«*EPA Inventory of U.S. Greenhouse Gas
Emissions and Sinks: 1990-2009
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EPA430-R-11-005
INVENTORY OF U.S. GREENHOUSE GAS EMISSIONS AND SINKS:
1990-2009
APRIL 15,2011
U.S. Environmental Protection Agency
1200 Pennsylvania Ave., N.W.
Washington, DC 20460
U.S.A.
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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 2009, 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 Mr. Brian Cook, Environmental Protection Agency, (202) 343-9135, cook.brianb@epa.gov.
For more information regarding climate change and greenhouse gas emissions, see the EPA web site at
.
Released for printing: April 15, 2011
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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 and Brian Cook. Ed Coe directed the work on
mobile combustion and transportation. Work on industrial process emissions was led by Mausami Desai. Work on
methane emissions from the energy sector was directed by Lisa Hanle and Kitty Sibold. Calculations for the waste
sector were led by Rachel Schmeltz. Tom Wirth directed work on the Agriculture, and together with Jennifer
Jenkins, directed work on the Land Use, Land-Use Change, and Forestry chapters. Work on emissions of HFCs,
PFCs, and SF6 was directed by Deborah Ottinger and Dave Godwin.
Within the EPA, other Offices also contributed data, analysis, and technical review for this report. The Office of
Transportation and Air Quality and the Office of Air Quality Planning and Standards provided analysis and review
for several of the source categories addressed in this report. The Office of Solid Waste and the Office of Research
and Development also contributed analysis and research.
The Energy Information Administration and the Department of Energy contributed invaluable data and analysis on
numerous energy-related topics. The U.S. Forest Service prepared the forest carbon inventory, and the Department
of Agriculture's Agricultural Research Service and the Natural Resource Ecology Laboratory at Colorado State
University contributed leading research on nitrous oxide and carbon fluxes from soils.
Other government agencies have contributed data as well, including the U.S. Geological Survey, the Federal
Highway Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department
of Commerce, the National Agricultural Statistics Service, the Federal Aviation Administration, and the Department
of Defense.
We would also like to thank Marian Martin Van Pelt, Randy Freed, and their staff at ICF International's Energy,
Environment, and Transportation Practice, including Don Robinson, Diana Pape, Susan Asam, Michael Grant,
Robert Lanza, Chris Steuer, Toby Mandel, Lauren Pederson, Joseph Herr, Jeremy Scharfenberg, Mollie Averyt,
Ashley Labrie, Hemant Mallya, Sandy Seastream, Douglas Sechler, Ashaya Basnyat, Kristen Schell, Victoria
Thompson, Mark Flugge, Paul Stewart, Tristan Kessler, Katrin Moffroid, Veronica Kennedy, Kaye Schultz, Seth
Greenburg, Larry O'Rourke, Rubab Bhangu, Deborah Harris, Emily Rowan, Roshni Rathi, Lauren Smith, Nikhil
Nadkarni, Caroline Cochran, Joseph Indvik, Aaron Sobel, and Neha Mukhi 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.
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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.
111
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Table of Contents
ACKNOWLEDGMENTS I
PREFACE Ill
TABLE OF CONTENTS V
LIST OF TABLES, FIGURES, AND BOXES VII
EXECUTIVE SUMMARY ES-1
Background Information ES-2
Recent Trends in U.S. Greenhouse Gas Emissions and Sinks ES-3
Overview of Sector Emissions and Trends ES-11
Other Information ES-14
1. INTRODUCTION 1-1
1.1. Background Information 1-2
1.2. Institutional Arrangements 1-9
1.3. Inventory Process 1-9
1.4. Methodology and Data Sources 1-11
1.5. Key Categories 1-12
1.6. Quality Assurance and Quality Control (QA/QC) 1-14
1.7. Uncertainty Analysis of Emission Estimates 1-16
1.8. Completeness 1-17
1.9. Organization of Report 1-17
2. TRENDS IN GREENHOUSE GAS EMISSIONS 2-1
2.1. Recent Trends in U.S. Greenhouse Gas Emissions and Sinks 2-1
2.2. Emissions by Economic Sector 2-16
2.3. Indirect Greenhouse Gas Emissions (CO, NOX, NMVOCs, and SO2) 2-25
3. ENERGY 3-1
3.1. Fossil Fuel Combustion (IPCC Source Category 1A) 3-3
3.2. Carbon Emitted from Non-Energy Uses of Fossil Fuels (IPCC Source Category 1A) 3-28
3.3. Incineration of Waste (IPCC Source Category lAla) 3-33
3.4. Coal Mining (IPCC Source Category IBla) 3-37
3.5. Abandoned Underground Coal Mines (IPCC Source Category IBla) 3-40
3.6. Natural Gas Systems (IPCC Source Category lB2b) 3-43
3.7. Petroleum Systems (IPCC Source Category lB2a) 3-49
3.8. Energy Sources of Indirect Greenhouse Gas Emissions 3-54
3.9. International Bunker Fuels (IPCC Source Category 1: Memo Items) 3-55
3.10. WoodBiomass andEthanol Consumption (IPCC Source Category 1A) 3-59
4. INDUSTRIAL PROCESSES 4-1
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4.1. Cement Production (IPCC Source Category 2A1) 4-4
4.2. Lime Production (IPCC Source Category 2A2) 4-7
4.3. Limestone and Dolomite Use (IPCC Source Category 2A3) 4-11
4.4. Soda Ash Production and Consumption (IPCC Source Category 2A4) 4-13
4.5. Ammonia Production (IPCC Source Category 2B1) and Urea Consumption 4-16
4.6. Nitric Acid Production (IPCC Source Category 2B2) 4-19
4.7. Adipic Acid Production (IPCC Source Category 2B3) 4-21
4.8. Silicon Carbide Production (IPCC Source Category 2B4) and Consumption 4-24
4.9. Petrochemical Production (IPCC Source Category 2B5) 4-26
4.10. Titanium Dioxide Production (IPCC Source Category 2B5) 4-29
4.11. Carbon Dioxide Consumption (IPCC Source Category 2B5) 4-31
4.12. Phosphoric Acid Production (IPCC Source Category 2B5) 4-33
4.13. Iron and Steel Production (IPCC Source Category 2C1) and Metallurgical Coke Production 4-37
4.14. Ferroalloy Production (IPCC Source Category 2C2) 4-45
4.15. Aluminum Production (IPCC Source Category 2C3) 4-47
4.16. Magnesium Production and Processing (IPCC Source Category 2C4) 4-52
4.17. Zinc Production (IPCC Source Category 2C5) 4-54
4.18. Lead Production (IPCC Source Category 2C5) 4-58
4.19. HCFC-22 Production (IPCC Source Category 2E1) 4-60
4.20. Substitution of Ozone Depleting Substances (IPCC Source Category 2F) 4-62
4.21. Semiconductor Manufacture (IPCC Source Category 2F6) 4-66
4.22. Electrical Transmission and Distribution (IPCC Source Category 2F7) 4-71
4.23. Industrial Sources of Indirect Greenhouse Gases 4-76
5. SOLVENT AND OTHER PRODUCT USE 5-1
5.1. Nitrous Oxide from Product Uses (IPCC Source Category 3D) 5-1
5.2. Indirect Greenhouse Gas Emissions from Solvent Use 5-3
6. AGRICULTURE 6-1
6.1. Enteric Fermentation (IPCC Source Category 4A) 6-2
6.2. Manure Management (IPCC Source Category 4B) 6-6
6.3. Rice Cultivation (IPCC Source Category 4C) 6-12
6.4. Agricultural Soil Management (IPCC Source Category 4D) 6-17
6.5. Field Burning of Agricultural Residues (IPCC Source Category 4F) 6-27
7. LAND USE, LAND-USE CHANGE, AND FORESTRY 7-1
7.1. Representation of the U.S. Land Base 7-4
7.2. Forest Land Remaining Forest Land 7-12
7.3. Land Converted to Forest Land (IPCC Source Category 5A2) 7-24
7.4. Cropland Remaining Cropland (IPCC Source Category 5B1) 7-24
vi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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7.5. Land Converted to Cropland (IPCC Source Category 5B2) 7-35
7.6. Grassland Remaining Grassland (IPCC Source Category 5C1) 7-38
7.7. Land Converted to Grassland (IPCC Source Category 5C2) 7-42
7.8. Wetlands Remaining Wetlands 7-45
7.9. Settlements Remaining Settlements 7-49
7.10. Land Converted to Settlements (Source Category 5E2) 7-55
7.11. Other (IPCC Source Category 5G) 7-55
8. WASTE 8-1
8.1. Landfills (IPCC Source Category 6A1) 8-2
8.2. Wastewater Treatment (IPCC Source Category 6B) 8-7
8.3. Composting (IPCC Source Category 6D) 8-18
8.4. Waste Sources of Indirect Greenhouse Gases 8-19
9. OTHER 9-1
10. RECALCULATIONS AND IMPROVEMENTS 10-1
11. REFERENCES 11-1
List of Tables, Figures, and Boxes
Tables
Table ES-1: Global Warming Potentials (100-Year Time Horizon) Used in this Report ES-3
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Tg CO2 Eq. or million metric tons CO2
Eq.) ES-4
Table ES-3: CO2 Emissions from Fossil Fuel Combustion by Fuel Consuming End-Use Sector (Tg or million metric
tonsCO2Eq.) ES-8
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/TPCC Sector (Tg or million
metric tons CO2 Eq.) ES-11
Table ES-5: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.)
ES-13
Table ES-6: Emissions from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.) ..ES-13
Table ES-7: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg or million metric tons CO2 Eq.)
ES-14
Table ES-8: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related Emissions Distributed
(Tg or million metric tons CO2 Eq.) ES-15
Table ES-9: Recent Trends in Various U.S. Data (Index 1990 = 100) ES-16
Table ES-10: Emissions of NOX, CO,NMVOCs, and SO2 (Gg) ES-17
Table 1-1: Global Atmospheric Concentration, Rate of Concentration Change, and Atmospheric Lifetime (years) of
Selected Greenhouse Gases 1-3
Table 1 -2: Global Warming Potentials and Atmospheric Lifetimes (Years) Used in this Report 1-7
Table 1-3: Comparison of 100-Year GWPs 1-8
Table 1-4: Key Categories for the United States (1990-2009) 1-13
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Table 1 -5. Estimated Overall Inventory Quantitative Uncertainty (Tg CO2 Eq. and Percent) 1-16
Table 1-6: IPCC Sector Descriptions 1-17
Table 1-7: List of Annexes 1-18
Table 2-1: Recent Trends inU.S. Greenhouse Gas Emissions and Sinks (Tg CO2Eq.) 2-3
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Gg) 2-5
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (Tg CO2 Eq.)... 2-7
Table 2-4: Emissions from Energy (Tg CO2Eq.) 2-8
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.) 2-9
Table 2-6: Emissions from Industrial Processes (TgCO2Eq.) 2-11
Table 2-7: N2O Emissions from Solvent and Other Product Use (Tg CO2 Eq.) 2-12
Table 2-8: Emissions from Agriculture (Tg CO2 Eq.) 2-13
Table 2-9: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (TgCO2Eq.) 2-14
Table 2-10: Emissions from Land Use, Land-Use Change, and Forestry (TgCO2Eq.) 2-14
Table 2-11: Emissions from Waste (Tg CO2 Eq.) 2-15
Table 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg CO2 Eq. and Percent of Total in
2009) 2-16
Table 2-13: Electricity Generation-Related Greenhouse Gas Emissions (Tg CO2Eq.) 2-19
Table 2-14: U.S Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-Related Emissions
Distributed (TgCO2Eq.) and Percent of Total in 2009 2-19
Table 2-15: Transportation-Related Greenhouse Gas Emissions (Tg CO2 Eq.) 2-22
Table 2-16: Recent Trends in Various U.S. Data (Index 1990 = 100) 2-25
Table 2-17: Emissions of NOX, CO, NMVOCs, and SO2 (Gg) 2-26
Table 3-1: CO2, CH4, and N2O Emissions from Energy (Tg CO2 Eq.) 3-1
Table 3-2: CO2, CH4, and N2O Emissions from Energy (Gg) 3-2
Table 3-3: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (Tg CO2 Eq.) 3-3
Table 3-4: CO2, CH4, and N2O Emissions fromFossil Fuel Combustion (Gg) 3-3
Table 3 -5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (Tg CO2 Eq.) 3-3
Table 3-6: Annual Change in CO2 Emissions and Total 2009 Emissions from Fossil Fuel Combustion for Selected
Fuels and Sectors (Tg CO2 Eq. and Percent) 3-4
Table 3 -7: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by Sector (Tg CO2 Eq.) 3-6
Table 3-8: CO2, CH4, and N2O Emissions fromFossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.) 3-7
Table 3-9: CO2 Emissions from Stationary Fossil Fuel Combustion (Tg CO2 Eq.) 3-8
Table 3-10: CH4 Emissions from Stationary Combustion (Tg CO2 Eq.) 3-9
Table 3-11: N2O Emissions from Stationary Combustion (Tg CO2 Eq.) 3-9
Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector (Tg CO2 Eq.)a 3-13
Table 3-13: CH4 Emissions from Mobile Combustion (Tg CO2 Eq.) 3-15
Table 3-14: N2O Emissions from Mobile Combustion (Tg CO2Eq.) 15
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (Tg CO2 Eq./QBtu) 19
viii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 3-16: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-related Fossil Fuel
Combustion by Fuel Type and Sector (Tg CO2 Eq. and Percent) 3-21
Table 3-17: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Energy-Related Stationary
Combustion, Including Biomass (Tg CO2Eq. and Percent) 3-24
Table 3-18: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Mobile Sources (Tg CO2
Eq. and Percent) 3-27
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (Tg CO2 Eq.) 3-28
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu) 3-29
Table 3-21: 2009 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions 3-30
Table 3-22: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-Energy Uses of Fossil Fuels
(Tg CO2 Eq. and Percent) 3-31
Table 3-23: Tier 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy Uses of Fossil Fuels
(Percent) 3-32
Table 3-24: CO2 and N2O Emissions from the Incineration of Waste (Tg CO2 Eq.) 3-34
Table 3-25: CO2 and N2O Emissions from the Incineration of Waste (Gg) 3-34
Table 3-26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted 3-36
Table 3-27: Tier 2 Quantitative Uncertainty Estimates for CO2 and N2O from the Incineration of Waste (Tg CO2 Eq.
and Percent) 3-36
Table 3-28: CH4 Emissions from Coal Mining (Tg CO2 Eq.) 3-37
Table 3-29: CH4 Emissions from Coal Mining (Gg) 3-38
Table 3-30: Coal Production (Thousand Metric Tons) 3-39
Table 3-31: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal Mining (Tg CO2 Eq. and
Percent) 3-39
Table 3-32: CH4 Emissions from Abandoned Coal Mines (Tg CO2Eq.) 3-40
Table 3-33: CH4 Emissions from Abandoned Coal Mines (Gg) 3-41
Table 3-34: Number of gassy abandoned mines occurring in U.S. basins grouped by class according to post-
abandonment state 3-42
Table 3-35: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Abandoned Underground Coal
Mines (Tg CO2 Eq. and Percent) 3-43
Table 3-36: CH4 Emissions from Natural Gas Systems (TgCO2Eq.)* 3-44
Table 3-37: CH4Emissions from Natural Gas Systems (Gg)* 3-44
Table 3-38: Non-combustion CO2 Emissions from Natural Gas Systems (TgCO2Eq.) 3-45
Table 3-39: Non-combustion CO2 Emissions from Natural Gas Systems (Gg) 3-45
Table 3-40: Tier 2 Quantitative Uncertainty Estimates for CH4 and Non-energy CO2 Emissions from Natural Gas
Systems (Tg CO2 Eq. and Percent) 3-46
Table 3-41: CH4 Emissions from Petroleum Systems (Tg CO2Eq.) 3-50
Table 3-42: CH4 Emissions from Petroleum Systems (Gg) 3-50
Table 3-43: CO2 Emissions from Petroleum Systems (Tg CO2Eq.) 3-50
Table 3-44: CO2 Emissions from Petroleum Systems (Gg) 3-50
Table 3-45: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum Systems (Tg CO2 Eq. and
Percent) 3-52
IX
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Table 3-46: Potential Emissions from CO2 Capture and Transport (Tg CO2 Eq.) 3-54
Table 3-47: Potential Emissions from CO2 Capture and Transport (Gg) 3-54
Table 3-48: NOX, CO, and NMVOC Emissions from Energy-Related Activities (Gg) 3-54
Table 3 -49: CO2, CH4, and N2O Emissions from International Bunker Fuels (Tg CO2 Eq.) 3-56
Table 3-50: CO2, CH4 andN2O Emissions from International Bunker Fuels (Gg) 3-56
Table 3-51: Aviation Jet Fuel Consumption for International Transport (Million Gallons) 3-57
Table 3-52: Marine Fuel Consumption for International Transport (Million Gallons) 3-58
Table 3-53: CO2 Emissions from Wood Consumption by End-Use Sector (Tg CO2 Eq.) 3-59
Table 3-54: CO2 Emissions from Wood Consumption by End-Use Sector (Gg) 3-60
Table 3-55: CO2 Emissions fromEthanol Consumption (Tg CO2 Eq.) 3-60
Table 3-56: CO2 Emissions from Ethanol Consumption (Gg) 3-60
Table 3-57: Woody Biomass Consumption by Sector (Trillion Btu) 3-60
Table 3-58: Ethanol Consumption by Sector (Trillion Btu) 3-61
Table 4-1: Emissions from Industrial Processes (TgCO2Eq.) 4-1
Table 4-2: Emissions from Industrial Processes (Gg) 4-2
Table 4-3: CO2 Emissions from Cement Production (Tg CO2 Eq. and Gg) 4-5
Table 4-4: Clinker Production (Gg) 4-6
Table 4-5: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement Production (Tg CO2 Eq. and
Percent) 4-6
Table 4-6: CO2 Emissions from Lime Production (Tg CO2Eq. andGg) 4-7
Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (Gg) 4-8
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated, and Dead-Burned-
Dolomite Lime Production (Gg) 4-9
Table 4-9: Adjusted Lime Production3 (Gg) 4-9
Table 4-10: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime Production (Tg CO2 Eq. and
Percent) 4-10
Table 4-11: CO2 Emissions from Limestone & Dolomite Use (TgCO2Eq.) 4-11
Table 4-12: CO2 Emissions from Limestone & Dolomite Use (Gg) 4-11
Table 4-13: Limestone and Dolomite Consumption (Thousand Metric Tons) 4-12
Table 4-14: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Limestone and Dolomite Use (Tg
CO2 Eq. and Percent) 4-13
Table 4-15: CO2 Emissions from Soda Ash Production and Consumption (Tg CO2 Eq.) 4-14
Table 4-16: CO2 Emissions from Soda Ash Production and Consumption (Gg) 4-14
Table 4-17: Soda Ash Production and Consumption (Gg) 4-15
Table 4-18: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash Production and
Consumption (Tg CO2Eq. and Percent) 4-16
Table 4-19: CO2 Emissions from Ammonia Production and Urea Consumption (Tg CO2 Eq.) 4-17
Table 4-20: CO2 Emissions from Ammonia Production and Urea Consumption (Gg) 4-17
Table 4-21: Ammonia Production, Urea Production, Urea Net Imports, and Urea Exports (Gg) 4-18
x Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 4-22: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ammonia Production and Urea
Consumption (Tg CO2Eq. and Percent) 4-19
Table 4-23: N2O Emissions from Nitric Acid Production (Tg CO2 Eq. and Gg) 4-20
Table 4-24: Nitric Acid Production (Gg) 4-20
Table 4-25: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Nitric Acid Production (Tg CO2 Eq.
and Percent) 4-21
Table 4-26: N2O Emissions from Adipic Acid Production (Tg CO2 Eq. and Gg) 4-22
Table 4-27: Adipic Acid Production (Gg) 4-23
Table 4-28: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Adipic Acid Production (Tg CO2
Eq. and Percent) 4-23
Table 4-29: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Tg CO2 Eq.) 4-24
Table 4-30: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Gg) 4-24
Table 4-31: Production and Consumption of Silicon Carbide (Metric Tons) 4-25
Table 4-32: Tier 2 Quantitative Uncertainty Estimates for CH4 and CO2 Emissions from Silicon Carbide Production
and Consumption (Tg CO2Eq. and Percent) 4-25
Table 4-33: CO2 and CH4 Emissions from Petrochemical Production (Tg CO2 Eq.) 4-26
Table 4-34: CO2 and CH4 Emissions from Petrochemical Production (Gg) 4-26
Table 4-35: Production of Selected Petrochemicals (Thousand Metric Tons) 4-27
Table 4-36: Carbon Black Feedstock (Primary Feedstock) and Natural Gas Feedstock (Secondary Feedstock)
Consumption (Thousand Metric Tons) 4-28
Table 4-37: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petrochemical Production and CO2
Emissions from Carbon Black Production (Tg CO2 Eq. and Percent) 4-28
Table 4-38: CO2 Emissions from Titanium Dioxide (Tg CO2 Eq. and Gg) 4-29
Table 4-39: Titanium Dioxide Production (Gg) 4-30
Table 4-40: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium Dioxide Production (Tg
CO2 Eq. and Percent) 4-31
Table 4-41: CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and Gg) 4-32
Table 4-42: CO2 Production (Gg CO2) and the Percent Used for Non-EOR Applications for Jackson Dome and
Bravo Dome 4-32
Table 4-43: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and
Percent) 4-33
Table 4-44: CO2 Emissions from Phosphoric Acid Production (Tg CO2 Eq. and Gg) 4-34
Table 4-45: Phosphate Rock Domestic Production, Exports, and Imports (Gg) 4-35
Table 4-46: Chemical Composition of Phosphate Rock (percent by weight) 4-35
Table 4-47: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Phosphoric Acid Production (Tg
CO2 Eq. and Percent) 4-36
Table 4-48: CO2 and CH4 Emissions from Metallurgical Coke Production (Tg CO2 Eq.) 4-38
Table 4-49: CO2 and CH4 Emissions from Metallurgical Coke Production (Gg) 4-38
Table 4-50: CO2 Emissions from Iron and Steel Production (Tg CO2 Eq.) 4-39
Table 4-51: CO2 Emissions from Iron and Steel Production (Gg) 4-39
XI
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Table 4-52: CH4 Emissions from Iron and Steel Production (Tg CO2 Eq.) 4-39
Table 4-53: CH4 Emissions from Iron and Steel Production (Gg) 4-39
Table 4-54: Material Carbon Contents for Metallurgical Coke Production 4-40
Table 4-55: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Metallurgical
Coke Production (Thousand Metric Tons) 4-40
Table 4-56: Production and Consumption Data for the Calculation of CO2 Emissions from Metallurgical Coke
Production (million ft3) 4-41
Table 4-57: CO2 Emission Factors for Sinter Production and Direct Reduced Iron Production 4-41
Table 4-58: Material Carbon Contents for Iron and Steel Production 4-41
Table 4-59: CH4 Emission Factors for Sinter and Pig Iron Production 4-42
Table 4-60: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Iron and Steel
Production (Thousand Metric Tons) 4-43
Table 4-61: Production and Consumption Data for the Calculation of CO2 Emissions from Iron and Steel
Production (million ft3 unless otherwise specified) 4-43
Table 4-62: Tier 2 Quantitative Uncertainty Estimates for CO2 and CH4 Emissions from Iron and Steel Production
and Metallurgical Coke Production (Tg. CO2Eq. and Percent) 4-44
Table 4-63: CO2 and CH4 Emissions from Ferroalloy Production (Tg CO2 Eq.) 4-45
Table 4-64: CO2 and CH4 Emissions from Ferroalloy Production (Gg) 4-46
Table 4-65: Production of Ferroalloys (Metric Tons) 4-46
Table 4-66: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ferroalloy Production (Tg CO2 Eq.
and Percent) 4-47
Table 4-67: CO2 Emissions from Aluminum Production (Tg CO2 Eq. and Gg) 4-48
Table 4-68: PFC Emissions from Aluminum Production (Tg CO2 Eq.) 4-48
Table 4-69: PFC Emissions from Aluminum Production (Gg) 4-48
Table 4-70: Production of Primary Aluminum (Gg) 4-50
Table 4-71: Tier 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from Aluminum Production (Tg
CO2 Eq. and Percent) 4-51
Table 4-72: SF6 Emissions from Magnesium Production and Processing (Tg CO2 Eq. and Gg) 4-52
Table 4-73: SF6 Emission Factors (kg SF6 per metric ton of magnesium) 4-53
Table 4-74: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Magnesium Production and
Processing (Tg CO2 Eq. and Percent) 4-54
Table 4-75: CO2 Emissions fromZinc Production (Tg CO2 Eq. and Gg) 4-55
Table 4-76: Zinc Production (Metric Tons) 4-56
Table 4-77: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc Production (Tg CO2 Eq. and
Percent) 4-57
Table 4-78: CO2 Emissions from Lead Production (Tg CO2 Eq. and Gg) 4-58
Table 4-79: Lead Production (Metric Tons) 4-59
Table 4-80: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead Production (Tg CO2 Eq. and
Percent) 4-59
Table 4-81: HFC-23 Emissions from HCFC-22 Production (Tg CO2 Eq. and Gg) 4-61
xii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 4-82: HCFC-22 Production (Gg) 4-61
Table 4-83: Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22 Production (Tg CO2 Eq. and
Percent) 4-62
Table 4-84: Emissions of HFCs and PFCs from ODS Substitutes (TgCO2Eq.) 4-62
Table 4-85: Emissions of HFCs and PFCs from ODS Substitution (Mg) 4-63
Table 4-86: Emissions of HFCs and PFCs from ODS Substitutes (Tg CO2 Eq.) by Sector 4-63
Table 4-87: Tier 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS Substitutes (Tg CO2
Eq. and Percent) 4-66
Table 4-88: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Tg CO2 Eq.) 4-67
Table 4-89: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Mg) 4-67
Table 4-90: Tier 2 Quantitative Uncertainty Estimates for HFC, PFC, and SF6 Emissions from Semiconductor
Manufacture (TgCO2Eq. and Percent) 4-71
Table 4-91: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Tg CO2 Eq.)
4-72
Table 4-92: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Gg) 4-72
Table 4-93: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Electrical Transmission and
Distribution (Tg CO2Eq. and percent) 4-75
Table 4-94: NOX, CO, and NMVOC Emissions from Industrial Processes (Gg) 4-76
Table 5-1: N2O Emissions from Solvent and Other Product Use (TgCO2Eq. andGg) 5-1
Table 5-2: N2O Production (Gg) 5-1
Table 5-3: N2O Emissions fromN2O Product Usage (Tg CO2 Eq. and Gg) 5-1
Table 5-4: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from N2O Product Usage (Tg CO2 Eq. and
Percent) 5-3
Table 5-5: Emissions of NOX, CO, and NMVOC from Solvent Use (Gg) 5-4
Table 6-1: Emissions from Agriculture (Tg CO2Eq.) 6-1
Table 6-2: Emissions from Agriculture (Gg) 6-1
Table 6-3: CH4 Emissions from Enteric Fermentation (Tg CO2Eq.) 6-2
Table 6-4: CH4 Emissions from Enteric Fermentation (Gg) 6-2
Table 6-5: Quantitative Uncertainty Estimates for CH4 Emissions from Enteric Fermentation (Tg CO2 Eq. and
Percent) 6-5
Table 6-6: CH4 and N2O Emissions from Manure Management (Tg CO2Eq.) 6-7
Table 6-7: CH4 and N2O Emissions from Manure Management (Gg) 6-8
Table 6-8: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O (Direct and Indirect) Emissions from Manure
Management (Tg CO2Eq. and Percent) 6-11
Table 6-9: CH4 Emissions from Rice Cultivation (Tg CO2 Eq.) 6-13
Table 6-10: CH4 Emissions fromRice Cultivation (Gg) 6-13
Table 6-11: Rice Areas Harvested (Hectares) 6-14
Table 6-12: Ratooned Area as Percent of Primary Growth Area 6-15
Table 6-13: Non-USDAData Sources for Rice Harvest Information 6-15
Table 6-14: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice Cultivation (Tg CO2 Eq. and
xiii
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Percent) 6-16
Table 6-15: N2O Emissions from Agricultural Soils (Tg CO2 Eq.) 6-17
Table 6-16: N2O Emissions from Agricultural Soils (Gg) 6-18
Table 6-17: Direct N2O Emissions from Agricultural Soils by Land Use Type and N Input Type (Tg CO2 Eq.)... 6-18
Table 6-18: Indirect N2O Emissions from all Land-Use Types (Tg CO2Eq.) 6-18
Table 6-19: Quantitative Uncertainty Estimates of N2O Emissions from Agricultural Soil Management in 2009 (Tg
CO2 Eq. and Percent) 6-26
Table 6-20: CH4 and N2O Emissions from Field Burning of Agricultural Residues (Tg CO2 Eq.) 6-28
Table 6-21: CH4, N2O, CO, and NOX Emissions from Field Burning of Agricultural Residues (Gg) 6-28
Table 6-22: Agricultural Crop Production (Gg of Product) 6-30
Table 6-23: U.S. Average Percent Crop Area Burned by Crop (Percent) 6-31
Table 6-24: Key Assumptions for Estimating Emissions from Field Burning of Agricultural Residues 6-31
Table 6-25: Greenhouse Gas Emission Ratios and Conversion Factors 6-31
Table 6-26: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Field Burning of
Agricultural Residues (Tg CO2Eq. and Percent) 6-31
Table 7-1: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.)
7-1
Table 7-2: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg C) 7-2
Table 7-3: Emissions from Land Use, Land-Use Change, and Forestry (Tg CO2Eq.) 7-2
Table 7-4: Emissions from Land Use, Land-Use Change, and Forestry (Gg) 7-3
Table 7-5: Size of Land Use and Land-Use Change Categories on Managed Land Area by Land Use and Land Use
Change Categories (thousands of hectares) 7-5
Table 7-6: Net Annual Changes inC Stocks (TgCO2/yr) in Forest and Harvested Wood Pools 7-14
Table 7-7: Net Annual Changes in C Stocks (Tg C/yr) in Forest and Harvested Wood Pools 7-14
Table 7-8: Forest area (1000 ha) and C Stocks (Tg C) in Forest and Harvested Wood Pools 7-15
Table 7-9: Estimates of CO2 (Tg/yr) emissions for the lower 48 states and Alaska1 7-16
Table 7-10: Tier 2 Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land Remaining Forest Land:
Changes in Forest C Stocks (Tg CO2Eq. and Percent) 7-19
Table 7-11: Estimated Non-CO2 Emissions from Forest Fires (TgCO2Eq.) for U.S. Forests1 7-21
Table 7-12: Estimated Non-CO2 Emissions from Forest Fires (Gg Gas) for U.S. Forests1 7-21
Table 7-13: Estimated Carbon Released from Forest Fires for U.S. Forests 7-22
Table 7-14: Tier 2 Quantitative Uncertainty Estimates of Non-CO2 Emissions from Forest Fires in Forest Land
Remaining Forest Land (Tg CO2Eq. and Percent) 7-22
Table 7-15: Direct N2O Fluxes from Soils in Forest Land Remaining Forest Land (Tg CO2 Eq. and Gg N2O).... 7-23
Table 7-16: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land Remaining Forest Land
(Tg CO2 Eq. and Percent) 7-24
Table 7-17: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg CO2 Eq.) 7-26
Table 7-18: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg C) 7-26
Table 7-19: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Cropland
Remaining Cropland (Tg CO2Eq. and Percent) 7-30
xiv Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 7-20: Emissions from Liming of Agricultural Soils (Tg CO2Eq.) 7-31
Table 7-21: Emissions from Liming of Agricultural Soils (TgC) 7-31
Table 7-22: Applied Minerals (Million Metric Tons) 7-32
Table 7-23: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming of Agricultural Soils (Tg
CO2 Eq. and Percent) 7-33
Table 7-24: CO2 Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg CO2 Eq.) 7-33
Table 7-25: CO2 Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg C) 7-33
Table 7-26: Applied Urea (Million Metric Tons) 7-34
Table 7-27: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization (Tg CO2 Eq. and Percent)
7-34
Table 7-28: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (Tg CO2 Eq.) 7-35
Table 7-29: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (Tg C) 7-35
Table 7-30: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Cropland (Tg CO2 Eq. and Percent) 7-37
Table 7-31: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg CO2 Eq.) 7-38
Table 7-32: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg C) 7-38
Table 7-33: Tier 2 Quantitative Uncertainty Estimates for C Stock Changes occurring within Grassland Remaining
Grassland (Tg CO2Eq. and Percent) 7-40
Table 7-34: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg CO2 Eq.) 7-42
Table 7-35: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg C) 7-42
Table 7-36: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Grassland (Tg CO2Eq. and Percent) 7-44
Table 7-37: Emissions fromPeatlandsRemaining Peatlands (Tg CO2Eq.) 7-46
Table 7-38: Emissions from Peatlands Remaining Peatlands (Gg) 7-46
Table 7-39: Peat Production of Lower 48 States (in thousands of Metric Tons) 7-47
Table 7-40: Peat Production of Alaska (in thousands of Cubic Meters) 7-47
Table 7-41: Tier-2 Quantitative Uncertainty Estimates for CO2 Emissions from Peatlands Remaining Peatlands
7-48
Table 7-42: Net C Flux from Urban Trees (Tg CO2 Eq. and Tg C) 7-49
Table 7-43: C Stocks (Metric Tons C), Annual C Sequestration (Metric Tons C/yr), Tree Cover (Percent), and
Annual C Sequestration per Area of Tree Cover (kg C/m2-yr)for 14 U.S. Cities 7-51
Table 7-44: Tier 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C Stocks in Urban Trees
(Tg CO2 Eq. and Percent) 7-52
Table 7-45: Direct N2O Fluxes from Soils in Settlements Remaining Settlements (Tg CO2Eq. and GgN2O) 7-53
Table 7-46: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements Remaining Settlements
(Tg CO2 Eq. and Percent) 7-54
Table 7-47: Net Changes in Yard Trimming and Food Scrap Stocks in Landfills (Tg CO2Eq.) 7-55
Table 7-48: Net Changes in Yard Trimming and Food Scrap Stocks in Landfills (Tg C) 7-56
Table 7-49: Moisture Content (%), C Storage Factor, Proportion of Initial C Sequestered (%), Initial C Content (%),
and Decay Rate (year"1) for Landfilled Yard Trimmings and Food Scraps in Landfills 7-58
xv
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Table 7-50: C Stocks in Yard Trimmings and Food Scraps in Landfills (Tg C) 7-58
Table 7-51: Tier 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard Trimmings and Food Scraps in
Landfills (Tg CO2 Eq. and Percent) 7-59
Table 8-1. Emissions from Waste (Tg CO2Eq.) 8-1
Table 8-2. Emissions from Waste (Gg) 8-2
Table 8-3. CH4 Emissions from Landfills (Tg CO2 Eq.) 8-3
Table 8-4. CH4 Emissions from Landfills (Gg) 8-3
Table 8-5. Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills (Tg CO2 Eq. and Percent)8-5
Table 8-6. CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Tg CO2 Eq.) 8-7
Table 8-7. CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Gg) 8-8
Table 8-8. U.S. Population (Millions) and Domestic Wastewater BOD5 Produced (Gg) 8-10
Table 8-9. Domestic Wastewater CH4 Emissions from Septic and Centralized Systems (2009) 8-10
Table 8-10. Industrial Wastewater CH4 Emissions by Sector (2009) 8-10
Table 8-11. U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and Petroleum Refining
Production (Tg) 8-10
Table 8-12. Variables Used to Calculate Percent Wastewater Treated Anaerobically by Industry (%) 8-11
Table 8-13. Wastewater Flow (nrVton) and BOD Production (g/L) for U.S. Vegetables, Fruits, and Juices Production
8-12
Table 8-14. U.S. Population (Millions), Available Protein (kg/person-year), and Protein Consumed (kg/person-year)
8-15
Table 8-15. Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater Treatment (Tg CO2 Eq.
and Percent) 8-16
Table 8-16. CH4 and N2O Emissions from Composting (Tg CO2 Eq.) 8-18
Table 8-17. CH4 and N2O Emissions from Composting (Gg) 8-18
Table 8-18: U.S. Waste Composted (Gg) 8-19
Table 8-19 : Tier 1 Quantitative Uncertainty Estimates for Emissions from Composting (Tg CO2 Eq. and Percent) 19
Table 8-20: Emissions of NOX, CO, and NMVOC from Waste (Gg) 8-20
Table 10-1: Revisions to U.S. Greenhouse Gas Emissions (Tg CO2 Eq.) 10-2
Table 10-2: Revisions to Net Flux of CO2 to the Atmosphere from Land Use, Land-Use Change, and Forestry (Tg
CO2Eq.) 10-4
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-4
Figure ES-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990 ES-4
Figure ES-4: 2009 Greenhouse Gas Emissions by Gas (percents based on Tg CO2 Eq.) ES-6
Figure ES-5: 2009 Sources of CO2 Emissions ES-7
Figure ES-6: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type ES-7
Figure ES-7: 2009 End-Use Sector Emissions of CO2, CH4, and N2O from Fossil Fuel Combustion ES-7
xvi Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Figure ES-8: 2009 Sources of CH4 Emissions ES-9
Figure ES-9: 2009 Sources of N2O Emissions ES-10
Figure ES-10: 2009 Sources of HFCs, PFCs, and SF6 Emissions ES-11
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector ES-11
Figure ES-12: 2009 U.S. Energy Consumption by Energy Source ES-12
Figure ES-13: Emissions Allocated to Economic Sectors ES-14
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors ES-16
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product ES-16
Figure ES-16: 2009 Key Categories ES-18
Figure 1-1: U.S. QA/QC Plan Summary ES-15
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas 1-1
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions 2-1
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990 2-1
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector 2-7
Figure 2-5: 2009 Energy Chapter Greenhouse Gas Sources 2-8
Figure 2-6: 2009 U.S. Fossil Carbon Flows (TgCO2Eq.) 2-8
Figure 2-7: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type 2-9
Figure 2-8: 2009 End-Use Sector Emissions from Fossil Fuel Combustion 2-10
Figure 2-9: 2009 Industrial Processes Chapter Greenhouse Gas Sources 2-11
Figure 2-10: 2009 Agriculture Chapter Greenhouse Gas Sources 2-13
Figure 2-11: 2009 Waste Chapter Greenhouse Gas Sources 2-15
Figure 2-12: Emissions Allocated to Economic Sectors 2-16
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors 2-19
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product 2-25
Figure 3-1: 2009 Energy Chapter Greenhouse Gas Sources 3-1
Figure 3-2: 2009 U.S. Fossil CarbonFlows (Tg CO2 Eq.) 3-1
Figure 3-3: 2009 U.S. Energy Consumption by Energy Source 3-5
Figure 3-4: U.S. Energy Consumption (QuadrillionBtu) 3-5
Figure 3-5: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type 3-5
Figure 3 -6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2009) 3-5
Figure 3 -7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2009) 3-5
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2009).... 3-6
Figure 3-9: Electricity Generation Retail Sales by End-Use Sector 3-10
Figure 3-10: Industrial Production Indices (Index 2002= 100) 3-11
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks, 1990-2008 3-13
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2008 3-13
Figure 3-13: Mobile Source CH4 and N2O Emissions 3-15
xvn
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Figure 3-14: U.S. Energy Consumption and Energy-Related CO2 Emissions Per Capita and Per Dollar GDP 3-20
Figure 4-1: 2009 Industrial Processes Chapter Greenhouse Gas Sources 4-1
Figure 6-1: 2009 Agriculture Chapter Greenhouse Gas Emission Sources 6-1
Figure 6-2: Sources and Pathways of N that Result in N2O Emissions from Agricultural Soil Management 6-17
Figure 6-3: Major Crops, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model, 1990-
2009 (TgCO2Eq./year) 6-19
Figure 6-4: Grasslands, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model, 1990-2009
(Tg CO2 Eq./year) 6-19
Figure 6-5: Major Crops, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the
DAYCENT Model, 1990-2009 (GgN/year) 6-19
Figure 6-6: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the
DAYCENT Model, 1990-2009 (GgN/year) 6-19
Figure 6-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using the DAYCENT
Simulation Model 6-26
Figure 7-1. Percent of Total Land Area in the General Land-Use Categories for 2009 7-6
Figure 7-2: Forest Sector Carbon Pools and Flows 7-13
Figure 7-3: Estimates of Net Annual Changes in C Stocks for Major C Pools 7-15
Figure 7-4: Average C Density in the Forest Tree Pool in the Conterminous United States, 2009 7-15
Figure 7-5: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Cropland Remaining Cropland 7-26
Figure 7-6: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Cropland Remaining Cropland 7-26
Figure 7-7: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009, Land
Converted to Cropland 7-36
Figure 7-8: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009, Land
Converted to Cropland 7-36
Figure 7-9: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Grassland Remaining Grassland 7-39
Figure 7-10: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Grassland Remaining Grassland 7-39
Figure 7-11: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Land Converted to Grassland 7-42
Figure 7-12: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Land Converted to Grassland 7-43
Figure 8-1: 2009 Waste Chapter Greenhouse Gas Sources 8-1
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-16
BoxES-3: Recalculations of Inventory Estimates ES-19
Box 1-1: Methodological approach for estimating and reporting U.S. emissions and sinks 1-2
xviii Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Box 1-2: The IPCC Fourth Assessment Report and Global Warming Potentials 1-8
Box 1-3 :IPCC Reference Approach 1-11
Box 2-1: Methodology for Aggregating Emissions by Economic Sector 2-23
Box 2-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data 2-25
Box 2-3: Sources and Effects of Sulfur Dioxide 2-27
Box 3 -1: Weather and Non-Fossil Energy Effects on CO2 from Fossil Fuel Combustion Trends 3-5
Box 3-2: Carbon Intensity of U.S. Energy Consumption 3-19
Box 3-3. Carbon Dioxide Transport, Injection, and Geological Storage 3-53
Box 6-1. Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions 6-20
Box 6-2: Comparison of Tier 2 U. S. Inventory Approach and IPCC (2006) Default Approach 6-29
Box 7-1: Methodological approach for estimating and reporting U.S. emissions and sinks 7-3
Box 7-2: CC>2 Emissions from Forest Fires 7-15
Box 7-3: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches 7-27
Box 8-1: Methodological approach for estimating and reporting U.S. emissions and sinks 8-1
Box 8-2: Biogenic Wastes in Landfills 8-6
xix
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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 2009. To ensure that the U.S. emissions inventory is comparable to those of other UNFCCC Parties, the
estimates presented here were calculated using methodologies consistent with those recommended in the Revised
1996 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), the IPCC Good Practice Guidance and Uncertainty Management in National
Greenhouse Gas Inventories (IPCC 2000), and the IPCC Good Practice Guidance for Land Use, Land-Use Change,
and Forestry (IPCC 2003). Additionally, the U.S. emission inventory has continued to incorporate new
methodologies and data from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
The structure of this report is consistent with the UNFCCC guidelines for inventory reporting.4 For most source
categories, the IPCC methodologies were expanded, resulting in a more comprehensive and detailed estimate of
emissions.
[BEGIN BOX]
Box ES-1: Methodological approach for estimating and reporting U.S. emissions and sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the IPCC.5 Additionally, the calculated emissions
and sinks in a given year for the U.S. 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
1 The term "anthropogenic", in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (IPCC/UNEP/OECD/IEA 1997).
2 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See.
3 Article 4(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/2006/sbsta/eng/09.pdf>.
5 See < http://www.ipcc-nggip.iges.or.jp/public/index.html>.
6 See.
Executive Summary ES-1
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are comparable. In this regard, U.S. emissions and sinks reported in this inventory report are comparable to
emissions and sinks reported by other countries. Emissions and sinks provided in this inventory do not preclude
alternative examinations, but rather this inventory report presents emissions and sinks in a common format
consistent with how countries are to report inventories under the UNFCCC. The report itself follows this
standardized format, and provides an explanation of the IPCC methods used to calculate emissions and sinks, and
the manner in which those calculations are conducted.
[END BOX]
Background Information
Naturally occurring greenhouse gases include water vapor, carbon dioxide (CO2), methane (CH4), nitrous oxide
(N2O), and ozone (O3). 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 their national greenhouse gas emission inventories.7 Some other fluorine-containing
halogenated substances—hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)—do
not deplete stratospheric ozone but are potent greenhouse gases. These latter substances are addressed by the
UNFCCC and accounted for in national greenhouse gas emission inventories.
There are also several gases that do not have a direct global warming effect but indirectly affect terrestrial and/or
solar radiation absorption by influencing the formation or destruction of greenhouse gases, including tropospheric
and stratospheric ozone. These gases include carbon monoxide (CO), oxides of nitrogen (NOX), and non-CH4
volatile organic compounds (NMVOCs). Aerosols, which are extremely small particles or liquid droplets, such as
those produced by sulfur dioxide (SO2) or elemental carbon emissions, can also affect the absorptive characteristics
of the atmosphere.
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 2005,
concentrations of these greenhouse gases have increased globally by 36, 148, and 18 percent, respectively (IPCC
2007).
Beginning in the 1950s, the use of CFCs and other stratospheric ozone depleting substances (ODS) increased by
nearly 10 percent per year until the mid-1980s, when international concern about ozone depletion led to the entry
into force of the Montreal Protocol. Since then, the production of ODS is being phased out. In recent years, use of
ODS substitutes such as HFCs and PFCs has grown as they begin to be phased in as replacements for CFCs and
HCFCs. Accordingly, atmospheric concentrations of these substitutes have been growing (IPCC 2007).
Global Warming Potentials
Gases in the atmosphere can contribute to the greenhouse effect both directly and indirectly. Direct effects occur
when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the
substance produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or
when a gas affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or
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.
7 Emissions estimates of CFCs, HCFCs, halons and other ozone-depleting substances are included in the annexes of the
Inventory report for informational purposes.
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.
ES-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
The GWP of a greenhouse gas is defined as the ratio of the time-integrated radiative forcing from the instantaneous
release of 1 kilogram (kg) of a trace substance relative to that of 1 kg of a reference gas (IPCC 2001). Direct
radiative effects occur when the gas itself is a greenhouse gas. The reference gas used is CO2, and therefore GWP-
weighted emissions are measured in teragrams (or million metric tons) of CO2 equivalent (Tg CO2 Eq.).9'10 All
gases in this Executive Summary are presented in units of Tg CO2 Eq.
The UNFCCC reporting guidelines for national inventories were updated in 2006,n but continue to require the use
of GWPs from the IPCC Second Assessment Report (SAR) (IPCC 1996). This requirement ensures that current
estimates of aggregate greenhouse gas emissions for 1990 to 2009 are consistent with estimates developed prior to
the publication of the IPCC Third Assessment Report (TAR) (IPCC 2001) and the IPCC Fourth Assessment Report
(AR4) (IPCC 2007). Therefore, to comply with international reporting standards under the UNFCCC, official
emission estimates are reported by the United States using SAR GWP values. All estimates are provided throughout
the report in both CO2 equivalents and unweighted units. A comparison of emission values using the SAR GWPs
versus the TAR and AR4 GWPs 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.
Table ES-1: Global Warming Potentials (100-Year Time Horizon) Used in this Report
Gas GWP
C02 1
CH4* 21
N2O 310
HFC-23 11,700
HFC-32 650
HFC-125 2,800
HFC-134a 1,300
HFC-143a 3,800
HFC-152a 140
HFC-227ea 2,900
HFC-236fa 6,300
HFC-4310mee 1,300
CF4 6,500
C2F6 9,200
C4F10 7,000
C6F14 7,400
SF6 23,900
Source: IPCC (1996)
* 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 the production of CO2 is not included.
Global warming potentials are not provided for CO, NOX, NMVOCs, SO2, and aerosols because there is no agreed-
upon method to estimate the contribution of gases that are short-lived in the atmosphere, spatially variable, or have
only indirect effects on radiative forcing (IPCC 1996).
Recent Trends in U.S. Greenhouse Gas Emissions and Sinks
In 2009, total U.S. greenhouse gas emissions were 6,633.2 Tg or million metric tons CO2 Eq. While total U.S.
emissions have increased by 7.3 percent from 1990 to 2009, emissions decreased from 2008 to 2009 by 6.1 percent
(427.9 Tg CO2 Eq.). This decrease was primarily due to (1) a decrease in economic output resulting in a decrease in
energy consumption across all sectors; and (2) a decrease in the carbon intensity of fuels used to generate electricity
due to fuel switching as the price of coal increased, and the price of natural gas decreased significantly. Since 1990,
U.S. emissions have increased at an average annual rate of 0.4 percent.
9 Carbon comprises 12/44fts of carbon dioxide by weight.
10 One teragram is equal to 1012 grams or one million metric tons.
11 See .
Executive Summary ES-3
-------
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 2009.
Figure ES-1: U.S. Greenhouse Gas Emissions by Gas
Figure ES-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
Figure ES-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
Table ES-2: Recent Trends in U.S.
Eq.)
Greenhouse Gas Emissions and Sinks (Tg CO2 Eq. or million metric tons CO2
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
Incineration of Waste
Ammonia Production and
Urea Consumption
Lime Production
Cropland Remaining
Cropland
Limestone and Dolomite Use
Soda Ash Production and
Consumption
Aluminum Production
Petrochemical Production
Carbon Dioxide
Consumption
Titanium Dioxide Production
Ferroalloy Production
Wetlands Remaining
Wetlands
Phosphoric Acid Production
Zinc Production
Lead Production
Petroleum Systems
Silicon Carbide Production
and Consumption
Land Use, Land-Use
1990 2000 2005
5,099.7 5,975.0 6,113.8
4,738.4 5,594.8| 5,753.2
1,820.8 2,296.9 1 2,402.1
1,485.9 1,809.5 1 1,896.6
846.5| 851.1 823.1
338.31 370.7
219.o| 230.8
27.9 35.9
118.6
99.5
37.6
33.3
8.0
16.8
11.5
7.1
5.1
4.1
6.8
3.3
1.4
1.2
2.2
1.0
1.5
07
144.9
85.9
29.9
40.4
11.1
16.4
14.1
7.5
5.1
4.2
6.1
4.5
1.4
357.9
223.5
50.0
143.4
65.9
29.9
45.2
12.5
12.8
14.4
7.9
6.8
4.2
4.1
4.2
1.3
1.8B 1.8
1.9l 1.4
l.ll 1.1
1.4l 1.4
i.o| 1.1
0.5 0.6H 0.6
0.6 Q.5M 0.5
0.2l 0.2
(861.5) (576.6)1 (1,056.5)
2006
6,021.1
5,653.1
2,346.4
1,878.1
848.2
321.5
208.6
50.3
145.6
68.8
30.8
45.8
12.5
12.3
15.1
7.9
8.0
4.2
3.8
3.8
1.7
1.8
1.5
0.9
1.2
1.1
0.6
0.5
0.2
(1,064.3)
2007
6,120.0
5,756.7
2,412.8
1,894.0
842.0
342.4
219.4
46.1
137.2
71.0
31.1
44.5
12.7
14.0
14.6
8.2
7.7
4.1
4.3
3.9
1.9
1.9
1.6
1.0
1.2
1.1
0.6
0.5
0.2
(1,060.9)
2008
5,921.4
5,565.9
2,360.9
1,789.9
802.9
348.2
224.2
39.8
141.0
66.0
32.8
40.5
12.2
11.9
14.3
8.7
6.3
4.1
4.5
3.4
1.8
1.8
1.6
1.0
1.2
1.2
0.6
0.5
0.2
(1,040.5)
2009
5,505.2
5,209.0
2,154.0
1,719.7
730.4
339.2
224.0
41.7
123.4
41.9
32.2
29.0
12.3
11.8
11.2
7.8
7.6
4.3
3.0
2.7
1.8
1.5
1.5
1.1
1.0
1.0
0.5
0.5
0.1
(1,015.1)
ES-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Change, and Forestry
(Sink)"
Biomass - Woodb 215.2
International Bunker Fuels0 111.
Biomass - Ethanof 4.2
CH4 674.9
Natural Gas Systems 189.8
Enteric Fermentation 132.1
Landfills 147.4
Coal Mining 84.1
Manure Management 31.7
Petroleum Systems 35.4
Wastewater Treatment 23.5
Forest Land Remaining
Forest Land 3.2
Rice Cultivation 7.1
Stationary Combustion 7.4
Abandoned Underground
Coal Mines 6.0
Mobile Combustion 4.7
Composting 0.3
Petrochemical Production 0.9
Iron and Steel Production &
Metallurgical Coke
Production 1.0
Field Burning of Agricultural
Residues 0.3
Ferroalloy Production +1
Silicon Carbide Production
and Consumption
Incineration of Waste +1
International Bunker Fuels0 0.2
N2O 315.2
Agricultural Soil
Management 197.8
Mobile Combustion 43.9
Manure Management 14.5
Nitric Acid Production 17.7
Stationary Combustion 12.8
Forest Land Remaining
Forest Land 2.7
Wastewater Treatment 3.7
N2O from Product Uses 4.4
Adipic Acid Production 15.8
Composting 0.4
Settlements Remaining
Settlements 1.0
Incineration of Waste 0.5
Field Burning of Agricultural
Residues 0.1
Wetlands Remaining
Wetlands +1
International Bunker Fuels0 1.1
HFCs 36.9
Substitution of Ozone
Depleting Substances'1 0.3
218.1
98.5
9.4M
218.1
98.5
IQA
209.3
136.5
111.7
60.4J
42.4J
31.5J
25.2
14.3
75I
6.6J
7.4J
3.4
1.3J
1.2
0.9
0.3
0.1
341.0
206.8
53.2
17.1
19.4
14.6
12.1
4.5
4.9
5.5
1.4
1.1
0.4
0.1
0.9
103.2
74.3
206.9
109.7
23.0
631.4
190.4
136.5
112.5
56.9
46.6
29.4
24.3
203.8
128.4
31.0
672.1
217.7
138.8
111.7
58.2
46.7
29.4
24.5
203.3
127.6
38.9
664.6
205.2
141.0
111.3
57.9
50.7
30.0
24.4
198.4
133.7
54.8
676.7
211.8
140.6
115.9
67.1
49.4
30.2
24.5
183.8
123.1
61.2
686.3
221.2
139.8
117.5
71.0
49.5
30.9
24.5
9.8
6.8
6.6
5.5
2.5
1.6
1.1
0.7
0.2
0.1
322.9
8.4
4.8
4.4
5.0
1.7
1.5
0.4
0.1
1.0
120.2
104.2
21.6
5.9
6.2
5.5
2.3
1.6
1.0
0.7
0.2
0.2
326.4
18.0
4.8
4.4
4.3
1.8
1.5
0.4
0.1
1.2
123.5
109.4
20.0
6.2
6.5
5.6
2.2
1.7
1.0
0.7
0.2
0.2
325.1
16.7
4.9
4.4
3.7
1.8
1.6
0.4
0.1
1.2
129.5
112.3
11.9
7.2
6.5
5.9
2.0
1.7
0.9
0.6
0.3
0.2
310.8
10.1
5.0
4.4
2.0
1.9
1.5
0.4
0.1
1.2
129.4
115.5
7.8
7.3
6.2
5.5
2.0
1.7
0.8
0.4
0.2
0.1
295.6
211.3
36.9
17.3
16.5
14.7
208.9
33.6
18.0
16.2
14.4
209.4
30.3
18.1
19.2
14.6
210.7
26.1
17.9
16.4
14.2
204.6
23.9
17.9
14.6
12.8
6.7
5.0
4.4
1.9
1.8
1.5
0.4
0.1
1.1
125.7
120.0
Executive Summary ES-5
-------
HCFC-22 Production 36.4 28.6 15.8 13.8 17.0 13.6 5.4
Semiconductor Manufacture 0.2B O.sB 0.2 0.3 0.3 0.3 0.3
PFCs 20.8 13.5 6.2 6.0 7.5 6.6 5.6
Semiconductor Manufacture 2.2 4.9B 3.2 3.5 3.7 4.0 4.0
Aluminum Production 18.sB 8.6B 3.0 2.5 3.8 2.7 1.6
SF6 34.4 20.1 19.0 17.9 16.7 16.1 14.8
Electrical Transmission and
Distribution 28.4 16.0 15.1 14.1 13.2
Magnesium Production and
I
Processing
Semiconductor Manufacture
Total
Net Emissions (Sources and
Sinks)
5.4
0.5
6,181.8
5,320.3
3.0
1.1
7,112.7
6,536.1
2.9
1.0
7,213.5
6,157.1
2.9
1.0
7,166.9
6,102.6
2.6
0.8
7,263.4
6,202.5
1.9
0.9
7,061.1
6,020.7
1.1
1.0
6,633.2
5,618.2
+ Does not exceed 0.05 Tg CO2 Eq.
a Parentheses indicate negative values or sequestration. The net CO2 flux total includes both emissions and sequestration, and
constitutes a net sink in the United States. Sinks are only included in net emissions total.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and
Forestry.
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.
Figure ES-4 illustrates the relative contribution of the direct greenhouse gases to total U.S. emissions in 2009. The
primary greenhouse gas emitted by human activities in the United States was CO2, representing approximately 83.0
percent of total greenhouse gas emissions. The largest source of CO2, and of overall greenhouse gas emissions, was
fossil fuel combustion. CH4 emissions, which have increased by 1.7 percent since 1990, resulted primarily from
natural gas systems, enteric fermentation associated with domestic livestock, and decomposition of wastes in
landfills. Agricultural soil management and mobile source fuel combustion were the major sources of N2O
emissions. Ozone depleting substance substitute emissions and emissions of HFC-23 during the production of
HCFC-22 were the primary contributors to aggregate HFC emissions. PFC emissions resulted as a by-product of
primary aluminum production and from semiconductor manufacturing, while electrical transmission and distribution
systems accounted for most SF6 emissions.
Figure ES-4: 2009 Greenhouse Gas Emissions by Gas (percents based on Tg CO2 Eq.)
Overall, from 1990 to 2009, total emissions of CO2 and CH4 increased by 405.5 Tg CO2 Eq. (8.0 percent) and 11.4
Tg CO2 Eq. (1.7 percent), respectively. Conversely, N2O emissions decreased by 19.6 Tg CO2 Eq. (6.2 percent).
During the same period, aggregate weighted emissions of HFCs, PFCs, and SF6 rose by 54.1 Tg CO2 Eq. (58.8
percent). From 1990 to 2009, HFCs increased by 88.8 Tg CO2 Eq. (240.41 percent), PFCs decreased by 15.1 Tg
CO2 Eq. (73.0 percent), and SF6 decreased by 19.5 Tg CO2 Eq. (56.8 percent). Despite being emitted in smaller
quantities relative to the other principal greenhouse gases, emissions of HFCs, PFCs, and SF6 are significant because
many of these gases have extremely high global warming potentials and, in the cases of PFCs and SF6, long
atmospheric lifetimes. Conversely, U.S. greenhouse gas emissions were partly offset by carbon sequestration in
forests, trees in urban areas, agricultural soils, and landfilled yard trimmings and food scraps, which, in aggregate,
offset 15.3 percent of total emissions in 2009. The following sections describe each gas' 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
ES-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
balanced. Since the Industrial Revolution (i.e., about 1750), global atmospheric concentrations of CO2 have risen
about 36 percent (IPCC 2007), principally due to the combustion of fossil fuels. Within the United States, fossil fuel
combustion accounted for 94.6 percent of CO2 emissions in 2009. Globally, approximately 30,313 Tg of CO2 were
added to the atmosphere through the combustion of fossil fuels in 2009, of which the United States accounted for
about 18 percent.12 Changes in land use and forestry practices can also emit CO2 (e.g., through conversion of forest
land to agricultural or urban use) or can act as a sink for CO2 (e.g., through net additions to forest biomass). In
addition to fossil-fuel combustion, several other sources emit significant quantities of CO2. These sources include,
but are not limited to non-energy use of fuels, iron and steel production and cement production (Figure ES-5).
Figure ES-5: 2009 Sources of CO2 Emissions
As the largest source of U.S. greenhouse gas emissions, CO2 from fossil fuel combustion has accounted for
approximately 78 percent of GWP-weighted emissions since 1990, growing slowly from 77 percent of total GWP-
weighted emissions in 1990 to 79 percent in 2009. Emissions of CO2 from fossil fuel combustion increased at an
average annual rate of 0.4 percent from 1990 to 2009. The fundamental factors influencing this trend include (1) a
generally growing domestic economy over the last 20 years, and (2) overall growth in emissions from electricity
generation and transportation activities. Between 1990 and 2009, CO2 emissions from fossil fuel combustion
increased from 4,738.4 Tg CO2 Eq. to 5,209.0 Tg CO2 Eq.—a 9.9 percent total increase over the twenty-year period.
From 2008 to 2009, these emissions decreased by 356.9 Tg CO2 Eq. (6.4 percent), the largest decrease in any year
over the twenty-year period.
Historically, changes in emissions from fossil fuel combustion have been the dominant factor affecting U.S.
emission trends. Changes in CO2 emissions from fossil fuel combustion are influenced by many long-term and
short-term factors, including population and economic growth, energy price fluctuations, technological changes, and
seasonal temperatures. 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 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: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Figure ES-7: 2009 End-Use Sector Emissions of CO2, CH4, and N2O from Fossil Fuel Combustion
The five major fuel consuming sectors contributing to CO2 emissions from fossil fuel combustion are electricity
generation, transportation, industrial, residential, and commercial. CO2 emissions are produced by the electricity
generation sector as they consume fossil fuel to provide electricity to one of the other four sectors, or "end-use"
sectors. For the discussion below, electricity generation emissions have been distributed to each end-use sector on
the basis of each sector's share of aggregate electricity consumption. This method of distributing emissions assumes
that each end-use sector consumes electricity that is generated from the national average mix of fuels according to
their carbon intensity. Emissions from electricity generation are also addressed separately after the end-use sectors
have been discussed.
12 Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2010 < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm> EIA (2010a).
Executive Summary ES-7
-------
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 CO2 emissions from fossil fuel combustion by end-use sector.
Table ES-3: CC>2 Emissions from Fossil Fuel Combustion by Fuel Consuming End-Use Sector (Tg or million metric
tons CO2 Eq.)
End-Use Sector
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories"
Total
Electricity Generation
1990
1,489.0
1,485.9 1
1,533.2 1
846.5
686.7
931.4
338.3
593.0
757.0
219.0
538.0
27.9
4,738.4
1,820.8
2000
1,813.0
1,809.5
1™
789.8
1,133.1
370.7 1
762.4
972.1
230.8
741.3
35.9
5,594.8
2,296.9
• 2005
1,901.3
1,896.6
14.7
1,560.0
823.1
737.0
1,214.7
1357.9
856.7
1,027.2
223.5
803.7
• 50.0
5,753.2
2,402.1
2006
1,882.6
1,878.1
4.5
1,560.2
848.2
712.0
1,152.4
321.5
830.8
1,007.6
208.6
799.0
50.3
5,653.1
2,346.4
2007
1,899.0
1,894.0
5.0
1,572.0
842.0
730.0
1,198.5
342.4
856.1
1,041.1
219.4
821.7
46.1
5,756.7
2,412.8
2008
1,794.6
1,789.9
4.7
1,517.7
802.9
714.8
1,182.2
348.2
834.0
1,031.6
224.2
807.4
39.8
5,565.9
2,360.9
2009
1,724.1
1,719.7
4.4
1,333.7
730.4
603.3
1,123.8
339.2
784.6
985.7
224.0
761.7
41.7
5,209.0
2,154.0
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. Transportation activities (excluding international bunker fuels) accounted for 33
percent of CC>2 emissions from fossil fuel combustion in 2009.13 Virtually all of the energy consumed in this end-
use sector came from petroleum products. Nearly 65 percent of the emissions resulted from gasoline consumption
for personal vehicle use. The remaining emissions came from other transportation activities, including the
combustion of diesel fuel in heavy-duty vehicles and jet fuel in aircraft. From 1990 to 2009, transportation
emissions rose by 16 percent due, in large part, to increased demand for travel and the stagnation of fuel efficiency
across the U.S. vehicle fleet. The number of vehicle miles traveled by light-duty motor vehicles (passenger cars and
light-duty trucks) increased 39 percent from 1990 to 2009, as a result of a confluence of factors including population
growth, economic growth, urban sprawl, and low fuel prices over much of this period.
Industrial End-Use Sector. Industrial CC>2 emissions, resulting both directly from the combustion of fossil fuels and
indirectly from the generation of electricity that is consumed by industry, accounted for 26 percent of CO2 from
fossil fuel combustion in 2009. Approximately 55 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 22
and 19 percent, respectively, of CO2 emissions from fossil fuel combustion in 2009. Both sectors relied heavily on
electricity for meeting energy demands, with 70 and 77 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 these end-use sectors
have increased 25 percent since 1990, due to increasing electricity consumption for lighting, heating, air
13 If emissions from international bunker fuels are included, the transportation end-use sector accounted for 35 percent of U.S.
emissions from fossil fuel combustion in 2009.
ES-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
conditioning, and operating appliances.
Electricity Generation. The United States relies on electricity to meet a significant portion of its energy demands.
Electricity generators consumed 36 percent of U.S. energy from fossil fuels and emitted 41 percent of the CO2 from
fossil fuel combustion in 2009. The type of fuel combusted by electricity generators has a significant effect on their
emissions. For example, some electricity is generated with low CO2 emitting energy technologies, particularly non-
fossil options such as nuclear, hydroelectric, or geothermal energy. However, electricity generators rely on coal for
over half of their total energy requirements and accounted for 95 percent of all coal consumed for energy in the
United States in 2009. Consequently, changes in electricity demand have a significant impact on coal consumption
and associated CO2 emissions.
Other significant CO2 trends included the following:
• CO2 emissions from non-energy use of fossil fuels have increased 4.7 Tg CO2 Eq. (4.0 percent) from 1990
through 2009. Emissions from non-energy uses of fossil fuels were 123.4 Tg CO2 Eq. in 2009, which
constituted 2.2 percent of total national CO2 emissions, approximately the same proportion as in 1990.
• CO2 emissions from iron and steel production and metallurgical coke production decreased by 24.1 Tg CO2
Eq. (36.6 percent) from 2008 to 2009, continuing a trend of decreasing emissions from 1990 through 2009
of 57.9 percent (57.7 Tg CO2 Eq.). This decline is due to the restructuring of the industry, technological
improvements, and increased scrap utilization.
• In 2009, CO2 emissions from cement production decreased by 11.5 Tg CO2 Eq. (28.4 percent) from 2008.
After decreasing in 1991 by two percent from 1990 levels, cement production emissions grew every year
through 2006; emissions decreased in the last three years. Overall, from 1990 to 2009, emissions from
cement production decreased by 12.8 percent, a decrease of 4.3 Tg CO2 Eq.
• Net CO2 uptake from Land Use, Land-Use Change, and Forestry increased by 153.5 Tg CO2 Eq. (17.8
percent) from 1990 through 2009. 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.
Methane Emissions
Methane (CH4) is more than 20 times as effective as CO2 at trapping heat in the atmosphere (IPCC 1996). Over the
last two hundred and fifty years, the concentration of CH4 in the atmosphere increased by 148 percent (IPCC 2007).
Anthropogenic sources of CH4 include natural gas and petroleum systems,, agricultural activities, landfills, coal
mining, wastewater treatment, stationary and mobile combustion, and certain industrial processes (see Figure ES-8).
Figure ES-8: 2009 Sources of CH4 Emissions
Some significant trends in U.S. emissions of CH4 include the following:
• In 2009, CH4 emissions from coal mining were 71.0 Tg CO2 Eq., a 3.9 Tg CO2 Eq. (5.8 percent) increase
over 2008 emission levels. The overall decline of 13.0 Tg CO2 Eq. (15.5 percent) from 1990 results from
the mining of less gassy coal from underground mines and the increased use of CH4 collected from
degasification systems.
• Natural gas systems were the largest anthropogenic source category of CH4 emissions in the United States
in 2009 with 221.2 Tg CO2 Eq. of CH4 emitted into the atmosphere. Those emissions have increased by
31.4 Tg CO2 Eq. (16.6 percent) since 1990. Methane emissions from this source increased 4 percent from
2008 to 2009 due to an increase in production and production wells.
• Enteric Fermentation is the second largest anthropogenic source of CH4 emissions in the United States. In
2009, enteric fermentation CH4 emissions were 139.8 Tg CO2 Eq. (20 percent of total CH4 emissions),
which represents an increase of 7.7 Tg CO2 Eq. (5.8 percent) since 1990.
Executive Summary ES-9
-------
• Methane emissions from manure management increased by 55.9 percent since 1990, from 31.7 Tg CO2 Eq.
in 1990 to 49.5 Tg CO2 Eq. in 2009. 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.
• Landfills are the third largest anthropogenic source of CH4 emissions in the United States, accounting for
17 percent of total CH4 emissions (117.5 Tg CO2 Eq.) in 2009. From 1990 to 2009, CH4 emissions from
landfills decreased by 29.9 Tg CO2 Eq. (20 percent), with small increases occurring in some interim years.
This downward trend in overall emissions is the result of increases in the amount of landfill gas collected
and combusted,14 which has more than offset the additional CH4 emissions resulting from an increase in the
amount of municipal solid waste landfilled.
Nitrous Oxide Emissions
N2O is produced by biological processes that occur in soil and water and by a variety of anthropogenic activities in
the agricultural, energy-related, industrial, and waste management fields. While total N2O emissions are much
lower than CO2 emissions, N2O is approximately 300 times more powerful than CO2 at trapping heat in the
atmosphere (IPCC 1996). Since 1750, the global atmospheric concentration of N2O has risen by approximately 18
percent (IPCC 2007). The main anthropogenic activities producing N2O in the United States are agricultural soil
management, fuel combustion in motor vehicles, manure management, nitric acid production and stationary fuel
combustion, (see Figure ES-9).
Figure ES-9: 2009 Sources of N2O Emissions
Some significant trends in U.S. emissions of N2O include the following:
• In 2009, N2O emissions from mobile combustion were 23.9 Tg CO2 Eq. (approximately 8.1 percent of U. S.
N2O emissions). From 1990 to 2009, N2O emissions from mobile combustion decreased by 45.6 percent.
However, from 1990 to 1998 emissions increased by 25.6 percent, due to control technologies that reduced
NOX emissions while increasing N2O emissions. Since 1998, newer control technologies have led to an
overall decline in N2O from this source.
• N2O emissions from adipic acid production were 1.9 Tg CO2 Eq. in 2009, and have decreased significantly
since 1996 from the widespread installation of pollution control measures. Emissions from adipic acid
production have decreased by 87.7 percent since 1990, and emissions from adipic acid production have
remained consistently lower than pre-1996 levels since 1998.
• Agricultural soils accounted for approximately 69.2 percent of N2O emissions in the United States in 2009.
Estimated emissions from this source in 2009 were 204.6 Tg CO2 Eq. Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2009, although overall emissions were 3.4 percent higher in
2009 than in 1990.
HFC, RFC, and SF6 Emissions
HFCs and PFCs are families of synthetic chemicals that are used as alternatives to ODS, which are being phased out
under the Montreal Protocol and Clean Air Act Amendments of 1990. HFCs and PFCs do not deplete the
stratospheric ozone layer, and are therefore acceptable alternatives under the Montreal Protocol.
These compounds, however, along with SF6, are potent greenhouse gases. In addition to having high global
warming potentials, SF6 and PFCs have extremely long atmospheric lifetimes, resulting in their essentially
irreversible accumulation in the atmosphere once emitted. Sulfur hexafluoride is the most potent greenhouse gas the
14 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.
ES-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC has evaluated (IPCC 1996).
Other emissive sources of these gases include electrical transmission and distribution systems, HCFC-22 production,
semiconductor manufacturing, aluminum production, and magnesium production and processing (see Figure ES-10).
Figure ES-10: 2009 Sources of HFCs, PFCs, and SF6 Emissions
Some significant trends in U.S. HFC, PFC, and SF6 emissions include the following:
• Emissions resulting from the substitution of ODS (e.g., CFCs) have been consistently increasing, from
small amounts in 1990 to 120.0 Tg CO2 Eq. in 2009. Emissions from ODS substitutes are both the largest
and the fastest growing source of HFC, PFC, and SF6 emissions. These emissions have been increasing as
phase-outs required under the Montreal Protocol come into effect, especially after 1994, when full market
penetration was made for the first generation of new technologies featuring ODS substitutes.
• HFC emissions from the production of HCFC-22 decreased by 85.2 percent (31.0 Tg CO2 Eq.) from 1990
through 2009, due to a steady decline in the emission rate of HFC-23 (i.e., the amount of HFC-23 emitted
per kilogram of HCFC-22 manufactured) and the use of thermal oxidation at some plants to reduce HFC-23
emissions.
• SF6 emissions from electric power transmission and distribution systems decreased by 54.8 percent (15.6
Tg CO2 Eq.) from 1990 to 2009, primarily because of higher purchase prices for SF6 and efforts by industry
to reduce emissions.
• PFC emissions from aluminum production decreased by 91.5 percent (17.0 Tg CO2 Eq.) from 1990 to
2009, due to both industry emission reduction efforts and lower domestic aluminum production.
Overview of Sector Emissions and Trends
In accordance with the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), and the 2003 UNFCCC Guidelines on Reporting and Review (UNFCCC 2003),
Figure ES-11 and Table ES-4 aggregate emissions and sinks by these chapters. Emissions of all gases can be
summed from each source category from IPCC guidance. Over the twenty-year period of 1990 to 2009, total
emissions in the Energy and Agriculture sectors grew by 463.3 Tg CO2 Eq. (9 percent), and 35.7 Tg CO2 Eq. (9
percent), respectively. Emissions decreased in the Industrial Processes, Waste, and Solvent and Other Product Use
sectors by 32.9 Tg CO2 Eq. (10 percent), 24.7 Tg CO2 Eq. (14 percent) and less than 0.1 Tg CO2 Eq. (0.4 percent),
respectively. Over the same period, estimates of net C sequestration in the Land Use, Land-Use Change, and
Forestry sector (magnitude of emissions plus CO2 flux from all LULUCF source categories) increased by 143.5 Tg
CO2Eq. (17 percent).
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (Tg or million
metric tons CO2 Eq.)
Chapter/IPCC Sector 1990J 2000J 2005 2006 2007 2008 2009
Energy 5,287.8 6,168.0 6,282.8 6,210.2 6,290.7 6,116.6 5,751.1
Industrial Processes 315.8 348.8 334.1 339.4 350.9 331.7 282.9
Solvent and Other Product Use 4.4B 4.9B 4.4 4.4 4.4 4.4 4.4
Agriculture 383.6 410.6 418.8 418.8 425.8 426.3 419.3
Land Use, Land-Use Change, and
Forestry (Emissions) IS.oB 36.3 28.6 49.8 47.5 33.2 25.0
Waste 175.2 143.9 144.9 144.4 144.1 149.0 150.5
Total Emissions 6,181.8 7,112.7 7,213.5 7,166.9 7,263.4 7,061.1 6,633.2
Net CO2 Flux from Land Use, Land- (861.5) (576.6) (1,056.5) (1,064.3) (1,060.9) (1,040.5) (1,015.1)
Executive Summary ES-11
-------
Use Change, and Forestry (Sinks)*
Net Emissions (Sources and Sinks) 5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2
* The net CO2 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total.
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. CO2 emissions for the period of 1990 through 2009. In 2009,
approximately 83 percent of the energy consumed in the United States (on a Btu basis) was produced through the
combustion of fossil fuels. The remaining 17 percent came from other energy sources such as hydropower, biomass,
nuclear, wind, and solar energy (see Figure ES-12). Energy-related activities are also responsible for CH4 and N2O
emissions (49 percent and 13 percent of total U.S. emissions of each gas, respectively). Overall, emission sources in
the Energy chapter account for a combined 87 percent of total U.S. greenhouse gas emissions in 2009.
Figure ES-12: 2009 U.S. Energy Consumption by Energy Source
Industrial Processes
The Industrial Processes chapter contains by-product or fugitive emissions of greenhouse gases from industrial
processes not directly related to energy activities such as fossil fuel combustion. For example, industrial processes
can chemically transform raw materials, which often release waste gases such as CO2, CH4, and N2O. These
processes include iron and steel production and metallurgical coke production, cement production, ammonia
production and urea consumption, lime production, limestone and dolomite use (e.g., flux stone, flue gas
desulfurization, and glass manufacturing), soda ash production and consumption, titanium dioxide production,
phosphoric acid production, ferroalloy production, CO2 consumption, silicon carbide production and consumption,
aluminum production, petrochemical production, nitric acid production, adipic acid production, lead production, and
zinc production. Additionally, emissions from industrial processes release HFCs, PFCs, and SF6. Overall, emission
sources in the Industrial Process chapter account for 4 percent of U.S. greenhouse gas emissions in 2009.
Solvent and Other Product Use
The Solvent and Other Product Use chapter contains greenhouse gas emissions that are produced as a by-product of
various solvent and other product uses. In the United States, emissions from N2O from product uses, the only source
of greenhouse gas emissions from this sector, accounted for about 0.1 percent of total U.S. anthropogenic
greenhouse gas emissions on a carbon equivalent basis in 2009.
Agriculture
The Agricultural chapter contains anthropogenic emissions from agricultural activities (except fuel combustion,
which is addressed in the Energy chapter, and agricultural CO2 fluxes, which are addressed in the Land Use, Land-
Use Change, and Forestry Chapter). Agricultural activities contribute directly to emissions of greenhouse gases
through a variety of processes, including the following source categories: enteric fermentation in domestic livestock,
livestock manure management, rice cultivation, agricultural soil management, and field burning of agricultural
residues. CH4 and N2O were the primary greenhouse gases emitted by agricultural activities. CH4 emissions from
enteric fermentation and manure management represented 20 percent and 7 percent of total CH4 emissions from
anthropogenic activities, respectively, in 2009. Agricultural soil management activities such as fertilizer application
and other cropping practices were the largest source of U.S. N2O emissions in 2009, accounting for 69 percent. In
2009, emission sources accounted for in the Agricultural chapters were responsible for 6.3 percent of total U.S.
greenhouse gas emissions.
ES-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Land Use, Land-Use Change, and Forestry
The Land Use, Land-Use Change, and Forestry chapter contains emissions of CH4 and N2O, and emissions and
removals of CO2 from forest management, other land-use activities, and land-use change. Forest management
practices, tree planting in urban areas, the management of agricultural soils, and the landfilling of yard trimmings
and food scraps resulted in a net uptake (sequestration) of C in the United States. Forests (including vegetation,
soils, and harvested wood) accounted for 85 percent of total 2009 net CO2 flux, urban trees accounted for 9 percent,
mineral and organic soil carbon stock changes accounted for 4 percent, and landfilled yard trimmings and food
scraps accounted for 1 percent of the total net flux in 2009. 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 5.5 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 2009 resulted in a net C sequestration of 1,015.1 Tg CO2 Eq.
(Table ES-5). This represents an offset of 18 percent of total U.S. CO2 emissions, or 15 percent of total greenhouse
gas emissions in 2009. Between 1990 and 2009, total land use, land-use change, and forestry net C flux resulted in a
17.8 percent increase in CO2 sequestration, primarily due to an increase in the rate of net C accumulation in forest C
stocks, particularly in aboveground and belowground tree biomass, and harvested wood pools. Annual C
accumulation in landfilled yard trimmings and food scraps slowed over this period, while the rate of annual C
accumulation increased in urban trees.
Table ES-5: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.)
Sink Category 1990 2000 2005 2006 2007 2008 2009
Forest Land Remaining Forest Land1
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements2
Other (Landfilled Yard Trimmings and
Food Scraps)
(681.1)
(29.4)
2.2m
(52.2)|
(19.8)|
(57.1)|
(24.2)
(378.3)
(30.2)
2 AM
(52.6)
(27.2)
(77.5)
(13.2)
(911.5)
(18.3)
5.9
(8.9)
(24.4)
(87.8)
(11.5)
(917.5)
(19.1)
5.9
(8.8)
(24.2)
(89.8)
(11.0)
(911.9)
(19.7)
5.9
(8.6)
(24.0)
(91.9)
(10.9)
(891.0)
(18.1)
5.9
(8.5)
(23.8)
(93.9)
(11.2)
(863.1)
(17.4)
5.9
(8.3)
(23.6)
(95.9)
(12.6)
Total (861.5) (576.6) (1,056.5) (1,064.3) (1,060.9) (1,040.5) (1,015.1)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Emissions from Land Use, Land-Use Change, and Forestry are shown in Table ES-6. The application of crushed
limestone and dolomite to managed land (i.e., liming of agricultural soils) and urea fertilization resulted in CO2
emissions of 7.8 Tg CO2 Eq. in 2009, an increase of 11 percent relative to 1990. The application of synthetic
fertilizers to forest and settlement soils in 2009 resulted in direct N2O emissions of 1.9 Tg CO2 Eq. Direct N2O
emissions from fertilizer application to forest soils have increased by 455 percent since 1990, but still account for a
relatively small portion of overall emissions. Additionally, direct N2O emissions from fertilizer application to
settlement soils increased by 55 percent since 1990. Forest fires resulted in CH4 emissions of 7.8 Tg CO2 Eq., and
in N2O emissions of 6.4 Tg CO2 Eq. in 2009. CO2 and N2O emissions from peatlands totaled 1.1 Tg CO2 Eq. and
less than 0.01 Tg CO2 Eq. in 2009, respectively.
Table ES-6: Emissions from Land Use, Land-Use Change, and Forestry (Tg or million metric tons CO2 Eq.)
Source Category 1990 2000 2005 2006 2007 2008 2009
CO2 8.1 8.8 8.9 8.8 9.2 9.6 8.9
Cropland Remaining Cropland: Liming of
Agricultural Soils 4.lU 43M 4.3 4.2 4.5 5.0 4.2
Cropland Remaining Cropland: Urea
Fertilization 2AM 3.2M 3.5 3.7 3.7 3.6 3.6
Executive Summary ES-13
-------
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
CH4
Forest Land Remaining Forest Land: Forest Fires
N2O
Forest Land Remaining Forest Land: Forest Fires
Forest Land Remaining Forest Land: Forest Soils
Settlements Remaining Settlements: Settlement
Soils l.o
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands +
Total 15.0 36.3 28.6 49.8 47.5 33.2 25.0
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Waste
The Waste chapter contains emissions from waste management activities (except incineration of waste, which is
addressed in the Energy chapter). Landfills were the largest source of anthropogenic greenhouse gas emissions in
the Waste chapter, accounting for just over 78 percent of this chapter's emissions, and 17 percent of total U.S. CH4
emissions.15 Additionally, wastewater treatment accounts for 20 percent of Waste emissions, 4 percent of U. S. CH4
emissions, and 2 percent of U.S. N2O emissions. Emissions of CH4 and N2O from composting are also accounted
for in this chapter; generating emissions of 1.7 Tg CO2 Eq. and 1.8 Tg CO2 Eq., respectively. Overall, emission
sources accounted for in the Waste chapter generated 2.3 percent of total U.S. greenhouse gas emissions in 2009.
Other Information
Emissions by Economic Sector
Throughout the Inventory of U.S. Greenhouse Gas Emissions and Sinks report, emission estimates are grouped into
six sectors (i.e., chapters) defined by the IPCC: Energy; Industrial Processes; Solvent Use; Agriculture; Land Use,
Land-Use Change, and Forestry; and Waste. While it is important to use this characterization for consistency with
UNFCCC reporting guidelines, it is also useful to allocate emissions into more commonly used sectoral categories.
This section reports emissions by the following economic sectors: Residential, Commercial, Industry,
Transportation, Electricity Generation, Agriculture, and U.S. Territories.
Table ES-7 summarizes emissions from each of these sectors, and Figure ES-13 shows the trend in emissions by
sector from 1990 to 2009.
Figure ES-13: Emissions Allocated to Economic Sectors
Table ES-7: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg or million metric tons CO2 Eq.)
Implied Sectors 1990 2000 2005 2006 2007 2008 2009
Electric Power Industry
Transportation
Industry
Agriculture
Commercial
Residential
U.S. Territories
1,868
1,545
1,564
429
395
345
33
.9
.2
.4
.0
.
.7
2,337
1,932
1,544
485
381
386
46
.6
.3
.0
1
.4
.2
nH
2.
2.
1.
,444.
,017.
,441.
493.
387.
371.
58.
.6
.4
.9
.2
.2
.0
2
2,388.2
1,994.4
1,497.3
516.7
375.2
335.8
59.3
2,454.0
2,003.8
1,483.0
520.7
389.6
358.9
53.5
2,400.7
1,890.7
1,446.9
503.9
403.5
367.1
48.4
2,193.0
1,812.4
1,322.7
490.0
409.5
360.1
45.5
15 Landfills also store carbon, due to incomplete degradation of organic materials such as wood products and yard trimmings, as
described in the Land-Use, Land-Use Change, and Forestry chapter of the Inventory report.
ES-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Total Emissions 6,181.8 7,112.7 7,213.5 7,166.9 7,263.4 7,061.1 6,633.2
Land Use, Land-Use Change, and
Forestry (Sinks) (861.5) (576.6) (1,056.5)(1,064.3)(1,060.9)(1,040.5)(1,015.1)
Net Emissions (Sources and Sinks) 5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2
Note: Totals may not sum due to independent rounding. Emissions include CO2, CH4, N2O, HFCs, PFCs, and SF6.
See Table 2-12 for more detailed data.
Using this categorization, emissions from electricity generation accounted for the largest portion (33 percent) of
U.S. greenhouse gas emissions in 2009. Transportation activities, in aggregate, accounted for the second largest
portion (27 percent), while emissions from industry accounted for the third largest portion (20 percent) of U.S.
greenhouse gas emissions in 2009. 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 20 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 7 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 sector accounted for 6 percent of emissions while the
residential sector accounted for 5 percent of emissions and U.S. territories accounted for 1 percent of emissions;
emissions from these sectors primarily consisted of CO2 emissions from fossil fuel combustion.
CO2 was also emitted and sequestered by a variety of activities related to forest management practices, tree planting
in urban areas, the management of agricultural soils, and landfilling of yard trimmings.
Electricity is ultimately consumed in the economic sectors described above. Table ES-8 presents greenhouse gas
emissions from economic sectors with emissions related to electricity generation distributed into end-use categories
(i.e., emissions from electricity generation are allocated to the economic sectors in which the electricity is
consumed). To distribute electricity emissions among end-use sectors, emissions from the source categories
assigned to electricity generation were allocated to the residential, commercial, industry, transportation, and
agriculture economic sectors according to retail sales of electricity.16 These source categories include CO2 from
fossil fuel combustion and the use of limestone and dolomite for flue gas desulfurization, CO2 and N2O from
incineration of waste, CH4 and N2O from stationary sources, and SF6 from electrical transmission and distribution
systems.
When emissions from electricity are distributed among these sectors, Industrial activities account for the largest
share of U.S. greenhouse gas emissions (29 percent) in 2009. Transportation is the second largest contributor to
total U.S. emissions (28 percent). The commercial and residential sectors contributed the next largest shares of total
U.S. greenhouse gas emissions in 2009. 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 2009.
Table ES-8: U.S Greenhouse Gas Emissions by Economic Sector with Electricity-Related Emissions Distributed
(Tg or million metric tons CO2 Eq.)
Implied Sectors
Industry
Transportation
Commercial
Residential
Agriculture
U.S. Territories
Total Emissions
Land Use, Land-Use Change,
2
1
6
1990
,238.3
,548.3|
947.7B
953.8|
460.ol
33.71
,181.8
(861.5)
2
1
1
1
7
2000
,314.4
,935.sB
,135.sB
,162.2B
518.41
46.0
,112.7
(576.6)
2
2
1
1
7
2005
,162.5
,022.2
,205.1
,242.9
522.7
58.2
,213.5
(1,056.5)
2
1
1
1
7
2006
,194.6
,999.0
,188.5
,181.5
544.1
59.3
,166.9
(1,064.3)
2007
2,192.9
2,008.9
1,225.3
1,229.6
553.2
53.5
7,263.4
(1,060.9)
2008
2,146.5
1,895.5
1,224.5
1,215.1
531.1
48.4
7,061.1
(1,040.5)
2009
1,910.9
1,816.9
1,184.9
1,158.9
516.0
45.5
6,633.2
(1,015.1)
16 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-15
-------
and Forestry (Sinks)
Net Emissions (Sources and
Sinks) 5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2
See Table 2-14 for more detailed data.
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors
[BEGIN BOX]
Box ES-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data
Total emissions can be compared to other economic and social indices to highlight changes over time. These
comparisons include: (1) emissions per unit of aggregate energy consumption, because energy-related activities are
the largest sources of emissions; (2) emissions per unit of fossil fuel consumption, because almost all energy-related
emissions involve the combustion of fossil fuels; (3) emissions per unit of electricity consumption, because the
electric power industry—utilities and nonutilities combined—was the largest source of U.S. greenhouse gas
emissions in 2009; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
and (5) emissions per capita.
Table ES-9 provides data on various statistics related to U.S. greenhouse gas emissions normalized to 1990 as a
baseline year. Greenhouse gas emissions in the United States have grown at an average annual rate of 0.4 percent
since 1990. This rate is slightly slower than that for total energy and for fossil fuel consumption, and much slower
than that for electricity consumption, overall gross domestic product and national population (see Figure ES-15).
Table ES-9: Recent Trends in Various U.S. Data (Index 1990
= 100)
Growth
Variable
GDPb
Electricity Consumption0
Fossil Fuel Consumption0
Energy Consumption0
Population"1
Greenhouse Gas Emissions6
1990
100
100
100
100
100
100 |
2000
140
127
117
116
113
115 H
2005
157
134
119
118
118
117
2006
162
135
117
118
120
116
2007
165
138
119
120
121
117
2008
165
138
116
118
122
114
2009
160
132
108
112
123
107
Rate3
2.5%
1.5%
0.5%
0.6%
1.1%
0.4%
a Average annual growth rate
b Gross Domestic Product in chained 2005 dollars (BEA 2010)
0 Energy content-weighted values (EIA 201 Ob)
d U.S. Census Bureau (2010)
e GWP-weighted values
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product
Source: BEA (2010), U.S. Census Bureau (2010), and emission estimates in this report.
[END BOX]
ES-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Indirect Greenhouse Gases (CO, NOX, NMVOCs, and S02)
The reporting requirements of the UNFCCC request that information be provided on indirect greenhouse gases,
which include CO, NOX, NMVOCs, and SO2. These gases do not have a direct global warming effect, but indirectly
affect terrestrial radiation absorption by influencing the formation and destruction of tropospheric and stratospheric
ozone, or, in the case of SO2, by affecting the absorptive characteristics of the atmosphere. Additionally, some of
these gases may react with other chemical compounds in the atmosphere to form compounds that are greenhouse
gases.
Since 1970, the United States has published estimates of annual emissions of CO, NOX, NMVOCs, and SO2 (EPA
2010, EPA 2009),18 which are regulated under the Clean Air Act. Table ES- 10 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 ES-10: Emissions of NOX, CO, NMVOCs, and SO2 (Gg)
Gas/Activity
NOX
Mobile Fossil Fuel Combustion
Stationary Fossil Fuel Combustion
Industrial Processes
Oil and Gas Activities
Incineration of Waste
Agricultural Burning
Solvent Use
Waste
CO
Mobile Fossil Fuel Combustion
Stationary Fossil Fuel Combustion
Industrial Processes
Incineration of Waste
Agricultural Burning
Oil and Gas Activities
Waste
Solvent Use
NMVOCs
Mobile Fossil Fuel Combustion
Solvent Use
Industrial Processes
Stationary Fossil Fuel Combustion
Oil and Gas Activities
Incineration of Waste
Waste
Agricultural Burning
S02
Stationary Fossil Fuel Combustion
Industrial Processes
Mobile Fossil Fuel Combustion
Oil and Gas Activities
Incineration of Waste
Waste
Solvent Use
1990
21
10
10
,707
,862
,023
591
139
82
8
1
0
130,038
119
5
4
20
10
5
2
,360
,000
,125
978
268
302
1
5
,930
,932
,216
,422
912
554
222
673
NA
20,935
18
1
,407
,307
793
390
38
0
0
2000
19
10
8
92
83
4
2
1
15
7
4
1
1
14
12
1
,116
,199
,053
626
111
114
8
3
2
,243
,559
,340
,216
,670
259
146
8
45
,227
,229
,384
,773
,077
388
257
119
NA
,830
,849
,031
632
287
29
1
1
2005
15,900
9,012
5,858
569
321
129
6
3
2
70,809
62,692
4,649
1,555
1,403
184
318
7
2
13,761
6,330
3,851
1,997
716
510
241
114
NA
13,466
11,541
831
889
181
24
1
0
2006
15,039
8,488
5,545
553
319
121
7
4
2
67,238
58,972
4,695
1,597
1,412
233
319
7
2
13,594
6,037
3,846
1,933
918
510
238
113
NA
12,388
10,612
818
750
182
24
1
0
2007
14,380
7,965
5,432
537
318
114
8
4
2
63,625
55,253
4,744
1,640
1,421
237
320
7
2
13,423
5,742
3,839
1,869
1,120
509
234
111
NA
11,799
10,172
807
611
184
24
1
0
2008
13,547
7,441
5,148
520
318
106
8
4
2
60,039
51,533
4,792
1,682
1,430
270
322
7
2
13,254
5,447
3,834
1,804
1,321
509
230
109
NA
10,368
8,891
795
472
187
23
1
0
2009
11,468
6,206
4,159
568
393
128
8
3
2
51,452
43,355
4,543
1,549
1,403
247
345
7
2
9,313
4,151
2,583
1,322
424
599
159
76
NA
8,599
7,167
798
455
154
24
1
0
17 See .
18 NOX and CO emission estimates from field burning of agricultural residues were estimated separately, and therefore not taken
from EPA (2008).
Executive Summary ES-17
-------
Agricultural Burning NA NA NA NA NA NA NA
Source: (EPA 2010, EPA 2009) except for estimates from field burning of agricultural residues.
NA (Not Available)
Note: Totals may not sum due to independent rounding.
Key Categories
The 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) defines a key category as a
"[source or sink category] that is prioritized within the national inventory system because its estimate has a
significant influence on a country's total inventory of direct greenhouse gases in terms of the absolute level of
emissions, the trend in emissions, or both."19 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 2009 emission estimates for the key categories as defined by a level analysis (i.e., the
contribution of each source or sink category to the total inventory level). The UNFCCC reporting guidelines request
that key category analyses be reported at an appropriate level of disaggregation, which may lead to source and sink
category names which differ from those used elsewhere in the inventory report. For more information regarding key
categories, see section 1.5 and Annex 1.
Figure ES-16: 2009 Key Categories
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 CO2 emissions from energy-related activities and cement processing, are
considered to have low uncertainties. For some other categories of emissions, however, a lack of data or an
incomplete understanding of how emissions are generated increases the uncertainty associated with the estimates
presented. Acquiring a better understanding of the uncertainty associated with inventory estimates is an important
step in helping to prioritize future work and improve the overall quality of the Inventory. Recognizing the benefit of
conducting an uncertainty analysis, the UNFCCC reporting guidelines follow the recommendations of the IPCC
Good Practice Guidance (IPCC 2000) and require that countries provide single estimates of uncertainty for source
and sink categories.
Currently, a qualitative discussion of uncertainty is presented for all source and sink categories. Within the
discussion of each emission source, specific factors affecting the uncertainty surrounding the estimates are
discussed. Most sources also contain a quantitative uncertainty assessment, in accordance with UNFCCC reporting
guidelines.
19 See Chapter 7 "Methodological Choice and Recalculation" in IPCC (2000).
ES-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
[BEGIN BOX]
BoxES-3: Recalculations of Inventory Estimates
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows the 2006 IPCC
Guidelines (IPCC 2006), which states, "Both methodological changes and refinements over time are an essential
part of improving inventory quality. It is good practice to change or refine methods" when: available data have
changed; the previously used method is not consistent with the IPCC guidelines for that category; a category has
become key; the previously used method is insufficient to reflect mitigation activities in a transparent manner; the
capacity for inventory preparation has increased; new inventory methods become available; and for correction of
errors." In general, recalculations are made to the U.S. greenhouse gas emission estimates either to incorporate new
methodologies or, most commonly, to update recent historical data.
In each Inventory report, the results of all methodology changes and historical data updates are presented in the
"Recalculations and Improvements" chapter; detailed descriptions of each recalculation are contained within each
source's description contained in the report, if applicable. In general, when methodological changes have been
implemented, the entire time series (in the case of the most recent inventory report, 1990 through 2009) has been
recalculated to reflect the change, per the 2006 IPCC Guidelines (IPCC 2006). Changes in historical data are
generally the result of changes in statistical data supplied by other agencies. References for the data are provided for
additional information.
[END BOX]
Executive Summary ES-19
-------
-------
I MFCs, PFCs, & SF6 Nitrous Oxide
8,000
7,000 -
6,000
5" 5,000 -
<-> 4,000
P
3,000
2,000
1,000
0
Methane
• Carbon Dioxide
6,633
a a a
OTHiNm-xj-m^Drxcoai
oooooooooo
(N(N(N(N(N(N(N(N(N(N
Figure ES-1: U.S. Greenhouse Gas Emissions by Gas
4% -,
2% -
0%
3.3%
2.8%
1.3%
-6.1%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Figure ES-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
1'082
-100 J
8888888888
Figure ES-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
-------
4.5%
MFCs, PFCs,
&SF6
2.2%
C02 as a Portion
of all Emissions
Figure ES-4: 2009 Greenhouse Gas Emissions by Gas (percents based on Tg C02 Eq.)
Fossil Fuel Combustion ^| ^^^B J
Non-Energy Use of Fuels ^^H
Iron and Steel Prod. & Metallurgical Coke Prod.
Natural Gas Systems
Cement Production
Incineration of Waste
Ammonia Production and Urea Consumption
Lime Production
Cropland Remaining Cropland
Limestone and Dolomite Use
Soda Ash Production and Consumption
Aluminum Production
Petrochemical Production
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Wetlands Remaining Wetlands
Phosphoric Acid Production
Zinc Production
Lead Production
Petroleum Systems
Silicon Carbide Production and Consumption
5,209
<0.5
<0.5
25
50
75 100
Tg CO2 Eq.
125
150
Figure ES-5: 2009 Sources of C02 Emissions
-------
1
2,500 -|
2,000
1,500 -
1,000 -
500
n
Relativ
by
{
42
by Fuel Type
2,154
224
Figure ES-6: 2009 C02 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Note: Electricity generation also includes emissions of less than 0.5 Tg CO2 Eq. from geothermal-based electricity generation.
2,000 -i
1,500 -
8 1,000 -
500 -
0 J
i From Direct Fossil Fuel Combustion
• From Electricity Consumption
990
42
1,750
1,340
1,132
h-
i/i
i.
(/)
I
Figure ES-7: 2009 End-Use Sector Emissions of C02, CH4, and N20 from Fossil Fuel Combustion
-------
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining Forest Land
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Prod. & Metallurgical Coke Prod.
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
Incineration of Waste
< 0.5
< 0.5
<0.5
<0.5
<0.5
CH4 as a Portion
of all Emissions
10.3%
O
25 50
Figure ES-8: 2009 Sources of CH4 Emissions
75 100 125 150 175 200 225
Tg CO2 Eq.
Agricultural Soil Management
Mobile Combustion
Manure Management
Nitric Acid Production
Stationary Combustion
Forest Land Remaining Forest Land
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Composting
Settlements Remaining Settlements
Incineration of Waste
Field Burning of Agricultural Residues
Wetlands Remaining Wetlands
205
N2O as a Portion
of all Emissions
4.5%
< 0.5
< 0.5
< 0.5
10
20 30
Tg CO2 Eq.
40
50
Figure ES-9: 2009 Sources of N20 Emissions
-------
Substitution of Ozone Depleting
Substances
Electrical Transmission and Distribution
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
I
120
MFCs, PFCs, and SF6 as a Portion
of all Emissions
2.2%
Magnesium Production and Processing
10
20 30
TgCO2Eq.
40
50
Figure ES-10: 2009 Sources of MFCs, PFCs, and SF6 Emissions
o
p
7,500
7,000
6,500
6,000
5,500
5,000
4,500
4,000
3,500
3,000 -
2,500
2,000 -
1,500
1,000
500
Industrial Processes
Agriculture
Waste
LULUCF (sources)
(500) -
(1,000) -
(1,500) -
Energy
Land
d Use, Land-Use Change and Forestry (
(sinks)
8888888888
Note: Relatively smaller amounts of GWP-weighted emissions are also emitted from the Solvent and Other Product Use
sectors
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
-------
Renewable
Energy
Nuclear Electric 8-2%_
Power
8.8%
Figure ES-12: 2009 U.S. Energy Consumption by Energy Source
2,500 -i
2,000 -
8
P 1,000
500 -
Electric
Power Industry
Transportation
Industry
Agriculture
I Commercial
Residential
8888888888
Figure ES-13: Emissions Allocated to Economic Sectors
Note: Does not include U.S. Territories.
-------
0
u
2,500 -i
2,000 -
1,000 -
500 -
^V Industry
Transportation
Commercial (gray)
Residential (black)
• Agriculture
rMfMfMrMrMrMrMrMrMrM
Figure ES-14: Emissions with Electricity Distributed to Economic Sectors
Note: Does not include U.S. Territories.
Real GDP
Population
Emissions
per capita
Emissions
per $GDP
TH IN m <*• m U) rx
CTi CTi CTi CTi CTi CTi CTi
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product
-------
C02 Emissions from Stationary Combustion - Coal
C02 Emissions from Mobile Combustion - Road
C02 Emissions from Stationary Combustion - Gas
C02 Emissions from Stationary Combustion - Oil
Fugitive Emissions from Natural Gas Systems |
Direct N20 Emissions from Agricultural Soil Management |
C02 Emissions from Mobile Combustion - Aviation |
CH4 Emissions from Enteric Fermentation |
C02 Emissions from Non-Energy Use of Fuels |
Emissions from Substitutes for Ozone Depleting Substances |
CH4 Emissions from Landfills |
C02 Emissions from Mobile Combustion: Other |
Fugitive Emissions from Coal Mining |
CH4 Emissions from Manure Management |
Indirect N20 Emissions from Applied Nitrogen |
C02 Em. from Iron and Steel Prod. & Metallurgical Coke Prod. |
C02 Emissions from Natural Gas Systems |
Fugitive Emissions from Petroleum Systems
CH4 Emissions from Wastewater Treatment
Non-C02 Emissions from Stationary Combustion
CH4 Emissions from Rice Cultivation
Key Categories as a Portion of All
Emissions
o
200 400 600
Figure ES-16: 2009 Key Categories
Notes: For a complete discussion of the key category analysis, see Annex 1.
Black bars indicate a Tier 1 level assessment key category.
Gray bars indicate a Tier 2 level assessment key category.
0 1,000 1,200 1,400 1,600 1,800 2,000
TgCO2Eq.
-------
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 2009. A summary of these estimates is provided in Table 2.1 and Table 2.2 by gas
and source category in the Trends in Greenhouse Gas Emissions chapter. The emission estimates in these tables are
presented on both a full molecular mass basis and on a Global Warming Potential (GWP) weighted basis in order to
show the relative contribution of each gas to global average radiative forcing.20 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."21'22
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.. ,"23 The United States views this report as an opportunity
to fulfill these commitments under the UNFCCC.
In 1988, preceding the creation of the UNFCCC, the World Meteorological Organization (WMO) and the United
Nations Environment Programme (UNEP) jointly established the Intergovernmental Panel on Climate Change
(IPCC). The role of the IPCC is to assess on a comprehensive, objective, open and transparent basis the scientific,
technical and socio-economic information relevant to understanding the scientific basis of risk of human-induced
climate change, its potential impacts and options for adaptation and mitigation (IPCC 2003). Under Working Group
1 of the IPCC, nearly 140 scientists and national experts from more than thirty countries collaborated in the creation
of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC/UNEP/OECD/IEA 1997) to
ensure that the emission inventories submitted to the UNFCCC are consistent and comparable between nations. The
IPCC accepted the Revised 1996 IPCC Guidelines at its Twelfth Session (Mexico City, September 11-13, 1996).
This report presents information in accordance with these guidelines. In addition, this Inventory is in accordance
with the IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories and
the Good Practice Guidance for Land Use, Land-Use Change, and Forestry, which further expanded upon the
methodologies in the Revised 1996 IPCC Guidelines. The IPCC has also accepted the 2006 Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) at its Twenty-Fifth Session (Mauritius, April 2006). The 2006 IPCC
Guidelines build on the previous bodies of work and includes new sources and gases ".. .as well as updates to the
previously published methods whenever scientific and technical knowledge have improved since the previous
guidelines were issued." Many of the methodological improvements presented in the 2006 Guidelines have been
adopted in this Inventory.
Overall, this inventory of anthropogenic greenhouse gas emissions provides a common and consistent mechanism
through which Parties to the UNFCCC can estimate emissions and compare the relative contribution of individual
sources, gases, and nations to climate change. The inventory provides a national estimate of sources and sinks for
the United States, including all states and U.S. territories24 . The structure of this report is consistent with the current
20 See the section below entitled Global Warming Potentials for an explanation of GWP values.
21 The term "anthropogenic", in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (IPCC/UNEP/OECD/IEA 1997).
22 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See . (UNEP/WMO 2000)
23 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
.
24 U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S. Pacific
Islands.
Introduction 1-1
-------
UNFCCC Guidelines on Annual Inventories (UNFCCC 2006).
[BEGIN BOX]
Box 1-1: Methodological approach for estimating and reporting U.S. emissions and sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the Intergovernmental Panel on Climate Change
(IPCC).25 Additionally, the calculated emissions and sinks in a given year for the U.S. are presented in a common
manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this international
agreement.26 The use of consistent methods to calculate emissions and sinks by all nations providing their
inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks
reported in this inventory report are comparable to emissions and sinks reported by other countries. Emissions and
sinks provided in this inventory do not preclude alternative examinations, but rather this inventory report presents
emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. The report itself follows this standardized format, and provides an explanation of the IPCC methods
used to calculate emissions and sinks, and the manner in which those calculations are conducted.
[END BOX]
1.1. Background Information
Science
For over the past 200 years, the burning of fossil fuels such as coal and oil, deforestation, and other sources have
caused the concentrations of heat-trapping "greenhouse gases" to increase significantly in our atmosphere. These
gases absorb some of the energy being radiated from the surface of the earth and trap it in the atmosphere,
essentially acting like a blanket that makes the earth's surface warmer than it would be otherwise.
Greenhouse gases are necessary to life as we know it, because without them the planet's surface would be about 60
°F cooler than present. But, as the concentrations of these gases continue to increase in the atmosphere, the Earth's
temperature is climbing above past levels. According to NOAA and NASA data, the Earth's average surface
temperature has increased by about 1.2 to 1.4 °F since 1900. The ten warmest years on record (since 1850) have all
occurred in the past 13 years (EPA 2009). Most of the warming in recent decades is very likely the result of human
activities. Other aspects of the climate are also changing such as rainfall patterns, snow and ice cover, and sea level.
If greenhouse gases continue to increase, climate models predict that the average temperature at the Earth's surface
could increase from 2.0 to 11.5 °F above 1990 levels by the end of this century (IPCC 2007). Scientists are certain
that human activities are changing the composition of the atmosphere, and that increasing the concentration of
greenhouse gases will change the planet's climate. But they are not sure by how much it will change, at what rate it
will change, or what the exact effects will be.27
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), and other trace gases in the
25 See .
26 See
27 For more information see
1-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
atmosphere that absorb the terrestrial radiation leaving the surface of the Earth (IPCC 2001). Changes in the
atmospheric concentrations of these greenhouse gases can alter the balance of energy transfers between the
atmosphere, space, land, and the oceans.28 A gauge of these changes is called radiative forcing, which is a measure
of the influence a factor has in altering the balance of incoming and outgoing energy in the Earth-atmosphere system
(IPCC 2001). Holding everything else constant, increases in greenhouse gas concentrations in the atmosphere will
produce positive radiative forcing (i.e., a net increase in the absorption of energy by the Earth).
Climate change can be driven by changes in the atmospheric concentrations of a number ofradiatively
active gases and aerosols. We have clear evidence that human activities have affected concentrations,
distributions and life cycles of these gases (IPCC 1996).
Naturally occurring greenhouse gases include water vapor, CO2, methane (CH4), nitrous oxide (N2O), and ozone
(O3). 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.29 Some other fluorine-containing halogenated substances—
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6)—do not deplete stratospheric
ozone but are potent greenhouse gases. These latter substances are addressed by the UNFCCC and accounted for in
national greenhouse gas inventories.
There are also several gases that, although they do not have a commonly agreed upon direct radiative forcing effect,
do influence the global radiation budget. These tropospheric gases include carbon monoxide (CO), nitrogen dioxide
(NO2), sulfur dioxide (SO2), and tropospheric (ground level) ozone 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 that are often composed of sulfur
compounds, carbonaceous combustion products, crustal materials and other human induced pollutants. They can
affect the absorptive characteristics of the atmosphere. Comparatively, however, the level of scientific
understanding of aerosols is still very low (IPCC 2001).
CO2, CH4, and N2O are continuously emitted to and removed from the atmosphere by natural processes on Earth.
Anthropogenic activities, however, can cause additional quantities of these and other greenhouse gases to be emitted
or sequestered, thereby changing their global average atmospheric concentrations. Natural activities such as
respiration by plants or animals and seasonal cycles of plant growth and decay are examples of processes that only
cycle carbon or nitrogen between the atmosphere and organic biomass. Such processes, except when directly or
indirectly perturbed out of equilibrium by anthropogenic activities, generally do not alter average atmospheric
greenhouse gas concentrations over decadal timeframes. Climatic changes resulting from anthropogenic activities,
however, could have positive or negative feedback effects on these natural systems. Atmospheric concentrations of
these gases, along with their rates of growth and atmospheric lifetimes, are presented in Table 1-1.
Table 1-1: Global Atmospheric Concentration, Rate of Concentration Change, and Atmospheric Lifetime (years) of
Selected Greenhouse Gases
Atmospheric Variable CO2 CH4 N2O SFg CF4
Pre-industrial atmospheric
concentration
Atmospheric concentration
Rate of concentration change
Atmospheric lifetime (years)
278 ppm
385 ppm
1.4ppm/yr
50-200d
0.715 ppm
1.741-1.865 ppnf
0.005 ppm/yrb
12e
0.270 ppm
0.321-0.322 ppma
0.26%/yr
114e
Oppt
5.6 ppt
Linear0
3,200
40 ppt
74 ppt
Linear0
>50,000
Source: Pre-industrial atmospheric concentrations and rate of concentration changes for all gases are from IPCC (2007). The
current atmospheric concentration for CO2 is from NOAA/ESRL (2009).
28 For more on the science of climate change, see NRC (2001).
29 Emissions estimates of CFCs, HCFCs, halons and other ozone-depleting substances are included in this document for
informational purposes.
Introduction 1-3
-------
a The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2006 through September 2007 (CDIAC 2009).
b The growth rate for atmospheric CH4 has been decreasing from 1.4 ppb/yr in 1984 to less than 0 ppb/yr in 2001,2004, and
2005.
0IPCC (2007) identifies the rate of concentration change for SF6 and CF4 as linear.
d No single lifetime can be defined for CO2 because of the different rates of uptake by different removal processes.
e This lifetime has been defined as an "adjustment time" that takes into account the indirect effect of the gas on its own residence
time.
A brief description of each greenhouse gas, its sources, and its role in the atmosphere is given below. The following
section then explains the concept of GWPs, which are assigned to individual gases as a measure of their relative
average global radiative forcing effect.
Water Vapor (H2O). Overall, the most abundant and dominant greenhouse gas in the atmosphere is water vapor.
Water vapor is neither long-lived nor well mixed in the atmosphere, varying spatially from 0 to 2 percent (IPCC
1996). In addition, atmospheric water can exist in several physical states including gaseous, liquid, and solid.
Human activities are not believed to affect directly the average global concentration of water vapor, but, the
radiative forcing produced by the increased concentrations of other greenhouse gases may indirectly affect the
hydrologic cycle. While a warmer atmosphere has an increased water holding capacity, increased concentrations of
water vapor affects the formation of clouds, which can both absorb and reflect solar and terrestrial radiation.
Aircraft contrails, which consist of water vapor and other aircraft emittants, are similar to clouds in their radiative
forcing effects (IPCC 1999).
Carbon Dioxide. 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 385 ppmv in 2008, a 37.5 percent increase (IPCC 2007
and NOAA/ESRL 2009) .30>31 The IPCC definitively states that "the present atmospheric CO2 increase is caused by
anthropogenic emissions of CO2" (IPCC 2001). The predominant source of anthropogenic CO2 emissions is the
combustion of fossil fuels. Forest clearing, other biomass burning, and some non-energy production processes (e.g.,
cement production) also emit notable quantities of CO2. In it's fourth assessment, the IPCC stated "most of the
observed increase in global average temperatures since the mid-20th century is very likely due to the observed
increased in anthropogenic greenhouse gas concentrations," of which CO2is the most important (IPCC 2007)
Methane. 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 143 percent since
1750, from a pre-industrial value of about 722 ppb to 1,741-1,865 ppb in 200732, although the rate of increase has
been declining. The IPCC has estimated that slightly more than half of the current CH4 flux to the atmosphere is
anthropogenic, from human activities such as agriculture, fossil fuel use, and waste disposal (IPCC 2007).
CH4 is removed from the atmosphere through a reaction with the hydroxyl radical (OH) and is ultimately converted
to CO2. Minor removal processes also include reaction with chlorine in the marine boundary layer, a soil sink, and
stratospheric reactions. Increasing emissions of CH4 reduce the concentration of OH, a feedback that may increase
the atmospheric lifetime of CH4 (IPCC 2001).
Nitrous Oxide. Anthropogenic sources of N2O emissions include agricultural soils, especially production of
30 The pre-industrial period is considered as the time preceding the year 1750 (IPCC 2001).
31 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).
32 The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2006 through September 2007 (CDIAC 2009)
1-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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 18
percent since 1750, from a pre-industrial value of about 270 ppb to 321-322 ppb in 200733, a concentration that has
not been exceeded during the last thousand years. N2O is primarily removed from the atmosphere by the photolytic
action of sunlight in the stratosphere (IPCC 2007).
Ozone. Ozone is present in both the upper stratosphere,34 where it shields the Earth from harmful levels of
ultraviolet radiation, and at lower concentrations in the troposphere,35 where it is the main component of
anthropogenic photochemical "smog." During the last two decades, emissions of anthropogenic chlorine and
bromine-containing halocarbons, such as CFCs, have depleted stratospheric ozone concentrations. This loss of
ozone in the stratosphere has resulted in negative radiative forcing, representing an indirect effect of anthropogenic
emissions of chlorine and bromine compounds (IPCC 1996). The depletion of stratospheric ozone and its radiative
forcing was expected to reach a maximum in about 2000 before starting to recover. As of IPCC's fourth
assessment,"whether or not recently observed changes in ozone trends are already indicative of recovery of the
global ozone layer is not yet clear." (IPCC 2007)
The past increase in tropospheric ozone, which is also a greenhouse gas, is estimated to provide the third largest
increase in direct radiative forcing since the pre-industrial era, behind CO2 and CH4. Tropospheric ozone is
produced from complex chemical reactions of volatile organic compounds mixing with NOX in the presence of
sunlight. The tropospheric concentrations of ozone and these other pollutants are short-lived and, therefore,
spatially variable. (IPCC 2001)
Halocarbons, Perfluorocarbons, and Sulfur Hexafluoride. Halocarbons are, for the most part, man-made chemicals
that have both direct and indirect radiative forcing effects. Halocarbons that contain chlorine (CFCs, HCFCs,
methyl chloroform, and carbon tetrachloride) and bromine (halons, methyl bromide, and hydrobromofluorocarbons
[HFCs]) result in stratospheric ozone depletion and are therefore controlled under the Montreal Protocol on
Substances that Deplete the Ozone Layer. Although CFCs and HCFCs include potent global warming gases, their
net radiative forcing effect on the atmosphere is reduced because they cause stratospheric ozone depletion, which
itself is an important greenhouse gas in addition to shielding the Earth from harmful levels of ultraviolet radiation.
Under the Montreal Protocol, the United States phased out the production and importation of halons by 1994 and of
CFCs by 1996. Under the Copenhagen Amendments to the Protocol, a cap was placed on the production and
importation of HCFCs by non-Article 536 countries beginning in 1996, and then followed by a complete phase-out
by the year 2030. While ozone depleting gases covered under the Montreal Protocol and its Amendments are not
covered by the UNFCCC; they are reported in this inventory under Annex 6.2 of this report for informational
purposes.
HFCs, PFCs, and SF6 are not ozone depleting substances, and therefore are not covered under the Montreal Protocol.
They are, however, powerful greenhouse gases. HFCs are primarily used as replacements for ozone depleting
substances but also emitted as a by-product of the HCFC-22 manufacturing process. Currently, they have a small
aggregate radiative forcing impact, but it is anticipated that their contribution to overall radiative forcing will
increase (IPCC 2001). PFCs and SF6 are predominantly emitted from various industrial processes including
aluminum smelting, semiconductor manufacturing, electric power transmission and distribution, and magnesium
33 The range is the annual arithmetic averages from a mid-latitude Northern-Hemisphere site and a mid-latitude Southern-
Hemisphere site for October 2006 through September 2007 (CDIAC 2009).
34 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.
35 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.
36 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.
Introduction 1-5
-------
casting. Currently, the radiative forcing impact of PFCs and SF6 is also small, but they have a significant growth
rate, extremely long atmospheric lifetimes, and are strong absorbers of infrared radiation, and therefore have the
potential to influence climate far into the future (IPCC 2001).
Carbon Monoxide. Carbon monoxide has an indirect radiative forcing effect by elevating concentrations of CH4 and
tropospheric ozone through chemical reactions with other atmospheric constituents (e.g., the hydroxyl radical, OH)
that would otherwise assist in destroying CH4 and tropospheric ozone. Carbon monoxide is created when carbon-
containing fuels are burned incompletely. Through natural processes in the atmosphere, it is eventually oxidized to
CO2. Carbon monoxide concentrations are both short-lived in the atmosphere and spatially variable.
Nitrogen Oxides. The primary climate change effects of nitrogen oxides (i.e., NO and NO2) are indirect and result
from their role in promoting the formation of ozone in the troposphere and, to a lesser degree, lower stratosphere,
where it has positive radiative forcing effects.37 Additionally, NOX emissions from aircraft are also likely to
decrease CH4 concentrations, thus having a negative radiative forcing effect (IPCC 1999). Nitrogen oxides are
created from lightning, soil microbial activity, biomass burning (both natural and anthropogenic fires) fuel
combustion, and, in the stratosphere, from the photo-degradation of N2O. Concentrations of NOX are both relatively
short-lived in the atmosphere and spatially variable.
Nonmethane Volatile Organic Compounds (NMVOCs). Non-CH4 volatile organic compounds include substances
such as propane, butane, and ethane. These compounds participate, along with NOX, in the formation of
tropospheric ozone and other photochemical oxidants. NMVOCs are emitted primarily from transportation and
industrial processes, as well as biomass burning and non-industrial consumption of organic solvents. Concentrations
of NMVOCs tend to be both short-lived in the atmosphere and spatially variable.
Aerosols. Aerosols are extremely small particles or liquid droplets found in the atmosphere. They can be produced
by natural events such as dust storms and volcanic activity, or by anthropogenic processes such as fuel combustion
and biomass burning. Aerosols affect radiative forcing differently than greenhouse gases, and their radiative effects
occur through direct and indirect mechanisms: directly by scattering and absorbing solar radiation; and indirectly by
increasing droplet counts that modify the formation, precipitation efficiency, and radiative properties of clouds.
Aerosols are removed from the atmosphere relatively rapidly by precipitation. Because aerosols generally have
short atmospheric lifetimes, and have concentrations and compositions that vary regionally, spatially, and
temporally, their contributions to radiative forcing are difficult to quantify (IPCC 2001).
The indirect radiative forcing from aerosols is typically divided into two effects. The first effect involves decreased
droplet size and increased droplet concentration resulting from an increase in airborne aerosols. The second effect
involves an increase in the water content and lifetime of clouds due to the effect of reduced droplet size on
precipitation efficiency (IPCC 2001). Recent research has placed a greater focus on the second indirect radiative
forcing effect of aerosols.
Various categories of aerosols exist, including naturally produced aerosols such as soil dust, sea salt, biogenic
aerosols, sulfates, and volcanic aerosols, and anthropogenically manufactured aerosols such as industrial dust and
carbonaceous38 aerosols (e.g., black carbon, organic carbon) from transportation, coal combustion, cement
manufacturing, waste incineration, and biomass burning.
The net effect of aerosols on radiative forcing is believed to be negative (i.e., net cooling effect on the climate),
although because they remain in the atmosphere for only days to weeks, their concentrations respond rapidly to
changes in emissions.39 Locally, the negative radiative forcing effects of aerosols can offset the positive forcing of
greenhouse gases (IPCC 1996). "However, the aerosol effects do not cancel the global-scale effects of the much
longer-lived greenhouse gases, and significant climate changes can still result" (IPCC 1996).
The IPCC's Third Assessment Report notes that "the indirect radiative effect of aerosols is now understood to also
37 NOX emissions injected higher in the stratosphere, primarily from fuel combustion emissions from high altitude supersonic
aircraft, can lead to stratospheric ozone depletion.
38 Carbonaceous aerosols are aerosols that are comprised mainly of organic substances and forms of black carbon (or soot)
(IPCC 2001).
39 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).
1-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
encompass effects on ice and mixed-phase clouds, but the magnitude of any such indirect effect is not known,
although it is likely to be positive" (IPCC 2001). Additionally, current research suggests that another constituent of
aerosols, black carbon, has a positive radiative forcing, and that its presence "in the atmosphere above highly
reflective surfaces such as snow and ice, or clouds, may cause a significant positive radiative forcing (IPCC 2007).
The primary anthropogenic emission sources of black carbon include diesel exhaust and openbiomass burning.
Global Warming Potentials
A global warming potential is a quantified measure of the globally averaged relative radiative forcing impacts of a
particular greenhouse gas (see Table 1-2). It is defined as the ratio of the time-integrated radiative forcing from the
instantaneous release of 1 kilogram (kg) of a trace substance relative to that of 1 kg of a reference gas (IPCC 2001).
Direct radiative effects occur when the gas itself absorbs radiation. Indirect radiative forcing occurs when chemical
transformations involving the original gas produce a gas or gases that are greenhouse gases, or when a gas
influences other radiatively important processes such as the atmospheric lifetimes of other gases. The reference gas
used is CO2, and therefore GWP weighted emissions are measured in teragrams of CO2 equivalent (Tg CO2 Eq.)40
The relationship between gigagrams (Gg) of a gas and Tg CO2 Eq. can be expressed as follows:
( T
Tg CO2 Eq = (Gg of gas) x (GWP) x i—
2 V ; V ' ll,OOOGg
where,
Tg CO2 Eq. = Teragrams of CO2 Equivalents
Gg = Gigagrams (equivalent to a thousand metric tons)
GWP = Global Warming Potential
Tg = Teragrams
GWP values allow for a comparison of the impacts of emissions and reductions of different gases. According to the
IPCC, GWPs typically have an uncertainty of ±35 percent. The parties to the UNFCCC have also agreed to use
GWPs based upon a 100-year time horizon although other time horizon values are available.
Greenhouse gas emissions and removals should be presented on a gas-by-gas basis in units of mass... In
addition, consistent with decision 2/CP.3, Parties should report aggregate emissions and removals of
greenhouse gases, expressed in CO2 equivalent terms at summary inventory level, using GWP values
provided by the IPCC in its Second Assessment Report... based on the effects of greenhouse gases over a
100-year time horizon.41
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, and SF6) tend to be
evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
The short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, ozone precursors (e.g., NOX, and
NMVOCs), and tropospheric aerosols (e.g., SO2 products and carbonaceous particles), however, vary regionally,
and consequently it is difficult to quantify their global radiative forcing impacts. No GWP values are attributed to
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 Atmospheric Lifetime GWP"
CO2 50-200 1
40 Carbon comprises 12/44ths of carbon dioxide by weight.
41 Framework Convention on Climate Change; ; 1 November 2002; Report of the
Conference of the Parties at its eighth session; held at New Delhi from 23 October to 1 November 2002; Addendum; Part One:
Action taken by the Conference of the Parties at its eighth session; Decision -/CP.8; Communications from Parties included in
Annex I to the Convention: Guidelines for the Preparation of National Communications by Parties Included in Annex I to the
Convention, Part 1: UNFCCC reporting guidelines on annual inventories; p. 7. (UNFCCC 2003)
Introduction 1-7
-------
CH4b
N2O
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2F6
C/fio
CsF14
SF6
12±3
120
264
5.6
32.6
14.6
48.3
1.5
36.5
209
17.1
50,000
10,000
2,600
3,200
3,200
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
7,000
7,400
23,900
Source: (IPCC 1996)
a 100-year time horizon
b The GWP of CH4 includes the direct effects and those indirect effects due to the production of tropospheric ozone and
stratospheric water vapor. The indirect effect due to the production of CO2 is not included.
[BEGIN BOX]
Box 1-2: The IPCC Fourth Assessment Report and Global Warming Potentials
In 2007, the IPCC published its Fourth Assessment Report (AR4), which provided an updated and more
comprehensive scientific assessment of climate change. Within this report, the GWPs of several gases were revised
relative to the SAR and the IPCC's Third Assessment Report (TAR) (IPCC 2001). Thus the GWPs used in this
report have been updated twice by the IPCC; although the SAR GWPs are used throughout this report, it is
interesting to review the changes to the GWPs and the impact such improved understanding has on the total GWP-
weighted emissions of the United States. Since the SAR and TAR, the IPCC has applied an improved calculation of
CO2 radiative forcing and an improved CO2 response function. The GWPs are drawn from IPCC/TEAP (2005) and
the TAR, with updates for those cases where new laboratory or radiative transfer results have been published.
Additionally, the atmospheric lifetimes of some gases have been recalculated. In addition, the values for radiative
forcing and lifetimes have been recalculated for a variety of halocarbons, which were not presented in the SAR.
Table 1-3 presents the new GWPs, relative to those presented in the SAR.
Table 1-3: Comparison of 100-Year GWPs
Gas
C02
CH4*
N2O
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-4310mee
CF4
C2F6
SAR
1
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
1,300
6,500
9,200
TAR
1
23
296
12,000
550
3,400
1,300
4,300
120
3,500
9,400
1,500
5,700
11,900
AR4
1
25
298
14,800
675
3,500
1,430
4,470
124
3,220
9,810
1,640
7,390
12,200
Change from
SAR
TAR
NC
2
(14)
300
(100)
600
NC
500
(20)
600
3,100
200
(800)
2,700
AR4
0
4
(12)
3,100
25
700
130
670
(16)
320
3,510
340
890
3,000
1-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
c^o
C6F14
SF6
7,000
7,400
23,900
8,600
9,000
22,200
8,860
9,300
22,800
1,600
1,600
(1,700)
1,860
1,900
(1,100)
Source: (IPCC 2007, IPCC 2001)
NC (No Change)
Note: Parentheses indicate negative values.
* 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.
To comply with international reporting standards under the UNFCCC, official emission estimates are reported by
the United States using SAR GWP values. The UNFCCC reporting guidelines for national inventories42 were
updated in 2002 but continue to require the use of GWPs from the SAR so that current estimates of aggregate
greenhouse gas emissions for 1990 through 2009 are consistent and comparable with estimates developed prior to
the publication of the TAR and AR4. For informational purposes, emission estimates that use the updated GWPs
are presented in detail in Annex 6.1 of this report. All estimates provided throughout this report are also presented
in unweighted units.
[END BOX]
1.2. Institutional 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, reviewing, or preparing portions of the U.S. Inventory—including federal and state government
authorities, research and academic institutions, industry associations, and private consultants.
Within EPA, the Office of Atmospheric Programs (OAP) is the lead office responsible for the emission calculations
provided in the Inventory, as well as the completion of the National Inventory Report and the Common Reporting
Format tables. The Office of Transportation and Air Quality (OTAQ) is also involved in calculating emissions for
the Inventory. While the U.S. Department of State officially submits the annual Inventory to the UNFCCC, EPA's
OAP serves as the focal point for technical questions and comments on the U. S. Inventory. The staff of OAP and
OTAQ coordinates the annual methodological choice, activity data collection, and emission calculations at the
individual source category level. Within OAP, an inventory coordinator compiles the entire Inventory into the
proper reporting format for submission to the UNFCCC, and is responsible for the collection and consistency of
cross-cutting issues in the Inventory.
Several other government agencies contribute to the collection and analysis of the underlying activity data used in
the Inventory calculations. Formal relationships exist between EPA and other U.S. agencies that provide official
data for use in the Inventory. The U.S. Department of Energy's Energy Information Administration provides
national fuel consumption data and the U.S. Department of Defense provides military fuel consumption and bunker
fuels. Informal relationships also exist with other U.S. agencies to provide activity data for use in EPA's emission
calculations. These include: the U.S. Department of Agriculture, the U.S. Geological Survey, the Federal Highway
Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department of
Commerce, the National Agricultural Statistics Service, and the Federal Aviation Administration. Academic and
research centers also provide activity data and calculations to EPA, as well as individual companies participating in
voluntary outreach efforts with EPA. Finally, the U.S. Department of State officially submits the Inventory to the
UNFCCC each April.
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
42
See.
Introduction 1-9
-------
compiling all emission estimates, and ensuring consistency and quality throughout the NIR and CRF tables.
Emission calculations for individual sources are the responsibility of individual source leads, who are most familiar
with each source category and the unique characteristics of its emissions profile. The individual source leads
determine the most appropriate methodology and collect the best activity data to use in the emission calculations,
based upon their expertise in the source category, as well as coordinating with researchers and contractors familiar
with the sources. A multi-stage process for collecting information from the individual source leads and producing
the Inventory is undertaken annually to compile all information and data.
Methodology Development, Data Collection, and Emissions and Sink Estimation
Source leads at EPA collect input data and, as necessary, evaluate or develop the estimation methodology for the
individual source categories. For most source categories, the methodology for the previous year is applied to the
new "current" year of the Inventory, and inventory analysts collect any new data or update data that have changed
from the previous year. If estimates for a new source category are being developed for the first time, or if the
methodology is changing for an existing source category (e.g., the United States is implementing a higher Tiered
approach for that source category), then the source category lead will develop a new methodology, gather the most
appropriate activity data and emission factors (or in some cases direct emission measurements) for the entire time
series, and conduct a special source-specific peer review process involving relevant experts from industry,
government, and universities.
Once the methodology is in place and the data are collected, the individual source leads calculate emissions and sink
estimates. The source leads then update or create the relevant text and accompanying annexes for the Inventory.
Source leads are also responsible for completing the relevant sectoral background tables of the Common Reporting
Format, conducting quality assurance and quality control (QA/QC) checks, and uncertainty analyses.
Summary Spreadsheet Compilation and Data Storage
The inventory coordinator at EPA collects the source categories' descriptive text and Annexes, and also aggregates
the emission estimates into a summary spreadsheet that links the individual source category spreadsheets together.
This summary sheet contains all of the essential data in one central location, in formats commonly used in the
Inventory document. In addition to the data from each source category, national trend and related data are also
gathered in the summary sheet for use in the Executive Summary, Introduction, and Recent Trends sections of the
Inventory report. Electronic copies of each year's summary spreadsheet, which contains all the emission and sink
estimates for the United States, are kept on a central server at EPA under the jurisdiction of the Inventory
coordinator.
National Inventory Report Preparation
The NIR is compiled from the sections developed by each individual source lead. In addition, the inventory
coordinator prepares a brief overview of each chapter that summarizes the emissions from all sources discussed in
the chapters. The inventory coordinator then carries out a key category analysis for the Inventory, consistent with
the IPCC Good Practice Guidance, IPCC Good Practice Guidance for Land Use, Land Use Change and Forestry,
and in accordance with the reporting requirements of the UNFCCC. Also at this time, the Introduction, Executive
Summary, and Recent Trends sections are drafted, to reflect the trends for the most recent year of the current
Inventory. The analysis of trends necessitates gathering supplemental data, including weather and temperature
conditions, economic activity and gross domestic product, population, atmospheric conditions, and the annual
consumption of electricity, energy, and fossil fuels. Changes in these data are used to explain the trends observed in
greenhouse gas emissions in the United States. Furthermore, specific factors that affect individual sectors are
researched and discussed. Many of the factors that affect emissions are included in the Inventory document as
separate analyses or side discussions in boxes within the text. Text boxes are also created to examine the data
aggregated in different ways than in the remainder of the document, such as a focus on transportation activities or
emissions from electricity generation. The document is prepared to match the specification of the UNFCCC
reporting guidelines for National Inventory Reports.
Common Reporting Format Table Compilation
The CRF tables are compiled from individual tables completed by each individual source lead, which contain source
1-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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emissions and activity data. The inventory coordinator integrates the source data into the UNFCCC's "CRF
Reporter" for the United States, assuring consistency across all sectoral tables. The summary reports for emissions,
methods, and emission factors used, the overview tables for completeness and quality of estimates, the recalculation
tables, the notation key completion tables, and the emission trends tables are then completed by the inventory
coordinator. Internal automated quality checks on the CRF Reporter, as well as reviews by the source leads, are
completed for the entire time series of CRF tables before submission.
QA/QC and Uncertainty
QA/QC and uncertainty analyses are supervised by the QA/QC and Uncertainty coordinators, who have general
oversight over the implementation of the QA/QC plan and the overall uncertainty analysis for the Inventory (see
sections on QA/QC and Uncertainty, below). These coordinators work closely with the source leads to ensure that a
consistent QA/QC plan and uncertainty analysis is implemented across all inventory sources. The inventory QA/QC
plan, detailed in a following section, is consistent with the quality assurance procedures outlined by EPA and IPCC.
Expert and Public Review Periods
During the Expert Review period, a first draft of the document is sent to a select list of technical experts outside of
EPA. The purpose of the Expert Review is to encourage feedback on the methodological and data sources used in
the current Inventory, especially for sources which have experienced any changes since the previous Inventory.
Once comments are received and addressed, a second draft of the document is released for public review by
publishing a notice in the U.S. Federal Register and posting the document on the EPA Web site. The Public Review
period allows for a 30 day comment period and is open to the entire U.S. public.
Final Submittal to UNFCCC and Document Printing
After the final revisions to incorporate any comments from the Expert Review and Public Review periods, EPA
prepares the final National Inventory Report and the accompanying Common Reporting Format Reporter database.
The U.S. Department of State sends the official submission of the U.S. Inventory to the UNFCCC. The document is
then formatted for printing, posted online, printed by the U.S. Government Printing Office, and made available for
the public.
1.4. Methodology and Data Sources
Emissions of greenhouse gases from various source and sink categories have been estimated using methodologies
that are consistent with the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997). In addition, the United States references the additional guidance provided in the
IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories (IPCC 2000),
the IPCC Good Practice Guidance for Land Use, Land-Use Change, and Forestry (IPCC 2003), and the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). To the extent possible, the present report relies
on published activity and emission factor data. Depending on the emission source category, activity data can
include fuel consumption or deliveries, vehicle-miles traveled, raw material processed, etc. Emission factors are
factors that relate quantities of emissions to an activity.
The IPCC methodologies provided in the Revised 1996 IPCC Guidelines represent baseline methodologies fora
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 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.
[BEGIN BOX]
Box 1-3: IPCC Reference Approach
Introduction 1-11
-------
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.
[END BOX]
1.5. Key Categories
The IPCC's Good Practice Guidance (IPCC 2000) defines a key category as a "[source or sink category] that is
prioritized within the national inventory system because its estimate has a significant influence on a country's total
inventory of direct greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both."43
By definition, key categories include those sources that have the greatest contribution to the absolute level of
national emissions. In addition, when an entire time series of emission estimates is prepared, a thorough
investigation of key categories must also account for the influence of trends and uncertainties of individual source
and sink categories. This analysis culls out source and sink categories that diverge from the overall trend in national
emissions. Finally, a qualitative evaluation of key categories is performed to capture any categories that were not
identified in any of the quantitative analyses.
A Tier 1 approach, as defined in the IPCC's Good Practice Guidance (IPCC 2000), was implemented to identify the
key categories for the United States. This analysis was performed twice; one analysis included sources and sinks
from the Land Use, Land-Use Change, and Forestry (LULUCF) sector, the other analysis did not include the
LULUCF categories. Following the Tier 1 approach, a Tier 2 approach, as defined in the IPCC's Good Practice
Guidance (IPCC 2000), was then implemented to identify any additional key categories not already identified in the
Tier 1 assessment. This analysis, which includes each source categories' uncertainty assessments (or proxies) in its
calculations, was also performed twice to include or exclude LULUCF categories.
In addition to conducting Tier 1 and 2 level and trend assessments, a qualitative assessment of the source categories,
as described in the IPCC's Good Practice Guidance (IPCC 2000), was conducted to capture any key categories that
were not identified by either quantitative method. One additional key category, international bunker fuels, was
identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine
international transport activities, and emissions from these fuels are reported separately from totals in accordance
with IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category
according to the Tier 1 approach. The amount of uncertainty associated with estimation of emissions from
international bunker fuels also supports the qualification of this source category as key, because it would qualify
bunker fuels as a key category according to the Tier 2 approach. Table 1-4 presents the key categories for the United
States (including and excluding LULUCF categories) using emissions and uncertainty data in this report, and ranked
according to their sector and global warming potential-weighted emissions in 2009. The table also indicates the
criteria used in identifying these categories (i.e., level, trend, Tier 1, Tier 2, and/or qualitative assessments). Annex
1 of this report provides additional information regarding the key categories in the United States and the
methodologies used to identify them.
43 See Chapter 7 "Methodological Choice and Recalculation" in IPCC (2000).
1-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 1-4: Key Categories for the United States (1990-2009)
IPCC Source Categories
Energy
CO2 Emissions from Stationary
Combustion - Coal
CO2 Emissions from Mobile
Combustion: Road
CO2 Emissions from Stationary
Combustion - Gas
CO2 Emissions from Stationary
Combustion - Oil
CO2 Emissions from Mobile
Combustion: Aviation
CO2 Emissions from Non-
Energy Use of Fuels
CO2 Emissions from Mobile
Combustion: Other
CO2 Emissions from Natural
Gas Systems
CO2 Emissions from Mobile
Combustion: Marine
Fugitive Emissions from
Natural Gas Systems
Fugitive Emissions from Coal
Mining
Fugitive Emissions from
Petroleum Systems
Non-CO2 Emissions from
Stationary Combustion
N2O Emissions from Mobile
Combustion: Road
Non-CO2 Emissions from
Stationary Combustion
International Bunker Fuelsb
Industrial Processes
CO2 Emissions from Iron and
Steel Production &
Metallurgical Coke
Production
CO2 Emissions from Cement
Production
CO2 Emissions from Ammonia
Production and Urea
Consumption
CO2 Emissions from
Aluminum Production
N2O Emissions from Nitric
Acid Production
N2O Emissions from Adipic
Acid Production
Emissions from Substitutes for
Ozone Depleting Substances
SF,; Emissions from Electrical
Transmission and
Distribution
HFC-23 Emissions from
HCFC-22 Production
Gas
CO2
C02
C02
CO2
CO2
CO2
CO2
C02
C02
CH4
CH4
CH4
CH4
N2O
N2O
Several
C02
CO2
C02
C02
N20
N2O
HiGWP
HiGWP
HiGWP
Tierl
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
• •
• •
Tier 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
• •
Qual"
•
2009
Emissions
(Tg C02
Eq.)
1,841.0
1,475.6
1,164.6
483.3
140.7
123.4
73.5
32.2
30.0
221.2
71.0
30.9
6.2
20.3
12.8
124.4
41.9
29.0
11.8
3.0
14.6
1.9
120.0
12.8
5.4
Introduction 1-13
-------
IPCC Source Categories
PFC Emissions from
Aluminum Production
SF6 Emissions from
Magnesium Production and
Processing
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
Waste
CH4 Emissions from Landfills
CH4 Emissions from
Wastewater Treatment
Land Use, Land Use Change,
and Forestry
CO2 Emissions from Changes
in Forest Carbon Stocks
CO2 Emissions from Urban
Trees
CO2 Emissions from Cropland
Remaining Cropland
CO2 Emissions from
Landfilled Yard Trimmings
and Food Scraps
CO2 Emissions from Grassland
Remaining Grassland
CH4 Emissions from Forest
Fires
N2O Emissions from Forest
Subtotal Without LULUCF
Total Emissions Without
LULUCF
Percent of Total Without
LULUCF
Subtotal With LULUCF
Total Emissions With
LULUCF
Percent of Total With
LULUCF
Gas
HiGWP
HiGWP
CH4
CH4
CH4
N2O
N2O
CH4
CH4
C02
C02
CO2
C02
C02
CH4
N2O
Tierl
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
• •
• •
• •
• • • •
• • • •
• • • •
• •
• •
•
•
Tier 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
•
• •
• •
'
• • • •
• • • •
• • • •
• •
• •
• •
• •
: :
• •
•
Qual"
2009
Emissions
(Tg C02
Eq.)
1.6
1.1
1398
49.5
7.3
160.2
44.4
117.5
24.5
(863.1)
(95.9)
(17.4)
(12.6)
(8.3)
7.8
6.4
6,512.7
6,608.2
99%
5,529.5
5,618.2
98%
Qualitative criteria.
bEmissions 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
1-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 1-1. These attributes include:
• specific detailed procedures and forms 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 the uncertainty of the
inventory estimates;
• expert review as well as QC—for both the inventory estimates and the Inventory (which is the primary
vehicle for disseminating the results of the inventory development process). In addition, the plan provides
for public review of the Inventory;
• both Tier 1 (general) and Tier 2 (source-specific) quality controls and checks, as recommended by IPCC
Good Practice Guidance;
• consideration of secondary data quality and source-specific quality checks (Tier 2 QC) in parallel and
coordination with the uncertainty assessment; the development of protocols and templates provides for
more structured communication and integration with the suppliers of secondary information;
• record-keeping provisions to track which procedures have been followed, and the results of the QA/QC and
uncertainty analysis, and contains feedback mechanisms for corrective action based on the results of the
investigations, thereby providing for continual data quality improvement and guided research efforts;
• implementation 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;
• a schedule for multi-year implementation; and
• promotion of coordination and interaction 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 plan itself is intended to be revised and reflect new
information that becomes available as the program develops, methods are improved, or additional
supporting documents become necessary.
In addition, based on the national QA/QC plan for the Inventory, source-specific QA/QC plans have been developed
for a number of sources. These plans follow the procedures outlined in the national QA/QC plan, tailoring the
procedures to the specific text and spreadsheets of the individual sources. For each greenhouse gas emissions source
or sink included in this Inventory, a minimum of a Tier 1 QA/QC analysis has been undertaken. Where QA/QC
activities for a particular source go beyond the minimum Tier 1 level, further explanation is provided within the
respective source category text.
The quality control activities described in the U.S. QA/QC plan occur throughout the inventory process; QA/QC is
not separate from, but is an integral part of, preparing the inventory. Quality control—in the form of both good
practices (such as documentation procedures) and checks on whether good practices and procedures are being
followed—is applied at every stage of inventory development and document preparation. In addition, quality
assurance occurs at two stages—an expert review and a public review. While both phases can significantly
contribute to inventory quality, the public review phase is also essential for promoting the openness of the inventory
development process and the transparency of the inventory data and methods.
The QA/QC plan guides the process of ensuring inventory quality by describing data and methodology checks,
developing processes governing peer review and public comments, and developing guidance on conducting an
analysis of the uncertainty surrounding the emission estimates. The QA/QC procedures also include feedback loops
and provide for corrective actions that are designed to improve the inventory estimates over time.
Figure 1-1: U.S. QA/QC Plan Summary
Introduction 1-15
-------
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 CO2
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. Despite these uncertainties, the UNFCCC
reporting guidelines follow the recommendation in the 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) and
require that countries provide single point estimates for each gas and emission or removal source category. Within
the discussion of each emission source, specific factors affecting the uncertainty associated with the estimates are
discussed.
Additional research in the following areas could help reduce uncertainty in the U.S. Inventory:
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions are not available at this time. In particular, emissions from some land-use
activities and industrial processes are not included in the inventory either because data are incomplete or
because methodologies do not exist for estimating emissions from these source categories. See Annex 5 of
this report for a discussion of the sources of greenhouse gas emissions and sinks excluded from this report.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the
accuracy of emission factors used to calculate emissions from a variety of sources. For example, the
accuracy of current emission factors applied to CH4 and N2O emissions from stationary and mobile
combustion is highly uncertain.
• Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources,
problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be
applied. For example, the ability to estimate emissions of SF6 from electrical transmission and distribution
is limited due to a lack of activity data regarding national SF6 consumption or average equipment leak
rates.
The overall uncertainty estimate for the U.S. greenhouse gas emissions inventory was developed using the IPCC
Tier 2 uncertainty estimation methodology. Estimates of quantitative uncertainty for the overall greenhouse gas
emissions inventory are shown below, in Table 1-5.
The IPCC provides good practice guidance on two approaches—Tier 1 and Tier 2—to estimating uncertainty for
individual source categories. Tier 2 uncertainty analysis, employing the Monte Carlo Stochastic Simulation
technique, was applied wherever data and resources permitted; further explanation is provided within the respective
source category text and in Annex 7. Consistent with the IPCC Good Practice Guidance (IPCC 2000), over a multi-
year timeframe, the United States expects to continue to improve the uncertainty estimates presented in this report.
Table 1-5. Estimated Overall Inventory Quantitative Uncertainty (Tg CO2 Eq. and Percent)
2009 Emission
Estimate"
Gas (Tg CO2 Eq.)
CO2
CH4e
N2Oe
PFC, HFC & SF6e
Total
Net Emissions (Sources and Sinks)
5,504.8
686.3
295.6
143.3
6,630.0
5,614.9
Uncertainty Range Relative
Emission Estimate1"
(Tg C02 Eq.) (%)
Lower
Boundd
5,436.6
623.9
261.7
134.5
6,584.2
5,512.3
Upper
Boundd
5,813.8
805.4
425.3
153.4
7,033.6
6,055.1
Lower
Bound
-1%
-9%
-11%
-6%
-1%
-2%
to Standard
Mean0 Deviation0
(Tg C02 Eq.)
Upper
Bound
6%
17%
44%
7%
6%
8%
5,622.5
702.8
334.2
143.7
6,803.2
5,785.4
97.5
45.3
42.1
4.8
115.0
139.1
Notes:
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative
uncertainty was performed this year. Thus the totals reported in this table exclude approximately 3.1 Tg CO2 Eq. of emissions for
1-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 2009.
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 reporter, 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 2009.
Although this report is intended to be comprehensive, certain sources have been identified yet excluded from the
estimates presented for various reasons. Generally speaking, sources not accounted for in this inventory are
excluded due to data limitations or a lack of thorough understanding of the emission process. The United States is
continually working to improve upon the understanding of such sources and seeking to find the data required to
estimate related emissions. As such improvements are implemented, new emission sources are quantified and
included in the Inventory. For a complete list of sources not included, see Annex 5 of this report.
1.9. Organization of Report
In accordance with the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/IEA 1997), and the 2006 UNFCCC Guidelines on Reporting and Review (UNFCCC 2006),
this Inventory of U.S. Greenhouse Gas Emissions and Sinks is segregated into six sector-specific chapters, listed
below in Table 1-6. In addition, chapters on Trends in Greenhouse Gas Emissions and Other information to be
considered as part of the U.S. Inventory submission are included.
Table 1-6: IPCC Sector Descriptions
Chapter/IPCC Sector Activities Included
Energy Emissions of all greenhouse gases resulting from stationary and
mobile energy activities including fuel combustion and fugitive fuel
emissions.
Industrial Processes By-product or fugitive emissions of greenhouse gases from
industrial processes not directly related to energy activities such as
fossil fuel combustion.
Solvent and Other Product Use Emissions, of primarily NMVOCs, resulting from the use of
solvents and N2O from product uses.
Agriculture Anthropogenic emissions from agricultural activities except fuel
combustion, which is addressed under Energy.
Land Use, Land-Use Change, Emissions and removals of CO2, CH4, and N2O from forest
and Forestry management, other land-use activities, and land-use change.
Waste Emissions from waste management activities.
Source: (IPCC/UNEP/OECD/IEA 1997)
Within each chapter, emissions are identified by the anthropogenic activity that is the source or sink of the
greenhouse gas emissions being estimated (e.g., coal mining). Overall, the following organizational structure is
consistently applied throughout this report:
Introduction 1-17
-------
Chapter/IPCC Sector: Overview of emission trends for each IPCC defined sector
SourC6 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: 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 CO2 from fossil fuel combustion relative to other sources because of its share of
emissions and its dominant influence on emission trends. For example, each energy consuming end-use sector (i.e.,
residential, commercial, industrial, and transportation), as well as the electricity generation sector, is described
individually. Additional information for certain source categories and other topics is also provided in several
Annexes listed in Table 1-7.
Table 1-7: List of Annexes
ANNEX 1 Key Category Analysis
ANNEX 2 Methodology and Data for Estimating CO2 Emissions from Fossil Fuel Combustion
2.1. Methodology for Estimating Emissions of CO2 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 CH4, N2O, and Indirect Greenhouse Gases from
Stationary Combustion
3.2. Methodology for Estimating Emissions of CH4, 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 CH4 Emissions from Coal Mining
3.4. Methodology for Estimating CH4 Emissions from Natural Gas Systems
3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum Systems
3.6. Methodology for Estimating CO2 and N2O Emissions from Incineration of Waste
3.7. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military
3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting
Substances
3.9. Methodology for Estimating CH4 Emissions from Enteric Fermentation
3.10. Methodology for Estimating CH4 and N2O Emissions from Manure Management
3.11. Methodology for Estimating N2O Emissions from Agricultural Soil Management
3.12. Methodology for Estimating Net Carbon Stock Changes in Forest Lands Remaining Forest Lands
3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral and Organic Soils on
Croplands and Grasslands
3.14. Methodology for Estimating CH4 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
1-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.7. Chemical Formulas
ANNEX 7 Uncertainty
7.1. Overview
7.2. Methodology and Results
7.3. Planned Improvements
7.4. Additional Information on Uncertainty Analyses by Source
Introduction 1-19
-------
-------
Figure 1: U.S. QA/QC Plan Summary
•Obtain data in electronic
format (if possible)
•Review spreadsheet
construction
•Avoid hardwiring
• Use data validation
• Protect cells
•Develop automatic
checkers for:
• Outliers, negative values, or
missing data
•triable types match values
•Time series consistency
•Maintain trackingtab for
status of gat he ring efforts
•Check input data fur-
transcription errors
•Inspect automatic checkers
•Identify spreadsheet
modifications that could
provide additional QA/QC
checks
Data Gathering
•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 con version factors
•Review spreadsheet
integrity
•Equations
•Units
•Input and output
•Develop automated
checkers for:
•Input ranges
•Calculations
•Emission aggregation
•Check dtations in
spreadsheet and textf en-
accuracy and style
•Check reference docket for
new citations
•Review documentation for
any data/methodology
changes
•Reproduce calculations
•Review time series for
consistency
• Review changes in data/
consistency with IPCC
methodology
•Corn mon starting versions
for each Inventory year
•Utilize unalterable
summary tab for each
source spreadsheet for
linkingto a master summary
spreadsheet
•Follow strict version
control procedures
• Document QA/QC
procedures
Data Documentation
Calculating Emissions
Cross-Cutting
Coordination
-------
-------
2. Trends in Greenhouse Gas Emissions
2.1. Recent Trends in U.S. Greenhouse Gas Emissions and Sinks
In 2009, total U.S. greenhouse gas emissions were 6,633.2 teragrams of carbon dioxide equivalents (Tg CO2 Eq.);
net emissions were 5,618.2 Tg CO2 Eq. reflecting the influence of sinks (net CO2 flux from Land Use, Land Use
Change, and Forestry).44 While total U.S. emissions have increased by 7.3 percent from 1990 to 2009, emissions
decreased from 2008 to 2009 by 6.1 percent (427.9 Tg CO2 Eq.). The following factors were primary contributors
to this decrease: (1) a decrease in economic output resulting in a decrease in energy consumption across all sectors;
and (2) a decrease in the carbon intensity of fuels used to generate electricity due to fuel switching as the price of
coal increased, and the price of natural gas decreased significantly.
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
As the largest contributor to U.S. greenhouse gas emissions, carbon dioxide (CO2) from fossil fuel combustion has
accounted for approximately 79 percent of global warming potential (GWP) weighted emissions since 1990, from
77 percent of total GWP-weighted emissions in 1990 to 79 percent in 2009. Emissions from this source category
grew by 9.9 percent (470.6 Tg CO2 Eq.) from 1990 to 2009 and were responsible for most of the increase in national
emissions during this period. From 2008 to 2009, these emissions decreased by 6.4 percent (356.9 Tg CO2 Eq.).
Historically, changes in emissions from fossil fuel combustion have been the dominant factor affecting U.S.
emission trends.
Changes in CO2 emissions from fossil fuel combustion are influenced by many long-term and short-term factors,
including population and economic growth, energy price fluctuations, technological changes, and seasonal
temperatures. On an annual basis, the overall consumption of fossil fuels in the United States fluctuates primarily in
response to changes in general economic conditions, energy prices, weather, and the availability of non-fossil
alternatives. For example, in a year with increased consumption of goods and services, low fuel prices, severe
summer and winter weather conditions, nuclear plant closures, and lower precipitation feeding hydroelectric dams,
there would likely be proportionally greater fossil fuel consumption than in a year with poor economic performance,
high fuel prices, mild temperatures, and increased output from nuclear and hydroelectric plants.
In the longer-term, energy consumption patterns respond to changes that affect the scale of consumption (e.g.,
population, number of cars, and size of houses), the efficiency with which energy is used in equipment (e.g., cars,
power plants, steel mills, and light bulbs) and behavioral choices (e.g., walking, bicycling, or telecommuting to work
instead of driving).
Energy-related CO2 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 CO2 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
2005.
From 2005 to 2006, emissions from fuel combustion decreased for the first time since 2000 to 2001. This decrease
occurred across all sectors, with the exception of the industrial sector and the U.S. Territories sector, due to a
44 Estimates are presented in units of teragrams of carbon dioxide equivalent (Tg CO2 Eq.), which weight each gas by its global
warming potential, or GWP, value. See section on global warming potentials in the Executive Summary.
Trends in Greenhouse Gas Emissions 2-1
-------
number of factors. The decrease in emissions from electricity generation is a result of a smaller share of electricity
generated by coal and a greater share generated by natural gas. Coal consumption for electricity generation
decreased by 1.3 percent while natural gas consumption for electricity generation increased by 6.0 percent in 2006
and nuclear power generation increased by less than 1 percent. The decrease in consumption of transportation fuels
is primarily a result of the restraint on fuel consumption caused by rising fuel prices, which directly resulted in a
decrease of petroleum consumption within this sector of about 1.1 percent in 2006. The significant decrease in
emissions from the residential sector is primarily a result of decreased electricity consumption due to increases in the
price of electricity, and warmer winter weather conditions compared to 2005. A moderate increase in industrial
sector emissions is the result of growth in industrial output and growth in the U.S. economy. Renewable fuels used
to generate electricity increased in 2006, with the greatest growth occurring in generation from wind by 48 percent.
After experiencing a decrease from 2005 to 2006, emissions from fuel combustion grew from 2006 to 2007 at a rate
somewhat higher than the average growth rate since 1990. There were a number of factors contributing to this
increase. More energy-intensive weather conditions in both the winter and summer resulted in an increase in
consumption of heating fuels, as well as an increase in the demand for electricity. This demand for electricity was
met with an increase in coal consumption of 1.7 percent, and with an increase in natural gas consumption of 9.9
percent. This increase in fossil fuel consumption, combined with a 14.7 percent decrease in hydropower generation
from 2006 to 2007, resulted in an increase in emissions in 2007. The increase in emissions from the residential and
commercial sectors is a result of increased electricity consumption due to warmer summer conditions and cooler
winter conditions compared to 2006. In addition to these more energy-intensive weather conditions, electricity
prices remained relatively stable compared to 2006, and natural gas prices decreased slightly. Emissions from the
industrial sector decreased compared to 2006 as a result of a decrease in industrial production and fossil fuels used
for electricity generation. Despite an overall decrease in electricity generation from renewable energy in 2007
driven by decreases in hydropower generation, wind and solar generation increased significantly.
Emissions from fossil fuel combustion decreased from 2007 to 2008. Several factors contributed to this decrease in
emissions. An increase in energy prices coupled with the economic downturn led to a decrease in energy demand
and a resulting decrease in emissions from 2007 to 2008. In 2008, the price of coal, natural gas, and petroleum used
to generate electricity, as well as the price of fuels used for transportation, increased significantly. As a result of this
price increase, coal, natural gas, and petroleum consumption used for electricity generation decreased by 1.4
percent, 2.5 percent, and 28.8 percent, respectively. The increase in the cost of fuels to generate electricity translated
into an increase in the price of electricity, leading to a decrease in electricity consumption across all sectors except
the commercial sector. The increase in transportation fuel prices led to a decrease in vehicle miles traveled (VMT)
and a 5.5 percent decrease in transportation fossil fuel combustion emissions from 2007 to 2008. Cooler weather
conditions in the summer led to a decrease in cooling degree days by 8.7 percent and a decrease in electricity
demand compared to 2007, whereas cooler winter conditions led to a 5.6 percent increase in heating degree days
compared to 2007 and a resulting increase in demand for heating fuels. The increased emissions from winter heating
energy demand was offset by a decrease in emissions from summer cooling related electricity demand. Lastly,
renewable energy45 consumption for electricity generation increased by 9.6 percent from 2007 to 2008, driven by a
significant increase in solar and wind energy consumption (of 19.4 percent and 60.2 percent, respectively). This
increase in renewable energy generation contributed to a decrease in the carbon intensity of electricity generation.
From 2008 to 2009, CO2 from fossil fuel combustion emissions experienced a decrease of 6.4 percent, the greatest
decrease of any year over the course of the twenty-year period. Various factors contributed to this decrease in
emissions. The continued economic downturn resulted in a 2.6 percent decrease in GDP, and a decrease in energy
consumption across all sectors. The economic downturn also impacted total industrial production and manufacturing
output, which decreased by 9.3 and 10.9 percent, respectively. In 2009, the price of coal used to generate electricity
increased, while the price of natural gas used to generate electricity decreased significantly. As a result, natural gas
was used for a greater share of electricity generation in 2009 than 2008, and coal was used for a smaller share. The
fuel switching from coal to natural gas and additional electricity generation from other energy sources in 2009,
which included a 6.8 percent increase in hydropower generation from the previous year, resulted in a decrease in
carbon intensity, and in turn, a decrease in emissions from electricity generation. From 2008 to 2009, industrial
sector emissions decreased significantly as a result of a decrease in output from energy-intensive industries of 16.6
45 Renewable energy, as defined in EIA's energy statistics, includes the following energy sources: hydroelectric power,
geothermal energy, biofuels, solar energy, and wind energy.
2-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
percent in nonmetallic mineral and 31.6 percent in primary metal industries. The residential and commercial sectors
only experienced minor decreases in emissions as summer and winter weather conditions were less energy-intensive
from 2008 to 2009, and the price of electricity only increased slightly. Heating degree days decreased slightly and
cooling degree days decreased by 3.8 percent from 2008 to 2009.
Overall, from 1990 to 2009, total emissions of CO2 and CH4 increased by 405.5 Tg CO2 Eq. (8.0 percent) and 11.4
Tg CO2 Eq. (1.7 percent), respectively, while N2O emissions decreased by 19.6 Tg CO2 Eq. (6.2 percent). During
the same period, aggregate weighted emissions of HFCs, PFCs, and SF6 rose by 54.1 Tg CO2 Eq. (58.8 percent).
Despite being emitted in smaller quantities relative to the other principal greenhouse gases, emissions of HFCs,
PFCs, and SF6 are significant because many of them have extremely high GWPs and, in the cases of PFCs and SF6,
long atmospheric lifetimes. Conversely, U.S. greenhouse gas emissions were partly offset by C sequestration in
managed forests, trees in urban areas, agricultural soils, and landfilled yard trimmings. These were estimated to
offset 15.3 percent of total emissions in 2009.
Table 2-1 summarizes emissions and sinks from all U.S. anthropogenic sources in weighted units of Tg CO2 Eq.,
while unweighted gas emissions and sinks in gigagrams (Gg) are provided in Table 2-2.
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Tg CO2 Eq.)
Gas/Source
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
1990
5,099.7
4,738.4
1,820.8
1,485.9
846.5
338.3%
2 19. Oft
27.9m
118.6
2000
5,975.0
5,594.sB
2,296.9^
1 ;
,S0P.5l
851.1
370.7M
230.8M
35.9M
144.9
6
5
2
1
2005
,113.8
,753.2
,402.1
,896.6
823.1
357.9
223.5
50.0
143.4
2006
6,021.1
5,653.1
2,346.4
1,878.1
848.2
321.5
208.6
50.3
145.6
6
5
2
1
2007
,120.0
,756.7
,412.8
,894.0
842.0
342.4
219.4
46.1
137.2
5
5
2
1
2008
,921.4
,565.9
,360.9
,789.9
802.9
348.2
224.2
39.8
141.0
2009
5,505.2
5,209.0
2,154.0
1, 719. 7
730.4
339.2
224.0
41.7
123.4
Iron and Steel Production &
Metallurgical Coke
Production 99.5 85.9
Natural Gas Systems 37.6 29.9
Cement Production 33.3 40.4
Incineration of Waste 8.01 11.1
Ammonia Production and
Urea Consumption 16.8 16.4
Lime Production 11.5 14.1
Cropland Remaining Cropland 7.11 7.51
Limestone and Dolomite Use ^. 1B ^. 1B
Soda Ash Production and
Consumption 4.lB 4.2B
Aluminum Production 6.8B 6.lB
Petrochemical Production 3.3 4.51
Carbon Dioxide Consumption 1-^B -^B
Titanium Dioxide Production 1.21 .81
Ferroalloy Production 2.21 .91
Wetlands Remaining Wetlands 1.01 .21
Phosphoric Acid Production 1.5 B -4 B
Zinc Production 0.7B -OB
Lead Production 0.5 B 0.6 B
Petroleum Systems 0.6B 0.5B
Silicon Carbide Production
and Consumption 0.4B 0.2 B
Land Use, Land-Use Change,
and Forestry (Sink)" (861.5) (576.6)
Biomass—Woodb 215.2 218.1
International Bunker Fuelsc 111.8 98.5
65.9
29.9
45.2
12.5
68.8
30.8
45.8
12.5
71.0
31.1
44.5
12.7
66.0
32.8
40.5
12.2
41.9
32.2
29.0
12.3
12.8
14.4
7.9
6.8
12.3
15.1
7.9
8.0
14.0
14.6
8.2
7.7
11.9
14.3
8.7
6.3
11.8
11.2
7.8
7.6
1.2
U
\.1
.3
.8
.4
.1
.4
.1
3.6
3.5
4.2 <•
3.8 i
3.8 :
1.7
1.8
1.5
0.9
1.2
1.1
0.6 (
0.5 (
\.\ <•
\3 *•
5.9 :
.9
.9
.6
.0
.2
.1
).6 (
).5 (
\.\
[.5
5.4
.8
.8
.6
.0
.2
.2
).6
).5
4.3
3.0
2.7
1.8
1.5
1.5
1.1
1.0
1.0
0.5
0.5
0.2
0.2
0.2
0.2
0.1
(1,056.5) (1,064.3) (1,060.9) (1,040.5) (1,015.1)
206.9 203.8 203.3 198.4 183.8
109.7 128.4 127.6 133.7 123.1
Trends in Greenhouse Gas Emissions
2-3
-------
Biomass — Ethanol
CH4
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining Forest
Land
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal
Mines
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Production &
Metallurgical Coke
Production
Field Burning of Agriculture
Residues
Ferroalloy Production
Silicon Carbide Production
and Consumption
Incineration of Waste
International Bunker Fuels0
N2O
Agricultural Soil Management
Mobile Combustion
Manure Management
Nitric Acid Production
Stationary Combustion
Forest Land Remaining Forest
Land
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Composting
Settlements Remaining
Settlements
Incineration of Waste
Field Burning of Agricultural
Residues
Wetlands Remaining Wetlands
International Bunker Fuels0
HFCs
Substitution of Ozone
Depleting Substances'1
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
4.2
674.9
189.81
132. 1 1
147.41
84. ll
31.7!
35.4
23.51
3.2
"
6.0
4.7
0.3
0.9
1.0
0.3
+1
+1
+1
0.2l
315.2
197.81
43.91
14.5
17.7
12.8
4.4
15.8
0.4
0.1
1.1
36.9
0.3
36.4
0.2
20.8
2.2l
18.5
34.4|
9.4
659.9
209.3
136.5
111.7
60.4
42.4 1
31.5J
25.2J
14.3 1
7.4
3.4
1.3
1.2
0.9
0.3
+
+
+
0.1
341.0
206.8 1
53.2
17.ll
19.4J
14.6J
12.1
45
4.9
5.5
1.4
0.1
0.9
103.2
74.31
28.61
O.sl
13.5
4.9J
8.6 1
20.1
23.0
631.4
190.4
136.5
112.5
56.9
46.6
29.4
24.3
9.8
16.8
6.6
5.5
2.5
1.6
1.1
0.7
0.2
+
+
+
0.1
322.9
211.3
36.9
17.3
16.5
14.7
18.4
4.8
4.4
5.0
1.7
11.5
0.4
0.1
1.0
120.2
104.2
15.8
0.2
6.2
3.2
3.0
19.0
31.0
672.1
217.7
138.8
111.7
58.2
46.7
29.4
24.5
21.6
5.9
6.2
5.5
2.3
1.6
1.0
0.7
0.2
+
+
+
0.2
326.4
208.9
33.6
18.0
16.2
14.4
18.0
4.8
4.4
4.3
1.8
1.5
0.4
0.1
+
1.2
123.5
109.4
13.8
0.3
6.0
3.5
2.5
17.9
38.9
664.6
205.2
141.0
111.3
57.9
50.7
30.0
24.4
20.0
6.2
6.5
5.6
2.2
1.7
1.0
0.7
0.2
+
+
+
0.2
325.1
209.4
30.3
18.1
19.2
14.6
16.7
4.9
4.4
3.7
1.8
1.6
0.4
0.1
+
1.2
129.5
112.3
17.0
0.3
7.5
3.7
3.8
16.7
54.8
676.7
211.8
140.6
115.9
67.1
49.4
30.2
24.5
11.9
7.2
6.5
5.9
2.0
1.7
0.9
0.6
0.3
+
+
+
0.2
310.8
210.7
26.1
17.9
16.4
14.2
10.1
5.0
4.4
2.0
1.9
1.5
0.4
0.1
+
1.2
129.4
115.5
13.6
0.3
6.6
4.0
2.7
16.1
61.2
686.3
221.2
139.8
117.5
71.0
49.5
30.9
24.5
7.8
7.3
6.2
5.5
2.0
1.7
0.8
0.4
0.2
+
+
+
0.1
295.6
204.6
23.9
17.9
14.6
12.8
6.7
5.0
4.4
1.9
1.8
1.5
0.4
0.1
+
1.1
125.7
120.0
5.4
0.3
5.6
4.0
1.6
14.8
2-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
5.4
0.5
3.0
1.1
15.1
2.9
1.0
14.1
2.9
1.0
13.2
2.6
0.8
13.3
1.9
0.9
12.8
1.1
1.0
Total
Net Emissions (Sources and
Sinks)
6,181.8 7,112.7 7,213.5 7,166.9 7,263.4 7,061.1 6,633.2
5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2
+ Does not exceed 0.05 Tg CO2 Eq.
a The net CO2 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Parentheses indicate negative values or sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and
Forestry.
0 Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (Gg)
Gas/Source
C02
Fossil Fuel Combustion
Electricity Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of Fuels
1990
5,099,719
4,738,4221
1,485,9371
846,47 5\
338,347
218,964
27,882
118,630
2000
5,974,991
5,594,848
2,296,894
I/
,809,514
851,094
370,666
230,828
35,853
144,933
6
5
2
1
2005
,113,751
,753,200
,402,142
,896,606
823,069
357,903
223,512
49,968
143,392
2006
6,021,089
5,653,116
2,346,406
1
,878,125
848,206
321,513
208,582
50,284
145,574
6
5
2
1
2007
,120,009
,756,746
,412,827
,893,994
842,048
342,397
219,356
46,123
137,233
2008
5,921,443
5,565,925
2,360,919
1,789,918
802,856
348,221
224,167
39,845
140,952
2009
5,505,204
5,208,981
2,154,025
1,719,685
730,422
339,203
223,993
41,652
123,356
Iron and Steel Production
& Metallurgical Coke
Production 99,528 85,9351 65,925 68,772 71,045 66,015 41,871
Natural Gas Systems 37,574| 29,877 29,902 30,755 31,050 32,828 32,171
Cement Production 33,278 40,405 45,197 45,792 44,538 40,531 29,018
Incineration of Waste 7,989 11,112 12,450 12,531 12,700 12,169 12,300
Ammonia Production and
Urea Consumption 16,831 16,402 12,849 12,300 14,038 11,949 11,797
Lime Production 11,5331 14,088 14,379 15,100 14,595 14,330 11,223
Cropland Remaining
Cropland 7,084 7,541 7,854 7,875 8,202 8,654 7,832
Limestone and Dolomite
Use 5,127 5,056 6,768 8,035 7,702 6,276 7,649
Soda Ash Production and
Consumption 4,141 4,181 4,228 4,162 4,140 4,111 4,265
Aluminum Production 6,831 6,086 4,142 3,801 4,251 4,477 3,009
Petrochemical Production 3,311 4,479 4,181 3,837 3,931 3,449 2,735
Carbon Dioxide
Consumption 1,416 1,421 1,321 1,709 1,867 1,780 1,763
Titanium Dioxide
Production 1,195 1,752 1,755 1,836 1,930 1,809 1,541
Ferroalloy Production 2,152 1,893 1,392 1,505 1,552 1,599 1,469
Wetlands Remaining
Wetlands 1,033 1,227 1,079 879 1,012 992 1,090
Phosphoric Acid
Production 1,529 1,382 1,386 1,167 1,166 1,187 1,035
Zinc Production 667 997| 1,088 1,088 1,081 1,230 966
Trends in Greenhouse Gas Emissions
2-5
-------
Lead Production
Petroleum Systems
Silicon Carbide
Production and
Consumption
Land Use, Land-Use
Change, and Forestry
(Sink)"
Biomass - Wootf
International Bunker
Fuels0
Biomass - Ethanof
CH4
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining
Forest Land
Rice Cultivation
Stationary Combustion
Abandoned Underground
Coal Mines
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Production
& Metallurgical Coke
Production
Field Burning of
Agricultural Residues
Ferroalloy Production
Silicon Carbide
Production and
Consumption
Incineration of Waste
International Bunker
Fuels0
N2O
Agricultural Soil
Management
Mobile Combustion
Manure Management
Nitric Acid Production
Stationary Combustion
Forest Land Remaining
Forest Land
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Composting
Settlements Remaining
Settlements
516 594 553
555 534| 490
375 248 219
(861,535) (576,588) (1,056,459)
215,186m 218,088 206,865
111,828 98,482 109,750
4,229 9,352 22,956
32,136 31,423 30,069
9,038 9,968 9,069
6,290 1 6,502
7,018| 5,317
4,003| 2,877
l,51l| 2,019
1,685| 1,501
1,118| 1,199
152
339
354
288
223
15
41
46
13
1
1
+
8
682
357
315
350
160
60
59
44
12
1
1
+
6
6,500
5,358
2,710
2,217
1,398
1,159
467
326
312
264
119
75
51
34
9
+
+
+
7
1,017 1,100 1 1,042
638
142
47
57
41
9
12
14
51
1
3
667
172
55
63
47
39
14
16
18
4
4
682
119
56
53
47
27
15
14
16
6
5
560
488
207
(1,064,330)
203,846
128,384
31,002
32,004
10,364
6,611
5,321
2,774
2,226
1,398
1,167
1,027
282
293
261
112
75
48
35
11
+
+
+
8
1,053
674
108
58
52
47
58
16
14
14
6
5
562
474
196
(1,060,882)
203,316
127,618
38,946
31,647
9,771
6,715
5,299
2,756
2,416
1,427
1,163
953
295
308
267
105
79
48
33
11
+
+
+
8
1,049
675
98
58
62
47
54
16
14
12
6
5
551
453
175
(1,040,461)
198,361
133,704
54,770
32,225
10,087
6,696
5,520
3,196
2,353
1,439
1,168
569
343
310
279
97
80
43
31
13
+
+
+
8
1,002
680
84
58
53
46
33
16
14
7
6
5
525
463
145
(1,015,074)
183,777
123,127
61,231
32,680
10,535
6,655
5,593
3,382
2,356
1,473
1,167
372
349
293
262
93
79
40
17
12
+
+
+
7
954
660
77
58
47
41
22
16
14
6
6
5
2-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Incineration of Waste
Field Burning of
Agricultural Residues
Wetlands Remaining
Wetlands
International Bunker
Fuels0
HFCs
Substitution of Ozone
Depleting Substances'1
HCFC-22 Production
Semiconductor
Manufacture
PFCs
Semiconductor
Manufacture
Aluminum Production
SF6
Electrical Transmission
and Distribution
Magnesium Production
and Processing
Semiconductor
Manufacture
3
M
M
3
M
M
M
1
3
M
M
2
M
M
M
1
3
M
M
1
M
M
M
1
4
M
M
1
M
M
M
1
4
M
M
1
M
M
M
1
4
M
M
1
M
M
M
1
4
M
M
M
M
M
1
+ Does not exceed 0.5 Gg.
M Mixture of multiple gases
a The net CO2 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Parentheses indicate negative values or sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and
Forestry
0 Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Emissions of all gases can be summed from each source category from Intergovernmental Panel on Climate Change
(IPCC) guidance. Over the twenty-year period of 1990 to 2009, total emissions in the Energy and Agriculture
sectors grew by 463.3 Tg CO2 Eq. (8.8 percent) and 35.7 Tg CO2 Eq. (9.3 percent), respectively. Emissions
decreased in the Industrial Processes, Waste, and Solvent and Other Product Use sectors by 32.9 Tg CO2 Eq. (10.4
percent), 24.7 Tg CO2 Eq. (14.1 percent) and less than 0.1 Tg CO2 Eq. (less than 0.4 percent), respectively. Over
the same period, estimates of net C sequestration in the Land Use, Land-Use Change, and Forestry sector increased
by 153.5 Tg CO2 Eq. (17.8 percent).
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (Tg CO2 Eq.)
Chapter/IPCC Sector
1990
2000
2005 2006
2007
2008
2009
Energy
Industrial Processes
Solvent and Other Product Use
Agriculture
Land Use, Land-Use Change,
and Forestry (Emissions)
Waste
5,287.8
315.8
4.4l
383.61
15.ol
175.2
6,168.0
348.8
4.9l
410.6
36.3
143.9
6,282.8
334.1
4.4
418.8
28.6
144.9
6,210.2
339.4
4.4
418.8
49.8
144.4
6,290.7
350.9
4.4
425.8
47.5
144.1
6,116.6
331.7
4.4
426.3
33.2
149.0
5,751.1
282.9
4.4
419.3
25.0
150.5
Trends in Greenhouse Gas Emissions
2-7
-------
Total Emissions
Net CO2 Flux from Land Use,
Land-Use Change, and
Forestry (Sinks)*
Net Emissions (Sources and
Sinks)
6,181.8 7,112.7 7,213.5 7,166.9 7,263.4 7,061.1 6,633.2
(861.5) (576.6) (1056.5) (1064.3) (1060.9) (1040.5) (1015.1)
5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2
The net CO2 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only
included in net emissions total. Please refer to Table 2-9 for a breakout by source.
Note: Totals may not sum due to independent rounding.
Note: Parentheses indicate negative values or sequestration.
Energy
Energy-related activities, primarily fossil fuel combustion, accounted for the vast majority of U.S. CO2 emissions for
the period of 1990 through 2009. In 2009, approximately 83 percent of the energy consumed in the United States
(on a Btu basis) was produced through the combustion of fossil fuels. The remaining 17 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 CO2 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 (49
percent and 13 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: 2009 Energy Chapter Greenhouse Gas Sources
Figure 2-6: 2009 U.S. Fossil Carbon Flows (Tg CO2 Eq.)
Table 2-4: Emissions from Energy (Tg CO2 Eq.)
Gas/Source
C02
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 Fuelsb
Biomass - Ethanol"
CH4
Natural Gas Systems
Coal Mining
Petroleum Systems
Stationary Combustion
Abandoned Underground
Coal Mines
Mobile Combustion
Incineration of Waste
1990
4,903.2
4,738.4
1,820.8
1,485.9
84(0 •
338.3B
219X)B
27.9B
118.61
37.6
8.0
0.6
215. 2M
in.sm
4.2M
327.4
189.81
84.1
35.4
7.4
6.0
4.7
+
2000
5,781.3
5,594.8
2,296.9
1,809.5
851. iB
370.7B
230.8B
35. 9l
144.91
29.9B
11.1
0.5l
2;s.;B
p&jB
9.4m
318.6
209.3B
60.4
31.5
6.6
3.4
+
2005
5,939.4
5,753.2
2,402.1
1,896.6
823.1
357.9
223.5
50.0
143.4
29.9
12.5
0.5
206. 9
109.7
23.0
291.3
190.4
56.9
29.4
6.6
5.5
2.5
+
2006
5,842.5
5,653.1
2,346.4
1,878.1
848.2
321.5
208.6
50.3
145.6
30.8
12.5
0.5
203.8
128.4
31.0
319.2
217.7
58.2
29.4
6.2
5.5
2.3
+
2007
5,938.2
5,756.7
2,412.8
1,894.0
842.0
342.4
219.4
46.1
137.2
31.1
12.7
0.5
203.3
127.6
38.9
307.3
205.2
57.9
30.0
6.5
5.6
2.2
+
2008
5,752.3
5,565.9
2,360.9
1,789.9
802.9
348.2
224.2
39.8
141.0
32.8
12.2
0.5
198.4
133.7
54.8
323.6
211.8
67.1
30.2
6.5
5.9
2.0
+
2009
5,377.3
5,209.0
2,154.0
1,719.7
730.4
339.2
224.0
41.7
123.4
32.2
12.3
0.5
183.8
123.1
61.2
336.8
221.2
71.0
30.9
6.2
5.5
2.0
+
2-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
International Bunker Fuels
N2O
Mobile Combustion
Stationary Combustion
Incineration of Waste
International Bunker Fuelsb
Total
0.2
57.2
43.9
12.8
0.5
1.1
5,287.8
o.M
68.1
53.2
14.6
0.4
0.9
6,168.0
0.1
52.1
36.9
14.7
0.4
1.0
6,282.8
0.
48.
33.
14.
0.
;.
6,210.
2
5
6
4
4
2
2
0.
45.
30.
14.
0.
;.
6,290.
2
1
3
6
4
2
7
a
40.
26.
14.
0.
;.
6,116.
2
7
1
2
4
2
6
0.1
37.0
23.9
12.8
0.4
1.1
5,751.1
+ Does not exceed 0.05 Tg CO2 Eq.
"Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use Change, and
Forestry
b Emissions from International Bunker Fuels are not included in totals.
Note: Totals may not sum due to independent rounding.
Carbon dioxide emissions from fossil fuel combustion are presented in Table 2-5 based on the underlying U.S.
energy consumer data collected by EIA. Estimates of CO2 emissions from fossil fuel combustion are calculated from
these EIA "end-use sectors" based on total consumption and appropriate fuel properties (any additional analysis and
refinement of the EIA data is further explained in the Energy chapter of this report). EIA's fuel consumption data
for the electric power sector comprises electricity-only and combined-heat-and-power (CHP) plants within the
NAICS 22 category whose primary business is to sell electricity, or electricity and heat, to the public (nonutility
power producers can be included in this sector as long as they meet they electric power sector definition). EIA
statistics for the industrial sector include fossil fuel consumption that occurs in the fields of manufacturing,
agriculture, mining, and construction. EIA's fuel consumption data for the transportation sector consists of all
vehicles whose primary purpose is transporting people and/or goods from one physical location to another. EIA's
fuel consumption data for the industrial sector consists of all facilities and equipment used for producing,
processing, or assembling goods (EIA includes generators that produce electricity and/or useful thermal output
primarily to support on-site industrial activities in this sector). EIA's fuel consumption data for the residential sector
consists of living quarters for private households. EIA's fuel consumption data for the commercial sector consists of
service-providing facilities and equipment from private and public organizations and businesses (EIA includes
generators that produce electricity and/or useful thermal output primarily to support the activities at commercial
establishments in this sector). Table 2-5, Figure 2-7, and Figure 2-8 summarize CO2 emissions from fossil fuel
combustion by end-use sector.
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.)
End-Use Sector 1990 2000 2005 2006 2007 2008 2009
Transportation
Combustion
Electricity
Industrial
Combustion
Electricity
Residential
Combustion
Electricity
Commercial
Combustion
Electricity
U.S. Territories
Total
Electricity Generation
1,489.0
1,485.9
3.0
1,533.2
846.5
686.7
931.4
338.3
593.0
757.0
219.0
538.0
27.9
4,738.4
1,820.8
1,813.0
1,809.5
3.4l
1,640.8
851.1
789.8
1,133.1
370.7
762.4|
972.1
230.8|
741.3|
35.9
5,594.8
2,296.9
1,901.3
1,896.6
4.7
1,560.0
823.1
737.0
1,214.7
357.9
856.7
1,027.2
223.5
803.7
50.0
5,753.2
2,402.1
1,882.6
1,878.1
4.5
1,560.2
848.2
712.0
1,152.4
321.5
830.8
1,007.6
208.6
799.0
50.3
5,653.1
2,346.4
1,899.0
1,894
5
.0
.0
1,572.0
842
730
1,198
342
856
1,041
219
821
46
5,756
.0
.0
.5
.4
.1
.1
.4
.7
.1
.7
2,412.8
1,794.6
1,789.9
4.7
1,517.7
802.9
714.8
1,182.2
348.2
834.0
1,031.6
224.2
807.4
39.8
5,565.9
2,360.9
1
1
,724
,719
4
.1
.7
.4
1,333.7
1
730
603
,123
339
784
985
224
761
41
.4
o
.J
.8
.2
.6
.7
.0
.7
.7
5,209.0
2
,154
.0
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.
Figure 2-7: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Trends in Greenhouse Gas Emissions 2-9
-------
Figure 2-8: 2009 End-Use Sector Emissions from Fossil Fuel Combustion
The main driver of emissions in the Energy sector is CO2 from fossil fuel combustion. The transportation end-use
sector accounted for 1,724.1 Tg CO2 Eq. in 2009 or approximately 33 percent of total CO2 emissions from fossil
fuel combustion, the largest share of any end-use sector.46 The industrial end-use sector accounted for 26 percent of
CO2 emissions from fossil fuel combustion. The residential and commercial end-use sectors accounted for an
average 22 and 19 percent, respectively, of CO2 emissions from fossil fuel combustion. Both end-use sectors were
heavily reliant on electricity for meeting energy needs, with electricity consumption for lighting, heating, air
conditioning, and operating appliances contributing 70 and 77 percent of emissions from the residential and
commercial end-use sectors, respectively. Significant trends in emissions from energy source categories over the
twenty-year period from 1990 through 2009 included the following:
• Total CO2 emissions from fossil fuel combustion increased from 4,738.4 Tg CO2 Eq. to 5,209.0 Tg CO2
Eq.—a 9.9 percent total increase over the twenty-year period. From 2008 to 2009, these emissions
decreased by 356.9 Tg CO2 Eq. (6.4 percent), the largest decrease of any year over the twenty-year period.
• CO2 emissions from non-energy use of fossil fuels increased 4.7 Tg CO2 Eq. (4.0 percent) from 1990
through 2009. Emissions from non-energy uses of fossil fuels were 123.4 Tg CO2 Eq. in 2009, which
constituted 2.2 percent of total national CO2 emissions.
• CO2 emissions from incineration of waste (12.3 Tg CO2 Eq. in 2009) increased by 4.3 Tg CO2 Eq. (54
percent) from 1990 through 2009, as the volume of plastics and other fossil carbon-containing materials in
municipal solid waste grew.
• CH4 emissions from coal mining were 71.0 Tg CO2 Eq. in 2009, a decline in emissions of 13.0 Tg CO2 Eq.
(15.5 percent) from 1990. This occurred as a result of the mining of less gassy coal from underground
mines and the increased use of CH4 collected from degasification systems.
• CH4 emissions from natural gas systems were 221.2 Tg CO2 Eq. in 2009; emissions have increased by 31.4
Tg CO2 Eq. (16.6 percent) since 1990.
• In 2009, N2O emissions from mobile combustion were 23.9 Tg CO2 Eq. (approximately 8.1 percent of U. S.
N2O emissions). From 1990 to 2009, N2O emissions from mobile combustion decreased by 45.6 percent.
However, from 1990 to 1998 emissions increased by 26 percent, due to control technologies that reduced
NOX emissions while increasing N2O emissions. Since 1998, newer control technologies have led to a
steady decline in N2O from this source.
Industrial Processes
Greenhouse gas emissions are produced as the by-products of many non-energy-related industrial activities. For
example, industrial processes can chemically transform raw materials, which often release waste gases such as CO2,
CH4, and N2O. These processes include iron and steel production and metallurgical coke production, cement
production, ammonia production and urea consumption, lime production, limestone and dolomite use (e.g., flux
stone, flue gas desulfurization, and glass manufacturing), soda ash production and consumption, titanium dioxide
production, phosphoric acid production, ferroalloy production, CO2 consumption, silicon carbide production and
consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production, lead
production, and zinc production (see Figure 2-9). Industrial processes also release HFCs, PFCs and SF6. In addition
to their use as ODS substitutes, HFCs, PFCs, SF6, and other fluorinated compounds are employed and emitted by a
number of other industrial sources in the United States. These industries include aluminum production, HCFC-22
production, semiconductor manufacture, electric power transmission and distribution, and magnesium metal
production and processing. Table 2-6 presents greenhouse gas emissions from industrial processes by source
category.
46 Note that electricity generation is the largest emitter of CO2 when electricity is not distributed among end-use sectors.
2-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Figure 2-9: 2009 Industrial Processes Chapter Greenhouse Gas Sources
Table 2-6: Emissions from Industrial Processes (Tg CO2 Eq.)
Gas/Source
C02
Iron and Steel Production &
Metallurgical Coke Production
Iron and Steel Production
Metallurgical Coke Production
Cement Production
Ammonia Production & Urea
Consumption
Lime Production
Limestone and Dolomite Use
Soda Ash Production and
Consumption
Aluminum Production
Petrochemical Production
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and
Consumption
CH4
Petrochemical Production
Iron and Steel Production &
Metallurgical Coke Production
Iron and Steel Production
Metallurgical Coke Production
Ferroalloy Production
Silicon Carbide Production and
Consumption
N2O
Nitric Acid Production
Adipic Acid Production
HFCs
Substitution of Ozone Depleting
Substances3
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and
Distribution
Magnesium Production and Processing
Semiconductor Manufacture
Total
1990
188.4
99.5J
97.11
2.5
33.3
16.8
11.5
5.1
4.1
6.8
3.3
1.4
1.2
2.2
1.5
0.7
0.5
0.4
1.9
0.9
1.0
1.0
+
+
+
33.5
17.7
15.8
36.9
0.3
36.4
0.2
20.8
2.2
18.5
34.4
28.4
5.4J
0.5J
315.8
200
184.
85.
83.
2
40.
16.
14.
5
4
6
4
1
1
1
1
1
0
0
2.
1
0
0.
24.
19.
5
103
74.
28.
0
13
4
8
20
16
3
1
348.
0
9
9
7
2
4!
4I
1
1
2I
1
sl
4I
8
9
4
6!
i
2
2I
9
9
+
+1
+1
9 1
4 1
5I
2!
3!
f>l
3
5
9
o
0
1
8
200
165.
65.
63.
2
45.
12.
14.
6
4
4
4
1
1
1
1
1
0
0
1.
1
0
0.
21.
16.
5
120.
104
15.
0.
6
3
3
19.
15.
2.
1.
334.
5
4
9
9
0
1
8
4
8
2
1
2
3
8
4
4
1
6
2
8
1
7
7
+
+
+
5
5
0
2
2
8
2
2
2
0
0
1
9
0
1
2006
169.9
68.8
66.9
1.9
45.8
12.3
15.1
8.0
4.2
3.8
3.8
.7
.8
.5
.2
.1
0.6
0.2
1.7
1.0
0.7
0.7
+
+
+
20.5
16.2
4.3
123.4
109.4
13.8
0.3
6.0
3.5
2.5
17.9
14.1
2.9
1.0
339.4
200
172.
71
69.
2
44
14
14
7
4
4
3
1
1
1
1
1
0
0
1.
1
0
0.
22.
19
3
129.
112
17
0
7
3
3
16.
13
2
0
350.
7
6
0
0
1
5
0
6
7
1
3
9
9
9
6
2
1
6
2
7
0
7
7
+
+
+
9
2
7
5
3
0
3
5
7
8
7
2
6
8
9
2008
159.5
66.0
63.7
2.3
40.5
11.9
14.3
6.3
4.1
4.5
3.4
.8
.8
.6
.2
.2
0.6
0.2
1.6
0.9
0.6
0.6
+
+
+
18.5
16.4
2.0
129.4
115.5
13.6
0.3
6.6
4.0
2.7
16.1
13.3
1.9
0.9
331.7
2009
119.0
41.9
40.9
1.0
29.0
11.8
11.2
7.6
4.3
3.0
2.7
.8
.5
.5
.0
.0
0.5
0.1
1.2
0.8
0.4
0.4
+
+
+
16.5
14.6
1.9
125.7
120.0
5.4
0.3
5.6
4.0
1.6
14.8
12.8
1.1
1.0
282.9
+ Does not exceed 0.05 Tg CO2 Eq.
a Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Trends in Greenhouse Gas Emissions
2-11
-------
Overall, emissions from industrial processes decreased by 10.4 percent from 1990 to 2009 due to decreases in
emissions from several industrial processes, such as iron and steel production and metallurgical coke production,
HCFC-22 production, aluminum production, adipic acid production, and electrical transmission and distribution.
Significant trends in emissions from industrial processes source categories over the twenty-year period from 1990
through 2009 included the following:
• Combined CO2 and CH4 emissions from iron and steel production and metallurgical coke production
decreased by 36.6 percent to 42.2 Tg CO2 Eq. from 2008 to 2009, and have declined overall by 58.2 Tg
CO2 Eq. (58.0 percent) from 1990 through 2009, due to restructuring of the industry, technological
improvements, and increased scrap utilization.
• CO2 emissions from ammonia production and urea consumption (11.8 Tg CO2 Eq. in 2009) have decreased
by 5.0 Tg CO2 Eq. (29.9 percent) since 1990, due to a decrease in domestic ammonia production. This
decrease in ammonia production is primarily attributed to market fluctuations.
• N2O emissions from adipic acid production were 1.9 Tg CO2 Eq. in 2009, and have decreased significantly
in recent years from the widespread installation of pollution control measures. Emissions from adipic acid
production have decreased by 87.7 percent since 1990 and by 89.0 percent since a peak in 1995.
• HFC emissions from ODS substitutes have been increasing from small amounts in 1990 to 120.0 Tg CO2
Eq. in 2009. This increase results from 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 accelerate over the next decade as
HCFCs—which are interim substitutes in many applications—are phased out under the provisions of the
Copenhagen Amendments to the Montreal Protocol.
• PFC emissions from aluminum production decreased by about 91.5 percent (17.0 Tg CO2 Eq.) from 1990
to 2009, due to both industry emission reduction efforts and lower domestic aluminum production.
Solvent and Other Product Use
Greenhouse gas emissions are produced as a by-product of various solvent and other product uses. In the United
States, N2O Emissions from Product Uses, the only source of greenhouse gas emissions from this sector, accounted
for 4.4 Tg CO2 Eq., or less than 0.1 percent of total U.S. emissions in 2009 (see Table 2-7).
Table 2-7: N2O Emissions from Solvent and Other Product Use (Tg CO2 Eq.)
Gas/Source
N2O
N2O from Product Uses
Total
1990
4.4
4.4
4.4
2000
4.9
4.9
4.9
2005
4.4
4.4
4.4
2006
4.4
4.4
4.4
2007
4.4
4.4
4.4
2008
4.4
4.4
4.4
2009
4.4
4.4
4.4
In 2009, N2O emissions from product uses constituted 1.5 percent of U.S. N2O emissions. From 1990 to 2009,
emissions from this source category decreased by just under 0.4 percent, though slight increases occurred in
intermediate years.
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 2009, agricultural activities were responsible for emissions of 419.3 Tg CO2 Eq., or 6.3 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 20.4 percent and 7.2 percent of total
CH4 emissions from anthropogenic activities, respectively, in 2009. Agricultural soil management activities, such as
fertilizer application and other cropping practices, were the largest source of U.S. N2O emissions in 2009,
accounting for 69.2 percent.
2-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Figure 2-10: 2009 Agriculture Chapter Greenhouse Gas Sources
Table 2-8: Emissions from Agriculture (Tg CO2 Eq.)
Gas/Source
CH4
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural
Residues
N2O
Agricultural Soil
Management
Manure Management
Field Burning of Agricultural
Residues
1990
171.2
132.1 1
31. 7H
7.1
0.3 •
212.4
197.8 •
14.51
0.1 1
2000
186.7
136.5
42.4
7.5 1
0.3 •
224.0
206.8
17.1
0.1
2005
190.1
136.5
46.6
6.8
0.2
228.7
211.3
17.3
0.1
2006
191.7
138.8
46.7
5.9
0.2
227.1
208.9
18.0
0.1
2007
198.2
141.0
50.7
6.2
0.2
227.6
209.4
18.1
0.1
2008
197.5
140.6
49.4
7.2
0.3
228.8
210.7
17.9
0.1
2009
196.8
139.8
49.5
7.3
0.2
222.5
204.6
17.9
0.1
Total 383.6 410.6 418.8 418.8 425.8 426.3 419.3
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from Agriculture include the following:
• Agricultural soils produced approximately 69 percent of N2O emissions in the United States in 2009.
Estimated emissions from this source in 2009 were 204.6 Tg CO2 Eq. Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2009, although overall emissions were 3.4 percent higher in
2009 than in 1990. Nitrous oxide emissions from this source have not shown any significant long-term
trend, as their estimation is highly sensitive to the amount of N applied to soils, which has not changed
significantly over the time-period, and to weather patterns and crop type.
• Enteric fermentation was the largest source of CH4 emissions in 2009, at 139.8 Tg CO2 Eq. Generally,
emissions decreased from 1996 to 2003, though with a slight increase in 2002. This trend was mainly due
to decreasing populations of both beef and dairy cattle and increased digestibility of feed for feedlot cattle.
Emissions increased from 2004 through 2007, as both dairy and beef populations increased and the
literature for dairy cow diets indicated a trend toward a decrease in feed digestibility for those years.
Emissions decreased again in 2008 and 2009 as beef cattle populations decreased again. During the
timeframe of this analysis, populations of sheep have decreased 49 percent since 1990 while horse
populations have increased over 87 percent, mostly since 1999. Goat and swine populations have increased
25 percent and 23 percent, respectively, during this timeframe.
• Overall, emissions from manure management increased 46 percent between 1990 and 2009. This
encompassed an increase of 56 percent for CH4, from 31.7 Tg CO2 Eq. in 1990 to 49.5 Tg CO2 Eq. in 2009;
and an increase of 23 percent for N2O, from 14.5 Tg CO2 Eq. in 1990 to 17.9 Tg CO2 Eq. in 2009. 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.
Land Use, Land-Use Change, and Forestry
When humans alter the terrestrial biosphere through land use, changes in land use, and land management practices,
they also alter the background carbon fluxes between biomass, soils, and the atmosphere. Forest management
practices, tree planting in urban areas, the management of agricultural soils, and the landfilling of yard trimmings
and food scraps have resulted in an uptake (sequestration) of carbon in the United States, which offset about 15
percent of total U.S. greenhouse gas emissions in 2009. Forests (including vegetation, soils, and harvested wood)
accounted for approximately 85 percent of total 2009 net CO2 flux, urban trees accounted for 9 percent, mineral and
organic soil carbon stock changes accounted for 4 percent, and landfilled yard trimmings and food scraps accounted
for 1 percent of the total net flux in 2009. 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
Trends in Greenhouse Gas Emissions 2-13
-------
sequester approximately 5.5 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 carbon and food scraps in landfills.
Land use, land-use change, and forestry activities in 2009 resulted in a net C sequestration of 1,015.1 Tg CO2 Eq.
(276.8 Tg C) (Table 2-9). This represents an offset of approximately 18 percent of total U.S. CO2 emissions, or 15
percent of total greenhouse gas emissions in 2009. Between 1990 and 2009, total land use, land-use change, and
forestry net C flux resulted in a 17.8 percent increase in CO2 sequestration.
Table 2-9: Net CO2 Flux from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.)
Sink Category
Forest Land Remaining Forest
Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining
Settlements
Other (Landfilled Yard
Trimmings and Food Scraps)
Total
1990
(681.1)
(29.4)
2.2|
(52.2)1
(19.8)1
(57.1)
(24.2)
(861.5)
2000
(378.3)1
(30.2)
2.4|
(52.6)
(27.2)
(77.5)
(13.2)
(576.6)
2005
(911.5)
(18.3)
5.9
(8.9)
(24.4)
(87.8)
(11.5)
(1,056.5)
2006
(917.5)
(19.1)
5.9
(8.8)
(24.2)
(89.8)
(11.0)
(1,064.3)
2007
(911.9)
(19.7)
5.9
(8.6)
(24.0)
(91.9)
(10.9)
(1,060.9)
2008
(891.0)
(18.1)
5.9
(8.5)
(23.8)
(93.9)
(11.2)
(1,040.5)
2009
(863.1)
(17.4)
5.9
(8.3)
(23.6)
(95.9)
(12.6)
(1,015.1)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Land use, land-use change, and forestry source categories also resulted in emissions of CO2, CH4, and N2O that are
not included in the net CO2 flux estimates presented in Table 2-9. The application of crushed limestone and
dolomite to managed land (i.e., soil liming) and urea fertilization resulted in CO2 emissions of 7.8 Tg CO2 Eq. in
2009, an increase of about 10.6 percent relative to 1990. Lands undergoing peat extraction resulted in CO2
emissions of 1.1 Tg CO2Eq. (1,090 Gg), andN2O emissions of less than 0.01 TgCO2Eq. N2O emissions from the
application of synthetic fertilizers to forest soils have increased from 0.1 Tg CO2 Eq. in 1990 to 0.4 Tg CO2 Eq. in
2009. Settlement soils in 2009 resulted in direct N2O emissions of 1.5 Tg CO2 Eq., a 55 percent increase relative to
1990. Emissions from forest fires in 2009 resulted in CH4 emissions of 7.8 Tg CO2 Eq., and in N2O emissions of 6.4
TgCO2Eq. (Table 2-10).
Table 2-10: Emissions from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.)
Source Category
C02
Cropland Remaining Cropland: Liming of
Agricultural Soils
Cropland Remaining Cropland: Urea Fertilization
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
CH4
Forest Land Remaining Forest Land: Forest Fires
N2O
Forest Land Remaining Forest Land: Forest Fires
Forest Land Remaining Forest Land: Forest Soils
Settlements Remaining Settlements: Settlement
Soils
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
Total
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
1990
8.1
4.7
2.4
1.0
3.2
3.2
3.7
2.6
0.1
1.0
+
15.0
2000
8.8
4.3
3.2
1
14.3
14.3 •
13.21
11.7|
0.4
1
+
36.3
2005
8.9
4.3
3.5
1.1
9.8
9.8
9.8
8.0
0.4
1.5
+
28.6
2006
8.8
4.2
3.7
0.9
21.6
21.6
19.5
17.6
0.4
1.5
+
49.8
2007
9.2
4.5
3.7
1.0
20.0
20.0
18.3
16.3
0.4
1.6
+
47.5
2008
9.6
5.0
3.6
1.0
11.9
11.9
11.6
9.8
0.4
1.5
+
33.2
2009
8.9
4.2
3.6
1.1
7.8
7.8
8.3
6.4
0.4
1.5
+
25.0
2-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Other significant trends from 1990 to 2009 in land use, land-use change, and forestry emissions include:
• Net C sequestration by forest land has increased by almost 27 percent. This is primarily due to increased
forest management and the effects of previous reforestation. The increase in intensive forest management
resulted in higher growth rates and higher biomass density. The tree planting and conservation efforts of
the 1970s and 1980s continue to have a significant impact on sequestration rates. Finally, the forested area
in the United States increased over the past 20 years, although only at an average rate of 0.21 percent per
year.
• Net sequestration of C by urban trees has increased by 68 percent over the period from 1990 to 2009. 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 48 percent since
1990. This is due in part to a decrease in the amount of yard trimmings and food scraps generated. In
addition, the proportion of yard trimmings and food scraps landfilled has decreased, as there has been a
significant rise in the number of municipal composting facilities in the United States.
Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 2-11). In 2009,
landfills were the third largest source of anthropogenic CH4 emissions, accounting for 17 percent of total U.S. CH4
emissions.47 Additionally, wastewater treatment accounts for 4 percent of U.S. CH4 emissions, and 2 percent of N2O
emissions. Emissions of CH4 and N2O from composting grew from 1990 to 2009, and resulted in emissions of 3.5
Tg CO2 Eq. in 2009. A summary of greenhouse gas emissions from the Waste chapter is presented in Table 2-11.
Figure 2-11: 2009 Waste Chapter Greenhouse Gas Sources
Overall, in 2009, waste activities generated emissions of 150.5 Tg CO2 Eq., or 2.3 percent of total U.S. greenhouse
gas emissions.
Table 2-11: Emissions from Waste (Tg CO2 Eq.)
Gas/Source
CH4
Landfills
Wastewater Treatment
Composting
N2O
Wastewater Treatment
Composting
Total
1990
171.2
147.4|
23. 5l
0.3
4.0
3.7
0.4
175.2
2000 •
138.1
111.71
25.21
1.3
5.9
4.5
1.4
143.9
2005
138.4
112.5
24.3
1.6
6.5
4.8
1.7
144.9
2006
137.8
111.7
24.5
1.6
6.6
4.8
1.8
144.4
2007
137.4
111.3
24.4
1.7
6.7
4.9
1.8
144.1
2008
142.1
115.9
24.5
1.7
6.8
5.0
1.9
149.0
2009
143.6
117.5
24.5
1.7
6.9
5.0
1.8
150.5
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from Waste include the following:
• Combined CO2 and CH4 emissions from composting have generally increased since 1990, from 0.7 Tg CO2
Eq. to 3.5 Tg CO2 Eq. in 2009, an over four-fold increase over the time series.
• From 1990 to 2009, net CH4 emissions from landfills decreased by 29.9 Tg CO2 Eq. (20 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,48 which has more than offset the
47 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.
48 Lhe 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.
Trends in Greenhouse Gas Emissions 2-15
-------
additional CH4 emissions resulting from an increase in the amount of municipal solid waste landfilled.
• From 1990 to 2009, CH4 and N2O emissions from wastewater treatment increased by 1.0 Tg CO2 Eq. (4.4
percent) and 1.3 Tg CO2 Eq. (36 percent), respectively.
2.2. Emissions by Economic Sector
Throughout this report, emission estimates are grouped into six sectors (i.e., chapters) defined by the IPCC and
detailed above: Energy; Industrial Processes; Solvent and Other Product Use; Agriculture; Land Use, Land-Use
Change, and Forestry; and Waste. While it is important to use this characterization for consistency with UNFCCC
reporting guidelines, it is also useful to allocate emissions into more commonly used sectoral categories. This
section reports emissions by the following U.S. economic sectors: residential, commercial, industry, transportation,
electricity generation, and agriculture, as well as U.S. territories.
Using this categorization, emissions from electricity generation accounted for the largest portion (33 percent) of
U.S. greenhouse gas emissions in 2009. Transportation activities, in aggregate, accounted for the second largest
portion (27 percent). Emissions from industry accounted for about 20 percent of U.S. greenhouse gas emissions in
2009. 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 20 percent of U.S. greenhouse gas emissions were contributed by the residential, agriculture, and
commercial sectors, plus emissions from U.S. territories. The residential sector accounted for 5 percent, and
primarily consisted of CO2 emissions from fossil fuel combustion. Activities related to agriculture accounted for
roughly 7 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 CO2
from fossil fuel combustion. The commercial sector accounted for roughly 6 percent of emissions, while U.S.
territories accounted for less than 1 percent.
CO2 was also emitted and sequestered (in the form of C) by a variety of activities related to forest management
practices, tree planting in urban areas, the management of agricultural soils, and landfilling of yard trimmings.
Table 2-12 presents a detailed breakdown of emissions from each of these economic sectors by source category, as
they are defined in this report. Figure 2-12 shows the trend in emissions by sector from 1990 to 2009.
Figure 2-12: Emissions Allocated to Economic Sectors
Table 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (Tg CO2 Eq. and Percent of Total in
2009)
Sector/Source
Electric Power Industry
CO2 from Fossil Fuel Combustion
Electrical Transmission and
Distribution
Incineration of Waste
Stationary Combustion
Limestone and Dolomite Use
Transportation
CO2 from Fossil Fuel Combustion
Substitution of Ozone Depleting
Substances
Mobile Combustion
Non-Energy Use of Fuels
Industry
CO2 from Fossil Fuel Combustion
1990
1,868.9
1,820.8
1,545.2
1,485.9
^.0-
1,564.4
815.4
2000
2,337
2,296
16
11
.6
.9
10.6|
2
1,932
1,809
55
55
12
1,544
812
,5|
.3
.5
'"•
.0
o
.J
2005
2,444.6
2,402
15
12
11
o
J
.1
.1
.9
.0
.4
2,017.4
1,896
72
37
10
1,441
776
.6
.9
.7
.2
.9
.3
2006
2,388.2
2,346.4
14.1
12.9
10.8
4.0
1,994.4
1,878.1
72.2
34.2
9.9
1,497.3
799.2
2007
2,454.0
2,412.8
13.2
13.1
11.0
3.9
2,003.8
1,894.0
68.8
30.7
10.2
1,483.0
793.6
2008
2,400.7
2,360.9
13.3
12.5
10.8
3.1
1,890.7
1,789.9
64.9
26.4
9.5
1,446.9
757.4
2009 Percent3
2,193
2,154
12
12
9
3
.0
.0
.8
.7
.7
.8
1,812.4
1,719
60
24
8
.7
.2
.0
.5
1,322.7
683
.8
33.1%
32.5%
0.2%
0.2%
0.1%
0.1%
27.3%
25.9%
0.9%
0.4%
0.1%
19.9%
10.3%
2-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Iron and Steel Production &
Metallurgical Coke Production
Petroleum Systems
Cement Production
Nitric Acid Production
Ammonia Production and Urea
Consumption
Lime Production
Substitution of Ozone Depleting
Substances
Abandoned Underground Coal Mines
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
N2O from Product Uses
Soda Ash Production and
Consumption
Limestone and Dolomite Use
Stationary Combustion
Petrochemical Production
Adipic Acid Production
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Mobile Combustion
Magnesium Production and
Processing
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and
Consumption
Agriculture
N2O from Agricultural Soil
Management
Enteric Fermentation
Manure Management
CO2 from Fossil Fuel Combustion
CH4 and N2O from Forest Fires
Rice Cultivation
Liming of Agricultural Soils
Urea Fertilization
CO2 and N2O from Managed
Peatlands
Mobile Combustion
N2O from Forest Soils
Field Burning of Agricultural
Residues
227.4
101.1
84.1
100.5
359
33.3
17.7
16.8
11.5
+
6.0
36.4
2.9
25.4
4.4
4.1
2.6
4.7
4.2
15.8
1.4
1.2
2.2
0.9
5.4
1.5
0.7
0.5
0.4
429.0
197.8
132.1
46.2
31.04
5.8
7.1
4.7
2.4
1.0
0.3
0.1
0.4
239.2
122.8
60.4
86.9
32.0
40.4
19.4
16.4
14.1
3.2
7.4
28.6
6.2
14.7
4.9
4.2
2.5
4.8
5.7
5.5
1.4
1.8
1.9
1.1
3.0
1.4
1.0
0.6
0.3
485.1
206.8
136.5
59.5
38.79
26.0
7.5
4.3
3.2
1.2
0.4
0.4
0.4
220.4
125.2
56.9
66.6
29.9
45.2
16.5
12.8
14.4
6.4
5.5
15.8
4.4
7.1
4.4
4.2
3.4
4.4
5.3
5.0
1.3
1.8
1.4
1.3
2.9
1.4
1.1
0.6
0.2
493.2
211.3
136.5
63.8
46.81
17.8
6.8
4.3
3.5
1.1
0.5
0.4
0.3
248.4
126.8
58.2
69.5
29.8
45.8
16.2
12.3
15.1
7.1
5.5
13.8
4.7
6.3
4.4
4.2
4.0
4.6
4.8
4.3
1.7
1.8
1.5
1.3
2.9
1.2
1.1
0.6
0.2
516.7
208.9
138.8
64.8
49.04
39.2
5.9
4.2
3.7
0.9
0.5
0.4
0.3
236.2
119.8
57.9
71.7
30.4
44.5
19.2
14.0
14.6
7.8
5.6
17.0
4.8
8.1
4.4
4.1
3.9
4.4
4.9
3.7
1.9
1.9
1.6
1.3
2.6
1.2
1.1
0.6
0.2
520.7
209.4
141.0
68.9
48.44
36.4
6.2
4.5
3.7
1.0
0.5
0.4
0.3
244.6
123.1
67.1
66.7
30.7
40.5
16.4
11.9
14.3
8.5
5.9
13.6
5.1
7.2
4.4
4.1
3.1
4.1
4.4
2.0
.8
.8
.6
.3
.9
.2
.2
0.6
0.2
503.9
210.7
140.6
67.3
45.44
21.7
7.2
5.0
3.6
1.0
0.5
0.4
0.4
253.4
111.1
71.0
42.2
31.4
29.0
14.6
11.8
11.2
10.9
5.5
5.4
5.3
4.6
4.4
4.3
3.8
3.6
3.6
1.9
1.8
1.5
1.5
1.3
1.1
1.0
1.0
0.5
0.2
490.0
204.6
139.8
67.3
46.66
14.2
7.3
4.2
3.6
1.1
0.5
0.4
0.4
3.8%
1.7%
1.1%
0.6%
0.5%
0.4%
0.2%
0.2%
0.2%
0.2%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
+
+
+
+
+
+
+
+
+
+
7.4%
3.1%
2.1%
1.0%
0.7%
0.2%
0.1%
0.1%
0.1%
+
+
+
+
Trends in Greenhouse Gas Emissions
2-17
-------
Stationary Combustion
Commercial
CO2 from Fossil Fuel Combustion
Landfills
Substitution of Ozone Depleting
Substances
Wastewater Treatment
Human Sewage
Composting
Stationary Combustion
Residential
CO2 from Fossil Fuel Combustion
Substitution of Ozone Depleting
Substances
Stationary Combustion
Settlement Soil Fertilization
U.S. Territories
CO2 from Fossil Fuel Combustion
Non-Energy Use of Fuels
Stationary Combustion
Total Emissions
+
395.5
219.0
147.4
+
23.5
3.7
0.7
1.3
345.1
338.3
279J
5.7
0.1
6,181.8
+1
381.4
230.8
111.7
5.4
25.2
4.5
2.6
1.3
386.2
370.7
10.1
43
11
46.0
35.9
110.0
01
7,112.7
+
387.2
223.5
112.5
17.6
24.3
4.8
3.3
1.2
371.0
357.9
7.3
4.3
1.5
58.2
50.0
8.1
0.2
7,213.5
+
375.2
208.6
111.7
21.1
24.5
4.8
3.3
1.2
335.8
321.5
8.9
3.9
1.5
59.3
50.3
8.8
0.2
7,166.9
+
389.6
219.4
111.3
24.9
24.4
4.9
3.5
1.2
358.9
342.4
10.7
4.2
1.6
53.5
46.1
7.2
0.2
7,263.4
+
403.5
224.2
115.9
29.1
24.5
5.0
3.5
1.2
367.1
348.2
12.9
4.4
1.5
48.4
39.8
8.4
0.2
7,061.1
+
409.5
224.0
117.5
33.7
24.5
5.0
3.5
1.2
360.1
339.2
15.1
4.2
1.5
45.5
41.7
3.7
0.2
6,633.2
+
6.2%
3.4%
1.8%
0.5%
0.4%
0.1%
0.1%
+
5.4%
5.1%
0.2%
0.1%
+
0.7%
0.6%
0.1%
+
100.0%
Sinks
(861.5) (576.6) (1,056.5) (1,064.3) (1,060.9) (1,040.5) (1,015.1) -15.3%
CO2 Flux from Forestsb
Urban Trees
CO2 Flux from Agricultural Soil
Carbon Stocks (99.2) (107.6)
Landfilled Yard Trimmings and Food
Scraps (24.2) (13.2)
(681.1) (378.3) (911.5) (917.5) (911.9) (891.0) (863.1) -13.0%
(57.1) (77.5) (87.8) (89.8) (91.9) (93.9) (95.9) -1.4%
(45.6) (46.1) (46.3) (44.4) (43.4) -0.7%
(11.5) (11.0) (10.9) (11.2) (12.6) -0.2%
Net Emissions
5,320.3 6,536.1 6,157.1 6,102.6 6,202.5 6,020.7 5,618.2 84.7%
Note: Includes all emissions of CO2, CH4, N2O, HFCs, PFCs, and SF6. Parentheses indicate negative values or sequestration.
Totals may not sum due to independent rounding.
ODS (Ozone Depleting Substances)
+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2009.
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 33 percent of total U.S. greenhouse gas
emissions in 2009. Emissions increased by 17 percent since 1990, as electricity demand grew and fossil fuels
remained the dominant energy source for generation. Electricity generation-related emissions decreased from 2008
to 2009 by 9 percent, primarily due to decreased CO2 emissions from fossil fuel combustion. The decrease in
electricity-related emissions was due to decreased economic output and the resulting decrease in electricity demand.
Electricity-related emissions also declined due to a decrease in the carbon intensity of fuels used to generate
electricity. This was caused by fuel switching as the price of coal increased and the price natural gas decreased
significantly. The fuel switching from coal to natural gas and additional electricity generation from other energy
sources in 2009, which included a 7 percent increase in hydropower generation from the previous year, resulted in a
decrease in carbon intensity, and in turn, a decrease in emissions from electricity generation. The electricity
generation sector in the United States is composed of traditional electric utilities as well as other entities, such as
power marketers and non-utility power producers. The majority of electricity generated by these entities was
2-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
through the combustion of coal in boilers to produce high-pressure steam that is passed through a turbine. Table
2-13 provides a detailed summary of emissions from electricity generation-related activities.
Table 2-13: Electricity Generation-Related Greenhouse Gas Emissions (Tg CO2 Eq.)
Gas/Fuel Type or Source
C02
CO2 from Fossil Fuel
Combustion
Coal
Natural Gas
Petroleum
Geothermal
Incineration of Waste
Limestone and Dolomite Use
CH4
Stationary Combustion*
Incineration of Waste
N2O
Stationary Combustion*
Incineration of Waste
SF6
Electrical Transmission and
Distribution
Total
1990
1,831.4
1,820.8
1,547.6
175.3
97. 5W
0.4
8.0
2.6
0.6
0.6
1
8.5
8.1
0.5
28.4
28.4
1,868.9
2000
2,310.5
2,296.9
1,927.4
280.8
88.4
0.4
11.1
2.5
0.7
0.7
+
10.4
10.0
0.4
16.0
16.0
2,337.6
2005
2,418.0
2,402.1
1,983.8
318.8
99.2
0.4
12.5
3.4
0.7
0.7
+
10.7
10.3
0.4
15.1
15.1
2,444.6
2006
2,363.0
2,346.4
1,953.7
338.0
54.4
0.4
12.5
4.0
0.7
0.7
+
10.5
10.1
0.4
14.1
14.1
2,388.2
2007
2,429.4
2,412.8
1,987.3
371.3
53.9
0.4
12.7
3.9
0.7
0.7
+
10.6
10.2
0.4
13.2
13.2
2,454.0
2008
2,376.2
2,360.9
1,959.4
361.9
39.2
0.4
12.2
3.1
0.7
0.7
+
10.4
10.1
0.4
13.3
13.3
2,400.7
2009
2,170.1
2,154.0
1,747.6
373.1
32.9
0.4
12.3
3.8
0.7
0.7
+
9.4
9.0
0.4
12.8
12.8
2,193.0
Note: Totals may not sum due to independent rounding.
* Includes only stationary combustion emissions related to the generation of electricity.
+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
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 retail sales of electricity (EIA 2010 and Duffield 2006). These three
source categories include CO2 from Fossil Fuel Combustion, CH4 and N2O from Stationary Combustion, and SF6
from Electrical Transmission and Distribution Systems.49
When emissions from electricity are distributed among these sectors, industry activities account for the largest share
of total U.S. greenhouse gas emissions (28.8 percent), followed closely by emissions from transportation (27.4
percent). Emissions from the residential and commercial sectors also increase substantially when emissions from
electricity are included. In all sectors except agriculture, CO2 accounts for more than 80 percent of greenhouse gas
emissions, primarily from the combustion of fossil fuels.
Table 2-14 presents a detailed breakdown of emissions from each of these economic sectors, with emissions from
electricity generation distributed to them. Figure 2-13 shows the trend in these emissions by sector from 1990 to
2009.
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors
Table 2-14: U.S Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-Related Emissions
Distributed (Tg CO2 Eq.) and Percent of Total in 2009
Sector/Gas
Industry
Direct Emissions
1990
2,238.3
1,564.4
2000
2,314.4
1,544.0
2005 2006 2007 2008 2009 Percent3
2,162.5 2,194.6 2,192.9 2,146.5 1,910.9 28.8%
1,441.9 1,497.3 1,483.0 1,446.9 1,322.7 19.9%
49 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.
Trends in Greenhouse Gas Emissions 2-19
-------
C02
CH4
N2O
MFCs, PFCs, and SF6
Electricity -Related
C02
CH4
N2O
SF6
Transportation
Direct Emissions
C02
CH4
N2O
HFCsb
Electricity -Related
C02
CH4
N2O
SF6
Commercial
Direct Emissions
C02
CH4
N2O
MFCs
Electricity -Related
C02
CH4
N2O
SF6
Residential
Direct Emissions
CO2
CH4
N2O
MFCs
Electricity -Related
C02
CH4
N2O
SF6
Agriculture
Direct Emissions
C02
CH4
N2O
Electricity -Related
C02
CH4
N2O
SF6
U.S. Territories
Total
1,140.5
318.8
41.8
63.3
673.9
660.3
0.2
3.1
10.2
1,548.3
1,545.2
1,497.8
4.5
42.9
+
3.1
3.1
+
+
+
947.7
395.5
219.0
172.1
4.4
+
552.2
541.1
0.2
2.5
8.4
953.8
345.1
338.3
4.4
2.1
0.3
608.7
596.5
0.2
2.8
9.2
460.0
429.0
39.2
174.5
215.3
31.0
30.4
+
0.1
0.5
33.7
6,181.8
1,147.9
312.5
34.0
49.6
770.4
761.5
0.2
3.4
5.3
1,935.8
1,932.3
1,821.6
3.1
51.9
55.7
3.5
3.5
+
+
+
1,135.8
381.4
230.8
139.0
6.2
5.4
754.4
745.7
0.2
3.3
5.2
1,162.2
386.2
370.7
3.4
2.1
10.1
775.9
767.0
0.2
3.4
5.3
518.4
485.1
47.6
201.1
236.4
33.3
32.9
+
0.1
0.2
46.0
7,112.7
1,093.8
285.7
30.0
32.5
720.5
712.7
0.2
3.2
4.5
2,022.2
2,017.4
1,906.8
2.2
35.5
72.9
4.8
4.8
+
+
+
1,205.1
3S7.2
223.5
139.3
6.8
17.6
817.9
809.0
0.2
3.6
5.1
1,242.9
371.0
357.9
3.4
2.4
7.3
871.9
862.4
0.3
3.8
5.4
522.7
493.2
55.7
200.1
237.4
29.4
29. 1
+
0.1
0.2
58.2
7,213.5
1,123.1
314.1
29. 1
31.0
697.3
689.9
0.2
3.1
4.1
1,999.0
1,994.4
1,888.0
2.0
32.1
72.2
4.6
4.6
+
+
+
1,188.5
375.2
208.6
138.7
6.9
21.1
813.2
804.7
0.2
3.6
4.8
1,181.5
335. 8
321.5
3.1
2.3
8.9
845.6
836.7
0.3
3.7
5.0
544.1
576.7
57.8
213.4
245.4
27.4
27.1
+
0.1
0.2
59.3
7,166.9
1,113.7
301.9
31.4
36.0
709.9
702.8
0.2
3.1
3.8
2,008.9
2,003.8
1,904.2
1.9
28.8
68.8
5.7
5.1
+
+
+
1,225.3
389.6
219.4
138.2
7.1
24.9
S35.7
827.4
0.2
3.6
4.5
1,229.6
35S.9
342.4
3.4
2.4
10.7
870.7
862.0
0.3
3.8
4.7
553.2
520.7
57.7
218.4
244.7
32.5
32.2
+
0.1
0.2
53.5
7,263.4
Note: Emissions from electricity generation are allocated based on aggregate electricity
Totals may not sum due to
1,070.1
318.1
26.8
31.9
699.7
692.5
0.2
3.0
3.9
1,895.5
1,890.7
1,799.4
1.7
24.6
64.9
4.7
4.7
+
+
+
1,224.5
403.5
224.2
143.1
7.2
29.1
821.0
812.7
0.2
3.6
4.6
1,215.1
367.7
348.2
3.5
2.4
12.9
848.1
839.4
0.2
3.7
4.7
531.1
503.9
55.1
209.6
239.2
27.2
26.9
+
0.1
0.2
48.4
7,061.1
942.7
331.2
24.5
24.2
588.3
582.2
0.2
2.5
3.4
1,816.9
1,812.4
1,728.2
1.6
22.4
60.2
4.5
4.5
+
+
+
1,184.9
409.5
224.0
144.5
7.2
33.7
775.4
767.4
0.2
3.3
4.5
1,158.9
360.1
339.2
3.4
2.4
15.1
798.8
790.5
0.2
3.4
4.7
516.0
490.0
55.6
204.8
229.7
25.9
25.7
+
0.1
0.2
45.5
6,633.2
14.2%
5.0%
0.4%
0.4%
8.9%
8.8%
+
+
0.1%
27.4%
27.3%
26.1%
+
0.3%
0.9%
0.1%
0.1%
+
+
+
17.9%
6.2%
3.4%
2.2%
0.1%
0.5%
11.7%
11.6%
+
+
0.1%
17.5%
5.4%
5.1%
0.1%
+
0.2%
12.0%
11.9%
+
0.1%
0.1%
7.8%
7.4%
0.8%
3.1%
3.5%
0.4%
0.4%
+
+
+
0.7%
100.0%
• consumption in each end-use sector.
independent rounding.
2-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
+ Does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
a Percent of total emissions for year 2009.
b Includes primarily HFC-134a.
Industry
The industrial end-use sector includes CO2 emissions from fossil fuel combustion from all manufacturing facilities,
in aggregate. This sector also includes emissions that are produced as a by-product of the non-energy-related
industrial process activities. The variety of activities producing these non-energy-related emissions includes
methane emissions from petroleum and natural gas systems, fugitive CH4 emissions from coal mining, by-product
CO2 emissions from cement manufacture, and HFC, PFC, and SF6 by-product emissions from semiconductor
manufacture, to name a few. Since 1990, industrial sector emissions have declined. The decline has occurred both
in direct emissions and indirect emissions associated with electricity use. However, the decline in direct emissions
has been sharper. In theory, emissions from the industrial end-use sector should be highly correlated with economic
growth and industrial output, but heating of industrial buildings and agricultural energy consumption are also
affected by weather conditions. In addition, structural changes within the U.S. economy that lead to shifts in
industrial output away from energy-intensive manufacturing products to less energy-intensive products (e.g., from
steel to computer equipment) also have a significant effect on industrial emissions.
Transportation
When electricity-related emissions are distributed to economic end-use sectors, transportation activities accounted
for 27 percent of U.S. greenhouse gas emissions in 2009. The largest sources of transportation greenhouse gases in
2009 were passenger cars (35 percent), light duty trucks, which include sport utility vehicles, pickup trucks, and
minivans (30 percent), freight trucks (20 percent) and commercial aircraft (6 percent). These figures include direct
emissions from fossil fuel combustion, as well as HFC emissions from mobile air conditioners and refrigerated
transport allocated to these vehicle types. Table 2-15 provides a detailed summary of greenhouse gas emissions from
transportation-related activities with electricity-related emissions included in the totals.
From 1990 to 2009, transportation emissions rose by 17 percent due, in large part, to increased demand for travel
and the stagnation of fuel efficiency across the U.S. vehicle fleet. The number of vehicle miles traveled by light-
duty motor vehicles (passenger cars and light-duty trucks) increased 39 percent from 1990 to 2009, as a result of a
confluence of factors including population growth, economic growth, urban sprawl, and low fuel prices over much
of this period.
From 2008 to 2009, CO2 emissions from the transportation end-use sector declined 4 percent. The decrease in
emissions can 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. Similarly, increased jet fuel prices were a factor in the 19 percent decrease in commercial aircraft
emissions since 2007.
Almost all of the energy consumed for transportation was supplied by petroleum-based products, with more than
half being related to gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially
diesel fuel for freight trucks and jet fuel for aircraft, accounted for the remainder. The primary driver of
transportation-related emissions was CO2 from fossil fuel combustion, which increased by 16 percent from 1990 to
2009. This rise in CO2 emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 60.2
Tg CO2 Eq. in 2009, led to an increase in overall emissions from transportation activities of 17 percent.
Although average fuel economy over this period increased slightly due primarily to the retirement of older vehicles,
average fuel economy among new vehicles sold annually gradually declined from 1990 to 2004. The decline in new
vehicle fuel economy between 1990 and 2004 reflected the increasing market share of light duty trucks, which grew
from about one-fifth of new vehicle sales in the 1970s to slightly over hah0 of the market by 2004. Increasing fuel
prices have since decreased the momentum of light duty truck sales, and average new vehicle fuel economy has
improved since 2005 as the market share of passenger cars increased. VMT growth among all passenger vehicles
has also been impacted, remaining stagnant from 2004 to 2007, compared to an average annual growth rate of 2.5
percent over the period 1990 to 2004. The recession supplemented the effect of increasing fuel prices in 2008 and
VMT declined by 2.1 percent, the first decrease in annual passenger vehicle VMT since 1990. Overall, VMT grew
by 0.2 percent in 2009. Gasoline fuel consumption increased slightly, while consumption of diesel fuel continued to
Trends in Greenhouse Gas Emissions 2-21
-------
decrease, due in part to a decrease in commercial activity and freight trucking as a result of the
Table 2-15: Transportation-Related Greenhouse Gas Emissions
Gas/Vehicle Type
Passenger Cars
C02
CH4
N2O
MFCs
Light-Duty Trucks
C02
CH4
N2O
MFCs
Medium- and Heavy-
Duty Trucks
C02
CH4
N2O
MFCs
Buses
C02
CH4
N2O
MFCs
Motorcycles
C02
CH4
N2O
Commercial Aircraft"
C02
CH4
N2O
Other Aircraft11
C02
CH4
N2O
Ships and Boats0
C02
CH4
N2O
MFCs
Rail
C02
CH4
N2O
MFCs
Other Emissions from
Electricity
Generation"1
Pipelines"
C02
Lubricants
C02
Total Transportation
International Bunker
1990
657.4
629.3 1
2.6
25.41
+
336.6
321. ll
1.4
14.ll
231.1 1
230.li
0.2l
0.8
+
8.4
.
135.4|
0.1
u.*m
45.1
44.5 •
+
0.6
+
39.0
38.5
O.ll
0.3
.:
36.0
36.ol
11.8
11.8
1,548.3
113.0
2000
695.3
644.2|
1.6
25.2
24.3 •
512.1
467.0|
1.1
22.4
21.?1
354.6
345.8|
O.ll
1.2
7.4
11.2
.
169.2|
0.1
U.J-
61.0
60.0
0.9
0.1
48.1
45.6
0.1
0.3
:
35.2
35.2
12.1
12.1
1,935.8
99.5
2005
709.5
662.3
1.1
17.8
28.4
551.3
505.9
0.7
13.7
31.0
408.4
396.0
0.1
1.1
11.1
12.0
11.8
+
+
0.2
1.7
1.6
+
+
162.8
161.2
0.1
1.5
35.9
35.5
0.1
0.3
45.2
44.5
+
0.6
+
53.0
50.3
0.1
0.4
2.2
0.1
32.2
32.2
10.2
10.2
2,022.2
110.9
economic recession.
(TgC02Eq.)
2006
682.9
639.1
1.0
15.7
27.1
564.0
519.5
0.7
12.6
31.2
418.6
406.1
0.1
1.1
11.4
12.3
12.0
+
+
0.3
1.9
1.9
+
+
138.5
137.1
0.1
1.3
35.1
34.7
0.1
0.3
48.4
47.7
+
0.7
+
55.1
52.4
0.1
0.4
2.2
0.1
32.3
32.3
9.9
9.9
1,999.0
129.7
2007
672.0
632.8
0.9
13.8
24.6
570.3
528.4
0.6
11.2
30.1
425.2
412.5
0.1
1.1
11.5
12.5
12.1
+
+
0.3
2.1
2.1
+
+
139.5
138.1
0.1
1.3
33.2
32.8
0.1
0.3
55.2
54.4
+
0.8
+
54.3
51.6
0.1
0.4
2.2
0.1
34.3
34.3
10.2
10.2
2,008.9
129.0
2008
632.5
597.9
0.8
11.7
22.1
553.8
515.1
0.6
9.5
28.6
403.1
390.4
0.1
1.0
11.6
12.2
11.8
+
+
0.4
2.2
2.1
+
+
123.4
122.2
0.1
1.2
35.2
34.8
0.1
0.3
37.1
36.6
+
0.5
+
50.6
47.9
0.1
0.4
2.3
0.1
35.7
35.7
9.5
9.5
1,895.4
135.1
2009
627.4
597.2
0.7
10.1
19.3
551.0
514.5
0.6
9.4
26.6
365.6
353.1
0.1
0.8
11.6
11.2
10.8
+
+
0.4
2.2
2.1
+
+
112.5
111.4
0.1
1.1
29.6
29.3
+
0.3
30.5
30.0
+
0.4
+
43.3
40.6
0.1
0.3
2.3
0.1
35.2
35.2
8.5
8.5
1,816.9
124.4
2-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Fuel/
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 8500 Ibs; medium- and heavy-duty trucks include vehicles larger than 8500 Ibs. HFC emissions
primarily reflect HFC-134a.
+ Does not exceed 0.05 Tg CO2 Eq.
a Consists of emissions from jet fuel consumed by domestic operations of commercial aircraft (no bunkers).
b Consists of emissions from jet fuel and aviation gasoline consumption by general aviation and military aircraft.
0 Fluctuations in emission estimates are associated with fluctuations in reported fuel consumption, and may reflect data collection
problems.
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 limestone
and dolomite use (from pollution control equipment installed in electricity generation plants).
e CO2 estimates reflect natural gas used to power pipelines, but not electricity. While the operation of pipelines produces CH4 and
N2O, these emissions are not directly attributed to pipelines in the US Inventory.
f Emissions from International Bunker Fuels include emissions from both civilian and military activities; these emissions are not
included in the transportation totals.
Commercial
The commercial sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
consumption of natural gas and petroleum products, primarily for heating and cooking needs. Energy-related
emissions from the residential and commercial sectors have generally been increasing since 1990, and are often
correlated with short-term fluctuations in energy consumption caused by weather conditions, rather than prevailing
economic conditions. Landfills and wastewater treatment are included in this sector, with landfill emissions
decreasing since 1990 and wastewater treatment emissions increasing slightly.
Residential
The residential sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
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 2009, agricultural soil management was the largest
source of N2O emissions, and enteric fermentation was the second largest source of CH4 emissions in the United
States. This sector also includes small amounts of CO2 emissions from fossil fuel combustion by motorized farm
equipment like tractors. The agriculture sector relies less heavily on electricity than the other sectors.
[BEGIN BOX]
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
Trends in Greenhouse Gas Emissions 2-23
-------
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 Limestone and Dolomite Use (from pollution control equipment installed in
electricity generation plants).
In the Transportation economic sector, the CO2 emissions from the combustion of fossil fuels included in the EIA
transportation fuel consuming sector are apportioned to this economic sector (additional analyses and refinement of
the EIA data is further explained in the Energy chapter of this report). Additional emissions are apportioned from
the CH4 and N2O from Mobile Combustion, based on the EIA transportation sector. Substitutes of Ozone Depleting
Substitutes are apportioned based on their specific end-uses within the source category, with emissions from
transportation refrigeration/air-conditioning systems to this economic sector. Finally, CO2 emissions from Non-
Energy Uses of Fossil Fuels identified as lubricants for transportation vehicles are included in the Transportation
economic sector.
For the Industry economic sector, the CO2 emissions from the combustion of fossil fuels included in the EIA
industrial fuel consuming sector, minus the agricultural use of fuel explained below, are apportioned to this
economic sector. Stationary and mobile combustion emissions of CH4 and N2O are also based on the EIA industrial
sector, minus emissions apportioned to the Agriculture economic sector described below. Substitutes of Ozone
Depleting Substitutes are apportioned based on their specific end-uses within the source category, with most
emissions falling within the Industry economic sector (minus emissions from the other economic sectors).
Additionally, all process-related emissions from sources with methods considered within the IPCC Industrial
Process guidance have been apportioned to this economic sector. This includes the process-related emissions (i.e.,
emissions from the actual process to make the material, not from fuels to power the plant) from such activities as
Cement Production, Iron and Steel Production and Metallurgical Coke Production, and Ammonia Production.
Additionally, fugitive emissions from energy production sources, such as Natural Gas Systems, Coal Mining, and
Petroleum Systems are included in the Industry economic sector. A portion of CO2 from Limestone and Dolomite
Use (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 Substitutes are apportioned based on their specific end-uses
within the source category, with emissions from residential air-conditioning systems to this economic sector. N2O
emissions from the Application of Fertilizers to developed land (termed "settlements" by the IPCC) are also
included in the Residential economic sector.
The Commercial economic sector includes the CO2 emissions from the combustion of fossil fuels reported in the
EIA commercial fuel consuming sector data. Stationary combustion emissions of CH4 and N2O are also based on the
EIA commercial sector. Substitutes of Ozone Depleting Substitutes are apportioned based on their specific end-uses
within the source category, with emissions from commercial refrigeration/air-conditioning systems to this economic
sector. Public works sources including direct CH4 from Landfills and CH4 and N2O from Wastewater Treatment and
2-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Composting are included in this economic sector.
[END BOX]
[BEGIN BOX]
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 2009; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
or (5) emissions per capita.
Table 2-16 provides data on various statistics related to U.S. greenhouse gas emissions normalized to 1990 as a
baseline year. Greenhouse gas emissions in the United States have grown at an average annual rate of 0.4 percent
since 1990. This rate is slightly slower than that for total energy consumption and growth in national population
since 1990 and much slower than that for electricity consumption and overall gross domestic product, respectively.
Total U.S. greenhouse gas emissions are growing at a rate similar to that of fossil fuel consumption since 1990 (see
Table 2-16).
Table 2-16: Recent Trends in Various U.S. Data (Index 1990 = 100)
Variable
GDPb
Electricity Consumption0
Fossil Fuel Consumption0
Energy Consumption0
Population"1
Greenhouse Gas Emissions6
1990
100
100
100
100
100
100
2000
140
127
117
116
113
115
2005
157
134
119
118
118
117
2006
162
135
117
118
120
116
2007
165
138
119
120
121
117
2008
165
138
116
118
122
114
2009
160
132
108
112
123
107
Growth
Rate"
2.5%
1.5%
0.5%
0.6%
1.1%
0.4%
a Average annual growth rate
b Gross Domestic Product in chained 2005 dollars (BEA 2010)
0 Energy-content-weighted values (EIA 2010)
d U.S. Census Bureau (2010)
e GWP-weighted values
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product
Source: BEA (2010), U.S. Census Bureau (2010), and emission estimates in this report.
[END BOX]
2.3. Indirect Greenhouse Gas Emissions (CO, NOX, NMVOCs, and
The reporting requirements of the UNFCCC50 request that information be provided on indirect greenhouse gases,
which include CO, NOX, NMVOCs, and SO2. These gases do not have a direct global warming effect, but indirectly
affect terrestrial radiation absorption by influencing the formation and destruction of tropospheric and stratospheric
ozone, or, in the case of SO2, by affecting the absorptive characteristics of the atmosphere. Additionally, some of
50
See.
Trends in Greenhouse Gas Emissions
2-25
-------
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.
Since 1970, the United States has published estimates of annual emissions of CO, NOX, NMVOCs, and SO2 (EPA
2010, EPA 2009),51 which are regulated under the Clean Air Act. Table 2-17 shows that fuel combustion accounts
for the majority of emissions of these indirect greenhouse gases. Industrial processes—such as the manufacture of
chemical and allied products, metals processing, and industrial uses of solvents—are also significant sources of CO,
NOX, and NMVOCs.
Table 2-17: Emissions of NOX, CO, NMVOCs, and SO2 (Gg)
Gas/Activity
NOX
Mobile Fossil Fuel
Combustion
1990
21,707
10,862
2000
19,116
10,199
2005
15,900
9,012
2006
15,039
8,488
2007
14,380
7,965
2008 2009
13,547 11,468
7,441 6,206
Stationary Fossil Fuel
Combustion 10,023
Industrial Processes 591
Oil and Gas Activities 139
Incineration of Waste 82
Agricultural Burning 8
Solvent Use 1
Waste 0
CO 130,038
Mobile Fossil Fuel
Combustion 119,360
Stationary Fossil Fuel
Combustion 5,000
Industrial Processes 4,125
Incineration of Waste 978
Agricultural Burning 268
Oil and Gas Activities 302
Waste 1
Solvent Use 5
NMVOCs 20,930
Mobile Fossil Fuel
Combustion 10,932
Solvent Use 5,216
Industrial Processes 2,422
Stationary Fossil Fuel
Combustion 912
8,053
626
111
114
3
92,243
83,559
4,340
2,216
1,670
259
146
8
45
15,227
7,229
4,384
1,773
1,077
5,858
569
321
129
6
3
2
70,809
5,545
553
319
121
7
4
2
67,238
62,692 58,972
5,432
537
318
114
8
4
2
63,625
55,253 51,533 43,355
5,148
520
318
106
8
4
2
60,039
4,159
568
393
128
8
3
2
51,452
4,649
1,555
1,403
184
318
7
2
13,761
6,330
3,851
1,997
716
4,695
1,597
1,412
233
319
7
2
13,594
6,037
3,846
1,933
918
4,744
1,640
1,421
237
320
7
2
13,423
5,742
3,839
1,869
1,120
4,792
1,682
1,430
270
322
7
2
13,254
5,447
3,834
1,804
1,321
4,543
1,549
1,403
247
345
7
2
9,313
4,151
2,583
1,322
424
51 NOX and CO emission estimates from field burning of agricultural residues were estimated separately, and therefore not taken
from EPA (2009) and EPA (2010).
2-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Oil and Gas Activities
Incineration of Waste
Waste
Agricultural Burning
S02
Stationary Fossil Fuel
Combustion
Industrial Processes
Mobile Fossil Fuel
Combustion
Oil and Gas Activities
Incineration of Waste
Waste
Solvent Use
Agricultural Burning
554
222
673
NA|
20,935
18,407
1,307
793
390
38
o
0
NA
388
257
119
NA|
14,830
12,849
1,031
632
287
29
1
NA |
510
241
114
NA
13,466
11,541
831
889
181
24
1
0
NA
510
238
113
NA
12,388
10,612
818
750
182
24
1
0
NA
509
234
111
NA
11,799
10,172
807
611
184
24
1
0
NA
509
230
109
NA
10,368
8,891
795
472
187
23
1
0
NA
599
159
76
NA
8,599
7,167
798
455
154
24
1
0
NA
Source: (EPA 2010, EPA 2009) except for estimates from field burning of agricultural residues.
NA (Not Available)
Note: Totals may not sum due to independent rounding.
[BEGIN BOX]
Box 2-3: Sources and Effects of Sulfur Dioxide
Sulfur dioxide (SO2) emitted into the atmosphere through natural and anthropogenic processes affects the earth's
radiative budget through its photochemical transformation into sulfate aerosols that can (1) scatter radiation from the
sun back to space, thereby reducing the radiation reaching the earth's surface; (2) affect cloud formation; and (3)
affect atmospheric chemical composition (e.g., by providing surfaces for heterogeneous chemical reactions). The
indirect effect of sulfur-derived aerosols on radiative forcing can be considered in two parts. The first indirect effect
is the aerosols' tendency to decrease water droplet size and increase water droplet concentration in the atmosphere.
The second indirect effect is the tendency of the reduction in cloud droplet size to affect precipitation by increasing
cloud lifetime and thickness. Although still highly uncertain, the radiative forcing estimates from both the first and
the second indirect effect are believed to be negative, as is the combined radiative forcing of the two (IPCC 2001).
However, because SO2 is short-lived and unevenly distributed in the atmosphere, its radiative forcing impacts are
highly uncertain.
Sulfur dioxide is also a major contributor to the formation of regional haze, which can cause significant increases in
acute and chronic respiratory diseases. Once SO2 is emitted, it is chemically transformed in the atmosphere and
returns to the earth as the primary source of acid rain. Because of these harmful effects, the United States has
regulated SO2 emissions in the Clean Air Act.
Electricity generation is the largest anthropogenic source of SO2 emissions in the United States, accounting for 83
percent in 2009. 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.
[END BOX]
Trends in Greenhouse Gas Emissions 2-27
-------
-------
• MFCs, PFCs, & SF
Methane
Nitrous Oxide
Figure 2-1: U.S. Greenhouse Gas Emissions by Gas
4% -]
2% -
3.3%
2.8%
-6.1%
1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Figure 2-2: Annual Percent Change in U.S. Greenhouse Gas Emissions
1,082
879
(N(N(N(N(N(N(N(N(N(N
Figure 2-3: Cumulative Change in Annual U.S. Greenhouse Gas Emissions Relative to 1990
-------
7,500
7,000
6,500
6,000
5,500
5,000
4,500
4,000
S 3,500
O 3,000
CT 2,500
!- 2,000
1,500
1,000
500
(500) -
(1,000)
(1,500) -J
Industrial Processes
Agriculture
Waste
LULUCF (sources)
X
Energy
Land Use, Land-Use Change and Forestry (sinks)
22222222228888888888
Note: Relatively smaller amounts of GWP-weighted emissions are also emitted from the Solvent and Other Product
Use sector
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector
Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems
Mobile Combustion
Stationary Combustion
Incineration of Waste
Abandoned Underground Coal Mines |
Energy as a Portion
of all Emissions
5,209
0 50
Figure 2-5: 2009 Energy Sector Greenhouse Gas Sources
100 150 200
Tg CO2 Eq.
250 300
-------
NED Emissions 1
Natural Gas Emissions
1,209
NEU Emissions 51
Non-Energy Use
Carbon Sequestered
183
Fossil Fue
Non-Energy Consumption
Use imports U.S.
33 Territories
42
Non-Energy Balancing Item
Use U.S.
Territories
4
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.
2-6 2009 U.S. (Tg C02 Eq.)
-------
2,500 -|
2,000 -
fi" 1,500 -
0
CT 1,000 -
h-
500 -
n -
Relative Contribu
by Fuel Type
•^B
42
•
2,154
224
| I I Ł &
H o S g
3 E
Figure 2-7: 2009 C02 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Note: Electricity generation also includes emissions of less than 0.5 Tg CO2 Eq. from geothermal-based electricity
Generation.
2
tj oi
1) C
2,000 -|
1,500 -
8 1,000 -
500 -
0 J
i From Direct Fossil Fuel Combustion
i From Electricity Consumption
990
42
1,132
1,340
1,750
Figure 2-8: 2009 End-Use Sector Emissions from Fossil Fuel Combustion
-------
Substitution of Ozone Depleting Substances
Iron and Steel Production & Metallurgical Coke Production
Cement Production
Nitric Acid Production
Electrical Transmission and Distribution
Ammonia Production and Urea Application
Lime Production
Limestone and Dolomite Use
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Soda Ash Production and Consumption
Petrochemical Production
Adipic Acid Production
Carbon Dioxide Consumption
Titanium Dioxide Production
Ferroalloy Production
Magnesium Production and Processing
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and Consumption
120
<0.5
Industrial Processes
as a Portion of all Emissions
4.3%
10
20
30
TgCO2Eq.
40
50
Figure 2-9: 2009 Industrial Processes Chapter Greenhouse Gas Sources
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation I
Field Burning of Agricultural Residues < 0.5
205
Agriculture as a Portion of all
Emissions
6.3%
O
0 50 100
TgCO2Eq.
Figure 2-10: 2009 Agriculture Chapter Greenhouse Gas Sources
150
-------
Landfills
Wastewater Treatment
Composting
Waste as a Portion of all Emissions
2.3%
I
0 25 50 75
TgCO2Eq.
Figure 2-11: 2009 Waste Chapter Greenhouse Gas Sources
100
125
2,500 -|
2,000 -
ff 1-500 -
8
P 1,000 -
500 -
Electric
Power Industry
Transportation
Industry
Agriculture
'• Commercial
Residential
Figure 2-12: Emissions Allocated to Economic Sectors
Note: Does not include U.S. Territories.
-------
2,500 -|
2,000 -
ci- 1,500 -
L1J
0
(_>
^ 1,000 -
500 -
Industry
Transportation
Commercial (gray)
Residential (black)
Agriculture
rMfMrMfMrMrMrMrMrMrM
Figure 2-13: Emissions with Electricity Distributed to Economic Sectors
Real GDP
Population
Emissions
per capita
Emissions
per $GDP
rsirsirsirsirsirsirsirsi
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product
-------
-------
3. Energy
Energy-related activities were the primary sources of U.S. anthropogenic greenhouse gas emissions, accounting for
86.7 percent of total greenhouse gas emissions on a carbon dioxide (CO2) equivalent basis52 in 2009. This included
98, 49, and 13 percent of the nation's CO2, methane (CH4), and nitrous oxide (N2O) emissions, respectively.
Energy-related CO2 emissions alone constituted 81 percent of national emissions from all sources on a CO2
equivalent basis, while the non-CO2 emissions from energy-related activities represented a much smaller portion of
total national emissions (5.6 percent collectively).
Emissions from fossil fuel combustion comprise the vast majority of energy-related emissions, with CO2 being the
primary gas emitted (see Figure 3-1). Globally, approximately 30,398 Tg of CO2 were added to the atmosphere
through the combustion of fossil fuels in 2009, of which the United States accounted for about 18 percent.53 Due to
their relative importance, fossil fuel combustion-related CO2 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, and mobile
fossil fuel combustion was the second largest source of N2O emissions in the United States.
Figure 3-1: 2009 Energy Chapter Greenhouse Gas Sources
Figure 3-2: 2009 U.S. Fossil Carbon Flows (Tg CO2 Eq.)
Energy-related activities other than fuel combustion, such as the production, transmission, storage, and distribution
of fossil fuels, also emit greenhouse gases. These emissions consist primarily of fugitive CH4 from natural gas
systems, petroleum systems, and coal mining.
Table 3-1 summarizes emissions from the Energy sector in units of teragrams (or million metric tons) of CO2
equivalents (Tg CO2 Eq.), while unweighted gas emissions in gigagrams (Gg) are provided in Table 3-2. Overall,
emissions due to energy-related activities were 5,751.1 TgCO2Eq. in 2009, an increase of 9 percent since 1990.
Table 3-1: CO2, CH4, and N2O Emissions from Energy
Gas/Source
1990
2000
CO2 4,903.2
Fossil Fuel Combustion 4,738.4
Electricity Generation 1,820.8
Transportation 1,485.9
Industrial 846.5
Residential 338.3
Commercial 219.0
U.S. Territories 27.9
Non-Energy Use of Fuels 118.6
Natural Gas Systems 37.6
Incineration of Waste 8.0
Petroleum Systems 0.6
Biomass - Wood" 215.2
International Bunker Fuels 111.8
Biomass - Ethanol 4.2
CH4 327.4
Natural Gas Systems _ 189.8
5,781.3
5,594.8
2,296.9
1,809.5
851.1
370.7
230.8
35.9
144.9
29.9
11.1
0.5
218.1
98.5
9.4
318.6
2005
5,939.4
5,753.2
2,402.1
1,896.6
823.1
357.9
223.5
50.0
143.4
29.9
12.5
0.5
206.9
109.7
23.0
291.3
190.4
2006
5,842.5
5,653.1
2,346.4
1,878.1
848.2
321.5
208.6
50.3
145.6
30.8
12.5
0.5
203. 8
128.4
31.0
319.2
217.7
2007
5,938.2
5,756.7
2,412.8
1,894.0
842.0
342.4
219.4
46.1
137.2
31.1
12.7
0.5
203.3
127.6
38.9
307.3
205.2
2008
5,752.3
5,565.9
2,360.9
1,789.9
802.9
348.2
224.2
39.8
141.0
32.8
12.2
0.5
198.4
133.7
54.8
323.6
211.8
2009
5,377.3
5,209.0
2,154.0
1,719.7
730.4
339.2
224.0
41.7
123.4
32.2
12.3
0.5
183.8
123.1
61.2
336.8
221.2
52 Estimates are presented in units of teragrams of carbon dioxide equivalent (Tg CO2 Eq.), which weight each gas by its global
warming potential, or GWP, value. See section on global warming potentials in the Executive Summary.
53 Global CO2 emissions from fossil fuel combustion were taken from Energy Information Administration International Energy
Statistics 2010 < http://tonto.eia.doe.gov/cfapps/ipdbproject/IEDIndex3.cfm> EIA (2010).
Energy 3-1
-------
Coal Mining 84.1
Petroleum Systems 35.4
Stationary Combustion 7.4
Abandoned Underground
Coal Mines 6.0
Mobile Combustion 4.7
Incineration of Waste +
International Bunker Fuels 0.2
N2O 57.2
Mobile Combustion 43.9
Stationary Combustion 12.8
Incineration of Waste 0.5
International Bunker Fuels 1.1
58.2
29.4
6.2
5.5
2.3
+
0.2
48.5
33.6
14.4
0.4
1.2
57.9
30.0
6.5
5.6
2.2
+
0.2
45.2
30.3
14.6
0.4
1.2
67.1
30.2
6.5
5.9
2.0
+
0.2
40.7
26.1
14.2
0.4
1.2
71.0
30.9
6.2
5.5
2.0
+
0.1
37.0
23.9
12.8
0.4
1.1
Total
5,287.8
6,168.0
6,282.8 6,210.2 6,290.7 6,116.6 5,751.1
+ Does not exceed 0.05 Tg CO2 Eq.
* 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: CO2, CH4, and N2O Emissions from Energy (Gg)
Gas/Source
1990
2000
2005
2006
2007
2008
2009
CO2 4,903,171 I I 5,781,303 5,939,434
Fossil Fuel Combustion 4,738,422 I I 5,594,848 I 5,753,200
Non-Energy Use of
Fuels 118,630 144,933 143,392
Natural Gas Systems 37,574 29,877 29,902
Incineration of Waste 7,989 I 11,112 12,450
Petroleum Systems 555 534 490
Biomass -Wood" 215,186 218,088 206,865
International Bunker
Fuels" 111,828 98,482 109,750
Biomass - Ethanol" 4,229 I 9,352 I 22,956
CH4 15,590 15,171 13,872
Natural Gas Systems 9,038 I 9,968 I 9,069
Coal Mining 4,003 I 2,877 I 2,710
Petroleum Systems 1,685 I 1,501 I 1,398
Stationary Combustion 354 315 312
Abandoned
Underground Coal
Mines 288 350 264
Mobile Combustion 223 160 119
Incineration of Waste + I + I +
International Bunker
N2O 185 220 168
Mobile Combustion 142 172 119
Stationary Combustion 41 47 47
Incineration of Waste 2 I 1 I 1
International Bunker
Fuels" 3 3 3
5,842,464 5,938,203 5,752,327 5,377,271
5,653,116 5,756,746 5,565,925 5,208,981
145,574
30,755
12,531
488
203,846
128,384
31,002
15,202
10,364
2,774
1,398
293
261
112
+
156
108
47
1
137,233
31,050
12,700
474
203,316
127,618
38,946
14,634
9,771
2,756
1,427
308
267
105
+
146
98
47
1
140,952
32,828
12,169
453
198,361
133,704
54,770
15,408
10,087
3,196
1,439
310
279
97
+
131
84
46
1
123,356
32,171
12,300
463
183,777
123,127
61,231
16,037
10,535
3,382
1,473
293
262
93
+
120
77
41
1
4
+ Does not exceed 0.05 Tg CO2 Eq.
* These values are presented for informational purposes only, in
reporting obligations, and are not included in the specific energy
elsewhere.
Note: Totals may not sum due to independent rounding.
line with IPCC methodological guidance and UNFCCC
sector contribution to the totals, and are already accounted for
3-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
3.1. Fossil Fuel Combustion (IPCC Source Category 1A)
Emissions from the combustion of fossil fuels for energy include the gases CO2, CH4, and N2O. Given that CO2 is
the primary gas emitted from fossil fuel combustion and represents the largest share of U.S. total emissions, CO2
emissions from fossil fuel combustion are discussed at the beginning of this section. Following that is a discussion
of emissions of all three gases from fossil fuel combustion presented by sectoral breakdowns. Methodologies for
estimating CO2 from fossil fuel combustion also differ from the estimation of CH4 and N2O emissions from
stationary combustion and mobile combustion. Thus, three separate descriptions of methodologies, uncertainties,
recalculations, and planned improvements are provided at the end of this section. Total CO2, CH4, and N2O
emissions from fossil fuel combustion are presented in Table 3-3 and Table 3-4.
Table 3-3: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (Tg CO2 Eq.)
Gas
C02
CH4
N2O
Total
1990
4,738.4
12.1
56.8
4,807.3
2000
5,594.8
10.0
67.7
5,627.6
2005
5,753.2
9.1
51.7
5,813.9
2006
5,653.1
8.5
48.1
5,709.7
2007
5,756.7
8.7
44.9
5,810.3
2008
5,565.9
8.5
40.4
5,614.8
2009
5,209.0
8.1
36.7
5,253.8
Note: Totals may not sum due to independent rounding.
Table 3-4: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion (Gg)
Gas
C02
CH4
N2O
1990
4,738,422
577
183 ^|
2000
5,594,848
476
219 ^|
2005
5,753,200
431
167
2006
5,653,116
405
155
2007
5,756,746
413
145
2008
5,565,925
407
130
2009
5,208,981
386
118
Note: Totals may not sum due to independent rounding.
C02 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 2009, CO2 emissions from
fossil fuel combustion decreased by 6.4 percent relative to the previous year. This decrease represents the largest
annual decrease in CO2 emissions from fossil fuel combustion for the twenty-year period.54 The decrease in CO2
emissions from fossil fuel combustion was a result of multiple factors including: (1) a decrease in economic output
resulting in a decrease in energy consumption across all sectors; (2) a decrease in the carbon intensity of fuels used
to generate electricity due to fuel switching as the price of coal increased, and the price natural gas decreased
significantly; and (3) an increase in non-fossil fuel consumption by approximately 2 percent. In 2009, CO2
emissions from fossil fuel combustion were 5,209.0 Tg CO2 Eq., or almost 10 percent above emissions in 1990 (see
Table 3-5).55
Table 3-5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (Tg CO2 Eq.)
Fuel/Sector 1990 2000 2005 2006 2007 2008 2009
Coal
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Natural Gas
1,718.4
3.0
12.0
155.3
NE
1,547.6
0.6
1,000.6
2,065.5
1.1
8.8 1
127.3
NE 1
1,927.4
0.9
1,217.4
r 2,112.3
0.8
9.3
115.3
NE
1,983.8
3.0
1,159.0
2,076.5
0.6
6.2
112.6
NE
1,953.7
3.4
1,141.3
2,106.0
0.7
6.7
107.0
NE
1,987.3
4.3
1,218.0
2,072.5
0.7
6.5
102.6
NE
1,959.4
3.3
1,226.0
1,841.0
0.6
5.8
83.4
NE
1,747.6
3.5
1,200.9
54 This decrease also represents the largest absolute and percentage decrease since the beginning of EIA's record of annual
energy consumption data, beginning in 1949 (EIA 2010a).
55 An additional discussion of fossil fuel emission trends is presented in the Trends in U.S. Greenhouse Gas Emissions Chapter.
Energy 3-3
-------
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Geothermal*
Total
238.0
142.1
409.1
36.0
175.3
NO
2,019.0
97.4
64.9
282.1
1,449.9
97.5
27.2
0.4
4,738.4
270
172
457
35
280
0
2,311
98
49
266
1,773
88
34
0
7 1
5 1
2 1
.6
.8
.7
.6
.8
.6
6 1
9
•4
2 1
.4
5,594.8
262.2
162.9
380.8
33.1
318.8
1.3
1 2,481.5
94.9
51.3
326.9
1,863.5
99.2
45.7
1 0.4
5,753.2
237.3
153.8
377.7
33.1
338.0
1.4
2,434.9
83.6
48.5
357.9
1,845.0
54.4
45.5
0.4
5,653.1
257.0
164.0
389.0
35.3
371.3
1.4
2,432.4
84.6
48.7
346.0
1,858.7
53.9
40.4
0.4
5,756.7
264.4
170.2
391.0
36.8
361.9
1.6
2,267.1
83.1
47.4
309.3
1,753.1
39.2
35.0
0.4
5,565.9
257.2
167.9
365.0
36.3
373.1
1.5
2,166.7
81.4
50.3
282.0
1,683.4
32.9
36.7
0.4
5,209.0
NE (Not estimated)
NO (Not occurring)
* Although not technically a fossil fuel, geothermal energy-related CO2 emissions are included for reporting purposes.
Note: Totals may not sum due to independent rounding.
Trends in CO2 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.56 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 CO2 Emissions and Total 2009 Emissions from Fossil Fuel Combustion for Selected
Fuels and Sectors (Tg CO2 Eq. and Percent)
Sector Fuel Type
Electricity Generation Coal
Electricity Generation Natural Gas
Electricity Generation Petroleum
Transportation a Petroleum
Residential Natural Gas
Commercial Natural Gas
Industrial Coal
Industrial Natural Gas
All Sectors b All Fuels b
2005 to 2006
-30.1 -1.5%
19.2 6.0%
.44.8 -45.2%
-18.5 -1.0%
-24.9 -9.5%
-9.1 -5.6%
-2.8 -2.4%
-3.1 -0.8%
-100.1 -1.7%
2006 to 2007
33.6 1.7%
33.3 9.9%
-0.5 -0.9%
13.7 0.7%
19.7 8.3%
10.2 6.6%
-5.6 -5.0%
11.3 3.0%
103.6 1.8%
2007 to 2008
-27.9 -1.4%
-9.3 -2.5%
-14.7 -27.2%
-105.6 -5.7%
7.4 2.9%
6.2 3.8%
-4.4 -4.1%
2.0 0.5%
-190.8 -3.3%
2008 to 2009
-211.7 -10.8%
11.1 3.1%
-6.3 -16.0%
-69.7 -4.0%
-7.3 -2.8%
-2.3 -1.3%
-19.2 -18.7%
-26.0 -6.6%
-356.9 -6.4%
Total 2009
1,747.6
373.1
32.9
1,683.4
257.2
167.9
83.4
365.0
5,209.0
a Excludes emissions from International Bunker Fuels.
b Includes fuels and sectors not shown in table.
56
Based on national aggregate carbon content of all coal, natural gas, and petroleum fuels combusted in the United States.
3-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
In the United States, 83 percent of the energy consumed in 2009 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 sources57 (8 percent), primarily
hydroelectric power and biofuels (EIA 2010). Specifically, petroleum supplied the largest share of domestic energy
demands, accounting for an average of 42 percent of total fossil fuel based energy consumption in 2009. Natural gas
and coal followed in order of importance, accounting for approximately 32 and 27 percent of total 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 2010).
Figure 3-3: 2009 U.S. Energy Consumption by Energy Source
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu)
Figure 3-5: 2009 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Fossil fuels are generally combusted for the purpose of producing energy for useful heat and work. During the
combustion process, the C stored in the fuels is oxidized and emitted as CO2 and smaller amounts of other gases,
including CH4, CO, and NMVOCs.58 These other C containing non-CO2 gases are emitted as a by-product of
incomplete fuel combustion, but are, for the most part, eventually oxidized to CO2 in the atmosphere. Therefore, it
is assumed that all of the C in fossil fuels used to produce energy is eventually converted to atmospheric CO2.
[BEGIN BOX]
Box 3-1: Weather and Non-Fossil Energy Effects on CO2 from Fossil Fuel Combustion Trends
In 2009, weather conditions remained constant in the winter and slightly cooler in the summer compared to 2008, as
heating degree days decreased slightly and cooling degree days decreased by 3.8 percent. Winter conditions were
relatively constant in 2009 compared to 2008, and the winter was slightly warmer than normal, with heating degree
days in the United States 0.7 percent below normal (see Figure 3-6). Summer conditions were slightly cooler in
2009 compared to 2008, and summer temperatures were slightly cooler than normal, with cooling degree days 1
percent below normal (see Figure 3-7) (EIA 2010).59
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2009)
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2009)
57 Renewable energy, as defined in EIA's energy statistics, includes the following energy sources: hydroelectric power,
geothermal energy, biofuels, solar energy, and wind energy
58 See the sections entitled Stationary Combustion and Mobile Combustion in this chapter for information on non-CO2 gas
emissions from fossil fuel combustion.
59 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 affect 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).
Energy 3-5
-------
Although no new U.S. nuclear power plants have been constructed in recent years, the utilization (i.e., capacity
factors60) of existing plants in 2009 remained high at just over 90 percent. Electricity output by hydroelectric power
plants increased in 2009 by approximately 6.8 percent. Electricity generated by nuclear plants in 2009 provided
nearly 3 times as much of the energy consumed in the United States as hydroelectric plants (EIA 2010). Nuclear,
hydroelectric, and wind power capacity factors since 1990 are shown in Figure 3-8.
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2009)
[END BOX]
Fossil Fuel Combustion Emissions by Sector
In addition to the CO2 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 CO2, CH4, and N2O emissions from fossil fuel
combustion by sector.
Table 3-7: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by Sector (Tg CO2 Eq.)
End-Use Sector 1990 2000 2005 2006 2007 2008
2009
Electricity Generation
C02
CH4
N2O
Transportation
C02
CH4
N2O
Industrial
C02
CH4
N2O
Residential
CO2
CH4
N2O
Commercial
C02
CH4
N2O
U.S. Territories*
Total
1,829.5
1,820
0
8
1,534
1,485
4
43
851
846
1
3
343
338
4
1
220
219
0
0
28
.8
.6
.1
.6
.9
.7
.9
.2
.5
.5
.2
.8
.3
.4
.1
.2
.0
.9
.4
.0
2,307.5
2,296
0
10
.9
7 1
.0
1,866.0
4,807.3
1,809
.5
3.4
53.2
855.9
851.1
1.6
3
375
370
3
0
232
230
0
0
36
.2
.0
7 1
.4
.9
.1 1
.8
.0
5,672.6
2,413.2
2,402.1
0.7
10.3
1,936.0
1,896.6
2.5
36.9
827.5
823.1
1.4
3.0
362.2
357.9
3.4
0.9
224.8
223.5
0.9
0.4
50.2
5,813.9
2,357.2
2,346.
0.
10.
4
7
1
1,914.1
1,878.
2.
33.
852.
848.
1.
3.
325.
321.
3.
0.
209.
208.
0.
0.
50.
5,709.
1
3
6
8
2
5
1
4
5
1
8
7
6
8
3
5
7
2,423.8
2,412.8
0.7
10.3
1,926.5
1,894.0
2.2
30.3
846.5
842.0
1.4
3.0
346.6
342.4
3.4
0.9
220.6
219.4
0.9
0.3
46.3
5,810.3
2,371.7
2,360.9
0.7
10.1
1,818.1
1,789.9
2.0
26.1
807.0
802.9
1.3
2.8
352.6
348.2
3.5
0.9
225.4
224.2
0.9
0.3
40.0
5,614.8
2,163.7
2,154.0
0.7
9.0
1,745.5
1,719.7
2.0
23.9
734.1
730.4
1.2
2.5
343.4
339.2
3.4
0.9
225.2
224.0
0.9
0.3
41.8
5,253.8
Note: Totals may not sum due to independent rounding. Emissions from fossil fuel combustion by electricity generation are
allocated based on aggregate national electricity consumption by each end-use sector.
* U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions from all fuel combustion
sources.
Other than CO2, gases emitted from stationary combustion include the greenhouse gases CH4 and N2O and the
60The 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 (201 Ob).
3-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
indirect greenhouse gases NOX, CO, and NMVOCs.61 CH4 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. CH4 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. CO
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. CH4 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.62 Emissions from U.S. territories are also calculated separately due to a lack of end-use-specific
consumption data. This method of distributing emissions assumes that 564 combustion sources focus on the
alternative method as presented in Table 3-8.
Table 3-8: CO2, CH4, and N2O Emissions from Fossil Fuel Combustion by End-Use Sector (Tg CO2 Eq.)
End-Use Sector 1990 2000 2005 2006 2007 2008 2009
Transportation
C02
CH4
N2O
Industrial
C02
CH4
N2O
Residential
C02
CH4
N2O
Commercial
CO2
CH4
N2O
U.S. Territories*
Total 4,807.3 5,672.6 5,813.9 5,709.7 5,810.3 5,614.8 5,253.8
Note: Totals may not sum due to independent rounding. Emissions from fossil fuel combustion by electricity generation are
allocated based on aggregate national electricity consumption by each end-use sector.
* U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions from all fuel combustion
61 Sulfur dioxide (SO2) emissions from stationary combustion are addressed in Annex 6.3.
62 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 3-7
-------
Stationary Combustion
The direct combustion of fuels by stationary sources in the electricity generation, industrial, commercial, and
residential sectors represent the greatest share of U.S. greenhouse gas emissions. Table 3-9 presents CO2 emissions
from fossil fuel combustion by stationary sources. The CO2 emitted is closely linked to the type of fuel being
combusted in each sector (see Methodology section for CO2 from fossil fuel combustion). Other than CO2, gases
emitted from stationary combustion include the greenhouse gases CH4 and N2O. Table 3-10 and Table 3-11 present
CH4 and N2O emissions from the combustion of fuels in stationary sources. CH4 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. CH4 emissions from stationary combustion are
primarily a function of the CH4 content of the fuel and combustion efficiency. Please refer to Table 3-7 for the
corresponding presentation of all direct emission sources of fuel combustion.
Table 3-9: CO2 Emissions from Stationary Fossil Fuel Combustion (Tg CO2 Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Natural Gas
Fuel Oil
Geothermal
Industrial
Coal
Natural Gas
Fuel Oil
Commercial
Coal
Natural Gas
Fuel Oil
Residential
Coal
Natural Gas
Fuel Oil
U.S. Territories
Coal
Natural Gas
Fuel Oil
Total
1990
1,820.8
1,547.6
175.3
97.5
0.4
846.5
155.3
409.1
282.1
219.0
12.0
142.1
64.9
338.3
3.0
238.0
97.4
27.9
0.6
NO 1
27.2
3,252.5
2000
2,296.
1,927.
280.
88.
0.
851.
127.
457.
266.
230.
8.
172.
49.
370.
1.
270.
98.
35.
0.
0.
34.
3,785.
9
4
8
4
4
1
3
2
6
8
8
5
6
7
1
7
8
9
9
7
2
3
2005
2,402.1
1,983.8
318.8
99.2
0.4
823.1
115.3
380.8
326.9
223.5
9.3
162.9
51.3
357.9
0.8
262.2
94.9
50.0
3.0
1.3
45.7
3,856.6
2006
2,346.4
1,953
338
54
0
848
112
377
357
208
6
153
48
321
0
237
83
50
3
1
45
3,775
.7
.0
.4
.4
.2
.6
.7
.9
.6
.2
.8
.5
.5
.6
.3
.6
.3
.4
.4
.5
.0
2007
2,412.8
1,987.3
371.3
53.9
0.4
842.0
107.0
389.0
346.0
219.4
6.7
164.0
48.7
342.4
0.7
257.0
84.6
46.1
4.3
1.4
40.4
3,862.8
2008
2,360.9
1,959.4
361.9
39.2
0.4
802.9
102.6
391.0
309.3
224.2
6.5
170.2
47.4
348.2
0.7
264.4
83.1
39.8
3.3
1.6
35.0
3,776.0
2009
2,154
1,747
373
32
0
730
83
365
282
224
5
167
50
339
0
257
81
41
3
1
36
.0
.6
.1
.9
.4
.4
.4
.0
.0
.0
.8
.9
.3
.2
.6
.2
.4
.7
.5
.5
.7
3,489.3
* U.S. Territories are not apportioned by sector, and emissions are from all fuel combustion sources (stationary and mobile) are
presented in this table.
3-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 3-10: CH4 Emissions from Stationary Combustion (Tg CO2 Eq.)
Sector/Fuel Type
Electricity Generation
Coal
Fuel Oil
Natural Gas
Wood
Industrial
Coal
Fuel Oil
Natural Gas
Wood
Commercial
Coal
Fuel Oil
Natural Gas
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
Fuel Oil
Natural Gas
Wood
Total
+ Does not exceed 0.05 Tg
Note: Totals may not sum
1990
0.6
1
0.1 1
1.5 1
0.3 1
0.2 1
0.2 1
0.9 1
0.9 1
0.2 1
0.4 1
4.4 1
0.2 1
1
+
7.4
CO2 Eq.
2000
0.7
1"'
+
6.6
2005
0.7
0.4
0.1
0.1
0.1
1.4
0.3
0.2
0.1
0.9
0.9
0.2
0.3
0.4
3.4
0.1
0.3
0.5
2.6
0.1
0.1
+
6.6
2006
0.7
0.4
0.1
0.1
1.5
0.3
0.2
0.1
0.9
0.8
0.1
0.3
0.4
3.1
+
0.3
0.4
2.3
0.1
0.1
+
6.2
2007
0.7
0.4
0.1
0.1
1.4
0.2
0.2
0.1
0.8
0.9
0.1
0.3
0.4
3.4
+
0.3
0.5
2.6
0.1
0.1
+
6.5
2008
0.7
0.4
0.1
0.1
1.3
0.2
0.2
0.1
0.8
0.9
0.1
0.3
0.4
3.5
+
0.3
0.5
2.7
0.1
0.1
+
6.5
2009
0.7
0.4
0.1
0.1
1.2
0.2
0.1
0.1
0.7
0.9
0.1
0.3
0.4
3.4
+
0.3
0.5
2.6
0.1
0.1
+
6.2
due to independent rounding.
Table 3-11: N2O Emissions from Stationary
Sector/Fuel Type
Electricity Generation
Coal
Fuel Oil
Natural Gas
Wood
Industrial
Coal
Fuel Oil
Natural Gas
Wood
Commercial
Coal
Fuel Oil
Natural Gas
Wood
Residential
Coal
Fuel Oil
Natural Gas
Wood
U.S. Territories
Coal
1990
8.1
7.6 1
0.2 1
0.2 1
3.2 1
0.8 1
0.5 1
0.2 1
1.7 1
0.4 1
0.1 1
0.2 1
0.1
0.1
1.1 1
+ 1
0.3 1
0.1
0.7 1
0.1 1
+
Combustion
2000
10.0
9.4 1
0.2 1
0.2 1
0.2 1
3.2 1
0.6 1
0.4 1
0.3 1
1.9 1
0.4 1
+ 1
0.1
0.1
0.1 1
0.9 1
+
0.3 1
0.2 1
0.5 1
0.1 1
+
(TgC02Eq.)
2005
10.3
9.7
0.2
0.2
0.2
3.0
0.6
0.5
0.2
1.7
0.4
+
0.1
0.1
0.1
0.9
+
0.3
0.1
0.5
0.1
+
2006
10.1
9.5
0.1
0.2
0.2
3.1
0.6
0.6
0.2
1.7
0.3
+
0.1
0.1
0.1
0.8
+
0.2
0.1
0.5
0.1
+
2007
10.2
9.7
0.1
0.2
0.2
3.0
0.5
0.6
0.2
1.7
0.3
+
0.1
0.1
0.1
0.9
+
0.2
0.1
0.5
0.1
+
2008
10.1
9.6
0.1
0.2
0.2
2.8
0.5
0.5
0.2
1.6
0.3
+
0.1
0.1
0.1
0.9
+
0.2
0.1
0.5
0.1
+
2009
9.0
8.5
0.1
0.2
0.2
2.5
0.4
0.4
0.2
1.4
0.3
+
0.1
0.1
0.1
0.9
+
0.2
0.1
0.5
0.1
+
Energy 3-9
-------
Fuel Oil 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Natural Gas + + + + + + +
Wood + + + + + + +_
Total 12.8 14.6 14.7 14.4 14.6 14.2 12.8
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Electricity Generation
The process of generating electricity is the single largest source of CO2 emissions in the United States, representing
39 percent of total CO2 emissions from all CO2 emissions sources across the United States. CH4 and N2O accounted
for a small portion of emissions from electricity generation, representing less than 0.1 percent and 0.4 percent,
respectively.63 Electricity generation also accounted for the largest share of CO2 emissions from fossil fuel
combustion, approximately 41 percent in 2009. CH4 and N2O from electricity generation represented 8 and 25
percent of emissions from CH4 and N2O emissions from fossil fuel combustion in 2009, 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).
Figure 3-9: Electricity Generation Retail Sales by End-Use Sector
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 electricity,64 while the other sectors consist of those producers that indicate their primary business is something
other than the production of electricity.
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 2009 decreased approximately 1.2 percent and 1.0 percent, respectively. The trend in
the commercial and residential sectors can largely be attributed to the decreased carbon intensity in the fuels used to
generate electricity for these sectors. In addition, electricity consumption in both sectors decreased as a result of the
less energy-intensive weather conditions compared to 2008. In 2009, the amount of electricity generated (in kWh)
decreased by 4 percent from the previous year. This decline was due to the economic downturn, a decrease in the
carbon intensity of fuels used to generate electricity due to fuel switching as the price of coal increased, and the
price of natural gas decreased significantly, and an increase in non-fossil fuel sources used to generate electricity. As
a result, CO2 emissions from the electric power sector decreased by 8.8 percent as the consumption of coal and
petroleum for electricity generation decreased by 10.8 percent and 16.6 percent, respectively, in 2009 and the
consumption of natural gas for electricity generation, increased by 3.1 percent. The decrease in C intensity of the
electricity supply (see Table 3-15) was the result of a decrease in the carbon intensity of fossil fuels consumed to
generate electricity and an increase in renewable generation of 5 percent spurred by a 28 percent increase in wind-
generated electricity.
63 Since emissions estimates for U.S. territories cannot be disaggregated by gas in Table 3-7and Table 3-8, the percentages for
CH4 and N2O exclude U.S. territory estimates.
64 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).
3-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Industrial Sector
The industrial sector accounted for 14 percent of CO2 emissions from fossil fuel combustion, 15 percent of CH4
emissions from fossil fuel combustion, and 7 percent of N2O emissions from fossil fuel combustion. CO2, CH4, and
N2O emissions resulted from the direct consumption of fossil fuels for steam and process heat production.
The industrial sector, per the underlying energy consumption data from EIA, includes activities such as
manufacturing, construction, mining, and agriculture. The largest of these activities in terms of energy consumption
is manufacturing, of which six industries—Petroleum Refineries, Chemicals, Paper, Primary Metals, Food, and
Nonmetallic Mineral Products—represent the vast majority of the energy use (EIA 2010 and EIA 2009c).
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.65 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 2008 to 2009, total industrial production and manufacturing output decreased by 9.3 and 10.9 percent,
respectively (FRB 2010). Over this period, output decreased across all production indices for Food, Petroleum
Refineries, Chemicals, Paper, Primary Metals, and Nonmetallic Mineral Products (see Figure 3-10).
Figure 3-10: Industrial Production Indices (Index 2002=100)
Despite the growth in industrial output (41 percent) and the overall U.S. economy (60 percent) from 1990 to 2009,
CO2 emissions from fossil fuel combustion in the industrial sector decreased by 13.7 percent over that time. 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 2009, CO2, CH4, and N2O
emissions from fossil fuel combustion and electricity use within the industrial end-use sector totaled 1,340.1 Tg CO2
Eq., or approximately 12.1 percent below 2008 emissions.
Residential and Commercial Sectors
The residential and commercial sectors accounted for 7 and 4 percent of CO2 emissions from fossil fuel combustion,
42 and 11 percent of CH4 emissions from fossil fuel combustion, and 2 and 1 percent of N2O emissions from fossil
fuel combustion, respectively. Emissions from these sectors were largely due to the direct consumption of natural
gas and petroleum products, primarily for heating and cooking needs. Coal consumption was a minor component of
energy use in both of these end-use sectors. In 2009, CO2, CH4, and N2O emissions from fossil fuel combustion and
electricity use within the residential and commercial end-use sectors were 1,131.6 Tg CO2 Eq. and 990.3Tg CO2
Eq., respectively. Total CO2, CH4, and N2O emissions from the residential and commercial sectors decreased by 4.9
and 4.5 percent from 2008 to 2009, 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).
Emissions from natural gas consumption represent about 76 and 75 percent of the direct fossil fuel CO2 emissions
from the residential and commercial sectors, respectively. In 2009, natural gas CO2 emissions from the residential
and commercial sectors decreased by 2.8 percent and 1.3 percent, respectively. The decrease in natural gas
emissions in both sectors is a result of less energy-intensive weather conditions in the United States compared to
65 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 misclassiiications of large commercial
customers likely cause the industrial end-use sector to appear to be more sensitive to weather conditions.
Energy 3-11
-------
2008.
