EPA 430-P-17-001
DRAFT Inventory of U.S. Greenhouse Gas
Emissions and Sinks:
1990-2015
February 14,2017
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
.
All data tables of this document for the full time series 1990 through 2015, inclusive, will be made available for the
final report published on April 15, 2017 at the internet site mentioned above.
FOR FURTHER INFORMATION
Contact Ms. Mausami Desai, Environmental Protection Agency, (202) 343-9381, desai.mausami@epa.gov.
Or Ms. Melissa Weitz, Environmental Protection Agency, (202) 343-9897, weitz.melissa@epa.gov.
For more information regarding climate change and greenhouse gas emissions, see the EPA web site at
.
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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 Vincent Camobreco. Amy Bunker, Sarah
Froman, Susan Burke, and Sarah Roberts directed the work on mobile combustion and transportation. Work on
industrial processes and product use emissions was led by Mausami Desai and John Steller. Work on fugitive
methane emissions from the energy sector was directed by Melissa Weitz and Cate Hight. Calculations for the waste
sector were led by Rachel Schmeltz. Tom Wirth directed work on the Agriculture and the Land Use, Land-Use
Change, and Forestry chapters, with support from John Steller. Work on emissions of HFCs, PFCs, SF6, and NF3
was directed by Deborah Ottinger and Dave Godwin.
Within the EPA, other Offices also contributed data, analysis, and technical review for this report. The Office of
Transportation and Air Quality and the Office of Air Quality Planning and Standards provided analysis and review
for several of the source categories addressed in this report. The Office of Solid Waste and the Office of Research
and Development also contributed analysis and research.
The Energy Information Administration and the Department of Energy contributed invaluable data and analysis on
numerous energy-related topics. The U.S. Forest Service prepared the forest carbon inventory, and the Department
of Agriculture's Agricultural Research Service and the Natural Resource Ecology Laboratory at Colorado State
University contributed leading research on nitrous oxide and carbon fluxes from soils. The National Oceanic and
Atmospheric Administration prepared the estimates of emissions from Coastal Wetlands.
Other government agencies have contributed data as well, including the U.S. Geological Survey, the Federal
Highway Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department
of Commerce, the National Agricultural Statistics Service, the Federal Aviation Administration, and the Department
of Defense.
We would also like to thank Marian Martin Van Pelt and the full Inventory team at ICF including Leslie Chinery,
Randy Freed, Diana Pape, Robert Lanza, Lauren Marti, Mollie Averyt, Mark Flugge, Larry O'Rourke, Deborah
Harris, Jonathan Cohen, Alexander Lataille, Sabrina Andrews, Bikash Acharya, Claire Boland, Rebecca Ferenchiak,
Kasey Knoell, Kevin Kurkul, Cory Jemison, Matt Lichtash, Jessica Kuna, Emily Kent, Emily Golla, Rani Murali,
Drew Stilson, Cara Blumenthal, Tim Storer, Louise Huttinger, and Jessica Klion for synthesizing this report and
preparing many of the individual analyses.
Finally, we thank the following teams for their significant analytical support: Eastern Research Group team (Casey
Pickering, Brandon Long, Clint Burklin, Gopi Manne, Deborah Bartram, Kara Edquist, Ami Aguiar and Brian
Guzzone); RTI International (Kate Bronstein, Meaghan McGrath); Raven Ridge Resources, and Ruby Canyon
Engineering Inc. (Michael Cote, Samantha Phillips, and Phillip Cunningham).
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1 Preface
2 The United States Environmental Protection Agency (EPA) prepares the official U.S. Inventory of Greenhouse Gas
3 Emissions and Sinks to comply with existing commitments under the United Nations Framework Convention on
4 Climate Change (UNFCCC). Under decision 3/CP.5 of the UNFCCC Conference of the Parties, national inventories
5 for UNFCCC Annex I parties should be provided to the UNFCCC Secretariat each year by April 15.
6 In an effort to engage the public and researchers across the country, the EPA has instituted an annual public review
7 and comment process for this document. The availability of the draft document is announced via Federal Register
8 Notice and is posted on the EPA web site. Copies are also mailed upon request. The public comment period is
9 generally limited to 30 days; however, comments received after the closure of the public comment period are
10 accepted and considered for the next edition of this annual report.
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, Table of Contents
2 TABLE OF CONTENTS IV
3 LIST OF TABLES, FIGURES, AND BOXES VII
4 EXECUTIVE SUMMARY ES-1
5 ES. 1 Background Information ES-2
6 ES.2 Recent Trends in U.S. Greenhouse Gas Emissions and Sinks ES-4
7 ES.3 Overview of Sector Emissions and Trends ES-17
8 ES.4 Other Information ES-22
9 1. INTRODUCTION 1-1
10 1.1 Background Information 1-3
11 1.2 National Inventory Arrangements 1-10
12 1.3 Inventory Process 1-13
13 1.4 Methodology and Data Sources 1-15
14 1.5 Key Categories 1-15
15 1.6 Quality Assurance and Quality Control (QA/QC) 1-19
16 1.7 Uncertainty Analysis of Emission Estimates 1-20
17 1.8 Completeness 1-22
18 1.9 Organization of Report 1-22
19 2. TRENDS IN GREENHOUSE GAS EMISSIONS 2-1
20 2.1 Recent Trends in U.S. Greenhouse Gas Emissions and Sinks 2-1
21 2.2 Emissions by Economic Sector 2-22
22 2.3 Indirect Greenhouse Gas Emissions (CO, NOx, NMVOCs, and SO2) 2-33
23 3. ENERGY 3-1
24 3.1 Fossil Fuel Combustion (IPCC Source Category 1A) 3-4
25 3.2 Carbon Emitted from Non-Energy Uses of Fossil Fuels (IPCC Source Category 1A) 3-41
26 3.3 Incineration of Waste (IPCC Source Category lAla) 3-48
27 3.4 Coal Mining (IPCC Source Category lBla) 3-52
28 3.5 Abandoned Underground Coal Mines (IPCC Source Category lBla) 3-57
29 3.6 Petroleum Systems (IPCC Source Category lB2a) 3-61
iv DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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3.7 Natural Gas Systems (IPCC Source Category lB2b) 3-71
3.8 Energy Sources of Indirect Greenhouse Gas Emissions 3-85
3.9 International Bunker Fuels (IPCC Source Category 1: Memo Items) 3-86
3.10 Wood Biomass and Ethanol Consumption (IPCC Source Category 1A) 3-91
4. INDUSTRIAL PROCESSES AND PRODUCT USE 4-1
4.1 Cement Production (IPCC Source Category 2A1) 4-7
4.2 Lime Production (IPCC Source Category 2A2) 4-10
4.3 Glass Production (IPCC Source Category 2A3) 4-15
4.4 Other Process Uses of Carbonates (IPCC Source Category 2A4) 4-18
4.5 Ammonia Production (IPCC Source Category 2B1) 4-21
4.6 Urea Consumption for Non-Agricultural Purposes 4-25
4.7 Nitric Acid Production (IPCC Source Category 2B2) 4-28
4.8 Adipic Acid Production (IPCC Source Category 2B3) 4-32
4.9 Silicon Carbide Production and Consumption (IPCC Source Category 2B5) 4-35
4.10 Titanium Dioxide Production (IPCC Source Category 2B6) 4-38
4.11 Soda Ash Production and Consumption (IPCC Source Category 2B7) 4-41
4.12 Petrochemical Production (IPCC Source Category 2B8) 4-44
4.13 HCFC-22 Production (IPCC Source Category 2B9a) - TO BE UPDATED FOR FINAL INVENTORY
REPORT 4-50
4.14 Carbon Dioxide Consumption (IPCC Source Category 2B10) 4-52
4.15 Phosphoric Acid Production (IPCC Source Category 2B10) 4-56
4.16 Iron and Steel Production (IPCC Source Category 2C1) and Metallurgical Coke Production 4-59
4.17 Ferroalloy Production (IPCC Source Category 2C2) 4-69
4.18 Aluminum Production (IPCC Source Category 2C3) 4-72
4.19 Magnesium Production and Processing (IPCC Source Category 2C4) 4-77
4.20 Lead Production (IPCC Source Category 2C5) 4-82
4.21 Zinc Production (IPCC Source Category 2C6) 4-85
4.22 Semiconductor Manufacture (IPCC Source Category 2E1) 4-90
4.23 Substitution of Ozone Depleting Substances (IPCC Source Category 2F) 4-101
4.24 Electrical Transmission and Distribution (IPCC Source Category 2G1) 4-108
4.25 Nitrous Oxide from Product Uses (IPCC Source Category 2G3) 4-115
4.26 Industrial Processes and Product Use Sources of Indirect Greenhouse Gases 4-118
5. AGRICULTURE 5-1
5.1 Enteric Fermentation (IPCC Source Category 3 A) 5-2
5.2 Manure Management (IPCC Source Category 3B) 5-8
5.3 Rice Cultivation (IPCC Source Category 3C) 5-15
5.4 Agricultural Soil Management (IPCC Source Category 3D) 5-20
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1 5.5 Liming (IPCC Source Category 3G) 5-34
2 5.6 Urea Fertilization (IPCC Source Category 3H) 5-37
3 5.7 Field Burning of Agricultural Residues (IPCC Source Category 3F) 5-39
4 6. LAND USE, LAND-USE CHANGE, AND FORESTRY 6-1
5 6.1 Representation of the U.S. Land Base 6-7
6 6.2 Forest Land Remaining Forest Land 6-20
7 6.3 Land Converted to Forest Land (IPCC Source Category 4A2) 6-41
8 6.4 Cropland Remaining Cropland (IPCC Source Category 4B1) 6-47
9 6.5 Land Converted to Cropland (IPCC Source Category 4B2) 6-56
10 6.6 Grassland Remaining Grassland (IPCC Source Category 4C1) 6-62
11 6.7 Land Converted to Grassland (IPCC Source Category 4C2) 6-71
12 6.8 Wetlands Remaining Wetlands (IPCC Source Category 4D1) 6-77
13 6.9 Land Converted to Wetlands (IPCC Source Category 4D2) 6-95
14 6.10 Settlements Remaining Settlements 6-98
15 6.11 Land Converted to Settlements (IPCC Source Category 3B5b) 6-114
16 6.12 Other Land Remaining Other Land (IPCC Source Category 4F1) 6-119
17 6.13 Land Converted to Other Land (IPCC Source Category 4F2) 6-119
18 7. WASTE 7-1
19 7.1 Landfills (IPCC Source Category 5A1) 7-3
20 7.2 Wastewater Treatment (IPCC Source Category 5D) 7-17
21 7.3 Composting (IPCC Source Category 5B1) 7-32
22 7.4 Waste Incineration (IPCC Source Category 5C1) 7-35
23 7.5 Waste Sources of Indirect Greenhouse Gases 7-35
24 8. OTHER 8-1
25 9. RECALCULATIONS AND IMPROVEMENTS 9-1
26 10. REFERENCES 10-1
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vi DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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 (MMT CO2 Eq.) ES-5
Table ES-3: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.) ES-11
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.)
ES-17
Table ES-5: U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and
Forestry (MMT CO Eq.) ES-21
Table ES-6: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.) ES-23
Table ES-7: U.S. Greenhouse Gas Emissions by Economic Sector with Electricity-Related Emissions Distributed
(MMTCO2 Eq.) ES-24
Table ES-8: Recent Trends in Various U.S. Data (Index 1990 = 100) ES-25
Table 1-1: Global Atmospheric Concentration, Rate of Concentration Change, and Atmospheric Lifetime of
Selected Greenhouse Gases 1-4
Table 1-2: Global Warming Potentials and Atmospheric Lifetimes (Years) Used in this Report 1-9
Table 1-3: Comparison of 100-Year GWP values 1-10
Table 1-4: Key Categories for the United States (1990-2015) 1-16
Table 1-5: Estimated Overall Inventory Quantitative Uncertainty (MMT CO2 Eq. and Percent) - TO BE UPDATED
FOR FINAL INVENTORY REPORT 1-21
Table 1-6: IPCC Sector Descriptions 1-23
Table 1-7: List of Annexes 1-23
Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT CO2 Eq.) 2-4
Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (kt) 2-6
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.) 2-
8
Table 2-4: Emissions from Energy (MMT CO2 Eq.) 2-11
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.) 2-12
Table 2-6: Emissions from Industrial Processes and Product Use (MMT CO2 Eq.) 2-15
Table 2-7: Emissions from Agriculture (MMT CO2 Eq.) 2-18
Table 2-8: U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and
Forestry (MMT CO2 Eq.) 2-20
U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq. and Percent of Total in
2-23
Table 2-9: Emissions from Waste (MMT CO2 Eq.) 2-22
Table 2-10
2015)
Table 2-11: Electricity Generation-Related Greenhouse Gas Emissions (MMT CO2 Eq.) 2-25
Table 2-12: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-Related Emissions
Distributed (MMT CO2 Eq.) and Percent of Total in 2015 2-27
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Table 2-13: Transportation-Related Greenhouse Gas Emissions (MMT CO2 Eq.) 2-29
Table 2-14: Recent Trends in Various U.S. Data (Index 1990 = 100) 2-32
Table 2-15: Emissions of NO,. CO, NMVOCs, and SO -(kt) 2-34
Table 3-1: CO2, CH4, and N20 Emissions from Energy (MMT CO2 Eq.) 3-2
Table 3-2: CO2, CH4, and N20 Emissions from Energy (kt) 3-3
Table 3-3: CO2, CH4, and N20 Emissions from Fossil Fuel Combustion (MMT CO2 Eq.) 3-4
Table 3-4: CO2, CH4, and N20 Emissions from Fossil Fuel Combustion (kt) 3-5
Table 3-5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (MMT CO2 Eq.) 3-5
Table 3-6: Annual Change in CO2 Emissions and Total 2015 Emissions fromFossil Fuel Combustion for Selected
Fuels and Sectors (MMT CO2 Eq. and Percent) 3-6
Table 3-7: CO2, CH4, andN20 Emissions fromFossil Fuel Combustion by Sector (MMT CO2 Eq.) 3-10
Table 3-8: CO2, CH4, andN20 Emissions fromFossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.).... 3-11
Table 3-9: CO2 Emissions from Stationary Fossil Fuel Combustion (MMT CO2 Eq.) 3-12
Table 3-10: CH4 Emissions from Stationary Combustion (MMT CO2 Eq.) 3-13
Table 3-11: N20 Emissions from Stationary Combustion (MMT CO2 Eq.) 3-13
Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector (MMT CO2 Eq.)... 3-21
Table 3-13: CH4 Emissions from Mobile Combustion (MMT CO2 Eq.) 3-24
Table 3-14: N2O Emissions from Mobile Combustion (MMT CO2 Eq.) 3-24
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (MMT CO2 Eq./QBtu) 3-30
Table 3-16: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-Related Fossil Fuel
Combustion by Fuel Type and Sector (MMT CO2 Eq. and Percent) 3-32
Table 3-17: Approach 2 Quantitative Uncertainty Estimates for CH4 and N20 Emissions from Energy-Related
Stationary Combustion, Including Biomass (MMT CO2 Eq. and Percent) 3-37
Table 3-18: Approach 2 Quantitative Uncertainty Estimates for CH4 and N20 Emissions from Mobile Sources
(MMT CO2 Eq. and Percent) 3-40
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (MMT CO2 Eq. and Percent) 3-42
Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu) 3-43
Table 3-21: 2015 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions 3-43
Table 3-22: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-Energy Uses of Fossil
Fuels (MMT CO2 Eq. and Percent) 3-45
Table 3-23: Approach 2 Quantitative Uncertainty Estimates for Storage Factors of Non-Energy Uses of Fossil Fuels
(Percent) 3-45
Table 3-24: CO2, CH4, andN20 Emissions from the Incineration of Waste (MMT CO2 Eq.) 3-48
Table 3-25: CO2, CH4, andN20 Emissions from the Incineration of Waste (kt) 3-49
Table 3-26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted (BioCycle dataset) 3-50
Table 3-27: Approach 2 Quantitative Uncertainty Estimates for CO2 andN20 from the Incineration of Waste (MMT
CO2 Eq. and Percent) 3-51
Table 3-28: Coal Production (kt) 3-53
Table 3-29: CH4 Emissions from Coal Mining (MMT CO2 Eq.) 3-53
viii DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 3-30: CH4 Emissions from Coal Mining (kt) 3-53
Table 3-31: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Coal Mining (MMT CO2 Eq.
and Percent) 3-56
Table 3-32: CH4 Emissions from Abandoned Coal Mines (MMT CO2 Eq.) 3-58
Table 3-33: CH4 Emissions from Abandoned Coal Mines (kt) 3-58
Table 3-34: Number of Gassy Abandoned Mines Present in U.S. Basins in 2015, grouped by Class according to
Post-Abandonment State 3-59
Table 3-35: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Abandoned Underground Coal
Mines (MMT CO2 Eq. and Percent) 3-60
Table 3-36: CH4 Emissions from Petroleum Systems (MMT CO2 Eq.) 3-62
Table 3-37: CH4 Emissions from Petroleum Systems (kt) 3-62
Table 3-38: CO2 Emissions from Petroleum Systems (MMT CO2 Eq.) 3-62
Table 3-39: CO2 Emissions from Petroleum Systems (kt) 3-63
Table 3-40: Oil Well Count Data 3-66
Table 3-41: National Tank Activity Data (Number of Tanks) by Category and National Emissions (Metric Tons
(II) 3-67
Table 3-42: National Equipment Counts for Fugitive Sources and National Emissions (Metric Tons CH4) 3-67
Table 3-43: Pneumatic Controller and Chemical Injection Pump National Equipment Counts and National Emissions
(Metric Tons CH4) 3-68
Table 3-44: Associated Gas Well Venting and Flaring National Emissions (Metric Tons CH4) 3-68
Table 3-45: Gas STAR Reductions (Metric Tons CH4) 3-70
Table 3-46: Potential Emissions from CO2 Capture and Extraction for EOR Operations (MMT CO2 Eq.) 3-71
Table 3-47: Potential Emissions from CO2 Capture and Extraction for EOR Operations (kt) 3-71
Table 3-48: CH4 Emissions from Natural Gas Systems (MMT CO2 Eq.)a 3-73
Table 3-49: CH4 Emissions from Natural Gas Systems (kt)a 3-73
Table 3-50: Calculated Potential CH4 and Captured/Combusted CH4 from Natural Gas Systems (MMT CO2 Eq.). 3-
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Table 3-51: Non-combustion CO2 Emissions from Natural Gas Systems (MMT CO2 Eq.) 3-74
Table 3-52: Non-combustion CO2 Emissions from Natural Gas Systems (kt) 3-74
Table 3-53: National Tank Activity Data (Number of Tanks) by Category and National Emissions (Metric Tons
CII) 3-78
Table 3-54: Gas Well Count Data 3-79
Table 3-55: National Equipment Counts for Fugitive Sources and National Emissions (Metric Tons CH4) 3-79
Table 3-56: Pneumatic Controller and Chemical Injection Pump National Equipment Counts and National Emissions
(Metric Tons CH4) 3-80
Table 3-57: National Liquids Unloading Activity Data by Category and National Emissions (Metric Tons CH4) 3-80
Table 3-58: National Gathering and Boosting Episodic Emission Activity Data (Number of Stations) and National
Emissions (Metric Tons CH4) 3-81
Table 3-59: CH4 Emissions from Processing Plants (Metric Tons CH4) 3-82
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Table 3-60: Previous (last year's) 1990-2014 Inventory Estimates for Processing Segment Emissions (Metric Tons
(II) 3-83
Table 3-61: Gas STAR Reductions (Metric Tons CH4) 3-84
Table 3-62: NOx, CO, and NMVOC Emissions from Energy-Related Activities (kt) 3-85
Table 3-63: CO2, CH4, and N20 Emissions from International Bunker Fuels (MMT CO2 Eq.) 3-87
Table 3-64: CO2, CH4, and N20 Emissions from International Bunker Fuels (kt) 3-88
Table 3-65: Aviation CO2 and N20 Emissions for International Transport (MMT CO2 Eq.) 3-88
Table 3-66: Aviation Jet Fuel Consumption for International Transport (Million Gallons) 3-89
Table 3-67: Marine Fuel Consumption for International Transport (Million Gallons) 3-89
Table 3-68: CO2 Emissions from Wood Consumption by End-Use Sector (MMT CO2 Eq.) 3-91
Table 3-69: CO2 Emissions from Wood Consumption by End-Use Sector (kt) 3-91
Table 3-70: CO2 Emissions from Ethanol Consumption (MMT CO2 Eq.) 3-92
Table 3-71: CO2 Emissions fromEthanol Consumption (kt) 3-92
Table 3-72: Woody Biomass Consumption by Sector (Trillion Btu) 3-93
Table 3-73: Ethanol Consumption by Sector (Trillion Btu) 3-93
Table 4-1: Emissions from Industrial Processes and Product Use (MMT CO2 Eq.) 4-2
Table 4-2: Emissions from Industrial Processes and Product Use (kt) 4-4
Table 4-3: CO2 Emissions from Cement Production (MMT CO2 Eq. and kt) 4-7
Table 4-4: Clinker Production (kt) 4-8
Table 4-5: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement Production (MMT CO2
Eq. and Percent) 4-9
Table 4-6: CO2 Emissions from Lime Production (MMT CO2 Eq. and kt) 4-10
Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (kt) 4-11
Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated, and Dead-Burned-
Dolomite Lime Production (kt) 4-12
Table 4-9: Adjusted Lime Production (kt) 4-12
Table 4-10: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime Production (MMT CO2
Eq. and Percent) 4-14
Table 4-11: CO2 Emissions from Glass Production (MMT CO2 Eq. and kt) 4-15
Table 4-12: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production (kt) 4-16
Table 4-13: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Glass Production (MMT CO2
Eq. and Percent) 4-17
Table 4-14: CO2 Emissions from Other Process Uses of Carbonates (MMT CO2 Eq.) 4-19
Table 4-15: CO2 Emissions from Other Process Uses of Carbonates (kt) 4-19
Table 4-16: Limestone and Dolomite Consumption (kt) 4-20
Table 4-17: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Other Process Uses of
Carbonates (MMT CO2 Eq. and Percent) 4-21
Table 4-18: CO2 Emissions from Ammonia Production (MMT CO2 Eq.) 4-22
Table 4-19: CO2 Emissions from Ammonia Production (kt) 4-23
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Table 4-20: Ammonia Production and Urea Production (kt) 4-24
Table 4-21: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ammonia Production (MMT
CO2 Eq. and Percent) 4-24
Table 4-22: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (MMT CO2 Eq.) 4-26
Table 4-23: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (kt) 4-26
Table 4-24: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (kt) 4-27
Table 4-25: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Urea Consumption for Non-
Agricultural Purposes (MMT CO2 Eq. and Percent) 4-27
Table 4-26: N2O Emissions from Nitric Acid Production (MMT CO2 Eq. and kt N2O) 4-28
Table 4-27: Nitric Acid Production (kt) 4-30
Table 4-28: Approach 2 Quantitative Uncertainty Estimates for N20 Emissions from Nitric Acid Production (MMT
CO2 Eq. and Percent) 4-31
Table 4-29: N2O Emissions from Adipic Acid Production (MMT CO2 Eq. and kt N2O) 4-32
Table 4-30: Adipic Acid Production (kt) 4-34
Table 4-31: Approach 2 Quantitative Uncertainty Estimates for N20 Emissions from Adipic Acid Production
(MMT CO2 Eq. and Percent) 4-35
Table 4-32: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (MMT CO2 Eq.) 4-36
Table 4-33: CO2 and CH4 Emissions from Silicon Carbide Production and Consumption (kt) 4-36
Table 4-34: Production and Consumption of Silicon Carbide (Metric Tons) 4-37
Table 4-35: Approach 2 Quantitative Uncertainty Estimates for CH4 and CO2 Emissions from Silicon Carbide
Production and Consumption (MMT CO2 Eq. and Percent) 4-37
Table 4-36: CO2 Emissions from Titanium Dioxide (MMT CO2 Eq. and kt) 4-38
Table 4-37: Titanium Dioxide Production (kt) 4-39
Table 4-38: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium Dioxide Production
(MMT CO2 Eq. and Percent) 4-40
Table 4-39: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(MMT C02 Eq.) 4-42
Table 4-40: CO2 Emissions from Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(kt) 4-42
Table 4-41: Soda Ash Production and Consumption Not Associated with Glass Manufacturing (kt) 4-43
Table 4-42: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash Production and
Consumption (MMT CO2 Eq. and Percent) 4-44
Table 4-43: CO2 and CH4 Emissions from Petrochemical Production (MMT CO2 Eq.) 4-46
Table 4-44: CO2 and CH4 Emissions from Petrochemical Production (kt) 4-46
Table 4-45: Production of Selected Petrochemicals (kt) 4-48
Table 4-46: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Petrochemical Production and
CO2 Emissions from Carbon Black Production (MMT CO2 Eq. and Percent) 4-49
Table 4-47: HFC-23 Emissions from HCFC-22 Production (MMT C02 Eq. and kt HFC-23) 4-50
Table 4-48: HCFC-22 Production (kt) 4-51
Table 4-49: Approach 2 Quantitative Uncertainty Estimates for HFC-23 Emissions from HCFC-22 Production
(MMT CO2 Eq. and Percent) 4-52
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Table 4-50: CO2 Emissions from CO2 Consumption (MMT CO2 Eq. and kt) 4-53
Table 4-51: CO2 Production (kt CO2) and the Percent Used for Non-EOR Applications 4-54
Table 4-52: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2 Consumption (MMT CO2
Eq. and Percent) 4-55
Table 4-53: CO2 Emissions from Phosphoric Acid Production (MMT CO2 Eq. and kt) 4-56
Table 4-54: Phosphate Rock Domestic Consumption, Exports, and Imports (kt) 4-57
Table 4-55: Chemical Composition of Phosphate Rock (Percent by Weight) 4-58
Table 4-56: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Phosphoric Acid Production
(MMT CO2 Eq. and Percent) 4-59
Table 4-57: CO2 Emissions from Metallurgical Coke Production (MMT CO2 Eq.) 4-60
Table 4-58: CO2 Emissions from Metallurgical Coke Production (kt) 4-61
Table 4-59: CO2 Emissions from Iron and Steel Production (MMT CO2 Eq.) 4-61
Table 4-60: CO2 Emissions from Iron and Steel Production (kt) 4-61
Table 4-61: CH4 Emissions from Iron and Steel Production (MMT CO2 Eq.) 4-61
Table 4-62: CH4 Emissions from Iron and Steel Production (kt) 4-62
Table 4-63: Material Carbon Contents for Metallurgical Coke Production 4-63
Table 4-64: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Metallurgical
Coke Production (Thousand Metric Tons) 4-63
Table 4-65: Production and Consumption Data for the Calculation of CO2 Emissions from Metallurgical Coke
Production (Million ft3) 4-64
Table 4-66: CO2 Emission Factors for Sinter Production, Direct Reduced Iron Production and Pellet Production4-64
Table 4-67: Material Carbon Contents for Iron and Steel Production 4-65
Table 4-68: CH4 Emission Factors for Sinter and Pig Iron Production 4-65
Table 4-69: Production and Consumption Data for the Calculation of CO2 and CH4 Emissions from Iron and Steel
Production (Thousand Metric Tons) 4-66
Table 4-70: Production and Consumption Data for the Calculation of CO2 Emissions from Iron and Steel
Production (Million ft3 unless otherwise specified) 4-67
Table 4-71: Approach 2 Quantitative Uncertainty Estimates for CO2 and CH4 Emissions from Iron and Steel
Production and Metallurgical Coke Production (MMT CO2 Eq. and Percent) 4-68
Table 4-72: CO2 and CH4 Emissions from Ferroalloy Production (MMT CO2 Eq.) 4-69
Table 4-73: CO2 and CH4 Emissions from Ferroalloy Production (kt) 4-70
Table 4-74: Production of Ferroalloys (Metric Tons) 4-71
Table 4-75: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Ferroalloy Production (MMT
CO2 Eq. and Percent) 4-72
Table 4-76: CO2 Emissions from Aluminum Production (MMT CO2 Eq. and kt) 4-73
Table 4-77: PFC Emissions from Aluminum Production (MMT CO2 Eq.) 4-73
Table 4-78: PFC Emissions from Aluminum Production (kt) 4-74
Table 4-79: Production of Primary Aluminum (kt) 4-76
Table 4-80: Approach 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from Aluminum
Production (MMT CO2 Eq. and Percent) 4-77
xii DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 4-81: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and Processing (MMT
C02 Eq.) 4-78
Table 4-82: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and Processing (kt)... 4-78
Table 4-83: SF6 Emission Factors (kg SF6 per Metric Ton of Magnesium) 4-80
Table 4-84: Approach 2 Quantitative Uncertainty Estimates for SF6, HFC-134a and CO2 Emissions from
Magnesium Production and Processing (MMT CO2 Eq. and Percent) 4-81
Table 4-85: CO2 Emissions from Lead Production (MMT CO2 Eq. and kt) 4-83
Table 4-86: Lead Production (Metric Tons) 4-84
Table 4-87: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead Production (MMT CO2
Eq. and Percent) 4-84
Table 4-88: Zinc Production (Metric Tons) 4-86
Table 4-89: CO2 Emissions from Zinc Production (MMT CO2 Eq. and kt) 4-87
Table 4-90: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc Production (MMT CO2
Eq. and Percent) 4-89
Table 4-91: PFC, HFC, SF6, NF3, and N20 Emissions from Semiconductor Manufacture (MMT CO2 Eq.) 4-91
Table 4-92: PFC, HFC, SF6, NF3, and N20 Emissions from Semiconductor Manufacture (kt) 4-92
Table 4-93: Approach 2 Quantitative Uncertainty Estimates for HFC, PFC, SF6, NF3 and N20 Emissions from
Semiconductor Manufacture (MMT CO2 Eq. and Percent) 4-99
Table 4-94: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2 Eq.) 4-101
Table 4-95: Emissions of HFCs and PFCs from ODS Substitution (Metric Tons) 4-101
Table 4-96: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2 Eq.) by Sector 4-102
Table 4-97: Approach 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from ODS Substitutes
(MMT CO2 Eq. and Percent) 4-104
Table 4-98: U.S. HFC Consumption (MMT CO Eq.) 4-106
Table 4-99: Averaged U.S. HFC Demand (MMT CO Eq.) 4-107
Table 4-100: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (MMT CO2 Eq.)
4-109
Table 4-101: SF6 Emissions from Electric Power Systems and Electrical Equipment Manufacturers (kt) 4-109
Table 4-102: Transmission Mile Coverage and Regression Coefficients (Percent) 4-112
Table 4-103: Approach 2 Quantitative Uncertainty Estimates for SF6 Emissions from Electrical Transmission and
Distribution (MMT CO2 Eq. and Percent) 4-114
Table 4-104: N2O Production (kt) 4-115
Table 4-105: N20 Emissions from N20 Product Usage (MMT CO2 Eq. and kt) 4-116
Table 4-106: Approach 2 Quantitative Uncertainty Estimates for N20 Emissions from N20 Product Usage (MMT
CO2 Eq. and Percent) 4-117
Table 4-107: NOx, CO, and NMVOC Emissions from Industrial Processes and Product Use (kt) 4-118
Table 5-1: Emissions from Agriculture (MMT CO2 Eq.) 5-2
Table 5-2: Emissions from Agriculture (kt) 5-2
Table 5-3: CH4 Emissions from Enteric Fermentation (MMT CO2 Eq.) 5-3
Table 5-4: CH4 Emissions from Enteric Fermentation (kt) 5-3
xiii
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Table 5-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Enteric Fermentation (MMT
CO2 Eq. and Percent) 5-6
Table 5-6: CH4 and N2O Emissions from Manure Management (MMT CChEq.) 5-9
Table 5-7: CH4 and N20 Emissions from Manure Management (kt) 5-10
Table 5-8: Approach 2 Quantitative Uncertainty Estimates for CH4 and N20 (Direct and Indirect) Emissions from
Manure Management (MMT CO2 Eq. and Percent) 5-13
Table 5-9: IPCC (2006) Implied Emission Factor Default Values Compared with Calculated Values for CH4 from
Manure Management (kg/head/year) 5-14
Table 5-10: CH4 Emissions from Rice Cultivation (MMT CO2 Eq.) 5-16
Table 5-11: CH4 Emissions from Rice Cultivation (kt) 5-17
Table 5-12: Rice Area Harvested (1,000 Hectares) 5-18
Table 5-13: Average Ratooned Area as Percent of Primary Growth Area (Percent) 5-18
Table 5-14: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Rice Cultivation (MMT CO2
Eq. and Percent) 5-19
Table 5-15: N20 Emissions from Agricultural Soils (MMT CO2 Eq.) 5-23
Table 5-16: N20 Emissions from Agricultural Soils (kt) 5-23
Table 5-17: Direct N20 Emissions from Agricultural Soils by Land Use Type and N Input Type (MMT CO2 Eq.) 5-
23
Table 5-18: Indirect N20 Emissions from Agricultural Soils (MMT CO2 Eq.) 5-24
Table 5-19: Quantitative Uncertainty Estimates of N20 Emissions from Agricultural Soil Management in 2015
(MMT CO2 Eq. and Percent) 5-32
Table 5-20: Emissions from Liming (MMT CO2 Eq.) 5-34
Table 5-21: Emissions from Liming (MMT C) 5-35
Table 5-22: Applied Minerals (MMT) 5-36
Table 5-23: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming (MMT CO2 Eq. and
Percent) 5-36
Table 5-24: CO2 Emissions from Urea Fertilization (MMT CO2 Eq.) 5-37
Table 5-25: CO2 Emissions from Urea Fertilization (MMT C) 5-37
Table 5-26: Applied Urea (MMT) 5-38
Table 5-27: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization (MMT CO2 Eq. and
Percent) 5-38
Table 5-28: CH4 and N20 Emissions from Field Burning of Agricultural Residues (MMT CO2 Eq.) 5-39
Table 5-29: CH4, N20, CO, and NOx Emissions from Field Burning of Agricultural Residues (kt) 5-40
Table 5-30: Agricultural Crop Production (kt of Product) 5-42
Table 5-31: U.S. Average Percent Crop Area Burned by Crop (Percent) 5-42
Table 5-32: Key Assumptions for Estimating Emissions from Field Burning of Agricultural Residues 5-43
Table 5-33: Greenhouse Gas Emission Ratios and Conversion Factors 5-43
Table 5-34: Approach 2 Quantitative Uncertainty Estimates for CH4 and N20 Emissions from Field Burning of
Agricultural Residues (MMT CO2 Eq. and Percent) 5-43
Table 6-1: C Stock Change from Land Use, Land-Use Change, and Forestry (MMT CO2 Eq.) 6-2
xiv DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 6-2: Emissions from Land Use, Land-Use Change, and Forestry by Gas (MMT CO2 Eq.) 6-3
Table 6-3: Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and Forestry by Land Use and
Land-Use Change Category (MMT CO2 Eq.) 6-3
Table 6-4: Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and Forestry by Gas (MMT CO2
Eq.) 6-4
Table 6-5: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry by Gas (kt) 6-6
Table 6-6: Managed and Unmanaged Land Area by Land-Use Categories for All 50 States (Thousands of Hectares)
6-8
Table 6-7
Hectares)
Land Use and Land-Use Change for the U.S. Managed Land Base for All 50 States (Thousands of
6-9
Table 6-8: Data Sources Used to Determine Land Use and Land Area for the Conterminous United States, Hawaii,
and Alaska 6-14
Table 6-9: Total Land Area (Hectares) by Land-Use Category for U.S. Territories 6-19
Table 6-10: Net CO2 Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools
(MMT C02 Eq.) 6-24
Table 6-11: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools
(MMT C) 6-24
Table 6-12: Forest Area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and Harvested Wood
Pools (MMT C) 6-25
Table 6-13: Estimates of CO2 (MMT per Year) Emissions from Forest Fires in the Conterminous 48 States and
Alaska3 6-27
Table 6-14: Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land Remaining Forest Land:
Changes in Forest C Stocks (MMT CO2 Eq. and Percent) 6-30
Table 6-15: Mean C Stocks, CO2 and CH4 Fluxes in Alaska between 2000 and 2009 6-33
Table 6-16: Non-CCh Emissions from Forest Fires (MMT CO2 Eq.)a 6-34
Table 6-17: Non-CCh Emissions from Forest Fires (kt)a 6-34
Table 6-18: Quantitative Uncertainty Estimates of Non-CCh Emissions from Forest Fires (MMT CO2 Eq. and
Percent)3 6-35
Table 6-19: N2O Emissions from N Additions to Soils3, b (MMT CO2 Eq. and kt N2O) 6-36
Table 6-20: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land Remaining Forest Land
and Land Converted to Forest Land (MMT CO2 Eq. and Percent) 6-38
Table 6-21: Estimated CO2 and Non-C02 Emissions onDrained Organic Forest Soils3 (MMT CO2 Eq.) 6-39
Table 6-22: Estimated CO2-C (MMT C) and Non-CCh Emissions on Drained Organic Forest Soils3 (kt) 6-39
Table 6-23: States identified as having Drained Organic Soils, Area of Forest on Drained Organic Soils, and
Sampling Error 6-40
Table 6-24: Quantitative Uncertainty Estimates for Annual CO2 and Non-C02 Emissions on Drained Organic Forest
Soils (MMT CO2 Eq. and Percent)3 6-41
Table 6-25: Net CO2 Flux from Forest C Pools in Land Converted to Forest Land by Land Use Change Category
(MMT C02 Eq.) 6-42
Table 6-26: Net C Flux from Forest C Pools in Land Converted to Forest Land by Land Use Change Category
(MMT C) 6-43
Table 6-27: Quantitative Uncertainty Estimates for Forest C Pool Stock Changes (MMT CO2 Eq. per Year) in 2015
from Land Converted to Forest Land by Land Use Change 6-45
xv
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Table 6-28: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT CO2 Eq.) 6-48
Table 6-29: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT C) 6-48
Table 6-30: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Cropland
Remaining Cropland (MMT CO2 Eq. and Percent) 6-54
Table 6-31: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland by Land Use Change
Category (MMT CO Eq.) 6-57
Table 6-32: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland (MMT C) 6-57
Table 6-33: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land
Converted to Cropland (MMT CO2 Eq. and Percent) 6-60
Table 6-34: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT CO2 Eq.) 6-63
Table 6-35: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT C) 6-63
Table 6-36: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring Within Grassland
Remaining Grassland (MMT CO2 Eq. and Percent) 6-67
Table 6-37: CH4 and N20 Emissions from Biomass Burning in Grassland (MMT CO2 Eq.) 6-68
Table 6-38: CH4, N20, CO, and NOx Emissions from Biomass Burning in Grassland (kt) 6-69
Table 6-39: Thousands of Grassland Hectares Burned Annually 6-69
Table 6-40: Uncertainty Estimates for Non-C02 Greenhouse Gas Emissions from Biomass Burning in Grassland
(MMT CO2 Eq. and Percent) 6-70
Table 6-41: Net CO2 Flux from Soil and Biomass C Stock Changes for Land Converted to Grassland (MMT CO2
Eq.) 6-72
Table 6-42: Net CO2 Flux from Soil and Biomass C Stock Changes for Land Converted to Grassland (MMT C).. 6-
72
Table 6-43: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes occurring within Land
Converted to Grassland (MMT CO2 Eq. and Percent) 6-75
Table 6-44: Emissions from PeatlandsRemaining Peatlands (MMT CO2 Eq.) 6-78
Table 6-45: Emissions from Peatlands Remaining Peatlands (kt) 6-78
Table 6-46: Peat Production of Lower 48 States (kt) 6-80
Table 6-47: Peat Production of Alaska (Thousand Cubic Meters) 6-80
Table 6-48: Approach 2 Quantitative Uncertainty Estimates for CO2, CH4, and N20 Emissions from Peatlands
Remaining Peatlands (MMT CO2 Eq. and Percent) 6-81
Table 6-49: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands Remaining Vegetated Coastal
Wetlands (MMT CO Eq.) 6-84
Table 6-50: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands Remaining Vegetated Coastal
Wetlands (MMT C) 6-84
Table 6-51: Net CHi Flux from Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands (MMT CO2
Eq.) 6-85
Table 6-52: Net CHi Flux from Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands (kt CH4). 6-85
Table 6-53: Approach 1 Quantitative Uncertainty Estimates for C Stock Changes occurring within Vegetated
Coastal Wetlands Remaining Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent) 6-86
Table 6-54: Approach 1 Quantitative Uncertainty Estimates for CH4 Emissions occurring within Vegetated Coastal
Wetlands Remaining Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent) 6-87
xvi DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 6-55: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands Converted to Unvegetated
Open Water Coastal Wetlands (MMT CO2 Eq.) 6-88
Table 6-56: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands Converted to Unvegetated
Open Water Coastal Wetlands (MMT C) 6-88
Table 6-57: Approach 1 Quantitative Uncertainty Estimates for Net CO2 Flux occurring within Vegetated Coastal
Wetlands Converted to Unvegetated Open Water Coastal Wetlands (MMT CO2 Eq. and Percent) 6-89
Table 6-58: Net CO2 Flux from Soil C Stock Changes from Unvegetated Open Water Coastal Wetlands Converted
to Vegetated Coastal Wetlands (MMT CO2 Eq.) 6-91
Table 6-59: Net CO2 Flux from Soil C Stock Changes from Unvegetated Open Water Coastal Wetlands Converted
to Vegetated Coastal Wetlands (MMT C) 6-91
Table 6-60: Approach 1 Quantitative Uncertainty Estimates for C Stock Changes occurring within Unvegetated
Open Water Coastal Wetlands Converted to Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent) 6-92
Table 6-61: Net N20 Flux from Aquaculture in Coastal Wetlands (MMT CO2 Eq.) 6-93
Table 6-62: Net N20 Flux from Aquaculture in Coastal Wetlands (kt N20) 6-93
Table 6-63: Approach 1 Quantitative Uncertainty Estimates for N20 Emissions for Aquaculture Production in
Coastal Wetlands (MMT CO2 Eq. and Percent) 6-94
Table 6-64: Net CO2 Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal Wetlands (MMT
C02 Eq.) 6-95
Table 6-65: Net CO2 Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal Wetlands (MMT C)
6-95
Table 6-66: Net CH4 Flux in Land Converted to Vegetated Coastal Wetlands (MMT CO2 Eq.) 6-96
Table 6-67: Net CH4 Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal Wetlands (kt CH4)6-
96
Table 6-68: Approach 1 Quantitative Uncertainty Estimates for Net CO2 Flux Changes occurring within Land
Converted to Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent) 6-97
Table 6-69: Approach 1 Quantitative Uncertainty Estimates for CH4 Emissions occurring within Land Converted to
Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent) 6-98
Table 6-70: Net CO2 Flux from Soil C Stock Changes in Settlements Remaining Settlements (MMT CO2 Eq.)... 6-99
Table 6-71: Net CO2 Flux from Soil C Stock Changes in Settlements Remaining Settlements (MMT C) 6-99
Table 6-72: Thousands of Hectares of Drained Organic Soils in Settlements Remaining Settlements 6-100
Table 6-73: Uncertainty Estimates for CO2 Emissions from Drained Organic Soils in Settlements Remaining
Settlements (MMT CO2 Eq. and Percent) 6-100
Table 6-74: Net C Flux from Urban Trees (MMT CO Eq. and MMT C) 6-102
Table 6-75: Annual C Sequestration (Metric Tons C/Year), Tree Cover (Percent), and Annual C Sequestration per
Area of Tree Cover (kg C/m2-yr) for 50 states plus the District of Columbia (2015) 6-104
Table 6-76: Approach 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C Stocks in Urban
Trees (MMT CO2 Eq. and Percent) 6-105
Table 6-77: N2O Fluxes from Soils in Settlements Remaining Settlements (MMT CO2 Eq. and kt N2O) 6-107
Table 6-78: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements Remaining Settlements
(MMT CO2 Eq. and Percent) 6-108
Table 6-79: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (MMT CO2 Eq.) 6-110
Table 6-80: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills (MMT C) 6-110
xvii
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Table 6-81: Moisture Contents, C Storage Factors (Proportions of Initial C Sequestered), Initial C Contents, and
Decay Rates for Yard Trimmings and Food Scraps in Landfills 6-112
Table 6-82: C Stocks in Yard Trimmings and Food Scraps in Landfills (MMT C) 6-113
Table 6-83: Approach 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard Trimmings and Food Scraps in
Landfills (MMT CO2 Eq. and Percent) 6-113
Table 6-84: Net CO2 Flux from Soil, Dead Organic Matter and Biomass C Stock Changes for Land Converted to
Settlements (MMT CO2 Eq.) 6-115
Table 6-85: Net CO2 Flux from Soil, Dead Organic Matter and Biomass C Stock Changes for Land Converted to
Settlements (MMT C) 6-115
Table 6-86: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes occurring within Land Converted
to Settlements (MMT CO2 Eq. and Percent) 6-118
Table 7-1: Emissions from Waste (MMT CO2 Eq.) 7-1
Table 7-2: Emissions from Waste (kt) 7-2
Table 7-3: CH4 Emissions from Landfills (MMT CO2 Eq.) 7-4
Table 7-4: CH4 Emissions from Landfills (kt) 7-5
Table 7-5: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Landfills (MMT CO2 Eq. and
Percent) 7-10
Table 7-6: Materials Discarded3 in the Municipal Waste Stream by Waste Type from 1990 to 2014 (Percent).... 7-16
Table 7-7: CH4 and N20 Emissions from Domestic and Industrial Wastewater Treatment (MMT CO2 Eq.) 7-18
Table 7-8: CH4 and N20 Emissions from Domestic and Industrial Wastewater Treatment (kt) 7-19
Table 7-9: U.S. Population (Millions) and Domestic Wastewater BOD5 Produced (kt) 7-21
Table 7-10: Domestic Wastewater CH4 Emissions from Septic and Centralized Systems (2015, MMT CO2 Eq. and
Percent) 7-22
Table 7-11: Industrial Wastewater CH4 Emissions by Sector (2015, MMT CO2 Eq. and Percent) 7-22
Table 7-12: U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and Petroleum Refining
Production (MMT) 7-22
Table 7-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by Industry (Percent) 7-24
Table 7-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables, Fruits, and Juices
Production 7-25
Table 7-15: U.S. Population (Millions), Population Served by Biological Denitrification (Millions), Fraction of
Population Served by Wastewater Treatment (Percent), Available Protein (kg/Person-Year), Protein Consumed
(kg/Person-Year), and Nitrogen Removed with Sludge (kt-N/Year) 7-29
Table 7-16: Approach 2 Quantitative Uncertainty Estimates for CH4 Emissions from Wastewater Treatment (MMT
CO2 Eq. and Percent) 7-30
Table 7-17: CH4 and N20 Emissions from Composting (MMT CO2 Eq.) 7-33
Table 7-18: CH4 and N20 Emissions from Composting (kt) 7-33
Table 7-19: U.S. Waste Composted (kt) 7-34
Table 7-20: Approach 1 Quantitative Uncertainty Estimates for Emissions from Composting (MMT CO2 Eq. and
Percent) 7-34
Table 7-21: Emissions of NOx, CO, and NMVOC from Waste (kt) 7-35
Table 9-1: Revisions to U.S. Greenhouse Gas Emissions (MMT CO2 Eq.) 9-4
xviii DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 9-2: Revisions to U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land Use, Land-Use
Change, and Forestry (MMT CO2 Eq.) 9-5
Figure ES-1: Gross U.S. Greenhouse Gas Emissions by Gas (MMT CO2 Eq.) ES-4
Figure ES-2: Annual Percent Change in Gross U.S. Greenhouse Gas Emissions Relative to the Previous Year ..ES-5
Figure ES-3: Cumulative Change in Annual Gross U.S. Greenhouse Gas Emissions Relative to 1990 (1990=0,
MM I CO- Eq.) ES-5
Figure ES-4: 2015 U.S. Greenhouse Gas Emissions by Gas (Percentages based on MMT CO2 Eq.) ES-8
Figure ES-5: 2015 Sources of CO2 Emissions (MMT CO2 Eq.) ES-9
Figure ES-6: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT CChEq.) ES-10
Figure ES-7: 2015 End-Use Sector Emissions of CO2 from Fossil Fuel Combustion (MMT CO2 Eq.) ES-10
Figure ES-8: 2015 Sources of CH4 Emissions (MMT CO2 Eq.) ES-13
Figure ES-9: 2015 Sources of N20 Emissions (MMT CO2 Eq.) ES-15
Figure ES-10: 2015 Sources of HFCs, PFCs, SF6, and NF3 Emissions (MMT CO2 Eq.) ES-16
Figure ES-11: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.) ES-17
Figure ES-12: 2015 U.S. Energy Consumption by Energy Source (Percent) ES-19
Figure ES-13: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.) ES-23
Figure ES-14: U.S. Greenhouse Gas Emissions with Electricity-Related Emissions Distributed to Economic Sectors
(MMTCO2 Eq.) ES-25
Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product (GDP)..ES-26
Figure ES-16: 2015 Key Categories (MMT CO Eq.) ES-27
Figure 1-1: National Inventory Arrangements Diagram 1-12
Figure 1-2: U.S. QA/QC Plan Summary 1-20
Figure 2-1: Gross U.S. Greenhouse Gas Emissions by Gas (MMT CO2 Eq.) 2-1
Figure 2-2: Annual Percent Change in Gross U.S. Greenhouse Gas Emissions Relative to the Previous Year 2-2
Figure 2-3: Cumulative Change in Annual Gross U.S. Greenhouse Gas Emissions Relative to 1990 (1990=0, MMT
C02 Eq.) 2-2
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2 Eq.) 2-8
Figure 2-5: 2015 Energy Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 2-10
Figure 2-6: 2015 U.S. Fossil Carbon Flows (MMT CO Eq.) 2-11
Figure 2-7: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT CO2 Eq.) 2-13
Figure 2-8: 2015 End-Use Sector Emissions of CO2 from Fossil Fuel Combustion (MMT CO2 Eq.) 2-13
Figure 2-9: 2015 Industrial Processes and Product Use Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 2-15
Figure 2-10: 2015 Agriculture Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 2-17
Figure 2-11: 2015 Waste Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 2-21
Figure 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.) 2-23
Figure 2-13: U.S. Greenhouse Gas Emissions with Electricity-Related Emissions Distributed to Economic Sectors
(MMT C02 Eq.) 2-26
XIX
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41
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic Product 2-33
Figure 3-1: 2015 Energy Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 3-1
Figure 3-2: 2015 U.S. Fossil Carbon Flows (MMT CO Eq.) 3-2
Figure 3-3: 2015 U.S. Energy Consumption by Energy Source (Percent) 3-7
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu) 3-7
Figure 3-5: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT CO2 Eq.) 3-8
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States (1950-2015, Index Normal
= 100) 3-9
Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States (1950-2015, Index Normal
= 100) 3-9
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United States (1990-2015,
Percent) 3-10
Figure 3-9: Electricity Generation (Billion kWh) and Emissions (MMT CO2 Eq.) 3-15
Figure 3-10: Electricity Generation Retail Sales by End-Use Sector (Billion kWh) 3-15
Figure 3-11: Industrial Production Indices (Index 2007=100) 3-17
Figure 3-12: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks, 1990-2015
(miles/gallon) 3-20
Figure 3-13: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2015 (Percent) 3-21
Figure 3-14: Mobile Source CH4 and N20 Emissions (MMT CO2 Eq.) 3-23
Figure 3-15: U.S. Energy Consumption and Energy-Related CO2 Emissions Per Capita and Per Dollar GDP 3-31
Figure 4-1: 2015 Industrial Processes and Product Use Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 4-2
Figure 4-2: U.S. HFC Consumption (MMT CO2 Eq.) 4-106
Figure 5-1: 2015 Agriculture Chapter Greenhouse Gas Emission Sources (MMT CO2 Eq.) 5-1
Figure 5-2: Total Net Annual CH4 Emissions from Rice Cultivation, 2015 (MMT CO2 Eq./Ycar) - TO BE
UPDATED FOR FINAL INVENTORY REPORT 5-17
Figure 5-3: Sources and Pathways of N that Result in N20 Emissions from Agricultural Soil Management 5-22
Figure 5-4: Crops, 2015 Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model (MMT CO2
Eq./year) 5-24
Figure 5-5: Grasslands, 2015 Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT Model (MMT
CO2 Eq./year) 5-25
Figure 5-6: Crops, 2015 Average Annual N Losses Leading to Indirect N20 Emissions Estimated Using the Tier 3
DAYCENT Model (kt N/year) - TO BE UPDATED FOR FINAL INVENTORY REPORT 5-25
Figure 5-7: Grasslands, 2015 Average Annual N Losses Leading to Indirect N20 Emissions Estimated Using the
Tier 3 DAYCENT Model (kt N/year) - TO BE UPDATED FOR FINAL INVENTORY REPORT 5-25
Figure 5-8: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using the DAYCENT
Simulation Model and IPCC Tier 1 Approach (kg N20 per ha per year) 5-33
Figure 6-1: Percent of Total Land Area for Each State in the General Land-Use Categories for 2015 6-10
Figure 6-2: Changes in Forest Area by Region for Forest Land Remaining Forest Land in the conterminous United
States and coastal Alaska (1990-2015, Million Hectares) 6-23
Figure 6-3: Estimated Net Annual Changes in C Stocks for All C Pools in Forest Land Remaining Forest Land in
the Conterminous U.S. and Coastal Alaska (MMT C per Year) 6-26
xx DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Figure 6-4: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2015,
Cropland Remaining Cropland 6-49
Figure 6-5: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2015,
Cropland Remaining Cropland 6-50
Figure 6-6: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management within States, 2015,
Grassland Remaining Grassland 6-64
Figure 6-7: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management within States, 2015,
Grassland Remaining Grassland 6-64
Figure 7-1: 2015 Waste Chapter Greenhouse Gas Sources (MMT CO2 Eq.) 7-1
Figure 7-2: Comparison of the Revised Inventory Methodology to EPA's GHGRP Subpart HH Emissions 7-13
Figure 7-3: Comparison of the 1990-2014 Inventory Methodology to the Revised Inventory Methodology 7-13
Figure 7-4: Management of Municipal Solid Waste in the United States, 2014 7-15
Figure 7-5: MSW Management Trends from 1990 to 2014 7-15
Figure 7-6: Percent of Recovered Degradable Materials from 1990 to 2014 (Percent) 7-16
Boxes
Box ES-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks ES-1
Box ES-2: Use of Ambient Measurements Systems for Validation of Emission Inventories ES-12
Box ES-3: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data ES-25
Box ES-4: Recalculations of Inventory Estimates ES-28
Box 1-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 1-2
Box 1-2: The IPCC Fifth Assessment Report and Global Warming Potentials 1-9
Box 1-3: IPCC Reference Approach 1-15
Box 2-1: Methodology for Aggregating Emissions by Economic Sector 2-31
Box 2-2: Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data 2-32
Box 2-3: Sources and Effects of Sulfur Dioxide 2-35
Box 3-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 3-3
Box 3-2: Energy Data from the Greenhouse Gas Reporting Program 3-4
Box 3-3: Weather and Non-Fossil Energy Effects on CO2 from Fossil Fuel Combustion Trends 3-8
Box 3-4: Uses of Greenhouse Gas Reporting Program Data and Improvements in Reporting Emissions from
Industrial Sector Fossil Fuel Combustion - TO BE UPDATED FOR FINAL INVENTORY REPORT 3-28
Box 3-5: Carbon Intensity of U.S. Energy Consumption 3-30
Box 3-6: Reporting of Lubricants, Waxes, and Asphalt and Road Oil Product Use in Energy Sector 3-47
Box 3-7: Carbon Dioxide Transport, Injection, and Geological Storage 3-70
Box 4-1: Industrial Processes Data from EPA's Greenhouse Gas Reporting Program 4-6
Box 5-1: Tier 1 vs. Tier 3 Approach for Estimating N2O Emissions 5-26
Box 5-2: Comparison of the Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 5-35
Box 5-3: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approach 5-41
Box 6-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sinks 6-6
xxi
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7
8
9
10
11
12
Box 6-2: Preliminary Estimates of Land Use in U.S. Territories 6-19
Box 6-3: CO2 Emissions from Forest Fires 6-26
Box 6-4: Preliminary Estimates of Historical Carbon Stock Change and Methane Emissions from Managed Land in
Alaska (Represents Mean for Years 2000 to 2009) 6-33
Box 6-5: Tier 3 Approach for Soil C Stocks Compared to Tier 1 or 2 Approaches 6-51
Box 6-6: Grassland Woody Biomass Analysis 6-68
Box 7-1: Waste Data from the Greenhouse Gas Reporting Program - TO BE UPDATED FOR FINAL
INVENTORY REPORT 7-2
Box 7-2: Nationwide Municipal Solid Waste Data Sources 7-14
Box 7-3: Overview of Municipal Solid Waste Management 7-15
Box 7-4: Description of a Modern, Managed Landfill 7-17
xxii DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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29
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 2015. To ensure that the U.S. emissions inventory is comparable to those of other UNFCCC Parties, the
estimates presented here were calculated using methodologies consistent with those recommended in the 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC
2006). The structure of this report is consistent with the UNFCCC guidelines for inventory reporting.4
Box ES-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sin
I
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the gross emissions total presented in this report for the United States excludes emissions and sinks
from LULUCF. The net emissions total presented in this report for the United States includes emissions and sinks
from LULUCF. All emissions and sinks are calculated using internationally-accepted methods provided by the
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 2006).
2 Article 2 of the Framework Convention on Climate Change published by the UNEPAVMO Information Unit on Climate
Change. See .
3 Article 4(1)(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 .
Executive Summary ES-1
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1 IPCC.5 Additionally, the calculated emissions and sinks in a given year for the United States are presented in a
2 common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this
3 international agreement.6 The use of consistent methods to calculate emissions and sinks by all nations providing
4 their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks
5 reported in this Inventory report are comparable to emissions and sinks reported by other countries. The report itself
6 follows this standardized format, and provides an explanation of the IPCC methods used to calculate emissions and
7 sinks, and the manner in which those calculations are conducted.
8 On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
9 reporting of greenhouse gases from large greenhouse gas emissions sources in the United States. Implementation of
10 40 CFR Part 98 is referred to as the Greenhouse Gas Reporting Program (GHGRP). 40 CFR part 98 applies to direct
11 greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject carbon dioxide (CO2)
12 underground for sequestration or other reasons.7 Reporting is at the facility level, except for certain suppliers of
13 fossil fuels and industrial greenhouse gases. The GHGRP dataset and the data presented in this Inventory report are
14 complementary.
15 The GHGRP data set continues to be an important resource for the Inventory, providing not only annual emissions
16 information, but also other annual information, such as activity data and emissions factors that can improve and
17 refine national emission estimates and trends over time. GHGRP data also allow EPA to disaggregate national
18 inventory estimates in new ways that can highlight differences across regions and sub-categories of emissions, along
19 with enhancing application of QA/QC procedures and assessment of uncertainties.
20 EPA uses annual GHGRP data in a number of category estimates and continues to analyze the data on an annual
21 basis, as applicable, for further use to improve the national estimates presented in this Inventory consistent with
22 IPCC guidance.8
23
24 ES.l Background Information
25 Greenhouse gases trap heat and make the planet warmer. The most important greenhouse gases directly emitted by
26 humans include carbon dioxide (CO2), methane (CH4), nitrous oxide (N20), and several other fluorine-containing
27 halogenated substances. Although the direct greenhouse gases CO2, CH4, and N20 occur naturally in the
28 atmosphere, human activities have changed their atmospheric concentrations. From the pre-industrial era (i.e.,
29 ending about 1750) to 2015, concentrations of these greenhouse gases have increased globally by 44, 162, and 21
30 percent, respectively (IPCC 2013 and NOAA/ESRL 2017). This annual report estimates the total national
31 greenhouse gas emissions and removals associated with human activities across the United States.
32 Global Warming Potentials
33 Gases in the atmosphere can contribute to climate change both directly and indirectly. Direct effects occur when the
34 gas itself absorbs radiation. Indirect radiative forcing occurs when chemical transformations of the substance
35 produce other greenhouse gases, when a gas influences the atmospheric lifetimes of other gases, and/or when a gas
36 affects atmospheric processes that alter the radiative balance of the earth (e.g., affect cloud formation or albedo).9
5 See .
6 See .
7 See and .
8 See
9 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.
2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 The IPCC developed the Global Warming Potential (GWP) concept to compare the ability of each greenhouse gas to
2 trap heat in the atmosphere relative to another gas.
3 The GWP of a greenhouse gas is defined as the ratio of the accumulated radiative forcing within a specific time
4 horizon caused by emitting 1 kilogram of the gas, relative to that of the reference gas CO2 (IPCC 2014). Direct
5 radiative effects occur when the gas itself is a greenhouse gas. The reference gas used is CO2, and therefore GWP-
6 weighted emissions are measured in million metric tons of CO2 equivalent (MMT CO2 Eq.).1011 All gases in this
7 Executive Summary are presented in units of MMT CO2 Eq. Emissions by gas in unweighted mass tons are provided
8 in the Trends chapter of this report.
9 UNFCCC reporting guidelines for national inventories require the use of GWP values from the IPCC Fourth
10 Assessment Report (AR4) (IPCC 2007).12 All estimates are provided throughout the report in both CO2 equivalents
11 and unweighted units. A comparison of emission values using the AR4 GWP values versus the SAR (IPCC 1996),
12 and the IPCC Fifth Assessment Report (AR5) (IPCC 2013) GWP values can be found in Chapter 1 and, in more
13 detail, in Annex 6.1 of this report. The GWP values used in this report are listed below in Table ES-1.
14
15 Table ES-1: Global Warming Potentials (100-Year Time Horizon) Used in this Report
Gas
GWP
CO2
1
CH4a
25
N2O
298
HFC-23
14,800
HFC-32
675
HFC-125
3,500
HFC-134a
1,430
HFC-143a
4,470
HFC-152a
124
HFC-227ea
3,220
HFC-236fa
9,810
HFC-4310mee
1,640
CF4
7,390
c2f6
12,200
C4F10
8,860
C6Fl4
9,300
SFe
22,800
NF3
17,200
Source: IPCC (2007)
a The CH4 GWP includes the direct
effects and those indirect effects due
to the production of tropospheric
ozone and stratospheric water vapor.
The indirect effect due to production
of CO2 is not included.
16
17
10 Carbon comprises 12/44 of carbon dioxide by weight.
11 One million metric ton is equal to 1012 grams or one teragram.
12 See .
Executive Summary ES-3
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7
8
9
10
11
12
13
14
15
16
17
18
ES.2 Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2015, total gross U.S. greenhouse gas emissions were 6,586.2 million metric tons (MMT) of CO2 Eq. Total U.S.
emissions have increased by 3.4 percent from 1990 to 2015, and emissions decreased from 2014 to 2015 by 2.2
percent (150.1 MMT CO2 Eq.). The decrease in total greenhouse gas emissions between 2014 and 2015 was driven
in large part by a decrease in CO2 emissions from fossil fuel combustion. The decrease in CO2 emissions from fossil
fuel combustion was a result of multiple factors, including: (1) substitution from coal to natural gas consumption in
the electric power sector; (2) wanner winter conditions in the first quarter of 2015 resulting in a decreased demand
for heating fuel in the residential and commercial sectors; and (3) a slight decrease in electricity demand. Lastly,
since 1990, U.S. emissions have increased at an average annual rate of 0.2 percent. Figure ES-1 through Figure ES-3
illustrate the overall trends in total U.S. emissions by gas, annual changes, and absolute change since 1990. Overall,
net emissions in 2015 were 11.2 percent below 2005 levels as shown in Table ES-2.
Table ES-2 provides a detailed summary of gross U.S. greenhouse gas emissions and sinks for 1990 through 2015.
Figure ES-1: Gross U.S. Greenhouse Gas Emissions by Gas (MMT CO2 Eq.)
¦ HFCs, PFCs, SF, and NF,
¦ Nitrous Oxide
¦ Methane
¦ Carbon Dioxide 6,909 6.966 7'021 7'053
6 534 6'625 6,712
6,367 6,312 6,424
7717 , 7,307 7,316 7249 7,351
7>zl7 7,098 7,134 7,174 7,1'
6,679 6,736
6,586
6,776
4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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5
6
7
8
9
10
11
12
13
Figure ES-2: Annual Percent Change in Gross U.S. Greenhouse Gas Emissions Relative to the
Previous Year
Figure ES-3: Cumulative Change in Annual Gross U.S. Greenhouse Gas Emissions Relative to
1990 (1990=0, MMT C02 Eq.)
940 949
¦>-ir\iro^Ln»£)r^cocr>
<7>cncr»cr>CT>cr>aScr>
chcricncriCT-icr.cricricn
fNjrvjfNr>Jrsir\irMrMrsjfNfNjr\irsirMrMfM
Table ES-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO,
5,121.4
6,129.7
5,567.5
5,359.5
5,512.1
5,561.8
5,410.6
Fossil Fuel Combustion
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Transportation
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Industrial
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Executive Summary ES-5
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Residential
338.3
357. n
325.5
282.5
329.7
345.4
319.6
Commercial
217.4
223.5
220.4
196.7
221.0
231.4
225.7
U.S. Territories
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Non-Energy Use of Fuels
117."
138.
108.5
105.5
122.0
117.2
127.0
Iron and Steel Production &
Metallurgical Coke Production
99."
66.5
59.9
54.2
52.2
57.5
47.9
Natural Gas Systems
37."
30.1
35.7
35.2
38.5
42.4
42.4
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.3
27.0
26.3
26.5
26.4
26.5
28.1
Lime Production
11."
14.6
14.0
13.8
14.0
14.2
13.3
Other Process Uses of Carbonates
4.9
6. '
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
Incineration of Waste
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Urea Fertilization
2.4
3.5
4.1
4.3
4.5
4.8
5.0
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Liming
4."
4. '
3.9
6.0
3.9
3.6
3.8
Petroleum Systems
3.6
3.9
4.2
3.9
3.7
3.6
3.6
Soda Ash Production and
Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Aluminum Production
6.8
4.1
3.3
3.4
3.3
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-
Agricultural Purposes
3.8
3 "
4.0
4.4
4.0
1.4
1.1
Phosphoric Acid Production
1.5
\.-
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Magnesium Production and
Processing
+
+ /
+
+
+
+
+
Wood Biomass and Ethanol
Consumption"
219.4
229. n
268.1
267.7
286.3
293.7
277.7
International Bunker Fuelsb
103.5
113.1
111.7
105.8
99.8
103.2
110.8
H4
786.1
685.4
673.4
667.2
659.6
659.4
654.9
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Natural Gas Systems
196.5
162.1
153.7
155.3
157.9
160.8
160.0
Landfills
179.6
134.
119.0
120.8
116.7
116.6
115.7
Manure Management
37.2
56.3
63.0
65.6
63.3
62.9
66.3
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Petroleum Systems
58.3
48.0
50.1
48.4
46.6
44.9
41.5
Wastewater Treatment
15."
16.0
15.3
15.1
14.9
14.8
14.8
Rice Cultivation
16.0
16.7
14.1
11.3
11.3
11.4
11.2
Stationary Combustion
8.5
7.4
7.1
6.6
8.0
8.1
7.0
Abandoned Underground Coal
Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Composting
0.4
1.9
1.9
1.9
2.0
2.1
2.1
Mobile Combustion
5.6
2.8
2.3
2.2
2.1
2.1
2.0
Field Burning of Agricultural
Residues
0.2
0.2
0.3
0.3
0.3
0.3
0.3
Petrochemical Production
0.2
0.1
+
0.1
0.1
0.1
0.2
Ferroalloy Production
-
+ /
+
+
+
+
+
6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Silicon Carbide Production and
silicon i^aroiue rrouucuon ana
Consumption
+ M
+ /
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
+ / 1
+ /
+
+
+
+
+
Incineration of Waste
+ /
+ * /'.
+
+
+
+
+
International Bunker Fuelsb
0.2
0.1
0.1
0.1
0.1
0.1
0.1
N2O
359/.
361.6
364.0
340.7
335.5
335.5
335.1
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Stationary Combustion
11.9
20.2
21.3
21.4
22.9
23.4
23.1
Manure Management
14.0
16.5
17.4
17.5
17.5
17.5
17.7
Mobile Combustion
41.2
35.7
22.8
20.4
18.5
16.6
15.4
Nitric Acid Production
12.1
11.3
10.9
10.5
10.7
10.9
11.6
Wastewater Treatment
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Composting
0.3
1
1.7
1.7
1.8
1.9
1.9
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
Semiconductor Manufacture
+ /
0.1
0.2
0.2
0.2
0.2
0.2
Field Burning of Agricultural
Residues
0.1
0.1
0.1
0.1
0.1
0.1
0.1
International Bunker Fuelsb
0.J
1.0
1.0
0.9
0.9
0.9
0.9
HFCs
46/.
120.0
154.4
155.9
159.0
166.7
174.1
Substitution of Ozone Depleting
Substances0
0.3
99.8
145.4
150.2
154.7
161.3
168.6
HCFC-22 Production
46.1
20.0
8.8
5.5
4.1
5.0
5.0
Semiconductor Manufacture
0.2
0.2
0.2
0.2
0.2
0.3
0.3
Magnesium Production and
Processing
0.0
0.0
+
+
0.1
0.1
0.1
PFCs
24.3
6.7
6.9
6.0
5.7
5.7
5.2
Semiconductor Manufacture
2.8
3.2
3.4
3.0
2.8
3.2
3.2
Aluminum Production
21.5
3.4
3.5
2.9
3.0
2.5
2.0
SF«
28.S
11.7
9.2
6.8
6.4
6.6
5.8
Electrical Transmission and
Distribution
23.1
8. '
6.0
4.8
4.6
4.8
4.2
Magnesium Production and
Processing
5.2
2
2.8
1.6
1.5
1.0
0.9
Semiconductor Manufacture
0.5
0
0.4
0.4
0.4
0.7
0.7
NF3
+
0.5
0.7
0.6
0.6
0.5
0.6
Semiconductor Manufacture
+
0-
0.7
0.6
0.6
0.5
0.6
Total Emissions
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
LULUCF Emissions'1
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change6'
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Total®
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
Net Emissions (Sources and Sinks)
5,917.6
7,000.3
6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 Eq.
a Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing Energy sector totals.
Net carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for Land Use, Land-Use
Change, and Forestry.
b Emissions from International Bunker Fuels are not included in totals.
c Small amounts of PFC emissions also result from this source.
d LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands; CH4 and N2O
emissions reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal
Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from
Forest Soils and Settlement Soils.
Executive Summary ES-7
-------
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
e LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements
Remaining Settlements, and Land Converted to Settlements. Refer to Table ES-5 for a breakout of emissions and
removals for Land Use, Land-Use Change, and Forestry by gas and source category.
fQuality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014,2015, which will
be updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to
Grassland sections in the LULUCF chapter of this report.
g Hie LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
Figure ES-4 illustrates the relative contribution of the direct greenhouse gases to total U.S. emissions in 2015. Note,
unless otherwise stated, all tables and figures provide total emissions without LULUCF. The primary greenhouse
gas emitted by human activities in the United States was CO2, representing approximately 82.2 percent of total
greenhouse gas emissions. The largest source of CO2, and of overall greenhouse gas emissions, was fossil fuel
combustion. Methane emissions, which have decreased by 16.7 percent since 1990, resulted primarily from enteric
fermentation associated with domestic livestock, natural gas systems, and decomposition of wastes in landfills.
Agricultural soil management, manure management, mobile source fuel combustion and stationary fuel combustion
were the major sources of N20 emissions. Ozone depleting substance substitute emissions and emissions of HFC-23
during the production of HCFC-22 were the primary contributors to aggregate hydrofluorocarbon (HFC) emissions.
Perfluorocarbon (PFC) emissions resulted as a byproduct of primary aluminum production and from semiconductor
manufacturing, electrical transmission and distribution systems accounted for most sulfur hexafluoride (SF6)
emissions, and semiconductor manufacturing is the only source of nitrogen trifluoride (NF3) emissions.
Figure ES-4: 2015 U.S. Greenhouse Gas Emissions by Gas (Percentages based on MMT CO2
Eq.)
HFCs, PFCs, SF« and NFi Subtotal
2.8%
NiO
5.1%
CH,
9.9%
CO*
82.2%
Overall, from 1990 to 2015, total emissions of CO2 increased by 289.2 MMT CO2 Eq. (5.6 percent), while total
emissions of CH4 decreased by 131.2 MMT C02Eq. (16.7 percent), and N20 emissions decreased by 24.5 MMT
CO2 Eq. (6.8 percent). During the same period, aggregate weighted emissions of HFCs, PFCs, SF6 and NF3 rose by
85.9 MMT CO2 Eq. (86.2 percent). From 1990 to 2015, HFCs increased by 127.5 MMT CO2 Eq. (273.8 percent),
PFCs decreased by 19.1 MMT CO2 Eq. (78.7 percent), SF6 decreased by 23.0 MMT CO2 Eq. (79.8 percent), and
NF3 increased by 0.5 MMT CO2 Eq. (1,057.0 percent). Despite being emitted in smaller quantities relative to the
other principal greenhouse gases, emissions of HFCs, PFCs, SF6 and NF3 are significant because many of these
gases have extremely high global wanning potentials and, in the cases of PFCs and SF6, long atmospheric lifetimes.
Conversely, U.S. greenhouse gas emissions were partly offset by carbon (C) sequestration in forests, trees in urban
areas, agricultural soils, landfilled yard trimmings and food scraps, and coastal wetlands, which in aggregate, offset
8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
5.9 percent of total emissions in 2015. The following sections describe each gas's contribution to total U.S.
greenhouse gas emissions in more detail.
Carbon Dioxide Emissions
The global carbon cycle is made up of large carbon flows and reservoirs. Billions of tons of carbon in the form of
CO2 are absorbed by oceans and living biomass (i.e., sinks) and are emitted to the atmosphere annually through
natural processes (i.e., sources). When in equilibrium, carbon fluxes among these various reservoirs are roughly
balanced.13 Since the Industrial Revolution (i.e., about 1750), global atmospheric concentrations of CO2 have risen
approximately 44 percent (IPCC 2013 and NOAA/ESRL 2017), principally due to the combustion of fossil fuels.
Within the United States, fossil fuel combustion accounted for 93.3 percent of CO2 emissions in 2015. Globally,
approximately 32,381 MMT of CO2 were added to the atmosphere through the combustion of fossil fuels in 2014, of
which the United States accounted for approximately 16 percent.14 Changes in land use and forestry practices can
lead to net CO2 emissions (e.g., through conversion of forest land to agricultural or urban use) or to a net sink for
CO2 (e.g., through net additions to forest biomass). Although fossil fuel combustion is the greatest source of CO2
emissions, there are 24 additional sources included in the Inventory (Figure ES-5).
Figure ES-5: 2015 Sources of CO2 Emissions (MMT CO2 Eq.)
Fossil Fuel Combustion
Non-Energy Use of Fuels
Iron and Steel Prod. & Metallurgical Coke Prod.
Natural Gas Systems
Cement Production
Petrochemical Production
Lime Production
Other Process Uses of Carbonates
Ammonia Production
Incineration of Waste
Urea Fertilization
Carbon Dioxide Consumption
Liming
Petroleum Systems
Soda Ash Production and Consumption
Aluminum Production
Ferroalloy Production
Titanium Dioxide Production
Glass Production
Urea Consumption for Non-Agricultural Purposes
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and Consumption
Magnesium Production and Processing
5,049
¦
¦
I
¦
I
I
I
<.05
<•05
COz as a Portion of all
Emissions
25
50
75
MMT CO, Eq.
100
125
150
Note: Fossil Fuel Combustion includes electricity generation, which also includes emissions of less than 0.05 MMT CO2 Eq.
from geothermal-based generation.
As the largest source of U.S. greenhouse gas emissions, CO2 from fossil fuel combustion has accounted for
approximately 77 percent of GWP-weighted emissions since 1990. The fundamental factors influencing emissions
13 Hie term "flux" is used to describe the net emissions of greenhouse gases accounting for both the emissions of CO2 to and the
removals of CO2 from the atmosphere. Removal of CO2 from the atmosphere is also referred to as "carbon sequestration."
14 Global CO2 emissions from fossil fuel combustion were taken from International Energy Agency CO: Emissions from Fossil
Fuels Combustion -Highlights IEA (2016). See .
Executive Summary ES-9
-------
1 levels include (1) changes in demand for energy and (2) a general decline in the carbon intensity of fuels combusted
2 for energy in recent years by most sectors of the economy. Between 1990 and 2015, CO2 emissions from fossil fuel
3 combustion increased from 4,740.7 MMT CO2 Eq. to 5,049.2 MMT CO2 Eq., a 6.5 percent total increase over the
4 twenty-six-year period. In addition CO2 emissions from fossil fuel combustion decreased from 2005 levels by 698.0
5 MMT CO2 Eq., a decrease of approximately 12.1 percent between 2005 to 2015. From 2014 to 2015, these
6 emissions decreased by 153.0 MMT CO2 Eq. (2.9 percent).
7 Historically, changes in emissions from fossil fuel combustion have been the dominant factor affecting U. S.
8 emission trends. Changes in CO2 emissions from fossil fuel combustion are influenced by many long-term and
9 short-term factors. Long-term factors include population and economic trends, technological changes, shifting
10 energy fuel choices, and various policies at the national, state, and local level. In the short term, the overall
11 consumption of fossil fuels in the United States fluctuates primarily in response to changes in general economic
12 conditions, energy prices, weather, and the availability of non-fossil alternatives.
13
14 Figure ES-6: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT
15 COz Eq.)
2,500
2,000
3 1,500
0
U
I-
1 1,000
500
0
U.S. Territories Commercial Residential Industrial Transportation Electricity Generation
16
17
18 Figure ES-7: 2015 End-Use Sector Emissions of CO2 from Fossil Fuel Combustion (MMT CO2
19 Eq.)
Relative Contribution by Fuel Type
H Petroleum
¦ Coal
¦ Natural Gas
I Geothermal
2,000"
¦ Direct Fossil Fuel Combustion
Indirect Fossil Fuel Combustion
1,737
1,500-
1,378
1,000-
1,004
889
500-
0
41
U.S. Territories Commercial Residential Industrial Transportation
21 The five major fuel consuming economic sectors contributing to CO2 emissions from fossil fuel combustion are
22 electricity generation transportation industrial, residential, and commercial. Carbon dioxide emissions are produced
23 by the electricity generation sector as they consume fossil fuel to provide electricity to one of the other four sectors.
10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
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.
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: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation
1,496.8
1,N')|.N
1,711.9
1,700.6
1,717.0
1,734.4
1,737.0
Combustion
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Electricity
3.0
4.7
4.3
3.9
4.0
4.1
3.7
Industrial
1,529.2
1,564.6
1,399.6
1,375.7
1,407.0
1,409.0
1,378.3
Combustion
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Electricity
686.7
7 i(S (S
624.7
592.8
594.7
593.2
549.6
Residential
931.4
1,214.1
1,116.2
1,007.8
1,064.6
1,080.1
1,003.8
Combustion
338.3
357.8
325.5
282.5
329.7
345.4
319.6
Electricity
593.0
856.3
790.7
725.3
734.9
734.7
684.3
Commercial
755.4
1.026.N
958.4
897.0
925.5
937.4
888.8
Combustion
217.4
22i s
220.4
196.7
221.0
231.4
225.7
Electricity
538.0
803.3
738.0
700.3
704.5
706.0
663.1
U.S. Territories3
27.9
4'V>
41.5
43.6
43.5
41.2
41.2
Total
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.')
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
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.
Notes: Combustion-related emissions from electricity generation are allocated based on aggregate national
electricity consumption by each end-use sector. Totals may not sum due to independent rounding.
Transportation End-Use Sector. When electricity-related emissions are distributed to economic end-use sectors,
transportation activities accounted for 34.4 percent of U.S. CO2 emissions from fossil fuel combustion in 2015. The
largest sources of transportation CO2 emissions in 2015 were passenger cars (41.9 percent), medium- and heavy-
duty trucks (23.6 percent), light-duty trucks, which include sport utility vehicles, pickup trucks, and minivans (17.5
percent), commercial aircraft (6.9 percent), rail (2.5 percent), other aircraft (2.3 percent), pipelines (2.2 percent), and
ships and boats (1.8 percent). Annex 3.2 presents the total emissions from all transportation and mobile sources,
including CO2, CH4, N20, and HFCs.
In terms of the overall trend, from 1990 to 2015, total transportation CO2 emissions rose by 16 percent due, in large
part, to increased demand for travel. The number of VMT by light-duty motor vehicles (i.e., passenger cars and
light-duty trucks) increased 42 percent from 1990 to 2015, as a result of a confluence of factors including population
growth, economic growth, urban sprawl, and low fuel prices during the beginning of this period. Almost all of the
energy consumed for transportation was supplied by petroleum-based products, with more than half being related to
gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially diesel fuel for freight
trucks and jet fuel for aircraft, accounted for the remainder.
Industrial End-Use Sector. Industrial CO2 emissions, resulting both directly from the combustion of fossil fuels and
indirectly from the generation of electricity that is consumed by industry, accounted for 27 percent of CO2 from
fossil fuel combustion in 2015. Approximately 60 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 declined since 1990. This decline is due to structural changes in the U.S.
Executive Summary ES-11
-------
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
31
32
33
34
35
36
37
38
39
40
41
42
43
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 20 and
18 percent, respectively, of CO2 emissions from fossil fuel combustion in 2015. Both sectors relied heavily on
electricity for meeting energy demands, with 68 and 75 percent, respectively, of their emissions attributable to
electricity consumption for lighting, heating, cooling, and operating appliances. The remaining emissions were due
to the consumption of natural gas and petroleum for heating and cooking. Emissions from the residential and
commercial end-use sectors have increased by 8 percent and 18 percent since 1990, respectively.
Electricity Generation. The United States relies on electricity to meet a significant portion of its energy demands.
Electricity generators consumed 34 percent of total U.S. energy uses from fossil fuels and emitted 38 percent of the
CO2 from fossil fuel combustion in 2015. The type of energy source used to generate electricity is the main factor
influencing emissions. For example, some electricity is generated through non-fossil fuel options such as nuclear,
hydroelectric, or geothermal energy. Including all electricity generation modes, electricity generators relied on coal
for approximately 33 percent of their total energy requirements in 2015.15 In addition, the coal used by electricity
generators accounted for 93 percent of all coal consumed for energy in the United States in 2015.16 Recently, a
decrease in the carbon intensity of fuels consumed to generate electricity has occurred due to a decrease in coal
consumption, and increased natural gas consumption and other generation sources. Including all electricity
generation modes, electricity generators used natural gas for approximately 33 percent of their total energy
requirements in 2015.17 Across the time series, changes in electricity demand and the carbon intensity of fuels used
for electricity generation have a significant impact on CO2 emissions. While emissions from the electric power
sector have increased by approximately 4 percent since 1990, the carbon intensity of the electric power sector, in
terms of CO2 Eq. per QBtu has significantly decreased by 16 percent during that same timeframe.
Other significant CO2 trends included the following:
• Carbon dioxide emissions from non-energy use of fossil fuels increased by 9.4 MMT CO2 Eq. (8.0 percent)
from 1990 through 2015. Emissions from non-energy uses of fossil fuels were 127.0 MMT CO2 Eq. in
2015, which constituted 2.3 percent of total national CO2 emissions, approximately the same proportion as
in 1990.
• Carbon dioxide emissions from iron and steel production and metallurgical coke production have decreased
by 51.8 MMT CO2 Eq. (51.9 percent) from 1990 through2015, due to restructuring of the industry,
technological improvements, and increased scrap steel utilization.
• Carbon dioxide emissions from ammonia production (10.8 MMT CO2 Eq. in 2015) decreased by 2.2 MMT
CO2 Eq. (17.2 percent) since 1990. Ammonia production relies on natural gas as both a feedstock and a
fuel, and as such, market fluctuations and volatility in natural gas prices affect the production of ammonia.
• Total C sequestration (i.e., net CO2 removals) in the LULUCF sector decreased by approximately 16.0
percent between 1990 and 2015. This decrease was primarily due to a decrease in the rate of net C
accumulation in forest C stocks and an increase in emissions from Land Converted to Grassland.
Box ES-2: Use of Ambient Measurements Systems for Validation of Emission Invento
i
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emission
inventories, the emissions and sinks presented in this report are organized by source and sink categories and
calculated using internationally-accepted methods provided by the IPCC.18 Several recent studies have measured
emissions at the national or regional level with results that sometimes differ from EPA's estimate of emissions. EPA
has engaged with researchers on how remote sensing, ambient measurement, and inverse modeling techniques for
greenhouse gas emissions could assist in improving the understanding of inventory estimates. In working with the
15 See .
16 See Table 6.2 Coal Consumption by Sector of EIA 2016.
17 See .
18 See .
12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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research community on ambient measurement and remote sensing techniques to improve national greenhouse gas
inventories, EPA relies upon guidance from the IPCC on the use of measurements and modeling to validate
emission inventories. 19An area of particular interest in EPA's outreach efforts is how ambient measurement data can
be used in a manner consistent with this Inventory report's transparency on its calculation methodologies, and the
ability of these techniques to attribute emissions and removals from remote sensing to anthropogenic sources, as
defined by the IPCC for this report, versus natural sources and sinks.
In an effort to improve the ability to compare the national-level greenhouse gas inventory with measurement results
that may be at other scales, a team at Harvard University along with EPA and other coauthors developed a gridded
inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly temporal resolution,
and detailed scale-dependent error characterization. The Inventory is designed to be consistent with the 1990 to 2014
U.S. EPA hwentory of U.S. Greenhouse Gas Emissions and Sinks estimates for the year 2012, which presents
national totals for different source types.20
Methane Emissions
Methane (CH4) is 25 times as effective as CO2 at trapping heat in the atmosphere (IPCC 2007). Over the last two
hundred and fifty years, the concentration of CH4 in the atmosphere increased by 162 percent (IPCC 2013 and
CDIAC 2016). 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: 2015 Sources of CHU Emissions (MMT CO2 Eq.)
Enteric Fermentation
Natural Gas Systems
Landfills
Manure Management
Coal Mining
Petroleum Systems
Wastewater Treatment
Rice Cultivation
Stationary Combustion
Abandoned Underground Coal Mines
Composting
Mobile Combustion
Field Burning of Agricultural Residues
Petrochemical Production
Ferroalloy Production
Silicon Carbide Production and Consumption
Iron and Steel Prod. & Metallurgical Coke Prod.
Incineration of Waste
I
¦
¦
I
I
< .05
< .05
< .05
< .05
< .05
< .05
0
CH< as a Portion of all
Emissions
9.9%
25
50
75 100
MMT C02 Eq.
125
150
175
Some significant trends in U.S. emissions of CH4 include the following:
19 See .
20 See.
Executive Summary ES-13
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46
• Enteric fermentation is the largest anthropogenic source of CH4 emissions in the United States. In 2015,
enteric fermentation CH4 emissions were 166.5 MMT CO2 Eq. (25.4 percent of total CH4 emissions),
which represents an increase of 2.4 MMT CO2 Eq. (1.5 percent) since 1990. This increase in emissions
from 1990 to 2015 generally follows the increasing trends in cattle populations. From 1990 to 1995,
emissions increased and then generally decreased from 1996 to 2004, mainly due to fluctuations in beef
cattle populations and increased digestibility of feed for feedlot cattle. Emissions increased from 2005 to
2007, as both dairy and beef populations increased. Research indicates that the feed digestibility of dairy
cow diets decreased during this period. Emissions decreased again from 2008 to 2015 as beef cattle
populations again decreased.
• Natural gas systems were the second largest anthropogenic source category of CH4 emissions in the United
States in 2015 with 160.0 MMT CO2 Eq. of CH4 emitted into the atmosphere. Those emissions have
decreased by 36.5 MMT CO2 Eq. (18.6 percent) since 1990. The decrease in CH4 emissions is largely due
to the decrease in emissions from transmission, storage, and distribution. The decrease in transmission and
storage emissions is largely due to reduced compressor station emissions (including emissions from
compressors and fugitives). The decrease in distribution emissions is largely attributed to increased use of
plastic piping, which has lower emissions than other pipe materials, and station upgrades at metering and
regulating (M&R) stations.
• Landfills are the third largest anthropogenic source of CH4 emissions in the United States (115.7 MMT
CO2 Eq.), accounting for 17.7 percent of total CH4 emissions in 2015. From 1990 to 2015, CH4 emissions
from landfills decreased by 63.8 MMT CO2 Eq. (35.6 percent), with small increases occurring in some
interim years. This downward trend in emissions coincided with increased landfill gas collection and
control systems, and a reduction of decomposable materials (i.e., paper and paperboard, food scraps, and
yard trimmings) discarded in MSW landfills over the time series,21 which has more than offset the
additional CH4 emissions resulting from an increase in the amount of municipal solid waste landfilled.
• Methane emissions from manure management, the fourth largest anthropogenic source of CH4 emissions in
the United States, increased by 78.3 percent since 1990, from 37.2 MMT CO2 Eq. in 1990 to 66.3 MMT
CO2 Eq. in 2015. 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.
• Methane emissions from petroleum systems in the United States (41.5 MMT CO2 Eq.) accounted for 6.3
percent of total CH4 emissions in 2015. From 1990 to 2015, CH4 emissions from petroleum systems
decreased by 16.8 MMT CO2 Eq. (or 28.8 percent). Production segment CH4 emissions have decreased by
around 8 percent from 2014 levels, primarily due to decreases in emissions from associated gas venting and
flaring.
Nitrous Oxide Emissions
Nitrous oxide (N20) 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 N20
emissions are much lower than CO2 emissions, N20 is approximately 300 times more powerful than CO2 at trapping
heat in the atmosphere (IPCC 2007). Since 1750, the global atmospheric concentration of N20 has risen by
approximately 21 percent (IPCC 2013 and CDIAC 2016). The main anthropogenic activities producing N20 in the
United States are agricultural soil management, stationary fuel combustion, fuel combustion in motor vehicles,
manure management, and nitric acid production (see Figure ES-9).
21 Carbon dioxide emissions from landfills are not included specifically in summing waste sector totals. Net carbon fluxes from
changes in biogenic carbon reservoirs and disposed wood products are accounted for in the estimates for LULUCF.
14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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32
NiO as a Portion of all
Emissions
Figure ES-9: 2015 Sources of N2O Emissions (MMT CO2 Eq.)
Management | 251
Stationary Combustion |
Manure Management
Mobile Combustion
Nitric Acid Production
Wastewater Treatment
AdipicAcid Production
N2O from Product Uses
Composting
Incineration of Waste
Semiconductor Manufacture
Field Burning of Agricultural Residues
10 15 20 25
MMT C02 Eq.
Some significant trends in U.S. emissions of N20 include the following:
• Agricultural soils accounted for approximately 75.0 percent of N20 emissions and 3.8 percent of total
emissions in the United States in 2015. Estimated emissions from this source in 2015 were 251.3 MMT
CO2 Eq. Annual N20 emissions from agricultural soils fluctuated between 1990 and 2015, although overall
emissions were 2.0 percent lower in 2015 than in 1990. Year-to-year fluctuations are largely a reflection of
annual variation in weather patterns, synthetic fertilizer use, and crop production.
• Nitrous oxide emissions from stationary combustion increased 11.2 MMT CO2 Eq. (94.0 percent) from
1990 through 2015. Nitrous oxide emissions from this source increased primarily as a result of an increase
in the number of coal fluidized bed boilers in the electric power sector.
• In 2015, total N20 emissions from manure management were estimated to be 17.7 MMT CO2 Eq.;
emissions were 14.0 MMT CO2 Eq. in 1990. These values include both direct and indirect N20 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 N20 emissions showed a 26.6 percent increase from 1990 to 2015 and a 1.1
percent increase from 2014 through 2015.
• Nitrous oxide emissions from mobile combustion decreased by 25.8 MMT CO2 Eq. (62.7 percent) from
1990 through 2015, primarily as a result of N20 national emission control standards and emission control
technologies for on-road vehicles.
• Nitrous oxide emissions from adipic acid production were 4.3 MMT CO2 Eq. in 2015, and have decreased
significantly since 1990 due to both the widespread installation of pollution control measures in the late
1990s and plant idling in the late 2000s. Emissions from adipic acid production have decreased by 72.0
percent since 1990 and by 74.8 percent since a peak in 1995.
HFC, PFC, SF6, and NF3 Emissions
Hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) are families of synthetic chemicals that are used as
alternatives to ozone depleting substances (ODS), which are being phased out under the Montreal Protocol and
Clean Air Act Amendments of 1990. Hydrofluorocarbons and PFCs do not deplete the stratospheric ozone layer,
and are therefore acceptable alternatives under the Montreal Protocol on Substances that Deplete the Ozone Layer.
These compounds, however, along with SF6 and NF3, are potent greenhouse gases. In addition to having high global
warming potentials, SF6 and PFCs have extremely long atmospheric lifetimes, resulting in their essentially
Executive Summary ES-15
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irreversible accumulation in the atmosphere once emitted. Sulfur hexafluoride is the most potent greenhouse gas the
IPCC lias evaluated (IPCC 2013).
Other emissive sources of these gases include HCFC-22 production electrical transmission and distribution systems,
semiconductor manufacturing, aluminum production and magnesium production and processing (see Figure ES-10).
Figure ES-10: 2015 Sources of HFCs, PFCs, SFe, and NF3 Emissions (MMT CO2 Eq.)
Substitution of Ozone Depleting Substances
HCFC-22 Production
Semiconductor Manufacture
Electrical Transmission and Distribution
Aluminum Production
Magnesium Production and Processing
169
HFCs, PFCs, SFo, and NF, as a
Portion of all Emissions
2.8%
10
MMT C02 Eq.
20
Some significant trends in U.S. HFC, PFC, SF6, and NF3 emissions include the following:
• Emissions resulting from the substitution of ODS (e.g., chlorofluorocarbons [CFCs]) have been
consistently increasing, from small amounts in 1990 to 168.6 MMT CO2 Eq. in 2015. This increase was in
large part the result of efforts to phase out CFCs and other ODS in the United States. In the short term, this
trend is expected to continue, and will likely continue over the next decade as hydrochlorofluorocarbons
(HCFCs), which are interim substitutes in many applications, are themselves phased out under the
provisions of the Copenhagen Amendments to the Montreal Protocol.
• GWP-weighted PFC, HFC, SF6, and NF3 emissions from semiconductor manufacture have increased by
34.3 percent from 1990 to 2015, due to industrial growth and the adoption of emission reduction
technologies. Within that time span, emissions peaked in 1999, the initial year of EPA's PFC
Reduction/Climate Partnership for the Semiconductor Industry, but have since declined to 4.8 MMT CO2
Eq. in 2015 (a 47.1 percent decrease relative to 1999).
• Sulfur hexafluoride emissions from electric power transmission and distribution systems decreased by 82.0
percent (19.0 MMT CO2 Eq.) from 1990 to 2015. There are two potential causes for this decrease: (1) a
sharp increase in the price of SF6 during the 1990s and (2) a growing awareness of the enviromnental
impact of SF6 emissions through programs such as EPA's SF6 Emission Reduction Partnership for Electric
Power Systems.
• Perfluorocarbon emissions from aluminum production decreased by 90.7 percent (19.5 MMT CO2 Eq.)
from 1990 to 2015. 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.
16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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ES.3 Overview of Sector Emissions and Trends
In accordance with the UNFCCC decision to set the 2006IPCC Guidelines for National Greenhouse Gas
Inventories (IPCC 2006) as the standard for Annex I countries at the Nineteenth Conference of the Parties
(UNFCCC 2014), Figure ES-11 and Table ES-4 aggregate emissions and sinks by the sectors defined by those
guidelines. Ov er the twenty-six-year period of 1990 to 2015, total emissions in the Energy, Industrial Processes and
Product Use, and Agriculture grew by 215.7 MMT CO2 Eq. (4.0 percent), 36.7 MMT CO2 Eq. (10.9 percent), and
27.0 MMT CO2 Eq. (5.5 percent), respectively. Over the same period, total emissions in the Waste sector decreased
by 59.9 MMT CO2 Eq. (30.1 percent) and estimates of net C sequestration in the Land Use, Land-Use Change, and
Forestry' (LULUCF) sector (magnitude of emissions plus CO2 removals from all LULUCF source categories)
decreased by 82.7 MMT CO2 Eq. (18.4 percent).
Figure ES-11:
Eq.)
U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2
7,500-
7,000-
6,500-
6,000-
5,500-
5,000-
. 4,500-
? 4,000 |
8 3,500-
| 3,000-
2,500-
2,000-
1,500-
1,000-
500-
0-
-500-
Industrial Processes and Product Use
Agriculture
Waste
LULUCF (emissions)
Energy
Land Use, Land-Use Cha
01
Table ES-4: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (MMT CO2 Eq.)
Chapter/IPCC Sector
1990
2005
2011
2012
2013
2014
2015
Energy
5,333.8
6,279.4
5,721.8
5,506.9
5,659.3
5,703.2
5,549.4
Fossil Fuel Combustion
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Natural Gas Systems
234.3
192.2
189.3
190.5
196.4
203.2
202.4
Non-Energy Use of Fuels
117.7
138.3
108.5
105.5
122.0
117.2
127.0
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Petroleum Systems
61.8
52.0
54.3
52.3
50.3
48.5
45.1
Stationary Combustion
20.4
27.6
28.4
28.0
30.9
31.5
30.1
Mobile Combustion
46.9
38.6
25.1
22.6
20.6
18.6
17.4
Incineration of Waste
8.4
12.9
10.9
10.7
10.7
10.9
11.0
Abandoned Underground Coal Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Industrial Processes and Product Use
338.3
351.6
369.7
359.5
362.4
378.1
375.1
Executive Summary ES-17
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Substitution of Ozone Depleting
Substances
0.3
99.8
145.4
150.2
154.7
161.3
168.6
Iron and Steel Production &
Metallurgical Coke Production
99.7
66.6
59.9
54.2
52.2
57.5
47.9
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.5
27.0
26.4
26.6
26.5
26.6
28.2
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
Nitric Acid Production
12.1
117
10.9
10.5
10.7
10.9
11.6
Other Process Uses of Carbonates
4.9
6.'
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
HCFC-22 Production
46.1
20.0
8.8
5.5
4.1
5.0
5.0
Semiconductor Manufacture
3.6
4"
4.9
4.5
4.1
5.0
5.0
Aluminum Production
28.3
7.6
6.8
6.4
6.2
5.4
4.8
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Electrical Transmission and
Distribution
23.1
8.'
6.0
4.8
4.6
4.8
4.2
Soda Ash Production and
Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-
Agricultural Purposes
3.8
3 ~
4.0
4.4
4.0
1.4
1.1
Magnesium Production and
Processing
5.2
2"
2.8
1.7
1.5
1.1
1.0
Phosphoric Acid Production
1.5
17
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Agriculture
495.3
526.4
541.9
525.9
516.9
514.7
522.3
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Manure Management
51.1
72.9
80.4
83.2
80.8
80.4
84.0
Rice Cultivation
16.0
16"
14.1
11.3
11.3
11.4
11.2
Urea Fertilization
2.4
37
4.1
4.3
4.5
4.8
5.0
Liming
4.7
47
3.9
6.0
3.9
3.6
3.8
Field Burning of Agricultural
Residues
0.3
07
0.4
0.4
0.4
0.4
0.4
Waste
199.3
158.2
142.6
144.4
140.4
140.2
139.4
Landfills
179.6
1347
119.0
120.8
116.7
116.6
115.7
Wastewater Treatment
19.1
20.4
20.1
19.9
19.8
19.7
19.7
Composting
0.7
37
3.5
3.7
3.9
4.0
4.0
Total Emissions3
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
Land Use, Land-Use Change, and
Forestry
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
Forest Land
(785.0)
(730.7)
(734.8)
(724.6)
(734.5)
(732.8)
(729.7)
Croplandb
59.8
16.1
16.2
13.9
12.9
13.8
14.5
Grassland15
241.2
329.9
286.0
273.6
302.4
302.9
302.3
Wetlands
(4.0)
(5.3)
(4.1)
(4.2)
(4.3)
(4.2)
(4.3)
Settlements
39.0
74"
61.6
53.7
53.1
51.6
50.7
Net Emissions (Sources and Sinks)0
5,917.6
7,000.3
6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
a Total emissions without LULUCF.
b Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for
18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to
Grassland sections in the LULUCF chapter of this report.
c Total emissions with LULUCF.
Notes: 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, and the use of fossil fuels for non-energy purposes.
Energy-related activities, primarily fossil fuel combustion, accounted for the vast majority of U.S. CO2 emissions for
the period of 1990 through 2015. In 2015, approximately 82 percent of the energy consumed in the United States (on
a Btu basis) was produced through the combustion of fossil fuels. The remaining 18 percent came from other energy
sources such as hydropower, biomass, nuclear, wind, and solar energy (see Figure ES-12). Energy-related activities
are also responsible for CH4 and N20 emissions (42 percent and 12 percent of total U.S. emissions of each gas,
respectively). Overall, emission sources in the Energy chapter account for a combined 84.3 percent of total U.S.
greenhouse gas emissions in 2015.
Figure ES-12: 2015 U.S. Energy Consumption by Energy Source (Percent)
8.6%
Nuclear Electric Power
9.7%
Renewable Energy
36.6%
Petroleum
16.2%
Coal
29.0%
Natural Gas
Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) chapter includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products.
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 N20. These processes include iron and steel production and metallurgical coke production cement
production ammonia production urea consumption lime production, other process uses of carbonates (e.g., flux
stone, flue gas desulfurization and glass manufacturing), soda ash production and consumption, titanium dioxide
production phosphoric acid production, ferroalloy production CO2 consumption silicon carbide production and
consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production, lead
production zinc production and N20 from product uses. Industrial processes also release HFCs, PFCs, SF6 and NF3
and other fluorinated compounds. In addition to the use of HFCs and some PFCs as ODS substitutes, HFCs, PFCs,
SF6, NF3, and other fluorinated compounds are employed and emitted by a number of other industrial sources in the
United States. These industries include aluminum production, HCFC-22 production, semiconductor manufacture,
electric power transmission and distribution and magnesium metal production and processing. Overall, emission
sources in the Industrial Process and Product Use chapter account for 5.7 percent of U.S. greenhouse gas emissions
in 2015.
Executive Summary ES-19
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38
Agriculture
The Agriculture chapter contains anthropogenic emissions from agricultural activities (except fuel combustion,
which is addressed in the Energy chapter, and agricultural CO2 fluxes, which are addressed in the Land Use, Land-
Use Change, and Forestry chapter). Agricultural activities contribute directly to emissions of greenhouse gases
through a variety of processes, including the following source categories: enteric fermentation in domestic livestock,
livestock manure management, rice cultivation, agricultural soil management, liming, urea fertilization, and field
burning of agricultural residues. CO2, CH4 and N20 were the primary greenhouse gases emitted by agricultural
activities. CO2 emissions from the application of crushed limestone and dolomite (i.e., soil liming) and urea
fertilization represented 0.2 percent of total CO2 emissions from anthropogenic activities. CH4 emissions from
enteric fermentation and manure management represented 25.4 percent and 10.1 percent of total CH4 emissions from
anthropogenic activities, respectively, in 2015. Agricultural soil management activities such as fertilizer application
and other cropping practices were the largest source of U.S. N20 emissions in 2015, accounting for 75.0 percent. In
2015, emission sources accounted for in the Agricultural chapters were responsible for 7.9 percent of total U.S.
greenhouse gas emissions.
Land Use, Land-Use Change, and Forestry
The Land Use, Land-Use Change, and Forestry chapter contains emissions of CH4 and N20, and emissions and
removals of CO2 from managed lands in the United States. Overall, managed land is a net sink for CO2 (C
sequestration) in the United States. The drivers of fluxes on managed lands include, for example, forest management
practices, tree planting in urban areas, the management of agricultural soils, the landfilling of yard trimmings and
food scraps, and activities that cause changes in C stocks in coastal wetlands. The main drivers for net forest
sequestration include net forest growth and increasing forest area, as well as a net accumulation of C stocks in
harvested wood pools. The net sequestration in Settlements Remaining Settlements, which occurs predominantly
from urban forests and landfilled yard trimmings and food scraps, is a result of net tree growth and increased urban
forest size, as well as long-term accumulation of yard trimmings and food scraps carbon in landfills.
Forest Land Remaining Forest Land (including vegetation, soils, and harvested wood) represented the largest
carbon sink from LULUCF, accounting for 77 percent of total 2015 negative C fluxes; Settlements Remaining
Settlements (urban trees and landfilled yard trimmings and food scraps) accounted for 12 percent; Land Converted to
Forest Land accounted for 9 percent; and Cropland Remaining Cropland, Wetlands Remaining Wetlands, and Land
Converted to Wetlands accounted for 3 percent of the total negative C fluxes in 2015. Conversely, Land Converted
to Grassland represented the largest carbon source from LULUCF, accounting for 61 percent of total 2015 positive
C fluxes, while Land Converted to Settlements accounted for 31 percent. Land Converted to Cropland accounted for
6 percent. Grassland Remaining Grassland accounted for 2 percent, and settlement soils in Settlements Remaining
Settlements accounted for less than 0.5 percent of the total positive C fluxes in 2015. Overall, positive C fluxes
totaled 481.6 MMT CO2 Eq. in 2015, while negative C fluxes totaled 868.5 MMT CO2 Eq. in 2015.
The LULUCF sector in 2015 resulted in a net increase in C stocks (i.e., net CO2 removals) of 386.8 MMT CO2 Eq.
(Table ES-5).22 This represents an offset of 5.9 percent of total (i.e., gross) greenhouse gas emissions in 2015.
Emissions from LULUCF activities in 2015 are 20.4 MMT CO2 Eq. and represent 0.3 percent of total greenhouse
gas emissions.23 Between 1990 and 2015, total C sequestration in the LULUCF sector decreased by 16.0 percent,
22 LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land
Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements,
and Land Converted to Settlements.
23 LULUCF emissions include the CO2, CH4, andN20 emissions from Peatlands Remaining Peatlands\ CH4 andN20 emissions
reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal Wetlands
Remaining Coastal Wetlands; CE[4 emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from Forest Soils and
Settlement Soils.
20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
primarily due to a decrease in the rate of net C accumulation in forests and an increase in emissions from Land
Converted to Grassland,24
Carbon dioxide removals are presented in Table ES-5 along with CO2, CH4, and N20 emissions for LULUCF source
categories. Lands undergoing peat extraction (i.e., Peatlands Remaining Peatlands) resulted in CO2 emissions of 0.8
MMT CO2 Eq. (763 kt of CO2). Forest fires were the largest source of CH4 emissions from LULUCF in 2015,
totaling 7.3 MMT CO2 Eq. (292 kt of CH4). Coastal Wetlands Remaining Coastal Wetlands resulted in CH4
emissions of 3.5 MMT CO2 Eq. (141 kt of CH4). Grassland fires resulted in CH4 emissions of 0.4 MMT CO2 Eq. (16
kt of CH4). Peatlands Remaining Peatlands and Land Converted to Wetlands resulted in CH4 emissions of less than
0.05 MMT C02 Eq.
Forest fires were also the largest source of N20 emissions from LULUCF in 2015, totaling 4.8 MMT CO2 Eq. (16 kt
of N20). Nitrous oxide emissions from fertilizer application to settlement soils in 2015 totaled to 2.6 MMT CO2 Eq.
(9 kt of N20). This represents an increase of 81.5 percent since 1990. Additionally, the application of synthetic
fertilizers to forest soils in 2015 resulted in N20 emissions of 0.5 MMT CO2 Eq. (2 kt of N20). Nitrous oxide
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. Grassland fires resulted in N20 emissions of 0.4 MMT CO2 Eq. (1 kt
of N2O). Coastal Wetlands Remaining Coastal Wetlands resulted in N2O emissions of 0.1 MMT CO2 Eq. (0.5 kt of
N2O), and Peatlands Remaining Peatlands resulted in N2O emissions of less than 0.05 MMT CO2 Eq.
Table ES-5: U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land Use, Land-
Use Change, and Forestry (MMT CO2 Eq.)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Net CO2 Fluxa
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
Forest Land Remaining Forest Landb
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
Land Converted to Forest Land
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Cropland Remaining Cropland0
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Land Converted to Cropland0
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Grassland Remaining Grassland0
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Land Converted to Grassland0
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Wetlands Remaining Wetlands
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
Land Converted to Wetlands
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Remaining Settlements
(86.2)
(91.4)
(98.7)
(99.2)
(99.8)
(101.2)
(102.1)
Land Converted to Settlements
123.8
163.6
157.6
150.2
150.2
150.2
150.2
CO2
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
1.1
1.1
0.9
0.8
0.8
0.8
0.8
CH4
6.7
13.3
11.2
14.9
11.0
11.2
11.2
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
3.2
9.4
6.8
10.8
7.2
7.3
7.3
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.8
0.6
0.2
0.4
0.4
Land Converted to Wetlands: Land
Converted to Coastal Wetlands
+
+
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
N2O
3.8
9.6
8.6
11.0
8.1
8.4
8.4
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
2.1
6.2
4.5
7.1
4.7
4.8
4.8
Settlements Remaining Settlements:
N2O Fluxes from Settlement Soils'1
1.4
2.5
2.6
2.7
2.6
2.6
2.6
24 Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014, 2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland sections
in the LULUCF chapter of this report.
Executive Summary ES-21
-------
Forest Land Remaining Forest Land:
N2O Fluxes from Forest Soilse
0.1
0.5
0.5
0.5
0.5
0.5
0.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.9
0.6
0.2
0.4
0.4
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
0.1
+
0.2
+
0.1
+
0.1
+
0.1
+
0.1
+
0.1
+
LULUCF Emissions'
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change3
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Total®
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements
Remaining Settlements, and Land Converted to Settlements.
b Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
c Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014, 2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, said Land Converted to Grassland
sections in the LULUCF chapter of this report.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to
Settlements.
e Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to
Forest Land.
f LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands, CH4 and N2O emissions
reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal Wetlands
Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from Forest Soils
and Settlement Soils.
8 The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
1 Waste
2 The Waste chapter contains emissions from waste management activities (except incineration of waste, which is
3 addressed in the Energy chapter). Landfills were the largest source of anthropogenic greenhouse gas emissions in the
4 Waste chapter, accounting for 83.0 percent of this chapter's emissions, and 17.7 percent of total U.S. CH4
5 emissions.25 Additionally, wastewater treatment accounts for 14.2 percent of Waste emissions, 2.3 percent of U.S.
6 CH4 emissions, and 1.5 percent of U.S. N20 emissions. Emissions of CH4 and N20 from composting are also
7 accounted for in this chapter, generating emissions of 2.1 MMT CO2 Eq. and 1.9 MMT CO2 Eq., respectively.
8 Overall, emission sources accounted for in the Waste chapter generated 2.1 percent of total U.S. greenhouse gas
9 emissions in 2015.
10 ES.4 Other Information
11 Emissions by Economic Sector
12 Throughout the Inventory of U.S. Greenhouse Gas Emissions and Sinks report, emission estimates are grouped into
13 five sectors (i.e., chapters) defined by the IPCC: Energy; Industrial Processes and Product Use; Agriculture;
14 LULUCF; and Waste. While it is important to use this characterization for consistency with UNFCCC reporting
25 Landfills also store carbon, due to incomplete degradation of organic materials such as harvest wood products, yard
trimmings, and food scraps, as described in the Land-Use, Land-Use Change, and Forestry chapter of the Inventory report.
22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 guidelines, it is also useful to characterize emissions according to commonly used economic sector categories:
2 residential, commercial, industry, transportation electricity generation, agriculture, and U.S. Territories.
3 Table ES-6 summarizes emissions from each of these economic sectors, and Figure ES-13 shows the trend in
4 emissions by sector from 1990 to 2015.
5 Figure ES-13: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.)
2,500-
Electric Power Industry
2,000-
Transportation
Industry
S i'500"
1,000-
Agriculture
Commercial (Red)
500-
Residential (Blue)
>sO
cn
o
o
o
-------
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
greenhouse gas emissions were contributed by, in order of magnitude, the agriculture, commercial, and residential
sectors, plus emissions from U.S. Territories. Activities related to agriculture accounted for 9 percent of U.S.
emissions; unlike other economic sectors, agricultural sector emissions were dominated by N20 emissions from
agricultural soil management and CH4 emissions from enteric fermentation. The commercial and residential sectors
accounted for 6 percent and 6 percent of emissions, respectively, 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, landfilling of yard trimmings, and changes in C stocks in coastal
wetlands.
Electricity is ultimately consumed in the economic sectors described above. Table ES-7 presents greenhouse gas
emissions from economic sectors with emissions related to electricity generation distributed into end-use categories
(i.e., emissions from electricity generation are allocated to the economic sectors in which the electricity is
consumed). To distribute electricity emissions among end-use sectors, emissions from the source categories assigned
to electricity generation were allocated to the residential, commercial, industry, transportation, and agriculture
economic sectors according to retail sales of electricity.26 These source categories include CO2 from fossil fuel
combustion and the use of limestone and dolomite for flue gas desulfurization, CO2 and N20 from incineration of
waste, CH4 and N20 from stationary sources, and SF6 from electrical transmission and distribution systems.
When emissions from electricity are distributed among these sectors, industrial activities and transportation account
for the largest shares of U.S. greenhouse gas emissions (30 percent and 27 percent, respectively) in 2015. The
residential and commercial sectors contributed the next largest shares of total U.S. greenhouse gas emissions in
2015. 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). 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 2015.
Table ES-7: U.S. Greenhouse Gas Emissions by Economic Sector with Electricity-Related
Emissions Distributed (MMT CO2 Eq.)
Implied Sectors
1990
2005
2011
2012
2013
2014
2015
Industry
2,297.1
2,180.3
1,973.5
1,926.0
1,976.9
1,986.7
1,956.2
Transportation
1,554.4
2,005.9
1,804.3
1,784.7
1,794.3
1,807.5
1,807.5
Commercial
968.4
1,217.6
1,158.1
1,100.6
1,128.5
1,142.7
1,094.0
Residential
951.5
1,241.3
1,161.5
1,057.2
1,122.0
1,143.7
1,071.5
Agriculture
561.5
612.4
633.1
620.6
609.9
610.8
612.0
U.S. Territories
33."
58.2
45.4
47.6
47.5
44.9
44.9
Total Emissions
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
LULUCF Sector Net Total3-"
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
Net Emissions (Sources and Sinks)
5,917.6
7,000.3
6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
a The LULUCF Sector Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals of
CO2 (i.e., sinks or negative emissions) from the atmosphere.
b Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland sections
in the LULUCF chapter of this report.
Notes: Emissions from electricity generation are allocated based on aggregate electricity consumption in each end-use sector.
Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
26 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.
24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Figure ES-14: U.S. Greenhouse Gas Emissions with Electricity-Related Emissions Distributed
to Economic Sectors (MMT CO2 Eq.)
2,500
Industry (Green)
2,000
Transportation (Purple^
"i 1,500
o
u
Commercial (Red)
z
Residential (Blue)
1,000
Agriculture
500
o
cn
v£>
CT«
CJi
O
O
O
O
O
00
o
o
o
T—I
o
o
-------
1
2 Figure ES-15: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
3 Product (GDP)
180-
Real GDP
170-
160-
150-
_ 140-
?»
I 120-
i-H
S 110-
"O
a 100-
Population
90-
Emissions per capita
so-
70-
Emissions per $GDP
60-
co
(T>
O
o
o
VO
o
o
CO
o
o
o
ro
o
o
s
o
in
o
o
rv
o
o
m
o
o
o
o
o
o
o
o
4
5
6 Source: BEA (2016), U.S. Census Bureau (2016), and emission estimates in this report.
7
s Key Categories
9 The 2006IPCC Guidelines (IPCC 2006) defines a key category as a "[category] that is prioritized within the
10 national inventory system because its estimate lias a significant influence on a country's total inventory of
11 greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals."27 By
12 definition key categories are sources or sinks that have the greatest contribution to the absolute overall level of
13 national emissions in any of the years covered by the time series. In addition when an entire time series of emission
14 estimates is prepared, a thorough investigation of key categories must also account for the influence of trends of
15 individual source and sink categories. Finally, a qualitative evaluation of key categories should be performed, in
16 order to capture any key categories that were not identified in either of the quantitative analyses.
17 Figure ES-16 presents 2015 emission estimates for the key categories as defined by a level analysis (i.e., the
18 contribution of each source or sink category to the total inventory level). The UNFCCC reporting guidelines request
19 that key category analyses be reported at an appropriate level of disaggregation which may lead to source and sink
20 category names which differ from those used elsewhere in the Inventory report. For more information regarding key
21 categories, see Section 1.5 - Key Categories and Annex 1.
27 See Chapter 4 "Methodological Choice and Identification of Key Categories" in IPCC (2006). See
26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
Figure ES-16: 2015 Key Categories (MMT CO2 Eq.)
C02 Emissions from Mobile Combustion: Road
C02 Emissions from Stationary Combustion - Coal - Electricity Generation
C02 Emissions from Stationary Combustion - Gas - Electricity Generation
C02 Emissions from Stationary Combustion - Gas - Industrial
C02 Emissions from Stationary Combustion - Oil - Industrial
C02 Emissions from Stationary Combustion - Gas - Residential
Direct N20 Emissions from Agricultural Soil Management
C02 Emissions from Stationary Combustion - Gas - Commercial
Emissions from Substitutes for Ozone Depleting Substances
CH4 Emissions from Enteric Fermentation
CH4 Emissions from Natural Gas Systems
C02 Emissions from Mobile Combustion: Aviation
C02 Emissions from Non-Energy Use of Fuels
CH4 Emissions from Landfills
C02 Emissions from Mobile Combustion: Other
C02 Emissions from Stationary Combustion - Oil - Residential
CH4 Emissions from Manure Management
C02 Emissions from Stationary Combustion - Coal - Industrial
Fugitive Emissions from Coal Mining
C02 Emissions from Iron and Steel Production & Metallurgical Coke Production
C02 Emissions from Stationary Combustion - Oil - Commercial
C02 Emissions from Natural Gas Systems
CH4 Emissions from Petroleum Systems
C02 Emissions from Cement Production
Indirect N20 Emissions from Applied Nitrogen
C02 Emissions from Stationary Combustion - Oil - U.S. Territories
Non-C02 Emissions from Stationary Combustion - Electricity Generation
0 200 400 600 800 1,000 1,200 1,400
MMT CO* Eq.
2
3 Note: For a complete discussion of the key category analysis, see Annex 1. Blue bars indicate either an Approach 1, or Approach
4 1 and Approach 2 level assessment key category. Gray bars indicate solely an Approach 2 level assessment key category.
5 Quality Assurance and Quality Control (QA/QC)
6 The United States seeks to continually improve the quality, transparency, and credibility of the Inventory of U.S.
7 Greenhouse Gas Emissions and Sinks. To assist in these efforts, the United States implemented a systematic
8 approach to QA/QC. The procedures followed for the Inventory have been formalized in accordance with the
9 Quality Assurance,'Duality Control and Uncertainty Management Plan (QA/QC Management Plan) for the
10 Inventory and the UNFCCC reporting guidelines.
11 Uncertainty Analysis of Emission Estimates
12 Uncertainty estimates are an essential element of a complete inventory of greenhouse gas emissions and removals,
13 because they help to prioritize future work and improve overall quality. Some of the current estimates, such as those
14 for CO: emissions from energy-related activities, are considered to have low uncertainties because the amount of
15 CO2 emitted is directly related to the amount of fuel consumed, the fraction of the fuel that is oxidized, and the
16 carbon content of the fuel. For some other categories of emissions, however, a lack of or missing data,
17 representativeness of data to real world conditions associated with the emissions/removal activities in United States,
18 sampling errors, and measurement errors are example factors that contribute to the uncertainty associated with the
19 estimates presented. Recognizing the benefit of conducting an uncertainty analysis, the UNFCCC reporting
20 guidelines follow the recommendations of the 2006IPCC Guidelines (IPCC 2006), Volume 1. Chapter 3 and require
21 that countries provide single estimates of uncertainty for source and sink categories.
Key Categories as a Portion of All
Emissions
Executive Summary ES-27
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
In addition to quantitative uncertainty assessments provided in accordance with UNFCCC reporting guidelines, 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.
ecaicuiations ot inventory tstimate
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows the 2006IPCC
Guidelines (IPCC 2006), which states, "Both methodological changes and refinements over time are an essential
part of improving inventory quality. It is good practice to change or refine methods when: available data have
changed; the previously used method is not consistent with the IPCC guidelines for that category; a category has
become key; the previously used method is insufficient to reflect mitigation activities in a transparent manner; the
capacity for inventory preparation has increased; new inventory methods become available; and for correction of
errors." In general, recalculations are made to the U.S. greenhouse gas emission estimates either to incorporate new
methodologies or, most commonly, to update recent historical data.
In each Inventory report, the results of all methodology changes and historical data updates are presented in the
Recalculations and Improvements chapter; detailed descriptions of each recalculation are contained within each
source's description contained in the report, if applicable. In general, when methodological changes have been
implemented, the entire time series (in the case of the most recent Inventory report, 1990 through 2014) 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.
28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
i. Introduction
This report presents estimates by the United States government of U.S. anthropogenic greenhouse gas emissions and
sinks for the years 1990 through 2015. 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 basis1 in order to
show the relative contribution of each gas to global average radiative forcing. This report also discusses the methods
and data used to calculate these emission estimates.
In 1992, the United States signed and ratified the United Nations Framework Convention on Climate Change
(UNFCCC). As stated in Article 2 of the UNFCCC, "The ultimate objective of this Convention and any related legal
instruments that the Conference of the Parties may adopt is to achieve, in accordance with the relevant provisions of
the Convention, stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent
dangerous anthropogenic interference with the climate system. Such a level should be achieved within a time-frame
sufficient to allow ecosystems to adapt naturally to climate change, to ensure that food production is not threatened
and to enable economic development to proceed in a sustainable manner."2'3
Parties to the Convention, by ratifying, "shall develop, periodically update, publish and make available... national
inventories of anthropogenic emissions by sources and removals by sinks of all greenhouse gases not controlled by
the Montreal Protocol, using comparable methodologies.. ."4 The United States views this report as an opportunity
to fulfill these commitments under the UNFCCC.
In 1988, preceding the creation of the UNFCCC, the World Meteorological Organization (WMO) and the United
Nations Environment Programme (UNEP) jointly established the Intergovernmental Panel on Climate Change
(IPCC). The role of the IPCC is to assess on a comprehensive, objective, open and transparent basis the scientific,
technical and socio-economic information relevant to understanding the scientific basis of risk of human-induced
climate change, its potential impacts and options for adaptation and mitigation (IPCC 2014). Under Working Group
1 of the IPCC, nearly 140 scientists and national experts from more than thirty countries collaborated in the creation
of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC/UNEP/OECD/IEA 1997) to
ensure that the emission inventories submitted to the UNFCCC are consistent and comparable between nations. The
IPCC Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories and the
IPCC Good Practice Guidance for Land Use, Land-Use Change, and Forestry further expanded upon the
methodologies in the Revised 1996 IPCC Guidelines. In 2006, the IPCC accepted the 2006 Guidelines for National
Greenhouse Gas Inventories at its Twenty-Fifth Session (Mauritius, April 2006). The 2006 IPCC Guidelines built
1 More information provided in "Global Warming Potentials" section of this chapter on the use of IPCC Fourth Assessment
Report (AR4) GWP values.
2 The term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of human
activities or are the result of natural processes that have been affected by human activities (IPCC 2006).
3 Article 2 of the Framework Convention on Climate Change published by the UNEP/WMO Information Unit on Climate
Change. See . (UNEP/WMO 2000)
4 Article 4(1)(a) of the United Nations Framework Convention on Climate Change (also identified in Article 12). Subsequent
decisions by the Conference of the Parties elaborated the role of Annex I Parties in preparing national inventories. See
.
Introduction 1-1
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upon the previous bodies of work and include new sources and gases ".. .as well as updates to the previously
published methods whenever scientific and technical knowledge have improved since the previous guidelines were
issued. The UNFCCC adopted the 2006IPCC Guidelines as the standard methodological approach for Annex I
countries at the Nineteenth Conference of the Parties (Warsaw, November 11-23, 2013). This report presents
information in accordance with these guidelines.
Overall, this Inventory of anthropogenic greenhouse gas emissions and sinks provides a common and consistent
mechanism through which Parties to the UNFCCC can estimate emissions and compare the relative contribution of
individual sources, gases, and nations to climate change. The Inventory provides a national estimate of sources and
sinks for the United States, including all states and U.S. Territories.5 The structure of this report is consistent with
the current UNFCCC Guidelines on Annual Inventories (UNFCCC 2014) for Parties included in Annex I of the
Convention.
Box 1-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sink
J
In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
inventories, the gross emissions total presented in this report for the United States excludes emissions and sinks
from LULUCF. The net emissions total presented in this report for the United States includes emissions and sinks
from LULUCF. All emissions and sinks are calculated using internationally-accepted methods consistent with the
IPCC Guidelines.6 Additionally, the calculated emissions and sinks in a given year for the United States are
presented in a common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under
this international agreement.7 The use of consistent methods to calculate emissions and sinks by all nations
providing their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions
and sinks reported in this Inventory are comparable to emissions and sinks reported by other countries. The report
itself follows this standardized format, and provides an explanation of the IPCC methods used to calculate emissions
and sinks, and the manner in which those calculations are conducted.
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
reporting of greenhouse gases from large greenhouse gas emissions sources in the United States. Implementation of
40 CFR Part 98 is referred to as the EPA's GHGRP. 40 CFR Part 98 applies to direct greenhouse gas emitters, fossil
fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for sequestration or other reasons.8
Reporting is at the facility level, except for certain suppliers of fossil fuels and industrial greenhouse gases. The
GHGRP dataset and the data presented in this Inventory are complementary.
The GHGRP data set continues to be an important resource for the Inventory, providing not only annual emissions
information, but also other annual information, such as activity data and emissions factors that can improve and
refine national emission estimates and trends over time. GHGRP data also allow EPA to disaggregate national
Inventory estimates in new ways that can highlight differences across regions and sub-categories of emissions. The
GHGRP will continue to enhance QA/QC procedures and assessment of uncertainties.
EPA continues to analyze the data on an annual basis to improve the national estimates presented in this Inventory
and uses that data for a number of categories consistent with IPCC guidance.9 EPA has integrated GHGRP
information for several categories10 this year and also identifies other categories11 where EPA plans to integrate
5 U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island, and other U.S. Pacific Islands.
6 See .
7 See .
8 See .
9 See
10 Energy Sector (Coal Mining, Stationary Combustion [Industrial Combustion Disaggregation], and Oil and Gas Systems);
Industrial Processes and Product Use (Adipic Acid Production, Aluminum Production, Carbon Dioxide Consumption, Electrical
Transmission and Distribution, HCFC-22 Production, Lime Production, Magnesium Production and Processing, ODS
Substitutes, Nitric Acid Production, Petrochemical Production, Semiconductor Manufacture); and Waste (Landfills).
11 Industrial Process and Product Use (Ammonia Production, Cement Production, and Other Fluorinated Gas Production)
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1 additional GHGRP data in the next edition of this report (see those categories Planned Improvement sections for
2 details).
3 1.1 Background Information
4 Science
5 For over the past 200 years, the burning of fossil fuels such as coal and oil, deforestation, land-use changes, and
6 other sources have caused the concentrations of heat-trapping "greenhouse gases" to increase significantly in our
7 atmosphere (NOAA 2017). These gases in the atmosphere absorb some of the energy being radiated from the
8 surface of the Earth and then re-radiate this energy with some returning to the Earth's surface, essentially acting like
9 a blanket that makes the Earth's surface warmer than it would be otherwise.
10 Greenhouse gases are necessary to life as we know it. Without greenhouse gases in the atmosphere, the planet's
11 surface would be about 60 degrees Fahrenheit cooler than present (EPA 2009). Carbon dioxide is also necessary for
12 plant growth. With emissions from biological and geological sources, there is a natural level of greenhouse gases
13 that is maintained in the atmosphere. But, as the concentrations of these gases continue to increase in from man-
14 made sources, the Earth's temperature is climbing above past levels. The Earth's average land and ocean surface
15 temperature has increased by about 1.2 to 1.9 degrees Fahrenheit since 1880. The last three decades have each been
16 the warmest decade successively at the Earth's surface since 1850 (IPCC 2013). Most of the warming in recent
17 decades is very likely the result of human activities. Other aspects of the climate are also changing such as rainfall
18 patterns, snow and ice cover, and sea level.
19 If greenhouse gases continue to increase, climate models predict that the average temperature at the Earth's surface
20 is likely to increase from 0.5 to 8.6 degrees Fahrenheit above 1986 through 2005 levels by the end of this century,
21 depending on future emissions (IPCC 2013). Scientists are certain that human activities are changing the
22 composition of the atmosphere, and that increasing the concentration of greenhouse gases will change the planet's
23 climate. However, they are not sure by how much it will change, at what rate it will change, or what the exact effects
24 will be.12
25 Greenhouse Gases
26 Although the Earth's atmosphere consists mainly of oxygen and nitrogen, neither plays a significant role in
27 enhancing the greenhouse effect because both are essentially transparent to terrestrial radiation. The greenhouse
28 effect is primarily a function of the concentration of water vapor, carbon dioxide (CO2), methane (CH4), nitrous
29 oxide (N20), and other trace gases in the atmosphere that absorb the terrestrial radiation leaving the surface of the
30 Earth (IPCC 2013). Changes in the atmospheric concentrations of these greenhouse gases can alter the balance of
31 energy transfers between the space and the earth system.13 A gauge of these changes is called radiative forcing,
32 which is a measure of the influence a perturbation has in altering the balance of incoming and outgoing energy in the
33 Earth-atmosphere system (IPCC 2013). Holding everything else constant, increases in greenhouse gas
34 concentrations in the atmosphere will produce positive radiative forcing (i.e., a net increase in the absorption of
3 5 energy by the Earth).
36 Human activities are continuing to affect the Earth's energy budget by changing the emissions and
37 resulting atmospheric concentrations of radiatively important gases and aerosols and by changing land
38 surface properties (IPCC 2013).
39 Naturally occurring greenhouse gases include water vapor, CO2, CH4, N20, and ozone (O3). Several classes of
40 halogenated substances that contain fluorine, chlorine, or bromine are also greenhouse gases, but they are, for the
12 For more information see .
13 For more on the science of climate change, see NRC (2012).
Introduction 1-3
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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.14 Some other fluorine-containing halogenated substances—hydrofluorocarbons (HFCs),
perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3)—do not deplete stratospheric
ozone but are potent greenhouse gases. These latter substances are addressed by the UNFCCC and accounted for in
national greenhouse gas inventories.
There are also several other substances that influence the global radiation budget but are short-lived and therefore
not well-mixed. These substances include carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxide (SO2), and
tropospheric (ground level) O3. Tropospheric ozone is formed by two precursor pollutants, volatile organic
compounds (VOCs) and nitrogen oxides (NOx) in the presence of ultraviolet light (sunlight).
Aerosols are extremely small particles or liquid droplets suspended in the Earth's atmosphere that are often
composed of sulfur compounds, carbonaceous combustion products (e.g., black carbon), crustal materials (e.g., dust)
and other human induced pollutants. They can affect the absorptive characteristics of the atmosphere (e.g., scattering
incoming sunlight away from the Earth's surface, or, in the case of black carbon, absorb sunlight) and can play a
role in affecting cloud formation and lifetime affecting the radiative forcing of clouds and precipitation patterns.
Comparatively, however, while the understanding of aerosols has increased in recent years, they still account for the
largest contribution to uncertainty estimates in global energy budgets (IPCC 2013).
Carbon dioxide, CH4, and N20 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 of Selected Greenhouse Gases
Atmospheric Variable
CO2
CH4
N2O
SF«
CF4
Pre-industrial atmospheric concentration
Atmospheric concentration
Rate of concentration change
Atmospheric lifetime (years)
280 ppm
404 ppma
2.4 ppm/yr
See footnotef
0.700 ppm
1.834 ppmb
5 ppb/yi4-6
12.4g
0.270 ppm
0.328 ppmb
0.8 ppb/yre
121®
Oppt
8.6 pptb
0.27 ppt/yre
3,200
40 ppt
79 pptc
0.7 ppt/yr6
50,000
a The atmospheric CO2 concentration is the 2016 annual average at the Mauna Loa, HI station (NOAA/ESRL 2017).
b The values presented are global 2015 annual average mole fractions (CDIAC 2016).
c The 2011 CF4 global mean atmospheric concentration is from the Advanced Global Atmospheric Gases Experiment (IPCC
2013).
d The growth rate for atmospheric CH4 decreased from over 10 ppb/yr in the 1980s to nearly zero in the early 2000s; recently, the
growth rate has been about 5 ppb/year.
e The rate of concentration change is the average rate of change between 2005 and 2011 (IPCC 2013).
f For a given amount of carbon dioxide emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by
the oceans and terrestrial vegetation, some fraction of the atmospheric increase will only slowly decrease over a number of years,
and a small portion of the increase will remain for many centuries or more.
B This lifetime has been defined as an "adjustment time" that takes into account the indirect effect of the gas on its own residence
time.
Source: Pre-industrial atmospheric concentrations, atmospheric lifetime, and rate of concentration changes for CH4, N2O, SFe, and
CF4 are from IPCC (2013). The rate of concentration change for CO2 is an average of the rates from 2011 through 2016 has
14 Emissions estimates of CFCs, HCFCs, halons and other ozone-depleting substances are included in this document for
informational purposes.
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fluctuated between 1.9 to 3.0 ppm per year over this period (NOAA/ESRL 2017).
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 (H20). Water vapor is the largest contributor to the natural greenhouse effect. Water vapor is
fundamentally different from other greenhouse gases in that it can condense and rain out when it reaches high
concentrations, and the total amount of water vapor in the atmosphere is in part a function of the Earth's
temperature. While some human activities such as evaporation from irrigated crops or power plant cooling release
water vapor into the air, this has been determined to have a negligible effect on climate (IPCC 2013). The lifetime of
water vapor in the troposphere is on the order of 10 days. Water vapor can also contribute to cloud formation, and
clouds can have both warming and cooling effects by either trapping or reflecting heat. Because of the relationship
between water vapor levels and temperature, water vapor and clouds serve as a feedback to climate change, such
that for any given increase in other greenhouse gases, the total warming is greater than would happen in the absence
of water vapor. Aircraft contrails, which consist of water vapor and other substances, are aviation-induced clouds
with the same radiative forcing effects as high-altitude cirrus clouds (IPCC 1999).
Carbon Dioxide (C02). 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. Carbon dioxide concentrations in the atmosphere increased from
approximately 280 parts per million by volume (ppmv) in pre-industrial times to 404 ppmv in 2016, a 44 percent
increase (IPCC 2013 and NOAA/ESRL 2017).1516 The IPCC definitively states that "the increase of CO2 ... is
caused by anthropogenic emissions from the use of fossil fuel as a source of energy and from land use and land use
changes, in particular agriculture" (IPCC 2013). The predominant source of anthropogenic CO2 emissions is the
combustion of fossil fuels. Forest clearing, other biomass burning, and some non-energy production processes (e.g.,
cement production) also emit notable quantities of CO2. In its Fifth Assessment Report, the IPCC stated "it is
extremely likely that more than half of the observed increase in global average surface temperature from 1951 to
2010 was caused by the anthropogenic increase in greenhouse gas concentrations and other anthropogenic forcings
together," of which CChis the most important (IPCC 2013).
Methane (CH4). Methane 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. Methane 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 162
percent since 1750, from a pre-industrial value of about 700 ppb to 1,834 ppb in 201517 although the rate of increase
decreased to near zero in the early 2000s, and has recently increased again to about 5 ppb/year. The IPCC has
estimated that slightly more than half of the current CH4 flux to the atmosphere is anthropogenic, from human
activities such as agriculture, fossil fuel use, and waste disposal (IPCC 2007).
Methane is primarily removed from the atmosphere through a reaction with the hydroxyl radical (OH) and is
ultimately converted to CO2. Minor removal processes also include reaction with chlorine in the marine boundary
layer, a soil sink, and stratospheric reactions. Increasing emissions of CH4 reduce the concentration of OH, a
feedback that increases the atmospheric lifetime of CH4 (IPCC 2013). Methane's reactions in the atmosphere also
lead to production of tropospheric ozone and stratospheric water vapor, both of which also contribute to climate
change.
15 The pre-industrial period is considered as the time preceding the year 1750 (IPCC 2013).
16 Carbon dioxide concentrations during the last 1,000 years of the pre-industrial period (i.e., 750 to 1750), a time of relative
climate stability, fluctuated by about +10 ppmv around 280 ppmv (IPCC 2013).
17 This value is the global 2015 annual average mole fraction (CDIAC 2016).
Introduction 1-5
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Nitrous Oxide (N20). Anthropogenic sources of N20 emissions include agricultural soils, especially production of
nitrogen-fixing crops and forages, the use of synthetic and manure fertilizers, and manure deposition by livestock;
fossil fuel combustion, especially from mobile combustion; adipic (nylon) and nitric acid production; wastewater
treatment and waste incineration; and biomass burning. The atmospheric concentration of N20 has increased by 21
percent since 1750, from a pre-industrial value of about 270 ppb to 328 ppb in 2015,18 a concentration that has not
been exceeded during the last 800 thousand years. Nitrous oxide is primarily removed from the atmosphere by the
photolytic action of sunlight in the stratosphere (IPCC 2013).