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 the Methodology section for CO2 from
fossil fuel combustion, this data is collected separately from the sectoral-level data available for the general
calculations. As sectoral information is not available for U.S. Territories, CO2, CH4, and N2O emissions are not
presented for U.S. Territories in the tables above, though the emissions will include some transportation and mobile
combustion sources.
Transportation Sector
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.
The transportation end-use sector accounted for 1,745.5 Tg CO2 Eq. in 2009, which represented 33 percent of CO2
emissions, 24 percent of CH4 emissions, and 65 percent of N2O emissions from fossil fuel combustion, respectively.
Fuel purchased in the U.S. for international aircraft and marine travel accounted for an additional 123.1 Tg CO2 in
2009; these emissions are recorded as international bunkers and are not included in U.S. totals according to
UNFCCC reporting protocols. Among domestic transportation sources, light duty vehicles (including passenger
cars and light-duty trucks) represented 64 percent of CO2 emissions, medium- and heavy-duty trucks 20 percent,
commercial aircraft 6 percent, and other sources 9 percent. Light-duty truck CO2 emissions increased by 60 percent
(193.4 Tg) from 1990 to 2009, representing the largest percentage increase of any transportation mode. General
aviation aircraft CO2 emissions also increased by nearly 60 percent (5.7 Tg) from 1990 to 2009. CO2 from the
domestic operation of commercial aircraft decreased by 18 percent (24.0 Tg) from 1990 to 2009. Across all
categories of aviation, CO2 emissions decreased by 21.6 percent (38.7 Tg) between 1990 and 2009. This includes a
59 percent (20.3 Tg) decrease in emissions from domestic military operations. For further information on all
greenhouse gas emissions from transportation sources, please refer to Annex 3.2. See Table 3-12 for a detailed
breakdown of CO2 emissions by mode and fuel type.
From 1990 to 2009, transportation emissions rose by 17 percent due, in large part, to increased demand for travel
and the stagnation of fuel efficiency across the U.S. vehicle fleet. The number of vehicle miles traveled by light-
duty motor vehicles (passenger cars and light-duty trucks) increased 39 percent from 1990 to 2009, as a result of a
confluence of factors including population growth, economic growth, urban sprawl, and low fuel prices over much
of this period.
From 2008 to 2009, CO2 emissions from the transportation end-use sector declined 4 percent. The decrease in
emissions can 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. Similarly, increased jet fuel prices were a factor in the 19 percent decrease in commercial aircraft
emissions since 2007.
Almost all of the energy consumed for transportation was supplied by petroleum-based products, with more than
hah0being related to gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially
diesel fuel for freight trucks and jet fuel for aircraft, accounted for the remainder. The primary driver of
transportation-related emissions was CO2 from fossil fuel combustion, which increased by 16 percent from 1990 to
2009. This rise in CO2 emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 60.2
Tg CO2 Eq. in 2009, led to an increase in overall emissions from transportation activities of 17 percent.
Transportation Fossil Fuel Combustion CO2 Emissions
Domestic transportation CO2 emissions increased by 16 percent (235.1 Tg) between 1990 and 2009, an annualized
increase of 0.8 percent. The 4 percent decline in emissions between 2008 and 2009 followed the previous year's
trend of decreasing emissions. Almost all of the energy consumed by the transportation sector is petroleum-based,
3-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
including motor gasoline, diesel fuel, jet fuel, and residual oil.66 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, N2O, CH4, and HFCs.
Carbon dioxide emissions from passenger cars and light-duty trucks totaled 1,111.7 Tg in 2009, an increase of 17
percent (161.3 Tg)from 1990. CO2 emissions from passenger cars and light-duty trucks peaked at 1,184.3 Tgin
2004, and since then have declined about 6 percent. Over the 1990s through early this decade, growth in vehicle
travel substantially outweighed improvements in vehicle fuel economy; however, the rate of Vehicle Miles Traveled
(VMT) growth slowed considerably starting in 2005 (and declined rapidly in 2008) while average vehicle fuel
economy increased. Among new vehicles sold annually, average fuel economy gradually declined from 1990 to
2004 (Figure 3-11), reflecting substantial growth in sales of light-duty trucks—in particular, growth in the market
share of sport utility vehicles—relative to passenger cars (Figure 3-12). New vehicle fuel economy improved
beginning in 2005, largely due to higher light-duty truck fuel economy standards, which have risen each year since
2005. The overall increase in fuel economy is also due to a slightly lower light-duty truck market share, which
peaked in 2004 at 52 percent and declined to 40 percent in 2009.
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks, 1990-2008
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2008
Light-duty truck67 CO2 emissions increased by 60 percent (193.4 Tg) from 1990 to 2009, representing the largest
percentage increase of any transportation mode. General aviation aircraft CO2 emissions also increased by nearly 60
percent (5.7 Tg) from 1990 to 2009. CO2 from the domestic operation of commercial aircraft decreased by 18
percent (24.0 Tg) from 1990 to 2009. Across all categories of aviation68, CO2 emissions decreased by 21.6 percent
(38.7 Tg) between 1990 and 2009. This includes a 59 percent (20.3 Tg) decrease in emissions from domestic
military operations. For further information on all greenhouse gas emissions from transportation sources, please
refer to Annex 3.2.
Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector (Tg CO2 Eq.)a
Fuel/Vehicle Type 1990 2000 2005 2006 2007 2008 2009
Gasoline 983.7 1,135.0 1,187.8 1,178.2 1,181.2 1,130.3 1,125.7
Passenger Cars 621.4 I 640.6 I 658.0 635.0 628.7 594.0 593.3
Light-Duty Trucks 309.1 I 446.4 I 478.7 491.5 500.1 486.5 485.9
Medium- and Heavy-Duty
Trucks'3 38.7 36.0 34.9 35.5 36.1 33.7 30.6
Buses 0.3 0.4 0.4 0.4 0.4 0.4 0.3
Motorcycles 1.7 1.8 1.6 1.9 2.1 2.1 2.1
Recreational Boats 12.4 9.8 14.1 14.0 13.9 13.5 13.4
Distillate Fuel Oil
(Diesel) 262.9 I 402.5 I 451.8 470.3 476.3 443.5 402.5
Passenger Cars 7.9 3.7 4.2 4.1 4.1 3.9 3.9
Light-Duty Trucks 11.5 20.1 25.8 26.8 27.3 26.9 26.7
Medium- and Heavy-Duty 190.5 309.6 360.6 370.1 376.1 356.0 321.8
66 Biofuel estimates are presented for informational purposes only in the Energy chapter, in line with IPCC
methodological guidance and UNFCCC reporting obligations. Net carbon fluxes from changes in biogenic carbon
reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see
Chapter 7). More information and additional analyses onbiofuels are available at EPA's "Renewable Fuels:
Regulations & Standards" web page: http://www.epa.gov/otaq/fuels/renewablefuels/regulations.htm
67Includes "light-duty trucks" fueled by gasoline, diesel and LPG.
68 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.
Energy 3-13
-------
Trucks"
Buses 8.0 10.2 10.6 10.8 10.8 10.3 9.3
Rail 35.5 42.1 45.6 47.8 46.6 43.2 36.2
Recreational Boats 2.0 2.7 3.1 3.2 3.3 0.9 3.5
Ships and Other Boats 7.5 14.1 8.1 7.5 8.2 2.2 1.2
Jet Fuel 176.2 I 199.8 I 194.2 169.5 168.7 155.1 138.8
Commercial Aircraft 135.4 I 169.2 I 161.2 137.1 138.1 122.2 111.4
Military Aircraft 34.4 21.1 18.1 16.4 16.1 16.3 14.1
General Aviation Aircraft 6.4 9.5 14.9 16.0 14.5 16.6 13.3
Fuelsc 46.4 58.8 56.7 74.6 73.8 75.5 69.4
Aviation Gasoline 3.1 2.5 2.4 2.3 2.2 2.0 1.8
General Aviation Aircraft 3.1 2.5 2.4 2.3 2.2 2.0 1.8
Residual Fuel Oil 22.6 33.3 19.3 23.0 29.0 19.9 12.0
Ships and Other Boatsd 22.6 33.3 19.3 23.0 29.0 19.9 12.0
Fuelsc 53.7 33.3 43.6 45.0 45.6 49.2 45.4
Natural Gas 36.0 35.6 33.1 33.1 35.3 36.8 36.3
Buses + I 0.4 0.8 0.8 1.0 1.1 1.1
Pipeline 36.0 35.2 32.2 32.3 34.3 35.7 35.2
LPG 1.4 0.7 1.7 1.7 1.4 2.4 2.5
Light-Duty Trucks 0.6 0.5 1.3 1.2 1.0 1.8 1.8
Medium- and Heavy-Duty
Trucks'3 0.8 0.3 0.4 0.5 0.4 0.7 0.7
Electricity 3.0 3.4 4.7 4.5 5.0 4.7 4.4
Rail 3.0 3.4 4.7 4.5 5.0 4.7 4.4
Total
1,489.0
1,813.0
1,901.3 1,882.6 1,899.0 1,794.6 1,724.1
Total (Including
Bunkers) c
1,600.8
1,911.4
2,011.1 2,011.0 2,026.6 1,928.3 1,847.2
a 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.
b Includes medium- and heavy-duty trucks over 8,500 Ibs.
0 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.
Note: Totals may not sum due to independent rounding.
Note: See section 3.10 of this chapter, in line with IPCC methodological guidance and UNFCCC reporting obligations, for more
information on ethanol.
+ Less than 0.05 Tg CO2 Eq.
- Unreported or zero
Mobile Fossil Fuel Combustion CFLt and N2O Emissions
Mobile combustion includes emissions of CH4 and N2O from all transportation sources identified in the U.S.
inventory with the exception of pipelines, which are stationary; mobile sources also include non-transportation
sources such as construction/mining equipment, agricultural equipment, vehicles used off-road, and other sources
(e.g., snowmobiles, lawnmowers, etc.). Annex 3.2 includes a summary of all emissions from both transportation
and mobile sources. Table 3-13 and Table 3-14 provide CH4 and N2O emission estimates in Tg CO2 Eq.69
' See Annex 3.2 for a complete time series of emission estimates for 1990 through 2009.
3-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Mobile combustion was responsible for a small portion of national CH4 emissions (0.3 percent) but was the second
largest source of U.S. N2O emissions (9 percent). From 1990 to 2009, mobile source CH4 emissions declined by 58
percent, to 2.0 Tg CO2 Eq. (93 Gg), due largely to control technologies employed in on-road vehicles since the mid-
1990s to reduce CO, NOX, NMVOC, and CH4 emissions. Mobile source emissions of N2O decreased by 46 percent,
to 23.9 Tg CO2 Eq. (77 Gg). Earlier generation control technologies initially resulted in higher N2O emissions,
causing a 26 percent increase in N2O emissions from mobile sources between 1990 and 1998. Improvements in
later-generation emission control technologies have reduced N2O output, resulting in a 50 percent decrease in
mobile source N2O emissions from 1998 to 2009 (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
Table 3-13: CH4 Emissions from Mobile Combustion (Tg CO2 Eq.)
Fuel Type/Vehicle Type" 1990 2000 2005
2006
2007 2008
2009
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Road
Non-Road
Ships and Boats
Rail
Aircraft
Agricultural Equipment13
Construction/Mining
Equipment0
Otherd
2.8
1.6l
1 if
'
1.9
1.1
0.7
0.1
1.7
1.0
0.6
0.1
1.6
0.9
0.6
0.1
1.4
0.8
0.6
0.1
1.3
0.7
0.6
0.1
0.6
+
0.1
0.2
0.1
0.1
0.1
0.1
0.6
+
0.1
0.1
0.1
0.1
0.1
0.1
0.5
0.
0.
0.
0.1
0.5
+
0.1
0.1
0.1
0.1
0.1
Total
4.7
3.4
2.5
2.3
2.2
2.0
2.0
a See Annex 3.2 for definitions of on-road vehicle types.
b Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in
agriculture.
0 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in
construction.
d "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad
equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg CO2 Eq.
Table 3-14: N2O Emissions from Mobile Combustion (Tg CO2 Eq.)
Fuel Type/Vehicle Type"
1990
2000
2005
2006 2007
2008
2009
Gasoline On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Motorcycles
32.1
17.7
13.6
29.0
15.7
12.5
0.7
25.5
13.7
11.1
0.7
21.8
11.7
9.5
0.6
19.9
10.0
9.3
0.5
Energy 3-15
-------
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-Duty
Trucks and Buses
Alternative Fuel On-Road
Non-Road
Ships and Boats
Rail
Aircraft
Agricultural Equipment13
Construction/Mining
Equipment0
Otherd
0.3
0.1
4.3
0.9
0.3
1.91
0.3J
0.4
0.3
0.3
0.2
4.3
0.6
0.4
1.9
0.4
0.5
0.6
0.3
0.3
0.2
4.2
0.7
0.4
1.6
0.4
0.5
0.6
0.3
0.3
0.2
4.3
0.8
0.4
1.6
0.4
0.5
0.6
0.3
0.3
0.2
3.8
0.5
0.3
1.5
0.4
0.5
0.6
0.3
0.3
0.2
3.6
0.4
0.3
1.3
0.4
0.5
0.6
Total
43.9
53.2
36.9
33.6
30.3
26.1
23.9
a See Annex 3.2 for definitions of on-road vehicle types.
b Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in
agriculture.
0 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in
construction.
d "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad
equipment, airport equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are
used off-road for commercial/industrial purposes.
Note: Totals may not sum due to independent rounding.
+ Less than 0.05 Tg CO2 Eq.
C02 from Fossil Fuel Combustion
Methodology
The methodology used by the United States for estimating CO2 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). 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 Energy Information
Administration (EIA) of the U.S. Department of Energy (DOE), primarily from the Monthly Energy
Review and published supplemental tables on petroleum product detail (EIA 2011). The EIA does not
include territories in its national energy statistics, so fuel consumption data for territories were collected
separately from Jacobs (2010).70
For consistency of reporting, the IPCC has recommended that countries report energy data using the
International Energy Agency (IEA) reporting convention and/or IEA data. Data in the IEA format are
presented "top down"—that is, energy consumption for fuel types and categories are estimated from energy
production data (accounting for imports, exports, stock changes, and losses). The resulting quantities are
referred to as "apparent consumption." The data collected in the United States by EIA on an annual basis
and used in this inventory are predominantly from mid-stream or conversion energy consumers such as
refiners and electric power generators. These annual surveys are supplemented with end-use energy
consumption surveys, such as the Manufacturing Energy Consumption Survey, that are conducted on a
periodic basis (every 4 years). These consumption data sets help inform the annual surveys to arrive at the
70 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 42 Lg CO2 Eq. in 2009.
3-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
national total and sectoral breakdowns for that total. 71
It is also important to note that U.S. fossil fuel energy statistics are generally presented using gross calorific
values (GCV) (i.e., higher heating values). Fuel consumption activity data presented here have not been
adjusted to correspond to international standards, which are to report energy statistics in terms of net
calorific values (NCV) (i.e., lower heating values).72
2. Subtract uses accounted for in the Industrial Processes chapter. Portions of the fuel consumption data for
seven fuel categories—coking coal, distillate fuel, industrial other coal, petroleum coke, natural gas,
residual fuel oil, and other oil—were reallocated to the industrial processes chapter, as they were consumed
during non-energy related industrial activity. To make these adjustments, additional data were collected
from AISI (2004 through 2010), Coffeyville (2010), U.S. Census Bureau (2010), EIA (2010c), USGS
(1991 through 2010), USGS (1994 through 2010), USGS (1995, 1998, 2000 through 2002, 2007, and
2009), USGS (1991 through 2009a), and USGS (1991 through 2009b).73
3. Adjust for conversion of fuels and exports ofCO2. Fossil fuel consumption estimates are adjusted
downward to exclude fuels created from other fossil fuels and exports of CO2.74 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.75 Since October 2000,
the Dakota Gasification Plant has been exporting CO2 to Canada by pipeline. Since this CO2 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
(2011) and data for synthetic natural gas were collected from EIA (2009b), and data for CO2 exports were
collected from the Dakota Gasification Company (2006), Fitzpatrick (2002), Erickson (2003), and EIA
(2007b).
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 the Federal Highway Administration's
(FHWA) VMT that indicated that the amount of distillate and motor gasoline consumption allocated to the
transportation sector in the EIA statistics should be adjusted. Therefore, for these estimates, the
transportation sector's distillate fuel and motor gasoline consumption was adjusted upward to match the
value obtained from the bottom-up analysis based on VMT. As the total distillate and motor gasoline
consumption estimate from EIA are considered to be accurate at the national level, the distillate
consumption totals for the residential, commercial, and industrial sectors were adjusted downward
proportionately. The data sources used in the bottom-up analysis of transportation fuel consumption include
AAR (2009 through 2010), Benson (2002 through 2004), DOE (1993 through 2010), EIA (2009a), EIA
(1991 through 2010), EPA (2009), and FHWA (1996 through 2010).76
71 See IPCC Reference Approach for estimating CO2 emissions from fossil fuel combustion in Annex 4 for a comparison of U.S.
estimates using top-down and bottom-up approaches.
72 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.
73 See sections on Iron and Steel Production and Metallurgical Coke Production, Ammonia Production and Urea Consumption,
Petrochemical Production, Titanium Dioxide Production, Ferroalloy Production, Aluminum Production, and Silicon Carbide
Production and Consumption in the Industrial Processes chapter.
74 Energy statistics from EIA(2010c) are already adjusted downward to account for ethanol added to motor gasoline, and biogas
in natural gas.
75 These adjustments are explained in greater detail in Annex 2.1.
76 FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in
data collection procedures. Based on data from FHWA's Traffic Volume Trends Program, the overall increase in VMT between
2008 and 2009 was estimated to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel
consumption by fuel type between 2008 and 2009, as described below.
Fuel use by vehicle class (also in the VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and
gasoline consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline was estimated
to grow by the rate of growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel
Energy 3-17
-------
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
byEIA(2011).
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).77 The Office of the Under Secretary of Defense (Installations and Environment) and the Defense
Energy Support Center (Defense Logistics Agency) of the U.S. Department of Defense (DoD) (DESC
2011) supplied data on military jet fuel and marine fuel use. Commercial jet fuel use was obtained from
FAA (2006 and 2009); residual and distillate fuel use for civilian marine bunkers was obtained from DOC
(1991 through 2010) for 1990 through 2001, 2007 and 2008, 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 mandatory
reporting rule (EPA 2010a). A discussion of the methodology used to develop the C content coefficients
are presented in Annexes 2.1 and 2.2.
8. Estimate CO2 Emissions. Total 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
fuel types other than jet fuel, fuel consumption data by vehicle type and transportation mode were used to
allocate emissions by fuel type calculated for the transportation end-use sector.
• For on-road vehicles, annual estimates of combined motor gasoline and diesel fuel consumption by
vehicle category were obtained from FHWA (1996 through 2010); for each vehicle category, the
percent gasoline, diesel, and other (e.g., CNG, LPG) fuel consumption are estimated using data from
DOE (1993 through 2010). Fuel use by vehicle class (found in the VM-1 table) was not available
from FHWA for 2009, but changes in overall diesel and gasoline consumption were released in Table
MF21. Fuel use in vehicle classes that were predominantly gasoline was estimated to grow by the rate
of growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly
diesel were estimated to fall by the same rate that diesel fuel consumption fell overall in 2009.
• For non-road vehicles, activity data were obtained from AAR (2009 through 2010), APTA (2007
through 2010), BEA (1991 through 2009), Benson (2002 through 2004), DOE (1993 through 2010),
was estimated to fall by the same rate that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle
classes based on these fuel consumption estimates, assuming no relative change in MPG between vehicle classes.
77 See International Bunker Fuels section in this chapter for a more detailed discussion.
3-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
DESC (2011), DOC (1991 through 2010), DOT (1991 through 2010), EIA (2009a), EIA (2009d), EIA
(2007a), EIA (2002), EIA (1991 through 2011), EPA (2010b), FAA (2008), and Gaffney (2007).
• For jet fuel used by aircraft, CO2 emissions were calculated directly based on reported consumption of
fuel as reported by EIA, and allocated to commercial aircraft using flight-specific fuel consumption
data from the Federal Aviation Administration's (FAA) Aviation Environmental Design Tool (AEDT)
(FAA 2011). 78 Allocation to domestic general aviation was made using FAA Aerospace Forecast
data, and allocation to domestic military uses was made using DoD data (see Annex 3.7).
Heat contents and densities were obtained from EIA (2010) and USAF (1998).79
[BEGIN BOX]
Box 3-2: 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
Tg CO2 Eq./QBtu for natural gas to upwards of 95 Tg CO2 Eq./QBtu for coal and petroleum coke.80 In general, the
C content per unit of energy of fossil fuels is the highest for coal products, followed by petroleum, and then natural
gas. The overall C intensity of the U.S. economy is thus dependent upon the quantity and combination of fuels and
other energy sources employed to meet demand.
Table 3-15 provides a time series of the C intensity for each sector of the U.S. economy. The time series
incorporates only the energy consumed from the direct combustion of fossil fuels in each sector. For example, the C
intensity for the residential sector does not include the energy from or emissions related to the consumption of
electricity for lighting. Looking only at this direct consumption of fossil fuels, the residential sector exhibited the
lowest C intensity, which is related to the large percentage of its energy derived from natural gas for heating. The C
intensity of the commercial sector has predominantly declined since 1990 as commercial businesses shift away from
petroleum to natural gas. The industrial sector was more dependent on petroleum and coal than either the residential
or commercial sectors, and thus had higher C intensities over this period. The C intensity of the transportation
sector was closely related to the C content of petroleum products (e.g., motor gasoline and jet fuel, both around 70
Tg CO2 Eq./EJ), which were the primary sources of energy. Lastly, the electricity generation sector had the highest
C intensity due to its heavy reliance on coal for generating electricity.
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (Tg CO2 Eq./QBtu)
Sector
Residential a
Commercial a
Industrial a
Transportation a
1990
57.4
59.2
64.3
71.1 |
2000
56.6
57.2
62.8
71.3
2005
56.6
57.5
64.3
71.4
2006
56.5
57.2
64.5
71.6
2007
56.3
57.1
64.0
71.9
2008
56.1
56.8
63.6
71.6
2009
56.0
56.9
63.2
71.5
78 Data for inventory years 2000 through 2005 were developed using the FAA's System for assessing Aviation's Global
Emissions (SAGE) model. That tool has been incorporated into the Aviation Environmental Design Tool (AEDT), which
calculates noise in addition to aircraft fuel burn and emissions for all commercial flights globally in a given year. Data for
inventory years 2006-2009 were developed using AEDT. The AEDT model dynamically models aircraft performance in space
and time to produce fuel bum, emissions and noise. Full flight gate-to-gate analyses are possible for study sizes ranging from a
single flight at an airport to scenarios at the regional, national, and global levels. AEDT is currently used by the U.S. government
to consider the interdependencies between aircraft-related fuel bum, noise and emissions.
79 For a more detailed description of the data sources used for the analysis of the transportation end use sector see the Mobile
Combustion (excluding CO2) and International Bunker Fuels sections of the Energy chapter, Annex 3.2, and Annex 3.7.
80 One exajoule (EJ) is equal to 1018 joules or 0.9478 QBtu.
Energy 3-19
-------
Electricity Generation b
U.S. Territories'
All Sectors c
87.3
73.0
73.0
86.2
1 72.5
I 73.0
85.8
73.4
73.5
85.4
73.5
73.5
84.7
73.8
73.3
84.9
73.3
73.1
83.7
73.1
72.4
a 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-year period of 1990 through 2009, 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 2009 was approximately 9 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 CO2 emissions per
dollar of gross domestic product (GDP) have both declined since 1990 (BEA 2010).
Figure 3-14: U.S. Energy Consumption and Energy-Related CO2 Emissions Per Capita and Per Dollar GDP
C intensity estimates were developed using nuclear and renewable energy data from EIA (2010), EPA (2010a), and
fossil fuel consumption data as discussed above and presented in Annex 2.1.
[END BOX]
Uncertainty and Time Series Consistency
For estimates of CO2 from fossil fuel combustion, the amount of CO2 emitted is directly related to the amount of
fuel consumed, the fraction of the fuel that is oxidized, and the carbon content of the fuel. Therefore, a careful
accounting of fossil fuel consumption by fuel type, average carbon contents of fossil fuels consumed, and
production of fossil fuel-based products with long-term carbon storage should yield an accurate estimate of 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 CO2 emission estimates is believed to be relatively small. See, for example,
Marland and Pippin (1990).
Although statistics of total fossil fuel and other energy consumption are relatively accurate, the allocation of this
consumption to individual end-use sectors (i.e., residential, commercial, industrial, and transportation) is less
certain. For example, for some fuels the sectoral allocations are based on price rates (i.e., tariffs), but a commercial
establishment may be able to negotiate an industrial rate or a small industrial establishment may end up paying an
industrial rate, leading to a misallocation of emissions. Also, the deregulation of the natural gas industry and the
more recent deregulation of the electric power industry have likely led to some minor problems in collecting
accurate energy statistics as firms in these industries have undergone significant restructuring.
To calculate the total CO2 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 for 2009. The amount
of CO2 emissions resulting from non-energy related fossil fuel use has been calculated separately and reported in the
Carbon Emitted from Non-Energy Uses of Fossil Fuels section of this report. These factors all contribute to the
uncertainty in the CO2 estimates. Detailed discussions on the uncertainties associated with C emitted from Non-
Energy Uses of Fossil Fuels can be found within that section of this chapter.
Various sources of uncertainty surround the estimation of emissions from international bunker fuels, which are
subtracted from the U.S. totals (see the detailed discussions on these uncertainties provided in the International
Bunker Fuels section of this chapter). Another source of uncertainty is fuel consumption by U.S. territories. The
3-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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
Tier 2 uncertainty estimation methodology, Monte Carlo 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.81 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.82
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).83 For purposes of this uncertainty analysis, each input variable was simulated 10,000 times through Monte
Carlo Sampling.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-16. Fossil fuel combustion
CO2 emissions in 2009 were estimated to be between 5,149.0 and 5,522.4 Tg CO2 Eq. at a 95 percent confidence
level. This indicates a range of 1 percent below to 6 percent above the 2009 emission estimate of 5,209.0.0 Tg CO2
Eq.
Table 3-16: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-related Fossil Fuel
Combustion by Fuel Type and Sector (Tg CO2 Eq. and Percent)
Fuel/Sector 2009 Emission Estimate Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Coalb
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
1,841.0
0.6
5.8
83.4
NE
1,747.6
3.5
Lower
Bound
1,779.3
0.6
5.5
80.5
NE
1,680.4
3.1
Upper
Bound
2,015.6
0.7
6.7
97.5
NE
1,915.8
4.2
Lower
Bound
-3%
-6%
-5%
-3%
NA
-4%
-12%
Upper
Bound
+9%
+15%
+15%
+17%
NA
+10%
+19%
81 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.
82 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.
83 Although, in general, random uncertainties are the main focus of statistical uncertainty analysis, when the uncertainty
estimates are elicited from experts, their estimates include both random and systematic uncertainties. Hence, both these types of
uncertainties are represented in this uncertainty analysis.
Energy 3-21
-------
Natural Gas b
Residential
Commercial
Industrial
Transportation
Electricity Generation
U.S. Territories
Petroleum b
Residential
Commercial
Industrial
Transportation
Electric Utilities
U.S. Territories
Total (excluding
Geothermal) b
Geothermal
1,200.9
257.2
167.9
365.0
36.3
373.1
1.5
2,166.7
81.4
50.3
282.0
1,683.4
32.9
36.7
5,208.6
0.4
1,209.4
250.0
163.2
374.9
35.2
362.3
1.3
2,067.2
76.9
47.9
231.2
1,598.6
31.5
33.8
5,148.76
NE
1,276.6
275.2
179.7
412.7
38.8
392.0
1.7
2,323.5
85.7
52.4
330.4
1,826.8
35.4
40.9
5,522.0
NE
+1%
-3%
-3%
+3%
-3%
-3%
-12%
-5%
-6%
-5%
-18%
-5%
-4%
-8%
-1%
NE
+6%
+7%
+7%
+13%
+7%
+5%
+17%
+7%
+5%
+4%
+17%
+9%
+7%
+11%
+6%
NE
Total (including
Geothermal) b'c
5,209.0
5,149.0
5,522.4
-1%
+6%
NA (Not Applicable)
NE (Not Estimated)
a Range of emission estimates predicted by Monte Carlo 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.
0 Geothermal emissions added for reporting purposes, but an uncertainty analysis was not performed for CO2 emissions from
geothermal production.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for CO2 from fossil fuel combustion was developed and implemented. This effort
included a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented
involved checks specifically focusing on the activity data and methodology used for estimating CO2 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 2011) updated energy consumption statistics across the time series.
These revisions primarily impacted the emission estimates for 2007 and 2008. In addition, the coal emissions for
U.S. Territories decreased from 2001 to 2008 due to the closure of a coal power plant in the U.S. Virgin Islands.
Overall, these changes resulted in an average annual increase of 0.5 Tg CO2 Eq. (less than 0.1 percent) in CO2
emissions from fossil fuel combustion for the period 1990 through 2008.
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.
Beginning in 2010, those facilities that emit over 25,000 tons of greenhouse gases (CO2 Eq.) from stationary
combustion across all sectors of the economy are required to calculate and report their greenhouse gas emissions to
3-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EPA through its Greenhouse Gas Reporting Program. These data will be used in future inventories to improve the
emission calculations through the use of these collected higher tier methodological data.
CH4 and N20 from Stationary Combustion
Methodology
CH4 and N2O emissions from stationary combustion were estimated by multiplying fossil fuel and wood
consumption data by emission factors (by sector and fuel type). National coal, natural gas, fuel oil, and wood
consumption data were grouped by sector: industrial, commercial, residential, electricity generation, and U.S.
territories. For the CH4 and N2O estimates, wood consumption data for the United States was obtained from EIA's
Annual Energy Review (EIA 2010). Fuel consumption data for coal, natural gas, and fuel oil for the United States
were obtained from EIA's Monthly Energy Review and unpublished supplemental tables on petroleum product
detail (EIA 2011). Because the United States does not include territories in its national energy statistics, fuel
consumption data for territories were provided separately by Jacobs (2010).84 Fuel consumption for the industrial
sector was adjusted to subtract out construction and agricultural use, which is reported under mobile sources.85
Construction and agricultural fuel use was obtained from EPA (2010a). Estimates for wood biomass consumption
for fuel combustion do not include wood wastes, liquors, municipal solid waste, tires, etc., that are reported as
biomass by EIA.
Emission factors for the four end-use sectors were provided by the 2006 IPCC 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.
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
CH4 emission estimates from stationary sources exhibit high uncertainty, primarily due to difficulties in calculating
emissions from wood combustion (i.e., fireplaces and wood stoves). The estimates of CH4 and N2O emissions
presented are based on broad indicators of emissions (i.e., fuel use multiplied by an aggregate emission factor for
different sectors), rather than specific emission processes (i.e., by combustion technology and type of emission
control).
An uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-recommended
Tier 2 uncertainty estimation methodology, Monte Carlo Simulation technique, with @RISK software.
The uncertainty estimation model for this source category was developed by integrating the CH4 and N2O stationary
source inventory estimation models with the model for CO2 from fossil fuel combustion to realistically characterize
the interaction (or endogenous correlation) between the variables of these three models. About 55 input variables
were simulated for the uncertainty analysis of this source category (about 20 from the CO2 emissions from fossil
fuel combustion inventory estimation model and about 35 from the stationary source inventory models).
In developing the uncertainty estimation model, uniform distribution was assumed for all activity-related input
variables and N2O emission factors, based on the SAIC/EIA (2001) report.86 For these variables, the uncertainty
84 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.
85 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.
86 SAIC/EIA (2001) characterizes the underlying probability density function for the input variables as a combination of uniform
and normal distributions (the former distribution to represent the bias component and the latter to represent the random
component). However, for purposes of the current uncertainty analysis, it was determined that uniform distribution was more
appropriate to characterize the probability density function underlying each of these variables.
Energy 3-23
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ranges were assigned to the input variables based on the data reported in SAIC/EIA (2001).87 However, the CH4
emission factors differ from those used by EIA. Since these factors were obtained from IPCC/UNEP/OECD/IEA
(1997), uncertainty ranges were assigned based on IPCC default uncertainty estimates (IPCC 2000).
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-17. Stationary combustion
CH4 emissions in 2009 (including biomass) were estimated to be between 4.1 and 14.0 Tg CO2 Eq. at a 95 percent
confidence level. This indicates a range of 34 percent below to 127 percent above the 2009 emission estimate of 6.2
Tg CO2 Eq.88 Stationary combustion N2O emissions in 2009 (including biomass) were estimated to be between 9.8
and 36.7 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 23 percent below to 187 percent
above the 2009 emissions estimate of 12.8 Tg CO2 Eq.
Table 3-17: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Energy-Related Stationary
Combustion, Including Biomass (Tg CO2 Eq. and Percent)
Source
Gas
2009 Emission
Estimate
(TgC02Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Stationary Combustion
Stationary Combustion
CH4
N2O
6.2
12.8
4.1 14.0 -34% +127%
9.8 36.7 -23% +187%
1 Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
The uncertainties associated with the emission estimates of CH4 and N2O are greater than those associated with
estimates of CO2 from fossil fuel combustion, which mainly rely on the carbon content of the fuel combusted.
Uncertainties in both CH4 and N2O estimates are due to the fact that emissions are estimated based on emission
factors representing only a limited subset of combustion conditions. For the indirect greenhouse gases, uncertainties
are partly due to assumptions concerning combustion technology types, age of equipment, emission factors used,
and activity data projections.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. 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
Historical CH4 and N2O emissions from stationary sources (excluding CO2) were revised due to a couple of changes,
mainly impacting 2007 and 2008 estimates. Slight changes to emission estimates for sectors are due to revised data
from EIA (2010). Wood consumption data in EIA (2011) were revised for the residential, commercial, and
industrial sectors for 2007 and 2008 as well as for the electric power sector for 2006 through 2008. The
combination of the methodological and historical data changes resulted in an average annual increase of 0.01 Tg
CO2 Eq. (0.2 percent) in CH4 emissions from stationary combustion and an average annual decrease of 0.08 Tg CO2
Eq. (0.5 percent) in N2O emissions from stationary combustion for the period 1990 through 2008.
87 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.
88 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.
3-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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.
Beginning in 2010, those facilities that emit over 25,000 tons of greenhouse gases (CO2 Eq.) from stationary
combustion across all sectors of the economy are required to calculate and report their greenhouse gas emissions to
EPA through its Greenhouse Gas Reporting Program. These data will be used in future inventories to improve the
emission calculations through the use of these collected higher tier methodological data.
CH4 and N20 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)89 are based on VMT and emission factors by vehicle and fuel type.
Emission factors for gasoline and diesel on-road vehicles utilizing Tier 2 and Low Emission Vehicle (LEV)
technologies were developed by ICF (2006b); all other gasoline and diesel on-road vehicle emissions factors were
developed by ICF (2004). These factors were derived from EPA, California Air Resources Board (CARD) and
Environment Canada laboratory test results of different vehicle and control technology types. The EPA, CARD and
Environment Canada tests were designed following the Federal Test Procedure (FTP), which covers three separate
driving segments, since vehicles emit varying amounts of greenhouse gases depending on the driving segment.
These driving segments are: (1) a transient driving cycle that includes cold start and running emissions, (2) a cycle
that represents running emissions only, and (3) a transient driving cycle that includes hot start and running
emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the
content of this bag was then analyzed to determine quantities of gases present. The emissions characteristics of
segment 2 were used to define running emissions, and subtracted from the total FTP emissions to determine start
emissions. These were then recombined based upon the ratio of start to running emissions for each vehicle class
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.90
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
89 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.
90 Additional information regarding the model can be found online at http://www.epa.gov/OMS/m6.htm.
Energy 3-25
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AFVs is provided in Annex 3.2.
Annual VMT data for 1990 through 2010 were obtained from the Federal Highway Administration's (FHWA)
Highway Performance Monitoring System database as reported in Highway Statistics (FHWA 1996 through
2010).91 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
2010) and information on total motor vehicle fuel consumption by fuel type from FHWA (1996 through 2010).
VMT for AFVs were taken from Browning (2003). The age distributions of the U.S. vehicle fleet were obtained
from EPA (2010a, 2000), and the average annual age-specific vehicle mileage accumulation of U.S. vehicles were
obtained from EPA (2000).
Control technology and standards data for on-road vehicles were obtained from EPA's Office of Transportation and
Air Quality (EPA 2007a, 2007b, 2000, 1998, and 1997) and Browning (2005). These technologies and standards are
defined in Annex 3.2, and were compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and
IPCC/UNEP/OECD/IEA (1997).
Non-Road Vehicles
To estimate emissions from non-road vehicles, fuel consumption data were employed as a measure of activity, and
multiplied by fuel-specific emission factors (in grams of N2O and CH4 per kilogram of fuel consumed).92 Activity
data were obtained from AAR (2009 through 2010), APTA (2007 through 2010), APTA (2006), BEA (1991 through
2005), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2008), DOE (1993 through 2010), DESC
(2011), DOT (1991 through 2010), EIA (2008a, 2007a, 2007b, 2002), EIA (2007 through 2010), EIA (1991 through
2011), EPA (2009), Esser (2003 through 2004), FAA (2011, 2010, and 2006), Gaffney (2007), and (2006 through
2010). Emission factors for non-road modes were taken from IPCC/UNEP/OECD/IEA (1997) and Browning
(2009).
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the mobile source sector using the IPCC-recommended Tier 2
uncertainty estimation methodology, Monte Carlo simulation technique, using @RISK software. The uncertainty
analysis was performed on 2009 estimates of CH4 and N2O emissions, incorporating probability distribution
functions associated with the major input variables. For the purposes of this analysis, the uncertainty was modeled
for the following four major sets of input variables: (1) vehicle miles traveled (VMT) data, by on-road vehicle and
fuel type and (2) emission factor data, by on-road vehicle, fuel, and control technology type, (3) fuel consumption,
data, by non-road vehicle and equipment type, and (4) emission factor data, by non-road vehicle and equipment
type.
Uncertainty analyses were not conducted for NOX, CO, or NMVOC emissions. Emission factors for these gases
have been extensively researched since emissions of these gases from motor vehicles are regulated in the United
States, and the uncertainty in these emission estimates is believed to be relatively low. However, a much higher
level of uncertainty is associated with CH4 and N2O emission factors, because emissions of these gases are not
regulated in the United States (and, therefore, there are not adequate 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 2009 were estimated to be between 1.8 and 2.2 Tg
CO2 Eq. at a 95 percent confidence level. This indicates a range of 9 percent below to 15 percent above the
corresponding 2009 emission estimate of 2.0 Tg CO2 Eq. Also at a 95 percent confidence level, mobile combustion
N2O emissions from mobile sources in 2009 were estimated to be between 20.5 and 27.9 Tg CO2 Eq., indicating a
range of 14 percent below to 17 percent above the corresponding 2009 emission estimate of 23.9 Tg CO2 Eq.
91 Fuel use by vehicle class (VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and gasoline
consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline were estimated to grow
by the rate of growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel were
estimated to fall by the same rate that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes
based on these fuel consumption estimates, assuming no relative change in MPG between vehicle classes.
92 The consumption of international bunker fuels is not included in these activity data, but is estimated separately under the
International Bunker Fuels source category.
3-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 3-18: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Mobile Sources (Tg CO2
Eq. and Percent)
Source
Gas
2009 Emission
Estimate3
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Bound Upper Bound Lower Bound Upper Bound
Mobile Sources
Mobile Sources
CH4
N2O
2.0
23.9
1.8 2.2 -9% +15%
20.5 27.9 -14% +17%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
This uncertainty analysis is a continuation of a multi-year process for developing quantitative uncertainty estimates
for this source category using the IPCC Tier 2 approach to uncertainty analysis. As a result, as new information
becomes available, uncertainty characterization of input variables may be improved and revised. For additional
information regarding uncertainty in emission estimates for CH4 and N2O please refer to the Uncertainty Annex.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2008. 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.
Recalculations Discussion
In order to ensure that these estimates are continuously improved, the calculation methodology is revised annually
based on comments from internal and external reviewers. Each year, a number of adjustments are made to the
methodologies used in calculating emissions in the current Inventory relative to previous Inventory reports. One of
the revisions that were made this year was incorporating motor vehicle age distribution from EPA's MOtor Vehicle
Emission Simulator (MOVES) model. MOVES is EPA's tool for estimating emissions from highway vehicles,
based on analysis of millions of emission test results and considerable advances in EPA's understanding of vehicle
emissions. Population data from the MOVES model was used to estimate the age distribution of motor vehicles in
the United States.
Planned Improvements
While the data used for this report represent the most accurate information available, four areas have been identified
that could potentially be improved in the short-term given available resources.
1. Develop updated emissions factors for diesel vehicles, motorcycle, and biodiesel vehicles. Previous
emission factors were based upon extrapolations from other vehicle classes and new test data from
Environment Canada and other sources may allow for better estimation of emission factors for these
vehicles.
2. Develop new emission factors for non-road equipment. The current inventory estimates for non-CO2
emissions from non-road sources are based on emission factors from IPCC guidelines published in 1996.
Recent data on non-road sources from Environment Canada and the California Air Resources Board will be
investigated in order to assess the feasibility of developing new N2O and CH4 emissions factors for non-
road equipment.
3. Examine the feasibility of estimating aircraft N2O and CH4 emissions by the number of takeoffs and
landings, instead of total fuel consumption. Various studies have indicated that aircraft N2O and CH4
Energy 3-27
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emissions are more dependent on aircraft takeoffs and landings than on total aircraft fuel consumption;
however, aircraft emissions are currently estimated from fuel consumption data. FAA's SAGE and AEDT
databases contain detailed data on takeoffs and landings for each calendar year starting in 2000, and could
potentially be used to conduct a Tier II analysis of aircraft emissions. This methodology will require a
detailed analysis of the number of takeoffs and landings by aircraft type on domestic trips, the development
of procedures to develop comparable estimates for years prior to 2000, and the dynamic interaction of
ambient air with aircraft exhausts is developed. The feasibility of this approach will be explored.
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
will be investigated. Continue to examine the use of EPA's MOVES model in the development of the inventory
estimates, including use for uncertainty analysis. Although the inventory uses some of the underlying data from
MOVES, such as vehicle age distributions by model year, MOVES is not used directly in calculating mobile source
emissions. As MOVES goes through additional testing and refinement, the use of MOVES will be further explored.
3.2. Carbon Emitted from Non-Energy Uses of Fossil Fuels (IPCC Source
Category 1A)
In addition to being combusted for energy, fossil fuels are also consumed for non-energy uses (NEU) in the United
States. The fuels used for these purposes are diverse, including natural gas, liquefied petroleum gases (LPG),
asphalt (a viscous liquid mixture of heavy crude oil distillates), petroleum coke (manufactured from heavy oil), and
coal (metallurgical) coke (manufactured from coking coal). The non-energy applications of these fuels are equally
diverse, including feedstocks for the manufacture of plastics, rubber, synthetic fibers and other materials; reducing
agents for the production of various metals and inorganic products; and non-energy products such as lubricants,
waxes, and asphalt (IPCC 2006).
CO2 emissions arise from non-energy uses via several pathways. Emissions may occur during the manufacture of a
product, as is the case in producing plastics or rubber from fuel-derived feedstocks. Additionally, emissions may
occur during the product's lifetime, such as during solvent use. Overall, throughout the time series and across all
uses, about 61 percent of the total C consumed for non-energy purposes was stored in products, and not released to
the atmosphere; the remaining 39 percent was emitted.
There are several areas in which non-energy uses of fossil fuels are closely related to other parts of the inventory.
For example, some of the NEU products release CO2 at the end of their commercial life when they are combusted
after disposal; these emissions are reported separately within the Energy chapter in the Incineration of Waste source
category. In addition, there is some overlap between fossil fuels consumed for non-energy uses and the fossil-
derived CO2 emissions accounted for in the Industrial Processes chapter, especially for fuels used as reducing
agents. To avoid double-counting, the "raw" non-energy fuel consumption data reported by EIA are modified to
account for these overlaps. There are also net exports of petrochemicals that are not completely accounted for in the
EIA data, and the inventory calculations make adjustments to address the effect of net exports on the mass of C in
non-energy applications.
As shown in Table 3-19, fossil fuel emissions in 2009 from the non-energy uses of fossil fuels were 123.4 Tg CO2
Eq., which constituted approximately 2 percent of overall fossil fuel emissions. In 2009, the consumption of fuels
for non-energy uses (after the adjustments described above) was 4,451.0 TBtu, an increase of 0.2 percent since 1990
(see Table 3-20). About 49.9 Tg of the C (182.8 Tg CO2 Eq.) in these fuels was stored, while the remaining 33.6 Tg
C (123.4 Tg CO2 Eq.) was emitted.
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (Tg CO2 Eq.)
Year
Potential Emissions
C Stored
Emissions as a % of Potential
Emissions
1990
310.8
192.2
38%
118.6
2000
383.6
238.6
38%
144.9
2005
381.6
238.3
38%
143.4
2006
381.7
236.1
38%
145.6
2007
370.1
232.8
37%
137.2
2008
344.9
204.0
41%
141.0
2009
306.1
182.8
40%
123.4
3-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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 (2011) (see Annex 2.1). Consumption of natural gas, LPG, pentanes plus, naphthas, other oils,
and special naphtha were adjusted to account for net exports of these products that are not reflected in the raw data
from EIA. Consumption values for industrial coking coal, petroleum coke, other oils, and natural gas in Table 3-20
and Table 3-21 have been adjusted to subtract non-energy uses that are included in the source categories of the
Industrial Processes chapter.93 Consumption values were also adjusted to subtract net exports of intermediary
chemicals.
For the remaining non-energy uses, the quantity of C stored was estimated by multiplying the potential emissions by
a storage factor.
• For several fuel types—petrochemical feedstocks (including natural gas for non-fertilizer uses, LPG,
pentanes plus, naphthas, other oils, still gas, special naphtha, and industrial other coal), asphalt and road oil,
lubricants, and waxes—U.S. data on C stocks and flows were used to develop C storage factors, calculated
as the ratio of (a) the C stored by the fuel's non-energy products to (b) the total C content of the fuel
consumed. A lifecycle approach was used in the development of these factors in order to account for losses
in the production process and during use. Because losses associated with municipal solid waste
management are handled separately in this sector under the Incineration of Waste source category, the
storage factors do not account for losses at the disposal end of the life cycle.
• For industrial coking coal and distillate fuel oil, storage factors were taken from IPCC/UNEP/OECD/IEA
(1997), which in turn draws from Marland and Rotty (1984).
• For the remaining fuel types (petroleum coke, miscellaneous products, and other petroleum), IPCC does not
provide guidance on storage factors, and assumptions were made based on the potential fate of C in the
respective NEU products.
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu)
Year 1990 2000 2005 2006 2007 2008 2009
Industry 4,181.1
Industrial Coking Coal +
Industrial Other Coal 8.2
Natural Gas to Chemical Plants 277.3
Asphalt & Road Oil 1,170.2
LPG 1,119.2
Lubricants 186.3
Pentanes Plus 77.5
Naphtha (<401°F) 325.9
Other Oil (>401 °F) 661.4
Still Gas 21.3
Petroleum Coke 54.8
Special Naphtha 100.8
Distillate Fuel Oil 7.0
Waxes 33.3
Miscellaneous Products 137.8
Transportation 176.0
Lubricants 176.0
U.S. Territories 86.7
Lubricants 0.7
5,214.4
53.0
12.4
420.3
1,275.7
1,607.0
189.9
229.3
593.7
527.0
12.6
353
94.4
11.7
33.1
119.2
179.4
179.4
152.2
3.1
5,174.4
79.8
11.9
397.0
1,323.2
1,444.0
160.2
146.3
679.6
514.8
67.7
128.8
60.9
16.0
31.4
112.8
151.3
151.3
121.9
4.6
5,163.2
62.3
12.4
407.7
1,261.2
1,488.6
156.1
105.5
618.1
573.4
57.2
172.2
68.9
17.5
26.1
136.0
147.4
147.4
133.4
6.2
5,060.7
1.7
12.4
412.5
1,197.0
1,483.0
161.2
132.7
542.6
669.2
44.2
155.9
75.5
17.5
21.9
133.5
152.2
152.2
108.4
5.9
4,671.9
28.4
12.4
395.2
1,012.0
1,409.6
149.6
114.9
467.3
599.2
47.3
174.4
83.2
17.5
19.1
142.0
141.3
141.3
126.7
2.7
4,267.7
6.1
12.4
366.0
873.1
1,446.2
134.5
93.4
450.7
392.5
133.9
133.0
44.2
17.5
12.2
151.8
127.1
127.1
56.3
1.0
93 These source categories include Iron and Steel Production, Lead Production, Zinc Production, Ammonia Manufacture, Carbon
Black Manufacture (included in Petrochemical Production), Titanium Dioxide Production, Ferroalloy Production, Silicon
Carbide Production, and Aluminum Production.
Energy 3-29
-------
Other Petroleum (Misc. Prod.)
86.0
149.1
117.3 127.2
102.5
124.1
55.2
Total
4,443.8
5,546.0
5,447.6 5,444.0 5,321.3 4,940.0 4,451.0
+ Does not exceed 0.05 TBtu
Note: To avoid double-counting, coal coke, petroleum coke, natural gas consumption, and other oils are adjusted for industrial
process consumption reported in the Industrial Processes sector. Natural gas, LPG, Pentanes Plus, Naphthas, Special Naphtha,
and Other Oils are adjusted to account for exports of chemical intermediates derived from these fuels. For residual oil (not
shown in the table), all non-energy use is assumed to be consumed in C black production, which is also reported in the Industrial
Processes chapter.
Note: Totals may not sum due to independent rounding.
Table 3-21: 2009 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions
Adjusted Carbon
Non-Energy Content Potential Carbon Carbon Carbon
Use" Coefficient Carbon Storage Stored Emissions Emissions
Sector/Fuel Type (TBtu) (TgC/QBtu) (TgC) Factor (TgC) (TgC) (TgCO2Eq.)
Industry
Industrial Coking Coal
Industrial Other Coal
Natural Gas to Chemical
Plants
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Naphtha (<401°F)
Other Oil (>401°F)
Still Gas
Petroleum Coke
Special Naphtha
Distillate Fuel Oil
Waxes
Miscellaneous Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum (Misc.
Prod.)
Total
4,267.7
6.1
12.4
366.0
873.1
1,446.2
134.5
93.4
450.7
392.5
133.9
133.0
44.2
17.5
12.2
151.8
127.1
127.1
56.3
1.0
55.2
4,451.0
-
31.00
25.82
14.47
20.55
17.06
20.20
19.10
18.55
20.17
17.51
27.85
19.74
20.17
19.80
20.31
-
20.20
-
20.20
20.00
-
79.8
0.2
0.3
5.3
17.9
24.7
2.7
1.8
8.4
7.9
2.3
3.7
0.9
0.4
0.2
3.1
2.6
2.6
1.1
+
1.1
83.5
-
0.10
0.58
0.58
1.00
0.58
0.09
0.58
0.58
0.58
0.58
0.30
0.58
0.50
0.58
0.00
-
0.09
-
0.09
0.10
-
49.5
0.0
0.2
3.1
17.9
14.3
0.2
1.0
4.9
4.6
1.4
1.1
0.5
0.2
0.1
0.0
0.2
0.2
0.1
+
0.1
49.9
30.3
0.2
0.1
2.2
0.1
10.3
2.5
0.7
3.5
3.3
1.0
2.6
0.4
0.2
0.1
3.1
2.3
2.3
1.0
+
1.0
33.6
111.1
0.6
0.5
8.1
0.3
37.9
9.0
2.7
12.9
12.2
3.6
9.5
1.3
0.6
0.4
11.3
8.5
8.5
3.7
0.1
3.6
123.4
+ Does not exceed 0.05 Tg
- Not applicable.
aTo avoid double counting, net exports have been deducted.
Note: Totals may not sum due to independent rounding.
Lastly, emissions were estimated by subtracting the C stored from the potential emissions (see Table 3-19). More
detail on the methodology for calculating storage and emissions from each of these sources is provided in Annex
2.3.
Where storage factors were calculated specifically for the United States, data were obtained on (1) products such as
asphalt, plastics, synthetic rubber, synthetic fibers, cleansers (soaps and detergents), pesticides, food additives,
antifreeze and deicers (glycols), and silicones; and (2) industrial releases including energy recovery, Toxics Release
Inventory (TRI) releases, hazardous waste incineration, and volatile organic compound, solvent, and non-
combustion CO emissions. Data were taken from a variety of industry sources, government reports, and expert
communications. Sources include EPA reports and databases such as compilations of air emission factors (EPA
2001), National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data (EPA 2010), Toxics Release
Inventory, 1998 (2000b), Biennial Reporting System (EPA 2004, 2007a), and pesticide sales and use estimates
3-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
(EPA 1998, 1999, 2002, 2004); the EIA Manufacturer's Energy Consumption Survey (MECS) (EIA 1994, 1997,
2001, 2005, 2010); the National Petrochemical & Refiners Association (NPRA 2002); the U.S. Bureau of the
Census (1999, 2004, 2009); Bank of Canada (2009); Financial Planning Association (2006); INEGI (2006); the
United States International Trade Commission (2011); Gosselin, Smith, and Hodge (1984); the Rubber
Manufacturers' Association (RMA 2009a,b); the International Institute of Synthetic Rubber Products (IISRP 2000,
2003); the Fiber Economics Bureau (FEE 2010); and the American Chemistry Council (ACC 2003-2010). Specific
data sources are listed in full detail in Annex 2.3.
Uncertainty and Time-Series Consistency
An uncertainty analysis was conducted to quantify the uncertainty surrounding the estimates of emissions and
storage factors from non-energy uses. This analysis, performed using @RISK software and the IPCC-recommended
Tier 2 methodology (Monte Carlo Simulation technique), provides for the specification of probability density
functions for key variables within a computational structure that mirrors the calculation of the inventory estimate.
The results presented below provide the 95 percent confidence interval, the range of values within which emissions
are likely to fall, for this source category.
As noted above, the non-energy use analysis is based on U.S.-specific storage factors for (1) feedstock materials
(natural gas, LPG, pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal), (2)
asphalt, (3) lubricants, and (4) waxes. For the remaining fuel types (the "other" category in Table 3-22 and Table
3-23), the storage factors were taken directly from the IPCC Guidelines for National Greenhouse Gas Inventories,
where available, and otherwise assumptions were made based on the potential fate of carbon in the respective NEU
products. To characterize uncertainty, five separate analyses were conducted, corresponding to each of the five
categories. In all cases, statistical analyses or expert judgments of uncertainty were not available directly from the
information sources for all the activity variables; thus, uncertainty estimates were determined using assumptions
based on source category knowledge.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-22 (emissions) and Table 3-23
(storage factors). Carbon emitted from non-energy uses of fossil fuels in 2009 was estimated to be between 97.6 and
135.3 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 21 percent below to 10 percent above
the 2009 emission estimate of 123.4 Tg CO2 Eq. The uncertainty in the emission estimates is a function of
uncertainty in both the quantity of fuel used for non-energy purposes and the storage factor.
Table 3-22: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-Energy Uses of Fossil Fuels
(Tg CO2 Eq. and Percent)
2009
Emission
Estimate
Source
Feedstocks
Asphalt
Lubricants
Waxes
Other
Total
Gas (Tg CO2 Eq.)
C02
C02
C02
C02
C02
C02
79.3
0.3
17.7
0.4
25.7
123.4
Uncertainty Range Relative to Emission Estimate"
(Tg CO2 Eq.) (%)
Lower Bound
63.4
0.1
14.6
0.3
10.3
97.6
Upper Bound
96.1
0.6
20.5
0.7
27.0
135.3
Lower Bound
-20%
-58%
-17%
-29%
-60%
-21%
Upper Bound
21%
119%
16%
74%
5%
10%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
NA (Not Applicable)
Energy 3-31
-------
Table 3-23: Tier 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy Uses of Fossil Fuels
(Percent)
Source
Feedstocks
Asphalt
Lubricants
Waxes
Other
2009 Storage
Gas Factor Uncertainty Range Relative to Emission Estimate"
(%) (%) (%, Relative)
CO2
CO2
CO2
CO2
CO2
58%
99.6%
9%
58%
17%
Lower
Bound
56%
99.1%
4%
49%
16%
Upper
Bound
60%
99.8%
17%
71%
66%
Lower
Bound
-3%
-0.5%
-57%
-15%
-3%
Upper
Bound
4%
0.3%
91%
22%
292%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval, as a
percentage of the inventory value (also expressed in percent terms).
In Table 3-23, feedstocks and asphalt contribute least to overall storage factor uncertainty on a percentage basis.
Although the feedstocks category—the largest use category in terms of total carbon flows—appears to have tight
confidence limits, this is to some extent an artifact of the way the uncertainty analysis was structured. As discussed
in Annex 2.3, the storage factor for feedstocks is based on an analysis of six fates that result in long-term storage
(e.g., plastics production), and eleven that result in emissions (e.g., volatile organic compound emissions). Rather
than modeling the total uncertainty around all of these fate processes, the current analysis addresses only the storage
fates, and assumes that all C that is not stored is emitted. As the production statistics that drive the storage values
are relatively well-characterized, this approach yields a result that is probably biased toward understating
uncertainty.
As is the case with the other uncertainty analyses discussed throughout this document, the uncertainty results above
address only those factors that can be readily quantified. More details on the uncertainty analysis are provided in
Annex 2.3.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. 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 2009 as well as their trends across
the time series.
Recalculations Discussion
In previous Inventories, the storage factor for asphalt was incorrectly assumed to be 100 percent. For the current
Inventory, it has been updated to 99.6 percent to reflect some loss of VOCs (see Annex 2.3 for more detailed
discussion).
Updates to the EIA Manufacturer's Energy Consumption Survey (MECS) for 2006 were released in the past year.
MECS data are only released once every four years and contribute to approximately 28 percent (as a time-weighted
average) of the C accounted for in feedstocks. MECS data are used to estimate the amount of C emitted from
energy recovery. Updating the energy recovery emission estimates with this new data affected emissions from 2003
3-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
through 2009, resulting in annual average increases of 7 percent from 2003 through 2009. In addition, the entire
energy recovery time series was recalculated to adjust for energy recovered from combustion of scrap tires. Carbon
emissions from scrap tires were inadvertently included in the energy recovery estimates; however, they are already
accounted for in the Incineration of Waste category.94 MECS data were adjusted to remove C from scrap tires used
as fuel in cement kilns, lime kilns, and electric arc furnaces. This adjustment resulted in decreases in emissions
across the entire time series. Emissions decreased by 0.3, 2.1, 1.3, and 1.5 percent for MECS-reporting years 1991,
1994, 1998, and 2002, respectively. Updating the energy recovery emission estimates with the 2006 MECS data
combined with adjusting for combustion of scrap tires increased the 2006 emission estimate by 9.5 percent. Overall,
emissions from energy recovery averaged over the entire time series increased by 1.2 percent when compared to last
year's inventory estimate because the increase resulting from updating the MECS data more than offsets the
decrease from adjusting for scrap tire combustion across the time series.
Planned Improvements
There are several improvements planned for the future:
• 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). EPA plans to better understand these trends to identify any mischaracterized or misreported fuel
consumption for non-energy uses.
• More accurate accounting of C in petrochemical feedstocks. 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. EPA plans to research this discrepancy by assessing the trends on both sides of the C balance. An
initial review of EIA (2011) data indicates that trends in LPG consumption for non-energy uses may largely
contribute to this discrepancy.
• More accurate accounting of C in imports and exports. As part of its effort to address the C balance
discrepancy, EPA will examine its import/export adjustment methodology to ensure that net exports of
intermediaries such as ethylene and propylene are fully accounted for.
• EPA recently researched updating the average carbon content of solvents, 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 carbon in solvents. EPA plans to
identify additional sources of solvents data in order to update the C content assumptions.
Finally, 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 and 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 conducting analyses of C fate similar to those described in Annex 2.3
or deferring to more updated default storage factors from IPCC where available.
3.3. Incineration of Waste (IPCC Source Category 1A1a)
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
94 From a regulatory-definition perspective combustion of scrap tires in cement kilns, lime kilns, and electric arc furnaces is not considered
"incineration;" however the use of the term "incineration" in this document also applies to the combustion of scrap tires and other materials for
energy recovery.
Energy 3-33
-------
and Matdes 2001, Kaufman etal. 2004, Simmons etal. 2006, van Haaren et al. 2010). In the context of this section,
waste includes all municipal solid waste (MSW) as well as tires. In the United States, almost all incineration of
MSW occurs at waste-to-energy facilities or industrial facilities where useful energy is recovered, and thus
emissions from waste incineration are accounted for in the Energy chapter. Similarly, tires are combusted for energy
recovery in industrial and utility boilers. Incineration of waste results in conversion of the organic inputs to CO2.
According to IPCC guidelines, when the CO2 emitted is of fossil origin, it is counted as a net anthropogenic
emission of CO2 to the atmosphere. Thus, the emissions from waste incineration are calculated by estimating the
quantity of waste combusted and the fraction of the waste that is C derived from fossil sources.
Most of the organic materials in municipal solid wastes are of biogenic origin (e.g., paper, yard trimmings), and
have their net C flows accounted for under the Land Use, Land-Use Change, and Forestry chapter. However, some
components—plastics, synthetic rubber, synthetic fibers, and carbon black—are of fossil origin. Plastics in the U.S.
waste stream are primarily in the form of containers, packaging, and durable goods. Rubber is found in durable
goods, such as carpets, and in non-durable goods, such as clothing and footwear. Fibers in municipal solid wastes
are predominantly from clothing and home furnishings. As noted above, tires (which contain rubber and carbon
black) are also considered a "non-hazardous" waste and are included in the waste incineration estimate, though
waste disposal practices for tires differ from municipal solid waste. Estimates on emissions from hazardous waste
incineration can be found in Annex 2.3 and are accounted for as part of the carbon mass balance for non-energy uses
of fossil fuels.
Approximately 26 million metric tons of MSW was incinerated in the United States in 2009 (EPA 2011). CO2
emissions from incineration of waste rose 54 percent since 1990, to an estimated 12.3 Tg CO2 Eq. (12,300 Gg) in
2009, as the volume of tires and other fossil C-containing materials in waste increased (see Table 3-24 and Table
3-25). Waste incineration is also a source of N2O and CH4 emissions (De Soete 1993; IPCC 2006). N2O emissions
from the incineration of waste were estimated to be 0.4 Tg CO2 Eq. (1 Gg N2O) in 2009, and have not changed
significantly since 1990. CH4 emissions from the incineration of waste were estimated to be less than 0.05 Tg CO2
Eq. (less than 0.5 Gg CH4) in 2009, and have not changed significantly since 1990.
Table 3-24: CO2 and N2O Emissions from the Incineration of Waste (Tg CO2 Eq.)
Gas/Waste Product
C02
Plastics
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in MSW
Synthetic Fibers
N2O
CH4
1990
8.0
5.6 1
0.3 1
0.4 1
0.9 1
0.8 1
0.5
2000
11.1
6.1 1
,.5
1.8
0.7 1
1.0 1
0.4 1
+ H
2005
12.5
6.9
1.6
2.0
0.8
1.2
0.4
+
2006
12.5
6.7
1.7
2.1
0.8
1.2
0.4
+
2007
12.7
6.7
1.8
2.3
0.8
1.2
0.4
+
2008
12.2
6.1
1.8
2.3
0.8
1.2
0.4
+
2009
12.3
6.2
1.8
2.3
0.8
1.2
0.4
+
Total
8.5
11.5
12.9
12.9
13.1
12.5
12.7
+ Does not exceed 0.05 Tg CO2 Eq.
Table 3 -25: CO2 and N2O Emissions from the Incineration of Waste (Gg)
Gas/Waste Product
CO2
Plastics
Synthetic Rubber in Tires
Carbon Black in Tires
Synthetic Rubber in MSW
Synthetic Fibers
N2O
CH4
1990
7,989
5,588
308
385
872 1
838 1
2 1
+ |
2000
11,112
6,104 1
1,454 1
1,818 1
689
1,046 1
1 1
+
2005
12,450
6,919
1,599
1,958
781
1,194
1
+
2006
12,531
6,722
1,712
2,113
775
1,208
1
+
2007
12,700
6,660
1,823
2,268
791
1,159
1
+
2008
12,169
6,148
1,823
2,268
770
1,161
1
+
2009
12,300
6,233
1,823
2,268
782
1,195
1
+
+ Does not exceed 0.5 Gg.
Methodology
Emissions of CO2 from the incineration of waste include CO2 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
3-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
were estimated by multiplying the amount of each material incinerated by the C content of the material and the
fraction oxidized (98 percent). Plastics incinerated in municipal solid wastes were categorized into seven plastic
resin types, each material having a discrete C content. Similarly, synthetic rubber is categorized into three product
types, and synthetic fibers were categorized into four product types, each having a discrete C content. Scrap tires
contain several types of synthetic rubber, as well as carbon black. Each type of synthetic rubber has a discrete C
content, and carbon black is 100 percent C. Emissions of CO2 were calculated based on the amount of scrap tires
used for fuel and the synthetic rubber and carbon black content of tires.
More detail on the methodology for calculating emissions from each of these waste incineration sources is provided
in Annex 3.6.
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 1999 through 2003, 2005 through 2011) and detailed unpublished backup data for some years not shown
in the reports (Schneider 2007). The proportion of total waste discarded that is incinerated was derived from data in
BioCycle's "State of Garbage in America" (van Haaren et al. 2010). The most recent data provides the proportion of
waste incinerated for 2008, so the corresponding proportion in 2009 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 Markets
in the United States, 2007 Edition (RMA 2009a). For 2008 and 2009, synthetic rubber mass in tires is assumed to be
equal to that in 2007 due to a lack of more recently available data.
Average C contents for the "Other" plastics category and synthetic rubber in municipal solid wastes were calculated
from 1998 and 2002 production statistics: carbon content for 1990 through 1998 is based on the 1998 value; content
for 1999 through 2001 is the average of 1998 and 2002 values; and 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 2009b).
The assumption that 98 percent of organic C is oxidized (which applies to all waste incineration categories for CO2
emissions) was reported in EPA's life cycle analysis of greenhouse gas emissions and sinks from management of
solid waste (EPA 2006).
Incineration of waste, including MSW, also results in emissions of N2O and CH4. These emissions were calculated
as a function of the total estimated mass of waste incinerated and an emission factor. As noted above, N2O and CH4
emissions are a function of total waste incinerated in each year; for 1990 through 2008, these data were derived from
the information published in BioCycle (van Haaren et al. 2010). Data on total waste incinerated was not available
for 2009, so this value was assumed to equal the most recent value available (2008). Table 3-26 provides data on
municipal solid waste discarded and percentage combusted for the total waste stream. According to Covanta Energy
(Bahor 2009) and confirmed by additional research based on ISWA (ERC 2009), all municipal solid waste
combustors in the United States are continuously fed stoker units. The emission factors of N2O and CH4 emissions
per quantity of municipal solid waste combusted are default emission factors for this technology type and were taken
from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
Energy 3-35
-------
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
2005
2006
2007
2008
2009
259,559,787
267,526,493
268,279,240
268,541,088
268,541,088"
25,973,520
25,853,401
24,788,539
23,674,017
23,674,017 a
10.0
9.7
9.2
8.8
8.8a
a Assumed equal to 2008 value.
Source: van Haaren et al. (2010).
Uncertainty and Time-Series Consistency
A Tier 2 Monte Carlo analysis was performed to determine the level of uncertainty surrounding the estimates of CO2
emissions and N2O emissions from the incineration of waste (given the very low emissions for CH4, no uncertainty
estimate was derived). IPCC Tier 2 analysis allows the specification of probability density functions for key
variables within a computational structure that mirrors the calculation of the inventory estimate. Uncertainty
estimates and distributions for waste generation variables (i.e., plastics, synthetic rubber, and textiles generation)
were obtained through a conversation with one of the authors of the Municipal Solid Waste in the United States
reports. Statistical analyses or expert judgments of uncertainty were not available directly from the information
sources for the other variables; thus, uncertainty estimates for these variables were determined using assumptions
based on source category knowledge and the known uncertainty estimates for the waste generation variables.
The uncertainties in the waste incineration emission estimates arise from both the assumptions applied to the data
and from the quality of the data. Key factors include MSW incineration rate; fraction oxidized; missing data on
waste composition; average C content of waste components; assumptions on the synthetic/biogenic C ratio; and
combustion conditions affecting N2O emissions. The highest levels of uncertainty surround the variables that are
based on assumptions (e.g., percent of clothing and footwear composed of synthetic rubber); the lowest levels of
uncertainty surround variables that were determined by quantitative measurements (e.g., combustion efficiency, C
content of C black).
The results of the Tier 2 quantitative uncertainty analysis are summarized inTable 3-27. Waste incineration CO2
emissions in 2009 were estimated to be between 9.8 and 15.2 Tg CO2 Eq. at a 95 percent confidence level. This
indicates a range of 21 percent below to 24 percent above the 2009 emission estimate of 12.3 Tg CO2 Eq. Also at a
95 percent confidence level, waste incineration N2O emissions in 2009 were estimated to be between 0.2 and 1.5 Tg
CO2 Eq. This indicates a range of 51 percent below to 320 percent above the 2009 emission estimate of 0.4 Tg CO2
Eq.
Table 3-27: Tier 2 Quantitative Uncertainty Estimates for CO2 and N2O from the Incineration of Waste (Tg CO2 Eq.
and Percent)
2009 Emission
Estimate
Source Gas (Tg CO2 Eq.)
Incineration of Waste CO2 12.3
Incineration of Waste N2O 0.4
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower
Bound Bound Bound
9.8 15.2 -21%
0.2 1.5 -51%
Upper
Bound
+24%
+320%
1 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
3-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
through 2009. 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
Several changes were made to input variables compared to the previous Inventory, resulting in an overall decrease in
the total emissions from the incineration of waste. Formerly, the percentage of overall rubber waste that is synthetic
(i.e., fossil-derived rather than biogenic) varied across the product categories, ranging from 25 percent for clothing
and footwear to 100 percent synthetic rubber for durable goods and containers and packaging. For the current
Inventory, this variable was updated to be 70 percent synthetic rubber for all four waste categories based on an
industry average (RMA, 2011). This change resulted in an average 1 percent decrease in CO2 emissions throughout
the time series. In addition, the percentage of waste incinerated was updated for 2008 based on data obtained from
The State of Garbage in America report (vanHaaren et al., 2010). Because the report is released every otheryear,
the percentage incinerated in 2007 was also updated using linear interpolation from the 2006 and 2008 values. The
change in the percentage incinerated, along with the change in the percentage synthetic rubber noted above,
decreased the 2007 and 2008 estimates by 4 percent and 7 percent, respectively, relative to the previous report.
Planned Improvements
Beginning in 2010, those facilities that emit over 25,000 tons of greenhouse gases (CO2 Eq.) from stationary
combustion across all sectors of the economy are required to calculate and report their greenhouse gas emissions to
EPA through its Greenhouse Gas Reporting Program. These data will be used in future inventories to improve the
emission calculations through the use of these collected higher tier methodological data.
Additional data sources for calculating the N2O and CH4 emission factors for U.S. incineration of waste may be
investigated.
3.4. Coal Mining (IPCC Source Category 1B1a)
Three types of coal mining related activities release CH4 to the atmosphere: underground mining, surface mining,
and post-mining (i.e., coal-handling) activities. Underground coal mines contribute the largest share of CH4
emissions. In 2009, 135 gassy underground coal mines in the United States employ ventilation systems to ensure
that CH4 levels remain within safe concentrations. These systems can exhaust significant amounts of CH4 to the
atmosphere in low concentrations. Additionally, 23 U.S. coal mines supplement ventilation systems with
degasification systems. Degasification systems are wells drilled from the surface or boreholes drilled inside the
mine that remove large volumes of CH4 before, during, or after mining. In 2009, 14 coal mines collected CH4 from
degasification systems and utilized this gas, thus reducing emissions to the atmosphere. Of these mines, 13 coal
mines sold CH4 to the natural gas pipeline and one coal mine used CH4 from its degasification system to heat mine
ventilation air on site. In addition, one of the coal mines that sold gas to pipelines also used CH4 to fuel a thermal
coal dryer. Surface coal mines also release CH4 as the overburden is removed and the coal is exposed, but the level
of emissions is much lower than from underground mines. Finally, some of the CH4 retained in the coal after
mining is released during processing, storage, and transport of the coal.
Total CH4 emissions in 2009 were estimated to be 71.0 Tg CO2 Eq. (3,382 Gg), a decline of 16 percent since 1990
(see Table 3-28 and Table 3-29). Of this amount, underground mines accounted for 71 percent, surface mines
accounted for 18 percent, and post-mining emissions accounted for 11 percent. The decline in CH4 emissions from
underground mines from 1996 to 2002 was the result of the reduction of overall coal production, the mining of less
gassy coal, and an increase in CH4 recovered and used. Since that time, underground coal production and the
associated methane emissions have remained fairly level, while surface coal production and its associated emissions
Energy 3-37
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have generally increased.
Table 3-28: CH4 Emissions from Coal Mining (Tg CO2 Eq.)
Activity
UG Mining
Liberated
Recovered & Used
Surface Mining
Post-Mining (UG)
Post-Mining (Surface)
Total
1990
62.3
67.9
(5.6)
12.0
7.7
2.0
84.1
2000
39.
54.
4
4
(14.9) |
12.
6.
2.
60.
0
4
2005
35
50
(15.
113
6
2
.0
.2
1)
.3
.4
.2
56.9
2006
35.7
54.3
(18.7)
14.0
6.3
2.3
58.2
2007
35.7
51.0
(15.3)
13.8
6.1
2.2
57.9
2008
44
60
(16.
14
6
2
67
.4
.5
1)
.3
.1
.3
.1
2009
50.4
67.0
(16.5)
12.9
5.6
2.1
71.0
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table 3-29: CH4 Emissions from Coal Mining (Gg)
Activity 1990 2000 2005 2006 2007 2008 2009
UG Mining 2,968 1,878 I 1,668 1,699 1,700 2,113 2,401
Liberated 3,234 I 2,588 I 2,389 2,588 2,427 2,881 3,189
Recovered & Used (265.9) I (710.4) (720.8) (889.4) (727.2) (768.0) (787.1)
Surface Mining 573.6 I 585.7 I 633.1 668.0 658.9 680.5 614.2
Post-Mining (UG) 368.3 I 318.1 I 305.9 298.5 289.6 292.0 266.7
Post-Mining (Surface) 93.2 95.2 102.9 108.5 107.1 110.6 99.8
Total 4,003 2,877 2,710 2,774 2,756 3,196 3,382
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 parts. The first part involves
estimating CH4 emissions from underground mines. Because of the availability of ventilation system measurements,
underground mine emissions can be estimated on a mine-by-mine basis and then summed to determine total
emissions. The second step involves estimating emissions from surface mines and post-mining activities by
multiplying basin-specific coal production by basin-specific emission factors.
Underground mines. Total CH4 emitted from underground mines was estimated as the sum of CH4 liberated from
ventilation systems and CH4 liberated by means of degasification systems, minus CH4 recovered and used. The
Mine Safety and Heath Administration (MSHA) samples CH4 emissions from ventilation systems for all mines with
detectable95 CH4 concentrations. These mine-by-mine measurements are used to estimate CH4 emissions from
ventilation systems.
Some of the higher-emitting underground mines also use degasification systems (e.g., wells or boreholes) that
remove CH4 before, during, or after mining. This CH4 can then be collected for use or vented to the atmosphere.
Various approaches were employed to estimate the quantity of CH4 collected by each of the twenty mines using
these systems, depending on available data. For example, some mines report to EPA the amount of CH4 liberated
from their degasification systems. For mines that sell recovered CH4 to a pipeline, pipeline sales data published by
state petroleum and natural gas agencies were used to estimate degasification emissions. For those mines for which
no other data are available, default recovery efficiency values were developed, depending on the type of
degasification system employed.
Finally, the amount of CH4 recovered by degasification systems and then used (i.e., not vented) was estimated. In
2009, 13 active coal mines sold recovered CH4 into the local gas pipeline networks and one coal mine used
recovered CH4 on site for heating. Emissions avoided for these projects were estimated using gas sales data reported
by various state agencies. For most mines with recovery systems, companies and state agencies provided individual
well production information, which was used to assign gas sales to a particular year. For the few remaining mines,
coal mine operators supplied information regarding the number of years in advance of mining that gas recovery
95 MSHA records coal mine CH4 readings with concentrations of greater than 50 ppm (parts per million) CELt. Readings below
this threshold are considered non-detectable.
3-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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occurs.
Surface Mines and Post-Mining Emissions. Surface mining and post-mining CH4 emissions were estimated by
multiplying basin-specific coal production, obtained from the Energy Information Administration's Annual Coal
Report (see Table 3-30) (EIA 2010), by basin-specific emission factors. Surface mining emission factors were
developed by assuming that surface mines emit two times as much CH4 as the average in situ CH4 content of the
coal. Revised data on in situ CH4 content and emissions factors are taken from EPA (2005), EPA (1996), and
AAPG (1984). This calculation accounts for CH4 released from the strata surrounding the coal seam. For post-
mining emissions, the emission factor was assumed to be 32.5 percent of the average in situ CH4 content of coals
mined in the basin.
Table 3-30: Coal Production (Thousand Metric Tons)
Year Underground Surface Total
1990
384,244
546,808 931,052
2005
2006
2007
2008
2009
334,398
325,697
319,139
323,932
301,241
691,448
728,447
720,023
737,832
671,475
1,025,846
1,054,144
1,039,162
1,061,764
972,716
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
recommended Tier 2 uncertainty estimation methodology. Because emission estimates from underground
ventilation systems were based on actual measurement data, uncertainty is relatively low. A degree of imprecision
was introduced because the measurements used were not continuous but rather an average of quarterly instantaneous
readings. Additionally, the measurement equipment used can be expected to have resulted in an average of 10
percent overestimation of annual CH4 emissions (Mutmansky and Wang 2000). Estimates of CH4 recovered by
degasification systems are relatively certain because many coal mine operators provided information on individual
well gas sales and mined through dates. Many of the recovery estimates use data on wells within 100 feet of a
mined area. Uncertainty also 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 currently
estimated.
Compared to underground mines, there is considerably more uncertainty associated with surface mining and post-
mining emissions because of the difficulty in developing accurate emission factors from field measurements.
However, since underground emissions comprise the majority of total coal mining emissions, the uncertainty
associated with underground emissions is the primary factor that determines overall uncertainty. The results of the
Tier 2 quantitative uncertainty analysis are summarized in Table 3-31. Coal mining CH4 emissions in 2009 were
estimated to be between 62.0 and 82.4 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 12.7
percent below to 16.1 percent above the 2009 emission estimate of 71.0 Tg CO2Eq.
Table 3-31: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal Mining (Tg CO2 Eq. and
Percent)
Source
2009 Emission
Estimate
Gas (Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Bound Upper Bound
Lower Bound Upper Bound
Coal Mining CH4
71.0
62.0
82.4
-12.7%
+16.1%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Energy 3-39
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Recalculations Discussion
For the current Inventory, there were some changes to pre-2009 emission estimates relative to the previous
Inventory. For the current Inventory, the conversion factor for converting short tons to metric tons was updated to
0.90718474 to be consistent with the number of significant digits used in other source categories. In the past, 0.9072
had been used. The factor was updated for all years, thus coal production estimates in Table 3-31 have changed
slightly.
Other changes include the recalculation of emissions avoided for two Jim Walter Resources (JWR) mines: Blue
Creek #4 Mine and Blue Creek #7 Mine. This resulted in changes to emissions avoided numbers for 2007 and 2008.
In 1998, 2000, 2001, 2002, 2003, and 2004, the emissions avoided for the Blacksville No. 2 mine in West Virginia
were assigned to Pennsylvania rather than West Virginia. These emissions avoided were correctly assigned to West
Virginia in the current Inventory; however, total emissions were not affected.
The emissions avoided for the Emerald and Cumberland mines were adjusted going back to 2006 based on
information provided by the project developer.
3.5. Abandoned Underground Coal Mines (IPCC Source Category 1B1a)
Underground coal mines contribute the largest share of CH4 emissions, with active underground mines the leading
source of underground emissions. However, mines also continue to release CH4 after closure. As mines mature and
coal seams are mined through, mines are closed and abandoned. Many are sealed and some flood through intrusion
of groundwater or surface water into the void. Shafts or portals are generally filled with gravel and capped with a
concrete seal, while vent pipes and boreholes are plugged in a manner similar to oil and gas wells. Some abandoned
mines are vented to the atmosphere to prevent the buildup of CH4 that may find its way to surface structures through
overburden fractures. As work stops within the mines, the CH4 liberation decreases but it does not stop completely.
Following an initial decline, abandoned mines can liberate CH4 at a near-steady rate over an extended period of
time, or, if flooded, produce gas for only a few years. The gas can migrate to the surface through the conduits
described above, particularly if they have not been sealed adequately. In addition, diffuse emissions can occur when
CH4 migrates to the surface through cracks and fissures in the strata overlying the coal mine. The following factors
influence abandoned mine emissions:
• Time since abandonment;
• Gas content and adsorption characteristics of coal;
• CH4 flow capacity of the mine;
• Mine flooding;
• Presence of vent holes; and
• Mine seals.
Gross abandoned mine CH4 emissions ranged from 6.0 to 9.1 Tg CO2 Eq. from 1990 through 2009, varying, in
general, by less than 1 to approximately 19 percent from year to year. Fluctuations were due mainly to the number
of mines closed during a given year as well as the magnitude of the emissions from those mines when active. Gross
abandoned mine emissions peaked in 1996 (9.1 Tg CO2 Eq.) due to the large number of mine closures from 1994 to
1996 (70 gassy mines closed during the three-year period). In spite of this rapid rise, abandoned mine emissions
have been generally on the decline since 1996. There were fewer than fifteen gassy mine closures during each of the
years from 1998 through 2009, with only ten closures in 2009. By 2009, gross abandoned mine emissions decreased
slightly to 8.5 Tg CO2 Eq. (see Table 3-32 and Table 3-33). Gross emissions are reduced by CH4 recovered and
used at 38 mines, resulting in net emissions in 2009 of 5.5 Tg CO2 Eq.
Table 3-32: CH4 Emissions from Abandoned Coal Mines (Tg CO2 Eq.)
Activity
Abandoned Underground Mines
Recovered & Used
Total
1990
6.0
0.0 •
6.0 |
2000
8.9
1.5 •
7.4
2005
7.0
1 1.5
| 5.5
2006
7.6
2.2
5.5
2007
8.9
3.3
5.6
2008
9.0
3.2
5.9
2009
8.5
3.0
5.5
Note: Totals may not sum due to independent rounding.
3-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 3-33: CH4 Emissions from Abandoned Coal Mines (Gg)
Activity
Abandoned Underground Mines
Recovered & Used
Total
1990
288
0
288
2000
422
72 •
350
2005
334
1 70
264
2006
364
103
261
2007
425
158
267
2008
430
150
279
2009
406
144
262
Note: Totals may not sum due to independent rounding.
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 mine, it is necessary to apply a decline function, initiated
upon abandonment, to that mine. In the analysis, mines were grouped by coal basin with the assumption that they
will generally have the same initial pressures, permeability and isotherm. As CH4 leaves the system, the reservoir
pressure, Pr, declines as described by the isotherm. The emission rate declines because the mine pressure (Pw) is
essentially constant at atmospheric pressure, for a vented mine, and the PI term is essentially constant at the
pressures of interest (atmospheric to 30 psia). A rate-time equation can be generated that can be used to predict
future emissions. This decline through time is hyperbolic in nature and can be empirically expressed as:
q =
where,
q = Gas rate at time t in mmcf/d
q; = Initial gas rate at time zero (to) in million cubic feet per day mmcfd)
b = The hyperbolic exponent, dimensionless
D! = Initial decline rate, 1/yr
t = Elapsed time from t0 (years)
This equation is applied to mines of various initial emission rates that have similar initial pressures, permeability and
adsorption isotherms (EPA 2003).
The decline curves created to model the gas emission rate of coal mines must account for factors that decrease the
rate of emission after mining activities cease, such as sealing and flooding. Based on field measurement data, it was
assumed that most U.S. mines prone to flooding will become completely flooded within eight years and therefore no
longer have any measurable CH4 emissions. Based on this assumption, an average decline rate for flooding mines
was established by fitting a decline curve to emissions from field measurements. An exponential equation was
developed from emissions data measured at eight abandoned mines known to be filling with water located in two of
the five basins. Using a least squares, curve-fitting algorithm, emissions data were matched to the exponential
equation shown below. There was not enough data to establish basin-specific equations as was done with the
vented, non-flooding mines (EPA 2003).
where,
q = Gas flow rate at time t in mcf/d
q, = Initial gas flow rate at time zero (to) in mcfd
Energy 3-41
-------
D = Decline rate, 1/yr
t = Elapsed time from t0 (years)
Seals have an inhibiting effect on the rate of flow of CH4 into the atmosphere compared to the rate that would be
emitted if the mine had an open vent. The total volume emitted will be the same, but will occur over a longer
period. The methodology, therefore, treats the emissions prediction from a sealed mine similar to emissions from a
vented mine, but uses a lower initial rate depending on the degree of sealing. The computational fluid dynamics
simulator was again used with the conceptual abandoned mine model to predict the decline curve for inhibited flow.
The percent sealed is defined as 100 x (i - (initial emissions from sealed mine / emission rate at abandonment prior
to sealing)). Significant differences are seen between 50 percent, 80 percent and 95 percent closure. These decline
curves were therefore used as the high, middle, and low values for emissions from sealed mines (EPA 2003).
For active coal mines, those mines producing over 100 mcfd account for 98 percent of all CH4 emissions. This same
relationship is assumed for abandoned mines. It was determined that 469 abandoned mines closing after 1972
produced emissions greater than 100 mcfd when active. Further, the status of 273 of the 469 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 were placed in
one of the three categories by applying a probability distribution analysis based on the known status of other mines
located in the same coal basin (EPA 2003).
Table 3-34: Number of gassy abandoned mines occurring 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
25
30
42
0
27
124
25
3
22
0
3
53
48
14
16
16
2
96
98
47
80
16
32
273
127
25
35
0
9
196
224
72
115
16
41
469
Inputs to the decline equation require the average emission rate and the date of abandonment. Generally this data is
available for mines abandoned after 1972; however, such data are largely unknown for mines closed before 1972.
Information that is readily available such as coal production by state and county are helpful, but do not provide
enough data to directly employ the methodology used to calculate emissions from mines abandoned after 1971. It is
assumed that pre-1972 mines are governed by the same physical, geologic, and hydrologic constraints that apply to
post-1972 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 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 emission rates during the 1970s (EPA 2003).
Abandoned mines 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 ventilated for the total CH4 liberation rate for 21 mines
that closed between 1992 and 2009. Since the sample of gassy mines (with active mine emissions greater than 100
mcfd) is assumed to account for 78 percent of the pre-1971 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 2009, emission totals were downwardly adjusted to reflect abandoned mine CH4 emissions
3-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
avoided from those mines. The inventory totals were not adjusted for abandoned mine reductions in 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
values that represent the highest and lowest quartile of the cumulative probability density function of each
parameter. Once the low, mid, and high values are selected, they are applied to a probability density function.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-35. Abandoned coal mines
CH4 emissions in 2009 were estimated to be between 4.0 and 7.3 Tg CO2 Eq. at a 95 percent confidence level. This
indicates a range of 27 percent below to 32 percent above the 2009 emission estimate of 5.5 Tg CO2 Eq. One of the
reasons for the relatively narrow range is that mine-specific data is used in the methodology. The largest degree of
uncertainty is associated with the unknown status mines (which account for 42 percent of the mines), with a ±57
percent uncertainty.
Table 3-35: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Abandoned Underground Coal
Mines (Tg CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission
Estimate Estimate"
Source Gas (TgCO2Eq.) (TgCO2Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Abandoned Underground
Coal Mines CH4 5.5 4.0 7.3 -27% +32%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
Recalculations Discussion
Changes in pre-2009 emissions avoided relative to the previous Inventory are due to the additions of pre-1972
Grayson Hills Energy and DTE Corinth projects, which were added to the current inventory. There were also two
abandoned mines added to the current Inventory, one abandoned in 2007 and one in 2008, which resulted in changes
in the liberated emissions relative to the previous report.
3.6. Natural Gas Systems (IPCC Source Category 1B2b)
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 221.2 Tg CO2
Eq. (10,535 Gg) of CH4 in 2009, a 17 percent increase over 1990 emissions (see Table 3-36 and Table 3-37), and
32.2 Tg CO2 Eq. (32,171 Gg) of non-combustion CO2 in 2009, a 14 percent decrease over 1990 emissions (see
Table 3-38 and Table 3-39). Improvements in management practices and technology, along with the replacement of
older equipment, have helped to stabilize emissions. Methane emissions increased since 2008 due to an increase in
production and production wells.
CH4 and non-combustion CO2 emissions from natural gas systems are generally process related, with normal
operations, routine maintenance, and system upsets being the primary contributors. Emissions from normal
operations include: natural gas engines and turbine uncombusted exhaust, bleed and discharge emissions from
pneumatic devices, and fugitive emissions from system components. Routine maintenance emissions originate from
Energy 3-43
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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 CO2 emissions
are discussed.
Field Production. In this initial stage, wells are used to withdraw raw gas from underground formations. Emissions
arise from the wells themselves, gathering pipelines, and well-site gas treatment facilities such as dehydrators and
separators. Emissions from pneumatic devices, well clean-ups, and gas well completions and re-completions with
hydraulic fracturing account for the majority of CH4 emissions. Flaring emissions account for the majority of the
non-combustion CO2 emissions. Emissions from field production accounted for approximately 59 percent of CH4
emissions and about 34 percent of non-combustion CO2 emissions from natural gas systems in 2009.
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 about 8 percent of CH4 emissions and approximately 66 percent of non-combustion CO2 emissions from
natural gas systems.
Transmission and Storage. Natural gas transmission involves high pressure, large diameter pipelines that transport
gas long distances from field production and processing areas to distribution systems or large volume customers
such as power plants or chemical plants. Compressor station facilities, which contain large reciprocating and turbine
compressors, are used to move the gas throughout the United States transmission system. Fugitive CH4 emissions
from these compressor stations and from metering and regulating stations account for the majority of the emissions
from this stage. Pneumatic devices and engine uncombusted exhaust 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 20 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.
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 over 1,208,000 miles of distribution mains in 2009, an increase from just over 944,000 miles in
1990 (OPS 2010b). Distribution system emissions, which account for approximately 13 percent of CH4 emissions
from natural gas systems and less than 1 percent of non-combustion CO2 emissions, result mainly from fugitive
emissions from gate stations and pipelines. An increased use of plastic piping, which has lower emissions than other
pipe materials, has reduced emissions from this stage. Distribution system CH4 emissions in 2009 were 13 percent
lower than 1990 levels.
Table 3-36: CH4 Emissions from Natural Gas Systems (Tg CO2 Eq.)*
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
89.2
18.0 1
49.2
33.4
189.8
2000
113.5
17.7
46.7
31.4
209.3
2005
105.4
14.3
41.4
29.3
190.4
2006
134.0
14.5
41.0
28.3
217.7
2007
118.2
15.1
42.5
29.4
205.2
2008
122.9
15.7
43.3
29.9
211.8
2009
130.3
17.5
44.4
29.0
221.2
*Including CH4 emission reductions achieved by the Natural Gas STAR program and NESHAP regulations.
Note: Totals may not sum due to independent rounding.
Table 3-37: CH4Emissions from Natural Gas Systems (Gg)*
Stage
Field Production
Processing
Transmission and
Storage
1990
4,248
855
2,344
2000
5,406
841
2,224 |
2005
5,021
681
1,973
2006
6,380
689
1,950
2007
5,628
717
2,025
2008
5,854
748
2,062
2009
6,205
834
2,115
3-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Distribution 1,591 1,497 1,395 1,346 1,402 1,423 1,381
Total 9,038 9,968 9,069 10,364 9,771 10,087 10,535
*Including CH4 emission reductions achieved by the Natural Gas STAR program and NESHAP regulations.
Note: Totals may not sum due to independent rounding.
Table 3-38: Non-combustion CO2 Emissions from Natural Gas Systems (Tg CO2 Eq.)
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
9.7
27.8
0.1 •
37.6 |
2000
6.4
23.3
0.1
+ •
29.9
2005
8.0
21.7
0.1
+
29.9
2006
9.4
21.2
0.1
+
30.8
2007
9.7
21.2
0.1
+
31.1
2008
11.3
21.4
0.1
+
32.8
2009
10.9
21.2
0.1
+
32.2
Note: Totals may not sum due to independent rounding.
+ Emissions are less than 0.1 Tg CO2 Eq.
Table 3-39: Non-combustion CO2 Emissions from Natural Gas Systems (Gg)
Stage
Field Production
Processing
Transmission and Storage
Distribution
Total
1990
9,704
27,763
62
46 1
37,574
2000
6,425
23,343
64
1 44
29,877
1
2005
8,050
21,746
64
41
29,902
2006
9
21
30
,438
,214
63
40
,755
2007
9,746
21,199
64
41
31,050
2008
11,336
21,385
65
42
32,828
2009
10
21
32
,877
,189
65
41
,171
Note: Totals may not sum due to independent rounding.
Methodology
The primary basis for estimates of CH4 and non-combustion-related CO2 emissions from the U.S. natural gas
industry is a detailed study by the Gas Research Institute and EPA (EPA/GRI1996). The EPA/GRI study developed
over 80 CH4 emission and activity factors to characterize emissions from the various components within the
operating stages of the U.S. natural gas system. The same activity factors were used to estimate both CH4 and non-
combustion CO2 emissions. However, the CH4 emission factors were adjusted for CO2 content when estimating
fugitive and vented non-combustion CO2 emissions. The EPA/GRI study was based on a combination of process
engineering studies and measurements at representative gas facilities. From this analysis, a 1992 emission estimate
was developed using the emission and activity factors, except where direct activity data was available (e.g., offshore
platform counts, processing plant counts, transmission pipeline miles, and distribution pipelines). For other years, a
set of industry activity factor drivers was developed that can be used to update activity factors. These drivers
include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and other
statistics that characterize the changes in the U.S. natural gas system infrastructure and operations.
Although the inventory primarily uses EPA/GRI emission factors, significant improvements were made to the
emissions estimates for three sources this year: gas well cleanups, condensate storage tanks and centrifugal
compressors. In addition, data for two sources not included in the EPA/GRI study - gas well completions and gas
well workovers (re-completions) with hydraulic fracturing- was added this year. In the case of gas well cleanups,
the methodology was revised to use a large sample of well and reservoir characteristics from the HPDI database
(HPDI2009) along with an engineering statics equation (EPA 2006a) to estimate the volume of natural gas
necessary to expel a liquid column choking the well production. The same sample E&P Tank sample runs for
condensate tank flashing emissions was used; however, the factor was improved by using a large sample distribution
of condensate production by gravity from the HPDI database (HPDI 2009) to weigh the sample simulation flashing
emissions rather than assuming a uniform distribution of condensate gravities. Additionally, TERC (TERC 2009)
data representing two regions was used in the emission factors for those two regions to estimate the effects of
separator dump valves malfunctioning and allowing natural gas to vent through the downstream storage tanks. The
EPA/GRI emission factor for centrifugal compressors sampled emissions at the seal face of wet seal compressors. A
World Gas Conference publication (WGC 2009) on the seal oil degassing vents was used to update this factor and to
also account for the emergence of dry seal centrifugal compressors (EPA 2006b), which eliminates seal oil
degassing vents and reduces overall emissions. Gas well completions and workovers with hydraulic fracturing were
Energy 3-45
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not common at the time the EPA/GRI survey was conducted. Since then, emissions data has become available
through Natural Gas STAR experiences and presentations (EPA 2004, 2007) as these activities became more
prevalent. The EPA/GRI study and previous Inventories did, however, include an estimate for well completions
without hydraulic fracturing under the source category Completion Flaring. The changes for gas well cleanups,
condensate storage tanks, centrifugal compressors, and gas well completions and gas well workovers (re-
completions) with hydraulic fracturing are described below in the Recalculations section. See Annex 3.4 for more
detailed information on the methodology and data used to calculate CH4 and non-combustion CO2 emissions from
natural gas systems.
Activity factor data were taken from the following sources: American Gas Association (AGA 1991-1998); Bureau
of Ocean Energy Management, Regulation and Enforcement (previous Minerals and Management Service)
(BOEMRE 2010a-d); Monthly Energy Review (EIA 2010f); Natural Gas Liquids Reserves Report (EIA 2005);
Natural Gas Monthly (EIA 2010b,c,e); the Natural Gas STAR Program annual emissions savings (EPA 2010); Oil
and Gas Journal (OGJ 1997-2010); Office of Pipeline Safety (OPS 2010a-b); Federal Energy Regulatory
Commission (FERC 2010) and other Energy Information Administration publications (EIA 2001, 2004, 2010a,d);
World Oil Magazine (2010a-b). 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 2009) and the Alabama State Oil and Gas Board (Alabama 2010). Other state
well data was taken from: American Association of Petroleum Geologists (AAPG 2004); Brookhaven College
(Brookhaven 2004); Kansas Geological Survey (Kansas 2010); Montana Board of Oil and Gas Conservation
(Montana 2010); Oklahoma Geological Survey (Oklahoma 2010); Morgan Stanley (Morgan Stanley 2005); Rocky
Mountain Production Report (Lippman 2003); New Mexico Oil Conservation Division (New Mexico 2010, 2005);
Texas Railroad Commission (Texas 20 lOa-d); Utah Division of Oil, Gas and Mining (Utah 2010). Emission factors
were taken from EPA/GRI (1996). GTFs Unconventional Natural Gas and Gas Composition Databases (GTI2001)
were used to adapt the CH4 emission factors into non-combustion related CO2 emission factors and adjust CH4
emission factors from the EPA/GRI survey. Methane compositions from GTI 2001 are adjusted year to year using
gross production by NEMS for oil and gas supply regions from the EIA. Therefore, emission factors may vary from
year to year due to slight changes in the methane composition for each NEMS oil and gas supply module region.
Additional information about CO2 content in transmission quality natural gas was obtained via the internet from
numerous U.S. transmission companies to help further develop the non-combustion CO2 emission factors.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted to determine the level of uncertainty surrounding estimates of
emissions from natural gas systems. Performed using @RISK software and the IPCC-recommended Tier 2
methodology (Monte Carlo Simulation technique), this analysis provides for the specification of probability density
functions for key variables within a computational structure that mirrors the calculation of the inventory estimate.
The @RISK model utilizes 1992 (base year) emissions to quantify the uncertainty associated with the emissions
estimates using the top twelve emission sources for the year 2009.
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 2009. The heterogeneous nature of the natural gas industry makes it difficult
to sample facilities that are completely representative of the entire industry. Because of this, scaling up from model
facilities introduces a degree of uncertainty. Additionally, highly variable emission rates were measured among
many system components, making the calculated average emission rates uncertain. The results of the Tier 2
quantitative uncertainty analysis are summarized in Table 3-40. Natural gas systems CH4 emissions in 2009 were
estimated to be between 179.1 and 287.6 Tg CO2 Eq. at a 95 percent confidence level. Natural gas systems non-
energy CO2 emissions in 2009 were estimated to be between 26.1 and 41.9 Tg CO2 Eq. at 95 percent confidence
level.
Table 3-40: Tier 2 Quantitative Uncertainty Estimates for CH4 and Non-energy CO2 Emissions from Natural Gas
Systems (Tg CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission Estimate"
Estimate
Source Gas (TgCO2Eq.)° (TgCO2Eq.) (%)
Lower
Bound0
Upper
Bound0
Lower
Bound0
Upper
Bound0
3-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Natural Gas Systems CH4 221.2 179.1 287.6 -19% +30%
>2
Natural Gas Systems'3 CO2 32.2 26.1 41.9 -19% +30%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
b An uncertainty analysis for the non-energy CO2 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 CO2 emissions.
0 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.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Discussion
A number of potential data sources were investigated to improve selected emission factors in the natural gas
industry. First, the HPDI database for well production and well properties was investigated for potential engineering
parameters to be used in engineering equations to develop a new emission factor for well cleanups (HPDI 2009).
The database was queried to obtain average well depth, shut-in pressure, well counts, and well production from each
basin. These parameters were used along with industry experiences to develop an engineering estimate of emissions
from each well in each basin of the sample data. The analysis led to a new emission factor for the gas well cleanup
source.
Additionally, industry experiences with hydraulic fracturing of tight formations for the completion or workover of
natural gas wells were reviewed to account for this source of emissions. Several Partners of the Natural Gas STAR
Program have reported recovering substantial volumes of natural gas that would have otherwise been vented
following completions or re-completions (workovers) involving hydraulic fracturing. This completion method,
which is a large emission source, was not characterized by the base EPA/GRI1996 study and has not been
accounted for in the national Inventory until this year.
A World Gas Conference paper (WGC 2009) gathered 48 sample measurements of centrifugal compressor wet seal
oil degassing emissions and published the results. The base year EPA/GRI 1996 study did not measure emissions
from the seal oil degassing vent. Instead seal face emissions were quantified and as such this emission source has
gone uncharacterized in the national Inventory until this year.
In some production areas the separator liquid level may drop too low such that the produced associated gas blows
through the dump valve and vents through the storage tank. These data were included where available for the
Inventory. More data will be necessary to potentially separate this source from storage tank flashing emissions and
also to represent the true scope of activity across the United States.
A number of other data sources for fugitive emission factors from the processing and transmission and storage
segments were reviewed. Several studies have been published since the EPA/GRI 1996 base year study that sample
emissions from the same common equipment components. The raw emissions data from these surveys can
potentially be combined with the raw data from the base year study to develop stronger emission factors. In addition
to common component leaks, several of these studies propose emission factors for pneumatic devices or other
sources. These studies require further review and thus the data are not included in the Inventory at this time.
Recalculations Discussion
Methodologies for gas well cleanups and condensate storage tanks were revised for the current Inventory, and new
sources of data for centrifugal compressors with wet seals, gas well completions with hydraulic fracturing, and gas
well workovers with hydraulic fracturing were used.
The largest increase in emissions relative to the previous Inventory was due to the revised emission factor for gas
well cleanups (also referred to in industry as gas well liquids unloading). HPDI well production and well property
sample data on well depth, shut-in pressure, and production rates were used in an engineering equation to re-
estimate the average unloading emissions by NEMS oil and gas module region for this source (HPDI 2009). This
methodological change increased emissions by more than 22 times while decreasing the substantial uncertainty that
was associated with the previous emission factor from the EPA/GRI 1996 study. The activity data remained the
same as the previous methodology. Emissions from non-Gas STAR Partners were not considered, nor was an
independent estimate of the scope of those emissions accounted for. Reductions beyond those reported from Natural
Energy 3-47
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Gas STAR Partners will be considered for inclusion in the next Inventory of sufficient data are available.
The next largest increase in emissions was due to the inclusion of gas well completions and workovers involving
hydraulic fracture (i.e. unconventional completions and workovers). The EPA/GRI 1996 study did not account for
this emerging technology and the source was previously unaccounted for in the Inventory. The Inventory did
account for completion flaring, however, this only includes emissions from completions without hydraulic fracturing
(i.e. conventional completions), which the EPA/GRI 1996 study assumes are mostly flared. Unlike completions and
workovers without hydraulic fracturing (i.e. conventional workovers), the high pressure venting of gas in order to
expel the large volumes of liquid used to fracture the well formation, results in a large emission of natural gas. The
Inventory tracks activity data for wells completed with hydraulic fracturing in each region. The gas well
completions with hydraulic fracturing was approximated using total number of producing gas wells completed with
hydraulic fracturing and the total number of shut-in gas wells completed with hydraulic fracturing from each year.
This approximation is made by taking the difference between the number of unconventional wells reported by EIA
for the current year and the previous year. Since drilling and hydraulic fracturing in unconventional (e.g. shale,
tight, and coal bed methane) formations is a relatively new technology, it is assumed that zero gas wells completed
with hydraulic fracturing are shut-in each year. This activity data was used along with a newly developed emission
factor to estimate emissions from these sources. It was assumed that approximately 50 percent of emissions from
gas well completions and workovers with hydraulic fracturing would be flared due to states such as Wyoming that
do not permit the venting of natural gas during well completions.
The same E&P Tank simulation data for hydrocarbon liquids above 45°API flashing emission in tanks was used as
in previous Inventories to estimate emissions from condensate tanks; however, these flashing emissions simulations
were coupled with a large sample of condensate production gravities from the HPDI database to improve the factor
to account for the average national distribution of condensate gravities. Previously, a simple average of simulation
results for each liquid gravity was used. Additionally, the TERC (2009) study provided a small sample of data
representing two regions in Texas where separator dump valve malfunctions were detected and measured. This data
was applied only to the regions represented by the study to account for this emission source.
Finally, WGC (2009) sample data on centrifugal compressor seal oil degassing vent rates was used to divide the
centrifugal compressors source in the processing and transmission and storage segments into two sources—
centrifugal compressors equipped with wet seals and centrifugal compressors equipped with dry seals. The seal oil
degassing vent (found with compressors using wet seals) was previously unaccounted for in the Inventory. This
improved methodology accounted for an increase in emissions from these sources between 50 and 100 percent.
Finally, the previous Inventory activity data are updated with revised values each year. However, the impact of
these changes was small compared to the changes described above.
The net effect of these changes was to increase total CH4 emissions from natural gas systems between 47 and 120
percent each year between 1990 and 2008 relative to the previous report. The natural gas production segment
accounted for the largest increases, largely due to the methodological changes to gas well cleanups and the addition
of gas well completions and workovers with hydraulic fracturing.
Planned Improvements
Emission reductions reported to Natural Gas STAR are deducted from the total sector emissions each year in the
natural gas systems inventory model to estimate emissions. These reported reductions often rely on Inventory
emission factors to quantify the extent of reductions. These reductions are also a source of uncertainty that is not
currently analyzed in the Inventory. Emissions reductions—in particular from gas well cleanups—may be
underestimated, and we intend to investigate whether additional data are available, and if appropriate, revisions to
more accurately account for emissions from natural gas systems will be incorporated into future inventories.
Additionally, accounting for the uncertainty of these reductions to more accurately provide upper and lower bounds
within the 95 percent confidence interval, will be investigated.
Separately, a larger study is currently underway to update selected compressor emission factors used in the national
inventory. Most of the activity factors and emission factors in the natural gas inventory are from the EPA/GRI
(1996) study. The current measurement-based study to develop updated emission factors for compressors is
intended to better reflect current national circumstances. Results from these studies are expected in 2011, and will
be incorporated into the Inventory, pending a peer review.
3-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Malfunctioning separator dump valves is not an occurrence isolated to the Texas counties in which the sample data
was obtained. New data will be reviewed as it becomes available on this emissions source and emissions will be
updated, as appropriate.
Data collected through EPA's Greenhouse Gas Reporting Program (40 CFR Part 98, Mandatory Reporting of
Greenhouse Gases; Final Rule, Subpart W) will be reviewed for potential improvements to the natural gas systems
emissions estimates. The rule will collect actual activity data using improved quantification methods from those
used in several of the studies which form the basis of this Inventory. Data collection for Subpart W began January
1, 2011 with emissions reporting beginning in 2012. These base year 2011 data will be reviewed for inclusion into a
future Inventory to improve the accuracy and reduce the uncertainty of the emission estimates.
3.7. Petroleum Systems (IPCC Source Category 1B2a)
CH4 emissions from petroleum systems are primarily associated with crude oil production, transportation, and
refining operations. During each of these activities, CH4 emissions are released to the atmosphere as fugitive
emissions, vented emissions, emissions from operational upsets, and emissions from fuel combustion. Fugitive and
vented CO2 emissions from petroleum systems are primarily associated with crude oil production and refining
operations but are negligible in transportation operations. Combusted CO2 emissions from fuels are already
accounted for in the Fossil Fuels Combustion source category, and hence have not been taken into account in the
Petroleum Systems source category. Total CH4 and CO2 emissions from petroleum systems in 2009 were 30.9 Tg
CO2 Eq. (1,473 Gg CH4) and 0.5 Tg CO2 (463 Gg), respectively. Since 1990, CH4 emissions have declined by 13
percent, due to industry efforts to reduce emissions and a decline in domestic oil production (see Table 3-4land
Table 3-42). CO2 emissions have also declined by 17 percent since 1990 due to similar reasons (see Table 3-43 and
Table 3-44).
Production Field Operations. Production field operations account for about 98 percent of total CH4 emissions from
petroleum systems. Vented CH4 from field operations account for over 90 percent of the emissions from the
production sector, unburned CH4 combustion emissions account for 6.4 percent, fugitive emissions are 3.4 percent,
and process upset emissions are slightly under two-tenths of a percent. The most dominant sources of emissions, in
order of magnitude, are shallow water offshore oil platforms, natural-gas-powered high bleed pneumatic devices, oil
tanks, natural-gas powered low bleed pneumatic devices, gas engines, deep water offshore platforms, and chemical
injection pumps. These seven sources alone emit about 94 percent of the production field operations emissions.
Offshore platform emissions are a combination of fugitive, vented, and unburned fuel combustion emissions from all
equipment housed on oil platforms producing oil and associated gas. Emissions from high and low-bleed pneumatics
occur when pressurized gas that is used for control devices is bled to the atmosphere as they cycle open and closed
to modulate the system. Emissions from oil tanks occur when the CH4 entrained in crude oil under pressure
volatilizes once the crude oil is put into storage tanks at atmospheric pressure. Emissions from gas engines are due
to unburned CH4 that vents with the exhaust. Emissions from chemical injection pumps are due to the 25 percent
that use associated gas to drive pneumatic pumps. The remaining six 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.
Vented CO2 associated with natural gas emissions from field operations account for 99 percent of the total CO2
emissions from this source category, while fugitive and process upsets together account for less than 1 percent of the
emissions. The most dominant sources of vented emissions are oil tanks, high bleed pneumatic devices, shallow
water offshore oil platforms, low bleed pneumatic devices, and chemical injection pumps. These five sources
together account for 98.5 percent of the non-combustion CO2 emissions from this source category, while the
remaining 1.5 percent of the emissions is distributed among 24 additional activities within the three categories:
vented, fugitive and process upsets.
Crude Oil Transportation. Crude oil transportation activities account for less than one half of one percent of total
CH4 emissions from the oil industry. Venting from tanks and marine vessel loading operations accounts for 61
percent of CH4 emissions from crude oil transportation. Fugitive emissions, almost entirely from floating roof tanks,
account for 19 percent. The remaining 20 percent is distributed among six additional sources within these two
categories. Emissions from pump engine drivers and heaters were not estimated due to lack of data.
Crude Oil Refining. Crude oil refining processes and systems account for slightly less than two percent of total CH4
emissions from the oil industry because most of the CH4 in crude oil is removed or escapes before the crude oil is
delivered to the refineries. There is an insignificant amount of CH4 in all refined products. Within refineries, vented
Energy 3-49
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emissions account for about 86 percent of the emissions, while both fugitive and combustion emissions account for
approximately seven percent each. Refinery system blowdowns for maintenance and the process of asphalt
blowing—with air, to harden the asphalt—are the primary venting contributors. Most of the fugitive CH4 emissions
from refineries are from leaks in the fuel gas system. Refinery combustion emissions include small amounts of
unburned CH4 in process heater stack emissions and unburned CH4 in engine exhausts and flares.
Asphalt blowing from crude oil refining accounts for 36 percent of the total non-combustion CO2 emissions in
petroleum systems.
Table 3-41: CH4 Emissions from Petroleum Systems (Tg CO2 Eq.)
Activity 1990 2000 2005
Production Field Operations 34.7 30.8 28.7
Pneumatic device venting 10.3 9.0 8.4
Tank venting 5.3 4.5 3.9
Combustion & process upsets 1.9 1.6 1.5
Misc. venting & fugitives 16.8 15.3 14.5
Wellhead fugitives 0.6 0.5 0.4
Crude Oil Transportation 0.1 0.1 0.1
Refining 0.5 0.6 0.6
Total 35.4 31.5 29.4
Note: Totals may not sum due to independent rounding.
Table 3-42: CH4 Emissions from Petroleum Systems (Gg)
Activity 1990 2000 2005
Production Field Operations 1,653 1,468 1,366
Pneumatic device venting 489 428 397
Tank venting 250 214 187
Combustion & process upsets 88 76 71
Misc. venting & fugitives 799 727 691
Wellhead fugitives 26 22 19
Crude Oil Transportation 7 1 5 1 5
Refining 25 28 28
Total 1,685 1,501 1,398
Note: Totals may not sum due to independent rounding.
Table 3-43 : CO2 Emissions from Petroleum Systems (Tg CO2 Eq.)
Activity 1990 2000 2005 2006
Production Field Operations 0.4 0.3 0.3 0.3
Pneumatic device venting +1 + + +
Tank venting 0.3 0.3 0.2 0.2
Misc. venting & fugitives +1 +1 + +
Wellhead fugitives +1 +1 + +
Crude Refining 0.2 0.2 0.2 0.2
Total 0.6 0.5 0.5 0.5
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 3-44: CO2 Emissions from Petroleum Systems (Gg)
Activity 1990 2000 2005 2006
Production Field Operations 376 323 285 285
Pneumatic device venting 27 24 22 22
Tank venting 328 281 246 246
Misc. venting & fugitives 18 17 16 16
Wellhead fugitives 1 1 1 1 1 1
Crude Refining 180 211 205 203
Total 555 534 490 488
2006
28.7
8.3
3.9
1.5
14.6
0.4
0.1
0.6
29.4
2006
1,365
396
188
71
693
17
5
28
1,398
2007
0.3
+
0.3
+
+
0.2
0.5
2007
292
22
252
16
1
182
474
2007
29.3
8.4
4.0
1.5
15.0
0.4
0.1
0.6
30.0
2007
1,396
398
192
72
714
20
5
27
1,427
2008
0.3
+
0.2
+
+
0.2
0.5
2008
288
23
247
16
1
165
453
2008
29.6
8.7
4.0
1.6
14.8
0.5
0.1
0.5
30.2
2008
1,409
416
189
75
707
23
5
25
1,439
2009
0.3
+
0.3
+
+
0.1
0.5
2009
319
23
278
16
1
144
463
2009
30.3
8.8
4.5
2.0
14.6
0.5
0.1
0.5
30.9
2009
1,444
419
212
94
696
23
5
24
1,473
Note: Totals may not sum due to independent rounding.
3-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Methodology
The methodology for estimating CH4 emissions from petroleum systems is a bottom-up approach, based on
comprehensive studies of CH4 emissions from U.S. petroleum systems (EPA 1996, EPA 1999). These studies
combined emission estimates from 64 activities occurring in petroleum systems from the oil wellhead through crude
oil refining, including 33 activities for crude oil production field operations, 11 for crude oil transportation activities,
and 20 for refining operations. Annex 3.5 provides greater detail on the emission estimates for these 64 activities.
The estimates of CH4 emissions from petroleum systems do not include emissions downstream of oil refineries
because these emissions are negligible.
The methodology for estimating CH4 emissions from the 64 oil industry activities employs emission factors initially
developed by EPA (1999). Activity factors for the years 1990 through 2009 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 their corresponding activity factor (e.g., equipment count or frequency of
activity). EPA (1999) provides emission factors for all activities except those related to offshore oil production and
field storage tanks. For offshore oil production, two emission factors were calculated using data collected over a
one-year period for all federal offshore platforms (EPA 2005, BOEMRE 2004). One emission factor is for oil
platforms in shallow water, and one emission factor is for oil platforms in deep water. Emission factors are held
constant for the period 1990 through 2009. The number of platforms in shallow water and the number of platforms
in deep water are used as activity factors and are taken from Bureau of Ocean Energy Management, Regulation, and
Enforcement (BOEMRE) (formerly Minerals Management Service) statistics (BOEMRE 2010a-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
2009).
For some years, complete activity factor data were not available. In such cases, one of three approaches was
employed. Where appropriate, the activity factor was calculated from related statistics using ratios developed for
EPA (1996). For example, EPA (1996) found that the number of heater treaters (a source of CH4 emissions) is
related to both number of producing wells and annual production. To estimate the activity factor for heater treaters,
reported statistics for wells and production were used, along with the ratios developed for EPA (1996). In other
cases, the activity factor was held constant from 1990 through 2009 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 factors. See Annex 3.5 for additional detail.
Among the more important references used to obtain activity factors are the Energy Information Administration
annual and monthly reports (EIA 1990 through 2010, 1995 through 2010, 1995 through 2010a-b), 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
reports (BOEMRE 2005, 2010a-c), analysis of BOEMRE data (EPA 2005, BOEMRE 2004), the Oil & Gas Journal
(OGJ 2010a,b), the Interstate Oil and Gas Compact Commission (IOGCC 2008), and the United States Army Corps
of Engineers (1995-2008).
The methodology for estimating CO2 emissions from petroleum systems combines vented, fugitive, and process
upset emissions sources from 29 activities for crude oil production field operations and one activity from petroleum
refining. Emissions are estimated for each activity by multiplying emission factors by their corresponding activity
factors. The emission factors for CO2 are estimated by multiplying the CH4 emission factors by a conversion factor,
which is the ratio of CO2 content and methane content in produced associated gas. The only exceptions to this
methodology are the emission factors for crude oil storage tanks, which are obtained from E&P Tank simulation
runs, and the emission factor for asphalt blowing, which was derived using the methodology and sample data from
API (2009).
Uncertainty and Time-Series Consistency
This section describes the analysis conducted to quantify uncertainty associated with the estimates of emissions from
petroleum systems. Performed using @RISK software and the IPCC-recommended Tier 2 methodology (Monte
Carlo Simulation technique), the method employed provides for the specification of probability density functions for
key variables within a computational structure that mirrors the calculation of the Inventory estimate. The results
provide the range within which, with 95 percent certainty, emissions from this source category are likely to fall.
Energy 3-51
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The detailed, bottom-up Inventory analysis used to evaluate U.S. petroleum systems reduces the uncertainty related
to the CH4 emission estimates in comparison to a top-down approach. However, some uncertainty still remains.
Emission factors and activity factors are based on a combination of measurements, equipment design data,
engineering calculations and studies, surveys of selected facilities and statistical reporting. Statistical uncertainties
arise from natural variation in measurements, equipment types, operational variability and survey and statistical
methodologies. Published activity factors are not available every year for all 64 activities analyzed for petroleum
systems; therefore, some are estimated. Because of the dominance of the seven major sources, which account for 92
percent of the total methane emissions, the uncertainty surrounding these seven sources has been estimated most
rigorously, and serves as the basis for determining the overall uncertainty of petroleum systems emission estimates.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 3-45. Petroleum systems CH4
emissions in 2009 were estimated to be between 23.5 and 76.9 Tg CO2 Eq., while CO2 emissions were estimated to
be between 0.4 and 1.2 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 24 percent below to
149 percent above the 2009 emission estimates of 30.9 and 0.5 Tg CO2 Eq. for CH4 and CO2, respectively.
Table 3-45: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petroleum Systems (Tg CO2 Eq. and
Percent)
Source
Gas
2009 Emission
Estimate
(TgC02Eq.)b
Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (%)
Lower Upper Lower Upper
Boundb Boundb Boundb Boundb
Petroleum Systems CH4 30.9 23.5 76.9 -24% 149%
Petroleum Systems CO2 0.5 0.4 L2 -24% 149%
a Range of 2009 relative uncertainty predicted by Monte Carlo 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Discussion
As part of QA/QC and verification activities done for the Inventory, potential improvements were identified, which
include a new emissions source associated with fixed roof storage tank emissions in the production segment. In
some production areas the separator liquid level may drop too low such that the produced associated gas blows
through the dump valve and vents through the storage tank. This data was included where available for the
Inventory (see Recalculation discussion below). More data will be necessary to potentially add this as a separate
source from storage tank flashing emissions and also to represent the true scope of activity across the United States.
Recalculations Discussion
Most revisions for the current Inventory relative to the previous report were due to updating previous years' data
with revised data from existing data sources. Well completion venting, well drilling, and offshore platform activity
factors were updated from existing data sources from 1990 onward.
Additionally, the emission factor for venting from fixed roof storage tanks in the crude oil production segment was
revised. Using the same E&P Tank sample data runs on crude oil gravities ranging up to 45°API, a new national
level flashing emissions factor was developed by using a large sample of production data, sorted by gravity,
available from the HPDI database.
A study prepared for the Texas Environmental Research Consortium measured emissions rates from several oil and
condensate tanks in Texas (TERC 2009). This data was plotted and compared to the flashing emissions simulated
via E&P Tank simulation. EPA observed that additional emissions beyond the flashing were present in
approximately 50 percent of the tanks. These emissions may be attributed to separator dump valves malfunctioning
or other methods of associated gas entering the tank and venting from the roof. Because the dataset was limited to
3-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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represent production from only 14 counties that represent 0.5 percent of U.S. production, the national emission
factor was scaled up such that only production from these counties is affected by the occurrence of associated gas
venting through the storage tank.
Planned Improvements
As noted above, nearly all emission factors used in the development of the petroleum systems estimates were taken
from EPA (1995, 1996, 1999), with the remaining emission factors taken from EPA default values (EPA 2005) and
a consensus of industry peer review panels. These emission factors will be reviewed as part of future Inventory
work. Results of this review and analysis will be incorporated into future inventories, as appropriate.
Malfunctioning separator dump valves is not an occurrence isolated to the Texas counties in which the sample data
was obtained. New data will be reviewed as they become available on this emissions source and emissions updated,
as appropriate.
Data collected through EPA's Greenhouse Gas Reporting Program will be reviewed for potential improvements to
petroleum systems emissions sources. The rule will collect actual activity data and improved quantification methods
from those used in several of the studies which form the basis of this Inventory. This data will be incorporated as
appropriate into the current Inventory to improve the accuracy and uncertainty of the emissions estimates. In
particular, EPA will investigate whether certain emissions sources currently accounted for in the Energy sector
should be separately accounted for in the petroleum systems inventory (e.g., CO2 process emissions from hydrogen
production).
In 2010, all U.S. petroleum refineries were required to collect information on their greenhouse gas emissions. This
data will be reported to EPA through its Greenhouse Gas Reporting Program in 2011. Data collected under this
program will be evaluated for use in future inventories to improve the calculation of national emissions from
petroleum systems.
[BEGIN BOX]
Box 3-3. Carbon Dioxide Transport, Injection, and Geological Storage
Carbon dioxide is produced, captured, transported, and used for Enhanced Oil Recovery (EOR) as well as
commercial and non-EOR industrial applications. This CO2 is produced from both naturally-occurring CO2
reservoirs and from industrial sources such as natural gas processing plants and ammonia plants. In the current
Inventory, emissions from naturally-produced CO2 are estimated based on the application.
In the current Inventory report, the CO2 that is used in non-EOR industrial and commercial applications (e.g., food
processing, chemical production) is assumed to be emitted to the atmosphere during its industrial use. These
emissions are discussed in the Carbon Dioxide Consumption section. The naturally-occurring CO2 used in EOR
operations is assumed to be fully sequestered. Additionally, all anthropogenic CO2 emitted from natural gas
processing and ammonia plants is assumed to be emitted to the atmosphere, regardless of whether the CO2 is
captured or not. These emissions are currently included in the Natural Gas Systems and the Ammonia Production
sections of the Inventory report, respectively.
IPCC (IPCC, 2006) included, for the first time, 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.
Beginning in 2010, facilities that conduct geologic sequestration of CO2 and all other facilities that inject CO2
underground will be required to calculate and report greenhouse gas data annually to EPA through its Greenhouse
Energy 3-53
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Gas Reporting Program. The Greenhouse Gas Reporting Rule requires greenhouse gas reporting from facilities that
inject CO2 underground for geologic sequestration, and requires greenhouse gas reporting from all other facilities
that inject CO2 underground for any reason, including enhanced oil and gas recovery. Beginning in 2010, facilities
conducting geologic sequestration of CO2 are required to develop and implement an EPA-approved site-specific
monitoring, reporting and verification (MRV) plan, and to report the amount of CO2 sequestered using a mass
balance approach. Data from this program, which will be reported to EPA in early 2012, for the 2011 calendar year,
will provide additional facility-specific information about the carbon capture, transport and storage chain, EPA
intends to evaluate that information closely and consider opportunities for improving our current inventory
estimates.
Preliminary estimates indicate that the amount of CO2 captured from industrial and natural sites is 47.3 Tg CO2
(47,340 Gg CO2) (see Table 3-46 and Table 3-47). 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.
Table 3-46: Potential Emissions from CO2 Capture and Transport (Tg CO2 Eq.)
Year 1990 2000 2005 2006 2007 2008 2009
Acid Gas Removal Plants 4.8 2.3 5.8 6.2 6.4 6.6 7.0
Naturally Occurring CO2 20.8 23.2 28.3 30.2 33.1 36.1 39.7
Ammonia Production Plants + I 0.7 0.7 0.7 0.7 0.6 0.6
Pipelines Transporting CO2 + + + + + + j_
Total 25.6 26.1 34.7 37.1 40.1 43.3 47.3
+ Does not exceed 0.05 Tg CO2 Eq.
Note; Totals may not sum due to independent rounding.
Table 3-47: Potential Emissions from CO2 Capture and Transport (Gg)
Year
Acid Gas Removal Plants
Naturally Occurring CO2
Ammonia Production Plants
Pipelines Transporting CO2
Total
1990
4,832
20,811
+
8
25,643
2000
2,264
23,208
676
8
26,149
2005
5,798
28,267
676
7
34,742
2006
6
30
37
,224
,224
676
7
,124
2007
6
33
40
,088
,086
676
7
,141
2008
6,630
36,102
580
8
43,311
2009
7,035
39,725
580
8
47,340
+ Does not exceed 0.5 Gg.
Note: Totals do not include emissions from pipelines transporting CO2
Note; Totals may not sum due to independent rounding.
[END BOX]
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 2009 are reported in Table 3-48.
Table 3-48: NOX, CO, and NMVOC Emissions from Energy-Related Activities (Gg)
Gas/Source
NOX
Mobile Combustion
Stationary Combustion
Oil and Gas Activities
Incineration of Waste
International Bunker Fuels*
CO
Mobile Combustion
1990
21,106
10,862
10,023
139
82 1
2,020
125,640 1
119,360 1
2000
18,477
10
,199
8,053
1
89
83
111
114
,344
,714
,559
2005
15,319
9,012
5,858
321
129
1,703
69,062
62,692
2006
14
8
5
1
65
58
,473
,488
,545
319
121
,793
,399
,972
2007
13,829
7
5
1
61
55
,965
,432
318
114
,791
,739
,253
2008
13,012
7,441
5,148
318
106
1,917
58,078
51,533
2009
10,887
6,206
4,159
393
128
1,651
49,647
43,355
Stationary Combustion 5,000 | 4,340 | 4,649 4,695 4,744 4,792 4,543
3-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Incineration of Waste
Oil and Gas Activities
International Bunker Fuels*
NMVOCs
Mobile Combustion
Stationary Combustion
Oil and Gas Activities
Incineration of Waste
International Bunker Fuels*
978 1
302 1
130
12,620
10,932
912
554 1
222 1
61 •
1,670
146
128
8,952
7,229
1,077
388
257
45
1,403
318
132
7,798
6,330
716
510
241
54
1,412
319
161
7,702
6,037
918
510
238
59
1,421
320
160
7,604
5,742
1,120
509
234
59
1,430
322
165
7,507
5,447
1,321
509
230
62
1,403
345
149
5,333
4,151
424
599
159
57
* These values are presented for informational purposes only and are not included in totals.
Note: Totals may not sum due to independent rounding.
Methodology
These emission estimates were obtained from preliminary data (EPA 2010, EPA 2009), and disaggregated based on
EPA (2003), which, in its final iteration, will be published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. Emissions were calculated either for individual categories or for many categories
combined, using basic activity data (e.g., the amount of raw material processed) as an indicator of emissions.
National activity data were collected for individual categories from various agencies. Depending on the category,
these basic activity data may include data on production, fuel deliveries, raw material processed, etc.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
Uncertainty and Time-Series Consistency
Uncertainties in these estimates are partly due to the accuracy of the emission factors 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 2009. 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.96 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).97
Greenhouse gases emitted from the combustion of international bunker fuels, like other fossil fuels, include CO2,
CH4 and N2O. Two transport modes are addressed under the IPCC definition of international bunker fuels: aviation
and marine.98 Emissions from ground transport activities—by road vehicles and trains—even when crossing
96 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).
97 Note that the definition of international bunker fuels used by the UNFCCC differs from that used by the International Civil
Aviation Organization.
98 Most emission related international aviation and marine regulations are under the rubric of the International Civil Aviation
Energy 3-55
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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."
Emissions of CO2 from aircraft are essentially a function of fuel use. CH4 and N2O emissions also depend upon
engine characteristics, flight conditions, and flight phase (i.e., take-off, climb, cruise, decent, and landing). CH4 is
the product of incomplete combustion and occur mainly during the landing and take-off phases. 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 2009 from the combustion of international bunker fuels from both
aviation and marine activities were 124.4 Tg CO2 Eq., or ten percent above emissions in 1990 (see Table 3-49 and
Table 3-50). Emissions from international flights and international shipping voyages departing from the United
States have increased by 49 percent and decreased by 18 percent, respectively, since 1990. The majority of these
emissions were in the form of CO2; however, small amounts of CH4 and N2O were also emitted.
Table 3-49: CO2, CH4, and N2O Emissions from International Bunker Fuels (Tg CO2 Eq.)
Gas/Mode 1990 2000 2005 2006 2007 2008 2009
CO2 111.8 98.5 109.7 128.4 127.6 133.7 123.1
Aviation 46.4 58.8 56.7 74.6 73.8 75.5 69.4
Marine 65.4 39.7 53.0 53.8 53.9 58.2 53.7
CH4 0.2 0.1 0.1 0.2 0.2 0.2 0.1
Aviation + I + I + + + + +
Marine 0.1 0.1 0.1 0.1 0.1 0.1 0.1
N2O 1.1 0.9 1.0 1.2 1.2 1.2 1.1
Aviation 0.5 0.6 0.6 0.8 0.8 0.8 0.7
Marine 0.5 03 0.4 0.4 0.4 0.5 0.4
Total 113.0 99.5 110.9 129.7 129.0 135.1 124.4
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
Table 3-50: CO2, CH4 and N2O Emissions from International Bunker Fuels (Gg)
Gas/Mode
CO2
Aviation
Marine
CH4
Aviation
1990
111,828
46,399
65,429
8 1
2
2000
98,482
58,785
39,697
I6!
2
2005
109,750
56,736
53,014
7
2
2006
128,384
74,552
53,832
8
2
2007
127,618
73,762
53,856
8
2
2008
133,704
75,508
58,196
8
2
2009
123,127
69,404
53,723
7
2
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).
99 Naphtha-type jet fuel was used in the past by the military in turbojet and turboprop aircraft engines.
3-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Marine
N2O
Aviation
Marine
_
2 ^|
_
1 ^|
5
3
2
1
5
4
2
1
5
4
2
1
6
4
2
1
5
4
2
1
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. C content and fraction
oxidized factors for jet fuel, distillate fuel oil, and residual fuel oil were taken directly from EIA and are presented in
Annex 2.1, Annex 2.2, and Annex 3.7 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
(2010) and USAF (1998), and heat content for jet fuel was taken from EIA (2010). A complete description of the
methodology and a listing of the various factors employed can be found in Annex 2.1. See Annex 3.7 for a specific
discussion on the methodology used for estimating emissions from international bunker fuel use by the U.S.
military.
Emission estimates for CH4 and N2O were calculated by multiplying emission factors by measures of fuel
consumption by fuel type and mode. Emission factors used in the calculations of CH4 and N2O emissions were
obtained from the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997). For aircraft emissions, the
following values, in units of grams of pollutant per kilogram of fuel consumed (g/kg), were employed: 0.09 for CH4
and 0.1 for N2O 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 aircraft fuel consumption for inventory years 2000 through 2005 were developed using the FAA's
System for assessing Aviation's Global Emissions (SAGE) model (FAA 2006). That tool has been subsequently
replaced by the Aviation Environmental Design Tool (AEDT), which calculates noise in addition to aircraft fuel
burn and emissions for flights globally in a given year (FAA 2010). Data for inventory years 2006 through 2009
were developed using AEDT.
International aviation bunker fuel consumption from 1990 to 2009 was calculated by assigning the difference
between the sum of domestic activity data (in Tbtu) from SAGE and the AEDT, and the reported EIA transportation
jet fuel consumption to the international bunker fuel category for jet fuel from EIA (2010). Data on U.S. Department
of Defense (DoD) aviation bunker fuels and total jet fuel consumed by the U.S. military was supplied by the Office
of the Under Secretary of Defense (Installations and Environment), DoD. Estimates of the percentage of each
Service's total operations that were international operations were developed by DoD. Military aviation bunkers
included international operations, operations conducted from naval vessels at sea, and operations conducted from
U.S. installations principally over international water in direct support of military operations at sea. Military
aviation bunker fuel emissions were estimated using military fuel and operations data synthesized from unpublished
data by the Defense Energy Support Center, under DoD's Defense Logistics Agency (DESC 2011). 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 3-51. See Annex 3.7 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 1991 through 2010) for 1990 through 2001, 2007, through
2009, 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 DESC (2011). The
total amount of fuel provided to naval vessels was reduced by 13 percent to account for fuel used while the vessels
were not-underway (i.e., in port). Data on the percentage of steaming hours underway versus not-underway were
provided by the U.S. Navy. These fuel consumption estimates are presented in. Table 3-52.
Table 3-51: Aviation Jet Fuel Consumption for International Transport (Million Gallons)
Nationality 1990 2000 2005 2006 2007 2008 2009
Energy 3-57
-------
U.S. and Foreign Carriers
U.S. Military
Total
4,934
862
5,796
6,157 1
480
6,638
5,943
• 462
6,405
7,809
400
8,209
7,726
410
8,137
7,909
386
8,295
7,270
368
7,638
Note: Totals may not sum due to independent rounding.
Table 3-52: Marine Fuel Consumption for International Transport (Million Gallons)
Fuel Type 1990 2000 2005 2006 2007 2008 2009
Residual Fuel Oil
Distillate Diesel Fuel & Other
U.S. Military Naval Fuels
Total
4,781
617
522
5,920
1 2,967
290
329 •
3,586
3,881
444
I 471
4,796
4,004
446
414
4,864
4,059
358
444
4,861
4,373
445
437
5,254
4,040
426
384
4,850
Note: Totals may not sum due to independent rounding.
Uncertainty and Time-Series Consistency
Emission estimates related to the consumption of international bunker fuels are subject to the same uncertainties as
those from domestic aviation and marine mobile combustion emissions; however, additional uncertainties result
from the difficulty in collecting accurate fuel consumption activity data for international transport activities separate
from domestic transport activities. 10° 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 CO2 in the Revised 1996 IPCC Guidelines is to use data by
specific aircraft type (IPCC/UNEP/OECD/IEA 1997). 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
100
See uncertainty discussions under Carbon Dioxide Emissions from Fossil Fuel Combustion.
3-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
near-ground level emissions of gases other than CO2.101
There is also concern as to the reliability of the existing DOC (1991 through 2010) 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 2008. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A source-specific QA/QC plan for international bunker fuels was developed and implemented. This effort included
a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved
checks specifically focusing on the activity data and emission factor sources and methodology used for estimating
CO2, CH4, and N2O from international bunker fuels in the United States. Emission totals for the different sectors
and fuels were compared and trends were investigated. No corrective actions were necessary.
Recalculations Discussion
Slight changes to emission estimates are due to revisions made to historical activity data for aviation jet fuel
consumption using the FAA's AEDT. These historical data changes resulted in changes to the emission estimates for
1990 through 2008 relative to the previous Inventory, which averaged to an annual decrease in emissions from
international bunker fuels of 0.13 Tg CO2Eq. (0.1 percent) in CO2 emissions, an annual decrease of less than 0.01
Tg CO2 Eq. (0.05 percent) in CH4 emissions, and an annual decrease of less than 0.01 Tg CO2 Eq. (0.1 percent) in
N2O emissions.
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
from corn and woody crops generates CO2 in addition to CH4 and N2O already covered in this chapter. In line with
the reporting requirements for inventories submitted under the UNFCCC, CO2 emissions from biomass combustion
have been estimated separately from fossil fuel CO2 emissions and are not directly included in the energy sector
contributions to U.S. totals. In accordance with IPCC methodological guidelines, any such emissions are calculated
by accounting for net carbon (C) fluxes from changes in biogenic C reservoirs in wooded or crop lands. For a more
complete description of this methodological approach, see the Land Use, Land-Use Change, and Forestry chapter
(Chapter 7), which accounts for the contribution of any resulting CO2 emissions to U. S. totals within the Land Use,
Land-Use Change and Forestry sector's approach.
In 2009, total CO2 emissions from the burning of woody biomass in the industrial, residential, commercial, and
electricity generation sectors were approximately 183.8 Tg CO2 Eq. (183,777 Gg) (see Table 3-53 and Table 3-54).
As the largest consumer of woody biomass, the industrial sector was responsible for 62 percent of the CO2 emissions
from this source. Emissions from this sector decreased from 2008 to 2009 due to a corresponding decrease in wood
consumption. The residential sector was the second largest emitter, constituting 24 percent of the total, while the
commercial and electricity generation sectors accounted for the remainder.
Table 3-53: CO2 Emissions from Wood Consumption by End-Use Sector (Tg CO2 Eq.)
End-Use Sector
Industrial
Residential
1990
135
59
o
.J
.8
2000
153.6
43.3
2005
136
44
o
.J
.3
2006
138.2
40.2
2007
132
44
.6
.3
2008
126.1
46.4
2009
114.2
44.3
101 U.S. aviation emission estimates for CO, NOX, and NMVOCs are reported by EPA's National Emission Inventory (NEI) Air
Pollutant Emission Trends web site, and reported under the Mobile Combustion section. It should be noted that these estimates
are based solely upon LTO cycles and consequently only capture near ground-level emissions, which are more relevant for air
quality evaluations. These estimates also include both domestic and international flights. Therefore, estimates reported under the
Mobile Combustion section overestimate IPCC-defined domestic CO, NOX, and NMVOC emissions by including landing and
take-off (LTO) cycles by aircraft on international flights, but underestimate because they do not include emissions from aircraft
on domestic flight segments at cruising altitudes. The estimates in Mobile Combustion are also likely to include emissions from
ocean-going vessels departing from U.S. ports on international voyages.
Energy 3-59
-------
Commercial 6.8 7.4 7.2 6.7 7.2 7.5 7.4
Electricity Generation 13.3 13.9 19.1 18.7 19.2 18.3 17.8
Total 215.2 218.1 206.9 203.8 203.3 198.4 183.8
Note: Totals may not sum due to independent rounding.
Table 3-54: CO2 Emissions from Wood Consumption by End-Use Sector (Gg)
End-Use Sector
Industrial
Residential
Commercial
Electricity Generation
Total
1990
135,348
59,808
6,779
13,252 •
215,186
2000
153,559
43,309
7,370 1
13,851
218,088
2005
136,269
44,340
7,182
19,074
206,865
2006
138,207
40,215
6,675
18,748
203,846
2007
132,642
44,340
7,159
19,175
203,316
2008
126,145
46,402
7,526
18,288
198,361
2009
114,222
44,340
7,406
17,809
183,777
Note: Totals may not sum due to independent rounding.
Biomass-derived fuel consumption in the United States transportation sector consisted primarily of ethanol use.
Ethanol is primarily produced from corn grown in the Midwest, and was used mostly in the Midwest and South.
Pure ethanol can be combusted, or it can be mixed with gasoline as a supplement or octane-enhancing agent. The
most common mixture is a 90 percent gasoline, 10 percent ethanol blend known as gasohol. Ethanol and ethanol
blends are often used to fuel public transport vehicles such as buses, or centrally fueled fleet vehicles.
In 2009, the United States consumed an estimated 894 trillion Btu of ethanol, and as a result, produced
approximately 61.2 Tg CO2 Eq. (61,231 Gg) (see Table 3-55 and Table 3-56 ) of CO2 emissions. Ethanol
production and consumption has grown steadily every year since 1990, with the exception of 1996 due to short corn
supplies and high prices in that year.
Table 3-55: CO2 Emissions from Ethanol Consumption (Tg CO2 Eq.)
End-Use Sector
Transportation
Industrial
Commercial
Total
1990
4.1
0.1 1
+
4.2
2000
9.2
0.1 1
+
9.4
2005
22.4
0.5
0.1
23.0
2006
30.3
0.7
0.1
31.0
2007
38.1
0.7
0.1
38.9
2008
53.8
0.8
0.1
54.8
2009
60.2
0.9
0.2
61.2
+ Does not exceed 0.05 Tg CO2 Eq.
Table 3-56: CO2 Emissions from Ethanol Consumption (Gg)
End-Use Sector
Transportation3
Industrial
Commercial
Total
1990
4,139 1
56
34
4,229
2000
9,239
87
26
9,352
2005
22,427
469
60
22,956
2006
30,255
662
86
31,002
2007
38,138
674
135
38,946
2008
53,827
798
146
54,770
2009
60,176
892
163
61,231
a See Annex 3.2, Table A-88 for additional information on transportation consumption of these fuels.
Methodology
Woody biomass emissions were estimated by applying two EIA gross heat contents (Lindstrom 2006) to U.S.
consumption data (EIA 2010) (see Table 3-57), 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 CO2 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 CO2 with 100 percent efficiency. The emissions from ethanol consumption were calculated by
applying an emission factor of 18.67 Tg C/QBtu (EPA 2010) to U.S. ethanol consumption estimates that were
provided in energy units (EIA 2010) (see Table 3-58).
Table 3-57: Woody Biomass Consumption by Sector (Trillion Btu)
End-Use Sector 1990 2000 2005 2006 2007 2008 2009
Industrial 1,442 1,636 1,452 1,472 1,413 1,344 1,217
3-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Residential
Commercial
580 420
66 7ll
Electricity Generation 129 134
Total
2,216 2,262
430 390 430
70 65 69
1 185 182 186
2,136 2,109 2,098
450
73
177
2,044
430
72
173
1,891
Table 3-58: Ethanol Consumption by Sector (Trillion Btu)
End-Use Sector
Transportation
Industrial
Commercial
Total
1990
60.5
0.8
0.5
61.8
2000
135.0
1.3
0.4
136.6
2005
327.6
6.8
1 0.9
335.3
2006
442.0
9.7
1.3
452.9
2007
557.1
9.8
2.0
568.9
2008
786.3
11.7
2.1
800.1
2009
879.0
13.0
2.4
894.5
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
Wood consumption values were revised for 2006 through 2008 based on updated information from EIA's Annual
Energy Review (EIA 2010). This adjustment of historical data for wood biomass consumption resulted in an average
annual decrease in emissions from wood biomass consumption of 0.8 Tg CO2 Eq. (0.4 percent) from 1990 through
2008. The C content coefficient for ethanol was also revised to be consistent with the carbon content coefficients
used for EPA's Mandatory Greenhouse Gas Reporting Rule. Slight adjustments were made to ethanol consumption
based on updated information from EIA (2010), which slightly decreased estimates for ethanol consumed. As a
result of these adjustments, average annual emissions from ethanol consumption increased by about 0.3 Tg CO2 Eq.
(1.9 percent) relative to the previous Inventory.
Energy 3-61
-------
-------
Fossil Fuel Combustion
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems •
Mobile Combustion I
Stationary Combustion I
Incineration of Waste I
Abandoned Underground Coal Mines |
Energy as a Portion
of all Emissions
5,209
50
100
150 200
Tg CO2 Eq.
Figure 3-1: 2009 Energy Chapter Greenhouse Gas Sources
250
300
-------
NED Emissions 1
Natural Gas Emissions
1,209
NEU Emissions 51
Non-Energy Use
Carbon Sequestered
183
Fossil Fue
Non-Energy Consumption
Use imports U.S.
33 Territories
42
Non-Energy Balancing Item
Use U.S.
Territories
4
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.
3-2 2009 U.S. (Tg C02 Eq.)
-------
Renewable
Nuclear Energy
Electric 8.2%
Power
8.8%
Figure 3-3: 2009 U.S. Energy Consumption by Energy Source
-------
120 -i
100 -
80 -
60 -
40 -
20 -
Total Energy
Fossil Fuels
Renewable & Nuclear
O^O^O^O^O^O^O^O^CTiO
i-Hi-li-li-li-li-li-li-li-li-
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu)
Note: Expressed as gross calorific values.
2,500 -i
2,000 -
m 1,500 -
8 1,000 -
500 -
0 -
Relative Contribution
by Fuel Type
2,154
42
224
Figure 3-5: 2009 C02 Emissions from Fossil Fuel Combustion by Sector and Fuel Type
Note: The electricity generation sector also includes emissions of less than 0.5 Tg CO2 Eq. from geothermal-based electricity generation.
-------
20 -,
-20 J
Normal
(4,524 Heating Degree Days)
LnLDLnLnLn^D^D^D^D^Dixixixixixcococococoo^o^o^o^CTiooooo
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2009)
Note: Climatological normal data are highlighted.
Statistical confidence interval for "normal" climatology period of 1971 through 2000.
99% Confidence
Normal
(1,242 Cooling Degree Days)
..... _ _ _ _ -
in m LO ix CTI
o o o o o
o o o o o
rsi rsi rsi rsi rsi
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2009)
Note: Climatological normal data are highlighted.
Statistical confidence interval for "normal" climatology period of 1971 through 2000.
-------
100
90
80
70
| 60
Ł• 50
30
20
10
0
Nuclear
Hydroelectric
Wind
88
8888888888
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2009)
1,400
1,300 -
1,200 -
1,100 -
1,000
900
800 J
Industrial
OOOOOOOOOO
-------
Total excluding Computers,
Communications Equipment, and
Semiconductors
Figure 3-10: Industrial Production Indexes (Index 2007=100)
22.5 -,
22.0 -
21.5 -
21.0 -
20.5 -
20.0 -
19.5 -
19.0 -
18.5 -
18.0 -
Model Year
Figure 3-11: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks, 1990-2009
-------
10,000
8,000 -
6,000 -
4,000 -
2,000 J
Passenger Cars
S? S? S?
CTi CT* CT*
rsirsirsirsirsirsirsirsirsirsi
Figure 3-12: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2009
60 -i
50 -
. 40 -
8 so H
20 -
10 -
N,O
CH4
Figure 3-13: Mobile Source CH4 and N20 Emissions
105 -i
i-irsim^i-Ln^Dixcoai
ooooooooo
rsirsirsirsirsirsirsirsirsi
CO^capita
65 J
CT> CT> CT> CT> CT> CT>
s s
Figure 3-14: U.S. Energy Consumption and Energy-Related C02 Emissions Per Capita and Per Dollar GDP
-------
4. Industrial Processes
Greenhouse gas emissions are produced as the by-products of various non-energy-related industrial activities. That
is, these emissions are produced from an industrial process itself and are not directly a result of energy consumed
during the process. For example, raw materials can be chemically transformed from one state to another. This
transformation can result in the release of greenhouse gases such as carbon dioxide (CO2), methane (CH4), and
nitrous oxide (N2O). The processes addressed in this chapter include iron and steel production and metallurgical
coke production, cement production, lime production, ammonia production and urea consumption, limestone and
dolomite consumption (e.g., flux stone, flue gas desulfurization, and glass manufacturing), soda ash production and
use, aluminum production, titanium dioxide production, CO2 consumption, ferroalloy production, phosphoric acid
production, zinc production, lead production, petrochemical production, silicon carbide production and
consumption, nitric acid production, and adipic acid production (see Figure 4-1).
Figure 4-1: 2009 Industrial Processes Chapter Greenhouse Gas Sources
In addition to the three greenhouse gases listed above, there are also industrial sources of man-made fluorinated
compounds called hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), and sulfur hexafluoride (SF6). The present
contribution of these gases to the radiative forcing effect of all anthropogenic greenhouse gases is small; however,
because of their extremely long lifetimes, many of them will continue to accumulate in the atmosphere as long as
emissions continue. In addition, many of these gases have high global warming potentials; SF6 is the most potent
greenhouse gas the Intergovernmental Panel on Climate Change (IPCC) has evaluated. Usage of HFCs is growing
rapidly since they are the primary substitutes for ozone depleting substances (ODSs), which are being phased-out
under the Montreal Protocol on Substances that Deplete the Ozone Layer. In addition to their use as ODS
substitutes, HFCs, PFCs, and SF6 are employed and emitted by a number of other industrial sources in the United
States. These industries include aluminum production, HCFC-22 production, semiconductor manufacture, electric
power transmission and distribution, and magnesium metal production and processing.
In 2009, industrial processes generated emissions of 282.9 teragrams of CO2 equivalent (Tg CO2 Eq.), or 4 percent
of total U.S. greenhouse gas emissions. CO2 emissions from all industrial processes were 119.0 Tg CO2 Eq.
(119,010 Gg) in 2009, or 2 percent of total U.S. CO2 emissions. CH4 emissions from industrial processes resulted in
emissions of approximately 1.2 Tg CO2 Eq. (58 Gg) in 2009, which was less than 1 percent of U.S. CH4 emissions.
N2O emissions from adipic acid and nitric acid production were 16.5 Tg CO2 Eq. (53 Gg) in 2009, or 6 percent of
total U.S. N2O emissions. In 2009 combined emissions of HFCs, PFCs and SF6 totaled 146.1 Tg CO2 Eq. Despite
the significant increase in HFC emissions associated with increased usage of ODSs, total emissions from industrial
processes in 2009 were less than 1990 for the first time since 1994. This decrease is primarily due to significant
reductions in emissions from iron and steel production, metallurgical coke production, ammonia production and urea
consumption, adipic acid production, HCFC-22 production, aluminum production and cement production.
Table 4-1 summarizes emissions for the Industrial Processes chapter in Tg CO2 Eq., while unweighted native gas
emissions in Gg are provided in Table 4-2. The source descriptions that follow in the chapter are presented in the
order as reported to the UNFCCC in the common reporting format tables, corresponding generally to: mineral
products, chemical production, metal production, and emissions from the uses of HFCs, PFCs, and SF6.
Table 4-1: Emissions from Industrial Processes (Tg CO2 Eq.)
Gas/Source 1990 2000 2005 2006 2007 2008 2009
CO2 188.4 184.9 165.4 169.9 172.6 159.5 119.0
Iron and Steel Production ani
Metallurgical Coke
Production 99.5 85.9 65.9 68.8 71.0 66.0 41.9
Iron and Steel Production 97.1 83.7 63.9 66.9 69.0 63.7 40.9
Metallurgical Coke
Production 2.5 2.2 2.0 1.9 2.1 2.3 1.0
Cement Production 33.3 40.4 45.2 45.8 44.5 40.5 29.0
Ammonia Production & Ure;
Consumption 16.8 16.4 12.8 12.3 14.0 11.9 11.8
Industrial Processes 4-1
-------
Lime Production 11.5
Limestone and Dolomite Use 5.1
Soda Ash Production and
Consumption 4.1
Aluminum Production 6.8
Petrochemical Production 3.3
Carbon Dioxide Consumptio 1.4
Titanium Dioxide Productior 1.2
Ferroalloy Production 2.2
Phosphoric Acid Production 1.5
Zinc Production 0.7
Lead Production 0.5
Silicon Carbide Production
and Consumption 0.4
CH4 1.9
Petrochemical Production 0.9
Iron and Steel Production an
Metallurgical Coke
Production 1.0
Iron and Steel Productior 1.0
Metallurgical Coke
Production +
Ferroalloy Production +
Silicon Carbide Production
and Consumption +
N2O 33.5
Nitric Acid Production 17.7
Adipic Acid Production 15.8
HFCs 36.9
Substitution of Ozone
Depleting Substances3 0.3
HCFC-22 Production 36.4
Semiconductor Manufacturii
HFCs 0.2
PFCs 20.8
Aluminum Production 18.5
Semiconductor Manufacturii
PFCs 2.2
SF6 34.4
Electrical Transmission and
Distribution 28.4
Semiconductor Manufacturii
SF6 0.5
Magnesium Production and
Processing 5.4
14.1
5.1
4.2
6.1
4.5
.4
.8
.9
.4
.0
0.6
0.2
2.2
1.2
0.9
0.9
24.9
19.4
5.5
103.2
74.3
28.6
0.3
13.5
8.6
4.9
20.1
16.0
1.1
3.0
14.4
6.8
4.2
4.1
4.2
1.3
1.8
1.4
1.4
1.1
0.6
0.2
1.8
1.1
0.7
0.7
21.5
16.5
5.0
120.2
104.2
15.8
0.2
6.2
3.0
3.2
19.0
15.1
1.0
2.9
15.1
8.0
4.2
3.8
3.8
1.7
1.8
1.5
1.2
1.1
0.6
0.2
1.7
1.0
0.7
0.7
20.5
16.2
4.3
123.5
109.4
13.8
0.3
6.0
2.5
3.5
17.9
14.1
1.0
2.9
14.6
7.7
4.1
4.3
3.9
1.9
1.9
1.6
1.2
1.1
0.6
0.2
1.7
1.0
0.7
0.7
22.9
19.2
3.7
129.5
112.3
17.0
0.3
7.5
3.8
3.7
16.7
13.2
0.8
2.6
14.3
6.3
4.1
4.5
3.4
1.8
1.8
1.6
1.2
1.2
0.6
0.2
1.6
0.9
0.6
0.6
18.5
16.4
2.0
129.4
115.5
13.6
0.3
6.7
2.7
4.0
16.1
13.3
0.9
1.9
11.2
7.6
4.3
3.0
2.7
.5
.5
.0
.0
0.5
0.1
1.2
0.8
0.4
0.4
16.5
14.6
1.9
125.7
120.0
5.4
0.3
5.6
1.6
4.0
14.8
1.0
1.1
Total
315.8
348.8
334.1
339.4
350.9
331.7
282.9
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
a Small amounts of PFC emissions also result from this source.
Table 4-2: Emissions from Industrial Processes (Gg)
Gas/Source
1990
2000
2005
2006
2007
2008 2009
CO2 188,431
Iron and Steel Production
and Metallurgical Coke
Production 99,528
184,919 165,384 169,870 172,592
85,935 65,925 68,772 71,045
159,470 119,010
66,015 41,871
4-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Iron and Steel
Production 97,058\
Metallurgical Coke
Production 2,47L
Cement Production 33,278|
Ammonia Production &
Urea Consumption 16,83 ll
Lime Production 11,533|
Limestone and Dolomite
Use 5,127
Soda Ash Production and
Consumption 4,141
Aluminum Production 6,831
Petrochemical Production 3,311
Carbon Dioxide
Consumption 1,416
Titanium Dioxide
Production 1,195
Ferroalloy Production 2,152
Phosphoric Acid
Production 1,529
Zinc Production 667
Lead Production 516
Silicon Carbide
Production and
Consumption 375
CH4 88
Petrochemical Production 41
Iron and Steel Production
and Metallurgical Coke
Production 46
Iron and Steel
Production 46
Metallurgical Coke
Production
Ferroalloy Production
Silicon Carbide
Production and
Consumption ll
N2O 108
Nitric Acid Production 57|
Adipic Acid Production 511
HFCs M|
Substitution of Ozone
Depleting Substances3 M|
HCFC-22 Production
Semiconductor
Manufacturing HFCs +1
PFCs M|
Aluminum Production M|
Semiconductor
Manufacturing PFCs Ml
SF6
Electrical Transmission
and Distribution
Semiconductor
83,740
2,195
40,405
16,402
14,088
5,056
4,181
6,086
4,479
1,421
1,752
1,893
63,882 66,852
2,043
45,197
12,849
14,379
6,768
4,228
4,142
4,181
1,321
1,755
1,392
1,919
45,792
12,300
15,100
8,035
4,162
3,801
3,837
1,709
1,836
1,505
68,991
2,054
44,538
14,038
14,595
7,702
4,140
4,251
3,931
1,867
1,930
1,552
63,682 40,914
2,334
40,531
11,949
14,330
6,276
4,111
4,477
3,449
1,780
1,809
1,599
956
29,018
11,797
11,223
7,649
4,265
3,009
2,735
1,763
1,541
1,469
1,382 1,386
997 1,088
594
248
104
59
44
44
1
1
80
63
18
M
M
2
M
M
M
1
1
553
219
86
51
34
34
+
+
69
53
16
M
M
1
M
M
M
1
1
1,167
1,088
560
207
83
48
35
35
+
+
66
52
14
M
M
1
M
M
M
1
1
1,166
1,081
562
196
82
48
33
33
+
+
74
62
12
M
M
1
M
M
M
1
1
1,187
1,230
551
175
75
43
31
31
+
+
60
53
7
M
M
1
M
M
M
1
1
1,035
966
525
145
58
40
17
17
+
+
53
47
6
M
M
+
M
M
M
1
1
Industrial Processes 4-3
-------
Manufacturing SF6
Magnesium Production
and Processing + + + + + + +_
+ Does not exceed 0.5 Gg
M (Mixture of gases)
Note: Totals may not sum due to independent rounding.
a Small amounts of PFC emissions also result from this source.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification Procedures
Tier 1 quality assurance and quality control procedures have been performed for all industrial process sources. For
industrial process sources of CO2 and CH4 emissions, a detailed plan was developed and implemented. This plan
was based on the overall U.S. strategy, but was tailored to include specific procedures recommended for these
sources. Two types of checks were performed using this plan: (1) general, or Tier 1, procedures that focus on annual
procedures and checks to be used when gathering, maintaining, handling, documenting, checking, and archiving the
data, supporting documents, and files, and (2) source-category specific, or Tier 2, procedures that focus on
procedures and checks of the emission factors, activity data, and methodologies used for estimating emissions from
the relevant industrial process sources. Examples of these procedures include checks to ensure that activity data and
emission estimates are consistent with historical trends; that, where possible, consistent and reputable data sources
are used across sources; that interpolation or extrapolation techniques are consistent across sources; and that
common datasets and factors are used where applicable.
The general method employed to estimate emissions for industrial processes, as recommended by the IPCC,
involves multiplying production data (or activity data) for each process by an emission factor per unit of production.
The uncertainty in the emission estimates is therefore generally a function of a combination of the uncertainties
surrounding the production and emission factor variables. Uncertainty of activity data and the associated probability
density functions for industrial processes CO2 sources were estimated based on expert assessment of available
qualitative and quantitative information. Uncertainty estimates and probability density functions for the emission
factors used to calculate emissions from this source were devised based on IPCC recommendations.
Activity data is obtained through a survey of manufacturers conducted by various organizations (specified within
each source); the uncertainty of the activity data is a function of the reliability of plant-level production data and is
influenced by the completeness of the survey response. The emission factors used were either 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 2009 inventory estimates from
industrial processes continues a multi-year process for developing credible quantitative uncertainty estimates for
these source categories using the IPCC Tier 2 approach. As the process continues, the type and the characteristics of
the actual probability density functions underlying the input variables are identified and better characterized
(resulting in development of more reliable inputs for the model, including accurate characterization of correlation
between variables), based primarily on expert judgment. Accordingly, the quantitative uncertainty estimates
reported in this section should be considered illustrative and as iterations of ongoing efforts to produce accurate
uncertainty estimates. The correlation among data used for estimating emissions for different sources can influence
the uncertainty analysis of each individual source. While the uncertainty analysis recognizes very significant
connections among sources, a more comprehensive approach that accounts for all linkages will be identified as the
uncertainty analysis moves forward.
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
4-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
the energy consumed in making the cement and the chemical process itself.102 Cement is produced in 36 states and
Puerto Rico. CO2 emitted from the chemical process of cement production is the second largest source of industrial
CO2 emissions in the United States.
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 CO2 in a process known as calcination or
calcining. A very small amount of carbonates other than CaCO3 and non-carbonates are also present in the raw
material; however, for calculation purposes all of the raw material is assumed to be CaCO3. Next, the lime is
combined with silica-containing materials to produce clinker (an intermediate product), with the earlier by-product
CO2 being released to the atmosphere. The clinker is then allowed to cool, mixed with a small amount of gypsum
and potentially other materials (e.g., slag), and used to make portland cement.103
In 2009, U.S. clinker production—including Puerto Rico—totaled 56,116 thousand metric tons (USGS 2011). The
resulting CO2 emissions were estimated to be 29.0 Tg CO2 Eq. (29,018 Gg) (see Table 4-3).
Table 4-3: CO2 Emissions from Cement Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 33.3 33,278
2005
2006
2007
2008
2009
45.2
45.8
44.5
40.5
29.0
45,197
45,792
44,538
40,531
29,018
Greenhouse gas emissions from cement production grew every year from 1991 through 2006, but have decreased
since. Emissions since 1990 have decreased by 13 percent. Emissions decreased significantly between 2008 and
2009, due to the economic recession and associated decrease in demand for construction materials. Cement
continues to be a critical component of the construction industry; therefore, the availability of public construction
funding, as well as overall economic conditions, have considerable influence on cement production.
Methodology
CO2 emissions from cement production are created by the chemical reaction of carbon-containing minerals (i.e.,
calcining limestone) in the cement kiln. While in the kiln, limestone is broken down into CO2 and lime with the
CO2 released to the atmosphere. The quantity of CO2 emitted during cement production is directly proportional to
the lime content of the clinker. During calcination, each mole of CaCO3 (i.e., limestone) heated in the clinker kiln
forms one mole of lime (CaO) and one mole of CO2:
CaCO3 + heat -» CaO + CO2
CO2 emissions were estimated by applying an emission factor, in tons of CO2 released per ton of clinker produced,
to the total amount of clinker produced. The emission factor used in this analysis is the product of the average lime
fraction for clinker of 65 percent and a constant reflecting the mass of CO2 released per unit of lime (van Oss 2008).
This calculation yields an emission factor of 0.51 tons of CO2 per ton of clinker produced, which was determined as
follows:
102 The CO2 emissions related to the consumption of energy for cement manufacture are accounted for under CO2 from Fossil
Fuel Combustion in the Energy chapter.
103 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). CO2 emissions that result from the production of
lime used to create masonry cement are included in the Lime Manufacture source category.
Industrial Processes 4-5
-------
EF = 0.6460 CaO x
Clinker
44.01 g/moleCO
56.08 g/moleCaO
= 0.5070 tons CO /tonclinker
During clinker production, some of the clinker precursor materials remain in the kiln as non-calcinated, partially
calcinated, or fully calcinated cement kiln dust (CKD). The emissions attributable to the calcinated portion of the
CKD are not accounted for by the clinker emission factor. The IPCC recommends that these additional CKD CO2
emissions should be estimated as two percent of the CO2 emissions calculated from clinker production.104 Total
cement production emissions were calculated by adding the emissions from clinker production to the emissions
assigned to CKD (IPCC 2006).105
The 1990 through 2009 activity data for clinker production (see Table 4-4) were obtained from USGS (US Bureau
of Mines 1990 through 1993, USGS 1995 through 2011). The data were compiled by USGS through questionnaires
sent to domestic clinker and cement manufacturing plants.
Table 4-4: Clinker Production (Gg)
Year Clinker
1990
64,355
2005
2006
2007
2008
2009
87,405
88,555
86,130
78,382
56,116
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 CaCO3, 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 2008). 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.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-5. 2009 CO2 emissions from
cement production were estimated to be between 25.3 and 33.0 Tg CO2 Eq. at the 95 percent confidence level. This
confidence level indicates a range of approximately 13 percent below and 14 percent above the emission estimate of
29.0 Tg CO2 Eq.
Table 4-5: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement Production (Tg CO2 Eq. and
Percent)
Source
2009 Emission
Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper
Lower Upper
104 Default IPCC clinker and CKD emission factors were verified through expert consultation with the Portland Cement
Association (PCA 2008) and van Oss (2008).
105 The two percent CO2 addition associated with CKD is included in the emission estimate for completeness. The cement
emission estimate also includes an assumption that all raw material is limestone (CaCO3) when in fact a small percentage is likely
composed of non-carbonate materials. Together these assumptions may result in a small emission overestimate (van Oss 2008).
4-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Bound Bound Bound Bound
Cement Production CO2 29.0 25.3 33.0 -13% +14%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Recalculations Discussion
Activity data for the time series was revised for the current Inventory. Specifically, clinker production data for 1995
through 2008 (excluding 2001) were revised to reflect published USGS data. In a given Inventory year, advance
clinker data is typically used. This data is typically finalized several years later by USGS. The published time
series was reviewed to ensure time series consistency. Published data generally differed from advance data by
approximately 1,000 metric tons, or 1 percent of the total. Details on the emission trends through time are described
in more detail in the Methodology section, above.
Planned Improvements
Future improvements to the cement source category involve evaluating facility level greenhouse gas emissions data
as a basis for improving emissions calculations from cement production. Beginning in 2010, all U.S. cement
production facilities are required to monitor, calculate and report their greenhouse gas emissions to EPA through its
Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions from facilities
based on use of higher tier methods and in particular assess how this data could be used to improve the overall
method for calculating emissions from the U.S. cement industry, including also improving emission factors for
clinker production and CKD.
4.2. Lime Production (IPCC Source Category 2A2)
Lime is an important manufactured product with many industrial, chemical, and environmental applications. Its
major uses are in steel making, flue gas desulfurization systems at coal-fired electric power plants, construction, and
water purification. 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]).
Lime production involves three main processes: stone preparation, calcination, and hydration. CO2 is generated
during the calcination stage, when limestone—mostly calcium carbonate (CaCO3)—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. 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.106 In certain additional applications, lime reabsorbs CO2
during use.
Lime production in the United States—including Puerto Rico—was reported to be 15,781 thousand metric tons in
2009 (USGS 2010). This production resulted in estimated CO2 emissions of 11.2 Tg CO2 Eq. (11,223 Gg) (see
Table 4-6 and Table 4-7).
Table 4-6: CO2 Emissions from Lime Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 11.5 11,533
2005
2006
2007
2008
2009
14.4
15.1
14.6
14.3
11.2
14,379
15,100
14,595
14,330
11,223
106 PQQ js obtained from the reaction of CO2 with calcium hydroxide. It is used as a filler and/or coating in the paper, food, and
plastic industries.
Industrial Processes 4-7
-------
Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (Gg)
Year Potential Recovered* Net Emissions
1990 12,004 471 11,533
2005
2006
2007
2008
2009
15,131
15,825
15,264
14,977
11,913
752
725
669
647
690
14,379
15,100
14,595
14,330
11,223
For sugar refining and PCC production.
Note: Totals may not sum due to rounding
Lime production in 2009 decreased by 21 percent compared to 2008, owing mostly to a significant downturn in
major markets such as construction and steel. Because of this significant downturn, overall lime production in 2009
was approximately equal to production in 1990. The contemporary lime market is approximately distributed across
five end-use categories as follows: environmental uses, 34 percent; metallurgical uses, 31 percent; chemical and
industrial uses, 25 percent; construction uses, 9 percent; and refractory dolomite, 1 percent. In the construction
sector, lime is used to improve durability in plaster, stucco, and mortars, as well as to stabilize soils. Consumption
for metallurgical uses accounted for 57 percent of the overall decrease in lime consumption (USGS 2010).
Methodology
During the calcination stage of lime production, CO2 is given off as a gas and normally exits the system with the
stack gas. To calculate emissions, the amounts of high-calcium and dolomitic lime produced were multiplied by
their respective emission factors. The emission factor is the product of a constant reflecting the mass of CO2
released per unit of lime and the average calcium plus magnesium oxide (CaO + MgO) content for lime (95 percent
for both types of lime) (IPCC 2006). The emission factors were calculated as follows:
For high-calcium lime:
[(44.01 g/mole CO2) - (56.08 g/mole CaO)] x (0.9500 CaO/lime) = 0.7455 g CO2/g lime
For dolomitic lime:
[(88.02 g/mole CO2) - (96.39 g/mole CaO)] x (0.9500 CaO/lime) = 0.8675 g CO2/g lime
Production was adjusted to remove the mass of chemically combined water found in hydrated lime, determined
according to the molecular weight ratios of H2O to (Ca(OH)2 and [Ca(OH)2«Mg(OH)2]) (IPCC 2000). These factors
set the chemically combined water content to 24.3 percent for high-calcium hydrated lime, and 27.2 percent for
dolomitic hydrated lime.
Lime emission estimates were multiplied by a factor of 1.02 to account for lime kiln dust (LKD), which is produced
as a by-product during the production of lime (IPCC 2006).
Lime emission estimates were further adjusted to account for PCC producers and sugar refineries that recover CO2
emitted by lime production facilities for use as an input into production or refining processes. For CO2 recovery by
sugar refineries, lime consumption estimates from USGS were multiplied by a CO2 recovery factor to determine the
total amount of CO2 recovered from lime production facilities. According to industry surveys, sugar refineries use
captured CO2 for 100 percent of their CO2 input (Lutter 2009). CO2 recovery by PCC producers was determined by
multiplying estimates for the percentage CO2 of production weight for PCC production at lime plants by a CO2
recovery factor based on the amount of purchased CO2 by PCC manufacturers (Prillaman 2008 through 2010). As
data were only available starting in 2007, CO2 recovery for the period 1990 through 2006 was extrapolated by
determining a ratio of PCC production at lime facilities to lime consumption for PCC (USGS 1992 through 2008).
Lime production data (high-calcium- and dolomitic-quicklime, high-calcium- and dolomitic-hydrated, and dead-
burned dolomite) for 1990 through 2009 (see Table 4-8) were obtained from USGS (1992 through 2010). Natural
4-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
hydraulic lime, which is produced from CaO and hydraulic calcium silicates, is not produced in the United States
(USGS 2009). Total lime production was adjusted to account for the water content of hydrated lime by converting
hydrate to oxide equivalent based on recommendations from the IPCC, and is presented in Table 4-9 (IPCC 2000).
The CaO and CaOMgO contents of lime were obtained from the IPCC (IPCC 2006). Since data for the individual
lime types (high calcium and dolomitic) was not provided prior to 1997, total lime production for 1990 through 1996
was calculated according to the three year distribution from 1997 to 1999.
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated, and Dead-Burned-
Dolomite Lime Production (Gg)
Year
High-Calcium
Quicklime
Dolomitic
Quicklime
High-Calcium
Hydrated
Dolomitic
Hydrated
Dead-Burned
Dolomite
1990
11,166
2,234
1,781
342
2005
2006
2007
2008
2009
14,100
15,000
14,700
14,900
11,800
2,990
2,950
2,700
2,310
1,830
2,220
2,370
2,240
2,070
1,690
474
409
352
358
261
200
200
200
200
200
Table 4-9: Adjusted Lime Production3 (Gg)
Year High-Calcium Dolomitic
1990
2005
2006
2007
2008
2009
12,514
15,781
16,794
16,396
16,467
13,079
2,809
3,535
3,448
3,156
2,771
2,220
1 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
these products and recovery rates for sugar refineries and PCC manufacturers located at lime plants. Although the
methodology accounts for various formulations of lime, it does not account for the trace impurities found in lime,
such as iron oxide, alumina, and silica. Due to differences in the limestone used as a raw material, a rigid
specification of lime material is impossible. As a result, few plants produce lime with exactly the same properties.
In addition, a portion of the CO2 emitted during lime production will actually be reabsorbed when the lime is
consumed. As noted above, lime has many different chemical, industrial, environmental, and construction
applications. In many processes, CO2 reacts with the lime to create calcium carbonate (e.g., water softening). CO2
reabsorption rates vary, however, depending on the application. For example, 100 percent of the lime used to
produce precipitated calcium carbonate reacts with CO2; whereas most of the lime used in steel making reacts with
impurities such as silica, sulfur, and aluminum compounds. A detailed accounting of lime use in the United States
and further research into the associated processes are required to quantify the amount of CO2 that is reabsorbed.107
In some cases, lime is generated from calcium carbonate by-products at pulp mills and water treatment plants.108
107 Representatives of the National Lime Association estimate that CO2 reabsorption that occurs from the use of lime may offset
as much as a quarter of the CO2 emissions from calcination (Males 2003).
108 Some carbide producers may also regenerate lime from their calcium hydroxide by-products, which does not result in
Industrial Processes 4-9
-------
The lime generated by these processes is not included in the USGS data for commercial lime consumption. In the
pulping industry, mostly using the Kraft (sulfate) pulping process, lime is consumed in order to causticize a process
liquor (green liquor) composed of sodium carbonate and sodium sulfide. The green liquor results from the dilution
of the smelt created by combustion of the black liquor where biogenic C is present from the wood. Kraft mills
recover the calcium carbonate "mud" after the causticizing operation and calcine it back into lime—thereby
generating CO2—for reuse in the pulping process. Although this re-generation of lime could be considered a lime
manufacturing process, the CO2 emitted during this process is mostly biogenic in origin, and therefore is not
included in the industrial processes totals (Miner and Upton 2002). In accordance with IPCC methodological
guidelines, any such emissions are calculated by accounting for net carbon (C) fluxes from changes in biogenic C
reservoirs in wooded or crop lands (see Chapter 7).
In the case of water treatment plants, lime is used in the softening process. Some large water treatment plants may
recover their waste calcium carbonate and calcine it into quicklime for reuse in the softening process. Further
research is necessary to determine the degree to which lime recycling is practiced by water treatment plants in the
United States.
Uncertainties also remain surrounding recovery rates used for sugar refining and PCC production. The recovery rate
for sugar refineries is based on two sugar beet processing and refining facilities located in California that use 100
percent recovered CO2 from lime plants (Lutter 2010). This analysis assumes that all sugar refineries located on-site
at lime plants also use 100 percent recovered CO2. The recovery rate for PCC producers located on-site at lime
plants is based on the 2009 value for PCC manufactured at commercial lime plants, given by the National Lime
Association (Prillaman 2010).
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-10. Lime CO2 emissions were
estimated to be between 10.4 and 12.3 Tg CO2 Eq. at the 95 percent confidence level. This confidence level
indicates a range of approximately 7 percent below and 10 percent above the emission estimate of 11.2 Tg CO2 Eq.
Table 4-10: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime Production (Tg CO2 Eq. and
Percent)
Source
2009 Emission
Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Bound Upper Bound Lower Bound Upper Bound
Lime Production CO2 11.2 10.4 12.3 -7% +10%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
No methodological or activity data changes to the time series were made to this source for the current Inventory.
Details on the emission trends through time are described in more detail in the Methodology section, above.
Planned Improvements
Future improvements to the lime source category involve evaluating facility level greenhouse gas emissions data as
a basis for improving emissions calculations from lime production. Beginning in 2010, all U.S. lime production
facilities are required to monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse
Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions from facilities based on use
of higher tier methods and in particular assess how this data could be used to improve the overall method for
calculating emissions from the U.S. lime industry, including improving emission factors for various lime types and
LKD.
Future improvements to the lime source category will also involve continued research into CO2 recovery associated
with lime use during sugar refining and precipitate calcium carbonate (PCC) production. Currently, two sugar
refining facilities in California have been identified to capture CO2 produced in lime kilns located on the same site
as the sugar refinery (Lutter 2010). Data on CO2 production by these lime facilities is unavailable. Future work will
emissions of CO2. In making calcium carbide, quicklime is mixed with coke and heated in electric furnaces. The regeneration of
lime in this process is done using a waste calcium hydroxide (hydrated lime) [CaC2 + 2H2O —> C2H2 + Ca(OH) 2], not calcium
carbonate [CaCO3]. Thus, the calcium hydroxide is heated in the kiln to simply expel the water [Ca(OH)2 + heat —> CaO + H2O]
and no CO2 is released.
4-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
include research to determine the number of sugar refineries that employ the carbonation technique, the percentage
of these that use captured CO2 from lime production facilities, and the amount of CO2 recovered per unit of lime
production. Future research will also aim to improve estimates of CO2 recovered as part of the PCC production
process using estimates of PCC production and CO2 inputs rather than lime consumption by PCC facilities.
4.3. Limestone and Dolomite Use (IPCC Source Category 2A3)
Limestone (CaCO3) and dolomite (CaCO3MgCO3)109 are basic raw materials used by a wide variety of industries,
including construction, agriculture, chemical, metallurgy, glass production, and environmental pollution control.
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. For some of these applications, limestone is heated sufficiently enough to calcine the
material and generate CO2 as a by-product. 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, or as a raw material in glass manufacturing and magnesium production.
In 2009, approximately 14,928 thousand metric tons of limestone and 3,020 thousand metric tons of dolomite were
consumed for these emissive applications. Overall, usage of limestone and dolomite resulted in aggregate CO2
emissions of 7.6 Tg CO2 Eq. (7,649 Gg) (see Table 4-1 land Table 4-12). Overall, emissions have increased 49
percent from 1990 through 2009.
Table 4-11: CO2 Emissions from Limestone & Dolomite Use (Tg CO2 Eq.)
Year Flux Stone Glass Making FGD
Magnesium
Production
Total
1990
2.6
2005
2006
2007
2008
2009
2.7
4.5
2.0
1.0
1.8
0.4
0.7
0.3
0.4
0.1
3.0
2.1
3.2
3.8
5.4
0.0
0.0
0.0
0.0
0.0
0.7
0.7
2.2
1.1
0.4
6.8
8.0
7.7
6.3
7.6
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.
Table 4-12: CO2 Emissions from Limestone & Dolomite Use (Gg)
Year Flux Stone Glass Making FGD
Magnesium
Production
Other Miscellaneous
Uses
1990
2,593
217
1,433
819
Total
5,127
5,056
2005
2006
2007
2008
2009
2,650
4,492
1,959
974
1,785
425
747
333
387
61
2,975
2,061
3,179
3,801
5,406
0
0
0
0
0
718
735
2,231
1,114
396
6,768
8,035
7,702
6,276
7,649
Methodology
CO2 emissions were calculated by multiplying the quantity of limestone or dolomite consumed by the average C
109 Limestone and dolomite are collectively referred to as limestone by the industry, and intermediate varieties are seldom
distinguished.
Industrial Processes 4-11
-------
content, 12.0 percent for limestone and 13.0 percent for dolomite (based on stoichiometry), and converting this
value to CO2. This methodology was used for flux stone, glass manufacturing, flue gas desulfurization systems,
chemical stone, mine dusting or acid water treatment, acid neutralization, and sugar refining and then converting to
CO2 using a molecular weight ratio. Flux stone used during the production of iron and steel was deducted from the
Limestone and Dolomite Use estimate and attributed to the Iron and Steel Production estimate.
Traditionally, the production of magnesium metal was the only other significant use of limestone and dolomite that
produced CO2 emissions. At the start of 2001, there were two magnesium production plants operating in the United
States and they used different production methods. One plant produced magnesium metal using a dolomitic process
that resulted in the release of CO2 emissions, while the other plant produced magnesium from magnesium chloride
using a CO2-emissions-free process called electrolytic reduction. However, the plant utilizing the dolomitic process
ceased its operations prior to the end of 2001, so beginning in 2002 there were no emissions from this particular sub-
use.
Consumption data for 1990 through 2008 of limestone and dolomite used for flux stone, glass manufacturing, flue
gas desulfurization systems, chemical stone, mine dusting or acid water treatment, acid neutralization, and sugar
refining (see Table 4-13) were obtained from the USGS Minerals Yearbook: Crushed Stone Annual Report (1995
through 2010a) and the U.S. Bureau of Mines (1991 & 1993a). Consumption data for 2009 were obtained from
personal communication with the USGS crushed stone commodity specialist (Willett 2010). The production
capacity data for 1990 through 2009of dolomitic magnesium metal also came from the USGS (1995 through 2010b)
and the U.S. Bureau of Mines (1990 through 1993b). The last plant in the United States that used the dolomitic
production process for magnesium metal closed in 2001. The USGS does not mention this process in the Minerals
Yearbook: Magnesium; therefore, it is assumed that this process continues to be non-existent in the United States
(USGS 2010b). 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 uses fraction of total
consumption in that year. *10
Table 4-13: Limestone and Dolomite Consumption (Thousand Metric Tons)
Activity
Flux Stone
Limestone
Dolomite
Glass Making
Limestone
Dolomite
FGD
Other Miscellaneous Uses
Total
1990
6,737
5,804
933
489
430
59
3,258
1,835
12,319
2000
6,283
4,151
2,132
843
843
0
4,061
1,640
12,826
2005
7,022
3,165
3,857
962
920
43
6,761
1,632
16,377
2006
11,030
5,208
5,822
1,693
1,629
64
4,683
1,671
19,078
2007
5,305
3,477
1,827
757
757
0
7,225
5,057
18,344
2008
3,253
1,970
1,283
879
879
0
8,639
2,531
15,302
2009
4,623
1,631
2,992
139
139
0
12,288
898
17,948
Notes: "Other miscellaneous uses" includes chemical stone, mine dusting or acid water treatment, acid neutralization, and sugar
refining. Zero values for limestone and dolomite consumption for glass making result during years when the USGS reports that
no limestone or dolomite are consumed for this use.
110This approach was recommended by USGS.
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Uncertainty and Time Series Consistency
The uncertainty levels presented in this section arise 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. Similarly, the quality of the limestone 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 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. The uncertainty of the estimates for limestone 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.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-14. Limestone and Dolomite
Use CO2 emissions were estimated to be between 6.6 and 9.1 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 13 percent below and 19 percent above the emission estimate of 7.6 Tg CO2 Eq.
Table 4-14: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Limestone and Dolomite Use (Tg
CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (TgC02Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Limestone and Dolomite Use CO2 7.6 6.6 9.1 -13% +19%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the limestone and dolomite source category involve research into the availability of
limestone and dolomite end-use data, including from EPA's new Greenhouse Gas Reporting Program. If sufficient
data are available, limestone and dolomite used as process materials in source categories included in future
inventories (e.g., glass production, other process use of carbonates) may be removed from this section and will be
reported under the appropriate source categories. Additionally, future improvements include revisiting the
methodology to distribute withheld data across emissive end-uses for all years to improve consistency of
calculations.
4.4. Soda Ash Production and Consumption (IPCC Source Category 2A4)
Soda ash (sodium carbonate, Na2CO3) 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. It is used primarily as an alkali,
either in glass manufacturing or simply as a material that reacts with and neutralizes acids or acidic substances.
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.
Only two states produce natural soda ash: Wyoming and California. Of these two states, only net emissions of CO2
Industrial Processes 4-13
-------
from Wyoming were calculated due to specifics regarding the production processes employed in the state.1ll
During the production process used in Wyoming, trona ore is calcined to produce crude soda ash. CO2 is generated
as a by-product of this reaction, and is eventually emitted into the atmosphere. In addition, CO2 may also be
released when soda ash is consumed.
In 2009, CO2 emissions from the production of soda ash from trona were approximately 1.7 Tg CO2 Eq. (1,733 Gg).
Soda ash consumption in the United States generated 2.5 Tg CO2 Eq. (2,532 Gg) in 2009. Total emissions from
soda ash production and consumption in 2009 were 4.3 Tg CO2 Eq. (4,265 Gg) (see Table 4-15 and Table 4-16).
Emissions have remained relatively constant with some fluctuations since 1990. These fluctuations were strongly
related to the behavior of the export market and the U.S. economy. Emissions from the production of soda ash from
trona in 2009 are currently proxied to emissions in 2008, due to lack of available data at time of publication.
Emissions in 2009 increased by approximately 4 percent from emissions in 2008, and have also increased overall by
3 percent since 1990.
Table 4-15: CO2 Emissions from Soda Ash Production and Consumption (Tg CO2 Eq.)
Year Production Consumption Total
1990
1.4
2.7
2005
2006
2007
2008
2009
1.7
1.6
1.7
1.7
1.7
2.6
2.5
2.5
2.4
2.5
4.2
4.2
4.1
4.1
4.3
Note: Totals may not sum due to independent rounding.
Table 4-16: CO2 Emissions from Soda Ash Production and Consumption (Gg)
Year Production Consumption Total
1990
1,431
2,710
4,141
2005
2006
2007
2008
2009
,655
,626
,675
,733
,733
2,573
2,536
2,465
2,378
2,532
4,228
4,162
4,140
4,111
4,265
Note: Totals may not sum due to independent rounding.
The United States represents about one-fourth of total world soda ash output. Based on final 2007 reported data, the
estimated distribution of soda ash by end-use in 2008 was glass making, 49 percent; chemical production, 30
percent; soap and detergent manufacturing, 8 percent; distributors, 5 percent; flue gas desulfurization, 2 percent;
water treatment, 2 percent; pulp and paper production, 2 percent; and miscellaneous, 3 percent (USGS 2009). The
same distribution by end-use is currently assumed for 2009, due to lack of available data at time of publication.
111 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 CO2 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 CO2 is generated as a by-product, the CO2 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, NaHCO3 was produced using soda ash feedstocks mined in
Wyoming and shipped to Colorado. Because the trona is 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.
4-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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. While Chinese soda ash production appears
to be stabilizing, U.S. competition in Asian markets is expected to continue. Despite this competition, U.S. soda ash
production is expected to increase by about 0.5 percent annually (USGS 2008).
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. CO2 and water are generated as by-products of the calcination process. CO2
emissions from the calcination of trona can be estimated based on the following chemical reaction:
2(Na3(CO3)(HCO3)'2H2O) -> 3Na2CO3 + 5H2O + CO2
[trona] [soda ash]
Based on this formula, approximately 10.27 metric tons of trona are required to generate one metric ton of CO2, or
an emission factor of 0.097 metric tons CO2 per metric ton trona (IPCC 2006). Thus, the 17.8 million metric tons of
trona mined in 2008 for soda ash production (USGS 2008) resulted in CO2 emissions of approximately 1.7 Tg CO2
Eq. (1,733 Gg). The same production and associated emissions estimates are assumed for 2009 due to lack of
available data at time of publication.
Once produced, most soda ash is consumed in glass and chemical production, with minor amounts in soap and
detergents, pulp and paper, flue gas desulfurization and water treatment. As soda ash is consumed for these
purposes, additional CO2 is usually emitted. In these applications, it is assumed that one mole of C is released for
every mole of soda ash used. Thus, approximately 0.113 metric tons of C (or 0.415 metric tons of CO2) are released
for every metric ton of soda ash consumed.
The activity data for trona production and soda ash consumption (see Table 4-17) were taken from USGS (1994
through 2008). Data for soda ash consumption in 2009 was taken from USGS (2010) Mineral Commodity Summary:
Soda Ash. Due to lack of 2009 trona production data at time of publication, the 2008 estimate is used as a proxy for
2009. Soda ash production and consumption data were collected by the USGS from voluntary surveys of the U.S.
soda ash industry.
Table 4-17: Soda Ash Production and Consumption (Gg)
Year Production' Consumption
1990 14,700 6,530
2005 17,000 6,200
2006 16,700 6,110
2007 17,200 5,940
2008 17,800 5,730
2009 17,800 6,100
Soda ash produced from trona ore only.
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. 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 information characterizing the emissions from each end-use is limited.
Therefore, there is uncertainty surrounding the emission factors from the consumption of soda ash.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-18. Soda Ash Production and
Consumption CO2 emissions were estimated to be between 4.0 and 4.6 Tg CO2 Eq. at the 95 percent confidence
level. This indicates a range of approximately 7 percent below and 7 percent above the emission estimate of 4.3 Tg
CO2 Eq.
Industrial Processes 4-15
-------
Table 4-18: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash Production and
Consumption (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Soda Ash Production
and Consumption CO2 4.3 4.0 4.6 -7% +7%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future inventories are anticipated to estimate emissions from glass production and other use of carbonates. These
inventories will extract soda ash consumed for glass production and other use of carbonates from the current soda
ash consumption emission estimates and include them under those sources.
In addition, future improvements to the soda ash production category involve evaluating facility level greenhouse
gas emissions data as a basis for improving emissions calculations from soda ash production. Beginning in 2010, all
U.S. soda ash production facilities are required to monitor, calculate and report their greenhouse gas emissions to
EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions
from facilities based on use of higher tier methods and in particular assess how this data could be used to improve
the overall method for calculating emissions from the U.S. soda ash production industry, including also improving
emission factors associated with trona consumption.
4.5. Ammonia Production (IPCC Source Category 2B1) and Urea Consumption
Emissions of CO2 occur during the production of synthetic ammonia, primarily through the use of natural gas as a
feedstock. The natural gas-based, naphtha-based, and petroleum coke-based processes produce CO2 and hydrogen
(H2), the latter of which is used in the production of ammonia. One N production plant located in Kansas is
producing ammonia from petroleum coke feedstock. In some plants the CO2 produced is captured and used to
produce urea. The brine electrolysis process for production of ammonia does not lead to process-based CO2
emissions.
There are five principal process steps in synthetic ammonia production from natural gas feedstock. The primary
reforming step converts CH4 to CO2, carbon monoxide (CO), and H2 in the presence of a catalyst. Only 30 to 40
percent of the CH4 feedstock to the primary reformer is converted to CO and CO2. 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. CO2 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.
The conversion process for conventional steam reforming of CH4, including primary and secondary reforming and
the shift conversion processes, is approximately as follows:
(catalyst)
0.88 CH4 + 1.26 Air + 1.24 H2O > 0.88 CO2 + N2 + 3 H2
N2 + 3 H2 HX 2 NH3
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.
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Not all of the CO2 produced in the production of ammonia is emitted directly to the atmosphere. Both ammonia and
CO2 are used as raw materials in the production of urea [CO(NH2)2], which is another type of nitrogenous fertilizer
that contains C as well as N. The chemical reaction that produces urea is:
2 NH3 + CO2 -> NH2COONH4 -» CO(NH2)2 + H2O
Urea is consumed for a variety of uses, including as a nitrogenous fertilizer, in urea-formaldehyde resins, and as a
deicing agent (TIG 2002). The C in the consumed urea is assumed to be released into the environment as CO2
during use. Therefore, the CO2 produced by ammonia production that is subsequently used in the production of urea
is still emitted during urea consumption. The majority of CO2 emissions associated with urea consumption are those
that result from its use as a fertilizer. These emissions are accounted for in the Cropland Remaining Cropland
section of the Land Use, Land-Use Change, and Forestry chapter. CO2 emissions associated with other uses of urea
are accounted for in this chapter. Net emissions of CO2 from ammonia production in 2009 were 11.8 Tg CO2 Eq.
(11,797 Gg), and are summarized in Table 4-19 and Table 4-20. Emissions of CO2 from urea consumed for non-
fertilizer purposes in 2009 totaled 3.9 Tg CO2 Eq. (3,942 Gg), and are summarized in Table 4-19 and Table 4-20.
The decrease in ammonia production in recent years is due to several factors, including market fluctuations and high
natural gas prices. Ammonia production relies on natural gas as both a feedstock and a fuel, and as such, domestic
producers are competing with imports from countries with lower gas prices. If natural gas prices remain high, it is
likely that domestically produced ammonia will continue to decrease with increasing ammonia imports (EEA 2004).
Table 4-19: CO2 Emissions from Ammonia Production and Urea Consumption (Tg CO2 Eq.)
Source
Ammonia Production
Urea Consumption3
Total
1990 •
13.0
3.8 •
16.8
2000
12.2
4.2
16.4
2005
9.2
1 3.7
12.8
2006
8.8
3.5
12.3
2007
9.1
5.0
14.0
2008
7.9
4.1
11.9
2009
7.9
3.9
11.8
Note: Totals may not sum due to independent rounding.
a Urea Consumption is for non-fertilizer purposes only. Urea consumed as a fertilizer is accounted for in the Land Use, Land-Use
Change, and Forestry chapter.
Table 4-20: CO2 Emissions from Ammonia Production and Urea Consumption (Gg)
Source 1990 2000 2005 2006 2007 2008 2009
Ammonia
Production
Urea Consumption3
Total
13,047
3,784
16,831
12,172
4,231
16,402
9,196
3,653
12,849
8,781
3,519
12,300
9,074
4,963
14,038
7,883
4,066
11,949
7,855
3,942
11,797
a Urea Consumption is for non-fertilizer purposes only. Urea consumed as a fertilizer is accounted for in the Land Use, Land-Use
Change, and Forestry chapter.
Methodology
The calculation methodology for non-combustion CO2 emissions from production of nitrogenous fertilizers from
natural gas feedstock is based on a CO2 emission factor published by the European Fertilizer Manufacturers
Association (EFMA). The selected EFMA factor is based on ammonia production technologies that are similar to
those employed in the United States. The CO2 emission factor (1.2 metric tons CO2/metric ton NH3) is applied to
the percent of total annual domestic ammonia production from natural gas feedstock. Emissions from fuels
consumed for energy purposes during the production of ammonia are accounted for in the Energy chapter.
Emissions of CO2 from ammonia production are then adjusted to account for the use of some of the CO2 produced
from ammonia production as a raw material in the production of urea. For each ton of urea produced, 8.8 of every
12 tons of CO2 are consumed and 6.8 of every 12 tons of ammonia are consumed (IPCC 2006, EFMA 2000). The
CO2 emissions reported for ammonia production are therefore reduced by a factor of 0.73 multiplied by total annual
domestic urea production. Total CO2 emissions resulting from nitrogenous fertilizer production do not change as a
result of this calculation, but some of the CO2 emissions are attributed to ammonia production and some of the CO2
emissions are attributed to urea consumption. Those CO2 emissions that result from the use of urea as a fertilizer are
accounted for in the Land Use, Land-Use Change, and Forestry chapter.
The total amount of urea consumed for non-agricultural purposes 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 and is reported in Table 4-21, from total U.S. production. Total urea production is estimated based on the
Industrial Processes 4-17
-------
amount of urea produced plus the sum of net urea imports and exports. CO2 emissions associated with urea that is
used for non-fertilizer purposes are estimated using a factor of 0.73 tons of CO2 per ton of urea consumed.
All 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. The CO2 emission factor for production of ammonia from petroleum coke
is based on plant specific data, wherein all C contained in the petroleum coke feedstock that is not used for urea
production is assumed to be emitted to the atmosphere as CO2 (Bark 2004). Ammonia and urea are assumed to be
manufactured in the same manufacturing complex, as both the raw materials needed for urea production are
produced by the ammonia production process. The CO2 emission factor (3.57 metric tons CO2/metric ton NH3) is
applied to the percent of total annual domestic ammonia production from petroleum coke feedstock.
The emission factor of 1.2 metric ton CO2/metric ton NH3 for production of ammonia from natural gas feedstock
was taken from the EFMA Best Available Techniques publication, Production of Ammonia (EFMA 1995). The
EFMA reported an emission factor range of 1.15 to 1.30 metric ton CO2/metric tonNH3, with 1.2 metric ton
CO2/metric ton NH3 as a typical value. Technologies (e.g., catalytic reforming process) associated with this factor
are found to closely resemble those employed in the U.S. 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 CO2/metric ton NH3 for production of ammonia from
petroleum coke feedstock was developed from plant-specific ammonia production data and petroleum coke
feedstock utilization data for the ammonia plant located in Kansas (Bark 2004). As noted earlier, emissions from
fuels consumed for energy purposes during the production of ammonia are accounted for in the Energy chapter.
Ammonia production data (see Table 4-21) was obtained from Coffeyville Resources (Coffeyville 2005, 2006,
2007a, 2007b, 2009, 2010) 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 was obtained from Coffeyville Resources
(Coffeyville 2005, 2006, 2007a, 2007b, 2009, 2010). Urea production data for 1990 through 2008 were obtained
from the Minerals Yearbook: Nitrogen (USGS 1994 through 2009). Urea production data for 2009 was obtained
from the U.S. Bureau of the Census (2010). Import data for urea were obtained from the U.S. Census Bureau
Current Industrial Reports Fertilizer Materials and Related Products annual and quarterly reports for 1997 through
2009 (U.S. Census Bureau 1998 through 2010), The Fertilizer Institute (TFI2002) for 1993 through 1996, and the
United States International Trade Commission Interactive Tariff and Trade DataWeb (U.S. ITC 2002) for 1990
through 1992 (see Table 4-21). Urea export data for 1990 through 2009 were taken from U.S. Fertilizer
Import/Exports fromUSDA Economic Research Service Data Sets (U.S. Department of Agriculture 2010).
Table 4-21: Ammonia Production, Urea Production, Urea Net Imports, and Urea Exports (Gg)
Year Ammonia Production Urea Production Urea Applied Urea Imports Urea Exports
as Fertilizer
1990
2000
2005
2006
2007
2008
2009
15,425
14,342
10,143
9,962
10,393
9,570
9,372
7,450
6,910
5,270
5,410
5,590
5,240
5,084
3,296
4,382
4,779
4,985
5,097
4,925
4,295
1,860
3,904
5,026
5,029
6,546
5,459
5,505
854
663
536
656
271
230
289
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 natural gas
feedstock consumption data for the U.S. ammonia industry as a whole, 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. It is also assumed that ammonia and urea are produced at collocated plants from the
4-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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same natural gas raw material.
Such recovery may or may not affect the overall estimate of CO2 emissions depending upon the end use to which the
recovered CO2 is applied. Further research is required to determine whether byproduct CO2 is being recovered from
other ammonia production plants for application to end uses that are not accounted for elsewhere.
Additional uncertainty is associated with the estimate of urea consumed for non-fertilizer purposes. Emissions
associated with this consumption are reported in this source category, while those associated with consumption as
fertilizer are reported in Cropland Remaining Cropland section of the Land Use, Land-Use Change, and Forestry
chapter. The amount of urea used for non-fertilizer purposes is estimated based on estimates of urea production, net
urea imports, and the amount of urea used as fertilizer. There is uncertainty associated with the accuracy of these
estimates as well as the fact that each estimate is obtained from a different data source.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-22. Ammonia Production and
Urea Consumption CO2 emissions were estimated to be between 10.9 and 12.7 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 7 percent below and 8 percent above the emission
estimate of 11.8 Tg CO2 Eq.
Table 4-22: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ammonia Production and Urea
Consumption (Tg CO2 Eq. and Percent)
Source
2009 Emission
Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Ammonia Production
and Urea Consumption CO2 11.8 10.9 12.7 -7% +8%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations Discussion
The uncertainty range (-7 percent/+8 percent) has decreased by 7 percent compared to the uncertainty range in the
previous Inventory (±11 percent), due to two stoichiometric variables being removed from the uncertainty analysis.
Planned Improvements
Future improvements to the ammonia production and urea consumption category involve evaluating facility level
greenhouse gas emissions data as a basis for improving emissions calculations from ammonia production.
Beginning in 2010, all U.S. ammonia production facilities are required to monitor, calculate and report their
greenhouse gas emissions to EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will
obtain data for 2010 emissions from facilities based on use of higher tier methods and in particular assess how this
data could be used to improve the overall method for calculating emissions from U.S. ammonia production.
Specifically, the planned improvements include assessing data to update the emission factors to include both fuel
and feedstock CO2 emissions and incorporate CO2 capture and storage. Methodologies will also be updated if
additional ammonia-production plants are found to use hydrocarbons other than natural gas for ammonia production.
Additional efforts will be made to find consistent data sources for urea consumption and to report emissions from
this consumption appropriately as defined.
4.6. Nitric Acid Production (IPCC Source Category 2B2)
Nitric acid (HNO3) is an inorganic compound used primarily to make synthetic commercial fertilizers. It is also a
major component in the production of adipic acid—a feedstock for nylon—and explosives. Virtually all of the nitric
acid produced in the United States is manufactured by the catalytic oxidation of ammonia (EPA 1997). During this
reaction, N2O is formed as a by-product and is released from reactor vents into the atmosphere.
Industrial Processes 4-19
-------
Currently, the nitric acid industry controls for emissions of NO and NO2 (i.e., NOX). As such, the industry in the US
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. NSCRs were widely installed in nitric plants built between 1971 and 1977. Approximately 25
percent of nitric acid plants use NSCR and they represent 15.3 percent of estimated national production (EPA
2010a). The remaining 84.7 percent of production occurs using SCR or extended absorption, neither of which is
known to reduce N2O emissions.
N2O emissions from this source were estimated to be 14.6 Tg CO2 Eq. (47 Gg) in 2009 (see Table 4-23). Emissions
from nitric acid production have decreased by 18 percent since 1990, with the trend in the time series closely
tracking the changes in production. Emissions decreased 11.4 percent between 2008 and 2009. Emissions have
decreased by 30.8 percent since 1997, the highest year of production in the time series.
Table 4-23: N2O Emissions from Nitric Acid Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq.
1990
2000
2005
2006
2007
2008
2009
17.7
19.4
16.5
16.2
19.2
16.4
14.6
Gg
57
63
53
52
62
53
47
Methodology
N2O emissions were calculated by multiplying nitric acid production by the amount of N2O emitted per unit of nitric
acid produced. The emission factor was determined as a weighted average of two known emission factors: 2 kg
N2O/metric ton HNO3 produced at plants using non-selective catalytic reduction (NSCR) systems and 9 kg
N2O/metric ton HNO3 produced at plants not equipped with NSCR (IPCC 2006). In the process of destroying NOX,
NSCR systems destroy 80 to 90 percent of the N2O, which is accounted for in the emission factor of 2 kg
N2O/metric ton HNO3. Approximately 25 percent of HNO3 plants in the United States are equipped with NSCR
representing 15.3 percent of estimated national production (EPA 2010a). Hence, the emission factor is equal to (9 x
0.847) + (2 x 0.153) = 7.9 kg N2O per metric ton HNO3.
Nitric acid production data for 1990 through 2002 were obtained from the U.S. Census Bureau, Current Industrial
Reports (2006). Production data for 2003 were obtained from the U.S. Census Bureau, Current Industrial Reports
(2008). Production data for 2004 through 2009 were obtained from the U.S. Census Bureau, Current Industrial
Reports (2010) (see Table 4-24).
Table 4-24: Nitric Acid Production (Gg)
Year
1990
2000
2005
2006
2007
2008
2009
Gg
7,195
7,900
6,711
6,572
7,827
6,686
5,924
Uncertainty and Time-Series Consistency
The overall uncertainty associated with the 2009 N2O emissions estimate from nitric acid production was calculated
using the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) Tier 2 methodology.
4-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Uncertainty associated with the parameters used to estimate N2O emissions included that of production data, the
share of U.S. nitric acid production attributable to each emission abatement technology over the time series, and the
emission factors applied to each abatement technology type.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 4-25. N2O emissions from nitric
acid production were estimated to be between 8.8 and 20.7 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 40 percent below to 42 percent above the 2009 emissions estimate of 14.6 Tg
CO2 Eq.
Table 4-25: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Nitric Acid Production (Tg CO2 Eq.
and Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Nitric Acid Production N2O 14.6
8.8 20.7 -40% +42%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the nitric acid production category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emissions calculations from nitric acid production. Beginning in 2010, all
U.S. nitric acid production facilities are required to monitor, calculate and report their greenhouse gas emissions to
EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions
from facilities based on use of higher tier methods and in particular assess how this data could be used to improve
the overall method for calculating emissions from U.S. nitric acid production. Specifically, the planned
improvements include assessing data to update the N2O emission factors, abatement utilization and destruction
factors, and the current share of nitric acid production attributable to various abatement technologies.
Recalculations Discussion
Historical estimates for N2O emissions from nitric acid production have been revised relative to the previous
Inventory based on updated information from EPA (2010) on abatement technologies in use and based on revised
production data published by the U.S. Census Bureau (2010). The previous Inventory assumed that approximately
17 percent of facilities accounting for less than 8 percent of national production were equipped with NSCR systems
(EPA 2010b). The current Inventory assumes that approximately 25 percent of facilities, accounting for roughly 15
percent of national production, were equipped with NSCR systems (EPA 2010a). This change resulted in a decrease
in the weighted average emission factor of 0.6 kg N2O/metric ton HNO3 (6.3 percent). Additionally, national nitric
acid production values for 1991, 1993-1995, 1997-1999, 2002, and 2008 have been updated relative to the previous
Inventory (US Census Bureau 2009, 2010). Revised production in 2008 contributed to an overall decrease in
emissions of 2.6 Tg CO2 Eq. (13.6 percent) in that year; revised production in the other historical years had a
negligible impact on emissions. Overall, changes relative to the previous Inventory resulted in an average annual
decrease in emissions of 1.3 Tg CO2Eq. (6.7 percent) for the period 1990 through 2008.
4.7. Adipic Acid Production (IPCC Source Category 2B3)
Adipic acid production is an anthropogenic source of N2O emissions. Worldwide, few adipic acid plants exist. The
United States and Europe are the major producers. In 2009, the United States had two companies with a total of
three adipic acid processes, two of which were operational (CW 2007; Desai 2010; VA DEQ 2009). The United
States accounts for the largest share of global adipic acid production capacity (30 percent), followed by the
European Union (29 percent) and China (22 percent) (SEI2010). Adipic acid is a white crystalline solid used in the
manufacture of synthetic fibers, plastics, coatings, urethane foams, elastomers, and synthetic lubricants.
Commercially, it is the most important of the aliphatic dicarboxylic acids, which are used to manufacture polyesters.
Industrial Processes 4-21
-------
84 percent of all adipic acid produced in the United States is used in the production of nylon 6,6; nine percent is
used in the production of polyester polyols; four percent is used in the production of plasticizers; and the remaining
four 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).
Adipic acid is produced through a two-stage process during which N2O is generated in the second stage. 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 by-product of the nitric acid oxidation stage and is emitted in the waste gas stream (Thiemens and Trogler
1991). Process emissions from the production of adipic acid vary with the types of technologies and level of
emission controls employed by a facility. In 1990, two of the three major adipic acid-producing plants had N2O
abatement technologies in place and, as of 1998, the three major adipic acid production facilities had control systems
in place (Reimer et al. 1999). One small plant, which last operated in April 2006 and represented approximately two
percent of production, did not control for N2O (VA DEQ 2009; ICIS 2007; VA DEQ 2006).
N2O emissions from adipic acid production were estimated to be 1.9 Tg CO2 Eq. (6 Gg) in 2009 (see Table 4-26).
National adipic acid production has increased by approximately 11 percent over the period of 1990 through 2009, to
roughly 820,000 metric tons. Over the same period, emissions have been reduced by 88 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 (Desai 2010).
Table 4-26: N2O Emissions from Adipic Acid Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990
2005
2006
2007
2008
2009
5.0
4.3
3.7
2.0
1.9
16
14
12
7
6
Methodology
Due to confidential business information, plant names are not provided in this section. The four adipic acid-
producing plants will henceforth be referred to as Plants 1 through 4.
For 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 (Desai 2010). These estimates were
based on continuous emissions monitoring equipment installed at the two facilities. In 2009, no Adipic acid
production occurred at Plant 1. For Plants 3 and 4, N2O emissions were calculated by multiplying adipic acid
production by an emission factor (i.e., N2O emitted per unit of adipic acid produced) and adjusting for the
percentage of N2O released as a result of plant-specific emission controls. On the basis of experiments, the overall
reaction stoichiometry for N2O production in the preparation of adipic acid was estimated at approximately 0.3
metric tons of N2O per metric ton of product (IPCC 2006). Emissions are estimated using the following equation:
N2O emissions = (production of adipic acid [metric tons {MT} of adipic acid]) x (0.3 MT N2O / MT adipic acid) x
(1 - [N2O destruction factor x abatement system utility factor])
The "N2O destruction factor" represents the percentage of N2O emissions that are destroyed by the installed
abatement technology. The "abatement system utility factor" represents the percentage of time that the abatement
equipment operates during the annual production period. Overall, in the United States, two of the plants employ
catalytic destruction (Plants 1 and 2), one plant employs thermal destruction (Plant 3), and the smallest plant used no
N2O abatement equipment (Plant 4). For Plant 3, which uses thermal destruction and for which no reported plant-
specific emissions are available, the N2O abatement system destruction factor is assumed to be 98.5 percent, and the
abatement system utility factor is assumed to be 97 percent (IPCC 2006).
4-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
From 1990 to 2003, plant-specific production data were estimated for Plant 3 where direct emission measurements
were not available. In order to calculate plant-specific production for this plant, national adipic acid production was
allocated to the plant level using the ratio of known plant capacity to total national capacity for all U.S. plants. The
estimated plant production for this plant was then used for calculating emissions as described above. For 2004 and
2006, actual plant production data were obtained and used for emission calculations (CW 2007; CW 2005). For
2005, interpolated national production was used for calculating emissions. Updated production data were not
available for Plant 3 for 2007 through 2009; therefore, production values for 2007 through 2009 were proxied using
2006 data.
For Plant 4, which last operated in April 2006 (VA DEQ 2009), plant-specific production data were obtained across
the time series from 1990 through 2008 (VA DEQ 2010). Since the plant has not operated since 2006, production in
2009 is assumed to be equal to the 2008 estimate, which was zero. The plant-specific production data were then used
for calculating emissions as described above.
National adipic acid production data (see Table 4-27) from 1990 through 2009 were obtained from the American
Chemistry Council (ACC 2010).
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 2005, the plant capacities for three plants were kept the same as the year 2000
capacities. Plant capacity for 1999 to 2005 for the one remaining plant was kept the same as 1998. For 2004 to
2009, although some plant capacity data are available (CW 1999, CMR 2001, ICIS 2007), they are not used to
calculate plant-specific production for these years because plant-specific production data for 2004 and 2006 are also
available and are used in our calculations instead (CW 2005, CW 2007).
Table 4-27: Adipic Acid Production (Gg)
Year Gg
1990
2005 903
2006 964
2007 930
2008 869
2009 819
Uncertainty and Time-Series Consistency
The overall uncertainty associated with the 2009 N2O emission estimate from adipic acid production was calculated
using the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) Tier 2 methodology.
Uncertainty associated with the parameters used to estimate N2O emissions included that of company specific
production data, emission factors for abated and unabated emissions, and company-specific historical emission
estimates.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 4-28. N2O emissions from
adipic acid production were estimated to be between 1.2 and 2.8 Tg CO2 Eq. at the 95 percent confidence level.
This indicates a range of approximately 40 percent below to 42 percent above the 2009 emission estimate of 1.9 Tg
CO2 Eq.
Table 4-28: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from Adipic Acid Production (Tg CO2
Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (TgC02Eq.) (%)
Industrial Processes 4-23
-------
Lower Upper Lower Upper
Bound Bound Bound Bound
Adipic Acid Production N2O L9 L2 2.8 -40% +42%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Recalculations
The current Inventory uses national production data from the ACC (2010) across the full time series. Previous
Inventories relied upon a variety of sources and linear interpolation for missing intervening years in the national
production time series. This change resulted in an average annual decrease in the national production estimate of
approximately 2 percent for the period 1990 through 2008 relative to the previous Inventory. Emissions decreased
by less than 0.1 percent over the same time period relative to the previous Inventory.
Planned Improvements
Future improvements to the adipic acid production category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emissions calculations from adipic acid production. Beginning in 2010, all
U.S. adipic acid production facilities are required to monitor, calculate and report their greenhouse gas emissions to
EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions
from facilities based on use of higher tier methods and in particular assess how this data could be used to improve
the overall method for calculating emissions from U.S. adipic acid production. Specifically, the planned
improvements include assessing data to update the N2O emission factors and update abatement utility and
destruction factors based on actual performance of the latest catalytic and thermal abatement equipment at plants
with continuous process and emission monitoring equipment.
4.8. Silicon Carbide Production (IPCC Source Category 2B4) and Consumption
CO2 and CH4 are emitted from the production112 of silicon carbide (SiC), a material used as an industrial abrasive.
To make SiC, quartz (SiO2) is reacted with C in the form of petroleum coke. A portion (about 35 percent) of the C
contained in the petroleum coke is retained in the SiC. The remaining C is emitted as CO2, CH4, or CO.
CO2 is also emitted from the consumption of SiC for metallurgical and other non-abrasive applications. 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 2006).
CO2 from SiC production and consumption in 2009 were 0.1 Tg CO2 Eq. (145 Gg) (USGS 2009). Approximately
63 percent of these emissions resulted from SiC production while the remainder results from SiC consumption. CH4
emissions from SiC production in 2009 were 0.01 Tg CO2 Eq. CH4 (0.4 Gg) (see Table 4-29 and Table 4-30).
Table 4-29: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Tg CO2 Eq.)
Year
C02
CH4
Total
1990
0.4
+
0.4
2000
0.2
+
0.3
2005
0.2
+
0.2
2006
0.2
+
0.2
2007
0.2
+
0.2
2008
0.2
+
0.2
2009
0.1
+
0.2
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 4-30: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (Gg)
Year 1990 2000 2005 2006 2007 2008 2009
112 Silicon carbide is produced for both abrasive and metallurgical applications in the United States. Production for metallurgical
applications is not available and therefore both CELt and CO2 estimates are based solely upon production estimates of silicon
carbide for abrasive applications.
4-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
C02
CH4
375
1
248
1
219
+
207
+
196
+
175
+
145
+
+ Does not exceed 0.5 Gg.
Methodology
Emissions of CO2 and CH4 from the production of SiC were calculated by multiplying annual SiC production by the
emission factors (2.62 metric tons CO2/metric ton SiC for CO2 and 11.6 kg CH^metric ton SiC for CH4) provided
by the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
Emissions of CO2 from silicon carbide consumption were 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
2009). The total SiC consumed in metallurgical and other non-abrasive uses was multiplied by the C content of SiC
(31.5 percent), which was determined according to the molecular weight ratio of SiC.
Production data for 1990 through 2008 were obtained from the Minerals Yearbook: Manufactured Abrasives (USGS
1991a through 2005a, 2007, and 2009). Production data for 2009 was taken from the Minerals Commodity
Summary: Abrasives (Manufactured) (USGS 2010). Silicon carbide consumption by major end use was obtained
from the Minerals Yearbook: Silicon (USGS 1991b through 2005b) (see Table 4-31) for years 1990 through 2004
and from the USGS Minerals Commodity Specialist for 2005 and 2006 (Corathers 2006, 2007). Silicon carbide
consumption by major end use data for 2009 is proxied using 2008 data due to unavailability of data at time of
publication. Net imports for the entire time series were obtained from the U.S. Census Bureau (2005 through 2010).
Table 4-31: Production and Consumption of Silicon Carbide (Metric Tons)
Year Production Consumption
1990
105,000
45,000
172,465
2005
2006
2007
2008
2009
35,000
35,000
35,000
35,000
35,000
220,149
199,937
179,741
144,928
92,280
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 some uncertainty associated with production, net
imports, and consumption data as well as the percent of total consumption that is attributed to metallurgical and
other non-abrasive uses.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-32. Silicon carbide production
and consumption CO2 emissions were estimated to be between 9 percent below and 9 percent above the emission
estimate of 0.2 Tg CO2 Eq. at the 95 percent confidence level. Silicon carbide production CH4 emissions were
estimated to be between 9 percent below and 9 percent above the emission estimate of 0.01 Tg CO2 Eq. at the 95
percent confidence level.
Table 4-32: Tier 2 Quantitative Uncertainty Estimates for CH4 and CO2 Emissions from Silicon Carbide Production
and Consumption (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Silicon Carbide Production CO2
0.2
0.13
0.16
-9%
+9%
Industrial Processes 4-25
-------
and Consumption
Silicon Carbide Production CH4 + + + -9% +9%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 Tg CO2 Eq. or 0.5 Gg.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the silicon carbide production source category include evaluating facility level greenhouse
gas emissions data as a basis for improving emissions calculations from silicon carbide production. Beginning in
2010, all U.S. silicon carbide production facilities are required to monitor, calculate and report their greenhouse gas
emissions to EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for
2010 emissions from facilities based on use of higher tier methods and in particular assess how this data could be
used to improve the overall method for calculating emissions from the U.S. silicon carbide production industry. 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. Additionally, as future improvement to the silicon carbide uncertainty analysis,
USGS Mineral Commodity Specialists will be contacted to verify the uncertainty range associated with silicon
carbide emissive utilization.
4.9. Petrochemical Production (IPCC Source Category 2B5)
The production of some petrochemicals results in the release of small amounts of CH4 and CO2 emissions.
Petrochemicals are chemicals isolated or derived from petroleum or natural gas. CH4 emissions are presented here
from the production of carbon black, ethylene, ethylene dichloride, and methanol, while CO2 emissions are
presented here for only carbon black production. The CO2 emissions from petrochemical processes other than
carbon black are currently included in the Carbon Stored in Products from Non-Energy Uses of Fossil Fuels Section
of the Energy chapter. The CO2 from carbon black production is included here to allow for the direct reporting of
CO2 emissions from the process and direct accounting of the feedstocks used in the process.
Carbon black is an intense black powder generated by the incomplete combustion of an aromatic petroleum or coal-
based feedstock. 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. Ethylene 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. Ethylene dichloride is
one of the first manufactured chlorinated hydrocarbons with reported production as early as 1795. In addition to
being an important intermediate in the synthesis of chlorinated hydrocarbons, ethylene dichloride is used as an
industrial solvent and as a fuel additive. Methanol is an alternative transportation fuel as well as a principle
ingredient in windshield wiper fluid, paints, solvents, refrigerants, and disinfectants. In addition, methanol-based
acetic acid is used in making PET plastics and polyester fibers.
Emissions of CO2 and CH4 from petrochemical production in 2009 were 2.7 Tg CO2 Eq. (2,735 Gg) and 0.8 Tg CO2
Eq. (40 Gg), respectively (see Table 4-33 and Table 4-34), totaling 3.6 Tg CO2 Eq. There has been an overall
decrease in CO2 emissions from carbon black production of 17 percent since 1990. CH4 emissions from
petrochemical production decreased by approximately two percent since 1990.
Table 4-33: CO2 and CH4 Emissions from Petrochemical Production (Tg CO2 Eq.)
Year
C02
CH4
Total
1990
3.3
0.9
4.2
2000
4.5
1.2
5.7
2005
4.2
1.1
5.3
2006
3.8
1.0
4.8
2007
3.9
1.0
4.9
2008
3.4
0.9
4.4
2009
2.7
0.8
3.6
Note: Totals may not sum due to independent rounding.
Table 4-34: CO2 and CH4 Emissions from Petrochemical Production (Gg)
Year 1990 2000 2005 2006 2007 2008 2009
4-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
CO2 3,311 I 4,479 I 4,181 3,837 3,931 3,449 2,735
CH4 41 59_H^_^_51 48 48 43 40
Methodology
Emissions of CH4 were calculated by multiplying annual estimates of chemical production by the appropriate
emission factor, as follows: 11 kg CH4/metric ton carbon black, 1 kg CH^metric ton ethylene, 0.4 kg CH^metric ton
ethylene dichloride,113 and 2 kg CHVmetric ton methanol. Although the production of other chemicals may also
result in CH4 emissions, insufficient data were available to estimate their emissions.
Emission factors were taken from the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997). Annual
production data (see Table 4-35) were obtained from the American Chemistry Council's Guide to the Business of
Chemistry (ACC 2002, 2003, 2005 through 2010) and the International Carbon Black Association (Johnson 2003,
2005 through 2010). Note that 2009 production data for Methanol was not available at time of publication, as such,
2008 methanol production is used as a proxy for 2009.
Table 4-35: Production of Selected Petrochemicals (Thousand Metric Tons)
Chemical
Carbon Black
Ethylene
Ethylene Dichloride
Methanol
1990 •
1
16
6
3
,307
,541
,282 1
,785
I 2000 •
1
24
9
5
,769
,970
,866
,221
2005
1,651
23,954
11,260
2,336
2006
1
25
9
1
,515
,000
,736
,123
2007
1,552
25,392
9,566
1,068
2008
1,362
22,539
8,981
1,136
2009
1,080
22,596
8,131
1,136
Almost all carbon black in the United States is produced from petroleum-based or coal-based feedstocks using the
"furnace black" process (European IPPC Bureau 2004). The furnace black process is a partial combustion process
in which a portion of the carbon black feedstock is combusted to provide energy to the process. Carbon black is also
produced in the United States by the thermal cracking of acetylene-containing feedstocks ("acetylene black
process") and by the thermal cracking of other hydrocarbons ("thermal black process"). One U.S carbon black plant
produces carbon black using the thermal black process, and one U.S. carbon black plant produces carbon black
using the acetylene black process (The Innovation Group 2004).
The furnace black process produces carbon black from "carbon black feedstock" (also referred to as "carbon black
oil"), which is a heavy aromatic oil that may be derived as a byproduct of either the petroleum refining process or
the metallurgical (coal) coke production process. For the production of both petroleum-derived and coal-derived
carbon black, the "primary feedstock" (i.e., carbon black feedstock) is injected into a furnace that is heated by a
"secondary feedstock" (generally natural gas). Both the natural gas secondary feedstock and a portion of the carbon
black feedstock are oxidized to provide heat to the production process and pyrolyze the remaining Carbon black
feedstock to carbon black. The "tail gas" from the furnace black process contains CO2, carbon monoxide, sulfur
compounds, CH4, and non-CH4 volatile organic compounds. A portion of the tail gas is generally burned for energy
recovery to heat the downstream carbon black product dryers. The remaining tail gas may also be burned for energy
recovery, flared, or vented uncontrolled to the atmosphere.
The calculation of the C lost during the production process is the basis for determining the amount of CO2 released
during the process. The C content of national carbon black production is subtracted from the total amount of C
contained in primary and secondary carbon black feedstock to find the amount of C lost during the production
process. It is assumed that the C lost in this process is emitted to the atmosphere as either CH4 or CO2. The C
content of the CH4 emissions, estimated as described above, is subtracted from the total C lost in the process to
calculate the amount of C emitted as CO2. The total amount of primary and secondary carbon black feedstock
consumed in the process (see Table 4-36) is estimated using a primary feedstock consumption factor and a
secondary feedstock consumption factor estimated from U.S. Census Bureau (1999, 2004, and 2007) data. The
average carbon black feedstock consumption factor for U.S. carbon black production is 1.69 metric tons of carbon
black feedstock consumed per metric ton of carbon black produced. The average natural gas consumption factor for
113 The emission factor obtained from IPCC/UNEP/OECD/IEA (1997), page 2.23 is assumed to have a misprint; the chemical
identified should be ethylene dichloride (C2H4C12) rather than dichloroethylene (C2H2C12).
Industrial Processes 4-27
-------
U.S. carbon black production is 321 normal cubic meters of natural gas consumed per metric ton of carbon black
produced. The amount of C contained in the primary and secondary feedstocks is calculated by applying the
respective C contents of the feedstocks to the respective levels of feedstock consumption (EIA 2003, 2004).
Table 4-36: Carbon Black Feedstock (Primary Feedstock) and Natural Gas Feedstock (Secondary Feedstock)
Consumption (Thousand Metric Tons)
Activity
Primary Feedstock
Secondary Feedstock
1990
2,213 1
284
2000
2,993 1
384
2005
2,794
| 359
2006
2,564
329
2007
2,627
337
2008
2,305
296
2009
1,828
235
For the purposes of emissions estimation, 100 percent of the primary carbon black feedstock is assumed to be
derived from petroleum refining byproducts. Carbon black feedstock derived from metallurgical (coal) coke
production (e.g., creosote oil) is also used for carbon black production; however, no data are available concerning
the annual consumption of coal-derived carbon black feedstock. Carbon black feedstock derived from petroleum
refining byproducts is assumed to be 89 percent elemental C (Srivastava et al. 1999). It is assumed that 100 percent
of the tail gas produced from the carbon black production process is combusted and that none of the tail gas is
vented to the atmosphere uncontrolled. The furnace black process is assumed to be the only process used for the
production of carbon black because of the lack of data concerning the relatively small amount of carbon black
produced using the acetylene black and thermal black processes. The carbon black produced from the furnace black
process is assumed to be 97 percent elemental C (Othmer et al. 1992).
Uncertainty and Time-Series Consistency
The CH4 emission factors used for petrochemical production are based on a limited number of studies. Using plant-
specific factors instead of average factors could increase the accuracy of the emission estimates; however, such data
were not available. There may also be other significant sources of CH4 arising from petrochemical production
activities that have not been included in these estimates.
The results of the quantitative uncertainty analysis for the CO2 emissions from carbon black production calculation
are based on feedstock consumption, import and export data, and carbon black production data. The composition of
carbon black feedstock varies depending upon the specific refinery production process, and therefore the assumption
that carbon black feedstock is 89 percent C gives rise to uncertainty. Also, no data are available concerning the
consumption of coal-derived carbon black feedstock, so CO2 emissions from the utilization of coal-based feedstock
are not included in the emission estimate. In addition, other data sources indicate that the amount of petroleum-
based feedstock used in carbon black production may be underreported by the U.S. Census Bureau. Finally, the
amount of carbon black produced from the thermal black process and acetylene black process, although estimated to
be a small percentage of the total production, is not known. Therefore, there is some uncertainty associated with the
assumption that all of the carbon black is produced using the furnace black process.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-37. Petrochemical production
CO2 emissions were estimated to be between 2.0 and 3.6 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 27 percent below to 31 percent above the emission estimate of 2.7 Tg CO2 Eq.
Petrochemical production CH4 emissions were estimated to be between 0.6 and 1.1 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 26 percent below to 27 percent above the emission
estimate of 0.8 Tg CO2 Eq.
Table 4-37: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petrochemical Production and CO2
Emissions from Carbon Black Production (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Petrochemical Production CO2
Petrochemical Production CH4
2.7
0.8
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower
Bound
2.0
0.6
Upper
Bound
3.6
1.1
Lower
Bound
-27%
-26%
Upper
Bound
+31%
+27%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
4-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the petrochemicals source category involve updating the methodology to use CH4 emission
factors for petrochemical production from the IPCC 2006 guidelines rather than the IPCC 1996 guidelines. Further
future improvements involve evaluating facility level greenhouse gas emissions data as a basis for improving
emissions calculations from petrochemical production. Beginning in 2010, all U.S. petrochemical production
facilities are required to monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse
Gas Reporting Program. Under the program, EPA will obtain data for 2010 emissions from facilities based on use of
higher tier methods and in particular assess how this data could be used to improve the overall method for
calculating emissions from the U.S. petrochemical production industry, for example using a Tier 2 methodology to
calculate emissions from the production of methanol, ethylene, propylene, ethylene dichloride, and ethylene oxide.
In addition, the planned improvements include assessing the data EPA obtains to update data sources for
acrylonitrile production in the United States.
4.10. Titanium Dioxide Production (IPCC Source Category 2B5)
Titanium dioxide (TiO2) is a metal oxide manufactured from titanium ore, and is principally used as a pigment.
Titanium dioxide is a principal ingredient in white paint, and is also used as a pigment in the manufacture of white
paper, foods, and other products. There are two processes for making TiO2: the chloride process and the sulfate
process. The chloride process uses petroleum coke and chlorine as raw materials and emits process-related CO2.
The sulfate process does not use petroleum coke or other forms of C as a raw material and does not emit CO2.
The chloride process is based on the following chemical reactions:
2 FeTiO3 + 7 C12 + 3 C -» 2 TiCL, + 2 FeCl3 + 3 CO2
2 TiCL, + 2 O2 -» 2 TiO2 + 4 C12
The C in the first chemical reaction is provided by petroleum coke, which is oxidized in the presence of the chlorine
and FeTiO3 (the Ti-containing ore) to form CO2. The majority of U.S. TiO2 was produced in the United States
through the chloride process, and a special grade of "calcined" petroleum coke is manufactured specifically for this
purpose.
Emissions of CO2 in 2009 were 1.5 TgCO2Eq. (1,541 Gg), which represents an increase of 29 percent since 1990
(see Table 4-38).
Table 4-3 8: CO2 Emissions from Titanium Dioxide (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990
1,195
2005
2006
2007
2008
2009
1.8
1.8
1.9
1.8
1.5
,755
,836
,930
,809
,541
Methodology
Emissions of CO2 from TiO2 production were calculated by multiplying annual TiO2 production by chloride-
process-specific emission factors.
Data were obtained for the total amount of TiO2 produced each year. For years previous 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
Industrial Processes 4-29
-------
had closed; therefore, 100 percent of post-2004 production uses the chloride process (USGS 2005). An emission
factor of 0.4 metric tons C/metric ton TiO2 was applied to the estimated chloride-process production. 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 C inputs. The amount of petroleum coke consumed annually in
TiO2 production was calculated based on the assumption that the calcined petroleum coke used in the process is 98.4
percent C and 1.6 percent inert materials (Nelson 1969).
The emission factor for the TiO2 chloride process was taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006). Titanium dioxide production data and the percentage of total TiO2
production capacity that is chloride process for 1990 through 2008 (see Table 4-39) were obtained through the
Minerals Yearbook: Titanium Annual Report (USGS 1991 through 2008). Production data in 2009 was obtained
from the Minerals Commodity Summary: Titanium and Titanium Dioxide (USGS 2010). Due to lack of available
2009 capacity data at the time of publication, the 2008 capacity estimate is used as a proxy for 2009. Percentage
chloride-process data were not available for 1990 through 1993, and 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 4-39: Titanium Dioxide Production (Gg)
Year Gg
1990 979
2005
2006
2007
2008
2009
,310
,370
,440
,350
,150
Uncertainty and Time-Series Consistency
Although some TiO2 may be produced using graphite or other C inputs, information and data regarding these
practices were not available. Titanium dioxide produced using graphite inputs, for example, may generate differing
amounts of CO2per 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.
Also, annual TiO2 is not reported by USGS by the type of production process used (chloride or sulfate). Only the
percentage of total production capacity by process is 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 (since, as of 2004, the last remaining sulfate-process plant in the United States
closed). This assumes that the chloride-process plants and sulfate-process plants operate at the same level of
utilization. Finally, the emission factor was applied uniformly to all chloride-process production, and no data were
available to account for differences in production efficiency among chloride-process plants. In calculating the
amount of petroleum coke consumed in chloride-process TiO2 production, literature data were used for petroleum
coke composition. Certain grades of petroleum coke are manufactured specifically for use in the TiO2 chloride
process; however, this composition information was not available.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-40. Titanium dioxide
consumption CO2 emissions were estimated to be between 1.4 and 1.7 Tg 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 1.5
Tg C02 Eq.
4-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 4-40: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium Dioxide Production (Tg
CO2 Eg. and Percent)
Source
Titanium Dioxide Production
Gas
C02
2009 Emission
Estimate
(Tg C02 Eq.)
1.5
Uncertainty Range Relative
(Tg C02 Eq.)
Lower Upper
Bound Bound
1.3 1.7
to Emission Estimate"
(%)
Lower Upper
Bound Bound
-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 time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the titanium dioxide production category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emissions calculations from titanium dioxide production. Beginning in
2010, all U.S. titanium dioxide production facilities using the chloride production process are required to monitor,
calculate and report their greenhouse gas emissions to EPA through its Greenhouse Gas Reporting Program. Under
the program, EPA will obtain data for 2010 emissions from facilities based on use of higher tier methods and in
particular assess how this data could be used to improve the overall method for calculating emissions from the U.S.
titanium dioxide production industry, including improving the emission factors. 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. Carbon Dioxide Consumption (IPCC Source Category 2B5)
CO2 is used for a variety of commercial applications, including food processing, chemical production, carbonated
beverage production, and refrigeration, and is also used in petroleum production for enhanced oil recovery (EOR).
CO2 used for EOR is injected into the underground reservoirs to increase the reservoir pressure to enable additional
petroleum to be produced.
For the most part, CO2 used in non-EOR applications will eventually be released to the atmosphere, and for the
purposes of this analysis CO2 used in commercial applications other than EOR is assumed to be emitted to the
atmosphere. CO2 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 by-product from the energy and industrial production
processes (e.g., ammonia production, fossil fuel combustion, ethanol production), and as a by-product 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 by-product 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. CO2 captured from biogenic
sources (e.g., ethanol production plants) is not included in the inventory. CO2 captured from crude oil and gas
production is used in EOR applications and is therefore reported in the Energy Chapter. Any CO2 captured from
industrial or energy production processes (e.g., ammonia plants, fossil fuel combustion) and used in non-EOR
applications is assumed to be emitted to the atmosphere. The CO2 emissions from such capture and use are
Industrial Processes 4-31
-------
therefore accounted for under Ammonia Production, Fossil Fuel Combustion, or other appropriate source category.
CO2 is produced as a by-product of crude oil and natural gas production. This CO2 is separated from the crude oil
and natural gas using gas processing equipment, and may be emitted directly to the atmosphere, or captured and
reinjected into underground formations, used for EOR, or sold for other commercial uses. A further discussion of
CO2 used in EOR is described in the Energy Chapter under the text box titled "Carbon Dioxide Transport, Injection,
and Geological Storage." The only CO2 consumption that is accounted for in this analysis is CO2 produced from
naturally-occurring CO2 reservoirs that is used in commercial applications other than EOR.
There are currently two facilities, one in Mississippi and one in New Mexico, producing CO2 from naturally
occurring CO2 reservoirs for use in both EOR and in other commercial applications (e.g., chemical manufacturing,
food production). There are other naturally occurring CO2 reservoirs, mostly located in the western UnitedStates.
Facilities are producing CO2 from these natural reservoirs, but they are only producing CO2 for EOR applications,
not for other commercial applications (Allis et al. 2000). CO2 production from these facilities is discussed in the
Energy Chapter.
In 2009, the amount of CO2 produced by the Mississippi and New Mexico facilities for commercial applications and
subsequently emitted to the atmosphere was 1.8 Tg CO2Eq. (1,763 Gg) (see Table 4-41). This amount represents a
decrease of one percent from the previous year and an increase of 24 percent since 1990. This increase was due to
an in increase in production at the Mississippi facility, despite the decrease in the percent of the facility's total
reported production that was used for commercial applications.
Table 4-41: CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 1.4 1,416
2005
2006
2007
2008
2009
1.3
1.7
1.9
1.8
1.8
1,321
1,709
1,867
1,780
1,763
Methodology
CO2 emission estimates for 1990 through 2009 were based on production data for the two 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 for the Jackson Dome, Mississippi facility and the percentage of total production that was used
for EOR and in non-EOR applications were obtained from the Advanced Resources Institute (ARI2006, 2007) for
1990 to 2000 and from the Annual Reports for Denbury Resources (Denbury Resources 2002 through 2010) for
2001 to 2009 (see Table 4-42). 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. CO2 production data for the Bravo Dome, New Mexico facility were obtained from the Advanced Resources
International, Inc. (ARI 1990 through 2010). The percentage of total production that was used for EOR and in non-
EOR applications were obtained from the New Mexico Bureau of Geology and Mineral Resources (Broadhead 2003
and New Mexico Bureau of Geology and Mineral Resources 2006).
Table 4-42: CO2 Production (Gg CO2) and the Percent Used for Non-EOR Applications for Jackson Dome and
114 There are currently four known electric power plants operating in the U.S. that capture CO2 for use as food-grade CO2 or
other industrial processes; however, insufficient data prevents estimating emissions from these activities as part of Carbon
Dioxide Consumption.
4-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Bravo Dome
Year Jackson Dome CO2
Production (Gg)
Jackson Dome %
Used for Non-EOR
Bravo Dome CO2
Production (Gg)
Bravo Dome % Used
for Non-EOR
1990
1,353
100%
6,301
2005
2006
2007
2008
2009
4,678
6,610
9,529
12,312
13,201
27%
25%
19%
14%
13%
5,799
5,613
5,605
5,605
4,639
1%
1%
1%
1%
1%
Uncertainty and Time-Series Consistency
Uncertainty is associated with the number of facilities that are currently producing CO2from 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 Tier 2 quantitative uncertainty analysis are summarized in Table 4-43. CO2 consumption CO2
emissions were estimated to be between 1.3 and 2.3 Tg CO2 Eq. at the 95 percent confidence level. This indicates a
range of approximately 26 percent below to 30 percent above the emission estimate of 1.8 Tg CO2 Eq.
Table 4-43: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2 Consumption (Tg CO2 Eq. and
Percent)
Source
Gas
2009 Emission
Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (%)
Lower Bound Upper Bound Lower Bound Upper Bound
CO2 Consumption
C02
1.8
1.3 2.3 -26% +30%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the Carbon Dioxide Consumption source category involve evaluating facility level
greenhouse gas emissions data as a basis for improving emissions calculations from carbon dioxide consumption.
Beginning in 2010, all U.S. CO2 producers are required to monitor, calculate and report the quantity of CO2 supplied
to EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for 2010 on CO2
supplied from facilities based on use of higher tier methods and in particular assess how this data could be used to
improve the overall method for calculating emissions from consumption of CO2
4.12. Phosphoric Acid Production (IPCC Source Category 2B5)
Phosphoric acid (H3PO4) is a basic raw material in the production of phosphate-based fertilizers. 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 production of phosphoric acid from phosphate rock produces
byproduct gypsum (CaSO4-2H2O), referred to as phosphogypsum.
The composition of natural phosphate rock varies depending upon the location where it is mined. Natural phosphate
Industrial Processes 4-33
-------
rock mined in the United States generally contains inorganic C in the form of calcium carbonate (limestone) and
also may contain organic C. The chemical composition of phosphate rock (francolite) mined in Florida is:
Ca10-x-y Nax Mgy (PO4)6_x(CO3)xF2+0.4x
The calcium carbonate component of the phosphate rock is integral to the phosphate rock chemistry. Phosphate
rock can also contain organic C that is physically incorporated into the mined rock but is not an integral component
of the phosphate rock chemistry. Phosphoric acid production from natural phosphate rock is a source of CO2
emissions, due to the chemical reaction of the inorganic C (calcium carbonate) component of the phosphate rock.
The phosphoric acid production process involves chemical reaction of the calcium phosphate (Ca3(PO4)2)
component of the phosphate rock with sulfuric acid (H2SO4) and recirculated phosphoric acid (H3PO4) (EFMA
2000). The primary chemical reactions for the production of phosphoric acid from phosphate rock are:
Ca3(PO4)2 + 4H3PO4 -> 3Ca(H2PO4)2
3Ca(H2PO4)2 + 3H2SO4 + 6H2O -> 3CaSO4 • 6H2O + 6H3PO4
The limestone (CaCO3) component of the phosphate rock reacts with the sulfuric acid in the phosphoric acid
production process to produce calcium sulfate (phosphogypsum) and CO2. The chemical reaction for the limestone-
sulfuric acid reaction is:
CaCO3+H2SO4 +H2O -> CaSO4 • 2H2O + CO2
Total marketable phosphate rock production in 2009 was 27.2 million metric tons (USGS 2010). Approximately
87 percent of domestic phosphate rock production was mined in Florida and North Carolina, while approximately 13
percent of production was mined in Idaho and Utah. Total imports of phosphate rock in 2009 were 1.8 million
metric tons (USGS 2010). The vast majority, 99 percent, of imported phosphate rock is sourced from Morocco
(USGS 2005). Marketable phosphate rock production, including domestic production and imports for consumption,
decreased by 13.6 percent between 2008 and 2009. Over the 1990 to 2009 period, production has decreased by 34
percent. Total CO2 emissions from phosphoric acid production were 1.0 Tg CO2 Eq. (1,035 Gg) in 2009 (see Table
4-44). According to USGS 2010, the weak market conditions of phosphate rock in the U.S. in 2009 were a result of
the global economic crisis that started in late 2008 and carried into 2009.
Table 4-44: CO2 Emissions from Phosphoric Acid Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 1.5 1,529
2005
2006
2007
2008
2009
1.4
1.2
1.2
1.2
1.0
1,386
1,167
1,166
1,187
1,035
Methodology
CO2 emissions from production of phosphoric acid from phosphate rock are calculated by multiplying the average
amount of calcium carbonate contained in the natural phosphate rock by the amount of phosphate rock that is used
annually to produce phosphoric acid, accounting for domestic production and net imports for consumption.
The CO2 emissions calculation methodology is based on the assumption that all of the inorganic C (calcium
carbonate) content of the phosphate rock reacts to CO2 in the phosphoric acid production process and is emitted with
the stack gas. The methodology also assumes that none of the organic C content of the phosphate rock is converted
to CO2 and that all of the organic C 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 4-45). For the years 1990, 1991,
4-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
1992, 2005, 2006, and 2007 only nationally aggregated mining data was reported by USGS. For these years, the
breakdown of phosphate rock mined in Florida and North Carolina, and the amount mined in Idaho and Utah, are
approximated using 1993 to 2004 data. Data for domestic production of phosphate rock, exports of phosphate rock
(primarily from Florida and North Carolina), and imports of phosphate rock for consumption for 1990 through 2008
were obtained from USGS Minerals Yearbook: Phosphate Rock (USGS 1994 through 2010). 2009 data were
obtained from USGS Minerals Commodity Summary: Phosphate Rock (USGS 2010). From 2004 through 2009, the
USGS reported no exports of phosphate rock from U.S. producers (USGS 2005 through 2010).
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 C, and phosphate rock
imported from Morocco contains approximately 1.46 percent inorganic C. Calcined phosphate rock mined in North
Carolina and Idaho contains approximately 0.41 percent and 0.27 percent inorganic C, respectively (see Table 4-46).
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 (87 percent of domestic production) and
carbonate content data for phosphate rock mined in Morocco are used to calculate CO2 emissions from consumption
of imported phosphate rock. The CO2 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). Carbonate content data for uncalcined phosphate rock mined in Idaho and Utah (13 percent
of domestic production) were not available, and carbonate content was therefore estimated from the carbonate
content data for calcined phosphate rock mined in Idaho.
Table 4-45: Phosphate Rock Domestic Production, Exports, and Imports (Gg)
Location/Year
U.S. Production3
FL&NC
ID&UT
Exports— FL & NC
Imports — Morocco
Total U.S.
Consumption
1990
49,800
42,494
7,306
6,240
451
44,011
2000
37,370
31,900
5,470
299
1,930
39,001
2005
36
31
4
2
38
,100
,227
,874
,630
,730
2006
30
26
4
2
32
,100
,037
,064
,420
,520
2007
29,700
25,691
4,010
2,670
32,370
2008
30,200
26,123
4,077
2,754
32,954
2009
27,200
23,528
3,672
1,800
29,000
a USGS does not disaggregate production data regionally (FL & NC and ID & UT) for 1990, 2005,2006, and 2007. Data for
those years are estimated based on the remaining time series distribution.
- Assumed equal to zero.
Table 4-46: Chemical Composition of Phosphate Rock (percent by weight)
Composition
Total Carbon (as C)
Inorganic Carbon (as C)
Organic Carbon (as C)
Inorganic Carbon (as CO2)
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
1.00
Morocco
1.56
1.46
0.10
5.00
Source: FIPR 2003
- Assumed equal to zero.
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 2009. For previous years in the time series,
USGS provided the data disaggregated regionally; however, beginning in 2006 only total U.S. phosphate rock
production were reported. Regional production for 2008 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 2008 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
Industrial Processes 4-35
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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 CO2 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. Another source of uncertainty is the disposition of the organic C
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 C is therefore not included in the
calculation of CO2 emissions from phosphoric acid production.
A third source of uncertainty is the assumption that all domestically-produced phosphate rock is used in phosphoric
acid production and used without first being calcined. Calcination of the phosphate rock would result in conversion
of some of the organic C in the phosphate rock into CO2. However, according to the USGS, only one producer in
Idaho is currently calcining phosphate rock, and no data were available concerning the annual production of this
single producer (USGS 2005). For available years, total production of phosphate rock in Utah and Idaho combined
amounts to approximately 13 percent of total domestic production on average (USGS 1994 through 2005).
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 CO2 in the elemental phosphorus production process. The calculation for CO2
emissions is based on the assumption that phosphate rock consumption, for purposes other than phosphoric acid
production, results in CO2 emissions from 100 percent of the inorganic C content in phosphate rock, but none from
the organic C content.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-47. Phosphoric acid
production CO2 emissions were estimated to be between 0.9 and 1.2 Tg CO2 Eq. at the 95 percent confidence level.
This indicates a range of approximately 18 percent below and 19 percent above the emission estimate of 1.0 Tg CO2
Eq.
Table 4-47: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Phosphoric Acid Production (Tg
CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (TgC02Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper Bound
Phosphoric Acid Production CO2 LO 0.9 L2 -18% +19%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the phosphoric acid production source category involve evaluating facility level greenhouse
gas emissions data as a basis for improving emissions calculations from phosphoric acid production. Beginning in
2010, all U.S. phosphoric acid producers are required to monitor, calculate and report their greenhouse gas
emissions to EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will obtain data for
2010 from facilities based on use of higher tier methods and assess how this data could be used to improve the
method for calculating emissions from the U.S. phosphoric acid production industry. Currently, data sources for the
carbonate content of the phosphate rock are limited. If additional data sources are found, this information will be
incorporated into future estimates. Additionally, as future improvement to the phosphoric acid uncertainty analysis,
USGS Mineral Commodity Specialists will be contacted to verify uncertainty ranges associated with phosphate rock
4-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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imports and exports.
4.13. Iron and Steel Production (IPCC Source Category 2C1) and Metallurgical
Coke Production
The production of iron and steel is an energy-intensive activity that also generates process-related emissions of CO2
and CH4. Process emissions occur at each step of steel production from the production of raw materials to the
refinement of iron to the making of crude steel. In the United States, steel is produced through both primary and
secondary processes. Historically, primary production—using a basic oxygen furnace (EOF) with pig iron as the
primary feedstock—has been the dominant method. But secondary production through the use scrap steel and
electric arc furnaces (EAFs) has increased significantly in recent years due to the economic advantages of steel
recycling, which has been driven by the increased availability of scrap steel. Total production of crude steel in the
United States in the time period between 2001 and 2008 ranged from a low of 99,321,000 tons to a high of
109,879,000 tons (2001 and 2004, respectively). But due to the decrease in demand caused by the global economic
downturn, crude steel production in the United States decreased to 65,460,000 tons in 2009 (AISI2010).
Metallurgical coke is an important input in the production of iron and steel. Coke is used to produce iron or pig iron
feedstock from raw iron ore. The production of metallurgical coke from coking coal occurs both on-site at
"integrated" iron and steel plants and off-site at "merchant" coke plants. Metallurgical coke is produced by heating
coking coal in a coke oven in a low-oxygen environment. The process drives off the volatile components of the
coking coal and produces coal (metallurgical) coke. Carbon containing byproducts of the metallurgical coke
manufacturing process include coke oven gas, coal tar, coke breeze (small-grade coke oven coke with particle size
<5mm) and light oil. Coke oven gas is recovered and used for underfiring the coke ovens and within the iron and
steel mill. Small amounts of coke oven gas are also sold as synthetic natural gas outside of iron and steel mills (and
are accounted for in the Energy chapter). Coal tar is used as a raw material to produce anodes used for primary
aluminum production, electric arc furnace (EAF) steel production, and other electrolytic processes, and also is used
in the production of other coal tar products. Light oil is sold to petroleum refiners who use the material as an
additive for gasoline. The metallurgical coke production process produces CO2 emissions and fugitive CH4
emissions.
Iron is produced by first reducing iron oxide (iron ore) with metallurgical coke in a blast furnace. Iron can be
introduced into the blast furnace in the form of raw iron ore, taconite pellets (9-16mm iron-containing spheres),
briquettes, or sinter. In addition to metallurgical coke and iron, other inputs to the blast furnace include natural gas,
fuel oil, and coke oven gas. The carbon in the metallurgical coke used in the blast furnace combines with oxides in
the iron ore in a reducing atmosphere to produce blast furnace gas containing carbon monoxide (CO) and CO2. The
CO is then converted and emitted as CO2 when combusted to either pre-heat the blast air used in the blast furnace or
for other purposes at the steel mill. This pig iron or crude iron that is produced from this process contains about 3 to
5 percent carbon by weight. The pig iron production process in a blast furnace produces CO2 emissions and fugitive
CH4 emissions.
Iron can also be produced through the direct reduction process; wherein, iron ore is reduced to metallic iron in the
solid state at process temperatures less than 1000°C. Direct reduced iron production results in process emissions of
CO2 and emissions of CH4 through the consumption of natural gas used during the reduction process.
Sintering is a thermal process by which fine iron-bearing particles, such as air emission control system dust, are
baked, which causes the material to agglomerate into roughly one-inch pellets that are then recharged into the blast
furnace for pig iron production. Iron ore particles may also be formed into larger pellets or briquettes by mechanical
means, and then agglomerated by heating. The agglomerate is then crushed and screened to produce an iron-bearing
feed that is charged into the blast furnace. The sintering process produces CO2 and fugitive CH4 emissions through
the consumption of carbonaceous inputs (e.g., coke breeze) during the sintering process.
Steel is produced from varying levels of pig iron and scrap steel in specialized EOF and EAF steel-making furnaces.
Carbon inputs to EOF steel-making furnaces include pig iron and scrap steel as well as natural gas, fuel oil, and
fluxes (e.g., limestone, dolomite). In a EOF, the carbon in iron and scrap steel combines with high-purity oxygen to
reduce the carbon content of the metal to the amount desired for the specified grade of steel. EAFs use carbon
electrodes, charge carbon and other materials (e.g., natural gas) to aid in melting metal inputs (primarily recycled
scrap steel), which are refined and alloyed to produce the desired grade of steel. CO2 emissions occur in BOFs
through the reduction process. In EAFs, CO2 emissions result primarily from the consumption of carbon electrodes
Industrial Processes 4-37
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and also from the consumption of supplemental materials used to augment the melting process.
In addition to the production processes mentioned above, CO2 is also generated at iron and steel mills through the
consumption of process by-products (e.g., blast furnace gas, coke oven gas) used for various purposes including
heating, annealing, and electricity generation. Process by-products sold for use as synthetic natural gas are deducted
and reported in the Energy chapter (emissions associated with natural gas and fuel oil consumption for these
purposes are reported in the Energy chapter).
The majority of CO2 emissions from the iron and steel production process come from the use of metallurgical coke
in the production of pig iron and from the consumption of other process by-products at the iron and steel mill, with
lesser amounts emitted from the use of flux and from the removal of carbon from pig iron used to produce steel.
Some carbon is also stored in the finished iron and steel products.
According to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006), the production of
metallurgical coke from coking coal is considered to be an energy use of fossil fuel and the use of coke in iron and
steel production is considered to be an industrial process source. Therefore, the Guidelines suggest that emissions
from the production of metallurgical coke should be reported separately in the Energy source, while emissions from
coke consumption in iron and steel production should be reported in the industrial process source. However, the
approaches and emission estimates for both metallurgical coke production and iron and steel production are both
presented here because the activity data used to estimate emissions from metallurgical coke production have
significant overlap with activity data used to estimate iron and steel production emissions. Further, some by-
products (e.g., coke oven gas) of the metallurgical coke production process are consumed during iron and steel
production, and some by-products of the iron and steel production process (e.g., blast furnace gas) are consumed
during metallurgical coke production. Emissions associated with the consumption of these by-products are
attributed to point of consumption. As an example, CO2 emissions associated with the combustion of coke oven gas
in the blast furnace during pig iron production are attributed to pig iron production. Emissions associated with the
use of conventional fuels (e.g., natural gas and fuel oil) for electricity generation, heating and annealing, or other
miscellaneous purposes downstream of the iron and steelmaking furnaces are reported in the Energy chapter.
Metallurgical Coke Production
Emissions of CO2 and CH4 from metallurgical coke production in 2009 were 1.0 Tg CO2 Eq. (956 Gg) and less than
0.002 Tg CO2 Eq. (less than 0.00003 Gg), respectively (see Table 4-48 and Table 4-49), totaling 1.0 Tg CO2 Eq.
Emissions decreased in 2009, and have decreased overall since 1990. In 2009, domestic coke production decreased
by 29 percent and has decreased overall since 1990. Coke production in 2009 was 46 percent lower than in 2000
and 60 percent below 1990. Overall, emissions from metallurgical coke production have declined by 61 percent (1.5
Tg CO2 Eq.) from 1990 to 2009.
Table 4-48: CO2 and CH4 Emissions from Metallurgical Coke Production (Tg CO2 Eq.)
Year
C02
CH4
Total
1990
2.5
+
2.5
2000
2.2
+
2.2
2005
2.0
+
2.0
2006
1.9
+
1.9
2007
2.1
+
2.1
2008
2.3
+
2.3
2009
1.0
+
1.0
+ Does not exceed 0.05 Tg CO2 Eq.
Table 4-49: CO2 and CH4 Emissions from Metallurgical Coke Production (Gg)
Year 1990 2000 2005 2006 2007 2008 2009
CO2 2,470 2,195 2,043 1,919 2,054 2,334 956
CH4 + ±M____t ± ± ± ±
+ Does not exceed 0.5 Gg
Iron and Steel Production
Emissions of CO2 and CH4 from iron and steel production in 2009 were 40.9 Tg CO2 Eq. (40,914 Gg) and 0.4 Tg
CO2 Eq. (17.4 Gg), respectively (see Table 4-50 through Table 4-53), totaling approximately 41 Tg CO2 Eq.
Emissions decreased in 2009—largely due to decreased steel production associated with the global economic
downturn—and have decreased overall since 1990 due to restructuring of the industry, technological improvements,
and increased scrap steel utilization. CO2 emission estimates include emissions from the consumption of
4-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 2009, domestic production of pig iron decreased by 44 percent. Overall, domestic pig iron production has
declined since the 1990s. Pig iron production in 2009 was 60 percent lower than in 2000 and 62 percent below
1990. CO2 emissions from steel production have declined by 15 percent (1.1 TgCO2Eq.) since 1990, while overall
CO2 emissions from iron and steel production have declined by 58 percent (56.1 Tg CO2 Eq.) from 1990 to 2009.
Table 4-50: CO2 Emissions from Iron and Steel Production (Tg CO2 Eq.)
Year
Sinter Production
Iron Production
Steel Production
Other Activities3
Total
1990
2.4
47.9
7.5
39.3
97.1
2000
2.2
33.8
7.9
39.9
83.7
2005
1.7
19.6
8.5
34.2
63.9
2006
1.4
23.9
8.9
32.6
66.9
2007
1.4
27.3
9.4
31.0
69.0
2008
1.3
25.7
7.5
29.1
63.7
2009
0.8
15.9
6.4
17.8
40.9
Note: Totals may not sum due to independent rounding.
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the steel mill other than consumption
in blast furnace, EAFs, or BOFs.
Table 4-51: CO2 Emissions from Iron and Steel Production (Gg)
Year
Sinter Production
Iron Production
Steel Production
Other Activities a
Total
1990
2,448
47,880
7,475 1
39,256
97,058
2000
2,158
33,818
7,887
39,877
83,740
2005
1,663
19,570
8,489
34,160
63,882
2006
1,418
23,928
8,924
32,583
66,852
2007
1,383
27,262
9,382
30,964
68,991
2008
1,299
25,696
7,541
29,146
63,682
2009
763
15,948
6,389
17,815
40,914
Note: Totals may not sum due to independent rounding.
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the steel mill other than consumption
in blast furnace, EAFs, or BOFs.
Table 4-52: CH4 Emissions from Iron and Steel Production (Tg CO2 Eq.)
Year
Sinter Production
Iron Production
Total
1990
+
0.9H
1.0
2000
+
0.9
0.9
2005
+
0.7
0.7
2006
+
0.7
0.7
2007
+
0.7
0.7
2008
+
0.6
0.6
2009
+
0.4
0.4
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 4-53: CH4 Emissions from Iron and Steel Production (Gg)
Year
Sinter Production
Iron Production
Total
1990
0.9
44.7
45.6
2000
«°:I
43.8 •
2005
0.6
33.5
34.1
2006
0.5
34.1
34.6
2007
0.5
32.7
33.2
2008
0.4
30.4
30.8
2009
0.3
17.1
17.4
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates presented in this chapter are based on the methodologies provided by the 2006 IPCC Guidelines
for National Greenhouse Gas Inventories (IPCC 2006), which 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.
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 lead and zinc (see Lead
Industrial Processes 4-39
-------
Production and Zinc Production in 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 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006) was utilized. The amount of carbon contained in
materials produced during the metallurgical coke production process (i.e., coke, coke breeze, coke oven gas, and
coal tar) is deducted from the amount of carbon contained in materials consumed during the metallurgical coke
production process (i.e., natural gas, blast furnace gas, coking coal). Light oil, which is produced during the
metallurgical coke production process, is excluded from the deductions due to data limitations. The amount of
carbon contained in these materials is calculated by multiplying the material-specific carbon content by the amount
of material consumed or produced (see Table 4-54). The amount of coal tar produced was approximated using a
production factor of 0.03 tons of coal tar per ton of coking coal consumed. The amount of coke breeze produced
was approximated using a production factor of 0.075 tons of coke breeze per ton of coking coal consumed. Data on
the consumption of carbonaceous materials (other than coking coal) as well as coke oven gas production were
available for integrated steel mills only (i.e., steel mills with co-located coke plants). Therefore, carbonaceous
material (other than coking coal) consumption and coke oven gas production were excluded from emission estimates
for merchant coke plants. Carbon contained in coke oven gas used for coke-oven underfiring was not included in
the deductions to avoid double-counting.
Table 4-54: Material Carbon Contents for Metallurgical Coke Production
Material kgC/kg
Coal Tar 0.62
Coke 0.83
Coke Breeze 0.83
Coking Coal 0.73
Material kg C/GJ
Coke Oven Gas 12.1
Blast Furnace Gas 70.8
Source: IPCC 2006, Table 4.3. Coke Oven Gas and Blast Furnace Gas, Table 1.3.
The production processes for metallurgical coke production results in fugitive emissions of CH4, which are emitted
via leaks in the production equipment rather than through the emission stacks or vents of the production plants. The
fugitive emissions were calculated by applying Tier 1 emission factors (0.1 g CH4 per metric ton) taken from the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) for metallurgical coke production.
Data relating to the mass of coking coal consumed at metallurgical coke plants and the mass of metallurgical coke
produced at coke plants were taken from the Energy Information Administration (EIA), Quarterly Coal Report
October through December (EIA 1998 through 2004) and January through March (EIA 2010a) (see Table 4-55).
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 20010) and through personal communications with AISI
(2008b) (see Table 4-56). The factor for the quantity of coal tar produced per ton of coking coal consumed was
provided by AISI (2008b). The factor for the quantity of coke breeze produced per ton of coking coal consumed
was obtained through Table 2-1 of the report Energy and Environmental Profile of the U.S. Iron and Steel Industry
(DOE 2000). Data on natural gas consumption and coke oven gas production at merchant coke plants were not
available and were excluded from the emission estimate. Carbon contents for coking coal, metallurgical coke, coal
tar, coke oven gas, and blast furnace gas were provided by the 2006 IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006). The C content for coke breeze was assumed to equal the C content of coke.
Table 4-55: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Metallurgical
Coke Production (Thousand Metric Tons)
Source/Activity Data 1990 2000 2005 2006 2007 2008 2009
Metallurgical Coke Production
Coking Coal Consumption at Coke
Plants
Coke Production at Coke Plants
Coal Breeze Production
Coal Tar Production
35,269
25,054
2,645
1,058
10,0//.
1,969
| 788
21,259
15,167
1,594
638
20,827
14,882
1,562
625
20,607
14,698
1,546
618
20,022
14,194
1,502
601
13,904
10,109
1,043
417
4-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 4-56: Production and Consumption Data for the Calculation of CO2 Emissions from Metallurgical Coke
Production (million ft3)
Source/Activity Data
Metallurgical Coke Production
Coke Oven Gas Production3
Natural Gas Consumption
Blast Furnace Gas Consumption
1990
250,767
599
24,602
2000
149,477
180 1
26,075
2005
114,213
2,996
4,460
2006
114
3
5
,386
,277
,505
2007
109
3
5
,912
,309
,144
2008
103
3
4
,191
,134
,829
2009
66
2
2
,155
,121
,435
a Includes coke oven gas used for purposes other than coke oven underfiring only.
Iron and Steel Production
Emissions of CO2 from sinter production and direct reduced iron production were estimated by multiplying total
national sinter production and the total national direct reduced iron production by Tier 1 CO2 emission factors (see
Table 4-57). Because estimates of sinter production and direct reduced iron production were not available,
production was assumed to equal consumption.
Table 4-57: CO2 Emission Factors for Sinter Production and Direct Reduced Iron Production
Material Produced Metric Ton
CO2/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, direct coal injection). The C contained in the pig iron, blast
furnace gas, and blast furnace inputs was estimated by multiplying the material-specific carbon content by each
material type (see Table 4-58). 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 carbon 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 carbon contained in inputs such as natural gas, coke oven
gas, fluxes, and pig iron. In each case, the C was calculated by multiplying material-specific carbon contents by
each material type (see Table 4-58). For EAFs, the amount of EAF anode consumed was approximated by
multiplying total EAF steel production by the amount of EAF anode consumed per metric ton of steel produced
(0.002 metric tons EAF anode per metric ton steel produced (AISI 2008b)). The amount of flux (e.g., limestone and
dolomite) used during steel manufacture was deducted from the Limestone and Dolomite Use source category to
avoid double-counting.
CO2 emissions from the consumption of blast furnace gas and coke oven gas for other activities occurring at the
steel mill were estimated by multiplying the amount of these materials consumed for these purposes by the material-
specific C content (see Table 4-58).
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 CO2 emissions from iron and steel
production (see Table 4-50and Table 4-51).
Table 4-58: Material Carbon Contents for Iron and Steel Production
Material
Coke
Direct Reduced Iron
Dolomite
EAF Carbon Electrodes
EAF Charge Carbon
Limestone
Pig Iron
kgC/kg
0.83
0.02
0.13
0.82
0.83
0.12
0.04
Industrial Processes 4-41
-------
Steel 0.01
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 sinter and pig iron result in fugitive emissions of CH4, which are emitted via leaks in
the production equipment rather than through the emission stacks or vents of the production plants. The fugitive
emissions were calculated by applying Tier 1 emission factors taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) for sinter production and the 1995 IPCC Guidelines
(IPCC/UNEP/OECD/IEA 1995) (see Table 4-59) for pig iron production. The production of direct reduced iron also
results in emissions of CH4 through the consumption of fossil fuels (e.g., natural gas); however, these emissions
estimates are excluded due to data limitations.
Table 4-59: CH4 Emission Factors for Sinter and Pig Iron Production
Material Produced Factor Unit
Pig Iron 0.9 g CH4/kg
Sinter 0.07 kg CH4/metric ton
Source: Sinter (IPCC 2006, Table 4.2), Pig Iron (IPCC/UNEP/OECD/IEA 1995, Table 2.2)
Sinter consumption and direct reduced iron consumption data were obtained from AISI's Annual Statistical Report
(AISI 2004 through 2010) and through personal communications with AISI (2008b) (see Table 4-60). Data on
direct reduced iron consumed in EAFs were not available for the years 1990, 1991, 1999, 2006, 2007, 2008, and
2009. EAF direct reduced iron consumption in 1990 and 1991 were assumed to equal consumption in 1992, and
consumption in 1999 was assumed to equal the average of 1998 and 2000. EAF consumption in 2006, 2007, 2008,
and 2009 were calculated by multiplying the total DRI consumption for all furnaces as provided in the 2009 AISI
Annual Statistical Report by the EAF share of total DRI consumption in 2005 (the most recent year that data was
available for EAF vs. EOF consumption of DRI). Data on direct reduced iron consumed in BOFs were not available
for the years 1990 through 1994, 1999, 2006, 2007, 2008, and 2009. EOF direct reduced iron consumption in 1990
through 1994 was assumed to equal consumption in 1995, and consumption in 1999 was assumed to equal the
average of 1998 and 2000. EOF consumption in 2006, 2007, and 2008 were calculated by multiplying the total DRI
consumption for all furnaces as provided in the 2009 AISI Annual Statistical Report by the EOF share of total DRI
consumption in 2005 (the most recent year that data was available for EAF vs. EOF consumption of DRI). The Tier
1 CO2 emission factors for sinter production and direct reduced iron production were obtained through the 2006
IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Data for pig iron production, coke, natural
gas, fuel oil, sinter, and pellets consumed in the blast furnace; pig iron production; and blast furnace gas produced at
the iron and steel mill and used in the metallurgical coke ovens and other steel mill activities were obtained from
AISI's Annual Statistical Report (AISI 2004 through 2010) and through personal communications with AISI
(2008b) (see Table 4-61). Data for EAF steel production, flux, EAF charge carbon, direct reduced iron, pig iron,
scrap steel, and natural gas consumption as well as EAF steel production were obtained from AISI's Annual
Statistical Report (AISI 2004 through 2010) and through personal communications with AISI (2011). The factor for
the quantity of EAF anode consumed per ton of EAF steel produced was provided by AISI (AISI 2008b). Data for
EOF steel production, flux, direct reduced iron, pig iron, scrap steel, natural gas, natural ore, pellet sinter
consumption as well as EOF steel production were obtained from AISI's Annual Statistical Report (AISI 2004
through 2010) and through personal communications with AISI (2008b). Because data on pig iron consumption and
scrap steel consumption in BOFs and EAFs were not available for 2006, 2007, and 2009, values for these years were
calculated by multiplying the total pig iron and scrap steel consumption for all furnaces as provided in the 2009 AISI
Annual Statistical Report by the EOF and EAF shares of total pig iron and scrap consumption in 2005 (the most
recent year that data was available for EAF vs. EOF consumption of pig iron and scrap steel). Because pig iron
consumption in EAFs was also not available in 2003 and 2004, the average of 2002 and 2005 pig iron consumption
data were used. Data on coke oven gas and blast furnace gas consumed at the iron and steel mill other than in the
EAF, EOF, or blast furnace were obtained from AISI's Annual Statistical Report (AISI 2004 through 2010) and
through personal communications with AISI (2008b). Data on blast furnace gas and coke oven gas sold for use as
synthetic natural gas were obtained through EIA's Natural Gas Annual 2009 (EIA 2010b). C contents for direct
reduced iron, EAF carbon electrodes, EAF charge carbon, limestone, dolomite, pig iron, and steel were provided by
4-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). The C contents for natural gas,
fuel oil, and direct injection coal as well as the heat contents for the same fuels were provided by EIA (1992, 2010c).
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 4-60: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Iron and Steel
Production (Thousand Metric Tons)
Source/Activity Data 1990
2000
2005
2006 2007 2008 2009
Sinter Production
Sinter Production 12,239J
Direct Reduced Iron
Production
Direct Reduced Iron
Production 936
Pig Iron Production
Coke Consumption 24,946
Pig Iron Production 49,669
Direct Injection Coal
Consumption 1,485
EAF Steel Production
EAF Anode and Charge
Carbon Consumption 67
Scrap Steel Consumption 35,7431
Flux Consumption 319
EAF Steel Production 3 3,5111
EOF Steel Production
Pig Iron Consumption 46,564
Scrap Steel Consumption 14,548
Flux Consumption 576
EOF Steel Production 43,9731
10,788
1,914
19,215
47,:
3,012
961
43,001
654
47,860
46,993
14,969
9781
53,965
8,315 7,088 6,914 6,497 3,814
1,633
13,832
37,222
2,573
1,127
37,558
695
52,194
32,115
11,612
582
42,705
1,497
2,087
1,769
1,243
14,684 15,039 14,251 8,572
37,904 36,337 33,730 19,019
2,526
2,734
2,578
1,674
1,245 1,214 1,109 845
38,033 40,845 40,824 35,472
671 567 680 476
56,071 57,004 52,791 36,700
32,638 33,773 29,322 23,134
11,759 12,628 8,029 6,641
610 408 431 318
42,119 41,099 39,105 22,659
Table 4-61: Production and Consumption Data for the Calculation of CO2 Emissions from Iron and Steel
Production (million ft3 unless otherwise specified)
Source/Activity Data 1990 2000 2005 2006 2007 2008
2009
Pig Iron Production
Natural Gas Consumption 56,273 91,798
Fuel Oil Consumption
(thousand gallons) 163,397B 120,921
Coke Oven Gas
Consumption 22,033 13,702
Blast Furnace Gas
Production 1,439,38(M 1,524,891
EAF Steel Production
Natural Gas Consumption 9,604 13,717
EOF Steel Production
Natural Gas Consumption 6,301 6,143
Coke Oven Gas
Consumption 3,851 640
Other Activities
Coke Oven Gas
Consumption 224,883| 135,135
Blast Furnace Gas
Consumption 1,414,778 1,498,816
59,844
16,170
16,557
58,344
87,702
16,649
56,112
84,498
16,239
1,299,980 1,236,526 1,173,588 1
14,959 16,070 16,337
5,026 5,827 11,740
524 559 525
97,132 97,178 93,148
1,295,520 1,231,021 1,168,444 1
53,349
55,552
15,336
,104,674
15,130
-4,304a
528
87,327
099,845
35,933
23,179
9,951
672,486
10,518
-2,670a
373
55,831
670,051
1 EPA is continuing to work with AISI to investigate why this value is negative.
Industrial Processes 4-43
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Uncertainty and Time-Series Consistency
The estimates of CO2 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 CO2 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 C 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. 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 (e.g.,
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 CO2 emissions. However, there are uncertainties associated
with each.
For the purposes of the CH4 calculation from iron and steel production it is assumed that all of the CH4 escapes as
fugitive emissions and that none of the CH4 is captured in stacks or vents. Additionally, the CO2 emissions
calculation is not corrected by subtracting the C content of the CH4, which means there may be a slight double
counting of C as both CO2 and CH4.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-62 for metallurgical coke
production and iron and steel production. Total CO2 emissions from metallurgical coke production and iron and
steel production were estimated to be between 35.2 and 48.4 Tg CO2 Eq. at the 95 percent confidence level. This
indicates arange of approximately 16 percent below and 16 percent above the emission estimate of 41.9 Tg CO2Eq.
Total CH4 emissions from metallurgical coke production and iron and steel production were estimated to be 0.4 Tg
CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 21 percent below and 23
percent above the emission estimate of 0.4 Tg CO2 Eq.
Table 4-62: Tier 2 Quantitative Uncertainty Estimates for CO2 and CH4 Emissions from Iron and Steel Production
and Metallurgical Coke Production (Tg. CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission
Source Gas Estimate Estimate"
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Metallurgical Coke & Iron and
Steel Production
Metallurgical Coke & Iron and
Steel Production
C02
CH4
41.9
0.4
35.2
0.3
48.4
0.4
-16%
-21%
+16%
+23%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
4-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Planned Improvements
Future improvements to the Iron and Steel production source category involve evaluating facility level greenhouse
gas emissions data as a basis for improving emissions calculations from iron and steel production. Beginning in
2010, all U.S. iron and steel producing facilities that emit over 25,000 tons of greenhouse gases (CO2 Eq.) are
required to monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse Gas
Reporting Program. Under the program, EPA will obtain data for 2010 from these facilities based on use of higher
tier methods and assess how this data could be used to improve the method for calculating emissions from the U.S.
iron and steel industry. Specifically, plans include attributing emissions 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. Additional 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.
Recalculations Discussion
In the previous Inventory, coal tar production and coke breeze production were incorrectly estimated by multiplying
the respective production factors by U.S. coke production at coke plants rather than U.S. coking coal consumption at
coke plants (to which the coal tar and coke breeze production factors should be applied). This issue has been
corrected and decreased the 1990 through 2008 emissions from metallurgical coke production by an average of 53
percent per year relative to the previous Inventory. The total 1990 through 2008 emissions for metallurgical coke
and iron and steel production decreased by an average of 3 percent per year relative to the previous Inventory.
4.14. Ferroalloy Production (IPCC Source Category 2C2)
CO2 and CH4 are emitted from the production of several ferroalloys. Ferroalloys are composites of iron and other
elements such as silicon, manganese, and chromium. When incorporated in alloy steels, ferroalloys are used to alter
the material properties of the steel. Estimates from two types of ferrosilicon (25 to 55 percent and 56 to 95 percent
silicon), silicon metal (about 98 percent silicon), and miscellaneous alloys (36 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. Subsequently, government information
disclosure rules prevent the publication of production data for these production facilities.
Similar to emissions from the production of iron and steel, CO2 is emitted when metallurgical coke is oxidized
during a high-temperature reaction with iron and the selected alloying element. Due to the strong reducing
environment, CO is initially produced, and eventually oxidized to CO2. A representative reaction equation for the
production of 50 percent ferrosilicon is given below:
Fe2O3 +2SiO2 +7C-»2FeSi+7CO
While most of the C contained in the process materials is released to the atmosphere as CO2, a percentage is also
released as CH4 and other volatiles. The amount of CH4 that is released is dependent on furnace efficiency,
operation technique, and control technology.
Emissions of CO2 from ferroalloy production in 2009 were 1.5 Tg CO2 Eq. (1,469 Gg) (see Table 4-63 and Table
4-64), which is a 32 percent reduction since 1990. Emissions of CH4 from ferroalloy production in 2009 were 0.01
Tg CO2 Eq. (0.406 Gg), which is a 40 percent decrease since 1990.
Table 4-63: CO2 and CH4 Emissions from Ferroalloy Production (Tg CO2 Eq.)
Year
C02
CH4
Total
1990
2.2
+
2.2
1 2000
1.9
| +
1 1-9
2005
1.4
+
1.4
2006
1.5
+
1.5
2007
1.6
+
1.6
2008
1.6
+
1.6
2009
1.5
+
1.6
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Industrial Processes 4-45
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Table 4-64: CO2 and CH4 Emissions from Ferroalloy Production (Gg)
Year
C02
CH4
1990
2,152 1
i •
• 2000
1,893 1
1 1 B
2005
1,392
+
2006
1,505
+
2007
1,552
+
2008
1,599
+
2009
1,469
+
Methodology
Emissions of CO2 and CH4 from ferroalloy production were calculated by multiplying annual ferroalloy production
by material-specific emission factors. Emission factors taken from the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) were applied to ferroalloy production. 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 (2.5 metric tons CO2/metric ton of alloy produced) and an emission factor for 65 percent silicon was applied for
CH4 (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 (4 metric tons
CO2/metric ton alloy produced and 1 kg CH4/metric ton of alloy produced, respectively). The emission factors for
silicon metal equaled 5 metric tons CO2/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 using an electric arc
furnace process (IPCC 2006), although some ferroalloys may have been produced with coking coal, wood, other
biomass, or graphite C 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.
Ferroalloy production data for 1990 through 2009 (see Table 4-65) were obtained from the USGS through personal
communications with the USGS Silicon Commodity Specialist (Corathers 2011) and through the Minerals
Yearbook: Silicon Annual Report (USGS 1991 through 2010). Because USGS does not provide estimates of silicon
metal production for 2006-2009, 2005 production data are used. Until 1999, the USGS reported production of
ferrosilicon containing 25 to 55 percent silicon separately from production of miscellaneous alloys containing 32 to
65 percent silicon; beginning in 1999, the USGS reported these as a single category (see Table 4-65). The
composition data for petroleum coke was obtained from Onder and Bagdoyan (1993).
Table 4-65: Production of Ferroalloys (Metric Tons)
Year
Ferrosilicon
25%-55%
Ferrosilicon
56%-95%
Silicon Metal
Misc. Alloys
32-65%
1990
321,385
109,566
145,744
72,442
2005
2006
2007
2008
2009
123,000
164,000
180,000
193,000
123,932
86,100
88,700
90,600
94,000
104,855
148,000
148,000
148,000
148,000
148,000
NA
NA
NA
NA
NA
NA (Not Available)
Uncertainty and Time-Series Consistency
Although some ferroalloys may be produced using wood or other biomass as a C source, 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 C is of biogenic origin.115 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 CO2 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.
115
Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
4-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Emissions of CH4 from ferroalloy production will vary depending on furnace specifics, such as type, operation
technique, and control technology. Higher heating temperatures and techniques such as sprinkle charging will
reduce CH4 emissions; however, specific furnace information was not available or included in the CH4 emission
estimates.
Also, annual ferroalloy production is now reported by the USGS in three broad categories: ferroalloys containing 25
to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95 percent silicon, and silicon
metal. It was assumed that the IPCC emission factors apply to all of the ferroalloy production processes, including
miscellaneous alloys. Finally, production data for silvery pig iron (alloys containing less than 25 percent silicon) are
not reported by the USGS to avoid disclosing company proprietary data. Emissions from this production category,
therefore, were not estimated.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-66. Ferroalloy production CO2
emissions were estimated to be between 1.3 and 1.7 Tg CO2 Eq. at the 95 percent confidence level. This indicates a
range of approximately 12 percent below and 13 percent above the emission estimate of 1.5 Tg 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 Tg CO2 Eq.
Table 4-66: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ferroalloy Production (Tg CO2 Eq.
and Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Ferroalloy Production CO2 1.5
Ferroalloy Production CH4 +
1.3 1.7 -12% +13%
+ + -12% +12%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
+ Does not exceed 0.05 Tg CO2 Eq.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the ferroalloy production source category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emissions calculations from ferroalloy production. Beginning in 2010, all
U.S. ferroalloy producing facilities that emit over 25,000 tons of greenhouse gases (CO2Eq.) are required to
monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse Gas Reporting
Program. Under the program, EPA will obtain data for 2010 from these facilities based on use of higher tier
methods and assess how this data could be used to improve the methodology and emissions factors for calculating
emissions from the U.S. ferroalloy industry, in particular, including emission estimates from production of
ferroalloys other than ferrosilicon and silicon metal. If data are available, emissions will be estimated for those
ferroalloys. Additionally, research will be conducted to determine whether data are available concerning raw
material consumption (e.g., coal coke, limestone and dolomite flux, etc.) for inclusion in ferroalloy production
emission estimates.
4.15. Aluminum Production (IPCC Source Category 2C3)
Aluminum is a light-weight, malleable, and corrosion-resistant metal that is used in many manufactured products,
including aircraft, automobiles, bicycles, and kitchen utensils. As of last reporting, the United States was the fourth
largest producer of primary aluminum, with approximately seven percent of the world total (USGS 2009a). The
United States was also a major importer of primary aluminum. The production of primary aluminum—in addition
to consuming large quantities of electricity—results in process-related emissions of CO2 and two perfluorocarbons
(PFCs): perfluoromethane (CF4) and perfluoroethane (C2F6).
CO2 is emitted during the aluminum smelting process when alumina (aluminum oxide, A12O3) is reduced to
aluminum using the Hall-Heroult reduction process. The reduction of the alumina occurs through electrolysis in a
Industrial Processes 4-47
-------
molten bath of natural or synthetic cryolite (Na3AlF6). The reduction cells contain a carbon lining that serves as the
cathode. Carbon is also contained in the anode, which can be a carbon mass of paste, coke briquettes, or prebaked
carbon blocks from petroleum coke. During reduction, most of this carbon is oxidized and released to the
atmosphere as CO2.
Process emissions of CO2 from aluminum production were estimated to be 3.0 Tg CO2 Eq. (3,009 Gg) in 2009 (see
Table 4-67). The carbon anodes consumed during aluminum production consist of petroleum coke and, to a minor
extent, coal tar pitch. The petroleum coke portion of the total CO2 process emissions from aluminum production is
considered to be a non-energy use of petroleum coke, and is accounted for here and not under the CO2 from Fossil
Fuel Combustion source category of the Energy sector. Similarly, the coal tar pitch portion of these CO2 process
emissions is accounted for here.