Ozone (03). Ozone is present in both the upper stratosphere,19 where it shields the Earth from harmful levels of
ultraviolet radiation, and at lower concentrations in the troposphere,20 where it is the main component of
anthropogenic photochemical "smog." During the last two decades, emissions of anthropogenic chlorine and
bromine-containing halocarbons, such as CFCs, have depleted stratospheric ozone concentrations. This loss of
ozone in the stratosphere has resulted in negative radiative forcing, representing an indirect effect of anthropogenic
emissions of chlorine and bromine compounds (IPCC 2013). The depletion of stratospheric ozone and its radiative
forcing was expected to reach a maximum in about 2000 before starting to recover.
The past increase in tropospheric ozone, which is also a greenhouse gas, is estimated to provide the fourth largest
increase in direct radiative forcing since the pre-industrial era, behind CO2, black carbon, and CH4. Tropospheric
ozone is produced from complex chemical reactions of volatile organic compounds (including CH4) mixing with
NOx in the presence of sunlight. The tropospheric concentrations of ozone and these other pollutants are short-lived
and, therefore, spatially variable (IPCC 2013).
Halocarbons, Sulfur Hexafluoride, and Nitrogen Trifluoride. Halocarbons are, for the most part, man-made
chemicals that have direct radiative forcing effects and could also have an indirect effect. Halocarbons that contain
chlorine (CFCs, HCFCs, methyl chloroform, and carbon tetrachloride) and bromine (halons, methyl bromide, and
hydrobromofluorocarbons) result in stratospheric ozone depletion and are therefore controlled under the Montreal
Protocol on Substances that Deplete the Ozone Layer. Although most CFCs and HCFCs are potent global warming
gases, their net radiative forcing effect on the atmosphere is reduced because they cause stratospheric ozone
depletion, which itself is a greenhouse gas but which also shields 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 521 countries, including the U.S., beginning in 1996, and then followed by
intermediate requirements and 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 for informational purposes.
Hydrofluorocarbons, PFCs, SF6, and NF3 are not ozone depleting substances. The most common HFCs are,
however, powerful greenhouse gases. Hydrofluorocarbons are primarily used as replacements for ozone depleting
substances but also emitted as a by-product of the HCFC-22 (chlorodifluoromethane) manufacturing process.
Currently, they have a small aggregate radiative forcing impact, but it is anticipated that without further controls
their contribution to overall radiative forcing will increase (IPCC 2013). An amendment to the Montreal Protocol
18 This value is the global 2015 annual average (CDIAC 2016).
19 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.
20 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.
21 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 of ten
additional years in the phase-out of ozone depleting substances.
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was adopted in 2016 which includes obligations for all countries to phase down the production and consumption of
HFCs.
Perfluorocarbons, SF6, and NF3 are predominantly emitted from various industrial processes including aluminum
smelting, semiconductor manufacturing, electric power transmission and distribution, and magnesium casting.
Currently, the radiative forcing impact of PFCs, SF6, and NF3 is also small, but they have a signifigant growth rate,
extremely long atmospheric lifetimes, and are strong absorbers of infrared radiation, and therefore have the potential
to influence climate far into the future (IPCC 2013).
Carbon Monoxide (CO). 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 (NOx). The primary climate change effects of nitrogen oxides (i.e., NO and NO2) are indirect and
result from their role in promoting the formation of ozone in the troposphere, are a precursor to nitrate particles (i.e.,
aerosols) and, to a lesser degree, lower stratosphere, where they have positive radiative forcing effects.22
Additionally, NOx emissions are also likely to decrease CH4 concentrations, thus having a negative radiative forcing
effect (IPCC 2013). Nitrogen oxides are created from lightning, soil microbial activity, biomass burning (both
natural and anthropogenic fires) fuel combustion, and, in the stratosphere, from the photo-degradation of N20.
Concentrations of NOx are both relatively short-lived in the atmosphere and spatially variable.
Non-methane Volatile Organic Compounds (NMVOCs). Non-methane volatile organic compounds include
substances such as propane, butane, and ethane. These compounds participate, along with NOx, in the formation of
tropospheric ozone and other photochemical oxidants. NMVOCs are emitted primarily from transportation and
industrial processes, as well as biomass burning and non-industrial consumption of organic solvents. Concentrations
of NMVOCs tend to be both short-lived in the atmosphere and spatially variable.
Aerosols. Aerosols are extremely small particles or liquid droplets found in the atmosphere that are either directly
emitted into or are created through chemical reactions in the Earth's atmosphere. Aerosols or their chemical
precursors can be emitted by natural events such as dust storms and volcanic activity, or by anthropogenic processes
such as fuel combustion and biomass burning. Various categories of aerosols exist, including naturally produced
aerosols such as soil dust, sea salt, biogenic aerosols, sulfates, nitrates, and volcanic aerosols, and anthropogenically
manufactured aerosols such as industrial dust and carbonaceous23 aerosols (e.g., black carbon, organic carbon) from
transportation, coal combustion, cement manufacturing, waste incineration, and biomass burning. Aerosols can be
removed from the atmosphere relatively rapidly by precipitation or through more complex processes under dry
conditions.
Aerosols affect radiative forcing differently than greenhouse gases. Their radiative effects occur through direct and
indirect mechanisms: directly by scattering and absorbing solar radiation (and to a lesser extent scattering,
absorption, and emission of terrestrial radiation); and indirectly by increasing cloud droplets and ice crystals that
modify the formation, precipitation efficiency, and radiative properties of clouds (IPCC 2013). Despite advances in
understanding of cloud-aerosol interactions, the contribution of aerosols to radiative forcing are difficult to quantify
because aerosols generally have short atmospheric lifetimes, and have number concentrations, size distributions, and
compositions that vary regionally, spatially, and temporally (IPCC 2013).
The net effect of aerosols on the Earth's radiative forcing is believed to be negative (i.e., net cooling effect on the
climate). In fact, "despite the large uncertainty ranges on aerosol forcing, there is high confidence that aerosols have
offset a substantial portion of GHG forcing" (IPCC 2013).24 Although because they remain in the atmosphere for
22 NOx emissions injected higher in the stratosphere, primarily from fuel combustion emissions from high altitude supersonic
aircraft, can lead to stratospheric ozone depletion.
23 Carbonaceous aerosols are aerosols that are comprised mainly of organic substances and forms of black carbon (or soot)
(IPCC 2013).
24 The IPCC (2013) defines high confidence as an indication of strong scientific evidence and agreement in this statement.
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only days to weeks, their concentrations respond rapidly to changes in emissions.25 Not all aerosols have a cooling
effect. Current research suggests that another constituent of aerosols, black carbon, has a positive radiative forcing
by heating the Earth's atmosphere and causing surface warming when deposited on ice and snow (IPCC 2013).
Black carbon also influences cloud development, but the direction and magnitude of this forcing is an area of active
research.
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 accumulated radiative forcing within a specific time
horizon caused by emitting 1 kilogram (kg) of the gas, relative to that of the reference gas CO2 (IPCC 2014). 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 million metric tons of CO2 equivalent (MMT
CO2 Eq.).26 The relationship between kilotons (kt) of a gas and MMT CO2 Eq. can be expressed as follows:
MMT CO2 Eq. = Million metric tons of CO2 equivalent
kt = kilotons (equivalent to a thousand metric tons)
GWP = Global warming potential
MMT = Million metric tons
GWP values allow for a comparison of the impacts of emissions and reductions of different gases. According to the
IPCC, GWPs typically have an uncertainty of ±35 percent. Parties to the UNFCCC have also agreed to use GWPs
based upon a 100-year time horizon, although other time horizon values are available.
... the global warming potential values used by Parties included in Annex I to the Convention (Annex I
Parties) to calculate the carbon dioxide equivalence of anthropogenic emissions by sources and removals
by sinks of greenhouse gases shall be those listed in the column entitled "Global warming potential for
given time horizon " in table 2.14 of the errata to the contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change, based on the effects of greenhouse
gases over a 100-year time horizon...27
Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N20, HFCs, PFCs, SF6, NF3) tend to
be evenly distributed throughout the atmosphere, and consequently global average concentrations can be
determined. The short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, ozone precursors
(e.g., NOx, and NMVOCs), and tropospheric aerosols (e.g., SO2 products and carbonaceous particles), however,
vary regionally, and consequently it is difficult to quantify their global radiative forcing impacts. Parties to the
UNFCCC have not agreed upon GWP values for these gases that are short-lived and spatially inhomogeneous in the
atmosphere.
25 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 2013).
26 Carbon comprises 12/44ths of carbon dioxide by weight.
27 Framework Convention on Climate Change; Available online at: ;
31 January 2014; Report of the Conference of the Parties at its nineteenth session; held in Warsaw from 11 to 23 November
2013; Addendum; Part two: Action taken by the Conference of the Parties at its nineteenth session; Decision 24/CP.19; Revision
of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention; p. 2. (UNFCCC
2014).
Global Warming Potentials
( MMT \
Eq. = (kt of gas) x (GWP) x oqq J
MMT CO-
where,
1-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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17
18
Table 1-2: Global Warming Potentials and Atmospheric Lifetimes (Years) Used in this Report
Gas
Atmospheric Lifetime
GWP
CO2
b
1
CH4a
12
25
N2O
114
298
HFC-23
270
14,800
HFC-32
4.9
675
HFC-125
29
3,500
HFC-134a
14
1,430
HFC-143a
52
4,470
HFC-152a
1.4
124
HFC-227ea
34.2
3,220
HFC-236fa
240
9,810
HFC-4310mee
15.9
1,640
CF4
50,000
7,390
C2F(5
10,000
12,200
O
O
2,600
8,860
C6Fl4
3,200
9,300
SF.
Introduction 1-9
-------
1 informational purposes, emission estimates that use GWPs from other IPCC Assessment Reports are presented in
2 detail in Annex 6.1 of this report.
3
4 Table 1-3: Comparison of 100-Year GWP values
AR5 with
Gas
SAR
AR4
AR5
feedbacksb
Comparison to AR4
SAR
AR5
AR5 with
feedbacks6
CO2
1
1
1
1
NC
NC
NC
CH4c
21
25
28
34
(4)
3
9
N2O
310
298
265
298
12
(33)
0
HFC-23
11,700
14,800
12,400
13,856
(3,100)
(2,400)
(944)
HFC-32
650
675
677
817
(25)
2
142
HFC-125
2,800
3,500
3,170
3,691
(700)
(330)
191
HFC-134a
1,300
1,430
1,300
1,549
(130)
(130)
119
HFC-143a
3,800
4,470
4,800
5,508
(670)
330
1,038
HFC-152a
140
124
138
167
16
14
43
HFC-227ea
2,900
3,220
3,350
3,860
(320)
130
640
HFC-236fa
6,300
9,810
8,060
8,998
(3,510)
(1,750)
(812)
HFC-4310mee
1,300
1,640
1,650
1,952
(340)
10
312
CF4
6,500
7,390
6,630
7,349
(890)
(760)
(41)
C2F6
9,200
12,200
11,100
12,340
(3,000)
(1,100)
140
C4F10
7,000
8,860
9,200
10,213
(1,860)
340
1,353
C6Fl4
7,400
9,300
7,910
8,780
(1,900)
(1,390)
(520)
SF«
23,900
22,800
23,500
26,087
1,100
700
3,287
NF3
NA
17,200
16,100
17,885
NA
(1,100)
685
NC (No Change)
NA (Not Applicable)
a The GWPs presented here are the ones most consistent with the methodology used in the AR4
report.
b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-
CO2 gases in order to be consistent with the approach used in calculating the CO2 lifetime.
Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to account
for the CO2 oxidation product.
c 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
only included in the value from AR5 that includes climate-carbon feedbacks.
Note: Parentheses indicate negative values.
Source: (IPCC 2013, IPCC 2007, IPCC 2001, IPCC 1996).
5
6 1.2 National Inventory Arrangements
7 The U.S. Environmental Protection Agency (EPA), in cooperation with other U.S. government agencies, prepares
8 the Inventory of U.S. Greenhouse Gas Emissions and Sinks. A wide range of agencies and individuals are involved
9 in supplying data to, planning methodological approaches and improvements, reviewing, or preparing portions of the
10 U.S. Inventory—including federal and state government authorities, research and academic institutions, industry
11 associations, and private consultants.
12 Within EPA, the Office of Atmospheric Programs (OAP) is the lead office responsible for the emission calculations
13 provided in the Inventory, as well as the completion of the National Inventory Report and the Common Reporting
14 Format tables. EPA's Office of Transportation and Air Quality (OTAQ) and Greenhouse Gas Reporting Program
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11
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13
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16
17
18
(GHGRP) (within OAP) are also involved in calculating emissions for the Inventory. While the U.S. Department of
State officially submits the annual Inventory to the UNFCCC, EPA's OAP serves as the Inventory focal point for
technical questions and comments on the U.S. Inventory. The staff of OAP and OTAQ coordinates the annual
methodological choice, activity data collection, and emission calculations at the individual source category level.
Within OAP, an inventory coordinator compiles the entire Inventory into the proper reporting format for submission
to the UNFCCC, and is responsible for the collection and consistency of cross-cutting issues in the Inventory.
Several other government agencies contribute to the collection and analysis of the underlying activity data used in
the Inventory calculations. Formal relationships exist between EPA and other U.S. agencies that provide official
data for use in the Inventory. The U.S. Department of Energy's Energy Information Administration provides
national fuel consumption data and the U.S. Department of Defense provides military fuel consumption and bunker
fuels. Informal relationships also exist with other U.S. agencies to provide activity data for use in EPA's emission
calculations. These include: the U.S. Department of Agriculture, the U.S. Geological Survey, the Federal Highway
Administration, the Department of Transportation, the Bureau of Transportation Statistics, the Department of
Commerce, the National Agricultural Statistics Service, and the Federal Aviation Administration. EPA also uses
emissions and other information (such as activity data) collected through the GHGRP. 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. Figure 1-1 diagrams the National Inventory Arrangements.
Introduction 1-11
-------
1 Figure 1-1: National Inventory Arrangements Diagram
Other U.S.
Government Agencies
U.S. Forest Service, National Oceanic and
Atmospheric Administration (NOAA)
United States Greenhouse Gas National Inventory Arrangements
United Nations
Framework Convention on
Climate Change
U.S. Department of State
U.S. Environmental
Protection Agency
Inventory Compiler
U.S. Environmental
Protection Agency
Energy
• Bureau of Transportation Statistics
• Energy Information Administration
• EPA GHGRP and Acid Rain Program
• EPA Office of Transportation and Air Quality
• Federal Aviation Administration
• Federal Highway Administration
• U.S. Department of Commerce
• U.S. Department of Defense
• U.S. Department of Energy
• U.S. Department of Transportation
Data Collection
Inventory Submission
Inventory Compilation
Emission Calculations
2
Agriculture and LULUCF
• Colorado State University
• National Agricultural Statistics Service
• U.S. Department of Agriculture
• U.S. Forest Service _
• U.S. Geological Survey |
BPI
Industrial Processes and Product Use
• Air-Conditioning, Heating, and Refrigeration Institute
• American Chemistry Council
• American Iron and Steel Institute
• EPA GHGRP
• U.S. Aluminum Association
• U.S. Department of Commerce
• U.S. Geological Survey Minerals
Information Center
Waste
• EPA GHGRP
• EPA Office of Land and Emergency
Management
1-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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i 1.3 Inventory Process
2 This section describes EPA's approach to preparing the annual U.S. Inventory, which consists of a National
3 Inventory Report (NIR) and Common Reporting Format (CRF) tables. The inventory coordinator at EPA is
4 responsible for compiling all emission estimates and ensuring consistency and quality throughout the NIR and CRF
5 tables. Emission calculations for individual sources are the responsibility of individual source leads, who are most
6 familiar with each source category and the unique characteristics of its emissions profile. The individual source
7 leads determine the most appropriate methodology and collect the best activity data to use in the emission
8 calculations, based upon their expertise in the source category, as well as coordinating with researchers and
9 contractors familiar with the sources. A multi-stage process for collecting information from the individual source
10 leads and producing the Inventory is undertaken annually to compile all information and data.
11 Methodology Development, Data Collection, and Emissions
12 and Sink Estimation
13 Source leads at EPA collect input data and, as necessary, evaluate or develop the estimation methodology for the
14 individual source categories. Because EPA has been preparing the Inventory for many years, for most source
15 categories, the methodology for the previous year is applied to the new "current" year of the Inventory, and
16 inventory analysts collect any new data or update data that have changed from the previous year. If estimates for a
17 new source category are being developed for the first time, or if the methodology is changing for an existing source
18 category (e.g., the United States is implementing a higher Tiered approach for that source category), then the source
19 category lead will develop a new methodology, gather the most appropriate activity data and emission factors (or in
20 some cases direct emission measurements) for the entire time series, and conduct a special source-specific review
21 process involving relevant experts from industry, government, and universities.
22 Once the methodology is in place and the data are collected, the individual source leads calculate emissions and sink
23 estimates. The source leads then update or create the relevant text and accompanying annexes for the Inventory.
24 Source leads are also responsible for completing the relevant sectoral background tables of the CRF, conducting
25 quality assurance and quality control (QA/QC) checks, and uncertainty analyses.
26 Summary Data Compilation and Storage
27 The inventory coordinator at EPA collects the source categories' descriptive text and Annexes, and also aggregates
28 the emission estimates into a summary spreadsheet that links the individual source category spreadsheets together.
29 This summary sheet contains all of the essential data in one central location, in formats commonly used in the
30 Inventory document. In addition to the data from each source category, national trend and related data are also
31 gathered in the summary sheet for use in the Executive Summary, Introduction, and Recent Trends sections of the
32 Inventory report. Electronic copies of each year's summary spreadsheet, which contains all the emission and sink
33 estimates for the United States, are kept on a central server at EPA under the jurisdiction of the inventory
34 coordinator.
35 National Inventory Report Preparation
36 The NIR is compiled from the sections developed by each individual source lead. In addition, the inventory
37 coordinator prepares a brief overview of each chapter that summarizes the emissions from all sources discussed in
38 the chapters. The inventory coordinator then carries out a key category analysis for the Inventory, consistent with the
39 2006IPCC Guidelines for National Greenhouse Gas Inventories, and in accordance with the reporting requirements
40 of the UNFCCC. Also at this time, the Introduction, Executive Summary, and Recent Trends sections are drafted, to
41 reflect the trends for the most recent year of the current Inventory. The analysis of trends necessitates gathering
42 supplemental data, including weather and temperature conditions, economic activity and gross domestic product,
43 population, atmospheric conditions, and the annual consumption of electricity, energy, and fossil fuels. Changes in
Introduction 1-13
-------
1 these data are used to explain the trends observed in greenhouse gas emissions in the United States. Furthermore,
2 specific factors that affect individual sectors are researched and discussed. Many of the factors that affect emissions
3 are included in the Inventory document as separate analyses or side discussions in boxes within the text. Text boxes
4 are also created to examine the data aggregated in different ways than in the remainder of the document, such as a
5 focus on transportation activities or emissions from electricity generation. The document is prepared to match the
6 specification of the UNFCCC reporting guidelines for National Inventory Reports.
7 Common Reporting Format Table Compilation
8 The CRF tables are compiled from individual tables completed by each individual source lead, which contain source
9 emissions and activity data. The inventory coordinator integrates the source data into the UNFCCC's "CRF
10 Reporter" for the United States, assuring consistency across all sectoral tables. The summary reports for emissions,
11 methods, and emission factors used, the overview tables for completeness and quality of estimates, the recalculation
12 tables, the notation key completion tables, and the emission trends tables are then completed by the inventory
13 coordinator. Internal automated quality checks on the CRF Reporter, as well as reviews by the source leads, are
14 completed for the entire time series of CRF tables before submission.
15 QA/QC and Uncertainty
16 QA/QC and uncertainty analyses are supervised by the QA/QC and uncertainty coordinators, who have general
17 oversight over the implementation of the QA/QC plan and the overall uncertainty analysis for the Inventory (see
18 sections on QA/QC and Uncertainty, below). These coordinators work closely with the source leads to ensure that a
19 consistent QA/QC plan and uncertainty analysis is implemented across all inventory sources. The inventory QA/QC
20 plan, detailed in a following section, is consistent with the quality assurance procedures outlined by EPA and IPCC.
21 The QA/QC and uncertainty findings also inform overall improvement planning, and specific improvements are
22 noted in the Planned Improvements sections of respective categories.
23 Expert and Public Review Periods
24 During the Expert Review period, a first draft of the document is sent to a select list of technical experts outside of
25 EPA. The purpose of the Expert Review is to encourage feedback on the methodological and data sources used in
26 the current Inventory, especially for sources which have experienced any changes since the previous Inventory.
27 Once comments are received and addressed, a second draft of the document is released for public review by
28 publishing a notice in the U.S. Federal Register and posting the document on the EPA Web site. The Public Review
29 period allows for a 30 day comment period and is open to the entire U.S. public. Comments may require further
30 discussion with experts or research, and specific Inventory improvements requiring further analysis as a result
31 comments are noted in categories Planned Improvement sections. See those sections for specific details.
32 Final Submittal to UNFCCC and Document Printing
33 After the final revisions to incorporate any comments from the Expert Review and Public Review periods, EPA
34 prepares the final National Inventory Report and the accompanying Common Reporting Format Reporter database.
35 The U.S. Department of State sends the official submission of the U.S. Inventory to the UNFCCC. The document is
36 then formatted and posted online, available for the public.1
1 See .
1-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
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 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). To a
great extent, this report makes use of published official economic and physical statistics for activity data and
emission factors. 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. For more information on data sources see Section 1.2 above, Box 1-1 on use of EPA's
GHGRP data, Section 1.2 above and categories methodology sections for more information on data sources. In
addition to official statistics, the report utilizes findings from academic studies, trade association surveys and
statistical reports, along with expert judgement, consistent with 2006 IPCC Guidelines.
The IPCC methodologies provided in the 2006 IPCC Guidelines represent foundational methodologies for a variety
of source categories, and many of these methodologies continue to be improved and refined as new research and
data become available. This report uses the IPCC methodologies when applicable, and supplements them with other
available country-specific methodologies and data where possible. Choices made regarding the methodologies and
data sources used are provided in conjunction with the discussion of each source category in the main body of the
report. Complete documentation is provided in the annexes on the detailed methodologies and data sources utilized
in the calculation of each source category.
Box 1-3: IPCC Reference Approach
I
The UNFCCC reporting guidelines require countries to complete a "top-down" reference approach for estimating
CO2 emissions from fossil fuel combustion in addition to their "bottom-up" sectoral methodology. This estimation
method uses alternative methodologies and different data sources than those contained in that section of the Energy
chapter. The reference approach estimates fossil fuel consumption by adjusting national aggregate fuel production
data for imports, exports, and stock changes rather than relying on end-user consumption surveys (see Annex 4 of
this report). The reference approach assumes that once carbon-based fuels are brought into a national economy, they
are either saved in some way (e.g., stored in products, kept in fuel stocks, or left unoxidized in ash) or combusted,
and therefore the carbon in them is oxidized and released into the atmosphere. Accounting for actual consumption of
fuels at the sectoral or sub-national level is not required.
1.5 Key Categories
The 2006IPCC Guidelines (IPCC 2006) defines a key category as a "[category] that is prioritized within the
national inventory system because its estimate has a significant influence on a country's total inventory of
greenhouse gases in terms of the absolute level, the trend, or the uncertainty in emissions and removals."2 By
definition, key categories include those categories that have the greatest contribution to the absolute level of national
emissions. In addition, when an entire time series of emission and removal estimates is prepared, a thorough
investigation of key categories must also account for the influence of trends and uncertainties of individual source
and sink categories. This analysis can identify source and sink categories that diverge from the overall trend in
national emissions. Finally, a qualitative evaluation of key categories is performed to capture any categories that
were not identified in any of the quantitative analyses.
Approach 1, as defined in the 2006 IPCC Guidelines (IPCC 2006), was implemented to identify the key categories
for the United States. This analysis was performed twice; one analysis included sources and sinks from the Land
2 See Chapter 4 Volume 1, "Methodological Choice and Identification of Key Categories" in IPCC (2006). See
.
Introduction 1-15
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Use, Land-Use Change, and Forestry (LULUCF) sector, the other analysis did not include the LULUCF categories.
Following Approach 1, Approach 2, as defined in the 2006IPCC Guidelines (IPCC 2006), was then implemented to
identify any additional key categories not already identified in Approach 1 assessment. This analysis, which includes
each source category's uncertainty assessments (or proxies) in its calculations, was also performed twice to include
or exclude LULUCF categories.
In addition to conducting Approach 1 and 2 level and trend assessments, a qualitative assessment of the source
categories, as described in the 2006 IPCC Guidelines (IPCC 2006), was conducted to capture any key categories that
were not identified by either quantitative method. One additional key category, international bunker fuels, was
identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine
international transport activities, and emissions from these fuels are reported separately from totals in accordance
with IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category
according to Approach 1. The amount of uncertainty associated with estimation of emissions from international
bunker fuels also supports the qualification of this source category as key, because it would qualify bunker fuels as a
key category according to Approach 2. Table 1-4 presents the key categories for the United States (including and
excluding LULUCF categories) using emissions and uncertainty data in this report, and ranked according to their
sector and global wanning potential (GWP)-weighted emissions in 2015. The table also indicates the criteria used in
identifying these categories (i.e., level, trend, Approach 1, Approach 2, and/or qualitative assessments). Annex 1 of
this report provides additional information regarding the key categories in the United States and the methodologies
used to identify them.
Table 1-4: Key Categories for the United States (1990-2015)
IPCC Source Categories
Gas
Approach 1
Approach 2
Quala
2015
Emissions
(MMT
CO2 Eq.)
Level
Trend
Level
Trend
Level
Trend
Level
Trend
Without
Without
With
With
Without
Without
With
With
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
Energy
CO2 Emissions from
Mobile Combustion:
( 1)
•
•
•
•
•
•
•
•
1,460.9
Road
CO: Emissions from
Stationary Combustion -
CO2
1,350.5
Coal - Electricity
Generation
CO: Emissions from
Stationary Combustion -
( 11
Gas - Electricity
Generation
C(): 1 'missions from
Stationary Combustion -
( 11
•
•
•
•
•
•
•
•
I'".-
Gas - Industrial
CO: Emissions from
Stationary Combustion -
( 11
•
•
•
•
•
•
()il - Industrial
CO2 Emissions from
Stationary Combustion -
CO2
•
•
•
•
•
•
252.8
Gas - Residential
CO: Emissions from
Stationary Combustion -
( 11
•
•
•
•
•
•
•
l~\l
Gas - Commercial
CO2 Emissions from
Mobile Combustion:
CO2
•
•
•
•
•
•
•
159.2
Aviation
C(): Emissions from
N011-Energy Use ol
( 1)
•
•
•
•
•
•
i:~."
fuels
1-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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CO2 Emissions from
Mobile Combustion:
CO2
•
.
81.6
Other
CO: Emissions from
Stationary Combustion -
( 11
•
.
...,s
()il - Residential
CO2 Emissions from
Stationary Combustion -
CO2
•
.
•
•
•
•
65.9
Coal - Industrial
CO: Emissions from
Stationary Combustion -
( 11
•
.
I"."*
Oil - Commercial
C( h I 'missions from
( 11
i:. 1
Natural lias Systems
CO2 Emissions from
Stationary Combustion -
( 11
•
.
'I.'-
Oil - U.S. Territories
CO2 Emissions from
Mobile Combustion:
CO2
•
.
31.6
Marine
CO2 Emissions from
Stationary Combustion -
( 11
:v_
Oil -1 ¦ leclricily
Generation
CO2 Emissions from
Stationary Combustion -
CO2
•
3.2
Gas - U.S. Territories
CO2 Emissions from
Stationary Combustion -
( 11
•
Coal - Commercial
CI h I 'missions from
Stationary Combustion -
( 11
•
•
MM
Coal - Residential
CI 11 Emissions from
CI Li
lou.u
Natural Gas Systems
fugitive Emissions from
CIIi
Coal Mining
CI 11 Emissions from
CI L|
11.-
Petroleum Systems
Non-C02 Emissions
from Stationary
ch4
•
•
•
•
3.9
Combustion - Residential
N011-CO2 Emissions
from Stationary
\ 11
i".-
Combustion -1 ¦lectricily
Generation
N2O Emissions from
Mobile Combustion:
N2O
•
.
•
•
11.3
Road
International Bunker
Fuels
Several
•
111.3
llldlisl|-i;il Pnnvssi-s ;iihI Pniduil I si-
CO2 Emissions from Iron
and Steel Production &
t ()
i-."
Metallurgical Coke
Production
CO2 Emissions from
CO2
39.6
Cement Production
CO2 Emissions from
Petrochemical
1 0
•
:x.i
Production
Introduction 1-17
-------
N2O Emissions from
N2O
4.3
Adipic Acid Production
Emissions lrom
Substitutes lor ()zone
IliGWP
•
•
•
•
•
•
168.6
Depleting Substances
I IFC-23 Emissions from
IliGWP
V"
I ICFC-22 Production
Pl'C Emissions from
IliGWP
Aluminum Production
SFe Emissions from
Electrical Transmission
HiGWP
2.0
and Distribution
Agriculture
CI 11 Emissions from
CI 11
I
Enteric fermentation
CI 11 Emissions from
CI 11
Manure Mammement
Direct N:() Emissions
from Agricultural Soil
\ ()
•
•
•
•
•
: i v ^
Management
indirect N;G Emissions
from Applied Nitrogen
JSI20
•
•
•
•
•
•
•
38.0
Waste
CI 11 Emissions from
ch4
I I-."
I .andlills
Land Use, Land-Use Change, and Forestry
CO2 Emissions from
Land Converted to
CO2
•
•
294.2
Grassland
CO: Emissions from
Land Converted to
1 <>.
•
1-".:
Settlements
CO2 Emissions from
Land Converted to
CO2
•
•
28.6
C ropland
CO2 Emissions from
Grassland Remaining
1 0
•
Grassland
CO2 Emissions from
Cropland Remaining
CO2
•
•
(14.0)
Cropland
CO2 Emissions from
I ,and Converted to
t ()
•
forest Land
C(): I Emissions from
Settlements Remaining
t ()
•
•
(102.1)
Settlements
CO2 Emissions from
forest Land Remaining
t ()
•
•
(667.0)
forest Land
CH4 Emissions from
CH4
7.3
Forest Fires
N2O Emissions from
\ ()
l.s
•
forest fires
Subtotal Without LULUCF
Tolal I".missiiiiis \\ iIh
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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
31
32
33
34
35
36
37
38
Total Emissions With LULUCF
6,219.8
IVrxi-nl ol Toial Willi l.l l.l ( 1
a Qualitative criteria.
b Emissions from this source not included in totals.
Note: Parentheses indicate negative values (or sequestration).
1.6 Quality Assurance and Quality Control
(QA/QC)
As part of efforts to achieve its stated goals for inventory quality, transparency, and credibility, the United States has
developed a quality assurance and quality control plan designed to check, document and improve the quality of its
inventory over time. QA/QC activities on the Inventory are undertaken within the framework of the U.S. Quality
Assurance/Quality Control and Uncertainty Management Plan (QA/QC plan) for the U.S. Greenhouse Gas
Inventory: Procedures Manual for QA/QC and Uncertainty Analysis.
Key attributes of the QA/QC plan are summarized in Figure 1-2. These attributes include:
• Procedures and Forms: detailed and specific systems that serve to standardize the process of documenting
and archiving information, as well as to guide the implementation of QA/QC and the analysis of
uncertainty
• Implementation of Procedures: application of QA/QC procedures throughout the whole inventory
development process from initial data collection, through preparation of the emission estimates, to
publication of the Inventory
• Quality Assurance: expert and public reviews for both the Inventory estimates and the Inventory report
(which is the primary vehicle for disseminating the results of the inventory development process). The
expert technical review conducted by the UNFCCC supplements these QA processes, consistent with the
2006IPCC Guidelines (IPCC 2006)
• Quality Control, consideration of secondary data and category-specific checks (Tier 2 QC) in parallel and
coordination with the uncertainty assessment; the development of protocols and templates, which provides
for more structured communication and integration with the suppliers of secondary information
• Tier 1 (general) and Tier 2 (category-specific) Checks: quality controls and checks, as recommended by
IPCC Good Practice Guidance and 2006 IPCC Guidelines (IPCC 2006)
• Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC,
uncertainty analysis, and feedback mechanisms for corrective action based on the results of the
investigations which provide for continual data quality improvement and guided research efforts
• Multi-Year Implementation', a schedule for coordinating the application of QA/QC procedures across
multiple years, especially for category-specific QC
• Interaction and Coordination: promoting communication within the EPA, across Federal agencies and
departments, state government programs, and research institutions and consulting firms involved in
supplying data or preparing estimates for the Inventory. The QA/QC Management Plan itself is intended to
be revised and reflect new information that becomes available as the program develops, methods are
improved, or additional supporting documents become necessary.
In addition, based on the national QA/QC plan for the Inventory, source-specific QA/QC plans have been developed
for a number of sources. These plans follow the procedures outlined in the national QA/QC plan, tailoring the
procedures to the specific text and spreadsheets of the individual sources. For each greenhouse gas emissions source
or sink included in this Inventory, a minimum of a Tier 1 QA/QC analysis has been undertaken. Where QA/QC
activities for a particular source go beyond the minimum Tier 1 level, and include category-specific checks (Tier 2)
Introduction 1-19
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
further explanation is provided within the respective source category text. Similarly, responses or updates based on
comments from the expert, public and the international technical expert reviews (e.g., UNFCCC) are also addressed
within the respective source category text.
The quality control activities described in the U.S. QA/QC plan occur throughout the inventory process; QA/QC is
not separate from, but is an integral part of, preparing the Inventory. Quality control—in the form of both good
practices (such as documentation procedures) and checks on whether good practices and procedures are being
followed—is applied at every stage of inventory development and document preparation. In addition, quality
assurance occurs at two stages—an expert review and a public review. While both phases can significantly
contribute to inventory quality, the public review phase is also essential for promoting the openness of the inventory
development process and the transparency of the inventory data and methods.
The QA/QC plan guides the process of ensuring inventory quality by describing data and methodology checks,
developing processes governing peer review and public comments, and developing guidance on conducting an
analysis of the uncertainty surrounding the emission estimates. The QA/QC procedures also include feedback loops
and provide for corrective actions that are designed to improve the inventory estimates over time.
Figure 1-2: U.S. QA/QC Plan Summary
03
C
<
>-
c
d)
>
c
03
C
<
CI
§
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
Variable types
match values
Time series
consistency
Maintain trackingtab for
status of gathering
efforts
Contact reports for non-
electroniccommunications
Provide cell references for
primary data elements
Obtain copies of all data
sources
~stand location of any
working/external
spreadsheets
Document assumptions
Clearly label parameters,
units, and conversion
factors
Review spreadsheet
integrity
• Equations
¦ Units
• Inputs and output
Develop automated
checkers for:
• Input ranges
¦ Calculations
• Emission aggregation
Check input data for
transcription errors
Inspect automatic
checkers
Identify spreadsheet
modifications that could
provide additional
QA/QC checks
Check citations in
spreadsheetandtextfor
accuracy and style
Check reference docketfor
new citations
Review documentation for
any data/ methodology
changes
Reproduce calculations
Review time series
consistency
Review changes in
data/consistency with IPCC
methodology
Common starting
versions for each
inventory year
Utilize unalterable
summary tab foreach
source spreadsheet for
linkingto a master
summary spreadsheet
Follow strict version
control procedures
Document QA/QC
procedures
Data Gathering
Data Documentation Calculating Emissions
Cross-Cutting
Coordination
1.7 Uncertainty Analysis of Emission Estimates
Uncertainty estimates are an essential element of a complete and transparent emissions inventory. Uncertainty
information is not intended to dispute the validity of the Inventory estimates, but to help prioritize efforts to improve
the accuracy of future Inventories and guide future decisions on methodological choice. While the U.S. Inventory
1-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
carbon dioxide (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. The
UNFCCC reporting guidelines follow the recommendation in the 2006IPCC Guidelines (IPCC 2006) and require
that countries provide single point estimates for each gas and emission or removal source category. Within the
discussion of each emission source, specific factors affecting the uncertainty associated with the estimates are
discussed.
Additional research in the following areas could help reduce uncertainty in the U.S. Inventory:
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions are not available at this time. In particular, emissions from some land-use
activities and industrial processes are not included in the inventory either because data are incomplete or
because methodologies do not exist for estimating emissions from these source categories. See Annex 5 of
this report for a discussion of the sources of greenhouse gas emissions and sinks excluded from this report.
• Improving the accuracy of emission factors. Further research is needed in some cases to improve the
accuracy of emission factors used to calculate emissions from a variety of sources. For example, the
accuracy of current emission factors applied to CH4 and N20 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 where more technology or process-specific
emission factors can be applied.
The overall uncertainty estimate for total U.S. greenhouse gas emissions was developed using the IPCC Approach 2
uncertainty estimation methodology. Estimates of quantitative uncertainty for the total U.S. greenhouse gas
emissions are shown below, in Table 1-5.
The IPCC provides good practice guidance on two approaches—Approach 1 and Approach 2—to estimating
uncertainty for individual source categories. Approach 2 uncertainty analysis, employing the Monte Carlo Stochastic
Simulation technique, was applied wherever data and resources permitted; further explanation is provided within the
respective source category text and in Annex 7. Consistent with the 2006 IPCC Guidelines (IPCC 2006), over a
multi-year timeframe, the United States expects to continue to improve the uncertainty estimates presented in this
report.
Table 1-5: Estimated Overall Inventory Quantitative Uncertainty (MMT CO2 Eq. and Percent)
- TO BE UPDATED FOR FINAL INVENTORY REPORT
2014 Emission
Uncertainty Range Relative to Emission
Standard
Estimate3
Estimateb
Mean0
Deviation0
Gas
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
(MMT CO2 Eq.)
Lower
Upper
Lower
Upper
Boundd
Boundd
Bound
Bound
CO2
5,555.6
5,459.4
5,830.0
-2%
5%
5,643.8
94.9
CH4e
730.8
674.3
917.5
-8%
26%
785.0
60.2
N2Oe
403.5
322.5
447.9
-20%
11%
378.6
32.2
PFC, HFC, SFo, andNF3e
175.3
172.3
190.9
-4%
6%
181.6
4.7
Total
6,865.2
6,765.4
7,223.9
-2%
5%
6,989.0
117.5
LULUCF Emissions'
24.6
12.8
38.9
-48%
58%
23.0
6.8
LULUCF Total Net Flux®
(787.0)
(1,051.4)
(647.8)
-18%
34%
(847.2)
102.9
LULUCF Sector Total"
(762.5)
(1,029.8)
(622.5)
-18%
35%
(824.2)
103.0
Net Emissions (Sources and
6,102.7
5,861.6
6,477.6
-4%
6%
6,164.8
156.7
Notes: Total emissions (excluding emissions for which uncertainty was not quantified) is presented without LULUCF. Net
emissions is presented with LULUCF.
Introduction 1-21
-------
¦' Emission estimates reported in this table correspond to emissions from only those source categories for which quantitative
uncertainly was performed this year. Thus the totals reported in this table exclude approximately 5.3 MM'f CO: Lq. of
emissions for which quantitative uncertainly was not assessed. 1 lence. these emission estimates do not match the linal total U.S.
greenhouse gas emission estimates presented in this Inventory.
'' The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5'1' percentile and the upper bound corresponding to 97.5'1' percentile.
c Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation ol'the simulated values from the mean.
¦' 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.
J The overall uncertainly estimates did not lake into account the uncertainly in the GWP values for CI 1i, N:() and high GWP
gases used in the inventory emission calculations for 2014.
1 UJUJCf emissions include the CO:, CI 11. and N:() emissions reported for Non-CO: Emissions from forest l'ires. N:() fluxes
from forest Soils. CO: I 'missions from Agricultural Liming. CO: 1'missions from Urea fertilization, Peatlands Remaining
Peatlands. and N:() fluxes Iroin Settlement Soils.
" Net C(): llux is the net C stock change from the follow ing categories: forest I .and Remaining forest I .and. I .and Converted to
forest Land, Cropland Remaining Cropland. Land Converted to Cropland. Grassland Remaining Grassland, Land Converted to
Grassland, Settlements Remaining Settlements, and Other.
'' The LUI.WCf Sector Total is the net sum of all emissions (i.e.. sources) of greenhouse gases to the atmosphere plus removals
of C(): (i.e.. sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
1 Emissions calculated for the U.S. Inventory reflect current best estimates; in some cases, however, estimates are
2 based on approximate methodologies, assumptions, and incomplete data. As new information becomes available in
3 the future, the United States will continue to improve and revise its emission estimates. See Annex 7 of this report
4 for further details on the U. S. process for estimating uncertainty associated with the emission estimates and for a
5 more detailed discussion of the limitations of the current analysis and plans for improvement. Annex 7 also includes
6 details on the uncertainty analysis performed for selected source categories.
7 1.8 Completeness
8 This report, along with its accompanying CRF tables, serves as a thorough assessment of the anthropogenic sources
9 and sinks of greenhouse gas emissions for the United States for the time series 1990 through 2015. This report is
10 intended to be comprehensive and includes the vast majority of emissions and removals identified as anthropogenic,
11 consistent with IPCC and UNFCCC guidelines. Certain smaller sources have been identified which were excluded
12 from the estimates presented for various reasons. Generally speaking, sources not accounted for in this Inventory are
13 excluded due to data limitations and assessment of significance in terms of overall national emissions per UNFCCC
14 reporting guidelines. The United States is continually working to improve upon the understanding of such sources
15 and seeking to find the data required to estimate related emissions. As such improvements are implemented, new
16 emission sources are quantified and included in the Inventory. For a complete list of sources not included, see
17 Annex 5 of this report.
is 1.9 Organization of Report
19 In accordance with the revision of the UNFCCC reporting guidelines agreed to at the nineteenth Conference of the
20 Parties (UNFCCC 2014), this Inventory of U.S. Greenhouse Gas Emissions and Sinks is segregated into five sector-
21 specific chapters, listed below in Table 1-6. In addition, chapters on Trends in Greenhouse Gas Emissions and Other
22 information to be considered as part of the U.S. Inventory submission are included.
1-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
Table 1-6: IPCC Sector Descriptions
Chapter/EPCC Sector
Activities Included
Energy
Industrial Processes and
Product Use
Agriculture
Land Use, Land-Use
Change, and Forestry
Waste
Emissions of all greenhouse gases resulting from stationary and mobile energy
activities including fuel combustion and fugitive fuel emissions, and non-
energy use of fossil fuels.
Emissions resulting from industrial processes and product use of greenhouse
gases.
Anthropogenic emissions from agricultural activities except fuel combustion,
which is addressed under Energy.
Emissions and removals of CO2, CH4, and N2O from forest management, other
land-use activities, and land-use change.
Emissions from waste management activities.
2 Within each chapter, emissions are identified by the anthropogenic activity that is the source or sink of the
3 greenhouse gas emissions being estimated (e.g., coal mining). Overall, the following organizational structure is
4 consistently applied throughout this report:
5 Chapter/IPCC Sector Overview of emission trends for each IPCC defined sector
6 Source category. Description of source pathway and emission trends.
7 Methodology: Description of analytical methods employed to produce emission estimates and identification of data
8 references, primarily for activity data and emission factors.
9 Uncertainty and Timeseries Consistency: A discussion and quantification of the uncertainty in emission estimates
10 and a discussion of time-series consistency.
11 QA/QC and Verification: A discussion on steps taken to QA/QC and verify the emission estimates, where beyond
12 the overall U.S. QA/QC plan, and any key findings.
13 Recalculations: A discussion of any data or methodological changes that necessitate a recalculation of previous
14 years' emission estimates, and the impact of the recalculation on the emission estimates, if applicable.
15 Planned Improvements: A discussion on any source-specific planned improvements, if applicable.
16 Special attention is given to CO2 from fossil fuel combustion relative to other sources because of its share of
17 emissions and its dominant influence on emission trends. For example, each energy consuming end-use sector (i.e.,
18 residential, commercial, industrial, and transportation), as well as the electricity generation sector, is described
19 individually. Additional information for certain source categories and other topics is also provided in several
20 Annexes listed in Table 1-7.
21 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 Emissions from Commercial Aircraft Jet Fuel Consumption
3.4. Methodology for Estimating CH4 Emissions from Coal Mining
3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum Systems
3.6. Methodology for Estimating CH4 Emissions from Natural Gas Systems
3.7. Methodology for Estimating CO2 and N2O Emissions from Incineration of Waste
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military
3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances
Introduction 1-23
-------
3.10.
Methodology for Estimating CH4 Emissions from Enteric Fermentation
3.11.
Methodology for Estimating CH4 and N2O Emissions from Manure Management
3.12.
Methodology for Estimating N2O Emissions, CH4 Emissions and Soil Organic C Stock Changes from
Agricultural Lands (Cropland and Grassland)
3.13.
Methodology for Estimating Net Carbon Stock Changes in Forest Land Remaining Forest Land and Land
Converted to Forest Land
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
6.7.
Chemical Formulas
ANNEX
7 Uncertainty
7.1.
Overview
7.2.
Methodology and Results
7.3.
Planned Improvements
ANNEX
8 QA/QC Procedures
8.1.
Background
8.2.
Purpose
8.3.
Assessment Factors
1-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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17
2. Trends in Greenhouse Gas Emissions
2.1 Recent Trends in U.S. Greenhouse Gas
Emissions and Sinks
In 2015, total gross U.S. greenhouse gas emissions were 6,586.2 MMT or million metric tons carbon dioxide (CO2)
Eq. Total U.S. emissions have increased by 3.4 percent from 1990 to 2015, and emissions decreased from 2014 to
2015 by 2.2 percent (150.1 MMT CO2 Eq.). The decrease in total greenhouse gas emissions between 2014 and 2015
was driven in large part by a decrease in CO2 emissions from fossil fuel combustion. The decrease in CO2 emissions
from fossil fuel combustion was a result of multiple factors, including: (1) substitution from coal to natural gas
consumption in the electric power sector; (2) warmer winter conditions in the first quarter of 2015 resulting in a
decreased demand for heating fuel in the residential and commercial sectors; and (3) a slight decrease in electricity
demand. Since 1990, U.S. emissions have increased at an average annual rate of 0.2 percent. Figure 2-1 through
Figure 2-3 illustrate the overall trend in total U.S. emissions by gas, annual changes, and absolute changes since
1990. Overall, net emissions in 2015 were 11.2 percent below 2005 levels as shown in Table 2-1.
Figure 2-1: Gross U.S. Greenhouse Gas Emissions by Gas (MMT CO2 Eq.)
8,000
7,000
6,000
d- 5,000
LU
o
I- 4,000
Z:
z
3,000
2,000
1,000
0
o*-HfNro^-Lnv£>r^coCT>o->-«rMmTi-Lnvor^coCT>o*-—I t—I 7—I fsj <~\l fN fN CM CM CM fN CM CsJ C\1 rsl fN fN fN fN
¦ HFCs, PFCs, SF6 and NFs
I Nitrous Oxide
¦ Methane
¦ Carbon Dioxide
Trends 2-1
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2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Figure 2-2: Annual Percent Change in Gross U.S. Greenhouse Gas Emissions Relative to the
Previous Year
4%
2%
0%
-2%
-4%
-6%
-8%
-0.9%
2.9%
3.3%
1.8% 1.7%a
— _ 1.4% 1.3%
III
1
O8%0^%05%
1 M
>.3%
1
:
0.5% 0.6%
m m
L.9 °/<
1
0.1%
L.4%
I
'0
1
¦
-0.9%
¦ |2.2%
¦
¦ 0.9%
ll
1 " || l| I
-2.1% ¦ -2.2%
-2.8% ¦
-3.5%
-6.2%
'-ifNiro^j-Lnusrvcocri
CTiCTiCTiCTiCXiOICTiO^O'i
a>cr>cr>CT>cncr>cr>
rM m m
_ - _________ o o o o
r\irNrMr\ir\irMr\ir\ir\ir\ir\irMrsjrNir\irN
Figure 2-3: Cumulative Change in Annual Gross U.S. Greenhouse Gas Emissions Relative to
1990 (1990=0, MMT COz Eq.)
1,200-
1,100-
1,000-
900-
800-
700-
600-
500-
400-
300-
200-
100-
0-
-100-
-200-
940 949
57
-55
'-HrMro^j-Lnvor^cocri
r\irNr\ir\lr\ir\ir\ir\irsjf\ir\irNr>jrMrsjrM
Overall, from 1990 to 2015, total emissions of CO2 increased by 289.2 MMT CO2 Eq. (5.6 percent), while total
emissions of methane (CH4) decreased by 131.2 MMT CO: Eq. (16.7 percent), and total emissions of nitrous oxide
(N2O) decreased by 24.5 MMT CO2 Eq. (6.8 percent). During the same period, aggregate weighted emissions of
hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF6), and nitrogen trifluoride (NF3) rose
by 85.9 MMT CO2 Eq. (86.2 percent). Despite being emitted in smaller quantities relative to the other principal
greenhouse gases, emissions of HFCs, PFCs, SF6, and NF3 are significant because many of them have extremely
high global wanning potentials (GWPs), and, in the cases of PFCs, SF6, and NF3, long atmospheric lifetimes.
Conversely, U.S. greenhouse gas emissions were partly offset by carbon (C) sequestration in managed forests, trees
in urban areas, agricultural soils, landfilled yard trimmings, and coastal wetlands. These were estimated to offset 5.9
percent of total emissions in 2015.
As the largest contributor to U.S. greenhouse gas emissions, CO2 from fossil fuel combustion lias accounted for
approximately 77 percent of GWP-weighted emissions for the entire time series since 1990. Emissions from this
source category grew by 6.5 percent (308.5 MMT CO2 Eq.) from 1990 to 2015 and were responsible for most of the
increase in national emissions during this period. In addition, CO2 emissions from fossil fuel combustion decreased
2-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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3
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5
6
7
8
9
10
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12
13
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15
16
17
18
19
20
21
22
23
24
25
26
27
28
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30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
from 2005 levels by 698.0 MMT CO2 Eq., a decrease of approximately 12.1 percent between 2005 to 2015. From
2014 to 2015, these emissions decreased by 2.9 percent (153.0 MMT 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, energy fuel choices,
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.
Energy-related CO2 emissions also depend on the type of fuel or energy consumed and its 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
2011.
From 2011 to 2012, CO2emissions from fossil fuel combustion decreased by 3.9 percent, with emissions from fossil
fuel combustion at their lowest level since 1994. This decrease from 2011 to 2012 is primarily a result of the
decrease in the carbon intensity of fuels used to generate electricity due to a slight increase in the price of coal, and a
significant decrease in the price of natural gas. The consumption of coal used to generate electricity decreased by
12.3 percent, while consumption of natural gas for electricity generation increased by 20.4 percent. Also, emissions
declined in the transportation sector largely due to a small increase in fuel efficiency across different transportation
modes and limited new demand for passenger transportation. In 2012, weather conditions remained fairly constant in
the summer and were much warmer in the winter compared to 2011, as cooling degree days increased by 1.7 percent
while heating degree days decreased 12.6 percent. This decrease in heating degree days resulted in a decreased
demand for heating fuel in the residential and commercial sector, which had a decrease in natural gas consumption
of 11.7 and 8.0 percent, respectively.
From 2012 to 2013, CO2 emissions from fossil fuel combustion increased by 2.6 percent. This increase is primarily a
result of the increased energy consumption in the residential and commercial sectors, as heating degree days
increased 18.5 percent in 2013 as compared to 2012. The cooler weather led to an increase of 17.1 and 12.9 percent
direct use of fuels in the residential and commercial sectors, respectively. In addition, there was an increase of 1.5
and 0.8 percent in electricity consumption in the residential and commercial sectors, respectively, due to regions that
heat their homes with electricity. The consumption of natural gas used to generate electricity decreased by 9.8
percent due to an increase in the price of natural gas. Electric power plants shifted some consumption from natural
gas to coal, and as a result increased coal consumption to generate electricity by 4.0 percent. Lastly, industrial
production increased 1.9 percent from 2012 to 2013, resulting in an increase in the in CO2 emissions from fossil fuel
combustion from the industrial sector by 3.7 percent.
From 2013 to 2014, CO2 emissions from fossil fuel combustion increased by 0.9 percent. This increase is primarily a
result of the increased energy consumption in the transportation (approximately 40 percent of increase), residential
(approximately 35 percent of increase), and commercial (approximately 27 percent of increase) sectors. In the
transportation sector, VMT increased by 1.3 percent resulting in increased fuel consumption across on-road
transportation modes. Heating degree days increased 1.9 percent in 2014 as compared to 2013, resulting in an
increased demand in heating fuels for the residential and commercial sectors. The cooler weather led to an increase
of 4.6 and 4.9 percent in direct use of fuels in the residential and commercial sectors, respectively. In addition, there
was an increase of 0.9 and 1.1 percent in electricity consumption in the residential and commercial sectors,
respectively, due to regions that heat their homes with electricity. Lastly, industrial production increased 2.9 percent
from 2013 to 2014, resulting in a slight increase in CO2 emissions from fossil fuel combustion from the industrial
sector by 0.4 percent. From the perspective of how these sector trends contributed to the overall 0.9 percent increase
from 2013 to 2014, the residential and commercial sectors were approximately 47 percent of the annual increase, the
transportation sector was 30 percent of the annual increase, and the industrial sector was just over 3 percent of the
2013 to 2014 increase in overall U.S. emissions.
From 2014 to 2015, CO2 emissions from fossil fuel combustion decreased by 2.9 percent, with emissions from fossil
fuel combustion at their lowest level since 1995. This decrease from 2014 to 2015 is primarily a result of the
decrease in the carbon intensity of fuels used to generate electricity due to significant decrease in the price of natural
gas. The consumption of coal used to generate electricity decreased by 13.9 percent, while consumption of natural
Trends 2-3
-------
1 gas for electricity generation increased by 18.7 percent. In 2015, weather conditions were much warmer in the
2 summer and winter compared to 2014, as cooling degree days increased by 14.6 percent while heating degree days
3 decreased 10.2 percent. This decrease in heating degree days resulted in a decreased demand for heating fuel in the
4 residential and commercial sector, which had a decrease in natural gas consumption of 9.0 and 7.4 percent,
5 respectively.
6 Table 2-1 summarizes emissions and sinks from all U.S. anthropogenic sources in weighted units of MMT CO2 Eq.,
7 while unweighted gas emissions and sinks in kilotons (kt) are provided in Table 2-2.
8 Table 2-1: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO,
5,121.4
6,129.7
5,567.5
5,359.5
5,512.1
5,561.8
5,410.6
Fossil Fuel Combustion
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Treimportation
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Industrial
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Residential
338.3
357.8
325.5
282.5
329.7
345.4
319.6
Commercial
217.4
223.5
220.4
196.7
221.0
231.4
225.7
U.S. Territories
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Non-Energy Use of Fuels
117.7
138.3
108.5
105.5
122.0
117.2
127.0
Iron and Steel Production &
Metallurgical Coke Production
99.7
66.5
59.9
54.2
52.2
57.5
47.9
Natural Gas Systems
37.7
30.1
35.7
35.2
38.5
42.4
42.4
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.3
27.0
26.3
26.5
26.4
26.5
28.1
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
Other Process Uses of Carbonates
4.9
6.3
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
Incineration of Waste
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Urea Fertilization
2.4
3.5
4.1
4.3
4.5
4.8
5.0
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Liming
4.7
4.3
3.9
6.0
3.9
3.6
3.8
Petroleum Systems
3.6
3.9
4.2
3.9
3.7
3.6
3.6
Soda Ash Production and
Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Aluminum Production
6.8
4.1
3.3
3.4
3.3
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-
Agricultural Purposes
3.8
3.7
4.0
4.4
4.0
1.4
1.1
Phosphoric Acid Production
1.5
1.3
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Magnesium Production and
Processing
+
+
+
+
+
+
+
Wood Biomass and Ethanol
Consumption"
219.4
229.8
268.1
267.7
286.3
293.7
277.7
International Bunker Fuelsb
103.5
113.1
111.7
105.8
99.8
103.2
110.8
CH,
786.1
685.4
673.4
667.2
659.6
659.4
654.9
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Natural Gas Systems
196.5
162.1
153.7
155.3
157.9
160.8
160.0
Landfills
179.6
134.3
119.0
120.8
116.7
116.6
115.7
Manure Management
37.2
56.3
63.0
65.6
63.3
62.9
66.3
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Petroleum Systems
58.3
48.0
50.1
48.4
46.6
44.9
41.5
2-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Wastewater Treatment
15.7
16.0
15.3
15.1
14.9
14.8
14.8
Rice Cultivation
16.0
16.7
14.1
11.3
11.3
11.4
11.2
Stationary Combustion
8.5
7.4
7.1
6.6
8.0
8.1
7.0
Abandoned Underground Coal
Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Composting
0.4
1.9
1.9
1.9
2.0
2.1
2.1
Mobile Combustion
5.6
2.8
2.3
2.2
2.1
2.1
2.0
Field Burning of Agricultural
Residues
0.2
0.2
0.3
0.3
0.3
0.3
0.3
Petrochemical Production
0.2
0.1
+
0.1
0.1
0.1
0.2
Ferroalloy Production
+
+
+
+
+
+
+
Silicon Carbide Production and
Consumption
+
+
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
+
+
+
+
+
+
+
Incineration of Waste
+
+
+
+
+
+
+
International Bunker Fuelsb
0.2
0.1
0.1
0.1
0.1
0.1
0.1
NjO
359.6
361.6
364.0
340.7
335.5
335.5
335.1
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Stationary Combustion
11.9
20.2
21.3
21.4
22.9
23.4
23.1
Manure Management
14.0
16.5
17.4
17.5
17.5
17.5
17.7
Mobile Combustion
41.2
35.7
22.8
20.4
18.5
16.6
15.4
Nitric Acid Production
12.1
11.3
10.9
10.5
10.7
10.9
11.6
Wastewater Treatment
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Composting
0.3
1.7
1.7
1.7
1.8
1.9
1.9
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
Semiconductor Manufacture
+
0.1
0.2
0.2
0.2
0.2
0.2
Field Burning of Agricultural
Residues
0.1
0.1
0.1
0.1
0.1
0.1
0.1
International Bunker Fuelsb
0.9
1.0
1.0
0.9
0.9
0.9
0.9
HFCs
46.6
120.0
154.4
155.9
159.0
166.7
174.1
Substitution of Ozone Depleting
Substances0
0.3
99.8
145.4
150.2
154.7
161.3
168.6
HCFC-22 Production
46.1
20.0
8.8
5.5
4.1
5.0
5.0
Semiconductor Manufacture
0.2
0.2
0.2
0.2
0.2
0.3
0.3
Magnesium Production and
Processing
0.0
0.0
+
+
0.1
0.1
0.1
PFCs
24.3
6.7
6.9
6.0
5.7
5.7
5.2
Semiconductor Manufacture
2.8
3.2
3.4
3.0
2.8
3.2
3.2
Aluminum Production
21.5
3.4
3.5
2.9
3.0
2.5
2.0
sf5
28.8
11.7
9.2
6.8
6.4
6.6
5.8
Electrical Transmission and
Distribution
23.1
8.3
6.0
4.8
4.6
4.8
4.2
Magnesium Production and
Processing
5.2
2.7
2.8
1.6
1.5
1.0
0.9
Semiconductor Manufacture
0.5
0.7
0.4
0.4
0.4
0.7
0.7
NF,
+
0.5
0.7
0.6
0.6
0.5
0.6
Semiconductor Manufacture
+
0.5
0.7
0.6
0.6
0.5
0.6
Total Emissions
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
LULUCF Emissions'1
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change^
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Totals
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
Net Emissions (Sources and Sinks)
5,917.6
7,000.3
6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 Eq.
Trends 2-5
-------
a Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing Energy sector
totals. Net carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for
LULUCF.
b Emissions from International Bunker Fuels are not included in totals.
c Small amounts of PFC emissions also result from this source.
d LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands, CH4 and
N2O emissions reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and
Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and
N2O Fluxes from Forest Soils and Settlement Soils.
e Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015,
which will be updated following public review. Corrected estimates are provided in footnotes of the emission
summary tables for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, and Land Converted to Grassland sections in the LULUCF chapter of this report.
f LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest
Land, Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands,
Settlements Remaining Settlements, and Land Converted to Settlements. Refer to Table 2-8 for a breakout of
emissions and removals for LULUCF by gas and source category.
B The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere
plus removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
1 Table 2-2: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO,
5,121.419
6,129,674
5,567,521
5,359,489
5,512,061
5,561,846
5,410,599
Fossil Fuel Combustion
4,740,671
5,747,142
5,227,690
5,024,685
5,157,583
5,202,139
5,049,159
Electricity Generation
1,820,SIS
2,400,874
2,157,688
2,022,181
2,038,122
2,038,018
1,900,673
Transportation
1,493,758
1,887,033
1,707,631
1,696,752
1,713,002
1,730,383
1,733,235
Industrial
842,473
827,999
774,951
782,929
812,228
815,758
828,797
Residential
338,347
357,834
325,537
282,540
329,674
345,390
319,565
Commercial
217,393
223,480
220,381
196,714
221,030
231,385
225,671
U.S. Territories
27,882
49,923
41,503
43,569
43,528
41,204
41,219
Non-Energy Use of Fuels
117,658
138,341
108,508
105,537
121,998
117,235
127,047
Iron and Steel Production &
Metallurgical Coke
Production
99,670
66,544
59,929
54,231
52,202
57,503
47,912
Natural Gas Systems
37,732
30,076
35,662
35,203
38,457
42,351
42,351
Cement Production
33,278
45,910
32,010
35,053
36,145
38,789
39,587
Petrochemical Production
21,326
26,972
26,338
26,501
26,395
26,496
28,062
Lime Production
11,700
14,552
13,982
13,785
14,028
14,210
13,342
Other Process Uses of
Carbonates
4,907
6,339
9,335
8,022
10,414
11,811
10,828
Ammonia Production
13,047
9,196
9,292
9,377
9,962
9,619
10,799
Incineration of Waste
7,950
12,469
10,564
10,379
10,398
10,608
10,676
Urea Fertilization
2,417
3,504
4,097
4,267
4,504
4,781
5,032
Carbon Dioxide Consumption
1,472
1,375
4,083
4,019
4,188
4,471
4,296
Liming
4,667
4,349
3,873
5,978
3,907
3,609
3,810
Petroleum Systems
3,553
3,927
4,192
3,876
3,693
3,567
3,567
Soda Ash Production and
Consumption
2,822
2,960
2,712
2,763
2,804
2,ill
2,789
Aluminum Production
6,831
4,142
3,292
3,439
3,255
2,833
2,767
Ferroalloy Production
2,152
1,392
1,735
1,903
1,785
1,914
1,960
Titanium Dioxide Production
1,195
1,755
1,729
1,528
1,715
1,688
1,554
Glass Production
1,535
1,928
1,299
1,248
1,317
1,336
1,299
Urea Consumption for Non-
Agricultural Purposes
3,784
3,653
4,030
4,407
4,014
1,380
1,128
Phosphoric Acid Production
1,529
1,342
1,171
1,118
1,149
1,038
1,007
Zinc Production
632
1,030
1,286
1,486
1,429
956
939
Lead Production
516
55o
538
527
546
509
504
2-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Silicon Carbide Production and
Consumption
375
219
170
158
169
173
180
Magnesium Production and
Processing
1
3
2
2
2
3
Wood Biomass and Ethanol
Consumption"
219,413
229,844
268,064
267,730
286,323
293,729
277,657
International Bunker Fuelsh
103,463
113,139
111,660
105,805
99,763
103,201
110,751
CH,
31,443
27,417
26,934
26,687
26,384
26,374
26,196
Enteric Fermentation
6,566
6,755
6,757
6,670
6,619
6,567
6,661
Natural Gas Systems
7,862
6,48.5
6,147
6,213
6,317
6,433
6,401
Landfills
7,182
5,372
4,760
4,834
4,669
4,663
4,628
Manure Management
1,486
2,254
2,519
2,625
2,530
2,514
2,651
Coal Mining
3,860
2,565
2,849
2,658
2,584
2,593
2,436
Petroleum Systems
2,330
1,921
2,004
1,935
1,864
1,796
1,660
Wastewater Treatment
627
639
613
604
597
592
591
Rice Cultivation
641
667
564
453
454
456
449
Stationary Combustion
339
296
283
265
320
324
278
Abandoned Underground Coal
Mines
288
264
257
249
249
253
256
Composting
15
7.5
75
77
81
84
84
Mobile Combustion
226
llo
91
87
85
82
82
Field Burning of Agricultural
Residues
9
8
11
11
11
11
11
Petrochemical Production
9
2
3
3
5
7
Ferroalloy Production
1
+ /
+
1
+
1
1
Silicon Carbide Production and
Consumption
1
-
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke
Production
1
1
+
+
+
+
+
Incineration of Waste
+
+ ** ,
+
+
+
+
+
International Bunker Fuelsb
7
.5
5
4
3
3
3
NjO
1,207
1,214
1,222
1,143
1,126
1,126
1,124
Agricultural Soil Management
861
872
906
853
841
839
843
Stationary Combustion
40
68
71
72
77
79
78
Manure Management
47
55
58
59
59
59
59
Mobile Combustion
138
120
77
68
62
56
52
Nitric Acid Production
41
38
37
35
36
37
39
Wastewater Treatment
11
15
16
16
16
16
17
Adipic Acid Production
51
24
34
19
13
18
14
N20 from Product Uses
14
14
14
14
14
14
14
Composting
1
6
6
6
6
6
6
Incineration of Waste
2
1
1
1
1
1
1
Semiconductor Manufacture
+
-
1
1
1
1
1
Field Burning of Agricultural
Residues
+
+ /
+
+
+
+
+
International Bunker Fuelsb
3
3
3
3
3
3
3
HFCs
M
IV1
M
M
M
M
M
Substitution of Ozone
Depleting Substances0
M
M
M
M
M
M
M
HCFC-22 Production
3
1
1
+
+
+
+
Semiconductor Manufacture
+
-
+
+
+
+
+
Magnesium Production and
Processing
0
0
+
+
+
+
+
PFCs
M
IV1
M
M
M
M
M
Semiconductor Manufacture
M
M
M
M
M
M
M
Aluminum Production
M
M
M
M
M
M
M
SFs
1
1
+
+
+
+
+
Trends 2-7
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacture
NF3
Semiconductor Manufacture
+ Does not exceed 0.5 kt.
M - Mixture of multiple gases
a Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing Energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for LULUCF.
b Emissions from International Bunker Fuels are not included in totals.
c Small amounts of PFC emissions also result from this source.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
Emissions of all gases can be summed from each source category into a set of five sectors defined by the
Intergovernmental Panel on Climate Change (IPCC). Figure 2-4 and Table 2-3 illustrate that over the twenty-six
year period of 1990 to 2015, total emissions in the Energy, Industrial Processes and Product Use, and Agriculture
sectors grew by 215.7 MMT CO2 Eq. (4.0 percent), 36.7 MMT CO2 Eq. (10.9 percent), and 27.0 MMT CO2 Eq. (5.5
percent), respectively. Emissions from the Waste sector decreased by 59.9 MMT CO2 Eq. (30.1 percent). Over the
same period, estimates of net C sequestration for the Land Use, Land-Use Change, and Forestry sector (magnitude
of emissions plus CO2 removals from all LULUCF categories) increased by 82.7 MMT CO2 Eq. (18.4 percent).
Figure 2-4: U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC Sector (MMT CO2
Eq.)
500-
000-
500-
000-
500-
000-
500-
000-
500-
000-
500-
000-
500-
000-
500-
0-1"
500-
Industrial Processes and Product Use
Agriculture
Waste
LULUCF (emissions)
Energy
Land Use, Land-Use Chang
Table 2-3: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks by Chapter/IPCC
Sector (MMT COz Eq.)
Chapter/IPCC Sector
1990
2005
2011
2012
2013
2014
2015
Energy
5,333.8
6,279.4
5,721.8
5,506.9
5,659.3
5,703.2
5,549.4
Fossil Fuel Combustion
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Natural Gas Systems
234.3
192.2
189.3
190.5
196.4
203.2
202.4
Non-Energy Use of Fuels
117.7
138.3
108.5
105.5
122.0
117.2
127.0
2-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Petroleum Systems
61.8
52.0
54.3
52.3
50.3
48.5
45.1
Stationary Combustion
20.4
27.6
28.4
28.0
30.9
31.5
30.1
Mobile Combustion
46.9
38.6
25.1
22.6
20.6
18.6
17.4
Incineration of Waste
8.4
12.9
10.9
10.7
10.7
10.9
11.0
Abandoned Underground Coal Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Industrial Processes and Product Use
338.3
351.6
369.7
359.5
362.4
378.1
375.1
Substitution of Ozone Depleting
Substances
0.3
99.8
145.4
150.2
154.7
161.3
168.6
Iron and Steel Production &
Metallurgical Coke Production
99.7
66.6
59.9
54.2
52.2
57.5
47.9
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.5
27.0
26.4
26.6
26.5
26.6
28.2
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
Nitric Acid Production
12.1
11.3
10.9
10.5
10.7
10.9
11.6
Other Process Uses of Carbonates
4.9
6.3
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
HCFC-22 Production
46.1
20.0
OO
OO
5.5
4.1
5.0
5.0
Semiconductor Manufacture
3.6
4."
4.9
4.5
4.1
5.0
5.0
Aluminum Production
28.3
7.6
6.8
6.4
6.2
5.4
4.8
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Electrical Transmission and
Distribution
23.1
8.3
6.0
4.8
4.6
4.8
4.2
Soda Ash Production and
Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-
Agricultural Purposes
3.8
37
4.0
4.4
4.0
1.4
1.1
Magnesium Production and
Processing
5.2
2"
2.8
1.7
1.5
1.1
1.0
Phosphoric Acid Production
1.5
17
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Agriculture
495.3
526.4
541.9
525.9
516.9
514.7
522.3
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Manure Management
51.1
72.9
80.4
83.2
80.8
80.4
84.0
Rice Cultivation
16.0
16.7
14.1
11.3
11.3
11.4
11.2
Urea Fertilization
2.4
3.5
4.1
4.3
4.5
4.8
5.0
Liming
4.7
4.3
3.9
6.0
3.9
3.6
3.8
Field Burning of Agricultural
Residues
0.3
0.3
0.4
0.4
0.4
0.4
0.4
Waste
199.3
158.2
142.6
144.4
140.4
140.2
139.4
Landfills
179.6
134.3
119.0
120.8
116.7
116.6
115.7
Wastewater Treatment
19.1
20.4
20.1
19.9
19.8
19.7
19.7
Composting
0.7
3.5
3.5
3.7
3.9
4.0
4.0
Total Emissions3
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
Land Use, Land-Use Change, and
Forestry
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
Forest Land
(785.0)
(730.7)
(734.8)
(724.6)
(734.5)
(732.8)
(729.7)
Croplandb
59.8
16.1
16.2
13.9
12.9
13.8
14.5
Grasslandb
241.2
329.9
286.0
273.6
302.4
302.9
302.3
Trends 2-9
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Wetlands
Settlements
(4.0) (5.3) (4.1)
39.0 74.7 61.6
(4.2)
53.7
(4.3)
53.1
(4.2)
51.6
(4.3)
50.7
Net Emission (Sources and Sinks)0
5,917.6 7,000.3 6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
Notes: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
a Total emissions without LULUCF.
b Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to
Grassland sections in the LULUCF chapter of this report.
c Net emissions with LULUCF.
Notes: Totals may not sum due to independent rounding. 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 2015. In 2015, approximately 82 percent of the energy consumed in the United States (on
a Btu basis) was produced through the combustion of fossil fuels. The remaining 18 percent came from other energy
sources such as hydropower, biomass, nuclear, wind, and solar energy (see Figure 2-5 and Figure 2-6). A discussion
of specific trends related to 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 N20 emissions (42 percent and 12
percent of total U.S. emissions of each gas, respectively). Table 2-4 presents greenhouse gas emissions from the
Energy chapter, by source and gas.
Figure 2-5: 2015 Energy Chapter Greenhouse Gas Sources (MMT CO2 Eq.)
Combustion I 5,049
Natural Gas Systems
Non-Energy Use of Fuels
Coal Mining
Petroleum Systems
Stationary Combustion
Mobile Combustion
Incineration of Waste
Abandoned Underground Coal Mines
Energy as a Portion of
all Emissions
83.1%
50
100
150
MMT C02 Eq.
200
250
300
2-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Figure 2-6: 2015 U.S. Fossil Carbon Flows (MMT CO2 Eq.)
2
NEU Emissions Y.
Bunkers
Energy Exports
NEU Emissions 6
Natural Gas Emissions
\,4M
Atmospheric
Emissions
5,373
1,476
Natural Gas Liquids,
Liquefied Refinery Gas,
& Other Liquids
Non-Energy Use
Carbon Sequestered
Fossi! Fuei
Energy
Imports
Non-Energy Balancing
Use U.S. 'tern
Territories (88;
Note; Totafs may not sum due to independent founding.
j Item" above accounts for the statistical
d unknowns in the reported data sets combined
4 Table 2-4: Emissions from Energy (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
4,907.6
5,932.0
< 5,386.6
5,179.7
5,332.1
5,375.9
5,232.8
Fossil Fuel Combustion
4,740.7
5,747.1
; 5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.9
5 2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Treimportation
1,493.8
1,887.0
\ 1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Industrial
842.5
828.0
] 775.0
782.9
812.2
815.8
828.8
Residential
338.3
357.8
1 325.5
282.5
329.7
345.4
319.6
Commercial
217.4
223.5
220.4
196.7
221.0
231.4
225.7
U.S. Territories
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Non-Energy Use of Fuels
117.7
138.3
108.5
105.5
122.0
117.2
127.0
Natural Gas Systems
37.7
30.1
35.7
35.2
38.5
42.4
42.4
Incineration of Waste
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Petroleum Systems
3.6
3.9
4.2
3.9
3.7
3.6
3.6
Biomass- Wood"
215.2
206.9
195.2
194.9
211.6
217.7
198.7
International Bunker Fuelsb
103.5
113.1
111.7
105.8
99.8
103.2
110.8
Biomass-Ethanol"
4.2
22.9
72.9
72.8
74.7
76.1
78.9
cm
372.6
291.1
290.8
285.2
285.5
287.0
277.8
Natural Gas Systems
196.5
162.1
153.7
155.3
157.9
160.8
160.0
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Petroleum Systems
58.3
48.0
50.1
48.4
46.6
44.9
41.5
Stationary Combustion
8.5
7.4
7.1
6.6
8.0
8.1
7.0
Abandoned Underground Coal
Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Mobile Combustion
5.6
2.8
2.3
2.2
2.1
2.1
2.0
Incineration of Waste
+
+
+
+
+
+
+
International Bunker Fuelsb
0.2
0.1
0.1
0.1
0.1
0.1
0.1
N2O
53.6
56.4
44.4
42.1
41.7
40.3
38.8
Stationary Combustion
11.9
20.2
21.3
21.4
22.9
23.4
23.1
Mobile Combustion
41.2
35.7
22.8
20.4
18.5
16.6
15.4
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
International Bunker Fuelsb
0.9
1.0
1.0
0.9
0.9
0.9
0.9
Total
5,333.8
6,279.4
5,721.8
5,506.9
5,659.3
5,703.2
5,549.4
Trends 2-11
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
+ Does not exceed 0.05 MMT CO2 Eq.
a Emissions from Wood Biomass and Ethanol Consumption are not included specifically in summing energy sector totals. Net
carbon fluxes from changes in biogenic carbon reservoirs are accounted for in the estimates for LULUCF.
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 the U.S. Energy Information Administration (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 are comprised of electricity-only
and combined-heat-and-power (CHP) plants within the North American Industry Classification System (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 consist of living quarters for private
households. EIA's fuel consumption data for the commercial sector consist of service-providing facilities and
equipment from private and public organizations and businesses (EIA includes generators that produce electricity
and/or useful thermal output primarily to support the activities at commercial establishments in this sector). Table
2-5 and Figure 2-7 summarize CO2 emissions from fossil fuel combustion by end-use sector. Figure 2-8 further
describes the total emissions from fossil fuel combustion, separated by end-use sector, including CH4 and N20 in
addition to CO2.
Table 2-5: CO2 Emissions from Fossil Fuel Combustion by End-Use Sector (MMT CO2 Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation
1,496.8
1,891.8
1,711.9
1,700.6
1,717.0
1,734.4
1,737.0
Combustion
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Electricity
3.0
4.7
4.3
3.9
4.0
4.1
3.7
Industrial
1,529.2
1,564.6
1,399.6
1,375.7
1,407.0
1,409.0
1,378.3
Combustion
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Electricity
686.7
736.6
624.7
592.8
594.7
593.2
549.6
Residential
931.4
1,214.1
1,116.2
1,007.8
1,064.6
1,080.1
1,003.8
Combustion
338.3
357.8
325.5
282.5
329.7
345.4
319.6
Electricity
593.0
856.3
790.7
725.3
734.9
734.7
684.3
Commercial
755.4
1,026.8
958.4
897.0
925.5
937.4
888.8
Combustion
217.4
223.5
220.4
196.7
221.0
231.4
225.7
Electricity
538.0
803.3
738.0
700.3
704.5
706.0
663.1
U.S. Territories3
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Total
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
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.
Notes: Combustion-related emissions from electricity generation are allocated based on aggregate national
electricity consumption by each end-use sector. Totals may not sum due to independent rounding.
2-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Figure 2-7: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT
COz Eq.)
c
u
t
2,500
2,000
1,500
1,000
500
Relative Contribution by Fuel Type
226
1,901
I Petroleum
I Coal
I Natural Gas
Geothermal
1,733
41
U.S. Territories
Commercial
Residential
Industrial
Transportation Electricity Generation
Figure 2-8: 2015 End-Use Sector Emissions of CO2 from Fossil Fuel Combustion (MMT CO2
Eq.)
2000
1500-
8 ioooH
I-
500-
I Direct Fossil Fuel Combustion
Indirect Fossil Fuel Combustion
1,737
1,378
1,004
U.S. Territories
Commercial
Residential
Industrial
Transportation
The main driver of emissions in the Energy sector is CO2 from fossil fuel combustion. Electricity generation is the
largest emitter of CO2, and electricity generators consumed 34 percent of U.S. energy from fossil fuels and emitted
38 percent of the CO2 from fossil fuel combustion in 2015. Electricity generation emissions can also be allocated to
the end-use sectors that are consuming that electricity, as presented in Table 2-5. The transportation end-use sector
accounted for 1,737.0 MMT CO2 Eq. in 2015 or approximately 34 percent of total CO2 emissions from fossil fuel
combustion. The industrial end-use sector accounted for 27 percent of CO2 emissions from fossil fuel combustion.
The residential and commercial end-use sectors accounted for 20 and 18 percent, respectively, of CO2 emissions
from fossil fuel combustion. Both of these end-use sectors were heavily reliant on electricity for meeting energy
needs, with electricity consumption for lighting, heating, air conditioning, and operating appliances contributing 68
and 75 percent of emissions from the residential and commercial end-use sectors, respectively. Significant trends in
emissions from energy source categories over the twenty six-year period from 1990 through 2015 included the
following:
• Total CO2 emissions from fossil fuel combustion increased from 4,740.7 MMT CO2 Eq. in 1990 to 5,049.2
MMT CO2 Eq. in 2015 - a 6.5 percent total increase over the twenty six-year period. From 2014 to 2015,
these emissions decreased by 153.0 MMT CO2 Eq. (2.9 percent).
Trends 2-13
-------
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
31
32
33
34
35
36
37
38
39
40
41
42
43
• Methane emissions from natural gas systems and petroleum systems (combined here) decreased from 254.8
MMT C02 Eq. in 1990 to 201.5 MMT C02 Eq. (53.3 MMT C02 Eq. or 20.9 percent) from 1990 to 2015.
Natural gas systems CH4 emissions decreased by 36.5 MMT CO2 Eq. (18.6 percent) since 1990, largely due
to a decrease in emissions from transmission, storage, and distribution. The decrease in transmission and
storage emissions is largely due to reduced compressor station emissions (including emissions from
compressors and fugitives). The decrease in distribution emissions is largely attributed to increased use of
plastic piping, which has lower emissions than other pipe materials, and station upgrades at metering and
regulating (M&R) stations. Petroleum systems CH4 emissions decreased by 16.8 MMT CO2 Eq. (or 28.8
percent) since 1990. This decrease is due primarily to decreases in emissions from associated gas venting.
• Carbon dioxide emissions from non-energy uses of fossil fuels increased by 9.4 MMT CO2 Eq. (8.0
percent) from 1990 through 2015. Emissions from non-energy uses of fossil fuels were 127.0 MMT CO2
Eq. in 2015, which constituted 2.3 percent of total national CO2 emissions, approximately the same
proportion as in 1990.
• Nitrous oxide emissions from stationary combustion increased by 11.2 MMT CO2 Eq. (94.0 percent) from
1990 through 2015. Nitrous oxide emissions from this source increased primarily as a result of an increase
in the number of coal fluidized bed boilers in the electric power sector.
• Nitrous oxide emissions from mobile combustion decreased by 25.8 MMT CO2 Eq. (62.7 percent) from
1990 through 2015, primarily as a result of N20 national emission control standards and emission control
technologies for on-road vehicles.
• Carbon dioxide emissions from incineration of waste (10.7 MMT CO2 Eq. in 2015) increased by 2.7 MMT
CO2 Eq. (34.3 percent) from 1990 through 2015, as the volume of plastics and other fossil carbon-
containing materials in municipal solid waste grew.
The decrease in CO2 emissions from fossil fuel combustion was a result of multiple factors, including: (1)
substitution from coal to natural gas consumption in the electric power sector; (2) warmer winter conditions in the
first quarter of 2015 resulting in a decreased demand for heating fuel in the residential and commercial sectors; and
(3) a slight decrease in electricity demand.
Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) chapter includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products.
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 N20. These processes include iron and steel production and metallurgical coke production, cement
production, ammonia production, urea consumption, lime production, other process uses of carbonates (e.g., flux
stone, flue gas desulfurization, and glass manufacturing), soda ash production and consumption, titanium dioxide
production, phosphoric acid production, ferroalloy production, CO2 consumption, silicon carbide production and
consumption, aluminum production, petrochemical production, nitric acid production, adipic acid production, lead
production, zinc production, and N20 from product uses (see Figure 2-9). Industrial processes also release HFCs,
PFCs, SF6, and NF3 and other fluorinated compounds. In addition to the use of HFCs and some PFCs as substitutes
for ozone depleting substances (ODS), fluorinated compounds such as HFCs, PFCs, SF6, NF3, and others 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.
2-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Figure 2-9: 2015 Industrial Processes and Product Use Chapter Greenhouse Gas Sources
2 (MMT CO2 Eq.)
Substitution of Ozone Depleting Substances
Iron and Steel Production & Metallurgical Coke Production
Cement Production
Petrochemical Production
Lime Production
Nitric Acid Production
Other Process Uses of Carbonates
Ammonia Production
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Carbon Dioxide Consumption
Adipic Acid Production
N»0 from Product Uses
Electrical Transmission and Distribution
Soda Ash Production and Consumption
Ferroalloy Production
Titanium Dioxide Production
Glass Production
Urea Consumption for Non-Agricultural Purposes
Magnesium Production and Processing
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and Consumption
169
Industrial Processes and Product
Use as a Portion of all Emissions
5.7%
30 40
MMT CO2 Eq.
50
60
70
Table 2-6: Emissions from Industrial Processes and Product Use (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
206.8
189.9
172.9
169.6
171.5
177.6
169.0
Iron and Steel Production & Metallurgical Coke
Production
99.7
66.5
59.9
54.2
52.2
57.5
47.9
Iron and Steel Production
97.2
64.5
58.5
53.7
50.4
55.5
45.1
Metallurgical Coke Production
2.5
2.0
1.4
0.5
1.8
2.0
2.8
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.3
27.0
26.3
26.5
26.4
26.5
28.1
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
Other Process Uses of Carbonates
4.9
6.3
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Soda Ash Production and Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Aluminum Production
6.8
4.1
3.3
3.4
3.3
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-Agricultural
Purposes
3.8
3.7
4.0
4.4
4.0
1.4
1.1
Phosphoric Acid Production
1.5
1.3
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Magnesium Production and Processing
+
+
+
+
+
+
+
Trends 2-15
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
ch4
0.3
0.1
0.1
0.1
0.1
0.2
0.2
Petrochemical Production
0.2
0.1
+
0.1
0.1
0.1
0.2
Ferroalloy Production
+
+ "/
+
+
+
+
+
Silicon Carbide Production and Consumption
+
+ /~
+
+
+
+
+
Iron and Steel Production & Metallurgical Coke
Production
+
+4
+
+
+
+
+
Iron and Steel Production
+
+/: •
+
+
+
+
+
Metallurgical Coke Production
0.0
0.0
0.0
0.0
0.0
0.0
0.0
N2O
31.6
22.S
25.6
20.4
19.0
20.8
20.3
Nitric Acid Production
12.1
in
10.9
10.5
10.7
10.9
11.6
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Semiconductor Manufacturing
+
0.1
0.2
0.2
0.2
0.2
0.2
HFCs
46.6
120.0
154.4
155.9
159.0
166.7
174.1
Substitution of Ozone Depleting Substances3
0.3
99.8
145.4
150.2
154.7
161.3
168.6
HCFC-22 Production
46.1
20.0
8.8
5.5
4.1
5.0
5.0
Semiconductor Manufacturing
0.2
0.2
0.2
0.2
0.2
0.3
0.3
Magnesium Production and Processing
0.0
0.0
+
+
0.1
0.1
0.1
PFCs
24.3
6.7
6.9
6.0
5.7
5.7
5.2
Semiconductor Manufacturing
2.8
3.2
3.4
3.0
2.8
3.2
3.2
Aluminum Production
21.5
3.4
3.5
2.9
3.0
2.5
2.0
SF«
28.8
11.7
9.2
6.8
6.4
6.6
5.8
Electrical Transmission and Distribution
23.1
8.'
6.0
4.8
4.6
4.8
4.2
Magnesium Production and Processing
5.2
2."
2.8
1.6
1.5
1.0
0.9
Semiconductor Manufacturing
0.5
0."
0.4
0.4
0.4
0.7
0.7
NF3
+
0.5
0.7
0.6
0.6
0.5
0.6
Semiconductor Manufacturing
+
0.5
0.7
0.6
0.6
0.5
0.6
Total
338.3
351.6
369.7
359.5
362.4
378.1
375.1
+ Does not exceed 0.05 MMT CO2 Eq.
a Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
Overall, emissions from the IPPU sector increased by 10.9 percent from 1990 to 2015. Significant trends in
emissions from IPPU source categories over the twenty six-year period from 1990 through 2015 included the
following:
• Hydrofluorocarbon emissions from ODS substitutes have been increasing from small amounts in 1990 to
168.6 MMT CO2 Eq. in 2015. This increase was in large part the result of efforts to phase out
chlorofluorocarbons (CFCs) and other ODSs in the United States. In the short term, this trend is expected to
continue, and will likely continue over the next decade as hydrochlorofluorocarbons (HCFCs), which are
interim substitutes in many applications, are themselves phased-out under the provisions of the
Copenhagen Amendments to the Montreal Protocol.
• Combined CO2 and CH4 emissions from iron and steel production and metallurgical coke production
decreased by 16.7 percent to 47.9 MMT CO2 Eq. from 2014 to 2015, and have declined overall by 51.8
MMT CO2 Eq. (51.9 percent) from 1990 through 2015, due to restructuring of the industry, technological
improvements, and increased scrap steel utilization.
• Carbon dioxide emissions from ammonia production (10.8 MMT CO2 Eq. in 2015) decreased by 2.2 MMT
CO2 Eq. (17.2 percent) since 1990. Ammonia production relies on natural gas as both a feedstock and a
fuel, and as such, market fluctuations and volatility in natural gas prices affect the production of ammonia.
• Urea consumption for non-agricultural purposes (1.1 MMT CO2 Eq. in 2015) decreased by 2.7 MMT CO2
Eq. (70.2 percent) since 1990. From 1990 to 2007, emissions increased by 31 percent to a peak of 4.9
MMT CO2 Eq., before decreasing by 77 percent to 2015 levels.
2-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
• In 2015, N20 emissions from product uses constituted 1.3 percent of U.S. N20 emissions. From 1990 to
2015, emissions from this source category decreased by 0.4 percent, though slight increases occurred in
intermediate years.
• Nitrous oxide emissions from adipic acid production were 4.3 MMT CO2 Eq. in 2015, and have decreased
significantly since 1990 due to both the widespread installation of pollution control measures in the late
1990s and plant idling in the late 2000s. Emissions from adipic acid production have decreased by 72.0
percent since 1990 and by 74.8 percent since a peak in 1995.
• PFC emissions from aluminum production decreased by 90.7 percent (19.5 MMT CO2 Eq.) from 1990 to
2015, due to both industry emission reduction efforts and lower domestic aluminum production.
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, liming, urea fertilization and field burning of agricultural residues.
In 2015, agricultural activities were responsible for emissions of 522.3 MMT CO2 Eq., or 7.9 percent of total U.S.
greenhouse gas emissions. Carbon dioxide, methane and nitrous oxide were the primary greenhouse gases emitted
by agricultural activities. Carbon dioxide emissions from the application of crushed limestone and dolomite (i.e., soil
liming) and urea fertilization represented 0.2 percent of total CO2 emissions from anthropogenic activities. Methane
emissions from enteric fermentation and manure management represented approximately 25.4 percent and 10.1
percent of total CH4 emissions from anthropogenic activities, respectively, in 2015. Agricultural soil management
activities, such as fertilizer use and other cropping practices, were the largest source of U.S. N20 emissions in 2015,
accounting for 75.0 percent. Figure 2-10 and Table 2-7 illustrate agricultural greenhouse gas emissions by source.
Figure 2-10: 2015 Agriculture Chapter Greenhouse Gas Sources (MMT CO2 Eq.)
Agriculture
Liming
Field Burning of Agricultural Residues
Agricultural Soil Management
Enteric Fermentation
Manure Management
Urea Fertilization
Rice Cultivation
Agriculture as a Portion of all
Emissions
0
50
100
MMT COz Eq.
150
Trends 2-17
-------
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
31
Table 2-7: Emissions from Agriculture (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
7.1
7.'J
8.0
10.2
8.4
8.4
8.8
Urea Fertilization
2.4
3-
4.1
4.3
4.5
4.8
5.0
Liming
4."
4.'
3.9
6.0
3.9
3.6
3.8
CH4
217.fi
242.1
246.3
244.0
240.4
238.7
244.3
Enteric Fermentation
164.2
00
168.9
166.7
165.5
164.2
166.5
Manure Management
37.2
56.'
63.0
65.6
63.3
62.9
66.3
Rice Cultivation
16.0
16"
14.1
11.3
11.3
11.4
11.2
Field Burning of Agricultural
Residues
0.2
0.2
0.3
0.3
0.3
0.3
0.3
N2O
270.fi
276.4
287.6
271.7
268.1
267.6
269.1
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Manure Management
14.0
16.
17.4
17.5
17.5
17.5
17.7
Field Burning of Agricultural
Residues
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Total
495.3
526.4
541.9
525.9
516.9
514.7
522.3
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from Agriculture source categories include the following:
• Agricultural soils produced approximately 75.0 percent of N20 emissions in the United States in 2015.
Estimated emissions from this source in 2015 were 251.3 MMT CO2 Eq. Annual N2O emissions from
agricultural soils fluctuated between 1990 and 2015, although overall emissions were 2.0 percent lower in
2015 than in 1990. Year-to-year fluctuations are largely a reflection of annual variation in weather patterns,
synthetic fertilizer use, and crop production.
• Enteric fermentation is the largest anthropogenic source of CH4 emissions in the United States. In 2015,
enteric fermentation CH4 emissions were 166.5 MMT CO2 Eq. (25.4 percent of total CH4 emissions),
which represents an increase of 2.4 MMT CO2 Eq. (1.5 percent) since 1990. This increase in emissions
from 1990 to 2015 in enteric fermentation generally follows the increasing trends in cattle populations.
From 1990 to 1995, emissions increased and then generally decreased from 1996 to 2004, mainly due to
fluctuations in beef cattle populations and increased digestibility of feed for feedlot cattle. Emissions
increased from 2005 to 2007, as both dairy and beef populations increased. Research indicates that the feed
digestibility of dairy cow diets decreased during this period. Emissions decreased again from 2008 to 2015
as beef cattle populations again decreased.
• Liming and urea fertilization are the only source of CO2 emissions from Agriculture. Estimated emissions
from these sources were 3.8 and 5.0 MMT CO2 Eq., respectively. Liming and urea fertilization emissions
increased by 5.6 percent and 5.3 percent, respectively, relative to 2014, and decreased by 18.4 percent and
increased by 108.2 percent, respectively since 1990.
• Overall, emissions from manure management increased 64.2 percent between 1990 and 2015. This
encompassed an increase of 78.3 percent for CH i. from 37.2 MMT CO2 Eq. in 1990 to 66.3 MMT CO2 Eq.
in 2015; and an increase of 26.6 percent for N2O, from 14.0 MMT CO2 Eq. in 1990 to 17.7 MMT CO2 Eq.
in 2015. The majority of the increase observed in CH4 resulted from swine and dairy cow manure, where
emissions increased 58 and 136 percent, respectively, from 1990 to 2015. From 2014 to 2015, there was a
5.4 percent increase in total CH4 emissions from manure management, mainly due to minor shifts in the
animal populations and the resultant effects on manure management system allocations.
Land Use, Land-Use Change, and Forestry
When humans alter the terrestrial biosphere through land use, changes in land use, and land management practices,
they also alter the background C fluxes between biomass, soils, and the atmosphere. Overall, managed land is a net
sink for CO2 (C sequestration) in the United States. The drivers of fluxes on managed lands include, for example,
2-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
37
38
forest management practices, tree planting in urban areas, the management of agricultural soils, the landfilling of
yard trimmings and food scraps, and activities that cause changes in C stocks in coastal wetlands. The main drivers
for net forest sequestration include net forest growth, increasing forest area, and a net accumulation of carbon stocks
in harvested wood pools. The net sequestration in Settlements Remaining Settlements, which occurs predominantly
from urban forests and landfilled yard trimmings and food scraps, is a result of net tree growth and increased urban
forest size, as well as long-term accumulation of yard trimmings and food scraps carbon in landfills.
Forest Land Remaining Forest Land (including vegetation, soils, and harvested wood) represented the largest
carbon sink from LULUCF, accounting for 77 percent of total 2015 negative C fluxes; Settlements Remaining
Settlements (urban trees and landfilled yard trimmings and food scraps) accounted for 12 percent; Land Converted to
Forest Land accounted for 9 percent; and Cropland Remaining Cropland, Wetlands Remaining Wetlands, and Land
Converted to Wetlands accounted for 3 percent of the total negative C fluxes in 2015. Conversely, Land Converted
to Grassland represented the largest carbon source from LULUCF, accounting for 61 percent of total 2015 positive
C fluxes, while Land Converted to Settlements accounted for 31 percent. Land Converted to Cropland accounted for
6 percent. Grassland Remaining Grassland accounted for 2 percent, and settlement soils in Settlements Remaining
Settlements accounted for less than 0.5 percent of the total positive C fluxes in 2015. Overall, positive C fluxes
totaled 481.6 MMT CO2 Eq. in 2015, while negative C fluxes totaled 868.5 MMT CO2 Eq. in 2015.
The LULUCF sector in 2015 resulted in a net increase in C stocks (i.e., net CO2 removals) of 386.8 MMT CO2 Eq.
(Table 2-3).1 This represents an offset of approximately 5.9 percent of total (i.e., gross) greenhouse gas emissions in
2015. Emissions from LULUCF activities in 2015 are 20.4 MMT CO2 Eq. and represent 0.3 percent of total
greenhouse gas emissions.2 Between 1990 and 2015, total C sequestration in the LULUCF sector decreased by 16.0
percent, primarily due to a decrease in the rate of net C accumulation in forests and an increase in emissions from
Land Converted to Grassland?
Carbon dioxide removals are presented in Table 2-8 along with CO2, CH4, and N20 emissions for LULUCF source
categories. Lands undergoing peat extraction (i.e., PeatlandsRemaining Peatlands) resulted in CO2 emissions of 0.8
MMT CO2 Eq. (763 kt of CO2). Forest fires were the largest source of CH4 emissions from LULUCF in 2015,
totaling 7.3 MMT CO2 Eq. (292 kt of CH4). Coastal Wetlands Remaining Coastal Wetlands resulted in CH4
emissions of 3.5 MMT CO2 Eq. (141 kt of CH4). Grassland fires resulted in CH4 emissions of 0.4 MMT CO2 Eq. (16
kt of CH4). Peatlands Remaining Peatlands and Land Converted to Wetlands resulted in CH4 emissions of less than
0.05 MMT C02 Eq.
Forest fires were also the largest source of N20 emissions from LULUCF in 2015, totaling 4.8 MMT CO2 Eq. (16 kt
of N20). Nitrous oxide emissions from fertilizer application to settlement soils in 2015 totaled to 2.6 MMT CO2 Eq.
(9 kt of N20). This represents an increase of 81.5 percent since 1990. Additionally, the application of synthetic
fertilizers to forest soils in 2015 resulted in N20 emissions of 0.5 MMT CO2 Eq. (2 kt of N20). Nitrous oxide
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. Grassland fires resulted in N20 emissions of 0.4 MMT CO2 Eq. (1 kt
of N2O). Coastal Wetlands Remaining Coastal Wetlands resulted in N2O emissions of 0.1 MMT CO2 Eq. (0.5 kt of
N2O), and Peatlands Remaining Peatlands resulted in N2O emissions of less than 0.05 MMT CO2 Eq. (see Table
2-8).
1 LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land
Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land
Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and
Land Converted to Settlements.
2 LULUCF emissions include the CO2, CH4, andN20 emissions from Peatlands Remaining Peatlands, CH4 andN20 emissions
reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal Wetlands Remaining
Coastal Wetlands; CFLi emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from Forest Soils and Settlement
Soils.
3 Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014, 2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland sections
in the LULUCF chapter of this report.
Trends 2-19
-------
1 Table 2-8: U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land Use, Land-
2 Use Change, and Forestry (MMT CO2 Eq.)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Net CO2 Fluxa
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
Forest Land Remaining Forest Landb
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
Land Converted to Forest Land
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Cropland Remaining Cropland0
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Land Converted to Cropland0
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Grassland Remaining Grassland0
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Land Converted to Grassland0
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Wetlands Remaining Wetlands
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
Land Converted to Wetlands
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Remaining Settlements
(86.2)
(91.4)
(98.7)
(99.2)
(99.8)
(101.2)
(102.1)
Land Converted to Settlements
123.8
163.6
157.6
150.2
150.2
150.2
150.2
CO2
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
1.1
1.1
0.9
0.8
0.8
0.8
0.8
CH4
6.7
13.3
11.2
14.9
11.0
11.2
11.2
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
3.2
9.4
6.8
10.8
7.2
7.3
7.3
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.8
0.6
0.2
0.4
0.4
Land Converted to Wetlands: Land
Converted to Coastal Wetlands
+
+
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
N2O
3.8
9.6
8.6
11.0
8.1
8.4
8.4
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
2.1
6.2
4.5
7.1
4.7
4.8
4.8
Settlements Remaining Settlements:
N2O Fluxes from Settlement Soils'1
1.4
2.5
2.6
2.7
2.6
2.6
2.6
Forest Land Remaining Forest Land:
N2O Fluxes from Forest Soilse
0.1
0.5
0.5
0.5
0.5
0.5
0.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.9
0.6
0.2
0.4
0.4
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
0.1
0.2
0.1
0.1
0.1
0.1
0.1
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
LULUCF Emissions'
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change3
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Totals
(449.1)
i (315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements
Remaining Settlements, and Land Converted to Settlements.
b Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
c Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014, 2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to
Grassland sections in the LULUCF chapter of this report.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to
Settlements.
e Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to
Fore st Land.
f LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands, CH4 and N2O
emissions reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal
2-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Wetlands Remaining Coastal Wetlands; CH4 emissions fromLawrf Converted to Coastal Wetlands; andN^O Fluxes from
Forest Soils and Settlement Soils.
g Hie LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Other significant trends from 1990 to 2015 in emissions from LULUCF categories include:
• Annual C sequestration by forest land (i.e., annual C stock accumulation in the five C pools for Forest
Land Remaining Forest Land and Land Converted to Forest Land) has decreased by approximately 6
percent since 1990. This is primarily due to decreased carbon sequestration from forest carbon stocks.
• Annual C sequestration from Settlements Remaining Settlements (which includes urban trees and landfilled
yard trimmings and food scraps) has increased by 18.4 percent over the period from 1990 to 2015. This is
primarily due to an increase in urbanized land area in the United States.
• Annual emissions from Land Converted to Grassland increased by approximately 20 percent from 1990 to
2015 due to losses in aboveground biomass, belowground bio mass, dead wood, and litter C stocks from
Forest Land Converted to Grassland.
• Annual emissions from Land Converted to Settlements increased by approximately 21 percent from 1990 to
2015 due to losses in aboveground biomass C stocks from Forest Land Converted to Settlements and
mineral soils C stocks from Grassland Converted to Settlements.
Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 2-11). In 2015,
landfills were the third-largest source of U.S. anthropogenic CH4 emissions, accounting for 17.7 percent of total
U.S. CH4 emissions.4 Additionally, wastewater treatment accounts for 14.2 percent of Waste emissions, 2.3 percent
of U.S. CH4 emissions, and 1.5 percent of N20 emissions. Emissions of CH4 and N20 from composting grew from
1990 to 2015, and resulted in emissions of 4.0 MMT CO2 Eq. in 2015. A summary of greenhouse gas emissions
from the Waste chapter is presented in Table 2-9.
Figure 2-11: 2015 Waste Chapter Greenhouse Gas Sources (MMT CO2 Eq.)
Landfills
Wastewater Treatment
Composting
Waste as a Portion of all Emissions
2.1%
0 20 40 60 80 100 120
MMT C02 Eq.
4 Landfills also store carbon, due to incomplete degradation of organic materials such as wood products and yard trimmings, as
described in the Land Use, Land-Use Change, and Forestry chapter.
Trends 2-21
-------
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
31
32
33
34
35
36
Overall, in 2015, waste activities generated emissions of 139.4 MMT CO2 Eq., or 2.1 percent of total U.S.
greenhouse gas emissions.
Table 2-9: Emissions from Waste (MMT CO2 Eq.)
Gas/Source
l*m
2005
2011
2012
2013
2014
2015
CH4
1 '>5.6
152.1
136.2
137.9
133.7
133.5
132.6
Landfills
179.6
134.3
119.0
120.8
116.7
116.6
115.7
Wastewater Treatment
15.7
16.0
15.3
15.1
14.9
14.8
14.8
Composting
0.4
1.9
1.9
1.9
2.0
2.1
2.1
N2O
3.7
6.1
6.4
6.6
6.7
6.8
6.9
Wastewater Treatment
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Composting
0 i
1.7
1.7
1.7
1.8
1.9
1.9
Total
I'J'U
15S.2
142.6
144.4
140.4
140.2
139.4
Note: Totals may not sum due to independent rounding.
Some significant trends in U.S. emissions from waste source categories include the following:
• From 1990 to 2015, net CH4 emissions from landfills decreased by 63.8 MMT CO2 Eq. (35.6 percent), with
small increases occurring in interim years. This downward trend in emissions coincided with increased
landfill gas collection and control systems, and a reduction of decomposable materials (i.e., paper and
paperboard, food scraps, and yard trimmings) discarded in MSW landfills over the time series.
• Combined CH4 and N2O emissions from composting have generally increased since 1990, from 0.7 MMT
CO2 Eq. to 4.0 MMT CO2 Eq. in 2015, which represents slightly more than a five-fold increase over the
time series. The growth in composting since the 1990s is attributable to primarily two factors: (1) steady
growth in population and residential housing, and (2) the enactment of legislation by state and local
governments that discouraged the disposal of yard trimmings in landfills.
• From 1990 to 2015, CH4 and N20 emissions from wastewater treatment decreased by 0.9 MMT CO2 Eq.
(5.8 percent) and increased by 1.6 MMT CO2 Eq. (47.0 percent), respectively. Methane emissions from
domestic wastewater treatment have decreased since 1999 due to decreasing percentages of wastewater
being treated in anaerobic systems, including reduced use of on-site septic systems and central anaerobic
treatment systems. Nitrous oxide emissions from wastewater treatment processes gradually increased
across the time series as a result of increasing U.S. population and protein consumption.
2.2 Emissions by Economic Sector
Throughout this report, emission estimates are grouped into five sectors (i.e., chapters) defined by the IPCC and
detailed above: Energy; Industrial Processes and Product Use; Agriculture; LULUCF; and Waste. While it is
important to use this characterization for consistency with UNFCCC reporting guidelines, it is also useful to
characterize emissions according to commonly used economic sector categories: 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 (29 percent) of
total U.S. greenhouse gas emissions in 2015. Transportation activities, in aggregate, accounted for the second largest
portion (27 percent). Emissions from industry accounted for about 22 percent of total U.S. greenhouse gas emissions
in 2015. Emissions from industry have in general declined over the past decade due to a number of factors, including
structural changes in the U.S. economy (i.e., shifts from a manufacturing-based to a service-based economy), fuel
switching, and efficiency improvements. The remaining 21 percent of U.S. greenhouse gas emissions were
contributed by the residential, agriculture, and commercial sectors, plus emissions from U.S. Territories. The
residential sector accounted for 6 percent, and primarily consisted of CO2 emissions from fossil fuel combustion.
Activities related to agriculture accounted for roughly 9 percent of U.S. emissions; unlike other economic sectors,
agricultural sector emissions were dominated by N20 emissions from agricultural soil management and CH4
2-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 emissions from enteric fermentation, rather than CO2 from fossil fuel combustion. The commercial sector accounted
2 for roughly 6 percent of emissions, while U.S. Territories accounted for less than 1 percent. Carbon dioxide was also
3 emitted and sequestered (in the form of C) by a variety of activities related to forest management practices, tree
4 planting in urban areas, the management of agricultural soils, landfilling of yard trimmings, and changes in C stocks
5 in coastal wetlands.
6 Table 2-10 presents a detailed breakdown of emissions from each of these economic sectors by source category, as
7 they are defined in this report. Figure 2-12 shows the trend in emissions by sector from 1990 to 2015.
8 Figure 2-12: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq.)
2,500-
Electric Power Industry
2,000-
Transportation
Industry
O
u
1-
z
z
1,000-
Agri culture
Commercial (Red)
500-
Residential (Blue)
o
o
o
00
o
o
o
rsj
CTi
en
cn
en
CM
o
o
Q S
o
ro
o
o
o
o
o
o
o
o
o
o
10
11
12 Table 2-10: U.S. Greenhouse Gas Emissions Allocated to Economic Sectors (MMT CO2 Eq. and
13 Percent of Total in 2015)
Sector/Source
1990
2005
2011
2012
2013
2014
2015
Percent3
Electric Power Industry
1,862.5
2,441.6
2,197.3
2,059.9
2,078.2
2,079.7
1,941.2
29.5%
CO2 from Fossil Fuel Combustion
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
28.9%
Stationary Combustion
7.7
16.5
18.0
18.2
19.5
20.0
19.9
0.3%
Incineration of Waste
8.4
12.9
10.9
10.7
10.7
10.9
11.0
0.2%
Other Process Uses of Carbonates
2.5
3.2
4.7
4.0
5.2
5.9
5.4
0.1%
Electrical Transmission and Distribution
23.1
8.3
6.0
4.8
4.6
4.8
4.2
0.1%
Transportation
1,551.3
2,001.0
1,800.0
1,780.7
1,790.2
1,803.4
1,803.7
27.4%
CO2 from Fossil Fuel Combustion
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
26.3%
Substitution of Ozone Depleting
Substances
+
67.1
60.2
55.1
49.8
47.2
45.1
0.7%
Mobile Combustion
45.7
36.8
23.2
20.6
18.6
16.6
15.4
0.2%
Non-Energy Use of Fuels
11.8
10.2
9.0
8.3
8.8
9.1
10.0
0.2%
Industry
1,629.5
1,469.3
1,378.5
1,365.2
1,412.8
1,426.0
1,436.7
21.8%
CO2 from Fossil Fuel Combustion
811.4
780.6
725.4
731.9
762.2
765.0
781.3
11.9%
Natural Gas Systems
234.3
192.2
189.3
190.5
196.4
203.2
202.4
3.1%
Non-Energy Use of Fuels
100.1
120.1
95.8
93.4
109.4
104.5
113.5
1.7%
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
0.9%
Iron and Steel Production
99.7
66.6
59.9
54.2
52.2
57.5
47.9
0.7%
Petroleum Systems
61.8
52.0
54.3
52.3
50.3
48.5
45.1
0.7%
Trends 2-23
-------
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
0.6%
Petrochemical Production
21.5
27.0
26.4
26.6
26.5
26.6
28.2
0.4%
Substitution of Ozone Depleting
Substances
+
7.4
17.1
18.8
20.4
22.3
24.8
0.4%
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
0.2%
Nitric Acid Production
12.1
11.3
10.9
10.5
10.7
10.9
11.6
0.2%
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
0.2%
Abandoned Underground Coal Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
0.1%
Other Process Uses of Carbonates
2.5
3.2
4.7
4.0
5.2
5.9
5.4
0.1%
HCFC-22 Production
46.1
20.0
OO
OO
5.5
4.1
5.0
5.0
0.1%
Semiconductor Manufacture
3.6
4.7
4.9
4.5
4.1
5.0
5.0
0.1%
Aluminum Production
28.3
7.6
6.8
6.4
6.2
5.4
4.8
0.1%
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
0.1%
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
0.1%
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
0.1%
Stationary Combustion
4.9
4.6
3.9
3.9
3.9
3.9
3.8
0.1%
Soda Ash Production and Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
+
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
+
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
+
Mobile Combustion
0.9
1.3
1.4
1.4
1.5
1.5
1.5
+
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
+
Urea Consumption for Non-Agricultural
Purposes
3.8
3.7
4.0
4.4
4.0
1.4
1.1
+
Magnesium Production and Processing
5.2
2.7
2.8
1.7
1.5
1.1
1.0
+
Phosphoric Acid Production
1.5
1.3
1.2
1.1
1.1
1.0
1.0
+
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
+
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
+
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
+
Agriculture
526.7
574.3
592.0
577.6
567.5
566.1
570.3
8.7%
N2O from Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
3.8%
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
2.5%
Manure Management
51.1
72.9
80.4
83.2
80.8
80.4
84.0
1.3%
CO2 from Fossil Fuel Combustion
31.0
47.4
49.6
51.1
50.0
50.8
47.5
0.7%
Rice Cultivation
16.0
16.7
14.1
11.3
11.3
11.4
11.2
0.2%
Urea Fertilization
2.4
3.5
4.1
4.3
4.5
4.8
5.0
0.1%
Liming
4.7
4.3
3.9
6.0
3.9
3.6
3.8
0.1%
Mobile Combustion
0.3
0.5
0.5
0.6
0.6
0.6
0.5
+
Field Burning of Agricultural Residues
0.3
0.3
0.4
0.4
0.4
0.4
0.4
+
Stationary Combustion
+
+
+
+
0.1
0.1
0.1
+
Commercial
418.1
400.7
406.5
387.3
410.1
422.2
416.7
6.3%
CO2 from Fossil Fuel Combustion
217.4
223.5
220.4
196.7
221.0
231.4
225.7
3.4%
Landfills
179.6
134.3
119.0
120.8
116.7
116.6
115.7
1.8%
Substitution of Ozone Depleting
Substances
+
17.6
42.1
44.9
47.4
49.2
50.2
0.8%
Wastewater Treatment
15.7
16.0
15.3
15.1
14.9
14.8
14.8
0.2%
Human Sewage
3.4
4.4
4.8
4.8
4.9
4.9
5.0
0.1%
Composting
0.7
3.5
3.5
3.7
3.9
4.0
4.0
0.1%
Stationary Combustion
1.4
1.4
1.4
1.2
1.3
1.4
1.4
+
Residential
344.9
370.4
356.3
318.4
372.6
394.0
372.7
5.7%
CO2 from Fossil Fuel Combustion
338.3
357.8
325.5
282.5
329.7
345.4
319.6
4.9%
Substitution of Ozone Depleting
Substances
0.3
7.7
25.9
31.4
37.0
42.6
48.4
0.7%
Stationary Combustion
6.3
4.9
4.9
4.5
5.9
6.0
4.7
0.1%
U.S. Territories
33.7
58.2
45.4
47.6
47.5
44.9
44.9
0.7%
CO2 from Fossil Fuel Combustion
27.9
49.9
41.5
43.6
43.5
41.2
41.2
0.6%
Non-Energy Use of Fuels
5.7
8.1
3.7
3.8
3.8
3.5
3.5
0.1%
Stationary Combustion
0.1
0.2
0.2
0.2
0.2
0.2
0.2
+
2-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Total Emissions 6,366.7 7,315.6
6,776.0 6,536.8 6,678.9 6,736.3 6,586.2 100.0%
LULUCF Sector Net Total"c (449.1) (315.3) i (375.1) (387.7) (370.4) (368.8) (366.4) (5.6%)
Net Emissions (Sources and Sinks) 5,917.6 7,000.3 6,400.9 6,149.1 6,308.5 6,367.5 6,219.8 94.4%
Note: Total emissions presented without LULUCF. Total net emissions presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 Eq. or 0.05 percent.
a Percent of total (gross) emissions excluding emissions from LULUCF for 2015.
b The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus removals of CO2
(i.e., sinks or negative emissions) from the atmosphere.
c Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014,2015, which will be updated
following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, sad Land Converted to Grassland sections in the LULUCF
chapter of this report.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or sequestration.
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 29 percent of total U.S. greenhouse gas
emissions in 2015. Emissions increased by 4 percent since 1990, as electricity demand grew and fossil fuels
remained the dominant energy source for generation. Electricity generation-related emissions decreased from 2014
to 2015 by 6.7 percent, primarily due to decreased CO2 emissions from fossil fuel combustion due to an increase in
natural gas consumption, and decreased coal consumption. Electricity sales to the residential and commercial end-
use sectors in 2015 decreased by 0.2 percent and increased by 0.6 percent, respectively. The trend in the residential
and commercial sectors can largely be attributed to warmer, less energy-intensive winter conditions compared to
2014. Electricity sales to the industrial sector in 2015 decreased by approximately 1.1 percent. Overall, in 2015, the
amount of electricity generated (in kWh) decreased by 0.2 percent from the previous year. This decrease in
generation contributed to a reduction in CO2 emissions from the electric power sector of 6.7 percent, as the
consumption of CCh-intensive coal for electricity generation decreased by 13.9 percent and natural gas generation
increased by 18.7 percent. The consumption of petroleum for electricity generation decreased by 6.6 percent in 2015
relative to 2014. Table 2-11 provides a detailed summary of emissions from electricity generation-related activities.
Table 2-11: Electricity Generation-Related Greenhouse Gas Emissions (MMT CO2 Eq.)
Gas/Fuel Type or Source
1990
2005
2011
2012
2013
2014
2015
CO2
1,831.2
2,416.5
2,172.9
2,036.6
2,053.7
2,054.5
1,916.8
Fossil Fuel Combustion
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Coal
1,547.6
1,983.8
1,722.7
1,511.2
1,571.3
1,569.1
1,350.5
Natural Gas
175.3
318.8
408.8
492.2
444.0
443.2
526.1
Petroleum
97.5
97.9
25.8
18.3
22.4
25.3
23.7
Geothermal
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Incineration of Waste
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Other Process Uses of
Carbonates
2.5
3.2
4.7
4.0
5.2
5.9
5.4
CH4
0.3
0.5
0.4
0.4
0.4
0.4
0.4
Stationary Sources
(Electricity Generation)
0.3
0.5
0.4
0.4
0.4
0.4
0.4
Incineration of Waste
+
+
+
+
+
+
+
N2O
7.8
16.4
17.9
18.1
19.4
19.9
19.8
Stationary Sources
(Electricity Generation)
7.4
16.0
17.6
17.8
19.1
19.6
19.5
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
SF«
23.1
8.3
6.0
4.8
4.6
4.8
4.2
Electrical Transmission and
Distribution
23.1
8.3
6.0
4.8
4.6
4.8
4.2
Trends 2-25
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
Total 1,862.5 2,441.6 2,197.3 2,059.9 2,078.2 2,079.7 1,941.2
+ Does not exceed 0.05 MMT CO2 Eq.
a Includes only stationary combustion emissions related to the generation of electricity.
Note: Totals may not sum due to independent rounding.
To distribute electricity emissions among economic end-use sectors, emissions from the source categories assigned
to the electricity generation sector were allocated to the residential, commercial, industry, transportation, and
agriculture economic sectors according to each economic sector's share of retail sales of electricity consumption
(EIA 2016 and Duffield 2006). These source categories include CO2 from Fossil Fuel Combustion, CH4 and N20
from Stationary Combustion. Incineration of Waste, Other Process Uses of Carbonates, and SF6 from Electrical
Transmission and Distribution Systems. Note that only 50 percent of the Other Process Uses of Carbonates
emissions were associated with electricity generation and distributed as described; the remainder of Other Process
Uses of Carbonates emissions were attributed to the industrial processes economic end-use sector.5
When emissions from electricity are distributed among these sectors, industrial activities account for the largest
share of total U.S. greenhouse gas emissions (29.7 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 2-12 presents a detailed breakdown of emissions from each of these economic sectors, with emissions from
electricity generation distributed to them. Figure 2-13 shows the trend in these emissions by sector from 1990 to
2015.
Figure 2-13: U.S. Greenhouse Gas Emissions with Electricity-Related Emissions Distributed
to Economic Sectors (MMT CO2 Eq.)
2,500
Industry (Green)
2,000
Transportation (Purple)
^ 1,500
O
u
l-
z
2:
Commercial (Red)
1,000
Residential (Blue)
Agriculture
500
00
cr>
o
o
o
vo
o
©
CO
o
o
o
rsi
cn
ro
o
o
s
o
LD
O
o
cn
o
o
LO
O
o
o
o
o
o
o
o
^ Emissions were not distributed to U.S. Territories, since the electricity generation sector only includes emissions related to the
generation of electricity in the 50 states and the District of Columbia.
2-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Table 2-12: U.S. Greenhouse Gas Emissions by Economic Sector and Gas with Electricity-
2 Related Emissions Distributed (MMT CO2 Eq.) and Percent of Total in 2015
Sector/Gas
1900
2005
2011
2012
2013
2014
2015
Percent3
Industry
2,297.1
2,180.3
1,973.5
1,926.0
1,976.9
1,986.7
1,956.2
29.7%
Direct Emissions
1,629.5
1,469.3
1,378.5
1,365.2
1,412.8
1,426.0
1,436.7
21.8%
CO2
1,157.1
1,121.4
1,029.2
1,029.9
1,080.1
1,087.1
1,104.3
16.8%
CH4
360.7
282.9
283.2
278.2
277.1
278.7
270.7
4.1%
N2O
35.4
26.7
29.2
24.0
22.7
24.5
24.0
0.4%
HFCs, PFCs, SFe.andNFs
76.3
38.2
36.8
33.1
32.9
35.7
37.7
0.6%
Electricity-Related
667.6
711.0
595.0
560.8
564.0
560.7
519.5
7.9%
CO2
656.4
703.7
588.4
554.4
557.4
553.9
513.0
7.8%
CH4
0.1
0.1
0.1
0.1
0.1
0.1
0.1
+
N2O
2.8
4.8
4.9
4.9
5.3
5.4
5.3
0.1%
SFe
8.3
2.4
1.6
1.3
1.2
1.3
1.1
+
Transportation
1,554.4
2,005.0
1,804.3
1,784.7
1,794.3
1,807.5
1,807.5
27.4%
Direct Emissions
1,551.3
2,001.0
1,800.0
1,780.7
1,790.2
1,803.4
1,803.7
27.4%
CO2
1,505.6
1,897.2
1,716.6
1,705.0
1,721.8
1,739.5
1,743.2
26.5%
CH4
5.4
2.4
1.9
1.8
1.7
1.7
1.6
+
N2O
40.3
34.3
21.3
18.8
16.9
15.0
13.7
0.2%
HFCsb
67.1
60.2
55.1
49.8
47.2
45.1
0.7%
Electricity-Related
3.1
4.S
4.3
3.9
4.1
4.1
3.8
0.1%
CO2
3.1
4.8
4.3
3.9
4.0
4.1
3.8
0.1%
CH4
+
; +*
+
+
+
+
+
+
N2O
'* + /
+
+
+
+
+
+
SFe
+
+
+
+
+
+
Commercial
96S.4
1,217.6
1,158.1
1,100.6
1,128.5
1,142.7
1,094.0
16.6%
Direct Emissions
41S.I
400.7
406.5
387.3
410.1
422.2
416.7
6.3%
CO2
217.4
223.5
220.4
196.7
221.0
231.4
225.7
3.4%
CH4
196.7
153.2
137.3
138.8
134.7
134.5
133.7
2.0%
N2O
4.1
6.4
6.7
6.8
7.0
7.1
7.2
0.1%
HFCs
17.6
42.1
44.9
47.4
49.2
50.2
0.8%
Electricity-Related
550.3
816.0
751.6
713.3
718.3
720.4
677.3
10.3%
CO2
541.1
808.5
743.3
705.3
709.9
711.7
668.8
10.2%
CH4
0.1
0.2
0.2
0.1
0.2
0.2
0.2
+
N2O
2.3
5.5
6.1
6.3
6.7
6.9
6.9
0.1%
SFe
6.8
2.8
2.0
1.7
1.6
1.7
1.4
+
Residential
951.5
1,241.3
1,161.5
1,057.2
1,122.0
1,143.7
1,071.5
16.3%
Direct Emissions
344.0
370.4
356.3
318.4
372.6
394.0
372.7
5.7%
CO2
338.3
357.8
325.5
282.5
329.7
345.4
319.6
4.9%
CH4
5.2
4.1
4.0
3.7
5.0
5.0
3.9
0.1%
N2O
1.0
0.9
0.8
0.7
1.0
1.0
0.8
+
HFCs
0.3
7.7
25.9
31.4
37.0
42.6
48.4
0.7%
Electricity-Related
606.6
870.S
805.2
738.8
749.3
749.8
698.8
10.6%
CO2
596.4
861.9
796.3
730.4
740.5
740.7
690.1
10.5%
CH4
0.1
0.2
0.2
0.2
0.2
0.2
0.2
+
N2O
2.5
5.8
6.6
6.5
7.0
7.2
7.1
0.1%
SFe
7.5
2.9
2.2
1.7
1.7
1.7
1.5
+
Agriculture
561.5
612.4
633.1
620.6
609.9
610.8
612.0
9.3%
Direct Emissions
526.7
574.3
592.0
577.6
567.5
566.1
570.3
8.7%
CO2
38.1
55.2
57.6
61.3
58.4
59.2
56.3
0.9%
CH4
21"."
242.3
246.5
244.2
240.6
238.9
244.5
3.7%
N2O
270.9
276.8
288.0
272.1
268.5
268.0
269.5
4.1%
Electricity-Related
34.S
38.1
41.1
43.1
42.4
44.7
41.7
0.6%
CO2
34.2
37.7
40.6
42.6
41.9
44.1
41.2
0.6%
CH4
+
1 +' /
+
+
+
+
+
+
N2O
0.1
0.3
0.3
0.4
0.4
0.4
0.4
+
SFe
0.4
0.1
0.1
0.1
0.1
0.1
0.1
+
U.S. Territories
33.7
58.2
45.4
47.6
47.5
44.9
44.9
0.7%
Trends 2-27
-------
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
Total Emissions
6,366.7
7,315.6
6,776.0
6,536.8
6,678.9
6,736.3
6,586.2
100.0%
LULUCF Sector Net Totalcd
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
(5.6%)
Net Emissions (Sources and
Sinks)
5,917.6
7,000.3
6,400.9
6,149.1
6,308.5
6,367.5
6,219.8
94.4%
Note: Total emissions presented without LULUCF. Net emissions presented with LULUCF.
+ Does not exceed 0.05 MMT CO2 Eq. or 0.05 percent.
a Percent of total gross emissions excluding emissions from LULUCF for year 2015.
b Includes primarily HFC-134a.
c The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere
plus removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
dQuality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015,
which will be updated following public review. Corrected estimates are provided in footnotes of the emission
summary tables for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland,
and Land Converted to Grassland sections in the LULUCF chapter of this report.
Notes: Emissions from electricity generation are allocated based on aggregate electricity consumption in each end-
use sector. Totals may not sum due to independent rounding.
Industry
The industry end-use sector includes CO2 emissions from fossil fuel combustion from all manufacturing facilities, in
aggregate. This end-use sector also includes emissions that are produced as a byproduct of the non-energy-related
industrial process activities. The variety of activities producing these non-energy-related emissions includes CH4
emissions from petroleum and natural gas systems, fugitive CH4 emissions from coal mining, by-product CO2
emissions from cement manufacture, and HFC, PFC, SF6, and NF3 byproduct emissions from semiconductor
manufacture, to name a few. Since 1990, industrial sector emissions have declined. The decline has occurred both in
direct emissions and indirect emissions associated with electricity use. In theory, emissions from the industrial end-
use sector should be highly correlated with economic growth and industrial output, but heating of industrial
buildings and agricultural energy consumption are also affected by weather conditions. In addition, structural
changes within the U.S. economy that lead to shifts in industrial output away from energy-intensive manufacturing
products to less energy-intensive products (e.g., from steel to computer equipment) also have a significant effect on
industrial emissions.
Transportation
When electricity-related emissions are distributed to economic end-use sectors, transportation activities accounted
for 27 percent of U.S. greenhouse gas emissions in 2015. The largest sources of transportation greenhouse gases in
2015 were passenger cars (41.5 percent), freight trucks (23.0 percent), light-duty trucks, which include sport utility
vehicles, pickup trucks, and minivans (18.3 percent), commercial aircraft (6.6 percent), rail (2.5 percent), other
aircraft (2.2 percent), pipelines (2.1 percent), and ships and boats (1.8 percent). These figures include direct CO2,
CH4, and N20 emissions from fossil fuel combustion used in transportation and emissions from non-energy use (i.e.,
lubricants) used in transportation, as well as HFC emissions from mobile air conditioners and refrigerated transport
allocated to these vehicle types.
In terms of the overall trend, from 1990 to 2015, total transportation emissions rose by 16 percent due, in large part,
to increased demand for travel as fleet wide light-duty vehicle fuel economy was relatively stable (average new
vehicle fuel economy declined slowly from 1990 through 2004 and then increased more rapidly from 2005 through
2015). The number of vehicle miles traveled by light-duty motor vehicles (passenger cars and light-duty trucks)
increased 42 percent from 1990 to 2015,6 as a result of a confluence of factors including population growth,
economic growth, urban sprawl, and periods of low fuel prices. The decline in new light-duty vehicle fuel economy
between 1990 and 2004 reflected the increasing market share of light-duty trucks, which grew from about 30 percent
6 VMT estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2016). Table VM-1 data
for 2015 have not been published yet, therefore 2015 mileage data is estimated using the 3.5 percent increase in FHWA Traffic
Volume Trends from 2014 to 2015.
2-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
of new vehicle sales in 1990 to 48 percent in 2004. Starting in 2005, the rate of VMT growth slowed while average
new vehicle fuel economy began to increase. Average new vehicle fuel economy has improved almost every year
since 2005, and the truck share has decreased to about 43 percent of new vehicles in Model Year (MY) 2015 (EPA
2016a). Table 2-13 provides a detailed summary of greenhouse gas emissions from transportation-related activities
with electricity-related emissions included in the totals. It is important to note that there was a change in methods
between 2014 and 2015 used to estimate gasoline consumption in the transportation sector. In the absence of this
change, CO2 emissions from passenger cars, light-duty trucks, and other on-road vehicles using gasoline would
likely have been higher in 2015.7
From 2008 to 2009, CO2 emissions from the transportation end-use sector declined 4.2 percent. The decrease in
emissions could largely be attributed to decreased economic activity in 2009 and an associated decline in the
demand for transportation. Modes such as medium- and heavy-duty trucks were significantly impacted by the
decline in freight transport. After reaching a decadal low in 2012, CO2 emissions from the transportation end-use
sector stabilized as the economic recovery gained strength.
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
2015. This rise in CO2 emissions, combined with an increase in HFCs from close to zero emissions in 1990 to 45.1
MMT CO2 Eq. in 2015, led to an increase in overall emissions from transportation activities of 16 percent.
Table 2-13: Transportation-Related Greenhouse Gas Emissions (MMT CO2 Eq.)
Gas/Vehicle
1990
2005
2011
2012
2013
2014
2015
Passenger Cars
656.7
708.7
774.1
767.7
763.0
762.4
749.8
CO2
629.3
660.1
736.9
735.5
735.5
737.7
727.1
CH4
3.2
1.2
1.2
1.1
1.1
1.0
1.0
N2O
24.1
15.7
12.1
10.5
9.2
7.8
6.9
HFCs
0.0
31.7
23.9
20.6
17.3
16.0
14.9
Light-Duty Trucks
335.2
552.2
331.5
325.1
322.2
337.2
331.5
CO2
320.7
503.3
293.8
290.5
290.8
308.0
304.2
CH4
1.7
0.9
0.4
0.4
0.3
0.3
0.3
N2O
12.8
14.7
5.6
4.9
4.3
4.0
3.6
HFCs
0.0
33.3
31.7
29.3
26.7
25.0
23.4
Medium- and Heavy-
Duty Trucks
231.4
399.2
389.3
389.9
397.1
408.4
416.2
CO2
230.4
396.3
384.7
384.9
391.6
402.9
410.4
CH4
0.3
0.1
0.1
0.1
0.1
0.1
0.1
N2O
0.7
1.2
1.1
1.0
1.0
0.9
0.9
HFCs
0.0
1.5
3.4
3.9
4.4
4.4
4.8
Buses
8.5
12.0
16.7
17.8
18.0
19.4
19.7
CO2
8.4
11."
16.2
17.3
17.5
18.9
19.2
CH4
+
+
+
+
+
+
N2O
+
0.1
0.1
0.1
0.1
0.1
HFCs
0.0
0.'
0.4
0.4
0.4
0.4
0.4
Motorcycles
1.8
1.7
3.6
4.2
4.0
3.9
3.8
CO2
1.7
1.6
3.6
4.1
3.9
3.8
3.7
CH4
+
+ £v;
+
+
+
+
+
N2O
+
+
+
+
+
+
Commercial Aircraft3
110.9
134.0
115.7
114.3
115.4
116.3
120.1
CO2
109.9
132.7
114.6
113.3
114.3
115.2
119.0
CH4
0.0
0.0
0.0
0.0
0.0
0.0
0.0
7 In 2016, FHWA changed its methods for estimating the share of motor gasoline used in on-highway and off-highway
applications. This resulted in an increase in the estimated off-highway motor gasoline consumption and subsequent decrease in
the on-highway motor gasoline consumption for 2015.
Trends 2-29
-------
n2o
1.0
1.2
1.1
1.0
1.1
1.1
1.1
Other Aircraftb
78.3
59.7
34.2
32.1
34.7
35.2
40.6
CO2
77.5
59.1
33.9
31.8
34.4
34.9
40.2
CH4
0.1
0.1
+
+
+
+
+
N2O
0.7
0.5
0.3
0.3
0.3
0.3
0.4
Ships and Boats0
44.9
44.9
46.4
40.1
39.4
28.6
32.2
CO2
44.3
44.3
45.5
39.3
38.7
28.0
31.6
CH4
+
+
+
+
+
+
+
N2O
0.6
0.6
0.8
0.7
0.7
0.5
0.6
HFCs
0.0
+
+
+
+
+
+
Rail
38.9
51.1
45.8
44.6
45.5
47.6
45.6
CO2
38.5
50.3
44.7
43.4
44.2
45.7
43.6
CH4
0.1
0.1
0.1
0.1
0.1
0.1
0.1
N2O
0.3
0.4
0.3
0.3
0.3
0.4
0.3
HFCs
0.0
0.3
0.7
0.8
0.9
1.4
1.6
Other Emissions from
Electricity Generation"1
0.1
+
+
+
+
+
+
Pipelines®
36.0
32.4
38.1
40.5
46.2
39.4
38.0
CO2
36.0
32.4
38.1
40.5
46.2
39.4
38.0
Lubricants
11.8
10.2
9.0
8.3
8.8
9.1
10.0
CO2
11.8
10.2
9.0
8.3
OO
OO
9.1
10.0
Total Transportation
1,554.4
2,005.9
1,804.3
1,784.7
1,794.3
1,807.5
1,807.5
International Bunker
Fuel/
104.5
114.2
112.8
106.8
100.7
104.2
111.8
+ Does not exceed 0.05 MMT 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.
c Fluctuations in emission estimates are associated with fluctuations in reported fuel consumption, and may reflect issues
with data sources.
d Other emissions from electricity generation are a result of waste incineration (as the majority of municipal solid waste is
combusted in "trash-to-steam" electricity generation plants), electrical transmission and distribution, and a portion of Other
Process Uses of Carbonates (from pollution control equipment installed in electricity generation plants).
e CO2 estimates reflect natural gas used to power pipelines, but not electricity. While the operation of pipelines produces
CH4 and N2O, these emissions are not directly attributed to pipelines in the U.S. Inventory.
f Emissions from International Bunker Fuels include emissions from both civilian and military activities; these emissions
are not included in the transportation totals.
Notes: Passenger cars and light-duty trucks include vehicles typically used for personal travel and less than 8,500 lbs;
medium- and heavy-duty trucks include vehicles larger than 8,500 lbs. HFC emissions primarily reflect HFC-134a. Totals
may not sum due to independent rounding.
1 Commercial
2 The commercial sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
3 lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
4 consumption of natural gas and petroleum products, primarily for heating and cooking needs. Energy-related
5 emissions from the residential and commercial sectors have generally been increasing since 1990, and are often
6 correlated with short-term fluctuations in energy consumption caused by weather conditions, rather than prevailing
7 economic conditions. Landfills and wastewater treatment are included in this sector, with landfill emissions
8 decreasing since 1990 and wastewater treatment emissions decreasing slightly.
9 Residential
10 The residential sector is heavily reliant on electricity for meeting energy needs, with electricity consumption for
11 lighting, heating, air conditioning, and operating appliances. The remaining emissions were largely due to the direct
12 consumption of natural gas and petroleum products, primarily for heating and cooking needs. Emissions from the
13 residential sectors have generally been increasing since 1990, and are often correlated with short-term fluctuations in
2-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
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 end-use sector includes a variety of processes, including enteric fermentation in domestic livestock,
livestock manure management, and agricultural soil management. In 2015, agricultural soil management was the
largest source of N20 emissions, and enteric fermentation was the largest source of CH4 emissions in the United
States. This sector also includes small amounts of CO2 emissions from fossil fuel combustion by motorized farm
equipment like tractors. The agriculture sector is less reliant on electricity than the other sectors.
Box 2-1: Methodology for Aggregating Emissions by Economic Sect
Ji
In presenting the Economic Sectors in the annual Inventory of U.S. Greenhouse Gas Emissions and Sinks, the
Inventory expands upon the standard IPCC sectors common for UNFCCC reporting. Discussing greenhouse gas
emissions relevant to U.S.-specific sectors improves communication of the report's findings.
In the Electricity Generation economic sector, CO2 emissions from the combustion of fossil fuels included in the
EIA electric utility fuel consuming sector are apportioned to this economic sector. Stationary combustion emissions
of CH4 and N20 are also based on the EIA electric utility sector. Additional sources include CO2, CH4, and N20
from waste incineration, as the majority of municipal solid waste is combusted in "trash-to-steam" electricity
generation plants. The Electricity Generation economic sector also includes SF6 from Electrical Transmission and
Distribution, and a portion of CO2 from Other Process Uses of Carbonates (from pollution control equipment
installed in electricity generation plants).
In the Transportation economic sector, the CO2 emissions from the combustion of fossil fuels included in the EIA
transportation fuel consuming sector are apportioned to this economic sector (additional analyses and refinement of
the EIA data is further explained in the Energy chapter of this report). Emissions of CH4 and N20 from Mobile
Combustion are also apportioned to this economic sector based on the EIA transportation fuel consuming sector.
Substitution of Ozone Depleting Substances emissions 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. The CH4 and N20 emissions from stationary and mobile combustion are also apportioned to this
economic sector based on the EIA industrial fuel consuming sector, minus emissions apportioned to the Agriculture
economic sector described below. Substitution of Ozone Depleting Substances emissions are apportioned based on
their specific end-uses within the source category, with most emissions falling within the Industry economic sector.
Additionally, all process-related emissions from sources with methods considered within the IPCC IPPU sector have
been apportioned to this economic sector. This includes the process-related emissions (i.e., emissions from the actual
process to make the material, not from fuels to power the plant) from such activities as Cement Production, Iron and
Steel Production and Metallurgical Coke Production, and Ammonia Production. Additionally, fugitive emissions
from energy production sources, such as Natural Gas Systems, Coal Mining, and Petroleum Systems are included in
the Industry economic sector. A portion of CO2 from Other Process Uses of Carbonates (from pollution control
equipment installed in large industrial facilities) are also included in the Industry economic sector. Finally, all
remaining CO2 emissions from Non-Energy Uses of Fossil Fuels are assumed to be industrial in nature (besides the
lubricants for transportation vehicles specified above), and are attributed to the Industry economic sector.
As agriculture equipment is included in EIA's industrial fuel consuming sector surveys, additional data is used to
extract the fuel used by agricultural equipment, to allow for accurate reporting in the Agriculture economic sector
from all sources of emissions, such as motorized farming equipment. Energy consumption estimates are obtained
from Department of Agriculture survey data, in combination with separate EIA fuel sales reports. This
supplementary data is used to apportion some of the CO2 emissions from fossil fuel combustion, and CH4 and N20
Trends 2-31
-------
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
31
32
33
34
35
emissions from stationary and mobile combustion, to the Agriculture economic sector. The other emission sources
included in this economic sector are intuitive for the agriculture sectors, such as N20 emissions from Agricultural
Soils, CH4 from Enteric Fermentation, CH4 and N20 from Manure Management, CH4 from Rice Cultivation, CO2
emissions from Liming and Urea Application, and CH4 and N20 from Forest Fires. Nitrous oxide 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 N20 are also based on the EIA residential fuel
consuming sector. Substitution of Ozone Depleting Substances are apportioned based on their specific end-uses
within the source category, with emissions from residential air-conditioning systems to this economic sector. Nitrous
oxide 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. Emissions of CH4 and N20 from Mobile Combustion are also
apportioned to this economic sector based on the EIA transportation fuel consuming sector. Substitution of Ozone
Depleting Substances emissions are apportioned based on their specific end-uses within the source category, with
emissions from commercial refrigeration/air-conditioning systems apportioned to this economic sector. Public works
sources including direct CH4 from Landfills and CH4 and N20 from Wastewater Treatment and Composting are also
included in this economic sector.
Recent Trends in Various U.S. Greenhouse Gas Emissions-Related Data
J
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 2015; (4) emissions per unit of total gross domestic product as a measure of national economic activity;
or (5) emissions per capita.
Table 2-14 provides data on various statistics related to U.S. greenhouse gas emissions normalized to 1990 as a
baseline year. Greenhouse gas emissions in the United States have grown at an average annual rate of 0.2 percent
since 1990. Since 1990, this rate is slightly slower than that for total energy and for fossil fuel consumption, and
much slower than that for electricity consumption, overall gross domestic product and national population (see
Table 2-14 and Figure 2-14).
Table 2-14: Recent Trends in Various U.S. Data (Index 1990 = 100)
Chapter/IPCC Sector
1990
2005
2011
2012
2013
2014
2015
Growth3
Greenhouse Gas Emissions'5
100
115
106
103
105
106
103
0.2%
Energy Consumption0
100
118
115
112
115
117
115
0.6%
Fossil Fuel Consumption0
100
119
110
107
110
111
110
0.4%
Electricity Consumption0
100
134
137
135
136
138
137
1.3%
GDPd
100
159
168
171
174
178
183
2.5%
Population6
100
118
125
126
126
127
128
1.0%
a Average annual growth rate
b GWP-weighted values
c Energy-content-weighted values (EIA 2016)
d Gross Domestic Product in chained 2009 dollars (BEA 2016)
e U.S. Census Bureau (2016)
2-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Figure 2-14: U.S. Greenhouse Gas Emissions Per Capita and Per Dollar of Gross Domestic
Product
180-
Real GDP
170-
160-
150-
_ M0-
? ,30-
| 120-
2 no-
¦o
a 100-
Population
90-
Emissions per capita
so-
70-
Emissions per $GDP
60-
co
CTi
O
O
O
cn
o
o
t-H
o
o
o
o
Source: BEA (2016), U.S. Census Bureau (2016), and emission estimates in this report.
2.3 Indirect Greenhouse Gas Emissions (CO,
N0X, NMVOCs, and S02)
The reporting requirements of the UNFCCC8 request that information be provided on indirect greenhouse gases,
which include CO, NOx, NMVOCs, and SO2. These gases do not have a direct global warming effect, but indirectly
affect terrestrial radiation absorption by influencing the formation and destruction of tropospheric and stratospheric
ozone, or, in the case of SO2, by affecting the absorptive characteristics of the atmosphere. Additionally, some of
these gases may react with other chemical compounds in the atmosphere to form compounds that are greenhouse
gases. Carbon monoxide is produced when carbon-containing fuels are combusted incompletely. Nitrogen oxides
(i.e., NO and NO2) are created by lightning, fires, fossil fuel combustion, and in the stratosphere from N20. Non-
methane 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 the interaction of CO with the hydroxyl radical—the major
8 See .
Trends 2-33
-------
1 atmospheric sink for CH4 emissions—to form CO2. Therefore, increased atmospheric concentrations of CO limit the
2 number of hydroxyl molecules (OH) available to destroy CH4.
3 Since 1970, the United States has published estimates of emissions of CO, NOx, NMVOCs, and SO2 (EPA 2015),9
4 which are regulated under the Clean Air Act. Table 2-15 shows that fuel combustion accounts for the majority of
5 emissions of these indirect greenhouse gases. Industrial processes—such as the manufacture of chemical and allied
6 products, metals processing, and industrial uses of solvents—are also significant sources of CO, NOx, and
7 NMVOCs.
8 Table 2-15: Emissions of NOx, CO, NMVOCs, and SO2 (kt)
Gas/Activity
1990
2005
2011
2012
2013
2014
2015
NOx
21,790
17,443
12,482
12,038
11,387
10,810
9,726
Mobile Fossil Fuel Combustion
10,862
10,295
7,294
6,871
6,448
6,024
5,172
Stationary Fossil Fuel Combustion
10,02:'
5,858
3,807
3,655
3,504
3,291
3,061
Oil and Gas Activities
139
321
622
663
704
745
745
Industrial Processes and Product Use
592
572
452
443
434
424
424
Forest Fires
80
239
172
276
185
188
188
Waste Combustion
82
128
73
82
91
100
100
Grassland Fires
5
21
54
39
13
27
27
Agricultural Burning
6
6
7
7
7
8
8
Waste
-
2
1
2
2
2
2
CO
132,877
75,570
52,586
54,119
48,620
46,922
44,954
Mobile Fossil Fuel Combustion
119,360
58,615
38,305
36,153
34,000
31,848
29,881
Forest Fires
2,832
8,486
6,136
9,815
6,655
6,642
6,642
Stationary Fossil Fuel Combustion
5,000
4,648
4,170
4,027
3,884
3,741
3,741
Industrial Processes and Product Use
978
1,403
1,003
1,318
1,632
1,947
1,947
Waste Combustion
4,129
1,557
1,229
1,246
1,262
1,273
1,273
Oil and Gas Activities
302
318
610
666
723
780
780
Grassland Fires
84
358
894
657
217
442
442
Agricultural Burning
191
178
234
232
239
240
239
Waste
1
7
5
6
8
9
9
NMVOCs
20,930
13,154
11,726
11,464
11,202
10,935
10,647
Industrial Processes and Product Use
7,638
5,849
3,929
3,861
3,793
3,723
3,723
Mobile Fossil Fuel Combustion
10,932
5,724
4,562
4,243
3,924
3,605
3,318
Oil and Gas Activities
554
510
2,517
2,651
2,786
2,921
2,921
Stationary Fossil Fuel Combustion
912
716
599
569
539
507
507
Waste Combustion
222
241
81
94
108
121
121
Waste
673
114
38
45
51
57
57
Agricultural Burning
NA
NA
NA
NA
NA
NA
NA
SO2
20,935
13,196
5,877
5,876
5,874
4,357
3,448
Stationary Fossil Fuel Combustion
18,40"
11,541
5,008
5,006
5,005
3,640
2,756
Industrial Processes and Product Use
1,30"
831
604
604
604
496
496
Mobile Fossil Fuel Combustion
390
180
108
108
108
93
93
Oil and Gas Activities
79'
619
142
142
142
95
70
Waste Combustion
38
25
15
15
15
32
32
Waste
+
1
+
+
+
1
1
Agricultural Burning
NA
NA
NA
NA
NA
NA
NA
+ Does not exceed 0.5 kt.
NA (Not Available)
Note: Totals may not sum due to independent rounding.
Source: (EPA 2015) except for estimates from Field Burning of Agricultural Residues.
9 NOx and CO emission estimates from Field Burning of Agricultural Residues were estimated separately, and therefore not
taken from EPA (2016b).
2-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
urces and Effects of Sulfur Diox
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 59.2
percent in 2012. Coal combustion contributes nearly all of those emissions (approximately 92 percent). Sulfur
dioxide emissions have decreased in recent years, primarily as a result of electric power generators switching from
high-sulfur to low-sulfur coal and installing flue gas desulfurization equipment.
Trends 2-35
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
3. Energy
Energy-related activities were the primary sources of U.S. anthropogenic greenhouse gas emissions, accounting for
84.3 percent of total greenhouse gas emissions on a carbon dioxide (CO2) equivalent basis in 2015.1 This included
97, 42, and 12 percent of the nation's CO2, methane (CH4), and nitrous oxide (N20) emissions, respectively. Energy-
related CO2 emissions alone constituted 79.5 percent of national emissions from all sources on a CO2 equivalent
basis, while the non-C02 emissions from energy-related activities represented a much smaller portion of total
national emissions (4.8 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 32,381 million metric tons (MMT) of CO2 were
added to the atmosphere through the combustion of fossil fuels in 2014, of which the United States accounted for
approximately 16 percent.2 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 N20. Stationary combustion of fossil fuels was the second largest source of N20
emissions in the United States and mobile fossil fuel combustion was the fourth largest source.
Figure 3-1: 2015 Energy Chapter Greenhouse Gas Sources (MMT CO2 Eq.)
Fossil Fuel Combustion
5,049
Natural Gas Systems I
Non-Energy Use of Fuels I
Coal Mining H
Petroleum Systems H
Stationary Combustion H
Mobile Combustion H
Incineration of Waste |
Abandoned Underground Coal Mines I
Energy as a Portion of
all Emissions
84.3%
50
100
150
MMT CO2 Eq.
200
250
300
1 Estimates are presented in units of million metric tons of carbon dioxide equivalent (MMT CO2 Eq.), which weight each gas by
its global wanning potential, or GWP, value. See section on global wanning potentials in the Executive Summary.
2 Global CO2 emissions from fossil fuel combustion were taken from International Energy Agency CO: Emissions from Fossil
Fuels Combustion - Highlights IEA (2016).
Energy 3-1
-------
Figure 3-2: 2015 U.S. Fossil Carbon Flows (MMT CO2 Eq.)
fossil Fuel
Energy Exports
International .
Bunkers .
NEU Emissions 12
Coal Emissions
1,435
NEU Emissions 6
Natural Gas Emissions
NEU Emissions 109
industrial
Non-Energy
Use Exports
Combustion
Emissions
1.423
Combustion
Emissions 1.464
Atmospnenc
Emissions
5373
Domestic
Fossil fuel
Production
4,695
Apparent
Consumption
5.496
Petroleum
Natural Gas
>..476
bffliSSfOnS
combustion
Emissions
Petroleum
1.439
Natural bas Liquids
Liquefied Refinery bas
& Other Liquids
Fossil Fuel
Energ;
imports
Petroleum
1308
Non-Energy Balancing
Use U.S. item
Territories (88i
Fossil Fust
Non-Energy consumption
Use Imports U.S.
HG 148
coa 27
emtones
Non-Energy Use
Carton Sequestered
Note: Totals may not sum due to independent rounding.
The "Balancing Item" above accounts for the statistical
imbalances and unknowns in the reported data sets combined
here..
NEU = Non-Energy Use
WG = Natural Gas
Energy-related activities other than fuel combustion, such as the production, transmission, storage, and distribution
of fossil fuels, also emit greenhouse gases. These emissions consist primarily of fugitive CH4 from natural gas
systems, petroleum systems, and coal mining. Table 3-1 summarizes emissions from the Energy sector in units of
MMT CO2 Eq., while unweighted gas emissions in kilotons (kt) are provided in Table 3-2. Overall, emissions due to
energy-related activities were 5,549.4 MMT CO2 Eq. in 2015,3 an increase of 4.0 percent since 1990.
Table 3-1: CO2, ChU, and N2O Emissions from Energy (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
4,907.6
5,932.0
5,386.6
5,179.7
5,332.1
5,375.9
5,232.8
Fossil Fuel Combustion
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
Electricity Generation
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Transportation
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
Industrial
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Residential
338.3
357.8
325.5
282.5
329.7
345.4
319.6
Commercial
217.4
223.5
220.4
196.7
221.0
231.4
225.7
U.S. Territories
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Non-Energy Use of Fuels
117.7
138.3
108.5
105.5
122.0
117.2
127.0
Natural Gas Systems
37.7
30.1
35.7
35.2
38.5
42.4
42.4
Incineration of Waste
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Petroleum Systems
3.6
3.9
4.2
3.9
3.7
3.6
3.6
Biomass-Wood"
215.2
206.9
195.2
194.9
211.6
217.7
198.7
International Bunker Fuels"
103.5
113.1
111.7
105.8
99.8
103.2
110.8
Biomass-Ethanol"
4.2
22.9
72.9
72.8
74.7
76.1
78.9
CH4
372.6
291.1
290.8
285.2
285.5
287.0
277.8
Natural Gas Systems
196.5
162.1
153.7
155.3
157.9
160.8
160.0
Petroleum Systems
58.3
48.0
50.1
48.4
46.6
44.9
41.5
Coal Mining
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Stationary Combustion
8.5
7.4
7.1
6.6
8.0
8.1
7.0
Abandoned Underground Coal
Mines
7.2
6.6
6.4
6.2
6.2
6.3
6.4
Mobile Combustion
5.6
2.8
2.3
2.2
2.1
2.1
2.0
Incineration of Waste
+
+
+
+
+
+
+
3 Following the revised reporting requirements under the UNFCCC, this Inventory report presents CO2 equivalent values based
on the IPCC Fourth Assessment Report (AR4) GWP values. See the Introduction chapter for more information.
3-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
International Bunker Fuels"
0.2
0.1
0.1
0.1
0.1
0.1
0.1
N2O
53.6
56.4
44.4
42.1
41.7
40.3
38.8
Stationary Combustion
11.9
20.2
21.3
21.4
22.9
23.4
23.1
Mobile Combustion
41.2
35.7
22.8
20.4
18.5
16.6
15.4
Incineration of Waste
0.5
0.4
0.3
0.3
0.3
0.3
0.3
International Bunker Fuels"
0.9
1.0
1.0
0.9
0.9
0.9
0.9
Total
5,333.8
6,279.4
5,721.8
5,506.9
5,659.3
5,703.2
5,549.4
+ Does not exceed 0.05 MMT CO2 Eq.
a These values are presented for informational purposes only, in line with IPCC methodological guidance and UNFCCC reporting
obligations, and are not included in the specific energy sector contribution to the totals, and are already accounted for elsewhere.
Note: Totals may not sum due to independent rounding.
Table 3-2: CO2, ChU, and N2O Emissions from Energy (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
4,907,563
5,931,954
5,386,617
5,179,680
5,332,130
5,375,900
5,232,800
Fossil Fuel Combustion
4,740,671
5,747,142
5,227,690
5,024,685
5,157,583
5,202,139
5,049,159
Non-Energy Use of Fuels
117,658
138,341
108,508
105,537
121,998
117,235
127,047
Natural Gas Systems
37,732
30,076
35,662
35,203
38,457
42,351
42,351
Incineration of Waste
7,950
12,469
10,564
10,379
10,398
10,608
10,676
Petroleum Systems
3,553
3,927
4,192
3,876
3,693
3,567
3,567
Biomass-Wood"
215,186
206,901
195,182
194,903
211,581
217,654
198,723
International Bunker Fuels"
103,463
113,139
111,660
105,805
99,763
103,201
110,751
Biomass-Ethanol"
4,227
22,943
72,881
72,827
74,743
76,075
78,934
cm
14,904
11,643
11,631
11,408
11,418
11,482
11,112
Natural Gas Systems
7,862
6,485
6,147
6,213
6,317
6,433
6,401
Petroleum Systems
2,330
1,921
2,004
1,935
1,864
1,796
1,660
Coal Mining
3,860
2,565
2,849
2,658
2,584
2,593
2,436
Stationary Combustion
339
296
283
265
320
324
278
Abandoned Underground
Coal Mines
288
264
257
249
249
253
256
Mobile Combustion
226
113
91
87
85
82
82
Incineration of Waste
+
+
+
+
+
+
+
International Bunker Fuels"
7
J
5
4
3
3
3
N2O
180
189
149
141
140
135
130
Stationary Combustion
40
68
71
72
77
79
78
Mobile Combustion
138
120
77
68
62
56
52
Incineration of Waste
2
1
1
1
1
1
1
International Bunker Fuels"
3
3
3
3
3
3
3
+ Does not exceed 0.5 kt
a These values are presented for informational purposes only, in line with IPCC methodological guidance and UNFCCC reporting
obligations, and are not included in the specific energy sector contribution to the totals, and are already accounted for elsewhere.
Note: Totals may not sum due to independent rounding.
I
In following the United Nations Framework Convention on Climate Change (UNFCCC) requirement under Article
4.1 to develop and submit national greenhouse gas emission inventories, the emissions and sinks presented in this
report and this chapter, are organized by source and sink categories and calculated using internationally-accepted
methods provided by the Intergovernmental Panel on Climate Change (IPCC). Additionally, the calculated
emissions and sinks in a given year for the United States are presented in a common manner in line with the
UNFCCC reporting guidelines for the reporting of inventories under this international agreement. The use of
consistent methods to calculate emissions and sinks by all nations providing their inventories to the UNFCCC
ensures that these reports are comparable. In this regard, U.S. emissions and sinks reported in this Inventory are
comparable to emissions and sinks reported by other countries. Emissions and sinks provided in this Inventory do
not preclude alternative examinations, but rather, this Inventory presents emissions and sinks in a common format
consistent with how countries are to report Inventories under the UNFCCC. The report itself, and this chapter,
follows this standardized format, and provides an explanation of the IPCC methods used to calculate emissions and
sinks, and the manner in which those calculations are conducted.
Energy 3-3
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9
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20
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25
26
27
28
29
30
31
32
33
34
35
36
Data from the Greenhouse Gas Reporting Prog
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule for the mandatory
reporting of greenhouse gases from large greenhouse gas emissions sources in the United States. Implementation of
40 CFR Part 98 is referred to as the Greenhouse Gas Reporting Program (GHGRP). 40 CFR Part 98 applies to direct
greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for
sequestration or other reasons. Reporting is at the facility level, except for certain suppliers of fossil fuels and
industrial greenhouse gases. 40 CFR part 98 requires reporting by 41 industrial categories. Data reporting by
affected facilities included the reporting of emissions from fuel combustion at that affected facility. In general, the
threshold for reporting is 25,000 metric tons or more of CO2 Eq. per year.
EPA's GHGRP dataset and the data presented in this Inventory report are complementary and, as indicated in the
respective Planned Improvements sections for source categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this Inventory (see, also, Box 3-4). Most
methodologies used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP, facilities collect
detailed information specific to their operations according to detailed measurement standards, which may differ with
the more aggregated data collected for the Inventory to estimate total, national U.S. emissions. It should be noted
that the definitions and provisions for reporting fuel types in EPA's GHGRP may differ from those used in the
Inventory in meeting the UNFCCC reporting guidelines. In line with the UNFCCC reporting guidelines, the
Inventory report is a comprehensive accounting of all emissions from fuel types identified in the IPCC guidelines
and provides a separate reporting of emissions from biomass. Further information on the reporting categorizations in
EPA's GHGRP and specific data caveats associated with monitoring methods in EPA's GHGRP has been provided
on the GHGRP website.
EPA presents the data collected by its GHGRP through a data publication tool that allows data to be viewed in
several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.
3.1 Fossil Fuel Combustion (IPCC Source
Categon
Emissions from the combustion of fossil fuels for energy include the gases CO2, CH4, and N20. 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 N20 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 N20
emissions from fossil fuel combustion are presented in Table 3-3 and Table 3-4.
Table 3-3: CO2, ChU, and N2O Emissions from Fossil Fuel Combustion (MMT CO2 Eq.)
Gas
1990
2005
2011
2012
2013
2014
2015
CO2
4,740.7
5,747.1
5.227.7
5,024.7
5,157.6
5,202.1
5,049.2
CH4
14.1
10.2
9.3
8.8
10.1
10.2
9.0
N2O
53.1
56.0
44.2
41.8
41.5
40.0
38.5
Total
4,807.9
5,813.4
5.281.2
5,075.3
5,209.2
5,252.3
5,096.7
Note: Totals may not sum due to independent rounding
3-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Table 3-4: CO2, ChU, and N2O Emissions from Fossil Fuel Combustion (kt)
Gas 1990 2005 2011 2012 2013 2014 2015
CO2 4,740,671 5,747,142 5,227,690 5,024,685 5,157,583 5,202,139 5,049,159
CH4 565 408 374 352 405 406 360
N2O 178 . 188 148 140 139 134 129
2 C02 from Fossil Fuel Combustion
3 Carbon dioxide is the primary gas emitted from fossil fuel combustion and represents the largest share of U.S. total
4 greenhouse gas emissions. Carbon dioxide emissions from fossil fuel combustion are presented in Table 3-5. In
5 2015, CO2 emissions from fossil fuel combustion decreased by 2.9 percent relative to the previous year. The
6 decrease in CO2 emissions from fossil fuel combustion was a result of multiple factors, including: (1) substitution
7 from coal to natural gas consumption in the electric power sector; (2) warmer winter conditions in the first quarter of
8 2015 resulting in a decreased demand for heating fuel in the residential and commercial sectors; and (3) a slight
9 decrease in electricity demand. In 2015, CO2 emissions from fossil fuel combustion were 5,049.2 MMT CO2 Eq., or
10 6.5 percent above emissions in 1990 (see Table 3-5).4
11 Table 3-5: CO2 Emissions from Fossil Fuel Combustion by Fuel Type and Sector (MMT CO2
12 Eq.)
Fuel/Sector
19'JO
2005
2011
2012
2013
2014
2015
Coal
1,718.4
2,112.3
1,813.9
1,592.8
1,654.4
1,652.0
1,422.7
Residential
3.0
0.8
NO
NO
NO
NO
NO
Commercial
12.0
9.3
5.8
4.1
3.9
3.8
2.9
Industrial
155.3
115.3
82.0
74.1
75.7
75.6
65.9
Transportation
NE
NE
NE
NE
NE
NE
NE
Electricity Generation
1,547.6
1,983.8
1,722.7
1,511.2
1,571.3
1,569.1
1,350.5
U.S. Territories
0.6
3.0
3.4
3.4
3.4
3.4
3.4
Natural Gas
1,000.3
1,166.7
1,291.5
1,352.6
1,391.2
1,422.2
1,463.8
Residential
238.0
262.2
254.7
224.8
266.2
277.9
252.8
Commercial
142.1
162.9
170.5
156.9
179.1
189.3
175.4
Industrial
408.9
388.5
417.3
434.8
451.9
468.4
467.5
Transportation
36.0
33.1
38.9
41.3
47.0
40.3
38.8
Electricity Generation
175.3
318.8
408.8
492.2
444.0
443.2
526.1
U.S. Territories
NO
1.3
1.4
2.6
3.0
3.2
3.2
Petroleum
2,021.5
2,467.8
2,121.9
2,078.9
2,111.6
2,127.5
2,162.3
Residential
97.4
94.9
70.9
57.7
63.4
67.5
66.8
Commercial
63.3
51.3
44.1
35.7
38.0
38.3
47.3
Industrial
278.3
324.2
275.7
274.1
284.6
271.7
295.5
Transportation
1,457.7
1,854.0
1,668.8
1,655.4
1,666.0
1,690.1
1,694.5
Electricity Generation
97.5
97.9
25.8
18.3
22.4
25.3
23.7
U.S. Territories
27.2
45.6
36.7
37.6
37.1
34.6
34.6
Geothermal3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Total
4,740.7
5,747.1
5,227.7
5,024.7
5,157.6
5,202.1
5,049.2
+ Does not exceed 0.05 MMT CO2 Eq.
NE (Not Estimated)
NO (Not Occurring)
aAlthough not technically a fossil fuel, geothermal energy-related CO2 emissions are included for reporting
purposes.
Note: Totals may not sum due to independent rounding.
4 An additional discussion of fossil fuel emission trends is presented in the Trends in U.S. Greenhouse Gas Emissions chapter.
Energy 3-5
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15
16
17
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19
20
21
22
23
24
25
26
27
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).
Carbon dioxide 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.5
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 2015 Emissions from Fossil Fuel
Combustion for Selected Fuels and Sectors (MMT CO2 Eq. and Percent)
Sector
Fuel Type
2011 to 2012
2012 to 2013
2013 to 2014
2014 to 2015
Total 2015
Electricity Generation
Coal
-211.5
-12.3%
60.1
4.0%
-2.2
-0.1%
-218.7
-13.9%
1,350.5
Electricity Generation
Natural Gas
83.5
20.4%
-48.3
-9.8%
-0.8
-0.2%
82.9
18.7%
526.1
Electricity Generation
Petroleum
-7.5
-29.0%
4.1
22.3%
2.9
12.8%
-1.6
-6.4%
23.7
Transportation3
Petroleum
-13.3
-0.8%
10.6
0.6%
24.1
1.4%
4.3
0.3%
1,694.5
Residential
Natural Gas
-29.8
-11.7%
41.4
18.4%
11.6
4.4%
-25.1
-9.0%
252.8
Commercial
Natural Gas
-13.6
-8.0%
22.3
14.2%
10.2
5.7%
-13.9
-7.4%
175.4
Industrial
Coal
-7.9
-9.7%
1.7
2.3%
-0.1
-0.1%
-9.8
-12.9%
65.9
Industrial
Natural Gas
17.5
4.2%
17.1
3.9%
16.5
3.7%
-0.9
-0.2%
467.5
All Sectorsb
All Fuelsb
-203.0
-3.9%
132.9
2.6%
44.6
0.9%
-153.0
-2.9%
5,049.2
a Excludes emissions from International Bunker Fuels.
b Includes fuels and sectors not shown in table.
Note: Totals may not sum due to independent rounding.
In the United States, 82 percent of the energy consumed in 2015 was produced through the combustion of fossil
fuels such as coal, natural gas, and petroleum (see Figure 3-3 and Figure 3-4). The remaining portion was supplied
by nuclear electric power (9 percent) and by a variety of renewable energy sources (10 percent), primarily
hydroelectric power, wind energy and biofuels (EIA 2016a).6 Specifically, petroleum supplied the largest share of
domestic energy demands, accounting for 37 percent of total U.S. energy consumption in 2015. Natural gas and coal
followed in order of energy demand importance, accounting for approximately 29 percent and 16 percent of total
U.S. energy consumption, respectively. Petroleum was consumed primarily in the transportation end-use sector and
the vast majority of coal was used in electricity generation. Natural gas was broadly consumed in all end-use sectors
except transportation (see Figure 3-5) (EIA 2016a).
5 Based on national aggregate carbon content of all coal, natural gas, and petroleum fuels combusted in the United States.
6 Renewable energy, as defined in EIA's energy statistics, includes the following energy sources: hydroelectric power,
geothermal energy, biofuels, solar energy, and wind energy.
3-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Figure 3-3: 2015 U.S. Energy Consumption by Energy Source (Percent)
Nuclear Electric Power
8.6%
Renewable Energy
9.7%
Petroeum
36.6%
Coal
16.2%
Natural Gas
29.0%
Figure 3-4: U.S. Energy Consumption (Quadrillion Btu)
120
100
CD
Of
80-
60
40-
20-
Total Energy
Fossil Fuels
CT\Cr>0">CTi0->0^(T>C7>0'>0^
cricr»oo^cj>cr>cr>Gi(j>o>
Renewable & Nuclear
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Energy 3-7
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Figure 3-5: 2015 CO2 Emissions from Fossil Fuel Combustion by Sector and Fuel Type (MMT
COz Eq.)
2,500
2,000
O
u
1,500
1,000
500
Relative Contribution by Fuel Type
1,901
1,733
Petroleum
Coal
Natural Gas
Geothermal
U.S. Territories Commercial
Residential Industrial Transportation Electricity
Generation
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.7 These other C containing non-CO; gases are emitted as a byproduct of
incomplete fuel combustion but are, for the most part, eventually oxidized to CO2 in the atmosphere. Therefore, it is
assumed all of the C in fossil fuels used to produce energy is eventually converted to atmospheric CO2.
Box 3-3: Weather and Non-Fossil Energy Effects on CO2 from Fossil Fuel Combustion Trends
In 2015, weather conditions, and a warm first and fourth quarter of the year in particular, caused a significant
decrease in energy demand for heating fuels and is reflected in the decreased residential emissions from 2014 to
2015. The United States in 2015 also experienced a wanner winter overall compared to 2014, as heating degree days
decreased (10.2 percent). Cooling degree days increased significantly by 14.6 percent and despite this increase in
cooling degree days, residential electricity demand decreased slightly. Warmer winter conditions compared to 2014
resulted in a decrease in the amount of energy required for heating, and heating degree days in the United States
were 9.7 percent below normal (see Figure 3-6). Summer conditions were significantly wanner in 2015 compared to
2014, with cooling degree days 22.5 percent above nonnal (see Figure 3-7) (EIA 2016a).8
7 See the sections entitled Stationary Combustion and Mobile Combustion in this chapter for information on 11011-CO2 gas
emissions from fossil fuel combustion.
8 Degree days are relative measurements of outdoor air temperature. Heating degree days are deviations of the mean daily
temperature below 65 degrees Fahrenheit, while cooling degree days are deviations of the mean daily temperature above 65
degrees Fahrenheit. Heating degree days have a considerably greater effect on energy demand and related emissions than do
cooling degree days. Excludes Alaska and Hawaii. Normals are based on data from 1971 through 2000. The variation in these
normals during this time period was +10 percent and +14 percent for heating and cooling degree days, respectively (99 percent
confidence interval).
3-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
Figure 3-6: Annual Deviations from Normal Heating Degree Days for the United States
(1950-2015, Index Normal = 100)
20
E
o
£
o
i—
c
o
*4—»
fD
>
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-10-
-20
Normal
(4,524 Heating Degree Days)
99% Confidence
Note: Climatological normal data are highlighted. Statistical confidence interval for
"normal" climatology period of 1981 through 2010.
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Figure 3-7: Annual Deviations from Normal Cooling Degree Days for the United States
(1950-2015, Index Normal = 100)
ro
E
E
o
30
20-
Normal
(1,242 cooling degree days)
99% Confidence
V
-20-
Note: Climatological normal data are highlighted. Statistical confidence
interval for "normal" climatology period of 1981 through 2010.
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Although no new U.S. nuclear power plants were brought online in 2015, the utilization (i.e., capacity factors)9 of
existing plants in 2015 remained high at 92 percent. Electricity output by hydroelectric power plants decreased in
2015 by approximately 4 percent. In recent years, the wind power sector lias been showing strong growth, such that,
on the margin, it is becoming a relatively important electricity source. Electricity generated by nuclear plants in
2015 provided more than 3 times as much of the energy generated in the United States from hydroelectric plants
(EIA 2016a). Nuclear, hydroelectric, and wind power capacity factors since 1990 are shown in Figure 3-8.
9 Hie 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 (2016a).
Energy 3-9
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1
2
Figure 3-8: Nuclear, Hydroelectric, and Wind Power Plant Capacity Factors in the United
States (1990-2015, Percent)
100
Nuclear
80-
•P
o-
60-
a
u
fD
LL
>>
Hydroelectric
fd 40-
Cl
fU
u
Wind
20-
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3
4
5 Fossil Fuel Combustion Emissions by Sector
6 In addition to the CO2 emitted from fossil fuel combustion, CH4 and N20 are emitted from stationary and mobile
7 combustion as well. Table 3-7 provides an overview of the CO2, CH4, and N20 emissions from fossil fuel
8 combustion by sector.
9 Table 3-7: CO2, ChU, and N2O Emissions from Fossil Fuel Combustion by Sector (MMT CO2
10 Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Electricity Generation
1,828.5
2,417.4
2,175.8
2,040.5
2,057.7
2,058.1
1,920.6
CO2
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
CH4
0.3
0.5
0.4
0.4
0.4
0.4
0.4
N2O
7.4
16.0
17.6
17.8
19.1
19.6
19.5
Transportation
1,540.6
1,925.6
1,732.7
1,719.3
1,733.6
1,749.0
1,750.7
CO2
1,493.8
1,887.0
1,707.6
1,696.8
1,713.0
1,730.4
1,733.2
CH4
5.6
2.8
2.3
2.2
2.1
2.1
2.0
N2O
41.2
35.7
22.8
20.4
18.5
16.6
15.4
Industrial
847.4
832.6
778.9
786.9
816.2
819.7
832.7
CO2
842.5
828.0
775.0
782.9
812.2
815.8
828.8
CH4
1.8
1.7
1.5
1.5
1.5
1.5
1.5
N2O
3.1
2.9
2.4
2.4
2.4
2.4
2.4
Residential
344.6
362.8
330.4
287.0
335.6
351.4
324.2
CO2
338.3
357.8
325.5
282.5
329.7
345.4
319.6
CH4
5.2
4.1
4.0
3.7
5.0
5.0
3.9
N2O
1.0
0.9
0.8
0.7
1.0
1.0
0.8
Commercial
218.8
224.9
221.7
197.9
222.4
232.8
227.1
CO2
217.4
223.5
220.4
196.7
221.0
231.4
225.7
CH4
1.0
1.1
1.0
0.9
1.0
1.1
1.1
N2O
0.4
0.3
0.3
0.3
0.3
0.3
0.3
3-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
U.S. Territories3 2&0 50.1 41.7 43.7 43.7 41.4 41.4
Total 4,807.9 5,813.4 5,281.2 5,075.3 5,209.2 5,252.3 5,096.7
a U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions from all fuel
combustion sources.
Notes: 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.
Other than CO2, gases emitted from stationary combustion include the greenhouse gases CH4 and N20 and the
indirect greenhouse gases NOx, CO, and NMVOCs.10 Methane and N20 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. Nitrous oxide emissions
from stationary combustion are closely related to air-fuel mixes and combustion temperatures, as well as the
characteristics of any pollution control equipment that is employed. Methane emissions from stationary combustion
are primarily a function of the CH4 content of the fuel and combustion efficiency.
Mobile combustion produces greenhouse gases other than CO2, including CH4, N20, and indirect greenhouse gases
including NOx, CO, and NMVOCs. As with stationary combustion, N20 and NOx emissions from mobile
combustion are closely related to fuel characteristics, air-fuel mixes, combustion temperatures, and the use of
pollution control equipment. N20 from mobile sources, in particular, can be formed by the catalytic processes used
to control NOx, CO, and hydrocarbon emissions. Carbon monoxide emissions from mobile combustion are
significantly affected by combustion efficiency and the presence of post-combustion emission controls. Carbon
monoxide emissions are highest when air-fuel mixtures have less oxygen than required for complete combustion.
These emissions occur especially in idle, low speed, and cold start conditions. Methane and NMVOC emissions
from motor vehicles are a function of the CH4 content of the motor fuel, the amount of hydrocarbons passing
uncombusted through the engine, and any post-combustion control of hydrocarbon emissions (such as catalytic
converters).
An alternative method of presenting combustion emissions is to allocate emissions associated with electricity
generation to the sectors in which it is used. Four end-use sectors were defined: industrial, transportation, residential,
and commercial. In the table below, electricity generation emissions have been distributed to each end-use sector
based upon the sector's share of national electricity consumption, with the exception of CH4 and N20 from
transportation.11 Emissions from U.S. Territories are also calculated separately due to a lack of end-use-specific
consumption data. This method assumes that emissions from combustion sources are distributed across the four end-
use sectors based on the ratio of electricity consumption in that sector. The results of this alternative method are
presented in Table 3-8.
Table 3-8: CO2, ChU, and N2O Emissions from Fossil Fuel Combustion by End-Use Sector
(MMT COz Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation
1,543.7
1,930.4
1,737.0
1,723.2
1,737.7
1,753.1
1,754.5
CO2
1,496.8
1,891.8
1,711.9
1,700.6
1,717.0
1,734.4
1,737.0
CH4
5.6
2.8
2.3
2.2
2.1
2.1
2.0
N2O
41.2
35.8
22.9
20.4
18.5
16.6
15.4
Industrial
1,537.0
1,574.2
1,408.8
1,385.0
1,416.6
1,418.8
1,378.2
CO2
1,529.2
1,564.6
1,399.6
1,375.7
1,407.0
1,409.0
1,368.7
CH4
2.0
1.9
1.6
1.6
1.6
1.6
1.6
N2O
5.9
7.8
7.6
7.7
8.0
8.2
8.0
Residential
940.2
1,224.9
1,127.7
1,018.8
1,077.6
1,093.4
1,020.2
CO2
931.4
1,214.1
1,116.2
1,007.8
1,064.6
1,080.1
1,008.3
CH4
5.4
4.2
4.2
3.9
5.1
5.2
4.0
N2O
3.4
6.6
7.3
7.1
7.9
8.1
7.9
10 Sulfur dioxide (SO2) emissions from stationary combustion are addressed in Annex 6.3.
11 Separate calculations were performed for transportation-related CH4 and N2O. The methodology used to calculate these
emissions are discussed in the mobile combustion section.
Energy 3-11
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Commercial
759.1
1,033.7
966.0
904.5
933.6
945.7
902.4
CO2
755.4
1,026.8
958.4
897.0
925.5
937.4
894.0
CH4
1.1
1.2
1.2
1.1
1.2
1.2
1.2
N2O
2.5
5.7
6.3
6.4
6.9
7.1
7.2
U.S. Territories3
28.0
50.1
41.7
43.7
43.7
41.4
41.4
Total
4,807.9
5,813.4
5,281.2
5,075.3
5,209.2
5,252.3
5,096.7
a U.S. Territories are not apportioned by sector, and emissions are total greenhouse gas emissions from all
fuel combustion sources.
Notes: 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.
Stationary Combustion
The direct combustion of fuels by stationary sources in the electricity generation, industrial, commercial, and
residential sectors represent the greatest share of U.S. greenhouse gas emissions. Table 3-9 presents CO2 emissions
from fossil fuel combustion by stationary sources. The CO2 emitted is closely linked to the type of fuel being
combusted in each sector (see Methodology section of CO2 from Fossil Fuel Combustion). Other than CO2, gases
emitted from stationary combustion include the greenhouse gases CH4 and N20. Table 3-10 and Table 3-11 present
CH4 and N20 emissions from the combustion of fuels in stationary sources.12 Methane and N20 emissions from
stationary combustion sources depend upon fuel characteristics, combustion technology, pollution control
equipment, ambient environmental conditions, and operation and maintenance practices. Nitrous oxide emissions
from stationary combustion are closely related to air-fuel mixes and combustion temperatures, as well as the
characteristics of any pollution control equipment that is employed. Methane emissions from stationary combustion
are primarily a function of the CH4 content of the fuel and combustion efficiency. The CH4 and N20 emission
estimation methodology was revised in 2010 to utilize the facility-specific technology and fuel use data reported to
EPA's Acid Rain Program (EPA 2016a) (see Methodology section for CH4 and N20 from Stationary Combustion).
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 (MMT CO2 Eq.)
Sector/Fuel Type
1990
2005
2011
2012
2013
2014
2015
Electricity Generation
1,820.8
2,400.9
2,157.7
2,022.2
2,038.1
2,038.0
1,900.7
Coal
1,547.6
1,983.8
1,722.7
1,511.2
1,571.3
1,569.1
1,350.5
Natural Gas
175.3
318.8
408.8
492.2
444.0
443.2
526.1
Fuel Oil
97.5
97.9
25.8
18.3
22.4
25.3
23.7
Geo thermal
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Industrial
842.5
828.0
775.0
782.9
812.2
815.8
828.8
Coal
155.3
115.3
82.0
74.1
75.7
75.6
65.9
Natural Gas
408.9
388.5
417.3
434.8
451.9
468.4
467.5
Fuel Oil
278.3
324.2
275.7
274.1
284.6
271.7
295.5
Commercial
217.4
223.5
220.4
196.7
221.0
231.4
225.7
Coal
12.0
9.3
5.8
4.1
3.9
3.8
2.9
Natural Gas
142.1
162.9
170.5
156.9
179.1
189.3
175.4
Fuel Oil
63.3
51.3
44.1
35.7
38.0
38.3
47.3
Residential
338.3
357.8
325.5
282.5
329.7
345.4
319.6
Coal
3.0
0.8
NO
NO
NO
NO
NO
Natural Gas
238.0
262.2
254.7
224.8
266.2
277.9
252.8
Fuel Oil
97.4
94.9
70.9
57.7
63.4
67.5
66.8
U.S. Territories
27.9
49.9
41.5
43.6
43.5
41.2
41.2
Coal
0.6
3.0
3.4
3.4
3.4
3.4
3.4
Natural Gas
NO
1.3
1.4
2.6
3.0
3.2
3.2
12
Since emission estimates for U.S. Territories cannot be disaggregated by gas in Table 3-10 and Table 3-11, the values for CH4
andN20 exclude U.S. Territory emissions.
3-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Fuel Oil 27.2 ; 45.6 36.7 37.6 37.1 34.6 34.6
Total 3,246.9 3.S60.1 3,520.1 3,327.9 3,444.6 3,471.8 3,315.9
+ Does not exceed 0.05 MMT CO2 Eq.
NO (Not Occurring)
Note: Totals may not sum due to independent rounding.
1 Table 3-10: ChU Emissions from Stationary Combustion (MMT CO2 Eq.)
Sector/Fuel Type
1990
2005
2011
2012
2013
2014
2015
Electric Power
0.3
0.5
0.4
0.4
0.4
0.4
0.4
Coal
0.3
0.3
0.3
0.2
0.2
0.2
0.2
Fuel Oil
+
+
+
+
+
+
+
Natural gas
0.1
0.1
0.2
0.2
0.2
0.2
0.2
Wood
+
+
+
+
+
+
+
Industrial
1.8
1.7
1.5
1.5
1.5
1.5
1.5
Coal
0.4
0.3
0.2
0.2
0.2
0.2
0.2
Fuel Oil
0.2
0.2
0.1
0.1
0.2
0.1
0.2
Natural gas
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Wood
1.0
1.0
0.9
1.0
0.9
0.9
0.9
Commercial
1.0
1.1
1.0
0.9
1.0
1.1
1.1
Coal
+
+
+
+
+
+
+
Fuel Oil
0.2
0.2
0.2
0.1
0.1
0.1
0.2
Natural gas
0.3
0.4
0.4
0.4
0.4
0.4
0.4
Wood
0.5
0.5
0.5
0.4
0.5
0.5
0.5
Residential
5.2
4.1
4.0
3.7
5.0
5.0
3.9
Coal
0.2
0.1
NO
NO
NO
NO
NO
Fuel Oil
0.3
0.3
0.3
0.2
0.2
0.2
0.2
Natural Gas
0.5
0.6
0.6
0.5
0.6
0.6
0.6
Wood
4.1
3.1
3.2
3.0
4.1
4.1
3.1
U.S. Territories
+
0.1
0.1
0.1
0.1
0.1
0.1
Coal
+
+
+
+
+
+
+
Fuel Oil
+
0.1
0.1
0.1
0.1
0.1
0.1
Natural Gas
NO
+
+
+
+
+
+
Wood
NO
NO
NO
NO
NO
NO
NO
Total
8.5
7.4
7.1
6.6
8.0
8.1
7.0
+ Does not exceed 0.05 MMT CO2 Eq.
NO (Not Occurring)
Note: Totals may not sum due to independent rounding.
2 Table 3-11: N2O Emissions from Stationary Combustion (MMT CO2 Eq.)
Sector/Fuel Type
1990
2005
2011
2012
2013
2014
2015
Electricity Generation
7.4
16.0
17.6
17.8
19.1
19.6
19.5
Coal
6.3
11.6
11.5
10.2
12.1
12.4
11.0
Fuel Oil
0.1
0.1
+
+
+
+
+
Natural Gas
1.0
4.3
6.1
7.5
7.0
7.2
8.4
Wood
+
+
+
+
+
+
+
Industrial
3.1
2.9
2.4
2.4
2.4
2.4
2.4
Coal
0.7
0.5
0.4
0.4
0.4
0.4
0.3
Fuel Oil
0.5
0.5
0.4
0.3
0.4
0.3
0.4
Natural Gas
0.2
0.2
0.2
0.2
0.2
0.3
0.2
Wood
1.6
1.6
1.5
1.5
1.5
1.5
1.5
Commercial
0.4
0.3
0.3
0.3
0.3
0.3
0.3
Coal
0.1
+
+
+
+
+
+
Fuel Oil
0.2
0.1
0.1
0.1
0.1
0.1
0.1
Natural Gas
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Energy 3-13
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
Wood
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Residential
1.0
0.9
0.8
0.7
1.0
1.0
0.8
Coal
+
+
NO
NO
NO
NO
NO
Fuel Oil
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Natural Gas
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Wood
0.7
0.5
0.5
0.5
0.7
0.7
0.5
U.S. Territories
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Coal
+
+
+
+
+
+
+
Fuel Oil
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Natural Gas
NO
+
+
+
+
+
+
Wood
NO
NO
NO
NO
NO
NO
NO
Total
11.9
20.2
21.3
21.4
22.9
23.4
23.1
+ Does not exceed 0.05 MMT CO2 Eq.
NO (Not Occurring)
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
35 percent of total CO2 emissions from all CO2 emissions sources across the United States. Methane and N20
accounted for a small portion of emissions from electricity generation, representing less than 0.1 percent and 1.0
percent, respectively. Electricity generation also accounted for the largest share of CO2 emissions from fossil fuel
combustion, approximately 37.6 percent in 2015. Methane and N20 from electricity generation represented 4.9 and
50.7 percent of total CH4 and N20 emissions from fossil fuel combustion in 2015, respectively.
While emissions from the electric power sector have increased by 4 percent since 1990, the carbon intensity of the
electric power sector, in terms of CO2 Eq. per QBtu has significantly decreased by 16 percent during that same
timeframe. This decarbonization of the electric power sector is a result of several key drivers. Coal-fired electricity
generation (in kilowatt-hours [kWh]) decreased from almost 54 percent of generation in 1990 to 34 percent in 2015.
This generation corresponded with an increase in natural gas and renewable energy generation, largely from wind
and solar energy. Natural gas generation (in kWh) represented 11 percent of electric power generation in 1990, and
increased over the 26 year period to represent 32 percent of electric power generation in 2015. This decoupling of
electricity generation and the resulting emissions is shown below in Figure 3-9.
Decreases in natural gas costs and the associated increase in natural gas generation, particularly between 2005 and
2015, was the main driver of the decrease in electric power sector carbon intensity. During this time period, the cost
of natural gas (in $/MMBtu) decreased by 51 percent while the cost of coal (in $/MMBtu) increased by 91 percent
(EIA 2016a). Between 1990 and 2015, renewable energy generation (in kWh) from solar and wind energy have
increased from 0.1 percent in 1990 to 5 percent in 2015, which also helped drive the decreases in the carbon
intensity of the electricity supply in the United States. This decrease in carbon intensity occurred even as total
electricity retail sales increased 39 percent, from 2,713 billion kWh in 1990 to 3,759 billion kWh in 2015.
3-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
Figure 3-9: Electricity Generation (Billion kWh) and Emissions (MMT CO* Eq.)
4,500 3,000
£
1
m
4,000
3,500
3,000
2,500
2,000
1,500
1,000
500
0
2,500
2,000
1,500
1,000
500
*»i *¦» *4 UN1 C-l
» » 6 8 s a a a a j a
oooSSooossgo
CN (N (N tN
Mrlnl rlPirtN^rj
I Coal Generation (billion kWh) (Left)
Nuctear Generation (billion kWh) (Left)
l Natural Gas Generation (bifion kWh) (Left)
> Petroleum Generation (bSBon kWh) (Left)
l Renewable Generation (tillion kVuti) (Left)
-Total Emissions (MMT C02 Eq.) (Right)
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 3-10).
Figure 3-10: Electricity Generation Retail Sales by End-Use Sector (Billion kWh)
1,500
Residential
1,400-
1,300-
Commercial
1,200-
C
0
1 1,100-
1,000-
Industrial
900-
800
o^HrMro^rLn^Dr^cocTiO^HrNim^rm^r^coc^OTHrMfn^-Ln
o^CT>cr>a><^o>o>o^oooooooooo^H —i —i •,—i ¦«—•
cna^cr>a^a^a^cr>a^c^crioooooooooooooooo
t—ii—it-ht-h-,—(t—I,—i^H^HT-HrNjrMrsirNJr\jrsjrNjrNjrvjfNr\jrMrMrNJrvjr\i
The electric power industry includes all power producers, consisting of both regulated utilities and non-utilities (e.g.
independent power producers, qualifying co-generators, 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
Energy 3-15
-------
1 power sector consists of electric utilities and independent power producers whose primary business is the production
2 of electricity, while the other sectors consist of those producers that indicate their primary business is something
3 other than the production of electricity.13
4 The industrial, residential, and commercial end-use sectors, as presented in Table 3-8, were reliant on electricity for
5 meeting energy needs. The residential and commercial end-use sectors were especially reliant on electricity
6 consumption for lighting, heating, air conditioning, and operating appliances. In 2015, electricity sales to the
7 residential end-use sector decreased by 0.5 percent and sales to the commercial end-use sector increased by 0.5
8 percent, respectively. The trend in the residential sector can largely be attributed to warmer, less energy-intensive
9 winter conditions while the trend in the commercial sector can largely be attributed to a growing economy compared
10 to 2014. Electricity sales to the industrial sector in 2015 decreased approximately 3.4 percent. Overall, in 2015, the
11 amount of electricity generated (in kWh) and the amount of electricity consumed (in kWh) decreased approximately
12 0.4 percent and 0.1 percent, respectively, relative to the previous year, while CO2 emissions from the electric power
13 sector decreased by 6.7 percent. The decrease in CO2 emissions was a result of a significant decrease in the
14 consumption of coal and increase in the consumption of natural gas for electricity generation by 13.9 percent and
15 18.7 percent, respectively, in 2015, andadecrease in the consumption of petroleum for electricity generation by 6.6
16 percent.
17 Industrial Sector
18 Industrial sector CO2, CH4, and N20, emissions accounted for 16, 16, and 6 percent of CO2, CH4, and N20,
19 emissions from fossil fuel combustion, respectively. Carbon dioxide, CH4, and N20 emissions resulted from the
20 direct consumption of fossil fuels for steam and process heat production.
21 The industrial sector, per the underlying energy consumption data from EI A, includes activities such as
22 manufacturing, construction, mining, and agriculture. The largest of these activities in terms of energy consumption
23 is manufacturing, of which six industries—Petroleum Refineries, Chemicals, Paper, Primary Metals, Food, and
24 Nonmetallic Mineral Products—represent the vast majority of the energy use (EIA 2016a and EIA 2009b).
25 In theory, emissions from the industrial sector should be highly correlated with economic growth and industrial
26 output, but heating of industrial buildings and agricultural energy consumption are also affected by weather
27 conditions.14 In addition, structural changes within the U.S. economy that lead to shifts in industrial output away
28 from energy-intensive manufacturing products to less energy-intensive products (e.g., from steel to computer
29 equipment) also have a significant effect on industrial emissions.
30 From 2014 to 2015, total industrial production and manufacturing output increased by 0.3 percent (FRB 2016). Over
31 this period, output increased across production indices for Food, Petroleum Refineries, Chemicals, and Nonmetallic
32 Mineral Products, and decreased slightly for Primary Metals and Paper (see Figure 3-11). Through EPA's
33 Greenhouse Gas Reporting Program (GHGRP), specific industrial sector trends can be discerned from the overall
34 total EIA industrial fuel consumption data used for these calculations.
35 I'iir eviiiiple. I'm 111 2d I 1 in > 14 ilie uuderK 11m I I \ dala showed increased cousunipikiii of Manual uas and a
36 decrease 111 pcimleiini liiels 1111 he industrial seclor I \' W (11 KikP dala hiuhliuhis lhal chemical niaiiiifacluriim and
37 iKiiiuielallie mineral producls were eouirihuiors in ihese Irentls
13 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).
14 Some commercial customers are large enough to obtain an industrial price for natural gas and/or electricity and are
consequently grouped with the industrial end-use sector in U.S. energy statistics. These misclassifications of large commercial
customers likely cause the industrial end-use sector to appear to be more sensitive to weather conditions.
15 Further details on industrial sector combustion emissions are provided by EPA's GHGRP. See
.
U.S. 1 CPA GIIGRP 2015 dala is not vet available. As a result, contributors to industrial trends have not yet been identified in
the current Inventory and will be updated lor the final Inventory report.
3-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Figure 3-11: Industrial Production Indices (Index 2007=100)
Total excluding Computers, Communications Equipment, and Semiconductors
Total Industrial
Stone, Clay & Glass Products
Chemicals
Petroleum Refineries
120-
100-
Primaty Metals
gv gi
& 0* iji
8
r-v
Si
a
s 8
8
s
Despite the growth in industrial output (62 percent) and the overall U.S. economy (83 percent) from 1990 to 2015,
CO2 emissions from fossil fuel combustion in the industrial sector decreased by 1.6 percent over the same time
series. A number of factors are believed to have caused this disparity between growth in industrial output and
decrease in industrial emissions, including: (1) more rapid growth in output from less energy-intensive industries
relative to traditional manufacturing industries, and (2) energy-intensive industries such as steel are employing new
methods, such as electric arc furnaces, that are less carbon intensive than the older methods. In 2015, CO2, CH4. and
N2O emissions from fossil fuel combustion and electricity use w ithin the industrial end-use sector totaled 1,378.2
MMT CO2 Eq., a 2.9 percent decrease from 2014 emissions.
Residential and Commercial Sectors
Residential and commercial sector CO2 emissions accounted for 6 and 4 percent of CO2 emissions from fossil fuel
combustion, CH4 emissions accounted for 43 and 12 percent of CH4 emissions from fossil fuel combustion, and N20
emissions accounted for 2 and 1 percent of N20 emissions from fossil fuel combustion, respectively. Emissions
Energy 3-17
-------
1 from these sectors were largely due to the direct consumption of natural gas and petroleum products, primarily for
2 heating and cooking needs. Coal consumption was a minor component of energy use in both of these end-use
3 sectors. In 2015, CO2, CH4, and N20 emissions from fossil fuel combustion and electricity use within the residential
4 and commercial end-use sectors were 1,020.2 MMT CO2 Eq. and 902.4 MMT CO2 Eq., respectively. Total CO2,
5 CH4, and N20 emissions from fossil fuel combustion and electricity use within the residential and commercial end-
6 use sectors decreased by 6.7 and 4.6 percent from 2014 to 2015, respectively.
7 Emissions from the residential and commercial sectors have generally been increasing since 1990, and are often
8 correlated with short-term fluctuations in energy consumption caused by weather conditions, rather than prevailing
9 economic conditions. In the long-term, both sectors are also affected by population growth, regional migration
10 trends, and changes in housing and building attributes (e.g., size and insulation).
11 In 2015, combustion emissions from natural gas consumption represent 79 and 78 percent of the direct fossil fuel
12 CO2 emissions from the residential and commercial sectors, respectively. Natural gas combustion CO2 emissions
13 from the residential and commercial sectors in 2015 decreased by 9.0 percent and 7.4 percent from 2014 levels,
14 respectively.
15 U.S. Territories
16 Emissions from U.S. Territories are based on the fuel consumption in American Samoa, Guam, Puerto Rico, U.S.
17 Virgin Islands, Wake Island, and other U.S. Pacific Islands. As described in the Methodology section of CO2 from
18 Fossil Fuel Combustion, this data is collected separately from the sectoral-level data available for the general
19 calculations. As sectoral information is not available for U.S. Territories, CO2, CH4, and N20 emissions are not
20 presented for U.S. Territories in the tables above, though the emissions will include some transportation and mobile
21 combustion sources.
22 Transportation Sector and Mobile Combustion
23 This discussion of transportation emissions follows the alternative method of presenting combustion emissions by
24 allocating emissions associated with electricity generation to the transportation end-use sector, as presented in Table
25 3-8. For direct emissions from transportation (i.e., not including emissions associated with the sector's electricity
26 consumption), please see Table 3-7.
27 Transportation End-Use Sector
28 The transportation end-use sector accounted for 1,754.5 MMT CO2 Eq. in 2015, which represented 34 percent of
29 CO2 emissions, 23 percent of CH4 emissions, and 40 percent of N20 emissions from fossil fuel combustion,
30 respectively.17 Fuel purchased in the United States for international aircraft and marine travel accounted for an
31 additional 111.8 MMT CO2 Eq. in 2015; these emissions are recorded as international bunkers and are not included
32 in U.S. totals according to UNFCCC reporting protocols.
33 From 1990 to 2015, transportation emissions from fossil fuel combustion rose by 14 percent due, in large part, to
34 increased demand for travel. The number of vehicle miles traveled (VMT) by light-duty motor vehicles (passenger
35 cars and light-duty trucks) increased 42 percent from 1990 to 2015,18 as a result of a confluence of factors including
36 population growth, economic growth, urban sprawl, and periods of low fuel prices.
17 Note that these totals include CO2, CH4 and N2O emissions from some sources in the U.S. Territories (ships and boats,
recreational boats, non-transportation mobile sources) and CH4 and N2O emissions from transportation rail electricity.
18VMT estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2016). Table VM-1 data
for 2015 has not been published yet, therefore 2015 mileage data is estimated using the 3.5 percent increase in FHWA Traffic
Volume Trends from 2014 to 2015.
3-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
From 2014 to 2015, CO2 emissions from the transportation end-use sector increased by 0.1 percent.19 20 The small
increase in emissions can be attributed to an increase in on-road distillate fuel oil and non-road fuel consumption,
particularly jet fuel, which is partly offset by a decrease in on-road motor gasoline consumption. It is important to
note, however, that the decrease in on-road motor gasoline consumption is likely due to a change in methods used to
estimate the share of gasoline used in on-road and non-road applications.21 Commercial aircraft emissions increased
slightly between 2014 and 2015, but have decreased 15 percent since 2007 (FAA 2017).22 Decreases in jet fuel
emissions (excluding bunkers) since 2007 are due in part to improved operational efficiency that results in more
direct flight routing, improvements in aircraft and engine technologies to reduce fuel burn and emissions, and the
accelerated retirement of older, less fuel efficient aircraft.
Almost all of the energy consumed for transportation was supplied by petroleum-based products, with more than
half being related to gasoline consumption in automobiles and other highway vehicles. Other fuel uses, especially
diesel fuel for freight trucks and jet fuel for aircraft, accounted for the remainder. The primary driver of
transportation-related emissions was CO2 from fossil fuel combustion, which increased by 16 percent from 1990 to
2015. Annex 3.2 presents the total emissions from all transportation and mobile sources, including CO2, N20, CH4,
and HFCs.
Transportation Fossil Fuel Combustion CO2 Emissions
Domestic transportation CO2 emissions increased by 16 percent (240.1 MMT CO2) between 1990 and 2015, an
annualized increase of 0.6 percent. Among domestic transportation sources, light-duty vehicles (including passenger
cars and light-duty trucks) represented 59 percent of CO2 emissions from fossil fuel combustion, medium- and
heavy-duty trucks and buses 25 percent, commercial aircraft 7 percent, and other sources 9 percent. See
Table 3-12 for a detailed breakdown of transportation CO2 emissions by mode and fuel type.
Almost all of the energy consumed by the transportation sector is petroleum-based, including motor gasoline, diesel
fuel, jet fuel, and residual oil. Carbon dioxide emissions from the combustion of ethanol and biodiesel for
transportation purposes, along with the emissions associated with the agricultural and industrial processes involved
in the production of biofuel, are captured in other Inventory sectors.23 Ethanol consumption from the transportation
sector has increased from 0.7 billion gallons in 1990 to 13.4 billion gallons in 2015, while biodiesel consumption
has increased from 0.01 billion gallons in 2001 to 1.5 billion gallons in 2015. For further information, see the section
on biofuel consumption at the end of this chapter and Table A-95 in Annex 3.2.
Carbon dioxide emissions from passenger cars and light-duty trucks totaled 1,031.3 MMT CO2 in 2015, an increase
of 9 percent (81.3 MMT CO2) from 1990 due, in large part, to increased demand for travel as fleet-wide light-duty
vehicle fuel economy was relatively stable (average new vehicle fuel economy declined slowly from 1990 through
2004 and then increased more rapidly from 2005 through 2015). Carbon dioxide emissions from passenger cars and
light-duty trucks peaked at 1,180.5 MMT CO2 in 2004, and since then have declined about 13 percent. The decline
19 Note that this value does not include lubricants.
20 Note that EPA plans to integrate new data for the final 1990 to 2015 Inventory. As a result, the estimate of CO2 emissions
from the transportation end-use sector will likely decrease from 2014 to 2015 in the final Inventory. See Planned Improvements
section for more detail.
21 In 2016, FHWA changed its methods for estimating the share of motor gasoline used in on-highway and off-highway
applications. This resulted in an increase in the estimated off-highway motor gasoline consumption and subsequent decrease in
the on-highway motor gasoline consumption for 2015. Among other updates, FHWA included lawn and garden equipment as
well as off-road recreational equipment it its estimates of off-highway gasoline consumption for the first time. If this gasoline
consumption had been attributed to the on-road sector in 2015, as it may have been in 2014 and previous years, the estimate of
on-road gasoline would have increased in this Inventory from 2014 to 2015.
22 Commercial aircraft, as modeled in FAA's AEDT (FAA 2017), consists of passenger aircraft, cargo, and other chartered
flights.
23 Biofuel estimates are presented in the Energy chapter for informational purposes only, in line with IPCC methodological
guidance and UNFCCC reporting obligations. Net carbon fluxes from changes in biogenic carbon reservoirs in croplands are
accounted for in the estimates for Land Use, Land-Use Change, and Forestry (see Chapter 6). More information and additional
analyses on biofuels are available at EPA's "Renewable Fuels: Regulations & Standards;" See
.
Energy 3-19
-------
1 in new light-duty vehicle fuel economy between 1990 and 2004 (Figure 3-12) reflected the increasing market share
2 of light-duty trucks, which grew from about 30 percent of new vehicle sales in 1990 to 48 percent in 2004. Starting
3 in 2005, the rate of VMT growth slowed while average new vehicle fuel economy began to increase. Average new
4 vehicle fuel economy lias improved almost every year since 2005, and the truck share has decreased to about 43
5 percent of new vehicles in model year 2015 (EPA 2016c).
6 Medium- and heavy-duty truck CO2 emissions increased by 78 percent from 1990 to 2015. This increase was largely
7 due to a substantial growth in medium- and heavy-duty truck VMT, which increased by 101 percent between 1990
8 and 2015.24 Carbon dioxide from the domestic operation of commercial aircraft increased by 8 percent (9.1 MMT
9 CO2) from 1990 to 20 1 5.25 Across all categories of aviation, excluding international bunkers, CO2 emissions
10 decreased by 15 percent (28.2 MMT CO2) between 1990 and 20 1 5.26 This includes a 58 percent (20.3 MMT CO2)
11 decrease in CO2 emissions from domestic military operations.
12 Transportation sources also produce CH4 and N20; these emissions are included in Table 3-13 and Table 3-14 and in
13 the CH4 and N20 from Mobile Combustion section. Annex 3.2 presents total emissions from all transportation and
14 mobile sources, including CO2, CH4, N20, and HFCs.
15 Figure 3-12: Sales-Weighted Fuel Economy of New Passenger Cars and Light-Duty Trucks,
16 1990-2015 (miles/gallon)
30.0.
29.0-
28.0"
27.0-
26.0"
25.0-
<3 24.0-
&
in 23.0-
QJ
Z 22.0-
21.0-
20.0-
19.0-
18.0-
17.0"
$
8
§
o
m
T—I
18 Source: EPA (2016c)
24 While FHWA data shows consistent growth in medium- and heavy-duty truck VMT over the 1990 to 2015 time period, part of
the growth reflects a method change for estimating VMT starting in 2007. This change in methodology in FHWA's VM-1 table
resulted in large changes in VMT by vehicle class, thus leading to a shift in VMT and emissions among on-road vehicle classes
in the 2007 to 2015 time period. During the time period prior to the method change (1990 to 2006), VMT for medium- and
heavy-duty trucks increased by 51 percent.
25 Commercial aircraft, as modeled in FAA's AEDT, consists of passenger aircraft, cargo, and other chartered flights.
26 Includes consumption of jet fuel and aviation gasoline. Does not include aircraft bunkers, which are not included in national
emission totals, in line with IPCC methodological guidance and UNFCCC reporting obligations.
3-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Figure 3-13: Sales of New Passenger Cars and Light-Duty Trucks, 1990-2015 (Percent)
ioo%"
75%-
% Passenger
50%-
% Light-Duty Trucks
25%-
0%
0,ICT10,»0^0'>(^0>0000000000*—,
cpio^crio^cno^chcnchcnoooooooooooooooo
^ ^HT-H^H^H^HT^^H^H^H^HrNirsJrsjrvjrsjrvjrsjrNjrNjrsirNjrsJrsjrsifNjr^
3 Source: EPA (2016c)
4
5 Table 3-12: CO2 Emissions from Fossil Fuel Combustion in Transportation End-Use Sector
6 (MMT COz Eq.)
Fuel/Vehicle Type
1990
2005
2011a
2012a
2013a
2014a
2015a
Gasolineb
983.5
1,183.7
1,068.8
1,064.7
1,065.6
1,083.8
1,068.1
Passenger Cars
621.4
655.9
732.8
731.4
731.4
733.5
722.8
Light-Duty Trucks
309.1
477.2
280.4
277.4
277.7
293.5
289.2
Medium- and Heavy-Duty Trucksc
38.7
34.8
38.9
38.7
39.5
40.0
39.4
Buses
0.3
0.4
0.7
0.8
0.8
0.9
0.9
Motorcycles
1.7
1.6
3.6
4.1
3.9
3.8
3.7
Recreational Boats'1
12.2
13.9
12.4
12.3
12.3
12.2
12.1
Distillate Fuel Oil (Diesel)b
262.9
457.5
430.0
427.5
433.9
447.5
460.7
Passenger Cars
7.9
4.2
4.1
4.1
4.1
4.1
4.2
Light-Duty Trucks
11.5
25.8
13.0
12.9
12.9
13.9
14.2
Medium- and Heavy-Duty Trucksc
190.5
360.2
344.4
344.4
350.0
361.1
369.3
Buses
8.0
10.6
14.4
15.4
15.5
16.9
17.2
Rail
35.5
45.5
40.4
39.5
40.1
41.6
39.9
Recreational Boats
2.0
3.2
3.6
3.7
3.7
3.8
3.9
Ships and Non-Recreational Boats6
7.5
8.0
10.1
7.5
7.5
6.2
12.0
International Bunker Fuel/
11.7
9.4
7.9
6.8
5.6
6.1
8.4
Jet Fuel
184.2
189.3
146.6
143.4
147.1
148.6
157.7
Commercial Aircraft8
109.9
132.7
114.6
113.3
114.3
115.2
119.0
Military Aircraft
35.0
19.4
11.6
12.1
11.0
15.4
14.7
General Aviation Aircraft
39.4
37.3
20.4
18.0
21.8
18.0
24.0
International Bunker Fuel/
38.0
60.1
64.8
64.5
65.7
69.4
71.8
International Bunker Fuels from
Commercial Aviation
30.0
55.6
61.7
61.4
62.8
66.3
68.6
Aviation Gasoline
3.1
2.4
1.9
1.7
1.5
1.5
1.5
General Aviation Aircraft
3.1
2.4
1.9
1.7
1.5
1.5
1.5
Energy 3-21
-------
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
31
32
33
34
35
Residual Fuel Oil
22.6
19.3
19.4
15.8
15.1
5.8
3.5
Ships and Boats6
22.6
19.3
19.4
15.8
15.1
5.8
3.5
International Bunker Fuel/
53.7
43.6
38.9
34.5
28.5
27.7
30.6
Natural Gas '
36.0
33.1
38.9
41.3
47.0
40.3
38.8
Passenger Cars
+
+
+
+
+
+
+
Light-Duty Trucks
+
+
+
+
+
+
+
Medium- and Heavy-Duty Trucks
+
+
+
+
+
+
+
Buses
+
0.6
0.8
0.8
0.8
0.8
0.8
Pipeline11
36.0
32.4
38.1
40.5
46.2
39.4
38.0
LPG J
1.4
1.7
2.1
2.3
2.7
2.9
3.0
Passenger Cars
+
+
+
+
+
+
0.1
Light-Duty Trucks
0.2
0.3
0.4
0.2
0.3
0.6
0.9
Medium- and Heavy-Duty Trucks0
1.1
1.3
1.4
1.8
2.1
1.9
1.7
Buses
0.1
0.1
0.2
0.3
0.4
0.3
0.3
Electricity
3.0
4.7
4.3
3.9
4.0
4.1
3.7
Rail
3.0
4.7
4.3
3.9
4.0
4.1
3.7
Ethanot
4.1
22.4
71.5
71.5
73.4
74.8
77.6
Total
1,496.8
1,891.8
1,711.9
1,700.6
1,717.0
1,734.4
1,737.0
Total (Including Bunkers)'
1,600.3
2,004.9
1,823.6
1,806.4
1,816.8
1,837.6
1,847.7
+ Does not exceed 0.05 MMT CO2 Eq.
a In 2011 FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These methodological
changes included how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase.
These changes were first incorporated for the 1990 through 2010 Inventory and apply to the 2007 through 2015 time period.
This resulted in large changes in VMT and fuel consumption data by vehicle class, thus leading to a shift in emissions among
on-road vehicle classes.
b Gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics Table MF-
21, MF-27, and VM-1 (FHWA 1996 through 2016). In 2016, FHWA changed its methods for estimating the share of motor
gasoline used in on-highway and off-highway applications. This resulted in an increase in the estimated off-highway motor
gasoline consumption and subsequent decrease in the on-highway motor gasoline consumption for 2015. Data from Table VM-1
is used to estimate the share of consumption between each on-road vehicle class. Since VM-1 data for 2015 has not been
published yet, fuel consumption shares from 2014 are used as a proxy for Public Review. These fuel consumption estimates are
combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A. 1 through A.6 (DOE 1993 through
2016). TEDB data for 2015 has not been published yet, therefore 2014 data is used as a proxy.
c Includes medium- and heavy-duty trucks over 8,500 lbs.
d In 2015, EPA incorporated the NONROAD2008 model into MOVES2014. The current Inventory uses the NONROAD
component of MOVES2014a for years 1999 through 2015.
e Note that large year over year fluctuations in emission estimates partially reflect nature of data collection for these sources.
f 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.
g Commercial aircraft, as modeled in FAA's AEDT, consists of passenger aircraft, cargo, and other chartered flights.
h Pipelines reflect CO2 emissions from natural gas powered pipelines transporting natural gas.
'Ethanol estimates are presented for informational purposes only. See Section 3.10 of this chapter and the estimates in Land Use,
Land-Use Change, and Forestry (see Chapter 6), in line with IPCC methodological guidance and UNFCCC reporting
obligations, for more information on ethanol.
J Transportation sector natural gas and LPG consumption are based on data from EIA (2016). In prior Inventory years, data from
DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG consumption. Since TEDB does
not include estimates for natural gas use by medium and heavy duty trucks or LPG use by passenger cars, EIA Alternative Fuel
Vehicle Data (Browning 2016) is now used to determine each vehicle class's share of the total natural gas and LPG
consumption. These changes were first incorporated in this year's Inventory and apply to the 1990 to 2015 time period.
Note: This table does not include emissions from non-transportation mobile sources, such as agricultural equipment and
construction/mining equipment; it also does not include emissions associated with electricity consumption by pipelines or
lubricants used in transportation. In addition, this table does not include CO2 emissions from U.S. Territories, since these are
covered in a separate chapter of the Inventory. Totals may not sum due to independent rounding.
3-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Mobile Fossil Fuel Combustion CH4 andN2O Emissions
0
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Mobile combustion includes emissions of CH4 and N20 from all transportation sources identified in the U.S.
Inventory with the exception of pipelines and electric locomotives;27 mobile sources also include non-transportation
sources such as construction/mining equipment, agricultural equipment, vehicles used off-road, and other sources
(e.g., snowmobiles, lawmnowers, etc.). 28 Annex 3.2 includes a summary of all emissions from both transportation
and mobile sources. Table 3-13 and Table 3-14 provide mobile fossil fuel CH4 and N20 emission estimates in MMT
C02 Eq 29
Mobile combustion was responsible for a small portion of national CH4 emissions (0.3 percent) but was the fourth
largest source of U.S. N20 emissions (4.6 percent). From 1990 to 2015, mobile source CH4 emissions declined by
64 percent, to 2.0 MMT CO2 Eq. (82 kt CH4), due largely to control technologies employed in on-road vehicles
since the mid-1990s to reduce CO, NOx, NMVOC, and CH4 emissions. Mobile source emissions of N20 decreased
by 63 percent, to 15.4 MMT CO2 Eq. (52 kt N2O). Earlier generation control technologies initially resulted in higher
N2O emissions, causing a 28 percent increase in N20 emissions from mobile sources between 1990 and 1997.
Improvements in later-generation emission control technologies have reduced N20 output, resulting in a 71 percent
decrease in mobile source N20 emissions from 1997 to 2015 (Figure 3-14). Overall, CH4 and N20 emissions were
predominantly from gasoline-fueled passenger cars and light-duty trucks.
Figure 3-14: Mobile Source ChU and N2O Emissions (MMT CO2 Eq.)
60~
O
u 30H
27 Emissions of CH4 from natural gas systems are reported separately. More information on the methodology used to calculate
these emissions are included in this chapter and Annex 3.4.
28 See the methodology sub-sections of the CO2 from Fossil Fuel Combustion and CH4 and N2O from Mobile Combustion
sections of this chapter. Note that N2O and CH4 emissions are reported using different categories than CO2. CO2 emissions are
reported by end-use sector (Transportation, Industrial, Commercial, Residential, U.S. Territories), and generally adhere to a top-
down approach to estimating emissions. CO2 emissions from non-transportation sources (e.g., lawn and garden equipment, farm
equipment, construction equipment) are allocated to their respective end-use sector (i.e., construction equipment CO2 emissions
are included in the Industrial end-use sector instead of the Transportation end-use sector). CH4 and N2O emissions are reported
using the "Mobile Combustion" category, which includes non-transportation mobile sources. CH4 and N2O emissions estimates
are bottom-up estimates, based on total activity (fuel use, VMT) and emissions factors by source and technology type. These
reporting schemes are in accordance with IPCC guidance. For informational purposes only, CO2 emissions from non-
transportation mobile sources are presented separately from their overall end-use sector in Annex 3.2.
29 See Annex 3.2 for a complete time series of emission estimates for 1990 through 2015.
Energy 3-23
-------
1
2 Table 3-13: ChU Emissions from Mobile Combustion (MMT CO2 Eq.)
Fuel Type/Vehicle Type3
1990
2005
2011
2012
2013
2014
2015
Gasoline On-Roadb
5.2
2.2
1.7
1.6
1.5
1.4
1.4
Passenger Cars
3.2
1.2
1.2
1.1
1.1
1.0
1.0
Light-Duty Trucks
1.7
0.9
0.4
0.4
0.3
0.3
0.3
Medium- and Heavy-Duty
Trucks and Buses
0.3
0.1
0.1
0.1
0.1
0.1
0.1
Motorcycles
+
+
+
+
+
+
+
Diesel On-Roadb
+
+
+
+
+
+
+
Passenger Cars
+
+
+
+
+
+
+
Light-Duty Trucks
+
+
+
+
+
+
+
Medium- and Heavy-Duty
Trucks and Buses
+
+
+
+
+
+
+
Alternative Fuel On-Road
+
+
+
+
+
+
+
Non-Roadc
0.4
0.5
0.5
0.6
0.6
0.6
0.6
Ships and Boats
+
+
+
+
+
+
+
Raild
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Aircraft
0.1
0.1
+
+
+
+
+
Agricultural Equipment6
0.1
0.2
0.2
0.2
0.2
0.2
0.2
Construction/Mining
Equipment
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Other8
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Total
5.6
2.8
2.3
2.2
2.1
2.1
2.0
+ Does not exceed 0.05 MMT CO2 Eq.
a See Annex 3.2 for definitions of on-road vehicle types.
b Gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table
VM-1 (FHWA 1996 through 2016). Table VM-1 data for 2015 has not been published yet, therefore 2015 mileage
data is estimated using the 3.5 percent increase in FHWA Traffic Volume Trends from 2014 to 2015. These mileage
estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A.l through
A.6 (DOE 1993 through 2016). TEDB data for 2015 has not been published yet, therefore 2014 data is used as a
proxy.
c In 2016, FHWA changed its methods for estimating the share of motor gasoline used in on-highway and off-
highway applications. This resulted in an increase in the estimated off-highway motor gasoline consumption and
subsequent decrease in the on-highway motor gasoline consumption for 2015.
d Rail emissions do not include emissions from electric powered locomotives. Class II and Class III diesel
consumption data for 2014 and 2015 are not available yet, therefore 2013 data is used as a proxy.
e Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road
in agriculture.
f Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used
off-road in construction.
B "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: In 2011, FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These
methodological changes included how vehicles are classified, moving from a system based on body-type to one that
is based on wheelbase. These changes were first incorporated for the 1990 through 2010 Inventory and apply to the
2007 through 2015 time period. This resulted in large changes in VMT and fuel consumption data by vehicle class,
thus leading to a shift in emissions among on-road vehicle classes. Totals may not sum due to independent rounding.
3 Table 3-14: N2O Emissions from Mobile Combustion (MMT CO2 Eq.)
Fuel Type/Vehicle Type3
1990
2005
2011
2012
2013
2014
2015
Gasoline On-Roadb
37.5
31.3
18.4
16.1
14.1
12.3
10.9
Passenger Cars
24.1
15.7
12.1
10.5
9.2
7.8
6.8
Light-Duty Trucks
12.8
14.7
5.6
4.9
4.3
4.0
3.5
3-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Medium- and Heavy-Duty
0.5
0.9
0.7
0.7
0.6
0.5
0.5
Trucks and Buses
Motorcycles
+
+
+
+
+
+
+
Diesel On-Roadb
0.2
0.3
0.4
0.4
0.4
0.4
0.4
Passenger Cars
.
+
+
+
+
+
+
+
Light-Duty Trucks
+
+
+
+
+
+
+
Medium- and Heavy-Duty
0.2
0.3
0.4
0.4
0.4
0.4
0.4
Trucks and Buses
Alternative Fuel On-Road
+
+
0.1
0.1
0.1
0.1
0.1
Non-Roadc
3.5
4.1
4.0
3.9
4.0
3.8
4.0
Ships and Boats
0.6
0.6
0.8
0.7
0.7
0.5
0.6
Raild
0.3
0.3
0.3
0.3
0.3
0.3
0.3
Aircraft
1.7
1.8
1.4
1.3
1.4
1.4
1.5
Agricultural Equipment6
0.2
0.4
0.4
0.4
0.4
0.4
0.4
Construction/Mining
0.3
0.5
0.6
0.6
0.6
0.6
0.6
Equipment
Other8
0.4
0.6
0.6
0.6
0.6
0.6
0.7
Total
41.2
35.7
22.8
20.4
18.5
16.6
15.4
+ Does not exceed 0.05 MMT CO2 Eq.
a See Annex 3.2 for definitions of on-road vehicle types.
b Gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1
(FHWA 1996 through 2016). Table VM-1 data for 2015 has not been published yet, therefore 2015 mileage data is
estimated using the 3.5 percent increase in FHWA Traffic Volume Trends from 2014 to 2015. These mileage estimates
are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables A. 1 through A.6 (DOE
1993 through 2016). TEDB data for 2015 has not been published yet, therefore 2014 data is used as a proxy.
c In 2016, FHWA changed its methods for estimating the share of motor gasoline used in on-highway and off-highway
applications. This resulted in an increase in the estimated off-highway motor gasoline consumption and subsequent
decrease in the on-highway motor gasoline consumption for 2015.
d Rail emissions do not include emissions from electric powered locomotives. Class II and Class III diesel consumption
data for 2014 and 2015 are not available yet, therefore 2013 data is used as a proxy.
e Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in
agriculture.
includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-
road in construction.
B "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: In 2011, FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These
methodological changes included how vehicles are classified, moving from a system based on body type to one that is
based on wheelbase. These changes were first incorporated for the 1990 through 2010 Inventory and apply to the 2007
through 2015 time period. This resulted in large changes in VMT and fuel consumption data by vehicle class, thus
leading to a shift in emissions among on-road vehicle classes. Totals may not sum due to independent rounding.
1 C02 from Fossil Fuel Combustion
2 Methodology
3 The methodology used by the United States for estimating CO2 emissions from fossil fuel combustion is
4 conceptually similar to the approach recommended by the IPCC for countries that intend to develop detailed,
5 sectoral-based emission estimates in line with a Tier 2 method in the 2006 IPCC Guidelines for National
6 Greenhouse Gas Inventories (IPCC 2006).30 The use of the most recently published calculation methodologies by
7 the IPCC, as contained in the 2006 IPCC Guidelines, is considered to improve the rigor and accuracy of this
30 The IPCC Tier 3B methodology is used for estimating emissions from commercial aircraft.
Energy 3-25
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34
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37
38
Inventory and is fully in line with IPCC Good Practice Guidance. A detailed description of the U.S. methodology is
presented in Annex 2.1, and is characterized by the following steps:
1. Determine total fuel consumption by fuel type and sector. Total fossil fuel consumption for each year is
estimated by aggregating consumption data by end-use sector (e.g., commercial, industrial, etc.), primary
fuel type (e.g., coal, petroleum, gas), and secondary fuel category (e.g., motor gasoline, distillate fuel oil,
etc.). Fuel consumption data for the United States were obtained directly from the EIA of the U.S.
Department of Energy (DOE), primarily from the Monthly Energy Review and published supplemental
tables on petroleum product detail (EIA 2016a). The EIA does not include territories in its national energy
statistics, so fuel consumption data for territories were collected separately from EIA's International
Energy Statistics (EIA 2014) and Jacobs (2010).31
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 four years). These consumption data sets help inform the annual surveys to arrive at
the national total and sectoral breakdowns for that total.32
Also, note that U.S. fossil fuel energy statistics are generally presented using gross calorific values (GCV)
(i.e., higher heating values). Fuel consumption activity data presented here have not been adjusted to
correspond to international standards, which are to report energy statistics in terms of net calorific values
(NCV) (i.e., lower heating values).33
2. Subtract uses accounted for in the Industrial Processes and Product Use chapter. Portions of the fuel
consumption data for seven fuel categories—coking coal, distillate fuel, industrial other coal, petroleum
coke, natural gas, residual fuel oil, and other oil—were reallocated to the Industrial Processes and Product
Use chapter, as they were consumed during non-energy related industrial activity. To make these
adjustments, additional data were collected from AISI (2004 through 2016), Colfeyville (2012), U.S.
Census Bureau (2001 through 2011), EIA (2016a, 2016b, 2016c), USAA (2008 through 2016), USGS
(1991 through 2015a), (USGS 2016a), USGS (2014 through 2016a), USGS (2014 through 2016b), USGS
(1995 through 2013), USGS (1995, 1998, 2000, 2001), USGS (2016b), USGS (20016c), USGS (2015a),
USGS (1991 through 2013), USGS (2016d), USGS (2015b), USGS (2014), USGS (1996 through 2013),
USGS (1991 through 2015b), USGS (2015 and 2016), USGS (1991 through 2015c).34
3. Adjust for conversion offuels and exports of CO 2. Fossil fuel consumption estimates are adjusted
downward to exclude fuels created from other fossil fuels and exports of CO2.35 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.36 Since October 2000, the
31 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 total emissions of 41.4 MMT CO2 Eq. in 2015.
32 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.
33 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.
34 See sections on Iron and Steel Production and Metallurgical Coke Production, Ammonia Production and Urea Consumption,
Petrochemical Production, Titanium Dioxide Production, Ferroalloy Production, Aluminum Production, and Silicon Carbide
Production and Consumption in the Industrial Processes and Product Use chapter.
35 Energy statistics from EIA (2016a) are already adjusted downward to account for ethanol added to motor gasoline, biodiesel
added to diesel fuel, and biogas in natural gas.
36 These adjustments are explained in greater detail in Annex 2.1.
3-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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39
40
41
42
43
44
45
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 and biodiesel were collected
from EIA (2016a), data for synthetic natural gas were collected from EIA (2016b), and data for CO2
exports were collected from the Eastman Gasification Services Company (2011), Dakota Gasification
Company (2006), Fitzpatrick (2002), Erickson (2003), EIA (2008) and DOE (2012).
4. Adjust Sectoral Allocation of Distillate Fuel Oil and Motor Gasoline. EPA had conducted a separate
bottom-up analysis of transportation fuel consumption based on data from the Federal Highway
Administration that indicated that the amount of distillate and motor gasoline consumption allocated to the
transportation sector in the EIA statistics should be adjusted. Therefore, for these estimates, the
transportation sector's distillate fuel and motor gasoline consumption was adjusted to match the value
obtained from the bottom-up analysis. As the total distillate and motor gasoline consumption estimate from
EIA are considered to be accurate at the national level, the distillate and motor gasoline consumption totals
for the residential, commercial, and industrial sectors were adjusted proportionately. The data sources used
in the bottom-up analysis of transportation fuel consumption include AAR (2008 through 2016), Benson
(2002 through 2004), DOE (1993 through 2016), EIA (2007), EIA (1991 through 2016), EPA (2016d), and
FHWA (1996 through 2016).37
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 Section 3.2 - Carbon Emitted and Stored in
Products from Non-Energy Uses of Fossil Fuels. Therefore, the amount of fuels used for non-energy
purposes was subtracted from total fuel consumption. Data on non-fuel consumption was provided by EIA
(2016a).
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).38 The Office of the Under Secretary of Defense (Installations and Environment) and the Defense
Logistics Agency Energy (DLA Energy) of the U.S. Department of Defense (DoD) (DLA Energy 2016)
supplied data on military jet fuel and marine fuel use. Commercial jet fuel use was obtained from FAA
(2017); residual and distillate fuel use for civilian marine bunkers was obtained from DOC (1991 through
2016) for 1990 through 2001 and 2007 through 2014, 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 in
Section 3.9 - International Bunker Fuels.
7. Determine the total C content of fuels consumed. Total C was estimated by multiplying the amount of fuel
consumed by the amount of C in each fuel. This total C estimate defines the maximum amount of C that
could potentially be released to the atmosphere if all of the C in each fuel was converted to CO2. The C
content coefficients used by the United States were obtained from EIA's Emissions of Greenhouse Gases in
the United States 2008 (EIA 2009a), and an EPA analysis of C content coefficients used in the GHGRP
37 Bottom-up gasoline and diesel highway vehicle fuel consumption estimates are based on data from FHWA Highway Statistics
Table MF-21, MF-27, and VM-1 (FHWA 1996 through 2016). In 2016, FHWA changed its methods for estimating the share of
motor gasoline used in on-highway and off-highway applications. This resulted in an increase in the estimated off-highway motor
gasoline consumption (including consumption in the commercial and industrial sectors of this Inventory) and subsequent
decrease in the on-highway motor gasoline consumption for 2015. Note that EPA plans to integrate new 2014 data for the final
1990 to 2015 Inventory report, which is discussed in the Planned Improvements section below.
38 See International Bunker Fuels section in this chapter for a more detailed discussion.
Energy 3-27
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31
(EPA 2010). A discussion of the methodology used to develop the C content coefficients are presented in
Annexes 2.1 and 2.2.
8. Estimate C02 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. Heat contents and densities
were obtained fromEIA (2016a) and USAF (1998).39
• For on-road vehicles, annual estimates of combined motor gasoline and diesel fuel consumption by
vehicle category were obtained fromFHWA (1996 through 2016); for each vehicle category, the
percent gasoline, diesel, and other (e.g., CNG, LPG) fuel consumption are estimated using data from
DOE (1993 through 2016). 40>41
• For non-road vehicles, activity data were obtained from AAR (2008 through 2016), APTA (2007
through 2016), APTA (2006), BEA (2016), Benson (2002 through 2004), DOE (1993 through 2016),
DLA Energy (2016), DOC (1991 through 2016), DOT (1991 through 2016), EIA (2009a), EIA
(2016a), EIA (2013), EIA (1991 through 2016), EPA (2016d),42 and Gaffney (2007).
• For jet fuel used by aircraft, CO2 emissions from commercial aircraft were developed by the U. S.
Federal Aviation Administration (FAA) using a Tier 3B methodology, consistent IPCC (2006) (see
Annex 3.3). Carbon dioxide emissions from other aircraft were calculated directly based on reported
consumption of fuel as reported by EIA. Allocation to domestic military uses was made using DoD
data (see Annex 3.8). General aviation jet fuel consumption is calculated as the remainder of total jet
fuel use (as determined by EIA) nets all other jet fuel use as determined by FAA and DoD. For more
information, see Annex 3.2.
Box 3-4: Uses of Greenhouse Gas
Industrial Sector Fossil Fuel Combi
- TO B Ul L All O FOR FINAL INVENTORY REPORT
\s described 1111 lie calculation niclhodolous. loial fossil liiel consumption lor each >ear is based on auureualed end-
use sector consumption published In 1 he M \ The a\ailabihis of facility -le\ el combustion emissions lliroimh I !l* Vs
39 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 3.8,
respectively.
40 Data from FHWA's Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. Since
VM-1 data for 2015 has not been published yet, fuel consumption shares from 2014 are used as a proxy for the current Inventory.
These fuel consumption estimates are combined with estimates of fuel shares by vehicle type from DOE's TEDB Annex Tables
A.l through A.6 (DOE 1993 through 2016). TEDB data for 2015 has not been published yet, therefore 2014 data is used as a
proxy. In 2011, FHWA changed its methods for estimating data in the VM-1 table. These methodological changes included how
vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first
incorporated for the 1990 through 2010 Inventory and apply to the 2007 through 2015 time period. This resulted in large changes
in VMT and fuel consumption data by vehicle class, thus leading to a shift in emissions among on-road vehicle classes.
41 Transportation sector natural gas and LPG consumption are based on data from EIA (2016). In prior Inventory years, data
from DOE TEDB was used to estimate each vehicle class's share of the total natural gas and LPG consumption. Since TEDB
does not include estimates for natural gas use by medium and heavy duty trucks or LPG use by passenger cars, EIA Alternative
Fuel Vehicle Data (Browning 2016) is now used to determine each vehicle class's share of the total natural gas and LPG
consumption. These changes were first incorporated in this year's Inventory and apply to the 1990 to 2015 time period.
42 In 2015, EPA incorporated the NONROAD2008 model into MOVES2014. The current Inventory uses the NONROAD
component of MOVES2014a for years 1999 through 2015.
3-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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( iicciihoiisc ( his kcporinm I'rournni i( il l( ikl'i li;is pi\»\ idcd ;iii opportunity u> heller chnmcleri/e llie indiisirinl
scclor's ciicrus coiisnnipiioii nnd emissions iii llie I micd Sinles. Miroimh n disnuureunlioii of I !l Vs incliisinnI sector
fuel consumption dnln from select industries
for IT \ 's (¦ 11( ikP 2(i Id ihroimh 2d 15 report iim \enrs. fncihiv -lc\ el fossil fuel combustion emissions reported
lliroimh llie (il I(iKlJ were cnlcuori/cd nnd disirihuied lo specific industry types In iiiih/.iim Incililv -repined \ \I( S
codes ins published b\ llie I S Census Uiirenin \s noicd prc\ kmisI\ iii iIiis report. I lie definitions ;md pro\ isions
for rcporiiim fuel l\ pes in I !l* Vs (il l( iklJ niehide some differences from llie ln\ cniory s use of I!I \ iinlionnl fuel
sinlisiies io meel llie I M'CCC reporiinu miidclincs l lie ll'CC lins pro\ ided miidnncc on nlminim I'nciliiy -lexel
reported fuels ;md fuel types published in iinlionnl cncruy statistics. which unided lins exercise.
This y enr's el lori represenis ;in nllempi lo nlmn. reconcile, nnd coordinnle llie Incilily -lex el report nm of fossil fuel
conibnsiioii emissions under IT \"s (¦ 1ICkP w nil ihe iinlionnl-lc\ el nppronch presenied in iliis repori ( oiisisieni
w nli rcconiniciidnlioiis for reporiiiiu ihe Inx eniory lo llie I \l;('('('. progress w;is nmde on cerinm fuel ly pes for
specific indiisiries nnd lins been included in ihe Common keporiinu I'ormnl iCkl'i inbles Mini nre snbiniiied lo ihe
I M'CCC nlonu wiili lins repori I'or llie ciirrcni exercise, llie elloris m reconciling fuels focused on si;mdnrd.
coninioii fuel types ic u.. nnlnrnl uns. disiillnle fuel oil. clc. i w here llie fuels mi I !l Vs iinlionnl sinlisiies nliuned well
w illi Incilily -lexel (il Kikl' dnln I'or iliese rensons. llie ciirreni iiiforninlion presenied in ihe ( kl' mbles should be
\ icwed ns ;iii mil in I nl tempi nl lins exercise \ddilionnl elloris will be nmde for Inline I nx eniory reporis io inipro\ e
llie ninppiim of fuel ly pes. nnd cxnniiiic wny s io reconcile nnd coordinnle nny differences belween Incilily -lex el dnln
nnd iinlionnl sinlisiies \ddilionnlly. this vein 's minis sis e.xpnnded lliis el lori ihronuh ihe lull lime series presenied in
llie ( kl' inbles \ i in I x scs were conducted liiiknm C ¦ 11( iklJ Incilily -lex el repori iiiu \\ il h l he iiiforninlion published by
I!I \ in lis \ 11!( S dnln in order lo disnuurcunlc llie l ull I lwo ihroimh 2d 15 lime series in llie ( kl ' inbles. Il is
bclic\cd Mini llie ciirrcni minis sis lins led lo improvements mi the preseiiinlion of dnln in ihe I memory. bin fnriher
work will be conducted, nnd future iniproveineiits will be renli/ed in subsequent lii\cniory reports
\ddilionnlly. lo nssist mi the disnuureunlion of indiistrinl fuel coiisnnipiioii. I !l A will now s\ iithesi/.e cncruy
coiisiimptioii dnln iismu the s;mie procedure ns is used for the Inst historicnl ibeiichmnrki y enr of the \mninl I mcruy
()ntlook i Al.()i I'liis procedure reorunni/es the niosi recent dnln from the Mniiiil'ncliiriim I ineruy ( oiisiiniptioii
Snrxcy (MIX'S) iconducted c\cry four y enrsi into the noniiiinl dnln snhniission yenr iisniu the snnie cncruy -
economy inieurnled model used lo produce the \l!() projections. the Vilionnl Iinerux Modclim: Sysieni (MAIS).
I T\ bchc\ es this 'nowcnsuiii:'' iechiiu|iie pro\ ides nil npproprinte estininle of eneruy coiisnnipiioii for the ( kl'
To nddress u;ips in the time series. I !l \ performs ;i \l A1S model projection. iisnm the Ml !CS bnseline snb-seclor
cncruy coiisnnipiioii l lie MAIS model nccoinils I'orchnimcs in I'nclors thnl influence iiidiistrinl sector eneruv
coiisnnipiioii. nnd lins ncccss to dnln w Inch niny be more recent Minn Ml!( S. such ns indiistrinl snb-seclor nincro
indiistrinl output lie. shipments) ;md fuel prices lis c\nhinliim the i mpncl of these Inclors on indiistrinl snbscclor
cncruy coiisnnipiioii. \l A1S enn nniicipnle chnimcs io the cncruy shnres occiirrnm post-MI !CS nnd enn pro\ ide n
wny lo npproprinlcly disnuureunle llie energy -relnled emissions dnln into the Ckl'.
While the fuel coiisnnipiioii \ nines for llie x nrions niniiiil'ncliiriim snb-seclors ;ire not directly snrx e\ed for nil \enrs.
lhe> represeni I I \'s besi estininle of historicnl coiisnnipiioii \ nines for iioii-\II !CS \enrs \loreo\ er. ns ;m mieurnl
pnri of encli \l.() piiblicnlion. I Ins s\ nihelic dnln series is 11 kelx io be mniiiinined consisieni w nli nil ;i\ mlnble I! IA
nnd iioii-I !l \ dnln sources c\ en ns the iniderls iiiu dnln sources e\ ol\ e for both niniiiifncliiriim nnd iioii-
mniiiil'ncliiriiiu indiisiries nlike
()i her sectors' fuel c(.
44 See .
45 See .
Energy 3-29
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36
Box 3-5: Carbon Intensity of U.S. Energy Consumptii
Fossil fuels are the dominant source of energy in the United States, and CO2 is the dominant greenhouse gas emitted
as a product from their combustion. Energy-related CO2 emissions are impacted by not only lower levels of energy
consumption but also by lowering the C intensity of the energy sources employed (e.g., fuel switching from coal to
natural gas). The amount of C emitted from the combustion of fossil fuels is dependent upon the C content of the
fuel and the fraction of that C that is oxidized. Fossil fuels vary in their average C content, ranging from about 53
MMT CO2 Eq./QBtu for natural gas to upwards of 95 MMT CO2 Eq./QBtu for coal and petroleum coke.46 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 the purposes of
following reporting guidelines and maintaining the focus of this section, renewable energy and nuclear electricity
and consumption are not included in the totals shown in Table 3-15 in order to focus attention on fossil fuel
combustion as detailed in this chapter. 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 MMT CO2 Eq./EJ), which were
the primary sources of energy. Lastly, the electricity generation sector had the highest C intensity due to its heavy
reliance on coal for generating electricity.
Table 3-15: Carbon Intensity from Direct Fossil Fuel Combustion by Sector (MMT CO2
Eq./QBtu)
Sector
1990
2005
2011
2012
2013
2014
2015
Residential3
57.4
56.6
55.7
55.5
55.3
55.4
55.6
Commercial3
59.1
57.5
56.5
56.1
55.8
55.7
56.2
Industrial3
64.3
64.3
62.4
62.0
61.8
61.5
61.5
Transportation3
71.1
71.4
71.5
71.5
71.4
71.5
71.5
Electricity Generation15
87.3
85.8
82.9
79.9
81.3
81.2
78.1
U.S. Territories0
73.0
73.4
73.1
72.4
72.1
71.9
71.9
All Sectors0
73.0
73.5
72.0
70.9
70.9
70.8
69.7
3 Does not include electricity or renewable energy consumption.
b Does not include electricity produced using nuclear or renewable energy.
c Does not include nuclear or renewable energy consumption.
Note: Excludes non-energy fuel use emissions and consumption.
For the time period of 1990 through about 2008, the C intensity of U.S. energy consumption was fairly constant, as
the proportion of fossil fuels used by the individual sectors did not change significantly over that time. Starting in
2008 the C intensity has decreased, reflecting the shift from coal to natural gas in the electricity sector during that
time period. Per capita energy consumption fluctuated little from 1990 to 2007, but in 2015 was approximately 10.2
percent below levels in 1990 (see Figure 3-15). To differentiate these estimates from those of Table 3-15, the C
intensity trend shown in Figure 3-15 and described below includes nuclear and renewable energy EIA data to
provide a comprehensive economy-wide picture of energy consumption. 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 2016).
46 One exajoule (EJ) is equal to 1018 joules or 0.9478 QBtu.
3-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
Figure 3-15: U.S. Energy Consumption and Energy-Related CO2 Emissions Per Capita and Per
Dollar GDP
110
100
COj/Energy Consumption
Energy Consumption/capita
O
o
COz/capita
X
CD
~o
c.
1—1
Energy Consumption/$GDP
m
1—1
cm cm cm
C intensity estimates were developed using nuclear and renewable energy data from EIA (2016a), EPA (2010), and
fossil fuel consumption data as discussed above and presented in Annex 2.1.
Uncertainty and Time-Series Consistency-TO BE UPDATED FOR FINAL
INVENTORY REPORT
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. 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 (Section 3.2). These factors all contribute to the
Energy 3-31
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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
31
32
33
34
IIMCCrlMIMlS III ilk.' ( '( ) CM I MIMICS I k'lMllcd discussions i»ll MlC IIMCCI'lMIMllCs MssoeiMled W llll ( ' Cllllllcd ll'OlM \oil-
l!llCI'U> I scs i)f 1'ossll I'llclscMII he I'oillld Wlllllll lIlMl sCCl Kill i»f I Ills cIlMplCI'
YmI'IOIIs siMIIVCs ill" IIMCCI'lMIMlS si I I'lXi 111 id MlC Csl i lllMl Kill (.if CM I IssKi I Is ll'DIII IIIICI'MMIIKIImI hllllkci llicls. W lllcll MI'C
siibii'Mclcd from I lie I S ioimIs iscc i lie delMilcd discussions tin ihese iniccriMiiiiics pro\ ided mi Section Vv
I iiici'iKii ki iki I Uniikcr I'liclsi \ iri| her source dI' iMicci"lMMii\ is fuel co Hsu iiipi ki ii In I S Terrilories. The I nil eel
SiMles docs iioi collect enerus statistics lor i is icrriiones Ml llic smiiic lc\ el of delMil ms lor I lie fills suites mikI I lie
Disiricl ill ('oliimbiM Thcrerorc. esiniiMliim hoili emissions ;nid hunker fuel coiisiiMipiion In ihese icrriiones is
difficult
I iiceriMiinics in ilie emission esiimimics prcseiiicd Mho\ e mIso rcsuli from ilie dMlM used lo mIIocmic ('() emissioiis
from llie li'MiisporiMlioii end-use sector lo nidi\ idiiMl \ chicle t\ pes mikI ti'Miispori modes In immiis cmscs. hoiiom-np
esiiiiiMlcs til' fuel consumption In \ chicle i\pe do not iiimIcIi muuicumic I'iicI-l\ pc estimimics from I!I \ 1'iirihcr
rescMich is plMinicd lo iiiipro\e I lie mIIocmiioii iiiio delMilcd irMiisporiMlion cud-use scclorciiiissiiins
The iineeilMi nl\ mmmKsis wms perrorincd In pri iiimia I'iicI l\ pe IoicmcIi cud-use sec lor. iisiiiu I lie IIHX'-recommended
\pproMch 2 iineei'lM i nl\ csIimimIioii niclhodolous. \1oiile ( mi'Io SiocIimsIic SimiiiImIioii 1ccIiiik|iic. w ii Ii r/RISk
soI'iwmi'c fortius nnccriMi iil\ esiiniMlioii. I lie iii\ciilor\ csIimimIioii model lor CO I'iomi fossil fuel comhiisiion \\;is
iiiicui'Mled willi llie relcNMiii \miimMcs I'iomi ilie in\eiiior\ csiiiiiMlioii model lor InicriiMlkiiimI Uniikcr I'ncls. to
i"cmIis|icmII> cliMi'Mclcri/e I lie iiiiei'Mclioii (or endogenous coitcImIiomi hem ecu I lie \ MiiMhles ii|" ihese mo models
\hoin I2u nipiii \ MiiMhles were modeled for CO Irom eiicruv -rehilcd I'ossil I'iicI Comhiisiioii i inchidiim Mhoni In
lor Moii-ciicrus I'iicI coiisnmpiioii mikI mIhmii 2d lor I nicriiMlkiiimI I >nnker I 'liclsi
In dcNclopmu I he iiiiccriM i ni\ esiiiMMlion model, uniform distributions were Mssnmed IoimII Mel in its -rekiled input
\ MiiMhles mikI emission I'Mclors. hMsed on the S\l(' III \ 12no 11 report 1 Tmnuukir dist rihiit ioiis were Mssmned lor
the o\idi/Mlioii liielors (or eiiiiihiistion ellieieneiesi The iineeriMinl\ iMimes were Mssmned lo the nipiii \Mi"iMhles
hMsed on ilie dMlM reporied in S \I(' l!l \ (2iio11 ;md on eon\ers;iiions wiili \mi"ioiis Muenes personnel |N
The iineeilMinl\ iMimes lor ilie mc1i\ il\-relMled nipiii \MiiMhles were l_\ pieM11\ ms\ iiimelrie Mromid llieir iii\eiiior\
esimiMles. llie niiceriMini\ iMimes lor ilie emissioiis hielors were s\ iiimelrie I Sims inr s\ sieniMlie iiiieeriMiiiiiesi
MssoeiMled willi ihese \ MriMhles Meeoimied lor much of l lie n iiceriMi lilies MssoeiMled w illi ihese \ MiiMhles i SAIC III \
2(iu I) ' |-'or purposes of this iiiieeriMiiils miimIs sis. cmcIi nipiii \ Mi iMhle wms sniiiilMled I(i.ooii limes ihronuh \lonie
( MI'lo SMlliplIll'-l.
I lie results of ilie \pproMeh 2 c|iimlitiimii\ e inieeriMinl\ miimK sis ;ire snniMiMi'i/ed in 'l ;ihle 1 (» I 'ossil fuel
eonihiisiion ( () eniissKHis in 2o 14 were esiiniMled lo he helween 5. h>2 4 miicI 5.45" 4 \I\1T CO I !i| mi m l>5 pereeni
eonfideiiee le\ el This indicMles m iMime iif 2 pereeni helow lo 5 percent mI>o\c I lie 2ol4 emission esiiniMle of 5.2ns 2
\l\llCO \x\
Table 3-16: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Energy-
Related Fossil Fuel Combustion by Fuel Type and Sector (MMT CO2 Eq. and Percent)
2014 rimissiiiii risiiniiiU' I iui-rl;iiim kiiii^i- Kihilivi' in l.missiim l'.slim;ik''
r'ucl/Swiur iMMK O: (MMT CO; |%|
I.I HUT I |>|KT I.IHMT I |)|KT
liiiiinil liiiiinil liiiunil Bound
47 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.
48 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.
49 Although, in general, random uncertainties are the main focus of statistical uncertainty analysis, when the uncertainty
estimates are elicited from experts, their estimates include both random and systematic uncertainties. Hence, both these types of
uncertainties are represented in this uncertainty analysis.
3-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
( Hill1'
1,653.7
I.5W..3
I.NO'J.I
-3"..
Residential
NK
NK
NK
NK
NK
Commercial
4.5
4.3
5.2
-5%
15%
Industrial
75.3
71.8
87.2
-5%
16%
Transportation
NK
NK
NK
NK
NK
Klectricily Generation
1,570.4
1,509.0
1,721.0
-4%
10%
U.S. Territories
3.4
3.0
4.0
-13%
19%
Villi nil (isis1'
W2r..r.
1.411.4
l.4'>2.7
-l"o
5"..
Residential
277.6
269.7
297.1
-3%
7%
Commercial
189.2
183.8
202.4
-3%
7%
Industrial
466.0
452.1
499.6
-3%
7%
Transportation
47.6
46.3
51.0
-3%
7%
1 Tectricity (ieneralion
443.2
430.4
465.6
-3%
5%
U.S. Territories
3.0
2.6
3.5
-12%
17%
IVlnik-um1'
2.127.5
I.W7.0
2.251.')
Residential
67.5
63.8
71.0
-5%
5%
Commercial
38.2
36.3
40.0
-5%
5%
Industrial
271.9
219.1
321.2
-19%
18%
Transportation
1.690.0
1.577.3
1.800.7
-7%
7%
Kleclric Utilities
25.3
24.1
27.3
-5%
8%
U.S. Territories
34.6
31.9
38.5
-8%
1 1%
Tnl.il ((.Alluding Gi-oilu-rm;!!)1'
5.207.N
5,102.0
5.457.0
-2"..
5"..
Geothermal
0.4
NK
NK
NK
NK
Tnl.il (including Gi-iiIIutiii.iI)1'''
5.20N.2
5.102.4
5.457.4
-2"..
5"..
NK (Not I estimated)
¦' Range of emission estimates predicted by Monte Carlo Stochastic Simulation lor a 95 percent confidence interval.
h The low and high estimates for total emissions were calculated separately through simulations and. hence, the low and
high emission estimates for the sub-source categories do not sum to total emissions.
c Geothermal emissions added for reporting purposes, but an uncertainly analysis was not performed for C(): emissions
from geothermal production.
1 Methodological recalculations were applied In llie enure lime scries in ensure lime-series cousisieucy from I'Wti
2 throimli 2<> 15 l)el;uls mi ilie emission lrends ihroimli lime ;ire described mi more del;nl in llie Melliodolouy seelion.
3 abo\ e
4 QA/QC and Verification
5 A source-specific QA/QC plan for CO2 from fossil fuel combustion was developed and implemented. This effort
6 included a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented
7 involved checks specifically focusing on the activity data and methodology used for estimating CO2 emissions from
8 fossil fuel combustion in the United States. Emission totals for the different sectors and fuels were compared and
9 trends were investigated to determine whether any corrective actions were needed. Minor corrective actions were
10 taken.
11 Recalculations Discussion
12 The Energy Information Administration (EIA 2016a) updated energy consumption statistics across the time series
13 relative to the previous Inventory. EIA revised 2014 natural gas consumption in all end-use sectors, 2011 through
14 2014 Liquefied Petroleum Gas (LPG) consumption in all end-use sectors, 2014 coal and natural gas consumption in
15 the electric power sector, 2014 coal consumption in the commercial sector, and 2013 distillate fuel consumption in
16 the industrial and transportation sectors. In 2016, EIA revised 2014 heat contents for coal, coal coke, and natural
17 gas.
18 Overall, these changes resulted in an average annual decrease of 0.2 MMT CO2 Eq. (less than 0.1 percent) in CO2
19 emissions from fossil fuel combustion for the period 1990 through 2014, relative to the previous Inventory.
Energy 3-33
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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
31
32
33
34
35
36
37
38
39
40
41
42
Planned Improvements
To reduce uncertainty of CO2 from fossil fuel combustion estimates for U.S. Territories, efforts will continue to
work with EIA and other agencies to improve the quality of the U.S. Territories data. This improvement is not all-
inclusive, and is part of an ongoing analysis and efforts to continually improve the CO2 from fossil fuel combustion
estimates. In addition, further expert elicitation may be conducted to better quantify the total uncertainty associated
with emissions from this source.
The availability of facility-level combustion emissions through EPA's GHGRP will continue to be examined to help
better characterize the industrial sector's energy consumption in the United States, and further classify total
industrial sector fossil fuel combustion emissions by business establishments according to industrial economic
activity type. Most methodologies used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP,
facilities collect detailed information specific to their operations according to detailed measurement standards,
which may differ with the more aggregated data collected for the Inventory to estimate total, national U.S.
emissions. In addition, and unlike the reporting requirements for this chapter under the UNFCCC reporting
guidelines, some facility-level fuel combustion emissions reported under the GHGRP may also include industrial
process emissions.50 In line with UNFCCC reporting guidelines, fuel combustion emissions are included in this
chapter, while process emissions are included in the Industrial Processes and Product Use chapter of this report. In
examining data from EPA's GHGRP that would be useful to improve the emission estimates for the CO2 from fossil
fuel combustion category, particular attention will also be made to ensure time series consistency, as the facility-
level reporting data from EPA's GHGRP are not available for all inventory years as reported in this Inventory.
Additional, analyses will be conducted to align reported facility-level fuel types and IPCC fuel types per the national
energy statistics. For example, efforts will be taken to incorporate updated industrial fuel consumption data from
EIA's Manufacturing Energy Consumption Survey (MECS), with updated data for 2014. Additional work will look
at CO2 emissions from biomass to ensure they are separated in the facility-level reported data, and maintaining
consistency with national energy statistics provided by EIA. In implementing improvements and integration of data
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
continue to be relied upon.51
An ongoing planned improvement is to develop improved estimates of domestic waterborne fuel consumption. The
Inventory estimates for residual and distillate fuel used by ships and boats is based in part on data on bunker fuel use
from the U.S. Department of Commerce. Domestic fuel consumption is estimated by subtracting fuel sold for
international use from the total sold in the United States. It may be possible to more accurately estimate domestic
fuel use and emissions by using detailed data on marine ship activity. The feasibility of using domestic marine
activity data to improve the estimates will continue to be investigated.
Lastly, an additional improvement for the final 1990 to 2015 Inventory is to update estimates of 2014 CO2 emissions
from on-road gasoline consumption with FHWA's 2014 Table MF-21 published in July 2016. Estimates in this draft
Inventory are based on the 2014 Table MF-21 published in September 2015. Gasoline consumption for on-road
transportation in 2014 is estimated to be higher in the more recent FHWA data, which will result in a larger decrease
in on-road gasoline consumption from 2014 to 2015 than what is estimated in the current Inventory. As a result, the
estimate of CO2 emissions from the overall transportation end-use sector will likely decrease from 2014 to 2015 in
the final Inventory. It is important to note, however, that the overall decrease in on-road motor gasoline consumption
from 2014 to 2015 is likely due to a change in FHWA methods used to estimate the share of gasoline used in on-
road and non-road applications in 2015. In absence of this method change, the estimate of on-road gasoline would
likely have increased in this Inventory from 2014 to 2015.
50 See .
51 See .
3-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 CH4 and N20 from Stationary Combustion
2 Methodology
3 Methane and N20 emissions from stationary combustion were estimated by multiplying fossil fuel and wood
4 consumption data by emission factors (by sector and fuel type for industrial, residential, commercial, and U.S.
5 Territories; and by fuel and technology type for the electric power sector). Beginning with the current Inventory
6 report, the electric power sector utilizes a Tier 2 methodology, whereas all other sectors utilize a Tier 1
7 methodology. The activity data and emission factors used are described in the following subsections.
8 Industrial, Residential, Commercial, and U.S. Territories
9 National coal, natural gas, fuel oil, and wood consumption data were grouped by sector: industrial, commercial,
10 residential, and U.S. Territories. For the CH4 and N20 estimates, wood consumption data for the United States was
11 obtained from EIA's Monthly Energy Review (EIA 2016a). Fuel consumption data for coal, natural gas, and fuel oil
12 for the United States were also obtained from EIA's Monthly Energy Review and unpublished supplemental tables
13 on petroleum product detail (EIA 2016a). Because the United States does not include territories in its national
14 energy statistics, fuel consumption data for territories were provided separately by EIA's International Energy
15 Statistics (EIA 2014) and Jacobs (2010).52 Fuel consumption for the industrial sector was adjusted to subtract out
16 construction and agricultural use, which is reported under mobile sources.53 Construction and agricultural fuel use
17 was obtained from EPA (2016c) and FHWA (1996 through 2016). Estimates for wood biomass consumption for fuel
18 combustion do not include wood wastes, liquors, municipal solid waste, tires, etc., that are reported as biomass by
19 EIA. Tier 1 default emission factors for these three end-use sectors were provided by the 2006IPCC Guidelines for
20 National Greenhouse Gas Inventories (IPCC 2006). U.S. Territories' emission factors were estimated using the U.S.
21 emission factors for the primary sector in which each fuel was combusted.
22 Electric Power Sector
23 The electric power sector now uses a Tier 2 emission estimation methodology as fuel consumption for the electricity
24 generation sector by control-technology type was obtained from EPA's Acid Rain Program Dataset (EPA 2016a).
25 This combustion technology- and fuel-use data was available by facility from 1996 to 2015. The Tier 2 emission
26 factors used were taken from IPCC (2006), which in turn are based on emission factors published by EPA.
27 Since there was a difference between the EPA (2016a) and EIA (2016a) total energy consumption estimates, the
28 remaining energy consumption from EIA (2016a) was apportioned to each combustion technology type and fuel
29 combination using a ratio of energy consumption by technology type from 1996 to 2015.
30 Energy consumption estimates were not available from 1990 to 1995 in the EPA (2016a) dataset, and as a result,
31 consumption was calculated using total electric power consumption from EIA (2016a) and the ratio of combustion
32 technology and fuel types from EPA (2016a). The consumption estimates from 1990 to 1995 were estimated by
33 applying the 1996 consumption ratio by combustion technology type to the total EIA consumption for each year
34 from 1990 to 1995. Emissions were estimated by multiplying fossil fuel and wood consumption by technology- and
35 fuel-specific Tier 2 IPCC emission factors.
36 Lastly, there were significant differences between wood biomass consumption in the electric power sector between
37 the EPA (2016a) and EIA (2016a) datasets. The higher wood biomass consumption from EIA (2016a) in the electric
38 power sector was distributed to the residential, commercial, and industrial sectors according to their percent share of
39 wood biomass energy consumption calculated from EIA (2016a).
52 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.
53 Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not
available to determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile
sources.
Energy 3-35
-------
1 More detailed information on the methodology for calculating emissions from stationary combustion, including
2 emission factors and activity data, is provided in Annex 3.1.
3 Uncertainty and Time-Series Consistency - TO BE UPDATED FOR FINAL
4 INVENTORY REPORT
5 Methane emission estimates from stationary sources exhibit high uncertainty, primarily due to difficulties in
6 calculating emissions from wood combustion (i.e., fireplaces and wood stoves). The estimates of CH4 and N20
7 emissions presented are based on broad indicators of emissions (i.e., fuel use multiplied by an aggregate emission
8 factor for different sectors), rather than specific emission processes (i.e., by combustion technology and type of
9 emission control).
10 An uncertainty analysis was performed by primary fuel type for each end-use sector, using the IPCC-recommended
11 Approach 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, with VvRISK
12 software.
13 The uncertainty estimation model for this source category was developed by integrating the CH4 and N20 stationary
14 source inventory estimation models with the model for CO2 from fossil fuel combustion to realistically characterize
15 the interaction (or endogenous correlation) between the variables of these three models. About 55 input variables
16 were simulated for the uncertainty analysis of this source category (about 20 from the CO2 emissions from fossil
17 fuel combustion inventory estimation model and about 35 from the stationary source inventory models).
18 In developing the uncertainty estimation model, uniform distribution was assumed for all activity-related input
19 variables and N20 emission factors, based on the SAIC/EIA (2001) report.54 For these variables, the uncertainty
20 ranges were assigned to the input variables based on the data reported in SAIC/EIA (2001).55 However, the CH4
21 emission factors differ from those used by EI A. These factors and uncertainty ranges are based on IPCC default
22 uncertainty estimates (IPCC 2006).
23 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-17. Stationary
24 combustion CH4 emissions in 2014 (including biomass) were estimated to be between 4.8 and 20.6 MMT CO2 Eq. at
25 a 95 percent confidence level. This indicates a range of 41 percent below to 155 percent above the 2014 emission
26 estimate of 8.1 MMT CO2 Eq.56 Stationary combustion N20 emissions in 2014 (including biomass) were estimated
27 to be between 17.9 and 34.2 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 24 percent
28 below to 46 percent above the 2014 emissions estimate of 23.4 MMT CO2 Eq.
54 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.
55 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.
56 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.
3-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Table 3-17: Approach 2 Quantitative Uncertainty Estimates for ChU and N2O Emissions from
2 Energy-Related Stationary Combustion, Including Biomass (MMT CO2 Eq. and Percent)
Si 111 nv
(¦;is
2014 rimissiiiii risiim.iU-
(MMT CO: l.(|.)
I iHvrl;iinl\ Ki-hiliu- In 11 missii 111
(MMTCO: Ki|.) ("..
I'lsliniiik-'1
)
I.I HUT I |1|KT I.IHUT
ISiimihI Bound liiniiid
I |1|KT
ISiimihI
Stationary Co
Stationary Co
mbustion
mbnstion
CI 11
\ ( 1
8.1
23.4
4.8 20.6 -41%
17.9 34.2 -24%
+ 155%
+46%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
3 The uiiceriaiiiiies associated wiili I lie emission csiiniales of CI I and N ();ire urealer llian lliose associated Willi
4 estimates of C() from fossil fuel combustion. which niaiiik rel> on llie carbon content iif llie fuel combusted
5 I uccriaiulics in holli CI I ;nid \ () es|im;iles are due lo llie fact lh;il emissions arc es|im;iled h;ised on emission
6 factors lepieseiiiinu onl\ ;i limned snhsel of conihiisiion eondilions f or the indirect uieenhoiise uases. uncertainties
7 are parik due lo assumptions couccruiim eonihiisiion leehiiolous i\ pes. aue of equipment emission factors used.
8 ;md acli\ il\ d;il;i projections
9 Methodological recalculations were applied lo llie enure lime-series lo ensure lime-series eonsisienes from I'wo
10 throimh 2d 15 I )el;nls on llie emission irends throimh lime ;ire described in more del;nl in ihe Melhodolouv seelion.
11 ;iho\e
12 QA/QC and Verification
13 A source-specific QA/QC plan for stationary combustion was developed and implemented. This effort included a
14 Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved
15 checks specifically focusing on the activity data and emission factor sources and methodology used for estimating
16 CH4, N20, and the indirect greenhouse gases from stationary combustion in the United States. Emission totals for
17 the different sectors and fuels were compared and trends were investigated.
is Recalculations Discussion
19 Methane and N20 emissions from stationary sources (excluding CO2) across the entire time series were revised due
20 revised data from EIA (2016a) and EPA (2016a) relative to the previous Inventory. The historical data changes
21 resulted in an average annual increase of less than 0.1 MMT CO2 Eq. (less than 0.1 percent) in CH4 emissions, and
22 an average annual decrease of less than 0.1 MMT CO2 Eq. (less than 0.1 percent) in N20 emissions from stationary
23 combustion for the period 1990 through 2014.
24 Planned Improvements
25 Several items are being evaluated to improve the CH4 and N20 emission estimates from stationary combustion and
26 to reduce uncertainty for U.S. Territories. Efforts will be taken to work with EIA and other agencies to improve the
27 quality of the U.S. Territories data. Because these data are not broken out by stationary and mobile uses, further
28 research will be aimed at trying to allocate consumption appropriately. In addition, the uncertainty of biomass
29 emissions will be further investigated since it was expected that the exclusion of biomass from the estimates would
30 reduce the uncertainty; and in actuality the exclusion of biomass increases the uncertainty. These improvements are
31 not all-inclusive, but are part of an ongoing analysis and efforts to continually improve these stationary combustion
32 estimates from U.S. Territories.
33 Future improvements to the CH4 and N20 from Stationary Combustion category involve continued research into the
34 availability of using CH4 and N20 from stationary combustion data from other sources, for example, data reported
35 under EPA's GHGRP. In examining data from EPA's GHGRP that would be useful to improve the emission
36 estimates for CH4 and N20 from Stationary Combustion category, particular attention will be made to ensure time
37 series consistency, as the facility-level reporting data from EPA's GHGRP are not available for all Inventory years
Energy 3-37
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1 as reported in this Inventory. In implementing improvements and integration of data from EPA's GHGRP, the latest
2 guidance from the IPCC on the use of facility-level data in national inventories will be relied upon.57
3 CH4 and N20 from Mobile Combustion
4 Methodology
5 Estimates of CH4 and N20 emissions from mobile combustion were calculated by multiplying emission factors by
6 measures of activity for each fuel and vehicle type (e.g., light-duty gasoline trucks). Activity data included vehicle
7 miles traveled (VMT) for on-road vehicles and fuel consumption for non-road mobile sources. The activity data and
8 emission factors used are described in the subsections that follow. A complete discussion of the methodology used to
9 estimate CH4 and N20 emissions from mobile combustion and the emission factors used in the calculations is provided
10 in Annex 3.2.
11 On-Road Vehicles
12 Estimates of CH4 and N20 emissions from gasoline and diesel on-road vehicles are based on VMT and emission
13 factors by vehicle type, fuel type, model year, and emission control technology. Emission estimates for alternative
14 fuel vehicles (AFVs) are based on VMT and emission factors by vehicle and fuel type.58
15 Emission factors for gasoline and diesel on-road vehicles utilizing Tier 2 and Low Emission Vehicle (LEV)
16 technologies were developed by ICF (2006b); all other gasoline and diesel on-road vehicle emissions factors were
17 developed by ICF (2004). These factors were derived from EPA, California Air Resources Board (CARB) and
18 Environment Canada laboratory test results of different vehicle and control technology types. The EPA, CARB and
19 Environment Canada tests were designed following the Federal Test Procedure (FTP), which covers three separate
20 driving segments, since vehicles emit varying amounts of greenhouse gases depending on the driving segment.
21 These driving segments are: (1) a transient driving cycle that includes cold start and running emissions, (2) a cycle
22 that represents running emissions only, and (3) a transient driving cycle that includes hot start and running
23 emissions. For each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the
24 content of this bag was then analyzed to determine quantities of gases present. The emissions characteristics of
25 segment 2 were used to define running emissions, and subtracted from the total FTP emissions to determine start
26 emissions. These were then recombined based upon the ratio of start to running emissions for each vehicle class
27 from MOBILE6.2, an EPA emission factor model that predicts gram per mile emissions of C02, CO, HC, NOx, and
28 PM from vehicles under various conditions, to approximate average driving characteristics.59
29 Emission factors for AFVs were first developed by ICF (2006a) after examining Argonne National Laboratory's
30 GREET 1.7-Transportation Fuel Cycle Model (ANL 2006) and Lipman and Delucchi (2002). These sources
31 describe AFV emission factors in terms of ratios to conventional vehicle emission factors. Ratios of AFV to
32 conventional vehicle emissions factors were then applied to estimated Tier 1 emissions factors from light-duty
33 gasoline vehicles to estimate light-duty AFVs. Emissions factors for heavy-duty AFVs were developed in relation to
34 gasoline heavy-duty vehicles. A complete discussion of the data source and methodology used to determine
35 emission factors from AFVs is provided in Annex 3.2.
36 Annual VMT data for 1990 through 2015 were obtained from the Federal Highway Administration's (FHWA)
37 Highway Performance Monitoring System database as reported in Highway Statistics (FHWA 1996 through
38 2016).60 VMT estimates were then allocated from FHWA's vehicle categories to fuel-specific vehicle categories
57 See .
58 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.
59 Additional information regarding the model can be found online at .
60 The source of VMT is FHWA Highway Statistics Table VM-1. Since Table VM-1 data for 2015 has not been published yet,
2015 VMT is estimated using the 3.5 percent increase in FHWA Traffic Volume Trends data from 2014 to 2015. In 2011,
FHWA changed its methods for estimating data in the VM-1 table. These methodological changes included how vehicles are
3-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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using the calculated shares of vehicle fuel use for each vehicle category by fuel type reported in DOE (1993 through
2016) and information on total motor vehicle fuel consumption by fuel type fromFHWA (1996 through 2016).
VMT for AFVs were estimated based on Browning (2016). The age distributions of the U.S. vehicle fleet were
obtained from EPA (2016b, 2000), and the average annual age-specific vehicle mileage accumulation of U.S.
vehicles were obtained from EPA (2016b).
Control technology and standards data for on-road vehicles were obtained from EPA's Office of Transportation and
Air Quality (EPA 2007a, 2007b, 2000, 1998, and 1997) and Browning (2005). These technologies and standards are
defined in Annex 3.2, and were compiled from EPA (1994a, 1994b, 1998, 1999a) and IPCC (2006).
Non-Road Vehicles
To estimate emissions from non-road vehicles, fuel consumption data were employed as a measure of activity, and
multiplied by fuel-specific emission factors (in grams of N20 and CH4 per kilogram of fuel consumed).61 Activity
data were obtained from AAR (2008 through 2016), APTA (2007 through 2016), APTA (2006), BEA (1991 through
2015), Benson (2002 through 2004), DHS (2008), DLA Energy (2015), DOC (1991 through 2015), DOE (1993
through 2015), DOT (1991 through 2016), EIA (2002, 2007, 2016a), EIA (2007 through 2016), EIA (1991 through
2016), EPA (2016b), Esser (2003 through 2004), FAA (2017), FHWA (1996 through 2016),62 Gaffney (2007), and
Whorton (2006 through 2014). Emission factors for non-road modes were taken from IPCC (2006) and Browning
(2009).
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis was conducted for the mobile source sector using the IPCC-recommended
Approach 2 uncertainty estimation methodology, Monte Carlo Stochastic Simulation technique, using @RISK
software. The uncertainty analysis was performed on 2015 estimates of CH4 and N20 emissions, incorporating
probability distribution functions associated with the major input variables. For the purposes of this analysis, the
uncertainty was modeled for the following four major sets of input variables: (1) VMT data, by on-road vehicle and
fuel type and (2) emission factor data, by on-road vehicle, fuel, and control technology type, (3) fuel consumption,
data, by non-road vehicle and equipment type, and (4) emission factor data, by non-road vehicle and equipment
type.
Uncertainty analyses were not conducted for NOx, CO, or NMVOC emissions. Emission factors for these gases have
been extensively researched since emissions of these gases from motor vehicles are regulated in the United States,
and the uncertainty in these emission estimates is believed to be relatively low. For more information, see Section
3.8 - Uncertainty Analysis of Emission Estimates. However, a much higher level of uncertainty is associated with
CH4 and N20 emission factors due to limited emission test data, and because, unlike C02 emissions, the emission
pathways of CH4 and N20 are highly complex.
Mobile combustion CH4 emissions from all mobile sources in 2015 were estimated to be between 1.7 and 2.6 MMT
CO2 Eq. at a 95 percent confidence level. This indicates a range of 18 percent below to 25 percent above the
corresponding 2015 emission estimate of 2.0 MMT CO2 Eq. Also at a 95 percent confidence level, mobile
combustion N20 emissions from mobile sources in 2015 were estimated to be between 13.4 and 18.1 MMT CO2
classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first incorporated
for the 1990 through 2010 Inventory and apply to the 2007 through 2015 time period. This resulted in large changes in VMT by
vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has
been replaced by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty
Vehicles, Long Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles from
the light truck category to the passenger vehicle category in this Inventory. These changes are reflected in a large drop in light-
truck emissions between 2006 and 2007.
61 The consumption of international bunker fuels is not included in these activity data, but is estimated separately under the
International Bunker Fuels source category.
62 In 2016, FHWA changed its methods for estimating the share of motor gasoline used in on-highway and off-highway
applications. This resulted in an increase in the estimated off-highway motor gasoline consumption and subsequent decrease in
the on-highway motor gasoline consumption for 2015.
Energy 3-39
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1 Eq., indicating a range of 13 percent below to 18 percent above the corresponding 2015 emission estimate of 15.4
2 MMT C02 Eq.
3 Table 3-18: Approach 2 Quantitative Uncertainty Estimates for ChU and N2O Emissions from
4 Mobile Sources (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate3
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower Upper
Bound
Bound
Bound Bound
Mobile Sources
CH4
2.0
1.7
2.6
-18% +25%
Mobile Sources
N2O
15.4
13.4
18.1
-13% +18%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
5 This uncertainty analysis is a continuation of a multi-year process for developing quantitative uncertainty estimates
6 for this source category using the IPCC Approach 2 uncertainty analysis. As a result, as new information becomes
7 available, uncertainty characterization of input variables may be improved and revised. For additional information
8 regarding uncertainty in emission estimates for CH4 and N20 please refer to the Uncertainty Annex.
9 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
10 through 2015 with two recent notable exceptions. First, an update to the method for estimating on-road VMT created
11 an inconsistency in on-road CH4 and N20 for the time periods 1990 to 2006 and 2007 to 2015. Second, an update to
12 the method for estimating share of motor gasoline used in on-highway and off-highway applications created an
13 inconsistency in non-road CH4 and N20 for the time periods 1990 to 2014 and 2015. Details on the emission trends
14 and methodological inconsistencies through time are described in more detail in the Methodology section, above.
15 QA/QC and Verification
16 A source-specific Quality Assurance/Quality Control plan for mobile combustion was developed and implemented.
17 This plan is based on the IPCC-recommended QA/QC Plan. The specific plan used for mobile combustion was
18 updated prior to collection and analysis of this current year of data. This effort included a Tier 1 analysis, as well as
19 portions of a Tier 2 analysis. The Tier 2 procedures focused on the emission factor and activity data sources, as well
20 as the methodology used for estimating emissions. These procedures included a qualitative assessment of the
21 emissions estimates to determine whether they appear consistent with the most recent activity data and emission
22 factors available. A comparison of historical emissions between the current Inventory and the previous Inventory
23 was also conducted to ensure that the changes in estimates were consistent with the changes in activity data and
24 emission factors.
25 Recalculations Discussion
26 Several updates were made to on-road CH4 and N20 emissions calculations this year resulting in a net increase to
27 CH4 and N20 emissions from mobile combustion relative to the previous Inventory. First several light-duty trucks
28 were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales
29 data. Second, which emission standards each vehicle type was assumed to have met were re-examined using
30 confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered
31 alternative fueled vehicles and therefore were not included in the engine technology breakouts. For this Inventory,
32 HEVs are now classified as gasoline vehicles across the entire time series. PHEVs (plug-in hybrid electric vehicles)
33 continue to be considered alternative fuel vehicles, as are electric vehicles. Estimates of alternative fuel vehicle
34 mileage for the last ten years were revised to reflect updates made to Energy Information Administration (EIA) data
35 on alternative fuel use and vehicle counts. Overall, these changes resulted in an average annual increase of 0.02
36 MMT C02 Eq. (1 percent) in CH4 emissions and an average annual increase of 0.5 MMT C02 Eq. (2 percent) in
37 N20 emissions from mobile combustion for the period 1990 through 2014, relative to the previous report.
3-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Planned Improvements
While the data used for this report represent the most accurate information available, several areas have been
identified that could potentially be improved in the near term given available resources.
• Examine the 2016 FHWA off-highway gasoline methodology change-which impacted estimates of
commercial, industrial, agriculture, and construction CH4 and N20 mobile emissions in 2015-to determine
whether we should adjust other methods for estimating non-road equipment emissions.
• Continue to explore potential improvements to estimates of domestic waterborne fuel consumption for
future Inventories. 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 continues to be
investigated. Additionally, the feasibility of including data from a broader range of domestic and
international sources for domestic bunker fuels, including data from studies such as the Third 1MO GHG
Study 2014, continues to be explored.
• Continue to examine the use of EPA's MOVES model in the development of the Inventory estimates,
including use for uncertainty analysis. Although the Inventory uses some of the underlying data from
MOVES, such as vehicle age distributions by model year, MOVES is not used directly in calculating
mobile source emissions. The use of MOVES is currently being evaluated to develop new emissions factors
for CH4 and N20, which may be integrated into the final version of this Inventory or future inventories.
Other approaches for updating CH4 and N20 emissions factors, including use of the latest GREET model,
are also being considered.
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).
Carbon dioxide 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 62 percent of the total C consumed for non-energy purposes was stored in products, and
not released to the atmosphere; the remaining 38 percent was emitted.
There are several areas in which non-energy uses of fossil fuels are closely related to other parts of this Inventory.
For example, some of the NEU products release C02 at the end of their commercial life when they are combusted
after disposal; these emissions are reported separately within the Energy chapter in the Incineration of Waste source
category. In addition, there is some overlap between fossil fuels consumed for non-energy uses and the fossil-
derived CO2 emissions accounted for in the Industrial Processes and Product Use chapter, especially for fuels used
as reducing agents. To avoid double-counting, the "raw" non-energy fuel consumption data reported by EIA are
modified to account for these overlaps. There are also net exports of petrochemicals that are not completely
accounted for in the EIA data, and the Inventory calculations adjust for the effect of net exports on the mass of C in
non-energy applications.
Energy 3-41
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27
28
29
30
31
32
As shown in Table 3-19, fossil fuel emissions in 2015 from the non-energy uses of fossil fuels were 127.0 MMT
CO2 Eq., which constituted approximately 2 percent of overall fossil fuel emissions. In 2015, the consumption of
fuels for non-energy uses (after the adjustments described above) was 4,961.4 TBtu, an increase of 10.8 percent
since 1990 (see Table 3-20). About 57.5 MMT (210.7 MMT CO2 Eq.) of the C in these fuels was stored, while the
remaining 34.6 MMT C (127.0 MMT CO2 Eq.) was emitted.
Table 3-19: CO2 Emissions from Non-Energy Use Fossil Fuel Consumption (MMT CO2 Eq. and
Percent)
Year
I'm
2005
2011
2012
2013
2014
2015
Potential Emissions
3I2.I
377.4
316.6
311.9
327.1
322.0
324.1
C Stored
194.5
239.1
208.1
206.5
205.1
205.1
209.3
Emissions as a % of Potential
38%
37%
34%
34%
37%
36%
35%
Emissions
117.7
138.3
108.5
105.5
122.0
117.2
127.0
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 EI A (2013, 2016) (see Annex 2.1). Consumption of natural gas, LPG, pentanes plus, naphthas, other
oils, and special naphtha were adjusted to account for net exports of these products that are not reflected in the raw
data from EIA. Consumption values for industrial coking coal, petroleum coke, other oils, and natural gas in Table
3-20 and Table 3-21 have been adjusted to subtract non-energy uses that are included in the source categories of the
Industrial Processes and Product Use chapter.63,64 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 the Energy sector under the Incineration of Waste source category,
the storage factors do not account for losses at the disposal end of the life cycle.
• For industrial coking coal and distillate fuel oil, storage factors were taken from IPCC (2006), which in turn
draws from Marland and Rotty (1984).
• For the remaining fuel types (petroleum coke, miscellaneous products, and other petroleum), IPCC does not
provide guidance on storage factors, and assumptions were made based on the potential fate of C in the
respective NEU products.
63 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.
64 Some degree of double counting may occur between these estimates of non-energy use of fuels and process emissions from
petrochemical production presented in the Industrial Processes and Produce Use sector. Data integration is not feasible at this
time as feedstock data from EIA used to estimate non-energy uses of fuels are aggregated by fuel type, rather than disaggregated
by both fuel type and particular industries (e.g., petrochemical production) as currently collected through EPA's GHGRP and
used for the petrochemical production category.