Table 4-67: CO2 Emissions from Aluminum Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990
6,831
2005
2006
2007
2008
2009
4.1
3.8
4.3
4.5
3.0
4,142
3,801
4,251
4,477
3,009
In addition to CO2 emissions, the aluminum production industry is also a source of PFC emissions. During the
smelting process, when the alumina ore content of the electrolytic bath falls below critical levels required for
electrolysis, rapid voltage increases occur, which are termed "anode effects." These anode effects cause carbon
from the anode and fluorine from the dissociated molten cryolite bath to combine, thereby producing fugitive
emissions of CF4 and C2F6. In general, the magnitude of emissions for a given smelter and level of production
depends on the frequency and duration of these anode effects. As the frequency and duration of the anode effects
increase, emissions increase.
Since 1990, emissions of CF4 and C2F6 have declined by 92 percent and 89 percent, respectively, to 1.3 Tg CO2 Eq.
of CF4 (0.20 Gg) and 0.30 Tg CO2 Eq. of C2F6 (0.032 Gg) in 2009, as shown in Table 4-68 and Table 4-69. 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. Since 1990, aluminum production has declined by
57 percent, while the combined CF4 and C2F6 emission rate (per metric ton of aluminum produced) has been reduced
by 80 percent.
Table 4-68: PFC Emissions from Aluminum Production (Tg CO2 Eq.)
Year CF4 C2F6 Total
1990
15.9
18.5
2005
2006
2007
2008
2009
2.5
2.1
3.2
2.2
1.3
0.4
0.4
0.6
0.5
0.3
3.0
2.5
3.8
2.7
1.6
Note: Totals may not sum due to independent rounding.
Table 4-69: PFC Emissions from Aluminum Production (Gg)
Year CF4 C2F6
1990
2.4
4-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
2006 0.3 +
2007 0.5 0.1
2008 0.3 0.1
2009 02 +
+ Does not exceed 0.05 Gg.
In 2009, U.S. primary aluminum production totaled approximately 1.7 million metric tons, a 35 percent decrease
from 2008 production levels (USAA 2010). In 2009, six companies managed production at 13 operational primary
aluminum smelters. Four smelters were closed the entire year, and demolition of one smelter that had been idle
since 2000 was completed in 2009. Of the operating smelters, three were temporarily idled during some fraction of
2009, and parts of four others were temporarily closed in 2009 (USGS 2010a). During 2009, U.S. primary
aluminum production was less for every month when compared to the corresponding month in 2008 (USGS 2009b,
USGS 2010b).
For 2010, total production during January through September was approximately 1.28 million metric tons, compared
to 1.32 million metric tons for the same period in 2009, only a 3 percent decrease (USGS 2010c). Based on the
similarity in production, process CO2 and PFC emissions are likely to be similar over this period in 2009 given no
significant changes in process controls at operational facilities.
Methodology
CO2 emissions released during aluminum production were estimated by combining individual partner reported data
with process-specific emissions modeling. These estimates are based on information gathered by EPA's Voluntary
Aluminum Industrial Partnership (VAIP) program.
Most of the CO2 emissions released during aluminum production occur during the electrolysis reaction of the carbon
anode, as described by the following reaction:
2A12O3 + 3C -» 4A1 + 3CO2
For prebake smelter technologies, CO2 is also emitted during the anode baking process. These emissions can
account for approximately 10 percent of total process CO2 emissions from prebake smelters.
Depending on the availability of smelter-specific data, the CO2 emitted from electrolysis at each smelter was
estimated from: (1) the smelter's annual anode consumption, (2) the smelter's annual aluminum production and rate
of anode consumption (per ton of aluminum produced) for previous and /or following years, or, (3) the smelter's
annual aluminum production and IPCC default CO2 emission factors. The first approach tracks the consumption and
C 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 total C consumption. 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 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 process data were not reported by these companies,
estimates were developed through linear interpolation, and/or assuming industry default values.
Industrial Processes 4-49
-------
In the absence of any previous 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 Soderberg and/or Prebake emission factors (metric ton of CO2 per
metric ton of aluminum produced) from IPCC (2006).
Aluminum production data for 10 out of 13 operating smelters were reported under the VAIP in 2009. Between
1990 and 2008, production data were provided 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 (USAA 2010), with allocation to specific smelters based on
reported production capacities (USGS 2009a).
PFC emissions from aluminum production were estimated using a per-unit production emission factor that is
expressed as a function of operating parameters (anode effect frequency and duration), as follows:
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)
This approach corresponds to either the Tier 3 or the Tier 2 approach in the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006), depending upon whether the slope-coefficient is smelter-specific (Tier 3)
or technology-specific (Tier 2). For 1990 through 2009, smelter-specific slope coefficients were available and were
used for smelters representing between 30 and 94 percent of U.S. primary aluminum production. The percentage
changed from year to year as some smelters closed or changed hands and as the production at remaining smelters
fluctuated. For smelters that did not report smelter-specific slope coefficients, IPCC technology-specific slope
coefficients were applied (IPCC 2000, 2006). The slope coefficients were combined with smelter-specific anode
effect data collected by aluminum companies and reported under the VAIP, to estimate emission factors over time.
For 1990 through 2009, smelter-specific anode effect data were available for smelters representing between 80 and
100 percent of U.S. primary aluminum production. Where smelter-specific anode effect data were not available,
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.
National primary aluminum production data for 2009 were obtained via the United States Aluminum Association
(USAA 2010). For 1990 through 2001, and 2006 (see Table 4-70) 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 2008 national aluminum production data were obtained from the USAA's Primary Aluminum Statistics
(USAA 2004, 2005, 2006, 2008, 2009).
Table 4-70: Production of Primary Aluminum (Gg)
Year Gg
1990 4,048
2005 2,478
2006 2,284
2007 2,560
2008 2,659
2009 1,727
4-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Uncertainty and Time Series Consistency
The overall uncertainties associated with the 2009 CO2, CF4, and C2F6 emission estimates were calculated using
Approach 2, as defined by IPCC (2006). For CO2, uncertainty was assigned to each of the parameters used to
estimate CO2 emissions. Uncertainty surrounding reported production data was assumed to be 1 percent (IPCC
2006). For additional variables, such as net C consumption, and sulfur and ash content in baked anodes, estimates
for uncertainties associated with reported and default data were obtained from IPCC (2006). A Monte Carlo
analysis was applied to estimate the overall uncertainty of the CO2 emission estimate for the U.S. aluminum industry
as a whole, and the results are provided below.
To estimate the uncertainty associated with emissions of CF4 and C2F6, the uncertainties associated with three
variables were estimated for each smelter: (1) the quantity of aluminum produced, (2) the anode effect minutes per
cell day (which may be reported directly or calculated as the product of anode effect frequency and anode effect
duration), and, (3) the smelter- or technology-specific slope coefficient. A Monte Carlo analysis was then applied to
estimate the overall uncertainty of the emission estimate for each smelter and for the U.S. aluminum industry as a
whole.
The results of this quantitative uncertainty analysis are summarized in Table 4-71. Aluminum production-related
CO2 emissions were estimated to be between 2.90 and 3.12 Tg CO2 Eq. at the 95 percent confidence level. This
indicates a range of approximately 4 percent below to 4 percent above the emission estimate of 3.01 Tg CO2 Eq.
Also, production-related CF4 emissions were estimated to be between 1.14 and 1.44 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 12 percent below to 12 percent above the emission
estimate of 1.29 Tg CO2 Eq. Finally, aluminum production-related C2F6 emissions were estimated to be between
0.25 and 0.35 Tg CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 17 percent
below to 19 percent above the emission estimate of 0.30 Tg CO2 Eq.
Table 4-71: Tier 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from Aluminum Production (Tg
CO2 Eq. and Percent)
Source Gas
Aluminum Production CO2
Aluminum Production CF4
Aluminum Production C2F6
_ ,. , Uncertainty Range Relative to 2009 Emission Estimate3
(Tg C02 Eq.) (Tg C02 Eq.) (%)
3.0
1.3
0.3
Lower Bound
2.9
1.1
0.2
Upper Bound
3.1
1.4
0.4
Lower Bound
-4%
-12%
-17%
Upper Bound
+4%
+12%
+19%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
The 2009 emission estimate was developed using either company-wide or site-specific PFC slope coefficients for all
but 1 of the 14 operating smelters where default IPCC (2006) slope data was used. In some cases, where smelters
are owned by one company, data have been reported on a company-wide basis as totals or weighted averages.
Consequently, in the Monte Carlo analysis, uncertainties in anode effect minutes per cell-day, slope coefficients, and
aluminum production have been applied to the company as a whole and not to each smelter. This probably
overestimates the uncertainty associated with the cumulative emissions from these smelters, because errors that were
in fact independent were treated as if they were correlated. It is therefore likely that the uncertainties calculated
above for the total U.S. 2009 emission estimates for CF4 and C2F6 are also overestimated.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Beginning in 2010, all primary U.S. aluminum producing facilities are required to monitor, calculate and report their
greenhouse gas emissions to EPA through its Greenhouse Gas Reporting Program. Under the program, EPA will
obtain data for 2010 from these facilities based on use of higher tier methods and assess how this data could be used
to improve the methodology and emissions factors for calculating emissions from the U.S. primary aluminum
production industry.
Industrial Processes 4-51
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4.16. Magnesium Production and Processing (IPCC Source Category 2C4)
The magnesium metal production and casting industry uses sulfur hexafluoride (SF6) as a cover gas to prevent the
rapid oxidation of molten magnesium in the presence of air. Sulfur hexafluoride has been used in this application
around the world for more than twenty-five years. A dilute gaseous mixture of SF6 with dry air and/or 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. Although alternative cover gases, such as AM-cover™ (containing
HFC-134a), Novec™ 612 and dilute SO2 systems can be used, many facilities in the United States are still using
traditional SF6 cover gas systems.
The magnesium industry emitted 1.1 Tg CO2 Eq. (0.04 Gg) of SF6 in 2009, representing a decrease of approximately
45 percent from 2008 emissions (See Table 4-72). The decrease can be attributed to die casting facilities in the
United States closing or halting production due to reduced demand from the American auto industry and other
industrial sectors (USGS 2010a). Production associated with primary and secondary facilities also dropped in 2009.
The significant reduction in emissions can also be attributed to industry efforts to switch to cover gas alternatives,
such as sulfur dioxide, as part of the EPA's SF6 Emission Reduction Partnership for the Magnesium Industry.
Table 4-72: SF6 Emissions from Magnesium Production and Processing (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 5.4
2005
2006
2007
2008
2009
2.9
2.9
2.6
1.9
1.1
0.1
0.1
0.1
0.1
0.04
Methodology
Emission estimates for the magnesium industry incorporate information provided by industry participants in EPA's
SF6 Emission Reduction Partnership for the Magnesium Industry. The Partnership started in 1999 and, currently,
participating companies represent 100 percent of U.S. primary and secondary production and 90 percent of the
casting sector production (i.e., die, sand, permanent mold, wrought, and anode casting). Absolute emissions for
1999 through 2009 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. When a partner did not report emissions, they were estimated based on the metal processed and
emission rate reported by that partner in previous and (if available) subsequent years. Where data for subsequent
years was not available, metal production and emissions rates were extrapolated based on the trend shown by
partners reporting in the current and previous years. When it was determined a Partner is no longer in production,
their metal production and emissions rates were set to zero if no activity information was available; in one case a
partner that closed mid-year was estimated to have produced 50 percent of the metal from the prior year.
Emission factors for 2002 to 2006 for sand casting activities were also acquired through the Partnership. For 2007,
2008 and 2009, the sand casting partner did not report and the reported emission factor from 2005 was utilized as
being representative of the industry. The 1999 through 2009 emissions from casting operations (other than die)
were estimated by multiplying emission factors (kg SF6 per metric ton of metal produced or processed) by the
amount of metal produced or consumed. The emission factors for casting activities are provided below in Table
4-73. The emission factors for primary production, secondary production and sand casting are withheld 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. U.S.
magnesium consumption (casting) data from 1990 through 2009 were available from the USGS (USGS 2002, 2003,
2005, 2006, 2007, 2008, 2010).
4-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 4-73: SF6 Emission Factors (kg SF6 per metric ton of magnesium)
Year Die Casting Permanent Mold Wrought Anodes
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2.14a
0.72
0.72
0.71
0.81
0.81
0.79
0.86
0.67
1.15b
1.77b
2
2
2
2
2
2
2
2
2
2
2
1
1
1
1
1
1
1
1
1
1
1
a This is a weighted average that includes an estimated emission factor of 5.2 kg SF6 per metric ton of magnesium for die casters
that did not participate in the Partnership in 1999. These die casters were assumed to be similar to partners that 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. In later years, die casters participating in the Partnership accounted for all U.S. die casting tracked by
USGS.
b The emission factor for die casting increased significantly between 2007 and 2008, and again between 2008 and 2009. These
increases occurred for two reasons. First, one of the die casters with a significant share of U.S. production that had used SF6 as a
cover gas and that had maintained a relatively low emission rate began using an alternative cover gas in 2008. Since the SF6
emission factor provided here is based only on die casting operations that use SF6 as a cover gas, the removal of the low-emitting
die caster from the SF6-using group increased the weighted average emission rate of that group. Second, one SF6-using die caster
experienced a significant leak in its cover gas distribution system in 2009 that resulted in an abnormally high SF6 emission rate.
To estimate emissions for 1990 through 1998, industry emission factors were multiplied by the corresponding metal
production and consumption (casting) statistics from USGS. The primary production emission factors were 1.2 kg
per metric ton for 1990 through 1993, and 1.1 kg per metric ton for 1994 through 1997. These factors were based on
information provided by U.S. primary producers. For die casting, an emission factor of 4.1 kg per metric ton was
used for the period 1990 through 1996. This factor was drawn from an international survey of die casters (Gjestland
& Magers 1996). For 1996 through 1998, the emission factors for primary production and die casting were 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 2002 through
2009 were provided by the Magnesium Partnership participants, and 1990 through 2001 emission factors for this
process were assumed to have been the same as the 2002 emission factor. The emission factor for secondary
production from 1990 through 1998 was assumed to be constant at the 1999 average partner value. 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 4-73.
Uncertainty
To estimate the uncertainty surrounding the estimated 2009 SF6 emissions from magnesium production and
processing, the uncertainties associated with three variables were estimated (1) emissions reported by magnesium
producers and processors that participate in the Magnesium Partnership, (2) emissions estimated for magnesium
producers and processors that participate in the Partnership but did not report this year, and (3) emissions estimated
for magnesium producers and processors that do not participate in the Partnership. An uncertainty of 5 percent was
assigned to the data reported by each participant in the Partnership. If partners did not report emissions data during
the current reporting year, SF6 emissions data were estimated using available emission factor and production
information reported in prior years; the extrapolation was based on the average trend for partners reporting in the
current reporting year and the year prior. The uncertainty associated with the SF6 usage estimate generated from the
extrapolated emission factor and production information was estimated to be 30 percent for each year of
extrapolation. The lone sand casting partner did not report in the past two reporting years 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 52 percent. For those
industry processes that are not represented in 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 4-53
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specific emission factors (see Table 4-73). The uncertainties associated with the emission factors and USGS-
reported statistics were assumed to be 75 percent and 25 percent, respectively. Emissions associated with sand
casting activities utilized a partner-reported emission factor with an uncertainty 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. Recent 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 Tier 2 quantitative uncertainty analysis are summarized in Table 4-74. SF6 emissions associated
with magnesium production and processing were estimated to be between 1.01 and 1.10 Tg CO2 Eq. at the 95
percent confidence level. This indicates a range of approximately 6 percent below to 5 percent above the 2008
emission estimate of 1.05 Tg CO2 Eq.
Table 4-74: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Magnesium Production and
Processing (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Magnesium Production SF6 1.05
1.01 1.10 -4% +4%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Recalculations Discussion
The uncertainty estimates for 2009 are lower relative to the previous inventory uncertainty estimate for 2008
emissions, which is likely due to the fact that emission estimates for 2009 are based more on actual reported data
than emission estimates for 2008 were in the 1990-2008 inventory, with two emission sources using projected
(highly uncertain) estimates.
Planned Improvements
Cover gas research conducted by the EPA 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 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC
2006)) that all SF6 utilized is emitted to the atmosphere. Additional research may lead to a revision of IPCC
Guidelines to reflect this phenomenon and until such time, developments in this sector will be monitored for
possible application to the inventory methodology. Another issue that will be addressed in future inventories is the
likely adoption of alternate cover gases by U.S. magnesium producers and processors. These cover gases, which
include AM-cover™ (containing HFC-134a) and Novec™ 612, have lower GWPs than SF6, and tend to quickly
degrade during their exposure to the molten metal. Magnesium producers and processors have already begun using
these cover gases for 2006 through 2009 in a limited fashion; because the amounts being used by companies on the
whole are low enough that they have a minor effect on the overall emissions from the industry, these emissions are
only being monitored and recorded at this time.
4.17. Zinc Production (IPCC Source Category 2C5)
Zinc production in the United States consists of both primary and secondary processes. Primary production in the
United States is conducted through the electrolytic process while secondary techniques used in the United States
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).
Of the primary and secondary processes used in the United States, only the electrothermic and Waelz kiln secondary
4-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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processes result in non-energy CO2 emissions (Viklund-White 2000).
During the electrothermic zinc production 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
added to the electric retort furnace reduces the zinc oxides and produces vaporized zinc, which is then captured in a
vacuum condenser. This reduction process produces non-energy CO2 emissions (Sjardin 2003).
In the Waelz kiln process, EAF dust, which is captured during the recycling of galvanized steel, enters a kiln along
with a reducing agent—often 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. Through
this process, approximately 0.33 metric ton of zinc is produced for every metric ton of EAF dust treated (Viklund-
White 2000).
In 2009, U.S. primary and secondary zinc production was estimated to total 286,000 metric tons (USGS 2010).
Since reported activity data for 2009 were not available for all necessary inputs in time for this publication,
production values in 2009 were assumed to equal 2008 values in some cases. The resulting emissions of CO2 from
zinc production in 2009 were estimated to be 0.97 Tg CO2 Eq. (966 Gg) (see Table 4-75). All 2009 CO2 emissions
resulted from secondary zinc production.
Table 4-75: CO2 Emissions from Zinc Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 0.7 667
2005
2006
2007
2008
2009
1.1
1.1
1.1
1.2
1.0
1088
1088
1081
1230
966
Emissions from zinc production in the U.S. have increased overall due to a gradual shift from non-emissive primary
production to emissive secondary production. In 2009, emissions were estimated to be 45 percent higher than they
were in 1990.
Methodology
Non-energy CO2 emissions from zinc production result from the electrothermic and Waelz kiln secondary
production processes, which both use metallurgical coke or other C-based materials as reductants. Sjardin (2003)
provides an emission factor of 0.43 metric tons CO2/metric ton zinc produced for emissive zinc production
processes; however, this emission factor is based on the Imperial Smelting Furnace production process. Because the
Imperial Smelting Furnace production process is not used in the United States, emission factors specific to
electrothermic and Waelz kiln processes were needed. Due to the limited amount of information available for these
electrothermic processes, only Waelz kiln process-specific emission factors were developed. These emission factors
were applied to both the Waelz kiln and electrothermic secondary zinc production processes.
A 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, 1.19 metric tons coke/metric ton
zinc produced (Viklund-White 2000), and the following equation:
I.l9metrictonscoke 0.85 metrictons C *& metric tons CO 3.70 metric tons CO
EF = x x — = —
Waelz Kiln metric tons zinc metric tons coke metric tons C metric tons zinc
In addition, a 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, 0.4 metric tons coke/metric ton EAF dust
Industrial Processes 4-55
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consumed (Viklund-White 2000), and the following equation:116
QAmetrictomcoke 0.85 metrictons C WmetrictonsCO 1.24 metric tons CO^
EF
— A A —
EAFDust metrictons EAFdust metric tons coke metrictonsC metric tons EAF Dust
The only companies in the United States that use emissive technology to produce secondary zinc products are
Horsehead Corp and Steel Dust Recycling. For Horsehead Corp, EAF dust is recycled in Waelz kilns at their
Beaumont, TX; Calumet, IL; Palmerton, PA; and Rockwood, TN facilities (and soon to be performed at their new
South Carolina facility). 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 Corp 2010). Steel Dust Recycling recycles
EAF dust into intermediate zinc products using Waelz kilns, and then sells the intermediate products to companies
who smelt it into refined products.
The total amount of EAF dust consumed by Horsehead Corp at their Waelz kilns was available from Horsehead
financial reports foryears 2006 through 2009 (Horsehead 2010). 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 1994 through 2010). The EAF dust consumption
values for each year were then multiplied by the 1.24 metric tons CO2/metric ton EAF dust consumed emission
factor to develop CO2 emission estimates for Horsehead's Waelz kiln facilities.
The amount of EAF dust consumed by the Steel Dust Recycling facility for 2008 and 2009 (the only two years it has
been in operation) was not publically available. Therefore, these consumption values were estimated by calculating
the 2008 and 2009 capacity utilization of Horsehead's Waelz kilns and multiplying this utilization ratio by the
capacity of Steel Dust Recycling's facility, which were available from the company (Steel Dust Recycling LLC
2010). The 1.24 metric tons CO2/metric ton EAF dust consumed emission factor was then applied to Steel Dust
Recycling's estimated EAF dust consumption to develop CO2 emission estimates for its Waelz kiln facility.
Refined zinc production levels for Horsehead's Monaca, PA facility (utilizing electrothermic technology) were
available from the company foryears 2005 through 2009 (Horsehead Corp 2010, Horsehead Corp 2008).
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 1994
through 2010). 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.
Table 4-76: Zinc Production (Metric Tons)
Year Primary Secondary
1990 262,704 95,708
2000 227,800 143,000
2005
2006
2007
2008
2009
191,120
113,000
121,000
125,000
125,000
156,000
156,000
157,000
161,000
161,000
116 For Waelz kiln based secondary zinc production, IPCC recommends the use of emission factors based on EAF dust
consumption 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 (IPCC 2006).
4-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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 estimate of the amount of EAF
dust consumed at a Waelz kiln facility operated in Alabama by Steel Dust Recycling LLC. Since actual EAF dust
consumption information is not available for the Steel Dust Recycling LLC facility, 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). Therefore,
there is uncertainty associated with the assumption that the capacity utilization of Steel Dust Recycling LLC's
Waelz kiln facility is equal to the capacity utilization of Horsehead's Waelz kiln facility. 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 Tier 2 quantitative uncertainty analysis are summarized in
Table 4-77. Zinc production CO2 emissions were estimated to be between 0.8 and 1.1 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of approximately 17 percent below and 18 percent above the emission
estimate of 1.0 Tg CO2 Eq.
Table 4-77: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc Production (Tg CO2 Eq. and
Percent)
2009 Emission
Source Gas Estimate
(TgC02Eq.)
Uncertainty Range Relative to Emission Estimate"
(TgC02Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Zinc Production CO2 1.0
0.8 1.1 -17% +18%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Future improvements to the zinc production source category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emissions calculations from zinc production. Beginning in 2010, all U.S.
zinc producing facilities (both primary and secondary) that emit over 25,000 tons of greenhouse gases (CO2 Eq.) are
required to monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse Gas
Reporting Program. Under the program, EPA will obtain data for 2010 from these facilities based on use of higher
tier methods and assess how this data could be used to improve the methodology and emissions factors for
calculating emissions from the U.S. zinc production industry.
Recalculations Discussion
The methodology for estimating CO2 emissions from zinc production was revised for the current Inventory based on
the availability of new data regarding secondary zinc production in the United States. The previous Inventory
methodology assumed that two facilities had produced zinc in the United States using emissive processes since
1990: Horsehead Corporation's Monaca, PA facility (electrothermic) and Horsehead Corporation's Palmerton, PA
facility (Waelz kiln). The 3.70 metric tons CO2/metric ton zinc emission factor was applied to the estimated refined
zinc production at the Monaca, PA electrothermic facility, and the 1.24 metric tons CO2/metric ton EAF dust
consumed emission factor was applied to the estimated EAF dust consumption at the Palmerton, PA Waelz kiln
facility. The annual zinc production (for the Monaca facility) and EAF dust consumption (for the Palmerton
Industrial Processes 4-57
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facility) were estimated using historic values that were published in articles for select years (extrapolation
techniques were used for years in which published data was not available). The Monaca, PA facility was assumed to
have closed in 2003 and not operated since.
New data for the industry showed that there were emissive zinc-producing facilities not being captured by the
previous Inventory methodology. The facilities that were not captured included three Horsehead Corp Waelz kiln
facilities in Beaumont, TX; Calumet, IL; and Rockwood, TN as well as a Waelz kiln facility commissioned in 2008
in Millport, AL by Steel Dust Recycling LLC. Also, research showed that the Monaca, PA facility only closed
temporarily in 2003 and has been operating every year since (the Monaca, PA facility produces refined zinc from
intermediary zinc products produced at Horsehead's other facilities). The updated methodology utilizes EAF dust
consumption values and secondary zinc production values released annually by the main secondary zinc producer in
the United States (Horsehead Corp.), and also includes the previously overlooked secondary zinc producing
facilities in the emission estimates.
As a result of the revised methodology, historical emission estimates decreased by an average of 11 percent between
1990 and 2002, while emission estimates increased by an average of 140 percent between 2003 and 2009. The
significant changes in emission estimates for years 2005 through 2008 were largely driven by Horsehead Corp's
Monaca, PA facility being captured in the emission calculations for these years.
4.18. Lead Production (IPCC Source Category 2C5)
Lead production in the United States consists of both primary and secondary processes—both of which emit CO2
(Sjardin 2003). Primary lead production, in the form of direct smelting, occurs at a just a single plant in Missouri.
Secondary production largely involves the recycling of lead acid batteries at approximately 21 separate smelters in
the United States. Fifteen of those secondary smelters have annual capacities of 15,000 tons or more and were
collectively responsible for 99 percent of secondary lead production in 2009 (USGS 2010). Secondary lead
production has increased in the United States over the past decade while primary lead production has decreased. In
2009, secondary lead production accounted for approximately 92 percent of total lead production (USGS 2011).
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). U.S. primary lead production
decreased by 24 percent from 2008 to 2009, and has decreased by 75 percent since 1990 (USGS 2011, USGS 1995).
Similar to primary lead production, CO2 emissions from secondary production result when a reducing agent, usually
metallurgical coke, is added to the smelter to aid in the reduction process. CO2 emissions from secondary production
also occur through the treatment of secondary raw materials (Sjardin 2003). U.S. secondary lead production
decreased from 2008 to 2009 by 3 percent, and has increased by 20 percent since 1990 (USGS 2011, USGS 1995).
At last reporting, the United States was the third largest mine producer of lead in the world, behind China and
Australia, accounting for 11 percent of world production in 2009 (USGS 2011). In 2009, U.S. primary and
secondary lead production totaled 1,213,000 metric tons (USGS 2011). The resulting emissions of CO2 from 2009
production were estimated to be 0.5 Tg CO2 Eq. (525 Gg) (see Table 4-78). The majority of 2009 lead production is
from secondary processes, which accounted for 95 percent of total 2009 CO2 emissions.
Table 4-78: CO2 Emissions from Lead Production (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 0.5 516
2005
2006
2007
2008
2009
0.6
0.6
0.6
0.6
0.5
553
560
562
551
525
After a gradual decrease in total emissions from 1990 to 1995, total emissions have gradually increased since 1995
4-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
and emissions in 2009 were two percent greater than in 1990. Although primary production has decreased
significantly (75 percent since 1990), secondary production has increased by about 20 percent over the same time
period. Since secondary production is more emissions-intensive, the increase in secondary production since 1990
has resulted in a net increase in emissions despite the sharp decrease in primary production (USGS 2011, USGS
1994).
Methodology
Non-energy CO2 emissions from lead production result from primary and secondary production processes that use
metallurgical coke or other C-based materials as reductants. For primary lead production using direct smelting,
Sjardin (2003) and the IPCC (2006) provide an emission factor of 0.25 metric tons CO2/metric ton lead. For
secondary lead production, Sjardin (2003) and IPCC (2006) provide an emission factor of 0.25 metric tons
CO2/metric ton lead for direct smelting as well as an emission factor of 0.2 metric tons CO2/metric ton lead
produced for the treatment of secondary raw materials (i.e., pretreatment of lead acid batteries). 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 CO2 emissions.
The 1990 through 2009 activity data for primary and secondary lead production (see Table 4-79) were obtained
through the USGS Mineral Yearbook: Lead (USGS 1994 through 2011).
Table 4-79: Lead Production (Metric Tons)
Year Primary Secondary
1990 404,000
922,000
2005
2006
2007
2008
2009
143,000
153,000
123,000
135,000
103,000
1,150,000
1,160,000
1,180,000
1,150,000
1,110,000
Uncertainty and Time-Series Consistency
Uncertainty associated with lead production relates to the emission factors and activity data used. The direct
smelting emission factor used in primary production is taken from Sjardin (2003) who averages the values provided
by three other studies (Dutrizac et al. 2000, Morris et al. 1983, Ullman 1997). For secondary production, Sjardin
(2003) adds a CO2 emission factor associated with battery treatment. The applicability of these emission factors to
plants in the United States is uncertain. There is also a smaller level of uncertainty associated with the accuracy of
primary and secondary production data provided by the USGS.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-80. Lead production CO2
emissions were estimated to be between 0.5 and 0.6 Tg CO2 Eq. at the 95 percent confidence level. This indicates a
range of approximately 14 percent below and 15 percent above the emission estimate of 0.5 Tg CO2 Eq.
Table 4-80: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead Production (Tg CO2 Eq. and
Percent)
Source
Lead Production
Gas
CO2
2009 Emission
Estimate
(Tg C02 Eq.)
0.5
Uncertainty Range Relative
(Tg C02 Eq.)
Lower Upper
Bound Bound
0.5 0.6
to Emission Estimate"
(%)
Lower Upper
Bound Bound
-14% +15%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-59
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Planned Improvements
Future improvements to the lead production source category involve evaluating facility level greenhouse gas
emissions data as a basis for improving emission calculations from lead production. Beginning in 2010, all U.S.
lead producing facilities (primary and secondary) that emit over 25,000 tons of greenhouse gases (CO2Eq.) are
required to monitor, calculate and report their greenhouse gas emissions to EPA through its Greenhouse Gas
Reporting Program. Under the Program, EPA will obtain data for 2010 from these facilities based on use of higher
tier methods and assess how this data could be used to improve the methodology and emissions factors for
calculating emissions from the U.S. lead production industry.
Recalculations Discussion
In previous Inventory reports, CO2 emissions from secondary lead production were estimated by multiplying
secondary lead production values from USGS by an emission factor of 0.2 metric tons CO2/metric ton lead
produced. This emission factor is provided by Sjardin (2003) and IPCC (2006) for the treatment of secondary raw
materials (i.e., pretreatment of lead acid batteries). Due to a misinterpretation of language in Sjardin (2003) and
IPCC (2006), this was the only emission factor applied to secondary lead production even though an emission factor
of 0.25 metric tons CO2/metric ton lead for direct smelting should have been applied as well. This issue has been
corrected for the current Inventory, and increased 1990 through 2008 emissions from lead production by an average
of 95 percent per year relative to the previous Inventory.
4.19. HCFC-22 Production (IPCC Source Category 2E1)
Trifluoromethane (HFC-23 or CHF3) is generated as a by-product 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. Because HCFC-22 depletes stratospheric ozone, its production for non-feedstock uses is scheduled to be
phased out by 2020 under the U.S. Clean Air Act.117 Feedstock production, however, is permitted to continue
indefinitely.
HCFC-22 is produced by the reaction of chloroform (CHC13) and hydrogen fluoride (HF) in the presence of a
catalyst, SbCl5. 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 (CHC12F), 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.
Emissions of HFC-23 in 2009 were estimated to be 5.4 Tg CO2 Eq. (0.5 Gg) (Table 4-81). This quantity represents
a 60 percent decrease from 2008 emissions and a 85 percent decline from 1990 emissions. The decrease from 2008
emissions was caused by a 27 percent decrease in HCFC-22 production and a 46 percent decrease in the HFC-23
emission rate. The decline from 1990 emissions is due to a 34 percent decrease in HCFC-22 production and a 78
percent decrease in the HFC-23 emission rate since 1990. The decrease in the emission rate is primarily attributable
to five factors: (a) five plants that did not capture and destroy the HFC-23 generated have ceased production of
HCFC-22 since 1990, (b) one plant that captures and destroys the HFC-23 generated began to produce HCFC-22, (c)
one plant implemented and documented a process change that reduced the amount of HFC-23 generated, and (d) the
same plant began recovering HFC-23, primarily for destruction and secondarily for sale, and (e) another plant began
destroying HFC-23. All three HCFC-22 production plants operating in the United States in 2009 used thermal
oxidation to significantly lower their HFC-23 emissions.
117 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]
4-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 4-81: HFC-23 Emissions from HCFC-22 Production (Tg CO2 Eq. and Gg)
Year TgCO2Eq. Gg
1990
36.4
2005
2006
2007
2008
2009
15.8
13.8
17.0
13.6
5.4
1
1
1
1
0.46
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 for National Greenhouse Gas
Inventories (IPCC 2006) were used. For the other three plants, the last of which closed in 1993, methods
comparable to the Tier 1 method in the 2006 IPCC Guidelines were used. Emissions from these three plants have
been calculated using the recommended emission factor for unoptimized plants operating before 1995 (0.04 kg
HCFC-23/kg HCFC-22 produced).
The five plants that have operated since 1994 measured concentrations of HFC-23 to estimate their emissions of
HFC-23. Plants using thermal oxidation to abate their HFC-23 emissions monitor the performance of their oxidizers
to verify that the HFC-23 is almost completely destroyed. Plants that release (or historically have released) some of
their byproduct HFC-23 periodically measure HFC-23 concentrations in the output stream using gas
chromatography. This information is combined with information on quantities of products (e.g., HCFC-22) to
estimate HFC-23 emissions.
In most years, including 2010, an industry association aggregates and reports to EPA country-level estimates of
HCFC-22 production and HFC-23 emissions (ARAP 1997, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,
2008, 2009, 2010). However, in 1997 and 2008, EPA (through a contractor) performed comprehensive reviews of
plant-level estimates of HFC-23 emissions and HCFC-22 production (RTI 1997; RTI 2008). These reviews enabled
EPA to review, update, and where necessary, correct U.S. totals, and also to perform plant-level uncertainty analyses
(Monte-Carlo simulations) for 1990, 1995, 2000, 2005, and 2006. Estimates of annual U.S. HCFC-22 production
are presented in Table 4-82.
Table 4-82: HCFC-22 Production (Gg)
Year Gg
1990
2005
2006
2007
2008
2009
156
154
162
126
91
Uncertainty and Time Series Consistency
The uncertainty analysis presented in this section was based on a plant-level Monte Carlo 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
Industrial Processes 4-61
-------
confidence interval for U.S. emissions of 6.8 percent below to 9.6 percent above the reported total.
Because plant-level emissions data for 2009 were not available, the relative errors yielded by the Monte Carlo
simulation for 2006 were applied to the U.S. emission estimate for 2009. The resulting estimates of absolute
uncertainty are likely to be 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 2008 and 2009 (because both HCFC-22 production and the HFC-23 emission rate
declined significantly), the two plants that contribute significantly to emissions were estimated to have similar
relative uncertainties in their 2006 (as well as 2005) emission estimates. Thus, changes in the relative contributions
of these two plants to total emissions are not likely to have a large impact on the uncertainty of the national emission
estimate.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-83. HFC-23 emissions from
HCFC-22 production were estimated to be between 5.0 and 5.9 Tg CO2 Eq. at the 95percent confidence level. This
indicates a range of approximately 7 percent below and 10 percent above the emission estimate of 5.4 Tg CO2 Eq.
Table 4-83: Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22 Production (Tg CO2 Eq. and
Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
HCFC-22 Production HFC-23 5.4
5.0 5.9 -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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Beginning in 2010, all U.S. HCFC-22 production facilities are required to calculate and report their greenhouse gas
emissions to EPA through its Greenhouse Gas Reporting Program. Data collected under this program will be used in
future inventories to improve the calculation of national emissions from HCFC-22 production
4.20. 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 1990.118 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
4-84 and Table 4-85.
Table 4-84: Emissions of HFCs and PFCs from OPS Substitutes (Tg CO2 Eq.)
Gas 1990 2000 2005 2006 2007 2008 2009
HFC-23 +1 +1 + + + + + ~
HFC-32 + +1 0.3 0.6 1.0 1.3 1.7
HFC-125 + 5.2 10.1 12.5 15.1 18.2 21.6
HFC-134a + 60.4 75.1 75.0 72.3 69.3 66.7
HFC-143a + 4.1 12.2 14.4 16.7 19.2 22.0
HFC-236fa + 0.5 0.8 0.8 0.9 0.9 0.9
118
[42U.S.C§7671,CAA§601]
4-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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CF4
Others*
Total
0.3
4.0
74.3
5.6
104.2
6.0
109.4
6.3
112.3
6.7
115.5
7.0
120.0
+ Does not exceed 0.05 Tg CO2 Eq.
* Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, C4F10, 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 C6F14.
Note: Totals may not sum due to independent rounding.
Table 4-85: Emissions of HFCs and PFCs from OPS Substitution (Mg)
Gas 1990 2000 2005 2006 2007
HFC-23
HFC-32
HFC-125
HFC-134a
HFC-143a
HFC-236fa
CF4
Others*
2008
1
971
4,453
57,728
3,782
131
2
M
1
1,465
5,393
55,603
4,402
136
2
M
2
1,977
6,486
53,294
5,044
141
2
M
2009
2
2,540
7,730
51,281
5,798
144
2
M
M (Mixture of Gases)
+ Does not exceed 0.5 Mg
* Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, C4F10, and PFC/PFPEs, the latter being a proxy for a
diverse collection of PFCs and perfluoropoly ethers (PFPEs) employed for solvent applications.
In 1990 and 1991, the only significant emissions of HFCs and PFCs as substitutes to ODSs were relatively small
amounts of HFC-152a—used as an aerosol propellant and also a component of the refrigerant blend R-500 used in
chillers—and HFC-134a in refrigeration end-uses. Beginning in 1992, HFC-134a was used in growing amounts as a
refrigerant in motor vehicle air-conditioners and in refrigerant blends such as R-404A.119 In 1993, the use of HFCs
in foam production began, and in 1994 these compounds also found applications as solvents. In 1995, ODS
substitutes for halons entered widespread use in the United States as halon production was phased-out.
The use and subsequent emissions of HFCs and PFCs as ODS substitutes has been increasing from small amounts in
1990 to 120.0 Tg CO2 Eq. in 2009. 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 accelerate 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 4-86 presents emissions of HFCs and PFCs as ODS substitutes by end-use sector for 1990 through 2009. The
end-use sectors that contributed the most toward emissions of HFCs and PFCs as ODS substitutes in 2009 include
refrigeration and air-conditioning (104.9 Tg CO2 Eq., or approximately 87 percent), aerosols (9.1 Tg CO2 Eq., or
approximately 8 percent), and foams (3.9 Tg CO2 Eq., or approximately 3 percent). Within the refrigeration and air-
conditioning end-use sector, motor vehicle air-conditioning was the highest emitting end-use (45.9 Tg CO2 Eq.),
followed by refrigerated retail food and transport. Each of the end-use sectors is described in more detail below.
Table 4-86: Emissions of HFCs and PFCs from ODS Substitutes (Tg CO2 Eq.) by Sector
Gas
1990
2000
2005
2006 2007 2008 2009
Refrigeration/Air Conditioning +
Aerosols 0.3
Foams +
Solvents +
Fire Protection +
I
93.1
7.3
1.9
1.3
0.5
97.6
7.7
2.1
1.3
0.6
99.8
8.2
2.3
1.3
0.7
102.3
8.6
2.5
1.3
0.7
104.9
9.1
3.9
1.3
0.8
Total
0.3
74.3
104.2 109.4 112.3 115.5 120.0
119 R-4Q4A contains HFC-125, HFC-143a, and HFC-134a.
Industrial Processes 4-63
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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-410A120, R-404A, and R-507A121. 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 committed
to replace 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 consumer aerosol products have not transitioned to HFCs, but to "not-in-kind" technologies,
such as solid roll-on deodorants and finger-pump sprays. The transition away from ODS in specialty aerosol
products has also led to the introduction of non-fluorocarbon alternatives (e.g., hydrocarbon propellants) in certain
applications, in addition to HFC-134a or HFC-152a. These propellants are released into the atmosphere as the
aerosol products are used.
Foams
CFCs and HCFCs have traditionally been used as foam blowing agents to produce polyurethane (PU), polystyrene,
polyolefin, and phenolic foams, which are used in a wide variety of products and applications. Since the Montreal
Protocol, flexible PU foams as well as other types of foam, such as polystyrene sheet, polyolefin, and phenolic foam,
have transitioned almost completely away from fluorocompounds, into alternatives such as CO2, methylene
chloride, and hydrocarbons. The majority of rigid PU foams have transitioned to HFCs—primarily HFC-134a and
HFC-245fa. Today, these HFCs are used to produce polyurethane appliance, PU commercial refrigeration, PU
spray, and PU panel foams—used in refrigerators, vending machines, roofing, wall insulation, garage doors, and
cold storage applications. In addition, HFC-152a is 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 or TCA), and to a lesser extent carbon tetrachloride (CC14) were
historically used as solvents in a wide range of cleaning applications, including precision, electronics, and metal
cleaning. Since their phaseout, metal cleaning end-use applications have primarily transitioned to non-fluorocarbon
solvents and not-in-kind processes. The precision and electronics cleaning end-uses have transitioned in part to high-
GWP gases, due to their high reliability, excellent compatibility, good stability, low toxicity, and selective solvency.
These applications rely on HFC-43 lOmee, HFC-365mfc, HFC-245fa, and to a lesser extent, PFCs. Electronics
cleaning involves removing flux residue that remains after a soldering operation for printed circuit boards and other
contamination-sensitive electronics applications. Precision cleaning may apply to either electronic components or to
metal surfaces, and is characterized by products, such as disk drives, gyroscopes, and optical components, that
require a high level of cleanliness and generally have complex shapes, small clearances, and other cleaning
120 R.41QA contains HFC-32 and HFC-125.
121 R-507A, also called R-507, contains HFC-125 and HFC-143a.
4-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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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-236ea 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, HFC-236fa, 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 nearly 60 different end-uses, the
model produces estimates of annual use and emissions of each compound. Further information on the Vintaging
Model is contained in Annex 3.8.
Uncertainty
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 nearly 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 5 other end-uses. These 26 end-uses comprise 97 percent of the total
emissions. 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 retail food equipment and refrigerated transport, as well as the percent of
non-MDI aerosol propellant that is HFC-152a.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-87. Substitution of ozone
depleting substances HFC and PFC emissions were estimated to be between 111.8 and 129.3 Tg CO2 Eq. at the 95
percent confidence level. This indicates a range of approximately 7 percent below to 8 percent above the emission
Industrial Processes 4-65
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estimate of 120.0 Tg CO2 Eq.
Table 4-87: Tier 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS Substitutes (Tg CO2
Eq. and Percent)
Source
2009 Emission
Gases Estimate
(Tg C02 Eq.)a
Uncertainty Range Relative to Emission Estimate1"
(Tg C02 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Substitution of Ozone
Depleting HFCs and
Substances PFCs 117.1 109.0 126.5 -7% +8%
a 2009 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, but not for other remaining categories. Therefore, because the
uncertainty associated with emissions from "other" ODS substitutes was not estimated, they were excluded in the estimates
reported in this table.
b Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Recalculations Discussion
An extensive review of the MDI aerosol, unitary air-conditioning, and domestic refrigerator foams markets resulted
in revisions to the Vintaging Model since the previous Inventory. For MDI aerosols, the charge size for both the
CFC and HFC propellants was revised. Based on research on substitutes and growth in the market, the percent of the
CFC market that transitions to HFCs over the time series and the overall size of the MDI market decreased. For
unitary air-conditioning, a review of air conditioner sales data reduced the quantity of air-conditioning equipment
introduced into the market for 1990 through!993 and 2008, while increasing the quantity of equipment sold into the
market for 1994 through 2009. A review of the domestic refrigerator foams market increased the quantity of
blowing agent consumed in the foam and decreased the quantity of blowing agent emitted during the foam
manufacturing process. Overall, these changes to the Vintaging Model increased greenhouse gas emissions on
average by 0.5 percent across the time series.
4.21. Semiconductor Manufacture (IPCC Source Category 2F6)
The semiconductor industry uses multiple long-lived fluorinated gases in plasma etching and plasma enhanced
chemical vapor deposition (PECVD) processes to produce semiconductor products. The gases most commonly
employed are trifluoromethane (HFC-23 or CHF3), perfluoromethane (CF4), perfluoroethane (C2F6), nitrogen
trifluoride (NF3), and sulfur hexafluoride (SF6), although other compounds such as perfluoropropane (C3F8) and
perfluorocyclobutane (c-C4F8) are also used. The exact combination of compounds is specific to the process
employed.
A single 300 mm silicon wafer that yields between 400 to 500 semiconductor products (devices or chips) may
require as many as 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
4-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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refractory metal films like tungsten.
For 2009, total weighted emissions of all fluorinated greenhouse gases by the U.S. semiconductor industry were
estimated to be 5.3 Tg CO2 Eq. Combined emissions of all fluorinated greenhouse gases are presented in Table 4-88
and Table 4-89 below for years 1990, 2000 and the period 2005 to 2009. The rapid growth of this industry and the
increasing complexity (growing number of layers)122 of semiconductor products led to an increase in emissions of
148 percent between 1990 and 1999, when emissions peaked at 7.2 Tg CO2 Eq. The emissions growth rate began to
slow after 1998, and emissions declined by 26 percent between 1999 and 2009. Together, industrial growth and
adoption of emissions reduction technologies, including but not limited to abatement technologies, resulted in a net
increase in emissions of 83 percent between 1990 and 2009.
Table 4-88: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Tg CO2 Eq.)
Year 1990 2000 2005 2006 2007 2008 2009
CF4 0.7 1.8 1.1 1.2 1.3 1.4 1.5
C2F6 1.5 3.0 2.0 2.2 2.3 2.4 2.5
C3F8 0.0 0.1 0.0 0.0 0.0 0.1 0.0
C4F8 0.0 0.0 0.1 0.1 0.1 0.1 0.0
HFC-23 0.2 0.3 0.2 0.3 0.3 0.3 0.3
SF6 0.5 1.1 1.0 1.0 0.8 0.9 1.0
NF3* OX) O2 0.4 (XT 0.5 0.6 0.5
Total 2.9 6.2 4.4 4.7 4.8 5.1 5.3
Note: Totals may not sum due to independent rounding.
* NF3 emissions are presented for informational purposes, using the AR4 GWP of 17,200, and are not included in totals.
Table 4-89: PFC, HFC, and SF6 Emissions from Semiconductor Manufacture (Mg)
Year 1990 2000 2005 2006 2007 2008 2009
CF4 115 281 168 181 198 216 227
C2F6 160 321 216 240 249 261 271
C3F8 0 I 18 5 5 6 13 5
CA 0 I 0 I 13 13 7 74
HFC-23 15 23 18 22 23 25 28
SF6 22 45 40 40 34 36 40
NF3 3 11 26 40 30 33 30
Methodology
Emissions are based on Partner reported emissions data received through the EPA's PFC Reduction/Climate
Partnership and the EPA's PFC Emissions Vintage Model (PEVM), a model which estimates industry emissions in
the absence of emission control strategies (Burton and Beizaie 2001).123 The availability and applicability of
Partner data differs across the 1990 through 2009 time series. Consequently, emissions from semiconductor
manufacturing were estimated using four distinct methods, one each for the periods 1990 through 1994, 1995
through 1999, 2000 through 2006, and 2007 through 2009.
1990 through 1994
From 1990 through 1994, Partnership data was unavailable and emissions were modeled using the PEVM (Burton
122 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.
123 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 report their PFC emissions to the EPA by
way of a third party, which aggregates the emissions.
Industrial Processes 4-67
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and Beizaie 2001).124 1990 to 1994 emissions are assumed to be uncontrolled, since reduction strategies such as
chemical substitution and abatement were yet to be developed.
PEVM is based on the recognition that PFC emissions from semiconductor manufacturing vary with: (1) the number
of layers that comprise different kinds of semiconductor devices, including both silicon wafer and metal
interconnect layers, and (2) silicon consumption (i.e., the area of semiconductors produced) for each kind of device.
The product of these two quantities, Total Manufactured Layer Area (TMLA), constitutes the activity data for
semiconductor manufacturing. PEVM also incorporates an emission factor that expresses emissions per unit of
layer-area. Emissions are estimated by multiplying TMLA by this emission factor.
PEVM incorporates information on the two attributes of semiconductor devices that affect the number of layers: (1)
linewidth technology (the smallest manufactured feature size), 125 and (2) product type (discrete, memory or
logic).126 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. 2010).
The emission factor in PEVM is the average of four historical emission factors, each derived by dividing the total
annual emissions reported by the Partners for each of the four years between 1996 and 1999 by the total TMLA
estimated for the Partners in each of those years. Over this period, the emission factors varied relatively little (i.e.,
the relative standard deviation for the average was 5 percent). Since Partners are believed not to have applied
significant emission reduction measures before 2000, the resulting average emission factor reflects uncontrolled
emissions. The emission factor is used to estimate world uncontrolled emissions using publicly available data on
world silicon consumption.
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 2010).
2000 through 2006
The emission estimate for the years 2000 through 2006—the period during which Partners began the consequential
application of PFC-reduction measures—was estimated using a combination of Partner reported emissions and
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
124 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.
125 By decreasing features of Integrated Circuit components, more components can be manufactured per device, which increases
its functionality. However, as those individual components shrink it requires more layers to interconnect them to achieve the
functionality. For example, a microprocessor manufactured with the smallest feature sizes (65 nm) might contain as many as 1
billion transistors and require as many as 11 layers of component interconnects to achieve functionality while a device
manufactured with 130 nm feature size might contain a few hundred million transistors and require 8 layers of component
interconnects (ITRS 2007).
126 Memoly 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.
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estimated using PEVM and the method described above. This is because non-Partners are assumed not to have
implemented any PFC-reduction measures, and PEVM models emissions without such measures. 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.127'128 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 2007 and Semiconductor Equipment and Materials Industry 2010). 129.130.131
2007 through 2009
For the years 2007 through 2009, emissions were also estimated using a combination of Partner reported emissions
and PEVM modeled emissions; however, two improvements were made to the estimation method employed for the
previous years in the time series. First, the 2007 through 2009 emission estimates account for the fact that Partners
and non-Partners employ different distributions of manufacturing technologies, with the Partners using
manufacturing technologies with greater transistor densities and therefore greater numbers of layers.132 Second, the
scope of the 2007 through 2009 estimates is expanded relative to the estimates for the years 2000 through 2006 to
include emissions from Research and Development (R&D) fabs. This was feasible through the use of more detailed
data published in the World Fab Forecast. PEVM databases are updated annually as described above. The
published world average capacity utilization for 2007 and 2008 was used for production fabs while in 2008 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 the emissions in 2009 for non-partners. PEVM estimates were
adjusted using technology weighted capacity shares that reflect relative influence of different utilization.
127 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.
128 Generally 5 percent or less of the fields needed to estimate TMLA shares are missing values in the World Fab Watch
databases. In the 2007 World Fab Watch database used to generate the 2006 non-Partner TMLA capacity share, these missing
values were replaced with the corresponding mean TMLA across fabs manufacturing similar classes of products. However, the
impact of replacing missing values on the non-Partner TMLA capacity share was inconsequential.
129 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.
130 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.
131 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.
132 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.
Industrial Processes 4-69
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Gas-Specific Emissions
Two different approaches were also used to estimate the distribution of emissions of specific fluorinated gases.
Before 1999, when 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. 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). For the 2000 through 2009 period, the 1990
through 1999 distribution was assumed to apply to the non-Partners. Partners, however, began reporting gas-
specific emissions during this period. Thus, gas-specific emissions for 2000 through 2009 were estimated by adding
the emissions reported by the Partners to those estimated for the non-Partners.
Data Sources
Partners estimate their emissions using a range of methods. For 2009, 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 for
National Greenhouse Inventories (IPCC 2006). Data used to develop emission estimates are attributed in part to
estimates provided by the members of the Partnership, and in part from data obtained from PEVM estimates.
Estimates of operating plant capacities and characteristics for Partners and non-Partners were derived from the
Semiconductor Equipment and Materials Industry (SEMI) World Fab Forecast (formerly World Fab Watch)
database (1996 through 2009) (e.g., Semiconductor Materials and Equipment Industry, 2010). Actual world
capacity utilizations for 2009 were obtained from Semiconductor International Capacity Statistics (SICAS) (SIA,
2009). Estimates of silicon consumed by linewidth from 1990 through 2009 were derived from information from
VLSI Research, Inc. (2010), and the number of layers per linewidth was obtained from International Technology
Roadmap for Semiconductors: 2006 Update (Burton and Beizaie 2001, ITRS 2007, ITRS 2008).
Uncertainty and Time Series Consistency
A quantitative uncertainty analysis of this source category was performed using the IPCC-recommended Tier 2
uncertainty estimation methodology, the Monte Carlo Stochastic Simulation technique. The equation used to
estimate uncertainty is:
U.S. emissions = ^Partnership gas-specific submittals + [(non-Partner share of World TMLA) x (PEVM Emission
Factor x World TMLA)]
The Monte Carlo analysis results presented below relied on estimates of uncertainty attributed to the four quantities
on the right side of the equation. Estimates of uncertainty for the four quantities were in turn developed using the
estimated uncertainties associated with the individual inputs to each quantity, error propagation analysis, Monte
Carlo simulation, and expert judgment. The relative uncertainty associated with World TMLA estimate in 2009 is
about ±10 percent, based on the uncertainty estimate obtained from discussions with VLSI, Inc. For the share of
World layer-weighted silicon capacity accounted for by non-Partners, a relative uncertainty of ±8 percent was
estimated based on a separate Monte Carlo simulation to account for the random occurrence of missing data in the
World Fab Watch database. For the aggregate PFC emissions data supplied to the partnership, a relative uncertainty
of ±50 percent was estimated for each gas-specific PFC emissions value reported by an individual Partner, and error
propagation techniques were used to estimate uncertainty for total Partnership gas-specific submittals.133 A relative
uncertainty of approximately ±10 percent was estimated for the PEVM emission factor, based on the standard
deviation of the 1996 to 1999 emission factors.134 All estimates of uncertainties are given at 95-percent confidence
intervals.
In developing estimates of uncertainty, consideration was also given to the nature and magnitude of the potential
bias that World activity data (i.e., World TMLA) might have in its estimates of the number of layers associated with
devices manufactured at each technology node. The result of a brief analysis indicated that U.S. TMLA overstates
the average number of layers across all product categories and all manufacturing technologies for 2004 by 0.12
layers or 2.9 percent. The same upward bias is assumed for World TMLA, and is represented in the uncertainty
analysis by deducting the absolute bias value from the World activity estimate when it is incorporated into the
133 Error propagation resulted in Partnership gas-specific uncertainties ranging from 17 to 27 percent
134 The average of 1996 to 1999 emission factor is used to derive the PEVM emission factor.
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Monte Carlo analysis.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-90. The emissions estimate for
total U.S. PFC emissions from semiconductor manufacturing were estimated to be between 4.8 and 5.9 Tg CO2 Eq.
at a 95 percent confidence level. This range represents 10 percent below to 11 percent above the 2009 emission
estimate of 5.3 Tg CO2 Eq. This range and the associated percentages apply to the estimate of total emissions rather
than those of individual gases. Uncertainties associated with individual gases will be somewhat higher than the
aggregate, but were not explicitly modeled.
Table 4-90: Tier 2 Quantitative Uncertainty Estimates for HFC, PFC, and SF6 Emissions from Semiconductor
Manufacture (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate" Uncertainty Range Relative to Emission Estimate1"
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Semiconductor HFC, PFC,
Manufacture and SF6 5.3
Lower
Bound0
4.8
Upper
Bound0
5.9
Lower
Bound
-10%
Upper
Bound
+11%
a Because the uncertainty analysis covered all emissions (including NF3), the emission estimate presented here does not match
that shown in Table 4-88.
b Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
0 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
With the exception of possible future updates to emission factors, the method to estimate non-Partner related
emissions (i.e., PEVM) is not expected to change. Future improvements to the national emission estimates will
primarily be associated with determining the portion of national emissions to attribute to Partner report totals (about
80 percent in recent years) and improvements in estimates of non-Partner totals. As the nature of the Partner reports
change through time and industry-wide reduction efforts increase, consideration will be given to what emission
reduction efforts—if any—are likely to be occurring at non-Partner facilities. Currently, none are assumed to occur.
Another point of consideration for future national emissions estimates is the inclusion of PFC emissions from heat
transfer fluid (HTF) loss to the atmosphere and the production of photovoltaic cells (PVs). Heat transfer fluids, of
which some are liquid perfluorinated compounds, are used during testing of semiconductor devices and,
increasingly, are used to manage heat during the manufacture of semiconductor devices. Evaporation of these fluids
is a source of emissions (EPA 2006). PFCs are also used during manufacture of PV cells that use silicon
technology, specifically, crystalline, polycrystalline, and amorphous silicon technologies. PV manufacture is
growing in the United States, and therefore may be expected to constitute a growing share of U.S. PFC emissions
from the electronics sector.
4.22. Electrical Transmission and Distribution (IPCC Source Category 2F7)
The largest use of SF6, both in the United States and internationally, is as an electrical insulator and interrupter in
equipment that transmits and distributes electricity (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. Sulfur hexafluoride has replaced
flammable insulating oils in many applications and allows for more compact substations in dense urban areas.
Fugitive emissions of SF6 can escape from gas-insulated substations and switchgear through seals, especially from
older equipment. The gas can also be released during equipment manufacturing, installation, servicing, and
disposal. Emissions of SF6 from equipment manufacturing and from electrical transmission and distribution systems
were estimated to be 12.8 Tg CO2 Eq. (0.5 Gg) in 2009. This quantity represents a 55 percent decrease from the
estimate for 1990 (see Table 4-91 and Table 4-92). This decrease is believed to have two causes: a sharp increase in
the price of SF6 during the 1990s and a growing awareness of the environmental impact of SF6 emissions through
Industrial Processes 4-71
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programs such as EPA's SF6 Emission Reduction Partnership for Electric Power Systems.
Table 4-91: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Tg CO2 Eq.)
Year Electric Power Electrical Equipment Total
Systems Manufacturers
1990
28.1
28.4
2005
2006
2007
2008
2009
14.1
13.1
12.4
12.1
12.1
1.1
1.0
0.8
1.3
0.7
15.1
14.1
13.2
13.3
12.8
Note: Totals may not sum due to independent rounding.
Table 4-92: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (Gg)
Year Emissions
1990
2005
2006
2007
2008
2009
0.6
0.6
0.6
0.6
0.5
Methodology
The estimates of emissions from Electric 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.
1999 through 2009 Emissions from Electric Power Systems
Emissions from electric power systems from 1999 to 2009 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; and, (2) the relationship between emissions and utilities' transmission miles as reported in the 2001, 2004,
2007, and 2010 Utility Data Institute (UDI) Directories of Electric Power Producers and Distributors (UDI2001,
2004, 2007, 2010). (Transmission miles are defined as the miles of lines carrying voltages above 34.5 kV.) Over
the period from 1999 to 2009, 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 47 percent of total
U.S. transmission miles. For each year, the emissions reported by or estimated for Partner utilities were added to the
emissions estimated for utilities that have never participated in the Partnership (i.e., non-Partners).135
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 2009, non-reporting
Partners accounted for approximately 8 percent of the total emissions attributed to Partner utilities.
Emissions from non-Partners in every year since 1999 were estimated using the results of a regression analysis that
showed that the emissions from reporting utilities were most strongly correlated with their transmission miles. The
results of this analysis are not surprising given that, in the United States, SF6 is contained primarily in transmission
135 Partners in EPA's SF6 Emission Reduction Partnership reduced their emissions by approximately 61% from 1999 to 2008.
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equipment rated above 34.5 kV. The equations were developed based on the 1999 SF6 emissions reported by a
subset of 42 Partner utilities (representing approximately 23 percent of U.S. transmission miles) and 2000
transmission mileage data obtained from the 2001 UDI Directory of Electric Power Producers and Distributors (UDI
2001). Two equations were developed, one for small and one for large utilities (i.e., with fewer or more 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 small and large transmission
networks. The same equations were used to estimate non-Partner emissions in 1999 and every year thereafter
because non-Partners were assumed not to have implemented any changes that would have resulted in reduced
emissions since 1999.
The regression equations are:
Non-Partner small utilities (fewer than 10,000 transmission miles, in kilograms):
Emissions (kg) = 1.001 x Transmission Miles
Non-Partner large utilities (more than 10,000 transmission miles, in kilograms):
Emissions (kg) = 0.58 x Transmission Miles
Data on transmission miles for each non-Partner utility for the years 2000, 2003, 2006, and 2009 were obtained from
the 2001, 2004, 2007, and 2010 UDI Directories of Electric Power Producers and Distributors, respectively (UDI
2001, 2004, 2007, 2010). The U.S. transmission system grew by over 25,000 miles between 2000 and 2003 and by
over 52,000 miles between 2003 and 2006. These periodic increases are assumed to have occurred gradually.
Therefore, transmission mileage was assumed to increase at an annual rate of 1.3 percent between 2000 and 2003
and 2.6 percent between 2003 and 2006. This growth rate slowed to 0.2% from 2006 to 2009 as transmission miles
increased by just 4,400 miles (approximately).
As a final step, total electric power system emissions 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) and the non-Partner emissions (determined using the 1999 regression
equations).
1990 through 1998 Emissions from Electric Power Systems
Because most participating utilities reported emissions only for 1999 through 2009, 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 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).136
(Although equation 7.3 of the IPCC Guidelines appears in the discussion of substitutes for ozone-depleting
substances, it is applicable to emissions from any long-lived pressurized equipment that is periodically serviced
during its lifetime.)
Emissions (kilograms SF6) = SF6 purchased to refill existing equipment (kilograms) + nameplate capacity of retiring
equipment (kilograms)137
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
136 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 SF6 during this time period, so it would not have been possible to conceal
sensitive sales information by aggregation.
137 Nameplate capacity is defined as the amount of SF6 within fully charged electrical equipment.
Industrial Processes 4-73
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electrical equipment manufacturers 40 years previous (e.g., in 2000, the nameplate capacity of retiring equipment
was assumed to equal 81.2 percent of the gas purchased in 1960). The remaining 18.8 percent was assumed to have
been emitted at the time of manufacture. The 18.8 percent emission factor is an average of IPCC default SF6
emission rates for Europe and Japan for 1995 (IPCC 2006). The 40-year lifetime for electrical equipment is also
based on IPCC (2006). The results of the two components of the above equation were then summed to yield
estimates of global SF6 emissions from 1990 through 1999.
U.S. emissions between 1990 and 1999 are assumed to follow the same trajectory as global emissions during this
period. To estimate U.S. emissions, global emissions for each year from 1990 through 1998 were divided by the
estimated global emissions from 1999. The result was a time series of factors that express each year's global
emissions as a multiple of 1999 global emissions. Historical U.S. emissions were estimated by multiplying the
factor for each respective year by the estimated U.S. emissions of SF6 from electric power systems in 1999
(estimated to be 15.0 Tg CO2 Eq.).
Two factors may affect the relationship between the RAND sales trends and actual global emission trends. One is
utilities' inventories of SF6 in storage containers. When SF6 prices rise, utilities are likely to deplete internal
inventories before purchasing new SF6 at the higher price, in which case SF6 sales will fall more quickly than
emissions. On the other hand, when SF6 prices fall, utilities are likely to purchase more SF6 to rebuild inventories,
in which case sales will rise more quickly than emissions. This effect was accounted for by applying 3-year
smoothing to utility SF6 sales data. The other factor that may affect the relationship between the RAND sales trends
and actual global emissions is the level of imports from and exports to Russia and China. SF6 production in these
countries is not included in the RAND survey and is not accounted for in any another manner by RAND. However,
atmospheric studies confirm that the downward trend in estimated global emissions between 1995 and 1998 was real
(see the Uncertainty discussion below).
1990 through 2009 Emissions from Manufacture of Electrical Equipment
The 1990 to 2009 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 2009 were estimated using Partner reported data and the total industry SF6 nameplate
capacity estimate (137.4 Tg CO2 Eq. in 2009). 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 2009 was
calculated. This ratio was then multiplied by the total industry nameplate capacity estimate 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
To estimate the uncertainty associated with emissions of SF6 from Electric Transmission and Distribution,
uncertainties associated with three quantities were estimated: (1) emissions from Partners, (2) emissions from non-
Partners, and (3) emissions from manufacturers of electrical equipment. A Monte Carlo analysis was then applied to
estimate the overall uncertainty of the emissions estimate.
Total emissions from the SF6 Emission Reduction Partnership include emissions from both reporting 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 5.3 percent. The uncertainty associated with extrapolated or interpolated emissions from non-
reporting Partners was assumed to be 20 percent.
There are two sources of uncertainty associated with the regression equations used to estimate emissions in 2009
from non-Partners: (1) uncertainty in the coefficients (as defined by the regression standard error estimate), and (2)
the uncertainty in total transmission miles for non-Partners. In addition, there is uncertainty associated with the
assumption that the emission factor used for non-Partner utilities (which accounted for approximately 57 percent of
U.S. transmission miles in 2009) will remain at levels defined by Partners who reported in 1999. However, the last
4-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
source of uncertainty was not modeled.
Uncertainties were also estimated regarding (1) the quantity of SF6 supplied with equipment by equipment
manufacturers, which is projected from Partner provided nameplate capacity data and industry SF6 nameplate
capacity estimates, and (2) the manufacturers' SF6 emissions rate.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 4-93. Electrical Transmission
and Distribution SF6 emissions were estimated to be between 10.2 and 15.7 Tg CO2 Eq. at the 95 percent confidence
level. This indicates a range of approximately 21 percent below and 22 percent above the emission estimate of 12.8
Tg C02 Eq.
Table 4-93: Tier 2 Quantitative Uncertainty Estimates for SF6 Emissions from Electrical Transmission and
Distribution (Tg CO2 Eq. and percent)
Source
2009 Emission
Gas Estimate
(TgC02Eq.)
Uncertainty Range Relative to 2009 Emission Estimate"
(TgC02Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Electrical Transmission
and Distribution SFg 12.8 10.2 15.7 -21% +22%
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, and emissions based on
atmospheric measurements declined by 27 percent over the same period.
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 1990 through
1999 emissions to EPA showed a downward trend beginning in the mid-1990s.
Recalculations Discussion
SF6 emission estimates for the period 1990 through 2008 were updated based on (1) new data from EPA's SF6
Emission Reduction Partnership; (2) revisions to interpolated and extrapolated non-reported Partner data; and (3) a
correction made to 2004 transmission mile data for a large Partnership utility that had been interpreted incorrectly
from the UDI database in previous years. Updating the 2004 transmission mile data for the Partner changed the
annual transmission mile growth rates used to extrapolate total U.S. transmission mile values for years in which a
UDI database was not purchased (including 1999). This recalculation impacted emission estimates in two ways.
First, the regression coefficients used to estimate emissions for non-Partners are based on 1999 transmission miles
and emissions for Partners that reported emissions in 1999, so the change in 1999 transmission miles affected the
regression coefficients. The result was that the regression coefficient for utilities with fewer than 10,000
transmission miles increased from 0.89 to 1.001 kg of emissions per transmission mile, while the regression
coefficient for utilities with more than 10,000 transmission miles increased very slightly from 0.577 to 0.578 kg of
emissions per transmission mile. The second impact of the updated annual transmission mile growth rates was that
the total non-Partner transmission miles that the regression coefficients are applied to were also affected. Based on
the revisions listed above, SF6 emissions from electric transmission and distribution increased between 4 to 9
percent for each year from 1990 through 2008.
In addition, the method for estimating potential emissions from the sector was updated for the 1990-2009 Inventory.
In previous years, potential emissions were assumed to equal total industry SF6 purchases, which were developed
from two components: (1) purchases by Partner utilities from bulk gas distributors, and (2) purchases by electrical
equipment manufacturers from bulk gas distributors. This previous method led to concerns of double-counting since
Partners sometimes were recording all SF6 received in cylinders from any source (including equipment
Industrial Processes 4-75
-------
manufacturers) as gas received from bulk distributors. Therefore, SF6 that was purchased by a utility from an
equipment manufacturer was sometimes counted as a purchase by both the equipment manufacturer and the utility.
The new method still assumes that potential emissions are equal to industry purchases, but estimates total purchases
for the industry by adding the total amount of gas purchased by all U.S. utilities from any source (bulk distributor or
equipment manufacturer) to estimated emissions from equipment manufacturers. It is assumed that all SF6 purchased
by equipment manufacturers is either emitted or sent to utilities.
4.23. Industrial Sources of Indirect Greenhouse Gases
In addition to the main greenhouse gases addressed above, many industrial processes generate emissions of indirect
greenhouse gases. Total emissions of nitrogen oxides (NOX), carbon monoxide (CO), and non-CH4 volatile organic
compounds (NMVOCs) from non-energy industrial processes from 1990 to 2009 are reported in Table 4-94.
Table 4-94: NOX, CO, and NMVOC Emissions from Industrial Processes (Gg)
Gas/Source 1990 1995 2000 2005 2006 2007 2008 2009
NOX 591 607 626 569 553 537 520 568
Other Industrial Processes 343 362 435 437 418 398 379 436
Chemical & Allied Product
Manufacturing 152 143 95 55 57 59 61 55
Metals Processing 88 89 81 60 61 62 62 60
Storage and Transport 3 I 5 I 14 15 15 16 16 15
Miscellaneous* 5 I 8 I 2J 22222
CO 4,125 I 3,959 I 2,216 I 1,555 1,597 1,640 1,682 1,549
Metals Processing 2,395 I 2,159 I 1,175 I 752 788 824 859 752
Other Industrial Processes 487 566 537 484 474 464 454 484
Chemical & Allied Product
Manufacturing 1,073 I 1,110 I 327 189 206 223 240 187
Storage and Transport 69 23 153 97 100 103 104 97
Miscellaneous* 101 102 23 32 30 27 25 29
NMVOCs 2,422 I 2,642 I 1,773 I 1,997 1,933 1,869 1,804 1,322
Storage and Transport 1,352 I 1,499 I 1,067 I 1,308 1,266 1,224 1,182 662
Other Industrial Processes 364 408 412 415 398 383 367 395
Chemical & Allied Product
Manufacturing 575 599 230 213 211 210 207 206
Metals Processing 111 113 61 44 44 43 42 44
Miscellaneous* 20 23 3 I 17 14 10 7 15
* Miscellaneous includes the following categories: catastrophic/accidental release, other combustion, health services, cooling
towers, and fugitive dust. It does not include agricultural fires or slash/prescribed burning, which are accounted for under the
Field Burning of Agricultural Residues source.
Note: Totals may not sum due to independent rounding.
Methodology
These emission estimates were obtained from preliminary data (EPA 2010, EPA 2009), and disaggregated based on
EPA (2003), which, in its final iteration, will be published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. Emissions were calculated either for individual categories or for many categories
combined, using basic activity data (e.g., the amount of raw material processed) as an indicator of emissions.
National activity data were collected for individual categories from various agencies. Depending on the category,
these basic activity data may include data on production, fuel deliveries, raw material processed, etc.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
4-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 2008. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Industrial Processes 4-77
-------
-------
Substitution of Ozone Depleting Substances
Iron and Steel Prod. & Metallurgical Coke Prod.
Cement Production
Nitric Acid Production
Electrical Transmission and Distribution
Ammonia Production and Urea Application
Lime Production
Limestone and Dolomite Use
HCFC-22 Production ^H
Semiconductor Manufacture ^^|
Aluminum Production ^^|
Soda Ash Production and Consumption ^^|
Petrochemical Production ^|
Adipic Acid Production |
Carbon Dioxide Consumption |
Titanium Dioxide Production |
Ferroalloy Production |
Magnesium Production and Processing |
Phosphoric Acid Production |
Zinc Production |
Lead Production |
Silicon Carbide Production and Consumption < 0.5
120
Industrial Processes
as a Portion of all Emissions
4.3%
10
20 30
TgC02Eq.
40
50
Figure 4-1: 2009 Industrial Processes Chapter Greenhouse Gas Sources
-------
-------
5. Solvent and Other Product Use
Greenhouse gas emissions are produced as a by-product of various solvent and other product uses. In the United
States, emissions from Nitrous Oxide (N2O) Product Uses, the only source of greenhouse gas emissions from this
sector, accounted for less than 0.1 percent of total U.S. anthropogenic greenhouse gas emissions on a CO2 equivalent
basis in 2009 (see Table 5-1). Indirect greenhouse gas emissions also result from solvent and other product use, and
are presented in Table 5-5 in gigagrams (Gg).
Table 5-1: N2O Emissions from Solvent and Other Product Use (Tg CO2 Eq. and Gg)
Gas/Source 1990 2000 2005 2006 2007 2008 2009
N2O from Product Uses
Tg
Gg
C02
Eq.
4.4 1 4.9
14 16
4.4
14
4.4
14
4.4
14
4.4
14
4.4
14
5.1. Nitrous Oxide from Product Uses (IPCC Source Category 3D)
N2O is a clear, colorless, oxidizing liquefied gas, with a slightly sweet odor. Two companies operate a total of five
N2O production facilities in the United States (Airgas 2007; FTC 2001). 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. As such, it is used to treat short-term pain, for sedation in minor elective surgeries, and
as an induction anesthetic. The second main use of N2O is as a propellant in pressure and aerosol products, the
largest application being pressure-packaged whipped cream. Small quantities of N2O also are used in the following
applications:
• Oxidizing agent and etchant used in semiconductor manufacturing;
• Oxidizing agent used, with acetylene, in atomic absorption spectrometry;
• Production of sodium azide, which is used to inflate airbags;
• Fuel oxidant in auto racing; and
• Oxidizing agent in blowtorches used by jewelers and others (Heydorn 1997).
Production of N2O in 2009 was approximately 15 Gg (Table 5-2).
Table 5-2: N2O Production (Gg)
Year Gg
1990
2005 15
2006 15
2007 15
2008 15
2009 15
N2O emissions were 4.4 Tg CO2 Eq. (14 Gg) in 2009 (Table 5-3). Production of N2O stabilized during the 1990s
because medical markets had found other substitutes for anesthetics, and more medical procedures were being
performed on an outpatient basis using local anesthetics that do not require N2O. The use of N2O as a propellant for
whipped cream has also stabilized due to the increased popularity of cream products packaged in reusable plastic
tubs (Heydorn 1997).
Table 5-3: N2O Emissions from N2O Product Usage (Tg CO2 Eq. and Gg)
Year Tg CO2 Eq. Gg
1990 4.4 14
Solvent and Other Product Use 5-1
-------
2000 4.9 16
2005
2006
2007
2008
2009
4.4
4.4
4.4
4.4
4.4
14
14
14
14
14
Methodology
Emissions from N2O product usage were calculated by first multiplying the total amount of N2O produced in the
United States by the share of the total quantity of N2O attributed to each end use. This value was then multiplied by
the associated emission rate for each end use. After the emissions were calculated for each end use, they were added
together to obtain a total estimate of N2O product usage emissions. Emissions were determined using the following
equation:
N2O Product Usage Emissions = Zi [Total U.S. Production of N2O] x [Share of Total Quantity of N2O Usage by
Sector i] x [Emissions Rate for Sector i]
where,
i = Sector.
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 2009, the medical/dental industry used an
estimated 89.5 percent of total N2O produced, followed by food processing propellants at 6.5 percent. All other
categories combined used the remainder of the N2O produced. This subcategory breakdown has changed only
slightly over the past decade. For instance, the small share of N2O usage in the production of sodium azide has
declined significantly during the 1990s. Due to the lack of information on the specific time period of the phase-out
in this market subcategory, most of the N2O usage for sodium azide production is assumed to have ceased after
1996, with the majority of its small share of the market assigned to the larger medical/dental consumption
subcategory (Heydorn 1997). The N2O was allocated across the following categories: medical applications, food
processing propellant, and sodium azide production (pre-1996). A usage emissions rate was then applied for each
sector to estimate the amount of N2O emitted.
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 foryears 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 unavailable data, production estimates foryears 2004 through 2009 were held at the 2003 value.
The 1996 share of the total quantity of N2O used by each subcategory was obtained from SRI Consulting's Nitrous
5-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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
unavailable data, the share of total quantity of N2O usage data for years 2004 through 2009 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 2009 N2O emission estimate from N2O product usage was calculated
using the IPCC Guidelines for National Greenhouse Gas Inventories (2006) Tier 2 methodology. Uncertainty
associated with the parameters used to estimate N2O emissions include production data, total market share of each
end use, and the emission factors applied to each end use, respectively.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 5-4. N2O emissions from N2O
product usage were estimated to be between 4.1 and 4.7 Tg CO2 Eq. at the 95 percent confidence level (or in 19 out
of 20 Monte Carlo Stochastic Simulations). This indicates a range of approximately 8 percent below to 8 percent
above the 2009 emissions estimate of 4.4 Tg CO2 Eq.
Table 5-4: Tier 2 Quantitative Uncertainty Estimates for N2O Emissions from N2O Product Usage (Tg CO2 Eq. and
Percent)
Source
N2O Product Usage
Gas
N2O
2009 Emission
Estimate
(TgC02Eq.)
4.4
Uncertainty
(TgCO
Lower
Bound
4.1
Range Relative to
2Eq.)
Upper
Bound
4.7
Emission Estimate"
Lower Upper
Bound Bound
-8% +8%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note that this uncertainty range (±8 percent) has increased by 12 percent compared to the uncertainty range in last
year's Inventory (±2 percent), due to a correction to the uncertainty input parameters. Furthermore, methodological
recalculations were applied to the entire time-series to ensure time-series consistency from 1990 through 2009.
Details on the emission trends through time-series are described in more detail in the Methodology section, above.
Planned Improvements
Planned improvements include a continued evaluation of alternative production statistics for cross verification, a
reassessment of N2O product use subcategories to accurately represent trends, investigation of production and use
cycles, and the potential need to incorporate a time lag between production and ultimate product use and resulting
release of N2O. Additionally, planned improvements include considering imports and exports of N2O for product
uses.
5.2. Indirect Greenhouse Gas Emissions from Solvent Use
The use of solvents and other chemical products can result in emissions of various ozone precursors (i.e., indirect
greenhouse gases).138 Non-CH4 volatile organic compounds (NMVOCs), commonly referred to as "hydrocarbons,"
are the primary gases emitted from most processes employing organic or petroleum based solvents. As some of
industrial applications also employ thermal incineration as a control technology, combustion by-products, such as
carbon monoxide (CO) and nitrogen oxides (NOX), are also reported with this source category. In the United States,
138 Solvent usage in the United States also results in the emission of small amounts of hydrofluorocarbons (HFCs) and
hydrofluoroethers (HFEs), which are included under Substitution of Ozone Depleting Substances in the Industrial Processes
chapter.
Solvent and Other Product Use 5-3
-------
4
4
emissions from solvents are primarily the result of solvent evaporation, whereby the lighter hydrocarbon molecules
in the solvents escape into the atmosphere. The evaporation process varies depending on different solvent uses and
solvent types. The major categories of solvent uses include: degreasing, graphic arts, surface coating, other
industrial uses of solvents (i.e., electronics, etc.), dry cleaning, and non-industrial uses (i.e., uses of paint thinner,
etc.).
Total emissions of NOX, NMVOCs, and CO from 1990 to 2009 are reported in Table 5-5.
Table 5-5: Emissions of NOX, CO, and NMVOC from Solvent Use (Gg)
Activity 1990 2000 2005 2006 2007 2008 2009
NOX
Surface Coating
Graphic Arts
Degreasing
Dry Cleaning
Other Industrial Processes3
Non-Industrial Processes'3
Other
CO
Surface Coating
Other Industrial Processes3
Dry Cleaning
Degreasing
Graphic Arts
Non-Industrial Processes'3
Other
NMVOCs
Surface Coating
Non-Industrial Processes'3
Degreasing
Dry Cleaning
Graphic Arts
Other Industrial Processes3
Other
3,846
1,575
1,444
280
230
193
36
3,839
1,573
1,441
280
229
193
87
36
3,834
1,571
1,439
279
229
193
87
36
2,583
1,058
970
188
154
130
59
24
3 Includes rubber and plastics manufacturing, and other miscellaneous applications.
b Includes cutback asphalt, pesticide application adhesives, consumer solvents, and other miscellaneous applications.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 Gg.
Methodology
Emissions were calculated by aggregating solvent use data based on information relating to solvent uses from
different applications such as degreasing, graphic arts, etc. Emission factors for each consumption category were
then applied to the data to estimate emissions. For example, emissions from surface coatings were mostly due to
solvent evaporation as the coatings solidify. By applying the appropriate solvent-specific emission factors to the
amount of solvents used for surface coatings, an estimate of emissions was obtained. Emissions of CO and NOX
result primarily from thermal and catalytic incineration of solvent-laden gas streams from painting booths, printing
operations, and oven exhaust.
These emission estimates were obtained from preliminary data (EPA 2010, EPA 2009), and disaggregated based on
EPA (2003), which, in its final iteration, will be published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. Emissions were calculated either for individual categories or for many categories
combined, using basic activity data (e.g., the amount of solvent purchased) as an indicator of emissions. National
activity data were collected for individual applications from various agencies.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
5-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
Uncertainty and Time-Series Consistency
Uncertainties in these estimates are partly due to the accuracy of the emission factors used and the reliability of
correlations between activity data and actual emissions.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Solvent and Other Product Use 5-5
-------
-------
6. Agriculture
Agricultural activities contribute directly to emissions of greenhouse gases through a variety of processes. This
chapter provides an assessment of non-carbon-dioxide emissions from the following source categories: enteric
fermentation in domestic livestock, livestock manure management, rice cultivation, agricultural soil management,
and field burning of agricultural residues (see Figure 6-1). Carbon dioxide (CO2) emissions and removals from
agriculture-related land-use activities, such as liming of agricultural soils and conversion of grassland to cultivated
land, are presented in the Land Use, Land-Use Change, and Forestry chapter. Carbon dioxide emissions from on-
farm energy use are accounted for in the Energy chapter.
Figure 6-1: 2009 Agriculture Chapter Greenhouse Gas Emission Sources
In 2009, the Agriculture sector was responsible for emissions of 419.3 teragrams of CO2 equivalents (Tg CO2 Eq.),
or 6.3 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 about 20 percent and 7 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 69 percent. Manure management and field burning of agricultural residues were also small sources
of N2O emissions.
Table 6-1 and Table 6-2 present emission estimates for the Agriculture sector. Between 1990 and 2009, CH4
emissions from agricultural activities increased by 14.9 percent, while N2O emissions fluctuated from year to year,
but overall increased by 4.8 percent.
Table 6-1: Emissions from Agriculture (Tg CO2 Eq.)
Gas/Source 1990 2000 2005 2006 2007 2008 2009
CH4 171.2 186.7 190.1 191.7 198.2 197.5 196.8
Enteric Fermentation 132.1 I 136.5 I 136.5 138.8 141.0 140.6 139.8
Manure Management 31.7 42.4 46.6 46.7 50.7 49.4 49.5
Rice Cultivation 7.1 7.5 6.8 5.9 6.2 7.2 7.3
Field Burning of
Agricultural Residues 0.3 0.3 0.2 0.2 0.2 0.3 0.2
N2O 212.4 I 224.0 I 228.7 227.1 227.6 228.8 222.5
Agricultural Soil
Management 197.8 I 206.8 I 211.3 208.9 209.4 210.7 204.6
Manure Management 14.5 17.1 17.3 18.0 18.1 17.9 17.9
Field Burning of
Agricultural Residues 0.1 0.1 0.1 0.1 0.1 (U 0.1
Total 383.6 410.6 418.8 418.8 425.8 426.3 419.3
Note: Totals may not sum due to independent rounding.
Table 6-2: Emissions from Agriculture (Gg)
Gas/Source 1990 2000 2005 2006 2007 2008 2009
CH4 8,153 8,890 9,052 9,129 9,437 9,405 9,372
Enteric Fermentation 6,290 6,502 I 6,500 6,611 6,715 6,696 6,655
Manure Management 1,511 I 2,019 I 2,217 2,226 2,416 2,353 2,356
Rice Cultivation 339 357 326 282 295 343 349
Field Burning of
Agricultural Residues 13 12 9 11 11 13 12
N2O 685 722 738 732 734 738 718
Agricultural Soil
Management 638 667 682 674 675 680 660
Agriculture 6-1
-------
Manure Management
Field Burning of
Agricultural Residues
55
56
+
58
+
58
+
58
+
58
+
+ Less than 0.5 Gg.
Note: Totals may not sum due to independent rounding.
6.1. Enteric Fermentation (IPCC Source Category 4A)
Methane is produced as part of normal digestive processes in animals. During digestion, microbes resident in an
animal's digestive system ferment food consumed by the animal. This microbial fermentation process, referred to as
enteric fermentation, produces CH4 as a byproduct, which can be exhaled or eructated by the animal. The amount of
CH4 produced and emitted by an individual animal depends primarily upon the animal's digestive system, and the
amount and type of feed it consumes.
Ruminant animals (e.g., cattle, buffalo, sheep, goats, and camels) are the major emitters of CH4 because of their
unique digestive system. Ruminants possess a rumen, or large "fore-stomach," in which microbial fermentation
breaks down the feed they consume into products that can be absorbed and metabolized. The microbial
fermentation that occurs in the rumen enables them to digest coarse plant material that non-ruminant animals cannot.
Ruminant animals, consequently, have the highest CH4 emissions among all animal types.
Non-ruminant animals (e.g., swine, horses, and mules) 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 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, and production (e.g., milk production, wool growth, pregnancy, or work).
Therefore, feed intake varies among animal types as well as among different management practices for individual
animal types (e.g., animals in feedlots or grazing on pasture).
Methane emission estimates from enteric fermentation are provided in Table 6-3 and Table 6-4. Total livestock CH4
emissions in 2009 were 139.8 Tg CO2 Eq. (6,655 Gg). Beef cattle remain the largest contributor of CH4 emissions
from enteric fermentation, accounting for 71 percent in 2009. Emissions from dairy cattle in 2009 accounted for 24
percent, and the remaining emissions were from horses, sheep, swine, and goats.
From 1990 to 2009, emissions from enteric fermentation have increased by 5.8 percent. Generally, emissions
decreased from 1996 to 2003, though with a slight increase in 2002. This trend was mainly due to decreasing
populations of both beef and dairy cattle and increased digestibility of feed for feedlot cattle. Emissions increased
from 2004 through 2007, as both dairy and beef populations have undergone increases and the literature for dairy
cow diets indicated a trend toward a decrease in feed digestibility for those years. Emissions decreased again in
2008 and 2009 as beef cattle populations again decreased. During the timeframe of this analysis, populations of
sheep have decreased 49 percent while horse populations have increased over 87 percent, mostly since 1999. Goat
and swine populations have increased 25 percent and 23 percent, respectively, during this timeframe.
Table 6-3: CH4 Emissions from Enteric Fermentation (Tg CO2 Eq.)
Livestock Type
1990
2000
2005
2006
2007
2008
2009
Beef Cattle
Dairy Cattle
Horses
Sheep
Swine
Goats
94.5
31.8
1.9
1.9
1.7
0.3
I I
99.3
30.4
3.5
1.0
1.9
0.3
100.9
31.1
3.6
1.0
1.9
0.3
101.6
32.4
3.6
1.0
2.1
0.3
100.7
32.9
3.6
1.0
2.1
0.3
99.6
33.2
3.6
1.0
2.1
0.3
Total
132.1
136.5
136.5
138.8
141.0
140.6
139.8
Note: Totals may not sum due to independent rounding.
Table 6-4: CH4 Emissions from Enteric Fermentation (Gg)
Livestock Type
1990
2000
2005
2006
2007
2008
2009
Beef Cattle
Dairy Cattle
4,502
1.513
4,790
1,460
4,731
1,449
4,803
1,479
4,837
1.544
4,796
1,564
4,742
1,581
6-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Horses
Sheep
Swine
Goats
Total
91
4
13
6,290
194
I
12
6,502
1166
49
92
14
6,500
171
50
93
15
6,611
171
49
98
16
6,715
171
48
101
16
6,696
171
46
99
16
6,655
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 except for bulls. Emission estimates for other domesticated animals
(horses, sheep, swine, goats, and bulls) 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 Steers)
o Cows
o Bulls
Calf birth rates, end-of-year population statistics, detailed feedlot placement information, and slaughter weight data
were used to create a transition matrix that models cohorts of individual animal types and their specific emission
profiles. The key variables tracked for each of the cattle population categories are described in Annex 3.9. These
variables include performance factors such as pregnancy and lactation as well as average weights and weight gain.
Annual cattle population data were obtained from the U.S. Department of Agriculture's (USDA) National
Agricultural Statistics Service (NASS) QuickStats database (USDA 2010).
Diet characteristics were estimated by region for U.S. dairy, beef, and feedlot cattle. These estimates 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 population
category. The IPCC recommends Ym values 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, rather than using the recommended IPCC values. The diet characterizations and estimation of DE and
Ym values were based on information from state agricultural extension specialists, a review of published forage
quality studies and scientific literature, expert opinion, and modeling of animal physiology. The diet characteristics
for dairy cattle were based on Donovan (1999) and an extensive review of nearly 20 years of literature. Dairy
replacement heifer diet assumptions were based on the observed relationship in the literature between dairy cow and
Agriculture 6-3
-------
dairy heifer diet characteristics. The diet assumptions for beef cattle were derived from NRC (2000). 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, DE values were based on diet information in NRC (2000) and Ym values were based on Johnson (2002).
Weight and weight gains for cattle were estimated from Holstein Association USA (2010), Enns (2008), Lippke et
al. (2000), Pinchack et al., (2004), Platter et al. (2003), Skogerboe et al. (2000), and expert opinion. See Annex 3.9
for more details on the method used to characterize cattle diets and weights in the United States.
To estimate CH4 emissions from all cattle types except bulls and calves younger than 7 months,139 the population
was divided into state, age, sub-type (i.e., dairy cows and replacements, beef cows and replacements, heifer and
steer stackers, and heifers and steers in feedlots), 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, 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, and heifer feedlot animals. To estimate
emissions from cattle, population data from the transition matrix were multiplied by the calculated emission factor
for each cattle type. More details are provided in Annex 3.9.
Emission estimates for other animal types were based on average emission factors representative of entire
populations of each animal type. Methane emissions from these animals accounted for a minor portion of total CH4
emissions from livestock in the United States from 1990 through 2009. Also, the variability in emission factors for
each of these other animal types (e.g., variability by age, production system, and feeding practice within each animal
type) is less than that for cattle. Annual livestock population data for these other livestock types, except horses and
goats, as well as feedlot placement information, were obtained for all years from USDA NASS (USDA 2010).
Horse population data were obtained from the Food and Agriculture Organization of the United Nations (FAO)
FAOSTAT database (FAO 2010), because USDA does not estimate U.S. horse populations annually. Goat
population data were obtained for 1992, 1997, 2002, and 2007 (USDA 2010); these data were interpolated and
extrapolated to derive estimates for the other years. Methane emissions from sheep, goats, swine, and horses 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. The
methodology is the same as that recommended by IPCC (2006).
See Annex 3.9 for more detailed information on the methodology and data used to calculate CH4 emissions from
enteric fermentation.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis for this source category was performed through the IPCC-recommended Tier 2
uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique as described in ICF (2003).
These uncertainty estimates were developed for the 1990 through 2001 Inventory report. No significant changes
occurred in the method of data collection, data estimation methodology, or other factors that influence the
uncertainty ranges around the 2009 activity data and emission factor input variables used in the current submission.
Consequently, these uncertainty estimates were directly applied to the 2009 emission estimates.
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 capture the fact that these variables cannot be negative.
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
139 Emissions from bulls are estimated using a Tier 1 approach because it is assumed there is minimal variation in population and
diets. Because calves younger than 7 months consume mainly milk and the IPCC recommends the use of methane conversion
factor of zero for all juveniles consuming only milk, this results in no methane emissions from this subcategory of cattle.
6-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
modeled. The exogenous correlation coefficients between the probability distributions of selected activity-related
variables were developed through expert judgment.
The uncertainty ranges associated with the activity data-related input variables were plus or minus 10 percent or
lower. However, for many emission factor-related input variables, the lower- and/or the upper-bound uncertainty
estimates were over 20 percent. The results of the quantitative uncertainty analysis are summarized in Table 6-5.
Enteric fermentation CH4 emissions in 2009 were estimated to be between 124.4 and 165.0 Tg CO2 Eq. at a 95
percent confidence level, which indicates a range of 11 percent below to 18 percent above the 2009 emission
estimate of 139.8 Tg CO2 Eq. Among the individual cattle sub-source categories, beef cattle account for the largest
amount of CH4 emissions as well as the largest degree of uncertainty in the inventory emission estimates. Among
non-cattle, horses account for the largest degree of uncertainty in the inventory emission estimates because there is a
higher degree of uncertainty among the FAO population estimates used for horses than for the USD A population
estimates used for swine, goats, and sheep.
Table 6-5: Quantitative Uncertainty Estimates for CH4 Emissions from Enteric Fermentation (Tg CO2 Eq. and
Percent)
Source Gas 2009 Emission Uncertainty Range Relative to Emission Estimate3'b
Estimate
(Tg C02 Eq.) (TgC02Eq.)
Enteric Fermentation
CH4
139.8
Lower
Bound
124.4
Upper
Bound
165.0
Lower
Bound
-11%
Upper
Bound
+18%
* 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 submitted in 2003 and
applied to the 2009 estimates.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2009. 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. Because there were no major
modifications to the CEFM for 2009, QA/QC emphasis for the current Inventory was placed on cleaning up
documentation and references within the model, and review of external data sources. For example, during the
course of the QA/QC activities for this source category, it was noted that the U.S. total for 2009 Cattle On Feed data
provided via USDA's Quickstats database did not match the total calculated from summing all individual states.
The appropriate party was contacted at USD A, and it was determined that data for New Mexico and North Carolina
were included individually, as well as within the "Other States" aggregate number, so they were being double
counted in the U.S. total. This issue was quickly resolved.
In addition, over the past few years, particular importance has been placed on harmonizing the data exchange
between the enteric fermentation and manure management source categories. The current inventory submission now
utilizes the transition matrix from the CEFM for estimating cattle populations and weights for both source
categories, and the CEFM is used to output volatile solids and nitrogen (N) excretion estimates using the diet
assumptions in the model in conjunction with the energy balance equations from the IPCC (2006). This approach
should complete the resolution of the discrepancies noted in previous reviews of these sectors, and facilitate the
QA/QC process for both of these source categories.
Recalculations Discussion
There were several modifications to the estimates relative to the previous Inventory that had an effect on emission
estimates, including the following:
• The average weight assumed for mature dairy cows has changed from the 1,550 pounds used in previous
inventories to 1,500 pounds (Johnson 2010; Holstein Association 2010).
• The USDA published revised estimates in several categories that affected historical emissions estimated for
Agriculture 6-5
-------
cattle and swine for 2008. Calves, beef replacements, and feedlot cattle all saw slight modifications to their
2008 populations, while swine population categories were modified so that the categories "<60 pounds" and
"60-119 pounds" were replaced with "<50 pounds" and "50-119" pounds. Additionally, 2008 lactation
estimates for Arkansas, Connecticut, Indiana, Nebraska, New Jersey, Oklahoma, South Carolina, and Vermont
were updated by USD A.
• For the 1990 through 2009 inventory, goat population data were taken from the 2007 Census of Agriculture.
For 2007 population values, the Census's 2007 "Total Goat" population for each state was used. Using the
2002 and 2007 data points, the population for the intervening years was interpolated, and the population for
2008 and 2009 were set equal to the population for 2007. The updated Census data resulted in a change in
population values from 2003 through 2008 as populations for these years were previously set equal to the 2002
population.
As a result of these changes, dairy cattle emissions decreased an average of 11.5 Gg (0.8 percent) per year and beef
cattle emissions decreased an average of 0.13 Gg (less than 0.01 percent) per year over the entire time series relative
to the previous Inventory. Historical emission estimates for 2008 increased by 1.3 percent for goats as a result of the
USDA population revisions described above.
Planned Improvements
Continued research and regular updates are necessary to maintain a current model of cattle diet characterization,
feedlot placement data, rates of weight gain and calving, among other data inputs. Ongoing revisions could include
some of the following options:
• Reviewing and updating the diet assumptions for foraging beef cattle;
• Estimating bull emissions using the IPCC Tier 2 approach;
• Updating input variables that are from older data sources, such as beef births by month and beef cow lactation
rates;
• The possible breakout of other animal types (i.e., sheep, swine, goats, horses) from national estimates to state-
level estimates; and
• Including bison in the estimates for other domesticated animals.
In addition, recent changes that have been implemented to the CEFM warrant an assessment of the current
uncertainty analysis; therefore, a revision of the quantitative uncertainty surrounding emission estimates from this
source category will be initiated.
6.2. Manure Management (IPCC Source Category 4B)
The management of livestock manure can produce anthropogenic CH4 and N2O emissions. Methane is produced by
the anaerobic decomposition of manure. 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.140 Indirect N2O emissions are produced
as result of the volatilization of N as NH3 and NOX and runoff and leaching of N during treatment, storage and
transportation.
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 materials in the manure tends to produce CH4.
When manure is handled as a solid (e.g., in stacks or dry lots) 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
140 Direct and indirect N2O emissions from dung and urine spread onto fields either directly as daily spread or after it is removed
from manure management systems (e.g., lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or
paddock lands are accounted for and discussed in the Agricultural Soil Management source category within the Agriculture
sector.
6-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 CH4 produced. 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
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 does not include any
leaching losses.
Estimates of CH4 emissions in 2009 were 49.5 Tg CO2 Eq. (2,356 Gg), 56 percent higher than in 1990. Emissions
increased on average by 0.9 Tg CO2 Eq. (2.5 percent) annually over this period. The majority of this increase was
from swine and dairy cow manure, where emissions increased 45 and 95 percent, respectively. Although the
majority of 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 limiting the application of manure nutrients have shifted manure
management practices at smaller dairies from daily spread to manure managed and stored on site. Although national
dairy animal populations have been generally decreasing, some states have seen increases in their dairy populations
as the industry becomes more concentrated in certain areas of the country. 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 facilities is translated into an increasing use of liquid manure management
systems, which have higher potential CH4 emissions than dry systems. This shift was accounted for by
incorporating state and WMS-specific CH4 conversion factor (MCF) values in combination with the 1992, 1997, and
2002 farm-size distribution data reported in the Census of Agriculture (USDA 2009a). Methane emissions from
sheep have decreased significantly since 1990 (a 54 percent decrease from 1990 to 2009); however, this is mainly
due to population changes. Overall, sheep contribute less than one percent of CH4 emissions from animal manure
management. From 2008 to 2009, there was a less than 1 percent increase in total CH4 emissions, due to minor
shifts in the animal populations and the resultant effects on manure management system allocations.
In 2009, total N2O emissions were estimated to be 17.9 Tg CO2 Eq. (58 Gg); in 1990, emissions were 14.5 Tg CO2
Eq. (47 Gg). These values include both direct and indirect N2O emissions from manure management. Nitrous oxide
emissions have remained fairly steady since 1990. Small changes in N2O emissions from individual animal groups
exhibit the same trends as the animal group populations, with the overall net effect that N2O emissions showed a 23
percent increase from 1990 to 2009 and a less than 1 percent decrease from 2008 through 2009.
Table 6-6 and Table 6-7 provide estimates of CH4 and N2O emissions from manure management by animal
category.
Table 6-6: CH4 and N2O Emissions from Manure Management (Tg CO2Eq.)
Gas/Animal Type 1990 2000 2005 2006 2007 2008 2009
CH4a 31.7 42.4 46.6 46.7 50.7 49.4 49.5
Dairy Cattle 12.6 18.9 21.4 21.7 24.2 24.1 24.5
BeefCattle 2.1 M 2.gl 2.8 2.9 2.9 2.8 2.7
Swine 13.1 17.5 19.0 18.7 20.3 19.3 19.0
Sheep O.l| O.l| 0.1 0.1 0.1 0.1 0.1
Agriculture 6-7
-------
Goats
Poultry
Horses
N20b
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
Total
05
14.5
5.3
15
0.2
46.2
,V,|
0.2
59.5
2.7
0.6
17.3
5.6
7.5
1.8
0.4
+
1.7
0.3
63.8
2.7
0.6
18.0
5.8
8.0
1.8
0.4
+
1.7
0.3
64.8
2.8
0.6
18.1
5.8
7.9
1.9
0.4
+
1.7
0.3
68.9
2.7
0.5
17.9
5.7
7.8
2.0
0.4
+
1.7
0.3
67.3
+ Less than 0.05 Tg CO2 Eq.
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.
Table 6-7: CH4 and N2O Emissions from Manure Management (Gg)
Gas/Animal Type
1990
2000
2005
2006
2007
2008
CH4a
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
N20b
Dairy Cattle
Beef Cattle
Swine
Sheep
Goats
Poultry
Horses
1,511
599
128
624
1
22
47
17
2,217
1,018
132
905
3
1
129
28
56
18
24
6
1
+
5
1
2,226
1,034
139
889
3
1
131
28
58
19
26
6
1
+
5
1
2,416
1,151
136
965
3
1
134
27
58
19
26
6
1
+
5
1
2,353
1,147
131
918
3
1
129
24
58
18
25
6
1
+
5
1
2.7
0.5
17.9
5.8
7.8
2.0
0.3
+
1.6
0.3
67.3
2009
2,356
1,168
130
903
3
1
127
24
58
19
25
6
1
+
5
1
+ Lessthan0.5Gg.
aAccounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
Includes both direct and indirect N2O emissions.
Note: Totals may not sum due to independent rounding.
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 for this Inventory. See Annex 3.10 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 waste management system (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
6-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
• 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 2009 for all livestock types, except horses and goats were
obtained from USDA 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
CEFM that models cohorts of individual animal types and their specific emission profiles. The key
variables tracked for each of the cattle population categories are described in Section 6.1 and in more detail
in Annex 3.9. Horse population data were obtained from the FAOSTAT database (FAO 2010). Goat
population data for 1992, 1997, 2002, and 2007 were obtained from the Census of Agriculture (USDA
2009a).
• The TAM is an annual average weight which was obtained for animal types other than cattle from
information in USD A' § Agricultural Waste Management Field Handbook (USDA 1996a), the American
Society of Agricultural Engineers, Standard D384.1 (ASAE 1999) and others (EPA 1992, Safley 2000,
ERG 2010a). For a description of the TAM used for cattle, please see section 6.1, Enteric Fermentation.
• WMS usage was estimated for swine and dairy cattle for different farm size categories using data from
USDA (USDA 1996b, 1998b, 2000a) 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 2000b, UEP 1999). For other animal types, manure management system
usage was based on previous estimates (EPA 1992).
• VS production rates for all cattle except for bulls and calves were calculated by head for each state and
animal type in the CEFM. VS production rates by animal mass for all other animals were determined using
data from USD A's Agricultural Waste Management Field Handbook (USDA 1996a, 2008) and data from
the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998).
• 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,
and 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.
• Anaerobic digestion system data were obtained from the EPA AgSTAR Program, including information
presented inihe AgSTAR Digest (EPA 2000, 2003, 2006). Anearobic digester emissions were calculated
based on estimated methane production and collection and destruction efficiency assumptions (ERG 2008).
To estimate CH4 emissions for cattle, 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. For animals other than cattle, the annual amount of VS
(kg per year) from manure excreted in each WMS was calculated for each animal type, state, and year. This
calculation multiplied the animal population (head) by the VS excretion rate (kg VS per 1,000 kg animal mass per
day), the TAM (kg animal mass per head) divided by 1,000, the WMS distribution (percent), and the number of days
per year (365.25).
The estimated amount of VS managed in each WMS was used to estimate the CH4 emissions (kg CH4 per year)
from each WMS. The amount of VS (kg per year) were multiplied by the maximum CH4 producing capacity of the
VS (B0) (m3 CH4 per kg VS), the MCF for that WMS (percent), and the density of CH4 (kg CH4 per m3 CH4). The
CH4 emissions for each WMS, state, and animal type were summed to determine the total U.S. CH4 emissions.
Nitrous Oxide Calculation Methods
The following inputs were used in the calculation of direct and indirect N2O emissions:
• Animal population data (by animal type and state);
Agriculture 6-9
-------
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 (EFvoiltailzatlon);
Indirect N2O emission factor for runoff and leaching (SP^off/iead,);
Fraction of nitrogen loss from volitalization of NH3 and NOX (Fracgas); and
Fraction of nitrogen loss from runoff and leaching
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 bulls and 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 (USD A 1996a, 2008) and data from the American
Society of Agricultural Engineers, Standard D384.1 (ASAE 1998).
• All N2O emission factors (direct and indirect) were taken from IPCC (2006).
• Country-specific estimates for the fraction of N loss from volatilization (Fracgas) and runoff and leaching
(FraCrunoff/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). FraCrunoff/ie^ung values were based on regional cattle runoff data from EPA's
Office of Water (EPA 2002b; see Annex 3.1).
To estimate N2O emissions for cattle, 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 animals other than cattle, 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 1000 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 (EFvoiatlilzatlon, 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/ie^h, 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 2003) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory report to determine the uncertainty associated with estimating CH4 and N2O emissions from
livestock manure management. The quantitative uncertainty analysis for this source category was performed in
2002 through the IPCC-recommended Tier 2 uncertainty estimation methodology, the Monte Carlo Stochastic
Simulation technique. The uncertainty analysis was developed based on the methods used to estimate CH4 and N2O
emissions from manure management systems. A normal probability distribution was assumed for each source data
category. The series of equations used were condensed into a single equation for each animal type and state. The
equations for each animal group contained four to five variables around which the uncertainty analysis was
6-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
performed for each state. These uncertainty estimates were directly applied to the 2009 emission estimates.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 6-8. Manure management CH4
emissions in 2009 were estimated to be between 40.6 and 59.4 Tg CO2 Eq. at a 95 percent confidence level, which
indicates a range of 18 percent below to 20 percent above the actual 2009 emission estimate of 49.5 Tg CO2 Eq. At
the 95 percent confidence level, N2O emissions were estimated to be between 15.0 and 22.1 Tg CO2 Eq. (or
approximately 16 percent below and 24 percent above the actual 2009 emission estimate of 17.9 Tg CO2 Eq.).
Table 6-8: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O (Direct and Indirect) Emissions from Manure
Management (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate
(Tg C02 Eq.)
Manure Management CH4 49.5
Manure Management N2O 17.9
Uncertainty Range Relative to Emission
Estimate"
(Tg C02 Eq.) (%)
Lower
Bound
40.6
15.0
Upper
Bound
59.4
22.1
Lower
Bound
-18%
-16%
Upper
Bound
+20%
+24%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
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 nitrogen excreted and the sum
of county estimates for the full time series.
Recalculations Discussion
The CEFM produces VS and Nex data for cattle that are used in the manure management inventory. As a result, all
changes to the CEFM described in Section 6.1 Enteric Fermentation contributed to changes in the VS and Nex data
utilized for calculating CH4 and N2O emissions from manure management. In addition, to standardize the estimates
of TAM between the CEFM and the manure management source category, the total VS and Nex estimates in units
of kg per head per year from the CEFM were used in the manure management calculations in the current Inventory.
With these changes, CH4 and N2O emission estimates from manure management systems are higher than reported in
the previous Inventory for both beef and dairy cattle. Methane emissions from beef and dairy cattle were higher by
7 and 24 percent, respectively, while N2O emissions were higher by 1 and 5 percent for beef and dairy cattle,
respectively, averaged over the 1990 to 2008 time series.
In addition to changes in cattle Nex and VS data, the VS and Nex for other animal types were updated using data
from USDA's updated Agricultural Waste Management Field Handbook (USD A 2008). Data from both the
previous Handbook and the updated the Handbook were used to create a time series of VS and Nex data across all
inventory years for all animals (ERG 2010b). The VS and Nex updates for all animals contributed to an average
emission increase of 9.5 percent for CH4 and 2.7 percent for N2O across the time series.
For the current Inventory, USDA population data were used that included updated market swine categories. USD A
changed the "market swine under 60 Ibs." category to "market swine under 50 Ibs." for years 2008 and 2009. In
addition, USDA changed the "market swine from 60-119 Ibs." to "market swine from 50-119 Ibs." for the same
years. This update resulted in a change in TAM estimates for those two swine categories which contributed to an
overall decrease in CH4 emissions from swine of 1.6 percent and an overall increase in N2O emissions from swine of
20.9 percent in 2008.
The goat population was updated to reflect the USDA 2007 Census of Agriculture. This change resulted in an
increase in both CH4and N2O emissions for goats from the years 2003 through 2008 by 13 percent and 16 percent on
average, respectively.
Agriculture 6-11
-------
Planned Improvements
A recent journal article (Lory et al., 2010) criticized the IPCC and EPA methodology used to estimate greenhouse
gas emissions from manure management. After review of the methodologies, EPA does not feel that any changes to
the IPCC inventory methodologies are required as a result of this article; for more specific information, please see
EPA's detailed response to the article (Bartram et al., 2010). EPA will continue to investigate any new or additional
data sources identified that contain updated information that can be used to improve the inventory emission
estimates. Also, EPA will continue to seek empirical data to compare inventory estimates to specific systems, in
order to improve the methodology used to estimate greenhouse gas emissions from manure management.
USDA's 2007 Census of Agriculture data are finalized and available. These data will be incorporated into the
county-level population estimates used for the Agricultural Soils source category and the estimates of MCF and
utilize it to update the WMS distributions for swine and dairy animals.
Due to time constraints, the temperature data used to estimate MCFs were not updated for the current Inventory.
Updated temperature data will be obtained and applied for subsequent Inventory reports.
The uncertainty analysis will be updated in the future to more accurately assess uncertainty of emission calculations.
This update is necessary due to the extensive changes in emission calculation methodology that was made in the
1990 through 2006 Inventory, including estimation of emissions at the WMS level and the use of new calculations
and variables for indirect N2O emissions.
6.3. Rice Cultivation (IPCC Source Category 4C)
Most of the world's rice, and all rice in the United States, is grown on flooded fields. When fields are flooded,
aerobic decomposition of organic material gradually depletes most of the oxygen present in the soil, causing
anaerobic soil conditions. Once the environment becomes anaerobic, CH4 is produced through anaerobic
decomposition of soil organic matter by methanogenic bacteria. As much as 60 to 90 percent of the CH4 produced is
oxidized by aerobic methanotrophic bacteria in the soil (some oxygen remains at the interfaces of soil and water, and
soil and root system) (Holzapfel-Pschorn et al. 1985, Sass et al. 1990). Some of the CH4 is also leached away as
dissolved CH4 in floodwater that percolates from the field. The remaining un-oxidized CH4 is transported from the
submerged soil to the atmosphere primarily by diffusive transport through the rice plants. Minor amounts of CH4
also escape from the soil via diffusion and bubbling through floodwaters.
The water management system under which rice is grown is one of the most important factors affecting CH4
emissions. Upland rice fields are not flooded, and therefore are not believed to produce CH4. In deepwater rice
fields (i.e., fields with flooding depths greater than one meter), the lower stems and roots of the rice plants are dead,
so the primary CH4 transport pathway to the atmosphere is blocked. The quantities of CH4 released from deepwater
fields, therefore, are believed to be significantly less than the quantities released from areas with shallower flooding
depths. Some flooded fields are drained periodically during the growing season, either intentionally or accidentally.
If water is drained and soils are allowed to dry sufficiently, CH4 emissions decrease or stop entirely. This is due to
soil aeration, which not only causes existing soil CH4 to oxidize but also inhibits further CH4 production in soils.
All rice in the United States is grown under continuously flooded conditions; none is grown under deepwater
conditions. Mid-season drainage does not occur except by accident (e.g., due to levee breach).
Other factors that influence CH4 emissions from flooded rice fields include fertilization practices (especially the use
of organic fertilizers), soil temperature, soil type, rice variety, and cultivation practices (e.g., tillage, seeding, and
weeding practices). The factors that determine the amount of organic material available to decompose (i.e., organic
fertilizer use, soil type, rice variety,141 and cultivation practices) are the most important variables influencing the
amount of CH4 emitted over the growing season; the total amount of CH4 released depends primarily on the amount
of organic substrate available. Soil temperature is known to be an important factor regulating the activity of
methanogenic bacteria, and therefore the rate of CH4 production. However, although temperature controls the
amount of time it takes to convert a given amount of organic material to CH4, that time is short relative to a growing
season, so the dependence of total emissions over an entire growing season on soil temperature is weak. The
application of synthetic fertilizers has also been found to influence CH4 emissions; in particular, both nitrate and
141 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.
6-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
sulfate fertilizers (e.g., ammonium nitrate and ammonium sulfate) appear to inhibit CH4 formation.
Rice is cultivated in eight states: Arkansas, California, Florida, Louisiana, Mississippi, Missouri, Oklahoma, and
Texas.142 Soil types, rice varieties, and cultivation practices for rice vary from state to state, and even from farm to
farm. However, most rice farmers apply organic fertilizers in the form of residue from the previous rice crop, which
is left standing, disked, or rolled into the fields. Most farmers also apply synthetic fertilizer to their fields, usually
urea. Nitrate and sulfate fertilizers are not commonly used in rice cultivation in the United States. In addition, the
climatic conditions of southwest Louisiana, Texas, and Florida often allow for a second, or ratoon, rice crop. Ratoon
crops are much less common or non-existent in Arkansas, California, Mississippi, Missouri, Oklahoma, and northern
areas of Louisiana. Methane emissions from ratoon crops have been found to be considerably higher than those
from the primary crop. This second rice crop is produced from regrowth of the stubble after the first crop has been
harvested. Because the first crop's stubble is left behind in ratooned fields, and there is no time delay between
cropping seasons (which would allow the stubble to decay aerobically), the amount of organic material that is
available for anaerobic decomposition is considerably higher than with the first (i.e., primary) crop.
Rice cultivation is a small source of CH4 in the United States (Table 6-9 and Table 6-10). In 2009, CH4 emissions
from rice cultivation were 7.3 Tg CO2 Eq. (349 Gg). Annual emissions fluctuated unevenly between the years 1990
and 2009, ranging from an annual decrease of 14 percent to an annual increase of 17 percent. There was an overall
decrease of 17 percent between 1990 and 2006, due to an overall decrease in primary crop area.143 However,
emission levels increased again by 24 percent between 2006 and 2009 due to a slight increase in rice crop area in all
states. The factors that affect the rice acreage in any year vary from state to state, although the price of rice relative
to competing crops is the primary controlling variable in most states.
Table 6-9: CH4 Emissions from Rice Cultivation (Tg CO2 Eq.)
State 1990 2000 2005 2006 2007 2008 2009
Primary
Arkansas
California
Florida
Louisiana
Mississippi
Missouri
Oklahoma
Texas
Ratoon
Arkansas
Florida
Louisiana
Texas
5.1
2.1
0.7
+
1.0
0.4
0.1
+
0.6
2.1
1.1
0.9
5.5
2.5
1.0
+
0.9
0.4
0.3
+
0.4
2.0
+
0.1
1.3
0.7
6.0
2.9
0.9
+
0.9
0.5
0.4
+
0.4
0.8
0.5
0.4
5.1
2.5
0.9
+
0.6
0.3
0.4
+
0.3
0.9
0.5
0.4
4.9
2.4
1.0
+
0.7
0.3
0.3
0.0
0.3
1.3
0.9
0.3
5.3
2.5
0.9
+
0.8
0.4
0.4
0.0
0.3
1.9
1.2
0.6
5.6
2.6
1.0
+
0.8
0.4
0.4
0.0
0.3
1.8
1.1
0.7
Total
7.1
7.5
6.8
5.9
6.2
7.2
7.3
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 6-10: CH4 Emissions from Rice Cultivation (Gg)
State
1990
2000
2005
2006
2007
2008
2009
Primary
Arkansas
California
Florida
Louisiana
241
102
34
1
46
II
287
139
45
1
45
241
119
44
1
29
235
113
45
1
32
254
119
44
1
39
265
125
47
1
39
142 A very small amount of rice is grown on about 20 acres in South Carolina; however, this amount was determined to be too
insignificant to warrant inclusion in national emission estimates.
143 The 14 percent decrease occurred between 2005 and 2006; the 17 percent increase happened between 1993 and 1994.
Agriculture 6-13
-------
Mississippi 21 19 22 16 16 19 21
Missouri 7 I 14 18 18 15 17 17
Oklahoma + I + I + + 0 + +
Texas 30 18 17 13 12 15 14
Ratoon 98 97 39 41 60 89 84
Arkansas + I + I 1 + + + +
Florida 2 I 2 I +11 1 2
Louisiana 52 61 22 22 42 59 51
Texas 45 34 17 18 16 29 3J_
Total 339 357 326 282 295 343 349
+ Lessthan0.5Gg
Note: Totals may not sum due to independent rounding.
Methodology
IPCC (2006) recommends using harvested rice areas, area-based daily emission factors (i.e., amount of CH4 emitted
per day per unit harvested area), and length of growing season to estimate annual CH4 emissions from rice
cultivation. This Inventory uses the recommended methodology and employs Tier 2 U.S.-specific emission factors
derived from rice field measurements. State-specific and daily emission factors were not available, however, so
average U.S. seasonal emission factors were used. Seasonal emissions have been found to be much higher for
ratooned crops than for primary crops, so emissions from ratooned and primary areas are estimated separately using
emission factors that are representative of the particular growing season. This approach is consistent with IPCC
(2006).
The harvested rice areas for the primary and ratoon crops in each state are presented in Table 6-11, and the area of
ratoon crop area as a percent of primary crop area is shown in Table 6-12. Primary crop areas for 1990 through
2009 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 2010). Source data for
non-USDA sources of primary and ratoon harvest areas are shown in Table 6-13. California, Mississippi, Missouri,
and Oklahoma have not ratooned rice over the period 1990 through 2009 (Guethle 1999 through 2010; Lee 2003
through 2007; Mutters 2002 through 2005; Street 1999 through 2003; Walker 2005, 2007 through 2008; Buehring
2009 through 2010).
Table 6-11: Rice
State/Crop
Arkansas
Primary
Ratoon3
California
Florida
Primary
Ratoon
Louisiana
Primary
Ratoon
Mississippi
Missouri
Oklahoma
Texas
Primary
Ratoon
Total Primary
Areas Harvested
1990
485,633
.I
"sl
220,558
66,1681
101,174
32,376B
6171
142,857
57,143
1,148,047
(Hectares)
2000
570,619B
_H
221,773B
7,801
3:"3
194,253B
77,701
88,223
68,393
283B
86,605
43,302
7,237,957
2005
661,675
662
212,869
4,565
0
212,465
27,620
106,435
86,605
271
81,344
21,963
1,366,228
2006
566,572
6
211,655
4,575
1,295
139,620
27,924
76,487
86,605
17
60,704
23,675
1,146,235
2007
536,220
5
215,702
6,242
1,873
152,975
53,541
76,487
72,036
0
58,681
21,125
1,118,343
2008
564,549
6
209,227
5,463
1,639
187,778
75,111
92,675
80,534
77
69,607
36,892
1,209,911
2009
594,901
6
225,010
5,664
2,266
187,778
65,722
98,341
80,939
0
68,798
39,903
1,261,431
6-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Total Ratoon
125,799
124,197
50,245
52,899
76,544 113,648 107,897
Total
1,273,847
1,362,148
1,416,473 1,199,135 1,194,887 1,323,559 1,369,328
a Arkansas ratooning occurred only in 1998, 1999, and 2005 through 2009.
Note: Totals may not sum due to independent rounding.
Table 6-12: Ratooned Area as Percent of Primary Growth Area
State
1990 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Arkansas 0% + + 0% 0.1% + + + +
Florida 50% 65% 41% 60% 54% 100% 77% 0% 28% 30% 30% 40%
Louisiana 30% 40% 30% 15% 35% 30% 13% 20% 35% 40% 35%
Texas 40% 50% 40% 37% 38% 35% 27% 39% 36% 53% 58%
+ Indicates ratooning rate less than 0.1 percent.
Table 6-13: Non-USD A Data Sources for Rice Harvest Information
State/Crop
1990 2000
2001
2002 2003 2004
2005 2006
2007 2008
2009
Arkansas
Ratoon
Wilson (2002 - 2007, 2009 - 2010)
Florida
Primary
Ratoon
Scheuneman
(1999-2001)
Scheuneman
(1999)
Deren
(2002)
Deren
(2002)
Kirstein (2003, 2006)
Kirstein(2003- Cantens
2004) (2005)
Gonzales(2006-2010)
Gonzales(2006-2010)
Louisiana
Ratoon
Bollich
(2000)
Linscombe (1999,
2001-2010)
Oklahoma
Primary
Lee (2003-2007)
Anderson (2008
-2010)
Texas
Ratoon
Klosterboer (1999 - 2003) Stansel (2004 -
2005)
Texas Ag Experiment Station (2006 -
2010)
To determine what CH4 emission factors should be used for the primary and ratoon crops, CH4 flux information
from rice field measurements in the United States was collected. Experiments that involved atypical or
nonrepresentative management practices (e.g., the application of nitrate or sulfate fertilizers, or other substances
believed to suppress CH4 formation), as well as experiments in which measurements were not made over an entire
flooding season or floodwaters were drained mid-season, were excluded from the analysis. The remaining
experimental results144 were then sorted by season (i.e., primary and ratoon) and type of fertilizer amendment (i.e.,
no fertilizer added, organic fertilizer added, and synthetic and organic fertilizer added). The experimental results
from primary crops with added synthetic and organic fertilizer (Bossio et al. 1999; Cicerone et al. 1992; Sass et al.
1991a, 1991b) were averaged to derive an emission factor for the primary crop, and the experimental results from
ratoon crops with added synthetic fertilizer (Lindau and Bollich 1993, Lindau et al. 1995) were averaged to derive
an emission factor for the ratoon crop. The resultant emission factor for the primary crop is 210 kg CH^hectare-
season, and the resultant emission factor for the ratoon crop is 780 kg CHVhectare-season.
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
144 In some of these remaining experiments, measurements from individual plots were excluded from the analysis because of the
aforementioned reasons. In addition, one measurement from the ratooned fields (i.e., the flux of 1,490 kg CH4/hectare-season in
Lindau and Bollich 1993) was excluded, because this emission rate is unusually high compared to other flux measurements in the
United States, as well as IPCC (2006) default emission factors.
Agriculture 6-15
-------
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 22 to 479 kg CH^hectare-season
and ratoon emissions ranged from 481 to 1,490 kg CH^hectare-season. The uncertainty distributions around the
primary and ratoon emission factors were derived using the distributions of the relevant primary or ratoon emission
factors available in the literature and described above. Variability about the rice emission factor means was not
normally distributed for either primary or ratooned crops, but rather skewed, with a tail trailing to the right of the
mean. A lognormal statistical distribution was, therefore, applied in the Tier 2 Monte Carlo analysis.
Other sources of uncertainty include the primary rice-cropped area for each state, percent of rice-cropped area that is
ratooned, 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. No uncertainties were calculated for the practice of flooding outside of the normal rice
season because CH4 flux measurements have not been undertaken over a sufficient geographic range or under a
broad enough range of representative conditions to account for this source in the emission estimates or its associated
uncertainty.
To quantify the uncertainties for emissions from rice cultivation, a Monte Carlo (Tier 2) uncertainty analysis was
performed using the information provided above. The results of the Tier 2 quantitative uncertainty analysis are
summarized in Table 6-14. Rice cultivation CH4 emissions in 2009 were estimated to be between 2.5 and 18.0 Tg
CO2 Eq. at a 95 percent confidence level, which indicates a range of 65 percent below to 146 percent above the
actual 2009 emission estimate of 7.3 Tg CO2 Eq.
Table 6-14: Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice Cultivation (Tg CO2 Eq. and
Percent)
Source Gas 2009 Emission
Estimate
(TgC02Eq.)
Rice Cultivation CH4 7.3
Uncertainty Range Relative to Emission Estimate"
(Tg C02 Eq.) (%)
Lower
Bound
2.5
Upper
Bound
18.0
Lower
Bound
-65%
Upper
Bound
+146%
* 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 2009. 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.
Planned Improvements
A possible future improvement is to create region-specific emission factors for rice cultivation. The current
methodology uses a nationwide average emission factor, derived from several studies done in a number of states.
The prospective improvement would take the same studies and average them by region, presumably resulting in
more spatially specific emission factors.
6-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.4. Agricultural Soil Management (IPCC Source Category 4D)
145
Nitrous oxide is produced naturally in soils through the microbial processes of nitrification and denitrification. A
number of agricultural activities increase mineral N availability in soils, thereby increasing the amount available for
nitrification and denitrification, and ultimately the amount of N2O emitted. These activities increase soil mineral N
either directly or indirectly (see Figure 6-2). Direct increases occur through a variety of management practices that
add or lead to greater release of mineral N to the soil, including fertilization; application of managed livestock
manure and other organic materials such as sewage sludge; deposition of manure on soils by domesticated animals
in pastures, rangelands, and paddocks (PRP) (i.e., by grazing animals and other animals whose manure is not
managed); production of N-fixing crops and forages; retention of crop residues; and drainage and cultivation of
organic cropland soils (i.e., soils with a high organic matter content, otherwise known as histosols).146 Other
agricultural soil management activities, including irrigation, drainage, tillage practices, and fallowing of land, can
influence N mineralization in soils and thereby affect direct emissions. Mineral N is also made available in soils
through decomposition of soil organic matter and plant litter, as well as asymbiotic fixation of N from the
atmosphere,147 and these processes are influenced by agricultural management through impacts on moisture and
temperature regimes in soils. These additional sources of mineral N are included at the recommendation of IPCC
(2006) for complete accounting of management impacts on greenhouse gas emissions, as discussed in the
Methodology section. Indirect emissions of N2O occur through two pathways: (1) volatilization and subsequent
atmospheric deposition of applied/mineralized N,148 and (2) surface runoff and leaching of applied/mineralized N
into groundwater and surface water. Direct emissions from agricultural lands (i.e., cropland and grassland) are
included in this section, while direct emissions from forest lands and settlements are presented in the Land Use,
Land-Use Change, and Forestry chapter. However, indirect N2O emissions from all land-uses (cropland, grassland,
forest lands, and settlements) are reported in this section.
Figure 6-2: Sources and Pathways of N that Result in N2O Emissions from Agricultural Soil Management
Agricultural soils produce the majority of N2O emissions in the United States. Estimated emissions from this source
in 2009 were 204.6 Tg CO2 Eq. (660 Gg N2O) (see Table 6-15 and Table 6-16). Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2009, although overall emissions were 3 percent higher in 2009 than
in 1990. Year-to-year fluctuations are largely a reflection of annual variation in weather patterns, synthetic fertilizer
use, and crop production. On average, cropland accounted for approximately 70 percent of total direct emissions,
while grassland accounted for approximately 30 percent. These percentages are about the same for indirect
emissions since forest lands and settlements account for such a small percentage of total indirect emissions.
Estimated direct and indirect N2O emissions by sub-source category are shown in Table 6-17 and Table 6-18.
Table 6-15: N2O Emissions from Agricultural Soils (Tg CO2 Eq.)
Activity 1990 2000 2005 2006 2007 2008 2009
Direct 153.8 162.6 167.5 163.7 165.1 166.6 160.2
Cropland 102.9 I 115.6 I 118.1 115.6 117.8 117.9 112.0
Grassland 50.9 47.1 49.4 48.1 47.3 48.7 48.2
Indirect (All Land-Use
Types) 44.0 44.1 43.9 45.2 44.3 44.1 44.4
145 Nitrification and denitrification are driven by the activity of microorganisms in soils. Nitrification is the aerobic microbial
oxidation of ammonium (NH4+) to nitrate (NO3~), 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).
146 Drainage and cultivation of organic soils in former wetlands enhances mineralization of N-rich organic matter, thereby
increasing N2O emissions from these soils.
147 Asymbiotic N fixation is the fixation of atmospheric N2 by bacteria living in soils that do not have a direct relationship with
plants.
148 These processes entail volatilization of applied or mineralized N as NH3 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 (HNO3), and NOX.
Agriculture 6-17
-------
Cropland
Grassland
Forest Land
Settlements
Total
0.3
197.8
I'll •
0.1
1 0.4
206.8
36.8
6.3
0.1
0.6
211.3
38.6
5.9
0.1
0.6
208.9
37.6
5.9
0.1
0.6
209.4
37.5
5.9
0.1
0.6
210.7
37.5
6.2
0.1
0.6
204.6
+ Less than 0.05 Tg CO2 Eq.
Table 6-16: N2O Emissions from Agricultural Soils (Gg)
Activity
Direct
Cropland
Grassland
Indirect (All Land-Use
Types)
Cropland
Grassland
Forest Land
Settlements
Total
1990
496
332 1
164 1
142 1
121 1
20 1
0 1
1
638
2000
525
152
142 1
122 1
19 1
+ 1
1 •
667
2005
540
381
159
142
119
20
+
2
682
2006
528
373
155
146
125
19
+
2
674
2007
533
380
152
143
121
19
+
2
675
2008
538
380
157
142
121
19
+
2
680
2009
517
361
155
143
121
20
+
2
660
+ Lessthan0.5GgN2O
Table 6-17: Direct N2O Emissions from Agricultural Soils by Land Use Type and N Input Type (Tg CO2 Eq.)
Activity 1990 2000 2005 2006 2007 2008
Total
153.8
162.6
167.5
163.7
165.1
166.6
2009
Cropland
Mineral Soils
102.9
100.1
115.6
112.7 1
118.1
115.2
115.6
112.7
117.8
114.9
117.9
115.0
112.0
109.1
Mineralization and
Asymbiotic Fixation 44.6 50.6 50.5 49.7 50.9 50.9 47.1
Synthetic Fertilizer 32.3 36.0 38.6 36.7 37.4 37.3 36.9
Residue N" 12.4 14.3 13.7 13.8 13.9 14.3 13.1
Organic Amendments1 10.8 11.8 12.3 12.5 12.8 12.5 12.1
Organic Soils 2.9 2.9 2.9 2.9 2.9 2.9 2.9
Grassland 50.9 47.1 49.4 48.1 47.3 48.7 48.2
Residue Nc 15.6 13.8 14.6 14.2 13.9 14.4 14.1
PRP Manure 8.1 7.9 8.2 8.1 8.0 8.2 7.9
Synthetic Fertilizer 3.9 3.9 4.1 4.0 3.9 4.0 3.9
Managed Manured 1.5 1.6 1.6 1.6 1.6 1.6 1.6
Sewage Sludge 0.3 0.4 0.5 0.5 0.5 0.5 0.5
Mineralization and Asymbiotic
Fixation 21.5 19.5 20.4 19.7 19.3 20.0 20.1
160.2
a Cropland residue N inputs include N in unharvested legumes as well as crop residue N.
b Organic amendment inputs include managed manure amendments, daily spread manure amendments, and commercial organic
fertilizers (i.e., dried blood, dried manure, tankage, compost, and other).
0 Grassland residue N inputs include N in ungrazed legumes as well as ungrazed grass residue N
d Accounts for managed manure and daily spread manure amendments that are applied to grassland soils.
Table 6-18: Indirect N2O Emissions from all Land-Use Types (Tg CO2 Eq.)
Activity
Cropland
Volatilization & Atm. Deposition
Surface Leaching & Run-Off
Grassland
Volatilization & Atm. Deposition
1990
37.5
11.6
25.8
6.1
5.1 |
2000
37.7
12.7
25.0
5.8
4.7
2005
36.8
13.1
23.7
6.3
4.8
2006
38.6
14.2
24.4
5.9
4.8
2007
37.6
12.8
24.9
5.9
4.7
2008
37.5
12.9
24.5
5.9
4.7
2009
37.5
13.4
24.1
6.2
4.7
6-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Surface Leaching & Run-Off
Forest Land
Volatilization & Atm. Deposition
Surface Leaching & Run-Off
Settlements
Volatilization & Atm. Deposition
Surface Leaching & Run-Off
Total
1.0
+ 1
+
_.-
0.1 1
0.2
44.0
0.3
44.1
11.5
0.1
+
0.1
0.6
0.2
0.4
43.9
1.1
0.1
+
0.1
0.6
0.2
0.4
45.2
1.2
0.1
+
0.1
0.6
0.2
0.4
44.3
1.2
0.1
+
0.1
0.6
0.2
0.4
44.1
1.5
0.1
+
0.1
0.6
0.2
0.4
44.4
+ Less than 0.05 Tg CO2 Eq.
Figure 6-3 through Figure 6-6 show regional patterns in direct N2O emissions, and also show N losses from
volatilization, leaching, and runoff that lead to indirect N2O emissions. Average annual emissions and N losses are
shown for croplands that produce major crops and from grasslands in each state. Direct N2O emissions from
croplands tend to be high in the Corn Belt (Illinois, Iowa, Indiana, Ohio, southern Minnesota, southern Wisconsin,
and eastern Nebraska), where a large portion of the land is used for growing highly fertilized corn and N-fixing
soybean crops. Direct emissions are also high in Missouri, Kansas, and Texas, primarily from irrigated cropping in
western Texas, dryland wheat in Kansas, and hay cropping in eastern Texas and Missouri. Direct emissions are low
in many parts of the eastern United States because a small portion of land is cultivated, and also low in many
western states where rainfall and access to irrigation water are limited.
Direct emissions (Tg CO2 Eq./state/year) from grasslands are highest in the central and western United States
(Figure 6-4) where a high proportion of the land is used for cattle grazing. Some areas in the Great Lake states, the
Northeast, and Southeast have moderate to low emissions even though emissions from these areas tend to be high on
a per unit area basis, because the total amount of grassland is much lower than in the central and western United
States.
Indirect emissions from croplands and grasslands (Figure 6-5 and Figure 6-6) show patterns similar to direct
emissions, because the factors that control direct emissions (N inputs, weather, soil type) also influence indirect
emissions. However, there are some exceptions, because the processes that contribute to indirect emissions (NO3~
leaching, N volatilization) do not respond in exactly the same manner as the processes that control direct emissions
(nitrification and denitrification). For example, coarser-textured soils facilitate relatively high indirect emissions in
Florida grasslands due to high rates of N volatilization and NO3" leaching, even though they have only moderate
rates of direct N2O emissions.
Figure 6-3: Major Crops, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model, 1990-
2009 (Tg CO2 Eq./year)
[Figure will be provided in public review]
Figure 6-4: Grasslands, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model, 1990-2009
(Tg CO2 Eq./year)
[Figure will be provided in public review]
Figure 6-5: Major Crops, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the
DAYCENT Model, 1990-2009 (GgN/year)
[Figure will be provided in public review]
Figure 6-6: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions Estimated Using the
DAYCENT Model, 1990-2009 (GgN/year)
[Figure will be provided in public review]
Methodology
The 2006IPCC Guidelines (IPCC 2006) divide the Agricultural Soil Management source category into four
components: (1) direct emissions due to N additions to cropland and grassland mineral soils, including synthetic
Agriculture 6-19
-------
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 drainage and cultivation of organic
cropland soils; (3) direct emissions from soils due to the deposition of manure by livestock on PRP grasslands; and
(4) indirect emissions from soils and water due to N additions and manure deposition to soils that lead to
volatilization, leaching, or runoff of N and subsequent conversion to N2O.
The United States has adopted recommendations from IPCC (2006) on methods for agricultural soil management.
These recommendations include (1) estimating the contribution of N from crop residues to indirect soil N2O
emissions; (2) adopting a revised emission factor for direct N2O emissions to the extent that Tier 1 methods are used
in the Inventory (described later in this section); (3) removing double counting of emissions from N-fixing crops
associated with the biological N fixation and crop residue N input categories; (4) using revised crop residue statistics
to compute N inputs to soils based on harvest yield data to the extent that Tier 1 methods are used in the Inventory;
(5) accounting for indirect as well as direct emissions from N made available via mineralization of soil organic
matter and litter, in addition to asymbiotic fixation149 (i.e., computing total emissions from managed land); and (6)
reporting all emissions from managed lands, largely because management affects all processes leading to soil N2O
emissions. One recommendation from IPCC (2006) that has not been adopted is the accounting of emissions from
pasture renewal, which involves occasional plowing to improve forage production. This practice is not common in
the United States, and is not estimated.
The methodology used to estimate emissions from agricultural soil management in the United States is based on a
combination of IPCC Tier 1 and 3 approaches. A Tier 3, process-based model (DAYCENT) was used to estimate
direct emissions from major crops on mineral (i.e., non-organic) soils; as well as most of the direct emissions from
grasslands. The Tier 3 approach has been specifically designed and tested to estimate N2O emissions in the United
States, accounting for more of the environmental and management influences on soil N2O emissions than the IPCC
Tier 1 method (see Box 6-1 for further elaboration). The Tier 1 IPCC (2006) methodology was used to estimate (1)
direct emissions from non-major crops on mineral soils (e.g., barley, oats, vegetables, and other crops); (2) the
portion of the grassland direct emissions that were not estimated with the Tier 3 DAYCENT model (i.e., federal
grasslands); and (3) direct emissions from drainage and cultivation of organic cropland soils. Indirect emissions
were also estimated with a combination of DAYCENT and the IPCC Tier 1 method.
In past Inventories, attempts were made to subtract "background" emissions that would presumably occur if the
lands were not managed. However, this approach is likely to be inaccurate for estimating the anthropogenic
influence on soil N2O emissions. Moreover, if background emissions could be measured or modeled based on
processes unaffected by anthropogenic activity, they would be a very small portion of the total emissions, due to the
high inputs of N to agricultural soils from fertilization and legume cropping. Given the recommendation from IPCC
(2006) and the influence of management on all processes leading to N2O emissions from soils in agricultural
systems, the decision was made to report total emissions from managed lands for this source category. Annex 3.11
provides more detailed information on the methodologies and data used to calculate N2O emissions from each
component.
[BEGIN BOX]
Box 6-1. Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions
The IPCC (2006) Tier 1 approach is based on multiplying activity data on different N inputs (e.g., synthetic
fertilizer, manure, N fixation, etc.) by the appropriate default IPCC emission factors to estimate N2O emissions on
an input-by-input basis. The Tier 1 approach requires a minimal amount of activity data, readily available in most
countries (e.g., total N applied to crops); calculations are simple; and the methodology is highly transparent. In
contrast, the Tier 3 approach developed for this Inventory employs a process-based model (i.e., DAYCENT) that
represents the interaction of N inputs and the environmental conditions at specific locations. Consequently, the Tier
149 N inputs from asymbiotic N fixation are not directly addressed in 2006 IPCC Guidelines, but are a component of the total
emissions from managed lands and are included in the Tier 3 approach developed for this source.
6-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
3 approach is likely to produce more accurate estimates; it accounts more comprehensively for land-use and
management impacts and their interaction with environmental factors (i.e., weather patterns and soil characteristics),
which will enhance or dampen anthropogenic influences. However, the Tier 3 approach requires more detailed
activity data (e.g., crop-specific N amendment rates), additional data inputs (e.g., daily weather, soil types, etc.), and
considerable computational resources and programming expertise. The Tier 3 methodology is less transparent, and
thus it is critical to evaluate the output of Tier 3 methods against measured data in order to demonstrate the
adequacy of the method for estimating emissions (IPCC 2006). Another important difference between the Tier 1
and Tier 3 approaches relates to assumptions regarding N cycling. Tier 1 assumes that N added to a system is
subject to N2O emissions only during that year and cannot be stored in soils and contribute to N2O emissions in
subsequent years. This is a simplifying assumption that is likely to create bias in estimated N2O emissions for a
specific year. In contrast, the process-based model used in the Tier 3 approach includes such legacy effects when N
added to soils is re-mineralized from soil organic matter and emitted as N2O during subsequent years.
[END BOX]
Direct N2O Emissions from Cropland Soils
Major Crop Types on Mineral Cropland Soils
The DAYCENT ecosystem model (Del Grosso et al. 2001, Parton et al. 1998) was used to estimate direct N2O
emissions from mineral cropland soils that are managed for production of major crops—specifically corn, soybeans,
wheat, alfalfa hay, other hay, sorghum, and cotton—representing approximately 90 percent of total croplands in the
United States. For these croplands, DAYCENT was used to simulate crop growth, soil organic matter
decomposition, greenhouse gas fluxes, and key biogeochemical processes affecting N2O emissions, and the
simulations were driven by model input data generated from daily weather records (Thornton et al. 1997, 2000;
Thornton and Running 1999), land management surveys (see citations below), and soil physical properties
determined from national soil surveys (Soil Survey Staff 2005). Note that the influence of land-use change on soil
N2O emissions was not addressed in this analysis, but is a planned improvement.
DAYCENT simulations were conducted for each major crop at the county scale in the United States. Simulating
N2O emissions at the county scale was facilitated by soil and weather data that were available for every county with
more than 100 acres of agricultural land, and by land management data (e.g., timing of planting, harvesting, and
intensity of cultivation) that were available at the agricultural-region level as defined by the Agricultural Sector
Model (McCarl et al. 1993). ASM has 63 agricultural regions in the contiguous United States. Most regions
correspond to one state, except for those states with greater heterogeneity in agricultural practices; in such cases,
more than one region is assigned to a state. While cropping systems were simulated for each county, the results best
represent emissions at regional (i.e., state) and national levels due to the regional scale of management data, which
include model parameters that determined the influence of management activities on soil N2O emissions (e.g., when
crops were planted/harvested).
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
Agriculture 6-21
-------
emissions with individual sources of N.
DAYCENT was used to estimate direct N2O emissions due to mineral N available from: (1) the application of
synthetic fertilizers; (2) the application of livestock manure; (3) the retention of crop residues (i.e., leaving residues
in the field after harvest instead of burning or collecting residues); and (4) mineralization of soil organic matter and
litter, in addition to asymbiotic fixation. Note that commercial organic fertilizers are addressed with the Tier 1
method because county-level application data would be needed to simulate applications in DAYCENT, and
currently data are only available at the national scale. The third and fourth sources are generated internally by the
DAYCENT model. For the first two practices, annual changes in soil mineral N due to anthropogenic activity were
obtained or derived from the following sources:
• Crop-specific N-fertilization rates: Data sources for fertilization rates include Alexander and Smith (1990),
Anonymous (1924), Battaglin and Goolsby (1994), Engle and Makela (1947), ERS (1994, 2003), Fraps and
Asbury (1931), Ibach and Adams (1967), Ibach et al. (1964), NFA (1946), NRIAI (2003), Ross and Mehring
(1938), Skinner (1931), Smalley et al. (1939), Taylor (1994), and USDA (1966, 1957, 1954, 1946).
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 (ERS 1997) with additional
data from other sources, including the National Agricultural Statistics Service (NASS 1992, 1999, 2004).
• Managed manure production and application to croplands and grasslands: Manure N amendments and daily
spread manure N amendments applied to croplands and grasslands (not including PRP manure) were
determined using USDA Manure N Management Databases for 1997 (Kellogg et al. 2000; Edmonds et al.
2003). Amendment data for 1997 were scaled to estimate values for other years based on the availability of
managed manure N for application to soils in 1997 relative to other years. The amount of available N from
managed manure for each livestock type was calculated as described in the Manure Management section
(Section 6.2) and Annex 3.10.
• Retention of crop residue, N mineralization from soil organic matter, and asymbiotic N fixation from the
atmosphere: 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, N fixation, mineralization of N from soil organic matter, and asymbiotic fixation are internally
generated by the model as part of the simulation. In other words, DAYCENT accounts for the influence of N
fixation, mineralization of N from soil organic matter, and retention of crop residue on N2O emissions, but these
are not model inputs. The DAYCENT simulations also accounted for the approximately 3 percent of grain 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, and Cibrowski 1996), and
therefore did not contribute to soil N2O emissions.
• Historical and modern crop rotation and management information (e.g., timing and type of cultivation, timing of
planting/harvest, etc.): These activity data were derived from Kurd (1930, 1929), Latta (1938), Iowa State
College Staff Members (1946), Bogue (1963), Hurt (1994), USDA (2000a) as extracted by Eve (2001) and
revised by Ogle (2002), CTIC (1998), Piper et al. (1924), Hardies and Hume (1927), Holmes (1902, 1929),
Spillman (1902, 1905, 1907, 1908), Chilcott (1910), Smith (1911), Kezer (ca. 1917), Hargreaves (1993), ERS
(2002), Warren (1911), Langston et al. (1922), Russell et al. (1922), Elliott and Tapp (1928), Elliott (1933),
Ellsworth (1929), Garey (1929), Hodges et al. (1930), Bonnen and Elliott (1931), Brenner et al. (2002, 2001),
and Smith et al. (2002).
DAYCENT simulations produced per-area estimates of N2O emissions (g N2O-N/m2) for major crops in each
county, which were multiplied by the cropland areas in each county to obtain county-scale emission estimates.
Cropland area data were from NASS (USDA 2010a, 2010b). The emission estimates by reported crop areas in the
county were scaled to the regions (and states for mapping purposes when there was more than one region in a state),
and the national estimate was calculated by summing results across all regions. DAYCENT is sensitive to
interannual variability in weather patterns and other controlling variables, so emissions associated with individual
activities vary through time even if the management practices remain the same (e.g., if N fertilization remains the
same for two years). In contrast, Tier 1 methods do not capture this variability and rather have a linear, monotonic
response that depends solely on management practices. DAYCENT's ability to capture these interactions between
management and environmental conditions produces more accurate estimates of N2O emissions than the Tier 1
method.
6-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Non-Major Crop Types on Mineral Cropland Soils
The IPCC (2006) Tier 1 methodology was used to estimate direct N2O emissions for mineral cropland soils that are
managed for production of non-major crop types, including barley, oats, tobacco, sugarcane, sugar beets,
sunflowers, millet, rice, peanuts, and other crops that were not included in the DAYCENT simulations. Estimates of
direct N2O emissions from N applications to non-major crop types 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;150 and (3) the retention of above- and below-
ground crop residues in agricultural fields (i.e., crop biomass that is not harvested). Non-manure organic
amendments were not included in the DAYCENT simulations because county-level data were not available.
Consequently, non-manure organic amendments, as well as additional manure that was not added to major crops in
the DAYCENT simulations, were included in the Tier 1 analysis. The influence of land-use change on soil N2O
emissions from non-major crops has not been addressed in this analysis, but is a planned improvement. The
following sources were used to derive activity data:
• A process-of-elimination approach was used to estimate synthetic N fertilizer additions for non-major crops,
because little information exists on their fertilizer application rates. The total amount of fertilizer used on farms
has been estimated by the USGS from sales records (Ruddy et al. 2006), and these data were aggregated to
obtain state-level N additions to farms. After subtracting the portion of fertilizer applied to major crops and
grasslands (see sections on Major Crops and Grasslands for information on data sources), the remainder of the
total fertilizer used on farms was assumed to be applied to non-major crops.
• A process-of-elimination approach was used to estimate manure N additions for non-major crops, because little
information exists on application rates for these crops. The amount of manure N applied to major crops and
grasslands was subtracted from total manure N available for land application (see sections on Major Crops and
Grasslands for information on data sources), and this difference was assumed to be applied to non-major crops.
• Non-manure, non-sewage-sludge 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 2010). Manure and sewage sludge components were subtracted
from total commercial organic fertilizers to avoid double counting.
• Crop residue N was derived by combining amounts of above- and below-ground biomass, which were
determined based on crop production yield statistics (USDA 1994, 1998, 2003, 2005, 2006, 2008, 2009, 2010a),
dry matter fractions (IPCC 2006), linear equations to estimate above-ground biomass given dry matter crop
yields from harvest (IPCC 2006), ratios of below-to-above-ground biomass (IPCC 2006), and N contents of the
residues (IPCC 2006). Approximately 3 percent of the crop residues were burned and therefore did not
contribute to soil N2O emissions, based on state inventory data (ILENR 1993, Oregon Department of Energy
1995, Noller 1996, Wisconsin Department of Natural Resources 1993, and Cibrowski 1996).
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 from non-major crop types.
Drainage and Cultivation of Organic Cropland Soils
The IPCC (2006) Tier 1 methods were used to estimate direct N2O emissions due to drainage and cultivation of
organic soils at a state scale. State-scale estimates of the total area of drained and cultivated organic soils were
obtained from the National Resources Inventory (NRI) (USDA 2000a, as extracted by Eve 2001 and amended by
Ogle 2002). Temperature data from Daly et al. (1994, 1998) were used to subdivide areas into temperate and sub-
tropical climates using the climate classification from IPCC (2006). Data were available for 1982, 1992 and 1997.
To estimate annual emissions, the total temperate area was multiplied by the IPCC default emission factor for
temperate regions, and the total sub-tropical area was multiplied by the average of the IPCC default emission factors
for temperate and tropical regions (IPCC 2006).
150 Commercial organic fertilizers include dried blood, tankage, compost, and other; dried manure and sewage sludge that are
used as commercial fertilizer have been excluded to avoid double counting. The dried manure N is counted with the non-
commercial manure applications, and sewage sludge is assumed to be applied only to grasslands.
Agriculture 6-23
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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 grasslands. Grasslands include pastures and rangelands used for
grass forage production, where the primary use is livestock grazing. Rangelands are typically extensive areas of
native grasslands that are not intensively managed, while pastures are often seeded grasslands, possibly following
tree removal, which may or may not be improved with practices such as irrigation and interseeding legumes.
DAYCENT was used to simulate county-scale N2O emissions from 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), 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 section for Major Crop Types on Mineral Cropland Soils. Managed manure N amendments to grasslands were
estimated from Edmonds et al. (2003) and adjusted for annual variation using data on the availability of managed
manure N for application to soils, according to methods described in the Manure Management section (Section 6.2)
and Annex 3.10. Biological N fixation is simulated within DAYCENT and therefore was not an input to the model.
Manure N deposition from grazing animals (i.e., PRP manure) is another key input of N to grasslands. The amounts
of PRP manure N applied on non-federal and federal grasslands in each county were based on the proportion of non-
federal to federal grassland area (See below for more information on area data). The amount of PRP manure applied
on non-federal grasslands was an input to the DAYCENT model (see Annex 3.10), and included approximately 91
percent of total PRP manure. The remainder of the PRP manure N excretions in each county was assumed to be
excreted on federal grasslands (i.e., DAYCENT simulations were only conducted for non-federal grasslands), and
the N2O emissions were estimated using the IPCC (2006) Tier 1 method with IPCC default emission factors.
Sewage sludge was assumed to be applied on grasslands because of the heavy metal content and other pollutants in
human waste that limit its use as an amendment to croplands. Sewage sludge application was estimated from data
compiled by EPA (1993, 1999, 2003), McFarland (2001), and NEBRA (2007). Sewage sludge data on soil
amendments to agricultural lands were only available at the national scale, and it was not possible to associate
application with specific soil conditions and weather at the county scale. Therefore, DAYCENT could not be used
to simulate the influence of sewage sludge amendments on N2O emissions from grassland soils, and consequently,
emissions from sewage sludge were estimated using the IPCC (2006) Tier 1 method.
Grassland area data were consistent with the Land Representation reported in Section 7.1. Data were obtained from
the U.S. Department of Agriculture National Resources Inventory (USDA 2000a, Nusser and Goebel 1997,
http://www.ncgc.nrcs.usda.gov/products/nri/index.htm) and the U.S. Geological Survey (USGS) National Land
Cover Dataset (NLCD, Vogelman et al. 2001, http://www.mrlc.gov), which were reconciled with the Forest
Inventory and Analysis Data (http://fia.fs.us/tools-data/data). The area data for pastures and rangeland were
aggregated to the county level to estimate non-federal and federal grassland areas.
DAYCENT simulations produced per-area estimates of N2O emissions (g N2O-N/m2) for pasture and rangelands,
which were multiplied by the non-federal grassland areas in each county. The county-scale N2O emission estimates
for non-federal grasslands were scaled to the 63 agricultural regions (and to the state level for mapping purposes if
there was more than one region in a state), and the national estimate was calculated by summing results across all
regions. Tier 1 estimates of N2O emissions for the PRP manure N deposited on federal grasslands and applied
sewage sludge N were produced by multiplying the N input by the appropriate emission factor. Tier 1 estimates for
emissions from manure N were calculated at the state level and aggregated to the entire country but emission from
sewage sludge N were calculated exclusively at the national scale.
Total Direct N2O Emissions from Cropland and Grassland Soils
Annual direct emissions from major and non-major crops on mineral cropland soils, from drainage and cultivation of
organic cropland soils, and from grassland soils were summed to obtain the total direct N2O emissions from
agricultural soil management (see Table 6-15 and Table 6-16).
Indirect N2O Emissions from Managed Soils of all Land-Use Types
This section describes the methods used for estimating indirect soil N2O emissions from all land-use types (i.e.,
6-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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croplands, grasslands, forest lands, and settlements). Indirect N2O emissions occur when mineral N made available
through anthropogenic activity is transported from the soil either in gaseous or aqueous forms and later converted
into N2O. There are two pathways leading to indirect emissions. The first pathway results from volatilization of N
as NOX and NH3 following application of synthetic fertilizer, organic amendments (e.g., manure, sewage sludge),
and deposition of PRP manure. N made available from mineralization of soil organic matter and 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 NO3") that was made available through anthropogenic activity on managed
lands, mineralization of soil organic matter, and asymbiotic fixation. The NO3" is subject to denitrification in water
bodies, which leads to N2O emissions. Regardless of the eventual location of the indirect N2O emissions, the
emissions are assigned to the original source of the N for reporting purposes, which here includes croplands,
grasslands, forest lands, and settlements.
Indirect N2O Emissions from Atmospheric Deposition of Volatilized N from Managed Soils
As in the direct emissions calculation, the Tier 3 DAYCENT model and IPCC (2006) Tier 1 methods were
combined to estimate the amount of N that was volatilized and eventually emitted as N2O. DAYCENT was used to
estimate N volatilization for land areas whose direct emissions were simulated with DAYCENT (i.e., major
croplands and most grasslands). The N inputs included are the same as described for direct N2O emissions in the
sections on major crops and grasslands. Nitrogen volatilization for all other areas was estimated using the Tier 1
method and default IPCC fractions for N subject to volatilization (i.e., N inputs on non-major croplands, PRP
manure N excretion on federal grasslands, sewage sludge application on grasslands). The Tier 1 method and default
fractions were also used to estimate N subject to volatilization from N inputs on settlements and forest lands (see the
Land Use, Land-Use Change, and Forestry chapter). For the volatilization data generated from both the DAYCENT
and Tier 1 approaches, the IPCC (2006) default emission factor was used to estimate indirect N2O emissions
occurring due to re-deposition of the volatilized N (Table 6-18).
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
used to produce major crops and most grasslands. 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 produce non-major crops, sewage sludge amendments on
grasslands, PRP manure N excreted on federal grasslands, and N inputs on settlements and forest lands. For both
the DAYCENT 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 DAYCENT and Tier 1
approaches, the IPCC (2006) default emission factor was used to estimate indirect N2O emissions that occur in
groundwater and waterways (Table 6-18).
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 calculated by DAYCENT; (2) the components of indirect emissions (N
volatilized and leached or runoff) calculated by DAYCENT; (3) direct emissions calculated with the IPCC (2006)
Tier 1 method; (4) the components of indirect emissions (N volatilized and leached or runoff) calculated with the
IPCC (2006) Tier 1 method; and (5) indirect emissions calculated with the IPCC (2006) Tier 1 method. Uncertainty
in direct emissions, which account for the majority of N2O emissions from agricultural management, as well as the
components of indirect emissions calculated by DAYCENT were estimated with a Monte Carlo Analysis,
addressing uncertainties in model inputs and structure (i.e., algorithms and parameterization) (Del Grosso et al.,
2010). Uncertainties in direct emissions calculated with the IPCC (2006) Tier 1 method, the proportion of
volatilization and leaching or runoff estimated with the IPCC (2006) Tier 1 method, and indirect N2O emissions
were estimated with a simple error propagation approach (IPCC 2006). Additional details on the uncertainty
Agriculture 6-25
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methods are provided in Annex 3.11.
Uncertainties from the Tier 1 and Tier 3 (i.e., DAYCENT) estimates were combined using simple error propagation
(IPCC 2006), and the results are summarized in Table 6-19. Agricultural direct soil N2O emissions in 2009 were
estimated to be between 118.3 and 250.6 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 26
percent below and 56 percent above the 2009 emission estimate of 160.2 Tg CO2 Eq. The indirect soil N2O
emissions in 2009 were estimated to range from 22.4 to 111.6 Tg CO2 Eq. at a 95 percent confidence level,
indicating an uncertainty of 50 percent below and 151 percent above the 2009 emission estimate of 44.4 Tg CO2 Eq.
Table 6-19: Quantitative Uncertainty Estimates of N2O Emissions from Agricultural Soil Management in 2009 (Tg
CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission
Source Gas Estimate Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Direct Soil N2O Emissions N2O
Indirect Soil N2O Emissions N2O
Lower
Bound
160.2 118.3
44.4 22.4
Upper
Bound
250.6
111.6
Lower
Bound
-26%
-50%
Upper
Bound
+56%
+151%
Note: Due to lack of data, uncertainties in areas for major crops, managed manure N production, PRP manure N production, other
organic fertilizer amendments, indirect losses of N in the DAYCENT simulations, and sewage sludge amendments to soils are
currently treated as certain; these sources of uncertainty will be included in future Inventories.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
For quality control, DAYCENT results for N2O emissions and NO3" leaching were compared with field data
representing various cropland and grassland systems, soil types, and climate patterns (Del Grosso et al. 2005, Del
Grosso et al. 2008), and further evaluated by comparing to emission estimates produced using the IPCC (2006) Tier
1 method for the same sites. Nitrous oxide measurement data were available for 11 sites in the United States and
one in Canada, representing 30 different combinations of fertilizer treatments and cultivation practices. DAYCENT
estimates of N2O emissions were closer to measured values at all sites compared to the IPCC Tier 1 estimate, except
for Colorado dryland cropping (Figure 6-7). In general, IPCC Tier 1 methodology tends to over-estimate emissions
when observed values are low and under-estimate emissions when observed values are high, while DAYCENT
estimates are less biased. This is not surprising because DAYCENT accounts for site-level factors (weather, soil
type) that influence N2O emissions. Nitrate leaching data were available for three sites in the United States
representing nine different combinations of fertilizer amendments. Linear regressions of simulated vs. observed
emission and leaching data yielded correlation coefficients of 0.89 and 0.94 for annual N2O emissions and NO3"
leaching, respectively. This comparison demonstrates that DAYCENT provides relatively high predictive capability
for N2O emissions and NO3" leaching, and is an improvement over the IPCC Tier 1 method (see additional
information in Annex 3.11).
Figure 6-7: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using the DAYCENT
Simulation Model
Spreadsheets containing input data and probability distribution functions required for DAYCENT simulations of
major croplands and grasslands and unit conversion factors were checked, as were the program scripts that were
used to run the Monte Carlo uncertainty analysis. Several errors were identified following re-organization of the
calculation spreadsheets, and corrective actions have been taken. In particular, some of the links between
spreadsheets were missing or needed to be modified. Spreadsheets containing input data, emission factors, and
calculations required for the Tier 1 approach were checked and no errors were found.
6-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Recalculations Discussion
Two major revisions were made in the Agricultural Soil Management section for the current Inventory.
First, the methodology used to estimate grassland areas was updated and revised to be consistent with the Land
Representation used in the Land Use, Land Use Change and Forestry sector (see Section 7.1). This led to an overall
decrease in grassland area, and lower emissions than reported in the prior Inventory. Second, the methodology used
to calculate livestock manure N was changed such that total manure N added to soils increased by approximately 11
percent (see Section 6.2 for details).
The recalculations had opposite impacts on the emissions, with less grassland area tending to decrease emissions and
higher manure N inputs tending to increase emissions. In some years emissions were higher overall, but on average,
these changes led to a lower amount of N2O emissions from agricultural soil management by about 1.5 percent over
the time series relative to the previous Inventory.
Planned Improvements
A key improvement is underway for Agricultural Soil Management to incorporate more land-use survey data from
the NRI (USDA 2000a) into the DAYCENT simulation analysis, beyond the area estimates for rangeland and
pasture that are currently used to estimate emissions from grasslands. NRI has a record of land-use activities since
1979 for all U.S. agricultural land, which is estimated at about 386 Mha. NASS is used as the basis for land-use
records in the current Inventory, and there are three major disadvantages to this dataset. First, most crops are grown
in rotation with other crops (e.g., corn-soybean), but NASS data provide no information regarding rotation histories.
In contrast, NRI is designed to track rotation histories, which is important because emissions from any particular
year can be influenced by the crop that was grown the previous year. Second, NASS does not conduct a complete
survey of cropland area each year, leading to gaps in the land base. NRI provides a complete history of cropland
areas for four out of every five years from 1979 to 1997, and then every year after 1998. Third, the current
inventory based on NASS does not quantify the influence of land-use change on emissions, which can be addressed
using the NRI survey records. NRI also provides additional information on pasture land management that can be
incorporated into the analysis (particularly the use of irrigation). Using NRI data will also make the Agricultural
Soil Management methods more consistent with the methods used to estimate C stock changes for agricultural soils.
The structure of model input files that contain land management data are currently being extensively revised to
facilitate use of the annualized NRI data. This improvement is planned for completion by the next Inventory.
Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural
Residues source category (Section 6.5). This year the methodology for Field Burning of Agricultural Residues was
significantly updated, but the changes were implemented too late for the new estimates of crop residues burned to be
incorporated into the DAYCENT runs for the Agricultural Soil Management source. Next year the estimates will be
reconciled; meanwhile the estimates presented in this section use the previous year's methodology for determining
crop residues burned.
Other planned improvements are minor but will lead to more accurate estimates, including updating DAYMET
weather data for more recent years following the release of new data, and using a rice-crop-specific emission factor
for N amendments to rice areas.
6.5. Field Burning of Agricultural Residues (IPCC Source Category 4F)
Farming activities produce large quantities of agricultural crop residues, and farmers use or dispose of these residues
in a variety of ways. For example, agricultural residues can be left on or plowed into the field; composted and then
applied to soils; landfilled; or burned in the field. Alternatively, they can be collected and used as fuel, animal
bedding material, supplemental animal feed, or construction material. Field burning of crop residues is not
considered a net source of CO2, because the C released to the atmosphere as CO2 during burning is assumed to be
reabsorbed during the next growing season. Crop residue burning is, however, a net source of CH4, N2O, CO, and
NOX, which are released during combustion.
Field burning is not a common method of agricultural residue disposal in the United States. The primary crop types
whose residues are typically burned in the United States are corn, cotton, lentils, rice, soybeans, sugarcane, and
wheat (McCarty 2009). In 2009, CH4 and N2O emissions from field burning were 0.2 Tg CO2 Eq. (12 Gg) and 0.1
Tg. CO2 Eq. (0.3 Gg), respectively. Annual emissions from this source over the period 1990 to 2009 have remained
Agriculture 6-27
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relatively constant, averaging approximately 0.2 Tg CO2 Eq. (1 Gg) of CH4 and 0.1 Tg CO2 Eq. (0.3 Gg) of N2O
(see Table 6-20 and Table 6-21).
Table 6-20: CH4 and N2O Emissions from Field Burning of Agricultural Residues (Tg CO2 Eq.)
Gas/Crop Type 1990 2000 2005 2006 2007 2008 2009
CH4 0.3 0.3 0.2 0.2 0.2 0.3 0.2
Corn
Cotton
Lentils
Rice + +| + +0.1
Soybeans
Sugarcane 0.1 0.1 + 0.1
Wheat 0.1 0.1 0.1 0.1 0.1 0.1 0.1
N2O 0.1 0.1 0.1 0.1 0.1 0.1 0.1
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
Total 0.4 0.4 0.3 0.3 0.3 0.4 0.4
+ Less than 0.05 Tg CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 6-21: CH4, N2O, CO, and NOX Emissions from Field Burning of Agricultural Residues (Gg)
Gas/Crop Type 1990 2000 2005 2006 2007 2008 2009
CH4
Corn
13
1 1
12
1 1
9
1
11
2
11
1
13
1
12
2
Cotton
Lentils
Rice 2j 2j 2 2 3 2 2
Soybeans 1 I 1 I 1 1 1 1 1
Sugarcane 3 I 2 I 1 3 1 2 2
Wheat 6 | 6 | 4 4 5 6 5
N2O
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
CO 268 259 184 233 237 270 247
NOX 8 8 6 7 8 8 8_
+ Less than 0.5 Gg
Note: Totals may not sum due to independent rounding.
Methodology
The Tier 2 methodology used for estimating greenhouse gas emissions from field burning of agricultural residues in
the United States is consistent with IPCC (2006) (for more details, see Box 6-2). In order to estimate the amounts of
C and N released during burning, the following equation was used:
C or N released = Ł over all crop types and states (Area Burned + Crop Area Harvested x Crop Production x
6-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Residue/Crop Ratio x Dry Matter Fraction x Burning Efficiency x Combustion Efficiency x Fraction of C or N)
where,
Area Burned = Total area of crop burned, by state
Crop Area Harvested = Total area of crop harvested, by state
Crop Production = Annual production of crop in Gg, by state
Residue/Crop Ratio = Amount of residue produced per unit of crop production, by state
Dry Matter Fraction = Amount of dry matter per unit of biomass for a crop
Fraction of C or N = Amount of C or N per unit of dry matter for a crop
Burning Efficiency = The proportion of prefire fuel biomass consumed151
Combustion Efficiency = The proportion of C or N released with respect to the total amount of C or N
available in the burned material, respectively151
Crop production and area harvested were available by state and year from USD A (2010) 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
(Emissions Ratio for C or N) x (Conversion Factor)
where,
Emissions Ratio = g CH4-C or CO-C/g C released, or g N2O-N or NOx-N/g N released
Conversion Factor = 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)
[BEGIN BOX]
Box 6-2: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach
This Inventory calculates emissions from Burning of Agricultural Residues using a Tier 2 methodology that is based
on rPCC/UNEP/OECD/TEA (1997) and incorporates crop- and country-specific emission factors and variables. The
equation used in this Inventory varies slightly in form from the one presented in the IPCC (2006) guidelines, but
both equations rely on the same underlying variables. The IPCC (2006) equation was developed to be broadly
applicable to all types of biomass burning, and, thus, is not specific to agricultural residues. IPCC (2006) default
factors are provided only for four crops (wheat, corn, rice, and sugarcane), while this Inventory analyzes emissions
from seven crops. A comparison of the methods and factors used in (1) the current Inventory and (2) the default
IPCC (2006) approach was undertaken to determine the magnitude of the difference in overall estimates resulting
from the two approaches. The IPCC (2006) approach was not used because crop-specific emission factors for N2O
were not available for all crops. In order to maintain consistency of methodology, the IPCC/UNEP/OECD/IEA
(1997) approach presented in the Methodology section was used.
The IPCC (2006) default approach resulted in 12 percent higher emissions of CH4 and 25 percent higher emissions
of N2O than the current estimates in this Inventory. It is reasonable to maintain the current methodology, since the
IPCC (2006) defaults are only available for four crops and are worldwide average estimates, while current inventory
estimates are based on U.S.-specific, crop-specific, published data.
[END BOX]
151 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).
Agriculture 6-29
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Crop production data for all crops except rice in Florida and Oklahoma were taken from USDA's QuickStats service
(USDA 2010). Rice production and area data for Florida and Oklahoma, which are not collected by USD A, were
estimated separately. Average primary and ratoon crop yields for Florida (Schueneman and Deren 2002) were
applied to Florida acreages (Schueneman 1999, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004, 2005;
Gonzalez 2007 through 2010), and crop yields for Arkansas (USDA 2010) were applied to Oklahoma acreages152
(Lee 2003 through 2006; Anderson 2008 through 2010). The production data for the crop types whose residues are
burned are presented in Table 6-22. 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 state153 from McCarty
(2010) for corn, cotton, lentils, rice, soybeans, sugarcane, and wheat.154 McCarty (2010) used remote sensing data
from Moderate Resolution Imaging Spectroradiometer (MODIS) to estimate area burned by crop. For the inventory
analysis, the state-level area burned data were divided by state-level crop area harvested data to estimate the percent
of crop area burned by crop and by state. The average fraction of area burned by crop across all states is shown in
Table 6-23. All crop area harvested data were from USDA (2010), except for rice acreage in Florida and Oklahoma,
which is not measured by USDA (Schueneman 1999, 2001; Deren 2002; Kirstein 2003, 2004; Cantens 2004, 2005;
Gonzalez 2007 through 2010; Lee 2003 through 2006; Anderson 2008 through 2010). 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 assumed to be equal to the average percent area burned from the 5 years for which data
were available. This average was taken at the crop and state level. Table 6-23 shows these percent area estimates
aggregated for the United States as a whole, at the crop level.
All residue/crop product mass ratios except sugarcane and cotton were obtained from Strehler and Stiitzle (1987).
The datum for sugarcane is from Kinoshita (1988) and that of cotton from Huang et al. (2007). The residue/crop
ratio for lentils was assumed to be equal to the average of the values for peas and beans. Residue dry matter
fractions for all crops except soybeans, lentils, and cotton were obtained from Turn et al. (1997). Soybean and lentil
dry matter fractions were obtained from Strehler and Stiitzle (1987); the value for lentil residue was assumed to
equal the value for bean straw. The cotton dry matter fraction was taken from Huang et al. (2007). The residue C
contents and N contents for all crops except soybeans and cotton are from Turn et al. (1997). The residue C content
for soybeans is the IPCC default (IPCC/UNEP/OECD/IEA 1997). The N content of soybeans is from Barnard and
Kristoferson (1985). The C and N contents of lentils were assumed to equal those of soybeans. The C and N
contents of cotton are from Lachnicht et al. (2004). These data are listed in Table 6-24. The burning efficiency was
assumed to be 93 percent, and the combustion efficiency was assumed to be 88 percent, for all crop types, except
sugarcane (EPA 1994). For sugarcane, the burning efficiency was assumed to be 81 percent (Kinoshita 1988) and
the combustion efficiency was assumed to be 68 percent (Turn et al. 1997). Emission ratios and conversion factors
for all gases (see Table 6-25) were taken from the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997).
Table 6-22: Agricultural Crop Production (Gg of Product)
Crop 1990 2000 2005 2006 2007 2008 2009
Corn3 201,534 251,854 282,263 267,503 331,177 307,142 333,011
Cotton 3,376 3,742 I 5,201 4,700 4,182 2,790 2,654
Lentils 40 137 238 147 166 109 266
Rice 7,114 I 8,705 I 10,132 8,843 9,033 9,272 9,972
Soybeans 52,416 75,055 83,507 87,001 72,859 80,749 91,417
Sugarcane 25,525 32,763 24,137 26,820 27,188 25,041 27,608
Wheat 74,292 60,641 M 57,243 49,217 55,821 68,016 60,366
a Corn for grain (i.e., excludes corn for silage).
152 Rice production yield data are not available for Oklahoma, so the Arkansas values are used as a proxy.
153 Alaska and Hawaii were excluded.
154 McCarty (2009) also examined emissions from burning of Kentucky bluegrass and a general "other crops/fallow" category,
but USDA crop area and production data were insufficient to estimate emissions from these crops using the methodology
employed in the Inventory. McCarty (2009) estimates that approximately 18 percent of crop residue emissions result from
burning of the Kentucky bluegrass and "other" categories.
6-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 6-23: U.S. Average Percent Crop Area Burned by Crop (Percent)
State
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
1990
+
3 H
2000
+ 1
\
40
3 1
2005
+
1
+
6
+
26
2
2006
+
1
2
8
+
56
o
6
2007
+
1
1
12
+
26
o
6
2008
+
2
1
9
+
39
o
6
2009
+
1
1
9
+
37
3
+ Less than 0.5 percent
Table 6-24: Key Assumptions for Estimating Emissions from Field Burning of Agricultural Residues
Crop Residue/Crop Dry Matter C Fraction N Fraction Burning Combustion
Ratio Fraction Efficiency Efficiency
(Fraction) (Fraction)
Corn
Cotton
Lentils
Rice
Soybeans
Sugarcane
Wheat
1.0
1.6
2.0
1.4
2.1
0.2
1.3
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
0.006
0.012
0.023
0.007
0.023
0.004
0.006
0.93
0.93
0.93
0.93
0.93
0.81
0.93
0.88
0.88
0.88
0.88
0.88
0.68
0.88
Table 6-25: Greenhouse Gas Emission Ratios and Conversion Factors
Gas
CH4:C
CO:C
N2O:N
NOX:N
Emission Ratio
0.0053
0.0603
0.007b
0.121b
Conversion
Factor
16/12
28/12
44/28
30/14
a Mass of C compound released (units of C) relative to mass of total C released from burning (units of C).
b Mass of N compound released (units of N) relative to mass of total N released from burning (units of N).
Uncertainty and Time-Series Consistency
Due to data and time limitations, uncertainty resulting from the fact that emissions from burning of Kentucky
bluegrass and "other" residues are not included in the emissions estimates was not incorporated into the uncertainty
analysis. The results of the Tier 2 Monte Carlo uncertainty analysis are summarized in Table 6-26. Methane
emissions from field burning of agricultural residues in 2009 were estimated to be between 0.15 and 0.35 Tg CO2
Eq. at a 95 percent confidence level. This indicates a range of 40 percent below and 42 percent above the 2009
emission estimate of 0.25 Tg CO2 Eq. Also at the 95 percent confidence level, N2O emissions were estimated to be
between 0.07 and 0.14 Tg CO2 Eq. (or approximately 30 percent below and 31 percent above the 2009 emission
estimate of 0.10 Tg CO2 Eq.).
Table 6-26: Tier 2 Quantitative Uncertainty Estimates for CH4 and N2O Emissions from Field Burning of
Agricultural Residues (Tg CO2 Eq. and Percent)
Source
Field Burning of A|
Field Burning of A|
Gas 2009 Emission Uncertainty Range Relative to
Estimate Emission Estimate"
(TgC02Eq.) (TgC02Eq.) (%)
picultural Residues
picultural Residues
CH4 0.25
N2O 0.10
Lower
Bound
0.15
0.07
Upper
Bound
0.35
0.14
Lower
Bound
-40%
-30%
Upper
Bound
+42%
+31%
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
Agriculture 6-31
-------
through 2009. 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 USDA (2010), leading to a percent area burned estimate of greater than 100 percent. In such
cases, it was assumed that the percent crop area burned for that state was 100 percent.
Recalculations Discussion
The methodology over the entire time series was revised relative to the previous Inventory to incorporate state- and
crop-level data on area burned from McCarty (2010). (1) Cotton and lentils were added as crops; peanuts and barley
were removed, because McCarty (2009) indicated that their residues are not burned in significant quantities in the
United States; (2) fraction of residue burned was calculated at the state and crop level based on McCarty (2010) and
USDA (2010) data, rather than a blanket application of 3 percent burned for all crops except rice and sugarcane, as
was used in the previous Inventory; (3) since data from McCarty (2010) were only available for 5 years, the percent
area burned for those 5 years was averaged by crop and state and used as an estimate for the remaining years in the
time series. Because the percent area burned was lower than previously assumed for almost all crops, these
recalculations have resulted in an average decrease in CH4 emissions of 71 percent and an average decrease in N2O
emissions of 79 percent across the time series, relative to the previous Inventory.
Planned Improvements
Further investigation will be made into inconsistent data from Florida and Oklahoma as mentioned in the QA/QC
and verification section, and attempts will be made to revise or further justify the assumption of 100 percent of area
burned for those crops and years where the estimated percent area burned exceeded 100 percent. The availability of
useable area harvested and other data for bluegrass and the "other crops" category in McCarty (2010) will also be
investigated, in order to try to incorporate these emissions into the Inventory.
6-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Agricultural Soil Management
Enteric Fermentation
Manure Management
Rice Cultivation I
Field Burning of Agricultural Residues < 0.5
Agriculture as a Portion of all
Emissions
6.3%
0 50 100 150
TgCO2Eq.
Figure 6-1: 2009 Agriculture Chapter Greenhouse Gas Sources
200
250
-------
Figure 6-2
Synthetic N Fertilizers
Synthetic N fertilizer applied to soil
Organic
Amendments
Includes both commercial and
non-co,mmercisl fertilizers (i.e.,
animal manure,compost,
sewage sludge, tankage, etc.)
Urine and Dunq from
Grazing Animals
Manure deposited on pasture, range,
and paddock
Crop Residues
Includes above- and belowground
residues for a II crops (non-N and N-
fixing (and from perennial forage
crops and pastures following renewal
Mineralization of
Soil Organic Matter
Includes N converted to mineral form
upon decomposition of soil organic
matter
Asymbiotic Fixation
Fixation of atmospheric N2 by bacteria
living in soils that do not have a direct
relationship with plants
This graphic illustrates the sources and pathways of nitrogen that result
in direct and indirect N20 emissions from soils using the methodologies
described in this Inventory. Emission pathways are shown with arrows.
On the lower right-hand side is a cut-away view of a representative
section of a managed soil; histosol cultivation is represented here.
-------
Figure 6-3
Major Crops, Average Annual Direct N20 Emissions Estimated Using the DAYCENT Model, 1990-2009
(Tg C02 Eq/year)
-------
Figure 6-4
Grasslands, Average Annual Direct N20 Emissions Estimated Using the DAYCENT Model, 1990-2009
(Tg C02 Eq./year)
Tg C02 Eq./year
H < 0.25
Zl 0.25 to 0.5
H 0.5 to 0.75
Zl 0.75 to 1
• 1to2
|2 to 4
• >4
-------
Figure 6-5
Major Crops, Average Annual N Losses Leading to Indirect N20 Emissions
Using the DAYCENT Model, 1990-2009 (Gg N/year)
-------
Figure 6-6
Grasslands, Average Annual N Losses Leading to Indirect N20 Emissions
Using the DAYCENT Model, 1990-2009 (Gg N/year)
-------
Figure 6-7
Comparison of Measured Emissions at Field Sites with Modeled Emissions
Using the DAYCENT Simulation Model
CO
T3
O)
O
40
35
30
25
20
15
10
5
0
D measured
DAYCENT
• IPCC
0°
0°
-------
-------
7. Land Use, Land-Use Change, and Forestry
This chapter provides an assessment of the net greenhouse gas flux155 resulting from the uses and changes in land
types and forests in the United States. The Intergovernmental Panel on Climate Change 2006 Guidelines for
National Greenhouse Gas Inventories (IPCC 2006) recommends reporting fluxes according to changes within and
conversions between certain land-use types termed forest land, cropland, grassland, and settlements (as well as
wetlands). The greenhouse gas flux from Forest Land Remaining Forest Land is reported using estimates of
changes in forest carbon (C) stocks, non-carbon dioxide (CO2) emissions from forest fires, and the application of
synthetic fertilizers to forest soils. The greenhouse gas flux reported in this chapter from agricultural lands (i.e.,
cropland and grassland) includes changes in organic C stocks in mineral and organic soils due to land use and
management, and emissions of CO2 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 fluxes from 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. CO2 fluxes from forest C stocks (except the
wood product components) and from agricultural soils (except the liming component) are calculated on an average
annual basis from data collected in intervals ranging from 1 to 10 years. The resulting annual averages are applied
to years between surveys. Calculations of non-CO2 emissions from forest fires are based on forest CO2 flux data.
For the landfilled yard trimmings and food scraps source, periodic solid waste survey data were interpolated so that
annual storage estimates could be derived. This flux has been applied to the entire time series, and periodic U.S.
census data on changes in urban area have been used to develop annual estimates of CO2 flux.
Land use, land-use change, and forestry activities in 2009 resulted in a net C sequestration of 1,015.1 Tg CO2 Eq.
(276.8 Tg C) (Table 7-1 and Table 7-2). This represents an offset of approximately 15.3 percent of total U.S. CO2
emissions. Total land use, land-use change, and forestry net C sequestration156 increased by approximately 17.8
percent between 1990 and 2009. This increase was primarily due to an increase in the rate of net C accumulation in
forest C stocks. Net C accumulation in Forest Land Remaining Forest Land, Land Converted to Grassland, and
Settlements Remaining Settlements increased, while net C accumulation in Cropland Remaining Cropland,
Grassland Remaining Grassland, and landfilled yard trimmings and food scraps slowed over this period. Emissions
from Land Converted to Cropland increased between 1990 and 2009.
Table 7-1: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.)
Sink Category 1990 2000 2005 2006 2007 2008 2009
Forest Land Remaining Forest
Land1 (681.1) (378.3) (911.5) (917.5) (911.9) (891.0) (863.1)
Cropland Remaining Cropland (29.4)B (30.2) (18.3) (19.1) (19.7) (18.1) (17.4)
Land Converted to Cropland 2.2B 2.4B 5.9 5.9 5.9 5.9 5.9
Grassland Remaining
Grassland (52.2) (52.6) (8.9) (8.8) (8.6) (8.5) (8.3)
Land Converted to Grassland (19.8)1 (27.2) (24.4) (24.2) (24.0) (23.8) (23.6)
Settlements Remaining
Settlements2 (57.1) (77.5) (87.8) (89.8) (91.9) (93.9) (95.9)
Other (Landfilled Yard
Trimmings and Food Scraps) (24.2) (13.2) (11.5) (11.0) (10.9) (11.2) (12.6)
Total (861.5) (576.6) (1,056.5) (1,064.3) (1,060.9) (1,040.5) (1,015.1)
Note: Parentheses indicate net sequestration. Totals may not sum due to independent rounding.
155 The term "flux" is used here to encompass both emissions of greenhouse gases to the atmosphere, and removal of C from the
atmosphere. Removal of C from the atmosphere is also referred to as "carbon sequestration."
156 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. This is also referred to as net C sequestration.
Land Use, Land-Use Change, and Forestry 7-1
-------
1 Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.
2 Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.
Table 7-2: Net CO2 Flux from Carbon Stock Changes in Land Use, Land-Use Change, and Forestry (Tg C)
Sink Category
Forest Land Remaining Forest
Land1
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining
Grassland
Land Converted to Grassland
Settlements Remaining
Settlements2
Other (Landfilled Yard
Trimmings and Food Scraps)
Total
1990
(185.7)
(8.0)
0.6
(14.2)1
(5.4)|
(15.6)1
(6.6)
(235.0)
2000
(103.2)
(8.2)
0.6
(14.3)
(7.4)
(21.1)
(3.6)
(157.3)
2005
(248.6)
(5.0)
1.6
(2.4)
(6.7)
(23.9)
(3.1)
(288.1)
2006
(250.2)
(5.2)
1.6
(2.4)
(6.6)
(24.5)
(3.0)
(290.3)
2007
(248.7)
(5.4)
1.6
(2.3)
(6.5)
(25.1)
(3.0)
(289.3)
2008
(243.0)
(4.9)
1.6
(2.3)
(6.5)
(25.6)
(3.1)
(283.8)
2009
(235.4)
(4.7)
1.6
(2.3)
(6.4)
(26.2)
(3.4)
(276.8)
Note: 1 Tg C = 1 teragram C = 1 million metric tons C. Parentheses indicate net sequestration. Totals may not sum due to
independent rounding.
1 Estimates include C stock changes on both Forest Land Remaining Forest Land and Land Converted to Forest Land.
2 Estimates include C stock changes on both Settlements Remaining Settlements and Land Converted to Settlements.
Emissions from Land Use, Land-Use Change, and Forestry are shown in Table 7-3 and Table 7-4. Liming of
agricultural soils and urea fertilization in 2009 resulted in CO2 emissions of 4.2 Tg CO2 Eq. (4,221 Gg) and 3.6 Tg
CO2 Eq. (3,612 Gg), respectively. Lands undergoing peat extraction (i.e., PeatlandsRemaining Peatlands) resulted
in CO2 emissions of 1.1 Tg CO2 Eq. (1,090 Gg), and nitrous oxide (N2O) emissions of less than 0.05 Tg CO2 Eq.
The application of synthetic fertilizers to forest soils in 2009 resulted in direct N2O emissions of 0.4 Tg CO2 Eq. (1
Gg). Direct N2O emissions from fertilizer application to forest soils have increased by 455 percent since 1990, but
still account for a relatively small portion of overall emissions. Additionally, direct N2O emissions from fertilizer
application to settlement soils in 2009 accounted for 1.5 Tg CO2 Eq. (5 Gg) in 2009. This represents an increase of
55 percent since 1990. Forest fires in 2009 resulted in methane (CH4) emissions of 7.8 Tg CO2 Eq. (372 Gg), and in
N2O emissions of 6.4 Tg CO2 Eq. (21 Gg).
Table 7-3: Emissions from Land Use, Land-Use Change, and Forestry (Tg CO2 Eq.)
Source Category 1990 2000 2005 2006 2007 2008 2009
CO2 8.1 8.8 8.9 8.8 9.2 9.6 8.9
Cropland Remaining Cropland:
Liming of Agricultural Soils 4.?B 43M 4.3 4.2 4.5 5.0 4.2
Urea Fertilization 2.4^ 3.2U 3.5 3.7 3.7 3.6 3.6
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands l.ol 1.2l 1.1 0.9 1.0 1.0 1.1
CH4 3.2B 14.3 9.8 21.6 20.0 11.9 7.8
Forest Land Remaining Forest
Land: Forest Fires 3.2M 14.3 9.8 21.6 20.0 11.9 7.8
N2O 3.7M 13.2 9.8 19.5 18.3 11.6 8.3
Forest Land Remaining Forest
Land: Forest Fires 2.6U 11.7 8.0 17.6 16.3 9.8 6.4
Forest Land Remaining Forest
Land: Forest Soils1 O.ll 0.4l 0.4 0.4 0.4 0.4 0.4
Settlements Remaining
Settlements: Settlement Soils2 l.ol l.ll 1.5 1.5 1.6 1.5 1.5
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands + + + + + + +
Total 15.0 36.3 28.6 49.8 47.5 33.2 25.0
+ Less than 0.05 Tg CO2 Eq.
Note: These estimates include direct emissions only. Indirect N2O emissions are reported in the Agriculture chapter. Totals may
7-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
not sum due to independent rounding.
1 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.
2 Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion.
Table 7-4: Emissions from Land Use, Land-Use Change, and Forestry (Gg)
Source Category 1990 2000 2005 2006
2007 2008 2009
8,117
C02
Cropland Remaining Cropland:
Liming of Agricultural Soils
Urea Fertilization
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands 1,033
4,667
2,417
8,768
4,328
3,214
1,227
682
8,933 8,754 9,214 9,646 8,922
4,349
3,504
1,079
467
4,220
3,656
879
1,027
1,027
63
57
4,464
3,738
1,012
953
953
59
53
5,042
3,612
992
569
569
37
31
4,221
3,612
1,090
372
372
27
21
CH4 152
Forest Land Remaining Forest
Land: Forest Fires 152
N2O 12|
Forest Land Remaining Forest
Land: Forest Fires
Forest Land Remaining Forest
Land: Forest Soils1
Settlements Remaining
Settlements: Settlement Soils2
Wetlands Remaining Wetlands:
Peatlands Remaining Peatlands
+ Less than 0.5 Gg
Note: These estimates include direct emissions only. Indirect N2O emissions are reported in the Agriculture chapter. Totals may
not sum due to independent rounding.
1 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.
2 Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements, and Land Converted to
Settlements, but not from land-use conversion.
[BEGIN BOX]
Box 7-1: Methodological approach for estimating and reporting U.S. emissions and sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the Intergovernmental Panel on Climate Change
(IPCC).157 Additionally, the calculated emissions and sinks in a given year for the U.S. are presented in a common
manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this international
agreement.158 The use of consistent methods to calculate emissions and sinks by all nations providing their
inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks
reported in this inventory report are comparable to emissions and sinks reported by other countries. Emissions and
sinks provided in this inventory do not preclude alternative examinations, but rather this inventory report presents
emissions and sinks in a common format consistent with how countries are to report inventories under the
UNFCCC. The report itself follows this standardized format, and provides an explanation of the IPCC methods
used to calculate emissions and sinks, and the manner in which those calculations are conducted.
[END BOX]
157 See http://www.ipcc-nggip.iges.or.jp/public/index.html.
158 Seehttp://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/5270.php.
Land Use, Land-Use Change, and Forestry 7-3
-------
7.1. 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 fluxes over the inventory time
series. This system should be consistent with IPCC (2006), such that all countries reporting on national greenhouse
gas fluxes to the UNFCCC should (1) describe the methods and definitions used to determine areas of managed and
unmanaged lands in the country, (2) describe and apply a consistent set of definitions for land-use categories over
the entire national land base and time series associated with the greenhouse gas inventory, such that increases in the
land areas within particular land-use categories are balanced by decreases in the land areas of other categories, and
(3) account for greenhouse gas fluxes on all managed lands. The implementation of such a system helps to ensure
that estimates of greenhouse gas fluxes are as accurate as possible. This section of the Inventory has been developed
in order to comply with this guidance.
Multiple databases are used to track land management in the United States, which are also used as the basis to
classify U.S. land area into the six IPCC land-use categories (i.e., Forest Land Remaining Forest Land, Cropland
Remaining Cropland, Grassland Remaining Grassland, Wetlands Remaining Wetlands, Settlements Remaining
Settlements and Other Land Remaining Other Land) and thirty land-use change categories (e.g., Cropland
Converted to Forest Land, Grassland Converted to Forest Land, Wetlands Converted to Forest Land, Settlements
Converted to Forest Land, Other Land Converted to Forest Lands)1^9 (IPCC 2006). The primary databases are the
U.S. Department of Agriculture (USDA) National Resources Inventory (NRI)160 and the USDA Forest Service
(USFS) Forest Inventory and Analysis (FIA)161 Database. The U.S. Geological Survey (USGS) National Land
Cover Dataset (NLCD)162 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 786 million hectares, and this entire land base is considered
managed.163 In 2009, the United States had a total of 274 million hectares of Forest Land (a 4 percent increase
since 1990), 163 million hectares of Cropland (down 4.4 percent since 1990), 258 million hectares of Grassland
(down 4.2 percent since 1990), 26 million hectares of Wetlands (down 4.9 percent since 1990), 49 million hectares
of Settlements (up 24.5 percent since 1990), and 14 million hectares of Other Land. It is important to note that the
land base formally classified for the Inventory (see Table 7-5) is considered managed. Alaska is not formally
included in the current land representation, but there is a planned improvement underway to include this portion of
the United States in future inventories. In addition, wetlands are not differentiated between managed and
unmanaged, although some wetlands would be unmanaged according to the U.S. definition (see definition later in
this section). Future improvements will include a differentiation between managed and unmanaged wetlands. In
addition, carbon stock changes are not currently estimated for the entire land base, which leads to discrepancies
between the area data presented here and in the subsequent sections of the NIR. Planned improvements are
underway or in development phases to conduct an inventory of carbon stock changes on all managed land (e.g.,
federal grasslands).
Dominant land uses vary by region, largely due to climate patterns, soil types, geology, proximity to coastal regions,
and historical settlement patterns, although all land-uses occur within each of the fifty states (Figure 7-1). Forest
Land tends to be more common in the eastern states, mountainous regions of the western United States, and Alaska.
Cropland is concentrated in the mid-continent region of the United States, and Grassland is more common in the
western United States. Wetlands are fairly ubiquitous throughout the United States, though they are more common
in the upper Midwest and eastern portions of the country. Settlements are more concentrated along the coastal
margins and in the eastern states.
159 Land-use category definitions are provided in the Methodology section.
160 jsjj^j data js available at .
161 FIA data is available at .
162 NLCD data is available at .
163 The current land representation does not include areas from Alaska or U.S. territories, but there are planned improvements to
include these regions in future reports.
7-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 7-5: Size of Land Use and Land-Use Change Categories on Managed Land Area by Land Use and Land Use
Change Categories (thousands of hectares)
Land Use & Land-
Use Change
Categories"
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 |
263
257
1
4
,878
,180
,266
,879
63
101
389
170,632 |
155
1
13
269
260
1
7
27
27
,433
,105
,298
163
470
162
,643
,064
,463
,502
230
129
255
,788
,179
138
134
286
<1
51
39,518
34
1
1
1
14
13
,742
,842
,373
,498
3
60
,385
,397
193
279
458
55
o
6
785,845
2000
268,790
253,080
2,793
11,347
201
268
1,102
164,401
144,004
1,101
17,834
264
886
311
263,092
245,460
3,048
13,303
373
255
653
27,560
26,155
378
348
633
o
6
43
47,558
34,055
5,480
3,599
4,183
29
212
14,443
12,286
506
440
1,085
115
11
785,845
H 2005
271,322
255
2
11
1
163
145
15
260
243
2
12
27
25
49
34
5
3
4
,444
,976
,122
205
303
,273
,192
,531
805
,513
234
825
283
,565
,839
,787
,632
339
255
714
,173
,701
401
351
675
o
6
43
,247
,975
,872
,673
,479
32
217
14,346
12
1
,104
559
499
,058
114
12
785,845
2006
272,107
256,181
2,983
11,157
205
304
1,276
163,178
145,518
804
15,513
234
825
283
260,012
243,395
2,773
12,541
338
253
712
26,983
25,519
398
348
672
3
42
49,238
34,966
5,872
3,672
4,479
32
217
14,327
12,087
559
499
1,057
114
12
785,845
2007
272,891
256,917
2,991
11,193
206
305
1,279
163,164
145,506
803
15,513
234
825
283
259,458
242,951
2,759
12,451
338
252
709
26,793
25,338
395
344
670
o
6
42
49,229
34,958
5,872
3,672
4,479
32
217
14,309
12,069
559
499
1,057
114
12
785,845
2008
273,677
257,655
2,998
11,229
207
306
1,282
163,151
145,493
802
15,512
234
825
283
258,904
242,506
2,745
12,360
337
250
706
26,603
25,157
393
341
668
o
6
42
49,220
34,949
5,871
3,672
4,479
32
217
14,290
12,051
559
499
1,056
114
12
785,845
2009
274,462
258,392
3,006
11,264
207
307
1,285
163,137
145,481
802
15,512
234
825
283
258,350
242,061
2,730
12,270
336
249
704
26,412
24,976
390
338
665
3
42
49,212
34,941
5,871
3,672
4,479
32
217
14,272
12,033
559
499
1,056
113
12
785,845
aThe abbreviations are "F" for Forest Land, "C" for Cropland, "G" for Grassland, "W" for Wetlands, "S" for Settlements, and
"O" for Other Lands. Lands remaining in the same land use category are identified with the land use abbreviation given twice
(e.g., "FF" is Forest Land Remaining Forest Land), and land use change categories are identified with the previous land use
abbreviation followed by the new land use abbreviation (e.g., "CF" is Cropland Converted to Forest Land).
Notes: All land areas reported in this table are considered managed. A planned improvement is underway to deal with an
exception for wetlands which includes both managed and unmanaged lands based on the definitions for the current U.S. Land
Representation Assessment. In addition, U. S. Lerritories have not been classified into land uses and are not included in the U. S.
Land Representation Assessment. See Planned Improvements for discussion on plans to include Alaska and territories in future
Inventories.
Land Use, Land-Use Change, and Forestry 7-5
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Figure 7-1. Percent of Total Land Area in the General Land-Use Categories for 2009
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 between the land-use categories that led to those
net changes. Approach 2 introduces tracking of individual land-use changes between the categories (e.g., Forest
Land to Cropland, Cropland to Forest Land, Grassland to Cropland, etc.), using surveys or other forms of data that
do not provide location data on specific parcels of land. Approach 3 extends Approach 2 by providing location data
on specific parcels of land, such as maps, along with the land-use history. The three approaches are not presented as
hierarchical tiers and are not mutually exclusive.
According to IPCC (2006), the approach or mix of approaches selected by an inventory agency should reflect
calculation needs and national circumstances. For this analysis, the 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. All of these datasets have a spatially-explicit time series of land-use data, and therefore
Approach 3 is used to provide a full representation of land use in the U.S. Inventory. 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 U.S. definitions of managed and unmanaged lands are similar to the basic IPCC (2006) definition of managed
land, but with some additional elaboration to reflect national circumstances. Based on the following definitions,
most lands in the United States are classified as managed:
• Managed Land: Land is considered managed if direct human intervention has influenced its condition.
Direct intervention 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. Managed land also includes legal protection of lands (e.g., wilderness, preserves, parks, etc.)
for conservation purposes (i.e., meets societal objectives).164
• Unmanaged Land: All other land is considered unmanaged. Unmanaged land is largely comprised of areas
inaccessible to human intervention due to the remoteness of the locations, or lands with essentially no
development interest or protection due to limited personal, commercial or social value. Though these lands
may be influenced indirectly by human actions such as atmospheric deposition of chemical species
164 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, however, due to limited data availability.
Wetlands are not characterized by use within the NPJ. 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. See the Planned
Improvements section of the Inventory for work being done to refine the Wetland area estimates.
7-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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produced in industry, they are not influenced by a direct human intervention.165
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
U.S. 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,166 while definitions of Cropland, Grassland, and Settlements are based on the NRI.167 The definitions for
Other Land and Wetlands are based on the IPCC (2006) definitions for these categories.
• Forest Land: A land-use category that includes areas at least 36.6m wide and 0.4 ha in size with at least 10
percent cover (or equivalent stocking) by live trees of any size, including land that formerly had such tree
cover and that will be naturally or artificially regenerated. Forest land includes transition zones, such as
areas between forest and non-forest lands that have at least 10 percent cover (or equivalent stocking) with
live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbelt strips
of trees must have a crown width of at least 36.6m and continuous length of at least 110.6 m to qualify as
forest land. Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if
they are less than 36.6 m wide or 0.4 ha in size, otherwise they are excluded from Forest Land and
classified as Settlements. Tree-covered areas in agricultural production settings, such as fruit orchards, or
tree-covered areas in urban settings, such as city parks, are not considered forest land (Smith et al. 2009).
NOTE: This definition applies to all U.S. lands and territories. However, at this time, data availability is
limited for remote or inaccessible areas such as interior Alaska
• 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.168 Cultivated crops include row crops or close-
grown crops and also hay or pasture in rotation with cultivated crops. Non-cultivated cropland includes
continuous hay, perennial crops (e.g., orchards) and horticultural cropland. Cropland also includes land
with alley cropping and windbreaks,169 as well as lands in temporary fallow or enrolled in conservation
reserve programs (i.e., set-asides170). 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, forbs, or shrubs suitable for grazing and browsing, and includes both pastures and native
rangelands.171 This includes areas where practices such as clearing, burning, chaining, and/or chemicals are
applied to maintain the grass vegetation. Savannas, some wetlands and deserts, in addition to tundra are
considered Grassland.172 Woody plant communities of low forbs and shrubs, such as mesquite, chaparral,
mountain shrub, and pinyon-juniper, are also classified as Grassland if they do not meet the criteria for
Forest Land. Grassland includes land managed with agroforestry practices such as silvipasture and
windbreaks, assuming the stand or woodlot does not meet the criteria for Forest Land. Roads through
165 There will be some areas that qualify as Forest Land or Grassland according to the land use criteria, but are classified as
unmanaged land due to the remoteness of their location.
166 See .
167 See .
168 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.
169 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.
170 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.
171 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.
172 IPCC (2006) guidelines do not include provisions to separate desert and tundra as land categories.
Land Use, Land-Use Change, and Forestry 7-7
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Grassland, including interstate highways, state highways, other paved roads, gravel roads, dirt roads, and
railroads are excluded from Grassland area estimates 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.
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 covered in other areas of the
IPCC guidance and/or the inventory, including Cropland (e.g., rice cultivation), Grassland, and Forest Land
(including drained or undrained forested wetlands).
• Settlements: A land-use category representing developed areas consisting of units of 0.25 acres (0.1 ha) or
more that includes residential, industrial, commercial, and institutional land; construction sites; public
administrative sites; railroad yards; cemeteries; airports; golf courses; sanitary landfills; sewage treatment
plants; water control structures and spillways; parks within urban and built-up areas; and highways,
railroads, and other transportation facilities. Also included are tracts of less than 10 acres (4.05 ha) that may
meet the definitions for Forest Land, Cropland, Grassland, or Other Land but are completely surrounded by
urban or built-up land, and so are included in the settlement category. Rural transportation corridors
located within other land uses (e.g., Forest Land, Cropland) are also included in Settlements.
• Other Land: A land-use category that includes bare soil, rock, ice, non-settlement transportation corridors,
and all land areas that do not fall into any of the other five land-use categories. It allows the total of
identified land areas to match the managed national area.
Land-Use Data Sources: Description and Application to U.S. Land Area Classification
U.S. Land-Use Data Sources
The three main data sources for land area and use data in the United States are the NRI, FIA, and the NLCD. For
the Inventory, the NRI is the official source of data on all land uses on non-federal lands (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
conducted by the USD A 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 U.S. Public Land Survey
(Nusser and Goebel 1997). Within a primary sample unit (typically a 160-acre [64.75 ha] square quarter-section),
three sample points are selected according to a restricted randomization procedure. Each point in the survey is
assigned an area weight (expansion factor) based on other known areas and land-use information (Nusser and
Goebel 1997). 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. This Inventory incorporates
data through 2003 from the NRI.
The FIA program, conducted by the USFS, is the official source of data on Forest Land area and management data
for the Inventory. 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
had been conducted periodically, with all plots in a state being measured at a frequency of every 5 to 14 years. A
new national plot design and annual sampling design was introduced by FIA about ten years ago. Most states,
though, have only recently been brought into this system. Annualized sampling means that a portion of plots
throughout each state is sampled each year, with the goal of measuring all plots once every 5 years. See Annex 3.12
to see the specific survey data available by state. The most recent year of available data varies state by state (2002
through 2009).
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Though NRI provides land-area data for both federal and non-federal lands, it only includes land-use data on non-
federal lands, and FIA only records data for forest land.173 Consequently, major gaps exist when the datasets are
combined, such as federal grassland operated by the Bureau of Land Management (BLM), USD A, and National
Park Service, as well as most of Alaska.174 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 and 2001, has been applied over the conterminous United States (Homer et al. 2007). The 2001
product also provides land use data that has been used for Hawaii federal lands. For this analysis, the NLCD
Retrofit Land Cover Change Product was used in order to represent both land use and land-use change for federal
lands in the conterminous U.S. (Homer et al. 2007). It is 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 (2005). This map
represents federal land boundaries in 2005, so as part of the analysis, the federal land area was adjusted annually
based on the NRI federal land area estimates (i.e., land is periodically transferred between federal and non-federal
ownership). Consequently, the portion of the land base categorized with NLCD data varied from year to year,
corresponding to an increase or decrease in the federal land base. The NLCD is strictly a source of land-cover
information, however, and does not provide the necessary site conditions, crop types, and management information
from which to estimate C stock changes on those lands.
Another step in the analysis is to address gaps as well as overlaps in the representation of the U.S. land base between
the Agricultural Carbon Stock Inventory (Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland) and Forest Land Carbon Stock Inventory (Forest Land
Remaining Forest Land and Land Converted to Forest Land), which are based on the NRI and FIA databases,
respectively. NRI and FIA have different criteria for classifying forest land and sampling designs, leading to
discrepancies in the resulting estimates of Forest Land area on non-federal land. Similarly, there are discrepancies
between the NLCD and FIA data for defining and classifying Forest Land on federal lands. Moreover, 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. The adjustments were made at a state-scale, and
it was assumed that the majority of the discrepancy in forest area was associated with an under- or over-prediction of
Grassland and Wetland area in the NRI and NLCD due to differences in Forest Land definitions. Specifically, the
Forest Land area for a given state according to the NRI and NLCD was adjusted to match the FIA estimates of
Forest Land for non-federal and federal land, respectively. In a second step, corresponding increases or decreases
were made in the area estimates of Grassland and Wetland from the NRI and NLCD, in order to balance the change
in forest area, and therefore not change the overall amount of managed land within an individual state. The
adjustments were based on the proportion of land within each of these land-use categories at the state-level, (i.e., a
higher proportion of Grassland led to a larger adjustment in Grassland area).
As part of Quality Assurance /Quality Control (QA/QC), the land base derived from the NRI, FIA and NLCD was
compared to the Topologically Integrated Geographic Encoding and Referencing (TIGER) survey (U.S. Census
Bureau 2010). The U.S. Census Bureau gathers data on the 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, which is critical for conducting emission inventories and is provided from the NRI and
FIA surveys. Consequently, the U.S. Census Survey was not adopted as the official land area estimate for the
Inventory. Rather, the NRI data were adopted because this database provides full coverage of land area and land use
173 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.
174 jjjg 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 U.S. Territories.
Land Use, Land-Use Change, and Forestry 7-9
-------
for the conterminous United States and Hawaii. Regardless, the total difference between the U.S. Census Survey
and the data sources used in the Inventory is about 25 million hectares for the total land base of about 786 million
hectares currently included in the Inventory, or a 3.1 percent difference. Much of this difference is associated with
open waters in coastal regions and the Great Lakes. NRI does not include as much of the area of open waters in
these regions as the U.S. Census Survey.
Approach for Combining Data Sources
The managed land base in the United States has been classified into the six IPCC land-use categories using
definitions175 developed to meet national circumstances, while adhering to IPCC (2006). In practice, the land was
initially classified into a variety of land-use categories using the NRI, FIA and NLCD, and then aggregated into the
thirty-six broad land use and land-use-change categories identified in IPCC (2006). Details on the approach used to
combine data sources for each land use are described below as are the gaps that will be reconciled as part of ongoing
planned improvements:
• 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, but NLCD has a new
product for Alaska that will be incorporated into the assessment as a planned improvement for future
reports. Forest Lands in U.S. territories are currently excluded from the analysis, but FIA surveys are
currently being conducted on U.S. territories and will become available in the future. NRI is being used in
the current report to provide Forest Land areas on non-federal lands in Hawaii. Currently, federal forest
land in Hawaii is evaluated with the 2001 NLCD, but FIA data will be collected in Hawaii in the future.
• Cropland: Cropland is classified using the NRI, which covers all non-federal lands within 49 states
(excluding Alaska), including state and local government-owned land as well as tribal lands. NRI is used
as the basis for both Cropland area data as well as to estimate C stocks and fluxes on Cropland. Croplands
in U.S. territories are excluded from both NRI data collection and the NLCD. NLCD has a new product for
Alaska that will be incorporated into the assessment as a planned improvement for future reports.
• Grassland: Grassland on non-federal lands is classified using the NRI within 49 states (excluding Alaska),
including state and local government-owned land as well as tribal lands. NRI is used as the basis for both
Grassland area data as well as to estimate C stocks and fluxes on Grassland. U.S. territories are excluded
from both NRI data collection and the current release of the NLCD product. Grassland on federal Bureau
of Land Management lands, Department of Defense lands, National Parks and within USFS lands are
covered by the NLCD. In addition, federal and non-federal grasslands in Alaska are currently excluded
from the analysis, but NLCD has a new product for Alaska that will be incorporated into the assessment for
future reports.
• Wetlands: NRI captures wetlands on non-federal lands within 49 states (excluding Alaska), while federal
wetlands are covered by the NLCD. Alaska and U.S. territories are excluded. This currently includes both
managed and unmanaged wetlands as no database has yet been applied to make this distinction. See
Planned Improvements for details.
• Settlements: The NRI captures non-federal settlement area in 49 states (excluding Alaska). If areas of
Forest Land or Grassland under 10 acres (4.05 ha) are contained within settlements or urban areas, they are
classified as Settlements (urban) in the NRI database. If these parcels exceed the 10 acre (4.05 ha)
threshold and are Grassland, they will be classified as such by NRI. Regardless of size, a forested area is
classified as non-forest by FIA if it is located within an urban area. Settlements on federal lands are
covered by NLCD. Settlements in U.S. territories are currently excluded from NRI and NLCD. NLCD has
a new product for Alaska that will be incorporated into the assessment as a planned improvement for future
reports.
• Other Land: Any land not falling into the other five land categories and, therefore, categorized as Other
Land is classified using the NRI for non-federal areas in the 49 states (excluding Alaska) and NLCD for the
federal lands. Other land in U.S. territories is excluded from the NLCD. NLCD has a new product for
175
Definitions are provided in the previous section.
7-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Alaska that will be incorporated into the assessment as a planned improvement for future reports.
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
initiated by distinguishing between managed and unmanaged lands. The managed lands are then assigned, from
highest to lowest priority, in the following manner:
Settlements > Cropland > Forest Land > Grassland > Wetlands > Other Land
Settlements are given the highest assignment priority because they are extremely heterogeneous with a mosaic of
patches that include buildings, infrastructure and travel corridors, but also open grass areas, forest patches, riparian
areas, and gardens. The latter examples could be classified as Grassland, Forest Land, Wetlands, and Cropland,
respectively, but when located in close proximity to settlement areas they tend to be managed in a unique manner
compared to non-settlement areas. Consequently, these areas are assigned to the Settlements land-use category.
Cropland is given the second assignment priority, because cropping practices tend to dominate management
activities on areas used to produce food, forage or fiber. The consequence of this ranking is that crops in rotation
with grass 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 and Other Land complete the list.
The assignment priority does not reflect the level of importance for reporting greenhouse gas emissions and
removals on managed land, but is intended to classify all areas into a single land use. Currently, the IPCC does not
make provisions in the guidelines for assigning land to multiple uses. For example, a Wetland is classified as Forest
Land if the area has sufficient tree cover to meet the stocking and stand size requirements. Similarly, Wetlands are
classified as Cropland if they are used for crop production, such as rice or cranberries. In either case, emissions
from Wetlands are included in the Inventory if human interventions are influencing emissions from Wetlands, in
accordance with the guidance provided in IPCC (2006).
Recalculations Discussion
No major revisions were made to the time series for the current Inventory. However, new data were incorporated
from FIA on forestland areas, which was used to make minor adjustments to the time series. FIA conducts a survey
of plots annually so that each plot is visited every 5 years (Note: some states have not initiated the annual sampling
regime, as discussed previously). Consequently, the time series is updated each year as new data are collected over
the 5 year cycles.
Planned Improvements
Area data by land-use category are not estimated for major portions of Alaska or any of the U.S. territories. A key
planned improvement is to incorporate land-use data from these areas into the Inventory. For Alaska, a new NLCD
2001 data product will be used to cover those land areas presently omitted. Fortunately, most of the managed land
in the United States is included in the current land-use statistics, but a complete accounting is a key goal for the near
future. Data sources will also be evaluated for representing land use on federal and non-federal lands in U.S.
territories.
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, and an analysis
is planned to develop region-specific adjustments.
There are also other databases that may need to be reconciled with the NPJ and NLCD datasets, particularly for
Settlements and Wetlands. Urban area estimates, used to produce C stock and flux estimates from urban trees, are
currently based on population data (1990 and 2000 U.S. Census data). Using the population statistics, "urban
clusters" are defined as areas with more than 500 people per square mile. The USFS is currently moving ahead with
Land Use, Land-Use Change, and Forestry 7-11
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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.
7.2. Forest Land Remaining Forest Land
Changes in Forest Carbon Stocks (IPCC Source Category 5A1)
For estimating C stocks or stock change (flux), C in forest ecosystems can be divided into the following five storage
pools (IPCC 2003):
• Aboveground biomass, which includes all living biomass above the soil including stem, stump, branches,
bark, seeds, and foliage. This category includes live understory.
• Belowground biomass, which includes all living biomass of coarse living roots greater than 2 mm diameter.
• Dead wood, which includes all non-living woody biomass either standing, lying on the ground (but not
including litter), or in the soil.
• Litter, which includes the litter, fumic, and humic layers, and all non-living biomass with a diameter less
than 7.5 cm at transect intersection, lying on the ground.
• Soil organic C (SOC), including all organic material in soil to a depth of 1 meter but excluding the coarse
roots of the aboveground pools.
In addition, there are two harvested wood pools necessary for estimating C flux:
• Harvested wood products (HWP) in use.
• HWP in solid waste disposal sites (SWDS).
C 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, growth, mortality, decomposition, and disturbances
such as fires or pest outbreaks) and anthropogenic activities (e.g., harvesting, thinning, clearing, 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 or 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 C of all vegetation C to the atmosphere. Instead, harvesting transfers a
portion of the C stored in wood to a "product pool." Once in a product pool, the C is emitted over time as CO2 when
the wood product combusts or decays. The rate of emission varies considerably among different product pools. For
example, if timber is harvested to produce energy, combustion releases C immediately. Conversely, if timber is
harvested and used as lumber in a house, it may be many decades or even centuries before the lumber decays and C
is released to the atmosphere. If wood products are disposed of in SWDS, the C contained in the wood may be
released many years or decades later, or may be stored almost permanently in the SWDS.
This section quantifies the net changes in C stocks in the five forest C pools and two harvested wood pools. The net
change in stocks for each pool is estimated, and then the changes in stocks are summed over all pools to estimate
total net flux. The focus on C implies that all C-based greenhouse gases are included, and the focus on stock change
suggests that specific ecosystem fluxes do not need to be separately itemized in this report. Disturbances from forest
fires and pest outbreaks are implicitly included in the net changes. For instance, an inventory conducted after fire
counts only the trees that are left. The change between inventories thus accounts for the C changes due to fires;
however, it may not be possible to attribute the changes to the disturbance specifically. The IPCC (2003)
recommends reporting C stocks according to several land-use types and conversions, specifically Forest Land
Remaining Forest Land and Land Converted to Forest Land. Currently, consistent datasets are just becoming
available for the conterminous United States to allow forest land conversions and forest land remaining forest land
to be identified, and research is ongoing to properly use that information based on research results. Thus, net
changes in all forest-related land, including non-forest land converted to forest and forests converted to non-forest,
are reported here.
7-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Forest C storage pools, and the flows between them via emissions, sequestration, and transfers, are shown in Figure
7-2. In the figure, boxes represent forest C storage pools and arrows represent flows between storage pools or
between storage pools and the atmosphere. Note that the boxes are not identical to the storage pools identified in
this chapter. The storage pools identified in this chapter have been refined in this graphic to better illustrate the
processes that result in transfers of C from one pool to another, and emissions to as well as uptake from the
atmosphere.
Figure 7-2: Forest Sector Carbon Pools and Flows
Approximately 33 percent (304 million hectares) of the U.S. land area is forested (Smith et al. 2009). The current
forest carbon inventory includes 271 million hectares in the conterminous 48 states (USDA Forest Service 2010a,
20 lOb) that are considered managed and are included in this inventory. An additional 6.1 million hectares of
southeast and south central Alaskan forest are inventoried and are included here. Three notable differences exist in
forest land defined in Smith et al. (2009) and the forest land included in this report, which is based on USDA Forest
Service (2010b). Survey data are not yet available from Hawaii and a large portion of interior Alaska, but estimates
of these areas are included in Smith et al. (2009). Alternately, survey data for west Texas has only recently become
available, and these forests contribute to overall carbon stock reported below. While Hawaii and U.S. territories
have relatively small areas of forest land and will thus probably 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 Forest Inventory and
Analysis (FIA) program of the U.S. Department of Agriculture (USDA) Forest Service or the National Resources
Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry et al. 2005).
Sixty-eight percent of U.S. forests (208 million hectares) are classified as timberland, meaning they meet minimum
levels of productivity. Nine percent of Alaska forests overall and 81 percent of forests in the conterminous United
States are classified as timberlands. Of the remaining nontimberland forests, 30 million hectares are reserved forest
lands (withdrawn by law from management for production of wood products) and 66 million hectares are lower
productivity forest lands (Smith et al. 2009). Historically, the timberlands in the conterminous 48 states have been
more frequently or intensively surveyed than other forest lands.
Forest land area declined by approximately 10 million hectares over the period from the early 1960s to the late
1980s. Since then, forest area has increased by about 12 million hectares. Current trends in forest area represent
average annual change of less than 0.2 percent. Given the low rate of change in U.S. forest land area, the major
influences on the current net C flux from forest land are management activities and the ongoing impacts of previous
land-use changes. These activities affect the net flux of C by altering the amount of C stored in forest ecosystems.
For example, intensified management of forests that leads to an increased rate of growth increases the eventual
biomass density of the forest, thereby increasing the uptake of C.176 Though harvesting forests removes much of the
aboveground C, on average the volume of annual net growth nationwide is about 72 percent higher than the volume
of annual removals on timberlands (Smith et al. 2009). The reversion of cropland to forest land increases C storage
in biomass, forest floor, and soils. The net effects of forest management and the effects of land-use change
involving forest land are captured in the estimates of C stocks and fluxes presented in this chapter.
In the United States, improved forest management practices, the regeneration of previously cleared forest areas, and
timber harvesting and use have resulted in net uptake (i.e., net sequestration) of C each year from 1990 through
2009. The rate of forest clearing begun in the 17th century following European settlement had slowed by the late
19th century. Through the later part of the 20th century many areas of previously forested land in the United States
were allowed to revert to forests or were actively reforested. The impacts of these land-use changes still influence C
fluxes from these forest lands. More recently, the 1970s and 1980s saw a resurgence of federally-sponsored forest
management programs (e.g., the Forestry Incentive Program) and soil conservation programs (e.g., the Conservation
Reserve Program), which have focused on tree planting, improving timber management activities, combating soil
erosion, and converting marginal cropland to forests. In addition to forest regeneration and management, forest
176 jjjg 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 7-13
-------
harvests have also affected net C fluxes. Because most of the timber harvested from U.S. forests is used in wood
products, and many discarded wood products are disposed of in SWDS rather than by incineration, significant
quantities of C in harvested wood are transferred to long-term storage pools rather than being released rapidly to the
atmosphere (Skog and Nicholson 1998, Skog 2008). The size of these long-term C storage pools has increased
during the last century.
Changes in C stocks in U.S. forests and harvested wood were estimated to account for net sequestration of 863 Tg
CO2Eq. (235 Tg C) in 2009 (Table 7-6, Table 7-7, and Table 7-8). 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, average C in forest ecosystem biomass (aboveground and belowground) increased from 67 to 73 Mg C/ha
between 1990 and 2010 (see Annex 3-12 for average C densities by specific regions and forest types). Continuous,
regular annual surveys are not available over the period for each state; therefore, estimates for non-survey years
were derived by interpolation between known data points. Survey years vary from state to state, and national
estimates are a composite of individual state surveys. Therefore, changes in sequestration over the interval 1990 to
2009 are the result of the sequences of new inventories for each state. C in forest ecosystem biomass had the
greatest effect on total change through increases in C density and total forest land. Management practices that
increase C stocks on forest land, as well as afforestation and reforestation efforts, influence the trends of increased C
densities in forests and increased forest land in the United States.
The decline in net additions to HWP carbon stocks continued though 2009 from the recent high point in 2006. This
is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to the decline in
housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was exceeded
by discards from uses. The result is a net reduction in the amount of HWP carbon that is held in products in use
during 2009. For 2009, additions to landfills still exceeded emissions from landfills and the net additions to landfills
have remained relatively stable. Overall, there were net carbon additions to HWP in use and in landfills combined
in 2009.
Table 7-6: Net Annual Changes in C Stocks (Tg CO2/yr) in Forest and Harvested Wood Pools
Carbon Pool 1990 2000 2005 2006 2007 2008 2009
Forest (549.3) (265.4) (806.1) (808.9) (808.9) (808.9) (808.9)
Aboveground
Biomass (360.0) (287.0) (447.9) (447.9) (447.9) (447.9) (447.9)
Belowground
Biomass
Dead Wood
Litter
Soil Organic
Carbon
Harvested Wood
Products in Use
SWDS
Total Net Flux
(70.9)
(31.6)
(32.2)
(54.7)
(131.8)
(64.8)
(67.0)
(681.1)
(57.5)
(12.9)
27.5
64.6
(112.9)
(47.0)
(65.9)
(378.3)
1(88.4)
(30.8)
(41.9)
(197.2)
(105.4)
(45.4)
1 (59.9)
1 (911.5)
(88.4)
(33.5)
(41.9)
(197.2)
(108.6)
(45.1)
(63.4)
(917.5)
(88.4)
(33.5)
(41.9)
(197.2)
(103.0)
(39.1)
(63.8)
(911.9)
(88.4)
(33.5)
(41.9)
(197.2)
(82.1)
(19.1)
(63.0)
(891.0)
(88.4)
(33.5)
(41.9)
(197.2)
(54.3)
6.8
(61.1)
(863.1)
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 3.12.
Harvested wood estimates are based on results from annual surveys and models. Totals may not sum due to independent
rounding.
Table 7-7: Net Annual Changes in C Stocks (Tg C/yr) in Forest and Harvested Wood Pools
Carbon Pool 1990 2000 2005 2006 2007 2008 2009
Forest (149.8) (72.4) (219.9) (220.6) (220.6) (220.6) (220.6)
Aboveground
Biomass (98.2) I (78.3) I (122.1) (122.1) (122.1) (122.1) (122.1)
Belowground
Biomass (19.3) I (15.7) I (24.1) (24.1) (24.1) (24.1) (24.1)
Dead Wood (8.6) (3.5) (8.4) (9.1) (9.1) (9.1) (9.1)
7-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Litter
Soil Organic C
Harvested Wood
Products in Use
SWDS
Total Net Flux
(8.8)
(14.9)
(35.9)
(17.7)
(18.3)
(185.7)
17.5
17.6
(30.8) 1
(12.8) 1
(18.0)
(103.2)
1(11.4)
(53.8)
(28.7)
(12.4)
1 (16.3)
| (248.6)
(11.4)
(53.8)
(29.6)
(12.3)
(17.3)
(250.2)
(11.4)
(53.8)
(28.1)
(10.7)
(17.4)
(248.7)
(11.4)
(53.8)
(22.4)
(5.2)
(17.2)
(243.0)
(11.4)
(53.8)
(14.8)
1.9
(16.7)
(235.4)
Note: Forest C stocks do not include forest stocks in U.S. territories, Hawaii, a portion of managed lands in Alaska, or trees on
non-forest land (e.g., urban trees, agroforestry systems). Parentheses indicate net C sequestration (i.e., a net removal of C from
the atmosphere). Total net flux is an estimate of the actual net flux between the total forest C pool and the atmosphere.
Harvested wood estimates are based on results from annual surveys and models. Totals may not sum due to independent
rounding.
Stock estimates for forest and harvested wood C storage pools are presented in Table 7-8. Together, the
aboveground live and forest soil pools account for a large proportion of total forest C stocks. C stocks in all non-soil
pools increased over time. Therefore, C sequestration was greater than C emissions from forests, as discussed
above. Figure 7-4 shows county-average C densities for live trees on forest land, including both above- and
belowground biomass.
Table 7-8: Forest area (1000 ha) and C Stocks (Tg C) in Forest and Harvested Wood Pools
Forest Area
(1000 ha)
Carbon Pools
(TgC)
Forest
Aboveground
Biomass
Belowground
Biomass
Dead Wood
Litter
Soil Organic C
Harvested
Wood
Products in Use
SWDS
Total C Stock
1990
269,137 1
42,783
15,072
2,995
2,960
4,791
16,96
1,859
1,231
628 •
44,643
2000
274,183 1
44,108
16,024
3,183
3,031
4,845
17,025
2,187
1,382
805 •
46,296
2005
276,769
44,886
16,536
3,285
3,060
4,862
17,143
2,325
1,436
890
47,211
2006
277,561
45,105
16,658
3,309
3,068
4,873
17,197
2,354
1,448
906
47,459
2007
278,354
45,326
16,780
3,333
3,077
4,885
17,251
2,383
1,460
923
47,710
2008
279,147
45,547
16,902
3,357
3,086
4,896
17,304
2,412
1,471
941
47,958
2009
279,939
45,767
17,024
3,381
3,096
4,908
17,358
2,434
1,476
958
48,201
2010
280,732
45,988
17,147
3,405
3,105
4,919
17,412
2,449
1,474
974
48,437
Note: Forest area estimates include portions of managed forests in Alaska for which survey data are available. Forest C stocks do
not include forest stocks in U.S. territories, Hawaii, a large portion of Alaska, or trees on non-forest land (e.g., urban trees,
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 et al. (2010)
and in Annex 3.12. Harvested wood estimates are based on results from annual surveys and models. Totals may not sum due to
independent rounding. Inventories are assumed to represent stocks as of January 1 of the inventory year. Flux is the net annual
change in stock. Thus, an estimate of flux for 2006 requires estimates of C stocks for 2006 and 2007.
Figure 7-3: Estimates of Net Annual Changes in C Stocks for Major C Pools
Figure 7-4: Average C Density in the Forest Tree Pool in the Conterminous United States, 2009
[BEGIN BOX]
Box 7-2: CO? Emissions from Forest Fires
Land Use, Land-Use Change, and Forestry 7-15
-------
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 disturbance removes C from the forest. The inventory data on which net C stock
estimates are based already reflect this C loss. Therefore, estimates of net annual changes in C stocks for U.S.
forestland already account for CO2 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 CO2 emissions from fire disturbance, these estimates are being highlighted here, using the full extent
of available data. Non-CO2 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. CO2 emissions for wildfires and prescribed fires in the lower 48 states and wildfires
in Alaska in 2009 were estimated to be 124.3 Tg CO2/yr. This amount is masked in the estimate of net annual forest
carbon stock change for 2009, however, because this net estimate accounts for the amount sequestered minus any
emissions.
Table 7-9: Estimates of CO2 (Tg/yr) emissions for the lower 48 states and Alaska1
Year
CO2 emitted
from Wildfires in
Lower 48 States
(Tg/yr)
CO2 emitted
from Prescribed
Fires in Lower 48
States (Tg/yr)
CO2 emitted
from Wildfires in
Alaska (Tg/yr)
Total CO2
emitted (Tg/yr)
1990
42.1
50.7
2005
2006
2007
2008
2009
131.0
313.6
284.1
169.0
97.1
24.8
29.3
34.0
20.8
27.3
f 155.9
f 342.9
f 318.1
f 189.8
f 124.3
+ Does not exceed 0.05 Tg CO2 Eq.
1 Note that these emissions have already been accounted for in the estimates of net annual changes in C stocks, which account for
the amount sequestered minus any emissions.
[END BOX]
Methodology and Data Sources
The methodology described herein is consistent with IPCC (2003, 2006) and IPCC/UNEP/OECD/IEA (1997).
Forest ecosystem C stocks and net annual C stock change are determined according to stock-difference methods,
which involve applying C estimation factors to forest inventory data and interpolating between successive
inventory-based estimates of C stocks. Harvested wood C estimates are 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 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 3.12 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 are made for the five IPCC C storage pools described above. All
estimates are based on data collected from the extensive array of permanent forest inventory plots in the United
States as well as models employed to fill gaps in field data. Carbon conversion factors are applied at the
disaggregated level of each inventory plot and then appropriately expanded to population estimates. A combination
7-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
of tiers as outlined by IPCC (2006) is used. The Tier 3 biomass C values are from forest inventory tree-level data.
The Tier 2 dead organic and soil C pools are based on empirical or process models from the inventory data. All
carbon conversion factors are specific to regions or individual states within the U.S., which are 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 USDA Forest Service FIA program
(Prayer and Furnival 1999, USDA Forest Service 2010b). Inventories include data collected on permanent
inventory plots on forest lands177 and are organized as a number of separate datasets, each representing a complete
inventory, or survey, of an individual state at a specified time. Some of the more recent annual inventories reported
for some states include "moving window" averages, which means that a portion—but not all—of the previous year's
inventory is updated each year (USDA Forest Service 2010d). Forest C calculations are organized according to
these state surveys, and the frequency of surveys varies by state. All available data sets 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 (2010b) as the
Forest Inventory and Analysis Database (FIADB) Version 4.0. However, to achieve consistent representation
(spatial and temporal), two other general sources of past FIA data are 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 mostly to
provide the data at or before 1990. An additional forest inventory data source is the Integrated Database (IDE),
which is a compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington
(Waddell and Hiserote 2005). These data were identified by Heath et al. (submitted) as the most appropriate non-
FIADB sources for these states and are included in this inventory. See USDA Forest Service (2010a) 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 in Annex 3.12.
Forest C stocks are estimated from inventory data by a collection of conversion factors and models (Birdsey and
Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et al. 2004, Smith et al. 2006), which have been
formalized in an FIADB-to-carbon calculator (Smith et al. 2010). The conversion factors and model coefficients are
categorized by region and forest type, and forest C stock estimates are calculated from application of these factors at
the scale of FIA inventory plots. The results are estimates of C density (Mg C per hectare) for six forest ecosystem
pools: live trees, standing dead trees, understory vegetation, down dead wood, forest floor, and soil organic matter.
The six carbon pools used in the FIADB-to-carbon calculator are aggregated to the 5 carbon pools defined by IPCC
(2006): aboveground biomass, belowground biomass, dead wood, litter, and soil organic matter. All non-soil pools
except forest floor are separated into aboveground and belowground components. The live tree and understory C
pools are pooled as biomass, and standing dead trees and down dead wood are pooled as dead wood, in accordance
with IPCC (2006).
Once plot-level C stocks are calculated as C densities on Forest Land Remaining Forest LandTor the five IPCC
(2006) reporting pools, the stocks are 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 are
summed to state or sub-state total C stocks. Annualized estimates of C stocks are developed by using available FIA
inventory data and interpolating or extrapolating to assign a C stock to each year in the 1990 through2010 time
series. Flux, or net annual stock change, is estimated by calculating the difference between two successive years and
applying the appropriate sign convention; net increases in ecosystem C are identified as negative flux. By
convention, inventories are assigned to represent stocks as of January 1 of the inventory year; an estimate of flux for
1996 requires 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 3.12.
177 Forest land in the United States includes land that is at least 10 percent stocked with trees of any size. Timberland is the most
productive type of forest land, which is on unreserved land and is producing or capable of producing crops of industrial wood.
Land Use, Land-Use Change, and Forestry 7-17
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Carbon in Biomass
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at
diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for
full-tree and aboveground-only biomass in order to estimate the belowground component. If inventory plots include
data on individual trees, tree C is based on Jenkins et al. (2003) and is a function of species and diameter. Some
inventory data do not provide measurements of individual trees; tree C in these plots is estimated from plot-level
volume of merchantable wood, or growing-stock volume, of live trees, which is calculated from updates of Smith et
al. (2003). These biomass conversion and expansion factors (BCEFs) are applied to about 3 percent of the inventory
records, all of which are pre-1998 data. Some inventory data, particularly some of the older datasets, may not
include sufficient information to calculate tree C because of incomplete or missing tree or volume data; C estimates
for these plots are based on averages from similar, but more complete, inventory data. This applies to an additional
2 percent of inventory records, which represent older (pre-1998) non-timberlands.
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 d.b.h. In the current inventory, it is assumed that 10
percent of total understory C mass is belowground. Estimates of C density are based on information in Birdsey
(1996). Understory frequently represents over 1 percent of C in biomass, but its contribution rarely exceeds 2
percent of the total.
Carbon in Dead Organic Matter
Dead organic matter is initially calculated as three separate pools with C stocks modeled from inventory data.
Estimates are specific to regions and forest types within each region, and stratification of forest land for dead
organic matter calculations is identical to that used for biomass through the state and sub-state use of FIA data as
discussed above. The two components of dead wood—standing dead trees and down dead wood—are estimated
separately. The standing dead tree C pools include aboveground and belowground (coarse root) mass and include
trees of at least 2.54 cm d.b.h. Calculations are BCEF-like factors based on updates of Smith et al. (2003). Down
dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not
attached to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. Ratios of
down dead wood to live tree are used to estimate this quantity. Litter C is the pool of organic C (also known as duff,
humus, and fine woody debris) above the mineral soil and includes woody fragments with diameters of up to 7.5 cm.
Estimates are based on equations of Smith and Heath (2002).
Carbon in Forest Soil
Soil organic C (SOC) 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 are based on the national STATSGO spatial database (USDA
1991), which includes region and soil type information. SOC determination is based on the general approach
described by Amichev and Galbraith (2004). Links to FIA inventory data were developed with the assistance of the
USDA Forest Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the soil C map.
This method produced mean SOC densities stratified by region and forest type group. It did not provide separate
estimates for mineral or organic soils but instead weighted their contribution to the overall average based on the
relative amount of each within forest land. Thus, forest SOC is a function of species and location, and net change
also depends on these two factors as total forest area changes. In this respect, SOC provides a country-specific
reference stock for 1990-present, but it does not reflect effects of past land use.
Harvested Wood Carbon
Estimates of the HWP contribution to forest C sinks and emissions (hereafter called "HWP Contribution") are based
on methods described in Skog (2008) using the WOODCARB II model. These methods are based on IPCC (2006)
guidance for estimating HWP C. IPCC (2006) provides methods that allow Parties to report HWP Contribution
using one of several different accounting approaches: production, stock change and atmospheric flow, as well as a
default method that assumes there is no change in HWP C stocks (see Annex 3.12 for more details about each
approach). The United States uses the production accounting approach to report HWP Contribution. Under the
production approach, C in exported wood is estimated as if it remains in the United States, and C in imported wood
is 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,
7-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
are also presented for comparison (see Annex 3.12). Annual estimates of change are calculated by tracking the
additions to and removals from the pool of products held in end uses (i.e., products in use such as housing or
publications) and the pool of products held in solid waste disposal sites (SWDS).
Solidwood products added to pools include lumber and panels. End-use categories for solidwood include single and
multifamily housing, alteration and repair of housing, and other end-uses. There is one product category and one
end-use category for paper. Additions to and removals from pools are tracked beginning in 1900, with the exception
that additions of softwood lumber to housing begins in 1800. Solidwood and paper product production and trade
data are from USDA Forest Service and other sources (Hair and Ulrich 1963; Hair 1958; USDC Bureau of Census;
1976; Ulrich, 1985, 1989; Steer 1948; AF&PA 2006a 2006b; Howard 2003, 2007). Estimates for disposal of
products reflect the change over time in the fraction of products discarded to SWDS (as opposed to burning or
recycling) and the fraction of SWDS that are in sanitary landfills versus dumps.
There are five annual HWP variables that are 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 yields the estimate for HWP Contribution under the production accounting
approach. A key assumption for estimating these variables is that products exported from the United States and held
in pools in other countries have the same half lives for products in use, the same percentage of discarded products
going to SWDS, and the same decay rates in SWDS as they would in the United States.
Uncertainty and Time Series Consistency
A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems as well as C in harvested
wood products through Monte Carlo simulation of the Methods described above and probabilistic sampling of C
conversion factors and inventory data. See Annex 3.12 for additional information. The 2009 flux estimate for forest
C stocks is estimated to be between -1,014 and -714 Tg CO2 Eq. at a 95 percent confidence level. This includes a
range of -662 to -959 Tg CO2 Eq. in forest ecosystems and -69 to -41 Tg CO2 Eq. for HWP.
Table 7-10: Tier 2 Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land Remaining Forest Land:
Changes in Forest C Stocks (Tg CO2 Eq. and Percent)
2009 Flux
Source Gas Estimate Uncertainty Range Relative to Flux
(TgC02
Eq.) (TgC02Eq.) (°,
Lower
Bound
Upper
Bound
Lower
Bound
Estimate"
o)
Upper
Bound
Forest Ecosystem CO2 (808.9) (959.4) (661.7) -19% -18%
Harvested Wood
Products CO2 (54.3) (68.6) (41.0) -27% -24%
Total Forest CO2 (863.1) (1,014.4) (713.9) -18% -17%
Note: Parentheses indicate negative values or net sequestration.
aRange 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
Land Use, Land-Use Change, and Forestry 7-19
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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 main purpose of the
FIA program has been to estimate areas, volume of growing stock, and timber products output and utilization
factors. 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
2010d).
Many key calculations for estimating current forest C stocks based on FIA data were developed to fill data gaps in
assessing forest carbon 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 3.12).
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 4.0, which are available at an FIA internet site (USDA Forest Service 2009b).
Agreement between the C datasets and the original inventories is important to verify accuracy of the data used.
Finally, C stock estimates were compared with previous inventory report estimates to ensure that any differences
could be explained by either new data or revised calculation methods (see the "Recalculations" discussion, below).
Estimates of the HWP variables and the HWP contribution under the production accounting approach use data from
U.S. Census and USDA Forest Service surveys of production and trade. Factors to convert wood and paper to units
C are based on estimates by industry and Forest Service published sources. The WOODCARB II model uses
estimation methods suggested by IPCC (2006). Estimates of annual C change in solidwood and paper products in
use were calibrated to meet two independent criteria. The first criterion is that the WOODCARB II model estimate
of C in houses standing in 2001 needs to match an independent estimate of C in housing based on U.S. Census and
USDA Forest Service survey data. Meeting the first criterion resulted in an estimated half life of about 80 years for
single family housing built in the 1920s, which is confirmed by other U.S. Census data on housing. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needs to match
EPA estimates of discards each year over the period 1990 to 2000 (EPA 2006). These criteria help reduce
uncertainty in estimates of annual change in C in products in use in the United States and, to a lesser degree, reduce
uncertainty in estimates of annual change in C in products made from wood harvested in the United States. In
addition, WOODCARB II landfill decay rates have been validated by ensuring that estimates of CH4 emissions from
landfills based on EPA (2006) data are reasonable in comparison with CH4 estimates based on WOODCARB II
landfill decay rates.
Recalculations Discussion
The basic models used to estimate forest ecosystem and HWP C stocks and change are unchanged from the previous
Inventory (Smith et al. 2010, Skog 2008). Many of the state-level estimates for 1990 through the present are
relatively similar to the values previously reported (EPA 2010). Recent forest inventory additions to the FIADB
include newer annual inventory data for most states including Oklahoma, which had the effect of increasing overall
net sequestration estimated for the interval from 2000 through 2008. An additional change to the FIADB was the
addition of some older periodic inventories for some southern states; these were incorporated into the calculations
but did not appreciably affect national trends. The addition of the IDE forest inventories for a part of the series for
California, Oregon, and Washington did affect recalculations for those states and the United States as a whole; it
tended to decrease net sequestration throughout the 1990 to 2008 interval. However, the decreased sequestration
associated with the use of the IDE was offset by the increased sequestration associated with newer annual inventory
data for the post-2000 interval.
Planned Improvements
The ongoing annual surveys by the FIA Program will improve precision of forest C estimates as new state surveys
7-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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become available (USDA Forest Service 2010b), particularly in western states. The annual surveys will eventually
include all states. To date, three states are not yet reporting any data from the annualized sampling design of FIA:
Hawaii, New Mexico and Wyoming. Estimates for these states are currently based on older, periodic data. Hawaii
and U.S. territories will also be included when appropriate forest C data are available. In addition, the more
intensive sampling of down dead wood, litter, and soil organic C on some of the permanent FIA plots continues and
will substantially improve resolution of C pools at the plot level for all U.S. forest land as this information becomes
available (Woodall et al. in press). Improved resolution, incorporating more of Alaska's forests, and using
annualized sampling data as it becomes available for those states currently not reporting are planned for future
reporting.
As more information becomes available about historical land use, the ongoing effects of changes in land use and
forest management will be better accounted for in estimates of soil C (Birdsey and Lewis 2003, Woodbury et al.
2006, Woodbury et al. 2007). Currently, soil C estimates are based on the assumption that soil C density depends
only on broad forest type group, not on land-use history, but long-term residual effects on soil and forest floor C
stocks are likely after land-use change. Estimates of such effects depend on identifying past land use changes
associated with forest lands.
Similarly, agroforestry practices, such as windbreaks or riparian forest buffers along waterways, are not currently
accounted for in the inventory. In order to properly account for the C stocks and fluxes associated with agroforestry,
research will be needed that provides the basis and tools for including these plantings in a nation-wide inventory, as
well as the means for entity-level reporting.
Non-C02 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 2009 were estimated to be 7.8 Tg CO2 Eq. of CH4 and 6.4 Tg CO2 Eq. of N2O, as shown in Table 7-11 and
Table 7-12. The estimates of non-CO2 emissions from forest fires account for wildfires in the lower 48 states and
Alaska as well as prescribed fires in the lower 48 states.
Table 7-11: Estimated Non-CO2 Emissions from Forest Fires (Tg CO2 Eq.) for U.S. Forests1
Gas
CH4
N2O
Total
1990
3.2
2.6
5.8
2000
14.3
11.7
26.0
2005
9.8
8.0
17.8
2006
21.6
17.6
39.2
2007
20.0
16.3
36.3
2008
11.9
9.8
21.7
2009
7.8
6.4
14.2
Calculated based on C emission estimates in Changes in Forest Carbon Stocks and default factors in IPCC (2003, 2006).
Table 7-12: Estimated Non-CO2 Emissions from Forest Fires (Gg Gas) for U.S. Forests1
Gas
CH4
N2O
1990
152
8
2000
682
38
2005
467
26
2006
1,027
57
2007
953
53
2008
569
31
2009
372
21
Calculated based on C emission estimates in Changes in Forest Carbon Stocks and default factors in IPCC (2003, 2006).
Methodology
The IPCC (2003) Tier 2 default methodology was used to calculate non-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. Estimates of CH4 and N2O emissions were calculated by multiplying the total
estimated CO2 emitted from forest burned by the gas-specific emissions ratios. CO2 emissions were estimated by
multiplying total C emitted (Table 7-13) 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 were:
CH4 Emissions = (C released) x 92.8% x (44/12) x (CH4 to CO2 emission ratio)
N2O Emissions = (C released) x 92.8% x (44/12) x (N2O to CO2 emission ratio)
Estimates for C emitted from forest fires are the same estimates used to generate estimates of CO2 presented earlier
in Box 7-1. Estimates for C emitted include emissions from wildfires in both Alaska and the lower 48 states as well
Land Use, Land-Use Change, and Forestry 7-21
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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 3.12 for more details on the methodology used to estimate C emitted from forest fires.
Table 7-13: Estimated Carbon Released from Forest Fires for U.S. Forests
Year C Emitted (Tg/yr)
1990
14.9
2005
2006
2007
2008
2009
45.8
100.8
93.5
55.8
36.5
Uncertainty and Time-Series Consistency
Non-CO2 gases emitted from forest fires depend on several variables, including: forest area for Alaska and the lower
48 states; average C densities for wildfires in Alaska, wildfires in the lower 48 states, and prescribed fires in the
lower 48 states; emission ratios; and combustion factor values (proportion of biomass consumed by fire). To
quantify the uncertainties for emissions from forest fires, a Monte Carlo (Tier 2) uncertainty analysis was performed
using information about the uncertainty surrounding each of these variables. The results of the Tier 2 quantitative
uncertainty analysis are summarized in Table 7-14.
Table 7-14: Tier 2 Quantitative Uncertainty Estimates of Non-CO2 Emissions from Forest Fires in Forest Land
Remaining Forest Land (Tg CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission
Source Gas Estimate Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Non-CO2 Emissions from Forest Fires CH4 7.8
Non-CO2 Emissions from Forest Fires N2O 6.4
Lower
Bound
2.2
1.8
Upper
Bound
19.2
15.7
Lower
Bound
-72%
-72%
Upper
Bound
+145%
+145%
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
control measures for forest fires included checking input data, documentation, and calculations to ensure data were
properly handled through the inventory process. Errors that were found during this process were corrected as
necessary.
Recalculations Discussion
This is the second year in which non-CO2 emissions were calculated using the 2006 IPCC default emission factors
for CH4 and N2O instead of the 2003 IPCC default emission factors. These default emission factors were converted
to CH4to CO2 and N2O to CO2 emission ratios and then multiplied by CO2 emissions to estimate CH4 and N2O
emissions. The previous 2003 IPCC methodology provides emission ratios that are multiplied by total carbon
emitted.
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
7-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
being conducted.
Direct N20 Fluxes from Forest Soils (IPCC Source Category 5A1)
Of the synthetic N fertilizers applied to soils in the United States, no more than one percent is applied to forest soils.
Application rates are similar to those occurring on cropped soils, but in any given year, only a small proportion of
total forested land receives N fertilizer. This is because forests are typically fertilized only twice during their
approximately 40-year growth cycle (once at planting and once approximately 20 years later). Thus, while the rate
of N fertilizer application for the area of forests that receives N fertilizer in any given year is relatively high, the
average annual application is quite low as inferred by dividing all forest land that may undergo N fertilization at
some point during its growing cycle by the amount of N fertilizer added to these forests in a given year. Direct N2O
emissions from forest soils in 2009 were 0.4 Tg CO2 Eq. (1 Gg). Emissions have increased by 455 percent from
1990 to 2009 as a result of an increase in the area of N fertilized pine plantations in the southeastern United States
and Douglas-fir timberland in western Washington and Oregon. Total forest soil N2O emissions are summarized in
Table 7-15.
Table 7-15: Direct N2O Fluxes from Soils in Forest Land Remaining Forest Land (Tg CO2 Eq. and Gg N2O)
Year Tg CO2 Eq. Gg
1990 0.1
2005
2006
2007
2008
2009
0.4
0.4
0.4
0.4
0.4
1.2
1.2
1.2
1.2
1.2
Note: These estimates include direct N2O emissions from N fertilizer additions only. Indirect N2O emissions from fertilizer
additions are reported in the Agriculture chapter. These estimates include emissions from both Forest Land Remaining Forest
Land and from Land Converted to Forest Land.
Methodology
The IPCC Tier 1 approach was used to estimate N2O from soils within Forest Land Remaining Forest Land.
According to U.S. Forest Service statistics for 1996 (USDA Forest Service 2001), approximately 75 percent of trees
planted were for timber, and about 60 percent of national total harvested forest area is in the southeastern United
States. Although southeastern pine plantations represent the majority of fertilized forests in the United States, this
Inventory also accounted for N fertilizer application to commercial Douglas-fir stands in western Oregon and
Washington. For the Southeast, estimates of direct N2O emissions from fertilizer applications to forests were based
on the area of pine plantations receiving fertilizer in the southeastern United States and estimated application rates
(Albaugh et al. 2007). Not accounting for fertilizer applied to non-pine plantations is justified because fertilization
is routine for pine forests but rare for hardwoods (Binkley et al. 1995). For each year, the area of pine receiving N
fertilizer was multiplied by the weighted average of the reported range of N fertilization rates (121 Ibs. N per acre).
Area data for pine plantations receiving fertilizer in the Southeast were not available for 2005, 2006, 2007 and 2008,
so data from 2004 were used for these years. For commercial forests in Oregon and Washington, only fertilizer
applied to Douglas-fir was accounted for, because the vast majority (~95 percent) of the total fertilizer applied to
forests in this region is applied to Douglas-fir (Briggs 2007). Estimates of total Douglas-fir area and the portion of
fertilized area were multiplied to obtain annual area estimates of fertilized Douglas-fir stands. The annual area
estimates were multiplied by the typical rate used in this region (200 Ibs. N per acre) to estimate total N applied
(Briggs 2007), and the total N applied to forests was multiplied by the IPCC (2006) default emission factor of 1
percent to estimate direct N2O emissions. The volatilization and leaching/runoff N fractions for forest land,
calculated according to the IPCC default factors of 10 percent and 30 percent, respectively, were included with the
indirect emissions in the Agricultural Soil Management source category (consistent with reporting guidance that all
indirect emissions are included in the Agricultural Soil Management source category).
Land Use, Land-Use Change, and Forestry 7-23
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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 level178 of uncertainty at ±50 percent, and area receiving fertilizer was
assigned a ±20 percent according to expert knowledge (Binkley 2004). IPCC (2006) provided estimates for the
uncertainty associated with direct N2O emission factor for synthetic N fertilizer application to soils. Quantitative
uncertainty of this source category was estimated through the IPCC-recommended Tier 2 uncertainty estimation
methodology. The uncertainty ranges around the 2005 activity data and emission factor input variables were
directly applied to the 2009 emissions estimates. The results of the quantitative uncertainty analysis are summarized
in Table 7-16. N2O fluxes from soils were estimated to be between 0.1 and 1.1 TgCO2Eq. at a 95 percent
confidence level. This indicates a range of 59 percent below and 211 percent above the 2009 emission estimate of
0.4 Tg CO2 Eq.
Table 7-16: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land Remaining Forest Land
(Tg CO2 Eq. and Percent)
2009 Emission Uncertainty Range Relative to Emission
Source Gas Estimate Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Forest Land Remaining Forest Land:
N2O Fluxes from Soils N2O 0.4 0.1 1.1 -59% +211%
Note: This estimate includes direct N2O emissions from N fertilizer additions to both Forest Land Remaining Forest Land and
Land Converted to Forest Land.
Planned Improvements
State-level area data will be acquired for southeastern pine plantations and northwestern Douglas-fir forests
receiving fertilizer to estimate soil N2O emission by state and provide information about regional variation in
emission patterns.
7.3. Land Converted to Forest Land (IPCC Source Category 5A2)
Land-use change is constantly occurring, and areas under a number of differing land-use types are converted to
forest each year, just as forest land is converted to other uses. However, the magnitude of these changes is not
currently known. Given the paucity of available land-use information relevant to this particular IPCC source
category, it is not possible to separate CO2 or N2O fluxes on Land Converted to Forest Land from fluxes on Forest
Land Remaining Forest Land at this time.
7.4. Cropland Remaining Cropland (IPCC Source Category 5B1)
Mineral and Organic Soil Carbon Stock Changes
Soils contain both organic and inorganic forms of C, but soil organic C (SOC) stocks are the main source and sink
for atmospheric CO2 in most soils. Changes in inorganic C stocks are typically minor. In addition, soil organic C is
178
Uncertainty is unknown for the fertilization rates so a conservative value of ±50% was used in the analysis.
7-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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the dominant organic C pool in cropland ecosystems, because biomass and dead organic matter have considerably
less C and those pools are relatively ephemeral. IPCC (2006) recommends reporting changes in soil organic C
stocks due to agricultural land-use and management activities on mineral and organic soils.179
Typical well-drained mineral soils contain from 1 to 6 percent organic C by weight, although mineral soils that are
saturated with water for substantial periods during the year may contain significantly more C (NRCS 1999).
Conversion of mineral soils from their native state to agricultural uses can cause as much as half of the SOC to be
decomposed and the C lost to the atmosphere. The rate and ultimate magnitude of C loss will depend on pre-
conversion conditions, conversion method and subsequent management practices, climate, and soil type. In the
tropics, 40 to 60 percent of the C loss generally occurs within the first 10 years following conversion; C stocks
continue to decline in subsequent decades but at a much slower rate. In temperate regions, C loss can continue for
several decades, reducing stocks by 20 to 40 percent of native C levels. Eventually, the soil can reach a new
equilibrium that reflects a balance between C inputs (e.g., decayed plant matter, roots, and organic amendments such
as manure and crop residues) and C loss through microbial decomposition of organic matter. However, land use,
management, and other conditions may change before the new equilibrium is reached. The quantity and quality of
organic matter inputs and their rate of decomposition are determined by the combined interaction of climate, soil
properties, and land use. Land use and agricultural practices such as clearing, drainage, tillage, planting, grazing,
crop residue management, fertilization, and flooding can modify both organic matter inputs and decomposition, and
thereby result in a net flux of C to or from the pool of soil C.
Organic soils, also referred to as histosols, include all soils with more than 12 to 20 percent organic C by weight,
depending on clay content (NRCS 1999, Brady and Weil 1999). The organic layer of these soils can be very deep
(i.e., several meters), forming under inundated conditions in which minimal decomposition of plant residue occurs.
When organic soils are prepared for crop production, they are drained and tilled, leading to aeration of the soil,
which accelerates the rate of decomposition and CO2 emissions. Because of the depth and richness of the organic
layers, C loss from drained organic soils can continue over long periods of time. The rate of CO2 emissions varies
depending on climate and composition (i.e., decomposability) of the organic matter. Also, the use of organic soils
for annual crop production leads to higher C loss rates than drainage of organic soils in grassland or forests, due to
deeper drainage and more intensive management practices in cropland (Armentano and Verhoeven 1990, as cited in
IPCC/UNEP/OECD/IEA 1997). Carbon losses are estimated from drained organic soils under both grassland and
cropland management in this Inventory.
Cropland Remaining Cropland includes all cropland in an inventory year that had been cropland for the last 20
years180 according to the USDA NRI land-use survey (USDA-NRCS 2000). The Inventory includes all privately-
owned croplands in the conterminous United States and Hawaii, but there is a minor amount of cropland on federal
lands that is not currently included in the estimation of C stock changes, leading to a discrepancy between the total
amount of managed area in Cropland Remaining Cropland (see Section 7.1) and the cropland area included in the
Inventory. It is important to note that plans are being made to include federal croplands in future C inventories.
The area of Cropland Remaining Cropland changes through time as land is converted to or from cropland
management. CO2 emissions and removals181 due to changes in mineral soil C stocks are estimated using a Tier 3
approach for the majority of annual crops. A Tier 2 IPCC method is used for the remaining crops (vegetables,
tobacco, perennial/horticultural crops, and rice) not included in the Tier 3 method. In addition, a Tier 2 method is
used for very gravelly, cobbly, or shaley soils (i.e., classified as soils that have greater than 35 percent of soil
volume comprised of gravel, cobbles, or shale) and for additional changes in mineral soil C stocks that were not
addressed with the Tier 3 approach (i.e., change in C stocks after 2003 due to Conservation Reserve Program
enrollment). Emissions from organic soils are estimated using a Tier 2 IPCC method.
Of the two sub-source categories, land-use and land management of mineral soils was the most important
component of total net C stock change between 1990 and 2009 (see Table 7-17 and Table 7-18). In 2009, mineral
soils were estimated to remove 45.1 Tg CO2 Eq. (12.3 Tg C). This rate of C storage in mineral soils represented
about a 20 percent decrease in the rate since the initial reporting year of 1990. Emissions from organic soils were
179 CO2 emissions associated with liming are also estimated but are included in a separate section of the report.
180 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.
181 Note that removals occur through crop and forage uptake of CO2 into biomass C that is later incorporated into soil pools.
Land Use, Land-Use Change, and Forestry 7-25
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27.7 Tg CO2 Eq. (7.5 Tg C) in 2009. In total, U.S. agricultural soils in Cropland Remaining Cropland removed
approximately 17.4 Tg CO2 Eq. (4.7 Tg C) in 2009.
Table 7-17: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg CO2 Eq.)
Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils (56.8) (57.9) I (45.9) (46.8) (47.3) (45.7) (45.1)
Organic Soils 27.4 27.7 27.7 27.7 27.7 27.7 27.7
Total Net Flux (29.4) (30.2) (18.3) (19.1) (19.7) (18.1) (17.4)
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
Table 7-18: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (Tg C)
Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils (15.5) I (15.8) (12.5) (12.8) (12.9) (12.5) (12.3)
Organic Soils 7.5 7.5 7.5 7.5 7.5 7.5 7.5
Total Net Flux (8.0) (8.2) (5.0) (5.2) (5.4) (4.9) (4.7)
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
The net reduction in soil C accumulation over the time series (39 percent from 1990 to 2009) was largely due to the
declining influence of annual cropland enrolled in the Conservation Reserve Program, which began in the late
1980s. However, there were still positive increases in C stocks from land enrolled in the reserve program, as well as
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 annual CO2 flux associated with C stock changes in mineral and organic soils is displayed
in Figure 7-5 and Figure 7-6. The highest rates of net C accumulation in mineral soils occurred in the Midwest,
which is the area with the largest amounts of cropland managed with conservation tillage. Rates were also high in
the Great Plains due to enrollment in the Conservation Reserve Program. Emission rates from drained organic soils
were highest along the southeastern coastal region, in the northeast central United States surrounding the Great
Lakes, and along the central and northern portions of the West Coast.
Figure 7-5: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Cropland Remaining Cropland
Figure 7-6: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Cropland Remaining Cropland
Methodology
The following section includes a description of the methodology used to estimate changes in soil C stocks due to: (1)
agricultural land-use and management activities on mineral soils; and (2) agricultural land-use and management
activities on organic soils for Cropland Remaining Cropland.
Soil C stock changes were estimated for Cropland Remaining Cropland (as well as agricultural land falling into the
IPCC categories Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland)
according to land-use histories recorded in the USDA National Resources Inventory (NRI) survey (USDA-NRCS
2000). The NRI is a statistically-based sample of all non-federal land, and includes approximately 260,000 points in
agricultural land for the conterminous United States and Hawaii.182 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
182
NRI points were classified as agricultural if under grassland or cropland management between 1990 and 2003.
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management information (e.g., crop type, soil attributes, and irrigation) were originally collected for each NRI point
on a 5-year cycle beginning in 1982. For cropland, data were collected for 4 out of 5 years in the cycle (i.e., 1979-
1982, 1984-1987, 1989-1992, and 1994-1997). However, the NRI program began collecting annual data in 1998,
and data are currently available through 2003. NRI points were classified as Cropland Remaining Cropland in a
given year between 1990 and 2009 if the land use had been cropland for 20 years. *83 Cropland includes all land
used to produce food and fiber, or forage that is harvested and used as feed (e.g., hay and silage).
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach was applied to estimate C stock changes for mineral soils used to produce a
majority of annual crops in the United States (Ogle et al. 2010). The remaining crops on mineral soils were
estimated using an IPCC Tier 2 method (Ogle et al. 2003), including vegetables, tobacco, perennial/horticultural
crops, rice, and crops 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 Century model, which is used for the Tier 3 method, has not been fully tested to address its adequacy for
estimating C stock changes associated with certain crops and rotations, as well as cobbly, gravelly, or shaley soils.
An additional stock change calculation was made for mineral soils using Tier 2 emission factors, accounting for
enrollment patterns in the Conservation Reserve Program after 2003, which was not addressed by the Tier 3
methods.
Further elaboration on the methodology and data used to estimate stock changes from mineral soils are described
below and in Annex 3.13.
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the Century biogeochemical model (Parton et al. 1987,
1988, 1994; Metherell et al. 1993), which simulates the dynamics of C and other elements in cropland, grassland,
forest, and savanna ecosystems. It uses monthly weather data as an input, along with information about soil physical
properties. Input data on land use and management are specified at monthly 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 computes 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 nutrient (N, K, S) elements. This method is more accurate than the Tier 1 and 2 approaches provided by the
IPCC, because the simulation model treats changes as continuous over time rather than the simplified discrete
changes represented in the default method (see Box 7-3 for additional information). National estimates were
obtained by simulating historical land-use and management patterns as recorded in the USDA National Resources
Inventory (NRI) survey.
[BEGIN BOX]
Box 7-3: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches
A Tier 3 model-based approach is used to inventory soil C stock changes on the majority of agricultural land with
mineral soils. This approach entails several fundamental differences compared to the IPCC Tier 1 or 2 methods,
which are based on a classification of land areas into a number of discrete classes based on a highly aggregated
classification of climate, soil, and management (i.e., only six climate regions, seven soil types and eleven
management systems occur in U.S. agricultural land under the IPCC classification). Input variables to the Tier 3
model, including climate, soils, and management activities (e.g., fertilization, crop species, tillage, etc.), are
represented in considerably more detail both temporally and spatially, and exhibit multi-dimensional interactions
through the more complex model structure compared with the IPCC Tier 1 or 2 approach. The spatial resolution of
183 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.
Land Use, Land-Use Change, and Forestry 7-27
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the analysis is also finer in the Tier 3 method compared to the lower tier methods as implemented in the United
States for previous Inventories (e.g., 3,037 counties versus 181 Major Land Resource Areas (MLRAs),
respectively).
In the Century model, soil C dynamics (and CO2 emissions and uptake) are treated as continuous variables, which
change on a monthly time step. Carbon emissions and removals are an outcome of plant production and
decomposition processes, which are simulated in the model structure. Thus, changes in soil C stocks are influenced
by not only changes in land use and management but also inter-annual climate variability and secondary feedbacks
between management activities, climate, and soils as they affect primary production and decomposition. This latter
characteristic constitutes one of the greatest differences between the methods, and forms the basis for a more
complete accounting of soil C stock changes in the Tier 3 approach compared with Tier 2 methodology.
Because the Tier 3 model simulates a continuous time period rather than the equilibrium step change used in the
IPCC methodology (Tier 1 and 2), the Tier 3 model addresses the delayed response of soils to management and
land-use changes. Delayed responses can occur due to variable weather patterns and other environmental
constraints that interact with land use and management and affect the time frame over which stock changes occur.
Moreover, the Tier 3 method also accounts for the overall effect of increasing yields and, hence, C input to soils that
have taken place across management systems and crop types within the United States. Productivity has increased by
1 to 2 percent annually over the past 4 to 5 decades for most major crops in the United States (Reilly and Fuglie
1998), which is believed to have led to increases in cropland soil C stocks (e.g., Allmaras et al. 2000). This is a
major difference from the IPCC-based Tier 1 and 2 approaches, in which trends in soil C stocks only capture
discrete changes in management and/or land use, rather than a longer term trend such as gradual increases in crop
productivity.
[END BOX]
Additional sources of activity data were used to supplement the land-use information from NRI. The Conservation
Technology Information Center (CTIC 1998) 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 USD A Economic Research
Service Cropping Practices Survey (ERS 1997) 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 USD A 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 3.13 for further details). Greater availability of managed
manure N relative to 1997 was, thus, assumed to increase the area amended with manure, while reduced availability
of manure N relative to 1997 was assumed to reduce the amended area. The amount of manure produced by each
livestock type was calculated for managed and unmanaged waste management systems based on methods described
in the Manure Management section (Section 6.2) and annex (Annex 3.10).
Manure amendments were an input to the Century Model based on manure N available for application from all
managed or unmanaged systems except Pasture/Range/Paddock.184 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
nitrous oxide emissions, volatilization of ammonia and NOX, runoff and leaching, and poultry manure used as a feed
supplement. More information on these losses is available in the description of the Manure Management source
category. For unmanaged systems, it is assumed that no N losses or additions occur prior to the application of
manure to the soil.
Monthly weather data were used as an input in the model simulations, based on an aggregation of gridded weather
data to the county scale from the Parameter-elevation Regressions on Independent Slopes Model (PRISM) database
184 Pasture/Range/Paddock manure additions to soils are addressed in the Grassland Remaining Grassland and Land Converted
to Grassland categories.
7-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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(Daly et al. 1994). Soil attributes, which were obtained from an NRI database, were assigned based on field visits
and soil series descriptions. Each NRI point was run 100 times as part of the uncertainty assessment, yielding a total
of over 18 million simulation runs for the analysis. Carbon stock estimates from Century were adjusted using a
structural uncertainty estimator accounting for uncertainty in model algorithms and parameter values (Ogle et al.
2007, 2010). C stocks and 95 percent confidence intervals were estimated for each year between 1990 and 2003, but
C stock changes from 2004 to 2009 were assumed to be similar to 2003 because no additional activity data are
currently available from the NRI for the latter years.
Tier 2 Approach
In the IPCC Tier 2 method, data on climate, soil types, land-use, and land management activity were used to classify
land area to apply appropriate stock change factors. MLRAs formed the base spatial unit for mapping climate
regions in the United States; each MLRA represents 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).
Reference C stocks were estimated using the National Soil Survey Characterization Database (NRCS 1997) with
cultivated cropland as the reference condition, rather than native vegetation as used in IPCC (2003, 2006).
Changing the reference condition was necessary because soil measurements under agricultural management are
much more common and easily identified in the National Soil Survey Characterization Database (NRCS 1997) than
those that are not considered cultivated cropland.
U.S.-specific stock change factors were derived from published literature to determine the impact of management
practices on SOC storage, including changes in tillage, cropping rotations and intensification, and land-use change
between cultivated and uncultivated conditions (Ogle et al. 2003, Ogle et al. 2006). U.S. factors associated with
organic matter amendments were not estimated because there were an insufficient number of studies to analyze
those impacts. Instead, factors from IPCC (2003) were used to estimate the effect of those activities. Euliss and
Gleason (2002) provided the data for computing the change in SOC storage resulting from restoration of wetland
enrolled in the Conservation Reserve Program.
Activity data were primarily based on the historical land-use/management patterns recorded in the NRI. Each NRI
point was classified by land use, soil type, climate region (using PRISM data, Daly et al. 1994) and management
condition. Classification of cropland area by tillage practice was based on data from the Conservation Tillage
Information Center (CTIC 1998, 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 previous methodology subsection on the
Tier 3 analysis for mineral soils.
Combining information from these data sources, SOC stocks for mineral soils were estimated 50,000 times for 1982,
1992, and 1997, using a Monte Carlo simulation approach and the 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). 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 2009 was determined by calculating the average annual change in stocks
between 1992 and 1997.
Additional Mineral C Stock Change
Annual C flux estimates for mineral soils between 1990 and 2009 were adjusted to account for additional C stock
changes associated with gains or losses in soil C after 2003 due to changes in Conservation Reserve Program
enrollment. The change in enrollment acreage relative to 2003 was based on data from USDA-FSA (2009) for 2004
through 2009, and 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 estimations using the IPCC method (see
Annex 3.13 for further discussion).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Cropland Remaining Cropland were estimated using the Tier 2
method provided in IPCC (2003, 2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC
Land Use, Land-Use Change, and Forestry 7-29
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rates. The final estimates included a measure of uncertainty as determined from the Monte Carlo simulation with
50,000 iterations. Emissions were based on the 1992 and 1997 Cropland Remaining Cropland areas from the 1997
National Resources Inventory (USDA-NRCS 2000). The annual flux estimated for 1992 was applied to 1990
through 1992, and the annual flux estimated for 1997 was applied to 1993 through 2009.
Uncertainty and Time-Series Consistency
Uncertainty associated with the Cropland Remaining Cropland land-use category was addressed for changes in
agricultural soil C stocks (including both mineral and organic soils). Uncertainty estimates are presented in Table
7-19 for 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 3.13 for further discussion). A combined
uncertainty estimate for changes in soil C stocks is also included. Uncertainty estimates from each component 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.
More details on how the individual uncertainties were developed are in Annex 3.13. The combined uncertainty for
soil C stocks in Cropland Remaining Cropland ranged from 172 percent below to 167 percent above the 2009 stock
change estimate of -17.4 Tg CO2 Eq.
Table 7-19: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Cropland
Remaining Cropland (Tg CO2 Eq. and Percent)
Source
Mineral Soil C Stocks: Cropland Remaining
Cropland, Tier 3 Inventory Methodology
Mineral Soil C Stocks: Cropland Remaining
Cropland, Tier 2 Inventory Methodology
Mineral Soil C Stocks: Cropland Remaining
Cropland (Change in CRP enrollment relative to
2003)
Organic Soil C Stocks: Cropland Remaining
Cropland, Tier 2 Inventory Methodology
2009 Flux
Estimate
(TgC02Eq.)
(42.3)
(3.0)
(0.3)
27.7
Uncertainty Range Relative to Flux
Estimate
(Tg C02 Eq.) (%)
Lower
Bound
(69.6)
(6.9)
(0.1)
15.8
Upper
Bound
(15.1)
0.8
(0.4)
36.9
Lower
Bound
-64%
-127%
-50%
-43%
Upper
Bound
+64%
+128%
+50%
+33%
Combined Uncertainty for Flux associated with
Agricultural Soil Carbon Stock Change in
Cropland Remaining Cropland
(17.4)
(47.3)
11.6
-172% +167%
Note: Parentheses indicate net sequestration. 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 2009. 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. As discussed in the uncertainty section, results were compared to field
measurements, and a statistical relationship was developed to assess uncertainties in the model's predictive
capability. The comparisons included over 40 long-term experiments, representing about 800 combinations of
management treatments across all of the sites (Ogle et al. 2007). Inventory reporting forms and text were reviewed
and revised as needed to correct transcription errors.
Planned Improvements
The first improvement is to update the Tier 2 inventory analysis with the latest annual National Resources Inventory
(NRI) data. While the land base for the Tier 3 approach uses the latest available data from the NRI, the Tier 2
portion of the Inventory has not updated and is based on the Revised 1997 NRI data product (USDA-NRCS 2000).
7-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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This improvement will extend the time series of the land use data from 1997 through 2003 for the Tier 2 portion of
the Inventory.
The second improvement is to incorporate remote sensing in the analysis for estimation of crop and forage
production, and conduct the Tier 3 assessment of soil C stock changes and soil nitrous oxide emissions in a single
analysis. Specifically, the Enhanced Vegetation Index (EVI) product that is derived from MODIS satellite imagery
is being used to refine the production estimation for the Tier 3 assessment framework based on the DAYCENT
simulation model. EVI reflects changes in plant "greenness" over the growing season and can be used to compute
production based on the light use efficiency of the crop or forage (Potter etal. 1993). In the current framework,
production is simulated based on the weather data, soil characteristics, and the genetic potential of the crop. While
this method produces reasonable results, remote sensing can be used to refine the productivity estimates and reduce
biases in crop production and subsequent C input to soil systems. It is anticipated that precision in the Tier 3
assessment framework will be increased by 25 percent or more with the new method. In addition, DAYCENT is
currently used for estimating soil nitrous oxide emissions in the Inventory, and can also be used to estimate soil
organic C stock changes using the same algorithms in the CENTURY model. Simulating both soil C stock changes
and nitrous oxide emissions in a single analysis will ensure consistency in the treatment of these sources, which are
coupled through the N and C cycles in agricultural systems.
C02 Emissions from Agricultural Liming
IPCC (2006) recommends reporting CO2 emissions from lime additions (in the form of crushed limestone (CaCO3)
and dolomite (CaMg(CO3)2) to agricultural soils. Limestone and dolomite are added by land managers to ameliorate
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,
climate regime, and the type of mineral applied. Emissions from liming have fluctuated over the past nineteen
years, ranging from 3.8 Tg CO2 Eq. to 5.0 Tg CO2 Eq. In 2009, liming of agricultural soils in the United States
resulted in emissions of 4.2 Tg CO2 Eq. (1.2 Tg C), representing about a 10 percent decrease in emissions since
1990 (see Table 7-20 and Table 7-21). The trend is driven entirely by the amount of lime and dolomite estimated to
have been applied to soils over the time period.
Table 7-20: Emissions from Liming of Agricultural Soils (Tg CO2 Eq.)
Source 1990 2000 2005 2006 2007 2008 2009
Liming of Soils1 4/7 4.3 4.3 4.2 4.5 5.0 4.2
Note: Shaded areas indicate values based on a combination of data and projections. All other values are based on data only.
1 Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to
Grassland, and Settlements Remaining Settlements.
Table 7-21: Emissions from Liming of Agricultural Soils (Tg C)
Source 1990 2000 2005 2006 2007 2008 2009
Liming of Soils1 L3 L2 1.2 1.2 1.2 1.4 1.2
Note: Shaded areas indicate values based on a combination of data and projections. All other values are based on data only.
1 Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to
Grassland, and Settlements Remaining Settlements.
Methodology
CO2 emissions from degradation of limestone and dolomite applied to agricultural soils were estimated using a Tier
2 methodology consistent with IPCC (2006). The annual amounts of limestone and dolomite applied (see Table
7-22) 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 River basin tends to occur under similar soil and rainfall regimes, and, thus, the
emission factor is appropriate for use across the United States (West 2008). The annual application rates of
limestone and dolomite were derived from estimates and industry statistics provided in the Minerals Yearbook and
Mineral Industry Surveys (Tepordei 1993 through 2006; Willett 2007a, b, 2009 through 2010; USGS 2008 through
Land Use, Land-Use Change, and Forestry 7-31
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2010). 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).
The "unspecified" and "estimated" amounts of crushed limestone and dolomite applied to agricultural soils were
calculated by multiplying the percentage of total "specified" limestone and dolomite production applied to
agricultural soils by the total amounts of "unspecified" and "estimated" limestone and dolomite production. In other
words, the proportion of total "unspecified" and "estimated" crushed limestone and dolomite that was applied to
agricultural soils (as opposed to other uses of the stone) was assumed to be proportionate to the amount of
"specified" crushed limestone and dolomite that was applied to agricultural soils. In addition, data were not
available for 1990, 1992, and 2009 on the fractions of total crushed stone production that were limestone and
dolomite, and on the fractions of limestone and dolomite production that were applied to soils. To estimate the 1990
and 1992 data, a set of average fractions were calculated using the 1991 and 1993 data. These average fractions
were applied to the quantity of "total crushed stone produced or used" reported for 1990 and 1992 in the 1994
Minerals Yearbook (Tepordei 1996). To estimate 2009 data, the previous year's fractions were applied to a 2009
estimate of total crushed stone presented in the USGS Mineral Industry Surveys: Crushed Stone and Sand and
Gravel in the First Quarter of 2010 (USGS 2010); thus, the 2009 data in Table 7-20 through Table 7-22 are shaded
to indicate that they are based on a combination of data and projections.
The primary source for limestone and dolomite activity data is the Minerals Yearbook, published by the Bureau of
Mines through 1994 and by the USGS from 1995 to the present. In 1994, the "Crushed Stone" chapter in the
Minerals Yearbook began rounding (to the nearest thousand metric tons) quantities for total crushed stone produced
or used. It then reported revised (rounded) quantities for each of the years from 1990 to 1993. In order to minimize
the inconsistencies in the activity data, these revised production numbers have been used in all of the subsequent
calculations. Since limestone and dolomite activity data are also available at the state level, the national-level
estimates reported here were broken out by state, although state-level estimates are not reported here.
Table 7-22: Applied Minerals (Million Metric Tons)
Mineral
Limestone
Dolomite
1990
19.01 1
2.36
2000
15.86
3.81 H
1 2005
18.09
| 1.85
2006
16.54
2.73
2007
17.46
2.92
2008
20.55 1
2.54
2009
17.20
2.13
Note: These numbers represent amounts applied to Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, and Settlements Remaining Settlements. Shaded areas indicate values
based on a combination of data and projections. All other values are based on data only.
Uncertainty and Time-Series Consistency
Uncertainty regarding limestone and dolomite activity data inputs was estimated at ±15 percent and assumed to be
uniformly distributed around the inventory estimate (Tepordei 2003b). 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 CO2 emissions (West 2005). The uncertainties associated with the
fraction of agricultural lime dissolved by nitric acid and the portion of bicarbonate that leaches through the soil were
each modeled as a smoothed triangular distribution between ranges of zero percent to 100 percent. The uncertainty
surrounding these two components largely drives the overall uncertainty estimates reported below. More
information on the uncertainty estimates for Liming of Agricultural Soils is contained within the Uncertainty Annex.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty of CO2 emissions from liming.
The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-23. CO2 emissions from
Liming of Agricultural Soils in 2008 were estimated to be between 0.1 and 8.4 Tg CO2 Eq. at the 95 percent
confidence level. This indicates a range of 97 percent below to 99 percent above the 2009 emission estimate of 4.2
Tg C02 Eq.
7-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table 7-23: Tier 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming of Agricultural Soils (Tg
CO2 Eq. and Percent)
2009 Emission
Source Estimate
Gas (Tg CO2 Eq.)
Uncertainty Range Relative to Emissions
Estimate3
(Tg CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Liming of Agricultural Soils1 CO2 4.2
0.1 8.4 -97% +99%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
1 Also includes emissions from liming on Land Converted to Cropland, Grassland Remaining Grassland, Land Converted to
Grassland, and Settlements Remaining Settlements.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC
analysis did not reveal any inaccuracies or incorrect input values.
Recalculations Discussion
Several adjustments were made in the current Inventory to improve the results. The quantity of applied minerals
reported in the previous Inventory for 2007 has been revised; the updated activity data for 2007 are approximately
1,480 thousand metric tons greater than the data used for the previous Inventory, consequently, the reported
emissions resulting from liming in 2007 increased by about 8.4 percent. In the previous Inventory, to estimate 2008
data, the previous year's fractions were applied to a 2008 estimate of total crushed stone presented in the USGS
Mineral Industry Surveys: Crushed Stone and Sand and Gravel in the First Quarter of 2009 (USGS 2009). 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 2008. These values have replaced those used in the previous Inventory
to calculate the quantity of minerals applied to soil and the emissions from liming. The updated activity data for
2008 are approximately 5,460 thousand metric tons greater than the data used in the previous Inventory. As a result,
the reported emissions from liming in 2008 increased by about 36 percent.
C02 Emissions from Urea Fertilization
The use of urea (CO(NH2)2) as fertilizer leads to emissions of CO2 that was fixed during the industrial production
process. Urea in the presence of water and urease enzymes is converted into ammonium (NH4+), hydroxyl ion
(OH"), and bicarbonate (HCO3~). The bicarbonate then evolves into CO2 and water. Emissions from urea
fertilization in the United States totaled 3.6 Tg CO2 Eq. (1.0 Tg C) in 2009 (Table 7-24 and Table 7-25). Emissions
from urea fertilization have grown 49 percent between 1990 and 2009, due to an increase in the use of urea as
fertilizer.
Table 7-24: CO2 Emissions from Urea Fertilization in Cropland Remaining, Cropland (Tg CO2 Eq.)
Source 1990 2000 2005 2006 2007 2008 2009
Urea Fertilization1 2.4 3.2 3.5 3.7 3.7 3.6 3.6
Note: Shaded areas indicate values based on a combination of data and projections. All other values are based on data only.
1 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.
Table 7-25: CO2 Emissions from Urea Fertilization in Cropland Remaining Cropland (Tg C)
Source 1990 2000 2005 2006 2007 2008 2009
Urea Fertilization1 (XT 0.9 1.0 1.0 1.0 1.0 1.0
Note: Shaded areas indicate values based on a combination of data and projections. All other values are based on data only.
1 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.
Land Use, Land-Use Change, and Forestry 7-33
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Methodology
Carbon dioxide emissions from the application of urea to agricultural soils were estimated using the IPCC (2006)
Tier 1 methodology. The annual amounts of urea fertilizer applied (see Table 7-26) were derived from state-level
fertilizer sales data provided in Commercial Fertilizers (TVA 1991, 1992, 1993, 1994; AAPFCO 1995 through
2010) and were multiplied by the default IPCC (2006) emission factor of 0.20, which is equal to the C content of
urea on an atomic weight basis. Because fertilizer sales data are reported in fertilizer years (July through June), a
calculation was performed to convert the data to calendar years (January through December). According to historic
monthly fertilizer use data (TVA 1992b), 65 percent of total fertilizer used in any fertilizer year is applied between
January and June of that calendar year, and 3 5 percent of total fertilizer used in any fertilizer year is applied between
July and December of the previous calendar year. Fertilizer sales data for the 2009 fertilizer year were not available
in time for publication. Accordingly, urea application in the 2009 fertilizer year was assumed to be equal to that of
the 2008 fertilizer year. Since 2010 fertilizer year data were not available, July through December 2009 fertilizer
consumption was assumed to be equal to July through December 2008 fertilizer consumption; thus, the 2009 data in
Table 7-24 through Table 7-26 are shaded to indicate that they are based on a combination of data and projections.
State-level estimates of CO2 emissions from the application of urea to agricultural soils were summed to estimate
total emissions for the entire United States.
Table 7-26: Applied Urea (Million Metric Tons)
1990 2000 2005 2006 2007 2008 2009
Urea Fertilizer1 3.30 4.38 4.78 4.98 5.10 4.92 4.92
Note: Shaded areas indicate values based on a combination of data and projections. All other values are based on data only.