3-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 3-20: Adjusted Consumption of Fossil Fuels for Non-Energy Uses (TBtu)
Year
1990
2005
2011
2012
2013
2014
2015
Industry
4,215.8
5,110.7
4,470.1
4,377.3
4,621.1
4,597.3
4,759.6
Industrial Coking Coal
0.0
80.4
60.8
132.5
119.3
48.2
121.4
Industrial Other Coal
8.2
11.9
10.3
10.3
10.3
10.3
10.3
Natural Gas to Chemical Plants
281.6
260.9
297.1
292.7
297.0
305.1
302.3
Asphalt & Road Oil
1,170.2
1,323.2
859.5
826.7
783.3
792.6
831.7
LPG
1,120.5
1,610.0
1,865.6
1,887.3
2,062.9
2,109.7
2,157.7
Lubricants
186.3
160.2
141.8
130.5
138.1
144.0
156.8
Pentanes Plus
117.6
95.-
26.4
40.3
45.4
43.5
78.4
Naphtha (<401 °F)
326.3
679.6
469.4
432.2
498.8
435.2
417.9
Other Oil (>401 °F)
662.1
499.4
368.2
267.4
209.1
236.2
216.8
Still Gas
36.7
67."
163.6
160.6
166.7
164.6
162.2
Petroleum Coke
27.2
105.2
0.0
0.0
0.0
0.0
0.0
Special Naphtha
100.9
60.9
21.8
14.1
96.6
104.5
97.0
Distillate Fuel Oil
7.0
11."
5.8
5.8
5.8
5.8
5.8
Waxes
33.3
31.4
15.1
15.3
16.5
14.8
12.4
Miscellaneous Products
137.8
112.8
164.7
161.6
171.2
182.7
188.9
Transportation
176.0
151.3
133.9
123.2
130.4
136.0
148.1
Lubricants
176.0
151."
133.9
123.2
130.4
136.0
148.1
U.S. Territories
86.7
121.')
56.7
58.1
57.4
53.6
53.6
Lubricants
0.7
4.6
1.0
1.0
1.0
1.0
1.0
Other Petroleum (Misc. Prod.)
86.0
117.'
55.7
57.1
56.4
52.6
52.6
Total
4,478.5
5,383.')
4,660.8
4,558.6
4,808.9
4,786.9
4,961.3
2 Table 3-21: 2015 Adjusted Non-Energy Use Fossil Fuel Consumption, Storage, and Emissions
Adjusted
Carbon
Non-Energy
Content
Potential
Storage
Carbon
Carbon
Carbon
Use3
Coefficient
Carbon
Factor
Stored
Emissions
Emissions
(MMT
(MMT
Sector/Fuel Type
(TBtu)
C/QBtu)
(MMT C)
(MMT C)
(MMT C)
CO2 Eq.)
Industry
4,759.6
NA
88.0
NA
57.1
31.0
113.5
Industrial Coking Coal
121.4
31.00
3.8
0.10
0.4
3.4
12.4
Industrial Other Coal
10.3
25.82
0.3
0.65
0.2
0.1
0.3
Natural Gas to
Chemical Plants
302.3
14.47
4.4
0.65
2.9
1.5
5.5
Asphalt & Road Oil
831.7
20.55
17.1
1.00
17.0
0.1
0.3
LPG
2157.7
17.06
36.8
0.65
24.1
12.7
46.5
Lubricants
156.8
20.20
3.2
0.09
0.3
2.9
10.5
Pentanes Plus
78.4
19.10
1.5
0.65
1.0
0.5
1.9
Naphtha (<401° F)
417.9
18.55
7.8
0.65
5.1
2.7
9.8
Other Oil (>401° F)
216.8
20.17
4.4
0.65
2.9
1.5
5.5
Still Gas
162.2
17.51
2.8
0.65
1.9
1.0
3.6
Petroleum Coke
+
27.85
+
0.30
+
+
+
Special Naphtha
97.0
19.74
1.9
0.65
1.3
0.7
2.4
Distillate Fuel Oil
5.8
20.17
0.1
0.50
0.1
0.1
0.2
Waxes
12.4
19.80
0.2
0.58
0.1
0.1
0.4
Miscellaneous Products
188.9
20.31
3.8
+
+
3.8
14.1
Transportation
148.1
NA
3.0
NA
0.3
2.7
10.0
Lubricants
148.1
20.20
3.0
0.09
0.3
2.7
10.0
U.S. Territories
53.6
NA
1.1
NA
0.1
1.0
3.5
Energy 3-43
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26
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32
33
34
35
36
37
38
39
40
41
Lubricants
1.0
20.20
+
0.09
+
+
0.1
Other Petroleum (Misc.
Prod.)
52.6
20.00
1.1
0.10
0.1
0.9
3.5
Total
4,961.3
92.1
57.5
34.6
127.0
+ Does not exceed 0.05 TBtu
NA - Not Applicable
a To avoid double counting, net exports have been deducted.
Note: Totals may not sum due to independent rounding.
Lastly, emissions were estimated by subtracting the C stored from the potential emissions (see Table 3-19). More
detail on the methodology for calculating storage and emissions from each of these sources is provided in Annex
2.3.
Where storage factors were calculated specifically for the United States, data were obtained on (1) products such as
asphalt, plastics, synthetic rubber, synthetic fibers, cleansers (soaps and detergents), pesticides, food additives,
antifreeze and deicers (glycols), and silicones; and (2) industrial releases including energy recovery. Toxics Release
Inventory (TRI) releases, hazardous waste incineration, and volatile organic compound, solvent, and non-
combustion CO emissions. Data were taken from a variety of industry sources, government reports, and expert
communications. Sources include EPA reports and databases such as compilations of air emission factors (EPA
2001), National Emissions Inventory (NEI) Air Pollutant Emissions Trends Data (EPA 2016a), Toxics Release
Inventory, 1998 (EPA 2000b), Biennial Reporting System (EPA 2000a, 2009), Resource Conser\>ation and Recovery
Act Information System (EPA 2013b, 2015b), pesticide sales and use estimates (EPA 1998, 1999, 2002, 2004, 2011),
and the Chemical Data Access Tool (EPA 2012); the EIA Manufacturer's Energy Consumption Survey (MECS)
(EIA 1994, 1997, 2001, 2005, 2010, 2013); the National Petrochemical & Refiners Association (NPRA 2002); the
U.S. Census Bureau (1999, 2004, 2009, 2014); Bank of Canada (2012, 2013, 2014, 2016); Financial Planning
Association (2006); INEGI (2006); the United States International Trade Commission (1990 through 2016);
Gosselin, Smith, and Hodge (1984); EPA's Municipal Solid Waste (MSW) Facts and Figures (EPA 2013a; 2014a,
2016b); the Rubber Manufacturers' Association (RMA 2009, 2011, 2014, 2016); the International Institute of
Synthetic Rubber Products (IISRP 2000, 2003); the Fiber Economics Bureau (FEB 2001, 2003, 2005, 2007, 2009,
2010, 2011, 2012, 2013); the EPA Chemical Data Access Tool (CDAT) (EPA 2014b); the American Chemistry
Council (ACC 2003 through 2011, 2013, 2014, 2015a); and the Guide to the Business of Chemistry (ACC 2012,
2015b, 2016). Specific data sources are listed in full detail in Annex 2.3.
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
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 VvRISK software and the IPCC-recommended
Approach 2 methodology (Monte Carlo Stochastic Simulation technique), provides for the specification of
probability density functions for key variables within a computational structure that mirrors the calculation of the
inventory estimate. The results presented below provide the 95 percent confidence interval, the range of values
within which emissions are likely to fall, for this source category.
As noted above, the non-energy use analysis is based on U.S.-specific storage factors for (1) feedstock materials
(natural gas, LPG, pentanes plus, naphthas, other oils, still gas, special naphthas, and other industrial coal), (2)
asphalt, (3) lubricants, and (4) waxes. For the remaining fuel types (the "other" category in Table 3-20 and Table
3-21), the storage factors were taken directly from IPCC (2006), where available, and otherwise assumptions were
made based on the potential fate of carbon in the respective NEU products. To characterize uncertainty, five separate
analyses were conducted, corresponding to each of the five categories. In all cases, statistical analyses or expert
judgments of uncertainty were not available directly from the information sources for all the activity variables; thus,
uncertainty estimates were determined using assumptions based on source category knowledge.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-22 (emissions) and Table
3-23 (storage factors). Carbon emitted from non-energy uses of fossil fuels in 2014 was estimated to be between
86.2 and 162.9 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 25 percent below to 42
3-44 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 pereenl nho\ e ilie 2(> 14 emission esiininle of I 14. ^ \1\1T('() l!q The imeeriniiily mi I lie emission esiininles is ;i
2 I'mielioii ill" inieeriniiils mi hiiili ilie i|iinnlil> of fuel used for iioii-enerus purposes nnd I lie siornue I";i«jUi|"
3 Table 3-22: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Non-
4 Energy Uses of Fossil Fuels (MMT CO2 Eq. and Percent)
Smiriv
(¦;is
2014 rimissiiin INlim.iU-
(MMT CO: l'.(|.)
I iiiirkiiim Kiinm' Rihiliu- in I'mission
(MM 1 ( (): i:i|.) ("i.
I'Nli m;i k"1
)
I.IHUT I ppiT
I.IHUT
I ppi r
lioll 11(1 1 $111111(1
lilllllld
liiilliul
Feedstocks
t ( )
75.1
49.6 125.3
-34%
67l/)
Asphalt
t ( )
0 i
0 1 0 6
_S7 (Zo
1 11%
1 .ubricants
t ( )
18.9
15.5 21.9
-18%
16%
Waxes
t ( )
0 "S
0.3 0.7
-28%
6
(Hlier
t ( )
19.6
14.1 21.7
-28%
11%
1 <>l;il
CO:
114.3
Sfi.2 U.2.'>
-25"...
1 *>»
¦4. • 0
" Raime of em
ission
estimates predicted by Monte C;
irlo Stochastic Simulation I'oi
' a 95 percent co
nlidence
interval.
Note: Totals 1
nay 11c
it sum due to independent ronndi
ng.
5 Table 3-23: Approach 2 Quantitative Uncertainty Estimates for Storage Factors of Non-
6 Energy Uses of Fossil Fuels (Percent)
Soil I'll'
2(114 Slur;i»i- I'";k1oi-
("..)
I iHi'i'liiinlN kiin^i' Rihiliu- in l.mission 1"siini;iit-'
("«) (V kiliiliM)
I.IHUT
l ppi r
I.IHUT I ppi'l"
liiiund
lilllllld
liiilliul lilllllld
Feedstocks
C(): 65%
S2%,
V>o/
-20% 10%
Asphalt
CO: 99.6%
99.1%
99.8%
-() *^% 0 2^%
1 .ubricants
C<): 9%
4%,
17%
-57% 88%
Waxes
CO: 58%
49%,
7( YYd
-15% 22%
()ther
C(): 4%,
4%,
24%
-3% 479%
•' Ranse of en
lission estimates predicted by Monte Ci
irlo Stocha
stic Simulation lor
a 95 percent confidence
interval, as ;
1 percentage of the inventory value (also
> expresset
I in percent terms).
7 In Tnhle '-2 V li.vdsliii.ks ;md ;isph;ili eoiuribiiie lensi 10 inernll siornue I'nelor inieeriniiiiy on ;i pereeninue h;isis
8 \llIkmiuIi 1 lie leedsioeks enleuory llie Inruesi use enleuors 111 terms oflolnl enrhon llims nppenrs 10 h;i\e liulil
9 eonfidenee hiiiiis. iliis is ui some e\lenl ;iii nrlifnel of ilie \xn\ I he imeeriniiiis ; 111; 11 \ sis uns siriielnred As diseussed
10 111 \nne\ 2 V 1 he siornue Inelor IV.11" leedsioeks is hnsed on ;i 11; 111; 11\sis of six I'nles ih;ii resiili 111 loim-ierni siornue
11 (en. pl;isiies prodiielioiu. ;ind ele\en lh;ii resiili in emissions ie u . \ olnlile orunme eonipoiind emissions) knllier
12 1 Ikiii modeling 1 he loinl iiiieerininis nroimd ;ill ofihese Inle proeesses. ilie eiirreni minissis addresses onl\ 1 he slornue
13 l;iles. ;ind ;issii mes ih;ii ;ill ( ih;ii is nol siored isemilled \s ilie prodiielion sinlisiies I h;i I dine I he siornue \ nines nre
14 relnli\el\ uell-ehnrneleri/ed. lliis npproneh \ ields n result llinl is probnhls hinsed lounrd iiiidersinlnm iiiieerininis
15 \s is 1 lie ense w ith ihe oilier iiiieerininis minis ses diseussed 1 IiixmiuIkiiii lliis doeiinieni. ihe iiiieerininis results nho\e
16 nddress those Inelors Mint en 11 he rendils qiinntil ied. More delnils 011 the iiiieerinuiis minis sis nre pros ided 111
17 \mie\ : ' '
18 \1elhodolouienl reenlenlnlions were npplied lo ilie enure lime series 10 ensure lime-series eoiisisienex from 1990
19 ihroimh 2(i 15 I)elnils 0111 he emission irends ihrondi lime nre desenhed 111 more delnil 1111he Melhodolous seelion.
20 nho\ e
Energy 3-45
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QA/QC and Verification
A source-specific Quality Assurance/Quality Control 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 verily that
none had changed or been removed. Import and export totals were compared for 2014 as well as their trends across
the time series.
Petrochemical input data reported by EIA will continue to be investigated in an attempt to address an input/output
discrepancy in the NEU model. Since 2001, the C accounted for in the feedstocks C balance outputs (i.e., storage
plus emissions) exceeds C inputs. Prior to 2001, the C balance inputs exceed outputs. Starting in 2001 through 2009,
outputs exceeded inputs. In 2010 and 2011, inputs exceeded outputs, and in 2012, outputs slightly exceeded inputs.
A portion of this discrepancy has been reduced and two strategies have been developed to address the remaining
portion (see Planned Improvements, below).
Recalculations Discussion
A number of updates to historical production values were included in the most recent Monthly Energy Review; these
have been populated throughout this document.
Planned Improvements
There are several improvements planned for the future:
• Analyzing the fuel and feedstock data from EPA's GHGRP to better disaggregate CO2 emissions in NEU
model and CO2 process emissions from petrochemical production.
• More accurate accounting of C in petrochemical feedstocks. EPA has worked with EIA to determine the
cause of input/output discrepancies in the C mass balance contained within the NEU model. In the future,
two strategies to reduce or eliminate this discrepancy will continue to be pursued. First, accounting of C in
imports and exports will be improved. The import/export adjustment methodology will be examined to
ensure that net exports of intermediaries such as ethylene and propylene are fully accounted for. Second,
the use of top-down C input calculation in estimating emissions will be reconsidered. Alternative
approaches that rely more substantially on the bottom-up C output calculation will be considered instead.
• Response to potential changes in NEU input data. In 2013 EIA initiated implementation of new data
reporting definitions for Natural Gas Liquids (NGL) and Liquefied Petroleum Gases (LPG); the new
definitions may affect the characterization of the input data that EIA provides for the NEU model and may
therefore result in the need for changes to the NEU methodology. EIA also obtains and applies proprietary
data for LPG inputs that are not directly applied as NEU input data because the data are proprietary. The
potential use of the proprietary data (in an aggregated, non-proprietary form) as inputs to the NEU model
will be investigated with EIA.
• Improving the uncertainty analysis. Most of the input parameter distributions are based on professional
judgment rather than rigorous statistical characterizations of uncertainty.
• Better characterizing flows of fossil C. Additional fates may be researched, including the fossil C load in
organic chemical wastewaters, plasticizers, adhesives, films, paints, and coatings. There is also a need to
further clarify the treatment of fuel additives and backflows (especially methyl tert-butyl ether, MTBE).
• Reviewing the trends in fossil fuel consumption for non-energy uses. Annual consumption for several fuel
3-46 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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types is highly variable across the time series, including industrial coking coal and other petroleum
(miscellaneous products). A better understanding of these trends will be pursued to identify any
mischaracterized or misreported fuel consumption for non-energy uses. For example, "miscellaneous
products" category includes miscellaneous products that are not reported elsewhere in the EIA data set. The
EIA does not have firm data concerning the amounts of various products that are being reported in the
"miscellaneous products" category; however, EIA has indicated that recovered sulfur from petroleum and
natural gas processing, and potentially also C black feedstock could be reported in this category. Recovered
sulfur would not be reported in the NEU calculation or elsewhere in the Inventory.
Updating the average C content of solvents was researched, since the entire time series depends on one
year's worth of solvent composition data. The data on C emissions from solvents that were readily
available do not provide composition data for all categories of solvent emissions and also have conflicting
definitions for volatile organic compounds, the source of emissive C in solvents. Additional sources of
solvents data will be investigated in order to update the C content assumptions.
Updating the average C content of cleansers (soaps and detergents) was researched; although production
and consumption data for cleansers are published every 5 years by the Census Bureau, the composition (C
content) of cleansers has not been recently updated. Recently available composition data sources may
facilitate updating the average C content for this category.
Revising the methodology for consumption, production, and C content of plastics was researched; because
of recent changes to the type of data publicly available for plastics, the NEU model for plastics applies data
obtained from personal communications. Potential revisions to the plastics methodology to account for the
recent changes in published data will be investigated.
Although U.S.-specific storage factors have been developed for feedstocks, asphalt, lubricants, and waxes,
default values from IPCC are still used for two of the non-energy fuel types (industrial coking coal,
distillate oil), and broad assumptions are being used for miscellaneous products and other petroleum. Over
the long term, there are plans to improve these storage factors by analyzing C fate similar to those
described in Annex 2.3 or deferring to more updated default storage factors from IPCC where available.
Reviewing the storage of carbon black across various sectors in the Inventory; in particular, the carbon
black abraded and stored in tires.
Box 3-6: Reporting of Lubricants, Waxes, and Asphalt and Road Oil Product Use in Energy Sector
I
IPCC (2006) provides methodological guidance to estimate emissions from the first use of fossil fuels as a product
for primary purposes other than combustion for energy purposes (including lubricants, paraffin waxes, bitumen /
asphalt, and solvents) under the Industrial Processes and Product Use (IPPU) sector. 65 In this Inventory, C storage
and C emissions from product use of lubricants, waxes, and asphalt and road oil are reported under the Energy sector
in the Carbon Emitted from Non-Energy Uses of Fossil Fuels source category (IPCC Source Category 1 A).66
The emissions are reported in the Energy sector, as opposed to the IPPU sector, to reflect national circumstances in
its choice of methodology and to increase transparency of this source category's unique country-specific data
sources and methodology. The country-specific methodology used for the Carbon Emitted from Non-Energy Uses of
Fossil Fuels source category is based on a carbon balance (i.e., C inputs-outputs) calculation of the aggregate
amount of fossil fuels used for non-energy uses, including inputs of lubricants, waxes, asphalt and road oil (see
Section 3.2, Table 3-21). For those inputs, U.S. country-specific data on C stocks and flows are used to develop
carbon storage factors, which are calculated as the ratio of the C stored by the fossil fuel non-energy products to the
total C content of the fuel consumed, taking into account losses in the production process and during product use.67
65 See Volume 3: Industrial Processes and Product Use, Chapter 5: Non-Energy Products from Fuels and Solvent Use of the
2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
66 Non-methane volatile organic compound (NMVOC) emissions from solvent use are reported separately in the IPPU sector,
following Chapter 5 of the 2006 IPCC Guidelines.
67 Data and calculations for lubricants and waxes and asphalt and road oil are in Annex 2.3 - Methodology and Data for
Estimating CO2 Emissions from Fossil Fuel Combustion.
Energy 3-47
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The country-specific methodology to reflect national circumstances starts with the aggregate amount of fossil fuels
used for non-energy uses and applies a C balance calculation, breaking out the C emissions from non-energy use of
lubricants, waxes, and asphalt and road oil. Due to U.S. national circumstances, reporting these C emissions
separately under IPPU would involve making artificial adjustments to both the C inputs and C outputs of the non-
energy use C balance. These artificial adjustments would also result in the C emissions for lubricants, waxes, and
asphalt and road oil being reported under IPPU, while the C storage for lubricants, waxes, and asphalt and road oil
would be reported under Energy. To avoid presenting an incomplete C balance and a less transparent approach for
the Carbon Emitted from Non-Energy Uses of Fossil Fuels source category calculation, the entire calculation of C
storage and C emissions is therefore conducted in the Non-Energy Uses of Fossil Fuels category calculation
methodology, and both the C storage and C emissions for lubricants, waxes, and asphalt and road oil are reported
under the Energy sector.
3.3 Incineration of Waste (IPCC Source
Category lAla)
Incineration is used to manage about 7 to 19 percent of the solid wastes generated in the United States, depending on
the source of the estimate and the scope of materials included in the definition of solid waste (EPA 2000; Goldstein
and Madtes 2001; Kaufman et al. 2004; Simmons et al. 2006; van Haaren et al. 2010). In the context of this section,
waste includes all municipal solid waste (MSW) as well as scrap 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, scrap tires are combusted for
energy recovery in industrial and utility boilers, pulp and paper mills, and cement kilns. 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 in scrap tires—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, scrap tires (which
contain synthetic rubber and carbon black) are also considered a "non-hazardous" waste and are included in the
waste incineration estimate, though waste disposal practices for tires differ from municipal solid waste. Estimates on
emissions from hazardous waste incineration can be found in Annex 2.3 and are accounted for as part of the C mass
balance for non-energy uses of fossil fuels.
Approximately 30.1 million metric tons of MSW were incinerated in the United States in 2014 (EPA 2016). Data for
the amount of MSW incinerated in 2015 were not available, so data for 2015 was assumed to be equal to data for
2014. CO2 emissions from incineration of waste rose 34 percent since 1990, to an estimated 10.7 MMT C02 Eq.
(10,676 kt) in 2015, as the volume of scrap tires and other fossil C-containing materials in waste increased (see
Table 3-24 and Table 3-25). Waste incineration is also a source of CH4 and N20 emissions (De Soete 1993; IPCC
2006). Methane emissions from the incineration of waste were estimated to be less than 0.05 MMT CO2 Eq. (less
than 0.5 kt CH4) in 2015, and have decreased by 32 percent since 1990. Nitrous oxide emissions from the
incineration of waste were estimated to be 0.3 MMT CO2 Eq. (1 kt N20) in 2015, and have decreased by 32 percent
since 1990.
Table 3-24: CO2, ChU, and N2O Emissions from the Incineration of Waste (MMT CO2 Eq.)
3-48 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Gas/Waste Product
1990
2005
2011
2012
2013
2014
2015
CO2
8.0
12.5
10.6
10.4
10.4
10.6
10.7
Plastics
5.6
6.9
5.8
5.7
5.8
5.9
5.9
Synthetic Rubber in Tires
0.3
1.6
1.4
1.3
1.2
1.2
1.2
Carbon Black in Tires
0.4
2.0
1.7
1.5
1.4
1.4
1.5
Synthetic Rubber in
MSW
0.9
0.8
0.7
0.7
0.7
0.7
0.7
Synthetic Fibers
0.8
1.2
1.1
1.1
1.3
1.3
1.3
CH4
+
+
+
+
+
+
+
N2O
0.5
0.4
0.3
0.3
0.3
0.3
0.3
Total
8.4
12.8
10.9
10.7
10.7
10.9
11.0
Table 3-25: CO2, ChU, and N2O Emissions from the Incineration of Waste (kt)
Gas/Waste Product
1990
2005
2011
2012
2013
2014
2015
CO2
7,950
12,469
10,564
10,379
10,398
10,608
10,676
Plastics
5,588
6,919
5,757
5,709
5,815
5,928
5,928
Synthetic Rubber in Tires
308
1,599
1,363
1,261
1,158
1,189
1,220
Carbon Black in Tires
385
1,958
1,663
1,537
1,412
1,449
1,487
Synthetic Rubber in
MSW
854
766
712
706
729
729
729
Synthetic Fibers
816
1,227
1,070
1,166
1,284
1,313
1,313
CH4
+
+
+
+
+
+
+
N2O
2
1
1
1
1
1
1
Methodology
Emissions of CO2 from the incineration of waste include CO2 generated by the incineration of plastics, synthetic
fibers, and synthetic rubber in MSW, as well as the incineration of synthetic rubber and carbon black in scrap tires.
The emission estimates are calculated for all four sources on a mass-basis based on the data available. These
emissions were estimated by multiplying the mass 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, carbon black, and synthetic fibers. 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 scrap tires. More detail on the
methodology for calculating emissions from each of these waste incineration sources is provided in Annex 3.7.
For each of the methods used to calculate CO2 emissions from the incineration of waste, data on the quantity of
product combusted and the C content of the product are needed. For plastics, synthetic rubber, and synthetic fibers in
MSW, the amount of specific materials discarded as municipal solid waste (i.e., the quantity generated minus the
quantity recycled) was taken from Municipal Solid Waste Generation, Recycling, and Disposal in the United States:
Facts and Figures (EPA 2000 through 2003, 2005 through 2014), and Advancing Sustainable Materials
Management: Facts and Figures: Assessing Trends in Material Generation, Recycling and Disposal in the United
States (EPA 2015, 2016) and detailed unpublished backup data for some years not shown in the reports (Schneider
2007). For 2015, the amount of MSW incinerated was assumed to be equal to that in 2014, due to the lack of
available data. The proportion of total waste discarded that is incinerated was derived from Shin (2014). Data on
total waste incinerated was not available in detail for 2012 through 2015, so these values were assumed to equal to
the 2011 value (Shin 2014). For synthetic rubber and carbon black in scrap tires, information was obtained from
U.S. Scrap Tire Management Summary for 2005 through 2015 data (RMA 2016). Average C contents for the
"Other" plastics category and synthetic rubber in municipal solid wastes were calculated from 1998 and 2002
production statistics: C content for 1990 through 1998 is based on the 1998 value; C content for 1999 through 2001
Energy 3-49
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1 is the average of 1998 and 2002 values; and C content for 2002 to date is based on the 2002 value. Carbon content
2 for synthetic fibers was calculated from a weighted average of production statistics from 1990 to date. Information
3 about scrap tire composition was taken from the Rubber Manufacturers' Association internet site (RMA 2012a). The
4 mass of incinerated material is multiplied by its C content to calculate the total amount of carbon stored.
5 The assumption that 98 percent of organic C is oxidized (which applies to all waste incineration categories for CO2
6 emissions) was reported in EPA's life cycle analysis of greenhouse gas emissions and sinks from management of
7 solid waste (EPA 2006). This percentage is multiplied by the carbon stored to estimate the amount of carbon
8 emitted.
9 Incineration of waste, including MSW, also results in emissions of CH4 and N20. These emissions were calculated
10 as a function of the total estimated mass of waste incinerated and emission factors. As noted above, CH4 and N20
11 emissions are a function of total waste incinerated in each year; for 1990 through 2008, these data were derived from
12 the information published in BioCvcle (van Haaren et al. 2010). Data for 2009 and 2010 were interpolated between
13 2008 and 2011 values. Data for 2011 were derived from Shin (2014). Data on total waste incinerated was not
14 available in the BioCvcle data set for 2012 through 2015, so these values were assumed to equal the 2011 BiocvcIe
15 data set value.
16 Table 3-26 provides data on municipal solid waste discarded and percentage combusted for the total waste stream.
17 The emission factors of N20 and CH4 emissions per quantity of municipal solid waste combusted are default
18 emission factors for the default continuously-fed stoker unit MSW incineration technology type and were taken from
19 IPCC (2006).
20 Table 3-26: Municipal Solid Waste Generation (Metric Tons) and Percent Combusted
21 (BioCycle dataset)
Incinerated (% of
Year Waste Discarded Waste Incinerated Discards)
1990 235,733,657 30,632,057 13.0%
2005 259,559,787 25,973,520 10.0%
2011 273,116,704 20,756,870 7.6%
2012 273,116,704a 20,756,870 7.6%
2013 273,116,704a 20,756,870 7.6%
2014 273,116,704a 20,756,870 7.6%
201 5 273,116,704a 20,756,870 7.6%
a Assumed equal to 2011 value.
Source: van Haaren et al. (2010)
22 Uncertainty and Time-Series Consistency - TO BE UPDATED
23 FOR FINAL INVENTORY REPORT
24 An Approach 2 Monte Carlo analysis was performed to determine the level of uncertainty surrounding the estimates
25 of CO2 emissions and N20 emissions from the incineration of waste (given the very low emissions for CH4, no
26 uncertainty estimate was derived). IPCC Approach 2 analysis allows the specification of probability density
27 functions for key variables within a computational structure that mirrors the calculation of the Inventory estimate.
28 Uncertainty estimates and distributions for waste generation variables (i.e., plastics, synthetic rubber, and textiles
29 generation) were obtained through a conversation with one of the authors of the Municipal Solid Waste in the
30 United States reports. Statistical analyses or expert judgments of uncertainty were not available directly from the
31 information sources for the other variables; thus, uncertainty estimates for these variables were determined using
32 assumptions based on source category knowledge and the known uncertainty estimates for the waste generation
33 variables.
34 The uncertainties in the waste incineration emission estimates arise from both the assumptions applied to the data
35 and from the quality of the data. Key factors include MSW incineration rate; fraction oxidized; missing data on
3-50 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 wasic co mposiiioii. ;i\ crnuc ( coiilcnl of wasic components; assumptions on I lie s\ nihclic hioucmc (' i~ilk»: and
2 conihiisiion conditions affectum \ C) emissions. 'I'hc hmhesi lc\ els of iiiiccriniiils surround llic \ arinhlcs lh;il arc
3 hascd on assumptions ic u. percent of cloi hum and fool wear composed ol" s\ nihelie ruhhen. I he lowesi lc\ els of
4 iiuccrlniuis surround \ arinhlcs ih;ii were dclcrniuicd In i|iinuiiinli\e measurements (e u . conihiisiion cflicicucs. ('
5 coulcul of (' hlnck)
6 The I'csulis of I he \ppronch 2 (|ii;inlil;ili\e iiiiccrinuin minis sis arc siinininri/cd in T;ihle ^-2~. Wasic iiiciiicrnliou
7 CO emissions in 2d 14 were csiuiinlcd lo he hclwccu X.5 and I 1.5 \1\11 'CO l!i| ;il ;i l>5 pereenl confidence le\ el.
8 This indicates ;i rnuuc of Id pcrccni helow lo 14 pereenl ;iho\ e I he 2d 14 emission csiini;iic of lM \ 1 \ 1" I' ('() I
9 \Isii ;ii ;i l)5 pereenl confidence lc\ el. w;isie incineration \ () emissions mi 2d 14 were esiini;iied lo he hclwccii d I
10 ;md d S M\| I ( () I !t| This indic;iles ;i rnuue of 5' pereenl helow lo l(>^ pereenl ;iho\ e I he 2d 14 emission csiini;ilc
11 oI'd ; \l\l I ( () \ x\
12 Table 3-27: Approach 2 Quantitative Uncertainty Estimates for CO2 and N2O from the
13 Incineration of Waste (MMT CO2 Eq. and Percent)
Si ill I'd'
(¦;is
2014 I'.niissiiin Kslim;iU-
(MM'I'CO: i:<|.»
I iHi-ri.iiim K;in|KT
Bound
Incineration of Waste
Incineration of Waste
t ( )
N (1
9.4
0 i
8.5 1 1.5
0.1 0.8
-10%
+ 14%
+ 163%
R;inge of emission estimates predicted by Monte Carlo Simulation lor a 95 percent confidence interval.
14 \lclhodolomc;il rcc;ilciil;iiioiis were ;ipplled lo ihc enure lime-series 10 ensure lime-series coiisisiencs from I'^'JD
15 ihroimh 2D 15 I )cl;iils on 1 he emission irends 1 IiixmiuIi lime ;ire described in more del;nl 1111 he Melhodolouv seclion.
16 ;iho\c
17 ration
18 A source-specific Quality Assurance/Quality Control plan was implemented for incineration of waste. This effort
19 included a Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented
20 involved checks specifically focusing on the activity data and specifically focused on the emission factor and
21 activity data sources and methodology used for estimating emissions from incineration of waste. Trends across the
22 time series were analyzed to determine whether any corrective actions were needed. Actions were taken to
23 streamline the activity data throughout the calculations on incineration of waste.
24 Recalculations r-vcussion
25 For the current Inventory, emission estimates for 2014 have been updated based on Advancing Sustainable
26 Materials Management: 2014 Fact Sheet (EPA 2016). The data used to calculate the percent incineration was not
27 updated in the current Inventory. Biocycle has not released a new State of Garbage in America Report since 2010
28 (with 2008 data), which used to be a semi-annual publication which publishes the results of the nation-wide MSW
29 survey. The results of the survey have been published in Shin (2014). This provided updated incineration data for
30 2011, so the generation and incineration data for 2012 through 2015 are assumed equivalent to the 2011 values. The
31 data for 2009 and 2010 were based on interpolations between 2008 and 2011.
32 A transcription error in 2013 plastics production data from EPA's Advancing Sustainable Materials Management:
33 Facts and Figures 2013: Assessing Trends in Material Generation, Recycling and Disposal in the United States
34 (EPA 2015) was identified and corrected. The amount of HDPE discarded in 2013 was misreported and the value
3 5 has been updated. This update results in updated emission estimate for the CO2 from Plastics for 2013.
36 Previously, the carbon content for synthetic fiber was assumed to be the weighted average of carbon contents of four
37 fiber types (polyester, nylon, olefin, and acrylic) based on 1999 fiber production data. This methodology has been
38 updated. A weighted average for the carbon content of synthetic fibers based on production data from 1990 through
39 2015 was developed for each year based on the amount of fiber produced. For each year, the weighted average
Energy 3-51
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36
37
38
39
40
41
42
43
44
carbon content was used to develop the amount of carbon emitted. This methodology update affects the synthetic
fiber CO2 estimates.
Planned Improvements
The availability of facility-level waste incineration data through EPA's Greenhouse Gas Reporting Program
(GHGRP) will be examined to help better characterize waste incineration operations in the United States. This
characterization could include future improvements as to the operations involved in waste incineration for energy,
whether in the power generation sector or the industrial sector. Additional examinations will be necessary as, unlike
the reporting requirements for this chapter under the UNFCCC reporting guidelines,68 some facility-level waste
incineration emissions reported under EPA's GHGRP may also include industrial process emissions. In line with
UNFCCC reporting guidelines, emissions for waste incineration with energy recovery are included in this chapter,
while process emissions are included in the Industrial Processes and Product Use chapter of this report. In
examining data from EPA's GHGRP that would be useful to improve the emission estimates for the waste
incineration category, particular attention will also be made to ensure time series consistency, as the facility-level
reporting data from EPA's GHGRP are not available for all inventory years as reported in this Inventory.
Additionally, analyses will focus on ensuring CO2 emissions from the biomass component of waste are separated in
the facility-level reported data, and on maintaining consistency with national waste generation and fate statistics
currently used to estimate total, national U.S. greenhouse gas emissions. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.69 GHGRP data is available for MSW combustors, which contains
information on the CO2, CH4, and N20 emissions from MSW combustion, plus the fraction of the emissions that are
biogenic. To calculate biogenic versus total CO2 emissions, a default biogenic fraction of 0.6 is used. The biogenic
fraction will be calculated using the current input data and assumptions to verily the current MSW emission
estimates.
If GHGRP data would not provide a more accurate estimate of the amount of solid waste combusted, new data
sources for the total MSW generated will be explored given that the data previously published semi-annually in
Biocycle (van Haaren et al. 2010) has ceased to be published, according to the authors. Equivalent data was derived
from Shin (2014) for 2011. A new methodology would be developed considering the available data within the
annual update of EPA's Advancing Sustainable Materials Management: Facts and Figures 2014: Assessing Trends
in Material Generation, Recycling and Disposal in the United States (EPA 2016) and a report from the
Environmental Research & Education Foundation (2016), MSW Management in the U.S.: 2010 & 2013, that has
data for 2010 and 2013. In developing the new methodology, appropriate assumptions would need to be made to
ensure that the MSW figures include the same boundaries. Consideration would also be made to be consistent with
calculations in other waste categories including landfilling and composting.
Additional improvements will be conducted to improve the transparency in the current reporting of waste
incineration. Currently, hazardous industrial waste incineration is included within the overall calculations for the
Carbon Emitted from Non-Energy Uses of Fossil Fuels source category. Waste incineration activities that do not
include energy recovery will be examined. Synthetic fibers within scrap tires are not included in this analysis and
will be explored for future Inventories. The carbon content of fibers within scrap tires would be used to calculate the
associated incineration emissions. Updated fiber content data from the Fiber Economics Bureau will also be
explored.
3.4 Coal Mining (IPCC Source Category lBla)
Three types of coal mining-related activities release CH4 to the atmosphere: underground mining, surface mining,
and post-mining (i.e., coal-handling) activities. While surface mines account for the majority of U.S. coal
production, underground coal mines contribute the largest share of CH4 emissions (see Table 3-29 and Table 3-30)
68 See .
69 See .
3-52 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 due to the higher CH4 content of coal in the deeper underground coal seams. In 2015, 305 underground coal mines
2 and 529 surface mines were operating in the United States. In recent years the total number of active coal mines in
3 the United States has declined. In 2015, the United States was the second largest coal producer in the world (812
4 MMT), after China (3,527 MMT) and followed by India (691 MMT) (IEA 2016).
5 Table 3-28: Coal Production (kt)
Year
Underground
Surface
Total
Number of Mines
Production
Number of Mines
Production
Number of Mines
Production
1990
1,683
384,244
1,656
546,808
3,339
931,052
2005
586
334.398
789
691.448
1,398
1,025,846
2011
508
313,529
788
684,807
1,296
998,337
2012
488
310,608
719
610,307
1,207
920,915
2013
395
309,546
637
581,270
1,032
890,815
2014
345
321,783
613
583,974
958
905,757
2015
305
278,342
529
534,127
834
812,469
6 Underground mines liberate CH4 from ventilation systems and from degasification systems. Ventilation systems
7 pump air through the mine workings to dilute noxious gases and ensure worker safety; these systems can exhaust
8 significant amounts of CH4 to the atmosphere in low concentrations. Degasification systems are wells drilled from
9 the surface or boreholes drilled inside the mine that remove large, often highly concentrated volumes of CH4 before,
10 during, or after mining. Some mines recover and use CH4 generated from ventilation and degasification systems,
11 thereby reducing emissions to the atmosphere.
12 Surface coal mines liberate CH4 as the overburden is removed and the coal is exposed to the atmosphere. CH4
13 emissions are normally a function of coal rank (a classification related to the percentage of carbon in the coal) and
14 depth. Surface coal mines typically produce lower-rank coals and remove less than 250 feet of overburden, so their
15 level of emissions is much lower than from underground mines.
16 In addition, CH4 is released during post-mining activities, as the coal is processed, transported, and stored for use.
17 Total CH4 emissions in 2015 were estimated to be 2,436 kt (60.9 MMT CO2 Eq.), a decline of 37 percent since 1990
18 (see Table 3-29 and Table 3-30). Of this amount, underground mines accounted for approximately 73 percent,
19 surface mines accounted for 14 percent, and post-mining emissions accounted for 13 percent.
20 Table 3-29: ChU Emissions from Coal Mining (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Underground (UG) Mining
74.2
42.0
50.2
47.3
46.2
46.4
44.6
Liberated
80.8
59.7
71.0
65.8
64.5
63.1
60.5
Recovered & Used
(6.6)
(17.7)
(20.8)
(18.5)
(18.3)
(16.7)
(15.9)
Surface Mining
10.8
11.9
11.6
10.3
9.7
9.6
8.7
Post-Mining (UG)
9.2
7.6
6.9
6.7
6.6
6.7
5.8
Post-Mining (Surface)
2.3
2.6
2.5
2.2
2.1
2.1
1.9
Total
96.5
64.1
71.2
66.5
64.6
64.8
60.9
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
21 Table 3-30: ChU Emissions from Coal Mining (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
UG Mining
2,968
1,682
2,008
1,891
1,849
1,854
1,783
Liberated
3,234
2,390
2,839
2,631
2,580
2,523
2,421
Recovered & Used
(266)
(708)
(831)
(740)
(730)
(668)
(638)
Surface Mining
430
475
465
410
388
386
347
Post-Mining (UG)
368
306
276
268
263
270
231
Post-Mining (Surface)
93
103
101
89
84
84
75
Total
3,860
2,565
2,849
2,658
2,584
2,593
2,436
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Energy 3-53
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Methodology
The methodology for estimating CH4 emissions from coal mining consists of two steps:
• Estimate emissions from underground mines. These emissions have two sources: ventilation systems and
degasification systems. They are estimated using mine-specific data, then summed to determine total CH4
liberated. The CH4 recovered and used is then subtracted from this total, resulting in an estimate of net
emissions to the atmosphere.
• Estimate CH4 emissions from surface mines and post-mining activities. Unlike the methodology for
underground mines, which uses mine-specific data, the methodology for estimating emissions from surface
mines and post-mining activities consists of multiplying basin-specific coal production by basin-specific gas
content and an emission factor.
Step 1: Estimate CH4 Liberated and CH4 Emitted from Underground Mines
Underground mines generate CH4 from ventilation systems and from degasification systems. Some mines recover
and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground
mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus
the CH4 recovered and used.
Step 1.1: Estimate CH4 Liberatedfrom Ventilation Systems
To estimate CH4 liberated from ventilation systems, EPA uses data collected through its Greenhouse Gas Reporting
Program (GHGRP) (subpart FF, "Underground Coal Mines"), data provided by the U.S. Mine Safety and Health
Administration (MSHA), and occasionally data collected from other sources on a site-specific level (e.g., state gas
production databases). Since 2011, the nation's "gassiest" underground coal mines—those that liberate more than
36,500,000 actual cubic feet of CH4 per year (about 14,700 MT CO2 Eq.)—have been required to report to EPA's
GHGRP (EPA 2016).70 Mines that report to the GHGRP must report quarterly measurements of CH4 emissions
from ventilation systems to EPA; they have the option of recording their own measurements, or using the
measurements taken by MSHA as part of that agency's quarterly safety inspections of all mines in the United States
with detectable CH4 concentrations.71
Since 2013, ventilation emission estimates have been calculated based on both GHGRP data submitted by
underground mines, and on quarterly measurement data obtained directly from MSHA for the remaining mines. The
quarterly measurements are used to determine the average daily emissions rate for the reporting year quarter.
Because not all mines report under the GHGRP, the emissions of the mines that do not report must be calculated
using the MSHA data. The MSHA data also serves as a quality assurance tool for validating GHGRP data.
Step 1.2: Estimate CH4 Liberatedfrom Degasification Systems
Particularly gassy underground mines also use degasification systems (e.g., wells or boreholes) to remove CH4
before, during, or after mining. This CH4 can then be collected for use or vented to the atmosphere. Twenty-six
mines used degasification systems in 2015, and the CH4 removed through these systems was reported to EPA's
GHGRP (EPA 2016). Based on the weekly measurements reported to EPA's GHGRP, degasification data
summaries for each mine were added together to estimate the CH4 liberated from degasification systems. Sixteen of
70 Underground coal mines report to EPA under Subpart FF of the GHGRP. In 2015, 123 underground coal mines reported to the
program.
71 MSHA records coal mine CH4 readings with concentrations of greater than 50 ppm (parts per million) CH4. Readings below
this threshold are considered non-detectable.
3-54 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 the 26 mines with degasification systems had operational CH4 recovery and use projects (see step 1.3 below), and
2 GHGRP reports show the remaining ten mines vented CH4from degasification systems to the atmosphere.72
3 Degasification volumes for the life of any pre-mining wells are attributed to the mine as emissions in the year in
4 which the well is mined through.73 EPA's GHGRP does not require gas production from virgin coal seams (coalbed
5 methane) to be reported by coal mines under subpart FF. Most pre-mining wells drilled from the surface are
6 considered coalbed methane wells and are reported under another subpart of the program (subpart W, "Petroleum
7 and Natural Gas Systems"). As a result, for the 10 mines with degasification systems that include pre-mining wells,
8 GHGRP information was supplemented with historical data from state gas well production databases (DMME 2016;
9 GSA 2016; WVGES 2016), as well as with mine-specific information regarding the dates on which the pre-mining
10 wells are mined through (JWR 2010; El Paso 2009).
11 Degasification information reported to EPA's GHGRP by underground coal mines was the primary source of data
12 used to develop estimates of CH4 liberated from degasification systems. Data reported to EPA's GHGRP were used
13 to estimate CH4 liberated from degasification systems at 21 of the 26 mines that employed degasification systems in
14 2015. For the other five mines (all with pre-mining wells from which CH4 was recovered), GHGRP data—along
15 with supplemental information from state gas production databases (DMME 2016; GSA 2016; WVGES 2016) —
16 were used to estimate CH4 liberated from degasification systems. For one mine, due to a lack of mine-provided
17 information used in prior years and a GHGRP reporting discrepancy, the CH4 liberated was based on both an
18 estimate from historical mine-provided CH4 recovery and use rates and state gas sales records.
19 Step 1.3: Estimate CH4 Recovered from Ventilation and Degasification Systems, and Utilized or
20 Destroyed (Emissions Avoided)
21 Sixteen mines had CH4 recovery and use projects in place in 2015. Fourteen of these mines sold the recovered CH4
22 to a pipeline, including one that also used CH4 to fuel a thermal coal dryer. In addition, one mine used recovered
23 CH4 for electrical power generation, and one used recovered CH4to heat mine ventilation air.
24 Ten of the 16 mines deployed degasification systems in 2015; for those mines, estimates of CH4 recovered from the
25 systems were exclusively based on GHGRP data. Based on weekly measurements, the GHGRP degasification
26 destruction data summaries for each mine were added together to estimate the CH4 recovered and used from
27 degasification systems.
28 All 10 mines with degasification systems used pre-mining wells as part of those systems, but only four of them
29 intersected pre-mining wells in 2015. GHGRP and supplemental data were used to estimate CH4 recovered and used
30 at two of these four mines; supplemental data alone (GSA 2016) were used for the other two mines, which reported
31 to EPA's GHGRP as a single entity. Supplemental information was used for these four mines because estimating
32 CH4 recovery and use from pre-mining wells requires additional data (not reported under subpart FF of EPA's
33 GHGRP; see discussion in step 1.2 above) to account for the emissions avoided. The supplemental data came from
34 state gas production databases as well as mine-specific information on the timing of mined-through pre-mining
35 wells.
36 GHGRP information was not used to estimate CH4 recovered and used at two mines. At one of these mines, a
37 portion of reported CH4 vented was applied to an ongoing mine air heating project. Because of a lack of mine-
38 provided information used in prior years and a GHGRP reporting discrepancy, the 2015 CH4 recovered and used
39 from pre-mining wells at the other mine was based on an estimate from historical mine-provided CH4 recovery and
40 use rates. Emissions recovered and used from the active mine degasification system were estimated based on a state
41 gas production data information system.
42 In 2015, one mine destroyed a portion of its CH4 emissions from ventilation systems using thermal oxidation
43 technology. The amount of CH4 recovered and destroyed by the project was determined through publicly-available
44 emission reduction project information (ACR 2016).
72 Several of the mines venting CH4 from degasification systems use a small portion the gas to fuel gob well blowers in remote
locations where electricity is not available. However, this CH4use is not considered to be a formal recovery and use project.
73 A well is "mined through" when coal mining development or the working face intersects the borehole or well.
Energy 3-55
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1 Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities
2 Mine-specific data are not available for estimating CH4 emissions from surface coal mines or for post-mining
3 activities. For surface mines, basin-specific coal production obtained from the Energy Information Administration's
4 Annual Coal Report (EIA 2016) was multiplied by basin-specific CH4 contents (EPA 1996, 2005) and a 150 percent
5 emission factor (to account for CH4from over- and under-burden) to estimate CH4 emissions (King 1994; Saghafi
6 2013). For post-mining activities, basin-specific coal production was multiplied by basin-specific gas contents and a
7 mid-range 32.5 percent emission factor for CH4 desorption during coal transportation and storage (Creedy 1993).
8 Basin-specific in situ gas content data were compiled from AAPG (1984) and USBM (1986).
9 Uncertainty and Time-Series Consistency
10 A quantitative uncertainty analysis was conducted for the coal mining source category using the IPCC-
11 recommended Approach 2 uncertainty estimation methodology. Because emission estimates from underground
12 ventilation systems were based on actual measurement data from EPA's GHGRP or from MSHA, uncertainty is
13 relatively low. A degree of imprecision was introduced because the ventilation air measurements used were not
14 continuous but rather quarterly instantaneous readings that were used to determine the average daily emissions rate
15 for the quarter. Additionally, the measurement equipment used can be expected to have resulted in an average of 10
16 percent overestimation of annual CH4 emissions (Mutmansky & Wang 2000). GHGRP data were used for a
17 significant number of the mines that reported their own measurements to the program beginning in 2013; however,
18 the equipment uncertainty is applied to both GHGRP and MSHA data.
19 Estimates of CH4 recovered by degasification systems are relatively certain for utilized CH4 because of the
20 availability of GHGRP data and gas sales information. Many of the recovery estimates use data on wells within 100
21 feet of a mined area. However, uncertainty exists concerning the radius of influence of each well. The number of
22 wells counted, and thus the avoided emissions, may vary if the drainage area is found to be larger or smaller than
23 estimated.
24 EPA's GHGRP requires weekly CH4 monitoring of mines that report degasification systems, and continuous CH4
25 monitoring is required for utilized CH4 on- or off-site. Since 2012, GHGRP data have been used to estimate CH4
26 emissions from vented degasification wells, reducing the uncertainty associated with prior MSHA estimates used for
27 this subsource. Beginning in 2013, GHGRP data were also used for determining CH4 recovery and use at mines
28 without publicly available gas usage or sales records, which has reduced the uncertainty from previous estimation
29 methods that were based on information from coal industry contacts.
30 In 2015 a level of uncertainty was introduced with the estimated values of recovered methane from two of the mines
31 with degasification systems. An increased level of uncertainty was applied to these two mines, but the change had
32 little impact on the overall uncertainty.
33 Surface mining and post-mining emissions are associated with considerably more uncertainty than underground
34 mines, because of the difficulty in developing accurate emission factors from field measurements. However, since
35 underground emissions constitute the majority of total coal mining emissions, the uncertainty associated with
36 underground emissions is the primary factor that determines overall uncertainty. The results of the Approach 2
37 quantitative uncertainty analysis are summarized in Table 3-31. Coal mining CH4 emissions in 2015 were estimated
38 to be between 53.3 and 70.6 MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 12.5 percent
39 below to 15.9 percent above the 2015 emission estimate of 60.9 MMT CO2 Eq.
40 Table 3-31: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from Coal
41 Mining (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Coal mining
CH4
60.9
53.3 70.6 -12.5% +15.9%
a Range of emission estimates predicted by Monte Carlo stochastic simulation for a 95 percent confidence interval.
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Methodological recalculations were applied to the entire time-series to ensure consistency from 1990 through 2015.
Details on the emission trends through time are described in more detail in the methodology section.
Recalculations Discussion
For the current Inventory, revisions were made to the 2013 and 2014 underground liberated and recovered
emissions. In 2014 recovered emissions reported to GHGRP for a mine located in Virginia were too high to be valid.
EPA estimated recovered emissions for this mine based on a five-year historical average. In 2016 EPA became
aware of the availability of the Virginia Division of Gas and Oil Data Information System (DGO DIS) and was able
to estimate recovered degasification emissions for the Virginia mine based on published well production. The well
production data was more accurate than the reported values in 2013, 2014, and 2015; thus 2013 and 2014 were
revised. The DGO DIS will continue to be used in future years until the GHGRP reported values can be verified for
this mine.
3.5 Abandoned Underground Coal Mines (IPCC
Source Category lBla)
Underground coal mines contribute the largest share of coal mine methane (CMM) emissions, with active
underground mines the leading source of underground emissions. However, mines also continue to release CH4 after
closure. As mines mature and coal seams are mined through, mines are closed and abandoned. Many are sealed and
some flood through intrusion of groundwater or surface water into the void. Shafts or portals are generally filled
with gravel and capped with a concrete seal, while vent pipes and boreholes are plugged in a manner similar to oil
and gas wells. Some abandoned mines are vented to the atmosphere to prevent the buildup of CH4 that may find its
way to surface structures through overburden fractures. As work stops within the mines, CH4 liberation decreases
but it does not stop completely. Following an initial decline, abandoned mines can liberate CH4 at a near-steady rate
over an extended period of time, or, if flooded, produce gas for only a few years. The gas can migrate to the surface
through the conduits described above, particularly if they have not been sealed adequately. In addition, diffuse
emissions can occur when CH4 migrates to the surface through cracks and fissures in the strata overlying the coal
mine. The following factors influence abandoned mine emissions:
• Time since abandonment;
• Gas content and adsorption characteristics of coal;
• CH4 flow capacity of the mine;
• Mine flooding;
• Presence of vent holes; and
• Mine seals.
Annual gross abandoned mine CH4 emissions ranged from 7.2 to 10.8 MMT CO2 Eq. from 1990 through 2015,
varying, in general, by less than 1 percent to approximately 19 percent from year to year. Fluctuations were due
mainly to the number of mines closed during a given year as well as the magnitude of the emissions from those
mines when active. Gross abandoned mine emissions peaked in 1996 (10.8 MMT CO2 Eq.) due to the large number
of gassy mine74 closures from 1994 to 1996 (72 gassy mines closed during the three-year period). In spite of this
rapid rise, abandoned mine emissions have been generally on the decline since 1996. Since 2002, there have been
fewer than twelve gassy mine closures each year. There were six gassy mine closures in 2015. In 2015, gross
abandoned mine emissions increased slightly from 8.7 to 9.0 MMT CO2 Eq. (see Table 3-32 and Table 3-33). Gross
emissions are reduced by CH4 recovered and used at 40 mines, resulting in net emissions in 2015 of 6.4 MMT CO2
Eq.
74 A mine is considered a "gassy" mine if it emits more than 100 thousand cubic feet of CH4 per day (100 mcfd).
Energy 3-57
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1 Table 3-32: ChU Emissions from Abandoned Coal Mines (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Abandoned Underground Mines
7.2
8.4
9.3
8.9
8.8
8.7
9.0
Recovered & Used
+
1.8 ?:¦<
2.9
2.7
2.6
2.4
2.6
Total
7.2
6.6
6.4
6.2
6.2
6.3
6.4
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
2
3 Table 3-33: ChU Emissions from Abandoned Coal Mines (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Abandoned Underground Mines
288
334
373
358
353
350
359
Recovered & Used
+
70
116
109
104
97
102
Total
288
264
257
249
249
253
256
+ Does not exceed 0.5 kt
Note: Totals may not sum due to independent rounding.
4 Methodology
5 Estimating CH4 emissions from an abandoned coal mine requires predicting the emissions of a mine from the time
6 of abandonment through the inventory year of interest. The flow of CH4 from the coal to the mine void is primarily
7 dependent on the mine's emissions when active and the extent to which the mine is flooded or sealed. The CH4
8 emission rate before abandonment reflects the gas content of the coal, the rate of coal mining, and the flow capacity
9 of the mine in much the same way as the initial rate of a water-free conventional gas well reflects the gas content of
10 the producing formation and the flow capacity of the well. A well or a mine which produces gas from a coal seam
11 and the surrounding strata will produce less gas through time as the reservoir of gas is depleted. Depletion of a
12 reservoir will follow a predictable pattern depending on the interplay of a variety of natural physical conditions
13 imposed on the reservoir. The depletion of a reservoir is commonly modeled by mathematical equations and mapped
14 as a type curve. Type curves which are referred to as decline curves have been developed for abandoned coal mines.
15 Existing data on abandoned mine emissions through time, although sparse, appear to fit the hyperbolic type of
16 decline curve used in forecasting production from natural gas wells.
17 In order to estimate CH4 emissions over time for a given abandoned mine, it is necessary to apply a decline function,
18 initiated upon abandonment, to that mine. In the analysis, mines were grouped by coal basin with the assumption
19 that they will generally have the same initial pressures, permeability and isotherm. As CH4 leaves the system, the
20 reservoir pressure (Pr) declines as described by the isotherm's characteristics. The emission rate declines because
21 the mine pressure (Pw) is essentially constant at atmospheric pressure for a vented mine, and the productivity index
22 (PI), which is expressed as the flow rate per unit of pressure change, is essentially constant at the pressures of
23 interest (atmospheric to 30 psia). The CH4 flow rate is determined by the laws of gas flow through porous media,
24 such as Darcy's Law. A rate-time equation can be generated that can be used to predict future emissions. This
25 decline through time is hyperbolic in nature and can be empirically expressed as:
26 q = gi(l + M>iO(_1/i0
27 where,
28
q
Gas flow rate at time t in million cubic feet per day (mmcfd)
29
Initial gas flow rate at time zero (tQ), mmcfd
30
b
The hyperbolic exponent, dimensionless
31
Dj
Initial decline rate, 1/yr
32
t
Elapsed time from tQ (years)
33 This equation is applied to mines of various initial emission rates that have similar initial pressures, permeability and
34 adsorption isotherms (EPA 2004).
3-58 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
The decline curves created to model the gas emission rate of coal mines must account for factors that decrease the
rate of emissions 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
will no longer have any measurable CH4 emissions. Based on this assumption, an average decline rate for flooded
mines was established by fitting a decline curve to emissions from field measurements. An exponential equation was
developed from emissions data measured at eight abandoned mines known to be filling with water located in two of
the five basins. Using a least squares, curve-fitting algorithm, emissions data were matched to the exponential
equation shown below. There was not enough data to establish basin-specific equations as was done with the vented,
non-flooding mines (EPA 2004).
q = qie^Dt)
where,
q = Gas flow rate at time t in mmcfd
qi = Initial gas flow rate at time zero (tQ), mmcfd
D = Decline rate, 1/yr
t = Elapsed time from to (years)
Seals have an inhibiting effect on the rate of flow of CH4 into the atmosphere compared to the flow rate that would
exist if the mine had an open vent. The total volume emitted will be the same, but emissions will occur over a longer
period of time. The methodology, therefore, treats the emissions prediction from a sealed mine similarly to the
emissions prediction from a vented mine, but uses a lower initial rate depending on the degree of sealing. A
computational fluid dynamics simulator was used with the conceptual abandoned mine model to predict the decline
curve for inhibited flow. The percent sealed is defined as 100 x (1 - [initial emissions from sealed mine / emission
rate at abandonment prior to sealing]). Significant differences are seen between 50 percent, 80 percent and 95
percent closure. These decline curves were therefore used as the high, middle, and low values for emissions from
sealed mines (EPA 2004).
For active coal mines, those mines producing over 100 thousand cubic feet per day (mcfd) account for about 98
percent of all CH4 emissions. This same relationship is assumed for abandoned mines. It was determined that the
524 abandoned mines closed after 1972 produced emissions greater than 100 mcfd when active. Further, the status
of 302 of the 524 mines (or 58 percent) is known to be either: 1) vented to the atmosphere; 2) sealed to some degree
(either earthen or concrete seals); or, 3) flooded (enough to inhibit CH4 flow to the atmosphere). The remaining 42
percent of the mines whose status is unknown were placed in one of these three categories by applying a probability
distribution analysis based on the known status of other mines located in the same coal basin (EPA 2004).
Table 3-34: Number of Gassy Abandoned Mines Present in U.S. Basins in 2015, grouped by
Class according to Post-Abandonment State
Basin
Sealed
Vented
Flooded
Total
Known
Unknown
Total Mines
Central Appl.
40
26
52
118
143
261
Illinois
34
3
14
51
30
81
Northern Appl.
46
22
16
84
39
123
Warrior Basin
0
0
16
16
0
16
Western Basins
28
3
2
33
10
43
Total
148
54
100
302
222
524
Inputs to the decline equation require the average emission rate and the date of abandonment. Generally this data is
available for mines abandoned after 1971; however, such data are largely unknown for mines closed before 1972.
Information that is readily available, such as coal production by state and county, is helpful but does not provide
enough data to directly employ the methodology used to calculate emissions from mines abandoned before 1972. It
is assumed that pre-1972 mines are governed by the same physical, geologic, and hydrologic constraints that apply
to post-1971 mines; thus, their emissions may be characterized by the same decline curves.
During the 1970s, 78 percent of CH4 emissions from coal mining came from seventeen counties in seven states. In
addition, mine closure dates were obtained for two states, Colorado and Illinois, for the hundred year period
extending from 1900 through 1999. The data were used to establish a frequency of mine closure histogram (by
Energy 3-59
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1 decade) and applied to the other five states with gassy mine closures. As a result, basin-specific decline curve
2 equations were applied to the 145 gassy coal mines estimated to have closed between 1920 and 1971 in the United
3 States, representing 78 percent of the emissions. State-specific, initial emission rates were used based on average
4 coal mine CH4 emissions rates during the 1970s (EPA 2004).
5 Abandoned mine emission estimates are based on all closed mines known to have active mine CH4 ventilation
6 emission rates greater than 100 mcfd at the time of abandonment. For example, for 1990 the analysis included 145
7 mines closed before 1972 and 258 mines closed between 1972 and 1990. Initial emission rates based on MSHA
8 reports, time of abandonment, and basin-specific decline curves influenced by a number of factors were used to
9 calculate annual emissions for each mine in the database (MSHA 2016). Coal mine degasification data are not
10 available for years prior to 1990, thus the initial emission rates used reflect ventilation emissions only for pre-1990
11 closures. CH4 degasification amounts were added to the quantity of CH4 vented to determine the total CH4 liberation
12 rate for all mines that closed between 1992 and 2015. Since the sample of gassy mines is assumed to account for 78
13 percent of the pre-1972 and 98 percent of the post-1971 abandoned mine emissions, the modeled results were
14 multiplied by 1.22 and 1.02 to account for all U.S. abandoned mine emissions.
15 From 1993 through 2015, emission totals were downwardly adjusted to reflect abandoned mine CH4 emissions
16 avoided from those mines. The Inventory totals were not adjusted for abandoned mine reductions from 1990 through
17 1992 because no data was reported for abandoned coal mining CH4 recovery projects during that time.
is Uncertainty and Time-Series Consistency
19 A quantitative uncertainty analysis was conducted to estimate the uncertainty surrounding the estimates of emissions
20 from abandoned underground coal mines. The uncertainty analysis described below provides for the specification of
21 probability density functions for key variables within a computational structure that mirrors the calculation of the
22 inventory estimate. The results provide the range within which, with 95 percent certainty, emissions from this source
23 category are likely to fall.
24 As discussed above, the parameters for which values must be estimated for each mine in order to predict its decline
25 curve are: 1) the coal's adsorption isotherm; 2) CH4 flow capacity as expressed by permeability; and 3) pressure at
26 abandonment. Because these parameters are not available for each mine, a methodological approach to estimating
27 emissions was used that generates a probability distribution of potential outcomes based on the most likely value and
28 the probable range of values for each parameter. The range of values is not meant to capture the extreme values, but
29 rather values that represent the highest and lowest quartile of the cumulative probability density function of each
30 parameter. Once the low, mid, and high values are selected, they are applied to a probability density function.
31 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 3-35. Annual abandoned
32 coal mine CH4 emissions in 2015 were estimated to be between 5.2 and 7.9 MMT CO2 Eq. at a 95 percent
33 confidence level. This indicates a range of 18 percent below to 24 percent above the 2015 emission estimate of 6.4
34 MMT CO2 Eq. One of the reasons for the relatively narrow range is that mine-specific data is available for use in the
35 methodology for mines closed after 1972. Emissions from mines closed prior to 1972 have the largest degree of
36 uncertainty because no mine-specific CH4 liberation rates exist.
37 Table 3-35: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from
38 Abandoned Underground Coal Mines (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Abandoned Underground
Coal Mines
CH4
6.4
5.2 7.9
-18% +24%
a Range of emission estimates predicted by Monte Carlo Simulation for a 95 percent confidence interval.
39
40 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
41 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
42 above.
3-60 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
3.6 Petroleum Systems (IPCC Source Category
lB2a)
EPA is seeking stakeholder feedback on a number of options for updating the emissions estimates in this section.
See Recalculations Discussion below for methods used in the development of the public review draft, and EPA's
memos on updates under consideration for other options being considered:
https://www.epa.gov/ghgemissions/updates-under-consideration-petroleum-and-natural-gas-systems-1990-2015-
ghg-inventory. It is likely that the methods presented here and the calculated emissions totals will change due to
revisions between this public review draft of the 1990-2015 Inventory and the final 1990-2015 Inventory, but
impacts on the total emissions estimate are expected to be minor. See the recalculations discussion below for more
details. Note that 2014 results for CO2 emissions are being used as a preliminary estimate for 2015 while EPA
reviews the activity data and estimation methods for consistency with data sources used for methane calculations.
Methane emissions from petroleum systems are primarily associated with onshore and offshore crude oil production,
transportation, and refining operations. During these activities, CH4 is released to the atmosphere as fugitive
emissions, vented emissions, emissions from operational upsets, and emissions from fuel combustion. Fugitive and
vented CO2 emissions from petroleum systems are primarily associated with crude oil production and refining
operations but are negligible in transportation operations. Total CH4 emissions from petroleum systems in 2015
were 41.5 MMT CO2 Eq. (1,660 kt). Total CO2 emissions from petroleum systems in 2015 were 3.0 MMT CO2 Eq.
(3,041 kt).
Production Field Operations. Production field operations account for approximately 98 percent of total CH4
emissions from petroleum systems. Vented CH4 from field operations account for approximately 85 percent of the
net emissions from the production sector, fugitive emissions are approximately 7 percent, uncombusted CH4
emissions (i.e., unburned fuel) account for approximately 8 percent, and process upset emissions are 0.2 percent.
The predominant sources of emissions from production field operations are pneumatic controllers, offshore oil
platforms, associated gas venting and flaring, oil tanks, gas engines, chemical injection pumps, hydraulically
fractured oil well completions, and oil wellheads. These sources alone emit over 90 percent of the production field
operations emissions. The remaining 10 percent of the emissions are distributed among around 20 additional
activities.
Since 1990, CH4 emissions from production field operations have decreased by nearly 30 percent. Production
segment methane emissions have decreased by around 8 percent from 2014 levels, primarily due to decreases in
emissions from associated gas venting and flaring.
Vented CO2 associated with production field operations account for approximately 99 percent of the total CO2
emissions from production field operations, while fugitive and process upsets together account for approximately 1
percent of the emissions. The principal sources of CO2 emissions are oil tanks, pneumatic controllers, chemical
injection pumps, and offshore oil platforms. These four sources together account for slightly over 97 percent of the
non-combustion CO2 emissions from production field operations, while the remaining 3 percent of the emissions is
distributed among around 20 additional activities. Due to the activity data source for CO2 from flaring, it is not
possible to develop separate estimates for flaring occurring in natural gas production and flaring occurring in oil
production. Total CO2 emissions from flaring for both natural gas and oil were 18.0 MMT CO2 in 2015 and are
included in the Natural Gas Systems estimates.
Crude Oil Transportation. Crude oil transportation activities account for approximately 1 percent of total CH4
emissions from the oil industry. Venting emissions, including from tanks, truck loading, rail loading, and marine
vessel loading operations account for 89 percent of CH4 emissions from crude oil transportation. Fugitive emissions,
almost entirely from floating roof tanks, account for approximately 11 percent of CH4 emissions from crude oil
transportation.
Since 1990, CH4 emissions from transportation have increased by 28 percent. However, because emissions from
crude oil transportation account for such a small percentage of the total emissions from the petroleum industry, this
has had little impact on the overall emissions. Methane emissions from transportation in 2015 increased by
approximately 2 percent from 2014 levels.
Energy 3-61
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
('rude Oil Refining. Crude oil refining processes and systems account for approximately 2 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,
incomplete combustion accounts for slightly over 51 percent of the CH4 emissions, while vented and fugitive
emissions account for approximately 34 and 15 percent, respectively. Flaring accounts for 82 percent of combustion
CH4 emissions. Refinery system blowdowns for maintenance and process vents are the primary venting contributors
(97 percent). Most of the fugitive CH4 emissions from refineries are from equipment leaks and storage tanks (87
percent).
Methane emissions from refining of crude oil have increased by approximately 7 percent since 1990; however,
similar to the transportation subcategory, this increase has had little effect on the overall emissions of CH4. Since
1990, CH4 emissions from crude oil refining have fluctuated between 24 and 28 kt.
Flare emissions from crude oil refining accounts for slightly more than 93 percent of the total CO2 emissions in
petroleum systems. Refinery CO2 emissions increased by approximately 9 percent from 1990 to 2015.
Table 3-36: ChU Emissions from Petroleum Systems (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Production Field Operations
(Potential)
57.5
48.1
50.4
48.7
46.5
44.9
41.4
Pneumatic controller venting
19.1
17.5
16.7
15.4
18.5
19.2
19.3
Offshore platforms
5.3
4.6
4.7
4.7
4.7
4.7
4.7
Associated gas venting and
flaring
17.7
14.8
16.6
15.1
9.3
6.2
3.8
Tank venting
8.4
4.0
3.2
3.3
3.4
3.5
3.5
Gas Engines
2.1
1.7
1.9
2.1
2.1
2.3
2.3
Production Voluntary
Reductions
-
(0.9)
(1.1)
(1.1)
(0.8)
(0.8)
(0.8)
Production Field Operations
(Net)
57.5
47.2
49.3
47.6
45.8
44.1
40.6
Crude Oil Transportation
0.2
0.1
0.1
0.2
0.2
0.2
0.2
Refining
0.6
0.7
0.7
0.7
0.6
0.6
0.6
Total
58.3
48.0
50.1
48.4
46.6
44.9
41.5
Notes: Totals may not sum due to independent rounding. Parentheses indicate emissions reductions.
able 3-37: ChU Emissions from Petroleum Systems (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Production Field Operations
(Potential)
2,300
1,925
2,016
1,948
1,862
1,795
1,657
Pneumatic controller venting
765
700
666
615
739
766
772
Offshore platforms
211
185
188
188
188
188
188
Associated gas venting and
flaring
708
592
666
605
372
246
153
Tank venting
335
161
127
133
137
141
138
Gas Engines
85
69
78
82
86
90
90
Production Voluntary
Reductions
-
(36)
(45)
(45)
(31)
(31)
(31)
Production Field Operations
(Net)
2,300
1,889
1,971
1,902
1,831
1,764
1,626
Crude Oil Transportation
7
5
5
6
7
8
8
Refining
24
27
28
27
26
24
26
Total
2,330
1,921
2,004
1,935
1,864
1,796
1,660
Notes: Totals may not sum due to independent rounding. Parentheses indicate emissions reductions.
Table 3-38: CO2 Emissions from Petroleum Systems (MMT CO2 Eq.)
3-62 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Activity 1990 2005 2011 2012 2013 2014 2015
Production Field Operations
Pneumatic controller venting
0.1
+
0.1
+
0.1
+
0.1
+
0.1
+
0.1
+
0.1
+
Tank venting
Misc. venting & fugitives
Wellhead fugitives
Process upsets
+
0.1
+
+
+
+
+
+
+
0.1
+
+
+
0.1
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Crude Refining
3.2
3.6
3.8
3.4
3.1
3.0
3.0
Total
3.3
3.7
3.9
3.5
3.2
3.0
3.0
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Note: 2014 data used as proxy for 2015 while EPA reviews the available activity data and estimation
methods.
1 Table 3-39: CO2 Emissions from Petroleum Systems (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Production Field Operations
117
99
104
99
94
90
90
Pneumatic controller venting
43
39
37
34
41
43
43
Tank venting
15
7
6
6
6
6
6
Misc. venting & fugitives
55
49
57
55
43
37
37
Wellhead fugitives
4
3
3
4
4
4
4
Process upsets
0.2
0.1
0.2
0.2
0.2
0.2
0.2
Crude Refining
3,172
3,600
3,811
3,420
3,146
2,951
2,951
Total
3,288
3,699
3,915
3,519
3,240
3,041
3,041
Note: Totals may not sum due to independent rounding.
Note: 2014 data used as proxy for 2015 while EPA reviews the available activity data and estimation
methods.
2 Methodology
3 The estimates of CH4 emissions from petroleum systems are largely based on GRI/EPA 1996, EPA 1999,
4 Drillinglnfo, and EPA's GHGRP data (RY 2010 through 2015). Petroleum Systems includes emission estimates for
5 activities occurring in petroleum systems from the oil wellhead through crude oil refining, including activities for
6 crude oil production field operations, crude oil transportation activities, and refining operations. Annex 3.5 provides
7 detail on the emission estimates for these activities. The estimates of CH4 emissions from petroleum systems do not
8 include emissions downstream of oil refineries because these emissions are considered to be negligible.
9 Emissions are estimated for each activity by multiplying emission factors (e.g., emission rate per equipment or per
10 activity) by the corresponding activity data (e.g., equipment count or frequency of activity).
11 References for emission factors include Methane Emissions from the Natural Gas Industry by the Gas Research
12 Institute and EPA (EPA/GRI 1996a-d), Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999),
13 Drillinglnfo (2015), consensus of industry peer review panels, BOEMRE and BOEM reports (BOEMRE 2004;
14 BOEM 2011), analysis of BOEMRE data (EPA 2005; BOEMRE 2004), and the GHGRP (2010 through 2015).
15 The emission factors for pneumatic controllers venting and chemical injection pumps were developed using EPA's
16 GHGRP data for reporting year 2014. The emission factors for tanks, and associated gas venting and flaring were
17 developed using EPA's GHGRP data for reporting year 2015. Emission factors for hydraulically fractured (HF) oil
18 well completions (controlled and uncontrolled) were developed using data analyzed for the 2015 NSPS OOOOa
19 proposal (EPA 2015a). For offshore oil production, two emission factors were calculated using data collected for all
20 federal offshore platforms (EPA 2015b; BOEM 2014), one for oil platforms in shallow water, and one for oil
21 platforms in deep water. For all sources, emission factors are held constant for the period 1990 through 2015.
22 Emission factors from EPA 1999 are used for all other production and transportation activities.
23 References for activity data include Drillinglnfo (2016), the Energy Information Administration annual and monthly
24 reports (EIA 1990 through 2016), (EIA 1995 through 2016a, 2016b), Methane Emissions from the Natural Gas
25 Industry by the Gas Research Institute and EPA (EPA/GRI 1996a-d), Estimates of Methane Emissions from the U.S.
Energy 3-63
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9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
Oil Industry (EPA 1999), consensus of industry peer review panels, BOEMRE and BOEM reports (BOEMRE 2004;
BOEM 2011), analysis of BOEMRE data (EPA 2005; BOEMRE 2004), the Oil & Gas Journal (OGJ 2016), the
Interstate Oil and Gas Compact Commission (IOGCC 2012), the United States Army Corps of Engineers, (1995
through 2016), and the GHGRP (2010 through 2015).
For many sources, complete activity data were not available for all years of the time series. In such cases, one of
three approaches was employed to estimate values, consistent with IPCC good practice. Where appropriate, the
activity data were calculated from related statistics using ratios developed based on EPA 1996, and/or GHGRP data.
For floating roof tanks, the activity data were held constant from 1990 through 2015 based on EPA (1999). In some
cases, activity data are developed by interpolating between recent data points (such as from GHGRP) and earlier
data points, such as from GRI 1996. Lastly, the previous year's data were used for domestic barges and tankers as
current year were not yet available. For offshore production, the number of platforms in shallow water and the
number of platforms in deep water are used as activity data and are taken from Bureau of Ocean Energy
Management (BOEM) (formerly Bureau of Ocean Energy Management, Regulation, and Enforcement [BOEMRE])
datasets (BOEM 2011a,b,c).
For petroleum refining activities, 2010 to 2015 emissions were directly obtained from EPA's GHGRP. All U.S.
refineries have been required to report CH4 and CO2 emissions for all major activities starting with emissions that
occurred in 2010. The national totals of these emissions for each activity were used for the 2010 to 2015 emissions.
The national emission totals for each activity were divided by refinery feed rates for those inventory years to
develop average activity-specific emission factors, which were used to estimate national emissions for each refinery
activity from 1990 to 2009 based on national refinery feed rates for each year (EPA 2015c).
The inventory estimate for Petroleum Systems takes into account Natural Gas STAR reductions. Voluntary
reductions included in the Petroleum Systems calculations were those reported to Natural Gas STAR for the
following activities: artificial lift - gas lift; artificial lift - use compression; artificial lift - use pumping unit;
consolidate crude oil production and water storage tanks; lower heater-treater temperature; re-inject gas for
enhanced oil recovery; re-inject gas into crude; and route casinghead gas to vapor recovery unit or compressor.
The methodology for estimating CO2 emissions from petroleum systems includes calculation of vented, fugitive, and
process upset emissions sources from 26 activities for crude oil production field operations and three activities from
petroleum refining. Generally, emissions are estimated for each activity by multiplying CO2 emission factors by
their corresponding activity data. The production field operations emission factors for CO2 are generally estimated
by multiplying the CH4 emission factors by a conversion factor, which is the ratio of CO2 content and CH4 content
in produced associated gas. One exception to this methodology are emission factors for offshore oil production
(shallow and deep water), which were derived using data from BOEM (EPA 2015b; BOEM 2014). For the three
petroleum refining activities (i.e., flares, asphalt blowing, and process vents); the CO2 emissions data for 2010 to
2014 were directly obtained from the GHGRP. The 2010 to 2013 CO2 emissions data from GHGRP along with the
refinery feed data for 2010 to 2013 were used to derive CO2 emission factors (i.e., sum of activity emissions/sum of
refinery feed) which were then applied to the annual refinery feed to estimate CO2 emissions for 1990 to 2009.
Uncertainty and Time-Series Consistency
EPA's planned uncertainty analysis is discussed in the Planned Improvements section.
New data available starting in 2010 for refineries and in 2011 for other sources have improved estimates of
emissions from Petroleum Systems. Many of the previously available data sets were collected in the 1990s. To
develop a consistent time series for 1990-2015, for sources with new data, EPA reviewed available information on
factors that may have resulted in changes over the time series (e.g. regulations, voluntary actions) and requested
stakeholder feedback on trends as well. For most sources, EPA developed annual data for 1993-2010 by
interpolating activity data or emission factors or both between 1992 and 2011 data points.
Information on time-series consistency for sources updated in this public review draft can be found in the
Recalculation Discussion below, with additional detail provided in the 2017 Production Memo. For information on
other sources, please see the Methodology Discussion above.
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QA/QC and Verification Discussion
The petroleum system emission estimates in the Inventory are continually being reviewed and assessed to determine
whether emission factors and activity factors accurately reflect current industry practices. A QA/QC analysis was
performed for data gathering and input, documentation, and calculation. QA/QC checks are consistently conducted
to minimize human error in the model calculations. EPA performs a thorough review of information associated with
new studies, GHGRP data, regulations, public webcasts, and the Natural Gas STAR Program to assess whether the
assumptions in the Inventory are consistent with current industry practices. The EPA has a multi-step data
verification process for GHGRP data, including automatic checks during data-entry, statistical analyses on
completed reports, and staff review of the reported data. Based on the results of the verification process, the EPA
follows up with facilities to resolve mistakes that may have occurred.
As in previous years, EPA conducted early engagement and communication with stakeholders on updates prior to
public review. In December 2016 and January 2017, EPA stakeholder webinars on GHG data for oil and gas. In
early 2017, EPA released memos detailing updates under consideration and requesting stakeholder feedback. EPA
discusses preliminary stakeholder feedback received in the public review draft. EPA continues to receive and review
feedback on the options presented, and may revise the recalculations in the final 1990-2015 Inventory based on that
feedback.
In recent years, several studies have measured emissions at the source level and at the national or regional level and
calculated emissions estimates that may differ from the GHG Inventory. There are a variety of potential uses of data
from new studies, including replacing a previous estimate or factor, verifying or QA of an existing estimate or
factor, and identifying areas for updates.
In general, there are two major types of studies related to oil and gas greenhouse gas data: studies that focus on
measurement or quantification of emissions from specific activities, processes and equipment, and studies that focus
on verification of estimates through inverse modeling. The first type of study can lead to direct improvements to or
verification of GHG Inventory estimates. In the past few years, EPA has reviewed and in many cases, incorporated
data from these data sources. The second type of study can provide general indications on potential over- and
under-estimates. A key challenge in using these types of studies to assess GHG Inventory results is having a relevant
basis for comparison. In an effort to improve the ability to compare the national-level GHG inventory with
measurement results that may be at other scales, a team at Harvard University along with EPA and other coauthors
developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly
temporal resolution, and detailed scale-dependent error characterization.75 The inventory is designed to be
consistent with the 2016 U.S. EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks (1990-2014) estimates
for the year 2012, which presents national totals for different source types.76
Recalculations Discussion
The EPA received information and data related to the emission estimates through the Inventory preparation process,
previous Inventories' formal public notice periods, GHGRP reporting, and new studies. The EPA carefully
evaluated relevant information available, and made updates to the production segment methodology for this public
review draft of the Inventory including revised well count, equipment count, and pneumatic controller activity data,
and revised activity and emissions data for tanks and associated gas venting and flaring.
In January 2017, the EPA released a draft memorandum, Inventory of U.S. Greenhouse Gas Emissions and Sinks
1990-2014: Revisions under Consideration for Natural Gas and Petroleum Systems Production Emissions, referred
to below as "2017 Production Memo," that discussed the changes under consideration and requested stakeholder
feedback on those changes.77 In this public review draft of the Inventory, EPA has selected from the options
presented in the 2017 Production Memo to develop emissions estimates. EPA continues to receive and review
75 See .
76 See .
77 See .
Energy 3-65
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1 feedback on the options presented, and it is likely that the methods presented here and the calculated emissions totals
2 will change due to revisions between this public review draft of the 2017 GHG Inventory (1990-2015) and the final
3 2017 GHG Inventory, but impacts on the total emissions estimate are expected to be minor.
4 The combined impact of revisions to 2014 petroleum production segment emissions, compared to the previous
5 Inventory, is a decrease in CH4 emissions from 67.4 to 44.1 MMT CO2 Eq. (23 MMT CO2 Eq., or 35 percent).
6 The recalculations resulted in an average increase in emission estimates across the 1990 through 2014 time series,
7 compared to the previous Inventory, of 3.2 MMT CO2 Eq, or 7 percent. The recalculations resulted in increases in
8 the emission estimate in early years of the time series, primarily due to recalculations related to associated gas
9 venting and flaring, and decreases in the emission estimate in later years of the time series, primarily due to
10 recalculations for pneumatic controllers.
11 Production
12 This section references the memorandum, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015:
13 Revisions under Consideration for Natural Gas and Petroleum Systems Production Emissions (2017 Production
14 Memo). This memorandum contains further details and documentation of recalculations.
15 Well Counts
16 EPA has used a more recent version of the Drillinglnfo data set to update well counts data in this public review draft
17 of the Inventory. For more information, see pages 19 through 20 of the 2017 Production Memo. This update
18 resulted in a decrease of 34 percent in oil well counts on average over the time series. EPA received feedback that
19 stakeholders generally support the revision as introducing more consistency with recently published well count
20 estimates. EPA continues to compare current estimates with stakeholder well counts also derived from Drillinglnfo,
21 investigate differences, and may further revise the well count estimates in the final Inventory, potentially resulting in
22 additional (but likely minor) decreases in calculated emissions from sources that rely on oil well counts for activity
23 data (e.g., pneumatic controllers, equipment leaks, and storage tanks).
24
25 Table 3-40: Oil Well Count Data
Oil Well Count
1990
2005
2011
2012
2013
2014
2015
Number of Oil Wells
595,109
497,744
560,005
584,515
601,670
619,818
607,559
Previous Estimated Number of
Oil Wells
904,675
764,371
838,899
867,375
884,652
898,268
NA
Percent Change in Counts
-34%
-35%
-33%
-33%
-32%
-31%
NA
NA - Not Applicable
26 Tanks
27 In the public review draft, EPA developed emissions estimates for oil tanks using GHGRP data and an equipment-
28 based approach. For more information, please see pages 5 through 14 of the 2017 Production Memo. Using 2015
29 GHGRP data, EPA developed a value for the number of large tanks per well (0.54) and the number of small tanks
30 per well (0.21), a fraction of tanks in each of five categories (large tanks with flares, large tanks with VRU,
31 uncontrolled large tanks, small tanks with flares, and small tanks without flares) for 2015, and corresponding
32 emission factors. The count of large and small tanks per well of 0.54 and 0.21, respectively, was applied to total oil
33 well counts for each year of the time series. The 2015 fraction of tanks in each control category was applied to tanks
34 for the years 2011 to 2015. For 1990, it was assumed that all tanks were in the uncontrolled categories. EPA then
35 linearly interpolated from 1990 to 2011 for each category. Category-specific emission factors developed from 2015
36 GHGRP data were applied for each year of the time series. EPA also developed a per-tank emission factor for
37 malfunctioning dump values. In the GHGRP, only large tanks report malfunctioning dump valves. EPA has applied
38 the emission factor to all large tanks for each year of the time series. EPA received some initial stakeholder feedback
39 on the updates under consideration for this source suggesting that the GHGRP methods for tanks could result in
40 potential underestimates in reported emissions, while other feedback suggested the GHGRP methods did not
41 underestimate tank emissions. EPA also received feedback from two commenters supporting the throughput-based
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1 approach over the tank count based approach (approach used here), noting that throughput more directly relates to
2 tank emission than the number of tanks. Based on this initial feedback, EPA is considering using the throughput-
3 based approach for the final 2017 GHG inventory. The throughput-based approach would result in lower national
4 methane emissions than the tank-based approach, with 2014 oil tank emissions around 50 percent lower than
5 emissions calculated using the tank-based approach presented here. As noted above, EPA continues to receive and
6 review feedback on the options presented and will also assess information received as public review comments.
7 Table 3-41: National Tank Activity Data (Number of Tanks) by Category and National
8 Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Large Tanks w/ Flares (Counts)
0
112,752
177,597
185,370
190,811
196,566
192,679
Large Tanks w/ VRU (Counts)
0
19,083
30,058
31,374
32,294
33,269
32,611
Large Tanks w/o Control
(Counts)
319,160
135,108
92,678
96,734
99,573
102,577
100,548
Small Tanks w/ Flares (Counts)
0
18,349
28,902
30,167
31,052
31,989
31,356
Small Tanks w/o Flares (Counts)
126,204
87,207
89,858
93,791
96,543
99,455
97,488
Total Emissions (MT)
335,031
160,949
127,406
132,983
136,885
141,014
138,225
Previous Estimated Emissions
(MT)
250,643
187,872
220,021
278,638
330,049
396,275
NA
Percent Change in Emissions
34%
-14%
-42%
-52%
-59%
-64%
NA
NA - Not Applicable
9 Equipment Counts (Fugitive Sources)
10 Additional reporting to GHGRP for RY2015 improved EPA's allocation of GHGRP equipment counts between
11 natural gas and petroleum for certain equipment leak category sources. EPA used the RY2015 reporting data to
12 develop improved counts of equipment per well. For more information, please see pages 20 through 22 of the 2017
13 Production Memo. For the public review draft, EPA developed per well counts using 2015 GHGRP and applied
14 those to national oil well counts for years 2011 through 2015. The per well counts for 1990 through 1992 were
15 retained from previous inventories, and counts for 1993 through 2010 were developed by linear interpolation.
16 Overall, the change decreased calculated emissions over the time series by around 12 percent, with the largest
17 changes in light crude separators.
18 Table 3-42: National Equipment Counts for Fugitive Sources and National Emissions (Metric
19 Tons Cm)
Activity Data/Emissions
1'WO
2005
2011
2012
2013
2014
2015
Separators (Heavy Crude)
12.575
18,730
22,047
23,011
23,687
24,401
23,919
(Counts)
Separators (Light Crude)
114.447
170,459
200,646
209,428
215,574
222,076
217,684
(Counts)
Heater/Treaters (Light Crude)
87.106
115,283
131,084
136,821
140,837
145,085
142,215
(Counts)
Headers (Heavy Crude) (Counts)
15,523
26,937
32,909
34,350
35,358
36,424
35,704
Headers (Light Crude) (Counts)
48.124
83,508
102,023
106,488
109,613
112,920
110,686
Total Emissions (MT)
26,5')3
38,563
45,054
47,026
48,406
49,866
48,880
Previous Estimated Emissions
28,420
45,244
54,139
55,977
57,092
57,970
NA
(MT)
Percent Change in Emissions
-(•" n
-15%
-17%
-16%
-15%
-14%
NA
NA - Not Applicable
20 Pneumatic Controllers and Chemical Injection Pumps
21 The changes to pneumatic controller and chemical injection pump equipment counts result from the changes in oil
22 well counts described above and from the improved estimate of the counts of oil wells in GHGRP, which improved
23 the activity factors of counts of controllers and pumps per oil well. The total per well counts of pneumatic
24 controllers and pumps were updated using year 2015 GHGRP data. These per well counts were applied to years
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12
13
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17
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2011 through 2015. For years 2011 through 2015, GHGRP year-specific data on fractions of pneumatic controllers
in each category (high bleed "HB", low bleed "LB", and intermittent "IB") were applied to the counts of pneumatic
controllers. The 1990 through 1992 per well counts of controllers in each category and pumps were retained for
1990 through 1992 and then the per well counts of pneumatic controllers in each category for 1993 through 2010
were developed by linearly interpolating from 1992 through 2011. Category-specific emissions factors developed
for the previous Inventory from year 2014 GHGRP data were applied throughout the time series. The recalculations
resulted in large decreases in total national counts, but only minor changes in the annual fractions of controllers in
each category.
Table 3-43: Pneumatic Controller and Chemical Injection Pump National Equipment Counts
and National Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Pneumatic Controllers
High Bleed (Counts)
163,860
95.653
55,418
38,649
22,991
19,921
20,143
Low Bleed (Counts)
304,312
273.073
256,148
286,470
182,646
176,810
159,783
Intermittent Bleed
(Counts)
-
155.315
249,378
260,377
397,042
424,126
428,652
Previous High Bleed
(Counts)
163,225
160.475
103,061
76,469
50,241
43,211
NA
Previous Low Bleed
(Counts)
303,132
460,289
495,938
494,211
337,406
300,940
NA
Previous Intermittent
Bleed Counts
-
284,053
533,112
599,859
806,207
868,079
NA
Total Emissions (MT)
765,061
700,2W.
666,087
614,545
739,177
766,390
771,571
Previous Estimated
Emissions (MT)
762,095
1,211,263
1,348,290
1,334,230
1,511,099
1,569,471
NA
Percent Change in
Emissions
0%
-42"..
-51%
-54%
-51%
-51%
N/A
Chemical Injection
Pumps (Counts)
New Pumps (Counts)
32,379
44,317
50,839
53,065
54,622
56,270
55,157
Previous Pumps
(Counts)
32,236
89,796
119,058
123,100
125,552
127,484
NA
Total Emissions (MT)
49,064
67.155
77,039
80,411
82,771
85,267
83,581
Previous Estimated
Emissions (MT)
48,849
136,071
180,413
186,537
190,253
193,181
NA
Percent Change in
Emissions
0%
-51"..
-57%
-57%
-56%
-56%
NA
NA - Not Applicable
Associated Gas Venting and Flaring
EPA developed a new estimate for associated gas venting and flaring, replacing its previous estimates for stripper
well venting. For more information, please see pages 14 through 19 of the 2017 Production Memo. For the public
review draft, EPA developed a total percentage of oil wells that vent and flare from 2015 GHGRP data (12 percent),
and applied that value to total national oil well counts for full time series. EPA then applied the GHGRP year-
specific split of that 12 percent between venting wells and flaring wells for years 2011 to 2015, and applied the 2011
split to each year from 1990 to 2011. Emission factors developed from year 2015 GHGRP data were applied for the
full time series. EPA then removed the "stripper well" line item that had been included in previous inventories as
those emissions are included in the updated estimates for associated gas venting and flaring.
Table 3-44: Associated Gas Well Venting and Flaring National Emissions (Metric Tons ChU)
Source 1990 2005 2011 2012 2013 2014 2015
Associated Gas Well Venting
Emissions (MT) 628,907 526,013 591,810 497,114 221,004 91,949 44,015
Associated Gas Well Flaring
Emissions (MT) 78,697 65,822 74,055 107,608 150,612 154,077 109,221
3-68 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Previous Estimated Emissions
from Stripper Wells (MT) 16,353 14,491 14,651 14,799 14,799 14,799 NA
NA - Not Applicable
1 Transportation
2 Recalculations due to updated activity data for quantity of petroleum transported by barge or tanker in the
3 transportation segment have resulted in an average increase in calculated emissions over the time series from this
4 segment of less than 0.01 percent.
5 Refining
6 Recalculations due to updated data, including resubmitted GHGRP data, in the refining segment have resulted in an
7 average increase in calculated emissions over the time series from this segment of less than 0.01 percent.
8 Planned Improvements
9 Plans for Final 2017 GHG Inventory
10 EPA continues to receive and review stakeholder feedback on the 2017 Production Memo. EPA will consider this
11 feedback, along with feedback on this public review draft as it develops the final Inventory.
12 EPA seeks feedback on the methods applied in this public review draft, on other options presented in the 2017
13 Production Memo, and on additional planned improvements under consideration discussed in this section.
14 Uncertainty
15 The most recent uncertainty analysis for the petroleum systems emission estimates in the Inventory was conducted
16 for the 1990 to 2009 Inventory that was released in 2011. Since the analysis was last conducted, several of the
17 methods used in the Inventory have changed, and industry practices and equipment have evolved. In addition, new
18 studies and other data sources such as those discussed in the sections below offer improvement to understanding and
19 quantifying the uncertainty of some emission source estimates. EPA is planning for the final Inventory an update to
20 the uncertainty analysis conducted for the Inventory published in 2011 to reflect the new information. It is difficult
21 to project whether updated uncertainty bounds around CH4 emission estimates would be wider, tighter, or about the
22 same as the current uncertainty bounds that were developed for the Inventory published in 2011 (i.e., minus 24
23 percent and plus 149 percent).
24 Gas STAR Reductions in Petroleum Systems Production Segment
25 The Inventory estimate for Petroleum Systems takes into account reductions reported to the Natural Gas STAR
26 program.78 Reductions included in the Petroleum Systems calculations are those reported to Natural Gas STAR for
27 the following activities: artificial lift - gas lift; artificial lift - use compression; artificial lift - use pumping unit;
28 consolidate crude oil production and water storage tanks; lower heater-treater temperature; re-inject gas for
29 enhanced oil recovery; re-inject gas into crude; and route casinghead gas to vapor recovery unit or compressor.
30 EPA is considering removing the Gas STAR reductions from its calculations for the Petroleum Systems Production
31 segment. In this public review draft of the Inventory, Gas STAR petroleum systems reductions reduce calculated
32 potential emissions by an average of 1 percent over the times series. Many emissions sources in the Inventory are
33 now calculated using net emissions approaches, with technology-specific activity data and emission factors, and
34 annual data from the GHGRP. It may not be necessary to adjust for the reductions and may result in double-counting
35 of reductions, and removing the reductions may improve transparency of the results and methods. However, EPA
78 See .
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30
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32
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34
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will review and potentially incorporate data from Gas STAR's Methane Challenge program as it becomes available
as part of process for compiling future inventories.
Table 3-45: Gas STAR Reductions (Metric Tons ChU)
Source
1990
2005
2011
2012
2013
2014
2015
Production Gas STAR
Reductions
-
(35,828)
(44,940)
(45,081)
(30,903)
(30,903)
(30,903)
Production Emissions
w/o Gas STAR
Reductions
2,300,010
1,924,554
2,015,621
1,947,513
1,861,6170
1,795,210
1,656,584
Production Emissions
w/ Gas STAR
Reductions
2,299,856
1,888,726
1,970,681
1,902,431
1,830,714
1,764,308
1,625,682
Information on Abandoned Wells
Abandoned wells are not currently included in the Inventory. EPA is seeking emission factors and national activity
data available to calculate these emissions. Commenters on previous inventories supported including this source
category, noted that the current data were limited, and suggested reviewing data that will become available in the
future. EPA has identified studies with data on abandoned wells (Townsend-Small et al. 2016 and Kang et al. 2016),
and EPA may provide an information box on this source (without including the estimate in emissions totals) in the
final Inventory and will consider including an estimate for this source in future inventories.
Plans for 2018 GHG Inventory (1990-2016) and Future GHG Inventories
EPA will review data available from the GHGRP, in particular new data on hydraulically fractured oil well
completions and workovers and new well-specific information, available in 2017 for the first time. EPA will
consider revising its method to take into account the new GHGRP data. EPA will continue to review CO2 data from
GHGRP and make updates consistent with CH4 updates as appropriate.
EPA will review data available from the recent Information Collection Request (ICR) for the oil and natural gas
industry79 for potential updates to the Inventory, including improving national-level activity data estimates, and will
assess new data received by the Methane Challenge Program on an ongoing basis, which may be used to confirm or
improve existing estimates and assumptions.
EPA continues to track studies that contain data that may be used to update the Inventory.
EPA seeks stakeholder feedback on these future plans.
Box 3-7: Carbon Dioxide Transport, Injection, and
Carbon dioxide is produced, captured, transported, and used for Enhanced Oil Recovery (EOR) as well as
commercial and non-EOR industrial applications. This CO2 is produced from both naturally-occurring CO2
reservoirs and from industrial sources such as natural gas processing plants and ammonia plants. In the Inventory,
emissions from naturally-produced CO2 are estimated based on the specific application.
In the Inventory, CO2 that is used in non-EOR industrial and commercial applications (e.g., food processing,
chemical production) is assumed to be emitted to the atmosphere during its industrial use. These emissions are
discussed in the Carbon Dioxide Consumption section. The naturally-occurring CO2 used in EOR operations is
assumed to be fully sequestered. Additionally, all anthropogenic CO2 emitted from natural gas processing and
ammonia plants is assumed to be emitted to the atmosphere, regardless of whether the CO2 is captured or not. These
emissions are currently included in the Natural Gas Systems and the Ammonia Production sections of the Inventory
report, respectively.
IPCC includes methodological guidance to estimate emissions from the capture, transport, injection, and geological
storage of CO2. The methodology is based on the principle that the carbon capture and storage system should be
79 https://www.epa.gov/controlling-air-pollution-oil-and-natural-gas-industry/oil-and-gas-industry-infonnation-requests
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handled in a complete and consistent manner across the entire Energy sector. The approach accounts for CO2
captured at natural and industrial sites as well as emissions from capture, transport, and use. For storage specifically,
a Tier 3 methodology is outlined for estimating and reporting emissions based on site-specific evaluations. However,
IPCC (IPCC 2006) notes that if a national regulatory process exists, emissions information available through that
process may support development of CO2 emissions estimates for geologic storage.
In the United States, facilities that produce CO2 for various end-use applications (including capture facilities such as
acid gas removal plants and ammonia plants), importers of CO2, exporters of CO2, facilities that conduct geologic
sequestration of CO2, and facilities that inject CO2 underground (including facilities conducting EOR), are required
to report greenhouse gas data annually to EPA through its GHGRP. Facilities conducting geologic sequestration of
CO2 are required to develop and implement an EPA-approved site-specific monitoring, reporting and verification
plan, and to report the amount of CO2 sequestered using a mass balance approach.
Available GHGRP data relevant for this inventory estimate consists of national-level annual quantities of CO2
captured and extracted for EOR applications for 2010 to 2015. For 2015, data from EPA's GHGRP (Subpart PP)
were unavailable for use in the current Inventory report due data confidentiality reasons. A linear trend extrapolation
was performed based on previous GHGRP reporting years (2010-2014) to estimate 2015 emissions.
EPA will continue to evaluate the availability of additional GHGRP data and other opportunities for improving the
emission estimates.
These estimates indicate that the amount of CO2 captured and extracted from industrial and natural sites for EOR
applications in 2015 is 61.0 MMT CO2 Eq. (60,988 kt) (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 Extraction for EOR Operations (MMT
COz Eq.)
Stage
1990
2005
2011
2012
2013
2014
2015
Capture Facilities
4.8
6
9.9
9.3
12.2
13.1
13.5
Extraction Facilities
20.8
28.3
48.4
48.9
47.0
46.2
47.5
Total
25.6
34.7
58.2
58.1
59.2
59.3
61.0
Table 3-47: Potential Emissions from CO2 Capture and Extraction for EOR Operations (kt)
Stage
1990
2005
2011
2012
2013
2014
2015
Capture Facilities
Extraction Facilities
4,832
20,811
6,475
28,267
9,877
48,370
9,267
48,869
12,205
46,984
13,093
46,225
13,483
47,505
Total
25,643
34,742
58,247
58,136
59,189
59,318
60,988
3.7 Natural Gas Systems (IPCC Source Category
lB2b)
EPA is seeking stakeholder feedback on a number of options for updating the emissions estimates in this section.
See Recalculations Discussion below for methods used in the development of the public review draft, and EPA's
memos on updates under consideration for other options being considered:
https://www.epa.gov/ghgemissions/updates-under-consideration-petroleum-and-natural-gas-systems-1990-2015-
ghg-inventory. It is likely that the methods presented here and the calculated emissions totals will change due to
revisions between this public review draft of the Inventory and the final Inventory, but impacts on the total emissions
estimate are expected to be minor. See the recalculations discussion below for more details. Note that 2014 results
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for CO2 emissions are being used as a preliminary estimate for 2015 while EPA reviews the activity data and
estimation methods for consistency with data sources used for methane calculations.
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 160.0 MMT
CO2 Eq. (6,401 kt) of CH4 in 2015, a 19 percent decrease compared to 1990 emissions, and a 1 percent decrease
compared to 2014 emissions (see Table 3-48, Table 3-49, and Table 3-50) and 42.4 MMT CO2 Eq. (42,351 kt) of
non-combustion CO2 in 2015, a 12 percent increase compared to 1990 emissions.
The 1990 to 2015 trend is not consistent across segments. Overall, the 1990 to 2015 decrease in CH4 emissions is
due primarily to the decrease in emissions from in the transmission/storage and distribution segments. Over the same
time period, the production segments saw increased methane emissions of 42 percent. Methane emissions in the
processing segment also decreased over the time series, by 10.2 MMT CO2 Eq., or 48 percent from 1990 levels.
Natural gas systems also emitted 42.4 MMT CO2 Eq. (42,351 kt) of non-combustion CO2 in 2015, a 12 percent
increase compared to 1990 emissions. The 1990 to 2015 increase in CO2 is due primarily to flaring; the volume of
gas flared increased 93 percent from 1990.
CH4 and non-combustion CO2 emissions from natural gas systems include those resulting from normal operations,
routine maintenance, and system upsets. Emissions from normal operations include: natural gas engine and turbine
uncombusted exhaust, bleed and discharge emissions from pneumatic controllers, and fugitive emissions from
system components. Routine maintenance emissions originate from pipelines, equipment, and wells during repair
and maintenance activities. Pressure surge relief systems and accidents can lead to system upset emissions. Below is
a characterization of the four major stages of the natural gas system. Each of the stages is described and the different
factors affecting CH4 and non-combustion CO2 emissions are discussed.
Production (including gathering and boosting). In the production stage, wells are used to withdraw raw gas from
underground formations. Emissions arise from the wells themselves, and well-site gas treatment facilities such as
dehydrators and separators. Gathering and boosting emission sources are not reported under a unique segment, but
are included within the production sector. The gathering and boosting segment of natural gas systems comprises
gathering and boosting stations (with multiple emission sources on site) and gathering pipelines. The gathering and
boosting stations receive natural gas from production sites and transfer it, via gathering pipelines, to transmission
pipelines or processing facilities (custody transfer points are typically used to segregate sources between each
segment). Emissions from production (including gathering and boosting) account for 65 percent of CH4 emissions
and 44 percent of non-combustion CO2 emissions from natural gas systems in 2015. Emissions from gathering
stations, pneumatic controllers, liquids unloading, and offshore platforms account for most of the CH4 emissions in
2015. Flaring emissions account for most of the non-combustion CO2 emissions. Due to the activity data source for
CO2 from flaring, it is not possible to develop separate estimates for flaring occurring in natural gas production and
flaring occurring in oil production. Total CO2 emissions from flaring for both natural gas and oil were 18.0 MMT
CO2 in 2015 and are included in the Natural Gas Systems estimates. CH4 emissions from production increased by 42
percent from 1990 to 2015, due primarily to increases in emissions from gathering and boosting stations (due to an
increase in the number of stations), increases in emissions from pneumatic controllers (due to an increase in the
number of controllers, particularly in the number of intermittent bleed controllers), and chemical injection pumps.
CO2 emissions from production increased 88 percent from 1990 to 2015 due primarily to increases in flaring.
Processing. In this stage, natural gas liquids and various other constituents from the raw gas are removed, resulting
in "pipeline quality" gas, which is injected into the transmission system. Fugitive CH4 emissions from compressors,
including compressor seals, are the primary emission source from this stage. Most of the non-combustion CO2
emissions come from acid gas removal (AGR) units, which are designed to remove CO2 from natural gas.
Processing plants account for 7 percent of CH4 emissions and 56 percent of non-combustion CO2 emissions from
natural gas systems. CH4 emissions from processing decreased by 48 percent from 1990 to 2015 as emissions from
equipment leaks and compressors (leaks and venting) decreased. CO2 emissions from processing decreased by 15
percent from 1990 to 2015, due to a decrease in acid gas removal emissions.
Transmission and Storage. Natural gas transmission involves high pressure, large diameter pipelines that transport
gas long distances from field production and processing areas to distribution systems or large volume customers
such as power plants or chemical plants. Compressor station facilities are used to move the gas throughout the U.S.
transmission system. Fugitive CH4 emissions from these compressor stations, and venting from pneumatic
controllers account for most of the emissions from this stage. Uncombusted engine exhaust and pipeline venting are
also sources of CH4 emissions from transmission. Natural gas is also injected and stored in underground formations,
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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). In 2015, emissions from the Aliso Canyon
leak event in Southern California contributed 2.0 MMT CO2 Eq. to transmission and storage emissions, around 6
percent of total emissions for this segment. Compressors and dehydrators are the primary contributors to emissions
from storage. CH4 emissions from the transmission and storage sector account for approximately 21 percent of
emissions from natural gas systems, while CO2 emissions from transmission and storage account for less than 1
percent of the non-combustion CO2 emissions from natural gas systems. CH4 emissions from this source decreased
by 42 percent from 1990 to 2015 due to reduced compressor station emissions (including emissions from
compressors and fugitives). CO2 emissions from transmission and storage have decreased by 37 percent from 1990
to 2015, also due to reduced compressor station emissions.
Distribution. Distribution pipelines take the high-pressure gas from the transmission system at "city gate" stations,
reduce the pressure and distribute the gas through primarily underground mains and service lines to individual end
users. There were 1,274,976 miles of distribution mains in 2015, an increase of over 330,000 miles since 1990
(PHMSA 2016a; PHMSA 2016b). Distribution system emissions, which account for 7 percent of CH4 emissions
from natural gas systems and less than 1 percent of non-combustion CO2 emissions, result mainly from fugitive
emissions from pipelines and stations. An increased use of plastic piping, which has lower emissions than other pipe
materials, has reduced both CH4 and CO2 emissions from this stage, as have station upgrades at metering and
regulating (M&R) stations. Distribution system CH4 emissions in 2015 were 75 percent lower than 1990 levels
(changed from 43.5 MMT CO2 Eq. to 11.0 MMT CO2 Eq.), while distribution CO2emissions in 2015 were 72
percent lower than 1990 levels (CO2 emission from this segment are less than 0.1 MMT CO2 Eq. across the time
series).
Total CH4 emissions for the four major stages of natural gas systems are shown in MMT CO2 Eq. (Table 3-48) and
kt (Table 3-49). Table 3-50 provides additional information on how the estimates in Table 3-46 were calculated.
Table 3-50 shows the calculated potential CH4 release (i.e., potential emissions before any controls are applied) from
each stage, and the amount of CH4 that is estimated to have been flared, captured, or otherwise controlled, and
therefore not emitted to the atmosphere. Subtracting the value for CH4 that is controlled, from the value for
calculated potential release of CH4, results in the total net emissions values. More disaggregated information on
potential emissions and emissions is available in Annex 3.6. See Methodology for Estimating CH4 and CO2
Emissions from Natural Gas Systems.
Table 3-48: ChU Emissions from Natural Gas Systems (MMT CO2 Eq.)a
Stage
l'WO
2005
2011
2012
2013
2014
2015
Field Production
73.1
97.1
103.6
106.0
105.0
106.5
104.2
Processing
21.3
12.1
10.1
10.1
10.9
11.1
11.1
Transmission and Storage
58.6
30.7
28.8
27.9
30.8
32.0
33.7
Distribution
43.5
22.1
11.1
11.3
11.2
11.2
11.0
Total
196.5
162.1
153.7
155.3
157.9
160.8
160.0
a These values represent CH4 emitted to the atmosphere. CH4 that is captured, flared, or otherwise
controlled (and not emitted to the atmosphere) has been calculated and removed from emission
totals.
Note: Totals may not sum due to independent rounding.
Table 3-49: ChU Emissions from Natural Gas Systems (kt)a
Stage
1990
2005
2011
2012
2013
2014
2015
Field Production
2,925
3.886
4,146
4,241
4,202
4,259
4,167
Processing
853
486
405
406
434
446
445
Transmission and Storage
2,343
1.230
1,152
1,116
1,232
1,282
1,349
Distribution
1,741
884
444
451
449
446
439
Total
7,862
6,485
6,147
6,213
6,317
6,433
6,401
a These values represent CH4 emitted to the atmosphere. CH4 that is captured, flared, or otherwise controlled (and
not emitted to the atmosphere) has been calculated and removed from emission totals.
Note: Totals may not sum due to independent rounding.
Table 3-50: Calculated Potential CH4 and Captu red/Com busted Cm from Natural Gas
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1990
2005
2011
2012
2013
2014
2015
Calculated Potential3
196.5
186.0
178.1
180.1
182.1
185.8
186.0
Field Production
73.1
103.7
114.8
117.0
116.6
118.8
117.4
Processing
21.3
12.1
10.1
10.1
10.9
11.1
11.1
Transmission and Storage
58.6
43.1
37.3
37.3
39.1
40.4
42.2
Distribution
43.5
23.3
12.6
12.4
12.2
12.2
12.0
Captured/Combusted
-
23.8
24.4
24.7
24.2
25.0
26.0
Field Production
-
6.6
11.1
10.9
11.6
12.3
13.2
Processing
-
-
-
-
-
-
-
Transmission and Storage
-
12.4
8.5
9.4
8.3
8.4
8.5
Distribution
-
1.2
1.5
1.1
1.0
1.0
1.0
Net Emissions
196.5
162.1
153.7
155.3
157.9
160.8
160.0
Field Production
73.1
97.1
103.6
106.0
105.0
106.5
104.2
Processing
21.3
12.1
10.1
10.1
10.9
11.1
11.1
Transmission and Storage
58.6
30.7
28.8
27.9
30.8
32.0
33.7
Distribution
43.5
22.1
11.1
11.3
11.2
11.2
11.0
+ Does not exceed 0.1 MMT CO2 Eq.
a In this context, "potential" means the total emissions calculated before voluntary reductions and regulatory
controls are applied.
Note: Totals may not sum due to independent rounding.
2 Table 3-51: Non-combustion CO2 Emissions from Natural Gas Systems (MMT CO2 Eq.)
Stage
1990
2005
2011
2012
2013
2014
2015
Field Production
8.3
14.1
13.7
16.6
18.6
18.6
Processing
27.8
21.7
21.5
21.5
21.8
23.7
23.7
Transmission and Storage
0.1
+
+
+
+
+
+
Distribution
0.1
+
+
+
+
+
+
Total
37.7
30.1
35.7
35.2
38.5
42.4
42.4
+ Does not exceed 0.1 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
3 Table 3-52: Non-combustion CO2 Emissions from Natural Gas Systems (kt)
Stage
1990
2005
2011
2012
2013
2014
2015
Field Production
9,857
8,260
14,146
13,684
16,649
18,585
18,585
Processing
27,763
21,746
21,466
21,469
21,756
23,713
23,713
Transmission and Storage
62
43
36
35
37
39
39
Distribution
50
t 27
15
14
14
14
14
Total
37,732
30,076
35,662
35,203
38,457
42,351
42,351
Note: Totals may not sum due to independent rounding.
4 Methodology
5 The methodology for natural gas emissions estimates presented in this public review draft of the Inventory involves
6 the calculation of CH4 and CO2 emissions for over 100 emissions sources, and then the summation of emissions for
7 each natural gas segment.
8 The approach for calculating emissions for natural gas systems generally involves the application of emission factors
9 to activity data. For many sources, the approach uses technology-specific emission factors or emission factors that
10 vary over time and take into account changes to technologies and practices, which are used to calculate net
11 emissions directly. For others, the approach uses what are considered "potential methane factors" and reduction data
12 to calculate net emissions.
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Emission Factors. A 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 (GRI) and EPA (EPA/GRI 1996). The
EPA/GRI study developed over 80 CH4 emission factors to characterize emissions from the various components
within the operating stages of the U.S. natural gas system. The EPA/GRI study was based on a combination of
process engineering studies, collection of activity data, and measurements at representative gas facilities conducted
in the early 1990s. Methane compositions from the Gas Technology Institute (GTI, formerly GRI) Unconventional
Natural Gas and Gas Composition Databases (GTI 2001) are adjusted year to year using gross production for oil and
gas supply National Energy Modeling System (NEMS) regions from the EIA. Therefore, emission factors may vary
from year to year due to slight changes in the CH4 composition for each NEMS oil and gas supply module region.
The emission factors used to estimate CH4 were also used to calculate non-combustion CO2 emissions. Data from
GTI 2001 were used to adapt the CH4 emission factors into non-combustion related CO2 emission factors.
Additional information about CO2 content in transmission quality natural gas was obtained from numerous U.S.
transmission companies to help further develop the non-combustion CO2 emission factors.
Another key source of emission factors is the Greenhouse Gas Reporting Program (GHGRP) Subpart W data (EPA
2016). In the production segment, GHGRP data were used to develop emission factors for gas well completions and
workovers (refracturing) with hydraulic fracturing, pneumatic controllers and chemical injection pumps, condensate
tanks, and liquids unloading. In the processing segment, GHGRP data were used to develop emission factors for
fugitives, compressors, flares, dehydrators, and blowdowns/venting. In the transmission and storage segment,
GHGRP data were used to develop factors for pneumatic controllers.
Other data sources used for emission factors include Marchese et al. for gathering stations, Zimmerle et al. for
transmission and storage station fugitives and compressors, and Lamb et al. for pipelines.
See Annex 3.6 for more detailed information on the methodology and data used to calculate CH4 and non-
combustion CO2 emissions from natural gas systems.
Activity Data. Activity data were taken from the following sources: Drillinglnfo, Inc (Drillinglnfo 2016); American
Gas Association (AGA 1991 through 1998); Bureau of Ocean Energy Management, Regulation and Enforcement
(previous Minerals and Management Service) (BOEMRE 201 la, 201 lb, 201 lc, 201 Id); Natural Gas Liquids
Reserves Report (EIA 2005); Natural Gas Monthly (EIA 2016a, 2016b, 2016c); the Natural Gas STAR Program
annual emissions savings (EPA 2013c); Oil and Gas Journal (OGJ 1997 through 2015); Pipeline and Hazardous
Materials Safety Administration (PHMSA 2016a, 2016b); Federal Energy Regulatory Commission (FERC 2015);
Greenhouse Gas Reporting Program (EPA 2016); other Energy Information Administration data and publications
(EIA 2001, 2004, 2012, 2014); (EPA 1999); Conservation Commission (Wyoming 2015); and the Alabama State
Oil and Gas Board (Alabama 2015).
For a few sources, recent direct activity data are not available. For these sources, either 2014 data was used as a
proxy for 2015 data, or a set of industry activity data drivers was developed and used to calculate activity data over
the time series. 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. More information on activity data and drivers is available in Annex 3.6.
Calculating Net Emissions. For most sources, emissions are calculated directly by applying emission factors to
activity data. However, for certain sectors, some sources are calculated using potential emission factors, and the
step of deducting CH4 that is not emitted from the total CH4 potential estimates to develop net CH4 emissions is
applied. To take into account use of such technologies, data, where available, are collected on both regulatory and
voluntary reductions. Regulatory actions addressed using this method include National Emission Standards for
Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents. Voluntary reductions included in the GHG
Inventory are those reported to Natural Gas STAR.
In this public review draft of the Inventory, EPA included an emissions estimate from the Aliso Canyon leak
event.80 EPA used the estimate of the leak published by the California Air Resources Board (99,650 MT for the
80 For more information on the Aliso Canyon event, and the measurements conducted of the leak, please see Ensuring Safe and
Reliable Underground Natural Gas Storage, Final Report of the Interagency Task Force on Natural Gas Storage Safety, available
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1 duration of the leak), adjusted to only include those emissions that occurred in 2015 (2016 emissions will be
2 included in next Inventory). The 2015 emissions estimate of 78,350 MT CH4 was added to the 2015 estimate of
3 fugitive emissions from storage wells, calculated with an emission factor approach, resulting in total emissions from
4 storage wells in 2015 of 92,590 MT CH4. For more information, please see Inventory of U.S. Greenhouse Gas
5 Emissions and Sinks 1990-2015: Update under Consideration for Storage Segment Emissions.*1 EPA continues to
6 seek stakeholder feedback on this update and on the memo.
7 Uncertainty and Time-Series Consistency
8 EPA's planned uncertainty analysis is discussed in the Planned Improvements section.
9 New data available starting in 2011 have improved estimates of emissions from Natural Gas Systems. Many of the
10 previously available data sets were collected in the 1990s. To develop a consistent time series for 1990-2015, for
11 sources with new data, EPA reviewed available information on factors that may have resulted in changes over the
12 time series (e.g. regulations, voluntary actions) and requested stakeholder feedback on trends as well. For most
13 sources, EPA developed annual data for 1993-2010 by interpolating activity data or emission factors or both
14 between 1992 and 2011 data points.
15 Information on time-series consistency for sources updated in this public review draft can be found in the
16 Recalculation Discussion below, with additional detail provided in the 2017 Production and Processing memos. For
17 information on other sources, please see the Methodology Discussion above.
is QA/QC and Verific cussion
19 The natural gas emission estimates in the Inventory are continually being reviewed and assessed to determine
20 whether emission factors and activity factors accurately reflect current industry practices. A QA/QC analysis was
21 performed for data gathering and input, documentation, and calculation. QA/QC checks are consistently conducted
22 to minimize human error in the model calculations. EPA performs a thorough review of information associated with
23 new studies, GHGRP data, regulations, public webcasts, and the Natural Gas STAR Program to assess whether the
24 assumptions in the Inventory are consistent with current industry practices. The EPA has a multi-step data
25 verification process for GHGRP data, including automatic checks during data-entry, statistical analyses on
26 completed reports, and staff review of the reported data. Based on the results of the verification process, the EPA
27 follows up with facilities to resolve mistakes that may have occurred.
28 As in previous years, EPA conducted early engagement and communication with stakeholders on updates prior to
29 public review. In December 2016 and January 2017, EPA stakeholder webinars on GHG data for oil and gas. In
30 early 2017, EPA released memos detailing updates under consideration and requesting stakeholder feedback. EPA
31 discusses preliminary stakeholder feedback received in the public review draft. EPA continues to receive and review
32 feedback on the options presented, and may revise the recalculations in the final 2017 GHG Inventory based on that
33 feedback.
34 In recent years, several studies have measured emissions at the source level and at the national or regional level and
35 calculated emissions estimates that may differ from the GHG Inventory. There are a variety of potential uses of data
36 from new studies, including replacing a previous estimate or factor, verifying or QA of an existing estimate or
37 factor, and identifying areas for updates.
38 In general, there are two major types of studies related to oil and gas greenhouse gas data: studies that focus on
39 measurement or quantification of emissions from specific activities, processes and equipment, and studies that focus
40 on verification of estimates through inverse modeling. The first type of study can lead to direct improvements to or
41 verification of GHG Inventory estimates. In the past few years, EPA has reviewed and in many cases, incorporated
at .
81 .
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1 data from these data sources. The second type of study can provide general indications on potential over- and
2 under-estimates. A key challenge in using these types of studies to assess GHG Inventory results is having a relevant
3 basis for comparison. In an effort to improve the ability to compare the national-level GHG inventory with
4 measurement results that may be at other scales, a team at Harvard University along with EPA and other coauthors
5 developed a gridded inventory of U.S. anthropogenic methane emissions with 0.1° x 0.1° spatial resolution, monthly
6 temporal resolution, and detailed scale-dependent error characterization.82 The inventory is designed to be
7 consistent with the 2016 Inventory of U.S. Greenhouse Gas Emissions and Sinks (1990-2014) estimates for the
8 year 2012, which presents national totals for different source types.83
9 Recalculations Discussion
10 The EPA received information and data related to the emission estimates through the Inventory preparation process,
11 previous Inventories' formal public notice periods, GHGRP data, and new studies. The EPA carefully evaluated
12 relevant information available, and made several updates in this public review draft of the Inventory, including
13 revisions to production segment activity and emissions data, gathering and boosting facility emissions, and
14 processing segment activity and emissions data. Additional information on inclusion of the Aliso Canyon emissions
15 can be found in the Methodology section above and in the 2017 Transmission and Storage Memo84 and not in the
16 Recalculation Discussion section as it did not involve recalculation of a previous year of the Inventory.
17 In January 2017, the EPA released draft memoranda that discussed the changes under consideration and requested
18 stakeholder feedback on those changes.85 In this public review draft of the 2017 GHG Inventory (1990-2015), EPA
19 has selected from the options presented in the 2017 Production and Processing memos to develop emissions
20 estimates. EPA continues to receive and review feedback on the options presented, and it is likely that the methods
21 presented here and the calculated emissions totals will change due to revisions between this public review draft of
22 the 1990-2015 Inventory and the final Inventory, but impacts on the total emissions estimate are expected to be
23 minor. The impact of all revisions to natural gas systems is a decrease of 15.3 MMT CO2 Eq., or 9 percent,
24 comparing the 2014 value from the previous Inventory to this public review draft Inventory. Over the time series,
25 the average change is a decrease of 15 MMT CO2 Eq., or 8 percent.
26 Recalculations for the production segment (including gathering and boosting facilities) resulted in a small decrease
27 in the 2014 CH4 emission estimate, from 109.0 MMT CO2 Eq. in the previous Inventory, to 106.5 MMT CO2 Eq. in
28 this public review draft of the Inventory, or 2 percent. Over the time series, the average change is an increase of 9.5
29 MMT CO2 Eq., or 10 percent.
30 Recalculations for the processing segment resulted in a decrease of 12.8 MMT CO2 Eq., or 54 percent comparing the
31 2014 value from the previous Inventory to this public review draft Inventory. Over the time series, the average
32 change was 28 percent.
33 Although there were no methodological updates to the transmission and storage segment, recalculations due to
34 updated data (e.g., GHGRP station counts, the GHGRP split between dry and wet seal centrifugal compressors, and
35 GHGRP pneumatic controller data) impacted emissions estimates, resulting in an average increase in calculated
36 emissions over the time series from this segment of around 24 metric tons CH4, or less than 0.01 percent.
37 Although there were no methodological updates to the distribution segment, recalculations due to updated data (e.g.,
38 GHGRP M&R station counts) impacted emissions estimates, resulting in an average increase in calculated emissions
39 over the time series from this segment of around 664 metric tons CH4, or 0.1 percent.
82 See .
83 See .
84 See
85 See Revisions under Consideration for Natural Gas and Petroleum Systems Production Emissions, and Revisions under
Consideration for Natural Gas Systems Processing Segment Emissions, available at
.
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Production
This section references the Inventory production segment supporting memoranda: "Revisions to Natural Gas and
Petroleum Production Emissions," (the "2017 Production Memo").86 This memorandum contains further details and
documentation of recalculations.
Tanks
In the public review draft, EPA developed emissions estimates for condensate tanks using GHGRP data and an
equipment-based approach. For more information, please see pages 5 through 14 of the 2017 Production Memo.
Using 2015 GHGRP data, EPA developed a value for large tanks per well (0.09) and small tanks per well (0.32), a
fraction of tanks in each of five categories (large tanks with flares, large tanks with VRU, uncontrolled large tanks,
small tanks with flares, and small tanks without flares) for 2015, and corresponding emission factors. The count of
large and small tanks per well of 0.09 and 0.32, respectively, was applied to total gas well counts for each year of
the time series. The 2015 fraction of tanks in each control category was applied to tanks for the years 2011 to 2015.
For 1990, it was assumed that 50 percent of large tanks were controlled with a flare and 50 percent were
uncontrolled, and all small tanks were in the uncontrolled category. EPA then linearly interpolated from 1990 to
2011 for each category. Category-specific emission factors developed from 2015 GHGRP data were applied for
each year of the time series. EPA also developed a per-tank emission factor for malfunctioning dump values. In the
GHGRP, only large tanks report malfunctioning dump valves. EPA has applied the emission factor to all large tanks
for each year of the time series. As the new method results in the direct calculation of net emissions, EPA removed
the tank reductions line item (an estimate of reductions from NESHAP). This revision on average resulted in a 74
percent decrease in the estimated emission for tanks across the time series. EPA received initial stakeholder
feedback on the updates under consideration for this source supporting the revision, but also received feedback
suggesting that the GHGRP methods for tanks could result in potential underestimates in reported emissions. EPA
received feedback from two commenters supporting the throughput-based approach over the tank count based
approach (the approach used here), noting that throughput more directly relates to tank emissions than the number of
tanks. Based on this initial feedback, EPA is considering using the throughput-based approach for the final 2017
GHG inventory. The throughput-based approach would result in lower national methane emissions than the tank-
based approach, with 2014 oil tank emissions around 30 percent lower than emissions calculated using the tank-
based approach presented here. As noted above, EPA continues to receive and review feedback on the options
presented and will also assess information received as public review comments.
Table 3-53: National Tank Activity Data (Number of Tanks) by Category and National
Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Large Tanks w/ Flares (Counts)
9,430
18,344
23,708
23,606
23,246
23,343
22,705
Large Tanks w/ VRU (Counts)
0
2,429
4,212
4,194
4,130
4,147
4,034
Large Tanks w/o Control
9,430
11,924
12,576
12,522
12,331
12,382
12,044
(Counts)
Small Tanks w/ Flares (Counts)
0
13,547
23,488
23,388
23,031
23,126
22,495
Small Tanks w/o Flares (Counts)
67,60"
103,660
121,669
121,150
119,301
119,797
116,523
Total Emissions (MT)
20,729
30,530
35,131
34,981
34,447
34,590
33,645
Previous Potential Emissions
93,224
119,191
229,284
259,121
312,185
303,711
NA
(MT)
Previous Regulatory Reductions
-
31,908
61,381
69,369
83,575
81,306
NA
(MT)
Previous Net Emissions (MT)
93,224
87,283
167,902
189,752
228,610
222,405
NA
Percent Change in Emissions
-78°/..
-65%
-79%
-82%
-85%
-84%
NA
NA - Not Applicable
86 See .
3-78 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
Well Counts
2 EPA has used a more recent version of the Drillinglnfo data set to update well counts data in this public review draft
3 of the Inventory. For more information, see pages 19 through 20 of the 2017 Production Memo. This update
4 resulted in a decrease of 1 percent in gas well counts on average over the time series. EPA received feedback that
5 stakeholders generally support the revision as introducing more consistency with recently published well count
6 estimates. EPA continues to compare current estimates with stakeholder well counts also derived from Drillinglnfo,
7 investigate differences, and may further revise the well count estimates in the final Inventory.
8 Table 3-54: Gas Well Count Data
Gas Well Count
1990
2005
2011
2012
2013
2014
2015
Number of Gas Wells
214,220
371,385
459,948
457,984
450,997
452,870
440,496
Previous Estimated Number of
Gas Wells
218,709
373,903
463,198
460,588
454,491
456,140
NA
Percent Change
-2%
-1%
-1%
-1%
-1%
-1%
NA
NA - Not Applicable
9 Equipment Counts (Fugitive Sources)
10 Additional reporting to GHGRP for RY2015 improved EPA's allocation of GHGRP equipment counts between
11 natural gas and petroleum for certain equipment leak category sources. EPA used the RY2015 reporting data to
12 develop improved counts of equipment per well. For more information, please see pages 20 to 22 of the 2017
13 Production Memo. For the public review draft, EPA developed per well counts using 2015 GHGRP and applied
14 those to national gas well counts for years 2011 through 2015. The per well counts for 1990 through 1992 were
15 retained from previous inventories, and counts for 1993 through 2010 were developed by linear interpolation.
16 Overall, the change decreased calculated emissions over the time series by around 11 percent, with the largest
17 decreases in meters/piping (20 percent), dehydrators (13 percent), and compressors (16 percent). Initial stakeholder
18 feedback supported this update.
19 Table 3-55: National Equipment Counts for Fugitive Sources and National Emissions (Metric
20 Tons cm)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Separators (Counts)
124,287
244,992
315,118
313,773
308,986
310,269
301,792
Heaters (Counts)
53,218
79,878
94,942
94,537
93,095
93,481
90,927
Dehydrators (Counts)
29,866
20,579
12,248
12,196
12,010
12,060
11,730
Meters/Piping (Counts)
197,710
321,723
394,384
392,700
386,709
388,315
377,705
Compressors (Counts)
18,870
29,888
36,005
35,852
35,305
35,451
34,483
Total Emissions (MT)
143,576
253,793
304,915
303,645
297,791
296,049
291,250
Previous Estimated Emissions
153,106
292,944
364,342
362,341
356,470
354,306
NA
(MT)
Percent Change in Emissions
-6%
-13%
-16%
-16%
-16%
-16%
NA
NA - Not Applicable
21 Pneumatic Controllers and Chemical Injection Pumps
22 Total per well counts of pneumatic controllers and chemical injection pump were updated using year 2015 GHGRP
23 data. The 2015 GHGRP data set allowed for improved estimates of the counts of gas wells in GHGRP, which
24 improved the activity factors of counts of controllers and pumps per gas well. These per well counts were applied to
25 years 2011 through 2015. For years 2011 through 2015, GHGRP year-specific data on fractions of pneumatic
26 controllers in each category (high bleed "HB", low bleed "LB", and intermittent "IB") were applied to the counts of
27 pneumatic controllers. The 1990 through 1992 per well counts of controllers in each category and pumps were
28 retained for 1990 through 1992 and then the per well counts of pneumatic controllers in each category and pumps
29 for 1993 through 2010 were developed by linear interpolating from 1992 through 2011. Category-specific emissions
30 factors developed from year 2014 GHGRP data were applied throughout the time series. The recalculations using
31 the latest GHGRP data resulted in only minor changes in the annual fractions of controllers in each category, and
32 only minor changes in total calculated emissions.
Energy 3-79
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1 Table 3-56: Pneumatic Controller and Chemical Injection Pump National Equipment Counts
2 and National Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Pneumatic Controllers
Low Bleed (Counts)
-
150,996
302,514
258,588
172,959
205,677
197,063
High Bleed (Counts)
78,780
106,371
83,315
71,494
40,950
30,219
22,628
Intermittent Bleed
(Counts)
146,306
359,640
479,475
531,527
634,555
616,091
609,018
Previous Low Bleed
(Counts)
-
138,223
276,586
239,734
144,443
226,280
NA
Previous High Bleed
(Counts)
80,776
106,689
86,310
76,418
42,050
29,006
NA
Previous Intermittent
Bleed (Counts)
150,013
360,379
484,942
526,908
645,408
579,633
NA
Total Emissions (MT)
568,862
1,041,120
1,148,601
1,169,765
1,180,634
1,110,653
1,065,233
Previous Estimated
Emissions
556,347
1,079,256
1,229,714
1,245,311
1,259,753
1,105,119
NA
Percent Change
2%
-4%
-7%
-6%
-6%
1%
NA
Chemical Injection
Pumps
New Pumps (Counts)
17,524
59,884
87,004
86,633
85,311
85,666
83,325
Previous Pumps (Counts)
17,805
58,094
84,538
84,061
82,948
83,249
NA
Total Emissions (MT)
30,552
95,296
132,348
131,783
129,772
130,311
126,751
Previous Estimated
A J A
Emissions
29,207
96,006
131,488
130,624
128,687
128,876
1\A.
Percent Change in
NA
Emissions
5%
-1%
1%
1%
1%
1%
NA - Not Applicable
3 Liquids Unloading
4 For the public review draft, EPA updated its estimates for liquids unloading to use data from GHGRP. For more
5 information, please see pages 23 to 25 of the 2017 Production Memo.
6 To develop this estimate, EPA retained the assumption that 56 percent of all gas wells conduct liquids unloading
7 (total percent of wells that vent for liquids unloading and wells that do not vent for liquids unloading (i.e., use of
8 non-emitting systems)) over the time series (developed from API/ANGA report). EPA also retained the assumption
9 that in 1990, all of the 56 percent of wells with liquids unloading issues vent without plunger lifts. For the years
10 2011 to 2015, EPA applied the 2015 GHGRP fraction of gas wells that vent for liquids unloading (16.8 percent), and
11 applied year-specific fractions of wells venting with plunger lifts and wells venting without plunger lifts. For years
12 1991 to 2010, EPA interpolated from the percentages of wells in each category for 1990 to 2011. For all years of the
13 time series, EPA applied average EFs calculated from 2011 to 2015 GHGRP data. The activity data assumptions and
14 emission factors were developed and applied at the national level, whereas, the previous year's Inventory calculated
15 emissions with regional factors.
16 The recalculation for liquids unloading emissions resulted in an average decrease of 329,102 MT or 45 percent over
17 the time series. The decrease in calculated emissions is much smaller in recent years (e.g., 12 percent for 2010
18 through 2014), than earlier years of the time series (e.g., 54 percent for 1990 through 1995). Initial stakeholder
19 feedback supported the use of liquids unloading data from GHGRP, but suggested developing regional activity
20 factors.
21 Table 3-57: National Liquids Unloading Activity Data by Category and National Emissions
22 (Metric Tons Cm)
Activity Data/Emissions 1990 2005 2011 2012 2013 2014 2015
Wells Venting w/o
Plunger Lifts (Counts) 120,499 76,775 29,627 32,687 35,778 35,114 29,896
3-80 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Emissions (w/o Plunger)
(MT)
372,283
237,196
91,534
100,988
110,536
108,485
92,365
Wells Venting With
Plunger Lifts (Counts)
-
27,430
47,559
44,170
39,906
40,885
44,026
Emissions (w/ Plunger)
(MT)
-
78,499
136,106
126,406
114,205
117,005
125,994
Total Emissions (MT)
372,283
315,695
227,640
227,393
224,742
225,490
218,359
Previous Estimated
Emissions (MT)
805,883
706,101
266,613
265,142
260,497
260,644
NA
Percent Change in
Emissions
-54%
-55%
-15%
-14%
-14%
-13%
NA
NA - Not Applicable
1 Gathering and Boosting Episodic Emissions
2 For the public review draft, EPA applied a factor developed in the Marchese study (37 metric tons CH4 per station)
3 to calculate emissions from gathering and boosting station episodic events. For more information, please see pages
4 25 to 26 of the 2017 Production Memo. This value was applied to all stations for each year of the time series. A
5 stakeholder comment received on this update expressed support for delaying this update and instead using data
6 reported to GHGRP as a basis for including an estimate in the next Inventory.
7 Table 3-58: National Gathering and Boosting Episodic Emission Activity Data (Number of
8 Stations) and National Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Gathering Stations (counts)
Total Emissions (MT)
Previous Estimated Emissions
2,565
94,905
NA
2,968
109,816
NA
4,246
157,102
NA
4,549
168,313
NA
4,638
171,606
NA
5,034
186,258
NA
5,276
195,212
NA
Percent Change in Emissions
NA
NA
NA
NA
NA
NA
NA
NA - Not Applicable
9 Processing
10 This section references the memo Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Updates
11 under Consideration for Natural Gas Systems Processing Segment Emissions (2017 Processing Memo) ,87
12 In this public review draft of the Inventory, EPA has selected from the options presented in the 2017 Processing
13 Memo to develop emissions estimates. EPA has received initial feedback (one comment) on the 2017 Processing
14 Memo suggesting that the GHGRP-based approaches would underestimate emissions and recommending use of the
15 Marchese et al. data set, using either site-level estimates or allocating Marchese site-level estimates to specific
16 sources using GHGRP emissions data. EPA continues to receive and review feedback on the options presented and
17 may make revisions to these estimates based on that feedback and feedback received on this public review draft.
18 The combined impact of revisions to 2014 processing segment emissions, compared to the previous Inventory, is a
19 decrease in CH4 emissions from 24.0 to 11.1 MMT CO2 Eq. (12.8 MMT CO2 Eq., or 54 percent).
20 The recalculations resulted in an average decrease in emission estimates across the 1990 to 2014 time series,
21 compared to the previous Inventory, of 5.7 MMT CO2 Eq., or 28 percent.
22 This section describes the approach that was used to calculate emissions in this public review draft. EPA will
23 continue to assess stakeholder feedback to develop updated estimates for the final Inventory.
24 Station Fugitives, Compressors, Flares and Dehydrators
25 GHGRP data were used to update the estimates for station fugitives, compressors, flares, and dehydrators. For more
87 See < https://www.epa.gOv/sites/production/files/2017-01/documents/2017_ghgi_ng_revision_under_consideration_-
_gas_proc_l. 17.pdf>.