'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.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 7-27 for Urea Fertilization. A Tier 2 Monte Carlo analysis was
completed. The largest source of uncertainty was the default emission factor, which assumes that 100 percent of the
C 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, thus, likely to be high. In addition, each
urea consumption data point has an associated uncertainty. Urea for non-fertilizer use, such as aircraft deicing, may
be included in consumption totals; it was determined through personal communication with Fertilizer Regulatory
Program Coordinator David L. Terry (2007), however, that this amount is most likely very small. Research into
aircraft deicing practices also confirmed that urea is used minimally in the industry; a 1992 survey found a known
annual usage of approximately 2,000 tons of urea for deicing; this would constitute 0.06 percent of the 1992
consumption of urea (EPA 2000). Similarly, surveys conducted from 2002 to 2005 indicate that total urea use for
deicing at U.S. airports is estimated to be 3,740 MT peryear, or less than 0.07 percent of the fertilizer total for 2007
(Itle 2009). Lastly, there is uncertainty surrounding the assumptions behind the calculation that converts fertilizer
years to calendar years. CO2 emissions from urea fertilization of agricultural soils in 2009 were estimated to be
between 2.1 and 3.7 Tg CO2 Eq. at the 95 percent confidence level. This indicates a range of 43 percent below to 3
percent above the 2009 emission estimate of 3.6 Tg CO2 Eq.
Table 7-27: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization (Tg CO2 Eq. and Percent)
2009 Emission
Estimate Uncertainty Range Relative to Emissions Estimate"
Source Gas (Tg CO2 Eq.) (Tg CO2 Eq.) (%)
Urea Fertilization
CO2 3.6
Lower Upper Lower
Bound Bound Bound
2.1 3.7 -43%
Upper
Bound
+3%
aRange of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: These numbers represent amounts applied to all agricultural land, including Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, Settlements Remaining Settlements, and Forest Land Remaining Forest
Land.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. 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: 1990-2009
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QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. Inventory reporting
forms and text were reviewed. No errors were found.
Recalculations Discussion
July to December 2007 urea application data were updated with assumptions for fertilizer year 2008, and the 2007
emission estimate was revised accordingly. The activity data decreased about 800,000 metric tons for 2007 and this
change resulted in an approximately 3 percent decrease in emissions in 2007 relative to the previous Inventory. In
the previous Inventory, the application for this period was calculated based on application during July to December
2006. January to June 2008 data were also used to update 2008 emission estimates. The activity data decreased
about 270,000 metric tons for 2008, resulting in an approximately 5 percent decrease in emissions in 2008 relative to
the previous Inventory.
Planned Improvements
The primary planned improvement is to investigate using a Tier 2 or Tier 3 approach, which would utilize country-
specific information to estimate a more precise emission factor.
7.5. Land Converted to Cropland (IPCC Source Category 5B2)
Land Converted to Cropland includes all cropland in an inventory year that had been another land use at any point
during the previous 20 years185 according to the USDA NRI land-use survey (USDA-NRCS 2000). Consequently,
lands are retained in this category for 20 years as recommended by the IPCC guidelines (IPCC 2006) unless there is
another land-use change. The Inventory includes all privately-owned croplands in the conterminous United States
and Hawaii, but there is a minor amount of cropland on federal lands that is not currently included in the estimation
of C stock changes, leading to a discrepancy between the total amount of managed area in Land Converted to
Cropland (see Section 7.1) and the cropland area included in the Inventory. It is important to note that plans are
being made to include these areas in future C inventories.
Background on agricultural C stock changes is provided in Cropland Remaining Cropland and will only be
summarized here for Land Converted to Cropland. Soils are the largest pool of C in agricultural land, and also have
the greatest potential for storage or release of C, because biomass and dead organic matter C pools are relatively
small and ephemeral compared with soils. The IPCC (2006) recommends reporting changes in soil organic C stocks
due to: (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and
management activities on organic soils.186
Land-use and management of mineral soils in Land Converted to Cropland generally led to relatively small
increases in soil C during the 1990s but the pattern changed to small losses of C through the latter part of the time
series (Table 7-28 and Table 7-29). The total rate of change in soil C stocks was 5.9 Tg CO2 Eq. (1.6 Tg C) in 2009.
Mineral soils were estimated to lose 3.3 Tg CO2 Eq. (0.9 Tg C) in 2009, while drainage and cultivation of organic
soils led to annual losses of 2.6 Tg CO2 Eq. (0.7 Tg C) in 2009.
Table 7-28: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (Tg CO2 Eq.)
Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils (0.3) (0.3) 3.3 3.3 3.3 3.3 3.3
Organic Soils 2A 2.6 2.6 2.6 2.6 2.6 2.6
Total Net Flux 2.2 2.4 5.9 5.9 5.9 5.9 5.9
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
Table 7-29: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (Tg C)
185 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.
186 CO2 emissions associated with liming are also estimated but included in a separate section of the report.
Land Use, Land-Use Change, and Forestry 7-35
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Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils (0.1) (0.1) 0.9 0.9 0.9 0.9 0.9
Organic Soils 0/7 0/7 0.7 0.7 0.7 0.7 0.7
Total Net Flux 0.6 0.6 1.6 1.6 1.6 1.6 1.6
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
The spatial variability in annual CO2 flux associated with C stock changes in mineral and organic soils for Land
Converted to Cropland is displayed in Figure 7-7 and Figure 7-8. While a large portion of the United States had net
losses of soil C for Land Converted to Cropland, there were some notable areas with net C accumulation in the
Great Plains, Midwest, mid-Atlantic states. These areas were gaining C following conversion, because the land had
been brought into hay production, including grass and legume hay, leading to enhanced plant production relative to
the previous land use, and thus higher C input to the soil. Emissions from organic soils were largest in California,
Florida, and the upper Midwest, which coincided with largest concentrations of cultivated organic soils in the United
States.
Figure 7-7: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Land Converted to Cropland
Figure 7-8: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009, Land
Converted to Cropland
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C stocks due
to agricultural land-use and management activities on mineral and organic soils for Land Converted to Cropland.
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 3.13.
Soil C stock changes were estimated for Land Converted to Cropland according to land-use histories recorded in the
USDA NRI survey (USDA-NRCS 2000). 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.
However, the NRI program initiated annual data collection in 1998, and the annual data are currently available
through 2003. NRI points were classified as Land Converted to Cropland in a given year between 1990 and 2009 if
the land use was cropland but had been another use during the previous 20 years. Cropland includes all land used to
produce food or fiber, or forage that is harvested and used as feed (e.g., hay and silage).
Mineral Soil Carbon Stock Changes
A Tier 3 model-based approach was applied to estimate C stock changes for soils on Land Converted to Cropland
used to produce a majority of all crops (Ogle et al. 2010). 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 vegetable, tobacco,
perennial/horticultural crops, and rice; land on very gravelly, cobbly, or shaley soils (greater than 35 percent by
volume); and land converted from forest or federal ownership.187
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the Century biogeochemical model for the Tier 3
187 Federal 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 2000).
7-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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methods. National estimates were obtained by using the model to simulate historical land-use change patterns as
recorded in the USDA National Resources Inventory (USDA-NRCS 2000). The methods used for Land Converted
to Cropland are the same as those described in the Tier 3 portion of Cropland Remaining Cropland section for
mineral soils (see Cropland Remaining Cropland Tier 3 methods section and Annex 3.13 for additional
information).
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 Cropland Remaining Cropland section for
mineral soils (see Cropland Remaining Cropland Tier 2 methods section for additional information).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Cropland were estimated using the Tier 2
method provided in IPCC (2003, 2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC
rates. The final estimates included a measure of uncertainty as determined from the Monte Carlo simulation with
50,000 iterations. Emissions were based on the 1992 and 1997 Land Converted to Cropland areas from the 1997
National Resources Inventory (USDA-NRCS 2000). The annual flux estimated for 1992 was applied to 1990
through 1992, and the annual flux estimated for 1997 was applied to 1993 through 2009.
Uncertainty and Time-Series Consistency
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 approaches were based on the same
method described for Cropland Remaining Cropland, except that the uncertainty inherent in the structure of the
Century model was not addressed. The uncertainty for annual C emission estimates from drained organic soils in
Land Converted to Cropland was estimated using the Tier 2 approach, as described in the Cropland Remaining
Cropland section.
Uncertainty estimates are presented in Table 7-30 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 3.13 for further discussion). A combined uncertainty estimate for changes in agricultural soil C stocks is also
included. Uncertainty estimates from each component 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 was estimated
to be 40 percent below and 36 percent above the inventory estimate of 5.9 Tg CO2 Eq.
Table 7-30: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Cropland (Tg CO2 Eq. and Percent)
Source
2009 Flux Uncertainty Range Relative to Flux
Estimate Estimate
(Tg C02 Eq.) (Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Mineral Soil C Stocks: Land Converted to
Cropland, Tier 3 Inventory Methodology
Mineral Soil C Stocks: Land Converted to
Cropland, Tier 2 Inventory Methodology
Organic Soil C Stocks: Land Converted to
Cropland, Tier 2 Inventory Methodology
(0.8)
4.1
2.6
(1.5)
2.3
1.2
(0.1)
5.8
3.7
-84%
-44%
-53%
+84%
+41%
+41%
Combined Uncertainty for Flux associated
with Soil Carbon Stock Change in Land
Converted to Cropland
5.9
3.5
8.1
-40%
+36%
Note: Parentheses indicate net sequestration. 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
Land Use, Land-Use Change, and Forestry 7-37
-------
above.
QA/QC and Verification
See QA/QC and Verification section under Cropland Remaining Cropland.
Planned Improvements
The empirically-based uncertainty estimator described in the Cropland Remaining Cropland section for the Tier 3
approach has not been developed to estimate uncertainties related to the structure of the Century model for Land
Converted to Cropland, but this is a planned improvement. This improvement will produce a more rigorous
assessment of uncertainty. See Planned Improvements section under Cropland Remaining Cropland^ additional
planned improvements.
7.6. Grassland Remaining Grassland (IPCC Source Category 5C1)
Grassland Remaining Grassland includes all grassland in an inventory year that had been grassland for the previous
20 years188 according to the USDA NRI land use survey (USDA-NRCS 2000). The Inventory includes all
privately-owned grasslands in the conterminous United States and Hawaii, but does not address changes in C stocks
for grasslands on federal lands, leading to a discrepancy between the total amount of managed area in Grassland
Remaining Grassland (see Section 7.1) and the grassland area included in the Inventory. While federal grasslands
probably have minimal changes in land management and C stocks, plans are being made to further evaluate and
potentially include these areas in future C inventories.
Background on agricultural C stock changes is provided in the Cropland Remaining Cropland section and will only
be summarized here for Grassland Remaining Grassland. Soils are the largest pool of C in agricultural land, and
also have the greatest potential for storage or release of C, because biomass and dead organic matter C pools are
relatively small and ephemeral compared to soils. IPCC (2006) recommends reporting changes in soil organic C
stocks due to: (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and
management activities on organic soils.189
Land-use and management of mineral soils in Grassland Remaining Grassland increased soil C, while organic soils
lost relatively small amounts of C in each year 1990 through 2009. Due to the pattern for mineral soils, the overall
trend was a gain in soil C over the time series although the rates varied from year to year, with a net removal of 8.3
Tg CO2 Eq. (2.3 Tg C) in 2009. There was considerable variation over the time series driven by variability in
weather patterns and associated interaction with land management activity. The change rates on per hectare basis
were small, however, even in the years with larger total changes in stocks. Overall, flux rates declined by 43.8 Tg
CO2 Eq. (12.0 Tg C) when comparing the net change in soil C from 1990 and 2009.
Table 7-31: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg CO2 Eq.)
Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils (56.0) (56.3) (12.6) (12.4) (12.3) (12.2) (12.0)
Organic Soils 3.9 3/7 3.7 3.7 3.7 3.7 3.7
Total Net Flux (52.2) (52.6) (8.9) (8.8) (8.6) (8.5) (8.3)
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
Table 7-32: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (Tg C)
Soil Type
Mineral Soils
Organic Soils
Total Net Flux
1990
(15.3)
1.1
(14.2)
1 2000
(15.3)
1 1.0
(14.3)
2005
(3.4)
1.0
(2.4)
2006
(3.4)
1.0
(2.4)
2007
(3.4)
1.0
(2.3)
2008
(3.3)
1.0
(2.3)
2009
(3.3)
1.0
(2.3)
188 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.
189 CO2 emissions associated with liming are also estimated but included in a separate section of the report.
7-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
The spatial variability in annual CO2 flux associated with C stock changes in mineral and organic soils is displayed
in Figure 7-9 and Figure 7-10. Grassland gained soil organic C in several regions during 2009, including the
Northeast, Midwest, Southwest and far western states; although these were relatively small increases in C on a per-
hectare basis. Emission rates from drained organic soils were highest along the southeastern coastal region, in the
northeast central United States surrounding the Great Lakes, and along the central and northern portions of the West
Coast.
Figure 7-9: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
Grassland Remaining Grassland
Figure 7-10: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Grassland Remaining Grassland
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C stocks due
to agricultural land-use and management activities on mineral and organic soils for Grassland Remaining
Grassland. Further elaboration on the methodologies and data used to estimate stock changes from mineral and
organic soils are provided in the Cropland Remaining Cropland section and Annex 3.13.
Soil C stock changes were estimated for Grassland Remaining Grassland according to land-use histories recorded in
the USDA NRI survey (USDA-NRCS 2000). 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.
However, the NRI program initiated annual data collection in 1998, and the annual data are currently available
through 2003. NRI points were classified as Grassland Remaining Grassland in a given year between 1990 and
2009 if the land use had been grassland for 20 years. Grassland includes pasture and rangeland used for grass forage
production, where the primary use is livestock grazing. Rangelands are typically extensive areas of native grassland
that are not intensively managed, while pastures are often seeded grassland, possibly following tree removal, that
may or may not be improved with practices such as irrigation and interseeding legumes.
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach 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 soil organic C stocks and stock changes for Grassland Remaining Grassland were estimated using the
Century biogeochemical model, as described in Cropland Remaining Cropland. Historical land-use and
management patterns were used in the Century simulations as recorded in the USDA National Resources Inventory
(NRI) survey, with supplemental information on fertilizer use and rates from the USDA Economic Research Service
Cropping Practices Survey (ERS 1997) 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 Annex
3.13 for further details). Greater availability of managed manure N relative to 1997 was, thus, assumed to increase
the area amended with manure, while reduced availability of manure N relative to 1997 was assumed to reduce the
Land Use, Land-Use Change, and Forestry 7-39
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amended area.
The amount of manure produced by each livestock type was calculated for managed and unmanaged waste
management systems based on methods described in the Manure Management Section (Section 6.2) and Annex
(Annex 3.10). In contrast to manure amendments, Pasture/Range/Paddock (PRP) manure N deposition was
estimated internally in the Century model, as part of the grassland system simulations (i.e., PRP manure deposition
was not an external input into the model). See the Tier 3 methods in Cropland Remaining Cropland section for
additional discussion on the Tier 3 methodology for mineral soils.
Tier 2 Approach
The Tier 2 approach is based on the same methods described in the Tier 2 portion of Cropland Remaining Cropland
section for mineral soils (see Cropland Remaining Cropland Tier 2 methods section and Annex 3.13 for additional
information).
Additional Mineral C Stock Change Calculations
Annual C flux estimates for mineral soils between 1990 and 2009 were adjusted to account for additional C stock
changes associated with sewage sludge amendments using a Tier 2 method. Estimates of the amounts of sewage
sludge N applied to agricultural land were derived from national data on sewage sludge generation, disposition, and
N content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from an
EPA report (EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey
(NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years. N application
rates from Kellogg et al. (2000) were used to determine the amount of area receiving sludge amendments. Although
sewage sludge can be added to land managed for other land uses, it was assumed that agricultural amendments occur
in grassland. Cropland is assumed to rarely be amended with sewage sludge due to the high metal content and other
pollutants in human waste. The soil C storage rate was estimated at 0.38 metric tons C per hectare per year for
sewage sludge amendments to grassland. The stock change rate is based on country-specific factors and the IPCC
default method (see Annex 3.13 for further discussion).
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Grassland Remaining Grassland were estimated using the Tier 2
method provided in IPCC (2003, 2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than
default IPCC rates. Emissions were based on the 1992 and 1997 Grassland Remaining Grassland areas from the
1997 National Resources Inventory (USDA-NRCS 2000). The annual flux estimated for 1992 was applied to 1990
through 1992, and the annual flux estimated for 1997 was applied to 1993 through 2009.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 7-33 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 3.13 for further discussion). A combined uncertainty estimate for changes in agricultural soil C stocks is also
included. Uncertainty estimates from each component 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 was
estimated to be 32 percent below and 25 percent above the inventory estimate of -8.3 Tg CO2 Eq.
Table 7-33: Tier 2 Quantitative Uncertainty Estimates for C Stock Changes occurring within Grassland Remaining
Grassland (Tg CO2 Eq. and Percent)
2009 Flux Uncertainty Range Relative to Flux
Estimate Estimate
Source (Tg CO2 Eq.) (Tg CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Mineral Soil C Stocks Grassland Remaining
Grassland, Tier 3 Methodology
(10.6)
(11.4)
(9.8)
-7%
+7%
7-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology (0.2)
Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology (Change in Soil
C due to Sewage Sludge Amendments) (1.2)
Organic Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology 3.7
(0.3)
0.0
-89% +127%
(1.9) (0.6) -50% +50%
1.2 5.5 -66% +49%
Combined Uncertainty for Flux Associated
with Agricultural Soil Carbon Stock Change
in Grassland Remaining Grassland
(8.3)
(11.0) (6.3) -32% +25%
Note: Parentheses indicate net sequestration. 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Uncertainties in Mineral Soil Carbon Stock Changes
The uncertainty analysis for Grassland Remaining Grassland using the Tier 3 approach and Tier 2 approach were
based on the same method described for Cropland Remaining Cropland, except that the uncertainty inherent in the
structure of the Century model was not addressed. See the Tier 3 approach for mineral soils under the Cropland
Remaining Cropland section for additional discussion.
A ±50 percent uncertainty was assumed for additional adjustments to the soil C stocks between 1990 and 2009 to
account for additional C stock changes associated with amending grassland soils with sewage sludge.
Uncertainties in Soil Carbon Stock Changes for Organic Soils
Uncertainty in C emissions from organic soils was estimated using country-specific factors and a Monte Carlo
analysis. Probability distribution functions for emission factors were derived from a synthesis of 10 studies, and
combined with uncertainties in the NRI land use and management data for organic soils in the Monte Carlo analysis.
See the Tier 2 section under minerals soils of Cropland Remaining Cropland for additional discussion.
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. A minor error was found in the post-processing results to compute the final
totals, which was corrected. No additional errors were found.
Recalculations Discussion
There were minor changes in the estimated area of grasslands associated with reconciling the forestland areas from
the Forest Inventory and Analysis (FIA) survey with the data from the National Resources Inventory (NRI) (see
section 7.1 for more information. The revised areas led to small changes in the soil C stock changes for Grassland
Remaining Grassland.
Planned Improvements
The main planned improvement for the next Inventory is to integrate the assessments of soil C stock changes and
soil N2O emissions into a single analysis. This improvement will ensure that the N and C cycles are treated
consistently in the Inventory, which is important because the cycles of these elements are linked through plant and
soil processes in agricultural lands. This improvement will include the development of an empirically-based
uncertainty analysis, which will provide a more rigorous assessment of uncertainty. See Planned Improvements
section under Cropland Remaining Cropland for additional planned improvements.
Land Use, Land-Use Change, and Forestry 7-41
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7.7. Land Converted to Grassland (IPCC Source Category 5C2)
Land Converted to Grassland includes all grassland in an inventory year that had been in another land use at any
point during the previous 20 years190 according to the USDA NRI land-use survey (USDA-NRCS 2000).
Consequently, lands are retained in this category for 20 years as recommended by IPCC (2006) unless there is
another land use change. The Inventory includes all privately-owned grasslands in the conterminous United States
and Hawaii, but does not address changes in C stocks for grasslands on federal lands, leading to a discrepancy
between the total amount of managed area for Land Converted to Grassland (see Section 7.1) and the grassland area
included in the Inventory. It is important to note that plans are being made to include these areas in future C
inventories.
Background on agricultural C stock changes is provided in Cropland Remaining Cropland and will only be
summarized here for Land Converted to Grassland. Soils are the largest pool of C in agricultural land, and also
have the greatest potential for storage or release of C, because biomass and dead organic matter C pools are
relatively small and ephemeral compared with soils. IPCC (2006) recommend reporting changes in soil organic C
stocks due to: (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-use and
management activities on organic soils.191
Land-use and management of mineral soils in Land Converted to Grassland led to an increase in soil C stocks from
1990 through 2009, which was largely due to annual cropland conversion to pasture (see Table 7-34 and Table
7-35). For example, the stock change rates were estimated to remove 20.3 Tg CO2 Eq./yr (5.5 Tg C) and 24.5 Tg
CO2 Eq./yr (6.7 Tg C) from mineral soils in 1990 and 2009, respectively. Drainage of organic soils for grazing
management led to losses varying from 0.5 to 0.9 Tg CO2 Eq./yr (0.1 to 0.2 Tg C).
Table 7-34: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg CO2 Eq.)
Soil Type 1990 2000 2005 2006 2007 2008 2009
Mineral Soils3 (20.3) (28.1) (25.3) (25.1) (24.9) (24.7) (24.5)
Organic Soils 0.5 0.9 0.9 0.9 0.9 0.9 0.9
Total Net Flux (19.8) (27.2) (24.4) (24.2) (24.0) (23.8) (23.6)
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
a Stock changes due to application of sewage sludge are reported in Grassland Remaining Grassland.
Table 7-35: Net CO2 Flux from Soil C Stock Changes for Land Converted to Grassland (Tg C)
Soil Type
Mineral Soils3
Organic Soils
Total Net Flux
1990
(5.5)
0.1
(5.4)
2000
(7.7)
0.2
(7.4)
2005
(6.9)
0.2
(6.7)
2006
(6.8)
0.2
(6.6)
2007
(6.8)
0.2
(6.5)
2008
(6.7)
0.2
(6.5)
2009
(6.7)
0.2
(6.4)
Note: Parentheses indicate net sequestration. Shaded areas indicate values based on a combination of historical data and
projections. All other values are based on historical data only. Totals may not sum due to independent rounding.
a Stock changes due to application of sewage sludge in Land Converted to Grassland are reported in Grassland Remaining
Grassland.
The spatial variability in annual CO2 flux associated with C stock changes in mineral soils is displayed in Figure
7-1 land Figure 7-12. Soil C stock increased in most states for Land Converted to Grassland. The largest gains
were in the South-Central region, Midwest, and northern Great Plains. The patterns were driven by conversion of
annual cropland into continuous pasture. Emissions from organic soils were largest in California, Florida, and the
upper Midwest, coinciding with largest concentrations of organic soils in the United States that are used for
agricultural production.
Figure 7-11: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2009,
190 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.
191 CO2 emissions associated with liming are also estimated but included in a separate section of the report.
7-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Land Converted to Grassland
Figure 7-12: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2009,
Land Converted to Grassland
Methodology
This section includes a brief description of the methodology used to estimate changes in soil C stocks due to
agricultural land-use and management activities on mineral soils for Land Converted to Grassland. Biomass C
stock changes are not explicitly included in this category but losses of associated with conversion of forest to
grassland are included in the Forest Land Remaining Forest Land section. Further elaboration on the methodologies
and data used to estimate stock changes from mineral and organic soils are provided in the Cropland Remaining
Cropland section and Annex 3.13.
Soil C stock changes were estimated for Land Converted to Grassland according to land-use histories recorded in
the USDA NRI survey (USDA-NRCS 2000). 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.
However, the NRI program initiated annual data collection in 1998, and the annual data are currently available
through 2003. NRI points were classified as Land Converted to Grassland in a given year between 1990 and 2009 if
the land use was grassland, but had been another use in the previous 20 years. Grassland includes pasture and
rangeland used for grass forage production, where the primary use is livestock grazing. Rangeland typically
includes extensive areas of native grassland that are not intensively managed, while pastures are often seeded
grassland, possibly following tree removal, that may or may not be improved with practices such as irrigation and
interseeding legumes.
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach 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, perennial/horticultural crops, and rice;
land areas with very gravelly, cobbly, or shaley soils (greater than 35 percent by volume); and land converted from
forest or federal ownership.192 A Tier 2 approach was also used to estimate additional changes in mineral soil C
stocks due to sewage sludge amendments. However, stock changes associated with sewage sludge amendments are
reported in the Grassland Remaining Grassland section.
Tier 3 Approach
Mineral SOC stocks and stock changes were estimated using the Century biogeochemical model as described for
Grassland Remaining Grassland. Historical land-use and management patterns were used in the Century
simulations as recorded in the NRI survey, with supplemental information on fertilizer use and rates from the USDA
Economic Research Service Cropping Practices Survey (ERS 1997) and the National Agricultural Statistics Service
(NASS 1992, 1999, 2004) (see Grassland Remaining Grassland Tier 3 methods section for additional information).
Tier 2 Approach
The Tier 2 approach used for Land Converted to Grassland on mineral soils is the same as described for Cropland
Remaining Cropland (See Cropland Remaining Cropland Tier 2 Approach and Annex 3.13 for additional
information).
192 Federal 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 2000).
Land Use, Land-Use Change, and Forestry 7-43
-------
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Grassland were estimated using the Tier 2
method provided in IPCC (2003, 2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than
default IPCC rates. Emissions were based on the 1992 and 1997 Land Converted to Grassland areas from the 1997
National Resources Inventory (USDA-NRCS 2000). The annual flux estimated for 1992 was applied to 1990
through 1992, and the annual flux estimated for 1997 was applied to 1993 through 2009.
Uncertainty and Time-Series Consistency
Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 approaches were based on the same
method described in Cropland Remaining Cropland, except that the uncertainty inherent in the structure of the
Century model was not addressed. The uncertainty or annual C emission estimates from drained organic soils in
Land Converted to Grassland was estimated using the Tier 2 approach, as described in the Cropland Remaining
Cropland section.
Uncertainty estimates are presented in Table 7-36 for each subsource (i.e., mineral soil C stocks and organic soil C
stocks), disaggregated to the level of the inventory methodology employed (i.e., Tier 2 and Tier 3). Uncertainty for
the portions of the Inventory estimated with Tier 2 and 3 approaches was derived using a Monte Carlo approach (see
Annex 3.13 for further discussion). A combined uncertainty estimate for changes in agricultural soil C stocks is also
included. Uncertainty estimates from each component 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
15 percent below to 15 percent above the 2009 estimate of -23.6 Tg CO2 Eq.
Table 7-36: Tier 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land Converted to
Grassland (Tg CO2 Eq. and Percent)
Source
Mineral Soil C Stocks: Land Converted to
Grassland, Tier 3 Inventory Methodology
Mineral Soil C Stocks: Land Converted to
Grassland, Tier 2 Inventory Methodology
Organic Soil C Stocks: Land Converted to
Grassland, Tier 2 Inventory Methodology
2009 Flux
Estimate
(TgC02Eq.)
(19.5)
(5.0)
0.9
Uncertainty Range Relative to Flux
Estimate
(TgC02Eq.)
Lower Upper
Bound Bound
(22.2) (16.7)
(7.0) (2.8)
0.2 1.8
(°/
Lower
Bound
-14%
-39%
-76%
°)
Upper
Bound
+14%
+43%
+104%
Combined Uncertainty for Flux associated with
Agricultural Soil Carbon Stocks in Land
Converted to Grassland
(23.6)
(27.0) (20.0) -15% +15%
Note: Parentheses indicate net sequestration. 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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
See the QA/QC and Verification section under Grassland Remaining Grassland.
Recalculations Discussion
There were minor changes in the current Inventory relative to the previous version in the estimated area of
grasslands associated with reconciling the forestland areas from the Forest Inventory and Analysis (FIA) survey with
the data from the National Resources Inventory (NRI) (see section 7.1 for more information). The revised areas led
to small changes in the soil C stock changes for Land Converted to Grassland.
7-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Planned Improvements
The main planned improvement for the next Inventory is to integrate the assessments of soil C stock changes and
soil nitrous oxide emissions into a single analysis. This improvement will ensure that the nitrogen and carbon cycles
are treated consistently in the national inventory, which is important because the cycles of these elements are linked
through plant and soil processes in agricultural lands. This improvement will include the development of an
empirically-based uncertainty analysis, which will provide a more rigorous assessment of uncertainty. See Planned
Improvements section under Cropland Remaining Cropland for additional planned improvements.
7.8. Wetlands Remaining Wetlands
Peatlands Remaining Peatlands
Emissions from Managed Peatlands
Managed peatlands are peatlands which have been cleared and drained for the production of peat. The production
cycle of a managed peatland has three phases: land conversion in preparation for peat extraction (e.g., draining, and
clearing surface biomass), extraction (which results in the emissions reported under Peatlands Remaining
Peatlands), and abandonment, restoration or conversion of the land to another use.
CO2 emissions from the removal of biomass and the decay of drained peat constitute the major greenhouse gas flux
from managed peatlands. Managed peatlands may also emit CH4 and N2O. The natural production of CH4 is largely
reduced but not entirely shut down when peatlands are drained in preparation for peat extraction (Strack et al., 2004
as cited in IPCC 2006); however, CH4 emissions are assumed to be insignificant under Tier 1 (IPCC, 2006). N2O
emissions from managed peatlands depend on site fertility. In addition, abandoned and restored peatlands continue
to release greenhouse gas emissions, and at present no methodology is provided by IPCC (2006) to estimate
greenhouse gas emissions or removals from restored peatlands. This inventory estimates both CO2 and N2O
emissions from Peatlands Remaining Peatlands in accordance with Tier 1 IPCC (2006) guidelines.
CO2 and N2O Emissions from Peatlands Remaining Peatlands
IPCC (2006) recommends reporting CO2 and N2O emissions from lands undergoing active peat extraction (i.e.,
Peatlands Remaining Peatlands) as part of the estimate for emissions from managed wetlands. Peatlands occur in
wetland areas where plant biomass has sunk to the bottom of water bodies and water-logged areas and exhausted the
oxygen supply below the water surface during the course of decay. Due to these anaerobic conditions, much of the
plant matter does not decompose but instead forms layers of peat over decades and centuries. In the United States,
peat is extracted for horticulture and landscaping growing media, and for a wide variety of industrial, personal care,
and other products. It has not been used for fuel in the United States for many decades. Peat is harvested from two
types of peat deposits in the United States: sphagnum bogs in northern states and wetlands in states further south.
The peat from sphagnum bogs in northern states, which is nutrient poor, is generally corrected for acidity and mixed
with fertilizer. Production from more southerly states is relatively coarse (i.e., fibrous) but nutrient rich.
IPCC (2006) recommends considering both on-site and off-site emissions when estimating CO2 emissions from
Peatlands Remaining Peatlands using the Tier 1 approach. Current methodologies estimate only on-site N2O
emissions, since off-site N2O estimates are complicated by the risk of double-counting emissions from nitrogen
fertilizers added to horticultural peat. On-site emissions from managed peatlands occur as the land is cleared of
vegetation and the underlying peat is exposed to sun and weather. As this occurs, some peat deposit is lost and CO2
is emitted from the oxidation of the peat. On-site N2O is emitted during draining depending on site fertility and if
the deposit contains significant amounts of organic nitrogen in inactive form. Draining land in preparation for peat
extraction allows bacteria to convert the nitrogen into nitrates which leach to the surface where they are reduced to
N2O.
Off-site CO2 emissions from managed peatlands occur from the horticultural and landscaping use of peat. CO2
emissions occur as the nutrient-poor (but now fertilizer-enriched) peat is used in bedding plants, other greenhouse
and plant nursery production, and by consumers, and as nutrient-rich (but relatively coarse) peat is used directly in
landscaping, athletic fields, golf courses, and plant nurseries. Most 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 predominately for horticultural purposes.
The magnitude of the CO2 emitted from peat depends on whether the peat has been extracted from nutrient-rich or
Land Use, Land-Use Change, and Forestry 7-45
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nutrient-poor peat deposits.
Total emissions from Peatlands Remaining Peatlands were estimated to be 1.095 Tg CO2 Eq. in 2009 (see Table
7-37) comprising 1.090 Tg CO2 Eq. (1,090 Gg) of CO2 and 0.005 Tg CO2 Eq. (0.016 Gg) of N2O. Total emissions
in 2009 were about 10 percent larger than total emissions in 2008, with the increase due to the higher peat
production reported in Alaska in 2009.
Total emissions from Peatlands Remaining Peatlands have fluctuated between 0.88 and 1.23 Tg CO2 Eq. across the
time series with a decreasing trend from 1990 until 1994 followed by an increasing trend through 2000. Since 2000,
total emissions show a decreasing trend until 2006 followed by an increasing trend in recent years. CO2 emissions
from Peatlands Remaining Peatlands have fluctuated between 0.88 and 1.23 Tg CO2 across the time series and drive
the trends in total emissions. N2O emissions remained close to zero across the time series, with a decreasing trend
from 1990 until 1995 followed by an increasing trend through 2000. N2O emissions decreased between 2000 and
2008, followed by a leveling off in 2009.
Table 7-37: Emissions from Peatlands Remaining Peatlands (Tg CO2 Eq.)
Gas
C02
N2O
Total
1990
1.0
+
1.0
2000
1.2
+
1.2
2005
1.1
+
1.1
2006
0.9
+
0.9
2007
1.0
+
1.0
2008
1.0
+
1.0
2009
1.1
+
1.1
+ Less than 0.01 Tg CO2 Eq.
Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does not take into account
imports, exports and stockpiles (i.e., apparent consumption).
Table 7-38: Emissions from Peatlands Remaining Peatlands (Gg)
Gas 1990 2000 2005 2006 2007 2008 2009
CO2 0)331,227 I 1,079 879 1,012 992 1,090
N2O + + + + + + +_
+ Less than 0.05 Gg
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).
Methodology
Off-Site CO2 Emissions
CO2 emissions from domestic peat production were estimated using a Tier 1 methodology consistent with IPCC
(2006). Off-site CO2 emissions from Peatlands Remaining Peatlands were calculated by apportioning the annual
weight of peat produced in the United States (Table 7-39) 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 carbon 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 and. Mineral Commodity Summaries from the U.S. Geological Survey (USGS 1991-2010).
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. The USGS often receives a response to the survey from
most of the smaller peat producers, but fewer of the larger ones. For example, of the four active operations
producing 23,000 or more metric tons per year, two did not respond to the survey in 2007. As a result, the USGS
estimates production from the non-respondent peat producers based on responses to previous surveys (responses
from 2004 and 2005, in the case above) or other sources.
The Alaska estimates rely on reported peat production from Alaska's annual Mineral Industry Reports (Szumigala et
al. 2010). Similar to the U.S. Geological Survey, Alaska's Mineral Industry Report methodology solicits voluntary
reporting of peat production from producers. However, the report does not estimate production for the non-reporting
producers, resulting in larger inter-annual variation in reported peat production from Alaska depending on the
number of producers who report in a given year (Szumigala 2011). In addition, in both the lower 48 states and
Alaska, large variations in peat production can also result from variations in precipitation and the subsequent
7-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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moisture conditions, since unusually wet years can hamper peat production (USGS 2010). The methodology
estimates Alaska emissions separately from lower 48 emissions because the state conducts its own mineral survey
and reports peat production by volume, rather than by weight (Table 7-40). However, volume production data was
used to calculate off-site CO2 emissions from Alaska applying the same methodology but with volume-specific
carbon fraction conversion factors from IPCC (2006).193
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. Therefore, off-
site CO2 emissions from the use of all horticultural peat within the United States are not accounted for using the Tier
1 approach. The United States has increasingly imported peat from Canada for horticultural purposes; from 2005 to
2008, imports of sphagnum moss (nutrient-poor) peat from Canada represented 97 percent of total U.S. peat imports
(USGS 2010). Most peat produced in the United States is reed-sedge peat, generally from southern states, which is
classified as nutrient rich by IPCC (2006). Higher-tier calculations of CO2 emissions from apparent consumption
would involve consideration of the percentages of peat types stockpiled (nutrient rich versus nutrient poor) as well
as the percentages of peat types imported and exported.
Table 7-39: Peat Production of Lower 48 States (in thousands of Metric Tons)
Type of Deposit
Nutrient-Rich
Nutrient-Poor
Total Production
1990
595.1 1
55.4
692.0
2000
728.6 1
63.4
792.0
1 2005
657.6
1 27.4
| 685.0
2006
529.0
22.0
551.0
2007
581.0
54.0
635.0
2008
559.7
55.4
615.0
2009
554.2
54.8
609.0
Sources: Minerals Yearbook: Peat (1990-2008 Reports), Mineral Commodity Summaries: Peat (1996-2009 Reports), and
Apodaca (2010). United States Geological Survey.
Table 7-40: Peat Production of Alaska (in thousands of Cubic Meters)
1990 2000 2005 2006 2007 2008 2009
Total Production 49.7 27.2 47.8 50.8 52.3 64.1 183.9
Sources: Alaska's Mineral Industry (1992-2009) Reports. Division of Geological & Geophysical Surveys, Alaska Department
of Natural Resources.
On-site CO2 Emissions
IPCC (2006) suggests basing the calculation of on-site emissions 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 method194 can extract up to 100 metric ton per
hectare per year (Cleary et al. 2005 as cited in IPCC 2006). The area of land managed for peat extraction in the
United States was estimated using nutrient-rich and nutrient-poor production data and the assumption that 100
metric tons of peat are extracted from a single hectare in a single year. The annual land area estimates were then
multiplied by the appropriate nutrient-rich or nutrient-poor IPCC (2006) default emission factor in order to calculate
on-site CO2 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 carbon 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
193 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).
194 jjjg 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).
Land Use, Land-Use Change, and Forestry 7-47
-------
States has been declining since 1990; therefore it seems reasonable to assume that no new areas are being cleared of
vegetation for managed peat extraction. Other changes in carbon stocks in living biomass on managed peatlands are
also assumed to be zero under the Tier 1 methodology (IPCC 2006).
On-site N2O Emissions
IPCC (2006) suggests basing the calculation of on-site N2O emissions 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 CO2 emissions methodology above details the calculation of area data from production data. In order to
estimate N2O emissions, the area of nutrient rich Peatlands Remaining Peatlands was multiplied by the appropriate
default emission factor taken from IPCC (2006).
Uncertainty
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. This
same uncertainty and distribution was assumed for the peat type production percentages. The uncertainty associated
with the Alaskan reported production data was assumed to be the same as the lower 48 states, or ± 25 percent with a
normal distribution. It should be noted that the Alaskan Department of Natural Resources estimate that around half
of producers do not respond to their survey with peat production data; therefore, the production numbers reported
are likely to underestimate Alaska peat production (Szumigala 2008). The uncertainty associated with the average
bulk density values was estimated to be ± 25 percent with a normal distribution (Apodaca 2008). IPCC (2006) gives
uncertainty values for the emissions factors for the area of peat deposits managed for peat extraction based on the
range of underlying data used to determine the emissions factors. The uncertainty associated with the emission
factors was assumed to be triangularly distributed. The uncertainty values surrounding the carbon fractions were
based on IPCC (2006) and the uncertainty was assumed to be uniformly distributed. Based on these values and
distributions, a Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty of CO2 and N2O
emissions from Peatlands Remaining Peatlands. The results of the Tier 2 quantitative uncertainty analysis are
summarized in Table 7-41. CO2 emissions from Peatlands Remaining Peatlands in 2009 were estimated to be
between 0.8 and 1.5 Tg CO2 Eq. at the 95 percent confidence level. This indicates a range of 30 percent below to 34
percent above the 2009 emission estimate of 1.1 Tg CO2 Eq. N2O emissions from Peatlands Remaining Peatlands
in 2009 were estimated to be between 0.001 and 0.007 Tg CO2 Eq. at the 95 percent confidence level. This indicates
a range of 74 percent below to 41 percent above the 2009 emission estimate of 0.005 Tg CO2 Eq.
Table 7-41: Tier-2 Quantitative Uncertainty Estimates for CO2 Emissions from Peatlands Remaining Peatlands
2009 Emissions Uncertainty Range Relative to Emissions
Estimate Estimate"
Source Gas (Tg CO2 Eq.) (Tg CO2 Eq.) (%)
Peatlands Remaining
Peatlands
C02 1.1
N20 +
Lower
Bound
0.8
+
Upper
Bound
1.5
+
Lower
Bound
-30%
-74%
Upper
Bound
34%
41%
+ Does not exceed 0.01 Tg CO2 Eq. or 0.5 Gg.
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. The QA/QC
analysis did not reveal any inaccuracies or incorrect input values.
Recalculations Discussion
The current Inventory represents the third Inventory report in which emissions from Peatlands Remaining Peatlands
are included . A revised 2008 estimate of peat production by volume for Alaska was reported in 2010 (Szumigala et
7-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
al. 2010). Updating the 2008 production data with this revised estimate led to a 5 percent increase over the previous
2008 emission estimate.
Planned Improvements
In order to further improve estimates of CO2 and N2O emissions from Peatlands Remaining Peatlands, future efforts
will consider options for obtaining better data on the quantity of peat harvested per hectare and the total area
undergoing peat extraction.
7.9. Settlements Remaining Settlements
Changes in Carbon Stocks in Urban Trees (IPCC Source Category 5E1)
Urban forests constitute a significant portion of the total U.S. tree canopy cover (Dwyer et al. 2000). Urban areas
(cities, towns, and villages) are estimated to cover over 4 percent of the United States (Nowak et al. 2005). With an
average tree canopy cover of 27 percent, urban areas account for approximately 3 percent of total tree cover in the
continental United States (Nowak et al. 2001). Trees in urban areas of the United States were estimated to account
for an average annual net sequestration of 76.5 Tg CO2 Eq. (20.9 Tg C) over the period from 1990 through 2009.
Net C flux from urban trees in 2009 was estimated to be -95.9 Tg CO2 Eq. (-26.2 Tg C). Annual estimates of CO2
flux (Table 7-42) were developed based on periodic (1990 and 2000) U.S. Census data on urbanized area. This
estimated urban 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 21 percent over the 1990 through 2009 time series—i.e., the Census
urban area is a subset of the Settlements area. 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 68 percent between 1990 and
2009 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.
Net C flux from urban trees is proportionately greater on an area basis than that of forests. This trend is primarily
the result of different net growth rates in urban areas versus forests—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. Areas covered by urban trees, therefore,
appear to have a greater C density than do forested areas (Nowak and Crane 2002).
Table 7-42: Net C Flux from Urban Trees (Tg CO2 Eq. and Tg C)
Year Tg CO2 Eq. Tg C
1990
2005
2006
2007
2008
2009
(87.8)
(89.8)
(91.9)
(93.9)
(95.9)
(23.9)
(24.5)
(25.1)
(25.6)
(26.2)
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 and Crane (2002) and Nowak (1994). In general, the methodology
used by Nowak and Crane (2002) to estimate net C sequestration in urban trees followed three steps. First, field
data from 14 cities were used to generate allometric estimates of biomass from measured tree dimensions. Second,
estimates of tree growth and biomass increment were generated from published literature and adjusted for tree
condition and land-use class to generate estimates of gross C sequestration in urban trees. Third, estimates of C
emissions due to mortality and decomposition were subtracted from gross C sequestration values to derive estimates
Land Use, Land-Use Change, and Forestry 7-49
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of net C sequestration. Sequestration estimates for these cities, in units of carbon sequestered per unit area of tree
cover, were then used to estimate urban forest C sequestration in the U.S. by using urban area estimates from U.S.
Census data and urban tree cover estimates from remote sensing data, an approach consistent with Nowak and Crane
(2002).
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.
Annual changes in net C flux from urban trees are based solely on changes in total urban area in the United States.
In order to generate the allometric relationships between tree dimensions and tree biomass, Nowak and Crane (2002)
and Nowak (1994, 2007c, 2009) collected field measurements in a number of U.S. cities between 1989 and 2002.
For a sample of trees in each of the cities in Table 7-43, 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 carbon 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 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. were analyzed using the U.S. Forest
Service's Urban Forest Effects (UFORE) model. UFORE is a computer model that uses standardized field data
from random plots in each city and local air pollution and meteorological data to quantify urban forest structure,
values of the urban forest, and environmental effects, including total C stored and annual C sequestration. UFORE
was used with field data from a stratified random sample of plots in each city to quantify the characteristics of the
urban forest. (Nowak et al. 2007a).
Gross C emissions result from tree death and removals. Estimates of gross 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 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 field data for 13 of the 14 cities are described in Nowak and Crane (2002), Nowak et al. (2007a), and references
cited therein. Data for the remaining city, Chicago, were taken from unpublished results (Nowak 2009). 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). A root-to-shoot ratio of 0.26 was taken from Cairns et al. (1997), and species- or genus-specific
moisture contents were taken from various literature sources (see Nowak 1994). Tree growth rates were taken from
existing literature. Average diameter growth was based on the following sources: estimates for trees in forest stands
came from Smith and Shifley (1984); estimates for trees on land uses with a park-like structure came from deVries
(1987); and estimates for more open-grown trees came from Nowak (1994). Formulas from Fleming (1988) formed
the basis for average height growth calculations. As described above, growth rates were adjusted to account for tree
condition. Growth factors for Atlanta, Boston, Freehold, Jersey City, Moorestown, New York, Philadelphia, and
Woodbridge were adjusted based on the typical growth conditions of different land-use categories (e.g., forest
stands, park-like stands). Growth factors for the more recent studies in Baltimore, Chicago, Minneapolis, San
7-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Francisco, Syracuse, and Washington were adjusted using an updated methodology based on the condition of each
individual tree, which is determined using tree competition factors (depending on whether it is open grown or
suppressed) (Nowak 2007b). 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 and Crane 2002).
Estimates of gross and net sequestration rates for each of the 14 cities (Table 7-43) were compiled in units of C
sequestration per unit area of tree canopy cover. These rates were used in conjunction with estimates of national
urban area and urban tree cover data to calculate national annual net C sequestration by urban trees for the United
States. This method was described in Nowak and Crane (2002) and has been modified to incorporate U.S. Census
data.
Specifically, urban area estimates were based on 1990 and 2000 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. Urban land area increased by
approximately 36 percent from 1990 to 2000; 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 both 1990 and 2000 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 represent a larger area than the Census-derived urban area estimates. However, the 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. Specifically, tree canopy cover of U.S. urban areas was estimated by Nowak et al.
(2001) to be 27 percent, assessed across Census-delineated urbanized areas, urban places, and places containing
urbanized area. This canopy cover percentage is multiplied by the urban area estimated for each year to produce an
estimate of national urban tree cover area.
Net annual C sequestration estimates were derived for the 14 cities by subtracting the gross annual emission
estimates from the gross annual sequestration estimates. The gross and net annual C sequestration values for each
city were divided by each city's area of tree cover to determine the average annual sequestration rates per unit of
tree area for each city. The median value for gross sequestration per unit area of tree cover (0.29 kg C/m2-yr) was
then multiplied by the estimate of national urban tree cover area to estimate national annual gross sequestration, per
the methods of Nowak and Crane (2002). To estimate national annual net sequestration, the estimate of national
annual gross sequestration was multiplied by the average of the ratios of net to gross sequestration (0.72) for those
cities that had both estimates. The urban tree cover estimates for each of the 14 cities and the United States were
obtained from Dwyer et al. (2000), Nowak et al. (2002), Nowak (2007a), and Nowak (2009). The urban area
estimates were taken from Nowak et al. (2005).
Table 7-43 : C Stocks (Metric Tons C), Annual C Sequestration (Metric
Annual C Sequestration per Area of Tree Cover (kg C/m2-yr) for 14 U.S
Pitv
\^llj
Atlanta, GA
Baltimore, MD
Boston, MA
Chicago, IL
Freehold, NJ
Jersey City, NJ
Minneapolis, MN
Moorestown, NJ
New York, NY
Philadelphia, PA
Carbon
Stocks
1,219,256
541,589
289,392
649,000
18,144
19,051
226,796
106,141
1,224,699
480,808
Gross Annual
Sequestration
42,093
14,696
9,525
22,800
494
807
8,074
3,411
38,374
14,606
Net Annual
Sequestration
32,169
9,261
6,966
16,100
318
577
4,265
2,577
20,786
10,530
Tree
Cover
36.7%
21.0%
22.3%
17.2%
34.4%
11.5%
26.4%
28.0%
20.9%
15.7%
Tons C/yr), Tree
. Cities
Gross Annual
Sequestration
per Area of
Tree Cover
0.34
0.35
0.30
0.22
0.28
0.18
0.20
0.32
0.23
0.27
Cover (Percent), and
Net Annual
Sequestration
per Area of
Tree Cover
0.26
0.22
0.22
0.16
0.18
0.13
0.11
0.24
0.12
0.20
Net: Gross
Annual
Sequestration
Ratio
0.76
0.63
0.73
0.71
0.64
0.71
0.53
0.76
0.54
0.72
Land Use, Land-Use Change, and Forestry 7-51
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San Francisco, CA
Syracuse, NY
Washington, DC
Woodbridge, NJ
175,994
156,943
477,179
145,150
4,627
4,917
14,696
5,044
4,152
4,270
11,661
3,663
11.9%
23.1%
28.6%
29.5%
0.33
0.33
0.32
0.28
Median: 0.29
0.29
0.29
0.26
0.21
0.90
0.87
0.79
0.73
Mean: 0.72
NA = not analyzed.
Sources: Nowak and Crane (2002), Nowak (2007a,c), and Nowak (2009).
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 14 U.S. cities. A 10
percent uncertainty was associated with urban area estimates while a 5 percent uncertainty was associated with
percent urban tree coverage. Both of these uncertainty estimates were based on expert judgment. Uncertainty
associated with estimates of gross and net C sequestration for each of the 14 U.S. cities was based on standard error
estimates for each of the city-level sequestration estimates reported by Nowak (2007c) and Nowak (2009). These
estimates are based on field data collected in each of the 14 U.S. cities, and uncertainty in these estimates increases
as they are scaled up to the national level.
Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition assumptions
used to calculate C sequestration and emission estimates (Nowak et al. 2002). These results also exclude changes in
soil C stocks, and there may be some overlap between the urban tree C estimates and the forest tree C estimates.
Due to data limitations, urban soil flux is not quantified as part of this analysis, while reconciliation of urban tree
and forest tree estimates will be addressed through the land-representation effort described in the Planned
Improvements section of this chapter.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the overall uncertainty of the sequestration
estimate. The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-44. The net C flux
from changes in C stocks in urban trees in 2009 was estimated to be between -116.8 and -77.7 Tg CO2 Eq. at a 95
percent confidence level. This indicates a range of 22 percent below and 19 percent above the 2009 flux estimate of
-95.9TgCO2Eq.
Table 7-44: Tier 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C Stocks in Urban Trees
(Tg CO2 Eq. and Percent)
Source
2009 Flux Estimate
Gas (Tg CO2 Eq.)
Uncertainty Range Relative to Flux Estimate
(Tg C02 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Changes in C Stocks
in Urban Trees
CO2
(95.9)
(116.8)
(77.7)
-22%
+19%
Note: Parentheses indicate negative values or net sequestration.
Details on the emission trends through time are described in more detail in the Methodology section, above.
QA/QC and Verification
The net C flux resulting from urban trees was predominately calculated using estimates of gross and net C
sequestration estimates for urban trees and urban tree coverage area published in the literature. The validity of these
data for their use in this section of the inventory was evaluated through correspondence established with an author of
the papers. Through this correspondence, the methods used to collect the urban tree sequestration and area data
were further clarified and the use of these data in the inventory was reviewed and validated (Nowak 2002a, 2007b,
2011).
Planned Improvements
A consistent representation of the managed land base in the United States is being developed. A component of this
effort, which is discussed at the beginning of the Land Use, Land-Use Change, and Forestry chapter, will involve
reconciling the overlap between urban forest and non-urban forest greenhouse gas inventories. It is highly likely
7-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
that 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 the inventory. The Forest Service is currently conducting research that will define urban area
boundaries and make it possible to distinguish forest from forested urban areas. Once those data become available,
they will be incorporated into estimates of net C flux resulting from urban trees.
Urban forest data for additional cities are expected in the near future, as are updated data for cities currently
included in the estimates. The use of these data will further refine the estimated median sequestration value. It may
also be possible to report C losses and gains separately in the future. It is currently not possible, since existing
studies estimate rather than measure natality or mortality; net sequestration estimates are based on assumptions
about whether dead trees are being removed, burned, or chipped. There is an effort underway to assess urban tree
loss to mortality and removals, which would allow for direct calculation of C losses and gains from observed rather
than estimated natality and mortality of trees.
Data from the 2010 U.S. Census is expected to provide updated U.S. urbanized area, which would allow for
refinement of the urban area time series. Revisions to urban area time series will result in revisions to prior years' C
flux estimates.
A revised average tree canopy cover percentage for U.S. urban areas is anticipated to become available in the peer-
reviewed literature in the near future, which would allow for updated C flux estimates. Furthermore, urban tree
cover data specific to each state is also expected in the near future. It may be possible to develop a set of state-
specific sequestration rates for more granular and regionally precise C flux estimates by coupling these data with
adjusted growth rates for each U.S. state. Future research may also enable more complete coverage of changes in the
C stock in urban trees for all Settlements land. To provide estimates for all Settlements, research would need to
establish the extent of overlap between Settlements and Census-defined urban areas, and would have to characterize
sequestration on non-urban Settlements land.
Direct N20 Fluxes from Settlement Soils (IPCC Source Category 5E1)
Of the synthetic N fertilizers applied to soils in the United States, approximately 2.5 percent are currently applied to
lawns, golf courses, and other landscaping occurring within settlement areas. Application rates are lower than those
occurring on cropped soils, and, therefore, account for a smaller proportion of total U.S. soil N2O emissions per unit
area. In addition to synthetic N fertilizers, a portion of surface applied sewage sludge is applied to settlement areas.
In 2009, N2O emissions from this source were 1.5 Tg CO2 Eq. (4.9 Gg). There was an overall increase of 55 percent
over the period from 1990 through 2009 due to a general increase in the application of synthetic N fertilizers to an
expanding settlement area. Interannual variability in these emissions is directly attributable to interannual variability
in total synthetic fertilizer consumption and sewage sludge applications in the United States. Emissions from this
source are summarized in Table 7-45.
Table 7-45: Direct N2O Fluxes from Soils in Settlements Remaining Settlements (Tg CO2 Eq. and Gg N2O)
Year Tg CO2 Eq. Gg
1990
1.0
2005
2006
2007
2008
2009
.5
.5
.6
.5
.5
4.7
4.8
5.1
4.9
4.9
Note: These estimates include direct N2O emissions from N fertilizer additions only. Indirect N2O emissions from fertilizer
additions are reported in the Agriculture chapter. These estimates include emissions from both Settlements Remaining
Settlements and from Land Converted to Settlements.
Methodology
For soils within Settlements Remaining Settlements, the IPCC Tier 1 approach was used to estimate soil N2O
emissions from synthetic N fertilizer and sewage sludge additions. Estimates of direct N2O emissions from soils in
settlements were based on the amount of N in synthetic commercial fertilizers applied to settlement soils, and the
Land Use, Land-Use Change, and Forestry 7-53
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amount of N in sewage sludge applied to non-agricultural land and surface disposal of sewage sludge (see Annex
3.11 for a detailed discussion of the methodology for estimating sewage sludge application).
Nitrogen applications to settlement soils are estimated using data compiled by the USGS (Ruddy et al. 2006). The
USGS estimated on-farm and non-farm fertilizer use is based on sales records at the county level from 1982 through
2001 (Ruddy et al. 2006). Non-farm N fertilizer was assumed to be applied to settlements and forest lands; values
for 2002 through 2008 were based on 2001 values adjusted for annual total N fertilizer sales in the United States
because there is no new activity data on application after 2001. Settlement application was calculated by subtracting
forest application from total non-farm fertilizer use. Sewage sludge applications were derived from national data on
sewage sludge generation, disposition, and N content (see Annex 3.11 for further detail). The total amount of N
resulting from these sources was multiplied by the IPCC default emission factor for applied N (1 percent) to
estimate direct N2O emissions (IPCC 2006). The volatilized and leached/runoff N fractions for settlements,
calculated with the IPCC default volatilization factors (10 or 20 percent, respectively, for synthetic or organic N
fertilizers) and leaching/runoff factor for wet areas (30 percent), were included with indirect emissions, as reported
in the N2O Emissions from Agricultural Soil Management source category of the Agriculture chapter (consistent
with reporting guidance that all indirect emissions are included in the Agricultural Soil Management source
category).
Uncertainty and Time-Series Consistency
The amount of N2O emitted from settlements depends not only on N inputs and fertilized area, but also on a large
number of variables, including organic C availability, oxygen gas partial pressure, soil moisture content, pH,
temperature, and irrigation/watering practices. The effect of the combined interaction of these variables on N2O flux
is complex and highly uncertain. The IPCC default methodology does not explicitly incorporate any of these
variables, except variations in fertilizer N and sewage sludge application rates. All settlement soils are treated
equivalently under this methodology.
Uncertainties exist in both the fertilizer N and sewage sludge application rates in addition to the emission factors.
Uncertainty in fertilizer N application was assigned a default level195 of ±50 percent. Uncertainty in the amounts of
sewage sludge applied to non-agricultural lands and used in surface disposal was derived from variability in several
factors, including: (1) N content of sewage sludge; (2) total sludge applied in 2000; (3) wastewater existing flow in
1996 and 2000; and (4) the sewage sludge disposal practice distributions to non-agricultural land application and
surface disposal. Uncertainty in the emission factors was provided by the IPCC (2006).
Quantitative uncertainty of this source category was estimated through the IPCC-recommended Tier 2 uncertainty
estimation methodology. The uncertainty ranges around the 2005 activity data and emission factor input variables
were directly applied to the 2009 emission estimates. The results of the quantitative uncertainty analysis are
summarized in Table 7-46. N2O emissions from soils in Settlements Remaining Settlements in 2009 were estimated
to be between 0.8 and 4.0 Tg CO2 Eq. at a 95 percent confidence level. This indicates a range of 49 percent below
to 163 percent above the 2009 emission estimate of 1.5 Tg CO2 Eq.
Table 7-46: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements Remaining Settlements
(Tg CO2 Eq. and Percent)
2009 Uncertainty Range Relative to Emission
Source Gas Emissions Estimate
(TgC02Eq.) (TgC02Eq.)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Settlements Remaining Settlements:
N2O Fluxes from Soils N2O L5 0.8 4.0 -49% 163%
Note: This estimate includes direct N2O emissions from N fertilizer additions to both Settlements Remaining
Settlements and from Land Converted to Settlements.
195 No uncertainty is provided with the USGS application data (Ruddy et al. 2006) so a conservative ±50% was used in the
analysis.
7-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Planned Improvements
A minor improvement is planned to update the uncertainty analysis for direct emissions from settlements to be
consistent with the most recent activity data for this source.
7.10. Land Converted to Settlements (Source Category 5E2)
Land-use change is constantly occurring, and land under a number of uses undergoes urbanization in the United
States each year. However, data on the amount of land converted to settlements is currently lacking. Given the lack
of available information relevant to this particular IPCC source category, it is not possible to separate CO2 or N2O
fluxes on Land Converted to Settlements from fluxes on Settlements Remaining Settlements at this time.
7.11. Other (IPCC Source Category 5G)
Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
In the United States, a significant change in C stocks results from the removal of yard trimmings (i.e., grass
clippings, leaves, and branches) and food scraps from settlements to be disposed in landfills. Yard trimmings 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. 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 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. C 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 therefore reported under the "Other" source category.
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 2011; Schneider 2007,
2008). Since then, programs banning or discouraging yard trimmings disposal have led to an increase in backyard
composting and the use of mulching mowers, and a consequent 5 percent decrease in the tonnage 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 33 percent in 2009. The net effect of the reduction in generation and the increase in composting is a 57
percent decrease in the quantity of yard trimmings disposed in landfills since 1990.
Food scraps generation has grown by 44 percent since 1990, and though the proportion of food scraps discarded in
landfills has decreased slightly from 82 percent in 1990 to 80 percent in 2009, the tonnage disposed in landfills has
increased considerably (by 40 percent). Overall, the decrease in the yard trimmings landfill disposal rate has more
than compensated for the increase in food scrap disposal in landfills, and the net result is a decrease in annual
landfill carbon storage from 24.2 Tg CO2 Eq. in 1990 to 12.6 Tg CO2 Eq. in 2009 (Table 7-47 and Table 7-48).
Table 7-47: Net Changes in Yard Trimming and Food Scrap Stocks in Landfills (Tg CO2 Eq.)
Carbon Pool 1990 2000 2005 2006 2007 2008 2009
Yard Trimmings (21.0) (8.8) (7.3) (7.5) (7.0) (7.3) (8.5)
Grass (1.8) (0.7) (0.6) (0.6) (0.6) (0.7) (0.8)
Leaves (9.0) (3.9) (3.3) (3.4) (3.2) (3.4) (3.9)
Branches (10.2) I (4.2) (3.3) (3.4) (3.2) (3.3) (3.8)
Food Scraps (3.2) (4.4) (4.3) (3.5) (3.9) (3.9) (4.1)
Total Net Flux (24.2) (13.2) (11.5) (11.0) (10.9) (11.2) (12.6)
Note: Totals may not sum due to independent rounding.
Land Use, Land-Use Change, and Forestry 7-55
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Table 7-48: Net Changes in Yard Trimming and Food Scrap Stocks in Landfills (Tg C)
Carbon Pool
Yard Trimmings
Grass
Leaves
Branches
Food Scraps
Total Net Flux
1990
(5.7)
(0.5)
(2.5)
(2.8)
(0.9)
(6.6)
2000
(2.4)
11
1 (1.2)
1 (3.6)
2005
(2.0)
1(0.2)
(0.9)
(0.9)
(1.2)
(3.1)
2006
(2.0)
(0.2)
(0.9)
(0.9)
(1.0)
(3.0)
2007
(1.9)
(0.2)
(0.9)
(0.9)
(1.1)
(3.0)
2008
(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)
Note: Totals may not sum due to independent rounding.
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 carbon in landfills can increase, with the net effect being a net atmospheric
removal of carbon. 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). C 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 landfilled in previous years that
decomposed.
To determine the total landfilled C stocks for a given year, the following were estimated: (1) the composition of the
yard trimmings; (2) the mass of yard trimmings and food scraps discarded in landfills; (3) the C storage factor of the
landfilled yard trimmings and food scraps; and (4) the rate of decomposition of the degradable C. The composition
of yard trimmings was assumed to be 30 percent grass clippings, 40 percent leaves, and 30 percent branches on a
wet weight basis (Oshins and Block 2000). The yard trimmings were subdivided, because each component has its
own unique adjusted C storage factor 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: Facts and Figures for 2009 (EPA
2011), which provides data for 1960, 1970, 1980, 1990, 2000, and 2005 through 2009. To provide data for some of
the missing years, detailed backup data were obtained from Schneider (2007, 2008). Remaining years in the time
series for which data were not provided were estimated using linear interpolation. The EPA (2011) report does not
subdivide discards of individual materials into volumes landfilled and combusted, although it provides an estimate
of the proportion of overall waste stream discards managed in landfills196 and combustors with energy recovery
(i.e., ranging from 100 percent and 0 percent, respectively, in 1960 to 81 percent and 19 percent in 2000); it is
assumed that the proportion of each individual material (food scraps, grass, leaves, branches) that is landfilled is the
same as the proportion across the overall waste stream.
The amount of C disposed of in landfills each year, starting in 1960, was estimated by converting the discarded
landfilled yard trimmings and food scraps from a wet weight to a dry weight basis, and then multiplying by the
initial (i.e., pre-decomposition) C content (as a fraction of dry weight). The dry weight of landfilled material was
calculated using dry weight to wet weight ratios (Tchobanoglous et al. 1993, cited by Barlaz 1998) and the initial C
contents and the C storage factors were determined by Barlaz (1998, 2005, 2008) (Table 7-49).
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
196 EPA (2011) 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.
7-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 a "seed" containing 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 "CS" in Table 7-49).
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,
resulting in emissions of CH4 and CO2 (the CH4 emissions resulting from decomposition of yard trimmings and food
scraps are accounted for in the "Waste" chapter). The degradable portion of the C is assumed to decay according to
first-order kinetics.
The first-order decay rates, k, for each component were derived from De la Cruz and Barlaz (2010). De la Cruz and
Barlaz (2010) calculate first-order decay rates using laboratory data published in Eleazer et al. (1997), and a
correction factor,/ is found so that the weighted average decay rate for all components is equal to the AP-42 default
decay rate (0.04) for mixed MSW for regions that receive more than 25 inches of rain annually. Because AP-42
values were developed using landfill data from approximately 1990, 1990 waste composition for the United States
from EPA's Characterization of Municipal Solid Waste in the United States: 1990 Update was used to calculate/
This correction factor is then multiplied by the Eleazer et al. (1997) decay rates of each waste component to develop
field-scale first-order decay rates.
De la Cruz and Barlaz (2010) also use other assumed initial decay rates for mixed MSW in place of the AP-42
default value based on different types of environments in which landfills in the United States are found, including
dry conditions (less than 25 inches of rain annually, &=0.02) and bioreactor landfill conditions (moisture is
controlled for rapid decomposition, &=0.12). The Landfills section of the Inventory (which estimates CH4
emissions) estimates the overall MSW decay rate by partitioning the U.S. landfill population into three categories,
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 yr"1, respectively.
De la Cruz and Barlaz (2010) calculate component-specific decay rates corresponding to the first value (0.020 yr"1),
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 yr"1 through linear
interpolation. A weighted national average component-specific decay rate was calculated by assuming that waste
generation is proportional to population (the same assumption used in the landfill methane emission estimate), based
on population data from the 2000 U.S. Census. The component-specific decay rates are shown in Table 7-49.
For each of the four materials (grass, leaves, branches, food scraps), the stock of C in landfills for any given year is
calculated according to the following formula:
LFC,,t = E W,n x (1 -MC,) x ICC, x {[CS, x ICC,] + [(1 - (CS, x ICC,)) x e^""']}
n
where,
/ = Year for which C stocks are being estimated (year),
/' = Waste type for which C stocks are being estimated (grass, leaves, branches, food scraps),
LFCit = Stock of C in landfills in year /, for waste /' (metric tons),
Win = Mass of waste /' disposed in landfills in year n (metric tons, wet weight),
n = Year in which the waste was disposed (year, where 1960 <«
-------
Ft=TLFCt-TLFC(t.
Thus, the C placed in a landfill in year n is tracked for each year / through the end of the inventory period (2009).
For example, disposal of food scraps in 1960 resulted in depositing about 1,135,000 metric tons of C. Of this
amount, 16 percent (179,000 metric tons) is persistent; the remaining 84 percent (956,000 metric tons) is degradable.
By 1965, more than half of the degradable portion (518,000 metric tons) decomposes, leaving a total of 617,000
metric tons (the persistent portion, plus the remainder of the degradable portion).
Continuing the example, by 2009, the total food scraps C originally disposed in 1960 had declined to 179,000 metric
tons (i.e., virtually all degradable C had decomposed). By summing the C remaining from 1960 with the C
remaining from food scraps disposed in subsequent years (1961 through 2009), the total landfill C from food scraps
in 2009 was 35.9 million metric tons. This value is then added to the C stock from grass, leaves, and branches to
calculate the total landfill C stock in 2009, yielding a value of 247.1 million metric tons (as shown in Table 7-50).
In exactly the same way total net flux is calculated for forest C and harvested wood products, the total net flux of
landfill C for yard trimmings and food scraps for a given year (Table 7-48) is the difference in the landfill C stock
for that year and the stock in the preceding year. For example, the net change in 2009 shown in Table 7-48 (3.4 Tg
C) is equal to the stock in 2009 (247.1 Tg C) minus the stock in 2008 (243.7 Tg C).
The C stocks calculated through this procedure are shown in Table 7-50.
Table 7-49: Moisture Content (%), C Storage Factor, Proportion of Initial C Sequestered (%), Initial C Content (%),
and Decay Rate (year'1) for Landfilled Yard Trimmings and Food Scraps in Landfills
Yard Trimmings
Food Scraps
Variable
Moisture Content (% H2O)
CS, proportion of initial C stored (%)
Initial C Content (%)
Decay Rate (year"1)
Grass
70
53
45
0.323
Leaves
30
85
46
0.185
Branches
10
77
49
0.016
70
16
51
0.156
Table 7-50: C Stocks in Yard Trimmings and Food Scraps in Landfills (Tg C)
Carbon Pool
Yard Trimmings
Branches
Leaves
Grass
Food Scraps
Total Carbon Stocks
1990
155.8
74.6
66.7
14.5
21.3
177.2
2000 I
191.9
92.4
82.4
17.2
27.0 1
218.9
2005
202.9
97.5
87.3
18.1
31.7
234.6
2006
205.0
98.5
88.3
18.2
32.7
237.6
2007
206.9
99.3
89.1
18.4
33.7
240.6
2008
208.9
100.2
90.1
18.6
34.8
243.7
2009
211.2
101.3
91.1
18.8
35.9
247.1
Note: Totals may not sum due to independent rounding.
Uncertainty and Time-Series Consistency
The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of
uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture
content, decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the
composition of the yard trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings
mixture). There are respective uncertainties associated with each of these factors.
A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the overall uncertainty of the sequestration
estimate. The results of the Tier 2 quantitative uncertainty analysis are summarized in Table 7-51. Total yard
trimmings and food scraps CO2 flux in 2009 was estimated to be between -21.2 and -6.2 Tg CO2 Eq. at a 95 percent
confidence level (or 19 of 20 Monte Carlo stochastic simulations). This indicates a range of 68 percent below to 51
percent above the 2009 flux estimate of -12.6 Tg CO2 Eq. More information on the uncertainty estimates for Yard
Trimmings and Food Scraps in Landfills is contained within the Uncertainty Annex.
7-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table 7-51: Tier 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard Trimmings and Food Scraps in
Landfills (Tg CO2 Eq. and Percent)
Source
2009 Flux
Estimate
Gas (Tg CO2 Eq.)
Uncertainty Range Relative to Flux Estimate"
(Tg C02 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Yard Trimmings and
Food Scraps CO2 (12.6) (21.2) (6.2) -68% +51%
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 2009. 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 and did not reveal
any systematic inaccuracies or incorrect input values.
Recalculations Discussion
First-order decay rate constants were updated based on De la Cruz and Barlaz (2010), as described in the
methodology section. Input data were updated for the years: 1990, 2000, 2005, and 2007 through 2009 based on the
updated values reported in Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts
and Figures for 2009 (EPA 2011). As a result, C storage estimates for those years were revised relative to the
previous Inventory. While data inputs for intervening years in the timeseries were not revised, overall C storage in
any given year is dependent on the previous year's storage (as shown in the second equation above), and so C
storage estimates for those years were also revised. These revisions resulted in an annual average increase in C
stored in landfills of 4.2 percent across the timeseries.
Planned Improvements
Future work is planned to evaluate the potential contribution of inorganic C, primarily in the form of carbonates, to
landfill sequestration, as well as the consistency between the estimates of C storage described in this chapter and the
estimates of landfill CH4 emissions described in the Waste chapter.
Land Use, Land-Use Change, and Forestry 7-59
-------
-------
Figure 7-1
Percent of Total Land Area in the General Land Use Categories for 2009
Croplands
Forest Lands
Grasslands
Settlements
Wetlands
Other Lands
10% M1%-30% •31%-50% B>50%
Note: Land use/land-use change categories were aggregated into the 6 general land-use categories based on the current use in 2009.
-------
Figure 7-2
Forest Sector Carbon Pools and Flows
Combustion from
forest fires (carbon
dioxide, methane)
Combustion from forest fires
(carbon dioxide, methane)
Harvest
Processing/ Residue
/^^Consumption
* ^4
Soil Organic
Material
Methane
Flaring
and
Utilization
Legend
Carbon Pool
Carbon transfer or flux
Combustion
Source: Heath et al. 2003
-------
Harvested Wood
Soil
Forest, Nonsoil
Total Net Change
Figure 7-3: Estimates of Net Annual Changes in C Stocks for Major C Pools
-------
Figure 7-4
Live Tree
Mg CO;, Eq./ha
I'".-. IFwin 200
-------
Figure 7-5
Total Net Annual C02 Flux for Mineral Soils Under Agricultural Management within States,
2009, 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.
-------
Figure 7-6
Total Net Annual C02 Flux for Organic Soils Under Agricultural Management within States,
2009, Cropland Remaining Cropland
O
0
Tg C02Eq./year
• >2
• 1to2
• 0.5 to 1
D 0.1 to 0.5
DO to 0.1
n No organic soils
Note: Values greater than zero represent emissions.
-------
Figure 7-7
Total Net Annual C02 Flux for Mineral Soils Under Agricultural Management within States,
2009, Land Converted to Cropland
o
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.
-------
Figure 7-8
Total Net Annual C02 Flux for Organic Soils Under Agricultural Management within States,
2009, Land Converted to Cropland
Tg C02Eq./year
• 0.5 to 1
D 0.1 to 0.5
Do to 0.1
EH No organic soils
Note: Values greater than zero represent emissions.
-------
Figure 7-9
Total Net Annual C02 Flux for Mineral Soils Under Agricultural Management within States,
2009, Grassland Remaining Grassland
Tg C02 Eq./year
D>0
D -0.1 to 0
D-0.5 to-0.1
• -1 to -0.5
• -2 to -1
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.
-------
Figure 7-10
Total Net Annual C02 Flux for Organic Soils Under Agricultural Management within States,
2009, Grassland Remaining Grassland
Tg C02Eq./year
|1to2
| 0.5 to 1
Q0.1 to 0.5
[]0 to 0.1
Q No organic soils
Note: Values greater than zero represent emissions.
-------
Figure 7-11
Total Net Annual C02 Flux for Mineral Soils Under Agricultural Management within States,
2009, 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.
-------
Figure 7-12
Total Net Annual C02 Flux for Organic Soils Under Agricultural Management within States,
2009, Land Converted to Grassland
Tg C02Eq./year
• 0.5 to 1
D 0.1 to 0.5
Do to 0.1
CH No organic soils
Note: Values greater than zero represent emissions.
-------
8. Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 8-1). Landfills
accounted for approximately 17 percent of total U.S. anthropogenic methane (CH4) emissions in 2009, the third
largest contribution of any CH4 source in the United States. Additionally, wastewater treatment and composting of
organic waste accounted for approximately 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 3 percent of total U.S. N2O emissions. Nitrogen
oxides (NOX), carbon monoxide (CO), and non-CH4 volatile organic compounds (NMVOCs) are emitted by waste
activities, and are addressed separately at the end of this chapter. A summary of greenhouse gas emissions from the
Waste chapter is presented in Table 8-1 and Table 8-2.
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 because virtually all of the combustion occurs in industrial and utility boilers that
recover energy. The incineration of waste in the United States in 2009 resulted in 12.7 Tg CO2 Eq. emissions, nearly
half of which is attributable to the combustion of plastics. For more details on emissions from the incineration of
waste, see Section 3.3.
Figure 8-1: 2009 Waste Chapter Greenhouse Gas Sources
[BEGIN BOX]
Box 8-1: Methodological approach for estimating and reporting U.S. emissions and sinks
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
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).197 Additionally, the calculated emissions and sinks in a given year for the U.S. are
presented in a common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under
this international agreement.198 The use of consistent methods to calculate emissions and sinks by all nations
providing their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions
and sinks reported in this inventory report are comparable to emissions and sinks reported by other countries.
Emissions and sinks provided in this Inventory do not preclude alternative examinations,199 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.
[END BOX]
Overall, in 2009, waste activities generated emissions of 150.5 Tg CO2 Eq., or just over 2 percent of total U.S.
greenhouse gas emissions.
Table 8-1. Emissions from Waste (Tg CO2 Eq.)
Gas/Source
CH4
Landfills
1990
171.2
147.41
2000
138.1
111.7
2005
138.4
112.5
2006
137.8
111.7
2007
137.4
111.3
2008
142.1
115.9
2009
143.6
117.5
197 See http://www.ipcc-nggip.iges.or.jp/public/index.html.
198 Seehttp://unfccc.int/national_reports/annex_i_ghg_inventories/national_inventories_submissions/items/5270.php.
199 For example, see http://www.epa.gov/aboutepa/oswer.html.
Waste 8-1
-------
Wastewater Treatment
Composting
N2O
Domestic Wastewater
Treatment
Composting
Total
23.5
0.3
I
3.7
0.4
175.2
25.2
1.3
1
4.5
1.4
143.9
24.3
1.6
6.5
4.8
1.7
144.9
24.5
1.6
6.6
4.8
1.8
144.4
24.4
1.7
6.7
4.9
1.8
144.1
24.5
1.7
6.8
5.0
1.9
149.0
24.5
1.7
6.9
5.0
1.8
150.5
Note: Totals may not sum due to independent rounding.
Table 8-2. Emissions from Waste (Gg)
Gas/Source
1990
2000
2005
2006
2007
2008
2009
CH4
Landfills
Wastewater Treatment
Composting
N2O
Domestic Wastewater
Treatment
Composting
6,563
5,321
1,167
75
21
16
6
6,541
5,299
1,163
79
22
16
6
6,769
5,520
1,168
80
22
16
6
6,840
5,593
1,167
79
22
16
6
Note: Totals may not sum due to independent rounding.
8.1. Landfills (IPCC Source Category 6A1)
In 2009, landfill CH4 emissions were approximately 117.5 Tg CO2 Eq. (5,593 Gg of CH4), representing the third
largest source of CH4 emissions in the United States, behind natural gas systems and enteric fermentation.
Emissions from municipal solid waste (MSW) landfills, which received about 64.5 percent of the total solid waste
generated in the United States, accounted for about 94 percent of total landfill emissions, while industrial landfills
accounted for the remainder. Approximately 1,800 operational landfills exist in the United States, with the largest
landfills receiving most of the waste and generating the majority of the CH4 (BioCycle 2006, adjusted to include
missing data from five states).
After being placed in a landfill, 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 CH4-producing anaerobic bacteria convert the
fermentation products into stabilized organic materials and biogas consisting of approximately 50 percent carbon
dioxide (CO2) and 50 percent CH4, by volume. Significant CH4 production typically begins one or two years after
waste disposal in a landfill and continues for 10 to 60 years or longer.
Methane emissions from landfills are a function of several factors, including: (1) the total amount of waste in MSW
landfills, which is related to total waste landfilled annually; (2) the characteristics of landfills receiving waste (i.e.,
composition of waste-in-place, size, climate); (3) the amount of CH4 that is recovered and either flared or used for
energy purposes; and (4) the amount of CH4 oxidized in landfills instead of being released into the atmosphere.
From 1990 to 2009, net CH4 emissions from landfills decreased by approximately 20 percent (see Table 8-3 and
Table 8-4). This net CH4 emissions decrease can be attributed to many factors, including changes in waste
composition, an increase in the amount of landfill gas collected and combusted, a higher frequency of composting,
and increased rates of recovery for degradeable materials (e.g, paper and paperboard).
The estimated annual quantity of waste placed in MSW landfills increased from about 209 Tg in 1990 to 297 Tg in
2009, an increase of 42 percent (see Annex 3.14). Despite increased waste disposal, the amount of decomposable
materials (i.e., paper and paperboard, food scraps, and yard trimmings) discarded in MSW landfills have decreased
by approximately 21 percent from 1990 to 2008 (EPA, 2009b). In addition, the amount of landfill gas collected and
combusted has increased. In 1990, for example, approximately 970 Gg of CH4 were recovered and combusted (i.e.,
used for energy or flared) from landfills, while in 2009, 7,208 Gg CH4 was combusted, which represents a 3 percent
increase in the quantity of CH4 recovered and combusted from 2008 levels. In 2009, an estimated 49 new landfill
gas-to-energy (LFGTE) projects and 32 new flares began operation.
8-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Over the past 9 years, however, the net CH4 emissions have fluctuated from year to year, but a slowly increasing
trend has been observed. 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.
Over the next several years, the total amount of municipal solid waste generated is expected to increase as the U.S.
population continues to grow. The percentage of waste landfilled, however, may decline due to increased recycling
and composting practices. In addition, the quantity of CH4 that is recovered and either flared or used for energy
purposes is expected to continue to increase as a result of 1996 federal regulations that require large municipal solid
waste 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 use 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).
Table 8-3. CH4 Emissions from Landfills (Tg CO2 Eq.)
Activity
MSW Landfills
Industrial Landfills
Recovered
Gas-to-Energy
Flared
Oxidized3
Total
1990
172.6 1
11.5
(13.6)
(6.7)
(16.4)
147.4
2000
206.9
14.3
(49.4)
(47.8)
(12.4)
111.7
2005
241.2
15.2
(56.5)
(74.9)
(12.5)
112.5
2006
248.1
15.3
(59.0)
(80.2)
(12.4)
111.7
2007
254.2
15.4
(63.7)
(82.3)
(12.4)
111.3
2008
260.3
15.5
(67.0)
(80.0)
(12.9)
115.9
2009
266.3
15.6
(72.0)
(79.4)
(13.1)
117.5
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
a Includes oxidation at both municipal and industrial landfills.
Table 8-4. CH4 Emissions from Landfills (G;
Activity 1990 2000 2005 2006 2007 2008 2009
MSW Landfills 8,219 I 9,854 I 11,486 11,813 12,107 12,395 12,679
Industrial Landfills 549 682 724 727 732 738 744
Gas-to-Energy (649) I (2,352) (2,691) (2,807) (3,033) (3,189) (3,429)
Flared (321) I (2,276) (3,566) (3,820) (3,918) (3,810) (3,779)
Oxidized3 (780) (591) (596) (592) (589) (614) (622)
Total 7,018 5,317 5,358 5,321 5,299 5,520 5,593
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
a Includes oxidation at municipal and industrial landfills.
Methodology
A detailed description of the methodology used to estimate CH4 emissions from landfills can be found in
Annex 3.14.
CH4 emissions from landfills were estimated as the CH4 produced from municipal solid waste landfills, plus the CH4
produced by industrial landfills, minus the CH4 recovered and combusted, minus the CH4 oxidized before being
released into the atmosphere:
CH4)S0l1(j Waste = [CH4)MSW + CH4jn
-------
potential (L0) and rate constant (k) 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 rate constant was found to increase with average annual rainfall; consequently, values of k
were developed for 3 ranges of rainfall. 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, and historical census data were
used to account for the shift in population to more arid areas over time. For further information, see Annex 3.14.
National landfill waste generation and disposal data for 2007, 2008, and 2009 were extrapolated based on BioCycle
data and the U.S. Census population from 2009. Data for 1989 through 2006 were obtained from BioCycle (2008).
Because BioCycle 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 (2010) and national per capita solid waste
generation from BioCycle (2008). Estimates of the annual quantity of waste landfilled for 1960 through 1988 were
obtained from EP A's Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to
Congress (EPA 1993) and an extensive landfill survey by the EPA's Office of Solid Waste in 1986 (EPA 1988).
Although waste placed in landfills in the 1940s and 1950s contributes very little to current CH4 generation, estimates
for those years were included in the first order decay model for completeness in accounting for CH4 generation rates
and are based on the population in those years and the per capita rate for land disposal for the 1960s. For
calculations in this inventory, wastes landfilled prior to 1980 were broken into two groups: wastes disposed in
landfills (Methane Conversion Factor, MCF, of 1) and those disposed in dumps (MCF of 0.6). Please see Annex
3.14 for more details.
The estimated landfill gas recovered per year was based on updated data collected from vendors of flaring
equipment, a database of landfill gas-to-energy (LFGTE) projects compiled by LMOP (EPA 2009a), and a database
maintained by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases (EIA
2007). As the EIA database only included data through 2006; 2007 to 2009 recovery for projects included in the
EIA database were assumed to be the same as in 2006. The three databases were carefully compared to identify
landfills that were in two or all three of the databases to avoid double counting reductions. Based on the information
provided by the EIA and flare vendor databases, the CH4 combusted by flares in operation from 1990 to 2009 was
estimated. This quantity likely underestimates flaring because these databases do not have information on all flares
in operation. Additionally, the EIA and LMOP databases provided data on landfill gas flow and energy generation
for landfills with LFGTE projects. If a landfill in the EIA database was also in the LMOP and/or the flare vendor
database, the emissions avoided were based on the EIA data because landfill owners or operators reported the
amount recovered based on measurements of gas flow and concentration, and the reporting accounted for changes
over time. If both flare data and LMOP recovery data were available for any of the remaining landfills (i.e., not in
the EIA database), then the emissions recovery was based on the LMOP data, which provides reported landfill-
specific data on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects. The
flare data, on the other hand, only provided a range of landfill gas flow for a given flare size. Given that each
LFGTE project is likely to also have a flare, double counting reductions from flares and LFGTE projects in the
LMOP database was avoided by subtracting emission reductions associated with LFGTE projects for which a flare
had not been identified from the emission reductions associated with flares. A further explanation of the
improvements made to estimate the landfill gas recovered for the current Inventory can be found in Annex 3.14.
A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4 emissions avoided. The value
for efficiency was selected based on the range of efficiencies (98 to 100 percent) recommended for flares in EPA's
AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4 (EPA 1998), efficiencies used to establish new
source performance standards (NSPS) for landfills, and in recommendations for closed flares used in LMOP.
Emissions from industrial landfills were estimated from activity data for industrial production (ERG 2010), waste
disposal factors, and the first order decay model. As over 99 percent of the organic waste placed in industrial
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). The amount of CH4 oxidized by the landfill
cover at both municipal and industrial landfills was assumed to be ten 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 landfills.
Uncertainty and Time-Series Consistency
Several types of uncertainty are associated with the estimates of CH4 emissions from landfills. The primary
8-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
uncertainty concerns the characterization of landfills. Information is not available on two fundamental factors
affecting CH4 production: the amount and composition of waste placed in every landfill for each year of its
operation. 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 landfills, are representative of U.S. landfills.
Additionally, the approach used to estimate the contribution of industrial wastes to total CH4 generation introduces
uncertainty. Aside from uncertainty in estimating CH4 generation potential, uncertainty exists in the estimates of
oxidation by cover soils. There is also uncertainty in the estimates of CH4 that is recovered by flaring and energy
projects. The IPCC default value of 10 percent for uncertainty in recovery estimates was used in the uncertainty
analysis when metering was in place (for about 64 percent of the CH4 estimated to be recovered). For flaring
without metered recovery data (approximately 34 percent of the CH4 estimated to be recovered), a much higher
uncertainty of approximately 50 percent was used (e.g., when recovery was estimated as 50 percent of the flare's
design capacity).
N2O emissions from the application of sewage sludge on landfills are not explicitly modeled as part of greenhouse
gas emissions from landfills. N2O emissions from sewage sludge applied to landfills would be relatively small
because the microbial environment in landfills 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, any
uncertainty or bias caused by not including N2O emissions from landfills is expected to be minimal.
The results of the IPCC Good Practice Guidance Tier 2 quantitative uncertainty analysis are summarized in Table
8-5. Landfill CH4 emissions in 2009 were estimated to be between 61.1 and 164.5 Tg CO2 Eq., which indicates a
range of 48 percent below to 40 percent above the 2009 emission estimate of 117.5 Tg CO2 Eq.
Table 8-5. Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills (Tg CO2 Eq. and Percent)
2009 Emission
Estimate Uncertainty Range Relative to Emission Estimate"
Source Gas (Tg CO2 Eq.) (Tg CO2 Eq.) (%)
Landfills
MSW
Industrial
CH4
CH4
CH4
117.5
103.4
14.1
Lower
Bound
61.1
61.0
10.2
Upper
Bound
164.5
167.5
17.1
Lower
Bound
-48%
-41%
-28%
Upper
Bound
+40%
+62%
+21%
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 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
A QA/QC analysis was performed for data gathering and input, documentation, and calculation. A primary focus of
the QA/QC checks was to ensure that CH4 recovery estimates were not double-counted. Both manual and electronic
checks were made to ensure that emission avoidance from each landfill was calculated in only one of the three
databases. The primary calculation spreadsheet is tailored from the IPCC waste model and has been verified
previously using the original, peer-reviewed IPCC waste model. All model input values were verified by secondary
QA/QC review.
Recalculations Discussion
In developing the current Inventory, a separate Monte Carlo analysis was conducted for MSW and industrial
landfills to better characterize the greater amount of uncertainty surrounding industrial waste data. Additional steps
were also taken to better characterize the food waste decay rate and the methodology for the flare correction factor.
A weighted component-specific decay rate for food waste of 0.156 yr"1 was used in the current Inventory as
recommended by ICF International (2009). This replaced the previous Inventory's default food waste decay rate of
0.185 yr"1 and resulted in a decrease of landfill emissions of less than 1 percent. The majority of changes in CH4
emissions from landfills over the time series resulted from improvements made to the flare correction factor to better
associate flares in the flare vendor database with a landfill and/or Landfill Gas to Energy (LFGTE) project in the
Waste 8-5
-------
EIA and LMOP databases.
The flare correction factor for the 1990 through 2008 Inventory report consisted of approximately 512 cases where
flares were not directly associated with a landfill and/or LFGTE project in the EIA and/or LMOP databases. For
these projects, CH4 avoided would be overestimated as both the CH4 avoided from flaring and the LFGTE project
would be counted. To abstain from overestimating emissions avoided from flaring, the CH4 avoided from flares with
no identified landfill or LFGTE project were determined and the flaring estimate from the flare vendor database was
reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis.
If comprehensive data on flares were available, the majority of LFGTE projects in the EIA and LMOP databases
would have an identified flare because it is assumed that most LFGTE projects have flares. However, given that the
flare vendor data only covers approximately 50 to 75 percent of the flare population, an associated flare was not
identified for all LFGTE projects. These LFGTE projects likely have flares; however, flares were unable to be
identified due to one of two reasons: (1) inadequate identifier information provided by the flare vendor; or (2) a lack
of the flare in the flare vendor database.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the current
Inventory to reduce the overall number of flares that were not matched (512) to landfills and/or LFGTE projects in
the EIA and LMOP databases. Each flare in the flare vendor database not associated with a LFGTE project in the
EIA or LMOP databases was investigated to determine if it could be matched to either a landfill in the EIA database
or a LFGTE project in the LMOP database. For some unmatched flares, the location information was missing or
incorrectly transferred to the flare vendor database. In other instances, the landfill names were slightly different
between what the flare vendor provided and the actual landfill name as listed in the EIA and/or LMOP databases.
It was found that a large majority of the unidentified flares are associated with landfills in LMOP that are currently
flaring, but are also considering LFGTE. These landfill projects considering a LFGTE project are labeled as
candidate, potential, or construction in the LMOP database. The flare vendor database was improved to match flares
with operational, shutdown as well as candidate, potential, and construction LFGTE projects, thereby reducing the
total number of unidentified flares in the flare vendor database, all of which are used in the flare correction factor.
The results of this effort significantly decreased the number of flares used in the flare correction factor from 512 to
27, impacted emission estimates for the entire time series, and resulted in an average annual decrease of 8.2 Tg CO2
Eq. (6.5 percent) in CH4 emissions from the Landfills source category for the period 1990 through 2008.
Planned Improvements
Beginning in 2010, 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.) will be required to calculate and
report their greenhouse gas emissions to EPA through its Greenhouse Gas Reporting Program (GHGRP). This
consists of the landfill, landfill gas collection systems, and landfill gas destruction devices, including flares. In
addition to reporting greenhouse gas information to EPA, landfill-specific characteristics such as annual waste
disposal quantity, waste composition data, surface area, and cover type must also be reported. The data collected
from the GHGRP will be used in future inventories to revise the parameters used in the CH4 generation calculations,
including degradeable organic carbon (DOC), the flare correction factor, the methane correction factor (MCF),
fraction of DOC dissimilated (DOCF), the destruction efficiency of flares, the oxidation factor (Ox), and the rate
constant (k). The addition of this higher tier data will improve the emission calculations to provide a more accurate
representation of gresnhouse gas emissions from MSW landfills.
[Begin Text Box]
Box 8-1: Biogenic Wastes in Landfills
Regarding the depositing of wastes of biogenic origin in landfills, empirical evidence shows that some of these
wastes degrade very slowly in landfills, and the C they contain is effectively sequestered in landfills over a period of
time (Barlaz 1998, 2006). Estimates of C removals from landfilling of forest products, yard trimmings, and food
scraps are further described in the Land Use, Land-Use Change, and Forestry chapter, based on methods presented
in IPCC (2003) and IPCC (2006).
[End Box]
8-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
8.2. Wastewater Treatment (IPCC Source Category 6B)
Wastewater treatment processes can produce anthropogenic CH4 and N2O emissions. Wastewater from domestic200
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 2009).
Soluble organic matter is generally removed using biological processes in which microorganisms consume the
organic matter for maintenance and growth. The resulting biomass (sludge) is removed from the effluent prior to
discharge to the receiving stream. Microorganisms can biodegrade soluble organic material in wastewater under
aerobic or anaerobic conditions, where the latter condition produces CH4. During collection and treatment,
wastewater may be accidentally or deliberately managed under anaerobic conditions. In addition, the sludge may be
further biodegraded under aerobic or anaerobic conditions. The generation of N2O may also result from the
treatment of domestic wastewater during both nitrification and denitrification of the N present, usually in the form of
urea, ammonia, and proteins. These compounds are converted to nitrate (NO3) through the aerobic process of
nitrification. Denitrification occurs under anoxic conditions (without free oxygen), and involves the biological
conversion of nitrate into dinitrogen gas (N2). N2O can be an intermediate product of both processes, but is more
often associated with denitrification.
The principal factor in determining the CH4 generation potential of wastewater is the amount of degradable organic
material in the wastewater. Common parameters used to measure the organic component of the wastewater are the
Biochemical Oxygen Demand (BOD) and Chemical Oxygen Demand (COD). Under the same conditions,
wastewater with higher COD (or BOD) concentrations will generally yield more CH4 than wastewater with lower
COD (or BOD) concentrations. BOD represents the amount of oxygen that would be required to completely
consume the organic matter contained in the wastewater through aerobic decomposition processes, while COD
measures the total material available for chemical oxidation (both biodegradable and non-biodegradable). Because
BOD is an aerobic parameter, it is preferable to use COD to estimate CH4 production. The principal factor in
determining the N2O generation potential of wastewater is the amount of N in the wastewater.
In 2009, CH4 emissions from domestic wastewater treatment were 16.0 Tg CO2 Eq. (760 Gg). Emissions gradually
increased 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. In 2009, CH4 emissions from industrial wastewater treatment were estimated to be 8.5 Tg CO2 Eq. (407
Gg). Industrial emission sources have increased across the time series through 1999 and then fluctuated up and
down with production changes associated with the treatment of wastewater from the pulp and paper manufacturing,
meat and poultry processing, fruit and vegetable processing, starch-based ethanol production, and petroleum refining
industries. Table 8-6 and Table 8-7 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 2009 emissions of N2O from centralized
wastewater treatment processes and from effluent were estimated to be 0.3 Tg CO2 Eq. (1 Gg) and 4.7 Tg CO2 Eq.
(15.2 Gg), respectively. Total N2O emissions from domestic wastewater were estimated to be 5.0 Tg CO2 Eq. (16.2
Gg). N2O emissions from wastewater treatment processes gradually increased across the time series as a result of
increasing U.S. population and protein consumption.
Table 8-6. CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Tg CO2 Eq.)
Activity 1990 2000 2005 2006 2007 2008 2009
200 Throughout the inventory, emissions from domestic wastewater also include any commercial and industrial wastewater
collected and co-treated with domestic wastewater.
Waste
-------
CH4
Domestic
Industrial*
N2O
Domestic
Total
23.5
16.4 1
1
3.7
27.2
25.2
16.8
18.4
4.5
4.5
29.6
24.3
16.2
8.2
4.8
4.8
29.1
24.5
16.0
8.5
4.8
4.8
29.3
24.4
15.9
8.5
4.9
4.9
29.3
24.5
15.8
8.6
5.0
5.0
29.5
24.5
16.0
8.5
5.0
5.0
29.5
Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit and vegetable processing,
starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
Table 8-7. CH4 and N2O Emissions from Domestic and Industrial Wastewater Treatment (Gg)
Activity 1990 2000 2005 2006 2007 2008 2009
CH4 1,118 1,199 1,159 1,167 1,163 1,168 1,167
Domestic 780 801 770 764 758 759 760
Industrial* 338 398 389 403 405 409 407
N2O 12 14 15 16 16 16 16
Domestic 12 14 15 16 16 16 16
* Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit and vegetable processing,
starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
Methodology
Domestic Wastewater CH4 Emission Estimates
Domestic wastewater CH4 emissions originate from both septic systems and from centralized treatment systems,
such as publicly owned treatment works (POTWs). Within these centralized systems, CH4 emissions can arise from
aerobic systems that are not well managed or that are designed to have periods of anaerobic activity (e.g.,
constructed wetlands), anaerobic systems (anaerobic lagoons and facultative lagoons), and from anaerobic digesters
when the captured biogas is not completely combusted. CH4 emissions from septic systems were estimated by
multiplying the total 5-day BOD (BOD5) produced in the United States by the percent of wastewater treated in
septic systems (20 percent), the maximum CH4 producing capacity for domestic wastewater (0.60 kg CH^g BOD),
and the CH4 correction factor (MCF) for septic systems (0.5). CH4 emissions from POTWs were estimated by
multiplying the total BOD5 produced in the United States by the percent of wastewater treated centrally (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 aerobic (zero or 0.3)
and anaerobic (0.8) systems with all aerobic systems assumed to be well-managed. CH4 emissions from anaerobic
digesters were estimated by multiplying the amount of biogas generated by wastewater sludge treated in anaerobic
digesters by the proportion of CH4 in digester biogas (0.65), the density of CH4 (662 g CH4/m3 CH4), and the
destruction efficiency associated with burning the biogas in an energy/thermal device (0.99). The methodological
equations are:
Emissions from Septic Systems = A
= (% onsite) x (total BOD5 produced) x (B0) x (MCF-septic) x 1/10A6
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) x 1/10A6
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) x 1/10A6
8-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Emissions from Anaerobic Digesters = D
= [(POTW_flow_AD) x (digester gas)/ (per capita flow)] x conversion to m3
ofCH4) x(l-DE) x l/lOA9
Where:
: (FRAC_CH4) x (365.25) x (density
Total CH4 Emissions (Gg) = A + B + C + D
% 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
Total BOD5 produced
Bo
MCF-septic
1/10A6
MCF-aerobic_not_well_man.
MCF-anaerobic
DE
POTW_flow
digester gas
AD
per capita flow
conversion to m3
FRAC_CH4
density of CH4
1/10A9
= 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
= 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)
= CH4 correction factor for septic systems (0.5)
= Conversion factor, kg to Gg
= CH4 correction factor for aerobic systems that are not well managed
(0.3)
= CH4 correction factor for anaerobic systems (0.8)
= CH4 destruction efficiency from flaring or burning in engine (0.99 for
enclosed flares)
= Wastewater influent flow to POTWs that have anaerobic digesters (gal)
= Cubic feet of digester gas produced per person per day (1.0
ft3/person/day) (Metcalf and Eddy 1991)
= Wastewater flow to POTW per person per day (100 gal/person/day)
= Conversion factor, ft3 to m3 (0.0283)
= Proportion CH4 in biogas (0.65)
= 662 (g CHVm3 CH4)
= Conversion factor, g to Gg
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2010) and
include the populations of the United States, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and
the Virgin Islands. Table 8-8 presents U.S. population and total BOD5 produced for 1990 through 2009, while Table
8-9 presents domestic wastewater CH4 emissions for both septic and centralized systems in 2009. 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, and 2009 American Housing Surveys conducted by the U.S.
Census Bureau (U.S. Census 2009), with data for intervening years obtained by linear interpolation. 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 2004a). Data for
intervening years were obtained by linear interpolation and the years 2004 through 2009 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 (1991 and 2003). The CH4 emission
factor (0.6 kg CH4/kg BOD5) and the MCFs were taken from IPCC (2006). The CH4 destruction efficiency for
methane recovered from sludge digestion operations, 99 percent, was selected based on the range of efficiencies (98
to 100 percent) recommended for flares in AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4 (EPA
1998), efficiencies used to establish new source performance standards (NSPS) for landfills, and in
recommendations for closed flares used by the Landfill Methane Outreach Program (LMOP). The cubic feet of
digester gas produced per person per day (1.0 ft3/person/day) and the proportion of CH4 in biogas (0.65) come from
Waste 8-9
-------
Metcalf and Eddy (1991). The wastewater flow to aPOTW(100 gal/person/day) was taken from the Great Lakes-
Upper Mississippi River Board of State and Provincial Public Health and Environmental Managers, "Recommended
Standards for Wastewater Facilities (Ten-State Standards)" (2004).
Table 8-8. U.S. Population (Millions) and Domestic Wastewater BOD5 Produced (Gg)
Year Population BODS
1990
254
8,333
2005
2006
2007
2008
2009
300
303
306
309
311
9,864
9,958
10,057
10,149
10,236
Source: U.S. Census Bureau (2010); Metcalf & Eddy 1991 and 2003.
Table 8-9. Domestic Wastewater CH4 Emissions from Septic and Centralized Systems (2009)
CH4 emissions (Tg CO2 Eq.) % of Domestic Wastewater CH4
Septic Systems
Centralized Systems
13.2
2.8
82.5%
17.5%
Total
16.0
100%
Note: Totals may not sum due to independent rounding.
Industrial Wastewater CH4 Emission Estimates
CH4 emissions 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. High volumes of wastewater generated and a high organic wastewater load were the main criteria.
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 2009 are displayed in Table 8-10 below. Table 8-11 contains production
data for these industries.
Table 8-10. Industrial Wastewater CH4 Emissions by Sector (2009)
CH4 emissions (Tg CO2 Eq.) % of Industrial Wastewater CH4
Pulp & Paper
Meat & Poultry
Petroleum Refineries
Fruit & Vegetables
Ethanol Refineries
4.1
3.6
0.6
0.1
0.1
48%
42%
7%
1%
1%
Total
8.5
100%
Note: Totals may not sum due to independent rounding.
Table 8-11. U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and Petroleum Refining
Production (Tg)
Year
Pulp and
Paper
Meat
(Live Weight
Killed)
Poultry
(Live Weight
Killed)
Vegetables,
Fruits and
Juices
Ethanol
Petroleum
Refining
1990
128.9
27.3
14.6
38.7
702.4
2005
2006
2007
2008
131.4
137.4
135.9
134.5
818.6
826.7
827.6
836.8
8-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
2009
137.0
5 O O
33.8
25.2
47.0
31.7
822.4
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 percentage of organic loading assumed to degrade
anaerobically, and the emission factor. Ratios of BOD: COD in various industrial wastewaters were obtained from
EPA (1997a) and used to estimate COD loadings. The B0 value used for all industries is the IPCC default value of
0.25 kg CH4/kg COD (IPCC 2006).
For each industry, the percent of plants in the industry that treat wastewater on site, the percent of plants that have a
primary treatment step prior to biological treatment, and the percent of plants that treat wastewater anaerobically
were defined. The percent of wastewater treated anaerobically onsite (TA) was estimated for both primary treatment
and secondary treatment. For plants that have primary treatment in place, an estimate of COD that is removed prior
to wastewater treatment in the anaerobic treatment units was incorporated.
The methodological equations are:
CH4 (industrial wastewater) = P x W x COD x %TA 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 (nrVmetric ton of product)
COD = Organics loading in wastewater (kg/m3)
%TA = Percent of wastewater treated anaerobically on site
%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
"/oWWaj = 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
"/oWW^ = Percent of wastewater treated anaerobically in anaerobic secondary treatment
"/oWW^t = percent of wastewater treated anaerobically in other secondary treatment
%CODS = percent of COD entering secondary treatment
B0 = Maximum CH4 producing potential of industrial wastewater (default value of
0.25 kg CH4/kg COD)
MCF = CH4 correction factor, indicating the extent to which the organic content
(measured as COD) degrades anaerobically
As described below, the values presented in Table 8-12 were used in the emission calculations.
Table 8-12. Variables Used to Calculate Percent Wastewater Treated Anaerobically by Industry (%)
Vjiriiiblc
%TAP
%TAS
%Plants0
%Plantsa
%Plantst
%wwap
%wwas
%wwat
Pulp
and
Paper
0
10.5
60
25
35
0
100
0
Meat
Processing
0
33
100
33
67
0
100
0
Poultry
Processing
0
25
100
25
75
0
100
0
Industry
Fruit/
Vegetable
Processing
0
4.2
11
5.5
5.5
0
100
0
Ethanol
Production
- Wet Mill
0
33.3
100
33.3
66.7
0
100
0
Ethanol
Production
- Dry Mill
0
75
100
75
25
0
100
0
Petroleum
Refining
0
100
100
100
0
0
100
0
Waste 8-11
-------
%CODP 100 100 100 100 100 100 100
%COD5 42 100 100 77 100 100 100
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. No anaerobic activity is assumed to occur in activated
sludge systems or aerated stabilization basins (note: although IPCC recognizes that some CH4 can be emitted from
anaerobic pockets, they recommend an MCF of zero). However, about 25 percent of the wastewater treatment
systems used in the United States are non-aerated stabilization basins. These basins are typically 10 to 25 feet deep.
These systems are classified as anaerobic deep lagoons (MCF = 0.8).
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). Published data from the American Forest and Paper Association,
data published by Paper Loop, and other published statistics were used to estimate production for 2002 through 2009
(Pulp and Paper 2005, 2006, and monthly reports from 2003 through 2008; Paper 360° 2007). The overall
wastewater outflow was estimated to be 85 nrYmetric ton, and the average BOD concentrations in raw wastewater
was estimated to be 0.4 gram BOD/liter (EPA 1997b, EPA 1993, World Bank 1999).
Meat and Poultry Processing. The meat and poultry processing industry makes extensive use of anaerobic lagoons
in sequence with screening, fat traps and dissolved air flotation when treating wastewater on site. About 33 percent
of meat processing operations (EPA 2002) and 25 percent of poultry processing operations (U.S. Poultry 2006)
perform on-site treatment in anaerobic lagoons. The IPCC default B0 of 0.25 kg CH4/kg COD and default MCF of
0.8 for anaerobic lagoons were used to estimate the CH4 produced from these on-site treatment systems. Production
data, in carcass weight and live weight killed for the meat and poultry industry, were obtained from the USDA
Agricultural Statistics Database and the Agricultural Statistics Annual Reports (USDA 2010). Data collected by
EPA's Office of Water provided estimates for wastewater flows into anaerobic lagoons: 5.3 and 12.5 nrVmetric 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.
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 CH/^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 2010) provided
production data for potatoes, other vegetables, citrus fruit, non-citrus fruit, and grapes processed for wine. Outflow
and BOD data, presented in Table 8-13, were obtained from EPA (1974) for potato, citrus fruit, and apple
processing, and from EPA (1975) for all other sectors.
Table 8-13. Wastewater Flow (nrVton) and BOD Production (g/L) for U.S. Vegetables, Fruits, and Juices Production
Commodity Wastewater Outflow (m3/ton) BOD (g/L)
Vegetables
Potatoes 10.27 1.765
Other Vegetables 8.74 0.801
8-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Fruit
Apples 3.66 1.371
Citrus 10.11 0.317
Non-citrus 12.42 1.204
Grapes (for wine) _ 2.78 _ 1.831
Ethanol Production. Ethanol, or ethyl alcohol, is produced primarily for use as a fuel component, but is also used in
industrial applications and in the manufacture of beverage alcohol. Ethanol can be produced from the fermentation
of sugar-based feedstocks (e.g., molasses and beets), starch- or grain-based feedstocks (e.g., corn, sorghum, and
beverage waste), and cellulosic biomass feedstocks (e.g., agricultural wastes, wood, and bagasse). Ethanol can also
be produced synthetically from ethylene or hydrogen and carbon monoxide. However, synthetic ethanol comprises
only about 2 percent of ethanol production, and although the Department of Energy predicts cellulosic ethanol to
greatly increase in the coming years, currently it is only in an experimental stage in the United States. According to
the Renewable Fuels Association, 82 percent of ethanol production facilities use corn as the sole feedstock and 7
percent of facilities use a combination of corn and another starch-based feedstock. The fermentation of corn is the
principal ethanol production process in the United States and is expected to increase through 2012, and potentially
more; therefore, emissions associated with wastewater treatment at starch-based ethanol production facilities were
estimated (ERG 2006).
Ethanol is produced from corn (or other starch-based feedstocks) primarily by two methods: wet milling and dry
milling. Historically, the majority of ethanol was produced by the wet milling process, but now the majority is
produced by the dry milling process. The wastewater generated at ethanol production facilities is handled in a
variety of ways. Dry milling facilities often combine the resulting evaporator condensate with other process
wastewaters, such as equipment wash water, scrubber water, and boiler blowdown and anaerobically treat this
wastewater using various types of digesters. Wet milling facilities often treat their steepwater condensate in
anaerobic systems followed by aerobic polishing systems. Wet milling facilities may treat the stillage (or processed
stillage) from the ethanol fermentation/distillation process separately or together with steepwater and/or wash water.
CH4 generated in anaerobic digesters is commonly collected and either flared or used as fuel in the ethanol
production process (ERG 2006).
Available information was compiled from the industry on wastewater generation rates, which ranged from 1.25
gallons per gallon ethanol produced (for dry milling) to 10 gallons per gallon ethanol produced (for wet milling)
(Ruocco 2006a,b; Merrick 1998; Donovan 1996; and NRBP 2001). COD concentrations were also found to be
about 3 g/L (Ruocco 2006a; Merrick 1998; White and Johnson 2003). The amount of wastewater treated
anaerobically was estimated, along with how much of the CH4 is recovered through the use of biomethanators (ERG
2006). CH4 emissions were then estimated as follows:
Methane = [Production x Flow x COD x 3.785 x ([%Plants0 x %WWa>p x %CODP] + [%Plantsa x %WWa,s
x%CODs] + [%PlantSt x %WWa,t x %CODS]) x B0 x MCF x % Not Recovered] + [Production x Flow x 3.785 x
COD x ([%Plants0 x %WWa,p x %CODP] + [%Plantsa x %WWa,s x %CODS] + [%Plantst x %WWa,t x %CODS])
B0 x MCF x (% Recovered) x (1-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%)
Waste 8
x
-------
B0 = 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 Gg
A time series of CH4 emissions for 1990 through 2009 was developed based on production data from the Renewable
Fuels Association (RFA 2010).
Petroleum Refining. Petroleum refining wastewater treatment operations produce CH4 emissions from anaerobic
wastewater treatment. The wastewater inventory section includes CH4 emissions from petroleum refining
wastewater treated on site under intended or unintended anaerobic conditions. Most facilities use aerated biological
systems, such as trickling filters or rotating biological contactors; these systems can also exhibit anaerobic
conditions that can result in the production of CH4. Oil/water separators are used as a primary treatment method;
however, it is unlikely that any COD is removed in this step.
Available information from the industry was compiled. The wastewater generation rate, from CARD (2007) and
Timm (1985), was determined to be 35 gallons per barrel of finished product. 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 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)
B0 = 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 2009 was developed based on production data from the Energy
Information Association (EIA 2010).
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 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 by-products in the conversion of nitrate to N gas in
anoxic biological treatment systems. Approximately 7 grams 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 grams 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 grams N2O per capita
8-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
per year.
N2O emissions from domestic wastewater were estimated using the following methodology:
= N2ONIT/DEMT + N2OwOUT MT/DEMT
N20NiT/DEMT= [(USpoPND) x EF2 x FJND-COM] * 1/10A9
N20WOUT MT/DEMT = {[(USPOP X WWTP) - USpoPNo] >< FrND-COM >< EFj} X 1/10A9
N2OEFFLuENT = {[((USpop - (0.9 x USPOPND)) x Protein x FNPR x FNON-CON x FrND-coM) - NSLUDGE] x EF3 x 44/28} x
1/10A6
where,
N2OTOTAL = Annual emissions of N2O (Gg)
N2OPLANT = N2O emissions from centralized wastewater treatment plants (Gg)
N2ONiT/DENiT = N2O emissions from centralized wastewater treatment plants with
nitrification/denitrification (Gg)
N2Owour NIT/DENIT = N2O emissions from centralized wastewater treatment plants without
nitrification/denitrification (Gg)
N2OEFFLUENT = N2O emissions from wastewater effluent discharged to aquatic environments (Gg)
USpop = U.S. population
USpopND = U.S. population that is served by biological denitrification (from CWNS)
WWTP = Fraction of population using WWTP (as opposed to septic systems)
EFj = Emission factor (3 .2 g N2O/person-year) - plant with no intentional denitrification
EF2 = Emission factor (7 g N2O/person-year) - plant with intentional denitrification
Protein = Annual per capita protein consumption (kg/person/year)
FNPR = Fraction of N in protein, default = 0.16 (kg N/kg protein)
FNON-CON = Factor for non-consumed protein added to wastewater (1.4)
FTND-COM =Factor for industrial and commercial co-discharged protein into the sewer system
(1.25)
NSLUDGE = N removed with sludge, kg N/yr
EF3 = Emission factor (0.005 kg N2O -N/kg sewage-N produced) - from effluent
0.9 = Amount of nitrogen removed by denitrification systems
44/28 = Molecular weight ratio of N2O to N2
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2010) 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, and 2009 American Housing Survey (U.S. Census
2009). Data for intervening years were obtained by linear interpolation. The emission factor (EF^ used to estimate
emissions from wastewater treatment was taken from IPCC (2006). Data on annual per capita protein intake were
provided by U.S. Department of Agriculture Economic Research Service (USDA 2009). Protein consumption data
for 2005 through 2009 were extrapolated from data for 1990 through 2004. Table 8-14 presents the data for U.S.
population and average protein intake. An emission factor to estimate emissions from effluent (EF3) has not been
specifically estimated for the United States, thus the default IPCC value (0.005 kg N2O-N/kg sewage-N produced)
was applied. The fraction of N in protein (0. 16 kg N/kg protein) was also obtained from IPCC (2006). The factor
for non-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 2009 were forecasted from the
rest of the time series. An estimate for the N removed as sludge (NSLUDGE) was obtained by determining the amount
of sludge disposed by incineration, by land application (agriculture or other), through surface disposal, in landfills,
or through ocean dumping. In 2009, 271 Gg N was removed with sludge.
Table 8-14. U.S. Population (Millions), Available Protein (kg/person-year), and Protein Consumed (kg/person-year)
Year _ Population Available Protein Protein Consumed
1990 254 38.7 29.6
Waste 8-15
-------
2005
2006
2007
2008
2009
300
303
306
309
311
41.7
41.9
42.1
42.2
42.4
32.1
32.1
32.2
32.4
32.5
Source: U.S. Census Bureau 2010, USDA 2009.
Uncertainty and Time-Series Consistency
The overall uncertainty associated with both the 2009 CH4 and N2O emission estimates from wastewater treatment
and discharge was calculated using the IPCC Good Practice Guidance Tier 2 methodology (2000). Uncertainty
associated with the parameters used to estimate CH4 emissions include that of numerous input variables used to
model emissions from domestic wastewater, and wastewater from pulp and paper manufacture, meat and poultry
processing, fruits and vegetable processing, ethanol production, and petroleum refining. Uncertainty associated with
the parameters used to estimate N2O emissions include that of sewage sludge disposal, total U.S. population,
average protein consumed per person, fraction of N in protein, non-consumption nitrogen factor, emission factors
per capita and per mass of sewage-N, and for the percentage of total population using centralized wastewater
treatment plants.
The results of this Tier 2 quantitative uncertainty analysis are summarized in Table 8-15. CH4 emissions from
wastewater treatment were estimated to be between 15.3 and 35.9 Tg CO2 Eq. at the 95 percent confidence level (or
in 19 out of 20 Monte Carlo Stochastic Simulations). This indicates a range of approximately 37 percent below to
47 percent above the 2009 emissions estimate of 24.5 Tg CO2 Eq. N2O emissions from wastewater treatment were
estimated to be between 1.2 and 9.7 Tg CO2 Eq., which indicates a range of approximately 76 percent below to 93
percent above the actual 2009 emissions estimate of 5.0 Tg CO2 Eq.
Table 8-15. Tier 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater Treatment (Tg CO2 Eq.
and Percent)
Source
Wastewater Treatment
Domestic
Industrial
Wastewater Treatment
2009 Emission
Gas Estimate
(Tg CO2 Eq.)
CH4
CH4
CH4
N2O
24.5
16.0
8.5
5.0
Uncertainty Range Relative to Emission
Estimate"
(Tg CO2 Eq.) (%)
Lower
Bound
15.3
7.6
5.1
1.2
Upper
Bound
35.9
26.6
13.1
9.7
Lower
Bound
-37%
-52%
-41%
-76%
Upper
Bound
+47%
+66%
+54%
+93%
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 2009. 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
8-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
• 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.
Planned Improvements Discussion
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 timeseries 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. Recently available CWNS data for 2008 also is being evaluated for incorporation into the
inventory. Due to significant changes in format, this dataset was unable to be included in the domestic wastewater
calculations for the current Inventory. However, EPA continues to evaluate ways to incorporate the updated data
into future years of the Inventory.
Currently, it is assumed that all aerobic 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.
A review of other industrial wastewater treatment sources for those industries believed to discharge significant loads
of BOD and COD has been ongoing. Food processing industries have the highest potential for CH4 generation due
to the waste characteristics generated, and the greater likelihood to treat the wastes anaerobically. However, in all
cases there is dated information available on U.S. treatment operations for these industries. A review of the organic
chemicals industry was conducted in April 2010, during which only 1987 data was readily identified. It was
concluded that current industry-level treatment system information is very difficult to obtain, as is time series data.
Based on the 1987 data, emissions from this source are small and are not a likely industry category for significant
CH4 emissions. Therefore, this industry has not been included in the Inventory and there are no near future plans to
do so. Similarly, the seafood processing industry was reviewed to estimate its potential to generate CH4. Due to
minimal anaerobic wastewater treatment operations at processing facilities, this industry was not selected for
inclusion in the Inventory. Other industries will be reviewed as necessary for inclusion in future years of the
Inventory.
Available data will be reviewed regarding anaerobic treatment at petroleum refineries. If necessary, the %TA for
this industry will be revised accordingly. Currently, all petroleum plants are assumed to have anaerobic treatment.
With respect to estimating N2O emissions, the default emission factor for indirect N2O from wastewater effluent and
direct N2O from centralized wastewater treatment facilities has a high uncertainty. Current research is being
conducted by the Water Environment Research Foundation (WERF) to measure N2O emissions from municipal
treatment systems. Such data will 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 expects WERF to publish a
final N2O generation report by the end of 2011. In addition, WERF recently conducted a study of greenhouse gas
emissions from septic systems located in California. This study concluded that the emission rate for methane and
nitrous oxide were 10.7 and 0.20 g/capita-d, respectively. EPA is currently reviewing the results of this study to
determine if the systems evaluated are representative of U.S. operations and if a country-specific factor for septic
systems can be introduced into the inventory. The effect would be to lower current estimates of CH4 emissions by
about half, and to include N2O emission estimates where previously none were calculated. In addition, more
investigation of new study results will be used to evaluate the method used to calculate N2O emissions associated
with effluent and whether septic systems are appropriately included in the calculation.
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
Waste 8-17
-------
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.
8.3. Composting (IPCC Source Category 6D)
Composting of organic waste, such as food waste, garden (yard) and park waste, and sludge, is common in the
United States. Advantages of composting include reduced volume in the waste material, stabilization of the waste,
and destruction of pathogens in the waste material. The end products of composting, depending on its quality, can
be recycled as fertilizer and soil amendment, or be disposed in a landfill.
Composting is an aerobic process and a large fraction of the degradable organic carbon in the waste material is
converted into carbon dioxide (CO2). Methane (CH4) is formed in anaerobic sections of the compost, but it is
oxidized to a large extent in the aerobic sections of the compost. Anaerobic sections are created in composting piles
when there is excessive moisture or inadequate aeration (or mixing) of the compost pile. The estimated CH4
released into the atmosphere ranges from less than 1 percent to a few percent of the initial C content in the material
(IPCC 2006). Composting can also produce nitrous oxide (N2O) emissions. The range of the estimated emissions
varies from less than 0.5 percent to 5 percent of the initial nitrogen content of the material (IPCC 2006).
From 1990 to 2009, the amount of material composted in the United States has increased from 3,810 Ggto 19,857
Gg, an increase of approximately 421 percent. From 2000 to 2009, the amount of material composted in the United
States has increased by approximately 33 percent. Emissions of CH4 and N2O from composting have increased by
the same percentage (see Table 8-16 and Table 8-17). In 2009, CH4 emissions from composting were 1.7 Tg CO2
Eq. (79 Gg), and N2O emissions from composting were 1.8 Tg CO2 Eq. (6 Gg). The wastes that are composted
include primarily 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 composting
waste quantities reported here do not include backyard composting. The growth in composting is attributable
primarily to two factors: (1) steady growth in population and residential housing, and (2) state and local
governments started enacting legislation that discouraged the disposal of yard trimmings in landfills. In 1992, 11
states and the District of Columbia had legislation in effect that banned or discouraged disposal of yard trimmings in
landfills. In 2005, 21 states and the District of Columbia, representing about 50 percent of the nation's population,
had enacted such legislation (EPA 2008).
Table 8-16. CH4 and N2O Emissions from Composting (Tg CO2 Eq.)
Activity
CH4
N2O
Total
1990
0.3
0.4
0.7
2000
1.3
1.4 •
| 2.7
2005
1.6
1 1.7
| 3.3
2006
1.6
1.8
3.3
2007
1.7
1.8
3.5
2008
1.7
1.9
3.5
2009
1.7
1.8
3.5
Table 8-17. CH4 and N2O Emissions from Composting (Gg)
Activity
CH4
N2O
1990
15
1
2000
60
4 I
2005
75
1 6
2006
75
6
2007
79
6
2008
80
6
2009
79
6
Methodology
CH4 and N2O emissions from composting depend on factors such as the type of waste composted, the amount and
type of supporting material (such as wood chips and peat) used, temperature, moisture content and aeration during
the process.
The emissions shown in Table 8-16 and Table 8-17 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):
E, = MxEF,
where,
8-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
E! = CH4 or N2O emissions from composting, Gg CH4 or N2O,
M = mass of organic waste composted in Gg,
EF; = emission factor for composting, 4 g CH4/kg of waste treated (wet basis) and 0.3 g
N2O/kg of waste treated (wet basis), and
i = designates either CH4 or N2O.
Estimates of the quantity of waste composted (M) are presented in Table 8-18. Estimates of the quantity composted
for 1990 and 1995 were taken from the Characterization of Municipal Solid Waste in the United States: 1996
Update (Franklin Associates 1997); estimates of the quantity composted for 2000, 2005, 2006, 2007, and 2008 were
taken from EPA's Municipal Solid Waste In The United States: 2008 Facts and Figures (EPA 2009); estimates of
the quantity composted for 2009 were calculated using the 2008 quantity composted.
Table 8-18: U.S. Waste Composted (Gg)
Activity 1990 2000 2005 2006 2007 2008 2009
Waste Composted 3,810 14,923 18,643 18,852 19,695 20,049 19,857
Source: Franklin Associates 1997 and EPA 2009.
Uncertainty and Time-Series Consistency
The estimated uncertainty from the 2006 IPCC Guidelines is ±50 percent for the Tier 1 methodology. Emissions
from composting in 2009 were estimated to be between 1.8 and 5.3 Tg CO2 Eq., which indicates a range of 50
percent below to 50 percent above the actual 2009 emission estimate of 3.5 Tg CO2 Eq. (see Table 8-19).
Table 8-19 : Tier 1 Quantitative Uncertainty Estimates for Emissions from Composting (Tg CO2 Eq. and Percent)
2009 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate
(Tg CO2 Eq.) (Tg CO2 Eq.) (%)
Composting
CH4, N2O
3.5
Lower
Bound
1.8
Upper
Bound
5.3
Lower
Bound
-50%
Upper
Bound
+50%
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2009. Details on the emission trends through time are described in more detail in the Methodology section,
above.
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.
8.4. 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 2009 are provided in Table 8-20.
Waste 8-19
-------
Table 8-20: Emissions of NOX, CO, and NMVOC from Waste (Gg)
Gas/Source 1990 2000 2005 2006 2007 2008 2009
NOX +1 2 2 2 2 2 2~
Landfills + I 2! 2 2 2 2 2
Wastewater Treatment + I + I + + + + +
Miscellaneous3 + I + I + + + + 0
CO ll8J77777
Landfills 1 I 7J 6 6 6 6 6
Wastewater Treatment + I 1 I + + + + +
Miscellaneous3 + I + I + + + + +
NMVOCs 673 119 114 113 111 109 76
Wastewater Treatment 57 51 49 49 48 47 33
Miscellaneous3 557 46 43 43 42 41 29
Landfills 58 22 22 21 21 21 14
a Miscellaneous includes TSDFs (Treatment, Storage, and Disposal Facilities under the Resource
Conservation and Recovery Act [42 U.S.C. § 6924, SWDA § 3004]) and other waste categories.
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.5 Gg.
Methodology
These emission estimates were obtained from preliminary data (EPA 2010, EPA 2009), and disaggregated based on
EPA (2003), which, in its final iteration, will be published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. Emission estimates of these gases were provided by sector, using a "top down"
estimating procedure—emissions were calculated either for individual sources or for many sources combined, using
basic activity data (e.g., the amount of raw material processed) as an indicator of emissions. National activity data
were collected for individual source categories from various agencies. Depending on the source category, these
basic activity data may include data on production, fuel deliveries, raw material processed, etc.
Uncertainty and Time-Series Consistency
No quantitative estimates of uncertainty were calculated for this source category. Methodological recalculations
were applied to the entire time-series to ensure time-series consistency from 1990 through 2009.
8-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Landfills
Wastewater Treatment
Composting
I
25
Waste as a Portion of all
Emissions
2.3%
50 75
TgCO2Eq.
100
125
Figure 8-1: 2009 Waste Chapter Greenhouse Gas Sources
-------
-------
9. Other
The United States does not report any greenhouse gas emissions under the Intergovernmental Panel on Climate
Change (IPCC) "Other" sector.
Other 9-1
-------
-------
10. Recalculations and Improvements
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows the 2006 IPCC
Guidelines (IPCC 2006), which states, "Both methodological changes and refinements over time are an essential
part of improving inventory quality. It is good practice to change or refine methods" when: available data have
changed; the previously used method is not consistent with the IPCC guidelines for that category; a category has
become key; the previously used method is insufficient to reflect mitigation activities in a transparent manner; the
capacity for inventory preparation has increased; new inventory methods become available; and for correction of
errors."
The results of all methodological changes and historical data updates are presented in this section; detailed
descriptions of each recalculation are contained within each source's description found in this report, if applicable.
Table 10-1 summarizes the quantitative effect of these changes on U.S. greenhouse gas emissions and Table 10-2
summarizes the quantitative effect on net CO2 flux to the atmosphere, both relative to the previously published U.S.
Inventory (i.e., the 1990 through 2008 report). These tables present the magnitude of these changes in units of
teragrams of carbon dioxide equivalent (Tg CO2 Eq.).
The Recalculations Discussion section of each source presents the details of each recalculation. In general, when
methodological changes have been implemented, the entire time series (i.e., 1990 through 2008) has been
recalculated to reflect the change, per IPCC (2006). Changes in historical data are generally the result of changes in
statistical data supplied by other agencies.
The following emission sources, which are listed in descending order of absolute average annual change in
emissions between 1990 and 2008, underwent some of the most important methodological and historical data
changes. A brief summary of the recalculations and/or improvements undertaken is provided for each emission
source.
• Natural Gas Systems (CH4). For the current Inventory, methodologies for gas well cleanups and condensate
storage tanks were revised, and new data sources for centrifugal compressors with wet seals, unconventional
gas well completions, and unconventional gas well workovers were used, relative to the previous Inventory. The
net effect of these changes was an increase in total CH4 emissions from natural gas systems of between 46.5 and
119.7 percent each year between 1990 and 2008, resulting in an overall annual average increase of 79.3 Tg CO2
Eq. (66.4 percent). The natural gas production segment accounted for the largest increases, largely due to the
methodological changes to gas well cleanups and the addition of unconventional gas well completions and
workovers.
• Landfills (CH^ Changes in CH4 emissions from Landfills relative to the previous Inventory resulted from
improvements made to better associate flares with the correct landfills or Landfill Gas to Energy projects across
the nation. In addition, steps were also taken to further characterize the food waste decay rate. A weighted
component-specific decay rate for food waste of 0.156 yr"1 was used in the current Inventory, replacing the
previous Inventory's default food waste decay rate of 0.185 yr"1 These revisions impacted emission estimates
for the entire time series and resulted in an average annual decrease of 8.3 Tg CO2 Eq. (6.5 percent) in CH4
emissions from Landfills for the period 1990 through 2008.
• Manure Management (CH4). Changes in CH4 emissions from Manure Management relative to the previous
Inventory resulted from several updates. Volatile solid production rates for all animal types were updated based
on data from the USD A and EPA's Cattle Enteric Fermentation Model. In addition, USD A data on swine were
re-categorized, which changed the typical animal mass for two categories. These changes impacted emission
estimates for the entire time series and resulted in an average annual increase of 3.5 Tg CO2 Eq. (9.4 percent) in
CH4 emissions from Manure Management across the entire time series relative to the previous Inventory.
• Agricultural Soil Management (N2O). Changes in N2O emissions from Agricultural Soil Management relative to
the previous Inventory resulted from methodological changes for estimating grassland areas and livestock
manure nitrogen. These recalculations have opposing effects on emissions; grassland area was reduced,
resulting in lower emissions, and livestock manure nitrogen increased, resulting in higher emissions. These
changes affected the entire time series, resulting in an average annual reduction in N2O emissions of 3.2 Tg CO2
Eq. (1.5 percent) for the period 1990 through 2008 relative to the previous Inventory.
Recalculations and Improvements 10-1
-------
• Iron and Steel Production & Metallurgical Coke Production (CO2). A calculation error in the previous
Inventory regarding coal tar production and coke breeze production estimates was corrected for the current
Inventory, resulting in an average annual decrease in CO2 emissions from Iron and Steel Production &
Metallurgical Coke Production of 2.2 Tg CO2 Eq. (2.7 percent) for the period 1990 through 2008.
• Non-Energy Uses of Fossil Fuels (CO 2). Updates to the El A Manufacturer's Energy Consumption Survey
(MECS) for 2006 resulted in changes to CO2 emissions from Non-Energy Uses of Fossil Fuels for 2003 through
2008 relative to the previous Inventory. Adjustments were made to the entire MECS time series to remove scrap
tire consumption for use as a fuel, which is associated with the Waste Incineration chapter. In addition,
emissions from synthetic rubber were revised across the entire time series. These changes impacted emission
estimates from 1990 through 2008 resulting in an average annual decrease in CO2 emissions of 1.4 Tg CO2 Eq.
(1.0 percent) across the entire time series.
• Petroleum Systems (CH4). Well completion venting, well drilling, and offshore platform activity factors were
updated relative to the previous Inventory from existing data sources from 1990 onward, and the emission
factor for venting from fixed roof storage tanks in the crude oil production segment was increased to reflect the
occurrence of gas venting through storage tanks. These changes affected the entire time series from Petroleum
Systems, resulting in an average annual increase in CH4 emissions of 1.3 Tg CO2 Eq. (4.3 percent) for the
period 1990 through 2008 relative to the previous report.
• Nitric Acid Production (N2O). Changes in N2O emission from Nitric Acid Production relative to the previous
Inventory resulted from updated information on abatement technologies in use at production facilities and
revised production data from the U.S. Census Bureau. These changes resulted in an average annual decrease in
N2O emissions of 1.3 Tg CO2 Eq. (6.7 percent) across the entire time series relative to the previous report.
• Electrical Transmission and Distribution (SF^. SF6 emission estimates for the period 1990 through 2008 were
updated relative to the previous Inventory based on (1) new data from EPA's SF6 Emission Reduction
Partnership; (2) revisions to interpolated and extrapolated non-reported Partner data; and (3) a correction made
to 2004 transmission mile data for a large Partnership utility that had been interpreted incorrectly from the UDI
database in previous years. In addition, the method for estimating potential emissions from the sector was
updated for the current Inventory to assume that all SF6 purchased by equipment manufacturers is either emitted
or sent to utilities. These changes affected the entire time series, resulting in an average annual increase of 1.2
Tg CO2 Eq. (6.6 percent) for the period 1990 through 2008 relative to the previous report.
• ForestlandRemaining Forestland (C Sink). Changes to the estimated carbon stored in Forestland Remaining
Forestland stemmed from recent additions to the Forest Inventory and Analysis Database (FIADB). Newer
annual inventory data for most states including Oklahoma, California, Oregon, and Washington were added.
Some older periodic inventories for some southern states were also updated. These changes resulted in an
average annual increase in carbon stored in forestland of 6.8 Tg CO2 Eq. (2.4 percent) for the period 1990
through 2008 relative to the previous inventory report.
Table 10-1: Revisions to U.S. Greenhouse Gas Emissions (Tg CO2 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
Incineration of Waste
1990
(1.1)
2.7
JNU
(0.1)
2000
(2.2)
1.5
1
1
(1.1)
(0.5)
3.2
NC
(1.2)
(2.2)
0.5
(0.8)
(0.2)
2005
5.3
(0.1)
+
1.3
(2.5)
(0.5)
2.3
(0.7)
6.9
(1.8)
0.4
(0.7)
(0.2)
2006
3.9
0.3
NC
1.4
(2.5)
(0.5)
2.6
(0.7)
4.2
(1.8)
1.2
(0.8)
(0.2)
2007
(0.2)
(0.3)
+
0.2
(0.2)
0.7
2.0
(3.0)
1.9
(1.8)
0.2
(0.7)
(0.6)
2008
0.2
(6.8)
(2.6)
4.7
(16.4)
5.5
4.7
(2.7)
6.8
(3.0)
2.9
(0.6)
(1.0)
10-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Ammonia Production and Urea Consumption
Lime Production
Cropland Remaining Cropland NCH NC
Limestone and Dolomite Use NCH NC
Soda Ash Production and Consumption NCH NC
Aluminum Production NCB NC
Petrochemical Production NCH NC
Carbon Dioxide Consumption NCH NC
Titanium Dioxide Production NCH NC
Ferroalloy Production NcB NC
Wetlands Remaining Wetlands NcB NC
Phosphoric Acid Production NcB NC
Zinc Production (0.3) (0.1)
Lead Production 0.2H 0.3
Petroleum Systems +H +|
Silicon Carbide Production and Consumption NCB NC
Land Use, Land-Use Change, and Forestry
(Sink)" 47.9 87.'.
Biomass - Wood" NCM NC
International Bunker Fuels"
Biomass - EthanoF
CH4
Natural Gas Systems
Enteric Fermentation i
Landfills i
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining Forest Land
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines NC
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Production & Metallurgical
Coke Production NC
Field Burning of Agricultural Residues (0.5)
Ferroalloy Production NC
Silicon Carbide Production and Consumption NC
Incineration of Waste NC
International Bunker Fuels"
N20 (7.1)
Agricultural Soil Management (5.7)
Mobile Combustion +1
Manure Management 0.1
Nitric Acid Production (1.2)
Stationary Combustion +1
Forest Land Remaining Forest Land +1
Wastewater Treatment +1
N2O from Product Uses NC
Adipic Acid Production +1
Composting NC
Settlements Remaining Settlements NC
Incineration of Waste NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
NC
0.6
0.3
NC
NC
NC
NC
NC
NC
NC
+
NC
NC
NC
NC
0.6
0.3
0.1
NC
(0.1)
NC
NC
NC
NC
NC
NC
NC
NC
NC
0.7
0.3
0.2
NC
1.0
(0.3)
NC
NC
NC
NC
NC
NC
0.1
+
0.8
0.3
NC
NC
NC
NC
(106.1)
NC
(0.8)
0.4
78.3
86.8
(0.2)
(13.1)
NC
4.3
1.1
+
NC
+
+
NC
NC
NC
(0.7)
NC
NC
NC
+
(5.4)
(4.5)
+
0.6
(1.1)
+
+
+
NC
NC
NC
NC
NC
(105.2)
(4.0)
(0.7)
0.5
103.9
114.6
(0.2)
(15.3)
+
4.4
1.1
+
NC
+
(0.1)
NC
NC
NC
(0.7)
NC
NC
NC
+
(3.1)
(2.3)
+
0.7
(1.1)
(0.1)
+
+
NC
NC
NC
NC
NC
(105.5)
(4.1)
0.6
0.7
95.4
105.7
(0.2)
(15.2)
(0.2)
4.9
1.2
+
NC
+
(0.1)
NC
NC
NC
(0.7)
NC
NC
+
+
(2.6)
(1.6)
+
0.8
(1.3)
(0.1)
+
+
NC
NC
NC
+
+
(100.1)
(0.1)
(1.5)
1.4
109.1
115.4
(0.2)
(10.4)
(0.5)
4.4
1.1
0.2
NC
(0.2)
+
+
NC
NC
(0.7)
NC
NC
+
+
(7.4)
(5.1)
+
0.8
(2.6)
+
+
+
NC
NC
+
(0.1)
+
Recalculations and Improvements 10-3
-------
Field Burning of Agricultural Residues (0.3) (0.4) (0.4) (0.4) (0.4) (0.4)
Wetlands Remaining Wetlands NcB NcB NC NC NC +
International Bunker Fuels" +1 +B + + + +
HFCs NC +B 1.0 1.6 2.1 2.5
Substitution of Ozone Depleting Substances NcB +B 1.0 1.6 2.1 2.5
HCFC-22 Production NcB NcB NC NC NC NC
Semiconductor Manufacture NcB NcB NC NC + +
PFCs NC NC NC NC + +
Semiconductor Manufacture NcB NcB NC NC + +
Aluminum Production Ncl NcB NC NC NC NC
SF6 l.sl l.ol 1.2 0.9 0.5 +
Electrical Transmission and Distribution l.sl l.ol 1.2 0.9 0.5 0.3
Magnesium Production and Processing NCB NCB + + + (0.1)
Semiconductor Manufacture NC NC NC NC + (0.2)
Net Change in Total Emissions'1 55.0 68.2 80.3 107.1 95.3 104.4
Percent Change 0.9% 1.0% 1.1% 1.5% 1.3% 1.5%
+ Absolute value does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
Parentheses indicate negative values
NC (No Change)
a Not included in emissions total.
b Excludes net CO2 flux from Land Use, Land-Use Change, and Forestry, and emissions from International Bunker Fuels.
Note: Totals may not sum due to independent rounding.
Table 10-2: Revisions to Net Flux of CO2 to the Atmosphere from Land Use, Land-Use Change, and Forestry (Tg
CO2Eq.)
Component: Net CO2 Flux From Land
Use, Land-Use Change, and Forestry
Forest Land Remaining Forest Land
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Settlements Remaining Settlements
Other
Net Change in Total Flux
Percent Change
1990
48.8
NC
NC
«..!)
NC
(0.7)
47.9
5.3%
2000 2005 2006 2007 2008
89.4 (105.0) (105.0) (105.0) (99.1)
NC NC NC NC NC
NC NC NC NC NC
+• 0.1 0.1 0.2 0.2
+• 0.2 0.3 0.3 0.4
NC NC NC NC NC
(1.9) (1.4) (0.6) (1.1) (1.7)
87.7 (106.1) (105.2) (105.5) (100.1)
13.2% (11.2%) (11.0%) (11.0%) (10.6%)
NC (No Change)
Note: Numbers in parentheses indicate a decrease in estimated net flux of CO2 to the atmosphere, or an increase in net
sequestration.
Note: Totals may not sum due to independent rounding.
+ Absolute value does not exceed 0.05 Tg CO2 Eq. or 0.05 percent.
10-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
11. References
Executive Summary
BEA (2010) 2009 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2009. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC.
July 29, 2010. Available online at < http://www.bea.gov/national/index.htnrfgdp >.
EIA (2010) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, September 2010, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2009/09).
EIA (2009) International Energy Annual 2007. Energy Information Administration (EIA), U.S. Department of
Energy. Washington, DC. Updated October 2008. Available online at
.
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United
Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available
online at . August 13, 2004.
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T.
Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.).
Cambridge University Press. Cambridge, United Kingdom.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T. Houghton, L.G. MeiraFilho, B.A. Callander, N. Harris, A. Kattenberg, andK. Maskell. (eds.). Cambridge
University Press. Cambridge, United Kingdom.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
UNFCCC (2003) National Communications: Greenhouse Gas Inventories from Parties included in Annex I to the
Convention, UNFCCC Guidelines on Reporting and Review. Conference of the Parties, Eighth Session, New Delhi.
(FCCC/CP/2002/8). March 28, 2003.
U.S. Census Bureau (2010) U.S. Census Bureau International Database (IDE). Available online at
. August 15, 2010.
Introduction
CDIAC (2009) "Recent Greenhouse Gas Concentrations." T.J. Biasing; DOI: 10.3334/CDIAC/atg.032. Available
online at . 23 February 2010.
References 11-1
-------
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 (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United
Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available
online at . August 13, 2004.
IPCC (2001) Climate Change 2001: The Scientific Basis. Intergovernmental Panel on Climate Change, J.T.
Houghton, Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, C.A. Johnson, and K. Maskell (eds.).
Cambridge University Press. Cambridge, United Kingdom.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change,, J.E. Penner, et al.
(eds.). Cambridge University Press. Cambridge, United Kingdom.
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T. Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell. (eds.). Cambridge
University Press. Cambridge, United Kingdom.
IPCC/TEAP (2005) Special Report: Safeguarding the Ozone Layer and the Global Climate System, Chapter 4:
Refrigeration. 2005. Available at http://www.auto-
ts.com/hcfc/technology%20option/Refrigeration/transport%20refrigeration.pdf
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobson, M.Z. (2001) "Strong Radiative Heating Due to the Mixing State of Black Carbon in Atmospheric
Aerosols." Nature, 409:695-697.
NOAA/ESRL (2009) "Trends in Atmospheric Carbon Dioxide." Available online at
. 11 January 2010.
UNEP/WMO (1999) Information Unit on Climate Change. Framework Convention on Climate Change. Available
online at .
UNFCCC (2006) Updated UNFCCC reporting guidelines on annual inventories following incorporation of the
provisions of decision 14/CP.ll. Note by the secretariat. (FCCC/SBSTA/2006/9). United Nations Office at Geneva,
Geneva.
UNFCCC (2006) Updated UNFCCC Reporting Guidelines on Annual Inventories Following Incorporation of the
Provisions of Decision 14/CP. 11. United Nations Framework Convention on Climate Change, Nairobi.
(FCCC/SBSTA/2006/9). August 16, 2006.
Trends in Greenhouse Gas Emissions
BEA (2010) 2009 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2009. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC.
July 29, 2010. Available online at < http://www.bea.gov/national/index.htm#gdp >.
Duffield, J. (2006) Personal communication. Jim Duffield, Office of Energy Policy and New Uses, USD A and
11-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Lauren Flinn, ICF International. December 2006.
EIA (2011) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, January 2011, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2011/01).
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at
IPCC (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 (2010) U.S. Census Bureau International Database (IDE). Available online at
. August 15, 2010.
Energy
EIA (2010) Indicators: CO2 Emissions. International Energy Statistics 2010. Energy Information Administration
.
Carbon Dioxide Emissions from Fossil Fuel Combustion
AAR (2009 through 2010) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, DC.
AISI (2004 through 2010) Annual Statistical Report, American Iron and Steel Institute, Washington, DC.
APTA (2007 through 2010) Public Transportation Fact Book. American Public Transportation Association,
Washington, DC. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, DC.
Available online at .
BEA (2010) 2009 Comprehensive Revision of the National Income and Product Accounts: Current-dollar and
"real" GDP, 1929-2009. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, DC.
July 29, 2010. Available online at < http://www.bea.gov/national/index.htnrfgdp >.
BEA (1991 through 2009) Unpublished BE-36 survey data. Bureau of Economic Analysis, U.S. Department of
Commerce. Washington, DC.
Benson, D. (2002 through 2004) Unpublished data. Upper Great Plains Transportation Institute, North Dakota State
University and American Short Line & Regional Railroad Association.
Coffeyville Resources Nitrogen Fertilizers (2010). 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.
DESC (2011) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
DOC (1991 through 2010) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries.
Form-563. Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, DC.
DOE (1993 through 2010) Transportation Energy Data Book. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-5198.
DOT (1991 through 2009) Fuel Cost and Consumption. Federal Aviation Administration, U.S. Department of
References 11-3
-------
Transportation, Bureau of Transportation Statistics, Washington, DC. DAI-10.
EIA (2011) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, January 2011, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2011/01).
EIA (2010a) U.S. Carbon Dioxide Emissions in 2009: A Retrospective Review. Energy Information Administration,
U.S. Department of Energy. Washington, DC. May 2010.
EIA (2010b) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2009). August 2010.
EIA (2010c) Quarterly Coal Report. Energy Information Administration, U.S. Department of Energy. Washington,
DC. DOE/EIA-0121.
EIA (2009a) Emissions of Greenhouse Gases in the United States 2008, Draft Report. Office of Integrated Analysis
and Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. DOE-EIA-
0573(2009).
EIA (2009b) Natural Gas Annual 2008. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0131(06). November 2009.
EIA (2009c) Manufacturing Consumption of Energy 2006. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Released July, 2009.
EIA (2007a) 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 (2007b) Historical Natural Gas Annual, 1930 - 2007. Energy Information Administration, U.S. Department of
Energy. Washington, DC.
EIA (2002) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at .
EIA (2001) U.S. Coal, Domestic and International Issues. Energy Information Administration, U.S. Department of
Energy. Washington, DC. March 2001.
EIA (1991 through 2009) Fuel Oil and Kerosene Sales. Energy Information Administration, U.S. Department of
Energy. Washington, DC. DOE/EIA-0535-annual.
EPA (2010a). 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.
EPA (2010b; NONROAD 2009a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
Erickson, T. (2003) Plains CO2 Reduction (PCOR) Partnership. Presented at the Regional Carbon Sequestration
Partnership Meeting Pittsburgh, Pennsylvania, Energy and Environmental Research Center, University of North
Dakota. November 3, 2003. Available online at .
FAA (2011) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2010.
FAA (2008). FAA Aerospace Forecasts Fiscal Years 2008-2025. Table 30 "General Aviation Aircraft Fuel
Consumption," Federal Aviation Administration. Available online at <
http://www.faa. gov/data_statistics/aviation/aerospace_forecasts/2007-
2020/media/FORECAST%20BOOK%20SM.pdf/>.
FAA (2006). System for assessing Aviation's Global Emission (SAGE) Model. Federal Aviation Administration's
Office of Aviation Policy, Planning, and Transportation Topics, 2006.
Fitzpatrick, E. (2002) The Weyburn Project: A Model for International Collaboration. Available online at
.
11-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
FHWA (1996 through 2010) Highway Statistics. Federal Highway Administration, U.S. Department of
Transportation, Washington, DC. Report FHWA-PL-96-023-annual. Available online at
.
FRB (2010) Industrial Production and Capacity Utilization. Federal Reserve Statistical Release, G.17, Federal
Reserve Board. Available online at June 25, 2010.
Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe
Aamidor, ICF International. December 17, 2007.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab
Bhangu, ICF International. U.S. Territories Fossil Fuel Consumption, 1990-2009. Unpublished. U.S. Energy
Information Administration. Washington, DC.
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, DC. June 22, 2001.
USAF (1998) Fuel Logistics Planning. U.S. Air Force: AFPAM23-221. May 1, 1998.
U.S. Bureau of the Census (2010), Current Industrial Reports Fertilizer Materials and Related Products: 2009
Summary. Available online at .
USGS (1991 through 20 \Q)Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (1994 through 20 lO)Minerals Yearbook: Lead Annual Report. U.S. Geological Survey, Reston, VA.
USGS (1995, 1998, 2000 through 2002, 2007, and 2009) Mineral Yearbook: Aluminum Annual Report. U.S.
Geological Survey, Reston, VA.
USGS (1991 through 2009a) Minerals Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
USGS (1991 through 2009b) Mineral Yearbook: Titanium Annual Report. U.S. Geological Survey, Reston, VA.
Stationary Combustion (excluding CO2)
EIA (2011) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, January 2011, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2011/01).
EIA (2010) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2009). August 2010.
EPA (2010a; NONROAD 2009a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. Available online at .
EPA (2010b). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (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 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
References 11-5
-------
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.,
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab
Bhangu, ICF International. U.S. Territories Fossil Fuel Consumption, 1990-2009. Unpublished. U.S. Energy
Information Administration. Washington, DC.
SAIC/EIA (2001) Monte Carlo Simulations of Uncertainty in U.S. Greenhouse Gas Emission Estimates. Final
Report. Prepared by Science Applications International Corporation (SAIC) for Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. June 22, 2001.
Mobile Combustion (excluding CO2)
AAR (2009 through 2010) Railroad Facts. Policy and Economics Department, Association of American Railroads,
Washington, DC.
ANL (2006) Argonne National Laboratory (2006) GREET model Version 1.7. June 2006.
APTA (2007 through 2010) Public Transportation Fact Book. American Public Transportation Association,
Washington, DC. Available online at .
APTA (2006) Commuter Rail National Totals. American Public Transportation Association, Washington, DC.
Available online at .
Benson, D. (2002 through 2004) Personal communication. Unpublished data developed by the Upper Great Plains
Transportation Institute, North Dakota State University and American Short Line & Regional Railroad Association.
BEA (1991 through 2005) Unpublished BE-36 survey data. Bureau of Economic Analysis (BEA), U.S. Department
of Commerce.
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.
DESC (2011) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
DOC (1991 through 2008) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries.
Form-563. Foreign Trade Division, Bureau of the Census, U.S. Department of Commerce. Washington, DC.
DOE (1993 through 2010) Transportation Energy Data Book. Office of Transportation Technologies, Center for
Transportation Analysis, Energy Division, Oak Ridge National Laboratory. ORNL-5198.
DOT (1991 through 2009) Fuel Cost and Consumption. Federal Aviation Administration, U.S. Department of
Transportation, Bureau of Transportation Statistics, Washington, DC. DAI-10.
EIA (2010) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy,
Washington, DC. July 2006. DOE/EIA-0384(2005).
EIA (2008a) "Table 3.1: World Petroleum Supply and Disposition." International Energy Annual. Energy
Information Administration, U.S. Department of Energy. Washington, DC. Available online at
.
11-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EIA (2007a) 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 (2007b) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, December 2007, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2007/12).
EIA (2007 through 2009) Natural Gas Annual. Energy Information Administration, U.S. Department of Energy,
Washington, DC. DOE/EIA-0131(06).
EIA (2002) Alternative Fuels Data Tables. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (1991 through 20II) Fuel Oil andKerosene Sales. Energy Information Administration, U.S. Department of
Energy, Washington, DC. DOE/EIA-0535-annual.
EPA (2008). "1970 - 2007 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2007a) Annual Certification Test Results Report. Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
EPA (2007b) Confidential engine family sales data submitted to EPA by manufacturers. Office of Transportation
and Air Quality, U.S. Environmental Protection Agency.
EPA (2010a) Motor Vehicle Emission Simulator (MOVES). Office of Transportation and Air Quality, U.S.
Environmental Protection Agency. Available online at .
EPA (20lOty NONROAD 2008a Model. Office of Transportation and Air Quality, U.S. Environmental Protection
Agency. 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 (1994&) 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.
FAA (2011) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2011.
References 11-7
-------
FAA (2009) FAA Aerospace Forecasts Fiscal Years 2009-2025. Table 30 "General Aviation Aircraft Fuel
Consumption," Federal Aviation Administration. Available online at <
http://www.faa.gov/data_research/aviation/aerospace_forecasts/2009-2025/media/Web%20GA%202009.xls>.
FAA (2006) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
System for Assessing Aviation's Global Emissions (SAGE). August 2006.
FHWA (1996 through 2010) Highway Statistics. Federal Highway Administration, U.S. Department of
Transportation, Washington, DC. Report FHWA-PL-96-023-annual. Available online at
.
Gaffney, J. (2007) Email Communication. John Gaffney, American Public Transportation Association and Joe
Aamidor, ICF International. December 17, 2007.
ICF (2006a) 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 (2006b) 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 (2004) Update of Methane and Nitrous Oxide Emission Factors for On-Highway Vehicles. Final Report to U.S.
Environmental Protection Agency. February 2004.
IPCC/UNEP/OECD/IEA (1997) Revised 1996IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Lipman, T. and M. Delucchi (2002) "Emissions of Nitrous Oxide and Methane from Conventional and Alternative
Fuel Motor Vehicles." Climate Change, 53:477-516.
Unnasch, S., L. Browning, and E. Kassoy (2001) Refinement of Selected Fuel-Cycle Emissions Analyses, Final
Report to ARE.
U.S. Census Bureau (2000) Vehicle Inventory and Use Survey. U.S. Census Bureau, Washington, DC. Database CD-
EC97-VIUS.
Whorton, D. (2006 through 2010) 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 (2010) "Guide to the Business of Chemistry, 2010" American Chemistry Council.
ACC (2003-2010) "PIPS Year-End Resin Statistics for 2009: Production, Sales and Captive Use,"
http://www.americanchemistry.com/s_acc/sec_policyissues.asp?CID=996&DID=6872
Bank of Canada (2009) Financial Markets Department Year Average of Exchange Rates. Available online at:
http://www.bankofcanada.ca/pdf/nraa09.pdf
EIA (2011) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, January 2011, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2011/01).
EIA (2010) EIA Manufacturing Consumption of Energy (MECS) 2006, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (2001) EIA Manufacturing Consumption of Energy (MECS) 1998, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EIA (1997) EIA Manufacturing Consumption of Energy (MECS) 1994, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
11-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EIA (1994) EIA Manufacturing Consumption of Energy (MECS) 1991, U.S. Department of Energy, Energy
Information Administration, Washington, DC.
EPA (2010). "1970 - 2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2007a) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts
Warehouse. Washington, DC. Available online at . Data for 2001-2007 are
current as of Sept. 9, 2009.
EPA (2007b) Municipal Solid Waste in the United States: Facts and Figures for 2006. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2006a)^4/> Emissions Trends - Continued Progress Through 2005. U.S. Environmental Protection Agency,
Washington DC. December 19, 2006. < http://www.epa.gov/air/airtrends/index.html>
EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/. Accessed September 2006.
EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6.
(http://www.epa.gov/oppbeadl/pestsales/99pestsales/market_estimates 1999.pdf). Accessed July 2003.
EPA (2001) AP 42, Volume I, Fifth Edition. Chapter 11: Mineral Products Industry. Available online at
http://www.epa.gov/ttn/chief/ap42/chll/index.html
EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, DC. Available online at .
EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental
Information, Office of Information Analysis and Access, Washington, DC. Available online at
.
EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates and
http://www.epa.gov/oppbeadl/pestsales/97pestsales/market_estimatesl997.pdf.
EPA (1998) EPA's Pesticides Industry Sales and Usage, 1994-1995 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/95pestsales/market_estimatesl995.pdf.
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 .
FEB (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions Chemical &
Engineering News, American Chemical Society, 11 July. Available online at http://www.cen-online.org.
FEB (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries, Chemical
& Engineering News, American Chemical Society, 7 July. Available online at http://www.cen-online.org.
FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and
products lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25
June. Available online at http://pubs.acs.org/cen
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available
online at: http://www.fpanet.org/global/planners/US_Canada_ex_rates.cfm. Accessed August 16, 2006.
Gosselin, Smith, and Hodge (1984), "Clinical Toxicology of Commercial Products." Fifth Edition, Williams &
Wilkins, Baltimore.
References 11-9
-------
Huurman, J. W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on Sectoral Energy
Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic
Rubber Producers, Inc. New Release; available online at: .
IISRP (2000) "Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and RMA" International
Institute of Synthetic Rubber Producers press release.
INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y
Clase de actividad. http://www.inegi.gob.mx/est/contenidos/espanol/proyectos/censos/ce2004/tb_manufacturas.asp.
Accessed August 15.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Marland, G., and R.M. Rotty (1984), "Carbon dioxide emissions from fossil fuels: A procedure for estimation and
results for 1950-1982", Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card
RMA (2009a). Scrap Tire Markets in the United States: 9th Biennial Report. Rubber Manufacturers Association,
Washington, DC. May 2009.
RMA (2009b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
http://www.rma.org/scrap_tires/scrap_tire_markets/scrap_tire_characteristics/ Accessed 17 September
2009. Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah
Shapiro of ICF International, January 10, 2007.
U.S. Bureau of the Census (2009) Soap and Other Detergent Manufacturing: 2007. Available online at
.
U.S. Bureau of the Census (2004) Soap and Other Detergent Manufacturing: 2002, Issued December 2004, EC02-
311-325611 (RV). Available online at .
U.S. Bureau of the Census (1999) Soap and Other Detergent Manufacturing: 1997, Available online at<
http://www.census.gov/epcd/www/ec97stat.htm>.
U.S. International Trade Commission (1990-2011) "Interactive Tariff and Trade DataWeb: Quick Query." Available
online at: http://dataweb.usitc.gov/ Accessed January 2011.
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.
11-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EPA (2007, 2008) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at
.
EPA (2006) Solid Waste Management and Greenhouse Gases: A Life-Cycle Assessment of Emissions and Sinks.
Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency. Washington, DC.
EPA (2000) Characterization of Municipal Solid Waste in the United States: Source Data on the 1999 Update.
Office of Solid Waste, U.S. Environmental Protection Agency. Washington, DC. EPA530-F-00-024.
Goldstein, N. and C. Madtes (2001) "13th Annual BioCycle Nationwide Survey: The State of Garbage in America."
BioCycles, JG Press, Emmaus, PA. December 2001.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Kaufman, et al. (2004) "14th Annual BioCycle Nationwide Survey: The State of Garbage in America 2004"
Biocycle, JG Press, Emmaus, PA. January, 2004.
RMA (2009a) U.S. Scrap Tire Markets in the United States: 9th Biennial Report. Rubber Manufacturers Association.
Washington, DC. May 2009.
RMA (2009b) "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.
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.
Coal Mining
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
DOE (1983) Methane Recovery from Coalbeds: A Potential Energy Source. U.S. Department of Energy.
DOE/METC/83-76.
EIA (2010) Annual Coal Report 1991-2009 (Formerly called Coal Industry Annual). Table 1. Energy Information
Administration, U.S. Department of Energy, Washington, DC.
EPA (1996) Evaluation and Analysis of Gas Content and Coal Properties of Major Coal Bearing Regions of the
United States. U.S. Environmental Protection Agency. EPA/600/R-96-065.
GRI(1988)^ Geologic Assessment of Natural Gas from Coal Seams. Topical Reports, Gas Research Institute 1986-
88.
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.
USBM (1986) Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins. Circular 9067,
U.S. Bureau of Mines.
Abandoned Underground Coal Mines
EPA (2003) Methane Emissions Estimates & Methodology for Abandoned Coal Mines in the U.S. Draft Final
Report. Washington, DC. June 2003.
Mutmansky, Jan M., and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual
Methane Emissions. Department of Energy and Geo-Environmental Engineering, Pennsylvania State University.
University Park, PA.
U.S. Department of Labor, Mine Health & Safety Administration (2007) Data Retrieval System. Available online at
.
References 11-11
-------
Natural Gas Systems
AAPG (2004) Shale Gas Exciting Again. American Association of Petroleum Geologists. Available online at
.
AGA (1991 through 1998) Gas Facts. American Gas Association. Washington, DC.
API (2005) "Table 12—Section III—Producing Oil Wells in the United States by State." In Basic Petroleum Data
Book. American Petroleum Institute, Volume XXV, Number 1. February 2005.
Alabama (2010) Alabama State Oil and Gas Board. Available online at .
BOEMRE (2010a) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior. Available online at
.
BOEMRE (20 lOb) Gulf of Mexico Region Products/Free Data. Bureau of Ocean Energy Management, Regulation
and Enforcement, U.S. Department of Interior. Available online at
.
BOEMRE (20 lOc) OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and Enforcement,
U.S. Department of Interior. Available online at .
BOEMRE (20lOd) Pacific OCS Region. Bureau of Ocean Energy Management, Regulation and Enforcement, U.S.
Department of Interior. Available online at
.
BOEMRE (2004) Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. OCS Study
MMS 2004-072.
Brookhaven (2004) Natural Gas Field Subject of Interest atBrookhaven College. Brookhaven College. Available
online at .
EIA (2010a) Number of Producing Gas and Gas Condensate Wells, 1989-2009, Natural Gas Navigator. Energy
Information Administration, U.S. Department of Energy, Washington, DC. Available online at
.
EIA (2010b) "Table 1—Summary of Natural Gas Supply and Disposition in the United States 1999-2010." Natural
Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available online
at .
EIA (2010c) "Table 2—Natural Gas Consumption in the United States." Natural Gas Monthly, Energy Information
Administration, U.S. Department of Energy, Washington, DC. Available online at .
EIA (2010d) Table 5.2. Monthly Energy Review. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2010e) "Table 6—Marketed Production of Natural Gas by State." Natural Gas Monthly, Energy Information
Administration, U.S. Department of Energy, Washington, DC. Available online at .
EIA (20101) U.S. Imports by Country. Energy Information Administration, U.S. Department of Energy,
Washington, DC. Available online at .
EIA (2005) "Table 5—U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves, 1977-2003." Energy
Information Administration, Department of Energy, Washington, DC.
EIA (2004) USING Markets and Uses. Energy Information Administration, U.S. Department of Energy,
Washington, DC. June 2004. Available online at .
EIA (2001) "Documentation of the Oil and Gas Supply Module (OGSM)." Energy Information Administration, U.S.
Department of Energy, Washington, DC. Available online at
.
EIA (1996) "Emissions of Greenhouse Gases in the United States" Carbon Dioxide Emissions. Energy Information
11-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Administration, U.S. Department of Energy, Washington, DC.
EPA (2010) Natural Gas STAR Reductions 1990-2009. Natural Gas STAR Program.
EPA (2007) Reducing Methane Emissions During Completions Operations. Natural Gas STAR Producer's
Technology Transfer Workshop. September 11, 2007. .
EPA (2006a) Installing Plunger Lift Systems in Gas Wells. Lessons Learned from Natural Gas STAR Partners.
October 2006. .
EPA (2006b) Replacing Wet Seals with Dry Seals in Centrifugal Compressors. Lessons Learned from Natural Gas
STAR Partners. October 2006. .
EPA (2004) Green Completions Natural Gas STAR Producer's Technology Transfer Workshop. September 21,
2004. .
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 (2010). North American LNG Terminals . Federal Energy Regulatory Commission, Washington, DC.
Available online at .
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition.
GRI-01/0136.
HPDI (2009) Production and Permit Data, October 2009.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buenida, K. Miwa, T Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
Kansas (2010) Kansas Geological Survey. Oil and Gas Production Data, All Wells, University of Kansas. Available
online at .
Lippman (2003) Rocky Mountain Region Second Quarter 2003 Production Report. Lippman Consulting, Inc.
Montana (2010) Montana Online Oil and Gas Information System. Montana Board of Oil and Gas Conservation,
Billing Office. Available online at .
Morgan Stanley (2005) Barnett Shale Update: None-Core Confidence Rises. Available online at
.
New Mexico (2005) Districts. Available online at .
OGJ (1997-2010) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK.
Oklahoma (2010) Oklahoma Petroleum Information Center—Coalbed-Methane Completions database. Oklahoma
Geological Survey. Available online at .
OPS (2010a) Natural Gas Transmission Pipeline Annual Mileage. Office of Pipeline Safety, U.S. Department of
Transportation, Washington, DC. Available online at .
OPS (2010b) Distribution Annuals Data. Office of Pipeline Safety, U.S. Department of Transportation, Washington,
DC. Available online at .
TERC (2009) VOC Emissions from Oil and Condensate Storage Tanks. Hendler, Albert, URS Corporation; Nunn,
Jim, COMM Engineering; Lundeen, Joe, Trimeric Corporation. Revised April 2, 2009. Available online at:
References 11-13
-------
.
Texas (2010a) Gas Well Counts by County. Texas Railroad Commission. Available online at
.
Texas (2010b) Oil Well Counts by County. Texas Railroad Commission. Available online at
.
Texas (2010c) The Barnett Shale Regional Report. Foster, Brad, Devon Energy, Texas Railroad Commission.
Available online at .
Texas (2010d) Oil and Gas District Boundaries. Texas Railroad Commission. Available online at
.
Utah (2010) Oil and Gas Data Download. Utah Division of Oil, Gas and Mining—Department of Natural Resources.
Available online at .
WGC (2009) "Methane's Role in Promoting Sustainable Development in the Oil and Natural Gas Industry."
October 2009.
World Oil Magazine (2010a) "Outlook 2009: Producing Gas Wells." 228(2). February 2010. Available online at
.
World Oil Magazine (2010b) "Outlook 2009: Producing Oil Wells." 228(2). February 2010. Available online at
.
Wyoming (2010) Wyoming Oil and Gas Conservation Commission. Available online at
.
Petroleum Systems
API (2009) Compendium of Greenhouse gas Emissions Methodologies for the Oil and Gas Industry. American
Petroleum Institute. Austin, TX, August 2009.
BOEMRE (2010a) OCS Platform Activity. Bureau of Ocean Energy Management, Regulation, and Enforcement,
U.S. Department of Interior. Available online at < http://www.boemre.gov/stats/ >.
BOEMRE (2010b) Platform Information and Data. Bureau of Ocean Energy Management, Regulation, and
Enforcement, U.S. Department of Interior. Available online at <
http://www.gomr.boemre.gov/homepg/pubinfo/freeasci/platform/freeplat.html>.
BOEMRE (2Q10c) Pacific OCS Region. Bureau of Ocean Energy Management, Regulation, and Enforcement, U.S.
Department of Interior. Available online at
.
BOEMRE (2005) Field and Reserve Information. Bureau of Ocean Energy Management, Regulation, and
Enforcement, U.S. Department of Interior. Available online at
< http://www.gomr.boemre.gov/homepg/offshore/fldresv/resvmenu.html >.
BOEMRE (2004). Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. Bureau of
Ocean Energy Management, Regulation, and Enforcement (formerly Minerals Management Service), U.S.
Department of Interior. OCS Study MMS 2004-072.
EIA (1990 through 2010) Refinery Capacity Report. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at
.
EIA (1995 through 20 IQ&) Annual Energy Review. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at .
EIA (1995 through 201 Ob) MonMy Energy Review. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at .
EIA (1995 through 2010) Petroleum Supply Annual. Volume 1. U.S Department of Energy Washington, DC.
Available online at:
11-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EPA (2005) Incorporating the Mineral Management Service Gulfwide Offshore Activities Data System (GOADS)
2000 data into the methane emissions inventories. Prepared by ICF International. U.S. Environmental Protection
Agency. 2005.
EPA (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF
International. Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA (1996) Methane Emissions from the U.S. Petroleum Industry (Draft). Prepared by Radian. U.S. Environmental
Protection Agency. June 1996.
EPA (1995) Compilation of Air Pollutant Emission Factors AP-42, Fifth Edition, Volume I: Stationary Point and
Area Sources. U.S. Environmental Protection Agency. Available online at
.
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 (2009) Production and Permit Data, October 2009.
IOGCC (2008) Marginal Wells: fuel for economic growth 2008 Report. Interstate Oil & Gas Compact Commission.
Available online at .
OGJ (2010a) Oil and Gas Journal 1990-2009. Pipeline Economics Issue, November 2010.
OGJ (2010b) Oil and Gas Journal 1990-2009. Worldwide Refining Issue, January 2010.
TERC (2009) VOC Emissions from Oil and Condensate Storage Tanks. Hendler, Albert, URS Corporation; Nunn,
Jim, COMM Engineering; Lundeen, Joe, Trimeric Corporation. Revised April 2, 2009. Available online at:
.
United States Army Corps of Engineers (1995-2008) Waterborne Commerce of the United States, Part 5: National
Summaries. U.S. Army Corps of Engineers. Washington, DC.
Energy Sources of Indirect Greenhouse Gases
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
International Bunker Fuels
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 .
References 11-15
-------
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.
DESC (2011) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense
Energy Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, DC.
DOC (1991 through 2010) Unpublished Report of Bunker Fuel Oil Laden on Vessels Cleared for Foreign Countries.
Form-563. Foreign Trade Division, Bureau of the Census, U. S. Department of Commerce. Washington, DC.
EIA (2010) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, October 2010, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2009/10).
FAA (2010) Personal Communication between FAA and Leif Hockstad for aviation emissions estimates from the
Aviation Environmental Design Tool (AEDT). January 2010.
FAA (2006). System for assessing Aviation's Global Emission (SAGE) Model. Federal Aviation Administration's
Office of Aviation Policy, Planning, and Transportation Topics, 2006.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
USAF (1998) Fuel Logistics Planning. U.S. Air Force pamphlet AFPAM23-221, May 1, 1998.
Wood Biomass and Ethanol Consumption
EIA (2010) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384 (2009). August 19, 2010.
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.
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.
Industrial Processes
Cement Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
USGS (1995 through 20U) Mineral Commodity Summaries - Cement. U.S. Geological Survey, Reston, VA.
U.S. Bureau of Mines (1990 through 1993) Minerals Yearbook: Cement Annual Report. U.S. Department of the
Interior, Washington, DC.
van Oss (2008) Personal communication. Hendrik van Oss, Commodity Specialist of the U.S. Geological Survey
and Erin Gray, ICF International. December 16, 2008.
Lime Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.,
11-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
Lutter (2009 through 2010). Personal communication. Karen Lutter, California Air Resources Board and Mausami
Desai, EPA. October 20, 2009; September 28, 2010.
Males, E. (2003) Memorandum from Eric Males, National Lime Association to Mr. William N. Irving & Mr. Leif
Hockstad, Environmental Protection Agency. March 6, 2003.
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 2010). Personal communication. Hunter Prillaman, National Lime Association and
Mausami Desai, EPA. November 5, 2008; October 19, 2009; October 21, 2010.
USGS (1992 through 2010)Minerals Yearbook: Lime. U.S. Geological Survey, Reston, VA.
Limestone and Dolomite Use
USGS (1995 through 20 IQa) Minerals Yearbook: Crushed Stone Annual Report. U.S. Geological Survey, Reston,
VA.
USGS (1995 through 201 Ob) Minerals Yearbook: Magnesium Annual Report. U.S. Geological Survey, Reston, VA.
U.S. Bureau of Mines (1991 & 1993a) Minerals Yearbook: Crushed Stone Annual Report. U.S. Department of the
Interior. Washington, DC.
U.S. Bureau of Mines (1990 through 1993b) Minerals Yearbook: Magnesium and Magnesium Compounds Annual
Report. U.S. Department of the Interior. Washington, DC.
Willett (2010) Personal communication. Jason Willett, Commodity Specialist of the U.S. Geological Survey and
Tristan Kessler, ICF International. September 23, 2010.
Soda Ash Production and Consumption
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.
USGS (2008) Minerals Yearbook: Soda Ash Annual Report. (Advance Release) U.S. Geological Survey, Reston,
VA.
USGS (2009 through 2010) Mineral Commodity Summary: Soda Ash. U.S. Geological Survey, Reston, VA.
USGS (1994 through 2007) Minerals Yearbook: Soda Ash Annual Report. U.S. Geological Survey, Reston, VA.
Ammonia Production and Urea Consumption
Bark (2004) CoffeyvilleNitrogen Plant Available online at
December 15, 2004.
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
.
EEA (2004) Natural Gas Issues for the U.S. Industrial and Power Generation Sectors. Submitted to National
Commission on Energy Policy.
EFMA (2000) Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry.
References 11-17
-------
Booklet No. 5 of 8: Production of Urea and Urea Ammonium Nitrate.
EFMA (1995) Production of Ammonia. European Fertilizer Manufacturers Association. March 1, 1995.
TFI (2002) U.S. Nitrogen Imports/Exports Table. The Fertilizer Institute. Available online at
. August 2002.
TIG (2002) Chemical Profiles- Urea. The Innovation Group. Available online at . September 2007.
U.S. Bureau of the Census (2010), Current Industrial Reports Fertilizer Materials and Related Products: 2009
Summary. Available online at
U.S. Census Bureau (2007) Current Industrial Reports Fertilizer Materials and Related Products: 2006 Summary.
Available online at < http://www.census.gOv/industry/l/mq325b065.pdf>.
U.S. Census Bureau (2006) Current Industrial Reports Fertilizer Materials and Related Products: 2005 Summary.
Available online at .
U.S. Census Bureau (2002, 2004, 2005) Current Industrial Reports Fertilizer Materials and Related Products:
Fourth Quarter Report Summary. Available online at .
U.S. Census Bureau (1998 through 2002b, 2003) Current Industrial Reports Fertilizer Materials and Related
Products: Annual Reports Summary. Available online at .
U.S. Census Bureau (2002a) Current Industrial Reports Fertilizer Materials and Related Products: First Quarter
2002. June 2002. Available online at .
U.S. Census Bureau (2002c) Current Industrial Reports Fertilizer Materials and Related Products: Third Quarter
2001. January 2002. Available online at .
U.S. Census Bureau (2001 a) Current Industrial Reports Fertilizer Materials and Related Products: Second Quarter
2001. September 2001. Available online at .
U.S. Census Bureau (1991 through 1994) Current Industrial Reports Fertilizer Materials Annual Report. Report No.
MQ28B. U.S. Census Bureau, Washington, DC.
U.S. Department of Agriculture (2009) Economic Research Service Data Sets, Data Sets, U.S. Fertilizer
Imports/Exports: Standard Tables. Available online at
.
USGS (1994 through 2009) Minerals Yearbook: Nitrogen. 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.
Nitric Acid Production
EPA (2010a) Draft Nitric Acid Database. U.S. Environmental Protection Agency, Office of Air and Radiation.
September, 2010.
EPA (20 lOb) Draft Nitric Acid Database. U.S. Environmental Protection Agency, Office of Air and Radiation.
March, 2010.
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.
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
11-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
US Census Bureau (2010) Personal communication between Hilda Ward (of U.S. Census Bureau) and Caroline
Cochran (of ICF International). October 26, 2010 and November 5, 2010.
US Census Bureau (2009) Current Industrial Reports. Fertilizers and Related Chemicals: 2009. "Table 1:
Summary of Production of Principle Fertilizers and Related Chemicals: 2009 and 2008." June, 2010. MQ325B(08)-
5. Available online at < http://www.census.gov/manufacturing/cir/historical_data/mq325b/index.html>.
US 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 < http://www.census.gov/manufacturing/cir/historical_data/mq325b/index.html>.
US 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 < http://www.census.gov/manufacturing/cir/historical_data/mq325b/index.html >.
US Census Bureau (2006) Current Industrial Reports., "Table 995: Inorganic Chemicals and Fertilizers." August,
2006. Series MAQ325A Available online at .
Adipic Acid Production
ACC (2010) "Business of Chemistry (Annual Data).xls." American Chemistry Council Guide to the Business of
Chemistry. August 2010.
C&EN (1995) "Production of Top 50 Chemicals Increased Substantially in 1994." Chemical & Engineering News,
73(15): 17. April 10, 1995.
C&EN (1994) "Top 50 Chemicals Production Rose Modestly Last Year." Chemical & Engineering News,
72(15): 13. April 11, 1994.
C&EN (1993) "Top 50 Chemicals Production Recovered Last Year." Chemical & Engineer ing News, 71(15): 11.
April 12, 1993.
C&EN (1992) "Production of Top 50 Chemicals Stagnates in 1991." Chemical & Engineering News, 70(15): 17.
April 13, 1992.
CMR (2001) "Chemical Profile: Adipic Acid." Chemical Market Reporter. July 16, 2001.
CMR (1998) "Chemical Profile: Adipic Acid." Chemical Market Reporter. June 15, 1998.
CW (2007) "Product Focus: Adipic Acid." Chemical Week. August 1-8, 2007.
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 (2009) Personal communication. Mausami Desai, U.S. Environmental Protection Agency and Joseph Herr,
ICF International. November 19, 2009.
Desai (2010) Personal communication. Mausami Desai, U.S. Environmental Protection Agency, and Caroline
Cochran, ICF International. November 8, 2010.
ICIS (2007) "Adipic Acid." ICIS Chemical Business Americas. July 9, 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.
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-COz 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.
References 11-19
-------
Thiemens, M.H., and W.C. Trogler (1991) "Nylon production; an unknown source of atmospheric nitrous oxide."
Science 251:932-934.
VA DEQ (2010) Personal communication. Stanley Faggert, Virgina Department of Environmental Quality and
Joseph Herr, ICF International. March 12, 2010.
VA DEQ (2009) Personal communication. Stanley Faggert, Virgina Department of Environmental Quality and
Joseph Herr, ICF International. October 26, 2009.
VA DEQ (2006) Virginia Title V Operating Permit. Honeywell International Inc. Hopewell Plant. Virginia
Department of Environmental Quality. Permit No. PRO50232. Effective January 1, 2007.
Silicon Carbide Production
Corathers, L. (2007) Personal communication between Lisa Corathers, Commodity Specialist, U.S. Geological
Survey and Michael Obeiter of ICF International. September 2007.
Corathers, L. (2006) Personal communication between Lisa Corathers, Commodity Specialist, U.S. Geological
Survey and Erin Fraser of ICF International. October 2006.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
U.S. Census Bureau (2005 through 2010) U.SInternational Trade Commission (USITC) Trade DataWeb.
Available online at .
USGS (2010) Minerals Commodity Summary: Abrasives (Manufactured). U.S. Geological Survey, Reston, VA.
USGS (1991a through 2009) Minerals Yearbook: Manufactured Abrasives Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (1991b through 2007) Minerals Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
Petrochemical Production
ACC (2002, 2003, 2005 through 2010) Guide to the Business of Chemistry. American Chemistry Council,
Arlington, VA.
EIA (2004) Annual Energy Review 2003. Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2003). September 2004.
EIA (2003) Emissions of Greenhouse Gases in the United States 2002. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy. Washington, DC. DOE-EIA-
0573(2002). February 2003.
European IPPC Bureau (2004) Draft Reference Document on Best Available Techniques in the Large Volumen
Inorganic Chemicals—Solid and Others Industry, Table 4.21. European Commission, 224. August 2004.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Johnson, G. L. (2010) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Caroline Cochran, ICF International. September 2010.
Johnson, G. L. (2009) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Jean Y. Kim, ICF International. October 2009.
Johnson, G. L. (2008) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Jean Y. Kim, ICF International. November 2008.
Johnson, G. L. (2007) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
Carbon Black Association (ICBA) and Tristan Kessler, ICF International. November 2007.
Johnson, G. L. (2006) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
11-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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.
Srivastava, Manoj, I.D. Singh, and Himmat Singh (1999) "Structural Characterization of Petroleum Based
Feedstocks for Carbon Black Production," Table-1. Petroleum Science and Technology 17(1&2):67-80.
The Innovation Group (2004) Carbon Black Plant Capacity. Available online at .
U.S. Census Bureau (2007) 2006 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC0731I3. June 2009.
U.S. Census Bureau (2004) 2002 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC02-311-325182. September 2004.
U.S. Census Bureau (1999) 1997 Economic Census: Manufacturing—Industry Series: Carbon Black Manufacturing.
Department of Commerce. Washington, DC. EC97M-3251F. August 1999.
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
67, July 8, 2010.
ARI (2007) CO2-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
DOE/EPA Workshop on the Economic and Environmental Implications of Global Energy Transitions."
Washington, DC. April 20-21, 2007.
ARI (2006) CO2-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
DOE/EPA Workshop on the Economic and Environmental Implications of Global Energy Transitions."
Washington, DC. April 20-21, 2006.
Broadhead (2003). Personal communication. Ron Broadhead, Principal Senior Petroleum Geologist and Adjunct
faculty, Earth and Environmental Sciences Department, New Mexico Bureau of Geology and Mineral Resources,
and Robin Pestrusak, ICF International. Septembers, 2003.
Denbury Resources Inc. (2002 through 2010) Annual Report: Form 10-K. Available online at
.
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. Retrieved from
http://geoinfo.nmt.edu/staff/broadhead/CO2.html#commercial
Phosphoric Acid Production
EFMA (2000) "Production of Phosphoric Acid." Best Available Techniques for Pollution Prevention and Control in
the European Fertilizer Industry. Booklet 4 of 8. European Fertilizer Manufacturers Association. Available online at
.
FIPR (2003) "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 (2003a) Florida Institute of Phosphate Research. Personal communication. Mr. Michael Lloyd, Laboratory
Manager, FIPR, Bartow, Florida, to Mr. Robert Lanza, ICF International. August 2003.
USGS (2010) Mineral Commodity Summary: Phosphate Rock. U.S. Geological Survey, Reston, VA.
References 11-21
-------
USGS (1994 through 2002, 2004 through 2010) Minerals Yearbook. Phosphate Rock Annual Report. U.S.
Geological Survey, Reston, VA.
Iron and Steel Production and Metallurgical Coke Production
AISI (2011) Personal communication, Mausami Desai, US EPA, and the American Iron and Steel Institute, January,
2011.
AISI (2004 through 2010) Annual Statistical Report, American Iron and Steel Institute, Washington, DC.
AISI (2008b) Personal communication, Mausami Desai, US EPA, and the American Iron and Steel Institute,
October 2008.
DOE (2000) Energy and Environmental Profile of the U.S. Iron and Steel Industry. Office of Industrial
Technologies, U.S. Department of Energy. August 2000. DOE/EE-0229.
EIA (2010a) Quarterly Coal Report: January-March 2010, Energy Information Administration, U.S. Department of
Energy. Washington, DC. DOE/EIA-0121.
EIA (2010b) Natural Gas Annual 2009, Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0131(09).
EIA (2QWc) Annual Energy Review 2009, Energy Information Administration, U.S. Department of Energy.
Washington, DC. DOE/EIA-0384(2009).
EIA (1998 through 2004) Quarterly Coal Report: October-December, Energy Information Administration, U.S.
Department of Energy. Washington, DC. DOE/EIA-0121.
EIA (1992) Coal and lignite production. EIA State Energy Data Report 1992, Energy Information Administration,
U.S. Department of Energy, Washington, DC.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC/UNEP/OECD/IEA (1995) "Volume 3: Greenhouse Gas Inventory Reference Manual. Table 2-T.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.
Ferroalloy Production
Corathers, L. (2011) Personal communication. Lisa Corathers, Commodity Specialist, U.S. Geological Survey and
Paul Stewart, ICF International. March 11, 2011..
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.
USGS (1991 through 2010) Minerals Yearbook: Silicon Annual Report. U.S. Geological Survey, Reston, VA.
Aluminum Production
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
USAA (2004, 2005, 2006) Primary Aluminum Statistics. U.S. Aluminum Association, Washington, DC.
11-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
USAA (2008, 2009) U.S. Primary Aluminum Production. U.S. Aluminum Association, Washington, DC.
USAA (2010) U.S. Primary Aluminum Production. U.S. Aluminum Association, Washington, DC.
USGS (1995, 1998, 2000, 2001, 2002) Minerals Yearbook: Aluminum Annual Report. U.S. Geological Survey,
Reston, VA.
USGS (2007) 2006Mineral Yearbook: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2009a) 2008Mineral Yearbook: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2009b) Mineral Industry Surveys: Aluminum in December 2008. U.S. Geological Survey, Reston, VA.
USGS (2010a) 2009 Mineral Commodity Summaries: Aluminum. U.S. Geological Survey, Reston, VA.
USGS (2010b) Mineral Industry Surveys: Aluminum in December 2009. U.S. Geological Survey, Reston, VA.
USGS (2010c) Mineral Industry Surveys: Aluminum in September 2010. U.S. Geological Survey, Reston, VA.
Magnesium Production and Processing
Bartos S., C. Laush, J. Scharfenberg, and R. Kantamaneni (2007) "Reducing greenhouse gas emissions from
magnesium die casting," Journal of Cleaner Production, 15: 979-987, March.
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 SF6 and the Environment: Emission Reduction
Strategies. San Diego, CA. November 21-22, 2002.
USGS (2002, 2003, 2005 through 2008, and 2010b) Minerals Yearbook: Magnesium Annual Report. U.S.
Geological Survey, Reston, VA. Available online at
.
USGS (2010a) Mineral Commodity Summaries: Magnesium Metal. U.S. Geological Survey, Reston, VA. Available
online at < http://minerals.usgs.gov/minerals/pubs/commodity/magnesium/mcs-2010-mgmet.pdf>.
Zinc Production
Horsehead Corp (2010). 10-k Annual Report for the Fiscal Year Ended December, 31 2009. Available at:
. Submitted March 16, 2010.
Horsehead Corp (2008). 10-k Annual Report for the Fiscal Year Ended December, 31 2007. Available at:
. Submitted March 31, 2008.
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.
Sjardin (2003) CC>2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and Inorganics
Industry. Copernicus Institute. Utrecht, the Netherlands.
Steel Dust Recycling LLC (2010). Available at < http://steeldust.com/home.htm>. Accessed October 18, 2010.
USGS (1994 through 2010) Minerals 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.
Lead Production
Dutrizac, J.E., V. Ramachandran, and J.A. Gonzalez (2000) Lead-Zinc 2000. The Minerals, Metals, and Materials
References 11-23
-------
Society.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Morris, D., F.R. Steward, and P. Evans (1983) Energy Efficiency of a Lead Smelter. Energy 8(5):337-349.
Sjardin, M. (2003) CO2 Emission Factors for Non-Energy Use in the Non-Ferrous Metal, Ferroalloys and
Inorganics Industry. Copernicus Institute. Utrecht, the Netherlands.
Ullman 's Encyclopedia of Industrial Chemistry: Fifth Edition (1997) Volume A5. John Wiley and Sons.
USGS (2010) Mineral Commodity Summary, Lead. U.S. Geological Survey, Reston, VA.
USGS (1994 through 20II) Minerals Yearbook: Lead Annual Report. U.S. Geological Survey, Reston, VA.
HCFC-22 Production
ARAP (2010) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. September 10, 2010.
ARAP (2009) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. September 21, 2009.
ARAP (2008) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. October 17, 2008.
ARAP (2007) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. October 2, 2007.
ARAP (2006) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. July 11, 2006.
ARAP (2005) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 9, 2005.
ARAP (2004) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. June 3, 2004.
ARAP (2003) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. August 18, 2003.
ARAP (2002) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 7, 2002.
ARAP (2001) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Deborah Ottinger of the U.S. Environmental Protection Agency. August 6, 2001.
ARAP (2000) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
Atmospheric Policy to Sally Rand of the U.S. Environmental Protection Agency. August 13, 2000.
ARAP (1999) Facsimile from Dave Stirpe, Executive Director, Alliance for Responsible Atmospheric Policy to
Deborah Ottinger Schaefer of the U.S. Environmental Protection Agency. September 23, 1999.
ARAP (1997) Letter from Dave Stirpe, Director, Alliance for Responsible Atmospheric Policy to Elizabeth Dutrow
of the U.S. Environmental Protection Agency. December 23, 1997.
IPCC (2006) 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.
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.
RTI (2008) "Verification of Emission Estimates of HFC-23 from the Production of HCFC-22 Emissions from 1990
11-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
through 2006." Report prepared by RTI International for the Climate Change Division. March, 2008.
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.
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.
ITRS (2007, 2008) International Technology Roadmap for Semiconductors: 2006 Update. January 2007;
International Technology Roadmap for Semiconductors: 2007 Edition, January 2008; Available online at
. Theses and earlier editions and updates are available at
. Information about the number of interconnect layers foryears 1990-2010 is contained in
Burton and Beizaie, 2001. PEVM is updated using new editions and updates of the ITRS, which are published
annually.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds. Institute for Global
Environmental Strategies (IGES). Hayama, Kanagawa, Japan.
Semiconductor Equipment and Materials Industry (2010) World Fab Forecast, November 2010 Edition.
Semiconductor Industry Association (SIA) (2009). STATS: SICAS Capacity and Utilization Rates Q1-Q4 2008, Ql-
Q4 2009. Available online at .
U.S. EPA (2006) Uses and Emissions of Liquid PFC Heat Transfer Fluids from the Electronics Sector. U.S.
Environmental Protection Agency, Washington, DC. EPA-430-R-06-901.
VLSI Research, Inc. (2010). Worldwide Silicon Demand by Wafer Size, by Linewidth and by Device Type, v. 9.09.
August 2010.
Electrical Transmission and Distribution
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.
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 SF6 and the Environment: Emission Reduction Strategies. RAND Environmental Science and Policy
Center, Scottsdale, AZ. December 1-3, 2004.
UDI (2010) 2010 UDI Directory of Electric Power Producers and Distributors, 118th Edition, Platts.
UDI (2007) 2007 UDI Directory of Electric Power Producers and Distributors, 115th Edition, Platts.
UDI (2004) 2004 UDI Directory of Electric Power Producers and Distributors, 112th Edition, Platts.
UDI (2001) 2007 UDI Directory of Electric Power Producers and Distributors, 109th Edition, Platts.
Industrial Sources of Indirect Greenhouse Gases
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 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
References 11-25
-------
online at
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
Solvent and Other Product Use
Nitrous Oxide from Product Uses
Airgas (2007) 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, 2007. 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 (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.
Tupman, M. (2003) Personal communication .Martin Tupman, Airgas Nitrous Oxide and Daniel Lieberman, ICF
International. August 8, 2003.
Tupman, M. (2002) Personal communication. Martin Tupman of Airgas Nitrous Oxide and Laxmi Palreddy, ICF
International. July 3, 2002.
Solvent Use
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
Agriculture
Enteric Fermentation
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.
11-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Enns, M. (2008) Personal Communication. Dr. Mark Enns, Colorado State University and staff at ICF International.
FAO (2010) FAOSTAT Statistical Database. Food and Agriculture Organization of the United Nations. Available
online at .
Galyean and Gleghorn (2001) Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas
Tech University. Available online at. June
2009.
Holstein Association (2010). History of the Holstein Breed (website). Available online at
. Accessed September 2010.
ICF (2006) Cattle Enteric Fermentation Model: Model Documentation. Prepared by ICF International for the
Environmental Protection Agency. June 2006.
ICF (2003) Uncertainty Analysis of 2001 Inventory Estimates of Methane Emissions from Livestock Enteric
Fermentation in the U.S. Memorandum from ICF International to the Environmental Protection Agency. May 2003.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change. Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
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
NRC (2000) Nutrient Requirements of Beef Cattle: Seventh Revised Edition: Update 2000. Table 11-1, Appendix
Table 1. National Research Council.
NRC (1999) 1996 Beef NRC: Appendix Table 22. National Research Council.
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.
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.
USDA (1996) Beef Cow/CalfHealth and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in
18 States. National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, DC. Available
online at . March 1996.
USDA (2010) Quick Stats: Agricultural Statistics Database. National Agriculture Statistics Service, U.S.
References 11-27
-------
Department of Agriculture. Washington, DC. Available online at
. Accessed June 28, 2010.
USDA:APHIS:VS (2002) Reference of 2002 Dairy Management Practices. National Animal Health Monitoring
System. Fort Collins, CO. Available online at .
USDA:APHIS:VS (1998) Beef '97. National Animal Health Monitoring System. Fort Collins, CO. Available online
at .
USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. National Animal Health Monitoring
System. Fort Collins, CO. Available online at .
USDA:APHIS:VS (1994) Beef Cow/Calf Health and Productivity Audit. National Animal Health Monitoring
System. Fort Collins, CO. Available online at .
USDA:APHIS:VS (1993) Beef Cow/Calf Health and Productivity Audit. National Animal Health Monitoring
System. 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, DC. May 31, 2000.
ASAE (1998) ASAE Standards 1998, 45* Edition. American Society of Agricultural Engineers. St. Joseph, MI.
Bartram, D., Itle, C. and Wirth, T. 2010. EPA Deliberations on: Journal of Environmental Quality article "An
Evaluation ofUSEPA Calculations of Greenhouse Gas Emissions from Anaerobic Lagoons." July 6, 2010.
Bryant, M.P., V.H. Varel, R.A. Frobish, and H.R. Isaacson (1976) In H.G. Schlegel (ed.); Seminar on Microbial
Energy Conversion. E. Goltz KG. Gottingen, Germany.
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 (2008) Climate Leaders Greenhouse Gas Inventory Protocol Offset Project Methodology for Project Type
Managing Manure with Biogas Recovery Systems. Available online at
.
EPA (2006) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
Winter 2006. Available online at . Retrieved July 2006.
EPA (2005) National Emission Inventory—Ammonia Emissions from Animal Agricultural Operations, Revised
Draft Report. U.S. Environmental Protection Agency. Washington, DC. April 22, 2005. Available online at
. Retrieved
August 2007.
EPA (2003) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
Winter 2003. Available online at . Retrieved July 2006.
EPA (2002a) Development Document for the Final Revisions to the National Pollutant Discharge Elimination
System (NPDES) Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (CAFOS).
U.S. Environmental Protection Agency. EPA-821-R-03-001. December 2002.
EPA (2002b) Cost Methodology for the Final Revisions to the National Pollutant Discharge Elimination System
Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations. U.S. Environmental
Protection Agency. EPA-821-R-03-004. December 2002.
EPA (2000) AgSTAR Digest. Office of Air and Radiation, U.S. Environmental Protection Agency. Washington, DC.
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.
11-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
July 19,2010.
ERG (2010b) "Updating Current Inventory Manure Characteristics new USDA 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 (2003) "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 (2001) Summary of development ofMDP Factor for methane conversion factor calculations. ERG, Lexington,
MA. September 2001.
ERG (2000a) Calculations: Percent Distribution of Manure for Waste Management Systems. ERG, Lexington, MA.
August 2000.
ERG (2000b) Discussion of Methodology for Estimating Animal Waste Characteristics (Summary of B0 Literature
Review). ERG, Lexington, MA. June 2000.
FAO (2010) Yearly U.S. total horse population data from the Food and Agriculture Organization of the United
Nations database. Available online at . August 2010.
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 of the ASAE,
25(1):226-230.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Johnson, D. (2000) Personal Communication. Dan Johnson, State Water Management Engineer, California Natural
Resource Conservation Service and Lee-Ann Tracy, ERG. June 23, 2000.
Lange, J. (2000) Personal Communication. JohnLange, Agricultural Statistician, U.S. Department of Agriculture,
National Agriculture Statistics Service and Lee-Ann Tracy, ERG. Washington, DC. May 8, 2000.
Lory, J.A., R.E. Massey, and J.M. Zulovich. 2010. An Evaluation of the USEPA Calculations of Greenhouse Gas
Emission from Anaerobic Lagoons. Journal of Environmental Quality, 39:776-783.
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. (2010) 1990-2009 Volatile Solids and Nitrogen Excretion Rates. Dataset to EPA from
ICF International. June 2009.
Morris, G.R. (1976) Anaerobic Fermentation of Animal Wastes: A Kinetic and Empirical Design Fermentation.
References 11-29
-------
M.S. Thesis. Cornell University.
NOAA (2009) National Climate Data Center (NCDC). Available online at
(for all states except Alaska and Hawaii) and (for Alaska and
Hawaii). June 2009.
Pederson, L., D. Pape and K. Moffroid (2007) 1990-2006 Volatile Solids and Nitrogen Excretion Rates, EPA
Contract GS-10F-0124J, Task Order 056-01. Memorandum to EPA from ICF International. August 2007.
Safley, L.M., Jr. and P.W. Westerman (1990) "Psychrophilic anaerobic digestion of animal manure: proposed design
methodology." Biological Wastes, 34:133-148.
Safley, L.M., Jr. (2000) Personal Communication. Deb Bartram, ERG and L.M. Safley, President, Agri-Waste
Technology. June and October 2000.
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 (2009a) 1992, 1997, 2002, and 2007 Census of Agriculture. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. Available online at . December
2009.
USDA (2009b) Published Estimates Database. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. Available online at . June 2009.
USDA (2009c) Chicken and Eggs 2008 Summary. National Agriculture Statistics Service, U.S. Department of
Agriculture. Washington, DC. February 2009. Available online at
.
USDA (2009d) Poultry - Production and Value 2008 Summary. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2009. Available online at
.
USDA (2008) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture.
USDA (2004a) Chicken and Eggs—Final Estimates 1998-2003. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. April 2004. Available online at
.
USDA (2004b) Poultry Production and Value—Final Estimates 1998-2002. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. April 2004. Available online at
.
USDA (2003) APHIS Sheep 2001,Parts I andIV. Available online at
.
USDA (2000a) National Animal Health Monitoring Systems (NAHMS) Dairy '96 Study. Stephen L. Ott, Animal and
Plant Health Inspection Service, U.S. Department of Agriculture. June 19, 2000.
USDA (2000b) Layers '99—Part II: References of1999 Table Egg Layer Management in the U.S. Animal and Plant
Health Inspection Service (APHIS), National Animal Health Monitoring System (NAHMS), U.S. Department of
Agriculture. January 2000.
USDA (1999) Poultry Production and Value—Final Estimates 1994-97. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, DC. March 1999. Available online at
.
11-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
USDA (1998a) Chicken and Eggs—Final Estimates 1994-97. National Agriculture Statistics Service, U.S.
Department of Agriculture. Washington, DC. December 1998. Available online at
.
USDA (1998b) National Animal Health Monitoring System's (NAHMS) Swine '95 Study. Eric Bush, Centers for
Epidemiology and Animal Health, U.S. Department of Agriculture.
USDA (1996a) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part
651. Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1996b) Swine '95: Grower/Finisher Part II: Reference of 1995 U.S. Grower/Finisher Health &
Management Practices. Animal Plant Health and Inspection Service, U.S. Department of Agriculture. Washington,
DC. June 1996.
Rice Cultivation
Anderson, M. (2008 through 2010). Email correspondence. Monte Anderson, Oklahoma Farm Service Agency 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 2010) Email correspondence. Nathan Buehring, Assistant Professor and Extension
Rice Specialist, Mississippi State University Delta Branch Exp. Station and ICF International.
Cantens, G. (2004 through 2005) Personal Communication. Janet Lewis, Assistant to Gaston Cantens, Vice
President of Corporate Relations, Florida Crystals Company and ICF International.
Cicerone R.J., C.C. Delwiche, S.C. Tyler, and P.R. Zimmerman (1992) "Methane Emissions from California Rice
Paddies with Varied Treatments." Global Biogeochemical Cycles, 6:233-248.
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.
Gonzalez, R. (2007 through 2010) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
Guethle, D. (1999 through 2010) Personal Communication. David Guethle, Agronomy Specialist, Missouri
Cooperative Extension Service and ICF International.
Holzapfel-Pschorn, A., R. Conrad, and W. Seiler (1985) "Production, Oxidation, and Emissions of Methane in Rice
Paddies." FEMSMicrobiology Ecology, 31:343-351.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
National Greenhouse Gas Inventories Programme, Intergovernmental Panel on Climate Change, Montreal. May
2000. IPCC-XWDoc. 10 (1.IV.2000).
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Kirstein, A. (2003 through 2004, 2006) Personal Communication. Arthur Kirstein, Coordinator, Agricultural
Economic Development Program, Palm Beach County Cooperative Extension Service, FL and ICF International.
Klosterboer, A. (1997, 1999 through 2003) Personal Communication. Arlen Klosterboer, retired Extension
Agronomist, Texas A&M University and ICF International. July 7, 2003.
Lancero, J. (2006 through 2010) Email correspondence. Jeff Lancero, California Air Resources Board and ICF
International.
Lee, D. (2003 through 2007) Email correspondence. Danny Lee, OK Farm Service Agency and ICF International.
References 11-31
-------
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 2010) Email correspondence. Steve Linscombe, Professor with the Rice
Research Station at Louisiana State University Agriculture Center and ICF International.
May hew, W. (1997) Personal Communication. Walter May hew, University of Arkansas, Little Rock and Holly
Simpkins, ICF Incorporated. November 24, 1997.
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.
Sacramento Valley Basinwide Air Pollution Control Council (2005, 2007) Report on the Conditional Rice Straw
Burning Permit Program. Available online at .
Saichuk, J. (1997) Personal Communication. John Saichuk, Louisiana State University and Holly Simpkins, ICF
Incorporated. November 24, 1997.
Sass, 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.
Stevens, G. (1997) Personal Communication. Gene Stevens, Extension Specialist, Missouri Commercial Agriculture
Program, Delta Research Center and Holly Simpkins, ICF Incorporated. December 17, 1997.
Street, J. (1997, 1999 through 2003) Personal Communication. Joe Street, Rice Specialist, Mississippi State
University, Delta Research Center and ICF International.
Texas Agricultural Experiment Station (2007 through 2010) Texas Rice Acreage by Variety. Agricultural Research
and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System. Available online at
.
Texas Agricultural Experiment Station (2006) 2005 - Texas Rice Crop Statistics Report. Agricultural Research and
Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, p. 8. Available online at
.
USDA (2005 through 2010) Crop Production Summary. National Agricultural Statistics Service, Agricultural
Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at
.
USDA (2003) Field Crops, Final Estimates 1997-2002. Statistical Bulletin No. 982. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . September 2005.
USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947 a. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . July 2001.
11-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
USDA (1994) Field Crops Final Estimates 1987-1992. Statistical Bulletin Number 896. National Agricultural
Statistics Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online
at . July 2001.
Walker, T. (2005, 2007 through 2008) Email correspondence. Tim Walker, Assistant Research Professor,
Mississippi State University Delta Branch Exp. Station and ICF International.
Wilson, C. (2002 through 2007, 2009 through 2010) Personal Communication. Dr. Chuck Wilson, Rice Specialist at
the University of Arkansas Cooperative Extension Service and ICF International.
Agricultural Soil Management
AAPFCO (1995 through 2000b, 2002 through 2009) Commercial Fertilizers. Association of American Plant Food
Control Officials. University of Kentucky, Lexington, KY.
AAPFCO (2000a) 1999-2000 Commercial Fertilizers Data, ASCII files. Available from David Terry, Secretary,
Association of American Plant Food Control Officials.
Alexander, R.B. and R.A. Smith (1990) County-Level Estimates of Nitrogen and Phosphorous Fertilizer Use in the
United States, 1945-1985. U.S. Geological Survey Open File Report 90-130.
Anonymous (1924) Fertilizer Used on Cotton, 1923-1924. "Miscellaneous Agricultural Statistics," Table 753. 1924
Yearbook of the Department of Agriculture, 1171.
Bastian, R. (2007) Personal Communication. Robert Bastian, Office of Water, U.S. Environmental Protection
Agency, Washington, DC and Victoria Thompson, ICF International. July 20, 2007.
Battaglin, W.A., andD.A. Goolsby (1994) Spatial Data in Geographic Information System Format on Agricultural
Chemical Use, Land Use, and Cropping Practices in the United States. U.S. Geological Survey Water-Resources
Investigations Report 94-4176.
Bogue A.G. (1963) From Prairie to Corn Belt: Farming on the Illinois and low a prairies in the Nineteenth Century.
The University of Chicago Press. Chicago, IL.
Bonnen C. A., and F.F. Elliott (1931) Type of Farming Are as in Texas. Bulletin Number 427, Texas Agricultural
Experiment Station, Agricultural and Mechanical College of Texas.
Brenner, J., K. Paustian, G. Bluhm, J. Cipra, M. Easter, R. Foulk, K. Killian, R. Moore, J. Schuler, P. Smith, and S.
Williams (2002) Quantifying the Change in Greenhouse Gas Emissions Due to Natural Resource Conservation
Practice Application in Nebraska. Colorado State University Natural Resource Ecology Laboratory and Natural
Resources Conservation Service, U.S. Department of Agriculture. Fort Collins, CO.
Brenner, J., K. Paustian., G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, T. Koutza, K. Killian, J. Schuler, S. Williams
(2001) Quantifying the Change in Greenhouse Gas Emissions Due to Natural Resource Conservation Practice
Application in Iowa. Final report to the Iowa Conservation Partnership. Colorado State University Natural
Resource Ecology Laboratory and U.S. Department of Agriculture Natural Resources Conservation Service. Fort
Collins, CO.
Chilcott E. C. (1910) A Study of Cultivation Methods and Crop Rotations for the Great Plains Area. Bureau of Plant
Industry Bulletin Number 187, U.S. Department of Agriculture. Government Printing Office. Washington, DC.
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29, 1996.
Cochran, W.G. (1977) Sampling Techniques, Third Edition. Wiley Publishing, New York.
CTIC (1998) 1998 Crop Residue Management Executive Summary. Conservation Technology Information Center.
Available online at .
Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, and P.A. Pasteris (1998) "Development of high-
quality spatial datasets for the United States." Proc., 1st International Conference on Geospatial Information in
Agriculture and Forestry, Lake Buena Vista, FL, 1-512-1-519. June 1-3, 1998.
Daly, C., R.P. Neilson, and D.L. Phillips (1994) "A statistical-topographic model for mapping climatological
precipitation over mountainous terrain." Journal of Applied Meteorology, 33:140-158.
References 11-33
-------
David, M.B., S.J. Del Grosso, X. Hu, G.F. Mclsaac, W.J. Parton, E.P. Marshall, C. Tonitto, and M.A. Youssef
(2009) "Modeling denitrification in a tile-drained, corn and soybean agroecosystem of Illinois, USA."
Biogeochemistry 93:7-30.
DAYMET (No date) Daily Surface Weather and Climatological Summaries. Numerical Terradynamic Simulation
Group (NTSG), University of Montana. Available online at .
Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) "15N isotopic crop residue cycling studies and modeling
suggest that IPCC methodologies to assess residue contributions to N2O-N emissions should be reevaluated."
Nutrient Cycling in Agroecosystems, DOI 10.1007/sl0705-009-9300-9.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010) "Estimating Uncertainty in N2O Emissions from US
Cropland Soils." Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.
Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating agricultural nitrous oxide emissions. EOS 89,
529-530.
Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005) "DAYCENT Model Analysis of Past and
Contemporary Soil N2O and Net Greenhouse Gas Flux for Major Crops in the USA." Soil Tillage and Research, 83:
9-24. doi: 10.1016/j.still.2005.02.007.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. Schimel (2001)
"Simulated Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In
Schaffer, M., L. Ma, S. Hansen, (eds.); Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC
Press. Boca Raton, Florida. 303-332.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, D.S. Ojima, A.E. Kulmala and S. Phongpan (2000) General model for
N2O and N2 gas emissions from soils due to denitrification. Global Biogeochem. Cycles, 14:1045-1060.
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, andRecordkeeping. Natural Resource Conservation Service, U.S. Department of Agriculture.
Elliott, F.F. (1933) Types of Farming in the United States. U.S. Department of Commerce. Government Printing
Office. Washington, DC.
Elliott, F.F. and J.W. Tapp (1928) Types of Farming in North Dakota. U.S. Department of Agriculture Technical
Bulletin Number 102.
Ellsworth, J.O. (1929) Types of Farming in Oklahoma. Agricultural Experiment Station Bulletin Number 181.
Oklahoma Agricultural and Mechanical College.
Engle, R.H. and B.R. Makela (1947) "Where is All the Fertilizer Going?" The National Fertilizer Association. The
Fertilizer Review, Vol. XXII, 6:7-10.
EPA (2003) Clean Watersheds Needs Survey 2000—Report to Congress, U.S. Environmental Protection Agency.
Washington, DC. Available online at .
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S.
Environmental Protection Agency. Available online at .
EPA (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S.
Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.
ERS (2003) Ag Chemical and Production Technology. Economic Research Service, U.S. Department of
Agriculture.
ERS (2002) Economic Research Service, U.S. Department of Agriculture. Available online at
.
ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, U.S. Department of Agriculture.
Available online at .
ERS (1994) Fertilizer Use and Price Statistics. Stock #86012. Economic Research Service, U.S. Department of
11-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Agriculture.
ERS (1988) Agricultural Resources—Inputs Situation and Outlook Report. AR-9. Economic Research Service, U.S.
Department of Agriculture.
Eve, M. (2001) E-mail correspondence. MarlenEve, Natural Resources Ecology Laboratory, Colorado State
University and Barbara Braatz and Caren Mintz, ICF International. Statistics on U.S. organic soil areas cultivated in
1982, 1992, and 1997, which were extracted from the 1997 National Resources Inventory. September 21, 2001.
Fraps, G.S. and S.E. Asbury (1931) Commercial Fertilizers in 1930-1931 and Their Uses. Agricultural Experiment
Station Bulletin Number 434. Agricultural and Mechanical College of Texas.
Hardies, E.W. and A.N. Hume (1927) Wheat in South Dakota. Agronomy Department Bulletin Number 222.
Agricultural Experiment Station, South Dakota State College of Agriculture and Mechanical Arts. Brookings, SD.
Garey, L.F. (1929) Types of Farming in Minnesota. Agricultural Experiment Station Bulletin Number 257.
University of Minnesota. St. Paul, MN.
Grant, W.R. andR.D. Krenz (1985) U. S. grain sorghum production practices and costs. Staff Report No. AGES
851024. National Economics Division, Economics Research Service, U.S. Department of Agriculture.
Hargreaves, M.W.M. (1993) Dry Farming in the Northern Great Plains: Years of Readjustment, 1920-1990.
University Press of Kansas. Lawrence, KS.
Hodges, J.A., F.F. Elliott, and W.E. Grimes (1930) Types of Farming in Kansas. Agricultural Experiment Station
Bulletin Number 251. Kansas State Agricultural College. Manhattan, KS.
Holmes C.L. (1929) Types of Farming in Iowa. Agricultural Experiment Station Bulletin Number 259. Iowa State
College of Agriculture and Mechanic Arts. Ames, IA.
Holmes O.K. (1902) "Practices in Crop Rotation." Yearbook of the Department of Agriculture, 519-532.
Kurd E.B. (1929) The Corn Enterprise in Iowa. Agricultural Experiment Station Bulletin Number 268. Iowa State
College of Agriculture and Mechanic Arts. Ames, IA.
Kurd E.B. (1930) Cropping Systems in Iowa Past and Present. Agricultural Experiment Station Bulletin Number
268. Iowa State College of Agriculture and Mechanic Arts. Ames, IA.
Hurt, R.D. (1994) American Agriculture: A Brief History. Iowa State University Press. Ames, IA.
Ibach, D.B. and J.R. Adams (1967) Fertilizer Use in the United States by Crops and Areas, 1964 Estimates.
Statistical Bulletin Number 408, U.S. Department of Agriculture.
Ibach, D.B., J.R. Adams, and E.I. Fox (1964) Commercial Fertilizer used on Crops and Pasture in the United
States, 1959 Estimates. U.S. Department of Agriculture Statistical Bulletin Number 348.
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.
Iowa State College Staff Members (1946)^4 Century of Farming in Iowa 1846-1946. The Iowa State College Press.
Ames, IA.
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.
Jaynes, D.B., T.S. Colvin, D.L. Karlen, C.A. Cambardella, and D.W. Meek (2001) "Nitrate
loss in subsurface draining as affected by nitrogen fertilizer rate." J. Environ. Qual. 30, 1305-1314.
Kellogg R.L., C.H. Lander, D.C. Moffitt, and N. Gollehon (2000) Manure Nutrients Relative to Capacity of
Cropland and Pastur eland to Assimilate Nutrients: Spatial and Temporal Trends for the UnitedStates. U.S.
Department of Agriculture Publication Number npsOO-0579.
Kezer A. (ca. 1917) Dry Farming in Colorado. Colorado State Board of Immigration, Denver, CO.
Kuchler, AW. (1964) "The Potential Natural Vegetation of the Conterminous United States." Amer. Geographical
References 11-35
-------
Soc. NY, Special Publication No. 36.
Langston C.W., L.M. Davis, C.A. Juve, O.C. Stine, A.E. Wight, A.J. Pistor, and C.F. Langworthy (1922) "The
Dairy Industry." Yearbook of the Department of Agriculture.
Latta, W.C. (1938) Outline History of Indiana Agriculture. Alpha Lambda Chapter of Epsilon Sigma Phi, Purdue
University, West Lafayette, IN.
McCarl, B.A., C.C. Chang, J.D. Atwood, and W.I. Nayda (1993) Documentation of ASM: The U.S. Agricultural
Sector Model, Technical Report TR-93. Agricultural Experimental Station, College Station, TX.
McFarland, M.J. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
Mosier, A.R. (2004) E-mail correspondence. Arvin Mosier, U.S. Department of Agriculture, Agricultural Research
Service and Stephen Del Grosso, Natural Resource Ecology Laboratory, Colorado State University, regarding the
uncertainty in estimates of N application rates for specific crops (±20). September 20, 2004.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a, National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgChl(99). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural
Statistics Service, U.S. Department of Agriculture. Available online at
.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association, July 21, 2007
NFA (1946) "Charting the Fertilizer Course: Results of NFA's Third Practice Survey." National Fertilizer
Association. The Fertilizer Review. Vol. XXI, 2:7-13.
Noller, J. (1996) Personal Communication. John Noller, Missouri Department of Natural Resources and Heike
Mainhardt, ICF Incorporated. July 30, 1996.
NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering
Service. Available online at .
NRIAI (2003) Regional Budget and Cost Information. U.S. Department of Agriculture, Natural Resources
Conservation Service, Natural Resources Inventory and Analysis Institute. Available online at
Ogle, S. (2002) E-mail correspondence. Stephen Ogle, Natural Resources Ecology Laboratory, Colorado State
University and Barbara Braatz, ICF International, concerning revised statistics on U.S. histosol areas cultivated in
1982, 1992, and 1997, which were extracted from the 1997 National Resources Inventory by MarlenEve. January 9,
2002.
Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, and K. Paustian. (2007) "Empirically-Based Uncertainty Associated
with Modeling Carbon Sequestration in Soils." Ecological Modeling 205:453-463.
Oregon Department of Energy (1995) Report on Reducing Oregon's Greenhouse Gas Emissions: Appendix D
Inventory and Technical Discussion. Oregon Department of Energy. Salem, OR.
Parton, W.J., M.D. Hartman, D.S. Ojima, andD.S. Schimel (1998) "DAYCENT: Its Land Surface Submodel:
Description and Testing". Glob. Planet. Chang. 19: 35-48.
Parton, W.J., E.A. Holland, S.J. Del Grosso, M.D. Hartman, R.E. Martin, A.R. Mosier, D.S. Ojima, and D.S.
Schimel (2001) Generalized model forNOx and N2O emissions from soils. Journal of Geophysical Research. 106
(D15):17403-17420.
Piper C.V., R.A. Oakley, H.N. Vinall, A.J. Pieters, W.J. Morse, W.J. Spillman, O.C. Stine, J.S. Cotton., G.A.
Collier, M.R Cooper, B.C. Parker, E.W. Sheets, and A.T. Semple (1924) "Hay." Yearbook of the Department of
11-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Agriculture, 285-376.
Ruddy B.C., D.L. Lorenz, and D.K. Mueller (2006) County-level estimates of nutrient inputs to the land surface of
the conterminous United States, 1982-2001. Scientific Investigations Report 2006-5012. US Department of the
Interior.
Ross W.H. and A.L. Mehring (1938) "Mixed Fertilizers." In Soils and Men. Agricultural Yearbook 1938. U.S.
Department of Agriculture.
Russell E.Z., S.S. Buckley, C.E. Baker, C.E. Gibbons, R.H. Wilcox, H.W. Hawthorne, S.W. Mendum, O.C. Stine,
O.K. Holmes, A.V. Swarthout, W.B. Bell, G.S. Jamieson, C.W. Warburton, and C.F. Langworthy (1922) Hog
Production and Marketing. Yearbook of the U.S. Department of Agriculture.
Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick (1986) "Estimating Generalized Soil-Water
Characteristics From Texture." Soil Sci. Soc. Am. J. 50:1031-1036.
Skinner, JJ. (1931) Fertilizers for Cotton Soils. Miscellaneous Publication Number 126. U.S. Department of
Agriculture.
Smalley, H.R., R.H. Engle, and H. Willett (1939) American Fertilizer Practices: Second Survey. The National
Fertilizer Association.
Smith C.B. (1911) Rotations in the Corn Belt. Yearbook of the Department of Agriculture, pp.325-336.
Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. Lamm, J. Schuler, and
S. Williams (2002) Quantifying the Change in Greenhouse Gas Emissions Due to Natural Resource Conservation
Practice Application in Indiana. Final Report to the Indiana Conservation Partnership, Colorado State University
Natural Resource Ecology Laboratory and U.S. Department of Agriculture Natural Resources Conservation Service,
Fort Collins, CO.
Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. (2005) State
Soil Geographic (STATSGO) Database for State. Available online at
.
Spillman W.J. (1908) Types of Farming in the United States. Yearbook of the Department of Agriculture, 351-366.
Spillman W.J. (1907) Cropping Systems for Stock Farms. Yearbook of the Department of Agriculture, 385-398.
Spillman W.J. (1905) Diversified Farming in the Cotton Belt. Yearbook of the Department of Agriculture, 193-218.
Spillman W.J. (1902) Systems of Farm Management in the United States. Yearbook of the Department of
Agriculture, 343-364.
Taylor, H.H. (1994) Fertilizer Use and Price Statistics: 1960-93. Resources and Technology Division, Economic
Research Service, U.S. Department of Agriculture, Statistical Bulletin Number 893.
Thornton, P.E., H. Hasenauer, and M.A. White (2000) "Simultaneous Estimation of Daily Solar Radiation and
Humidity from Observed Temperature and Precipitation: An Application Over Complex Terrain in Austria."
Agricultural and Forest Meteorology 104:255-271.
Thornton, P.E. and S.W. Running (1999) "An Improved Algorithm for Estimating Incident Daily Solar Radiation
from Measurements of Temperature, Humidity, and Precipitation." Agriculture and Forest Meteorology. 93: 211-
228.
Thornton, P.E., S.W. Running, and M.A. White (1997) "Generating Surfaces of Daily Meteorology Variables Over
Large Regions of Complex Terrain." Journal of Hydrology. 190:214-251.
TVA (1991 through 1992a, 1993 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle
Shoals, AL.
TVA (1992b) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL.
USDA (2010a) Crop Production 2009 Summary, National Agricultural Statistics Service, Agricultural Statistics
Board, U.S. Department of Agriculture, Washington, DC. Available online at ,
USDA (2010b) Quick Stats: U.S. & All States Data - Crops. National Agricultural Statistics Service, U.S.
References 11-37
-------
Department of Agriculture. Washington, DC. U.S. Department of Agriculture, National Agricultural Statistics
Service. Washington, D.C., Available online at
USD A (2003, 2005 through 2006, 2008 through 2009) Crop Production Summary, National Agricultural Statistics
Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at
,
USDA (2000a) 1997 National Resources Inventory. National Agricultural Statistics Service, U.S. Department of
Agriculture. Washington, DC. Available online at .
USDA (200Gb) Agricultural Statistics 2000. National Agricultural Statistics Service, U.S. Department of
Agriculture. Washington, DC. Available online at .
USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947a. National Agricultural
Statistics Service, U.S. Department of Agriculture. Washington, DC. Available online at
. Accessed July 2001.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1994) Field Crops: Final Estimates, 1987-1992. Statistical Bulletin Number 896, National Agriculture
Statistics Service, U.S. Department of Agriculture. Washington, DC. Available online at
.
USDA (1966) Consumption of Commercial Fertilizers and Primary Plant Nutrients in the United States, 1850-1964
and By States, 1945-1964. Statistical Bulletin Number 375, Statistical Reporting Service, U.S. Department of
Agriculture.
USDA (1957) Fertilizer Usedon Crops and Pastures in the United States—1954 Estimates. Statistical Bulletin
Number 216, Agricultural Research Service, U.S. Department of Agriculture.
USDA (1954) Fertilizer Use and Crop Yields in the United States. Agricultural Handbook Number 68, the Fertilizer
Work Group, U.S. Department of Agriculture.
USDA (1946) Fertilizers and Lime in the United States. Miscellaneous Publication Number 586, U.S. Department
of Agriculture.
VEMAP (1995) Members (J.M. Melillo, J. Borchers, J. Chaney, H. Fisher, S. Fox, A. Haxeltine, A. Janetos, D.W.
Kicklighter, T.G.F. Kittel, A.D. McGuire, R. McKeown, R. Neilson, R. Nemani, D.S. Ojima, T. Painter, Y. Pan,
W.J. Parton, L. Pierce, L. Pitelka, C. Prentice, B. Rizzo, N.A. Rosenbloom, S. Running, D.S. Schimel, S. Sitch, T.
Smith, I. Woodward). "Vegetation/Ecosystem Modeling and Analysis Project (VEMAP): Comparing Biogeography
and Biogeochemistry Models in a Continental-Scale Study of Terrestrial Ecosystem Responses to Climate Change
and CC>2 Doubling." Global Biogeochemical Cycles, 9:407-437.
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.
Warren J.A. (1911) Agriculture in the Central Part of the Semiarid Portion of the Great Plains. Bulletin Number
215, Bureau of Plant Industry, U.S. Department of Agriculture.
Williams, S.A. (2006) Data compiled for the Consortium for Agricultural Soils Mitigation of Greenhouse Gases
(CASMGS) from an unpublished manuscript. Natural Resource Ecology Laboratory, Colorado State University.
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 2010) Email correspondence. Monte Anderson, Oklahoma Farm Service Agency and
ICF International. July 12, 2010.
Ashman (2008). Email communication. Janet Ashman, Hawaii Agriculture Research Center and Victoria
Thompson, ICF International. Ms. Ashman cited an August 2004 report prepared for the U.S. Dept. of Energy by
11-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
the Hawaiian Commercial & Sugar Co., "Closed-Loop Biomass Co-Firing--Pilot-Scale and Full-Scale Test Results."
Barnard, G., and L. Kristoferson (1985) Agricultural Residues as Fuel in the Third World. Earthscan Energy
Information Programme and the Beijer Institute of the Royal Swedish Academy of Sciences. London, England.
Cantens, G. (2004 through 2005) Personal Communication. Janet Lewis, Assistant to Gaston Cantens, Vice
President of Corporate Relations, Florida Crystals Company and ICF International.
Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike
Mainhardt, ICF Incorporated. July 29, 1996.
Deren, C. (2002) Personal communication. Dr. Chris Deren, Everglades Research and Education Centre at the
University of Florida and Caren Mintz, ICF International. August 15, 2002.
EPA (1994) International Anthropogenic Methane Emissions: Estimates for 1990, Report to Congress. EPA 230-R-
93-010. Office of Policy Planning and Evaluation, U.S. Environmental Protection Agency, Washington, DC.
EPA (1992) Prescribed Burning Background Document and Technical Information Document for Prescribed
Burning Best Available Control Measures. Office of Air Quality Planning and Standards, U.S. Environmental
Protection Agency. Research Triangle Park, NC. EPA-450/2-92-003.
Gonzalez, R. (2007 through 2010) Email correspondence. Rene Gonzalez, Plant Manager, Sem-Chi Rice Company
and ICF International.
Guethle, D. (2009) Email correspondence. David Guethle, Agronomy Specialist, Missouri Cooperative Extension
Service and Sarah Menassian, ICF International. July 20, 2009.
Huang, Y., W. Zhang, W. Sun, and X. Zheng (2007) "Net Primary Production of Chinese Croplands from 1950 to
1999." Ecological Applications, 17(3):692-701.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCC-
XVI/Doc. 10 (1.IV.2000). May 2000.
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Jenkins, B.M., S.Q. Turn, and R.B. Williams (1992) "Atmospheric emissions from agricultural burning in
California: determination of burn fractions, distribution factors, and crop specific contributions." Agriculture,
Ecosystems and Environment 38:313-330.
Kinoshita, C.M. (1988) "Composition and processing of burned and unburned cane in Hawaii." Intl. Sugar Jnl.
90:1070,34-37.
Kirstein, A. (2003 through 2004) Personal Communication. Arthur Kirstein, Coordinator, Agricultural Economic
Development Program, Palm Beach County Cooperative Extension Service, Florida and ICF International.
Lachnicht, S.L., P.P. Hendrix, R.L. Potter, D.C. Coleman, and D.A. Crossley Jr. (2004) "Winter decomposition of
transgenic cotton residue in conventional-till and no-till systems." Applied Soil Ecology, 27:135-142.
Lee, D. (2003 through 2007) Email correspondence. Danny Lee, OK Farm Service Agency and ICF International.
McCarty, J.L. (2010) Agricultural Residue Burning in the Contiguous United States by Crop Type and State.
Geographic Information Systems (GIS) Data provided to the EPA Climate Change Division by George Pouliot,
Atmospheric Modeling and Analysis Division, EPA. Dr. McCarty's research was supported by the NRI Air Quality
Program of the Cooperative State Research, Education, and Extension Service, USD A, under Agreement No.
20063511216669 and the NASA Earth System Science Fellowship.
McCarty, J.L. (2009) Seasonal and Inter annual Variability of Emissions from Crop Residue Burning in the
Contiguous United States. Dissertation. University of Maryland, College Park.
References 11-39
-------
Schueneman, T. (1999, 2001) Personal Communication. Tom Schueneman, Agricultural Extension Agent, Palm
Beach County, FL and ICF International. July 30, 2001.
Schueneman, T.J. and C.W. Deren (2002) "An Overview of the Florida Rice Industry." SS-AGR-77, Agronomy
Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of
Florida. Revised November 2002.
Strehler, A., and W. Stiitzle (1987) "Biomass Residues." In Hall, D.O. and Overend, R.P. (eds.) Biomass. John
Wiley and Sons, Ltd. Chichester, UK.
Turn, S.Q., B.M. Jenkins, J.C. Chow, L.C. Pritchett, D. Campbell, T. Cahill, and S.A. Whalen (1997) "Elemental
characterization of paniculate matter emitted from biomass burning: Wind tunnel derived source profiles for
herbaceous and wood fuels." Journal of Geophysical Research 102(D3):3683-3699.
USDA (2010) Quick Stats: U.S. & All States Data; Crops; Production and Area Harvested; 1990 - 2009. National
Agricultural Statistics Service, U.S. Department of Agriculture. Washington, DC. U.S. Department of Agriculture,
National Agricultural Statistics Service. Washington, D.C., Available online at .
Walker, T. (2008) Email correspondence. Tim Walker, Assistant Research Professor, Mississippi State University
Delta Branch Exp. Station and Sarah Menassian, ICF International. July 25, 2008.
Land Use, Land-Use Change, and Forestry
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
Representation of the U.S. Land Base
Homer, C., J. Dewitz, J. Fry, M. Coan, N. Hossain, C. Larson, N. Herold, A. McKerrow, J.N. VanDriel and J.
Wickham. 2007. Completion of the 2001 National Land Cover Database for the Conterminous United States,
Photogrammetric Engineering and Remote Sensing, Vol. 73, No. 4, pp 337-341.
IPCC (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.
National Atlas (2005) Federal Lands of the United States. National Atlas of the United States, U.S. Department of
the Interior, Washington DC. Available online 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, DC.
U.S. Census Bureau (2010) Topologically Integrated Geographic Encoding and Referencing (TIGER) system
shapefiles. U.S. Census Bureau, Washington, DC. Available online at .
Forest Land Remaining Forest Land: Changes in Forest Carbon Stocks
AF&PA. 2006a (and earlier). Statistical roundup. (Monthly). Washington, DC: American Forest & Paper
Association.
AF&PA. 2006b (and earlier). Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest &
Paper Association.
Amichev, B. Y. and J. M. Galbraith (2004) "A Revised Methodology for Estimation of Forest Soil Carbon from
Spatial Soils and Forest Inventory Data Sets." Environmental Management 33(Suppl. 1):S74-S86.
Barlaz, M.A. (1998) "Carbon storage during biodegradation of municipal solid waste components in laboratory-
scale landfills." GlobalBiogeochemical Cycles 12 (2):373-380.
11-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Bechtold, W.A.; Patterson, P.L. 2005. The enhanced forest inventory and analysis program—national sampling
design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture Forest
Service, Southern Research Station. 85 p.
Birdsey, R.A., andL.S. Heath (1995) "Carbon Changes in U.S. Forests." In Productivity of'America's Forests and
Climate Change. Gen. Tech. Rep. RM-271. Rocky Mountain Forest and Range Experiment Station, Forest Service,
U.S. Department of Agriculture. Fort Collins, CO, 56-70.
Birdsey, R. (1996) "Carbon Storage for Major Forest Types and Regions in the Conterminous United States." In
R.N. Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for
Mitigating Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263).
Birdsey, R., and L. S. Heath (2001) "Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux."
In 5*0/7 Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-135.
Birdsey, R. A., and G. M. Lewis (2003) "Current and Historical Trends in Use, Management, and Disturbance of
U.S. Forestlands." In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The Potential of U.S. Forest
Soils to Sequester Carbon and Mitigate the Greenhouse Effect. CRC Press, New York, 15-34.
Eleazer, W.E., W.S. Odle, III, Y.S. Wang, and M.A. Barlaz (1997) "Biodegradability of municipal solid waste
components in laboratory-scale landfills." Env. Sci. Tech. 31(3):911-917.
EPA (2010) Inventory ofU. S. Greenhouse Gas Emissions and Sinks: 1990-2008. EPA, Office of Atmospheric
Programs. Washington, DC.
EPA (2006) Municipal solid waste in the United States: 2005 Facts and figures. Office of Solid Waste, U.S.
Environmental Protection Agency. Washington, DC. (5306P) EPA530-R-06-011. Available online at
.
Prayer, W.E., and G.M. Furnival (1999) "Forest Survey Sampling Designs: A History." Journal of Forestry 97(12):
4-10.
Freed, R. (2004) Open-dump and Landfill timeline spreadsheet (unpublished). ICF International. Washington, DC.
Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products - 1963. U.S. Department of
Agriculture Forest Service, Misc Publication No. 953. Washington, DC.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of
Agriculture Forest Service, Washington, DC.
Heath, L. (2007) Email communication between Kim Klunich, EPA, and Linda Heath, U.S. Forest Service.
November 9, 2007.
Heath, L.S. (2006a) Email correspondence. Linda Heath, U.S. Department of Agriculture Forest Service and
Kimberly Klunich, U.S. Environmental Protection Agency regarding the 95 percent CI for forest area estimates (±
0.24%) and average carbon density for Lower 48 States (± 0.4%). October 26, 2006.
Heath, L.S. (2006b) Email correspondence. Linda Heath, U.S. Department of Agriculture Forest Service and
Kimberly Klunich, U.S. Environmental Protection Agency regarding the 95 percent CI for average carbon density
for Alaska (±1.2%). October 27, 2006.
Heath, L.S., J.E., Smith, and R. A. Birdsey (2003) Carbon Trends in U. S. Forestlands: A Context for the Role of
Soils in Forest Carbon Sequestration. In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The Potential
ofU. S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect. Lewis Publishers (CRC Press). Boca
Raton, FL, 35-45.
Heath, L. S., and J.E. Smith (2000) "Soil Carbon Accounting and Assumptions for Forestry and Forest-related
Land Use Change." In The Impact of Climate Change on America's Forests. Joyce, L.A., and Birdsey, R. A. Gen.
Tech. Rep. RMRS-59. Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture. Fort
Collins, CO, 89-101.
Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, andK. L. Waddell (submitted) Carbon stocks on forested
lands of the United States, with emphasis on USD A Forest Service ownership.
References 11-41
-------
Howard, James L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap.
FPL-RP-615. Madison, WI: USD A, Forest Service, Forest Products Laboratory. Available online at
.
Howard, James L. (2007) U.S. timber production, trade, consumption, and price statistics 1965 to 2005. Res. Pap.
FPL-RP-637. Madison, WI: USD A, Forest Service, Forest Products Laboratory.
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 .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
Jenkins, J,C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) "National-scale biomass estimators for United
States tree species." Forest Science 49(1): 12-35.
Johnson, D. W., and P. S. Curtis (2001) "Effects of Forest Management on Soil C and N Storage: Meta Analysis."
Forest Ecology and Management 140:227-238.
Melosi, M. (2000) The Sanitary City. Johns Hopkins University Press. Baltimore, MD.
Melosi, M. (1981) Garbage in The Cities: Refuse Reform and the Environment: 1880-1980. Texas A&M Press.
Micales, J.A. and K.E. Skog (1997) "The decomposition of forest products in landfills." International
Biodeterioration & Biodegradation. 39(2-3): 145-158.
National Association of State Foresters (2007a) State Forestry Statistics 1998 Report. Available online at
March 2008.
National Association of State Foresters (2007b) State Forestry Statistics 2002 Report. Available online at
March 2008.
National Association of State Foresters (2007c) State Forestry Statistics 2004 Report. Available online at
March 2008.
Perry, C.H., C.W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an
accounting of "working trees". In: "Moving Agroforestry into the Mainstream." Proc. 9th N. Am. Agroforestry
Conf., Brooks, K.N. and Ffolliott, P.P. (eds). 12-15 June 2005, Rochester, MN [CD-ROM]. Dept. of Forest
Resources, Univ. Minnesota, St. Paul, MN, 12 p. Available online at (verified 23
Sept 2006).
Phillips, D.L., S.L. Brown, P.E. Schroeder, and R.A. Birdsey (2000) "Toward Error Analysis of Large-Scale Forest
Carbon Budgets." Global Ecology andBiogeography 9:305-313.
Skog, K.E., and G.A. Nicholson (1998) "Carbon Cycling Through Wood Products: The Role of Wood and Paper
Products in Carbon Sequestration." Forest Products Journal 48:75-83.
Skog, K.E., K. Pingoud, and J.E. Smith (2004) "A method countries can use to estimate changes in carbon stored in
harvested wood products and the uncertainty of such estimates." Environmental Management 33(Suppl. 1):S65-S73.
Skog, K.E. (2008) "Sequestration of carbon in harvested wood products for the United States." Forest Products
Journal 58:56-72.
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., and L.S.Heath (In press) "Exploring the assumed invariance of implied emission factors for forest
biomass in greenhouse gas inventories." Environmental Science & Policy doi: 10.1016/j.envsci.2009.10.002
11-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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, USD A Forest Service, Northeastern
Research Station, Newtown Square, PA.
Smith, J. E., L. S. Heath, and P. B. Woodbury (2004) "How to estimate forest carbon for large areas from inventory
data." Journal of Forestry 102:25-31.
Smith, W. B., P. D. Miles, C. H. Perry, and S. A. Pugh (2009) Forest Resources of the United States, 2007. General
Technical Report WO-78, U.S. Department of Agriculture Forest Service, Washington Office.
Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User's Guide: Forestland
Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of
Agriculture Forest Service, Northern Research Station, in press.
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture
Forest Service. Washington, DC.
Ulrich, Alice (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub.
1453, U.S. Department of Agriculture Forest Service. Washington, DC.
Ulrich, A.H. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA
Miscellaneous Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77.
USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1.
Washington, DC.
USDA Forest Service (2010a) Forest Inventory and Analysis National Program: User Information. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at .
Accessed 07 October 2010.
USDA Forest Service. (2010b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at http://199.128.173.17/fiadb4-
downloads/datamart.html Accessed 07 October 2010.
USDA Forest Service. (2010c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
. Accessed 07 October 2010.
USDA Forest Service (2010d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://fia.fs.fed.us/library/database-documentation/ > Accessed 07 October 2010.
USDA Forest Service (2008) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://www.fia.fs.fed.us/library/database-documentation/>. Accessed 15 December 2009.
USDA Forest Service (1992) "1984-1990 Wildfire Statistics." Prepared by State and Private Forestry Fire and
Aviation Management Staff. Facsimile from Helene Cleveland, USDA Forest Service, to ICF International. January
30, 2008.
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.
Waddell, K., and B. Hiserote. 2005. The PNW-FIA Integrated Database User Guide: A database of forest inventory
information for California, Oregon, and Washington. Forest Inventory and Analysis Program, Pacific Northwest
Research Station, Portland, Oregon, USA.Woodall, C.W., Amacher, M.C., Bechtold, W.A., Coulston, J.W., Jovan,
S., Perry, C.H., Randolph, K.C., Schulz, B.K., Smith, G.C., Tkacz, B, and Will-Wolf, S. (in press) "Status and future
of the forest health indicators program of the USA" Environmental Monitoring and Assessment.
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.
Woodbury, P.B., L.S. Heath, and J.E. Smith (2007) Effects of land use change on soil carbon cycling in the
References 11-43
-------
conterminous United States from 1900 to 2050, Global Biogeochem. Cycles, 21, GB3006,
doi: 10.1029/2007GB002950.
Forest Land Remaining Forest Land: Non-CO2 Emissions from Forest Fires
Alaska Department of Natural Resources (2008) Division of Forestry. "Fire Statistics." Available online at
October 2008.
Alaska Interagency Coordination Center (AICC) (2010). Alaska Fire Season 2008. Available online at <
http://fire.ak.blm.gov/content/aicc/stats/2009.pdf>
Heath, L. (2008) Phone communication between Kim Klunich, EPA, and Linda Heath, U.S. Forest Service.
November 24, 2008.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
National Interagency Fire Center (2010) "Fire Information—Wildland Fire Statistics. Total Wildland Fires and
Acres (1960-2009)." Available online at November 2010.
National Association of State Foresters (1998) State Forestry Statistics 1998 Report. Available online at
October 2008.
National Association of State Foresters (2002) State Forestry Statistics 2002 Report. Available online at
October 2008.
National Association of State Foresters (2004) State Forestry Statistics 2004 Report. Available online at
October 2008.
National Association of State Foresters (2008) State Forestry Statistics 2006 Report. Available online at
February 2009.
Smith, J. (2008a) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 3,
2008.
Smith, J. (2008b) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 8,
2008.
Smith, J. (2008c) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 16,
2008.
Smith, J. (2009) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, January 30,
2009.
USDA Forest Service (2010a) Forest Inventory and Analysis National Program: User Information. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at
Accessed 07 October 2010.
USDA Forest Service. (2010b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at < http://199.128.173.17/fiadb4-
downloads/datamart.html> Accessed 07 October 2010.
USDA Forest Service. (2010c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
Accessed 07 October 2010.
USDA Forest Service (2010d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://fia.fs.fed.us/library/database-documentation/ > Accessed 07 October 2010.
11-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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.
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.
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.
USDA Forest Service (2001) U.S. Forest Facts and Historical Trends. FS-696. U.S. Department of Agriculture
Forest Service, Washington, DC. Available online at .
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land
Converted to Grassland: Changes in Agricultural Soil Carbon Stocks
Allmaras, R.R., H.H. Schomberg, C.L. Douglas, Jr., and T.H. Dao (2000) "Soil organic carbon sequestration
potential of adopting conservation tillage in U.S. croplands." J Soil Water Conserv 55:365-373.
Armentano, T.V. and J.T.A. Verhoeven (1990) "Biogeochemical Cycles: Global," in B.C. Patten, et al. (eds.);
Wetlands and Shallow Continental Water Bodies. SPB Academic Publishing. The Hague, the Netherlands, Vol. I,
281-311.
Bastian, R. (2007) Personal Communication. Robert Bastian, Office of Water, U.S. Environmental Protection
Agency, Washington, DC and Victoria Thompson, ICF International. July 20, 2007.
Brady, N.C. and R.R. Weil (1999) The Nature and Properties of Soils. Prentice Hall. Upper Saddle River, NJ, 881.
CTIC (1998) "1998 Crop Residue Management Executive Summary." Conservation Technology Information
Center. West Lafayette, IN.
Daly, C., R.P. Neilson, and D.L. Phillips (1994) "A Statistical-Topographic Model for Mapping Climatological
Precipitation Over Mountainous Terrain." Journal of Applied Meteorology 33:140-158.
Dean, W. E., and E. Gorham (1998) Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands.
Geology 26:535-538.
Easter, M., S. Williams, and S. Ogle. (2008) Gap-filling NRI data for the Soil C Inventory. Natural Resource
Ecology Laboratory, Colorado State University, Fort Collins, CO. Report provided to the U.S. Environmental
Protection Agency, Tom Wirth.
Edmonds, L., R. L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J.
Schaefer (2003) "Costs associated with development and implementation of Comprehensive Nutrient Management
Plans." Part I—Nutrient management, land treatment, manure and wastewater handling and storage, and
recordkeeping. Natural Resources Conservation Service, U.S. Department of Agriculture. Available online at
.
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S.
References 11-45
-------
Environmental Protection Agency. Available online at .
EPA (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S.
Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.
ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department of
Agriculture. Available online at .
ERS (1988) Agricultural Resources—Inputs Situation and Outlook Report. AR-9. Economic Research Service, U.S.
Department of Agriculture.
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.
Eve, M. (2001) E-mail correspondence. MarlenEve, Natural Resources Ecology Laboratory, Colorado State
University and Barbara Braatz and Caren Mintz, ICF International. Statistics on U.S. organic soil areas cultivated in
1982, 1992, and 1997, which were extracted from the 1997 National Resources Inventory. September 21, 2001.
Grant, W.R. andR.D. Krenz (1985) U. S. grain sorghum production practices and costs. Staff Report No. AGES
851024. National Economics Division, Economics Research Service, U.S. Department of Agriculture.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental
Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13,
2004. Available online at .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency, Paris, France.
Kellogg, R.L., C.H. Lander, D.C. Moffitt, and N. Gollehon (2000) Manure Nutrients Relative to the Capacity of
Cropland and Pastur eland to Assimilate Nutrients: Spatial and Temporal Trends for the United States. U.S.
Department of Agriculture, Washington, DC. Publication number npsOO-0579.
McFarland, M.J. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12.
Metherell, A.K., L.A. Harding, C.V. Cole, and W.J. Parton (1993) "CENTURY Soil Organic Matter Model
Environment." Agroecosystem version 4.0. Technical documentation, GPSR Tech. Report No. 4, USDA/ARS, Ft.
Collins, CO.
NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgChl(04)a. National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgCHl(99). National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgChl(92). National Agricultural
Statistics Service, U.S. Department of Agriculture, Washington, DC. Available online at
.
NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and
Residuals Association, July 21, 2007
NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering
Service. Available online at .
NRCS (1999) 5*0/7 Taxonomy: A basic system of soil classification for making and interpreting soil surveys, 2nd
Edition. Agricultural Handbook Number 436, Natural Resources Conservation Service, U.S. Department of
11-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Agriculture, Washington, DC.
NRCS (1997) "National Soil Survey Laboratory Characterization Data," Digital Data, Natural Resources
Conservation Service, U.S. Department of Agriculture. Lincoln, NE.
NRCS (1981) Land Resource Regions and Major Land Resource Areas of the United States, USDA Agriculture
Handbook 296, United States Department of Agriculture, Natural Resources Conservation Service, National Soil
Survey Cente., Lincoln, NE, pp. 156.
Nusser, S.M., JJ. Goebel (1997) The national resources inventory: a long term monitoring programme.
Environmental and Ecological Statistics, 4, 181-204.
Ogle, S.M., FJ. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian (2009) "Scale and uncertainty in
modeled soil organic carbon stock changes for US croplands using a process-based model." Global Change Biology,
in press.
Ogle, S.M., FJ. 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., FJ. Breidt, and K. Paustian. (2006) "Bias and variance in model results due to spatial scaling of
measurements for parameterization in regional assessments." Global Change Biology 12:516-523.
Ogle, S.M., M.D. Eve, F J. Breidt, and K. Paustian (2003) "Uncertainty in estimating land use and management
impacts on soil organic carbon storage for U.S. agroecosystems between 1982 and 1997." Global Change Biology
9:1521-1542.
Ogle, S., M. Eve, M. Sperrow, FJ. Breidt, and K. Paustian (2002) Agricultural Soil C Emissions, 1990-2001:
Documentation to Accompany EPA Inventory Tables. Natural Resources Ecology Laboratory, Fort Collins, CO.
Provided in an e-mail from Stephen Ogle, NREL to Barbara Braatz, ICF International. September 23, 2002
Parton, W J., D.S. 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., 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.
Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993)
"Terrestrial ecosystem production: a process model based on global satellite and surface data." Global
Biogeochemical Cycles 7:811-841.
Reilly, J.M. and K.O. Fuglie. (1998) "Future yield growth in field crops: What evidence exists?" Soil Till Res
47:275-290.
USDA-FSA (2007) Conservation Reserve Program Summary and Enrollment Statistics FY 2006. U.S. Department
of Agriculture, Farm Service Agency, Washington, DC, Available online at
http://www.fsa.usda.gov/Internet/FSA_File/06rpt.pdf.
USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651.
Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.
USDA (1966) Consumption of Commercial Fertilizers and Primary Plant Nutrients in the United States, 1850-1964
and By States, 1945-1964. Statistical Bulletin Number 375, Statistical Reporting Service, U.S. Department of
Agriculture.
USDA (1951) Fertilizer Used on Crops and Pastures in the United States—1954 Estimates. Statistical Bulletin
Number 216, Agricultural Research Service, U.S. Department of Agriculture.
USDA (1954) Fertilizer Use and Crop Yields in the United States. Agricultural Handbook Number 68, the Fertilizer
Work Group, U.S. Department of Agriculture.
USDA-NRCS (2000) Digital Data And Summary Report: 1997 National Resources Inventory. Revised December
References 11-47
-------
2000. Resources Inventory Division, Natural Resources Conservation Service, United States Department of
Agriculture, Beltsville, MD.
Peatlands Remaining Peatlands: CO2 and N2O Emissions from Peatlands Remaining Peatlands
Apodaca, L. (2008) E-mail correspondence. Lori Apodaca, Peat Commodity Specialist, USGS and Emily Rowan,
ICF International. October and November.
Cleary, I, N. Roulet and T.R. Moore (2005) "Greenhouse gas emissions from Canadian peat extraction, 1990-2000:
A life-cycle analysis." Ambio 34:456-461.
Division of Geological & Geophysical Surveys (DGGS). Various authors. (1997-2008) Alaska's Mineral Industry.
Division of Geological & Geophysical Surveys, Alaska Department of Natural Resources, Fairbanks, AK.
Available online at .
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Szumigala, D. J. (2008). Phone conversation. Dr. David Szumigala, Division of Geological and Geophysical
Surveys, Alaska Department of Natural Resources and Emily Rowan, ICF International. October 17th, 2008.
Szumigala, D.J., R.A. Hughes and L.A. Harbo (2009) Alaska's mineral industry 2008: A summary. Division of
Geological & Geophysical Surveys, Alaska Department of Natural Resources, Fairbanks, AK. Available online at
.
United States Geological Survey (USGS) (2010) Mineral commodity summaries 2010. United States Geological
Survey, Reston, VA. Available online at < http://minerals.usgs.gov/minerals/pubs/mcs/>.
United States Geological Survey (USGS). Various authors. (1996-2009) Mineral Commodity Summaries: Peat.
United States Geological Survey, Reston, VA. Available online at
.
United States Geological Survey (USGS) (1991-2008) Minerals Yearbook: Peat. United States Geological Survey,
Reston, VA. Available online at .
Liming and Urea
AAPFCO (1995 through 2000a, 2002 through 2010) Commercial Fertilizers. Association of American Plant Food
Control Officials. University of Kentucky. Lexington, KY.
AAPFCO (2000b) 1999-2000 Commercial Fertilizers Data, ASCII files. Available from David Terry, Secretary,
AAPFCO.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Me, Cortney (2009). Email correspondence. Cortney Itle, ERG and Tom Wirth, U.S. Environmental Protection
Agency on the amount of urea used in aircraft deicing. January 7, 2009.
Tepordei, V. V. (1994) "Crushed Stone," In Minerals Yearbook 1992. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1279-1303.
Tepordei, V.V. (1993) "Crushed Stone," InMinerals Yearbook 1991. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1469-1511.
Tepordei, V.V. (1995) "Crushed Stone," InMinerals Yearbook 1993. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. pp. 1107-1147.
11-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Tepordei, V.V. (1996) "Crashed Stone," InMinerals Yearbook 1994. U.S. Department of the Interior/Bureau of
Mines, Washington, DC. Available online at
http://minerals.usgs.gov/minerals/pubs/commodity/stone_crashed/index.html#mis>. Accessed August 2000.
Tepordei, V.V. (1997 through 2006) "Crashed Stone," In Minerals Yearbook. U.S. Department of the Interior/U.S.
Geological Survey. Washington, DC. Available online at
.
Tepordei, Valentin V. (2003b) Personal communication. Valentin Tepordei, U.S. Geological Survey and ICF
Consulting, August 18, 2003.
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.
U.S. EPA. (2000) Preliminary Data Summary: Airport Deicing Operations (Revised). EPA-821-R-00-016. August
2000.
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
.
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 (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 (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
.
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.
West, Tristram O. (2008). Email correspondence. Tristram West, Environmental Sciences Division, Oak Ridge
National Laboratory, U.S. Department of Energy and Nikhil Nadkarni, ICF International on suitability of liming
emission factor for the entire United States. June 9, 2008.
Willett, J.C. (2007a) "Crashed Stone," InMinerals Yearbook 2005. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2007.
Willett, J.C. (2007b) "Crashed Stone," InMinerals Yearbook 2006. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2008.
Willett, J.C. (2009) "Crashed Stone," InMinerals Yearbook 2007. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2009.
Willett, J.C. (2010) "Crashed Stone," InMinerals Yearbook 2008. U.S. Department of the Interior/U.S. Geological
Survey, Washington, DC. Available online at
. Accessed August 2010.
Settlements Remaining Settlements: Changes in Carbon Stocks in Urban Trees
Cairns, M. A., S. Brown, E.H. Helmer, and G.A. Baumgardner (1997) "Root Biomass Allocation in the World's
Upland Forests." Oceologia 111:1-11.
References 11-49
-------
deVries, R.E. (1987)^4 Preliminary Investigation of the Growth and Longevity of Trees in Central Park. M.S.
thesis, Rutgers University, New Brunswick, NJ.
Dwyer, J.F., DJ. Nowak, M.H. Noble, and S.M Sisinni (2000) Connecting People with Ecosystems in the 21st
Century: An Assessment of Our Nation's Urban Forests. General Technical Report PNW-GTR-490, U.S.
Department of Agriculture, Forest Service, Pacific Northwest Research Station, Portland, OR.
Fleming, L.E. (1988) Growth Estimation of Street Trees in Central New Jersey. M.S. thesis, Rutgers University,
New Brunswick, NJ.
IPCC (2006) 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, Jen 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) Personal Communication. David Nowak, USDA Forest Service and Susan Asam, ICF
International. September 25, 2007.
Nowak, D.J. (2007c) E-mail correspondence regarding revised sequestration values and standard errors for
sequestration values. David Nowak, USDA Forest Service to Susan Asam, ICF International. October 31, 2007.
Nowak, D.J. (2002a) E-mail correspondence containing information on possible urban tree carbon and forest carbon
overlap. David Nowak, USDA Forest Service to Barbara Braatz, ICF International. January 10, 2002.
Nowak, D.J. (2002b) E-mail correspondence regarding significant digits. David Nowak, USDA Forest Service to
Barbara Braatz, ICF International. October 29, 2002.
Nowak, D.J. (1994) "Atmospheric Carbon Dioxide Reduction by Chicago's Urban Forest." In: Chicago's Urban
Forest Ecosystem: Results of the Chicago Urban Forest Climate Project. E.G. McPherson, D.J. Nowak, and R. A.
Rowntree (eds.). General Technical Report NE-186. U.S. Department of Agriculture Forest Service, Radnor, PA.
pp. 83-94.
Nowak, D.J. (1986) "Silvics of an Urban Tree Species: Norway Maple (Acer platanoides L.)." M.S. thesis, College
of Environmental Science and Forestry, State University of New York, Syracuse, NY.
Nowak, D.J. 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., M.H. Noble, S.M. Sisinni, and J.F. Dwyer (2001) "Assessing the U.S. Urban Forest Resource."
Journal of Forestry 99(3):37-42.
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.
Settlements Remaining Settlements: N2O Fluxes from Soils
Albaugh, T.J., Allen, H.L., Fox, T.R. (2007) Historical Patterns of Forest Fertilization in the Southeastern United
States from 1969 to 2004. Southern Journal of Applied Forestry, 31, 129-137(9)
11-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
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 Biodegradation of Municipal Solid Waste Components in Laboratory-
Scale Landfills." Global Biogeochemical Cycles 12:373-380.
Eleazer, W.E., W.S. Odle, Y. Wang, and M.A. Barlaz (1997) "Biodegradability of Municipal Solid Waste
Components in Laboratory-Scale Landfills." Environmental Science Technology 31:911-917.
EPA (2006 through 20II) Municipal Solid Waste in the United States: Facts and Figures. U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC. Available online at <
http://www.epa.gov/osw/nonhaz/municipal/msw99.htm>.
EPA (2005) Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures for
2003. U.S. Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
Available online at < http://www.epa.gov/osw/nonhaz/municipal/pubs/msw03rpt.pdf>.
EPA (2005a) Municipal Solid Waste in the United States: 2003 Data Tables. U.S. Environmental Protection
Agency, Office of Solid Waste and Emergency Response, Washington, DC. Available online at <
http://www.epa.gov/osw/nonhaz/municipal/pubs/03data.pdf>.
EPA (2003) Characterization of Municipal Solid Waste in the United States: 2001 Update. (Draft.) U.S.
Environmental Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
EPA (1999) Characterization of Municipal Solid Waste in the United States: 1998 Update. U.S. Environmental
Protection Agency, Office of Solid Waste and Emergency Response, Washington, DC.
IPCC (2006) 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.). Available online at
.
Oshins, C. and D. Block (2000) "Feedstock Composition at Composting Sites." Biocycle 41(9):31-34.
Schneider, S. (2007, 2008) E-mail correspondence. Shelly Schneider, Franklin Associates, A Division of ERG and
Sarah Shapiro, ICF International.
Tchobanoglous, G., H. Theisen, and S.A. Vigil (1993) Integrated Solid Waste Management, 1st edition. McGraw-
Hill, NY. Cited by Barlaz (1998).
Waste
Landfills
40 CFR Part 60, Subpart Cc (2005) Emission Guidelines and Compliance Times for Municipal Solid Waste
References 11-51
-------
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. (1998) "Carbon Storage During Biodegradation of Municipal Solid Waste Components in Laboratory-
scale Landfills." Global Biogeochemical Cycles, 12(2): 373-380, June 1998.
Barlaz, M.A. (2006) "Forest Products Decomposition in Municipal Solid Waste Landfills." Waste Management,
26(4): 321-333.
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.
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 <
http://www.jgpress.com/archives/_free/001782.html>
Czepiel, P., B. Mosher, P. Crill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane
Emissions." Journal of Geophysical Research, 101(D11):16721-16730.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
EPA (2009a) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2009.
EPA (2009b) Municipal Solid Waste Generation, Recycling, and Disposal in the United States Detailed Tables and
Figures for 2008. November 2009. Available online at <
http://www.epa.gov/osw/nonhaz/municipal/pubs/msw2008data.pdf>.
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste
Landfills. November 1998.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, DC. EPA/530-SW-88-011. September 1988.
ERG (2010). Production Data Supplied by ERG for 1990-2009 for Pulp and Paper, Fruits and Vegetables, and
Meat. July.
ICF International (2009). Updating Component-specific, First-order Decay Rates Used in Estimating Changes in
Yard Trimmings and Food Scrap Carbon Stocks in Landfills (Deliverable under EPA Contract Number EP-W-07-
068, Task Order 054, Task 03). Memorandum to M. Weitz (EPA), November 12, 2009.
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.
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.
Mancinelli, R. and C. McKay (1985) "Me thane-Oxidizing Bacteria in Sanitary Landfills." Proc. First Symposium
on Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, 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 (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from
11-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Landfills. Memorandum to E. Scheehle (EPA), August 26, 2004.
Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill
Methane Recovery. Publication No. GR-LG 0075. March 1998.
U.S. Bureau of Census (2010) International Database. August 2010. Available online at
.
Wastewater Treatment
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.
CARB (2007) Attachments C TO F - Supplemental Materials Document for Staff Report: Initial Statement of
Reasons for Rulemaking, Mandatory Reporting of Greenhouse Gas Emissions Pursuant to the California Global
Warming Solutions Act of 2006 (Assembly Bill 32), Attachment E: Technical Attachment on Development of
Emissions Reporting Requirements for Oil Refineries and Hydrogen Plants. California Environmental Protection
Agency Air Resources Board, dated October 19, 2007, http://www.arb.ca.gov/regact/2007/ghg2007/suppisor.pdf.
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.
El A. (2010) Energy Information Administration. U.S. Refinery and Blender Net Production of Crude Oil and
Petroleum Products (Thousand Barrels). Available online at: http://tonto.eia.doe.gov/dnav/pet/hist/mttrpusla.htm.
Accessed: August 2010.
EPA (1974) Development Document for Effluent Limitations Guidelines and New Source Performance Standards
for the Apple, Citrus, and Potato Processing Segment of the Canned and Preserved Fruits and Vegetables Point
Source Category. Office of Water, U.S. Environmental Protection Agency, Washington, DC, EPA-440/l-74-027-a.
March 1974.
EPA (1975) Development Document for Interim Final and Proposed Effluent Limitations Guidelines and New
Source Performance Standards for the Fruits, Vegetables, and Specialties Segment of the Canned and Preserved
Fruits and Vegetables Point Source Category. United States Environmental Protection Agency, Office of Water.
EPA-440/1-75-046. Washington DC, October 1975.
EPA (1992) Clean Watersheds Needs Survey 1992-Report to Congress. Office of Wastewater Management, U.S.
Environmental Protection Agency. Washington, DC.
EPA (1993) Development Document for the Proposed Effluent Limitations Guidelines and Standards for the Pulp,
Paper and Paperboard Point Source Category. EPA-821-R-93-019. Office of Water, U.S. Environmental
Protection Agency. Washington, DC. October 1993.
EPA (1996) 1996 Clean Water Needs Survey Report to Congress. Assessment of Needs for Publicly Owned
Wastewater Treatment Facilities, Correction of Combined Sewer Overflows, and Management of Storm Water and
Nonpoint Source Pollution in the United States. Office of Wastewater Management, U.S. Environmental Protection
Agency. Washington, DC. Available online at . Accessed
July 2007.
EPA (1997a) Estimates of Global Greenhouse Gas Emissions from Industrial and Domestic Wastewater Treatment.
EPA-600/R-97-091. Office of Policy, Planning, and Evaluation, U.S. Environmental Protection Agency.
Washington, DC,. September 1997.
EPA (1997b) 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, DC. October 1997.
EPA (1998) "AP-42 Compilation of Air Pollutant Emission Factors." Chapter 2.4, Table 2.4-3, page 2.4-13.
Available online at .
EPA (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste and Emergency
References 11-53
-------
Response, U.S. Environmental Protection Agency. Washington, DC, EPA530-R-99-009. September 1999.
EPA (2000) Clean Watersheds Needs Survey 2000 - Report to Congress. Office of Wastewater Management, U.S.
Environmental Protection Agency. Washington, DC. Available online at
. Accessed July 2007.
EPA (2002) 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, DC,. January 2002.
EPA (2004a) Clean Watersheds Needs Survey 2004-Report to Congress. U.S. Environmental Protection Agency,
Office of Wastewater Management. Washington, DC.
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.
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 (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCC-
XVI/Doc. 10 (1.IV.2000). May.
Lockwood-Post (2002) Lockwood-Post's Directory of Pulp, Paper and Allied Trades, Miller-Freeman Publications.
San Francisco, CA.
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.
Metcalf& Eddy, Inc. (2003) Wastewater Engineering: Treatment, Disposal and Reuse, 4th ed. McGrawHill
Publishing.
Metcalf & Eddy, Inc. (1991) Wastewater Engineering: Treatment, Disposal and Reuse, 3rd ed. McGraw Hill
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.
Paper 360° (2007) "U.S. production rises slightly in December." March 2007. Available online at <
http://www.thefreelibrary.com/U.S.+production+rises+slightly+in+December.(The+Pulse)-a0161909243>.
Accessed June 2007.
Pulp and Paper (2006) "AF&PA projects more capacity losses this year, small gains in 2007-08." April 2006.
Pulp and Paper (2005) "U.S. paper/board production rises in 2004 to 91.47 million tons." April 2005.
Pulp and Paper (2003-2008) "Month in Statistics." January 2003-September 2008.
Renewable Fuels Association (2010) Historic U.S. Fuel Ethanol Production. Available online at
. Accessed August 2010.
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
11-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Emissions Estimate." July 2001.
Timm, C.M. (1985) Water use, conservation and wastewater treatment alternatives for oil refineries in New Mexico.
NMERDI-2-72-4628.
U.S. Census Bureau (2009) "American Housing Survey." Table 1A-4: Selected Equipment and Plumbing-All
Housing Units. From 1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, and 2009 reports. Available
online at . Accessed August 2010.
U.S. Census Bureau (2010) International Database. Available online at and
. Accessed August 2010.
USDA (2009) Economic Research Service. U.S. Food Supply: Nutrients and Other Food Components, Per Capita
Per Day. Washington DC. Available online at . Accessed August 2010.
USDA (2010) National Agricultural Statistics Service. Washington, DC. Available online at
and
. Accessed August 2010.
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.
World Bank (1999) Pollution Prevention and Abatement Handbook 1998, Toward Cleaner Production. The
International Bank for Reconstruction and Development, The World Bank, Washington, DC. ISBN 0-8213-3638-X.
Composting
EPA (2009) Municipal Solid Waste in the United States: 2008 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
EPA (2008) Municipal Solid Waste in the United States: 2007 Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available online at
.
Franklin Associates (1997) Characterization of Municipal Solid Waste in the United States: 1996 Update. Report
prepared for the U.S. Environmental Protection Agency, Municipal and Industrial Solid Waste Division by Franklin
Associates, Ltd., Prairie Village, KS. EPA530-R-97-015. June 1997.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
Waste Sources of Indirect Greenhouse Gas Emissions
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions
Inventory (NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available
online at
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and
the Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards,
U.S. Environmental Protection Agency. Research Triangle Park, NC. October 1997.
References 11-55
-------
Annexes
The following seven annexes provide additional information related to the material presented in the main body of
this report as directed in the UNFCCC Guidelines on Reporting and Review (GE.03-60887). Annex 1 contains an analysis
of the key categories of emissions discussed in this report and a review of the methodology used to identify those key
categories. Annex 2 describes the methodologies used to estimate CO2 emissions from fossil fuel combustion, the carbon
content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3
discusses the methodologies used for a number of individual source categories in greater detail than was presented in the
main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC
reference approach for estimating CO2 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the
inclusion of an emission source category and discusses some of the sources that are excluded from U. S. estimates. Annex
6 provides a range of additional information that is relevant to the contents of this report. Finally, Annex 7 provides data
on the uncertainty of the emission estimates included in this report.
ANNEX 1 Key Category Analysis 3
ANNEX 2 Methodology and Data for Estimating C02 Emissions from Fossil Fuel Combustion 21
2.1. Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion 21
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 48
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels 84
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories 109
3.1. Methodology for Estimating Emissions of ChU, N20, and Indirect Greenhouse Gases from Stationary Combustion 109
3.2. Methodology for Estimating Emissions of ChU, N20, and Indirect Greenhouse Gases from Mobile Combustion and
Methodology for and Supplemental Information on Transportation-Related GHG Emissions 116
3.3. Methodology for Estimating ChU Emissions from Coal Mining 139
3.4. Methodology for Estimating ChU and C02 Emissions from Natural Gas Systems 147
3.5. Methodology for Estimating ChU and C02 Emissions from Petroleum Systems 165
3.6. Methodology for Estimating C02, N20 and ChU Emissions from the Incineration of Waste 170
3.7. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military 176
3.8. Methodology for Estimating HFC and RFC Emissions from Substitution of Ozone Depleting Substances 181
3.9. Methodology for Estimating ChU Emissions from Enteric Fermentation 199
3.10. Methodology for Estimating ChU and N20 Emissions from Manure Management 211
3.11. Methodology for Estimating N20 Emissions from Agricultural Soil Management 235
3.12. Methodology for Estimating Net Carbon Stock Changes in Forest Lands Remaining Forest Lands 254
3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral and Organic Soils on Cropland and Grassland 285
3.14. Methodology for Estimating ChU Emissions from Landfills 304
ANNEX 4 IPCC Reference Approach for Estimating C02 Emissions from Fossil Fuel Combustion 313
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 323
ANNEX 6 Additional Information 325
6.1. Global Warming Potential Values 325
6.2. Ozone Depleting Substance Emissions 333
6.3. Sulfur Dioxide Emissions 335
6.4. Complete List of Source Categories 337
6.5. Constants, Units, and Conversions 338
6.6. Abbreviations 341
6.7. Chemical Formulas 345
ANNEX 7 Uncertainty 349
7.1. Overview 349
7.2. Methodology and Results 349
7.3. Planned Improvements 355
7.4. Additional Information on Uncertainty Analyses by Source 358
A-l
-------
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
ANNEX 1 Key Category Analysis
The United States has identified national key categories based on the estimates presented in this report. The
IPCC's Good Practice Guidance (IPCC 2000) describes a key category as a "[category] that is prioritized within the
national inventory system because its estimate has a significant influence on a country's total inventory of direct
greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both."1 By definition, key
categories are sources or sinks that have the greatest contribution to the absolute overall level of national emissions in any
of the years covered by the time series. In addition, when an entire time series of emission estimates is prepared, a
determination of key categories must also account for the influence of the trends of individual categories. Therefore, a
trend assessment is conducted to identify source and sink categories for which significant uncertainty in the estimate
would have considerable effects on overall emission trends. Finally, a qualitative evaluation of key categories should be
performed, in order to capture any key categories that were not identified in either of the quantitative analyses, but can be
considered key because of the unique country-specific estimation methods.
The methodology for conducting a key category analysis, as defined by IPCC's Good Practice Guidance (IPCC
2000), IPCC's Good Practice Guidance for Land Use, Land-Use Change, and Forestry (IPCC 2003), and IPCC's 2006
Guidelines for National Greenhouse Gas Inventories IPCC (2006); includes:
• Tier 1 approach (including both level and trend assessments);
• Tier 2 approach (including both level and trend assessments, and incorporating uncertainty analysis); and
• Qualitative approach.
This Annex presents an analysis of key categories, both for sources only and also for sources and sinks (i.e.,
including LULUCF); discusses Tier 1, Tier 2, and qualitative approaches to identifying key categories; provides level and
trend assessment equations; and provides a brief statistical evaluation of IPCC's quantitative methodologies for defining
key categories. Table A- 1 presents the key categories for the United States (including and excluding LULUCF categories)
using emissions and uncertainty data in this report, and ranked according to their sector and global warming potential-
weighted emissions in 2009. The table also indicates the criteria used in identifying these categories (i.e., level, trend, Tier
1, Tier 2, and/or qualitative assessments).
A-3
-------
Table A-1: Key Source Categories for the United States 11990-20091
IPCC Source Categories
Gas
Tier 1
Level Trend Trend
Without Without Level With With
LULUCF LULUCF LULUCF LULUCF
Tier 2
Level Trend
Without Without
LULUCF LULUCF
Level Trend
With With
LULUCF LULUCF
Quaf
2009
Emissions
(Tg C02 Eq.)
Energy
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
CO2 Emissions from Mobile Combustion: Aviation
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Marine
Fugitive Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
Fugitive Emissions from Petroleum Systems
Non-CO2 Emissions from Stationary Combustion
N2O Emissions from Mobile Combustion: Road
Non-CO2 Emissions from Stationary Combustion
International Bunker Fuels
Industrial Processes
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Cement Production
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Aluminum Production
N2O Emissions from Nitric Acid Production
N2O Emissions from Adipic Acid Production
Emissions from Substitutes for Ozone Depleting Substances
SF6 Emissions from Electrical Transmission and Distribution
HFC-23 Emissions from HCFC-22 Production
PFC Emissions from Aluminum Production
SF6 Emissions from Magnesium Production and Processing
C02
C02
N20
N2O
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
Agriculture
CFLi Emissions from Enteric Fermentation
CFLi Emissions from Manure Management
CFLi Emissions from Rice Cultivation
Direct N2O Emissions from Agricultural Soil Management
Indirect N2O Emissions from Applied Nitrogen
CH4
CH4
CH4
N20
N2O
160.2
44.4
Waste
A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC Source Categories
CFL, Emissions from Landfills
CFLt Emissions from Wastewater Treatment
Land Use, Land Use Change, and Forestry
CO2 Emissions from Changes in Forest Carbon Stocks
CO2 Emissions from Urban Trees
CO2 Emissions from Cropland Remaining Cropland
CO2 Emissions from Landfilled Yard Trimmings and Food
Scraps
CO2 Emissions from Grassland Remaining Grassland
CFLt Emissions from Forest Fires
N2O Emissions from Forest Fires
Subtotal Without LULUCF
Total Emissions Without LULUCF
Percent of Total Without LULUCF
Subtotal With LULUCF
Total Emissions With LULUCF
Percent of Total With LULUCF
Gas
CH4
CH4
C02
C02
C02
C02
CO2
CH4
N20
Tier 1
Level Trend Trend
Without Without Level With With
LULUCF LULUCF LULUCF LULUCF
• • • •
• •
• •
•
•
'
Tier 2
Level Trend Level Trend
Without Without With With
LULUCF LULUCF LULUCF LULUCF
• • • •
• •
• •
• •
'
• •
• •
• •
•
Quaf
2009
Emissions
(Tg C02 Eq.)
117.5
24.5
(863.1)
(95.9)
(17.4)
(12.6)
(8.3)
7.8
6.4
6,512.7
6,608.2
99%
5,529.5
5,618.2
98%
"Qualitative criteria.
'Emissions from this source not included in totals.
Note: Parentheses indicate negative values (or sequestration).Table A-2 provides a complete listing of source categories by IPCC sector, along with notations on the criteria used in identifying key
categories, without LULUCF sources and sinks. Similarly, Table A-3 provides a complete listing of source and sink categories by IPCC sector, along with notations on the criteria used in identifying key
categories, including LULUCF sources and sinks. The notations refer specifically to the year(s) in the inventory time series (i.e., 1990 to 2009) in which each source category reached the threshold for
being a key category based on either a Tier 1 or Tier 2 level assessment.
A-5
-------
In addition to conducting Tier 1 and 2 level and trend assessments, a qualitative assessment of the source
categories, as described in the IPCC's Good Practice Guidance (IPCC 2000), was conducted to capture any key categories
that were not identified by any quantitative method. One additional key category, international bunker fuels, was
identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine
international transport activities, and emissions from these fuels are reported separately from totals in accordance with
IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category according to
the Tier 1 approach. The amount of uncertainty associated with estimation of emissions from international bunker fuels
also supports the qualification of this source category as key, which would qualify it as a key category according to the
Tier 2 approach.
Table A-2: U.S Greenhouse Gas Inventory Source Categories without LULUCF
IPCC Source Categories
Energy
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
CO2 Emissions from Mobile Combustion: Aviation
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Marine
CO2 Emissions from Incineration of Waste
CO2 Emissions from Petroleum Systems
CO2 Emissions from Stationary Combustion - Geothermal
Energy
Fugitive Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
Fugitive Emissions from Petroleum Systems
Non-CO2 Emissions from Stationary Combustion
Fugitive Emissions from Abandoned Underground Coal
Mines
CH4 Emissions from Mobile Combustion: Road
CH4 Emissions from Mobile Combustion: Other
CH4 Emissions from Mobile Combustion: Aviation
CH4 Emissions from Mobile Combustion: Marine
CH4 Emissions from Incineration of Waste
N2O Emissions from Mobile Combustion: Road
Non-CO2 Emissions from Stationary Combustion
N2O Emissions from Mobile Combustion: Other
N2O Emissions from Mobile Combustion: Aviation
N2O Emissions from Mobile Combustion: Marine
N2O Emissions from Incineration of Waste
International Bunker Fuels0
Industrial Processes
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Cement Production
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Lime Production
CO2 Emissions from Limestone and Dolomite Use
CO2 Emissions from Soda Ash Production and
Consumption
CO2 Emissions from Aluminum Production
CO2 Emissions from Petrochemical Production
CO2 Emissions from Carbon Dioxide Consumption
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Phosphoric Acid Production
Direct
GHG
C02
C02
C02
C02
C02
C02
C02
C02
CO2
C02
C02
C02
CH4
CH4
CH4
CH4
CH4
CFL,
CFL,
CFL,
CFL,
CH4
N2O
N20
N20
N2O
N20
N2O
Several
C02
C02
C02
C02
CO2
C02
CO2
C02
C02
C02
C02
C02
2009
Emissions
(Tg CO2 Key ID
Eq.) Category? Criteria3
1,841.0 • LiL2
1,475.6 • L,T,L2T2
1,164.6 • LiTiL2T2
483.3 • LiTiL2T2
140.7 • LiTiL2T2
123.4 • Li L2
73.5 • LiTi
32.2 • LiTiL2T2
30.0 • LjTi
12.3
0.5
0.4
221.2 • LiTiL2T2
71.0 • LiTiL2T2
30.9 • LiTiL2T2
6.2 • T2
5.5
1.4
0.4
0.1
+
+
20.3 • LiTiT2
12.8 • L2
1.8
1.3
0.4
0.4
124.4 • Q
41.9 • LiTiL2T2
29.0 • Ti
11.8 • Ti
11.2
7.6
4.3
3.0
2.7
1.8
1.5
1.5
1.0
Level in which
year(s)?b
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990,2009,
1990
1990, 2009
1990, 2009
1990,, 2009
1990
1990, 2009
19902, 2009
A-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC Source Categories
CO2 Emissions from Zinc Production
CO2 Emissions from Lead Production
CO2 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Petrochemical Production
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH4 Emissions from Ferroalloy Production
CH4 Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Nitric Acid Production
N2O Emissions from Product Uses
N2O Emissions from Adipic Acid Production
Emissions from Substitutes for Ozone Depleting
Substances
SFe Emissions from Electrical Transmission and
Distribution
HFC-23 Emissions from HCFC-22 Production
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
PFC Emissions from Aluminum Production
SFe Emissions from Magnesium Production and Processing
Agriculture
CFLt Emissions from Enteric Fermentation
CH4 Emissions from Manure Management
CH4 Emissions from Rice Cultivation
CH4 Emissions from Field Burning of Agricultural
Residues
Direct N2O Emissions from Agricultural Soil Management
Indirect N2O Emissions from Applied Nitrogen
N2O Emissions from Manure Management
N2O Emissions from Field Burning of Agricultural
Residues
Waste
CH4 Emissions from Landfills
CH4 Emissions from Wastewater Treatment
CH4 Emissions from Composting
N2O Emissions from Wastewater Treatment
N2O Emissions from Composting
Direct
GHG
C02
C02
C02
CH4
CH4
CH4
CH4
N2O
N20
N2O
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
HiGWP
CH4
CH4
CH4
CH4
N20
N2O
N20
N2O
CH4
CH4
CH4
N2O
N20
2009
Emissions
(Tg CO2 Key ID
Eq.) Category? Criteria3
1.0
0.5
0.1
0.8
0.4
+
+
14.6 • T2
4.4
1.9 • TiT2
120.0 • LiTiT2
12.8 • TiT2
5.4 • LjTjT,
5.3
1.6 • TjT2
1.1 • Ti
139.8 • Li L2
49.5 • LjT^
7.3 • L2
0.2
160.2 • LiTiL2T2
44.4 • LiL2T2
17.9
0.1
117.5 • LiTiL2T2
24.5 • L2
1.7
5.0
1.8
Level in which
year(s)?b
2009
1990
1990, 2009
2009
2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
a For the ID criteria, L refers to a key category identified through a level assessment; T refers to a key category identified through a trend
assessment and the subscripted number refers to either a Tier 1 or Tier 2 assessment (e.g., L2 designates a source is a key category for a Tier 2
level assessment).
b If the source is a key category for both LI and L2 (as designated in the ID criteria column), it is a key category for both assessments in the years
provided unless noted by a subscript, in which case it is a key category for that assessment in that year only (.e.g., 19902 designates a source is a
key category for the Tier 2 assessment only in 1990).
c Emissions from these sources not included in totals.
+ Does not exceed 0.05 Tg CO2 Eq.
Note: LULUCF sources and sinks are not included in this analysis.
Table A-3: U.S Greenhouse Gas Inventory Source Categories with LULUCF
IPCC Source Categories
Energy
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
CO2 Emissions from Mobile Combustion: Aviation
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Mobile Combustion: Other
Gas
C02
C02
CO2
C02
C02
C02
C02
2009
Emissions
(Tg CO2 Key
Eq.) Category?