Energy 3-81
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
information, see pages 5 to 10, and 12 to 16 of the 2017 Processing Memo. Linear interpolation was used to create
time series consistency between earlier years' emission factors and activity factors (1990 through 1992) that
generally rely on data from GRI/EPA 1996 and the GHGRP emission and activity factors for recent years. However,
the plant fugitive emission factors in previous Inventories included plant fugitives but not compressor fugitives, and
separate emission factors were applied for compressor emissions (including compressor fugitive and vented
sources). There is also some overlap between those categories and the flare and dehydrator categories. Because of
these differences, the two sets of emission factors (GRI/EPA and factors calculated from GHGRP) cannot be
directly compared. For the purpose of interpolating for the time series, in this public review draft of the Inventory,
the EPA calculated plant-level emission factors for processing stations that include plant and compressor fugitive
sources, compressor vented sources, flares, and dehydrators. The previous Inventory emission factors were used for
1990 through 1992; emission factors from GHGRP were used for 2011 through 2015. Emission factors for 1993
through 2010 were developed through linear interpolation.
In this public review draft of the Inventory, the EPA incorporated GHGRP average values of reciprocating and
centrifugal compressors per processing plant, using year 2015 data. These values were applied for 2011 through
2015. GHGRP data for 2011 through 2015 are used to develop year-specific splits between centrifugal compressor
seal types (wet versus dry seals). GHGRP year 2015 data were used to develop emission factors on a per-plant basis
for fugitives, flares, and dehydrators, and a per-compressor basis for compressors. Emission factors for dry seal
centrifugal compressors were developed using GHGRP data supplemented with the previous Inventory emission
factor for dry seal emissions.
In order to create time series consistency between earlier years' per plant compressor count estimates (1990 to 1992)
and the most recent years' per plant compressor count estimates (2011 to 2015) that were calculated using GHGRP
data, compressor counts for the years 1993 through 2010 were calculated using linear interpolation between the data
endpoints of 1992 and 2011.
The overall impact of using revised emissions data and activity data from GHGRP is a decrease in emissions for
fugitives and compressors. For the year 2014, the calculated CH4 emissions decrease due to use of revised emission
factors and activity data for processing plant fugitives, compressor venting, flares, and dehydrators is approximately
17.2 MMT C02 Eq.
Gas Engines and Turbines
In the public review draft, the estimates for gas engines and gas turbines were updated to incorporate data from
GHGRP. For more information, please see pages 10 to 11 of the 2017 Processing Memo. GHGRP data were used to
develop an updated value for million horsepower-hours (MMHPhr) per plant for both gas engines and gas turbines.
These values were applied to plant counts for years 2011 to 2015. The previous estimates of MMHPhr per plant
were retained for 1990 through 1992, and values for 1993 to 2010 were developed by linear interpolation between
the 1992 and 2011 values. EPA retained the previous Inventory emission factor and applied it for all years of the
time series. The recalculation for gas engines resulted in an average increase in the estimate of 24,496 MT, or 16
percent over the time series. The recalculation for gas turbines resulted in an average decrease in the estimate of 395
MT, or 8 percent over the time series.
Blowdown Venting
In the public review draft, the estimate for blowdown venting was updated to incorporate data from GHGRP. For
more information, please see pages 11 to 12 of the 2017 Processing Memo. A per-plant emission factor was
developed from 2015 GHGRP data, and applied to plant counts for years 2011 through 2015. The previous emission
factors were retained for 1990 through 1992, and values for 1993 through 2010 were developed by linear
interpolation between the 1992 and 2011 values. The recalculation resulted in an average decrease in the estimate of
7,769 MT or 17 percent over the time series.
Table 3-59: ChU Emissions from Processing Plants (Metric Tons ChU)
Activity Data/Emissions 1990 2005 2011 2012 2013 2014 2015
Plant Total Emissions (MT) 633,867 245,798 143,187 143,341 153,160 157,063 156,252
(Overlapping Sources)
Plant Fugitives (MT) 14,625 14,625 15,687 16,097 16,097
3-82 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Reciprocating Compressors
64,413
64,413
69,089
70,896
70,896
(MT)
Centrifugal Compressors (Wet
22,061
22,387
22,767
23,143
21,428
Seals) (MT)
Centrifugal Compressors (Dry
6,959
6,787
7,936
8,260
9,165
Seals) (MT)
Flares (MT)
19,776
19,776
21,212
21,767
21,767
Dehydrators (MT)
15,353
15,353
16,468
16,899
16,899
Gas Engines (MT)
137,102
187,639
211,002
211,002
226,322
232,241
232,241
Gas Turbines (MT)
3,861
3,875
3,883
3,883
4,165
4,274
4,274
AGR Vents (MT)
16,494
12,267
13,134
13,134
14,088
14,456
14,456
Pneumatic Controllers (MT)
2,414
1,796
1,923
1,923
2,062
2,116
2,116
Blowdowns/Venting (MT)
59,507
34,586
32,251
32,251
34,593
35,497
35,497
Total Processing Emissions
853,245
485,962
405,380
405,534
434,390
445,648
444,837
(MT)
NA - Not Applicable
2 Table 3-60: Previous (last year's) 1990-2014 Inventory Estimates for Processing Segment
3 Emissions (Metric Tons ChU)
Activity Data/Emissions
1990
2005
2011
2012
2013
2014
2015
Previous Plant Total
Emissions (Overlapping
Sources) (MT)
633,867
621,625
761,618
793,031
800,622
843,513
NA
Previous Plants (MT)
42,295
31,457
33,681
33,681
36,126
37,126
NA
Previous Recip. Compressors
(MT)
324,939
327,869
420,871
442,077
445,551
473,829
NA
Previous Centrifugal
Compressors (Wet Seals)
(MT)
240,293
229,237
236,115
237,683
237,940
240,031
NA
Previous Centrifugal
Compressors (Dry Seals)
(MT)
-
6,483
36,835
43,755
44,889
54,117
NA
Previous Kimray Pumps
(MT)
3,678
3,712
4,764
5,005
5,044
5,364
NA
Previous Dehydrator Vents
(MT)
22,662
22,866
29,352
30,831
31,073
33,045
NA
Previous Gas Engines (MT)
137,102
138,338
177,578
186,526
187,991
199,923
NA
Previous Gas Turbines (MT)
3,861
3,896
5,001
5,253
5,294
5,630
NA
Previous AGR Vents (MT)
16,494
12,267
13,134
13,134
14,088
14,478
NA
Previous Pneumatic
Controllers (MT)
2,414
1,796
1,923
1,923
2,062
2,119
NA
Previous
Blowdowns/Venting (MT)
59,507
44,259
47,387
47,387
50,827
52,235
NA
Previous-Total Potential
Emissions (MT)
853,245
822,180
1,006,640
1,047,252
1,060,884
1,117,897
NA
Previous-Gas STAR
Reductions (MT)
(1,488)
(155,501)
(140,368)
(140,449)
(140,744)
(140,797)
NA
Previous-Total Net
Emissions (MT)
851,757
666,679
866,272
906,804
920,141
977,100
NA
NA - Not Applicable
4 Gas STAR Reductions in the Processing Segment
5 The EPA's approach for revising the Inventory methodology to incorporate GHGRP data in the processing segment
6 resulted in net emissions being directly calculated for all sources in each year of the time series. This obviated the
7 need to apply Gas STAR reductions data for these sources. EPA has removed the Gas STAR reductions for the
Energy 3-83
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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
31
32
33
processing segment. Over the 1990 to 2015 time series, annual Gas STAR reductions averaged 76,828 MT CH4, or
1.9 MMTC02 Eq.
Planned Improvements
Plans for Final Inventory
EPA continues to receive and review stakeholder feedback on the 2017 Production Memo, the 2017 Processing
Memo, and the 2017 storage revisions to incorporate Aliso Canyon emissions. EPA will consider this feedback,
along with feedback on this public review draft as it develops the final Inventory.
EPA seeks feedback on the methods applied in this public review draft, on other options presented in the memos,
and on additional planned improvements under consideration discussed in this section.
Uncertainty
The most recent uncertainty analysis for the natural gas systems emission estimates in the Inventory was conducted
for the 1990 to 2009 Inventory that was released in 2011. Since the analysis was last conducted, several of the
methods used in the Inventory have changed, and industry practices and equipment have evolved. In addition, new
studies and other data sources such as those discussed in the sections below offer improvement to understanding and
quantifying the uncertainty of some emission source estimates. EPA is planning for the final Inventory an update to
the uncertainty analysis conducted for the Inventory published in 2011 to reflect the new information. It is difficult
to project whether updated uncertainty bounds around CH4 emission estimates would be wider, tighter, or about the
same as the current uncertainty bounds that were developed for the Inventory published in 2011 (i.e., minus 19
percent and plus 30 percent).
Gas STAR Reductions in the Production Segment
The Inventory estimate for the Production Segment takes into account reductions reported to the Natural Gas STAR
program.88 EPA is considering removing the Gas STAR reductions from its calculations for the production segment.
In this public review draft of the Inventory, Gas STAR production segment reductions reduce calculated potential
emissions by an average of 5 percent over the times series. Many emissions sources in the Inventory are now
calculated using net emissions approaches, with technology-specific activity data and emission factors, and annual
data from the GHGRP. It may not be necessary to adjust for the reductions and may result in double-counting of
reductions, and removing the reductions may improve transparency of the results and methods. However, EPA will
review and potentially incorporate data from Gas STAR's Methane Challenge progam as it becomes availableas part
of process for compiling future inventories.
Table 3-61: Gas STAR Reductions (Metric Tons ChU)
Source
1990
2005
2011 2012 2013 2014
2015
Production Gas STAR
Reductions
Production Emissions
w/o Gas STAR
Reductions
Production Emissions
w/ Gas STAR
Reductions
2,925,162
2,925,162
(263,654)
4,149,308
(448,833) (440,790) (463,903) (494,184) (530,058)
4,590,938 4,678,607 4,665,013 4,752,783 4,696,873
3,885,654 4,142,104 4,237,817 4,201,110 4,258,599 4,166,815
If implemented, this change would increase the production segment estimate by an average of 5 percent over the
time series. There are more Gas STAR reductions in later years of the time series due to an assumption that ongoing
activities are added at the same rate in years with incomplete data. As such this change would impact the trend. For
88 See .
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1 example, in this public review draft, emissions from production decrease by 7 percent from 2005 to 2015; removing
2 the Gas STAR reductions would change this decrease to 15 percent. The change from 2014 to 2015, a decrease of 2
3 percent in the public review draft, would change to a decrease of 1 percent in the final Inventory, if this change is
4 implemented.
5 Information on Abandoned Wells
6 Abandoned wells are not currently included in the Inventory. EPA is seeking emission factors and national activity
7 data available to calculate these emissions. Commenters on previous inventories supported including this source
8 category, noted that the current data were limited, and suggested reviewing data that will become available in the
9 future. EPA has identified studies with data on abandoned wells (Townsend-Small et al. 2016 and Kang et al. 2016),
10 and EPA may provide an information box on this source (without including the estimate in emissions totals) in the
11 final Inventory and will consider including an estimate for this source in future inventories.
12 Plans for 1990-2016 and Future Inventories
13 EPA seeks stakeholder feedback on these future plans.
14 EPA will review data available from GHGRP, in particular new data on gathering and boosting stations, gathering
15 pipelines, and transmission pipeline blowdowns and new well-specific information, available in 2017 for the first
16 time. EPA will consider revising its method to take into account the new GHGRP data. EPA will continue to review
17 CO2 data from GHGRP and make updates consistent with CH4 updates as appropriate.
18 EPA will review data available from the recent Information Collection Request (ICR) for the oil and natural gas
19 industry89 for potential updates to the Inventory, including improving national-level activity data estimates, and will
20 assess new data received by the Methane Challenge Program on an ongoing basis, which may be used to confirm or
21 improve existing estimates and assumptions.
22 EPA continues to track studies that contain data that may be used to update the Inventory.
23 Key studies in progress include DOE-funded work on the following sources: vintage and new plastic pipelines
24 (distribution segment), industrial meters (distribution segment), and sources within the gathering and storage
25 segments.90
26 3.8 Energy Sources of Indirect Greenhouse Gas
27 Emissions
28 In addition to the main greenhouse gases addressed above, many energy-related activities generate emissions of
29 indirect greenhouse gases. Total emissions of nitrogen oxides (NOx), carbon monoxide (CO), and non-CH4 volatile
30 organic compounds (NMVOCs) from energy-related activities from 1990 to 2015 are reported in Table 3-62.
31 Table 3-62: NOx, CO, and NMVOC Emissions from Energy-Related Activities (kt)
Gas/Activity
1990
2005
2011
2012
2013
2014
2015
NOx
21,106
16,602
11,796
11,271
10,747
10,161
9,078
Mobile Fossil Fuel Combustion
10,862
10,295
j 7,294
6,871
6,448
6,024
5,172
Stationary Fossil Fuel Combustion
10,023
5,858
i 3,807
3,655
3,504
3,291
3,061
Oil and Gas Activities
139
321
j 622
663
704
745
745
Waste Combustion
82
128
1 73
82
91
100
100
International Bunker Fuels"
1,956
1,704
s 1,553
1,398
1,139
1,138
1,225
89 See .
90 See .
Energy 3-85
-------
CO
125,640
64,985
44,088
42,164
40,239
38,315
36,348
Mobile Fossil Fuel Combustion
119,360
58,615
j 38,305
36,153
34,000
31,848
29,881
Stationary Fossil Fuel Combustion
5,000
4,648
i 4,170
4,027
3,884
3,741
3,741
Waste Combustion
978
1,403
1,003
1,318
1,632
1,947
1,947
Oil and Gas Activities
302
318
i 610
666
723
780
780
International Bunker Fuels"
103
133
137
133
129
135
141
NMVOCs
12,620
7,191
7,759
7,558
7,357
7,154
6,867
Mobile Fossil Fuel Combustion
10,932
5,724
! 4,562
4,243
3,924
3,605
3,318
Oil and Gas Activities
554
510
; 2,517
2,651
2,786
2,921
2,921
Stationary Fossil Fuel Combustion
912
716
599
569
539
507
507
Waste Combustion
222
241
! 81
94
108
121
121
International Bunker Fuels"
57
54
51
46
41
42
47
a These values are presented for informational purposes only and are not included in totals.
Note: Totals may not sum due to independent rounding.
1 Methodology
2 Emission estimates for 1990 through 2015 were obtained from data published on the National Emission Inventory
3 (NEI) Air Pollutant Emission Trends web site (EPA 2016), and disaggregated based on EPA (2003). Emission
4 estimates for 2012 and 2013 for non-electric generating units (EGU) and non-mobile sources are held constant from
5 2011 in EPA (2016). Emissions were calculated either for individual categories or for many categories combined,
6 using basic activity data (e.g., the amount of raw material processed) as an indicator of emissions. National activity
7 data were collected for individual applications from various agencies.
8 Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
9 activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
10 AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
11 variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
12 Program emissions inventory, and other EPA databases.
13 Uncertainty and Time-Series Consistency
14 Uncertainties in these estimates are partly due to the accuracy of the emission factors used and accurate estimates of
15 activity data. A quantitative uncertainty analysis was not performed.
16 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
17 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
18 above.
w 3.9 International Bunker Fuels (IPCC Source
20 Category 1: Memo Items)
21 Emissions resulting from the combustion of fuels used for international transport activities, termed international
22 bunker fuels under the UNFCCC, are not included in national emission totals, but are reported separately based upon
23 location of fuel sales. The decision to report emissions from international bunker fuels separately, instead of
24 allocating them to a particular country, was made by the Intergovernmental Negotiating Committee in establishing
3-86 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
the Framework Convention on Climate Change.91 These decisions are reflected in the IPCC methodological
guidance, including IPCC (2006), 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).92
Two transport modes are addressed under the IPCC definition of international bunker fuels: aviation and marine.93
Greenhouse gases emitted from the combustion of international bunker fuels, like other fossil fuels, include CO2,
CH4 and N20 for marine transport modes, and CO2 and N20 for aviation transport modes. Emissions from ground
transport activities—by road vehicles and trains—even when crossing international borders are allocated to the
country where the fuel was loaded into the vehicle and, therefore, are not counted as bunker fuel emissions.
The 2006 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 2006 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 2006 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.94
Emissions of CO2 from aircraft are essentially a function of fuel use. Nitrous oxide emissions also depend upon
engine characteristics, flight conditions, and flight phase (i.e., take-off, climb, cruise, decent, and landing). Recent
data suggest that little or no CH4 is emitted by modern engines (Anderson et al. 2011), and as a result, CH4
emissions from this category are considered zero. In jet engines, N20 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. Carbon dioxide is the primary greenhouse gas emitted from marine shipping.
Overall, aggregate greenhouse gas emissions in 2015 from the combustion of international bunker fuels from both
aviation and marine activities were 111.8 MMT CO2 Eq., or 7.0 percent above emissions in 1990 (see Table 3-63
and Table 3-64). Emissions from international flights and international shipping voyages departing from the United
States have increased by 88.8 percent and decreased by 40.6 percent, respectively, since 1990. The majority of these
emissions were in the form of CO2; however, small amounts of CH4 (from marine transport modes) and N20 were
also emitted.
Table 3-63: CO2, ChU, and N2O Emissions from International Bunker Fuels (MMT CO2 Eq.)
Gas/Mode
l'WO
2005
2011
2012
2013
2014
2015
CO2
103.5
113.1
111.7
105.8
99.8
103.2
110.8
Aviation
38.0
60.1
64.8
64.5
65.7
69.4
71.8
Commercial
30.0
55.6
61.7
61.4
62.8
66.3
68.6
Military
8.1
4.5
3.1
3.1
2.9
3.1
3.2
Marine
65.4
53.0
46.9
41.3
34.1
33.8
38.9
CH4
0.2
0.1
0.1
0.1
0.1
0.1
0.1
91 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. lc).
92 Note that the definition of international bunker fuels used by the UNFCCC differs from that used by the International Civil
Aviation Organization.
93 Most emission related international aviation and marine regulations are under the rubric of the International Civil Aviation
Organization (ICAO) or the International Maritime Organization (IMO), which develop international codes, recommendations,
and conventions, such as the International Convention of the Prevention of Pollution from Ships (MARPOL).
94 Naphtha-type jet fuel was used in the past by the military in turbojet and turboprop aircraft engines.
Energy 3-87
-------
Aviation3 + + + + + + +
Marine
0.2
0.1
0.1
0.1
0.1
0.1
0.1
N2O
O.'J
1.0
1.0
0.9
0.9
0.9
0.9
Aviation
0.4
0.6
0.6
0.6
0.6
0.7
0.7
Marine
0.5
0.4
0.4
0.3
0.2
0.2
0.3
Total
104.5
114.2
112.8
106.8
100.7
104.2
111.8
aCH4 emissions from aviation are estimated to be zero.
Notes: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
1 Table 3-64: CO2, ChU, and N2O Emissions from International Bunker Fuels (kt)
Gas/Mode
1990
2005
2011
2012
2013
2014
2015
CO2
103,463
113,139
111,660
105,805
99,763
103,201
110,751
Aviation
38,034
60,125
64,790
64,524
65,664
69,411
71,805
Marine
65,429
53,014
46,870
41,281
34,099
33,791
38,946
CH4
7
5
5
4
3
3
3
Aviation3
0
0
0
0
0
0
0
Marine
7
5
5
4
3
3
3
N2O
3
3
3
3
3
3
3
Aviation
1
2
2
2
2
2
2
Marine
2
1
1
1
1
1
1
aCH4 emissions from aviation are estimated to be zero.
Notes: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
2 Table 3-65: Aviation CO2 and N2O Emissions for International Transport (MMT CO2 Eq.)
Aviation Mode
1990
2005
2011
2012
2013
2014
2015
Commercial Aircraft
30.0
55.6
61.7
61.4
62.8
66.3
68.6
Military Aircraft
8.1
4.5
3.1
3.1
2.9
3.1
3.2
Total
38.0
60.1
64.8
64.5
65.7
69.4
71.8
Notes: Totals may not sum due to independent rounding. Includes aircraft cruise altitude emissions.
3 Methodology
4 Emissions of CO2 were estimated by applying C content and fraction oxidized factors to fuel consumption activity
5 data. This approach is analogous to that described under Section 3.1- CO2 from Fossil Fuel Combustion. Carbon
6 content and fraction oxidized factors for jet fuel, distillate fuel oil, and residual fuel oil were taken directly from EIA
7 and are presented in Annex 2.1, Annex 2.2, and Annex 3.8 of this Inventory. Density conversions were taken from
8 Chevron (2000), ASTM (1989), and USAF (1998). Heat content for distillate fuel oil and residual fuel oil were
9 taken from EIA (2016) and USAF (1998), and heat content for jet fuel was taken from EIA (2016). A complete
10 description of the methodology and a listing of the various factors employed can be found in Annex 2.1. See Annex
11 3.8 for a specific discussion on the methodology used for estimating emissions from international bunker fuel use by
12 the U.S. military.
13 Emission estimates for CH4 and N20 were calculated by multiplying emission factors by measures of fuel
14 consumption by fuel type and mode. Emission factors used in the calculations of CH4 and N20 emissions were
15 obtained from the 2006IPCC Guidelines (IPCC 2006). For aircraft emissions, the following value, in units of grams
16 of pollutant per kilogram of fuel consumed (g/kg), was employed: 0.1 for N20 (IPCC 2006). For marine vessels
17 consuming either distillate diesel or residual fuel oil the following values (g/MJ), were employed: 0.32 for CH4 and
18 0.08 for N20. Activity data for aviation included solely jet fuel consumption statistics, while the marine mode
19 included both distillate diesel and residual fuel oil.
20 Activity data on domestic and international aircraft fuel consumption were developed by the U.S. Federal Aviation
21 Administration (FAA) using radar-informed data from the FAA Enhanced Traffic Management System (ETMS) for
3-88 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
31
32
33
34
35
36
37
1990, 2000 through 2015 as modeled with the Aviation Environmental Design Tool (AEDT). This bottom-up
approach is built from modeling dynamic aircraft performance for each flight occurring within an individual
calendar year. The analysis incorporates data on the aircraft type, date, flight identifier, departure time, arrival time,
departure airport, arrival airport, ground delay at each airport, and real-world flight trajectories. To generate results
for a given flight within AEDT, the radar-informed aircraft data is correlated with engine and aircraft performance
data to calculate fuel burn and exhaust emissions. Information on exhaust emissions for in-production aircraft
engines comes from the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank
(EDB). This bottom-up approach is in accordance with the Tier 3B method from the 2006IPCC Guidelines (IPCC
2006).
International aviation CO2 estimates for 1990 and 2000 through 2015 are obtained from FAA's AEDT model (FAA
2017). The radar-informed method that was used to estimate CO2 emissions for commercial aircraft for 1990, and
2000 through 2015 is not possible for 1991 through 1999 because the radar data set is not available for years prior to
2000. FAA developed OAG schedule-informed inventories modeled with AEDT and great circle trajectories for
1990, 2000 and 2010. Because fuel consumption and CO2 emission estimates for years 1991 through 1999 are
unavailable, consumption estimates for these years were calculated using fuel consumption estimates from the
Bureau of Transportation Statistics (DOT 1991 through 2013), adjusted based on 2000 through 2005 data.
Data on U.S. Department of Defense (DoD) aviation bunker fuels and total jet fuel consumed by the U.S. military
was supplied by the Office of the Under Secretary of Defense (Installations and Environment), DoD. Estimates of
the percentage of each Service's total operations that were international operations were developed by DoD.
Military aviation bunkers included international operations, operations conducted from naval vessels at sea, and
operations conducted from U.S. installations principally over international water in direct support of military
operations at sea. Military aviation bunker fuel emissions were estimated using military fuel and operations data
synthesized from unpublished data from DoD's Defense Logistics Agency Energy (DLA Energy 2016). 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 3-66. See Annex 3.8 for additional discussion of military data.
Activity data on distillate diesel and residual fuel oil consumption by cargo or passenger carrying marine vessels
departing from U.S. ports were taken from unpublished data collected by the Foreign Trade Division of the U.S.
Department of Commerce's Bureau of the Census (DOC 2016) for 1990 through 2001, 2007 through 2015, and the
Department of Homeland Security's Bunker Report for 2003 through 2006 (DHS 2008). Fuel consumption data for
2002 was interpolated due to inconsistencies in reported fuel consumption data. Activity data on distillate diesel
consumption by military vessels departing from U.S. ports were provided by DLA Energy (2016). The total amount
of fuel provided to naval vessels was reduced by 21 percent to account for fuel used while the vessels were not-
underway (i.e., in port). Data on the percentage of steaming hours underway versus not-underway were provided by
the U.S. Navy. These fuel consumption estimates are presented in Table 3-67.
Table 3-66: Aviation Jet Fuel Consumption for International Transport (Million Gallons)
Nationality
1990
2005
2011
2012
2013
2014
2015
U.S. and Foreign Carriers
3,222
5,983
6,634
6,604
6,748
7,126
7,383
U.S. Military
862
462
319
321
294
318
327
Total
4,084
6,445
6,953
6,925
7,042
7,445
7,711
Note: Totals may not sum due to independent rounding.
ble 3-67: Marine Fuel Consumption for International Transport (Million Gallons)
Fuel Type
1990
2005
2011
2012
2013
2014
2015
Residual Fuel Oil
4,781
3,881
3,463
3,069
2,537
2,466
2,718
Distillate Diesel Fuel & Other
617
444
393
280
235
261
492
U.S. Military Naval Fuels
522
471
382
381
308
331
326
Total
5,920
4,796
4,237
3,730
3,081
3,058
3,536
Note: Totals may not sum due to independent rounding.
Energy 3-89
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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
31
32
33
34
35
36
37
38
39
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
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.95 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, govermnents, 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 lias 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 2006IPCC Guidelines (IPCC 2006) is to use data by
specific aircraft type, number of individual flights and, ideally, movement data to better differentiate between
domestic and international aviation and to facilitate estimating the effects of changes in technologies. The IPCC also
recommends that cruise altitude emissions be estimated separately using fuel consumption data, while landing and
take-off (LTO) cycle data be used to estimate near-ground level emissions of gases other than CO2.96
95 See uncertainty discussions under Carbon Dioxide Emissions from Fossil Fuel Combustion.
96 U.S. aviation emission estimates for CO, NOx, and NMVOCs are reported by EPA's National Emission Inventory (NEI) Air
Pollutant Emission Trends website, 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-detined 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.
3-90 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 There is also concern reuardum ihe reliability of I he e\isinm DOC < 2< > I <• > d:il:i on murine \ essel fuel consumption
2 repined ;il I S eiisiiims stations due id ihe smuilicaui deuree of iiiier-;imiu;il \ ariatiou
3 Methodological recalculations were ;ipplied to ihe enure lime-series to ensure time-series consistency from I'wn
4 throimh 2<> 15 I)el;nls on ihe emission trends ihrouuh time :ire described in more del;ul in the \1elhodolou\ section.
5 ;ibo\ e
6 QA/QC and Verification
7 A source-specific QA/QC plan for international bunker fuels was developed and implemented. This effort included a
8 Tier 1 analysis, as well as portions of a Tier 2 analysis. The Tier 2 procedures that were implemented involved
9 checks specifically focusing on the activity data and emission factor sources and methodology used for estimating
10 CO2, CH4, and N20 from international bunker fuels in the United States. Emission totals for the different sectors and
11 fuels were compared and trends were investigated. No corrective actions were necessary.
12 Planned improvements
13 The feasibility of including data from a broader range of domestic and international sources for bunker fuels,
14 including data from studies such as the Third 1MO GHG Study 2014 (IMO 2014), is being considered.
15 3.10 Wood Biomass and Ethanol
is Consumption (IPCC Source Category 1A)
17 The combustion of biomass fuels such as wood, charcoal, and wood waste and biomass-based fuels such as ethanol,
18 biogas, and biodiesel generates CO2 in addition to CH4 and N20 already covered in this chapter. In line with the
19 reporting requirements for inventories submitted under the UNFCCC, CO2 emissions from biomass combustion
20 have been estimated separately from fossil fuel CO2 emissions and are not directly included in the energy sector
21 contributions to U.S. totals. In accordance with IPCC methodological guidelines, any such emissions are calculated
22 by accounting for net carbon (C) fluxes from changes in biogenic C reservoirs in wooded or crop lands. For a more
23 complete description of this methodological approach, see the Land Use, Land-Use Change, and Forestry chapter
24 (Chapter 6), which accounts for the contribution of any resulting CO2 emissions to U.S. totals within the Land Use,
25 Land-Use Change, and Forestry sector's approach.
26 In 2015, total CO2 emissions from the burning of woody biomass in the industrial, residential, commercial, and
27 electricity generation sectors were approximately 198.7 MMT CO2 Eq. (198,723 kt) (see Table 3-68 and Table
28 3-69). As the largest consumer of woody biomass, the industrial sector was responsible for 61.2 percent of the CO2
29 emissions from this source. The residential sector was the second largest emitter, constituting 22.4 percent of the
30 total, while the commercial and electricity generation sectors accounted for the remainder.
31 Table 3-68: CO2 Emissions from Wood Consumption by End-Use Sector (MMT CO2 Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Industrial
135.3
136.3
122.9
125.7
123.1
124.4
121.6
Residential
59.8
44.3
46.4
43.3
59.8
59.8
44.5
Commercial
6.8
7.2
7.1
6.3
7.2
7.6
7.5
Electricity Generation
13.3
19.1
18.8
19.6
21.4
25.9
25.1
Total
215.2
206.9
195.2
194.9
211.6
217.7
198.7
Note: Totals may not sum due to independent rounding.
32 Table 3-69: CO2 Emissions from Wood Consumption by End-Use Sector (kt)
End-Use Sector 1990 2005 2011 2012 2013 2014 2015
Industrial 135,348 136,269 4 122,865 125,724 123,149 124,369 121,564
Energy 3-91
-------
Residential 59,808 44,340 46,402 43,309 59,808 59,808 44,497
Commercial 6,779 7,218 7,131 6,257 7,235 7,569 7,517
Electricity Generation 13,252 19,074 18,784 19,612 21,389 25,908 25,146
Total 215,186 206,901 195,182 194,903 211,581 217,654 198,723
Note: Totals may not sum due to independent rounding.
1 The transportation sector is responsible for most of the ethanol consumption in the United States. Ethanol is
2 currently produced primarily from corn grown in the Midwest, but it can be produced from a variety of biomass
3 feedstocks. Most ethanol for transportation use is blended with gasoline to create a 90 percent gasoline, 10 percent
4 by volume ethanol blend known as E-10 or gasohol.
5 In 2015, the United States consumed an estimated 1,153.1 trillion Btu of ethanol, and as a result, produced
6 approximately 78.9 MMT CO2 Eq. (78,934 kt) (see Table 3-70 and Table 3-71) of CO2 emissions. Ethanol
7 production and consumption has grown significantly since 1990 due to the favorable economics of blending ethanol
8 into gasoline and federal policies that have encouraged use of renewable fuels.
9 Table 3-70: CO2 Emissions from Ethanol Consumption (MMT CO2 Eq.)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation3
4.1
22.4
71.5
71.5
73.4
74.8
77.6
Industrial
0.1
0.5
1.1
1.1
1.2
1.0
1.0
Commercial
+
0.1
0.2
0.2
0.2
0.3
0.3
Total
4.2
22.9
72.9
72.8
74.7
76.1
78.9
+ Does not exceed 0.05 MMT CO2 Eq.
a See Annex 3.2, Table A-94 for additional information on transportation consumption of these fuels.
Note: Totals may not sum due to independent rounding.
10 Table 3-71: CO2 Emissions from Ethanol Consumption (kt)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation3
4,136
22,414
71,537
71,510
73,359
74,810
77,622
Industrial
56
468
1,146
1,142
1,202
987
1,025
Commercial
34
60
198
175
183
277
288
Total
4,227
22,943
72,881
72,827
74,743
76,075
78,934
a See Annex 3.2, Table A-94 for additional information on transportation consumption of these fuels.
Note: Totals may not sum due to independent rounding.
11 Methodology
12 Woody biomass emissions were estimated by applying two EIA gross heat contents (Lindstrom 2006) to U.S.
13 consumption data (EIA 2016) (see Table 3-72), provided in energy units for the industrial, residential, commercial,
14 and electric generation sectors. One heat content (16.95 MMBtu/MT wood and wood waste) was applied to the
15 industrial sector's consumption, while the other heat content (15.43 MMBtu/MT wood and wood waste) was applied
16 to the consumption data for the other sectors. An EIA emission factor of 0.434 MT C/MT wood (Lindstrom 2006)
17 was then applied to the resulting quantities of woody biomass to obtain CO2 emission estimates. It was assumed that
18 the woody biomass contains black liquor and other wood wastes, has a moisture content of 12 percent, and is
19 converted into CO2 with 100 percent efficiency. The emissions from ethanol consumption were calculated by
20 applying an emission factor of 18.7 MMT C/QBtu (EPA 2010) to U.S. ethanol consumption estimates that were
21 provided in energy units (EIA 2016) (see Table 3-73).
3-92 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Table 3-72: Woody Biomass Consumption by Sector (Trillion Btu)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Industrial
1,441.9
1,451.7
1,308.9
1,339.4
1,312.0
1,325.0
1,295.1
Residential
580.0
430.0
450.0
420.0
580.0
580.0
431.5
Commercial
65.7
70.0
69.2
60.7
70.2
73.4
72.9
Electricity Generation
128.5
185.0
182.2
190.2
207.4
251.3
243.9
Total
2,216.2
2,136.7
2,010.2
2,010.3
2,169.5
2,229.6
2,043.3
Note: Totals may not sum due to independent rounding.
2 Table 3-73: Ethanol Consumption by Sector (Trillion Btu)
End-Use Sector
1990
2005
2011
2012
2013
2014
2015
Transportation
60.4
327.4
1,045.0
1,044.6
1,071.6
1,092.8
1,133.9
Industrial
0.8
6.8
16.7
16.7
17.6
14.4
15.0
Commercial
0.5
0.9
2.9
2.6
2.7
4.1
4.2
Total
61.7
335.1
1,064.6
1,063.8
1,091.8
1,111.3
1,153.1
Note: Totals may not sum due to independent rounding.
3 Uncertainty and Time-Series Consistency - TO BE UPDATED
4 FOR FINAL INVENTORY REPORT
5 It is assumed that the combustion efficiency for woody biomass is 100 percent, which is believed to be an
6 overestimate of the efficiency of wood combustion processes in the United States. Decreasing the combustion
7 efficiency would decrease emission estimates. Additionally, the heat content applied to the consumption of woody
8 biomass in the residential, commercial, and electric power sectors is unlikely to be a completely accurate
9 representation of the heat content for all the different types of woody biomass consumed within these sectors.
10 Emission estimates from ethanol production are more certain than estimates from woody biomass consumption due
11 to better activity data collection methods and uniform combustion techniques.
12 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
13 through 2014. Details on the emission trends through time are described in more detail in the Methodology section,
14 above.
15 Recalculations Discussion
16 Wood consumption values for 2014 were not revised relative to the previous Inventory, as there were no historical
17 revisions fromEIA's Monthly Energy Review (EIA 2016).
is Planned Improvements
19 The availability of facility-level combustion emissions through EPA's Greenhouse Gas Reporting Program
20 (GHGRP) will be examined to help better characterize the industrial sector's energy consumption in the United
21 States, and further classify woody biomass consumption by business establishments according to industrial
22 economic activity type. Most methodologies used in EPA's GHGRP are consistent with IPCC, though for EPA's
23 GHGRP, facilities collect detailed information specific to their operations according to detailed measurement
24 standards, which may differ with the more aggregated data collected for the Inventory to estimate total, national U.S.
25 emissions. In addition and unlike the reporting requirements for this chapter under the UNFCCC reporting
26 guidelines, some facility-level fuel combustion emissions reported under the GHGRP may also include industrial
27 process emissions.97 In line with UNFCCC reporting guidelines, fuel combustion emissions are included in this
28 chapter, while process emissions are included in the Industrial Processes and Product Use chapter of this report. In
97 See .
Energy 3-93
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
examining data from EPA's GHGRP that would be useful to improve the emission estimates for the CO2 from
biomass combustion category, particular attention will also be made to ensure time series consistency, as the
facility-level reporting data from EPA's GHGRP are not available for all inventory years as reported in this
Inventory. Additionally, analyses will focus on aligning reported facility-level fuel types and IPCC fuel types per the
national energy statistics, ensuring CO2 emissions from biomass are separated in the facility-level reported data, and
maintaining consistency with national energy statistics provided by EIA. In implementing improvements and
integration of data from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in
national inventories will be relied upon.98
Currently emission estimates from biomass and biomass based fuels included in this inventory are limited to woody
biomass and ethanol. Other forms of biomass-based fuel consumption include biogas and biodiesel. An effort will be
made to examine sources of data for biogas and biodiesel including data from EIA for possible inclusion. EIA
(2016) natural gas data deducts biogas used in the natural gas supply. EIA (2016) diesel data does not deduct for
biodiesel, so the fuel consumption data must subtract biodiesel consumption.
98 See .
3-94 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
4. Industrial Processes and Product Use
The Industrial Processes and Product Use (IPPU) chapter includes greenhouse gas emissions occurring from
industrial processes and from the use of greenhouse gases in products. The industrial processes and product use
categories included in this chapter are presented in Figure 4-1.
Greenhouse gas emissions are produced as the byproducts of various non-energy-related industrial activities. That is,
these emissions are produced either from an industrial process itself, and are not directly a result of energy
consumed during the process. For example, raw materials can be chemically or physically transformed from one
state to another. This transformation can result in the release of greenhouse gases such as carbon dioxide (CO2),
methane (CH4), and nitrous oxide (N20). The processes included in this chapter include iron and steel production
and metallurgical coke production, cement production, lime production, other process uses of carbonates (e.g., flux
stone, flue gas desulfurization, and glass manufacturing), ammonia production and urea consumption, petrochemical
production, aluminum production, soda ash production and use, titanium dioxide production, CO2 consumption,
ferroalloy production, glass production, zinc production, phosphoric acid production, lead production, silicon
carbide production and consumption, nitric acid production, and adipic acid production.
In addition, greenhouse gases are often used in products or by end-consumers. These gases include industrial
sources of man-made compounds such as hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride
(SF6), nitrogen trifluoride (NF3), as well as N20. The present contribution of HFCs, PFCs, SF6, and NF3 gases to the
radiative forcing effect of all anthropogenic greenhouse gases is small; however, because of their extremely long
lifetimes, many of them will continue to accumulate in the atmosphere as long as emissions continue. In addition,
many of these gases have high global warming potentials; SF6 is the most potent greenhouse gas the
Intergovernmental Panel on Climate Change (IPCC) has evaluated. Use of HFCs is growing rapidly since they are
the primary substitutes for ozone depleting substances (ODSs), which are being phased-out under the Montreal
Protocol on Substances that Deplete the Ozone Layer. Hydrofluorocarbons, PFCs, SF6, and NF3 are employed and
emitted by a number of other industrial sources in the United States such as aluminum production, HCFC-22
production, semiconductor manufacture, electric power transmission and distribution, and magnesium metal
production and processing. Nitrous oxide is emitted by the production of adipic acid and nitric acid, semiconductor
manufacturing, end-consumers in product uses through the administration of anesthetics, and by industry as a
propellant in aerosol products.
In 2015, IPPU generated emissions of 375.1 million metric tons of CO2 equivalent (MMT CO2 Eq.), or 5.7 percent
of total U.S. greenhouse gas emissions. Carbon dioxide emissions from all industrial processes were 169.0 MMT
CO2 Eq. (168,956 kt CO2) in2015, or 3.1 percent of total U.S. CO2 emissions. Methane emissions from industrial
processes resulted in emissions of approximately 0.2 MMT CO2 Eq. (9 kt CH4) in 2015, which was less than 1
percent of U.S. CH4 emissions. Nitrous oxide emissions from IPPU were 20.3 MMT CO2 Eq. (68 kt N2O) in 2015,
or 6.1 percent of total U.S. N20 emissions. In 2015 combined emissions of HFCs, PFCs, SF6, and NF3 totaled 185.6
MMT CO2 Eq. Total emissions from IPPU in 2015 were 10.9 percent more than 1990 emissions. Indirect
greenhouse gas emissions also result from IPPU, and are presented in Table 4-107 in kilotons (kt).
Industrial Processes and Product Use 4-1
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
Figure 4-1: 2015 Industrial Processes and Product Use Chapter Greenhouse Gas Sources
(MMT COz Eq.)
Substitution of Ozone Depleting Substances
Iron and Steel Production & Metallurgical Coke Production
Cement Production
Petrochemical Production
Lime Production
Nitric Acid Production
Other Process Uses of Carbonates
Ammonia Production
HCFC-22 Production
Semiconductor Manufacture
Aluminum Production
Carbon Dioxide Consumption
Adipic Acid Production
NzO from Product Uses
Electrical Transmission and Distribution
Soda Ash Production and Consumption
Ferroalloy Production
Titanium Dioxide Production
Glass Production
Urea Consumption for Non-Agricultural Purposes
Magnesium Production and Processing
Phosphoric Acid Production
Zinc Production
Lead Production
Silicon Carbide Production and Consumption
MMT CO, Eq.
The increase in overall IPPU emissions since 1990 reflects a range of emission trends among the emission sources.
Emissions resulting from most types of metal production have declined significantly since 1990, largely due to
production shifting to other countries, but also due to transitions to less-emissive methods of production (in the case
of iron and steel) and to improved practices (in the case of PFC emissions from aluminum production). Emissions
from mineral sources have either increased or not changed significantly since 1990 but largely track economic
cycles, while CO2 and CH4 emissions from chemical sources have either decreased or not changed significantly.
Hydrofluorocarbon emissions from the substitution of ODS have increased drastically since 1990, while the
emission trends of HFCs, PFCs, SF6, and NF3 from other sources are mixed. Nitrous oxide emissions from the
production of adipic and nitric acid have decreased, while N20 emissions from product uses has remained nearly
constant over time. Trends are explained further within each emission source category throughout the chapter.
Table 4-1 summarizes emissions for the IPPU chapter in MMT CO2 Eq. using IPCC Fourth Assessment Report
(AR4) GWP values, following the requirements of the revised United Nations Framework Convention on Climate
Change (UNFCCC) reporting guidelines for national inventories (IPCC 2007).1 Unweighted native gas emissions in
kt are also provided in Table 4-2. The source descriptions that follow in the chapter are presented in the order as
reported to the UNFCCC in the common reporting format tables, corresponding generally to: mineral products,
chemical production, metal production, and emissions from the uses of HFCs, PFCs, SF6, and NF3.
Table 4-1: Emissions from Industrial Processes and Product Use (MMT CO2 Eq.)
Gas/Source 1990 2005 2011 2012 2013 2014 2015
1 See .
Industrial Processes and Product
Use as a Portion of all Emissions
4-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
co2
206.8
189.9
172.9
169.6
171.5
177.6
169.0
Iron and Steel Production &
Metallurgical Coke Production
99.7
66.5
59.9
54.2
52.2
57.5
47.9
Iron and Steel Production
97.2
64.5
58.5
53.7
50.4
55.5
45.1
Metallurgical Coke Production
2.5
2.0
1.4
0.5
1.8
2.0
2.8
Cement Production
33.3
45.9
32.0
35.1
36.1
38.8
39.6
Petrochemical Production
21.3
27.0
26.3
26.5
26.4
26.5
28.1
Lime Production
11.7
14.6
14.0
13.8
14.0
14.2
13.3
Other Process Uses of Carbonates
4.9
6.3
9.3
8.0
10.4
11.8
10.8
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
Carbon Dioxide Consumption
1.5
1.4
4.1
4.0
4.2
4.5
4.3
Soda Ash Production and
Consumption
2.8
3.0
2.7
2.8
2.8
2.8
2.8
Aluminum Production
6.8
4.1
3.3
3.4
3.3
2.8
2.8
Ferroalloy Production
2.2
1.4
1.7
1.9
1.8
1.9
2.0
Titanium Dioxide Production
1.2
1.8
1.7
1.5
1.7
1.7
1.6
Glass Production
1.5
1.9
1.3
1.2
1.3
1.3
1.3
Urea Consumption for Non-
Agricultural Purposes
3.8
3.7
4.0
4.4
4.0
1.4
1.1
Phosphoric Acid Production
1.5
1.3
1.2
1.1
1.1
1.0
1.0
Zinc Production
0.6
1.0
1.3
1.5
1.4
1.0
0.9
Lead Production
0.5
0.6
0.5
0.5
0.5
0.5
0.5
Silicon Carbide Production and
Consumption
0.4
0.2
0.2
0.2
0.2
0.2
0.2
Magnesium Production and
Processing
+
+
+
+
+
+
+
CH4
0.3
0.1
0.1
0.1
0.1
0.2
0.2
Petrochemical Production
0.2
0.1
+
0.1
0.1
0.1
0.2
Ferroalloy Production
+
+
+
+
+
+
+
Silicon Carbide Production and
Consumption
+
+
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
+
+
+
+
+
+
+
Iron and Steel Production
+
+
+
+
+
+
+
Metallurgical Coke Production
0.0
0.0
0.0
0.0
0.0
0.0
0.0
N2O
31.6
22.8
25.6
20.4
19.0
20.8
20.3
Nitric Acid Production
12.1
11.3
10.9
10.5
10.7
10.9
11.6
Adipic Acid Production
15.2
7.1
10.2
5.5
3.9
5.4
4.3
NjO from Product Uses
4.2
4.2
4.2
4.2
4.2
4.2
4.2
Semiconductor Manufacturing
+
0.1
0.2
0.2
0.2
0.2
0.2
HFCs
46.6
120.0
154.4
155.9
159.0
166.7
174.1
Substitution of Ozone Depleting
Substances3
0.3
99.8
145.4
150.2
154.7
161.3
168.6
HCFC-22 Production
46.1
20.0
8.8
5.5
4.1
5.0
5.0
Semiconductor Manufacturing
0.2
0.2
0.2
0.2
0.2
0.3
0.3
Magnesium Production and
Processing
0.0
0.0
+
+
0.1
0.1
0.1
PFCs
24.3
6.7
6.9
6.0
5.7
5.7
5.2
Semiconductor Manufacturing
2.8
3.2
3.4
3.0
2.8
3.2
3.2
Aluminum Production
21.5
3.4
3.5
2.9
3.0
2.5
2.0
SF«
28.8
11.7
9.2
6.8
6.4
6.6
5.8
Electrical Transmission and
Distribution
23.1
8.3
6.0
4.8
4.6
4.8
4.2
Magnesium Production and
Processing
5.2
2.7
2.8
1.6
1.5
1.0
0.9
Semiconductor Manufacturing
0.5
0.7
0.4
0.4
0.4
0.7
0.7
NF3
+
0.5
0.7
0.6
0.6
0.5
0.6
Semiconductor Manufacturing
+
0.5
0.7
0.6
0.6
0.5
0.6
Total 338.3 351.6 369.7 359.5 362.4 378.1 375.1
Industrial Processes and Product Use 4-3
-------
+ Does not exceed 0.05 MMT CO2 Eq.
a Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
1 Table 4-2: Emissions from Industrial Processes and Product Use (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
206,772
189,866
172,934
169,564
171,520
177,556
168,956
Iron and Steel Production &
Metallurgical Coke Production
99,670
66,544
59,929
54,231
52,202
57,503
47,912
Iron and Steel Production
97,16'
64,500
58,503
53,687
50,379
55,489
45,073
Metallurgical Coke Production
2,503
2,044
1,426
543
1,824
2,014
2,839
Cement Production
33,278
45,910
32,010
35,053
36,145
38,789
39,587
Petrochemical Production
21,326
26,972
26,338
26,501
26,395
26,496
28,062
Lime Production
11,700
14,552
13,982
13,785
14,028
14,210
13,342
Other Process Uses of Carbonates
4,90"
6,339
9,335
8,022
10,414
11,811
10,828
Ammonia Production
13,04"
9,196
9,292
9,377
9,962
9,619
10,799
Carbon Dioxide Consumption
1,472
1,375
4,083
4,019
4,188
4,471
4,296
Soda Ash Production and
Consumption
2,822
2,960
2,712
2,763
2,804
2,ill
2,789
Aluminum Production
6,831
4,142
3,292
3,439
3,255
2,833
2,767
Ferroalloy Production
2,152
1,392
1,735
1,903
1,785
1,914
1,960
Titanium Dioxide Production
1,195
1,755
1,729
1,528
1,715
1,688
1,554
Glass Production
1,535
1,928
1,299
1,248
1,317
1,336
1,299
Urea Consumption for Non-
Agricultural Purposes
3,784
3,653
4,030
4,407
4,014
1,380
1,128
Phosphoric Acid Production
1,529
1,342
1,171
1,118
1,149
1,038
1,007
Zinc Production
632
1,030
1,286
1,486
1,429
956
939
Lead Production
516
553
538
527
546
509
504
Silicon Carbide Production and
Consumption
37.5
219
170
158
169
173
180
Magnesium Production and
Processing
1
3
3
2
2
2
3
CH4
12
4
3
4
4
6
9
Petrochemical Production
9
3
2
3
3
5
7
Ferroalloy Production
1
+
+
1
+
1
1
Silicon Carbide Production and
Consumption
1
+
+
+
+
+
+
Iron and Steel Production &
Metallurgical Coke Production
1
1
+
+
+
+
+
Iron and Steel Production
/
1
+
+
+
+
+
Metallurgical Coke Production
0
0
0
0
0
0
0
N2O
106
76
86
69
64
70
68
Nitric Acid Production
41
38
37
35
36
37
39
Adipic Acid Production
51
24
34
19
13
18
14
N20 from Product Uses
14
14
14
14
14
14
14
Semiconductor Manufacturing
-
+
1
1
1
1
1
HFCs
M
M
M
M
M
M
M
Substitution of Ozone Depleting
Substances3
M
M
M
M
M
M
M
HCFC-22 Production
1
1
+
+
+
+
Semiconductor Manufacturing
-
+
+
+
+
+
+
Magnesium Production and
Processing
0
0
+
+
+
+
+
PFCs
M
M
M
M
M
M
M
Semiconductor Manufacturing
M
M
M
M
M
M
M
Aluminum Production
M
M
M
M
M
M
M
4-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
37
38
39
SF«
Electrical Transmission and
Distribution
Magnesium Production and
Processing
Semiconductor Manufacturing
NF3
Semiconductor Manufacturing
+ Does not exceed 0.5 kt.
M (Mixture of gases)
a Small amounts of PFC emissions also result from this source.
Note: Totals may not sum due to independent rounding.
The UNFCCC incorporated the 2006IPCC Guidelines for National Greenhouse Gas Inventories (2006IPCC
Guidelines) as the standard for Annex I countries at the Nineteenth Conference of the Parties (Warsaw, November
11-23, 2013). This chapter presents emission estimates calculated in accordance with the methodological guidance
provided in these guidelines.
QA/QC and Verification Procedures - TO BE UPDATED FOR
FINAL INVENTORY REPORT
For industrial processes and product use sources, a detailed QA/QC plan was developed and implemented for
specific categories. This plan was based on the overall The Quality Assurance/Quality Control and Uncertainty
Management Plan for the U.S. Greenhouse Gas Inventory (QA/QC Management Plan), but was tailored to include
specific procedures recommended for these sources. Two types of checks were performed using this plan: (1)
general, or Tier 1, procedures consistent with Volume 1, Chapter 6 of the 2006 IPCC GL that focus on annual
procedures and checks to be used when gathering, maintaining, handling, documenting, checking, and archiving the
data, supporting documents, and files; and (2) source-category specific, or Tier 2, procedures that focus on checks
and comparisons of the emission factors, activity data, and methodologies used for estimating emissions from the
relevant industrial process and product use sources. Examples of these procedures include checks to ensure that
activity data and emission estimates are consistent with historical trends; that, where possible, consistent and
reputable data sources are used across sources; that interpolation or extrapolation techniques are consistent across
sources; and that common datasets and factors are used where applicable. Tier 1 quality assurance and quality
control procedures and calculation-related QC (category-specific. Tier 2) have been performed for all industrial
process and product use sources. Additional Tier 2 procedures were performed for more significant emission
categories or sources where significant methodological and data updates have taken place, consistent with the IPCC
Good Practice Guidelines. Application of these procedures and updates/improvements as a result of QA processes
(expert, public, and UNFCCC technical expert reviews) are described further within respective source categories,
such as ODS Substitutes in dedicated comparison sections, or the recalculations, and planned improvement sections.
For most industrial process and product use categories, activity data is obtained via aggregation of facility level data
fromEPA's Greenhouse Gas Reporting Program, national commodity surveys conducted by U.S. Geologic Survey
Mineral's Information Center, U.S. Department of Energy (DOE), U.S. Census Bureau, industry associations such
as Air-Conditioning, Heating, and Refrigeration Institute (AHRI), American Chemistry Council (ACC), and
American Iron and Steel Institute, (specified within each source); the uncertainty of the activity data is a function of
the reliability of reported plant-level production data and is influenced by the completeness of the survey response.
The emission factors used are derived from EPA GHGRP, application of IPCC defaults. IPCC default factors are
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 tilings,
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 or may not represent U.S. national averages. Additional
assumptions are described within each source.
The uncertainty analysis performed to quantify uncertainties associated with the 2015 emission estimates from
industrial processes and product use continues a multi-year process for developing credible quantitative uncertainty
1 1 + + + +
1 + + + + +
+ + + + + +
+ + + + + +
+ + + + + +
+ + + + + +
Industrial Processes and Product Use 4-5
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1
2
3
4
5
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
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22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
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.
Box 4-1: Industrial Processes Data from EPA's Gree
On October 30, 2009, the U.S. Environmental Protection Agency (EPA) published a rule requiring annual reporting
of greenhouse gas data from large greenhouse gas emissions sources in the United States. Implementation of the
rule, codified at 40 CFR part 98, is referred to as EPA's Greenhouse Gas Reporting Program (GHGRP). The rule
applies to direct greenhouse gas emitters, fossil fuel suppliers, industrial gas suppliers, and facilities that inject CO2
underground for sequestration or other reasons and requires reporting by sources or suppliers in 41 industrial
categories. Annual reporting is at the facility level, except for certain suppliers of fossil fuels and industrial
greenhouse gases. In general, the threshold for reporting is 25,000 metric tons or more of CO2 Eq. per year, but
reporting is required for all facilities in some industries. Calendar year 2010 was the first year for which data were
reported for facilities subject to 40 CFR part 98, though some source categories first reported data for calendar year
2011.
EPA's GHGRP dataset and the data presented in this Inventory are complementary. EPA presents the data collected
by EPA's GHGRP through a data publication tool (ghgdata.epa.gov) that allows data to be viewed in several
formats, including maps, tables, charts, and graphs for individual facilities or groups of facilities. Most
methodologies used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP, facilities collect
detailed information specific to their operations according to detailed measurement standards. This may differ from
the more aggregated data collected for the Inventory to estimate total, national U.S. emissions. It should be noted
that the definitions for source categories in EPA's GHGRP may differ from those used in this Inventory in meeting
the UNFCCC reporting guidelines (IPCC 2011). In line with the UNFCCC reporting guidelines, the Inventory is a
comprehensive accounting of all emissions from source categories identified in the IPCC (2006) guidelines. Further
information on the reporting categorizations in EPA's GHGRP and specific data caveats associated with monitoring
methods in EPA's GHGRP has been provided on the EPA's GHGRP website.
For certain source categories in this Inventory (e.g., nitric acid production and petrochemical production), EPA has
also integrated data values that have been calculated by aggregating EPA's GHGRP data that are considered
confidential business information (CBI) at the facility level. EPA, with industry engagement, has put forth criteria to
confirm that a given data aggregation shields underlying CBI from public disclosure. EPA is publishing only data
values that meet these aggregation criteria.2 Specific uses of aggregated facility-level data are described in the
respective methodological sections. For other source categories in this chapter, as indicated in the respective planned
improvements sections, EPA is continuing to analyze how facility-level GHGRP data may be used to improve the
national estimates presented in this Inventory, giving particular consideration to ensuring time series consistency and
completeness. As stated previously in the Introduction chapter, this year EPA has integrated GHGRP information
for various Industrial Processes and Product Use categories3 and also identified places where EPA plans to integrate
additional GHGRP data in additional categories4 (see those categories Planned Improvement sections for details).
The GHGRP dataset is a particularly important annual resource and will continue to be important for improving
emissions estimates from Industrial Process and Product Use in future Inventory reports.
2 U.S. EPA Greenhouse Gas Reporting Program. Developments on Publication of Aggregated Greenhouse Gas Data, November
25, 2014. See .
3 Adipic Acid Production, Aluminum Production, Carbon Dioxide Consumption, Electrical Transmission and Distribution,
HCFC-22 Production, Lime Production, Magnesium Production and Processing, ODS Substitutes, Nitric Acid Production,
Petrochemical Production, and Semiconductor Manufacture.
4 Ammonia Production, Cement Production, Glass Production and Other fluorinated gas production.
4-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
2 4.1 Cement Production (IPCC Source Category
3 2A1)
4 Cement production is an energy- and raw material-intensive process that results in the generation of carbon dioxide
5 (CO2) from both the energy consumed in making the cement and the chemical process itself. Emissions from fuels
6 consumed for energy purposes during the production of cement are accounted for in the Energy chapter.
7 During the cement production process, calcium carbonate (CaCCh) is heated in a cement kiln at a temperature of
8 about 1,450 degrees Celsius (2,700 degrees Fahrenheit) to form lime (i.e., calcium oxide or CaO) and CO2 in a
9 process known as calcination or calcining. The quantity of CO2 emitted during cement production is directly
10 proportional to the lime content of the clinker. During calcination, each mole of limestone (CaCCh) heated in the
11 clinker kiln forms one mole of lime (CaO) and one mole of CO2:
12 CaC03 + heat -» CaO + C02
13 Next, the lime is combined with silica-containing materials to produce clinker (an intermediate product), with the
14 earlier byproduct CO2 being released to the atmosphere. The clinker is then allowed to cool, mixed with a small
15 amount of gypsum and potentially other materials (e.g., slag, etc.), and used to make Portland cement.5
16 Carbon dioxide emitted from the chemical process of cement production is the second largest source of industrial
17 CO2 emissions in the United States. Cement is produced in 34 states and Puerto Rico. Texas, California, Missouri,
18 Florida, and Alabama were the five leading cement-producing states in 2015 and accounted for nearly 50 percent of
19 total U.S. production (USGS 2016b). Clinker production in 2015 increased approximately 2 percent from 2014
20 levels as cement sales continued to increase in 2015, but at a more moderate rate compared to 2014. In 2015, U.S.
21 clinker production totaled 76,555 kilotons (USGS 2016a). The resulting CO2 emissions were estimated to be 39.6
22 MMT C02 Eq. (39,587 kt) (see Table 4-3).
23 Table 4-3: CO2 Emissions from Cement Production (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
33.3
33,278
2005
45.9
45.910
2011
32.0
32,010
2012
35.1
35,053
2013
36.1
36,145
2014
38.8
38,789
2015
39.6
39,587
24 Greenhouse gas emissions from cement production increased every year from 1991 through 2006 (with the
25 exception of a slight decrease in 1997), but decreased in the following years until 2009. Emissions from cement
26 production were at their lowest levels in 2009 (2009 emissions are approximately 28 percent lower than 2008
27 emissions and 12 percent lower than 1990). Since 2010, emissions have increased by roughly 27 percent. In 2015,
28 emissions from cement production increased by 2 percent from 2014 levels.
5 Approximately three percent of total clinker production is used to produce masonry cement, which is produced using
plasticizers (e.g., ground limestone, lime, etc.) and Portland cement (USGS 2011). Carbon dioxide emissions that result from the
production of lime used to create masonry cement are included in the Lime Manufacture source category.
Industrial Processes and Product Use 4-7
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25
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27
28
29
30
31
32
Emissions since 1990 have increased by 19 percent. Emissions decreased significantly between 2008 and 2009, due
to the economic recession and associated decrease in demand for construction materials. Emissions increased
slightly from 2009 levels in 2010, and continued to gradually increase during the 2011 through 2015 time period due
to increasing consumption. Cement continues to be a critical component of the construction industry; therefore, the
availability of public and private construction funding, as well as overall economic conditions, have considerable
impact on the level of cement production.
Methodology
Carbon dioxide emissions were estimated using the Tier 2 methodology from the 2006IPCC Guidelines. The Tier 2
methodology was used because detailed and complete data (including weights and composition) for carbonate(s)
consumed in clinker production are not available, and thus a rigorous Tier 3 approach is impractical. Tier 2 specifies
the use of aggregated plant or national clinker production data and an emission factor, which is the product of the
average lime fraction for clinker of 65 percent and a constant reflecting the mass of CO2 released per unit of lime.
The U.S. Geological Survey (USGS) mineral commodity expert for cement has confirmed that this is a reasonable
assumption for the United States (VanOss 2013a). This calculation yields an emission factor of 0.51 tons of CO2 per
ton of clinker produced, which was determined as follows:
EFciinker = 0.6460 CaO X [(44.01 g/mole CO2) (56.08 g/mole CaO)] = 0.5070 tons CCh/ton clinker
During clinker production, some of the clinker precursor materials remain in the kiln as non-calcinated, partially
calcinated, or fully calcinated cement kiln dust (CKD). The emissions attributable to the calcinated portion of the
CKD are not accounted for by the clinker emission factor. The IPCC recommends that these additional CKD CO2
emissions should be estimated as two percent of the CO2 emissions calculated from clinker production (when data
on CKD generation are not available). Total cement production emissions were calculated by adding the emissions
from clinker production to the emissions assigned to CKD (IPCC 2006).
Furthermore, small amounts of impurities (i.e., not calcium carbonate) may exist in the raw limestone used to
produce clinker. The proportion of these impurities is generally minimal, although a small amount (1 to 2 percent) of
magnesium oxide (MgO) may be desirable as a flux. Per the IPCC Tier 2 methodology, a correction for MgO is not
used, since the amount of MgO from carbonate is likely very small and the assumption of a 100 percent carbonate
source of CaO already yields an overestimation of emissions (IPCC 2006). The 1990 through 2012 activity data for
clinker production (see Table 4-4) were obtained from USGS (Van Oss 2013b). Clinker production data for 2013
through 2015 were also obtained from USGS (USGS 2016a). The data were compiled by USGS (to the nearest ton)
through questionnaires sent to domestic clinker and cement manufacturing plants, including the facilities in Puerto
Rico.
Table 4-4: Clinker Production (kt)
Year Clinker
1990 64,355
2005 88.783
2011 61,903
2012 67,788
2013 69,900
2014 75,012
201 5 76,555
Notes: Clinker production from 1990 through 2015
includes Puerto Rico. Data were obtained from USGS
(Van Oss 2013a; USGS 2016), whose original data source
was USGS and U.S. Bureau of Mines Minerals Yearbooks
(2014 data obtained from mineral industry surveys for
cement in September 2015; 2015 data obtained from
mineral industry surveys for cement in January 2016).
4-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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7
8
9
10
11
12
13
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22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
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 CaCCb, when a small percentage likely consists of other carbonate and non-
carbonate raw materials. The lime content of clinker varies from 60 to 67 percent; 65 percent is used as a
representative value (Van Oss 2013a). CKD loss can range from 1.5 to 8 percent depending upon plant
specifications. Additionally, some amount of CO2 is reabsorbed when the cement is used for construction. As
cement reacts with water, alkaline substances such as calcium hydroxide are formed. During this curing process,
these compounds may react with CO2 in the atmosphere to create calcium carbonate. This reaction only occurs in
roughly the outer 0.2 inches of surface area. Because the amount of CO2 reabsorbed is thought to be minimal, it was
not estimated.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-5. Based on the
uncertainties associated with total U.S. clinker production, the CO2 emission factor for clinker production, and the
emission factor for additional CO2 emissions from CKD, 2015 CO2 emissions from cement production were
estimated to be between 37.2 and 42.0 MMT CO2 Eq. at the 95 percent confidence level. This confidence level
indicates a range of approximately 6 percent below and 6 percent above the emission estimate of 39.6 MMT CO2
Eq.
Table 4-5: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Cement
Production (MMT CO2 Eq. and Percent)
_ 2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Cement Production CO2 39.6
37.2
42.0
-6%
+6%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for
a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
In response to comments from the Portland Cement Association (PCA) during the prior public review and UNFCCC
expert technical reviews, EPA is continuing to evaluate and analyze data reported under EPA's GHGRP that would
be useful to improve the emission estimates for the Cement Production source category. EPA held a technical
meeting with PCA in August 2016 to review inventory methods and available data from the GHGRP data set. Most
cement production facilities reporting under EPA's GHGRP use Continuous Emission Monitoring Systems (CEMS)
to monitor and report CO2 emissions, thus reporting combined process and combustion emissions from kilns. In the
near-term, EPA will assess use of aggregated activity data on clinker production reported by all facilities starting in
calendar year 2014, first reported to EPA in March 2015. In assessing use of this and other data from EPA's
GHGRP, particular attention will be made to ensure time series consistency of the emissions estimates presented in
future Inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as facility-level reporting
data from EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar year 2010,
are not available for all inventory years (i.e., 1990 through 2009) as required for this Inventory. This will be
documented along with application of category-specific QC to compare activity data from GHGRP with existing
data from USGS. In implementing improvements and integration of data from EPA's GHGRP, the latest guidance
from the IPCC on the use of facility-level data in national inventories will be relied upon, in addition to category
Industrial Processes and Product Use 4-9
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1 specific QC methods recommended by 2006IPCC Guidelines.6 EPA's long-term improvement plan includes
2 continued assessment and feasibility to use additional GHGRP information, in particular disaggregating aggregated
3 GHGRP emissions consistent with IPCC and UNFCCC guidelines to present both national process and combustion
4 emissions streams. This longer-term planned analysis is still in development and has not been updated for this
5 current inventory report.
6 4.2 Lime Production fSPCC Source Category
, 2A2)
8 Lime is an important manufactured product with many industrial, chemical, and environmental applications. Lime
9 production involves three main processes: stone preparation, calcination, and hydration. Carbon dioxide (CO2) is
10 generated during the calcination stage, when limestone—mostly calcium carbonate (CaCCh)—is roasted at high
11 temperatures in a kiln to produce calcium oxide (CaO) and CO2. The CO2 is given off as a gas and is normally
12 emitted to the atmosphere.
13 CaCO3 —> CaO + C02
14 Some of the CO2 generated during the production process, however, is recovered at some facilities for use in sugar
15 refining and precipitated calcium carbonate (PCC) production.7 Emissions from fuels consumed for energy purposes
16 during the production of lime are accounted for in the Energy chapter.
17 For U.S. operations, the term "lime" actually refers to a variety of chemical compounds. These include CaO, or
18 high-calcium quicklime; calcium hydroxide (Ca(OH)2), or hydrated lime; dolomitic quicklime ([CaOMgO]); and
19 dolomitic hydrate ([Ca(OH)2'MgO] or [Ca(OH)2'Mg(OH)2]).
20 The current lime market is approximately distributed across five end-use categories as follows: metallurgical uses,
21 37 percent; environmental uses, 31 percent; chemical and industrial uses, 22 percent; construction uses, 9 percent;
22 and refractory dolomite, 1 percent (USGS 2016b). The major uses are in steel making, flue gas desulfurization
23 systems at coal-fired electric power plants, construction, and water treatment, as well as uses in mining, pulp and
24 paper and precipitated calcium carbonate manufacturing. Lime is also used as a CO2 scrubber, and there has been
25 experimentation on the use of lime to capture CO2 from electric power plants.
26 Lime production in the United States—including Puerto Rico—was reported to be 18,279 kilotons in 2015
27 (Corathers 2017). At year-end 2015, there were 77 operating primary lime plants in the United States, including
28 Puerto Rico8. Principal lime producing states are Missouri, Alabama, Kentucky, Ohio, Texas (USGS 2016a).
29 U.S. lime production resulted in estimated net CO2 emissions of 13.3 MMT CO2 Eq. (13,342 kt) (see Table 4-6 and
30 Table 4-7). The trends in CO2 emissions from lime production are directly proportional to trends in production,
31 which are described below.
32 Table 4-6: CO2 Emissions from Lime Production (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
11.7
11,700
2005
14.6
14.552
2011
14.0
13,982
6 See .
7 PCC is 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.
8 In 2015, 74 operating primary lime facilities in the United States reported to the EPA Greenhouse Gas Reporting Program.
4-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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2012 13.8 13,785
2013 14.0 14,028
2014 14.2 14,210
201 5 113 13,342
1 Table 4-7: Potential, Recovered, and Net CO2 Emissions from Lime Production (kt)
Year
Potential
Recovered3
Net Emissions
1990
11,959
259
11,700
2005
15.074
522
14.552
2011
14,389
407
13,982
2012
14,258
473
13,785
2013
14,495
467
14,028
2014
14,715
505
14,210
2015
13,764
422
13,342
a For sugar refining and PCC production.
Note: Totals may not sum due to independent rounding.
2 In 2015, lime production decreased compared to 2014 levels (decrease of about 6 percent) at 18,279 kilotons, owing
3 to decreased consumption by the U.S. nonferrous metallurgical industries (primarily copper) and steel industries
4 (Corathers 2017; USGS 2016a).
5 Methodology
6 To calculate emissions, the amounts of high-calcium and dolomitic lime produced were multiplied by their
7 respective emission factors using the Tier 2 approach from the 2006IPCC Guidelines. The emission factor is the
8 product of the stoichiometric ratio between CO2 and CaO, and the average CaO and MgO content for lime. The CaO
9 and MgO content for lime is assumed to be 95 percent for both high-calcium and dolomitic lime (IPCC 2006). The
10 emission factors were calculated as follows:
11 For high-calcium lime:
12 [(44.01 g/mole C02) h- (56.08 g/mole CaO)] x (0.9500 CaO/lime) = 0.7455 g C02/g lime
13 For dolomitic lime:
14 [(88.02 g/mole C02) h- (96.39 g/mole CaO)] x (0.9500 CaO/lime) = 0.8675 g C02/g lime
15 Production was adjusted to remove the mass of chemically combined water found in hydrated lime, determined
16 according to the molecular weight ratios of H20 to (Ca(OH)2 and |Ca(OH)2*IVIg(OH)2|) (IPCC 2006). These factors
17 set the chemically combined water content to 24.3 percent for high-calcium hydrated lime, and 27.2 percent for
18 dolomitic hydrated lime.
19 The 2006 IPCC Guidelines (Tier 2 method) also recommends accounting for emissions from lime kiln dust (LKD)
20 through application of a correction factor. LKD is a byproduct of the lime manufacturing process typically not
21 recycled back to kilns. LKD is a very fine-grained material and is especially useful for applications requiring very
22 small particle size. Most common LKD applications include soil reclamation and agriculture. Currently, data on
23 annual LKD production is not readily available to develop a country specific correction factor. Lime emission
24 estimates were multiplied by a factor of 1.02 to account for emissions from LKD (IPCC 2006). See the Planned
25 Improvements section associated with efforts to improve uncertainty analysis and emission estimates associated with
26 LKD.
27 Lime emission estimates were further adjusted to account for the amount of CO2 captured for use in on-site
28 processes. All the domestic lime facilities are required to report these data to EPA under its GHGRP. The total
29 national-level annual amount of CO2 captured for on-site process use was obtained from EPA's GHGRP (EPA
Industrial Processes and Product Use 4-11
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1 2016) based on reported facility level data for years 2010 through 2015. The amount of CO2 captured/recovered for
2 on-site process use is deducted from the total potential emissions (i.e., from lime production and LKD). The net lime
3 emissions are presented in Table 4-6 and Table 4-7. GHGRP data on CO2 removals (i.e., CO2 captured/recovered)
4 was available only for 2010 through 2015. Since EPA's GHGRP data are not available for 1990 through 2009, IPCC
5 "splicing" techniques were used as per the 2006 IPCC Guidelines on time series consistency (IPCC 2006, Volume 1,
6 Chapter 5).
7 Lime production data (by type, high-calcium- and dolomitic-quicklime, high-calcium- and dolomitic-hydrated, and
8 dead-burned dolomite) for 1990 through 2015 (see Table 4-8) were obtained from the U.S. Geological Survey
9 (USGS) (USGS 2016b; Corathers 2017) annual reports and are compiled by USGS to the nearest ton. Natural
10 hydraulic lime, which is produced from CaO and hydraulic calcium silicates, is not manufactured in the United
11 States (USGS 2011). Total lime production was adjusted to account for the water content of hydrated lime by
12 converting hydrate to oxide equivalent based on recommendations from the IPCC, and is presented in Table 4-9
13 (IPCC 2006). The CaO and CaOMgO contents of lime were obtained from the IPCC (IPCC 2006). Since data for
14 the individual lime types (high calcium and dolomitic) were not provided prior to 1997, total lime production for
15 1990 through 1996 was calculated according to the three year distribution from 1997 to 1999.
16 Table 4-8: High-Calcium- and Dolomitic-Quicklime, High-Calcium- and Dolomitic-Hydrated,
17 and Dead-Burned-Dolomite Lime Production (kt)
High-Calcium Dolomitic High-Calcium Dolomitic Dead-Burned
Year Quicklime Quicklime Hydrated Hydrated Dolomite
1990 11,166 2,234 1,781 319 342
2005 14,100 2,990 2,220 474 200
2011
13,900
2,690
2,010
230
200
2012
13,600
2,790
2,000
253
200
2013
13,800
2,850
2,050
260
200
2014
14,100
2,740
2,190
279
200
2015
13,100
2,550
2,150
279
200
18 Table 4-9: Adjusted Lime Production (kt)
Year High-Calcium Dolomitic
1990 12,466 2,800
2005 15,721 3,522
2011
15,367
3,051
2012
15,075
3,076
2013
15,297
3,252
2014
15,699
3,135
2015
14,670
2,945
Note: Minus water content of hydrated lime.
19
20
21
22
23
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
The uncertainties contained in these estimates can be attributed to slight differences in the chemical composition of
lime products and CO2 recovery rates for on-site process use over the time series. Although the methodology
accounts for various formulations of lime, it does not account for the trace impurities found in lime, such as iron
4-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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42
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, especially at captive lime production facilities. As noted above, lime lias many different chemical,
industrial, enviromnental, and construction applications. In many processes, CO2 reacts with the lime to create
calcium carbonate (e.g., water softening). Carbon dioxide reabsorption rates vary, however, depending on the
application. For example, 100 percent of the lime used to produce precipitated calcium carbonate reacts with CO2;
whereas most of the lime used in steel making reacts with impurities such as silica, sulfur, and aluminum
compounds. Quantifying the amount of CO2 that is reabsorbed would require a detailed accounting of lime use in the
United States and additional information about the associated processes where both the lime and byproduct CO2 are
"reused" are required to quantify the amount of CO2 that is reabsorbed. Research conducted thus far has not yielded
the necessary information to quantify CO2 reabsorption rates.9 However, some additional information on the amount
of CO2 consumed on site at lime facilities has been obtained from EPA's GHGRP.
In some cases, lime is generated from calcium carbonate byproducts at pulp mills and water treatment plants.10 The
lime generated by these processes is included in the USGS data for commercial lime consumption. In the pulping
industry, mostly using the Kraft (sulfate) pulping process, lime is consumed in order to causticize a process liquor
(green liquor) composed of sodium carbonate and sodium sulfide. The green liquor results from the dilution of the
smelt created by combustion of the black liquor where biogenic carbon (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 C fluxes from changes in biogenic C reservoirs
in wooded or crop lands (see the Land Use, Land-Use Change, and Forestry chapter).
In the case of water treatment plants, lime is used in the softening process. Some large water treatment plants may
recover their waste calcium carbonate and calcine it into quicklime for reuse in the softening process. Further
research is necessary to determine the degree to which lime recycling is practiced by water treatment plants in the
United States.
Another uncertainty is the assumption that calcination emissions for LKD are around 2 percent. The National Lime
Association (NLA) lias commented that the estimates of emissions from LKD in the United States could be closer to
6 percent. They also note that additional emissions (approximately 2 percent) may also be generated through
production of other byproducts/wastes (off-spec lime that is not recycled, scrubber sludge) at lime plants (Seeger
2013). There is limited data publicly available on LKD generation rates and also quantities, types of other
byproducts/wastes produced at lime facilities. Further research and data is needed to improve understanding of
additional calcination emissions to consider revising the current assumptions that are based on IPCC guidelines. In
preparing estimates for the current inventory, EPA initiated a dialogue with NLA to discuss data needs to generate a
country specific LKD factor and is reviewing the information provided by NLA. More information can be found in
the Planned Improvements section below.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-10. Lime CO2 emissions
for 2015 were estimated to be between 13.8 and 14.5 MMT CO2 Eq. at the 95 percent confidence level. This
confidence level indicates a range of approximately 3 percent below and 3 percent above the emission estimate of
14.1 MMT C02Eq.
9 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).
10 Some carbide producers may also regenerate lime from their calcium hydroxide byproducts, which does not result in
emissions of CO2. In making calcium carbide, quicklime is mixed with coke and heated in electric furnaces. The regeneration of
lime in this process is done using a waste calcium hydroxide (hydrated lime) [CaC2 + 2H2O —» C2H2 + Ca(OH) 2], not calcium
carbonate [CaCCb]. 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.
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Table 4-10: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lime
Production (MMT CO2 Eq. and Percent)
Smiriv
(¦iis
2015 l.missiim l!siiiii;iU-
(MMT CO: Kil l
I iKirliiiiil\ K;iii Kmissimi
(MMT CO: l.(|.)
l'.slim;ik''
("..)
I.I HUT I |)|KT I.IHUT
lillllllll lillllllll lillllllll
I |)|KT
Hound
I.ime l'n
)dnction
t ()
14.1
13.8 14.5 -3%
+ i%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation lor a l)5 percent conlidence interval.
Methodological recalculations were applied Id ilie enure lime scries id ensure eoiisisieiics 111 emissions from I'wo
lliroimh 2n|5 l)el;iilson i lie emission trends ihroimh lime arc described 111 more del;iil 1111 lie Melliodolous seel ion.
;ibo\ e
Recalculations Discussion
Updated data from Lisa Corathers (U.S. Geological Survey) (Corathers 2017) resulted in High-Calcium Quicklime
production data changes for 2014 and Dolomitic Quicklime production data changes for 2013 and 2014, as shown in
Table 4-8.
Recovered emissions shown in Table 4-7 were updated using aggregated GHGRP data from 2010 to 2015. This data
changed slightly from previous Inventory reports due to the adoption of new rounding technique to maintain
consistency with other data sets. Both of these data updates resulted in changes to emissions estimates across the
time-series (2011 to 2015) of less than 1 percent.
Planned Improvements
Future improvements involve finishing review of data to improve current assumptions associated with emissions
from production of LKD and other byproducts/wastes as discussed in the Uncertainty and Time-Series Consistency
section per comments from the NLA provided during the public review of the draft 2015 Inventory. In response to
comments, EPA met with NLA on April 7, 2015 to outline specific information required to apply IPCC methods to
develop a country-specific correction factor to more accurately estimate emissions from production of LKD. In
response to this technical meeting, in January and February 2016, NLA compiled and shared historical emissions
information reported by member facilities on an annual basis under voluntary reporting initiatives over 2002 through
2011 associated with generation of total calcined byproducts and LKD (LKD reporting only differentiated starting in
2010). This emissions information was reported on a voluntary basis consistent with NLA's facility-level reporting
protocol also recently provided. EPA has reviewed the information provided by NLA and plans to work with them
to address need for EPA's analysis, as there is limited information across the time series. Due to limited resources
and need for additional QA of information, this planned improvement is still in process and has not been
incorporated into this current Inventory report. As an interim step, EPA plans to update the qualitative description of
uncertainty to reflect the information provided by NLA in the final report.
In addition, EPA plans to also review GHGRP emissions and activity data reported to EPA under Subpart S, in
particular review of aggregated activity data on lime production, by type. Particular attention will be made to also
ensuring time series consistency of the emissions estimates presented in future Inventory reports, consistent with
IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the
program's initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory
years (i.e., 1990 through 2009) as required for this Inventory. In implementing improvements and integration of data
from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
be relied upon.11
11 See .
4-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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4.3 Glass Production (IPCC Source Category
2 A3)
Glass production is an energy and raw-material intensive process that results in the generation of CO2 from both the
energy consumed in making glass and the glass process itself. Emissions from fuels consumed for energy purposes
during the production of glass are accounted for in the Energy sector.
Glass production employs a variety of raw materials in a glass-batch. These include formers, fluxes, stabilizers, and
sometimes colorants. The major raw materials (i.e., fluxes and stabilizers) which emit process-related carbon
dioxide (CO2) emissions during the glass melting process are limestone, dolomite, and soda ash. The main former in
all types of glass is silica (SiCh). Other major formers in glass include feldspar and boric acid (i.e., borax). Fluxes
are added to lower the temperature at which the batch melts. Most commonly used flux materials are soda ash
(sodium carbonate, Na2CC>3) and potash (potassium carbonate, K20). Stabilizers are used to make glass more
chemically stable and to keep the finished glass from dissolving and/or falling apart. Commonly used stabilizing
agents in glass production are limestone (CaCCh). dolomite (CaCO^MgCO;,)- alumina (AI2O3), magnesia (MgO),
barium carbonate (BaCCh). strontium carbonate (SrCCh). lithium carbonate (Li2C03), and zirconia (Z1O2) (OIT
2002). Glass makers also use a certain amount of recycled scrap glass (cullet), which comes from in-house return of
glassware broken in the process or other glass spillage or retention such as recycling or cullet broker services.
The raw materials (primarily limestone, dolomite and soda ash) release CO2 emissions in a complex high-
temperature chemical reaction during the glass melting process. This process is not directly comparable to the
calcination process used in lime manufacturing, cement manufacturing, and process uses of carbonates (i.e.,
limestone/dolomite use), but has the same net effect in terms of CO2 emissions (IPCC 2006). The U.S. glass industry
can be divided into four main categories: containers, flat (window) glass, fiber glass, and specialty glass. The
majority of commercial glass produced is container and flat glass (EPA 2009). The United States is one of the major
global exporters of glass. Domestically, demand comes mainly from the construction, auto, bottling, and container
industries. There are over 1,500 companies that manufacture glass in the United States, with the largest being
Corning, Guardian Industries, Owens-Illinois, and PPG Industries.12
In 2015, 341 kilotons of limestone and 2,390 kilotons of soda ash were consumed for glass production (USGS
2015c; Willett 2017). Dolomite consumption data for glass manufacturing was reported to be zero for 2015. Use of
limestone and soda ash in glass production resulted in aggregate CO2 emissions of 1.3 MMT CO2 Eq. (1,299 kt) (see
Table 4-11). Overall, emissions have decreased 15 percent from 1990 through 2015.
Emissions in 2015 decreased approximately 3 percent from 2014 levels while, in general, emissions from glass
production have remained relatively constant over the time series with some fluctuations since 1990. In general,
these fluctuations were related to the behavior of the export market and the U.S. economy. Specifically, the extended
downturn in residential and commercial construction and automotive industries between 2008 and 2010 resulted in
reduced consumption of glass products, causing a drop in global demand for limestone/dolomite and soda ash, and a
corresponding decrease in emissions. Furthermore, the glass container sector is one of the leading soda ash
consuming sectors in the United States. Some commercial food and beverage package manufacturers are shifting
from glass containers towards lighter and more cost effective polyethylene terephthalate (PET) based containers,
putting downward pressure on domestic consumption of soda ash (USGS 1995 through 2015c).
Table 4-11: CO2 Emissions from Glass Production (MMT CO2 Eq. and kt)
Year MMT CO2 Eq.
1000 1.5 1,535
2005 1.0 1.028
12 Excerpt from Glass & Glass Product Manufacturing Industry Profile, First Research. Available online at:
.
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2011 1.3 1,299
2012 1.2 1,248
2013 1.3 1,317
2014 1.3 1,336
201 5 y 1,299
Note: Totals may not sum due to
independent rounding
Methodology
Carbon dioxide emissions were calculated based on the 2006IPCC Guidelines Tier 3 method by multiplying the
quantity of input carbonates (limestone, dolomite, and soda ash) by the carbonate-based emission factor (in metric
tons CO^metric ton carbonate): limestone, 0.43971; dolomite, 0.47732; and soda ash, 0.41492.
Consumption data for 1990 through 2015 of limestone, dolomite, and soda ash used for glass manufacturing were
obtained from the U.S. Geological Survey (USGS) Minerals Yearbook: Crushed Stone Annual Report (1995 through
2015b), 2015 preliminary data from the USGS Crushed Stone Commodity Expert (Willett 2017), the USGS
Minerals Yearbook: Soda Ash Annual Report (1995 through 2015) (USGS 1995 through 2015c), USGS Mineral
Industry Surveys for Soda Ash in January 2015 (USGS 2015a) and the U.S. Bureau of Mines (1991 and 1993a),
which are reported to the nearest ton. During 1990 and 1992, the USGS did not conduct a detailed survey of
limestone and dolomite consumption by end-use. Consumption for 1990 was estimated by applying the 1991
percentages of total limestone and dolomite use constituted by the individual limestone and dolomite uses to 1990
total use. Similarly, the 1992 consumption figures were approximated by applying an average of the 1991 and 1993
percentages of total limestone and dolomite use constituted by the individual limestone and dolomite uses to the
1992 total.
Additionally, each year the USGS withholds data on certain limestone and dolomite end-uses due to confidentiality
agreements regarding company proprietary data. For the purposes of this analysis, emissive end-uses that contained
withheld data were estimated using one of the following techniques: (1) the value for all the withheld data points for
limestone or dolomite use was distributed evenly to all withheld end-uses; or (2) the average percent of total
limestone or dolomite for the withheld end-use in the preceding and succeeding years.
There is a large quantity of limestone and dolomite reported to the USGS under the categories "unspecified-
reported" and "unspecified-estimated." A portion of this consumption is believed to be limestone or dolomite used
for glass manufacturing. The quantities listed under the "unspecified" categories were, therefore, allocated to glass
manufacturing according to the percent limestone or dolomite consumption for glass manufacturing end use for that
year.13
Based on the 2015 reported data, the estimated distribution of soda ash consumption for glass production compared
to total domestic soda ash consumption is 48 percent (USGS 1995 through 2015c).
Table 4-12: Limestone, Dolomite, and Soda Ash Consumption Used in Glass Production (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Limestone
430
920
614
555
693
775
341
Dolomite
59
541
0
0
0
0
0
Soda Ash
3,177
3,050
2,480
2,420
2,440
2,410
2,390
Total
3,666
4,511
3,094
2,975
3,133
3,185
2,731
13 This approach was recommended by USGS.
4-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Uncertainty and Time-Series Consistency - TO BE UPDATED
2 FOR FINAL INVENTORY REPORT
3 The uncertainty levels presented in this section arise in part due to variations in the chemical composition of
4 limestone used in glass production. In addition to calcium carbonate, limestone may contain smaller amounts of
5 magnesia, silica, and sulfur, among other minerals (potassium carbonate, strontium carbonate and barium carbonate,
6 and dead burned dolomite). Similarly, the quality of the limestone (and mix of carbonates) used for glass
7 manufacturing will depend on the type of glass being manufactured.
8 The estimates below also account for uncertainty associated with activity data. Large fluctuations in reported
9 consumption exist, reflecting year-to-year changes in the number of survey responders. The uncertainty resulting
10 from a shifting survey population is exacerbated by the gaps in the time series of reports. The accuracy of
11 distribution by end use is also uncertain because this value is reported by the manufacturer of the input carbonates
12 (limestone, dolomite & soda ash) and not the end user. For 2015, there has been no reported consumption of
13 dolomite for glass manufacturing. This data has been reported to USGS by dolomite manufacturers and not end-
14 users (i.e., glass manufacturers). There is a high uncertainty associated with this estimate, as dolomite is a major raw
15 material consumed in glass production. Additionally, there is significant inherent uncertainty associated with
16 estimating withheld data points for specific end uses of limestone and dolomite. The uncertainty of the estimates for
17 limestone and dolomite used in glass making is especially high. Lastly, much of the limestone consumed in the
18 United States is reported as "other unspecified uses;" therefore, it is difficult to accurately allocate this unspecified
19 quantity to the correct end-uses. Further research is needed into alternate and more complete sources of data on
20 carbonate-based raw material consumption by the glass industry.
21 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-13. In 2015, glass
22 production CO2 emissions were estimated to be between 1.3 and 1.4 MMT CO2 Eq. at the 95 percent confidence
23 level. This indicates a range of approximately 4 percent below and 5 percent above the emission estimate of 1.3
24 MMT CO2 Eq.
25 Table 4-13: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Glass
26 Production (MMT CO2 Eq. and Percent)
Source Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Glass Production CO2
1.3
1.3
1.4
-4%
+5%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
27 Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
28 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
29 above.
30 Recalculations Discussion
31 Limestone and dolomite consumption data for 2014 was revised relative to the previous Inventory based on the
32 preliminary data obtained directly from the USGS Crush Stone Commodity expert, Jason Willett (Willett 2017). In
33 the previous Inventory (i.e., 1990 through 2014), preliminary data were used for 2014 and updated for the current
34 Inventory. The published time series was reviewed to ensure time series consistency. This update caused a decrease
35 in 2014 emissions of less than 1 percent compared to 2014 emissions from the previous inventory (i.e., 1990 through
36 2014).
37 Planned Improvements
38 As noted in the prior reports, current publicly available activity data shows consumption of only limestone and soda
39 ash for glass manufacturing. While limestone and soda ash are the predominant carbonates used in glass
Industrial Processes and Product Use 4-17
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41
manufacturing, there are other carbonates that are also consumed for glass manufacturing, although in smaller
quantities. EPA has initiated review of activity data on carbonate consumption, by type by the glass industry from
EPA's Greenhouse Gas Reporting Program (GHGRP) reported annually since 2010.
EPA has initiated review of this activity data and is hopes to finalize assessment for future integration of data
reported under EPA's GHGRP this spring to improve the completeness of emission estimates and facilitate
category-specific QC for the Glass Production source category. EPA's GHGRP has an emission threshold for
reporting, so the assessment will consider the completeness of carbonate consumption data for glass production in
the United States. Particular attention will also be made to also ensuring time series consistency of the emissions
estimates presented in future Inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as
the facility-level reporting data from EPA's GHGRP, with the program's initial requirements for reporting of
emissions in calendar year 2010, are not available for all inventory years (i.e., 1990 through 2009) as required for
this Inventory. In implementing improvements and integration of data from EPA's GHGRP, the latest guidance
from the IPCC on the use of facility-level data in national inventories will be relied upon.14 These planned
improvements are ongoing and EPA hopes to also initiate research into other sources of activity data for carbonate
consumption by the glass industry.
4.4 Other Process Uses of Carbonates (IPCC
Source Category 2A4)
Limestone (CaCCh). dolomite (CaCChMgCCh).'5 and other carbonates such as soda ash, magnesite, and siderite are
basic materials used by a wide variety of industries, including construction, agriculture, chemical, metallurgy, glass
production, and environmental pollution control. This section addresses only limestone and dolomite use. For
industrial applications, carbonates such as limestone and dolomite are heated sufficiently enough to calcine the
material and generate CO2 as a byproduct.
CaCO3 —> CaO + C02
MgC03 —> MgO + C02
Examples of such applications include limestone used as a flux or purifier in metallurgical furnaces, as a sorbent in
flue gas desulfurization (FGD) systems for utility and industrial plants, and as a raw material for the production of
glass, lime, and cement. Emissions from limestone and dolomite used in other process sectors such as cement, lime,
glass production, and iron and steel, are excluded from this section and reported under their respective source
categories (e.g., Section 4.3, Glass Production). Emission from soda ash consumption is reported under respective
categories (e.g., Glass Manufacturing (IPCC Source Category 2A3) and Soda Ash Production and Consumption
(IPCC Source Category 2B7)). Emissions from fuels consumed for energy purposes during these processes are
accounted for in the Energy chapter.
Limestone is widely distributed throughout the world in deposits of varying sizes and degrees of purity. Large
deposits of limestone occur in nearly every state in the United States, and significant quantities are extracted for
industrial applications. In 2014, the leading limestone producing states are Texas, Missouri, Florida, Ohio, and
Kentucky, which contribute 43 percent of the total U.S. output (USGS 1995a through 2015). Similarly, dolomite
deposits are also widespread throughout the world. Dolomite deposits are found in the United States, Canada, Mexico,
Europe, Africa, and Brazil. In the United States, the leading dolomite producing states are Illinois, Pennsylvania, and
New York, which contribute 55 percent of the total 2014 U.S. output (USGS 1995a through 2015).
In 2015, 22,322 kt of limestone and 3,244 kt of dolomite were consumed for these emissive applications, excluding
glass manufacturing (Willett 2017). Usage of limestone and dolomite resulted in aggregate CO2 emissions of 10.8
14 See .
15 Limestone and dolomite are collectively referred to as limestone by the industry, and intermediate varieties are seldom
distinguished.
4-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 MMT CO2 Eq. (10,828 kt) (see Table 4-14 and Table 4-15). While 2015 emissions have decreased 8 percent
2 compared to 2014, overall emissions have increased 121 percent from 1990 through 2015.
3 Table 4-14: CO2 Emissions from Other Process Uses of Carbonates (MMT CO2 Eq.)
Other
Magnesium Miscellaneous
Year Flux Stone FGD Production Uses3 Total
1990 2.6 1.4 0.1 0.8 4.9
2005 2.6 3.0 0.0 0.7 6.3
2011 1.5 5.4 0.0 2.4 9.3
2012 1.1 5.8 0.0 1.1 8.0
2013 2.3 6.3 0.0 1.8 10.4
2014 2.9 7.1 0.0 1.8 11.8
2015 3.0 7.3 0.0 0.5 10.8
a "Other miscellaneous uses" include chemical stone, mine dusting or acid water
treatment, acid neutralization, and sugar refining.
Note: Totals may not sum due to independent rounding.
4 Table 4-15: CO2 Emissions from Other Process Uses of Carbonates (kt)
Other
Magnesium Miscellaneous
Year Flux Stone FGD Production Uses3 Total
1990 2,592 1,432 64 819 4,907
2005 2,649 2.973 0 718 6.339
2011
1,467
5,420
0
2,449
9,335
2012
1,077
5,797
0
1,148
8,022
2013
2,307
6,309
0
1,798
10,414
2014
2,911
7,111
0
1,790
11,811
2015
3,031
7,335
0
462
10,828
a "Other miscellaneous uses" include chemical stone, mine dusting or acid water
treatment, acid neutralization, and sugar refining.
Note: Totals may not sum due to independent rounding.
5 Methodology
6 Carbon dioxide emissions were calculated based on the 2006IPCC Guidelines Tier 2 method by multiplying the
7 quantity of limestone or dolomite consumed by the emission factor for limestone or dolomite calcination,
8 respectively, Table 2.1-limestone: 0.43971 metric ton CCh/metric ton carbonate, and dolomite: 0.47732 metric ton
9 CCVmetric ton carbonate.16 This methodology was used for flux stone, flue gas desulfurization systems, chemical
10 stone, mine dusting or acid water treatment, acid neutralization, and sugar refining. Flux stone used during the
11 production of iron and steel was deducted from the Other Process Uses of Carbonates source category estimate and
12 attributed to the Iron and Steel Production source category estimate. Similarly, limestone and dolomite consumption
13 for glass manufacturing, cement, and lime manufacturing are excluded from this category and attributed to their
14 respective categories.
15 Historically, the production of magnesium metal was the only other significant use of limestone and dolomite that
16 produced CO2 emissions. At the end of 2001, the sole magnesium production plant operating in the United States
16 2006IPCC Guidelines, Volume 3: Chapter 2.
Industrial Processes and Product Use 4-19
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1 that produced magnesium metal using a dolomitic process that resulted in the release of CO2 emissions ceased its
2 operations (USGS 1995b through 2012; USGS 2013).
3 Consumption data for 1990 through 2015 of limestone and dolomite used for flux stone, flue gas desulfurization
4 systems, chemical stone, mine dusting or acid water treatment, acid neutralization and sugar refining (see Table
5 4-16) were obtained from the U.S. Geological Survey (USGS) Minerals Yearbook: Crushed Stone Annual Report
6 (1995a through 2015), preliminary data for 2015 from USGS Crushed Stone Commodity Expert (Willett 2017),
7 American Iron and Steel Institute limestone and dolomite consumption data (AISI2016), and the U.S. Bureau of
8 Mines (1991 and 1993a), which are reported to the nearest ton. The production capacity data for 1990 through 2015
9 of dolomitic magnesium metal also came from the USGS (1995b through 2012; USGS 2013) and the U.S. Bureau of
10 Mines (1990 through 1993b). During 1990 and 1992, the USGS did not conduct a detailed survey of limestone and
11 dolomite consumption by end-use. Consumption for 1990 was estimated by applying the 1991 percentages of total
12 limestone and dolomite use constituted by the individual limestone and dolomite uses to 1990 total use. Similarly,
13 the 1992 consumption figures were approximated by applying an average of the 1991 and 1993 percentages of total
14 limestone and dolomite use constituted by the individual limestone and dolomite uses to the 1992 total.
15 Additionally, each year the USGS withholds data on certain limestone and dolomite end-uses due to confidentiality
16 agreements regarding company proprietary data. For the purposes of this analysis, emissive end-uses that contained
17 withheld data were estimated using one of the following techniques: (1) the value for all the withheld data points for
18 limestone or dolomite use was distributed evenly to all withheld end-uses; (2) the average percent of total limestone
19 or dolomite for the withheld end-use in the preceding and succeeding years; or (3) the average fraction of total
20 limestone or dolomite for the end-use over the entire time period.
21 There is a large quantity of crushed stone reported to the USGS under the category "unspecified uses." A portion of
22 this consumption is believed to be limestone or dolomite used for emissive end uses. The quantity listed for
23 "unspecified uses" was, therefore, allocated to each reported end-use according to each end-use's fraction of total
24 consumption in that year.17
25 Table 4-16: Limestone and Dolomite Consumption (kt)
Activity 1990
2005
2011 2012 2013 2014 2015
Flux Stone 6,737
Limestone 5,804
Dolomite 933
FGD 3,258
Other Miscellaneous Uses 1,835
7,022
3,165
3,857
6,761
1,632
4,396 3,666 6,345 7,599 7,834
2,531 3,108 4,380 4,243 4,590
1,865 559 1,965 3,356 3,244
12,326 13,185 14,347 16,171 16,680
5,548 2,610 3,973 4,069 1,052
Total 11,830
15,415
22,270 19,461 24,665 27,839 25,566
26
27
28
29
30
31
32
33
34
35
36
37
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
The uncertainty levels presented in this section account for uncertainty associated with activity data. Data on
limestone and dolomite consumption are collected by USGS through voluntary national surveys. USGS contacts the
mines (i.e., producers of various types of crushed stone) for annual sales data. Data on other carbonate consumption
are not readily available. The producers report the annual quantity sold to various end-users/industry types. USGS
estimates the historical response rate for the crushed stone survey to be approximately 70 percent, the rest is
estimated by USGS. Large fluctuations in reported consumption exist, reflecting year-to-year changes in the number
of survey responders. The uncertainty resulting from a shifting survey population is exacerbated by the gaps in the
time series of reports. The accuracy of distribution by end use is also uncertain because this value is reported by the
producer/mines and not the end user. Additionally, there is significant inherent uncertainty associated with
estimating withheld data points for specific end uses of limestone and dolomite. Lastly, much of the limestone
17 This approach was recommended by USGS, the data collection agency.
4-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 consumed in llie I uiled Sink's is reported ;is 'oilier unspecified uses." ilicivTuiv. il is difficult In nccurnlcK nllocnle
2 llns unspecified t|iinulil> In llie correct end-uses
3 I ueei'lniuis in I lie esiiiunles nlso n rises in pnri due In \ nrinlious in I lie clicmicnl composition of limestone In
4 nddiliou Ui enleiuni cnrbounle. limestone nin\ column siiinller ;uikm11ils (if ninmiesin. silien. nud sulfur. ninoim oilier
5 niiiieinls The evict specilicnlious fur limestone i»r dolomite used ns llu\ sioue \nr\ w illi llie p\ ronielnllurmcnl
6 process nud llie kind of ore processed.
7 l lie results of llie \pproncli 2 c|ii;iillil;ili\ e uiicerlniun minis sis nre sunininri/ed mi Tnhle 4-1 ~ ( nrbou dioxide
8 emissions from oilier process uses of enrbounlcs mi 2d 15 were esiminled lo he between Id ~ nud 14 t) \ 1 \ 1 T ('() I a|
9 nl llie pereeni eoiifideuee lex el fins mdienles n i.iuue of nppro\ininlel> 12 pereeni helow nud 15 percent nho\e
10 the emission esiimnle of 12 I \l\f I ( () Ia|
11 Table 4-17: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Other
12 Process Uses of Carbonates (MMT CO2 Eq. and Percent)
2015 1" missiiiii
Smiriv
(¦iis
I'slimiik-
I luiTl.iiim ki-hiliM'In I'iiiiissiiin
r.slim;ik''
1 MM'I'CO: i:<|.)
(MM 1 ( (): l.(|.) <"
I.IHUT I |>|KT 1 .ON IT
I |>|KT
liiiund Bound limind
ISiiiiihI
Other Process Uses
t (I
12.1
of Carbonates
Runge of emission estimates predicted by Monte Carlo Stochastic Simulation Ibr a 95 percent confidence interval.
13 Mclliodolomcnl rccniculnlious were npplied lo llie enure lime series 10 ensure coiisisieucs iu eniissious fnini I'Wt)
14 ilirouuli 2d 15 Delnils 0111 lie emission ireuds ilirouuli lime nre described 111 niiire dclnil 1111 lie Melliodolous secliou.
15 nbo\c
16 rvcuaicuianuii.> ui^tu33iui 1
17 Limestone and dolomite consumption data, by end-use, for 2014 was updated relative to the previous Inventory
18 based on the preliminary data provided by USGS Crush Stone Commodity expert, Jason Willett. In the previous
19 Inventory (i.e., 1990 through 2014), preliminary data were used for 2014 and updated forthe current Inventory. The
20 published time series was reviewed to ensure time series consistency. This update caused a decrease in total
21 limestone and dolomite consumption for emissive end uses in 2014 by approximately 2 percent.
22 Planned Improvements
23 Pending available resources, this section will integrate and present emissions from soda ash consumption for other
24 chemical uses (non-glass production). Currently, in this document, these estimates are presented along with
25 emissions from soda ash production (IPCC Category 2B7). This improvement is planned for the final version of this
26 year's inventory report and is not incorporated into this public review draft.
27 4.5 Ammonia Production (IPCC Source
28 Category 2B1)
29 Emissions of carbon dioxide (CO2) occur during the production of synthetic ammonia, primarily through the use of
30 natural gas, petroleum coke, or naphtha as a feedstock. The natural gas-, naphtha-, and petroleum coke-based
31 processes produce CO2 and hydrogen (H2), the latter of which is used in the production of ammonia. The brine
32 electrolysis process for production of ammonia does not lead to process-based CO2 emissions. Emissions from fuels
33 consumed for energy purposes during the production of ammonia are accounted for in the Energy chapter.
Industrial Processes and Product Use 4-21
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In the United States, the majority of ammonia is produced using a natural gas feedstock; however, one synthetic
ammonia production plant located in Kansas is producing ammonia from petroleum coke feedstock. In some U.S.
plants, some of the CO2 produced by the process is captured and used to produce urea rather than being emitted to
the atmosphere. There are approximately 13 companies operating 26 ammonia producing facilities in 17 states.
More than 55 percent of domestic ammonia production capacity is concentrated in the states of Louisiana (29
percent), Oklahoma (20 percent), and Texas (6 percent) (USGS 2016).
There are five principal process steps in synthetic ammonia production from natural gas feedstock. The primary
reforming step converts methane (CH4) to CO2, carbon monoxide (CO), and H2 in the presence of a catalyst. Only
30 to 40 percent of the CH4 feedstock to the primary reformer is converted to CO and CO2 in this step of the
process. The secondary reforming step converts the remaining CH4 feedstock to CO and CO2. The CO in the process
gas from the secondary reforming step (representing approximately 15 percent of the process gas) is converted to
CO2 in the presence of a catalyst, water, and air in the shift conversion step. Carbon dioxide is removed from the
process gas by the shift conversion process, and the hydrogen gas is combined with the nitrogen (N2) gas in the
process gas during the ammonia synthesis step to produce ammonia. The CO2 is included in a waste gas stream with
other process impurities and is absorbed by a scrubber solution. In regenerating the scrubber solution, CO2 is
released from the solution.
The conversion process for conventional steam reforming of CH4, including the primary and secondary reforming
and the shift conversion processes, is approximately as follows:
0.88C7/4 + 1.26Air +1.24H20 0.88C02 + N2 +3H2
N2 + 3H2 -> 2NH3
To produce synthetic ammonia from petroleum coke, the petroleum coke is gasified and converted to CO2 and H2.
These gases are separated, and the H2 is used as a feedstock to the ammonia production process, where it is reacted
with N2 to form ammonia.
Not all of the CO2 produced during the production of ammonia is emitted directly to the atmosphere. Some of the
ammonia and some of the CO2 produced by the synthetic ammonia process are used as raw materials in the
production of urea [CO(NH2)2], which has a variety of agricultural and industrial applications.
The chemical reaction that produces urea is:
2NH3+ C02 -> NH2COONH4 -> CO(NH2)2 +h2o
Only the CO2 emitted directly to the atmosphere from the synthetic ammonia production process is accounted for in
determining emissions from ammonia production. The CO2 that is captured during the ammonia production process
and used to produce urea does not contribute to the CO2 emission estimates for ammonia production presented in
this section. Instead, CO2 emissions resulting from the consumption of urea are attributed to the urea consumption or
urea application source category (under the assumption that the carbon stored in the urea during its manufacture is
released into the environment during its consumption or application). Emissions of CO2 resulting from agricultural
applications of urea are accounted for in the Agriculture chapter. Previously, these emission estimates from the
agricultural application of urea were accounted for in the Cropland Remaining Cropland section of the Land Use,
Land Use Change, and Forestry chapter. Emissions of CO2 resulting from non-agricultural applications of urea (e.g.,
use as a feedstock in chemical production processes) are accounted for in the Urea Consumption for Non-
Agricultural Purposes section of this chapter.
Total emissions of CO2 from ammonia production in 2015 were 10.8 MMT CO2 Eq. (10,799 kt), and are
summarized in Table 4-18 and Table 4-19. Ammonia production relies on natural gas as both a feedstock and a fuel,
and as such, market fluctuations and volatility in natural gas prices affect the production of ammonia. Since 1990,
emissions from ammonia production have decreased by 17 percent. Emissions in 2015 have increased by
approximately 12 percent from the 2014 levels.
Table 4-18: CO2 Emissions from Ammonia Production (MMT CO2 Eq.)
Source
1990
2005
2011
2012
2013
2014
2015
Ammonia Production
13.0
9.2
9.3
9.4
10.0
9.6
10.8
Total
13.0
9.2
9.3
9.4
10.0
9.6
10.8
4-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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44
Table 4-19: CO2 Emissions from Ammonia Production (kt)
Source
1990
2005
2011
2012
2013
2014
2015
Ammonia Production
13,047
9,196
9,292
9,377
9,962
9,619
10,799
Total
13,047
9,196
9,292
9,377
9,962
9.619
10,799
Methodology
Carbon dioxide emissions from production of synthetic ammonia from natural gas feedstock is based on the 2006
IPCC Guidelines (IPCC 2006) Tier 1 and 2 method. A country-specific emission factor is developed and applied to
national ammonia production to estimate emissions. The method uses a CO2 emission factor published by the
European Fertilizer Manufacturers Association (EFMA) that is based on natural gas-based ammonia production
technologies that are similar to those employed in the United States. This CO2 emission factor of 1.2 metric tons
CCVmetric ton NH3 (EFMA 2000a) is applied to the percent of total annual domestic ammonia production from
natural gas feedstock.
Emissions of CO2 from ammonia production are then adjusted to account for the use of some of the CO2 produced
from ammonia production as a raw material in the production of urea. The CO2 emissions reported for ammonia
production are reduced by a factor of 0.733 multiplied by total annual domestic urea production. This corresponds to
a stoichiometric CCh/urea factor of 44/60, assuming complete conversion of ammonia (NH3) and CO2 to urea (IPCC
2006; EFMA 2000b).
All synthetic ammonia production and subsequent urea production are assumed to be from the same process—
conventional catalytic reforming of natural gas feedstock, with the exception of ammonia production from
petroleum coke feedstock at one plant located in Kansas. Annual ammonia and urea production are shown in Table
4-20. The CO2 emission factor for production of ammonia from petroleum coke is based on plant-specific data,
wherein all carbon contained in the petroleum coke feedstock that is not used for urea production is assumed to be
emitted to the atmosphere as CO2 (Bark 2004). Ammonia and urea are assumed to be manufactured in the same
manufacturing complex, as both the raw materials needed for urea production are produced by the ammonia
production process. The CO2 emission factor of 3.57 metric tons CCh/metric ton NH3 for the petroleum coke
feedstock process (Bark 2004) is applied to the percent of total annual domestic ammonia production from
petroleum coke feedstock.
The emission factor of 1.2 metric ton CCh/metric ton NH3 for production of ammonia from natural gas feedstock
was taken from the EFMA Best Available Techniques publication, Production of Ammonia (EFMA 2000a). The
EFMA reported an emission factor range of 1.15 to 1.30 metric ton C02/metric ton NH3, with 1.2 metric ton
CCVmetric ton NH3 as a typical value (EFMA 2000a). Technologies (e.g., catalytic reforming process, etc.)
associated with this factor are found to closely resemble those employed in the United States for use of natural gas
as a feedstock. The EFMA reference also indicates that more than 99 percent of the CH4 feedstock to the catalytic
reforming process is ultimately converted to CO2. As noted earlier, emissions from fuels consumed for energy
purposes during the production of ammonia are accounted for in the Energy chapter. The total ammonia production
data for 2011 through 2015 were obtained from American Chemistry Council (2016). For years before 2011,
ammonia production data (see Table 4-20) were obtained from Coffeyville Resources (Coffeyville 2005, 2006,
2007a, 2007b, 2009, 2010, 2011, and 2012) and the Census Bureau of the U.S. Department of Commerce (U.S.
Census Bureau 1991 through 1994, 1998 through 2011) as reported in Current Industrial Reports Fertilizer Materials
and Related Products annual and quarterly reports. Urea-ammonia nitrate production from petroleum coke for years
through 2011 was obtained from Coffeyville Resources (Coffeyville 2005, 2006, 2007a, 2007b, 2009, 2010, 2011,
and 2012), and from CVR Energy, Inc. Annual Report (CVR 2012 ,2014, 2015, and 2016) for 2012, 2013, 2014,
and 2015. Urea production data for 1990 through 2008 were obtained from the Minerals Yearbook: Nitrogen (USGS
1994 through 2009). Urea production data for 2009 through 2010 were obtained from the U.S. Census Bureau (U.S.
Census Bureau 2010 and 2011). The U.S. Census Bureau ceased collection of urea production statistics, and urea
production data for 2011, 2012, 2013 and 2014 were obtained from the Minerals Yearbook: Nitrogen (USGS 2015,
2016). USGS urea production data for 2015 was not yet published and so 2014 data were used as a proxy for 2014.
Industrial Processes and Product Use 4-23
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26
Table 4-20: Ammonia Production and Urea Production (kt)
Ammonia Urea
Production Production
1990 15,425 7,450
2005 10,143 5,270
2011 10,325 5,430
2012 10,305 5,220
2013 10,930 5,480
2014 10.515 5,230
201 5 11,505 5,230
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
The uncertainties presented in this section are primarily due to how accurately the emission factor used represents an
average across all ammonia plants using natural gas feedstock. Uncertainties are also associated with ammonia
production estimates and the assumption that all ammonia production and subsequent urea production was from the
same process—conventional catalytic reforming of natural gas feedstock, with the exception of one ammonia
production plant located in Kansas that is manufacturing ammonia from petroleum coke feedstock. Uncertainty is
also associated with the representativeness of the emission factor used for the petroleum coke-based ammonia
process. It is also assumed that ammonia and urea are produced at collocated plants from the same natural gas raw
material.
Recovery of CO2 from ammonia production plants for purposes other than urea production (e.g., commercial sale,
etc.) has not been considered in estimating the CO2 emissions from ammonia production as data concerning the
disposition of recovered CO2 are not available. 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.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-21. Carbon dioxide
emissions from ammonia production in 2015 were estimated to be between 8.7 and 10.2 MMT CO2 Eq. at the 95
percent confidence level. This indicates a range of approximately 8 percent below and 8 percent above the emission
estimate of 9.4 MMT CO2 Eq.
Table 4-21: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from
Ammonia Production (MMT CO2 Eq. and Percent)
Source Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Ammonia Production CO2
9.4
8.7
10.2
-8%
+8%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
4-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Recalculations Discussion
Production estimates for urea production for 2014 were updated relative to the previous Inventory using information
obtained from the recent 2014 Minerals Yearbook: Nitrogen (USGS 2016). For the previous version of the Inventory
(i.e., 1990 through 2014), 2013 data was used as a proxy for 2014 as the 2014 data were not published prior to the
previous Inventory report. This update resulted in a slight increase of emissions by approximately 2 percent for 2014
relative to the previous Inventory.
Planned Improvements
Future improvements involve continuing to evaluate and analyze data reported under EPA's GHGRP to improve the
emission estimates for the Ammonia Production source category, in particular new data from updated reporting
requirements finalized in October of 2014 (79 FR 63750) and December 2016 (81 FR 89188)18, that include facility-
level ammonia production data, will be included in future reports if the data meets GHGRP CBI aggregation criteria.
Particular attention will be made to ensure time series consistency of the emissions estimates presented in future
Inventory reports, along with application of appropriate category-specific QC procedures consistent with IPCC and
UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the program's
initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory years (i.e.,
1990 through 2009) as required for this Inventory. In implementing improvements and integration of data from
EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will be
relied upon.19 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. Due to limited resources, this planned improvement is still in development and so is not incorporated
into this Inventory.
4.6 Urea Consumption for Non-Agricultural
Purposes
Urea is produced using ammonia and carbon dioxide (CO2) as raw materials. All urea produced in the United States
is assumed to be produced at ammonia production facilities where both ammonia and CO2 are generated. There are
approximately 20 of these facilities operating in the United States.
The chemical reaction that produces urea is:
2NH3+ C02 -> NH2COONH4 -> CO(NH2)2 +h2o
This section accounts for CO2 emissions associated with urea consumed exclusively for non-agricultural purposes.
Carbon dioxide emissions associated with urea consumed for fertilizer are accounted for in the Agriculture chapter.
Urea is used as a nitrogenous fertilizer for agricultural applications and also in a variety of industrial applications.
The industrial applications of urea include its use in adhesives, binders, sealants, resins, fillers, analytical reagents,
catalysts, intermediates, solvents, dyestuffs, fragrances, deodorizers, flavoring agents, humectants and dehydrating
agents, formulation components, monomers, paint and coating additives, photosensitive agents, and surface
treatments agents. In addition, urea is used for abating nitrogen oxide (NOx) emissions from coal-fired power plants
and diesel transportation motors.
Emissions of CO2 from urea consumed for non-agricultural purposes in 2015 were estimated to be 1.1 MMT CO2
Eq. (1,128 kt), and are summarized in Table 4-22 and Table 4-23. 2015 data on urea production data, urea exports
18 See .
19 See .
Industrial Processes and Product Use 4-25
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1 and imports are not yet published. 2014 data has been used as proxy for 2015. Net CO2 emissions from urea
2 consumption for non-agricultural purposes in 2015 have decreased by approximately 71 percent from 1990.
3 Table 4-22: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (MMT CO2
4 Eq.)
Source
1990
2005
2011
2012
2013
2014
2015
Urea Consumption
3.8
3.7
4.0
4.4
4.0
1.4
1.1
Total 3.8 3.7 4.0 4.4 4.0 1.4 1.1
5 Table 4-23: CO2 Emissions from Urea Consumption for Non-Agricultural Purposes (kt)
Source 1990 2005 2011 2012 2013 2014 2015
Urea Consumption 3,784 3,653 4,030 4,407 4,014 1,380 1,128
Total 3,784 3,653 4,030 4,407 4,014 1,380 1,128
6 Methodology
7 Emissions of CO2 resulting from urea consumption for non-agricultural purposes are estimated by multiplying the
8 amount of urea consumed in the United States for non-agricultural purposes by a factor representing the amount of
9 CO2 used as a raw material to produce the urea. This method is based on the assumption that all of the carbon in
10 urea is released into the environment as CO2 during use, and consistent with the 2006IPCC Guidelines.
11 The amount of urea consumed for non-agricultural purposes in the United States is estimated by deducting the
12 quantity of urea fertilizer applied to agricultural lands, which is obtained directly from the Agriculture chapter (see
13 Table 5-24) and is reported in Table 4-24, from the total domestic supply of urea. In previous Inventory reports, the
14 quantity of urea fertilizer applied to agricultural lands was obtained directly from the Cropland Remaining Cropland
15 section of the Land Use, Land Use Change, and Forestry chapter. The domestic supply of urea is estimated based on
16 the amount of urea produced plus the sum of net urea imports and exports. A factor of 0.733 tons of CO2 per ton of
17 urea consumed is then applied to the resulting supply of urea for non-agricultural purposes to estimate CO2
18 emissions from the amount of urea consumed for non-agricultural purposes. The 0.733 tons of CO2 per ton of urea
19 emission factor is based on the stoichiometry of producing urea from ammonia and CO2. This corresponds to a
20 stoichiometric CCh/urea factor of 44/60, assuming complete conversion of NH3 and CO2 to urea (IPCC 2006; EFMA
21 2000).
22 Urea production data for 1990 through 2008 were obtained from the Minerals Yearbook: Nitrogen (USGS 1994
23 through 2009). Urea production data for 2009 through 2010 were obtained from the U.S. Census Bureau (2011). The
24 U.S. Census Bureau ceased collection of urea production statistics in 2011, therefore, urea production data for 2011,
25 2012, 2013 and 2014 were obtained from the Minerals Yearbook: Nitrogen (USGS 2014 through 2016). Urea
26 production data for 2015 are not yet publicly available and so 2014 data have been used as proxy.
27 Urea import data for 2015 are not yet publicly available and so 2014 data have been used as proxy. Urea import data
28 for 2014 were obtained from the Minerals Yearbook: Nitrogen (USGS 2016). Urea import data for 2011 and 2012
29 were taken from U.S. Fertilizer Import/Exports from the United States Department of Agriculture (USDA)
30 Economic Research Service Data Sets (U.S. Department of Agriculture 2012). Urea import data for the previous
31 years were obtained from the U. S. Census Bureau Current Industrial Reports Fertilizer Materials and Related
32 Products annual and quarterly reports for 1997 through 2010 (U.S. Census Bureau 2001 through 2011), The
33 Fertilizer Institute (TFI 2002) for 1993 through 1996, and the United States International Trade Commission
34 Interactive Tariff and Trade DataWeb (U.S. ITC 2002) for 1990 through 1992 (see Table 4-24). Urea export data for
35 2015 are not yet publicly available and so 2014 data have been used as proxy. Urea export data for 2014 were
36 obtained from the Minerals Yearbook: Nitrogen (USGS 2016). Urea export data for 1990 through 2012 were taken
37 from U.S. Fertilizer Import/Exports from USD A Economic Research Service Data Sets (U.S. Department of
38 Agriculture 2012).
4-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 4-24: Urea Production, Urea Applied as Fertilizer, Urea Imports, and Urea Exports (kt)
Year Urea Urea Applied Urea Urea
Production as Fertilizer Imports Exports
1990 7,450 3,296 1,860 854
2005 5,270 4,779 5,026 536
2011 5,430 5,587 5,860 207
2012 5,220 5,819 6,944 336
2013 5,480 6,141 6,470 335
2014 5,230 6,520 3,510 339
201 5 5,230 6,862 3,510 339
2 Uncertainty and Time-Series Consistency - TO BE UPDATED
3 FOR FINAL INVENTORY REPORT
4 There is limited publicly-available data on the quantities of urea produced and consumed for non-agricultural
5 purposes. Therefore, the amount of urea used for non-agricultural purposes is estimated based on a balance that
6 relies on estimates of urea production, urea imports, urea exports, and the amount of urea used as fertilizer. The
7 primary uncertainties associated with this source category are associated with the accuracy of these estimates as well
8 as the fact that each estimate is obtained from a different data source. Because urea production estimates are no
9 longer available from the USGS, there is additional uncertainty associated with urea produced beginning in 2011.
10 There is also uncertainty associated with the assumption that all of the carbon in urea is released into the
11 environment as CO2 during use.
12 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-25. Carbon dioxide
13 emissions associated with urea consumption for non-agricultural purposes were estimated to be between 3.5 and 4.5
14 MMT CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 12 percent below and 12
15 percent above the emission estimate of 4.0 MMT CO2 Eq.
16 Table 4-25: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Urea
17 Consumption for Non-Agricultural Purposes (MMT CO2 Eq. and Percent)
Source
_ 2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper
Lower Upper
Bound Bound
Bound Bound
Urea Consumption for
Non-Agricultural
CO2 4.0
3.5 4.5
-12% +12%
Purposes
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
18 Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
19 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
20 above.
21 Recalculations Discussion
22 Production estimates for total urea production and estimates for urea exports and imports for 2014 were updated
23 using information obtained from the Minerals Yearbook: Nitrogen (USGS 2016). This update, as well as the urea
24 consumption update included below, resulted in a significant decrease in urea imports for 2014, resulting in a
25 decrease of the 2014 emission estimate relative to the previous report of approximately 66 percent. In addition, this
26 update also resulted in an update to the urea export value for 2013.
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The amount of urea consumed for agricultural purposes (used for calculating urea consumption for non-agricultural
purposes) in the United States for the years 2011 through 2014 was revised based updated urea application estimates
in a new AAPFCO report (2016) obtained from the Agriculture chapter (see Table 5-24). These updates resulted in a
decrease in the emission estimate relative to the previous report of approximately 5 percent in 2013 and 66 percent
in 2014, as previously described. As stated previously in the Methodology section, in previous inventory reports the
quantity of urea fertilizer applied to agricultural lands was obtained directly from the Cropland Remaining Cropland
section of the Land Use, Land Use Change, and Forestry chapter, and has been moved to the Agriculture chapter for
this report.
4.7 Nitric Acid Production (IPCC Source
Category 2B2)
Nitrous oxide (N20) is emitted during the production of nitric acid (HNO3), 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 high-
temperature catalytic oxidation of ammonia (EPA 1998). There are two different nitric acid production methods:
weak nitric acid and high-strength nitric acid. The first method utilizes oxidation, condensation, and absorption to
produce nitric acid at concentrations between 30 and 70 percent nitric acid. High-strength acid (90 percent or greater
nitric acid) can be produced from dehydrating, bleaching, condensing, and absorption of the weak nitric acid. The
basic process technology for producing nitric acid has not changed significantly over time. Most U.S. plants were
built between 1960 and 2000. As of 2015, there were 34 active weak nitric acid production plants, including one
high-strength nitric acid production plant in the United States (EPA 2010; EPA 2016).
During this reaction, N20 is formed as a byproduct and is released from reactor vents into the atmosphere.
Emissions from fuels consumed for energy purposes during the production of nitric acid are accounted for in the
Energy chapter.
Nitric acid is made from the reaction of ammonia (NH3) with oxygen (O2) in two stages. The overall reaction is:
4NH3 + 802 -> 4HNO:i +4H20
Currently, the nitric acid industry controls emissions of NO and NO2 (i.e., NOx). As such, the industry in the United
States uses a combination of non-selective catalytic reduction (NSCR) and selective catalytic reduction (SCR)
technologies. In the process of destroying NOx, NSCR systems are also very effective at destroying N20. However,
NSCR units are generally not preferred in modern plants because of high energy costs and associated high gas
temperatures. NSCR systems were installed in nitric plants built between 1971 and 1977 with NSCRs installed at
approximately one-third of the weak acid production plants. U.S. facilities are using both tertiary (i.e., NSCR) and
secondary controls (i.e., alternate catalysts).
Nitrous oxide emissions from this source were estimated to be 11.6 MMT CO2 Eq. (3 9 kt of N20) in 2015 (see
Table 4-26). Emissions from nitric acid production have decreased by 5 percent since 1990, with the trend in the
time series closely tracking the changes in production. Emissions have decreased by 19 percent since 1997, the
highest year of production in the time series.
Table 4-26: N2O Emissions from Nitric Acid Production (MMT CO2 Eq. and kt N2O)
Year
MMT CO2 Eq.
kt N2O
1990
12.1
41
2005
1 1.3
38
2011 10.9 37
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29
30
31
32
33
34
2012
2013
2014
2015
10.5
10.7
10.9
11.6
35
36
37
39
Methodology
Emissions of N20 were calculated using the estimation methods provided by the 2006IPCC Guidelines and country
specific methods from EPA's GHGRP. The 2006 IPCC Guidelines Tier 2 method was used to estimate emissions
from nitric acid production for 1990 through 2009, and a country-specific approach similar to the IPCC Tier 3
method was used to estimate N20 emissions for 2010 through 2015.
Process N20 emissions and nitric acid production data were obtained directly from EPA's GHGRP for 2010 through
2015 by aggregating reported facility-level data (EPA 2016). In the United States, all nitric acid facilities producing
weak nitric acid (30 to 70 percent in strength) are required to report annual greenhouse gas emissions data to EPA as
per the requirements of its GHGRP. As of 2015, there were 34 facilities that reported to EPA, including the known
single high-strength nitric acid production facility in the United States (EPA 2016). All nitric acid (weak acid)
facilities are required to calculate process emissions using a site-specific emission factor developed through annual
performance testing under typical operating conditions or by directly measuring N20 emissions using monitoring
equipment.20 The high-strength nitric acid facility also reports N20 emissions associated with weak acid production
and this may capture all relevant emissions, pending additional further EPA research. More details on the
calculation, monitoring and QA/QC methods applicable to Nitric Acid facilities can be found under Subpart V:
Nitric Acid Production of the regulation. Part 98.21 EPA verifies annual facility-level GHGRP reports through a
multi-step process (e.g., combination of electronic checks and manual reviews) to identify potential errors and
ensure that data submitted to EPA are accurate, complete, and consistent.22
To calculate emissions from 2010 through 2015, EPA's GHGRP nitric acid production data is utilized to develop
weighted country specific emission factors used to calculate emissions estimates. Based on aggregated nitric acid
production data by abatement type (i.e., with, without) provided by EPA's GHGRP, the percent of production values
and associated emissions of nitric acid with and without abatement technologies are calculated. These percentages
are the basis for developing the country specific weighted emission factors which vary from year to year based on
the amount of nitric acid production with and without abatement technologies.
Using EPA's GHGRP data for 20 1 0,23 country-specific N20 emission factors were calculated for nitric acid
production with abatement and without abatement (i.e., controlled and uncontrolled emission factors), as previous
stated. The following 2010 emission factors were derived for production with abatement and without abatement: 3.3
kg N20/metric ton HNO3 produced at plants using abatement technologies (e.g., tertiary systems such as NSCR
systems) and 5.99 kg N20/metric ton HNO3 produced at plants not equipped with abatement technology. Country-
specific weighted emission factors were derived by weighting these emission factors by percent production with
abatement and without abatement over time periods 1990 through 2008 and 2009. These weighted emission factors
were used to estimate N20 emissions from nitric acid production for years prior to the availability of EPA's GHGRP
20 Facilities must use standard methods, either EPA Method 320 or ASTM D6348-03 and must follow associated QA/QC
procedures consistent during these performance test consistent with category-specific QC of direct emission measurements.
21 See .
22 See .
23 National N2O process emissions, national production, and national share of nitric acid production with abatement and without
abatement technology was aggregated from the GHGRP facility-level data for2010to2015(i.e., percent production with and
without abatement).
2010 through 2015
1990 through 2009
Industrial Processes and Product Use 4-29
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33
34
35
36
data (i.e., 1990 through 2008 and 2009). A separate weighted factor is included for 2009 due to data availability for
that year. At that time, EPA had initiated compilation of a nitric acid database to improve estimation of emissions
from this industry and obtained updated information on application of controls via review of permits and outreach
with facilities and trade associations. The research indicated recent installation of abatement technologies at
additional facilities.
Based on the available data, it was assumed that emission factors for 2010 would be more representative of
operating conditions in 1990 through 2009 than more recent years. Initial review of historical data indicates that
percent production with and without abatement can change over time and also year over year due to changes in
application of facility-level abatement technologies, maintenance of abatement technologies, and also due to plant
closures and start-ups (EPA 2012, 2013; Desai 2012; CAR 2013). The installation dates of N20 abatement
technologies are not known at most facilities, but it is assumed that facilities reporting abatement technology use
have had this technology installed and operational for the duration of the time series considered in this report
(especially NSCRs).
The country-specific weighted N20 emission factors were used in conjunction with annual production to estimate
N20 emissions for 1990 through 2009, using the following equations:
El — Pi X EFWelgfr):eCl:l
EFweighted,i = X EFc) + (%PUnc,i X EFunc)\
where,
E = Annual N20 Emissions for year i (kg/yr)
Pi = Annual nitric acid production for year i (metric tons HNO3)
EF weighted,i = Weighted N20 emission factor for year i (kg N20/metric ton HNO3)
%Pc,i = Percent national production of HNO3 with N20 abatement technology (%)
EFC = N20 emission factor, with abatement technology (kg N20/metric ton HNO3)
%Punc,i = Percent national production of HNO3 without N20 abatement technology (%)
EFunc = N20 emission factor, without abatement technology (kg N20/metric ton HNO3)
i = year from 1990 through 2009
For 2009: Weighted N20 emission factor - 5.46 kg N20/metric ton HNO3.
For 1990 through 2008: Weighted N20 emission factor - 5.66 kg N20/metric ton HNO3.
Nitric acid production data for the United States for 1990 through 2009 were obtained from the U.S. Census Bureau
(U.S. Census Bureau 2008, 2009, 2010a, 2010b) (see Table 4-27). Publicly-available information on plant-level
abatement technologies was used to estimate the shares of nitric acid production with and without abatement for
2008 and 2009 (EPA 2012, 2013; Desai 2012; CAR 2013). EPA has previously conducted a review of operating
permits to obtain more current information due to the lack of publicly-available data on use of abatement
technologies for 1990 through 2007, as stated previously; therefore, the share of national production with and
without abatement for 2008 was assumed to be constant for 1990 through 2007.
Table 4-27: Nitric Acid Production (kt)
Year kt
1990 7,200
2005 6.710
2011 7,600
2012 7,460
2013 7,580
2014 7,660
2015 7,210
4-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Uncertainty and Time-Series Consistency - TO BE UPDATED
2 FOR FINAL INVENTORY REPORT
3 Uncertainty associated with the parameters used to estimate N20 emissions includes the share of U.S. nitric acid
4 production attributable to each emission abatement technology over the time series (especially prior to 2010), and
5 the associated emission factors applied to each abatement technology type. While some information has been
6 obtained through outreach with industry associations, limited information is available over the time series
7 (especially prior to 2010) for a variety of facility level variables, including plant specific production levels, plant
8 production technology (e.g., low, high pressure, etc.), and abatement technology type, installation date of abatement
9 technology, and accurate destruction and removal efficiency rates. Production data prior to 2010 were obtained from
10 National Census Bureau, which does not provide uncertainty estimates with their data. Facilities reporting to
11 GHGRP must measure production using equipment and practices used for accounting purposes. At this time EPA
12 does not estimate uncertainty of the aggregated facility-level information.
13 To maintain consistency across the time-series and with the rounding approaches taken by other data sets, a new
14 rounding approach was performed for the GHGRP Subpart V: Nitric Acid data. This resulted in production data
15 changes across the time-series of 2010 to 2015, in which GHGRP data lias been utilized. The results of this update
16 have had an insignificant impact on the emissions estimates across the 2010 to 2015 time-series.
17 The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-28. Nitrous oxide
18 emissions from nitric acid production were estimated were estimated to be between 10.4 and 11.5 MMT CO2 Eq. at
19 the 95 percent confidence level. This indicates a range of approximately 5 percent below to 5 percent above the
20 2015 emissions estimate of 11.6 MMT CO2 Eq.
21 Table 4-28: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from Nitric
22 Acid Production (MMT CO2 Eq. and Percent)
Source
Ci as
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Nitric Acid
Production
n2o
11.6
10.4 11.5
-5% +5%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a
95 percent confidence interval.
23 Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
24 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
25 above.
26 Planned Improvements
27 Pending resources, EPA is considering both near-term and long-term improvement to estimates and associated
28 characterization of uncertainty. In the short-term, with 6 years of EPA's GHGRP data EPA hopes to complete
29 updates of category-specific QC procedures to potentially also improve both qualitative and quantitative uncertainty
30 estimates. Longer term, in 2020 EPA anticipates having information from GHGRP facilities on the installation date
31 of any N2O abatement equipment, per recent revisions finalized in December 2016 to EPA's GHGRP. This
32 information will enable more accurate estimation of N20 emissions from nitric-acid production over the time-series.
33
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4.8 Adipic Acid Production (IPCC Source
Category 2B3)
Adipic acid is produced through a two-stage process during which nitrous oxide (N20) is generated in the second
stage. Emissions from fuels consumed for energy purposes during the production of adipic acid are accounted for in
the Energy chapter. The first stage of manufacturing usually involves the oxidation of cyclohexane to form a
cyclohexanone/ cyclohexanol mixture. The second stage involves oxidizing this mixture with nitric acid to produce
adipic acid. Nitrous oxide is generated as a byproduct of the nitric acid oxidation stage and is emitted in the waste
gas stream (Thiemens and Trogler 1991). The second stage is represented by the following chemical reaction:
(iCH2)5CO(cyclohexanone) + (CH2)5CHOH (cyclohexanol) + wHN03
-» HOOC(CH2)4COOH(adipic acid) + xN20 + yH20
Process emissions from the production of adipic acid vary with the types of technologies and level of emission
controls employed by a facility. In 1990, two major adipic acid-producing plants had N20 abatement technologies in
place and, as of 1998, three major adipic acid production facilities had control systems in place (Reimer et al. 1999).
One small plant, which last operated in April 2006 and represented approximately two percent of production, did not
control for N20 (VA DEQ 2009; ICIS 2007; VA DEQ 2006). In 2014, catalytic reduction, non-selective catalytic
reduction (NSCR) and thermal reduction abatement technologies were applied as N20 abatement measures at adipic
acid facilities (EPA 2016).
Worldwide, only a few adipic acid plants exist. The United States, Europe, and China are the major producers. In
2015, the United States had two companies with a total of three adipic acid production facilities (two in Texas and
one in Florida), all of which were operational (EPA 2016). The United States accounts for the largest share of global
adipic acid production capacity (30 percent), followed by the European Union (29 percent) and China (22 percent)
(SEI 2010). Adipic acid is a white crystalline solid used in the manufacture of synthetic fibers, plastics, coatings,
urethane foams, elastomers, and synthetic lubricants. Commercially, it is the most important of the aliphatic
dicarboxylic acids, which are used to manufacture polyesters. Eighty-four percent of all adipic acid produced in the
United States is used in the production of nylon 6,6; 9 percent is used in the production of polyester polyols; 4
percent is used in the production of plasticizers; and the remaining 4 percent is accounted for by other uses,
including unsaturated polyester resins and food applications (ICIS 2007). Food grade adipic acid is used to provide
some foods with a "tangy" flavor (Thiemens and Trogler 1991).
Nitrous oxide emissions from adipic acid production were estimated to be 4.3 MMT CO2 Eq. (14 kt N20) in 2015
(see Table 4-29). National adipic acid production has increased by approximately 40 percent over the period of 1990
through 2015, to approximately 1,055,000 metric tons (ACC 2016). Over the period 1990 through 2015, emissions
have been reduced by 72 percent due to both the widespread installation of pollution control measures in the late
1990s and plant idling in the late 2000s. In April 2006, the smallest of the four facilities ceased production of adipic
acid (VA DEQ 2009); furthermore, one of the major adipic acid production facilities was not operational in 2009 or
2010 (Desai 2010). All three remaining facilities were in operation in 2015. Very little information on annual trends
in the activity data exist for adipic acid.
Table 4-29: N2O Emissions from Adipic Acid Production (MMT CO2 Eq. and kt N2O)
Year
MMT CO2 Eq.
kt N2O
1990
15.2
51
2005
7.1
24
2011
10.2
34
2012
5.5
19
2013
3.9
13
2014
5.4
18
4-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
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5
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23
24
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29
30
31
32
33
34
35
36
37
38
2015
4.3
14
Methodology
Emissions are estimated using both Tier 2 and Tier 3 methods consistent with the 2006IPCC Guidelines. Due to
confidential business information, plant names are not provided in this section. Therefore, the four adipic acid-
producing facilities will be referred to as Plants 1 through 4. Plant 4 was closed in April 2006. Overall, as noted
above, the three plants that are currently operating facilities use abatement equipment. Plants 1 and 2 employ
catalytic destruction and Plant 3 employs thermal destruction.
2010 through 2015
All emission estimates for 2010 through 2015 were obtained through analysis of EPA's GHGRP data (EPA 2014
through 2016), which is consistent with the 2006 IPCC Guidelines Tier 3 method. Facility-level greenhouse gas
emissions data were obtained from the GHGRP for the years 2010 through 2015 (EPA 2014 through 2016) and
aggregated to national N20 emissions. Consistent with IPCC Tier 3 methods, all adipic acid production facilities are
required to calculate emissions using a facility-specific emission factor developed through annual performance
testing under typical operating conditions or by directly measuring N20 emissions using monitoring equipment.24
More information on the calculation, monitoring and QA/QC methods for process N20 emissions applicable to
adipic acid production facilities under Subpart E can be found in the electronic code of federal regulations.25 EPA
verifies annual facility-level GHGRP reports through a multi-step process (e.g. combination of electronic checks and
manual reviews) to identify potential errors and ensure that data submitted to EPA are accurate, complete, and
consistent.26
1990 through 2009
For years prior to EPA's GHGRP reporting, for both Plants 1 and 2, 1990 to 2009 emission estimates were obtained
directly from the plant engineer and account for reductions due to control systems in place at these plants during the
time series. These prior estimates are considered confidential business information and hence are not published
(Desai 2010). These estimates were based on continuous process monitoring equipment installed at the two
facilities. In 2009 and 2010, no adipic acid production occurred at Plant 1 per reporting to EPA's GHGRP (EPA
2012; Desai 2011b).
For the Plant 4, 1990 through 2009 N20 emissions were estimated using the following Tier 2 equation from the
2006 IPCC Guidelines until shutdown of the plant in 2006:
Eaa = Qaa X EFaa X (1 - [DF X UF])
where,
Eaa
N20 emissions from adipic acid production, metric tons
Qaa
Quantity of adipic acid produced, metric tons
EFaa
Emission factor, metric ton N20/metric ton adipic acid produced
DF
N20 destruction factor
UF
Abatement system utility factor
The adipic acid production is multiplied by an emission factor (i.e., N20 emitted per unit of adipic acid produced),
which has been estimated, based on experiments that the reaction stoichiometry for N20 production in the
preparation of adipic acid, to be approximately 0.3 metric tons of N20 per metric ton of product (IPCC 2006). The
"N20 destruction factor" in the equation represents the percentage of N20 emissions that are destroyed by the
24 Facilities must use standard methods, either EPA Method 320 or ASTM D6348-03 and must follow associated QA/QC
procedures consistent during these performance test consistent with category-specific QC of direct emission measurements.
25 See .
26 See .
Industrial Processes and Product Use 4-33
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1 installed abatement technology. The "abatement system utility factor" represents the percentage of time that the
2 abatement equipment operates during the annual production period. No abatement equipment was installed at the
3 Inolex/Allied Signal facility, which last operated in April 2006 (VA DEQ 2009). Plant-specific production data for
4 this facility were obtained across the time series from 1990 through 2006 from the Virginia Department of
5 Enviromnental Quality (VA DEQ 2010). The plant-specific production data were then used for calculating
6 emissions as described above.
7 For Plant 3, 2005 through 2009 emissions were obtained directly from the plant (Desai 201 la). For 1990 through
8 2004, emissions were estimated using plant-specific production data and the IPCC factors as described above for
9 Plant 4. Plant-level adipic acid production for 1990 through 2003 was estimated by allocating national adipic acid
10 production data to the plant level using the ratio of known plant capacity to total national capacity for all U.S. plants
11 (ACC 2016; CMR 2001, 1998; CW 1999; C&EN 1992 through 1995). For 2004, actual plant production data were
12 obtained and used for emission calculations (CW 2005).
13 Plant capacities for 1990 through 1994 were obtained from Chemical & Engineering News, "Facts and Figures" and
14 "Production of Top 50 Chemicals" (C&EN 1992 through 1995). Plant capacities for 1995 and 1996 were kept the
15 same as 1994 data. The 1997 plant capacities were taken from Chemical Market Reporter, "Chemical Profile:
16 Adipic Acid" (CMR 1998). The 1998 plant capacities for all four plants and 1999 plant capacities for three of the
17 plants were obtained from Chemical Week, Product Focus: Adipic Acid/Adiponitrile (CW 1999). Plant capacities for
18 2000 for three of the plants were updated using Chemical Market Reporter. "Chemical Profile: Adipic Acid" (CMR
19 2001). For 2001 through 2003, the plant capacities for three plants were kept the same as the year 2000 capacities.
20 Plant capacity for 1999 to 2003 for the one remaining plant was kept the same as 1998.
21 National adipic acid production data (see Table 4-30) from 1990 through 2015 were obtained from the American
22 Chemistry Council (ACC 2016).
23 Table 4-30: Adipic Acid Production (kt)
Year
kt
1990
755
2005
865
2011
840
2012
950
2013
980
2014
1,025
2015
1,055
24 Uncertainty and Time-Series Consistency - TO BE UPDATED
25 FOR FINAL INVENTORY REPORT
26 Uncertainty associated with N20 emission estimates includes the methods used by companies to monitor and
27 estimate emissions. While some information lias been obtained through outreach with facilities, limited information
28 is available over the time series on these methods, abatement technology destruction and removal efficiency rates
29 and plant specific production levels.
30 The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-31. Nitrous oxide
31 emissions from adipic acid production for 2015 were estimated to be between 5.2 and 5.6 MMT CO2 Eq. at the 95
32 percent confidence level. These values indicate a range of approximately 4 percent below to 4 percent above the
33 2015 emission estimate of 4.3 MMT CO2 Eq.
4-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 4-31: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from Adipic
2 Acid Production (MMT CO2 Eq. and Percent)
Smiriv
(.
2015 1'mission r.siim;iU-
,!IS (MM 1 ( (): i:t|.)
I IH'lThliim K:
(MMT CO:
.m»i' Ki-I;iliw- In 1! miss in 11
l".c|.) ("..)
I'siiniiik'1
I.ONIT
Bound
I |)|KT
lit hi ml
I.I HUT
Bound
I |)|KT
Bound
Adipic Ac:
id l'rodnc
lion >
•):() 4.3
S 2
5 (S
-4%
+4%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation lor a l)5 percent confidence interval.
3 Methodological recalculations were applied u» ihe enure lime series u» ensure eoiisisieiics 111 emissions from I)
4 1 IiixmiuIi 2d 15 I )el;nls 011 ihe emission ire nils ihroimh lime ;ire described 111 more detail in llie Melhodolouv seel 1011.
5 ;ihn\ e
« 4.9 Silicon Carbide Production and
7 Consumption (IPCC Source Category 2B5)
8 Carbon dioxide (CO2) and methane (CH4) are emitted from the production of silicon carbide (SiC), a material used
9 as an industrial abrasive. Silicon carbide is produced for abrasive, metallurgical, and other non-abrasive applications
10 in the United States. Production for metallurgical and other non-abrasive applications is not available and therefore
11 both CO2 and CH4 estimates are based solely upon production estimates of silicon carbide for abrasive applications.
12 Emissions from fuels consumed for energy purposes during the production of silicon carbide are accounted for in the
13 Energy chapter.
14 To produce SiC, silica sand or quartz (SiCh) is reacted with carbon in the form of petroleum coke. A portion (about
15 35 percent) of the carbon contained in the petroleum coke is retained in the SiC. The remaining carbon is emitted as
16 CO2, CH4, or carbon monoxide (CO). The overall reaction is shown below (but in practice it does not proceed
17 according to stoichiometry):
18 Si02 + 3C -> SiC + 2CO (+ 02 -> 2C02)
19 Carbon dioxide is also emitted from the consumption of SiC for metallurgical and other non-abrasive applications.
20 Markets for manufactured abrasives, including SiC, are heavily influenced by activity in the U.S. manufacturing
21 sector, especially in the aerospace, automotive, furniture, housing, and steel manufacturing sectors. The U.S.
22 Geological Survey (USGS) reports that a portion (approximately 50 percent) of SiC is used in metallurgical and
23 other non-abrasive applications, primarily in iron and steel production (USGS 2006a). As a result of the economic
24 downturn in 2008 and 2009, demand for SiC decreased in those years. Low cost imports, particularly from China,
25 combined with high relative operating costs for domestic producers, continue to put downward pressure on the
26 production of SiC in the United States. However, demand for SiC consumption in the United States has recovered
27 somewhat from its low in 2009 (USGS 2012a). Abrasive-grade silicon carbide was manufactured at a two facilities
28 in 2015 (USGS 2016).
29 Carbon dioxide emissions from SiC production and consumption in 2015 were 0.2 MMT CO2 Eq. (180 kt CO2) (see
30 Table 4-32 and Table 4-33). Approximately 51 percent of these emissions resulted from SiC production while the
31 remainder resulted from SiC consumption. Methane emissions from SiC production in 2015 were 0.01 MMT CO2
32 Eq. (0.4 kt CH4) (see Table 4-32 and Table 4-33). Emissions have fluctuated in recent years, but 2015 emissions are
33 about 52 percent lower than emissions in 1990.
Industrial Processes and Product Use 4-35
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1 Table 4-32: CO2 and ChU Emissions from Silicon Carbide Production and Consumption (MMT
2 COz Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
CO2
CH4
0.4
+
0.2
+
0.2
+
0.2
+
0.2
+
0.2
+
0.2
+
Total
0.4
0.2
0.2
0.2
0.2
0.2
0.2
+ Does not exceed 0.05 MMT CO2 Eq.
3 Table 4-33: CO2 and ChU Emissions from Silicon Carbide Production and Consumption (kt)
Year
1990
2005
2011
2012
2013
2014
2015
CO2
CH4
375
]
219
+
170
+
158
+
169
+
173
+
180
+
+ Does not exceed 0.5 kt.
4 Methodology
5 Emissions of CO2 and CH4 from the production of SiC were calculated27 using the Tier 1 method provided by the
6 2006IPCC Guidelines. Annual estimates of SiC production were multiplied by the appropriate emission factor, as
7 shown below:
8
9
10 where,
11 Esc,C02
12 EF sc,co2
13 QSc
14 ESC)ch4
15 EFSC,CH4
16
17 Emission factors were taken from the 2006 IPCC Guidelines:
18 • 2.62 metric tons CCVmetric ton SiC
19 • 11.6 kg CH i/mctric ton SiC
20 Emissions of CO2 from silicon carbide consumption for metallurgical uses were calculated by multiplying the
21 annual utilization of SiC for metallurgical uses (reported annually in the USGS Minerals Yearbook: Silicon) by the
22 carbon content of SiC (31.5 percent), which was determined according to the molecular weight ratio of SiC.
23 Emissions of CO2 from silicon carbide consumption for other non-abrasive uses were calculated by multiplying the
24 annual SiC consumption for non-abrasive uses by the carbon content of SiC (31.5 percent). The annual SiC
25 consumption for non-abrasive uses was calculated by multiplying the annual SiC consumption (production plus net
26 imports) by the percent used in metallurgical and other non-abrasive uses (50 percent) (USGS 2006a) and then
27 subtracting the SiC consumption for metallurgical use.
27 EPA has not integrated aggregated facility-level GHGRP information to inform these estimates. The aggregated information
(e.g., activity data and emissions) associated with silicon carbide did not meet criteria to shield underlying confidential business
information (CBI) from public disclosure.
Esc,C02 ~ EFsc,co2 x Qsc
/I metric ton\
Esc,CH4 = EFSCjCH4 x Qsc x ^ 10QQkg )
CO2 emissions from production of SiC, metric tons
Emission factor for production of SiC, metric ton CCh/metric ton SiC
Quantity of SiC produced, metric tons
CH4 emissions from production of SiC, metric tons
Emission factor for production of SiC, kilogram CH4/metric ton SiC
4-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Production data for 1990 through 2013 were obtained from the Minerals Yearbook: Manufactured Abrasives (USGS
2 1991a through 2015). Production data for 2014 and 2015 were obtained from the Minerals Industry Sur\>evs:
3 Abrasives (Manufactured) (USGS 2016). Silicon carbide consumption by major end use for 1990 through 2012 were
4 obtained from the Minerals Yearbook: Silicon (USGS 1991b through 2013) (see Table 4-34). In the previous report,
5 silicon carbide consumption data for 2013 and 2014 were not yet publicly available so 2012 data were used as
6 proxy. In this year's report, 2013 and 2014 data are available and were recalculated to remove proxy data. 2015
7 silicon carbide consumption data is not yet published by the USGS, resulting in the use of 2014 data as a proxy. Net
8 imports and exports for the entire time series were obtained from the U.S. Census Bureau (2005 through 2016).
9 Table 4-34: Production and Consumption of Silicon Carbide (Metric Tons)
Year
Production
Consumption
1990
105,000
172,465
2005
35,000
220,149
2011
35,000
136,222
2012
35,000
114,265
2013
35,000
134,055
2014
35,000
140,733
2015
35,000
153,475
10 Uncertainty and Time-Series Consistency - TO BE UPDATED
11 FOR FINAL INVENTORY REPORT
12 There is uncertainty associated with the emission factors used because they are based on stoichiometry as opposed to
13 monitoring of actual SiC production plants. An alternative would be to calculate emissions based on the quantity of
14 petroleum coke used during the production process rather than on the amount of silicon carbide produced. However,
15 these data were not available. For CH4, there is also uncertainty associated with the hydrogen-containing volatile
16 compounds in the petroleum coke (IPCC 2006). There is also uncertainty associated with the use or destruction of
17 methane generated from the process in addition to uncertainty associated with levels of production, net imports,
18 consumption levels, and the percent of total consumption that is attributed to metallurgical and other non-abrasive
19 uses.
20 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-35. Silicon carbide
21 production and consumption CO2 emissions from 2015 were estimated to be between 9 percent below and 9 percent
22 above the emission estimate of 0.17 MMT CO2 Eq. at the 95 percent confidence level. Silicon carbide production
23 CH4 emissions were estimated to be between 9 percent below and 10 percent above the emission estimate of 0.01
24 MMT CO2 Eq. at the 95 percent confidence level.
25 Table 4-35: Approach 2 Quantitative Uncertainty Estimates for ChU and CO2 Emissions from
26 Silicon Carbide Production and Consumption (MMT CO2 Eq. and Percent)
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
Source
Gas
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Silicon Carbide Production
and Consumption
CO2
0.17
0.16
0.19
-9%
+9%
Silicon Carbide Production
CH4
+
+
+
-9%
+10%
+ Does not exceed 0.05 MMT CO2 Eq.
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Industrial Processes and Product Use 4-37
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1 \lcllkH.kilii!!ic;il recalculations were applied u> llic enure lime scries ii» ensure consistcncs in emissions lioni I'wo
2 ihroimh 2d 15 I )clails on i lie emission trends I IiixmiuIi lime arc described in more detail in I lie \ lei lK>tK>k>u\ section.
3 alxn e
4 4.10 Titanium Dioxide Production (IPCC Source
5 Category 2B6)
6 Titanium dioxide (TiCh) is manufactured using one of two processes: the chloride process and the sulfate process.
7 The chloride process uses petroleum coke and chlorine as raw materials and emits process-related carbon dioxide
8 (CO2). Emissions from fuels consumed for energy purposes during the production of titanium dioxide are accounted
9 for in the Energy chapter. The chloride process is based on the following chemical reactions:
10 2FeTi03 +7Cl2 +3C -> 2TiCl4 +2FeCl3 +3C02
11 2TiCl4 + 202 ->2Ti02 +4Cl2
12 The sulfate process does not use petroleum coke or other forms of carbon as a raw material and does not emit CO2.
13 The C in the first chemical reaction is provided by petroleum coke, which is oxidized in the presence of the chlorine
14 and FeTiCb (rutile ore) to form CO2. Since 2004, all TiC>2 produced in the United States has been produced using the
15 chloride process, and a special grade of "calcined" petroleum coke is manufactured specifically for this purpose.
16 The principal use of TiC>2 is as a pigment in white paint, lacquers, and varnishes; it is also used as a pigment in the
17 manufacture of plastics, paper, and other products. In 2015, U.S. TiC>2 production totaled 1,160,000 metric tons
18 (USGS 2016). There were a total six plants producing TiC>2 in the United States—two located in Mississippi, and
19 single plants located in Delaware, Louisiana, Ohio, and Tennessee.
20 Emissions of CO2 from titanium dioxide production in 2015 were estimated to be 1.6 MMT CO2 Eq. (1,554 kt CO2),
21 which represents an increase of 30 percent since 1990 (see Table 4-36). Compared to 2014, emissions from titanium
22 dioxide production decreased by 8 percent in 2015 due to an 8 percent decrease in production.
23 Table 4-36: CO2 Emissions from Titanium Dioxide (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
1.2
1,195
2005
1.8
1,755
2011
1.7
1,729
2012
1.5
1,528
2013
1.7
1,715
2014
1.7
1,688
2015
1.6
1,554
24 Methodology
25 Emissions of CO2 from TiC>2 production were calculated by multiplying annual national TiC>2 production by chloride
26 process-specific emission factors using a Tier 1 approach provided in 2006 IPCC Guidelines. The Tier 1 equation is
27 as follows:
28 Etd = EFtd X Qtd
29 where,
4-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Etd = CO2 emissions from TiO: production, metric tons
2 EFtd = Emission factor (chloride process), metric ton CCh/mctric ton TiO:
3 Qtd = Quantity ofTiO: produced
4 Data were obtained for the total amount of TiO: produced each year. For years prior to 2004, it was assumed that
5 TiO: was produced using the chloride process and the sulfate process in the same ratio as the ratio of the total U.S.
6 production capacity for each process. As of 2004, the last remaining sulfate process plant in the United States
7 closed; therefore, 100 percent of post-2004 production uses the chloride process (USGS 2005b). The percentage of
8 production from the chloride process is estimated at 100 percent since 2004. An emission factor of 1.34 metric tons
9 CCh/mctric ton TiO: was applied to the estimated chloride-process production (IPCC 2006). It was assumed that all
10 TiO: produced using the chloride process was produced using petroleum coke, although some TiO: may have been
11 produced with graphite or other carbon inputs.
12 The emission factor for the TiO: chloride process was taken from the 2006 IPCC Guidelines. Titanium dioxide
13 production data and the percentage of total TiO: production capacity that is chloride process for 1990 through 2013
14 (see Table 4-37:) were obtained through the Minerals Yearbook: Titanium Annual Report (USGS 1991 through
15 2015b). Production data for 2015 was obtained from the Minerals Commodity Summary: Titanium and Titanium
16 Dioxide (USGS 2016).28 Data on the percentage of total TiO: production capacity that is chloride process were not
17 available for 1990 through 1993, so data from the 1994 USGS Minerals Yearbook were used for these years.
18 Because a sulfate process plant closed in September 2001, the chloride process percentage for 2001 was estimated
19 based on a discussion with Joseph Gambogi (2002). By 2002, only one sulfate process plant remained online in the
20 United States and this plant closed in 2004 (USGS 2005b).
21 Table 4-37: Titanium Dioxide Production (kt)
Year
kt
1990
979
2005
1,310
2011
1,290
2012
1,140
2013
1,280
2014
1,260
2015
1,160
22
23
24
25
26
27
28
29
30
31
32
33
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
Each year, the U.S. Geological Survey (USGS) collects titanium industry data for titanium mineral and pigment
production operations. If TiO: pigment plants do not respond, production from the operations is estimated on the
basis of prior year production levels and industry trends. Variability in response rates varies from 67 to 100 percent
of TiO: pigment plants over the time series.
Although some TiO: may be produced using graphite or other carbon inputs, information and data regarding these
practices were not available. Titanium dioxide produced using graphite inputs, for example, may generate differing
amounts of CO2 per unit of TiO: 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 TiO: produced, sufficient data were not available
to do so.
28 EPA has not integrated aggregated facility-level GHGRP information for Titanium Dioxide production facilities (40 CFR Part
98 Subpart EE). The relevant aggregated information (activity data, emission factor) from these facilities did not meet criteria to
shield underlying CBI from public disclosure.
Industrial Processes and Product Use 4-39
-------
1 \s til' 2t)()4. i lie l;is| iviikii mi ii'-i sul Ink-process plnul in I lie I mied Slnlcs closed Since nmiiinl 11() production wns
2 not reported h\ I S( iS In I lie i\ pe i»f production process used (chloride or siillnlei prior id >< >4 mid oul\ I lie
3 pcrccuinuc of loinl production cnpncih In process wns re purled, llie pereeni of loinl 11() production cnpncilv llinl
4 wns nlirihnled lo llie chloride process wns multiplied In loinl I K) prodnclK 12 percent below nnd I ' percent nbo\e the emission
13 estimnte of I S \I\M ( () \\\.
14 Table 4-38: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Titanium
15 Dioxide Production (MMT CO2 Eq. and Percent)
Smiriv
(¦;is
2015 r.missiiin l!slim;iU-
(MM'I'CO: l.(|.)
1 iHvii.iiim Ki-l;iliM- in llniissinn
(MM 1 C (): l.(|.) ("
l!slilll;ik-''
••I
l.fHUT I |)|KT 1.IHUT
1 {uiiihI Bound 1 {uiiihI
I |)|KT
1 $111111(1
Titanium Dioxide Prod
•uction
t ( )
1.8
1.5 2.0 -12%
+ 13%
Runge of emission estimates predicted by Monte Curio Stochastic Simulation lor a 95 percent confidence interval.
16 Mclhodolomcnl rccnlculnlioiis were npplied lo the entire lime series to ensure coiisisteucs 111 emissions from llJl>t>
17 ihroimh 2(> 15 I)et;uls tin the emission trends ihroimh time nre described 111 more dclnil 111 the \lelhodolou\ section.
18 nho\c.
19 Recalculations Discussion
20 Production data for 2014 was updated relative to the previous Inventory based on recently published data in the
21 USGS 2016Minerals Commodity Summaries: Titanium and Titanium Dioxide (USGS 2016). This resulted in a 4
22 percent decrease in 2014 CO2 emissions from TiCh production relative to the previous report.
23 Planned Improvements
24 Planned improvements include researching the significance of titanium-slag production in electric furnaces and
25 synthetic-rutile production using the Becher process in the United States. Significant use of these production
26 processes will be included in future Inventory reports. This planned improvement is still in development by the EPA
27 and is not included in this report. Pending available resources, EPA will also evaluate use of GHGRP data to
28 improve category-specific QC consistent with both Volume 1, Chapter 6 of 2006IPCC Guidelines and the latest
29 IPCC guidance on the use of facility-level data in national inventories.29
29 See .
4-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 4.11 Soda Ash Production and Consumption
2 (IPCC Source Category 2B7)
3 Carbon dioxide (CO2) is generated as a byproduct of calcining trona ore to produce soda ash, and is eventually
4 emitted into the atmosphere. In addition, CO2 may also be released when soda ash is consumed. Emissions from
5 fuels consumed for energy purposes during the production and consumption of soda ash are accounted for in the
6 Energy sector.
7 Calcining involves placing crushed trona ore into a kiln to convert sodium bicarbonate into crude sodium carbonate
8 that will later be filtered into pure soda ash. The emission of CO2 during trona-based production is based on the
9 following reaction:
10 2Na2C03 • NaHC03 • 2H20(Trona) -» 3Na2C03(Soda Ash) + 5H20 + C02
11 Soda ash (sodium carbonate, Na2CC>3) is a white crystalline solid that is readily soluble in water and strongly
12 alkaline. Commercial soda ash is used as a raw material in a variety of industrial processes and in many familiar
13 consumer products such as glass, soap and detergents, paper, textiles, and food. (Emissions from soda ash used in
14 glass production are reported under Section 4.3, Glass Production (IPCC Source Category 2A3). Glass production is
15 its own source category and historical soda ash consumption figures have been adjusted to reflect this change.) After
16 glass manufacturing, soda ash is used primarily to manufacture many sodium-based inorganic chemicals, including
17 sodium bicarbonate, sodium chromates, sodium phosphates, and sodium silicates (USGS 2015b). Internationally,
18 two types of soda ash are produced, natural and synthetic. The United States produces only natural soda ash and is
19 second only to China in total soda ash production. Trona is the principal ore from which natural soda ash is made.
20 The United States represents about one-fourth of total world soda ash output. Only two states produce natural soda
21 ash: Wyoming and California. Of these two states, only net emissions of CO2 from Wyoming were calculated due to
22 specifics regarding the production processes employed in the state.30 Based on preliminary 2015 reported data, the
23 estimated distribution of soda ash by end-use in 2015 (excluding glass production) was chemical production, 58
24 percent; soap and detergent manufacturing, 13 percent; distributors, 11 percent; flue gas desulfurization, 9 percent;
25 other uses, 5 percent; water treatment, 3 percent; and pulp and paper production, 2 percent (USGS 2015b).31
26 U.S. natural soda ash is competitive in world markets because the majority of the world output of soda ash is made
27 synthetically. Although the United States continues to be a major supplier of world soda ash, China, which
28 surpassed the United States in soda ash production in 2003, is the world's leading producer.
29 In 2015, CO2 emissions from the production of soda ash from trona were approximately 1.7 MMT CO2 Eq. (1,714 kt
30 CO2). Soda ash consumption in the United States generated l.lMMTC02Eq. (1,075 kt CO2) in 2015. Total
31 emissions from soda ash production and consumption in 2015 were 2.8 MMT CO2 Eq. (2,789 kt CO2) (see Table
32 4-39 and Table 4-40).
33 Total emissions from soda ash production and consumption in 2015 decreased by approximately 1 percent from
34 emissions in 2014, and have stayed approximately the same as 1990 levels.
35 Emissions have remained relatively constant over the time series with some fluctuations since 1990. In general,
36 these fluctuations were related to the behavior of the export market and the U.S. economy. The U.S. soda ash
30 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 byproduct, 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, sodium bicarbonate was produced using soda ash feedstocks
mined in Wyoming and shipped to Colorado. Prior to 2004, because the trona was mined in Wyoming, the production numbers
given by the USGS included the feedstocks mined in Wyoming and shipped to Colorado. In this way, the sodium bicarbonate
production that took place in Colorado was accounted for in the Wyoming numbers.
31 Percentages may not add up to 100 percent due to independent rounding.
Industrial Processes and Product Use 4-41
-------
1 industry continued a trend of increased production and value in 2015 since experiencing a decline in domestic and
2 export sales caused by adverse global economic conditions in 2009. The annual average unit value of soda ash set a
3 record high in 2012, and soda ash exports increased as well, accounting for 55 percent of total production (USGS
4 2015b).
5 Table 4-39: CO2 Emissions from Soda Ash Production and Consumption Not Associated with
6 Glass Manufacturing (MMT CO2 Eq.)
Year
Production
Consumption
Total
1990
1.4
1.4
2.8
2005
1.7
1.3
3.0
2011
1.6
2.7
2012
1.7
2.8
2013
1.7
2.8
2014
1.7
2.8
2015
1.7
2.8
Note: Totals may not sum due to independent rounding.
7 Table 4-40: CO2 Emissions from Soda Ash Production and Consumption Not Associated with
8 Glass Manufacturing (kt)
Year
Production
Consumption
Total
1990
1,431
1,390
2,822
2005
1.655
1.305
2.960
2011
1,607
1,105
2,712
2012
1,665
1,097
2,763
2013
1,694
1,109
2,804
2014
1,685
1,143
2,827
2015
1,714
1,075
2,789
Note: Totals may not sum due to independent rounding.
9 Methodology
10 During the production process, trona ore is calcined in a rotary kiln and chemically transformed into a crude soda
11 ash that requires further processing. Carbon dioxide and water are generated as byproducts of the calcination
12 process. Carbon dioxide emissions from the calcination of trona can be estimated based on the chemical reaction
13 shown above. Based on this formula, which is consistent with an IPCC Tier 1 approach, approximately 10.27 metric
14 tons of trona are required to generate one metric ton of CO2, or an emission factor of 0.0974 metric tons CO2 per
15 metric ton trona (IPCC 2006). Thus, the 17.6 million metric tons of trona mined in 2015 for soda ash production
16 (USGS 2015b) resulted in CO2 emissions of approximately 1.7 MMT CO2 Eq. (1,714 kt).
17 Once produced, most soda ash is consumed in chemical and soap production, with minor amounts in pulp and paper,
18 flue gas desulfurization, and water treatment (excluding soda ash consumption for glass manufacturing). As soda ash
19 is consumed for these purposes, additional CO2 is usually emitted. In these applications, it is assumed that one mole
20 of carbon is released for every mole of soda ash used. Thus, approximately 0.113 metric tons of carbon (or 0.415
21 metric tons of CO2) are released for every metric ton of soda ash consumed. In future Inventories, consistent with
22 the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, emissions from soda ash consumption in
23 chemical production processes will be reported under Section 4.4 Other Process Uses of Carbonates (IPCC Category
24 2A4).
4-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
The activity data for trona production and soda ash consumption (see Table 4-41) for 1990 to 2015 were obtained
from the U.S. Geological Survey (USGS) Minerals Yearbook for Soda Ash (1994 through 2015b) and USGS
Mineral Industry Sun'evs for Soda Ash (USGS 2016). Soda ash production and consumption data32 were collected
by the USGS from voluntary surveys of the U.S. soda ash industry. The U.S. Enviromnental Protection Agency
(EPA) will continue to analyze and assess opportunities to use facility-level data fromEPA's Greenhouse Gas
Reporting Program (GHGRP) to improve the emission estimates for Soda Ash Production source category
consistent with IPCC33 and UNFCCC guidelines.
Table 4-41: Soda Ash Production and Consumption Not Associated with Glass Manufacturing
(kt)
Year
Production3
Consumption6
1990
14,700
3,351
2005
17,000
3,144
2011
16,500
2,663
2012
17,100
2,645
2013
17,400
2,674
2014
17,300
2,754
2015
17,600
2,591
a Soda ash produced from trona ore only.
b Soda ash consumption is sales reported by
producers which exclude imports. Historically,
imported soda ash is less than 1 percent of the
total U.S. consumption (Kostick 2012).
Uncertainty and Time-Series Consistency -TO BE UPDATED
FOR FINAL INVENTORY REPORT
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 for trona-based soda ash production.
EPA plans to work with other entities to reassess the uncertainty of these emission factors and activity data based on
the most recent information and data. Through EPA's GHGRP, EPA is aware of one facility producing soda ash
from a liquid alkaline feedstock process. Soda ash production data was collected by the USGS from voluntary
surveys. A survey request was sent to each of the five soda ash producers, all of which responded, representing 100
percent of the total production data (USGS 2016). One source of uncertainty is the purity of the trona ore used for
manufacturing soda ash. The emission factor used for this estimate assumes the ore is 100 percent pure, and likely
overestimates the emissions from soda ash manufacture. The average water-soluble sodium carbonate-bicarbonate
content for ore mined in Wyoming ranges from 85.5 to 93.8 percent (USGS 1995). For emissions from soda ash
consumption, the primary source of uncertainty, however, results from the fact that these emissions are dependent
upon the type of processing employed by each end-use. Specific emission factors for each end-use are not available,
so a Tier 1 default emission factor is used for all end uses. Therefore, there is uncertainty surrounding the emission
factors from the consumption of soda ash. Additional uncertainty comes from the reported consumption and
allocation of consumption within sectors that is collected on a quarterly basis by the USGS. Efforts have been made
to categorize company sales within the correct end-use sector.
32 EPA has assessed feasibility of using emissions information (including activity data) from EPA's GHGRP program; however
at this time, the aggregated information associated with production of soda ash did not meet criteria to shield underlying
confidential business information (CBI) from public disclosure.
33 See .
Industrial Processes and Product Use 4-43
-------
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
31
32
33
34
The lesiills nl" llie \pproach 2 i|ii;inlil;ili\e iiuccriaiuls aiials sis ;iiv suniniai'i/ed in Table 4-42. Soda Ash hodiicliou
and ( ousiinipiioii ( () c 1111 ssi i.i i is I'm' 2(> 15 were esiinialed In be between 2 5 ;md 2.*> \1\1I CO I !c| ;il I lie l>5 percent
confidence le\el This indicates ;i raime of appi'o\inialel\ ~ percent below ;md <> percent abo\e the emission estimate
nl": S \1\1I ( () \x\
Table 4-42: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Soda Ash
Production and Consumption (MMT CO2 Eq. and Percent)
2015 riniissiiin r.siim.iu- I iui-ri;iiim K;mm- Ki-hiiiu- in Kmissimi 1'sii 111:1 ic-1
"l"lV "!IS (MM T CO: i:<|.) (MM 1 ( ():
I.I HUT
Bound
I |)|KT
Bull 11(1
I.IHUT I |1|KT
ISlllllllI lillllllll
Soda Ash Production ,,,,
C( h 2 8 2 1
and Consumption
2 9
-1^/)
;i Range of emission estimates predicted by Monte Carlo Stochastic Simulation lor a
95 percent co
nlidence interval.
Methodological recalculations were applied In 1 he enure lime series in ensure consistence
ihroiiuh 2o 15 1 )elails mi 1 he emission 1 rends ihrouuh lime are described 111 more delail 111
111 emissions from 1 lwo
1 lie Melhodolous section.
abo\ e
Planned Improvements
Time and resources permitting, soda ash consumed for other chemical uses will be extracted from the current soda
ash consumption emission estimates and included under those sources or Other Process Uses of Carbonates (IPCC
Category 2A4) for the final report of the current Inventory cycle (i.e., 1990 through 2015). In addition, EPA plans to
incorporate the use of EPA's GHGRP data for conducting category-specific QC of emission estimates consistent
with both Volume 1, Chapter 6 of 2006 IPCC Guidelines and the latest IPCC guidance on the use of facility-level
data in national inventories.34
4.12 Petrochemical Production (IPCC Source
Category 2B8)
The production of some petrochemicals results in the release of small amounts of carbon dioxide (CO2) and methane
(CH4) emissions. Petrochemicals are chemicals isolated or derived from petroleum or natural gas. Carbon dioxide
emissions from the production of acrylonitrile, carbon black, ethylene, ethylene dichloride, ethylene oxide, and
methanol, and CH4 emissions from the production of methanol and acrylonitrile are presented here and reported
under IPCC Source Category 2B8. The petrochemical industry uses primary fossil fuels (i.e., natural gas, coal,
petroleum, etc.) for non-fuel purposes in the production of carbon black and other petrochemicals. Emissions from
fuels and feedstocks transferred out of the system for use in energy purposes (e.g., indirect or direct process heat or
steam production) are currently accounted for in the Energy sector.
Worldwide more than 90 percent of acrylonitrile (vinyl cyanide, C3H3N) is made by way of direct ammoxidation of
propylene with ammonia (NH3) and oxygen over a catalyst. This process is referred to as the SOHIO process
after the Standard Oil Company of Ohio (SOHIO) (IPCC 2006). The primary use of acrylonitrile is as the raw
material for the manufacture of acrylic and modacrylic fibers. Other major uses include the production of plastics
(acrylonitrile-butadiene-styrene [ABS] and styrene-acrylonitrile [SAN]), nitrile rubbers, nitrile barrier resins,
adiponitrile, and acrylamide. All U.S. acrylonitrile facilities use the SOHIO process (AN 2014). The SOHIO process
involves a fluidized bed reaction of chemical-grade propylene, ammonia, and oxygen over a catalyst. The process
produces acrylonitrile as its primary product and the process yield depends on the type of catalyst used and the
34 See .
4-44 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
process configuration. The ammoxidation process also produces byproduct CO2, carbon monoxide (CO), and water
from the direct oxidation of the propylene feedstock, and produces other hydrocarbons from side reactions in the
ammoxidation process.
Carbon black is a black powder generated by the incomplete combustion of an aromatic petroleum- or coal-based
feedstock at a high temperature. Most carbon black produced in the United States is added to rubber to impart
strength and abrasion resistance, and the tire industry is by far the largest consumer. The other major use of carbon
black is as a pigment. The predominant process used in the United States is the furnace black (or oil furnace)
process. In the furnace black process, carbon black oil (a heavy aromatic liquid) is continuously injected into the
combustion zone of a natural gas-fired furnace. Furnace heat is provided by the natural gas and a portion of the
carbon black feedstock; the remaining portion of the carbon black feedstock is pyrolyzed to carbon black. The
resultant CO2 and uncombusted CH4 emissions are released from thermal incinerators used as control devices,
process dryers, and equipment leaks. Carbon black is also produced in the United States by the thermal cracking of
acetylene-containing feedstocks (i.e., acetylene black process), by the thermal cracking of other hydrocarbons (i.e.,
thermal black process), and by the open burning of carbon black feedstock (i.e., lamp black process); each of these
process are used at only one U.S. plant each (EPA 2000).
Ethylene (C2H4) is consumed in the production processes of the plastics industry including polymers such as high,
low, and linear low density polyethylene (HDPE, LDPE, LLDPE); polyvinyl chloride (PVC); ethylene dichloride;
ethylene oxide; and ethylbenzene. Virtually all ethylene is produced from steam cracking of ethane, propane, butane,
naphtha, gas oil, and other feedstocks. The representative chemical equation for steam cracking of ethane to ethylene
is shown below:
C2H6 -» C2H4 + H2
Small amounts of CH4 are also generated from the steam cracking process. In addition, CO2 and CH4 emissions are
also generated from combustion units.
Ethylene dichloride (C2H4CI2) is used to produce vinyl chloride monomer, which is the precursor to polyvinyl
chloride (PVC). Ethylene dichloride was used as a fuel additive until 1996 when leaded gasoline was phased out.
Ethylene dichloride is produced from ethylene by either direct chlorination, oxychlorination, or a combination of the
two processes (i.e., the "balanced process"); most U.S. facilities use the balanced process. The direct chlorination
and oxychlorination reactions are shown below:
C2H4 + Cl2 -» C2H4Cl2 (direct chlorination)
C2H4 + -02 + 2HCI -» C2H4Cl2 + 2H20 (oxychlorination)
C2H4 + 3 02 —> 2C02 + 2 H20 (direct oxidation of ethylene during oxychlorination)
In addition to the byproduct CO2 produced from the direction oxidation of the ethylene feedstock, CO2 and CH4
emissions are also generated from combustion units.
Ethylene oxide (C2H4O) is used in the manufacture of glycols, glycol ethers, alcohols, and amines. Approximately
70 percent of ethylene oxide produced worldwide is used in the manufacture of glycols, including monoethylene
glycol. Ethylene oxide is produced by reacting ethylene with oxygen over a catalyst. The oxygen may be supplied to
the process through either an air (air process) or a pure oxygen stream (oxygen process). The byproduct CO2 from
the direct oxidation of the ethylene feedstock is removed from the process vent stream using a recycled carbonate
solution, and the recovered CO2 may be vented to the atmosphere or recovered for further utilization in other
sectors, such as food production (IPCC 2006). The combined ethylene oxide reaction and byproduct CO2 reaction is
exothermic and generates heat, which is recovered to produce steam for the process. The ethylene oxide process also
produces other liquid and off-gas byproducts (e.g., ethane, etc.) that may be burned for energy recovery within the
process. Almost all facilities, except one in Texas, use the oxygen process to manufacture ethylene oxide (EPA
2008).
Methanol (CH3OH) is a chemical feedstock most often converted into formaldehyde, acetic acid and olefins. It is
also an alternative transportation fuel, as well as an additive used by municipal wastewater treatment facilities in the
denitrification of wastewater. Methanol is most commonly synthesized from a synthesis gas (i.e., "syngas" - a
mixture containing H2, CO, and CO2) using a heterogeneous catalyst. There are a number of process techniques that
can be used to produce syngas. Worldwide, steam reforming of natural gas is the most common method; however, in
Industrial Processes and Product Use 4-45
-------
1 the United States only two facilities use steam reforming of natural gas. Other syngas production processes in the
2 United States include partial oxidation of natural gas and coal gasification.
3 Emissions of CO2 and CH4 from petrochemical production in 2015 were 28.1 MMT CChEq. (28,062 kt CO2) and
4 0.2 MMT CO2 Eq. (7 kt CH4), respectively (see Table 4-43 and Table 4-44). Since 1990, total CO2 emissions from
5 petrochemical production increased by 32 percent. Methane emissions from petrochemical (methanol and
6 acrylonitrile) production have decreased by approximately 18 percent since 1990, given declining production.
7 Table 4-43: CO2 and ChU Emissions from Petrochemical Production (MMT CO2 Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
CO2
21.3
27.0
26.3
26.5
26.4
26.5
28.1
CH4
0.2
0.1
+
0.1
0.1
0.1
0.2
Total
21.5
27.1
26.4
26.6
26.5
26.6
28.3
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
8
9 Table 4-44: CO2 and ChU Emissions from Petrochemical Production (kt)
Year
l'WO
2005
2011
2012
2013
2014
2015
CO2
CH4
21,326
26,972
3
26,338
2
26,501
3
26,395
3
26,496
5
28,062
7
10 Methodology
11 Emissions of CO2 and CH4 were calculated using the estimation methods provided by the 2006IPCC Guidelines
12 and country-specific methods from EPA's Greenhouse Gas Reporting Program (GHGRP). The 2006 IPCC
13 Guidelines Tier 1 method was used to estimate CO2 and CH4 emissions from production of acrylonitrile and
14 methanol,35 and a country-specific approach similar to the IPCC Tier 2 method was used to estimate CO2 emissions
15 from carbon black, ethylene, ethylene oxide, and ethylene dichloride. The Tier 2 method for petrochemicals is a total
16 feedstock C mass balance method used to estimate total CO2 emissions, but is not applicable for estimating CH4
17 emissions. As noted in the 2006 IPCC Guidelines, the total feedstock C mass balance method (Tier 2) is based on
18 the assumption that all of the C input to the process is converted either into primary and secondary products or into
19 CO2. Further, the guideline states that while the total C mass balance method estimates total C emissions from the
20 process but does not directly provide an estimate of the amount of the total C emissions emitted as CO2, CH4, or
21 non-CH4 volatile organic compounds (NMVOCs). This method accounts for all the C as CO2, including CH4. Note,
22 a subset of facilities reporting under EPA's GHGRP use alternate methods to the C balance approach (e.g.,
23 Continuous Emission Monitoring Systems (CEMS) or other engineering approaches) to monitor CO2 emissions and
24 these facilities are required to also report CH4 and N20 emissions. Preliminary analysis of aggregated annual reports
25 shows that these emissions are less than 500 kt/year. EPA's GHGRP is currently reviewing this data to facilitate
26 category-specific QC and EPA plans to include in the final report (i.e., 1990 through 2015), pending completion of
27 the analysis, time and resources.
28 Carbon Black, Ethylene, Ethylene Dichloride and Ethylene Oxide
29 2010 through 2015
30 Carbon dioxide emissions and national production were aggregated directly from EPA's GHGRP dataset for 2010
31 through 2015 (EPA GHGRP 2016). In 2015, EPA's GHGRP data reported CO2 emissions of 3,260,000 metric tons
35 EPA has not integrated aggregated facility-level GHGRP information for acrylonitrile and methanol production. The
aggregated information associated with production of these petrochemicals did not meet criteria to shield underlying CBI from
public disclosure.
4-46 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
from carbon black production; 20,100,000 metric tons of CChfrom ethylene production; 398,000 metric tons of CO2
from ethylene dichloride production; and 1,200,000 metric tons of CO2 from ethylene oxide production. These
emissions reflect application of a country-specific approach similar to the IPCC Tier 2 method and were used to
estimate CO2 emissions from the production of carbon black, ethylene, ethylene dichloride, and ethylene oxide.
Since 2010, EPA's GHGRP, under Subpart X, requires all domestic producers of petrochemicals to report annual
emissions and supplemental emissions information (e.g., production data, etc.) to facilitate verification of reported
emissions. Under EPA's GHGRP, petrochemical production facilities are required to use either a mass balance
approach or CEMS to measure and report emissions for each petrochemical process unit to estimate facility-level
process CO2 emissions. The mass balance method is used by most facilities36 and assumes that all the carbon input
is converted into primary and secondary products, byproducts, or is emitted to the atmosphere as CO2. To apply the
mass balance, facilities must measure the volume or mass of each gaseous and liquid feedstock and product, mass
rate of each solid feedstock and product, and carbon content of each feedstock and product for each process unit and
sum for their facility. More details on the greenhouse gas calculation, monitoring and QA/QC methods applicable to
petrochemical facilities can be found under Subpart X (Petrochemical Production) of the regulation (40 CFR Part
98).37 EPA verifies annual facility-level GHGRP reports through a multi-step process (e.g. combination of
electronic checks and manual reviews) to identify potential errors and ensure that data submitted to EPA are
accurate, complete, and consistent.38
1990 through 2009
For prior years, for these petrochemical types, an average national CO2 emission factor was calculated based on the
2010 through 2013 EPA's GHGRP data and applied to production for earlier years in the time series (i.e., 1990
through 2009) to estimate CO2 emissions from carbon black, ethylene, ethylene dichloride, and ethylene oxide.
Carbon dioxide emission factors were derived from EPA's GHGRP data by dividing annual CO2 emissions for
petrochemical type "i" with annual production for petrochemical type "i" and then averaging the derived emission
factors obtained for each calendar year 2010 through 2015. The average emission factors for each petrochemical
type were applied across all prior years because petrochemical production processes in the United States have not
changed significantly since 1990, though some operational efficiencies have been implemented at facilities over the
time series.
The average country-specific CO2 emission factors that were calculated from the 2010 through 2015 GHGRP data
are as follows:
• 2.62 metric tons CCVmetric ton carbon black produced
• 0.78 metric tons CCVmetric ton ethylene produced
• 0.040 metric tons CCVmetric ton ethylene dichloride produced
• 0.44 metric tons CCVmetric ton ethylene oxide produced
Annual production data for carbon black for 1990 through 2009 were obtained from the International Carbon Black
Association (Johnson 2003 and 2005 through 2010). Annual production data for ethylene and ethylene dichloride for
36 A few facilities producing ethylene dichloride used CO2 CEMS, those CO2 emissions have been included in the aggregated
GHGRP emissions presented here. For ethylene production processes, nearly all process emissions are from the combustion of
process off-gas. Under EPA's GHGRP, Subpart X, ethylene facilities can report CO2 emissions from burning of process gases
using the optional combustion methodology for ethylene production processes, which is requires estimating emissions based on
fuel quantity and carbon contents of the fuel. This is consistent with the 2006 IPCC Guidelines (p. 3.57) which recommends
including combustion emissions from fuels obtained from feedstocks (e.g., off-gases) in petrochemical production under in the
IPPU sector. In 2014, for example, this represented about 20 of the 80 reporting facilities. In addition to CO2, these facilities are
required to report emissions of CH4 and N2O from combustion of ethylene process off-gas in flares. Both facilities using CEMS
(consistent with a Tier 3 approach) and those using the optional combustion methodology are also required to report emissions of
CH4 and N2O from combustion of petrochemical process-off gases and flares, as applicable. Preliminary analysis of the
aggregated reported CH4 and N2O emissions from facilities using CEMS and the optional combustion methodology suggests that
these annual emissions are less than 500 kt/yr so not significant enough to prioritize for inclusion in the report at this time.
Pending resources and significance, EPA may include these emissions in future reports to enhance completeness.
37 See .
38 See .
Industrial Processes and Product Use 4-47
-------
1 1990 through 2009 were obtained from the American Chemistry Council's (ACC's) Guide to the Business of
2 Chemistry (ACC 2002, 2003, 2005 through 2011). Annual production data for ethylene oxide were obtained from
3 ACC's U.S. Chemical Industry Statistical Handbook for 2003 through 2009 (ACC 2014a) and from ACC's Business
4 of Chemistry for 1990 through 2002 (ACC 2014b). As noted above, annual 2010 through 2015 production data for
5 carbon black, ethylene, ethylene dichloride, and ethylene oxide, were obtained from EPA's GHGRP.
6 Acrylonitrile
7 Carbon dioxide and methane emissions from acrylonitrile production were estimated using the Tier 1 method in the
8 2006IPCC Guidelines. Annual acrylonitrile production data were used with IPCC default Tier 1 CO2 and CH4
9 emission factors to estimate emissions for 1990 through 2015. Emission factors used to estimate acrylonitrile
10 production emissions are as follows:
11 • 0.18 kg CH4/metric ton acrylonitrile produced
12 • 1.00 metric tons CCh/mctric ton acrylonitrile produced
13
14 Annual acrylonitrile production data for 1990 through 2015 were obtained from ACC's Business of Chemistry (ACC
15 2016).
16 Methanol
17 Carbon dioxide and methane emissions from methanol production were estimated using Tier 1 method in the 2006
18 IPCC Guidelines. Annual methanol production data were used with IPCC default Tier 1 CO2 and CH4 emission
19 factors to estimate emissions for 1990 through 2015. Emission factors used to estimate methanol production
20 emissions are as follows:
21 • 2.3 kg CH4/metric ton methanol produced
22 • 0.67 metric tons CCh/mctric ton methanol produced
23
24 Annual methanol production data for 1990 through 2015 were obtained from the ACC's Business of Chemistry
25 (ACC 2016).
26 Table 4-45: Production of Selected Petrochemicals (kt)
Chemical
1990
2005
2011
2012
2013
2014
2015
Carbon Black
1,307
1.651
1,340
1,280
1,230
1,210
1,220
Ethylene
16,542
23,975
25,100
24,800
25,300
25,500
26,900
Ethylene Dichloride
6,283
11.260
8,620
11,300
11,500
11,300
11,300
Ethylene Oxide
2,429
3.220
3,010
3,110
3,150
3,140
3,240
Acrylonitrile
1,214
1.325
1,055
1,220
1,075
1,095
1,050
Methanol
3,750
1.225
700
995
1,235
2,105
3,065
27 -TO BE UPDATED
28 FOR FINAL INVENTORY REPORT
29 The ( 11 ;ind ('() emission factors used lor acr\ lomirilc unci methanol production arc based on ;i limited number of
30 studies I sum plant-spec ilie factors instead of default or ;i\ crauc factors could increase I lie accurals of 1 lie emission
31 esiiniaies. howc\cr. such dala were uol a\ailablc lor 1 lie ciirrcni lii\cuior\ rcpori
32 The resul is of 1 lie i|iiauiiiali\ c iiiiccriaiuls aiials sis lor ilie ('() emissions from carbon black producliou. el 11\ leue.
33 el li\ leue dichloride. and el 11\ lene o\idc arc based 011 rcpoilcd (il l( iRR dala. keler lo ilie Mcthodolous sccliou for
34 more details 011 how iliese cniissioiis were calculated and reported lo I!R Vs (il l( iRR There is sonic iiiiccrtaiuis 111
35 1 he applicability olThc a\crauc emission factors forcach pctrochcniical i\pe across all prior \ ears While
36 pclrochcniical producliou processes 1111 lie I lined Sialcs ha\e 1101 chaimcd simiificauiK since I'J'Jii. sonic
37 opcralioual efficiencies ha\c been implcniciiicd al facililics o\cr 1 lie lime scries
4-48 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 The results nl" llie \pprnach 2 quaulilali\e iiuccriaiuls ;iii;iI> sis ;iiv suniniari/ed in I ahle 4-4(> I'el rne lien ileal
2 prndiielinu ("() cniissmus limn 2u 15 were esiinialed In he between 25 ' and 2~.S \ 1 \ 1'I' ('() I !«.| al the l>5 percent
3 cniilidcuce le\el This indicates ;i raime nl apprn\inialel> 5 percent helnw In 5 percent ahn\e the emission csiim;iic
4 ill' 2<> 5 \1\1T CO I !q IVirncheniical prnductinii CI I emissimis I'rnni 2o 15 were esiinialed in he helween <>.<>5 ;ind
5 ii 15 \1\1l 'CO l!q ;il l he l>5 pereenl cniilidcuce le\ el This indicates a rauue nl'apprn\inialel> 55 pereenl helnw In
6 45 percent ahn\e the eniissinii estimate nl'u I MMTCO l!q
7 Table 4-46: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from
8 Petrochemical Production and CO2 Emissions from Carbon Black Production (MMT CO2 Eq.
9 and Percent)
2015 1" missiiiii
Smiriv
(¦;is
I'sliniiik-
I luvrhiiim
Ki'hiliM' In lliiiissiiiii
I'sliniiik-'
(MMT CO: i:<|.)
(MMTCO:
.11' .
( '"I
I.IHUT
li
0.15
_
+45%,
Range of emission estimates predicted by Monle Carlo Stochastic Simulation lor a 95 percent confidence interval.
10 Melhndnlnmcal ree;ileiil;iluiiis were applied In llie entire lime series in ensure eniisisieues 111 eniissinus I'rnni ll>lJt>
11 llirmmh 2 <) 15 l)el;nls nil I he eniissmii trends ihrnuuh lime ;ire described 111 ninre del;nl 1111 he Melhndnlnus seelinu.
12 ;ihn\e.
13 I v» ut i to. 111H ussion
14 CO2 emissions data were obtained from the EPA's GHGRP CBI aggregation analysis and updated from 2010
15 through 2015. In addition, this update included adjusted rounding and altering of significant figures for these years.
16 As a result of the rounding, some reported petrochemical emissions in metric tons (MT CO2) increased and some
17 decreases across the 2010 to 2015 time series; however, when converted to million metric tons (MMT CO2) this
18 change became insignificant in its effect on total annual emissions compared to the previous Inventory report.
19 PI •lUIU-.-j fhi'l'l titi'tll'
20 Improvements include completing category-specific QC of activity data and EFs, along with further assessment of
21 CH4 and N20 emissions to enhance completeness in reporting of emissions from petrochemical production, pending
22 resources, significance and time series consistency considerations.
23 Pending resources, a secondary potential improvement for this source category would focus on continuing to
24 analyze the fuel and feedstock data from EPA's GHGRP to better disaggregate energy-related emissions and
25 allocate them more accurately between the Energy and IPPU sectors of the Inventory. Some degree of double
26 counting may occur between CO2 estimates of non-energy use of fuels in the energy sector and CO2 process
27 emissions from petrochemical production in this sector. Data integration is not feasible at this time as feedstock data
28 from the Energy Information Administration (EIA) used to estimate non-energy uses of fuels are aggregated by fuel
29 type, rather than disaggregated by both fuel type and particular industries (e.g., petrochemical production). EPA,
30 through its GHGRP, currently does not collect complete data on quantities of fuel consumed as feedstocks by
31 petrochemical producers, only feedstock type. Recent revisions to reporting requirements finalized in 2014 and 2016
32 (79 FR 63750; 81 FR 89188)39 may address this issue in future reporting years for the GHGRP data allowing for
33 easier data integration between the non-energy uses of fuels category and the petrochemicals category presented in
39 https://www.epa.gov/ghgreporting/historical-rulemakings
Industrial Processes and Product Use 4-49
-------
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
31
32
33
34
35
this chapter. EPA's GHGRP has initiated analysis of reported petrochemical feedstocks but further QC is required.
This planned improvement is currently under development and has not been instituted into the current Inventory.
4.13 HCFC-22 Production (IPCC Source
Category 2B9a) - TO BE UPDATED FOR
FINAL INVENTORY REPORT
Trifluoromethane (HFC-23 or CHF3) is generated as a byproduct during the manufacture of chlorodifluoromethane
(HCFC-22), which is primarily employed in refrigeration and air conditioning systems and as a chemical feedstock
for manufacturing synthetic polymers. Between 1990 and 2000, U.S. production of HCFC-22 increased significantly
as HCFC-22 replaced chlorofluorocarbons (CFCs) in many applications. Between 2000 and 2007, U.S. production
fluctuated but generally remained above 1990 levels. In 2008 and 2009, U.S. production declined markedly and lias
remained near 2009 levels since. Because HCFC-22 depletes stratospheric ozone, its production for non-feedstock
uses is scheduled to be phased out by 2020 under the U.S. Clean Air Act.40 Feedstock production, however, is
permitted to continue indefinitely.
HCFC-22 is produced by the reaction of chloroform (CHCI3) and hydrogen fluoride (HF) in the presence of a
catalyst, SbCk 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 (CHCI2F), HCFC-22
(CHCIF2), HFC-23 (CHF3), HC1, chloroform, and HF. The under-fluorinated intermediates (HCFC-21) and
chloroform are then condensed and returned to the reactor, along with residual catalyst, to undergo further
fluorination. The final vapors leaving the condenser are primarily HCFC-22, HFC-23, HC1 and residual HF. The
HC1 is recovered as a useful byproduct, and the HF is removed. Once separated from HCFC-22, the HFC-23 may be
released to the atmosphere, recaptured for use in a limited number of applications, or destroyed.
Two facilities produced HCFC-22 in the U.S. in 2014. Emissions of HFC-23 from this activity in 2014 were
estimated to be 5.0 MMT CO2 Eq. (0.3 kt) (see Table 4-47). This quantity represents a 23 percent increase from
2013 emissions and an 89 percent decline from 1990 emissions. The increase from 2013 emissions and the decrease
from 1990 emissions were caused primarily by changes in the HFC-23 emission rate (kg HFC-23 emitted/kg HCFC-
22 produced). The long-term decrease in the emission rate is primarily attributable to six factors: (a) five plants that
did not capture and destroy the HFC-23 generated have ceased production of HCFC-22 since 1990; (b) one plant
that captures and destroys the HFC-23 generated began to produce HCFC-22; (c) one plant implemented and
documented a process change that reduced the amount of HFC-23 generated; (d) the same plant began recovering
HFC-23, primarily for destruction and secondarily for sale; (e) another plant began destroying HFC-23; and (f) the
same plant, whose emission factor was higher than that of the other two plants, ceased production of HCFC-22 in
2013.
Table 4-47: HFC-23 Emissions from HCFC-22 Production (MMT COz Eq. and kt HFC-23)
Year MMTCChEq. kt HFC-23
1990 46.1 3
2005 20.0 1
2010 8.0 0.5
2011 8.8 0.6
2012 5.5 0.4
40 As construed, interpreted, and applied in the terms and conditions of the Montreal Protocol on Substances that Deplete the
Ozone Layer. [42 U.S.C. §7671m(b), CAA §614]
4-50 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
2013
2014
4.1
5.0
0.3
0.3
1 ivietnoaoiogf
2 To esiimnle MIX -2 ' emissions for l'i\ e ill" I lie eidil 11( IX -22 plnnis ih;ii li;i\ e o pern led in llie I in led Sinlcs since
3 I wo methods conipnrnhle In I lie Tier ' methods mi I lie 2nnf- ll'< '<'' iui.Lliihw (ll'CC 2oo<>) were used. I ninssious
4 for 2(i |(i Miroimh 2d 14 were ohiniued lliroimh reports snhniiiied In I S IK IX -22 production I'neililies in 14' Vs
5 (ireeiilkinse (ins Kcporium I'rournni i(il l( iklJi I P Vs (il l( ikl' ninndnles ilinl nil 11(T'( -22 producliou Incililies
6 report I heir niiminl emissions ii|" MIX -2 ' frnm IK IX -22 producliou processes mid MIX -2 ' destruction processes
7 I'icn lousk . dnln were ohiniued In I P \ Miroimh collnhornliou w illi mi industry nssocinliou llinl receis ed \ oluuinriK
8 reporied IKIX-22 producliou nud MIX-2 ^ emissions nuuunIK froninlll S 11( l'C-22 producers from iwo
9 Miroimh 2n(ilJ. These emissions were nuureunled nud reported lo 14' \ on nil niiminl hnsis.
10 I \ir i he ill her three pin ills, l lie Insi of w Inch closed in I '¦>'¦> V methods conipnrnhle lo I he Tier I mel Ik>cI in I lie 2un/-
11 ll'< '<' < ini,!i./iih> were used I Emissions from iliese lliree plnuis hn\ e heeu cnlculnled iisiuu I lie recommended
12 emission fnctor I'nr iiiKipiinii/ed plnnis opcrnliuu before IW5 (<) i)4 ku 1K TX -2^ ku 11( l'C-22 produced)
13 The Ine plnnis Mini hn\ e opernled since IW4 niensure (or. liir ilie plnuis Mini hn\ e since closed, niensuredi
14 coiiccuirnlious of 111 ¦"( -2 ' to esiimnle I heir eniissKius til' MIX -2 ' I'lnuis usiiiu llierninl o\idnliou to nhnle llieir MIX'-
15 2' emissions iiki mu>r I lie pcrforninuce of llieir oxidizers to \ en l\ llinl llie 111 ¦"(" -2 3 isnlnuisi coniplelcK destroyed
16 Mnuis Mint relense (or historicnlK hn\e relensedi some ol' llieir h\ pixiclucl 111 ¦"( '-2 3 periodicals niensure MIX -2^
17 coiiceuirnlious in lhe oiiipui sirenni usiuu uns clironinlournphs This iiiroriunliou is combined w illi iiilliriunlkiii on
18 qiinuiilies til' products ie u.. 1l( IX -221 lo esiimnle I MX -2 ' eniissions
19 l o esiimnle I'wo ihroimh 2()(eniissions. reporis rrom nil iiidnsirs nssocinliou were used Mini nuureunled 11('IX"-22
20 producliou nud 111X -2 3 eniissions rrom nil I S IK IX -22 producers nnd reporied llieni lo IP \ ( \k \l' llJl>~. |w»j.
21 2ooo. 2oo1. 2oo2. 2<>oV 2oo4. 2oi>5. 2o(K>. 2oo". 2oos. 2ooij. 2oKM To esiimnle 2olo lliroimh 2o 14 enussioiis.
22 I'ncilils -le\ el dnln (iiicludum hoili 11( "I X -22 producliou nud 111X -2 3 emissions) reporied ihroimh llie 14' Vs
23 (i II (iklJ were minis /ed lu 1w~ nud 2oos. conipreheusi\ e res lews ol' plnui-lc\ el esiininles of MIX -2 ' eniissions
24 nud IK IX -22 producliou were performed (k II ll>l>~. K'I'l 2ooSi The ll>l>~ nud 2oox res lews eunhled I S loinls lo
25 he res icwed. updnled. nud where uecessniy correcled. nnd nlso for plnni-le\el uuccriniuis nunlvses (\louie-( nrlo
26 siniiilnliousi io he perlornied for I wo. |w5. 2ooo. 2oo5. nud 2o()(> I !siininles of niiiiunl I S. IKTC-22 producliou
27 nre preseuied mi Tnhle 4-4S
28 Table 4-48: HCFC-22 Production (kt)
U-:ir
kl
1990
139
2005
156
20I0
|0|
20I I
I 10
20I2
96
2013
l
20I4
I
Note: I ICT'C-22 production in 2013 and 2014 is
considered Confidential Business Information
(CI JI) as there were only two producers of
I ICl'C-22 in 2013 and 2014.
29 Uncertainty and Time-Series Consistency
30 l lie iiuccriniuis minis sis preseuied mi Mils section wns hnsed on n plnui-lc\ el Monte ( nrlo Siochnsiic Siniiilnliou for
31 2o(K> l lie Monte ( nrlo nnnlvsis used esiininles of lhe uiiccriniiiiics iu llie nidix idunl snnnhles mi encli plnui's
32 esi minium procedure This minis sis wns hnsed on l lie ueuernliou of lo.ooo rnndoni snniples of model inpiiis from I lie
Industrial Processes and Product Use 4-51
-------
1 prohahilils dcnsiis functions forcach nipiil \ normal probability dcusiis fuucliou was assumed lorall
2 measurements mid biases e\cept llie equipment leak estimates IV»r one plant. ;i lou-uornial probability density
3 fuucliou was used for this plant's equipment leak estimates The simulation for )(• \ iclilcd ;i l>5-pcrccul
4 coiil ulciicc mHcia ;il IV.il" I S. emissions of <> S percent below in (> percent abo\ c I lie reported li 14
9 (hee;inse hoili 11( l'( -22 production mid the 111"(' -2" emission rule declined simnricmilK i. llie mo pkinls ih;ii
10 coiiirihnie simiiric;inil\ lo emissions were esiini;iied lo h;i\e sinnkir rel;ili\e iniceri;iiiiiies in iheir 2nn<> i;is well ;is
11 2do5) emission esiini;iies Thus, climmcs in llie rel;ili\ e coiiirihiiiions of these iwo plmils io ioi;il eniissuiiis ;ire noi
12 likels lo h;i\e;i kirue imp;icl on llie iiiiceri;iinl\ ol'llic ii;ilu sis ;ire snnini;iri/.ed in T;ihle 4-4 MIX -2 ^ emissions
14 from I l(T( -22 prodiiclion were es|ini;iled lo he helween 4 " ;nul 5 5 \I\IT CO I !i| ;ii llie percent confidence
15 lc\ el l lns indicates ;i r;mue of ;ippro\ini;iiel\ ~ percent helow iiinl Id percent ;iho\e the emission esiini;iie of 5.D
16 \I\IT'CO I a|
17 Table 4-49: Approach 2 Quantitative Uncertainty Estimates for HFC-23 Emissions from
18 HCFC-22 Production (MMT CO2 Eq. and Percent)
Si ill I'l l"
(¦iis
2014 riiiiissimi risiini;iii'
(MMT CO; Ku.)
I iuiTl;iinl\ kiin^i'ki-liiliu'In l.mission l.siim;ik''
(MM 1 ('(); Ku.l (",..)
I.I HUT I |)|)IT I.IHUT I |)|)IT
liiiund 1 {iiiiihI Bound liinind
1ICTC-22 Pro.
due lion
1 ll'C-23
()
4.7 5.5 -7% +10%
R;mge of emissions rellects a 95 percent confidence interval.
19 \1clhodolomc;il rec;ilcnl;ilKiiis were applied u> the enure unie-series u> ensure unie-series coiisisiencs from I'J'Jii
20 ihroimh 2d 15 I )el;nls on the emission trends ihroimh lime ;ire descrihed 111 more del;nl 111 llie \lelhoikilou\ seclion.
21 ;iho\e
22 4.14 Carbon Dioxide Consumption (IPCC
23 Source Category 2B10)
24 Carbon dioxide (CO2) is used for a variety of commercial applications, including food processing, chemical
25 production, carbonated beverage production, and refrigeration, and is also used in petroleum production for
26 enhanced oil recovery (EOR). Carbon dioxide used for EOR is injected underground to enable additional petroleum
27 to be produced. For the purposes of this analysis, CO2 used in commercial applications other than EOR is assumed
28 to be emitted to the atmosphere. Carbon dioxide used in EOR applications is discussed in the Energy chapter under
29 "Carbon Capture and Storage, including Enhanced Oil Recovery" and is not discussed in this section.
30 Carbon dioxide is produced from naturally-occurring CO2 reservoirs, as a byproduct from the energy and industrial
31 production processes (e.g., ammonia production, fossil fuel combustion, ethanol production), and as a byproduct
32 from the production of crude oil and natural gas, which contain naturally occurring CO2 as a component. Only CO2
33 produced from naturally occurring CO2 reservoirs, and as a byproduct from energy and industrial processes, and
34 used in industrial applications other than EOR is included in this analysis. Carbon dioxide captured from biogenic
35 sources (e.g., ethanol production plants) is not included in the Inventory. Carbon dioxide captured from crude oil
36 and gas production is used in EOR applications and is therefore reported in the Energy chapter.
37 Carbon dioxide is produced as a byproduct of crude oil and natural gas production. This CO2 is separated from the
38 crude oil and natural gas using gas processing equipment, and may be emitted directly to the atmosphere, or
39 captured and reinjected into underground formations, used for EOR, or sold for other commercial uses. A further
4-52 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 discussion of CO2 used in EOR is described in the Energy chapter in Box 3-7 titled "Carbon Dioxide Transport,
2 Injection, and Geological Storage."
3 In 2015, the amount of CO2 produced and captured for commercial applications and subsequently emitted to the
4 atmosphere was 4.3 MMT CChEq. (4,296 kt) (see Table 4-50). This is a decrease of approximately 4 percent from
5 2014 levels and an increase of approximately 192 percent since 1990. The 2015 emissions estimate is based on a
6 linear extrapolation correlated with the trend found in the EPA's GHGRP data, as described in the Methodology
7 section below.
8 Table 4-50: CO2 Emissions from CO2 Consumption (MMT CO2 Eq. and kt)
Year MMT CO2 Eq. kt
1990 1.5 1,472
2005 1.4 1,375
2011 4.1 4,083
2012 4.0 4,019
2013 4.2 4,188
2014 4.5 4,471
2015 4.3 4,296
9 Methodology
10 Carbon dioxide emission estimates for 1990 through 2015 were based on the quantity of CO2 extracted and
11 transferred for industrial applications (i.e., non-EOR end-uses). Some of the CO2 produced by these facilities is used
12 for EOR and some is used in other commercial applications (e.g., chemical manufacturing, food production). It is
13 assumed that 100 percent of the CO2 production used in commercial applications other than EOR is eventually
14 released into the atmosphere.
15 2010 through 2015
16 For 2010 through 2014, data from EPA's Greenhouse Gas Reporting Program (GHGRP) (Subpart PP) were
17 aggregated from facility-level reports to develop a national-level estimate for use in the Inventory (EPA GHGRP
18 2016). Facilities report CO2 extracted or produced from natural reservoirs and industrial sites, and CO2 captured
19 from energy and industrial processes and transferred to various end-use applications to EPA's GHGRP. This
20 analysis includes only reported CO2 transferred to food and beverage end-uses. EPA is continuing to analyze and
21 assess integration of CO2 transferred to other end-uses to enhance the completeness of estimates under this source
22 category. Other end-uses include industrial applications, such as metal fabrication. EPA is analyzing the information
23 reported to ensure that other end-use data excludes non-emissive applications and publication will not reveal
24 confidential business information (CBI). Reporters subject to EPA's GHGRP Subpart PP are also required to report
25 the quantity of CO2 that is imported and/or exported. Currently, these data are not publicly available through the
26 GHGRP due to data confidentiality reasons and hence are excluded from this analysis.
27 Facilities subject to Subpart PP of EPA's GHGRP are required to measure CO2 extracted or produced. More details
28 on the calculation and monitoring methods applicable to extraction and production facilities can be found under
29 Subpart PP: Suppliers of Carbon Dioxide of the regulation, Part 98.41 The number of facilities that reported data to
30 EPA's GHGRP Subpart PP (Suppliers of Carbon Dioxide) for 2010 through 2014 is much higher (ranging from 44
31 to 48) than the number of facilities included in the Inventory for the 1990 to 2009 time period prior to the
32 availability of GHGRP data (4 facilities). The difference is largely due to the fact the 1990 to 2009 data includes
33 only CO2 transferred to end-use applications from naturally occurring CO2 reservoirs and excludes industrial sites.
41 See .
Industrial Processes and Product Use 4-53
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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
For 2015, data from EPA's GHGRP (Subpart PP) was unavailable for use in the current Inventory report due to data
confidentiality reasons. A linear trend extrapolation was performed based on previous GHGRP reporting years
(2010 to 2014) to estimate 2015 emissions. This time-series recalculation is consistent with Volume 1, Chapter 5 of
the 2006IPCC Guidelines for National Greenhouse Gas Inventories.
1990 through 2009
For 1990 through 2009, data from EPA's GHGRP are not available. For this time period, CO2 production data from
four naturally-occurring CO2 reservoirs were used to estimate annual CO2 emissions. These facilities were Jackson
Dome in Mississippi, Brave and West Bravo Domes in New Mexico, and McCallum Dome in Colorado. The
facilities in Mississippi and New Mexico produced CO2 for use in both EOR and in other commercial applications
(e.g., chemical manufacturing, food production). The fourth facility in Colorado (McCallum Dome) produced CO2
for commercial applications only (New Mexico Bureau of Geology and Mineral Resources 2006).
Carbon dioxide production data and the percentage of production that was used for non-EOR applications for the
Jackson Dome, Mississippi facility were obtained from Advanced Resources International (ARI 2006, 2007) for
1990 to 2000, and from the Annual Reports of Denbury Resources (Denbury Resources 2002 through 2010) for
2001 to 2009 (see Table 4-51). Denbury Resources reported the average CO2 production in units of MMCF CO2 per
day for 2001 through 2009 and reported the percentage of the total average annual production that was used for
EOR. Production from 1990 to 1999 was set equal to 2000 production, due to lack of publicly available production
data for 1990 through 1999. Carbon dioxide production data for the Bravo Dome and West Bravo Dome were
obtained from ARI for 1990 through 2009 (ARI 1990 to 2010). Data for the West Bravo Dome facility were only
available for 2009. The percentage of total production that was used for non-EOR applications for the Bravo Dome
and West Bravo Dome facilities for 1990 through 2009 were obtained from New Mexico Bureau of Geology and
Mineral Resources (Broadhead 2003; New Mexico Bureau of Geology and Mineral Resources 2006). Production
data for the McCallum Dome (Jackson County), Colorado facility were obtained from the Colorado Oil and Gas
Conservation Commission (COGCC) for 1999 through 2009 (COGCC 2014). Production data for 1990 to 1998 and
percentage of production used for EOR were assumed to be the same as for 1999, due to lack of publicly-available
data.
Table 4-51: CO2 Production (kt CO2) and the Percent Used for Non-EOR Applications
Year
Jackson Dome,
Bravo Dome,
West Bravo
McCallum
Total CO2
%
MS
NM
Dome, NM CO2
Dome, CO
Production
Non-
CO2 Production
CO2 Production
Production
CO2 Production
from Extraction
EOR3
(kt) (% Non-
(kt) (% Non-
(kt) (% Non-
(kt) (% Non-
and Capture
EOR)
EOR)
EOR)
EOR)
Facilities (kt)
1000
1.344(100%)
63(1%,)
65 (100%i)
NA
NA
2005
1,254 (27%)
58(1%)
+
63 (100%)
NA
NA
2011
NA
NA
NA
NA
66,241
6%
2012
NA
NA
NA
NA
66,326
6%
2013
NA
NA
NA
NA
68,435
6%
2014
NA
NA
NA
NA
72,000
6%
2015
NA
NA
NA
NA
72,560
6%
+ Does not exceed 0.5 percent.
a Includes only food & beverage applications.
NA (Not available). For 2010 through 2015, the publicly available GHGRP data were aggregated at the national level.
Facility-level data are not publicly available from EPA's GHGRP.
4-54 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Uncertainty and Time-Series Consistency - TO BE UPDATED
2 FOR FINAL INVENTORY REPORT
3 There is uncertainty associated with the data reported through EPA's GHGRP. Specifically, there is uncertainty
4 associated with the amount of CO2 consumed for food and beverage applications given a threshold for reporting
5 under GHGRP applicable to those reporting under Subpart PP. in addition to the exclusion of the amount of CO2
6 transferred to all other end-use categories. This latter category might include CO2 quantities that are being used for
7 non-EOR industrial applications such as firefighting. Second, uncertainty is associated with the exclusion of
8 imports/exports data for CO2 suppliers. Currently these data are not publicly available through EPA's GHGRP and
9 hence are excluded from this analysis.
10 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-52. Carbon dioxide
11 consumption CO2 emissions for 2015 were estimated to be between 3.9 and 5.1 MMT CO2 Eq. at the 95 percent
12 confidence level. This indicates a range of approximately 12 percent below to 13 percent above the emission
13 estimate of 4.5 MMT CO2 Eq.
14 Table 4-52: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from CO2
15 Consumption (MMT CO2 Eq. and Percent)
_ 2015 Emission Estimate
Source Gas x
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
:%)
1
Lower Upper
Lower
Upper
Bound Bound
Bound
Bound
CO2 Consumption CO2 4.5
3.9 5.1
-12%
+13%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interva
1.
16 Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
17 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
18 above.
19 Planned Improvements
20 EPA will continue to evaluate the potential to include additional GHGRP data on other emissive end-uses to
21 improve accuracy and completeness of estimates for this source category. Particular attention will be made to
22 ensuring time series consistency of the emissions estimates presented in future Inventory reports, consistent with
23 IPCC and UNFCCC guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the
24 program's initial requirements for reporting of emissions in calendar year 2010, are not available for all inventory
25 years (i.e., 1990 through 2009) as required for this Inventory. In implementing improvements and integration of data
26 from EPA's GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will
27 be relied upon.42 These improvements, in addition to updating the time-series when new data is available, are still in
28 process and will be incorporated into future Inventory reports.
42 See .
Industrial Processes and Product Use 4-55
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1 4.15 Phosphoric Acid Production (IPCC Source
2 Category 2B10)
3 Phosphoric acid (H3PO4) is a basic raw material used in the production of phosphate-based fertilizers. Phosphoric
4 acid production from natural phosphate rock is a source of carbon dioxide (CO2) emissions, due to the chemical
5 reaction of the inorganic carbon (calcium carbonate) component of the phosphate rock.
6 Phosphate rock is mined in Florida and North Carolina, which account for about 80 percent of total domestic output,
7 as well as in Idaho and Utah and is used primarily as a raw material for wet-process phosphoric acid production
8 (USGS 2016). The composition of natural phosphate rock varies depending upon the location where it is mined.
9 Natural phosphate rock mined in the United States generally contains inorganic carbon in the form of calcium
10 carbonate (limestone) and also may contain organic carbon. The calcium carbonate component of the phosphate
11 rock is integral to the phosphate rock chemistry. Phosphate rock can also contain organic carbon that is physically
12 incorporated into the mined rock but is not an integral component of the phosphate rock chemistry.
13 The phosphoric acid production process involves chemical reaction of the calcium phosphate (Ca3(P04)2)
14 component of the phosphate rock with sulfuric acid (H2SO4) and recirculated phosphoric acid (H3PO4) (EFMA
15 2000). However, the generation of CO2 is due to the associated limestone-sulfuric acid reaction, as shown below:
16 CaCO3 + //2SO4 + H2O —> CctSOq • 2H2O + CO2
17 Total U.S. phosphate rock production sold or used in 2015 was 27.6 million metric tons (USGS 2016). Total imports
18 of phosphate rock to the United States in 2015 were approximately 1.9 million metric tons (USGS 2016). Most of
19 the imported phosphate rock (64 percent) is from Morocco, with the remaining 36 percent being from Peru (USGS
20 2016). All phosphate rock mining companies are vertically integrated with fertilizer plants that produce phosphoric
21 acid located near the mines. Some additional phosphoric acid production facilities are located in Texas, Louisiana,
22 and Mississippi that used imported phosphate rock.
23 Over the 1990 to 2015 period, domestic production has decreased by nearly 47 percent. Total CO2 emissions from
24 phosphoric acid production were 1.0 MMT CO2 Eq. (1,007 kt CO2) in 2015 (see Table 4-53). Domestic
25 consumption of phosphate rock in 2015 was estimated to have decreased by approximately 2 percent over 2014
26 levels, owing to producers drawing from higher than average inventories and the closure of a mine in Florida.
27 Domestic consumption also decreased because of lower phosphoric acid production (USGS 2016).
28 Table 4-53: CO2 Emissions from Phosphoric Acid Production (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
1.5
1.529
2005
1.3
1.342
2011
1.2
1,171
2012
1.1
1,118
2013
1.1
1,149
2014
1.0
1,038
2015
1.0
1,007
29 Methodology
30 Carbon dioxide emissions from production of phosphoric acid from phosphate rock are estimated by multiplying the
31 average amount of inorganic carbon (expressed as CO2) contained in the natural phosphate rock as calcium
32 carbonate by the amount of phosphate rock that is used annually to produce phosphoric acid, accounting for
33 domestic production and net imports for consumption. The estimation methodology is as follows:
34 F = C x O
u-r '-'pa °pr Vpr
4-56 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
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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
31
32
33
34
35
36
37
38
where,
Epa = CO2 emissions from phosphoric acid production, metric tons
Cpr = Average amount of carbon (expressed as CO2) in natural phosphate rock, metric ton CO2/
metric ton phosphate rock
QPr = Quantity of phosphate rock used to produce phosphoric acid
The CO2 emissions calculation methodology is based on the assumption that all of the inorganic C (calcium
carbonate) content of the phosphate rock reacts to produce 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 U.S. Geological Survey (USGS) Mineral Yearbook: Phosphate Rock disaggregated
phosphate rock mined annually in Florida and North Carolina from phosphate rock mined annually in Idaho and
Utah, and reported the annual amounts of phosphate rock exported and imported for consumption (see Table 4-54).
For the years 1990 through 1992, and 2005 through 2015, only nationally aggregated mining data was reported by
USGS. For the years 1990, 1991, and 1992, the breakdown of phosphate rock mined in Florida and North Carolina,
and the amount mined in Idaho and Utah, are approximated using average share of U.S. production in those states
from 1993 to 2004 data. For the years 2005 through 2015, the same approximation method is used, but the share of
U.S. production in those states data were obtained from the USGS commodity specialist for phosphate rock (USGS
2012). Data for domestic sales or consumption of phosphate rock, exports of phosphate rock (primarily from Florida
and North Carolina), and imports of phosphate rock for consumption for 1990 through 2015 were obtained from
USGS Minerals Yearbook: Phosphate Rock (USGS 1994 through 2015b), and from USGS Minerals Commodity
Summaries: Phosphate Rock in 2016 (USGS 2016). From 2004 through 2015, the USGS reported no exports of
phosphate rock from U.S. producers (USGS 2005 through 2015b).
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 2003a). Phosphate rock mined in Florida contains approximately 1 percent inorganic C, and phosphate rock
imported from Morocco contains approximately 1.46 percent inorganic carbon. Calcined phosphate rock mined in
North Carolina and Idaho contains approximately 0.41 percent and 0.27 percent inorganic C, respectively (see Table
4-55).
Carbonate content data for phosphate rock mined in Florida are used to calculate the CO2 emissions from
consumption of phosphate rock mined in Florida and North Carolina (80 percent of domestic production) and
carbonate content data for phosphate rock mined in Morocco are used to calculate 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). The USGS confirmed that no significant quantity of domestic production of phosphate rock
is in the calcined form (USGS 2012).
Table 4-54: Phosphate Rock Domestic Consumption, Exports, and Imports (kt)
Location/Year
1990
2005
2011
2012
2013
2014
2015
U.S. Domestic
Consumption
49,800
35,200
28,600
27,300
28,800
26,700
26,500
FLandNC
42,494
28,160
22,880
21,840
23,040
21,360
21,200
ID and UT
7,306
7,040
5,720
5,460
5,760
5,340
5,300
Exports—FL and NC
6,240
0
0
0
0
0
0
Imports
451
2,630
3,750
3,570
3,170
2,390
1,900
TotalU.S. Consumption
44,011
37,830
32,350
30,870
31,970
29,090
28,400
Industrial Processes and Product Use 4-57
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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
31
32
33
34
35
36
37
38
39
40
Table 4-55: Chemical Composition of Phosphate Rock (Percent by Weight)
Central
North
North Carolina
Idaho
Composition
Florida
Florida
(calcined)
(calcined)
Morocco
Total Carbon (as C)
1.60
1.76
0.76
0.60
1.56
Inorganic Carbon (as C)
1.00
0.93
0.41
0.27
1.46
Organic Carbon (as C)
0.60
0.83
0.35
0.00
0.10
Inorganic Carbon (as CO2)
3.67
3.43
1.50
1.00
5.00
Source: FIPR (2003a).
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
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 2015. For previous years in the time series,
USGS provided the data disaggregated regionally; however, beginning in 2006, only total U.S. phosphate rock
production was reported. Regional production for 2015 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 2015 regional production data represents actual production in those regions. Total
U.S. phosphate rock production data are not considered to be a significant source of uncertainty because all the
domestic phosphate rock producers report their annual production to the USGS. Data for exports of phosphate rock
used in the emission calculation are reported by phosphate rock producers and are not considered to be a significant
source of uncertainty. Data for imports for consumption are based on international trade data collected by the U.S.
Census Bureau. These U.S. government economic data are not considered to be a significant source of uncertainty.
An additional source of uncertainty in the calculation of 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. The Inventory relies on one study (FIPR 2003a) of chemical
composition of the phosphate rock; limited data are available beyond this study. Another source of uncertainty is the
disposition of the organic carbon content of the phosphate rock. A representative of the Florida Institute of
Phosphate Research (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 2003b). Organic carbon 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 air permit information available to
the public, at least one facility has calcining units permitted for operation (NCDENR 2013).
Finally, USGS indicated that approximately 7 percent of domestically-produced phosphate rock is used to
manufacture elemental phosphorus and other phosphorus-based chemicals, rather than phosphoric acid (USGS
2006). According to USGS, there is only one domestic producer of elemental phosphorus, in Idaho, and no data
were available concerning the annual production of this single producer. Elemental phosphorus is produced by
reducing phosphate rock with coal coke, and it is therefore assumed that 100 percent of the carbonate content of the
phosphate rock will be converted to 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 carbon content in phosphate rock, but none
from the organic carbon content.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-56. 2015 phosphoric acid
production CO2 emissions were estimated to be between 0.9 and 1.4 MMT CO2 Eq. at the 95 percent confidence
level. This indicates a range of approximately 19 percent below and 20 percent above the emission estimate of 1.1
MMT CO2 Eq.
4-58 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 4-56: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from
2 Phosphoric Acid Production (MMT CO2 Eq. and Percent)
Si ill I'l l"
(¦;is
2015 rimissiiiii I'.sliniiili'
(MMT CO: l.i|.)
I iuiTl;iinl\ Ki-hiliu-In llinissiiiii
(MM 1 CO: l.i|.) ("i.)
I'slimuli'1
I.I HUT I |)|KT I.I HUT
liiiiinil Bull nil liinind
I |)|KT
ISmiml
Phosphoric Acid Pi
induction
t ( )
I.I
0.9 1.4 -19%
+20%
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
3 \1ciIk ilie enlire lime series u» ensure ciiiisisieiies in emissions from I99U
4 1 IiixmiuIi 2 <> 15 I )el;nls tin ihe emission ire nils ihroimh lime ;ire described 111 more detail 111 llie Melliodolouv seeliou.
5 aho\ e
6 Recalculations Discussion
7 Relative to the previous Inventory, the phosphate rock import data for 2011 through 2014 were revised based on
8 updated data publicly available from USGS (USGS 2016). This revision resulted in a change in emission estimates
9 ranging from approximately 2 to 5 percent across the time series of 2011 to 2014 compared to the previous
10 inventory report.
11 Planned Improvements
12 EPA continues to evaluate potential improvements to the Inventory estimates for this source category, which include
13 direct integration of EPA's Greenhouse Gas Reporting Program (GHGRP) data for 2010 through 2015 and the use
14 of reported GHGRP data to update the inorganic C content of phosphate rock for prior years. Confidentiality of CBI
15 is being assessed, in addition to the applicability of EPA's GHGRP data for the averaged inorganic C content data
16 (by region) from 2010 through 2015 to inform estimates in prior years in the required time series (i.e., 1990 through
17 2009). In implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the
18 IPCC on the use of facility-level data in national inventories will be relied upon.43 This planned improvement is still
19 in development by EPA and have not been implemented into the current inventory report.
20 4.16 Iron and Steel Production (IPCC Source
21 Category 2C1) and Metallurgical Coke
22 Production
23 Iron and steel production is a multi-step process that generates process-related emissions of carbon dioxide (CO2)
24 and methane (CH4) as raw materials are refined into iron and then transformed into crude steel. Emissions from
25 conventional fuels (e.g., natural gas, fuel oil) consumed for energy purposes during the production of iron and steel
26 are accounted for in the Energy chapter.
27 Iron and steel production includes six distinct production processes: coke production, sinter production, direct
28 reduced iron (DRI) production, pig iron production, electric arc furnace (EAF) steel production, and basic oxygen
29 furnace (BOF) steel production. The number of production processes at a particular plant is dependent upon the
30 specific plant configuration. In addition to the production processes mentioned above, CO2 is also generated at iron
31 and steel mills through the consumption of process byproducts (e.g., blast furnace gas, coke oven gas) used for
43 See .
Industrial Processes and Product Use 4-59
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
various purposes including heating, annealing, and electricity generation. Process byproducts sold for use as
synthetic natural gas are deducted and reported in the Energy chapter. In general, CO2 emissions are generated in
these production processes through the reduction and consumption of various carbon-containing inputs (e.g., ore,
scrap, flux, coke byproducts). In addition, fugitive CH4 emissions can also be generated from these processes but
also sinter, direct iron and pellet production.
Currently, there are between 15 and 20 integrated iron and steel steelmaking facilities that utilize BOFs to refine and
produce steel from iron and more than 100 steelmaking facilities that utilize EAFs to produce steel primarily from
recycled ferrous scrap. In addition, there are 18 cokemaking facilities, of which 7 facilities are co-located with
integrated iron and steel facilities. Slightly more than 62 percent of the raw steel produced in the United States is
produced in one of seven states: Alabama, Arkansas, Indiana, Kentucky, Mississippi, Ohio, and Tennessee (AISI
2016a).
Total production of crude steel in the United States between 2000 and 2008 ranged from a low of 99,320,000 tons to
a high of 109,880,000 tons (2001 and 2004, respectively). Due to the decrease in demand caused by the global
economic downturn (particularly from the automotive industry), crude steel production in the United States sharply
decreased to 65,459,000 tons in 2009. In 2010, crude steel production rebounded to 88,731,000 tons as economic
conditions improved and then continued to increase to 95,237,000 tons in 2011 and 97,769,000 tons in 2012; crude
steel production slightly decreased to 95,766,000 tons in 2013 and then slightly increased to 97,195,000 tons in 2014
(AISI 2016a); crude steel production decreased to 86,912,000 tons in 2015, a decrease of roughly 10 percent from
2014 levels. The United States was the fourth largest producer of raw steel in the world, behind China, Japan and
India, accounting for approximately 4.9 percent of world production in 2015 (AISI 2016a).
The majority of CO2 emissions from the iron and steel production process come from the use of coke in the
production of pig iron and from the consumption of other process byproducts, with lesser amounts emitted from the
use of flux and from the removal of carbon from pig iron used to produce steel.
According to the 2006IPCC Guidelines, the production of metallurgical coke from coking coal is considered to be
an energy use of fossil fuel and the use of coke in iron and steel production is considered to be an industrial process
source. Therefore, the 2006 IPCC Guidelines suggest that emissions from the production of metallurgical coke
should be reported separately in the Energy sector, while emissions from coke consumption in iron and steel
production should be reported in the Industrial Processes and Product Use sector. However, the approaches and
emission estimates for both metallurgical coke production and iron and steel production are both presented here
because much of the relevant activity data is used to estimate emissions from both metallurgical coke production and
iron and steel production. For example, some byproducts (e.g., coke oven gas) of the metallurgical coke production
process are consumed during iron and steel production, and some byproducts of the iron and steel production
process (e.g., blast furnace gas) are consumed during metallurgical coke production. Emissions associated with the
consumption of these byproducts are attributed at the point of consumption. Emissions associated with the use of
conventional fuels (e.g., natural gas, fuel oil) 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 from metallurgical coke production in 2015 were 2.8 MMT CO2 Eq. (2,839 kt CO2) (see Table
4-57 and Table 4-58). Emissions increased in 2015 from 2014 levels and have increased overall since 1990. In the
previous inventory, 2014 domestic coke production data were not published, so 2013 data was used as a proxy. In
this report, domestic coke production data for 2015 was available and so 2014 data were not used as proxy for 2015,
differing from the previous inventory report. 2014 published domestic coke production data were also updated. Coke
production in 2015 was 34 percent lower than in 2000 and 50 percent below 1990. Overall, emissions from
metallurgical coke production have increased by 13 percent (0.3 MMT CO2 Eq.) from 1990 to 2015.
Table 4-57: CO2 Emissions from Metallurgical Coke Production (MMT CO2 Eq.)
Gas
1990
2005
2011
2012
2013
2014
2015
CO2
2.5
2.0
1.4
0.5
1.8
2.0
2.8
Total
2.5
2.0
1.4
0.5
1.8
2.0
2.8
4-60 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 4-58: CO2 Emissions from Metallurgical Coke Production (kt)
Gas
1990
2005
2011
2012 2013
2014
20T5
CO2
2,503
2,044
1,426
543 1,824
2,014
2,839
3
4 Iron and Steel Production
5 Emissions of CO2 and CH4 from iron and steel production in 2015 were 45.1 MMT CO2 Eq. (45,075 kt) and 0.0086
6 MMT CO2 Eq. (0.3 kt), respectively (see Table 4-59 through Table 4-62), totaling approximately 45.1 MMT CO2
7 Eq. Emissions decreased in 2015 and have decreased overall since 1990 due to restructuring of the industry,
8 technological improvements, and increased scrap steel utilization. Carbon dioxide emission estimates include
9 emissions from the consumption of carbonaceous materials in the blast furnace, EAF, and BOF, as well as blast
10 furnace gas and coke oven gas consumption for other activities at the steel mill.
11 In 2015, domestic production of pig iron decreased by 13 percent from 2014 levels. Overall, domestic pig iron
12 production has declined since the 1990s. Pig iron production in 2015 was 47 percent lower than in 2000 and 49
13 percent below 1990. Carbon dioxide emissions from steel production have increased by 2 percent (0.1 MMT CO2
14 Eq.) since 1990, while overall CO2 emissions from iron and steel production have declined by 54 percent (52.1
15 MMT C02Eq.) from 1990 to 2015.
16 Table 4-59: CO2 Emissions from Iron and Steel Production (MMT CO2 Eq.)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Sinter Production
2.4
1.7
1.2
1.2
1.1
1.1
1.0
Iron Production
45.6
17.5
18.4
10.9
11.9
18.6
11.7
Steel Production
7.9
9.4
9.3
9.9
8.6
7.8
8.1
Other Activities3
41.2
35.9
29.7
31.7
28.7
27.9
24.3
Total
97.2
64.5
58.5
53.7
50.4
55.5
45.1
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the steel
mill other than consumption in blast furnace, EAFs, or BOFs.
Note: Totals may not sum due to independent rounding.
17 Table 4-60: CO2 Emissions from Iron and Steel Production (kt)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Sinter Production
2,448
1,663
1,188
1,159
1,117
1,104
1,016
Iron Production
45,592
17,545
18,376
10,918
11,935
18,629
11,696
Steel Production
7,933
9,356
9,255
9,860
8,617
7,845
8,082
Other Activitiesa
41,193
4 35,934
29,683
31,750
28,709
27,911
24,282
Total
97,167
64,500
58,503
53,687
50,379
55,489
45,075
a Includes emissions from blast furnace gas and coke oven gas combustion for activities at the steel
mill other than consumption in blast furnace, EAFs, or BOFs.
Note: Totals may not sum due to independent rounding.
18 Table 4-61: Cm Emissions from Iron and Steel Production (MMT CO2 Eq.)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Sinter Production + + ' + + + + +
Total
+
+
+
+
+
+
+
+ Does not exceed 0.05 MMT CO2 Eq.
Industrial Processes and Product Use 4-61
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9
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12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
Table 4-62: ChU Emissions from Iron and Steel Production (kt)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Sinter Production
0.9
0.6 i
0.4
0.4
0.4
0.4
0.3
Total 0.9 0,r. 0.4 0.4 0.4 0.4 0.3
Methodology
Emission estimates presented in this chapter are largely based on Tier 2 methodologies provided by the 2006IPCC
Guidelines. These Tier 2 methodologies call for a mass balance accounting of the carbonaceous inputs and outputs
during the iron and steel production process and the metallurgical coke production process. Tier 1 methods are used
for certain iron and steel production processes (i.e., sinter production, pellet production and DRI production) for
which available data are insufficient for utilizing a Tier 2 method.
The Tier 2 methodology equation is as follows:
Eco2 ~
^(<2a X Ca) - ^(<2fc X Cb)
44
12
where,
Ec02
a
b
Qa
ca
Qb
Cb
44/12
Emissions from coke, pig iron, EAF steel, or BOF steel production, metric tons
Input material a
Output material b
Quantity of input material a, metric tons
Carbon content of input material a, metric tons C/metric ton material
Quantity of output material b, metric tons
Carbon content of output material b, metric tons C/metric ton material
Stoichiometric ratio of CO2 to C
The Tier 1 methodology equations are as follows:
ES,P = Qsx EFs,p
Ed,C02 = Qd X EFd,C02
Ep,C02 = Qp X EFp,co2
where,
ES)P
Emissions from sinter production process for pollutant p (CO2 or CH4), metric ton
Qs
Quantity of sinter produced, metric tons
EFs,p
Emission factor for pollutant p (CO2 or CH4), metric ton /Vmctric ton sinter
Ed,C02 =
Emissions from DRI production process for CO2, metric ton
Qd
Quantity of DRI produced, metric tons
EFd,co2 =
Emission factor for CO2, metric ton CCh/metric ton DRI
QP
Quantity of pellets produced, metric tons
EFp,co2 -
Emission factor for CO2, metric ton CCh/metric ton pellets produced
Metallurgical Coke Production
Coking coal is used to manufacture metallurgical coke that is used primarily as a reducing agent in the production of
iron and steel, but is also used in the production of other metals including zinc and lead (see Zinc Production and
Lead Production sections of this chapter). Emissions associated with producing metallurgical coke from coking coal
are estimated and reported separately from emissions that result from the iron and steel production process. To
estimate emissions from metallurgical coke production, a Tier 2 method provided by the 2006 IPCC Guidelines was
utilized. The amount of carbon contained in materials produced during the metallurgical coke production process
4-62 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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30
31
32
33
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36
37
(i.e., coke, coke breeze, coke oven gas, and coal tar) is deducted from the amount of carbon contained in materials
consumed during the metallurgical coke production process (i.e., natural gas, blast furnace gas, and coking coal).
Light oil, which is produced during the metallurgical coke production process, is excluded from the deductions due
to data limitations. The amount of carbon contained in these materials is calculated by multiplying the material-
specific carbon content by the amount of material consumed or produced (see Table 4-63). The amount of coal tar
produced was approximated using a production factor of 0.03 tons of coal tar per ton of coking coal consumed. The
amount of coke breeze produced was approximated using a production factor of 0.075 tons of coke breeze per ton of
coking coal consumed (AISI 2008; DOE 2000). Data on the consumption of carbonaceous materials (other than
coking coal) as well as coke oven gas production were available for integrated steel mills only (i.e., steel mills with
co-located coke plants). Therefore, carbonaceous material (other than coking coal) consumption and coke oven gas
production were excluded from emission estimates for merchant coke plants. Carbon contained in coke oven gas
used for coke-oven underfiring was not included in the deductions to avoid double-counting.
Table 4-63: Material Carbon Contents for Metallurgical Coke Production
Material
kgC/kg
Coal Tar
0.62
Coke
0.83
Coke Breeze
0.83
Coking Coal
0.73
Material
kgC/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.
Although the 2006 IPCC Guidelines provide a Tier 1 CH4 emission factor for metallurgical coke production (i.e.,
0.1 g CH4 per metric ton of coke production), it is not appropriate to use because CO2 emissions were estimated
using the Tier 2 mass balance methodology. The mass balance methodology makes a basic assumption that all
carbon that enters the metallurgical coke production process either exits the process as part of a carbon-containing
output or as CO2 emissions. This is consistent with a preliminary assessment of aggregated facility-level greenhouse
gas CH4 emissions reported by coke production facilities under EPA's GHGRP. The assessment indicates that CH4
emissions from coke production are below 500 kt or 0.05 percent of total national emissions. Pending resources and
significance, EPA will assess the possibility of including these emissions in future reports to enhance completeness
and has not incorporated these emissions into this report.
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 2016a) (see Table 4-64). Data on the volume of natural gas
consumption, blast furnace gas consumption, and coke oven gas production for metallurgical coke production at
integrated steel mills were obtained from the American Iron and Steel Institute (AISI), Annual Statistical Report
(AISI 2004 through 2016a) and through personal communications with AISI (AISI 2008) (see Table 4-65). The
factor for the quantity of coal tar produced per ton of coking coal consumed was provided by AISI (AISI 2008). 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. The C content for coke breeze was assumed to equal the C content
of coke.
Table 4-64: Production and Consumption Data for the Calculation of CO2 and ChU Emissions
from Metallurgical Coke Production (Thousand Metric Tons)
Source/Activity Data 1990 2005 2011 2012 2013 2014 2015
Metallurgical Coke Production
Coking Coal Consumption at Coke Plants 35,269 21,259 /^ 19,445 18,825 19,481 19,321 17,879
Coke Production at Coke Plants 25,054 I5.l(v 13,989 13,764 13,898 13,748 12,479
Industrial Processes and Product Use 4-63
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Coal Breeze Production
Coal Tar Production
2,645 1.594 1,458 1,412 1,461 1,449 1,341
1,058 638 ji 583 565 584 580 536
1 Table 4-65: Production and Consumption Data for the Calculation of CO2 Emissions from
2 Metallurgical Coke Production (Million ft3)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Metallurgical Coke Production
Coke Oven Gas Production
250,767
114,213
109,044
113,772
108,162
102,899
84,336
Natural Gas Consumption
599
2,996
3,175
3,267
3,247
3,039
2,338
Blast Furnace Gas Consumption
24,602
4,460
3,853
4,351
4,255
4,346
4,185
3 Iron and Steel Production
4 Emissions of CO2 from sinter production, direct reduced iron production and pellet production were estimated by
5 multiplying total national sinter production and the total national direct reduced iron production by Tier 1 CO2
6 emission factors (see Table 4-66). Because estimates of sinter production, direct reduced iron production and pellet
7 production were not available, production was assumed to equal consumption.
8 Table 4-66: CO2 Emission Factors for Sinter Production, Direct Reduced Iron Production and
9 Pellet Production
Metric Ton
Material Produced
CCh/Metric Ton
Sinter
0.2
Direct Reduced Iron
0.7
Pellet Production
0.03
Source: IPCC (2006), Table 4.1.
10 To estimate emissions from pig iron production in the blast furnace, the amount of carbon contained in the produced
11 pig iron and blast furnace gas were deducted from the amount of carbon contained in inputs (i.e., metallurgical coke,
12 sinter, natural ore, pellets, natural gas, fuel oil, coke oven gas, and direct coal injection). The carbon contained in the
13 pig iron, blast furnace gas, and blast furnace inputs was estimated by multiplying the material-specific C content by
14 each material type (see Table 4-67). Carbon in blast furnace gas used to pre-heat the blast furnace air is combusted
15 to form CO2 during this process. Carbon contained in blast furnace gas used as a blast furnace input was not
16 included in the deductions to avoid double-counting.
17 Emissions from steel production in EAFs were estimated by deducting the carbon contained in the steel produced
18 from the carbon contained in the EAF anode, charge carbon, and scrap steel added to the EAF. Small amounts of
19 carbon from direct reduced iron, pig iron, and flux additions to the EAFs were also included in the EAF calculation.
20 For BOFs, estimates of carbon contained in BOF steel were deducted from C contained in inputs such as natural gas,
21 coke oven gas, fluxes, and pig iron. In each case, the carbon was calculated by multiplying material-specific carbon
22 contents by each material type (see Table 4-67). For EAFs, the amount of EAF anode consumed was approximated
23 by multiplying total EAF steel production by the amount of EAF anode consumed per metric ton of steel produced
24 (0.002 metric tons EAF anode per metric ton steel produced [AISI2008]). The amount of flux (e.g., limestone and
25 dolomite) used during steel manufacture was deducted from the "Other Process Uses of Carbonates" source category
26 (IPCC Source Category 2A4) to avoid double-counting.
27 Carbon dioxide emissions from the consumption of blast furnace gas and coke oven gas for other activities occurring
28 at the steel mill were estimated by multiplying the amount of these materials consumed for these purposes by the
29 material-specific carbon content (see Table 4-67).
30 Carbon dioxide emissions associated with the sinter production, direct reduced iron production, pig iron production,
31 steel production, and other steel mill activities were summed to calculate the total CO2 emissions from iron and steel
32 production (see Table 4-59 and Table 4-60).
4-64 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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34
Table 4-67: Material Carbon Contents for Iron and Steel Production
Material
kgC/kg
Coke
0.83
Direct Reduced Iron
0.02
Dolomite
0.13
EAF Carbon Electrodes
0.82
EAF Charge Carbon
0.83
Limestone
0.12
Pig Iron
0.04
Steel
0.01
Material
kgC/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 process for sinter 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 taken from the 2006 IPCC Guidelines for sinter production (see Table
4-68). Although the 1995 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1995) provide a Tier 1 CH4 emission factor
for pig iron production, it is not appropriate to use because CO2 emissions were estimated using the Tier 2 mass
balance methodology. The mass balance methodology makes a basic assumption that all carbon that enters the pig
iron production process either exits the process as part of a carbon-containing output or as CO2 emissions; the
estimation of CH4 emissions is precluded. A preliminary analysis of facility-level emissions reported during iron
production further supports this assumption and indicates that CH4 emissions are below 500 kt CO2 Eq. and well
below 0.05 percent of total national emissions. The production of direct reduced iron also results in emissions of
CH4 through the consumption of fossil fuels (e.g., natural gas, etc.); however, these emission estimates are excluded
due to data limitations. Pending further analysis and resources, EPA may include these emissions in future reports to
enhance completeness. EPA is still assessing the possibility of including these emissions in future reports and have
not included this data into the current report.
Table 4-68: ChU Emission Factors for Sinter and Pig Iron Production
Material Produced
Factor
Unit
Sinter
0.07
kg CEU/metric ton
Source: IPCC (2006), Table 4.2.
Sinter consumption and pellet consumption data for 1990 through 2015 were obtained from MSVs Annual
Statistical Report (AISI2004 through 2016a) and through personal communications with AISI (AISI2008) (see
Table 4-69). In general, direct reduced iron (DRI) consumption data were obtained from the U.S. Geological Survey
(USGS) Minerals Yearbook- Iron and Steel Scrap (USGS 1991 through 2015) and personal communication with
the USGS Iron and Steel Commodity Specialist (Fenton 2015). However, data for DRI consumed inEAFs were not
available for the years 1990 and 1991. EAF DRI consumption in 1990 and 1991 was calculated by multiplying the
total DRI consumption for all furnaces by the EAF share of total DRI consumption in 1992. Also, data for DRI
consumed in BOFs were not available for the years 1990 through 1993. BOF DRI consumption in 1990 through
1993 was calculated by multiplying the total DRI consumption for all furnaces (excluding EAFs and cupola) by the
BOF share of total DRI consumption (excluding EAFs and cupola) in 1994.
The Tier 1 CO2 emission factors for sinter production, direct reduced iron production and pellet production were
obtained through the 2006 IPCC Guidelines (IPCC 2006). Time series data for pig iron production, coke, natural
gas, fuel oil, sinter, and pellets consumed in the blast furnace; pig iron production; and blast furnace gas produced at
the iron and steel mill and used in the metallurgical coke ovens and other steel mill activities were obtained from
AISI's Annual Statistical Report (AISI 2004 through 2016a) and through personal communications with AISI (AISI
2008) (see Table 4-69 and Table 4-70).
Data for EAF steel production, flux, EAF charge carbon, and natural gas consumption were obtained from AISI's
Annual Statistical Report (AISI 2004 through 2016a) and through personal communications with AISI (AISI 2006
Industrial Processes and Product Use 4-65
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17
18
through 2016b and AISI 2008). The factor for the quantity of EAF anode consumed per ton of EAF steel produced
was provided by AISI (AISI 2008). Data for BOF steel production, flux, natural gas, natural ore, pellet, sinter
consumption as well as BOF steel production were obtained from AISI's Animal Statistical Report (AISI 2004
through 2016a) and through personal communications with AISI (AISI 2008). Data for EAF and BOF scrap steel,
pig iron, and DRI consumption were obtained from the USGS Minerals Yearbook - Iron and Steel Scrap (USGS
1991 through 2015). Data on coke oven gas and blast furnace gas consumed at the iron and steel mill (other than in
the EAF, BOF, or blastfurnace) were obtained from AISI's Annua! Statistical Report (AISI 2004 through 2016a)
and through personal communications with AISI (AISI 2008).
Data on blast furnace gas and coke oven gas sold for use as synthetic natural gas were obtained from EIA's Natural
Gas Annual (EIA 2016b). Carbon contents for direct reduced iron, EAF carbon electrodes, EAF charge carbon,
limestone, dolomite, pig iron, and steel were provided by the 2006IPCC Guidelines. The carbon contents for natural
gas, fuel oil, and direct injection coal were obtained from EIA (EIA 2016c) and EPA (EPA 2010). Heat contents for
fuel oil and direct injection coal were obtained from EIA (EIA 1992, 2011); natural gas heat content was obtained
from Table 37 of AISI's Annual Statistical Report (AISI 2004 through 2016a). Heat contents for coke oven gas and
blast furnace gas were provided in Table 37 of AISI's Annual Statistical Report (AISI 2004 through 2016a) and
confirmed by AISI staff (Carroll 2016).
Table 4-69: Production and Consumption Data for the Calculation of CO2 and ChU Emissions
from Iron and Steel Production (Thousand Metric Tons)
Source/Activity Data
19W
20115
2011
2012
2013
2014
2015
Sinter Production
Sinter Production
12,239
8,315
5,941
5,795
5,583
5,521
5,079
Direct Reduced Iron
Production
Direct Reduced Iron
Production
516
1,303
1,582
3,530
3,350
4,790
4,790
Pellet Production
Pellet Production
60,563
50,096
36,041
39,288
38,198
37,538
32,146
Pig Iron Production
Coke Consumption
24,946
13,832
11,962
9,571
9,308
11,136
7,969
Pig Iron Production
49,669
37,222
30,228
32,063
30,309
29,375
25,436
Direct Injection Coal
Consumption
1,485
2,5^
2,604
2,802
2,675
2,425
2,275
EAF Steel Production
EAF Anode and Charge
Carbon Consumption
67
1,127
1,257
1,318
1,122
1,127
1,116
Scrap Steel
Consumption
42,691
46,600
50,500
50,900
47,300
48,873
48,873
Flux Consumption
319
695
726
748
771
771
726
EAF Steel Production
33,511
52,194
52,108
52,415
52,641
55,174
49,451
BOF Steel Production
Pig Iron Consumption
47,307
34,400
31,300
31,500
29,600
23,755
23,755
Scrap Steel
Consumption
14,713
11,400
8,800
8,350
7,890
5,917
5,917
Flux Consumption
576
582
454
476
454
454
454
BOF Steel Production
43,9^
42,705
34,291
36,282
34,238
33,000
29,396
4-66 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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29
30
Table 4-70: Production and Consumption Data for the Calculation of CO2 Emissions from
Iron and Steel Production (Million ft3 unless otherwise specified)
Source/Activity Data
1990
2005
2011
2012
2013
2014
2015
Pig Iron Production
Natural Gas
Consumption
56,273
59,844
59,132
62,469
48,812
47,734
43,294
Fuel Oil Consumption
(thousand gallons)
163,397
16,170
21,378
19,240
17,468
16,674
9,326
Coke Oven Gas
Consumption
22,033
16,557
17,772
18,608
17,710
16,896
13,921
Blast Furnace Gas
Production
1,439,380
1,299,980
1,063,326
1,139,578
1,026,973
1,000,536
874,670
EAF Steel Production
Natural Gas
Consumption
15,905
19,985
6,263
11,145
10,514
9,622
8,751
BOF Steel Production
Coke Oven Gas
Consumption
3,851
524
554
568
568
524
386
Other Activities
Coke Oven Gas
Consumption
224,883
97,132
90,718
94,596
89,884
85,479
70,029
Blast Furnace Gas
Consumption
1,414,778
1,295,520
1,059,473
1,135,227
1,022,718
996,190
870,485
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
The estimates of CO2 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. Current estimates include estimates from pellet consumption, but exclude emissions
from pellet production. For the Final draft of this Inventory report (i.e., 1990 through 2015), pellet production will
be included in emissions estimates. There is uncertainty associated with the assumption that pellet production, direct
reduced iron and sinter consumption are equal to production. There is uncertainty with the representativeness of the
associated IPCC default emission factors. 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, when consumed in the blast furnace. For EAF steel production, there is
uncertainty associated with the amount of EAF anode and charge carbon consumed due to inconsistent data
throughout the time series. Also for EAF steel production, there is uncertainty associated with the assumption that
100 percent of the natural gas attributed to "steelmaking furnaces" by AISI is process-related and nothing is
combusted for energy purposes. Uncertainty is also associated with the use of process gases such as blast furnace
gas and coke oven gas. Data are not available to differentiate between the use of these gases for processes at the
steel mill versus for energy generation (i.e., electricity and steam generation); therefore, all consumption is attributed
Industrial Processes and Product Use 4-67
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27
28
29
30
31
32
33
34
In iroil ;iik.l skvl production These d;il;i ;md carbon emileiils produce ;i rel;ili\ el> accurate es|ini;ile of C()
emissions. llowe\er. lliercare iiiieeri;niilies associated Willi each
The results of llie \pproach 2 i|ii;inlil;ili\ e iiiiccriaiuls ;in;il> sis arc siiiiiin;iri/ed in T;ihle 4-~ I fur niclallurmcal coke
production and iron and si eel production Tolal CO emissions Iroin nielallurmcal coke production and iron and sieel
production fur 2u 15 were esiiin;iied in he between 4" 2 ;md <¦ ^ (> M\11 ( () I !q ;il llie l>5 pereenl confidence le\el
fins indie;iles ;i raime of approximate^ 15 pereenl below ;md 15 pereenl aho\e llie emission esliniale of 55 4 \1\ff
CO I !q lolal CI I emissions from niclallurmcal coke production and iron and sieel production lor 2d 15 were
esiini;iied lo he between u oux and <> u | \|\f| (() l!q ;u ihel>5 pereenl eonfidenee lex el I Ins indie;iles ;i ranue of
approximate^ ll> pereenl helow ;md ll> pereenl ;iho\e llie emission eslinuile of t> no1) \l\ff CO I !q.
Table 4-71: Approach 2 Quantitative Uncertainty Estimates for CO2 and ChU Emissions from
Iron and Steel Production and Metallurgical Coke Production (MMT CO2 Eq. and Percent)
Si HI I'l l'
2015 I'lmissiiiii l.slim;ili'
" (MM 1 ( (): Kci.)
I niiThiiim K;mm-Kil;iii\i'In l-~missii>11 1"siim;iit-'
(MM 1 ( (): Kii.) ("..)
I.I HUT I |)|KT I.I HUT I |)|KT
liiillllll liiillllll liiillllll liiillllll
Metallurgical Cc
and Steel Prodi
Metallurgical Cc
and Steel Prodi
>ke & Iron
.iction
>ke & Iron
.iction
CO: 55.4
CI 11 +
47.2 63.6 -15% +15%
+ + -19%, +19%
Does not exceed 0.05 MMT CO: Kq.
Range of emission estimates predicted by Monte Carlo Stochastic Simulation lor a l)5 percent confidence interval.
\1elhodolome;il ree;ileiil;iiions were applied lo llie enure lime series 10 ensure eonsisieiies 111 emissions from I'Wt)
lliroiiuh 2d 15 l)el;iilsoii llie emission trends ihronuh lime ;ire described 111 niorcdel;iil 1111 he Melhodolouv section.
:iho\ e
Recalculations Discussion
Updated data was obtained for 2014 direct reduced iron production (USGS 2015), 2014 process inputs for
metallurgical coke production, outputs of U.S. metallurgical coke production and direct reduced iron consumption
for BOF steel production (EIA 2016a). These revisions resulted in an increase of 2014 CO2 emissions estimates
from metallurgical coke production and 2014 CO2 emissions estimates from iron and steel production by 4 percent
each compared to the previous inventory report.
Planned Improvements
Future improvements involve improving activity data and emission factor sources for estimating CO2 and CH4
emissions from pellet production. The EPA has identified a potential activity data source for national-level pellet
production and plans to update estimates based on this data for the final draft of this year's inventory report (i.e.,
1990 through 2015), pending schedule and resources. The EPA has reported pellet production activity data into
Table 4-69 of the methodology section, as shown above, but has not incorporated that data into emissions estimates.
EPA will also evaluate and analyze data reported under EPA's GHGRP to improve the emission estimates for this
and other Iron and Steel Production process categories. Particular attention will be made to ensure time series
consistency of the emissions estimates presented in future Inventory reports, consistent with IPCC and UNFCCC
guidelines. This is required as the facility-level reporting data from EPA's GHGRP, with the program's initial
requirements for reporting of emissions in calendar year 2010, are not available for all inventory years (i.e., 1990
through 2009) as required for this Inventory. In implementing improvements and integration of data from EPA's
GHGRP, the latest guidance from the IPCC on the use of facility-level data in national inventories will be relied
upon.44
44 See .
4-68 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Additional improvements include accounting for emission estimates for the production of metallurgical coke to the
2 Energy chapter as well as identifying the amount of carbonaceous materials, other than coking coal, consumed at
3 merchant coke plants. Other potential improvements include identifying the amount of coal used for direct injection
4 and the amount of coke breeze, coal tar, and light oil produced during coke production. Efforts will also be made to
5 identify information to better characterize emissions from the use of process gases and fuels within the Energy and
6 Industrial Processes and Product Use chapters. This planned improvement is still in development and is not included
7 in this current inventory report.
s 4.17 Ferroalloy Production (IPCC Source
9 Category 2C2)
10 Carbon dioxide (CO2) and methane (CH4) are emitted from the production of several ferroalloys. Ferroalloys are
11 composites of iron (Fe) and other elements such as silicon (Si), manganese (Mn), and chromium (Cr). Emissions
12 from fuels consumed for energy purposes during the production of ferroalloys are accounted for in the Energy
13 chapter. Emissions from the production of two types of ferrosilicon (25 to 55 percent and 56 to 95 percent silicon),
14 silicon metal (96 to 99 percent silicon), and miscellaneous alloys (32 to 65 percent silicon) have been calculated.
15 Emissions from the production of ferrochromium and ferromanganese are not included here because of the small
16 number of manufacturers of these materials in the United States, and therefore, government information disclosure
17 rules prevent the publication of production data for these production facilities.
18 Similar to emissions from the production of iron and steel, CO2 is emitted when metallurgical coke is oxidized
19 during a high-temperature reaction with iron and the selected alloying element. Due to the strong reducing
20 environment, CO is initially produced, and eventually oxidized to CO2. A representative reaction equation for the
21 production of 50 percent ferrosilicon (FeSi) is given below:
22 Fe203 + 2Si02 + 7C —> 2FeSi + 7CO
23 While most of the carbon contained in the process materials is released to the atmosphere as CO2, a percentage is
24 also released as CH4 and other volatiles. The amount of CH4 that is released is dependent on furnace efficiency,
25 operation technique, and control technology.
26 When incorporated in alloy steels, ferroalloys are used to alter the material properties of the steel. Ferroalloys are
27 used primarily by the iron and steel industry, and production trends closely follow that of the iron and steel industry.
28 Twelve companies in the United States produce ferroalloys (USGS 2016a).
29 Emissions of CO2 from ferroalloy production in 2015 were 2.0 MMT CO2 Eq. (1,960 kt CO2) (see Table 4-72 and
30 Table 4-73), which is a 9 percent reduction since 1990. Emissions of CHi from ferroalloy production in 2015 were
31 0.01 MMT CO2 Eq. (0.5 kt CH4), which is a 19 percent decrease since 1990.
32 Table 4-72: CO2 and ChU Emissions from Ferroalloy Production (MMT CO2 Eq.)
Gas
1'WO
2005
2011
2012
2013
2014
2015
CO2
CH4
1.4
+
1.7
1.9
+
OO +
1.9
+
2.0
+
Total
*> *>
1.4
1.7
1.9
1.8
1.9
2.0
+ Does not exceed 0.05 MMT CO2 Eq.
Industrial Processes and Product Use 4-69
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1
Table 4-73: CO2 and ChU Emissions from Ferroalloy Production (kt)
Gas
1990
2005
2011
2012
2013
2014
2015
CO2
CH4
2,152
1
1,392
+
1,735
+
1,903
1
1,785
+
1,914
1
1,960
1
+ Does not exceed 0.5 kt.
2 Methodology
3 Emissions of CO2 and CH4 from ferroalloy production were calculated45 using a Tier 1 method from the 2006IPCC
4 Guidelines by multiplying annual ferroalloy production by material-specific default emission factors provided by
5 IPCC (IPCC 2006). The Tier 1 equations for CO2 and CH4 emissions are as follows:
6
7 Eco2 = Yj
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5
6
7
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9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Silicon (USGS 2014, 2015b, 2016b). The following data were available from the USGS publications for the time-
series:
• Ferrosilicon, 25 to 55 percent Si: Annual production data were available from 1990 through 2010.
• Ferrosilicon 56 to 95 percent Si: Annual production data were available from 1990 through 2010.
• Silicon Metal: Annual production data were available from 1990 through 2005. The production data for
2005 were used as proxy for 2006 through 2010.
• Miscellaneous Alloys, 32 to 65 percent Si: Annual production data were available from 1990 through 1999.
Starting 2000, USGS reported miscellaneous alloys and ferrosilicon containing 25 to 55 percent silicon as a
single category.
Starting with the 2011 publication USGS reported all the ferroalloy production data as a single category (i.e.. Total
Silicon Materials Production). This is due to the small number of ferroalloy manufacturers in the United States and
government information disclosure rules. Ferroalloy product shares developed from the 2010 production data (i.e.,
ferroalloy product production/total ferroalloy production) were used with the total silicon materials production
quantity to estimate the production quantity by ferroalloy product type for 2011 through 2015 (USGS 2013, 2014,
2015b, 2016b).
Table 4-74: Production of Ferroalloys (Metric Tons)
Year Ferrosilicon Ferrosilicon Silicon Metal Misc. Alloys
25%-55% 56%-95% 32-65%
1990 321,385 109,566 145,744 72,442
2005 123,000 86,100 148,000 NA
2011
159,667
140,883
154,450
NA
2012
175,108
154,507
169,385
NA
2013
164,229
144,908
158,862
NA
2014
176,161
155,436
170,404
NA
2015
180,372
159,151
174,477
NA
NA - Not Available for product type, aggregated along with ferrosilicon (25-55% Si)
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
Annual ferroalloy production was reported by the USGS in three broad categories until the 2010 publication:
ferroalloys containing 25 to 55 percent silicon (including miscellaneous alloys), ferroalloys containing 56 to 95
percent silicon and silicon metal (through 2005 only, 2005 value used as proxy for 2005 through 2010). Starting
with the 2011 Minerals Yearbook, USGS started reporting all the ferroalloy production under a single category: total
silicon materials production. The total silicon materials quantity was allocated across the three categories based on
the 2010 production shares for the three categories. Refer to the Methodology section for further details.
Additionally, production data for silvery pig iron (alloys containing less than 25 percent silicon) are not reported by
the USGS to avoid disclosing proprietary company data. Emissions from this production category, therefore, were
not estimated.
Also, some ferroalloys may be produced using wood or other biomass as a primary or secondary carbon source
(carbonaceous reductants), however information and data regarding these practices were not available. Emissions
from ferroalloys produced with wood or other biomass would not be counted under this source because wood-based
carbon is of biogenic origin.46 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
46 Emissions and sinks of biogenic carbon are accounted for in the Land Use, Land-Use Change, and Forestry chapter.
Industrial Processes and Product Use 4-71
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1 aucul used mi llie process. ralhcr lli;in I lie ;iiih5 perceui confidence
9 le\ el This iudic;iles ;i raimc of appro\inialcly 12 pcrccul helow ;iud 12 perceui ;iho\e I he emission esiini;iie of I
10 \l\ITCO I !i| Ferroalloy prodiielion CI I emissions were csiinialcd lo he hclwccu a rauue of approMinalcly 12
11 perceui helow ;iud 12 perceui ;iho\e llie emission esiim;iie ol'u ii| \1\11 (() Fq
12 Table 4-75: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from
13 Ferroalloy Production (MMT CO2 Eq. and Percent)
Smiriv
(¦;is
2015 Emission l'.siiin;iU'
(MMT CO: l.(|.)
I iHvii;iinl> Ki-l;iliM- In Emission
(MM 1 ( 6: l.t|.) ("..)
l;.slini;ik':l
I.I HUT I |)|KT I.I IN IT
ISiiiiihI Bound liiiuiid
I |)|KT
ISiiiiihI
Ferroalloy 1
Ferroallov 1
'reduction
'reduction
I ( )
CM 11
1.9
1.7 2.1 -12%
+ + -12%
+ 12%
+ 12%
-Does not exceed 0.05 MMTCO: Fq.
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
14 Methodological rcc;ilcul;ilious were ;ipplled lo llie eulire lime series 10 ensure cousisicucy 111 emissions from I'wo
15 ihroimh 2d 15 I )el;nls 011 llie emission ireuds ihroimh lime ;ire described 111 more del;iil 111 llie Mclhodolouy secliou.
16 ;iho\c
17 Planned Improvements
18 Pending available resources and prioritization of improvements for more significant sources, EPA will continue to
19 evaluate and analyze data reported under EPA's GHGRP that would be useful to improve the emission estimates and
20 category-specific QC procedures for the Ferroalloy Production source category. Given the small number of
21 facilities, particular attention will be made to ensure time series consistency of the emissions estimates presented in
22 future Inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-level
23 reporting data from EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar
24 year 2010, are not available for all inventory years (i.e., 1990 through 2009) as required for this Inventory. In
25 implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
26 use of facility-level data in national inventories will be relied upon.47 EPA is still assessing the possibility of
27 incorporating this planned improvement into the annual inventory report and has not included these data sets into the
28 current inventory report.
29 4.18 Aluminum Production (IPCC Source
30 Category 2C3)
31 Aluminum is a light-weight, malleable, and corrosion-resistant metal that is used in many manufactured products,
32 including aircraft, automobiles, bicycles, and kitchen utensils. As of recent reporting, the United States was the
47 See .
4-72 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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23
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29
30
31
32
33
eighth largest producer of primary aluminum, with approximately 3 percent of the world total production (USGS
2016). 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 carbon dioxide (CO2)
and two perfluorocarbons (PFCs): perfluoromethane (CF4) and perfluoroethane (C2F6).
Carbon dioxide is emitted during the aluminum smelting process when alumina (aluminum oxide, AI2O3) is reduced
to aluminum using the Hall-Heroult reduction process. The reduction of the alumina occurs through electrolysis in a
molten bath of natural or synthetic cryolite (Na3AlF6). The reduction cells contain a carbon (C) lining that serves as
the cathode. Carbon is also contained in the anode, which can be a C mass of paste, coke briquettes, or prebaked C
blocks from petroleum coke. During reduction, most of this C is oxidized and released to the atmosphere as CO2.
Process emissions of CO2 from aluminum production were estimated to be 2.8 MMT CO2 Eq. (2,767 kt) in 2015
(see Table 4-76). The C anodes consumed during aluminum production consist of petroleum coke and, to a minor
extent, coal tar pitch. The petroleum coke portion of the total 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 4-76: CO2 Emissions from Aluminum Production (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
6.8
6,831
2005
4.1
4.142
2011
3.3
3,292
2012
3.4
3,439
2013
3.3
3,255
2014
2.8
2,833
2015
2.8
2,767
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 C 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 86 percent, respectively, to 1.5 MMT CO2
Eq. of CF4 (0.3 kt) and 0.5 MMT C02 Eq. of C2F6 (0.04 kt) in 2015, as shown in Table 4-77 and Table 4-78. This
decline is due both to reductions in domestic aluminum production and to actions taken by aluminum smelting
companies to reduce the frequency and duration of anode effects. These actions include technology and operational
changes such as employee training, use of computer monitoring, and changes in alumina feeding techniques. Since
1990, aluminum production has declined by 61 percent, while the combined CF4 and C2F6 emission rate (per metric
ton of aluminum produced) has been reduced by 76 percent. Emissions decreased by approximately 21 percent
between 2014 and 2015 due to decreases in aluminum production and in the rate of CF4 and C2F6 emissions per
metric ton of aluminum produced.
Table 4-77: PFC Emissions from Aluminum Production (MMT CO2 Eq.)
Year
CF4
C2F6
Total
1990
17.9
3.5
21.5
2005
2.9
0.6
3.4
2011
2.7
0.8
3.5
2012
2.3
0.7
2.9
2013
2.3
0.7
3.0
Industrial Processes and Product Use 4-73
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
2014 1.9 0.6 2.5
2015 1.5 0.5 2.0
Note: Totals may not sum due to
independent rounding.
Table 4-78: PFC Emissions from Aluminum Production (kt)
Year CF4 C2F6
1990 2.4 0.3
2005 0.4
+
2011 0.4
2012 0.4
2013 0.3
2014 0.3
2015 0.3
0.1
0.1
0.1
0.1
+
+ Does not exceed 0.05 kt.
In 2015, U.S. primary aluminum production totaled approximately 1.6 million metric tons, a 7 percent decrease from
2014 production levels (USAA 2016a). In 2015, three companies managed production at eight operational primary
aluminum smelters. One smelter remained on standby throughout the year, and two non-operating smelters were
permanently shut down during 2015 (USGS 2016a). During 2015, monthly U.S. primary aluminum production was
lower for every month in 2015, when compared to the corresponding months in 2014 (USGS 2016b; USGS 2015).
For 2016, total production for the January to August period was approximately 0.6 million metric tons compared to
1.1 million metric tons for the same period in 2015, a 48 percent decrease (USAA 2016b). Based on the decrease in
production, process CO2 and PFC emissions are likely to be lower in 2016 compared to 2015 if there are no
significant changes in process controls at operational facilities.
Process CO2 and PFC (i.e., CF4 and C2F6) emission estimates from primary aluminum production for 2010 through
2015 are available from EPA's Greenhouse Gas Reporting Program (GHGRP)—Subpart F (Aluminum Production)
(EPA 2016). Under EPA's GHGRP, facilities began reporting primary aluminum production process emissions (for
2010) in 2011; as a result, GHGRP data (for 2010 through 2015) are available to be incorporated into the Inventory.
EPA's GHGRP mandates that all facilities that contain an aluminum production process must report: CF4 and C2F6
emissions from anode effects in all prebake and Soderberg electrolysis cells, CO2 emissions from anode
consumption during electrolysis in all prebake and Soderberg cells, and all CO2 emissions from onsite anode baking.
To estimate the process emissions, EPA's GHGRP uses the process-specific equations (and certain technology-
specific defaults) detailed in subpart F (aluminum production).48 These equations are based on the Tier 2/Tier 3
IPCC (2006) methods for primary aluminum production, and Tier 1 methods when estimating missing data
elements. It should be noted that the same methods (i.e., 2006 IPCC Guidelines) were used for estimating the
emissions prior to the availability of the reported GHGRP data in the Inventory.
Process CO2 Emissions from Anode Consumption and Anode Baking
Carbon dioxide emission estimates for the years prior to the introduction of EPA's GHGRP in 2010 were estimated
2006 IPCC Guidelines methods, but individual facility reported data were combined with process-specific emissions
modeling. These estimates were based on information previously gathered from EPA's Voluntary Aluminum
Industrial Partnership (VAIP) program, U.S. Geological Survey (USGS) Mineral Commodity reviews, and The
48 Code of Federal Regulations, Title 40: Protection of Environment, Part 98: Mandatory Greenhouse Gas Reporting, Subpart
F—Aluminum Production. See .
Methodology
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32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
Aluminum Association (USAA) statistics, among other sources. Since pre- and post-GHGRP estimates use the same
methodology, emission estimates are comparable across the time series.
Most of the CO2 emissions released during aluminum production occur during the electrolysis reaction of the C
anode, as described by the following reaction:
2AI2O3 + 3C -> 4A1 + 3C02
For prebake smelter technologies, CO2 is also emitted during the anode baking process. These emissions can
account for approximately 10 percent of total process CO2 emissions from prebake smelters.
Depending on the availability of smelter-specific data, the CO2 emitted from electrolysis at each smelter was
estimated from: (1) the smelter's annual anode consumption, (2) the smelter's annual aluminum production and rate
of anode consumption (per ton of aluminum produced) for previous and/or following years, or (3) the smelter's
annual aluminum production and IPCC default CO2 emission factors. The first approach tracks the consumption and
carbon content of the anode, assuming that all C in the anode is converted to CO2. Sulfur, ash, and other impurities
in the anode are subtracted from the anode consumption to arrive at a C consumption figure. This approach
corresponds to either the IPCC Tier 2 or Tier 3 method, depending on whether smelter-specific data on anode
impurities are used. The second approach interpolates smelter-specific anode consumption rates to estimate
emissions during years for which anode consumption data are not available. This approach avoids substantial errors
and discontinuities that could be introduced by reverting to Tier 1 methods for those years. The last approach
corresponds to the IPCC Tier 1 method (IPCC 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 Soderberg cells, the process formula accounts for the weight of paste consumed per
metric ton of aluminum produced, and pitch properties, including sulfur, hydrogen, and ash content.
Through the VAIP, anode consumption (and some anode impurity) data have been reported for 1990, 2000, 2003,
2004, 2005, 2006, 2007, 2008, and 2009. Where available, smelter-specific process data reported under the VAIP
were used; however, if the data were incomplete or unavailable, information was supplemented using industry
average values recommended by IPCC (2006). Smelter-specific CO2 process data were provided by 18 of the 23
operating smelters in 1990 and 2000, by 14 out of 16 operating smelters in 2003 and 2004, 14 out of 15 operating
smelters in 2005, 13 out of 14 operating smelters in 2006, 5 out of 14 operating smelters in 2007 and 2008, and 3 out
of 13 operating smelters in 2009. For years where CO2 emissions data or CO2 process data were not reported by
these companies, estimates were developed through linear interpolation, and/or assuming representative (e.g.,
previously reported or industry default) values.
In the absence of any previous historical smelter specific process data (i.e., 1 out of 13 smelters in 2009; 1 out of 14
smelters in 2006, 2007, and 2008; 1 out of 15 smelters in 2005; and 5 out of 23 smelters between 1990 and 2003),
CO2 emission estimates were estimated using Tier 1 Soderberg and/or Prebake emission factors (metric ton of CO2
per metric ton of aluminum produced) from IPCC (2006).
Process PFC Emissions from Anode Effects
Smelter-specific PFC emissions from aluminum production for 2010 through 2015 were reported to EPA under its
GHGRP. To estimate their PFC emissions and report them under EPA's GHGRP, smelters use an approach identical
to the Tier 3 approach in the 2006 IPCC Guidelines (IPCC 2006). Specifically, they use a smelter-specific slope
coefficient as well as smelter-specific operating data to estimate an emission factor using the following equation
PFC = SxAE
AE = F xD
where,
PFC = CF4 or C2F6, kg/MT aluminum
Industrial Processes and Product Use 4-75
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34
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36
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39
S = Slope coefficient, PFC/AE
AE = Anode effect, minutes/cell-day
F = Anode effect frequency per cell-day
D = Anode effect duration, minutes
They then multiply this emission factor by aluminum production to estimate PFC emissions. All U.S. aluminum
smelters are required to report their emissions under EPA's GHGRP.
Perfluorocarbon emissions for the years prior to 2010 were estimated using the same equation, but the slope-factor
used for some smelters was technology-specific rather than smelter-specific, making the method a Tier 2 rather than
a Tier 3 approach for those smelters. Emissions and background data were reported to EPA under the VAIP. For
1990 through 2009, smelter-specific slope coefficients were available and were used for smelters representing
between 30 and 94 percent of U. S. primary aluminum production. The percentage changed from year to year as
some smelters closed or changed hands and as the production at remaining smelters fluctuated. For smelters that did
not report smelter-specific slope coefficients, IPCC technology-specific slope coefficients were applied (IPCC
2006). The slope coefficients were combined with smelter-specific anode effect data collected by aluminum
companies and reported under the VAIP to estimate emission factors over time. For 1990 through 2009, smelter-
specific anode effect data were available for smelters representing between 80 and 100 percent of U.S. primary
aluminum production. Where smelter-specific anode effect data were not available, representative values (e.g.,
previously reported or industry averages) were used.
For all smelters, emission factors were multiplied by annual production to estimate annual emissions at the smelter
level. For 1990 through 2009, smelter-specific production data were available for smelters representing between 30
and 100 percent of U.S. primary aluminum production. (For the years after 2000, this percentage was near the high
end of the range.) Production at non-reporting smelters was estimated by calculating the difference between the
production reported under VAIP and the total U.S. production supplied by USGS or USAA, and then allocating this
difference to non-reporting smelters in proportion to their production capacity. Emissions were then aggregated
across smelters to estimate national emissions.
Between 1990 and 2009, production data were provided under the VAIP by 21 of the 23 U.S. smelters that operated
during at least part of that period. For the non-reporting smelters, production was estimated based on the difference
between reporting smelters and national aluminum production levels (USGS and USAA 1990 through 2009), with
allocation to specific smelters based on reported production capacities (USGS 1990 through 2009).
National primary aluminum production data for 2015 were obtained via USAA (USAA 2016a). For 1990 through
2001, and 2006 (see Table 4-79) data were obtained from USGS Mineral Industry Surveys: Aluminum Annual
Report (USGS 1995, 1998, 2000, 2001, 2002, 2007). For 2002 through 2005, and 2007 through 2014, national
aluminum production data were obtained from the USAA's Primary Aluminum Statistics (USAA 2004 through
2006, 2008 through 2015).
Table 4-79: Production of Primary Aluminum (kt)
Year
kt
1990
4,048
2005
2,478
2011
1,986
2012
2,070
2013
1,948
2014
1,710
2015
1,587
Uncertainty and Time-Series Consistency
Uncertainty was assigned to the CO2, CF4, and C2F6 emission values reported by each individual facility to EPA's
GHGRP. As previously mentioned, the methods for estimating emissions for EPA's GHGRP and this report are the
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1 same, and follow the 2006IPCC Guidelines methodology. As a result, it was possible to assign uncertainty bounds
2 (and distributions) based on an analysis of the uncertainty associated with the facility-specific emissions estimated
3 for previous Inventory years. Uncertainty surrounding the reported CO2, CF4, and C2F6 emission values were
4 determined to have a normal distribution with uncertainty ranges of ±6, ±16, and ±20 percent, respectively. A Monte
5 Carlo analysis was applied to estimate the overall uncertainty of the CO2, CF4, and C2F6 emission estimates for the
6 U.S. aluminum industry as a whole, and the results are provided below.
7 The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-80. Aluminum
8 production-related CO2 emissions were estimated to be between 2.7 and 2.8 MMT CO2 Eq. at the 95 percent
9 confidence level. This indicates a range of approximately 2 percent below to 2 percent above the emission estimate
10 of 2.8 MMT CO2 Eq. Also, production-related CF4 emissions were estimated to be between 1.4 and 1.6 MMT CO2
11 Eq. at the 95 percent confidence level. This indicates a range of approximately 7 percent below to 7 percent above
12 the emission estimate of 1.5 MMT CO2 Eq. Finally, aluminum production-related C2F6 emissions were estimated to
13 be between 0.4 and 0.6 MMT CO2 Eq. at the 95 percent confidence level. This indicates a range of approximately 13
14 percent below to 13 percent above the emission estimate of 0.5 MMT CO2 Eq.