1,841.0
1,475.6
1,164.6
483.3
140.7
123.4
73.5
ID
Criteria3
LiTiL2T2
LiTiL2T2
LjTjL,2
LiTi
Level in which
year(s)?b
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
A-7
-------
IPCC Source Categories
CO2 Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Marine
CO2 Emissions from Incineration of Waste
CO2 Emissions from Petroleum Systems
CO2 Emissions from Stationary Combustion - Geothermal
Energy
Fugitive Emissions from Natural Gas Systems
Fugitive Emissions from Coal Mining
Fugitive Emissions from Petroleum Systems
Non-CO2 Emissions from Stationary Combustion
Fugitive Emissions from Abandoned Underground Coal
Mines
CFLt Emissions from Mobile Combustion: Road
CH4 Emissions from Mobile Combustion: Other
CH4 Emissions from Mobile Combustion: Aviation
CFLt Emissions from Mobile Combustion: Marine
CH4 Emissions from Incineration of Waste
N2O Emissions from Mobile Combustion: Road
Non-CO2 Emissions from Stationary Combustion
N2O Emissions from Mobile Combustion: Other
N2O Emissions from Mobile Combustion: Aviation
N2O Emissions from Mobile Combustion: Marine
N2O Emissions from Incineration of Waste
International Bunker Fuels0
Industrial Processes
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Cement Production
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Lime Production
CO2 Emissions from Limestone and Dolomite Use
CO2 Emissions from Soda Ash Production and
Consumption
CO2 Emissions from Aluminum Production
CO2 Emissions from Petrochemical Production
CO2 Emissions from Carbon Dioxide Consumption
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Zinc Production
CO2 Emissions from Lead Production
CO2 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Petrochemical Production
CH4 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH4 Emissions from Ferroalloy Production
CH4 Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Nitric Acid Production
N2O Emissions from Product Uses
N2O Emissions from Adipic Acid Production
Emissions from Substitutes for Ozone Depleting
Substances
SFe Emissions from Electrical Transmission and
Distribution
HFC-23 Emissions from HCFC-22 Production
Gas
C02
C02
CO2
C02
C02
CH4
CH4
CH4
CH4
CH4
CH,
CH4
CFL,
CFL,
CH4
N20
N2O
N20
N20
N2O
N20
Several
C02
C02
C02
C02
CO2
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
CH4
CH4
CH4
N2O
N20
N20
HiGWP
HiGWP
HiGWP
2009
Emissions
(Tg CO2 Key ID
Eq.) Category? Criteria3
32.2 • LiTiL2T2
30.0 • LiTi
12.3
0.5
0.4
221.2 • LiTiL2T2
71.0 • LiTiL2T2
30.9 • LiTiL2T2
6.2 • T2
5.5
1.4
0.4
0.1
+
+
20.3 • LjTjT,
12.8 • L2
1.8
1.3
0.4
0.4
124.4 • Q
41.9 • LiTiL2T2
29.0 • Li Ti
11.8 • Ti
11.2
7.6
4.3
3.0
2.7
1.8
1.5
1.5
1.0
1.0
0.5
0.1
0.8
0.4
+
+
14.6 • Ti
4.4
1.9 • TjT2
120.0 • LiTiL2T2
12.8 • TiT2
5.4 • LiTiT2
Level in which
year(s)?b
19902, 2009
1990
1990, 2009
1990, 2009
1990, 2009
1990
1990, 2009
19902, 2009
1990
2009
1990
A-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC Source Categories
PFC, HFC, and SF6 Emissions from Semiconductor
Manufacture
PFC Emissions from Aluminum Production
SF6 Emissions from Magnesium Production and Processing
Agriculture
CH4 Emissions from Enteric Fermentation
CH4 Emissions from Manure Management
CH4 Emissions from Rice Cultivation
CH4 Emissions from Field Burning of Agricultural
Residues
Direct N2O Emissions from Agricultural Soil Management
Indirect N2O Emissions from Applied Nitrogen
N2O Emissions from Manure Management
N2O Emissions from Field Burning of Agricultural
Residues
Waste
CH4 Emissions from Landfills
CH4 Emissions from Wastewater Treatment
CH4 Emissions from Composting
N2O Emissions from Wastewater Treatment
N2O Emissions from Composting
Land Use, Land Use Change, and Forestry
CO2 Emissions from Changes in Forest Carbon Stocks
CO2 Emissions from Urban Trees
CO2 Emissions from Land Converted to Grassland
CO2 Emissions from Cropland Remaining Cropland
CO2 Emissions from Landfilled Yard Trimmings and Food
Scraps
CO2 Emissions from Grassland Remaining Grassland
CO2 Emissions from Land Converted to Cropland
CO2 Emissions from Liming of Agricultural Soils
CO2 Emissions from Urea Fertilization
CO2 Emissions from Wetlands Remaining Wetlands
CH4 Emissions from Forest Fires
N2O Emissions from Forest Fires
N2O Emissions from Settlement Soils
N2O Emissions from Forest Soils
N2O Emissions from Wetlands Remaining Wetlands
Gas
HiGWP
HiGWP
HiGWP
CH4
CH4
CH4
CH4
N2O
N20
N2O
N20
CH4
CH4
CH4
N20
N2O
C02
C02
C02
C02
C02
C02
C02
C02
C02
CO2
CH4
N2O
N2O
N20
N2O
2009
Emissions
(Tg CO2 Key ID
Eq.) Category? Criteria3
5.3
1.6 • TI
1.1 • Ti
139.8 • Li L2
49.5 • Li Ti T2
7.3 • L2
0.2
160.2 • LiTiL2T2
44.4 • LiL2T2
17.9
0.1
117.5 • LiTiL2T2
24.5 • L2
1.7
5.0
1.8
(863.1) • LiTiL2T2
(95.9) • LiTiL2T2
(23.6)
(17.4) • L2T!T2
(12.6) • L2TiT2
(8.3) • LiTiL2T2
5.9
4.2
3.6
1.1
7.8 • L2T2
6.4 • T2
1.5
0.4
+
Level in which
year(s)?b
1990, 2009
1990, 2009
2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990, 2009
1990
1990
2009
" For the ID criteria, L refers to a key category identified through a level assessment; T refers to a key category identified through a trend
assessment and the subscripted number refers to either a Tier 1 or Tier 2 assessment (e.g., L2 designates a source is a key category for a Tier 2
level assessment).
b If the source is a key category for both LI and L2 (as designated in the ID criteria column), it is a key category for both assessments in the years
provided unless noted by a subscript, in which case it is a key category only for that assessment in only that year (.e.g., 19902 designates a source
is a key category for the Tier 2 assessment only in 1990).
c Emissions from these sources not included in totals.
+ Does not exceed 0.05 Tg CO2 Eq.
Note: Parentheses indicate negative values (or sequestration).
Evaluation of Key Categories
Level Assessment
When using a Tier 1 approach for the level assessment, a predetermined cumulative emissions threshold is used
to identify key categories. When source and sink categories are sorted in order of decreasing absolute emissions, those
that fall at the top of the list and cumulatively account for 95 percent of emissions are considered key categories. The 95
percent threshold in the IPCC Good Practice Guidance (IPCC 2000) was designed to establish a general level where the
key category analysis covers approximately 75 to 92 percent of inventory uncertainty.
Including the Tier 2 approach provides additional insight into why certain source categories are considered key,
and how to prioritize inventory improvements. In the Tier 2 approach, the level assessment for each category from the Tier
1 approach is multiplied by its percent relative uncertainty. If the uncertainty reported is asymmetrical, the absolute value
A-9
-------
of the larger uncertainty is used. Uncertainty is not estimated for the following sources: CO2 emissions from stationary
combustion - geothermal energy; CO2 emissions from mobile combustion by mode of transportation; CH4 and N2O
emissions from mobile combustion by mode of off-road transportation; and CH4 from the incineration of waste. While
CO2 emissions from geothermal energy are included in the overall emissions estimate, they are not an official IPCC source
category. As a result, there are no guidelines to associate uncertainty with the emissions estimate; therefore, an uncertainty
analysis was not conducted. The uncertainty associated with CO2 from mobile combustion is applied to each mode's
emissions estimate, and the uncertainty associated with off-road vehicle CH4 and N2O emissions are applied to both CH4
and N2O emissions from aviation, marine, and other sources. No uncertainty was associated with CH4 emissions from
waste incineration because emissions are less than 0.05 Gg CH4 and an uncertainty analysis was not conducted. When
source and sink categories are sorted in decreasing order of this calculation, those that fall at the top of the list and
cumulatively account for 90 percent of emissions are considered key categories. The key categories identified by the Tier
2 level assessment may differ from those identified by the Tier 1 assessment. The final set of key categories includes all
source and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
It is important to note that a key category analysis can be sensitive to the definitions of the source and sink
categories. If a large source category is split into many subcategories, then the subcategories may have contributions to
the total inventory that are too small for those source categories to be considered key. Similarly, a collection of small,
non-key source categories adding up to less than 5 percent of total emissions could become key source categories if those
source categories were aggregated into a single source category. The United States has attempted to define source and
sink categories by the conventions which would allow comparison with other international key categories, while still
maintaining the category definitions that constitute how the emissions estimates were calculated for this report. As such,
some of the category names used in the key category analysis may differ from the names used in the main body of the
report. Additionally, the United States accounts for some source categories, including fossil fuel feedstocks, international
bunkers, and emissions from U.S. territories, that are derived from unique data sources using country-specific
methodologies.
Table A- 4 through Table A- 7 contain the 1990 and 2009 level assessments for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are
shaded dark gray. Additional key categories identified by the Tier 2 assessment are shaded light gray.
Trend Assessment
The Tier 1 approach for trend assessment is defined as the product of the source or sink category level
assessment and the absolute difference between the source or sink category trend and the total trend. In turn, the source or
sink category trend is defined as the change in emissions from the base year to the current year, as a percentage of current
year emissions from that source or sink category. The total trend is the percentage change in total inventory emissions
from the base year to the current year.
Thus, the source or sink category trend assessment will be large if the source or sink category represents a large
percentage of emissions and/or has a trend that is quite different from the overall inventory trend. To determine key
categories, the trend assessments are sorted in decreasing order, so that the source or sink categories with the highest trend
assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all categories
that fall within that cumulative 95 percent are considered key categories.
For the Tier 2 approach, the trend assessment for each category from the Tier 1 approach is multiplied by its
percent relative uncertainty. If the uncertainty reported is asymmetrical, the larger uncertainty is used. When source and
sink categories are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively
account for 90 percent of emissions are considered key categories. The key categories identified by the Tier 2 trend
assessment may differ from those identified by the Tier 1 assessment. The final set of key categories includes all source
and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
Table A- 8 and Table A- 9 contain the 1990 through 2009 trend assessment for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are
shaded dark gray. Additional key categories identified by the Tier 2 assessment are shaded light gray
A-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-4:1990 Key Source Category Tier land Tier 2 Analysis—Level Assessment, without LULUCF
IPCC Source Categories
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
Fugitive Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Aviation
Direct N2O Emissions from Agricultural Soil Management
CFLt Emissions from Landfills
CFLt Emissions from Enteric Fermentation
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Iron and Steel Production & Metallurgical
Coke Production
Fugitive Emissions from Coal Mining
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Mobile Combustion: Marine
Indirect N2O Emissions from Applied Nitrogen
N2O Emissions from Mobile Combustion: Road
CO2 Emissions from Natural Gas Systems
HFC-23 Emissions from HCFC-22 Production
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Cement Production
CH4 Emissions from Manure Management
SF6 Emissions from Electrical Transmission and Distribution
CFLt Emissions from Wastewater Treatment
PFC Emissions from Aluminum Production
N2O Emissions from Nitric Acid Production
CO2 Emissions from Ammonia Production and Urea
Consumption
N2O Emissions from Adipic Acid Production
N2O Emissions from Manure Management
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Lime Production
CO2 Emissions from Incineration of Waste
Non-CO2 Emissions from Stationary Combustion
CFL, Emissions from Rice Cultivation
CO2 Emissions from Aluminum Production
Fugitive Emissions from Abandoned Underground Coal Mines
SFe Emissions from Magnesium Production and Processing
CO2 Emissions from Limestone and Dolomite Use
N2O Emissions from Product Uses
CFL( Emissions from Mobile Combustion: Road
CO2 Emissions from Soda Ash Production and Consumption
N2O Emissions from Wastewater Treatment
CO2 Emissions from Petrochemical Production
PFC, HFC, and SF6 Emissions from Semiconductor
Manufacture
CO2 Emissions from Ferroalloy Production
N2O Emissions from Mobile Combustion: Aviation
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Carbon Dioxide Consumption
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Titanium Dioxide Production
CH4 Emissions from Iron and Steel Production & Metallurgical
Coke Production
CH4 Emissions from Petrochemical Production
CO2 Emissions from Zinc Production
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Petroleum Systems
CO2 Emissions from Lead Production
N2O Emissions from Incineration of Waste
CO2 Emissions from Stationary Combustion - Geothermal
Direct
GHG
C02
C02
C02
C02
CFL,
C02
N2O
CFL,
CFL,
C02
C02
CFL,
C02
C02
N2O
N20
C02
HFCs
CFL,
C02
CFL,
SF6
CFL,
PFCs
N20
C02
N2O
N2O
N20
C02
CO2
CFL,
CFL,
C02
CFL,
SF6
C02
N2O
CFL,
C02
N2O
CO2
Several
C02
N20
C02
C02
N2O
C02
CH4
CFL,
C02
N2O
C02
C02
N2O
C02
1990
Estimate
(Tg CO2 Tier 1 Level Cumulative Tier 2 Level
Eq.) Assessment Total Uncertainty3 Assessment
1,718.4
1,188.9
964.5
569.1
189.8
179.3
153.8
147.4
132.1
118.6
99.5
84.1
73.3
44.5
44.0
40.3
37.6
36.4
35.4
33.3
31.7
28.4
23.5
18.5
17.7
16.8
15.8
14.5
12.8
11.5
8.0
7.4
7.1
6.8
6.0
5.4
5.1
4.4
4.2
4.1
3.7
3.3
2.9
2.2
1.7
1.5
1.4
1.3
1.2
1.0
0.9
0.7
0.6
0.6
0.5
0.5
0.4
0.28
0.19
0.16
0.09
0.03
0.03
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.28
0.47
0.63
0.72
0.75
0.78
0.80
0.83
0.85
0.87
0.89
0.90
0.91
0.92
0.93
0.93
0.94
0.94
0.95
0.96
0.96
0.96
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
9%
8%
6%
7%
30%
8%
56%
48%
18%
21%
16%
16%
8%
8%
151%
17%
30%
10%
149%
14%
20%
22%
47%
11%
42%
8%
42%
24%
187%
10%
24%
127%
146%
4%
32%
4%
19%
8%
15%
7%
93%
31%
11%
13%
68%
19%
30%
47%
13%
23%
27%
18%
57%
149%
15%
320%
NE
0.026
0.016
0.010
0.007
0.009
0.002
0.014
0.012
0.004
0.004
0.003
0.002
0.001
0.001
0.011
0.001
0.002
0.001
0.009
0.001
0.001
0.001
0.002
<0.001
0.001
0.001
0.001
0.001
0.004
O.001
O.001
0.002
0.002
0.001
0.001
O.001
0.001
O.001
O.001
0.001
0.001
O.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
A-ll
-------
IPCC Source Categories
Energy
CO2 Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Composting
Emissions from Substitutes for Ozone Depleting Substances
CFL, Emissions from Composting
CH4 Emissions from Mobile Combustion: Other
CH4 Emissions from Field Burning of Agricultural Residues
CH4 Emissions from Mobile Combustion: Aviation
N2O Emissions from Field Burning of Agricultural Residues
CFLt Emissions from Silicon Carbide Production and
Consumption
CFLt Emissions from Mobile Combustion: Marine
CH4 Emissions from Ferroalloy Production
CH4 Emissions from Incineration of Waste
Direct
GHG
CO2
N20
Several
CFL,
CFL,
CFL,
CFL,
N20
CFL,
CFL,
CFL,
CFL,
1990
Estimate
(Tg C02
Eq.)
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.1
+
+
+
+
Tier 1 Level
Assessment
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Cumulative
Total
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Uncertainty3
9%
50%
8%
50%
47%
42%
61%
31%
9%
73%
12%
NE
Tier 2 Level
Assessment
O.001
0.001
O.001
0.001
0.001
O.001
0.001
0.001
0.001
O.001
O.001
0.001
Note: LULUCF sources and sinks are not included in this analysis.
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg CO2 Eq.
Table A- 5: 1990 Key Source CategoryTieM and Tier 2 Analysis— Level Assessment, with LULUCF
IPCC Source Categories
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Changes in Forest Carbon Stocks
CO2 Emissions from Stationary Combustion - Oil
Fugitive Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Aviation
Direct N2O Emissions from Agricultural Soil Management
CFLt Emissions from Landfills
CFLt Emissions from Enteric Fermentation
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Iron and Steel Production & Metallurgical
Coke Production
Fugitive Emissions from Coal Mining
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Urban Trees
CO2 Emissions from Grassland Remaining Grassland
CO2 Emissions from Mobile Combustion: Marine
Indirect N2O Emissions from Applied Nitrogen
N2O Emissions from Mobile Combustion: Road
CO2 Emissions from Natural Gas Systems
HFC-23 Emissions from HCFC-22 Production
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Cement Production
CFLt Emissions from Manure Management
CO2 Emissions from Cropland Remaining Cropland
SFe Emissions from Electrical Transmission and Distribution
CO2 Emissions from Landfilled Yard Trimmings and Food
Scraps
CFLt Emissions from Wastewater Treatment
CO2 Emissions from Land Converted to Grassland
PFC Emissions from Aluminum Production
N2O Emissions from Nitric Acid Production
CO2 Emissions from Ammonia Production and Urea
Consumption
Direct
GHG
C02
CO2
CO2
C02
CO2
CFL,
C02
N20
CFL,
CFL,
CO2
CO2
CFL,
C02
CO2
C02
C02
N20
N20
C02
HFCs
CFL,
CO2
cm
C02
SF6
C02
CFL,
C02
PFCs
N20
CO2
1990
Estimate
(TgC02
Eq.)
1,718.4
1,188.9
964.5
681.1
569.1
189.8
179.3
153.8
147.4
132.1
118.6
99.5
84.1
73.3
57.1
52.2
44.5
44.0
40.3
37.6
36.4
35.4
33.3
31.7
29.4
28.4
24.2
23.5
19.8
18.5
17.7
16.8
Tier 1 Level
Assessment
0.24
0.17
0.14
0.10
0.08
0.03
0.03
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Cumulative
Total
0.24
0.41
0.55
0.65
0.73
0.75
0.78
0.80
0.82
0.84
0.86
0.87
0.88
0.89
0.90
0.91
0.92
0.92
0.93
0.93
0.94
0.94
0.95
0.95
0.96
0.96
0.96
0.97
0.97
0.97
0.98
0.98
Uncertainty3
9%
8%
6%
18%
7%
30%
8%
56%
48%
18%
21%
16%
16%
8%
22%
32%
8%
151%
17%
30%
10%
149%
14%
20%
172%
22%
68%
47%
15%
11%
42%
8%
Tier 2 Level
Assessment
0.023
0.014
0.009
0.017
0.006
0.008
0.002
0.012
0.010
0.003
0.004
0.002
0.002
0.001
0.002
0.002
0.001
0.009
0.001
0.002
0.001
0.007
0.001
0.001
0.007
0.001
0.002
0.002
0.001
0.001
0.001
O.001
A-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
1990
Estimate
Direct (Tg COi Tier 1 Level Cumulative
IPCC Source Categories
N2O Emissions from Adipic Acid Production
N2O Emissions from Manure Management
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Lime Production
CO2 Emissions from Incineration of Waste
Non-CO2 Emissions from Stationary Combustion
CFLt Emissions from Rice Cultivation
CO2 Emissions from Aluminum Production
Fugitive Emissions from Abandoned Underground Coal Mines
SF6 Emissions from Magnesium Production and Processing
CO2 Emissions from Limestone and Dolomite Use
CO2 Emissions from Liming of Agricultural Soils
N2O Emissions from Product Uses
CH4 Emissions from Mobile Combustion: Road
CO2 Emissions from Soda Ash Production and Consumption
N2O Emissions from Wastewater Treatment
CO2 Emissions from Petrochemical Production
CFLt Emissions from Forest Fires
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
N2O Emissions from Forest Fires
CO2 Emissions from Urea Fertilization
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Land Converted to Cropland
N2O Emissions from Mobile Combustion: Aviation
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Carbon Dioxide Consumption
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Wetlands Remaining Wetlands
N2O Emissions from Settlement Soils
CFLt Emissions from Iron and Steel Production & Metallurgical
Coke Production
CFLt Emissions from Petrochemical Production
CO2 Emissions from Zinc Production
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Petroleum Systems
CO2 Emissions from Lead Production
N2O Emissions from Incineration of Waste
CO2 Emissions from Stationary Combustion - Geothermal
Energy
CO2 Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Composting
Emissions from Substitutes for Ozone Depleting Substances
CFLi Emissions from Composting
CFLt Emissions from Mobile Combustion: Other
CFLt Emissions from Field Burning of Agricultural Residues
CFLt Emissions from Mobile Combustion: Aviation
N2O Emissions from Field Burning of Agricultural Residues
N2O Emissions from Forest Soils
CH4 Emissions from Silicon Carbide Production and
Consumption
CFLt Emissions from Mobile Combustion: Marine
CFLt Emissions from Ferroalloy Production
N2O Emissions from Wetlands Remaining Wetlands
CFLi Emissions from Incineration of Waste
GHG
N20
N20
N2O
C02
CO2
CFL,
CFL,
CO2
CFL,
SF6
CO2
C02
N20
CFL,
C02
N20
C02
CH4
Several
N20
CO2
C02
CO2
N2O
C02
CO2
N20
C02
C02
N20
CH4
CH4
CO2
N20
CO2
CO2
N20
CO2
CO2
N20
Several
CH4
CH4
CH4
CH,
N20
N20
CH4
CLL
CH4
N20
CH4
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical,
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg CO2 Eq.
Eq.) Assessment
15.8
14.5
12.8
11.5
8.0
7.4
7.1
6.8
6.0
5.4
5.1
4.7
4.4
4.2
4.1
3.7
3.3
3.2
2.9
2.6
2.4
2.2
2.2
1.7
1.5
1.4
1.3
1.2
1.0
1.0
1.0
0.9
0.7
0.6
0.6
0.5
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.2
0.1
0.1
+
+
+
+
+
the uncertainty
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
given here
Tier 2 Level
Total Uncertainty3 Assessment
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
is the larger and always
42%
24%
187%
10%
24%
127%
146%
4%
32%
4%
19%
99%
8%
15%
7%
93%
31%
145%
11%
145%
43%
13%
40%
68%
19%
30%
47%
13%
34%
163%
23%
27%
18%
57%
149%
15%
320%
NE
9%
50%
8%
50%
47%
42%
61%
31%
211%
9%
73%
12%
74%
NE
positive.
0.001
O.001
0.003
<0.001
O.001
0.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
A-13
-------
Table A- 6:2009 Key Source CategoryTier 1 and Tier 2 Analysis—Level Assessment, without LULUCF
IPCC Source Categories
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
Fugitive Emissions from Natural Gas Systems
Direct N2O Emissions from Agricultural Soil Management
CO2 Emissions from Mobile Combustion: Aviation
CFLt Emissions from Enteric Fermentation
CO2 Emissions from Non-Energy Use of Fuels
Emissions from Substitutes for Ozone Depleting Substances
CFLt Emissions from Landfills
CO2 Emissions from Mobile Combustion: Other
Fugitive Emissions from Coal Mining
CFLt Emissions from Manure Management
Indirect N2O Emissions from Applied Nitrogen
|O2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
O2 Emissions from Natural Gas Systems
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Mobile Combustion: Marine
CO2 Emissions from Cement Production
CFLt Emissions from Wastewater Treatment
N2O Emissions from Mobile Combustion: Road
N2O Emissions from Manure Management
N2O Emissions from Nitric Acid Production
SFe Emissions from Electrical Transmission and Distribution
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Incineration of Waste
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Lime Production
CO2 Emissions from Limestone and Dolomite Use
CFLt Emissions from Rice Cultivation
Non-CO2 Emissions from Stationary Combustion
Fugitive Emissions from Abandoned Underground Coal Mines
HFC-23 Emissions from HCFC-22 Production
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
N2O Emissions from Wastewater Treatment
N2O Emissions from Product Uses
CO2 Emissions from Soda Ash Production and Consumption
CO2 Emissions from Aluminum Production
CO2 Emissions from Petrochemical Production
N2O Emissions from Adipic Acid Production
N2O Emissions from Composting
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Carbon Dioxide Consumption
CFLt Emissions from Composting
PFC Emissions from Aluminum Production
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Ferroalloy Production
CFLt Emissions from Mobile Combustion: Road
N2O Emissions from Mobile Combustion: Aviation
SF6 Emissions from Magnesium Production and Processing
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Zinc Production
CFLt Emissions from Petrochemical Production
CO2 Emissions from Lead Production
Direct
GHG
C02
CO2
C02
C02
CFL,
N20
CO2
CFL,
C02
Several
CFL,
C02
CFL,
CFL,
N20
C02
CO2
CFL,
C02
CO2
CH4
N20
N20
N20
SF6
N20
C02
C02
CO2
CO2
CFL,
CFL,
CFL,
HFCs
Several
N20
N20
C02
CO2
CO2
N20
N20
N2O
C02
CFL,
PFCs
C02
CO2
CFL,
N20
SF6
C02
CO2
CFL,
C02
2009
Estimate
(TgC02
Eq.)
1,841.0
1,475.6
1,164.6
483.3
221.2
160.2
140.7
139.8
123.4
120.0
117.5
73.5
71.0
49.5
44.4
41.9
32.2
30.9
30.0
29.0
24.5
20.3
17.9
14.6
12.8
12.8
12.3
11.8
11.2
7.6
7.3
6.2
5.5
5.4
5.3
5.0
4.4
4.3
3.0
2.7
1.9
1.8
1.8
1.8
1.7
1.6
1.5
1.5
1.4
1.3
1.1
1.0
1.0
0.8
0.5
Tier 1 Level
Assessment
0.28
0.22
0.18
0.07
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
Cumulative
Total
0.28
0.50
0.68
0.75
0.78
0.81
0.83
0.85
0.87
0.89
0.91
0.92
0.93
0.94
0.94
0.95
0.95
0.96
0.96
0.97
0.97
0.97
0.98
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
Uncertainty3
9%
8%
6%
7%
30%
56%
8%
18%
21%
8%
48%
8%
16%
20%
151%
16%
30%
149%
8%
14%
47%
17%
24%
42%
22%
187%
24%
8%
10%
19%
146%
127%
32%
10%
11%
93%
8%
7%
4%
31%
42%
50%
47%
30%
50%
11%
13%
13%
15%
68%
4%
19%
18%
27%
15%
Tier 2 Level
Assessment
0.026
0.019
0.011
0.005
0.010
0.014
0.002
0.004
0.004
0.002
0.009
0.001
0.002
0.001
0.010
0.001
0.001
0.007
<0.001
0.001
0.002
0.001
0.001
0.001
O.001
0.004
0.001
0.001
O.001
O.001
0.002
0.001
O.001
0.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
O.001
0.001
A-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC Source Categories
CO2 Emissions from Petroleum Systems
N2O Emissions from Mobile Combustion: Marine
CH, Emissions from Mobile Combustion: Other
CO2 Emissions from Stationary Combustion - Geothermal
Energy
N2O Emissions from Incineration of Waste
CH, Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH, Emissions from Field Burning of Agricultural Residues
CO2 Emissions from Silicon Carbide Production and
Consumption
CFLt Emissions from Mobile Combustion: Aviation
N2O Emissions from Field Burning of Agricultural Residues
CFL( Emissions from Mobile Combustion: Marine
CFLt Emissions from Ferroalloy Production
CFLt Emissions from Silicon Carbide Production and
Consumption
CFLt Emissions from Incineration of Waste
Direct
GHG
C02
N20
CH,
CO2
N20
CH,
CH,
CO2
CH,
N20
CH,
CH,
CH,
CH,
2009
Estimate
(Tg CO2 Tier 1 Level Cumulative Tier 2 Level
Eq.) Assessment Total Uncertainty3 Assessment
0.5
0.4
0.4
0.4
0.4
0.4
0.2
0.1
0.1
0.1
+
+
+
+
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
149%
57%
47%
NE
320%
23%
42%
9%
61%
31%
73%
12%
9%
NE
<0.001
O.001
O.001
O.001
0.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
0.001
0.001
Note: LULUCF sources and sinks are not included in this analysis.
a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive.
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg CO2 Eq.
Table A-7:2009 Key Source Category Tier land Tier 2 Analysis—Level Assessment with LULUCF
IPCC Source Categories
CO2 Emissions from Stationary Combustion - Coal
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Changes in Forest Carbon Stocks
CO2 Emissions from Stationary Combustion - Oil
Fugitive Emissions from Natural Gas Systems
Direct N2O Emissions from Agricultural Soil Management
CO2 Emissions from Mobile Combustion: Aviation
CH, Emissions from Enteric Fermentation
CO2 Emissions from Non-Energy Use of Fuels
Emissions from Substitutes for Ozone Depleting Substances
CH, Emissions from Landfills
CO2 Emissions from Urban Trees
CO2 Emissions from Mobile Combustion: Other
Fugitive Emissions from Coal Mining
CH, Emissions from Manure Management
Indirect N2O Emissions from Applied Nitrogen
CO2 Emissions from Iron and Steel Production & Metallurgical
Coke Production
CO2 Emissions from Natural Gas Systems
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Mobile Combustion: Marine
CO2 Emissions from Cement Production
CH, Emissions from Wastewater Treatment
CO2 Emissions from Land Converted to Grassland
N2O Emissions from Mobile Combustion: Road
N2O Emissions from Manure Management
CO2 Emissions from Cropland Remaining Cropland
N2O Emissions from Nitric Acid Production
SFe Emissions from Electrical Transmission and Distribution
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Landfilled Yard Trimmings and Food
Scraps
CO2 Emissions from Incineration of Waste
Direct
GHG
C02
CO2
C02
C02
CO2
CH,
N20
CO2
CH,
CO2
Several
CH,
CO2
C02
CH,
CH,
N20
CO2
CO:
CH,
CO2
C02
CH,
C02
N20
N2O
C02
N20
SF6
N20
C02
C02
2009
Estimate
(Tg CO2 Tier 1 Level Cumulative Tier 2 Level
Eq.) Assessment Total Uncertainty3 Assessment
1,841.0
1,475.6
1,164.6
863.1
483.3
221.2
160.2
140.7
139.8
123.4
120.0
117.5
95.9
73.5
71.0
49.5
44.4
41.9
32.2
30.9
30.0
29.0
24.5
23.6
20.3
17.9
17.4
14.6
12.8
12.8
12.6
12.3
0.24
0.19
0.15
0.11
0.06
0.03
0.02
0.02
0.02
0.02
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
O.01
O.01
0.01
O.01
0.01
0.01
O.01
0.01
0.01
O.01
0.01
O.01
0.01
0.24
0.43
0.58
0.70
0.76
0.79
0.81
0.83
0.85
0.86
0.88
0.89
0.91
0.92
0.93
0.93
0.94
0.94
0.95
0.95
0.96
0.96
0.96
0.97
0.97
0.97
0.97
0.97
0.98
0.98
0.98
0.98
9%
8%
6%
18%
7%
30%
56%
8%
18%
21%
8%
48%
22%
8%
16%
20%
151%
16%
30%
149%
8%
14%
47%
15%
17%
24%
172%
42%
22%
187%
68%
24%
0.023
0.016
0.010
0.020
0.005
0.009
0.012
0.002
0.003
0.003
0.001
0.007
0.003
0.001
0.001
0.001
0.009
0.001
0.001
0.006
O.001
0.001
0.001
0.001
0.001
0.001
0.004
0.001
O.001
0.003
0.001
0.001
A-15
-------
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Lime Production
CO2 Emissions from Grassland Remaining Grassland
CFL Emissions from Forest Fires
CO2 Emissions from Limestone and Dolomite Use
CFLt Emissions from Rice Cultivation
N2O Emissions from Forest Fires
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Land Converted to Cropland
Fugitive Emissions from Abandoned Underground Coal Mines
HFC-23 Emissions from HCFC-22 Production
PFC, HFC, and SF6 Emissions from Semiconductor
Manufacture
N2O Emissions from Wastewater Treatment
N2O Emissions from Product Uses
CO2 Emissions from Soda Ash Production and Consumption
CO2 Emissions from Liming of Agricultural Soils
CO2 Emissions from Urea Fertilization
CO2 Emissions from Aluminum Production
CO2 Emissions from Petrochemical Production
N2O Emissions from Adipic Acid Production
N2O Emissions from Composting
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Carbon Dioxide Consumption
CFL Emissions from Composting
PFC Emissions from Aluminum Production
CO2 Emissions from Titanium Dioxide Production
N2O Emissions from Settlement Soils
CO2 Emissions from Ferroalloy Production
CFL; Emissions from Mobile Combustion: Road
N2O Emissions from Mobile Combustion: Aviation
CO2 Emissions from Wetlands Remaining Wetlands
SFe Emissions from Magnesium Production and Processing
CO2 Emissions from Phosphoric Acid Production
CO2 Emissions from Zinc Production
CFLt Emissions from Petrochemical Production
CO2 Emissions from Lead Production
CO2 Emissions from Petroleum Systems
N2O Emissions from Mobile Combustion: Marine
CH4 Emissions from Mobile Combustion: Other
CO2 Emissions from Stationary Combustion - Geothermal
Energy
N2O Emissions from Incineration of Waste
CFL; Emissions from Iron and Steel Production & Metallurgical
Coke Production
N2O Emissions from Forest Soils
CFL Emissions from Field Burning of Agricultural Residues
CO2 Emissions from Silicon Carbide Production and
Consumption
CFL Emissions from Mobile Combustion: Aviation
N2O Emissions from Field Burning of Agricultural Residues
CFL Emissions from Mobile Combustion: Marine
CH4 Emissions from Ferroalloy Production
CFL Emissions from Silicon Carbide Production and
Consumption
N2O Emissions from Wetlands Remaining Wetlands
CFL Emissions from Incineration of Waste
C02
C02
CO2
CFL
CO2
CFL
N20
CFL
CO2
CFL
HFCs
Several
N2O
N20
C02
CO2
C02
CO2
CO2
N20
N2O
N20
C02
CFL
PFCs
C02
N2O
C02
CFL
N2O
C02
SF6
C02
C02
CFL
C02
C02
N2O
CFL
CO2
N20
CFL
N20
CFL
C02
CFL
N20
CFL
CIL
CFL
N20
CFL
11.8
11.2
8.3
7.8
7.6
7.3
6.4
6.2
5.9
5.5
5.4
5.3
5.0
4.4
4.3
4.2
3.6
3.0
2.7
1.9
1.8
1.8
1.8
1.7
1.6
1.5
1.5
1.5
1.4
1.3
1.1
1.1
1.0
1.0
0.8
0.5
0.5
0.4
0.4
0.4
0.4
0.4
0.4
0.2
0.1
0.1
0.1
+
+
+
+
+
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
1.00
8%
10%
32%
145%
19%
146%
145%
127%
40%
32%
10%
11%
93%
8%
7%
99%
43%
4%
31%
42%
50%
47%
30%
50%
11%
13%
163%
13%
15%
68%
34%
4%
19%
18%
27%
15%
149%
57%
47%
NE
320%
23%
211%
42%
9%
61%
31%
73%
12%
9%
74%
NE
<0.001
0.001
O.001
0.001
O.001
0.001
0.001
0.001
O.001
0.001
O.001
0.001
0.001
0.001
0.001
0.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
0.001
0.001
0.001
O.001
0.001
O.001
0.001
O.001
O.001
O.001
0.001
0.001
" Percent relative uncertainty. If the corresponding uncertainty is asymmetrical,
NE Uncertainty not estimated.
+ Does not exceed 0.05 Tg CO2 Eq.
the uncertainty given here is the larger and always positive.
A-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-8:1990-2009 KeySourceCategorcTieM and 2 Analysis—Trend Assessment, without LULUCF
IPCC Source Categories
CO2 Emissions from Mobile Combustion: Road
CO2 Emissions from Stationary Combustion - Gas
CO2 Emissions from Stationary Combustion - Oil
Emissions from Substitutes for Ozone Depleting Substances
CO2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Mobile Combustion: Aviation
CFL, Emissions from Landfills
HFC-23 Emissions from HCFC-22 Production
N2O Emissions from Mobile Combustion: Road
Fugitive Emissions from Coal Mining
PFC Emissions from Aluminum Production
Fugitive Emissions from Natural Gas Systems
CO2 Emissions from Mobile Combustion: Marine
SFe Emissions from Electrical Transmission and
Distribution
CFLt Emissions from Manure Management
N2O Emissions from Adipic Acid Production
CO2 Emissions from Natural Gas Systems
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Cement Production
CO2 Emissions from Ammonia Production and Urea
Consumption
CO2 Emissions from Mobile Combustion: Other
SF6 Emissions from Magnesium Production and Processing
Direct N2O Emissions from Agricultural Soil Management
N2O Emissions from Nitric Acid Production
CO2 Emissions from Aluminum Production
CO2 Emissions from Non-Energy Use of Fuels
CO2 Emissions from Incineration of Waste
CFLt Emissions from Mobile Combustion: Road
Indirect N2O Emissions from Applied Nitrogen
N2O Emissions from Manure Management
PFC, HFC, and SF6 Emissions from Semiconductor
Manufacture
CO2 Emissions from Limestone and Dolomite Use
Non-CO2 Emissions from Stationary Combustion
CH4 Emissions from Enteric Fermentation
N2O Emissions from Composting
CFLt Emissions from Composting
CO2 Emissions from Lime Production
N2O Emissions from Wastewater Treatment
Fugitive Emissions from Abandoned Underground Coal
Mines
Non-CO2 Emissions from Stationary Combustion
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Petrochemical Production
CFL, Emissions from Iron and Steel Production &
Metallurgical Coke Production
CH4 Emissions from Wastewater Treatment
CO2 Emissions from Phosphoric Acid Production
N2O Emissions from Mobile Combustion: Aviation
N2O Emissions from Mobile Combustion: Other
CO2 Emissions from Stationary Combustion - Coal
N2O Emissions from Product Uses
CFLt Emissions from Rice Cultivation
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Silicon Carbide Production and
Consumption
Direct
GHG
CO2
CO2
C02
Several
C02
CO2
CFL,
HFCs
N20
CFL,
PFCs
CFL,
C02
SF6
CFL,
N20
C02
CFL,
CO2
C02
CO2
SF6
N20
N2O
C02
CO2
C02
CFL,
N20
N20
Several
C02
CFL,
CFL,
N20
CFL,
C02
N20
CFL,
N20
CO2
C02
CFL,
CFL,
CO2
N20
N20
CO2
N20
CH4
CO2
CO2
1990
Estimate
(TgC02
Eq.)
1,188.9
964.5
569.1
0.3
99.5
179.3
147.4
36.4
40.3
84.1
18.5
189.8
44.5
28.4
31.7
15.8
37.6
35.4
33.3
16.8
73.3
5.4
153.8
17.7
6.8
118.6
8.0
4.2
44.0
14.5
2.9
5.1
7.4
132.1
0.4
0.3
11.5
3.7
6.0
12.8
2.2
3.3
1.0
23.5
1.5
1.7
1.3
1,718.4
4.4
7.1
1.2
0.4
2009
Estimate Percent Cumulative
(Tg CO2 Tier 1 Trend Tier 2 Trend Contribution Contribution
Eq.) Assessment Assessment to Trend (%) to Trend (%)
1,475.6
1,164.6
483.3
120.0
41.9
140.7
117.5
5.4
20.3
71.0
1.6
221.2
30.0
12.8
49.5
1.9
32.2
30.9
29.0
11.8
73.5
1.1
160.2
14.6
3.0
123.4
12.3
1.4
44.4
17.9
5.3
7.6
6.2
139.8
1.8
1.7
11.2
5.0
5.5
12.8
1.5
2.7
0.4
24.5
1.0
1.3
1.8
1,841.0
4.4
7.3
1.5
0.1
0.03
0.02
0.02
0.02
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.002
0.001
0.001
0.001
0.001
0.001
0.003
<0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
0.001
O.001
O.001
20.1 20
13.1 3
12.6 4
11.9 5
6.5 6
5.1 6
4.0 7
3.4 7
2.3 7
1.9 8
1.8 8
1.8 8
1.8 8
1.8 8
1.5 9
1.5 9
0.8 92
0.7 93
0.7 93
0.6 94
0.5 94
0.5 95
0.5 95
0.4 96
0.4 96
0.4 97
0.4 97
0.3 97
0.3 98
0.2 98
0.2 98
0.2 98
0.2 98
0.2 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 99
0.1 100
0.1 100
0.1 100
0.1 100
O.I 100
0.1 100
0.1 100
O.I 100
O.I 100
A-17
-------
Direct
IPCC Source Categories
CO2 Emissions from Zinc Production
CO2 Emissions from Carbon Dioxide Consumption
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Soda Ash Production and
Consumption
N2O Emissions from Incineration of Waste
CO2 Emissions from Petroleum Systems
CH4 Emissions from Mobile Combustion: Other
CH4 Emissions from Petrochemical Production
CH4 Emissions from Mobile Combustion: Aviation
CH4 Emissions from Field Burning of Agricultural Residues
CO2 Emissions from Stationary Combustion - Geothermal
Energy
CO2 Emissions from Lead Production
CH4 Emissions from Silicon Carbide Production and
Consumption
CH4 Emissions from Ferroalloy Production
N2O Emissions from Field Burning of Agricultural
Residues
CFLt Emissions from Mobile Combustion: Marine
CH4 Emissions from Incineration of Waste
Note: LULUCF sources and sinks are not included in this analysis
+ Does not exceed 0.05 Tg CO2 Eq.
GHG
C02
CO2
N2O
CO2
N20
C02
CH4
CH4
CFL,
CH4
CO2
CO2
CH4
CH4
N20
CFL,
CH4
1990
Estimate
(TgC02
Eq.)
0.7
1.4
0.6
4.1
0.5
0.6
0.3
0.9
0.2
0.3
0.4
0.5
+
+
0.1
+
+
2009
Estimate Percent
(Tg CO2 Tier 1 Trend Tier 2 Trend Contribution
Eq.)
1.0
1.8
0.4
4.3
0.4
0.5
0.4
0.8
0.1
0.2
0.4
0.5
+
+
0.1
+
+
Assessment Assessment to Trend (%)
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
<0.001
0.001
O.001
0.001
0.001
O.001
O.001
O.001
O.001
0.001
O.001
0.001
0.001
0.1
O.I
O.I
O.I
0.1
0.1
O.I
0.1
0.1
O.I
O.I
O.I
O.I
0.1
O.I
0.1
0.1
Cumulative
Contribution
to Trend (%)
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
Table A- 9: 1990-2009 Key Source Category Tier land 2 Analysis— Trend Assessment, with LULUGF
IPCC Source Categories
!O2 Emissions from Mobile Combustion: Road
O2 Emissions from Stationary Combustion - Oil
O2 Emissions from Changes in Forest Carbon Stocks
missions from Substitutes for Ozone Depleting Substances
CO2 Emissions from Stationary Combustion - Gas
[O2 Emissions from Iron and Steel Production &
Metallurgical Coke Production
O2 Emissions from Mobile Combustion: Aviation
O2 Emissions from Grassland Remaining Grassland
CFLt Emissions from Landfills
HFC-23 Emissions from HCFC-22 Production
CO2 Emissions from Urban Trees
CO2 Emissions from Stationary Combustion - Coal
N2O Emissions from Mobile Combustion: Road
Fugitive Emissions from Coal Mining
PFC Emissions from Aluminum Production
CO2 Emissions from Mobile Combustion: Marine
SF6 Emissions from Electrical Transmission and
Distribution
N2O Emissions from Adipic Acid Production
CFLt Emissions from Manure Management
Fugitive Emissions from Natural Gas Systems
CO2 Emissions from Cropland Remaining Cropland
CO2 Emissions from Landfilled Yard Trimmings and Food
Scraps
CO2 Emissions from Natural Gas Systems
Fugitive Emissions from Petroleum Systems
CO2 Emissions from Cement Production
Direct N2O Emissions from Agricultural Soil Management
CO2 Emissions from Ammonia Production and Urea
Consumption
Direct
GHG
C02
C02
C02
Several
C02
C02
C02
CO2
CFL,
HFCs
C02
C02
N20
CFL,
PFCs
C02
SF6
N2O
CH4
CH4
C02
C02
C02
CH4
C02
N20
CO2
1990
Estimate
(Tg C02
Eq.)
1,188.9
569.1
681.1
0.3
964.5
99.5
179.3
52.2
147.4
36.4
57.1
1,718.4
40.3
84.1
18.5
44.5
28.4
15.8
31.7
189.8
29.4
24.2
37.6
35.4
33.3
153.8
16.8
2009
Estimate
(Tg C02
Eq.)
1,475.6
483.3
863.1
120.0
1,164.6
41.9
140.7
8.3
117.5
5.4
95.9
1,841.0
20.3
71.0
1.6
30.0
12.8
1.9
49.5
221.2
17.4
12.6
32.2
30.9
29.0
160.2
11.8
Tier 1 Trend Tier
2 Trend
Assessment Assessment
0.02
0.02
0.01
0.01
0.01
0.01
0.01
0.01
0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
0.002
0.001
0.003
0.001
0.001
0.001
0.001
0.002
0.002
O.001
0.001
0.001
O.001
0.001
0.001
O.001
O.001
0.001
0.001
0.001
0.003
0.001
0.001
0.001
O.001
0.001
O.001
Percent
Contribution
to Trend (%)
14.4
10.6
9.7
9.4
9.1
5.2
4.3
3.8
3.4
2.7
2.7
2.1
1.8
1.6
1.5
1.4
1.4
1.2
1.2
1.2
1.1
1.1
0.7
0.6
0.6
0.6
0.5
Cumulative
Contribution
to Trend (%)
14
25
35
44
53
58
63
67
70
73
75
77
79
81
82
84
85
86
87
89
90
91
92
92
93
93
94
A-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
IPCC Source Categories
CO2 Emissions from Mobile Combustion: Other
CO2 Emissions from Non-Energy Use of Fuels
SF6 Emissions from Magnesium Production and Processing
N2O Emissions from Nitric Acid Production
CO2 Emissions from Aluminum Production
CFLt Emissions from Forest Fires
CH4 Emissions from Enteric Fermentation
CO2 Emissions from Incineration of Waste
CO2 Emissions from Land Converted to Cropland
N2O Emissions from Forest Fires
Indirect N2O Emissions from Applied Nitrogen
CH4 Emissions from Mobile Combustion: Road
PFC, HFC, and SFe Emissions from Semiconductor
Manufacture
N2O Emissions from Manure Management
CO2 Emissions from Limestone and Dolomite Use
CO2 Emissions from Land Converted to Grassland
Non-CO2 Emissions from Stationary Combustion
N2O Emissions from Composting
CFLt Emissions from Composting
CO2 Emissions from Lime Production
Non-CO2 Emissions from Stationary Combustion
Fugitive Emissions from Abandoned Underground Coal
Mines
N2O Emissions from Wastewater Treatment
CFLt Emissions from Wastewater Treatment
CO2 Emissions from Urea Fertilization
CO2 Emissions from Ferroalloy Production
CO2 Emissions from Petrochemical Production
CO2 Emissions from Liming of Agricultural Soils
CFLt Emissions from Iron and Steel Production &
Metallurgical Coke Production
CO2 Emissions from Phosphoric Acid Production
N2O Emissions from Mobile Combustion: Aviation
N2O Emissions from Settlement Soils
N2O Emissions from Mobile Combustion: Other
CFLt Emissions from Rice Cultivation
N2O Emissions from Product Uses
N2O Emissions from Forest Soils
CO2 Emissions from Silicon Carbide Production and
Consumption
CO2 Emissions from Titanium Dioxide Production
CO2 Emissions from Zinc Production
CO2 Emissions from Soda Ash Production and
Consumption
N2O Emissions from Mobile Combustion: Marine
CO2 Emissions from Carbon Dioxide Consumption
N2O Emissions from Incineration of Waste
CO2 Emissions from Petroleum Systems
CFLt Emissions from Petrochemical Production
CFLt Emissions from Mobile Combustion: Other
CFLt Emissions from Mobile Combustion: Aviation
CFLt Emissions from Field Burning of Agricultural Residues
CO2 Emissions from Stationary Combustion - Geothermal
Energy
CO2 Emissions from Lead Production
CO2 Emissions from Wetlands Remaining Wetlands
CFLt Emissions from Silicon Carbide Production and
Consumption
CFLt Emissions from Ferroalloy Production
N2O Emissions from Field Burning of Agricultural
Residues
1990 2009
Estimate Estimate Percent Cumulative
Direct (Tg COi (Tg COi Tier 1 Trend Tier 2 Trend Contribution Contribution
GHG Eq.) Eq.) Assessment Assessment to Trend (%) to Trend (%)
C02
C02
SF6
N20
C02
CFL,
CFL,
C02
C02
N20
N20
CFL,
Several
N20
C02
C02
CFL,
N20
CFL,
C02
N20
CFL,
N2O
CFL,
C02
C02
C02
C02
CFL,
C02
N20
N20
N2O
CFL,
N20
N20
C02
C02
C02
C02
N20
CO2
N20
C02
CFL,
CFL,
CFL,
CFL,
C02
C02
C02
CH4
CFL,
N20
73.3
118.6
5.4
17.7
6.8
3.2
132.1
8.0
2.2
2.6
44.0
4.2
2.9
14.5
5.1
19.8
7.4
0.4
0.3
11.5
12.8
6.0
3.7
23.5
2.4
2.2
3.3
4.7
1.0
1.5
1.7
1.0
1.3
7.1
4.4
0.1
0.4
1.2
0.7
4.1
0.6
1.4
0.5
0.6
0.9
0.3
0.2
0.3
0.4
0.5
1.0
+
+
0.1
73.5
123.4
1.1
14.6
3.0
7.8
139.8
12.3
5.9
6.4
44.4
1.4
5.3
17.9
7.6
23.6
6.2
1.8
1.7
11.2
12.8
5.5
5.0
24.5
3.6
1.5
2.7
4.2
0.4
1.0
1.3
1.5
1.8
7.3
4.4
0.4
0.1
1.5
1.0
4.3
0.4
1.8
0.4
0.5
0.8
0.4
0.1
0.2
0.4
0.5
1.1
+
+
0.1
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.01
<0.001
<0.001
<0.001
<0.001
O.001
0.001
<0.001
<0.001
0.001
0.001
0.001
0.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
0.001
O.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
0.001
O.001
O.001
0.001
O.001
0.001
O.001
0.001
0.001
0.001
O.001
O.001
0.001
O.001
0.001
0.001
O.001
0.001
O.001
0.001
O.001
0.001
O.001
0.001
0.5
0.4
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
O.I
0.1
O.I
O.I
0.1
O.I
0.1
O.I
0.1
0.1
0.1
O.I
O.I
0.1
O.I
0.1
0.1
O.I
0.1
O.I
0.1
O.I
0.1
O.I
0.1
94
95
95
95
96
96
96
97
97
97
98
98
98
98
98
98
99
99
99
99
99
99
99
99
99
99
99
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
100
A-19
-------
IPCC Source Categories
CHt Emissions from Mobile Combustion: Marine
N2O Emissions from Wetlands Remaining Wetlands
CH) Emissions from Incineration of Waste
Direct
GHG
CH4
N20
CH,
1990
Estimate
(Tg C02
Eq.)
+
+
+
2009
Estimate
(Tg C02
Eq.)
+
+
+
Tier 1 Trend
Assessment
<0.01
<0.01
<0.01
Tier 2 Trend
Assessment
<0.001
<0.001
<0.001
Percent
Contribution
to Trend (%)
<0.1
<0.1
<0.1
Cumulative
Contribution
to Trend (%)
100
100
100
+ Does not exceed 0.05 Tg CO2 Eq.
References
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara,
and K. Tanabe (eds.). Hayama, Kanagawa, Japan.
IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available online
at . August 13, 2004.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme.
A-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
ANNEX 2 Methodology and Data for
Estimating CO2 Emissions from Fossil
Fuel Combustion
2.1. Methodology for Estimating Emissions of COi from Fossil Fuel
Combustion
Carbon dioxide (CO2) emissions from fossil fuel combustion were estimated using a "bottom-up" methodology
characterized by seven steps. These steps are described below.
Step 1: Determine Total Fuel Consumption by Fuel Type and Sector
The bottom-up methodology used by the United States for estimating CO2 emissions from fossil fuel combustion
is conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change (IPCC) for
countries that intend to develop detailed, sectoral-based emission estimates (IPCC/UNEP/OECD/IEA 1997). Total
consumption data and adjustments to consumption are presented in Columns 2 through 13 of Table A- 10.
Adjusted consumption data are presented in Columns 2 through 8 of Table A- 11 through Table A- 30 with totals
by fuel type in Column 8 and totals by end-use sector in the last rows. Fuel consumption data for the bottom-up approach
were obtained directly from the Energy Information Administration (EIA) of the U.S. Department of Energy. These data
were first gathered in physical units, and then converted to their energy equivalents (see the Constants, Units, and
Conversions Annex). The EIA data were collected through a variety of consumption surveys at the point of delivery or
use and qualified with survey data on fuel production, imports, exports, and stock changes. Individual data elements were
supplied by a variety of sources within EIA. Most information was taken from published reports, although some data were
drawn from unpublished energy studies and databases maintained by EIA.
Energy consumption data were aggregated by sector (i.e., residential, commercial, industrial, transportation,
electricity generation, and U.S. territories), primary fuel type (e.g., coal, natural gas, and petroleum), and secondary fuel
type (e.g., motor gasoline, distillate fuel, etc.). The 2009 total adjusted energy consumption across all sectors, including
territories, and energy types was 71,949.8 trillion British thermal units (TBtu), as indicated in the last entry of Column 8 in
Table A- 11. This total excludes fuel used for non-energy purposes and fuel consumed as international bunkers, both of
which were deducted in earlier steps.
Electricity consumption information was allocated to each sector based on EIA's distribution of electricity retail
sales to ultimate customers (i.e., residential, commercial, industrial, and other). Because the "other" fuel use includes sales
to both the commercial and transportation sectors, EIA's limited transportation electricity use data were subtracted from
"other" electricity use and also reported separately. This total was consequently combined with the commercial electricity
data. Further information on these electricity end uses is described in EIA's Annual Energy Review (EIA 201 Ob).
There are also three basic differences between the consumption data presented in Table A- 11 through Table A-
30 and those recommended in the IPCC emission inventory methodology.
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV)1 rather than the
lower heating values (LHV)2 reflected in the IPCC emission inventory methodology. This convention is followed because
data obtained from EIA are based on HHV. Of note, however, is that EIA renewable energy statistics are often published
using LHV. The difference between the two conventions relates to the treatment of the heat energy that is consumed in the
process of evaporating the water contained in the fuel. The simplified convention used by the International Energy
Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and coal and by 0.9 for
natural gas.
Also referred to as Gross Calorific Values (GCV).
2 Also referred to as Net Calorific Values (NCV).
A-21
-------
Second, while EIA's energy use data for the United States includes only the 50 U.S. states and the District of
Columbia, the data reported to the UNFCCC are to include energy consumption within territories. Therefore,
consumption estimates for U.S. territories3 were added to domestic consumption of fossil fuels. Energy consumption data
from U.S. territories are presented in Column 7 of Table A- 11 through Table A- 30. It is reported separately from
domestic sectoral consumption, because it is collected separately by EIA with no sectoral disaggregation.
Third, there were a number of modifications made in this report that may cause consumption information herein
to differ from figures given in the cited literature. These are (1) the reallocation of select amounts of coking coal,
petroleum coke, natural gas, residual fuel oil, and other oil (>401 F) for processes accounted for in the Industrial Processes
chapter, (2) corrections for synthetic natural gas production, (3) subtraction of other fuels used for non-energy purposes,
and (4) subtraction of international bunker fuels. These adjustments are described in the following steps.
Step 2: Subtract uses accounted for in the Industrial Processes chapter.
Portions of the fuel consumption data for seven fuel categories—coking coal, distillate fuel, industrial other coal,
petroleum coke, natural gas, residual fuel oil, and other oil (>401 F)—were reallocated to the Industrial Processes chapter,
as these portions were consumed as raw materials during non-energy related industrial processes. Emissions from these
fuels used as raw materials are presented in the Industrial Processes chapter, and are removed from the energy and non-
energy consumption estimates within the Energy chapter.
• Coking coal, also called "coal coke," is used as a raw material (specifically as a reducing agent) in the blast
furnace process to produce iron and steel, lead, and zinc and therefore is not used as a fuel for this process.
• Similarly, petroleum coke is used in multiple processes as a raw material, and is thus not used as a fuel in
those applications. The processes in which petroleum coke is used include (1) ferroalloy production, (2)
aluminum production (for the production of C anodes and cathodes), (3) titanium dioxide production (in the
chloride process), (4) ammonia production, and (5) silicon carbide.
• Natural gas consumption is used for the production of ammonia, and blast furnace and coke oven gas used in
iron and steel production.
• Residual fuel oil and other oil (>40 IF) are both used in the production of C black.
• Natural gas, distillate fuel, coal, and metallurgical coke are used to produce pig iron through the reduction of
iron ore in the production of iron and steel.
Step 3: Adjust for Conversion of Fossil Fuels and Exports
First, a portion of industrial "other" coal that is accounted for in EIA coal combustion statistics is actually used to
make "synthetic natural gas" via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The plant
produces synthetic natural gas and byproduct CO2. The synthetic natural gas enters the natural gas distribution system.
Since October 2000, a portion of the CO2 produced by the coal gasification plant has been exported to Canada by pipeline.
The remainder of the CO2 byproduct from the plant is released to the atmosphere. The energy in this synthetic natural gas
enters the natural gas distribution stream, and is accounted for in EIA natural gas combustion statistics. Because this
energy of the synthetic natural gas is already accounted for as natural gas combustion, this amount of energy is deducted
from the industrial coal consumption statistics to avoid double counting. The exported CO2 is not emitted to the
atmosphere in the United States, and therefore the energy used to produce this amount of CO2 is subtracted from industrial
other coal.
Step 4: Subtract Consumption for Non-Energy Use
U.S. aggregate energy statistics include consumption of fossil fuels for non-energy purposes. Depending on the end-use,
non-energy uses of fossil fuels can result in long term storage of some or all of the C contained in the fuel. For example,
asphalt made from petroleum can sequester up to 100 percent of the C contained in the petroleum feedstock for extended
periods of time. Other non-energy fossil fuel products, such as lubricants or plastics also store C, but can lose or emit
3 Fuel consumption by U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island,
and other U.S. Pacific Islands) is included in this report
A-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
some of this C when they are used and/or burned as waste.4 As the emission pathways of C used for non-energy purposes
are vastly different than fuel combustion, these emissions are estimated separately in the Carbon Emitted in Products from
Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for non-energy purposes,
shown in Table A-31, was subtracted from total fuel consumption.
Step 5: Subtract Consumption of International Bunker Fuels
Emissions from international transport activities, or international bunker fuel consumption, are not included in
national totals, as required by the IPCC (IPCC/UNEP/OECD/FEA 1997). There is currently disagreement internationally
as to how these emissions should be allocated, and until this issue is resolved, countries are asked to report them
separately. EIA energy statistics, however, include these bunker fuels—-jet fuel for aircraft, and distillate fuel oil and
residual fuel oil for marine shipping—as part of fuel consumption by the transportation end-use sector. Therefore, the
amount of consumption for international bunker fuels was estimated and subtracted from total fuel consumption (see Table
A-32). Emissions from international bunker fuels have been estimated separately and not included in national totals.5
Step 6: Determine the C Content of All Fuels
The C content of combusted fossil fuels was estimated by multiplying adjusted energy consumption (Columns 2
through 8 of Table A- 11 through Table A- 30) by fuel-specific C content coefficients (see Table A- 33 and Table A- 34)
that reflect the amount of C per unit of energy in each fuel. The C content coefficients used in the U.S. inventory were
derived by EIA from detailed fuel information and are similar to the C content coefficients contained in the IPCC's default
methodology (iPCC/UNEP/OECD/iEA 1997), with modifications reflecting fuel qualities specific to the United States.
Step 7: Estimate CO2 Emissions
Actual CO2 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas,
geothermal) and consuming sector (i.e., residential, commercial, industrial, transportation, electricity generation, and U.S.
territories). Emission estimates are expressed in teragrams of carbon dioxide equivalents (Tg CO2 Eq.). To convert from
C content to CO2 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent based on
guidance in IPCC (2006).
To determine total emissions by final end-use sector, emissions from electricity generation were distributed to
each end-use sector according to its share of aggregate electricity consumption (see Table A-35). This pro-rated approach
to allocating emissions from electricity generation may overestimate or underestimate emissions for particular sectors due
to differences in the average C content of fuel mixes burned to generate electricity.
4 See the Waste Incineration section of the Energy chapter and the Waste Incineration Annex for a discussion of emissions from the
combustion of plastics in the municipal solid waste stream.
5 Refer to the International Bunker Fuels section of the Energy chapter for a description of the methodology for distinguishing between
bunker and non-bunker fuel consumption.
A-23
-------
Table A-10:2009 Energy Consumption Data by Fuel Type (TBtu) and Adjusted Energy Consumption Data
1 23456789
10
11
12
13
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Coking Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend
Components
Crude Oil
MoGas Blend
Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
6.8 61.5
6.8
61.5
4,852.1 3,168.3
1,196.5 708.5
591.7 406.3
26.2 5.3
578.7 165.4
68.8
62.4
0.3
6,055.5 3,938.3
Total Consumption (TBtu)a
Ind. Trans. Elec.
899.3
6.1
893.2
7,275.6
7,865.3
873.1
1,085.5
4.9
1,878.4
134.5
376.3
102.7
(0.8)
151.8
471.9
424.8
127.5
799.0
1,455.0
46.2
(77.8)
12.2
16,040.1
NE 18,296.2
NE
18,296.2
684.2 7,038.5
25,177.1 389.8
26.6
5,555.0 70.6
2,883.3
41.2
127.1
15,779.7
764.2 180.7
138.6
51.9
25,861.3 25,776.4
Terr.
38.2
38.2
27.4
560.3
74.3
41.7
7.6
14.9
1.0
199.8
165.7
55.2
625.9
Total
19,302.0
6.8
61.5
6.1
893.2
NE
18,296.2
38.2
23,046.2
35,897.5
873.1
26.6
7,783.4
2,925.0
43.9
2,678.6
262.6
16,424.7
1,275.7
(0.8)
207.1
471.9
424.8
127.5
937.9
1,455.0
46.2
(77.8)
12.2
51.9
78,297.5
Adjustments (TBtu)"
Bunker
Fuel
1,678.1
112.3
961.0
604.8
1,678.1
Unadjusted NEU Consumption
Ind. Trans. Terr.
18.5
6.1
12.4
388.4
4,079.4 127.1 56.3
873.1
17.5
1,520.5
134.5 127.1 1.0
151.8 55.2
471.9
424.8
97.7
195.4
133.9
46.2
12.2
4,486.3 127.1 56.3
Total Adjusted
Consumption
19,283.4
6.8
61.5
880.8
18,296.2
38.2
22,657.8
29,956.7
26.6
7,653.6
1,964.0
43.9
1,158.1
16,424.7
670.9
(0.8)
29.9
742.4
1,321.1
(77.8)
51.9
71,949.8
3 Expressed as gross calorific values (i.e., higher heating values).
Adjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption (see Table A-32).
NE (Not Estimated)
A-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-11:2009 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
6.8 61.5
6.8
61.5
4,852.1 3,168.3
1,196.5 708.5
591.7 406.3
26.2 5.3
578.7 165.4
68.8
62.4
0.3
6,055.5 3,938.3
Adjusted
Ind.
880.8
880.8
6,887.2
3,785.9
1,068.1
4.9
357.9
376.3
102.7
(0.8)
29.9
603.6
1,321.1
(77.8)
11,553.8
Consumption (TBtu)a
Trans. Elec.
NE
NE
684.2
23,371.9
26.6
5,442.7
1,922.3
41.2
15,779.7
159.4
24,056.2
18,296.2
18,296.2
7,038.5
389.8
70.6
180.7
138.6
51.9
25,776.4
Terr.
38.2
38.2
27.4
504.0
74.3
41.7
7.6
14.9
199.8
165.7
569.6
Total
19,283.4
6.8
61.5
880.8
18,296.2
38.2
22,657.8
29,956.7
26.6
7,653.6
1,964.0
43.9
1,158.1
16,424.7
670.9
(0.8)
29.9
742.4
1,321.1
(77.8)
51.9
71,949.8
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
0.6 5.8
0.6
5.8
257.2 167.9
81.4 50.3
43.8 30.0
1.9 0.4
35.7 10.2
4.9
4.7
0.0
339.2 224.0
83.4
83.4
365.0
282.0
79.0
0.4
22.1
26.8
7.7
(0.1)
2.1
61.6
88.1
(5.8)
730.4
NE 1,747.6
1,747.6
36.3 373.1
1,683.4 32.9
1.8
402.5 5.2
138.8
2.5
1,125.7
12.0 13.6
14.2
0.4
1,719.7 2,154.0
Use
Terr.
3.5
3.5
1.5
36.7
5.5
3.0
0.6
0.9
14.3
12.4
41.7
Total
1,841.0
0.6
5.8
83.4
NE
1,747.6
3.5
1,200.9
2,166.7
1.8
566.0
141.8
3.2
71.5
1,171.7
50.4
(0.1)
2.1
75.8
88.1
(5.8)
0.4
5,209.0
a Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels,
32).
NE (Not Estimated)
conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption (see Table A-
A-25
-------
Table A-12:2008 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
7.7 69.1
7.7
69.1
4,989.4 3,211.3
1,214.7 669.3
640.7 355.9
21.3 4.3
552.7 158.0
77.7
73.2
0.3
6,211.7 3,949.7
Adjusted
Ind.
1,083.4
1,083.4
7,377.7
4,165.2
1,197.9
4.0
382.0
424.9
135.6
0.1
35.8
615.6
1,423.0
(53.7)
12,626.2
Consumption (TBtu)a
Trans. Elec.
NE
NE
694.5
24,319.9
28.3
5,996.5
2,147.3
39.4
15,843.4
265.0
25,014.4
20,513.0
20,513.0
6,828.9
467.7
73.1
240.4
154.2
51.0
27,860.5
Terr. Total
35.7 21,708.7
7.7
69.1
1,083.4
20,513.0
35.7 35.7
29.3 23,130.9
478.6 31,315.4
28.3
94.3 8,358.3
34.9 2,182.2
5.8 35.3
15.7 1,147.8
134.0 16,480.1
193.9 908.1
0.1
35.8
770.1
1,423.0
(53.7)
51.0
543.5 76,206.1
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
0.7 6.5
0.7
6.5
264.4 170.2
83.1 47.4
47.4 26.3
1.6 0.3
34.1 9.7
5.5
5.5
0.0
348.2 224.2
102.6
102.6
391.0
309.3
88.6
0.3
23.6
30.3
10.2
0.0
2.5
62.9
94.9
(4.0)
802.9
NE
36.8
1,753.1
2.0
443.5
155.1
2.4
1,130.3
19.9
1,789.9
1,959.4
1,959.4
361.9
39.2
5.4
18.1
15.7
0.4
2,360.9
Use
Terr.
3.3
3.3
1.6
35.0
7.0
2.5
0.4
1.0
9.6
14.6
39.8
Total
2,072.5
0.7
6.5
102.6
NE
1,959.4
3.3
1,226.0
2,267.1
2.0
618.2
157.6
2.6
70.8
1,175.7
68.2
0.0
2.5
78.6
94.9
(4.0)
0.4
5,565.9
a Expressed as gross calorific values (i.e., higher heating values).
b Adjustments are subtracted from total consumption estimates and include biofuels,
32).
NE (Not Estimated)
conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption (see Table A-
A-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-13:2007 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
7.8 70.0
7.8
70.0
4,849.6 3,094.1
1,224.9 680.7
697.3 368.7
43.9 9.2
483.7 121.4
105.6
75.4
0.4
6,082.2 3,844.8
Adjusted
Ind.
1,130.3
1,130.3
7,339.6
4,679.9
1,185.5
13.4
563.7
528.7
130.4
1.8
42.0
666.7
1,482.6
65.2
13,149.8
Consumption (TBtu)a
Trans. Elec.
NE
NE
665.4
25,685.8
31.6
6,439.7
2,336.3
21.9
16,470.1
386.1
26,351.2
20,807.7
20,807.7
7,005.2
657.1
89.3
396.6
171.2
49.9
28,520.0
Terr. Total
46.5 22,062.4
7.8
70.0
1,130.3
20,807.7
46.5 46.5
26.8 22,980.6
551.7 33,480.1
31.6
136.5 8,917.0
55.5 2,391.8
5.2 71.8
11.6 1,202.3
157.0 17,261.4
185.9 1,174.4
1.8
42.0
838.3
1,482.6
65.2
49.9
625.0 78,572.9
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
0.7 6.7
0.7
6.7
257.0 164.0
84.6 48.7
51.6 27.3
3.2 0.7
29.8 7.5
7.6
5.7
0.0
342.4 219.4
107.0
107.0
389.0
346.0
87.7
1.0
34.8
37.9
9.8
0.1
2.9
68.1
98.9
4.8
842.0
NE
35.3
1,858.7
2.2
476.3
168.7
1.4
1,181.2
29.0
1,894.0
1,987.3
1,987.3
371.3
53.9
6.6
29.8
17.5
0.4
2,412.8
Use
Terr.
4.3
4.3
1.4
40.4
10.1
4.0
0.4
0.7
11.3
14.0
46.1
Total
2,106.0
0.7
6.7
107.0
NE
1,987.3
4.3
1,218.0
2,432.4
2.2
659.5
172.7
5.3
74.2
1,238.0
88.2
0.1
2.9
85.6
98.9
4.8
0.4
5,756.7
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-27
-------
Table A-14:2006 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
6.4 64.8
6.4
64.8
4,475.9 2,901.7
1,204.9 677.7
693.0 390.5
66.4 15.2
445.5 123.2
73.4
75.3
0.3
5,687.2 3,644.2
Adjusted
Ind.
1,188.3
1,188.3
7,125.0
4,842.3
1,199.4
29.6
587.5
567.0
176.4
0.6
32.8
682.5
1,496.1
70.3
13,155.6
Consumption (TBtu)a
Trans. Elec.
NE
NE
625.0
25,589.8
33.4
6,358.6
2,347.1
27.5
16,516.8
306.3
26,214.7
20,461.9
20,461.9
6,375.1
648.1
73.7
360.5
213.9
49.7
27,534.8
Terr. Total
36.9 21,758.2
6.4
64.8
1,188.3
20,461.9
36.9 36.9
26.1 21,528.8
620.9 33,583.6
33.4
90.2 8,805.3
76.1 2,423.2
4.4 115.5
6.6 1,190.2
188.8 17,345.9
254.8 1,173.3
0.6
32.8
896.7
1,496.1
70.3
49.7
683.9 76,920.4
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
0.6 6.2
0.6
6.2
237.3 153.8
83.6 48.5
51.3 28.9
4.9 1.1
27.5 7.6
5.2
5.7
0.0
321.5 208.6
112.6
112.6
377.7
357.9
88.7
2.2
36.3
40.4
13.2
0.0
2.3
69.7
99.8
5.2
848.2
NE
33.1
1,845.0
2.3
470.3
169.5
1.7
1,178.2
23.0
1,878.1
1,953.7
1,953.7
338.0
54.4
5.4
27.1
21.8
0.4
2,346.4
Use
Terr.
3.4
3.4
1.4
45.5
6.7
5.5
0.3
0.4
13.5
19.1
50.3
Total
2,076.5
0.6
6.2
112.6
NE
1,953.7
3.4
1,141.3
2,434.9
2.3
651.2
175.0
8.5
73.5
1,237.4
88.1
0.0
2.3
91.6
99.8
5.2
0.4
5,653.1
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-15:2005 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
8.4 97.0
8.4
97.0
4,946.4 3,073.2
1,369.2 716.0
772.6 404.7
83.8 21.6
512.9 131.4
42.2
115.8
0.3
6,324.0 3,886.2
Adjusted
Ind.
1,219.1
1,219.1
7,184.9
4401 3
1,124.5
39.1
521.0
328.4
223.2
8.3
45.9
678.6
1,429.4
2.8
12,805.3
Consumption (TBtu)a
Trans. Elec.
NE
NE
623.9
25,932.6
35.4
6,193.8
2,689.2
28.2
16,729.6
256.4
26,556.5
20,737.2
20,737.2
6,014.5
1,234.5
114.6
876.5
243.5
50.1
28,036.4
Terr. Total
32.7 22,094.5
8.4
97.0
1,219.1
20,737.2
32.7 32.7
24.3 21,867.2
623.3 34,276.9
35.4
121.3 8,731.6
66.0 2,755.2
5.8 150.2
0.8 1,194.2
194.3 17,294.4
235.2 1,707.2
8.3
45.9
922.3
1,429.4
2.8
50.1
680.4 78,288.8
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
0.8 9.3
0.8
9.3
262.2 162.9
94.9 51.3
57.1 29.9
6.1 1.6
31.7 8.1
3.0
8.7
0.0
357.9 223.5
115.3
115.3
380.8
326.9
83.2
2.9
32.2
23.3
16.8
0.6
3.2
69.3
95.4
0.2
823.1
NE
33.1
1,863.5
2.4
458.1
194.2
1.7
1,187.8
19.3
1,896.6
1,983.8
1,983.8
318.8
99.2
8.5
65.8
24.9
0.4
2,402.1
Use
Terr.
3.0
3.0
1.3
45.7
9.0
4.8
0.4
0.0
13.8
17.7
50.0
Total
2,112.3
0.8
9.3
115.3
NE
1,983.8
3.0
1,159.0
2,481.5
2.4
645.8
199.0
11.0
73.7
1,227.9
128.2
0.6
3.2
94.2
95.4
0.2
0.4
5,753.2
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-29
-------
Table A-16:2004 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
11.4 102.9
11.4
102.9
4,980.8 3,201.0
1,474.6 766.7
878.1 447.0
84.8 20.5
511.7 152.0
24.4
122.5
0.3
6,466.8 4,070.6
Adjusted
Ind.
1,262.0
1,262.0
7,802.3
47876
1,136.6
28.2
564.8
203.4
195.3
10.6
52.1
683.9
1,483.3
(75.6)
13,346.9
Consumption (TBtu)a
Trans. Elec.
NE
NE
602.0
25,529.7
31.2
5,917.7
2,525.0
19.1
16,850.3
186.4
26,131.6
20,305.0
20,305.0
5,594.9
1,212.4
111.3
879.0
222.1
50.5
27,162.9
Terr. Total
32.0 21,713.3
11.4
102.9
1,262.0
20,305.0
32.0 32.0
24.6 22,205.6
653.6 33,919.5
31.2
134.4 8,625.2
68.8 2,593.7
6.0 139.5
0.8 1,248.4
198.6 17,276.7
245.0 1,628.2
10.6
52.1
906.2
1,483.3
(75.6)
50.5
710.2 77,889.0
Emissions'" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
1.1 9.8
1.1
9.8
264.1 169.7
102.7 54.9
64.9 33.1
6.2 1.5
31.6 9.4
1.7
9.2
0.0
367.9 234.4
118.3
118.3
413.7
317.7
84.1
2.1
34.9
14.5
14.7
0.7
3.6
69.8
99.0
(5.6)
849.6
NE
31.9
1,834.9
2.2
437.7
182.4
1.2
1,197.6
14.0
1,866.8
1,943.1
1,943.1
296.7
96.9
8.2
66.0
22.7
0.4
2,337.0
Use
Terr.
2.9
2.9
1.3
47.9
9.9
5.0
0.4
0.0
14.1
18.4
52.2
Total
2,075.1
1.1
9.8
118.3
NE
1,943.1
2.9
1,177.5
2,455.0
2.2
637.9
187.3
10.2
77.1
1,227.9
122.3
0.7
3.6
92.5
99.0
(5.6)
0.4
5,708.0
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-17:2003 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.2 82.0
12.2
82.0
5,209.4 3,260.9
1,466.1 762.1
851.3 453.1
70.3 18.6
544.5 156.9
22.0
111.1
0.3
6,687.8 4,104.9
Adjusted
Ind.
1,248.8
1,248.8
7,806.1
4,018.7
1,054.1
24.1
484.6
119.2
171.7
7.5
51.7
678.9
1,477.3
(50.4)
13,073.5
Consumption (TBtu)a
Trans. Elec.
NE
NE
627.4
24,937.8
30.2
5,710.9
2,480.8
16.5
16,600.2
99.1
25,565.3
20,184.7
20,184.7
5,246.2
1,205.0
161.0
869.4
174.7
49.2
26,685.2
Terr. Total
33.9 21,561.7
12.2
82.0
1,248.8
20,184.7
33.9 33.9
26.9 22,177.0
621.8 33,011.5
30.2
120.5 8,350.8
76.1 2,556.9
10.7 123.7
10.5 1,213.0
210.1 16,951.6
193.9 1,445.2
7.5
51.7
853.9
1,477.3
(50.4)
49.2
682.6 76,799.3
Emissions'" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
1.2 7.8
1.2
7.8
275.9 172.7
101.8 54.5
63.0 33.5
5.1 1.4
33.7 9.7
1.6
8.3
0.0
378.8 235.0
117.0
117.0
413.4
299.3
78.0
1.8
30.0
8.5
12.9
0.5
3.6
69.3
98.6
(3.7)
829.6
NE
33.2
1,790.4
2.1
422.4
179.2
1.0
1,178.3
7.4
1,823.6
1,931.0
1,931.0
277.8
95.0
11.9
65.3
17.8
0.4
2,304.2
Use
Terr.
3.1
3.1
1.4
45.3
8.9
5.5
0.8
0.7
14.9
14.6
49.9
Total
2,060.1
1.2
7.8
117.0
NE
1,931.0
3.1
1,174.3
2,386.3
2.1
617.6
184.7
9.1
75.0
1,203.2
108.5
0.5
3.6
87.2
98.6
(3.7)
0.4
5,621.1
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-31
-------
Table A-18:2002 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.2 89.8
12.2
89.8
5,014.5 3,225.0
1,358.9 645.4
761.8 393.4
59.9 15.9
537.1 140.8
15.2
79.8
0.2
6,385.6 3,960.3
Adjusted
Ind.
1,243.7
1,243.7
8,100.0
3,860.0
1,048.4
13.8
539.3
104.2
146.1
7.5
52.4
680.7
1,403.3
(135.7)
13,203.7
Consumption (TBtu)a
Trans. Elec.
NE
NE
701.6
24,844.0
33.7
5,595.9
2,448.5
14.3
16,523.7
227.9
25,545.6
19,782.8
19,782.8
5,766.8
961.3
127.4
658.7
175.2
49.4
26,560.3
Terr. Total
10.8 21,139.3
12.2
89.8
1,243.7
19,782.8
10.8 10.8
22.8 22,830.7
556.8 32,226.5
33.7
92.8 8,019.7
61.8 2,510.2
8.2 97.9
11.2 1,242.7
189.4 16,832.5
193.6 1,306.1
7.5
52.4
856.1
1,403.3
(135.7)
49.4
590.5 76,245.9
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
1.2 8.6
1.2
8.6
265.8 170.9
93.9 46.1
56.3 29.1
4.4 1.2
33.2 8.7
1.1
6.0
0.0
360.9 225.6
116.6
116.6
429.3
287.5
77.5
1.0
33.3
7.4
11.0
0.5
3.7
69.5
93.6
(10.1)
833.4
NE
37.2
1,785.1
2.3
413.9
176.8
0.9
1,174.1
17.1
1,822.3
1,889.9
1,889.9
305.7
76.8
9.4
49.5
17.9
0.4
2,272.7
Use
Terr.
1.0
1.0
1.2
40.6
6.9
4.5
0.6
0.7
13.5
14.5
42.8
Total
2,017.2
1.2
8.6
116.6
NE
1,889.9
1.0
1,210.1
2,329.9
2.3
593.1
181.3
7.2
76.8
1,196.0
98.1
0.5
3.7
87.4
93.6
(10.1)
0.4
5,557.6
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-19:2001 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
12.0 96.9
12.0
96.9
4,889.0 3,097.3
1,463.0 718.6
842.2 471.4
95.1 31.4
525.7 142.7
3.1
69.9
0.2
6,364.0 3,912.7
Adjusted
Ind.
1,358.4
1,358.4
7,933.2
39377
1,181.8
23.2
461.0
24.3
156.1
6.1
61.6
663.4
1,430.7
(75.4)
13,224.3
Consumption (TBtu)a
Trans. Elec.
NE
NE
658.0
24,428.5
34.9
5,417.0
2,605.2
13.7
16,198.3
159.5
25,086.6
19,613.7
19,613.7
5,458.1
1,276.6
170.5
1,002.8
103.2
46.9
26,395.2
Terr.
3.8
3.8
22.9
632.2
109.4
98.9
0.9
7.0
187.6
228.4
658.9
Total
21,084.8
12.0
96.9
1,358.4
19,613.7
3.8
22,058.5
32,451.6
34.9
8,192.3
2,704.0
150.6
1,150.1
16,413.2
1,616.7
6.1
61.6
766.8
1,430.7
(75.4)
46.9
75,641.7
Emissions" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.1 9.2
1.1
9.2
259.1 164.2
101.8 51.5
62.3 34.9
7.0 2.3
32.5 8.8
0.2
5.2
0.0
362.0 224.9
127.8
127.8
420.5
293.4
87.4
1.7
28.5
1.7
11.7
0.4
4.3
67.7
95.5
(5.6)
841.7
NE
34.9
1,752.8
2.4
400.6
188.1
0.8
1,148.7
12.0
1,787.6
1,869.8 0.4
1,869.8
0.4
289.3 1.2
98.5 46.2
12.6 8.1
7.1
0.1
0.4
13.3
75.3 17.2
10.5
0.4
2,257.9 47.8
Total
2,008.4
1.1
9.2
127.8
NE
1,869.8
0.4
1,169.1
2,344.0
2.4
605.9
195.3
11.0
71.1
1,164.0
121.4
0.4
4.3
78.3
95.5
(5.6)
0.4
5,521.9
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-33
-------
Table A-20:2000 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Adjusted
Res. Comm. Ind.
11.4 91.9 1,348.8
11.4
91.9
1,348.8
5,104.6 3,251.6 8,619.2
1,427.5 694.0 3,586.6
778.0 422.2 1,003.7
94.6 29.7 15.6
554.9 150.4 562.6
91.6 190.3
3.8
106.5
0.2 669.5
1,435.6
(401.2)
6,543.5 4,037.4 13,554.5
Consumption (TBtu)a
Trans. Elec.
NE
NE
672.0
24,715.2
36.3
5,442.4
2,766.4
11.9
16,014.8
443.5
25,387.2
20,220.2
20,220.2
5,293.4
1,144.3
174.8
870.8
98.6
48.1
26,706.0
Terr.
10.3
10.3
12.7
471.7
71.3
74.1
2.4
8.0
185.1
130.9
494.6
Total
21,682.4
11.4
91.9
1,348.8
20,220.2
10.3
22,953.5
32,039.3
36.3
7,892.5
2,840.5
142.2
1,287.8
16,199.8
1,727.1
3.8
106.5
768.3
1,435.6
(401.2)
48.1
76,723.2
Emissions" (Tg CO2 Eq.) from Energy
Res. Comm. Ind. Trans. Elec.
1.1 8.8
1.1
8.8
270.7 172.5
98.8 49.6
57.5 31.2
6.9 2.2
34.4 9.3
6.9
0.0
370.7 230.8
127.3
127.3
457.2
266.6
74.2
1.1
34.9
14.3
0.3
7.5
68.4
95.8
(29.7)
851.1
NE
35.6
1,773.9
2.5
402.5
199.8
0.7
1,135.0
33.3
1,809.5
1,927.4
1,927.4
280.8
88.4
12.9
65.4
10.1
0.4
2,296.9
Use
Terr.
0.9
0.9
0.7
34.2
5.3
5.3
0.2
0.5
13.1
9.8
35.9
Total
2,065.5
1.1
8.8
127.3
NE
1,927.4
0.9
1,217.4
2,311.6
2.5
583.7
205.1
10.4
79.8
1,148.1
129.7
0.3
7.5
78.5
95.8
(29.7)
0.4
5,594.8
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-21:1999 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Adjusted
Res. Comm. Ind.
14.0 102.5 1,372.8
14.0
102.5
1,372.8
4,834.9 3,115.0 8,401.6
1,342.1 613.9 3,465.8
705.0 373.4 983.4
111.2 26.9 12.8
526.0 140.2 395.9
73.3 154.0
6.4
103.5
0.1 676.5
1,421.1
(287.9)
6,191.0 3,831.5 13,240.1
Consumption (TBtu)a
Trans. Elec.
NE
NE
675.3
24,071.1
39.2
5,251.3
2,677.5
14.3
15,913.1
175.7
24,746.4
19,279.5
19,279.5
4,902.1
1,211.4
140.1
958.7
112.5
50.6
25,443.6
Terr. Total
10.2 20,779.0
14.0
102.5
1,372.8
19,279.5
10.2 10.2
21,929.0
461.0 31,165.3
39.2
79.4 7,532.6
59.5 2,737.1
3.7 154.7
8.3 1,084.6
164.0 16,077.2
146.0 1,507.8
6.4
103.5
789.1
1,421.1
(287.9)
50.6
471.2 73,923.8
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.3 9.8
1.3
9.8
256.3 165.1
92.8 43.8
52.1 27.6
8.1 2.0
32.5 8.7
5.5
0.0
350.5 218.7
129.9
129.9
445.4
260.0
72.7
0.9
24.5
11.6
0.4
7.2
69.1
94.8
(21.3)
835.3
NE
35.8
1,725.9
2.7
388.4
193.4
0.9
1,127.4
13.2
1,761.7
1,836.4 0.9
1,836.4
0.9
259.9
93.8 33.5
10.4 5.9
4.3
0.3
0.5
11.6
72.0 11.0
11.5
0.4
2,190.5 34.5
Total
1,978.3
1.3
9.8
129.9
NE
1,836.4
0.9
1,162.6
2,249.9
2.7
557.1
197.7
11.3
67.1
1,139.0
113.2
0.4
7.2
80.6
94.8
(21.3)
0.4
5,391.1
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-35
-------
Table A-22:1998 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
11.5 93.4
11.5
93.4
4,646.1 3,083.0
1,207.3 609.1
675.1 375.1
108.3 31.2
423.9 117.6
85.2
0.1
5,865.0 3,785.5
Adjusted
Ind.
1,470.8
1,470.8
8,826.5
3,379.3
1,027.9
22.1
271.6
173.3
4.0
89.7
667.5
1,437.3
(313.9)
13,676.6
Consumption (TBtu)a
Trans. Elec.
NE
NE
666.1
23,154.5
35.5
4,955.2
2,483.9
17.6
15,583.4
78.9
23,820.6
19,215.7
19,215.7
4,674.9
1,306.2
135.7
1,047.0
123.6
50.4
25,247.2
Terr. Total
10.5 20,802.0
11.5
93.4
1,470.8
19,215.7
10.5 10.5
21,896.6
445.4 30,102.0
35.5
71.9 7,240.8
59.9 2,543.7
6.3 167.8
5.9 836.7
160.3 15,743.8
141.1 1,525.5
4.0
89.7
791.2
1,437.3
(313.9)
50.4
456.0 72,850.9
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.1 8.9
1.1
8.9
246.0 163.3
84.0 43.7
49.9 27.7
7.9 2.3
26.1 7.2
6.4
0.0
331.1 215.9
139.1
139.1
467.4
254.7
76.0
1.6
16.7
13.0
0.3
6.3
68.2
95.9
(23.3)
861.2
NE
35.3
1,661.9
2.5
366.5
179.4
1.1
1,106.6
5.9
1,697.2
1,828.2 1.0
1,828.2
1.0
247.6
101.3 32.4
10.0 5.3
4.3
0.5
0.4
11.4
78.6 10.6
12.6
0.4
2,177.4 33.4
Total
1,978.3
1.1
8.9
139.1
NE
1,828.2
1.0
1,159.5
2,178.0
2.5
535.5
183.7
12.3
51.6
1,118.0
114.6
0.3
6.3
80.8
95.9
(23.3)
0.4
5,316.2
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-23:1997 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
16.0 129.4
16.0
129.4
5,092.9 3,285.3
1,333.5 655.1
785.9 398.9
92.9 24.6
454.8 120.2
111.2
0.1
6,442.5 4,069.8
Adjusted
Ind.
1,457.0
1,457.0
9,052.1
3,754.9
1,053.7
18.8
429.9
235.6
9.1
4.6
30.0
631.1
1,445.1
(102.9)
14,264.1
Consumption (TBtu)a
Trans. Elec.
NE
NE
780.3
22,649.5
39.7
4,802.2
2,509.5
14.2
15,147.5
136.5
23,429.8
18,904.5
18,904.5
4,125.5
926.8
110.6
714.6
101.6
50.2
24,007.1
Terr. Total
10.4 20,517.4
16.0
129.4
1,457.0
18,904.5
10.4 10.4
22,336.2
445.3 29,765.2
39.7
81.6 7,232.7
62.1 2,571.6
4.0 140.3
6.5 1,025.7
160.0 15,307.5
131.1 1,329.0
9.1
4.6
30.0
732.8
1,445.1
(102.9)
50.2
455.7 72,669.1
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.5 12.3
1.5
12.3
270.1 174.2
93.0 47.1
58.1 29.5
6.8 1.8
28.1 7.4
8.4
0.0
364.6 233.6
137.6
137.6
480.0
279.8
77.9
1.4
26.5
17.7
0.6
0.3
2.1
64.4
96.4
(7.6)
897.4
NE
41.4
1,625.3
2.7
355.2
181.2
0.9
1,075.0
10.3
1,666.6
1,797.0 1.0
1,797.0
1.0
218.8
72.2 32.4
8.2 6.0
4.5
0.3
0.4
11.4
53.7 9.8
10.4
0.4
2,088.4 33.4
Total
1,949.4
1.5
12.3
137.6
NE
1,797.0
1.0
1,184.4
2,149.8
2.7
534.9
185.7
10.3
63.3
1,086.4
99.8
0.6
0.3
2.1
74.8
96.4
(7.6)
0.4
5,284.0
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-37
-------
Table A-24:1996 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
16.6 121.6
16.6
121.6
5,354.4 3,226.3
1,396.6 718.3
839.0 437.6
88.8 21.0
468.7 122.4
137.2
0.1
6,767.5 4,066.2
Adjusted
Ind.
1,454.9
1,454.9
9,048.6
3,760.8
1,046.7
18.3
401.7
281.7
7.0
13.7
38.5
629.0
1,437.1
(112.8)
14,264.4
Consumption (TBtu)a
Trans. Elec.
NE
NE
736.9
22,406.2
37.4
4,599.0
2,459.9
15.6
14,979.4
314.9
23,143.1
18,429.0
18,429.0
3,862.4
817.4
109.4
628.4
79.6
48.9
23,157.7
Terr.
10.3
10.3
434.6
76.5
78.5
3.0
7.3
151.4
118.0
445.0
Total
20,032.4
16.6
121.6
1,454.9
18,429.0
10.3
22,228.7
29,533.9
37.4
7,108.1
2,538.4
131.1
1,015.8
15,130.8
1,480.1
7.0
13.7
38.5
708.7
1,437.1
(112.8)
48.9
71,843.9
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.6 11.6
1.6
11.6
283.9 171.1
97.5 51.8
62.1 32.4
6.5 1.5
28.9 7.5
10.3
0.0
383.0 234.5
137.4
137.4
479.8
280.6
77.4
1.3
24.8
21.2
0.5
1.0
2.7
64.2
95.9
(8.4)
897.8
NE
39.1
1,608.0
2.6
340.1
177.7
1.0
1,063.0
23.6
1,647.1
1,752.4 1.0
1,752.4
1.0
204.8
63.4 31.6
8.1 5.7
5.7
0.2
0.5
10.7
47.2 8.9
8.1
0.4
2,021.0 32.5
Total
1,903.9
1.6
11.6
137.4
NE
1,752.4
1.0
1,178.7
2,132.8
2.6
525.7
183.3
9.6
62.6
1,073.8
111.1
0.5
1.0
2.7
72.4
95.9
(8.4)
0.4
5,215.9
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-25:1995 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
17.5 116.8
17.5
116.8
4,954.2 3,096.0
1,260.9 694.0
791.8 419.1
74.3 22.1
394.8 108.7
2.5
141.5
0.1
6,232.5 3,906.8
Adjusted
Ind.
1,526.9
1,526.9
8,736.2
3,417.3
966.7
15.4
403.4
27.2
284.7
5.3
14.5
34.5
609.1
1,377.3
(320.9)
13,680.4
Consumption (TBtu)a
Trans. Elec.
NE
NE
724.0
21,916.2
39.6
4,383.3
2,409.8
17.7
14,678.5
387.3
22,640.1
17,466.3
17,466.3
4,302.0
754.6
108.1
566.0
80.6
45.6
22,568.5
Terr. Total
10.2 19,137.7
17.5
116.8
1,526.9
17,466.3
10.2 10.2
21,812.3
461.8 28,504.7
39.6
89.5 6,758.4
75.7 2,485.5
3.6 115.4
5.6 930.2
146.7 14,854.9
140.7 1,520.1
5.3
14.5
34.5
689.8
1,377.3
(320.9)
45.6
472.0 69,500.3
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
1.7 11.2
1.7
11.2
262.7 164.2
88.3 50.1
58.6 31.0
5.4 1.6
24.4 6.7
0.2
10.6
0.0
352.7 225.5
144.4
144.4
463.3
255.0
71.5
1.1
24.9
1.9
21.4
0.4
1.1
2.4
62.2
91.9
(23.8)
862.7
NE
38.4
1,569.8
2.7
324.2
170.9
1.1
1,041.8
29.1
1,608.2
1,660.7 0.9
1,660.7
0.9
228.1
58.7 33.6
8.0 6.6
5.4
0.3
0.3
10.4
42.5 10.6
8.2
0.3
1,947.9 34.5
Total
1,819.0
1.7
11.2
144.4
NE
1,660.7
0.9
1,156.6
2,055.6
2.7
499.8
176.3
8.4
57.4
1,054.3
114.2
0.4
1.1
2.4
70.4
91.9
(23.8)
0.3
5,031.5
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-39
-------
Table A-26:1994 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
20.8 118.1
20.8
118.1
4,959.8 2,962.0
1,305.3 745.9
856.7 447.1
64.9 19.5
383.7 107.3
171.9
0.1
6,286.0 3,826.0
Adjusted
Ind.
1,594.9
1,594.9
8,290.4
3,631.6
975.8
16.9
423.1
368.4
6.1
18.7
80.8
607.9
1,413.2
(279.2)
13,516.8
Consumption (TBtu)a
Trans. Elec.
NE
NE
708.5
21,379.1
38.1
4,187.0
2,360.6
34.0
14,401.3
358.1
22,087.7
17,260.9
17,260.9
3,977.3
1,058.8
120.1
869.0
69.7
53.0
22,350.0
Terr. Total
10.0 19,004.7
20.8
118.1
1,594.9
17,260.9
10.0 10.0
20,898.0
506.3 28,627.0
38.1
118.8 6,705.5
65.8 2,426.4
3.0 104.3
7.3 955.3
147.4 14,548.7
164.1 1,931.5
6.1
18.7
80.8
677.7
1,413.2
(279.2)
53.0
516.3 68,582.8
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
2.0 11.3
2.0
11.3
262.9 157.0
91.8 54.0
63.4 33.1
4.8 1.4
23.7 6.6
12.9
0.0
356.7 222.3
150.7
150.7
439.4
270.3
72.2
1.2
26.1
27.7
0.4
1.4
5.7
62.1
94.3
(20.7)
860.4
NE
37.6
1,531.3
2.6
309.7
167.5
2.1
1,022.5
26.9
1,568.8
1,638.8 0.9
1,638.8
0.9
210.8
81.3 36.9
8.9 8.8
4.7
0.2
0.4
10.5
65.3 12.3
7.1
0.4
1,931.2 37.8
Total
1,803.7
2.0
11.3
150.7
NE
1,638.8
0.9
1,107.6
2,065.6
2.6
495.9
172.2
7.6
59.0
1,033.0
145.0
0.4
1.4
5.7
69.2
94.3
(20.7)
0.4
4,977.4
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-27:1993 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
25.7 117.3
25.7
117.3
5,063.3 2,923.3
1,348.5 743.4
883.3 447.2
75.6 14.0
389.6 109.2
172.7
0.2
6,437.5 3,783.9
Adjusted
Ind.
1,585.0
1,585.0
8,281.4
3,508.4
990.1
13.1
412.2
391.2
0.1
21.2
56.4
618.4
1,401.8
(396.0)
13,374.7
Consumption (TBtu)a
Trans. Elec.
NE
NE
644.7
20,859.5
38.4
3,889.4
2,306.9
20.2
14,237.0
367.5
21,504.2
17,195.9
17,195.9
3,537.5
1,123.8
86.5
958.6
78.6
57.3
21,914.5
Terr.
9.6
9.6
459.9
104.9
62.1
3.8
4.9
128.3
155.9
469.5
Total
18,933.5
25.7
117.3
1,585.0
17,195.9
9.6
20,450.1
28,043.3
38.4
6,401.3
2,369.0
106.5
936.2
14,365.3
2,046.0
0.1
21.2
56.4
697.2
1,401.8
(396.0)
57.3
67,484.3
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
2.5 11.3
2.5
11.3
268.4 155.0
94.9 53.8
65.3 33.1
5.5 1.0
24.0 6.7
13.0
0.0
365.8 220.1
149.8
149.8
439.0
261.9
73.2
1.0
25.4
29.4
0.0
1.6
3.9
63.1
93.5
(29.3)
850.6
NE
34.2
1,497.8
2.7
287.6
163.8
1.2
1,014.8
27.6
1,531.9
1,632.5 0.9
1,632.5
0.9
187.5
86.4 33.6
6.4 7.8
4.4
0.3
0.3
9.1
72.0 11.7
8.0
0.4
1,906.9 34.5
Total
1,796.9
2.5
11.3
149.8
NE
1,632.5
0.9
1,084.2
2,028.4
2.7
473.4
168.3
7.8
57.8
1,023.9
153.6
0.0
1.6
3.9
71.2
93.5
(29.3)
0.4
4,909.8
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-41
-------
Table A-28:1992 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
25.6 116.6
25.6
116.6
4,804.6 2,871.2
1,365.8 788.9
931.4 481.7
65.0 11.1
369.4 106.9
189.1
0.1
6,196.0 3,776.7
Adjusted
Ind.
1,554.1
1,554.1
8,129.3
3,850.2
1,028.5
9.8
441.8
328.1
0.2
27.4
75.7
261.0
596.9
1,435.7
(354.8)
13,533.6
Consumption (TBtu)a
Trans. Elec.
NE
NE
608.1
20,366.5
41.1
3,665.7
2,267.7
19.4
13,972.5
400.1
20,974.6
16,465.6
16,465.6
3,511.5
990.7
73.5
872.2
45.0
55.1
21,022.9
Terr. Total
8.8 18,170.6
25.6
116.6
1,554.1
16,465.6
8.8 8.8
19,924.7
444.9 27,807.1
41.1
91.8 6,272.7
61.3 2,329.0
3.3 89.2
11.9 949.4
122.1 14,094.7
154.6 1,944.0
0.2
27.4
75.7
261.0
642.0
1,435.7
(354.8)
55.1
453.7 65,957.5
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
2.5 11.3
2.5
11.3
254.5 152.1
96.5 57.3
68.9 35.6
4.8 0.8
22.8 6.6
14.2
0.0
353.5 220.6
147.4
147.4
430.7
284.9
76.1
0.7
27.3
24.6
0.0
2.0
5.4
18.3
61.0
95.8
(26.3)
863.0
NE
32.2
1,465.0
2.8
271.1
161.2
1.2
998.6
30.0
1,497.3
1,569.6 0.8
1,569.6
0.8
186.0
75.5 32.5
5.4 6.8
4.4
0.2
0.7
8.7
65.5 11.6
4.6
0.4
1,831.5 33.3
Total
1,731.5
2.5
11.3
147.4
NE
1,569.6
0.8
1,055.6
2,011.6
2.8
463.9
165.6
6.5
58.7
1,007.4
146.0
0.0
2.0
5.4
18.3
65.6
95.8
(26.3)
0.4
4,799.1
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-29:1991 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
25.4 115.5
25.4
115.5
4,667.2 2,795.4
1,381.5 903.6
931.0 517.7
72.3 12.1
378.1 108.2
53.7
211.9
6,074.0 3,814.5
Adjusted
Ind.
1,602.2
1,602.2
7,828.1
3,578 0
1,050.8
11.4
342.2
122.0
270.9
(0.1)
38.9
(25.9)
249.2
564.2
1,404.5
(450.2)
13,008.3
Consumption (TBtu)a
Trans. Elec.
NE
NE
620.3
19,671.2
41.7
3,449.7
2,329.0
21.1
13,605.2
224.4
20,291.5
16,249.7
16,249.7
3,377.4
1,198.3
83.6
1,085.3
29.3
54.5
20,879.8
Terr. Total
7.7 18,000.5
25.4
115.5
1,602.2
16,249.7
7.7 7.7
19,288.4
425.4 27,158.0
41.7
71.4 6,104.1
78.2 2,407.2
2.8 98.6
13.8 863.5
124.7 13,905.6
134.6 1,927.2
(0.1)
38.9
(25.9)
249.2
593.5
1,404.5
(450.2)
54.5
433.2 64,501.4
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
2.4 11.1
2.4
11.1
247.3 148.1
97.5 65.6
68.9 38.3
5.3 0.9
23.3 6.7
3.8
15.9
347.2 224.8
152.1
152.1
414.7
265.2
11.1
0.8
21.1
8.7
20.3
(0.0)
2.9
(1.8)
17.5
57.6
93.7
(33.3)
832.0
NE
32.9
1,411.6
2.9
255.1
165.7
1.3
969.7
16.9
1,444.4
1,548.2 0.7
1,548.2
0.7
178.9
90.7 30.9
6.2 5.3
5.6
0.2
0.9
8.9
81.5 10.1
3.0
0.4
1,818.2 31.6
Total
1,714.5
2.4
11.1
152.1
NE
1,548.2
0.7
1,021.9
1,961.4
2.9
451.4
171.2
7.2
53.3
991.1
144.7
(0.0)
2.9
(1.8)
17.5
60.6
93.7
(33.3)
0.4
4,698.2
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-43
-------
Table A- 30:1990 Energy Consumption Data and Clh Emissions from Fossil Fuel Combustion by Fuel Type
1 23456789
10
11
12
13
14
15
Fuel Type
Total Coal
Residential Coal
Commercial Coal
Industrial Other Coal
Transportation Coal
Electric Power Coal
U.S. Territory Coal (bit)
Natural Gas
Total Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuel
Kerosene
LPG
Lubricants
Motor Gasoline
Residual Fuel
Other Petroleum
AvGas Blend Components
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils
Waxes
Geothermal
TOTAL (All Fuels)
Res. Comm.
31.1 124.5
31.1
124.5
4,490.9 2,682.2
1,375.2 891.4
959.2 525.4
63.9 11.8
352.1 102.3
22.1
229.8
5,897.2 3,698.1
Adjusted
Ind.
1,640.1
1,640.1
7,720.8
38109
1,098.5
12.3
380.2
36.8
364.1
0.2
50.9
53.7
167.8
563.5
1,451.9
(369.0)
13,171.7
Consumption (TBtu)a
Trans. Elec.
NE
NE
679.9
20,213.2
45.0
3,554.8
2,477.2
22.9
13,813.0
300.3
20,893.1
16,261.0
16,261.0
3,308.5
1,289.4
96.5
1,162.6
30.4
52.7
20,911.6
Terr. Total
7.0 18,063.6
31.1
124.5
1,640.1
16,261.0
7.0 7.0
18,882.3
374.8 27,955.0
45.0
74.0 6,308.4
61.0 2,538.2
2.6 90.6
14.4 871.9
101.0 13,972.8
121.8 2,178.7
0.2
50.9
53.7
167.8
593.9
1,451.9
(369.0)
52.7
381.9 64,953.5
Emissions'" (Tg CO2 Eq.) from Energy Use
Res. Comm. Ind. Trans. Elec. Terr.
3.0 12.0
3.0
12.0
238.0 142.1
97.4 64.9
70.9 38.9
4.7 0.9
21.8 6.3
1.6
17.3
338.3 219.0
155.3
155.3
409.1
282.1
81.2
0.9
23.5
2.6
27.3
0.0
3.8
3.8
11.7
57.5
96.9
(27.3)
846.5
NE
36.0
1,449.9
3.1
262.9
176.2
1.4
983.7
22.6
1,485.9
1,547.6 0.6
1,547.6
0.6
175.3
97.5 27.2
7.1 5.5
4.3
0.2
0.9
7.2
87.3 9.2
3.1
0.4
1,820.8 27.9
Total
1,718.4
3.0
12.0
155.3
NE
1,547.6
0.6
1,000.6
2,019.0
3.1
466.5
180.5
6.6
53.9
995.1
163.6
0.0
3.8
3.8
11.7
60.6
96.9
(27.3)
0.4
4,738.4
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-31), and international bunker fuel consumption
(see Table A-32).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
NE (Not Estimated)
A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-31: Unadjusted Non-Energy Fuel Consumption tTBtul
Sector/Fuel Type
Industry
Industrial Coking Coal
Industrial Other Coal
Natural Gas to Chemical Plants,
Other Uses
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Still Gas
Petroleum Coke
Special Naphtha
Other (Wax/Misc.)
Distillate Fuel Oil
Waxes
Miscellaneous Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum (Misc. Prod.)
Total
1990
4,509.4
1
8.2!
301.?B
1,170.2
1,201.4
186.3
82.6 •
347. 8 1
753.9 •
21.3 •
150.7 1
107. ll
1
33.3 •
137. 8 I
176.0
176.0 •
86.7
0.7
86.0
4,772.1
1995
5,194.9
37.2
11.3
357.2
1,178.2
1,586.9
177.8
303.4
373.0
801.0
40.1
112.2
70.8
8.0
40.6
97.1
167.9
167.9
90.8
2.0
88.8
5,453.6
1996
5,270.5
23.5
11.4
360.3
1,175.9
1,652.0
172.5
316.5
479.3
729.6
0.0
127.8
74.5
9.2
48.7
89.0
163.0
163.0
121.7
1.5
120.2
5,555.1
1997
5,493.3
0.0
11.2
386.7
1,223.6
1,670.4
182.3
298.9
536.4
861.3
2.1
96.3
72.3
10.4
43.7
97.8
172.1
172.1
131.6
2.5
129.1
5,797.0
1998
5,723.5
10.4
10.4
426.6
1,262.6
1,144.4
190.8
204.3
584.0
818.7
0.0
191.0
107.3
11.7
42.4
119.0
180.2
180.2
135.0
1.3
133.8
6,038.7
1999
5,981.5
39.5
11.1
436.6
1,324.4
1,820.7
192.8
261.4
502.1
811.1
16.1
259.3
145.4
11.7
37.4
111.9
182.1
182.1
139.3
1.4
138.0
6,302.9
2000
5,607.6
53.0
12.4
438.4
1,275.7
1,665.4
189.9
236.7
613.5
722.2
12.6
126.6
97.4
11.7
33.1
119.2
179.4
179.4
152.2
3.1
149.1
5,939.1
2001
5,266.7
24.3
11.3
407.7
1,256.9
1,553.4
174.0
201.6
493.7
662.4
35.8
194.2
78.5
11.7
36.3
124.9
164.3
164.3
80.3
0.0
80.3
5,511.3
2002
5,350.5
39.8
12.0
380.7
1,240.0
1,620.3
171.9
171.4
582.6
632.1
57.8
161.4
102.4
11.7
32.2
134.2
162.4
162.4
140.2
3.0
137.2
5,653.0
2003
5,308.0
51.2
11.9
384.4
1,219.5
1,545.1
159.0
169.1
613.0
699.4
59.0
145.8
80.5
13.1
31.0
126.0
150.1
150.1
123.5
4.9
118.6
5,581.6
2004
5,845.9
167.1
11.9
403.8
1,303.8
1,576.4
161.0
170.4
749.4
779.5
62.9
249.7
51.0
14.6
30.8
113.4
152.1
152.1
110.8
5.1
105.7
6,108.7
2005
5,532.4
79.8
11.9
411.3
1,323.2
1,488.1
160.2
150.3
698.7
708.0
67.7
210.5
62.5
16.0
31.4
112.8
151.3
151.3
121.9
4.6
117.3
5,805.7
2006
5,510.7
62.3
12.4
416.8
1,261.2
1,516.8
156.1
107.3
628.9
790.6
57.2
251.5
70.1
17.5
26.1
136.0
147.4
147.4
133.4
6.2
127.2
5,791.5
2007
5,324.7
1.7
12.4
432.1
1,197.0
1,542.2
161.2
137.4
562.5
744.1
44.2
239.2
78.0
17.5
21.9
133.5
152.2
152.2
108.4
5.9
102.5
5,585.4
2008
4,855.6
28.4
12.4
406.0
1,012.0
1,442.1
149.6
117.2
477.2
647.8
47.3
252.1
84.9
17.5
19.1
142.0
141.3
141.3
126.7
2.7
124.1
5,123.6
2009
4,486.3
6.1
12.4
388.4
873.1
1,520.5
134.5
97.7
471.9
424.8
133.9
195.4
46.2
17.5
12.2
151.8
127.1
127.1
56.3
1.0
55.2
4,669.7
Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for in the Industrial Processes
Chapter.
+ Does not exceed 0.05 TBtu.
Table A-32: International Bunker Fuel Consumption (TBtu)
Fuel Type
Marine Residual Fuel Oil
Marine Distillate Fuel Oil & Other
Aviation Jet Fuel
Total
1990
715.7
158.0
652.3
1,526.0
1995
523.2
125.7
722.4
1,371.4
1996 1997
536.4 575.2
114.1 125.5
814.3 798.7
1,464.8 1,499.4
1998 1999
594.8 489.7
158.8 113.6
872.9 784.3
1,626.5 1,387.6
2000 2001
444.1 426.0
85.9 72.4
813.9 820.8
1,343.9 1,319.3
2002
448.9
82.6
891.9
1,423.3
2003 2004
471.8 553.1
103.9 143.6
784.6 857.6
1,360.3 1,554.2
2005 2006
581.0 599.4
126.9 119.3
785.6 1,032.3
1,493.5 1,750.9
2007
607.5
111.3
1,021.3
1,740.2
2008
654.6
122.2
1,045.5
1,822.3
2009
604.8
112.3
961.0
1,678.1
A-45
-------
Table A- 33: Key Assumptions for Estimating Clh Emissions
C Content Coefficient
Fuel Type (Tg C/QBtu)
Coal
Residential Coal [a]
Commercial Coal [a]
Industrial Coking Coal [a]
Industrial Other Coal [a]
Electric Power Coal [a]
U.S. Territory Coal (bit) 25.14
Pipeline Natural Gas [a]
Flare Gas a 15.31
Petroleum
Asphalt & Road Oil 20.55
Aviation Gasoline 18.86
Distillate Fuel Oil No. 1 19.98
Distillate Fuel Oil No. 2b 20.17
Distillate Fuel Oil No. 4 20.47
Jet Fuel [a]
Kerosene 19.96
LPG (energy use) [a]
LPG (non-energy use) [a]
Lubricants 20.20
Motor Gasoline [a]
Residual Fuel Oil No. 5 19.89
Residual Fuel Oil No. 6b 20.48
Other Petroleum
AvGas Blend Components 18.87
Crude Oil [a]
MoGas Blend Components [a]
Misc. Products [a]
Misc. Products (Territories) [a]
Naphtha (<401 deg. F) 18.55
Other Oil (>401 deg. F) 20.17
PentanesPlus 19.10
Petroleum Coke 27.85
Still Gas 18.20
Special Naphtha 19.74
Unfinished Oils [a]
Waxes 19.80
Geothermal 2.05
a Flare gas is not used in the CO2 from fossil fuel combustion calculations and is presented for informational purposes only.
b Distillate fuel oil No.2 and residual fuel oil No. 6 are used in the CO2 from fossil fuel combustion calculations, and other oil types are presented
for informational purposes only. An additional discussion on the derivation of these carbon content coefficients is presented in Annex 2.2.
Sources: C coefficients from EIA (2009b) and EPA 2010a.
[a] These coefficients vary annually due to fluctuations in fuel quality (see Table A- 34)
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 34: Annually Variable C Content Coefficients by Year [Tg C/QBtul
Fuel Type
Residential Coal
Commercial Coal
Industrial Coking Coal
Industrial Other Coal
Electric Power Coal
Pipeline Natural Gas
LPG (energy use)
LPG (non-energy use)
Motor Gasoline
Jet Fuel
MoGas Blend
Components
Misc. Products
Unfinished Oils
Crude Oil
1990 1995 1996 1997 1998 1999
26.20 26.13 26.04 25.90 26.07 25.98
26.20B 26.13 26.04 25.90 26.07 25.98
25.531 25.57 25.56 25.59 25.62 25.59
25.821 25.80 25.75 25.75 25.79 25.80
25.96B 25.93 25.93 25.93 25.95 25.98
14.45 1 14.46 14.46 14.46 14.44 14.46
16.861 16.82 16.82 16.84 16.81 16.86
17.06B 17.09 17.10 17.08 17.08 17.07
19.42 • 19.36 19.35 19.36 19.37 19.32
19.40 • 19.34 19.70 19.70 19.70 19.70
19.42B 19.36 19.35 19.36 19.37 19.32
20.15B 20.21 20.23 20.22 20.22 20.17
20.151 20.21 20.23 20.22 20.22 20.17
20.15 20.21 20.23 20.22 20.22 20.17
2000
26.01
26.01
25.63
25.74
26.00
14.47
16.89
17.09
19.33
19.70
19.33
20.22
20.22
20.22
2001
26.00
26.00
25.63
25.66
26.00
14.46
16.87
17.10
19.34
19.70
19.34
20.27
20.27
20.27
*U.S. EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal
2002
25.98
25.98
25.65
25.57
26.05
14.46
16.85
17.09
19.38
19.70
19.38
20.28
20.28
20.28
2003
26.04
26.04
25.63
25.55
26.09
14.44
16.86
17.09
19.36
19.70
19.36
20.25
20.25
20.25
2004
25.91
25.91
25.63
25.56
26.10
14.46
16.84
17.07
19.38
19.70
19.38
20.31
20.31
20.31
2005
26.09
26.09
25.60
25.80
26.09
14.46
16.84
17.06
19.36
19.70
19.36
20.31
20.31
20.31
2006
26.29
26.29
25.60
25.84
26.04
14.46
16.83
17.06
19.45
19.70
19.45
20.28
20.28
20.28
2007
25.94
25.94
25.61
25.82
26.05
14.46
16.82
17.05
19.56
19.70
19.56
20.28
20.28
20.28
2008
25.71*
25.71
25.61
25.82
26.05
14.46
16.83
17.06
19.46
19.70
19.46
20.31
20.31
20.31
2009
25.71*
25.71
25.61
25.82
26.05
14.46
16.83
17.06
19.46
19.70
19.46
20.31
20.31
20.31
in the residential sector is extremely limited. Therefore, the number cited here is
developed from commercial/institutional consumption.
Source: EPA(2010a)
Table A-35: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours)
End-Use Sector
Residential
Commercial
Industrial
Transportation
Total
1990 1995 1996 1997 1998 1999
924 1,043 1,083 1,076 1,130 ,145
838 953 980 1,027 1,078 1,104
1,070 1,163 1,186 1,194 1,212 1,230
5 55555
2,837 3,164 3,254 3,302 3,425 3,484
2000
1,192
1,159
1,235
5
3,592
2001
1,202
1,191
1,159
6
3,557
2002
1,265
1,205
,156
6
3,632
2003
1,276
1,199
1,181
7
3,662
2004
1,292
1,230
1,186
7
3,716
2005
1,359
1,275
1,169
8
3,811
2006
1,352
1,300
1,158
7
3,817
2007
1,392
1,336
1,187
8
3,924
2008
1,380
1,336
1,183
8
3,906
2009
1,363
1,323
1,048
8
3,741
Note: Does not include the U.S. territories.
Source: EIA(2010b)
A-47
-------
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels
This sub-annex presents the background and methodology for estimating the carbon (C) content of fossil fuels
combusted in the United States. The C content of a particular fossil fuel represents the maximum potential emissions to
the atmosphere if all C in the fuel is oxidized during combustion. The C content coefficients used in past editions of this
report were developed using methods first outlined in the U.S. Energy Information Administration's (EIA) Emissions of
Greenhouse Gases in the United States: 1987-1992 (1994) and were developed primarily by EIA. For this report, EPA
has updated many of the C content coefficients based on carbon dioxide emission factors developed for the Mandatory
Reporting of Greenhouse Gases Rule, signed in September 2009 (EPA, 2009b, 2010). This sub-annex describes an
updated methodology for estimating the C content of natural gas, and presents a time-series analysis of changes in U.S. C
content coefficients for coal, petroleum products and natural gas. A summary of C content coefficients used in this report
appears in Table A- 36.
Though the methods for estimating C contents for coal, natural gas, and petroleum products differ in their
details, they each follow the same basic approach. First, because C coefficients are presented in terms of mass per unit
energy (i.e., teragrams C per quadrillion Btu or Tg C/QBtu), those fuels that are typically described in volumetric units
(petroleum products and natural gas) are converted to units of mass using an estimated density. Second, C contents are
derived from fuel sample data, using descriptive statistics to estimate the C share of the fuel by weight. The heat content
of the fuel is then estimated based on the sample data, or where sample data are unavailable or unrepresentative, by default
values that reflect the characteristics of the fuel as defined by market requirements. A discussion of each fuel appears
below.
The C content of coal is described first because approximately one-third of all U.S. C emissions from fossil fuel
combustion are associated with coal consumption. The methods and sources for estimating the C content of natural gas
are provided next. Approximately one-fifth of U.S. greenhouse gas emissions from fossil fuel combustion are attributable
to natural gas consumption. Finally, this sub-annex examines C contents of petroleum products. U.S. energy consumption
statistics account for more than 20 different petroleum products.
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 36: Carbon Content Coefficients Used in this Report tTg Carhon/QBtul
Fuel Type
Coal
Residential Coaf
Commercial Coal3
Industrial Coking Coala
Industrial Other Coaf
Utility Coaf 'b
Pipeline Natural Gasc
Flare Gas
Petroleum
Asphalt and Road Oil
Aviation Gasoline
Distillate Fuel Oil No. 1
Distillate Fuel Oil No. 2
Distillate Fuel Oil No. 4
Jet Fuel"
Kerosene
LPG (energy use)c
LPG (non-energy use)c
Lubricants
Motor Gasoline0
Residual Fuel No. 5
Residual Fuel No. 6
Other Petroleum
Av Gas Blend Comp.
Mo Gas Blend Compc
Crude Oilc
Misc. Products0
Misc. Products (Terr.)
Naphtha (<401 deg. F)
Other oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils0
Waxes
Other Wax and Misc.
Geothermal
1990
26.20
26.20
25.53
25.82
25.96
14.45
15.31
20.55
18.86
19.98
20.17
20.47
19.73
19.96
16.86
17.06
20.20
19.42
19.89
20.48
18.87
19.42
20.15
20.15
20.15
18.55
20.17
19.10
27.85
18.20
19.74
20.15
19.80
19.80
2.05
1995
26.13
26.13
25.57
25.80
25.93
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.82
17.09
20.20
19.36
19.89
20.48
18.87
19.36
20.21
20.21
20.21
18.55
20.17
19.10
27.85
18.20
19.74
20.21
19.80
19.80
2.05
1996
26.04
26.04
25.56
25.75
25.93
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.82
17.10
20.20
19.35
19.89
20.48
18.87
19.35
20.23
20.23
20.23
18.55
20.17
19.10
27.85
18.20
19.74
20.23
19.80
19.80
2.05
1997
25.90
25.90
25.59
25.75
25.93
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.84
17.08
20.20
19.36
19.89
20.48
18.87
19.36
20.22
20.22
20.22
18.55
20.17
19.10
27.85
18.20
19.74
20.22
19.80
19.80
2.05
1998
26.07
26.07
25.62
25.79
25.95
14.44
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.81
17.08
20.20
19.37
19.89
20.48
18.87
19.37
20.22
20.22
20.22
18.55
20.17
19.10
27.85
18.20
19.74
20.22
19.80
19.80
2.05
1999
25.98
25.98
25.59
25.80
25.98
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.86
17.07
20.20
19.32
19.89
20.48
18.87
19.32
20.17
20.17
20.17
18.55
20.17
19.10
27.85
18.20
19.74
20.17
19.80
19.80
2.05
2000
26.01
26.01
25.63
25.74
26.00
14.47
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.89
17.09
20.20
19.33
19.89
20.48
18.87
19.33
20.22
20.22
20.22
18.55
20.17
19.10
27.85
18.20
19.74
20.22
19.80
19.80
2.05
2001
26.00
26.00
25.63
25.66
26.00
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.87
17.10
20.20
19.34
19.89
20.48
18.87
19.34
20.27
20.27
20.27
18.55
20.17
19.10
27.85
18.20
19.74
20.27
19.80
19.80
2.05
2002
25.98
25.98
25.65
25.57
26.05
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.85
17.09
20.20
19.38
19.89
20.48
18.87
19.38
20.28
20.28
20.28
18.55
20.17
19.10
27.85
18.20
19.74
20.28
19.80
19.80
2.05
2003
26.04
26.04
25.63
25.55
26.09
14.44
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.86
17.09
20.20
19.36
19.89
20.48
18.87
19.36
20.25
20.25
20.25
18.55
20.17
19.10
27.85
18.20
19.74
20.25
19.80
19.80
2.05
2004
25.91
25.91
25.63
25.56
26.10
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.84
17.07
20.20
19.38
19.89
20.48
18.87
19.38
20.31
20.31
20.31
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
2.05
2005
26.09
26.09
25.60
25.80
26.09
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.84
17.06
20.20
19.36
19.89
20.48
18.87
19.36
20.31
20.31
20.31
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
2.05
2006
26.29
26.29
25.60
25.84
26.04
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.45
19.89
20.48
18.87
19.45
20.28
20.28
20.28
18.55
20.17
19.10
27.85
18.20
19.74
20.28
19.80
19.80
2.05
2007
25.94
25.94
25.61
25.82
26.05
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.82
17.05
20.20
19.56
19.89
20.48
18.87
19.56
20.28
20.28
20.28
18.55
20.17
19.10
27.85
18.20
19.74
20.28
19.80
19.80
2.05
2008
25.71*
25.71
25.61
25.82
26.05
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
18.87
19.46
20.31
20.31
20.31
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
2.05
2009
25.71*
25.71
25.61
25.82
26.05
14.46
15.31
20.55
18.86
19.98
20.17
20.47
19.70
19.96
16.83
17.06
20.20
19.46
19.89
20.48
18.87
19.46
20.31
20.31
20.31
18.55
20.17
19.10
27.85
18.20
19.74
20.31
19.80
19.80
2.05
*U.S. EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore, the number cited here is
developed from commercial/institutional consumption.
aC contents vary annually based on changes in annual mix of production and end-use consumption of coal from each producing state.
bC content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of heat released when a
fuel is burned. Coal, crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and hydrogen combine with oxygen in the air to produce
CO2 and water. Some of the energy released in burning goes into transforming the water into steam and is usually lost. The amount of heat spent in transforming the water into steam is counted as part of
A-49
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gross heat content. Lower heating value (net heating value), in contrast, does not include the heat spent in transforming the water into steam. Using a simplified methodology based on International
Energy Agency defaults, higher heating value can be converted to lower heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by 0.90. Carbon content
coefficients are presented in higher heating value because U.S. energy statistics are reported by higher heating value.
CC contents vary annually based on changes in fuel composition.
A-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Coal
Approximately one-third of all U.S. CO2 emissions from fossil fuel combustion are associated with coal
consumption. Although the IPCC guidelines provide C contents for coal according to rank, it was necessary to develop C
content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the
C content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each
consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies
over time. A time series of C contents by coal rank and consuming sector appears in Table A- 37.6
Methodology
The methodology for developing C contents for coal by consuming sector consists of four steps. An additional
step has been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
Step 1. Determine carbon contents by rank and by state of origin
C contents by rank and state of origin are estimated on the basis of 7,092 coal samples, 6,588 of which were
collected by the U.S. Geological Survey (USGS 1998) and 504 samples that come from the Pennsylvania State University
database (PSU 2010). These coal samples are classified according to rank and state of origin. For each rank in each state,
the average heat content and C content of the coal samples are calculated based on the proximate (heat) and ultimate
(percent carbon) analyses of the samples. Dividing the C content (reported in pounds CO2) by the heat content (reported
in million Btu or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of Tg
C/QBtu.
Step 2. Determine weighted average carbon content by state
C contents by rank and origin calculated in Step 1 are then weighted by the annual share of state production that
was each rank. State production by rank is obtained from the EIA. This step yields a single carbon content per state that
varies annually based on production. However, most coal-producing states produce only one rank of coal. For these states
the weighted factor equals the carbon content calculated in Step 1 and is constant across the time series.
Step 3. Allocate sectoral consumption by state of origin
U.S. energy statistics7 through 2007 provide data on the origin of coal used in four areas: 1) the electric power
industry, 2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use sectors.8 Because
U.S. energy statistics do not provide the distribution of coal rank consumed by each consuming sector, it is assumed that
each sector consumes a representative mixture of coal ranks from a particular state that matches the mixture of all coal
produced in that state during the year. Thus, the weighted state-level factor developed in Step 2 is applied.
Step 4. Weight sectoral carbon contents to reflect the rank and state of origin of coal consumed
Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's
weighted C content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a
national sectoral C content.
Csector ~~ Sstatel xCstatel ~"~ Sstate2xCstate2 +• • • • + Sstate50xCstate50
Where,
Csector = The C content by consuming sector;
For a comparison to earlier estimated carbon contents please see Chronology and Explanation of Changes in Individual Carbon
Content Coefficients of Fossil Fuels near the end of this annex.
7 U.S. Energy Information Administration (EIA). Coal Distribution - Annual (2001-2008); and Coal Industry Annual (1990-2000).
o
Beginning in 2008, the EIA collects and reports data on commercial and institutional coal consumption, rather than residential and
commercial consumption. Thus, the residential / commercial coal coefficient reported in Table A- 36 for 2009 represents the mix of coal
consumed by commercial and institutional users. Currently, only an extremely small amount of coal is consumed in the U.S. Residential
Sector.
A-51
-------
Sstate = The portion of consuming sector coal consumption attributed to production from a given state;
Cstate = The estimated weighted C content of all ranks produced in a given state.
A-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A- 37: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank tTg C/QBtul 0990-20091
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential/
Commercial
Coal Rank
Anthracite
Bituminous
Sub-bituminous
Lignite
1990
25.96
25.53
25.82
26.20
28.28
25.38
26.50
26.58
1995
25.93
25.57
25.80
26.13
28.28
25.42
26.50
26.59
1996
25.93
25.56
25.75
26.04
28.28
25.43
26.50
26.58
1997
25.93
25.59
25.75
25.90
28.28
25.43
26.50
26.59
1998
25.95
25.62
25.79
26.07
28.28
25.43
26.50
26.59
1999
25.98
25.59
25.80
25.98
28.28
25.44
26.50
26.60
2000
26.00
25.63
25.74
26.01
28.28
25.45
26.49
26.61
2001
26.00
25.63
25.66
26.00
28.28
25.46
26.50
26.62
2002
26.05
25.65
25.57
25.98
28.28
25.46
26.50
26.63
2003
26.09
25.63
25.55
26.04
28.28
25.45
26.50
26.62
2004
26.10
25.63
25.56
25.91
28.28
25.45
26.50
26.62
2005
26.09
25.60
25.80
26.09
28.28
25.45
26.50
26.62
2006
26.04
25.60
25.84
26.29
28.28
25.45
26.50
26.62
2007
26.05
25.61
25.82
25.94
28.28
25.45
26.50
26.64
2008
26.05
25.61
25.82
25.71
28.28
25.44
26.50
26.65
2009
26.05
25.61
25.82
25.71
28.28
25.44
26.50
26.65
" In 2008, the EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
Sources: C content coefficients calculated from USGS (1998) and PSU (2010); data presented in EPA (2010b).
A-53
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Step 5. Develop national-level carbon contents by rank for comparison to IPCC defaults
Although not used to calculate emissions, national-level C contents by rank are more easily compared to C
contents of other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank
developed under Step 1 by overall coal production by state and rank. Each state-level C content by rank is multiplied by
the share of national production of that rank that each state represents. The resulting partial C contents are then summed
across all states to generate an overall C content for each rank.
Nrank = Prankl x Qankl + Prank2 X Qank2 +• • • + PranknX Crankn
Where,
Nrank = The national C content by rank;
Prank = The portion of U. S. coal production of a given rank attributed to each state; and
Qank = The estimated C content of a given rank in each state.
Data Sources
The ultimate analysis of coal samples was based on the 7,092 coal samples, 6,588 of which are from USGS
(1998) and 504 that come from the Pennsylvania State University Coal Database (PSU 2010). Data contained in the
USGS's CoalQual Database are derived primarily from samples taken between 1973 and 1989, and were largely reported
in State Geological Surveys. Data in the PSU Coal Database are mainly from samples collected by PSU since 1967 and
are housed at the PSU Sample Bank. Only the subset of PSU samples that are whole-seam channel samples are included
in the development of carbon factors in order to increase data accuracy.
Data on coal consumption by sector and state of origin, as well as coal production by state and rank, were
obtained from EIA. The EIA's Annual Coal Report is the source for state coal production by rank from 2001-2008. In
prior years, the EIA reported this data in its Coal Industry Annual. Data for coal consumption by state of origin and
consuming sector for 2001 to 2008 was obtained from the EIA's Coal Distribution—Annual. For 1990-2000, end-use data
was obtained from the Coal Industry Annual.
Uncertainty
C contents vary considerably by state. Bituminous coal production and sub-bituminous coal production
represented 47.3 percent and 46.1 percent of total U.S. supply in 2008, respectively. State average C content coefficients
for bituminous coal vary from a low of 85.59 kg CO2 per MMBtu in Texas to a high of 105.21 kg CO2 per MMBtu in
Montana. However, Texas bituminous coal is considered anomalous,9 has not been produced since 2004 and production
since 1990 peaked at just 446,000 short tons in 1996. The next lowest average emission factor for bituminous coal is
found in Western Kentucky (91.36 kg CO2 per MMBtu). In 2000, Montana produced no bituminous coal and Western
Kentucky production accounted for just 4.5 percent of overall bituminous production. In 2008, more than 60 percent of
bituminous coal was produced in three states: West Virginia, Kentucky (predominantly from the Eastern production
region), and Pennsylvania, and this share has remained fairly constant since 1990. These three states show a variation in C
content for bituminous coals of +0.7 percent, based on more than 2,000 samples (see Table A-38).
Similarly, the C content coefficients for sub-bituminous coal range from 91.29 kg CO2 per MMBtu in Utah to
98.10 kg CO2 per MMBtu in Alaska. However, Utah has no recorded production of sub-bituminous coal since 1990.
Production of sub-bituminous coal in Alaska has made up less than 0.7 percent of total sub-bituminous production since
1990, with even this small share declining over time. Wyoming has represented between 75 percent and 87 percent of
total sub-bituminous coal production in the United States in each year since 1990. Thus, the C content coefficient for
Wyoming (97.22 kg CO2 per MMBtu), based on 455 samples, dominates the national average.
The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5
percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and
Pennsylvania was +1.2 percent or less for each state. The large number of samples and the low variability within the
sample set of the states that represent the predominant source of supply of U.S. coal suggest that the uncertainty in this
factor is very low, on the order of+1.0 percent.
9 See, for example: San Filipo, 1999. USGS. (U.S. Geological Survey Open-File Report 99-301), Ch. 4.
A-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to
generate national average carbon emission factors as well as county-level factors. This study's rank-based national
average factors have a maximum deviation from the factors developed here by EPA of -0.55 percent, which is for lignite
(range: -0.55 to +0.1 percent). This corroboration further supports the assertion of minimal uncertainty in the application
of the rank-based factors derived for the purposes of this Inventory.
Table A-38: Variability in Carbon Content Coefficients by Rank Across States (Kilograms C02 Per MMBtu)
State
Alabama
Alaska
Arizona
Arkansas
Colorado
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maryland
Massachusetts
Michigan
Mississippi
Missouri
Montana
Nevada
New Mexico
North Dakota
Ohio
Oklahoma
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wyoming
U.S. Average
Number of
Samples
951
91
15
80
318
35
1
57
146
100
29
897
1
47
3
3
8
111
309
2
185
202
674
63
861
61
64
169
470
18
612
503
7,092
Bituminous
92.84
98.33
93.94
96.36
94.37
95.01
92.33
92.65
91.87
90.91
92.61
94.29
-
92.88
91.71
105.21
94.41
94.29
-
91.84
92.33
93.33
92.82
85.59
95.75
93.51
94.53
93.84
94.80
93.13
Sub-
bituminous
-
98.10
97.34
-
96.52
94.90
-
-
-
-
-
-
-
-
97.73
-
94.89
93.97
-
-
-
-
94.19
91.29
97.36
97.22
96.94
Anthracite
-
-
-
-
-
-
-
-
-
-
-
-
114.82
-
103.60
-
103.92
-
-
-
103.68
-
-
-
98.54
102.53
104.29
Lignite
99.10
98.65
-
94.97
101.10
-
-
-
-
-
96.01
-
-
98.19
99.40
99.86
-
99.48
-
-
-
-
94.47
-
106.55
98.63
Notes: - Indicates No Sample Data Available.
Sources: Calculated from USGS (1998), and PSU (2010); data presented in EPA (2010).
Natural Gas
Natural gas is predominantly composed of methane, which is 75 percent C by weight and contains 14.2 Tg
C/QBtu (higher heating value), but it may also contain many other compounds that can lower or raise its overall C content.
These other compounds may be divided into two classes: 1) natural gas liquids (NGLs), and 2) non-hydrocarbon gases.
The most common NGLs are ethane (C2H6), propane (C3H8), butane (C4H10), and, to a lesser extent, pentane (C5H12) and
hexane (C6H14). Because the NGLs have more C atoms than methane (which has only one), their presence increases the
overall C content of natural gas. NGLs have a commercial value greater than that of methane, and therefore are usually
separated from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a
petrochemical feedstock, propane and butane have diverse uses, and natural gasoline contributes to the gasoline/naphtha
"octane pool," used primarily to make motor gasoline.
10 A term used in the gas processing industry to refer to a mixture of liquid hydrocarbons (mostly pentanes and heavier hydrocarbons)
extracted from natural gas.
A-55
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Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as CO2, nitrogen, helium and
other noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total,
but there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon
gases in raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial
enough to reduce the energy content of the gas below pipeline specifications (see Step 1, below). Otherwise, inert gases
will usually be left in the natural gas. Because the raw gas that is usually flared (see Step 2, below) contains NGLs and
CO2, it will typically have a higher overall C content than gas that has been processed and moved to end-use customers via
transmission and distribution pipelines.
Methodology
The methodology for estimating the C contents of pipeline and flared natural gas can be described in five steps.
Step 1. Define pipeline-quality natural gas
In the United States, pipeline-quality natural gas is required to have an energy content greater than 970 Btu per
cubic foot, but less than 1,100 Btu per cubic foot. Hydrogen sulfide content must be negligible. Typical pipeline-quality
natural gas is about 95 percent methane, 3 percent NGLs, and 2 percent non-hydrocarbon gases, of which approximately
halfisCO2.
However, there remains a range of gas compositions that are consistent with pipeline specifications. The
minimum C content coefficient for natural gas would match that for pure methane, which equates to an energy content of
1,005 Btu per standard cubic foot. Gas compositions with higher or lower Btu content tend to have higher C emission
factors, because the "low" Btu gas has a higher content of inert gases (including CO2 offset with more NGLs), while
"high" Btu gas tends to have more NGLs.
flared:
Step 2. Define flared gas
Every year, a certain amount of natural gas is flared in the United States. There are several reasons that gas is
• There may be no market for some batches of natural gas, the amount may be too small or too variable, or the
quality might be too poor to justify treating the gas and transporting it to market (such is the case when gas
contains large shares of CO2). Most natural gas that is flared for these reasons is "rich" associated gas, with
relatively high energy content, high NGL content, and a high C content.
• Gas treatment plants may flare substantial volumes of natural gas because of "process upsets," because the gas is
"off spec," or possibly as part of an emissions control system. Gas flared at processing plants may be of variable
quality.
Data on the energy content of flare gas, as reported by states to EIA, indicate an average energy content of 1,130
Btu per standard cubic foot (EIA 1994). Flare gas may have an even higher energy content than reported by EIA since
rich associated gas can have energy contents as high as 1,300 to 1,400 Btu per cubic foot.
Step 3. Determine a relationship between carbon content and heat content
A relationship between C content and heat content may be used to develop a C content coefficient for natural gas
consumed in the United States. In 1994, EIA examined the composition (including C contents) of 6,743 samples of
pipeline-quality natural gas from utilities and/or pipeline companies in 26 cities located in 19 states. To demonstrate that
these samples were representative of actual natural gas "as consumed" in the United States, their heat content was
compared to that of the national average. For the most recent year, the average heat content of natural gas consumed in the
United States was 1,029 Btu per cubic foot, and has varied by less than 1 percent (1,027 to 1,029 Btu per cubic foot) over
the past 5 years. Meanwhile, the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the median
heat content was 1,031 Btu per cubic foot. Thus, the average heat content of the sample set falls well within the typical
range of natural gas consumed in the United States, suggesting that these samples continue to be representative of natural
gas "as consumed" in the United States. The average and median composition of these samples appear in Table A-39.
A-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-39: Composition of Natural Gas (Percent)
Compound
Methane
Ethane
Propane
Higher Hydrocarbons
Non-hydrocarbons
Higher Heating Value (Btu per cubic foot)
Average
93.07
3.21
0.59
0.32
2.81
1,027
Median
95.00
2.79
0.48
0.30
1.43
1,031
Source: Gas Technology Institute (1992).
Carbon contents were calculated for a series of sub-samples based on their CO2 content and heat content. Carbon
contents were calculated for the groups of samples with less than 1.0 percent (n=5,181) and less than 1.5 percent CO2 only
(n=6,522) and those with less than 1.0 or 1.5 percent CO2 and less than 1,050 Btu/cf (n= 4,888 and 6,166, respectively).
These stratifications were chosen to exclude samples with CO2 content and heat contents outside the range of pipeline-
quality natural gas. In addition, hexane was removed from the samples since it is usually stripped out of raw natural gas
before delivery because it is a valuable natural gas liquid used as a feedstock for gasoline. The average carbon contents
for the four separate sub-samples are shown below in Table A-40.
Table A-40: Carbon Content of Pipeline-Quality Natural Gas by CQ2 and Heat Content (Tg C/QBtu)
Sample Average Carbon Content
Full Sample 14.48
<1.0%CO2 14.43
<1.5%CO2 14.47
< 1.0 % CO2 and <1,050 Btu/cf 14.42
< 1.5 % CO2 and <1,050 Btu/cf 14.47
Source: EPA (2010).
Step 4. Apply carbon content coefficients developed in Step 3 to pipeline natural gas
A regression analysis was performed on the sub-samples in to further examine the relationship between carbon
content and heat content. The regression used carbon content as the dependent variable and heat content as the
independent variable. The resulting R-squared values11 for each of the sub-samples ranged from 0.79 for samples with
less than 1.5% CO2 and under 1,050 Btu/cf to 0.91 for samples containing less than 1.0% CO2 only. However, the sub-
sample with less than 1.5% CO2 and 1,050 Btu/cf was chosen as the representative sample for two reasons. First, it most
accurately reflects the range of CO2 content and heat content of pipeline quality natural gas. Secondly, the R-squared
value, although it is the lowest of the sub-groups tested, remains relatively high. This high R-squared indicates a low
percentage of variation in C content as related to heat content. The regression for this sub-sample resulted in the following
equation:
C Content = (0.011 x Heat Content) + 3.5341
This equation was used to estimate the annual predicted carbon content of natural gas from 1990 to 2009 based
on the EIA's national average pipeline-quality gas heat content for each year. The table of average carbon contents for
each year is shown below in Table A-41.
Table A-41: Carbon Content Coefficients for Natural Gas tTg Carhon/QBtul
Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Natural Gas 14.45 14.46 14.46 14.46 14.44 14.46 14.47 14.46 14.46 14.44 14.46 14.46 14.46 14.46 14.46 14.46
Source: EPA (2010)
Step 5. Apply carbon content coefficients developed in Step 3 to flare gas
Selecting a C content coefficient for flare gas was much more difficult than for pipeline natural gas, because of
the uncertainty of its composition and of the combustion efficiency of the flare. Because EIA estimates the heat content of
flare gas at 1,130 Btu per cubic foot, the average C content for samples with more than 1,100 Btu per cubic foot (n=18)
was chosen as the relevant sub-sample from which to calculate a flare gas carbon content. It should be noted that the
sample dataset did not include any samples with more than 1,130 Btu per cubic foot.
R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
A-57
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Hexane was not removed from flare gas samples since it is assumed that natural gas liquids are present in
samples with higher heat contents. Carbon contents were calculated for each sample with a heat content of more than
1,100 Btu per cubic foot. The simple average carbon content for the sample sub-set representing flare gas is shown below
in Table A-42.
Table A-42: Carbon Content of Flare Gas tTg C/QBtul
Relevant Sub-Sample Average Carbon Content
>l,100Btu/cf 15.31
Source: EPA (2010)
Data Sources
Natural gas samples were obtained from the Gas Technology Institute (1992). Average heat content data for
natural gas consumed in the United States was taken from EIA (2009a).
Uncertainty
The assignment of C content coefficients for natural gas, and particularly for flare gas, requires more subjective
judgment than the methodology used for coal. This subjective judgment may introduce additional uncertainty.
Figure A-l shows the relationship between the calculated C content for each natural gas sample and its energy
content. This figure illustrates the relatively restricted range of variation in both the energy content (which varies by about
6 percent from average) and the C emission coefficient of natural gas (which varies by about 5 percent). Thus, the
knowledge that gas has been sold via pipeline to an end-use consumer allows its C emission coefficient to be predicted
with an accuracy of+5.0 percent.
Figure A-1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
Natural gas suppliers may achieve the same overall energy content from a wide variety of methane, higher
hydrocarbon, and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu
value. In fact, the variation in C content for a single Btu value may be nearly as great as the variation for the whole
sample. As a result, while energy content has some predictive value, the specific energy content does not substantially
improve the accuracy of an estimated C content coefficient beyond the +5.0 percent offered with the knowledge that it is
of pipeline-quality.
The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients
tend to have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure methane. Samples
with a greater proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu
per cubic foot, along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have
lower energy content, but they usually contain carbon dioxide as one of the inert gases and, consequently, also tend to
have higher emission coefficients (see left side of Figure A-l).
For the full sample (n=6,743), the average C content of a cubic foot of gas was 14.48 Tg C/QBtu (see Table A-
41). Additionally, a regression analysis using the full sample produced a predicted C content of 14.49 Tg C/QBtu based
on a heat content of 1,029 btu/cf (the average heat content in the U.S. for the most recent year). However, these two
values include an upward influence on the resulting carbon content that is caused by inclusion in the sample set of the
samples that contain large amounts of inert carbon dioxide and those samples with more than 1,050 Btu per cubic foot that
contain an unusually large amount of NGLs. Because typical gas consumed in the United States does not contain such a
large amount of carbon dioxide or natural gas liquids, a carbon content of 14.47 Tg C/QBtu, based on samples with less
than 1.5 percent carbon dioxide and less than 1,050 Btu per cubic foot, better represents the pipeline-quality fuels typically
consumed.
Petroleum
There are four critical determinants of the C content coefficient for a petroleum-based fuel:
A-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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• The density of the fuel (e.g., the weight in kilograms of one barrel of fuel);
• The fraction by mass of the product that consists of hydrocarbons, and the fraction of non-hydrocarbon
impurities;
• The specific types of 'families' of hydrocarbons that make up the hydrocarbon portion of the fuel; and
• The heat content of the fuel.
Cfuel = (Dfuelx Sfuel)/Efuei
Where,
CM = The C content coefficient of the fuel;
DM = The density of the fuel;
Sad = The share of the fuel that is C; and
Efud = The heat content of the fuel.
Most of the density, carbon share or heat contents applied to calculate the carbon coefficients for petroleum
products that are described in this sub-Annex and applied to this emissions inventory have been updated for this edition of
the report. These changes have been made where necessary to increase the accuracy of the underlying data or to align the
petroleum properties data used in this report with that developed for use in the Mandatory Reporting of Greenhouse Gases
Rule (EPA 2009b).
Petroleum products vary between 5.6 degrees API gravity (dense products such as asphalt and road oil) and 247
degrees (ethane).12 This is a range in density of 60 to 150 kilograms per barrel, or +50 percent. The variation in C content,
however, is much smaller (+5 to 7 percent) for products produced by standard distillation refining: ethane is 80 percent C
by weight, while petroleum coke is 90 to 92 percent C. This tightly bound range of C contents can be explained by basic
petroleum chemistry (see below). Additional refining can increase carbon contents. Calcined coke, for example, is formed
by heat treating petroleum coke to about 1600 degrees Kelvin (calcining), to expel volatile materials and increase the
percentage of elemental carbon. This product can contain as much as 97 to 99 percent carbon. Calcined coke is mainly
used in the aluminum and steel industry to produce carbon anodes.
Petroleum Chemistry
Crude oil and petroleum products are typically mixtures of several hundred distinct compounds, predominantly
hydrocarbons. All hydrocarbons contain hydrogen and C in various proportions. When crude oil is distilled into
petroleum products, it is sorted into fractions by the boiling temperature of these hundreds of organic compounds. Boiling
temperature is strongly correlated with the number of C atoms in each molecule. Petroleum products consisting of
relatively simple molecules and few C atoms have low boiling temperatures, while larger molecules with more C atoms
have higher boiling temperatures.
Products that boil off at higher temperatures are usually more dense, which implies greater C content as well.
Petroleum products with higher C contents, in general, have lower energy content per unit mass and higher energy content
per unit volume than products with lower C contents. Empirical research led to the establishment of a set of quantitative
relationships between density, energy content per unit weight and volume, and C and hydrogen content. Figure A-2
compares C content coefficients calculated on the basis of the derived formula with actual C content coefficients for a
range of crude oils, fuel oils, petroleum products, and pure hydrocarbons. The actual fuel samples were drawn from the
sources described below in the discussions of individual petroleum products.
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon
Density
API gravity is an arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the American
Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter the
compound. Light crude oils generally exceed 38 degrees API and heavy crude oils are all crude oils with an API gravity of 22 degrees or
below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. API gravity can be calculated with the
following formula: API Gravity = (141.5/Specific Gravity) — 131.5. Specific gravity is the density of a material relative to that of water.
At standard temperature and pressure, there are 62.36 pounds of water per cubic foot, or 8.337 pounds water per gallon.
A-59
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The derived empirical relationship between C content per unit heat and density is based on the types of
hydrocarbons most frequently encountered. Petroleum fuels can vary from this relationship due to non-hydrocarbon
impurities and variations in molecular structure among classes of hydrocarbons. In the absence of more exact information,
this empirical relationship offers a good indication of C content.
Non-hydrocarbon Impurities
Most fuels contain a certain share of non-hydrocarbon material. This is also primarily true of crude oils and fuel
oils. The most common impurity is sulfur, which typically accounts for between 0.5 and 4 percent of the mass of most
crude oils, and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain
appreciable quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and
oxygen content of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have much
lower levels of impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the residual oil
fraction. Light products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content of heavy
fuel oils can often be several percent lower than that of lighter fuels, due entirely to the presence of non-hydrocarbons.
Variations in Hydrocarbon Classes
Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density
and C content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in
order to give petroleum products distinct properties. The main classes of hydrocarbons are described below.
Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass.
Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2. Paraffins include ethane (C2H6), propane
(C3H8), butane (C4H10), and octane (C8H18). As the chemical formula suggests, the C content of the paraffins increases
with their C number: ethane is 79.89 percent C by weight, octane 84.12 percent. As the size of paraffin molecules
increases, the C content approaches the limiting value of 85.7 percent asymptotical (see Figure A-3).
Cycloparaffms. Cycloparaffins are similar to paraffins, except that the C molecules form ring structures rather
than straight chains, and consequently require two fewer hydrogen molecules than paraffins. Cycloparaffins always have
the general formula CnH2n and are 85.63 percent C by mass, regardless of molecular size.
Olefms. Olefins are a very reactive and unstable form of paraffin: a straight chain with two carbon atoms double
bonded together (thus are unsaturated) compared to the carbon atoms in a paraffin (which are saturated with hydrogen)..
They are never found in crude oil but are created in moderate quantities by the refining process. Gasoline, for example,
may contain between 2 and 20 percent olefins. They also have the general formula CnH2n, and hence are also always 85.63
percent C by weight. Propylene (C3H6), a common intermediate petrochemical product, is an olefin.
Aromatics. Aromatics are very reactive hydrocarbons that are relatively uncommon in crude oil (10 percent or
less). Light aromatics increase the octane level in gasoline, and consequently are deliberately created by catalytic
reforming of heavy naphtha. Aromatics also take the form of ring structures with some double bonds between C atoms.
The most common aromatics are benzene (C6H6), toluene (C7H8), and xylene (C8H10). The general formula for aromatics
is CnH2n_6. Benzene is 92.26 percent C by mass, while xylene is 90.51 percent C by mass and toluene is 91.25 percent C by
mass. Unlike the other hydrocarbon families, the C content of aromatics declines asymptotically toward 85.7 percent with
increasing C number and density (see Figure A-3).
Polynuclear Aromatics. Polynuclear aromatics are large molecules with a multiple ring structure and few
hydrogen atoms, such as naphthalene (C10H8 and 93.71 percent C by mass) and anthracene (C14H10 and 97.7 percent C).
They are relatively rare but do appear in heavier petroleum products.
Figure A-3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules containing
2 to 4 C atoms are all natural gas liquids; hydrocarbons with 5 to IOC atoms are predominantly found in naphtha and
gasoline; and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to make diesel
fuel, kerosene and jet fuel. Larger molecules which can be vacuum distilled may be used as lubricants, waxes, and residual
fuel oil or cracked and blended into the gasoline or distillate pools.
A-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
If one knows nothing about the composition of a particular petroleum product, assuming that it is 85.7 percent C
by mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be
most likely to be correct for crude oils and fuel oils. The C content of lighter products is more affected by the shares of
paraffins and aromatics in the blend.
Energy Content of Petroleum Products
The exact energy content (gross heat of combustion) of petroleum products is not generally known. EIA
estimates energy consumption in Btu on the basis of a set of industry-standard conversion factors. These conversion
factors are generally accurate to within 3 to 5 percent.
Individual Petroleum Products
The United States maintains data on the consumption of more than 20 separate petroleum products and product
categories. The C contents, heat contents, and density for each product are provided below in Table A-43. A description
of the methods and data sources for estimating the key parameters for each individual petroleum product appears below.
Table A-43: Carbon Content Coefficients and Underlying Data for Petroleum Products
Fuel
Motor Gasoline
LPG(total)
LPG (energy use)
LPG (non-energy use)
Jet Fuel
Distillate Fuel No. 1
Distillate Fuel No. 2
Distillate Fuel No. 4
Residual Fuel No. 5
Residual Fuel No. 6
Asphalt and Road Oil
Lubricants
Naphtha (< 400 deg. F) c
Other Oils (>400deg.F)c
Aviation Gas
Kerosene
Petroleum Coke
Special Naphtha
Petroleum Waxes
Still Gas
Crude Oil
Unfinished Oils
Miscellaneous Products
Pentanes Plus
2008 Carbon
Content Gross Heat of Combustion Density
(Tg C/QBtu) (MMBtu/Barrel) (API Gravity)
19.46
16.97
16.83
17.06
19.70
19.98
20.17
20.47
19.89
20.48
20.55
20.20
18.55
20.17
18.86
19.96
27.85
19.74
19.80
18.20
20.31
20.31
20.31
19.10
a
b
b
b
5.670
5.822
5.809
6.135
5.879
6.317
6.636
6.065
5.248
5.825
5.048
5.825
6.024
5.248
5.537
6.000
5.800
5.825
5.796
4.620
a
b
b
b
42.0
35.3
35.8
23.2
33.0
15.5
5.6
25.7
62.4
35.8
69.0
35.3
-
52.0
43.3
-
31.2
31.2
31.2
81.3
Percent
Carbon
a
b
b
b
86.30
86.40
87.30
86.47
85.67
84.67
83.47
85.80
84.11
87.30
85.00
86.40
92.28
84.75
85.30
77.70
85.49
85.49
85.49
83.63
" Calculation of the carbon content coefficient for motor gasoline in 2008 uses separate higher heating values for conventional and reformulated
gasoline of 5.253 and 5.150, respectively (EIA2008a). Densities and carbon shares (percent carbon) are annually variable and separated by both
fuel formulation and grade, see Motor Gasoline and Blending Components, below, for details.
b LPG is a blend of multiple paraffmic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density
and C content, see Table A-46.
c Petrochemical feedstocks have been split into naphthas and other oils for this inventory report. Parameters presented are for naphthas with a boiling
temperature less than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher boiling points. They are assumed to have the same
characteristics as distillate fuel oil no. 2.
- No sample data available
Sources: EIA (1994), EIA (2009a), EPA (2009b), and EPA (2010).
A-61
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Motor Gasoline and Motor Gasoline Blending Components
Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of
additives, blended to form a fuel suitable for use in spark-ignition engines.13 "Motor Gasoline" includes conventional
gasoline; all types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline.
Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly
20 percent of all U.S. CO2 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-users) for
several types of finished gasoline over the 1990-2008 time period: regular, mid-grade and premium conventional gasoline
(all years) and regular, mid-grade and premium reformulated gasoline (November 1994 to 2008). Leaded and oxygenated
gasoline are not separately included in the data used for this report.14
The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for
gasoline, ranging from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a
range of possible C and energy contents per barrel. Table A- 44 reflects changes in the density of gasoline over time and
across grades and formulations of gasoline through 2008.
Table A-44: Motor Gasoline Density, 1990-2009 [Degrees API1
Fuel Grade
Conventional
Low Octane
High Octane
Conventional
Low Octane
High Octane
Reformulated
Low Octane
High Octane
Reformulated
Low Octane
High Octane
1990 1995
-Winter Grade
62 0 59 8
59.0 58.0
- Summer Grade
58.2 56.1
55.5 55.1
-Winter Grade
NA 61.9
NA 59.9
- Summer Grade
NA 585
NA 56.7
1996
606
585
•>64
553
693
608
580
"i/X
1997
61 5
543
y/ 1
564
69 1
69 1
588
^84
1998
61 8
600
Y/h
557
697
61 4
584
^8^
1999
61 6
603
Y/V
574
697
61 0
584
i/X
2000
61 6
547
56 8
558
697
61 1
584
J
-------
Table A-45: Characteristics of Major Reformulated Fuel Additives
Additive
MTBE
ETBE
TAME
DIPE
Ethanol(100%)
Density (Degrees
API)
58.6
58.5
51.2
62.7
45.8
Carbon Share (Percent)
68.13
70.53
70.53
70.53
52.14
Source: EPA, 2009b.
Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there
has been a shift away from the addition of MTBE, TAME, ETBE and DIPE and towards the use of ethanol as a fuel
oxygenate.15 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
lower carbon share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME.
The density of ethanol was calculated by fitting density data at 10 degree intervals to a polynomial of order two and then
using the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent denatured
ethanol (denatured by 2% hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the U.S. and does not
require any modification to vehicle engines or fuel systems. The average gallon of reformulated alcohol blend gasoline in
2008 contained 8.6 percent ethanol (by volume). As of 2010, ten States require the use of ethanol-blended fuel.16 Ethanol
blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United States but can only be used in specially
designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the United States is produced using corn as
feedstock,17 although production pathways utilizing agricultural waste, woody biomass and other resources are in
development.
Methodology
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both
standard gasoline and reformulated gasoline were obtained from the EIA.
Step 2. Develop carbon content coefficients for each grade and type
Annual C content coefficients for each gasoline grade, type and season are derived from four parameters for each
constituent of the finished gasoline blend: the volumetric share of each constituent,18 the density of the constituent, share
of the constituent19 that is C; and the energy content of a gallon of the relevant formulation of gasoline. The percent by
mass of each constituent of each gasoline type was calculated using percent by volume data from NIPER and the density
of each constituent. The ether additives listed in Table A-45 are accounted for in both reformulated fuels and conventional
fuels, to the extent that they were present in the fuel. From 2006 onward, reformulated fuel mass percentages are
calculated from their constituents, net of the share provided by ethanol. C content coefficients were then derived from the
calculated percent by mass values by weighting the carbon share of each constituent by its contribution to the total mass of
the finished motor gasoline product.
Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
The C content for each grade, type and season of fuel is multiplied by the share of annual consumption
represented by the grade and fuel type during the relevant time period. Individual coefficients are then summed and
totaled to yield an overall C content coefficient for each year.
15 The annual motor gasoline carbon contents that are applied for this inventory do not include the carbon contributed by the
ethanol contained in reformulated fuels. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic carbon reservoirs in
croplands are accounted for in the estimates for Land Use, Land-Use Change and Forestry.
16 Ethanol.org. http://www.ethanol.org/index.php?id=79&parentid=26. Retrieved 2-19-2010.
17 "Ethanol Market Penetration". Alternative Fuels and Advanced Vehicles Data Center, US DOE.
http://www.afdc.energy.gov/afdc/ethanol/market.html. Retrieved 2-19-2010.
18 Calculations account for the properties of the individual constituents of gasoline, including, as applicable to the fuel grade and
type: aromatics (excluding benzene), olefins, benzene, saturates, MTBE, TAME, ETBE, DIPE and ethanol.
19 Saturates are assumed to be octane and aromatics are assumed to be toluene.
A-63
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Data Sources
Data for the density of motor gasoline were derived from the National Institute for Petroleum and Energy
Research (NIPER) (1990 through 2009). Data on the characteristics of reformulated gasoline, including C share, were also
taken from NIPER (1990 through 2009).
Standard heat contents for motor gasoline of 5.253 MMBtu per barrel conventional gasoline and 5.150 MMBtu
per barrel reformulated gasoline20 were adopted from EIA (2009a).
Uncertainty
The uncertainty underlying the C content coefficients for motor gasoline has three underlying sources: the
uncertainty in the averages published by NIPER, uncertainty in the C shares assumed in the EPA's analysis to be
representative of the constituent hydrocarbon classes within gasoline (aromatics, olefins and saturates) and uncertainty in
the heat contents applied.
A variable number of samples are used each year to determine the average percent by volume share of each
hydrocarbon within each grade, season and formulation of gasoline that are obtained from NIPER. The total number of
samples analyzed for each seasonal NIPER report varies from approximately 730 to over 1,800 samples over the period
from 1990 through 2008. The number of samples analyzed that underlie the calculation of the average make-up of each
seasonal formulation and grade varies from approximately 50 to over 400, with the greatest number of samples each
season being of conventional, regular or premium gasoline. Further, not all sample data submitted to NIPER contains data
for each of the properties, such that the number of samples underlying each constituent average value for each season,
grade and formulation may be variable within the single gasoline type (e.g., of the 1,073 samples for which some data was
obtained for gasoline sold in Winter 1995-1996, benzene content was provided for all samples, while olefin, aromatic and
saturate content was provided for just 736 of those samples).
The distribution of sample origin collected for the NIPER report and the calculation of national averages are not
reflective of sales volumes. The publication of simple, rather than sales-weighted averages to represent national average
values increases the uncertainty in their application to the calculation of carbon content factors for the purposes of this
inventory. Further, data for each sample is submitted voluntarily, which may also affect their representativeness.
Additionally, because the simple average constituent shares are calculated based upon data that have been
renormalized to account for the share of ethers and alcohols, total average volume shares may not equal 100 percent.
The simple average for each hydrocarbon constituent is contained within a range of values that are as wide as
+74.57-63 percent of the mean across the Winter 2007-2008 and +49.6/-51.3 percent across the Summer 2008 samples of
conventional, regular grade gasoline. However, these wide ranges exist for benzene, which generally accounts for only 1
percent, by volume, of each gallon. In contrast, saturates, the class of hydrocarbon that contribute the largest share, by
volume, ranges only +6.47-6.5 percent for the same set of Winter samples and +15.77-8.8 percent for the Summer samples.
Secondly, EPA's calculation of C content factors for each gasoline type includes the following assumptions: for
the purposes of assigning a carbon share to each compound in the blend, aromatic content (other than benzene) is assumed
to be toluene and saturated hydrocarbons are assumed to be octane. All olefins have the same carbon share because they
all have a molecular formula in the form CnH2n, so the C share applied to the olefin portion of the total gasoline blend does
not increase the level of uncertainty in the calculation. These assumptions are based upon the use of octane and octane
isomers as the primary saturates and toluene as the primary non-benzene aromatic in U.S. motor gasoline blends. The
octane rating of a particular blend is based upon the equivalent iso-octane to heptane ratio, which is achieved through
significant octane content relative to the other saturates. Aside from benzene, U.S. gasolines will include toluene as a
major aromatic component, so toluene may be assumed a reasonable representative of total non-benzene aromatic content
(EPA 2009a).
For each hydrocarbon category, the assumed C content lies within a range of possible values for all such
hydrocarbons. Among saturated hydrocarbons, the C share of octane (84.12 percent) is at the high end of the range while
ethane is represents the low end of the range (79.89 percent C). Total saturates constitute from 40 to 95 percent by volume
of a given gasoline blend. For aromatics, toluene (91.25 percent C) lies in the middle of the possible range. This range is
bounded by cumene (89.94 percent C) and naphthalene (93.71 percent C). Total aromatics may make up between 3 and
50 percent by volume of any given gasoline blend. The range of these potential values contributes to the uncertainty
surrounding the final calculated carbon factors.
20 The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
A-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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However, as demonstrated above in Figure A-3, the amount of variation in C content of gasoline is restricted by
the compounds in the fuel to +4 percent. Further, despite variation in sampling survey response, sample size and annually
variable fuel formulation requirements, the observed variation in the annual weighted motor gasoline coefficients
estimated for this inventory is +0.8 percent over 1990 through 2008.
The third primary contributor to uncertainty is the assumed heat content. The heat contents are industry
standards established many years ago. The heat contents are standard conversion factors used by EIA to convert
volumetric energy data to energy units. Because the heat contents of fuels change over time, without necessarily and
directly altering their volume, the conversion of known volumetric data to energy units may introduce bias. Thus, a more
precise approach to estimating emissions factors would be to calculate C content per unit of volume, rather than per unit of
energy. Adopting this approach, however, makes it difficult to compare U.S. C content coefficients with those of other
nations.
The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also
changing. However, that change within any season grade has been less than 1 percent over the decade. Of greater concern
is the use of a standardized heat content across grades that show a variation in density of +1.5 percent from the mean for
conventional gasoline and +1.0 percent for reformulated fuels.
Jet Fuel
Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the
United States: "naphtha-based" jet fuels and "kerosene-based" jet fuels. In 1989, 13 percent of U.S. consumption was
naphtha-based fuel, with the remainder kerosene-based jet fuel. In 1993, the U.S. Department of Defense began a
conversion from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand for
reformulated motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel
represented less than one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in this
report prior to 1996 represents a consumption-weighted combination of the naphtha-based and kerosene-based
coefficients. From 1996 to 2008, only the kerosene-based portion of total consumption is considered significant.
Methodology
Step 1. Estimate the carbon content for naphtha-based jet fuels
Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its characteristics
are limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the acceptable API
gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per barrel based on EIA
industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent (Martel and Angello
1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent.
Step 2. Estimate the carbon content for kerosene-based jet fuels
The density of kerosene-based jet fuels was estimated at 42 degrees API and the carbon share at 86.3 percent.
The density estimate was based on 38 fuel samples examined by NIPER. Carbon share was estimated on the basis of a
hydrogen content of 13.6 percent found in fuel samples taken in 1959 and reported by Martel and Angello, and on an
assumed sulfur content of 0.1 percent. The EIA's standard heat content of 5.670 MMBtu per barrel was adopted for
kerosene-based jet fuel.
Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type of fuel (1990-1995
only)
For years 1990 through!995, the C content for each jet fuel type (naphtha-based, kerosene-based) is multiplied
by the share of overall consumption of that fuel type, as reported by EIA (2009a). Individual coefficients are then summed
and totaled to yield an overall C content coefficient. Only the kerosene-based C coefficient is reflected in the overall jet
fuel coefficient for 1996 through 2008.
Data Sources
Data on the C content of naphtha-based jet fuel was taken from C.R. Martel and L.C. Angello (1977). Data on
the density of naphtha-based jet fuel was taken from ASTM (1985). Standard heat contents for kerosene and naphtha-
based jet fuels were adopted from EIA (2009a). Data on the C content of kerosene-based jet fuel is based on C.R. Martel
andL.C. Angello (1977) and the density is derived from NIPER (1993).
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Uncertainty
Variability in jet fuel is relatively small with the average C share of kerosene-based jet fuel varying by less than
+1 percent and the density varying by +1 percent. This is because the ratio of fuel mass to useful energy must be tightly
bounded to maximize safety and range. There is more uncertainty associated with the density and C share of naphtha-
based jet fuel because sample data were unavailable and default values were used. This uncertainty has only a small
impact on the overall uncertainty of the C content coefficient for jet fuels, however, because naphtha-based jet fuel
represents a small and declining share of total jet fuel consumption in the United States and is treated as negligible when
calculating carbon content factors for 1996 onwards.
Distillate Fuel
Distillate fuel is a general classification for diesel fuels and fuel oils. Products known as No. 1, No. 2, and No. 4
diesel fuel are used in on-highway diesel engines, such as those in trucks and automobiles, as well as off-highway engines,
such as those in railroad locomotives and agricultural machinery. No. 1, No. 2, and No. 4 fuel oils are also used for space
heating and electric power generation.
Methodology
For this inventory, separate C coefficients have been estimated for each of the three distillates, although the level
of aggregation of U.S. energy statistics requires that a single coefficient is used to represent all three grades in inventory
calculations. In past inventories, the emission coefficient was only determined for distillate No. 2. Distillate No. 2
remains the representative grade applied to the distillate class for calculation purposes. Coefficients developed for Nos. 1
and 4 distillate are provided for informational purposes. The C share each distillate is drawn from Perry's Chemical
Engineers' Handbook, 8th Ed. (Green & Perry 2008). Each C share was combined with individual heat contents of 5.822,
5.809 and 6.135 MMBtu per barrel, respectively for distillates No. 1, No. 2 and No. 4, and densities of 35.3, 35.8 and 23.2
degrees API to calculate C coefficients for each distillate type.
Data Sources
Densities for distillate Nos. 1 and 2 were derived from Alliance of Automobile Manufacturers, Diesel Survey -
Winter 2008 (AAM 2009). Densities are based on four, and 144 samples, respectively. The density of distillate fuel oil
No. 4 is taken from Perry's Chemical Engineer's Handbook, 8 Ed. (Green & Perry, 2008), Table 24-6.
Heat contents are adopted from EPA (2009b). And carbon shares for each distillate are from Perry's Chemical
Engineers' Handbook (Green & Perry 2008), Table 24-6.
Uncertainty
The primary source of uncertainty for the estimated C content of distillate fuel is the selection of No. 2 distillate
as the typical distillate fuel oil or diesel fuel. No.2 fuel oil is generally consumed for home heating. No.l distillate is
generally less dense and if it is consumed in large portions for mobile sources, the application of the C content estimated
for No. 2 for this report is likely to be too high when applied to both Nos. 1 and 2 distillates. The opposite is true of the
application of a coefficient based upon the properties of No. 2 to the consumption of No. 4 distillate, which is of a
significantly higher density and thus, has a higher C coefficient despite its lower C share. The overall effect on
uncertainty from applying a single factor will depend on the relative annual consumption of each distillate.
The densities applied to the calculation of each carbon factor are an underlying a source of uncertainty. While
the density of No. 1 distillate is based upon just four samples, the factor applied to all distillates in the inventory estimates
(that for No. 2 oil) is based on a much larger sample size (144). Given the range of densities for these three distillate fuel
classes (0.1342 to 0.1452 MT/bbl at 60°F), the uncertainty associated with the assumed density of distillate fuels is
predominately a result of the use of No. 2 to represent all distillate consumption. There is also a small amount of
uncertainty in the No. 2 distillate density itself. This is due to the possible variation across seasonal diesel formulations
and fuel grades and between stationary and transport applications within the No. 2 distillate classification. The range of
the density of the samples of No. 2 diesel (regular grade, 15ppm sulfur) is ±2.5 percent from the mean, while the range in
density across the small sample set of No. 1 diesel is -2.1 to +1.6 percent of the mean. Samples from AAM (2009) of
Premium No. 2 diesel (n=5) and higher sulfur (500 ppm S) regular diesel (n=2), which are also consumed in the U.S., each
have nominally higher average densities (+1.3 percent and +0.6 percent, respectively) than do the low-sulfur regular diesel
samples that underlie the density applied in this inventory.
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The use of the 144 AAM samples to define the density of No. 2 distillate (and those four samples used to define
that of No. 1 distillate) may introduce additional uncertainty because the samples were collected from just one season of
on-road fuel production (Winter 2008). Despite the limited sample frame, the average No. 2 density calculated from the
samples is applied to the calculation of a uniform C coefficient applicable for all years of the inventory and for all types of
distillate consumption. The ASTM standards for each grade of diesel fuel oil do not include a required range in which the
density must lie, and the density (as well as heat content and carbon share) may vary according to the additives in each
seasonal blend and the sulfur content of each sub-grade.
However, previous studies also show relatively low variation in density across samples of No. 2 and across all
distillates, supporting the application of a single No. 2 density to all U.S. distillate consumption. The average density
calculated from samples analyzed by the EIA in 1994 (n=7) differs only very slightly from the value applied for the
purposes of this inventory (-0.12 percent for No. 2 distillate). Further, the difference between the mean density applied to
this inventory (No. 2 only) and that calculated from EIA samples of all distillates, regardless of grade, is also near zero (-
0.06 percent, based on n=14, of distillates Nos. 1, 2 and 4 combined).
A C share of 87.30 percent is applied to No. 2 distillate, while No. 1 and No. 4 have C shares estimated at 86.40
and 86.47 percent, respectively. Again, the application of parameters specific to No. 2 to the consumption of all three
distillates contributes to an increased level of uncertainty in the overall coefficient and emissions estimate and its broad
application. For comparison, four No. 1 fuel oil samples obtained by EIA (1994) contained an average of 86.19 percent C,
while seven samples No. 2 fuel oil from the same EIA analysis showed an average of 86.60 percent C. Additionally, three
samples of No. 4 distillate indicate an average C share of 85.81 percent. The range of C share observed across the seven
No. 2 samples is 86.1 to 87.5 percent, and across all samples (all three grades, n=14) the range is 85.3 to 87.5 percent C.
There also exists an uncertainty of+1 percent in the share of C in No. 2 based on the limited sample size.
Residual Fuel
Residual fuel is a general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after
the distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Residual fuel conforms to ASTM
Specifications D 396 and D 975 and Federal Specification VV-F-815C. No. 5, a residual fuel oil of medium viscosity, is
also known as Navy Special and is defined in Military Specification MIL-F-859E, including Amendment 2 (NATO
Symbol F-770). It is used in steam-powered vessels in government service and inshore power plants. No. 6 fuel oil
includes Bunker C fuel oil and is used for the production of electric power, space heating, vessel bunkering, and various
industrial purposes.
In the United States, electric utilities purchase about one-third of the residual oil consumed. A somewhat larger
share is used for vessel bunkering, and the balance is used in the commercial and industrial sectors. The residual oil
(defined as No. 6 fuel oil) consumed by electric utilities has an energy content of 6.287 MMBtu per barrel (EIA 2008a)
and an average sulfur content of 1 percent (EIA 2001). This implies a density of about 17 degrees API.
Methodology
Because U.S. energy consumption statistics are available only as an aggregate of Nos. 5 and 6 residual oil, a
single coefficient must be used to represent the full residual fuel category. As in earlier editions of this report, residual
fuel oil has been defined as No. 6 fuel oil, due to the majority of residual consumed in the United States being No. 6.
However, for this report, a separate coefficient for fuel oil No. 5 has also been developed for informational purposes.
Densities of 33.0 and 15.5 degrees API were adopted when developing the C content coefficients for Nos. 5 and 6,
respectively (Wauquier, J.-P., ed. 1995; Green & Perry, ed. 2008).
The estimated C share of fuel oil No. 5 is 85.67 percent, based on an average of 12 ultimate analyses of samples
of fuel oil (EIA 1994). An average share of C in No. 6 residual oil of 84.67 percent by mass was used, based on Perry's,
8th Ed. (Green & Perry 2008).
Data Sources
Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008).
Data on the C share of fuel oil No. 5 was adopted from EIA (1994), and the density of No. 5 was obtained from
Wauquier, J.-P., ed. (1995).
Heat contents for both Nos. 5 and 6 fuel oil are adopted from EPA (2009b).
Uncertainty
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Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between the
density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur impurities,
which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities of several
percent are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on the
uncertainty of C share estimation than it does on density. This is because these impurities do provide some Btu content to
the fuel, but they are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents would have a
different total carbon share than a fuel oil that is closer to pure hydrocarbon. This contributes to the uncertainty of the
estimation of an average C share and C coefficient for these varied fuels.
The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the
mean density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the
uncertainty associated with the C content of residual fuel is probably +1 percent.
Liquefied Petroleum Gases (LPG)
EIA identifies four categories of paraffinic hydrocarbons as LPG: ethane, propane, isobutane, and n-butane.
Because each of these compounds is a pure paraffinic hydrocarbon, their C shares are easily derived by taking into account
the atomic weight of C (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of propane,
C3H8, is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C content
coefficients to be calculated directly. Table A-46 summarizes the physical characteristic of LPG.
Table A-46: Physical Characteristics of Liquefied Petroleum Gases
Compound
Ethane
Propane
Isobutane
n-butane
Chemical
Formula
C2H6
C3H8
C4H10
C4H10
Density (Barrels
Per Metric Ton)
11.55
12.76
11.42
10.98
Carbon Content
(Percent)
79.89
81.71
82.66
82.66
Energy Content
(MMBtu/Barrel)
3.082
3.836
3.974
4.326
Carbon Content
Coefficient (Tg
C/QBtu)
17.16
16.76
17.77
17.75
Source: Densities - CRC Handbook of Chemistry and Physics (2008/09); Carbon Contents - derived from the atomic weights of the elements;
Energy Contents - EPA (2009b). All values are for the compound in liquid form. The density and energy content of ethane are for refrigerated
ethane (-89 degrees C). Values for n-butane are for pressurized butane (-25 degrees C).
Methodology
Step 1. Assign carbon content coefficients to each pure paraffinic compound
Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in
the U.S. energy statistics category, Liquefied Petroleum Gases.
Step 2. Weight individual LPG coefficients for share of fuel use consumption
A C content coefficient for LPG used as fuel is developed based on the consumption mix of the individual
compounds reported in U.S. energy statistics.
Step 3. Weight individual LPG coefficients for share of non-fuel use consumption
The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While
the majority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel
applications. A C content coefficient for LPG used for non-fuel applications is developed based on the consumption mix
of the individual compounds reported in U.S. energy statistics.
Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
consumption
The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A- 47.
Data Sources
Data on C share was derived via calculations based on atomic weights of each element of the four individual
compounds Densities are from the CRC Handbook of Chemistry and Physics, 89th Ed.. The energy content of each LPG is
A-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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from the EPA (2009b). LPG consumption was based on data obtained from API (1990 through 2008) and EIA (2009b).
Non-fuel use of LPG was obtained from API (1990 through 2008).
Uncertainty
Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical
constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any
uncertainty is associated with the collection of data tabulating fuel- and non-fuel consumption in U.S. energy statistics.
This uncertainty is likely less than +3 percent.
Table A- 47: Consumption and Carbon Content Coefficients of Liquefied Petroleum Gases, 1990-2009
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008 2009
Energy Consumption (QBtu)
Fuel Use
Ethane
Propane
Butane
Isobutane
Non-Fuel
Use
Ethane
Propane
Butane
Isobutane
0.88
0.04 1
0.77 •
0.06 I
0.01 1
1.35 1
0.71 1
0.51 1
0.11 1
0.02
Carbon Content (Tg
Fuel Use
Non-Fuel
Use
16.86
17.06
0.94
0.03
0.86
0.03
0.02
1.78
0.89
0.67
0.15
0.08
C/QBtu)
16.82
17.09
1.02
0.04
0.94
0.03
0.02
1.87
0.97
0.64
0.15
0.10
16.82
17.10
1.04
0.07
0.92
0.03
0.02
1.88
0.93
0.71
0.13
0.11
16.84
17.08
0.84
0.00
0.80
0.02
0.02
1.96
0.96
0.77
0.12
0.12
16.81
17.08
1.08
0.01
0.97
0.06
0.05
2.06
1.08
0.77
0.12
0.09
16.86
17.07
1.31
0.10
1.07
0.07
0.06
1.90
1.04
0.65
0.11
0.09
16.89
17.09
1.16
0.06
1.00
0.06
0.04
1.77
0.96
0.59
0.13
0.09
16.87
17.10
1.25
0.06
1.10
0.05
0.04
1.85
1.00
0.64
0.12
0.08
16.85
17.09
1.22
0.06
1.07
0.06
0.03
1.75
0.92
0.63
0.13
0.07
16.86
17.09
1.26
0.06
1.12
0.06
0.01
1.80
0.97
0.66
0.13
0.03
16.84
17.07
1.21
0.06
1.08
0.05
0.01
1.70
0.91
0.63
0.12
0.03
16.84
17.06
1.19
0.06
1.07
0.05
0.01
1.74
0.98
0.63
0.12
0.02
16.83
17.06
1.20
0.07
1.09
0.05
0.00
1.78
1.03
0.64
0.11
0.01
16.82
17.05
1.13 1.13
0.06 0.07
1.02 1.02
0.05 0.03
0.00 0.01
1.67 1.80
0.95 1.12
0.60 0.60
0.12 0.08
0.00 0.01
16.83 16.82
17.06 17.06
Sources: Fuel use of LPG based on data from EIA (2009b) and API (1990 through 2007). Non-fuel use of LPG from
API (1990 through 2008). Volumes converted using the energy contents provided in Table A-46. C contents from EPA
(2010).
Aviation Gasoline
Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile
hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating
engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation
gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
approximately 0.1 percent of all consumption.
The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content
to a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must
have as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as
high anti-knock quality.
Methodology
A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of
5.048 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon,
based on the relationship between heat content and density of petroleum liquids, as described in Thermal Properties of
Petroleum Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5
percent iso-octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation
gasoline is 0.05 percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible
and excluded for the purposes of this analysis. This yielded a C share of 85.00 percent and a C content coefficient of
18.86 Tg C/QBtu.
Data Sources
Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
Uncertainty
The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The
uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum
products examined because no ultimate analyses of samples are available. Given the requirements for safe operation of
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piston-powered aircraft the composition of aviation gas is well bounded and the uncertainty of the C content coefficient is
likely to be +5 percent.
Still Gas
Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a
refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and
secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different
processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified
and sold as pipeline-quality natural gas. For the purposes of this inventory, the coefficient derived here is only applied to
still gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as
well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous
byproducts, it is difficult to determine what represents typical still gas.
Methodology
The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share of
still gas was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon
share of 77.7 percent. The density of still gas was estimated to be 0.1405 metric tons per barrel based on its heat content
(from EIA 2008a) and the relationship between heat content and density that is described by the U.S. Department of
Commerce, Bureau of Standards (DOC 1929).
Data Sources
The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405
metric tons per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from
EIA (2009a).
Uncertainty
The EIA obtained data on four samples of still gas. Table A-48 below shows the composition of those samples.
Table A-48: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas
Sample Hydrogen Methane Ethane Propane Btu Per Cubic Carbon Content
Foot (Tg C/QBtu)
One
Two
Three
Four
12.7
34.7
72.0
17.0
28.1
20.5
12.8
31.0
17.1
20.5
10.3
16.2
11.9
6.7
3.8
2.4
1,388
1,143
672
1,100
17.51
14.33
10.23
15.99
Sources: EIA (2008b).
Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
uncertain. Gas streams with a large free hydrogen content are likely to be used as refinery or chemical feedstocks.
Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is less
likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content coefficient
used for this report is probably at the high end of the plausible range given that it is higher than the greatest sample-based
C content in Table A-48.
Asphalt
Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of carbon
dioxide emissions. It is derived from a class of hydrocarbons called "asphaltenes," which are abundant in some crude oils
but not in others. Asphaltenes have oxygen and nitrogen atoms bound into their molecular structure, so that they tend to
have lower C contents than do other hydrocarbons.
Methodology
Ultimate analyses of twelve samples of asphalts showed an average C content of 83.47 percent. The EIA
standard Btu content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables
show a density of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content
coefficient of 20.55 Tg C/QBtu.
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Data Sources
A standard heat content for asphalt was adopted from EIA (2009a). The density of asphalt was determined by
the ASTM (1985). C share is adopted from analyses in EIA (2008b).
Uncertainty
The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and
sulfur, with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for
sulfur. Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely
to be accurate to +5 percent.
Lubricants
Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing
materials used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be
produced either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder
oil to those used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range
of product compositions and end uses within this category.
Methodology
The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API, or 0.1428 metric
tons per barrel. Ultimate analysis of a single sample of motor oil yielded a C content of 85.80 percent. A standard heat
content of 6.065 MMBtu per barrel was adopted from EIA. These factors produce a C content coefficient of 20.20 Tg
C/QBtu.
Data Sources
A standard heat content was adopted from the EIA (2009a). The carbon content of lubricants is adopted from
ultimate analysis of one sample of motor oil (EPA 2009a). The density of lubricating oils was determined by ASTM
(1985).
Uncertainty
Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product
compositions and end uses in this category combined with an inability to establish the shares of the various products
captured under this category in U.S. energy statistics. Because lubricants may be produced from either the distillate or
residual fractions during refineries, the possible C content coefficients range from 19.89 Tg C/QBtu to 21.48 Tg C/QBtu
or an uncertainty band from-1.5 percent to + 1.4 percent of the estimated value.
Petrochemical Feedstocks
U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling
temperature below 400 degrees Fahrenheit, generally called "naphtha," and those with a boiling temperature 401 degrees
Fahrenheit and above, referred to as "other oils" for the purposes of this inventory.
Methodology
The C content of these petrochemical feedstocks are estimated independently according to the following steps.
Step 1. Estimate the carbon content coefficient for naphtha
Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content and
octane rating), "straight-run" naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five
samples of naphtha were examined and showed an average C share of 84.11 percent. A density of 62.4 degrees API
gravity was taken from the Handbook of Petroleum Refining Processes, 3r ed. The standard EIA heat content of 5.248
MMBtu per barrel is used to estimate a C content coefficient of 18.55 Tg C/QBtu.
Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400
degrees Fahrenheit and above ("other oils ")
The boiling temperature of this product places it into the "middle distillate" fraction in the refining process, and
EIA estimates that these petrochemical feedstocks have the same heat content as distillate fuel No. 2. Thus, the C content
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coefficient of 20.17 Tg C/QBtu used for distillate fuel No. 2 is also adopted for this portion of the petrochemical
feedstocks category.
Data Sources
Naphthas: Data on the C content was taken from Unzelman (1992). Density is from Meyers (2004). A standard
heat content for naphthas was adopted from EIA (2009a). Other oils: See Distillate Fuel, Distillate No.2.
Uncertainty
Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity
of the purchaser, who are presumed to be a chemical company or a petrochemical unit co-located on the refinery grounds.
Naphthas are defined, for the purposes of U.S. energy statistics, as those naphtha products destined for use as a
petrochemical feedstock. Because naphthas are also commonly used to produce motor gasoline, there exists a
considerable degree of uncertainty about the exact composition of petrochemical feedstocks.
Different naphthas are distinguished by their density and by the share of paraffins, isoparaffins, olefins,
naphthenes and aromatics contained in the oil. Naphtha from the same crude oil fraction may have vastly different
properties depending on the source of the crude. Two different samples of Egyptian crude, for example, produced two
straight run naphthas having naphthene and paraffin contents (percent volume) that differ by 18.1 and 17.5 percent,
respectively (Matar and Hatch, 2000).
Naphthas are typically used either as a petrochemical feedstock or a gasoline feedstock, with lighter paraffinic
naphthas going to petrochemical production. Naphthas that are rich in aromatics and naphthenes tend to be reformed or
blended into gasoline. Thus, the product category encompasses a range of possible fuel compositions, creating a range of
possible C shares and densities. The uncertainty associated with the calculated C content of naphthas is primarily a
function of the uncertainty that underlies the average carbon share calculation, which is based on a limited number of
samples. Two additional samples cited by the EIA (1994) have a range of 83.80 to 84.42 percent C.
The uncertainty of the carbon content for other oils is based upon the assumption of distillate oil No. 2 as a
product representative of the ill-defined classification of "other oils," and from the calculation of the C content of No. 2
itself (see "Distillate Fuels," above). While No. 2 distillate is used as a proxy for "other oils" for the purposes of this
inventory's carbon coefficient, important differences exist between these two petroleum products, contributing some
uncertainty to the cross-application. Other oils are defined herein as those "oils with a boiling range equal to or greater
than 401°F that are generally intended for use as a petrochemical feedstock and are not defined elsewhere." For
comparison, various material safety data sheets (MSDSs) published by producers of distillate No. 2 indicate a boiling
range for this product of 320-700 degrees Fahrenheit. The relatively open definition of the classification "other oils"
leaves room for potentially significant variation in the heating value, density and carbon share properties of each feedstock
oil having a boiling point above 400 degrees Fahrenheit, creating a large band of uncertainty beyond that associated with
the C factor for distillate No. 2.
Kerosene
A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use as
a light source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel. Kerosene is
generally comparable to No.l distillate oil.
Methodology
The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the
physical characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40
percent were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content
coefficient of 19.96 Tg C/QBtu.
Data Sources
A standard heat content for distillate No. 1 was adopted from EIA (2009a).
Uncertainty
Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for
kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent
lower. If kerosene is more aptly compared to No. 2 distillate oil, then the true C content coefficient is likely to be about
A-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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1.1 percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes are do have some
variation in their properties. For example, the boiling range of kerosene is 250-550 degrees Fahrenheit, whereas No. 1 oils
typically boil over a range from 350-615 degrees Fahrenheit. The properties of individual kerosenes will vary with their
use and particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons having
9 to 16 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking processes and
additives contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
Petroleum Coke
Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid,
usually has a C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
Methodology
Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The ASTM
standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu per barrel
assumed. Together, these factors produced an estimated C content coefficient of 27.85 Tg C/QBtu.
Data Sources
C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken
from the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
Uncertainty
The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two
factors: the use of only two samples to establish C contents and a standard heat content which may be too low. Together,
these uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
Special Naphtha
Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial
hexane and four classes of solvent: Stoddard solvent, used in dry cleaning; high flash point solvent, used as an industrial
paint because of its slow evaporative characteristics; odorless solvent, most often used for residential paints; and high
solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage, requiring
the development of multiple coefficients.
Methodology
The method for estimating the C content coefficient of special naphtha includes three steps.
Step 1. Estimate the carbon content coefficient for hexane
Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is
83.7 degrees API or 5All pounds per gallon and its derived C content coefficient is 21.40 Tg C/QBtu.
Step 2. Estimate the carbon contents ofnon-hexane special naphthas
The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion
above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point
solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall
1977). These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-
49, below.
Table A-49: Characteristics of Non-hexane Special Naphthas
Aromatic Content
Special Naphtha
Odorless Solvent
Stoddard Solvent
High Flash Point
Mineral Spirits
(Percent)
1
15
15
30
Density
(Degrees API)
55.0
47.9
47.6
43.6
Carbon Share
(Percent Mass)
84.51
84.44
84.70
85.83
Carbon Content
(Tg C/QBtu)
19.41
20.11
20.17
20.99
Sources: EIA (2008b) and Boldt and Hall (1977).
Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
A-73
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EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions
coefficient for special naphthas is 19.74 Tg C/QBtu.
Data Sources
A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
adopted from Boldt and Hall (1977).
Uncertainty
The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation
of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of —1.7 percent to
+8.4 percent.
Petroleum Waxes
The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at
77 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline
waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have
longer C chains and more variation in their chemical bonds than paraffin waxes.
Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic
compounds with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is
estimated at 45 degrees API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic
compounds with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is
estimated at 36.7 degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products
Handbook.
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of
paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption
is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 percent, and the
weighted average density is 6.75 pounds per gallon. EIA's standard heat content for waxes is 5.537 MMBtu per barrel.
These inputs yield a C content coefficient for petroleum waxes of 19.80 Tg C/QBtu.
Data Sources
Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10
samples found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2009a).
Uncertainty
Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and
microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to + 1 percent because of the
nearly uniform relationship between C and other elements in petroleum waxes broadly defined.
Crude Oil, Unfinished Oils, and Miscellaneous Products
U.S. energy statistics include several categories of petroleum products designed to ensure that reported refinery
accounts "balance" and cover any "loopholes" in the taxonomy of petroleum products. These categories include crude oil,
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products account for
less than one percent of oil consumption, and unfinished oils are a balancing item that may show negative consumption.
For C accounting purposes, it was assumed that all these products have the same C content as crude oil.
Methodology
EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a
method of estimating C content based on this information, results of ultimate analyses of 182 crude oil samples were
collected. Within the sample set, C content ranged from 82 to 88 percent C, but almost all samples fell between 84 percent
and 86 percent C. The density and sulfur content of the crude oil data were regressed on the C content, producing the
following equation:
Percent C = 76.99 + (10.19 x Specific Gravity) + (-0.76 x Sulfur Content)
Absent the term representing sulfur content, the equation had an R-squared of only 0.35.21 When C content was
adjusted to exclude sulfur, the R-squared value rose to 0.65. While sulfur is the most important non-hydrocarbon
impurity, nitrogen and oxygen can also be significant, but they do not seem to be correlated with either density or sulfur
content. Restating these results, density accounts for about 35 percent of the variation in C content, impurities account for
about 30 percent of the variation, and the remaining 35 percent is accounted for by other factors, including (presumably)
the degree to which aromatics and polynuclear aromatics are present in the crude oil. Applying this equation to the 2008
crude oil quality data (30.21 degrees API and 1.47 percent sulfur) produces an estimated C content of 84.79 percent.
Applying the density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced
an emissions coefficient of 20.31 Tg C/QBtu.
Data Sources
C content was derived from 182 crude oil samples, including 150 samples from U.S. National Research Council
(1927). A standard heat content for crude oil was adopted from EIA (2009a).
Uncertainty
The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be
explained by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since
unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is
likely to be bounded by the range of petroleum products described in this chapter at +10 percent.
Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil
Fuels
Coal
Original 1994 Analysis
A set of 5,426 coal samples from the EIA coal analysis file were used to develop carbon content estimates. The
results from that sample set appear below in Table A-50. The EIA Coal Analysis File was originally developed by the U.S.
Bureau of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United
States. Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S.
government laboratories.
Table A-50: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990 - 2000 [Tg C/QBtul
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential/Commercial
Coal Rank
Anthracite
25.68
25.51
25.58
25.92
28.13
25.69
25.51
25.59
26.00
28.13
25.69
25.51
25.62
26.13
28.13
26.71
25.51
25.61
25.97
28.13
25.72
25.52
25.63
25.95
28.13
25.74
25.53
25.63
26.00
28.13
25.74
25.55
25.61
25.92
28.13
25.76
25.56
25.63
26.00
28.13
25.76
25.56
25.63
26.00
28.13
25.76
25.56
25.63
26.00
28.13
25.76
25.56
25.63
26.00
28.13
21 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
A-75
-------
Bituminous
Subbituminous
Lignite
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
25.37
26.24
26.62
Sources: Emission factors be consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal, " U.S. Energy
Information Administration, Quarterly Coal Report, January-March 1994. (Washington, DC, 1994) and Emission factors by rank from Science
Applications International Corporation, "Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task Report,"
Prepared for the U.S. Energy Information Administration, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
2002 Update
The methodology employed for these estimates was unchanged from previous years; however, the underlying
coal data sample set was updated. A new database, CoalQual 2.0 (1998), compiled by the U.S. Geological Survey was
adopted for the updated analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state
affiliates between 1973 and 1989. The decision to switch to the sample data contained in the USGS CoalQual database
from the EIA database was made because the samples contained in the USGS database were collected and analyzed more
recently than those obtained by EIA from the Bureau of Mines. The new coefficients developed in the 2002 revision were
in use through the 1990-2007 Inventory and are provided in Table A-51, below.
Table A-51: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990 - 2000 [Tg C/QBtul
1990 1991 1992 1993 1994 1995 1996 1997
1998 1999
2000
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential/Commercial
Coal Rank
Anthracite
Bituminous
Sub-bituminous
Lignite
25.68
25.51
25.58
25.92
28.26
25.43
26.50
26.19
25.69
25.51
25.60
26.00
28.26
25.45
26.49
26.21
25.69
25.51
25.62
26.13
28.26
25.44
26.49
26.22
25.71
25.51
25.61
25.97
28.26
25.45
26.48
26.21
25.72
25.52
25.63
25.95
28.26
25.46
26.49
26.24
25.74
25.53
25.63
26.00
28.26
25.47
26.49
26.22
25.74
25.55
25.61
25.92
28.26
25.47
26.49
26.17
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.20
25.76
25.56
25.63
26.00
28.26
25.47
26.49
26.23
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.26
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by SAIC, 2007.
2007 Update
The analysis of the USGS Coal Qual data was updated in 2007 to make a technical correction that affected the
value for lignite and those sectors which consume lignite Table A-52 contains the annual coefficients that resulted from
the 2007 analysis.
A-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A-52: Carbon Content Coefficients for Coal by Consuming Sector and Coal Ranh.1990-2007 ITg C/QBtul
Consuming Sector
Electric Power
Industrial Coking
Other Industrial
Residential/Commercial
Coal Rank
Anthracite
Bituminous
Sub-bituminous
Lignite
1990 1995
25.68 25.74
25.51 25.53
25.58 25.63
25.92 26.00
28.26 28.26
25.43 25.47
26.50 26.49
26.19 26.22
1996
25.74
25.55
25.61
25.92
28.26
25.47
26.49
26.17
1997
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.20
1998
25.76
25.56
25.63
26.00
28.26
25.47
26.49
26.23
1999
25.76
25.56
25.63
26.00
28.26
25.48
26.49
26.26
2000
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2001
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2002
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2003
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2004
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2005
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2006
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.30
2007
25.76
25.56
25.63
26.00
28.26
25.49
26.48
26.57
Sources: Data from USGS, U.S. Coal Quality Database Version 2.0 (1998) and analysis prepared by (SAIC 2007).
A-77
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2010 Update
The estimated annual C content coefficients for coal by rank and sector of consumption were updated again in
2010. Sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to the 6,588
USGS samples to create a new database of 7,092 samples. The same analytical method used in the 2002 update was
applied using these additional samples to calculate revised state-level carbon contents for each coal rank and then for
national average consumption by end-use sector and by rank.
Natural Gas
A revised analytical methodology underlies the natural gas coefficients used in this report. Prior to the current
Inventory, descriptive statistics were used to stratify 6,743 samples of pipeline quality natural gas by heat content and then
to determine the average C content of natural gas at the national average heat content (EIA 1994). The same coefficient
was applied to all pipeline natural gas consumption for all years, because U.S. energy statistics showed a range of national
average heat contents of pipeline gas of only 1,025 to 1,031 Btu per cubic foot (1 percent) from 1990 through!994. A
separate factor was developed in the same manner for all flared gas. In the previous Inventory, a weighted national
average C content was calculated using the average C contents for each sub-sample of gas that conformed with an
individual state's typical cubic foot of natural gas since there is regional variation in energy content. The result was a
weighted national average of 14.47 Tg C/QBtu.
The current Inventory is revised to make use of the same set of samples, but utilizes a regression equation, as
described above, of sample-based heat content and carbon content data in order to calculate annually-variable national
average C content coefficients based on annual national average heat contents for pipeline natural gas and for flare gas. In
addition, the revised analysis calculates an average C content from all samples with less than 1.5 percent CO2 and less than
1,050 Btu/cf (samples most closely approximating the makeup of pipeline quality natural gas). The result was identical to
the previous weighted national average of 14.47 Tg C/QBtu. The average C contents from the 1994 calculations are
presented in Table A-53 below for comparison.
Table A-53: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (Tg C/QBtu]
Sample Average Carbon Content
GRI Full Sample 14.51
Greater than 1,000 Btu 14.47
1,025 to 1,035 Btu 14.45
975 to 1,000 Btu 14.73
1,000 to 1,025 Btu 14.43
1,025 to 1,050 Btu 14.47
1,050 to 1,075 Btu 14.58
1,075 to 1,100 Btu 14.65
Greater than 1,100 Btu 14.92
Weighted National Average 14.47
Source: EIA (1994).
Petroleum Products
All of the petroleum product carbon coefficients except that for Aviation Gasoline Blending Components have
been updated for the current Inventory. EPA is updating these factors to better align the fuel properties data that underlie
the Inventory factors with those published in the Mandatory Reporting of Greenhouse Gases Rule (EPA 2009b), Suppliers
of Petroleum Products (MM) and Stationary Combustion (C) subparts. The coefficients that were applied in the previous
report are provided in Table A-49 below. Specifically, each of the coefficients used in this report have been calculated
from updated density and C share data, largely adopted from analyses undertaken for the Rule (EPA 2009b). In some
cases, the heat content applied to the conversion to a carbon-per-unit-energy basis has also been updated. Additionally,
the category Misc. Products (Territories), which is based upon the coefficients calculated for crude oil, has been allowed to
vary annually with the crude oil coefficient. The petrochemical feedstock category has been eliminated for this report
because the constituent products - naphthas and other oils - are estimated independently. Further, although the level of
aggregation of U.S. energy statistics currently limits the application of coefficients for residual and distillate fuels to these
two generic classifications, individual coefficients for the five major types of fuel oil (Nos. 1, 2, 4, 5 and 6) have been
estimated for the current report and are presented in Table A-43 above. Each of the C coefficients applied in the previous
Inventory is provided below for comparison (Table A-54).
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A-54: Carbon Content Coefficients and Underlying Data for Petroleum Products
Fuel
Motor Gasoline
LPG(total)
LPG (energy use)
LPG (non-energy use)
Jet Fuel
Distillate Fuel
Residual Fuel
Asphalt and Road Oil
Lubricants
Petrochemical
Feedstocks
Aviation Gas
Kerosene
Petroleum Coke
Special Naphtha
Petroleum Waxes
Still Gas
Crude Oil
Unfinished Oils
Miscellaneous Products
Pentanes Plus
Natural Gasoline
2007 Carbon
Content
(Tg C/QBtu)
19.33
16.99
17.18
16.76
19.33
19.95
21.49
20.62
20.24
19.37
18.87
19.72
27.85
19.86
19.81
17.51
20.33
20.33
20.33
18.24
14.47
Gross Heat of
Combustion
(MMBtu/Barrel)
5.219
a
a
a
5.670
5.825
6.287
6.636
6.065
5.248b
5.048
5.670
6.024
5.248
5.537
6.000
5.800
5.825
5.796
4.620
4.620
Density
(API Gravity)
59.1
a
a
a
42.0
35.5
11.0
5.6
25.6
67. lb
69.0
41.4
-
51.2
43.3
-
30.5
30.5
30.5
81.7
81.7
Percent
Carbon
86.60
a
a
a
86.30
86.34
85.68
83.47
85.80
84.1 lb
85.00
86.01
92.28
84.76
85.29
-
85.49
85.49
85.49
83.70
83.70
" LPG is a blend of multiple paraffmic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density
and C content, see Lable A-46.
b Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with higher
boiling points are assumed to have the same characteristics as distillate fuel.
- No sample data available
Sources: EIA (1994), EIA (2008a), and SAIC (2007).
Additional revisions to the inventory's carbon coefficients since 1990 are detailed below.
Jet Fuel
1995 Update
Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71
Tg C/QBtu to 19.33 Tg C/QBtu. This downward revision was the result of a shift in the sample set used from one
collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based
on an EIA standard and probably yields a downward bias in the revised C content coefficient.
2008 Inventory Update
The coefficient was revised again for the 2008 inventory, returning to Martel and Angello and NIPER as the
source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in order to align
the coefficients used for this report with the values used in the Mandatory Reporting of Greenhouse Gases Rule (EPA
2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate for the
Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients across the
types of jet fuel in use in the U.S. The factor will be revisited in future inventories in light of data received from reporting
entities in response to the Rule.
Liquefied Petroleum Gases (LPG)
The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the
underlying compounds: ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus— assumed
to have the characteristics of hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed
A-79
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pentanes plus from this fuel category. Because pentanes plus is relatively rich in C per unit of energy, its removal from
the consumption mix lowered the C content coefficient for LPG from 17.26 Tg C/QBtu to 16.99 Tg C/QBtu. In 1998, EIA
began separating LPG consumption into two categories: energy use and non-fuel use and providing individual coefficients
for each. Because LPG for fuel use typically contains higher proportions of propane than LPG for non-fuel use, the C
content coefficient for fuel use was 1.8 to 2.5 percent higher than the coefficient for non-fuel use in previous Inventories
(see Table A-49).
However, for the current update of the LPG coefficients, the assumptions that underlie the selection of density
and heat content data for each pure LPG compound have been updated, leading to a significant revision of the assumed
properties of ethane. For this report, the physical characteristics of ethane, which constitutes over 90 percent of LPG
consumption for non-fuel uses, have been updated to reflect ethane that is in (refrigerated) liquid form. Previously, the
share of ethane was included using the density and energy content of gaseous ethane. Table A-55, below, compares the
values applied for each of the compounds under the two sets of coefficient calculations. The carbon share of each pure
compound was also updated by using more precise values for each compound's molecular weight.
Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use is now higher
than that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under
the revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each
year than is that for LPGs consumed for fuel (energy) uses.
Table A-55: Physical Characteristics of Liquefied Petroleum Gases
Compound
Ethane
Propane
Isobutane
n-butane
Chemical
Formula
C2H6
C3H8
C4H10
C4H10
1990-2007
Density
(bbl / MT)
16.88
12.44
11.20
10.79
Updated
Density
(bbl / MT)
11.55
12.76
11.42
10.98
1990-2007
Energy
Content
(MMBtu/bbl)
2.916
3.824
4.162
4.328
Updated
Energy
Content
(MMBtu/bbl)
3.082
3.836
3.974
4.326
1990-2007
C Content
Coefficient (Tg
C/QBtu)
16.25
17.20
17.75
17.72
Updated
C Content
Coefficient (Tg
C/QBtu)
17.16
16.76
17.77
17.75
Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All values are for
the compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for
pressurized butane (-25 degrees C). Values in previous editions of this inventory: Gurthrie (1960).
Motor Gasoline
The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives
in a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects
of the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change
resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in
1995. In 2005-2006 reformulated fuels containing ethers began to be phased out nationally. Ethanol was added to
gasoline blends as a replacement oxygenate, leading to another shift in gasoline density (see Table A- 44), in the list and
proportion of constituents that form the blend and in the blended C share based on those constituents.
A-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-56: Carbon Content Coefficients for Petroleum Products.1990-2007ITgC/QBtul
Fuel Type
Petroleum
Asphalt and Road Oil
Aviation Gasoline
Distillate Fuel Oil
Jet Fuela
Kerosene
LPG (energy use)a
LPG (non-energy use)a
Lubricants
Motor Gasoline3
Residual Fuel
Other Petroleum
Av Gas Blend Comp.
Mo Gas Blend Compa
Crude Oila
Misc. Products3
Misc. Products (Terr.)
Naphtha (<401 deg. F)
Other oil (>401 deg. F)
Pentanes Plus
Petrochemical Feed.
Petroleum Coke
Still Gas
Special Naphtha
Unfinished Oils"
Waxes
Other Wax and Misc.
1990
20.62
18.87
19.95
19.40
19.72
17.21
16.83
20.24
19.41
21.49
18.87
19.41
2( >.!(,•
20.161
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.161
19.81
19.81
1995
20.62
18.87
19.95
19.34
19.72
17.20
16.87
20.24
19.38
21.49
18.87
19.38
20.23
20.23
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.23
19.81
19.81
1996
20.62
18.87
19.95
19.33
19.72
17.20
16.86
20.24
19.36
21.49
18.87
19.36
20.25
20.25
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.25
19.81
19.81
1997
20.62
18.87
19.95
19.33
19.72
17.18
16.88
20.24
19.35
21.49
18.87
19.35
20.24
20.24
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.24
19.81
19.81
1998
20.62
18.87
19.95
19.33
19.72
17.23
16.88
20.24
19.33
21.49
18.87
19.33
20.24
20.24
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.24
19.81
19.81
1999
20.62
18.87
19.95
19.33
19.72
17.25
16.84
20.24
19.33
21.49
18.87
19.33
20.19
20.19
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.19
19.81
19.81
2000
20.62
18.87
19.95
19.33
19.72
17.20
16.81
20.24
19.34
21.49
18.87
19.34
20.23
20.23
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.23
19.81
19.81
2001
20.62
18.87
19.95
19.33
19.72
17.21
16.83
20.24
19.34
21.49
18.87
19.34
20.29
20.29
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.29
19.81
19.81
2002
20.62
18.87
19.95
19.33
19.72
17.20
16.82
20.24
19.35
21.49
18.87
19.35
20.30
20.30
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.30
19.81
19.81
2003
20.62
18.87
19.95
19.33
19.72
17.21
16.84
20.24
19.33
21.49
18.87
19.33
20.28
20.28
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.28
19.81
19.81
2004
20.62
18.87
19.95
19.33
19.72
17.20
16.81
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2005
20.62
18.87
19.95
19.33
19.72
17.19
16.81
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2006
20.62
18.87
19.95
19.33
19.72
17.19
16.78
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
2007
20.62
18.87
19.95
19.33
19.72
17.18
16.76
20.24
19.33
21.49
18.87
19.33
20.33
20.33
20.00
18.14
19.95
18.24
19.37
27.85
17.51
19.86
20.33
19.81
19.81
aC contents vary annually based on changes in fuel composition.
bC content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of heat released when a
fuel is burned. Coal, crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and hydrogen combine with oxygen in the air to produce
CO2 and water. Some of the energy released in burning goes into transforming the water into steam and is usually lost. The amount of heat spent in transforming the water into steam is counted as part of
gross heat content. Lower heating value (net heating value), in contrast, does not include the heat spent in transforming the water into steam. Using a simplified methodology based on International
Energy Agency defaults, higher heating value can be converted to lower heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by 0.90. Carbon content
coefficients are presented in higher heating value because U.S. energy statistics are reported by higher heating value.
A-81
-------
References
AAM (2009). Diesel Survey. Alliance of Automobile Manufacturers, Winter 2008.
American Gas Association (1974) Gas Engineer's Handbook, New York, NY, Industrial Press, pp. 3/71, 3.87.
API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
API (1988) Alcohols and Ethers: A Technical Assessment of Their Applications as Fuels and Fuel Components, American
Petroleum Institute, API 4261.
ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
Materials. Philadelphia, PA.
Black, F. and L. High (1979) "Methodology for Determining Particulate and Gaseous Diesel Emissions," in, The
Measurement and Control of Diesel Particulate Emissions, Society of Automotive Engineers, p. 128.
Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for
Testing and Materials, p. 30.
Chemical Rubber Company (CRC), (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide,
Cleveland, OH: CRC Press.
DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
Washington, DC. pp. 16-21.
EIA (2001-2009b) Coal Distribution - Annual, U.S. Department of Energy, Energy Information Administration.
Washington, DC. DOE/EIA.
EIA (2008a) Monthly Energy Review, September 2007 and Published Supplemental Tables on Petroleum Product detail.
Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-003 5(2007/9).
EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006).
October 2008.
EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, DC.
DOE/EIA-0384(2008).
EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington,
DC. Available online at <
http://www.eia.doe.gov/oil_gas/petroleum/dataj)ublications/petroleum_supply_annual/psa_volumel/psa_volumel.ht
ml>.
EIA (2001-2009a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration. Washington,
DC. DOE/EIA 0584.
EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington,
DC. August 2001. Available online at .
EIA (1990-2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration. Washington,
DC. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, DC. November, 1994. DOE/EIA 0573.
EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
(unpublished manuscript, April 1993).
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air
and Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2009a), "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air
and Radiation. 30 January, 2009.
A-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EPA (2009b). Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-
2278, 30 September, 2009.
Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. Chowdiah, and
A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. GRI-
92/0123. March 1992.
Guthrie, V.B. (ed.) (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY,
McGraw-Hill.
Hadaller, O.J. and A.M. Momenthy (1990) The Characteristics of Future Fuels, Part 1, "Conventional Heat Fuels".
Seattle, WA, Boeing Corp. September 1990. pp. 46-50.
Intergovernmental Panel on Climate Change (IPCC), 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
Prepared by the National Greenhouse Gas Inventories Programme (Japan, 2006)
Matar, S. andL. Hatch (2000), Chemistry of Petrochemical Processes, 2n Ed. Gulf Publishing Company: Houston.
Martel, C.R., and L.C. Angello (1977) "Hydrogen Content as a Measure of the Combustion Performance of Hydrocarbon
Fuels," in Current Research in Petroleum Fuels, Volume I. New York, NY, MSS Information Company, p. 116.
Martin, S.W. (1960) "Petroleum Coke," in Virgil Guthrie (ed.), Petroleum Processing Handbook, New York, NY,
McGraw-Hill, pp. 14-15.
Meyers, (2004), Handbook of Petroleum Refining Processes, 3rd ed., NY, NY: McGraw Hill.
National Institute for Petroleum and Energy Research (1990 through 2009) Motor Gasolines, Summer and Motor
Gasolines, Winter.
NIPER (1993). C. Dickson, Aviation Turbine Fuels, 1992, NIPER-179 PPS93/2 (Bartlesville, OK: National Institute for
Petroleum and Energy Research, March 1993).
Pennsylvania State University (PSU) (2010). Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
Quick, Jeffrey (2010). "Carbon Dioxide Emission Factors for U.S. Coal by Origin and Destination," Environmental
Science & Technology, Forthcoming.
SAIC (2007) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation,
Market Policies Branch.
U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and
Technology, New York, NY, McGraw-Hill.
Unzelman, G.H. (1992) "A Sticky Point for Refiners: FCC Gasoline and the Complex Model," Fuel Reformulation,
July/August 1992, p. 29.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
Wauquier, J., ed. (1995). Petroleum Refining, Crude Oil, Petroleum Products and Process Flowsheets (Editions Technip -
Pans, 1995) pg.225, Table 5.16.
A-83
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2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of
Fossil Fuels
Carbon (C) storage associated with the non-energy use of fossil fuels was calculated by multiplying each fuel's
potential emissions (i.e., each fuel's total C content) by a fuel-specific storage factor, as listed in Table A-57. The
remaining C—i.e., that which is not stored—is emitted. This sub-annex explains the methods and data sources employed
in developing the storage factors for petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer uses,
LPG, pentanes plus, naphthas, other oils, still gas, special naphtha), asphalt and road oil, lubricants, waxes, and
miscellaneous products. The storage factors22 for the remaining non-energy fuel uses are either based on values
recommended for use by IPCC (2006), or when these were not available, assumptions based on the potential fate of C in
the respective NEU products.
Table A-57: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type Storage Factor (%)
Industry
Industrial Coking Coala
Industrial Other Coal b
Natural Gas to Chemical Plants b
Asphalt & Road Oil
LPGb
Lubricants
Pentanes Plus b
Naphtha (<401deg.F)b
Other Oil (>401 deg. F) b
Still Gas b
Petroleum Coke0
Special Naphtha b
Distillate Fuel Oil
Waxes
Miscellaneous Products
Transportation
Lubricants
U.S. Territories
Lubricants
Other Petroleum (Misc. Prod.)
10
58
58
100
58
9
58
58
58
58
30
58
50
58
0
9
_
9
10
- Not applicable
a Includes processes for which specific coking coal consumption and emission factor data are not available. Consumption of coking coal for
production of iron and steel is covered in the Industrial Processes chapter.
b The storage factor listed is the value for 2009. As described in this annex, the factor varies over time.
c Assumes petroleum coke consumption is for pigments. Consumption of petroleum coke for production of primary aluminum anodes, electric arc
furnace anodes, titanium dioxide, ammonia, urea, and ferroalloys is covered in the Industrial Processes chapter.
The following sections describe the non-energy uses in greater detail, outlining the methods employed and data
used in estimating each storage factor. Several of the fuel types tracked by EIA are used in organic chemical synthesis and
in other manufacturing processes, and are referred to collectively as "petrochemical feedstocks." Because the methods and
data used to analyze them overlap, they are handled as a group and are discussed first. Discussions of the storage factors
for asphalt and road oil, lubricants, waxes, and miscellaneous products follow.
22 Throughout this section, references to "storage factors" represent the proportion of carbon stored.
A-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Petrochemical Feedstocks
Petrochemical feedstocks—industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus,
naphthas, other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals and
products. Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a few
of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e.,
predominantly CH4) to heavier, more complex naphthas and other oils.23
After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted
approximately 189.1 Tg CO2 Eq., or 62% percent, of the 306.1 Tg CO2 Eq. of non-energy fuel consumption in 2009. For
2009 the storage factor for the eight fuel categories was 58 percent. In other words, of the net consumption, 58 percent
was destined for long-term storage in products—including products subsequently combusted for waste disposal—while
the remaining 42 percent was emitted to the atmosphere directly as CO2 (e.g., through combustion of industrial
byproducts) or indirectly as CO2 precursors (e.g., through evaporative product use). The indirect emissions include a
variety of organic gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which eventually oxidize
into CO2 in the atmosphere. The derivation of the storage factor is described in the following sections.
Methodology and Data Sources
The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C
content for the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of feedstocks.
One aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were grouped
because of the overlap of their derivative products. EHie to the many reaction pathways involved in producing
petrochemical products (or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their
parent fuel feedstocks.
Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the
major categories of petrochemical feedstocks. EIA's Petroleum Supply Annual publication tracks imports and exports of
petrochemical feedstocks, including butanes, butylenes, ethane, ethylene, propane, propylene, LPG, and naphthas (i.e.,
most of the large volume primary chemicals produced by petroleum refineries). These imports and exports are already
factored into the U.S. fuel consumption statistics. However, EIA does not track imports and exports of chemical
intermediates and products produced by the chemical industry (e.g., xylenes, vinyl chloride), which are derived from the
primary chemicals produced by the refineries. These products represent very large flows of C derived from fossil fuels
(i.e., fossil C), so estimates of net flows not already considered in EIA's dataset were developed for the entire time series
from 1990 to 2009.
The approach to estimate imports and exports involves three steps, listed here and then described in more detail
below:
Step 1. Identify commodities derived from petrochemical feedstocks, and calculate net import/export for each.
Step 2. Estimate the C content for each commodity.
Step 3. Sum the net C imports/exports across all commodities.
Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA)
and U.S. Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC).
NPRA provided a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile
import-export data for periodic reports issued to NPRA's membership on trade issues. Additional feedstock commodities
were identified by HTS code in the BoC data system and included in the net import/export analysis.
One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are
fuels and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRA-
supplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of
petrochemical feedstocks. Although MTBE and related ether imports are included in the published NPRA data, these
commodities are not included in the total net imports/exports calculated here, because it is assumed that they are fuel
additives and do not contribute to domestic petrochemical feedstocks. Net exports of MTBE and related ethers are also
not included in the totals, as these commodities are considered to be refinery products that are already accounted for in the
23 Naphthas are compounds distilled from petroleum containing 4 to 12 carbon atoms per molecule and having a boiling point less than
401° F. "Other oils" are distillates containing 12 to 25 carbon atoms per molecule and having a boiling point greater than 401° F.
A-85
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EIA data. Imports and exports of commodities for which production and consumption data are provided by EIA (e.g.,
butane, ethylene, and liquefied petroleum gases) are also not included in the totals, to avoid double-counting.
Another difficulty is that one must be careful to assure that there is not double-counting of imports and exports in
the data set. Other parts of the mass balance (described later) provide information on C flows, in some cases based on
production data and in other cases based on consumption data. Production data relates only to production within the
country; consumption data incorporates information on imports and exports as well as production. Because many
commodities are emissive in their use, but not necessarily their production, consumption data is appropriately used in
calculations for emissive fates. For purposes of developing an overall mass balance on U.S. non-energy uses of C, for
those materials that are non-emissive (e.g., plastics), production data is most applicable. And for purposes of adjusting the
mass balance to incorporate C flows associated with imports and exports, it was necessary to carefully review whether or
not the mass balance already incorporated cross-boundary flows (through the use of consumption data), and to adjust the
import/export balance accordingly.
The BoC trade statistics are publicly available24 and cover a complete time series from 1990 to 2009. These
statistics include information on imports and exports of thousands of commodities. After collecting information on annual
flows of the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each
commodity from its chemical formula. In cases where the imports and exports were expressed in units of volume, rather
than mass, they were converted to mass based on the commodities' densities.
Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in
Table A-58. As shown in the table, the United States has been a net exporter of chemical intermediates and products
throughout the 1990 to 2009 period.
Table A-58: Net Exports of Petrochemical Feedstocks, 1990 - 2009 tTg Clh Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
NetExports 12.0 11.1 8.3 1.8 7.3 14.8 20.2 6.5 4.1 8.4 4.5 9.0
After adjusting for imports and exports, the C budget is adjusted for the quantity of C that is used in the
Industrial Processes sector of the Inventory. Fossil fuels used for non-energy purposes in industrial processes—and for
which C emissions and storage have been characterized through mass balance calculations and/or emission factors that
directly link the non-energy use fossil fuel raw material and the industrial process product—are not included in the non-
energy use sector. These industrial processes (and their non-energy use fossil fuel raw materials) include iron and steel
(coal coke), primary aluminum (petroleum coke), titanium oxide (petroleum coke), ferroalloys (petroleum coke), and
ammonia and urea (petroleum coke and natural gas).
For each year of the Inventory, the total C content of non-energy uses was calculated by starting with the EIA
estimate of non-energy use, and reducing it by the adjustment factor for net exports (see Table A-58) to yield net domestic
fuel consumption for non-energy. The balance was apportioned to either stored C or emissive C, based on a storage factor.
The overall storage factor for the feedstocks was determined by developing a mass balance on the C in
feedstocks, and characterizing products, uses, and environmental releases as resulting in either storage or emissions. The
total C in the system was estimated by multiplying net domestic consumption for non-energy by the C content of each of
the feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still
gas, special naphtha). C content values for the fuel feedstocks are discussed in the Estimating Emissions from Fossil Fuel
Combustion and Estimating the Carbon Content from Fossil Fuel Combustion Annexes.
Next, C pools and releases in a variety of industrial releases, energy recovery processes, and products were
characterized. The C fate categories are plastics, energy recovery, synthetic rubber, synthetic fibers, organic solvents, C
black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and
deicers (glycols), and silicones.25
24 See the U.S International Trade Commission (USITC) Trade Dataweb at .
25 For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges
associated with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions
could have been accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they
were included here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
A-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage
factor is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of
stored C and emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for
some years in the time series.26 To address this mass imbalance, the storage factor was calculated as C storage divided by
total C outputs (rather than C storage divided by C inputs).
Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
consumed in the United States insofar as emissions of CO2 from waste combustion are accounted for separately in the
Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
The following sections provide details on the calculation steps, assumptions, and data sources employed in
estimating and classifying the C in each product and waste shown in Table A-59. Summing the C stored and dividing it by
total C outputs yields the overall storage factor, as shown in the following equation for 2009:
Overall Storage Factor = C Stored / (C Stored + C Emitted) =
129.3 Tg CO2Eq. / (129.3 + 93.4) Tg CO2 Eq. = 58%
Table A-59: C Stored and Emitted by Products from Feedstocks in 2009 (Tg GO? Eq.)
C Stored C Emitted
Product/Waste Type (Tg CO2 Eq.) (Tg CO2 Eq.)
Industrial Releases
TRI Releases
Industrial VOCs
Non-combustion CO
Hazardous Waste Incin.
Energy Recovery
Products
Plastics
Synthetic Rubber
Antifreeze and deicers
Abraded tire rubber
Food additives
Silicones
Synthetic Fiber
Pesticides
Soaps, shampoos, detergents
Solvent VOCs
Total
0.1
0.1
129.2
111.1
10.7
0.5
6.7
0.3
129.3
4.4
1.0
2.3
0.7
0.4
74.8
14.2
0.9
0.3
0.8
0.2
6.7
5.3
93.4
- Not applicable
Note: Totals may not sum due to independent rounding.
The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each
is discussed below.
Industrial Releases
Industrial releases include toxic chemicals reported through the Toxics Release Inventory, industrial emissions of
volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions from
hazardous waste incineration.
Overall, there was fairly close agreement between inputs and outputs: for the entire 1990 through 2009 time series, inputs exceeded
outputs by a time-weighted average of 0.2 percent. During the period 1990 through 2000, carbon inputs exceeded carbon outputs (i.e.,
the sum of carbon stored and carbon emitted) by a time-weighted average of 10 percent. For those years, the assumption was made that
the "missing" carbon was lost through fates leading to emissions. This discrepancy shifted during the period from 2001 through 2009, in
which carbon outputs exceeded carbon inputs by a time-weighted average of 12 percent.
A-87
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TRI Releases
Fossil-derived C is found in many toxic substances released by industrial facilities. The Toxics Release
Inventory (TRI), maintained by EPA, tracks these releases by chemical and environmental release medium (i.e., land, air,
or water) on a biennial basis (EPA 2000b). By examining the C contents and receiving media for the top 35 toxic
chemicals released, which account for 90 percent of the total mass of chemicals, the quantity of C stored and emitted in the
form of toxic releases can be estimated.
The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular
formula. The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further
subdivided into air emissions, surface water discharges, underground injection, and releases to land; the latter is further
broken down to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."27 The
C released in each disposal location is provided in Table A-60.
Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition
of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were
assumed to result in an ultimate fate of emission as CO2 (i.e., a storage factor of zero was applied to these categories.) The
release allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average storage
factor for the on-site C and apply it to the off-site releases.
For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C
content value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the
remaining 10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
Data on TRI releases for the full 1990 through 2009 time series were not readily available. Since this category is
small (less than 1 Tg C emitted and stored), the 1998 value was applied for the entire time series.
Table A-60:1998 TRI Releases by Disposal Location [Gg GO? Eq.l
Carbon Stored Carbon Emitted
Disposal Location (Gg CO2 Eq.) (Gg CO2 Eq.)
Air Emissions - 924.0
Surface Water Discharges - 6.7
Underground Injection 89.4
RCRA Subtitle C Landfill Disposal 1.4
Other On-Site Land Releases - 15.9
Off-site Releases 6A 36.0
Total 97.2 982.6
- Not applicable
Note: Totals may not sum due to independent rounding.
Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained from preliminary
data (EPA 2010, EPA 2009b), and disaggregated based on EPA (2003b), which, in its final iteration, will be published on
the National Emission Inventory (NEI) Air Pollutant Emission Trends web site. The 1990-2009 Trends data include
information on NMVOC emissions by end-use category; some of these fall into the heading of "industrial releases" in
Table A-60 above, and others are related to "product use"; for ease of discussion, both are covered here. The end-use
categories that represent "Industrial NMVOC Emissions" include some chemical and allied products, certain petroleum
related industries, and other industrial processes. NMVOC emissions from solvent utilization (product use) were
considered to be a result of non-energy use of petrochemical feedstocks. These categories were used to distinguish non-
energy uses from energy uses; other categories where VOCs could be emitted due to combustion of fossil fuels were
excluded to avoid double counting.
Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs,
assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The
assumptions for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ
significantly. For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect
Only the top 9 chemicals had their land releases separated into RCRA Landfills and Other Land Disposal. For the remaining
chemicals, it was assumed that the ratio of disposal in these two categories was equal to the carbon-weighted average of the land disposal
fate of the top 9 chemicals (i.e., 8 percent attributed to RCRA Landfills and 92 percent in the "Other" category).
A-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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the C content of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the
Trends Report. The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85
percent based upon C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents
approximately 90 percent, depending upon substitution).
An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent
emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use
categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes.
The percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual
compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent
emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a single individual
compound was selected to represent each category, and the C content of the category was estimated based on the C content
of the representative compound. The overall C content of the solvent evaporation emissions for 1998, estimated to be 56
percent, is assumed to be constant across the entire time series.
The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-61 for 1990
through 2009. Solvent evaporation emissions in 2009 were 5.3 Tg CO2Eq., and industrial NMVOC emissions in 2009
were 2.3 Tg CO2Eq.
Table A-61: Industrial and Solvent NMVOC Emissions
Industrial NMVOCs3
NMVOCs ('000 Short Tons)
Carbon Content (%)
Carbon Emitted (Tg CO2 Eq.)
1990
1,279
85%
3.6
1995
1,358
85%
3.8
2000
803
85%
2.3
2005
824
85%
2.3
2006
806
85%
2.3
2007
788
85%
2.2
2008
769
85%
2.2
2009
829
85%
2.3
Solvent Evaporation1"
Solvents ('000 Short Tons)
Carbon Content (%)
Carbon Emitted (Tg CO2 Eq.)
5,750
56%
10.8
6,183
56%
11.6
4,832
56%
9.0
4,245
56%
7.9
4,239
56%
7.9
4,232
56%
7.9
4,226
56%
7.9
2,847
56%
5.3
"Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes categories.
b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other industrial processes,
and non-industrial processes.
Non-Combustion Carbon Monoxide Emissions
Carbon monoxide (CO) emissions data were also obtained from the NEI preliminary data (EPA 2010, EPA
2009b), and disaggregated based on EPA (2003b).. There are three categories of CO emissions in the report that are
classified as process-related emissions not related to fuel combustion. These include chemical and allied products
manufacturing, metals processing, and other industrial processes. Some of these CO emissions are accounted for in the
Industrial Processes section of this report, and are therefore not accounted for in this section. These include total C
emissions from the primary aluminum, titanium dioxide, iron and steel, and ferroalloys production processes. The total C
(CO and CO2) emissions from oil and gas production, petroleum refining, and asphalt manufacturing are also accounted
for elsewhere in this Inventory. Biogenic emissions (e.g., pulp and paper process emissions) are accounted for in the Land
Use, Land-Use Change and Forestry chapter and excluded from calculation of CO emissions in this section. Those CO
emissions that are not accounted for elsewhere are considered to be byproducts of non-fuel use of feedstocks, and are thus
included in the calculation of the petrochemical feedstocks storage factor. Table A-62 lists the CO emissions that remain
after taking into account the exclusions listed above.
Table A-62: Non-Combustion Carbon Monoxide Emissions3
1990 1995 2000 2005 2006 2007 2008 2009
Thousand short tons CO 489 481 623 461 469 477 484 461
Carbon Emitted (Tg CO2 Eq.) 0/7 0/7 0.9 0.7 0.7 0.7 0.7 0.7
Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other industrial processes
categories.
A-89
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Hazardous Waste Incineration
Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).28
Industrial wastes, such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air
pollution control, are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or toxic
according to standardized tests or studies conducted by the EPA.
Hazardous wastes must be treated prior to disposal according to the federal regulations established under the
authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for
those wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking,
combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management, and
boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the
hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy
recovery calculations described below, they are not included as part of hazardous waste incineration.
EPA's Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these
reports provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this
information in its Biennial Reporting System (BRS) database (EPA 2000a, 2007a). Combusted hazardous wastes are
identified based on EPA-defined management system types M041 through M049 (incineration). Combusted quantities are
grouped into four representative waste form categories based on the form codes reported in the BRS: aqueous liquids,
organic liquids and sludges, organic solids, and inorganic solids. To relate hazardous waste quantities to C emissions,
"fuel equivalent" factors were derived for hazardous waste by assuming that the hazardous wastes are simple mixtures of a
common fuel, water, and noncombustible ash. For liquids and sludges, crude oil is used as the fuel equivalent and coal is
used to represent solids.
Fuel equivalent factors were multiplied by the tons of waste incinerated to obtain the tons of fuel equivalent.
Multiplying the tons of fuel equivalent by the C content factors (discussed in the Estimating the Carbon Content from
Fossil Fuel Combustion Annex) yields tons of C emitted. Implied C content is calculated by dividing the tons of C
emitted by the associated tons of waste incinerated.
Waste quantity data for hazardous wastes were obtained from EPA's BRS database for reporting years 1989,
1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, and 2007 (EPA 2000a, 2007a). Values for 2008 and 2009 were held
constant at the 2007 level. Combusted waste quantities were obtained from Form GM (Generation and Management) for
wastes burned on site and Form WR (Wastes Received) for waste received from off-site for combustion. For each of the
waste types, assumptions were developed on average waste composition (see Table A-63). Regulations require
incinerators to achieve at least 99.99 percent destruction of organics; this formed the basis for assuming the fraction of C
oxidized. Emissions from hazardous waste incineration in 2007 were 0.4 Tg CO2 Eq. Table A-64 lists the CO2 emissions
from hazardous waste incineration.
Table A-63: Assumed Composition of Combusted Hazardous Waste by Weight [Percent!
Waste Type Water (%) Noncombustibles (%) Fuel Equivalent (%)
Aqueous Waste 90 5 5
Organic Liquids and 40 20 40
Sludges
Organic Solids 20 40 40
Inorganic Solids 20 70 10
Table A-64: C02 Emitted from Hazardous Waste Incineration [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
CO2 Emissions 1.1 1.7 1.0 0.6 0.6 0.6 0.6 0.6 0.5 0.4 0.4 0.4
Energy Recovery
The amount of feedstocks combusted for energy recovery was estimated from data included in EIA's
Manufacturers Energy Consumption Survey (MEGS) for 1991, 1994, 1998, 2002, and 2006 (EIA 1994, 1997, 2001, 2005,
2010a). Some fraction of the fossil C exiting refineries and designated for use for feedstock purposes actually ends up
being combusted for energy recovery (despite the designation of feedstocks as a "non-energy" use) because the chemical
reactions in which fuel feedstocks are used are not 100 percent efficient. These chemical reactions may generate
28 [42 U.S.C. §6924, SDWA §3004]
A-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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unreacted raw material feedstocks or generate byproducts that have a high energy content. The chemical industry and
many downstream industries are energy-intensive and often have boilers or other energy recovery units on-site, and thus
these unreacted feedstocks or byproducts are often combusted for energy recovery. Also, as noted above in the section on
hazardous waste incineration, regulations provide a strong incentive—and in some cases require—burning of organic
wastes generated from chemical production processes.
Information available from the MECS include data on the consumption for energy recovery of "other" fuels in
the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors. These
"other" fuels include refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and
blast furnace gases; scrap tires; and other uncharacterized fuels. Fuel use of petroleum coke is included separately in the
fuel use data provided annually by EIA, and energy recovery of coke oven gas and blast furnace gas (i.e., byproducts of
the iron and steel production process) is addressed in the Iron and Steel production section in the Industrial Processes
chapter. Consumption of refinery still gas in the refinery sector is also included separately in the fuel use data from EIA.
The combustion of scrap tires in cement kilns, lime kilns, and electric arc furnaces is accounted for in the Waste
Incineration chapter; data from the Rubber Manufacturers Association (RMA 2009a) were used to difference out energy
recovery from scrap tires in these industries. Consumption of net steam, assumed to be generated from fossil fuel
combustion, is also included separately in the fuel use data from EIA. Therefore, these categories of "other" fuels are
addressed elsewhere in the Inventory and not considered as part of the petrochemical feedstocks energy recovery analysis.
The remaining categories of fuels, including waste gas; waste oils, tars, and related materials; and other uncharacterized
fuels are assumed to be petrochemical feedstocks burned for energy recovery (see Table A-65). The conversion factors
listed in the Estimating Emissions from Fossil Fuel Combustion Annex were used to convert the Btu values for each fuel
feedstock to Tg CO2. Petrochemical feedstocks combusted for energy recovery corresponded to 42.5 Tg CO2 Eq. in 1991,
35.1 Tg CO2 Eq. in 1994, 58.0 Tg CO2 Eq. in 1998, 70.6 Tg CO2 in 2002, and 74.8 in 2006. Values for petrochemical
feedstocks burned for energy recovery for years between 1991 and 1994, between 1994 and 1998, between 1998 and 2002,
and between 2002 and 2006 have been estimated by linear interpolation. The value for 1990 is assumed to be the same as
the value for 1991, and values for years subsequent to 2006 are assumed to be the same as the value for 2006 (Table A-
66).
Table A-65: Summary of 2006 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery [Trillion Btul
Subsector and Industry
Printing and Related Support
Petroleum and Coal Products
Chemicals
Plastics and Rubber Products
Nonmetallic Mineral Products
Primary Metals
Fabricated Metal Products
Machinery
Computer and Electronic Products
Electrical Equip., Appliances,
Components
Transportation Equipment
Furniture and Related Products
Miscellaneous
Total (Trillion Btu)
Average C Content (Tg/QBtu)
Fraction Oxidized
Total C (Tg)
NAICS
CODE
323
324
325
326
327
331
332
333
334
335
336
337
339
Waste Gasa
0
0
459
0
1
1
0
0
0
0
5
0
0
466
18.14
1
8.45
Waste
Oils/Tars"
0
7
15
0
20
0
0
0
0
0
0
0
0
42
20.62
1
0.87
Refinery
Still Gasc
0
1,482
0
0
0
0
0
0
0
0
0
0
0
1482
17.51
1
25.95
Net
Steam*1
0
110
168
0
1
17
0
1
0
0
0
0
0
297
0
0
0.00
Other
Fuels6
0
90
435
0
14
20
0
1
1
0
11
0
0
572
19.37
1
11.08
Total C (Tg) (ex. still gas from
refining)
8.45
0.87
11.08
a C content: Waste Gas is assumed to be same as naphtha <401 deg. F
b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil
c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate
d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate
e C content: "Other" is assumed to be the same as petrochemical feedstocks
Table A-66: Carbon Emitted from Fuels Burned for Energy Recovery [Tg Clh Eq.l
1990
1995
40.8
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
C Emissions 42.5 40.
64.3 67.4 70.6 71.6 72.7 73.7 74.S
74.8
74.5
74.S
A-91
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Products
More C is found in products than in industrial releases or energy recovery. The principal types of products are
plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze
and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases
of NMVOCs; the other product types are discussed below.
Plastics
Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as
published in Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through
direct communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for
2006 and subsequent years were taken from the American Chemistry Council (ACC 2007 through 2009 supplemented by
Vallianos 2011). Production was organized by resin type (see Table A-67) and by year. Several of the resin categories
included production from Canada and/or Mexico, in addition to the U.S. values for part of the time series. The production
data for the affected resins and years were corrected using an economic adjustment factor, based on the percent of North
American production value in this industry sector accounted for by the United States. A C content was then assigned for
each resin. These C contents were based on molecular formulae and are listed in Table A-68 and Table A-69. In cases
where the resin type is generic, referring to a group of chemicals and not a single polymer (e.g., phenolic resins, other
styrenic resins), a representative compound was chosen. For other resins, a weighted C content of 68 percent was assumed
(i.e., it was assumed that these resins had the same content as those for which a representative compound could be
assigned).
There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. As
noted in the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste
stream can be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in the
mass balance for feedstocks (described in this annex) to avoid double-counting.
Table A-67:2009 Plastic Resin Production tTg dry weight! and C Stored tTg Clh Eq.l
2009 Production3 Carbon Stored
Resin Type (Tg dry weight) (Tg CO2 Eq.)
Epoxy
Urea
Melamine
Phenolic
Low-Density Polyethylene (LDPE)
Linear Low-Density Polyethylene (LLDPE)
High Density Polyethylene (HOPE)
Polypropylene (PP)
Acrylonitrile-butadiene-styrene (ABS)
Other Styrenics0
Polystyrene (PS)
Nylon
Polyvinyl chloride (PVC)b
Thermoplastic Polyester
All Other (including Polyester (unsaturated))
Total
0.24
0.47
0.47
1.44
2.74
5.39
6.97
6.65
0.37
0.48
2.00
0.38
5.24
3.24
4.61
40.70
0.7
0.6
0.5
4.0
8.6
17.0
21.9
20.9
1.2
1.6
6.8
0.9
7.4
7.4
11.6
111.1
" Production estimates provided by the American Chemistry Council include Canadian production for Urea, Melamine, Phenolic, LDPE, LLDPE,
HOPE, PP, ABS, SAN, Other Styrenics, PS, Nylon, PVC, and Thermoplastic Polyester, and Mexican production for PP, ABS, SAN, Other
Styrenics, Nylon, and Thermoplastic Polyester. Values have been adjusted to account just for U.S. production.
b Includes copolymers
c Includes Styrene-acrylonitrile (SAN)
Note: Totals may not sum due to independent rounding.
Table A-68: Assigned C Contents of Plastic Resins [% by weightl
c
Resin Type Content Source of C Content Assumption
Epoxy 76% Typical epoxy resin made from epichlorhydrin and bisphenol A
Polyester (Unsaturated) 63% Poly (ethylene terephthalate) (PET)
Urea 34% 50% carbamal, 50% N-(hydroxymethyl) urea *
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Melamine
Phenolic
Low-Density Polyethylene (LDPE)
Linear Low-Density Polyethylene (LLDPE)
High Density Polyethylene (HOPE)
Polypropylene (PP)
Acrylonitrile-Butadiene-Styrene (ABS)
Styrene-Acrylonitrile (SAN)
Other Styrenics
Polystyrene (PS)
Nylon
Polyvinyl Chloride (PVC)
Thermoplastic Polyester
All Other
29% Trimethylol melamine *
77% Phenol
86% Polyethylene
86% Polyethylene
86% Polyethylene
86% Polypropylene
85% 50% styrene, 25% acrylonitrile, 25% butadiene
80% 50% styrene, 50% acrylonitrile
92% Polystyrene
92% Polystyrene
65% Average of nylon resins (see Table A-69)
38% Polyvinyl chloride
63% Polyethylene terephthalate
69% Weighted average of other resin production
*Does not include alcoholic hydrogens.
Table A-69: Major Nylon Resins and their C Contents (% by weight)
Resin
Nylon 6
Nylon 6,6
Nylon 4
Nylon 6,10
Nylon 6,11
Nylon 6,12
Nylon 1 1
C Content
64%
64%
52%
68%
69%
70%
72%
Synthetic Rubber
Data on synthetic rubber in tires were derived from data on the scrap tire market and the composition of scrap
tires from the Rubber Manufacturers' Association (RMA). The market information is presented in the report Scrap Tire
Markets in the United States 2007 Edition (RMA 2009a), while the tire composition information is from the "Scrap Tires,
Facts and Figures" section of the organization's website (RMA 2009b). Data on synthetic rubber in other products
(durable goods, nondurable goods, and containers and packaging) were obtained from EPA's Municipal Solid Waste in the
United States reports (1996 through 2003a, 2005, 2007b, and 2008, 2009a, 2011) and detailed unpublished backup data
for some years not shown in the Characterization of Municipal Solid Waste in the United States reports (Schneider 2007).
The abraded rubber from scrap passenger tires was assumed to be 2.5 Ibs per scrap tire, while the abraded rubber from
scrap commercial tires was assumed to be 10 Ibs per scrap tire. Data on abraded rubber weight were obtained by
calculating the average weight difference between new and scrap tires (RMA 2009b).
A C content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic
rubber) was assigned based on the weighted average of C contents (based on molecular formula) by elastomer type
consumed in 1998, 2001, and 2002 (see Table A-70). The 1998 consumption data were obtained from the International
Institute of Synthetic Rubber Producers (IISRP) press release "Synthetic Rubber Use Growth to Continue Through 2004,
Says IISRP and RMA" (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release,
"IISRP Forecasts Moderate Growth in North America to 2007" (IISRP 2003).
The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire
rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion of
rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter.
Table A-70:2002 Rubber Consumption tGgl and C Content [%1
Elastomer Type
SBR Solid
Poly butadiene
Ethylene Propylene
Polychloroprene
NBR Solid
Polyisoprene
Others
Weighted Average
Total
2002 Consumption (Gg)*
768
583
301
54
84
58
367
-
2,215
C Content
91%
89%
86%
59%
77%
88%
88%
90%
-
A-93
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* Includes consumption in Canada.
- Not applicable
Note: Totals may not sum due to independent rounding.
Synthetic Fibers
Annual synthetic fiber production data were obtained from the Fiber Economics Bureau, as published in
Chemical & Engineering News (FEE 2001, 2003, 2005, 2007, 2009, 2010). These data are organized by year and fiber
type. For each fiber, a C content was assigned based on molecular formula (see Table A-71). For polyester, the C content
for poly (ethylene terephthalate) (PET) was used as a representative compound. For nylon, the average C content of nylon
6 and nylon 6,6 was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such as acetate and
rayon, have been omitted from the synthetic fibers' C accounting displayed here because much of their C is of biogenic
origin and carbon fluxes from biogenic compounds are accounted for in the Land Use, Land-Use Change and Forestry
chapter. These fibers account for only 4 percent of overall fiber production by weight.
There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that
emissions related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste
section of the Energy chapter.
Table A-71:2009 Fiber Production tTgl, C Content [%1. and C Stored tTg C0? Eq.l
Production C Stored
Fiber Type (Tg) C Content (TgCO2Eq.)
Polyester
Nylon
Olefin
Acrylic
Total
0.9
0.6
1.0
+
2.5
63%
64%
86%
68%
-
2.14
1.46
3.00
0.07
6.68
+ Less than 0.05 Tg
- Not applicable
Note: Totals may not sum due to independent rounding
Pesticides
Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, and 2000/2001 Pesticides
Industry Sales and Usage Market Estimates (EPA 1998, 1999, 2002, 2004) reports. The most recent data available were
for 2001, so it was assumed that the 2002 through 2009 consumption was equal to that of 2001. Active ingredient
compound names and consumption weights were available for the top 25 agriculturally-used pesticides and top 10
pesticides used in the home and garden and the industry/commercial/government categories. The report provides a range
of consumption for each active ingredient; the midpoint was used to represent actual consumption. Each of these
compounds was assigned a C content value based on molecular formula. If the compound contained aromatic rings
substituted with chlorine or other halogens, then the compound was considered persistent and the C in the compound was
assumed to be stored. All other pesticides were assumed to release their C to the atmosphere. Over one-third of 2001 total
pesticide active ingredient consumption was not specified by chemical type in the Sales and Usage report (EPA 2004).
This unspecified portion of the active ingredient consumption was treated as a single chemical and assigned a C content
and a storage factor based on the weighted average of the known chemicals' values.
Table A-72: Active Ingredient Consumption in Pesticides [Million Ihs.l and C Emitted and Stored [Tg C0? Eq.) in 2001
Active Ingredient C Emitted C Stored
Pesticide Use* (Million Ibs.) (Tg CO2 Eq.) (Tg CO2 Eq.)
Agricultural Uses
Non-Agricultural Uses
Home & Garden
Industry /Go v't/Commercial
Other
Total
458.5
84.5
38.5
46.0
345.0
888.0
0.1
+
+
0.1
0.2
0.2
+
+
0.1
0.3
+ Less than 0.05 Tg CO2 Eq.
*2001 estimates (EPA2004b).
Note: Totals may not sum due to independent rounding.
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Soaps, Shampoos, and Detergents
Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain fossil
C. All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was
assumed to be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of cleansers.
For this analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo,
laundry detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption of
household personal cleansers were obtained from the U.S. Census Bureau 1992, 1997, 2002, and 2007 Economic Census
(U.S. Bureau of the Census 1994, 1999, 2004, 2009). Consumption values for 1990 and 1991 were assumed to be the
same as the 1992 value; consumption was interpolated between 1992 and 1997, 1997 and 2002, and 2002 and 2007;
consumption for 2008 and 2009 was assumed to equal the 2007 value. Cleanser consumption values were adjusted by
import and export data to develop US consumption estimates.
Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each
product's overall C content was then derived from the composition and contents of its ingredients. From these values the
mean C content for cleansers was calculated to be 21.9 percent.
The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds)
and/or terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry detergents,
heavy duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report provides dollar
values for the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid." The categories for
which both quantity and value data are available is a subset of total production. Those categories that presented both
quantity and value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were
extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid29 cleanser
categories, respectively.
Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C
content (21.9 percent) by this value yielded an estimate of 4.6 Tg CO2 Eq. in cleansers for 1997. For all subsequent years,
it was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2009,
value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent manufacturing
(Bureau of Labor Statistics 2009). The ratio of value of shipments to carbon content was then applied to arrive at total
carbon content of cleansers. For 1992, 2002, and 2007 the estimates are 3.6 Tg CO2 Eq., 5.1 Tg CO2 Eq., 7.6 Tg CO2 Eq.,
respectively. Estimates for other years are based on these values as described above, and are shown in Table A-73.
Table A-73: C Emitted from Utilization of Soaps, Shampoos, and Detergents [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
C Emissions 3.6 4.2 4.5 4.8 5.1 5.7 6.2 6.7 7.0 7.6 7.1 6.7
Antifreeze and Deicers
Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are
used as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol
compounds are assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater
treatment process to CO2 or to otherwise biodegrade to CO2. Glycols are water soluble and degrade rapidly in the
environment (Howard 1993).
Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide
to the Business of Chemistry (ACC 2010). Import and export data were used to adjust annual production data to annual
consumption data. The percentage of the annual consumption of each glycol compound used for antifreeze and deicing
applications was estimated from Chemical Profiles data published on The Innovation Group website and from similar data
published in the Chemical Market Reporter, which became ICIS Chemical Business in 2005. Production data for
propylene glycol, diethylene glycol, and triethylene glycol are no longer reported in the Guide to the Business of
Chemistry, so data from ICIS Chemical Business on total demand was used with import and export data to estimate
production of these chemicals.
The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted as
CO2. Emissions of CO2 from utilization of antifreeze and deicers are summarized in Table A-74.
29 A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
A-95
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Table A-74: C Emitted from Utilization of Antifreeze and Deicers tTg Clh Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
C Emissions L2 1.3 1.5 1.2 1.3 1.2 1.4 1.2 1.3 1.2 1.0 0.9
Food Additives
Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring
agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds.
These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants
where they are degraded by the wastewater treatment processes to CO2 or to otherwise biodegrade to CO2. Certain food
additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic feedstocks. Food
additives that are derived from biogenic feedstocks are accounted for in the Land Use, Land-Use Change and Forestry
chapter.
Annual production data for food additive compounds were obtained from the Guide to the Business of Chemistry
(ACC 2010). Import and export data were used to adjust annual production data to annual consumption data. The
percentage of the annual consumption of food additive compounds was estimated from Chemical Profiles data published
on The Innovation Group website30 and from similar data published in the Chemical Market Reporter, which became ICIS
Chemical Business in 2005.31 Production data for several food additive compounds are no longer reported in the Guide to
the Business of Chemistry, so data from ICIS Chemical Business on total demand was used with import and export data to
estimate production of these chemicals. The consumption of synthetic food additives is assumed to be 100 percent emitted
as CO2. Emissions of CO2 from utilization of synthetic food additives are summarized in Table A-75.
Table A-75: C Emitted from Utilization of Food Additives [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
C Emissions 0.6 0.7 0.7 0.6 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8
Silicones
Silicone compounds (e.g., polymethyl siloxane) are used as sealants and in manufactured products. Silicone
compounds are manufactured from petrochemical feedstocks including methyl chloride. It is assumed that petrochemical
feedstocks used to manufacture silicones are incorporated into the silicone products and not emitted as CO2 in the
manufacturing process. It is also assumed that the C contained in the silicone products is stored, and not emitted as CO2.
Annual production data for each silicone manufacturing compound were obtained from the Guide to the Business
of Chemistry (ACC 2010). Import and export data were used to adjust annual production data to annual consumption data.
The percentage of the annual consumption of each silicone manufacturing compound was estimated from Chemical
Profiles data published on The Innovation Group website and from similar data published in the Chemical Market
Reporter, which became ICIS Chemical Business in 2005. The consumption of silicone manufacturing compounds is
assumed to be 100 percent stored, and not emitted as CO2. Storage of silicone manufacturing compounds is summarized
in Table A-76.
Table A-76: C Stored in Silicone Products [Tg C0? Eq.l
1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
C Storage 0_3 0.4 0.4 0.4 0.5 0.5 0.5 0.4 0.5 0.5 0.5 0.5
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the feedstocks C storage factor and the quantity of C emitted from feedstocks in 2009. The
Tier 2 analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of
uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates
were determined using assumptions based on source category knowledge. Uncertainty estimates for production data (the
majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20 percent in the
underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of imports and
30 http://www.the-innovation-group.com/ChemProfiles
3! http://www.icis.com/home/default.aspx
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exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils (>401 deg. F) to
reflect the additional uncertainty in the assignment of part of the production quantity to industrial processes. A relatively
narrow uniform distribution ±1 percent to ±15 percent, depending on the fuel type) was applied to each C coefficient.
The Monte Carlo analysis produced a storage factor distribution that approximates a normal curve, with a mean
of 58 percent, a standard deviation of 1 percent, and the 95 percent confidence interval of 56 percent and 60 percent. This
compares to the calculated Inventory estimate of 58 percent. The analysis produced a C emission distribution
approximating a normal curve with a mean of 78.8 Tg CO2 Eq., standard deviation of 8.5 Tg CO2 Eq., and 95 percent
confidence limits of 63.4 and 96.1 Tg CO2 Eq. This compares with a calculated Inventory estimate of 79.3 Tg CO2 Eq.
The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as a
result of the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on an
analysis of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions (e.g.,
volatile organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate processes,
the current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As the production
statistics that drive the storage factors are relatively well-characterized, this approach yields a result that is probably biased
toward understating uncertainty.
As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the characterization
of C flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other coal, natural gas for
non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on assuming that the
ultimate fates of all of these fuel types —in terms of storage and emissions—are similar. In addition, there are
uncertainties associated with the simplifying assumptions made for each end use category C estimate. Generally, the
estimate for a product is subject to one or more of the following uncertainties:
• The value used for estimating the C content has been assumed or assigned based upon a representative
compound.
• The split between C storage and emission has been assumed based on an examination of the environmental fate
of the products in each end use category.
• Environmental fates leading to emissions are assumed to operate rapidly, i.e., emissions are assumed to occur
within one year of when the fossil C enters the non-energy mass balance. Some of the pathways that lead to
emissions as CO2 may actually take place on a time-scale of several years or decades. By attributing the
emissions to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a non-
energy fuel use and thus starts being tracked by EIA), this approach has the effect of "front-end loading" the
emission profile.
Another cross-cutting source of uncertainty is that for several sources the amount of C stored or emitted was
calculated based on data for only a single year. This specific year may not be representative of storage for the entire
Inventory period. Sources of uncertainty associated with specific elements of the analysis are discussed below.
Import and export data for petrochemical feedstocks were obtained from EIA, the National Petroleum Refiners
Association, and the U.S. BoC for the major categories of petrochemical feedstocks (EIA 2001a, NPRA 2001, and U.S.
BoC 2006). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle
differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is
accurate and the approach used here may underestimate or overestimate net exports of C.
Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for
energy uses. However, for those fuels where IPCC storage factors are used, this "oxidation factor" may be inherent in the
storage factor applied when calculating emissions from non-energy consumption, which would result in a double-counting
of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore application of
oxidation factors to non-energy uses may result in a slight underestimation of C emissions from non-energy uses.
The major uncertainty in using the TRI data is the possibility of double counting emissions that are already
accounted for in the NMVOC data (see above) and in the storage and emission assumptions used. The approach for
predicting environmental fate simplifies some complex processes, and the balance between storage and emissions is very
sensitive to the assumptions on fate. Extrapolating from known to unknown characteristics also introduces uncertainty.
The two extrapolations with the greatest uncertainty are: 1) that the release media and fate of the off-site releases were
assumed to be the same as for on-site releases, and 2) that the C content of the least frequent 10 percent of TRI releases
was assumed to be the same as for the chemicals comprising 90 percent of the releases. However, the contribution of
these chemicals to the overall estimate is small. The off-site releases only account for 3 percent of the total releases, by
weight, and, by definition, the less frequent compounds only account for 10 percent of the total releases.
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The principal sources of uncertainty in estimating CO2 emissions from solvent evaporation and industrial
NMVOC emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial
NMVOC emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate
or overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific solvent
compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content for
industrial emissions has more uncertainty, however, as it is calculated from the average C content of an average volatile
organic compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a).
Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the composition
of combusted hazardous wastes, including the characterization that hazardous wastes are similar to mixtures of water,
noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C that enters
hazardous waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash—but given that
the destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99 percent, this is a very
minor source of uncertainty. C emission estimates from hazardous waste should be considered central value estimates that
are likely to be accurate to within +50 percent.
The amount of feedstocks combusted for energy recovery was estimated from data included in the Manufacturers
Energy Consumption Surveys (MEGS) for 1991, 1994, 1998, 2002, and 2006 (EIA 1994, 1997, 2001, 2005, 2010a).
MECS is a comprehensive survey that is conducted every four years and intended to represent U.S. industry as a whole,
but because EIA does not receive data from all manufacturers (i.e., it is a sample rather than a census), EIA must
extrapolate from the sample. Also, the "other" fuels are identified in the MECS data in broad categories, including
refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and blast furnace gases; and
other uncharacterized fuels. Moreover, the industries using these "other" fuels are also identified only in broad categories,
including the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing
sectors. The "other" fuel consumption data are reported in BTUs (energy units) and there is uncertainty concerning the
selection of a specific conversion factor for each broad "other" fuel category to convert energy units to mass units. Taken
as a whole, the estimate of energy recovery emissions probably introduces more uncertainty than any other element of the
non-energy analysis.
Uncertainty in the C storage estimate for plastics arises primarily from four factors. First, production of some
plastic resins is not tracked directly and must be estimated based on other market data. Second, the raw data on
production for several resins include Canadian and/or Mexican production and may overestimate the amount of plastic
produced from U.S. fuel feedstocks; this analysis includes adjustments to "back out" the Canadian and Mexican values,
but these adjustments are approximate. Third, the assumed C content values are estimates for representative compounds,
and thus do not account for the many formulations of resins available. This uncertainty is greater for resin categories that
are generic (e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g., polypropylene,
polyethylene). Fourth, the assumption that all of the C contained in plastics is stored ignores certain end uses (e.g.,
adhesives and coatings) where the resin may be released to the atmosphere; however, these end-uses are likely to be small
relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The
value does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable goods,
or containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also uncertainties as to
the assignment of C content values; however, they are much smaller than in the case of plastics. There are probably fewer
variations in rubber formulations than in plastics, and the range of potential C content values is much narrower. Lastly,
assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or degradation during
product lifetimes. However, the proportion of the total C that is released to the atmosphere during use is probably
negligible.
A small degree of uncertainty arises from the assignment of C content values in textiles; however, the magnitude
of this uncertainty is less than that for plastics or rubber. Although there is considerable variation in final textile products,
the stock fiber formulations are standardized and proscribed explicitly by the Federal Trade Commission.
For pesticides, the largest source of uncertainty involves the assumption that an active ingredient's C is either 0
percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-ingredient
molecular structure, and not on compound-specific environmental data. The mechanism by which a compound is bound
or released from soils is very complicated and can be affected by many variables, including the type of crop, temperature,
application method, and harvesting practice. Another smaller source of uncertainty arises from the C content values
applied to the unaccounted for portion of active ingredient. C contents vary widely among pesticides, from 7 to 72
percent, and the remaining pesticides may have a chemical make-up that is very different from the 32 pesticides that have
A-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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been examined. Additionally, pesticide consumption data were only available for 1987, 1993, 1995, 1997, 1999, and
2001; the majority of the time series data were interpolated or held constant at the latest (2001) value. Another source of
uncertainty is that only the "active" ingredients of pesticides are considered in the calculations; the "inactive" ingredients
may also be derived from petrochemical feedstocks.
It is important to note that development of this uncertainty analysis is a multi-year process. The current
feedstocks analysis examines NEU fuels that end in storage fates Thus only C stored in pesticides, plastics, synthetic
fibers, synthetic rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in
the uncertainty estimate above. In the future this analysis will be expanded to include the uncertainty surrounding emitted
fates in addition to the storage fates. Estimates of variable uncertainty will also be refined where possible to include fewer
assumptions. With these major changes in future Inventories, the uncertainty estimate is expected to change, and likely
increase. An increase in the uncertainty estimate in the coming years will not indicate that the Inventory calculations have
become less certain, but rather that the methods for estimating uncertainty have become more comprehensive; thus,
potential future changes in the results of this analysis will reflect a change in the uncertainty analysis, not a change in the
Inventory quality.
Asphalt and Road Oil
Asphalt is one of the principal non-energy uses of fossil fuels. The term "asphalt" generally refers to a mixture
of asphalt cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this analysis,
"asphalt" is used interchangeably with asphalt cement, a residue of crude oil. Though minor amounts of C are emitted
during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below.
Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of
asphalt paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. HMA, which
makes up 90 percent of total asphalt paving (EPA 2001), contains asphalt cement mixed with an aggregate of rock
materials. Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha).
Emulsified asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt
cement, accounting for approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C
in asphalt roofing; it was assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type
was multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic
compounds [VOCs], carbon monoxide [CO], poly cyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs],
and phenol) from HMA paving, using emission factors reported in EPA (2001) and total HMA production.32 The next
step was to estimate the C content of the organic emissions. This calculation was based on the C content of CO and
phenol, and an assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of
(1) the proportion of asphalt cement in asphalt paving, assumed to be 8 percent asphalt cement content based on EPA
(2001), and (2) the proportion of C in asphalt cement. For the latter factor, all paving types were characterized as having a
mass fraction of 85 percent C in asphalt cement, based on the assumption that asphalt is primarily composed of saturated
paraffinic hydrocarbons. By combining these estimates, the result is that over 99.99 percent of the C in asphalt cement
was retained (i.e., stored), and less than 0.01 percent was emitted.
Cut-back asphalt is produced in three forms: rapid, medium, and slow cure. The production processes for all
three forms emit C primarily from the volatile petroleum distillate used in the process as a diluent to thin the asphalt
cement so that it can be applied more readily (EPA 2001).
A mass balance on C losses from asphalt was constructed by first estimating the amount of carbon emitted as
VOCs. Values for medium cure asphalt are used to represent all cut-back asphalt. The average weight of distillates used in
medium cure cut-back asphalt (35 percent) is multiplied by the loss rate (as emissions of VOCs) of 70 percent from the
Emissions Inventory Guidebook to arrive at an estimate that 25 percent of the diluent is emitted (Environment Canada
2006). Next, the fraction of C in the asphalt/ diluent mix that is emitted was estimated, assuming 85 percent C content;
this yields an overall storage factor of 93.5 percent for cut-back asphalt.
32 The emission factors are expressed as a function of asphalt paving tonnage (i.e., including the rock aggregate as well as the asphalt
cement).
A-99
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One caveat associated with this calculation is that it is possible that the carbon flows for asphalt and diluent
(volatile petroleum distillate) are accounted for separately in the EIA statistics on fossil fuel flows, and thus the mass
balance calculation may need to re-map the system boundaries to correctly account for carbon flows. EPA plans to re-
evaluate this calculation in the future.
It was assumed that there was no loss of C from emulsified asphalt (i.e., the storage factor is 100 percent) based
on personal communication with an expert from Akzo Nobel Coatings, Inc. (James 2000).
Data on asphalt and road oil consumption and C content factors were supplied by EIA. Hot mix asphalt
production and emissions factors, and the asphalt cement content of HMA were obtained from "Hot Mix Asphalt Plants
Emissions Assessment Report" from EPA's AP-42 (EPA 2001) publication. The consumption data for cut-back and
emulsified asphalts were taken from a Moulthrop, et al. study used as guidance for estimating air pollutant emissions from
paving processes (EIIP 2001). "Asphalt Paving Operation" AP-42 (EPA 2001) provided the emissions source information
used in the calculation of the C storage factor for cut-back asphalt. The storage factor for emulsified asphalt was provided
by Alan James of Akzo Nobel Coatings, Inc. (James 2000).
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2009. The Tier 2
analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of
uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates
were determined using assumptions based on source category knowledge. Uncertainty estimates for asphalt production
were assumed to be ±20 percent, while the asphalt property variables were assumed to have narrower distributions. A
narrow uniform distribution, with maximum 5 percent uncertainty (± 5 percent) around the mean, was applied to the C
content coefficient.
The Monte Carlo analysis produced a tight distribution of storage factor values, with the 95 percent confidence
interval of 99.1 percent and 99.8 percent, with the mean value of 99.5 percent. This compares to the storage factor value
used in the Inventory of 99.6 percent. The analysis produced a C emission distribution with a mean of 0.31 Tg CO2 Eq.,
standard deviation of 0.13 and 95 percent confidence limits of 0.12 Tg CO2 Eq. and 0.63 Tg CO2 Eq. This compares with
an Inventory calculated estimate of 0.29 Tg CO2Eq.
The principal source of uncertainty is that the available data are from short-term studies of emissions associated
with the production and application of asphalt. As a practical matter, the cement in asphalt deteriorates over time,
contributing to the need for periodic re-paving. Whether this deterioration is due to physical erosion of the cement and
continued storage of C in a refractory form or physicochemical degradation and eventual release of CO2 is uncertain.
Long-term studies may reveal higher lifetime emissions rates associated with degradation.
Many of the values used in the analysis are also uncertain and are based on estimates and professional judgment.
For example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the range is
variable—from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly 2000).
Over this range, the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly, changes
in the assumed C content of asphalt cement would have only a minor effect.
The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994. More
recent trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in emulsified
asphalt use as a substitute. This change in trend would indicate an overestimate of emissions from asphalt.
Future improvements to this uncertainty analysis, and to the overall estimation of a storage factor for asphalt,
include characterizing the long-term fate of asphalt.
Lubricants
Lubricants are used in industrial and transportation applications. They can be subdivided into oils and greases,
which differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate.
According to EIA (2010b), the C content from U.S. production of lubricants in 2009 was approximately 5.3 Tg C. Based
on apportioning oils and greases to various environmental fates, and characterizing those fates as resulting in either long-
term storage or emissions, the overall C storage factor was estimated to be 9.2 percent; thus, emissions in 2009 were about
4.8 TgC, or 17.7TgCO2Eq.
A-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Methodology and Data Sources
For each lubricant category, a storage factor was derived by identifying disposal fates and applying assumptions
as to the disposition of the C for each practice. An overall lubricant C storage factor was calculated by taking a
production-weighted average of the oil and grease storage factors.
Oils
Regulation of used oil in the United States has changed dramatically over the past 20 years.33 The effect of these
regulations and policies has been to restrict landfilling and dumping, and to encourage collection of used oil. The
economics of the petroleum industry have generally not favored re-refining—instead, most of the used oil that has been
collected has been combusted.
Table A-77 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an estimate
of the proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is combustion, either
during initial use or after collection as used oil. Combustion results in 99 percent oxidation to CO2 (EIIP 1999), with
correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm sewers, primarily
by "do-it-yourselfers" who change their own oil, is another fate that results in conversion to CO2 given that the releases are
generally small and most of the oil is biodegraded (based on the observation that land farming—application to soil—is one
of the most frequently used methods for degrading refinery wastes). In the landfill environment, which tends to be
anaerobic within municipal landfills, it is assumed that 90 percent of the oil persists in an undegraded form, based on
analogy with the persistence of petroleum in native petroleum-bearing strata, which is also anaerobic. Re-refining adds a
recycling loop to the fate of oil. Re-refined oil was assumed to have a storage factor equal to the weighted average for the
other fates (i.e., after re-refining, the oil would have the same probability of combustion, landfilling, or dumping as virgin
oil), that is, it was assumed that about 97 percent of the C in re-refined oil is ultimately oxidized. Because of the
dominance of fates that result in eventual release as CO2, only about 3 percent of the C in oil lubricants goes into long-
term storage.
Table A-77: Commercial and Environmental Fate of Oil Lubricants (Percent)
Fate of Oil Portion of Total Oil
Combusted During Use
Not Combusted During Use
Combusted as Used Oil
Dumped on the ground or in storm sewers
Landfilled
Re-refined into lube oil base stock and other products
Weighted Average
20%
80%
64%
6%
2%
8%
C Stored
0.2%
2.7%
0.6%
1.8%
0.2%
2.9%
* (e.g., in boilers or space heaters)
- Not applicable
Greases
Table A-78 provides analogous estimates for lubricant greases. Unlike oils, grease is generally not combusted
during use, and combustion for energy recovery and re-refining is thought to be negligible. Although little is known about
the fate of waste grease, it was assumed that 90 percent of the non-combusted portion is landfilled, and the remainder is
dumped onto the ground or storm sewers. Because much of the waste grease will be in containers that render it relatively
inaccessible to biodegradation, and because greases contain longer chain paraffins, which are more persistent than oils, it
was assumed that 90 percent and 50 percent of the C in landfilled and dumped grease, respectively, would be stored. The
overall storage factor is 82 percent for grease.
33 For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" web site ()
has over 50 entries on used oil regulation and policy for 1994 through 2000.
A-101
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Table A-78: Commercial and Environmental Fate of Grease Lubricants [Percent!
Fate of Grease Portion of Total Grease C Stored
Combusted During Use 5% 0.1%
Not Combusted During Use 95% 81.7%
Landfilled 86% 77.0%
Dumped on the ground or in storm
sewers 10% 4.8%
Weighted Average - 81.8%
- Not applicable
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average for
lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S.
Census Bureau does maintain records of the value of production of lubricating oils and lubricating greases. These were
retrieved from the relevant industry series summaries from the 1997 Economic Census (Census Bureau 1999). Assuming
that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil, 8 percent
grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an overall
storage factor of 9.2 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from
lubricants in 2009. The Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for the activity variables; thus,
uncertainty estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for
oil and grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty
estimates for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6
percent uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4
percent and 17 percent around a mean value of 10 percent. This compares to the calculated Inventory estimate of 9
percent. The analysis produced a C emission distribution approximating a normal curve with a mean of 17.5 Tg CO2 Eq.,
standard deviation of 1.5 and 95 percent confidence limits of 14.6 Tg CO2 Eq. and 20.5 Tg CO2 Eq.. This compares with
an Inventory calculated estimate of 17.7 Tg CO2Eq..
The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use,
post-use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment.
There is no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate
of the commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but
also introduce uncertainty in the estimate.
The assumption that the mass of oil and grease can be divided according to their value also introduces
uncertainty. Given the large difference between the storage factors for oil and grease, changes in their share of total
lubricant production have a large effect on the weighted storage factor.
Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored
include further refinement of the uncertainty estimates for the individual activity variables.
Waxes
Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature
increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived from animals,
plants, and lignite [coal] are also used. An analysis of wax end uses in the United States, and the fate of C in these uses,
suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
A-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Methodology and Data Sources
The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year
by end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in
packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest
growing end use, is candles, followed by construction materials and firelogs. Table A-79 categorizes some of the wax end
uses, which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically
modified wax substances, and other miscellaneous wax uses (NPRA 2002)
Table A-79: Emissive and Non-emissive [Storage! Fates of Waxes: Uses by Fate and Percent of Total Mass
Use
Packaging
Non-packaging
Candles
Construction Materials
Firelogs
Cosmetics
Plastics
Tires/Rubber
Hot Melts
Chemically Modified
Other
Total
Emissive
6%
36%
18%
4%
7%
1%
1%
1%
1%
0%
2%
42%
Non-emissive
24%
34%
2%
14%
+
2%
2%
1%
1%
1%
9%
58%
+ Does not exceed 0.5 percent
A C storage factor for each wax end use was estimated and then summed across all end uses to provide an overall
C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax
products are assumed to have the same C content. Table A-80 categorizes wax end uses identified by the NPRA, and lists
each end use's estimated C storage factor.
Table A-80: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total G Mass Stored
Percent of Percent of C Percent of Total
Total Wax Stored C Mass Stored
Use Mass
Packaging
Non-Packaging
Candles
Construction Materials
Firelogs
Cosmetics
Plastics
Tires/Rubber
Hot Melts
Chemically Modified
Other
Total
30%
20%
18%
7%
3%
3%
3%
3%
1%
12%
100%
79%
10%
79%
1%
79%
79%
47%
50%
79%
79%
NA
24%
2%
14%
2%
2%
1%
1%
1%
9%
58%
+ Does not exceed 0.5 percent
Source, mass percentages: NPRA 2002. Estimates of percent stored are based on professional judgment, ICF Consulting.
Note: Totals may not sum due to independent rounding.
Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and
explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles
combust during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90
percent of C contained in candles is emitted as CO2. In firelogs, petroleum wax is used as a binder and as a fuel, and is
combusted during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C
contained in hotmelts is assumed to be emitted as CO2 as heat is applied to these products during use. It is estimated that
50 percent of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30
percent of annual wax production (NPRA 2002).
All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
assumed to remain in the product (i.e., it is assumed that there are no emissions of CO2 from wax during the production of
the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
A-103
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corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
degrade rapidly in an aerobic environment, producing CO2; however, because packaging products ultimately enter landfills
typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill.
In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as
particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product,
which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in
landfills or be combusted (EPA 2003).
The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to
enter the municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
assigned a C storage factor of 79 percent.
Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately
recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of
rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for
waxes, wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the estimates of the wax C storage factor and the quantity of C emitted from wax in 2009. A Tier 2 analysis
was performed to allow the specification of probability density functions for key variables, within a computational
structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty
were not available directly from the information sources for the activity variables; thus, uncertainty estimates were
determined using assumptions based on source category knowledge. Uncertainty estimates for wax variables were
assumed to have a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied to
total consumption of waxes and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values fell
within the range of 49 percent and 71 percent, around the mean value of about 60 percent. This compares to the calculated
Inventory estimate of 58 percent. The analysis produced an emission distribution, with the 95 percent confidence interval
values of 0.3 Tg CO2 Eq. and 0.7 Tg CO2 Eq., with a mean value of 0.4 Tg CO2 Eq. This compares with a calculated
Inventory estimate of 0.4 Tg CO2 Eq., which falls within the range of 95 percent confidence limits established by this
quantitative uncertainty analysis. Uncertainty associated with the wax storage factor is considerable due to several
assumptions pertaining to wax imports/exports, consumption, and fates.
Miscellaneous Products
Miscellaneous products are defined by the U.S. Energy Information Administration as: "all finished [petroleum]
products not classified elsewhere, e.g., petrolatum; lube refining byproducts (e.g., aromatic extracts and tars); absorption
oils; ram-jet fuel; petroleum rocket fuel; synthetic natural gas feedstocks; and specialty oils."
Methodology and Data Sources
Data are not available concerning the distribution of each of the above-listed subcategories within the
"miscellaneous products" category. However, based on the anticipated disposition of the products in each subcategory, it is
assumed that all of the C content of miscellaneous products is emitted rather than stored. Petrolatum and specialty oils
(which include greases) are likely to end up in solid waste or wastewater streams rather than in durable products, and
would be emitted through waste treatment. Absorption oil is used in natural gas processing and is not a feedstock for
manufacture of durable products Jet fuel and rocket fuel are assumed to be combusted in use, and synthetic natural gas
feedstocks are assumed to be converted to synthetic natural gas that is also combusted in use. Lube refining byproducts
could potentially be used as feedstocks for manufacture of durable goods, but such byproducts are more likely to be used
in emissive uses. Lube refining byproducts and absorption oils are liquids and are precluded from disposal in landfills.
Because no sequestering end uses of any of the miscellaneous products subcategories have been identified, a zero percent
storage factor is assigned to miscellaneous products. According to EIA (2010b), the C content of miscellaneous petroleum
A-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
products in 2009 was approximately 20.3 Tg C/QBtu. One hundred percent of the C content is assumed to be emitted to
the atmosphere, where it is oxidized to CO2.
Uncertainty
A separate uncertainty analysis was not conducted for miscellaneous products, though this category was included
in the uncertainty analysis of other non-energy uses discussed in the following section.
Other Non-Energy Uses
The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking
coal and distillate fuel oil, storage factors were taken from IPCC (2006), which in turn draws from Marland and Rotty
(1984). These factors are 0.1 and 0.5, respectively.
IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke, miscellaneous
products, and other petroleum), and assumptions were made based on the potential fate of C in the respective NEUs.
Specifically, the storage factor for petroleum coke is 0.3, based on information from Huurman (2006) indicating that
petroleum coke is used in the Netherlands for production of pigments, with 30% being stored long-term. EIA defines
"miscellaneous products" as "all finished products not classified elsewhere (e.g., petrolatum, lube refining byproducts
(aromatic extracts and tars), absorption oils, ram-jet fuel, petroleum rocket fuels, synthetic natural gas feedstocks, and
specialty oils)." All of these uses are emissive, and therefore the storage factor for miscellaneous products is set at zero
(EIA 20010b). The "other petroleum" category is reported by U.S. territories and accounts mostly for the same products
as miscellaneous products, but probably also includes some asphalt, known to be non-emissive. The exact amount of
asphalt or any of the other miscellaneous products is confidential business information, but based on judgment the storage
factor for this category was estimated at 0.1.
For all these fuel types, the overall methodology simply involves multiplying C content by a storage factor,
yielding an estimate of the mass of C stored. To provide a complete analysis of uncertainty for the entire NEU
subcategory, the uncertainty around the estimate of "other" NEUs was characterized, as discussed below.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty
surrounding the weighted average of the remaining fuels' C storage factors and the total quantity of C emitted from these
other fuels in 2009. A Tier 2 analysis was performed to allow the specification of probability density functions for key
variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or
expert judgments of uncertainty were not available directly from the information sources for some of the activity variables;
thus, uncertainty estimates were determined using assumptions based on source category knowledge. A uniform
distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to be
normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest uncertainty
range of 10 percent (±10%) around the inventory value, was applied to coking coal and miscellaneous products. C
coefficients for distillate fuel oil ranged from 18.5 to 21.1 Tg C/QBtu. The fuel-specific storage factors were assigned
wide triangular distributions indicating greater uncertainty.
The Monte Carlo analysis produced a storage factor distribution with 95 percent confidence limits of 16 percent
and 66 percent, with a mean of 39 percent. This compares to the Inventory calculation of weighted average (across the
various fuels) storage factor of about 17 percent. The analysis produced an emission distribution, with the 95 percent
confidence limit of 10.3 Tg CO2 Eq. and 27.0 Tg CO2 Eq., and a mean of 18.9 Tg CO2 Eq.. This compares with the
Inventory estimate of 25.7 Tg CO2 Eq., which falls closer to the upper boundary of the 95 percent confidence limit. The
uncertainty analysis results are driven primarily by the very broad uncertainty inputs for the storage factors.
References
ACC (2010) "Guide to the Business of Chemistry, 2010" American Chemistry Council.
ACC (2007-2009) "PIPS Year-End Resin Statistics: Production, Sales and Captive Use,"
http://www.americanchemistry.com/s_acc/secj)olicyissues.asp?CID=996&DID=6872
APC (2003-2006) "APC Year-End Statistics," http://www.americanplasticscouncil.org/s_apc/docs/1700/1678.pdf
APC (2001) as cited in ACS (2001) "Production: slow gains in output of chemicals and products lagged behind U.S.
economy as a whole" Chemical & Engineering News.
A-105
-------
APC (2000) Facts and Figures, Chemical & Engineering News, June 26,2000.
Bank of Canada (2009) Financial Markets Department Year Average of Exchange Rates. Available online at:
http: //www. bankofcanada. ca/pdf/nraaO 9. pdf
Bureau of Labor Statistics (2009) Producer Price Index Industry Data: Soap and Other Detergent Manufacturing.
Available online at: http://data.bls.gov:8080/PDQ/outside.jsp?survey=pc
Davie, IN., J.P. Winter, andR.P. Varoney (1995) "Decomposition of Coated Papers from a Quick Service Restaurant."
Technical Association for Pulp andPaper Industry Journal. Vol 78 (5): 127-130.
Davie, IN. (1993) "Compostability of Petroleum Wax-based Coatings." Technical Association for Pulp and Paper
Industry Journal. Vol 76 (2): 167-170.
EIA (2010a) EIA Manufacturing Consumption of Energy (MECS) 2006, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (2010b) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, September 2010, Energy
Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2010/9).
EIA (2005) EIA Manufacturing Consumption of Energy (MECS) 2002, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (2001) EIA Manufacturing Consumption of Energy (MECS) 1998, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (1997) EIA Manufacturing Consumption of Energy (MECS) 1994, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
EIA (1994) EIA Manufacturing Consumption of Energy (MECS) 1991, U.S. Department of Energy, Energy Information
Administration, Washington, DC.
Eldredge-Roebuck (2000) Personal communication between Joe Casola, ICF Consulting and Brandt Eldredge-Roebuck,
American Plastics Council, 11 July 2000.
EIIP (2001) "Area Sources" Asphalt Paving, Emissions Inventory Improvement Program: State and Taerritorial Air
Pollution Program Administrators/Association of Local Air Pollution Control Officials and US EPA, EIIP Document
Series Vol. Ill, Ch. 17. (STAPPA/ALAPCO/EPA), Washington DC, January 2001.
http://www.epa.gov/ttn/chief/eiip/techreport/volume03/index.html.
EIIP (1999) Methods for Estimating Greenhouse Gas Emissions from Combustion of Fossil Fuels. Emissions Inventory
Improvement Program: State and Territorial Air Pollution Program Administrators/Association of Local Air Pollution
Control Officials and U.S. Environmental Protection Agency, EIIP Document Series Volume VIII, Chapter 1,
STAPPA/ALAPCO/EPA, Washington, DC. August 2000. Available online at
. 28 August 2000.
Environment Canada (2006). Emissions Inventory Guidebook v 1.3. Criteria Air Contaminants Division: Quebec, Canada.
Available online at: http://www.eea.europa.eu/publications/EMEPCORTNAIR5/B461 lvsl.3.pdf.
EPA (1996-2003a, 2005, 2007b, 2008, 2009a, 201V) Municipal Solid Waste in the United States: Facts and Figures.
Office of Solid Waste and Emergency Response, U.S. Environmental Protection Agency, Washington, DC. Available
online at .
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009b). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available online at
EPA (2007a) Biennial Reporting System (BRS) Database. U.S. Environmental Protection Agency, Envirofacts
Warehouse. Washington, DC. Available online at . Data for 2001-2007 are
current as of Sept. 9, 2009.
EPA (2006a) Air Emissions Trends - Continued Progress Through 2005. U.S. Environmental Protection Agency,
Washington DC. December 19, 2006.
A-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
EPA (2004) EPA's Pesticides Industry Sales and Usage, 2000 and 2001 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/. Accessed September 2006.
EPA (2003b) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (2002) EPA's Pesticides Industry Sales and Usage, 1998 and 1999 Market Estimates, table 3.6.
(http://www.epa.gov/oppbeadl/pestsales/99pestsales/market_estimatesl999.pdf). Accessed July 2003.
EPA (2001) AP 42, Volume I, Fifth Edition. Chapter 11: Mineral Products Industry. Available online at
http: //www. epa. gov/ttn/chief/ap42/ch 11 /index. html
EPA (2000a) Biennial Reporting System (BRS). U.S. Environmental Protection Agency, Envirofacts Warehouse.
Washington, DC. Available online at .
EPA (2000b) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental
Information, Office of Information Analysis and Access, Washington, DC. Available online at
.
EPA (1999) EPA's Pesticides Industry Sales and Usage, 1996-1997 Market Estimates and
http://www.epa.gov/oppbeadl/pestsales/97pestsales/market_estimates 1997.pdf.
EPA (1998) EPA's Pesticides Industry Sales and Usage, 1994-1995 Market Estimates
http://www.epa.gov/oppbeadl/pestsales/95pestsales/market_estimates 1995.pdf.
FEB (2010) Fiber Economics Bureau, as cited in C&EN (2010) Output Declines in U.S., Europe. Chemical &
Engineering News, American Chemical Society, 6 July. Available online at http://www.cen-online.org.
FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions. Chemical &
Engineering News, American Chemical Society, 6 July. Available online at http://www.cen-online.org.
FEB (2007) Fiber Economics Bureau, as cited in C&EN (2007) Gains in Chemical Output Continue. Chemical &
Engineering News, American Chemical Society. July 2, 2007. Available online at .