15 Table 4-80: Approach 2 Quantitative Uncertainty Estimates for CO2 and PFC Emissions from
16 Aluminum Production (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Aluminum Production
CO2
2.8
2.7
2.8
-2%
+2%
Aluminum Production
CF4
1.5
1.4
1.6
-7%
+7%
Aluminum Production
C2F6
0.5
0.4
0.6
-13%
+13%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
17
18 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
19 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
20 above.
21 QA/QC and Verification
22 Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
23 control measures for the Aluminum Production category included checking input data, documentation, and
24 calculations to ensure data were properly handled through the inventory process. Errors that were found during this
25 process were corrected as necessary.
26 4.19 Magnesium Production and Processing
27 (IPCC Source Category 2C4)
28 The magnesium metal production and casting industry uses sulfur hexafluoride (SF6) as a cover gas to prevent the
29 rapid oxidation of molten magnesium in the presence of air. Sulfur hexafluoride has been used in this application
30 around the world for more than thirty years. A dilute gaseous mixture of SF6 with dry air and/or carbon dioxide
31 (CO2) is blown over molten magnesium metal to induce and stabilize the formation of a protective crust. A small
32 portion of the SF6 reacts with the magnesium to form a thin molecular film of mostly magnesium oxide and
33 magnesium fluoride. The amount of SF6 reacting in magnesium production and processing is considered to be
34 negligible and thus all SF6 used is assumed to be emitted into the atmosphere. Alternative cover gases, such as AM-
35 cover™ (containing HFC-134a), Novec™ 612 (FK-5-1-12) and dilute sulfur dioxide (SO2) systems can, and are
36 being used by some facilities in the United States. However, many facilities in the United States are still using
37 traditional SF6 cover gas systems.
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The magnesium industry emitted 0.9 MMT CO2 Eq. (0.04 kt) of SF6, 0.09 MMT CO2 Eq. (0.06 kt) of HFC-134a,
and 0.003 MMT CO2 Eq. (2.6 kt) of CO2 in 2015. This represents a decrease of approximately 5 percent from total
2014 emissions (see Table 4-81). The decrease can be attributed to reduction in primary and die casting SF6
emissions between 2014 and 2015 as reported through EPA's Greenhouse Gas Reporting Program (GHGRP). In
2015, SF6 emissions decreased by 7 percent. The reduction in SF6 emissions is likely due in part to decreased
production from reporting facilities in 2015. The decrease in SF6 emissions can also be attributed to continuing
industry efforts to utilize SF6 alternatives, such as HFC-134a, Novec™612 and SO2, to reduce greenhouse gas
emissions. In 2015, total HFC-134a emissions increased from 0.08 MMT CO2 Eq. to 0.09 MMT CO2 Eq., or a 19
percent increase as compared to 2014 emissions. This is mainly attributable to the increased use of this alternative
for primary production. FK 5-1-12 emissions increased by 8 percent from 0.0050 kt to 0.0054 kt. The emissions of
the carrier gas, CO2, increased from 2.3 kt in 2014 to 2.6 kt in 2015.
Table 4-81: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and
Processing (MMT CO2 Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
SFe
5.2
2.7
2.8
1.6
1.5
1.0
0.9
HFC-134a
0.0
0.0
+
+
0.1
0.1
0.1
CO2
+
+
+
+
+
+
+
FK 5-1-12
0.0
0.0
+
+
+
+
+
Total3
5.2
2.7
2.8
1.7
1.5
1.1
1.0
+ Does not exceed 0.05 MMT CO2 Eq.
a Total does not include FK 5-1-12. FK-5-1-12 values shown for informational purposes
only.
Note: Totals may not sum due to independent rounding.
Table 4-82: SF6, HFC-134a, FK 5-1-12 and CO2 Emissions from Magnesium Production and
Processing (kt)
Year
1990
2005
2011
2012
2013
2014
2015
SFe
0.2
0.1
0.1
0.1
0.1
+
+
HFC-134a
0.0
0.0
+
+
0.1
0.1
0.1
CO2
1.4
2.9
3.1
2.3
2.1
2.3
2.6
FK 5-1-12
0.0
0.0
+
+
+
+
+
+ Does not exceed 0.05 kt.
Methodology
Emission estimates for the magnesium industry incorporate information provided by industry participants in EPA's
SF6 Emission Reduction Partnership for the Magnesium Industry as well as emissions data reported through Subpart
T (Magnesium Production and Processing) of the EPA's GHGRP. The Partnership started in 1999 and, in 2010,
participating companies represented 100 percent of U.S. primary and secondary production and 16 percent of the
casting sector production (i.e., die, sand, permanent mold, wrought, and anode casting). SF6 Emissions for 1999
through 2010 from primary production, secondary production (i.e., recycling), and die casting were generally
reported by Partnership participants. Partners reported their SF6 consumption, which is assumed to be equivalent to
emissions. Along with SF6, some Partners also reported their HFC-134a and FK 5-1-12 usage, which is also
assumed to be equal to emissions. 2010 was the last reporting year under the Partnership. Emissions data for 2011
through 2015 are obtained through EPA's GHGRP. Under the program, owners or operators of facilities that have a
magnesium production or casting process must report emissions from use of cover or carrier gases, which include
SF6, HFC-134a, FK 5-1-12 and CO2. Consequently, cover and carrier gas emissions from magnesium production
and processing were estimated for three time periods, depending on the source of the emissions data: 1990 through
1998, 1999 through 2010, and 2011 through 2015. The methodologies described below also make use of magnesium
production data published by the U.S. Geological Survey (USGS).
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1990 through 1998
To estimate emissions for 1990 through 1998, industry SF6 emission factors were multiplied by the corresponding
metal production and consumption (casting) statistics from USGS. For this period, it was assumed that there was no
use of HFC-134a or FK 5-1-12 cover gases and hence emissions were not estimated for these alternatives.
Sulfur hexafluoride emission factors from 1990 through 1998 were based on a number of sources and assumptions.
Emission factors for primary production were available from U.S. primary producers for 1994 and 1995. The
primary production emission factors were 1.2 kg SF6 per metric ton for 1990 through 1993, and 1.1 kg SF6 per
metric ton for 1994 through 1997. The emission factor for secondary production from 1990 through 1998 was
assumed to be constant at the 1999 average Partner value. An emission factor for die casting of 4.1 kg SF6 per metric
ton, which was available for the mid-1990s from an international survey (Gjestland and Magers 1996), was used for
years 1990 through 1996. For 1996 through 1998, the emission factor for die casting was assumed to decline linearly
to the level estimated based on Partner reports in 1999. This assumption is consistent with the trend in SF6 sales to
the magnesium sector that is reported in the RAND survey of major SF6 manufacturers, which shows a decline of 70
percent from 1996 to 1999 (RAND 2002). Sand casting emission factors for 1990 through 2001 were assumed to be
the same as the 2002 emission factor. The emission factors for the other processes (i.e., permanent mold, wrought,
and anode casting), about which less is known, were assumed to remain constant at levels defined in Table 4-81.
These emission factors for the other processes (i.e., permanent mold, wrought, and anode casting) were based on
discussions with industry representatives.
The quantities of CO2 carrier gas used for each production type have been estimated using the 1999 estimated CO2
emissions data and the annual calculated rate of change of SF6 use in the 1990 through 1999 time period. For each
year and production type, the rate of change of SF6 use between the current year and the subsequent year was first
estimated. This rate of change is then applied to the CO2 emissions of the subsequent year to determine the CO2
emission of the current year. The emissions of carrier gases for permanent mold, wrought and anode processes are
not estimated in this Inventory.
1999 through 2010
The 1999 through 2010 emissions from primary and secondary production are based on information provided by
EPA's industry Partners. In some instances, there were years of missing Partner data, including SF6 consumption
and metal processed. For these situations, emissions were estimated through interpolation where possible, or by
holding company-reported emissions (as well as production) constant from the previous year. For alternative cover
gases, including HFC-134a and FK 5-1-12, mainly reported data was relied upon. That is, unless a Partner reported
using an alternative cover gas, it was not assumed it was used. Emissions of alternate gases were also estimated
through linear interpolation where possible.
The die casting emission estimates for 1999 through 2010 are also based on information supplied by industry
Partners. When a Partner was determined to be no longer in production, its metal production and usage rates were
set to zero. Missing data on emissions or metal input was either interpolated or held constant at the last available
reported value. In 1999 and from 2008 through 2010, Partners did not account for all die casting tracked by U SGS,
and, therefore, it was necessary to estimate the emissions of die casters who were not Partners. For 1999, die casters
who were not Partners were assumed to be similar to Partners who cast small parts. Due to process requirements,
these casters consume larger quantities of SF6 per metric ton of processed magnesium than casters that process large
parts. Consequently, emission estimates from this group of die casters were developed using an average emission
factor of 5.2 kg SF6 per metric ton of magnesium. This emission factor was developed using magnesium production
and SF6 usage data for the year 1999. For 2008 through 2010, the characteristics of the die casters who were not
Partners were not well known, and therefore the emission factor for these die casters was set equal to 3.0 kg SF6 per
metric ton of magnesium, the average of the emission factors reported over the same period by the die casters who
were Partners.
The emissions from other casting operations were estimated by multiplying emission factors (kg SF6 per metric ton
of metal produced or processed) by the amount of metal produced or consumed from USGS, with the exception of
some years for which Partner sand casting emissions data are available. The emission factors for sand casting
activities were acquired through the data reported by the Partnership for 2002 to 2006. For 1999 through 2001, the
sand casting emission factor was held constant at the 2002 Partner-reported level. For 2007 through 2010, the sand
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1 casting Partner did not report and the reported emission factor from 2005 was applied to the Partner and to all other
2 sand casters. Activity data for 2005 was obtained from USGS (USGS 2005b).
3 The emission factors for primary production, secondary production and sand casting for the 1999 to 2010 are not
4 published to protect company-specific production information. However, the emission factor for primary production
5 has not risen above the average 1995 Partner value of 1.1 kg SF6 per metric ton. The emission factors for the other
6 industry sectors (i.e., permanent mold, wrought, and anode casting) were based on discussions with industry
7 representatives. The emission factors for casting activities are provided below in Table 4-83.
8 The emissions of HFC-134a and FK-5-1-12 were included in the estimates for only instances where Partners
9 reported that information to the Partnership. Emissions of these alternative cover gases were not estimated for
10 instances where emissions were not reported.
11 Carbon dioxide carrier gas emissions were estimated using the emission factors developed based on EPA's GHGRP -
12 reported carrier gas and cover gas data, by production type. It was assumed that the use of carrier gas, by production
13 type, is proportional to the use of cover gases. Therefore, an emission factor, in kg CO2 per kg cover gas and
14 weighted by the cover gases used, was developed for each of the production types. EPA's GHGRP data on which
15 these emissions factors are based was available for primary, secondary, die casting and sand casting. The emission
16 factors were applied to the total quantity of all cover gases used (SF6, HFC-134a, and FK-5-1-12) by production
17 type in this time period. Carrier gas emissions for the 1999 through 2010 time period were only estimated for those
18 Partner companies that reported using CO2 as a carrier gas through the GHGRP. Using this approach helped ensure
19 time series consistency. The emissions of carrier gases for permanent mold, wrought and anode processes are not
20 estimated in this Inventory.
21 Table 4-83: SF6 Emission Factors (kg SF6 per Metric Ton of Magnesium)
Year Die Casting" Permanent Mold Wrought Anodes
1999
2.14b
2
1 1
2000
0.72
2
1 1
2001
0.72
2
1 1
2002
0.71
2
1 1
2003
0.81
2
1 1
2004
0.79
2
1 1
2005
0.77
2
1 1
2006
0.88
2
1 1
2007
0.64
2
1 1
2008
0.10
2
1 1
2009
2.30
2
1 1
2010
2.94
2
1 1
a Weighted average includes all die casters, Partners and non-Partners. For
the majority of the time series (2000 to 2007), Partners made up 100 percent
of die casters in the United States.
b Weighted average that includes an estimated emission factor of 5.2 kg SF6
per metric ton of magnesium for die casters that do not participate in the
Partnership.
22 2011 through 2015
23 For 2011 through 2015, for the primary and secondary producers, EPA's GHGRP-reported cover and carrier gases
24 emissions data were used. For die and sand casting, some emissions data was obtained through EPA's GHGRP. The
25 balance of the emissions for these industry segments were estimated based on previous Partner reporting (i.e., for
26 Partners that did not report emissions through EPA's GHGRP) or were estimated by multiplying emission factors by
27 the amount of metal produced or consumed. Partners who did not report through EPA's GHGRP were assumed to
28 have continued to emit SF6 at the last reported level, which was from 2010 in most cases. All Partners were assumed
29 to have continued to consume magnesium at the last reported level. Where the total metal consumption estimated for
30 the Partners fell below the U.S. total reported by USGS, the difference was multiplied by the emission factors
31 discussed in the section above. For the other types of production and processing (i.e., permanent mold, wrought, and
32 anode casting), emissions were estimated by multiplying the industry emission factors with the metal production or
33 consumption statistics obtained from USGS (USGS 2015).
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Uncertainty and Time-Series Consistency
Uncertainty surrounding the total estimated emissions in 2015 is attributed to the uncertainties around SF6, HFC-
134a, and CO2 emission estimates. To estimate the uncertainty surrounding the estimated 2015 SF6 emissions from
magnesium production and processing, the uncertainties associated with three variables were estimated: (1)
emissions reported by magnesium producers and processors for 2015 through EPA's GHGRP, (2) emissions
estimated for magnesium producers and processors that reported via the Partnership in prior years but did not report
2015 emissions through EPA's GHGRP, and (3) emissions estimated for magnesium producers and processors that
did not participate in the Partnership or report through EPA's GHGRP. An uncertainty of 5 percent was assigned to
the emissions (usage) data reported by each GHGRP reporter for all the cover and carrier gases (per the 2006IPCC
Guidelines). If facilities did not report emissions data during the current reporting year through EPA's GHGRP
reporting program, SF6 emissions data were held constant at the most recent available value reported through the
Partnership. The uncertainty associated with these values was estimated to be 30 percent for each year of
extrapolation. Alternate cover gas and carrier gases data was set equal to zero if the facilities did not report via the
GHGRP program. One known sand caster (the lone Partner) has not reported since 2007 and its activity and
emission factor were held constant at 2005 levels due to a reporting anomaly in 2006 because of malfunctions at the
facility. The uncertainty associated with the SF6 usage for the sand casting Partner was 90 percent. As with the non-
reporting facility, the uncertainty with this value was estimated to be 30 percent for each year of extrapolation,
increasing the uncertainty this year. For those industry processes that are not represented in the Partnership, such as
permanent mold and wrought casting, SF6 emissions were estimated using production and consumption statistics
reported by USGS and estimated process-specific emission factors (see Table 4-84). The uncertainties associated
with the emission factors and USGS-reported statistics were assumed to be 75 percent and 25 percent, respectively.
Emissions associated with die casting and sand casting activities utilized emission factors based on Partner reported
data with an uncertainties of 75 percent. In general, where precise quantitative information was not available on the
uncertainty of a parameter, a conservative (upper-bound) value was used.
Additional uncertainties exist in these estimates that are not addressed in this methodology, such as the basic
assumption that SF6 neither reacts nor decomposes during use. The melt surface reactions and high temperatures
associated with molten magnesium could potentially cause some gas degradation. Previous measurement studies
have identified SF6 cover gas degradation in die casting applications on the order of 20 percent (Bartos et al. 2007).
Sulfur hexafluoride may also be used as a cover gas for the casting of molten aluminum with high magnesium
content; however, the extent to which this technique is used in the United States is unknown.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-84. Total emissions
associated with magnesium production and processing were estimated to be between 1.0 and 1.1 MMT CO2 Eq. at
the 95 percent confidence level. This indicates a range of approximately 6 percent below to 6 percent above the
2015 emission estimate of 1.0 MMT CO2 Eq. The uncertainty estimates for 2015 are smaller relative to the
uncertainty reported for 2014 in the previous Inventory report. In the previous Inventory, the emissions factor of die-
casting had a significant impact on the uncertainty because of relatively high emissions from the facilities that do not
report under the EPA's GHGRP. This year, there was a decrease in production from non-GHGRP reporting die
casting facilities, lowering the uncertainty bounds on the total emission estimate.
Table 4-84: Approach 2 Quantitative Uncertainty Estimates for SFe, HFC-134a and CO2
Emissions from Magnesium Production and Processing (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
SFe, HFC-
Magnesium
Production
134a and
CO2
1.0
1.0
1.1
-6%
+6%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
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1 Recalculations Discussion
2 For one GHGRP-reporting facility, a recalculation for 2014 CO2 emissions was performed to ensure methodological
3 consistency and based on the availability of new data. The CO2 emissions for this facility in 2014 were previously
4 held constant at 2013 levels based on data reported through the EPA's GHGRP. Since the facility reported 2015
5 data, but did report in 2014, the estimate of 2014 emissions has been revised by interpolating the reported emissions
6 between 2013 and 2015, reported via EPA's GHGRP. This has caused a slight decrease in the CO2 emissions for
7 2014 compared to the previous Inventory.
8 One facility revised its GHGRP reported data for 2014 HFC-134a emissions, resulting in a decrease in overall 2014
9 emissions.
10 A facility that had not previously reported under the GHGRP reported 2014 and 2015 SF6 die-casting emissions in
11 2016. Since production levels were held constant from 2014 data, the resulting adjustment to non-GHGRP casting
12 production led to a slight decrease in 2014 SF6 emissions.
13 Planned Improvements
14 Cover gas research conducted over the last decade has found that SF6 used for magnesium melt protection can have
15 degradation rates on the order of 20 percent in die casting applications (Bartos et al. 2007). Current emission
16 estimates assume (per the 2006IPCC Guidelines) that all SF6 utilized is emitted to the atmosphere. Additional
17 research may lead to a revision of the 2006 IPCC Guidelines to reflect this phenomenon and until such time,
18 developments in this sector will be monitored for possible application to the inventory methodology.
19 Usage and emission details of carrier gases in permanent mold, wrought and anode processes will be researched as
20 part of a future inventory. Based on this research, it will be determined if CO2 carrier gas emissions are to be
21 estimated.
22 4.20 Lead Production (IPCC Source Category
23 2C5)
24 Lead production in the United States consists of both primary and secondary processes—both of which emit carbon
25 dioxide (CO2) (Sjardin 2003). Emissions from fuels consumed for energy purposes during the production of lead are
26 accounted for in the Energy chapter.
27 Primary production of lead through the direct smelting of lead concentrate produces CO2 emissions as the lead
28 concentrates are reduced in a furnace using metallurgical coke (Sjardin 2003). Primary lead production, in the form
29 of direct smelting, previously occurred at a single smelter in Missouri. This primary lead smelter was closed at the
30 end of 2013. In 2014, the smelter processed a small amount of residual lead during demolition of the site (USGS
31 2015). In 2015, the smelter processed no lead (USGS 2016).
32 Similar to primary lead production, CO2 emissions from secondary lead production result when a reducing agent,
33 usually metallurgical coke, is added to the smelter to aid in the reduction process. Carbon dioxide emissions from
34 secondary production also occur through the treatment of secondary raw materials (Sjardin 2003). Secondary
35 production primarily involves the recycling of lead acid batteries and post-consumer scrap at secondary smelters. Of
36 all the domestic secondary smelters operational in 2015, 11 smelters had capacities of 30,000 tons or more and were
37 collectively responsible for more than 95 percent of secondary lead production in 2015 (USGS 2016). Secondary
38 lead production has increased in the United States over the past decade while primary lead production has decreased
39 to production levels of zero. In 2015, secondary lead production accounted for 100 percent of total lead production.
40 As was the case in 2014, the lead-acid battery industry accounted for about 90 percent of the reported U.S. lead
41 consumption in 2015 (USGS 2016).
42 In 2015, total secondary lead production in the United States was slightly lower than that in 2014. Increased
43 production at a couple of smelters was expected to be offset by temporary closure of one smelter. In 2014, a
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producer temporarily shut down operations of a lead smelter in Vernon, CA (90,000 metric ton capacity smelter) due
to environmental concerns from state regulators. As stated in the previous inventory report, the company intended to
restart operations in 2015, after making improvements to the plant, but closed the plant instead. In 2015, one
secondary producer announced plans to build a new plant in Nevada capable of producing high-purity lead for use in
advanced lead-acid batteries; this plant is expected to be built in 2016. Increases in exports of spent lead-acid
batteries in recent years have decreased the amount of scrap available to secondary smelters (USGS 2016).
U.S. primary lead production reached production levels of zero, a decrease of 100 percent from 2014 to 2015, and
has also decreased by 100 percent since 1990. This is due to the closure of the only domestic primary lead smelter in
2013 (year-end). In 2015, U.S. secondary lead production dropped slightly from 2014 levels (decrease of 1 percent),
and has increased by 21 percent since 1990 (USGS 1995 through 2013, 2014, 2015, 2016).
In 2015, U.S. primary and secondary lead production totaled 1,120,000 metric tons (USGS 2016). The resulting
emissions of CO2 from 2015 lead production were estimated to be 0.5 MMT CO2 Eq. (504 kt) (see Table 4-85). All
of the 2015 lead production is from secondary processes, which accounted for 100 percent of total 2015 CO2
emissions from lead production. At last reporting, the United States was the third largest mine producer of lead in
the world, behind China and Australia, accounting for approximately 8 percent of world production in 2015 (USGS
2016.
Table 4-85: CO2 Emissions from Lead Production (MMT CO2 Eq. and kt)
Year
MMT CO2 Eq.
kt
1990
0.5
516
2005
0.6
2011
0.5
538
2012
0.5
527
2013
0.5
546
2014
0.5
509
2015
0.5
504
After a steady increase in total emissions from 1995 to 2000, total emissions have gradually decreased since 2000
and are currently lower than 1990 levels.
The methods used to estimate emissions for lead production49 are based on Sjardin's work (Sjardin 2003) for lead
production emissions and Tier 1 methods from the 2006IPCC Guidelines. The Tier 1 equation is as follows:
C02 Emissions = (DS x EFDS) + (5 x EFS)
For primary lead production using direct smelting, Sjardin (2003) and the IPCC (2006) provide an emission factor of
0.25 metric tons CCVmetric ton lead. For secondary lead production, Sjardin (2003) and IPCC (2006) provide an
emission factor of 0.25 metric tons CCh/metric ton lead for direct smelting, as well as an emission factor of 0.2
metric tons CCh/metric ton lead produced for the treatment of secondary raw materials (i.e., pretreatment of lead
49 EPA has not integrated aggregated facility-level Greenhouse Gas Reporting Program (GHGRP) information to inform these
estimates. The aggregated information (e.g., activity data and emissions) associated with Lead Production did not meet criteria to
shield underlying confidential business information (CBI) from public disclosure.
Methodology
where,
DS
S
EFds
EFS
Lead produced by direct smelting, metric ton
Lead produced from secondary materials
Emission factor for direct Smelting, metric tons CCh/metric ton lead product
Emission factor for secondary materials, metric tons CCh/metric ton lead product
Industrial Processes and Product Use 4-83
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1 acid batteries). Since the secondary production of lead involves both the use of the direct smelting process and the
2 treatment of secondary raw materials, Sjardin recommends an additive emission factor to be used in conjunction
3 with the secondary lead production quantity. The direct smelting factor (0.25) and the sum of the direct smelting and
4 pretreatment emission factors (0.45) are multiplied by total U.S. primary and secondary lead production
5 respectively, to estimate CO2 emissions.
6 The 1990 through 2015 activity data for primary and secondary lead production (see Table 4-86) were obtained from
7 the U.S. Geological Survey (USGS 1995 through 2013, 2014, 2015, 2016).
8 Table 4-86: Lead Production (Metric Tons)
Year Primary Secondary
1990 404,000 922,000
2005 143,000 1,150,000
2011
118,000
1,130,000
2012
111,000
1,110,000
2013
114,000
1,150,000
2014
1,000
1,130,000
2015
0
1,120,000
9 Uncertainty and Time-Series Consistency - TO BE UPDATED
10 FOR FINAL INVENTORY REPORT
11 Uncertainty associated with lead production relates to the emission factors and activity data used. The direct
12 smelting emission factor used in primary production is taken from Sjardin (2003) who averaged the values provided
13 by three other studies (Dutrizac et al. 2000; Morris et al. 1983; Ullman 1997). For secondary production Sjardin
14 (2003) added a CO2 emission factor associated with battery treatment. The applicability of these emission factors to
15 plants in the United States is uncertain. There is also a smaller level of uncertainty associated with the accuracy of
16 primary and secondary production data provided by the USGS. This information is collected by USGS via voluntary
17 surveys.
18 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-87. Lead production CO2
19 emissions in 2015 were estimated to be between 0.4 and 0.6 MMT CO2 Eq. at the 95 percent confidence level. This
20 indicates a range of approximately 15 percent below and 16 percent above the emission estimate of 0.5 MMT CO2
21 Eq.
22 Table 4-87: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Lead
23 Production (MMT CO2 Eq. and Percent)
Source Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Lead Production CO2
0.5
0.4
0.6
-15%
+16%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval
24 Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
25 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
26 above.
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Recalculations Discussion
For the current Inventory, primary and secondary lead production quantities were revised to reflect the most recent
USGS publication (USGS 2016). This change resulted in a 2 percent increase in the 2014 emission estimate
compared to the previous Inventory report.
Planned Improvements
Pending resources and prioritization of improvements for more significant sources, EPA will continue to evaluate
and analyze data reported under EPA's GHGRP that would be useful to improve the emission estimates and
category specific QC for the Lead Production source category, in particular considering completeness of reported
lead production given the reporting threshold. Given the small number of facilities in the US, particular attention
will be made to risks for disclosing CBI and ensuring time series consistency of the emissions estimates presented in
future Inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-level
reporting data from EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar
year 2010, are not available for all inventory years (i.e., 1990 through 2009) as required for this Inventory. In
implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
use of facility-level data in national inventories will be relied upon.50 EPA is still assessing the possibility of
including this planned improvement in future Inventory reports.
4.21 Zinc Production (IPCC Source Category
2C6)
Zinc production in the United States consists of both primary and secondary processes. Of the primary and
secondary processes used in the United States, only the electrothermic and Waelz kiln secondary processes result in
non-energy carbon dioxide (CO2) emissions (Viklund-White 2000). Emissions from fuels consumed for energy
purposes during the production of zinc are accounted for in the Energy chapter.
The majority of zinc produced in the United States is used for galvanizing. Galvanizing is a process where zinc
coating is applied to steel in order to prevent corrosion. Zinc is used extensively for galvanizing operations in the
automotive and construction industry. Zinc is also used in the production of zinc alloys and brass and bronze alloys
(e.g., brass mills, copper foundries, and copper ingot manufacturing). Zinc compounds and dust are also used, to a
lesser extent, by the agriculture, chemicals, paint, and rubber industries.
Primary production in the United States is conducted through the electrolytic process, while secondary techniques
include the electrothermic and Waelz kiln processes, as well as a range of other metallurgical, hydrometallurgical,
and pyrometallurgical processes. Worldwide primary zinc production also employs a pyrometallurgical process
using the Imperial Smelting Furnace process; however, this process is not used in the United States (Sjardin 2003).
In the electrothermic process, roasted zinc concentrate and secondary zinc products enter a sinter feed where they
are burned to remove impurities before entering an electric retort furnace. Metallurgical coke is added to the electric
retort furnace as a carbon-containing reductant. This concentration step, using metallurgical coke and high
temperatures, reduces the zinc oxides and produces vaporized zinc, which is then captured in a vacuum condenser.
This reduction process also generates non-energy CO2 emissions.
ZnO + C -» Zn(gas) + C02 (Reaction 1)
ZnO + CO -» Zn(gas) + C02 (Reaction 2)
In the Waelz kiln process, electric arc furnace (EAF) dust, which is captured during the recycling of galvanized
steel, enters a kiln along with a reducing agent (typically carbon-containing metallurgical coke). When kiln
50 See .
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temperatures reach approximately 1,100 to 1,200 degrees Celsius, zinc fumes are produced, which are combusted
with air entering the kiln. This combustion forms zinc oxide, which is collected in a baghouse or electrostatic
precipitator, and is then leached to remove chloride and fluoride. The use of carbon-containing metallurgical coke in
a high-temperature fuming process results in non-energy CO2 emissions. Through this process, approximately 0.33
metric tons of zinc is produced for every metric ton of EAF dust treated (Viklund-White 2000).
The only companies in the United States that use emissive technology to produce secondary zinc products are
Horsehead, PIZO, and Steel Dust Recycling. For Horsehead, EAF dust is recycled in Waelz kilns at their Beaumont,
TX; Calumet, IL; Palmerton, PA; Rockwood, TN; and Barnwell, SC facilities. These Waelz kiln facilities produce
intermediate zinc products (crude zinc oxide or calcine), most of which was transported to their Monaca, PA facility
where the products were smelted into refined zinc using electro thermic technology. In April 2014, Horsehead
permanently shut down their Monaca smelter. This was replaced by their new facility in Mooresboro, NC. The new
Mooresboro facility uses a hydrometallurgical process (i.e., solvent extraction with electrowinning technology) to
produce zinc products. The current capacity of the new facility is 155,000 short tons, with plans to expand to
170,000 short tons peryear. During the fourth quarter of 2015, the Mooresboro facility was only operating at
approximately 25 percent of capacity (Horsehead 2016). Direct consumption of coal, coke, and natural gas have
been replaced with electricity consumption at the new Mooresboro facility. The new facility is reported to have a
significantly lower greenhouse gas and other air emissions than the Monaca smelter (Horsehead 2012b).
The Mooresboro facility uses leaching and solvent extraction (SX) technology combined with electrowinning,
melting, and casting technology. In this process, Waelz Oxide (WOX) is first washed in water to remove soluble
elements such as chlorine, potassium, and sodium, and then is leached in a sulfuric acid solution to dissolve the
contained zinc creating a pregnant liquor solution (PLS). The PLS is then processed in a solvent extraction step in
which zinc is selectively extracted from the PLS using an organic solvent creating a purified zinc-loaded electrolyte
solution. The loaded electrolyte solution is then fed into the electrowinning process in which electrical energy is
applied across a series of anodes and cathodes submerged in the electrolyte solution causing the zinc to deposit on
the surfaces of the cathodes. As the zinc metal builds up on these surfaces, the cathodes are periodically harvested in
order to strip the zinc from their surfaces (Horsehead 2015). Hydrometallurgical production processes are assumed
to be non-emissive since no carbon is used in these processes (Sjardin 2003).
PIZO and Steel Dust Recycling recycle EAF dust into intermediate zinc products using Waelz kilns, and then sell
the intermediate products to companies who smelt it into refined products.
In 2015, U.S. primary and secondary refined zinc production were estimated to total 175,000 metric tons (USGS
2016 (see Table 4-88). Domestic zinc mine production increased slightly in 2015 compared to 2014 levels, primarily
owing mostly to the reopening of the Pend Oreille Mine in Washington in late 2014. The mine was expected to
reach full production by yearend 2015. Zinc metal production decreased slightly due to a decline in secondary
production; in 2014, Horsehead closed its smelter in Monaca, PA, while starting up its new recycling facility in
Mooresboro, NC. However, the new facility experienced continued delays in ramp-up efforts due to technical issues
(USGS 2016). Primary zinc production (primary slab zinc) increased by 14 percent in 2015, while, secondary zinc
production in 2015 decreased by 29 percent relative to 2014.
Emissions of CO2 from zinc production in 2015 were estimated to be 0.9 MMT CO2 Eq. (933 kt CO2) (see Table
4-89). All 2015 CO2 emissions resulted from secondary zinc production processes. Emissions from zinc production
in the United States have increased overall since 1990 due to a gradual shift from non-emissive primary production
to emissive secondary production. In 2015, emissions were estimated to be 48 percent higher than they were in
1990.
Table 4-88: Zinc Production (Metric Tons)
Year
Primary
Secondary
1990
262,704
95,708
2005
191.120
156,000
2011
110,000
138,000
2012
114,000
147,000
2013
106,000
127,000
2014
110,000
70,000
2015
125,000
50,000
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Table 4-89: CO2 Emissions from Zinc Production (MMT CO2 Eq. and kt)
Year MMT CO2 Eg. kt
1990 0.6 632
2005 1.0 1,030
2011 1.3 1,286
2012 1.5 1,486
2013 1.4 1,429
2014 1.0 956
2015 0.9 933
Methodology
The methods used to estimate non-energy CO2 emissions from zinc production51 using the electrothermic primary
production and Waelz kiln secondary production processes are based on Tier 1 methods from the 2006IPCC
Guidelines (IPCC 2006). The Tier 1 equation used to estimate emissions from zinc production is as follows:
Eco2 ~ Zn x EFdefault
where,
ECo2 = CO2 emissions from zinc production, metric tons
Zn = Quantity of zinc produced, metric tons
EFdefeuit = Default emission factor, metric tons CCh/metric ton zinc produced
The Tier 1 emission factors provided by IPCC for Waelz kiln-based secondary production were derived from coke
consumption factors and other data presented in Vikland-White (2000). These coke consumption factors as well as
other inputs used to develop the Waelz kiln emission factors are shown below. IPCC does not provide an emission
factor for electrothermic processes due to limited information; therefore, the Waelz kiln-specific emission factors
were also applied to zinc produced from electrothermic processes. Starting in 2014, refined zinc produced in the
United States used hydrometallurgical processes and is assumed to be non-emissive.
For Waelz kiln-based production, IPCC recommends the use of emission factors based on EAF dust consumption, if
possible, rather than the amount of zinc produced since the amount of reduction materials used is more directly
dependent on the amount of EAF dust consumed. Since only a portion of emissive zinc production facilities
consume EAF dust, the emission factor based on zinc production is applied to the non-EAF dust consuming
facilities while the emission factor based on EAF dust consumption is applied to EAF dust consuming facilities.
The Waelz kiln emission factor based on the amount of zinc produced was developed based on the amount of
metallurgical coke consumed for non-energy purposes per ton of zinc produced (i.e., 1.19 metric tons coke/metric
ton zinc produced) (Viklund-White 2000), and the following equation:
1.19 metric tons coke 0.85 metric tons C 3.67 metric tons C02 3.70 metric tons C02
t-j ^ aelz KlL~h ~~ ¦ ¦ ^ ^ ~~ . .
metric tons zinc metric tons coke metric tons C metric tons zinc
The Waelz kiln emission factor based on the amount of EAF dust consumed was developed based on the amount of
metallurgical coke consumed per ton of EAF dust consumed (i.e., 0.4 metric tons coke/metric ton EAF dust
consumed) (Viklund-White 2000), and the following equation:
51 EPA has not integrated aggregated facility-level Greenhouse Gas Reporting Program (GHGRP) information to inform these
estimates. The aggregated information (e.g., activity data and emissions) associated with Zinc Production did not meet criteria to
shield underlying confidential business information (CBI) from public disclosure.
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48
OA metric tons coke 0.85 metric tons C 3.67 metric tons C02 1.24 metric tons C02
EFgAp Qtic? ^ ^
metric tons EAF Dust metric tons coke metric tons C metric tons EAF Dust
The total amount of EAF dust consumed by Horsehead at their Waelz kilns was available from Horsehead financial
reports foryears 2006 through 2015 (Horsehead 2007, 2008, 2010a, 2011, 2012a, 2013, 2014, 2015, and 2016).
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 the U.S. Geological Survey (USGS) Minerals
Yearbook: Zinc (USGS 1995 through 2006). The EAF dust consumption values for each year were then multiplied
by the 1.24 metric tons CO:/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 Steel Dust Recycling (SDR) and their total production capacity were
obtained from SDR's facility in Alabama for the years 2011 through 2015 (SDR 2012, 2014, 2015, and 2017).
SDR's facility in Alabama underwent expansion in 2011 to include a second unit (operational since early- to mid-
2012). SDR's facility lias been operational since 2008. Annual consumption data for SDR was not publicly available
for the years 2008, 2009, and 2010. These data were estimated using data for Horsehead's Waelz kilns for 2008
through 2010 (Horsehead 2007, 2008, 2010a, 2010b, and 2011). Annual capacity utilization ratios were calculated
using Horsehead's annual consumption and total capacity for the years 2008 through 2010. Horsehead's annual
capacity utilization ratios were multiplied with SDR's total capacity to estimate SDR's consumption for each of the
years, 2008 through 2010 (SDR 2013).
PIZO Technologies Worldwide LLC's facility in Arkansas lias been operational since 2009. The amount of EAF
dust consumed by PIZO's facility for 2009 through 2015 was not publicly available. EAF dust consumption for
PIZO's facility for 2009 and 2010 were estimated by calculating annual capacity utilization of Horsehead's Waelz
kilns and multiplying this utilization ratio by PIZO's total capacity (PIZO 2012). EAF dust consumption for PIZO's
facility for 2011 through 2015 were estimated by applying the average annual capacity utilization rates for
Horsehead and SDR (Grupo PROMAX) to PIZO's annual capacity (Horsehead 2012, 2013, 2014, 2015, and 2016;
SDR 2012, 2014 and 2017; PIZO 2012, 2014 and 2017). The 1.24 metric tons CO:/metric ton EAF dust consumed
emission factor was then applied to PIZO's and Steel Dust Recycling's estimated EAF dust consumption to develop
CO2 emission estimates for those Waelz kiln facilities.
Refined zinc production levels for Horsehead's Monaca, PA facility (utilizing electrothermic technology) were
available from the company foryears 2005 tlirough2013 (Horsehead 2008, 2011, 2012, 2013, and 2014). The
Monaca facility was permanently shut down in April 2014 and was replaced by Horsehead's new facility in
Mooresboro, NC. The new facility uses hydrometallurgical process to produce refined zinc products. This process is
assumed to be non-emissive. Production levels for 1990 through 2004 were extrapolated using the percentage
changes in annual refined zinc production at secondary smelters in the United States as provided by USGS Minerals
Yearbook: Zinc (USGS 1995 through 2005). The 3.70 metric tons COi/metric ton zinc emission factor was then
applied to the Monaca facility's production levels to estimate CO2 emissions for the facility. The Waelz kiln
production emission factor was applied in this case rather than the EAF dust consumption emission factor since
Horsehead's Monaca facility did not consume EAF dust.
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
The uncertainty associated with these estimates is two-fold, relating to activity data and emission factors used.
First, there is uncertainty associated with the amount of EAF dust consumed in the United States to produce
secondary zinc using emission-intensive Waelz kilns. The estimate for the total amount of EAF dust consumed in
Waelz kilns is based on (1) an EAF dust consumption value reported annually by Horsehead Corporation as part of
its financial reporting to the Securities and Exchange Commission (SEC), and (2) an EAF dust consumption value
obtained from the Waelz kiln facility operated in Alabama by Steel Dust Recycling LLC. Since actual EAF dust
consumption information is not available for PIZO's facility (2009 through 2010) and SDR's facility (2008 through
2010), the amount is estimated by multiplying the EAF dust recycling capacity of the facility (available from the
company's website) by the capacity utilization factor for Horsehead Corporation (which is available from
Horsehead's financial reports). Also, the EAF dust consumption for PIZO's facility for 2011 through 2013 was
4-88 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 cslinialcd h\ milllipl\ iim llie ;i\ crauc capacils unli/aliou I'aclor dc\ eloped from I lorschcad (orp ;md SI )K's ;iiiiui;il
2 capacil> unli/aliou rales In KI/.O's I! \l 'diisi rec\cliuu capacil> Therefore. I here is iiiiccriaiuls associated w nil llie
3 assumption used lii estimate KI/O and SI )K's ;iiinii;il I! \l' diisi consumption \ allies (except SDK 's I! \l' diisi
4 consumption for > I I lliroimh 2u IV w Inch were obtained from SDK's rccscliuu facilits mi \l;ih;im;ii
5 Second. I lie iv is uiiccrlaiiits associated w illi 1 lie emission factors used li» estimate ('() emissions from secondary
6 /ine production processes The \\;iel/ kiln emission factors arc h;ised on materials h;il;mees for niclallurmcal coke
7 ;md I !.\l; diisi consumed ;is pro\ ided In Yikhuid-Whiic (2uuu) Therefore, the ;iccur;ic\ of iliese emission factors
8 depend upon ihe ;iccur;ic> olThese m;iieri;ils h;il;mces l);ii;i limii;ilioiis pre\euted llie dc\elopmeiil oreniissiou
9 factors lor llie eleciroiherniic process Therefore. emission factors lor ihe \\ ;iel/ kiln process were ;ipplled lo hoili
10 eleciroiherniic ;md Wacl/ kiln prodiicliou processes.
11 The results oi l lie Approach 2 i|ii;nilil;ili\ e uuccri;iiiil\ ;iu;il\ sis ;ire sunini;iri/ed in Table ) /.me production ('()
12 emissions from 2015 were estimated lo he helweeu <> X ;iud I 2 \I\IT CO I !i|. ;il llie l>5 perceni confidence lc\ el.
13 I'liis indicates a r;iuue of ;ippro\ini;ilel> ll> percent helow ;iud 21 percent ;iho\c the eniission estim;ile of I o \I\1T
14 CO Ia|
15 Table 4-90: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Zinc
16 Production (MMT CO2 Eq. and Percent)
2015 I'liiiissiiui
Si ill I'l l'
(¦;is I'sliniiik'
I iui'i'l;iiiil\ Kiin^i-Kiliilivi'In l.missiun l!siim;ik-:l
(MMT CO: Kii.l
(MMT CO: l.i|.)
1 0 \
( •<»)
I.I HUT I |)|KT
I.I HUT I |)|KT
lii 111 lid Bound
IShuihI lion ml
/.inc Production
CO: 1.0
0.8 1.2
-19% +21 %
¦' Range of emissii
;>n estimates predicted by Mi
inte Carlo Stochastic Simulation for
a l)5 percent confidence interval.
17 Methodological recalculations were npplicd lo llie eulire lime series 10 ensure coiisisieucv 111 emissions from I'wo
18 throimh 2d 15 I )el;nls 011 llie emission ireuds ihroimh lime ;ire described iu more dcl;iil 1111he Melhodolouv seel 1011.
19 ;iho\e
20 Planned Improvements
21 Pending resources and prioritization of improvements for more significant sources, EPA will continue to evaluate
22 and analyze data reported under EPA's GHGRP that would be useful to improve the emission estimates and
23 category specific QC for the Zinc Production source category, in particular considering completeness of reported
24 zinc production given the reporting threshold. Given the small number of facilities in the US, particular attention
25 will be made to risks for disclosing CBI and ensuring time series consistency of the emissions estimates presented in
26 future Inventory reports, consistent with IPCC and UNFCCC guidelines. This is required as the facility-level
27 reporting data from EPA's GHGRP, with the program's initial requirements for reporting of emissions in calendar
28 year 2010, are not available for all inventory years (i.e., 1990 through 2009) as required for this Inventory. In
29 implementing improvements and integration of data from EPA's GHGRP, the latest guidance from the IPCC on the
30 use of facility-level data in national inventories will be relied upon.52 EPA is still assessing the possibility of
31 including this planned improvement in future Inventory reports.
52 See .
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4.22 Semiconductor Manufacture (IPCC Source
Category 2E1)
3 The semiconductor industry uses multiple greenhouse gases (GHGs) in its manufacturing processes. These include
4 long-lived fluorinated greenhouse gases used for plasma etching and chamber cleaning, fluorinated heat transfer
5 fluids used for temperature control and other applications, and nitrous oxide (N20) used to produce thin films
6 through chemical vapor deposition.
7 The gases most commonly employed in plasma etching and chamber cleaning are trifluoromethane (HFC-23 or
8 CHF3), perfluoromethane (CF4), perfluoroethane (C2F6), nitrogen trifluoride (NF3), and sulfur hexafluoride (SF6),
9 although other fluorinated compounds such as perfluoropropane (C3F8) and perfluorocyclobutane (c-C4F8) are also
10 used. The exact combination of compounds is specific to the process employed.
11 A single 300 mm silicon wafer that yields between 400 to 600 semiconductor products (devices or chips) may
12 require more than 100 distinct fluorinated-gas-using process steps, principally to deposit and pattern dielectric films.
13 Plasma etching (or patterning) of dielectric films, such as silicon dioxide and silicon nitride, is performed to provide
14 pathways for conducting material to connect individual circuit components in each device. The patterning process
15 uses plasma-generated fluorine atoms, which chemically react with exposed dielectric film to selectively remove the
16 desired portions of the film. The material removed as well as undissociated fluorinated gases flow into waste
17 streams and, unless emission abatement systems are employed, into the atmosphere. Plasma enhanced chemical
18 vapor deposition (PECVD) chambers, used for depositing dielectric films, are cleaned periodically using fluorinated
19 and other gases. During the cleaning cycle the gas is converted to fluorine atoms in plasma, which etches away
20 residual material from chamber walls, electrodes, and chamber hardware. Undissociated fluorinated gases and other
21 products pass from the chamber to waste streams and, unless abatement systems are employed, into the atmosphere.
22 In addition to emissions of unreacted gases, some fluorinated compounds can also be transformed in the plasma
23 processes into different fluorinated compounds which are then exhausted, unless abated, into the atmosphere. For
24 example, when C2F6 is used in cleaning or etching, CF4 is generated and emitted as a process by-product. In some
25 cases, emissions of the by-product gas can rival or even exceed emissions of the input gas, as is the case for NF3
26 used in remote plasma chamber cleaning, which generates CF4 as a by-product.
27 Besides dielectric film etching and PECVD chamber cleaning, much smaller quantities of fluorinated gases are used
28 to etch polysilicon films and refractory metal films like tungsten.
29 N20 is used in manufacturing semiconductor devices to produce thin films by CVD and nitridation processes as well
30 as for N-doping of compound semiconductors and reaction chamber conditioning (Doering 2000).
31 Liquid perfluorinated compounds are also used as heat transfer fluids (HTFs) for temperature control, device testing,
32 cleaning substrate surfaces and other parts, and soldering in certain types of semiconductor manufacturing
33 production processes. Leakage and evaporation of these fluids during use is a source of fluorinated gas emissions
34 (U.S. EPA 2006). Unweighted HTF emissions consist primarily of perfluorinated amines, hydrofluoroethers,
35 perfluoropolyethers, and perfluoroalkylmorpholines. With the exception of the hydrofluoroethers, all of these
36 compounds are very long-lived in the atmosphere and have GWPs near 10,000.53
37 For 2015, total GWP-weighted emissions of all fluorinated greenhouse gases and nitrous oxide from deposition,
38 etching, and chamber cleaning processes in the U.S. semiconductor industry were estimated to be 5.0 MMT CO2 Eq.
39 Total emissions of all greenhouse gases other than HTFs are presented in Table 4-91 and Table 4-92 below for the
53 The GWP of PFPMIE, a perfluoropolyether used as an HTF, is included in the Fourth Assessment Report with a value of
10,300. The GWPs of the perfluorinated amines and perfluoroalkylmorpholines that are used as HTFs have not been evaluated in
the peer-reviewed literature. However, evaluations by the manufacturer indicate that their GWPs are near 10,000 (78 FR 20632),
which is expected given that these compounds are both saturated and fully fluorinated. EPA assigns a default GWP of 10,000 to
compounds that are both saturated and fully fluorinated and that do not have chemical-specific GWPs in either the Fourth or the
Fifth Assessment Reports.
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years 1990, 2005, and the period 2010 to 2015. (HTF emissions are presented separately, as discussed below.) The
rapid growth of this industry and the increasing complexity (growing number of layers)54 of semiconductor products
led to an increase in emissions of 153 percent between 1990 and 1999, when emissions peaked at 9.1 MMT CO2 Eq.
Emissions began to decline after 1999, declining by 45 percent between 1999 and 2015. Together, industrial growth,
adoption of emissions reduction technologies (including but not limited to abatement technologies), and shifts in gas
usages resulted in a net increase in emissions of 40 percent between 1990 and 2015.
In 2010, the industry was still recovering from slowed economic activity which began in 2008. Between 2010 and
2011 fluorinated gas and N20 emissions increased by 24 percent; reductions in emissions of 9 percent were then
observed between both 2011 and 2012, and 2012 and 2013. Emissions increased in 2014, by 24 percent compared to
2013, and stayed similar in 2015, decreasing by less than 1 percent compared to 2014. As discussed below, this
apparent increase between 2013 and 2014 is likely to be an artifact of a change in the emission factors applied by
facilities that report their emissions to EPA under the Greenhouse Gas Reporting Program (GHGRP).
Facility emissions of HTFs from semiconductor manufacturing are reported to EPA under the GHGRP, and are
available for the years 2011 through 2015. These emissions are provided for informational purposes and not
included in the Inventory totals presented in Table 4-91. It is important to note that the HTF emissions presented in
these tables represent a sum of HTF emissions, in CO2 Eq., from facilities that report under the GHGRP, or only
those facilities whose emissions exceed 25,000 metric tons annually. The HTF emissions in 2011 were 0.75 MMT
CO2 Eq., with a high of 0.92 MMT CO2 Eq. in 2012. Emissions in 2013 were the lowest at 0.62 MMT CO2 Eq.
while the emissions in 2014 and 2015 are comparable at 0.79 MMT CO2 Eq. and 0.76 MMT CO2 Eq. respectively.
Emissions from one facility contribute significantly to the high emissions in 2012, while the decrease in emissions
of the same facility contributes to the lowest emissions in 2013. Emissions in 2014 and 2015 are slightly higher due
to a higher number of total reporting facilities. These new facilities represent 1.4 percent of total HTF emissions in
2014, and 1.7 percent in 2015. Additionally, an analysis of the available data reported to the GHGRP indicates that
HTF emissions account for anywhere between 13 percent and 17 percent of total annual emissions (F-GHG, N20
and HTFs) from semiconductor manufacturing.55
Table 4-91: PFC, HFC, SFe, NF3, and N2O Emissions from Semiconductor Manufacture (MMT
COz Eq.)
Year
1WO
2005
2010
2011
2012
2013
2014
2015
CF4
0 S
1.1
1.1
1.4
1.3
1.2
1.6
1.5
C2F6
2 11
2.0
1.4
1.8
1.6
1.4
1.5
1.4
C3F8
0.1
0.1
0.2
0.1
0.1
0.1
0.1
C4F8
0 0
0.1
+
0.1
0.1
0.1
0.1
0.1
HFC-23
u:
0.2
0.2
0.2
0.2
0.2
0.3
0.3
SFe
(i 5
0.7
0.4
0.4
0.4
0.4
0.7
0.7
NF3
0.5
0.5
0.7
0.6
0.6
0.5
0.6
Total F-GHGs
3.(1
4.6
3.8
4.7
4.3
3.9
4.8
4.8
N2O
0.1
0.1
0.2
0.2
0.2
0.2
0.2
Total
3.(1
4.7
4.0
4.9
4.5
4.1
5.0
5.0
HTFsa
0.0
0.0
0.0
0.7
0.9
0.6
0.8
0.8
+ Does not exceed 0.05 MMT CO2 Eq.
a The HTF emissions from 1990 to 2010 are not estimated and reported as 0. HTF use
in semiconductor manufacturing began in early 2000s, and by 2005 started to
penetrate applications in electronics manufacturing (U.S. EPA 2006). Emissions are
not estimated for those years due to lack of reliable emission factors and activity data
for HTFs.
54 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.
55 Emissions data for HTFs (in tons of gas) from the semiconductor industry from 2011 through 2015 were obtained from the
EPA GHGRP annual facility emissions reports.
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Note: Totals may not sum due to independent rounding. HTF emissions presented
represent the GHGRP-reporting facilities only, or approximately 97 percent of the
U.S. industry, based on total manufactured layer area in 2015.
Table 4-92: PFC, HFC, SFe, NF3, and N2O Emissions from Semiconductor Manufacture (kt)
Year
19'HI
2005
2010
2011
2012
2013
2014
2015
CF4
0 I I
0.14
0.15
0.19
0.17
0.16
0.21
0.21
C2F6
0 l(.
0.16
0.12
0.14
0.13
0.11
0.12
0.12
C3F8
+
+
+
+
+
+
+
C4F8
0 0
+
+
+
+
+
+
+
HFC-23
+
+
+
+
+
+
+
SFe
+
+
+
+
+
+
+
NF3
+
+
+
+
+
+
+
N2O
0 i:
0.41
0.49
0.79
0.65
0.60
0.74
0.82
+ Does not exceed 0.05 kt.
Methodology
Emissions are based on data reported through Subpart I, Electronics Manufacture, of EPA's GHGRP, Partner
reported emissions data received through the EPA's PFC56 Reduction/Climate Partnership, EPA's PFC Emissions
Vintage Model (PEVM)—a model that estimates industry emissions in the absence of emission control strategies
(Burton and Beizaie 2001),57 and estimates of industry activity (i.e., total manufactured layer area). The availability
and applicability of reported emissions data from the EPA Partnership and EPA's GHGRP, and activity data differs
across the 1990 through 2015 time series. Consequently, fluorinated greenhouse gas (F-GHG) emissions from
semiconductor manufacturing were estimated using six distinct methods, one each for the periods 1990 through
1994, 1995 through 1999, 2000 through 2006, 2007 through 2010, 2011 through 2012 and 2015, and 2013 through
2014. Nitrous oxide emissions were estimated using four distinct methods, one each for the period 1990 through
1994, 1995 through 2010, 2011 through 2012 and 2015, and 2013 through 2014.
1990 through 1994
From 1990 through 1994, Partnership data were unavailable and emissions were modeled using PEVM (Burton and
Beizaie 2001).58 The 1990 to 1994 emissions are assumed to be uncontrolled, since reduction strategies such as
chemical substitution and abatement were yet to be developed.
PEVM is based on the recognition that fluorinated greenhouse gas emissions from semiconductor manufacturing
vary with: (1) the number of layers that comprise different kinds of semiconductor devices, including both silicon
wafer and metal interconnect layers, and (2) silicon consumption (i.e., the area of semiconductors produced) for
each kind of device. The product of these two quantities, Total Manufactured Layer Area (TMLA), constitutes the
activity data for semiconductor manufacturing. PEVM also incorporates an emission factor that expresses emissions
per unit of manufactured layer-area. Emissions are estimated by multiplying TMLA by this emission factor.
56 In the context of the EPA Partnership and PEVM, PFC refers to perfluorocompounds, not perfluorocarbons.
57 A Partner refers to a participant in the U.S. EPA PFC Reduction/Climate Partnership for the Semiconductor Industry.
Through a Memorandum of Understanding (MoU) with the EPA, Partners voluntarily reported their PFC emissions to the EPA
by way of a third party, which aggregated the emissions through 2010.
58 Various versions of the PEVM exist to reflect changing industrial practices. From 1990 to 1994 emissions estimates are from
PEVM vl.0, 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.
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PEVM incorporates information on the two attributes of semiconductor devices that affect the number of layers: (1)
linewidth technology (the smallest manufactured feature size),59 and (2) product type (discrete, memory or logic).60
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 (IC) specific to product type (Burton and Beizaie 2001; ITRS 2007). PEVM derives historical consumption
of silicon (i.e., square inches) by linewidth technology from published data on annual wafer starts and average wafer
size (VLSI Research, Inc. 2012).
The emission factor in PEVM is the average of four historical emission factors, each derived by dividing the total
annual emissions reported by the Partners for each of the four years between 1996 and 1999 by the total TMLA
estimated for the Partners in each of those years. Over this period, the emission factors varied relatively little (i.e.,
the relative standard deviation for the average was 5 percent). Since Partners are believed not to have applied
significant emission reduction measures before 2000, the resulting average emission factor reflects uncontrolled
emissions. The emission factor is used to estimate world uncontrolled emissions using publicly-available data on
world silicon consumption.
As it was assumed for this time period that there was no consequential adoption of fluorinated-gas-reducing
measures, a fixed distribution of fluorinated-gas use was assumed to apply to the entire U.S. industry to estimate
gas-specific emissions. This distribution was based upon the average fluorinated-gas purchases made by
semiconductor manufacturers during this period and the application of IPCC default emission factors for each gas
(Burton and Beizaie 2001).
To estimate N20 emissions, it is assumed the proportion of N20 emissions estimated for 1995 (discussed below)
remained constant for the period of 1990 through 1994.
1995 through 1999
For 1995 through 1999, total U.S. emissions were extrapolated from the total annual emissions reported by the
Partners (1995 through 1999). Partner-reported emissions are considered more representative (e.g., in terms of
capacity utilization in a given year) than PEVM estimated emissions, and are used to generate total U.S. emissions
when applicable. The emissions reported by the Partners were divided by the ratio of the total capacity of the plants
operated by the Partners and the total capacity of all of the semiconductor plants in the United States; this ratio
represents the share of capacity attributable to the Partnership. This method assumes that Partners and non-Partners
have identical capacity utilizations and distributions of manufacturing technologies. Plant capacity data is contained
in the World Fab Forecast (WFF) database and its predecessors, which is updated quarterly (Semiconductor
Equipment and Materials Industry 2012 and 2013). Gas-specific emissions were estimated using the same method as
for 1990 through 1994.
For this time period, the N20 emissions were estimated using an emission factor that is applied to the annual, total
U.S. TMLA manufactured. The emission factor was developed using a regression-through-the-origin (RTO) model:
GHGRP reported N20 emissions were regressed against the corresponding TMLA of facilities that reported no use
of abatement systems. Details on EPA's GHGRP reported emissions and development of emission factor using the
RTO model are presented in the 2011 through 2012 section. The total U.S. TMLA was estimated using PEVM.
59 By decreasing features of Integrated Circuit components, more components can be manufactured per device, which increases
its functionality. However, as those individual components shrink it requires more layers to interconnect them to achieve the
functionality. For example, a microprocessor manufactured with 65 nm feature sizes might contain as many as 1 billion
transistors and require as many as 11 layers of component interconnects to achieve functionality, while a device manufactured
with 130 nm feature size might contain a few hundred million transistors and require 8 layers of component interconnects (ITRS
2007).
60 Memory devices manufactured with the same feature sizes as microprocessors (a logic device) require approximately one-half
the number of interconnect layers, whereas discrete devices require only a silicon base layer and no interconnect layers (ITRS
2007). Since discrete devices did not start using PFCs appreciably until 2004, they are only accounted for in the PEVM
emissions estimates from 2004 onwards.
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2000 through 2006
Emissions for the years 2000 through 2006—the period during which Partners began the consequential application
of fluorinated greenhouse gas-reduction measures—were estimated using a combination of Partner-reported
emissions and adjusted PEVM modeled emissions. The emissions reported by Partners for each year were accepted
as the quantity emitted from the share of the industry represented by those Partners. Remaining emissions, those
from non-Partners, were estimated using PEVM, with one change. To ensure time series consistency and to reflect
the increasing use of remote clean technology (which increases the efficiency of the production process while
lowering emissions of fluorinated greenhouse gases), the average non-Partner emission factor (PEVM emission
factor) was assumed to begin declining gradually during this period. Specifically, the non-Partner emission factor for
each year was determined by linear interpolation, using the end points of 1999 (the original PEVM emission factor)
and 2011 (a new emission factor determined for the non-Partner population based on GHGRP-reported data,
described below).
The portion of the U.S. total attributed to non-Partners is obtained by multiplying PEVM's total U.S. emissions
figure by the non-Partner share of U.S. total silicon capacity for each year as described above.61 Gas-specific
emissions from non-Partners were estimated using linear interpolation of gas-specific emission distribution of 1999
(assumed same as total U.S. Industry in 1994) and 2011 (calculated from a subset of non-Partner facilities from
GHGRP reported emissions data). Annual updates to PEVM reflect published figures for actual silicon consumption
from VLSI Research, Inc., revisions and additions to the world population of semiconductor manufacturing plants,
and changes in IC fabrication practices within the semiconductor industry (see ITRS 2008 and Semiconductor
Equipment and Materials Industry 2011).62,63,64
The N20 emissions were estimated using the same methodology as the 1995 through 1999 methodology.
2007 through 2010
For the years 2007 through 2010, emissions were also estimated using a combination of Partner reported emissions
and adjusted PEVM modeled emissions to provide estimates for non-Partners; however, two improvements were
made to the estimation method employed for the previous years in the time series. First, the 2007 through 2010
emission estimates account for the fact that Partners and non-Partners employ different distributions of
manufacturing technologies, with the Partners using manufacturing technologies with greater transistor densities and
61 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.
62 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 WFF 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 (FQSV) to determine the average design
capacity over the 2006 period.
63 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.
64 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.
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therefore greater numbers of layers.65 Second, the scope of the 2007 through 2010 estimates was expanded relative
to the estimates for the years 2000 through 2006 to include emissions from research and development (R&D) fabs.
This additional enhancement was feasible through the use of more detailed data published in the WFF. PEVM
databases were updated annually as described above. The published world average capacity utilization for 2007
through 2010 was used for production fabs, while for R&D fabs a 20 percent figure was assumed (SIA 2009).
In addition, publicly-available actual utilization data was used to account for differences in fab utilization for
manufacturers of discrete and IC products for 2010 emissions for non-Partners. The Semiconductor Capacity
Utilization (SICAS) Reports from SIA provides the global semiconductor industry capacity and utilization,
differentiated by discrete and IC products (SIA, 2009 through 2011). PEVM estimates were adjusted using
technology-weighted capacity shares that reflect the relative influence of different utilization. Gas-specific emissions
for non-Partners were estimated using the same method as for 2000 through 2006.
The N20 emissions were estimated using the same methodology as the 1995 through 1999 methodology.
2011 through 2012 and 2015
The fifth method for estimating emissions from semiconductor manufacturing covers the period 2011 through 2012
and 2015. This methodology does not include emissions of non-reporting populations for the years 2013 and 2014
because for the non-reporting population, emission factors and facility-specific production were not estimated. The
emissions for these time periods are estimated using a sixth method described below. This methodology differs from
previous years because the EPA's Partnership with the semiconductor industry ended (in 2010) and reporting under
EPA's GHGRP began. Manufacturers whose estimated uncontrolled emissions equal or exceed 25,000 MT CO2 Eq.
per year (based on default F-GHG-specific emission factors and total capacity in terms of substrate area) are
required to report their emissions to EPA. This population of reporters to EPA's GHGRP included both historical
Partners of EPA's PFC Reduction/Climate Partnership as well as non-Partners. In EPA's GHGRP, the population of
non-Partner facilities also included manufacturers that use GaAs technology in addition to Si technology.66
Emissions from the population of manufacturers that were below the reporting threshold were also estimated for this
time period using EPA-developed emission factors and estimates of facility-specific production obtained from WFF.
Inventory totals reflect the emissions from both populations.
Under EPA's GHGRP, semiconductor manufacturing facilities report emissions of F-GHGs used in etch and clean
processes as well as emissions of fluorinated heat transfer fluids. (Fluorinated heat transfer fluids are used to control
process temperatures, thermally test devices, and clean substrate surfaces, among other applications.) They also
report N20 emissions from CVD and other processes. The F-GHGs and N20 were aggregated, by gas, across all
semiconductor manufacturing GHGRP reporters to calculate gas-specific emissions for the GHGRP-reporting
segment of the U.S. industry. At this time, emissions that result from heat transfer fluid use are not included in the
total emission estimates from semiconductor manufacturing, though GHGRP reported emissions have been
compiled and presented for informational purposes in Table 4-91.
For the segment of the semiconductor industry that is below EPA's GHGRP reporting threshold, and for R&D
facilities, which are not covered by EPA's GHGRP, emission estimates are based on EPA-developed emission
factors for the F-GHGs and N20 and estimates of manufacturing activity. The new emission factors (in units of mass
of CO2 Eq./TMLA [MSI]) are based on the emissions reported by facilities under EPA's GHGRP and TMLA
estimates for these facilities from the WFF (SEMI 2012, SEMI 2013, and SEMI 2016). In a refinement of the
method used in prior years to estimate emissions for the non-Partner population, different emission factors were
developed for different subpopulations of fabs, one for facilities that manufacture devices on Si wafers and one for
facilities that manufacture on GaAs wafers. An analysis of the emission factors of reporting fabs showed that the
65 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.
66 GaAs and Si technologies refer to the wafer on which devices are manufactured, which use the same PFCs but in different
ways.
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characteristics that had the largest impacts on emission factors were the substrate (i.e., Si or GaAs) used at the fab,
whether the fab contained R&D activities, and whether the fab reported using point-of-use fluorinated greenhouse
gas abatement.67 For each of these groups, a subpopulation-specific emission factor was obtained using a
regression-through-the-origin (RTO) model: facility-reported aggregate emissions of seven F-GHGs (CF4, C2F6,
C3F8, C4F8, CHF3, SF6 and NF3)68 were regressed against the corresponding TMLA to estimate an aggregate F-GHG
emissions factor (CO2 Eq./MSI TMLA), and facility-reported N20 emissions were regressed against the
corresponding TMLA to estimate a N20 emissions factor (CO2 Eq./MSI TMLA). For each subpopulation, the slope
of the RTO model is the emission factor for that subpopulation. To estimate emissions from fabs that are solely
doing research and development (R&D) or are Pilot fabs (i.e., fabs that are excluded from subpart I reporting
requirements), emission factors were estimated based on GHGRP reporting fabs containing R&D activities. EPA
applied a scaling factor of 1.15 to the slope of the RTO model to estimate the emission factor applicable to the non-
reporting fabs that are only R&D or Pilot fabs. This was done as R&D activities lead to use of more F-GHGs and
N20 for development of chips that are not counted towards the final estimated TMLA. Hence, it is assumed that the
fabs with only R&D activities use 15 percent more F-GHGs and N20 per TMLA.
For 2011, 2012 and 2015, estimates of TMLA relied on the capacity utilization of the fabs published by the U.S.
Census Bureau's Historical Data Quarterly Survey of Plant Capacity Utilization (USCB 2011, 2012, 2016). Similar
to the assumption for 2007 through 2010, facilities with only R&D activities were assumed to utilize only 20 percent
of their manufacturing capacity. All other facilities in the United States are assumed to utilize the average percent of
the manufacturing capacity without distinguishing whether fabs produce discrete products or logic products.
Non-reporting fabs were then broken out into similar subpopulations. Information on the technology and R&D
activities of non-reporting fabs was available through the WFF. Information on the use of point-of-use abatement by
non-reporting fabs was not available; thus, EPA conservatively assumed that non-reporting facilities did not use
point-of-use abatement. The appropriate emission factor was applied to the total TMLA of each subpopulation of
non-reporting facilities to estimate the GWP-weighted emissions of that subpopulation.
Gas-specific, GWP-weighted emissions for each subpopulation of non-reporting facilities were estimated using the
corresponding reported distribution of gas-specific, GWP-weighted emissions from which the aggregate emission
factors, based on GHGRP reported data, were developed. Estimated in this manner, the non-reporting population
accounted for 10, 11, and 6 percent of U.S. emissions in 2011, 2012, and 2015, respectively. EPA's GHGRP-
reported emissions and the calculated non-reporting population emissions are summed to estimate the total
emissions from semiconductor manufacturing.
The methodology used for this time period included emissions from facilities employing Si- and GaAs-using
technologies. The use of GaAs technology became evident via analysis of GHGRP emissions and WFF data.
However, no adjustment of pre-2011 emissions was made because (1) the use of these technologies appears
relatively new, (2) in the aggregate these emissions make a relatively small contribution to total industry emissions
(i.e., 3 percent in 2015), and (3) it would require a large effort to retroactively adjust pre-2011 emissions.
2013 through 2014
For the years 2013 through 2014, as for 2011, 2012, and 2015, F-GHG andN20 emissions data received through
EPA's GHGRP were aggregated, by gas, across all semiconductor-manufacturing GHGRP reporters to calculate
gas-specific emissions for the GHGRP-reporting segment of the U.S. industry. An updated methodology that does
not depend on the WFF derived activity data was used to estimate emissions for the segment of the industry that are
not covered by EPA's GHGRP. For the facilities that did not report to the GHGRP (i.e., which are below EPA's
GHGRP reporting threshold or are R&D facilities), emissions were estimated based on the proportion of total U.S.
emissions attributed to non-reporters for 2011, 2012 and 2015. EPA first estimated this proportion for both F-GHGs
and N20 for 2011, 2012, and 2015, resulting in one proportion for F-GHGs and one for N20, and then applied the
average of these years' proportions to the 2013 and 2014 GHGRP reported emissions to estimate the non-reporters'
67 For the non-reporting segment of the industry using GaAs technology, emissions were estimated only for those fabs that
manufactured the same products as manufactured by reporters. The products manufactured were categorized as discrete
(emissions did not scale up with decreasing feature size).
68 Only seven gases were aggregated because inclusion of F-GHGs that are not reported in the inventory results in
overestimation of emission factor that is applied to the various non-reporting subpopulations.
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emissions. Fluorinated gas-specific, GWP-weighted emissions for non-reporters were estimated using the
corresponding reported distribution of gas-specific, GWP-weighted emissions reported through EPA's GHGRP for
2013 and 2014 respectively.
Data Sources
GHGRP reporters, which consist of EPA Partners and non-Partners, estimated their emissions using a default
emission factor method established by EPA. Like the Tier 2b Method in the 2006IPCC Guidelines, this method
uses different emission and by-product generation factors for different F-GHGs and process types, but it goes
beyond the Tier 2b Method by requiring use of updated factors for different wafer sizes (i.e., 300 mm vs. 150 and
200 mm) and CVD clean subtypes (in situ thermal, in situ thermal, and remote plasma). Starting with 2014 reported
emissions, EPA's GHGRP required semiconductor manufacturers to apply updated emission factors to estimate their
F-GHG emissions (40 CFRPart 98). For the years 2011 to 2013 reported emissions, semiconductor manufacturers
used older emission factors to estimate their F-GHG Emissions (Federal Register / Vol. 75, No. 230 / Wednesday,
December 1, 2010, 74829). GHGRP-reporting facilities are estimated to have accounted for about 92 percent of F-
GHG emissions and 95 percent of N20 emissions from U.S. semiconductor manufacturing between 2011 and 2015.
Historically, partners estimated and reported their emissions using a range of methods and uneven documentation. 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. Partners are estimated to have accounted for between 56 and 79 percent of F-GHG
emissions from U.S. semiconductor manufacturing between 1995 and 2010, with the percentage declining in recent
years as Partners increasingly implemented abatement measures. Estimates of operating plant capacities and
characteristics for Partners and non-Partners were derived from the Semiconductor Equipment and Materials
Industry (SEMI) WFF (formerly World Fab Watch) database (1996 through 2012 and 2015) (e.g., Semiconductor
Materials and Equipment Industry, 2016). Actual worldwide capacity utilizations for 2008 through 2010 were
obtained from Semiconductor International Capacity Statistics (SICAS) (SIA 2009 through 2011). Estimates of the
number of layers for each linewidth was obtained from International Technology Roadmap for Semiconductors:
2013 Edition (Burton and Beizaie 2001; ITRS 2007; ITRS 2008; ITRS 2011; ITRS 2013). PEVM utilized the WFF,
SICAS, and ITRS, as well as historical silicon consumption estimates published by VLSI. Actual quarterly U.S.
capacity utilizations for 2011, 2012 and 2015 were obtained from the U.S. Census Bureau's Historical Data
Quarterly Survey of Plant Capacity Utilization (USCB 2011; 2012; 2015).
Uncertainty and Time-Serii insistency
A quantitative uncertainty analysis of this source category was performed using the IPCC-recommended Approach 2
uncertainty estimation methodology, the Monte Carlo Stochastic Simulation technique. The equation used to
estimate uncertainty is:
Total Emissions (Et) = GHGRP Reported F-GHG Emissions (Er,f-ghg) + Non-Reporters' Estimated F-GHG
Emissions (Enr.f-ghg) + GHGRP Reported N2O Emissions (Er.nzo) + Non-Reporters' Estimated N2O Emissions
(Enr,N2o)
where Er and Enr denote totals for the indicated subcategories of emissions for F-GHG and N20, respectively.
The uncertainty in ET presented in Table 4-93 below results from the convolution of four distributions of emissions,
each reflecting separate estimates of possible values of Erj-ghg, Er,N20, Err,f-ghg, and E\ r.\2I ,. The approach and
methods for estimating each distribution and combining them to arrive at the reported 95 percent confidence interval
(CI) are described in the remainder of this section.
The uncertainty estimate of Er, f-ghg, or GHGRP-reported F-GHG emissions, is developed based on gas-specific
uncertainty estimates of emissions for two industry segments, one processing 200 mm wafers and one processing
300 mm wafers. Uncertainties in emissions for each gas and industry segment were developed during the assessment
of emission estimation methods for the subpart I GHGRP rulemaking in 2012 (see Technical Support for
Modifications to the Fluorinated Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities
Industrial Processes and Product Use 4-97
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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
31
32
33
34
35
36
37
38
39
40
41
42
under Subpart I, docket EPA-HQ-OAR-2011-0028).69 The 2012 analysis did not take into account the use of
abatement. For the industry segment that processed 200 mm wafers, estimates of uncertainties at a 95 percent CI
ranged from ±29 percent for C3F8 to ±10 percent for CF4. For the corresponding 300 mm industry segment,
estimates of the 95 percent CI ranged from ±36 percent for C4F8 to ±16 percent for CF4. These gas and wafer-
specific uncertainty estimates are applied to the total emissions of the facilities that did not abate emissions as
reported under EPA's GHGRP.
For those facilities reporting abatement of emissions under EPA's GHGRP, estimates of uncertainties for the no
abatement industry segments are modified to reflect the use of full abatement (abatement of all gases from all
cleaning and etching equipment) and partial abatement. These assumptions used to develop uncertainties for the
partial and full abatement facilities are identical for 200 mm and 300 mm wafer processing facilities. For all
facilities reporting gas abatement, a triangular distribution of destruction or removal efficiency is assumed for each
gas. The triangular distributions range from an asymmetric and highly uncertain distribution of zero percent
minimum to 90 percent maximum with 70 percent most likely value for CF4 to a symmetric and less uncertain
distribution of 85 percent minimum to 95 percent maximum with 90 percent most likely value for C4F8, NF3, and
SF6. For facilities reporting partial abatement, the distribution of fraction of the gas fed through the abatement
device, for each gas, is assumed to be triangularly distributed as well. It is assumed that no more than 50 percent of
the gases area abated (i.e., the maximum value) and that 50 percent is the most likely value and the minimum is zero
percent. Consideration of abatement then resulted in four additional industry segments, two 200-mm wafer-
processing segments (one fully and one partially abating each gas) and two 300-mm wafer-processing segment (one
fully and the other partially abating each gas). Gas-specific emission uncertainties were estimated by convolving the
distributions of unabated emissions with the appropriate distribution of abatement efficiency for fully and partially
abated facilities using a Montel Carlo simulation.
The uncertainty in Er f-ghg is obtained by allocating the estimates of uncertainties to the total GHGRP-reported
emissions from each of the six industry segments, and then running a Monte Carlo simulation which results in the 95
percent CI for emissions from GHGRP reporting facilities (Erf-ghg).
The uncertainty in ERjN2o is obtained by assuming that the uncertainty in the emissions reported by each of the
GHGRP reporting facilities results from the uncertainty in quantity of N20 consumed and the N20 emission factor
(or utilization). Similar to analyses completed for subpart I (see Technical Support for Modifications to the
Fluorinated Greenhouse Gas Emission Estimation Method Option for Semiconductor Facilities under Subpart I,
docket EPA-HQ-OAR-2011-0028), the uncertainty of N20 consumed was assumed to be 20 percent. Consumption
of N20 for GHGRP reporting facilities was estimated by back- calculating from emissions reported and assuming no
abatement. The quantity of N20 utilized (the complement of the emission factor) was assumed to have a triangular
distribution with a minimum value of zero percent, mode of 20 percent and maximum value of 84 percent. The
minimum was selected based on physical limitations, the mode was set equivalent to the subpart I default N20
utilization rate for chemical vapor deposition, and the maximum was set equal to the maximum utilization rate found
in ISMI Analysis of Nitrous Oxide Survey Data (ISMI, 2009). The inputs were used to simulate emissions for each
of the GHGRP reporting, N20-emitting facilities. The uncertainty for the total reported N20 emissions was then
estimated by combining the uncertainties of each of the facilities reported emissions using Monte Carlo simulation.
The estimate of uncertainty in ENr,f-ghg and E\ r.\ 2( , entailed developing estimates of uncertainties for the emissions
factors for each non-reporting sub-category and the corresponding estimates of TMLA.
The uncertainty in TMLA depends on the uncertainty of two variables—an estimate of the uncertainty in the average
annual capacity utilization for each level of production of fabs (e.g., full scale or R&D production) and a
69 OnNovember 13, 2013, EPA published a final rule revising subpartl (Electronics Manufacturing) of the GHGRP (78 FR
68162). The revised rule includes updated default emission factors and updated default destruction and removal efficiencies that
are slightly different from those that semiconductor manufacturers were required to use to report their 2012 emissions. The
uncertainty analyses that were performed during the development of the revised rule focused on these updated defaults, but are
expected to be reasonably representative of the uncertainties associated with the older defaults, particularly for estimates at the
country level. (They may somewhat underestimate the uncertainties associated with the older defaults at the facility level.) For
simplicity, the 2012 estimates are assumed to be unbiased although in some cases, the updated (and therefore more
representative) defaults are higher or lower than the older defaults. Multiple models and sensitivity scenarios were run for the
subpart I analysis. The uncertainty analysis presented here made use of the Input gas and wafer size model (Model 1) under the
following conditions: Year = 2010, f = 20, n = SIA3.
4-98 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 corresponding estimate of the uncertainty in the number of layers manufactured. For both variables, the distributions
2 of capacity utilizations and number of manufactured layers are assumed triangular for all categories of non-reporting
3 fabs. For production fabs and for facilities that manufacture discrete devices, the most probable utilization is
4 assumed to be 82 percent, with the highest and lowest utilization assumed to be 89 percent, and 70 percent,
5 respectively. The most probable values for utilization for R&D facilities are assumed to be 20 percent, with the
6 highest utilization at 30 percent, and the lowest utilization at 10 percent. For the triangular distributions that govern
7 the number of possible layers manufactured, it is assumed the most probable value is one layer less than reported in
8 the ITRS; the smallest number varied by technology generation between one and two layers less than given in the
9 ITRS and largest number of layers corresponded to the figure given in the ITRS.
10 The uncertainty bounds for the average capacity utilization and the number of layers manufactured are used as
11 inputs in a separate Monte Carlo simulation to estimate the uncertainty around the TMLA of both individual
12 facilities as well as the total non-reporting TMLA of each sub-population.
13 The uncertainty around the emission factors for each non-reporting category of facilities is dependent on the
14 uncertainty of the total emissions (MMT CO2 Eq. units) and the TMLA of each reporting facility in that category.
15 For each subpopulation of reporting facilities, total emissions were regressed on TMLA (with an intercept forced to
16 zero) for 10,000 emissions and 10,000 TMLA values in a Monte Carlo simulation, which results in 10,000 total
17 regression coefficients (emission factors). The 2.5th and the 97.5th percentile of these emission factors are
18 determined and the bounds are assigned as the percent difference from the estimated emission factor.
19 For simplicity, the results of the Monte Carlo simulations on the bounds of the gas- and wafer size-specific
20 emissions as well as the TMLA and emission factors are assumed to be normally distributed and the uncertainty
21 bounds are assigned at 1.96 standard deviations around the estimated mean. The departures from normality were
22 observed to be small.
23 The final step in estimating the uncertainty in emissions of non-reporting facilities is convolving the distribution of
24 emission factors with the distribution of TMLA using Monte Carlo simulation.
25 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-93, which is also obtained
26 by convolving—using Monte Carlo simulation—the distributions of emissions for each reporting and non-reporting
27 facility. The emissions estimate for total U.S. F-GHG and N20 emissions from semiconductor manufacturing were
28 estimated to be between 4.8 and 5.3 MMT CO2 Eq. at a 95 percent confidence level. This range represents 5 percent
29 below to 5 percent above the 2015 emission estimate of 5.0 MMT CO2 Eq. This range and the associated
30 percentages apply to the estimate of total emissions rather than those of individual gases. Uncertainties associated
31 with individual gases will be somewhat higher than the aggregate, but were not explicitly modeled.
32 Table 4-93: Approach 2 Quantitative Uncertainty Estimates for HFC, PFC, SFe, NF3 and N2O
33 Emissions from Semiconductor Manufacture (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Boundb Boundb
Lower Upper
Bound Bound
Semiconductor
Manufacture
HFC, PFC, SFe,
NF3, andN20
5.0
4.8 5.3
-5% 5%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
b Absolute lower and upper bounds were calculated using the corresponding lower and upper bounds in percentages.
34 The emissions reported under EPA's GHGRP for 2014 and 2015, which are included in the overall emissions
35 estimated for 2014 and 2015, were based on an updated set of default emission factors. This may have affected the
36 trend seen between 2013 and 2014 (a 13 percent increase), which reversed the trend seen between 2011 and 2013.
37 As discussed in the Planned Improvements section, EPA is considering further analysis to determine how much, if
38 any, of the 2013 to 2014 trend may be attributable to the updated factors.
Industrial Processes and Product Use 4-99
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1 Recalculations Discussion
2 Emissions from 2011 through 2014 were updated to reflect updated emissions reporting in EPA's GHGRP. Further,
3 gas-process specific non-reporter emission factors were updated to reflect the historical changes in GHGRP data as
4 well as updated manufacturing utilizations.
5 Planned Improvements
6 This Inventory contains estimates of seven fluorinated gases for semiconductor manufacturing and N20. However,
7 other fluorinated gases (e.g., C\FX) are used in relatively smaller amounts. Previously, emissions data for these other
8 fluorinated gases was not reported through the EPA Partnership. Through EPA's GHGRP, these data, as well as heat
9 transfer fluid emission data, are available. Therefore, a point of consideration for future Inventory reports is the
10 inclusion of other fluorinated gases, and emissions from heat transfer fluid (HTF) loss to the atmosphere.
11 Emissions from fluorinated heat transfer fluids, of which some are liquid perfluorinated compounds, are presented in
12 Table 4-91 for informational purposes for 2011 through 2015, and are based upon GHGRP-reported emissions. The
13 GHGRP-reported HTF emissions along with WFF database could be used to develop emission factors for identified
14 subpopulations. Further research needs to be done to determine if the same subpopulations identified in developing
15 new emission factors for F-GHGs are applicable or new subpopulations have to be studied as HTFs are used
16 primarily by manufacturers of wafer size 300 mm and above. Currently, HTF emissions have only been estimated
17 for those years for which there was reported data available. In the future, back casting could be applied to determine
18 HTF emissions for the semiconductor industry prior to 2011.
19 Along with more emissions information for semiconductor manufacturing, EPA's GHGRP requires the reporting of
20 emissions from other types of electronics manufacturing, including micro-electro-mechanical systems (MEMs), flat
21 panel displays, and photovoltaic cells. There currently are five MEMs manufacturers and no flat panel displays and
22 photovoltaic cell manufacturing facilities reporting to EPA's GHGRP. The MEMs manufacturers also report
23 emissions from semiconductor manufacturing and do not distinguish between these two types of manufacturing in
24 their report; thus, emissions from MEMs manufacturers are included in the totals here. Emissions from
25 manufacturing of flat panel displays and photovoltaic cells may be included in future Inventory reports; however,
26 estimation methodologies would need to be developed.
27 The Inventory methodology uses data reported through the EPA Partnership (for earlier years) and EPA's GHGRP
28 (for later years) to extrapolate the emissions of the non-reporting population. While these techniques are well
29 developed, the understanding of the relationship between the reporting and non-reporting populations is limited.
30 Further analysis of the reporting and non-reporting populations could aid in the accuracy of the non-reporting
31 population extrapolation in future years.
32 The Inventory uses utilization from two different sources for various time periods-SEMI to develop PEVM and to
33 estimate non-Partner emissions for the period 1995 to 2010 and U.S. Census Bureau for 2011 through 2014. SEMI
34 reported global capacity utilization for manufacturers through 2011. U.S. Census Bureau capacity utilization include
35 U.S. semiconductor manufacturers as well as assemblers. Further analysis on the impacts of using a new and
36 different source of utilization data could prove to be useful in better understanding of industry trends and impacts of
37 utilization data sources on historical emission estimates.
38 Starting with 2014 reported emissions, EPA's GHGRP required semiconductor manufacturers to apply updated
39 emission factors to estimate their F-GHG emissions. EPA is planning to investigate whether and how much this
40 change may have affected the trend seen in estimated emissions between 2013 and 2014.
4-100 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 4.23 Substitution of Ozone Depleting
2 Substances (IPCC Source Category 2F)
3 Hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs) are used as alternatives to several classes of ozone-
4 depleting substances (ODSs) that are being phased out under the terms of the Montreal Protocol and the Clean Air
5 Act Amendments of 1990.70 Ozone depleting substances—chlorofluorocarbons (CFCs), halons, carbon
6 tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—are used in a variety of industrial
7 applications including refrigeration and air conditioning equipment, solvent cleaning, foam production, sterilization,
8 fire extinguishing, and aerosols. Although HFCs and PFCs are not harmful to the stratospheric ozone layer, they are
9 potent greenhouse gases. Emission estimates for HFCs and PFCs used as substitutes for ODSs are provided in Table
10 4-94 and Table 4-95.
11 Table 4-94: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2 Eq.)
Gas
1990
2005
2011
2012
2013
2014
2015
HFC-23
+
+
+
+
+
+
+
HFC-32
+
0.3
3.4
4.4
5.4
6.4
7.5
HFC-125
+
9.5
37.2
43.6
49.9
55.9
61.9
HFC-134a
+
73.4
72.5
67.8
62.8
60.8
59.1
HFC-143a
+
9.4
22.5
24.4
26.0
27.2
28.0
HFC-236fa
+
1.2
1.4
1.5
1.5
1.4
1.3
CF4
+
+
+
+
+
+
+
Others3
0.3
5.9
8.2
8.6
9.0
9.5
10.8
Total
0.3
99.8
145.4
150.2
154.7
161.3
168.6
+ Does not exceed 0.05 MMT CO2 Eq.
a Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-43-10mee, HFO-1234yf, C4F10, and
PFC/PFPEs, the latter being a proxy for a diverse collection of PFCs and perfluoropoly ethers (PFPEs)
employed for solvent applications. For estimating purposes, the GWP value used for PFC/PFPEs was based
upon CeFi4.
Note: Totals may not sum due to independent rounding.
12 Table 4-95: Emissions of HFCs and PFCs from ODS Substitution (Metric Tons)
Gas
1990
2005
2011
2012
2013
2014
2015
HFC-23
+
1
2
2
2
3
3
HFC-32
+
511
5,032
6,479
7,985
9,475
11,052
HFC-125
+
2,701
10,626
12,445
14,259
15,974
17,686
HFC-134a
+
51,304
50,731
47,396
43,906
42,495
41,296
HFC-143a
+
2,108
5,034
5,451
5,813
6,088
6,273
HFC-236fa
+
125
147
148
151
148
135
CF4
+
2
4
4
4
4
4
Others3
M
M
M
M
M
M
M
+ Does not exceed 0.5 MT.
M (Mixture of Gases)
a Others include HFC-152a, HFC-227ea, HFC-245fa, HFC-43-10mee, HFO-1234yf, C4F10, and PFC/PFPEs,
the latter being a proxy for a diverse collection of PFCs and perfluoropolyethers (PFPEs) employed for
solvent applications.
13
14 In 1990 and 1991, the only significant emissions of HFCs and PFCs as substitutes to ODSs were relatively small
15 amounts of HFC-152a—used as an aerosol propellant and also a component of the refrigerant blend R-500 used in
16 chillers—and HFC-134a in refrigeration end-uses. Beginning in 1992, HFC-134a was used in growing amounts as a
70 [42 U.S.C § 7671, CAA Title VI]
Industrial Processes and Product Use 4-101
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1 refrigerant in motor vehicle air-conditioners and in refrigerant blends such as R-404A.71 In 1993, the use of HFCs in
2 foam production began, and in 1994 ODS substitutes for halons entered widespread use in the United States as halon
3 production was phased-out. In 1995, these compounds also found applications as solvents.
4 The use and subsequent emissions of HFCs and PFCs as ODS substitutes has been increasing from small amounts in
5 1990 to 168.6 MMT CO2 Eq. emitted in 2015. This increase was in large part the result of efforts to phase out CFCs
6 and other ODSs in the United States. In the short term, this trend is expected to continue, and will likely continue
7 over the next decade as HCFCs, which are interim substitutes in many applications, are themselves phased-out under
8 the provisions of the Copenhagen Amendments to the Montreal Protocol. Improvements in the technologies
9 associated with the use of these gases and the introduction of alternative gases and technologies, however, may help
10 to offset this anticipated increase in emissions.
11 Table 4-96 presents emissions of HFCs and PFCs as ODS substitutes by end-use sector for 1990 through 2015. The
12 end-use sectors that contributed the most toward emissions of HFCs and PFCs as ODS substitutes in 2015 include
13 refrigeration and air-conditioning (144.9 MMT CO2 Eq., or approximately 86 percent), aerosols (11.0 MMT CO2
14 Eq., or approximately 7 percent), and foams (9.4 MMT CO2 Eq., or approximately 6 percent). Within the
15 refrigeration and air-conditioning end-use sector, motor vehicle air-conditioning was the highest emitting end-use
16 (38.3 MMT CO2 Eq.), followed by refrigerated retail food and refrigerated transport. Each of the end-use sectors is
17 described in more detail below.
18 Table 4-96: Emissions of HFCs and PFCs from ODS Substitutes (MMT CO2 Eq.) by Sector
Sector
1990
2005
2011
2012
2013
2014
2015
Refrigeration/Air
+
87.8
125.9
130.0
133.6
139.2
144.9
Conditioning
Aerosols
0.3
7.6
10.1
10.3
10.5
10.8
11.0
Foams
+
2.1
6.4
6.9
7.5
8.0
9.4
Solvents
+
1.7
1.7
1.7
1.8
1.8
1.8
Fire Protection
+
0.7
1.2
1.3
1.3
1.4
1.5
Total
0.3
99.8
145.4
150.2
154.7
161.3
168.6
19 + Does not exceed 0.05 MMT CO2 Eq.
20 Note: Totals may not sum due to independent rounding.
21 Refrigeration/Air Conditioning
22 The refrigeration and air-conditioning sector includes a wide variety of equipment types that have historically used
23 CFCs or HCFCs. End-uses within this sector include motor vehicle air-conditioning, retail food refrigeration,
24 refrigerated transport (e.g., ship holds, truck trailers, railway freight cars), household refrigeration, residential and
25 small commercial air-conditioning and heat pumps, chillers (large comfort cooling), cold storage facilities, and
26 industrial process refrigeration (e.g., systems used in food processing, chemical, petrochemical, pharmaceutical, oil
27 and gas, and metallurgical industries). As the ODS phaseout has taken effect, most equipment has been retrofitted or
28 replaced to use HFC-based substitutes. Common HFCs in use today in refrigeration/air-conditioning equipment are
29 HFC-134a, R-410A,72 R-404A, and R-507A.73 Lower -GWP options such as HFO-1234yf in motor vehicle air-
30 conditioning, R-717 (ammonia) in cold storage and industrial applications, and R-744 (carbon dioxide) and
31 HFC/HFO blends in retail food refrigeration, are also being used. These refrigerants are emitted to the atmosphere
32 during equipment manufacture and operation (as a result of component failure, leaks, and purges), as well as at
33 servicing and disposal events.
34 Aerosols
35 Aerosol propellants are used in metered dose inhalers (MDIs) and a variety of personal care products and
36 technical/specialty products (e.g., duster sprays and safety horns). Many pharmaceutical companies that produce
37 MDIs—a type of inhaled therapy used to treat asthma and chronic obstructive pulmonary disease—have replaced
71 R-404A contains HFC-125, HFC-143a, andHFC-134a.
72 R-410A contains HFC-32 and HFC-125.
73 R-507A, also called R-507, contains HFC-125 and HFC-143a.
4-102 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
the use of CFCs with HFC-propellant alternatives. The earliest ozone-friendly MDIs were produced with HFC-134a,
but the industry has started to use HFC-227ea as well. Conversely, since the use of CFC propellants was banned in
1978, most non-medical consumer aerosol products have not transitioned to HFCs, but to "not-in-kind"
technologies, such as solid roll-on deodorants and finger-pump sprays. The transition away from ODS in specialty
aerosol products has also led to the introduction of non-fluorocarbon alternatives (e.g., hydrocarbon propellants) in
certain applications, in addition to HFC-134a or HFC-152a. Other low-GWP options such as HFO-1234ze(E) are
being used as well. These propellants are released into the atmosphere as the aerosol products are used.
Foams
Chlorofluorocarbons 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
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 PU appliance, PU commercial refrigeration, PU spray, and PU panel
foams—used in refrigerators, vending machines, roofing, wall insulation, garage doors, and cold storage
applications. In addition, HFC-152a, HFC-134a and CO2 are used to produce polystyrene sheet/board foam, which is
used in food packaging and building insulation. Low-GWP fluorinated foam blowing agents in use include HFO-
1234ze(E) and -1233zd(E). 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
Chlorofluorocarbons, methyl chloroform (1,1,1-trichloroethane or TCA), and to a lesser extent carbon tetrachloride
(CCI4) were historically used as solvents in a wide range of cleaning applications, including precision, electronics,
and metal cleaning. Since their phaseout, metal cleaning end-use applications have primarily transitioned to non-
fluorocarbon solvents and not-in-kind processes. The precision and electronics cleaning end-uses have transitioned
in part to high-GWP gases, due to their high reliability, excellent compatibility, good stability, low toxicity, and
selective solvency. These applications rely on HFC-43-10mee, HFC-365mfc, HFC-245fa, and to a lesser extent,
PFCs. Electronics cleaning involves removing flux residue that remains after a soldering operation for printed circuit
boards and other contamination-sensitive electronics applications. Precision cleaning may apply to either electronic
components or to metal surfaces, and is characterized by products, such as disk drives, gyroscopes, and optical
components, that require a high level of cleanliness and generally have complex shapes, small clearances, and other
cleaning challenges. The use of solvents yields fugitive emissions of these HFCs and PFCs.
Fire Protection
Fire protection applications include portable fire extinguishers ("streaming" applications) that originally used halon
1211, and total flooding applications that originally used halon 1301, as well as some halon 2402. Since the
production and import of virgin halons were banned in the United States in 1994, the halon replacement agent of
choice in the streaming sector has been dry chemical, although HFC-236fa is also used to a limited extent. In the
total flooding sector, HFC-227ea has emerged as the primary replacement for halon 1301 in applications that require
clean agents. Other HFCs, such as HFC-23 and HFC-125, are used in smaller amounts. The majority of HFC-227ea
in total flooding systems is used to protect essential electronics, as well as in civil aviation, military mobile weapons
systems, oil/gas/other process industries, and merchant shipping. Fluoroketone FK-5-1-12 is also used as a low-
GWP option and 2-BTP is being considered. As fire protection equipment is tested or deployed, emissions of these
HFCs occur.
Methodology
"•J" m
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
Industrial Processes and Product Use 4-103
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1 modeled estimates of the quantity of equipment or products sold each year containing these chemicals and the
2 amount of the chemical required to manufacture and/or maintain equipment and products over time. Emissions for
3 each end-use were estimated by applying annual leak rates and release profiles, which account for the lag in
4 emissions from equipment as they leak over time. By aggregating the data for 65 different end-uses, the model
5 produces estimates of annual use and emissions of each compound. Further information on the Vintaging Model is
6 contained in Annex 3.9.
7 Uncertainty and Time-Series Consistency - TO BE UPDATED
s FOR FINAL INVENTORY REPORT
9 Given that emissions of ODS substitutes occur from thousands of different kinds of equipment and from millions of
10 point and mobile sources throughout the United States, emission estimates must be made using analytical tools such
11 as the Vintaging Model or the methods outlined in IPCC (2006). Though the model is more comprehensive than the
12 IPCC default methodology, significant uncertainties still exist with regard to the levels of equipment sales,
13 equipment characteristics, and end-use emissions profiles that were used to estimate annual emissions for the
14 various compounds.
15 The Vintaging Model estimates emissions from 65 end-uses. The uncertainty analysis, however, quantifies the level
16 of uncertainty associated with the aggregate emissions resulting from the top 21 end-uses, comprising over 95
17 percent of the total emissions, and 6 other end-uses. These 27 end-uses comprise 97 percent of the total emissions,
18 equivalent to 156.4 MMT CO2 Eq. In an effort to improve the uncertainty analysis, additional end-uses are added
19 annually, with the intention that over time uncertainty for all emissions from the Vintaging Model will be fully
20 characterized. Any end-uses included in previous years' uncertainty analysis were included in the current
21 uncertainty analysis, whether or not those end-uses were included in the top 95 percent of emissions from ODS
22 substitutes.
23 In order to calculate uncertainty, functional forms were developed to simplify some of the complex "vintaging"
24 aspects of some end-use sectors, especially with respect to refrigeration and air-conditioning, and to a lesser degree,
25 fire extinguishing. These sectors calculate emissions based on the entire lifetime of equipment, not just equipment
26 put into commission in the current year, thereby necessitating simplifying equations. The functional forms used
27 variables that included growth rates, emission factors, transition from ODSs, change in charge size as a result of the
28 transition disposal quantities, disposal emission rates, and either stock for the current year or original ODS
29 consumption. Uncertainty was estimated around each variable within the functional forms based on expert
30 judgment, and a Monte Carlo analysis was performed. The most significant sources of uncertainty for this source
31 category include the emission factors for residential unitary air-conditioners, as well as the percent of non-MDI
32 aerosol propellant that is HFC-152a.
33 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-97. Substitution of ozone
34 depleting substances HFC and PFC emissions were estimated to be between 154.2 and 172.5 MMT CO2 Eq. at the
35 95 percent confidence level. This indicates a range of approximately 1.4 percent below to 10.3 percent above the
36 emission estimate of 156.4 MMT CO2 Eq., which comprises 97 percent of total emissions.
37 Table 4-97: Approach 2 Quantitative Uncertainty Estimates for HFC and PFC Emissions from
38 ODS Substitutes (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gases
Estimate
(MMT CO2 Eq.)a
Uncertainty Range Relative to Emission Estimateb
(MMT CO2 Eq.) (%)
Lower Upper
Lower Upper
Bound Bound
Bound Bound
Substitution of Ozone
Depleting Substances
HFCsand
PFCs
156.4
154.2 172.5
-1.4% +10.3%
39 a 2015 emission estimates and the uncertainty range presented in this table correspond to selected end-uses within the aerosols,
40 foams, solvents, lire extinguishing agents, and refrigerants sectors that comprise 97 percent of total emissions, but not for other
41 remaining categories. Therefore, because the uncertainty associated with emissions from "other" ODS substitutes was not
42 estimated, they were excluded in the uncertainty estimates reported in this table.
43 b Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
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MciIk 15. Delails mi ilie emission iivih.K lliroimli lime ;ire described in more del;nl in ihe Mellindiilouv scclioii.
:ibn\ e
Comparison of Reported Consumption to Modeled Consumption of HFCs
Data from EPA's Greenhouse Gas Reporting Program (GHGRP) was also used to perform quality control on the
modeled emissions from this source category. To do so, consumption patterns demonstrated through data reported
under GHGRP Subpart 00: Suppliers of Industrial Greenhouse Gases and Subpart QQ: Imports and Exports of
Equipment Pre-charged with Fluorinated GHGs or Containing Fluorinated GHGs in Closed-cell Foams were
compared to the modeled demand for new saturated HFCs (excluding HFC-23) used as ODS substitutes from the
Vintaging Model. The collection of data from suppliers of HFCs enables EPA to calculate the reporters' aggregated
net supply-the sum of the quantities of chemical produced or imported into the United States less the sum of the
quantities of chemical transformed (used as a feedstock in the production of other chemicals), destroyed, or exported
from the United States.74 This allows for a quality control check on emissions from this source because the
Vintaging Model uses modeled demand for new chemical as a proxy for total amount supplied, which is similar to
net supply, as an input to the emission calculations in the model.
Reported Net Supply (GHGRP Top-Down Estimate)
Under EPA's GHGRP, suppliers (i.e., producers, importers, and exporters) of HFCs under Subpart 00 began
annually reporting their production, transformation, destruction, imports, and exports to EPA in 2011 (for supply
that occurred in 2010) and suppliers of HFCs under Subpart QQ began annually reporting their imports and exports
to EPA in 2012 (for supply that occurred in 2011). Beginning in 2015, bulk consumption data for aggregated HFCs
reported under Subpart 00 were made publicly available under EPA's GHGRP. Data include all saturated HFCs
(except HFC-23) reported to EPA across the GHGRP-reporting time series (2010 through 2015). The data include
all 26 such saturated HFCs listed in Table A-l of 40 CFR Part 98, where regulations for EPA's GHGRP are
promulgated, though not all species were reported in each reporting year. For the first time in 2016, net imports of
HFCs contained in pre-charged equipment or closed-cell foams reported under Subpart QQ were made publicly
available under EPA's GHGRP across the GHGRP-reporting time series (2010 through 2015).
Modeled Consumption (VintagingModel Bottom-Up Estimate)
The Vintaging Model, used to estimate emissions from this source category, calculates chemical demand based on
the quantity of equipment and products sold, serviced and retired each year, and the amount of the chemical required
to manufacture and/or maintain the equipment and products.75 It is assumed that the total demand equals the amount
supplied by either new production, chemical import, or quantities recovered (usually reclaimed) and placed back on
the market. In the Vintaging Model, demand for new chemical, as a proxy for consumption, is calculated as any
chemical demand (either for new equipment or for servicing existing equipment) that cannot be met through
recycled or recovered material. No distinction is made in the Vintaging Model between whether that need is met
through domestic production or imports. To calculate emissions, the Vintaging Model estimates the quantity
released from equipment over time. Thus, verifying the Vintaging Model's calculated consumption against GHGRP
reported data is one way to check the Vintaging Model's emission estimates.
There are ten saturated HFC species modeled in the Vintaging Model: HFC-23, HFC-32, HFC-125, HFC-134a,
HFC-143a, HFC-152a, HFC-227ea, HFC-236fa, HFC-245fa, and HFC-43-10mee. For the purposes of this
comparison, only nine HFC species are included (HFC-23 is excluded), to more closely align with the aggregated
total reported under EPA's GHGRP. While some amounts of less-used saturated HFCs, including isomers of those
74 Chemical that is exported, transformed, or destroyed—unless otherwise imported back to the United States—will never be
emitted in the United States.
75 The model builds an inventory of the in-use stock of equipment and products and ODSs and HFCs in each of the sub-
applications. Emissions are subsequently estimated by applying annual and disposal emission rates to each population of
equipment and products.
Industrial Processes and Product Use 4-105
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1 included in the Vintaging Model, are reportable under EPA's GHGRP, the data are believed to represent an amount
2 comparable to the modeled estimates as a quality control check.
3 Comparison Results and Discussion
4 Comparing the estimates of consumption from these two approaches (i.e., reported and modeled) ultimately supports
5 and improves estimates of emissions, as noted in the 2006IPCC Guidelines for National Greenhouse Gas
6 Inventories (which refer to fluorinated greenhouse gas consumption based on supplies as "potential emissions"):
7 [W]hen considered along with estimates of actual emissions, the potential emissions approach can assist in
8 validation of completeness of sources covered and as a QC check by comparing total domestic
9 consumption as calculated in this 'potential emissions approach' per compound with the sum of all activity
10 data of the various uses (IPCC 2006).
11 Table 4-98 and Figure 4-2 compare the net supply of saturated HFCs (excluding HFC-23) in MMT CO2 Eq. as
12 determined from Subpart 00 (industrial GHG suppliers) and Subpart QQ (supply of HFCs in products) of EPA's
13 GHGRP for the years 2010 through 2015 and the chemical demand as calculated by the Vintaging Model for the
14 same time series.
15 Table 4-98: U.S. HFC Consumption (MMT COz Eq.)
2010
2011
2012
2013
2014
2015
Reported Net Supply (GHGRP)
235
249
245
295
279
290
Industrial GHG Suppliers
235
24 f
227
278
254
264
Imports of HFCs in Products
N/Aa
7
18
17
25
26
Modeled Supply (Vintaging Model)
256
256
273
278
282
285
Percent Difference
9%
3%
11%
-6%
1%
-2%
16 a Importers and exporters of fluorinated gases in products were not required to report until 2011.
17
18 Figure 4-2: U.S. HFC Consumption (MMT CO2 Eq.)
350
300
250
cr
LU
fN 200
O
u
2 150
100
50
0
2010 2011 2012 2013 2014 2015
¦ Reported BulkSupply ¦ Reported Imports in Products ¦ Modeled Consumption
19
20 As shown the estimates from the Vintaging Model are generally higher than the GHGRP estimates by an average of
21 3 percent across the time series (i.e., 2010 through 2015). Potential reasons for these differences include:
22 • The Vintaging Model includes fewer HFCs than are reported to EPA's GHGRP. However, the additional
23 reported HFCs represent a small fraction of total HFC use for this source category, both in GWP-weighted
24 and unweighted terms, and as such, it is not expected that the additional HFCs reported to EPA are a major
25 driver for the difference between the two sets of estimates. To the extent lower-GWP isomers were used in
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lieu of the modeled chemicals (e.g., HFC-134 instead of HFC-134a), lower CO2 Eq. amounts in the EPA's
GHGRP data compared to the modeled estimates would be expected.
• Because the top-down data are reported at the time of actual production or import and the bottom-up data
are calculated at the time of actual placement on the market, there could be a temporal discrepancy when
comparing data. Because the EPA's GHGRP data generally increases over time (although some year-to-
year variations exist) and the Vintaging Model estimates also increase, EPA would expect the modeled
estimates to be slightly lower than the corresponding GHGRP data due to this temporal effect.
• Under EPA's GHGRP, all facilities that produce HFCs are required to report their quantities, whereas
importers or exporters of HFCs or pre-charged equipment and closed-cell foams that contain HFCs are only
required to report if either their total imports or their total exports of greenhouse gases are greater than or
equal to 25,000 metric tons of CO2 Eq. per year. Thus, some imports may not be accounted for in the
GHGRP data. On the other hand, some exports might also not be accounted for in this data.
• In some years, imports and exports may be greater than consumption because the excess is being used to
increase chemical or equipment stockpiles; in other years, the opposite may hold true. Similarly, relocation
of manufacturing facilities or recovery from the recession could contribute to variability in imports or
exports. Averaging imports and exports over multiple years can minimize the impact of such fluctuations.
For example, when the 2012 and 2013 net additions to the supply are averaged, as shown in Table 4-99, the
percent difference between the consumption estimates decreases compared to the 2012-only estimates.
Table 4-99: Averaged U.S. HFC Demand (MMT CCh Eq.)
2010-2011 Avg.
2011-2012 Avg.
2012-2013 Avg.
2013-2014 Avg.
2014-2015 Avg.
Reported Net Supply (GHGRP)
242
247
270
287
284
Modeled Demand (Vintaging Model)
256
264
275
280
284
Percent Difference
6%
7%
2%
-2%
0%
• The Vintaging Model does not reflect the dynamic nature of reported HFC consumption, with significant
differences seen in each year. Whereas the Vintaging Model projects a slowly increasing overall demand,
actual consumption for specific chemicals or equipment may vary over time and could even switch from
positive to negative (indicating more chemical exported, transformed, or destroyed than produced or
imported in a given year). Furthermore, consumption as calculated in the Vintaging Model is a function of
demand not met by disposal recovery. If, in any given year, a significant number of units are disposed,
there will be a large amount of additional recovery in that year that can cause an unexpected and not
modeled decrease in demand and thus a decrease in consumption. On the other hand, if market, economic,
or other factors cause less than expected disposal and recovery, actual supply would decrease, and hence
consumption would increase to meet that demand not satisfied by recovered quantities, increasing the
GHGRP data and bringing those totals closer to the Vintaging Model estimates.
• The Vintaging Model is used to estimate the emissions that occur in the United States. As such, all
equipment or products that contain ODS or alternatives, including saturated HFCs, are assumed to consume
and emit chemicals equally as like equipment or products originally produced in the United States. The
GHGRP data from Subpart OO (industrial GHG suppliers) includes HFCs produced or imported and used
to fill or manufacture products that are then exported from the United States. The Vintaging Model
estimates of demand and supply are not meant to incorporate such chemical. Likewise, chemicals may be
used outside the United States to create products or charge equipment that is then imported to and used in
the United States. The Vintaging Model estimates of demand and supply are meant to capture this
chemical, as it will lead to emissions inside the United States. The GHGRP data from Subpart QQ (supply
of HFCs in products) accounts for some of these differences; however, the scope of Subpart QQ does not
cover all such equipment or products and the chemical contained therein. Depending on whether the United
States is a net importer or net exporter of such chemical, this factor may account for some of the difference
shown above or might lead to a further discrepancy.
One factor, however, would only lead to modeled estimates to be even higher than the estimates shown and hence
for most years higher than EPA's GHGRP data:
• Saturated HFCs are also known to be used as a cover gas in the production of magnesium. The Vintaging
Model estimates here do not include the amount of HFCs for this use, but rather only the amount for uses
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that traditionally were served by ODS. Nonetheless, EPA expects that this supply not included in the
Vintaging Model estimates to be very small compared to the ODS substitute use for the years analyzed. An
indication of the different magnitudes of these categories is seen in the fact that the 2015 emissions from
that non-modeled source (ii I \l\ll (() I !q i are much smaller than those for the ODS substitute sector
(168.6 MMTC02 Eq).
Using a Tier 2 bottom-up modeling methodology to estimate emissions requires assumptions and expert judgement.
Comparing the Vintaging Model's estimates to GHGRP reported estimates, particularly for more widely used
chemicals, can help validate the model but it is expected that the model will have limitations. This comparison
shows that Vintaging Model consumption estimates are well within the same order of magnitude as the actual
consumption data as reported to EPA's GHGRP although the differences in reported net supply and modeled
demand are still significant in some of the years. Although it can be difficult to capture the observed market
variability, the Vintaging Model is periodically reviewed and updated to ensure that the model reflects the current
and future trajectory of ODS and ODS substitutes across all end-uses and the Vintaging Model will continue to be
compared to available top-down estimates in order to ensure the model accurately estimates HFC consumption and
emissions.
Recalculations Discussion
For the current Inventory, reviews of the foams sector resulted in revisions to the Vintaging Model since the
previous Inventory report. Methodological recalculations were applied to the entire time-series to ensure time-series
consistency from 1990 through 2015. For the foams sector, assumptions regarding lifetimes and loss rates were
revised based on a review of 2006IPCC Guidelines for National Greenhouse Gas Inventories. Combined, these
assumption changes decreased C02-equivalent greenhouse gas emissions on average by 0.2 percent between 1990
and 2015.
Planned Improvements
Future improvements to the Vintaging Model are planned for the refrigeration and air-conditioning, foam, and
aerosols sectors. A refrigerated food processing and dispensing equipment end-use may be added to the refrigeration
and air-conditioning sector, in order to capture a portion of the retail food market that may not be adequately
encompassed by the small retail food end-use. In addition, end-uses representing medium-duty and heavy-duty
vehicle and truck air conditioners may be added to the refrigeration and air-conditioning sector.
New vintages will be added for the motor vehicle air-conditioning, large retail food, medium retail food, small retail
food, vending machines, cold storage, household refrigerators and freezers, aerosols, and multiple foam end-uses.
These vintages will include transitions to low-GWP alternatives as companies begin to comply with rules issued
under EPA's Significant New Alternatives Policy (SNAP) Program These updates to the Vintaging Model are
anticipated to have the greatest impact on the estimates of greenhouse gas emissions for the refrigeration and air-
conditioning and foams sectors in the near term, and are also anticipated to have an increasingly larger impact in
future years as the low-GWP alternatives penetrate the U.S. market.
4.24 Electrical Transmission and Distribution
(IPCC Source Category 2G1)
The largest use of sulfur hexafluoride (SF6), both in the United States and internationally, is as an electrical insulator
and interrupter in equipment that transmits and distributes electricity (RAND 2004). The gas has been employed by
the electric power industry in the United States since the 1950s because of its dielectric strength and arc-quenching
characteristics. It is used in gas-insulated substations, circuit breakers, and other switchgear. SF6 has replaced
flammable insulating oils in many applications and allows for more compact substations in dense urban areas.
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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 4.2 MMT CO2 Eq. (0.2 kt) in 2015. This quantity represents an 82 percent decrease from the
estimate for 1990 (see Table 4-100 and Table 4-101). There are two potential causes for this decrease: a sharp
increase in the price of SF6 during the 1990s and a growing awareness of the magnitude and environmental impact
of SF6 emissions through programs such as EPA's voluntary SF6 Emission Reduction Partnership for Electric Power
Systems (Partnership) and EPA's GHGRP. Utilities participating in the Partnership have lowered their emission
factor (kg SF6 emitted per kg of nameplate capacity) by more than 85 percent since the Partnership began in 1999. A
recent examination of the SF6 emissions reported by electric power systems to EPA's GHGRP revealed that SF6
emissions from reporters have decreased by 28 percent from 2011 to 20 1 5,76 with much of the reduction seen from
utilities that are not participants in the Partnership. These utilities may be making relatively large reductions in
emissions as they take advantage of relatively large and/or inexpensive emission reduction opportunities (i.e., "low
hanging fruit," such as replacing major leaking circuit breakers) that Partners have already taken advantage of under
the voluntary program (Ottinger et al. 2014).
Table 4-100: SF6 Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (MMT CO2 Eq.)
Electric Power Electrical Equipment
Year Systems Manufacturers Total
1990 22.8 0.3 23.1
2005 7.7 0.5 8.3
2011 5.3 0.7 6.0
2012 4.5 0.3 4.8
2013 4.2 0.4 4.6
2014 4.5 0.4 4.8
201 5 3J) 03 42
Note: Totals may not sum due to independent rounding.
Table 4-101: SF6 Emissions from Electric Power Systems and Electrical Equipment
Manufacturers (kt)
Year Emissions
1990 1.0
2005 0.4
2011 0.3
2012 0.2
2013 0.2
2014 0.2
2015 0.2
Methodology
The estimates of emissions from Electrical Transmission and Distribution are comprised of emissions from electric
power systems and emissions from the manufacture of electrical equipment. The methodologies for estimating both
sets of emissions are described below.
76 Analysis of emission trends from the GHGRP is imperfect due to an inconsistent group of reporters year to year.
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1990 through 1998 Emissions from Electric Power Systems
Emissions from electric power systems from 1990 through 1998 were estimated based on (1) the emissions
estimated for this source category in 1999, which, as discussed in the next section, were based on the emissions
reported during the first year of EPA's SF6 Emission Reduction Partnership for Electric Power Systems
(Partnership), and (2) the RAND survey of global SF6 emissions. Because most utilities participating in the
Partnership reported emissions only for 1999 through 2011, modeling was used to estimate SF6 emissions from
electric power systems for the years 1990 through 1998. To perform this modeling, U.S. emissions were assumed to
follow the same trajectory as global emissions from this source during the 1990 to 1999 period. To estimate global
emissions, the RAND survey of global SF6 sales were used, together with the following equation for estimating
emissions, which is derived from the mass-balance equation for chemical emissions (Volume 3, Equation 7.3) in the
2006IPCC Guidelines77 (Although Equation 7.3 of the 2006IPCC Guidelines appears in the discussion of
substitutes for ozone-depleting substances, it is applicable to emissions from any long-lived pressurized equipment
that is periodically serviced during its lifetime.)
Emissions (kilograms SF6) = SF6 purchased to refill existing equipment (kilograms) + nameplate capacity of retiring
equipment (kilograms)78
Note that the above equation holds whether the gas from retiring equipment is released or recaptured; if the gas is
recaptured, it is used to refill existing equipment, thereby lowering the amount of SF6 purchased by utilities for this
purpose.
Gas purchases by utilities and equipment manufacturers from 1961 through 2003 are available from the RAND
(2004) survey. To estimate the quantity of SF6 released or recovered from retiring equipment, the nameplate
capacity of retiring equipment in a given year was assumed to equal 81.2 percent of the amount of gas purchased by
electrical equipment manufacturers 40 years previous (e.g., in 2000, the nameplate capacity of retiring equipment
was assumed to equal 81.2 percent of the gas purchased in 1960). The remaining 18.8 percent was assumed to have
been emitted at the time of manufacture. The 18.8 percent emission factor is an average of IPCC default SF6
emission rates for Europe and Japan for 1995 (IPCC 2006). The 40-year lifetime for electrical equipment is also
based on IPCC (2006). The results of the two components of the above equation were then summed to yield
estimates of global SF6 emissions from 1990 through 1999.
U.S. emissions between 1990 and 1999 are assumed to follow the same trajectory as global emissions during this
period. To estimate U.S. emissions, global emissions for each year from 1990 through 1998 were divided by the
estimated global emissions from 1999. The result was a time series of factors that express each year's global
emissions as a multiple of 1999 global emissions. Historical U.S. emissions were estimated by multiplying the factor
for each respective year by the estimated U.S. emissions of SF6 from electric power systems in 1999 (estimated to be
14.3 MMT C02 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).
77 Ideally, sales to utilities in the U.S. between 1990 and 1999 would be used as a model. However, this information was not
available. There were only two U.S. manufacturers of SFe during this time period, so it would not have been possible to conceal
sensitive sales information by aggregation.
78 Nameplate capacity is defined as the amount of SFe within fully charged electrical equipment.
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1999 through 2015 Emissions from Electric Power Systems
Emissions from electric power systems from 1999 to 2015 were estimated based on: (1) reporting from utilities
participating in EPA's SF6 Emission Reduction Partnership for Electric Power Systems (Partners), which began in
1999; (2) reporting from utilities covered by the EPA's GHGRP, which began in 2012 for emissions occurring in
2011 (GHGRP-Only Reporters); and (3) the relationship between utilities' reported emissions and their transmission
miles as reported in the 2001, 2004, 2007, 2010, and 2013 Utility Data Institute (UDI) Directories of Electric Power
Producers and Distributors (UDI 2001, 2004, 2007, 2010, 2013), which was applied to the electric power systems
that do not report to EPA (Non-Reporters). (Transmission miles are defined as the miles of lines carrying voltages
above 34.5 kV).
Partners
Over the period from 1999 to 2015, Partner utilities, which for inventory purposes are defined as utilities that either
currently are or previously have been part of the Partnership,79 represented 48 percent, on average, of total U.S.
transmission miles. Partner utilities estimated their emissions using a Tier 3 utility-level mass balance approach
(IPCC 2006). If a Partner utility did not provide data for a particular year, emissions were interpolated between
years for which data were available or extrapolated based on Partner-specific transmission mile growth rates. In
2012, many Partners began reporting their emissions (for 2011 and later years) through EPA's GHGRP (discussed
further below) rather than through the Partnership. In 2015, approximately 0.6 percent of the total emissions
attributed to Partner utilities were reported through Partnership reports. Approximately 95 percent of the total
emissions attributed to Partner utilities were reported and verified through EPA's GHGRP. Partners without verified
2015 data accounted for approximately 5 percent of the total emissions attributed to Partner utilities.80
GHGRP-Only Reporters
EPA's GHGRP requires users of SF6 in electric power systems to report emissions if the facility has a total SF6
nameplate capacity that exceeds 17,820 pounds. (This quantity is the nameplate capacity that would result in annual
SF6 emissions equal to 25,000 metric tons of CO2 equivalent at the historical emission rate reported under the
Partnership.) As under the Partnership, electric power systems that report their SF6 emissions under EPA's GHGRP
are required to use the Tier 3 utility-level mass-balance approach. Many Partners began reporting their emissions
through EPA's GHGRP in 2012 (reporting emissions for 2011 and later years) because their nameplate capacity
exceeded the reporting threshold. Partners who did not report through EPA's GHGRP continued to report through
the Partnership.
In addition, many non-Partners began reporting to EPA for the first time through its GHGRP in 2012. Non-Partner
emissions reported and verified under EPA's GHGRP were compiled to form a new category of reported data
(GHGRP-Only Reporters). GHGRP-Only Reporters accounted for 20 percent of U.S. transmission miles and 24
percent of estimated U.S. emissions from electric power system in 2015.81
79 For the 2015 inventory, partners who had reported three years or less of data prior to 2006 were removed. Most of these
partners had been removed from the list of current Partners, but remained in the inventory due to the extrapolation methodology
for non-reporting partners.
80 It should be noted that data reported through EPA's GHGRP must go through a verification process; only data verified as of
September 1, 2016 could be used in the emission estimates for 2015. For Partners whose GHGRP data was not yet verified,
emissions were extrapolated based upon historical Partner-specific transmission mile growth rates, and those Partners are
included in the 'non-reporting Partners' category.
For electric power systems, verification involved a series of electronic range, completeness, and algorithm checks for each report
submitted. In addition, EPA manually reviewed the reported data and compared each facility's reported transmission miles with
the corresponding quantity in the UDI 2013 database (UDI 2013). In the first year of GHGRP reporting, EPA followed up with
reporters where the discrepancy between the reported miles and the miles published by UDI was greater than 10 percent, with a
goal to improve data quality. Only GHGRP data verified as of September 1, 2016 was included in the emission estimates for
2011,2012, 2013,2014 and 2015.
81 GHGRP-reported and Partner transmission miles from a number of facilities were equal to zero with non-zero emissions.
These facilities emissions were added to the emissions totals for their respective parent companies when identifiable and not
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Non-Reporters
Emissions from Non-Reporters (i.e., utilities other than Partners and GHGRP-Only Reporters) in every year since
1999 were estimated using the results of a regression analysis that correlated emissions from reporting utilities
(using verified data from both Partners and GHGRP-Only Reporters) with their transmission miles.82 As noted
above, non-Partner emissions were reported to the EPA for the first time through its GHGRP in 2012 (representing
2011 emissions). This set of reported data was of particular interest because it provided insight into the emission rate
of non-Partners, which previously was assumed to be equal to the historical (1999) emission rate of Partners.
Specifically, emissions were estimated for Non-Reporters as follows:
• Non-Reporters. 1999 to 2011: First, the 2011 emission rates (per kg nameplate capacity and per
transmission mile) reported by Partners and GHGRP-Only Reporters were reviewed to determine whether
there was a statistically significant difference between these two groups. Transmission mileage data for
2011 was reported through GHGRP, with the exception of transmission mileage data for Partners that did
not report through GHGRP, which was obtained from UDI. It was determined that there is no statistically
significant difference between the emission rates of Partners and GHGRP-Only reporters; therefore, Partner
and GHGRP-Only reported data for 2011 were combined to develop regression equations to estimate the
emissions of Non-Reporters. Historical emissions from Non-Reporters were estimated by linearly
interpolating between the 1999 regression coefficients (based on 1999 Partner data) and the 2011
regression coefficients.
• Non-Reporters. 2012 to Present: It was determined that there continued to be no statistically significant
difference between the emission rates reported by Partners and by GHGRP-Only Reporters. Therefore, the
emissions data from both groups were combined to develop regression equations for 2012. This was
repeated for 2013, 2014, and 2015 using Partner and GHGRP-Only Reporter data for each year.
o The 2015 regression equation for utilities was developed based on the emissions reported by a subset
of Partner utilities and GHGRP-Only utilities (representing approximately 66 percent of total U.S.
transmission miles). The regression equation for 2015 is:
Emissions (kg) = 0.166 x Transmission Miles
Table 4-102 below shows the percentage of transmission miles covered by reporters (i.e., associated with reported
data) and the regression coefficient for 1999 (the first year data was reported), and for 2011 through 2015 (the first
three years with GHGRP reported data). The coefficient decreased between 2014 and 2015.
Table 4-102: Transmission Mile Coverage and Regression Coefficients (Percent)
1999
2011 2012 2013 2014 2015
Percentage of Miles Covered by Reporters
Regression Coefficient3
50
0.71
68 68 68 68 66
0.26 0.23 0.22 0.22 0.17
a Regression coefficient is defined as emissions (in kg) divided by transmission miles.
Data on transmission miles for each Non-Reporter for the years 2000, 2003, 2006, and 2009, and 2012 were
obtained from the 2001, 2004, 2007, 2010, and 2013 UDI Directories of Electric Power Producers and Distributors,
respectively (UDI 2001, 2004, 2007, 2010, 2013). The U.S. transmission system grew by over 25,000 miles between
2000 and 2003 yet declined by almost 4,000 miles between 2003 and 2006. Given these fluctuations, periodic
increases are assumed to occur gradually. Therefore, transmission mileage was assumed to increase at an annual rate
of 1.2 percent between 2000 and 2003 and decrease by -0.20 percent between 2003 and 2006. This growth rate grew
to 3 percent from 2006 to 2009 as transmission miles increased by more than 59,000 miles. The annual growth rate
included in the regression equation when not identifiable or applicable. Other facilities reported non-zero transmission miles with
zero emissions, or zero transmission miles and zero emissions. These facilities were not included in the development of the
regression equations (discussed further below). These emissions are already implicitly accounted for in the relationship between
transmission miles and emissions.
82 In the United States, SF6 is contained primarily in transmission equipment rated above 34.5 kV.
4-112 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 for 2009 through 2012 was calculated to be 2.0 percent as transmission miles grew by approximately 43,000 during
2 this time period.
3 Total Industry Emissions
4 As a final step, total electric power system emissions from 1999 through 2015 were determined for each year by
5 summing the Partner reported and estimated emissions (reported data was available through the EPA's SF6 Emission
6 Reduction Partnership for Electric Power Systems), the GHGRP-Only reported emissions, and the non-reporting
7 utilities' emissions (determined using the regression equations).
8 1990 through 2015 Emissions from Manufacture of Electrical Equipment
9 The 1990 to 2015 emission estimates for original equipment manufacturers (OEMs) were derived by assuming that
10 manufacturing emissions equal some percent of the quantity of SF6 provided with new equipment(described below).
11 The 2011 to 2015 emission estimates for OEMs were obtained from GHGRP Subpart SS emissions, as well as
12 assumptions on the percent share of emissions from GHGRP reporters. The quantity of SF6 provided with new
13 equipment was estimated based on statistics compiled by the National Electrical Manufacturers Association
14 (NEMA). These statistics were provided for 1990 to 2000; the quantities of SF6 provided with new equipment for
15 2001 to 2015 were estimated using Partner reported data and the total industry SF6 nameplate capacity estimate
16 (194.6 MMT CO2 Eq. in 2015). Specifically, the ratio of new nameplate capacity to total nameplate capacity of a
17 subset of Partners for which new nameplate capacity data was available from 1999 to 2015 was calculated. These
18 ratios were then multiplied by the total industry nameplate capacity estimate for each year to derive the amount of
19 SF6 provided with new equipment for the entire industry. Emission rates for the time period 1990 to 2000 was
20 assumed to be 10 percent of the quantity of SF6 provided with new equipment. The 10 percent emission rate is the
21 average of the "ideal" and "realistic" manufacturing emission rates (4 percent and 17 percent, respectively)
22 identified in a paper prepared under the auspices of the International Council on Large Electric Systems (CIGRE) in
23 February 2002 (O'Connell et al. 2002). For the time period 2001 to 2010, the emission rates were estimated by
24 interpolating the emissions rates between 2000 and 2011. The emission rate for 2011 was estimated using the SF6
25 emissions from Subpart SS reporters, and an assumption that these reported emissions account for a conservative
26 estimate of 50 percent of the total emissions from OEMs. The emissions were divided by the total quantity of SF6
27 provided with new equipment in 2011 to get an emission rate. Emissions from 2011 to 2015 are obtained from
28 GHGRP Subpart SS emissions and scaled up to assume that GHGRP Subpart SS reporters constitute only 50 percent
29 of the emissions.
30 Uncertainty and Time-Series Consistency - TO BE UPDATED
31 FOR FINAL INVENTORY REPORT
32 To estimate the uncertainty associated with emissions of SF6 from Electrical Transmission and Distribution
33 uncertainties associated with four quantities were estimated: (1) emissions from Partners, (2) emissions from
34 GHGRP-Only Reporters, (3) emissions from Non-Reporters, and (4) emissions from manufacturers of electrical
35 equipment. A Monte Carlo analysis was then applied to estimate the overall uncertainty of the emissions estimate.
36 Total emissions from the SF6 Emission Reduction Partnership include emissions from both reporting (through the
37 Partnership or GHGRP) and non-reporting Partners. For reporting Partners, individual Partner-reported SF6 data was
38 assumed to have an uncertainty of 10 percent. Based on a Monte Carlo analysis, the cumulative uncertainty of all
39 Partner-reported data was estimated to be 4.7 percent. The uncertainty associated with extrapolated or interpolated
40 emissions from non-reporting Partners was assumed to be 20 percent.
41 For GHGRP-Only Reporters, reported SF6 data was assumed to have an uncertainty of 20 percent.83 Based on a
42 Monte Carlo analysis, the cumulative uncertainty of all GHGRP-Only reported data was estimated to be 6.1 percent.
43 There are two sources of uncertainty associated with the regression equations used to estimate emissions in 2013
44 from Non-Reporters: (1) uncertainty in the coefficients (as defined by the regression standard error estimate), and
83 Uncertainty is assumed to be higher for the GHGRP-Only category, because 2011 is the first year that those utilities have
reported to EPA.
Industrial Processes and Product Use 4-113
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1 (2) the uncertainty in total transmission miles for Non-Reporters. Uncertainties were also estimated regarding (1) the
2 quantity of SF6 supplied with equipment by equipment manufacturers, which is projected from Partner provided
3 nameplate capacity data and industry SF6 nameplate capacity estimates, and (2) the manufacturers' SF6 emissions
4 rate.
5 The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 4-103. Electrical
6 Transmission and Distribution SF6 emissions were estimated to be between 4.6 and 6.9 MMT CO2 Eq. at the 95
7 percent confidence level. This indicates a range of approximately 17 percent below and 23 percent above the
8 emission estimate of 5.6 MMT CO2 Eq.
9 Table 4-103: Approach 2 Quantitative Uncertainty Estimates for SF6 Emissions from
10 Electrical Transmission and Distribution (MMT CO2 Eq. and Percent)
2014 Emission
Source
Gas
Estimate
Uncertainty Range Relative to 2014 Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Lower Upper
Bound Bound
Bound Bound
Electrical Transmission
and Distribution
SFo
5.6
4.6 6.9
-17% +23%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
11 In addition to the uncertainty quantified above, there is uncertainty associated with using global SF6 sales data to
12 estimate U.S. emission trends from 1990 through 1999. However, the trend in global emissions implied by sales of
13 SF6 appears to reflect the trend in global emissions implied by changing SF6 concentrations in the atmosphere. That
14 is, emissions based on global sales declined by 29 percent between 1995 and 1998 (RAND 2004), and emissions
15 based on atmospheric measurements declined by 17 percent over the same period (Levin et al. 2010).
16 Several pieces of evidence indicate that U.S. SF6 emissions were reduced as global emissions were reduced. First,
17 the decreases in sales and emissions coincided with a sharp increase in the price of SF6 that occurred in the mid-
18 1990s and that affected the United States as well as the rest of the world. A representative from DILO, a major
19 manufacturer of SF6 recycling equipment, stated that most U.S. utilities began recycling rather than venting SF6
20 within two years of the price rise. Finally, the emissions reported by the one U.S. utility that reported its emissions
21 for all the years from 1990 through 1999 under the Partnership showed a downward trend beginning in the mid-
22 1990s.
23 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
24 through 2014. Details on the emission trends through time are described in more detail in the Methodology section,
25 above.
26 Recalculations Discussion
27 The historical emissions estimated for this source category have undergone some revisions. SF6 emission estimates
28 for the period 1990 through 2014 were updated relative to the previous report based on revisions to interpolated and
29 extrapolated non-reported Partner data.84 For the current Inventory, historical estimates for the period 2011 through
30 2014 were also updated relative to the previous report based on revisions to reported historical data in EPA's
31 GHGRP. The regression coefficients to estimate emissions from non-reporting utilities was adjusted between the
32 years 1999 and 2014 due to methodology updates,85 and as a result, there were changes to the emissions from non-
33 reporting utilities. Emissions estimates for OEMs were updated to incorporate Subpart SS reported emissions and
34 assumptions on the percent share of emissions from EPA's GHGRP reporters.
84 Hie earlier year estimates within the time series (i.e., 1990-1998) were updated based on revisions to the 1999 U.S. emission
estimate because emissions for 1990-1998 are estimated by multiplying a series of annual factors by the estimated U.S. emissions
of SFo from electric power systems in 1999 (see Methodology section).
85 This year, a statistical test was performed to evaluate the difference between "large" and "non-large" reporters. It was
determined that there is no statistically significant between the two groups. The regression analysis conducted for estimating non-
reporter emissions estimates was updated to remove the "large" versus "non-large" designation.
4-114 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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30
As a result of the recalculations, SF6 emissions from electrical transmission and distribution decreased by 14 percent
for 2014 relative to the previous report. On average, the change in SF6 emission estimates for the entire time series is
approximately 14 percent per year.
Planned Improvements
EPA is continuing research to improve the methodology for estimating non-reporter nameplate capacity. The current
methodology uses Beginning of Year Nameplate Capacity and the Net Increase in Nameplate Capacity for the
GHGRP-only reporters, which aggregates a small portion of hermetically sealed equipment and high-voltage
equipment. More research is needed to determine the impact of removing the Net Increase in Nameplate Capacity.
4.25 Nitrous Oxide from Product Uses flPCC
Source Category 2G3)
Nitrous oxide (N20) is a clear, colorless, oxidizing liquefied gas, with a slightly sweet odor which is used in a wide
variety of specialized product uses and applications. The amount of N20 that is actually emitted depends upon the
specific product use or application.
There are a total of three N20 production facilities currently operating in the United States (Ottinger 2014). Nitrous
oxide is primarily used in carrier gases with oxygen to administer more potent inhalation anesthetics for general
anesthesia, and as an anesthetic in various dental and veterinary applications. The second main use of N20 is as a
propellant in pressure and aerosol products, the largest application being pressure-packaged whipped cream. Small
quantities of N20 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 N20 in 2015 was approximately 15 kt (see Table 4-104).
Table 4-104: N2O Production (kt)
Year kt
1990 16
2005 15
2011 15
2012 15
2013 15
2014 15
2015 15
Nitrous oxide emissions were 4.2 MMT C02 Eq. (14 kt N20) in 2015 (see Table 4-105). Production of N20
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 N20. The use of
N20 as a propellant for whipped cream has also stabilized due to the increased popularity of cream products
packaged in reusable plastic tubs (Heydorn 1997).
Industrial Processes and Product Use 4-115
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1
Table 4-105: N2O Emissions from N2O Product Usage (MMT CO2 Eq. and kt)
Year MMT CO2 Eq. kt
1000 4.2 14
2005 4.2 14
2011 4.2 14
2012 4.2 14
2013 4.2 14
2014 4.2 14
2015 4.2 14
2 Methodology
3 Emissions from N20 product uses were estimated using the following equation:
4 Epu = x $a x ERa)
a
5 where,
6
Epu
N20 emissions from product uses, metric tons
7
P
Total U.S. production of N20, metric tons
8
a
specific application
9
sa
Share of N20 usage by application a
10
ERa
Emission rate for application a, percent
11 The share of total quantity of N20 usage by end-use represents the share of national N20 produced that is used by
12 the specific subcategory (e.g., anesthesia, food processing). In 2015, the medical/dental industry used an estimated
13 86.5 percent of total N20 produced, followed by food processing propellants at 6.5 percent. All other categories
14 combined used the remainder of the N20 produced. This subcategory breakdown has changed only slightly over the
15 past decade. For instance, the small share of N20 usage in the production of sodium azide has declined significantly
16 during the 1990s. Due to the lack of information on the specific time period of the phase-out in this market
17 subcategory, most of the N20 usage for sodium azide production is assumed to have ceased after 1996, with the
18 majority of its small share of the market assigned to the larger medical/dental consumption subcategory (Heydorn
19 1997). The N20 was allocated across the following categories: medical applications, food processing propellant, and
20 sodium azide production (pre-1996). A usage emissions rate was then applied for each sector to estimate the amount
21 of N20 emitted.
22 Only the medical/dental and food propellant subcategories were estimated to release emissions into the atmosphere,
23 and therefore these subcategories were the only usage subcategories with emission rates. For the medical/dental
24 subcategory, due to the poor solubility of N20 in blood and other tissues, none of the N20 is assumed to be
25 metabolized during anesthesia and quickly leaves the body in exhaled breath. Therefore, an emission factor of 100
26 percent was used for this subcategory (IPCC 2006). For N20 used as a propellant in pressurized and aerosol food
27 products, none of the N20 is reacted during the process and all of the N20 is emitted to the atmosphere, resulting in
28 an emission factor of 100 percent for this subcategory (IPCC 2006). For the remaining subcategories, all of the N20
29 is consumed/reacted during the process, and therefore the emission rate was considered to be zero percent (Tupman
30 2002).
31 The 1990 through 1992 N20 production data were obtained from SRI Consulting's Nitrous Oxide, North America
32 report (Heydorn 1997). Nitrous oxide production data for 1993 through 1995 were not available. Production data for
33 1996 was specified as a range in two data sources (Heydorn 1997; Tupman 2002). In particular, for 1996, Heydorn
34 (1997) estimates N20 production to range between 13.6 and 18.1 thousand metric tons. Tupman (2003) provided a
35 narrower range (15.9 to 18.1 thousand metric tons) for 1996 that falls within the production bounds described by
36 Heydorn (1997). Tupman (2003) data are considered more industry-specific and current. Therefore, the midpoint of
37 the narrower production range was used to estimate N20 emissions for years 1993 through 2001 (Tupman 2003).