FEB (2005) Fiber Economics Bureau, as cited in C&EN (2005) Production: Growth in Most Regions. Chemical &
Engineering News, American Chemical Society, 11 July. Available online at http://www.cen-online.org.
FEB (2003) Fiber Economics Bureau, as cited in C&EN (2003) Production Inches Up in Most Countries. Chemical &
Engineering News, American Chemical Society, 7 July. Available online at http://www.cen-online.org.
FEB (2001) Fiber Economics Bureau, as cited in ACS (2001) Production: slow gains in output of chemicals and products
lagged behind U.S. economy as a whole Chemical & Engineering News, American Chemical Society, 25 June.
Available online at http://pubs.acs.org/cen
Financial Planning Association (2006) Canada/US Cross-Border Tools: US/Canada Exchange Rates. Available online at:
http://www.fpanet.org/global/planners/US_Canada_ex_rates.cfm. Accessed August 16, 2006.
Gosselin, Smith, and Hodge (1984), "Clinical Toxicology of Commercial Products." Fifth Edition, Williams & Wilkins,
Baltimore.
Huurman, J. W.F. (2006) Recalculation of Dutch Stationary Greenhouse Gas Emissions Based on sectoral Energy
Statistics 1990-2002. Statistics Netherlands, Voorburg, The Netherlands.
IGI (2002) 100 Industry Applications. The International Group Inc. Available online at:
http://www.igiwax.com/100_apps.html Toronto, Ontario.
IISRP (2003) "IISRP Forecasts Moderate Growth in North America to 2007" International Institute of Synthetic Rubber
Producers, Inc. New Release; available online at: .
IISRP (2000) "Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and RMA" International Institute of
Synthetic Rubber Producers press release.
INEGI (2006) Produccion bruta total de las unidades economicas manufactureras por Subsector, Rama, Subrama y Clase
de actividad. http://www.megi.gob.mx/est/contenidos/espanol/proyectos/censos/ce2004/tb_manufacturas.asp.
Accessed August 15.
A-107
-------
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K. Tanabe, eds.; Institute for Global Environmental
Strategies (IGES). Hayama, Kanagawa, Japan.
iPCC/UNEP/OECD/TEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency. Paris, France.
James, A. (2000) Personal communication between Suzanne Bratis of ICF International and Alan James of Akzo Nobel
Coatings, Inc. July 2000. (Tel: 614-294-3361).
Kelly (2000) Personal communication between Tom Smith, ICF Consulting and Peter Kelly, Asphalt Roofing
Manufacturers Association, August 2000.
Maguire (2004) Personal communication with J. Maguire, National Petrochemicals and Refiners Association. August -
September 2004.
Marland, G., and R.M. Rotty (1984), "Carbon dioxide emissions from fossil fuels: A procedure for estimation and results
for 1950-1982", Tellus 36b:232-261.
NPRA (2002) North American Wax - A Report Card
Rinehart, T. (2000) Personal communication between Thomas Rinehart of U.S. Environmental Protection Agency, Office
of Solid Waste, and Randall Freed of ICF International. July 2000. (Tel: 703-308-4309).
RMA (2009a). Scrap Tire Markets in the United States: 9' Biennial Report. Rubber Manufacturers Association,
Washington, DC. May 2009.
RMA (2009b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
http://www.rma.org/scrap_tires/scrap_tire_markets/scrap_tire_characteristics/ Accessed 17 September 2009.
Schneider, S. (2007) E-mail between Shelly Schneider of Franklin Associates (a division of ERG) and Sarah Shapiro of
ICF International, January 10, 2007.
SPI (2000) The Society of the Plastics Industry Website, http://www.plasticsindustry.org/industry/stat3.htm, Accessed 28
June 2000.
U.S. Bureau of the Census (1994, 1999, 2004, 2009) 1992, 1997, 2002 and 2007 Economic Census. Available online at:
http://factfinder.census.gov/servlet/DatasetMainPageServlet? j)rogram=ECN&_submenuId=&_lang=en&_ts=
U.S. International Trade Commission (1990-2008) http://dataweb.usitc.gov/Accessed September 20
U.S. International Trade Commission (1990-2011) "Interactive Tariff and Trade DataWeb: Quick Query." Available
online at: http://dataweb.usitc.gov/ Accessed January 2011.
Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the
American Chemistry Council, 4 January 2011.
A-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Figure A-1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database
16.0 -i
= National Average
14.0
i I I
970 990 1,010 1,030 1,050 1,070
Energy Content (Btu per Cubic Foot)
1,090
\
1,110
I
1,130
Source: EIA (1994) Energy Information Administration, Emissions of Greenhouse Gases in the United States 1987-1992, U.S. Department of
Energy, Washington, DC, November, 1994, DOE/EIA0573, Appendix A.
-------
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon
Density
24 "I
22 -
l!
•- .1
11
(J c
g€
I"
(J 01
20 -
16 -
'Refer mate
• Llg hi Refo rrnate
HeaĄy Refo rmate
• Catalytic Naphthas
i-hexane ~T~
i-pentane
n-butane j-butane
P ro py le ne
-j- Propane
I
) 15
I
30
I
45
I
60
+ =
Paraffin
Hydro carbons
I
75 90
I I :
105 120 135
I
150
16
Hydrocarbon Density (API Gravity)
Source: Carbon content factors for paraffins are calculated based on the properties of hydrocarbons in V. Guthrie (ed.), Petroleum Products
Handbook (New York: McGraw Hill, 1960) p. 33. Carbon content factors from other petroleum products are drawn from sources described
below. Relationship between density and emission factors based on the relationship between density and energy content in U.S. Department of
Commerce, National Bureau of Standards, Thermal Properties of Petroleum Products, Miscellaneous Publication, No. 97 (Washington, D.C.,
1929), pp.16-21, and relationship between energy content and fuel composition in S. Ringen, J. Lanum, and P.P. Miknis, "Calculating Heating
Values from the Elemental Composition of Fossil Fuels,' Fuel, Vol. 58 (January 1979), p.69.
-------
Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100 -,
95 -
-C
c
85 -
90 -
75 —
70
1 Paraffins
T Cyc lo paraffi re
T Anornatlcs
Benzene V
To I ue ne V n
Xylene
Cyclo pentane
ttttffttttttttfffffttttttttfftttt
n- pe ntane •
"Butane
1 Propane
"Ethane
Metfnare
Gasoline Jet Fuel
LPQ Naphtha Kerosene Diesel
UfceOil FielOil
1
5
1
10
I
15
I
20
I
25
I
30
I
35
NurnberofCarbonAtorns In Molecule
Source: J.M. Hunt, Petroleum Geochemistry and Geology (San Francisco, CA, W.H. Freeman and Company, 1979), pp. 31-37.
-------
-------
ANNEX 3 Methodological Descriptions for
Additional Source or Sink Categories
3.1. Methodology for Estimating Emissions of CELt, NiO, and Indirect
Greenhouse Gases from Stationary Combustion
Estimates of CH4 and N2O Emissions
Methane (CH4) and nitrous oxide (N2O) emissions from stationary combustion were estimated using IPCC
emission factors and methods. Estimates were obtained by multiplying emission factors—by sector and fuel type—by
fossil fuel and wood consumption data. This "top-down" methodology is characterized by two basic steps, described
below. Data are presented in Table A-81 through Table A-85.
Step 1: Determine Energy Consumption by Sector and Fuel Type
Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial,
residential, electric power, and U.S. territories. For CH4 and N2O, estimates were based upon consumption of coal, gas,
oil, and wood. Energy consumption data for the United States were obtained from EIA's Monthly Energy Review,
January 2010 and Published Supplemental Tables on Petroleum Product detail (EIA 2011). Wood consumption data for
the United States was obtained from EIA's Annual Energy Review (EIA 2010). Because the United States does not
include territories in its national energy statistics, fuel consumption data for territories were collected separately from the
EIA from Jacobs (2010).34 Fuel consumption for the industrial sector was adjusted to subtract out construction and
agricultural use, which is reported under mobile sources.35 Construction and agricultural fuel use was obtained from EPA
(2010). The energy consumption data by sector were then adjusted from higher to lower heating values by multiplying by
0.9 for natural gas and wood and by 0.95 for coal and petroleum fuel. This is a simplified convention used by the
International Energy Agency. Table A-81 provides annual energy consumption data for the years 1990 through 2009.
Step 2: Determine the Amount of CH4 and N2O Emitted
Activity data for each sector and fuel type were then multiplied by emission factors to obtain emission estimates.
Emission factors for the residential, commercial, industrial, and electric power sectors were taken from the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). These N2O emission factors by fuel type (consistent
across sectors) were also assumed for U.S. territories. The CH4 emission factors by fuel type for U.S. territories were
estimated based on the emission factor for the primary sector in which each fuel was combusted. Table A-82 provides
emission factors used for each sector and fuel type.
Estimates of NOX, CO, and NMVOC Emissions
Emissions estimates for NOX, CO, and NMVOCs were obtained from preliminary data (EPA 2010b, EPA 2009)
and disaggregated based on EPA (2003), which, in its final iteration, will be published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site.
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels
(i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which
includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of
NOX, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions
were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity
data (e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the
individual categories were obtained from various sources. Depending upon the category, these activity data may include
34 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.
35 Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available to
determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources.
A-109
-------
fuel consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in
conjunction with emission factors that relate the quantity of emissions to the activity. Table A-83 through Table A-85
present indirect greenhouse gas emission estimates for 1990 through 2008.
The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2009) is
represented by the following equation:
Ep>s = As x EFP,S x (l - cyiOO)
Where,
E = Emissions
p = Pollutant
s = Source category
A = Activity level
EF = Emission factor
C = Percent control efficiency
The EPA currently derives the overall emission control efficiency of a category from a variety of sources,
including published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory,
and other EPA databases. The U.S. approach for estimating emissions of NOX, CO, and NMVOCs from stationary
combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/iEA 1997).
A-110 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-81: Fuel Consumption by Stationary Combustion for Calculating CHa and M Emissions tTBtul
Fuel/End-Use Sector
Coal
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Wood
Residential
Commercial
Industrial
Electric Power
U.S. Territories
1990
18,064
3l|
124
1,640 •
16.26 ll
1,ZO? —
375
18,202
4,49 ll
2,682 •
7,72 ll
3,309B
2,216 1
580
66 •
1,442(
1291
NE
1995
19,138
17
117
1,527
17,466
10
5,425
1,261
694
2,254
755
462
21,088
4,954
3,096
8,736
4,302
0
2,370
520
72
1,652
125
NE
1996
20,032
17
122
1,455
18,429
10
5,933
1,397
718
2,566
817
435
21,492
5,354
3,226
9,049
3,862
0
2,437
540
76
1,683
138
NE
1997
20,517
16
129
1,457
18,905
10
5,879
1,334
655
2,519
927
445
21,556
5,093
3,285
9,052
4,126
0
2,371
430
73
1,731
137
NE
1998
20,802
12
93
1,471
19,216
11
5,697
1,207
609
2,129
1,306
445
21,230
4,646
3,083
8,827
4,675
0
2,184
380
64
1,603
137
NE
1999
20,779
14
103
1,373
19,279
10
5,844
1,342
614
2,216
1,211
461
21,254
4,835
3,115
8,402
4,902
0
2,214
390
67
1,620
138
NE
2000
21,682
11
92
1,349
20,220
10
6,047
1,427
694
2,310
1,144
472
22,281
5,105
3,252
8,619
5,293
13
2,262
420
71
1,636
134
NE
2001
21,085
12
97
1,358
19,614
4
6,657
1,463
719
2,567
1,277
632
21,400
4,889
3,097
7,933
5,458
23
2,006
370
67
1,443
126
NE
2002
21,139
12
90
1,244
19,783
11
5,978
1,359
645
2,456
961
557
22,129
5,014
3,225
8,100
5,767
23
1,995
380
69
1,396
150
NE
2003
21,562
12
82
1,249
20,185
34
6,630
1,466
762
2,576
1,205
622
21,550
5,209
3,261
7,806
5,246
27
2,002
400
71
1,363
167
NE
2004
21,713
11
103
1,262
20,305
32
6,881
1,475
767
2,774
1,212
654
21,604
4,981
3,201
7,802
5,595
25
2,121
410
70
1,476
165
NE
2005
22,095
8
97
1,219
20,737
33
6,812
1,369
716
2,869
1,235
623
21,243
4,946
3,073
7,185
6,015
24
2,136
430
70
1,452
185
NE
2006
21,758
6
65
1,188
20,462
37
6,413
1,205
678
3,261
648
621
20,904
4,476
2,902
7,125
6,375
26
2,109
390
65
1,472
182
NE
2007
22,062
8
70
1,130
20,808
47
6,217
1,225
681
3,103
657
552
22,315
4,850
3,094
7,340
7,005
27
2,098
430
69
1,413
186
NE
2008
21,709
8
69
1,083
20,513
36
5,439
1,215
669
2,609
468
479
22,436
4,989
3,211
7,378
6,829
29
2,044
450
73
1,344
177
NE
2009
19,283
7
61
881
18,296
38
4,994
1,197
708
2,195
390
504
21,974
4,852
3,168
6,887
7,038
27
1,891
430
72
1,217
173
NE
NE (Not Estimated)
Note: Totals may not sum due to independent rounding.
A-lll
-------
Table A-82: CHa and N20 Emission Factors by Fuel Type and Sector Ig/GJ)36
Fuel/End-Use Sector
Coal
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Petroleum
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
Electric Power
U.S. Territories
Wood
Residential
Commercial
Industrial
Electric Power
U.S. Territories
CH4
300
10
10
1
1
10
10
3
3
5
5
5
1
1
1
300
300
30
30
NA
N2O
1.5
1.5
1.5
1.5
1.5
0.6
0.6
0.6
0.6
0.6
0.1
0.1
0.1
0.1
0.1
4.0
4.0
4.0
4.0
NA
NA (Not Applicable)
GJ (Gigajoule) = 10 joules. One joule = 9.486xlO'4 Btu
A-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-83: NOx Emissions from Stationary Combustion tGgl
Sector/Fuel Type
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Industrial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal Combustion
Commercial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Residential
Coalb
Fuel Oil"
Natural Gasb
Wood
Other Fuels3
Total
1990
6,045
5,119l
200
513
NA
NA
213
2,559
530
240
877
NA
119
792
671
88
181
NA
366
749
NA
NA
NA
42
707
10,023
1995
5,792
5,061
87
510
NA
NA
134
2,650
541
224
999
NA
111
774
607
35
94
210
NA
269
813
NA
NA
NA
44
769
9,862
1996
5,581
5,079
107
248
5
NA
142
2,666
490
203
900
NA
109
965
734
30
86
224
NA
394
726
NA
NA
NA
27
699
9,707
1997
5,683
5,118
131
277
6
NA
150
2,614
487
196
880
NA
103
948
539
32
88
229
NA
191
699
NA
NA
NA
27
671
9,534
1998
5,637
4,932
202
329
24
NA
149
2,570
475
190
869
NA
104
932
510
34
73
220
NA
183
651
NA
NA
NA
27
624
9,369
1999
5,183
4,437
179
393
33
NA
141
2,283
475
190
706
NA
100
813
483
23
54
156
NA
249
441
NA
NA
NA
25
416
8,390
2000
4,829
4,130
147
376
36
NA
140
2,278
484
166
710
NA
109
809
507
21
52
161
NA
273
439
NA
NA
NA
21
417
8,053
2001
4,453
3,802
149
325
37
NA
140
2,296
518
153
711
NA
116
798
428
21
52
165
NA
189
446
NA
NA
NA
22
424
7,623
2002
4,265
3,634
142
310
36
NA
143
1,699
384
114
526
NA
86
591
438
19
50
157
NA
213
423
NA
NA
NA
21
402
6,825
2003
3,988
3,398
133
290
33
NA
134
1,638
370
110
507
NA
82
569
456
19
49
156
NA
231
421
NA
NA
NA
21
400
6,503
2004
3,711
3,162
124
270
31
NA
124
1,577
356
105
488
NA
79
548
473
19
49
156
NA
249
420
NA
NA
NA
21
399
6,181
2005
3,434
2,926
114
250
29
NA
115
1,515
342
101
469
NA
76
527
490
19
49
155
NA
267
418
NA
NA
NA
20
397
5,858
2006
3,121
2,659
104
227
26
NA
105
1,520
343
102
471
NA
77
528
486
19
49
155
NA
263
417
NA
NA
NA
20
397
5,545
2007
3,007
2,562
100
219
25
NA
101
1,525
344
102
472
NA
77
530
483
19
49
155
NA
261
417
NA
NA
NA
20
396
5,432
2008
2,722
2,319
91
198
23
NA
91
1,530
345
102
474
NA
77
532
479
19
49
154
NA
257
416
NA
NA
NA
20
396
5,148
2009
1,766
1,505
59
128
15
NA
59
1,478
334
99
458
NA
74
514
501
19
49
154
NA
280
414
NA
NA
NA
20
394
4,159
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-res
b Residential coal, fuel oil, and natural gas emissions are included in the '
Note: Totals may not sum due to independent rounding.
idential wood (EPA 2003, 2009, 201 Ob).
'Other Fuels" category (EPA 2003, 2009, 201 Ob).
Table A-84: CO Emissions from Stationary Combustion tGgl
Sector/Fuel Type
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal
Combustion
Industrial
Coal
1990
329
213
18
46
NA
NA
52
797
95
1995
337
227
9
49
NA
NA
52
958
88
1996
369
228
11
72
NA
7
52
1,078
100
1997
385
233
13
76
NA
8
54
1,054
99
1998
410
220
17
88
NA
30
55
1,044
96
1999
450
187
36
150
NA
24
52
1,100
114
2000
439
221
27
96
NA
31
63
1,106
118
2001
439
220
28
92
NA
32
67
1,137
125
2002
595
298
38
125
NA
44
91
1,149
127
2003
590
296
37
124
NA
43
90
1,115
123
2004
586
293
37
123
NA
43
89
1,080
119
2005
582
292
37
122
NA
43
89
1,045
115
2006
598
300
38
126
NA
44
91
1,064
117
2007
616
308
39
129
NA
45
94
1,084
119
2008
633
317
40
133
NA
46
97
1,103
121
2009
631
316
40
132
NA
46
96
1,030
113
A-113
-------
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal
Combustion
Commercial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Residential
Coal"
Fuel Oil"
Natural Gasb
Wood
Other Fuels3
Total
67
205
NA
253
177
205
13
40
NA
136
3,668
NA
NA
NA
3,430 •
238
5,000
64
313
NA
270
222
211
14
17
49
NA
132
3,877
NA
NA
NA
3,629
248
5,383
49
307
NA
316
305
122
13
17
58
NA
34
2,364
NA
NA
NA
2,133
231
3,934
47
307
NA
302
299
126
13
18
59
NA
36
2,361
NA
NA
NA
2,133
229
3,926
46
305
NA
303
294
122
14
15
57
NA
36
2,352
NA
NA
NA
2,133
220
3,928
54
350
NA
286
296
151
16
17
81
NA
36
3,323
NA
NA
NA
3,094
229
5,024
48
355
NA
300
285
151
14
17
83
NA
36
2,644
NA
NA
NA
2,416
228
4,340
45
366
NA
321
279
154
13
17
84
NA
38
2,648
NA
NA
NA
2,424
224
4,377
46
370
NA
325
282
177
15
20
97
NA
44
3,044
NA
NA
NA
2,787
257
4,965
44
359
NA
315
274
173
15
19
95
NA
43
2,981
NA
NA
NA
2,730
252
4,860
43
347
NA
305
265
169
15
19
93
NA
42
2,919
NA
NA
NA
2,672
246
4,753
42
336
NA
296
257
166
14
19
91
NA
41
2,856
NA
NA
NA
2,615
241
4,649
42
342
NA
301
261
166
15
19
91
NA
42
2,867
NA
NA
NA
2,624
242
4,695
43
349
NA
307
266
167
15
19
92
NA
42
2,878
NA
NA
NA
2,635
243
4,744
44
355
NA
312
271
168
15
19
92
NA
42
2,889
NA
NA
NA
2,645
244
4,792
41
331
NA
291
253
158
14
18
87
NA
40
2,725
NA
NA
NA
2,495
230
4,543
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 201 Ob).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2003, 2009, 201 Ob).
Note: Totals may not sum due to independent rounding.
Table A-85: NMVOC Emissions from Stationary Combustion tGgl
Sector/Fuel Type
Electric Power
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal
Combustion
Industrial
Coal
Fuel Oil
Natural gas
Wood
Other Fuels3
Internal
Combustion
Commercial
Coal
Fuel Oil
1990
43
24
5
2
NA
NA
165
7I
11
52
NA
46
49
18
1
1
1995
40
26
2
2
NA
NA
9
187
5
11
66
NA
45
60
21
1
3
1996
44
25
3
7
NA
+
9
163
6
8
54
NA
33
63
22
1
3
1997
47
26
4
7
NA
+
10
160
6
7
54
NA
31
62
22
1
3
1998
51
26
5
9
NA
1
10
159
6
7
54
NA
31
61
21
1
3
1999
49
25
4
9
NA
2
10
156
9
10
52
NA
26
60
25
1
3
2000
56
27
4
12
NA
2
11
157
9
9
53
NA
27
58
28
1
4
2001
55
26
4
12
NA
2
11
160
10
9
54
NA
29
57
29
1
4
2002
44
21
4
10
NA
1
8
138
9
7
47
NA
25
49
61
1
6
2003
44
21
4
10
NA
1
8
132
9
7
45
NA
24
47
53
1
5
2004
44
21
4
10
NA
1
8
126
8
7
43
NA
23
45
45
1
3
2005
44
21
3
10
NA
1
8
121
8
6
41
NA
22
43
33
1
2
2006
44
21
4
10
NA
1
8
120
8
6
41
NA
22
43
36
1
3
2007
45
22
4
10
NA
1
9
119
8
6
40
NA
22
42
38
1
4
2008
45
22
4
10
NA
1
9
118
8
6
40
NA
22
42
40
1
5
2009
46
22
4
10
NA
1
9
110
7
6
37
NA
20
39
23
+
1
A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Natural gas
Wood
Other Fuels3
Residential
Coal"
Fuel Oilb
Natural Gas"
Wood
Other Fuels3
Total
7
NA
8
686
NA
NA
NA
651
35
912
10
NA
8
725
NA
NA
NA
688
37
973
13
NA
5
788
NA
NA
NA
756
33
1,018
13
NA
5
788
NA
NA
NA
757
32
1,017
12
NA
5
786
NA
NA
NA
756
30
1,016
11
NA
10
815
NA
NA
NA
794
21
1,045
14
NA
9
837
NA
NA
NA
809
27
1,077
14
NA
10
837
NA
NA
NA
809
27
1,081
23
NA
31
1,341
NA
NA
NA
1,298
43
1,585
18
NA
29
1,066
NA
NA
NA
1,032
35
1,296
14
NA
27
792
NA
NA
NA
767
26
1,008
9
NA
22
519
NA
NA
NA
502
17
716
12
NA
19
719
NA
NA
NA
695
23
918
16
NA
17
918
NA
NA
NA
888
30
1,120
19
NA
15
1,117
NA
NA
NA
1,081
36
1,321
4
NA
18
244
NA
NA
NA
236
8
424
NA (Not Applicable)
+ Does not exceed 1 Gg.
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 201 Ob).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2003, 2009, 201 Ob).
Note: Totals may not sum due to independent rounding.
A-115
-------
3.2. Methodology for Estimating Emissions of CH4, NiO, and Indirect
Greenhouse Gases from Mobile Combustion and Methodology for and
Supplemental Information on Transportation-Related GHG Emissions
Estimating COj Emissions by Transportation Mode
Transportation-related CO2 emissions, as presented in the Carbon Dioxide Emissions from Fossil Fuel
Combustion section of the Energy chapter, were calculated using the methodology described in Annex 2.1. This section
provides additional information on the data sources and approach used for each transportation fuel type. As noted in
Annex 2.1, CO2 emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor
gasoline based on data sources for individual modes of transportation (considered a "bottom up" approach). For most
other fuel and energy types (i.e., jet fuel, aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), CO2
emissions were calculated based on transportation sector-wide fuel consumption estimates from the Energy Information
Administration (EIA 2007b and EIA 2007 through 2009) and apportioned to individual modes (considered a "top down"
approach).
Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of
"bottom up" data would be more accurate for diesel fuel and motor gasoline, based on the availability of reliable
transportation-specific data sources. A "bottom up" diesel calculation was implemented in the 1990-2005 Inventory, and a
bottom-up gasoline calculation was introduced in the 1990-2006 Inventory for the calculation of emissions from on-road
vehicles. Motor gasoline and diesel consumption data for on-road vehicles come from FHWA's Highway Statistics, Table
VM-1 (FHWA 1996 through 2009), and are based on federal and state fuel tax records. These fuel consumption estimates
were then combined with estimates of fuel shares by vehicle type from DOE's Transportation Energy Data Book (DOE
1993 through 2009) to develop an estimate of fuel consumption for each vehicle type (i.e., passenger cars, light-duty
trucks, buses, medium- and heavy-duty trucks, motorcycles). The on-road gas and diesel fuel consumption estimates by
vehicle type were then adjusted for each year so that the sum of gasoline and diesel fuel consumption across all vehicle
categories matched the fuel consumption estimates in Highway Statistics' Tables MF-21 and MF-27 (FHWA 1996 through
2010). Table MF-21 provided fuel consumption estimates for the most current Inventory year; Table MF-27 provided fuel
consumption estimates for years 1990-2008. This resulted in a final estimate of motor gasoline and diesel fuel use by
vehicle type, consistent with the FHWA total for on-road motor gasoline and diesel fuel use.
Estimates of diesel fuel consumption from rail were taken from the Association of American Railroads (AAR
2008 through 2010) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2010 and
APTA 2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through
2004) and Whorton (2006 through 2009) for Class II and III railroads, and DOE's Transportation Energy Data Book
(DOE 1993 through 2010) for passenger rail. Estimates of diesel from ships and boats were taken from EIA's Fuel Oil
and Kerosene Sales (1991 through 2011).
Since EIA's total fuel consumption estimate for each fuel type is considered to be accurate at the national level,
adjustments needed to be made in the estimates for other sectors to equal the EIA total. In the case of motor gasoline,
estimates of fuel use by recreational boats come from EPA's NONROAD Model (EPA 201 Ob), and these estimates along
with those from other sectors (e.g., commercial sector, industrial sector) were adjusted. Similarly, to ensure consistency
with EIA's total diesel estimate for all sectors, the diesel consumption totals for the residential, commercial, and industrial
sectors were adjusted downward proportionately.
As noted above, for fuels other than motor gasoline and diesel, EIA's transportation sector total was apportioned
to specific transportation sources. For jet fuel, estimates come from: DOT (1991 through 2010) and FAA (2011, 2006) for
commercial aircraft, FAA (2009) for general aviation aircraft, and DESC (2008) for military aircraft. Estimates for
biofuels, including ethanol and biodiesel were discussed separately and were not apportioned to specific transportation
sources. Consumption estimates for biofuels were calculated based on data from the Energy Information Administration
(EIA2010b).
Table A-86 displays estimated fuel consumption by fuel and vehicle type. Table A-87 displays estimated energy
consumption by fuel and vehicle type. The values in both of these tables correspond to the figures used to calculate CO2
emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy
estimates by fuel type, with activity data used to apportion consumption to the various modes of transport. For motor
gasoline, the figures do not include ethanol blended with gasoline; although ethanol is included in FHWA's totals for
reported motor gasoline use. Ethanol is a biofuel and in order to be in line with IPCC methodological guidance and
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
UNFCCC reporting obligations, net carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the
estimates for Land Use, Land-Use Change and Forestry chapter, not in Energy chapter totals. Ethanol and biodiesel
consumption estimates are shown separately in Table A-88.
Table A-86. Fuel Consumption by Fuel and Vehicle Type [million gallons unless otherwise specified!3
Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009
Motor Gasoline1"
Passenger Cars
Light-Duty Trucks
Motorcycles
Buses
110,440.9
69,762.4
34,698.4
194.0
38.9 •
118,216.0 1
67,948.0
44,369.3
200.3
41.7 •
129,101.8
72,859.7 1
50,773.8 I
209.8
43.6
137,294.2
76,059.6
55,336.4
183.4
41.9
132,944.7
71,645.8
55,459.3
212.2
41.7
132,543.8
70,548.0
56,117.9
230.9
43.6
127,524.7
67,022.7
54,889.7
241.5
42.8
127,012.2
66,945.0
54,826.0
241.2
38.9
Medium- and Heavy-
Duty Trucks 4,349.8
Recreational Boats0 1,397.3
Distillate Fuel Oil (Diesel
Fuel) 25,631.2
Passenger Cars 771.2
Light-Duty Trucks 1,118.5
Buses 781.0
Medium- and Heavy-
Duty Trucks
Recreational Boats
Ships and Other Boats
Rail
Jet Fueld
Commercial Aircraft
General Aviation Aircraft
Military Aircraft
Aviation Gasoline
General Aviation Aircraft
Residual Fuel Oild e
Ships and Other Boats
Natural Gas d (million
cubic feet)
Passenger Cars
Light-Duty Trucks
Buses
Pipelines
LPGd
Buses
Light-Duty Trucks
Medium- and Heavy-
Duty Trucks
Electricity
Rail
18,574.2
190.4
735.3
3,460.6
18,349.7
14,103.4
662.9
3,583.4
374.2
374.2
2,006.2
2,006.2
0.7
4,071.6
1,585.1
31,604.6
764.9
1,452.0
851.1
23,240.7
228.1
1,204.3
3,863.5
17,850.3
14,795.9
559.8
2,494.6
329.3
329.3
2,587.4
2,587.4
4,095.5
1,119.3
39,241.4
356.1
1,960.6
996.6
30,179.9
265.6
1,376.8
4,105.9
20,491.9
17,352.8
972.0
2,167.1
301.9
301.9
2,962.5
2,962.5
0.7
3,960.1
1,597.9
4,008.3
1,577.4
4,046.5
1,556.9
3,803.7
1,524.2
0.6
138.4
1.5
48.8
5,381.9
5,381.9
0.6
0.6
308.7
1.0
233.2
74.6
7,506.4
7,506.4
0.6
0.6
320.0
1.0
229.5
89.5
7,357.6
7,357.6
0.6
0.6
256.7
184.7
72.0
8,172.6
8,172.6
0.7
0.7
459.5
334.0
125.5
7,699.6
7,699.6
3,454.2
1,506.8
44,659.2
414.0
2,518.4
1,030.1
45,847.6
403.2
2,609.9
1,048.5
46,432.3
399.7
2,658.5
1,048.5
43,236.5
382.4
2,618.5
1,007.3
39,243.4
380.2
2,603.2
910.4
35,160.0
305.4
785.3
4,446.1
19,919.9
16,532.7
1,526.7
1,860.5
294.2
294.2
1,713.1
1,713.1
36,078.5
313.4
729.5
4,664.5
17,386.1
14,065.0
1,642.6
1,678.5
278.3
278.3
2,046.3
2,046.3
36,665.4
321.4
799.8
4,538.9
17,305.8
14,163.9
1,485.6
1,656.3
262.8
262.8
2,579.4
2,579.4
34,706.1
87.8
218.4
4,216.0
15,906.2
12,533.8
1,705.7
1,666.8
235.3
235.3
1,770.5
1,770.5
31,368.6
337.5
118.8
3,524.8
14,239.3
11,425.1
1,364.3
1,449.9
221.0
221.0
1,064.8
1,064.8
0.7
0.6
487.5
354.3
133.2
7,688.5
7,688.5
FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection
procedures. Based on data from FHWA's Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated
to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as
described below.
Fuel use by vehicle class (also in the VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and gasoline
consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline were estimated to grow by the rate of
growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel were estimated to fall by the same rate
that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates,
assuming no relative change in MPG between vehicle classes.
b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-
Use Change and Forestry chapter.
c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline
estimates.
d Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes.
e Fluctuations in reported fuel consumption may reflect data collection problems.
+ Less than 0.05 million gallons or 0.05 million cubic feet
- Unreported or zero
Table A-87: Energy Consumption by Fuel and Vehicle Type tThtul
Fuel/Vehicle Type
1990
1995
2000
2005
2006
2007
2008
2009
A-117
-------
Motor Gasoline" 13,813.0
Passenger Cars 8,725.3
Light-Duty Tracks 4,339.8
Motorcycles 24.3
Buses 4.9
Medium- and Heavy-
Duty Tracks 544.0
Recreational Boats0 174.8
Distillate Fuel Oil
(Diesel Fuel) 3,554.8
Passenger Cars 107.0
Light-Duty Tracks 155.1
Buses 108.3
Medium- and Heavy-
Duty Tracks 2,576.1
Recreational Boats 26.4
Ships and Other Boats 102.0
Rail 480.0
Jet Fuel0 2,477.2
Commercial Aircraft 1,904.0
General Aviation
Aircraft 89.5
Military Aircraft 483.8
Aviation Gasoline11 45.0
General Aviation
Aircraft 45.0
Residual Fuel Oil4 e 300.3
Ships and Other Boats 300.3
Natural Gasd 679.9
Passenger Cars
Light-Duty Tracks
Buses
Pipelines 679.9
LPGd 22.9
Buses
Light-Duty Tracks 9.2
Medium- and Heavy-
Duty Tracks 13.7
Electricity*1 16.2
Rail 16.2
14,678.5
8,436.9
5,509.2
24.9
5.2
505.6
196.8
4,383.3
106.1
201.4
118.0
3,223.3
31.6
167.0
535.8
2,409.8
1,997.4
75.6
336.8
39.6
39.6
387.3
387.3
724.0
1.9
0.1
1.0
721.0
17.7
0.1
8.5
9.1
17.0
17.0
16,014.8
9,038.1
6,298.4
26.0
5.4
508.0
138.8
5,442.4
49.4
271.9
138.2
4,185.7
36.8
190.9
569.4
2,766.4
2,342.6
131.2
292.6
36.3
36.3
443.5
443.5
672.0
8.3
663.7
11.9
0.1
7.6
4.2
18.4
18.4
16,729.6
9,268.0
6,742.8
22.8
5.1
492.2
198.6
6,193.8
57.4
349.3
142.9
4,876.4
42.4
108.9
616.6
2,689.2
2,231.9
206.1
251.2
35.4
35.4
256.4
256.4
623.9
15.8
608.1
28.2
0.1
21.3
6.8
25.6
25.6
16,516.8
8,901.1
6,890.2
26.4
5.2
498.0
196.0
6,358.6
55.9
362.0
145.4
5,003.7
43.5
101.2
646.9
2,347.1
1,898.8
221.8
226.6
33.4
33.4
306.3
306.3
625.0
15.5
609.4
27.5
0.1
19.7
7.7
25.1
25.1
16,470.1
8,766.4
6,973.3
28.7
5.4
502.8
193.5
6,439.7
55.4
368.7
145.4
5,085.1
44.6
110.9
629.5
2,336.3
1,912.1
200.6
223.6
31.6
31.6
386.1
386.1
665.4
18.6
646.8
21.9
15.8
6.2
27.9
27.9
15,843.4
8,326.8
6,819.4
30.0
5.3
472.6
189.4
5,996.5
53.0
363.2
139.7
4,813.4
12.2
30.3
584.7
2,147.3
1,692.1
230.3
225.0
28.3
28.3
265.0
265.0
694.5
20.7
673.8
39.4
28.6
10.8
26.1
26.1
15,779.7
8,317.1
6,811.5
30.0
4.8
429.1
187.2
5,442.7
52.7
361.0
126.3
4,350.5
46.8
16.5
488.9
1,922.3
1,542.4
184.2
195.7
26.6
26.6
159.4
159.4
684.2
20.8
663.5
41.2
30.0
11.3
26.1
26.1
Total
20,909.3
22,657.1
25,405.6
26,582.1 26,239.8 26,379.1 25,040.5 24,082.3
FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection
procedures. Based on data from FHWA's Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated
to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as
described below.
Fuel use by vehicle class (also in the VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and gasoline
consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline were estimated to grow by the rate of
growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel were estimated to fall by the same rate
that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates,
assuming no relative change in MPG between vehicle classes.
b Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, Land-
Use Change and Forestry chapter.
c Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline
estimates.
d Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes
e Fluctuations in reported fuel consumption may reflect data collection problems.
- Unreported or zero
Table A-88. Biofuel Consumption by Fuel Type [million gallons!
Fuel Type
1990
1995
2000
2005
2006
2007
2008
2009
Ethanol
Biodiesel
717.5
NA
1,335.8
NA
1,601.7
NA
3,888.0
137.5
5,245.0
394.6
6,611.6
542.3
9,331.5
484.9
10,432.2
514.0
A-118 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Estimates of CH4 and NjO Emissions
Mobile source emissions of greenhouse gases other than CO2 are reported by transport mode (e.g., road, rail,
aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N2O were derived using a
methodology similar to that outlined in the Revised 1996IPCC Guidelines (IPCC/UNEP/OECD/TEA 1997).
Activity data were obtained from a number of U.S. government agencies and other publications. Depending on
the category, these basic activity data included fuel consumption and vehicle miles traveled (VMT). These estimates were
then multiplied by emission factors, expressed as grams per unit of fuel consumed or per vehicle mile.
Methodology for On-Road Gasoline and Diesel Vehicles
Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year
VMT by vehicle type (e.g., passenger cars, light-duty trucks, medium- and heavy-duty trucks,37 buses, and
motorcycles) were obtained from the Federal Highway Administration's (FHWA) Highway Statistics (FHWA 1996
through 2009). As these vehicle categories are not fuel-specific, VMT for each vehicle type was disaggregated by fuel
type (gasoline, diesel) so that the appropriate emission factors could be applied. VMT from Highway Statistics Table VM-
1 (FHWA 1996 through 2009) was allocated to fuel types (gasoline, diesel, other) using historical estimates of fuel shares
reported in the Appendix to the Transportation Energy Data Book (DOE 1993 through 2010). These fuel shares are drawn
from various sources, including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the
American Public Transportation Association. The fuel shares were first adjusted proportionately so that the gasoline and
diesel shares for each vehicle type summed to 100 percent in order to develop an interim estimate of VMT for each
vehicle/fuel type category that summed to the total national VMT estimate. VMT for alternative fuel vehicles (AFVs) was
calculated separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from AFVs
were then subtracted from the appropriate interim VMT estimates to develop the final VMT estimates by vehicle/fuel type
category.38 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 89
and Table A- 90, respectively.
Total VMT for each on-road category (i.e., gasoline passenger cars, light-duty gasoline trucks, heavy-duty
gasoline vehicles, diesel passenger cars, light-duty diesel trucks, medium- and heavy-duty diesel vehicles, and
motorcycles) were distributed across 31 model years shown for 2009 in Table A- 93. Distributions for 1990-2007 are
presented in the Inventory Docket. This distribution was derived by weighting the appropriate age distribution of the U.S.
vehicle fleet according to vehicle registrations by the average annual age-specific vehicle mileage accumulation of U.S.
vehicles. Age distribution values were obtained from EPA's MOBILE6 model for all years before 1999 (EPA 2000) and
EPA's MOVES model for years 1999 forward (EPA 2010a).39 Age-specific vehicle mileage accumulation was obtained
from EPA's MOBILE6 model (EPA 2000).
Step 2: Allocate VMT Data to Control Technology Type
VMT by vehicle type for each model year was distributed across various control technologies as shown in Table
A- 97 through Table A- 100. The categories "EPA Tier 0" and "EPA Tier 1" were used instead of the early three-way
catalyst and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines. EPA
Tier 0, EPA Tier 1, EPA Tier 2, and LEV refer to U.S. emission regulations, rather than control technologies; however,
each does correspond to particular combinations of control technologies and engine design. EPA Tier 2 and its
predecessors EPA Tier 1 and Tier 0 apply to vehicles equipped with three-way catalysts. The introduction of "early three-
way catalysts," and "advanced three-way catalysts," as described in the Revised 1996 IPCC Guidelines, roughly
correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).40 EPA Tier 2 regulations affect
vehicles produced starting in 2004 and are responsible for a noticeable decrease in N2O emissions compared EPA Tier 1
emissions technology (EPA 1999b).
Medium-duty trucks include vehicles with a gross vehicle weight rating (GVWR) of 8,500 to 14,000 Ibs. while heavy-duty trucks include
those with a GVWR of over 14,000 Ibs.
o o
In Inventories through 2002, gasoline-electric hybrid vehicles were considered part of an "alternative fuel and advanced technology" category.
However, vehicles are now only separated into gasoline, diesel, or alternative fuel categories, and gas-electric hybrids are now considered within
the gasoline vehicle category.
Age distributions were held constant for the period 1990-1998, and reflect a 25-year vehicle age span. EPA (2010) provides a variable age
distribution and 31-year vehicle age span beginning in year 1999.
40 For further description, see "Definitions of Emission Control Technologies and Standards" section of this annex.
A-119
-------
Control technology assignments for light and heavy-duty conventional fuel vehicles for model years 1972 (when
regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through 2007 were
determined using confidential engine family sales data submitted to EPA (EPA 2007b). Vehicle classes and emission
standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA
2007a). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier 0, EPA
Tier 1, Tier 2, and LEV standards. Assignments for 1996 and 1997 were estimated based on the fact that EPA Tier 1
standards for light-duty vehicles were fully phased in by 1996. Tier 2 began initial phase-in by 2004.
Step 3: Determine CH4 and N2O Emission Factors by Vehicle, Fuel, and Control Technology Type
Emission factors for gasoline and diesel on-road vehicles were developed by ICF (2004). These factors were
based on EPA and CARB laboratory test results of different vehicle and control technology types. The EPA and CARB
tests were designed following the Federal Test Procedure (FTP), which covers three separate driving segments, since
vehicles emit varying amounts of GHGs depending on the driving segment. These driving segments are: (1) a transient
driving cycle that includes cold start and running emissions, (2) a cycle that represents running emissions only, and (3) a
transient driving cycle that includes hot start and running emissions. For each test run, a bag was affixed to the tailpipe of
the vehicle and the exhaust was collected; the content of this bag was later analyzed to determine quantities of gases
present. The emission characteristics of segment 2 was used to define running emissions, and subtracted from the total
FTP emissions to determine start emissions. These were then recombined based upon MOBILE6.2's ratio of start to
running emissions for each vehicle class to approximate average driving characteristics.
Step 4: Determine the Amount of CH4 and N2O Emitted by Vehicle, Fuel, and Control Technology Type
Emissions of CH4 and N2O were then calculated by multiplying total VMT by vehicle, fuel, and control
technology type by the emission factors developed in Step 3.
Methodology for Alternative Fuel Vehicles (AFVs)
Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from "VMT Projections for
Alternative Fueled and Advanced Technology Vehicles through 2025" (Browning 2003). Alternative Fuels include
Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol, and
Electric Vehicles (battery powered). Most of the vehicles that use these fuels run on an Internal Combustion Engine (ICE)
powered by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or diesel),
or some combination.41 The data obtained include vehicle fuel use and total number of vehicles in use from 1992 through
2007. Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly,
fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the
equivalent amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel
economy of a given technology to that of conventional gasoline or diesel vehicles) were taken from full fuel cycle studies
done for the California Air Resources Board (Unnasch and Browning, Kassoy 2001). These ratios were used to estimate
fuel economy in miles per gasoline gallon equivalent for each alternative fuel and vehicle type. Energy use per fuel type
was then divided among the various weight categories and vehicle technologies that use that fuel. Total VMT per vehicle
type for each calendar year was then determined by dividing the energy usage by the fuel economy. Note that for AFVs
capable of running on both/either traditional and alternative fuels, the VMT given reflects only those miles driven that
were powered by the alternative fuel, as explained in Browning (2003). VMT estimates for AFVs by vehicle category
(passenger car, light-duty truck, heavy-duty vehicles) are shown in Table A- 91, while more detailed estimates of VMT by
control technology are shown in Table A- 92.
Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol or methanol;
some vehicles are also designed to run on gasoline or an alternative fuel - either natural gas or LPG - but not at the same time, while other
vehicles are designed to run on E85 (85% ethanol) or gasoline, or any mixture of the two. Heavy-duty vehicles are more likely to run on a
combination of diesel fuel and either natural gas, LPG, ethanol, or methanol.
A-120 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Step 2: Determine CH4 and N2O Emission Factors by Vehicle and Alternative Fuel Type
CH4 and N2O emission factors for alternative fuel vehicles (AFVs) are calculated according to studies by
Argonne National Laboratory (2006) and Lipman & Delucchi (2002), and are reported in ICF (2006a). In these studies,
N2O and CH4 emissions for AFVs were expressed as a multiplier corresponding to conventional vehicle counterpart
emissions. Emission estimates in these studies represent the current AFV fleet and were compared against Tier 1
emissions from light-duty gasoline vehicles to develop new multipliers. Alternative fuel heavy-duty vehicles were
compared against gasoline heavy-duty vehicles as most alternative fuel heavy-duty vehicles use catalytic after treatment
and perform more like gasoline vehicles than diesel vehicles. These emission factors are shown in Table A- 102.
Step 3: Determine the Amount of CH4 and N2O Emitted by Vehicle and Fuel Type
Emissions of CH4 and N2O were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by
the appropriate emission factors (Step 2).
Methodology for Non-Road Mobile Sources
CH4 and N2O emissions from non-road mobile sources were estimated by applying emission factors to the
amount of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel
type, as shown in Table A- 96. Consumption data for ships and other boats (i.e., vessel bunkering) were obtained from
DHS (2008) and EIA (1991 through 2011) for distillate fuel, and DHS (2008) and EIA (2007b) for residual fuel; marine
transport fuel consumption data for U.S. territories (EIA 2008b, EIA 1991 through 2011) were added to domestic
consumption, and this total was reduced by the amount of fuel used for international bunkers. *• Gasoline consumption by
recreational boats was obtained from EPA's NONROAD model (EPA 2010). Annual diesel consumption for Class I rail
was obtained from the Association of American Railroads (AAR) (2008), diesel consumption from commuter rail was
obtained from APTA (2007 through 2010) and Gaffney (2007), and consumption by Class II and III rail was provided by
Benson (2002 through 2004) and Whorton (2006 through 2009). Diesel consumption by commuter and intercity rail was
obtained from DOE (1993 through 2010). Data on the consumption of jet fuel and aviation gasoline in aircraft were
obtained from EIA (2007b), as described in Annex 2.1: Methodology for Estimating Emissions of CO2 from Fossil Fuel
Combustion, and were reduced by the amount allocated to international bunker fuels. Pipeline fuel consumption was
obtained from EIA (2007 through 2010) (note: pipelines are a transportation source but are stationary, not mobile,
sources). Data on fuel consumption by all non-transportation mobile sources'*-' were obtained from EPA's NONROAD
model (EPA 2010) and from FHWA (1996 through 2010) for gasoline consumption for trucks used off-road.
Emissions of CH4 and N2O from non-road mobile sources were calculated by multiplying U.S. default emission
factors in the Revised 1996 IPCC Guidelines (iPCC/UNEP/OECD/iEA 1997) by activity data for each source type (see
Table A- 103).
Estimates of NOX, CO, and NMVOC Emissions
The emission estimates of NOX, CO, and NMVOCs from mobile combustion (transportation) were obtained from
preliminary data (EPA 2010, EPA 2009), which, in final iteration, will be published on the EPA's National Emission
Inventory (NEI) Air Pollutant Emission Trends web site. This EPA report provides emission estimates for these gases by
fuel type using a procedure whereby emissions were calculated using basic activity data, such as amount of fuel delivered
or miles traveled, as indicators of emissions.
Table A- 104 through Table A- 106 provides complete emission estimates for 1990 through 2009.
Table A- 89: Vehicle Miles Traveled for Gasoline On-Road Vehicles HO9 Miles)3
Year
1990
1991
1992
Passenger
Cars
1391.2
1341.7
1354.8
Light-Duty
Trucks
554.3
627.2
682.9
Heavy-Duty
Vehicles
25.4
25.0
24.8
Motorcycles
9.6
9.2
9.6
See International Bunker Fuels section of the Energy Chapter.
"Non-transportation mobile sources" are defined as any vehicle or equipment not used on the traditional road system, but excluding aircraft,
rail and watercraft. This category includes snowmobiles, golf carts, riding lawn mowers, agricultural equipment, and trucks used for off-road
purposes, among others.
A-121
-------
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1356.5
1387.5
1420.6
1454.7
1488.5
1536.6
1559.1
1591.5
1619.3
1649.2
1662.6
1690.2
1698.6
1680.8
1662.7
1,606.4
1,621.1
720.5
738.8
762.5
788.0
820.8
836.8
867.4
886.7
904.9
925.8
943.0
984.2
997.5
1037.2
1065.6
1,062.0
1,071.7
24.5
25.0
24.7
24.0
23.6
23.6
23.8
23.6
23.2
23.1
23.5
23.8
24.1
24.2
24.7
24.7
22.7
9.9
10.2
9.8
9.9
10.1
10.3
10.6
10.5
9.6
9.6
9.6
10.1
10.5
12.0
13.6
14.5
14.6
Source: Derived from FHWA (1996 through 2009).
a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection
procedures. Based on data from FHWA's Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated
to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009
Table A- 90: Vehicle Miles Traveled for Diesel On-Read Vehicles HO9 Miles)3
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Passenger Light-Duty Heavy-Duty
Cars Trucks Vehicles
16.9
16.3
16.5
17.9
18.3
17.3
14.7
13.5
12.4
9.4
8.0
8.1
8.3
8.3
8.4
8.4
8.3
8.1
7.8
7.9
19.7
21.6
23.4
24.7
25.3
26.9
27.8
29.0
30.5
32.6
35.2
37.0
38.9
39.6
41.3
41.9
43.5
44.6
44.4
44.8
125.5
129.3
133.5
140.3
150.5
158.7
164.3
173.4
178.4
185.3
188.0
191.1
196.4
199.1
201.6
202.8
201.4
204.7
205.5
187.8
Source: Derived from FHWA (1996 through 2009).
" FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection
procedures. Based on data from FHWA's Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated
to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009
Table A- 91: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (10 Miles)
Year
1990
1991
1992
1993
1994
1995
1996
1997
Passenger Light-Duty Heavy-Duty
Cars Trucks Vehicles
0.2
0.2
0.3
0.3
0.3
0.4
0.5
0.6
0.7
0.6
0.6
0.6
0.5
0.6
0.7
0.9
1.1
1.0
0.9
1.3
1.2
1.2
1.2
1.3
A-122 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
1998 0.6
1999 0.6
2000 0.8
2001 0.9
2002 1.0
2003 1.2
2004 1.3
2005 1.4
2006 1.5
2007 1.6
2008 1.7
2009 1.8
1.0
1.0
1.2
1.2
1.3
1.4
1.6
1.6
1.8
2.0
2.2
2.3
1.4
1.3
1.5
1.8
2.0
2.1
2.2
2.1
3.7
4.6
4.3
4.8
Source: Derived from Browning (2003).
Table A-92: DetailedVehicle Miles Traveled forAlternative Fuel On-Read Vehicles (10e Miles)
Vehicle Type
Passenger Cars
Methanol-Flex Fuel ICE
Ethanol-Flex Fuel ICE
CNG ICE
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
NEVs
Electric Vehicle
Light-Duty Trucks
Ethanol-Flex Fuel ICE
CNG ICE
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
Electric Vehicle
Medium-Duty Trucks
CNG Bi-fuel
LPG ICE
LPG Bi-fuel
Biodiesel (BD20)
Heavy-Duty Trucks
Neat Methanol ICE
Neat Ethanol ICE
CNG ICE
LPG ICE
LPG Bi-fuel
LNG
Biodiesel (BD20)
Buses
Neat Methanol ICE
Neat Ethanol ICE
CNG ICE
LPG ICE
LNG
Biodiesel (BD20)
Electric
Total VMT
1990
206.3
_
+
10.6
28.2
20.6
146.9
660.7
-
10.9
24.2
56.9
568.7
508.0
2.3
24.3
481.4
523.9
3.0
12.7
36.3
471.9
41.4
1.9
0.1
11.2
28.2
-
-
1,940.3
1995
400.6
40.9
2.2
28.0
75.1
40.3
201.7
5.2
7.2
606.8
1.3
29.6
71.0
48.5
449.4
7.1
458.4
20.1
20.0
418.3
627.0
7.8
2.0
32.2
46.3
531.9
6.9
80.5
3.7
2.2
37.5
30.9
4.3
-
2.0
2,173.4
2000
788.1
13.2
120.4
68.9 1
202.9
41.9 1
197.6
8.2 1
62.4 1
72.6 1
1,162.0
122.6
145.9
280.1
58.4
511.9
8.2
35.0 1
629.6
117.0
29.7
475.9
7.0
712.3
01 1
83.7 1
48.3 1
529.7
22.2 1
28.3
111.9
1
1
53.4 1
35.6 1
13.3 1
45 1
5.1 •
3,403.9
2005
1,362.2
_
399.1
118.8
253.3
31.3
175.1
93.8
135.5
155.2
1,640.0
415.0
163.8
348.2
57.3
502.3
76.7
76.4
831.4
300.7
36.8
444.8
49.1
1,084.9
218.7
69.0
569.4
117.3
110.4
207.4
-
-
122.9
34.6
27.8
15.6
6.5
5,125.9
2006
1,481.2
_
429.8
125.0
275.0
24.9
149.1
186.4
135.7
155.2
1,840.3
518.7
169.3
365.8
54.6
491.4
163.8
76.4
866.4
314.2
33.8
362.5
156.0
2,495.7
252.8
65.5
524.1
126.5
1,526.8
349.7
-
-
123.4
35.1
28.1
156.8
6.2
7,033.3
2007
1,606.3
_
504.4
129.2
295.3
18.5
123.0
246.1
136.0
153.8
1,992.0
657.7
175.4
389.2
38.4
438.5
219.2
73.1
866.9
337.4
31.3
289.1
209.1
3,337.8
332.1
65.3
512.9
141.8
2,285.7
406.1
-
-
131.7
33.5
25.1
209.1
6.3
8,209.0
2008
1,662.3
_
554.2
129.7
306.5
16.5
112.0
259.3
136.2
147.8
2,153.0
788.5
181.8
405.2
36.1
422.1
240.9
77.8
921.0
370.8
30.9
284.3
235.0
3,024.7
495.3
64.8
505.2
158.1
1,801.4
412.3
-
-
139.2
33.7
21.1
210.9
6.7
8,173.3
2009
1,813.0
_
661.0
135.3
330.4
11.8
94.4
295.0
136.5
148.7
2,299.7
921.7
186.3
432.9
32.9
383.6
263.0
78.4
924.0
396.8
31.0
223.2
272.9
3,435.3
516.8
64.8
502.2
174.2
2,177.3
422.1
-
-
148.8
33.9
18.8
212.5
7.3
8,894.0
Source: Derived from Browning (2003).
Note: Throughout the rest of this
Inventory, medium-duty trucks are grouped with heavy-duty trucks;
they are reported separately here because
these two categories may run on a slightly different range of fuel types.
+ Less than 0.05 million vehicle miles traveled
- Unreported or zero
A-123
-------
Table A- 93: Age Distribution by Vehicle/Fuel Type tor On-Road Vehicles.3 2009
Vehicle Age LDGV LDGT HDGV LDDV" LDDT HDDV
MC
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
5.2%
5.8%
6.4%
6.6%
6.4%
6.2%
6.1%
6.4%
6.4%
6.5%
6.0%
5.3%
4.9%
4.3%
3.7%
3.1%
2.3%
1.8%
1.4%
1.2%
1.0%
0.8%
0.6%
0.5%
0.4%
0.3%
0.2%
0.1%
0.1%
0.1%
0.1%
100%
4.0%
5.0%
7.4%
7.5%
7.9%
7.6%
7.1%
6.7%
6.3%
5.8%
5.0%
4.5%
3.8%
3.4%
2.9%
2.6%
2.1%
1.6%
1.3%
1.3%
1.1%
1.1%
0.9%
0.8%
0.7%
0.5%
0.3%
0.2%
0.2%
0.2%
0.2%
100%
5.2%
5.7%
5.9%
6.0%
6.0%
4.7%
3.8%
3.3%
3.9%
4.3%
4.3%
3.5%
2.7%
2.7%
3.2%
3.3%
2.5%
1.8%
1.8%
2.3%
3.1%
2.9%
2.5%
3.1%
2.4%
1.5%
2.3%
1.3%
1.2%
1.4%
1.6%
100%
4.6%
5.2%
5.8%
5.9%
5.7%
5.5%
5.5%
5.7%
5.7%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
0.0%
1.0%
1.1%
1.2%
1.6%
1.6%
0.8%
0.6%
57%
4.1%
5.1%
6.8%
6.5%
6.8%
10.0%
7.2%
7.7%
8.0%
6.6%
7.1%
1.7%
4.3%
2.9%
2.8%
2.4%
1.8%
1.2%
1.0%
0.9%
0.8%
0.6%
0.5%
0.7%
0.6%
0.6%
0.5%
0.5%
0.1%
0.2%
0.1%
100%
5.6%
6.2%
6.5%
7.9%
7.6%
6.0%
4.6%
4.2%
4.6%
5.5%
5.8%
4.5%
3.5%
3.3%
3.7%
3.2%
2.4%
1.6%
1.6%
1.8%
2.0%
1.7%
1.3%
1.4%
1.0%
0.6%
0.6%
0.3%
0.3%
0.3%
0.3%
100%
6.2%
12.2%
11.0%
10.4%
9.1%
7.7%
6.6%
5.8%
5.0%
4.0%
3.0%
2.5%
2.4%
2.1%
1.6%
1.9%
1.5%
1.3%
1.0%
0.8%
0.6%
0.5%
0.5%
0.4%
0.4%
0.3%
0.2%
0.2%
0.2%
0.2%
0.3%
100%
Source: EPA (2010).
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV
(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC
(motorcycles).
b According to EPA's MOVES model, sales of diesel passenger cars in model years 9-23 was very small compared to total passenger car sales, so
the calculated fraction of these vehicles in these model years was stored as zero.
Table A- 94: Annual Average Vehicle Mileage Accumulation per Vehiclea [miles!
Vehicle Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
LDGV
14,910
14,174
13,475
12,810
12,178
11,577
11,006
10,463
9,947
9,456
8,989
8,546
8,124
7,723
LDGT
19,906
18,707
17,559
16,462
15,413
14,411
13,454
12,541
11,671
10,843
10,055
9,306
8,597
7,925
HDGV
20,218
18,935
17,100
16,611
15,560
14,576
13,655
12,793
11,987
11,231
10,524
9,863
9,243
8,662
LDDV
14,910
14,174
13,475
12,810
12,178
11,577
11,006
10,463
9,947
9,456
8,989
8,546
8,124
7,723
LDDT
26,371
24,137
22,095
20,228
18,521
16,960
15,533
14,227
13,032
11,939
10,939
10,024
9,186
8,420
HDDV
28,787
26,304
24,038
21,968
20,078
18,351
16,775
15,334
14,019
12,817
11,719
10,716
9,799
8,962
MCb
4,786
4,475
4,164
3,853
3,543
3,232
2,921
2,611
2,300
1,989
1,678
1,368
1,368
1,368
A-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
7,342
6,980
6,636
6,308
5,997
5,701
5,420
5,152
4,898
4,656
4,427
4,427
4,427
4,427
4,427
4,427
4,427
7,290
6,690
6,127
5,598
5,103
4,642
4,214
3,818
3,455
3,123
2,822
2,822
2,822
2,822
2,822
2,822
2,822
8,028
7,610
7,133
6,687
6,269
5,877
5,510
5,166
4,844
4,542
4,259
4,259
4,259
4,259
4,259
4,259
4,259
7,342
6,980
6,636
6,308
5,997
5,701
5,420
5,152
4,898
4,656
4,427
4,427
4,427
4,427
4,427
4,427
4,427
7,718
7,075
6,487
5,948
5,454
5,002
4,588
4,209
3,861
3,542
3,250
3,250
3,250
3,250
3,250
3,250
3,250
8,196
7,497
6,857
6,273
5,739
5,250
4,804
4,396
4,023
3,681
3,369
3,369
3,369
3,369
3,369
3,369
3,369
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
1,368
Source: EPA (2000).
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV
(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC
(motorcycles).
b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and therefore
are presented in aggregate.
Table A-95: VHT Distribution by Vehicle Age and Vehicle/Fuel Type.3 2009
Vehicle Age
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
Total
LDGV
7.56%
8.03%
8.49%
8.23%
7.65%
7.00%
6.59%
6.51%
6.24%
6.04%
5.25%
4.45%
3.92%
3.28%
2.63%
2.11%
1.52%
1.10%
0.81%
0.67%
0.52%
0.41%
0.29%
0.23%
0.16%
0.11%
0.07%
0.05%
0.04%
0.03%
0.03%
100%
LDGT
6.52%
7.67%
10.68%
10.14%
9.95%
8.98%
7.87%
6.85%
6.07%
5.20%
4.14%
3.40%
2.65%
2.20%
1.71%
1.42%
1.05%
0.75%
0.54%
0.49%
0.40%
0.33%
0.26%
0.20%
0.15%
0.12%
0.08%
0.05%
0.04%
0.04%
0.05%
100%
HDGV
9.50%
9.80%
9.15%
8.97%
8.40%
6.23%
4.64%
3.83%
4.20%
4.34%
4.08%
3.14%
2.26%
2.15%
2.33%
2.30%
1.60%
1.09%
1.01%
1.21%
1.56%
1.36%
1.08%
1.29%
0.94%
0.56%
0.87%
0.48%
0.48%
0.53%
0.62%
100%
LDDV"
10.78%
11.44%
12.11%
11.74%
10.90%
9.98%
9.39%
9.28%
8.90%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.00%
0.69%
0.78%
0.86%
1.11%
1.12%
0.54%
0.38%
100%
LDDT
7.43%
8.51%
10.26%
9.01%
8.65%
11.59%
7.68%
7.54%
7.14%
5.43%
5.32%
1.19%
2.69%
1.69%
1.46%
1.15%
0.80%
0.51%
0.36%
0.32%
0.24%
0.16%
0.13%
0.16%
0.13%
0.13%
0.12%
0.11%
0.03%
0.04%
0.02%
100%
HDDV
10.67%
10.80%
10.30%
11.49%
10.06%
7.30%
5.15%
4.27%
4.27%
4.69%
4.46%
3.18%
2.27%
1.95%
2.00%
1.57%
1.08%
0.68%
0.60%
0.64%
0.64%
0.50%
0.36%
0.33%
0.22%
0.14%
0.13%
0.07%
0.06%
0.06%
0.06%
100%
MC
9.53%
17.45%
14.68%
12.87%
10.36%
8.02%
6.13%
4.88%
3.65%
2.52%
1.60%
1.12%
1.07%
0.94%
0.71%
0.81%
0.66%
0.55%
0.44%
0.36%
0.26%
0.22%
0.22%
0.18%
0.15%
0.14%
0.10%
0.08%
0.08%
0.09%
0.14%
100%
Note: Estimated by weighting data in Table A- 93 by data in Table A- 94.
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV
(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC
(motorcycles).
b According to EPA's MOVES model, sales of diesel passenger cars in model years 9-23 was very small compared to total passenger car sales, so
the calculated fraction of these vehicles in these model years was stored as zero.
A-125
-------
Table A- 96: Fuel Consumption for Off-Road Sources by Fuel Type [million gallons!
Vehicle Type/Year
Aircraft3
Aviation Gasoline
Jet Fuel
Ships and Other
Boats
Diesel
Gasoline
Residual
Construction/
Mining
Equipment1"
Diesel
Gasoline
Agricultural
Equipment0
Diesel
Gasoline
Rail
Diesel
Other"
Diesel
Gasoline
Total
1990
18,723.9
374.2
18,349.7
4,507.2
1,043.1
1,403.4
2,060.7
4,160.3
3,674.4
485.9
3,133.7
2,320.9
812.8
3,460.6
3,460.6
5,916.5
1,423.3
4,493.2
39,902.2
1995
18,179.6
329.3
17,850.3
5,789.1
1,545.7
1,597.3
2,646.1
4,834.7
4,386.9
447.8
3,698.3
2,771.6
926.7
3,863.5
3,863.5
6,524.8
1,720.0
4,804.7
42,890.0 H
2000
20,793.7
301.9
20,491.9
6,430.6
1,750.1
1,652.9
3,027.5
5,439.1
5,094.9
344.2
3,874.5
3,222.3
652.3
4,105.9
4,105.9
6,826.1
2,016.0
4,810.2
47,470.0
2005
20,214.1
294.2
19,919.9
4,882.7
1,471.4
1,629.9
1,781.4
6,520.5
5,823.4
697.1
4,715.0
3,637.2
1,077.8
4,446.1
4,446.1
8,281.2
2,340.3
5,940.9
49,059.5
2006
17,664.4
278.3
17,386.1
5,144.6
1,410.5
1,619.6
2,114.5
6,656.4
5,968.2
688.3
4,947.8
3,719.1
1,228.7
4,664.5
4,664.5
8,396.1
2,405.4
5,990.7
47,473.8
2007
17,568.6
262.8
17,305.8
5,747.1
1,490.7
1,609.6
2,646.8
6,683.7
6,112.9
570.8
4,861.5
3,800.9
1,060.6
4,538.9
4,538.9
8,255.6
2,470.6
5,785.0
47,655.5
2008
16,141.6
235.3
15,906.2
4,113.4
675.7
1,599.8
1,837.9
6,835.0
6,257.7
577.3
4,516.6
3,882.8
633.8
4,216.0
4,216.0
8,386.7
2,535.8
5,850.9
44,209.2
2009
14,460.2
221.0
14,239.3
3,548.7
825.7
1,590.7
1,132.3
6,960.0
6,402.5
557.5
4,640.9
3,964.6
676.3
3,524.8
3,524.8
8,481.6
2,601.0
5,880.7
41,616.4
Sources: AAR (2008 through 2009), APIA (2007 through 2009), BEA (1991 through 2005), Benson (2002 through 2004), DHS (2008), DOC
(1991 through 2008), DESC (2008), DOE (1993 through 2009), DOT (1991 through 2009), EIA (2002), EIA (2007b), EIA (2008), EIA (2007
through 2008), EIA (1991 through 2011), EPA (2007b), FAA (200), FAA (2006), Gaffney (2007), and Whorton (2006 through 2009).
a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d "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.
Table A- 97: Control Technology Assignments for Gasoline Passenger Cars (Percent of VMT1
Model Years
1973-1974
1975
1976-1977
1978-1979
1980
1981
1982
1983
1984-1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Non-catalyst Oxidation EPA Tier 0 EPA Tier 1
100%
20% 80%
15% 85%
10% 90%
5% 88% 7%
15% 85%
14% 86%
12% 88%
100%
60% 40%
20% 80%
1% 97%
0.5% 96.5%
<1% 87%
<1% 67%
44%
3%
1%
<1%
<1%
.
.
.
.
-
LEV
-
-
-
-
-
-
-
-
-
-
-
2%
3%
13%
33%
56%
97%
99%
87%
41%
38%
18%
4%
2%
-
EPA Tier 2
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
13%
59%
62%
82%
96%
98%
100%
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.
A-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
- Not applicable.
Table A- 98: Control Technology Assignments for Gasoline Light-Duty Trucks (Percent of VMTla
Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV" EPA Tier 2
1973-1974
1975
1976
1977-1978
1979-1980
1981
1982
1983
1984
1985
1986
1987-1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
100%
30% 70%
20% 80%
25% 75%
20% 80%
95% 5%
90% 10%
80% 20%
70% 30%
60% 40%
50% 50%
5% 95%
60% 40%
20% 80%
100%
100%
80%
57%
65%
1%
10%
<1%
.
.
.
.
.
-
-
-
-
-
-
-
20%
43%
35%
99%
90%
53%
72%
38%
25%
14%
-
-
-
-
-
-
-
-
-
-
-
-
-
47%
28%
62%
75%
86%
100%
100%
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
" Detailed descriptions of emissions control technologies are provided in the following section of this annex.
b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission reductions with certain
types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can reduce the total number of LEVs they
need to build to meet a specified emission average for all of their vehicles in a given model year.
- Not applicable.
Table A- 99: Control Technology Assignments for Gasoline Heavy-DutyVehicles [Percent of VMTP
Model Years
<1981
1982-1984
1985-1986
1987
1988-1989
1990-1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Uncontrolled Non-catalyst Oxidation
100%
95% - 5%
95% 5%
70% 15%
60% 25%
45% 30%
25%
10%
.
.
.
.
.
.
.
.
.
.
.
-
EPA Tier 0 EPA Tier 1
-
-
-
15%
15%
25%
10% 65%
5% 85%
96%
78%
54%
64%
69%
65%
5%
-
-
-
-
-
LEV"
-
-
-
-
-
-
-
-
4%
22%
46%
36%
31%
30%
37%
23%
22%
21%
20%
19%
EPA Tier 2
-
-
-
-
-
-
-
-
-
-
-
-
-
5%
59%
77%
78%
79%
80%
81%
Sources: EPA (1998), EPA (2007a), and EPA (2007b).
a Detailed descriptions of emissions control technologies are provided in the following section of this annex.
b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions with certain
types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can reduce the total number of LEVs
they need to build to meet a specified emission average for all of their vehicles in a given model year.
A-127
-------
- Not applicable.
Table A-100: Control Technology Assignments for Diesel On-Road Vehicles and Motorcycles
Vehicle Type/Control Technology Model Years
Diesel Passenger Cars and Light-Duty Trucks
Uncontrolled 1960-1982
Moderate control 1983-1995
Advanced control 1996-2009
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled 1960-1990
Moderate control 1991 -2003
Advanced control 2004-2006
After-treatment 2007-2009
Motorcycles
Uncontrolled 1960-1995
Non-catalyst controls 1996-2009
Source: EPA (1998) and Browning (2005)
Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex.
Table A-101: Emission Factors for CH* and H?0 for On-Road Vehicles
Vehicle Type/Control
Technology
N20
(g/mi)
CH4
(g/mi)
Gasoline Passenger Cars
EPA Tier 2
Low Emission Vehicles
EPA Tier la
EPA Tier 0 a
Oxidation Catalyst
Non-Catalyst Control
Uncontrolled
Gasoline Light-Duty Trucks
EPA Tier 2
Low Emission Vehicles
EPA Tier la
EPA Tier Oa
Oxidation Catalyst
Non-Catalyst Control
Uncontrolled
Gasoline Heavy-Duty Vehicles
EPA Tier 2
Low Emission Vehicles
EPA Tier la
EPA Tier Oa
Oxidation Catalyst
Non-Catalyst Control
Uncontrolled
Diesel Passenger Cars
Advanced
Moderate
Uncontrolled
Diesel Light-Duty Trucks
Advanced
Moderate
Uncontrolled
Diesel Medium- and Heavy-Duty
Trucks and Buses
Aftertreatment
Advanced
Moderate
Uncontrolled
Motorcycles
Non-Catalyst Control
Uncontrolled
0.0036
0.0150
0.0429
0.0647
0.0504
0.0197
0.0197
0.0066
0.0157
0.0871
0.1056
0.0639
0.0218
0.0220
0.0134
0.0320
0.1750
0.2135
0.1317
0.0473
0.0497
0.0010
0.0010
0.0012
0.0015
0.0014
0.0017
0.0048
0.0048
0.0048
0.0048
0.0069
0.0087
0.0173
0.0105
0.0271
0.0704
0.1355
0.1696
0.1780
0.0163
0.0148
0.0452
0.0776
0.1516
0.1908
0.2024
0.0333
0.0303
0.0655
0.2630
0.2356
0.4181
0.4604
0.0005
0.0005
0.0006
0.0010
0.0009
0.0011
0.0051
0.0051
0.0051
0.0051
0.0672
0.0899
Source: ICF (2006b and 2004).
A-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
a The categories "EPA Tier 0" and "EPA Tier 1" were substituted for the early three-way catalyst and advanced three-way catalyst categories,
respectively, as defined in the Revised 1996IPCC Guidelines. Detailed descriptions of emissions control technologies are provided at the end of
this annex.
Table A-102: Emission Factors for Clh and M for Alternative Fuel Vehicles (g/mil
N2O
CH4
Light Duty Vehicles
Methanol
CNG
LPG
Ethanol
Biodiesel (BD20)
Medium- and Heavy -Duty Trucks
Methanol
CNG
LNG
LPG
Ethanol
Biodiesel (BD20)
Buses
Methanol
CNG
Ethanol
Biodiesel (BD20)
0.067
0.050
0.067
0.067
0.001
0.175
0.175
0.175
0.175
0.175
0.005
0.175
0.175
0.175
0.005
0.018
0.737
0.037
0.055
0.0005
0.066
1.966
1.966
0.066
0.197
0.005
0.066
1.966
0.197
0.005
Source: Developed by ICE (2006a) using ANL (2006) and Lipman and Delucchi (2002).
Table A-103: Emission Factors for Clh and M Emissions from Non-Road Mobile Combustion (g/kg fuel)
Vehicle Type/Fuel Type
Ships and Boats
Residual
Gasoline
Diesel
Rail
Diesel
Agricultural Equipment3
Gasoline
Diesel
Construction/Mining
Equipment0
Gasoline
Diesel
Other Non-Road
All "Other" Categories0
Aircraft
Jet Fuel
Aviation Gasoline
N2O
0.16
0.08
0.14
0.08
0.08
0.08
0.08
0.08
0.08
0.10
0.04
CH4
0.03
0.23
0.02
0.25
0.45
0.45
0.18
0.18
0.18
0.087
2.64
Source: IPCC/UNEP/OECD/IEA (1997) and ICE (2009).
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport
equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for
commercial/industrial purposes.
Table A-104: NOx Emissions from Mobile Combustion tugl
Fuel Type/Vehicle Type
Gasoline On-Road
Passenger Cars
Light-Duty Tracks
Medium- and Heavy-
Duty Tracks and Buses
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Tracks
1990
5,746
3,847
1,364
515 1
20 1
2,956
39
20 |
1995
4,560
2,752
1,325
469 1
3,493
1
2000
3,812
2,084
1,303
411 1
13 1
3,803
1
2005
3,102
1,692
1,073
326
11
2,787
5
5
2006
2,897
1,581
1,002
304
11
2,603
5
4
2007
2,693
1,469
931
283
10
2,419
4
4
2008
2,488
1,357
860
261
9
2,235
4
4
2009
2,212
1,207
765
232
8
1,987
3
3
A-129
-------
Medium- and Heavy-
Duty Tracks and Buses
Alternative Fuel On-
RoacT
Non-Road
Ships and Boats
Rail
Aircraftb
Agricultural Equipment0
Construction/Mining
Equipment4
Other6
Total
2,897
IE
2,160
402
338
25
437
641
318
10,862
3,462
IE
2,483
488
433 1
31 1
478 1
697 1
357
10,536
„,,
407
10,199
2,778
IE
3,122
646
576
47
565
811
478
9,012
2,594
IE
2,988
618
551
45
540
776
457
8,488
2,411
IE
2,853
590
527
43
516
741
437
7,965
2,228
IE
2,718
562
502
41
491
706
416
7,441
1,981
IE
2,007
415
371
30
363
521
307
6,206
a NOX emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
'"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.
Note: Totals may not sum due to independent rounding.
IE = Included Elsewhere
Table A-105: CO Emissions from Mobile Combustion [Ggl
Fuel Type/Vehicle Type
1990
1995
2000
2005
2006
2007
2008
2009
Gasoline On-Road 98,328
Passenger Cars 60,757
Light-Duty Tracks 29,237
Medium- and Heavy-
Duty Tracks and Buses 8,093
Motorcycles 240
Diesel On-Road 1,696
Passenger Cars 35
Light-Duty Tracks 22
Medium- and Heavy-
Duty Tracks and Buses 1,639
Alternative Fuel On-
Roada IE
Non-Road 19,337
Ships and Boats 1,559
Rail 85
Aircraft" 217
Agricultural Equipment0 581
Construction/Mining
Equipment4 1,090
Other6 15,805
74,673
42,065
27,048
5,404
155
1,424
18
16
1,391
IE
21,533
1,781
93
224
628
II
1,132
17,676
60,657
32,867
24,532
3,104
154
1,088
7
6
1,075
IE
21,814
1,825
90
245
626
1,047
17,981
43,374
24,166
17,264
1,844
100
665
4
4
656
IE
18,652
1,534
75
196
520
872
15,455
40,492
22,560
16,117
1,721
94
621
4
4
613
IE
17,859
1,469
72
187
498
835
14,798
37,610
20,954
14,969
1,599
87
576
4
3
569
IE
17,067
1,404
69
179
476
798
14,141
34,727
19,348
13,822
1,476
80
532
4
3
525
IE
16,274
1,339
66
171
454
761
13,484
34,199
19,054
13,612
1,454
79
524
4
3
517
IE
8,633
710
35
91
241
404
7,153
Total
119,360
97,630
83,559
62,692 58,972 55,253 51,533 43,355
a NOX emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
'"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.
Note: Totals may not sum due to independent rounding.
IE = Included Elsewhere
Table A-106: NHVOCs Emissions from Mobile Combustion [Ggl
Fuel Type/Vehicle Type
Gasoline On-Road
Passenger Cars
Light-Duty Tracks
Medium- and Heavy-
Duty Tracks and Buses
1990
8,110
5,120
2,374
575 1
1995
5,819
3,394
2,019
382
2000
4,615
2,610
1,750
232
2005
3,558
1,987
1,382
171
2006
3,358
1,875
1,304
162
2007
3,158
1,764
1,226
152
2008
2,958
1,652
1,149
142
2009
2,878
1,607
1,118
138
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Motorcycles
Diesel On-Road
Passenger Cars
Light-Duty Trucks
Medium- and Heavy-
Duty Trucks and Buses
Alternative Fuel On-
RoacT
Non-Road
Ships and Boats
Rail
Aircraftb
Agricultural Equipment0
Construction/Mining
Equipment
Other6
Total
42
406
16
14
377
IE
2,415
608
33
28
85
149
1,512
10,932
24
304
8
9
286
IE
2,622
739
36
28
86
152
1,580
8,745
23
216
3
4
209
IE
2,399
744
35
24
76
130
1,390
7,229
18
172
3
3
167
IE
2,600
798
39
21
79
137
1,527
6,330
17
163
2
3
157
IE
2,516
772
37
20
77
132
1,477
6,037
16
153
2
3
148
IE
2,430
746
36
20
74
128
1,427
5,742
15
143
2
3
139
IE
2,346
720
35
19
71
123
1,378
5,447
15
139
2
2
135
IE
1,134
348
17
9
35
60
666
4,151
" NOX emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
'"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.
Note: Totals may not sum due to independent rounding.
IE = Included Elsewhere
Definitions of Emission Control Technologies and Standards
The N2O and CH4 emission factors used depend on the emission standards in place and the corresponding level
of control technology for each vehicle type. Table A- 97 through Table A- 100 show the years in which these technologies
or standards were in place and the penetration level for each vehicle type. These categories are defined below.
Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as
uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel
vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.
Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.
Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model
years (1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in
motorcycles beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions
through adjustments to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas
recirculation (EGR) valves, which also helps meet vehicle NOX standards.
Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, and was the most
common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to
1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot
function properly with leaded gasoline.
A-131
-------
EPA Tier 0
This emission standard from the Clean Air Act was met through the implementation of early "three-way"
catalysts, therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in
the early 1980s, and remained common until 1994. This more sophisticated emission control system improves the
efficiency of the catalyst by converting CO and HC to CO2 and H2O, reducing NOX to nitrogen and oxygen, and using an
on-board diagnostic computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system
(carburetor or fuel injection) with electronic "trim" (also known as a "closed-loop system"). New cars with three-way
catalysts met the Clean Air Act's amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4
g/mileby 1981 andNOxto 1.0 g/mile by 1981.
EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOX
emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent
reduction from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for
NOX, and 0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use
of more advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These
advanced emission control systems included advanced three-way catalysts, electronically controlled fuel injection and
ignition timing, EGR, and air injection.
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOX
emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks.
Manufacturers can meet this average emission level by producing vehicles in 11 emission "Bins", the three highest of
which expire in 2006. These new emission levels represent a 77 to 95% reduction in emissions from the EPA Tier 1
standard set in 1994. Emission reductions were met through the use of more advanced emission control systems and lower
sulfur fuels and are applied to vehicles beginning in 2004. These advanced emission control systems include improved
combustion, advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air
injection.
Low Emission Vehicles (LEV)
This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to light-
duty gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel
injection with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts with
secondary air injection. LEVs as defined here include transitional low-emission vehicles (TLEVs), low emission vehicles,
ultra-low emission vehicles (ULEVs) and super ultra-low emission vehicles (SULEVs). In this analysis, all categories of
LEVs are treated the same due to the fact that there are very limited CH4 or N2O emission factor data for LEVs to
distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel
and advanced technology vehicle assessments.
Diesel Emission Controls
Below are the two levels of emissions control for diesel vehicles.
Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles
(generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were
implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and
1994.
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control technologies.
These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place
from 1996 through 2006.
Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction
(SCR) systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of
emission control necessary to comply with standards in place from 2007 on.
Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
This section of this Annex includes supplemental information on the contribution of transportation and other
mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are
generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate CO2, N2O, CH4,
and HFC emissions. Although the inventory is not required to provide detail beyond what is contained in the body of this
report, the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex,
within the annex that details the calculation methods and data used for non- CO2 calculations, is to provide all
transportation estimates presented throughout the repot in one place.
This section of this Annex reports total greenhouse gas emissions from transportation and other (non-
transportation) mobile sources in CO2 equivalents, with information on the contribution by greenhouse gas and by mode,
vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of
CO2 from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational
vehicles), and to provide more detailed breakdowns of emissions by source.
Estimation of CO2 from Non-Transportation Mobile Sources
The estimates of N2O and CH4 from fuel combustion presented in the Energy chapter of the inventory include
both transportation sources and other mobile sources. Other mobile sources include construction/mining equipment,
agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but do
not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of
CO2 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the agricultural,
industrial, and commercial sectors. In order to provide comparable information on transportation and mobile sources,
Table A- 107 provides estimates of CO2 from these other mobile sources, developed from EPA's NONROAD model and
FHWA's Highway Statistics. These other mobile source estimates were developed using the same fuel consumption data
utilized in developing the N2O and CH4 estimates.
Table A-107: C02 Emissions from Non-Transportation Mobile Sources tTg Clh Eq.l
Fuel Type/Vehicle Type
Agricultural Equipment3
Construction/Mining
Equipment11
Other Sources0
Total
1990
31.0
42.0
54.5
127.6
1995
36.6
48.9 1
59.8
145.4
2000
38.8
55.3
62.8
156.9
2005
46,
65,
76,
188.
.8
.9
.2
,9
2006
49.0
67.3
77.6
193.9
2007
48.4
67.8
76.7
193.0
2008
45.4
69.3
77.7
192.4
2009
46.7
70.6
78.6
195.9
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport
equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for
commercial/industrial purposes.
Estimation of HFC Emissions from Transportation Sources
In addition to CO2, N2O and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are
emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and
purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions;
Mobile AC represents the emissions from air conditioning units in passenger cars and light-duty trucks, Comfort Cooling
represents the emissions from air conditioning units in passenger trains and buses, and Refrigerated Transport represents
the emissions from units used to cool freight during transportation.
Table A- 108 below presents these HFC emissions. Table A- 109 presents all transportation and mobile source
greenhouse gas emissions, including HFC emissions.
A-133
-------
Table A-108: HFC Emissions from Transportation Sources
Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009
Mobile AC T 16?7 4
-------
Greenhouse gas emissions from aircraft decreased 22 percent between 1990 and 2009. Emissions from military
aircraft decreased 59 percent and commercial aircraft emissions rose 2 percent between 1990 and 2007 then dropped 19
percent from 2007 to 2009.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 197.7 Tg CO2 Eq. in 2009. Together, these sources emitted more
greenhouse gases than ships and boats, rail, and pipelines combined. Emissions from non-transportation mobile sources
increased rapidly, growing approximately 54 percent between 1990 and 2009. CH4 and N2O emissions from these sources
are included in the "Mobile Combustion" section and CO2 emissions are included in the relevant economic sectors.
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas
Table A- 110 presents estimates of greenhouse gas emissions from transportation and other mobile sources
broken down by greenhouse gas. As this table shows, CO2 accounts for the vast majority of transportation greenhouse gas
emissions (approximately 96 percent in 2009). Emissions of CO2 from transportation and mobile sources increased by
300.2 Tg CO2 Eq. between 1990 and 2009. In contrast, the combined emissions of CH4 and N2O decreased by 22.7 Tg
CO2 Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant
emissions. Meanwhile, HFC emissions from mobile air conditioners and refrigerated transport increased from virtually
no emissions in 1990 to 60.2 Tg CO2 Eq. in 2009 as these chemicals were phased in as substitutes for ozone depleting
substances. It should be noted, however, that the ozone depleting substances that HFCs replaced are also powerful
greenhouse gases, but are not included in national greenhouse gas inventories due to their mandated phase out.
Greenhouse Gas Emissions from Freight and Passenger Transportation
Table A- 111 and Table A- 112present greenhouse gas estimates from transportation, broken down into the
passenger and freight categories. Passenger modes include light-duty vehicles, buses, passenger rail, aircraft (general
aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A- 111.
Freight modes include medium- and heavy-duty trucks, freight rail, refrigerated transport, waterborne freight vessels,
pipelines, and commercial aircraft and are illustrated in Table A- 112. Commercial aircraft do carry some freight, in
addition to passengers, and for this Inventory, the emissions have been split between passenger and freight transportation.
(In previous Inventories, all commercial aircraft emissions were considered passenger transportation.) The amount of
commercial aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on freight
shipped by commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to weigh an
average of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger weight carried
were used to determine percent shares which were used to split the total commercial aircraft emissions estimates. The
remaining transportation and mobile emissions were from sources not considered to be either freight or passenger modes
(e.g., construction/mining and agricultural equipment, lubricants).
The estimates in these tables are derived from the estimates presented in Table A- 109. In addition, estimates of
fuel consumption from DOE (1993 through 2010) were used to allocate rail emissions between passenger and freight
categories.
In 2009, passenger transportation modes emitted 1,322.1 Tg CO2 Eq., while freight transportation modes emitted
475.3 Tg CO2 Eq. Since 1990, the rate of growth of greenhouse gas emissions from freight sources has been 7 percent
higher than emissions from passenger sources, due largely to the rapid increase in emissions associated with medium- and
heavy-duty trucks.
Table A-109: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources [Tg GO? Eq.l
Mode / Vehicle
Type / Fuel Type
Transportation
Total3
On-Road Vehicles
Passenger Cars
Gasoline
Diesel
AFVs
HFCs from
1990
1,548.3
1,235.2
657.4
649.4
7.9
__
1995
1,698.5
1,371.3
646.0
627.8
7.9
,„., __
2000
1,935.8
1,575.1
695.3
667.3
3.7
__
2005
2,022.2
1,682.9
709.5
676.9
4.2
+
28.4
2006
1,999.0
1,679.7
682.9
651.6
4.1
+
27.1
2007
2,008.9
1,682.1
672.0
643.3
4.1
+
24.6
2008
1,895.4
1,603.8
632.5
606.5
3.9
+
22.1
2009
1,816.9
1,557.4
627.4
604.1
3.9
+
19.3
Percent
Change
1990-2009
17%
26%
-5%
-7%
-51%
551%
NA
The decline in CFC emissions is not captured in the official transportation estimates.
A-135
-------
Mobile AC
Light-Duly
Trucks
Gasoline
Diesel
AFVs
HFCs from
Mobile AC
Medium- and
Heavy-Duty
Trucks
Gasoline
Diesel
AFVs
HFCs from
Refrigerated
Transport
Buses
Gasoline
Diesel
AFVs
HFCs from
Comfort
Cooling
Motorcycles
Gasoline
Aircraft
Commercial
Aircraft
Jet Fuel
General Aviation
Aircraft
Jet Fuel
Aviation Gasoline
Military Aircraft
Jet Fuel
Ships and Boats'"
Gasoline
Distillate Fuel
Residual Fuel
HFCs from
Refrigerated
Transport
Rail
Distillate Fuel
Electricity
HFCs from
Comfort Cooling
HFCs from
Refrigerated
Transport
Other Emissions
from Rail
Electricity Use
Pipelines0
Natural Gas
Other
Transportation
Lubricants
Non-
Transportation
Mobile Total
Agricultural
Equipment11
336.6
324.5
11.5
0.6
+
231.1
39.5
190.7
0.9
+
8.4
0.4
8.0
+
+
1.8
1.8
181.2
136.8
136.8
9.6
6.4
3.2
34.8
34.8
45.1
12.6
9.6
22.9
-
39.0
35.8
3.1
+
+
0.1
36.0
36.0
11.8
11.8
128.8
31.4
436.6 512.1 551.3
414.6 469.8 493.0
14.9 20.1 25.9
0.5 0.5 1.4
6.5
277.8
36.8
238.6
0.6
1.7
9.2
0.4
8.7
0.1
+
1.8
1.8
175.4
21.7 31.0
354.6 408.4
37.0 35.8
309.9 361.0
0.3 0.5
7.4 11.1
11.2 12.0
0.4 0.4
10.2 10.6
0.5 0.9
0.1 0.2
1.9 1.7
1.9 1.7
204.4 198.7
143.1 170.9 162.8
143.1 170.9 162.8
8.2
5.4
2.8
24.1
24.1
58.6
14.1
14.9
29.5
+
43.7
40.0
3.1
+
0.5
0.1
38.2
38.2
11.3
11.3
12.1
9.6
2.6
21.3
21.3
61.0
10.0
17.1
33.8
0.1
48.1
42.5
3.5
.:
+
35.2
35.2
12.1
12.1
17.5
15.0
2.5
18.3
18.3
45.2
14.2
11.4
19.6
+
53.0
46.0
4.8
+
2.1
0.1
32.3
32.3
10.2
10.2
146.8 158.3 190.7
39.2 | 47.3
564.0
504.7
26.8
1.3
31.2
418.6
36.3
370.4
0.6
11.4
12.3
0.4
10.8
0.8
0.3
1.9
1.9
173.6
138.5
138.5
18.5
16.2
2.4
16.5
16.5
48.4
14.1
10.9
23.4
+
55.1
48.3
4.5
+
2.2
0.1
32.3
32.3
9.9
9.9
195.8
49.6
570.3
511.8
27.3
1.0
30.1
425.2
36.8
376.4
0.5
11.5
12.5
0.4
10.8
1.0
0.3
2.1
2.1
172.7
139.5
139.5
16.9
14.6
2.2
16.3
16.3
55.2
14.0
11.7
29.5
+
54.3
47.0
5.0
+
2.2
0.1
34.3
34.3
10.2
10.2
194.8
49.0
553.8
496.5
26.9
1.8
28.6
403.1
34.4
356.3
0.8
11.6
12.2
0.4
10.3
1.1
0.4
2.2
2.2
158.7
123.4
123.4
18.8
16.8
2.0
16.4
16.4
37.1
13.6
3.2
20.2
+
50.6
43.6
4.7
+
2.2
0.1
35.7
35.7
9.5
9.5
194.2
45.9
551.0
495.8
26.7
1.9
26.6
365.6
31.1
322.0
0.8
11.6
11.2
0.4
9.3
1.1
0.4
2.2
2.2
142.1
112.5
112.5
15.3
13.4
1.9
14.3
14.3
30.5
13.5
4.8
12.2
+
43.3
36.5
4.4
+
2.2
0.1
35.2
35.2
8.5
8.5
197.7
47.2
64%
53%
133%
227%
NA
58%
-21%
69%
-6%
NA
34%
-1%
17%
40569%
NA
24%
24%
-22%
-18%
-18%
59%
109%
-41%
-59%
-59%
-32%
7%
-50%
-47%
NA
11%
2%
45%
NA
NA
-9%
-2%
-2%
-28%
-28%
54%
50%
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Gasoline 7.3
Diesel 24.1
Construction/
Mining
Equipment6 42.4
Gasoline 4.4
Diesel 38.0
Other Equipment' 55.0
Gasoline 40.3
Diesel 14.7
Transportation
and Non-
Transportation
Mobile Total 1,677.1
"-
8.3
28.7
49.4
4.0
45.4
60.4
42.6
1,845.3
5.8
33.4
55.8
3.1
52.7
63.4
42.5
20.9
2,094.1
9.6
37.7
66.5
6.2
60.3
76.9
52.7
24.2
11.0
38.6
67.9
6.2
61.8
78.3
53.4
24.9
9.6
39.4
68.4
5.1
63.3
77.4
51.8
25.6
5.7
40.3
69.9
5.2
64.8
78.4
52.2
26.2
6.1
41.1
71.2
5.0
66.3
79.3
52.4
26.9
2,212.9 2,194.8 2,203.6 2,089.7 2,014.7
-17%
71%
68%
14%
74%
44%
30%
83%
20%
a Not including emissions from international bunker fuels.
b Fluctuations in emission estimates reflect data collection problems.
c Includes only CO2 from natural gas used to power pipelines; does not include emissions from electricity use or non-CO2 gases.
d Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
e Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
f "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport
equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for
commercial/industrial purposes.
+ Less than 0.05 Tg CO2 Eq.
- Unreported or zero
NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
Table A-110: Transportation and Mobile Source Emissions by Gas [Tg GO? Eq.l
Gas
C02
N2O
CH4
HFC
Total
1990
1,628.4
43.9
4.7
1,677.0
1995
1,767.9
54.0
4.3 1
19.0
1,845.2
2000
1,981.9 1
53.2
3.4
55.7 U
2,094.1
2005
2,100.4
36.9
2.5
72.9
2,212.8
2006
2,086.5
33.6
2.2
72.2
2,194.7
2007
2,102.2
30.3
2.2
68.8
2,203.5
2008
1,996.5
26.1
2.0
64.9
2,089.6
2009
1,928.6
23.9
2.0
60.2
2,014.6
Percent
Change
1990-
2009
18%
-46%
-58%
NA
20%
- Unreported or zero
NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type,1990 to 2009 (Tg G02 Eq.)
Tahle A-111: Greenhouse Gas Emissions from Passenger Transportation [Tg C0? Eq.l
Vehicle Type
1990
On-Road
Vehicles
Passenger Cars
Light-Duty
Trucks
Buses
Motorcycles
Aircraft
General Aviation
Commercial
Aircraft
Recreational
Boats
Passenger Rail
1,004.1
657.4
336.6
8.4
1.8
122.7
9.6
113.1
14.5
4.4
1995
1,093.5
646.0
436.6
9.2
1.8
126.5
8.2
118.3
16.4
4.5
1,220.5
695.3
512.1
11.2
1.9
153.4
12.1
141.3
12.7
5.2
2005
1,274.5
709.5
2006
1,261.1
682.9
2007
1,256.9
672.0
2008
1,200.8
632.5
2009
1,191.7
627.4
Percent
Change
1990-
2009
19%
-5%
551.3
12.0
1.7
154.4
17.5
17.4
6.2
564.0
12.3
1.9
135.3
18.5
116.7
17.3
6.0
570.3
12.5
2.1
136.0
16.9
119.2
17.3
6.5
553.8
12.2
2.2
124.3
18.8
105.5
14.5
6.2
551.0
11.2
2.2
111.4
15.3
96.1
16.9
6.0
64%
34%
24%
-9%
59%
17%
37%
A-137
-------
Total
1,145.7
1,241.0
1,391.8
1,452.5 1,419.7 1,416.8 1,345.8 1,326.1
16%
Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included
in these estimates.
Table A-112: Greenhouse Gas Emissions from Domestic Freight Transportation tTg Clh Eq.l
By Mode
Trucking
Freight Rail
Ships and Other Boats
Pipelines
Commercial Aircraft
Total
1990
231.1
34.5
30.6 1
36.0 1
23.7
356.0
1995
277.8
39.1
42.2
38.2
24.8
422.1
2000
354.6
42.8
48.3 1
35.2 1
29.6 •
510.5
2005
408.4
46.7
27.9
32.2
26.0
541.1
2006
418,
49,
31,
32,
21,
552.
.6
.0
.1
.3
.8
,8
2007
425.2
47.8
37.9
34.3
20.3
565.5
2008
403.1
44.5
22.6
35.7
18.0
523.7
2009
365.6
37.2
13.5
35.2
16.4
467.9
Percent
Change
1990-
2009
58%
8%
-56%
-2%
-31%
31%
Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included
in these estimates.
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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3.3. Methodology for Estimating CELi Emissions from Coal Mining
The methodology for estimating CH4 emissions from coal mining consists of two distinct steps. The first step
addresses emissions from underground mines. For these mines, 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 for surface
mines and post-mining activities. In contrast to the methodology for underground mines, which uses mine-specific data,
the surface mine and post-mining activities analysis consists of multiplying basin-specific coal production by basin-
specific emission factors.
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 CH4 generated from degasification systems, thereby reducing emissions to the atmosphere. Total CH4
emitted from underground mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from
degasification systems, minus CH4 recovered and used.
Step 1.1: Estimate CH4 Liberated from Ventilation Systems
All coal mines with detectable CH4 emissions45 use ventilation systems to ensure that CH4 levels remain within
safe concentrations. Many coal mines do not have detectable levels of CH4, while others emit several million cubic feet
per day (MMCFD) from their ventilation systems. On a quarterly basis, the U.S. Mine Safety and Health Administration
(MSHA) measures CH4 emissions levels at underground mines. MSHA maintains a database of measurement data from
all underground mines with detectable levels of CH4 in their ventilation air. Based on the four quarterly measurements,
MSHA estimates average daily CH4 liberated at each of the underground mines with detectable emissions.
For the years 1990 through 1996, 1998 through 2006, 2008 and 2009, MSHA emissions data were obtained for a
large but incomplete subset of all mines with detectable emissions. This subset includes mines emitting at least 0.1
MMCFD for some years and at least 0.5 MMCFD for other years, as shown in Table A- 113. Well over 90 percent of all
ventilation emissions were concentrated in these subsets. For 1997 and 2007, the complete MSHA databases for all 586
mines (in 1997) and 730 mines (in 2007) with detectable CH4 emissions were obtained. These mines were assumed to
account for 100 percent of CH4 liberated from underground mines. Using the complete database from 1997, the
proportion of total emissions accounted for by mines emitting less than 0.1 MMCFD or 0.5 MMCFD was estimated (see
Table A- 113). The proportion was then applied to the years 1990 through 2006 to account for the less than 5 percent of
ventilation emissions coming from mines without MSHA data.
For 1990 through 1999, average daily CH4 emissions were multiplied by the number of days in the year (i.e., coal
mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For 2000 through 2009,
MSHA provided quarterly emissions. The average daily CH4 emissions were multiplied by the number of days
corresponding to the number of quarters the mine vent was operating. For example, if the mine vent was operational in
one out of the four quarters, the average daily CH4 emissions were multiplied by 92 days. Total ventilation emissions for a
particular year were estimated by summing emissions from individual mines.
45 MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane. Readings below
this threshold are considered non-detectable.
A-139
-------
Table A-113: Mine-Specific Data Used to Estimate Ventilation Emissions
Year Individual Mine Data Used
1990 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1991 1990 Emissions Factors Used Instead of Mine-Specific Data
1992 1990 Emissions Factors Used Instead of Mine-Specific Data
1993 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1994 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1995 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)*
1996 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)*
1997 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
1998 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
1999 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2000 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2001 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2002 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2003 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2004 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2005 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2006 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)*
2007 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
2008 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
2009 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**
* Factor derived from a complete set of individual mine data collected for 1997.
* * Factor derived from a complete set of individual mine data collected for 2007.
Step 1.2: Estimate CH4 Liberated from Degasification Systems
Coal mines use several different types of degasification systems to remove CH4, including vertical wells and
horizontal boreholes to recover CH4 prior to mining of the coal seam. Gob wells and cross-measure boreholes recover
CH4 from the overburden (i.e., GOB area) after mining of the seam (primarily in longwall mines).
MSHA collects information about the presence and type of degasification systems in some mines, but does not
collect quantitative data on the amount of CH4 liberated. Thus, the methodology estimated degasification emissions on a
mine-by-mine basis based on other sources of available data. Many of the coal mines employing degasification systems
have provided EPA with information regarding CH4 liberated from their degasification systems. For these mines, this
reported information was used as the estimate. In other cases in which mines sell CH4 recovered from degasification
systems to a pipeline, gas sales were used to estimate CH4 liberated from degasification systems (see Step 1.3). Finally,
for those mines that do not sell CH4 to a pipeline and have not provided information to EPA, CH4 liberated from
degasification systems was estimated based on the type of system employed. For example, for coal mines employing gob
wells and horizontal boreholes, the methodology assumes that degasification emissions account for 40 percent of total CFLt
liberated from the mine.
Step 1.3: Estimate CH4 Recovered from Degasification Systems and Utilized (Emissions Avoided)
In 2009, fourteen active coal mines had CH4 recovery and use projects. Thirteen mines sold the recovered CH4
to a pipeline, one used the CH4 on site to heat mine ventilation air, and one of the coal mines used CH4 to generate
electricity. One of the mines that sold gas to a pipeline also used CH4 to fuel a thermal coal dryer. In order to calculate
emissions avoided from pipeline sales, information was needed regarding the amount of gas recovered and the number of
years in advance of mining that wells were drilled. Several state agencies provided gas sales data, which were used to
estimate emissions avoided for these projects. Additionally, coal mine operators provided information on gas sales and/or
the number of years in advance of mining. Emissions avoided were attributed to the year in which the coal seam was
mined. For example, if a coal mine recovered and sold CH4 using a vertical well drilled five years in advance of mining,
the emissions avoided associated with those gas sales (cumulative production) were attributed to the well at the time it was
mined through (e.g., five years of gas production). Where individual well data is not available, estimated percentages of
the operator's annual gas sales within the field around the coal mine are attributed to emissions avoidance. For some
mines, individual well data were used to assign gas sales to the appropriate emissions avoided year. In most cases, coal
mine operators provided this information, which was then used to estimate emissions avoided for a particular year.
Additionally, several state agencies provided production data for individual wells.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Step 2: Estimate CH, 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 and post-mining activities, basin-specific coal production was multiplied by a basin-specific
emission factor to determine CH4 emissions.
Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
The first step in estimating CH4 emissions from surface mining and post-mining activities was to define the
geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was
conducted by coal basin as defined in Table A- 114, which presents coal basin definitions by basin and by state.
The Energy Information Administration's (EIA) Annual Coal Report includes state- and county-specific
underground and surface coal production by year. To calculate production by basin, the state level data were grouped into
coal basins using the basin definitions listed in Table A- 114. For two states—West Virginia and Kentucky—county-level
production data was used for the basin assignments because coal production occurred from geologically distinct coal
basins within these states. Table A- 115 presents the coal production data aggregated by basin.
Step 2.2: Estimate Emissions Factors for Each Emissions Type
Emission factors for surface mined coal were developed from the in situ CH4 content of the surface coal in each
basin. Based on an analysis presented in EPA (1993), surface mining emission factors were estimated to be from 1 to 3
times the average in situ CH4 content in the basin. For this analysis, the surface mining emission factor was determined to
be twice the in situ CH4 content in the basin. Furthermore, the post-mining emission factors used were estimated to be 25
to 40 percent of the average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was
determined to be 32.5 percent of the in situ CH4 content in the basin. Table A- 116 presents the average in situ content for
each basin, along with the resulting emission factor estimates.
Step 2.3: Estimate CH4 Emitted
The total amount of CH4 emitted was calculated by multiplying the coal production in each basin by the
appropriate emission factors.
Total annual CH4 emissions are equal to the sum of underground mine emissions plus surface mine emissions
plus post-mining emissions. Table A- 117 and Table A- 118 present estimates of CH4 liberated, used, and emitted for
1990 through 2008. Table A- 119 provides emissions by state.
Table A-114: Coal Basin Definitions by Basin and by State
Basin States
Northern Appalachian Basin Maryland, Ohio, Pennsylvania, West Virginia North
Central Appalachian Basin Kentucky East, Tennessee, Virginia, West Virginia South
Warrior Basin Alabama, Mississippi
Illinois Basin Illinois, Indiana, Kentucky West
South West and Rockies Basin Arizona, California, Colorado, New Mexico, Utah
North Great Plains Basin Montana, North Dakota, Wyoming
West Interior Basin Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Northwest Basin Alaska, Washington
State Basin
Alabama Warrior Basin
Alaska Northwest Basin
Arizona South West and Rockies Basin
Arkansas West Interior Basin
California South West and Rockies Basin
Colorado South West and Rockies Basin
Illinois Illinois Basin
Indiana Illinois Basin
Iowa West Interior Basin
Kansas West Interior Basin
Kentucky East Central Appalachian Basin
Kentucky West Illinois Basin
Louisiana West Interior Basin
Maryland Northern Appalachian Basin
A-141
-------
Mississippi Warrior Basin
Missouri West Interior Basin
Montana North Great Plains Basin
New Mexico South West and Rockies Basin
North Dakota North Great Plains Basin
Ohio Northern Appalachian Basin
Oklahoma West Interior Basin
Pennsylvania. Northern Appalachian Basin
Tennessee Central Appalachian Basin
Texas West Interior Basin
Utah South West and Rockies Basin
Virginia Central Appalachian Basin
Washington Northwest Basin
West Virginia South Central Appalachian Basin
West Virginia North Northern Appalachian Basin
Wyoming North Great Plains Basin
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A-115: Annual Goal Production (Thousand Short Tons)
Basin
Underground Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
Surface Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
Total Coal
Production
N. Appalachia
Cent. Appalachia
Warrior
Illinois
S. West/Rockies
N. Great Plains
West Interior
Northwest
1990
423,556
103,865
198,4 um
17,53 lB
69,167B
32,754H
i, 122m
105
-
602,753
60,761
94,343B
11,4131
72,OOoB
43,863
249,356B
64,310
6,707
931,068
164,626
292J55B
28,944B
141,167B
76,6 I?B
251,078
64,4 15m
6,707
1994
399,102
100,122
170,893
14,471
69,050
41,681
2,738
147
-
634,401
44,960
106,129
8,795
51,868
49,119
308,279
58,791
6,460
937,594
145,082
277,022
23,266
120,918
90,800
311,017
58,938
6,460
1995
396,249
98,103
166,495
17,605
69,009
42,994
2,018
25
-
636,726
39,372
106,250
7,036
40,376
46,643
331,367
59,116
6,566
937,115
137,475
272,745
24,641
109,385
89,637
333,385
59,141
6,566
1996
409,850
106,729
171,845
18,217
67,046
43,088
2,788
137
-
654,007
39,788
108,869
6,420
44,754
43,814
343,404
60,912
6,046
965,131
146,517
280,714
24,637
111,800
86,902
346,192
61,049
6,046
1997
420,657
112,135
177,720
18,505
64,728
44,503
2,854
212
-
669,271
40,179
113,275
5,963
46,862
48,374
349,612
59,061
5,945
988,783
152,314
290,995
24,468
111,590
92,877
352,466
59,273
5,945
1998
417,729
116,718
171,279
17,316
64,463
45,983
1,723
247
-
699,806
41,043
108,345
5,697
45,715
49,635
385,438
57,951
5,982
1,013,828
157,761
279,624
23,013
110,178
95,618
387,161
58,198
5,982
1999
391,791
107,575
157,058
14,799
63,529
46,957
1,673
200
-
708,639
33,928
107,507
4,723
40,474
50,349
407,683
58,309
5,666
998,310
141,503
264,565
19,522
104,003
97,306
409,356
58,509
5,666
2000
372,766
105,374
150,584
15,895
53,720
45,742
1,210
241
-
700,608
34,908
110,479
4,252
33,631
49,587
407,670
54,170
5,911
973,765
140,282
261,063
20,147
87,351
95,329
408,880
54,411
5,911
2001
380,627
107,025
152,457
15,172
54,364
51,193
-
415.623
-
745,306
35,334
116,983
4,796
40,894
52,180
438,367
50,613
6,138
1,021,446
142,360
269,440
19,967
95,258
103,373
438,367
51,028
6,138
2002
357,384
98,643
137,224
14,916
54,016
52,121
-
464
-
735,912
30,088
111,340
6,320
39,380
50,006
441,346
50,459
6,973
991,838
128,731
248,564
21,236
93,396
102,127
441,346
50,923
6,973
2003
352,785
98,369
130,724
15,375
51,780
56,111
32
394
-
717,869
27,370
99,419
8,437
36,675
41,237
444,007
53,411
7,313
971,297
125,739
230,143
23,812
88,455
97,348
444,039
53,805
7,313
2004
367,558
106,915
128,559
16,114
56,320
59,039
201
410
-
743,553
28,174
103,968
9,742
34,016
42,558
466,224
51,706
7,165
1,008,000
135,089
232,527
25,856
90,336
101,597
466,425
52,116
7,165
2005
368,611
111,151
123,083
13,295
59,180
60,865
572
465
-
762,191
28,873
112,222
11,599
33,702
42,756
474,056
52,263
6,720
1,025,864
140,024
235,305
24,894
92,882
103,621
474,628
52,728
6,720
2006
359,020
107,827
117,738
10,737
61,726
59,670
840
482
-
802,975
28,376
118,388
11,889
33,362
36,798
518,136
52,021
4,005
1,054,162
136,203
236,126
22,626
95,088
96,468
518,976
52,503
4,005
2007
351,791
106,024
110,103
11,462
61,924
58,815
2,869
594
-
793,689
26,121
116,226
11,410
33,736
34,310
523,695
46,867
1,324
1,039,178
132,143
226,328
22,872
95,660
93,125
526,564
47,462
1,324
2008
357,074
105,228
114,998
12,281
64,609
55,781
3,669
508
-
813,321
30,413
118,962
11,172
34,266
34,283
538,387
44,361
1,477
1,170,395
135,641
233,960
23,453
98,875
90,064
542,056
44,869
1,477
2009
332,061
99,629
98,689
11,505
67,186
50,416
4,248
388
-
740,175
26,552
97,778
10,731
34,837
32,167
496,290
39,960
1,860
1,072,236
126,181
196,467
22,236
102,023
82,583
500,538
40,348
1,860
Source for 1990-2009 data: EIA (1990 through 2009), Annual Coal Report. U.S. Department of Energy, Washington, DC, Table 1.
Note: Totals may not sum due to independent rounding.
A-143
-------
Table A- 116: Goal Surface and Post-Mining CH* Emission Factors (ft3 per Short Ton)
Surface Average Underground Average Surface Mine Post-Mining Post Mining
Basin in situ Content In situ Content Factors Surface Factors Underground
Northern Appalachia
Central Appalachia (WV)
Central Appalachia (VA)
Central Appalachia (E KY)
Warrior
Illinois
Rockies (Piceance Basin)
Rockies (Uinta Basin)
Rockies (San Juan Basin)
Rockies (Green River Basin)
Rockies (Raton Basin)
N. Great Plains (WY, MT)
N. Great Plains (ND)
West Interior (Forest City, Cherokee
Basins)
West Interior (Arkoma Basin)
West Interior (Gulf Coast Basin)
Northwest (AK)
Northwest (WA)
59.5
24.9
24.9
24.9
30.7
34.3
33.1
16.0
7.3
33.1
33.1
20.0
5.6
34.3
74.5
11.0
16.0
16.0
138.4
136.8
399.1
61.4
266.7
64.3
196.4
99.4
104.8
247.2
127.9
15.8
15.8
64.3
331.2
127.9
160.0
47.3
119.0
49.8
49.8
49.8
61.4
68.6
66.2
32.0
14.6
66.2
66.2
40.0
11.2
68.6
149.0
22.0
32.0
32.0
19.3
8.1
8.1
8.1
10.0
11.1
10.8
5.2
2.4
10.8
10.8
6.5
1.8
11.1
24.2
3.6
1.8
5.2
45.0
44.5
129.7
20.0
86.7
20.9
63.8
32.3
34.1
80.3
41.6
5.1
5.1
20.9
107.6
41.6
52.0
15.4
Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination ofthe Gas Contents ofU.S. CoalBasins, 1983 U.S. DOE
Report (DOE/METC/83-76), Methane Recovery from Coalbeds: A Potential Energy Source, 1986-88 Gas Research Institute Topical Reports, A
Geologic Assessment of Natural Gas from Coal Seams; Surface Mines Emissions Assessment, U.S. EPA Draft Report, November 2005.
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-117: Underground Coal Mining Clh Emissions [Billion Cubic Feetl
Activity 1990 1991 1992 1993 1994 1995
Ventilation Output 112 NA NA 95 96 100
Adjustment Factor for Mine
1996
90
1997 1998 1999 2000
96
94 92 87
Data* 97.8% NA NA 97.8% 97.8% 91.4% 91.4% 100.0% 97.8% 97.8% 97.8%
Adjusted Ventilation Output 114 NA NA 97 98 109
Degasification System
Liberated 54 NA NA 45 46 36
Total Underground
Liberated 168 164 162 142 144 146
Recovered & Used (14) (15) (17) (23) (27) (30)
Total 154 150 145 120 117 116
* Refer to Table A- 113.
Note: Totals may not sum due to independent rounding.
Table A-118: Total Goal Mining Clh Emissions (Billion Cubic Feet)
Activity 1990 1991 1992 1993 1994
Underground Mining 154 150 145 120 117
Surface Mining 30 28 28 28 29
Post-Mining (Underground) 19 18 18 16 17
Post-Mining (Surface) 55 545
Total 208 201 195 167 168
Note: Totals may not sum due to independent rounding.
99
52
150
(37)
113
1995
116
28
17
5
166
96
43
139
(28)
111
1996
113
29
18
5
165
96 94 89
49 40 45
146 134 135
(35) (31) (37)
110 103 98
1997 1998 1999
111 110 103
30 31 31
18 18 17
555
164 165 156
2001
84
97.8%
86
49
135
(41)
95
2000
98
30
17
5
149
2002
79
2003 2004
76 83
97.8% 97.8% 97.8%
80
51
131
(43)
88
2001
95
33
17
5
149
77 84
50 45
127 130
(38) (40)
89 90
2002 2003
88 89
32 31
16 16
5 5
140 141
2005
75
97.8%
77
48
124
(37)
87
2004
90
32
16
5
144
2006
79
97.8%
80
54
134
(46)
88
2005
87
33
16
5
141
2007
81
100.0%
81
44
125
(37)
88
2006
88
35
15
6
144
2008
100
2009
114
99.0% 99.0%
101
49
150
(40)
110
2007 2008
88 110
34 35
15 15
6 6
143 166
115
51
166
(41)
125
2009
125
32
14
5
176
Table A- 119: Total Goal Mining CJh Emissions by State (Million Cubic Feet)
State 1990 1994 1995 1996 1997
Alabama 32,272 29,630 33,735 29,556 26,426
Alaska 63 1 58 63 55 54
Arizona 192 222 203 177 199
Arkansas 7 1 8 5 4 3
California 1 -
Colorado 10,325 9,192 7,582 5,972 9,189
Illinois 10,502 10,585 11,563 10,876 8,534
Indiana 2,795 2,495 2,025 2,192 2,742
Iowa 3oB 4 ...
Kansas 57 B 23 23 19 29
Kentucky 10,956 11,259 10,269 8,987 10,451
Louisiana 81 1 89 95 82 91
Maryland 519B 237 237 259 267
Mississippi -1
Missouri 211 1 67 44 57 32
Montana 1,749 1,936 1,834 1,756 1,906
Mew Mexico 451 1 679 586 408 459
North Dakota 38oB 420 392 389 385
Ohio 5,065 4,583 4,189 4,068 4,349
Oklahoma 285 1 359 323 286 385
1998
26,440
50
192
4
9,181
7,847
2,878
+
27
10,005
82
251
0
30
1,992
489
389
4,350
395
1999
25,702
58
200
4
9,390
7,810
2,650
+
33
9,561
76
225
1
31
1,911
497
405
3,914
469
2000 2001
23,342 21,896
61 56
223 228
2 2
10,808 11,117
8,542 7,270
2,231 3,373
+ +
16 14
9,105 9,363
94 95
331 340
57 43
35 29
1,783 1,820
464 630
407 397
3,519 3,619
454 620
2002
18,686
43
217
2
12,082
5,972
3,496
+
16
8,464
97
401
165
20
1,738
1,280
401
2,831
660
2003
19,288
40
205
1
13,216
4,744
3,821
+
12
8,028
103
391
264
43
1,719
1,864
401
2,649
620
2004
18,246
56
216
1
12,582
5,798
3,531
+
6
7,926
97
411
256
46
1,853
2,052
390
3,183
849
2005
15,912
54
205
1
13,608
6,586
3,702
+
14
7,494
106
421
254
48
1,870
3,001
390
3,385
877
2006
14,699
53
139
4
13,102
6,954
4,029
+
34
9,135
105
435
271
31
1,931
2,970
396
3,413
658
2007
17,159
49
135
144
13,180
4,493
4,347
+
33
9,278
80
261
253
19
2,016
2,660
385
2,672
774
2008
21,120
55
136
237
12,998
7,759
5,452
-
18
10,641
98
325
203
20
2,076
3,479
386
3,959
970
2009
23,975
69
127
119
14,100
7,322
6,155
-
15
12,617
94
273
246
36
1,804
3,904
390
4,746
646
A-145
-------
Pennsylvania
Tennessee
Texas
Utah
Virginia
Washington
West Virginia
Wyoming
Total
22,735 •
296
\A2(M
3,587 •
46,13?B
1861
49,039 1
8,496 1
207,844
24,024
101
1,339
2,626
26,742
182
30,664
10,912
168,433
25,611
112
1,347
2,570
20,059
181
30,552
12,185
165,785
26,440
143
1,411
2,810
19,771
170
36,384
12,838
165,109
30,026
148
1,364
3,566
16,851
167
33,554
12,994
164,171
30,888
116
1,345
3,859
13,978
173
35,566
14,549
165,075
24,867
119
1,357
3,633
13,321
153
33,599
15,607
155,568
24,830
99
1,240
2,816
12,065
159
30,563
15,725
149,371
22,252
142
1,152
2,080
11,506
172
33,985
17,147
149,348
19,668
142
1,157
2,709
11,227
217
31,405
17,352
140,449
20,281
124
1,215
3,408
11,906
232
28,474
17,497
140,544
20,020
136
1,173
5,253
11,389
210
29,465
18,435
143,581
18,289
140
1,175
4,787
8,790
196
30,612
18,784
140,698
18,727
117
1,165
5,445
9,830
96
29,510
20,752
144,004
19,519
120
1,073
3,678
10,118
+
29,654
20,974
143,076
21,044
105
998
5,524
9,334
-
37,406
21,601
165,945
23,216
84
898
5,449
8,172
-
41,241
19,903
175,598
- Zero Cubic Feet
+ Does not exceed 0.5 Million Cubic Feet
Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining activities. The following states have neither underground nor surface
mining and thus report no emissions as a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, Minnesota, Nebraska, Nevada, New
Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota, Vermont, and Wisconsin.
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
3.4. Methodology for Estimating CH4 and COi Emissions from Natural Gas
Systems
The following steps were used to estimate CH4 and non-energy CO2 emissions from natural gas systems.
Step 1: Calculate Emission Estimates for Base Year 1992 Using EPA Adjusted GRI/EPA Study
The first step in estimating CH4 and non-energy related (i.e., fugitive, vented and flared) CO2 emissions from
natural gas systems was to develop a detailed base year estimate of emissions. The study by EPA/GRI (1996) divides the
industry into four stages to construct a detailed emission inventory for the year 1992. These stages include: field
production, processing, transmission and storage (i.e., both underground and liquefied gas storage), and distribution. This
study produced emission factors and activity data for over 100 different emission sources within the natural gas system.
Emissions for 1992 were estimated by multiplying activity levels by emission factors for each system component and then
summing by stage.
Since publication activity data for some of the components in the system have been updated based on publicly
available data. For other sources where annual activity data are not available, a set of industry activity factor drivers was
developed that can be used to update activity data. Table A-120 through Table A-123 display the 2009 activity levels,
CH4 emission factors, and CH4 emissions for each stage. These data are shown to illustrate the kind of data used to
calculate CH4 and non-energy CO2 emissions from all stages. Many emission factors determined by EPA/GRI (1996)
were assumed to be representative of emissions from each source type over the period 1990 through 2009.
However, several emission factors have been updated since publication of the EPA/GRI 1996 study. Notably,
emission factors for gas well cleanups (EPA 2006a, HPDI 2009), condensate storage tanks (EPA 1999, HPDI 2009, TERC
2009), and centrifugal compressors (EPA 2006b, WGC 2009) have been revised. Emissions for gas well completions and
workovers (re-completions) with hydraulic fracturing (i.e. unconventional) (EPA 2004, 2007), which were not included in
the EPA/GRI study, have also been added. The EPA/GRI study and Inventory did, however, include an estimate for well
completions without hydraulic fracturing under the source category Completion Flaring.. The revised 2009 emission factor
for centrifugal compressors will be reevaluated in the next Inventory cycle.
For most sources, the CH4 emission factors were adjusted for CO2 content when estimating fugitive and vented
non-energy CO2 emissions. In the case of non-energy CO2 emissions from flared sources, acid gas removal units and
condensate tanks, specific industry data related to those sources was used to derive their respective emission factors.
The activity levels and CH4 emission factors in Table A-120 are arranged into regions designated by the National
Energy Modeling System (NEMS) for Oil and Gas Supply. NEMS for Oil and Gas Supply splits the continental United
States into 6 regions: Northeast, Midcontinent, Rocky Mountain, South West, West Coast and Gulf Coast. GRI, however,
does not evaluate activity data for each of these regions separately. The GRI national AF estimates were allocated to the
NEMS oil and gas supply module regions using the NEMS regional gas well counts to national well count ratios.
Step 2: Collect Aggregate Statistics on Main Driver Variables
As detailed data on each of the over 100 sources were not available for the period 1990 through 2009, activity
levels were estimated using aggregate statistics on key drivers, including: number of producing wells (EIA 2010a-b, New
Mexico 2010a-b, Texas 2010a-b), number of gas plants (AGA 1991-1997; OGJ 1998-2010), number of shallow and deep
offshore platforms (BOEMRE 2010a-d), miles of transmission pipeline (OPS 2010a), miles of distribution pipeline (OPS
2010b), miles of distribution services (OPS 201 Ob), and energy consumption (EIA 2010c). Table A-124 provides the
activity levels of some of the key drivers in the natural gas analysis.
Step 3: Estimate CH, Emissions for Each Year and Stage
Emissions from each stage of the natural gas industry were estimated by multiplying the activity factors by the
appropriate emission factors, summing all sources for each stage and then accounting for CH4 reductions reported to the
Natural Gas STAR Program and CH4 reductions resulting from regulations such as National Emission Standards for
Hazardous Air Pollutants (NESHAP) regulations.
Industry partners report CH4 emission reductions by project to the Natural Gas STAR Program. The reductions
are estimates using actual measurement data or equipment-specific emission factors. The reductions undergo quality
assurance and quality control checks to identify errors, inconsistencies, or irregular data before being incorporated into the
Inventory. These quality assurance and quality control checks include matching Natural Gas STAR reported reductions to
A-147
-------
specific inventory sources to make sure that a reported reduction for one source is not greater than the emission estimate
for that source. Total emissions are estimated by adding the emission estimates from each stage. The base year of the
inventory is 1992; therefore any reductions reported for 1992 or earlier are considered to be already included in the base-
year emission factors and are not subtracted from the inventory estimate. If the reported reduction occurred between 1990
and 1992, then the reduction is added back into the estimate for the appropriate year(s). The reductions are also adjusted
to remove the sunsetting time period which is relevant to Natural Gas STAR accounting but not the Inventory. For
example, replacing a gas-assisted pump with an electric pump permanently reduces the vented methane emissions from
that source, even after the Natural Gas STAR sunsetting period. CH4 emission reductions from the Natural Gas STAR
Program beyond the efforts reflected in the 1992 base year are summarized in Table A-125.
The 1990 Clean Air Act (CAA) sets the limits on the amount of hazardous air pollutants (HAPs) that can be
emitted in the United States. The NESHAP regulations set the standards to limit emissions of HAPs. The emission sources
are required to use the Maximum Achievable Control Technology, giving the operators flexibility to choose the type of
control measure(s) to implement. In regards to the oil and natural gas industry, the NESHAP regulation addresses HAPs
from the oil and natural gas production sectors and the natural gas transmission and storage sectors of the industry.
Though the regulation deals specifically with HAPs reductions, methane emissions are also reduced.
The NESHAP regulation requires that glycol dehydration unit vents and storage tanks that have HAP emissions
and exceed a gas throughput and liquids throughput value, respectively, be connected to a closed loop emission control
system that reduces emissions by 95 percent. Also, gas processing plants exceeding the threshold natural gas throughput
limit are required to routinely implement Leak Detection and Survey (LDAR) programs. The emissions reductions
achieved as a result of NESHAP regulations were estimated using data provided in the Federal Register Background
Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the
enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control
measures already in place that would be impacted by the regulation. The reductions are representative of the control
measures in both the oil and natural gas industry. CH4 emission reductions from regulations, such as NESHAP, are
summarized in Table A-126.
Additionally, some states, such as Wyoming, may require that natural gas produced during well completions not
be vented. In these regions emissions from natural gas well completions and re-completions are either recovered for sales
or must be flared. The volume of gas recovered by bringing equipment to the wellsite for the treatment and injection of
the produced completion gas into the sales pipeline is reported by Natural Gas STAR. The remaining volume of
completion gas from states that do not allowing the venting of this gas is flared.
Step 4: Estimate CO2 Emissions for Each Year and Stage
The same procedure for estimating CH4 emissions holds true for estimating non-energy related CO2 emissions,
except the emission estimates are not corrected for reductions due to the Natural Gas STAR program or other regulations.
Produced natural gas is composed of primarily CH4, but as shown in Table A-131, the natural gas contains, in
some cases, as much as 8 percent CO2. The same vented and fugitive emissions of natural gas that led to CH4 emissions
also contain a certain volume of CO2. Accordingly, the CO2 emissions for each sector can be estimated using the same
methane activity and emissions factors for these vented and fugitive sources. The primary difference is that EPA/GRI
emission factors are adjusted for CO2 content in each sector.
Using the default CH4 content of produced natural gas from EPA/GRI (1996) of 78.8 percent, the corresponding
amount of CO2 emissions from the production sector can be estimated. Each sector of the natural gas system has varying
CO2 contents, similar to the way the CH4 content varies among the sectors. Table A-131 shows the CO2 content for the
different well types in the production sector of the natural gas system. In the estimation of CO2 emissions from the
production sector, the production sector CH4 emission factors were used as a basis to estimate CO2 emissions; however,
they were converted to CO2 emission factors by multiplication by a conversion factor. This conversion factor is the ratio
of CO2 content in the gas stream (in this case the production concentrations provided in Table A-131) to the corresponding
CH4 content of the same gas stream. The three exceptions to this methodology are CO2 emissions from flares, acid gas
removal units, and condensate tanks. In the case of flare emissions, a direct CO2 emission factor from EIA (1996) was
used. This emission factor was applied to the portion of offshore gas that is not vented and all of the gas reported as
vented and flared onshore by EIA. The amount of CO2 emissions from an acid gas unit in a processing plant is equal to
the difference in CO2 concentrations between produced natural gas and pipeline quality gas applied to the throughput of
the plant. This methodology was applied to the national gas throughput using national average CO2 concentrations in
produced gas (3.45 percent) and transmission quality gas (1 percent). For condensate tanks, a series of E&P Tank (EPA
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
1999) simulations provide the total CO2 vented per barrel of condensate throughput from fixed roof tank flash gas for
condensate gravities of API 45 degree and higher. The ratios of emissions to throughput were used to estimate the CO2
emission factor for condensate passing through fixed roof tanks. The detailed source emission estimates for CH4 and CO2
from the production sector are presented in Table A-127 and
Table A-132, respectively.
In the processing sector, the CO2 content of the natural gas remains the same as the CO2 content in the
production sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only
minimally treated after being produced and then transported to natural gas processing plants via gathering pipelines. The
CO2 content in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline
quality gas. The EPA/GRI study estimates the average CH4 content of natural gas in the processing sector to be 87 percent
CH4. Consequently, the processing sector CH4 emission factors were proportioned to reflect the CO2 content of either
produced natural gas or pipeline quality gas using the same methodology as the production sector. The detailed source
emission estimates for CH4 and CO2 from the processing sector are presented in Table A-128 and Table A-133,
respectively.
For the transmission sector, CO2 content in natural gas transmission pipelines was estimated for the top twenty
transmission pipeline companies in the United States (separate analyses identified the top twenty companies based on gas
throughput and total pipeline miles). The weighted average CO2 content in the transmission pipeline quality gas in both
cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the CO2
emissions for the transmission sector the CH4 emission factors were proportioned from the 93.4 percent CH4 reported in
EPA/GRI (1996) to reflect the 1 percent CO2 content found in transmission quality natural gas. The detailed source
emissions estimates for CH4 and CO2 for the transmission sector are presented in Table A-129 and Table A-134,
respectively.
The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the
transmission sector. The CH4 content (93.4 percent) and CO2 content (1 percent) are identical due to the absence of any
further treatment between sector boundaries. Thus, the CH4 emissions factors were converted to CO2 emission factors
using the same methodology as discussed for the transmission sector. The detailed source emission estimates for CH4 and
CO2 for the distribution sector are presented in Table A-130 and Table A-135, respectively.
Because Partners report only CH4 emission reductions to the Natural Gas STAR Program, there was no need to
adjust for the Natural Gas STAR program in the CO2 emissions estimates for any of the sectors in the natural gas system.
The impact of regulations, such as NESHAP, on CO2 emission reductions are not currently addressed in the CO2 emission
estimates.
Table A-120:2009 Data and Clh Emissions tMgl for the Natural Gas Production Stage
Activity
Gas Wells
NE - Associated Gas Wells1'2
NE - Non-associated Gas Wells (less
Unconventional)
NE - Unconventional Gas Wells3
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
2009 EPA Inventory Values
Activity Data
Emission Factor
Emissions (Mg)
46,9 14 wells
183,834 wells
0 wells
368 heaters
130,522 separators
1,213 dehydrators
9,230 meters
184 compressors
24 compressors
3 stations
87,900 miles
266 completions/yr
0 completions/yr
8, 170 Wells
89,343 controllers
919 active pumps
NA
7.59scfd/well
NEscfd/well
15.13scfd/heater
0.96scfd/sep
23.15scfd/dehy
9.59scfd/meter
284.95 scfd/comp
16,182 scfd/comp
8,776.43 scfd/station
56.57 scfd/mile
780 scf/comp
7,694,435 scf/comp
2,706 scf/well
367 scfd/device
264 scfd/pump
NA
9,804.22
0
39.10
877.52
197.33
622.25
368.26
2,730.18
185.09
34,954.19
4.00
0
425.89
230,608.23
1,705.46
A-149
-------
Activity
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Slowdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
2009 EPA Inventory Values
Activity Data
354,971 MMscf/yr
398,396 MMscf/yr
0 MMbbl/yr
0 MMbbl/yr
OMMHPhr
7,997 workovers/yr
0 workovers/yr
75,923 LP Gas Wells
132, 103 vessels
87,900 miles (gathering)
1 84 compressors
1 84 compressors
3 52,630 PRV
2 1,975 miles
Midcontinent
Gas Wells
MC - Associated Gas Wells1'2
MC - Non-associated Gas Wells
MC - Unconventional Gas Wells
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Slowdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
72,935 wells
73,9 13 wells
13, 277 wells
3 5, 399 heaters
37,928 separators
12,518dehydrators
119,028 meters
9,852 compressors
16 compressors
2 stations
68,424 miles
126 completions/yr
575 completions/yr
3,875 wells
135,232 controllers
12,381 active pumps
3,664,068 MMscf/yr
4, 112,309 MMscf/yr
13 MMbbl/yr
13 MMbbl/yr
16,304 MMHPhr
3,793 workovers/yr
1,328 workovers/yr
30,526 LP Gas Wells
85,845 vessels
68,424 miles (gathering)
9,852 compressors
9,852 compressors
167,248 PRV
17, 106 miles
Rocky Mountain
Gas Wells
Emission Factor
1,056 scf/MMscf
293.3scf/MMscf
21.87scf/bbl
4.37scf/bbl
0.26 scf/HPhr
2,612scf/w.o.
7,694,435 scf/w.o.
1,36 1,786 scfy/LP well
83 scfy/vessel
329 scfy/mile
4,0 16 scfy/comp
8,985 scfy/comp
36 scfy/PRV
712scf/mile
^^M
NA
7.54scfd/well
4.71 scfd/well
15.08scfd/heater
0.95 scfd/sep
96.70 scfd/dehy
9.56scfd/meter
284.13scfd/comp
16,135 scfd/comp
8,751. 13 scfd/station
56.40 scfd/mile
778 scf/comp
7,672,247 scf/comp
2,699 scf/well
366 scfd/device
263 scfd/pump
1,053 scf/MMscf
292.5 scf/MMscf
302.75 scf/bbl
60.55 scf/bbl
0.25 scf/HPhr
2,604 scf/w.o.
7,672,247 scf/w.o.
7 11, 3 85 scfy/LP well
83 scfy/vessel
328 scfy/mile
4,005 scfy/comp
8,960 scfy/comp
36 scfy/PRV
710 scf/mile
^^H
Emissions (Mg)
7,217.80
2,250.58
0
0
0
402.26
0
1,991,319.86
211.21
556.73
14.22
31.81
245.75
301.34
^m
NA
3,918.89
439.78
3,753.52
254.26
8,510.31
8,001.30
19,679.53
1,814.87
123.04
27,130.93
1.89
84,966.30
201.41
348,046.02
22,905.82
74,288.52
23,163.89
75,803.69
15,160.74
79,973.26
190.24
196,190.87
418,245.79
136.85
432.13
759.96
1,700.15
116.22
233.90
•
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity
RM - Associated Gas Wells1'2
RM - Non-associated Gas Wells
RM - Unconventional Gas Wells
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Blawdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Coal Bed Methane
Powder River
2009 EPA Inventory Values
Activity Data
14,948 wells
75,4 18 wells
23 ,4 17 wells
45,069 heaters
49,3 19 separators
14,190dehydrators
114, 129 meters
10,971 compressors
32 compressors
4 stations
125,062 miles
143 completions/yr
0 completions/yr
4,393 wells
144,695 controllers
17,593 active pumps
4, 153,459 MMscf/yr
4,661, 570 MMscf/yr
12 MMbbl/yr
12 MMbbl/yr
1 8,481 MMHPhr
4,299 workovers/yr
2,342 workovers/yr
31, 148 LP Gas Wells
108,578 vessels
125,062 miles (gathering)
10,971 compressors
10,971 compressors
189,586 PRV
3 1,266 miles
gal produced
28,577,932,572 water
South West
Gas Wells
SW - Associated Gas Wells1'2
SW - Non-associated Gas Wells
SW - Unconventional Gas Wells
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
56,810wells
27,802 wells
13,740 wells
11, 25 8 heaters
23,347 separators
5,964dehydrators
50,678 meters
5,650 compressors
16 compressors
2 stations
60,894 miles
60 completions/yr
3,594 completions/yr
1,846 wells
55, 168 controllers
2,534 active pumps
Emission Factor
NA
35.62scfd/well
6.97scfd/well
57.43 scfd/heater
121.42scfd/sep
90.68 scfd/dehy
52.65 scfd/meter
266.44 scfd/comp
15,131scfd/comp
8,206.34 scfd/station
52.89 scfd/mile
729 scf/comp
7,1 94,624 scf/comp
2,531scf/well
343 scfd/device
247 scfd/pump
987 scf/MMscf
274.3 scf/MMscf
21.87scf/bbl
4.37scf/bbl
0.24 scf/HPhr
2,442 scf/w.o.
7, 194,624 scf/w.o.
7 10,583 scfy/LP well
78 scfy/vessel
307 scfy/mile
3,756 scfy/comp
8,402 scfy/comp
34 scfy/PRV
666 scf/mile
Gg/gallon water
2.002E-09 drainage
^^^M
NA
37.24scfd/well
36.52scfd/well
58.97 scfd/heater
124.68 scfd/sep
93. 12 scfd/dehy
54.06 scfd/meter
273.59 scfd/comp
15,536 scfd/comp
8,426.34 scfd/station
54.31 scfd/mile
749 scf/comp
7,3 87,499 scf/comp
2,598 scf/well
353 scfd/device
253 scfd/pump
Emissions (Mg)
NA
18,882.55
1,147.38
18,196.66
42,097.53
9,046.43
42,238.56
20,549.03
3,403.78
230.76
46,501.51
2.01
0
214.10
349,219.02
30,521.75
78,968.46
24,623.14
5,054.59
1,010.92
85,011.32
202.20
324,485.75
426,283.58
162.32
740.65
793.54
1,775.27
123.54
400.89
57,494.47
^H
NA
7,278.09
3,527.07
4,667.26
20,462.48
3,904.30
19,258.70
10,866.09
1,747.51
118.47
23,249.07
0.87
511,365.92
92.40
136,716.45
4,514.24
A-151
-------
Activity
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Slowdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
2009 EPA Inventory Values
Activity Data
1,745,768 MMscf/yr
1,959,335 MMscf/yr
7 MMbbl/yr
7 MMbbl/yr
7,768 MMHPhr
1 ,807 workovers/yr
1 ,374 workovers/yr
11, 482 LP Gas Wells
40,569 vessels
60,894 miles (gathering)
5,650 compressors
5,650 compressors
79,686 PRV
15, 224 miles
West Coast
Gas Wells
WC - Associated Gas Wells1'2
WC - Non-associated Gas Wells
WC - Unconventional Gas Wells3
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Blawdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
24,548 wells
1,786 wells
0 wells
1,786 heaters
1,304 separators
256 dehydrators
3, 364 meters
2,074 compressors
8 compressors
1 stations
16, 367 miles
3 completions/yr
0 completions/yr
79 wells
1,790 con trailers
1,2 13 active pumps
75,055 MMscf/yr
84 ,237 MMscf/yr
0 MMbbl/yr
0 MMbbl/yr
334 MMHPhr
78 workovers/yr
0 workovers/yr
738 LP Gas Wells
3,346 vessels
16,367 miles (gathering)
2,074 compressors
2,074 compressors
3, 426 PRV
4,092 miles
GulfCoast
Gas Wells
Emission Factor
l,014scf/MMscf
281.6scf/MMscf
302.75 scf/bbl
60.55 scf/bbl
0.25 scf/HPhr
2,507 scf/w.o.
7,387,499 scf/w.o.
864,974 scfy/LP well
80 scfy/vessel
316scfy/mile
3,856 scfy/comp
8,627 scfy/comp
35 scfy/PRV
684 scf/mile
^^^H
NA
42.49 scfd/well
NE
67.29 scfd/heater
142.27 scfd/sep
106.25 scfd/dehy
61.68 scfd/meter
3 12. 19 scfd/comp
17,728 scfd/comp
9,615. 15 scfd/station
61.97scfd/mile
855 scf/comp
8,429,754 scf/comp
2,965 scf/well
402 scfd/device
289 scfd/pump
l,157scf/MMscf
321.3scf/MMscf
2 1.87 scf/bbl
4.37 scf/bbl
0.28 scf/HPhr
2,861 scf/w.o.
8,429,754 scf/w.o.
1,49 1,925 scfy/LP well
91 scfy/vessel
360 scfy/mile
4,400 scfy/comp
9,844 scfy/comp
40 scfy/PRV
780 scf/mile
^^^m
Emissions (Mg)
34,081.57
10,626.97
40,817.37
8,163.47
36,689.57
87.27
195,497.16
191,288.32
62.27
370.30
419.62
938.74
53.32
200.44
^m
NA
533.50
0
844.89
1,303.93
191.54
1,458.59
4,550.68
997.03
67.59
7,130.46
0.04
0
4.53
5,060.58
2,465.10
1,671.97
521.34
0
0
1,799.92
4.30
0
21,195.07
5.86
113.57
175.73
393.14
2.62
61.48
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity
2009 EPA Inventory Values
Activity Data
Emission Factor
Emissions (Mg)
GC - Associated Gas Wells '
GC - Non-associated Gas Wells
GC - Unconventional Gas Wells3
Field Separation Equipment
Heaters
Separators
Dehydrators
Meters/Piping
Gathering Compressors
Small Reciprocating Compressors
Large Reciprocating Compressors
Large Reciprocating Stations
Pipeline Leaks
Drilling and Well Completion
Completion Flaring
Unconventional Gas Well Completions
Well Drilling
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without Control Devices
Condensate Tanks with Control Devices
Compressor Exhaust Vented
Gas Engines
Well Workovers
Conventional Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas Wells)
Slowdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Coal Bed Methane
Black Warrior
Offshore Platforms
Shallow water Gas Platforms (GoM and
Pacific)
Deepwater Gas Platforms (GoM and
Pacific)
27,134 wells
75,046 wells
0 wells
16,810 heaters
49,381 separators
10,775 dehydrators
87,505 meters
6,079 compressors
24 compressors
3 stations
98,419 miles
109 completions/yr
0 completions/yr
3,335 wells
52,157 controllers
2,477 active pumps
3,153,749 MMscf/yr
3,539,56 IMMscf/yr
33 MMbbl/yr
33 MMbbl/yr
14,033 MMHPhr
3,265 workovers/yr
0 workovers/yr
30,994 LP Gas Wells
76,966 vessels
98,419 miles (gathering)
6,079 compressors
6,079 compressors
143,954 PRV
24,605 miles
5,026 wells
Shallow water
2,072 gas platforms
Deepwater gas
40 platforms
NA
40.97 scfd/well
NEscfd/well
64.88 scfd/heater
137.17scfd/sep
102.45 scfd/dehy
59.48 scfd/meter
301.01 scfd/comp
17,094 scfd/comp
9,270.90 scfd/station
59.75 scfd/mile
824 scf/comp
8,127,942 scf/comp
2,859 scf/well
388 scfd/device
279 scfd/pump
l,115scf/MMscf
309.8scf/MMscf
21.87scf/bbl
4.37 scfbbl
0.27 scf/HPhr
2,759 scf/w.o.
8,127,942 scf/w.o.
2,522,975 scfy/LP well
88 scfy/vessel
347 scfy/mile
4,243 scfy/comp
9,492 scfy/comp
38 scfy/PRV
752 scf/mile
0.0023 Gg/well
19,178scfd/platform
79,452 scfd/platform
NA
21,613.40
0
7,667.69
47,618.06
7,760.09
36,586.28
12,863.02
2,884.00
195.52
41,342.16
1.73
0
183.66
142,210.39
4,853.92
67,739.71
21,121.91
13,900.13
2,780.03
72,923.33
173.49
0
1,506,084
129.99
658.48
496.73
1,111.26
105.98
356.41
11,693.03
279,356
22,389
Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems model. Here the oil wells count is used as a driver
only.
2 NA = not applicable; i.e. this data is not applicable for the Natural Gas systems model.
3 NE = not estimate; some emission factors were not estimated because there is no corresponding activity for that region.
Table A-121:2009 Data and Clh Emissions tMgl for the Natural Gas Processing Stage
2009 EPA Inventory Values
Activity
Plants
Recip. Compressors
Centrifugal Compressors (wet
seals)
Centrifugal Compressors (dry
seals)
Compressor Exhaust
Gas Engines
Gas Turbines
Activity Data
578
4,876
646
140
35,030
41,535
plants
compressors
compressors
compressors
MMHPhr
MMHPhr
Emission Factor
7,906
11,196
51,370
25,189
0.24
0.01
scfd/plant
scfd/comp
scfd/comp
scfd/comp
scf/HPhr
scf/HPhr
Emissions(Mg)
32,124
383,767
233,371
24,725
161,923
4,560
A-153
-------
Activity
AGR Vents
Kimray Pumps
Dehydrator Vents
Pneumatic Devices
Blowdowns/Venting
2009 EPA Inventory Values
Activity Data
293
1,269,018
11,432,591
578
578
AGR units
MMscf/yr
MMscf/yr
gas plants
gas plants
Emission Factor
6,083
178
122
164,721
4,060
scfd/AGR
scf/MMscf
scf/MMscf
scfy/plant
Mscfy/plant
Emissions(Mg)
12,527
4,344
26,764
1,834
45,197
Table A-122:2009 Data and Clh Emissions tMgl for the Natural Gas Transmission Stage
Activity
Pipeline Leaks
Compressor Stations
(Transmission)
Station
Recip Compressor
Centrifugal Compressor (wet
seals)
Centrifugal Compressor (dry seals)
Compressor Stations (Storage)
Station
Recip Compressor
Centrifugal Compressor (wet
seals)
Centrifugal Compressor (dry seals)
Wells (Storage)
M&R (Trans. Co. Interconnect)
M& R (Farm Taps + Direct Sales)
Dehydrator vents (Transmission)
Dehydrator vents (Storage)
Compressor Exhaust
Engines (Transmission)
Turbines (Transmission)
Engines (Storage)
Turbines (Storage)
Generators (Engines)
Generators (Turbines)
Pneumatic Devices Trans + Stor
Pneumatic Devices Trans
Pneumatic Devices Storage
Routine Maintenance/Upsets
Pipeline venting
Station Venting Trans + Storage
Station Venting Transmission
Station Venting Storage
LNG Storage
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
LNG Import Terminals
LNG Stations
LNG Reciprocating Compressors
LNG Centrifugal Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
2009 EPA Inventory Values
Activity
301,546
1,790
7,197
667
55
392
1,152
84
29
18,267
2,684
79,592
1,141,012
2,029,824
45,546
10,868
4,995
1,755
2,229
26
70,458
15,421
301,546
1,790
392
70
270
64
718
113
70
9
44
8
1,904
461
Data
miles
Stations
Compressors
Compressors
Compressors
Stations
Compressors
Compressors
Compressors
Wells
Stations
Stations
MMscf/Year
MMscf/Year
MMHPhr
MMHPhr
MMHPhr
MMHPhr
MMHPhr
MMHPhr
Devices
Devices
Miles
Compressor
Stations
Compressor
Stations
Stations
Compressors
Compressors
MMHPhr
MMHPhr
Stations
Stations
Compressors
Compressors
MMHPhr
MMHPhr
Emission
1.55
8,778
15,205
50,222
32,208
21,507
21,116
45,441
31,989
115
3,984
31
94
117
0.24
0.01
0.24
0.01
0.24
0.01
162,197
162,197
32
4,359
4,359
21,507
21,116
30,573
0.24
0.01
4,359
21,507
21,116
30,573
0.24
0.01
Factor
Scfd/ mile
Scfd/station
Scfd/ comp
Scfd/ comp
Scfd/ comp
Scfd/ comp
Scfd/ comp
Scfd/ comp
Scfd/ comp
Scfd/ comp
scfd/station
scfd/station
scf/MMscf
scf/MMscf
scf/HPhr
scf/HPhr
scf/HPhr
scf/HPhr
scf/HPhr
scf/HPhr
Scfy/device
Scfy/device
Mscfy/mile
Mscfy/station
Mscfy/station
scfd/station
scfd/comp
scfd/comp
scf/HPhr
scf/HPhr
Mscfy/station
scfd/station
scfd/comp
scfd/comp
scf/HPhr
scf/HPhr
Emissions(Mg)
3,294
110,458
769,264
235,445
12,436
59,230
171,007
26,819
6,532
14,704
75,180
17,457
2,060
4,581
210,531
1,193
23,091
193
10,302
3
220,104
48,174
183,816
150,278
32,890
10,623
40,147
13,766
3,320
12
5,899
1,376
6,561
1,676
8,802
51
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity
LNG Station Venting
2009 EPA Inventory Values
Activity Data Emission Factor
9 Stations 4,359 Mscfy/station
Emissions(Mg)
764
Table A-123: 2009 Data and CH* Emissions (Mg) for the Natural Gas Distribution Stage
Activity
Pipeline Leaks
Mains — Cast Iron
Mains — Unprotected steel
Mains — Protected steel
Mains — Plastic
Services — Unprotected steel
Services Protected steel
Services — Plastic
Services — Copper
Meter/Regulator (City Gates)
M&R >300
M&R 100-300
M&R <100
Reg >300
R- Vault >300
Reg 100-300
R- Vault 100-300
Reg 40- 100
R- Vault 40- 100
Reg <40
Customer Meters
Residential
Commercial/Industry
Routine Maintenance
Pressure Relief Valve
Releases
Pipeline Slowdown
Upsets
Mishaps (Dig-ins)
2009 EPA Inventory Values
Activity Data
35,429 miles
67,331 miles
484,337 miles
621,404 miles
5,218,497 services
15,389,666 services
42,601,520 services
1,083,539 services
3,945 stations
14,398 stations
7,696 stations
4,314 stations
2,533 stations
13,049 stations
5,863 stations
39,159 stations
34,726 stations
16,604 stations
40,646,493 outdr meters
4,121,452 meters
1,208,501 mile main
1,316,917 miles
1,316,917 miles
Emission Factor
239 Mscf/mile-yr
110 Mscf/mile-yr
3 Mscf/mile-yr
10 Mscf/mile-yr
2 Mscf/service
0.2 Mscf/service
0.01 Mscf/service
0.3 Mscf/service
180 scfh/station
96 scfh/station
4 scfh/station
162 scfh/station
1 scfh/station
4 1 scfh/station
0.2 scfh/station
1 scfh/station
0.1 scfh/station
0. 1 scfh/station
143 scfy/meter
48 scfy/meter
0. 1 Mscf/mile
0. 1 Mscfy/mile
2 Mscfy/mile
Emissions(Mg)
162,880
142,893
28,609
118,605
170,941
52,314
7,629
5,308
119,685
232,233
5,596
117,825
556
89,166
178
6,871
507
373
112,160
3,802
1,164
2,587
40,328
A-155
-------
Table A-124: Key Activity Data Drivers
Variable
Transmission Pipelines Length
Wells
NE— Associated Gas Wells*
NE — Non-associated Gas Wells*
MC — Associated Gas Wells*
MC — Non-associated Gas Wells*
RM — Associated Gas Wells*
RM — Non-associated Gas Wells*
SW — Associated Gas Wells*
SW— Non-associated Gas Wells*
WC— Associated Gas Wells*
WC— Non-associated Gas Wells*
GC— Associated Gas Wells*
GC— Non-associated Gas Wells*
Platforms
Gulf of Mexico and Pacific OCS Off-
shore Platforms
GoM and Pacific OCS Deep Water
Platforms
Gas Plants
Distribution Services
Steel — Unprotected
Steel — Protected
Plastic
Copper
Distribution Mains
Cast Iron
Steel — Unprotected
Steel — Protected
Plastic
Units
miles
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# wells
# platforms
# platforms
# gas plants
# of services
# of services
# of services
# of services
# of services
miles
miles
miles
miles
miles
1990
291,990
68,261
124,241
64,379 B
53,940 1
13,7491
24,339
69,339
24,217 1
20,672 1
1,292 1
36,279 1
41,753
3,943 1
17
761
47,883,083
7,633,526 B
19,781,581 1
18,879,865 1
1,588,111
944,157
58,292 1
108,941
465,538
311,386
1992
291,468
67,489 1
129,157
70,640 B
59,358 1
14, 142 B
26,323
68,130
22,609
19,819
1,254B
35,376
37,307 B
3,966 B
19
732
49,142,008
7,138,563 B
19,742,086 B
20,692,674 B
1,568,685
888,925
52,917 B
99,619 B
469,106
267,283
1995
296,947
66,102B
129,789
72,483 B
65,585 B
13,745 B
32,668 B
59,954B
27,392 B
19,109 B
I.I I4B
34,792 B
41,978 B
3,981 B
23
675
54,644,033
6,151,653 B
21,002,455 B
26,044,545 B
1,445,380
1,001,706
50,625
94,058 B
503,288
353,735
2000 2005
298,957 300,663
58,671
143,922
67,880
51,217
12,328
64,539
54,830
32,346
20,494
1,338
32,497
48,316
4,020
38
46,471
158,238
65,652
71,379
13,327
72,438
55,502
34,194
21,562
1,424
28,090
57,600
3,911
59
585 566
56,761,042 58,556,335
5,675,520 B 5,308,375
17,855,560 B 15,883,423
31,795,871 1 36,152,277
1, 434,09 ll 1,212,260
1,048,485 1,093,909
44,750 37,371
82,800 B 69,291
471,510 461,459
449,425 525,788
2006
300,458
47,034
164,322
65,903
73,914
13,437
75,170
54,550
35,417
22,189
1,503
27,319
60,715
3,914
67
571
62,255,435
5,642,470
15,732,037
39,632,313
1,248,615
1,209,419
36,977
71,738
481,811
618,893
2007
301,180
46,646
172,493
69,234
80,650
12,021
70,532
55,251
38,049
22,110
1,506
26,234
68,188
3,839
63
574
63,524,388
5,448,804
15,756,048
41,092,515
1,227,021
1,198,585
37,669
69,525
489,815
601,575
2008
303,388
47,088
174,682
72,622
82,705
14,763
91,395
56,787
40,176
24,271
1,693
27,661
72,047
3,761
65
577
63,559,296
5,388,623
15,456,866
41,573,069
1,140,738
1,188,714
36,462
69,374
479,502
603,377
2009
301,546
46,914
183,834
72,935
87,190
14,948
98,835
56,810
41,542
24,548
1,786
27,134
75,046
3,583
68
578
64,293,222
5,218,497
15,389,666
42,601,520
1,083,539
1,208,501
35,429
67,331
484,337
621,404
* NEMS (National Energy Modeling System) projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic and financial factors, world
energy markets, resource availability and costs, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-125: CHa Reductions Derived from the Natural Gas STAR Program tCgl
Process 1992* 1995 2000 2005
Production 0 1 75
Processing 0 1 5 1
Transmission and Storage 0 1 121
Distribution 0 19 1
Total 0 220
2006
318 I 1,425 1,471
23 1 129
258 1 500
27 35
140
480
48
626 2,090 2,138
2007
1,863
139
454
35
2,492
2008
2,239
145
440
28
2,852
2009
1,993
83
367
41
2,484
*Reductions are relative to 1992; therefore, there are zero reductions in 1992.
Note: These reductions will not match the Natural Gas STAR program reductions. These numbers are adjusted for reductions prior to the 1992
base year, and do not include a sunsetting period. Totals may not sum due to independent rounding.
Table A-126: CJh Reductions Derived from Regulations (Gg)
Process 1990 1995
Production 9.8 1 24.1
Processing NA NA
Transmission and Storage NA NA
Distribution NA NA
Total 9.8 24.1
NA Not applicable
2000 2005
303.6 278.4
12.9 12.1
NA NA
NA NA
316.5 290.5
2006
1,572
12.4
NA
NA
1,584
2007
783.5
13.0
NA
NA
796.5
2008
966.5
13.7
NA
NA
980.2
2009
733.7
14.2
NA
NA
747.9
Note: NESHAP regulations went into effect in 1999. Totals may not sum due to independent rounding.
Table A-127: CH* Emission Estimates from the Natural Gas Production Stage Excluding Reductions from the Natural Gas
STAR Program and Regulations (Gg)
Activity 1990 1992 1995
Normal Fugitives
Associated Gas Wells
Non-Associated Gas
Wells (less
conventional) 30.82 31.68
Unconventional Gas
Wells 0.01 0.02
Field Separation
Equipment
Heaters 12.57 13.28
Separators 43.83 44.16
Dehydrators 14.75 15.76
Meter/ Piping 43.61 44.95
Gathering Compressors
Small Reciprocating
Comp. 30.22 32.15
Large Reciprocating
Comp. 7.50 8.56
Large Reciprocating
Stations 0.51 0.58
Pipeline Leaks 98.03 102.22
Vented and Combusted
Drilling and Well
Completion
Completion Flaring 0.01 0.01
Unconventional Gas
Well Completions 16.48 23.53
Well Drilling 0.74 0.54
Coal Bed Methane
Powder River 0.04 B 0.47
Black Warrior 2.72 4.84
Normal Operations
Pneumatic Device
Vents 569.76 607.88
Chemical Injection
Pumps 26.89 29.38
Kimray Pumps 131.82 140.65
Dehydrator Vents 41.10 43.86
Condensate Tank Vents
54.79
17.89
54.89
38.27
9.80
0.66 B
114.49
„.„,
31.66
0.53B
1.48 •
6.25 B
697.6 1|
35.04
159.45
49.72
2000 2005
48.05
0.26
23.56
76.51
20.77
73.80
45.57
11.70
0.79
133.58
0.01
422.38
1.01
28.76
6.82
810.83
43.44
185.01
57.69
52.88
0.53
26.90
86.43
23.95
83.74
53.85
12.60
0.85
148.45
0.01
2006
50.86
1.32
28.01
90.24
24.93
87.18
56.10
12.59
0.85
152.97
0.01
203.69 2,502.62
1.63
46.38
9.88
954.88
51.88
213.32
66.52
1.85
56.38
10.45
991.35 1
53.93
222.04
69.24
2007
52.48
1.53
28.36
94.09
26.17
90.23
58.49
12.71
0.86
160.02
0.01
855.93 1
1.90
54.60
11.09
,043.32 1
54.93
233.36
72.76
2008
58.63
1.67
33.09
106.73
28.07
102.50
65.19
13.58
0.92
172.60
0.01
,089.76
1.93
57.58
11.69
,145.65 1
62.93
250.28
78.04
2009
62.03
1.59
35.17
112.61
29.61
108.17
68.88
13.58
0.92
180.31
0.01
596.33
1.12
57.49
11.69
,211.86
66.97
263.97
82.31
A-157
-------
Activity
Condensate Tanks
without Control
Device
Condensate Tanks
with Control
Device
Compressor Exhaust
Vented
Gas Engines 119.06
Well Workovers
Conventional Gas
Wells 0.56
Unconventional Gas
Wells 2.92
Well Clean Ups (LP
Gas Wells) 2,651.65
Slowdowns
Vessel BD 0.36
Pipeline BD 1.56
Compressor BD 1.17
Compressor Starts 2.61
Upsets
Pressure Relief
Valves 0.34
Mishaps 0.85
Offshore
Offshore water Gas
Platforms (GoM &
Pacific) 290.64
Deepwater Gas
Platforms (GoM &
Pacific)
Total
1995
2000
2005 2006 2007 2008 2009
15.54 11.91
4,360
.56
.91
.48
.58
.16
.26
.38
.63
.24
.78
.35
.88
30
.94
?n
58.09
11.62
151.43
0.64
16.07
2,931. 40 1
n.42 B
1.821
1.481
3.3 ll
0.391
0.99B
307.56
7.40 H
67.47
13.49
180.03
0.73
98.40
3,330.18
0.49
2.13
1.76
3.94
0.45
1.15
324.06
99.33
19.87
215.10
0.84
241.50
3,755.97
0.56
2.36
2.08
4.65
0.51
1.28
321.98
12.81 20.43
109.76
21.95
224.16
0.87
492.00
3,804.83
0.58
2.44
2.17
4.85
0.53
1.32
321.51
23.20
114.96
22.99
235.19
0.94
574.10
4,134.53
0.62
2.55
2.26
5.05
0.57
1.38
305.25
21.10
135.58
27.12
261.65
1.00
685.67
4,333.13
0.67
2.75
2.52
5.63
0.61
1.49
298.98
21.78
135.58
27.12
276.40
1.06
716.17
4,554.42
0.71
2.87
2.66
5.95
0.65
1.55
279.36
22.39
dsio (. ms &T>z. q dit s ?7d q n^q s q"?i
Note: Totals may not sum due to independent rounding.
Table A-128: CH* Emission Estimates from the Natural Gas Processing Plants Excluding Reductions from the Natural Gas
STAR Program and Regulations tCgl
Activity
1990
1992
1995
2000
2005
2006
2007
2008
2009
Normal Fugitives
Plants 42.30 40.68
Reciprocating
Compressors 324.94 I 324.74
Centrifugal Compressors
(wet seals) 240.29 | 240.15
Centrifugal
Compressors
(dry seals)
Vented and
Combusted
Normal Operations
Compressor
Exhaust
Gas Engines 137.10 I 137.02
Gas Turbines 3.86 3.86
AGR Vents 16.49 15.87
Kimray Pumps 3.68 3.68
Dehydrator Vents 22.66 22.65
Pneumatic Devices 2.41 2.32
Routine Maintenance
Blowdowns/Venting 59.51 57.24
37.52
338.42
248.60
0.81
32.51
349.51
251.32
3.50
31.46 31.74 31.90 32.07 32.12
327.87 337.12 351.55 371.25 383.77
229.24 229.92 230.99 232.45 233.37
6.48
9.50
14.21
20.64
24.73
142.79
4.02
14.63
3.83
23.60
2.14
52.78
147.47
4.15
12.68
3.96
24.38
1.86
45.74
138.34
3.90
12.27
3.71
22.87
1.80
44.26
142.24
4.01
12.38
3.82
23.51
1.81
44.65
148.33
4.18
12.44
3.98
24.52
1.82
44.88
156.64
4.41
12.51
4.20
25.89
1.83
45.12
161.92
4.56
12.53
4.34
26.76
1.83
45.20
Total
853.24
848.20
869.16
877.08
822.18 840.69 868.81
907.01
931.14
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Note: Totals may not sum due to independent rounding.
Table A-129: CH* Emission Estimates from the Natural Gas Transmission and Storage Excluding Reductions from the
Natural Gas STAR Program and Regulations tugl
Activity
1990
1992
1995
2000
2005
2006
2007
2008
Fugitives
Pipelines Leaks
Compressor Stations
(Transmission)
Station
Recip Compressor
Centrifugal Compressor
(wet seals)
Centrifugal Compressor
(dry seals)
Compressor Stations
(Storage)
Station
Recip Compressor
Centrifugal
Compressor (wet seals)
Centrifugal Compressor
(dry seals)
Wells (Storage)
M&R (Trans. Co.
Interconnect)
M&R (Farm Taps +
Direct Sales)
Vented and Combusted
Normal Operation
Dehydrator Vents
3.19
3.24
3.271
109.51
762.66
245.59
4.53
62.17
179.62
34.41
2.54
15.431
74.531
17.31
3.28
3.28
3.29
3.31
60.06
173.53
30.91
4.10
14.91
74.96
17.41
54.36
157.05
27.39
4.10
13.49
74.91
17.39
58.76
60.62
169.67 175.16
27.65 27.78
5.72
14.59
75.09
17.44
6.53
15.05
75.64
17.56
2009
3.29
110.13 110.06 110.32 111.13 110.46
767.01 766.49 768.33 773.96 769.26
236.93 236.75 236.81 237.00 235.44
11.01 11.01 11.36 12.44 12.44
59.23
171.01
26.82
6.53
14.70
75.18
17.46
(Transmission)
Dehydrator Vents
(Storage)
Compressor Exhaust
Engines (Transmission)
Turbines (Transmission)
Engines (Storage)
Turbines (Storage)
Generators (Engines)
Generators (Turbines)
Pneumatic Devices
Trans+Stor
Pneumatic Devices Trans
Pneumatic Devices
Storage
Routine
Maintenance/Upsets
Pipeline Venting
Station venting
Trans+Storage
Station Venting
Transmission
Station Venting Storage
LNG Storage
LNG Stations
LNG Reciprocating
Compressors
LNG Centrifugal
Compressors
LNG Compressor Exhaust
LNG Engines
LNG Turbines
LNG Station Venting
1.99 1
4.23
176.92 1
1.00
21.30
0.18
8.66
0.00 1
213.13
44.44 I
177.991
145.52B
30.34
9.24 1
34.50
11.78
3.21
0.01
5.13
1.99 2.03
4.51 4.67
2.04H
4.8ll
186.65 | 204.91 | 215.30
1.06
22.75
0.19
9.13
0.00
212.75
47.46
177.67
145. 26
32.41
9.45
35.37
12.09
3.23
0.01
5.25
1.16
23.53
0.20
10.03
0.00
216.75
49.09
181.01
147.99
33.51
9.77
36.67
12.55
3.26
0.01
5.43
1.22U
24.24
0.2ol
10.54
o.ool
218.21
50.56
ws.yy™
34.52
10.30
J.JU
O.Oll
5.72^
2.05
4.65
203.10
1.15
23.41
0.20
9.94
0.00
219.46
48.85
183.28
149.84
33.35
10.62
40.15
13.77
3.32
0.01
5.90
2.05
4.20
200.09
1.13
21.19
0.18
9.79
0.00
219.31
44.21
183.15
149.74
30.18
10.62
40.15
13.77
3.32
0.01
5.90
2.06
4.54
213.12
1.21
22.91
0.19
10.43
0.00
219.84
47.79
183.59
150.10
32.63
10.62
40.15
13.77
3.32
0.01
5.90
2.07
4.69
214.32
1.21
23.63
0.20
10.49
0.00
221.45
49.31
184.94
151.20
33.66
10.62
40.15
13.77
3.32
0.01
5.90
2.06
4.58
210.53
1.19
23.09
0.19
10.30
0.00
220.10
48.17
183.82
150.28
32.89
10.62
40.15
13.77
3.32
0.01
5.90
A-159
-------
Activity 1990 1992 1995 2000
LNG Import Terminals
LNG Stations 0.21 0.21 0.21 0.21
LNG Reciprocating
Compressors 1.01 1.01 1.01 1.01
LNG Centrifugal
Compressors 0.26 0.26 0.26 0.26
LNG Compressor Exhaust
LNG Engines 1.74 0.96 0.49 4.41
LNG Turbines1 0.01 0.00 0.00 0.03
LNG Station Venting 0.12 0.12 0.12 B 0.12
2005
0.42
2.02
0.52
12.18
0.07
0.24
2006
0.42
2.02
0.52
11.28
0.07
0.24
2007
0.42
2.02
0.52
14.81
0.09
0.24
2008
1.06
5.05
1.29
6.91
0.04
0.59
2009
1.38
6.56
1.68
0.05
0.76
Total
2,344
2,376
2,442
2,482
2,473 2,430 2,479 2,502 2,482
Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
Table A-130: CH* Emission Estimates from the Natural Gas Distribution Stage Excluding Reductions from the Natural Gas STAR
Program tCgl
Activity
Pipeline Leaks
Mains — Cast Iron
Mains — Unprotected steel
Mains — Protected steel
Mains — Plastic
Services — Unprotected steel
Services Protected steel
Services — Plastic
Services — Copper
Meter/Regulator (City Gates)
M&R >300
M&R 100-300
M&R <100
Reg >300
R- Vault >300
Reg 100-300
R- Vault 100-300
Reg 40- 100
R- Vault 40- 100
Reg <40
Customer Meters
Residential
Commercial/Industry
Routine Maintenance
Pressure Relief Valve Releases
Pipeline Slowdown
Upsets
Mishaps (Dig-ins)
Total
1990
267.991
23 1.2o!
27.50
59.43!
250.05!
67.24!
3.38
110.4ll
214.25!
5.161
108.7o!
0.5l!
82.26
0.161
6.34
0.47
0.34
103.47B
3.97
0.91
2.39
37.20
1,591
1992
243 .2s!
211.42I
27.7l!
51.021
233.84!
67. Ill
3.71
7.68
117.93!
228.82!
5.5l!
116.09!
0.55!
87.86
0.18
6.77
0.50
0.37
110. 5ll
4.25
0.86
2.55
39.74
1,568
1995
232.74!
199.62!
29.73!
67.52!
201. 5ll
71.39!
4.66
7.08
121.96!
236.64!
5.7ol
120.06!
0.57
90.86
0.18
7.00!
0.52
0.38
114.291
4.78
0.96
2.64
41.09
1,562
2000
205.731
175.72!
27.85!
85.781
185.9ll
60.7o!
5.69
7.02
125.62!
243 .76!
5.87!
123.67!
0.581
93.59
0.19!
7.21
0.53
0.39
117.72!
4.66
1.01
2.72
42.33
1,524
2005
171.81
147.05
27.26
100.36
173.89
53.99
6.47
5.94
121.36
235.49
5.67
119.48
0.56
90.42
0.18
6.97
0.51
0.38
113.73
3.95
1.05
2.62
40.89
1,430
2006
170.00
152.25
28.46
118.13
184.83
53.48
7.10
6.12
109.84
213.13
5.14
108.13
0.51
81.83
0.16
6.31
0.47
0.34
102.93
3.85
1.16
2.37
37.01
1,394
2007
173.18
147.55
28.93
114.82
178.49
53.56
7.36
6.01
118.73
230.38
5.55
116.88
0.55
88.45
0.18
6.82
0.50
0.37
111.26
3.98
1.15
2.57
40.01
1,437
2008
167.63
147.23
28.32
115.16
176.51
52.54
7.44
5.59
122.50
237.70
5.73
120.60
0.57
91.26
0.18
7.03
0.52
0.38
114.80
4.03
1.14
2.65
41.28
1,451
2009
162.88
142.89
28.61
118.61
170.94
52.31
7.63
5.31
119.68
232.23
5.60
117.83
0.56
89.17
0.18
6.87
0.51
0.37
112.16
3.80
1.16
2.59
40.33
1,422
Note: Totals may not sum due to independent rounding.
Table A-131: U.S. Production Sector Clh Content in Natural Gas by HEMS Region and Natural Gas Well type
U.S. Region
Well Types
Conventional
Un-conventional
All types
North
East
0.92%
7.42%
3.04%
Midcontinen
t
0.79%
0.31%
0.79%
Gulf Coast
2.17%
0.23%
2.17%
South West
3.81%
NA
3.81%
Rocky
Mountain
7.95%
0.64%
7.58%
West Coast
0.16%
NA
0.16%
Lower-48 States
3.41%
4.83%
3.45%
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-132: C02 Emission Estimates from the Natural Gas Production Stage tGgl
Activity
Normal Fugitives
Gas Wells
Non-Associated Gas Wells
Unconventional Gas Wells
Field Separation Equipment
Heaters
Separators
Dehydrators
Meter/ Piping
Gathering Compressors
Small Reciprocating Comp.
Large Reciprocating Comp.
Large Reciprocating
Stations
Pipeline Leaks
Vented and Combusted
Drilling and Well Completion
Completion Flaring2
Unconventional Gas Well
Completions
Well Drilling
Coal Bed Methane
Powder River1
Black Warrior1
Normal Operations
Pneumatic Device Vents
Chemical Injection Pumps
Kimray Pumps
Dehydrator Vents
Condensate Tank Vents
Condensate Tanks without
Control Device
Condensate Tanks with
Control Device
Compressor Exhaust Vented
Gas Engines1
Well Workovers
Gas Wells
Unconventional Gas Wells
Well Clean Ups (LP Gas
Wells)
Slowdowns
Vessel BD
Pipeline BD
Compressor BD
Compressor Starts
Upsets
Pressure Relief Valves
Mishaps
Flaring Emissions - Onshore
Offshore
Offshore water Gas Platforms
(GoM & Pacific)
Deepwater Gas Platforms
(GoM & Pacific)
Flaring Emissions - Offshore
Total
1990
3.56
0.00
1.86
6.04
1.50
5.85
2.95
0.69B
0.05
10.75B
0.00
2.19
0.07
NE 1
NE 1
58.49B
2.95B
13.4lB
4.1gB
10.25 B
2.05
NE 1
0.06
0.47
251.4lB
0.04
0.17
o.l iB
0.25
0.03
0.09
9,092.72
1.48
0.03
230.37
9,704
1992
3.57
0.00
1.91
5.97
1.54
5.85
2.98
0.91
0.06
10.85
0.00
1.58
0.05
NE
NE
60.24
3.11
13.71
4.28
9.43
1.89
NE
0.06
0.84
252.88
0.04
0.17
0.12
0.26
0.03
0.09
10,172.49
1.51
0.03
163.13
10,720
1995
4.25
0.00
2.32
7.17
1.73
6.95
3.57
1.02
0.07
11.99
0.00
5.27
0.05
NE
NE
69.38
3.75
15.43
4.81
8.79
1.76
NE
0.06
2.35
273.66
0.04
0.19
0.14
0.31
0.04
0.10
17,167.79
1.56
0.04
197.22
17,792
2000
6.46
0.01
3.95 B
11.29 B
2.58B
10.90 B
5.46
1.33
0.09
16.04 B
0.00
122.20 B
0.12
NE 1
NE 1
2005
7.00
0.01
4.44
12.68
2.83
12.21
6.17
1.40
0.09
17.64
0.00
21.29
0.18
NE
NE
101.86J 114.87
6.33 •
22.9 iB
7.14B
9.34
1.87
NE 1
0.08
22.89 B
7.18
25.15
7.84
10.25
2.05
NE
0.09
43.99
339.56 375.89
0.06 0.06
0.26 B 0.28
0.21 0.24
5
0.47
0.05
n.uB
0.53
0.06
0.15
,525.04 7,193.00
1.64
1.63
0.07 0.10
204.31 180.66
6,425
8,050
2006
6.18
0.03
4.61
13.20
2.94
12.69
6.41
1.40
0.09
18.21
0.00
748.86
0.21
NE
NE
119.29
7.45
26.12
8.15
11.26
2.25
NE
0.10
118.88
367.69
0.07
0.29
0.25
0.55
0.06
0.16
7,812.35
1.63
0.12
146.48
9,438
2007
5.86
0.03
4.48
13.16
2.93
12.57
6.38
1.40
0.09
18.25
0.00
166.74
0.20
NE
NE
118.75
7.20
26.05
8.12
11.17
2.23
NE
0.10
135.55
377.12
0.07
0.29
0.25
0.55
0.06
0.16
8,664.25
1.55
0.11
160.05
9,746
2008
7.33
0.03
5.54
15.78
3.38
15.14
7.63
1.62
0.11
20.80
0.00
108.05
0.22
NE
NE
139.81
8.94
30.04
9.37
11.90
2.38
NE
0.11
146.36
413.61
0.08
0.33
0.29
0.66
0.07
0.18
10,024.82
1.52
0.11
360.00
11,336
2009
8.00
0.03
5.95
16.82
3.60
16.15
8.15
1.62
0.11
21.94
0.00
85.45
0.13
NE
NE
149.18
9.61
31.99
9.98
11.90
2.38
NE
0.12
145.24
439.94
0.08
0.35
0.31
0.70
0.07
0.19
9,545.39
1.42
0.11
360.00
10,877
Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.
2 Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
A-161
-------
Table A-133: C02 Emission Estimates from the Natural Gas Processing Plants tGgl
Activity
Normal Fugitives
Plants — Before CO2 removal
Plants - After CO2 removal
Reciprocating Compressors -
Before CO2 removal
Reciprocating Compressors -
After CO2 removal
Centrifugal Compressors (wet
seals) - Before CO2 removal
Centrifugal Compressors (wet
seals) —After CO2 removal
Centrifugal Compressors (dry
seals) — Before CO2 removal
Centrifugal Compressors (dry
seals) -After CO2 removal
Vented and Combusted
Normal Operations
Compressor Exhaust
Gas Engines1
Gas Turbines
AGR Vents
Kimray Pumps
Dehydrator Vents
Pneumatic Devices
Routine Maintenance
Slowdown s/Venting
Total
1990 1992 1995
2.56
0.57
19.67
4.37
14.55
3.23
0
0
NE
NE
27,708
0.39
2.42
0.29
2.46 2.27 1
0.55
19.66
4.36
14.54
3.23
0
0
NE
NE
26,652
0.39
2.42
0.27
o.som
20.49
4.55
15.05
3.341
o.osB
0.01
NE!
NE!
24,577
0.41
2.52B
0.25 •
6.36 6.12 5.64
27,763 26,706 24,632
2000
1.97B
0.44
21.161
4.70
15.2ll
3.38
0.21
0.05
NE
NE|
23,288
0.421
2.61
0.22
4_89_H
23,343
2005
1.90
0.42
19.85
4.41
13.88
3.08
0.39
0.09.
NE
NE
21,694
0.40
2.45
0.21
4.73
21,746
2006
1.92
0.43
20.41
4.53
13.92
3.09
0.58
0.13
NE
NE
21,161
0.41
2.51
0.21
4.78
21,214
2007
1.93
0.43
21.28
4.72
13.98
3.10
0.86
0.19
NE
NE
21,144
0.43
2.62
0.22
4.80
21,199
2008
1.94
0.43
22.47
4.99
14.07
3.12
1.25
0.28
NE
NE
21,328
0.45
2.77
0.22
4.83
21,385
2009
1.94
0.43
23.23
5.16
14.13
3.14
1.50
0.33
NE
NE
21,130
0.46
2.86
0.22
4.83
21,189
1 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.
Note: Totals may not sum due to independent rounding.
Table A-134: C02 Emission Estimates from the Natural Gas Transmission and Storage tGgl
Activity
1990
1992
1995
2000
2005 2006 2007 2008 2009
Fugitives
Pipelines Leaks
Compressor Stations
(Transmission)
Station
Recip Compressor
Centrifugal Compressor (wet
seals)
Centrifugal Compressor (dry
seals)
Compressor Stations (Storage)
Station
Recip Compressor
Centrifugal Compressor (wet
seals)
Centrifugal Compressor (dry
seals)
Wells (Storage)
M&R (Trans. Co. Interconnect)
M&R (Farm Taps + Direct Sales)
Vented and Combusted
Normal Operation
Dehydrator Vents
(Transmission)
Dehydrator Vents (Storage)
Compressor Exhaust
Engines (Transmission)1
Turbines (Transmission)1
0.09
0.96
0
1.02
0
0.39 0.42
2.10 2.10
0.49 0.49
0.09
3.14
21.85
7.20
0.02
1.74
5.03
1.05
0.43
2.14
0.50
0.091
3.16J
22.00|
7.081
0.13
1.791
5.18J
0.991
0.07
0.45
2 1
0.50
0.061
0.14|
NE
NE
0.09 0.09
3.18
22.12
6.83
0.32
1.73
5.01
0.89
0.12
0.43
2.16
0.50
3.17
22.11
6.83
0.32
1.57
4.53
0.79
0.12
0.39
2.16
0.50
0.09 0.10 0.10
3.18
22.16
6.83
0.33
1.69
4.89
0.17
0.42
2.17
0.50
3.21
22.33
6.84
0.36
1.75
5.05
3.19
22.19
6.79
0.36
1.71
4.93
0.77
0.19 0.19
0.43
2.18
0.51
0.42
2.17
0.50
0.06
0.13
NE
NE
0.06
0.12
NE
NE
0.06
0.13
NE
NE
0.06
0.14
NE
NE
0.06
0.13
NE
NE
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity
1990
1992
1995
2000
2005 2006
2007
2008 2009
Engines (Storage) NE NE
Turbines (Storage)1 NE NE
Generators (Engines)1 NE NE
Generators (Turbines)1 NE NE
Pneumatic Devices Trans+Stor
Pneumatic Devices Trans 6.151 6.14
Pneumatic Devices Storage 1.28B 1.37
Routine Maintenance/Upsets
Pipeline Venting 5.13 5.13
Station venting Trans+Storage
Station Venting Transmission 4.20 4.19
Station Venting Storage 0.881 0.93
LNG Storage
LNG Stations 0.31
LNG Reciprocating Compressors 1.16
LNG Centrifugal Compressors 0.39
LNG Compressor Exhaust
LNG Engines1 NE
LNG Turbines1 NE
LNG Station Venting 0.17
LNG Import Terminals
LNG Stations 0.01 0.01
LNG Reciprocating Compressors 0.03H 0.03
LNG Centrifugal Compressors 0.011 0.01
LNG Compressor Exhaust
LNG Engines1 NE NE
LNG Turbines1 NE NE
LNG Station Venting2 0.00 0.00
I I
0.32
1.18
0.411
NE
NE
ai8
5.22
NE
NE
NE
NE
6.291
1.461
5.261
4.301
1.001
0.351
1.301
0.451
NE
NE
0.19|
0.01
0.03
0.01
NE
NE
0.00
NE
NE
NE
NE
6.33
1.41
5.29
4.32
0.96
0.36
1.34
0.46
NE
NE
0.20
0.01
0.07
0.02
NE
NE
0.01
NE
NE
NE
NE
6.33
1.28
5.28
4.32
0.87
0.36
1.34
0.46
NE
NE
0.20
0.01
0.07
0.02
NE
NE
0.01
NE
NE
NE
NE
6.34
1.38
5.30
4.33
0.94
0.36
1.34
0.46
NE
NE
0.20
0.01
0.07
0.02
NE
NE
0.01
NE
NE
NE
NE
6.39
1.42
5.33
4.36
0.97
0.36
1.34
0.46
NE
NE
0.20
0.04
0.17
0.04
NE
NE
0.02
NE
NE
NE
NE
6.35
1.39
5.30
4.33
0.95
0.36
1.34
0.46
NE
NE
0.20
0.05
0.22
0.06
NE
NE
0.03
Total
61.75
62.37
63.73
64.44
64.36 63.31 64.18 65.04 64.55
1 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.
2 Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
Table A-135: C02 Emission Estimates from the Natural Gas Distribution Stage tGgl
Activity
Pipeline Leaks
Mains — Cast Iron
Mains — Unprotected steel
Mains — Protected steel
Mains — Plastic
Total Pipeline Miles
Services — Unprotected steel
Services Protected steel
Services — Plastic
Services — Copper
Meter/Regulator (City
Gates)
M&R>300
M&R 100-300
M&R<100
Reg >300
R- Vault >300
Reg 100-300
R- Vault 100-300
Reg 40- 100
R- Vault 40- 100
Reg <40
Customer Meters
Residential
Commercial/Industry
Routine Maintenance
1990
7.73
6.67
0.79
1.71
7.21
1.94
0.10
0.22
3.18
6.18
0.15
3.14
0.01
2.37
0.00
0.18
0.01
0.01
2.98
0.11
1992
7.02B
6.1ol
0.80 B
1.471
6.75
1.94B
O.llB
0.221
3.40
6.60 B
0.161
3.35
0.02|
2.53|
o.oiB
0.20B
0.01
O.OlB
3.191
0.12
1995
6.71
5.76
0.86
1.95
5.81
2.06
0.13
0.20
3.52
6.83
0.161
3.46
0.02
2.62
0.01
0.20
0.01
0.01
3.30
0.14
2000
5.93
5.07
0.80
,„•
5.36
1.75
0.161
0.20
3.62
7.03
0.17
0.02
2.70
0.01
0.21
0.02
0.01
2005
4.96
4.24
0.79
2.89
5.02
1.56
0.19
0.17
3.50
6.79
0.16
3.45
0.02
2.61
0.01
0.20
0.01
0.01
3.28
0.11
2006
4.90
4.39
0.82
3.41
5.33
1.54
0.20
0.18
3.17
6.15
0.15
3.12
0.01
2.36
0.00
0.18
0.01
0.01
2.97
0.11
2007
5.00
4.26
0.83
3.31
5.15
1.54
0.21
0.17
3.42
6.65
0.16
3.37
0.02
2.55
0.01
0.20
0.01
0.01
3.21
0.11
2008
4.84
4.25
0.82
3.32
5.09
1.52
0.21
0.16
3.53
6.86
0.17
3.48
0.02
2.63
0.01
0.20
0.01
0.01
3.31
0.12
2009
4.70
4.12
0.83
3.42
4.93
1.51
0.22
0.15
3.45
6.70
0.16
3.40
0.02
2.57
0.01
0.20
0.01
0.01
3.24
0.11
A-163
-------
Activity
Pressure Relief Valve
Releases
Pipeline Slowdown
Upsets
Mishaps (Dig-ins)
Total
1990
0.03
0.07
1.07
45.90
1992
0.02
0.07
1.15
45.24
1995
0.03
0.08
1.19
45.05
2000
0.03
0.08
1.22
43.97
2005
0.03
0.08
1.18
41.25
2006
0.03
0.07
1.07
40.20
2007
0.03
0.07
1.15
41.46
2008
0.03
0.08
1.19
41.85
2009
0.03
0.07
1.16
41.03
Emissions are not actually 0, but too small to show at this level of precision.
Note: Totals may not sum due to independent rounding.
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
3.5. Methodology for Estimating CELi and COi Emissions from Petroleum
Systems
The methodology for estimating CH4 and non-combustion CO2 emissions from petroleum systems is based on
the 1999 EPA draft report, Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999) and the study, Methane
Emissions from the U.S. Petroleum Industry (EPA/GRI 1996). Sixty-four activities that emit CH4 and thirty activities that
emit non-combustion CO2 from petroleum systems were examined from these reports. Most of the activities analyzed
involve crude oil production field operations, which accounted for about 98 percent of total oil industry CH4 emissions.
Crude transportation and refining accounted for the remaining CH4 emissions of less than one half and about one and a
half percent, respectively. Non-combustion CO2 emissions were analyzed for production operations and asphalt blowing
in refining operations. Non-combustion CO2 emissions from transportation operations are not included because they are
negligible. The following steps were taken to estimate CH4and CO2 emissions from petroleum systems.
Step 1: Determine Emission Factors for all Activities
The CH4 emission factors for the majority of the activities for 1995 are taken from the 1999 EPA draft report,
which contained the most recent and comprehensive determination of CH4 emission factors for the 64 CH4-emitting
activities in the oil industry at that time. Emission factors for pneumatic devices in the production sector were recalculated
in 2002 using emissions data in the EPA/GRI 1996c study. The gas engine emission factor is taken from the EPA/GRI
1996b study. The oil tank venting emission factor is taken from the API E&P Tank Calc weighted average for API gravity
less than 45 API degrees with the distribution of gravities taken from a sample of production data from the HPDI database.
Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the Gulf-wide Offshore
Activity Data System (GOADS) report (EPA 2005, BOEMRE 2004). The emission factors determined for 1995 were
assumed to be representative of emissions from each source type over the period 1990 through 2009. Therefore, the same
emission factors are used for each year throughout this period.
The CO2 emission factors were derived from the corresponding source CH4 emission factors. The amount of CO2
in the crude oil stream changes as it passes through various equipment in petroleum production operations. As a result,
four distinct stages/streams with varying CO2 contents exist. The four streams that are used to estimate the emissions
factors are the associated gas stream separated from crude oil, hydrocarbons flashed out from crude oil (such as in storage
tanks), whole crude oil itself when it leaks downstream, and gas emissions from offshore oil platforms. The standard
approach used to estimate CO2 emission factors was to use the existing CH4 emissions factors and multiply them by a
conversion factor, which is the ratio of CO2 content to methane content for the particular stream. Ratios of CO2 to CH4
volume in emissions are presented in Table A- 5. The two exceptions are the emissions factor for storage tanks, which is
estimated using API E&P Tank Calc simulation runs of tank emissions for crude oil of different gravities less than 45 API
degrees; and the emissions factor for uncontrolled asphalt blowing, which is estimated using the data and methods
provided by API (2009).
Step 2: Determine Activity Levels for Each Year
Activity levels change from year to year. Some factors change in proportion to crude oil rates: production,
transportation, refinery runs. Some change in proportion to the number of facilities: oil wells, petroleum refineries. Some
factors change proportional to both the rate and number of facilities.
For most sources, activity levels found in the EPA/GRI 1996 for the 1995 base year are extrapolated to other
years using publicly-available data sources. For the remaining sources, the activity levels are obtained directly from
publicly available data and are not extrapolated from the 1995 base year.
For both sets of data, a determination was made on a case-by-case basis as to which measure of petroleum
industry activity best reflects the change in annual activity. Publicly-reported data from the Bureau of Ocean Energy
Management, Regulation and Enforcement (BOEMRE), Energy Information Administration (EIA), American Petroleum
Institute (API), the Oil & Gas Journal (O&GJ), the Interstate Oil and Gas Compact Commission (IOGCC), and the U.S
Army Corps of Engineers (USAGE) were used to extrapolate the activity levels from the base year to each year between
1990 and 2009. Data used include total domestic crude oil production, number of domestic crude oil wells, total imports
and exports of crude oil, total petroleum refinery crude runs, and number of oil-producing offshore platforms. The activity
data for the total crude transported in the transportation sector is not available. In this case, all the crude oil that was
transported was assumed to go to refineries. Therefore, the activity data for the refining sector was used also for the
transportation sector. In the few cases where no data was located, oil industry data based on expert judgment was used. In
the case of non-combustion CO2 emission sources, the activity factors remain the same as for CH4 emission sources.
A-165
-------
Step 3: Estimate Methane and Carbon Dioxide Emissions for Each Activity for Each Year
Annual CH4 emissions from each of the 64 petroleum system activities and CO2 emissions from the 30 petroleum
system activities were estimated by multiplying the activity data for each year by the corresponding emission factor.
These annual emissions for each activity were then summed to estimate the total annual CH4 and CO2 emissions,
respectively. Table A- 140 provides the CO2 content in natural gas for equipment in different crude streams to estimate
CO2 emission factors using CH4emsslon factors.
Table A-136, Table A-137, Table A-138, and Table A-141 provide 2009 activity factors, emission factors, and
emission estimates and Table A-139 and Table A-142 provide a summary of emission estimates for the years 1990, 1995,
2000, and 2005 through 2009. Table A- 140 provides the CO2 content in natural gas for equipment in different crude
streams to estimate CO2 emission factors using CH4 emission factors.
Table A-136:2009 CH* Emissions from Petroleum Production Field Operations
Activity/Equipment
2009 EPA Inventory Values
Emission Factor
Vented Emissions
Oil Tanks
Pneumatic Devices, High Bleed
Pneumatic Devices, Low Bleed
Chemical Injection Pumps
Vessel Slowdowns
Compressor Slowdowns
Compressor Starts
Stripper wells
Well Completion Venting
Well Workovers
Pipeline Pigging
Offshore Platforms, Shallow water
Oil, fugitive, vented and combusted
Offshore Platforms, Deepwater oil,
fugitive, vented and combusted
7.39 scf of CH4/bbl crude
330 scfd CH4/device
52 scfd CH4/device
248 scfd CH4/pump
78 scfy CH4/vessel
3,775 scf/yr of CH4/compressor
8,443 scf/yr. of CH4/compressor
2,345 scf/yr of CH4/stripper well
733 scf/completion
96 scf CH4/workover
2.40 scfd of CH4/pig station
54,795 scfd CH4/platform
260,274 scfd CH4/platform
Fugitive Emissions
Oil Wellheads (heavy crude)
Oil Wellheads (light crude)
Separators (heavy crude)
Separators (light crude)
Heater/Treaters (light crude)
Headers (heavy crude)1
Headers (light crude)
Floating Roof Tanks
Compressors
Large Compressors
Sales Areas
Pipelines
Well Drilling
Battery Pumps
0.13 scfd/well
16.6 scfd/well
0.15 scfd CH4/separator
14 scfd CH4/separator
19 scfd CH4/heater
0.08 scfd CH4/header
1 1 scfd CH4/header
338,306 scf CH4/floating roof
tank/yr.
100 scfd CH4/compressor
16,360 scfd CH4/compressor
41 scfCH4/loading
NE scfd of CH4/mile of
pipeline
NE scfd of CH4/oil well
drilled
0.24 scfdofCH4/pump
Combustion Emissions
Gas Engines
Heaters
Well Drilling
0.24 scf CH4/HP-hr
0.52 scf CH4/bbl
2,453 scf CH4/well drilled
Activity Factor
1,490 MMbbl/yr (non stripper
wells)
139,797 No. of high-bleed
devices
259,624 No. of low-bleed
devices
28,166 No. of pumps
182,451 No. of vessels
2,473 No. of compressors
2,473 No. of compressors
3 17,230 No. of stripper wells
vented
1 1,804 Oil well completions
39,450 Oil well workovers
0 No. of crude pig
stations
1,441 No. of shallow water oil
platforms
28 No. of deep water oil
platforms
14,718 No. of hvy. crude wells
194,052 No. of It. crude wells
10,740 No. of hvy. crude seps.
97,745 No. of It. crude seps.
73,966 No. of heater treaters
13,721 No. of hvy. crude hdrs.
42,536 No. of It. crude hdrs.
24 No. of floating roof
tanks
2,473 No. of compressors
0 No. of large comprs.
1,630,377 Loadings/year
1 1 ,249 Miles of gathering line
13,255 No. of oil wells drilled
157,800 No. of battery pumps
15,577 MMHP-hr
1956.8 MMbbl/yr
13,255 Oil wells drilled
Emi s si on s
(Bcf/yr)
67.625
11.016
16.859
4.928
2.550
0.014
0.009
0.021
0.744
0.009
0.004
-
28.823
2.649
2.539
0.001
1.178
0.001
0.494
0.518
0.000
0.169
0.008
0.090
-
0.066
NE
NE
0.014
4.7926
3.739
1.020
0.033
Emi s si on s
(Gg/yr)
1,300
211.8
324.2
94.76
49.04
0.274
0.179
0.401
14.31
0.166
0.073
-
554.3
50.93
49
0.013
22.65
0.012
9.504
9.965
0.007
3.241
0.159
1.736
-
1.271
NE
NE
0.266
92.22
71.89
19.61
0.625
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity/Equipment
Flares
2009 EPA Inventory Values
Emission Factor
20 scf CH4/Mcf flared
Process Upset Emissions
Pressure Relief Valves 35 scf/yr/PR valve
Well Blowouts Onshore 2.5 MMscf/blowout
Total
Activity Factor
242,355 Mcf flared/yr
163,449 No. of PR valves
44.2 No. of blowouts/yr
Emissions
(Bcf/yr)
0.005
0.116
0.006
0.110
75.08
Emissions
(Gg/yr)
0.932
2.233
0.109
2.124
1,444
- Zero Emissions
1 Emissions are not actually 0, but too small to show at this level of precision.
NE: Not estimated for lack of data
Table A-137:2009 CH4 Emissions from Petroleum Transportation
Activity/Equipment
Vented Emissions
Tanks
Truck Loading
Marine Loading
Rail Loading
Pump Station
Maintenance1
Pipeline Pigging
Fugitive Emissions
Pump Stations
Pipelines
Floating Roof Tanks
Combustion Emissions
Pump Engine Drivers
Heaters
Total
Emission
Factor Units
0.021 scf CH4/yr/bbl of crude delivered to
refineries
0.520 scf CH4/yr/bbl of crude transported by
truck
2.544 scf CH4/1000 gal. crude marine loadings
0.520 scf CH4/yr/bbl of crude transported by
rail
36.80 scf CH4/station/yr
39 scfd of CH4/pig station
25 scf CH4/mile/yr.
NE scf CH4/bbl crude transported by
pipeline
58,965 scf CH4/floating roof tank/yr.
0.24 scf CH4/hp-hr
0.521 scf CH4/bbl burned
Activity
Factor Units
5,233 MMbbl crude feed/yr
67.8 MMbbl trans, by
truck
19,609,664 1,000 gal. /yr loaded
4.2 MMbbl Crude by
rail/yr
496 No. of pump stations
992 No. of pig stations
49 585 No. of miles of crude
p/1
6,43 1 MMbbl crude piped
824 No. of floating roof
tanks
NE No. of hp-hrs
NE No. of bbl Burned
Emissions
(Bcf/yr)
0.209
0.108
0.035
0.050
0.002
0.000
0.014
0.050
0.001
NE
0.049
NE
NE
NE
0.259
Emissions
(Gg/yr)
4.024
2.073
0.678
0.959
0.042
0.000
0.271
0.958
0.024
NE
0.934
NE
NE
NE
4.982
1 Emissions are not actually 0, but too small to show at this level of precision.
NE: Not estimated for lack of data
Table A-138:2009 Clh Emissions from Petroleum Refining
Activity/Equipment
2009 EPA Inventory Values
Emission Factor
Vented Emissions
Tanks
System Slowdowns
Asphalt Blowing
20.6 scf CH4/Mbbl
137 scf CH4/Mbbl
2,555 scf CH4/Mbbl
Fugitive Emissions
Fuel Gas System
Floating Roof Tanks
Wastewater Treating
Cooling Towers
439 Mscf CH4/refmery/yr
587 scf CH4/floating roof
tank/yr.
1.88 scfCH4/Mbbl
2.36 scf CH4/Mbbl
Combustion Emissions
Atmospheric Distillation
Vacuum Distillation
3.61 scfCH4/Mbbl
3.61 scfCH4/Mbbl
Activity Factor
1,839 Mbbl/calendar day heavy
crude feed
14,336 Mbbl/calendar day refinery
feed
359 Mbbl/calendar day
production
150 Refineries
767 No. of floating roof tanks
14,336 Mbbl/calendar day refinery
feed
14,336 Mbbl/calendar day refinery
feed
14,659 Mbbl/calendar day refinery
feed
6,503 Mbbl/calendar day feed
Emissions Emissions
(Bcf/yr) (Gg/yr)
1.065
0.014
0.716
0.335
0.088
0.066
0.000
0.010
0.012
0.090
0.019
0.009
20.48
0.266
13.77
6.445
1.702
1.266
0.009
0.189
0.237
1.723
0.371
0.165
A-167
-------
2009 EPA Inventory Values
„.._, »^-^_Tn>. Emissions Emissions
A ^ •<, m t. Emission Factor Activity Factor ._ ,. . ,„ . .
Activity/Equipment J (Bcf/yr) (Gg/yr)
Thermal Operations 6.01 scfCH4/Mbbl 2,034 Mbbl/calendar day feed
Catalytic Cracking 5 . 17 scf CH4/Mbbl 4,694 Mbbl/calendar day feed
Catalytic Reforming 7.22 scf CH4/Mbbl 2,970 Mbbl/calendar day feed
Catalytic Hydrocracking 7.22 scf CH4/Mbbl 1,393 Mbbl/calendar day feed
Hydrorefming 2 . 1 7 scf CH4/Mbbl 2,067 Mbbl/calendar day feed
Hydrotreating 6.50 scf CH4/Mbbl 9,480 Mbbl/calendar day feed
Alkylation/Polymerization 12.6 scfCH4/Mbbl 1,027 Mbbl/calendar day feed
Aromatics/Isomeration 1.80 scf CH4/Mbbl 956 Mbbl/calendar day feed
Lube Oil Processing 0.00 scf CH4/Mbbl 165 Mbbl/calendar day feed
Engines 0.006 scf CH4/hp-hr 1,128 MMhp-hr/yr
Flares 0.189 scf CH4/Mbbl 14,336 Mbbl/calendar day refinery
feed
Total
Emissions are not actually 0, but too small to show at this level of precision.
Table A-139: Summary of OH* Emissions from Petroleum Systems IGgl
Activity 1990 1995 2000 2005 2006 2007 2008 2009
Production Field
Operations 1,653 1,557 1,468 1,366 1,365 1,396 1,409 1,444
Pneumatic device venting 489 463 • 428 397 396 398 416 419
Tank venting 25oB 226M 214M 187 188 192 189 212
Combustion & process
upsets 88l 82l 76 • 71 71 72 75 94
Misc. venting & fugitives 799 • 762 • 727 • 691 693 714 707 696
Wellhead fugitives 26 1 25 1 22 1 19 17 20 23 23
Crude Oil Transportation 7 H 6 5 55555
Refining 25 26 28 28 28 27 25 24
Total 1,685 1,589 1,501 1,398 1,398 1,427 1,439 1,473
Note: Totals may not sum due to independent rounding.
Table A-140: Ratios of CJh to OH* Volume in Emissions from Petroleum Production Field Operations
Whole Crude, . .,,.,,-, T , ™ , ^
T, , c, ' Associated Gas Tank Flash Gas
Post-Separator
Ratio %C02 / "/oCH, 0.052 0.020 0.017
Table A-141: 2009 CJh Emissions from Petroleum Production Field Operations and Petroleum Refining
0.004 0.086
0.009 0.170
0.008 0.150
0.004 0.071
0.002 0.031
0.022 0.432
0.005 0.091
0.001 0.012
0.000 0.000
0.006 0.124
0.001 0.019
1.243 23.90
Offshore
0.004
2009 EPA Inventory Values
» ^ -i. IT ± Emission Factor Activity Factor
Activity/Equipment J
Vented Emissions
Oil Tanks 3.53 scf of CO2/bbl crude 1 ,490 MMbbl/yr (non stripper
wells)
Pneumatic Devices, High Bleed 6.704 scfd CO2/device 139,797 No. of high-bleed
devices
Pneumatic Devices, Low Bleed 1.055 scfd CO2/device 259,624 No. of low-bleed devices
Chemical Injection Pumps 5.033 scfd CO2/pump 28,166 No. of pumps
Vessel Slowdowns1 1.583 scfy CO2/vessel 182,451 No. of vessels
Compressor Slowdowns 77 scf/yr of CO2/compressor 2,473 No. of compressors
Compressor Starts1 171 scf/yr. of CO2/compressor 2,473 No. of compressors
Stripper wells 48 scf/yr of CO2/stripper well 317,230 No. of stripper wells
vented
Well Completion Venting1 14.87 scf/completion 11,804 Oil well completions
Well Workovers1 1.95 scf CO2/workover 39,450 Oil well workovers
Pipeline Pigging NE scfd of CO2/pig station NE No. of crude pig stations
Offshore Platforms, Shallow water 358 scfd CO2/platform 1,441 No. of shallow water oil
Oil, fugitive, vented and combusted platforms
Offshore Platforms, Deepwater oil, 1,701 scfd CO2/platform 28 No. of deep water oil
Emissions Emissions
(Bcf/yr) (Gg/yr)
5.972
5.256
0.342
0.100
0.052
0.000
0.000
0.000
0.015
0.000
0.000
NE
0.188
0.017
316.5
278.6
18.13
5.299
2.742
0.015
0.010
0.022
0.800
0.009
0.004
NE
9.984
0.917
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Activity/Equipment
fugitive, vented and combusted
2009 EPA Inventory Values
Emission Factor
Activity Factor
(Bcf/yr)
(Gg/yr)
platforms
Fugitive Emissions
Oil Wellheads (heavy crude)1
Oil Wellheads (light crude)
Separators (heavy crude)
Separators (light crude)
Heater/Treaters (light crude)
Headers (heavy crude)1
Headers (light crude)
Floating Roof Tanks
Compressors
Large Compressors
Sales Areas
Pipelines
Well Drilling
Battery Pumps
0.003 scfd/well
0.337 scfd/well
0.003 scfd CO2/separator
0.281 scfd CO2/separator
0.319 scfd CO2/heater
0.002 scfd CO2/header
0.220 scfd CO2/header
17,490 scfCO2/floatingroof
tank/yr.
2.029 scfd CO2/compressor
332 scfd CO2/compressor
2.096 scf CO2/loading
NE scfd of CO2/mile of
pipeline
NE scfd of CO2/oil well
drilled
0.012 scfd of CO2/pump
Process Upset Emissions
Pressure Relief Valves
Well Blowouts Onshore
1.794 scf/yr/PR valve
0.051 MMscf/blowout
Refining Emissions
Asphalt Blowing
20,736 scf CO2/Mbbl
Total
1 Emissions are not actually 0, but too small to show at this level of precision.
2 Asphalt Blowing emissions are the only significant vented emissions from the refining
14,718 No. of hvy. crude wells
194,052 No. of It. crude wells
10,740 No. of hvy. crude seps.
97,745 No. of It. crude seps.
73,966 No. of heater treaters
13,721 No. of hvy. crude hdrs.
42,536 No. of It. crude hdrs.
24 No. of floating roof tanks
2,473 No. of compressors
0 No. of large comprs.
1,630,377 Loadings/year
1 1 ,249 Miles of gathering line
13,255 No. of oil wells drilled
157,800 No. of battery pumps
163,499 No. of PR valves
44 No. of blowouts/yr
359 Mbbl/calendar day
production
0.052
0.000
0.024
0.000
0.010
0.009
0.000
0.003
0.000
0.002
0.000
0.003
NE
NE
0.001
0.003
0.000
0.002
2.720
2.720
8.747
2.776
0.001
1.267
0.001
0.531
0.456
0.000
0.181
0.023
0.097
0.000
0.181
NE
NE
0.038
0.134
0.016
0.119
144.2
144.2
463.6
sector; other sources are too small to show at this level of
precision.
NE: Not estimated for lack of data
Energy use CO2 emissions not estimated to avoid double counting with fossil fuel combustion
Table A-142: Summary of Clh Emissions from Petroleum Systems tGgl
Activity
1990
1995
2000
2005 2006 2007 2008 2009
Production Field
Operations
Pneumatic device venting
Tank venting
Misc. venting & fugitives
Wellhead fugitives
Refining
Asphalt Blowing
187
211
285 292 288 319
22 22 23 23
246 252 247 278
16 16 16 16
1
1
1
1
205 203 182 165 144
Total
555
528
534
490 488 474 453 463
A-169
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3.6. Methodology for Estimating COi, NiO and CH4 Emissions from the
Incineration of Waste
Emissions of CO2 from the incineration of waste include CO2 generated by the incineration of plastics, synthetic
rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of
synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also
results in emissions of N2O and CH4. The methodology for calculating emissions from each of these waste incineration
sources is described in this Annex.
CCh from Plastics Incineration
In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures
reports (EPA 1999 through 2003, 2005 through 2011), the flows of plastics in the U.S. waste stream are reported for seven
resin categories. For 2009, the quantity generated, recovered, and discarded for each resin is shown in Table A-143. The
data set for 1990 through 2009 is incomplete, and several assumptions were employed to bridge the data gaps. The EPA
reports do not provide estimates for individual materials landfilled and incinerated, although they do provide such an
estimate for the waste stream as a whole. To estimate the quantity of plastics landfilled and incinerated, total discards
were apportioned based on the proportions of landfilling and incineration for the entire U.S. waste stream for each year in
the time series according to Biocycle's State of Garbage in America (van Haaren et al. 2010). For those years when
distribution by resin category was not reported (1990 through 1994), total values were apportioned according to 1995 (the
closest year) distribution ratios. Generation and recovery figures for 2002 and 2004 were linearly interpolated between
surrounding years' data.
Table A-143:2008 Plastics in the Municipal Solid Waste Stream by Resin tGgl
Waste Pathway
Generation
Recovery
Discard
Landfill
Combustion
Recovery*
Discard*
Landfill*
Combustion*
PET
3,202
662
2,540
2,316
224
21%
79%
72%
7%
HOPE
4,727
535
4,191
3,822
369
11%
89%
81%
8%
PVC
1,016
0
1,016
926
90
0%
100%
91%
9%
LDPE/
LLDPE
5,715
290
5,425
4,947
478
5%
95%
87%
8%
PP
5,017
45
4,971
4,533
438
1%
99%
90%
9%
PS
2,241
18
2,223
2,027
196
1%
99%
90%
9%
Other
5,144
372
4,772
4,351
421
7%
93%
85%
8%
Total
27,261
1,923
25,338
23,104
2,234
7%
93%
85%
8%
*As a percent of waste generation.
Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density polyethylene),
PVC (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene).
Fossil fuel-based CO2 emissions were calculated as the product of plastic combusted, C content, and fraction
oxidized (see Table A-144). The C content of each of the six types of plastics is listed, with the value for "other plastics"
assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent.
Table A-144:2008 Plastics Incinerated tGgl, Carbon Content [%1. Fraction Oxidized [%1 and Carbon Incinerated tGgl
Factor
Quantity Combusted
Carbon Content of Resin
Fraction Oxidized
Carbon in Resin Combusted
Emissions (Tg CO2 Eq.)
PET
224
63%
98%
137
0.5
HDPE
369
86%
98%
310
1.1
PVC
90
38%
98%
34
0.1
LDPE/
LLDPE
478
86%
98%
402
1.5
PP
438
86%
98%
368
1.3
PS
196
92%
98%
177
0.6
Other3
421
66%
98%
271
1.0
Total
2,234
-
1,700
6.2
" Weighted average of other plastics produced.
Note: Totals may not sum due to independent rounding.
from Incineration of Synthetic Rubber and Carbon Black in Tires
Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
content of the tires. Scrap Tire Markets in the United States: 9th Biennial Report (RMA 2009a) reports that 2,484.4
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
thousand of the 4,105.8 thousand tons of scrap tires generated in 2007 (approximately 61 percent of generation) were used
for fuel purposes. Using RMA's estimates of average tire composition and weight, the mass of synthetic rubber and C
black in scrap tires was determined:
• Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C contents
of the major elastomers used in new tire consumption.46 Table A- 145 shows consumption and C content of
elastomers used for tires and other products in 2002, the most recent year for which data are available.
• C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying (a) the mass of scrap tires incinerated by (b) the total C content of the synthetic rubber and C black
portions of scrap tires and (c) by a 98 percent oxidation factor yielded CO2 emissions, as shown in Table A- 146. The
disposal rate of rubber in tires (0.5 Tg C/yr) is smaller than the consumption rate for tires based on summing the
elastomers listed in Table A- 145 (1.3 Tg/yr); this is due to the fact that much of the rubber is lost through tire wear during
the product's lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and
fuel use for 1990 through 2007 were taken from RMA 2006 and RMA 2009a; when data were not reported, they were
linearly interpolated between bracketing years' data or, for the ends of time series, set equal to the closest year with
reported data.
RMA 2009a changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap tire.
As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert the
previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006,
2009b).
Table A-145: Elastomers Consumed in 2002 tGgl
Elastomer
Styrene butadiene rubber solid
For Tires
For Other Products*
Poly butadiene
For Tires
For Other Products
Ethylene Propylene
For Tires
For Other Products
Polychloroprene
For Tires
For Other Products
Nitrile butadiene rubber solid
For Tires
For Other Products
Polyisoprene
For Tires
For Other Products
Others
For Tires
For Other Products
Total
For Tires
Consumed
768
660
108
583
408
175
301
6
295
54
0
54
84
1
83
58
48
10
367
184
184
2,215
1,307
Carbon
Content
91%
91%
91%
89%
89%
89%
86%
86%
86%
59%
59%
59%
77%
77%
77%
88%
88%
88%
88%
88%
88%
-
-
Carbon Equivalent
700
602
98
518
363
155
258
5
253
32
0
32
65
1
64
51
42
9
323
161
161
1,950
1,174
*Used to calculate C content of non-tire rubber products in municipal solid waste.
- Not applicable
Note: Totals may not sum due to independent rounding.
46 The carbon content of tires (1,158 Gg C) divided by the mass of rubber in tires (1,285 Gg) equals 90 percent.
A-171
-------
Table A-146: Scrap Tire Constituents and Clh Emissions from Scrap Tire Incineration in 2007
Weight of Material Fraction Emissions (Tg
Material (Tg) Oxidized Carbon Content CO2Eq.)
Synthetic Rubber
Carbon Black
Total
0.5 98% 90%
0.6 98% 100%
1.1
1.8
2.3
4.1
- Not applicable
from Incineration of Synthetic Rubber in Municipal Solid Waste
Similar to the methodology for scrap tires, CO2 emissions from synthetic rubber in MSW were estimated by
multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the
MSW stream was estimated from generation and recycling data47 provided in the Municipal Solid Waste Generation,
Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011)
and unpublished backup data (Schneider 2007). The reports divide rubber found in MSW into three product categories:
other durables (not including tires), non-durables (which includes clothing and footwear and other non-durables), and
containers and packaging. EPA (2011) did not report rubber found in the product category "containers and packaging;"
however, containers and packaging from miscellaneous material types were reported for 2009. As a result, EPA assumes
that rubber containers and packaging are reported under the "miscellaneous" category; and therefore, the quantity reported
for 2009 was set equal to the quantity reported for 2008. Since there was negligible recovery for these product types, all
the waste generated is considered to be discarded. Similar to the plastics method, discards were apportioned into
landfilling and incineration based on their relative proportions, for each year, for the entire U.S. waste stream. The report
aggregates rubber and leather in the MSW stream; an assumed synthetic rubber content of 70% was assigned to each
product type, as shown in Table A-147.48 A C content of 85 percent was assigned to synthetic rubber for all product types
(based on the weighted average C content of rubber consumed for non-tire uses), and a 98 percent fraction oxidized was
assumed.
Table A-147: Rubber and Leather in Municipal Solid Waste in 2009
Product Type
Durables (not Tires)
Non-Durables
Clothing and Footwear
Other Non-Durables
Containers and Packaging
Total
Incinerated Synthetic Carbon Content Fraction Emissions
(Gg) Rubber (%) (%) Oxidized (%) (TgCO2Eq.)
271
85
63
22
2
358
70%
70%
70%
70%
-
85%
85%
85%
85%
-
98%
98%
98%
98%
-
0.8
0.3
0.2
0.1
+
1.1
+ Less than 0.05 Tg CO2 Eq.
- Not applicable.
from Incineration of Synthetic Fibers
CO2 emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber discarded
annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data provided in the
Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999
through 2003, 2005 through 2011) for textiles. Production data for the synthetic fibers was based on data from the
American Chemical Society (FEB 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the
amount recovered from (b) the waste generated (see Table A-148). As with the other materials in the MSW stream,
discards were apportioned based on the annually variable proportions of landfilling and incineration for the entire U.S.
waste stream as found in van Haaren et al. (2010). It was assumed that approximately 55 percent of the fiber was synthetic
in origin, based on information received from the Fiber Economics Bureau (DeZan 2000). An average C content of 70
percent was assigned to synthetic fiber using the production-weighted average of the C contents of the four major fiber
Discards = Generation minus recycling.
48 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
types (polyester, nylon, olefin, and acrylic) produced in 1999 (see Table A-149). The equation relating CO2 emissions to
the amount of textiles combusted is shown below.
CO2 Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (Gg) x
(Percent of Total Fiber that is Synthetic) x (Average C Content of Synthetic Fiber) x
(44g C02/12 g C)
Table A-148: Synthetic Textiles in MSW tGgl
Year
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Generation
2,884
3,674
3,832
4,090
4,269
4,498
4,686
4,870
5,123
5,297
5,451
5,649
5,893
5,927
6,170
6,319
Recovery
328
447
472
526
556
611
655
715
750
774
884
918
933
953
948
943
Discards
2,557
3,227
3,361
3,564
3,713
3,887
4,031
4,155
4,373
4,522
4,567
4,731
4,959
4,974
5,222
5376
Incineration
332
442
467
458
407
406
415
432
459
472
473
473
479
460
460
474
Table A-149: Synthetic Fiber Production in 1999
Fiber Production (Tg) Carbon Content
Polyester 1.8 63%
Nylon 1.2 64%
Olefin 1.4 86%
Acrylic 0_1 68%
Total 4.5 70%
NjO and CH4 from Incineration of Waste
Estimates of N2O emissions from the incineration of waste in the United States are based on the methodology
outlined in the EPA's Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid
Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003,
2005 through 2011) and unpublished backup data (Schneider 2007). According to this methodology, emissions of N2O
from waste incineration are the product of the mass of waste incinerated, an emission factor of N2O emitted per unit mass
of waste incinerated, and an N2O emissions control removal efficiency. The mass of waste incinerated was derived from
the information published in BioCycle (van Haaren et al. 2010). For waste incineration in the United States, an emission
factor of 50 g N2O/metric ton MSW based on the 2006 IPCC Guidelines and an estimated emissions control removal
efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW incinerators in the United States use
continuously-fed stoker technology (Bahor 2009, ERG 2009).
Estimates of CH4 emissions from the incineration of waste in the United States are based on the methodology
outlined in IPCC's 2006 Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this
methodology, emissions of CH4 from waste incineration are the product of the mass of waste incinerated and an emission
factor of CH4 emitted per unit mass of waste incinerated. Similar to the N2O emissions methodology, the mass of waste
incinerated was derived from the information published in Biocycle (van Haaren et al. 2010). For waste incineration in the
United States, an emission factor of 0.20 kg CH4/Gg MSW was used based on the 2006 IPCC Guidelines and assuming
that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009, ERC 2009). No
information was available on the mass of waste incinerated from Biocycle in 2009, so the value was assumed to remain
constant at the 2008 level.
Despite the differences in methodology and data sources, the two series of references (EPA's and BioCycle's)
provide estimates of total solid waste incinerated that are relatively consistent (see Table A-150).
A-173
-------
Table A-150: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons)
Year
EPA
BioCycle
1990
28,939,680
30,632,057
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
32,241,888
32,740,848
33,294,240
31,216,752
30,881,088
30,599,856
30,481,920
30,255,120
30,028,320
28,585,872
28,685,664
28,985,040
29,003,184
28,622,160
26,317,872
29,639,040
29,707,171
27,798,368
25,489,893
24,296,249
25,974,978
25,942,036a
25,802,917
25,930,542"
26,037,823
25,973,520°
25,853,401
24,788,539d
23,674,017
NA
NA (Not Available)
a Interpolated between 2000 and 2002 values.
b Interpolated between 2002 and 2004 values.
c Interpolated between 2004 and 2006 values.
11 Interpolated between 2006 and 2008 values
References
ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008). "16th Annual
BioCycle Nationwide Survey: The State of Garbage in America" Biocycle, JG Press, Emmaus, PA. December.
Astlett Rubber Inc. (n.d.) Material Safety Data Sheet, Carbon Black. Available online at:
. Accessed 5 November 2009.
Bahor, B (2009) Covanta Energy's public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions and
Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA.
DeZan, D.(2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF
Consulting. 4 August 2000.
Energy Recovery Council (2009). "2007 Directory of Waste-to-Energy Plants in the United States," accessed September
29, 2009.
EPA (2005 through 20011) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid Waste and
Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at
.
EPA (1999 through 2003) Characterization of Municipal Solid Waste in the United States: Source Data Update. Office of
Solid Waste, U.S. Environmental Protection Agency. Washington, DC.
EPA (1995). Compilation of Air Pollutant Emission Factors, AP-42. Fifth Edition, Vol. I: Stationary Point and Area
Sources, Introduction. Office of Air Quality Planning and Standards. Research Triangle Park, NC. October.
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 .
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.
RMA (2009a) Scrap Tire Markets in the United States: 9th Biennial Report. Rubber Manufacturers Association.
Washington, DC. May 2009.
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
RMA (2009b) "Scrap Tire Markets: Facts and Figures - Scrap Tire Characteristics." Available online at:
Accessed 17 September 2009.
RMA (2002 through 2006) U.S. Scrap Tire Markets. Rubber Manufacturers Association. Washington, DC. Available
online at: .
Schneider, S. (2007) E-mail communication Shelly Schneider, Franklin Associates to Sarah Shapiro, ICF International., A
Division of ERG. January 10, 2007.
STMC (1990 through 1997) Scrap Tire Use/Disposal Study. Rubber Manufacturers Association: Scrap Tire Management
Council. Available online at: .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.
A-175
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3.7. Methodology for Estimating Emissions from International Bunker Fuels
used by the U.S. Military
Bunker fuel emissions estimates for the Department of Defense (DoD) are developed using data generated by the
Defense Energy Support Center (DESC) for aviation and naval fuels. The DESC of the Defense Logistics Agency (DLA)
prepared a special report based on data in the Fuels Automated System (FAS), a database that recently replaced the
Defense Fuels Automated Management System (DFAMS). Data for intermediate fuel oil, however, currently remains in
the original DFAMS database. DFAMS/FAS contains data for 1995 through 2009, but the data set was not complete for
years prior to 1995. Fuel quantities for 1990 to 1994 were estimated based on a back-calculation of the 1995 DFAMS
values using DLA aviation and marine fuel procurement data. The back-calculation was refined in 1999 to better account
for the jet fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995.
Step 1: Omit Extra-Territorial Fuel Deliveries
Beginning with the complete FAS data set for each year, the first step in the development of DoD-related
emissions from international bunker fuels was to identify data that would be representative of international bunker fuel
consumption as that term is defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within
the United States or its territories and used in international maritime or aviation transport). Therefore, fuel data were
categorized by the location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e.,
sales abroad).
Step 2: Allocate JP-8 between Aviation and Land-based Vehicles
As a result of DoD49 and NATO50 policies on implementing the Single Fuel For the Battlefield concept, DoD
activities have been increasingly replacing diesel fuel with JP8 (a type of jet fuel) in compression ignition and turbine
engines in land-based equipment. Based on this concept and examination of all data describing jet fuel used in land-based
vehicles, it was determined that a portion of JP8 consumption should be attributed to ground vehicle use. Based on
available Service data and expert judgment, it was determined that a small fraction of the total JP8 use should be
reallocated from the aviation subtotal to a new land-based jet fuel category for 1997 and subsequent years. The amount of
JP8 reallocated was determined to be between 1.78 and 2.7 times the amount of diesel fuel used, depending on the Service.
As a result of this reallocation, the JP8 use reported for aviation will be reduced and the total fuel use for land-based
equipment will increase. DoD's total fuel use will not change.
Table A-151 displays DoD's consumption of fuels that remain at the completion of Step 1, summarized by fuel
type. Table A-151 reflects the adjustments for jet fuel used in land-based equipment, as described above.
Step 3: Omit Land-Based Fuels
Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were also omitted for the purpose of
calculating international bunker fuels. The remaining fuels, listed below, were considered potential DoD international
bunker fuels.
• Marine: naval distillate fuel (F76), marine gas oil (MGO), and intermediate fuel oil (IFO).
• Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB).
Step 4: Omit Fuel Transactions Received by Military Services that are not Considered to be International
Bunker Fuels
Next, the records were sorted by Military Service. The following assumptions were used regarding bunker fuel
use by Service, leaving only the Navy and Air Force as users of military international bunker fuels.
49 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Directive 4140.25, DoD Management Policy for Energy Commodities and
Related Services, 1999.
NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipments Using Compression Ignition or Turbine
Engines, 1987.
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
• Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential
international bunker fuel. Fuel consumed in international aviation or marine transport was included in the
bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a
country's borders was not considered a bunker fuel.
• Based on discussions with the Army staff, only an extremely small percentage of Army aviation emissions,
and none of its watercraft emissions, qualified as bunker fuel emissions. The magnitude of these emissions
was judged to be insignificant when compared to Air Force and Navy emissions. Based on this, Army
bunker fuel emissions were assumed to be zero.
• Marine Corps aircraft operating while embarked consumed fuel reported as delivered to the Navy. Bunker
fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel estimates.
Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero.
• Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased
fuel from DESC were assumed to be zero.
Step 5: Determine Bunker Fuel Percentages
Next it was necessary to determine what percent of the marine and aviation fuels were used as international
bunker fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States and
end in a foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations
principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights).
For the Air Force, a bunker fuel weighted average was calculated based on flying hours by major command. International
flights were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In
addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel per hour of
flight than most tactical aircraft. The Air Force bunker fuel percentage was determined to be 13.2 percent. This
percentage was multiplied by total annual Air Force aviation fuel delivered for U.S. activities, producing an estimate for
international bunker fuel consumed by the Air Force. The Naval Aviation bunker fuel percentage of total fuel was
calculated using flying hour data from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year
1998, and estimates of bunker fuel percent of flights provided by the fleet. The Naval Aviation bunker fuel percentage,
determined to be 40.4 percent, was multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding
total Navy aviation bunker fuel consumed.
For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels. In 2000, the
Navy reported that 79 percent of vessel operations were underway, while the remaining 21 percent of operations occurred
in port (i.e., pierside). Therefore, the Navy maritime bunker fuel percentage was determined to be 79 percent. The
percentage of time underway may vary from year-to-year. For example, for years prior to 2000, the bunker fuel
percentage was 87 percent. Table A-152 and Table A-153 display DoD bunker fuel use totals for the Navy and Air Force.
Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine GHG emissions. CO2 emissions
from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels,
respectively.
The rows labeled "U.S. Military" and "U.S. Military Naval Fuels" in the tables in the International Bunker Fuels
section of the Energy Chapter were based on the totals provided in Table A-152 and Table A-153, below. CO2 emissions
from aviation bunkers and distillate marine bunkers presented in Table A-156, and are based on emissions from fuels
tallied in Table A-152 and Table A-153.
A-177
-------
Table A-151: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons)
Vehicle Type/Fuel
Aviation
Total Jet Fuels
JP8
JP5
Other Jet Fuels
Aviation Gasoline
Marine
Middle Distillate (MGO)
Naval Distillate (F76)
Intermediate Fuel Oil (IFO)b
Other0
Diesel
Gasoline
Jet Fuel d
Total (Including Bunkers)
1990
4,598.4
4,598.4B
285.?B
1,025 .4 •
3,287.3
686.8
686. sB
717.lB
93.0
624. ll
+
6,002.4
1995
3,099.9
3,099.9
2,182.8
691.2
225.9
+
438.9
+
438.9
+
310.9
119.9
191.1
+
3,849.8
1996
2,941.9
2,941.9
2,253.1
615.8
72.9
+
493.3
38.5
449.0
5.9
276.9
126.1
150.8
+
3,712.1
1997
2,685.6
2,685.6
2,072.0
552.8
60.9
+
639.8
47.5
583.4
9.0
263.3
132.6
119.0
11.7
3,588.8
1998
2,741.4
2,741.4
2,122.5
515.6
103.3
+
674.2
51.1
608.4
14.7
256.8
139.5
93.9
23.4
3,672.4
1999
2,635.2
2,635.2
2,066.5
505.5
63.3
+
598.9
49.2
542.9
6.7
256.0
146.8
74.1
35.0
3,490.1
2000
2,664.4
2,664.4
2,122.7
472.1
69.6
+
454.4
48.3
398.0
8.1
248.2
126.6
74.8
46.7
3,367.0
2001
2,900.6
2,900.6
2,326.2
503.2
71.2
+
418.4
33.0
369.1
16.3
109.8
26.6
24.7
58.4
3,428.8
2002
2,609.8
2,609.6
2,091.4
442.2
76.1
0.1
455.8
41.2
395.1
19.5
211.1
57.7
27.5
125.9
3,276.7
2003
2,615.0
2,614.9
2,094.3
409.1
111.4
0.1
609.1
88.1
460.9
60.2
221.2
60.8
26.5
133.9
3,445.3
2004
2,703.1
2,703.1
2,126.2
433.7
143.2
+
704.5
71.2
583.5
49.9
170.9
46.4
19.4
105.1
3,578.5
2005
2,338.1
2,338.0
1,838.8
421.6
77.6
0.1
604.9
54.0
525.9
25.0
205.6
56.8
24.3
124.4
3,148.6
2006
2,092.0
2,091.9
1,709.3
325.5
57.0
0.1
531.6
45.8
453.6
32.2
107.3
30.6
11.7
65.0
2,730.9
2007
2,081.0
2,080.9
1,618.5
376.1
86.3
0.2
572.8
45.7
516.0
11.1
169.0
47.3
19.2
102.6
2,822.8
2008
2,067.8
2,067.7
1,616.2
362.2
89.2
0.1
563.4
55.2
483.4
24.9
173.6
49.1
19.7
104.8
2,804.9
2009
1,831.1
1,830.9
1,374.9
361.4
94.6
0.2
500.5
56.8
412.5
31.2
174.6
49.0
19.7
105.9
2,506.2
Note: Totals may not sum due to independent rounding.
a Includes fuel consumption in the United States and U.S. Territories.
b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the military Services for 1990 and 1996. The 1991 through 1995 data points were interpolated from the Service
inventory data. The 1997 through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors used for other diesel and gasoline were 5.2 and -21.1
percent, respectively. However, prior diesel fuel estimates from 1997 through 2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced the diesel fuel
consumption in land-based vehicles. Data sets for other diesel and gasoline consumed by the military in 2000 were estimated based on ground fuels consumption trends. This method produced a result
that was more consistent with expected consumption for 2000. In 2001, other gasoline and diesel fuel totals were generated by DESC.
d The fraction of jet fuel consumed in land-based vehicles was estimated using Service data, DESC data, and expert judgment.
+ Does not exceed 0.05 million gallons.
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-152: Total U.S. Military Aviation Bunker Fuel (Million Gallons)
Fuel Type/Service
JP8
Navy
Air Force
JP5
Navy
Air Force
JP4
Navy
Air Force
JAA
Navy
Air Force
JA1
Navy
Air Force
JAB
Navy
Air Force
Navy Subtotal
Air Force Subtotal
Total
1990
56.7
56.7
+
370.5
365.31
5.3
420.8
420.8
1
+
430.5
431.3
861.8
1995
300.4
38.3
262.2
249.8
246.3
3.5
21.5
+
21.5
9.2
5.7
3.5
+
+
+
+
+
+
290.2
290.7
580.9
1996
308.8
39.8
269.0
219.4
216.1
3.3
1.1
+
1.1
10.3
6.6
3.7
+
+
+
+
+
+
262.5
277.0
539.5
1997
292.0
46.9
245.1
194.2
191.2
3.0
0.1
+
0.1
9.4
5.9
3.5
+
+
+
+
+
+
244.0
251.7
495.6
1998
306.4
53.8
252.6
184.4
181.4
3.0
+
+
+
10.8
6.6
4.2
+
+
+
+
+
+
241.8
259.9
501.7
1999
301.4
55.5
245.9
175.4
170.6
4.8
+
+
+
10.8
6.3
4.5
+
+
+
+
+
+
232.4
255.2
487.5
2000
307.6
53.4
254.2
160.3
155.6
4.7
+
+
+
12.5
7.9
4.5
+
+
+
+
+
+
216.9
263.5
480.4
2001
341.2
73.8
267.4
169.7
163.7
6.1
+
+
+
12.6
8.0
4.6
0.1
+
0.1
+
+
+
245.5
278.1
523.6
2002
309.5
86.6
222.9
158.3
153.0
5.3
+
+
+
13.7
9.8
3.8
0.6
+
0.6
+
+
+
249.4
232.7
482.1
2003
305.1
76.3
228.7
146.1
141.3
4.9
+
+
+
21.7
15.5
6.2
0.2
+
0.2
+
+
+
233.1
239.9
473.0
2004
309.8
79.2
230.6
157.9
153.8
4.1
+
+
+
30.0
21.5
8.6
0.5
+
0.5
+
+
+
254.4
243.7
498.1
2005
285.6
70.9
214.7
160.6
156.9
3.7
+
+
+
15.5
11.6
3.9
0.5
+
0.5
+
+
+
239.4
222.9
462.3
2006
262.5
64.7
197.8
125.0
122.8
2.3
+
+
+
11.7
9.1
2.6
0.4
+
0.4
+
+
+
196.6
203.1
399.7
2007
249.1
62.7
186.5
144.5
141.8
2.7
+
+
+
15.6
11.7
3.9
1.1
0.1
1.0
+
+
+
216.3
194.0
410.3
2008
229.4
59.2
170.3
139.2
136.5
2.6
+
+
+
16.8
12.5
4.3
1.0
0.1
0.8
+
+
+
208.3
178.1
386.3
2009
212.5
56.4
156.1
136.9
133.4
3.5
+
0.0
0.0
18.1
12.3
5.8
0.5
+
0.6
+
+
+
202.1
166.1
368.1
+ Does not exceed 0.05 million gallons.
Note: Totals may not sum due to independent rounding.
Table A-153: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)
Marine Distillates
Navy — MGO
Navy — F76
Navy— IFO
Total
1990
+
522. 4 •
+
522.4
1995
+
333.8
| +
333.8
1996
30.3
331.9
4.6
366.8
1997
35.6
441.7
7.1
484.3
1998
31.9
474.2
11.6
517.7
1999
39.7
466.0
5.3
511.0
2000
23.8
298.6
6.4
328.8
2001
22.5
282.6
12.9
318.0
2002
27.1
305.6
15.4
348.2
2003
63.7
347.8
47.5
459.0
2004
56.2
434.4
39.4
530.0
2005
38.0
413.1
19.7
470.7
2006
33.0
355.9
25.4
414.3
2007
31.6
404.1
8.8
444.4
2008
40.9
376.9
19.0
436.7
2009
39.9
320.0
24.1
384.0
+ Does not exceed 0.005 million gallons.
Note: Totals may not sum due to independent rounding.
A-179
-------
Table A-154: Aviation and Marine Carbon Contents tTg Carhon/QBtul and Fraction Oxidized
Mode (Fuel)
Aviation (Jet Fuel)
Marine (Distillate)
Marine (Residual)
Carbon Content
Coefficient
Variable
20.17
20.48
Fraction
Oxidized
1.00
1.00
1.00
Source: EPA (2010) and IPCC (2006)
Table A-155: Annual Variable Carbon Content Coefficient for Jet FueHTg Carhon/QBtul _
Fuel _ 1990 _ 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Jet Fuel 19.40 19.34 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70
Source: EPA (2010)
Table A-156: Total U.S. DoD Clh Emissions from Bunker Fuels tTg GO? Eq.l
Mode
Aviation
Marine
Total
1990
8.1
5.4
13.4
1995
5.5
3.4
9.0
1996
5.2
3.8
9.0
1997
4.8
5.0
9.8
1998
4.9
5.3
10.2
1999 2000
4.7 4.7
5.2 3.4
10.0 8.0
2001 2002
5.1 4.7
3.3 3.6
8.3 8.3
2003 2004
4.6 4.8
4.7 5.4
9.3 10.3
2005 2006
4.5 3.9
4.8 4.2
9.3 8.1
2007
4.0
4.6
8.5
2008 2009
3.8 3.6
4.5 3.9
8.2 7.5
Note: Totals may not sum due to independent rounding.
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of
Ozone Depleting Substances
Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a
country-specific modeling approach. The Vintaging Model was developed as a tool for estimating the annual chemical
emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal
Protocol and the United States' Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—
chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—
has been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these
industries have moved toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the
Vintaging Model has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the
decline of ODS consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute end-use sectors: air-conditioning and
refrigeration, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 59 independently modeled
end-uses. The model requires information on the market growth for each of the end-uses, as well as a history of the
market transition from ODS to alternatives, as well as the characteristics of each end-use such as market size or charge
sizes and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to
each of its substitutes.
The model, named for its method of tracking the emissions of annual "vintages" of new equipment that enter into
service, is a "bottom-up" model. It models the consumption of chemicals based on estimates of the quantity of equipment
or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain
the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of
the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a "business-as-
usual" baseline case, and does not incorporate measures to reduce or eliminate the emissions of these gases other than
those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying annual leak rates,
service emission rates, and disposal emission rates to each population of equipment. By aggregating the emission and
consumption output from the different end-uses, the model produces estimates of total annual use and emissions of each
chemical.
The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System
maintained by the Stratospheric Protection Division and information from submissions to EPA under the Significant New
Alternatives Policy (SNAP) program. Published sources include documents prepared by the United Nations Environment
Programme (UNEP) Technical Options Committees, reports from the Alternative Fluorocarbons Environmental
Acceptability Study (AFEAS), and conference proceedings from the International Conferences on Ozone Protection
Technologies and Earth Technologies Forums. EPA also coordinates extensively with numerous trade associations and
individual companies. For example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and
Refrigeration Institute; the Association of Home Appliance Manufacturers; the American Automobile Manufacturers
Association; and many of their member companies have provided valuable information over the years. In some instances
the unpublished information that the EPA uses in the model is classified as Confidential Business Information (CBI). The
annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be inferred. Full public
disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has been entrusted to the
EPA.
The following sections discuss the emission equations used in the Vintaging Model for each broad end-use
category. These equations are applied separately for each chemical used within each of the different end-uses. In the
majority of these end-uses, more than one ODS substitute chemical is used.
In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market.
Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated
ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which
alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given
alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated
for each chemical used within each end-use.
Methodology
The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:
A-181
-------
1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken
from published sources and industry experts.
2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed
characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate
the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this
simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to
comply with the ODS phase-out.
3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of
substitutes that are required each year for the manufacture, installation, use, or servicing of products. The emissions are
estimated from the emission profile for each vintage of equipment or product in each end-use. By aggregating the
emissions from each vintage, a time profile of emissions from each end-use is developed.
Each set of end-uses is discussed in more detail in the following sections.
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into two categories: emissions
during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the
time of discard. Two separate steps are required to calculate the lifetime emissions from leakage and service, and the
emissions resulting from disposal of the equipment. For any given year, these lifetime emissions (for existing equipment)
and disposal emissions (from discarded equipment) are summed to calculate the total emissions from refrigeration and air-
conditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak,
service, and disposal emission rates.
Step 1: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation
and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
Esj = (la + ls) x E Qcj.i+1 for i = ;->Ł
Where:
Es = Emissions from Equipment Serviced. Emissions in yeary from normal leakage and servicing (including
recharging) of equipment.
4 = Annual Leak Rate. Average annual leak rate during normal equipment operation (expressed as a
percentage of total chemical charge).
4 = Service Leak Rate. Average leakage during equipment servicing (expressed as a percentage of total
chemical charge).
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new
equipment in a given year by weight.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 2: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
atmosphere when that vintage is discarded. Disposal emissions are thus a function of the quantity of chemical contained
in the retiring equipment fleet and the proportion of chemical released at disposal:
Edj = Qcj.k+1 x [1 - (HW x re)}
Where:
Ed = Emissions from Equipment Disposed. Emissions in yeary from the disposal of equipment.
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new
equipment in yearj-k+1, by weight.
rm = Chemical Remaining. Amount of chemical remaining in equipment at the time of disposal (expressed as
a percentage of total chemical charge).
re = Chemical Recovery Rate. Amount of chemical that is recovered just prior to disposal (expressed as a
percentage of chemical remaining at disposal (rm)).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 3: Calculate total emissions
Finally, lifetime and disposal emissions are summed to provide an estimate of total emissions.
Where:
E = Total Emissions. Emissions from refrigeration and air conditioning equipment in year/
Es = Emissions from Equipment Serviced. Emissions in year j from leakage and servicing (including
recharging) of equipment.
Ed = Emissions from Equipment Disposed. Emissions in yeary from the disposal of equipment.
j = Year of emission.
Assumptions
The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS
are presented in Table A- 157, below. As new technologies replace older ones, it is generally assumed that there are
improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is
assumed to grow independently, according to annual growth rates.
A-183
-------
Table A-157: Refrigeration andAir-Conditioning Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Centrifugal Chillers
CFC-11
CFC-12
R-500
CFC-114
HCFC-123
HCFC-22
HFC-134a
HFC-134a
HCFC-22
HCFC-123
HFC-134a
HCFC-22
HCFC-123
HFC-236fa
1993
1991
1992
1992
1991
1993
1992
1991
1993
1993
1993
1993
1993
1994
1994
1994
1994
1994
1994
1994
45%
16%
39%
53%
16%
31%
53%
16%
31%
100%
Unknown
HFC-134a
None
None
HFC-134a
Unknown
None
HFC-134a
Unknown
HFC-236fa
2000
2000
2000
1996
2010
2010
2010
1996
100%
100%
100%
100%
None
None
None
HFC-134a
1998
2009
100%
0.5%
0.5%
0.5%
0.2%
Cold Storage
CFC-12
HCFC-22
R-502
HCFC-22
R-404A
R-507
HCFC-22
HCFC-22
R-404A
R-507
1990
1994
1994
1992
1990
1993
1994
1993
1996
1996
1993
1993
1996
1996
65%
26%
9%
100%
40%
45%
15%
R-404A
R-507
None
None
R-404A
R-507
R-404A
R-507
R-404A
R-507
Non-
ODP/GWP
None
None
1996
1996
1996
1996
2009
2009
1996
1996
1996
2010
2010
2009
2009
2010
2010
2010
2010
2010
75%
25%
8%
3%
68%
23%
38%
12%
50%
None
None
None
None
None
None
None
None
None
2.5%
2.5%
2.5%
Commercial Unitary Air Conditioners (Large)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
R-407C
R-410A
R-407C
R-410A
2001
2006
2006
2009
2009
2005
2009
2009
2010
2010
5%
1%
9%
5%
81%
None
None
None
None
None
0.8%
Commercial Unitary Air Conditioners (Small)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
R-410A
R-410A
R-410A
1996
2001
2006
2009
2000
2005
2009
2010
3%
18%
8%
71%
None
None
None
None
0.8%
Dehumidifiers
HCFC-22 |HFC-134a | 1997
1997
89% [None
1
0.2%
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Initial
Market
Segment
Primary Substitute
Name of
Substitute
R-410A
Start
Date
2007
Date of Full
Penetration in
New
Equipment
2010
Maximum
Market
Penetration
11%
Secondary Substitute
Name of
Substitute
None
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Ice Makers
CFG- 12 |HFC-134a
1993
1995
100% [None
I 2.5%
Industrial Process Refrigeration
CFC-11
CFC-12
HCFC-22
HCFC-123
HFC-134a
HCFC-22
HCFC-22
HCFC-123
HFC-134a
R-401A
HCFC-22
1992
1992
1991
1991
1992
1992
1995
1992
1994
1994
1994
1994
1994
1994
1996
1993
70%
15%
15%
10%
35%
50%
5%
100%
Unknown
None
HFC-134a
HFC-134a
R-404A
R-410A
R-507
Unknown
None
HFC-134a
HFC-134a
R-404A
R-410A
R-507
HFC-134a
R-404A
R-410A
R-507
1995
1995
1995
1999
1995
1997
1995
1995
1999
1995
2009
2009
2009
2009
2010
2010
2010
2010
2010
2000
2009
2009
2009
2009
2010
2010
2010
2010
100%
15%
50%
20%
15%
100%
2%
5%
2%
2%
14%
45%
18%
14%
None
None
None
None
None
None
None
None
None
None
None
None
None
None
2.5%
2.5%
2.5%
Mobile Air Conditioners (Passenger Cars)
CFC-12 |HFC-134a
1992
1994
100% [None
I
I
I 1.9%
Mobile Air Conditioners (Light Duty Trucks)
CFC-12 |HFC-134a
1993
1994
100% [None
I
I
I -0.4%
Mobile Air Conditioners (School and Tour Buses)
CFC-12
HCFC-22
HFC-134a
1994
1994
1995
1997
0.5%
99.5%
HFC-134a
None
2006
2007
100%
None
2.6%
Mobile Air Conditioners (Transit Buses)
HCFC-22 |HFC-134a | 1995
2009
100% [None
I
I
I 2.6%
Mobile Air Conditioners (Trains)
HCFC-22
HFC-134a
R-407C
2002
2002
2009
2009
50%
50%
None
None
2.6%
Packaged Terminal Air Conditioners and Heat Pumps
HCFC-22
R-410A
R-410A
2006
2009
2009
2010
10%
90%
None
None
0.8%
Positive Displacement Chillers
HCFC-22
HCFC-22
1996
1996
100%
HFC-134a
R-407C
2000
2000
2009
2009
9%
1%
R-407C
R-410A
None
2010
2010
2020
2020
60%
40%
0.5%
A-185
-------
Initial
Market
Segment
CFC-12
Primary Substitute
Name of
Substitute
HCFC-22
Start
Date
1993
Date of Full
Penetration in
New
Equipment
1993
Maximum
Market
Penetration
100%
Secondary Substitute
Name of
Substitute
HFC-134a
R-407C
HFC-134a
R-407C
HFC-134a
R-407C
Start
Date
2009
2009
2000
2000
2009
2009
Date of Full
Penetration in
New
Equipment
2010
2010
2009
2009
2010
2010
Maximum
Market
Penetration
81%
9%
9%
1%
81%
9%
Tertiary Substitute
Name of
Substitute
R-407C
R-410A
None
R-407C
R-410A
None
R-407C
R-410A
None
Start
Date
2010
2010
2010
2010
2010
2010
Date of Full
Penetration in
New
Equipment
2020
2020
2020
2020
2020
2020
Maximum
Market
Penetration
60%
40%
60%
40%
60%
40%
Growth
Rate
0.2%
Refrigerated Appliances
CFC-12 |HFC-134a
1994
1995
100%
|HFC-134a
2005
2005
100%
I None
I 0.5%
Residential Unitary Air Conditioners
HCFC-22
HCFC-22
R-410A
R-410A
R-410A
2006
2000
2000
2006
2006
2005
2006
2006
70%
5%
5%
20%
R-410A
R-407C
R-410A
R-410A
None
None
2007
2010
2010
2006
2010
2010
2010
2006
29%
14%
57%
100%
None
R-410A
None
None
2011
2015
100%
0.8%
Retail Food (Large)
CFC-12
R-502
R-502
HCFC-22
1988
1990
1990
1993
100%
100%
HCFC-22
R-404A
R-507
R-404A
R-507
R-404A
R-507
1991
1995
1995
2000
2000
2005
2005
1993
2000
2000
2005
2005
2010
2010
100%
17.5%
7.5%
31.5%
13.5%
18%
12%
R-404A
R-507
R-404A
R-507
R-404A
R-507
None
None
None
None
None
None
1995
1995
2000
2000
2005
2005
2000
2000
2005
2005
2010
2010
17.5%
7.5%
31.5%
13.5%
18%
12%
1.7%
1.7%
Retail Food (Medium)
HCFC-22
R-404A
R-507
R-404A
R-507
R-404A
R-507
1995
1995
2000
2000
2005
2005
2000
2000
2005
2005
2010
2010
17.5%
7.5%
31.5%
13.5%
18%
12%
None
None
None
None
None
None
1.7%
Retail Food (Small)
CFC-12
HCFC-22
1990
1993
90%
HFC-134a
1993
1995
90%
C02
2010
2010
5%
1.7%
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Initial
Market
Segment
Primary Substitute
Name of
Substitute
R-404A
R-507
Start
Date
1993
1993
Date of Full
Penetration in
New
Equipment
1996
1996
Maximum
Market
Penetration
7.5%
2.5%
Secondary Substitute
Name of
Substitute
R-404A
R-507
None
None
Start
Date
2000
2000
Date of Full
Penetration in
New
Equipment
2009
2009
Maximum
Market
Penetration
7.5%
2.5%
Tertiary Substitute
Name of
Substitute
None
None
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Transport Refrigeration
CFC-12
R-502
HFC-134a
HCFC-22
HFC-134a
R-404A
1993
1993
1993
1993
1995
1995
1995
1995
98%
2%
55%
45%
None
HFC-134a
None
None
1995
1999
100%
None
2.5%
2.5%
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
R-410A
HFC-134a
R-407C
R-410A
HFC-134a
R-407C
R-410A
2000
2000
2000
2006
2006
2009
2009
2009
2006
2006
2009
2009
2009
2010
2010
2010
5%
5%
2%
2.5%
4.5%
18%
22.5%
40.5%
None
None
None
None
None
None
None
None
0.8%
Window Units
HCFC-22
R-407C
R-407C
R-410A
R-410A
2003
2009
2003
2009
2009
2010
2009
2010
3%
35%
7%
55%
None
None
None
None
5.0%
A-187
-------
Table A- 158 presents the average equipment lifetimes and annual HFC emission rates (for servicing and leaks)
for each end-use assumed by the Vintaging Model.
Table A-158. Refrigeration and Air-conditioning Lifetime Assumptions
End-Use
Centrifugal Chillers
Cold Storage
Commercial Unitary A/C
Dehumidifiers
Ice Makers
Industrial Process Refrigeration
Mobile Air Conditioners
Positive Displacement Chillers
PTAC/PTHP
Retail Food
Refrigerated Appliances
Residential Unitary A/C
Transport Refrigeration
Water & Ground Source Heat Pumps
Window Units
Lifetime
(Years)
20-27
20-25
15
11
20
25
5-12
20
12
15-20
14
15
12
20
12
HFC Emission Rates
(%)
2.0- 10.9
15.0
7.9-8.6
0.5
3.0
3.6- 12.3
2.3- 18.0
0.5-1.5
3.9
7.8-29.9
0.6
7.2-8.3
20.6-27.9
3.9
0.6
Aerosols
ODSs, HFCs and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle
releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are
modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols
was banned in 1978, and many products transitioned to hydrocarbons or "not-in-kind" technologies, such as solid
deodorants and finger-pump hair sprays. However, MDIs can continue to use CFCs as propellants because their use has
been deemed essential. Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in
MDIs are limited to the treatment of asthma and chronic obstructive pulmonary disease.
All HFCs and PFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is
currently no aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the
atmosphere. The following equation describes the emissions from the aerosols sector.
Ej = Qcj
Where:
E = Emissions. Total emissions of a specific chemical in year 7 from use in aerosol products, by weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol products sold in year
j, by weight.
= Year of emission.
Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital
medical devices and specialty consumer products, are presented in Table A- 159.
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-159. Aerosol Product Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
MDIs
CFC Mix*
HFC-134a
Non-ODP/GWP
CFC Mix*
1997
1998
2000
1997
2007
2000
6%
7%
87%
None
None
HFC-134a
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
HFC-134a
HFC-227ea
2002
2003
2006
2010
2010
2011
2011
2014
2014
2002
2009
2009
2011
2011
2012
2012
2014
2014
34%
47%
5%
6%
1%
3%
0.3%
3%
0.3%
0.8%
Consumer Aerosols (Non-MDIs)
NA**
HFC-152a
HFC-134a
1990
1995
1991
1995
50%
50%
None
HFC-152a
HFC-152a
1997
2001
1998
2005
44%
36%
2.0%
*CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs
from 1993 to 2008.
"Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that
is now using HFC propellants is modeled.
Solvents
ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need
to be cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving
the item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid
phase and are not emitted as gas. Thus, emissions are considered "incomplete," and are a fixed percentage of the amount
of solvent consumed in a year. The remainder of the consumed solvent is assumed to be reused or disposed without being
released to the atmosphere. The following equation calculates emissions from solvent applications.
Where:
Emissions. Total emissions of a specific chemical in yeary from use in solvent applications, by weight.
Percent Leakage. The percentage of the total chemical that is leaked to the atmosphere, assumed to be
90 percent.
Qc = Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent applications in the
year j, by weight.
j = Year of emission.
Transition Assumptions
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals
cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A- 160.
A-189
-------
Table A-160. Solvent Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Growth
Rate
Adhesives
CH3CCI3 |Non-ODP/GWP | 1994
1995
100% [None
I 2.0%
Electronics
CFC-113
CH3CCI3
Semi-Aqueous
HCFC-225ca/cb
HFC-4310mee
HFE-7100
nPB
Methyl Siloxanes
No-Clean
Non-ODP/GWP
PFC/PFPE
1994
1994
1995
1994
1992
1992
1992
1996
1996
1995
1995
1996
1995
1996
1996
1996
1997
1997
52%
0.2%
0.7%
0.7%
5%
0.8%
40%
99.8%
0.2%
None
Unknown
None
None
None
None
None
None
Non-ODP/GWP
Non-ODP/GWP
2000
2005
2003
2009
90%
10%
2.0%
2.0%
Metals
CH3CCI3
CFC-113
CCI4
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1992
1992
1992
1996
1996
1996
100%
100%
100%
None
None
None
2.0%
2.0%
2.0%
Precision
CH3CCI3
CFC-113
Non-ODP/GWP
HFC-4310mee
PFC/PFPE
Non-ODP/GWP
HCFC-225ca/cb
HFE-7100
1995
1995
1995
1995
1995
1995
1996
1996
1996
1996
1996
1996
99.3%
0.6%
0.1%
96%
1%
3%
None
None
Non-ODP/GWP
Non-ODP/GWP
None
Unknown
None
2000
2005
2003
2009
90%
10%
2.0%
2.0%
Non-ODP/GWP includes chemicals with 0 ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives such as
"no clean" technologies.
Fire Extinguishing
ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming"
applications as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are
generally built to be leak-tight, some leaks do occur and of course emissions occur when the agent is released. Total
emissions from fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in
operation at a given time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate
for an average equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are
assumed to be 3.5 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of
chemical is assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a
single year, accounting for all fire protection equipment in operation in that year. Each fire protection agent is modeled
separately. In the Vintaging Model, streaming applications have a 12-year lifetime and flooding applications have a 20-
year lifetime.
• = r
Qcj.i+1 for i=l
Where:
E = Emissions. Total emissions of a specific chemical in yeary for streaming fire extinguishing equipment,
by weight.
r = Percent Released. The percentage of the total chemical in operation that is released to the atmosphere.
Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire extinguishing equipment in
a given year, j-i+l, by weight.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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k = Lifetime. The average lifetime of the equipment.
Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A- 161.
Table A-161. Fire Extinguishing Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Growth
Rate
Flooding Agents
Halon-1301
Halon-1301*
HFC-23
HFC-227ea
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
C4Flo
HFC-125
1994
1994
1994
1994
1995
1998
1994
1997
1994
1999
1999
1994
2034
2027
1999
2006
4%
0.2%
18%
46%
10%
10%
1%
11%
Unknown
None
FK-5-1-12
HFC-125
FK-5-1-12
None
None
FK-5-1-12
None
2003
2001
2003
2003
2010
2008
2010
2003
10%
10%
7%
100%
2.2%
Streaming Agents
Halon-1211
Halon-1211*
HFC-236fa
Halotron
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1992
1997
1994
1993
1995
1999
1992
1999
1997
1994
2024
2018
5%
3%
4%
58%
20%
10%
Unknown
None
Non-ODP/GWP
HFC-236fa
None
None
None
2015
2015
2015
2015
25%
75%
3.0%
* Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled with stockpiled or
recovered supplies.
Foam Blowing
ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels
around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate,
increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell
or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams
are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of
the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
equation.
Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following
Where:
Enij = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing
losses, by weight.
Im = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For open-cell foams,
Im is 100%.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
j = Year of emission.
A-191
-------
Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as
presented in the following equation.
EUJ = lu x -L QCJ.I+I for i=l—>k
Where:
Eitj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses
during use, by weight.
lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
/ = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following
equation.
Edj = Id x QCj.k
Where:
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.
Id = Loss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-Disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while
the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane
foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal,
calculated as presented in the following equation.
Epj = Ip x 2j Qcj.m for m =k-^>k + 32
Where:
Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight.
Ip = Leak Rate. Percent of original blowing agent emitted post disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a
given year.
k = Lifetime. The average lifetime of foam product.
m = Counter. Runs from lifetime (k) to (k+26).
j = Year of emission.
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Step 5: Calculate total emissions (open-cell and closed-cell foams)
To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as
presented in the following equation.
Ej = Em/ + EUJ + Edj + Epj
Where:
Ej = Total Emissions. Total emissions of a specific chemical in yeary, by weight.
Em = Emissions from manufacturing. Total emissions of a specific chemical in year7 due to manufacturing
losses, by weight.
EUJ = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses
during use, by weight.
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.
Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight.
Assumptions
The Vintaging Model contains 13 foam types, whose transition assumptions away from ODS and growth rates
are presented in Table A- 162. The emission profiles of these 13 foam types are shown in Table A- 163.
A-193
-------
Table A-162. Foam Blowing Market Transition Assumptions
Initial
Market
Segment
Primary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Tertiary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
Commercial Refrigeration Foam
CFC-11
HCFC-141b
HCFC-142b
HCFC-22
1989
1989
1989
1996
1996
1996
40%
8%
52%
HFC-245fa
Non-ODP/GWP
Non-ODP/GWP
HFC-245fa
Non-ODP/GWP
HFC-245fa
2002
2002
2009
2009
2009
2009
2003
2003
2010
2010
2010
2010
80%
20%
80%
20%
80%
20%
None
None
None
None
None
None
6.0%
Flexible PU Foam: Integral Skin Foam
CFC-11
HCFC-141b
1989
1990
100%
HFC-134a
HFC-134a
CO2
C02
1993
1994
1993
1994
1996
1996
1996
1996
25%
25%
25%
25%
None
None
None
None
2.0%
Flexible PU Foam: Slabstock Foam, Moulded Foam
CFC-11
Non-ODP/GWP
1992
1992
100%
None
2.0%
Phenolic Foam
CFC-11 |HCFC-141b
1989
1990
100% 1 Non-ODP/GWP
1992
1992
100% |None
2.0%
Polyolefin Foam
CFC-114
HFC-152a
HCFC-142b
1989
1989
1993
1993
10%
90%
Non-ODP/GWP
Non-ODP/GWP
2005
1994
2010
1996
100%
100%
None
None
2.0%
PU and PIR Rigid: Boardstock
CFC-11
HCFC-141b
1993
1996
100%
Non-ODP/GWP
HC/HFC-245fa
Blend
2000
2000
2003
2003
95%
5%
None
None
6.0%
PU Rigid: Domestic Refrigerator and Freezer Insulation
CFC-11
HCFC-141b
1993
1995
100%
HFC-134a
HFC-245fa
HFC-245fa
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
1996
2001
2006
2002
2006
2009
2001
2003
2009
2005
2009
2014
7%
50%
10%
10%
3%
20%
Non-ODP/GWP
Non-ODP/GWP
Non-ODP/GWP
None
None
None
2002
2015
2015
2003
2029
2029
100%
100%
100%
0.8%
PU Rigid: One Component Foam
CFC-12
HCFC-142b/22
Blend
1989
1996
70%
Non-ODP/GWP 2009 2010 80%
HFC-134a 2009 2010 10%
HFC-152a 2009 2010 10%
None
None
None
4.0%
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Initial
Market
Segment
Primary Substitute
Name of
Substitute
HCFC-22
Start
Date
1989
Date of Full
Penetration
in New
Equipment
1996
Maximum
Market
Penetration
30%
Secondary Substitute
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment
Maximum
Market
Penetration
Non-ODP/GWP 2009 2010 80%
HFC-134a 2009 2010 10%
HFC-152a 2009 2010 10%
Tertiary Substitute
Name of
Substitute
None
None
None
Start
Date
Date of Full
Penetration in
New
Equipment
Maximum
Market
Penetration
Growth
Rate
PU Rigid: Other: Slabstock Foam
CFC-11
HCFC-141b
1989
1996
100%
C02
Non-ODP/GWP
HCFC-22
1999
2001
2003
2003
2003
2003
45%
45%
10%
None
None
Non-ODP/GWP
2009
2010
100%
2.0%
PU Rigid: Sandwich Panels: Continuous and Discontinuous
CFC-11
HCFC-141b
HCFC-22
1989
1989
1996
1996
82%
18%
HCFC-22/Water
Blend
HFC-245fa/C02
Blend
Non-ODP/GWP
HFC-134a
HFC-245fa/CO2
Blend
Non-ODP/GWP
C02
HFC-134a
2001
2002
2001
2002
2009
2009
2009
2009
2003
2004
2004
2004
2010
2010
2010
2010
20%
20%
40%
20%
40%
20%
20%
20%
HFC Blend
Non-ODP/GWP
None
None
None
None
None
None
None
2009
2009
2010
2010
50%
50%
6.0%
PU Rigid: Spray Foam
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
HFC-245fa/C02
Blend
Non-ODP/GWP
2002
2002
2001
2003
2003
2003
30%
60%
10%
None
None
None
6.0%
XPS: Boardstock Foam
CFC-12
HCFC-142b/22
Blend
HCFC-142b
1989
1989
1994
1994
10%
90%
HFC-134a
HFC-152a
C02
Non-ODP/GWP
HFC-134a
HFC-152a
C02
Non-ODP/GWP
2009
2009
2009
2009
2009
2009
2009
2009
2010
2010
2010
2010
2010
2010
2010
2010
70%
10%
10%
10%
70%
10%
10%
10%
None
None
None
None
None
None
None
None
2.5%
XPS: Sheet Foam
CFC-12
C02
Non-ODP/GWP
1989
1989
1994
1994
1%
99%
None
C02
HFC-152a
1995
1995
1999
1999
9%
10%
None
None
2.0%
A-195
-------
Table A-163. Emission profile for the foam end-uses
Foam End-Use
Flexible PU Foam: Slabstock Foam, Moulded Foam
Commercial Refrigeration
Rigid PU: Spray Foam
Rigid PU: Slabstock and Other
Phenolic Foam
Polyolefin Foam
Rigid PU: One Component Foam
XPS: Sheet Foam*
XPS: Boardstock Foam
Flexible PU Foam: Integral Skin Foam
Rigid PU: Domestic Refrigerator and Freezer
Insulation*
PU and PIR Rigid: Boardstock
PU Sandwich Panels: Continuous and Discontinuous
Loss at
Manufacturing (%)
100
6
15
37.5
23
95
100
40
25
95
4
6
5.5
Annual
Leakage Rate
(%)
0
0.25
1.5
0.75
0.875
2.5
0
2
0.75
2.5
0.25
1
0.5
Leakage
Lifetime
(years)
1
15
56
15
32
2
1
25
50
2
14
50
50
Loss at
Disposal (%)
0
90.25
1
51.25
49
0
0
0
37.5
0
40.0
44
69.5
Total*
(%)
100
100
100
100
100
100
100
90
100
100
47.5
100
100
PIR (Polyisicyanurate)
PU (Polyurethane)
XPS (Extruded Polystyrene)
*In general, total emissions from foam end-uses are assumed to be 100 percent, although work is underway to investigate that assumption. In the
XPS Sheet/Insulation Board end-use, the source of emission rates and lifetimes did not yield 100 percent emission; it is unclear at this time
whether that was intentional. In the Rigid PU Appliance Foam end-use, the source of emission rates and lifetimes did not yield 100 percent
emission; the remainder is anticipated to be emitted at a rate of 2.0%/year post-disposal for the next 26 years.
Sterilization
Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a
blend of 12% ethylene oxide (EtO) and 88% CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes the
equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a single
end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to be
emitted in that year, as shown in the following equation.
Where:
E = Emissions. Total emissions of a specific chemical in year j from use in sterilization equipment, by
weight.
Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization equipment in yeary, by
weight.
j = Year of emission.
Assumptions
The Vintaging Model contains 1 sterilization end-use, whose transition assumptions away from ODS and growth
rates are presented in Table A- 164.
Table A-164. Sterilization Market Transition Assumptions
Initial Market
Segment
12/88
Primary Substitute
Name of Substitute
EtO
Non-ODP/GWP
HCFC/EtO Blends
Start
Date
1994
1994
1993
Date of Full
Penetration in
New Equipment
1995
1995
1994
Maximum
Market
Penetration
95%
1%
4%
Growth
Rate
2.0%
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Model Output
By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions
for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by -chemical basis,
J J / J '
summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in
metric tons and in teragrams of CO2 equivalents (Tg CO2 Eq.). The conversion of metric tons of chemical to Tg CO2 Eq.
is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more
accurate information becomes available, the model is adjusted in such a way that both past and future emission estimates
are often altered.
Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been
consumed in an application or sub-application and that which has already been released" (IPCC 2006). For any given
year, the bank is equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus
chemical in new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount
added to replace chemical emitted prior to the given year, as shown in the following equation:
BcJ=BcJ_rQdJ+QPj+Ee-Qr
Where:
BCJ = Bank of Chemical. Total bank of a specific chemical in year j, by weight.
Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in equipment
disposed of in year j, by weight.
Op/ = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical that is entering the
market in year j, by weight.
Ee = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is emitted during year j
but is not replaced in that year. The Vintaging Model assumes all chemical emitted from refrigeration,
air conditioning and fire extinguishing equipment is replaced in the year it is emitted, hence this term is
zero for all sectors except foam blowing.
Qr = Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical that is used to
replace emissions that occurred prior to year j. The Vintaging Model assumes all chemical emitted
from refrigeration, air conditioning and fire extinguishing equipment is replaced in the year it is
emitted, hence this term is zero for all sectors.
= Year of emission.
Table A- 165 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990-2009.
A-197
-------
Table A-165. Banks of ODS and ODS Substitutes, 1990-2009 [Mil
1990
CFC
HCFC
653,338
235,625
454,719
HFC
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
628,584
602,580
578,017
554,078
531,714
513,896
500,430
489,582
482,541
479,890
878,343
947,180
999,467
1,033,925
1,067,106
1,103,085
1,135,099
1,160,421
1,174,094
1,165,278
181,693
210,207
241,960
280,053
318,505
354,544
390,237
425,719
457,860
498,695
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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3.9. Methodology for Estimating CELi Emissions from Enteric Fermentation
Methane emissions from enteric fermentation were estimated for five livestock categories: cattle, horses, sheep,
swine, and goats. Emissions from cattle represent the majority of U.S. emissions from enteric fermentation; consequently,
the more detailed IPCC Tier 2 methodology was used to estimate emissions from all cattle (except for bulls). The IPCC
Tier 1 methodology was used to estimate emissions from bulls and the other types of livestock.
Estimate Methane Emissions from Cattle
This section describes the process used to estimate methane emissions from cattle enteric fermentation using the
Cattle Enteric Fermentation Model (CEFM).51 The CEFM, based on recommendations provided in
IPCC/UNEP/OECD/TEA (1997), IPCC (2000) and IPCC (2006), uses information on population, energy requirements,
digestible energy, and methane conversion rates to estimate methane emissions.52 The emission methodology consists of
the following three steps: (1) characterize the cattle population to account for animal population categories with different
emission profiles; (2) characterize cattle diets to generate information needed to estimate emission factors; and (3) estimate
emissions using these data and the IPCC Tier 2 equations.
Step 1: Characterize U.S. Cattle Population
The national cattle population estimates in the inventory submission are based on data obtained from the U.S.
Department of Agriculture's (USDA) National Agricultural Statistics Service Quick Stats database (USDA 2010). A
summary of the annual average populations upon which all livestock-related emissions are based is provided in Table A-
166. Cattle populations used in the Enteric Fermentation sector were estimated using the cattle transition matrix in the
CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from
birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while taking into
account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates. The use of
supplemental USDA data and the cattle transition matrix in the CEFM results in the cattle population estimates for this
sector differing slightly from the January 1 or July 1 USDA point estimates and the cattle population data obtained from
the Food and Agriculture Organization of the United Nations (FAO).
Table A-166: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2009
Livestock Type
Calves 0-6 months
Dairy
Dairy Cows
Dairy Replacements 7-11 months
Dairy Replacements 12-23 months
Beef
Bulls
Beef Cows
Beef Replacements 7-11 months
Beef Replacements 12-23 months
Steer Stackers
Heifer Stackers
Feedlot Cattle
1990
22,561
10,0151
l,214l
2,915B
2,16ol
32,455 !
1,269|
2,961 M
10,321 !
5,946
9,549
1995
23,499
9,482 1
1,216!
2,892 1
2,385 1
35,190|
l,493l
3,637 1
11,716|
6,699!
11,064
2000
22,569
9,183l
1,196!
2,812|
2,293l
33,575 •
1,313!
3,097 1
8,724 1
5,371 !
13,006
2005
21,678
9,004
1,257
2,905
2,214
32,674
1,363
3,171
8,185
5,015
12,652
2006
21,621
9,104
1,277
3,017
2,258
32,703
1,380
3,294
8,248
5,041
13,526
2007
21,483
9,145
1,299
3,043
2,214
32,644
1,349
3,276
8,302
4,966
13,404
2008
21,155
9,257
1,304
3,097
2,207
32,435
1,312
3,169
8,233
4,868
13,070
2009
20,940
9,333
1,325
3,101
2,184
31,712
1,287
3,098
8,501
5,051
12,964
Note: Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the January
estimates downloaded from USDA (2010).
The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an
estimate of the population age and weight structure by cattle type on a monthly basis. Since cattle often do not remain in a
single population type for an entire year (e.g., calves become stackers, stackers become feedlot animals), and emission
Emissions from bulls are estimated in the CEFM using a Tier 1 approach based on published population statistics and national average
emission factors because the variation in diets and within year population is assumed to be minimal. The IPCC recommends the use of a
methane conversion factor of zero for calves, because they consume mainly milk, therefore this results in no methane emissions from
calves through 6 months.
52 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
A-199
-------
profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation
model to obtain more accurate emission estimates than would be available from annual point estimates of population (such
as available from USDA statistics) and weight for each cattle type.
The transition matrix tracks both dairy and beef populations, and divides the populations into males and females,
and subdivides the population further into specific cattle groupings for replacement, stacker, feedlot, and mature animals.
The matrix is based primarily on two types of data: population statistics and weight statistics (including target weights,
slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of animals over
time by month. The matrix also relies on supplementary data, such as feedlot placement statistics, slaughter statistics,
death rates, and calving rates.
The basic method for tracking population of animals per category is based on the number of births (or graduates)
into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category
(such as stackers to feedlot placements).
Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This
level of detail accounts for the variability in CH4 emissions associated with each life stage. Given that a stage can last less
than one year (e.g., beef calves are weaned at 7 months), each is modeled on a per-month basis. The type of cattle also
impacts CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition matrix for each of three
separate lifecycle phases, 1) calves, 2) replacements and stackers, and 3) feedlot animals. In addition, the number of
mature cows is tracked for both dairy and beef stock. Each lifecycle is discussed separately below, and the categories
tracked are listed in Table A-167.
Table A-167: Cattle Population Categories Used for Estimating Clh Emissions
Dairy Cattle Beef Cattle
Calves Calves
Heifer Replacements Heifer Replacements
Cows Heifer and Steer Stackers
Animals in Feedlots (Heifers & Steers)
Cows
Bulls*
* Bulls (beef and dairy) are accounted for in a single category.
The key variables tracked for each of these cattle population categories (except bulls) are as follows:
Calves. The number of animals born on a monthly basis was used to initiate monthly cohorts and to determine
population age structure. The number of calves born each month was obtained by multiplying annual births by the
percentage of births by month. Annual birth information for each year was taken from USDA (2010). For dairy cows, the
number of births is assumed to be distributed equally throughout the year (approximately 8.3 percent per month), beef
births are distributed according to Table A-168, based on estimates from the National Animal Health Monitoring System
(NAHMS) (USDA/APHIS/VS 1998, 1994, 1993). To determine whether calves were born to dairy or beef cows, the dairy
cow calving rate (USDA/APHIS/VS 2002, USDA/APHIS/VS 1996) was multiplied by the total dairy cow population to
determine the number of births attributable to dairy cows, with the remainder assumed to be attributable to beef cows.
Total annual calf births are obtained from USDA, and distributed into monthly cohorts by cattle type (beef or dairy). Calf
growth is modeled by month, based on estimated monthly weight gain for each cohort (approximatey 61 pounds per
month). Total calf population is modified through time to account for veal calf slaughter at 4 months and a calf death loss
of 0.35 percent annually (distributed across age cohorts up to six months of age). An example of a transition matrix for
calves is shown in Table A-169. Note that calves age one through six months available in January have been tracked
through the model based on births and death loss from the previous year.
Table A-168: Estimated Beef Cow Births by Month
Jan Feb Mar Apr May Jim Jul Aug Sep Oct Nov Dec
7% 15% 28% 22% 9% 3% 2% 2% 3% 4% 3% 3%
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-169: Example of Monthly Average Populations from Calf Transition Matrix
Age
(month)
6
5
4
3
2
1
0
Jan
1,207
1,199
1,494
1,751
1,684
1,666
2,665
Feb
1,199
1,494
1,751
1,684
1,666
2,665
5,005
Mar
1,494
1,751
1,684
1,666
2,665
5,005
8,720
Apr
1,751
1,684
1,666
2,665
5,005
8,720
7,062
May
1,684
1,666
2,665
5,005
8,720
7,062
3,279
Jun
1,666
2,665
5,005
8,720
7,062
3,279
1,631
Jul
2,665
5,005
8,720
7,062
3,279
1,631
1,212
Aug
5,005
8,720
7,062
3,279
1,631
1,212
1,202
Sep
8,720
7,062
3,279
1,631
1,212
1,202
1,495
Oct
7,062
3,279
1,631
1,212
1,202
1,495
1,748
Nov
3,279
1,631
1,212
1,202
1,495
1,748
1,680
Dec
1,631
1,212
1,202
1,495
1,748
1,680
1,661
Replacements and Stockers. At seven months of age, calves 'graduate' and are separated into the applicable
cattle types. First the number of replacements required for beef and dairy cattle are calculated based on estimated death
losses and population changes between beginning and end of year population estimates. All steer, and remaining heifers
(after subtracting required replacements), are considered 'stackers,' that is backgrounding animals that are eligible for
placement into feedlots as they reach the appropriate weight class. During the stacker phase animals are subtracted out of
the transition matrix for placement into feedlots based on feedlot placement statistics from USDA (2010).
The data and calculations that occur for the stacker category include matrices that estimate the population of
backgrounding heifers and steer, as well as a matrix for total combined stackers. The matrices start with the beginning of
year populations in January and model the progression of each cohort. The age structure of the January population is based
on estimated births by month from the previous two years, although in order to balance the population properly, an
adjustment is added that slightly reduces population percentages in the older populations. The populations are modified
through addition of graduating calves (month 7, bottom row of Table A-170) and subtraction through death loss and
animals placed in feedlots. Eventually, an entire cohort population of stackers may be zero, indicating that the complete
cohort has been transitioned into feedlots. An example of the transition matrix for stackers is shown in Table A-170.
Table A-170: Example of Monthly Average Populations from Stocker Transition Matrix
Age
(month)
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
Jan
202
353
286
133
66
49
49
60
71
68
67
1,076
2,027
3,532
2,860
1,328
4,450
Feb
214
174
81
40
30
30
37
43
41
41
918
1,774
3,105
2,542
1,204
4,083
878
Mar
126
59
29
22
22
27
31
30
30
669
1,607
2,813
2,297
1,082
3,588
770
871
Apr
47
23
17
17
21
25
24
24
531
1,324
2,471
2,017
962
3,229
607
617
1,060
May
20
15
15
18
21
20
20
453
1,160
2,162
1,796
857
2,765
507
451
664
1,295
Jun
12
12
15
18
17
17
379
1,004
1,867
1,547
716
2,242
365
253
425
915
1,232
Jul
10
13
15
14
14
321
882
1,636
1,355
625
1,669
282
179
209
504
782
1,230
Aug
11
13
12
12
273
779
1,442
1,193
551
1,337
49
49
82
261
464
831
2,114
Sep
9
9
9
197
620
1,141
942
435
985
49
49
61
91
213
429
1,682
4,188
Oct
7
7
153
526
965
795
368
779
49
49
61
71
76
128
1,353
3,596
7,484
Nov
5
103
421
765
629
291
546
49
49
61
71
68
72
1,151
2,916
6,832
6,012
Dec
51
311
559
457
212
305
49
49
61
71
68
67
1,094
2,683
6,195
5,605
2,665
In order to ensure a balanced population of both stackers and placements, additional data tables are utilized in the
stacker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the total
number of animals within the population that are available to be placed in feedlots and the actual feedlot placement
statistics provided by USDA (2010). In cases where there are discrepancies between the USDA estimated placements by
weight class and the calculated animals available by weight, the model pulls available stackers from one higher weight
category if available. If there are still not enough animals to fulfill requirements the model pulls animals from the next
lower category. Note that in the current time series, this method was able to ensure that total placement data matched
USDA estimates, and no shortfalls have occurred.
In addition, average weights were tracked for each monthly age group using starting weight and monthly weight
gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target
weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be
88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 Ibs. Other reported target weights
A-201
-------
were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight was
taken from measurements provided by a major British Bos taurus breed (Enns 2008). Beef replacement weight was
calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at 24 months. As dairy weights
are not a trait that is typically tracked, mature weight for dairy cows was estimated at 1,500 for all years, based on a
personal communication with Kris Johnson (2010) and an estimate from Holstein Association USA (2010). Dairy
replacement at 15 months was assumed to be 875 Ibs and replacement at 24 months is 1,300 Ibs. Live slaughter weights
were derived from dressed slaughter weight data for each year (USDA 2010). The annual typical animal mass for each
livestock type are presented in Table A- 171
Weight gain for stacker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from
average daily estimates from Lippke et al. 2000, Pinchack et al., 2004, Platter et al., 2003, and Skogerboe et al., 2000 for
2000 through 2007. Interim years were calculated linearly, as shown in Table A-172, and weight gain was held constant
starting in 2008. Live slaughter weight was estimated as dressed weight divided by 0.63. This ratio represents the dressed
weight (i.e. weight of the carcass after removal of the internal organs), to the live weight (i.e. weight taken immediately
before slaughter). Table A-172 provides weights and weight gains that vary by year in the CEFM.
Table A-171: Typical Animal Mass that Varies by Year (Ibs)
Year/Cattle Dairy Dairy
Type Cows Replacements
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
1,500
900
898
897
899
898
898
898
900
897
899
897
898
897
900
897
895
898
897
898
897
Beef Beef
Cows Replacements
1,221
1,225
1,263
1,280
1,280
1,282
1,285
1,286
1,296
1,292
1,272
1,272
1,276
1,308
1,323
1,327
1,341
1,348
1,348
1,348
820
822
841
852
854
858
859
861
866
862
849
850
852
872
878
880
890
895
895
895
Steer
Stackers
692
695
714
721
721
735
739
737
736
731
720
726
726
719
719
718
725
721
721
731
Heifer
Stackers
652
656
673
683
689
701
707
708
710
709
702
707
708
702
702
706
713
707
705
715
Steer
Feedlot
923
975
984
930
944
947
940
939
957
960
961
963
982
973
967
975
984
992
1,000
1,007
Heifer
Feedlot
846
867
878
864
876
880
878
877
892
895
899
901
915
905
905
917
925
928
939
948
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-172: Weight Gains that Vary by Year Ubsl
Year/Cattle Steer Stackers to 12 Steer Stackers to 24 Heifer Stackers to 12 Heifer Stackers to 24
Type months(lbs/day) months (Ibs/day) months(lbs/day) months(lbs/day)
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1.53
1.56
1.59
1.62
1.65
1.68
1.71
1.74
1.77
1.80
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.23
1.29
1.35
1.41
1.47
1.53
1.59
1.65
1.71
1.77
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.23
1.29
1.35
1.41
1.47
1.53
1.59
1.65
1.71
1.77
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.08
1.15
1.23
1.30
1.38
1.45
1.53
1.60
1.68
1.75
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
1.83
Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al. (2003), Skogerboe et al. (2000)
Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the
stacker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number
of animals from the stacker cohorts into the feedlot populations to match the reported placement data. After animals are
placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become acclimated to
the new feed type, during this time weight gain is estimated to be 2.8 to 3 pounds per day (Johnson 1999). Animals are
then switched to a finishing diet for a period of time before they are slaughtered. Weight gain during finishing diets is
estimated to be 3 to 3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the
start weight (i.e., placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target
weight (as determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are
tracked for inclusion in the following year's emission and population counts. For 1990 to 1995, only the total placement
data were available, therefore placements for each weight category (displayed in Table A-173) for those years are based on
the average of monthly placements from the 1996 to 1998 reported figures. Placement data is available by weight class for
all years from 1996 onward. Table A-173 provides a summary of the reported feedlot placement statistics for 2009.
Table A-173: Feedlot Placements in the United States for 2009 (Number of animals placed in Thousand Head)
Weight
Placed When:
< 600 Ibs
600 - 700 Ibs
700 - 800 Ibs
> 800 Ibs
Total
Jan
380
505
553
420
1,858
Feb
320
385
538
435
1,678
Mar
305
340
593
570
1,808
Apr
355
315
405
525
1,600
May
395
305
433
505
1,638
Jun
315
290
371
415
1,391
Jul
455
365
458
585
1,863
Aug
435
395
514
775
2,119
Sep
490
450
593
855
2,388
Oct
615
645
579
635
2,474
Nov Dec
510 430
565 420
394 401
375 295
1,844 1,546
Source: USDA (2010).
Note: Totals may not sum due to independent rounding.
Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for
particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed
on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4
emissions. A weighted average percent of pregnant cows each month was estimated using information on births by month
and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was
estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This
process results in a range of weighted monthly lactation estimates expressed as Ibs/animal/month. The monthly estimates
from January to December are 3.3, 5.1, 8.7, 12.0, 13.6, 13.3, 11.7, 9.3, 6.9, 4.4, 3.0, and 2.8 Ibs milk/animal/day. Annual
estimates for dairy cattle were taken from USDA milk production statistics. Dairy lactation estimates for 1990 through
2009 are shown in
A-203
-------
Table A-174. Beef and dairy cow and bull populations are assumed to remain relatively static throughout the
year, as large fluctuations in population size are assumed to not occur. These estimates are taken from the USDA
beginning and end of year population datasets.
Table A-174: Dairy Lactation by State libs/year/cow)
State/Year
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
1990
12,214
13,300
17,500
11,841
18,456
17,182
15,606
13,667
14,033
12,973
13,604
16,475
14,707
14,590
15,118
12,576
10,947
11,605
14,619
13,461
14,871
15,394
14,127
12,081
13,632
13,542
13,866
16,400
15,100
13,538
18,815
14,658
15,220
12,624
13,767
12,327
16,273
14,726
14,250
12,771
12,257
11,825
14,350
15,838
14,528
14,213
18,532
11,250
13,973
12,337
1995
14,176
17,000 1
19,735 1
12,150 1
19,573 1
18,687 1
16,438 1
14,500 1
14,698 1
15,550 1
13,654 1
18,147 1
15,887 1
15,375 1
16,124 1
14,390 1
12,469 1
11,908 1
16,025 1
14,725 1
16,000 1
17,071 1
15,894 1
12,909 1
14,158 1
15,000 1
14,797 1
18,128
16,300
13,913 1
18,969 1
16,501 1
16,314 1
13,094 1
15,917 1
13,611 1
17,289 1
16,492 1
14,773 1
14,481 1
13,398 1
13,740 1
15,244 1
16,739 1
16,210 1
15,116 1
20,091 1
12,667 1
15,397 1
13,197
2000
13,920
14,500
21,820
12,436
21,130
21,618
17,778
14,747
15,688
16,284
14,358
20,816
17,450
16,568
18,298
16,923
12,841
12,034
17,128
16,083
17,091
19,017
17,777
15,028
14,662
17,789
16,513
19,000
17,333
15,250
20,944
17,378
16,746
14,292
17,027
14,440
18,222
18,081
15,667
16,087
15,516
14,789
16,503
17,573
17,199
15,833
22,644
15,588
17,306
13,571
2005
14,000
12,273
22,679
13,545
21,404
22,577
19,200
16,622
16,591
17,259
12,889
22,332
18,827
20,295
20,641
20,505
12,896
12,400
18,030
16,099
17,059
21,635
18,091
15,280
16,026
19,579
17,950
21,680
18,875
16,000
21,192
18,639
18,741
14,182
17,567
16,480
18,876
18,722
17,000
16,000
17,741
15,743
19,646
18,875
18,469
16,990
23,270
14,923
18,500
14,878
2006
14,500
12,250
22,855
12,900
21,815
23,155
19,316
16,286
16,447
18,234
13,256
22,346
19,252
19,861
20,127
20,938
13,296
12,375
17,938
17,281
17,375
22,234
18,598
14,957
16,000
18,632
18,328
20,148
19,533
16,182
21,853
18,879
18,510
14,688
17,737
16,630
19,000
19,390
17,273
16,294
18,580
15,657
19,501
20,314
18,383
17,363
23,055
15,385
18,824
17,612
2007
15,154
14,667
23,260
12,941
22,440
22,932
19,211
16,618
16,832
18,169
12,241
22,513
18,612
20,307
20,085
19,882
13,889
12,034
17,788
18,121
17,000
22,761
18,817
15,429
14,982
18,500
18,220
20,481
19,333
16,800
21,958
19,303
19,188
15,310
18,109
16,580
19,417
19,422
16,455
17,889
19,306
15,857
18,982
20,376
18,079
17,530
23,239
15,000
19,310
18,831
2008
15,333
12,000
23,382
12,400
22,344
22,930
19,158
16,923
17,167
17,829
10,882
22,432
18,569
19,683
19,995
20,641
13,444
12,269
18,273
18,375
16,933
22,180
18,927
14,550
14,682
18,412
18,672
20,704
19,933
16,900
23,269
19,859
18,979
16,077
18,321
16,578
19,772
19,262
18,091
17,889
19,956
16,068
20,134
20,894
18,400
17,612
23,344
15,083
19,546
19,386
2009
14,909
10,000
23,028
12,615
22,000
23,089
18,684
17,000
18,061
18,600
14,200
22,091
18,873
20,137
20,367
21,085
14,190
11,870
18,061
18,255
17,571
22,445
19,230
13,889
14,654
19,933
19,721
21,821
19,533
17,889
24,320
20,071
19,644
16,739
18,744
16,983
19,719
19,360
17,818
18,765
20,128
16,232
20,898
20,988
18,289
18,083
23,171
14,727
20,079
18,811
Source: USDA (2010).
* Beef lactation data were developed using the methodology described in Step 1.
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Step 2: Characterize U.S. Cattle Population Diets
To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and
CH4 conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population categories,
data were collected on diets considered representative of different regions. For both grazing animals and animals being
fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists
and from USDA (1996). The data for each of the diets (e.g., proportions of different feed constituents, such as hay or
grains) were used to determine feed chemical composition for use in estimating digestible energy and Ym for each animal
type.
DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle
and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different
regions and animal types in the United States, digestible energy and Ym values unique to the United States53 were
developed. Digestible energy and Ym values were estimated for each cattle population category, for each year in the time
series based on physiological modeling, published values, and/or expert opinion.
For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input
required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final
weight, age at start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data used to
simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals with
similar characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999) that
represents physiological processes in the ruminant animals and diet characteristics from USDA (1996). The Donovan and
Baldwin model accounts for differing diets (i.e., grain-based or forage-based), so that Ym values for the variable feeding
characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review of dairy diets was
conducted and nearly 250 diets were analyzed from 1990 through 2008 across 23 states. Kebreab et al (2008). conducted
an evaluation of models and found that the COWPOLL model was the best model for estimating Ym for dairy. Therefore,
the COWPOLL model was used to estimate Ym values for each of the diets. Due to the variability associated with cattle
diets, a function based on the national trend observed from the analysis of the diary diets was used to calculate 1991 and
beyond values based on the regional 1990 Ym values from Donovan and Baldwin.. The resulting scaling factor is shown
below:
( 1 22 ^ ( 1 22 ^
Ym = Ym(l990)EXP . l'2* \IEXP\- L22
(Year -1980) J ^ (l 990 -1980)y
DE values for dairy cows were estimated from the literature search based on the on annual trends observed in the
recent data collection effort. The regional variability observed in the literature search was not statistically significant, and
therefore DE was not varied by region.
Considerably less data was available for dairy heifers, so assumptions were based on the relationship of the
collected data literature on dairy heifers to the data on dairy cow diets. From this relationship, DE was estimated as the
mature cow DE minus three percent, and Ym was estimated as that of the mature dairy cow plus 0.1 percent.
To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted
DE values for a combination of forage and supplemental diets. Where DE values were not available for specific feed
types, total digestible nutrients (TDN) as a percent of dry matter (DM) intake was used as a proxy for DE, as listed in NRC
(2000). Forage diets make up the majority of beef cattle diets, and two separate regional DE values were used to account
for the generally lower forage quality in the western United States. For all non-western beef cattle, the forage DE was an
average of the seasonal values for Grass Pasture diets listed in Appendix Table 1 of the NRC (2000). This resulted in a
DE of 64.7 percent for the forage portion of the diet for all beef cattle in regions other than the west. For beef cattle in the
west, the forage DE was calculated as the seasonal average for meadow and range diets listed in Appendix Table 1 of the
NRC (2000). This resulted in a DE for the forage portion of the western region diet of 58.5 percent. In addition, it was
assumed that each region fed a supplemental diet, as shown in Table A- 175. By weighting the calculated DE values from
the forage and supplemental diets, the DE values for the composite diet were calculated.54 The percent of each diet that is
assumed to be supplemental and the forage DE values used for each region, and the resulting weighted DE values are
53 In some cases, the Ym values used for this analysis extend beyond the range provided by the IPCC. However, EPA believes that these
values are representative for the U.S. due to research conducted to characterize the diets of U.S. cattle and assess the Ym values
associated with different animal performance and feed characteristics in the United States.
For example, in California the forage DE of 64.7 was used for 95 percent of the grazing cattle diet and a supplemented diet DE of 69.3
percent was used for five percent of the diet, for a total weighted DE of 64.9 percent, as shown in Table A-176.
A-205
-------
shown in Table A-176. These values are used for steer and heifer stackers and beef replacements. Finally, for beef cows,
the DE value was adjusted downward by two percent to reflect the lower digestibility diets of the mature beef cow based
on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002).
For feedlot animals, DE and Ym values for 1990 were taken from Johnson (1999). . In the CEFM, values for
1991 through 1999 were linearly extrapolated based on values for 1990 and 2000. DE and Ym values for 2000 onwards
were estimated using the MOLLY model as described in Kebreab et al (2008). In addition, feedlot animals are assumed
to spend the first 25 days in the feedlot on a "step-up" diet to become accustomed to the higher quality feedlot diets. The
step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE and Ym values.
Table A-177 shows the regional DE and Ym for U.S. cattle in each region for 2009.
Table A-175: DE Values and Representative Regional Diets (Percent of Diet for each Region) for the Supplemental Diet of
Grazing Reef Cattle
Feed
Alfalfa Hay
Barley
Bermuda
Bermuda Hay
Corn
Corn Silage
Cotton Seed Meal
Grass Hay
Orchard
Soybean Meal
Supplement
Sorghum
Soybean Hulls
Timothy Hay
Whole Cotton
Seed
Wheat Middlings
Wheat
Weighted Total
Source of TON
(NRC 2000)
Table 11-1, feed #4
Table 11-1, feed #12
Table 11-1, feed #17
Table 11-1, feed #17
Table 11-1, feed #38
Table 11-1, feed #39
Table 11-1, feed #42
Table la, feed #129,
147, 148
Table 11-1, feed #61
Table 11-1, feed #70
Table 11-1, feed #67
Table 11-1, feed #69
Table 11-1, feed #77
Table 11-1, feed #41
Table la, feed #433
Table 11-1, feed #83
Unweighted
TON
orDE California3
59.6% 65%
86.3% 10%
48.5%
48.5%
88.1% 10%
71.2%
74.4%
53.7%
53.5%
OQ 1 O/
oJ. lyo
81.3%
76.4%
55.5%
89.2% 5%
83.0%
87.2% 10%
69%
Northern
Great
West Plains
30% 30%
15%
10% 25%
25%
40%
CQ/ «0/
Jyo JYO
15%
65% 74%
Southcentral Northeast Midwest Southeast
29% 12% 30%
35%
40%
11% 13% 13%
20% 20%
7%
30%
40%
^o/
J70
20%
7%
50%
5%
13%
62% 65% 65% 59%
Source of representative regional diets: Donovan (1999).
"Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western
region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH,
ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the
Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC,
and FL.
Tahle A-176: Percent of Each Diet that is Supplemental and the Resulting DE Values for Each Region
Region3
West
Northeast
Southcentral
Midwest
Northern Great Plains
Southeast
California
Percent
Supplement
10
15
10
15
15
5
5
Percent Forage
90
85
90
85
85
95
95
Forage DE Used
59
65
65
65
65
65
65
Calculated Weighted
Average DE
59
65
64
65
66
64
65
Sources: Percent of total diet that is supplemental diet, Donovan (1999); Forage DE, NRC (2000).
"Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western
region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH,
ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the
Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC,
and FL.
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-177: Regional Digestible Energy [DEI and Clh Conversion Rates tYJ for Cattle in 2009
Animal Type
BeefRepl. Heif.
Dairy Repl. Heif.
Steer Stackers
Heifer Stackers
Steer Feedlot
Heifer Feedlot
Beef Cows
Dairy Cows
Steer Step-Up
Heifer Step-Up
Data
DEa
Y b
1 m
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
DE
Ym
California c
65
6.5%
64
6.0%
65
6.5%
65
6.5%
83
3.9%
83
3.9%
63
6.5%
67
5.9%
72
5.2%
72
5.2%
West
59
6.5%
64
6.0%
59
6.5%
59
6.5%
83
3.9%
83
3.9%
57
6.5%
67
5.9%
72
5.2%
72
5.2%
Northern
Great Plains
66
6.5%
64
5.7%
66
6.5%
66
6.5%
83
3.9%
83
3.9%
64
6.5%
67
5.6%
72
5.2%
72
5.2%
Southcentral
64
6.5%
64
6.5%
64
6.5%
64
6.5%
83
3.9%
83
3.9%
62
6.5%
67
6.4%
72
5.2%
72
5.2%
Northeast
65
6.5%
64
6.4%
65
6.5%
65
6.5%
83
3.9%
83
3.9%
63
6.5%
67
6.3%
72
5.2%
72
5.2%
Midwest
65
6.5%
64
5.7%
65
6.5%
65
6.5%
83
3.9%
83
3.9%
63
6.5%
67
5.6%
72
5.2%
72
5.2%
Southeast
64
6.5%
64
7.0%
64
6.5%
64
6.5%
83
3.9%
83
3.9%
62
6.5%
67
6.9%
72
5.2%
72
5.2%
" Digestible Energy in units of percent of GE (M J/Day).
b Methane Conversion Rate is the fraction of GE in feed converted to methane.
c Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western
region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH,
ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the
Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC,
and FL.
Step 3: Estimate CHt Emissions from Cattle
Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the IPCC (2006)
equations, b) determine an emission factor using the GE values and other factors, and c) sum the daily emissions for each
animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. The necessary data
values for each state and animal type include:
• Body Weight (kg)
• Weight Gain (kg/day)
• Net Energy for Activity (Ca, MJ/day)55
• Standard Reference Weight (kg)56
• Milk Production (kg/day)
• Milk Fat (percent of fat in milk = 4)
• Pregnancy (percent of population that is pregnant)
• DE (percent of gross energy intake digestible)
• Ym (the fraction of gross energy converted to CH4)
• Population
Step 3a: Determine Gross Energy, GE
As shown in the following equation, gross energy (GE) is derived based on the net energy estimates and the feed
characteristics. Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require
the NEi factor). All net energy equations are provided in IPCC (2006).
Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain
grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the
year.
56 Standard Reference Weight is the mature weight of a female animal of the animal type being estimated, used in the model to account
for breed potential.
A-207
-------
'NEm + NEa + NE, + NEwork + NEp \ + ( NEg"
REM \+(REG
v X V y
DE%
100
Where,
GE = Gross energy (MJ/day)
NEm = Net energy required by the animal for maintenance (MJ/day)
NEa = Net energy for animal activity (MJ/day)
NE! = Net energy for lactation (MJ/day)
NEwork = Net energy for work (MJ/day)
NEP = Net energy required for pregnancy (MJ/day)
REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed
NEg = Net energy needed for growth (MJ/day)
REG = Ratio of net energy available for growth in a diet to digestible energy consumed
DE = Digestible energy expressed as a percent of gross energy (percent)
Step 3b: Determine Emission Factor
The emission factor (DayEmit) was determined using the gross energy value and the methane conversion factor
(Ym) for each category. This relationship is shown in the following equation:
x Y
DayEmit =
55.65
Where,
DayEmit = Emission factor (kg CH4/head/day)
GE = Gross energy intake (MJ/head/day)
Ym = CH4 conversion rate, which is the fraction of gross energy in feed converted to CH4 (%)
55.65 = A factor for the energy content of methane (MJ/kg CH4)
The daily emission factors were estimated for each animal type and state, calculated national emission factors are
shown by animal type in Table A-178.
Table A-178: Calculated National Emission Factors for Cattle by Animal Type [kg Clh/head/vearl
Cattle Type _ 1990 1995 2000 2005 2006 2007 2008 2009
Dairy
Cows 124 • 125 • 1321 133 134 139 139 140
Replacements 7-11
months 48 1 46 1 46 1 45 45 46 46 46
Replacements 12-23
months 73 • 69 1 70 • 67 67 70 69 70
Beef
Bulls 53 • 53 • 53 • 53 53 53 53 53
Cows 89 1 92l 9ll 94 94 94 94 94
Replacements 7-11
months 54 1 57 • 57 • 59 60 60 60 60
Replacements 12-23
months 63 • 66 • 66 • 68 69 69 69 69
Steer Stackers 5sl 56 1 5gl 58 58 58 57 57
Heifer Stackers 51 • 56 • 60 • 59 59 59 59 59
Feedlot Cattle _ 39 _ 38 _ 39 _ 39 39 42 42 43
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Step 3c: Estimate Total Emissions
Emissions were summed for each month and for each state population category using the daily emission factor
for a representative animal and the number of animals in the category. The following equation was used:
Where,
DayEmitstate
Days/Month
SubPopstate
Emissionsstate = DayEmitstate x Days/Month x SubPopsta
The emission factor for the subcategory and state (kg CH^head/day)
The number of days in the month
The number of animals in the subcategory and state during the month
This process was repeated for each month, and the totals for each state subcategory were summed to achieve an
emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The
estimates for each of the 10 subcategories of cattle are listed in Table A-179. The emissions for each subcategory were
then summed to estimate total emissions from beef cattle and dairy cattle for the entire year.
Table A-179: CHa Emissions from Cattle tGgl
Cattle Type 1990
2005 2006 2007 2008 2009
Dairy 1,513
Cows 1,242
Replacements 7-11 months 5 81
Replacements 12-23
months 212
Beef 4,502
Bulls 114
Cows 2,887
Replacements 7-11 months 69
1,440
1,1831
56
201
5,128
126
3,223
85
1,460
1,209
55
14,790
3,059B
:
1,449
1,197
56
196
4,731
117
3,056
1,479
1,219
57
203
4,803
120
3,079
82
1,544
1,271
60
213
4,837
117
3,083
81
1,564
1,289
60
214
4,796
117
3,065
79
1,581
1,304
61
216
4,742
116
2,995
77
Replacements iz-zj
months 1881
Steer Stackers 563 1
Heifer
Stackers 305 1
Feedlot Cattle 375
24lB 204B 217
66 ll 508 • 472
374 • 322 • 298
416 502 489
228
475
298
521
228
478
294
556
220
473
288
554
215
489
298
553
Total
6,015
6,567
6,251
6,180 6,282 6,381 6,374 6,323
Notes: Totals may not sum due to independent rounding. Because calves younger than 7 months consume mainly milk the IPCC recommends the
use of methane conversion factor of zero, resulting in no methane emissions from this subcategory of cattle.
Emission Estimates from Other Livestock
All livestock population data, except for horses, were taken from the U.S. Department of Agriculture (USDA)
National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2009). Table A- 183of the Manure
Management Annex shows the population data for all livestock species that were used for estimating all livestock-related
emissions. For each animal category, the USDA publishes monthly, annual, and multi-year livestock population and
production estimates. All data were downloaded from the USDA-NASS agricultural database (USDA 2010). The Food
and Agriculture Organization (FAO) publishes horse population data. These data were accessed from the FAOSTAT
database (FAO 2010). Methane emissions from sheep, goats, swine, and horses were estimated by multiplying published
national population estimates by the IPCC emission factor for each year. Table A- 180 shows the emission factors used for
these other livestock.
Enteric fermentation emissions from all livestock types are shown in Table A-181 and Table A-182.
Table A-180: Emission Factors for Other Livestock [kg CHJhead/yearl
Livestock Type _ Emission Factor _
Sheep
Goats
Horses
Swine
8
5
18
1.5
Source: IPCC (2006)
A-209
-------
Table A-181: CHa Emissions from Enteric Fermentation tTg C02 Eq.l
Livestock Type
Beef Cattle
Dairy Cattle
Horses
Sheep
Swine
Goats
Total
1990
94.5
31.8
!•
0.3
132.1
1995
107.7
30.2
i:i
0.2
143.5
2000
100.6
30.7
2.0
1.2
1.9
0.3
136.5
2001
99.9
30.5
2.1
1.2
1.9
0.3
135.7
2002
100.0
30.6
2.3
1.1
1.9
0.3
136.1
2003
100.0
28.4
2.6
1.1
1.9
0.3
134.4
2004
98.3
29.9
3.0
1.0
1.9
0.3
134.4
2005
99.3
30.4
3.5
1.0
1.9
0.3
136.5
2006
100.9
31.1
3.6
1.0
1.9
0.3
138.8
2007
101.6
32.4
3.6
1.0
2.1
0.3
141.0
2008
100.7
32.9
3.6
1.0
2.1
0.3
140.6
2009
99.6
33.2
3.6
1.0
2.1
0.3
139.8
Note: Totals may not sum due to independent rounding.
Table A-182: CH* Emissions from Enteric Fermentation tGgl
Livestock
Type
Beef Cattle
Dairy Cattle
Horses
Sheep
Swine
Goats
Total
1990
4,502
1,5 13 •
9ll
1
6,290 H
1995 H
5,128
l,44ol
92
88
12
6,832
2000
4,790
1,460
94
56
88
12
6,502
2001
4,757
1,453
99
55
88
12
6,464
2002
4,761
1,457
108
53
90
13
6,481
2003
4,762
1,354
126
51
90
13
6,396
2004
4,680
1,422
144
49
91
14
6,400
2005
4,731
1,449
166
49
92
15
6,500
2006
4,803
1,479
171
50
93
15
6,611
2007
4,837
1,544
171
49
98
16
6,715
2008
4,796
1,564
171
48
101
16
6,696
2009
4,742
1,581
171
46
99
16
6,655
Note: Totals may not sum due to independent rounding.
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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3.10. Methodology for Estimating CH4 and N2O Emissions from Manure
Management
The following steps were used to estimate methane (CH4) and nitrous oxide (N2O) emissions from the
management of livestock manure. Nitrous oxide emissions associated with pasture, range, or paddock systems and daily
spread systems are included in the emission estimates for Agricultural Soil Management (see the Agricultural Soils
Management Annex).
Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2009 for all livestock types, except horses and goats were
obtained from the USDA National Agricultural Statistics Service (NASS). The population data used in the emissions
calculations for cattle, swine, and sheep were downloaded from the USDA NASS Population Estimates Database (USDA
2009a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998a, 1999, 2004a,
2004b, 2010a, and 201 Ob). Horse population data were obtained from the Food and Agriculture Organization (FAO)
FAOSTAT database (FAO 2010). Goat population data for 1992, 1997, 2002, and 2007 were obtained from the Census of
Agriculture (USDA 2009a). Additional data sources used and adjustments to these data sets are described below.
Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories
from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the
year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered;
therefore, to develop the best estimate for the annual animal population, the populations and the individual characteristics,
such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle Enteric
Fermentation Model (CEFM). For animals that have relatively static populations throughout the year, such as mature
cows and bulls, the January 1 values were used. For animals that have flucatuating populations throughout the year, such
as calves and growing heifers and steer, the populations are modeled based on a transition matrix that uses annual
population data from USDA along with USDA data on animal births, placement into feedlots, and slaughter statistics.
Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under
23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over
(greater than 82 kg). The average of the quarterly data was used in the emissions calculations. For states where only
December inventory is reported, the December data were used directly.
Sheep: Population data for lamb and sheep on feed are not available after 1993 (USDA 1994). The number of
lamb and sheep on feed for 1994 through 2009 were calculated using the average of the percent of lamb and sheep on feed
from 1990 through 1993. In addition, all of the sheep and lamb "on feed" are not necessarily on "feedlots;" they may be
on pasture/crop residue supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop
residue were provided only for lamb in 1993. To calculate the populations of sheep and lamb in feedlots for all years, it
was assumed that the percentage of sheep and lamb on feed that are in feedlots versus pasture/crop residue is the same as
that for lambs in 1993 (Anderson 2000).
Goats: Annual goat population data by state were available for 1992, 1997, 2002, and 2007 (USDA 2009a). The
data for 1992 were used for 1990 through 1992 and the data for 2007 were used for 2007 through 2009. Data for 1993
through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated based on the 1992, 1997, and 2002 Census
data.
Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one
year old), broilers, other chickens, and turkeys (USDA 1995a, 1995b, 1998a, 1999, 2004a, 2004b, 2010a, and 2010b). The
annual population data for boilers and turkeys were adjusted for the turnover (i.e., slaughter) rate (Lange 2000). All
poultry population data were adjusted to account for states that report non-disclosed populations to USDA NASS. The
combined populations of the states reporting non-disclosed populations are reported as "other" states. State populations
for the non-disclosed states were estimated by equally distributing the population attributed to "other" states to each of the
non-disclosed states.
Horses: The FAO publishes annual total U.S. horse population, which were accessed from the FAOSTAT
database (FAO 2010). State horse population data were estimated using state population distributions from the 1992,
1997, and 2002 Census of Agriculture and the FAO national population data. A summary of the livestock population
characterization data used to calculate CH4 and N2O emissions is presented in Table A- 183.
A-211
-------
Step 2: Waste Characteristics Data
Methane and N2O emissions calculations are based on the following animal characteristics for each relevant
livestock population:
• Volatile solids (VS) excretion rate;
• Maximum methane producing capacity (B0) for U.S. animal waste;
• Nitrogen excretion rate (Nex); and
• Typical animal mass (TAM).
Table A- 184 presents a summary of the waste characteristics used in the emissions estimates. Published sources
were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population
data discussed in Step 1. The USDA's Agricultural Waste Management Field Handbook (AWMFH; USDA 1996a, 2008)
is one of the primary sources of waste characteristics. Data from the 1996 and 2008 USDA AWMFH were used to
estimate VS and Nex for most animal groups across the time series of the inventory, as shown in Table A-3. In some
cases, data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) were used to supplement
the USDA data. The VS and Nex data for breeding swine are from a combination of the types of animals that make up this
animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding swine is typically
broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars (Safley 2000). Due to the change
in USDA reporting of hens and pullets, new nitrogen and VS excretion rates were calculated for the combined population
of hens and pullets; a weighted average rate was calculated based on hen and pullet population data from 1990 to 2004.
The method for calculating VS excretion and Nex from beef and dairy cows, heifers, and steers is based on the
relationship between animal performance characteristics such as diet, lactation, and weight gain and energy utilization.
The method used is outlined by the IPCC Tier II methodology, and is modeled in the enteric fermentation portion of the
inventory in order to take advantage of the detailed diet and animal performance data assembled as part of the Tier II
analysis for cattle.
Volatile solids content of manure is the fraction of the diet consumed by cattle that is not digested and thus
excreted as fecal material; fecal material combined with urinary excretions constitutes manure. The enteric fermentation
model requires the estimation of gross energy intake and its fractional digestibility to estimate enteric CH4 emissions (see
the Enteric Fermentation Annex for details on the enteric energy model). These two inputs are used to calculate the
indigestible energy per animal unit as gross energy minus digestible energy plus the amount of gross energy for urinary
energy excretion per animal unit (2 or 4 percent). This value is then converted to VS production per animal unit using the
typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting out the ash content of
manure. The current equation recommended by the 2006 IPCC Guidelines is:
VS production (kg) = [(GE - DE) + (UE x GE)] x l~ASH
Where,
GE = Gross energy intake (MJ)
DE = Digestible energy (MJ)
(UE x GE) = Urinary energy expressed as fraction of GE, assumed to be 0.04 except for
feedlots which are reduced 0.02 as a result of the high grain content of their diet.
ASH = Ash content of manure calculated as a fraction of the dry matter feed intake
(assumed to be 0.08).
18.45 = Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is
relatively constant across a wide range of forage and grain-based feeds
commonly consumed by livestock.
Nitrogen uptake in cattle is carried out through dietary protein intake. However, when feed intake of protein
exceeds the nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate
the nitrogen excreted by each animal type, the cattle enteric fermentation model (CEFM) utilizes the energy balance
calculations recommended by the IPCC (2006) for gross energy and the energy required for growth along with inputs of
weight gain, milk production, and the percent of crude protein in the diets. The total nitrogen excreted is measured in the
CEFM as nitrogen consumed minus nitrogen retained by the animal for growth and in milk. The basic equation for
calculating Nex is shown below, followed by the equations for each of the constituent parts.
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Where,
excreted
consumed
^ growth
Nmlk
excreted consumed \ growth milk )
- Daily N excreted per animal, kg per animal per day.
= Daily N intake per animal, kg per animal per day
= Nitrogen retained by the animal for growth, kg per animal per day
= Nitrogen retained in milk, kg per animal per day
The equation for nitrogen consumed is based on the 2006 IPCC Guidelines, and is estimated as:
GE ,
18.45
fCP%\
100
6.25
V ) _
consumed
Where:
N consumed = Daily N intake per animal, kg per animal per day
GE = Gross energy intake, as calculated in the CEFM, MJ per animal per day
18.45 = Conversion factor for dietary GE per kg of dry matter, MJ per kg.
CP% = Percent crude protein in diet, input into the CEFM
6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The portion of consumed nitrogen that is retained as product equals the nitrogen required for weight gain plus
that in milk. The nitrogen retained in body weight gain by stackers, replacements, or feedlot animals is calculated using
the net energy for growth (NEg), weight gain (WG), and other conversion factors and constants. The equation matches
current 2006 IPCC Guidelines recommendations, and is as follows:
N
\WG*
[ (7.03*NEg)~]
WG
1000
6.25
Where,
N
growth
WG
268
7.03
NEg
1,000
6.25
growth
= Nitrogen retained by the animal for growth, kg per animal per day
= Daily weight gain of the animal, kg per day
= Constant from 2006 IPCC Guidelines
= Constant from 2006 IPCC Guidelines
= Net energy required for growth, MJ per animal per day
= Conversion from grams to kilograms
= Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N
The N content of milk produced also currently matches the 2006 IPCC Guidelines. Milk nitrogen retained as
product is calculated using the following equation:
milk *
•** milk
6.38
A-213
-------
Where,
N nak = Nitrogen retained in milk, kg per animal per day
milk = Milk production, kg per day
pr% = Percent protein in milk, estimated from the fat content as 1.9 + 0.4 * %Fat
100 = Conversion from percent to value (e.g., 4% to 0.04)
6.38 = Conversion from kg Protein to kg N
The VS and N equations above were used to calculate VS and Nex rates for each state, cattle type, and year.
Table A- 186 presents the state-specific VS and Nex production rates used for cattle in 2009.
Step 3: Waste Management System Usage Data
Table A- 187 summarizes 2009 manure distribution data among waste management systems (WMS) at beef
feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining
animal types (beef cattle not on feed, sheep, horses, and goats) is managed on pasture, range, or paddocks, on drylot, or
with solids storage systems. Additional information on the development of the manure distribution estimates for each
animal type is presented below. Definitions of each WMS type are presented in Table A- 188.
Beef Cattle and Dairy Heifers: The beef feedlot and dairy heifer WMS data were developed using information
from EPA's Office of Water's engineering cost analyses conducted to support the development of effluent limitations
guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and state contacts
supporting this work and additional contacts with the national USDA office to estimate the percent of beef steers and
heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots. Therefore, for these animal
groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition, there is a small amount of
manure contained in runoff, which may or may not be collected in runoff ponds. The runoff from feedlots was calculated
by region in Calculations: Percent Distribution of Manure for Waste Management Systems (ERG 2000b) and was used to
estimate the percentage of manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3
percent. The percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent. The
remaining population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock systems,
which are utilized for the majority of the population of beef cattle in the country.
Dairy Cows: The WMS data for dairy cows were developed using data from the Census of Agriculture, EPA's
Office of Water, USDA, and expert sources. Farm-size distribution data are reported in the 1992, 1997, and 2002 Census
of Agriculture (USDA 2009a). It was assumed that the data provided for 1992 were the same as that for 1990 and 1991,
and data provided for 2002 were the same as that for 2003 through 2009. Data for 1993 through 1996 and 1998 through
2001 were extrapolated using the 1992, 1997, and 2002 data. The percent of waste by system was estimated using the
USDA data broken out by geographic region and farm size.
Based on EPA site visits and state contacts, manure from dairy cows at medium (200 through 700 head) and
large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In addition, they
may have a solids separator in place prior to their storage component. Estimates of the percent of farms that use each type
of system (by geographic region) were developed by EPA's Office of Water, and were used to estimate the percent of
waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage (separated solids)
(EPA 2002b). Manure management system data for small (fewer than 200 head) dairies were obtained from USDA
(2000a). These operations are more likely to use liquid/slurry and solid storage management systems than anaerobic
lagoon systems. The reported manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earth-
basin, and aerated lagoon), anaerobic lagoon, and solid storage (includes manure pack, outside storage, and inside storage).
Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from
personal communications with personnel from several organizations. These organizations include state NRCS offices,
state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000,
Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure
management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, and 2002
(USDA 2009a) were used in conjunction with the state data obtained from personal communications to determine regional
percentages of total dairy cattle and dairy waste that are managed using these systems. These percentages were applied to
the total annual dairy cow and heifer state population data for 1990 through 2009, which were obtained from the USDA
NASS (USDA 2009b).
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies
reported using more than one type of manure management system. Due to limitations in how USDA reports the manure
management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure is
typically partitioned to use only one manure management system, rather than transferred between several different
systems. Emissions estimates are only calculated for the final manure management system used for each portion of
manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total of
100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep
pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent
(i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided
by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).
Swine: The distribution of manure managed in each WMS was estimated using data from a USDA report and
EPA's Office of Water site visits (USDA 1998, ERG 2000a). For operations with less than 200 head, manure
management system data were obtained from USDA (USDA 1998). It was assumed that those operations use pasture,
range, or paddock systems. For swine operations with greater than 200 head, the percent of waste managed in each system
was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data
reported in the 1992, 1997, and 2002 Census of Agriculture (USDA 2009a) were used to determine the percentage of all
swine utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992
were the same as that for 1990 and 1991, and data provided for 2002 were the same as that for 2003 through 2009. Data
for 1993 through 1996 and 1998 through 2001 were extrapolated using the 1992, 1997, and 2002 data. The reported
manure management systems were deep pit, liquid/slurry (includes above- and below-ground slurry), anaerobic lagoon,
and solid storage (includes solids separated from liquids).
Some swine operations reported using more than one management system; therefore, the total percent of systems
reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the
manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled
in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another,
which could result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into
the WMS distribution, using the same method as described above for dairy operations.
Sheep: Waste management system data for sheep were obtained from USDA NASS sheep report for years 1990
through 1993 (USDA 1994). Data for 2001 are obtained from USDA APHIS sheep report (USDA 2003). The data for
years 1994-2000 are calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for
years 2002 and beyond are assumed to be the same as 2001. It was assumed that all sheep manure not deposited in feedlots
was deposited on pasture, range, or paddock lands (Anderson 2000).
Goats and Horses: Waste management system data for 1990 to 2009 were obtained from Appendix H of Global
Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that all manure not deposited in
pasture, range, or paddock lands was managed in dry systems.
Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: Waste
management system data for 1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure
(EPA 1992). These data were also used to represent 1990 and 1991. The percentage of layer operations using a shallow
pit flush house with anaerobic lagoon or high-rise house without bedding was obtained for 1999 from a United Egg
Producers voluntary survey (UEP 1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA,
IN, MN, MO, NC, NE, OH, PA, TX, and WA) with USDA data (USDA 2000b). It was assumed that the change in
system usage between 1990 and 1999 is proportionally distributed among those years of the inventory. It was assumed
that system usage in 2000 through 2009 was equal to that estimated for 1999. Data collected for EPA's Office of Water,
including information collected during site visits (EPA 2002b), were used to estimate the distribution of waste by
management system and animal type.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from Global
Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one percent of poultry waste is
deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed to be deposited in operations
with bedding management.
Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N2O emission factors (EFs) used in the emission calculations were
determined using the methodologies presented below.
A-215
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Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in
Table A- 189. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual
average temperature data. Each state and year in the inventory was assigned a climate classification of cool, temperate or
warm.
For anaerobic lagoons and other liquid systems a climate-based approach based on the van't Hoff-Arrhenius
equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for long-
term retention time. This approach is consistent with the recently revised guidelines from IPCC (IPCC 2006). The van't
Hoff-Arrhenius equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman
1990):
/ = e
Where,
Tj =303.15K
T2 = Ambient temperature (K) for climate zone (in this case, a weighted value for each state)
E = Activation energy constant (15,175 cal/mol)
R = Ideal gas constant (1.987 cal/Kmol)
The factor/represents the proportion of VS that are biologically available for conversion to CH4 based on the
temperature of the system. For those animal populations using liquid manure management systems or manure runoff
ponds (i.e., dairy cow, dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on
the counties where the specific animal population resides (i.e., the temperatures were weighted based on the percent of
animals located in each county). The average county and state temperature data were obtained from the National Climate
Data Center (NCAA 2009). County population data were calculated from state-level population data from NASS and
county-state distribution data from the 1992, 1997, and 2002 Census data (USDA 2009a). County population distribution
data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996
were extrapolated based on 1992 and 1997 data; county population data for 1998 through 2001 were extrapolated based on
1997 and 2002 data; and county population data for 2003 to 2009 were assumed to be the same as 2002.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:
• The weighted-average temperature for a state is calculated using the population estimates and average monthly
temperature in each county.
• Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius "f' factor, using the equation
presented above. A minimum temperature of 5°C is used for uncovered anaerobic lagoons and 7.5°C is used for
liquid/slurry and deep pit systems.
• Monthly production of VS added to the system is estimated based on the number of animals present.
• For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This
factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from
anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents a variety of factors that
may affect methane production in lagoon systems.
• The amount of VS available for conversion to CH4 is assumed to be equal to the amount of VS produced during
the month (from Step 3). For anaerobic lagoons, the amount of VS available also includes VS that may remain
in the system from previous months.
• The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the
"f' factor.
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount
available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once
per year, occurring after the month of September.
The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by
the maximum CH4 potential of the waste (B0).
The annual MCF is then calculated as:
CH4 generated annual
MCFannual
VS produced annualxBo
Where,
MCF mnual = Methane conversion factor
VS produced am^i = Volatile solids excreted annually
B0 = Maximum CH4 producing potential of the waste
In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from
the previous year are available in the lagoon system in the next year. For example, the VS from October, November, and
December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006.
Following this procedure, the resulting MCF for lagoons accounts for temperature variation throughout the year, residual
VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to
CH4. It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation
doesn't reflect the VS carry-over.
The liquid system MCFs are presented in Table A-8 by state, WMS, and animal group for 2009.
Nitrous Oxide Emission Factors
Direct N2O emission factors for manure management systems (kg N2O-N/kg excreted N) were set equal to the
most recent default IPCC factors (IPCC 2006), presented in Table A- 191.
Indirect N2O emission factors account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and
NOX (FraCgas) and runoff/leaching (FraCjunofgieach)- IPCC default indirect N2O emission factors were used to estimate
indirect N2O emissions. These factors are 0.010 kg N2O-N/kg N for volatilization and 0.0075 kg N2O/kg N for
runoff/leaching.
EPA developed country-specific estimates of nitrogen losses for Fracgas and Frac^^f-pieach for the U.S. The vast
majority of volatilization losses are NH3. Although there are also some small losses of NOX, no quantified estimates were
available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. Therefore,
FraCgaS values were based on WMS-specific volatilization values estimated from U.S. EPA's National Emission Inventory
- Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracrlmog7ieaCh, EPA used data from
EPA's Office of Water that estimate the amount of runoff from beef, dairy, and heifer operations in five geographic
regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, WMS, and
region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses; therefore, FraCj^of^each
was set equal to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A-
192.
Step 5: CH4 and N2O Emission Calculations
To calculate methane emissions for animals other than cattle, EPA first estimated the amount of volatile solids
excreted in manure that is managed in each WMS:
VS excreted state)AnimalWMS = Population state Ammal x-x VSx WMSx365.25
Where,
VS excreted state> Ammai, WMS = Amount of VS excreted in manure managed in each WMS for each
animal type (kg/yr)
A-217
-------
Population state Ammai = Annual average state animal population by animal type (head)
TAM = Typical animal mass (kg)
VS = Volatile solids production rate (kg VS/1000 kg animal mass/day)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
365.25 = Days per year
Using the CEFM VS data for cattle, the amount of VS excreted was calculated using the following equation:
VS excreted state ^^ w^ = Population state ^^ x VS x WMS
Where,
VS excreted statei Ammai, WMS = Amount of VS excreted in manure managed in each WMS for each
animal type (kg/yr)
Population state Animai = Annual average state animal population by animal type (head)
VS = Volatile solids production rate (kg VS/animal/year)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
For all animals, the estimated amount of VS was used to calculate methane emissions using the following
equation:
CH4 = Ł(VSeXCreted State,Anmal,WMS *BQ xMCFxO.662)
State, Animal, WMS
Where,
CH4 = CH4 emissions (kg CHVyr)
VS excreted WMS state = Amount of VS excreted in manure managed in each WMS (kg/yr)
B0 = Maximum CH4 producing capacity (m3 CHykg VS)
MCF annnai, state> WMS = MCF for the animal group, state and WMS (percent)
0.662 = Density of methane at 25° C (kg CH4/m3 CH4)
EPA developed a calculation to estimate the amount of CH4 emitted from anaerobic digestion (AD) systems
utilizing CH4 capture and combustion technology. First, EPA assumed that AD systems produce 90 percent of the
maximum CH4 producing capacity. This value is applied for all climate regions and AD system types. However, EPA
realizes that the actual amount of CH4 produced by each AD system is very variable and will change based on operational
and climate conditions and an assumption of 90 percent is likely overestimating CH4 production from some systems and
underestimating CH4 production in other systems. The CH4 production of AD systems is calculated using the equation
below:
CH4 Production AD^^ = Population ADADSystem x -- x VS x Bo x 0.662 x 365.25 x 0.90
ADSystem
Where,
CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr)
Population AD state = Number of animals on a particular AD system
VS = Volatile solids production rate (kg VS/1000 kg animal mass-day)
TAM = Typical Animal Mass (kg/head)
B0 = Maximum CH4 producing capacity (CH4 m3/kg VS)
0.662 = Density of methane at 25° C (kg CH4/m3 CH4)
365.25 = Days/year
0.90 = CH4 production factor for AD systems
Next, EPA considered the collection efficiency (CE) and destruction efficiency (DE) of the AD system. The CE
of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD systems was
assumed to be 99 percent (EPA 2008). The CH4 DE from flaring or burning in an engine was assumed to be 98 percent;
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
r . i M
[CH 4 Production AD AD system X ^1 - CE AD system )\
therefore, the amount of CH4 that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The
amount of CH4 produced by systems with anaerobic digestion was calculated with the following equation:
r[cH4 Production AD ADsystemxCEADsystemx(l-DE)]
CH 4 Emissions AD = Z
State, Animal, AD Systems
Where:
CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr)
CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr)
CEAD SyStem = Collection efficiency of the AD system, varies by AD system type
DE = Destruction efficiency of the AD system, 0.98 for all systems
In addition to methane emissions, EPA also estimated total N2O emissions from manure management systems.
Total N2O emissions were calculated by summing direct and indirect N2O emissions. The first step in estimating direct
and indirect N2O emissions was calculating the amount of nitrogen excreted in manure and managed in each WMS for
animals other than cattle using the following equation:
N excreted state> Ammal> ^ = Population state> Ammal x WMS x —— x Nex x 365.25
Where,
N excreted state> Ammai, WMS = Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Population state = Annual average state animal population by animal type (head)
WMS = Distribution of manure by waste management system for each animal
type in a state (percent)
TAM = Typical animal mass (kg)
Nex = Total Kjeldahl nitrogen excretion rate (kg N/1000 kg animal mass/day)
365.25 = Days per year
Using the CEFMNex data for cattle, the amount of N excreted was calculated using the following equation:
N excreted SMe, Ammal,WMS = Population SMeAnmial x WMS x Nex
Where,
N excreted state, Ammd, WMS = Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Population state = Annual average state animal population by animal type (head)
WMS = Distribution of manure by waste management system for each animal
type in a state (percent)
Nex = Total Kjeldahl nitrogen excretion rate (kg N/animal/year)
For all animals, direct N2O emissions were calculated as follows:
44
Direct N20 = Ł N excreted state> Am
mal WMS
x
>
State, Ammal, WMS V *• ° .
Where,
Direct N2O = Direct N2O emissions (kg N2O/yr)
N excreted state, Ammd, WMS = Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
EFwMs = Direct N2O emission factor from IPCC guidelines (kg N2O-N /kg N)
44/28 = Conversion factor of N2O-N to N2O
A-219
-------
Indirect N2O emissions were calculated for all animals with the following equation:
Indirect N2<3 =
State, Animal, WMS
Nexcretedstate)AmmalWMS x-
FracgaSjWMS 44
1AA volatilization ^o
1UU Zo
Nexcretedstat AmmalWMS x
Fracmno:
ff/leach, WMS
100
xEF,
runnoff/leach
44
28
Where,
Indirect N2O
N excreted state, Animal, WMS
FracgaS)WMS
FraCrunoffileach, WM S
-t^-T volatilization
-t^-T runofiTleach
44/28
Indirect N2O emissions (kg N2O/yr)
Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Nitrogen lost through volatilization in each WMS
Nitrogen lost through runoff and leaching in each WMS; data were not
available for leaching so the value reflects only runoff
Emission factor for volatilization (0.010 kg N2O-N/kg N)
Emission factor for runoff/leaching (0.0075 kg N2O-N/kg N)
Conversion factor of N2O-N to N2O
Emission estimates of CH4 and N2O by animal type are presented for all years of the inventory in Table A- 193
and Table A- 194, respectively. Emission estimates for 2009 are presented by animal type and state in Table A- 195 and
Table A- 196, respectively.
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A-183: Livestock Population (1,000 Head)
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine1
Market <50 Ib.
Market 50-1 19 Ib.
Market 120- 179 Ib.
Market >1 80 Ib.
Breeding
Beef Cattle2
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
Sheep
Sheep On Feed
Sheep NOF
Goats
Poultry3
Hens>l yr.
Pullets
Chickens
Broilers
Turkeys
Horses
1990
14,144
10,015
4,129i
53,941
18,359
11,734
9,440 I
7,5 10 I
6,899 I
87,228 •
6,357 I
3,192l
2,160
22,561
10,182
10,321
32,455
11,358
l,18oi
10,178
2,516
1,537,074
273,467
73,167
6,545
1,066,209
117,685
5,069 H
1995
13,590
9,482 •
4,108 I
58,899 I
19,656 I
12,836 I
10,545 I
8,937 I
6,926 I
95,683 •
7,233 I
3,831 •
2,385 1
23,499
11,829
11,716
35,190
8,989 I
1,771 I
7,218 I
2,357
1,826,977
299,071
81,369
7,637 I
1,331,940
106,960
5,130
2000
13,191
9,183
4,008
58,864
19,574
12,926
10,748
9,385
6,231
89,948
8,304
4,702
2,293
22,569
9,781
8,724
33,575
7,036
2,963
4,073
2,419
2,033,123
333,593
95,159
8,088
1,506,127
90,155
5,240
2001
13,217
9,172
4,045
58,913
19,659
12,900
10,708
9,465
6,181
89,118
7,932
4,569
2,274
22,389
9,832
8,724
33,398
6,908
3,256
3,652
2,475
2,060,398
340,317
95,656
8,126
1,525,413
90,887
5,500
2002
13,165
9,106
4,060
60,028
19,863
13,284
11,013
9,738
6,129
89,102
8,116
4,557
2,244
22,325
9,843
8,883
33,134
6,623
3,143
3,480
2,530
2,097,691
340,209
95,289
8,353
1,562,015
91,826
6,000
2003
13,215
9,142
4,073
59,827
19,929
13,138
11,050
9,701
6,011
88,232
8,416
4,676
2,248
21,997
9,564
8,347
32,983
6,321
3,049
3,272
2,652
2,085,268
340,979
100,346
8,439
1,544,155
91,349
7,000
2004
13,021
8,988
4,033
60,735
20,222
13,400
11,227
9,922
5,963
86,441
8,018
4,521
2,201
21,781
9,321
8,067
32,531
6,105
2,943
3,162
2,774
2,130,877
343,922
101,429
8,248
1,589,209
88,069
8,000
2005
13,165
9,004
4,162
61,073
20,228
13,519
11,336
9,997
5,993
86,954
8,116
4,536
2,214
21,678
9,550
8,185
32,674
6,135
2,982
3,153
2,897
2,150,410
348,203
96,809
8,289
1,613,091
84,018
9,200
2006
13,398
9,104
4,294
61,887
20,514
13,727
11,443
10,113
6,090
88,070
8,724
4,801
2,258
21,621
9,716
8,248
32,703
6,230
3,043
3,187
3,019
2,154,236
349,888
96,596
7,938
1,612,327
87,487
9,500
2007
13,487
9,145
4,343
65,417
21,812
14,557
12,185
10,673
6,190
87,639
8,674
4,730
2,214
21,483
9,592
8,302
32,644
6,120
3,000
3,120
3,141
2,166,936
346,613
103,816
8,164
1,619,400
88,943
9,500
2008
13,658
9,257
4,401
67,408
19,964
17,219
12,931
11,193
6,102
86,450
8,481
4,589
2,207
21,155
9,350
8,233
32,435
5,950
2,911
3,039
3,141
2,175,990
339,859
99,458
7,589
1,638,055
91,029
9,500
2009
13,759
9,333
4,426
66,142
19,559
17,077
12,517
11,084
5,905
85,736
8,450
4,514
2,184
20,940
9,436
8,501
31,712
5,747
2,805
2,942
3,141
2,086,699
339,526
101,588
8,496
1,554,636
82,453
9,500
Note: Totals may not sum due to independent rounding.
1 Prior to 2008, the Market <50 Ibs category was <60 Ibs and the Market 50-119 Ibs category was Market 60-119 Ibs; USDA updated the categories to be more consistent with international animal
categories.
2 NOF = Not on Feed
3 Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
A-221
-------
Table A-184: Waste Characteristics Data
Animal Group
Dairy Cows
Dairy Heifers
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
Market Swine <50 Ibs.
Market Swine <60 Ibs.
Market Swine 50-1 19 Ibs.
Market Swine 60-1 19 Ibs.
Market Swine 120-179 Ibs.
Market Swine >180 Ibs.
Breeding Swine
Feedlot Sheep
NOF Sheep
Goats
Horses
Hens >/= 1 yr
Pullets
Other Chickens
TAM (kg)
680
406-408
419-457
384-430
750
118
296-406
314-335
554-611
13
16
39
41
68
91
198
25
80
64
450
1.8
1.8
1.8
TAM
Source
Enteric
Fermentation
Table A- 171
Enteric
Fermentation
Table A- 171
Enteric
Fermentation
Table A- 171
Enteric
Fermentation
Table A- 171
Shuyler 2000
USDA 1996a
Enteric
Fermentation
Table A- 171
Enteric
Fermentation
Table A- 171
Enteric
Fermentation
Table A- 171
ERG2010a
Safley 2000
ERG2010a
Safley 2000
Safley 2000
Safley 2000
Safley 2000
EPA 1992
EPA 1992
ASAE 1999
ASAE 1999
ASAE 1999
ASAE 1999
ASAE 1999
Total Kjeldahl
Nitrogen, Nex
(kg/day per
1,000 kg mass)
Table A- 185
Table A- 185
Table A- 185
Table A- 185
Table A- 186
Table A- 186
Table A- 185
Table A- 185
Table A- 185
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Maximum
Methane
Generation
Potential, B0
Nel (m3CH4/kgVS
Source added)
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
USDA 1996a
USDA 1996a, 2008
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
ASAE 1998, USDA 2008
ASAE 1998, USDA 2008
ASAE 1998
ASAE 1998, USDA 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
0.24
0.17
0.33
0.33
0.17
0.17
0.17
0.17
0.17
0.48
0.48
0.48
0.48
0.48
0.48
0.48
0.36
0.19
0.17
0.33
0.39
0.39
0.39
Volatile Solids, VS
Bo (kg/day per 1,000 kg
Source mass)
Morris 1976
Bryant et. al. 1976
Hashimoto 1981
Hashimoto 1981
Hashimoto 1981
Hashimoto 1981
Hashimoto 1981
Hashimoto 1981
Hashimoto 1981
Hashimoto 1984
Hashimoto 1984
Hashimoto 1984
Hashimoto 1984
Hashimoto 1984
Hashimoto 1984
Hashimoto 1984
EPA 1992
EPA 1992
EPA 1992
EPA 1992
Hill 1982
Hill 1982
Hill 1982
Table A- 185
Table A- 185
Table A- 185
Table A- 185
Table A- 186
Table A- 186
Table A- 185
Table A- 185
Table A- 185
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
Table A- 186
VS
Source
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
USDA 1996a
USDA 1996a, 2008
Moffroid and Pape, 2010
Moffroid and Pape, 2010
Moffroid and Pape, 2010
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
ASAE 1998, USDA 2008
ASAE 1998, USDA 2008
ASAE 1998
ASAE 1998, USDA 2008
USDA 1996a, 2008
USDA 1996a, 2008
USDA 1996a, 2008
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Broilers 0.9 ASAE 1999 Table A-186 USDA 1996a, 2008 0.36 Hill 1984 Table A-186 USDA 1996a, 2008
Turkeys 6.8 ASAE 1999 Table A-186 USDA 1996a, 2008 0.36 Hill 1984 Table A-186 USDA 1996a, 2008
A-223
-------
Table A-185: Estimated Volatile Solids and Nitrogen Excreted Production Rate by year for Animals Other Than Cattle [hg/day/1000 Kg animal massl
Animal Type
VS
Market Swine
<50 Ib.
Market Swine 50-
1191b.
Market Swine
120- 179 Ib.
Market Swine
>1801b.
Breeding Swine
NOF Bulls
NOF Calves
Sheep
Goats
Hens>l yr.
Pullets
Chickens
Broilers
Turkeys
Horses
Nex
Market Swine
<50 Ib.
Market Swine
50-1 19 Ib.
Market Swine
120- 179 Ib.
Market Swine
>1801b.
Breeding Swine
NOF Bulls
NOF Calves
Sheep
Goats
Hens >1 yr.
Pullets
Chickens
Broilers
Turkeys
Horses
1990
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1991
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1992
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1993
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1994
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1995
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1996
8.8
5.4
5.4
5.4
2.6
6.04
6.41
9.2
9.5
10.091
10.091
10.8
15
9.7
10
0.6
0.42
0.42
0.42
0.235
0.31
0.3
0.42
0.45
0.695
0.695
0.83
1.1
0.74
0.3
1997
8.8
5.4
5.4
5.4
2.611
6.04
6.5175
9.2
9.5
10.097
10.097
10.817
15.167
9.596
10
0.627
0.43
0.43
0.43
0.232
0.31
0.313
0.42
0.45
0.703
0.703
0.853
1.088
0.730
0.3
1998
8.8
5.4
5.4
5.4
2.623
6.04
6.625
9.2
9.5
10.103
10.103
10.833
15.333
9.492
10
0.653
0.44
0.44
0.44
0.230
0.31
0.325
0.42
0.45
0.711
0.711
0.875
1.077
0.721
0.3
1999
8.8
5.4
5.4
5.4
2.634
6.04
6.733
9.11
9.5
10.109
10.109
10.85
15.5
9.388
9.61
0.68
0.45
0.45
0.45
0.227
0.31
0.338
0.423
0.45
0.719
0.719
0.898
1.065
0.711
0.295
2000
8.8
5.4
5.4
5.4
2.645
6.04
6.84
9.02
9.5
10.115
10.115
10.867
15.667
9.283
9.22
0.707
0.46
0.46
0.46
0.224
0.31
0.35
0.426
0.45
0.727
0.727
0.92
1.053
0.702
0.289
2001
8.8
5.4
5.4
5.4
2.656
6.04
6.948
8.93
9.5
10.121
10.121
10.883
15.833
9.179
8.83
0.733
0.47
0.47
0.47
0.221
0.31
0.363
0.429
0.45
0.735
0.735
0.943
1.042
0.692
0.284
2002
8.8
5.4
5.4
5.4
2.668
6.04
7.055
8.84
9.5
10.1265
10.1265
10.9
16
9.075
8.44
0.76
0.48
0.48
0.48
0.219
0.31
0.375
0.432
0.45
0.743
0.743
0.965
1.03
0.683
0.278
2003
8.8
5.4
5.4
5.4
2.679
6.04
7.163
8.75
9.5
10.132
10.132
10.917
16.167
8.971
8.05
0.787
0.49
0.49
0.49
0.216
0.31
0.388
0.435
0.45
0.750
0.750
0.988
1.018
0.673
0.273
2004
8.8
5.4
5.4
5.4
2.69
6.04
7.27
8.66
9.5
10.138
10.138
10.933
16.333
8.867
7.66
0.813
0.5
0.5
0.5
0.213
0.31
0.4
0.438
0.45
0.758
0.758
1.01
1.007
0.663
0.267
2005
8.8
5.4
5.4
5.4
2.701
6.04
7.378
8.57
9.5
10.144
10.144
10.95
16.5
8.7625
7.27
0.84
0.51
0.51
0.51
0.211
0.31
0.413
0.441
0.45
0.766
0.766
1.033
0.995
0.654
0.262
2006
8.8
5.4
5.4
5.4
2.713
6.04
7.485
8.48
9.5
10.150
10.150
10.967
16.667
8.658
6.88
0.867
0.52
0.52
0.52
0.208
0.31
0.425
0.444
0.45
0.774
0.774
1.055
0.983
0.644
0.256
2007
8.8
5.4
5.4
5.4
2.724
6.04
7.593
8.39
9.5
10.156
10.156
10.983
16.833
8.554
6.49
0.893
0.53
0.53
0.53
0.205
0.31
0.438
0.447
0.45
0.782
0.782
1.078
0.972
0.635
0.251
2008
8.8
5.4
5.4
5.4
2.735
6.04
7.7
8.3
9.5
10.162
10.162
11
17
8.45
6.1
0.92
0.54
0.54
0.54
0.203
0.31
0.45
0.45
0.45
0.79
0.79
1.1
0.96
0.625
0.245
2009
8.8
5.4
5.4
5.4
2.735
6.04
7.7
8.3
9.5
10.162
10.162
11
17
8.45
6.1
0.92
0.54
0.54
0.54
0.203
0.31
0.45
0.45
0.45
0.79
0.79
1.1
0.96
0.625
0.245
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 186: Estimated Volatile Solids
[hg/animal/yearl
and Nitrogen Excreted Production Rate by State for Cattle for 2009
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North
Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South
Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Volatile Solids
Dairy Cow
2,267.41
1,846.07
2,819.52
2,046.05
2,723.70
2,824.09
2,494.94
2,369.10
2,502.90
2,543.18
2,159.89
2,749.48
2,509.01
2,603.48
2,620.71
2,674.30
2,213.71
1,990.33
2,448.34
2,462.83
2,411.79
2,775.94
2,535.74
2,191.18
2,193.82
2,588.27
2,572.43
2,729.35
2,558.38
2,435.51
2,916.03
2,598.56
2,621.22
2,349.60
2,499.38
2,372.40
2,572.28
2,545.40
2,430.23
2,555.49
2,602.79
2,366.26
2,664.94
2,667.08
2,465.40
2,504.58
2,830.17
2,199.28
2,599.14
2,504.44
Dairy
Heifers
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
1,251.47
BeefNOF
Cow
1,680.43
2,158.74
2,158.74
1,675.36
1,638.85
1,546.94
1,652.50
1,652.50
1,680.43
1,680.43
2,158.74
2,158.74
1,653.05
1,653.05
1,653.05
1,546.94
1,680.43
1,675.36
1,652.50
1,652.50
1,652.50
1,653.05
1,653.05
1,680.43
1,653.05
1,546.94
1,546.94
2,158.74
1,652.50
1,652.50
2,158.74
1,652.50
1,680.43
1,546.94
1,653.05
1,675.36
2,158.74
1,652.50
1,652.50
1,680.43
1,546.94
1,680.43
1,675.36
2,158.74
1,652.50
1,680.43
2,158.74
1,652.50
1,653.05
1,546.94
BeefNOF
Heifers
1,103.73
1,475.35
1,440.02
1,093.22
1,042.04
951.51
1,093.00
1,062.76
1,117.18
1,103.56
1,450.79
1,392.62
1,037.84
1,050.80
1,022.85
945.66
1,092.37
1,112.51
1,080.42
1,066.48
1,080.42
1,045.14
1,047.02
1,103.12
1,067.85
1,003.22
950.97
1,428.01
1,073.63
1,068.91
1,417.32
1,050.97
1,103.07
985.19
1,061.17
1,066.91
1,414.00
1,050.97
1,095.96
1,107.64
970.02
1,100.05
1,058.67
1,424.78
1,055.51
1,097.87
1,401.02
1,071.08
1,067.78
989.11
BeefNOF
Steer
979.63
1,287.98
1,287.98
976.43
953.48
896.01
962.06
962.06
979.63
979.63
1,287.98
1,287.98
962.40
962.40
962.40
896.01
979.63
976.43
962.06
962.06
962.06
962.40
962.40
979.63
962.40
896.01
896.01
1,287.98
962.06
962.06
1,287.98
962.06
979.63
896.01
962.40
976.43
1,287.98
962.06
962.06
979.63
896.01
979.63
976.43
1,287.98
962.06
979.63
1,287.98
962.06
962.40
896.01
Beef OF
Heifers
673.81
758.17
674.59
731.19
688.37
685.35
674.37
740.93
723.70
706.46
706.46
679.06
664.58
661.25
675.86
684.40
731.19
714.03
692.36
641.27
706.46
674.88
690.51
680.89
681.24
657.92
680.84
691.23
706.46
745.24
667.61
675.38
731.19
671.24
668.17
672.96
671.24
671.24
675.44
688.56
674.32
671.24
678.95
719.20
727.14
675.38
674.99
617.96
676.24
671.24
Beef OF
Steer
656.14
723.58
656.77
702.01
667.78
665.37
656.59
709.80
696.02
682.24
682.24
660.34
648.76
646.10
657.78
664.60
702.01
688.29
670.97
630.13
682.24
656.99
669.49
661.80
662.08
643.44
661.76
670.06
682.24
713.24
651.18
657.39
702.01
654.09
651.63
655.46
654.09
654.09
657.44
667.94
656.55
654.09
660.25
692.43
698.78
657.39
657.08
611.49
658.08
654.09
Nitrogen Excreted
Beef Beef Beef Beef Beef
Dairy Dairy NOF NOF NOF OF OF
Cow Heifers Cow Heifers Steer Heifers Steer
135.78 68.85 70.16 48.18 40.04 53.69 55.01
115.86 68.85 84.14 60.31 49.59 61.06 61.25
157.68 68.85 84.14 58.35 49.59 53.76 55.07
124.61 68.85 72.93 49.63 41.72 58.70 59.26
152.93 68.85 71.23 46.75 40.63 54.96 56.09
157.87 68.85 71.65 44.97 40.74 54.70 55.87
143.73 68.85 68.93 47.85 39.24 53.74 55.06
138.33 68.85 68.93 45.95 39.24 59.55 59.98
145.89 68.85 70.16 49.02 40.04 58.05 58.70
147.62 68.85 70.16 48.17 40.04 56.54 57.43
129.34 68.85 84.14 58.95 49.59 56.54 57.43
154.67 68.85 84.14 55.73 49.59 54.15 55.40
144.34 68.85 70.73 45.55 40.34 52.88 54.33
148.40 68.85 70.73 46.38 40.34 52.59 54.09
149.14 68.85 70.73 44.59 40.34 53.87 55.17
151.44 68.85 71.65 44.57 40.74 54.61 55.80
133.47 68.85 70.16 47.47 40.04 58.70 59.26
122.21 68.85 72.93 50.88 41.72 57.20 57.99
141.73 68.85 68.93 47.06 39.24 55.31 56.39
142.36 68.85 68.93 46.18 39.24 50.85 52.61
140.16 68.85 68.93 47.06 39.24 56.54 57.43
155.80 68.85 70.73 46.02 40.34 53.78 55.09
145.49 68.85 70.73 46.14 40.34 55.15 56.25
132.50 68.85 70.16 48.14 40.04 54.31 55.54
130.80 68.85 70.73 47.47 40.34 54.34 55.56
147.74 68.85 71.65 48.53 40.74 52.30 53.84
147.06 68.85 71.65 44.93 40.74 54.30 55.53
153.80 68.85 84.14 57.69 49.59 55.21 56.30
146.46 68.85 68.93 46.63 39.24 56.54 57.43
141.18 68.85 68.93 46.34 39.24 59.93 60.30
161.82 68.85 84.14 57.10 49.59 53.15 54.56
148.18 68.85 68.93 45.21 39.24 53.83 55.13
150.97 68.85 70.16 48.14 40.04 58.70 59.26
137.49 68.85 71.65 47.29 40.74 53.46 54.83
143.92 68.85 70.73 47.04 40.34 53.20 54.60
138.62 68.85 72.93 47.94 41.72 53.61 54.95
147.06 68.85 84.14 56.91 49.59 53.46 54.83
145.90 68.85 68.93 45.21 39.24 53.46 54.83
140.95 68.85 68.93 48.03 39.24 53.83 55.14
148.15 68.85 70.16 48.42 40.04 54.98 56.11
148.37 68.85 71.65 46.25 40.74 53.73 55.05
140.02 68.85 70.16 47.95 40.04 53.46 54.83
151.19 68.85 72.93 47.41 41.72 54.14 55.40
151.13 68.85 84.14 57.51 49.59 57.66 58.37
142.47 68.85 68.93 45.49 39.24 58.35 58.96
145.96 68.85 70.16 47.81 40.04 53.83 55.13
158.13 68.85 84.14 56.19 49.59 53.79 55.10
131.04 68.85 68.93 46.47 39.24 48.81 50.89
148.21 68.85 70.73 47.46 40.34 53.90 55.19
144.14 68.85 71.65 47.56 40.74 53.46 54.83
Source: Moffroid and Pape, 2010.
A-225
-------
Table A-187:2009 Manure Distribution Among Waste Management Systems by Operation [Percent!
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
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
Beef Feedlots
Dry Liquid/
Lot2 Slurry2
100 1.3
100 1.3
100 0.4
100 1.3
100 1.3
100 0.4
100 1
100 1
100 1.3
100 1.3
100 1.3
100 0.4
100 0.6
100 0.6
100 0.6
100 0.6
100 1
100 1.3
100 1
100 1
100 1
100 0.6
100 0.6
100 1.3
100 0.6
100 0.4
100 0.6
100 0.4
100 1
100 1
100 0.4
100 1
100 1
100 0.6
100 0.6
100 0.4
100 1.3
100 1
Dairies1
Daily Solid Liquid/ Anaerobic Deep
Spread Storage Slurry Lagoon Pit
45 17 11 10 17 0
4 6 28 24 30 8
0 10 9 20 61 0
57 15 11 7 10 1
1 11 9 21 58 0
11 12 24 62 1
6 43 17 20 12 2
6 44 19 19 10 2
17 22 8 15 39 0
40 18 10 11 21 0
1 0 11 21 67 0
0 1 12 23 63 1
5 8 43 26 13 5
8 13 35 24 16 3
6 10 41 25 14 4
3 5 28 33 29 3
61 14 14 6 22
60 14 10 6 91
7 45 20 17 92
7 44 23 15 83
7 45 24 15 63
3 6 32 33 22 4
6 10 44 24 12 5
57 15 10 7 10 0
8 14 48 18 65
3 4 25 26 36 6
4 6 35 30 21 4
0 0 11 24 64 0
7 44 21 16 93
8 45 24 14 63
0 10 9 19 61 0
7 45 20 16 10 2
54 15 12 11 61
6 11 45 22 12 4
8 14 41 23 11 4
0 6 25 23 40 6
20 0 13 21 44 2
9 47 25 12 52
Dairy Heifer Facilities
Daily Dry Liquid/ ^^
Spread Lot Slurry
17 38 0 45
6 90 1 4
10 90 0 0
15 28 0 57
11 88 1 1
1 98 0 1
43 51 0 6
44 50 0 6
22 61 1 17
18 42 0 40
0 99 1 1
1 99 0 0
8 87 0 5
13 79 0 8
10 83 0 6
5 92 0 3
14 24 0 61
14 26 0 60
45 48 0 7
44 49 0 7
45 47 0 7
6 91 0 3
10 84 0 6
15 28 0 57
14 77 0 8
4 93 0 3
6 90 0 4
0 99 0 0
44 49 0 7
45 47 0 8
10 90 0 0
45 48 0 7
15 31 0 54
11 83 0 6
14 78 0 8
6 94 0 0
0 80 1 20
47 44 0 9
Swine Operations1
_ Solid Liquid/ Anaerobic Deep
Pasture „ „, T _. r
Storage Slurry Lagoon Pit
548 51 32
53 2 13 10 22
14 3 5 50 27
4 4 12 46 35
13 3 8 47 29
2 5 26 17 50
57 2 12 9 20
12 4 24 18 42
71 1 8 6 14
8 4 10 47 32
23 3 18 21 34
46 3 15 10 26
2 5 28 15 50
3 5 28 15 50
1 4 12 48 35
2 5 28 13 51
5 4 12 45 34
54 2 13 10 22
73 1 7 6 13
21 4 21 16 38
31 3 19 14 32
5 5 25 17 48
2 5 26 18 49
246 57 31
3 5 28 14 51
6 5 25 17 47
3 5 28 15 50
35 2 4 39 20
57 2 12 9 20
31 3 19 14 33
92 0 2 24
20 4 21 15 40
046 58 31
9 5 24 17 45
7 4 27 15 47
1 46 58 31
58 2 12 9 20
5 5 25 18 47
Layer Operations
. , . Poultry
Anaerobic . ,
T without
La2°°n Litter
41.9 58.1
25 75
60 40
0 100
12 88
60 40
5 95
5 95
41.9 58.1
41.9 58.1
25 75
60 40
2 98
0 100
0 100
2 98
5 95
60 40
5 95
5 95
5 95
2 98
0 100
60 40
0 100
60 40
2 98
0 100
5 95
5 95
60 40
5 95
41.9 58.1
2 98
0 100
60 40
25 75
0 100
Broiler and
Turkey
Operations
Poultry
Pasture with
Litter
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
State
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Beef Feedlots
Dry Liquid/
Lot2 Slurry2
100 1
100 1.3
100 0.6
100 1
100 0.4
100 0.4
100 1
100 1
100 1.3
100 1
100 0.6
100 0.4
Dairies1
„ , Daily Solid Liquid/ Anaerobic Deep
Pasture J , *
Spread Storage Slurry Lagoon Pit
9 47 26 12 43
54 15 89 13 0
5 8 38 28 18 4
59 15 12 9 42
0 8 13 24 53 2
11 17 26 51 3
7 44 19 17 10 2
57 15 12 9 42
17 0 11 22 49 1
7 45 23 16 73
7 12 42 24 12 4
7 12 22 23 30 6
Dairy Heifer Facilities
Daily Dry Liquid/ p f. 2
Spread2 Lot2 Slurry2
47 44 0 9
15 31 0 54
8 87 0 5
15 26 0 59
8 92 0 0
1 98 0 1
44 49 0 7
15 28 0 57
0 83 1 17
45 48 0 7
12 82 0 7
12 81 0 7
Swine Operations1
„ , Solid Liquid/ Anaerobic Deep
Pasture , *
Storage Slurry Lagoon Pit
56 2 12 9 21
649 50 32
3 5 26 18 48
10 4 12 41 33
736 54 29
1 6 26 17 51
86 1 4 37
347 55 31
37 3 17 12 31
58 2 11 8 21
13 4 24 17 42
3 5 26 17 49
Layer Operations
. , . Poultry
Anaerobic . „ ,
without
La2°°n Litter
5 95
60 40
2 98
5 95
12 88
60 40
5 95
5 95
12 88
5 95
2 98
60 40
Broiler and
Turkey
Operations
Poultry
Pasture with
Litter
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
1 99
In the methane inventory for manure management, the percent of dairy cows and swine with anaerobic digestion systems is estimated using data from EPA's AgSTAR Program.
Because manure from beef feedlots and dairy heifers maybe managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generates
emissions is greater than 100 percent.
A-227
-------
Table A-188: Manure Management System Descriptions
Manure Management System Description1
Pasture
Daily Spread
Solid Storage
Dry Lot
Liquid/ Slurry
Anaerobic Lagoon
The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. N2O
emissions from deposited manure are covered under the N2O from Agricultural Soils category.
Manure is routinely removed from a confinement facility and is applied to cropland or pasture within
24 hours of excretion. N2O emissions during storage and treatment are assumed to be zero. N2O
emissions from land application are covered under the Agricultural Soils category.
The storage of manure, typically for a period of several months, in unconfmed piles or stacks.
Manure is able to be stacked due to the presence of a sufficient amount of bedding material or loss of
moisture by evaporation.
A paved or unpaved open confinement area without any significant vegetative cover where
accumulating manure may be removed periodically. Dry lots are most typically found in dry climates
but also are used in humid climates.
Manure is stored as excreted or with some minimal addition of water to facilitate handling and is
stored in either tanks or earthen ponds, usually for periods less than one year.
Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage.
Lagoon supernatant is usually used to remove manure from the associated confinement facilities to
the lagoon. Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater),
depending on the climate region, the volatile solids loading rate, and other operational factors.
Anaerobic lagoons accumulate sludge over time, diminishing treatment capacity. Lagoons must be
cleaned out once every 5 to 15 years, and the sludge is typically applied to agricultural lands. The
water from the lagoon may be recycled as flush water or used to irrigate and fertilize fields. Lagoons
are sometimes used in combination with a solids separator, typically for dairy waste. Solids
separators help control the buildup of nondegradable material such as straw or other bedding
materials.
Animal excreta with or without straw are collected and anaerobically digested in a large containment
vessel or covered lagoon. Digesters are designed and operated for waste stabilization by the
microbial reduction of complex organic compounds to CO2 and CH4, which is captured and flared or
used as a fuel.
Collection and storage of manure usually with little or no added water typically below a slatted floor
in an enclosed animal confinement facility. Typical storage periods range from 5 to 12 months, after
which manure is removed from the pit and transferred to a treatment system or applied to land.
Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw, peanut
hulls, or other products, depending on availability. The bedding absorbs moisture and dilutes the
manure produced by the birds. Litter is typically cleaned out completely once a year. These manure
systems are typically used for all poultry breeder flocks and for the production of meat type chickens
(broilers) and other fowl.
In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no
bedding to absorb moisture. The ventilation system dries the manure as it is stored. When designed
and operated properly, this high-rise system is a form of passive windrow composting.
Manure management system descriptions are from the 2006IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture,
Forestry and Other Land Use, Chapter 10: Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development
Document for the Final Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for
Concentrated Animal Feeding Operations (EPA-821-R-03-001, December 2002).
Table A-189: Methane Conversion Factors (percent) for Dry Systems
Anaerobic Digester
Deep Pit
Poultry with Litter
Poultry without Litter
Waste Management System
Aerobic Treatment
Cattle Deep Litter (<1 month)
Cattle Deep Litter (>1 month)
Composting - In Vessel
Composting - Static Pile
Composting-Extensive/ Passive
Composting-Intensive
Daily Spread
Dry Lot
Fuel
Cool Climate MCF
0
0.03
0.21
0.005
0.005
0.005
0.005
0.001
0.01
0.1
Temperate Climate MCF
0
0.03
0.44
0.005
0.005
0.01
0.01
0.005
0.015
0.1
Warm Climate MCF
0
0.3
0.76
0.005
0.005
0.015
0.015
0.01
0.05
0.1
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Waste Management System Cool Climate MCF Temperate Climate MCF
Pasture
Poultry with bedding
Poultry without bedding
Solid Storage
0.01
0.015
0.015
0.02
0.015
0.015
0.015
0.04
Warm Climate MCF
0.02
0.015
0.015
0.05
Table A- 190: Methane Conversion Factors by State for Liquid Systems for 2009 (percent)
State
Dairy
Swine
Beef Poultry
Anaerobic Liquid/Slurry Anaerobic Liquid/Slurry Liquid/Slurry Anaerobic
Lagoon and Deep Pit
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
0.76
0.46
0.80
0.74
0.75
0.63
0.68
0.74
0.78
0.76
0.77
0.67
0.71
0.70
0.68
0.73
0.74
0.77
0.63
0.72
0.67
0.67
0.66
0.76
0.73
0.59
0.70
0.71
0.63
0.72
0.72
0.65
0.73
0.65
0.70
0.75
0.62
0.69
0.69
0.76
0.68
0.74
0.76
0.67
0.63
0.71
0.62
0.69
0.66
0.59
0.39
0.14
0.62
0.34
0.35
0.21
0.24
0.31
0.52
0.39
0.58
0.23
0.28
0.27
0.24
0.30
0.31
0.46
0.20
0.29
0.23
0.23
0.23
0.41
0.30
0.19
0.26
0.26
0.20
0.28
0.30
0.22
0.31
0.21
0.26
0.37
0.20
0.25
0.26
0.38
0.24
0.31
0.44
0.23
0.21
0.28
0.20
0.26
0.22
0.18
Lagoon and Deep Pit
0.76
0.46
0.75
0.75
0.74
0.67
0.68
0.74
0.78
0.76
0.77
0.65
0.71
0.71
0.69
0.73
0.74
0.77
0.63
0.73
0.68
0.68
0.67
0.76
0.72
0.61
0.70
0.71
0.65
0.72
0.71
0.66
0.75
0.65
0.71
0.74
0.63
0.70
0.69
0.76
0.68
0.75
0.76
0.66
0.63
0.73
0.64
0.70
0.67
0.64
0.38
0.14
0.37
0.36
0.32
0.24
0.24
0.31
0.50
0.38
0.58
0.22
0.28
0.27
0.25
0.30
0.31
0.46
0.20
0.30
0.24
0.24
0.23
0.39
0.30
0.20
0.26
0.27
0.22
0.29
0.28
0.22
0.37
0.21
0.27
0.35
0.21
0.26
0.26
0.39
0.24
0.34
0.43
0.22
0.21
0.31
0.21
0.26
0.23
0.21
0.40
0.14
0.48
0.34
0.42
0.22
0.25
0.31
0.51
0.38
0.58
0.22
0.27
0.27
0.25
0.30
0.31
0.47
0.20
0.30
0.24
0.23
0.23
0.41
0.30
0.20
0.25
0.26
0.20
0.28
0.28
0.22
0.32
0.21
0.26
0.34
0.22
0.26
0.26
0.39
0.24
0.32
0.37
0.23
0.21
0.28
0.22
0.26
0.23
0.21
Lagoon
0.76
0.46
0.76
0.74
0.75
0.63
0.69
0.74
0.78
0.75
0.77
0.65
0.71
0.71
0.69
0.73
0.74
0.77
0.63
0.73
0.67
0.68
0.66
0.76
0.73
0.61
0.70
0.71
0.64
0.72
0.68
0.67
0.73
0.64
0.71
0.75
0.64
0.71
0.69
0.76
0.68
0.74
0.77
0.66
0.63
0.72
0.63
0.69
0.67
0.62
A-229
-------
Table A-191: Direct Nitrous Oxide Emission Factors for 2009 (kg N20-N/kg Kjdl N)
Waste Management System
Aerobic Treatment (forced aeration)
Aerobic Treatment (natural aeration)
Anaerobic Digester
Anaerobic Lagoon
Cattle Deep Bed (active mix)
Cattle Deep Bed (no mix)
Composting in vessel
Composting intensive
Composting passive
Composting static
Daily Spread
Deep Pit
Dry Lot
Fuel
Liquid/Slurry
Pasture
Poultry with bedding
Poultry without bedding
Solid Storage
Direct N2O
Emission
Factor
0.005
0.01
0
0
0.07
0.01
0.006
0.1
0.01
0.006
0
0.002
0.02
0
0.005
0
0.001
0.001
0.005
Table A- 192: Indirect Nitrous Oxide Loss Factors (percent)
Animal Type
Beef Cattle
Beef Cattle
Beef Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Dairy Cattle
Goats
Goats
Horses
Horses
Poultry
Poultry
Poultry
Poultry
Poultry
Poultry
Sheep
Sheep
Swine
Swine
Swine
Swine
Swine
1 Data for nitrogen losses
Waste Management
System
Dry Lot
Liquid/Slurry
Pasture
Anaerobic Lagoon
Daily Spread
Deep Pit
Dry Lot
Liquid/Slurry
Pasture
Solid Storage
Dry Lot
Pasture
Dry Lot
Pasture
Anaerobic Lagoon
Liquid/Slurry
Pasture
Poultry with bedding
Poultry without
bedding
Solid Storage
Dry Lot
Pasture
Anaerobic Lagoon
Deep Pit
Liquid/Slurry
Pasture
Solid Storage
Runoff/Leaching Nitrogen Loss
Volatilization
Nitrogen Loss
23
26
0
43
10
24
15
26
0
27
23
0
23
0
54
26
0
26
34
8
23
0
58
34
26
0
45
Central
1.1
0
0
0.2
0
0
0.6
0.2
0
0.2
1.1
0
0
0
0.2
0.2
0
0
0
0
1.1
0
0.2
0
0.2
0
0
due to leaching were not available, so the values represent only
Pacific
3.9
0
0
0.8
0
0
2
0.8
0
0
3.9
0
0
0
0.8
0.8
0
0
0
0
3.9
0
0.8
0
0.8
0
0
nitrogen
Mid-
Atlantic
3.6
0
0
0.7
0
0
1.8
0.7
0
0
3.6
0
0
0
0.7
0.7
0
0
0
0
3.6
0
0.7
0
0.7
0
0
Midwest
1.9
0
0
0.4
0
0
0.9
0.4
0
0
1.9
0
0
0
0.4
0.4
0
0
0
0
1.9
0
0.4
0
0.4
0
0
South
4.3
0
0
0.9
0
0
2.2
0.9
0
0
4.3
0
0
0
0.9
0.9
0
0
0
0
4.3
0
0.9
0
0.9
0
0
losses due to runoff.
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-193: Methane Emissions from Livestock Manure Management [Ggla
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine
Market Swine
Market <50 Ibs.
Market 50-1 19 Ibs.
Market 120- 179 Ibs.
Market > 180 Ibs.
Breeding Swine
Beef Cattle
Feedlot Steers
Feedlot Heifers
NOF Bulls
NOF Calves
NOF Heifers
NOF Steers
NOF Cows
Sheep
Goats
Poultry
Hens>l yr.
Total Pullets
Chickens
Broilers
Turkeys
Horses
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
599 615 598 623 663 693 702 734 781 854
592 608 591 616 656 686 695 727 774 846
7777777777
624 676 639 680 741 764 730 783 892 849
484 524 500 534 585 608 582 626 720 692
102 110 104 109 119 121 116 125 141 133
101 111 105 110 120 124 117 127 144 138
136 147 140 151 164 170 163 175 201 193
145 156 152 165 182 194 185 198 235 229
140 152 139 146 156 155 148 157 172 157
128 128 131 131 137 140 139 136 139 139
14 14 14 13 14 14 14 13 13 14
7777888888
5555555555
8888998899
12 12 13 14 14 15 15 14 14 14
12 12 13 14 13 14 14 13 13 12
69 69 70 71 74 76 76 74 76 76
7776655554
1111111111
131 132 127 131 131 128 126 128 130 126
73 72 70 73 72 69 68 67 70 66
25 26 23 23 23 22 21 23 23 21
4444443344
19 20 20 21 22 23 24 25 26 27
10 10 10 10 9 9 9 9 8 7
22 22 21 21 21 21 21 21 22 21
2000
900
893
7
834
680
131
136
189
225
155
133
15
9
5
9
13
11
71
4
1
127
67
22
3
28
7
20
2001
960
952
7
854
696
134
138
192
232
158
135
15
9
5
9
13
11
73
4
1
131
70
22
3
28
7
20
2002
997
990
7
879
720
137
144
199
240
158
133
15
9
5
9
13
11
71
4
1
130
67
22
4
29
7
21
2003
856
850
6
857
703
135
140
196
233
154
133
16
9
5
9
13
10
71
4
1
130
68
22
4
29
7
24
2004
969
962
7
851
701
134
140
194
233
150
130
15
9
5
9
12
10
71
3
1
129
66
23
3
30
7
26
2005
1018
1011
7
905
745
141
148
208
248
160
132
15
9
5
9
13
10
71
3
1
129
66
22
3
31
7
28
2006
1034
1027
7
889
730
139
146
203
243
159
139
16
9
5
10
13
10
74
3
1
131
66
23
3
32
7
28
2007
1151
1143
8
965
798
152
159
223
264
166
136
16
9
5
10
13
10
73
3
1
134
67
25
3
32
7
27
2008
1147
1139
8
918
763
107
170
223
262
155
131
16
9
5
9
12
10
70
3
1
129
64
23
3
33
7
24
2009
1168
1159
8
903
753
105
169
218
260
150
130
16
9
5
9
12
10
69
3
1
127
64
23
4
31
6
24
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
Table A- 194: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (Gg)
Animal Type
Dairy Cattle
Dairy Cows
Dairy Heifer
Swine
Market Swine
Market <50 Ibs.
Market 50- 119 Ibs.
Market 120- 179 Ibs.
Market > 180 Ibs.
Breeding Swine
Beef Cattle
Feedlot Steers
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
17.1 17.01 17.03 17.27 17.4 17.7 17.7 17.9 18.0 17.6
10.0 10.07 9.97 10.05 10.1 10.3 10.3 10.4 10.5 10.2
7.0 6.93 7.06 7.22 7.3 7.4 7.4 7.4 7.5 7.4
4.0 4.18 4.33 4.37 4.6 4.5 4.4 4.7 5.1 5.0
3.0 3.11 3.26 3.29 3.5 3.5 3.3 3.6 4.0 4.1
0.6 0.59 0.61 0.60 0.6 0.6 0.6 0.7 0.7 0.7
0.6 0.67 0.70 0.70 0.7 0.7 0.7 0.8 0.8 0.8
0.9 0.90 0.94 0.95 1.0 1.0 1.0 1.0 1.1 1.1
0.9 0.95 1.02 1.04 1.1 1.1 1.1 1.2 1.3 1.3
1.0 1.07 1.08 1.07 1.1 1.1 1.0 1.1 1.1 1.0
19.8 20.33 20.15 19.10 21.0 21.8 21.5 21.5 21.7 24.1
13.4 13.67 13.56 12.83 13.9 14.4 14.0 13.9 14.1 15.5
2000
17.9
10.5
7.5
5.0
4.1
0.8
0.8
1.1
1.3
0.9
25.0
16.1
2001
18.2
10.5
7.7
5.1
4.2
0.8
0.8
1.2
1.4
0.9
24.1
15.4
2002
18.5
10.6
7.8
5.3
4.4
0.8
0.9
1.2
1.5
0.9
24.8
16.1
2003
16.5
9.6
7.0
5.4
4.5
0.9
0.9
1.3
1.5
0.9
25.1
16.3
2004
17.8
10.2
7.5
5.6
4.7
0.9
0.9
1.3
1.5
0.9
23.7
15.3
2005
18.2
10.4
7.8
5.7
4.8
0.9
1.0
1.4
1.6
0.9
24.1
15.5
2006
18.7
10.7
8.1
5.9
5.0
1.0
1.0
1.4
1.6
0.9
25.8
16.8
2007
18.8
10.7
8.1
6.3
5.4
1.1
1.1
1.5
1.8
0.9
25.6
16.7
2008
18.5
10.5
8.0
6.4
5.6
0.8
1.3
1.6
1.9
0.8
25.2
16.5
2009
18.7
10.7
8.0
6.3
5.5
0.8
1.2
1.6
1.9
0.8
25.2
16.5
A-231
-------
Feedlot Heifers
Sheep
Goats
Poultry
Hens>l yr.
Total Pullets
Chickens
Broilers
Turkeys
Horses
6.4
0.4
0.1
4.7
1.0
0.3
0.0
2.2
1.2
0.7
6.65
0.41
0.07
4.79
1.02
0.29
0.03
2.26
1.19
0.70
6.60
0.44
0.07
4.88
1.02
0.29
0.03
2.36
1.18
0.71
6.27
0.44
0.07
4.96
1.03
0.29
0.03
2.46
1.14
0.71
7.0
0.6
0.1
5.1
1.0
0.3
0.0
2.6
1.1
0.7
7.4
0.7
0.1
5.1
1.0
0.3
0.0
2.7
1.1
0.7
7.4
0.8
0.1
5.3
1.0
0.3
0.0
2.8
1.1
0.7
7.6
0.9
0.1
5.3
1.1
0.3
0.0
2.8
1.1
0.7
7.6
0.9
0.1
5.3
1.1
0.3
0.0
2.9
1.0
0.7
8.5
1.0
0.1
5.3
1.1
0.3
0.0
2.9
0.9
0.7
8.9
1.1
0.1
5.3
1.1
0.3
0.0
2.9
0.9
0.7
8.7
1.2
0.1
5.3
1.2
0.3
0.0
2.9
0.9
0.7
8.8
1.2
0.1
5.4
1.2
0.3
0.0
3.0
0.9
0.8
8.8
1.2
0.1
5.3
1.2
0.4
0.0
2.9
0.9
0.9
8.4
1.1
0.1
5.4
1.2
0.4
0.0
2.9
0.8
1.0
8.5
1.2
0.1
5.4
1.3
0.4
0.0
3.0
0.8
1.1
9.0
1.2
0.1
5.4
1.3
0.4
0.0
2.9
0.8
1.1
8.9
1.2
0.1
5.4
1.3
0.4
0.0
2.9
0.8
1.1
8.7
1.2
0.1
5.4
1.3
0.4
0.0
2.9
0.8
1.1
8.6
1.1
0.1
5.1
1.3
0.4
0.0
2.7
0.7
1.1
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-195: Methane Emissions by State from Livestock Manure Management for 2009tGgla
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Beef on
Feedlots
0.0091
0.0001
0.8202
0.0042
1.3793
1.5272
0.0002
0.0002
0.0049
0.0078
0.0014
0.3405
0.2797
0.1859
2.0285
3.8479
0.0190
0.0043
0.0003
0.0202
0.0002
0.2544
0.4411
0.0088
0.0965
0.0645
3.9357
0.0092
0.0001
0.0003
0.2468
0.0481
0.0038
0.1065
0.3035
0.5381
0.1395
0.1272
+
0.0025
0.6037
0.0071
6.2856
0.0392
0.0005
0.0505
0.2798
0.0142
0.3705
0.1025
Beef Not
on Feed
2.5130
0.0214
1.3914
2.3427
3.5354
2.2547
0.0214
0.0127
3.3731
2.0242
0.3871
1.7547
1.1587
0.6572
3.3553
5.2668
2.8498
1.8598
0.0387
0.1159
0.0237
0.4613
1.5054
1.8861
5.1365
3.4419
5.7938
0.7279
0.0181
0.0283
1.6482
0.3778
1.0137
2.1409
0.9541
6.0089
1.7579
0.6633
0.0042
0.7246
4.0757
2.4632
22.3466
1.1290
0.0547
1.6854
1.0249
0.5231
1.1936
1.6831
Dairy Cow
0.7878
0.0336
52.2827
0.4741
401.7280
25.9551
0.9206
0.3593
17.9106
6.0643
0.3521
113.6925
7.1004
9.9651
15.5070
17.1442
1.5234
0.8950
1.3237
2.5013
0.4876
34.7198
28.1062
0.8255
4.7540
1.6729
5.8908
6.3173
0.6294
0.3514
77.4893
27.1686
1.7797
1.3253
16.8066
9.8078
13.5532
15.4864
0.0310
1.0262
7.4168
1.5864
93.4873
14.7238
5.2580
2.7183
37.9185
0.3899
70.4229
0.6483
Dairy
Heifer
0.0133
0.0003
0.1192
0.0095
2.0424
0.0901
0.0186
0.0041
0.0998
0.0614
0.0029
0.4155
0.0951
0.1057
0.2062
0.1058
0.0881
0.0194
0.0296
0.0453
0.0117
0.2332
0.4485
0.0190
0.0650
0.0149
0.0319
0.0153
0.0123
0.0087
0.2155
0.5322
0.0354
0.0313
0.1837
0.0684
0.0933
0.4585
0.0009
0.0186
0.0632
0.0796
0.5075
0.0679
0.1005
0.0776
0.1870
0.0051
1.0204
0.0075
Swine —
Market
1.8870
0.0036
2.2931
1.5543
1.5433
4.7356
0.0086
0.0533
0.0643
2.6177
0.1295
0.1240
36.9889
31.3953
264.5844
18.8129
5.0055
0.0565
0.0078
0.2579
0.0643
8.3149
58.7581
5.8277
25.0029
1.1172
25.5224
0.0285
0.0107
0.0531
0.0015
0.4586
151.1504
0.8294
16.4061
32.2081
0.0571
10.2107
0.0049
4.2497
10.1931
2.6957
14.7215
4.0454
0.0032
5.7130
0.0989
0.0223
2.4065
0.2398
Swine —
Breedin
g
0.5402
0.0019
0.6103
2.1073
0.2113
2.7422
0.0054
0.0427
0.0530
1.0343
0.0854
0.0714
9.7843
5.4684
28.7171
3.5728
1.0488
0.0306
0.0058
0.0736
0.0141
1.9980
10.6826
1.2846
7.4122
0.3543
7.3081
0.0121
0.0034
0.0160
0.0010
0.1625
35.4253
0.6165
3.1993
14.2156
0.0239
1.8110
0.0039
0.4742
2.8293
0.4871
2.4491
1.0264
0.0018
0.8152
0.0570
0.0137
0.8365
0.2827
Layer
8.8601
0.1801
0.6572
0.5189
4.2273
3.1055
0.2664
0.0727
6.8326
15.2216
0.1325
0.5614
0.2512
0.8165
1.7071
0.0416
0.5920
2.1219
0.3816
0.2180
0.0106
0.6120
0.3644
8.2188
0.2482
0.3486
0.5819
0.0214
0.0659
0.0715
0.5926
0.4220
11.0133
0.0390
0.9148
3.5948
0.8226
0.7260
0.0696
5.0711
0.1373
0.2492
4.3726
2.7412
0.0188
0.4380
1.1358
0.0971
0.2969
0.0085
Broiler
3.6406
0.0000
0.0000
3.8171
0.2654
+
0.2645
0.8381
0.1526
4.8018
+
+
+
0.2645
0.2645
+
1.1114
0.2654
+
1.0567
+
0.2645
0.1622
2.8818
0.2645
+
0.2645
+
+
+
+
0.2645
2.7590
+
0.2042
0.8181
0.2645
0.5557
+
0.8637
+
0.6867
2.4289
+
+
0.8717
0.2645
0.2994
0.1658
+
Turkey
0.0528
0.0526
0.0528
0.7252
0.3751
0.0526
0.0526
0.0526
0.0528
0.0528
0.0528
0.0526
0.0526
0.3739
0.0526
0.0526
0.0526
0.0528
0.0526
0.0526
0.0526
0.0526
0.0526
0.0528
0.4611
0.0526
0.0526
0.0526
0.0526
0.0526
0.0526
0.0526
0.8878
0.0526
0.1296
0.0526
0.0526
0.2243
0.0526
0.2976
0.1122
0.0526
0.0528
0.0798
0.0526
0.4237
0.0526
0.0823
0.0526
0.0526
Sheep
0.0090
0.0060
0.1057
0.0090
0.4652
0.1927
0.0038
0.0060
0.0090
0.0090
0.0090
0.0987
0.0273
0.0235
0.0940
0.0376
0.0188
0.0090
0.0038
0.0060
0.0038
0.0367
0.0658
0.0090
0.0390
0.1198
0.0334
0.0315
0.0038
0.0060
0.0564
0.0291
0.0197
0.0413
0.0611
0.0376
0.1034
0.0470
0.0038
0.0090
0.1433
0.0160
0.6132
0.1363
0.0038
0.0352
0.0249
0.0155
0.0399
0.1973
Goats
0.0302
0.0001
0.0158
0.0190
0.0490
0.0122
0.0011
0.0009
0.0216
0.0315
0.0034
0.0044
0.0084
0.0118
0.0140
0.0124
0.0245
0.0081
0.0015
0.0042
0.0021
0.0070
0.0092
0.0115
0.0241
0.0031
0.0086
0.0030
0.0010
0.0027
0.0089
0.0100
0.0369
0.0011
0.0174
0.0313
0.0095
0.0148
0.0002
0.0163
0.0027
0.0327
0.4273
0.0043
0.0016
0.0158
0.0082
0.0070
0.0140
0.0021
Horses
0.6750
0.0096
0.5327
0.6119
1.4005
0.6150
0.0595
0.0205
0.9338
0.5944
0.0507
0.3824
0.4106
0.4193
0.3719
0.4644
0.9063
0.4687
0.0628
0.1588
0.1062
0.5225
0.4657
0.5058
0.7706
0.5437
0.3390
0.0950
0.0511
0.1549
0.2770
0.4391
0.6074
0.2312
0.6158
0.8553
0.4620
0.6010
0.0180
0.3354
0.3628
0.7329
3.4006
0.3088
0.0686
0.4668
0.4636
0.1949
0.6197
0.4158
+ Emission estimate is less than 0.00005 Gg.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
A-233
-------
Table A-196: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2009
[Ggl
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Beef
Feedlot-
Heifer
0.0021
+
0.2208
0.0014
0.3129
0.6402
0.0001
0.0001
0.0011
0.0018
0.0003
0.1429
0.1096
0.0727
0.8062
1.4902
0.0068
0.0010
0.0001
0.0071
0.0001
0.1022
0.1778
0.0020
0.0375
0.0270
1.5630
0.0038
+
0.0001
0.1006
0.0181
0.0014
0.0431
0.1194
0.2153
0.0497
0.0465
+
0.0006
0.2413
0.0025
1.7394
0.0165
0.0002
0.0181
0.1000
0.0051
0.1489
0.0429
Beef
Feedlot-
Steers
0.0040
+
0.4235
0.0026
0.5977
1.2240
0.0001
0.0002
0.0020
0.0035
0.0006
0.2737
0.2108
0.1399
1.5456
2.8499
0.0129
0.0018
0.0002
0.0137
0.0001
0.1959
0.3394
0.0039
0.0718
0.0520
2.9921
0.0073
0.0001
0.0002
0.1934
0.0347
0.0026
0.0827
0.2294
0.4131
0.0954
0.0892
+
0.0011
0.4627
0.0048
3.3315
0.0313
0.0003
0.0347
0.1917
0.0099
0.2855
0.0824
Dairy
Cow
0.0060
0.0006
0.2285
0.0050
2.1407
0.1751
0.0149
0.0049
0.0924
0.0434
0.0018
0.7334
0.1408
0.2096
0.2924
0.1782
0.0316
0.0081
0.0244
0.0427
0.0107
0.5270
0.6319
0.0071
0.1234
0.0213
0.0829
0.0368
0.0117
0.0070
0.4144
0.4805
0.0228
0.0314
0.3446
0.0760
0.1223
0.3984
0.0008
0.0076
0.1309
0.0238
0.5319
0.1145
0.1044
0.0430
0.2872
0.0077
1.6751
0.0079
Dairy
Heifer
0.0045
0.0004
0.1056
0.0033
1.6247
0.1381
0.0133
0.0027
0.0506
0.0228
0.0023
0.6375
0.1205
0.1218
0.2542
0.1401
0.0288
0.0043
0.0204
0.0300
0.0078
0.3146
0.5617
0.0047
0.0724
0.0218
0.0423
0.0233
0.0087
0.0055
0.2955
0.3629
0.0144
0.0387
0.2115
0.0657
0.1036
0.2817
0.0005
0.0051
0.0819
0.0279
0.4742
0.1031
0.0703
0.0295
0.2164
0.0034
1.2450
0.0094
Swine —
Market
0.0099
0.0001
0.0116
0.0088
0.0085
0.0493
0.0001
0.0004
0.0004
0.0141
0.0007
0.0013
0.3498
0.3033
1.7628
0.1705
0.0299
0.0003
0.0001
0.0022
0.0006
0.0858
0.6070
0.0291
0.2284
0.0134
0.2541
0.0002
0.0001
0.0005
+
0.0049
0.7694
0.0092
0.1571
0.1696
0.0006
0.0966
+
0.0220
0.1030
0.0157
0.0833
0.0599
+
0.0317
0.0011
0.0002
0.0246
0.0045
Swine —
Breeding
0.0021
+
0.0024
0.0087
0.0008
0.0212
+
0.0003
0.0002
0.0041
0.0003
0.0005
0.0678
0.0388
0.1407
0.0239
0.0046
0.0001
+
0.0005
0.0001
0.0152
0.0812
0.0047
0.0499
0.0031
0.0536
0.0001
+
0.0001
+
0.0013
0.1326
0.0050
0.0225
0.0546
0.0002
0.0127
+
0.0018
0.0210
0.0021
0.0102
0.0109
+
0.0033
0.0005
0.0001
0.0063
0.0039
Layer
0.0639
0.0031
0.0034
0.0736
0.0873
0.0192
0.0116
0.0030
0.0469
0.1106
0.0014
0.0034
0.0181
0.1134
0.2370
0.0030
0.0246
0.0111
0.0176
0.0091
0.0005
0.0453
0.0506
0.0430
0.0346
0.0022
0.0425
0.0030
0.0030
0.0030
0.0034
0.0186
0.0811
0.0030
0.1270
0.0190
0.0106
0.1009
0.0030
0.0265
0.0101
0.0105
0.0892
0.0163
0.0009
0.0186
0.0274
0.0043
0.0221
0.0001
Broiler
0.3221
+
+
0.3377
0.0235
+
0.0235
0.0744
0.0135
0.4248
+
+
+
0.0235
0.0235
+
0.0987
0.0235
+
0.0938
+
0.0235
0.0144
0.2550
0.0235
+
0.0235
+
+
+
+
0.0235
0.2441
+
0.0181
0.0726
0.0235
0.0493
+
0.0764
+
0.0610
0.2149
+
+
0.0774
0.0235
0.0266
0.0147
+
Turkey
0.0061
0.0061
0.0061
0.0840
0.0435
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0435
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0536
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.0061
0.1029
0.0061
0.0151
0.0061
0.0061
0.0261
0.0061
0.0345
0.0130
0.0061
0.0061
0.0093
0.0061
0.0493
0.0061
0.0096
0.0061
0.0061
Sheep
0.0049
0.0016
0.0165
0.0042
0.0822
0.0452
0.0030
0.0049
0.0049
0.0049
0.0016
0.0232
0.0190
0.0164
0.0656
0.0262
0.0152
0.0042
0.0030
0.0049
0.0030
0.0256
0.0459
0.0049
0.0272
0.0281
0.0233
0.0074
0.0030
0.0049
0.0132
0.0236
0.0107
0.0289
0.0493
0.0262
0.0274
0.0381
0.0030
0.0049
0.1001
0.0130
0.0959
0.0320
0.0030
0.0286
0.0066
0.0126
0.0279
0.0463
Goats
0.0024
+
0.0012
0.0015
0.0039
0.0014
0.0001
0.0001
0.0017
0.0025
0.0003
0.0005
0.0010
0.0014
0.0017
0.0015
0.0029
0.0006
0.0002
0.0005
0.0002
0.0008
0.0011
0.0009
0.0029
0.0004
0.0010
0.0004
0.0001
0.0003
0.0011
0.0012
0.0029
0.0001
0.0021
0.0037
0.0011
0.0018
+
0.0013
0.0003
0.0039
0.0337
0.0005
0.0002
0.0019
0.0010
0.0008
0.0017
0.0002
Horses
0.0232
0.0005
0.0183
0.0210
0.0481
0.0317
0.0031
0.0011
0.0321
0.0204
0.0017
0.0197
0.0212
0.0216
0.0192
0.0239
0.0467
0.0161
0.0032
0.0082
0.0055
0.0269
0.0240
0.0174
0.0397
0.0280
0.0175
0.0049
0.0026
0.0080
0.0143
0.0226
0.0209
0.0119
0.0317
0.0441
0.0238
0.0310
0.0009
0.0115
0.0187
0.0378
0.1168
0.0159
0.0035
0.0241
0.0239
0.0100
0.0319
0.0214
+ Emission estimate is less than 0.00005 Gg.
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
3.11. Methodology for Estimating NiO Emissions from Agricultural Soil
Management
Nitrous oxide emissions from agricultural soils result from the interaction of the natural processes of
denitrification and nitrification with management practices that add or release mineral nitrogen (N) in the soil profile.
Emissions can occur directly in the soil where the N is made available or can be transported to another location following
volatilization, leaching, or runoff, and then converted into N2O.
A combination of Tier 1 and Tier 3 approaches was used to estimate direct and indirect N2O emissions from
agricultural soils. The process-based biogeochemical model DAYCENT (a Tier 3 approach) was used to estimate N2O
emissions resulting from croplands on mineral soils that were used to produce major crops, while the IPCC (2006) Tier 1
methodology was applied to estimate N2O emissions for non-major crop types on mineral soils. The Tier 1 method was
also used to estimate direct N2O emissions due to drainage and cultivation of organic cropland soils. Direct N2O
emissions from grasslands were estimated by using a combination of DAYCENT and IPCC (2006) Tier 1 methods. A
combination of DAYCENT and Tier 1 methods was also used to estimate indirect emissions from all managed lands (i.e.,
croplands, grasslands, forest lands, and settlements). Specifically, the amount of N volatilized from soils, as well as
leaching or transport of nitrate (NO3") off-site in surface runoff waters was computed by DAYCENT for the direct
emission analyses, while IPCC default factors were used to estimate N transport for the analyses using the Tier 1
methodology. The indirect N2O emissions resulting from off-site transport of N were then computed using the IPCC
(2006) Tier 1 default emission factor. Overall, the Tier 3 approach is used to estimate approximately 85 percent of direct
soil emissions and 70 percent of total soil N2O emissions associated with agricultural soil management in the United
States.
DAYCENT (Del Grosso et al. 2001, Parton et al. 1998) simulates biogeochemical N fluxes between the
atmosphere, vegetation, and soil, allowing for a more complete estimation of N2O emissions than IPCC Tier 1 methods by
accounting for the influence of environmental conditions including soil characteristics and weather patterns, specific crop
and forage qualities that influence the N cycle, and management practices at a daily time step. For example, plant growth
is controlled by nutrient availability, water, and temperature stress; moreover, growth removes mineral N from the soil
before it can potentially be converted into N2O. Nutrient supply is a function of external nutrient additions as well as litter
and soil organic matter (SOM) decomposition rates, and increasing decomposition can lead to greater N2O emissions by
enhancing mineral N availability in soils. In this model-based assessment framework, daily maximum/minimum
temperature and precipitation, timing and description of management events (e.g., fertilization, tillage, harvest), and soil
texture data are model inputs to DAYCENT, which form the basis to simulate key processes and generate robust estimates
of N2O emissions from soils. Key processes simulated within sub-models of DAYCENT include plant production, organic
matter formation and decomposition, soil water and soil temperature regimes by layer, and nitrification and denitrification
processes (Figure A- 7). Comparison of model results and plot level data show that DAYCENT reliably simulates crop
yields, soil organic matter levels, and trace gas fluxes for a number of native and managed systems (Del Grosso et al.
2001, 2005). Comparisons with measured data showed that DAYCENT estimated emissions more accurately and
precisely than the IPCC Tier 1 methodology (Figure A- 8). The linear regression of simulated vs. measured emissions for
DAYCENT had higher r2 and a fitted line closer to a perfect 1:1 relationship between measured and modeled N2O
emissions (Del Grosso et al. 2005, 2008). This is not surprising, since DAYCENT includes site-specific factors (climate,
soil properties, and previous management) that influence N2O emissions. Furthermore, DAYCENT also simulated NO3"
leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error =
69 percent) (Del Grosso et al. 2005). Thus, the Tier 3 approach has reduced uncertainties in the agricultural soil
management section relative to earlier Inventory years where the IPCC Tier 1 method was used. The latest operational
version of DAYCENT has several improvements, including (1) elimination of the influence of labile (i.e., easily
decomposable by microbes) C availability on surface litter denitrification rates, (2) incorporation of precipitation events on
surface litter denitrification, and (3) having the wettest soil layer within the rooting zone control plant transpiration.
[Begin Text Box]
Box1. DAYGENT Model Simulation of Nitrification and Denitrification
The DAYCENT model simulates the two biogeochemical processes, nitrification and denitrification, that result
in N2O emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm of
soil, while denitrification is calculated for the entire soil profile. The equations and key parameters controlling N2O
emissions from nitrification and denitrification are described below.
A-235
-------
Nitrification is controlled by soil ammonium (NH4+) concentration, water filled pore space (WFPS), temperature
(t), and pH according to the following equation:
Nit = NH4 x Kmax x F(t) x F(WFPS) x F(pH)
where,
Nit = the soil nitrification rate (g N/m /day)
NH4 = the model-derived soil ammonium concentration (g N/m2)
Kmax = the maximum fraction of NH4+ nitrified (Kmax = 0.10/day)
F(t) = the effect of soil temperature on nitrification (Figure A- 5a)
F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A- 5b)
F(pFŁ) = the effect of soil pH on nitrification (Figure A- 5c)
The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O.
N2O emissions from denitrification are a function of soil NO3" concentration, WFPS, heterotrophic (i.e.,
microbial) respiration, and texture. Denitrification is calculated for each soil layer in the profile, and N2O emissions from
each layer are summed to obtain total soil emissions. The model assumes that denitrification rates are controlled by the
availability of soil NO3" (electron acceptor), labile C compounds (electron donor) and oxygen (competing electron
acceptor). Heterotrophic soil respiration is used as a proxy for labile C availability, while oxygen availability is a function
of soil physical properties that influence gas diffusivity, soil WFPS, and oxygen demand. The model selects the minimum
of the NO3" and CO2 functions to establish a maximum potential denitrification rate for particular levels of electron
acceptor and C substrate and accounts for limitations of oxygen availability to estimate daily denitrification rates
according to the following equation:
Den = min[F(CO2), F(NO3)] x F(WFPS)
where,
Den = the soil denitrification rate (|_ig N/g soil/day)
F(CO2) = a function relating N gas flux to soil respiration (Figure A- 6a)
F(NO3) = a function relating N gas flux to nitrate levels (Figure A- 5b)
F(WFPS) = a dimensionless multiplier (Figure A- 6c).
The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (DFc):
x inflection = 0.90 - M(CO2)
where,
M = a multiplier that is a function of DFC.
Respiration has a much stronger effect on the water curve in clay soils with low DFC than in loam or sandy soils
with high DFC (Figure A- 6c). The model assumes that microsites in fine-textured soils can become anaerobic at relatively
low water contents when oxygen demand is high.
After calculating total N gas flux, the ratio of N2/N2O is estimated so that total N gas emissions can be
partitioned between N2O and N2:
RN2/N2o = Fr(N03/C02) x Fr(WFPS).
where,
RN2/N20 = the ratio of N2/N2O
Fr(NO3/CO2) = a function estimating the impact of the availability of electron donor relative to substrate
Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N2O.
For Fr(NO3/CO2), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be
in the form of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2.
[End Box]
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Figure A- 5: Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
Figure A- 6: Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on
Denitrification Rates
There are five steps in estimating direct N2O emissions from cropland and grassland soils, and indirect N2O
emissions from volatilization, leaching, and runoff from all managed lands (i.e., croplands, grasslands, forest lands, and
settlements). First, the activity data are derived from a combination of land-use, livestock, crop, and grassland
management surveys, as well as expert knowledge. In the second, third, and fourth steps, direct and indirect N2O
emissions are estimated using DAYCENT and/or the Tier 1 method. In the fifth step, total emissions are computed by
summing all components. The remainder of this annex describes the methods underlying each step.
Step 1: Derive Activity Data
The activity data requirements vary for major crops, non-major crops, grasslands, organic cropland soils,
settlements and forest lands. Activity data were derived for direct and indirect N2O emission calculations as described
below.
Step la: Activity Data for Direct Emissions from Crop Production on Mineral Soils
Nitrous oxide emissions from mineral cropland soils include emissions from both major and non-major cropping
systems and were estimated using a Tier 3 and Tier 1 approach, respectively.
Major Crop Types: Tier 3 DAYCENT Simulations
The activity data requirements for estimating N2O emissions from major crop types (corn, soybeans, wheat,
alfalfa hay, other hay, sorghum, and cotton) include the following: (1) crop-specific mineral N fertilizer rates and timing,
(2) crop-specific manure amendment N rates and timing, (3) other N inputs, (4) crop-specific land management
information, (5) native vegetation, (6) daily weather data for every county, (7) sub-county-level soil texture data, and (8)
county-level crop areas. The United States was divided into 63 agricultural regions based on common cropping practices
as defined by McCarl et al. (1993), and data were assembled and provided as inputs to the DAYCENT biogeochemical
ecosystem model.
Synthetic N Fertilizer Application: Data on N fertilizer rates were obtained primarily from the U.S. Department
of Agriculture-Economic Research Service 1995 Cropping Practices Survey (ERS 1997). In this survey, data on
inorganic N fertilization rates were collected for major crops (corn, cotton, soybeans, and wheat) in the high production
states during 1995. It is assumed that the fertilization rates have not changed much during the Inventory reporting period,
which is confirmed by the sales data showing relatively minor change in the amount of fertilizer sold for on-farm use
across the time series of this Inventory (Ruddy et al. 2006). The trend and therefore the rates and uncertainties reflected in
the 1995 survey data are considered representative for 1990 through 2008 (trends will be re-evaluated when new
fertilization data are released by U.S. Department of Agriculture). Note that all wheat data were combined into one
category and assumed to represent small grains in aggregate. Estimates for sorghum fertilizer rates were derived from
corn fertilizer rates using a ratio of national average corn fertilizer rates to national average sorghum fertilizer rates, which
were derived from additional publications (NASS 1992, 1999, 2004; ERS 1988; Grant and Krenz 1985; USDA 1954,
1957, 1966). Alfalfa hay is assumed to not be fertilized, but grass hay is fertilized according to rates from published farm
enterprise budgets (NRIAI 2003).
The ERS survey parameter "TOT N' (total amount of N applied per acre), with a small number of records
deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for
each region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state with
a minimum of 15 data points for irrigated and rainfed, respectively. If a state was not surveyed for a particular crop or if
fewer than 15 data points existed for one of the categories, then data were aggregated to U.S. Department of Agriculture
Farm Production Regions in order to estimate a mean and standard deviation for fertilization rates (Farm Production
Regions are groups of states in the United States with similar agricultural commodities). If Farm Production Region data
were not available, crop data were aggregated to the entire United States (all major states surveyed) to estimate a mean and
standard deviation for a particular crop in a state lacking sufficient data. Standard deviations for fertilizer rates were used
A-237
-------
to construct probability distribution functions (PDFs) with log-normal densities in order to address uncertainties in
application rates (see Step 2a for discussion of uncertainty methods). Total fertilizer application data are found in Table
A- 197.
Simulations were conducted for the period prior to 1990 in order to initialize the DAYCENT model (see Step
2a), and estimates for crop-specific regional fertilizer rates prior to 1990 were based largely on extrapolation/interpolation
of fertilizer rates from the years with available data. For crops in some agricultural regions, little or no data were
available, and, therefore, a geographic regional mean was used to simulate N fertilization rates (e.g., no data were
available from Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the
southeastern United States were used to simulate fertilization to corn fields in this state).
Managed Livestock Manure57 N Amendment Rates and Timing: County-level manure addition estimates have
been derived from manure N addition rates developed by the Natural Resources Conservation Service (NRCS) (Edmonds
et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, NRCS has coupled
estimates of manure N produced with estimates of manure N recoverability by animal waste management system to
produce county-level estimates of manure N applied to cropland and pasture. Edmonds et al. (2003) defined a hierarchy
that included 24 crops, cropland used as pasture, and permanent pasture. They estimated the area amended with manure
and application rates in 1997 for both manure-producing farms and manure-receiving farms within a county and for two
scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation
(Edmonds et al. 2003). The goal of nutrient management plans is to apply manure nutrients at a rate meeting plant
demand, thus limiting leaching losses of nutrients to groundwater and waterways. For DAYCENT simulations, the
baseline scenario estimates have been used as the basis for manure amendment applications under the assumption that
Comprehensive Nutrient Management Plans have not been fully implemented. This is a conservative assumption because
it allows for higher leaching rates due to some over-application of manure to soils. The rates for manure-producing farms
and manure-receiving farms have been area-weighted and combined to produce a single county-level estimate for the
amount of land amended with manure and the manure N application rate for each crop in each county. Several of the
crops in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all
small grain crops have been combined into one category. In order to address uncertainty in these data, uniform probability
distributions were constructed based on the proportion of land receiving manure versus the amount not receiving manure
for each crop type and pasture. For example, if 20 percent of land producing corn in a county was amended with manure,
randomly drawing a value equal to or greater than 0 and less than 20 would lead to a simulation with a manure
amendment, while drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the
simulation (see Step 2a for further discussion of uncertainty methods).
Edmonds et al. (2003) only provides manure application rate data for 1997, but the amount of managed manure
available for soil application changes annually, so the area amended with manure was adjusted relative to 1997 to account
for all the manure available for application in other years. Specifically, the manure N available for application in other
years was divided by the manure N available in 1997. If the ratio was greater than 1, there was more manure N available
in that county relative to the amount in 1997, and so it was assumed a larger area was amended with manure. In contrast,
ratios less than one implied less area was amended with manure because there was a lower amount available in the year
compared to 1997. The amendment area in each county for 1997 was multiplied by the ratio to reflect the impact of
manure N availability on the area amended. The amount of managed manure N available for application to soils was
calculated by determining the populations of animals that were on feedlots or otherwise housed in order to collect and
manage the manure, as described in the Manure Management section (Section 6.2) and annex (Annex 3.10).
To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003), carbon-
nitrogen (C:N) ratios for livestock-specific manure types were adapted from the Agricultural Waste Management Field
Handbook (USDA 1996), On-Farm Composting Handbook (NRAES 1992), and recoverability factors provided by
Edmonds et al (2003). The C:N ratios were applied to county-level estimates of manure N excreted by animal type and
management system to produce a weighted county average C:N ratio for manure amendments. The average C:N ratio was
used to determine the associated C input for crop amendments derived from Edmonds et al. (2003).
To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a cropland
soil, crop-specific reduction factors were derived from mineral fertilization data for land amended with manure versus land
57 For purposes of the Inventory, total livestock manure is divided into two general categories: (1) managed manure, and (2) unmanaged
manure. Managed manure includes manure that is stored in manure management systems such as pits and lagoons, as well as manure
applied to soils through daily spread manure operations. Unmanaged manure encompasses all manure deposited on soils by animals on
PRP.
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not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). Mineral N fertilization rates were
reduced for crops receiving manure N based on a fraction of the amount of manure N applied, depending on the crop and
whether it was irrigated or rainfed. The reduction factors were randomly selected from PDFs with normal densities in
order to address uncertainties in the dependence between manure amendments and mineral fertilizer application.
Manure N that was not applied to major crops and grassland was assumed to be applied to non-major crop types.
The fate of manure N is summarized in Table A- 198.
Residue N Inputs: Residue N input is estimated as part of the DAYCENT simulation and is not an input to the
model. Unlike the Tier 1 approach, N inputs from crop residues are not considered activity data in the DAYCENT
simulations because N availability from this source is simulated by the model based on N uptake during crop growth
according to environmental and management conditions, such as temperature, precipitation, and edaphic (i.e., soil)
characteristics, in combination with the harvest practices. That is, while the model accounts for the contribution of N from
crop residues to the soil profile and subsequent N2O emissions, this source of mineral soil N is not activity data in the
sense that it is not a model input. Similarly, N from mineralization of soil organic matter and asymbiotic N fixation are
also simulated by the model. The simulated total N inputs of above- and below-ground residue N and fixed N that was not
harvested and not burned (the DAYCENT simulations assumed that 3 percent of non-harvested above ground residues for
grain crops were burned) are provided in Table A- 199.
Other N Inputs: Other N inputs are estimated within the DAYCENT simulation, and thus input data are not
required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the
atmosphere. The influence of additional inputs of N are estimated in the simulations so that there is full accounting of all
emissions from managed lands, as recommended by IPCC (2006). The simulated total N inputs from other sources are
provided in Table A- 199.
Crop Rotation and Land Management Information. Data were obtained on specific timing and type of
cultivation, timing of planting/harvest, and crop rotation schedules for the 63 agricultural regions (Hurd 1930, 1929, Latta
1938, Iowa State College Staff Members 1946, Bogue 1963, Hurt 1994, USDA 2000a, 2000b, CTIC 1998, Piper et al.
1924, Hardies and Hume 1927, Holmes 1902, 1929, Spillman 1902, 1905, 1907, 1908, Chilcott 1910, Smith 1911, Kezer
ca 1917, Hargreaves 1993, ERS 2002, Warren 1911, Langston et al. 1922, Russell et al. 1922, Elliott and Tapp 1928,
Elliott 1933, Ellsworth 1929, Garey 1929, Holmes 1929, Hodges et al. 1930, Bonnen and Elliott 1931, Brenner et al. 2002,
2001, Smith et al. 2002). As with N fertilizer and manure additions, data were not complete, so regional averages were
used to fill spatial gaps in the datasets and interpolation/extrapolation was used to fill temporal gaps. The amount of
agricultural residue burning was based on state inventory data (ILENR 1993, Oregon Department of Energy 1995, Noller
1996, Wisconsin Department of Natural Resources 1993, and Cibrowski 1996).
Native Vegetation by County. Pre-agricultural land cover for each county was designated according to the
potential native vegetation used in the Vegetation-Ecosystem Modeling and Analysis Project (VEMAP 1995), which was
based on the Kuchler (1964) Potential Vegetation Map for the conterminous United States.
Daily Weather Data by County. Daily maximum/minimum temperature and precipitation data were obtained
from the DAYMET model, which generates daily surface precipitation, temperature, and other meteorological data at 1
km2 resolution driven by weather station observations and an elevation model (Thornton et al. 2000 and 1997, Thornton
and Running 1999, DAYMET no date). It is necessary to use computer-generated weather data because weather station
data do not exist in each county, and moreover weather station data are for a point in space, while the DAYMET uses this
information with interpolation algorithms to derive weather patterns for areas between these stations. DAYMET weather
data are available for the United States at 1 km2 resolution for 1980 through 2003. For each county, DAYMET weather
data were selected from the 1 km2 cell that occurred in agricultural lands according the National Land Cover Dataset
(Vogelman et al. 2001). The grid cells formed the basis for county-scale PDFs based on the frequency of cells with same
weather patterns. Separate PDFs were developed for cropland, pasture/hay land, and rangeland. A weather record was
then randomly selected from the PDFs in each iteration of the Monte Carlo analysis to represent variation in precipitation
and temperature at the county scale. Weather data were randomly selected from the previous 23 years to represent 2004
through 2009, accounting for uncertainty in the weather during the years that have no data. The time series will be
updated when new weather data are available.
Soil Properties by County. Soil texture data required by DAYCENT were obtained from STATSGO (Soil Survey
Staff 2005). Observed data for soil hydraulic properties needed for model inputs were not available, so they were
calculated from STATSGO (Soil Survey Staff 2005) texture class and Saxton et al.'s (1986) hydraulic properties
calculator. Similar to the weather data, soil types within the STATSGO map that occurred in agricultural lands according
to the National Land Cover Dataset (Vogelman et al. 2001) were used to form a county-scale PDF. Specifically, the PDFs
were an area-weighted proportion for the extent of overlap between STATSGO map units and agricultural land. Separate
PDFs were developed for cropland, pasture/hay land, and rangeland. Individual soil types were randomly selected from
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the PDFs during each iteration of the Monte Carlo analysis to represent variation in soil texture and depth at the county
scale.
Crop Areas by County. County-level total crop area data were downloaded from the NASS web site for the
years 1990 through 2009 (USDA 2010a, 201 Ob), and these data formed the basis to scale emissions from individual crop
types to an entire county.
Non-Major Crop Types: Tier 1 Method
The activity data required for calculating emissions from non-major crop types include: (1) the amount of
mineral N in synthetic fertilizers that are applied annually, (2) managed manure N, (3) the amount of N in other
commercial organic fertilizers and (4) the amount of N in the above- and below-ground residue retained on and in soils of
all non-major crops.
Application of Synthetic Commercial Fertilizers: A process-of-elimination approach was used to estimate
synthetic N fertilizer additions to non-major crop types. The total amount of fertilizer used on farms has been estimated
by the USGS from 1990-2001 on a county scale from fertilizer sales data (Ruddy et al. 2006). For 2002-2009, county-
level fertilizer used on farms was adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995
through 2009). In addition, fertilizer application data are available for major crops and grasslands (discussed in Step 1
sections for Major Crops and Grasslands). Thus, the amount of N applied to non-major crops was assumed to be the
remainder of the fertilizer used on farms after subtracting the amount applied to major crops and grasslands. The
differences were aggregated to the state level and PDFs were derived based on uncertainties in the amount of N applied to
major crops and grasslands. Total fertilizer application is found in Table A- 200.
Manure and Other Commercial Organic Fertilizers:58 Manure N applied to non-major crops was estimated
using the activity data described for major crops (Table A- 198). Estimates of total national annual N additions from other
commercial organic fertilizers were derived from organic fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995
through 2009). AAPFCO fertilizer data were not yet available for 2009, so 2008 values were used as a placeholder until
data become available. Commercial organic fertilizers include dried blood, tankage, compost, and other; dried manure and
sewage sludge that are used as commercial fertilizer were subtracted from totals to avoid double counting. The dried
manure N is counted with the non-commercial manure applications, and sewage sludge is assumed to be applied only to
grasslands. The organic fertilizer data, which are recorded in mass units of fertilizer, had to be converted to mass units of
N by multiplying the consumption values by the average organic fertilizer N contents provided in the annual fertilizer
publications. These N contents are weighted average values, and vary from year to year (ranging from 2.3 percent to 3.9
percent over the period 1990 through 2008). The fertilizer consumption data are recorded in "fertilizer year" totals, (i.e.,
July to June), but were converted to calendar year totals. This was done by assuming that approximately 35 percent of
fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b). July to December
values were not available for calendar year 2008 so a "least squares line" statistical extrapolation using the previous 14
years of data was used to arrive at an approximate value. PDFs were derived for the organic fertilizer applications
assuming a default ±50 percent uncertainty. Annual consumption of other organic fertilizers is presented in Table A- 201.
Retention of Crop Residue: Annual crop yield (metric tons per hectare) and area harvested (hectare) statistics for
non-major N-fixing crops, including bean and pulse crops, were taken from U.S. Department of Agriculture crop
production reports (USDA 1994, 1998, 2003, 2005, 2006, 2008, 2009, 2010a,b). Crop yield per hectare and area planted
were multiplied to determine total crop yield for each crop, which was then converted to tons of dry matter product using
the residue dry matter fractions shown in
Table A- 202. Dry matter yield was then converted to tons of above- and below-ground biomass N. Above-
ground biomass was calculated by using linear equations to estimate above-ground biomass given dry matter crop yields,
and below-ground biomass was calculated by multiplying above-ground biomass by the below-to-above-ground biomass
ratio. N inputs were estimated by multiplying above- and below-ground biomass by respective N concentrations. All
ratios and equations used to calculate residue N inputs (shown in Table A- 203) are from IPCC (2006) and Williams
(2006). PDFs were derived assuming a ±50 percent uncertainty in the yield estimates (NASS does not provide
uncertainty), along with uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of
Other commercial organic fertilizers include, dried blood, dried manure, tankage, compost, sewage, and other minor organic fertilizer
types, but manure and sewage sludge have been excluded in order to avoid double-counting and ensure consistency across the Inventory
as these inputs are calculated using alternative data sources and methods.
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below-ground to above-ground biomass, and residue N fractions. The resulting annual biomass N inputs are presented in
Table A-203.
Step Ib: Activity Data for Direct Emissions from Drainage and Cultivation of Organic Cropland Soils
Tier 1 Method
Estimates and associated uncertainty for the area of drained and cultivated organic cropland soils in 1992 and
1997 were obtained from the U.S. Department of Agriculture 1997 National Resources Inventory (USDA 2000a, as
extracted by Eve 2001, and revised by Ogle 2002).59 These areas were grouped by broad climatic region60 using
temperature and precipitation estimates from Daly et al. (1994, 1998) and then further aggregated to derive total land in
temperate and sub-tropical regions. Areas for 1992 were assumed to represent 1990 through 1992 and areas for 1997 were
assumed to represent 1993 through 2009 (Table A- 204).
Step Ic: Activity Data for Direct Emissions from Grassland Management
N2O emissions from non-federal grasslands were estimated using DAYCENT. DAYCENT simulations
addressed the influence of legume seeding, managed manure N amendments, unmanaged manure N excreted by livestock
and deposited directly onto pasture, range, and paddock (PRP) soils, and synthetic fertilizer applications. N2O emissions
from PRP manure N deposition on federal grasslands and sewage sludge amendments to agricultural soils were addressed
using the Tier 1 method.
Tier 3 DAYCENT Simulations
Activity data for DAYCENT simulations of grasslands (i.e., climate, soils, and N inputs) were based on the same
sources as those used for major crop types described in Step la. In addition to the data sources used for major crops,
county-level area data on non-federal pasture and rangeland (i.e., mostly privately-owned) were needed for U.S.
grasslands. This information was based on U.S. Land Representation Analysis for Land Use, Land Use Change and
Forestry sector (See Section 7.1), and included data compiled from the U.S. Department of Agriculture National
Resources Inventory (USDA 2000a, Nusser and Goebel 1997, http://www.ncgc.nrcs.usda.gov/products/nri/index.htm), the
USDA Forest Service (USFS) Forest Inventory and Analysis Database (FIA, http://fia.fs.fed .us/tools-data/data) and the
U.S. Geological Survey (USGS) National Land Cover Dataset (NLCD, Vogelman et al. 2001, http://www.mrlc.gov).
Grassland on non-federal lands is classified using the NRI and grassland on federal lands is classified using the NLCD.
Grassland area data from the NRI and NLCD were adjusted to achieve consistency with FIA estimates of Forest Land.
Another key source of N for grasslands is PRP manure N deposition. Activity data for PRP manure N excretion from dairy
cattle, beef cattle, swine, sheep, goats, poultry, and horses were derived from multiple sources as described in the Manure
Management section (Section 6.2) and Annex 3.10. The amount of PRP manure N deposited on non-federal grasslands
(non-federal grasslands are mostly under private ownership) was based on the relative proportion of federal and non-
federal grasslands in each county based on the U.S. Land Representation Analysis (See Section 7.1) . For example, if 75
percent of the grasslands in a county were non-federal then 75 percent of PRP manure N was assumed to be deposited on
non-federal grasslands.
Nitrogen fixation by legumes, and N residue inputs from senesced grass litter were included as sources of N to
the soil, and were estimated in the DAYCENT simulations as a function of vegetation type, weather, and soil properties.
Similar to the methodology for major crops, "other N inputs" were simulated within the DAYCENT model framework in
order to estimate all greenhouse gas emissions from managed lands (IPCC 2006), including mineralization from
decomposition of soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Decomposition
rates are a function of litter quality and quantity, soil texture, water content and temperature, and other factors. Total
annual amounts of PRP manure N, mineral N fertilizer application, manure N amendments, forage legume N and "other N
inputs" can be found in Table A- 205.
59 These areas do not include Alaska, but Alaska's cropland area accounts for less than 0.1 percent of total U.S. cropland area, so this
omission is not significant.
60 The climatic regions were: (1) cold temperate, dry, (2) cold temperate, moist, (3) sub-tropical, dry, (4) sub-tropical, moist, (5) warm
temperate, dry, and (6) warm temperate, moist.
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Tier 1 Method: Additional Direct Soil N2O Emissions
The Tier 1 method was used to estimate emissions from PRP manure that were not simulated with DAYCENT,
in addition to emissions due to sewage sludge amendments to agricultural soils.
PRP Manure: PRP manure N data were derived using methods described in the Manure Management section
(Section 6.2) and Annex 3.10. The amount of PRP manure N deposited on federal grasslands was based on the relative
proportion of federal to non-federal grassland area in each county. As discussed in the Tier 3 DAYCENT Simulations
section, the area data were based on the U.S. Department of Agriculture's National Resources Inventory (USDA 2000a)
and the National Land Cover Dataset (Vogelman et al. 2001), respectively, and were reconciled with the Forest Inventory
and Analysis dataset in order to produce the U.S. Land Representation (See Section 7.1). Soil N2O emissions from the
proportion of PRP manure N deposited on federal grasslands were estimated with the Tier 1 method.
Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or private
wastewater treatment works and is typically used as a soil amendment or is sent to waste disposal facilities such as
landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to grasslands.
Estimates of the amounts of sewage sludge N applied to agricultural lands 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. Sewage
sludge generation data are not available after 2004 (Bastian 2007), so the 1990 through 2004 data were linearly
extrapolated for the most recent years. The total sludge generation estimates were then converted to units of N by
applying an average N content of 3.9 percent (McFarland 2001), and disaggregated into use and disposal practices using
historical data in EPA (1993) and NEBRA (2007). The use and disposal practices were agricultural land application, other
land application, surface disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The
resulting estimates of sewage sludge N applied to agricultural land were used here; the estimates of sewage sludge N
applied to other land and surface-disposed were used in estimating N2O fluxes from soils in Settlements Remaining
Settlements (see section 7.5 of the Land Use, Land-Use Change, and Forestry chapter). Sewage sludge disposal data are
provided in Table A- 206.
Step Id: Activity Data for Indirect N2O Emissions from Managed Soils of all Land-Use Types and Managed
Manure Systems
Volatilization of N that was applied or deposited as synthetic fertilizer, livestock manure, sewage sludge, and
other organic amendments leads to emissions of NH3 and NOX to the atmosphere. In turn, this N is returned to soils
through atmospheric deposition, thereby increasing mineral N availability and enhancing N2O production. Additional N is
lost from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N
losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N2O. However, N
leaching was assumed to be an insignificant source of indirect N2O in cropland and grassland systems where the amount of
precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. These areas are typically semi-
arid to arid, and nitrate leaching to groundwater is a relatively uncommon event; moreover IPCC (2006) recommends
limiting the amount of nitrate leaching assumed to be a source of indirect N2O emissions based on precipitation, irrigation
and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, sewage
sludge N, and other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral
soils, and may be found in Table A- 197 through Table A- 201, Table A- 203, and Table A- 206. The activity data for
computing direct and indirect N2O emissions from settlements and forest lands are described in the Land Use, Land-Use
Change, and Forestry chapter.
Using the DAYCENT model, volatilization and leaching/surface run-off of N from soils was computed internally
for major crop types and non-federal grasslands. DAYCENT simulates the processes leading to these losses of N based on
environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation,
harvest), and soil N availability. Note that the DAYCENT method accounts for losses of N from all anthropogenic
activity, not just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1
methodology. Similarly, the N available for producing indirect emissions resulting from grassland management as well as
deposited PRP manure was also estimated by DAYCENT. Estimated leaching losses of N from DAYCENT were not used
in the indirect N2O calculation if the amount of precipitation plus irrigation did not exceed 80 percent of the potential
evapotranspiration. Volatilized losses of N were summed for each day in the annual cycle to provide an estimate of the
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amount of N subject to indirect N2O emissions. In addition, the daily losses of N through leaching and runoff in overland
flow were summed for the annual cycle. Uncertainty in the estimates was derived from uncertainties in the activity data
for the N inputs (i.e., fertilizer and organic amendments; see Step la for further information)
The Tier 1 method was used to estimate N losses from mineral soils due to volatilization and leaching/runoff for
non-major crop types, forestland, settlements, sewage sludge applications, and PRP manure on federal grasslands not
accounted for by DAYCENT simulations. To estimate volatilized losses, synthetic fertilizers, manure, sewage sludge, and
other organic N inputs were multiplied by the fraction subject to gaseous losses using the respective default values of 0.1
kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N
ranged from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic
amendments (IPCC 2006). Leaching/runoff losses of N were estimated by summing the N additions from synthetic and
other organic fertilizers, manure, sewage sludge, and above- and below-ground crop residues, and then multiplying by the
default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty from 0.1-0.8 kg NO3-N/kg
N (IPCC 2006). However, N leaching was assumed to be an insignificant source of indirect N2O emissions if the amount
of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. PDFs were derived for each
of the N inputs in the same manner as direct N2O emissions, discussed in Steps la and Ic.
Volatilized N was summed for losses from major crop types, minor crop types, grasslands, settlements, and
forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching and surface runoff were summed
to obtain the total losses for this pathway.
Step 2: Estimate Direct N2O Emissions from Cropland Soils
In this step, N2O emissions were calculated for major crop types and non-major crop types on mineral soils, in
addition to emissions associated with drainage and cultivation of organic soils.
Step 2a: Direct N2O Emissions from Cropland Mineral Soils
Two methods were used to estimate direct N2O emissions from N additions and crop production on mineral soils.
The DAYCENT ecosystem model was used to estimate emissions from major crop types, while the Tier 1 methodology
was used to estimate emissions from crops considered non-major types, which are grown on a considerably smaller
portion of land than the major types.
Major Crops: Tier 3 DAYCENT Simulations
Simulations were performed over three major time periods for each county in the United States using the
DAYCENT model. The first time period was used for simulation of native vegetation up to date of cultivation in the
county (1 A.D. to plow out). Plow out was assumed to occur between 1600 and 1850, depending on the state in which the
county lies. Simulation of at least 1600 years of native vegetation was needed to initialize soil organic matter (SOM)
pools in the model. The second time period of the simulation started at plowout and represents historical agricultural
practices up to the modern period (plow out to 1970). Simulation of the historical cropping period was needed to establish
modern day SOM levels, which is important because N2O emissions are sensitive to the amount of SOM. Lastly,
simulations were performed for the modern agricultural period (1971 through 2009).
Corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton are defined as major crops and were
simulated in every county where they were grown. These crops represent approximately 90 percent of total principal
cropland in the United States as defined by the U.S. Department of Agriculture (USDA 2003). Overall, the DAYCENT
simulations included approximately 86 percent of total cropland area. For rotations that include a cycle that repeats every
two or more years (e.g., corn/soybeans, wheat/corn/fallow), different simulations were performed where each phase of the
rotation was simulated every year. For example, 3 rotations were simulated in regions where wheat/corn/fallow cropping
is a dominant rotation—one with wheat grown the first year, a second with corn the first year and a third with fallow the
first year. This ensured that each crop was represented during each year in one of the three simulations. In cases where
the same crop was grown in the same year in two or more distinct rotations for a region, N2O emissions were averaged
across the different rotations to obtain a value for that crop. Emissions from cultivated fallow land were also included.
Fallow area was assumed to be equal to winter wheat area in regions where winter wheat/fallow rotations are the dominant
land management for winter wheat.
The simulations reported here assumed conventional tillage cultivation, gradual improvement of cultivars, and
gradual increases in fertilizer application until 1989. Note that there is a planned improvement to incorporate use of
conservation tillage in the United States into this Inventory. The productivity of cultivars (cultivated varieties) has
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steadily improved over the last century and therefore it is unrealistic to assume that modern varieties of crops, such as
corn, are identical to the popular varieties grown in 1900 in terms of yield potential, N demand, etc. Realistic simulations
of historical land management and vegetation type are important because they influence present day soil C and N levels,
which influence present-day N cycling and associated N2O emissions.
Uncertainty estimation was an integral part of this analysis; uncertainty in the input data for the county-scale
simulations and structural uncertainty associated with the DAYCENT model predictions were both addressed (Del Grosso
et al. 2010). In the first step, a Monte Carlo Analysis was used to propagate input data uncertainty through the modeling
process. Thus, input data were randomly selected from PDFs for weather records, soil type, mineral N fertilization rate,
and organic amendments. See Step la for additional discussion about the PDFs. After selecting a set of inputs for a
county, DAYCENT was used to simulate each crop and then the process was repeated until 100 iterations were completed.
Due to the computationally intensive requirements for DAYCENT, it was not possible to simulate every county with the
Monte Carlo Analysis. Two counties were selected from each of the 63 agricultural regions, and additional counties were
added based on the variance in N2O emissions across regions from the past year's Inventory, using a Neyman allocation
(Cochran 1977). A Neyman allocation is based on the variance in N2O emissions across the 63 regions; regions with
larger variances were allocated a larger number of counties for the Monte Carlo Analysis. A total of 300 counties were
included in the Monte Carlo Analysis, which is approximately 10 percent of all counties with agricultural land. In
addition, all counties were simulated once based on the dominant conditions from the PDFs (i.e., most common soil type,
weather condition, manure amendment, and mineral fertilizer rate).
In the second step of the uncertainty analysis, a structural uncertainty estimator was developed to account for
uncertainty inherent in model formulation and parameterization using an empirically-based procedure described by Ogle et
al. (2007). The procedure is based on developing a statistical relationship between modeled results and field
measurements. Specifically, DAYCENT was used to simulate 11 agricultural experiments with 108 treatments, and the
results were analyzed using a linear-mixed effect model in which measurements were statistically modeled as a function of
simulated emissions. Simulated DAYCENT emissions were a highly significant predictor of the measurements, with a p-
value of O.01. Several other variables were tested in the statistical model to evaluate if DAYCENT exhibited bias under
certain conditions related to climate, soil types, and management practices. The type of crop or grassland was significant
at an alpha level of 0.05, demonstrating that DAYCENT tended to over-estimate emissions for small grains systems and
grassland, but was accurate in predicting the N2O emissions for other crops. Random effects were included in the model
to capture the dependence in time series and data collected from the same site, which were needed to estimate appropriate
standard deviations for parameter coefficients.
The structural uncertainty estimator accounted for bias and prediction error in the DAYCENT model results, as
well as random error associated with fine-scale emission predictions in counties over a time series from 1990 to 2009. To
apply the uncertainty estimator, DAYCENT N2O emission estimates were used as an input to the linear mixed effect
model after randomly selecting statistical parameter coefficients from their joint probability distribution, in addition to
random draws from PDFs representing the uncertainty due to site, site by year random effects and the residual error from
the linear-mixed effect model (Del Grosso et al. 2010).
In DAYCENT, once N enters the plant/soil system, the model cannot distinguish among the original sources of
the N to determine which management activity led to specific N2O emissions. This means, for example, that N2O
emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop
residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each crop in a county,
the N inputs in a simulation were determined for anthropogenic practices discussed in IPCC (2006), including synthetic
mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage
of N input for anthropogenic practices was divided by the total N input, and this proportion was used to determine the
amount of N2O emissions assigned to each of the practices.61 For example, if 70 percent of the mineral N made available
in the soil was due to mineral fertilization, then 70 percent of the N2O emissions were assigned to this practice. The
remainder of soil N2O emissions is reported under "other N inputs," which includes mineralization due to decomposition
of soil organic matter and litter, as well as asymbiotic fixation of mineral N in soils from the atmosphere. Asymbiotic N
fixation by soil bacteria is a minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems.
Mineralization of soil organic matter is a more significant source of N, but is still typically less than half of the amount of
N made available in the soil compared to fertilization, manure amendments, and symbiotic fixation. Accounting for the
61 This method is a simplification of reality to allow partitioning of N2O emissions, as it assumes that all N inputs have an identical
chance of being converted to N2O. This is unlikely to be the case, but DAYCENT does not track N2O emissions by source of mineral N
so this approximation is the only approach that can be used for partitioning N2O emissions by source of N input. Moreover, this
approach is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for mostN sources (e.g., PRP).
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influence of "other N inputs" was necessary in order to meet the recommendation of reporting all emissions from managed
lands (IPCC 2006). While this method allows for attribution of N2O emissions to the individual N inputs to the soils, it is
important to realize that sources such as synthetic fertilization may have a larger impact on N2O emissions than would be
suggested by the associated level of N input for this source (Delgado et al. 2009). Further research will be needed to
improve upon this attribution method, however.
The final N2O emission estimate was determined by summing the estimates from the single simulation conducted
in each county for the dominant condition to the 63 regions. Estimates were then adjusted to account for the difference
between the emissions estimated in the Monte Carlo analysis and the dominant condition simulation on a region-by-region
basis (i.e., if the Monte Carlo mean was slightly higher than the dominant condition among the counties included in the
Monte Carlo analysis, the total emission estimate for the region would be raised by the difference) (Del Grosso et al.
2010). In turn, regional values were summed to produce the national total. The uncertainty was based on the variance in
simulated N2O emissions for the iterations in the Monte Carlo Analysis and the variance associated with difference
between the means from the Monte Carlo Analysis and the simulated N2O emissions for the dominant condition, expressed
as a 95 percent confidence interval (Del Grosso et al. 2010).
Non-Major Crops: Tier 1 Method
To estimate direct N2O emissions from N additions to non-major crops, the amount of N in applied synthetic
fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) was added to N
inputs from crop residues, and the resulting annual totals were multiplied by the IPCC default emission factor of 0.01 kg
N2O-N/kg N (IPCC 2006). The uncertainty was determined based on simple error propagation methods (IPCC 2006).
The uncertainty in the default emission factor ranged from 0.3-3.0 kg N2O-N/kg N (IPCC 2006). Uncertainty in activity
data is ± 20 percent for fertilizer additions (Mosier 2004).62 Uncertainties in the emission factor and fertilizer additions
were combined with uncertainty in the equations used to calculate residue N additions from above- and below-ground
biomass dry matter and N concentration to derive overall uncertainty.
Step 2b: Direct N2O Emissions Due to Drainage and Cultivation of Organic Cropland Soils
To estimate annual N2O emissions from drainage and cultivation of organic soils, the area of cultivated organic
soils in temperate regions was multiplied by the IPCC (2006) default emission factor for temperate soils and the
corresponding area in sub-tropical regions was multiplied by the average (12 kg N2O-N/ha cultivated) of IPCC (2006)
default emission factors for temperate (8 kg N2O-N/ha cultivated) and tropical (16 kg N2O-N/ha cultivated) organic soils.
The uncertainty was determined based on simple error propagation methods (IPCC 2006), including uncertainty in the
default emission factor ranging from 2-24 kg N2O-N/ha (IPCC 2006).
Step 2c: Estimate Total Direct N2O Emissions from Cropland Soils
In this step, total direct N2O emissions from cropland soils are calculated by summing direct emissions on
mineral soils with emissions resulting from the drainage and cultivation of organic soils (i.e., histosols) (Table A- 207).
Uncertainties were combined using the simple error propagation method (IPCC 2006).
Step 3: Estimate Direct N2O Emissions from Grasslands
DAYCENT was used to estimate direct N2O emissions from soils in non-federal grasslands (pastures and
rangeland), and the Tier 1 method was used for federal grasslands. Managed pastures were simulated with DAYCENT by
assuming that the vegetation mix includes forage legumes and grasses, and that grazing intensity was moderate to heavy.
Rangelands were simulated without forage legumes and grazing intensity was assumed to be light to moderate. The
methodology used to conduct the DAYCENT simulations of grasslands was similar to that for major crop types described
above in Step 2a, including the analysis addressing uncertainty in the model inputs and model structure. Carbon and
nitrogen additions to grasslands from grazing animals were obtained from county level animal excretion data and area data
for federal and non-federal grasslands, as described in Steplc.
Note that due to lack of data, uncertainties in managed manure N production, PRP manure N production, other commercial organic
fertilizer amendments, indirect losses of N in the DAYCENT simulations, and sewage sludge amendments to soils are currently treated
as certain; these sources of uncertainty will be included in future Inventories.
A-245
-------
The Tier 1 method was used to estimate emissions from N excreted by livestock on federal lands (i.e., PRP
manure N). The Tier 1 method was also used to estimate emissions from sewage sludge application to grasslands. These
two sources of N inputs to soils were multiplied by the IPCC (2006) default emission factors (0.01 kg N2O-N/kg N from
sludge and horse, sheep, and goat manure, and 0.02 kg N2O-N/kg N from cattle, swine, and poultry manure) to estimate
N2O emissions. This emission estimate was summed with the DAYCENT simulated emissions to provide the national
total for direct N2O losses from grasslands (Table A- 208). The uncertainty was determined based on the Tier 1 error
propagation methods provided by the IPCC (2006) with uncertainty in the default emission factor ranging from 0.007 to
0.06 kg N2O-N/kg N (IPCC 2006).
Step 4: Estimate Indirect N2O Emissions for All Land-Use Types
In this step, N2O emissions were calculated for the two indirect emission pathways (N2O emissions due to
volatilization, and N2O emissions due to leaching and runoff of N), which were then summed to yield total indirect N2O
emissions from croplands, grasslands, forest lands, and settlements.
Step 4a: Indirect Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP
manure, were calculated according to the amount of mineral N that was transported in gaseous forms from the soil profile
and later emitted as soil N2O following atmospheric deposition. See Step Id for additional information about the methods
used to compute N losses due to volatilization. The estimated N volatilized for all land-use and livestock activities was
multiplied by the IPCC default emission factor of 0.01 kg N2O-N/kg N (IPCC 2006) to compute total N2O emissions from
volatilization. The resulting estimates are provided in Table A- 209. The uncertainty was determined using simple error
propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the
default emission factor ranging from 0.002-0.05 kg N2O-N/kg N (IPCC 2006).
Step 4b: Indirect Emissions Due to Leaching and Runoff
The amount of mineral N (i.e., synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
mineralization, asymbiotic fixation) that was transported from the soil profile in aqueous form was used to calculate
indirect emissions from (1) leaching of mineral N from soils and (2) losses in runoff of water associated with overland
flow. See Step Id for additional information about the methods used to compute N losses from soils due to leaching and
runoff in overland water flows.
The total amount of N transported from soil profiles through leaching and surface runoff was multiplied by the
IPCC default emission factor of 0.0075 kg N2O-N/kg N (IPCC 2006) to estimate emissions for this source. The resulting
emission estimates are provided in Table A- 210. The uncertainty was determined based on simple error propagation
methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg N2O-N/kg N
(IPCC 2006).
Step 5: Estimate Total N2O Emissions for U.S. Soils
Total emissions were estimated by adding total direct emissions (from major crop types and non-major crop
types on mineral cropland soils, drainage and cultivation of organic soils, and grassland management) to indirect
emissions for all land use and management activities. U.S. national estimates for this source category are provided in
Table A- 210. Uncertainties in the final estimate were combined using simple error propagation methods (IPCC 2006),
and expressed as a 95 percent confidence interval.
Direct and indirect emissions of soil N2O vary regionally in both croplands and grasslands as a function of N
inputs, weather, and soil type. A little more than half of the total N2O emissions from major crops occur in Iowa, Illinois,
Nebraska, Minnesota, Texas, Kansas and Indiana where N inputs associated with corn rotations are high or where large
land areas are cropped (Table A- 211). On a per area unit basis, direct N2O emissions are also high in many of the
Mississippi River Basin states where there are also high N input to corn and soybean crops (Figure A- 9). Emissions are
also high in some western and New England states. Only a small portion of the land in these regions is used for crop
production, but management and conditions lead to higher emissions on a per unit area basis than other regions. For
example, emissions are high in California, Arizona, and other western states due to intensive irrigation management
systems. For some New England states, emissions are high on a per unit area because subsurface soil layers remain frozen
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
when surface soil layers thaw in spring. This creates saturated conditions near the surface that facilitate denitrification and
N2O emissions. Indirect emissions tend to be high on an area basis in the central and eastern United States because
relatively high rainfall facilitates N losses from leaching and runoff and in some western states where irrigation can
contribute to leaching and runoff (Figure A- 10).
Direct and indirect emissions from grasslands are typically lower than those from croplands (Table A- 211,
Figure A- 11, and Figure A- 12) because N inputs tend to be lower, particularly from synthetic fertilizer. Texas was by far
the highest emitter for this category, followed by Nebraska, Montana, Oklahoma, New Mexico Colorado and South
Dakota. On a per area unit basis, emissions are lower in the western United States because grasslands in the East and
Central regoins are more intensively managed (legume seeding, fertilization) while western rangelands receive few, if any,
N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically irrigated so
minimal leaching and runoff of N occurs in these grasslands.
Figure A- 7: DAYGENT Model Flow Diagram
Figure A- 8: Comparisons of Results from DAYGENT Model and IPGG Tier 1 Method with Measurements of Soil H?0 Emissions
Figure A- 9: Major Crops, Average Annual Direct H?0 Emissions, Estimated Using the DAYGENT Model, 1990-2009 (Metric
Tons G02 Eq./ha/year)
Figure A-10: Major Crops, Average Annual N Losses Leading to Indirect H?0 Emissions, Estimated Using the DAYCENT Model,
1990-2009 (kg N/ha/year)
Figure A-11: Grasslands, Average Annual Direct H?0 Emissions, Estimated Using the DAYCENT Model, 1990-2009 (Metric
Tons G02 Eq./ha/year]
Figure A-12: Grasslands, Average Annual N Losses Leading to Indirect H?0 Emissions, Estimated Using the DAYGENT Model,
1990-2009 (kg N/ha/year)
A-247
-------
Table A-197: Synthetic Fertilizer N Added to Major Crops [Gg HI
1990 1995 1996 1997 1998 1999 2000 2001
Fertilizer N 7,468 7,307 7,915 7,705 7,641 7,412 7,575 7,450
2002 2003
2004
7,547 7,370 7,355
2005
7,000
2006
6,855
2007
7,428
2008
6,663
2009
6194
Table A- 198: Fate of Livestock Manure Nitrogen (Gg N)
Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003
Managed Manure N Applied
to Major Crops and
Grasslands3"" 993> 968 1,038 1,063 1,116 1,103 1,099 1
Managed Manure N Applied
toNon-MajorCropsb 1,473 1,618 1,568 1,563 1,519 1,561 1,587 1
Managed Manure N Applied
to Grasslands 446 • 515 514 503 542 478 463
Pasture, Range, & Paddock
Manure N 4,083 4,458 4,412 4,296 4,237 4,158 4,056 4
Total 6,994 7,559 7,532 7,426 7,414 7,300 7,204 7
a Accounts for N volatilized and leached/runoff during treatment, storage and transport before soil application.
b Includes managed manure and daily spread manure amendments
c Totals may not sum exactly due to rounding.
Table A- 199: Crop Residue N and 9ther N Inputs to Major Crops as Simulated by DAYGENT (Gg N)
Activity 1990 1995 1996 1997 1998 1999 2000 2001
Residue Na 2,982 3,305 3,322 3,329 3,099 3,659 3,450 3,151
Mineralization &
Asymbiotic Fixation 12,406 12,010 12,590 12,811 14,166 12,926 13,548 13,520
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Table A- 200: Synthetic Fertilizer N Added to Non-Major Crops IGg N)
1990 1995 1996 1997 1998 1999 2000 2001
FertilizerN 1,076 1,884 1,326 1,638 1,669 1,841 1,546 1,571
,086 1,164 1,079
,583 1,585 1,549
466 470 470
,043 4,049 4,072
,177 7,268 7,171
2002 2003
3,253 3,407
2004
1,077
1,598
472
4,077
7,223
2004
3,231
13,238 13,216 13,361
2002 2003
1,540 1,838
2004
1,905
2005
1,036
1,687
475
4,157
7,355
2005
3,041
12,740
2005
2,155
2006
847
1,878
479
4,209
7,413
2006
3,186
12,800
2006
2,039
2007
1,195
1,655
475
4,201
7,527
2007
3,164
2008
1,110
1,649
474
4,170
7,403
2008
3,210
13,084 12,943
2007
1,692
2008
2,229
2009
948
1,722
462
4,123
7,255
2009
2,996
12,345
2009
2,653
Table A- 201: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N)
1990 1995 1996 1997 1998 1999 2000 2001 2002
Other Commercial Organic Fertilizer Na 4 10 13 14 12 11
978
2003 2004 2005 2006 2007
8
9
10
12 15
2008
13
2009
13
a Includes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double counting.
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A- 202: Key Assumptions for Production of Non-Major Crops and Retention of Crop Residues
Dry Matter
Fraction of
Harvested
Crop Product
Peanuts for Nuts
Dry Edible Beans
Dry Edible Peas
Austrian Winter
Peas
Lentils
Wrinkled Seed Peas
Barley
Oats
Rye
Millet
Rice
0.94
0.90
0.91
0.91
0.91
0.91
0.89
0.89
0.88
0.90
0.89
Above-ground Residue
Slope Intercept
1.07
0.36
1.13
1.13
1.13
1.13
0.98
0.91
1.09
1.43
0.95
1.54
0.68
0.85
0.85
0.85
0.85
0.59
0.89
0.88
0.14
2.46
Ratio of Residue N Fraction
Below-ground
Residue to
Above-ground
Biomass Above-ground Below-ground
0.20
0.19
0.19
0.19
0.19
0.19
0.22
0.25
0.22
0.22
0.16
0.016
0.010
0.008
0.008
0.008
0.008
0.007
0.007
0.005
0.007
0.007
0.014
0.010
0.008
0.008
0.008
0.008
0.014
0.008
0.011
0.009
0.009
Table A- 203: Nitrogen in Crop Residues Retained on Soils Producing Non-Major Crops (Gg N)
Crop
Peanuts for Nuts
Dry Edible Beans
Dry Edible Peas
Austrian Winter Peas
Lentils
Wrinkled Seed Peas
Barley
Oats
Rye
Millet
Rice
Total
1990
64
1
9
112
55
9
7
80
378
1995
63
16
11
8
9
9
96
29
9
7
87
343
1996
65
15
9
8
9
8
105
28
9
7
86
349
1997
64
16
11
8
9
8
96
30
8
7
91
348
1998
68
16
11
8
9
8
94
30
9
7
91
352
1999
66
16
10
8
9
8
74
27
9
7
99
336
2000
61
15
10
8
10
8
86
28
9
3
94
331
2001
71
13
10
8
10
8
68
24
8
7
103
330
2002
61
16
10
8
9
8
63
23
8
2
101
311
2003
69
14
11
8
9
8
76
27
9
5
97
333
2004
71
13
14
8
10
9
76
23
8
6
109
347
2005
77
15
15
8
11
8
59
23
8
5
106
336
2006
63
14
8
8
10
8
51
20
8
4
95
290
2007
65
15
8
8
10
8
59
20
8
6
96
304
2008
79
15
14
8
9
8
66
20
8
6
99
332
2009
65
14
17
8
11
9
63
20
8
4
105
324
A-249
-------
Table A- 204: Drained and Cultivated Organic Soil Area (Thousand Hectares)
Year Temperate Area Sub-Tropical Area
1990 444
1995 450
1996 450
1997 450
1998 450
1999 450
2000 450
2001 450
2002 450
2003 450
2004 450
2005 450
2006 450
2007 450
2008 450
2009 450
194
^H
196
196
196
196
196
196
196
196
196
196
196
196
196
196
196
Table A- 205: Synthetic Fertilizer N, PRP Manure N, Organic Manure N Amendment, Forage Legume N, and Other N Inputs Simulated with the DAYGENT Model (Gg N)
N Source 1990 1995
Fertilizer N 9,678 9,662
PRP Manure N 3,718 4,065
Managed Manure 1,438 1,483
Residue Na 11,164 11,560
Mineralization &
Asymbiotic
Fixation 23,864 23,237
1996
10,245
4,021
1,552
11,054
23,649
1997
9,973
3,906
1,566
11,069
24,172
a Residue N inputs include unharvested fixed N from legumes as well as
1998
9,891
3,856
1,658
10,667
25,721
1999
9,628
3,787
1,581
12,264
23,703
2000
9,747
3,697
1,561
11,056
24,190
2001
9,620
3,688
1,552
11,078
24,412
2002
9,723
3,701
1,634
10,631
23,731
2003
9,553
3,728
1,549
11,234
23,942
2004 2005 2006 2007
9,536 9,213 9,088 9,656
3,731 3,806 3,856 3,850
1,549 1,511 1,326 1,670
10,855 10,731 10,840 10,875
24,175 23,635 23,549 23,830
2008
8,871
3,819
1,584
10,890
23,731
2009
8,385
3,781
1,410
10,577
23,285
crop residue N.
Table A- 200: Sewage Sludge Nitrogen by Disposal Practice (Gg N)
Disposal Practice 1990
Applied to Agricultural Soils 52 1
Other Land Application 25 1
Surface Disposal 20
Total 98
1995
69
28
16
113
1996
72
29
15
116
1997
75
29
14
118
1998
78
29
13
121
1999
81
30
12
122
2000
30
30
10
124
2001
86
30
9
125
2002
89
30
8
127
2003 2004 2005 2006 2007
91 94 98 101 104
30 30 31 31 32
65544
128 130 134 136 139
2008
106
32
3
141
2009
109
32
3
144
Note: Totals may not sum due to independent rounding.
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 207: Direct IhO Emissions from Cropland Soils tTg Clh Eq.l
Activity
Mineral Soils
Major Crops
Synthetic Fertilizer
Managed Manure
Residue Na
Mineralization and Asymbiotic
Fixation
Non-Major Crops
Synthetic Fertilizer
Managed Manure and Other
Organic Commercial Fertilizer
Residue N
Organic Soils
Total*
+ Less than 0.05 Tg CO2 Eq.
1990
100.1
85.8
10.6
44.6
14.3
1
1.8
2.9
102.9
1995
107.1
88.3
27.4
3.6
12.4
44.8
18.8
9.2
7.9
1.7
2.9
110.0
1996
115.5
99.6
31.7
4.2
13.5
50.2
15.9
6.5
7.7
1.7
2.9
118.4
1997
112.9
95.6
29.6
4.1
12.8
49.0
17.4
8.0
7.7
1.7
2.9
115.8
1998 1999 2000 2001 2002 2003
116.5 111.1 112.7 120.1 112.5 109.7
99.2 92.9 95.7 103.1 95.7 91.5
29.4 27.4 28.4 30.4 28.8 27.0
4.1 4.0 4.0 4.4 4.3 3.9
11.6 13.7 12.7 12.7 12.4 12.4
54.0 47.7 50.6 55.5 50.2 48.3
17.3 18.3 16.9 17.0 16.8 18.2
8.1 9.0 7.5 7.7 7.5 9.0
7.5 7.7 7.8 7.7 7.8 7.6
1.7 1.6 1.6 1.6 1.5 1.6
2.9 2.9 2.9 2.9 2.9 2.9
119.4 114.0 115.6 123.0 115.4 112.6
2004
116.2
97.4
28.7
4.1
12.6
52.1
18.8
9.3
7.8
1.7
2.9
119.1
2005
115.2
94.8
28.1
4.1
12.1
50.5
20.4
10.5
8.3
1.6
2.9
118.1
2006
112.7
92.1
26.7
3.3
12.4
49.7
20.6
9.9
9.2
1.4
2.9
115.6
2007
114.9
97.1
29.1
4.6
12.4
50.9
17.9
8.2
8.1
1.5
2.9
117.8
2008
115.0
94.4
26.5
4.4
12.6
50.9
20.6
10.9
8.1
1.6
2.9
117.9
2009
109.1
86.1
23.9
3.6
11.5
47.1
23.0
12.9
8.5
1.6
2.9
112.0
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
Table A- 208: Direct N?0 Emissions from Grasslands (Tg GO
DAYCENT
Synthetic Fertilizer
PRP Manure
Managed Manure
Residue Na
Mineralization and
Asymbiotic Fixation
Tier 1
PRP Manure
Sewage Sludge
Total
1990 1995
47.1 45.2
3.9 4.1
4.6 4.7
1.5 1.8
15.6 14.6
21.5 20.0
3.7 4.1
3.5 3.7
0.3 0.3
50.9 49.2
1996
49.4
4.5
5.2
2.0
15.5
22.3
4.1
3.7
0.4
53.5
Eq.l
1997
46.2
4.1
4.7
1.8
14.6
21.2
4.0
3.7
0.4
50.3
1998
47.3
4.2
4.6
1.8
14.5
22.2
4.0
3.6
0.4
51.3
1999 2000 2001 2002 2003
42.9 43.3 47.3 47.5 42.6
3.7 3.9 4.1 4.3 3.8
4.4 4.5 4.7 4.7 4.4
1.5 1.6 1.7 1.8 1.5
14.9 13.8 15.5 15.3 13.9
18.4 19.5 21.2 21.3 19.0
3.9 3.8 3.7 3.7 3.7
3.5 3.4 3.3 3.3 3.2
0.4 0.4 0.4 0.4 0.4
46.8 47.1 51.0 51.2 46.3
2004
44.5
4.0
4.6
1.6
14.2
20.1
3.7
3.3
0.5
48.2
2005
45.6
4.1
4.9
1.6
14.6
20.4
3.8
3.3
0.5
49.4
2006
44.3
4.0
4.8
1.6
14.2
19.7
3.8
3.3
0.5
48.1
2007
43.4
3.9
4.7
1.6
13.9
19.3
3.8
3.3
0.5
47.3
2008
44.9
4.0
4.9
1.6
14.4
20.0
3.8
3.3
0.5
48.7
2009
44.4
3.9
4.7
1.6
14.1
20.1
3.8
3.2
0.5
48.2
+ Less than 0.05 Tg CO2 Eq.
a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-251
-------
Table A- 209: Indirect M Emissions tTg Clh Eq.l
Activity
Volatilization and Atm. Deposition
Croplands
Settlements
Forest Land
Grasslands
Surface Leaching & Run-off
Croplands
Settlements
Forest Land
Grasslands
Total
+ Less than 0.05 Tg CO2 Eq.
1990 1995
16.9 18.2
11.6 12.8
0.1 0.2
5.1 5.2
27.1 30.3
25.8 29.1
0.2 0.3
+ +
1.0 0.8
44.0 48.5
1996
17.6
12.4
0.2
5.0
29.8
28.1
0.3
0.1
1.4
47.4
1997
17.9
12.6
0.2
5.1
25.8
24.4
0.3
0.1
0.9
43.7
Table A- 210: Total M Emissions from Agricultural Soil Management (Tg Clh
Activity
Total Direct
Direct Emissions from Mineral
Cropland Soils
Synthetic Fertilizer
Organic Amendment3
Residue Nb
Mineralization and Asymbiotic Fixation
Direct Emissions from Drained
Organic Cropland Soils
Direct Emissions from Grasslands*
Synthetic Mineral Fertilizer
PRP Manure*
Managed Manure
Sewage Sludge
Residueb
Mineralization and Asymbiotic Fixation
Total Indirect
Volatilization
Leaching/Runoff
Total Emissions
1990 1995
153.8 159.2
100.1 107.1
32.3 36.6
10.8 11.6
12.4 14.0
44.6 44.8
2.9 2.9
50.9 49.2
3.9 4.1
8.1 8.4
1.5 1.8
0.3 0.3
15.6 14.6
21.5 20.0
44.0 48.5
16.9 18.2
27.1 30.3
197.8 207.6
1996
171.9
115.5
38.2
11.9
15.2
50.2
2.9
53.5
4.5
8.9
2.0
0.4
15.5
22.3
47.4
17.6
29.8
219.3
1997
166.1
112.9
37.6
11.8
14.5
49.0
2.9
50.3
4.1
8.4
1.8
0.4
14.6
21.2
43.7
17.9
25.8
209.7
1998
17.8
12.4
0.2
5.2
29.1
27.5
0.3
0.1
1.3
46.9
Eq.)
1998
170.7
116.5
37.5
11.6
13.4
54.0
2.9
51.3
4.2
8.2
1.8
0.4
14.5
22.2
46.9
17.8
29.1
217.6
1999 2000
17.8 17.6
12.6 12.7
0.2 0.1
5.0 4.7
24.5 26.5
23.0 25.0
0.3 0.3
0.1 0.1
1.0 1.2
42.3 44.1
1999 2000
160.9 162.6
111.1 112.7
36.4 36.0
11.7 11.8
15.3 14.3
47.7 50.6
2.9 2.9
46.8 47.1
3.7 3.9
7.9 7.9
1.5 1.6
0.4 0.4
14.9 13.8
18.4 19.5
42.3 44.1
17.8 17.6
24.5 26.5
203.2 206.8
2001 2002
17.6 17.5
12.5 12.5
0.2 0.2
4.9 4.7
28.8 24.8
27.2 23.2
0.3 0.3
0.1 0.1
1.1 1.2
46.3 42.3
2001 2002
174.0 166.6
120.1 112.5
38.1 36.3
12.2 12.1
14.3 13.9
55.5 50.2
2.9 2.9
51.0 51.2
4.1 4.3
8.1 8.0
1.7 1.8
0.4 0.4
15.5 15.3
21.2 21.3
46.3 42.3
17.6 17.5
28.8 24.8
220.4 208.8
2003
17.6
12.5
0.2
4.8
25.9
24.0
0.4
0.1
1.4
43.5
2003
158.9
109.7
35.9
11.5
14.0
48.3
2.9
46.3
3.8
7.6
1.5
0.4
13.9
19.0
43.5
17.6
25.9
202.4
2004
17.8
12.8
0.2
4.8
26.7
24.9
0.4
0.1
1.3
44.5
2004
167.3
116.2
37.9
12.0
14.3
52.1
2.9
48.2
4.0
7.8
1.6
0.5
14.2
20.1
44.5
17.8
26.7
211.8
2005
18.2
13.1
0.2
4.8
25.7
23.7
0.4
0.1
1.5
43.9
2005
167.5
115.2
38.6
12.3
13.7
50.5
2.9
49.4
4.1
8.2
1.6
0.5
14.6
20.4
43.9
18.2
25.7
211.3
2006
19.3
14.2
0.2
4.8
25.9
24.4
0.4
0.1
1.1
45.2
2006
163.7
112.7
36.7
12.5
13.8
49.7
2.9
48.1
4.0
8.1
1.6
0.5
14.2
19.7
45.2
19.3
25.9
208.9
2007
17.7
12.8
0.2
4.7
26.6
24.9
0.4
0.1
1.2
44.3
2007
165.1
114.9
37.4
12.8
13.9
50.9
2.9
47.3
3.9
8.0
1.6
0.5
13.9
19.3
44.3
17.7
26.6
209.4
2008 2009
17.8 18.3
12.9 13.4
0.2 0.2
4.7 4.7
26.3 26.1
24.5 24.1
0.4 0.4
0.1 0.1
1.2 1.5
44.1 44.4
2008 2009
166.6 160.2
115.0 109.1
37.3 36.9
12.5 12.1
14.3 13.1
50.9 47.1
2.9 2.9
48.7 48.2
4.0 3.9
8.2 7.9
1.6 1.6
0.5 0.5
14.4 14.1
20.0 20.1
44.1 44.4
17.8 18.3
26.3 26.1
210.7 204.6
+ Less than 0.05 Tg CO2 Eq.
a Organic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e., dried blood, tankage, compost, and other).
b Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 211: Total 2009 M Emissions (Direct and Indirect! from Agricultural Soil Management by State tTg Clh Eq.l
State
AL
AR
AZ
CA
CO
CT
DE
FL
GA
HIS
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
Croplands
0.67
3.07
0.91
6.96
1.23
0.57
0.77
2.02
1.22
0.00
15.79
2.19
11.86
7.55
5.66
2.00
1.58
0.13
1.03
0.22
4.48
9.71
6.07
2.44
2.01
2.38
5.46
6.38
0.06
1.61
0.54
0.31
3.19
7.37
1.21
1.12
3.05
0.02
0.66
3.25
1.60
5.32
0.64
4.26
0.46
2.28
6.64
0.44
0.51
Grasslands
0.71
0.76
0.93
1.80
2.46
0.03
0.01
1.03
0.36
n.e.
0.85
1.57
0.37
0.30
2.21
1.14
0.54
0.02
0.09
0.02
0.37
0.80
1.82
0.42
3.09
0.65
1.17
2.98
0.01
0.02
2.91
0.61
0.33
0.49
3.02
1.43
0.12
0.00
0.15
2.53
0.77
8.93
0.92
0.84
0.06
1.00
0.77
0.21
1.95
Settlements
0.03
0.02
0.02
0.19
0.02
0.02
0.01
0.22
0.01
n.e.
0.07
0.02
0.12
0.07
0.07
0.03
0.04
0.04
0.05
0.01
0.08
0.03
0.07
0.03
0.01
0.05
0.03
0.07
0.01
0.07
0.01
0.01
0.07
0.11
0.03
0.01
0.06
0.01
0.03
0.02
0.05
0.09
0.01
0.06
0.00
0.03
0.04
0.01
0.01
Forest Lands Total
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
n.e.
1.42
3.85
1.86
8.95
3.71
0.62
0.78
3.27
1.59
1.59
16.71
3.78
12.34
7.92
7.94
3.17
2.16
0.19
1.18
0.25
4.92
10.54
7.96
2.90
5.11
3.08
6.66
9.43
0.08
1.71
3.46
0.93
3.60
7.97
4.27
2.56
3.24
0.03
0.85
5.79
2.42
14.34
1.57
5.16
0.52
3.31
7.45
0.66
n.e. 2.47
Lower
Bound
0.87
2.47
0.95
5.73
2.36
0.36
0.45
2.28
1.02
1.02
11.99
2.34
8.81
5.05
5.58
1.91
1.65
0.13
0.30
0.17
3.50
7.38
5.34
1.81
3.21
2.14
4.46
6.72
0.06
0.34
1.66
0.04
2.35
5.67
1.97
1.60
1.77
0.02
0.16
3.97
1.46
8.26
1.06
3.34
0.37
1.24
5.23
0.36
1.69
Upper
Bound
2.51
7.37
4.58
17.46
6.14
1.58
2.38
5.58
3.23
3.23
33.98
7.60
19.28
12.74
12.16
5.69
3.44
0.77
3.27
1.36
8.35
16.41
12.65
6.33
8.22
6.28
10.67
14.63
0.41
5.10
6.09
9.01
7.02
12.54
7.83
5.42
7.60
0.58
2.10
9.35
6.22
22.86
3.24
12.17
1.11
7.93
12.13
1.62
4.02
1 Emissions from non-manure organic N inputs for minor crops were not estimated (n.e.) at the state level.
2 Emissions from sewage sludge applied to grasslands and were not estimated (n.e.) at the state level
3 Emissions from sewage sludge applied to settlements were not estimated (n.e.) at the state level.
4 Forestland emissions were not estimated (n.e.) at the state level.
N2O emissions are not reported for Hawaii except from cropland organic soils.
A-253
-------
3.12. Methodology for Estimating Net Carbon Stock Changes in Forest
Lands Remaining Forest Lands
This sub-annex expands on the methodology used to calculate net changes in carbon (C) stocks in forest
ecosystems and in harvested wood products. Some of the details of C conversion factors and procedures for calculating
net CO2 flux for forests are provided below; full details of selected topics may be found in the cited references.
Carbon Stocks and Net Changes in Forest Ecosystem Carbon Stocks
At least two forest inventories exist for most forest land in the United States. C stocks are estimated based on
data from each inventory, at the level of permanent inventory plots. C per hectare (for a sample location) is multiplied by
the total number of hectares that the plot represents, and then totals are summed for an area of interest, such as the state of
Maine. Net annual C stock changes are calculated by taking the difference between the inventories and dividing by the
number of years between the inventories for a selected state or sub-state area.
Forest inventory data
The estimates of forest C stocks are based on data derived from forest inventory surveys. Forest inventory data
were obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Prayer and Furnival 1999).
FIA data include remote sensing information and collection of measurements in the field at sample locations called plots.
Tree measurements include diameter and species. On a subset of plots, additional measurements or samples are taken of
down dead wood, litter, and soil C; however, these are not yet available nationwide for C estimation. The field protocols
are thoroughly documented and available for download from the USDA Forest Service (2010c). The inventory was
designed for timber volume estimation rather than C stock estimation, so most C pools are not measured directly or are
sampled only on a subset of plots. Bechtold and Patterson (2005) provide the estimation procedures for standard forest
inventory results. The data are freely available for download at USDA Forest Service (2010b) as the Forest Inventory and
Analysis Database (FIADB) Version 4.0; these data are the primary sources of forest inventory used to estimate forest C
stocks.
Forest surveys have begun in the U.S. territories and in Hawaii. Meanwhile this inventory assumes that these
areas account for a net C change of zero. Survey data are available for the temperate oceanic ecoregion of Alaska
(southeast and south central). Inventory data are publicly available for 6.1 million hectares of forest land, and these
inventoried lands, comprising 12% of the total forest land in Alaska, contribute to the forest carbon stocks presented here.
Agroforestry systems are also not currently accounted for in the U.S. Inventory, since they are not explicitly
inventoried by either of the two primary national natural resource inventory programs: the FIA program of the USDA
Forest Service and the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry
et al. 2005). The majority of these tree-based practices do not meet the size and definitions for forests within each of these
resource inventories. The size characteristics that exclude them from inventories also allow these systems to provide their
many services without taking the land out of agricultural production, making them an appealing C sequestration option.
Agroforestry in the United States has been defined as "intensive land-use management that optimizes the benefits
(physical, biological, ecological, economic, social) from bio-physical interactions created when trees and/or shrubs are
deliberately combined with crops and/or livestock" (Gold et al. 2000). In the United States, there are six categories of
agroforestry practices: riparian forest buffers, windbreaks, alley cropping, silvopasture, forest farming and special
applications. These practices are used to address many issues facing agricultural lands, such as economic diversification,
habitat fragmentation, and water quality. While providing these services and regardless of intent, these tree-based
plantings will also reduce atmospheric CO2. This occurs directly through CO2 sequestration into woody biomass, and
indirectly through enhancement of agricultural production, trapping wind-blown and surface runoff sediments, and/or
reducing CO2 emissions through fuel-use savings (Quam et al. 1992). The effects of these individual practices can
potentially be quite large when taken into account within a whole-farm or within an aggregating larger entity (i.e., state-
level) (Quam et al. 1992, Schoeneberger 2006). One estimate of the sequestration potential through agroforestry practices
in the United States is 90.3 Mt C/year by 2025 (Nair and Nair 2003).
63
More information on agroforestry practices can be found online at .
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Summing state-level C stocks to calculate United States net Cflux in forest ecosystems
The overall approach for determining forest C stocks and stock change is based on methodology and algorithms
coded into the computer tool described in Smith et al. (2010). It focuses on estimating forest C stocks based on data from
two or more forest surveys conducted several years apart for each state or sub-state. There are generally two or more
surveys available for download for each state. C stocks are calculated separately for each state based on available
inventories conducted since 1990 and for the inventory closest to, but prior to, 1990 if such data are available and
consistent with these methods. This approach ensures that the period 1990 (the base year) to present can be adequately
represented. Surveys conducted prior to and in the early to mid 1990s focused on land capable of supporting timber
production (timberland). As a result, information on less productive forest land or lands reserved from harvest was
limited. Inventory field crews periodically measured all the plots in a state at a frequency of every 5 to 14 years.
Generally, forests in states with fast-growing (and therefore rapidly changing) forests tended to be surveyed more often
than states with slower-growing (and therefore slowly changing) forests. Older surveys for some states, particularly in the
West, also have National Forest System (NFS) lands or reserved lands that were surveyed at different times than
productive, privately-owned forest land in the state. Periodic data for each state thus became available at irregular
intervals and determining the year of data collection associated with the survey can sometimes be difficult.
Table A-212: Source of Unique Forest Inventory and Average Year of Field Survey Used to Estimate Statewide Carbon
Stocks
State/Substatea
Alabama
Alaska, non-reserved Southcentral
Alaska, non-reserved Southeast
Alaska, reserved Southcentral
Alaska, reserved Southeast
Arizona, NFS non- woodlands
Arizona, NFS woodlands
Arizona, non -NFS non -woodlands
Arizona, non -NFS woodlands
Arkansas
Source of Inventory Data,
Report/Inventory Yearb
FIADB 4.0, 1982
FIADB 4.0, 1990
FIADB 4.0, 2000
FIADB 4.0, 2005
FIADB 4.0, 2009
FIADB 4.0, 2009
FIADB 4.0, 2009
FIADB 4.0, 2009
1987 RPA
FIADB 4.0, 1999
FIADB 4.0, 2009
1987 RPA
FIADB 4.0, 1999
FIADB 4.0, 2009
FIADB 4.0, 1985
FIADB 4.0, 1999
FIADB 4.0, 2009
FIADB 4.0, 1999
FIADB 4.0, 2009
FIADB 4.0, 1988
FIADB 4.0, 1995
Average Year Assigned
to Inventory0
1982
1990
1999
2003
2007
2007
2006
2006
1985
1996
2005
1984
1996
2005
1986
1996
2006
1990
2006
1988
1996
64 Forest land is defined as land at least 120 feet wide and 1 acre in size with at least 10 percent cover (or equivalent stocking by
live trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. .
Forest land includes transition zones, such as areas between forest and nonforest lands that have at least 10 percent cover (or
equivalent stocking) with live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbelt
strips of trees must have a crown width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land.
Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if they are less than 120 feet wide or an
acre in size. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings,
such as city parks, are not considered forest land. (Smith et al. 2009) Timberland is the most productive type of forest land, which
is on unreserved land and is producing or capable of producing crops of industrial wood. Productivity is at a minimum rate of 20
cubic feet of industrial wood per acre per year (Woudenberg and Farrenkopf 1995). There are about 203 million hectares of
timberland in the conterminous United States, which represents 81 percent of all forest lands over the same area (Smith et al.
2009).
A-255
-------
California, NFS
California, non-NFS
Colorado, NFS non- woodlands
Colorado, NFS woodlands
Colorado, non -NFS non- woodlands
Colorado, non -NFS woodlands
Connecticut
Delaware
Florida
Georgia
Idaho, Caribou-Targhee NF
Idaho, Kootenai NF
Idaho, non-NFS non-woodlands
Idaho, non -NFS woodlands
Idaho, other NFS
Idaho, Payette NF
Idaho, Salmon-Challis NF
Idaho, Sawtooth NF
Illinois
FIADB 4.0, 2005
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
1997 RPA
FIADB 4.0, 2009
1997 RPA
FIADB 4.0, 1984
FIADB 4.0, 2009
Westwide, 1983
FIADB 4.0, 2009
Westwide, 1983
FIADB 4.0, 2009
FIADB 4.0, 1985
FIADB 4.0, 1998
FIADB 4.0, 2007
FIADB 4.0, 1986
FIADB 4.0, 1999
FIADB 4.0, 2008
FIADB 4.0, 1987
FIADB 4.0, 1995
FIADB 4.0, 2007
FIADB 4.0, 1989
FIADB 4.0, 1997
FIADB 4.0, 2004
FIADB 4.0, 2008
Westwide, 1991
FIADB 4.0, 2009
1987 RPA
FIADB 4.0, 1991
FIADB 4.0, 2009
FIADB 4.0, 1991
FIADB 4.0, 2009
FIADB 4.0, 1991
FIADB 4.0, 2009
Westwide, 1991
FIADB 4.0, 1991
FIADB 4.0, 2009
1987 RPA
FIADB 4.0, 2009
1987 RPA
FIADB 4.0, 2009
Westwide, 1991
FIADB 4.0, 1991
FIADB 4.0, 2009
FIADB 4.0, 1985
FIADB 4.0, 1998
FIADB 4.0, 2005
2003
2007
1997
2006
1993
2006
1981
2006
1975
1997
2006
1980
2006
1983
2006
1985
1998
2006
1986
1999
2007
1987
1995
2005
1989
1997
2001
2006
1992
2007
1988
1995
2007
1990
2007
1982
2007
1988
2000
2007
1982
2007
1978
2007
1983
1996
2007
1985
1998
2004
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
FIADB 4.0, 2008
2006
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana, NFS
Montana, non-NFS non-reserved
Montana, non-NFS reserved
Nebraska
FIADB 4.0, 1986
FIADB 4.0, 1998
FIADB 4.0, 2003
FIADB 4.0, 2008
FIADB 4.0, 1990
FIADB 4.0, 2003
FIADB 4.0, 2008
FIADB 4.0, 1981
FIADB 4.0, 1994
FIADB 4.0, 2005
FIADB 4.0, 2008
FIADB 4.0, 1988
FIADB 4.0, 2004
FIADB 4.0, 2007
FIADB 4.0, 1984
FIADB 4.0, 1991
FIADB 4.0, 2005
Eastwide, 1982
FIADB 4.0, 1995
FIADB 4.0, 2003
FIADB 4.0, 2008
FIADB 4.0, 1986
FIADB 4.0, 1999
FIADB 4.0, 2008
FIADB 4.0, 1985
FIADB 4.0, 1998
FIADB 4.0, 2007
FIADB 4.0, 1980
FIADB 4.0, 1993
FIADB 4.0, 2004
FIADB 4.0, 2008
FIADB 4.0, 1990
FIADB 4.0, 2003
FIADB 4.0, 2008
FIADB 4.0, 1987
FIADB 4.0, 1994
FIADB 4.0, 2006
FIADB 4.0, 1989
FIADB 4.0, 2003
FIADB 4.0, 2008
1987 RPA
FIADB 4.0, 1989
FIADB 4.0, 2009
FIADB 4.0, 1989
FIADB 4.0, 2009
1997 RPA
FIADB 4.0, 2009
FIADB 4.0, 1983
FIADB 4.0, 1994
1986
1998
2001
2007
1990
2002
2006
1981
1994
2003
2007
1987
2002
2005
1984
1991
2004
1983
1995
2002
2007
1986
2000
2007
1985
1998
2006
1980
1993
2003
2006
1989
2001
2006
1987
1994
2007
1988
2002
2006
1988
1996
2007
1989
2006
1990
2007
1983
1995
A-257
-------
Nevada, NFS non- woodlands
Nevada, NFS woodlands
Nevada, non -NFS non -woodlands
Nevada, non -NFS woodlands
New Hampshire
New Jersey
New Mexico, NFS non -woodlands
New Mexico, NFS woodlands
New Mexico, non -NFS non -woodlands
New Mexico, non -NFS woodlands
New York, non-reserved
New York, reserved
North Carolina
North Dakota
Ohio
Oklahoma, Central & West
Oklahoma, East
Oregon, NFS East
Oregon, NFS West
Oregon, non-NFS East
FIADB 4.0, 2005
FIADB 4.0, 2008
1987 RPA
FIADB 4.0, 1989
FIADB 4.0, 2005
1987 RPA
FIADB 4.0, 1989
FIADB 4.0, 2005
1997 RPA
FIADB 4.0, 2005
FIADB 4.0, 1989
FIADB 4.0, 2005
FIADB 4.0, 1983
FIADB 4.0, 1997
FIADB 4.0, 2007
FIADB 4.0, 1987
FIADB 4.0, 1999
FIADB 4.0, 2008
1987 RPA
FIADB 4.0, 1999
1987 RPA
FIADB 4.0, 1999
FIADB 4.0, 1987
FIADB 4.0, 1999
FIADB 4.0, 1999
Eastwide, 1980
FIADB 4.0, 1993
FIADB 4.0, 2007
1987 RPA
FIADB 4.0, 2007
FIADB 4.0, 1984
FIADB 4.0, 1990
FIADB 4.0, 2002
FIADB 4.0, 2007
FIADB 4.0, 1980
FIADB 4.0, 1995
FIADB 4.0, 2005
FIADB 4.0, 2008
FIADB 4.0, 1991
FIADB 4.0, 2006
FIADB 4.0, 1989
FIADB 4.0, 1986
FIADB 4.0, 1993
FIADB 4.0, 2008
IDE, 1990s
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
Westwide, 1992
IDE, 1990s
2004
2006
1974
1997
2005
1978
1997
2005
1985
2005
1980
2005
1983
1997
2005
1987
1999
2007
1986
1997
1986
1997
1987
1999
1989
1981
1993
2005
1988
2005
1984
1990
2001
2006
1979
1995
2003
2007
1991
2005
1989
1986
1993
2008
1995
2006
1996
2006
1991
1999
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
FIADB 4.0, 2009
2005
Oregon, non-NFS West
Pennsylvania
Rhode Island
South Carolina
South Dakota, NFS
South Dakota, non-NFS
Tennessee
Texas, Central & West
Texas, East
Utah, non-woodlands
Utah, woodlands
Vermont
Virginia
Washington, NFS East
Washington, NFS West
Washington, non-NFS East
Washington, non-NFS West
Westwide, 1992
IDE, 1990s
FIADB 4.0, 2009
FIADB 4.0, 1989
FIADB 4.0, 2004
FIADB 4.0, 2008
FIADB 4.0, 1985
FIADB 4.0, 1998
FIADB 4.0, 2007
FIADB 4.0, 1986
FIADB 4.0, 1993
FIADB 4.0, 2001
FIADB 4.0, 2006
1997 RPA
FIADB 4.0, 1995
FIADB 4.0, 2005
FIADB 4.0, 2008
1987 RPA
FIADB 4.0, 1995
FIADB 4.0, 2005
FIADB 4.0, 2008
FIADB 4.0, 1989
FIADB 4.0, 1999
FIADB 4.0, 2004
FIADB 4.0, 2007
FIADB 4.0, 2007
FIADB 4.0, 1986
FIADB 4.0, 1992
FIADB 4.0, 2003
FIADB 4.0, 2008
FIADB 4.0, 1993
FIADB 4.0, 2009
FIADB 4.0, 1993
FIADB 4.0, 2009
FIADB 4.0, 1983
FIADB 4.0, 1997
FIADB 4.0, 2007
FIADB 4.0, 1985
FIADB 4.0, 1992
FIADB 4.0, 2001
FIADB 4.0, 2007
IDE, 1990s
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
IDE, 1990s
FIADB 4.0, 2009
1989
1997
2006
1990
2003
2007
1985
1999
2006
1986
1993
2001
2005
1986
1999
2004
2006
1986
1995
2004
2007
1989
1998
2003
2006
2006
1986
1992
2003
2006
1993
2005
1994
2005
1983
1997
2006
1985
1991
2000
2005
1996
2006
1996
2006
1992
2006
1990
2006
A-259
-------
West Virginia
Wisconsin
Wyoming, NFS
Wyoming, non-NFS non-reserved non-
woodlands
Wyoming, non-NFS non-reserved woodlands
Wyoming, non-NFS reserved
FIADB 4.0, 1989
FIADB 4.0, 2000
FIADB 4.0, 2008
FIADB 4.0, 1983
FIADB 4.0, 1996
FIADB 4.0, 2004
FIADB 4.0, 2008
1997 RPA
FIADB 4.0, 2000
FIADB 4.0, 1984
FIADB 4.0, 2000
FIADB 4.0, 1984
FIADB 4.0, 2000
1997 RPA
FIADB 4.0, 2000
1988
2001
2007
1982
1995
2002
2006
1982
2000
1984
2002
1984
2002
1985
2000
a Substate areas (Smith et al. 2010) include National Forests (NFS), all forest ownerships except National Forest (non-NFS), woodlands (forest
land dominated by woodland species, such as pinyon and juniper, where stocking cannot be determined (USDA Forest Service 2010c)), non-
woodlands (used for clarity to emphasize that woodlands are classified separately), reserved (forest land withdrawn from timber utilization
through statute, administrative regulation, or designation, Smith et al. (2009)), and non-reserved (forest land that is not reserved, used for clarity).
Some National Forests are listed individually by name, e.g., Payette NF. Oregon and Washington were divided into eastern and western forests
(east or west of the crest of the Cascade Mountains). Oklahoma and Texas are divided into East versus Central & West according to forest
inventroy survey units (USDA Forest Service 2010d). Alaska is represented by a portion of forest land, in the southcentral and southeast part of
the state.
b FIADB 4.0 is the current, publicly available, format of FIA inventory data, and these files were downloaded from the Internet September 12,
2010 (USDA Forest Service 2010b). IDE (Integrated Database) data are a compilation of periodic inventory data from the 1990s for California,
Oregon, and Washington (Waddell and Hiserote 2005). Eastwide (Hansen et al. 1992) and Westwide (Woudenberg and Farrenkopf 1995)
inventory data are formats that predate the FIADB data. RPA data are periodic national summaries. The year is the nominal, or reporting, year
associated with each dataset.
c Average year is based on average measurement year of forest land survey plots and rounded to the nearest integer year.
A new national plot design and annualized sampling (USDA Forest Service 2010a) was introduced by FIA with
most new surveys beginning after 1998. These surveys include sampling of all forest land including reserved and lower
productivity lands. Most states have annualized inventory data available as of September 2010. Annualized sampling
means that a portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to
10 years, depending on the region of the United States. The full unique set of data with all measured plots, such that each
plot has been measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable,
unbiased samples of forest inventory, but with higher sampling errors than the full cycle. After all plots have been
measured once, the sequence continues with remeasurement of the first year's plots, starting the next new cycle. Most
Eastern states have completed the first full cycle of the annualized inventories and are providing annual updates to the
state's forest inventory with each year's remeasurements, such that one plot's measurements are included in subsequent
year's annual updates. Thus, annually updated estimates of forest C stocks are accurate, but estimates of stock change
cannot utilize the annually updated inventory measurements directly, as there is redundancy in the data used to generate
the annual updates of C stock. For example, a typical annual inventory update for an eastern state will include new data
from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that included in the
previous year's annual update. The interpretation and use of the sequence of annual inventory updates can affect trends in
annualized stock and stock change. In general, the C stock and stock change calculations use annual inventory summaries
(updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update is the
exception because it is included in stock change calculations if at least half of the plots in a state include new
measurements. Table A-212 lists the specific surveys used in this report, and this list can be compared with the full set of
summaries available for download (USDA Forest Service 201 Ob).
For each pool in each state in each year, C stocks are estimated by linear interpolation between survey years.
Similarly, fluxes, or net stock changes, are estimated for each pool in each state by dividing the difference between two
successive stocks by the number of intervening years between surveys. Thus, the number of separate stock change
estimates for each state or sub-state is one less than the number of available inventories. Annual estimates of stock and net
change since the most recent survey are based on linear extrapolation. C stock and flux estimates for each pool are
summed over all forest land in all states as identified in Table A-212 to form estimates for the United States. Summed net
annual stock change and stock are presented in Table A-213 and Table A-214, respectively. Table A-214 also provides an
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
estimate of forest area based on the interpolation and extrapolation procedure described above. Estimated net stock
change of non-soil forest ecosystem carbon for 2009 for each of the states is shown in Table A-215, which also includes
estimated forest area and total non-soil forest carbon stock. The state-level forest areas and carbon stocks are from the
most recent inventory available (USDA Forest Service 2010a), and the estimate for net stock change is the mean of the
2000 through 2009 estimates from the carbon calculator (Smith et al. 2010).
A-261
-------
Table A-213: Net Annual Changes in Carbon Stocks tTg C yrl in Forest and Harvested Wood Pools.1990-2009
Carbon Pool
Forest
Live,
aboveground
Live,
belowground
Dead Wood
Litter
Soil Organic
Carbon
Harvested Wood
Products in Use
SWDS
Total Net Flux
Table A-214:
1990 1991 1992 1993 1994 1995 1996 1997 1998
(149.8) (151.4) (149.8) (139.5) (147.1) (148.4) (130.3) (128.6) (102.8)
(98.2) (99.9) (100.5) (97.4) (101.5) (102.8) (92.7) (95.8) (84.7)
(19.3) (19.7) (19.8) (19.3) (20.1) (20.5) (18.4) (19.0) (16.9)
(8.6) (8.6) (8.3) (7.6) (7.7) (7.7) (7.1) (6.2) (4.8)
(8.8) (8.8) (9.0) (8.1) (7.6) (6.0) (5.1) (5.1) (0.5)
(14.9) (14.4) (12.1) (7.2) (10.2) (11.4) (7.1) (2.5) 4.1
(35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1)
(17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4)
(18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7)
(185.7) (185.1) (183.6) (172.4) (180.5) (180.7) (160.9) (160.7) (133.9)
Carbon Stocks (Tg G) in Forest and Harvested Wood Pools.1990-2010
1990 1991 1992 1993 1994 1995 1996 1997 1998
1999
(76.9)
(78.3)
(15.7)
(4.0)
4.9
16.2
(32.5)
(14.1)
(18.4)
(109.4)
1999
2000
(72.4)
(78.3)
(15.7)
(3.5)
7.5
17.6
(30.8)
(12.8)
(18.0)
(103.2)
2000
2001
(113.0)
(89.3)
(17.9)
(4.0)
1.8
(3.7)
(25.5)
(8.7)
(16.8)
(138.5)
2001
2002
(162.3)
(103.0)
(20.5)
(5.2)
(5.3)
(28.3)
(26.8)
(9.6)
(17.2)
(189.1)
2002
2003
(210.3)
(118.9)
(23.5)
(7.6)
(9.9)
(50.3)
(25.9)
(9.7)
(16.2)
(236.1)
2003
2004
(219.6)
(122.1)
(24.1)
(8.1)
(11.4)
(53.8)
(28.7)
(12.4)
(16.3)
(248.3)
2004
2005
(219.9)
(122.1)
(24.1)
(8.4)
(11.4)
(53.8)
(28.7)
(12.4)
(16.3)
(248.6)
2005
2006
(220.6)
(122.1)
(24.1)
(9.1)
(11.4)
(53.8)
(29.6)
(12.3)
(17.3)
(250.2)
2006
2007 2008
(220.6) (220.6)
(122.1) (122.1)
(24.1) (24.1)
(9.1) (9.1)
(11.4) (11.4)
(53.8) (53.8)
(28.1) (22.4)
(10.7) (5.2)
(17.4) (17.2)
(248.7) (243.0)
2007 2008
2009
(220.6)
(122.1)
(24.1)
(9.1)
(11.4)
(53.8)
(14.8)
1.9
(16.7)
(235.4)
2009 2010
Forest Area
(1000 ha)
Carbon Pool
Forest
Live,
aboveground
Live,
belowground
Dead Wood
Litter
Soil
Organic
Carbon
Harvested
Wood
Products in
Use
SWDS
269,137 269,764 270,395 270,992 271,535 272,071 272,602 273,081 273,553 273,950 274,183 274,386 274,717 275,234 275,976 276,769 277,561 278,354 279,147 279,939 280,732
42,783 42,933 43,085 43,234 43,374 43,521 43,669 43,800 43,928
15,072 15,171 15,271 15,371 15,468 15,570 15,673 15,765 15,861
2,995 3,014 3,034 3,053 3,073 3,093 3,113 3,132 3,151
2,960 2,969 2,978 2,986 2,994 3,001 3,009 3,016 3,022
4,791 4,799 4,808 4,817 4,825 4,833 4,839 4,844 4,849
16,965 16,980 16,995 17,007 17,014 17,024 17,035 17,043 17,045
1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124
1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355
628 646 665 683 701 718 735 752 769
44,031 44,108 44,180 44,293 44,456 44,666 44,886 45,105 45,326 45,547 45,767 45,988
15,946 16,024 16,102 16,192 16,295 16,414 16,536 16,658 16,780 16,902 17,024 17,147
3,168 3,183 3,199 3,217 3,237 3,261 3,285 3,309 3,333 3,357 3,381 3,405
3,027 3,031 3,035 3,039 3,044 3,052 3,060 3,068 3,077 3,086 3,096 3,105
4,850 4,845 4,837 4,835 4,841 4,851 4,862 4,873 4,885 4,896 4,908 4,919
17,041 17,025 17,007 17,011 17,039 17,089 17,143 17,197 17,251 17,304 17,358 17,412
2,155 2,187 2,218 2,244 2,271 2,296 2,325 2,354 2,383 2,412 2,434 2,449
1,368 1,382 1,395 1,404 1,413 1,423 1,436 1,448 1,460 1,471 1,476 1,474
787 805 823 840 857 873 890 906 923 941 958 974
Total Carbon
Stock
44,643 44,828 45,014 45,197 45,370 45,550 45,731 45,892 46,052 46,186 46,296 46,399 46,537 46,726 46,962 47,211 47,459 47,710 47,958 48,201 48,437
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-215: State-level forest area, carbon stock, and net annual stock change. Estimates are forest ecosystem carbon
and do not include harvested wood
Mean year of
Mean net annual
Nonsoil C nonsoil stock
field data Forest area stock (Mt change 2000-2009
State
Alabama
Alaska
Arizona
Arkansas
California
Colorado
Connecticut
Delaware
Florida
Georgia
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
Wyoming
Table A-216
collection
2006
2007
2006
2007
2006
2006
2007
2007
2005
2006
2007
2006
2007
2006
2007
2005
2004
2007
2007
2007
2006
2006
2007
2006
2007
2006
2005
2006
2007
1994
2006
2006
2007
2007
2003
2006
2007
2007
2006
2007
2006
2006
2005
2007
2006
2006
2007
2006
2001
shows average C
based on forest lands in this Inventory.
described below to the
(1000 ha)
9,219
6,132
7,572
7,560
13,333
9,294
698
143
6,838
10,046
8,656
1,942
1,920
1,227
902
5,006
5,722
7,146
1,009
1,222
8,021
6,876
7,941
6,231
10,356
538
4,520
1,944
810
6,753
7,669
7,521
289
3,255
3,235
12,163
6,739
141
5,239
753
5,659
24,363
7,396
1,857
6,400
9,060
4,846
6,757
4,633
density values
These values
most recent inventory per state
Q
682
1,217
436
644
2,165
915
89
18
471
802
1,053
215
225
113
74
502
447
741
131
166
826
536
610
605
1,132
44
247
254
86
484
943
727
20
353
237
2,007
804
17
440
51
576
977
510
250
683
1,711
592
620
468
(Mt C/yr)
(1.2)
0.0
2.0
(3.6)
(5.2)
(5.2)
0.7
(0.1)
(5.0)
(7.7)
2.1
(5.3)
(3.7)
(2.8)
(1.5)
(5.3)
0.1
(1.9)
(1.2)
(0.4)
(6.5)
(4.1)
(6.2)
(10.5)
(4.2)
(0.6)
(0.8)
(0.5)
(0.0)
(3.8)
(6.3)
(6.8)
0.2
(3.0)
(0.8)
(8.2)
(7.0)
(0.0)
(5.7)
(0.9)
(3.8)
(2.4)
(3.4)
(0.3)
(3.4)
(10.0)
(4.6)
(6.0)
(1.1)
for forest ecosystem C pools according to region and forest types
were calculated
as available 12
by applying plot-level C estimation procedures as
September 2010 (USDA 2010b). C density values
A-263
-------
reflect the most recent survey for each state as available in the FIADB, not potential maximum C storage. C densities are
affected by the distribution of stand sizes within a forest type, which can range from regenerating to mature stands. A
large proportion of young stands in a particular forest type are likely to reduce the regional average for C density.
Table A-216: Average carbon density (Mg G/ha) by carbon pool and forest area (1000 ha) according to region and forest
type, based on the most recent inventory survey available for each state from FIA, corresponding to an average year of
2006
Region
(States)
Forest Types
Above- Below-
ground ground
Biomass Biomass
Dead
Wood
Litter
Carbon Density (Mg C/ha)
Northeast
Soil
Organic
Carbon
Forest
Area
(1,000 ha)
(CT,DE,MA,MD,ME,NH,NJ,NY,OH,PA,RI,VT,WV)
White/Red/Jack Pine
Spruce/Fir
Oak/Pine
Oak/Hickory
Elm/Ash/Cottonwood
Maple/Beech/Birch
Aspen/Birch
Minor Types and Nonstocked
All
Northern Lake States
(MI,MN,WI)
White/Red/Jack Pine
Spruce/Fir
Oak/Hickory
Elm/Ash/Cottonwood
Maple/Beech/Birch
Aspen/Birch
Minor Types and Nonstocked
All
Northern Prairie States
(IA,IL,IN,KS,MO,ND,NE,SD)
Ponderosa Pine
Oak/Pine
Oak/Hickory
Elm/Ash/Cottonwood
Minor Types and Nonstocked
All
Ponderosa Pine
South Central
(AL,AR,KY,LA,MS,OK,TN,TX)
Loblolly/Shortleaf Pine
Pinyon/Juniper
Oak/Pine
Oak/Hickory
Oak/Gum/Cypress
Elm/Ash/Cottonwood
Woodland Hardwoods
Minor Types and Nonstocked
All
Loblolly/Shortleaf Pine
Southeast
(FL,GA,NC,SC,VA)
Longleaf/Slash Pine
Loblolly/Shortleaf Pine
Oak/Pine
Oak/Hickory
Oak/Gum/Cypress
Elm/Ash/Cottonwood
Minor Types and Nonstocked
All
Coastal Alaska
(approximately 12 percent of
91.6
52.7
77.0
82.5
59.3
77.9
48.1
48.2
74.5
54.1
40.5
70.4
52.4
74.1
40.9
34.5
54.5
40.3
52.0
69.8
74.7
41.6
66.0
40.3
40.6
45.8
16.4
48.1
58.5
76.2
51.9
10.3
32.3
45.9
45.8
34.7
48.6
52.6
73.1
74.2
57.9
45.7
59.0
19.0
11.2
15.2
15.6
11.3
15.0
9.5
9.5
14.4
11.3
8.6
13.3
10.0
14.2
7.9
6.8
10.7
8.5
10.1
13.2
14.0
8.1
12.5
8.5
8.3
9.4
3.2
9.4
11.0
14.5
9.8
1.7
6.2
8.8
9.4
7.1
10.0
10.3
13.8
14.3
10.9
8.7
11.5
11.9
13.3
9.8
11.1
9.9
13.3
9.3
10.3
11.9
9.0
8.6
10.8
9.2
11.5
9.5
8.8
9.9
7.1
7.5
9.7
11.8
8.3
9.7
7.1
5.5
7.0
1.9
6.6
6.8
9.2
7.1
0.9
5.8
5.9
7.0
6.2
8.5
6.7
8.9
9.7
9.7
9.0
8.4
13.7
30.7
27.4
8.1
6.9
27.1
8.6
10.9
17.8
12.3
33.1
7.9
7.5
27.3
8.3
18.0
16.4
14.3
25.5
7.7
6.8
17.9
9.5
14.3
10.9
9.6
12.2
9.3
6.4
6.5
5.9
5.0
7.1
7.4
9.6
9.7
9.6
9.3
6.4
6.5
5.6
5.8
7.9
78.1
98.0
66.9
53.1
111.7
69.6
87.4
73.7
69.0
120.8
261.8
97.1
179.9
134.3
146.1
122.6
152.3
48.5
40.1
49.4
83.2
60.3
54.5
48.5
55.5
41.9
37.7
41.7
38.6
52.8
49.9
65.0
54.3
45.5
41.9
110.0
72.9
61.4
45.3
158.0
95.7
107.0
79.2
1,584
2,970
1,234
13,007
1,450
13,673
1,704
1,855
37,478
1,821
3,213
3,815
2,118
4,301
5,272
1,113
21,654
576
551
9,570
1,874
1,231
13,803
576
1,198
13,256
3,894
5,115
24,619
5,131
3,441
8,977
4,271
68,704
13,256
4,139
9,137
4,054
12,014
4,551
760
1,389
36,044
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
forest land in Alaska)
Spruce/Fir
Fir/Spruce/Mountain
Hemlock
Hemlock/Sitka Spruce
Aspen/Birch
Minor Types and Nonstocked
All
Spruce/Fir
Pacific Northwest, Westside
(Western OR and WA)
Douglas-fir
Fir/Spruce/Mountain Hemlock
Hemlock/Sitka Spruce
Alder/Maple
Minor Types and Nonstocked
All
Pacific Northwest, Eastside
(Eastern OR and WA)
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Western Larch
Other Western Softwoods
Minor Types and Nonstocked
All
Pacific Southwest
(CA)
Pinyon/Juniper
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Redwood
Other Western Softwoods
California Mixed Conifer
Western Oak
Tanoak/Laurel
Minor Types and Nonstocked
All
Rocky Mountain, North
(ID,MT)
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Western Larch
Other Western Softwoods
Aspen/Birch
Minor Types and Nonstocked
Rocky Mountain, South
(AZ,CO,NM,NV,UT,WY)
24.2
5.0
5.6
33.8
62.1
367
96.0
142.2
42.0
40.0
105.5
24.2
20.3
30.0
8.0
7.8
22.2
5.0
26.8
37.5
9.1
10.7
28.2
5.6
43.2
50.5
10.6
19.5
42.5
33.8
62.1
116.3
42.5
75.7
86.5
62.1
2,233
2,754
310
469
6,132
367
146.2
150.1
174.7
84.8
68.9
135.6
77.5
48.8
95.3
41.2
63.5
15.7
36.0
57.1
24.1
162.8
63.7
158.0
219.6
33.7
127.8
67.0
132.0
57.8
92.0
30.7
31.7
36.8
16.6
13.7
28.3
16.2
10.2
20.1
8.7
13.3
3.0
7.1
11.9
4.7
33.8
13.2
33.4
45.9
6.6
26.8
12.8
26.0
11.7
18.7
39.0
41.6
50.0
24.2
18.0
37.0
19.5
11.2
27.8
12.0
19.2
4.5
16.1
15.8
2.3
37.3
15.0
45.0
48.1
7.6
32.8
7.9
20.1
17.7
20.0
32.0
38.3
37.8
7.6
13.5
28.8
36.3
22.5
37.9
21.1
35.7
36.2
25.1
30.1
21.1
35.7
22.4
38.3
60.5
37.5
37.9
29.7
28.0
25.2
31.9
94.8
62.1
116.3
115.2
85.9
95.5
94.8
50.7
62.1
52.0
45.1
78.8
81.5
68.4
26.3
40.1
41.3
51.9
53.8
49.8
49.8
27.6
27.6
37.0
38.6
5,956
1,187
1,566
1,189
1,216
11,114
2,089
2,742
1,781
1,041
204
1,252
999
10,109
742
442
899
824
299
806
3,159
3,791
830
1,540
13,333
72.8
40.0
67.6
50.3
60.1
44.8
31.8
28.4
15.4
8.3
14.3
10.7
12.7
9.4
6.0
5.7
14.5
8.2
22.1
10.9
15.4
6.9
12.5
17.0
37.0
22.9
37.4
23.1
36.3
39.3
26.8
22.5
38.8
34.3
44.1
37.2
34.2
31.4
56.6
42.9
5,587
1,865
4,471
2,761
492
649
533
2,655
Pinyon/Juniper
Douglas-fir
Ponderosa Pine
Fir/Spruce/Mountain Hemlock
Lodgepole Pine
Aspen/Birch
Woodland Hardwoods
Minor Types and Nonstocked
All
United States (forest land
included in Inventory)
22.9
74.7
47.3
79.6
52.0
56.9
19.0
18.7
36.3
60.0
4.7
15.9
10.0
16.9
11.1
11.0
3.6
3.6
7.5
11.9
1.0
16.9
8.2
22.9
13.0
13.2
4.6
7.9
6.9
11.1
21.1
38.1
23.6
38.8
24.0
28.5
28.2
22.6
25.4
17.6
19.7
30.9
24.1
31.5
27.0
58.8
25.9
25.4
25.8
62.0
18,738
1,797
3,570
4,262
2,024
2,555
4,135
3,088
40,168
277,552
Note: The forest area values in this table do not equal the forest area values reported in Table A-214, because the forest area values in this table
are estimated using the most recent dataset per state, with an average year of 2006. The time series of forest area values reported in Table A-214,
A-265
-------
in contrast, is constructed following the CCT methods used to construct the carbon stock series. The forest area values reported in Table A-214
and Table A-216 would only be identical if all states were measured simultaneously or they all had identical rates of change.
The Inventory is derived primarily from the current FIADB 4.0 data (USDA Forest Service 2010b), but it also
draws on older FIA survey data where necessary. The Resources Planning Act Assessment (RPA) database, which
includes periodic summaries of state inventories, is one example. Information about the RPA data is available on the
Internet (USDA Forest Service 2010a, see Program Features), and compilations of analytical estimates based on these
databases are found in Waddell et al. (1989) and Smith et al. (2001). The basic difference between the RPA database and
the FIADB is that the FIADB includes some informative additional details such as individual-tree data. Having only plot-
level information (such as volume per hectare) limits the conversion to biomass. This does not constitute a substantial
difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et al. 2004). In the past, FIA
made their data available in tree-level Eastwide (Hansen et al. 1992) or Westwide (Woudenberg and Farrenkopf 1995)
formats, which included inventories for Eastern and Western states, respectively. The current Inventory estimates rely in
part on older tree-level data that are not available on the current FIADB site. The Integrated Database (IDE) is a
compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington (Waddell and
Hiserote 2005). These data were identified by Heath et al. (submitted) as the most appropriate non-FIADB sources for
these states.
An historical focus of the FIA program was to provide information on timber resources of the United States. For
this reason, prior to 1998, some forest land, which were less productive or reserved (i.e., land where harvesting was
prohibited by law), were less intensively surveyed. This generally meant that on these less productive lands, forest type
and area were identified but data were not collected on individual tree measurements. The practical effect that this
evolution in inventories has had on estimating forest C stocks from 1990 through the present is that some older surveys of
lands do not have the stand-level values for merchantable volume of wood or stand age. Any data gaps identified in the
surveys taken before 1998 were filled by assigning average C densities calculated from the more complete, later
inventories from the respective states. The overall effect of this necessary approach to generate estimates for C stock is
that no net change in C density occurs on those lands with gaps in past surveys.
Estimating C stocks from forest inventory data
For each inventory summary in each state, data are converted to C units or augmented by other ecological data.
This collection of conversion factors and models used for inventory-based forest carbon estimates (Smith et al. 2010,
Heath et al. in press) was intitially developed as an offshoot of the forest carbon simulation model FORCARB (Heath et
al. 2010) and is incorporated into a number of applications (Birdsey and Heath 1995, Birdsey and Heath 2001, Heath et al.
2003, Smith et al. 2004, Smith et al. 2007, Hoover and Rebain 2008). The conversion factors and model coefficients are
usually categorized by region, and forest type. Classifications for both region and forest type are subject to change
depending on the particular coefficient set. Thus, region and type are specifically defined for each set of estimates.
Factors are applied to the survey data at the scale of FIA inventory plots. The results are estimates of C density (Mg per
hectare) for the various forest pools. C density for live trees, standing dead trees, understory vegetation, down dead wood,
litter, and soil organic matter are estimated. All non-soil pools except litter can be separated into aboveground and
belowground components. The live tree and understory C pools are pooled as biomass in this inventory. Similarly,
standing dead trees and down dead wood are pooled as dead wood in this inventory. C stocks and fluxes for Forest Land
Remaining Forest Land are reported in pools following IPCC (2003).
Live tree C pools
The tree C pools include aboveground and belowground (coarse root) C mass of live trees. Separate estimates
are made for full-tree and aboveground-only biomass to estimate the belowground component. Most tree C estimates are
based on Jenkins et al. (2003) and are functions of species groups and diameter. For example, the equation for estimating
aboveground biomass for a live tree of a species in the aspen/alder/cottonwood/willow group is:
Biomass (kg dry weight) = e(-2'2094 + 23867 *ln(dlameter))
Diameter is cm at diameter breast height (d.b.h.), which is measured at 1.37 m above the forest floor. C is
calculated by multiplying biomass by 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 1997). A
full set of coefficients can be found in Jenkins et al. (2003; Table 4). Belowground root biomass is estimated as a ratio of
roots to total aboveground biomass. The equation for ratio of root biomass of a live tree in the
aspen/alder/cottonwood/willow group is:
-n ,• (-1.6911 +0.8160/diameter)
Ratio = e^ ;
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Belowground biomass is calculated by multiplying the ratio by total aboveground biomass. A full set of
coefficients can be found in Jenkins et al. (2003; Table 6). The C per tree is summed for each plot, and multiplied by the
appropriate expansion factors to obtain a C stock estimate for the plot.
Some inventory data do not provide measurements of individual trees; tree C in these plots is estimated from
plot-level growing stock volume of live trees and equations given in Table A-217 and Table A-218. These equations are
updates of those in Smith et al. (2003), modified to reduce error and correspond to common forest types defined by
inventories. Separate estimates are made for whole-tree and aboveground-only biomass based on forest type group and
region. The belowground portion is determined as the difference between the two estimates. C density is estimated based
on the growing stock volume of the plot, where growing stock includes live trees of commercial species meeting specified
standards. Only trees 12.7 cm (5 inches) d.b.h. and larger are included in growing stock volume. The full sets of
coefficients are in Table A-217 and Table A-218. For example, the total C in tree biomass per hectare of aspen-birch in
the North averages 8.1 Mg C/ha if growing-stock volume is zero. If growing-stock volume is greater than zero, the
estimate is in two parts. Average C density of non-growing-stock trees (sapling and cull trees) is 14.3 Mg C/ha, and the
equation for C in growing-stock trees is:
Growing-stock trees (Mg C/ha) = e(-°337 + ln(vohme) *M33)
Units for volume are m /ha.
Table A-217: Coefficients for estimating carbon density of live trees (above- and below-ground, MgC/ha) by region and
type for plot-level data such as RPA data3
Region1"
North
Pacific
Coast
Rocky
Mountain
Forest type group0
Aspen/Birch
Elm/Ash/Cottonwood
Maple/Beech/Birch
Oak/Hickory
Hardwood minor types
Oak/Pine
Ponderosa Pine & Exotic
Softwood
Loblolly/ShortleafPine
Spruce/Fir
White/Red/Jack Pine
Softwood minor types
Non-stocked
Alder/Maple
Other Western Hardwoods
Tanoak/Laurel
Western Oak
Hardwood minor types
California Mixed Conifer
Douglas-fir
Fir/Spruce/Mt. Hemlock
Hemlock/Sitka Spruce
Lodgepole Pine
Pinyon/Juniper
Ponderosa Pine
Western Larch
Softwood minor types
RPA Western Hardwoods
Non-stocked
Aspen/Birch
Harwood minor types
Douglas-fir
Fir/Spruce/Mt. Hemlock
Lodgepole Pine
Other Western Softwoods
Ponderosa Pine
Softwood minor types
RPA Western Hardwood
Pinyon/Juniper
Carbon density,
if Growing Stock
Volume (CSV) = 0
8.138
16.187
6.938
13.083
10.376
4.079
2.595
6.277
6.424
3.908
6.277
1.054
8.425
8.425
8.425
8.425
8.425
10.102
2.752
10.102
10.102
10.102
22.552
10.102
10.102
10.102
8.425
0.880
4.594
4.866
1.987
1.987
1.080
1.987
1.987
1.987
13.714
22.927
C density for non-
Growing Stock
(GS), if CSV > 0
14.335
18.707
17.054
15.914
14.127
15.473
6.895
9.766
16.903
12.117
17.234
1.238
4.444
10.483
10.203
7.400
4.802
4.727
4.961
6.462
8.034
5.733
5.065
2.262
5.254
6.771
7.460
0.300
9.516
11.844
5.363
6.693
8.051
12.217
5.574
5.496
11.678
23.301
Coefficient
A
-0.337
-0.206
-0.170
-0.079
0.002
-0.146
-0.074
-0.415
-0.487
-0.349
-0.380
-0.174
0.056
0.041
-0.167
0.344
0.333
0.137
0.180
0.171
0.085
-0.129
-0.070
0.145
-0.264
0.466
0.302
0.049
0.324
0.266
0.331
0.065
0.003
0.361
0.382
-0.152
0.246
0.254
Coefficient B
0.933
0.920
0.925
0.932
0.890
0.908
0.886
0.943
0.947
0.924
0.970
0.866
0.828
0.864
0.917
0.850
0.770
0.843
0.834
0.834
0.830
0.857
0.842
0.813
0.853
0.783
0.831
0.806
0.792
0.814
0.825
0.825
0.804
0.796
0.771
0.836
0.807
0.794
A-267
-------
West. Oak/Other West.
Hardwoods
Non-stocked
Elm/Ash/Cottonwood
Oak/Gum/Cypress
Oak/Hickory
Hardwood minor types
South Oak/Pine
Loblolly/ShortleafPine
Longleaf/Slash Pine
Softwood minor types
Non-stocked
14.441
1.111
12.841
7.176
14.594
47.316
4.106
3.892
4.441
7.161
0.467
18.544
0.568
21.633
23.919
20.007
40.194
17.933
12.466
8.694
20.189
0.943
0.215
0.257
-0.144
-0.216
-0.031
-0.442
-0.086
0.206
0.110
-0.085
0.019
0.796
0.732
0.896
0.907
0.886
0.960
0.858
0.773
0.772
0.868
0.734
a Prediction of C in growing-stock trees is based on exp(A + B* ln(growing stock volume)).
b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast
(CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX,
VA).
c Forest type groups are identified in FIADB and RPA data (Smith et al. (2001, USDA Forest Service 2010d).
Table A-218: Coefficients for estimating carbon density of live trees (aboveground only, MgC/ha) by region and type for
plot-level data such as RPA data3
Region1"
North
Pacific
Coast
Rocky
Mountain
South
Carbon density, „ , . ,
., „ . „, ' C density for non-
if Growing Stock rs .f ^ ^ ft
Forest type group Volume (GSV)=0
Aspen/Birch
Elm/Ash/Cottonwood
Maple/Beech/Birch
Oak/Hickory
Hardwood minor types
Oak/Pine
Ponderosa Pine & Exotic Softwood
Loblolly/Shortleaf Pine
Spruce/Fir
White/Red/Jack Pine
Softwood minor types
Non-stocked
Alder/Maple
Other Western Hardwoods
Tanoak/Laurel
Western Oak
Hardwood minor types
California Mixed Conifer
Douglas-fir
Fir/Spruce/Mt. Hemlock
Hemlock/Sitka Spruce
Lodgepole Pine
Pinyon/Juniper
Ponderosa Pine
Western Larch
Softwood minor types
RPA Western Hardwoods
Non-stocked
Aspen/Birch
Harwood minor types
Douglas-fir
Fir/Spruce/Mt. Hemlock
Lodgepole Pine
Other Western Softwoods
Ponderosa Pine
Softwood minor types
RPA Western Hardwood
Pinyon/Juniper
West. Oak/Other West. Hardwoods
Non-stocked
Elm/Ash/Cottonwood
Oak/Gum/Cypress
Oak/Hickory
6.697
13.585
5.762
10.960
8.647
3.368
2.116
5.098
5.206
3.174
5.098
0.880
7.006
7.006
7.006
7.006
7.006
8.309
2.235
8.309
8.309
8.309
18.583
8.309
8.309
8.309
7.006
0.724
3.798
4.027
1.616
1.616
0.871
1.616
1.616
1.616
11.341
18.867
11.942
0.916
10.749
5.987
12.223
'
11.880
15.653
14.219
13.306
11.796
12.881
5.671
8.070
13.833
10.010
14.246
1.032
3.676
8.709
8.469
6.163
3.974
3.883
4.072
5.285
6.586
4.674
4.170
1.849
4.282
5.563
6.202
0.247
7.914
9.936
4.388
5.466
6.571
10.031
4.569
4.473
9.704
19.173
15.353
0.466
18.129
20.004
16.731
Coefficient
A
-0.521
-0.387
-0.352
-0.260
-0.166
-0.335
-0.269
-0.620
-0.684
-0.548
-0.570
-0.357
-0.138
-0.154
-0.355
0.167
0.136
-0.061
-0.017
-0.027
-0.113
-0.327
-0.263
-0.053
-0.461
0.267
0.119
-0.146
0.139
0.084
0.134
-0.133
-0.195
0.165
0.185
-0.350
0.054
0.059
0.021
0.061
-0.323
-0.400
-0.215
Coefficient
B
0.934
0.922
0.926
0.933
0.888
0.909
0.886
0.946
0.948
0.926
0.971
0.866
0.830
0.867
0.918
0.850
0.773
0.844
0.835
0.835
0.831
0.858
0.842
0.814
0.853
0.784
0.831
0.808
0.793
0.815
0.826
0.826
0.805
0.797
0.772
0.837
0.809
0.794
0.796
0.733
0.897
0.909
0.888
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Hardwood minor types
Oak/Pine
Loblolly/ShortleafPine
Longleaf/Slash Pine
Softwood minor types
Non-stocked
39.737
3.394
3.172
3.634
5.893
0.388
33.739
14.923
10.288
7.176
16.751
0.788
-0.631
-0.277
0.012
-0.088
-0.280
-0.171
0.964
0.859
0.773
0.773
0.869
0.735
" Prediction of aboveground C in growing-stock trees is based on exp(A + B* ln(growing stock volume).
b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast
(CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX,
VA).
c Forest type groups are identified in FIADB and RPA data (Smith et al. 2001, USDA Forest Service 2010d).
Understory vegetation
Understory vegetation is a minor component of biomass. Understory vegetation is defined as all biomass of
undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this inventory, it is assumed
that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is near the lower range of
temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: ratios are likely to
be lower for light-limited understory vegetation as compared with larger trees, and a greater proportion of all root mass
will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots.
These were fit to the equation:
Ratio = e^"B''ln(live tree c density)
In this equation, "ratio" is the ratio of understory C density (Mg C/ha) to live tree C density (above- and below-
ground) in Mg C/ha. An additional coefficient is provided as a maximum ratio; that is, any estimate predicted from the
equation that is greater than the maximum ratio is set equal to the maximum ratio. A full set of coefficients is in Table A-
219. Regions and forest types are the same classifications described in Smith et al. (2003). As an example, the basic
calculation for understory C in aspen-birch forests in the Northeast is:
Understory (Mg C/ha) = (live tree C density) x e(0'855 - L03 *ln(tree c denslty)
This calculation is followed by three possible modifications. First, the maximum value for the ratio is set to 2.02
(see value in column "maximum ratio"); this also applies to stands with zero tree C, which is undefined in the above
equation. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked and piny on/juniper stands are set
to coefficient A, which is a carbon density (Mg C/ha) for these types only.
Table A-219: Coefficients for estimating the ratio of carbon
MgC/ha)a by region and forest type. The ratio is multiplied
vegetation
density of understory vegetation (above- and belowground,
by tree carbon density on each plot to produce understory
Region Forest Type
NE
NLS
NFS
Aspen-Birch
MBB/Other Hardwood
Oak-Hickory
Oak-Pine
Other Pine
Spruce-Fir
White-Red-Jack Pine
Nonstocked
Aspen-Birch
Lowland Hardwood
Maple-Beech-Birch
Oak-Hickory
Pine
Spruce-Fir
Nonstocked
Conifer
Lowland Hardwood
Maple-Beech-Birch
Oak-Hickory
Oak-Pine
A
0.855
0.892
0.842
1.960
2.149
0.825
1.000
2.020
0.777
0.650
0.863
0.965
0.740
1.656
1.928
1.189
1.370
1.126
1.139
2.014
_ Maximum
" .. c
ratio
1.032
1.079
1.053
1.235
1.268
1.121
1.116
2.020
1.018
0.997
1.120
1.091
1.014
1.318
1.928
1.190
1.177
1.201
1.138
1.215
2.023
2.076
2.057
4.203
4.191
2.140
2.098
2.060
2.023
2.037
2.129
2.072
2.046
2.136
2.117
2.114
2.055
2.130
2.072
4.185
A-269
-------
Non stocked
2.052
2.052
2.072
PSW
PWE
PWW
RMN
RMS
SC
SE
Douglas-fir
Fir- Spruce
Hardwoods
Other Conifer
Pinyon- Juniper
Redwood
Non stocked
Douglas-fir
Fir- Spruce
Hardwoods
Lodgepole Pine
Pinyon- Juniper
Ponderosa Pine
Non stocked
Douglas-fir
Fir- Spruce
Other Conifer
Other Hardwoods
Red Alder
Western Hemlock
Non stocked
Douglas-fir
Fir- Spruce
Hardwoods
Lodgepole Pine
Other Conifer
Pinyon- Juniper
Ponderosa Pine
Non stocked
Douglas-fir
Fir- Spruce
Hardwoods
Lodgepole Pine
Other Conifer
Pinyon- Juniper
Ponderosa Pine
Non stocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
Oak-Pine
Planted Pine
Upland Hardwood
Non stocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
Oak-Pine
Planted Pine
Upland Hardwood
Non stocked
2.084
1.983
1.571
4.032
4.430
2.513
4.431
1.544
1.583
1.900
1.790
2.708
1.768
4.315
1.727
1.770
2.874
2.157
2.094
2.081
4.401
2.342
2.129
1.860
2.571
2.614
2.708
2.099
4.430
5.145
2.861
1.858
3.305
2.134
2.757
3.214
4.243
0.917
1.601
2.166
1.903
1.489
2.089
4.044
0.834
1.601
1.752
1.642
1.470
1.903
4.033
1.201
1.268
1.038
1.785
4.430
1.312
4.431
1.064
1.156
1.133
1.257
2.708
1.213
4.315
1.108
1.164
1.534
1.220
1.230
1.218
4.401
1.360
1.315
1.110
1.500
1.518
2.708
1.344
4.430
2.232
1.568
1.110
1.737
1.382
2.757
1.732
4.243
1.109
1.129
1.260
1.190
1.037
1.235
4.044
1.089
1.129
1.155
1.117
1.036
1.191
4.033
4.626
4.806
4.745
4.768
4.820
4.698
4.626
4.626
4.806
4.745
4.823
4.820
4.768
4.626
4.609
4.807
4.768
4.745
4.745
4.693
4.589
4.731
4.749
4.745
4.773
4.821
4.820
4.776
4.773
4.829
4.822
4.745
4.797
4.821
4.820
4.820
4.797
1.842
4.191
4.161
4.173
4.124
4.170
4.170
1.842
4.191
4.178
4.195
4.141
4.182
4.182
"Prediction of ratio of understory C to live tree C is based on the equation: Ratio=exp(A-B*ln(tree_carbon_tph)), where "ratio" is the ratio of
understory C density to live tree (above-and below- ground) C density, and "tree_carbon_density" is live tree (above-and below- ground) C
density in Mg C/ha.
b Regions and types as defined in Smith et al. (2003)
'Maximum ratio: any estimate predicted from the equation that is greater than the maximum ratio is set equal to the maximum ratio.
Dead Wood
The standing dead tree C pools include aboveground and belowground (coarse root) mass. Estimates for
standing dead tree C are not based on newly available FIA standing dead tree data because updated methodology is not yet
available for integrating dead wood carbon estimates based on both older (which do not have consistent measurements of
dead wood) and newer surveys. Instead, the estimates are based on a ratio of growing stock volume of live trees, stratified
by region and forest type groups, applied at the FIA plot-level. The standing dead tree equations estimate mass; they are
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
converted to C mass by multiplying by 0.5. An example calculation for standing dead tree C in aspen-birch forests in the
Northeast is:
Dry weight of dead trees (Mg/ha) = 1.0 x (growing stock volume)0 4"
It is multiplied by 0.5 to obtain Mg C/ha. All coefficients are provided in Table A-220. Note that nonstocked
stands are assigned a constant C density (the value of Coefficient A).
Down dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are
not attached to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. Ratio estimates
of down dead wood to live tree biomass were developed using FORCARB2 simulations and applied at the plot level
(Smith et al. 2004). Estimates for down dead wood correspond to the region and forest type classifications described in
Smith et al. (2003). A full set of ratios is provided in Table A-221. An additional component of down dead wood is a
regional average estimate of logging residue based on Smith et al. (2006) applied at the plot level. These are based on a
regional average C density at age zero and first order decay; initial densities and decay coefficients are provided in Table
A-222. These amounts are added to explicitly account for down dead wood following harvest. In practice, this
modification resulted in minor changes to the estimates. Example calculations of the two components for down dead
wood in 25-year-old aspen-birch forests in the Northeast are:
C density (Mg C/ha) = (live tree C density, above- and below-ground) x (0.078) = 7.8% of live tree C
Conversion to C units is not necessary because the live tree value is already in terms of C.
C density additional for logging residue (Mg C/ha) = 13.9 x e("25/12'r> = 1.8 (Mg C/ha)
Table A-220: Coefficients for estimating standing dead tree carbon [HgC/hal by region and forest type group3
Region
MTN
MTS
NC
NE
PC
sc
Forest type group0
Douglas-fir
Fir/Spruce/Mt. Hemlock
Hemlock/Sitka Spruce
Lodgepole Pine
Ponderosa Pine
Nonstocked
Douglas-fir
Fir/Spruce/Mt. Hemlock
Lodgepole Pine
Ponderosa Pine
Nonstocked
Aspen/Birch
Elm/Ash/Cottonwood
Maple/Beech/Birch
Planted Pine
Oak/Hickory
Oak/Pine
Spruce/Fir
White/Red/Jack Pine
Nonstocked
Aspen/Birch
Elm/Ash/Cottonwood
Maple/Beech/Birch
Oak/Hickory
Oak/Pine
Spruce/Fir
White/Red/Jack Pine
Nonstocked
California Mixed Conifer
Douglas-fir
Douglas-fir Planted
Fir/Spruce/Mt. Hemlock
Hemlock/Sitka Spruce
Redwood
Nonstocked
Elm/Ash/Cottonwood
Loblolly/Shortleaf Pine
Coefficient A
3.935
4.550
1.000
1.177
1.000
12.855
2.200
6.923
1.177
1.944
4.232
1.962
3.755
3.442
1.000
2.949
1.364
1.320
2.844
2.634
1.000
4.992
3.041
3.332
1.725
5.893
2.841
2.876
1.000
1.237
10.145
4.235
1.546
5.385
7.377
2.393
1.203
Coefficient B
0.312
0.358
0.569
0.501
0.455
-
0.460
0.293
0.501
0.292
-
0.400
0.253
0.219
0.298
0.236
0.394
0.472
0.266
-
0.499
0.134
0.306
0.191
0.311
0.190
0.254
-
0.608
0.559
0.112
0.415
0.562
0.287
-
0.284
0.271
A-271
-------
SE
South
West
Loblolly/Shortleaf Planted
Oak/Gum/Cypress
Oak/Hickory
Oak/Pine
Non stocked
Elm/Ash/Cotton wood
Loblolly/Shortleaf Pine
Loblolly/Shortleaf Planted
Oak/Gum/Cypress
Oak/Hickory
Oak/Pine
Non stocked
Longleaf/Slash Pine
Longleaf/Slash Planted
Alder/Maple
Aspen/Birch
Pinyon/Juniper
Tan oak/Laurel
Western Hardwood/Woodlands
Western Larch
Western Oak
1.000
4.234
2.396
1.133
0.286
1.358
1.000
1.000
1.770
2.256
1.000
0.349
1.000
1.000
2.190
3.062
3.163
1.000
5.595
2.049
1.996
0.138
0.121
0.186
0.337
—
0.476
0.324
0.265
0.329
0.257
0.351
—
0.184
0.106
0.466
0.376
0.100
0.593
0.181
0.449
0.348
"Standing dead tree C is based on the equation: mass (Mg/ha) = A* (live-tree growing stock volume)AB. Note that nonstocked stands are assigned
a constant C density (the value listed under coefficient A). Note that the standing dead tree equations are for biomass. To convert to C mass,
multiply by 0.5.
'Regions are PC (CA,OR-West,WA-West), MTN (OR-East,WA-East,ID,MT), MTS (AZ,CO,NM,NV,UT,WY), West (regions PC, MTN, and
MTS), NC (IA, IL, IN, KS, MI, MN, MO, ND, NE, SD, WI), NE (CT, DE, MA, MD, ME, NH, NJ, NY, OH, PA, RI, VT, WV), SC (AL, AR,
KY, LA, MS, OK, TN, TX), SE (FL, GA, NC, SC, VA), and South (regions SC and SE).
'Forest types are described in FIADB documentation (USDA Forest Service, 20 lOd). Minor forest types within a region that are not explicitly
defined/listed in the table of coefficients are assigned to a similar hardwood or softwood forest type.
Table A-221: Ratio for estimating down dead wood by region and forest type. The ratio is multiplied by the live tree carbon
density on a plot to produce down dead wood carbon density [HgC/hal
Region3
NE
NLS
NFS
PSW
PWE
Forest type3
Aspen-Birch
MBB/Other Hardwood
Oak-Hickory
Oak-Pine
Other Pine
Spruce-Fir
White-Red-Jack Pine
Nonstocked
Aspen-Birch
Lowland Hardwood
Maple-Beech-Birch
Oak-Hickory
Pine
Spruce-Fir
Nonstocked
Conifer
Lowland Hardwood
Maple-Beech-Birch
Oak-Hickory
Oak-Pine
Nonstocked
Douglas-fir
Fir- Spruce
Hardwoods
Other Conifer
Pinyon-Juniper
Redwood
Nonstocked
Douglas-fir
Fir- Spruce
Hardwoods
Ratio
0.078
0.071
0.068
0.061
0.065
0.092
0.055
0.019
0.081
0.061
0.076
0.077
0.072
0.087
0.027
0.073
0.069
0.063
0.068
0.069
0.026
0.091
0.109
0.042
0.100
0.031
0.108
0.022
0.103
0.106
0.027
. , ,. Forest type (cont'd)
(cont'd) Jr v '
Douglas-fir
Fir- Spruce
Other Conifer
PWW Other Hardwoods
Red Alder
Western Hemlock
Nonstocked
Douglas-fir
Fir- Spruce
Hardwoods
RMN Lodgepole Pine
Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Douglas-fir
Fir- Spruce
Hardwoods
Lodgepole Pine
KM^ ^^ ^ ...
Other Conifer
Pinyon-Juniper
Ponderosa Pine
Nonstocked
Bottomland Hardwood
Misc. Conifer
Natural Pine
SC Oak-Pine
Planted Pine
Upland Hardwood
Nonstocked
SE Bottomland Hardwood
Ratio
(cont'd)
0.100
0.090
0.073
0.062
0.095
0.099
0.020
0.062
0.100
0.112
0.058
0.060
0.030
0.087
0.018
0.077
0.079
0.064
0.098
0.060
0.030
0.082
0.020
0.063
0.068
0.068
0.072
0.077
0.067
0.013
0.064
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Lodgepole Pine
Pinyon-Juniper
Ponderosa Pine
Non stocked
0.093
0.032
0.103
0.024
Misc. Conifer
Natural Pine
Oak-Pine
Planted Pine
Upland Hardwood
Non stocked
0.081
0.081
0.063
0.075
0.059
0.012
" Regions and types as defined in Smith et al. (2003).
Table A-222: Coefficients for estimating logging residue component of down dead wood.
Initial Carbon
Forest Type Group1" Density
Region" (softwood/hardwood) (Mg/ha) Decay Coefficient
Alaska
Alaska
NE
NE
NLS
NLS
NFS
NFS
PSW
PSW
PWE
PWE
PWW
PWW
RMN
RMN
RMS
RMS
SC
SC
SE
SE
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
hardwood
softwood
6.9
8.6
13.9
12.1
9.1
7.2
9.6
6.4
9.8
17.5
3.3
9.5
18.1
23.6
7.2
9.0
5.1
3.7
4.2
5.5
6.4
7.3
12.1
32.3
12.1
17.9
12.1
17.9
12.1
17.9
12.1
32.3
12.1
32.3
12.1
32.3
43.5
18.1
43.5
18.1
8.9
17.9
8.9
17.9
a Regions are defined in Smith et al. (2003) with the addition of coastal Alaska.
b Forest types are according to majority hardwood or softwood species.
Litter carbon
C of the litter layer is currently sampled on a subset of the FIA plots. However, these data are not yet available
electronically for general application to all inventories in Table A-l. Litter C is the pool of organic C (including material
known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with diameters of up
to 7.5 cm. Estimates therefore continue to be based on equations of Smith and Heath (2002) and applied at the plot level.
The equations describe processes for decay or loss of forest floor following harvest and the net accumulation of new forest
floor material following stand growth. For example, total forest floor C at a given number of years after a clearcut harvest
for aspen-birch forests in the North is:
Total forest floor C (Mg C/ha) = (18.4xyears)/(53.7+years) + 10.2x e(-yeals/92)
See Table 4 of Smith and Heath (2002) for the complete set of coefficients. Note that these are direct estimates
of C density; the 0.5 conversion does not apply to litter.
Soil organic carbon
Soil organic carbon (SOC) is currently sampled to a 20 cm depth on subsets of FIA plots, however, these data are
not available for the entire United States. Thus, estimates of SOC are based on the national STATSGO spatial database
(USDA 1991), and the general approach described by Amichev and Galbraith (2004). In their procedure, SOC was
calculated for the conterminous United States using the STATSGO database, and data gaps were filled by representative
values from similar soils. Links to region and forest type groups were developed with the assistance of the USDA Forest
Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the soil C map. The average SOC
densities are provided in Table A-216.
A-273
-------
Carbon in Harvested Wood Products
Estimates of the harvested wood product (HWP) contribution to forest C sinks and emissions (hereafter called
"HWP Contribution") are based on methods described in Skog (2008) using the WOODCARB II model. These methods
are based on IPCC (2006) guidance for estimating HWP carbon. The 2006 IPCC Guidelines provide methods that allow
Parties to report HWP Contribution using one of several different accounting approaches: production, stock change, and
atmospheric flow, as well as a default method. The various approaches are described below. The approaches differ in
how HWP Contribution is allocated based on production or consumption as well as what processes (atmospheric fluxes or
stock changes) are emphasized.
• Production approach: Accounts for the net changes in carbon stocks in forests and in the wood products
pool, but attributes both to the producing country.
• Stock change approach: Accounts for changes in the product pool within the boundaries of the consuming
country.
• Atmospheric flow approach: Accounts for net emissions or removals of carbon to and from the atmosphere
within national boundaries. C removal due to forest growth is accounted for in the producing country while
C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming country.
• Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption
be justified if this is how a Party is choosing to report.
The United States uses the production accounting approach (as in previous years) to report HWP Contribution (Table A-
223). Though reported U.S. HWP estimates are based on the production approach, estimates resulting from use of the two
alternative approaches—the stock change and atmospheric flow approaches—are also presented for comparison (see Table
A-224). Annual estimates of change are calculated by tracking the additions to and removals from the pool of products
held in end uses (i.e., products in use such as housing or publications) and the pool of products held in solid waste disposal
sites (SWDS).
Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and
atmospheric flow approaches for imports and exports are provided in Table A-223. The HWP variables estimated are:
(1 A) annual change of C in wood and paper products in use in the United States,
(IB) annual change of C in wood and paper products in SWDS in the United States,
(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.
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-223: Harvested wood products from wood harvested in United States—Annual additions of carbon to stocks and total stocks
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010
Net carbon additions per year (Tg C per year)
Total Harvested
wood carbon (35.9)
(17.7)
(14.4)
(3.3)
Products in use
Solid wood
products
Paper products
Products in
SWDS
Solid wood
products
Paper products
Total Carbon stocks (Tg C)
Total Harvested wood
carbon 1,859
Products in use 1,231
(33.8) (33.8) (32.9)
(14.9) (16.3) (15.0)
(11.9) (12.6) (12.2)
(3.1) (3.7) (2.8)
(33.4) (32.3) (30.6) (32.0)
(15.9) (15.1) (14.1) (14.7)
(12.1) (11.2) (11.5) (11.8)
(3.8) (3.8) (2.6) (3.0)
(31.1) (32.5) (30.8) (25.5) (26.8) (25.9) (28.7) (28.7)
(13.4) (14.1) (12.8) (8.7) (9.6) (9.7) (12.4) (12.4)
(11.4) (12.1) (11.9) (10.1) (10.7) (10.1) (11.6) (11.9)
(2.0) (2.0) (1.0) 1.4 1.1 0.4 (0.8) (0.5)
(29.6) (28.1) (22.4) (14.8)
(12.3) (10.7) (5.2) 1.9
(10.6) (8.7) (4.4) 0.6
(1.7) (2.0) (0.8) 1.3
(18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16.3) (16.3) (17.3) (17.4) (17.2) (16.7)
(9.9)
(8.3)
(11.1) (9.5) (9.7)
(7.7) (7.9) (8.3)
(9.8) (10.7) (10.6) (10.3)
(7.7) (6.5) (6.0) (6.9)
(10.2) (10.6) (10.7) (10.7) (11.1) (11.1) (11.3) (11.5)
(7.5) (7.8) (7.3) (6.0) (6.1) (5.1) (5.0) (4.8)
(11.6) (11.7) (11.5) (11.2)
(5.7) (5.7) (5.7) (5.4)
1,895
1,249
1,929
1,264
1,963
1,280
1,996 2,029 2,061 2,092 2,124 2,155 2,187 2,218 2,244 2,271 2,296 2,325
1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413 1,423 1,436
2,354 2,383 2,412 2,434 2,449
1,448 1,460 1,471 1,476 1,474
Products in SWDS
628 646 665 683
701
718 735 752 769 787 805 823 840 857 873 890 906 923 941 958
974
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-224: Comparison of Net Annual Change in Harvested Wood Products Carbon Stocks Using Alternative Accounting Approaches
HWP Contribution to LULUCF Emissions/ removals (Tg CO2 Eq.)
Inventory Stock Change Atmospheric Flow Production
Year Approach Approach Approach
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
(129.6)
(116.3)
(120.0)
(126.8)
(130.0)
(126.0)
(122.3)
(131.4)
(139.8)
(149.4)
(143.2)
(128.3)
(135.6)
(134.6)
(163.0)
(161.4)
(138.6)
(115.4)
(138.4)
(131.4)
(131.6)
(127.8)
(129.9)
(128.0)
(122.5)
(127.4)
(122.7)
(127.3)
(120.3)
(100.3)
(103.1)
(99.2)
(109.1)
(109.0)
(114.2)
(112.1)
(131.8)
(123.8)
(123.8)
(120.7)
(122.5)
(118.4)
(112.2)
(117.3)
(114.1)
(119.1)
(112.9)
(93.4)
(98.2)
(94.8)
(105.3)
(105.4)
(108.6)
(103.0)
A-275
-------
2008
2009
(78.4)
(41.9)
(94.3)
(68.3)
(82.1)
(54.3)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-225: Harvested Wood Products Sectoral Background Data for LULUGF— United States (production approach)
Inventory
year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Note: 1C HWPDC
atmosphere).
1A
Annual
Change in
stock of HWP
in use from
consumption
ACnwp m DC
17,044
13,129
15,718
16,957
18,221
17,307
17,018
18,756
20,311
22,035
20,491
17,295
18,629
19,180
26,384
25,777
19,010
12,999
3,589
(5,401)
= H + PM - PEX -
IB
Annual
Change in
stock of HWP
in SWDS from
consumption
ACHWP SWDS DC
18,308
18,602
17,006
17,627
17,221
17,051
16,348
17,090
17,818
18,714
18,560
17,691
18,357
17,532
18,077
18,249
18,780
18,497
17,786
16,825
2A
Annual
Change in
stock of
HWP in
use
produced
from
domestic
harvest
AC HWP m
DH
17,659
14,940
16,334
14,971
15,930
15,065
14,092
14,740
13,376
14,123
12,828
8,711
9,570
9,676
12,429
12,394
12,308
10,673
5,203
(1,857)
AC HWP IU DC ~ AC HWP SWDS DC AND
2B
Annual
Change in
stock of HWP
in SWDS
produced
from
domestic
harvest
ACHWP SWDS
DH
18,278
18,812
17,427
17,949
17,479
17,229
16,513
17,263
17,735
18,353
17,962
16,774
17,207
16,186
16,298
16,347
17,302
17,409
17,188
16,652
|C HWPDH = H -
3
Annual
Imports of
wood, and
paper
products +
wood fuel,
pulp,
recovered
paper,
roundwood/
chips
PIM
12,680
11,552
12,856
14,512
15,685
16,712
16,691
17,983
19,722
21,266
22,426
22,975
24,604
25,962
31,650
31,714
25,485
21,603
16,822
13,281
AC HWPIUDH - AC
4
Annual
Exports of
wood, and
paper
products +
wood fuel,
pulp,
recovered
paper,
roundwood/
chips
PEX
15,078
15,667
16,032
14,788
15,665
17,266
16,733
16,877
15,057
15,245
16,185
15,336
15,744
16,303
16,948
17,423
18,836
20,670
21,156
20,485
5
Annual
Domestic
Harvest
H
142,297
144,435
139,389
134,554
134,750
137,027
134,477
135,439
135,021
134,939
134,458
128,621
127,567
124,949
130,460
131,711
127,064
120,922
108,339
95,130
6
Annual release
of carbon to
the
atmosphere
from HWP
consumption
(from
fuelwood &
products in
use and
products in
SWDS)
TC-HWPDC
104,547
108,588
103,489
99,694
99,328
102,115
101,069
100,699
101,558
100,211
101,648
101,274
99,441
97,896
100,700
101,976
95,922
90,360
82,630
76,501
7
Annual release
of carbon to the
atmosphere from
HWP (including
firewood) where
wood came from
domestic harvest
(from products in
use and products
in SWDS )
TCnwpDH
GgC /yr
106,359
110,682
105,627
101,633
101,342
104,733
103,872
103,436
103,911
102,464
103,667
103,136
100,791
99,086
101,733
102,971
97,454
92,840
85,948
80,334
HWP SWDS DH • Parentheses indicate net C sequestration (i.e., a net removal of C
8
HWP
Contribution
to AFOLU
CO2
emissions/
removals
Gg CO2 /yr
(131,772)
(123,758)
(123,791)
(120,708)
(122,498)
(118,411)
(112,219)
(117,344)
(114,071)
(119,078)
(112,898)
(93,447)
(98,179)
(94,828)
(105,332)
(105,382)
(108,567)
(102,967)
(82,101)
(54,250)
from the
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Annual estimates of variables 1A, IB, 2A and 2B were calculated by tracking the additions to and removals from
the pool of products held in end uses (e.g., products in uses such as housing or publications) and the pool of products held
in SWDS. In the case of variables 2A and 2B, the pools include products exported and held in other countries and the
pools in the United States exclude products made from wood harvested in other countries. Solidwood products added to
pools include lumber and panels. End-use categories for solidwood include single and multifamily housing, alteration and
repair of housing, and other end uses. There is one product category and one end-use category for paper. Additions to and
removals from pools are tracked beginning in 1900, with the exception that additions of softwood lumber to housing
begins in 1800. Solidwood and paper product production and trade data are from USDA Forest Service and other sources
(Hair and Ulnch 1963; Hair 1958; USDC Bureau of Census 1976; Ulnch, 1985, 1989; Steer 1948; AF&PA 2006a, 2006b;
Howard 2003).
The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order
(exponential) decay curves with given half-lives (time at which half of amount placed in use will have been discarded
from use). Half-lives for products in use, determined after calibration of the model to meet two criteria, are shown in
Table A-226. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to
match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA
estimates of discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 1 A, and to
a lesser extent variable 2A. The calibration also determines the amounts going to SWDS. In addition ,WOODCARB II
landfill decay rates have been validated by making sure that estimates of methane emissions from landfills based on EPA
data are reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Table A-227. Estimates of IB and 2B also reflect the
change over time in the fraction of products discarded to SWDS (versus burning or recycling) and the fraction of SWDS
that are sanitary landfills versus dumps.
Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key
assumption for estimating these variables is that products exported from the United States and held in pools in other
countries have the same half lives for products in use, the same percentage of discarded products going to SWDS, and the
same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in Table
A-213 and Table A-214. The decline in net additions to HWP carbon stocks continued though 2009 from the recent high
point in 2006. This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to
the decline in housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was
exceeded by discards from uses. The result is a net reduction in the amount of HWP carbon that is held in products in use
during 2009. For 2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have
remained relatively stable. Overall, there were net carbon additions to HWP in use and in landfills combined.
Table A-226: Half-life of solidwood and paper products in end uses
Parameter Value Units
Half life of wood in single family housing 1920 and before 78.0 Years
Half life of wood in single family housing 1920-1939 78.0 Years
Half life of wood in single family housing 1940-1959 80.0 Years
Half life of wood in single family housing 1960-1979 81.9 Years
Half life of wood in single family housing 1980 + 83.9 Years
Ratio of multifamily half live to single family half life 0.61
Ratio of repair and alterations half life to single family half life 0.30
Half life for other solidwood product in end uses 38.0 Years
Half life of paper in end uses 2.54 Years
Source: Skog, K.E. (2008) "Sequestration of carbon in harvested wood products for the United States." Forest Products Journal 58:56-72.
Table A-227: Parameters determining decay of wood and paper in SWDS
Parameter Value Units
Percentage of wood and paper in dumps that is subject to decay 100%
Percentage of wood in landfills that is subject to decay 23%
Percentage of paper in landfills that is subject to decay 56%
Half life of wood in landfills/dumps (portion subject to decay) 29 Years
Half life of paper in landfills/ dumps (portion subject to decay) 14.5 Years
Source: Skog, K.E. (2008) "Sequestration of carbon in harvested wood products for the United States." Forest Products Journal 58:56-72
A-277
-------
Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see uncertainty table in LULUCF chapter) are consistent
with the IPCC-recommended Tier 2 methodology (IPCC 2006). Separate analyses are produced for forest ecosystem and
HWP flux. The uncertainty estimates are from Monte Carlo simulations of the respective models and input data. Methods
generally follow those described in Heath and Smith (2000), Smith and Heath (2000), and Skog et al. (2004).
Uncertainties surrounding input data or model processes are quantified as probability distribution functions (PDFs), so that
a series of sample values can be randomly selected from the distributions. Model simulations are repeated a large number
of times to numerically simulate the effect of the random PDF selections on estimated total C flux. The separate results
from the ecosystem and HWP simulations are pooled for total uncertainty (see uncertainty table in LULUCF chapter).
Uncertainty surrounding current net C flux in forest ecosystems is based on the value for 2007 as obtained from
the Monte Carlo simulation. C stocks are based on forest condition level (plot-level) calculations, and, therefore,
uncertainty analysis starts probabilistic sampling at the plot level. Uncertainty surrounding C density (Mg/ha) is defined
for each of six C pools for each inventory plot. Live trees are generally assigned normal PDFs, which are defined
according to variability information in Jenkins et al. (2003) and the species and number of trees measured on each FIA
plot. Plot-level live tree C estimates from RPA data are based on volume; these PDFs also include an additional level of
uncertainty based on their respective regression equations. Similarly, the normally-distributed PDFs for standing dead
trees are based on both volume regression and the individual-tree uncertainties related to the Jenkins et al. (2003) based
estimates. Definitions of these normal distributions, which centered on expected values, depend on region, type, and
specific plot information. Where data gaps—tree or volume—are identified for older inventory data, corresponding
averages from later data are applied for live and standing dead tree C densities. Uniform PDFs with a range of ±90
percent of the average are used for these plots.
Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of
information available about these estimates. Down dead wood, understory, and litter are assigned triangular distributions
with the mean at the expected value for each plot and the minimum and mode at 10 percent of the expected value. The use
of these PDFs skewed to the right reflects the assumption that a small proportion of plots will have relatively high C
densities. Joint sampling of PDFs is specified for two pairs of samples: understory PDF sampling is slightly negatively
correlated with live tree PDF sampling, and down dead wood sampling is slightly positively correlated with live tree
sampling. This also reflects the structure of the estimates, which are dependent on live tree C. Soil organic C is defined as
a uniform PDF at ±50 percent of the mean. Sub-state or state total carbon stocks associated with each survey are the
cumulative sum of random samples from the plot-level PDFs, which are then appropriately expanded to population
estimates. These expected values for each carbon pool include uncertainty associated with sampling, which is also
incorporated in the Monte Carlo simulation. Sampling errors are determined according to methods described for the
FIADB (Bechtold and Patterson 2005), are normally distributed, and are assigned a slight positive correlation between
successive surveys for Monte Carlo sampling. More recent annual inventories are assigned higher sampling correlation
between successive surveys based on the proportion of plot data jointly included in each. Errors for older inventory data
are not available, and these surveys are assigned values consistent with those obtained from the FIADB.
Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling
is the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the
production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated the
effect of uncertainty in 13 sources, including production and trade data and parameters used to make the estimate.
Uncertain data and parameters include data on production and trade and factors to convert them to C, the Census-based
estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to S WDS for 1990 to 2000, the limits on
decay of wood and paper in SWDS, the decay rate (half-life) of wood and paper in SWDS, the proportion of products
produced in the United States made with wood harvested in the United States, and the rate of storage of wood and paper C
in other countries that came from United States harvest, compared to storage in the United States.
A total of ten thousand samples are drawn from the PDF input to separately determine uncertainties about forest
ecosystem and HWP flux before they are combined for a quantitative estimate of total forest carbon uncertainty (see
uncertainty table in LULUCF chapter). Again this year, true Monte Carlo sampling is used for the forest ecosystem
estimates (in contrast to Latin hypercube sampling, which was used in some previous estimates), and a part of the QA/QC
process includes verifying that the PDFs are adequately sampled.
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Emissions from fires
C02
As stated in other sections, the forest inventory approach implicitly accounts for emissions due to disturbances.
Net C stock change is estimated by subtracting consecutive C stock estimates. A disturbance removes C from the forest.
The inventory data, on which net C stock estimates are based, already reflects the C loss because only C remaining in the
forest is estimated. Estimating the CO2 emissions from a disturbance such as fire and adding those emissions to the net
CO2 change in forests would result in double-counting the loss from fire because the inventory data already reflect the
loss. There is interest, however, in the size of the CO2 emissions from disturbances such as fire. The IPCC (2003)
methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate emissions from forest
fires.
The same methodology was used to estimate emissions from both wildfires and prescribed fires occurring in the
lower 48 states. Wildfire area statistics are available, but they include non-forest land, such as shrublands and grasslands.
It was thus necessary to develop a rudimentary estimate of the percent of area burned in forest by multiplying the reported
area burned by a ratio of total forest land area to the total area considered to be under protection from fire. Data on total
area of forest land were obtained from FIA (USDA Forest Service 201 Ob). Data on "total area considered to be under
protection from fire" were available at the state level and obtained for the year 1990 from 1984-1990 Wildfire Statistics
prepared by the U.S. Forest Service (USDA Forest Service 1992). Data for years 1998, 2002, 2004, and 2006 were
obtained from the National Association of State Foresters (NASF 2008, 2007a, 2007b, 2007c). For states where data were
available for all four years, the 1990 value was assumed for years 1990 to 1994, values for 1998 were assumed for years
1995 to 1998, values for 2002 were assumed for years 1999 to 2002, values for 2004 were assumed for years 2003 and
2004, and values for 2006 were assumed for years 2005 to 2009. For states where data were available for all years except
2002, 2004 data were assumed for years 1999 to 2004. For states where data were available for all years except 2004,
2006 data were assumed for 2003 through 2008. For years where data were available for all years except 2006, 2004 data
were assumed for years 2003 to 2008. Since no 1998 value was available for Alaska, the 1990 value was assumed for
years 1990 to 1997, the 2002 value was assumed for years 1998 to 2002, and the 2004 value was assumed for years 2003
and 2005 to 2009. Data for 1990, 1998, and 2006 were available for New Mexico, so the 1990 value was assumed for
years 1990-1995, while the 1998 value was assumed for year 1996 through 2001, and 2006 data were assumed for all
remaining years. Data for 1990, 1998, and 2002 were available for Illinois, so the 1990 value was assumed for years
1990-1995, while the 1998 value was assumed for years 1995 through 2001, and the 2002 value was assumed for all
remaining years. Total forestland area for the lower 48 states was divided by total area considered to be under protection
from wildfire for the lower 48 states across the 1990 to 2009 time series to create ratios that were then applied to reported
area burned to estimate the area of forestland burned for the lower 48 states. The ratio was applied to area burned from
wildland fires and prescribed fires occurring in the lower 48 states. Reported area burned data for prescribed fires was
available from 1998 to 2009 from the National Interagency Fire Center (MFC 2010). Data for the year 1998 was assumed
for years 1990 to 1997.
Forest area burned data for Alaska are from the Alaska Department of Natural Resources (Alaska Department of
Natural Resources 2008) or the Alaska Interagency Coordination Center (Alaska Interagency Coordination Center 2010).
Data are acres of land which experienced fire activity on forest service land. Based on personal communication with
USFS, forest areas under the protection of USFS serve as a proxy for coastal areas, which is where the majority of forest
fires in Alaska occur (Heath 2008). According to expert judgment, the coastal area of Alaska included in this Inventory is
mostly temperate rainforest and, therefore, there is little call for prescribed burns (Smith 2008a). It was, thus, assumed
that reported area burned for prescribed fires covers only prescribed fires in the lower 48 states.
The average C density in the lower 48 states for aboveground biomass C, dead wood C, and litter layer is 91
Mg/ha, according to data from FIA. A default value of 0.45 from IPCC (2006) was assumed for the amount of biomass
burned by wildfire (combustion factor value). Thus, approximately 41.0 Mg C/ha is estimated to be emitted by wildfire.
For Alaska, an average C density of 179 Mg/ha was used based on data from FIA; this translates into 80.6 Mg C/ha
emitted. Based on data from the U.S. Forest Service, a value of 30 Mg/ha was used as the average C density for
prescribed fires, where the average C density is adjusted to reflect the fuel load included in dead wood and litter only and
the thought that prescribed fires only occur in the lower 48 states (Smith 2008a). Thus, prescribed fires are estimated to
emit 13.5 Mg C/ha. Estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting
estimates are displayed in Table A-228. C estimates were multiplied by 92.8 percent to account for the proportion of
carbon emitted as CO2 and by 3.67 to yield CO2 units. Total CO2 emissions for wildfires and prescribed fires in the lower
48 states and wildfires in Alaska in 2009 were estimated to be 127.0 Tg/yr.
A-279
-------
Table A-228: Areas (hectares) from wildfire statistics and corresponding estimates of carbon and Clh (Tg/yr) emissions
for wildfires and prescribed fires in the lower 48 states and wildfires in Alaska1
Lower 48 States
Wildfires
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Reported
area burned2
(ha)
579,589
486,807
785,892
438,865
1,540,987
727,051
2,212,309
335,914
489,246
1,869,918
2,685,981
1,356,830
2,023,976
1,358,986
637,258
1,629,067
3,888,011
3,512,122
2,099,842
1,201,996
Forest area
burned3 (ha)
302,372
254,618
412,111
230,704
811,927
418,819
1,313,377
199,827
291,647
1,123,023
1,615,551
817,280
1,217,243
718,172
342,999
940,471
2,250,696
2,038,630
1,212,446
711,766
Carbon
emitted
(Tg/yr)
12
10
17
9
33
17
54
8
12
46
66
33
50
29
14
39
92
83
50
29
C02
emitted
(Tg/yr)
42
35
57
32
113
58
183
28
41
156
225
114
170
100
48
131
314
284
169
99
Reported
area
burned2
(ha)
355,432
355,432
355,432
355,432
355,432
355,432
355,432
355,432
355,432
806,780
77,789
667,428
1,086,503
1,147,695
996,453
934,965
1,100,966
1,274,383
783,068
1,024,306
Prescribed
Forest
area
burned3
(ha)
185,429
185,904
186,384
186,845
187,273
204,747
211,008
211,437
211,878
484,530
46,788
402,022
653,436
606,513
536,332
539,761
637,328
739,723
452,142
606,547
Fires
Carbon
emitted
(Tg/yr)
3
3
3
3
3
3
3
3
3
7
1
5
9
8
7
7
9
10
6
8
C02
emitted
(Tg/yr)
9
9
9
9
9
9
10
10
10
22
2
18
30
28
25
25
29
34
21
28
Alaska
Wildfires
Forest
area
burned4
(acres)
8
557
47
110
23
7
103
33
2
7
1
2,078
28
17
23
353
8
2
1
22
Forest
area Carbon
burned emitted
(ha) (Tg/yr)
3 0.0003
225 0.0182
19 0.0015
45 0.0036
9 0.0007
3 0.0002
42 0.0034
13 0.0011
1 0.0001
3 0.0002
1 0.0000
841 0.0677
11 0.0009
7 0.0006
9 0.0008
143 0.0115
3 0.0003
1 0.0001
0 0.0000
9 0.0007
C02
emitted
(Tg/yr)
0.0009
0.0618
0.0052
0.0122
0.0025
0.0008
0.0115
0.0036
0.0002
0.0007
0.0001
0.2305
0.0031
0.0019
0.0026
0.0392
0.0009
0.0002
0.0001
0.0024
1 Note that these emissions have already been accounted for in the estimates of net annual changes in carbon stocks, which accounts for the
amount sequestered minus any emissions, including the assumption that combusted wood may continue to decay through time.
2 National Interagency Fire Center (2010).
3 Ratios calculated using forest land area estimates from FIA (USDA Forest Service 2010b) and wildland area under protection estimates from
USDA Forest Service (1992) and the National Association of State Foresters (2007).
4 1990-2007 Alaskan forest fires data are from the Alaska Department of Natural Resources (2008). 2008 data are from Alaska Interagency
Coordination Center (2009).
Non-CO2
Emissions of non-CO2 gases from forest fires were estimated using the default IPCC (2003) methodology, IPCC
(2006) emission ratios, and default IPCC (2006) combustion factor for wildfires. Emissions estimates for CH4 and N2O
are calculated by multiplying the total estimated CO2 emitted from forest burned by gas-specific emissions ratios and
conversion factors. The equations used are:
CH4 Emissions = (CO2 released) x 92.8% x (44/12) x (CH4 to CO2 emission ratio)
N2O Emissions = (CO2 released) x 92.8% x (44/12) x (N2O to CO2 emission ratio)
The resulting estimates are presented in Table A-229.
Table A-229: Estimated carbon released and estimates of non-Clh emissions (Tg/yr) for U.S.forests1
Year
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
C emitted (Tg/yr)
14.886
12.954
19.394
11.973
35.777
19.915
56.635
11.038
14.803
52.529
66.789
38.963
58.668
37.598
CH4 emitted
(Tg/yr)
0.152
0.132
0.198
0.122
0.365
0.203
0.577
0.113
0.151
0.535
0.681
0.397
0.598
0.383
N2O
(Tg/yr)
0.008
0.007
0.011
0.007
0.020
0.011
0.032
0.006
0.008
0.030
0.038
0.022
0.033
0.021
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
2004 21.287 0.217 0.012
2005 45.811 0.467 0.026
2006 100.770 1.027 0.057
2007 93.468 0.953 0.053
2008 55.754 0.568 0.031
2009 37.336 0.381 0.021
1 Calculated based on C emission estimates in Table A-228 and default factors in IPCC (2003, 2006)
References
AF&PA. 2006a (and earlier). Statistical roundup. (Monthly). Washington, DC: American Forest & Paper
Association.
AF&PA. 2006b (and earlier). Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest &
Paper Association.
Alaska Department of Natural Resources (2008) Division of Forestry. "Fire Statistics." Available online at
October 2008.
Alaska Interagency Coordination Center (AICC) (2010). "2009 Fire Season Statistics." Available online at <
http://fire.ak.blm.gov/content/aicc/stats/2009.pdf>
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.
Bechtold, W.A.; Patterson, P.L. 2005. The enhanced forest inventory and analysis program—national sampling
design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture Forest
Service, Southern Research Station. 85 p.
Birdsey, R. A., and L.S. Heath (1995) "Carbon Changes in U.S. Forests." In Productivity of America's Forests and
Climate Change. Gen. Tech. Rep. RM-271. Rocky Mountain Forest and Range Experiment Station, Forest
Service, U.S. Department of Agriculture. Fort Collins, CO, 56-70.
Birdsey, R. (1996) "Carbon Storage for Major Forest Types and Regions in the Conterminous United States." In
R.N. Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for
Mitigating Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263).
Birdsey, R., and L. S. Heath (2001) "Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux."
In Soil Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-
135.
Prayer, W.E., and G.M. Furnival (1999) "Forest Survey Sampling Designs: A History." Journal of Forestry 97(12):
4-10.
Gold, M. A., W. J. Rietveld, H. E. Garrett, and R. F. Fisher. (2000) Agroforestry Nomenclature, Concepts, and
Practices for the United States. In H.E. Garrett, W. J. Rietveld, and R.F. Fisher, editors. North American
Agroforestry: An Integrated Science and Practice. ASA. Madison, Wisconsin, 63-77.
Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products - 1963. U.S. Department of
Agriculture Forest Service, Misc Publication No. 953. Washington, DC.
Hair, D. (1958) "Historical forestry statistics of the United States." Statistical Bull. 228. U.S. Department of
Agriculture Forest Service, Washington, DC.
Hansen, M.H., T Frieswyk, J.F. Glover, J.F. Kelly (1992) The Eastwide Forest Inventory Data Base: Users Manual.
General Technical Report NC-151. U.S. Department of Agriculture, Forest Service, North Central Forest
Experiment Station. St. Paul, MN.
Heath, L.S., J.E., Smith, and R.A. Birdsey (2003) Carbon Trends in U. S. Forestlands: A Context for the Role of
Soils in Forest Carbon Sequestration. In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lai, editors. The
Potential ofU. S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect. Lewis Publishers (CRC
Press). Boca Raton, FL, 35-45.
Heath, L. S., and J.E. Smith (2000) "Soil Carbon Accounting and Assumptions for Forestry and Forest-related
A-281
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Land Use Change." In The Impact of Climate Change on America's Forests. Joyce, L. A., and Birdsey, R. A. Gen.
Tech. Rep. RMRS-59. Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture. Fort
Collins, CO, 89-101.
Heath, L.S., M.C. Nichols, J.E. Smith, and J.R. Mills. 2010. FORCARB2: An updated version of the U.S. Forest
Carbon Budget Model. Gen. Tech. Rep. NRS-67.USDA Forest Service, Northern Research Station, Newtown
Square, PA. 52 p. [CD-ROM].
Heath, L.S., J.E. Smith, K.E. Skog, D. Nowak, and C. Woodall. In press. Managed forest carbon estimates for the
U.S. Greenhouse Gas Inventory, 1990-2008. Journal of Forestry.
Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, and K. L. Waddell (submitted). Carbon stocks on forested
lands of the United States, with emphasis on USDA Forest Service ownership.Hoover, C., and S. Rebain. 2008.
The Kane Experimental Forest carbon inventory: Carbon reporting with FVS. Pages 17-22 in R.N. Havis and
N.L. Crookston, comps. 2008. Third Forest Vegetation Simulator Conference; 2007 February 13-15; Fort Collins,
CO. Proceedings RMRS-P-54. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado.
Howard, James L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap.
FPL-RP-615. Madison, WI: USDA, Forest Service, Forest Products Laboratory. Available online at
.
IPCC (2006) 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 .
IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories.
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for
Economic Co-Operation and Development, International Energy Agency. Paris, France.
Jenkins, J,C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) "National-scale biomass estimators for United
States tree species." Forest Science 49(1): 12-35.
Nair, P.K.R. and V.D. Nair. (2003) Carbon storage in North American Agroforestry systems. In Kimble J., Heath
L.S., Birdsey R.A., Lai R., editors. The potential of U.S. forest soils to sequester carbon and mitigate the
greenhouse effect. CRC Press. Boca Raton, FL, 333-346.
National Interagency Fire Center (2010) "Fire Information—Wildland Fire Statistics. Total Wildland Fires and
Acres (1960-2009)." Available online at November 2010.
National Association of State Foresters (2008) State Forestry Statistics 2006 Report. Available online at <
http://www.stateforesters.org/files/2006%20State%20Forestry%20Statistics-Web-Final.pdf> October 2008.
National Association of State Foresters (2007a) State Forestry Statistics 1998 Report. Available online at
March 2008.
National Association of State Foresters (2007b) State Forestry Statistics 2002 Report. Available online at
March 2008.
National Association of State Foresters (2007c) State Forestry Statistics 2004 Report. Available online at
March 2008.
Perry, C.H., C.W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an
accounting of "working trees". In: "Moving Agroforestry into the Mainstream." Proc. 9th N. Am. Agroforestry
Conf., Brooks, K.N. and Ffolliott, P.P. (eds). 12-15 June 2005, Rochester, MN [CD-ROM]. Dept. of Forest
Resources, Univ. Minnesota, St. Paul, MN, 12 p. Available online at (verified
23 Sept 2006).
Quam, V.C., J. Gardner, J.R. Brandle, and T.K. Boes (1992) Windbreaks in Sustainable Agricultural Systems. EC-
91-1772. University of Nebraska Extension. Lincoln, NE.
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Schoeneberger, PJ. (2006) Landscape Classification. U.S. Department of Agriculture. Lincoln, NE.
Skog, K.E., K. Pingoud, and J.E. Smith (2004) "A method countries can use to estimate changes in carbon stored in
harvested wood products and the uncertainty of such estimates." Environmental Management 33(Suppl. 1):S65-
S73.
Skog, K.E. (2008) "Sequestration of carbon in harvested wood products for the United States." Forest Products
Journal 58:56-72.
Smith, J. (2008a) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 3,
2008.
Smith, J. and L.S. Heath (2000) Considerations for interpreting probabilistic estimates of uncertainty of forest
carbon. In Joyce, L.A., Birdsey, R., editors. The impact of climate change on America's forests: a technical
document supporting the 2000 USDA Forest Service RPA Assessment. Gen. Tech. Rep. RMRS-GTR-59. U.S.
Department of Agriculture, Forest Service, Rocky Mountain Research Station. Fort Collins, CO, 102-111.
Smith, J.E., and L.S. Heath (2002) "A model of forest floor carbon mass for United States forest types." Res. Paper
NE-722. USDA Forest Service, Northeastern Research Station, Newtown Square, PA.
Smith, W. B., J. S. Vissage, D. R. Darr, and R. M. Sheffield (2001) Forest Resources of the United States, 1997.
General Technical Report NC-219, U.S. Department of Agriculture Forest Service, North Central Research
Station, St. Paul, MN.
Smith, J. E., L. S. Heath, and J. C. Jenkins (2003) Forest Volume-to-Biomass Models and Estimates of Mass for Live
and Standing Dead Trees of U.S. Forests. General Technical Report NE-298, USDA Forest Service, Northeastern
Research Station, Newtown Square, PA.
Smith, J. E., L. S. Heath, and P. B. Woodbury (2004) "How to estimate forest carbon for large areas from inventory
data." Journal of Forestry 102:25-31.
Smith, J.E., L.S. Heath, K.E. Skog, R.A. Birdsey. (2006) Methods for calculating forest ecosystem and harvested
carbon with standard estimates for forest types of the United States. Gen. Tech. Rep. NE-343. U.S. Department of
Agriculture, Forest Service, Northeastern Research Station. Newtown Square, PA.
Smith, J.E., L.S. Heath, and M.C. Nichols (2007). U.S. Forest Carbon Calculation Tool User's Guide: Forestland
Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13, U.S. Department of
Agriculture Forest Service, Northern Research Station.
Smith, W. B., P. D. Miles, C. H. Perry, and S. A. Pugh (2009) Forest Resources of the United States, 2007. General
Technical Report WO-78, U.S. Department of Agriculture Forest Service, Washington Office.
Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User's Guide: Forestland
Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of
Agriculture Forest Service, Northern Research Station.
Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture
Forest Service. Washington, DC.
Ulrich, Alice (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub.
1453, U.S. Department of Agriculture Forest Service. Washington, DC.
Ulrich, A.H. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA
Miscellaneous Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77.
USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1.
Washington, DC.
USDA Forest Service (1992) "1984-1990 Wildfire Statistics." Prepared by State and Private Forestry Fire and
Aviation Management Staff. Facsimile from Helene Cleveland, USDA Forest Service, to ICF International.
January 30, 2008.
USDA Forest Service (2010a) Forest Inventory and Analysis National Program: User Information. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at
-------
data/docs/default.asp>. Accessed 07 October 2010.
USDA Forest Service. (2010b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department
of Agriculture Forest Service. Washington, DC. Available online at . Accessed 07 October 2010.
USDA Forest Service. (2010c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods
and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at
. Accessed 07 October 2010.
USDA Forest Service (2010d) Forest Inventory and Analysis National Program, FIA library: Database
Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at <
http://www.fia.fs.fed.us/library/database-documentation/ >. Accessed 07 October 2010.
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.
Waddell, K. L., D. D. Oswald, and D. S. Powell. (1989) Forest statistics of the United States, 1987. Resource
Bulletin PNW-168. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland,
OR.
Waddell, K., and B. Hiserote. 2005. The PNW-FIA Integrated Database User Guide: A database of forest inventory
information for California, Oregon, and Washington. Forest Inventory and Analysis Program, Pacific Northwest
Research Station, Portland, Oregon, USA.
Woudenberg, S.W. and T.O. Farrenkopf (1995) The Westwide forest inventory data base: user's manual. General
Technical Report INT-GTR-317. U.S. Department of Agriculture Forest Service, Intermountain Research Station.
Ogden, UT.
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3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral
and Organic Soils on Cropland and Grassland
This sub-annex describes the methodologies used to calculate annual carbon (C) stock changes from mineral and
organic soils under agricultural management, including Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to Grassland. Three types of methodologies were applied: (1) a
Tier 3 approach, employing the Century simulation model, (2) Tier 2 methods with country-specific stock change and
emission factors; and (3) Tier 2 methods for estimating additional changes in mineral soil C stocks due to sewage sludge
additions to soils and enrollment changes in the Conservation Reserve Program (CRP) after 2003.
The Inventory uses a Tier 3 approach to estimate soil C stock changes for the majority of agricultural lands. This
approach has several advantages over the IPCC Tier 1 or 2 approaches:
• It utilizes actual weather data at county scales, rather than a broad climate region classification, enabling
quantification of inter-annual variability in C fluxes at finer spatial scales;
• The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C
dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;
• The simulation approach provides a more detailed representation of management influences and their
interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods; and
• Soil C changes are estimated on a more continuous basis (monthly) as a function of the interaction of
climate, soil, and land management, compared with the linear change between the start and end of the
inventory that is used with the Tier 1 and 2 methods.
The Century model was chosen as an appropriate tool for a Tier 3 approach based on several criteria:
• The model was developed in the United States and has been extensively tested and verified for U.S.
conditions. In addition, the model has been widely used by researchers and agencies in many other parts of
the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India,
Jordan, Kenya, Mexico).
• The model is capable of simulating cropland, grassland, forest, and savanna ecosystems, and land-use
transitions between these different land uses. It is, thus, well suited to model land-use change effects.
• The model was designed to simulate all major types of management practices that influence soil C
dynamics, with the exception of cultivated organic soils and a few crops that have not been parameterized
for Century simulations (e.g., rice, perennial/horticultural crops, and tobacco). For these latter cases, an
IPCC Tier 2 method has been used.
• Much of the data needed for the model was obtainable from existing national databases. The exceptions are
CRP enrollment after 2003 and sewage sludge amendments to soils, which are not known at a sufficient
resolution to use the Tier 3 model. Soil C stock changes associated with these practices are addressed with a
Tier 2 method.
Century Model Description
The Century model simulates C (and also N, P, and S) dynamics, soil temperature, and water dynamics for
cropland, grassland, forest, and savanna (mixed forest-grassland) systems. For this analysis, only C and N dynamics have
been included for several reasons: to simplify the analysis and reduce data requirements, and because P and S interactions
are less important as determinants of land-use- and management-induced changes in soil C stocks for U.S. agricultural
systems.
The model has four main components: (1) soil organic matter and nutrient dynamics; (2) plant growth processes;
(3) water and temperature dynamics; and (4) management practices. The model was designed to work with readily
available input data: monthly weather data (e.g., temperature and precipitation); soil physical properties (e.g., soil texture,
drainage condition, rooting depth); and information about land use/land cover (e.g., vegetation attributes) and management
activities (see below). The model operates on a monthly time step (with weekly time steps used for soil water dynamics).
Dynamics of organic C and N (Figure A-13) are simulated for the surface and subsurface litter pools and the top
20 cm of the soil profile; mineral N dynamics are simulated through the whole soil profile. Organic C and N stocks are
represented by two plant litter pools (labelled metabolic and structural) and three soil organic matter (SOM) pools
(labelled active, slow, and passive). The metabolic litter pool represents the easily decomposable constituents of plant
residues, while the structural litter pool is composed of more recalcitrant, ligno-cellulose plant materials. The three SOM
pools represent a gradient in decomposability, from active SOM (representing microbial biomass and associated
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metabolites) having a rapid turnover (months to years), to passive SOM (representing highly processed, humified,
condensed decomposition products), which is highly recalcitrant, with mean residence times on the order of several
hundred years. The slow pool represents decomposition products of intermediate stability, having a mean residence time
on the order of decades and is the fraction that tends to change the most in terms of C content in response to changes in
land use and management. Soil texture influences turnover rates of the slow and passive pools The clay and silt-sized
mineral fraction of the soil provides physical protection from microbial attack, leading to slower decomposition and
greater SOM stabilization in finely textured soils. Soil temperature and moisture, tillage disturbance, aeration, and other
factors influence the decomposition and loss of C from the soil organic matter pools.
Figure A-13: Flow diagram of Carbon submodel (A) and Nitrogen submodel (B)
The plant-growth submodel simulates C assimilation through photosynthesis, N uptake, dry matter production,
partitioning of C within the crop or forage, senescence, and mortality. The primary function of the growth submodel is to
estimate the amount, type, and timing of organic matter inputs to soil and to represent the influence of the plant on soil
water, temperature, and N balance. Yield and removal of harvested biomass are also simulated. Separate submodels are
designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and woody vegetation (i.e., trees and scrub).
Only the herbaceous plant submodel is currently used in the Inventory. Maximum monthly net primary production (NPP)
rate (a parameter of crop and forage species/variety, specified in the model input files) is modified by air temperature and
available water to estimate a potential monthly NPP, which is then further subject to nutrient limitations in order to
estimate actual NPP and biomass allocation.
The soil-water balance submodel calculates water balance components and changes in soil water availability,
which influences both plant growth and decomposition/nutrient cycling processes. The moisture content of soils are
simulated through a multi-layer profile based on precipitation, snow accumulation and melting, interception, soil and
canopy evaporation, transpiration, soil water movement, runoff, and drainage.
The final main component of the model is the management submodel, which includes options for specifying crop
type, crop sequence (e.g., rotation), tillage, fertilization, organic matter addition (e.g., manure amendments), harvest (with
variable residue removal), drainage, irrigation, burning, and grazing intensity. An input "schedule" file is used to simulate
the timing of management activities and temporal trends; schedules can be organized into discrete time blocks to define a
repeated sequence of events (e.g., a crop rotation or a frequency of disturbance such as a burning cycle for perennial
grassland). Management options can be specified for any month of a year within a scheduling block, where management
codes point to operation-specific parameter files (referred to as *.100 files), which contain the information used to
simulate management effects within the model process algorithms. User-specified management activities can be defined
by adding to or editing the contents of the *. 100 files. Additional details of the model formulation are given in Parton et
al. (1987, 1988, 1994) and Metherell et al. (1993), and archived copies of the model source code are available.
The model has been tested for application in U.S. agricultural lands and has been shown to capture the general
trends in C storage across approximately 870 field plots from 47 experimental sites (Figure A-14). Some biases and
imprecision were found in predictions of soil organic C, which is reflected in the uncertainty associated with Century
model results as described in Step 2b of this sub-annex. Additional discussion is provided in Ogle et al. (2007, 2010)
Figure A-14: Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century
Model
IPCC Tier 2 Method Description
The IPCC Tier 2 method has been developed to estimate C stock changes and CO2 fluxes between soils and the
atmosphere based on land-use and management activity (IPCC 2003, 2006; Ogle et al. 2003). For mineral soils (i.e., all
soil orders from the USDA taxonomic classification except Histosols), the Tier 2 method uses reference C values to
establish baseline C stocks that are modified based on agricultural activities using land-use change, tillage, and input
factors. The standard IPCC approach was modified to use agricultural SOC stocks as the reference condition, rather than
uncultivated soils under native vegetation. This modification was needed because soil measurements under agricultural
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management are much more common and easily identified in the National Soil Survey Characterization Database (NRCS
1997). Measurements of soils under native vegetation are uncommon in the major agricultural regions of the United States
because most of the area has been converted into cropland. In addition, country-specific factors were derived for land-use
change, tillage, and input factors.
Organic soils used for agricultural production are treated in a separate calculation. These soils are made up of
deep (greater than 30 cm) layers of organic material that can decompose at a steady rate over several decades following
drainage for crop production or grazing (IPCC 2006). The IPCC approach uses an emission factor to estimate annual
losses of CO2 from cultivated organic soils, rather than an explicit stock change approach.
Methodological Steps for Derivation of Soil Organic C Stock Change Estimates
The inventory of soil C stock changes in U.S. agricultural land combines Tier 2 and 3 approaches. A simulation-
based Tier 3 approach was used to estimate soil C changes for most agricultural land (approximately 90 percent of total
cropland and grassland) comprising the dominant cropping and grazing systems in the United States, for which the model
has been well-tested. Estimates for the remaining area, comprising less-common crop systems (e.g., horticultural,
vegetable, tobacco, rice), land converted between non-agricultural and agricultural uses, and all agricultural land occurring
on drained organic soils, were developed using the Tier 2 approach. Tier 2 methods were also used to estimate additional
changes in mineral soil C stocks due to sewage sludge additions to soils, and enrollment changes in the Conservation
Reserve Program after 2003. Most of the activity data sources were common to the Tier 2 and Tier 3 approaches, and,
hence, they are described in an integrated manner below. Additional activity data required for the methods are described
in adjoining sections, followed by the computation steps.
Step 1: Derive Activity Data
Activity data were compiled for the Tier 3 Century biogeochemical model and Tier 2 IPCC methods, including
climate data, soil characteristics, and land-use/management activity data. The first step was to obtain land-
use/management activity data, and determine the land base for areas under agricultural management. The areas modeled
with Century and those estimated with the Tier 2 IPCC method were also subdivided. Finally, additional data, specific to
each method, were collected on other key management activities (e.g., tillage management, fertilizer and manure addition
rates) and environmental conditions (e.g., climate and soil characteristics).
Step la: Determine the Land Base and Classify Management Systems
Land Base—The National Resources Inventory (NRI) provided the basis for identifying the U.S. agricultural
land base on non-federal lands, and classifying parcels into Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to Grassland (USDA-NRCS 2000). Note that the Inventory does
not include estimates of C stock changes for grasslands and a minor amout of croplands on federal lands, even though
these areas are part of the managed land base for the United States. C stock changes on federal croplands and grasslands
will be further evaluated and included in future inventories. The NRI has a stratified multi-stage sampling design, where
primary sample units are stratified on the basis of county and township boundaries defined by the U. S. Public Land Survey
(Nusser and Goebel 1997). Within a primary sample unit, typically a 160-acre (64.75 ha) square quarter-section, three
sample points are selected according to a restricted randomization procedure. Each point in the survey is assigned an area
weight (expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). In principle,
the expansion factors represent the amount of area with the land use and land use change history that is the same as the
point location. It is important to note that the NRI is a sampling of land use, and therefore there is some uncertainty
associated with scaling the point data to a region or the country using the expansion factors. In general, those uncertainties
decline at larger scales, such as states compared to smaller county units, because of a larger sample size. An extensive
amount of soils, land-use, and land management data have been collected through the survey, which occurs every five
years (Nusser et al. 1998).65 Primary sources for data include aerial photography and remote sensing imagery as well as
field visits and county office records. The annual NRI data product provides crop data for most years between 1979 and
2003, with the exception of 1983, 1988, and 1993. These years were gap-filled using an automated set of rules so that
cropping sequences were filled with the most likely crop type given the historical cropping pattern at each NRI point
location. Grassland data were reported on 5-year increments prior to 1998, but it was assumed that the land use was also
grassland between the years of data collection (see Easter et al. 2008 for more information).
65 In the current Inventory, NRI data only provide land-use and management statistics through 2003, but additional data will be
incorporated in the future to extend the time series of land use and management data.
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NRI points were included in the land base for the agricultural soil C inventory if they were identified as cropland
or grassland between 1990 and 2003 (Table A-230). The most recent national-level data available for NRI were for
2003; and so the designation for 2003 was extended to 2009 in order to provide C stock changes over the entire time
series. An additional modification was made to the time series from 2004 to 2009 for Grassland Remaining Grassland and
Land Converted to Grassland associated with the modification of NRI data with the Forest Inventory and Analysis
Dataset. Overall, more than 260,000 NRI points were included in the inventory calculations, and the total agricultural land
base varied from 370 to 367 million hectares from 1990 through 2009. Each NRI point represents a specific land parcel
based upon the weighted expansion factors.
For each year, land parcels were subdivided into Cropland Remaining Cropland, Land Converted to Cropland,
Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a
specific year were classified as Cropland Remaining Cropland if they had been cropland for at least 20 years. Similarly
land parcels under grassland management in a specific year of the inventory were classified as Grassland Remaining
Grassland if they had been designated as grassland for at least 20 years.67 Otherwise, land parcels were classified as Land
Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands
are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland) for 20
years as recommended by the IPCC guidelines (IPCC 2006).
Table A-230: Total Land Areas for the Agricultural Soil C Inventory. Subdivided by Land Use Categories [Million Hectares!
Land Areas (106 ha)
Category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Mineral Soils
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Non-Agricultural Uses3
Organic Soils
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Non-Agricultural Uses3
Total
166.38
14.01
176.03
10.10
2.46
0.70
0.07
0.50
0.04
0.04
370.32
166.14
14.24
175.67
10.23
2.46
0.70
0.07
0.50
0.04
0.04
370.10
165.89
14.41
175.38
10.44
2.46
0.70
0.07
0.50
0.04
0.04
369.93
163.32
14.23
172.75
9.94
8.08
0.72
0.03
0.48
0.07
0.05
369.66
162,
15,
171,
10,
8,
0,
0,
.27
.71
.52
.51
.08
.72
.03
0.48
0,
0,
369.
.07
.05
43
161.95
15.88
171.43
10.77
8.08
0.72
0.03
0.48
0.07
0.05
369.45
161.61
16.24
171.20
10.91
8.08
0.72
0.03
0.48
0.07
0.05
369.40
161.26
16.37
171.12
11.27
8.08
0.72
0.03
0.48
0.07
0.05
369.44
158.40
17.79
169.79
13.73
8.08
0.72
0.03
0.48
0.07
0.05
369.13
158.67
17.43
169.70
13.90
8.08
0.72
0.03
0.48
0.07
0.05
369.12
Category
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Mineral Soils
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Non-Agricultural Uses3
Organic Soils
Cropland Remaining Cropland
Land Converted to Cropland
Grassland Remaining Grassland
Land Converted to Grassland
Non-Agricultural Uses3
Total
158.83
17.09
169.65
14.24
8.08
0.72
0.03
0.48
0.07
0.05
369.24
158,
16,
169,
14,
8,
0,
0,
.87
.83
.50
.64
.08
.72
.03
0.48
0,
0,
.07
.05
369.27
159.56
16.09
169.97
14.28
8.08
0.72
0.03
0.48
0.07
0.05
369.32
160.72
14.87
170.26
13.98
8.08
0.72
0.03
0.48
0.07
0.05
369.25
160.72
14.87
170.04
13.91
8.08
0.72
0.03
0.48
0.07
0.05
368.95
160.72
14.87
169.78
13.83
8.08
0.72
0.03
0.48
0.07
0.05
368.62
160.72
14.87
169.52
13.74
8.08
0.72
0.03
0.48
0.07
0.05
368.27
160.72
14.87
169.25
13.66
8.08
0.72
0.03
0.48
0.07
0.05
367.93
160.72
14.87
168.99
13.58
8.08
0.72
0.03
0.48
0.07
0.05
367.58
160.72
14.87
168.73
13.50
8.08
0.72
0.03
0.48
0.07
0.05
367.23
a The non-agricultural uses were converted to or from cropland or grassland between 1990 and 2003.
Subdivide Land Base for Tier 2 and 3 Inventory Appro-aches-The Tier 3 method based on application of the
Century model was used to model NRI points on most mineral soils. Parcels of land that were not simulated with Century
were allocated to the Tier 2 approach, including (1) land parcels occurring on organic soils; (2) land parcels that included
non-agricultural uses such as forest and federal lands in one or more years of the inventory;68 (3) land parcels on mineral
Includes non-federal lands only, because federal lands are not classified into land uses as part of the NRI survey (i.e, they are only
designated as federal lands).
67 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and consequently the
classifcations were based on less than 20 years from 1990 to 2001.
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soils that were very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent of soil volume
comprised of gravel, cobbles, or shale); or (4) land parcels that were used to produce vegetables, perennial/horticultural
crops, tobacco or rice, which was either grown continuously or in rotation with other crops. Century has not been fully
tested for non-major crops, horticultural or perennial crops, rice and agricultural use of organic soils. In addition, Century
has not been adequately tested for soils with a high gravel, cobble, or shale content, or fully tested for the transitions
between agricultural and non-agricultural uses.
Table A-231: Total Land Area Estimated with Tier 2a and 3 Inventory Approaches (Million Hectares)
Land Areas (106 ha)
Year Tier 2* Tier 3 Total
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
47.27
47.27
47.27
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
47.25
323.05
322.83
322.66
322.41
322.17
322.19
322.14
322.19
321.88
321.87
321.98
322.01
322.07
321.99
321.70
321.36
321.02
320.67
320.32
319.98
370.32
370.10
369.93
369.66
369.43
369.45
369.40
369.44
369.13
369.12
369.24
369.27
369.32
369.25
368.95
368.62
368.27
367.93
367.58
367.23
a Land use data for 1998-2003 are based on the Revised 1997 NRI data product for the Tier 2 method. Consequently, area data estimates in this
table are not used for the Tier 2 portion of the Inventory.
Management System Classification—NRI points on mineral soils were classified into specific crop rotations,
continuous pasture/rangeland, and other non-agricultural uses for the Tier 2 inventory analysis based on the survey data
(Table A-232). NRI points were assigned to IPCC input categories (low, medium, high, and high with organic
amendments) according to the classification provided in IPCC (2006). In addition, NRI differentiates between improved
and unimproved grassland, where improvements include irrigation and interseeding of legumes. In order to estimate
uncertainties, PDFs for the NRI land-use data were constructed as multivariate normal based on the total area estimates for
each land-use/management category and associated covariance matrix. Through this approach, dependencies in land use
were taken into account resulting from the likelihood that current use is correlated with past use.
For the Tier 3 inventory estimates, the actual cropping and grassland histories were simulated with the Century
model so it was not necessary to classify NRI points into management systems. Uncertainty in the areas associated with
each management system was determined from the estimated sampling variance from the NRI survey (Nusser and Goebel
1997). See Step 2b for additional discussion.
Table A-232: Total Land Areas by Land-Use and Management System for the Tier 2 Approach [Million Hectares)
Land Areas (106 ha)
Land-Use/Management System 1990-92 (Tier 2) 1993-2009 (Tier 2)
Cropland Systems
Irrigated Crops
Continuous Row Crops
Continuous Small Grains
Continuous Row Crops and Small Grains
Row Crops in Rotation with Hay and/or Pasture
Small Grains in Rotation with Hay and/or Pasture
Row Crops and Small Grains in Rotation with Hay and/or Pasture
31.53
7.27
4.12
1.25
2.30
0.30
0.06
0.03
29.25
6.91
3.63
1.04
1.95
0.23
0.06
0.04
Federal land is treated as forest or nominal grassland for purposes of these calculations, although the specific use is not identified in
the NRI survey (USDA-NRCS 2000). Future inventories will include C estimation for the disaggegrated land use and land use change
categories on federal lands.
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Vegetable Crops
Low Residue Annual Crops (e.g., Tobacco or Cotton)
Small Grains with Fallow
Row Crops and Small Grains with Fallow
Row Crops with Fallow
Miscellaneous Crop Rotations
Continuous Rice
Rice in Rotation with other crops
Continuous Perennial or Horticultural Crops
Continuous Hay
Continuous Hay with Legumes or Irrigation
CRP
Aquaculture
Grassland Systems
Rangeland
Continuous Pasture
Continuous Pasture with Legumes or Irrigation (i.e., improved)
CRP
Non-Agricultural Systems
Forest
Federal
Water
Settlements
Miscellaneous
Total
2.90
0.87
2.01
1.72
0.52
0.54
0.34
1.78
2.57
0.59
1.31
1.03
0.01
12.02
5.98
3.76
2.25
0.02
2.46
1.53
0.01
0.11
0.04
0.77
46.01
3.16
1.03
1.31
1.80
0.34
0.43
0.31
1.91
2.50
0.50
1.12
0.96
0.01
8.68
5.16
2.49
1.03
0.00
8.08
3.95
0.05
0.25
2.46
1.36
46.01
Organic soils are also categorized into land-use systems based on drainage (IPCC 2006). Undrained soils are
treated as having no loss of organic C. Drained soils are subdivided into those used for cultivated cropland, which are
assumed to have high drainage and greater losses of C, and those used for managed pasture, which are assumed to have
less drainage and smaller losses of C. Overall, the area of organic soils drained for cropland and grassland has remained
relatively stable since 1992 (see Table A-233).
Table A-233: Total Land Areas for Drained Organic Soils By Land Management Category and Climate Region (Million
Hectares!
Land Areas (106 ha)
IPCC Land-Use Category for Organic Soils
Undrained
Managed Pasture (Low Drainage)
Cultivated Cropland (High Drainage)
Other Land Usesa
Total
Cold Temperate
1992 1997
0.07
0.42
0.33
0.02
0.84
0.06
0.42
0.34
0.01
0.84
Warm Temperate
1992 1997
0.0020
0.0136
0.0971
0.0002
0.11
0.0017
0.0119
0.0974
0.0017
0.11
Tropical
1992 1997
0.12 0.09
0.07 0.08
0.19 0.20
0.00 0.02
0.39 0.39
"Urban, water, and miscellaneous non-cropland, which are part of the agricultural land base because these areas were converted from or into
agricultural land uses during the 1990s.
Step Ib: Obtain Additional Management Activity Data for the Tier 3 Century Model
Tillage Practices—Tillage practices were estimated for each cropping system based on data compiled by the
Conservation Technology Information Center (CTIC 1998). CTIC compiles data on cropland area under five tillage
classes by major crop species and year for each county. Because the surveys involve county-level aggregate area, they do
not fully characterize tillage practices as they are applied within a management sequence (e.g., crop rotation). This is
particularly true for area estimates of cropland under no-till, which include a relatively high proportion of "intermittent"
no-till, where no-till in one year may be followed by tillage in a subsequent year. For example, a common practice in
maize-soybean rotations is to use tillage in the maize crop while no-till is used for soybean, such that no-till practices are
not continuous in time. Estimates of the area under continuous no-till were provided by experts at CTIC to account for
intermittent tillage activity and its impact on soil C (Towery 2001).
Tillage practices were grouped into 3 categories: full, reduced, and no-tillage. Full tillage was defined as multiple
tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface residue
coverage. This definition corresponds to the intensive tillage and "reduced" tillage systems as defined by CTIC (1998).
No-till was defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-till is applied
to all crops in the rotation. Reduced tillage made up the remainder of the cultivated area, including mulch tillage and ridge
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tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for different
crops, rotations, and regions to represent the three tillage classes were derived from the 1995 Cropping Practices Survey
by the Economic Research Service (ERS 1997).
Tillage data were further processed to construct probability distribution functions (PDFs) using CTIC tillage
data. Transitions between tillage systems were based on observed county-level changes in the frequency distribution of
the area under full, reduced, and no-till from the 1980s through 2004. Generally, the fraction of full tillage decreased
during this time span, with concomitant increases in reduced till and no-till management. Transitions that were modeled
and applied to NRI points occurring within a county were full tillage to reduced and no-till, and reduced tillage to no-till.
The remaining amount of cropland was assumed to have no change in tillage (e.g., full tillage remained in full tillage).
Transition matrices were constructed from CTIC data to represent tillage changes for three time periods, 1980-1989, 1990-
1999, 2000-2009. Areas in each of the three tillage classes—full till (FT), reduced till (RT), no-till (NT)—in 1989 (the
first year the CTIC data were available) were used for the first time period, data from 1997 were used for the second time
period, and data from 2004 were used for the last time period. Percentage areas of cropland in each county were
calculated for each possible transition (e.g., FT^FT, FT^RT, FT^NT, RT^RT, RT^NT) to obtain a probability for
each tillage transition at an NRI point. Since continuous NT constituted < 1 percent of total cropland prior to 1990, there
were no transitions for NT^FT or NT^NT. Uniform probability distributions were established for each tillage scenario
in the county. For example, a particular crop rotation had 80 percent chance of remaining in full tillage over the two
decades, a 15 percent chance of a transition from full to reduced tillage and a 5 percent chance of a transition from full to
no-till. The uniform distribution was subdivided into three segments with random draws in the Monte Carlo simulation
(discussed in Step 2b) leading to full tillage over the entire time period if the value was greater than or equal to 0 and less
than 80, a transition from full to reduced till if the random draw was equal to or greater than 80 and less than 95, or a
transition from full to no-till if the draw was greater than or equal to 95. See step 2b for additional discussion of the
uncertainty analysis.
Mineral Fertilizer Application—Data on nitrogen fertilizer rates were obtained primarily from USDA's
Economic Research Service's 1995 Cropping Practices Survey (ERS 1997). In this survey, data on inorganic nitrogen
fertilization rates were collected for major crops (corn, cotton, soybeans, potatoes, winter wheat, durum wheat, and other
spring wheat) in the key crop producing states. Note that all wheat data were combined into one category and assumed to
represent small grains in general. Estimates for sorghum fertilizer rates were derived from corn rates using a ratio of
national average corn fertilizer rates to national average sorghum fertilizer rates derived from additional publications
(NASS 2004, 1999, 1992; ERS 1988; Grant and Krenz 1985; USDA 1954, 1957, 1966).
The ERS survey parameter "TOT N" (total amount of N applied per acre), with a small number of records
deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for
a region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state at the
finest resolution available. State-level data were produced for surveyed states if a minimum of 15 data points existed for
each of the two categories (irrigated and rainfed). If a state was not surveyed for a particular crop or if fewer than 15 data
points existed for one of the categories, then data at the Farm Production Region level were substituted. If Farm
Production Region data were not available, then U.S.-level estimates (all major states surveyed) were used in the
simulation for that particular crop in the state lacking sufficient data. Note that standard deviations for fertilizer rates on
log scale were used to construct PDFs on a log-normal scale, in order to address uncertainties in application rates (see Step
2b for discussion of uncertainty methods).
Manure Application—County-level manure N addition estimates were obtained from the Natural Resources
Conservation Service (Edmonds et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of
Agriculture, NRCS has coupled estimates of manure nitrogen produced with estimates of manure nitrogen recoverability
by animal waste management system to produce county-level estimates of manure nitrogen applied to cropland and
pasture. Edmonds et al. (2003) defined a hierarchy of land use systems to which manure is applied, that included 24 crops,
cropland used as pasture, and permanent pasture. They estimated the area amended with manure and manure nitrogen
application rates in 1997 for both manure-producing farms and manure-receiving farms within a county, for two
scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation.
The application rates for the baseline scenario were used in the inventory under the assumption that Comprehensive
Nutrient Management Plans have not been fully implemented.
In order to derive estimates of manure application rates over time, the availability of managed manure N for
application to soils (which are available annually) was used to adjust the amount of area amended with manure on a county
scale (Note: Edmonds et al. (2003) only provide information on application rates for 1997). Specifically, the estimated
available managed manure N in another year was divided by the managed manure N available in 1997. The amendment
area in a county for 1997 was then multiplied by the ratio to reflect the probability of manure amendments based on the
variation in available manure N across time. If more managed manure N was available in a given year for a county
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relative to the amount available in 1997 (ratio > 1), it was assumed that there was a higher probability of a manure
amendment. In contrast, if less managed manure N was available (ratio < 1), the probability of an amendment declined in
comparison to 1997. A detailed description of the derivation of the managed manure N availability data is provided in the
Manure Management section (Section 6.2) and Annex (Annex 3.10). Managed manure N availability in the 1980s was
based on USDA estimates (Kellogg et al. 2000) after adjusting for relative differences in manure N production between
the USDA dataset and estimates derived from the method described in Annex 3.10. Unmanaged manure classified as
pasture/range/paddock manure was assumed to have negligible impacts on soil C stocks because of the tradeoff between
reduced litterfall C versus C ingested by livestock and deposited on soils in manure.
For Century simulations, the amended areas were averaged for three time periods (1980-1989, 1990-1999, and
2000-2009) similar to the tillage transitions. Rates for manure-producing farms and manure-receiving farms have been
area-weighted and combined to produce a manure nitrogen application rate for each crop in a county. Several of the crops
in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all small
grain crops have been combined into one category. In order to address uncertainty, uniform probability distributions were
constructed based on the proportion of land receiving manure versus the amount not receiving manure for each crop type
and pasture. For example, if the 20 percent of land producing corn in a county was amended with manure, randomly
drawing a value equal to or greater than 0 and less than 20 would lead to simulation with a manure amendment, while
drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2b
for further discussion of uncertainty methods).
To estimate the C inputs associated with the manure N application rates (from Edmonds et al. 2003), C:N ratios
for various manure types (based on animal species and manure management system) were estimated from data in the
Agricultural Waste Management Field Handbook (USDA 1996) and the On-Farm Composting Handbook (NRAES 1992).
Weighted county-average C:N ratios for total manure applied were then calculated based on the C:N ratio and the manure
N production rate for each manure type reported in the county. Manure C addition rates were then calculated by
multiplying the county-average manure C:N ratio by the manure N application rates.
To account for the common practice of reducing inorganic nitrogen fertilizer inputs when manure is added to a
cropland soil, a set of crop-specific reduction factors were derived from mineral fertilization data for land amended with
manure versus land not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). In the
simulations, mineral N fertilization rates were reduced for crops receiving manure nitrogen based on a fraction of the
amount of manure nitrogen applied, depending on the crop and whether it was irrigated or a rainfed system. The reduction
factors were selected from PDFs with normal densities in order to address uncertainties in this dependence between
manure amendments and mineral fertilizer application.
Irrigation—NRI differentiates between irrigated and non-irrigated land but does not provide more detailed
information on the type and intensity of irrigation. Hence, irrigation was modeled by assuming that applied water was
sufficient to meet full crop demand (i.e., irrigation plus precipitation equaled potential evapotranspiration during the
growing season).
Step Ic—Obtain Additional Management Activity Data for Tier 2IPCC Method
Tillage Practices—PDFs were constructed for the CTIC tillage data, as bivariate normal on a log-ratio scale to
reflect negative dependence among tillage classes. This structure ensured that simulated tillage percentages were non-
negative and summed to 100 percent. CTIC data do not differentiate between continuous and intermittent use of no-
tillage, which is important for estimating SOC storage. Thus, regionally based estimates for continuous no-tillage (defined
as 5 or more years of continuous use) were modified based on consultation with CTIC experts, as discussed in Step la
(downward adjustment of total no-tillage acres reported, Towery 2001).
Manure Amendments—Manure management is also a key practice in agricultural lands, with organic
amendments leading to significant increases in SOC storage. USDA provides information on the amount of land amended
with manure for 1997 based on manure production data and field-scale surveys detailing application rates that had been
collected in the Census of Agriculture (Edmonds et al. 2003). Similar to the Century model discussion in Steplb, the
amount of land receiving manure was based on the estimates provided by Edmonds et al. (2003), as a proportion of crop
and grassland amended with manure within individual climate regions. The resulting proportions were used to re-classify
a portion of crop and grassland into a new management category. Specifically, a portion of medium input cropping
systems was re-classified as high input, and a portion of the high input systems was re-classified as high input with
amendment. In grassland systems, the estimated proportions for land amended with manure were used to re-classify a
portion of nominally-managed grassland as improved, and a portion of improved grassland as improved with high input.
These classification approaches are consistent with the IPCC inventory methodology (IPCC 2003, 2006). Uncertainties in
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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the amount of land amended with manure were based on the sample variance at the climate region scale, assuming normal
density PDFs (i.e., variance of the climate region estimates, which were derived from county-scale proportions).
Wetland Reserve—Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern
Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service. The area
of restored wetlands was estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide
reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to
estimate uncertainty. Consequently, a ±50 percent range was used to construct the PDFs for the uncertainty analysis.
Step Id—Obtain Management Activity Data to Compute Additional Changes in Soil Organic C Stocks in
Mineral Soils Due to Sewage Sludge Applications and CRP Enrollment after 2003
Two additional influences on soil organic C stocks in mineral soils were estimated using a Tier 2 method,
including: sewage sludge additions to agricultural soils and changes in enrollment for the Conservation Reserve Program
after 2003.
Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from an EPA
report (EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey (NEBRA 2007).
These values were linearly interpolated to estimate values for the intervening years. Sewage sludge generation data are
not available for 2005 onwards (Bastian 2007), so the 1990 through 2004 data were linearly extrapolated for the most
recent years. The total sludge generation estimates were then converted to units of N by applying an average N content of
3.9 percent (McFarland 2001), and disaggregated into use and disposal practices using historical data in EPA (1993) and
NEBRA (2007). The use and disposal practices were agricultural land application, other land application, surface disposal,
incineration, landfilling, ocean dumping (ended in 1992), and other disposal. Sewage sludge N was assumed to be applied
at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and
removed at harvest, representing the recommended application rate for manure amendments. This capacity varies from
year to year, because it is based on specific crop yields during the respective year (Kellogg et al. 2000). Total sewage
sludge N available for application was divided by the assimilative capacity to estimate the total land area over which
sewage sludge had been applied. The resulting estimates were used for the estimation of soil C stock change.
The change in enrollment for the Conservation Reserve Program after 2003 was based on the amount of land
under active contracts from 2004 through 2009 relative to 2003 (USDA-FSA 2010).
Step le: Obtain Climate and Soils Data
Tier 3 Century Model—Monthly weather data (temperature and precipitation) from the PRISM database
(Parameter-elevation Regressions on Independent Slopes Model) (Daly et al. 1994) were used as an input to the Century
model simulations for the period 1895 through 2003. PRISM is based on observed weather data from the National
Weather Service network database and statistical models for interpolation and orographic corrections. The primary
database consists of approximately 4x4 km grid cells. These data were averaged (weighted by area) for each county in the
United States, so that counties are the finest spatial scale represented in the Century simulations.
Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) were the main soil
variables used as input to the Century model. Other soil characteristics needed in the simulation, such as field capacity
and wilting-point water contents, were estimated from soil texture data using pedo-transfer functions available in the
model. Soil input data are derived from the NRI database, which contain descriptions for the soil type at each NRI point
(used to specify land-use and management time series-see below). The data are based on field measurements collected as
part of soil survey and mapping. Soils are classified according to "soil-series," which is the most detailed taxonomic level
used for soil mapping in the United States. Surface soil texture and hydric condition were obtained from the soil attribute
table in the NRI database. Texture is one of the main controls on soil C turnover and stabilization in the Century model,
which uses particle size fractions of sand (50-2,000 |im), silt (2-50 |im), and clay (< 2 um) as inputs. NRI points were
assigned to one of twelve texture classes for the simulations. Hydric condition specifies whether soils are poorly-drained,
and hence prone to water-logging, or moderately to well-drained (non-hydric), in their native (pre-cultivation) condition.69
Poorly drained soils can be subject to anaerobic (lack of oxygen) conditions if water inputs (precipitation and irrigation)
exceed water losses from drainage and evapotranspiration. Depending on moisture conditions, hydric soils can range from
being fully aerobic to completely anaerobic, varying over the year. Decomposition rates are modified according to a linear
Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
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function that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic conditions (default
parameters in Century).
IPCC Tier 2 Method—The IPCC inventory methodology for agricultural soils divides climate into eight distinct
zones based upon average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006)
(Table A-234). Six of these climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001).
Table A-234: Characteristics of the IPGG Climate Zones that Occur in the United States
Climate Zone
Cold Temperate, Dry
Cold Temperate, Moist
Warm Temperate, Dry
Warm Temperate, Moist
Sub-Tropical, Dry*
Sub-Tropical, Moist (w/short dry season)3
Annual Average
Temperature (°C)
<10
< 10
10-20
10-20
>20
>20
Average Annual Precipitation
(mm)
< Potential Evapotranspiration
> Potential Evapotranspiration
<600
> Potential Evapotranspiration
< 1,000
1,000-2,000
Length of Dry Season
(months)
NA
NA
NA
NA
Usually long
<5
a The climate characteristics listed in the table for these zones are those that correspond to the tropical dry and tropical moist zones of the IPCC.
They have been renamed "sub-tropical" here.
Mean climate (1961-1990) variables from the PRISM data set (Daly et al. 1994) were used to classify climate
zones. Mean annual precipitation and annual temperature data were averaged (weighted by area) for each of the 4x4 km
grid cells occurring within a MLRA region. These averages were used to assign a climate zone to each MLRA according
to the IPCC climate classification (Figure A-15). MLRAs represent geographic units with relatively similar soils, climate,
water resources, and land uses; and there are approximately 180 MLRAs in the United States (NRCS 1981).
Figure A-15: Major Land Resource Areas by IPGG Climate Zone
Soils were classified into one of seven classes based upon texture, morphology, and ability to store organic
matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks,
representing estimates from conventionally managed cropland, were computed for each of the mineral soil types across the
various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS
1997) (Table A-235). These stocks are used in conjunction with management factors to compute the change in SOC
stocks that result from management and land-use activity. PDFs, which represent the variability in the stock estimates,
were constructed as normal densities based on the mean and variance from the pedon data. Pedon locations were clumped
in various parts of the country, which reduces the statistical independence of individual pedon estimates. To account for
this lack of independence, samples from each climate by soil zone were tested for spatial autocorrelation using the
Moran's I test, and variance terms were inflated by 10 percent for all zones with significant p-values.
Table A-235: U.S. Soil Groupings Based on the IPGG Categories and Dominant Taxonomic Soil, and Reference Carbon
Stocks (Metric Tons C/ha)
Reference Carbon Stock in Climate Regions
IPCC Inventory USDA Taxonomic Soil
Soil Categories Orders
High Clay Activity Vertisols, Mollisols,
Mineral Soils Inceptisols, Aridisols, and
high base status Alfisols
Low Clay Activity Ultisols, Oxisols, acidic
Mineral Soils Alfisols, and many Entisols
Sandy Soils Any soils with greater than 70
percent sand and less than
8 percent clay (often
Entisols)
Volcanic Soils Andisols
Spodic Soils Spodosols
Aquic Soils Soils with Aquic suborder
Cold
Temperate,
Dry
42 (n =
45 (n =
24 (n
124 (n =
133)
= 37)
= 5)
= 12)
86 (n=20)
86 (n
= 4)
Cold
Temperate,
Moist
65 (n =
52 (n =
526)
113)
40 (n = 43)
114(n
= 2)
74 (n = 13)
89 (n =
161)
Warm
Temperate,
Dry
37 (n = 203)
25 (n = 86)
16 (n = 19)
124 (n = 12)
86 (n=20)
48 (n = 26)
Warm
Temperate,
Moist
51 (n =
40 (n =
30 (n =
424)
300)
102)
124 (n = 12)
107 (n
51 (n =
= 7)
300)
Sub-
Tropical,
Dry
42 (n
39 (n
33 (n =
124 (n
= 26)
= 13)
= 186)
= 12)
86 (n=20)
63 (n =
= 503)
Sub-
Tropical,
Moist
57 (n =
47 (n
50 (n =
128 (n
12)
= 7)
18)
= 9)
86 (n=20)
48 (n =
12)
Hydric soils are primarily subject to anaerobic conditions outside the plant growing season (i.e., in the absence of active plant water
uptake). Soils that are water-logged during much of the year are typically classified as organic soils (e.g., peat), which are not simulated
with the Century model.
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Organic Soils' Histosols NA NA NA NA NA NA
" C stocks are not needed for organic soils.
Notes: C stocks are for the top 30 cm of the soil profile, and were estimated from pedon data available in the National Soil Survey
Characterization database (NRCS 1997); sample size provided in parentheses (i.e., 'n' values refer to sample size).
Step 2: Estimate Organic C Stock Changes for Agricultural Lands on Mineral Soils Simulated with the
Tier 3 Century Model
This methodology description is divided into two sub-steps. First, the model was used to establish the initial
conditions and C stocks for 1979, which was the last year before the NRI survey was initiated. In the second sub-step,
Century was used to estimate changes in soil organic C stocks based on the land-use and management histories recorded in
the NRI (USDA-NRCS 2000), including the reporting period starting in 1990.
Step 2a: Simulate Initial Conditions (Pre-NRI Conditions)
Century model initialization involves two steps, with the goal of estimating the most accurate stock for the pre-
NRI history, and the distribution of organic C among the pools represented in the model (e.g., Structural, Metabolic,
Active, Slow, Passive). Each pool has a different turnover rate (representing the heterogeneous nature of soil organic
matter), and the amount of C in each pool at any point in time influences the forward trajectory of the total soil organic C
storage. There is currently no national set of soil C measurements that can be used for establishing initial conditions in the
model. Sensitivity analysis of the Century model showed that the rate of change of soil organic matter is relatively
insensitive to the amount of total soil organic C but is highly sensitive to the relative distribution of C among different
pools (Parton et al. 1987). By simulating the historical land use prior to the inventory period, initial pool distributions are
estimated in an unbiased way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation,
with long-term mean climate based on 30-yr averages of the PRISM data (1960-1990), and the soil physical attributes for
the NRI points. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United States.
The model was run for 7,000 years to represent a pre-settlement era and achieve a steady-state condition.
The second step is to run the model for the period of time from settlement to the beginning of the NRI survey,
representing the influence of historic land-use change and management, particularly the conversion of native vegetation to
agricultural uses. This encompasses a varying time period from land conversion (depending on historical settlement
patterns) to 1979. The information on historical cropping practices used for Century simulations was gathered from a
variety of sources, ranging from the historical accounts of farming practices reported in the literature (e.g., Miner 1998) to
national level databases (e.g., NASS 2004). A detailed description of the data sources and assumptions used in
constructing the base history scenarios of agricultural practices can be found in Williams and Paustian (2005).
Step 2b—Estimate Soil Organic C Stock Changes and Uncertainties
After estimating model initialization, the model is used to simulate the NRI land use and management histories
from 1979 through 2003.71 The simulation system incorporates a dedicated MySQL database server and a 24-node
parallel processing computer cluster. Input/output operations are managed by a set of run executive programs written in
PERL. The assessment framework for this analysis is illustrated in Figure A-16.
Figure A-16: Uncertainty in Data Inputs
Evaluating uncertainty was an integral part of the analysis, and included three components: (1) uncertainty in the
main activity data inputs affecting soil C balance (input uncertainty); (2) uncertainty in the model formulation and
parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling
uncertainty) (Ogle et al. 2010). For component 1, input uncertainty was evaluated for fertilization management, manure
applications, and tillage, which are the primary management activity data that were supplemental to the NRI observations
and have significant influence on soil C dynamics. As described in Step Ib, PDFs were derived from surveys at the
county scale in most cases. To represent uncertainty in these inputs, a Monte-Carlo Analysis was used with 100 iterations
for each NRI cluster-point in which random draws were made from PDFs for fertilizer, manure application, and tillage.
71 The estimated soil C stock change in 2003 is currently assumed to represent the changes between 2004 and 2009. New estimates will
be available in the future to extend the time series of land use and management data.
A-295
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As described above, an adjustment factor was also selected from PDFs with normal densities to represent the dependence
between manure amendments and N fertilizer application rates. The total number of Century simulations was over 12
million for the Monte Carlo Analysis with 100 iterations.
The second component dealt with uncertainty inherent in model formulation and parameterization. An
empirically-based procedure was employed to develop a structural uncertainty estimator from the relationship between
modeled results and field measurements from agricultural experiments (Ogle et al. 2007). The Century model was
initialized for 45 long-term field experiments with over 800 treatments in which soil C was measured under a variety of
management conditions (e.g., variation in crop rotation, tillage, fertilization rates, manure amendments). These studies
were obtained from an extensive search of published studies. All studies located in North America that met minimum
criteria of having sufficient site-level information and experimental designs were used, including C stock estimates,
texture data, experimental designs with control plots, and land-use and management records for the experimental time
period and pre-experiment condition. The inputs to the model were essentially known in the simulations for the long-term
experiments, and, therefore, the analysis was designed to evaluate uncertainties associated with the model structure (i.e.,
model algorithms and parameterization).
The relationship between modeled soil C stocks and field measurements was statistically analyzed using linear-
mixed effect modeling techniques. Additional fixed effects were included in the mixed effect model if they explained
significant variation in the relationship between modeled and measured stocks (i.e., if they met an alpha level of 0.05 for
significance). Several variables were tested including: land-use class; type of tillage; cropping system; geographic
location; climate; soil texture; time since the management change; original land cover (i.e., forest or grassland); grain
harvest as predicted by the model compared to the experimental values; and variation in fertilizer and residue
management. The final model included variables for organic matter amendments, fertilizer rates, inclusion of hay/pasture
in cropping rotations, use of no-till, and inclusion of bare fallow in the rotation, which were significant at an alpha level of
0.05. These fixed effects were used to make an adjustment to modeled values due to biases that were creating significant
mismatches between the modeled and measured stock values. Random effects captured the statistical dependence (i.e., the
data are not fully independent) in time series and data collected from the same long-term experimental site. Accounting
for this statistical dependency is needed to estimate appropriate standard deviations for parameter coefficients.
A Monte Carlo approach was used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for the
statistical equation (i.e., fixed effects) were selected from their joint probability distribution, as well as random error
associated with fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixed-
effect model. The stock estimate and associated management information was then used as input into the equation, and
adjusted stock values were computed for each C stock estimate produced in the evaluation of input uncertainty for
Cropland Remaining Cropland (Component 1 of the uncertainty analysis). Note that the uncertainty estimator needs
further development for application to Grassland Remaining Grassland and the land-use change categories. This
development is a planned improvement for the soil C inventory. The variance of the adjusted C stock estimates were
computed from the 100 simulated values from the Monte Carlo analysis.
The third element was the uncertainty associated with scaling the Century results for each NRI point to the entire
land base, using the expansion factors provided with the NRI database. The expansion factors represent the number of
hectares associated with the land-use and management history for a particular point. This uncertainty was determined by
computing the variances of the expanded estimates, accounting for the two-stage sampling design of the NRI.
For the land base that was simulated with the Century model, soil organic C stocks ranged from losses of 4.25 Tg
CO2 Eq. to gains of 68.05 Tg CO2 Eq. annually, depending on the land-use/land-use change category and inventory time
period. Estimates and uncertainties are provided in Table A-236.
Table A-236: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with
the Tier 3 Century Model-Based Approach [Tg Clh Eq.l
Year
1990
1991
1992
1993
1994
1995
Cropland Remaining
Cropland
Estimate 95% CI
(55.19) (11 1.72) to 1.33
(57.50) (90.49) to (24.51)
(68.05) (99. 81) to (36.28)
(64.25) (95.93) to (32.56)
(62.38) (94.48) to (30.29)
(47.58) (83.91)to(11.25)
Land Converted to
Cropland
Estimate 95% CI
(4.43) (5.06) to (3. 80)
(4.30) (4.93) to (3.68)
(4.75) (5.38) to (4. 13)
(8.66) (9.34) to (7.98)
(13. 42) to
(12.61) (11.80)
(3.84) (4. 51) to (3. 17)
Grassland Remaining
Grassland
Estimate 95% CI
(55.10) (57. 18) to
(53.01)
(29.87) to
(28.04) (26.21)
(9.84) (10. 82) to (8. 85)
(1.48) (3.29) to 0.33
(68.28) to
(66.74) (65.20)
(29.45) (30.78) to
(28.13)
Land Converted to Grassland
Estimate 95% CI
(15.75)
(15.06)
(13.83)
(13.52)
(17.87)
(18.21)
(18.43) to (13.07)
(17. 58) to (12.54)
(16.23) to (11.44)
(16.09) to (10.94)
(20.62) to (15. 12)
(20. 80) to (15.61)
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
(54.99)
(54.08)
(44.34)
(29.72)
(54.83)
(37.57)
(36.14)
(42.32)
(42.32)
(42.32)
(42.32)
(42.32)
(42.32)
(42.32)
(83. 65) to (26.32)
(8 1.53) to (26.63)
(77.06) to (11.61)
(58.21) to (1.23)
(85. 86) to (23. 80)
(68.05) to (7.08)
(67.43) to (4.85)
(69.67) to (14.98)
(69.67) to (14.98)
(69.67) to (14.98)
(69.67) to (14.98)
(69.67) to (14.98)
(69.67) to (14.98)
(69.67) to (14.98)
(4.55)
(4.21)
(10.77)
(3.30)
(4.41)
(2.53)
(1.97)
(0.84)
(0.84)
(0.84)
(0.84)
(0.84)
(0.84)
(0.84)
(5.24) to (3. 85)
(4.94) to (3.48)
(11. 60) to (9. 94)
(4.02) to (2.59)
(5. 15) to (3. 67)
(3 .24) to (1.83)
(2.68) to (1.25)
(1.54) to (0.13)
(1.54) to (0.13)
(1.54) to (0.13)
(1.54) to (0.13)
(1.54) to (0.13)
(1.54) to (0.13)
(1.54) to (0.13)
4.25
(20.44)
1.46
(18.92)
(55.15)
(27.34)
(46.81)
(11.65)
(11.49)
(11.32)
(11.14)
(10.97)
(10.79)
(10.62)
3.00 to 5. 50
(2 1.73) to
(19.14)
2.49 to 0.43
(19.78) to
(18.06)
(56.04) to
(54.26)
(28. 17) to
(26.51)
(47.62) to
(46.00)
(12. 50) to
(10.79)
(12.34) to
(10.64)
(12. 17) to
(10.47)
(11. 99) to
(10.29)
(11. 82) to
(10.12)
(11. 65) to (9.94)
(11. 47) to (9.77)
(15.19)
(19.90)
(17.33)
(23.95)
(23.11)
(24.03)
(22.72)
(20.69)
(20.51)
(20.31)
(20.10)
(19.90)
(19.69)
(19.48)
(17.78) to (12.60)
(22.64) to (17. 16)
(20.33) to (14.33)
(26.64) to (21.26)
(26.26) to (19.97)
(27.01) to (21.05)
(25.70) to (19.73)
(23. 50) to (17.89)
(23.31) to (17.70)
(23. 11) to (17.50)
(22.90) to (17.30)
(22.70) to (17.09)
(22. 49) to (16. 89)
(22.29) to (16.68)
Note: Does not include the change in storage resulting from the annual application of sewage sludge, or the additional Conservation Reserve
Program enrollment.
Step 3: Estimate C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2
Approach, in Addition to CO2 Emissions from Agricultural Lands on Drained Organic Soils
Mineral and organic soil calculations were made for each climate by soil zone across the United States. Mineral
stock values were derived for non-major crop rotations and land converted from non-agricultural uses to cropland in 1982,
1992, and 1997 based on the land-use and management activity data in conjunction with appropriate reference C stocks,
land-use change, tillage, input, and wetland restoration factors. C losses from organic soils were computed based on 1992
and 1997 land use and management in conjunction with the appropriate C loss rate. Each input to the inventory
calculations for the Tier 2 approach had some level of uncertainty that was quantified in PDFs, including the land-use and
management activity data, reference C stocks, and management factors. A Monte Carlo Analysis was used to quantify
uncertainty in SOC change for the inventory period based on uncertainty in the inputs. Input values were randomly
selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent confidence
interval for the inventory results.
Step 3a: Derive Mineral Soil Stock Change and Organic Soil Emission Factors
Stock change factors representative of U.S. conditions were estimated from published studies (Ogle et al. 2003,
Ogle et al. 2006). The numerical factors quantify the impact of changing land use and management on SOC storage in
mineral soils, including tillage practices, cropping rotation or intensification, and land conversions between cultivated and
native conditions (including set-asides in the Conservation Reserve Program), as well as the net loss of SOC from organic
soils attributed to agricultural production on drained soils. Studies from the United States and Canada were used in this
analysis under the assumption that they would best represent management impacts for the Inventory.
For mineral soils, studies had to report SOC stocks (or information to compute stocks), depth of sampling, and
the number of years since a management change to be included in the analysis. The data were analyzed using linear
mixed-effect modeling, accounting for both fixed and random effects. Fixed effects included depth, number of years since
a management change, climate, and the type of management change (e.g., reduced tillage vs. no-till). For depth
increments, the data were not aggregated for the C stock measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and
10-30 cm) was included as a separate point in the dataset. Similarly, time series data were not aggregated in these
datasets. Consequently, random effects were needed to account for the dependence in time series data and the dependence
among data points representing different depth increments from the same study. Factors were estimated for the effect of
management practices at 20 years for the top 30 cm of the soil (Table A-237). Variance was calculated for each of the
U.S. factor values, and used to construct PDFs with a normal density. In the IPCC method, specific factor values are
given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which
A-297
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influences the C balance of soils. Specifically, higher stocks are associated with increased productivity and C inputs
(relative to native grassland) on improved grassland with both medium and high input.72 Organic amendments in annual
cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in rice cultivation are
associated with reduced decomposition due to periodic flooding. There were insufficient field studies to derive factor
values for these systems from the published literature, and, thus, estimates from IPCC (2003) were used under the
assumption that they would best approximate the impacts, given the lack of sufficient data to derive U.S.-specific factors.
A measure of uncertainty was provided for these factors in IPCC (2003), which was used to construct PDFs.
Table A-237: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated with
Management Impacts on Mineral Soils
Land-Use Change Factors
Cultivated3
General Uncult.M>(n=251)
Set-Aside3 (n= 142)
Improved Grassland Factors'
Medium Input
High Input
Wetland Rice Production Factor0
Tillage Factors
Conv. Till
Red. Till (n=93)
No-till (n=2 12)
Cropland Input Factors
Low (n=85)
Medium
High (n=22)
High with amendment0
IPCC
default
1
1.4
1.25
1.1
Na
1.1
1
1.05
1.1
0.9
1
1.1
1.2
Warm Moist
Climate
1
1.42±0.06
1.31±0.06
1.14±0.06
1.11±0.04
1.1
1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
U.S. Factor
Warm Dry Cool Moist
Climate Climate
1
1.37±0.05
1.26±0.04
1.14±0.06
1.11±0.04
1.1
1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
1
1.24±0.06
1.14±0.06
1.14±0.06
1.11±0.04
1.1
1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
Cool Dry
Oimate
1
1.20±0.06
1.10±0.05
1.14±0.06
1.11±0.04
1.1
1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
Note: The "n" values refer to sample size.
a Factors in the IPCC documentation (IPCC 2006) were converted to represent changes in SOC storage from a cultivated condition rather than a
native condition.
b Default factor was higher for aquic soils at 1.7. The U.S. analysis showed no significant differences between aquic and non-aquic soils, so a
single U.S. factor was estimated for all soil types.
c U.S.-specific factors were not estimated for land improvements, rice production, or high input with amendment because of few studies
addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland
rice production in the US. Factors provided in IPCC (2003) were used as the best estimates of these impacts.
Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to
higher water tables and inundation of the soil for at least part of the year. A stock change factor was estimated assessing
the difference in SOC storage between restored and unrestored wetlands enrolled in the Conservation Reserve Program
(Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the first 10 years (Table A-
238). A PDF with a normal density was constructed from these data based on results from a linear regression model.
Following the initial increase of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The
mass accumulation rate of organic C was estimated using annual sedimentation rates (cm/yr) in combination with percent
organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate
are described in Dean and Gorham (1998); the resulting rate and variance were used to construct a PDF with a normal
density (Table A-238).
Table A-238: Factor Estimate for the Initial and Subsequent Increase in Organic Soil C Following Wetland Restoration of
Conservation Reserve Program
Variable
Value
Factor (Initial Increase—First 10 Years) 1.22±0.18
Mass Accumulation (After Initial 10 Years) 0.79±0.05 Mg C/ha-yr
Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002).
In addition, C loss rates were estimated for cultivated organic soils based on subsidence studies in the United
States and Canada (Table A-239). PDFs were constructed as normal densities based on the mean C loss rates and
associated variances.
72
Improved grasslands are identified in the 1991 National Resources Inventory as grasslands that were irrigated or seeded with legumes,
in addition to those reclassified as improved with manure amendments.
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Table A-239: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPGG Default
Rates (Metric Ton G/ha-yr)
Region
Cropland
IPCC U.S. Revised
Grassland
IPCC U.S. Revised
Cold Temperate, Dry & Cold Temperate, Moist
Warm Temperate, Dry & Warm Temperate, Moist
Sub-Tropical, Dry & Sub-Tropical, Moist
1
10
20
11.2±2.5
14.0±2.5
14.0±3.3
0.25
2.5
5
2.8±0.5a
3.5±0.8a
3.5±0.8a
a There were not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values
for cropland, which was an assumption used for the IPCC default organic soil C losses on grassland.
Step 3b: Estimate Annual Changes in Mineral Soil Organic C Stocks and CO2 Emissions from Organic Soils
In accordance with IPCC methodology, annual changes in mineral soil C were calculated by subtracting the
beginning stock from the ending stock and then dividing by 20.73 For this analysis, the base inventory estimate for 1990
through 1992 is the annual average of 1992 stock minus the 1982 stock. The annual average change between 1993 and
2009 is the difference between the 1997 and 1992 C stocks. Using the Monte Carlo approach, SOC stock change for
mineral soils was estimated 50,000 times between 1982 and 1992, and between 1992 and 1997. From the final
distribution of 50,000 values, a 95 percent confidence interval was generated based on the simulated values at the 2.5 and
97.5 percentiles in the distribution (Ogle et al. 2003).
For organic soils, annual losses of CO2 were estimated for 1992 and 1997 by applying the Monte Carlo approach
to 1992 and 1997 land-use data in the United States. The results for 1992 were applied to the years 1990 through 1992,
and the results for 1997 were applied to the years 1993 through 2009.
Mineral soils for the land base estimated with the Tier 2 approach accumulated about 1.7 to 3.0 Tg CO2 Eq.
annually in Cropland Remaining Cropland, while mineral soils in Land Converted to Cropland lost C at a rate of about
4.1 to 4.2 Tg CO2 Eq. annually. Minerals soils in Grassland Remaining Grassland had small gains of about 0.2 to 0.3 Tg
CO2 Eq. annually and sequestered from 4.6 to 5.0 Tg CO2 Eq. annually in Land Converted to Grassland. Organic soils lost
about 27.4 to 27.7 Tg CO2 Eq. annually in Cropland Remaining Cropland and 2.4 to 2.6 Tg CO2 Eq. annually in Land
Converted to Cropland, as well as an additional 3.7 to 3.9 Tg CO2 Eq. annually in Grassland Remaining Grassland (Table
A-240) and 0.5 to 0.9 Tg CO2 Eq. annually in Land Converted to Grassland. Estimates and uncertainties are provided in
Table A-240.
Table A-240: Annual Change in Soil Organic Carbon Stocks (05% Confidence Interval) for the Land Rase Estimated with
the Tier 2 Analysis using U.S. Factor Values, Reference Carbon Stocks, and Carbon Loss Rates (Tg Clh EgVyrl
Year
Mineral Soils
1990-1992
1993-2009
Organic
Soils
1990-1992
1993-2009
Cropland Remaining
Cropland
Estimate 95% CI
(1.65)
(3.01)
27.43
27.68
(2.6) to 5. 8
(6.9) to 0.8
18.3 to 39.4
18. 5 to
39.5
Land Converted to
Cropland
Estimate 95% CI
4.18
4.14
2.42
2.64
2. 5 to 6.0
2. 5 to 6.0
1.4 to 3. 8
1.5 to 4.0
Grassland Remaining
Grassland*
Estimate 95% CI
(0.33)
(0.15)
3.85
3.69
(0.6) to (0.1)
(0.4) to 0.04
1.97 to 6.4
1.9 to 6.1
Land Converted to
Grassland*
Estimate 95% CI
(4.55)
(4.99)
0.47
0.88
(6.5) to (2.7)
(7.2) to (2.9)
0.22 to 0.8
0.4 to 1.5
Preliminary estimates that will be finalized after public review period following completion of quality control measures.
Step 4: Compute Additional Changes in Soil Organic C Stocks Due to Organic Amendments and CRP
Enrollment after 2003
There are two additional land-use and management activities in U.S. agricultural lands that were not estimated in
Steps 2 and 3. The first activity involved the application of sewage sludge to agricultural lands. Minimal data exist on
where and how much sewage sludge is applied to U.S. agricultural soils, but national estimates of mineral soil land area
receiving sewage sludge can be approximated based on sewage sludge N production data, and the assumption that
amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). It was assumed that
sewage sludge for agricultural land application was applied to grassland because of the high heavy metal content and other
pollutants found in human waste, which limits its application to crops. The impact of organic amendments on SOC was
The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
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calculated as 0.38 metric tonnes C/ha-yr. This rate is based on the IPCC default method and country-specific factors (see
Table A- 241), by calculating the effect of converting nominal, medium-input grassland to high input improved grassland
(assuming a reference C stock of 50 metric tonnes C/ha, which represents a mid-range value for the dominant cropland
soils in the United States, the land use factor for grassland (1.4) and the country-specific factor of 1.11 for high input
improved grassland, with the change in stocks occurring over a 20 year (default value) time period; i.e., [50 x 1.4* 1.11 —
50 x 1.4] / 20 = 0.38). From 1990 through 2009, sewage sludge applications in agricultural lands increased SOC storage
from 0.6 to 1.3 Tg CO2 Eq./year (Table A- 241). A nominal ±50 percent uncertainty was attached to these estimates due
to limited information on application and the rate of change in soil C stock change with sewage sludge amendments.
The second activity was the change in enrollment for the Conservation Reserve Program after 2003 for mineral
soils. Relative to the enrollment in 2003, the total area in the Conservation Reserve Program decreased from 2004 to
2009, leading to a reduction in enrollment of 0.16 million ha over the five-year period (USDA-FSA 2009). An average
annual change in SOC of 0.5 metric tonnes C/ha-yr was used to estimate the effect of the enrollment changes. This rate is
based on the IPCC default method and country-specific factors (see Table A-237) by calculating the impact of setting
aside a medium input cropping system in the Conservation Reserve Program (assuming a reference C stock of 50 metric
tonnes C/ha, which represents a mid-range value for the dominant cropland soils in the United States and the average
country-specific factor of 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 yr
(default value) time period; i.e., [50 x 1.2 - 50] / 20 = 0.5). While increases in enrollment from 2004 to 2009 generated
additional accumulation of CO2 Eq. annually, reductions in enrollment in 2009 caused emissions of 0.29 Tg CO2 Eq
(Table A-242). A nominal ±50 percent uncertainty was also attached to these estimates due to limited information about
the enrollment trends at subregional scales, which creates uncertainty in the rate of the soil C stock change (stock change
factors for set-aside lands vary by climate region).
Step 5: Compute Net CO2 Emissions and Removals from Agricultural Lands
The sum of total CO2 emissions and removals from the Tier 3 Century Model Approach (Step 2), Tier 2 IPCC
Methods (Step 3) and additional land-use and management considerations (Step 4) are presented in Table A-242. Overall,
there was a net accumulation of 99.2 Tg CO2 Eq. in 1990 for agricultural soils, and this rate had decreased by the end of
the reporting period in 2009 to 43.4 Tg CO2 Eq.
The total stock change (as seen in the Land Use, Land-Use Change, and Forestry chapter) as well as per hectare
rate of change varies among the states (Figure A-17 and Figure A-18). On a per hectare basis, the highest rates of C
accumulation occurred in the Northeast, Midwest, northern Great Plains, and Northwest. The states with highest total
amounts of C sequestration were Iowa, Illinois, Missouri, Montana, Oklahoma, North Dakota, and South Dakota (Table
A- 243). For organic soils, emission rates were highest in the regions that contain the majority of the drained organic
soils, including California, Florida, Michigan, Minnesota, and New York. On a per hectare basis, the emission rate
patterns were very similar to the total emissions in each state, with the highest rates in those regions with warmer climates
and a larger proportion of the drained organic soil managed for crop production.
Figure A-17: Net G Stock Change, per Hectare.for Mineral Soils Under Agricultural Management, 2009
Figure A-18: Net G Stock Change, per Hectare.for Organic Soils Under Agricultural Management, 2009
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 241: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of Sewage Sludge to Mineral Soils
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Sewage Sludge N Applied
to Agricultural Land (Mg
N)a 52,198 55,658 59,250 62,977 65,966 69,001 72,081 75,195 78,353 80,932 83,523 86,124 88,736 91,358 93,991 98,081 100,887 103,682 106,468 109,245
Assimilative Capacity (Mg
N/ha)b 0.120 0.120 0.120 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122
Area covered by Available
Sewage Sludge N(ha)c 434,985 463,816 493,746 516,202 540,707 565,583 590,828 616,357 642,240 663,381 684,612 705,932 727,341 748,836 770,418 803,942 826,940 849,851 872,686 895,452
Average Annual Rate of C
storage (Mg C/ha-yr)
0.38 0.38 0.38 0.38
0.38
0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38
0.38 0.38
0.38
Contribution to Soil C
(TgC02/yr)e'f (0.61) (0.65) (0.69) (0.72) (0.75) (0.79) (0.82) (0.86) (0.89) (0.92) (0.95) (0.98) (1.01) (1.04) (1.07) (1.12) (1.15) (1.18) (1.22) (1.25)
Values in parentheses indicate net C storage.
aN applied to soils described in Step Id.
b Assimilative Capacity is the national average amount of manure-derived N that can be applied on cropland without buildup of nutrients in the soil (Kellogg et al., 2000).
c Area covered by sewage sludge N available for application to soils is the available N applied at the assimilative capacity rate. The 1992 assimilative capacity rate was applied to 1990 - 1992 and the 1997 rate was
applied to 1993-2009.
d Annual rate of C storage based on national average increase in C storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr)
° Contribution to Soil C is estimated as the product of the area covered by the available sewage sludge N and the average annual C storage attributed to an organic matter amendment.
f Some small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands.
Table A-242: Annual Soil G Stock Change in Cropland Remaining Cropland\.WE\, Land Converted to Cropland\\K$\, Grassland Remaining Grassland\SM\, and Land
Converted to Grassland [\R^\, in U.S. Agricultural Soils tTg Clh Eq.l
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
(55.0)
(4.5)
4.2
(15.2)
(54.1)
(4.2)
(20.4)
(19.9)
(44.3)
(10.8)
1.5
(17.3)
(29.7)
(3.3)
(18.9)
(24.0)
(54.8)
(4.4)
(55.2)
(23.1)
(37.6)
(2.5)
(27.3)
(24.0)
(36.1)
(2.0)
(46.8)
(22.7)
(42.3)
(0.8)
(11.7)
(20.7)
Net emissions based on Tier 3 Century-based analysis (Step 2)
CRC (55.2) (57.5) (68.0) (64.2) (62.4) (47.6)
LCC (4.4) (4.3) (4.8) (8.7) (12.6) (3.8)
GRG (55.1) (28.0) (9.8) (1.5) (66.7) (29.5)
LCG (15.8) (15.1) (13.8) (13.5) (17.9) (18.2)
Net emissions based on the IPCC Tier 2 analysis (Step 3)
Mineral
Soils
CRC
LCC
GRG
LCG
Organic Soils
CRC
LCC
GRG
LCG
Additional changes in net emissions from mineral soils based on application of sewage sludge to agricultural land (Step 4)
GRG (0.6) (0.6) (0.7) (0.7) (0.8) (0.8) (0.8) (0.9) (0.9) (0.9) (1.0) (1.0) (1.0) (1.0)
Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4)
CRC . . .
Total Stock Changes by Land Use/Land-Use Change Category (Step 5)
(42.3) (42.3)
(0.8) (0.8)
(11.5) (11.3)
(20.5) (20.3)
(42.3)
(0.8)
(11.1)
(20.1)
(42.3)
(0.8)
(11.0)
(19.9)
(42.3) (42.3)
(0.8) (0.8)
(10.8) (10.6)
(19.7) (19.5)
(1.6)
4.2
(0.3)
(4.5)
27.4
2.4
3.9
0.5
(1.6)
4.2
(0.3)
(4.5)
27.4
2.4
3.9
0.5
(1.6)
4.2
(0.3)
(4.5)
27.4
2.4
3.9
0.5
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(3.0)
4.1
(0.2)
(5.0)
27.7
2.6
3.7
0.9
(1.1) (1.1) (1.2) (1.2) (1.2) (1.3)
(0.4) (0.6) (1.4) (2.0) (0.4) 0.3
A-301
-------
CRC
LCC
GRG
LCG
Total *
(29.4)
2.2
(52.2)
(19.8)
(99.2)
(31.7) (42.3)
2.3 1.8
(25.2) (7.0)
(19.1) (17.9)
(73.7) (65.3)
(39.6) (37.7)
(1.9) (5.8)
1.3 (64.0)
(17.6) (22.0)
(57.8) (129.5)
(22.9) (30.3)
2.9 2.2
(26.7) 7.0
(22.3) (19.3)
(69.0) (40.4)
(29.4) (19.7)
2.6 (4.0)
(17.8) 4.1
(24.0) (21.4)
(68.6) (41.0)
(5.1) (30.2)
3.5 2.4
(16.3) (52.6)
(28.0) (27.2)
(46.0) (107.6)
(12.9) (11.5)
4.2 4.8
(24.8) (44.3)
(28.1) (26.8)
(61.6) (77.8)
(17.7) (18.1)
5.9 5.9
(9.2) (9.0)
(24.8) (24.6)
(45.7) (45.8)
(18.3) (19.1)
5.9 5.9
(8.9) (8.8)
(24.4) (24.2)
(45.6) (46.1)
(19.7)
5.9
(8.6)
(24.0)
(46.3)
(18.1) (17.4)
5.9 5.9
(8.5) (8.3)
(23.8) (23.6)
(44.4) (43.4)
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 243: Soil C Stock Change for Mineral and Organic Soils during 2009 within individual states (Tg G02 EqJ)
State Mineral Soil Organic Soil
AL
AR
AZ
CA
CO
CT
DE
FL
GA
HI
IA
ID
IL
IN
KS
KY
LA
MA
MD
ME
MI
MN
MO
MS
MT
NC
ND
NE
NH
NJ
NM
NV
NY
OH
OK
OR
PA
RI
SC
SD
TN
TX
UT
VA
VT
WA
WI
WV
WY
(0.45)
(1.09)
(0.80)
(0.16)
(0.54)
(0.06)
0.02
0.26
(0.21)
(0.02)
(4.67)
(1.53)
(5.16)
(1.72)
(2.34)
(1.90)
(0.93)
(0.02)
(0.19)
(0.17)
(2.28)
(2.57)
(9.54)
(1.19)
(6.10)
(0.35)
(6.03)
(2.03)
(0.04)
(0.09)
(1.18)
(0.23)
(1.83)
(2.91)
(6.48)
(2.13)
(1.85)
(0.01)
0.05
(5.86)
(1.85)
5.46
(0.02)
(0.50)
(0.28)
(2.19)
(2.48)
(0.41)
0.17
-
-
-
2.29
0.00
-
-
10.84
-
0.25
0.75
0.11
0.54
2.93
-
-
0.07
0.03
0.03
-
2.72
7.30
-
0.00
0.11
2.25
-
-
0.01
0.01
-
0.00
0.61
0.42
-
0.12
0.01
0.00
0.04
-
-
-
-
0.02
0.00
0.26
2.88
-
0.01
Total
(0.45)
(1.09)
(0. 80)
2.14
(0.54)
(0.06)
0.02
11.10
(0.21)
0.24
(3.92)
(1.43)
(4.61)
1.21
(2.34)
(1.90)
(0.86)
0.01
(0.16)
(0.17)
0.43
4.73
(9.54)
(1.18)
(5.99)
1.90
(6.03)
(2.03)
(0.03)
(0.08)
(1.18)
(0.23)
(1.23)
(2.49)
(6.48)
(2.00)
(1.84)
(0.00)
0.09
(5.86)
(1.85)
5.46
(0.02)
(0.48)
(0.27)
(1.93)
0.41
(0.41)
0.18
Note: Parentheses indicate net C accumulation. Estimates do not include soil C stock change associated with CRP enrollment after 2003 or
sewage sludge application to soils, which were only estimated at the national scale. The sum of state results will not match the national results
because state results are generated in a separate programming package, the sewage sludge and CRP enrollment after 2003 are not included, and
differences arise due to rounding of values in this table.
A-303
-------
3.14. Methodology for Estimating CELi Emissions from Landfills
Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in
solid waste. By volume, landfill gas is about half CH4 and half CO2.74 The amount and rate of CH4 generation depends
upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment.
Not all CH4 generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared
or utilized for energy, thus oxidizing to CO2 during combustion. Of the remaining CH4, a portion oxidizes to CO2 as it
travels through the top layer of the landfill cover. In general, landfill-related CO2 emissions are of biogenic origin and
primarily result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes. 75To
estimate the amount of CH4 produced in a landfill in a given year, information is needed on the type and quantity of waste
in the landfill, as well as the landfill characteristics (e.g., size, aridity, waste density). This information is not available for
the majority landfills in the United States. Consequently, to estimate CH4 generation, a methodology was developed based
on the quantity of waste placed in landfills nationwide each year, the first order decay model, and model parameters from
the analysis of measured CH4 generation rates for U.S. landfills with gas recovery systems.
From various studies and surveys of the generation and disposal of solid waste, estimates of the amount of waste
placed in MSW and industrial landfills were developed. A database of measured CH4 generation rates at landfills with gas
recovery systems was compiled and analyzed. The results of this analysis and other studies were used to develop an
estimate of the CH4 generation potential for use in the first order decay model. In addition, the analysis and other studies
provided estimates of the CH4 generation rate constant as a function of precipitation. The first order decay model was
applied to annual waste disposal estimates for each year and for three ranges of precipitation to estimate CH4 generation
rates nationwide for the years of interest. Based on the organic content of industrial wastes and the estimates of the
fraction of these wastes sent to industrial landfills, CH4 emissions from industrial landfills were also estimated using the
first order decay model. Total CH4 emissions were estimated by adding the CH4 from MSW and industrial landfills and
subtracting the amounts recovered for energy or flaring and the amount oxidized in the soil. The steps taken to estimate
CH4 emissions from U.S. landfills for the years 1990 through 2009 are discussed in greater detail below.
Figure A-19 presents the CH4 emissions process—from waste generation to emissions—in graphical format.
Step 1: Estimate Annual Quantities of Solid Waste Placed in Landfills
For 1989 to 2009, estimates of the annual quantity of waste placed in MSW landfills were developed from a
survey of State agencies as reported in BioCycle's State of Garbage in America (BioCycle 2008), adjusted to include U.S.
territories.76 The BioCycle survey is the only continually updated nationwide survey of waste disposed in landfills in the
United States.
Table A-244 shows estimates of waste quantities contributing to CH4 emissions. The table shows BioCycle
estimates of total waste landfilled each year from 1990 through 2000, 2002, 2004, and 2006 adjusted for U.S. territories.
A linear interpolation was used for 2001, 2003, 2005, 2007, 2008, and 2009 because there are no BioCycle surveys for
those years. The next BioCycle survey will be published in 2011 representing 2008 data, at which time, the waste
landfilled for 2007, 2008, and 2009 will be updated.
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
Table A-244: Solid Waste in MSW Landfills Contributing to Clh Emissions tTg unless otherwise noted!
Description
Total Waste Generated3
Percent of Wastes Landfilleda
1990
271
77% |
1995
302
63%
2000
377
61%
2001 2002
416 455
63% 66%
2003
462
65%
2004
470
64%
2005
459
64%
2006
448
65%
2007
452
65%
2008
456
65%
2009
460
65%
Typically, landfill gas also contains small amounts of nitrogen, oxygen, and hydrogen, less than 1 percent nonmethane volatile
organic compounds (NMVOCs), and trace amounts of inorganic compounds.
75 See Box 8-1 "Biogenic Emissions and Sinks of Carbon" in the Waste chapter for additional background on how biogenic emissions of
landfill CO2 are addressed in the U.S. Inventory.
76 Since the BioCycle survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using population data for
the U.S territories (U.S. Census Bureau 2010) and the per capita rate for waste landfilled from BioCycle (2008).
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Total Wastes Landfilleda 209M 190• 230 263 298 300 301 285 289 291 294 297
Waste in Place (30 years)" 4,674 5,075 I 5,400 5,488 5,608 5,759 5,909 6,058 6,198 6,328 6,458 6,587
Waste Contributing to
Emissions' 6,815 7,796 8,828 9,092 9,390 9,690 9,991 10,286 10,574 10,866 11,160 11,457
a Source: BioCycle (2008), adjusted for missing U.S. territories using U.S. Census Bureau (2010) population data and per capita disposal rate
from BioCycle. The data, originally reported in short tons, are converted to metric tons. Estimates shown for 2001 and 2003 are based on an
interpolation because there were no surveys in 2001 and 2003; estimates shown for 2005, 2007, 2008, and 2009 based on the increase in
population.
b This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the CH4 generation. Values
are based on EPA (1993).
c This estimate represents the cumulative amount of waste that has been placed in landfills from 1940 to the year indicated and is the sum of the
annual disposal rates used in the first order decay model. Values are based on EPA (1993).
Estimates of the annual quantity of waste placed in landfills from 1960 through 1988 were developed from
EPA's 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW landfills (EPA 1988). Based on the national
survey and estimates of the growth of commercial, residential and other wastes, the annual quantity of waste placed in
landfills averaged 127 million metric tons in the 1960s, 154 million metric tons in the 1970s, 190 million metric tons in the
1990s, and 285 million metric tons in the 2000's. Estimates of waste placed in landfills in the 1940s and 1950s were
developed based on U.S. population for each year and the per capital disposal rates from the 1960s.
Step 2: Estimate CH, Generation at Municipal Solid Waste Landfills
The CH4 generation was estimated from the integrated form of the first order decay (FOD) model using the
procedures and spreadsheets from IPCC (2006) for estimating CH4 emissions from solid waste disposal. The form of the
FOD model that was applied incorporates a time delay of 6 months after waste disposal before the generation of CH4
begins.
The input parameters needed for the FOD model equations are the mass of waste disposed each year, which was
discussed in the previous section, degradable organic carbon (DOC), and the decay rate constant (k). The DOC is
determined from the CH4 generation potential (L0 in m CH4/Mg waste), which is discussed in more detail in subsequent
paragraphs, and the following equation:
DOC = [L0 x 6.74 x 10'4] H- [F x 16/12 x DOCf x MCF]
Where,
DOC = degradable organic carbon (fraction, Gg C/Gg waste),
L0 = CH4 generation potential (m3 CH4/Mg waste),
6.74 x 10'4 = CH4 density (Mg/m3),
F = fraction of CH4 by volume in generated landfill gas (equal to 0.5)
16/12 = molecular weight ratio CH4/C,
DOCf = fraction of DOC that can decompose in the anaerobic conditions in the landfill (fraction equal
to 0.5 for MSW), and
MCF = methane correction factor for year of disposal (fraction equal to 1 for anaerobic managed sites).
The DOC value used in the CH4 generation estimates from MSW landfills is 0.203 based on the CH4 generation
potential of 100 m3 CH4/Mg waste as described below. Data from a set of 52 representative landfills across the U.S. in
different precipitation ranges were chosen to evaluate L0, and ultimately the country-specific DOC value. The 2004
Chartwell Municipal Solid Waste Facility Directory confirmed that each of the 52 landfills chosen accepted or accepts
both MSW and construction and demolition (C&D) waste (Chartwell 2004; RTI 2009).
The methane generation potential (L0) varies with the amount of organic content of the waste material. A higher
L0 occurrs with a higher content of organic waste. Waste composition data is not collected for all landfills nationwide; thus
a default value must be used. Values for L0 were evaluated from landfill gas recovery data for this set of 52 landfills,
which resulted in a best fit value for L0 of 99 m3/Mg of waste (RTI 2004). This value compares favorably with a range of
50 to 162 (midrange of 106) m /Mg presented by Peer, Thorneloe, and Epperson (1993); a range of 87 to 91 m /Mg from a
detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA 1998); and a value
of 100 m3/Mg recommended in EPA's compilation of emission factors (EPA 1998; EPA 2008) based on data from 21
landfills. Based on the results from these studies, a value of 100 m3/Mg appears to be a reasonable best estimate to use in
the FOD model for the national inventory.
A-305
-------
The FOD model was applied to the gas recovery data for the 52 landfills to calculate the rate constant (k) directly
for L0 = 100 m /Mg. The rate constant was found to increase with annual average precipitation; consequently, average
values of k were developed for three ranges of precipitation, shown in Table A- 245 and recommended in EPA's
compilation of emission factors (EPA 2008).
Table A- 245. Average Values for Rate Constant [hi by Precipitation Range [yr1]
Precipitation range (inches/year) k(yr )
<20 0.020
20-40 0.038
>40 0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's compilation
of emission factors (EPA 1998; EPA, 2008) recommends a value of 0.02 yr"1 for arid areas (less than 20 inches/year of
precipitation) and 0.04 yr"1 for non-arid areas. The SWANA study of 18 landfills reported a range in values of k from 0.03
to 0.06 yr" based on CH4 recovery data collected generally in the time frame of 1986 to 1995.
Using data collected primarily for the year 2000, the distribution of waste in place versus precipitation was
developed from over 400 landfills (RTI 2004). A distribution was also developed for population vs. precipitation for
comparison. The two distributions were very similar and indicated that population in areas or regions with a given
precipitation range was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using U.S.
Census data and rainfall data, the distributions of population versus rainfall were developed for each Census decade from
1950 through 2000. The distributions showed that the U.S. population has shifted to more arid areas over the past several
decades. Consequently, the population distribution was used to apportion the waste landfilled in each decade according to
the precipitation ranges developed for k, as shown in Table A-246.
Table A-246. Percent of U.S. Population within Precipitation Ranges [%1
Precipitation Range (inches/year)
<20
20-40
>40
1950
11
40
49
1960
13
39
48
1970
14
38
48
1980
16
36
48
1990
19
34
47
2000
20
33
47
Source: RTI (2004) using population data from the U.S. Bureau of Census and precipitation data from the National Climatic Data Center's
National Oceanic and Atmospheric Administration.
In developing the Inventory, the proportion of waste disposed of in managed landfills versus open dumps prior to
1980 was re-evaluated. Based on the historical data presented by Mintz et al. (2003), a timeline was developed for the
transition from the use of open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, it
was estimated that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94
percent was managed in open dumps. Between 1940 and 1980, the fraction of waste land disposed transitioned towards
managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For wastes disposed of in
dumps, a methane correction factor (MCF) of 0.6 was used based on the recommended IPCC default value for
uncharacterized land disposal (IPCC 2006); this MCF is equivalent to assuming 50 percent of the open dumps are deep
and 50 percent are shallow. The recommended IPCC default value for the MCF for managed landfills of 1 was used for
the managed landfills (IPCC 2006).
Step 3: Estimate CH, Generation at Industrial Landfills
Industrial landfills receive waste from factories, processing plants, and other manufacturing activities. In
national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven
percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). For the 1990
through 2007 and current inventories, the methodology was updated and improved by using activity factors (industrial
production levels) to estimate the amount of industrial waste landfilled each year and by applying the FOD model to
estimate CH4 generation. A nationwide survey of industrial waste landfills found that over 99 percent of the organic waste
placed in industrial landfills originated from two industries: food processing (meat, vegetables, fruits) and pulp and paper
(EPA 1993). Data for annual nationwide production for the food processing and pulp and paper industries were taken
from industry and government sources for recent years; estimates were developed for production for the earlier years for
which data were not available. For the pulp and paper industry, production data published by the Lockwood-Post's
Directory (ERG 2010) and U.S. Department of Agriculture (2010) were the primary sources for years 1965 through 2009.
An extrapolation based on U.S. real gross domestic product was used for years 1940 through 1964. For the food
processing industry, production levels were obtained or developed from the U.S. Department of Agriculture (2010) for the
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
years 1990 through 2009 (ERG 2010). An extrapolation based on U.S. population was used for the years 1940 through
1989.
In addition to production data for the pulp and paper and food processing industries, the following inputs were
needed to use the FOD model for estimating CH4 generation from industrial landfills: 1) quantity of waste that is disposed
in industrial landfills (as a function of production), 2) CH4 generation potential (L0) or DOC, and 3) FOD decay constant
(k). Research into waste generation and disposal in landfills for the pulp and paper industry indicated that the quantity of
waste landfilled was about 0.050 Mg/Mg of product compared to 0.046 Mg/Mg product for the food processing industry
(Weitz and Banner 2006). These factors were applied to estimates of annual production to estimate annual waste disposal
in landfills. Estimates for DOC were derived from available data (Kraft and Orender, 1993; NCASI 2008; Flores et al.
1999). The DOC value for industrial pulp and paper waste is estimated as 0.20 (L0 of 99 m3/Mg); the DOC value for
industrial food waste is estimated as 0.26 (L0 of 128 m3/Mg) (Coburn 2008). Estimates for k were taken from the default
values in the 2006 IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is 0.19
yr" , and the value given for paper waste is 0.06 yr" . A literature review was conducted for the current inventory year with
the intent of updating values for Lo and k in the pulp and paper industry. Insufficient data was obtained to warrant a
change for the current inventory year, but ongoing efforts may result in a U.S. industry-specific value rather than a default
IPCC value in future inventory years.
As with MSW landfills, a similar trend in disposal practices from open dumps to managed landfills was expected
for industrial landfills; therefore, the same time line that was developed for MSW landfills was applied to the industrial
landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste land disposed transitioned
from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed landfills in 1980 and on.
For wastes disposed of in dumps, an MCF of 0.6 was used and for wastes disposed of in managed landfills, an MCF of 1
was used, based on the recommended IPCC default values (IPCC 2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CH4 generation
from industrial landfills.
Step 4: Estimate CH, Emissions Avoided
The estimate of CH4 emissions avoided (e.g., combusted) was based on landfill-specific data on landfill gas-to-
energy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4
emissions avoided. The value for efficiency was selected based on the range of efficiencies (98 to 100 percent)
recommended for flares in EPA's "AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4," efficiencies used
to establish new source performance standards (NSPS) for landfills, and in recommendations for closed flares used in the
Landfill Methane Outreach Program (LMOP).
Step 4a: Estimate CH4 Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects
The quantity of CH4 avoided due to LFGTE systems was estimated based on information from two sources: (1)
a database maintained by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases
(EIA 2007) and (2) a database compiled by LMOP (EPA 2009). The EIA database included location information for
landfills with LFGTE projects, estimates of CH4 reductions, descriptions of the projects, and information on the
methodology used to determine the CH4 reductions. Generally the CH4 reductions for each reporting year were based on
the measured amount of landfill gas collected and the percent CH4 in the gas. For the LMOP database, data on landfill gas
flow and energy generation (i.e., MW capacity) were used to estimate the total direct CH4 emissions avoided due to the
LFGTE project. Detailed information on the landfill name, owner or operator, city, and state were available for both the
EIA and LMOP databases; consequently, it was straightforward to identify landfills that were in both databases. The EIA
database was given priority because reductions were reported for each year and were based on direct measurements.
Landfills in the LMOP database that were also in the EIA database were dropped to avoid double counting.
Step 4b: Estimate CH4 Emissions Avoided Through Flaring
The quantity of CH4 flared was based on data from the EIA database and on information provided by flaring
equipment vendors. To avoid double-counting, flares associated with landfills in the EIA and LMOP databases were
excluded from the flare vendor database. As with the LFGTE projects, reductions from flaring landfill gas in the EIA
database were based on measuring the volume of gas collected and the percent of CH4 in the gas. The information
provided by the flare vendors included information on the number of flares, flare design flow rates or flare dimensions,
year of installation, and generally the city and state location of the landfill. When a range of design flare flow rates was
provided by the flare vendor, the median landfill gas flow rate was used to estimate CH4 recovered from each remaining
A-307
-------
flare (i.e., for each flare not associated with a landfill in the EIA or LMOP databases). Several vendors provided
information on the size of the flare rather than the flare design gas flow rate. To estimate a median flare gas flow rate for
flares associated with these vendors, the size of the flare was matched with the size and corresponding flow rates provided
by other vendors. Some flare vendors reported the maximum capacity of the flare. An analysis of flare capacity versus
measured CH4 flow rates from the EIA database showed that the flares operated at 51 percent of capacity when averaged
over the time series and at 72 percent of capacity for the highest flow rate for a given year. For those cases when the flare
vendor supplied maximum capacity, the actual flow was estimated as 50 percent of capacity. Total CH4 avoided through
flaring from the flare vendor database was estimated by summing the estimates of CH4 recovered by each flare for each
year.
Step 4c: Reduce CH4 Emissions Avoided Through Flaring
As mentioned in Step 4b, flares in the flare vendor database associated with landfills in the EIA and LMOP
databases were excluded from the flare reduction estimates in the flare vendor database. If comprehensive data on flares
were available, each LFGTE project in the EIA and LMOP databases would have an identified flare because it is assumed
that most LFGTE projects have flares. However, given that the flare vendor data only covers approximately 50 to 75
percent of the flare population, an associated flare was not identified for all LFGTE projects. These LFGTE projects
likely have flares, yet flares were unable to be identified for one of two reasons: 1) inadequate identifier information in the
flare vendor data; or 2) a lack of the flare in the flare vendor database. For those projects for which a flare was not
identified due to inadequate information, CH4 avoided would be overestimated, as both the CH4 avoided from flaring and
the LFGTE project would be counted. To avoid overestimating emissions avoided from flaring, the CH4 avoided from
LFGTE projects with no identified flares was determined and the flaring estimate from the flare vendor database was
reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis. This step likely underestimates
CH4 avoided due to flaring but was applied to be conservative in the estimates of CH4 emissions avoided.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990-
2009 Inventory to reduce the total number of flares in the flare vendor database that were not matched (512) to landfills
and/or LFGTE projects in the EIA and LMOP databases. Each flare in the flare vendor database not associated with a
LFGTE project in the EIA or LMOP databases was investigated to determine if it could be matched to either a landfill in
the EIA database or a LFGTE project in the LMOP database. For some unmatched flares, the location information was
missing or incorrectly transferred to the flare vendor database. In other instances, the landfill names were slightly different
between what the flare vendor provided and the actual landfill name as listed in the EIA and LMOP databases.
It was found that a large majority of the unmatched flares are associated with landfills in LMOP that are
currently flaring, but are also considering LFGTE. These landfills projects considering a LFGTE project are labeled as
candidate, potential, or construction in the LMOP database. The flare vendor database was improved to match flares with
operational, shutdown as well as candidate, potential, and construction LFGTE projects, thereby reducing the total number
of unidentified flares in the flare vendor database, all of which are used in the flare correction factor. The results of this
effort significantly decreased the number of flares used in the flare correction factor, and consequently, increased
recovered flare emissions, and decreased net emissions from landfills for the 1990-2009 Inventory. The revised state-by-
state flare correction factors were applied to the entire 1990 to 2009 time series for the current Inventory year.
Step 5: Estimate CH, Oxidation
A portion of the CH4 escaping from a landfill oxidizes to CO2 in the top layer of the soil. The amount of
oxidation depends upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed
that of the CH4 generated, minus the amount of gas recovered for flaring or LFGTE projects, 10 percent was oxidized in
the soil (Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The factor of 10 percent is consistent
with the value recommended in the 2006 IPCC revised guidelines for managed and covered landfills, and was therefore
applied to the estimates of CH4 generation minus recovery for both MSW and industrial landfills
Literature reviews were conducted in 2006 and 2010 to provide recommendations for the most appropriate
oxidation rate assumptions. It was found that oxidation values are highly variable and range from zero to over 100 percent
(i.e., the landfill is considered to be an atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric
metane down through the cover). There is considerable uncertainty and variability surrounding estimates of oxidation
because it is difficult to measure and varies considerably with the thickness and type of the cover material, climate, and the
presence of cracks/fissures in the cover material through which methane can escape. IPCC (2006) notes that test results
from field and laboratory studies may lead to over-estimations of oxidation in landfill cover soils, because they largely
determine oxidation using uniform and homogeneous soil layers. In addition, a number of studies note that gas escapes
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
more readily through the side slopes of a landfill as compared to moving through the cover thus complicating the
correlation between oxidation and cover type or gas recovery.
Spokas et al (2005), in particular, helps to illustrate expected patterns (e.g., seasonality of generation,
effectiveness of gas recovery) associated with landfill methane production and flux through the cover system. This study
also highlights the large variability in oxidation between and within sites and ultimately reports oxidation ranging between
4-50 percent. All but one of the test sites had an active gas recovery system in place. For landfills with gas collection
systems, there have been studies to show that gas recovery increases oxidation because it slows the flux of methane
through the cover system. Although this may be true, there does not appear to be enough data to support the premise that
landfills with gas recovery systems increase oxidation. This is demonstrated by the Spokas et al (2005) data where the
oxidation rates were about the same for a landfill site with and without gas recovery. However, the site also had a thin
temporary cover so the oxidation would be less than a site with a final cover system. Based on this and other studies, there
does not appear to be adequate justification for increasing the default oxidation value for landfills with gas recovery.
Changing the oxidation rate from 10 percent to 20 percent has a minimal impact on the overall inventory results.
Changing the oxidation rate from 10 percent to 60 percent has a more significant impact on the overall inventory results,
lowering the overall inventory by as much as 15 to 20 percent.
The current default oxidation factor of 10 percent is recommended for all landfills in the Inventory until more
reliable, peer-reviewed data is available about the influence of climate, cover type, and gas recovery is better understood.
Step 6: Estimate Total CH, Emissions
Total CH4 emissions were calculated by adding emissions from MSW and industrial landfills, and subtracting
CH4 recovered and oxidized, as shown in Table A- 247.
A-309
-------
Table A- 247: CHa Emissions from Landfills tGgl
Activity
MSW Generation
Industrial Generation
Potential Emissions
Landfill Gas-to-Energy
Flare
Emissions Avoided
Oxidation at MSW Landfills
Oxidation at Industrial Landfills
Net Emissions
1990
8,219
549
8,768
(649)
(321)
(970)
(725)
(55)
7,018
1995
9,132
611
9,742
(1,070)
(1,298)
(2,368)
(676)
(61)
6,637
2000
9,854
682
10,536
(2,352)
I (2,276)
(4,628)
(523)
(68)
| 5,317
2001
10,068
695
10,763
(2,548)
(2,491)
(5,039)
(503)
(70)
5,152
2002
10,367
710
11,069
(2,589)
(2,738)
(5,326)
(504)
(70)
5,169
2003
10,754
710
11,464
(2,563)
(2,886)
(5,448)
(531)
(71)
5,414
2004
11,127
716
11,842
(2,673)
(3,364)
(6,037)
(509)
(72)
5,224
2005
11,486
724
12,210
(2,691)
(3,566)
(6,257)
(523)
(73)
5,358
2006
11,813
727
12,540
(2,807)
(3,820)
(6,628)
(518)
(73)
5,321
2007
12,107
732
12,839
(3,033)
(3,918)
(6,951)
(516)
(73)
5,299
2008
12,395
738
13,133
(3,189)
(3,810)
(7,000)
(540)
(74)
5,520
2009
12,679
744
13,423
(3,429)
(3,779)
(7,208)
(547)
(74)
5,593
Note: Totals may not sum exactly to the last significant figure due to rounding.
Note: MSW generation in
represents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation.
Note: Parentheses denote negative values.
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
References
BioCycle (2008) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N.
Themelis. BioCycle. December 2008. Available online at < http://www.jgpress.corn/archives/_free/001782.html>
Czepiel, P., B. Mosher, P. Grill, and R. Harriss (1996) "Quantifying the Effect of Oxidation on Landfill Methane
Emissions." Journal of Geophysical Research, 101(D11): 16721-16730.
Coburn, J. (2008) "Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food
Industry." Memorandum to M. Weitz (EPA), October 13, 2008.
EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at
.
Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group.
EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S.
Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993.
EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste
Landfills. November 1998.
EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency.
Washington, DC. EPA/530-SW-88-011. September 1988.
EPA (2008). Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste
Landfills. October 2008.
EPA (2009) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2009.
ERG (2010). Production Data Supplied by ERG for 1990-2009 for Pulp and Paper, Fruits and Vegetables, and Meat.
July.
Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (1999) "Quantification and Characterization of Food Processing
Wastes/Residues." Compost Science & Utilization, 7(1): 63-71.
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.
Jensen, J.E.F., and R. Pipatti (2002) "CH4 Emissions from Solid Waste Disposal." Background paper for the Good
Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories.
Kraft, D.L. and H.C. Orender (1993) "Considerations for Using Sludge as a Fuel." Tappi Journal, 76(3): 175-183.
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.
Mintz C., R. Freed, and M. Walsh (2003) "Timeline of Anaerobic Land Disposal of Solid Waste." Memorandum to T.
Wirth (EPA) and K. Skog (USDA), December 31, 2003.
National Council for Air and Stream Improvement, Inc. (NCASI) (2008) "Calculations Documenting the Greenhouse Gas
Emissions from the Pulp and Paper Industry." Memorandum to R. Nicholson (RTI), May 21, 2008.
Peer, R., S. Thorneloe, and D. Epperson (1993) "A Comparison of Methods for Estimating Global Methane Emissions
from Landfills." Chemosphere, 26(1-4):387-400.
RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills.
Memorandum to E. Scheehle (EPA), August 26, 2004.
RTI (2009) GHG Inventory Improvement - Construction & Demolition Waste DOC and Lo Value. Memorandum to R.
Schmeltz, April 15,2010.
Spokas, K.; Bogner, J.; Chanton, J.P.; Morcet, M.; Aran, C.; Graff, C.; Moreau-Le Golvan, Y.; and I. Hebe (2006).
Methane Mass Balance at Three Landfill Sites: What is the Efficiency of Capture by Gas Collection Systems? Waste
Management, 26(5): 516-525.
A-311
-------
Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane
Recovery. Publication No. GR-LG 0075. March 1998.
U.S. Bureau of Census (2010) International Data Base. August 2010. Available online at
.
Weitz, K. and M. Banner (2006) "Methane Emissions for Industrial Landfills." Memorandum to M. Weitz (EPA),
September 5, 2006.
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2009 (Tg CO2 Eq.)
cr
LLJ
O
Ol
2,500 -,
2,000 -
1,500 -
1,000
500
8
Non-Transportation Mobile Sources
Mobile AC, Refrig. Transport, Lubricants
Boats/Ships, Rail, and Pipelines
T-I rsl
en en
en en
01
en
en
ID
en
en
rx
en
en
a s
O
O
rsl
rsl
O
O
rsl
01
O
O
rsl
ID
o
o
rsi
rx
o
o o
rsl rsl
8 g
-------
Figure A-5
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
10
20
30
Soil Temperature
40
50
60
line
WFPS
1.2-
1 -
0.8-
0.6-
0.4-
0.2-
0.
10
PH
-------
Figure A-6
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
a.
o
35-
30-
25-
20-
15-
10-
5-
0
0
200
N03 jug N/g soil
300
400
a.
o
C02 jug C/g soil/day
blay-low resp
loam-low resp
20
60
100
WFPS %
-------
DAYCENT MODEL
S=soil type
V=vegtype
L=land use
PLANT
OMPONENTS
i
•y
i'
i
i
N03
0-1 cm
1-4 cm
4-15 cm
15-30 cm
etc.
NH4+
0-15 cm
•* x
* ^
\
DEAD PLANT
MATERIAL
STRUCTURAL
METABOLIC
i
Decomp\L — ON,
R^l
CO,
V '
SOM
ACTIVE
0.5-lyr
SLOW
10-50 yr
PASSIVE
1000-5000 yr
Figure A-7: DAYCENT model flow diagram
-------
D)
O
40 n
35 -
30 -
20 -
15 -
10 -
5 -
0 -
. DAYCEISTT
» IPCC
1-1 line
Linear (DAYCEISTT)
- • Linear (IPCC)
- - Linear (1-1 line)
y = 1.0765x - 0.6936
R2 = 0.9587
„ S -
y"=0.1688x+3.6132
R2 = 0.3931
0
10
20
NjO gN ha-1 day1
30
40
Figure A-8: Comparisons of Results from DAYCENT Model and IPCC Tier 1
Method with Measurements of Soil N20 Emissions
-------
Figure A-9
Major Crops, Average Annual Direct N20 Emissions Estimated Using the DAYCENT Model,
1990-2009 (Metric Tons C02 Eq./ha/year)
Metric Tons C02 Eq./ha/year
D<0.4
D 0.4-0.5
D 0.5-0.6
D 0.6-0.7
• 0.7-0.8
• 0.8-1
• 1
-------
Figure A-10
Major Crops, Average Annual N losses Leading to Indirect N20 Emissions Estimated Using the DAYCENT Model,
1990-2009 (kg N/ha/year)
kg N/ha/year
H < 10
H 10 to 20
H 20 to 40
H 40 to 60
• 60 to 80
II 80 to 100
• > 100
-------
Figure A-11
Grasslands, Average Annual Direct N20 Emissions Estimated Using the DAYCENT Model,
1990-2009 (Metric Tons C02 Eq./ha/year)
Metric Tons C02 Eq./ha/year
D<0.1
D 0.1-0.2
D 0.2-0.3
D 0.3-0.4
• 0.4-0.5
• 0.5-0.6
• >0.6
-------
Figure A-12
Grasslands, Average Annual N Losses Leading to Indirect N20 Emissions Estimated Using the DAYCENT Model,
1990-2009 (kg N/ha/year)
kg N/ha/year
D<5
D 5 to 10
n 10 to 15
D 15 to 20
• 20 to 30
• 30 to 50
• >50
-------
Figure A-13
Moisture, Temperature,
Nutrients,
Genetic potential
*
SURFACE
STRUCTURAL
C
STRUCC(1)
^•*k
. SURFACE
METABOLIC
C
MŁTABC(1)
BELOWGROUND
STFUJCTURAL
C
STHUCCT3
BELOWGROUND
METABOLIC
C
METABOO)
M - multiplier for effects of moisture,
temperature, cultivation
LEACH - H2O leached below 30 cm
SOMTC = SOM1C(2)*SOM2C*SOM3C
tSTRUC C<2)+METftB C<2)
SOMSC = SOM1C(2)*SOM2C*SOM3C
HI = multiplier for effects or moisture,
temperature, cultivation
THETMNd) - Annual Net N Mineralization
TMINRLd) - Total Soil Mineral N
SOMTE(I) - Total Soil Organic N
SOMSEd) - SOM1E(2,1) + SOM2E(1)
*SOM3E(1)
| ffLA"vER+l
AVER
I
Stormf
STREAM!^
-------
Figure A-14
Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century Model
•8
90-
70-
50-
V*
30-
30
50
70
90
Sqrt Modeled Soil Organic Carbon (g C m~2)
-------
Figure A-15
o
This figure shows the IPCC climate zone assigned to each of the 180 Major Land Resource Areas (MLRAs) in the United States, based on
PRISM climate data averaged for each MRLA.
-------
Figure A-16
I Manure additions I I Fertilizer rates | I Tillage practices I
Monthly weather I f-
I Sous data p-
.j\ /[L. ^
1 1
1 Run C ontrol 1 Databases t~
^ s r*i
1 "
1 Century 1
1
: Model uncertainty/"^ :
1 estimator ~U \ — •
. NJRI iinrertainfv _ / r\.,« .. /
|PCCaip|J\_ //
Uncertainty in data inputs (i.e. fertilizer, manure and tillage practices) are estimated using a Monte-Carlo procedure with 100 random draws from input
data probability distributions, for each NRI point simulated. Model uncertainty is estimated through an empirical-based approach. Uncertainty in the
land representation of NRI is estimated from the statistics compiled from the NRI surveys to determine the land area expansion factors, which are used
to upscale data to the national level.
-------
Figure A-17
Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2009
• o
Metric Tons C02 Eq./ha/year
D>0
D-0.05 toO
D-0.1 to-0.05
• -0.25to-0.1
•-0.5 to -0.25
• <-0.5
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.
-------
Figure A-18
Net C Stock Changes, per Hectare, for Organic Soils Under Agricultural Management, 2009
Metric Tons C02 Eq./year
• >1
• 0.5 to 1
D 0.25 to 0.5
DO.1toO.25
Do to 0.1
O organic soils
Note: Values greater than zero represent emissions.
-------
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
MSW and
Industrial
Waste
Generated"
MSW and
Industrial
Waste
Landfilled"
Recovered
for
Energy6
Total
Methane
Generated
at
Landfills'
Non-
recovered
Methane
Generated
at Landfills
Total
Methane
Emitted
a BioCycle 2006 for MSW and activity factors for industrial waste.
b 1960 through 1988 based on EPA 1988 and EPA 1993; 1989 through 2006 based on BioCycle 2006.
c 2006 IPCC Guidelines - First Order Decay Model.
11 EIA 2007 and flare vendor database.
e EIA 2007 and EPA (LMOP) 2007.
f2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996
-------
ANNEX 4 IPCC Reference Approach for
Estimating CO2 Emissions from Fossil
Fuel Combustion
It is possible to estimate carbon dioxide (CO2) emissions from fossil fuel consumption using alternative
methodologies and different data sources than those described in the Estimating Emissions from Fossil Fuel Combustion
Annex. For example, the UNFCCC reporting guidelines request that countries, in addition to their "bottom-up" sectoral
methodology, complete a "top-down" Reference Approach for estimating CO2 emissions from fossil fuel combustion.
Section 1.3 of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reporting Instructions states,
"If a detailed, Sectoral Approach for energy has been used for the estimation of CO2 from fuel combustion you are still
asked to complete... the Reference Approach... for verification purposes" (IPCC/UNEP/OECD/IEA 1997). This reference
method estimates fossil fuel consumption by adjusting national aggregate fuel production data for imports, exports, and
stock changes rather than relying on end-user consumption surveys. The basic principle is that once C-based fuels are
brought into a national economy, they are either saved in some way (e.g., stored in products, kept in fuel stocks, or left
unoxidized in ash) or combusted, and therefore the C in them is oxidized and released into the atmosphere. Accounting
for actual consumption of fuels at the sectoral or sub-national level is not required. The following discussion provides the
detailed calculations for estimating CO2 emissions from fossil fuel combustion from the United States using the IPCC-
recommended Reference Approach.
Step 1: Collect and Assemble Data in Proper Format
To ensure the comparability of national inventories, the IPCC has recommended that countries report energy data
using the International Energy Agency (IEA) reporting convention. National energy statistics were collected in physical
units from several EIA documents in order to obtain the necessary data on production, imports, exports, and stock
changes.
It was necessary to make a number of modifications to these data to generate more accurate apparent
consumption estimates of these fuels. The first modification adjusts for consumption of fossil fuel feedstocks accounted
for in the Industrial Processes chapter, which include the following: unspecified coal for coal coke used in iron and steel
production; natural gas, distillate fuel, and coal used in iron and steel production; natural gas used for ammonia
production; petroleum coke used in the production of aluminum, ferroalloys, titanium dioxide, ammonia, and silicon
carbide; and other oil and residual fuel oil used in the manufacture of C black. The second modification adjusts for the
fact that EIA energy statistics include synthetic natural gas in coal and natural gas data. The third modification adjusts for
the inclusion of ethanol in motor gasoline statistics. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic
carbon reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see
Chapter 7). The fourth modification adjusts for consumption of bunker fuels, which refer to quantities of fuels used for
international transportation estimated separately from U.S. totals. The fifth modification consists of the addition of U.S.
territories data that are typically excluded from the national aggregate energy statistics. The territories include Puerto
Rico, U.S. Virgin Islands, Guam, American Samoa, Wake Island, and U.S. Pacific Islands. These data, as well as the
production, import, export, and stock change statistics, are presented in Table A- 248.
The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of CO2 emissions,
fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics are
typically provided in physical units (as in Table A- 248 for 2009), they were converted to units of energy before CO2
emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion factors provided
by EIA. These factors and their data sources are displayed in Table A- 249. The resulting fuel type-specific energy data
for 2009 are provided in Table A- 250.
Step 2: Estimate Apparent Fuel Consumption
The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the country.
This requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes. In this
way, C enters an economy through energy production and imports (and decreases in fuel stocks) and is transferred out of
A-313
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the country through exports (and increases in fuel stocks). Thus, apparent consumption of primary fuels (including crude
oil, natural gas liquids, anthracite, bituminous, subbituminous and lignite coal, and natural gas) can be calculated as
follows:
Apparent Consumption = Production + Imports - Exports - Stock Change
Flows of secondary fuels (e.g., gasoline, residual fuel, coke) should be added to primary apparent consumption.
The production of secondary fuels, however, should be ignored in the calculations of apparent consumption since the C
contained in these fuels is already accounted for in the supply of primary fuels from which they were derived (e.g., the
estimate for apparent consumption of crude oil already contains the C from which gasoline would be refined). Flows of
secondary fuels should therefore be calculated as follows:
Secondary Consumption = Imports - Exports - Stock Change
Note that this calculation can result in negative numbers for apparent consumption of secondary fuels. This
result is perfectly acceptable since it merely indicates a net export or stock increase in the country of that fuel when
domestic production is not considered.
Next, the apparent consumption and secondary consumption need to be adjusted for feedstock uses of fuels
accounted for in the Industrial Processes chapter, international bunker fuels, and U.S. territory fuel consumption. Bunker
fuels and feedstocks accounted for in the Industrial Processes chapter are subtracted from these estimates, while fuel
consumption in U.S. territories is added.
The IPCC Reference Approach calls for estimating apparent fuel consumption before converting to a common
energy unit. However, certain primary fuels in the United States (e.g., natural gas and steam coal) have separate
conversion factors for production, imports, exports, and stock changes. In these cases, it is not appropriate to multiply
apparent consumption by a single conversion factor since each of its components has different heat contents. Therefore,
United States fuel statistics were converted to their heat equivalents before estimating apparent consumption. Results are
provided in Table A- 249.
Step 3: Estimate Carbon Emissions
Once apparent consumption is estimated, the remaining calculations are similar to those for the "bottom-up"
Sectoral Approach (see Estimating Emissions from Fossil Fuel Combustion Annex). Potential CO2 emissions were
estimated using fuel-specific C coefficients (see Table A- 250).77 The C in products from non-energy uses of fossil fuels
(e.g., plastics or asphalt) was then estimated and subtracted (see Table A-252). This step differs from the Sectoral
Approach in that emissions from both fuel combustion and non-energy uses are accounted for in this approach. Finally, to
obtain actual CO2 emissions, net emissions were adjusted for any C that remained unoxidized as a result of incomplete
combustion (e.g., C contained in ash or soot).78 The fraction oxidized was assumed to be 100 percent for petroleum, coal,
and natural gas based on guidance in IPCC (2006) (see Estimating Emissions from Fossil Fuel Combustion Annex).
Step 4: Convert to CO2 Emissions
Because the IPCC reporting guidelines recommend that countries report greenhouse gas emissions on a full
molecular weight basis, the final step in estimating CO2 emissions from fossil fuel consumption was converting from units
of C to units of CO2. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of CO2 to C (44/12) to
obtain total CO2 emitted from fossil fuel combustion in teragrams (Tg). The results are contained in Table A- 251.
Comparison Between Sectoral and Reference Approaches
These two alternative approaches can both produce reliable estimates that are comparable within a few percent.
Note that the reference approach includes emissions from non-energy uses. Therefore, these totals should be compared to
the aggregation of fuel use and emission totals from Emissions of CO2 from Fossil Fuel Combustion and Carbon Emitted
from Non-Energy Uses of Fossil Fuels Annexes. These two sections together are henceforth referred to as the Sectoral
77 Carbon coefficients from EIA were used wherever possible. Because EIA did not provide coefficients for coal, the IPCC-
recommended emission factors were used in the top-down calculations for these fuels. See notes in
for more specific source information.
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Approach. Other than this distinction, the major difference between methodologies employed by each approach lies in the
energy data used to derive C emissions (i.e., the actual surveyed consumption for the Sectoral Approach versus apparent
consumption derived for the Reference Approach). In theory, both approaches should yield identical results. In practice,
however, slight discrepancies occur. For the United States, these differences are discussed below.
Differences in Total Amount of Energy Consumed
Table A-254 summarizes the differences between the Reference and Sectoral approaches in estimating total
energy consumption in the United States. Although theoretically the two methods should arrive at the same estimate for
U.S. energy consumption, the Reference Approach provides an energy total that is 1.2 percent lower than the Sectoral
Approach for 2009. The greatest differences lie in lower estimates for petroleum consumption for the Reference Approach
(4.2 percent) and higher estimates for natural gas consumption for the Reference Approach (2.5 percent).
There are several potential sources for the discrepancies in consumption estimates:
• Product Definitions. The fuel categories in the Reference Approach are different from those used in the
Sectoral Approach, particularly for petroleum. For example, the Reference Approach estimates apparent
consumption for crude oil. Crude oil is not typically consumed directly, but refined into other products. As
a result, the United States does not focus on estimating the energy content of the various grades of crude oil,
but rather estimating the energy content of the various products resulting from crude oil refining. The
United States does not believe that estimating apparent consumption for crude oil, and the resulting energy
content of the crude oil, is the most reliable method for the United States to estimate its energy consumption.
Other differences in product definitions include using sector-specific coal statistics in the Sectoral Approach
(i.e., residential, commercial, industrial coking, industrial other, and transportation coal), while the
Reference Approach characterizes coal by rank (i.e. anthracite, bituminous, etc.). Also, the liquefied
petroleum gas (LPG) statistics used in the bottom-up calculations are actually a composite category
composed of natural gas liquids (NGL) and LPG.
• Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for
categories such as "crude oil" where the key statistics are derived from thousands of producers in the United
States and abroad.
• Possible inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the United
States believes that its emphasis on collection of detailed energy consumption data is a more accurate
methodology for the United States to obtain reliable energy data. Therefore, top-down statistics used in the
Reference Approach may not be as accurately collected as bottom-up statistics applied to the Sectoral
Approach.
• Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral
Approach uses reported consumption estimates. While these numbers should be equal, there always seems
to be a slight difference that is often accounted for in energy statistics as a "balancing item."
Differences in Estimated CO2 Emissions
Given these differences in energy consumption data, the next step for each methodology involved estimating
emissions of CO2. Table A-255 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2009, the Reference Approach resulted in a 1.2 percent lower estimate of energy
consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference
Approach was 0.8 percent lower. Estimates of natural gas emissions from the Reference Approach are higher (2.6
percent), and coal and petroleum emission estimates are lower (0.4 percent and 3.0 percent, respectively) than the Sectoral
Approach. Potential reasons for these differences may include:
• Product Definitions. Coal data is aggregated differently in each methodology, as noted above. The format
used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach.
Also, the Reference Approach relies on a "crude oil" category for determining petroleum-related emissions.
Given the many sources of crude oil in the United States, it is not an easy matter to track potential
differences in C content between many different sources of crude; particularly since information on the C
content of crude oil is not regularly collected.
• Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by
IPCC (iPCC/UNEP/OECD/TEA 1997), while the Sectoral Approach uses annually updated category-specific
coefficients by sector that are likely to be more accurate. Also, as noted above, the C coefficient for crude
A-315
-------
oil is more uncertain than that for specific secondary petroleum products, given the many sources and grades
of crude oil consumed in the United States.
Although the two approaches produce similar results, the United States believes that the "bottom-up" Sectoral
Approach provides a more accurate assessment of CO2 emissions at the fuel level. This improvement in accuracy is
largely a result of the data collection techniques used in the United States, where there has been more emphasis on
obtaining the detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy
flow data used in the Reference Approach. The United States believes that it is valuable to understand both methods.
References
EIA (2011) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, January 2011, Energy Information
Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2011/01).
EIA (2010) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy, Washington,
DC. DOE/EIA-0384(2009). August, 2010.
EIA (1995-2010) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington,
DC, Volume I. DOE/EIA-0340.
EPA (2010). Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
IPCC (2006), 2006IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES,
Japan.
iPCC/UNEP/OECD/TEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency.
SAIC (2004) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation,
Market Policies Branch.
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 248:2009 U.S. Energy Statistics [Physical Units!
Fuel Category (Units)
Solid Fuels (Thousand Short Tons)
Gas Fuels (Million Cubic Feet)
Liquid Fuels (Thousand Barrels)
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified Coal
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Fuel
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Production
1,934
493,661
504,679
72,477
20,213,850
1,956,596
697,124
53,635
Imports
a
a
a
a
347
22,639
3,984,233
3,289,675
70,794
515,169
81,536
73
1,143
29,488
82,172
120,936
18,014
6,543
30,323
4,638
7,006
1,497
8,023
80
Exports
a
a
a
a
1,307
59,097
1,005,724
15,985
50,681
23,625
71,326
1,681
25,230
214,411
151,606
142,821
8,116
20,692
2,165
9,950
515
Stock
U.S.
Change Adjustment Bunkers Territories
a
a
a
a
(140)
33,711
(34,253)
24,132
(14,281)
22,558
(13,387)
(158)
228
5,428
19,951
1,111
(297)
(349)
(115)
(317)
(1,805)
(71)
(2,303)
(689)
440
4,643
16,926
249,817
171,416
173,089
552 19,286
10,000 96,195
10,355
2,528
1,712
26,723
3,883
38,289
1,344
7,360
12,755
26,353
172
9,522
[a] Included in Unspecified Coal
Data Sources: Solid and Gas Fuels: EIA(2011); Liquid Fuels: EIA (1995-2010).
A-317
-------
Table A- 249: Conversion Factors to Energy Units (Heat Equivalents]
Fuel Category (Units) Fuel Type
Solid Fuels (Million Btu/Short Ton) Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Natural Gas (BTU/Cubic Foot)
Liquid Fuels (Million Btu/Barrel) Grade Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Stock U.S.
Production Imports Exports Change Adjustment Bunkers Territories
22.57
23.89
17.14
12.87
25.00
25.00
1,026 1,025
5.80 5.99
3.69 3.69
5.83 5.83
5.22 5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
25.63
25.97
1,009
5.80
3.69
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
25.00
20.86
1,026
5.80
3.69
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
28.16
12.87
25.85
1,025
5.80
3.69
5.83
5.22 5.22
5.05
5.67
5.55
5.83 5.83
6.29 6.29
5.25
6.02 6.02
5.83 5.83
5.25
6.07
5.54
6.64
6.00
5.80
25.14
1,026
5.80
3.69
5.83
5.22
5.05
5.67
5.67
5.83
6.29
5.25
6.02
5.83
5.25
6.07
5.54
6.64
6.00
5.80
Data Sources: Coal and lignite production: EIA (2010); Unspecified Solid Fuels: EIA (2011); Coke, Natural Gas and Petroleum Products: EIA (2011).
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A- 250:2009 Apparent Consumption of Fossil Fuels (TBtu)
Fuel Category
Solid Fuels
Gas Fuels
Liquid Fuels
Total
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
Production Imports
43.7
11,793.6
8,650.2
932.5
8.7
566.0
20,739.4 4,083.8
11,348.3 19,701.9
2,572.4 261.2
3,000.9
279.9 425.5
0.4
6.5
167.2
478.7
760.3
94.5
39.4
176.6
24.3
42.5
8.3
53.2
0.5
56,359.8 29,900.3
Stock U.S. Apparent
Exports Change Adjustment Bunkers Territories Consumption
33.5
1,534.9
1,014.8
92.7
187.0
137.6
372.2
9.5
143.1
1,248.9
953.1
0.0
860.4
42.6
125.5
12.0
66.0
3.0
6,836.8
(3.5)
703.2
(35.1)
140.0
(52.7)
131.4
(69.9)
(0.8)
1.3
30.8
116.2
7.0
(1.6)
(2.1)
(0.7)
(1.7)
(10.9)
(0.4)
(15.3)
(4.0)
931.3
12.4
59.7
437.4
256.1
894.5
961.0
3.2 112.3
62.9 604.8
62.4
14.7
1,803.3 1,678.1
43.0
27.4
14.3
199.8
7.6
41.7
74.3
165.7
1.0
55.2
630.1
43.7
11,793.6
8,637.8
872.8
(21.3)
(2,066.6)
23,615.0
30,817.4
2,713.6
2,731.8
(291.7)
1.2
3.3
(925.9)
(927.8)
(701.8)
96.1
(881.2)
162.6
(16.6)
(71.0)
(3.3)
2.5
0.0
56.7
75,640.9
Note: Totals may not sum due to independent rounding.
A-319
-------
Table A-251:2009 Potential C02 Emissions
Apparent Consumption Carbon Coefficients Potential Emissions
Fuel Category
Solid Fuels
Gas Fuels
Liquid Fuels
Total
Fuel Type
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
Unspecified
Natural Gas
Crude Oil
Nat Gas Liquids and LRGs
Other Liquids
Motor Gasoline
Aviation Gasoline
Kerosene
Jet Fuel
Distillate Fuel
Residual Oil
Naphtha for petrochemical feedstocks
Petroleum Coke
Other Oil for petrochemical feedstocks
Special Naphthas
Lubricants
Waxes
Asphalt/Road Oil
Still Gas
Misc. Products
(QBtu) (Tg Carbon/QBtu) (Tg CO2 Eq.)
0.044
11.794
8.638
0.873
(0.021)
(2.067)
23.615
30.817
2.714
2.732
(0.292)
0.001
0.003
(0.926)
(0.928)
(0.702)
0.096
(0.881)
0.163
(0.017)
(0.071)
(0.003)
0.002
-
0.057
28.28
25.44
26.50
26.65
31.00
25.34
14.46
20.31
16.90
20.31
19.46
18.86
19.96
19.70
20.17
20.48
18.55
27.85
20.17
19.74
20.20
19.80
20.55
18.20
20.31
4.5
1,100.2
839.3
85.3
(2.4)
(192.0)
1,251.6
2,294.5
168.2
203.4
(20.8)
0.1
0.2
(66.9)
(68.6)
(52.7)
6.5
(90.0)
12.0
(1.2)
(5.3)
(0.2)
0.2
0.0
4.2
5,470.2
Data Sources: C content coefficients by coal rank from USGS (1998) and SAIC (2004); Unspecified Solid Fuels, EIA (2011), Natural Gas and Liquid Fuels: EPA (2010).
Note: Totals may not sum due to independent rounding.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Table A-252:2009 Non-Energy Carbon Stored in Products
Fuel Type
Coal
Natural Gas
Asphalt & Road Oil
LPG
Lubricants
Pentanes Plus
Petrochemical Feedstocks
Petroleum Coke
Special Naphtha
Waxes/Mi sc.
Misc. U.S. Territories Petroleum
Total
Consumption
for Non-
Energy Use
(TBtu)
6.1
366.0
873.1
1,446.2
262.6
93.4
[a]
133.0
44.2
[a]
[a]
Carbon
Coefficients
(Tg
Carbon/QBtu)
31.00
14.46
20.55
17.06
20.20
19.10
[a]
27.85
19.74
[a]
[a]
Carbon
Content
(Tg Carbon)
0.2
5.3
17.9
24.7
5.3
1.8
[a]
3.7
0.9
[a]
[a]
Fraction
Sequestered
0.10
0.58
1.00
0.58
0.09
0.58
[a]
0.30
0.58
[a]
[a]
Carbon
Stored (Tg
C02 Eq.)
0.07
11.26
65.51
52.53
1.79
3.80
40.33
4.08
1.86
1.16
0.41
182.8
[a] Values for Misc. U.S. Territories Petroleum, Petrochemical Feedstocks and Waxes/Misc. are not shown because these categories are
aggregates of numerous smaller components.
Note: Totals may not sum due to independent rounding.
Table A-253:2009 Reference Approach Clh Emissions from Fossil Fuel Consumption [Tg C0? Eq. unless otherwise noted!
Potential Carbon Net Fraction Total
Fuel Category Emissions Sequestered Emissions Oxidized Emissions
Coal
Petroleum
Natural Gas
Total
1,834.9
2,383.7
1,251.6
5,470.2
0.1
171.4
11.3
182.8
1,834.8
2,212.2
1,240.4
5,287.4
100.0%
100.0%
100.0%
-
1,834.8
2,212.2
1,240.4
5,287.4
Note: Totals may not sum due to independent rounding.
A-321
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Table A-254: Fuel Consumption in the United States by Estimating Approach tTBtul
Approach 1990 1995 1996 1997
Sectoral 69,673.0 74,908.3 77,350.2 78,415.8
Coal 18,071. B! 19,186.2 20,067.4 20,528.6
Natural Gas 19,184.oB 22,169.5 22,589.0 22,722.9
Petroleum 32,417.2B 33,552.6 34,693.8 35,164.4
Reference (Apparent) 68,941.1 73,951.5 76,275.2 77,822.5
Coal 17,572.2 18,566.7 19,424.8 20,104.6
Natural Gas 19,672.0 22,274.0 22,695.8 22,827.8
Petroleum 31,696.8 33,110.8 34,154.5 34,890.0
Difference -1.1% -1.3% -1.4% -0.8%
Coal -2.8% -3.2% -3.2% -2.1%
Natural Gas 2.5% 0.5% 0.5% 0.5%
Petroleum -2.2% -1.3% -1.6% -0.8%
1998
78,839.3
20,822.8
22,323.2
35,693.3
77,838.5
19,980.4
22,403.3
35,454.8
-1.3%
-4.0%
0.4%
-0.7%
1999
80,176.1
20,829.6
22,365.5
36,981.0
79,082.3
20,029.1
22,458.4
36,594.7
-1.4%
-3.8%
0.4%
-1.0%
2000
82,614.3
21,747.8
23,391.9
37,474.6
81,543.6
20,956.8
23,483.9
37,102.9
-1.3%
-3.6%
0.4%
-1.0%
2001
81,106.1
21,120.4
22,466.2
37,519.6
80,610.7
20,709.5
22,535.5
37,365.7
-0.6%
-1.9%
0.3%
-0.4%
2002
81,849.5
21,191.2
23,211.4
37,446.9
81,307.1
20,796.7
23,276.0
37,234.3
-0.7%
-1.9%
0.3%
-0.6%
2003
82,331.7
21,624.8
22,561.3
38,145.6
81,732.9
21,080.5
22,630.3
38,022.1
-0.7%
-2.5%
0.3%
-0.3%
2004
83,947.2
21,892.4
22,609.4
39,445.4
83,416.2
21,734.5
22,676.2
39,005.6
-0.6%
-0.7%
0.3%
-1.1%
2005
84,044.3
22,186.3
22,278.5
39,579.5
83,495.8
21,985.5
22,344.8
39,165.4
-0.7%
-0.9%
0.3%
-1.0%
2006
82,662.2
21,832.9
21,945.6
38,883.7
81,854.4
21,532.4
22,014.7
38,307.3
-1.0%
-1.4%
0.3%
-1.5%
2007
84,108.4
22,076.5
23,412.6
38,619.3
83,727.5
21,586.3
23,473.0
38,668.1
-0.5%
-2.2%
0.3%
0.1%
2008 2009
81,278.7 76,567.5
21,749.5 19,302.0
23,536.9 23,046.2
35,992.3 34,219.4
79,932.4 75,640.9
21,388.5 19,259.9
23,609.6 23,615.0
34,934.3 32,766.0
-1.7% -1.2%
-1.7% -0.2%
0.3% 2.5%
-2.9% -4.2%
* Includes U.S. territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
Table A-255: G02 Emissions from Fossil Fuel Combustion by Estimating Approach (Tg CJh Eq.)
Approach 1990 1995 1996 1997
Sectoral 4,856.7 5,170.4 5,352.3 5,428.0
Coal 17187 1 823 3 1 906 8 1 949 9
Natural Gas 1 006 8 1 164 7 1 186 5 1 193 1
Petroleum 2,131.1 2,182.4 2,259.0 2,285.0
Reference (Apparent) 4,802.2 5,130.6 5,303.0 5,421.9
Coal 1,654.1 1,756.2 1,837.0 1,901.8
Natural Gas 1,034.0 1,171.4 1,193.3 1,200.0
Petroleum 2,114.1 2,202.9 2,272.7 2,320.1
Difference -1.1% -0.8% -0.9% -0.1%
Coal -3.8% -3.7% -3.7% -2.5%
Natural Gas 2.7% 0.6% 0.6% 0.6%
Petroleum -0.8% 0.9% 0.6% 1.5%
1998
5,474.5
1 979 8
1 1693
2,325.4
5,427.4
1,894.4
1,174.5
2,358.5
-0.9%
-4.3%
0.4%
1.4%
1999
5,558.6
1 982 8
1 1724
2,403.4
5,501.3
1,902.9
1,178.3
2,420.1
-1.0%
-4.0%
0.5%
0.7%
2000
5,739.4
2071 4
12264
2,441.7
5,686.5
1,989.0
1,232.2
2,465.2
-0.9%
-4.0%
0.5%
1.0%
2001
5,655.4
2011 3
1 1777
2,466.5
5,650.1
1,967.6
1,181.5
2,501.0
-0.1%
-2.2%
0.3%
1.4%
2002
5,693.2
2021 7
1 2176
2,453.9
5,690.1
1,976.4
1,221.9
2,491.9
-0.1%
-2.2%
0.3%
1.5%
2003
5,752.1
2065 8
1 181 8
2,504.6
5,741.1
2,002.2
1,187.0
2,551.8
-0.2%
-3.1%
0.4%
1.9%
2004
5,858.9
20927
1 1849
2,581.3
5,872.1
2,065.2
1,190.7
2,616.3
0.2%
-1.3%
0.5%
1.4%
2005
5,896.2
2 1209
1 167 5
2,607.9
5,891.3
2,088.1
1,171.7
2,631.5
-0.1%
-1.5%
0.4%
0.9%
2006
5,798.3
20834
1 1499
2,565.0
5,770.6
2,049.5
1,154.0
2,567.1
-0.5%
-1.6%
0.4%
0.1%
2007
5,893.6
2 1066
1 2266
2,560.4
5,880.8
2,054.3
1,230.8
2,595.6
-0.2%
-2.5%
0.3%
1.4%
2008 2009
5,706.5 5,331.9
2 075 9 1 842 1
1 234 8 1 209 1
2,395.8 2,280.8
5,630.2 5,287.4
2,036.7 1,834.8
1,239.2 1,240.4
2,354.2 2,212.2
-1.3% -0.8%
-1.9% -0.4%
0.4% 2.6%
-1.7% -3.0%
* Includes U.S. territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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ANNEX 5 Assessment of the Sources and
Sinks of Greenhouse Gas Emissions Not
Included
Although this report is intended to be a comprehensive assessment of anthropogenic79 sources and sinks of
greenhouse gas emissions for the United States, certain sources have been identified but not included in the estimates
presented for various reasons. Before discussing these sources, however, it is important to note that processes or activities
that are not anthropogenic in origin or do not result in a net source or sink of greenhouse gas emissions are intentionally
excluded from a national inventory of anthropogenic greenhouse gas emissions, in line with guidance from the IPCC in
their guidelines for national inventories.
Given a source category that is both anthropogenic and results in net greenhouse gas emissions, reasons for not
including a source related to an anthropogenic activity include one or more of the following:
• Though an estimating method has been developed, data were not adequately available to calculate
emissions.
• Emissions were implicitly accounted for within another source category (e.g., CO2 from Fossil Fuel
Combustion).
It is also important to note that the United States believes that the sources discussed below are very low in
comparison with the overall estimate of total U.S. greenhouse gas emissions, and not including them introduces a very
minor bias. In general, the emission sources described in this annex are for source categories with methodologies
introduced in the 2006 IPCC Guidelines for which data collection has not been sufficient to pursue an initial estimation of
greenhouse gases.
N2O from Caprolactam Production
Caprolactam is a widely used chemical intermediate, primarily to produce nylon-6. All processes for producing
caprolactam involve the catalytic oxidation of ammonia, with N2O being produced as a by-product.. More research is
required to determine this source's significance because there is currently insufficient information available on
caprolactam production to estimate emissions in the United States.
CO2 and CH4 from Calcium Carbide Production
CO2 is formed by the oxidation of petroleum coke in the production of calcium carbide. These CO2 emissions
are implicitly accounted for in the storage factor calculation for the non-energy use of petroleum coke in the Energy
chapter. There is currently not sufficient data on coke consumption for calcium carbide production to estimate emissions
from this source.
CO2 from Graphite Consumption in Ferroalloy and Steel Production
Emissions from "graphite," "wood," or "biomass" in calculating CO2 emissions from ferroalloy production, iron
and steel production or other "Industrial Processes" included in Chapter 4 of the inventory are not explicitly calculated. It
is assumed that 100 percent of the C used in ferroalloy production is derived from petroleum coke and that all of the C
used in iron and steel production is derived from coal coke or petroleum coke. It is also assumed that all of the C used in
lead and zinc production is derived from coal coke. It is possible that some non-coke C is used in the production of
ferroalloys, lead, zinc, and iron and steel, but no data are available to conduct inventory calculations for sources of C other
than petroleum coke and coal coke used in these processes.
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 ("2006 IPCC Guidelines for National Greenhouse Gas
Inventories").
A-323
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Non-fuel uses of coal coke and petroleum coke are accounted for in the Industrial Process chapter, either directly
for iron and steel, aluminum, ferroalloy, lead, zinc, and titanium dioxide production, or indirectly by applying a storage
factor to "uncharacterized" non-fuel uses of petroleum coke and coal coke. Non-fuel uses of wood and biomass are not
accounted for in the Energy or Industrial Process chapters, as all uses of wood and biomass are accounted for in the Land
Use, Land-Use Change, and Forestry chapter. It is assumed for the purposes of the CO2 emission calculation that no wood
or other biogenic C is used in any of these industrial processes. Some biogenic C may be used in these industrial
processes but sufficient data to estimate emissions are not available.
Consumption of either natural or synthetic graphite is not explicitly accounted for in the Industrial Process
chapter. It is assumed that all of the C used in manufacturing C anodes for production of aluminum, ferroalloys, and
electric arc furnace (EAF) steel are derived directly from petroleum coke and coal tar pitch (a coal coke byproduct), not
from natural graphite or synthetic graphite sources. Some amount of C used in these industrial processes may be derived
from natural or synthetic graphite sources, but sufficient data to estimate emissions are not currently available.
Miscellaneous SF6 Uses
Sulfur hexafluoride (SF6) is used in several applications for which estimates have not been provided in this
inventory. Sulfur hexafluoride may be emitted from the production, leakage, and dismantling of radar, tracer, and night
vision equipment. Emissions from this source are believed to be minor, and no data were available for estimating the
emissions. Sulfur hexafluoride may be used in foam insulation, for dry etching, in laser systems, for indoor air quality
testing, for laboratory hood testing, for chromatography, in tandem accelerators, in loudspeakers, in shock absorbers, and
for certain biomedical applications. Emissions from this source are believed to be minor, and no data were available for
estimating the emissions. Sulfur hexafluoride may be emitted from the production, breakage, or leakage of soundproof
double-glazed windows. Emissions from this source are believed to be minor, and no data were available for estimating
the emissions. Sulfur hexafluoride may be emitted from applications involving the production of sport shoes, tires, and
tennis balls. Emissions from this source are believed to be minor, and no data were available for estimating the emissions.
Sulfur hexafluoride may be emitted from applications involving tracer gasses to detect leakage from pressure vessels and
as a tracer gas in the open air. Emissions from this source are believed to be minor, and no data were available for
estimating the emissions.
CO2 from Non-Hazardous Industrial Waste Incineration
Waste incineration is incorporated in two sections of the energy chapter of the inventory: in the section on CO2
emissions from waste incineration, and in the calculation of emissions and storage from non-energy uses of fossil fuels.
The former section addresses fossil-derived materials (such as plastics) that are discarded as part of the municipal
wastestream and combusted (generally for energy recovery). The latter addresses two types of combustion: hazardous
waste incineration of organic materials (assumed to be fossil-derived), in which regulated wastes are burned without
energy recovery, and burning of fossil-derived materials for energy recovery. There is one potentially important category
of waste incineration that is not included in our calculus: industrial non-hazardous waste, burned for disposal (rather than
energy recovery). Data are not readily available for this source; further research is needed to estimate the magnitude of
CO2 emissions.
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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ANNEX 6 Additional Information
6.1. Global Warming Potential Values
Global Warming Potentials (GWPs) are intended as a quantified measure of the globally averaged relative
radiative forcing impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcing—both direct and
indirect effects—integrated over a period of time from the emission of a unit mass of gas relative to some reference gas
(IPCC 1996). Carbon dioxide (CO2) was chosen as this reference gas. Direct effects occur when the gas itself is a
greenhouse gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas
or gases that are greenhouse gases, or when a gas influences other radiatively important processes such as the atmospheric
lifetimes of other gases. The relationship between gigagrams (Gg) of a gas and teragrams of CO2 equivalents (Tg CO2
Eq.) can be expressed as follows:
Tg CO 2 Eq = (Gg of gas) x (GWP) > •' Tg '
,1,000 GgJ
Where,
Tg CO2 Eq. = Teragrams of Carbon Dioxide Equivalents
Gg = Gigagrams (equivalent to a thousand metric tons)
GWP = Global Warming Potential
Tg = Teragrams
GWP values allow policy makers to compare the impacts of emissions and reductions of different gases.
According to the IPCC, GWPs typically have an uncertainty of +35 percent, though some GWPs have larger uncertainty
than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the parties to the
UNFCCC have agreed to use consistent GWPs from the IPCC Second Assessment Report (SAR), based upon a 100 year
time horizon, although other time horizon values are available (see Table A-256).
In addition to communicating emissions in units of mass, Parties may choose also to use global
warming potentials (GWPs) to reflect their inventories and projections in carbon dioxide-equivalent terms, using
information provided by the Intergovernmental Panel on Climate Change (IPCC) in its Second Assessment
Report. Any use of GWPs should be based on the effects of the greenhouse gases over a 100-year time horizon.
In addition, Parties may also use other time horizons.80
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, and SF6) tend to
be evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
However, the short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect greenhouse gases
(e.g., NOX and NMVOCs), and tropospheric aerosols (e.g., SO2 products and black carbon) vary spatially, and
consequently it is difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to
these gases that are short-lived and spatially inhomogeneous in the atmosphere.
Table A-256: Global Warming Potentials 1GWP1 and Atmospheric Lifetimes [Years! of Gases Used in this Report
Gas Atmospheric Lifetime 100-year GWP" 20-year GWP 500-year GWP
Carbon dioxide (CO2)
Methane (CH4)b
Nitrous oxide (N2O)
HFC-23
HFC-32
HFC- 125
HFC-134a
HFC-143a
50-200
12±3
120
264
5.6
32.6
14.6
48.3
1
21
310
11,700
650
2,800
1,300
3,800
1
56
280
9,100
2,100
4,600
3,400
5,000
1
6.5
170
9,800
200
920
420
1,400
80 Framework Convention on Climate Change; FCCC/CP/1996/15/Add.l; 29 October 1996; Report of the Conference of the Parties at
its second session; held at Geneva from 8 to 19 July 1996; Addendum; Part Two: Action taken by the Conference of the Parties at its
second session; Decision 9/CP.2; Communications from Parties included in Annex I to the Convention: guidelines, schedule and process
for consideration; Annex: Revised Guidelines for the Preparation of National Communications by Parties Included in Annex I to the
Convention; p. 18. FCCC (1996)
A-325
-------
HFC-152a
HFC-227ea
HFC-236fa
HFC-43-10mee
CF4
C2F6
C3F8
C4Flo
c-C4F8
C5F12
C6F14
SF6
1.5
36.5
209
17.1
50,000
10,000
2,600
2,600
3,200
4,100
3,200
3,200
140
2,900
6,300
1,300
6,500
9,200
7,000
7,000
8,700
7,500
7,400
23,900
460
4,300
5,100
3,000
4,400
6,200
4,800
4,800
6,000
5,100
5,000
16,300
42
950
4,700
400
10,000
14,000
10,100
10,100
12,700
11,000
10,700
34,900
Source: IPCC (1996)
a GWPs used in this report are calculated over 100 year time horizon.
b The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water
vapor. The indirect effect due to the production of CO2 is not included.
Table A-257 presents direct and net (i.e., direct and indirect) GWPs for ozone-depleting substances (ODSs).
Ozone-depleting substances directly absorb infrared radiation and contribute to positive radiative forcing; however, their
effect as ozone-depleters also leads to a negative radiative forcing because ozone itself is a potent greenhouse gas. There
is considerable uncertainty regarding this indirect effect; therefore, a range of net GWPs is provided for ozone depleting
substances. The IPCC Guidelines and the UNFCCC do not include reporting instructions for estimating emissions of
ODSs because their use is being phased-out under the Montreal Protocol (see note below Table A-257). The effects of
these compounds on radiative forcing are not addressed in this report
Table A-257: Net 100-year Global Warming Potentials for Select Ozone Depleting Substances*
Gas
CFC-11
CFC-12
CFC-113
HCFC-22
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
CHC13
CC14
CH3Br
Halon-1211
Halon-1301
Direct
4,600
10,600
6,000
1,700
120
620
700
2,400
140
1,800
5
1,300
6,900
Netnin
(600)
7,300
2,200
1,400
20
480
(5)
1,900
(560)
(3,900)
(2,600)
(24,000)
(76,000)
Netmax
3,600
9,900
5,200
1,700
100
590
570
2,300
0
660
(500)
(3,600)
(9,300)
Source: IPCC (2001)
Parentheses indicate negative values.
Note: Because these compounds have been shown to deplete stratospheric ozone, they are typically referred to as ozone depleting substances
(ODSs). However, they are also potent greenhouse gases. Recognizing the harmful effects of these compounds on the ozone layer, in 1987 many
governments signed the Montreal Protocol on Substances that Deplete the Ozone Layer to limit the production and importation of a number of
CFCs and other halogenated compounds. The United States furthered its commitment to phase-out ODSs by signing and ratifying the
Copenhagen Amendments to the Montreal Protocol in 1992. Under these amendments, the United States committed to ending the production and
importation of halons by 1994, and CFCs by 1996. .
The IPCC has published its Fourth Assessment Report (AR4), providing the most current and comprehensive
scientific assessment of climate change (IPCC 2007). Within this report, the GWPs of several gases were revised relative
to the SAR and the IPCC's Third Assessment Report (TAR) (IPCC 2001). Thus the GWPs used in this report have been
updated twice by the IPCC; although the SAR GWPs are used throughout this report, it is informative to review the
changes to the GWPs and the impact such improved understanding has on the total GWP-weighted emissions of the
United States. All GWPs use CO2 as a reference gas; a change in the radiative efficiency of CO2 thus impacts the GWP of
all other greenhouse gases. Since the SAR and TAR, the IPCC has applied an improved calculation of CO2 radiative
forcing and an improved CO2 response function. The GWPs are drawn from IPCC/TEAP (2005) and the TAR, with
updates for those cases where new laboratory or radiative transfer results have been published. Additionally, the
atmospheric lifetimes of some gases have been recalculated. Because the revised radiative forcing of CO2 is about 8
percent lower than that in the TAR, the GWPs of the other gases relative to CO2 tend to be larger, taking into account
revisions in lifetimes. However, there were some instances in which other variables, such as the radiative efficiency or the
chemical lifetime, were altered that resulted in further increases or decreases in particular GWP values. In addition, the
values for radiative forcing and lifetimes have been calculated for a variety of halocarbons, which were not presented in
the SAR. Updates in some well-mixed HFC compounds (including HFC-23, HFC-32, HFC-134a, and HFC-227ea) for
A-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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AR4 result from investigation into radiative efficiencies in these compounds, with some GWPs changing by up to 40
percent; with this change, the uncertainties associated with these well-mixed HFCs are thought to be approximately 12
percent.
Table A- 258 compares the lifetimes and GWPs for the SAR, TAR, and AR4.
Table A- 258: Comparison of GWPs and lifetimes used in the SAR, TAR, and AR4
Gas
Carbon dioxide (CO2)
Methane (CH4)b
Nitrous oxide (N2O)
Hydrofluorocarbons
HFC-23
HFC-32
HFC- 125
HFC-134a
HFC-143a
HFC-152a
HFC-227ea
HFC-236fa
HFC-245fa
HFC-365mfc
HFC-43-10mee
Fully Fluorinated
Species
SF6
CF4
C2F6
C3F8
C4Flo
c-C4F8
C5F12
C6F14
Others"
NF3
Lifetime (years)
SAR TAR
50-200
12±3
120
264
5.6
32.6
14.6
48.3
1.5
36.5
209
NA
NA
17.1
3,200
50,000
10,000
2,600
2,600
3,200
4,100
3,200
NA
5-200a
8.4/12c
120/114°
260
5.0
29
13.8
52
1.4
33.0
220
7.2
9.9
15
3,200
50,000
10,000
2,600
2,600
3,200
4,100
3,200
740
AR4
5-200a
8.7/12c
120/1 14 c
270
4.9
29
14
52
1.4
34.2
240
7.6
6.6
15.9
3200
50,000
10,000
2,600
2,600
3,200
4,100
3,200
740
GWP (100 year)
SAR TAR
1
21
310
11,700
650
2,800
1,300
3,800
140
2,900
6,300
NA
NA
1,300
23,900
6,500
9,200
7,000
7,000
8,700
7,500
7,400
NA
1
23
296
12,000
550
3,400
1,300
4,300
120
3,500
9,400
950
890
1,500
22,200
5,700
11,900
8,600
8,600
10,000
8,900
9,000
10,800
AR4
1
25
298
14,800
675
3,500
1,430
4,470
124
3,220
9,810
1,030
794
1,640
22,800
7,390
12,200
8,830
8,860
10,300
9,160
9,300
17,200
Difference (re
TAR TAR(%)
NC
2
(14)
300
(100)
600
NC
500
(20)
600
3,100
NA
NA
200
(1,700)
(800)
2,700
1,600
1,600
1,300
1,400
1,600
NA
NC
10%
(5%)
3%
(15%)
21%
NC
13%
(14%)
21%
49%
NA
NA
15%
(7%)
(12%)
29%
23%
23%
15%
19%
22%
NA
lative to SAR)
AR4 AR4
NC
4
(12)
3,100
25
700
130
670
(16)
320
3,510
NA
NA
340
(1,100)
890
3,000
1,830
1,860
1,600
1,660
1,900
NA
(%)
NC
19%
(4%)
26%
4%
25%
10%
18%
(11%)
11%
56%
NA
NA
26%
(5%)
14%
33%
26%
27%
18%
22%
26%
NA
NC (No Change)
NA (Not Applicable)
a No single lifetime can be determined for CO2. (See IPCC 2001)
b The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water
vapor. The indirect effect due to the production of CO2 is not included.
c Methane and nitrous oxide have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean
atmospheric lifetime (LT) is given first, followed by perturbation time (PT).
d Gases whose lifetime has been determined only via indirect means or for whom there is uncertainty over the loss process.
Note: Parentheses indicate negative values.
Source: IPCC (2001)
The choice of GWPs between the SAR, TAR, and AR4 has an impact on both the overall emissions estimated by
the inventory, as well as the trend in emissions over time. To summarize, Table A-259 shows the overall trend in U.S.
greenhouse gas emissions, by gas, from 1990 through 2009 using the three GWP sets. The table also presents the impact
of TAR and AR4 GWPs on the total emissions for 1990 and for 2009.
Table A-259: Effects on U.S. Greenhouse Gas Emissions Using TAR, SAR, and AR4 GWPstTg Cinq.)
Gas Trend from 1990 to 2009 Revisions to Annual Estimates (relative to SAR)
C02
CH4
N20
HFCs, PFCs, and SF6*
Total
Percent Change
SAR
405.5
11.4
(19.6)
54.1
451.4
7.3%
TAR
405.5
12.5
(18.7)
59.8
459.1
7.4%
AR4
405.5
13.6
(18.8)
57.3
457.6
7.3%
TAR
AR4
1990
NC
64.3
(14.2)
(2.3)
47.7
0.8%
NC
128.5
(12.2)
11.8
128.1
2.1%
TAR
AR4
2009
NC
65.4
(13.4)
3.3
55.3
0.8%
NC
130.7
(11.4)
15.0
134.3
2.0%
NC (No Change)
* Includes NF3
Note: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values.
A-327
-------
When the GWPs from the AR4 are applied to the emission estimates presented in this report, total emissions for
the year 2009 are 6,767.5 Tg CO2 Eq., as compared to 6,633.2 Tg CO2 Eq. when the GWPs from the SAR are used (a 2.0
percent difference). Table A-260 provides a detailed summary of U.S. greenhouse gas emissions and sinks for 1990
through 2009, using the GWPs from the AR4. The percent change in emissions is equal to the percent change in the GWP;
however, in cases where multiple gases are emitted in varying amounts the percent change is variable over the years, such
as with substitutes for ozone depleting substances. Table A-261 summarizes the resulting change in emissions from SAR
to AR4 GWPs for 1990 through 2009 including the percent change for 2009.
Table A-260: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the AR4 GWPs [Tg C0? Eq.l
Gas/Source
C02
Fossil Fuel
Combustion
Electricity
Generation
Transportation
Industrial
Residential
Commercial
U.S. Territories
Non-Energy Use of
Fuels
Iron and Steel
Production &
Metallurgical
Coke Production
Natural Gas Systems
Cement Production
Incineration of Waste
Ammonia Production
and Urea
Consumption
Lime Production
Cropland Remaining
Cropland
Limestone and
Dolomite Use
Soda Ash Production
and Consumption
Aluminum Production
Petrochemical
Production
Carbon Dioxide
Consumption
Titanium Dioxide
Production
Ferroalloy Production
Wetlands Remaining
Wetlands
Phosphoric Acid
Production
Zinc Production
Lead Production
Petroleum Systems
Silicon Carbide
Production and
Consumption
Land Use, Land-Use
Change, and
Forestry (Sink)"
WoodBiomass and
Ethanol
Consumption
1990 2000 2005
5,099.7 5,975.0 6,113.8
4,738.4 5,594.8
1,820.81 2,296.9
1,485.91 1,809.5
846. sl 851.1
338.31 370.7
219.o| 230.8
27.9
118.6
99.5
37.6
33.3
8.0
16.8
11.5
7.1
5.1
4.1
6.8
3.3
1.4
1.2
2.2
1.0
1.5
0.7
0.5
0.6
0.4
35.9
144.9
85.9
29.9
40.4
11.1
16.4
14.1
7.5
5.1
4.2
6.1
4.5
1.4
1.8
1.9
1.2
1.4
1.0
0.6
0.5
0.2
fS6^.5)l (576.6)
227.4
5,735.2
2,402.1
1,896.6
823.1
357.9
223.5
50.0
143.4
65.9
29.9
45.2
12.5
12.8
14.4
7.9
6.8
4.2
4.1
4.2
1.3
1.8
1.4
1.1
1.4
1.1
0.6
0.5
0.2
(1,056.5)
229.8
2006
6,021.1
5,653.1
2,346.4
1,878.1
848.2
321.5
208.6
50.3
145.6
68.8
30.8
45.8
12.5
12.3
15.1
7.9
8.0
4.2
3.8
3.8
1.7
1.8
1.5
0.9
1.2
1.1
0.6
0.5
0.2
(1,064.3)
234.8
2007
6,120.0
5,756.7
2,412.8
1,894.0
842.0
342.4
219.4
46.1
137.2
71.0
31.1
44.5
12.7
14.0
14.6
8.2
7.7
4.1
4.3
3.9
1.9
1.9
1.6
1.0
1.2
1.1
0.6
0.5
0.2
(1,060.9)
242.3
2008
5,921.4
5,565.9
2,360.9
1,789.9
802.9
348.2
224.2
39.8
141.0
66.0
32.8
40.5
12.2
11.9
14.3
8.7
6.3
4.1
4.5
3.4
1.8
1.8
1.6
1.0
1.2
1.2
0.6
0.5
0.2
(1,040.5)
253.1
2009
5,505.2
5,209.0
2,154.0
1,719.7
730.4
339.2
224.0
41.7
123.4
41.9
32.2
29.0
12.3
11.8
11.2
7.8
7.6
4.3
3.0
2.7
1.8
1.5
1.5
1.1
1.0
1.0
0.5
0.5
0.1
(1,015.1)
245.0
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
International Bunker
Fuels'
CH4
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater
Treatment
Forest Land
Remaining Forest
Land
Rice Cultivation
Stationary
Combustion
Abandoned
Underground
Coal Mines
Mobile Combustion
Composting
Petrochemical
Production
Iron and Steel
Production &
Metallurgical
Coke Production
Field Burning of
Agricultural
Residues
Ferroalloy Production
Silicon Carbide
Production and
Consumption
Incineration of Waste
International Bunker
Fuels'
N2O
Agricultural Soil
Management
Mobile Combustion
Manure Management
Nitric Acid
Production
Stationary
Combustion
Forest Land
Remaining Forest
Land
Wastewater
Treatment
N2O from Product
Uses
Adipic Acid
Production
Composting
Settlements
Remaining
Settlements
Incineration of Waste
Field Burning of
Agricultural
Residues
Wetlands Remaining
Wetlands
111.8\ 98.5 109.7
803.4 785.6 751.7
226.0 1 249.2 226.7
157.31 162.5
175.5! 132.9
100. l! 71.9
37.8
42.1
28.0
3.8
8.5
8.8
7.2
5.6
0.4
1.0
1.1
0.3
+
+
+
0.2
303.0
50.5
37.5
30.0
17.0
8.9
7.9
8.8
4.0
1.5
1.5
1.1
0.3
+
+
+
0.1
327.8
190.1 ! 198.8
42.2! 51.1
13.9
17.0
12.3
2.6
3.5
4.2
15.2
0.3
0.9
0.5
0.1
+
16.4
18.7
14.0
11.6
4.3
4.7
5.3
1.3
1.1
0.4
0.1
+
162.5
133.9
67.7
55.4
35.0
29.0
11.7
8.2
7.8
6.6
3.0
1.9
1.3
0.9
0.2
+
+
+
0.2
310.4
203.2
35.5
16.6
15.9
14.1
8.0
4.6
4.2
4.8
1.7
1.4
0.4
0.1
+
128.4
800.1
259.1
165.3
133.0
69.3
55.6
34.9
29.2
25.7
7.0
7.3
6.5
2.8
1.9
1.2
0.9
0.3
+
+
+
0.2
313.8
200.8
32.3
17.3
15.5
13.9
17.3
4.6
4.2
4.1
1.7
1.4
0.4
0.1
+
127.6
791.2
244.3
167.9
132.5
68.9
60.4
35.7
29.1
23.8
7.4
7.7
6.7
2.6
2.0
1.2
0.8
0.3
+
+
+
0.2
312.5
201.3
29.1
17.4
18.5
14.0
16.1
4.7
4.2
3.5
1.8
1.5
0.4
0.1
+
133.7
805.6
252.2
167.4
138.0
79.9
58.8
36.0
29.2
14.2
8.6
7.7
7.0
2.4
2.0
1.1
0.8
0.3
+
+
+
0.2
298.7
202.6
25.1
17.2
15.8
13.6
9.7
4.8
4.2
2.0
1.8
1.5
0.4
0.1
+
123.1
817.0
263.4
166.4
139.8
84.6
58.9
36.8
29.2
9.3
8.7
7.3
6.6
2.3
2.0
1.0
0.4
0.3
+
+
+
0.2
284.2
196.7
23.0
17.2
14.0
12.3
6.5
4.8
4.2
1.9
1.8
1.5
0.4
0.1
+
A-329
-------
International Bunker
Fuels'
HFCs, PFCs, and
SF6
HFCs
Substitution of Ozone
Depleting
Substances4
HCFC-22 Production
Semiconductor
Manufacture
PFCs
Semiconductor
Manufacture
Aluminum Production
SF6
Electrical
Transmission and
Distribution
Magnesium
Production and
Processing
Semiconductor
Manufacture
Total
i.om 0.9
103.8 152.4
46.6 116.9
0.3
46.1
0.2
24.4
2.9
21.6
32.8
27.1
5.2
0.5
80.4
36.2
0.3
16.2
6.3
9.9
19.2
15.3
2.9
1.0
160.1
134.0
113.7
20.0
0.3
7.9
4.5
3.4
18.1
14.4
2.8
1.0 0.9
6,309.9 7,240.7 7,336.0
1.1
162.5
137.4
119.6
17.5
0.3
8.0
5.1
2.9
17.1
13.4
2.7
0.9
7,297.5
1.1
170.4
144.9
123.1
21.5
0.3
9.6
5.1
4.5
15.9
12.6
2.5
0.8
7,394.1
1.2
168.6
144.5
126.9
17.2
0.4
8.7
5.5
3.2
15.4
12.7
1.8
0.8
7,194.4
1.1
161.1
139.5
132.3
6.8
0.4
7.5
5.6
1.9
14.2
12.2
1.0
0.9
6,767.5
+ Does not exceed 0.05 Tg CO2 Eq.
a The net CO2 flux total includes both emissions and sequestration, and constitutes a sink in the United States. Sinks are only included in net
emissions total. Parentheses indicate negative values or sequestration.
b Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
c Emissions from International Bunker Fuels are not included in totals.
d Small amounts of PEC emissions also result from this source.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-261: Change in U.S. Greenhouse Gas Emissions and Sinks Using SAR us. AR4 GWPs [Tg GO? Eq.l
Gas/Source
CO2
CH4
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining Forest
Land
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal
Mines
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Production &
Metallurgical Coke Production
Field Burning of Agricultural
Residues
Ferroalloy Production
Silicon Carbide Production and
Consumption
Incineration of Waste
International Bunker Fuelsa
N2O
1990
NC
128.5
36.2
25.2
28.1
16.0
6.ol
(>.?•
4.5
o.e|
1.4
1.4
1.2
0.9l
E
0.2
;
•
(12.2)1
2000
NC
125.7
39.9
26.0
21.3
11.5
8.1
e.oB
4.8
2.7
1.4
1.3
1.4
o.e|
0.2
0,
0.2
•
(13.2)1
2005
NC
120.3
36.3
26.0
21.4
10.8
8.9
5.6
4.6
1.9
1.3
1.2
1.1
0.5
0.3
0.2
0.1
+
+
+
+
+
(12.5)
2006
NC
128.0
41.5
26.4
21.3
11.1
8.9
5.6
4.7
4.1
1.1
1.2
1.0
0.4
0.3
0.2
0.1
+
+
+
+
+
(12.6)
2007
NC
126.6
39.1
26.9
21.2
11.0
9.7
5.7
4.7
3.8
1.2
1.2
1.1
0.4
0.3
0.2
0.1
+
+
+
+
+
(12.6)
2008
NC
128.9
40.3
26.8
22.1
12.8
9.4
5.8
4.7
2.3
1.4
1.2
1.1
0.4
0.3
0.2
0.1
0.1
+
+
+
+
(12.0)
2009
NC
130.7
42.1
26.6
22.4
13.5
9.4
5.9
4.7
1.5
1.4
1.2
1.0
0.4
0.3
0.2
0.1
+
+
+
+
+
(11.4)
Percent
Change
in 2009
NC
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
19%
(4%)
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Agricultural Soil Management
Mobile Combustion
Manure Management
Nitric Acid Production
Stationary Combustion
Forest Land Remaining Forest
Land
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Composting
Settlements Remaining
Settlements
Incineration of Waste
Field Burning of Agricultural
Residues
Wetlands Remaining Wetlands
International Bunker Fuels"
HFCs, PFCs, and SF6
HFCs
Substitution of Ozone Depleting
Substances
HCFC-22 Production
Semiconductor Manufacture
PFCs
Semiconductor Manufacture
Aluminum Production
SF6
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
Total
(7.7)
(1.7)
(0.6)
(0.7)
(0.5)
(0.1)1
(0.1)
(0.2)
(0.6)
<+>
(+)
1
(+)
(+)
(+)
11.8
9.7
(+)
9.7
0.6
3.0
(1.6)
(1.3)
(0.3)
(+)
128.1
(8.0)
(2.1)
(0.7)
(0.8)
(0.6)1
(0.5)
(0.2)
(0.2)
(0.2)
(0.1)
(+)
<+,
(+)
(+)
(+)
15.6
13.7
6.1
7.6
S
(0.9)
(0.7)
(0.1)1
(+)
128.1
(8.2)
(1.4)
(0.7)
(0.6)
(0.6)
(0.3)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(+)
(+)
(+)
(+)
14.6
13.8
9.5
4.2
0.1
1.8
1.3
0.5
(0.9)
(0.7)
(0.1)
(+)
122.4
(8.1)
(1.3)
(0.7)
(0.6)
(0.6)
(0.7)
(0.2)
(0.2)
(0.2)
(0.1)
(0.1)
(+)
(+)
(+)
(+)
15.1
14.0
10.2
3.7
0.1
2.0
1.6
0.4
(0.8)
(0.6)
(0.1)
(+)
130.5
(8.1)
(1.2)
(0.7)
(0.7)
(0.6)
(0.6)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(+)
(+)
(+)
(+)
16.7
15.4
10.8
4.5
0.1
2.1
1.5
0.6
(0-8)
(0.6)
(0.1)
(+)
130.7
(8.2)
(1.0)
(0.7)
(0.6)
(0.5)
(0.4)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(+)
(+)
(+)
(+)
16.4
15.1
11.4
3.6
0.1
2.0
1.6
0.5
(0.7)
(0.6)
(0.1)
(+)
133.3
(7.9)
(0.9)
(0.7)
(0.6)
(0.5)
(0.3)
(0.2)
(0.2)
(0.1)
(0.1)
(0.1)
(+)
(+)
(+)
(+)
15.0
13.8
12.3
1.4
0.1
1.9
1.6
0.3
(0.7)
(0.6)
(+)
(+)
134.3
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
(4%)
10%
11%
10%
26%
26%
33%
40%
17%
(5%)
(5%)
(5%)
(5%)
2.0%
NC (No change)
+ Does not exceed 0.05 Tg CO2 Eq.
a Emissions from International Bunker Fuels are not included in totals.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-262 below shows a comparison of total emissions estimates by sector using both the IPCC SAR and
AR4 GWP values. For most sectors, the change in emissions was minimal. The effect on emissions from waste was by
far the greatest (18 percent in 2009), due the predominance of CH4 emissions in this sector. Emissions from all other
sectors were comprised of mainly CO2 or a mix of gases, which moderated the effect of the changes.
Table A-262: Comparison of Emissions by Sector using IPCC SAR and AR4 GWP Values [Tg Clh Eq.l
Sector
1990
2000
2005
2006
2007
2008
Energy
SAR GWP (Used in Inventory) 5,287.8
AR4 GWP, Updated 5,347.9
Difference (%) 1.1%
Industrial Processes
SAR GWP (Used in Inventory) 315.
AR4 GWP, Updated 326.6
Difference (%) 3.4%
Solvent and Other Product Use
SAR GWP (Used in Inventory) 4.4
AR4 GWP, Updated 4.2
Difference (%) (3.9%)
Agriculture
SAR GWP (Used in Inventory) 383.6
AR4 GWP, Updated 408.0
Difference (%) 6.4%
Land Use, Land-Use Change, and
Forestry
16,168.0
6,226.1
0.9% •
348.8
363.9
4.3%l
4.9J
4.7l
(3.9%)
410.6J
437.51
6.5% I
6,282.8 6,210.2
6,336.3 6,269.1
0.9% 0.9%
334.1
348.2
4.2%
4.4
4.2
(3.9%)
339.4
354.1
4.3%
4.4
4.2
(3.9%)
418.8 418.8
446.1 446.5
6.5% 6.6%
6,290.7
6,347.5
0.9%
350.9
367.1
4.6%
4.4
4.2
(3.9%)
425.8
454.7
6.8%
331.7
347.7
4.8%
426.3
455.0
6.7%
2009
6,116.6 5,751.1
6,176.7 5,813.8
1.0% 1.1%
282.9
297.5
5.2%
4.4 4.4
4.2 4.2
(3.9%) (3.9%)
419.3
448.2
6.9%
A-331
-------
SAR GWP (Used in
AR4 GWP, Updated
Difference (%)
Waste
SAR GWP (Used in
AR4 GWP, Updated
Difference (%)
Inventory)
Inventory)
(846.6)
(846. Ill
(0.1%)1
175.2B
207.?B
18.5%
(540.3)
(539.3)
(0.2%)
143.9
170.0
18.1%
(1,027.9)
(1,027.5)
0.0%
144.9
171.0
• 18.0%
(1,014.5)
(1,012.1)
(0.2%)
144.4
170.4
18.0%
(1,013.4)
(1,011.3)
(0.2%)
144.1
170.0
18.0%
(1,007.3)
(1,006.4)
(0.1%)
149.0
175.8
18.0%
(990.1)
(990.0)
(0.0%)
150.5
177.6
18.0%
Net Emissions (Sources and Sinks)
SAR GWP (Used in
AR4 GWP
Difference (%)
Inventory)
5,320.3
5,448.4
2.4%
6,536.1
6,662.9
1.9%
6,157.1
6,278.4
H 2.0%
6,102.6
6,232.3
2.1%
6,202.5
6,332.2
2.1%
6,020.7
6,153.0
2.2%
5,618.2
5,751.3
2.4%
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Overall, these revisions to GWP values do not have a significant effect on U.S. emission trends, as shown in
Table A-263 and Table A-264.
Table A-263: Change in U.S. Greenhouse Gas Emissions and Sinks Using TAR us. AR4 GWPs tTg Clh Eq.l
Gas
1990
2000
2005 2006 2007 2008
2009
C02
CH4
N2O
HFCs
PFCs*
NC
64.3
2.0
8.8
4.5
0.9
NC
II
NC
60.1
2.1
12.5
1.2
0.5
NC
64.0
2.1
12.2
1.2
0.4
NC
63.3
2.1
13.0
1.5
0.4
NC
64.4
2.0
12.2
1.2
0.4
NC
65.4
1.9
10.3
1.0
0.4
Total
80.4
81.7
76.3
80.0
80.2 80.3
78.9
NC (No change)
* Includes NF3
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Table A-264: Change in U.S. Greenhouse Gas Emissions Using TAR us. AR4 GWPs [Percent!
Gas/Source
C02
CH4
N2O
HFCs
Substitution of Ozone Depleting
Substances
HCFC-22 Production"
Semiconductor Manufacture0
PFCs
Semiconductor Manufacture0
Aluminum Production3
SF6
Total
1990
NC
8.7%
0.7%
23.2%
3.3%
23.3%
23.3%
22.4%
10.0%
24.2%
2.7%
1.3%
2000
NC
8.7%
0.7%
13.0%
8.8%
23.3%
23.3%
19.9%
11.3%
26.0%
2.7%
1.8%
. _
2005
NC
8.7%
0.7%
10.2%
8.2%
23.3%
23.3%
17.7%
13.1%
24.4%
2.7%
1.7%
2006
NC
8.7%
0.7%
9.8%
8.0%
23.3%
23.3%
17.6%
14.2%
24.2%
2.7%
1.8%
2007
NC
8.7%
0.7%
9.8%
7.7%
23.3%
23.3%
17.8%
13.4%
23.4%
2.7%
1.8%
2008
NC
8.7%
0.7%
9.2%
7.5%
23.3%
23.3%
16.8%
13.6%
22.9%
2.7%
1.9%
2009
8.
NC
7%
0.7%
7.
7
23
23
15.
13
22
2.
2.
9%
.2%
.3%
.3%
8%
.7%
.8%
7%
0%
NC (No change)
a PFC emissions from CF4 and C2F6
b HFC-23 emitted
c Emissions from HFC-23, CF4, C2F6, C3F8, SF6, and the addition of NF3
Note: Excludes Sinks. Parentheses indicate negative values.
References
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 (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.
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere,81 where it shields the earth from harmful levels of ultraviolet radiation,
and at lower concentrations in the troposphere,82 where it is the main component of anthropogenic photochemical "smog."
Chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs),
along with certain other chlorine and bromine containing compounds, have been found to deplete the ozone levels in the
stratosphere. These compounds are commonly referred to as ozone depleting substances (ODSs). If left unchecked,
stratospheric ozone depletion could result in a dangerous increase of ultraviolet radiation reaching the earth's surface. In
1987, nations around the world signed the Montreal Protocol on Substances that Deplete the Ozone Layer. This landmark
agreement created an international framework for limiting, and ultimately eliminating, the production of most ozone
depleting substances. ODSs have historically been used in a variety of industrial applications, including refrigeration and
air conditioning, foam blowing, fire extinguishing, as an aerosol propellant, sterilization, and solvent cleaning.
In the United States, the Clean Air Act Amendments of 1990 provide the legal instrument for implementation of
the Montreal Protocol controls. The Clean Air Act classifies ozone depleting substances as either Class I or Class II,
depending upon the ozone depletion potential (ODP) of the compound.83 The production of CFCs, halons, carbon
tetrachloride, and methyl chloroform—all Class I substances—has already ended in the United States. However, large
amounts of these chemicals remain in existing equipment,84 and stockpiles of the ODSs, as well as material recovered
from equipment being decommissioned, are used for maintaining the existing equipment. As a result, emissions of Class I
compounds will continue, albeit in ever decreasing amounts, for many more years. Class II designated substances, all of
which are hydrochlorofluorocarbons (HCFCs), have been, or are being, phased out at later dates than Class I compounds
because they have lower ozone depletion potentials. These compounds served, and in some cases continue to serve, as
interim replacements for Class I compounds in many industrial applications. The use and emissions of HCFCs in the
United States is anticipated to continue for several decades as equipment that use Class I as equipment that use Class I
substances and Class II substances are retired from use. Under current controls, however, the production for domestic use
of all HCFCs in the United States will end by the year 2030.
In addition to contributing to ozone depletion, CFCs, halons, carbon tetrachloride, methyl chloroform, and
HCFCs are also potent greenhouse gases. However, the depletion of the ozone layer has a cooling effect on the climate
that counteracts the direct warming from tropospheric emissions of ODSs. Stratospheric ozone influences the earth's
radiative balance by absorption and emission of longwave radiation from the troposphere as well as absorption of
shortwave radiation from the sun; overall, stratospheric ozone has a warming effect.
The IPCC has prepared both direct GWPs and net (combined direct warming and indirect cooling) GWP ranges
for some of the most common ozone depleting substances (IPCC 1996). See Global Warming Potential Values Annex for
a listing of the net GWP values for ODS.
Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting
substances, the United States believes that no inventory is complete without the inclusion of these compounds. Emission
estimates for several ozone depleting substances are provided in Table A- 265.
Table A- 265: Emissions of Ozone Depleting Substances tGgl
Compound 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Class I
CFC-11 29.oB 12.ll 12.7 12.7 12.6 12.7 12.4 12.2 12.2 11.1 9.9 8.6
81 The stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of
atmospheric ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in a
region commonly called the ozone layer.
82 The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial regions
(i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in the
atmosphere and is the site for weather processes including most of the water vapor and clouds.
83 Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other substances that may deplete
stratospheric ozone but which have an ODP of less than 0.2 are Class II.
84 Older refrigeration and air-conditioning equipment, fire extinguishing systems, meter-dose inhalers, and foam products blown
with CFCs/HCFCs may still contain ODS.
A-333
-------
CFC-12 106.5 65.0B 32.9 26.7 21.3 16.6 12.4 8.6 6.3 5.8 4.9 4.0
CFC-113 59.4 11.5 + + + + + + + + + +
CFC-114 5.4B 1.11 0.5 0.4 0.4 0.3 0.3 0.2 0.2 0.2 0.1 0.1
CFC-115 5.sB 5.3l 4.1 3.6 3.2 2.7 2.0 1.3 0.6 0.2 0.1 0.1
Carbon Tetrachloride 4.31 0.91 + + + + + + + + + +
Methyl Chloroform 222.5 72.1 + + + + + + + + + +
Halon-1211 l.o I l.o I 1.1 0.9 0.8 0.7 0.7 0.7 0.7 0.6 0.6 0.6
Halon-1301 l.o| l.o| 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.1 0.9 0.8
Class II
HCFC-22
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
HCFC-225ca/cb
55.0
0.3
1.2
4.0
3.6
0.1
71.9
0.6
1.5
7.0
2.7
0.2
74.8 77.9 79.4 81.3 81.7 83.2 83.7 84.7 84.5
0.6 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8
1.5 1.5 1.5 1.5 1.5 1.5 1.6 1.6 1.6
6.7 5.6 4.0 4.1 4.2 4.3 5.5 6.9 8.4
2.8 2.9 3.0 3.2 3.3 3.4 3.5 3.7 2.5
0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3
+ Does not exceed 0.05 Gg.
Methodology and Data Sources
Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named
for its method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up"
model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold,
serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment.
The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the equipment in
each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission
rates to each population of equipment. By aggregating the emission and consumption output from the different end-uses,
the model produces estimates of total annual use and emissions of each chemical. Please see HFC and PFC Emissions
from Substitution of Ozone Depleting Substances Annex of this Inventory for a more detailed discussion of the Vintaging
Model.
Uncertainties
Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics,
and end-use emissions profiles that are used by these models. Please see the ODS Substitutes section of this report for a
more detailed description of the uncertainties that exist in the Vintaging Model.
References
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.
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.3. Sulfur Dioxide Emissions
Sulfur dioxide (SO2), emitted into the atmosphere through natural and anthropogenic processes, affects the
Earth's radiative budget through photochemical transformation into sulfate aerosols that can (1) scatter sunlight back to
space, thereby reducing the radiation reaching the Earth's surface; (2) affect cloud formation; and (3) affect atmospheric
chemical composition (e.g., stratospheric ozone, by providing surfaces for heterogeneous chemical reactions). The overall
effect of SO2-derived aerosols on radiative forcing is believed to be negative (IPCC 2007). However, because SO2 is
short-lived and unevenly distributed through the atmosphere, its radiative forcing impacts are highly uncertain. Sulfur
dioxide emissions have been provided below in Table A-266.
The major source of SO2 emissions in the United States is the burning of sulfur containing fuels, mainly coal.
Metal smelting and other industrial processes also release significant quantities of SO2. The largest contributor to U.S.
emissions of SO2 is electricity generation, accounting for 60 percent of total SO2 emissions in 2009 (see Table A-267);
coal combustion accounted for approximately 92 percent of that total. The second largest source was industrial fuel
combustion, which produced 18 percent of 2009 SO2 emissions. Overall, SO2 emissions in the United States decreased by
59 percent from 1990 to 2009. The majority of this decline came from reductions from electricity generation, primarily
due to increased consumption of low sulfur coal from surface mines in western states.
Sulfur dioxide is important for reasons other than its effect on radiative forcing. It is a major contributor to the
formation of urban smog and acid rain. As a contributor to urban smog, high concentrations of SO2 can cause significant
increases in acute and chronic respiratory diseases. In addition, once SO2 is emitted, it is chemically transformed in the
atmosphere and returns to earth as the primary contributor to acid deposition, or acid rain. Acid rain has been found to
accelerate the decay of building materials and paints, and to cause the acidification of lakes and streams and damage trees.
As a result of these harmful effects, the United States has regulated the emissions of SO2 under the Clean Air Act. The
EPA has also developed a strategy to control these emissions via four programs: (1) the National Ambient Air Quality
Standards program,85 (2) New Source Performance Standards,86 (3) the New Source Review/Prevention of Significant
Deterioration Program,87 and (4) the sulfur dioxide allowance program.88
Table A-266: S02 Emissions [Ggl
Sector/Source
1990
2000
2005 2006 2007 2008 2009
Energy 19,628 13,797
Stationary Combustion 18,407 12,849
Mobile Combustion 7931 632
Oil and Gas Activities 390 • 287
Waste Combustion 381 29
Industrial Processes 1,307 1,031
Chemical Manufacturing 269B 307
Metals Processing 6591 284
Storage and Transport 61 5
Other Industrial Processes 3621 372
Miscellaneous* ' ' I ^4
Solvent Use +1 1
Degreasing +H +
Graphic Arts + B +
Dry Cleaning NAB +
Surface Coating +H +
Other Industrial +1 ll
Non-industrial NAB NA
Agriculture NAB NA
Agricultural Burning NAB NA
Waste +B ll
Landfills +• l|
Wastewater Treatment +B
Miscellaneous Waste +
12,634
11,541
889
181
24
831
228
159
3
327
114
11,568
10,612
750
182
24
818
230
167
3
318
102
10,991
10,172
611
184
24
807
230
176
4
308
89
9,573
8,891
472
187
23
795
231
184
4
298
77
NA
NA
NA
1
1
NA
NA
NA
1
1
NA
NA
NA
1
1
NA
NA
NA
1
1
7,800
7,167
455
154
24
798
222
158
2
324
91
NA
NA
NA
1
1
Total
20,935
14,830
13,466 12,388 11,799 10,368 8,599
85 [42 U.S.C § 7409, CAA § 109]
86[42U.S.C§7411, CAA§111]
[42 U.S.C § 7473, CAA § 163]
' [42 U.S.C § 7651, CAA § 401]
87
A-335
-------
Source: Data taken from EPA (2005) and disaggregated based on EPA (2003).
* Miscellaneous includes other combustion and fugitive dust categories.
+ Does not exceed 0.5 Gg
NA (Not Available)
Note: Totals may not sum due to independent rounding.
Table A-267: S02 Emissions from Electricity Generation tGgl
Fuel Type
Coal
Petroleum
Natural Gas
Misc. Internal Combustion
Other
Total
1990
13,808
580
1
45
NA
14,433
2000
9,620
428 •
157 •
54|
78
10,338
2005
8,680
458
174
57
71
9,439
2006
7,846
414
157
52
64
8,532
2007
7,459
393
149
49
61
8,111
2008
6,300
332
126
41
51
6,851
2009
4,718
249
94
31
38
5,131
Source: Data taken from EPA (2009 and 2010) and disaggregated based on EPA (2003).
Note: Totals may not sum due to independent rounding.
References
EPA (2010). "2009 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory (NEI)
Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards.
EPA (2009). "1970 - 2008 Average annual emissions, all criteria pollutants in MS Excel." National Emissions Inventory
(NEI) Air Pollutant Emissions Trends Data. Office of Air Quality Planning and Standards. Available online at
.
EPA (2003) E-mail correspondence. 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 (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.
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.4. Complete List of Source Categories
Chapter/Source
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding CO2)
Mobile Combustion (excluding CO2)
Coal Mining
Abandoned Underground Coal Mines
Natural Gas Systems
Petroleum Systems
Incineration of Waste
Industrial Processes
Titanium Dioxide Production
Aluminum Production
Iron and Steel Production
Ferroalloy Production
Ammonia Production and Urea Consumption
Cement Production
Lime Production
Limestone and Dolomite Use
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Phosphoric Acid Production
Petrochemical Production
Silicon Carbide Production and Consumption
Lead Production
Zinc Production
Adipic Acid Production
Nitric Acid Production
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
Electrical Transmission and Distributing
Magnesium Production and Processing
Solvent and Other Product Use
N2O Product Usage
Agriculture
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Agricultural Soil Management
Land Use, Land-Use Change, and Forestry
CO2 Flux
Cropland Remaining Cropland
Settlements Remaining Settlements
Forestland Remaining Forestland
Wetlands Remaining Wetlands
Waste
Landfills
Wastewater Treatment
Composting
CO2
C02
CH4, N2O, CO, NO*, NMVOC
CH4, N2O, CO, NO*, NMVOC
CH4
CH4
CH4
CH4
CO2,CH4,N2O
C02
C02, CF4, C2F6
C02, CH4
CO2, CH4
CO2
C02
C02
C02
C02
C02
C02
CH4, C02
CH4, CO2
C02
C02
N2O
N20
HFCs, PFCsa
HFC-23
HFCs, PFCs, SF6b
SF6
SF6
CO, NOx, NMVOC
N20
CH4
CH4, N2O
CH4
CH4, N2O
N20, CO, NOx
CO2 (sink)
C02
N20
CH4, N2O
C02, N20
CH4
CH4, N20
CH4, N2O
"Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and
PFC/PFPEs.
b Includes such gases as HFC-23, CF4, C2F& SF6.
A-337
-------
6.5. Constants, Units, and Conversions
Metric Prefixes
Although most activity data for the United States is gathered in customary U.S. units, these units are converted
into metric units per international reporting guidelines. Table A- 268 provides a guide for determining the magnitude of
metric units.
Table A- 268: Guide to Metric Unit Prefixes
Prefix/Symbol
atto (a)
femto (f)
pico (p)
nano (n)
micro (\i )
milli (m)
centi (c)
deci (d)
deca (da)
hecto (h)
kilo (k)
mega (M)
giga (G)
tera (T)
peta (P)
exa (E)
Factor
io-18
io-15
io-12
io-9
ID'6
1C'3
1Q-2
1C'1
10
IO2
IO3
IO6
IO9
IO12
IO15
IO18
Unit Conversions
1 kilogram =
1 pound =
1 short ton =
1 metric ton =
1 cubic meter =
1 cubic foot =
1 U.S. gallon
1 barrel (bbl)
1 barrel (bbl)
1 liter
1 foot
1 meter =
1 mile =
1 kilometer =
1 acre =
1 square mile =
2.205 pounds
0.454 kilograms
2,000 pounds = 0.9072 metric tons
1,000 kilograms = 1.1023 short tons
35.3 15 cubic feet
0.02832 cubic meters
3.7854 12 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
0.3048 meters
3.28 feet
1.609 kilometers
0.622 miles
43,560 square feet = 0.4047 hectares = 4,047 square meters
2.589988 square kilometers
To convert degrees Fahrenheit to degrees Celsius, subtract 32 and multiply by 5/9
To convert degrees Celsius to Kelvin, add 273.15 to the number of Celsius degrees
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
Density Conversions89
Methane
Carbon dioxide
Natural gas liquids
Unfinished oils
Alcohol
Liquefied petroleum gas
Aviation gasoline
Naphtha jet fuel
Kerosene jet fuel
Motor gasoline
Kerosene
Naphtha
Distillate
Residual oil
Lubricants
Bitumen
Waxes
Petroleum coke
Petrochemical feedstocks
Special naphtha
Miscellaneous products
1 cubic meter =
1 cubic meter =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
1 metric ton =
0.67606 kilograms
1.85387 kilograms
11. 6 barrels =
7.46 barrels
7.94 barrels
11. 6 barrels =
8.9 barrels
8.27 barrels
7.93 barrels
8.53 barrels
7.73 barrels =
8.22 barrels
7.46 barrels
6.66 barrels =
7.06 barrels =
6.06 barrels =
7.87 barrels
5. 51 barrels =
7.46 barrels
8.53 barrels
8.00 barrels
1,844.2 liters
1,186.04 liters
1,262.36 liters
1,844.2 liters
1,4 15. Oliters
1,314. 82 liters
1,260.72 liters
1,356. 16 liters
1,228.97 liters
1,306.87 liters
1,186.04 liters
1,058.85 liters
1,122. 45 liters
963.46 liters
1,25 1.23 liters
876.02 liters
1,186.04 liters
1,356. 16 liters
1,27 1.90 liters
Energy Conversions
Converting Various Energy Units to Joules
The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the
energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (10 ) joules. A British
thermal unit (Btu, the customary U. S. energy unit) is the quantity of heat required to raise the temperature of one pound of
water one degree Fahrenheit at or near 39.2 Fahrenheit.
1TJ =
2.388x10" calories
23.88 metric tons of crude oil equivalent
947.8 million Btus
277,800 kilowatt-hours
Converting Various Physical Units to Energy Units
Data on the production and consumption of fuels are first gathered in physical units. These units must be
converted to their energy equivalents. The conversion factors in Table A-269 can be used as default factors, if local data
are not available. See Appendix A of ^\A? $, Annual Energy Review 2009 (EIA 2010) for more detailed information on the
energy content of various fuels.
'Reference: EIA (2007)
A-339
-------
Table A-269: Conversion Factors to Energy Units (Heat Equivalents)
Fuel Type (Units) Factor
Solid Fuels (Million Btu/Short ton)
Anthracite coal 22.5 73
Bituminous coal 23.89
Sub-bituminous coal 17.14
Lignite 12.866
Coke 24.8
Natural Gas (Btu/Cubic foot) 1,027
Liquid Fuels (Million Btu/Barrel)
Motor gasoline 5.150
Aviation gasoline 5.048
Kerosene 5.670
Jet fuel, kerosene-type 5.670
Distillate fuel 5.825
Residual oil 6.287
Naphtha for petrochemicals 5.248
Petroleum coke 6.024
Other oil for petrochemicals 5.825
Special naphthas 5.248
Lubricants 6.065
Waxes 5.537
Asphalt 6.636
Still gas 6.000
Misc. products 5.796
Note: For petroleum and natural gas, Annual Energy Review 2009 (EIA 2010). For coal ranks, State Energy Data Report 1992 (EIA 1993). All
values are given in higher heating values (gross calorific values).
References
EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-
0573(2006).
EIA (2007b) Annual Energy Review 2009. Energy Information Administration, U.S. Department of Energy, Washington,
DC. DOE/EIA-0384(2009). August 2010.
EIA (1998) Emissions of Greenhouse Gases in the United States, DOE/EIA-0573(97), Energy Information
Administration, U.S. Department of Energy. Washington, DC. October.
EIA (1993) State Energy Data Report 1992, DOE/EIA-0214(93), Energy Information Administration, U.S. Department of
Energy. Washington, DC. December.
A-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
6.6. Abbreviations
AAPFCO
ABS
AFEAS
AFV
AGA
AHEF
AISI
ANL
APC
API
ASAE
ASTM
BCEF
BEA
BoC
BODS
EOF
BRS
BTS
Btu
C&EN
CAAA
CAPP
CARB
CBI
CEFM
CFC
CFR
CKD
CMA
CMOP
CNG
CRF
CRP
CTIC
CVD
DE
DESC
DFAMS
DM
DOC
DoD
DOE
DOI
DOT
EAF
EF
EFMA
EJ
EGR
EIA
EIIP
EOR
EPA
ERS
FAA
FAO
FCCC
FEE
FGD
FHWA
FIA
FIPR
American Association of Plant Food Control Officials
Acrylonitrile Butadiene Styrene
Alternative Fluorocarbon Environmental Acceptability Study
Alternative Fuel Vehicle
American Gas Association
Atmospheric and Health Effect Framework
American Iron and Steel Institute
Argonne National Laboratory
American Plastics Council
American Petroleum Institute
American Society of Agricultural Engineers
American Society for Testing and Materials
Biomass conversion and expansion factors
Bureau of Economic Analysis, U.S. Department of Commerce
Bureau of Census
Biochemical oxygen demand over a 5-day period
Basic Oxygen Furnace
Biennial Reporting System
Bureau of Transportation Statistics, U.S. Department of Transportation
British thermal unit
Chemical and Engineering News
Clean Air Act Amendments of 1990
Canadian Association of Petroleum Producers
California Air Resources Board
Confidential Business Information
Cattle Enteric Fermentation Model
Chlorofluorocarbon
Code of Federal Regulations
Cement Kiln Dust
Chemical Manufacturer's Association
Coalbed Methane Outreach Program
Compressed Natural Gas
Common Reporting Format
Conservation Reserve Program
Conservation Technology Information Center
Chemical vapor deposition
Digestible Energy
Defense Energy Support Center-DoD's defense logistics agency
Defense Fuels Automated Management System
Dry Matter
U.S. Department of Commerce
U.S. Department of Defense
U.S. Department of Energy
U.S. Department of the Interior
U.S. Department of Transportation
Electric Arc Furnace
Emission Factor
European Fertilizer Manufacturers Association
Exajoule
Exhaust Gas Recirculation
Energy Information Administration, U.S. Department of Energy
Emissions Inventory Improvement Program
Enhanced oil recovery
U.S. Environmental Protection Agency
Economic Research Service
Federal Aviation Administration
Food and Agricultural Organization
Framework Convention on Climate Change
Fiber Economics Bureau
Flue Gas Desulphurization
Federal Highway Administration
Forest Inventory and Analysis
Florida Institute of Phosphate Research
A-341
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FTP
GCV
GDP
Gg
GHG
GRI
GSAM
GWP
HBFC
HC
HCFC
HDDV
HDGV
HOPE
HFC
HFE
HHV
HMA
HTF
HTS
HWP
ICAO
IEA
IFO
IISRP
ILENR
IMO
IPAA
IPCC
JWR
LDDT
LDDV
LDGT
LDGV
LDPE
LEV
LFG
LFGTE
LHV
LLDPE
LMOP
LNG
LPG
LTO
LULUCF
MC
MCF
MCFD
MGO
MLRA
MMCFD
MMS
MMTCE
MSHA
MSW
MTBE
NAHMS
NAICS
NAPAP
NASS
NCV
NEU
NEV
NGL
NIR
NMVOC
Federal Test Procedure
Gross calorific value
Gross domestic product
Gigagram
Greenhouse gas
Gas Research Institute
Gas Systems Analysis Model
Global warming potential
Hydrobromofluorocarbon
Hydrocarbon
Hydrochlorofluorocarbon
Heavy duty diesel vehicle
Heavy duty gas vehicle
High density polyethylene
Hydrofluorocarbon
Hydrofluoroethers
Higher Heating Value
Hot Mix Asphalt
Heat Transfer Fluid
Harmonized Tariff Schedule
Harvested wood product
International Civil Aviation Organization
International Energy Association
Intermediate Fuel Oil
International Institute of Synthetic Rubber Products
Illinois Department of Energy and Natural Resources
International Maritime Organization
Independent Petroleum Association of America
Intergovernmental Panel on Climate Change
Jim Walters Resources
Light duty diesel truck
Light duty diesel vehicle
Light duty gas truck
Light duty gas vehicle
Low density polyethylene
Low emission vehicles
Landfill gas
Landfill gas-to-energy
Lower Heating Value
Linear low density polyethylene
EPA's Landfill Methane Outreach Program
Liquefied Natural Gas
Liquefied petroleum gas(es)
Landing and take-off
Land use, land-use change, and forestry
Motorcycle
Methane conversion factor
Thousand Cubic Feet Per Day
Marine Gas Oil
Major Land Resource Area
Million Cubic Feet Per Day
Minerals Management Service
Million metric tons carbon equivalent
Mine Safety and Health Administration
Municipal solid waste
Methyl Tertiary Butyl Ether
National Animal Health Monitoring System
North American Industry Classification System
National Acid Precipitation and Assessment Program
USDA's National Agriculture Statistics Service
Net calorific value
Non-Energy Use
Neighborhood Electric Vehicle
Natural Gas Liquids
National Inventory Report
Non-methane volatile organic compound
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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NOx
NPRA
NRC
NRCS
NRI
NSCEP
NSCR
NWS
GAP
OAQPS
OOP
ODS
OECD
OEM
QMS
ORNL
OSHA
OTA
OTAQ
PAH
PCC
PDF
PECVD
PET
PEVM
PFC
PFPE
POTW
Ppbv
Ppmv
Pptv
PRP
PS
PSU
PU
PVC
QA/QC
QBtu
RCRA
SAE
SAGE
SAN
SAR
SCR
SNAP
SNG
SOC
STMC
SULEV
SWANA
TAM
TAME
TAR
TBtu
TON
TFI
TgC02Eq.
TJ
TLEV
TMLA
TRI
TSDF
TVA
UDA
U.S.
U.S. ITC
Nitrogen Oxides
National Petroleum and Refiners Association
National Research Council
Natural Resources Conservation Service
National Resources Inventory
National Service Center for Environmental Publications
Non-selective catalytic reduction
National Weather Service
EPA Office of Atmospheric Programs
EPA Office of Air Quality Planning and Standards
Ozone Depleting Potential
Ozone depleting substances
Organization of Economic Co-operation and Development
Original equipment manufacturers
EPA Office of Mobile Sources
Oak Ridge National Laboratory
Occupational Safety and Health Administration
Office of Technology Assessment
EPA Office of Transportation and Air-Quality
Polycyclic Aromatic Hydrocarbons
Precipitate calcium carbonate
Probability Density Function
Plasma enhanced chemical vapor deposition
Polyethylene Terephthalate
PFC Emissions Vintage Model
Perfluorocarbon
Perfluoropolyether
Publicly Owned Treatment Works
Parts per billion (10 ) by volume
Parts per million(106) by volume
Parts per trillion (10 ) by volume
Pasture/Range/Paddock
Polystyrene
Primary Sample Unit
Polyurethane
Polyvinyl chloride
Quality Assurance and Quality Control
Quadrillion Btu
Resource Conservation and Recovery Act
Society of Automotive Engineers
System for assessing Aviation's Global Emissions
Styrene Acrylonitrile
IPCC Second Assessment Report
Selective catalytic reduction
Significant New Alternative Policy Program
Synthetic natural gas
Soil Organic Carbon
Scrap Tire Management Council
Super Ultra Low Emissions Vehicle
Solid Waste Association of North America
Typical Animal Mass
Tertiary Amyl Methyl Ether
IPCC Third Assessment Report
Trillion Btu
Total Digestible Nutrients
The Fertilizer Institute
Teragrams carbon dioxide equivalent
Terajoule
Traditional Low Emissions Vehicle
Total Manufactured Layer Area
Toxic Release Inventory
Hazardous waste treatment, storage, and disposal facility
Tennessee Valley Authority
Utility Data Institute
United States
United States International Trade Commission
A-343
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UEP United Egg Producers
ULEV Ultra Low Emission Vehicle
UNEP United Nations Environmental Programme
UNFCCC United Nations Framework Convention on Climate Change
USAF United States Air Force
USDA United States Department of Agriculture
USFS United States Forest Service
USGS United States Geological Survey
VAIP EPA's Voluntary Aluminum Industrial Partnership
VKT Vehicle kilometers traveled
VMT Vehicle miles traveled
VOCs Volatile Organic Compounds
VS Volatile Solids
WIP Waste In Place
WMO World Meteorological Organization
WMS Waste Management Systems
ZEVs Zero Emissions Vehicles
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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6.7. Chemical Formulas
Table A-270: Guide to Chemical Formulas
Symbol
Name
Al
A12O3
Br
C
CH4
C2H6
C3H8
CF4
C2F6
c-C3F6
C3F8
c-C4F8
C4Flo
C5Fi2
C6F14
CF3I
CFC13
CF2C12
CF3C1
C2F3C13
CC13CF3
C2F4C12
C2F5C1
CHC12F
CHF2C1
C2F3HC12
C2F4HC1
C2FH3C12
C2H3F2C1
CF3CF2CHC12
CC1F2CF2CHC1F
CC14
CHC1CC12
CC12CC12
CH3C1
CH3CC13
CH2C12
CHC13
CHF3
CH2F2
CH3F
C2HF5
C2H2F4
CH2FCF3
C2H3F3
C2H3F3
CH2FCH2F
C2H4F2
CH3CH2F
C3HF7
CF3CF2CH2F
CF3CHFCHF2
C3H2F6
C3H3F5
CHF2CH2CF3
CF3CH2CF2CH3
C5H2Fio
CF3OCHF2
CF2HOCF2H
CH3OCF3
CF3CHFOCF3
Aluminum
Aluminum Oxide
Bromine
Carbon
Methane
Ethane
Propane
Perfluoromethane
Perfluoroethane, hexafluoroethane
Perfluorocyclopropane
Perfluoropropane
Perfluorocyclobutane
Perfluorobutane
Perfluoropentane
Perfluorohexane
Trifluoroiodomethane
Trichlorofluoromethane (CFC-11)
Dichlorodifluoromethane (CFC-12)
Chlorotrifluoromethane (CFC-13)
Trichlorotrifluoroethane (CFC-113)*
CFC-113a*
Dichlorotetrafluoroethane (CFC-114)
Chloropentafluoroethane (CFC-115)
HCFC-21
Chlorodifluoromethane (HCFC-22)
HCFC-123
HCFC-124
HCFC-141b
HCFC-142b
HCFC-225ca
HCFC-225cb
Carbon tetrachloride
Trichloroethylene
Perchloroethylene, tetrachloroethene
Methylchloride
Methylchloroform
Methylenechloride
Chloroform, trichloromethane
HFC-23
HFC-32
HFC-41
HFC-125
HFC-134
HFC-134a
HFC-143*
HFC-143a*
HFC-152*
HFC-152a*
HFC-161
HFC-227ea
HFC-236cb
HFC-236ea
HFC-236fa
HFC-245ca
HFC-245fa
HFC-365mfc
HFC-43-10mee
HFE-125
HFE-134
HFE-143a
HFE-227ea
A-345
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CF3CHC1OCHF2
CF3CHFOCHF2
CF3CH2OCF3
CF3CF2OCH3
CHF2CH2OCF3
CF3CH2OCHF2
CHF2CF2OCH3
CF3CH2OCH3
CF3CF2OCF2CHF2
CF3CF2OCH2CF3
CF3CF2CF2OCH3
CF3CF2OCH2CHF2
CF3CHFCF2OCH3
CHF2CF2CF2OCH3
CHF2CF2OCH2CHF2
CHF2CF2CH2OCHF2
CF3CF2CH2OCH3
CHF2CF2OCH2CH3
041* gOOris
C4F9OC2H5
CHF2OCF2OC2F4OCHF2
CHF2OCF2OCHF2
CHF2OCF2CF2OCHF2
CH3OCH3
CH2Br2
CH2BrCl
CHBr3
CHBrF2
CH3Br
CF2BrCl
CF3Br(CBrF3)
CF3I
CO
CO2
CaCO3
CaMg(C03)2
CaO
Cl
F
Fe
Fe203
FeSi
H, H2
H20
H202
OH
N,N2
NH3
NFL,+
HN03
NF3
N20
NO
NO2
N03
Na
Na2C03
Na3AlF6
0,02
03
S
H2SO4
SF6
SF5CF3
SO2
Si
HCFE-235da2
HFE-236ea2
HFE-236fa
HFE-245cb2
HFE-245fal
HFE-245fa2
HFE-254cb2
HFE-263fb2
HFE-329mcc2
HFE-338mcf2
HFE-347mcc3
HFE-347mcf2
HFE-356mec3
HFE-356pcc3
HFE-356pcf2
HFE-356pcf3
HFE-365mcf3
HFE-374pcf2
HFE-7100
HFE-7200
H-Galden 1040x
HG-10
HG-01
Dimethyl ether
Dibromomethane
Dibromochloromethane
Tribromomethane
Bromodifluoromethane
Methylbromide
Bromodichloromethane (Halon 1211)
Bromotrifluoromethane (Halon 1301)
FIC-13I1
Carbon monoxide
Carbon dioxide
Calcium carbonate, Limestone
Dolomite
Calcium oxide, Lime
atomic Chlorine
Fluorine
Iron
Ferric oxide
Ferrosilicon
atomic Hydrogen, molecular Hydrogen
Water
Hydrogen peroxide
Hydroxyl
atomic Nitrogen, molecular Nitrogen
Ammonia
Ammonium ion
Nitric acid
Nitrogen trifluoride
Nitrous oxide
Nitric oxide
Nitrogen dioxide
Nitrate radical
Sodium
Sodium carbonate, soda ash
Synthetic cryolite
atomic Oxygen, molecular Oxygen
Ozone
atomic Sulfur
Sulfuric acid
Sulfur hexafluoride
Trifluoromethylsulphur pentafluoride
Sulfur dioxide
Silicon
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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SiC Silicon carbide
SiO2 Quartz
* Distinct isomers.
A-347
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A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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ANNEX 7 Uncertainty
The annual U.S. Inventory presents the best effort to produce estimates for greenhouse gas source and sink
categories in the United States. These estimates were generated according to the UNFCCC reporting guidelines, following
the recommendations set forth in the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC/UNEP/OECD/TEA 1997), the IPCC Good Practice Guidance (IPCC 2000), the Good Practice Guidance for Land
Use, Land-Use Change and Forestry (IPCC 2003), and the 2006 Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). This Annex provides an overview of the uncertainty analysis conducted to support the U.S. Inventory,
describes the sources of uncertainty characterized throughout the Inventory associated with various source categories
(including emissions and sinks), and describes the methods through which uncertainty information was collected,
quantified, and presented.
7.1. Overview
The current inventory emission estimates for some source categories, such as for CO2 Emissions from Fossil Fuel
Combustion, have relatively low level of uncertainty associated with them. However, for some other source categories, the
inventory emission estimates are considered less certain. The two major types of uncertainty associated with these
emission estimates are (1) model uncertainty, which arises when the emission and/or removal estimation models used in
developing the inventory estimates do not fully and accurately characterize the respective emission and/or removal
processes (due to a lack of technical details or other resources), resulting in the use of incorrect or incomplete estimation
methodologies and (2) parameter uncertainty, which arises due to a lack of precise input data such as emission factors and
activity data.
The model uncertainty can be partially analyzed by comparing the model results with those of other models
developed to characterize the same emission (or removal) process, after taking into account the differences in their
conceptual framework, capabilities, data and assumptions. However, it would be very difficult—if not impossible—to
quantify the model uncertainty associated with the emission estimates (primarily because, in most cases, only a single
model has been developed to estimate emissions from any one source). Therefore, model uncertainty was not quantified in
this report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the individual source category
description and inventory estimation methodology.
Parameter uncertainty is, therefore, the principal type and source of uncertainty associated with the national
inventory emission estimates and is the main focus of the quantitative uncertainty analyses in this report. Parameter
uncertainty has been quantified for all of the emission sources and sinks in the U.S. Inventory, with the exception of one
very small emission source category, CH4 emissions from Incineration of Waste, which was included in the 1990-2008
National GHG Inventory for the first time, and two other source categories International Bunker Fuels and biomass energy
consumption) whose emissions are not included in the Inventory totals.
The primary purpose of the uncertainty analysis conducted in support of the U.S. Inventory is (i) to determine the
quantitative uncertainty associated with the emission (and removal) estimates presented in the main body of this report
[based on the uncertainty associated with the input parameters used in the emission (and removal) estimation
methodologies] and (ii) to evaluate the relative importance of the input parameters in contributing to uncertainty in the
associated source category inventory estimate and in the overall inventory estimate. Thus, the U.S. Inventory uncertainty
analysis provides a strong foundation for developing future improvements and revisions to the Inventory estimation
process. For each source category, the analysis highlights opportunities for changes to data measurement, data collection,
and calculation methodologies. These are presented in the "Planned Improvements" sections of each source category's
discussion in the main body of the report.
7.2. Methodology and Results
The United States has developed a quality assurance and quality control (QA/QC) and uncertainty management
plan (EPA 2002) in accordance with the IPCC Good Practice Guidance (IPCC 2000). Like the QA/QC plan, the
uncertainty management plan is part of a continually evolving process. The uncertainty management plan provides for a
quantitative assessment of the inventory analysis itself, thereby contributing to continuing efforts to understand both what
causes uncertainty and how to improve inventory quality. Although the plan provides both general and specific guidelines
for implementing quantitative uncertainty analysis, its components are intended to evolve over time, consistent with the
A-349
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inventory estimation process. The U.S. plan includes procedures and guidelines, and forms and templates, for developing
quantitative assessments of uncertainty in the national Inventory estimates (EPA 2002).
The IPCC Good Practice Guidance recommends two approaches—Tier 1 and Tier 2—for developing
quantitative estimates of uncertainty in the inventory estimate of individual source categories and the overall inventory. Of
these, the Tier 2 approach is both more flexible and reliable than Tier 1; both methods are described in the next section.
The United States is in the process of implementing a multi-year strategy to develop quantitative estimates of uncertainty
for all source categories using the Tier 2 approach. For the current Inventory, a Tier 2 approach was implemented for all
source categories with the exception of Composting and parts of Agricultural Soil Management source categories.
The current Inventory reflects significant improvements over the previous publication in the extent to which the
Tier 2 approach to uncertainty analysis was adopted. Each of the new Tier 2 analyses reflects additional detail and
characterization of input parameters using statistical data collection, expert elicitation methods and more informed
judgment. In following the UNFCCC requirement under Article 4.1, emissions from International Bunker Fuels and
Indirect Greenhouse Gas Emissions are not included in the total emissions estimated for the U.S. Inventory; therefore, no
quantitative uncertainty estimates have been developed for these source categories. Emissions from biomass combustion
are accounted for implicitly in the LULUCF chapter through the calculation of changes in carbon stocks. The Energy
sector does provide an estimate of CO2 emissions from bioenergy consumption provided as a memo item for informational
purposes in line with the UNFCCC reporting requirements.
Tier 1 and Tier 2 Approach
The Tier 1 method for estimating uncertainty is based on the error propagation equation. This equation combines
the uncertainty associated with the activity data and the uncertainty associated with the emission (or the other) factors.
The Tier 1 approach is applicable where emissions (or removals) are usually estimated as the product of an activity value
and an emission factor or as the sum of individual sub-source category values. Inherent in employing the Tier 1 method
are the assumptions that, for each source category, (i) both the activity data and the emission factor values are
approximately normally distributed, (ii) the coefficient of variation (i.e., the ratio of the standard deviation to the mean)
associated with each input variable is less than 30 percent, and (iii) the input variables within and across (sub-) source
categories are not correlated (i.e., value of each variable is independent of the values of other variables).
The Tier 2 method is preferred (i) if the uncertainty associated with the input variables is significantly large, (ii)
if the distributions underlying the input variables are not normal, (iii) if the estimates of uncertainty associated with the
input variables are correlated, and/or (iv) if a sophisticated estimation methodology and/or several input variables are used
to characterize the emission (or removal) process correctly. In practice, the Tier 2 is the preferred method of uncertainty
analysis for all source categories where sufficient and reliable data are available to characterize the uncertainty of the input
variables.
The Tier 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the Monte
Carlo method). Under this method, estimates of emissions (or removals) for a particular source category are generated
many times (equal to the number of simulations specified) using an uncertainty model, which is an emission (or removal)
estimation equation that imitates or is the same as the inventory estimation model for a particular source category. These
estimates are generated using the respective, randomly-selected values for the constituent input variables using
commercially available simulation software such as @RISK or Crystal Ball.
Characterization of Uncertainty in Input Variables
Both Tier 1 and Tier 2 uncertainty analyses require that all the input variables are well-characterized in terms of
their Probability Density Functions (PDFs). In the absence of particularly convincing data measurements, sufficient data
samples, or expert judgments that determined otherwise, the PDFs incorporated in the current source category uncertainty
analyses were limited to normal, lognormal, uniform, triangular, and beta distributions. The choice among these five
PDFs depended largely on the observed or measured data and expert judgment.
However, because the input variables that determine the emissions from the Fossil Fuel Combustion and the International
Bunker Fuels source categories are correlated, uncertainty associated with the activity variables in the International Bunker Fuels
was taken into account in estimating the uncertainty associated with the Fossil Fuel Combustion.
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Source Category Inventory Uncertainty Estimates
Discussion surrounding the input parameters and sources of uncertainty for each source category appears in the
body of this report. Table A-271 summarizes results based on assessments of source category-level uncertainty. The table
presents base year (1990 or 1995) and current year (2009) emissions for each source category. The combined uncertainty
(at the 95 percent confidence interval) for each source category is expressed as the percentage deviation above and below
the total 2009 emissions estimated for that source category. Source category trend uncertainty is described subsequently
in this Appendix.
Table A-271: Summary Results of Source Category Uncertainty Analyses
Source Category
C02
Fossil Fuel Combustion0
Non-Energy Use of Fuels
Natural Gas Systems
Cement Production
Lime Production
Limestone and Dolomite Use
Soda Ash Production and Consumption
Carbon Dioxide Consumption
Incineration of Waste
Titanium Dioxide Production
Aluminum Production
Iron and Steel Production and Metallurgical Coke
Production
Ferroalloy Production
Ammonia Production and Urea Consumption
Phosphoric Acid Production
Petrochemical Production
Silicon Carbide Production and Consumption
Lead Production
Zinc Production
Cropland Remaining Cropland
Wetlands Remaining Wetlands
Petroleum Systems
Land Use, Land-Use Change, and Forestry (Sink/
Biomass - Wood
International Bunker Fuels"
Biomass - Ethanol
CH4
Stationary Combustion
Mobile Combustion
Coal Mining
Abandoned Underground Coal Mines
Natural Gas Systems
Petroleum Systems
Petrochemical Production
Silicon Carbide Production and Consumption
Iron and Steel Production and Metallurgical Coke
Production
Ferroalloy Production
Enteric Fermentation
Manure Management
Rice Cultivation
Field Burning of Agricultural Residues
Forest Land Remaining Forest Land
Landfills
Wastewater Treatment
Base Year ^n«o u • • a
_ . . -a 2009 Emissions
Emissions '
Tg CO2 Eq.
5,099.3
4,738.0
118.6
37.6
33.3
11.5
5.1
4.1
1.4
8.0
1.2
6.8
99.5
2.2
16.8
1.5
3.3
0.4
0.5
0.7
7.1
1.0
0.6
(861.5)
215.2
111.8
4.2
674.9
7.4
4.7
84.1
6.0
189.8
35.4
0.9
+
1.0
+
132.1
31.7
7.1
0.3
3.2
147.4
23.5
Tg CO2 Eq.
5,504.8
5,208.6
123.4
32.2
29.0
11.2
7.6
4.3
1.8
12.3
1.5
3.0
41.9
1.5
11.8
1.0
2.7
0.1
0.5
1.0
7.8
1.1
0.5
(1,015.1)
183.8
123.1
61.2
686.3
6.2
2.0
71.0
5.5
221.2
30.9
0.8
+
0.4
+
139.8
49.5
7.3
0.2
7.8
117.5
24.5
2009 Uncertainty"
Low
-1%
-1%
-
-
-
-7%
-
-7%
-
-
-
-4%
-
16%
-
-7%
-
-
-9%
-
-
-
-
-
15%
NE
NE
NE
-9%
-
-9%
-
-
-
-
-
-9%
-
21%
-
-
-
-
-
-
-
High
6%
6%
10%
30%
14%
10%
19%
7%
30%
24%
13%
4%
16%
13%
8%
19%
31%
9%
15%
18%
50%
34%
149%
-15%
NE
NE
NE
17%
127%
15%
16%
32%
30%
149%
27%
9%
23%
12%
18%
20%
146%
42%
145%
40%
47%
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Composting
Incineration of Waste
International Bunker Fuels"
N2O
Stationary Combustion
Mobile Combustion
Adipic Acid Production
Nitric Acid Production
Manure Management
Agricultural Soil Management
Field Burning of Agricultural Residues
Wastewater Treatment
N2O from Product Uses
Incineration of Waste
Settlements Remaining Settlements
Forest Land Remaining Forest Land
Composting
Wetlands Remaining Wetlands
International Bunker Fuels"
HFCs, PFCs, and SF6
Substitution of Ozone Depleting Substances8
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Electrical Transmission and Distribution
Magnesium Production and Processing
Total11
Net Emissions (Sources and Sinks)11
0.3
+
0.2
315.2
12.8
43.9
15.8
17.7
14.5
197.8
0.1
3.7
4.4
0.5
1.0
2.7
0.4
+
1.1
120.7
29.0
36.4
2.9
18.5
28.4
5.4
6,210.0
5,348.5
1.7
+
0.1
295.6
12.8
23.9
1.9
14.6
17.9
204.6
0.1
5.0
4.4
0.4
1.5
6.7
1.8
+
1.1
143.3
117.1
5.4
5.3
1.6
12.8
1.1
6,630.0
5,614.9
-
NE
NE
-
-
-
-
-
-
-
-
-
-8%
-
-
-
-
-
NE
-6%
-7%
-7%
-
-
-
-3%
-1%
-2%
50%
NE
NE
44%
187%
17%
42%
42%
24%
54%
31%
93%
8%
320%
163%
139%
50%
41%
NE
7%
8%
10%
11%
11%
22%
4%
6%
8%
Notes:
Totals may not sum due to independent rounding.
*Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen 1995.
+ Does not exceed 0.05 Tg CO2 Eq.
NE Not Estimated
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 for 2009 in this table exclude approximately 3.2 Tg CO2 Eq. of emissions for which quantitative
uncertainty was not assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in
this Inventory. All uncertainty estimates correspond only to the totals reported in this table.
b The uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th percentile and the upper
bound corresponding to 97.5th percentile.
c This source category's inventory estimates exclude CO2 emissions from geothermal sources, as quantitative uncertainty analysis was not
performed for that sub-source category. Hence, for this source category, the emissions reported in this table do not match the emission estimates
presented in the Energy chapter of the Inventory.
d Sinks are only included in Net Emissions.
e Emissions from International Bunker Fuels are not included in the totals.
'Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
8 This source category's inventory estimate for 2009 excludes 2.9 Tg of CO2 Eq. from several very small emission sources, as uncertainty
associated with those sources was not assessed. Hence, for this source category, the emissions reported in this table do not match the emission
estimates presented in the Industrial Processes chapter of the Inventory.
h Totals exclude emissions for which uncertainty was not quantified. The Base Year emissions correspond to 1990 estimates for all source
categories, with the exception of Substitution of ODS, for which the estimates correspond to 1995. Similarly, the total for HFCs, PFCs, and SF6
for the Base Year includes 1995 emission estimates for Substitution of ODS and 1990 emission estimates for all other source categories.
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate for the U.S. greenhouse gas emissions inventory was developed using the
IPCC Tier 2 uncertainty estimation methodology. The uncertainty models of all the emission source categories could not
be directly integrated to develop the overall uncertainty estimates due to software constraints in integrating multiple, large
uncertainty models. Therefore, an alternative approach was adopted to develop the overall uncertainty estimates. The
Monte Carlo simulation output data for each emission source category uncertainty analysis were combined by type of gas
and the probability distributions were fitted to the combined simulation output data, where such simulated output data
were available. If such detailed output data were not available for particular emissions sources, individual probability
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
-------
distributions were assigned to those source category emission estimates based on the most detailed data available from the
quantitative uncertainty analysis performed.
For the Composting and for parts of Agricultural Soil Management source categories, Tier 1 uncertainty results
were used in the overall uncertainty analysis estimation. However, for all other emission sources (excluding international
bunker fuels, CO2 from biomass combustion, and CH4 from incineration of waste), Tier 2 uncertainty results were used in
the overall uncertainty estimation.
The overall uncertainty model results indicate that the 2009 U.S. greenhouse gas emissions are estimated to be
within the range of approximately 6,584 to 7,034 Tg CO2 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of -1 percent to 6 percent with respect to the total U.S. greenhouse gas emission estimate of
approximately 6,630 Tg CO2 Eq. The uncertainty interval associated with total CO2 emissions, which constitute about 83
percent of the total U.S. greenhouse gas emissions in 2009, ranges from -1 percent to 6 percent of total CO2 emissions
estimated. The results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions
ranges from -9 percent to 17 percent, uncertainty associated with the total inventory N2O emission estimate ranges from -
11 percent to 44 percent, and uncertainty associated with high GWP gas emissions ranges from -6 percent to 7 percent.
A summary of the overall quantitative uncertainty estimates is shown below.
Table A-272. Quantitative Uncertainty Assessment of Overall National Inventory Emissions (Tg C0? Eq. and Percent)
2009 Emission Standard
Estimate3 Uncertainty Range Relative to Emission Estimate Mean0 Deviation0
Gas (TgCO2Eq.) (Tg CO2 Eq.) (%) (Tg CO2 Eq.)
Lower Lower
Bound Upper Bound Bound Upper Bound
CO2
CH4e
N20e
PFC, HFC & SF6e
Total
Net Emissions (Sources and
Sinks)
5,504.8
686.3
295.6
143.3
6,630.0
5,614.9
5,436.6
623.9
261.7
134.5
6,584.2
5,512.3
5,813.8
805.4
425.3
153.4
7,033.6
6,055.1
-1%
-9%
-11%
-6%
-1%
-2%
6%
17%
44%
7%
6%
8%
5,622.5
702.8
334.2
143.7
6,803.2
5,785.4
97.5
45.3
42.1
4.8
115.0
139.1
Notes:
a Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative uncertainty was
performed this year. Thus the totals reported in this table exclude approximately 3.2 Tg CO2 Eq. of emissions for which quantitative uncertainty
was not assessed. Hence, these emission estimates do not match the final total U.S. greenhouse gas emission estimates presented in this
Inventory.
b The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th
percentile and the upper bound corresponding to 97.5th percentile.
c Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of deviation of the
simulated values from the mean.
d The lower and upper bound emission estimates for the sub-source categories do not sum to total emissions because the low and high estimates
for total emissions were calculated separately through simulations.
e The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N2O and high GWP gases used in the
inventory emission calculations for 2009.
Trend Uncertainty
In addition to the estimates of uncertainty associated with the current year's emission estimates, this Annex also
presents the estimates of trend uncertainty. The IPCC Good Practice Guidance defines trend as the difference in emissions
between the base year (i.e., 1990) and the current year (i.e., 2009) inventory estimates. However, for purposes of
understanding the concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change in
the emissions (or removal) estimated for the current year, relative to the emission (or removal) estimated for the base year.
The uncertainty associated with this emission trend is referred to as trend uncertainty.
Under the Tier 1 approach, the trend uncertainty for a source category is estimated using the sensitivity of the
calculated difference between the base year and the current year (i.e., 2009) emissions to an incremental (i.e., 1 percent)
increase in one or both of these values for that source category. The two sensitivities are expressed as percentages: Type
A sensitivity highlights the effect on the difference between the base and the current year emissions caused by a 1 percent
change in both, while Type B sensitivity highlights the effect caused by a change to only the current year's emissions.
Both sensitivities are simplifications introduced in order to analyze the correlation between the base and the current year
estimates. Once calculated, the two sensitivities are combined using the error propagation equation to estimate the overall
trend uncertainty.
A-353
-------
Under the Tier 2 approach, the trend uncertainty is estimated using Monte Carlo Stochastic Simulation technique.
The trend uncertainty analysis takes into account the fact that the base and the current year estimates often share input
variables. For purposes of the current Inventory, a simple approach has been adopted, under which the base year source
category emissions (or removals) are assumed to exhibit the same uncertainty characteristics as the current year emissions
(or removals). Source category-specific PDFs for base year estimates were developed using current year (i.e., 2009)
uncertainty output data. These were adjusted to account for differences in magnitude between the two years' inventory
estimates. Then, for each source category, a trend uncertainty estimate was developed using the Monte Carlo method.
The overall inventory trend uncertainty estimate was developed by combining all source category-specific trend
uncertainty estimates. These preliminary trend uncertainty estimates present the range of likely change from base year to
2009, and are shown in Table A- 273.
Table A- 273. Quantitative Assessment of Trend Uncertainty tTg GO? Eg. and Percent!
Gas/Source
Base Year . . a
„ . . "a 2009 Emissions
Emissions '
Emissions
Trend a
Trend Range" "
(Tg CO2 Eq.) (%) (%)
C02
Fossil Fuel Combustion0
Non-Energy Use of Fuels
Iron and Steel Production and Metallurgical Coke
Production
Natural Gas Systems
Cement Production
Incineration of Waste
Ammonia Production and Urea Consumption
Lime Production
Cropland Remaining Cropland
Limestone and Dolomite Use
Soda Ash Production and Consumption
Aluminum Production
Petrochemical Production
Carbon Dioxide Consumption
Ferroalloy Production
Titanium Dioxide Production
Wetlands Remaining Wetlands
Phosphoric Acid Production
Zinc Production
Petroleum Systems
Lead Production
Silicon Carbide Production and Consumption
Land Use, Land-Use Change, and Forestry (Sink)
Biomass - Wood
International Bunker Fuels"
Biomass — Ethanof
CH4
Natural Gas Systems
Enteric Fermentation
Landfills
Coal Mining
Manure Management
Petroleum Systems
Wastewater Treatment
Forest Land Remaining Forest Land
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines
Mobile Combustion
Composting
Petrochemical Production
Iron and Steel Production and Metallurgical Coke
Production
Field Burning of Agricultural Residues
Ferroalloy Production
Silicon Carbide Production and Consumption
5,099.3
4,738.0
118.6
99.5
37.6
33.3
8.0
16.8
11.5
7.1
5.1
4.1
6.8
3.3
1.4
2.2
1.2
1.0
1.5
0.7
0.6
0.5
0.4
(861.5)
215.2
111.8
4.2
674.9
189.8
132.1
147.4
84.1
31.7
35.4
23.5
3.2
7.1
7.4
6.0
4.7
0.3
0.9
1.0
0.3
+
+
5,504.8
5,208.6
123.4
41.9
32.2
29.0
12.3
11.8
11.2
7.8
7.6
4.3
3.0
2.7
1.8
1.5
1.5
1.1
1.0
1.0
0.5
0.5
0.1
(1,015.1)
183.8
123.1
61.2
686.3
221.2
139.8
117.5
71.0
49.5
30.9
24.5
7.8
7.3
6.2
5.5
2.0
1.7
0.8
0.4
0.2
+
+
8%
10%
4%
-58%
-14%
-13%
54%
-30%
-3%
11%
49%
3%
-56%
-17%
24%
-32%
29%
5%
-32%
45%
-17%
2%
-61%
18%
-15%
10%
1348%
2%
17%
6%
-20%
-16%
56%
-13%
4%
144%
3%
-17%
-9%
-58%
421%
-2%
-62%
-8%
-40%
-67%
Lower
Bound
3%
5%
-17%
-67%
-39%
-28%
12%
-37%
-14%
-56%
20%
-7%
-58%
-46%
-17%
-43%
7%
-33%
-48%
12%
-64%
-18%
-66%
-5%
NE
NE
NE
-15%
-17%
-14%
-61%
-32%
19%
-62%
-42%
-50%
-75%
-67%
-40%
-65%
131%
-34%
-72%
-51%
-99%
-71%
Upper
Bound
13%
16%
31%
-47%
20%
5%
111%
-22%
10%
181%
85%
14%
-53%
27%
88%
-18%
55%
67%
-11%
86%
90%
25%
-56%
46%
NE
NE
NE
22%
64%
30%
63%
4%
105%
98%
91%
1110%
317%
109%
38%
-51%
1061%
44%
-48%
72%
-99%
-62%
A-354 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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Incineration of Waste
International Bunker Fuels'
N20g
Agricultural Soil Management
Mobile Combustion
Manure Management
Nitric Acid Production
Stationary Combustion
Forest Land Remaining Forest Land
Wastewater Treatment
N2O from Product Uses
Adipic Acid Production
Composting
Settlements Remaining Settlements
Incineration of Waste
Field Burning of Agricultural Residues
Wetlands Remaining Wetlands
International Bunker Fuels'
HFCs, PFCs, and SF6
Substitution of Ozone Depleting Substances8
Electrical Transmission and Distribution
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Magnesium Production and Processing
Total11
Net Emissions (Sources and Sinks)11
+
0.2
315.2
197.8
43.9
14.5
17.7
12.8
2.7
3.7
4.4
15.8
0.4
1.0
0.5
0.1
+
1.1
120.7
29.0
28.4
36.4
2.9
18.5
5.4
6,210.0
5,348.5
+
0.1
295.6
204.6
23.9
17.9
14.6
12.8
6.7
5.0
4.4
1.9
1.8
1.5
0.4
0.1
+
1.1
143.3
117.1
12.8
5.4
5.3
1.6
1.1
6,630.0
5,614.9
-23%
-14%
-6%
3%
-46%
23%
-18%
0%
152%
36%
0%
-88%
421%
55%
-23%
3%
-7%
4%
19%
304%
-55%
-85%
83%
-91%
-81%
7%
5%
NE
NE
-32%
-36%
-56%
-7%
-56%
-65%
-40%
-72%
-12%
-93%
135%
-51%
-83%
-33%
-72%
NE
9%
264%
-66%
-87%
58%
-93%
-82%
2%
-2%
NE
NE
31%
65%
-32%
63%
50%
183%
977%
499%
12%
-77%
1042%
392%
245%
60%
225%
NE
29%
349%
-39%
-83%
111%
-90%
-80%
12%
12%
Notes:
Totals may not sum due to independent rounding.
*Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen to use 1995.
+ Does not exceed 0.05 Tg CO2 Eq.
NE Not Estimated
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 for 2009 exclude approximately 3.2 Tg CO2 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. Emissions trends and the emission range were calculated based on the emissions estimates reported in this table and, therefore,
may differ from the emissions trends reported elsewhere in this Inventory.
b The trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to 2.5th percentile value
and the upper bound corresponding to 97.5th percentile value.
c This source category's inventory estimates exclude CO2 emissions from geothermal sources, as quantitative uncertainty analysis was not
performed for that sub-source category. Hence, for this source category, the emissions reported in this table do not match the emission estimates
presented in the Energy chapter of the Inventory.
d Sinks are only included in Net Emissions.
e Emissions from International Bunker Fuels are not included in the emission totals and emission trend estimates.
'Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals.
8 This source category's inventory estimate for 2009 excludes about 2.9 Tg of CO2 Eq. from several very small emission sources, as uncertainty
associated with those sources was not assessed. Hence, for this source category, the emissions reported in this table do not match the emission
estimates presented in the Industrial Processes chapter of the Inventory.
h Totals exclude emissions for which uncertainty was not quantified. The Base Year emissions correspond to 1990 estimates for all source
categories, with the exception of Substitution of ODS, for which the estimates correspond to 1995. Similarly, the total for HFCs, PFCs, and SF6
for the Base Year includes 1995 emission estimates for Substitution of ODS and 1990 emission estimates for all other source categories.
7.3. Planned Improvements
Identifying the sources of uncertainty in the emission and sink estimates of the Inventory and quantifying the
magnitude of the associated uncertainty is the crucial first step towards improving those estimates. Quantitative
assessment of the parameter uncertainty may also provide information about the relative importance of input parameters
(such as activity data and emission factors), based on their relative contribution to the uncertainty within the source
category estimates. Such information can be used to prioritize resources with a goal of reducing uncertainty over time
within or among inventory source categories and their input parameters. In the current Inventory, potential sources of
model uncertainty have been identified for some emission source categories, and uncertainty estimates based on their
parameters' uncertainty have been developed for all the emission source categories, with the exception of CH4 from
incineration of waste, which is a minor emission source category newly added to the Inventory starting with the 2008
business year, and the international bunker fuels and wood biomass and ethanol combustion source categories, which are
A-355
-------
not included in the energy sector totals. Emissions from biomass and ethanol combustion however are accounted for
implicitly in the LULUCF chapter through the calculation of changes in carbon stocks. The Energy sector does provide an
estimate of CO2 emissions from bioenergy consumption provided as a memo item for informational purposes.
Specific areas that require further research include:
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile
sources, could not be developed at this time either because data are incomplete or because methodologies do not
exist for estimating emissions from these source categories. See Annex 5 of this report for a discussion of the
sources of greenhouse gas emissions and sinks excluded from this report. In the future, efforts will focus on
estimating emissions from excluded emission sources and developing uncertainty estimates for all source
categories for which emissions are estimated.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the accuracy of
emission factors used to calculate emissions from a variety of sources. For example, the accuracy of current
emission factors applied to CH4 and N2O emissions from stationary and mobile combustion are highly uncertain.
• Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources,
problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be applied.
For example, the ability to estimate emissions of SF6 from electrical transmission and distribution is limited due
to a lack of activity data regarding national SF6 consumption or average equipment leak rates.
In improving the quality of uncertainty estimates the following include areas that deserve further attention:
• Refine Source Category and Overall Uncertainty Estimates. For many individual source categories, further
research is needed to more accurately characterize PDFs that surround emissions modeling input variables. This
might involve using measured or published statistics or implementing rigorous elicitation protocol to elicit expert
judgments, if published or measured data are not available.
• Include GWP uncertainty in the estimation of Overall level and trend uncertainty. The current year's Inventory
does not include the uncertainty associated with the GWP values in the estimation of the overall uncertainty for
the Inventory. Including this source would contribute to a better characterization of overall uncertainty and help
assess the level of attention that this source of uncertainty warrants in the future.
• Improve characterization of trend uncertainty associated with base year Inventory estimates. The
characterization of base year uncertainty estimates could be improved, by developing explicit uncertainty models
for the base year. This would then improve the analysis of trend uncertainty. However, not all of the simplifying
assumptions described in the "Trend Uncertainty" section above may be eliminated through this process due to a
lack of availability of more appropriate data.
7.4. Additional Information on Uncertainty Analyses by Source
The quantitative uncertainty estimates associated with each emission and sink source category are reported in
each chapter of this Inventory following the discussions of inventory estimates and their estimation methodology. This
section provides additional descriptions of the uncertainty analyses performed for some of the sources, including the
models and methods used to calculate the emission estimates and the potential sources of uncertainty surrounding them.
These sources are organized below in the same order as the sources in each chapter of the main section of this Inventory.
To avoid repetition, the following uncertainty analysis discussions of individual source categories do not include
descriptions of these source categories. Hence, to better understand the details provided below, refer to the respective
chapters and sections in the main section of this Inventory, as needed. All uncertainty estimates are reported relative to the
2009 Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
Energy
The uncertainty analysis descriptions in this section correspond to some source categories included in the Energy
Chapter of the Inventory.
Mobile Combustion (excluding CO2)
Mobile combustion emissions of CH4 and N2O per vehicle mile traveled vary significantly due to fuel type and
composition, technology type, operating speeds and conditions, type of emission control equipment, equipment age, and
operating and maintenance practices.
A-356 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009
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The primary activity data for on-road vehicles, VMT, are collected and analyzed each year by government
agencies. To determine the uncertainty associated with the activity data used in the calculations of CH4 and N2O
emissions, the agencies and the experts that supply the data were contacted. Because few of these sources were able to
provide quantitative estimates of uncertainty, expert judgment was used to assess the quantitative uncertainty associated
with the activity data.
The estimates of VMT for on-road vehicles by vehicle type in the U.S. were provided by the FHWA (1996
through 2009), and were generated through a cooperative process between the FHWA and the state and the local
governments. These estimates are subject to several possible sources of error, such as unregistered vehicles, and
measurement and estimation errors. These VMT were apportioned by fuel type, based on data from DOE (1993 through
2009), and then allocated to individual model years using temporal profiles of both the vehicle fleet by age and vehicle
usage by model year in the U.S. provided by EPA (2007c) and EPA (2000). While the uncertainty associated with the total
national VMT is believed to be low, the uncertainty within individual source categories was considered to be higher due to
the uncertainty associated with apportioning total VMT into individual vehicle categories, by fuel type, technology type,
and by equipment age. It was assumed that smaller sources had greater percentage uncertainty and vice-versa. Another
source of uncertainty in the estimates occurs due to differences in the FHWA and the EPA data sources. For example, the
FHWA data are used for defining vehicle types and for developing the estimates of VMT by vehicle type; whereas, the
estimates of VMT by fuel types are calculated using EPA's definition of vehicle categories (which differ from those of the
FHWA).
The emission factors for on-road vehicles used in the Inventory were obtained from ICF (2006b) and ICF (2004).
These factors were based on laboratory testing of vehicles. While the controlled testing environment simulates real
driving conditions, emission results from such testing can only approximate real world conditions and emissions. For
some vehicle and control technology types, because the testing did not yield statistically significant results within the 95
percent confidence interval, expert judgment was adopted in developing the emission factors. In those cases, the missing
emission factors were extrapolated based on the data available on the other emission factors and the emissions factors
available for similar vehicle and control technology type. For example, if light duty trucks with an oxidation catalyst has
no testing (or not significant testing) results and if light duty cars with an oxidation catalyst had testing results, the CH4
and the N2O emission factors for the trucks were calculated from the corresponding emissions factors for the car based
upon the ratio of CO2 emissions per mile for the car to the truck.
A total of 111 highway data input variables were simulated through Monte Carlo Simulation technique using
@RISK software. Variables included VMT and emission factors for individual conventional and alternative fuel vehicle
categories and technologies. In developing the uncertainty estimation model, a normal distribution was assumed for all
but two activity-related input variables (e.g., VMT); in the case of the two input variables, buses and percent of diesel
combination trucks, triangular distributions were assumed. The dependencies and other correlations among the activity
data were incorporated into the model to ensure consistency in the model specification and simulation. Emission factors
were assigned uniform distributions, with the upper and the lower bounds assigned based on 95 percent confidence
intervals of laboratory test data. In cases where data did not yield statistically significant results within the 95 percent
confidence interval, estimates of upper and lower bounds were determined using expert judgments. For biodiesel vehicles,
because no test data were available, consistent with the assumptions underlying the ANL GREET model, their N2O and
CH4 emissions were assumed to be same as those for diesel vehicles of similar types. For other alternative fuel vehicles
(AFVs), uncertainty estimates were developed based on conventional fuel vehicle emission factors and applicable
multipliers, as described in the ICF's AFV emission factors memorandum to EPA (ICF 2006a). The results of the
quantitative uncertainty analysis are reported as quantitative uncertainty estimates following the mobile source category
emissions description in the Energy Chapter of this Inventory.
Emissions from non-road vehicles account for 23 percent of CH4 emissions from mobile sources and 13 percent
of N2O emissions from mobile sources in 2009. A quantitative analysis of uncertainty in the inventory estimates of
emissions from non-road vehicles was performed for the first time for the 2009 inventory. Sources of uncertainty for non-
road vehicles were investigated by examining the underlying uncertainty of emission factors and fuel consumption data.
A non-road uncertainty assessment module was developed independently and integrated with the highway mobile
uncertainty model to facilitate a more comprehensive quantitative analysis of uncertainty for all mobile sources.
The fuel consumption data for non-road vehicles were obtained from several sources. Estimates of fuel
consumption for non-road vehicles (i.e., equipment used for agriculture, construction, lawn and garden, railroad, airport
ground support, etc., as well as recreational vehicles) were generated by the EPA's NONROAD model (EPA 2009). This
model estimates fuel consumption based on estimated equipment/vehicle use (in hours) and average fuel consumed per
hour of use. Since the fuel estimates are not based upon documented fuel sales or consumption, a fair degree of
uncertainty accompanies these estimates. Estimates of distillate fuel sales for ships and boats were obtained from EIA's
Fuel Oil and Kerosene Sales (EIA 1991 through 20010). These estimates have a moderate level of uncertainty since
A-357
-------
EIA's estimates are based on survey data and reflect sales to economic sectors, which may include use by both mobile and
non-mobile sources within a sector. Domestic consumption of residual fuel by ships and boats is obtained from EIA
(200 lOa). These estimates fluctuate widely from year to year, and are believed to be highly uncertain. In addition,
estimates of distillate and residual fuel sales for ships and boats are adjusted for bunker fuel consumption, which
introduces an additional (and much higher) level of uncertainty. Jet fuel and aviation gasoline consumption data are
obtained from DOT (1991 through 20010), EIA (2007a), FAA (20010), and FAA (2006). Additionally, all jet fuel
consumption in the transportation sector is assumed to be consumed by aircraft. Some jet fuel may also be used for other
purposes such as blending with diesel fuel or heating oil. In calculating CH4 emissions from aircraft, an average emission
factor is applied to total jet fuel consumption. This average emission factor takes into account the fact that CH4 emissions
occur only during the landing and take-off (LTO) cycles, with no CH4 being emitted during the cruise cycle. However, a
better approach would be to apply emission factors based on the number of LTO cycles.
Emission factors for non-road modes were taken from IPCC/UNEP/OECD/iEA (1997) and Browning (2009).
These emission factors are based on laboratory test data and expert judgment, and have similar sources of uncertainty as
the on-road emission factors for uncontrolled vehicles.
A total of 79 non-road data input variables were simulated in the non-road uncertainty assessment module using
@RISK software. To determine the uncertainty associated with the non-road fuel consumption data, the agencies and
experts that supply the data were contacted. Since few of these sources were able to provide quantitative estimates of
uncertainty, expert judgment was used to assess the quantitative uncertainty associated with the fuel consumption data. A
normal distribution was assumed for all non-road activity-related input (fuel use) variables, and the activity variables were
assumed to be independent of each other. Uncertainty estimates for non-road emissions factors were developed based on
laboratory test data and expert judgement. Beta-PERT distributions were assumed for the emissions factor variables, and
correlations among the data were incorporated into the model to ensure consistency in model specification.
The results of the quantitative uncertainty analysis are reported as quantitative uncertainty estimates following
the mobile source category emissions description in the Energy Chapter of this Inventory.
Incineration of Waste
The upper and lower bounds of uncertainty in the CO2 emissions estimate for Incineration of Waste are 24
percent and -21 percent respectively, and in the N2O emission estimates are 320 percent and -51 percent respectively,
relative to the respective 2009 emission estimates, at the 95 percent confidence interval. The uncertainties in the waste
combustion emission estimates arise from both the assumptions applied to the data and from the quality of the data. Key
factors include MSW combustion rate, fraction oxidized, missing data on MSW composition, average carbon content of
MSW components, assumptions on the synthetic/biogenic carbon ratio, and combustion conditions affecting N2O
emissions. For more information on emission estimates from MSW combustion, please refer to the Incineration of Waste
section of the Energy chapter. The highest levels of uncertainty surround the variables, whose estimates were developed
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, carbon
content of carbon black). Important sources of uncertainty are as follows:
• MSW Combustion Rate. A source of uncertainty affecting both fossil CO2 and N2O emissions is the estimate of
the MSW combustion rate. The BioCyde (Glenn 1999, Goldstein and Matdes 2000, Goldstein and Matdes 2001,
Kaufman et al. 2004a, Kaufman et al. 2004b, Simmons et al. 2006, Arsova et al. 2008, van Haaren et al. 2010)
estimate of total waste combustion, used for the N2O and CH4 emissions estimates, and waste incineration rate,
used for the CO2 emissions estimate are based on a survey of state officials, who use differing definitions of solid
waste and who draw from a variety of sources of varying reliability and accuracy. The survey methodology
changed significantly in 2003 and thus the results reported for 2002 are not directly comparable to the earlier
results (Kaufman et al. 2004a, 2004b), introducing further uncertainty.
• Fraction Oxidized. Another source of uncertainty for the CO2 emissions estimate is fraction oxidized. Municipal
waste combustors vary considerably in their efficiency as a function of waste type, moisture content, combustion
conditions, and other factors. A value of 98 percent was assumed for this analysis.
• Missing Data on Municipal Solid Waste Composition. Disposal rates have been interpolated when there is an
incomplete interval within a time series. Where data are not available for years at the end of a time series, they
are set equal to the most recent years for which estimates are available.
• Average Carbon Contents. Average carbon contents were applied to the mass of "Other" plastics combusted,
synthetic rubber in tires and municipal solid waste, and synthetic fibers. These average values were estimated
from the average carbon content of the known products recently produced. The actual carbon content of the
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combusted waste may differ from this estimate depending on differences in the chemical formulation between
the known and unspecified materials, and differences between the composition of the material disposed and that
produced. For rubber, this uncertainty ranges from 51 to 91 percent; for plastics, it may be more significant, as
their carbon contents range from 38 to 92 percent. However, overall, this is a small source of uncertainty.
• Synthetic/'Biogenic Assumptions. A portion of the fiber and rubber in municipal solid waste is biogenic in origin.
Assumptions have been made concerning the allocation between synthetic and biogenic materials based
primarily on expert judgment.
• Combustion Conditions Affecting N2O Emissions. Because insufficient data exist to provide detailed estimates of
N2O emissions for individual combustion facilities, the estimates presented exhibit high uncertainty. The
emission factor for N2O from municipal solid waste combustion facilities used in the analysis corresponds to the
default emission factor for continuously fed stoker units found in IPCC (2006). Using this emission factor
assumes that all waste combustors in the United States use continuously fed stoker technology, which is
uncertain. Due to a lack of information on the control of N2O emissions from MSW combustion facilities in the
United States, the estimate of zero percent for N2O emissions control removal efficiency also exhibits
uncertainty.
Industrial Processes
The uncertainty analysis descriptions in this section correspond to some source categories included in the
Industrial Processes Chapter of the Inventory.
Ammonia Manufacture and Urea Consumption
The uncertainty upper and lower bounds of the emission estimate for Ammonia Manufacture and Urea
Consumption were 8 percent and -7 percent, respectively, at the 95 percent confidence interval. The European Fertilizer
Manufacturer's Association (EFMA) reported an emission factor range of 1.15 to 1.30 ton CO2/ton NH3, with 1.2 ton
CO2/ton NH3 reported as a typical value. The actual emission factor depends upon the amount of air used in the ammonia
production process, with 1.15 ton CO2/ton NH3 being the approximate stoichiometric minimum that is achievable for the
conventional reforming process. By using natural gas consumption data for each ammonia plant, more accurate estimates
of CO2 emissions from ammonia production could be calculated. However, these consumption data are often considered
confidential. Also, natural gas is consumed at ammonia plants both as a feedstock to the reforming process and for
generating process heat and steam. Natural gas consumption data, if available, would need to be divided into feedstock
use (non-energy) and process heat and steam (fuel) use, as CO2 emissions from fuel use and non-energy use are calculated
separately.91
Natural gas feedstock consumption data for the U.S. ammonia industry as a whole are available from the Energy
Information Administration (EIA) Manufacturers Energy Consumption Survey (MECS) for the years 1985, 1988, 1991,
1994 and 1998 (EIA 1994, 1998). These feedstock consumption data collectively correspond to an effective average
emission factor of 1.0 ton CO2/ton NH3, which appears to be below the stoichiometric minimum that is achievable for the
conventional steam reforming process. The EIA data for natural gas consumption for the years 1994 and 1998 correspond
more closely to the CO2 emissions calculated using the EFMA emission factor than do data for previous years. The 1994
and 1998 data alone yield an effective emission factor of 1.1 ton CO2/tonNH3, corresponding to CO2 emissions estimates
that are approximately 1.5 Tg CO2 Eq. below the estimates calculated using the EFMA emission factor of 1.2 ton CO2/ton
NH3. Natural gas feedstock consumption data are not available from EIA for other years, and data for 1991 and previous
91 It appears that the IPCC emission factor for ammonia production of 1.5 ton CO2 per ton ammonia may include both CO2
emissions from the natural gas feedstock to the process and some CO2 emissions from the natural gas used to generate process
heat and steam for the process. Table 2-5, Ammonia Production Emission Factors, in Volume 3 of the Revised 1996 IPCC
Guidelines for National Greenhouse Gas Inventories Reference Manual (IPCC 1997) includes two emission factors, one reported
for Norway and one reported for Canada. The footnotes to the table indicate that the factor for Norway does not include natural
gas used as fuel but that it is unclear whether the factor for Canada includes natural gas used as fuel. However, the factors for
Norway and Canada are nearly identical (1.5 and 1.6 tons CO2 per ton ammonia, respectively) and it is likely that if one value
does not include fuel use, the other value also does not. For the conventional steam reforming process, however, the EFMA
reports an emission factor range for feedstock CO2 of 1.15 to 1.30 ton per ton (with a typical value of 1.2 ton per ton) and an
emission factor for fuel CO2 of 0.5 tons per ton. This corresponds to a total CO2 emission factor for the ammonia production
process, including both feedstock CO2 and process heat CO2, of 1.7 ton per ton, which is closer to the emission factors reported in
the IPCC 1996 Reference Guidelines than to the feedstock-only CO2 emission factor of 1.2 ton CO2 per ton ammonia reported by
the EFMA. Because it appears that the emission factors cited in the IPCC Guidelines may actually include natural gas used as
fuel, we use the 1.2 tons/ton emission factor developed by the EFMA.
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years may underestimate feedstock natural gas consumption, and therefore the EFMA emission factor was used to estimate
CO2 emissions from ammonia production, rather than EIA data.
Research indicates that there is only one U.S. plant that manufactures ammonia from petroleum coke. CO2
emissions from this plant are explicitly accounted for in the Inventory estimates. No data for ammonia plants using
naphtha or other feedstocks other than natural gas have been identified. Therefore, all other CO2 emissions from ammonia
plants are calculated using the emission factor for natural gas feedstock. However, actual emissions may differ because
processes other than catalytic steam reformation and feedstocks other than natural gas may have been used for ammonia
production. Urea is also used for other purposes than as a nitrogenous fertilizer. Currently, urea used as a nitrogenous
fertilizer is accounted for in the LULUCF chapter. Research has identified one ammonia production plant that is
recovering byproduct CO2 for use in EOR. Such CO2 is currently assumed to remain sequestered (see the section of this
chapter on CO2 Consumption); however, time series data for the amount of CO2 recovered from this plant are not available
and therefore all of the CO2 produced by this plant is assumed to be emitted to the atmosphere and allocated to Ammonia
Manufacture.
Phosphoric Acid Production
The uncertainty upper and lower bounds of the emissions estimate for Phosphoric Acid Production were 19
percent and -18 percent, respectively, at the 95 percent confidence interval. Factors such as the composition of phosphate
rock affect CO2 emissions from phosphoric acid production. For more information on how emissions estimates were
calculated, please refer to the Phosphoric Acid Production section of the Industrial Processes chapter. Only one set of data
from the Florida Institute of Phosphate Research (FIPR) was available for the composition of phosphate rock mined
domestically and imported, and data for uncalcined phosphate rock mined in North Carolina and Idaho were unavailable.
Inorganic carbon content (as CO2) of phosphate rock could vary ±1 percent, resulting in a variation in CO2 emissions of
±20 percent.
Organic C is not included in the calculation of CO2 emissions from phosphoric acid production. However, if, for
example, 50 percent of the organic carbon content of the phosphate rock were to be emitted as CO2 in the phosphoric acid
production process, the CO2 emission estimate would increase by approximately 50 percent. If it is assumed that 100
percent of the reported domestic production of phosphate rock for Idaho and Utah was first calcined, and it is assumed that
50 percent of the organic carbon content of the total production for Idaho and Utah was converted to CO2 in the
calcination process, the CO2 emission estimate would increase on the order of 10 percent. If it were assumed that there are
zero emissions from other uses of phosphate rock, CO2 emissions would fall 10 percent.
Iron and Steel & Metallurgical Coke Production
The uncertainty upper and lower bounds of the CO2 emission estimate for Iron and Steel & Metallurgical Coke
Production were 16 percent and -16 percent, respectively, at the 95 percent confidence interval. Factors such as the
composition of C anodes and the C content of pig iron and crude steel affect CO2 emissions from Iron and Steel
Production. For more information on emission estimates, please refer to the Iron and Steel Production section of the
Industrial Processes chapter. Simplifying assumptions were made concerning the composition of C anodes, (80 percent
petroleum coke and 20 percent coal tar). For example, within the aluminum industry, the coal tar pitch content of anodes
can vary from 15 percent in prebaked anodes to 24 to 28 percent in Soderberg anode pastes (DOE 1997). An average
value was assumed and applied to all carbon anodes utilized during aluminum and steel production. It was also assumed
that the C contents of all pig iron and crude steel have carbon contents of 4 percent and 1 percent, respectively. The
carbon content of pig iron can vary between 3.6 and 4.4 percent, while crude steel can have a carbon content of up to 2
percent, although it is typically less than 1 percent (IPCC 2000).
Aluminum Production
The uncertainty upper and lower bounds of the PFCs emission estimate for Aluminum Production were 11
percent and -11 percent, respectively, at the 95 percent confidence interval. The uncertainties associated with three
variables were estimated for each smelter: (1) the quantity of aluminum produced, (2) the anode effect minutes per cell day
(which may be reported directly or calculated as the product of anode effect frequency and anode effect duration), and (3)
the smelter- or technology-specific slope coefficient (or weight fraction). For more information on the effect of these
variables on PFC emissions, please refer the Aluminum Production section of the Industrial Processes chapter. All three
types of data are assumed to be characterized by a normal distribution. The uncertainty in aluminum production estimates
was assumed to be 1 percent for reported data (IPCC 2006). For reported anode effect frequency and duration data, the
uncertainties were assumed to be 2 percent and 5 percent, respectively (Kantamaneni et al. 2001). For calculated smelter-
specific CF4 and C2F6 slope coefficients the uncertainties were assumed to be 15 percent (IPCC 2006). For smelters
applying technology-specific slope coefficients or weight fractions, the uncertainty in the coefficients was based on the
standard deviation of the individual measurements used to determine the average value given by the IPCC guidance for
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technology-specific (Tier 2) slope coefficients. Consequently, the uncertainty value assigned to the technology-specific
slope coefficients for CF4 for CWPB was 0.036, and for C2F6 for CWPB was 0.060. (The uncertainty in the technology-
specific CF4 slope coefficent for CWPB is reported as 6 percent in IPCC (2006), but was increased to 50 percent in this
analysis to better account for measurement uncertainty for an individual facility. The uncertainty in PFC emissions for
CWPB facilities (the best behaved of the technology types) is about 50 percent for any given facility using the Tier 2
calculation.) In general, where precise quantitative information was not available on the uncertainty of a parameter, an
upper-bound value was used.
Magnesium Production
The uncertainty information below pertains to the emission estimates presented in the Magnesium Production
section of the Industrial Processes chapter. Please refer to that section for more information about this source. The
uncertainty upper and lower bounds of the emissions estimate for Magnesium Production were 4 percent and -4 percent,
respectively, at the 95 percent confidence interval. An uncertainty of 5 percent was assigned to the data reported by each
participant in the Partnership. If Partners did not report emissions data during the current reporting year, SF6 emissions
data were estimated using available emission factor and production information reported in prior years; the extrapolation
was based on the average trend for Partners reporting in the current reporting year and the year prior. The uncertainty
associated with the SF6 usage estimate generated from the extrapolated emission factor and production information was
determined using a sum of squares method. A 5 percent uncertainty for the year the Partner last reported was assumed and
a 30% uncertainty for each subsequent year was assumed. For those industry processes that are not represented in
Partnership, such as permanent mold and wrought casting, SF6 emissions were estimated using production and
consumption statistics reported by USGS and estimated process-specific emission factors (see Table 4-73). The
uncertainties associated with the emission factors and USGS-reported statistics were assumed to be 75 percent and 25
percent, respectively. Emissions associated with sand casting activities not entirely captured by the Partnership utilized a
Partner-reported emission factor with an uncertainty 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.
Electrical Transmission and Distribution
The uncertainty upper and lower bounds of the emissions estimate for Electrical Transmission and Distribution at
the 95 percent confidence interval were 22 percent and -21 percent, respectively. Uncertainty associated with emissions of
SF6 from electrical transmission and distribution stem from the following three quantities: (1) emissions from partners, (2)
emissions from non-partners, and (3) emissions from manufacturers of electrical equipment. The uncertainty of partner
emissions is related to whether the partner emissions are reported or estimated. For reported partner emissions, individual
partner submitted SF6 data was assumed to have an uncertainty of 10 percent. Based on a Monte Carlo analysis, the
cumulative uncertainty of the total partner reported data was estimated to be 5.3 percent. For partner-estimated emissions,
the uncertainty associated with emissions extrapolated or interpolated from reported emissions data was assumed to be 20
percent. There are two sources of uncertainty that contribute to the non-partner emissions uncertainty. The first is the
uncertainty in the coefficients of the regression equations used to estimate emissions from non-partners, and the second is
the uncertainty in the total transmission miles for non-partners—the independent variable in the regression equation. The
uncertainty in the coefficients (as defined by the regression standard error estimate) is estimated to be ±20 percent for
small utilities and ±64 percent for large utilities, while the uncertainty in the transmission miles is assumed to be 10
percent. For equipment manufacturers, the quantity of SF6 charged into equipment by equipment manufacturers is
estimated using partner reported new nameplate capacity data and the estimate for the total industry nameplate capacity.
The quantity of SF6 charged into equipment in 2009 is estimated to have an uncertainty of 70.2 percent, and is derived
from the uncertainty in partner reported new nameplate capacity (estimated as 4.2 percent using error propagation) and the
uncertainty in the estimate for U.S. total nameplate capacity (assumed to be 70 percent).
A Monte Carlo analysis was applied to estimate the overall uncertainty of the 2009 emission estimate for SF6
from electrical transmission and distribution. For each defined parameter (i.e., regression coefficient, transmission
mileage, partner-reported and partner-estimated SF6 emissions data for electric power systems; and SF6 emission rate and
statistics for manufacturers), random variables were selected from probability density functions, all assumed to have
normal distributions about the mean.
Agriculture
The uncertainty analysis descriptions in this section correspond to some source categories included in the
Agriculture Chapter of the Inventory.
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Manure Management
The uncertainty information below pertains to the emission estimates presented in the Manure Management
section of the Agriculture chapter. Please refer to that section for information about various manure management systems
and their effect on emissions from this source. The uncertainty upper and lower bounds of the CH4 emissions estimate for
Manure Management were 20 percent and -18 percent, respectively, at the 95 percent confidence interval. The primary
factors that contribute to the uncertainty in the emission estimates are a lack of information on the usage of various manure
management systems in each regional location and the exact CH4 generating characteristics of each type of manure
management system. Because of significant shifts in the swine and dairy sectors toward larger farms, it is believed that
increasing amounts of manure are being managed in liquid manure management systems. The existing estimates reflect
these shifts in the weighted MCFs based on 1992, 1997, and 2002 farm-size data. However, the assumption of a direct
relationship between farm size and liquid system usage may not apply in all cases and may vary based on geographic
location. In addition, the CH4 generating characteristics of each manure management system type are based on relatively
few laboratory and field measurements, and may not match the diversity of conditions under which manure is managed
nationally.
Previously, IPCC published a default range of MCFs for anaerobic lagoon systems of 0 to 100 percent, reflecting
the wide range in performance that may be achieved with these systems (IPCC 2000). There exist relatively few data
points on which to determine country-specific MCFs for these systems. In the United States, many livestock waste
treatment systems classified as anaerobic lagoons are actually holding ponds that are substantially organically overloaded
and therefore not producing CH4 at the same rate as a properly designed lagoon. In addition, these systems may not be
well operated, contributing to higher loading rates when sludge is allowed to enter the treatment portion of the lagoon or
the lagoon volume is pumped too low to allow treatment to occur. Rather than setting the MCF for all anaerobic lagoon
systems in the United States based on data available from optimized lagoon systems, a MCF methodology utilizing the
van't Hoff-Arrhenius equation was developed to more closely match observed system performance and account for the
affect of temperature on system performance.
The MCF methodology used in the inventory includes a factor to account for management and design practices
that result in the loss of VS from the management system. This factor is currently estimated based on data from anaerobic
lagoons in temperate climates, and from only three systems. However, this methodology is intended to account for
systems across a range of management practices.
Uncertainty also exists with the maximum CH4 producing potential of VS excreted by different animal groups
(i.e., B0). The B0 values used in the CH4 calculations are published values for U.S. animal waste. However, there are
several studies that provide a range of B0 values for certain animals, including dairy and swine. The B0 values chosen for
dairy assign separate values for dairy cows and dairy heifers to better represent the feeding regimens of these animal
groups. For example, dairy heifers do not receive an abundance of high energy feed and consequently, dairy heifer
manure will not produce as much CH4 as manure from a milking cow. However, the data available for B0 values are
sparse, and do not necessarily reflect the rapid changes that have occurred in this industry with respect to feed regimens.
Rice Cultivation
The uncertainty upper and lower bounds of the emission estimate for Rice Cultivation were 146 percent and -65
percent, respectively, at the 95 percent confidence interval. Factors such as primary rice-cropped area, ratooning, and
flooding affect greenhouse gas emissions from this source. For more information on emissions estimates for Rice
Cultivation, please refer to that section in the Agriculture Chapter. Uncertainty associated with primary rice-cropped area
for each state was assumed to range from 1 percent to 5 percent of the mean area based on expert judgment. A normal
distribution of uncertainty, truncated to avoid negative values, was assumed about the mean for areas.
Ratooned area data are an additional source of uncertainty. Although ratooning accounts for only 5 to 10 percent
of the total rice-cropped area, it is responsible for about 15 to 30 percent of total emissions. For states that have never
reported any ratooning, it is assumed with complete certainty that no ratooning occurred in 2009. For states that regularly
report ratooning, uncertainty is estimated to be between 3 percent and 5 percent (based on expert judgment) and is
assumed to have a normal distribution, truncated to avoid negative values. For Arkansas, which reported significant
ratooning in 1998 and 1999 only, a triangular distribution was assumed, with a lower boundary of 0 percent ratooning and
an upper boundary of 0.034 percent ratooning based on the maximum ratooned area reported in 1998 and 1999.
The practice of flooding outside of the normal rice season is also an uncertainty. According to agricultural
extension agents, all of the rice-growing states practice this on some part of their rice acreage. Estimates of these areas
range from 5 to 68 percent of the rice acreage. Fields are flooded for a variety of reasons: to provide habitat for waterfowl,
to provide ponds for crawfish production, and to aid in rice straw decomposition. To date, however, CH4 flux
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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.
Agricultural Soil Management
The uncertainty information below pertains to the emission estimates presented in the Agricultural Soil
Management section of the Agriculture chapter. Please refer to that section for information about this source. For direct
emissions calculated using DAYCENT, uncertainty in the results was attributed to model inputs (i.e., activity data,
weather and soil conditions) and the structure of the model (i.e., underlying model equations and parameterization). A
Monte Carlo analysis was implemented to address these uncertainties and propagate errors through the modeling process
(Del Grosso et al, 2010). The analysis was conducted using probability distribution functions (PDFs) for weather, soil
characteristics, and N inputs to simulate direct N2O emissions for each crop- or grassland type in a county. A joint PDF
was used to address the structural uncertainty for direct N2O emissions from crops, which was derived using an
empirically-based method (Ogle et al. 2007). This same Monte Carlo analysis was used to derive uncertainty for the
volatilization, runoff, and leaching of N that had been estimated with DAYCENT. County-scale PDFs for weather were
based on the variation in temperature and precipitation as represented in DAYMET weather data grid cells (1x1 km)
occurring in croplands and grasslands in a county. The National Land Cover Dataset (Vogelman et al. 2001) provided the
data on distribution of croplands and grasslands. Similarly, county-scale PDFs for soil characteristics were based on
STATSGO Soil Map Units (Soil Survey Staff 2005), that occurred in croplands and grasslands. PDFs for fertilizer were
derived from survey data for major U.S. crops, both irrigated and rainfed (ERS 1997; NASS 2004, 1999, 1992; Grant and
Krenz 1985). State-level PDFs were developed for each crop if a minimum of 15 data points existed for each of the two
categories (irrigated and rainfed). Where data were insufficient at the state-level, PDFs were developed for multi-state
Farm Production Regions. Uncertainty in manure application for specific crops was incorporated into the analysis based
on total manure available for application in each county, a weighted average application rate, and the crop-specific land
area amended with manure for 1997 (compiled from USDA data on animal numbers, manure production, storage
practices, application rates and associated land areas receiving manure amendments; see Edmonds et al. 2003). Together
with the total area for each crop within a county, the result yielded a probability that a given crop in a specific county
would either receive manure or not in the Monte Carlo analysis. A ratio of manure N available for application in each year
of the inventory relative to 1997 was used to adjust the amount of area amended with manure, under the assumption that
changing the amount of manure N available for application would lead to a proportional change in amended area (see the
section on Major Crop Types on Mineral Soils for data sources on manure N availability). If soils were amended with
manure, a reduction factor was applied to the N fertilization rate accounting for the interaction between fertilization and
manure N amendments (i.e., producers reduce mineral fertilization rates if applying manure). Reduction factors were
randomly selected from probability distribution factors based on relationships between manure N application and fertilizer
rates from USDA cropping survey data (ERS 1997).
An empirically-based uncertainty estimator was developed to assess the uncertainty in model structure associated
with its' algorithms and parameterization, using a method described by Ogle et al. (2007). This estimator was based on a
linear mixed-effect modeling analysis comparing N2O emission estimates from eight agricultural experiments with 50
treatments. Although the dataset was relatively small, modeled emissions were significantly related to measurements with
a p-value of less than 0.01. Random effects were included to capture the dependence in time series and data collected
from the same experimental site, which were needed to estimate appropriate standard deviations for parameter
coefficients. The model structural uncertainty estimator, accounted for bias and prediction error in the DAYCENT model
results, as well as random error associated with fine-scale emission predictions in counties over a time series from 1990 to
2009. Note that the current application only addresses structural uncertainty in cropland estimates; further development
will be needed to address this uncertainty in model estimates for grasslands, which is a planned improvement as more soil
N2O measurement data become available for grassland sites. In general, DAYCENT tended to underestimate emissions if
the rates were above 6 g N2O/ha/day (Del Grosso et al., 2010). Model structural uncertainty was not assessed for N
volatilization and leaching/runoff, because sufficient data from field experiments were not available.
A simple error propagation method (IPCC 2006) was used to estimate uncertainties for direct emissions
estimated with Tier 1 methods, including management of non-major crops (mineral fertilization, crop residues, organic
fertilizers) and N inputs that were not addressed in the DAYCENT simulations (i.e., sewage sludge N, PRP manure N
excreted on federal grasslands). Similarly, indirect emissions from N inputs that were not simulated with DAYCENT
were calculated according to the IPCC methodology using the simple error propagation method (IPCC 2006). PDFs for
the proportion of N subject to volatilization, leaching and runoff, as well as indirect N2O emission factors were based on
IPCC (2006), and PDFs for the activity data were based on the uncertainties associated underlying survey information and
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calculations.92 For lands simulated by DAYCENT, uncertainty in indirect emissions was derived using the simple error
propagation approach, combining uncertainty from the DAYCENT outputs for N volatilization and leaching/runoff with
uncertainty in the indirect N2O emission factors (IPCC 2006).
Field Burning of Agricultural Residues
The uncertainty upper and lower bounds of the CH4 emission estimate for Field Burning of Agricultural Residues
were 42 percent and -40 percent, respectively, and of the N2O emissions estimate were 31 percent and -30 percent
respectively, at the 95 percent confidence interval. Variables such as crop production, residue/crop product ratios, and
burning and combustion efficiencies affect greenhouse gas emission estimates for Field Burning of Agricultural Residues.
For more information on emission estimates, please refer to the Field Burning of Agricultural Residues section of the
Agriculture Chapter. The uncertainty in production for all crops considered here is estimated to be 5 percent, based on
expert judgment. The uncertainty in area burned was 7 percent, based on McCarty (2009). Residue/crop product ratios
can vary among cultivars. Generic residue/crop product ratios, rather than ratios specific to the United States, have been
used for all crops except sugarcane. An uncertainty of 10 percent was applied to the residue/crop product ratios for all
crops except for cotton, which was 55 percent. Based on the range given for measurements of soybean dry matter fraction
(Strehler and Stiltzle 1987), residue dry matter contents were assigned an uncertainty of 3.1 percent for all crop types,
except for cotton and lentils, which were 10 and 4.4 percent, respectively. Burning and combustion efficiencies were
assigned an uncertainty of 5 percent based on expert judgment.
The N2O emission ratio was estimated to have an uncertainty of 28.6 percent based on the range reported in
IPCC/UNEP/OECD/IEA (1997). The uncertainty estimated for the CH4 emission ratio was 40 percent based on the range
of ratios reported in IPCCAJNEP/OECD/IEA (1997).
Land Use, Land-Use Change, and Forestry
Forest Land Remaining Forest Land
Changes in Forest Carbon Stocks
Forest area data from the USDA Forest Service and C density data affect total net flux of forest C estimates. For
more information on net forest C flux, please refer to the Changes in Forest Carbon Stocks section of the Land Use, Land-
Use Change, and Forestry (LULUCF) chapter. The USDA Forest Service inventories are designed to be accurate within 3
percent at the 67 percent confidence level (one standard error) per 405,000 ha (1 million acres) of timberland (USDA
Forest Service 2006c). For larger areas, the uncertainty in area is concomitantly less, and precision at plot levels is greater.
An analysis of uncertainty in growing stock volume data for timber producing land in the Southeast by Phillips et al.
(2000) found that nearly all of the uncertainty in their analysis was due to sampling rather than the regression equations
used to estimate volume from tree height and diameter. The quantitative uncertainty analysis summarized here primarily
focuses on uncertainties associated with the estimates of specific C stocks at the plot level and does not address error in
tree diameters or volumes.
Estimates for stand-level C pools are derived from extrapolations of site-specific studies to all forest land,
because survey data on these pools are not generally available. Such extrapolation introduces uncertainty because
available studies may not adequately represent regional or national averages. Uncertainty may also arise due to: (1)
modeling errors (e.g., relying on coefficients or relationships that are not well known); and (2) errors in converting
estimates from one reporting unit to another (Birdsey and Heath 1995). An important source of uncertainty is that there is
little consensus from available data sets on the effect of land-use change and forest management activities (such as
harvest) on soil C stocks. For example, while Johnson and Curtis (2001) found little or no net change in soil C following
harvest, on average, across a number of studies, many of the individual studies did show differences. Heath and Smith
(2000) noted that the experimental design in a number of soil studies limited their usefulness for determining effects of
harvesting on soil C. Because soil C stocks are large, estimates need to be very precise, since even small relative changes
in soil C sum to large differences when integrated over large areas. The soil C stock and stock change estimates presented
here are based on the assumption that soil C density for each broad forest type group stays constant over time. The state of
information and modeling are improving in this regard (Woodbury et al. 2006, 2007); the effects of land use and of
changes in land use and forest management will be better accounted for in future estimates of soil C.
Uncertainty in estimates about the HWP Contribution is based on Monte Carlo simulation of the production
approach. The uncertainty analysis is based on Skog et al. (2004), with later revisions made in conjunction with overall
revisions in the HWP model (Skog in preparation). The uncertainty analysis for HWP includes an evaluation of the effect
92
With the exception of organic fertilizers and crop yields, which were assumed to have a default ±50 percent uncertainty.
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of uncertainty in 13 sources including production and trade data, factors to convert products to quantities of C, rates at
which wood and paper are discarded, and rates and limits for decay of wood and paper in SWDS.
Direct N2O fluxes from Forest Soils
The uncertainty upper and lower bounds of the emissions estimate for Direct N2O Fluxes from Forest Soils were
211 percent and -59 percent, respectively, at the 95 percent confidence interval. Variables such as the emission factor for
synthetic fertilizer applied to soil, and the area of forest land receiving fertilizer affect Direct N2O fluxes from Forest Soils.
For more information, please refer to that section of the LULUCF chapter. The uncertainty range of the IPCC (2006)
default emission factor for synthetic fertilizer applied to soil, ranges from 0.3 to 3 percent. Because IPCC does not
provide further information on whether this range represents the 95 percent confidence interval or the absolute minimum
and maximum values, a triangular distribution was used to represent the uncertainty of the emission factor. The
uncertainty in the area of forest land receiving fertilizer was conservatively estimated at ±20 percent and in fertilization
rates at ±50 percent (Binkley 2004).
Cropland Remaining Cropland
The uncertainty information below pertains to the emission estimates presented in the Cropland Remaining
Cropland section of the LULUCF chapter. Please refer to that section for information about this source. The uncertainty
upper and lower bounds of the emissions estimate for Cropland Remaining Cropland were -172 percent and 167 percent,
respectively, at the 95 percent confidence interval. Probability Distribution Functions (PDFs) for fertilizer were based on
survey data for major U.S. crops, both irrigated and rainfed (ERS 1997; NASS 2004, 1999, 1992; Grant and Krenz 1985).
State-level PDFs were developed for each crop if a minimum of 15 data points existed for each of the two categories
(irrigated and rainfed). Where data were insufficient at the state-level, PDFs were developed for multi-state Farm
Production Regions. Uncertainty in manure applications for specific crops was incorporated in the analysis based on total
manure available for use in each county, a weighted average application rate, and the crop-specific land area amended with
manure (compiled from USDA data on animal numbers, manure production, storage practices, application rates and
associated land areas receiving manure amendments; see Edmonds et al. 2003). Together with the total area for each crop
within a county, this yielded a probability that a given crop at a specific NRI point would either receive manure or not. A
ratio of managed manure N production in each year of the inventory relative to 1997 was used to adjust the probability of
an area receiving an amendment, under the assumption that greater or less managed manure N production would lead to a
proportional change in amended area (see Tier 3 Methods Section for data sources on manure N production). Manure
amendment areas were averaged across decades to produce the PDF for the Monte Carlo Analysis (i.e., 1980-1989, 1990-
2000). If soils were amended with manure, a reduction factor was applied to the N fertilization rate accounting for the
interaction between fertilization and manure N amendments (i.e., producers often reduce mineral fertilization rates if
applying manure). Reduction factors were randomly selected from probability distribution factors based on relationships
between manure N application and fertilizer rates (ERS 1997). For tillage uncertainty, transition matrices were
constructed from CTIC data to represent tillage changes for two time periods, combining the first two and the second two
management blocks (i.e., 1980-1989, 1990-2000). A Monte Carlo analysis was conducted with 100 iterations in which
inputs values were randomly drawn from the PDFs to simulate the soil C stocks for each NRI cluster of points (i.e.,
inventory points in the same county were grouped into clusters if they had the same land-use/management history and soil
type) using the Century model.
An empirically-based uncertainty estimator was developed to assess uncertainty in model structure associated
with the algorithms and parameterization. The estimator was based on a linear mixed effect modeling analysis comparing
modeled soil C stocks with field measurements from 45 long-term agricultural experiments with over 800 treatments,
representing a variety of tillage, cropping, and fertilizer management practices (Ogle et al. 2006b). The final model
included variables for organic matter amendments, N fertilizer rates, inclusion of hay/pasture in cropping rotations, use of
no-till, setting-aside cropland from production and inclusion of bare fallow in the rotation. Each of these variables were
found to be significant at a 95 percent probability level, and accounted for statistically significant biases in the modeled
estimates from Century. For example, Century tended to under-estimate the influence of organic amendments on soil C
storage, so a variable was added to adjust the estimate from Century. Random effects captured the dependence in time
series and data collected from the same long-term experimental site, which were needed to estimate appropriate standard
deviations for parameter coefficients. For each C stock estimate from the Monte Carlo analysis, the structural uncertainty
estimator was applied to adjust the value accounting for bias and prediction error in the modeled values. The structural
uncertainty estimator was applied by randomly drawing parameter coefficients from their joint probability distribution, in
addition to random draws from PDFs representing the uncertainty due to site and site by year random effects. Finally,
uncertainty in the land-use and management statistics from the NRI were incorporated into the analysis based on the
sampling variance for the clusters of NRI points.
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The NRI has a two-stage sampling design that allowed PDFs to be constructed assuming a multivariate normal
distribution accounting for dependencies in activity data. PDFs for the tillage activity data, as provided by the CTIC, were
constructed on a bivariate normal distribution with a log-ratio scale, accounting for the negative dependence among the
proportions of land under conventional and conservation tillage practices. PDFs for the agricultural areas receiving
manure were derived assuming a normal distribution from county-scale area amendment estimates derived from the
USDA Census of Agriculture (Edmonds et al. 2003). Lastly, enrollment in wetland restoration programs was estimated
from contract agreements, but due to a lack of information on the margin of error, PDFs were constructed assuming a
nominal ±50 percent uncertainty range.
Mineral Soil Carbon Stock Changes
Tier 3 Approach
The uncertainty information below pertains to the emission estimates presented in the Mineral Soil Carbon Stock
Changes section of the LULUCF chapter. Please refer to that section for information about this source. The uncertainty
analysis for the Tier 3 Century inventory had three components: 1) a Monte Carlo approach to address uncertainties in
model inputs, 2) an empirically-based approach for quantifying uncertainty inherent in the structure of the Century model,
and 3) scaling uncertainty associated with the NRI survey (i.e., scaling from the individual NRI points to the entire U.S.
agricultural land base using the expansion factors).
For the model input uncertainty, probability distribution functions (PDFs) were developed for fertilizer rates,
manure application and tillage practices. An empirically-based uncertainty estimator was developed to assess uncertainty
in model structure associated with the algorithms and parameterization. The estimator was based on a linear mixed effect
modeling analysis comparing modeled soil C stocks with field measurements from 45 long-term agricultural experiments
with over 800 treatments, representing a variety of tillage, cropping, and fertilizer management practices (Ogle et al.
2007). The final model included variables for organic matter amendments, N fertilizer rates, inclusion of hay/pasture in
cropping rotations, use of no-till, setting-aside cropland from production, and inclusion of bare fallow in the rotation.
Each of these variables were found to be significant at a 0.05 alpha level, and accounted for statistically significant biases
in modeled estimates from the Century model. Uncertainty in land-use and management statistics from the NRI were
incorporated into the analysis based on the sampling variance for the clusters of NRI points.
Tier 2 Approach
For the Tier 2 IPCC method, a Monte Carlo approach was used (Ogle et al. 2003). PDFs for stock change factors
were derived from a synthesis of 91 published studies, which addressed the impact of management on SOC storage.
Uncertainties in land-use and management activity data were also derived from a statistical analysis.
Additional Mineral C Stock Change Calculations
A ±50 percent uncertainty was assumed for additional adjustments to the mineral soil C stocks between 1990 and
2006, accounting for additional C stock changes associated gains or losses in C sequestration after 1997 due to changes in
Conservation Reserve Program enrollment.
Organic Soil Carbon Stock Changes
Uncertainty in C emissions from organic soils was estimated in the same manner described for mineral soil using
the Tier 2 method and Monte Carlo analysis. PDFs for emission factors were derived from a synthesis of 10 studies, and
combined with uncertainties in the NRI land use and management data for organic soils in the Monte Carlo analysis.
Please refer to the Organic Soil C Stock Changes section of the LULUCF chapter for more information on C emissions
from organic soils.
CO2 Emissions from Liming
The uncertainty information below pertains to the emission estimates presented in the Mineral Soil Carbon Stock
Changes section of the LULUCF chapter. Please refer to that section for information about liming activity data and the
emission factors used for this source. A Monte Carlo (Tier 2) uncertainty analysis was applied to estimate the uncertainty
of CO2 emissions from liming. Uncertainties in the estimates of emissions from liming result from both the emission
factors and the activity data. The emission factors used for limestone and dolomite take into account the fate of C
following application to soils, including: dissolution of liming constituents; leaching of bicarbonates into the soil and
transport to the ocean; and emissions to the atmosphere (West and McBride 2005). The C accounting behind these
emission factors entails assumptions about several uncertain factors. First, it is uncertain what fraction of agricultural lime
is dissolved by nitric acid (HNO3)—a process that releases CO2—and what portion reacts with carbonic acid (H2CO3),
resulting in the uptake of CO2. The fractions can vary depending on soil pH and N fertilizer use. The second major source
of uncertainty is the fraction of bicarbonate (HCO3") that leaches through the soil profile and is transported into
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groundwater, which can eventually be transferred into rivers and into the ocean. This fraction can vary depending on the
soil pH and whether calcium (Ca +) and magnesium (Mg +) liming constituents that might otherwise accompany HCO3",
are taken up by crops, remain in the upper soil profile, or are transported through or out of the soil profile. Finally, the
emission factors do not account for the time that is needed for leaching and transport processes to occur.
There are several sources of uncertainty in the limestone and dolomite activity data. When reporting data to the
USGS (or U.S. Bureau of Mines), some producers do not distinguish between limestone and dolomite. In these cases, data
are reported as limestone, so this reporting could lead to an overestimation of limestone and an underestimation of
dolomite. In addition, the total quantity of crushed stone listed each year in the Minerals Yearbook excludes American
Samoa, Guam, Puerto Rico, and the U.S. Virgin Islands.
Land Converted to Cropland
Tier 2 Approach
The uncertainty upper and lower bounds of the emissions estimate for Land Converted to Cropland were 36
percent and -40 percent, respectively, at the 95 percent confidence interval. The uncertainty analysis for Land Converted
to Cropland using the Tier 2 approach was based on the same method described for Cropland Remaining Cropland.
Mineral and Organic Soil Carbon Stock Changes
The quantitative estimates of uncertainty presented above are missing several components. This section
qualitatively describes these contributors to overall uncertainty. The agricultural soil C inventory has undergone several
improvements during the past few years, such as the development of the Tier 3 inventory method to estimate mineral soil
C stock changes for the majority of U.S. cropland. However, some limitations remain in the analysis. First, the current
agricultural soil C inventory includes some points designated as non-agricultural land-uses in the NRI if the points were
categorized as cropland in either 1992 or 1997, but were urban, water, or miscellaneous non-cropland (e.g., roads and
barren areas) in another year. The impact on soil organic C storage that results from converting non-agricultural uses to
cropland is not well-understood, and therefore, those points were not included in the calculations for mineral soils
(emissions from organic soils, however, were computed for those points in the years that they were designated as an
agricultural use). Similarly, the effect of aquaculture (e.g., rice cultivation followed by crayfish production in flooded
fields) on soil C stocks has not been estimated due to a lack of experimental data. Second, the current estimates may
underestimate losses of C from organic soils because the 1997 National Resources Inventory was not designed as a soil
survey and organic soils frequently occur as relatively small inclusions within major soil types. Lastly, the IPCC Tier 2
methodology does not take into account changes in SOC stocks due to pre-1982 land use and land-use change.
Grassland Remaining Grassland
Tier 2 Approach
The uncertainty upper and lower bounds of the emissions estimate for Grassland Remaining Grassland were -32
percent and 25 percent, respectively, at the 95 percent confidence interval. The uncertainty analysis for Grassland
Remaining Grassland using the Tier 2 approach was based on the same method described for Cropland Remaining
Cropland.
Additional Uncertainties in Mineral and Organic Soil C Stock Changes
The quantitative estimates of uncertainty presented above are missing several components. This section
qualitatively describes these contributors to overall uncertainty. Minimal data exist on where and how much sewage
sludge has been applied to U.S. agricultural land and the accounting of this activity appears to be much more difficult than
the related-activity of using manure to amend agricultural soils. Consequently, there is considerable uncertainty in the
application of sewage sludge, which is assumed to be applied to Grassland Remaining Grassland. However, some sludge
may be applied to other agricultural land, but there is not sufficient information to further subdivide application among the
agricultural land use/land-use change categories. Another limitation is that the current estimates may underestimate losses
of C from organic soils because the 1997 National Resources Inventory was not designed as a soil survey and organic soils
frequently occur as relatively small inclusions within major soil types. Lastly, the IPCC Tier 2 methodology does not take
into account changes in SOC stocks due to pre-1982 land use and land-use change.
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Land Converted to Grassland
Tier 2 Approach
The uncertainty upper and lower bounds of the emissions estimate for Land Converted to Grassland were -15
percent and 15 percent, respectively, at the 95 percent confidence interval, The uncertainty analysis for Land Converted to
Grassland using the Tier 2 approach was based on the same method described for Cropland Remaining Cropland. See the
Tier 2 section under minerals soils in the Cropland Remaining Cropland section for additional discussion.
Additional Uncertainties in Mineral and Organic Soil Carbon Stock Changes
The quantitative estimates of uncertainty presented above are missing several components. This section
qualitatively describes these contributors to overall uncertainty. The agricultural soil C inventory has undergone several
improvements during the past few years, such as the development of the Tier 3 inventory method to estimate mineral soil
C stock changes for the majority of U.S. grassland. However, some limitations remain in the analysis. First, the current
agricultural soil C inventory includes some points designated as non-agricultural land-uses in the NRI if the points were
categorized as agricultural land use in either 1992 or 1997, but were urban, water, or miscellaneous non-cropland (e.g.,
roads and barren areas) in another year. The impact on SOC storage that results from converting non-agricultural uses to
grassland is not well-understood, and therefore, those points were not included in the calculations for mineral soils
(emissions from organic soils, however, were computed for those points in the years that they were designated as
grassland). Second, the current estimates may underestimate losses of C from organic soils because the 1997 National
Resources Inventory was not designed as a soil survey and organic soils frequently occur as relatively small inclusions
within major soil types. Lastly, this IPCC Tier 2 methodology does not take into account changes in SOC stocks due to
pre-1982 land use and land-use change.
Settlements Remaining Settlements
N2O Fluxes from Settlement Soil
The uncertainty information below pertains to the emission estimates presented in the N2O Fluxes from
Settlement Soils section of the LULUCF chapter. Please refer to that section for information about synthetic fertilizer N,
the amounts of sewage sludge applied to non-agricultural lands, and other variables that affect this source. The uncertainty
upper and lower bounds of the emissions estimate for N2O fluxes from Settlement Soil were 163 percent and -49 percent,
respectively, at the 95 percent confidence interval. The uncertainty range for the IPCC's default emission factor for
mineral and organic N additions applied to soil ranges from 0.3 to 3 percent (IPCC 2006). Because the IPCC does not
provide further information on whether this range represents the 95 percent confidence interval or the absolute minimum
and maximum values, a triangular distribution was used to represent the uncertainty of the emission factor.
The uncertainty in the total amount of synthetic fertilizer N applied in the United States was estimated to be ±3
percent (Terry 2005). The uncertainty in the amount of synthetic fertilizer N applied to settlement soils was
conservatively estimated to be ±50 percent, since no uncertainty was provided in Ruddy et al. (2006). The uncertainty in
the amounts of sewage sludge applied to non-agricultural lands and used in surface disposal was based on the uncertainty
of the following data points: (1) N content of sewage sludge; (2) total sludge applied in 2000; (3) wastewater existing
flow in 1996, 2000, and 2004; and (4) the sewage sludge disposal practice distributions to non-agricultural land
application and surface disposal.
(1) The value assumed for N content of sewage sludge could range from around 0.1 percent to around 17 percent
(McFarland 2001). Because information was not available on the distribution, a triangular distribution was
assumed based on IPCC guidelines.
(2) The uncertainty in the total amount of sludge applied in 2000 was based on a comparison with similar data
available from other publications, which were all within 3 percent of the value used in the Inventory calculations
(BioCycle 2000, NRC 2002, WEF 1997, Bastian 1997). The distribution was estimated to be normal based on
expert opinion (Boucher 2006).
(3) The uncertainty in the wastewater existing flow values for 1996 and 2000 was estimated at 0.0625 percent with a
lognormal distribution (Plastino 2006).
(4) The uncertainty in the sewage sludge disposal practice distributions was based on a comparison with similar data
available from other publications, which were at most 12 percent different than the distribution for non-
agricultural land application used in the Inventory calculations and at most 69 percent different than the
distribution for surface disposal used in the Inventory calculations (Biocycle 2000, NRC 2002).
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Other
The uncertainty analysis descriptions in this section correspond to Changes in Yard Trimming and Food Scrap
Carbon Stocks in Landfills source category included in the Other Chapter of the Inventory.
Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
The uncertainty ranges were assigned based on expert judgment and are assumed to be normally distributed
around the inventory estimate, except for the values for decomposition rate, proportion of C stored, and the decay rates.
The uncertainty ranges associated with these values are highlighted separately in this section.
The uncertainty range selected for input variables for the proportions of both grass and leaves in yard trimmings
was 20 to 60 percent. The initial C content for grass, leaves, and food scraps (all expressed as percentages in the
calculations for the inventory) were plus or minus 10 percent. For the moisture content of branches (where the inventory
estimate is 10 percent), the uncertainty range was assumed to be 5 to 30 percent, within a lognormal distribution.
The uncertainty ranges associated with the disposal of grass, leaves, branches, and food scraps were bound at 50
percent to 150 percent of the inventory estimates. The proportion of C stored in grass, leaves, branches, and food scraps
was assumed to vary plus or minus 20 percent from the best estimate, with a uniform distribution. The proportion of C
stored in food scraps was truncated at a lower bound of 2 percent.
Finally, the uncertainty ranges assigned to the decay rates of grass, leaves, branches, and food scraps were
developed based on De la Cruz, 2010. The minimum value corresponds to projected conditions if all landfills are in a dry
environment; the maximum value corresponds to bioreactor conditions. A triangular distribution is applied to each of these
variables.
References
EPA (2002). Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas
Inventory: Procedures Manual for Quality Assurance/Quality Control and Uncertainty Analysis, U.S. Greenhouse
Gas Inventory Program, U.S. Environmental Protection Agency, Office of Atmospheric Programs, EPA 430-R-02-
007B, June 2002.
iPCC/UNEP/OECD/TEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic
Co-Operation and Development, International Energy Agency.
IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories,
Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCC-
XVI/Doc. 10 (1.IV.2000), May 2000.
iPCC/UNEP/OECD/TEA (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Paris:
Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic Co-
Operation and Development, International Energy Agency.
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