4-116 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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19
20
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23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
The 2002 and 2003 N20 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 N20 production to range between 13.6 and 15.9 thousand metric tons. Due to the
unavailability of data, production estimates for years 2004 through 2015 were held constant at the 2003 value.
The 1996 share of the total quantity of N20 used by each subcategory was obtained from SRI Consulting's Nitrous
Oxide, North America report (Heydorn 1997). The 1990 through 1995 share of total quantity of N20 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 N20 usage by sector was obtained from communication with a N20 industry expert (Tupman 2002).
The 2002 and 2003 share of total quantity of N20 usage by sector was obtained from CGA (2002, 2003). Due to the
unavailability of data, the share of total quantity of N20 usage data for years 2004 through 2015 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 N20 industry expert
(Tupman 2002). The emissions rate for all other subcategories was obtained from communication with a N20
industry expert (Tupman 2002). The emissions rate for the medical/dental subcategory was obtained from the 2006
IPCC Guidelines.
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REVIEW
The overall uncertainty associated with the 2015 N20 emission estimate from N20 product usage was calculated
using the 2006 IPCC Guidelines (2006) Approach 2 methodology. Uncertainty associated with the parameters used
to estimate N20 emissions include production data, total market share of each end use, and the emission factors
applied to each end use, respectively.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 4-106. Nitrous oxide
emissions from N20 product usage were estimated to be between 3.2 and 5.2 MMT C02 Eq. at the 95 percent
confidence level. This indicates a range of approximately 24 percent below to 24 percent above the emission
estimate of 4.2 MMT C02 Eq.
Table 4-106: Approach 2 Quantitative Uncertainty Estimates for N2O Emissions from N2O
Product Usage (MMT CO2 Eq. and Percent)
Source Gas
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
N2O from Product Uses N2O
4.2
3.2
5.2
-24%
+24%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time series to ensure consistency in emissions from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Planned Improvements
Pending resources, planned improvements include a continued evaluation of alternative production statistics for
cross verification a reassessment of N20 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 N20. Additionally, planned improvements include considering imports and exports of
N20 for product uses. Finally, for future Inventories EPA will examine data from EPA's GHGRP to improve the
emission estimates for the N20 product use subcategory. Particular attention will be made to ensure aggregated
information can be published without disclosing CBI and time series consistency, as the facility-level reporting data
from EPA's GHGRP are not available for all inventory years as required in this Inventory. EPA is still assessing the
Industrial Processes and Product Use 4-117
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1 possibility of incorporating aggregated CBI data to estimate emissions; however, this planned improvement is still in
2 development and not incorporated in the current inventory report.
3 4.26 Industrial Processes and Product Use
4 Sources of Indirect Greenhouse Gases
5 In addition to the main greenhouse gases addressed above, many industrial processes can result in emissions of
6 various ozone precursors (i.e., indirect greenhouse gases). As some of industrial applications also employ thermal
7 incineration as a control technology, combustion byproducts, such as carbon monoxide (CO) and nitrogen oxides
8 (NOx), are also reported with this source category. Non-CH4 volatile organic compounds (NMVOCs), commonly
9 referred to as "hydrocarbons," are the primary gases emitted from most processes employing organic or petroleum
10 based products, and can also result from the product storage and handling. Accidental releases of greenhouse gases
11 associated with product use and handling can constitute major emissions in this category. In the United States,
12 emissions from product use are primarily the result of solvent evaporation, whereby the lighter hydrocarbon
13 molecules in the solvents escape into the atmosphere. The major categories of product uses include: degreasing,
14 graphic arts, surface coating, other industrial uses of solvents (e.g., electronics), dry cleaning, and non-industrial
15 uses (e.g., uses of paint thinner). Product usage in the United States also results in the emission of small amounts of
16 hydrofluorocarbons (HFCs) and hydrofluoroethers (HFEs), which are included under Substitution of Ozone
17 Depleting Substances in this chapter.
18 Total emissions of NOx, CO, and NMVOCs from non-energy industrial processes and product use from 1990 to
19 2015 are reported in Table 4-107.
20 Table 4-107: NOx, CO, and NMVOC Emissions from Industrial Processes and Product Use (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
NOx
592
572
452
443
434
424
424
Industrial Processes
Other Industrial Processes
343
43"
320
315
311
306
306
Metals Processing
88
60
64
65
66
68
68
Chemical and Allied Product
Manufacturing
152
55
47
45
44
42
42
Storage and Transport
3
15
18
14
10
5
5
Miscellaneous3
5
2
3
3
3
2
2
Product Uses
Surface Coating
1
1
1
1
1
1
Graphic Arts
+
0
0
0
0
0
0
Degreasing
+
0
0
0
0
0
0
Dry Cleaning
+
0
0
0
0
0
0
Other Industrial Processesb
+
0
0
0
0
0
0
Non-Industrial Processes0
+
0
0
0
0
0
0
Other
NA
0
0
0
0
0
0
CO
4,129
1,557
1,229
1,246
1,262
1,273
1,273
Industrial Processes
Metals Processing
2,395
752
695
647
600
553
553
Other Industrial Processes
487
484
306
388
470
551
551
Chemical and Allied Product
Manufacturing
1,073
189
152
140
128
117
117
Miscellaneous3
101
32
51
49
48
42
42
Storage and Transport
69
9"
25
19
14
9
9
Product Uses
Surface Coating
+
2
2
2
2
1
1
Other Industrial Processes'5
4
0
0
0
0
0
0
Dry Cleaning
+
0
0
0
0
0
0
Degreasing
+
0
0
0
0
0
0
4-118 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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2
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4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
Graphic Arts
+
0
0
0
0
0
0
Non-Industrial Processes0
+
0
0
0
0
0
0
Other
NA
0
0
0
0
0
0
NMVOCs
7,638
5,849
3,929
3,861
3,793
3,723
3,723
Industrial Processes
Storage and Transport
1,352
1,308
947
837
727
618
618
Other Industrial Processes
364
414
298
306
313
320
320
Chemical and Allied Product
Manufacturing
575
213
76
73
70
68
68
Metals Processing
111
45
31
29
28
26
26
Miscellaneous3
20
r
27
27
26
23
23
Product Uses
Surface Coating
2,289
1,578
1,045
1,061
1,077
1,093
1,093
Non-Industrial Processes0
1,724
1,446
957
972
987
1,002
1,002
Degreasing
675
280
186
189
191
194
194
Dry Cleaning
195
230
152
155
157
160
160
Graphic Arts
249
194
128
130
132
134
134
Other Industrial Processes'5
85
88
58
59
60
61
61
Other
+
36
24
24
24
25
25
+ Does not exceed 0.5 kt
NA (Not Available)
a Miscellaneous includes the following categories: catastrophic/accidental release, other combustion, health
services, cooling towers, and fugitive dust. It does not include agricultural fires or slash/prescribed burning,
which are accounted for under the Field Burning of Agricultural Residues source.
b Includes rubber and plastics manufacturing, and other miscellaneous applications.
c Includes cutback asphalt, pesticide application adhesives, consumer solvents, and other miscellaneous
applications.
Note: Totals may not sum due to independent rounding.
Methodology
Emission estimates for 1990 through 2015 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2016), and disaggregated based on EPA (2003). Data were
collected for emissions of CO, NOx, volatile organic compounds (VOCs), and sulfur dioxide (SO2) from metals
processing, chemical manufacturing, other industrial processes, transport and storage, and miscellaneous sources.
Emission estimates for 2012 and 2013 for non-electric generating units (EGU) are held constant from 2011 in EPA
(2016). Emission estimates for 2012 and 2013 for non-mobile sources are recalculated emissions by interpolation
from 2015 in EPA (2016). Emissions were calculated either for individual source categories or for many categories
combined, using basic activity data (e.g., the amount of raw material processed or the amount of solvent purchased)
as an indicator of emissions. National activity data were collected for individual categories from various agencies.
Depending on the category, these basic activity data may include data on production, fuel deliveries, raw material
processed, etc.
Emissions for product use were calculated by aggregating product use data based on information relating to product
uses from different applications such as degreasing, graphic arts, etc. Emission factors for each consumption
category were then applied to the data to estimate emissions. For example, emissions from surface coatings were
mostly due to solvent evaporation as the coatings solidify. By applying the appropriate product-specific emission
factors to the amount of products used for surface coatings, an estimate of NMVOC emissions was obtained.
Emissions of CO and NOx under product use result primarily from thermal and catalytic incineration of solvent-
laden gas streams from painting booths, printing operations, and oven exhaust.
Activity data were used in conjunction with emission factors, which together relate the quantity of emissions to the
activity. Emission factors are generally available from the EPA's Compilation of Air Pollutant Emission Factors,
AP-42 (EPA 1997). The EPA currently derives the overall emission control efficiency of a source category from a
variety of information sources, including published reports, the 1985 National Acid Precipitation and Assessment
Program emissions inventory, and other EPA databases.
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i Uncertainty and Time-Series Consistency
2 Uncertainties in these estimates are partly due to the accuracy of the emission factors and activity data used. A
3 quantitative uncertainty analysis was not performed.
4 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
5 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
6 above.
7
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5. Agriculture
Agricultural activities contribute directly to emissions of greenhouse gases through a variety of processes. This
chapter provides an assessment of non-carbon dioxide (CO2) 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, as well as CO2 emissions from liming and urea fertilization (see Figure
5-1). Additional CO2 emissions and removals from agriculture-related land-use and management activities, such as
cultivation of cropland and conversion of grassland to cropland, are presented in the Land Use, Land-Use Change,
and Forestry chapter. Carbon dioxide emissions from on-fann energy use are accounted for in the Energy chapter.
Figure 5-1: 2015 Agriculture Chapter Greenhouse Gas Emission Sources (MMT CO2 Eq.)
Agricultural Soil
Management
Enteric Fermentation
Manure Management
Rice Cultivation
Urea Fertilization
Liming
Field Burning of | < 0.5
Agricultural Residues
0
251
Agriculture as a Portion of all Emissions
7.9%
20
40
60
80 100
MMT CO* Eq.
120
140
160 180
In 2015, the Agriculture sector was responsible for emissions of 522.3 MMT CO2 Eq.,1 or 7.9 percent of total U.S.
greenhouse gas emissions. Carbon dioxide, methane (CH4), and nitrous oxide (N20) were the primary greenhouse
gases emitted by agricultural activities. Methane emissions from enteric fermentation and manure management
represent 25.4 percent and 10.1 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. Emissions of N20 by agricultural soil management
through activities such as fertilizer application and other agricultural practices that increased nitrogen availability in
the soil were the largest source of U.S. N20 emissions, accounting for 75.0 percent. Manure management and field
burning of agricultural residues were also small sources of N20 emissions. Urea fertilization and liming each
accounted for 0.1 percent of total CO2 emissions from anthropogenic activities.
1 Following the current reporting requirements under the United Nations Framework Convention on Climate Change (UNFCCC),
this Inventory report presents CO2 equivalent values based on the IPCC Fourth Assessment Report (AR4) GWP values. See the
Introduction chapter for more information.
Agriculture 5-1
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1 Table 5-1 and Table 5-2 present emission estimates for the Agriculture sector. Between 1990 and 2015, CO2 and
2 CH4 emissions from agricultural activities increased by 24.8 percent and 12.3 percent, respectively, while N20
3 emissions fluctuated from year to year, but overall decreased by 0.6 percent.
4 Table 5-1: Emissions from Agriculture (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
7.1
7.9
8.0
10.2
8.4
8.4
8.8
Urea Fertilization
2.4
3
4.1
4.3
4.5
4.8
5.0
Liming
4.7
4.3
3.9
6.0
3.9
3.6
3.8
CH4
217.6
242.1
246.3
244.0
240.4
238.7
244.3
Enteric Fermentation
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Manure Management
37.2
56.3
63.0
65.6
63.3
62.9
66.3
Rice Cultivation
16.0
16.7
14.1
11.3
11.3
11.4
11.2
Field Burning of Agricultural Residues
0.2
0.2
0.3
0.3
0.3
0.3
0.3
N2O
270.6
276.4
287.6
271.7
268.1
267.6
269.1
Agricultural Soil Management
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Manure Management
14.0
16.5
17.4
17.5
17.5
17.5
17.7
Field Burning of Agricultural Residues
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Total
495.3
526.4
541.9
525.9
516.9
514.7
522.3
Note: Totals may not sum due to independent rounding.
ible 5-2: Emissions from Agriculture (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CO2
7,084
7,854
7,970
10,245
8,411
8,391
8,842
Urea Fertilization
2,417
3,504
4,097
4,267
4,504
4,781
5,032
Liming
4,667
4,349
3,873
5,978
3,907
3,609
3,810
CH4
8,702
9,684
9,851
9,760
9,615
9,548
9,772
Enteric Fermentation
6,566
6,755
6,757
6,670
6,619
6,567
6,661
Manure Management
1,486
2,254
2,519
2,625
2,530
2,514
2,651
Rice Cultivation
641
667
564
453
454
456
449
Field Burning of Agricultural Residues
9
8
11
11
11
11
11
N2O
908
928
965
912
900
898
903
Agricultural Soil Management
861
872
906
853
841
839
843
Manure Management
47
55
58
59
59
59
59
Field Burning of Agricultural Residues
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
Note: Totals may not sum due to independent rounding.
6
7 5.1 Enteric Fermentation (IPCC Source
s Category 3A)
9 Methane is produced as part of normal digestive processes in animals. During digestion, microbes resident in an
10 animal's digestive system ferment food consumed by the animal. This microbial fermentation process, referred to as
11 enteric fermentation, produces CH4 as a byproduct, which can be exhaled or eructated by the animal. The amount of
12 CH4 produced and emitted by an individual animal depends primarily upon the animal's digestive system, and the
13 amount and type of feed it consumes.
14 Ruminant animals (e.g., cattle, buffalo, sheep, goats, and camels) are the major emitters of CH4 because of their
15 unique digestive system. Ruminants possess a rumen, or large "fore-stomach," in which microbial fermentation
16 breaks down the feed they consume into products that can be absorbed and metabolized. The microbial fermentation
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1 that occurs in the rumen enables them to digest coarse plant material that non-ruminant animals cannot. Ruminant
2 animals, consequently, have the highest CH4 emissions per unit of body mass among all animal types.
3 Non-ruminant animals (e.g., swine, horses, and mules and asses) also produce CH4 emissions through enteric
4 fermentation, although this microbial fermentation occurs in the large intestine. These non-ruminants emit
5 significantly less CH4 on a per-animal-mass basis than ruminants because the capacity of the large intestine to
6 produce CH4 is lower.
7 In addition to the type of digestive system, an animal's feed quality and feed intake also affect CH4 emissions. In
8 general, lower feed quality and/or higher feed intake leads to higher CH4 emissions. Feed intake is positively
9 correlated to animal size, growth rate, level of activity and production (e.g., milk production, wool growth,
10 pregnancy, or work). Therefore, feed intake varies among animal types as well as among different management
11 practices for individual animal types (e.g., animals in feedlots or grazing on pasture).
12 Methane emission estimates from enteric fermentation are provided in Table 5-3 and Table 5-4. Total livestock CH4
13 emissions in 2015 were 166.5 MMT CChEq. (6,661 kt). Beef cattle remain the largest contributor of CH4 emissions
14 from enteric fermentation, accounting for 71 percent in 2015. Emissions from dairy cattle in 2015 accounted for 26
15 percent, and the remaining emissions were from horses, sheep, swine, goats, American bison, mules and asses.
16 Table 5-3: ChU Emissions from Enteric Fermentation (MMT CO2 Eq.)
Livestock Type
1990
2005
2011
2012
2013
2014
2015
Beef Cattle
119.1
125.2
121.8
119.1
118.0
116.5
118.1
Dairy Cattle
39.4
37.6
41.1
41.7
41.6
42.0
42.6
Swine
2.0
2.3
2.5
2.5
2.5
2.4
2.6
Horses
1.0
1.7
1.7
1.6
1.6
1.6
1.5
Sheep
2.3
1.2
1.1
1.1
1.1
1.0
1.1
Goats
0.3
0.4
0.3
0.3
0.3
0.3
0.3
American Bison
0.1
0.4
0.3
0.3
0.3
0.3
0.3
Mules and Asses
+
0.1
0.1
0.1
0.1
0.1
0.1
Total
164.2
168.9
168.9
166.7
165.5
164.2
166.5
Note: Totals may not sum due to independent rounding.
+ Does not exceed 0.05 MMT CO2 Eq.
Table 5-4: ChU Emissions from Enteric Fermentation (kt)
Livestock Type
1990
2005
2011
2012
2013
2014
2015
Beef Cattle
4,763
5,007
4,873
4,763
4,722
4,660
4,724
Dairy Cattle
1,574
1,503
1,645
1,670
1,664
1,679
1,706
Swine
81
92
98
100
98
96
102
Horses
40
70
67
65
64
62
61
Sheep
91
49
44
43
43
42
42
Goats
13
14
14
13
13
12
12
American Bison
4
17
14
13
13
12
13
Mules and Asses
1
2
3
3
3
3
3
Total
6,566
6,755
6,757
6,670
6,619
6,567
6,661
Note: Totals may not sum due to independent rounding.
19 From 1990 to 2015, emissions from enteric fermentation have increased by 1.5 percent. While emissions generally
20 follow trends in cattle populations, over the long term there are exceptions as population decreases have been
21 coupled with production increases or minor decreases. For example, beef cattle emissions decreased 0.8 percent
22 from 1990 to 2015, while beef cattle populations actually declined by 7 percent and beef production increased
23 (USDA 2016), and while dairy emissions increased 8.3 percent over the entire time series, the population has
24 declined by 4 percent and milk production increased 40 percent (USDA 2016). This trend indicates that while
25 emission factors per head are increasing, emission factors per unit of product are going down.
26 Generally, from 1990 to 1995 emissions from beef increased and then decreased from 1996 to 2004. These trends
27 were mainly due to fluctuations in beef cattle populations and increased digestibility of feed for feedlot cattle. Beef
28 cattle emissions generally increased from 2004 to 2007, as beef populations underwent increases and an extensive
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literature review indicated a trend toward a decrease in feed digestibility for those years. Beef cattle emissions
decreased again from 2008 to 2015 as populations again decreased. Emissions from dairy cattle generally trended
downward from 1990 to 2004, along with an overall dairy population decline during the same period. Similar to beef
cattle, dairy cattle emissions rose from 2004 to 2007 due to population increases and a decrease in feed digestibility
(based on an analysis of more than 350 dairy cow diets). Dairy cattle emissions have continued to trend upward
since 2007, in line with dairy population increases. Regarding trends in other animals, populations of sheep have
steadily declined, with an overall decrease of 54 percent since 1990. Horse populations are 56 percent greater than
they were in 1990, but their numbers have been declining by about 2 percent annually since 2007. Goat populations
increased by about 20 percent through 2007 but have since dropped below 1990 numbers, while swine populations
have increased 19 percent since 1990. The population of American bison more than tripled over the 1990-2015 time
period, while mules and asses have more than quadrupled.
Methodology
Livestock enteric fermentation 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 enteric fermentation CH4 emissions from livestock in the United States. A more detailed
methodology (i.e., IPCC Tier 2) was therefore applied to estimate emissions for all cattle. Emission estimates for
other domesticated animals (horses, sheep, swine, goats, American bison, and mules and asses) were handled using a
less detailed approach (i.e., IPCC Tier 1).
While the large diversity of animal management practices cannot be precisely characterized and evaluated,
significant scientific literature exists that provides the necessary data to estimate cattle emissions using the IPCC
Tier 2 approach. The Cattle Enteric Fermentation Model (CEFM), developed by EPA and used to estimate cattle
CH4 emissions from enteric fermentation, incorporates this information and other analyses of livestock population,
feeding practices, and production characteristics.
National cattle population statistics were disaggregated into the following cattle sub-populations:
• Dairy Cattle
o Calves
o Heifer Replacements
o Cows
• Beef Cattle
o Calves
o Heifer Replacements
o Heifer and Steer Stackers
o Animals in Feedlots (Heifers and Steer)
o Cows
o Bulls
Calf birth rates, end-of-year population statistics, detailed feedlot placement information, and slaughter weight data
were used to create a transition matrix that models cohorts of individual animal types and their specific emission
profiles. The key variables tracked for each of the cattle population categories are described in Annex 3.10. These
variables include performance factors such as pregnancy and lactation as well as average weights and weight gain.
Annual cattle population data were obtained from the U.S. Department of Agriculture's (USDA) National
Agricultural Statistics Service (NASS) QuickStats database (USDA 2016).
Diet characteristics were estimated by region for dairy, foraging beef, and feedlot beef cattle. These diet
characteristics were used to calculate digestible energy (DE) values (expressed as the percent of gross energy intake
digested by the animal) and CH4 conversion rates (Ym) (expressed as the fraction of gross energy converted to CH4)
for each regional population category. The IPCC recommends Ym ranges of 3.0±1.0 percent for feedlot cattle and
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6.5±1.0 percent for other well-fed cattle consuming temperate-climate feed types (IPCC 2006). Given the
availability of detailed diet information for different regions and animal types in the United States, DE and Ym
values unique to the United States were developed. The diet characterizations and estimation of DE and Ym values
were based on information from state agricultural extension specialists, a review of published forage quality studies
and scientific literature, expert opinion, and modeling of animal physiology.
The diet characteristics for dairy cattle were based on Donovan (1999) and an extensive review of nearly 20 years of
literature from 1990 through 2009. Estimates of DE were national averages based on the feed components of the
diets observed in the literature for the following year groupings: 1990-1993, 1994-1998, 1999-2003, 2004-2006,
2007, and 2008 onward.2 Base year Ym values by region were estimated using Donovan (1999). As described in
ERG (2016), a ruminant digestion model (COWPOLL, as selected in Kebreab et al. 2008) was used to evaluate Ym
for each diet evaluated from the literature, and a function was developed to adjust regional values over time based
on the national trend. Dairy replacement heifer diet assumptions were based on the observed relationship in the
literature between dairy cow and dairy heifer diet characteristics.
For feedlot animals, the DE and Ym values used for 1990 were recommended by Johnson (1999). Values for DE and
Ym for 1991 through 1999 were linearly extrapolated based on the 1990 and 2000 data. DE and Ym values for 2000
onwards were based on survey data in Galyean and Gleghorn (2001) and Vasconcelos and Galyean (2007).
For grazing beef cattle, Ym values were based on Johnson (2002), DE values for 1990 through 2006 were based on
specific diet components estimated from Donovan (1999), and DE values from 2007 onwards were developed from
an analysis by Archibeque (2011), based on diet information in Preston (2010) and USDA-APHIS:VS (2010).
Weight and weight gains for cattle were estimated from Holstein (2010), Doren et al. (1989), Enns (2008), Lippke et
al. (2000), Pinchack et al. (2004), Platter et al. (2003), Skogerboe et al. (2000), and expert opinion. See Annex 3.10
for more details on the method used to characterize cattle diets and weights in the United States.
Calves younger than 4 months are not included in emission estimates because calves consume mainly milk and the
IPCC recommends the use of a Ym of zero for all juveniles consuming only milk. Diets for calves aged 4 to 6
months are assumed to go through a gradual weaning from milk decreasing to 75 percent at 4 months, 50 percent at
age 5 months, and 25 percent at age 6 months. The portion of the diet made up with milk still results in zero
emissions. For the remainder of the diet, beef calf DE and Ym are set equivalent to those of beef replacement heifers,
while dairy calf DE is set equal to that of dairy replacement heifers and dairy calf Ym is provided at 4 and 7 months
of age by Soliva (2006). Estimates of Ym for 5 and 6 month old dairy calves are linearly interpolated from the values
provided for 4 and 7 months.
To estimate CH4 emissions, the population was divided into state, age, sub-type (i.e., dairy cows and replacements,
beef cows and replacements, heifer and steer stackers, heifers and steers in feedlots, bulls, beef calves 4 to 6 months,
and dairy calves 4 to 6 months), and production (i.e., pregnant, lactating) groupings to more fully capture differences
in CH4 emissions from these animal types. The transition matrix was used to simulate the age and weight structure
of each sub-type on a monthly basis in order to more accurately reflect the fluctuations that occur throughout the
year. Cattle diet characteristics were then used in conjunction with Tier 2 equations from IPCC (2006) to produce
CH4 emission factors for the following cattle types: dairy cows, beef cows, dairy replacements, beef replacements,
steer stackers, heifer stackers, steer feedlot animals, heifer feedlot animals, bulls, and calves. To estimate emissions
from cattle, monthly population data from the transition matrix were multiplied by the calculated emission factor for
each cattle type. More details are provided in Annex 3.10.
Emission estimates for other animal types were based on average emission factors representative of entire
populations of each animal type. Methane emissions from these animals accounted for a minor portion of total CH4
emissions from livestock in the United States from 1990 through 2015. Additionally, the variability in emission
factors for each of these other animal types (e.g., variability by age, production system, and feeding practice within
each animal type) is less than that for cattle. Annual livestock population data for sheep; swine; goats; horses; mules
and asses; and American bison were obtained for available years fromUSDA NASS (USDA 2016). Horse, goat and
mule and ass population data were available for 1987, 1992, 1997, 2002, 2007, and 2012 (USDA 1992, 1997, 2016);
the remaining years between 1990 and 2015 were interpolated and extrapolated from the available estimates (with
the exception of goat populations being held constant between 1990 and 1992). American bison population
2 Due to inconsistencies in the 2003 literature values, the 2002 values were used for 2003, as well.
Agriculture 5-5
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estimates were available from USDA for 2002, 2007, and 2012 (USDA 2016) and from the National Bison
Association (1999) for 1990 through 1999. Additional years were based on observed trends from the National Bison
Association (1999), interpolation between known data points, and extrapolation beyond 2012, as described in more
detail in Annex 3.10. Methane emissions from sheep, goats, swine, horses, American bison, and mules and asses
were estimated by using emission factors utilized in Crutzen et al. (1986, cited in IPCC 2006). These emission
factors are representative of typical animal sizes, feed intakes, and feed characteristics in developed countries. For
American bison the emission factor for buffalo was used and adjusted based on the ratio of live weights to the 0.75
power. The methodology is the same as that recommended by IPCC (2006).
See Annex 3.10 for more detailed information on the methodology and data used to calculate CH4 emissions from
enteric fermentation.
Uncertainty and Time-Series Consistency
A quantitative uncertainty analysis for this source category was performed using the IPCC-recommended Approach
2 uncertainty estimation methodology based on a Monte Carlo Stochastic Simulation technique as described in ICF
(2003). These uncertainty estimates were developed for the 1990 through 2001 Inventory report (i.e., 2003
submission to the UNFCCC). There have been no significant changes to the methodology since that time;
consequently, these uncertainty estimates were directly applied to the 2015 emission estimates in this Inventory
report.
A total of 185 primary input variables (177 for cattle and 8 for non-cattle) were identified as key input variables for
the uncertainty analysis. A normal distribution was assumed for almost all activity- and emission factor-related input
variables. Triangular distributions were assigned to three input variables (specifically, cow-birth ratios for the three
most recent years included in the 2001 model run) to ensure only positive values would be simulated. For some key
input variables, the uncertainty ranges around their estimates (used for inventory estimation) were collected from
published documents and other public sources; others were based on expert opinion and best estimates. In addition,
both endogenous and exogenous correlations between selected primary input variables were modeled. The
exogenous correlation coefficients between the probability distributions of selected activity-related variables were
developed through expert judgment.
The uncertainty ranges associated with the activity data-related input variables were plus or minus 10 percent or
lower. However, for many emission factor-related input variables, the lower- and/or the upper-bound uncertainty
estimates were over 20 percent. The results of the quantitative uncertainty analysis are summarized in Table 5-5.
Based on this analysis, enteric fermentation CH4 emissions in 2015 were estimated to be between 148.2 and 196.5
MMT CO2 Eq. at a 95 percent confidence level, which indicates a range of 11 percent below to 18 percent above the
2015 emission estimate of 166.5 MMT CO2 Eq. Among the individual cattle sub-source categories, beef cattle
account for the largest amount of CH4 emissions, as well as the largest degree of uncertainty in the emission
estimates—due mainly to the difficulty in estimating the diet characteristics for grazing members of this animal
group. Among non-cattle, horses represent the largest percent of uncertainty in the previous uncertainty analysis
because the Food and Agricultural Organization of the United Nations (FAO) population estimates used for horses
at that time had a higher degree of uncertainty than for the USDA population estimates used for swine, goats, and
sheep. The horse populations are now from the same USDA source as the other animal types, and therefore the
uncertainty range around horses is likely overestimated. Cattle calves, American bison, mules and asses were
excluded from the initial uncertainty estimate because they were not included in emission estimates at that time.
Table 5-5: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from Enteric
Fermentation (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3'b'c
(MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Enteric Fermentation
CH4
166.5
148.2 196.5 -11% +18%
a Range of emissions estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
5-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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b Note that the relative uncertainty range was estimated with respect to the 2001 emission estimates from the 2003
submission and applied to the 2015 estimates.
c The overall uncertainty calculated in 2003, and applied to the 2015 emission estimate, did not include uncertainty
estimates for calves, American bison, and mules and asses. Additionally, for bulls the emissions estimate was based
on the Tier 1 methodology. Since bull emissions are now estimated using the Tier 2 method, the uncertainty
surrounding their estimates is likely lower than indicated by the previous uncertainty analysis.
1 Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
2 through 2015. Details on the emission trends through time are described in more detail in the Methodology section.
3 QA/QC and Verification
4 In order to ensure the quality of the emission estimates from enteric fermentation, the IPCC Tier 1 and Tier 2
5 Quality Assurance/Quality Control (QA/QC) procedures were implemented consistent with the U.S. QA/QC plan
6 (EPA 2002). Tier 2 QA procedures included independent peer review of emission estimates. Over the past few
7 years, particular importance has been placed on harmonizing the data exchange between the enteric fermentation
8 and manure management source categories. The current Inventory now utilizes the transition matrix from the CEFM
9 for estimating cattle populations and weights for both source categories, and the CEFM is used to output volatile
10 solids and nitrogen excretion estimates using the diet assumptions in the model in conjunction with the energy
11 balance equations from the IPCC (2006). This approach facilitates the QA/QC process for both of these source
12 categories.
13 Recalculations Discussion
14 For the current Inventory, differences can be seen in emission estimates for years prior to 2015 when compared
15 against the same years in the previous Inventory—from 2008 through 2011, as well as 2014. These recalculations
16 were due to changes made to historical data and corrections made to erroneous formulas in the CEFM. No
17 modifications were made to the methodology.
18 Revisions to input data include the following:
19 • The USDA published minor revisions in several categories that affected historical emissions estimated for cattle
20 for 2008 and subsequent years, including the following:
21 o Calf birth data were revised for 2008 and 2014;
22 o Dairy cow milk production values were revised for several states for 2014;
23 o Slaughter values were revised for 2014 for steers and heifers;
24 • The USDA also revised population estimates for some categories of non-cattle animals, which affected
25 historical emissions estimated for "other" livestock. Changes included:
26 o Revised 2014 populations for market and breeding swine in some states; and
27 o Revised 2013 populations of sheep for some states.
28 In addition to these changes in input data, a miscount of the number of states included in the cattle on feed total for
29 "other states" in 2011 was corrected. This resulted in revised 2011 estimates for feedlot cattle in 19 states.
30 These recalculations had an insignificant impact on the overall emission estimates.
31 Planned Improvements
32 Continued research and regular updates are necessary to maintain an emissions inventory that reflects the current
33 base of knowledge. Future improvements for enteric fermentation could include some of the following options:
34 • Further research to improve the estimation of dry matter intake (as gross energy intake) using data from
35 appropriate production systems;
Agriculture 5-7
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• Updating input variables that are from older data sources, such as beef births by month and beef cow lactation
rates;
• Investigation of the availability of annual data for the DE, Ym, and crude protein values of specific diet and feed
components for foraging and feedlot animals;
• Further investigation on additional sources or methodologies for estimating DE for dairy, given the many
challenges in characterizing dairy diets;
• Further evaluation of the assumptions about weights and weight gains for beef cows, such that trends beyond
2007 are updated, rather than held constant;
• Further evaluation of the estimated weight for dairy cows (i.e., 1,500 lbs) that is based solely on Holstein cows
as mature dairy cow weight is likely slightly overestimated, based on knowledge of the breeds of dairy cows in
the United States;
• Potentially updating to a Tier 2 methodology for other animal types (i.e., sheep, swine, goats, horses);
• Investigation of methodologies and emission factors for including enteric fermentation emission estimates from
poultry;
• Comparison of the current CEFM processing of animal population data to estimates developed using annual
average populations to determine if the model could be simplified to use annual population data; and
• Recent changes that have been implemented to the CEFM warrant an assessment of the current uncertainty
analysis; therefore, a revision of the quantitative uncertainty surrounding emission estimates from this source
category will be initiated.
5.2 Manure Management (IPCC Source
Category 3B)
The treatment, storage, and transportation of livestock manure can produce anthropogenic CH4 and N20 emissions.
Methane is produced by the anaerobic decomposition of manure. Nitrous oxide emissions are produced through both
direct and indirect pathways. Direct N20 emissions are produced as part of the nitrogen (N) cycle through the
nitrification and denitrification of the organic N in livestock dung and urine.3 There are two pathways for indirect
N20 emissions. The first is the result of the volatilization of N in manure (as NH3 and NOx) and the subsequent
deposition of these gases and their products (NH4+ and NO3") onto soils and the surface of lakes and other waters.
The second pathway is the runoff and leaching of N from manure to the groundwater below, in riparian zones
receiving drain or runoff water, or in the ditches, streams, rivers, and estuaries into which the land drainage water
eventually flows.
When livestock or poultry manure are stored or treated in systems that promote anaerobic conditions (e.g., as a
liquid/slurry in lagoons, ponds, tanks, or pits), the decomposition of the volatile solids component in the manure
tends to produce CH4. When manure is handled as a solid (e.g., in stacks or drylots) or deposited on pasture, range,
or paddock lands, it tends to decompose aerobically and produce little or no CH4. Ambient temperature, moisture,
and manure storage or residency time affect the amount of CH4 produced because they influence the growth of the
bacteria responsible for CH4 formation. For non-liquid-based manure systems, moist conditions (which are a
function of rainfall and humidity) can promote CH4 production. Manure composition, which varies by animal diet,
growth rate, and type, including the animal's digestive system, also affects the amount of CH4 produced. In general,
the greater the energy content of the feed, the greater the potential for CH4 emissions. However, some higher-energy
3 Direct and indirect N2O emissions from dung and urine spread onto fields either directly as daily spread or after it is removed
from manure management systems (i.e., lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or
paddock lands are accounted for and discussed in the Agricultural Soil Management source category within the Agriculture
sector.
5-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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feeds also are more digestible than lower quality forages, which can result in less overall waste excreted from the
animal.
The production of direct N20 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
N20 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 N20 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 N20 in the waste management system (WMS). Indirect N20 emissions are
produced when nitrogen is lost from the system through volatilization (as NH3 or NOx) or through runoff and
leaching. The vast majority of volatilization losses from these operations are NH3. Although there are also some
small losses of NOx, there are no quantified estimates available for use, so losses due to volatilization are only based
on NH3 loss factors. Runoff losses would be expected from operations that house animals or store manure in a
manner that is exposed to weather. Runoff losses are also specific to the type of animal housed on the operation due
to differences in manure characteristics. Little information is known about leaching from manure management
systems as most research focuses on leaching from land application systems. Since leaching losses are expected to
be minimal, leaching losses are coupled with runoff losses and the runoff/leaching estimate provided in this chapter
does not account for any leaching losses.
Estimates of CH4 emissions from manure management in 2015 were 66.3 MMT C02 Eq. (2,651 kt); in 1990,
emissions were 37.2 MMT C02 Eq. (1,486 kt). This represents a 78 percent increase in emissions from 1990.
Emissions increased on average by 1.1 MMT C02 Eq. (3.0 percent) annually over this period. The majority of this
increase is due to swine and dairy cow manure, where emissions increased 58 and 136 percent, respectively. From
2014 to 2015, there was a 5.4 percent increase in total CH4 emissions from manure management, mainly due to an
increase in larger farms and animal populations, as well a shifting of manure management to liquid systems with
increasing farm size.
Although the majority of managed manure in the United States is handled as a solid, producing little CH4, the
general trend in manure management, particularly for dairy and swine (which are both shifting towards larger
facilities), is one of increasing use of liquid systems. Also, new regulations controlling the application of manure
nutrients to land have shifted manure management practices at smaller dairies from daily spread systems to storage
and management of the manure on site. Although national dairy animal populations have generally been decreasing
since 1990, some states have seen increases in their dairy populations as the industry becomes more concentrated in
certain areas of the country and the number of animals contained on each facility increases. These areas of
concentration, such as California, New Mexico, and Idaho, tend to utilize more liquid-based systems to manage
(flush or scrape) and store manure. Thus the shift toward larger dairy and swine facilities has translated into an
increasing use of liquid manure management systems, which have higher potential CH4 emissions than dry systems.
This significant shift in both the dairy and swine industries was accounted for by incorporating state and WMS-
specific CH4 conversion factor (MCF) values in combination with the 1992, 1997, 2002, 2007 and 2012 farm-size
distribution data reported in the Census of Agriculture (USDA 2016d).
In 2015, total N20 emissions from manure management were estimated to be 17.7 MMT C02 Eq. (59 kt); in 1990,
emissions were 14.0 MMT C02 Eq. (47 kt). These values include both direct and indirect N20 emissions from
manure management. Nitrous oxide emissions have remained fairly steady since 1990. Small changes inN20
emissions from individual animal groups exhibit the same trends as the animal group populations, with the overall
net effect that N20 emissions showed a 27 percent increase from 1990 to 2015 and a 1.1 percent increase from 2014
through 2015. Overall shifts toward liquid systems have driven down the emissions per unit of nitrogen excreted.
Table 5-6 and Table 5-7 provide estimates of CH4 and N20 emissions from manure management by animal
category.
Table 5-6: ChU and N2O Emissions from Manure Management (MMT CO2 Eq.)
Gas/Animal Type 1990 2005 2011 2012 2013 2014 20l?
CH4a 37.2 56.3 63.0 65.6 63.3 62.9 66.3
Dairy Cattle 14.7 : 26.4 32.4 34.3 33.4 34.0 34.8
Agriculture 5-9
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Beef Cattle
3.1
3.3
3.3
3.2
3.0
3.0
3.1
Swine
15.6
22.9
23.7
24.5
23.2
22.2
24.6
Sheep
0.2
0.1
0.1
0.1
0.1
0.1
0.1
Goats
+5*
+
+
+
+
+
+
Poultry
3.'
3.2
3.2
3.2
3.2
3.3
3.4
Horses
0.2
0.3
0.2
0.2
0.2
0.2
0.2
American Bison
+y
+
+
+
+
+
+
Mules and Asses
+ /
+
+
+
+
+
+
N2Ob
14.0
16.5
17.4
17.5
17.5
17.5
17.7
Dairy Cattle
5. '
5.6
5.8
5.9
5.9
5.9
6.1
Beef Cattle
5.9
7.2
7.7
7.7
7.7
7.8
7.7
Swine
1.2
1.7
1.9
1.9
1.9
1.8
2.0
Sheep
0.1
0.3
0.3
0.3
0.3
0.3
0.3
Goats
+ /
+
+
+
+
+
+
Poultry
1.4
1.6
1.5
1.6
1.6
1.6
1.6
Horses
0.1
0.1
0.1
0.1
0.1
0.1
0.1
American Bison
NA
NA
NA
NA
NA
NA
NA
Mules and Asses
+
+
+
+
+
+
Total
51.1
72.9
80.4
83.2
80.8
80.4
84.0
+ Does not exceed 0.05 MMT CO2 Eq.
NA - Not available
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic
digesters.
b Includes both direct and indirect N2O emissions.
Notes: Totals may not sum due to independent rounding. American bison are maintained entirely
on unmanaged WMS; there are no American bison N2O emissions from managed systems.
1 Table 5-7: ChU and N2O Emissions from Manure Management (kt)
Gas/Animal Type
1990
2005
2011
2012
2013
2014
2015
CH4a
1,486
2,254
2,519
2,625
2,530
2,514
2,651
Dairy Cattle
590
1,057
1,297
1,373
1,338
1,361
1,391
Beef Cattle
126
133
131
128
121
120
126
Swine
622
916
949
982
930
890
985
Sheep
7
3
3
3
3
3
3
Goats
1
1
1
1
1
1
1
Poultry
131
129
127
128
128
131
135
Horses
9
12
10
10
9
9
9
American Bison
+
+
+
+
+
+
+
Mules and Asses
+
+
+
+
+
+
+
N2Ob
47
55
58
59
59
59
59
Dairy Cattle
18
19
19
20
20
20
20
Beef Cattle
20
24
26
26
26
26
26
Swine
4
6
6
6
6
6
7
Sheep
+
1
1
1
1
1
1
Goats
+
+
+
+
+
+
+
Poultry
5
5
5
5
5
5
5
Horses
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
Mules and Asses
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
NA - Not available
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic
digesters.
b Includes both direct and indirect N2O emissions.
Notes: Totals may not sum due to independent rounding. American bison are maintained entirely
on unmanaged WMS; there are no American bison N2O emissions from managed systems.
5-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
Methodology
The methodologies presented in IPCC (2006) form the basis of the CH4 and N20 emission estimates for each animal
type. This section presents a summary of the methodologies used to estimate CH4 and N20 emissions from manure
management. See Annex 3.11 for more detailed information on the methodology and data used to calculate CH4 and
N20 emissions from manure management.
Methane Calculation Methods
The following inputs were used in the calculation of CH4 emissions:
• Animal population data (by animal type and state);
• Typical animal mass (TAM) data (by animal type);
• Portion of manure managed in each WMS, by state and animal type;
• Volatile solids (VS) production rate (by animal type and state or United States);
• Methane producing potential (B0) of the volatile solids (by animal type); and
• Methane conversion factors (MCF), the extent to which the CH4 producing potential is realized for each
type of WMS (by state and manure management system, including the impacts of any biogas collection
efforts).
Methane emissions were estimated by first determining activity data, including animal population, TAM, WMS
usage, and waste characteristics. The activity data sources are described below:
• Annual animal population data for 1990 through 2015 for all livestock types, except goats, horses, mules
and asses, and American bison were obtained from the USDA NASS. For cattle, the USDA populations
were utilized in conjunction with birth rates, detailed feedlot placement information, and slaughter weight
data to create the transition matrix in the CEFM that models cohorts of individual animal types and their
specific emission profiles. The key variables tracked for each of the cattle population categories are
described in Section 5.1 and in more detail in Annex 3.11. Goat population data for 1992, 1997, 2002,
2007, and 2012; horse and mule and ass population data for 1987, 1992, 1997, 2002, 2007, and 2012; and
American bison population for 2002, 2007 and 2012 were obtained from the Census of Agriculture (USDA
2014a). American bison population data for 1990 through 1999 were obtained from the National Bison
Association (1999).
• The TAM is an annual average weight that was obtained for animal types other than cattle from
information in USDA's Agricultural Waste Management Field Handbook (USDA 1996), the American
Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) and others (Meagher 1986; EPA 1992;
Safley 2000; ERG 2003b; IPCC 2006; ERG 2010a). For a description of the TAM used for cattle, see
Section 5.1.
• WMS usage was estimated for swine and dairy cattle for different farm size categories using state and
regional data from USDA (USDA APHIS 1996; Bush 1998; Ott 2000; USDA 2016d) and EPA (ERG
2000a; EPA 2002a and 2002b). For beef cattle and poultry, manure management system usage data were
not tied to farm size but were based on other data sources (ERG 2000a; USDA APHIS 2000; UEP 1999).
For other animal types, manure management system usage was based on previous estimates (EPA 1992).
American bison WMS usage was assumed to be the same as not on feed (NOF) cattle, while mules and
asses were assumed to be the same as horses.
• VS production rates for all cattle except for calves were calculated by head for each state and animal type
in the CEFM. VS production rates by animal mass for all other animals were determined using data from
USDA's Agricultural Waste Management Field Handbook (USDA 1996 and 2008; ERG 2010b and 2010c)
and data that was not available in the most recent Handbook were obtained from the American Society of
Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006IPCC Guidelines (IPCC 2006).
American bison VS production was assumed to be the same as NOF bulls.
Agriculture 5-11
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• The maximum CH4-producing capacity of the VS (B0) was determined for each animal type based on
literature values (Morris 1976; Bryant et al. 1976; Hashimoto 1981; Hashimoto 1984; EPA 1992; Hill
1982; Hill 1984).
• MCFs for dry systems were set equal to default IPCC factors based on state climate for each year (IPCC
2006). MCFs for liquid/slurry, anaerobic lagoon, and deep pit systems were calculated based on the
forecast performance of biological systems relative to temperature changes as predicted in the van't Hoff-
Arrhenius equation which is consistent with IPCC (2006) Tier 2 methodology.
• Data from anaerobic digestion systems with CH4 capture and combustion were obtained from the EPA
AgSTAR Program, including information presented in the AgSTAR Digest (EPA 2000, 2003, 2006) and the
AgSTAR project database (EPA 2016). Anaerobic digester emissions were calculated based on estimated
methane production and collection and destruction efficiency assumptions (ERG 2008).
• For all cattle except for calves, the estimated amount of VS (kg per animal-year) managed in each WMS
for each animal type, state, and year were taken from the CEFM, assuming American bison VS production
to be the same as NOF bulls. For animals other than cattle, the annual amount of VS (kg per year) from
manure excreted in each WMS was calculated for each animal type, state, and year. This calculation
multiplied the animal population (head) by the VS excretion rate (kg VS per 1,000 kg animal mass per
day), the TAM (kg animal mass per head) divided by 1,000, the WMS distribution (percent), and the
number of days per year (365.25).
The estimated amount of VS managed in each WMS was used to estimate the CH4 emissions (kg CH4 per year)
from each WMS. The amount of VS (kg per year) were multiplied by the maximum CH4 producing capacity of the
VS (B0) (m3 CH4 per kg VS), the MCF for that WMS (percent), and the density of CH4 (kg CH4per m3 CH4). The
CH4 emissions for each WMS, state, and animal type were summed to determine the total U.S. CH4 emissions.
Nitrous Oxide Calculation Methods
The following inputs were used in the calculation of direct and indirect N20 emissions:
• Animal population data (by animal type and state);
• TAM data (by animal type);
• Portion of manure managed in each WMS (by state and animal type);
• Total Kjeldahl N excretion rate (Nex);
• Direct N20 emission factor (EFwms);
• Indirect N20 emission factor for volatilization (EFvoiatuization);
• Indirect N20 emission factor for runoff and leaching (EFninorrie„ch):
• Fraction of N loss from volatilization of NH3 and NOx (Fracgas); and
• Fraction of N loss from runoff and leaching (FraCnmoff/ieach).
Nitrous oxide emissions were estimated by first determining activity data, including animal population, TAM, WMS
usage, and waste characteristics. The activity data sources (except for population, TAM, and WMS, which were
described above) are described below:
• Nex rates for all cattle except for calves were calculated by head for each state and animal type in the
CEFM. Nex rates by animal mass for all other animals were determined using data from USD A's
Agricultural Waste Management Field Handbook (USDA 1996 and 2008; ERG 2010b and 2010c) and data
from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) and IPCC (2006).
American bison Nex rates were assumed to be the same as NOF bulls.4
• All N20 emission factors (direct and indirect) were taken from IPCC (2006). These data are appropriate
because they were developed using U.S. data.
4 The N2O emissions from N excreted (Nex) by American bison on grazing lands are accounted for and discussed in the
Agricultural Soil Management source category and included under pasture, range and paddock (PRP) emissions. Because
American bison are maintained entirely on unmanaged WMS and N2O emissions from unmanaged WMS are not included in the
Manure Management category, there are no N2O emissions from American bison included in the Manure Management category.
5-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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• Country-specific estimates for the fraction of N loss from volatilization (Fracgas) and runoff and leaching
(FraCmnoff/ieach) were developed. Fracgas values were based on WMS-specific volatilization values as
estimated from EP A's National Emission Inventory - Ammonia Emissions from Animal Agriculture
Operations (EPA 2005). FraCmnoff/ieachmg values were based on regional cattle runoff data from EPA's Office
of Water (EPA 2002b; see Annex 3.11).
To estimate N20 emissions for cattle (except for calves), the estimated amount of N excreted (kg per animal-year)
that is managed in each WMS for each animal type, state, and year were taken from the CEFM. For calves and other
animals, the amount of N excreted (kg per year) in manure in each WMS for each animal type, state, and year was
calculated. The population (head) for each state and animal was multiplied by TAM (kg animal mass per head)
divided by 1,000, the nitrogen excretion rate (Nex, in kg N per 1,000 kg animal mass per day), WMS distribution
(percent), and the number of days per year.
Direct N20 emissions were calculated by multiplying the amount of N excreted (kg per year) in each WMS by the
N20 direct emission factor for that WMS (EFwms, in kg N20-N per kg N) and the conversion factor of N20-N to
N20. These emissions were summed over state, animal, and WMS to determine the total direct N20 emissions (kg of
N20 per year).
Next, indirect N20 emissions from volatilization (kg N20 per year) were calculated by multiplying the amount of N
excreted (kg per year) in each WMS by the fraction of N lost through volatilization (Fractas) divided by 100, and the
emission factor for volatilization (EFvoiatiiization, in kg N20 per kg N), and the conversion factor of N20-N to N20.
Indirect N20 emissions from runoff and leaching (kg N20 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 (FraCnmoff/ieach)
divided by 100, and the emission factor for runoff and leaching (EFrunoff/ieach, in kg N20 per kg N), and the
conversion factor of N20-N to N20. The indirect N20 emissions from volatilization and runoff and leaching were
summed to determine the total indirect N20 emissions.
The direct and indirect N20 emissions were summed to determine total N20 emissions (kg N20 per year).
Uncertainty and Time-Series Consistency
An analysis (ERG 2003a) was conducted for the manure management emission estimates presented in the 1990
through 2001 Inventory report (i.e., 2003 submission to the UNFCCC) to determine the uncertainty associated with
estimating CH4 and N20 emissions from livestock manure management. The quantitative uncertainty analysis for
this source category was performed in 2002 through the IPCC-recommended Approach 2 uncertainty estimation
methodology, the Monte Carlo Stochastic Simulation technique. The uncertainty analysis was developed based on
the methods used to estimate CH4 and N20 emissions from manure management systems. A normal probability
distribution was assumed for each source data category. The series of equations used were condensed into a single
equation for each animal type and state. The equations for each animal group contained four to five variables around
which the uncertainty analysis was performed for each state. These uncertainty estimates were directly applied to the
2015 emission estimates as there have not been significant changes in the methodology since that time.
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 5-8. Manure management
CH4 emissions in 2015 were estimated to be between 54.3 and 79.5 MMT C02 Eq. at a 95 percent confidence level,
which indicates a range of 18 percent below to 20 percent above the actual 2015 emission estimate of 66.3 MMT
C02 Eq. At the 95 percent confidence level, N20 emissions were estimated to be between 14.9 and 22.0 MMT C02
Eq. (or approximately 16 percent below and 24 percent above the actual 2015 emission estimate of 17.7 MMT C02
Eq.).
Table 5-8: Approach 2 Quantitative Uncertainty Estimates for CH4 and N2O (Direct and
Indirect) Emissions from Manure Management (MMT CO2 Eq. and Percent)
2015 Emission
Source Gas Estimate Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Manure Management CH4 66.3 54.3 79.5 -18% 20%
Agriculture 5-13
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Manure Management N2O 17.7 14.9 22.0 -16% 24%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section.
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 N20 emissions from managed systems and CH4
emissions from livestock manure. All errors identified were corrected. Order of magnitude checks were also
conducted, and corrections made where needed. Manure N data were checked by comparing state-level data with
bottom up estimates derived at the county level and summed to the state level. Similarly, a comparison was made by
animal and WMS type for the full time series, between national level estimates for N excreted and the sum of county
estimates for the full time series.
Any updated data, including population, are validated by experts to ensure the changes are representative of the best
available U.S.-specific data. The U.S.-specific values for TAM, Nex, VS, B0, and MCF were also compared to the
IPCC default values and validated by experts. Although significant differences exist in some instances, these
differences are due to the use of U.S.-specific data and the differences in U.S. agriculture as compared to other
countries. The U.S. manure management emission estimates use the most reliable country-specific data, which are
more representative of U.S. animals and systems than the IPCC (2006) default values.
For additional verification, the implied CH4 emission factors for manure management (kg of CH4 per head per year)
were compared against the default IPCC (2006) values. Table 5-9 presents the implied emission factors of kg of CH4
per head per year used for the manure management emission estimates as well as the IPCC (2006) default emission
factors. The U.S. implied emission factors fall within the range of the IPCC (2006) default values, except in the case
of sheep, goats, and some years for horses and dairy cattle. The U.S. implied emission factors are greater than the
IPCC (2006) default value for those animals due to the use of U.S.-specific data for typical animal mass and VS
excretion. There is an increase in implied emission factors for dairy and swine across the time series. This increase
reflects the dairy and swine industry trend towards larger farm sizes; large farms are more likely to manage manure
as a liquid and therefore produce more CH4 emissions.
Table 5-9: IPCC (2006) Implied Emission Factor Default Values Compared with Calculated
Values for ChU from Manure Management (kg/head/year)
IPCC Default
CH4 Emission Implied CHt Emission Factors (kg/head/year)
(kg/head/year)
1990
2005
2011
2012
2013
2014
2015
Dairy Cattle
48-112
30.2
59.4
70.3
73.9
72.3
73.4
74.0
Beef Cattle
1-2
1.5
1.6
1.7
1.7
1.6
1.6
1.7
Swine
10-45
11.5
15.0
14.5
14.8
14.2
13.8
14.5
Sheep
0.19-0.37
0.6
0.6
0.5
0.5
0.5
0.5
0.5
Goats
0.13-0.26
0.4
0.3
0.3
0.3
0.3
0.3
0.3
Poultry
0.02-1.4
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Horses
1.56-3.13
4.3
3.1
2.6
2.7
2.5
2.5
2.6
American Bison
NA
1.8
2.0
2.1
2.1
2.0
2.0
2.1
Mules and Asses
0.76-1.14
0.9
1.0
1.0
1.0
0.9
0.9
1.0
NA - Not Applicable
In addition, default IPCC (2006) emission factors for N20 were compared to the U.S. Inventory implied N20
emission factors. Default N20 emission factors from the 2006 IPCC Guidelines were used to estimate N20 emission
from each WMS in conjunction with U.S.-specific Nex values. The implied emission factors differed from the U.S.
Inventory values due to the use of U.S.-specific Nex values and differences in populations present in each WMS
throughout the time series.
5-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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44
45
Recalculations Discussion
The CEFM produces population, VS and Nex data for cattle, excepting calves, that are used in the manure
management inventory. As a result, all changes to the CEFM described in Section 5.1 contributed to changes in the
population, VS and Nex data used for calculating CH4 and N20 cattle emissions from manure management. In
addition, the manure management emission estimates included the following recalculations relative to the previous
Inventory:
• State animal populations were updated to reflect updated USDA NASS datasets, which resulted in
population changes for:
o Poultry in 2014;
o Sheep in 2013;
o Dairy heifers in 2008 through 2010, and 2014;
o NOF cattle 2008 through 2010, and 2014;
o OF cattle for 2008 through 2012, and 2014;
o Both beef and dairy calves in 2008, 2009, and 2014; and
o Swine in 2014 (USDA 2016a).
• WMS distribution data were updated with Census of Agriculture farm size distribution data, which resulted
in WMS distribution changes for dairy cows and swine for 2008 through 2014, and poultry in 2010 (USDA
2016d).
• Temperature data were updated which resulted in MCF changes for goats, horses, mules, and sheep in
2014, as well as dairy cattle, beef cattle, swine and poultry from 2013 through 2014 (NOAA 2016).
• Anaerobic digester data were updated to reflect updated EPA AgSTAR datasets, which resulted in VS
distribution changes for dairy cows, swine, and poultry from 2011 through 2014 (EPA 2016).
These changes impacted CH4 emission estimates for 2008 through 2014, overall increasing annual estimations from
0.6 to 3.1 percent. Dairy cow methane emissions increased by 1.0 percent in 2008 up to a 5.6 percent increase in
2014. Swine increased CH4 emissions by about 0.2 percent in 2008 up to a 1.0 percent increase in 2013, but
decreased CH4 emissions in 2014 by about 0.7 percent.
Planned Improvements
The uncertainty analysis for manure management will be updated in future Inventories to more accurately assess
uncertainty of emission calculations. This update is necessary due to the extensive changes in emission calculation
methodology, including estimation of emissions at the WMS level and the use of new calculations and variables for
indirect N20 emissions.
Potential data sources (such as the USDA Agricultural Resource Management Survey) for updated WMS
distribution estimates have been reviewed and discussed with USDA. EPA is working with USDA to obtain these
data sources for potential use in future Inventory reports. In addition, future Inventory reports may present emissions
on a monthly basis to show seasonal emission changes for each WMS; this update would help compare these
Inventory data to other data and models.
5.3 Rice Cultivation (IPCC Source Category 3C)
Most of the world's rice is grown on flooded fields (Baicich 2013), and flooding creates anaerobic conditions that
foster CH4 production through a process known as methanogenesis. Approximately 60 to 90 percent of the CH4
produced by methanogenie bacteria is oxidized in the soil and converted to CChby methanotrophic bacteria. The
remainder is emitted to the atmosphere (Holzapfel-Pschorn et al. 1985; Sass et al. 1990) or transported as dissolved
CH4 into groundwater and waterways (Neue et al. 1997). Methane is transported to the atmosphere primarily
through the rice plants, but some CH4 also escapes via ebullition (i.e., bubbling through the water) and to a much
lesser extent by diffusion through the water (van Bodegom et al. 2001).
Agriculture 5-15
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Water management is arguably the most important factor affecting CH4 emissions, and improved water management
has the largest potential to mitigate emissions (Yan et al. 2009). Upland rice fields are not flooded, and therefore do
not produce CH4, but large amounts of CH4 can be emitted in continuously irrigated fields, which is the most
common practices in the United States (USDA 2012). Single or multiple aeration events with drainage of a field
during the growing season can significantly reduce these emissions (Wassmann et al. 2000a), but drainage may also
increase N20 emissions. Deepwater rice fields (i.e., fields with flooding depths greater than one meter, such as
natural wetlands) tend to have less living stems reaching the soil, thus reducing the amount of CH4 transport to the
atmosphere through the plant compared to shallow-flooded systems (Sass 2001).
Other management practices also influence CH4 emissions from flooded rice fields including rice residue straw
management and application of organic amendments, in addition to cultivar selection due to differences in the
amount of root exudates5 among rice varieties (Neue et al. 1997). These practices influence the amount of organic
matter available for methanogenesis, and some practices, such as mulching rice straw or composting organic
amendments, can reduce the amount of labile carbon and limit CH4 emissions (Wassmann et al. 2000b). Fertilization
practices also influences CH4 emissions, particularly the use of fertilizers with sulfate (Wassmann et al. 2000b;
Linquist et al. 2012). Other environmental variables also impact the methanogenesis process such as soil
temperature and soil type. Soil temperature is an important factor regulating the activity of methanogenic bacteria
which in turn affects the rate of CH4 production. Soil texture influences decomposition of soil organic matter, but is
also thought to have an impact on oxidation of CH4 in the soil (Sass et al. 1994).
Rice is currently cultivated in twelve states, including Arkansas, California, Florida, Illinois, Kentucky, Louisiana,
Minnesota, Mississippi, Missouri, New York, South Carolina, Tennessee and Texas. Soil types, rice varieties, and
cultivation practices vary across the United States, but most farmers apply fertilizers and do not harvest crop
residues. In addition, a second, ratoon rice crop is sometimes grown in the Southeast. Ratoon crops are produced
from regrowth of the stubble remaining after the harvest of the first rice crop. Methane emissions from ratoon crops
are higher than those from the primary crops due to the increased amount of labile organic matter available for
anaerobic decomposition in the form of relatively fresh crop residue straw. Emissions tend to be higher in rice fields
if the residues have been in the field for less than 30 days before planting the next rice crop (Lindau and Bollich
1993; IPCC 2006; Wang et al. 2013).
Overall, rice cultivation is a minor source of CH4 emissions in the United States relative to other source categories
(see Table 5-10 and Table 5-11). In 2015, CH4 emissions from rice cultivation were 11.2 MMT CO2 Eq. (449 kt).
Annual emissions fluctuate between 1990 and 2015, and emissions in 2015 represented a 30 percent decrease
compared to 1990. Variation in emissions is largely due to differences in the amount of rice harvested areas over
time. In Arkansas rice harvested areas increased by 2 percent from 1990 to 2015, while rice harvested area declined
in California, Louisiana and Texas by 2 percent, 41 percent and 78 percent respectively (see Table 5-12).
Table 5-10: ChU Emissions from Rice Cultivation (MMT CO2 Eq.)
State
1990
2005
2011
2012
2013
2014
2015
Arkansas
3.3
4.7
4.0
3.8
3.8
3.7
3.8
California
2.0
2.1
1.9
2.0
2.0
2.0
2.0
Florida
+
0.1
+
+
+
+
+
Illinois
+
+
+
+
+
+
+
Kentucky
+
+
+
+
+
+
+
Louisiana
6.1
6.5
5.6
3.9
3.9
4.0
3.8
Minnesota
+
+
+
+
+
+
+
Mississippi
0.6
0.6
0.3
0.5
0.5
0.5
0.5
Missouri
0.'
0.6
0.4
0.3
0.3
0.3
0.3
New York
+
+
+
+
+
+
+
South Carolina
+
+
+
+
+
+
+
Tennessee
+ yl
+
+
+
+
+
+
Texas
3.7
2.1
1.8
0.9
0.9
0.9
0.9
Total
16.0
16.7
14.1
11.3
11.3
11.4
11.2
5 The roots of rice plants add organic material to the soil through a process called "root exudation." Root exudation is thought to
enhance decomposition of the soil organic matter and release nutrients that the plant can absorb and use to stimulate more
production. The amount of root exudate produced by a rice plant over a growing season varies among rice varieties.
5-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 5-11: ChU Emissions from Rice Cultivation (kt)
State
1990
2005
2011
2012
2013
2014
2015
Arkansas
132
187
162
151
151
150
150
California
81
82
75
81
81
81
82
Florida
+
3
+
+
+
+
+
Illinois
+
+
+
+
+
+
+
Kentucky
+
+
+
+
+
+
+
Louisiana
246
261
226
156
156
159
152
Minnesota
1
2
1
1
1
1
1
Mississippi
23
23
11
19
19
19
19
Missouri
12
22
15
12
12
12
12
New York
+
+
+
+
+
+
+
South Carolina
+
+
+
+
+
+
+
Tennessee
+
+
+
+
+
+
+
Texas
146
86
74
34
34
34
34
Total
641
667
564
453
454
456
449
+ Does not exceed 0.5 kt.
Note: Totals may not sum due to independent rounding.
Figure 5-2: Total Net Annual ChU Emissions from Rice Cultivation, 2015 (MMT CO2 Eq./Year)
- TO BE UPDATED FOR FINAL INVENTORY REPORT
Methodology
The methodology used to estimate CH4 emissions from rice cultivation is based on a combination of IPCC Tier 1
and 3 approaches. The Tier 3 method utilizes a process-based model (DAYCENT) to estimate CH4 emissions from
rice cultivation (Cheng et al. 2013), and has been tested in the United States (See Annex 3.12) and Asia (Cheng et al.
2013, 2014). The model simulates hydrological conditions and thermal regimes, organic matter decomposition, root
exudation, rice plant growth and its influence on oxidation of CH4, as well as CH4 transport through the plant and
via ebullition (Cheng et al. 2013). The method simulates the influence of organic amendments and rice straw
management on methanogenesis in the flooded soils. In addition to CH4 emissions, DAYCENT simulates soil C
stock changes and N20 emissions (Parton et al. 1987 and 1998; Del Grosso et al. 2010), and allows for a seamless
set of simulations for crop rotations that include both rice and non-rice crops.
The Tier 1 method is applied to estimate CH4 emissions from rice when grown in rotation with crops that are not
simulated by DAYCENT, such as vegetables and perennial/horticultural crops. The Tier 1 method is also used for
areas converted between agriculture (i.e., cropland and grassland) and other land uses, such as forest land, wetland,
and settlements. In addition, the Tier 1 method is used to estimate CH4 emissions from organic soils (i.e., Histosols)
and from areas with very gravelly, cobbly, or shaley soils (greater than 35 percent by volume). The Tier 3 method
using DAYCENT lias not been folly tested for estimating emissions associated with these crops and rotations, land
uses, as well as organic soils or cobbly, gravelly, and shaley mineral soils.
The Tier 1 method for estimating CH4 emissions from rice production utilizes a default base emission rate and
scaling factors (IPCC 2006). The base emission factor represents emissions for continuously flooded fields with no
organic amendments. Scaling factors are used to adjust for water management and organic amendments that differ
from continuous flooding with no organic amendments. The method accounts for pre-season and growing season
flooding; types and amounts of organic amendments; and the number of rice production seasons within a single year
(i.e., single cropping, ratooning, etc.). The Tier 1 analysis is implemented in the Agriculture and Land Use National
Greenhouse Gas Inventory (ALU) software (Ogle et al. 2016).6
6 See .
Agriculture 5-17
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Rice cultivation areas are based on cropping and land use histories recorded in the USD A National Resources
Inventory (NRI) survey (USDA-NRCS 2015). The NRI is a statistically-based sample of all non-federal land, and
includes 380,956 survey points of which 1,588 are in locations with rice cultivation at the end of the NRI time
series. The Tier 3 method is used to estimate CH4 emissions from 1,393 of the NRI survey locations, and the
remaining 195 survey locations are estimated with the Tier 1 method. Each NRI survey point is associated with an
"expansion factor" that allows scaling of CH4 emissions from NRI points to the entire country (i.e., each expansion
factor represents the amount of area with the same land-use/management history as the sample point). Land-use and
some management information in the NRI (e.g., crop type, soil attributes, and irrigation) were collected on a 5-year
cycle beginning in 1982, along with cropping rotation data in 4 out of 5 years for each 5 year time period (i.e., 1979
to 1982, 1984 to 1987, 1989 to 1992, and 1994 to 1997). The NRI program began collecting annual data in 1998,
with data currently available through 2012 (USDA-NRCS 2015). This Inventory only uses NRI data through 2012
because newer data are not available, but will be incorporated when additional years of data are released by USDA-
NRCS. The harvested rice areas in each state are presented in Table 5-12.
Table 5-12: Rice Area Harvested (1,000 Hectares)
State/Crop
1990
2005
2011
2012
2013
2014
2015
Arkansas
599
796
642
613
613
613
613
California
248
24"
249
244
244
244
244
Florida
0
11
0
0
0
0
0
Illinois
0
0
0
0
0
0
0
Kentucky
0
0
0
0
0
0
0
Louisiana
380
402
318
226
226
226
226
Minnesota
4
10
5
6
6
6
6
Mississippi
119
115
53
92
92
92
92
Missouri
47
93
64
46
46
46
46
New York
1
0
0
0
0
0
0
South Carolina
0
0
0
0
0
0
0
Tennessee
0
1
0
0
0
0
0
Texas
300
150
120
66
66
66
66
Total
1,698
1,82ft
1,451
1,292
1,292
1,292
1,292
Notes: Totals may not sum due to independent rounding. States are included if NRI reports rice areas at any time
between 1990 and 2012.
The Southeastern states have sufficient growing periods for a ratoon crop in some years. For example, in Arkansas,
the length of growing season is occasionally sufficient for ratoon crops on an average of 1 percent of the rice fields.
No data are available about ratoon crops in Missouri or Mississippi, and the average amount of ratooning in
Arkansas was assigned to these states. Ratoon cropping occurs much more frequently in Louisiana (LSU 2015 for
years 2000 through 2013, 2015) and Texas (TAMU 2015 foryears 1993 through 2014), averaging 32 percent and 45
percent of rice acres planted, respectively. Florida also has a large fraction of area with a ratoon crop (49 percent).
Ratoon rice crops are not grown in California. Ratooned crop area as a percent of primary crop area is presented in
Table 5-13.
Table 5-13: Average Ratooned Area as Percent of Primary Growth Area (Percent)
State
1990-2015
Arkansas3
1%
California
0%
Floridab
49%
Louisiana0
32%
Mississippi3
1%
Missouri3
0%
Texas'1
45%
3 Arkansas: 1990-2000 (Slaton 1999 through 2001); 2001-2011 (Wilson 2002 through 2007,2009 through 2012); 2012-2013
(Hardke 2013, 2014).
b Florida - Ratoon: 1990-2000 (Schueneman 1997, 1999 through 2001); 2001 (Deren 2002); 2002-2003 (Kirstein 2003 through
2004,2006); 2004 (Cantens 2004 through 2005); 2005-2013 (Gonzalez 2007 through 2014)
c Louisiana: 1990-2013 (Linscombe 1999,2001 through 2014).
5-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 dTexas: 1990-2002 (Klosterboer 1997, 1999 through 2003); 2003-2004 (Stansel 2004 through 2005); 2005 (Texas Agricultural
2 Experiment Station 2006); 2006-2013 (Texas Agricultural Experiment Station 2007 through 2014).
3
4 While rice crop production in the United States includes a minor amount of land with mid-season drainage or
5 alternate wet-dry periods, the majority of rice growers use continuously flooded water management systems (Hardke
6 2015; UCCE 2015; Hollier 1999; Way et al. 2014). Therefore, continuous flooding was assumed in the DAYCENT
7 simulations and the Tier 1 method. Variation in flooding can be incorporated in future Inventories if water
8 management data are collected.
9 Winter flooding is another key practice associated with water management in rice fields, and the impact of winter
10 flooding on CH4 emissions is addressed in the Tier 3 and Tier 1 analyses. Flooding is used to prepare fields for the
11 next growing season, and to create waterfowl habitat (Young 2013; Miller et al. 2010; Fleskes et al. 2005).
12 Fitzgerald et al. (2000) suggests that as much as 50 percent of the annual emissions may occur during the winter
13 flood. Winter flooding is a common practice with an average of 34 percent of fields managed with winter flooding
14 in California (Miller et al. 2010; Fleskes et al. 2005), and approximately 21 percent of the fields managed with
15 winter flooding in Arkansas (Wilson and Branson 2005 and 2006; Wilson and Runsick 2007 and 2008; Wilson et al.
16 2009 and 2010; Hardke and Wilson 2013 and 2014; Hardke 2015). No data are available on winter flooding for
17 Texas, Louisiana, Florida, Missouri, or Mississippi. For these states, the average amount of flooding is assumed to
18 be similar to Arkansas. In addition, the amount of flooding is assumed to be relatively constant over the Inventory
19 time period.
20 Uncertainty and Time-Series Consistency
21 Sources of uncertainty in the Tier 3 method include management practices, uncertainties in model structure (i.e.,
22 algorithms and parameterization), and variance associated with the NRI sample. Sources of uncertainty in the IPCC
23 (2006) Tier 1 method include the emission factors, management practices, and variance associated with the NRI
24 sample. A Monte Carlo analysis was used to propagate uncertainties in the Tier 1 and 3 methods, and the
25 uncertainties from each approach are combined to produce the final CH4 emissions estimate using simple error
26 propagation (IPCC 2006). Additional details on the uncertainty methods are provided in Annex 3.12. Rice
27 cultivation CH4 emissions in 2015 were estimated to be between 8.1 and 14.4 MMT CO2 Eq. at a 95 percent
28 confidence level, which indicates a range of 28 percent below to 28 percent above the actual 2015 emission estimate
29 of 11.2 MMT C02 Eq. (see Table 5-14).
30 Table 5-14: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from Rice
31 Cultivation (MMT CO2 Eq. and Percent)
Source
Inventory
Method
Gas
2015 Emission
Estimate
Uncertainty Range Relative to Emission
Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower Upper
Bound
Bound
Bound Bound
Rice Cultivation
Tier 3
CH4
9.9
6.8
13.1
-32% +32%
Rice Cultivation
Tier 1
ch4
1.3
0.9
1.9
-30% +46%
Rice Cultivation
Total
ch4
11.2
8.1
14.4
-28% +28%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
32 Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
3 3 through 2015. Details on the emission trends through time are described in more detail in the Methodology section.
34 QA/QC and Verification
35 Quality control measures include checking input data, model scripts, and results to ensure data are properly handled
36 throughout the inventory process. Inventory reporting forms and text are reviewed and revised as needed to correct
37 transcription errors. No errors were found in the reporting forms and text.
38 Model results are compared to field measurements to verily if results adequately represent CH4 emissions. The
39 comparisons included over 15 long-term experiments, representing about 80 combinations of management
Agriculture 5-19
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treatments across all of the sites. A statistical relationship was developed to assess uncertainties in the model
structure and adjust for model bias and assess precision in the resulting estimates (methods are described in Ogle et
al. 2007). See Annex 3.12 for more information.
Recalculations Discussion
Methodological recalculations in the current Inventory are associated with the following improvements: (1)
DAYCENT model development to improve the simulation of soil temperature; (2) improvements in the cropping
and land use histories that are simulated in DAYCENT between 1950 and 1979, which generate initial values for the
state variables in the model and (3) driving the DAYCENT simulations with updated input data for land use and
management from the National Resources Inventory, which revised the time series from 1990 through 2012. These
changes resulted in an increase in emissions of approximately 25 percent on average relative to the previous
Inventory and an increase in uncertainty from confidence interval with a lower bound and upper bound of 17 percent
to a confidence interval with an upper and lower bound of 28 percent.
Planned Improvements
A major improvement is underway to update the time series of management data with information from the USDA-
NRCS Conservation Effects Assessment Program (CEAP). This improvement will fill several gaps in the
management data including more specific data on fertilizer rates, updated tillage practices, water management,
organic amendments and more information on planting and harvesting dates. This improvement is expected to be
completed for the 1990 through 2017 Inventory (2019 submission to the UNFCCC). However, the timeline may be
extended if there are insufficient resources to fund this improvement.
5.4 Agricultural Soil Management (IPCC Source
Category 3D)
Nitrous oxide is naturally produced in soils through the microbial processes of nitrification and denitrification that is
driven by the availability of mineral nitrogen (N) (Firestone and Davidson 1989).7 Mineral N is made available in
soils through decomposition of soil organic matter and plant litter, as well as asymbiotic fixation of N from the
atmosphere.8 A number of agricultural activities increase mineral N availability in soils that lead to direct N20
emissions from nitrification and denitrification at the site of a management activity (see Figure 5-3) (Mosier et al.
1998), including N fertilization; application of managed livestock manure and other organic materials such as
sewage sludge; deposition of manure on soils by domesticated animals in pastures, rangelands, and paddocks (PRP)
(i.e., by grazing animals and other animals whose manure is not managed); production of N-fixing crops and
forages; retention of crop residues; and drainage of organic soils (i.e., soils with a high organic matter content,
otherwise known as Histosols9) (IPCC 2006). Additionally, agricultural soil management activities, including
irrigation, drainage, tillage practices, and fallowing of land, can influence N mineralization by impacting moisture
and temperature regimes in soils. Indirect emissions of N20 occur when N is transported from a site and is
7 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).
8 Asymbiotic N fixation is the fixation of atmospheric N2 by bacteria living in soils that do not have a direct relationship with
plants.
9 Drainage of organic soils in former wetlands enhances mineralization of N-rich organic matter, thereby increasing N2O
emissions from these soils.
5-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 subsequently converted to N20; there are two pathways for indirect emissions: (1) volatilization and subsequent
2 atmospheric deposition of applied/mineralized N, and (2) surface runoff and leaching of applied/mineralized N into
3 groundwater and surface water.10
4 Direct and indirect emissions from agricultural lands are included in this section (i.e., cropland and grassland as
5 defined in Section 6.1 Representation of the U.S. Land Base; N20 emissions from Forest Land and Settlements soils
6 are found in Sections 6.2 and 6.10, respectively). The U.S. Inventory includes all greenhouse gas emissions from
7 managed land based on guidance in IPCC (2006), and consequently N mineralization from decomposition of soil
8 organic matter and asymbiotic N fixation are also included in this section to fully address emissions from the
9 managed land base (see Methodology section for more information).
10 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,
in addition to leaching and runoff of NO3" that is converted to N2O in aquatic systems.
Agriculture 5-21
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Figure 5-3: Sources and Pathways of N that Result in N2O Emissions from Agricultural Soil
Management
Sources and Pathways of N that Result in NjO Emissions from Agricultural Soil Management
N Volatilization
Synthetic N Fertilizers
fEfmiTZER
Synthetic IM fertilizer applied tosoil
N Inputs to
Managed Soils
Organic
Amendments
Direct N20
Emissions
Includes both commercial and
non-co,m mercisl fertilizers (i.e.,
animal manure, com post
sewage sludge, tankage etc.)
N Volatilization
and Deposition
Urine and Dung from
Grazing Animals
Indirect N20
Emissions
Manure deposited on pasture rang^
and paddock
Crop Residues
Ind udes above- and belowground
residues for all crops (non-N and In-
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
Asymbiotic Fixation
Fixation of atm ospheric by bacteria
living in soilsthat do not have a direct
relationshipwith plants
Histosol
Cultivation
This graphic illustrates the sources and pathways of nitrogen that result
in direct and indirect N,0 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.
Groundwater
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1 Agricultural soils produce the majority of N20 emissions in the United States. Estimated emissions from this source
2 in 2015 are 251.3 MMT CO2 Eq. (843 kt) (see Table 5-15 and Table 5-16). Annual N2O emissions from agricultural
3 soils fluctuated between 1990 and 2015, although overall emissions are 2.0 percent lower in 2015 than in 1990.
4 Year-to-year fluctuations are largely a reflection of annual variation in weather patterns, synthetic fertilizer use, and
5 crop production. From 1990 to 2015, on average cropland accounted for approximately 70 percent of total direct
6 emissions, while grassland accounted for approximately 30 percent. The percentages for indirect emissions, on
7 average, are approximately 83 percent of indirect emissions from croplands and 17 percent for grasslands. Estimated
8 direct and indirect N20 emissions by sub-source category are shown in Table 5-17 and Table 5-18.
9 Table 5-15: N2O Emissions from Agricultural Soils (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Direct
212.0
218.4
220.4
215.6
212.8
212.4
213.3
Cropland
147.5
153.9
158.3
156.7
154.2
154.3
154.6
Grassland
64.5
64.6
62.1
59.0
58.6
58.1
58.7
Indirect
44.6
41.4
49.7
38.4
37.7
37.6
38.0
Cropland
37.0
34.4
41.9
31.6
30.9
30.8
31.1
Grassland
7.6
7.0
7.8
6.9
6.8
6.7
6.9
Total
256.6
259.8
270.1
254.1
250.5
250.0
251.3
Note: Totals may not sum due to independent rounding.
10 Table 5-16: N2O Emissions from Agricultural Soils (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Direct
711
733
740
724
714
713
716
Cropland
495
516
531
526
518
518
519
Grassland
217
217
208
198
197
195
197
Indirect
150
139
167
129
126
126
127
Cropland
124
115
141
106
104
103
104
Grassland
25
23
26
23
23
23
23
Total
861
872
906
853
841
839
843
Note: Totals may not sum due to independent rounding.
11 Table 5-17: Direct N2O Emissions from Agricultural Soils by Land Use Type and N Input Type
12 (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Cropland
147.5
153.9
158.3
156.7
154.2
154.3
154.6
Mineral Soils
144.1
150.6
155.1
153.5
151.0
151.1
151.4
Synthetic Fertilizer
53.6
54.6
58.0
60.4
58.3
58.2
58.3
Organic Amendment3
10.0
10.9
11.2
11.3
11.3
11.2
11.4
Residue Nb
22.1
22.9
23.9
23.5
23.7
23.8
23.9
Mineralization and
Asymbiotic Fixation
58.4
62.2
62.1
58.2
57.8
57.8
57.8
Drained Organic Soils
3.3
3.3
3.2
3.2
3.2
3.2
3.2
Grassland
64.5
64.6
62.1
59.0
58.6
58.1
58.7
Mineral Soils
61.3
61.1
58.8
55.7
55.3
54.9
55.4
Synthetic Fertilizer
0.9
0.8
0.8
0.7
0.7
0.7
0.7
PRP Manure
16.1
13.8
13.6
13.3
13.0
12.5
13.2
Managed Manurec
0.9
1.1
1.1
1.1
1.1
1.1
1.1
Sewage Sludge
0.2
0.5
0.5
0.6
0.6
0.6
0.6
Residue Nd
14.5
15.8
14.8
14.2
14.2
14.2
14.2
Mineralization and
Asymbiotic Fixation
28.5
29.2
28.1
25.8
25.8
25.8
25.7
Drained Organic Soils
3.3
3.5
3.3
3.3
3.3
3.3
3.3
Total
212.0
218.5
220.4
215.6
212.8
212.4
213.3
a Organic amendment inputs include managed manure, daily spread manure, and commercial organic
fertilizers (i.e., dried blood, dried manure, tankage, compost, and other).
b Cropland residue N inputs include N in unharvested legumes as well as crop residue N.
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c Managed manure inputs include managed manure and daily spread manure amendments that are applied to
grassland soils.
d Grassland residue N inputs include N in ungrazed legumes as well as ungrazed grass residue N.
Table 5-18: Indirect N2O Emissions from Agricultural Soils (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
Cropland
Volatilization & Atm.
37.0
34.4
41.9
31.6
30.9
30.8
31.1
Deposition
12.0
13.0
12.9
12.7
12.5
12.5
12.7
Surface I caching & Run-Off
25.0
21.4
29.0
18.9
18.4
18.4
18.4
Grassland
7.6
7.0
7.8
6.9
6.8
6.7
6.9
Volatilization & Atm.
Deposition
4.3
4.5
4.2
4.2
4.2
4.2
4.2
Surface Leaching & Run-Off
3.2
2.5
3.5
2.6
2.6
2.6
2.6
Total
44.6
41.4
49.7
38.4
37.7
37.6
38.0
Note: Totals may not sum due to independent rounding.
Figure 5-4 and Figure 5-5 show regional patterns for direct N20 emissions for croplands and grasslands, and Figure
5-6 and Figure 5-7 show N losses from volatilization leaching, and runoff that lead to indirect N20 emissions.
Annual emissions and N losses in 2015 are shown for the Tier 3 Approach only.
Figure 5-4: Crops, 2015 Annual Direct N2O Emissions Estimated Using the Tier 3 DAYCENT
Model (MMT COz Eq./year)
Direct N20 emissions from croplands tend to be high in the Corn Belt (Illinois, Iowa, Indiana, Ohio, southern
Minnesota and Wisconsin, and eastern Nebraska), where a large portion of the land is used for growing highly
fertilized corn and N-fixing soybean crops (see Figure 5-4). Emissions are also high in the Lower Mississippi River
Basin from Missouri to Louisiana, and highly productive irrigated areas, such as Platte River, which flows from
Colorado through Nebraska, Snake River Valley in Idaho and the Central Valley in California. Direct emissions are
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low in many parts of the eastern United States because only a small portion of land is cultivated as well as in many-
western states where rainfall and access to irrigation water are limited.
Direct emissions from grasslands are highest in the southeast, particularly Kentucky and Tennessee, in additional to
areas in east Texas and Iowa, where there tends to be higher rates of manure amendments on a relatively small
amount of pasture, compared to other regions of the United States. However, total emissions from grasslands tend to
be higher in the Great Plains and western United States (see Figure 5-5) where a high proportion of the land is used
for cattle grazing.
Figure 5-5: Grasslands, 2015 Annual Direct N2O Emissions Estimated Using the Tier 3
DAYCENT Model (MMT CO2 Eq./year)
Figure 5-6: Crops, 2015 Average Annual N Losses Leading to Indirect N2O Emissions
Estimated Using the Tier 3 DAYCENT Model (kt N/year) - TO BE UPDATED FOR FINAL
INVENTORY REPORT
Figure 5-7: Grasslands, 2015 Average Annual N Losses Leading to Indirect N2O Emissions
Estimated Using the Tier 3 DAYCENT Model (kt N/year) - TO BE UPDATED FOR FINAL
INVENTORY REPORT
Methodology
The 2006IPCC Guidelines (IPCC 2006) divide emissions from the agricultural soil management source category
into five components, including (1) direct emissions fromN additions to cropland and grassland mineral soils from
sy nthetic fertilizers, sewage sludge applications, crop residues, organic amendments, and biological N fixation
associated with planting of legumes on cropland and grassland soils; (2) direct emissions from soil organic matter
mineralization due to land use and management change; (3) direct emissions from drainage of organic soils in
croplands and grasslands; (4) direct emissions from soils due to manure deposited by livestock on PRP grasslands;
and (5) indirect emissions from soils and water from N additions and manure deposition to soils that lead to
volatilization, leaching, or runoff of N and subsequent conversion to N20. The United States reports on total
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emissions from all managed land, which is a proxy for anthropogenic impacts on greenhouse gas emissions (IPCC
2006), including direct and indirect N20 emissions from asymbiotic fixation11 and mineralization of soil organic
matter and litter. One recommendation from IPCC (2006) that has not been completely adopted is the estimation of
emissions from grassland pasture renewal, which involves occasional plowing to improve forage production in
pastures. Currently no data are available to address pasture renewal.
Direct N2O Emissions
The methodology used to estimate direct N20 emissions from agricultural soil management in the United States is
based on a combination of IPCC Tier 1 and 3 approaches (IPCC 2006; Del Grosso et al. 2010). A Tier 3 process-
based model (DAYCENT) is used to estimate direct emissions from a variety of crops that are grown on mineral
(i.e., non-organic) soils, as well as the direct emissions from non-federal grasslands with the exception of sewage
sludge amendments (Del Grosso et al. 2010). The Tier 3 approach has been specifically designed and tested to
estimate N20 emissions in the United States, accounting for more of the environmental and management influences
on soil N20 emissions than the IPCC Tier 1 method (see Box 5-1 for further elaboration). Moreover, the Tier 3
approach allows for the Inventory to address direct N20 emissions and soil C stock changes from mineral cropland
soils in a single analysis. Carbon and N dynamics are linked in plant-soil systems through biogeochemical processes
of microbial decomposition and plant production (McGill and Cole 1981). Coupling the two source categories (i.e.,
agricultural soil C and N20) in a single inventory analysis ensures that there is consistent activity data and treatment
of the processes, and interactions are taken into account between C and N cycling in soils.
The Tier 3 approach is based on the cropping and land use histories recorded in the USDA National Resources
Inventory (NRI) (USDA-NRCS 2015). The NRI is a statistically-based sample of all non-federal land,12 and
includes 363,286 points on agricultural land for the conterminous United States that are included in the Tier 3
method. The Tier 1 approach is used to estimate the emissions from the remaining 205,487 in the NRI survey that
are designated as cropland or grassland (discussed later in this section). Each point is associated with an "expansion
factor" that allows scaling of N20 emissions from NRI points to the entire country (i.e., each expansion factor
represents the amount of area with the same land-use/management history as the sample point). Each NRI point was
sampled on a 5-year cycle from 1982 until 1997. For cropland, data were collected in 4 out of 5 years in the cycle
(i.e., 1979 through 1982, 1984 through 1987, 1989 through 1992, and 1994 through 1997). In 1998, the NRI
program began collecting annual data, and the annual data are currently available through 2012 (USDA-NRCS
2015).
Box 5-1: Tier 1 vs. Tier 3 Approach for Estimating N2O Emission
i
The IPCC (2006) Tier 1 approach is based on multiplying activity data on different N inputs (i.e., synthetic fertilizer,
manure, N fixation, etc.) by the appropriate default IPCC emission factors to estimate N20 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 fortius Inventory employs a process-based model (i.e., DAYCENT) that represents the
interaction of N inputs, land use and management, as well as environmental conditions at specific locations.
Consequently, the Tier 3 approach produces more accurate estimates; it accounts more comprehensively for land-use
and management impacts and their interaction with environmental factors (i.e., weather patterns and soil
characteristics), which will enhance or dampen anthropogenic influences. However, the Tier 3 approach requires
more detailed activity data (e.g., crop-specific N amendment rates), additional data inputs (i.e., 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 that the method is an improvement over lower tier methods 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 N20 emissions only during that year and cannot be stored in
11 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.
12 The NRI survey does include sample points on federal lands, but the program does not collect data from those sample
locations.
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soils and contribute to N20 emissions in subsequent years. This is a simplifying assumption that is likely to create
bias in estimated N20 emissions for a specific year. In contrast, the process-based model used in the Tier 3 approach
includes the legacy effect of N added to soils in previous years that is re-mineralized from soil organic matter and
emitted as N20 during subsequent years.
DAYCENT is used to estimate N20 emissions associated with production of alfalfa hay, barley, corn, cotton, dry
beans, grass hay, grass-clover hay, lentils, oats, onions, peanuts, peas, potatoes, rice, sorghum, soybeans, sugar
beets, sunflowers, tobacco, tomatoes, and wheat, but is not applied to estimate N20 emissions from other crops or
rotations with other crops,13 such as sugarcane, some vegetables, tobacco, and perennial/horticultural crops. Areas
that are converted between agriculture (i.e., cropland and grassland) and other land uses, such as forest land, wetland
and settlements, are not simulated with DAYCENT. DAYCENT is also not used to estimate emissions from land
areas with very gravelly, cobbly, or shaley soils in the topsoil (greater than 35 percent by volume in the top 30cm of
the soil profile), or to estimate emissions from drained organic soils (Histosols). The Tier 3 method has not been
fully tested for estimating N20 emissions associated with these crops and rotations, land uses, as well as organic
soils or cobbly, gravelly, and shaley mineral soils. In addition, federal grassland areas are not simulated with
DAYCENT due to limited activity on land use histories. For areas that are not included in the DAYCENT
simulations, the Tier 1IPCC (2006) methodology is used to estimate (1) direct emissions from crops on mineral
soils that are not simulated by DAYCENT; (2) direct emissions from PRP on federal grasslands; and (3) direct
emissions from drained organic soils in croplands and grasslands.
Tier 3 Approach for Mineral Cropland Soils
The DAYCENT biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001 and 2011) is used to estimate
direct N20 emissions from mineral cropland soils that are managed for production of a wide variety of crops (see list
in previous paragraph) based on the cropping histories in the 2012 NRI (USDA-NRCS 2015). Crops simulated by
DAYCENT are grown on approximately 91 percent of total cropland area in the United States. For agricultural
systems in the central region of the United States, crop production for key crops (i.e., corn, soybeans, sorghum,
cotton, and wheat) is simulated in DAYCENT with a NASA-CASA production algorithm (Potter et al. 1993; Potter
et al. 2007) using the Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI)
products, MOD13Q1 and MYD13Q1, with a pixel resolution of 250m.14
DAYCENT is used to estimate direct N20 emissions due to mineral N available from the following sources: (1) the
application of synthetic fertilizers; (2) the application of livestock manure; (3) the retention of crop residues and
subsequent mineralization of N during microbial decomposition (i.e., leaving residues in the field after harvest
instead of burning or collecting residues); (4) mineralization of soil organic matter; and (5) asymbiotic fixation. Note
that commercial organic fertilizers (TVA 1991 through 1994; AAPFCO 1995 through 2016) are addressed with the
Tier 1 method because county-level application data would be needed to simulate applications in DAYCENT, and
currently data are only available at the national scale. The third and fourth sources are generated internally by the
DAYCENT model.
Synthetic fertilizer data are based on fertilizer use and rates by crop type for different regions of the United States
and are obtained primarily from the USDA Economic Research Service. The data collection program was known as
the Cropping Practices Surveys through 1995 (USDA-ERS 1997), and then became the Agricultural Resource
Management Surveys (ARMS) (USDA-ERS 2015). Additional data are compiled through other sources particularly
the National Agricultural Statistics Service (NASS 1992, 1999, 2004). Frequency and rates of livestock manure
application to cropland during 1997 are estimated from data compiled by the USDA Natural Resources
Conservation Service (Edmonds et al. 2003), and then adjusted using county-level estimates of manure available for
application in other years. The adjustments are based on county-scale ratios of manure available for application to
soils in other years relative to 1997 (see Annex 3.12 for further details). Greater availability of managed manure N
relative to 1997 is assumed to increase the area amended with manure, while reduced availability of manure N
13 A small proportion of the major commodity crop production, such as corn and wheat, is included in the Tier 1 analysis because
these crops are rotated with other crops or land uses (e.g., forest lands) that are not simulated by DAYCENT.
14 See .
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relative to 1997 is assumed to reduce the amended area. Data on the county-level N available for application is
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 N20 emissions,
volatilization of ammonia and NOx, runoff and leaching, and poultry manure used as a feed supplement. For
unmanaged systems, it is assumed that no N losses or additions occur prior to the application of manure to the soil.
More information on livestock manure production is available in Section 5.2: Manure Management and Annex 3.11.
The IPCC approach considers crop residue N and N mineralized from soil organic matter as activity data. However,
they are not treated as activity data in DAYCENT simulations because residue production, symbiotic N fixation
(e.g., legumes), mineralization of N from soil organic matter, and asymbiotic N fixation are internally generated by
the model as part of the simulation. In other words, DAYCENT accounts for the influence of symbiotic N fixation,
mineralization of N from soil organic matter and crop residue retained in the field, and asymbiotic N fixation on
N20 emissions, but these are not model inputs. The N20 emissions from crop residues are reduced by approximately
3 percent (the assumed average burned portion for crop residues in the U.S.) to avoid double-counting associated
with non-C02 greenhouse gas emissions from agricultural residue burning. The estimate of residue burning is based
on state inventory data (ILENR 1993; Oregon Department of Energy 1995; Noller 1996; Wisconsin Department of
Natural Resources 1993; Cibrowski 1996).
Additional sources of data are used to supplement the mineral N (USDA-ERS 1997, 2011), livestock manure
(Edmonds et al. 2003), and land-use information (USDA-NRCS 2015). The Conservation Technology Information
Center (CTIC 2004) provides annual data on tillage activity with adjustments for long-term adoption of no-till
agriculture (Towery 2001). Tillage has an influence on soil organic matter decomposition and subsequent soil N20
emissions. The time series of tillage data from CTIC began in 1989 and ended in 2004, so further changes in tillage
practices since 2004 are not currently captured in the Inventory and practices used in 2004 are assumed to apply for
subsequent years. Daily weather data are used as an input in the model simulations, based on gridded weather data at
a 32 km scale from the North America Regional Reanalysis Product (NARR) (Mesinger et al. 2006). Soil attributes
are obtained from the Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2011).
Each NRI point is run 100 times as part of the uncertainty assessment, yielding a total of over 18 million simulations
for the analysis. Soil N20 emission estimates from DAYCENT are adjusted using a structural uncertainty estimator
to account for uncertainty in model algorithms and parameter values (Del Grosso et al. 2010). Soil N20 emissions
and associated 95 percent confidence intervals are estimated for each year between 1990 and 2012, but emissions
from 2013 to 2015 are assumed to be similar to 2012. Annual data are currently available through 2012 (USDA-
NRCS 2015), and the Inventory time series will be updated in the future as new NRI data are released.
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 N20 production (nitrification and denitrification). It is not
possible to partition N20 emissions into each anthropogenic activity directly from model outputs due to the
complexity of the interactions (e.g., N20 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, or made available through decomposition of soil organic matter and plant litter, as well as
asymbiotic fixation of N from the atmosphere, is determined for each N source and then divided by the total amount
of mineral N in the soil according to the DAYCENT model simulation. The percentages are then multiplied by the
total of direct N20 emissions in order to approximate the portion attributed to N management 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 N20, 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 N20 emissions with individual sources of N.
Tier 1 Approach for Mineral Cropland Soils
The IPCC (2006) Tier 1 methodology is used to estimate direct N20 emissions for mineral cropland soils that are not
simulated by DAYCENT (e.g., DAYCENT has not been parametrized to simulate all crop types and some soil types
such as Histosols). For the Tier 1 Approach, estimates of direct N20 emissions from N applications are based on
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mineral soil N that is made available from the following practices: (1) the application of synthetic commercial
fertilizers; (2) application of managed manure and non-manure commercial organic fertilizers; and (3)
decomposition and mineralization of nitrogen from above- and below-ground crop residues in agricultural fields
(i.e., crop biomass that is not harvested). Non-manure commercial organic amendments are only included in the Tier
1 analysis because these data are not available at the county-level, which is necessary for the DAYCENT
simulations.15 Consequently, all commercial organic fertilizer, as well as manure that is not added to crops in the
DAYCENT simulations, are included in the Tier 1 analysis. The following sources are used to derive activity data:
• A process-of-elimination approach is used to estimate synthetic N fertilizer additions for crop areas not
simulated by DAYCENT. The total amount of fertilizer used on farms has been estimated at the county- level
by the USGS from sales records (Ruddy et al. 2006), and these data are aggregated to obtain state-level N
additions to farms. For 2002 through 2015, state-level fertilizer for on-farm use is adjusted based on annual
fluctuations in total U.S. fertilizer sales (AAPFCO 1995 through 2007; AAPFCO 2008 through 2016).16 After
subtracting the portion of fertilizer applied to crops and grasslands simulated by DAYCENT (see Tier 3
Approach for Mineral Cropland Soils and Direct N20 Emissions from Grassland Soils Sections for information
on data sources), the remainder of the total fertilizer used on farms is assumed to be applied to crops that are not
simulated by DAYCENT.
• Similarly, a process-of-elimination approach is used to estimate manure N additions for crops that are not
simulated by DAYCENT. The amount of manure N applied in the Tier 3 approach to crops and grasslands is
subtracted from total manure N available for land application (see Tier 3 Approach for Mineral Cropland Soils
and Direct N20 Emissions from Grassland Soils Sections for information on data sources), and this difference is
assumed to be applied to crops that are not simulated by DAYCENT.
• Commercial organic fertilizer additions are based on organic fertilizer consumption statistics, which are
converted to units of N using average organic fertilizer N content (TVA 1991 through 1994, AAPFCO 1995
through 2016). Commercial fertilizers do include some manure and sewage sludge, but the amounts are
removed from the commercial fertilizer data to avoid double counting with the manure N dataset described
above and the sewage sludge amendment data discussed later in this section.
• Crop residue N is derived by combining amounts of above- and below-ground biomass, which are determined
based on NRI crop area data (USDA-NRCS 2013), crop production yield statistics (USDA-NASS 2015), 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). N inputs from residue were reduced by 3 percent to account for average residue burning
portions in the United States.
The total increase in soil mineral N from applied fertilizers and crop residues is multiplied by the IPCC (2006)
default emission factor to derive an estimate of direct N20 emissions using the Tier 1 Approach.
Drainage of Organic Soils in Croplands and Grasslands
The IPCC (2006) Tier 1 methods are used to estimate direct N20 emissions due to drainage of organic soils in
croplands or grasslands at a state scale. State-scale estimates of the total area of drained organic soils are obtained
from the 2012 NRI (USDA-NRCS 2015) using soils data from the Soil Survey Geographic Database (SSURGO)
(Soil Survey Staff 2011). Temperature data from Daly et al. (1994 and 1998) are used to subdivide areas into
temperate and tropical climates using the climate classification from IPCC (2006). Annual data are available
between 1990 and 2012. Emissions are assumed to be similar to 2012 from 2013 to 2015 because no additional
activity data are currently available from the NRI for the latter years. To estimate annual emissions, the total
temperate area is multiplied by the IPCC default emission factor for temperate regions, and the total tropical area is
multiplied by the IPCC default emission factor for tropical regions (IPCC 2006).
Direct N2O Emissions from Grassland Soils
15 Commercial organic fertilizers include dried blood, tankage, compost, and other, but the dried manure and sewage sludge is
removed from the dataset in order to avoid double counting with other datasets that are used for manure N and sewage sludge.
16 Values are not available for 2014 through 2015 so a "least squares line" statistical extrapolation using the previous 5 years of
data is used to arrive at an approximate value for 2014 through 2015.
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As with N20 from croplands, the Tier 3 process-based DAYCENT model and Tier 1 method described in IPCC
(2006) are combined to estimate emissions from non-federal grasslands and PRP manure N additions for federal
grasslands, respectively. Grassland includes pasture and rangeland that produce grass forage primarily for livestock
grazing. Rangelands are typically extensive areas of native grassland that are not intensively managed, while
pastures are typically seeded grassland (possibly following tree removal) that may also have additional management,
such as irrigation, fertilization, or interseeding legumes. DAYCENT is used to simulate N20 emissions from NRI
survey locations (USDA-NRCS 2015) on non-federal grasslands resulting from manure deposited by livestock
directly onto pastures and rangelands (i.e., PRP manure), N fixation from legume seeding, managed manure
amendments (i.e., manure other than PRP manure such as Daily Spread), and synthetic fertilizer application. Other
N inputs are simulated within the DAYCENT framework, including N input from mineralization due to
decomposition of soil organic matter and N inputs from senesced grass litter, as well as asymbiotic fixation of N
from the atmosphere. The simulations used the same weather, soil, and synthetic N fertilizer data as discussed under
the Tier 3 Approach in the Mineral Cropland Soils section. Mineral N fertilization rates are based on Carbon
Sequestration Rural Appraisals (CSRA) conducted by the USDA-NRCS (USDA-NRCS, unpublished data). The
CSRA was a solicitation of expert knowledge from USDA-NRCS staff throughout the United States to support the
national greenhouse gas inventory. Managed manure N amendments to grasslands are 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 5.2) and Annex 3.11. Biological
N fixation is simulated within DAYCENT, and therefore is not an input to the model.
Manure N deposition from grazing animals in PRP systems (i.e., PRP manure) is another key input of N to
grasslands. The amounts of PRP manure N applied on non-federal grasslands for each NRI point are based on
amount of N excreted by livestock in PRP systems. The total amount of N excreted in each county is divided by the
grassland area to estimate the N input rate associated with PRP manure. The resulting input rates are used in the
DAYCENT simulations. DAYCENT simulations of non-federal grasslands accounted for approximately 78 percent
of total PRP manure N in aggregate across the country. The remainder of the PRP manure N in each state is assumed
to be excreted on federal grasslands, and the N20 emissions are estimated using the IPCC (2006) Tier 1 method with
IPCC default emission factors. Sewage sludge is 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 is estimated from data compiled by EPA (1993, 1999, 2003), McFarland (2001), and NEBRA (2007).
Sewage sludge data on soil amendments to agricultural lands are only available at the national scale, and it is 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 N20 emissions from
grassland soils, and consequently, emissions from sewage sludge are estimated using the IPCC (2006) Tier 1
method.
Grassland area data are obtained from the U.S. Department of Agriculture NRI (Nusser and Goebel 1997; USDA-
NRCS 2015) and the U. S. Geological Survey (USGS) National Land Cover Dataset (Fry et al. 2011; Homer et al.
2007; Homer et al. 2015), which are reconciled with the Forest Inventory and Analysis Data. The area data for
pastures and rangeland are aggregated to the county level to estimate non-federal and federal grassland areas.
N20 emissions for the PRP manure N deposited on federal grasslands and applied sewage sludge N are estimated
using the Tier 1 method by multiplying the N input by the default emission factor. Emissions from manure N are
estimated at the state level and aggregated to the entire country, but emissions from sewage sludge N are calculated
exclusively at the national scale.
As previously mentioned, each NRI point is simulated 100 times as part of the uncertainty assessment, yielding a
total of over 18 million simulation runs for the analysis. Soil N20 emission estimates from DAYCENT are adjusted
using a structural uncertainty estimator accounting for uncertainty in model algorithms and parameter values (Del
Grosso et al. 2010). Soil N20 emissions and 95 percent confidence intervals are estimated for each year between
1990 and 2012, but emissions from 2013 to 2015 are assumed to be similar to 2012. The annual data are currently
available through 2012 (USDA-NRCS 2015) and will be updated when additional data are released.
5-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Total Direct N2O Emissions from Cropland and Grassland Soils
Annual direct emissions from the Tier 1 and 3 approaches for mineral and drained organic soils occurring in both
croplands and grasslands are summed to obtain the total direct N20 emissions from agricultural soil management
(see Table 5-15 and Table 5-16).
Indirect N2O Emissions
This section describes the methods used for estimating indirect soil N20 emissions from croplands and grasslands.
Indirect N20 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 N20. 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. Nitrogen made
available from mineralization of soil organic matter and residue, including N incorporated into crops and forage
from symbiotic N fixation, and input of N from asymbiotic fixation also contributes to volatilized N emissions.
Volatilized N can be returned to soils through atmospheric deposition, and a portion of the deposited N is emitted to
the atmosphere as N20. The second pathway occurs via leaching and runoff of soil N (primarily in the form of NO3")
that is made available through anthropogenic activity on managed lands, mineralization of soil organic matter and
residue, including N incorporated into crops and forage from symbiotic N fixation, and inputs of N into the soil from
asymbiotic fixation. The NO3" is subject to denitrification in water bodies, which leads to N20 emissions. Regardless
of the eventual location of the indirect N20 emissions, the emissions are assigned to the original source of the N for
reporting purposes, which here includes croplands and grasslands.
Indirect N2O Emissions from Atmospheric Deposition of Volatilized N
The Tier 3 DAYCENT model and IPCC (2006) Tier 1 methods are combined to estimate the amount of N that is
volatilized and eventually emitted as N20. DAYCENT is used to estimate N volatilization for land areas whose
direct emissions are simulated with DAYCENT (i.e., most commodity and some specialty crops and most
grasslands). The N inputs included are the same as described for direct N20 emissions in the Tier 3 Approach for
Mineral Cropland Soils and Direct N20 Emissions from Grassland Soils sections. Nitrogen volatilization from all
other areas is estimated using the Tier 1 method and default IPCC fractions for N subject to volatilization (i.e., N
inputs on croplands not simulated by DAYCENT, PRP manure N excreted on federal grasslands, sewage sludge
application on grasslands). For the volatilization data generated from both the DAYCENT and Tier 1 approaches,
the IPCC (2006) default emission factor is used to estimate indirect N20 emissions occurring due to re-deposition of
the volatilized N (see Table 5-18).
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 are combined to estimate the amount of N that is subject to leaching and surface runoff into water
bodies, and eventually emitted as N20. DAYCENT is used to simulate the amount of N transported from lands in
the Tier 3 Approach. Nitrogen transport from all other areas is 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 are not simulated by DAYCENT, sewage sludge amendments on grasslands, and PRP
manure N excreted on federal grasslands. For both the DAYCENT Tier 3 and IPCC (2006) Tier 1 methods, nitrate
leaching is assumed to be an insignificant source of indirect N20 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
Agriculture 5-31
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1 data estimated by the Tier 3 and Tier 1 approaches, the IPCC (2006) default emission factor is used to estimate
2 indirect N20 emissions that occur in groundwater and waterways (see Table 5-18).
3 Uncertainty and Time-Series Consistency
4 Uncertainty is estimated for each of the following five components of N20 emissions from agricultural soil
5 management: (1) direct emissions simulated by DAYCENT; (2) the components of indirect emissions (N volatilized
6 and leached or runoff) simulated by DAYCENT; (3) direct emissions calculated with the IPCC (2006) Tier 1
7 method; (4) the components of indirect emissions (N volatilized and leached or runoff) calculated with the IPCC
8 (2006) Tier 1 method; and (5) indirect emissions estimated with the IPCC (2006) Tier 1 method. Uncertainty in
9 direct emissions, which account for the majority of N20 emissions from agricultural management, as well as the
10 components of indirect emissions calculated by DAYCENT are estimated with a Monte Carlo Analysis, addressing
11 uncertainties in model inputs and structure (i.e., algorithms and parameterization) (Del Grosso et al. 2010).
12 Uncertainties in direct emissions calculated with the IPCC (2006) Tier 1 method, the proportion of volatilization and
13 leaching or runoff estimated with the IPCC (2006) Tier 1 method, and indirect N20 emissions are estimated with a
14 simple error propagation approach (IPCC 2006). In addition, uncertainties from the Approach 1 and Approach 3
15 (i.e., DAYCENT) estimates are combined using simple error propagation (IPCC 2006). Additional details on the
16 uncertainty methods are provided in Annex 3.12. Table 5-19 shows the combined uncertainty for direct soil N20
17 emissions ranged from 16 percent below to 26 percent above the 2015 emissions estimate of 213.3 MMT C02 Eq.,
18 and the combined uncertainty for indirect soil N20 emissions range from 46 percent below to 155 percent above the
19 2015 estimate of 38.0 MMT C02 Eq.
20 Table 5-19: Quantitative Uncertainty Estimates of N2O Emissions from Agricultural Soil
21 Management in 2015 (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Direct Soil N2O Emissions
N2O
213.3
179.7 267.9
-16% 26%
Indirect Soil N2O Emissions
N2O
38.0
20.5 96.8
-46% 155%
Notes: Due to lack of data, uncertainties in managed manure N production, PRP manure N production, other organic
fertilizer amendments, and sewage sludge amendments to soils are currently treated as certain; these sources of
uncertainty will be included in future Inventory reports.
22 Additional uncertainty is associated with an incomplete estimation of N20 emissions from managed croplands and
23 grasslands in Hawaii and Alaska. The Inventory currently includes the N20 emissions from mineral fertilizer and
24 PRP N additions in Alaska and Hawaii, and drained organic soils in Hawaii. Land areas used for agriculture in
25 Alaska and Hawaii are small relative to major commodity cropping states in the conterminous United States, so the
26 emissions are likely to be small for the other sources of N (e.g., crop residue inputs), which are not currently
27 included in the Inventory.
28 Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
29 through 2015. Details on the emission trends through time are described in more detail in the Methodology section.
30 QA/QC and Verification
31 DAYCENT results for N20 emissions and NO3" leaching are compared with field data representing various cropland
32 and grassland systems, soil types, and climate patterns (Del Grosso et al. 2005; Del Grosso et al. 2008), and further
33 evaluated by comparing the model results to emission estimates produced using the IPCC (2006) Tier 1 method for
34 the same sites. Nitrous oxide measurement data are available for 41 sites, which mostly occur in the United States,
35 with five in Europe and three in Australia, representing over 200 different combinations of fertilizer treatments and
36 cultivation practices. Nitrate leaching data are available for four sites in the United States, representing 10 different
37 combinations of fertilizer amendments/tillage practices. DAYCENT estimates of N20 emissions are closer to
38 measured values at most sites compared to the IPCC Tier 1 estimate (see Figure 5-8). In general, the IPCC Tier 1
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methodology tends to over-estimate emissions when observed values are low and under-estimate emissions when
observed values are high while DAYCENT estimates have less bias. DAYCENT accounts for key site-level factors
(i.e., weather, soil characteristics, and management) that are not addressed in the IPCC Tier 1 method, and thus the
model is better able to represent the variability in N20 emissions. DAYCENT does have a tendency to under-
estimate very high N20 emission rates; and estimates are adjusted using the statistical model derived from the
comparison of model estimates to measurements (see Annex 3.12 for more information). Regardless, the comparison
demonstrates that DAYCENT provides relatively high predictive capability for N:0 emissions, and is an
improvement over the IPCC Tier 1 method.
Figure 5-8: Comparison of Measured Emissions at Field Sites and Modeled Emissions Using
the DAYCENT Simulation Model and IPCC Tier 1 Approach (kg N2O per ha per year)
ro 5
~ Measured
~ DayCent
¦ IPCC
Spreadsheets containing input data and probability distribution functions required for DAYCENT simulations of
croplands and grasslands and unit conversion factors have been checked, in addition to the program scripts that are
used to run the Monte Carlo uncertainty analysis. Links between spreadsheets have been checked, updated, and
corrected when necessary. Spreadsheets containing input data, emission factors, and calculations required for the
Tier 1 method have been checked and updated as needed.
Recalculations Discussion
Methodological recalculations in the current Inventory are associated with the following improvements: (1) driving
the DAYCENT simulations with updated input data for land management from the National Resources Inventory
extending the time series through 2012; (2) modifying the number of experimental study sites used to quantify
model uncertainty for direct N20 emissions; (3) DAYCENT model development to improve the simulation of soil
temperature; (4) improvements in the cropping and land use histories that are simulated in DAYCENT between
1950 and 1979 that generate initial values for the model state variables, such as initial soil organic C stock values;
and (5) implementing a more robust set of model output variables that enabled a more accurate and detailed
accounting of N from synthetic fertilizers, managed manure, and PRP manure applied to grasslands. These changes
resulted in a decrease in emissions of approximately 14.4 percent on average relative to the previous Inventory and
an increase in the upper bound of the 95 percent confidence interval for direct N20 emissions from 24 to 31 percent.
The differences in emissions and uncertainty are mainly due to modifying the number of study sites used to quantify
model uncertainty.
Agriculture 5-33
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1 Planned improvements
2 Several planned improvements are underway. The DAYCENT biogeochemical model will be improved with a better
3 representation of plant phenology, particularly senescence events following grain filling in crops. In addition, crop
4 parameters associated with temperature effects on plant production will be further improved in DAYCENT with
5 additional model calibration. Model development is underway to represent the influence of nitrification inhibitors
6 and slow-release fertilizers (e.g., polymer-coated fertilizers) on N20 emissions. An improved representation of
7 drainage as well as freeze-thaw cycles are also under development. Experimental study sites will continue to be
8 added for quantifying model structural uncertainty, and studies that have continuous (daily) measurements of N20
9 (e.g., Scheer et al. 2013) will be given priority.
10 The time series of management data will be updated with information from the USDA-NRCS Conservation Effects
11 Assessment Program (CEAP). This improvement will fill several gaps in the management data including more
12 specific data on fertilizer rates, updated tillage practices, and more information on planting and harvesting dates for
13 crops.
14 Improvements are underway to simulate crop residue burning in the DAYCENT model based on the amount of crop
15 residues burned according to the data that is used in the Field Burning of Agricultural Residues source category (see
16 Section 5.7).
17 Alaska and Hawaii are not included for all sources in the current Inventory for agricultural soil management, with
18 the exception of N20 emissions from drained organic soils in croplands and grasslands for Hawaii, synthetic
19 fertilizer and PRP N amendments for grasslands in Alaska and Hawaii. A planned improvement over the next two
20 years is to add the remaining sources for these states into the Inventory analysis.
21 There is also an improvement based on updating the Tier 1 emission factor for N20 emissions from drained organic
22 soils by using the revised factor in the 2013 Supplement to the 2006IPCC Guidelines for National Greenhouse Gas
23 Inventories: Wetlands (IPCC 2013).
24 All of these improvements are expected to be completed for the 1990 through 2017 Inventory (2019 submission to
25 the UNFCCC). However, the time line may be extended if there are insufficient resources to fund all or part of these
26 planned improvements.
27 5.5 Liming (IPCC Source Category 3G)
28 Crushed limestone (CaCCh) and dolomite (CaMg(C03)2) are added to soils by land managers to increase soil pH
29 (i.e., to reduce acidification). Carbon dioxide emissions occur as these compounds react with hydrogen ions in soils.
30 The rate and ultimate magnitude of degradation of applied limestone and dolomite depends on the soil conditions,
31 soil type, climate regime, and whether limestone or dolomite is applied. Emissions from liming of soils have
32 fluctuated over the past 25 years in the United States, ranging from 3.6 MMT C02 Eq. to 6.0 MMT C02 Eq. In
33 2015, liming of soils in the United States resulted in emissions of 3.8 MMT C02 Eq. (1.0 MMT C), representing an
34 18 percent decrease in emissions since 1990 (see Table 5-20 and Table 5-21). The trend is driven by the amount of
35 limestone and dolomite applied to soils over the time period.
36 Table 5-20: Emissions from Liming (MMT CO2 Eq.)
Source
1990
2005
2011
2012
2013
2014
2015
Limestone
4.1
3.9
3.4
4.5
3.6
3.3
3.5
Dolomite
0.6
0.4
0.4
1.5
0.3
0.3
0.3
Total
4.7
4.3
3.9
6.0
3.9
3.6
3.8
Note: Totals may not sum due to independent rounding.
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Table 5-21: Emissions from Liming (MMT C)
Source
1990
2005
2011
2012
2013
2014
2015
Limestone
1.1
1.1
0.9
1.2
1.0
0.9
0.9
Dolomite
0.2
0.1
0.1
0.4
0.1
0.1
0.1
Total
1.3
1.2
1.1
1.6
1.1
1.0
1.0
Note: Totals may not sum due to independent rounding.
Methodology
Carbon dioxide emissions from application of limestone and dolomite to soils were estimated using a Tier 2
methodology consistent with IPCC (2006). The annual amounts of limestone and dolomite applied (see Table 5-22)
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 carbonates that are transported from soils through hydrological processes
and eventually deposited in ocean basins (West and McBride 2005). This analysis of lime dissolution is based on
studies in the Mississippi River basin, where the vast majority of lime application occurs in the United States (West
2008). Moreover, much of the remaining lime application is occurring under similar precipitation regimes, and so
the emission factors are considered a reasonable approximation for all lime application in the United States (West
2008).
The annual application rates of limestone and dolomite were derived from estimates and industry statistics provided
in the Minerals Yearbook and Mineral Industry Surveys (Tepordei 1993 through 2006; Willett 2007a, 2007b, 2009,
2010, 2011a, 2011b, 2013a, 2014, 2015 and 2016; USGS 2008 through 2016). The U.S. Geological Survey (USGS;
U.S. Bureau of Mines prior to 1997) compiled production and use information through surveys of crushed stone
manufacturers. However, manufacturers provided different levels of detail in survey responses so the estimates of
total crushed limestone and dolomite production and use were divided into three components: (1) production by end-
use, as reported by manufacturers (i.e., "specified" production); (2) production reported by manufacturers without
end-uses specified (i.e., "unspecified" production); and (3) estimated additional production by manufacturers who
did not respond to the survey (i.e., "estimated" production).
Box 5-2: Comparison
1
. Inventory Approach and IPCC (2006)
I
Emissions from liming of soils were estimated using a Tier 2 methodology based on emission factors specific to the
United States that are lower than the IPCC (2006) emission default factors. Most lime application in the United
States occurs in the Mississippi River basin, or in areas that have similar soil and rainfall regimes as the Mississippi
River basin. Under these conditions, a significant portion of dissolved agricultural lime leaches through the soil into
groundwater. Groundwater moves into channels and is transported to larger rives and eventually the ocean where
CaCCb precipitates to the ocean floor (West and McBride 2005). The U.S.-specific emission factors (0.059 metric
ton C/metric ton limestone and 0.064 metric ton C/metric ton dolomite) are about half of the IPCC (2006) emission
factors (0.12 metric ton C/metric ton limestone and 0.13 metric ton C/metric ton dolomite). For comparison, the
2015 U.S. emission estimate from liming of soils is 3.8 MMT CO2 Eq. using the U.S.-specific factors. In contrast,
emissions would be estimated at 7.8 MMT CO2 Eq. using the IPCC (2006) default emission factors.
Data on "specified" limestone and dolomite amounts were used directly in the emission calculation because the end
use is provided by the manufacturers and can be used to directly determine the amount applied to soils. However, it
is not possible to determine directly how much of the limestone and dolomite is applied to soils for manufacturer
surveys in the "unspecified" and "estimated" categories. For these categories, the amounts of crushed limestone and
dolomite applied to soils were determined by multiplying the percentage of total "specified" limestone and dolomite
production that is applied to 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 soils is proportional to the amount of total "specified" crushed limestone and dolomite that was
applied to soils.
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1 In addition, data were not available for 1990, 1992, and 2015 on the fractions of total crushed stone production that
2 were limestone and dolomite, and on the fractions of limestone and dolomite production that were applied to soils.
3 To estimate the 1990 and 1992 data, a set of average fractions were calculated using the 1991 and 1993 data. These
4 average fractions were applied to the quantity of "total crushed stone produced or used" reported for 1990 and 1992
5 in the 1994 Minerals Yearbook (Tepordei 1996). To estimate 2015 data, 2014 fractions were applied to a 2015
6 estimate of total crushed stone presented in the USGS Mineral Industry Surveys: Crushed Stone and Sand and
1 Gravel in the First Quarter of 2016 (USGS 2016).
8 The primary source for limestone and dolomite activity data is the Minerals Yearbook, published by the Bureau of
9 Mines through 1994 and by the USGS from 1995 to the present. In 1994, the "Crushed Stone" chapter in the
10 Minerals Yearbook began rounding (to the nearest thousand metric tons) quantities for total crushed stone produced
11 or used. It then reported revised (rounded) quantities for each of the years from 1990 to 1993. In order to minimize
12 the inconsistencies in the activity data, these revised production numbers have been used in all of the subsequent
13 calculations.
14 Table 5-22: Applied Minerals (MMT)
Mineral
1990
2005
2011
2012
2013
2014
2015
Limestone
19.0
18.1
15.9
20.8
16.4
15.3
16.1
Dolomite
2.4
1.9
1.9
6.3
1.5
1.3
1.4
15 Uncertainty and Time-Series Consistency
16 Uncertainty regarding the amount of limestone and dolomite applied to soils was estimated at ±15 percent with
17 normal densities (Tepordei 2003; Willett 2013b). Analysis of the uncertainty associated with the emission factors
18 included the fraction of lime dissolved by nitric acid versus the fraction that reacts with carbonic acid, and the
19 portion of bicarbonate that leaches through the soil and is transported to the ocean. Uncertainty regarding the time
20 associated with leaching and transport was not addressed in this analysis, but is assumed to be a relatively small
21 contributor to the overall uncertainty (West 2005). The probability distribution functions for the fraction of lime
22 dissolved by nitric acid and the portion of bicarbonate that leaches through the soil were represented as smoothed
23 triangular distributions between ranges of zero and 100 percent of the estimates. The uncertainty surrounding these
24 two components largely drives the overall uncertainty.
25 A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the uncertainty in CO2 emissions from
26 liming. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 5-23. Carbon
27 dioxide emissions from carbonate lime application to soils in 2015 were estimated to be between -0.4 and 7.2 MMT
28 CO2 Eq. at the 95 percent confidence level. This confidence interval represents a range of 111 percent below to 88
29 percent above the 2015 emission estimate of 3.8 MMT CO2 Eq.
30 Table 5-23: Approach 2 Quantitative Uncertainty Estimates for CO2 Emissions from Liming
31 (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Liming
CO2
3.8
(0.4) 7.2
-111% +88%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
32 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
3 3 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
34 above.
5-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 QA/QC and Verification
2 A source-specific QA/QC plan for liming has been developed and implemented, and the quality control effort
3 focused on the Tier 1 procedures for this Inventory. No errors were found.
4 Recalculations Discussion
5 Adjustments were made in the current Inventory to improve the results. First, limestone and dolomite application
6 data for 2014 were approximated in the previous Inventory using a ratio of total crushed stone for 2014 relative to
7 2015 (similar to 2015 in the current Inventory). The estimates for 2014 were updated with the recently published
8 datafromUSGS (2016). With this revision in the activity data, the emissions decreased by 12.8 percent in 2014
9 relative to the previous Inventory.
10 5.6 Urea Fertilization (IPCC Source Category
11 3H)
12 The use of urea (CO(NH2)2) as a fertilizer leads to CO2 emissions through the release of CO2 that was fixed during
13 the industrial production process. In the presence of water and urease enzymes, urea is converted into ammonium
14 (NH4+), hydroxyl ion (OH), and bicarbonate (HCO3 ). The bicarbonate then evolves into CO2 and water. Emissions
15 from urea fertilization in the United States totaled 5.0 MMT CO2 Eq. (1.4 MMT C) in 2015 (Table 5-24 and Table
16 5-25). Due to an increase in application of urea fertilizers between 1990 and 2015, CO2 emissions have increased by
17 108 percent from this management activity.
18 Table 5-24: CO2 Emissions from Urea Fertilization (MMT CO2 Eq.)
Source 1990 2005 2011 2012 2013 2014 2015
Urea Fertilization 2.4 3.5 41 43 45 48 51)
19 Table 5-25: CO2 Emissions from Urea Fertilization (MMT C)
Source 1990 2005 2011 2012 2013 2014 2015
Urea Fertilization 0.7 1.0 1.1 1.2 1.2 1.3 1.4
20 Methodology
21 Carbon dioxide emissions from the application of urea to agricultural soils were estimated using the IPCC (2006)
22 Tier 1 methodology. The method assumes that all CO2 fixed during the industrial production process of urea are
23 released after application. The annual amounts of urea applied to croplands (see Table 5-26) were derived from the
24 state-level fertilizer sales data provided in Commercial Fertilizer reports (TVA 1991, 1992, 1993, 1994; AAPFCO
25 1995 through 2016). These amounts were multiplied by the default IPCC (2006) emission factor (0.20 metric tons of
26 C per metric ton of urea), which is equal to the C content of urea on an atomic weight basis. Because fertilizer sales
27 data are reported in fertilizer years (July previous year through June current year), a calculation was performed to
28 convert the data to calendar years (January through December). According to monthly fertilizer use data (TVA
29 1992b), 35 percent of total fertilizer used in any fertilizer year is applied between July and December of the previous
30 calendar year, and 65 percent is applied between January and June of the current calendar year. For example, in the
31 2000 fertilizer year, 35 percent of the fertilizer was applied in July through December 1999, and 65 percent was
32 applied in January through June 2000.
Agriculture 5-37
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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
31
32
33
34
35
Fertilizer sales data for the 2014 and 2015 fertilizer years (i.e., July 2013 through June 2014 and July 2014 through
June 2015) were not available fortius Inventory. Therefore, urea application in the 2014 and 2015 fertilizer years
were estimated using a linear, least squares trend of consumption over the data from the previous five years (2009
through 2013) at the state level. A trend of five years was chosen as opposed to a longer trend as it best captures the
current inter-state and inter-annual variability in consumption. 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. The
fertilizer year data is then converted into calendar year data using the method described above.
Table 5-26: Applied Urea (MMT)
1990
2005
2011
2012
2013
2014
2015
Urea Fertilizer3
3.3
4.8
5.6
5.8
6.1
6.5
6.9
a These numbers represent amounts applied to all agricultural land, including Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land
Converted to Grassland, Settlements Remaining Settlements, Forest Land Remaining Forest Land
and Land Converted to Forest Land, as it is not currently possible to apportion the data by land-
use category.
Uncertainty and Time-Series Consistency
Uncertainty estimates are presented in Table 5-27 for urea fertilization. An Approach 2 Monte Carlo analysis was
completed. The largest source of uncertainty was the default emission factor, which assumes that 100 percent of the
C in CO(NH2)2 applied to soils is ultimately emitted into the environment as CO2. This factor does not incorporate
the possibility that some of the C may be retained in the soil, and therefore the uncertainty range was set from 0
percent emissions to the maximum emission value of 100 percent using a triangular distribution. In addition, urea
consumption data also have uncertainty that is propagated through the emission calculation using a Monte Carlo
simulation approach as described by the IPCC (2006). Carbon dioxide emissions from urea fertilization of
agricultural soils in 2015 were estimated to be between 2.9 and 5.2 MMT CO2 Eq. at the 95 percent confidence
level. This indicates a range of 43 percent below to 3 percent above the 2015 emission estimate of 5.0 MMT CO2
Eq.
Table 5-27: Quantitative Uncertainty Estimates for CO2 Emissions from Urea Fertilization
(MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Urea Fertilization
CO2
5.0
2.9 5.2
-43% 3%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
There are additional uncertainties that are not quantified in this analysis. Urea for non-fertilizer use, such as aircraft
deicing, may be included in consumption totals, but the amount is likely very small. For example, research on
aircraft deicing practices is consistent with this assumption based on a 1992 survey that 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 metric tons per year, or less than 0.07 percent of the fertilizer total for 2007 (Itle 2009). In
addition, there is uncertainty surrounding the underlying assumptions behind the calculation that converts fertilizer
years to calendar years. These uncertainties are negligible over multiple years, however, because an over- or under-
estimated value in one calendar year is addressed with corresponding increase or decrease in the value for the
subsequent year.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
5-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 QA/QC and Verification
2 A source-specific QA/QC plan for Urea Fertilization has been developed and implemented, and no errors were
3 found.
4 Recalculations Discussion
5 Recalculations resulted from updated urea application estimates in a new AAPFCO report (2016). Specifically, the
6 2012, 2013 and 2014 activity data (i.e., amount of urea applied) for the states of Alabama and Virginia were
7 updated. This resulted in an emissions increase for the United States of 1 percent in 2012, 3.7 percent in 2013 and
8 5.9 percent in 2014.
9 5.7 Field Burning of Agricultural Residues (IPCC
10 Source Category 3F)
11 Crop production creates large quantities of agricultural crop residues, which farmers manage in a variety of ways.
12 For example, crop residues can be left in the field and possibly incorporated into the soil with tillage; collected and
13 used as fuel, animal bedding material, supplemental animal feed, or construction material; composted and applied to
14 soils; transported to landfills; or burned in the field. Field burning of crop residues is not considered a net source of
15 CO2 emissions because the C released to the atmosphere as CO2 during burning is reabsorbed during the next
16 growing season by the crop. However, crop residue burning is a net source of CH4, N20, CO, and NOx, which are
17 released during combustion.
18 In the United States, field burning of agricultural residues commonly occurs in southeastern states, the Great Plains,
19 and the Pacific Northwest (McCarty 2011). The primary crops that are managed with residue burning include corn,
20 cotton, lentils, rice, soybeans, sugarcane, and wheat (McCarty 2009). Rice, sugarcane, and wheat residues account
21 for approximately 70 percent of all crop residue burning and emissions (McCarty 2011). In 2015, CH4 and N20
22 emissions from field burning of agricultural residues were 0.3 MMT CO2 Eq. (11 kt) and 0.1 MMT. CO2 Eq. (0.3
23 kt), respectively (see Table 5-28 and Table 5-29). Annual emissions of CH4 and N20 have increased from 1990 to
24 2015 by 25 percent and 23 percent, respectively. The increase in emissions over time is due to larger amounts of
25 residue production with higher yielding crop varieties and fuel loads.
26 Table 5-28: ChU and N2O Emissions from Field Burning of Agricultural Residues (MMT CO2
27 Eq.)
Gas/Crop Type
1990
2005
2011
2012
2013
2014
2015
CH4
0.2
0.2
0.3
0.3
0.3
0.3
0.3
Wheat
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Rice
0.1
+
0.1
0.1
0.1
0.1
+
Sugarcane
Corn
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Cotton
+
+
+
+
+
+
+
Soybeans
Lentil
+
+
+
+
+
+
+
+
+
+
+
+
+
+
N2O
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Wheat
+
+
+
+
+
+
+
Rice
+
+
+
+
+
+
+
Sugarcane
Corn
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Cotton
+
+
+
+
+
+
+
Soybeans
Lentil
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Total 0.3 0.3 0.4 0.4 0.4 0.4 0.4
Agriculture 5-39
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
+ Does not exceed 0.05 MMT CO2 Eq.
Note: Totals may not sum due to independent rounding.
Table 5-29: ChU, N2O, CO, and NOx Emissions from Field Burning of Agricultural Residues
(kt)
Gas/Crop Type
1990
2005
2011
2012
2013
2014
2015
CH4
10
8
11
11
11
11
11
Wheat
>
4
5
5
5
5
5
Rice
2
2
2
2
2
2
2
Sugarcane
Corn
1
1
1
1
1
1
1
1
1
1
2
2
2
2
Soybeans
Lentil
1
+ *>>'
1
+
1
+
1
+
1
+
1
+
1
+
Cotton
+ y;,:
+
+
+
+
+
+
N2O
+
+
+
+
+
+
+
Wheat
+ r|.-
+
+
+
+
+
+
Rice
+ >'s.
+
+
+
+
+
+
Sugarcane
Corn
+
+ K;-
+
+
+
+
+
+
+
+
+
+
+
+
Cotton
+ $'t
+
+
+
+
+
+
Soybeans
Lentil
+
+ f%
+
+
+
+
+
+
+
+
+
+
+
+
CO
202
177
233
234
238
238
239
NOx
(,
6
8
8
8
8
8
+ Does not exceed 0.5 kt.
Methodology
A U.S.-specific Tier 2 method was used to estimate greenhouse gas emissions from field burning of agricultural
residues (for more details comparing the U.S.-specific approach to the IPCC (2006) default approach, see Box 5-3).
In order to estimate the amounts of C and N released during burning, the following equation was used:
C or N released = £ for all crop types and states
AB
CAH x CP x RCR x DMF x BE x CE x (FC or FN)
where,
= Total area of crop burned, by state
= Total area of crop harvested, by state
= Annual production of crop in kt, by state
= Amount of residue produced per unit of crop production
= Amount of dry matter per unit of bio mass for a crop
= Amount of C or N per unit of dry matter for a crop
= The proportion of prefire fuel biomass consumed17
= The proportion of C or N released with respect to the total amount of C or N
available in the burned material, respectively
Crop Production and Crop Area Harvested were available by state and year from USD A (2016) for all crops (except
rice in Florida and Oklahoma, as detailed below). The amount of C or N released was used in the following equation
to determine the CH4, CO, N20, and NOx emissions from the Field Burning of Agricultural Residues:
Area Burned (AB)
Crop Area Harvested (CAH)
Crop Production (CP)
Residue: Crop Ratio (RCR)
Dry Matter Fraction (DMF)
Fraction of C or N (FC or FN)
Burning Efficiency (BE)
Combustion Efficiency (CE)
17 In IPCC/UNEP/OECD/IEA (1997), the equation for C orN released contains the variable 'fraction oxidized in burning'. This
variable is equivalent to (burning efficiency x combustion efficiency).
5-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
CH4 and CO, or N2O and NOx Emissions from Field Burning of Agricultural Residues =
C or N Released x ER x CF
where,
Emissions Ratio (ER)
Conversion Factor (CF)
g CH4-C or CO-C/g C released, or g N20-N or NOx-N/g N released
conversion, by molecular weight ratio, of CH4-C to C (16/12), or CO-C to C
(28/12), or NzO-N to N (44/28), or NOx-N to N (30/14)
Box 5-3: Comparison of Tier 2 U.S. Inventory Approach and IPCC (2006) Default Approac
Emissions from Field Burning of Agricultural Residues were calculated using a Tier 2 methodology that is based on
the method developed by the IPCC/UNEP/OECD/IEA (1997) and incorporates crop- and country-specific emission
factors and variables. The rationale for using the IPCC/UNEP/OECD/IEA (1997) approach rather than the method
provided in the 2006 IPCC Guidelines is as follows: (1) the equations from both guidelines rely on the same
underlying variables (though the formats differ); (2) the IPCC (2006) equation was developed to be broadly
applicable to all types of biomass burning, and, thus, is not specific to agricultural residues; and (3) the IPCC (2006)
default factors are provided only for four crops (corn, rice, sugarcane, and wheat) while this Inventory includes
emissions from seven crops (corn, cotton, lentils, rice, soybeans, sugarcane, and wheat).
A comparison of the methods and factors used in: (1) the current Inventory and (2) the default IPCC (2006)
approach was undertaken in the 1990 through 2015 Inventory report to determine the difference in overall estimates
between the two approaches. To estimate greenhouse gas emissions from field burning of agricultural residues using
the IPCC (2006) methodology, the following equation—cf. IPCC (2006) Equation 2.27—was used:
The IPCC (2006) Tier 1 method approach that utilizes default mass of fuel values resulted in 1 percent higher
emissions of CH4 and 14 percent higher emissions of N20 compared to this Inventory. If U.S.-specific data are used
to derive the Mass of Fuel (Mb) from USD A NASS statistics (USDA 2016), i.e., Tier 2 method, then the IPCC
(2006) method resulted in 28 percent higher emissions of CH4 and 44 percent higher emissions of N20 compared to
this Inventory. This larger difference is attributable to lower combustion efficiency values in
IPCC/UNEP/OECD/IEA (1997). In particular, sugarcane has a much lower combustion efficiency value in the
earlier guidelines. A lower value is justified because sugarcane is burned prior to harvesting and has a higher
moisture content that reduces the combustion efficiency, unlike most other crops (IPCC/UNEP/OECD/IEA 1997).
IPCC (2006) does not address the unique burning regime of sugarcane. Overall, the IPCC/UNEP/OECD/IEA (1997)
method is considered more appropriate for U.S. conditions because it is more flexible for incorporating country-
specific data compared to IPCC (2006) approach.
Crop yield data (except rice in Florida) were based on USDA's QuickStats (USDA 2016), and crop area data were
based on the 2012 NRI (USDA-NRCS 2015). In order to estimate total crop production, the crop yield data from
USDA Quick Stats crop yields was multiplied by the NRI crop areas. Rice yield data for Florida was estimated
separately because yield data were not collected by USDA. Total rice production for Florida was determined using
NRI crop areas and total yields were based on average primary and ratoon rice yields from Schueneman and Deren
(2002). Relative proportions of ratoon crops were derived from information in several publications (Schueneman
1999, 2000, 2001; Deren2002; Kirstein2003, 2004; Cantens 2004, 2005; Gonzalez 2007 through2014). The
production data for the crop types whose residues are burned are presented in Table 5-30. Crop weight by bushel
was obtained from Murphy (1993).
Emissions (kt) = AB x (MbX Cf) x Gef x 10~6
where,
Area Burned (AB)
Mass Burned (MB x Cf)
Total area of crop burned (ha)
IPCC (2006) default fuel biomass consumption (metric tons dry matter burnt
ha-1) and US-Specific Values using NASS Statistics (USDA 2016)
IPCC (2006) emission factor (g kg1 dry matter burnt)
Emission Factor (Gef)
Agriculture 5-41
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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
The fraction of crop area burned was calculated using data on area burned by crop type and state18 from McCarty
(2010) for corn, cotton, lentils, rice, soybeans, sugarcane, and wheat.19 McCarty (2010) used remote sensing data
from MODIS to estimate area burned by crop. State-level area burned data were divided by state-level crop area
harvested data to estimate the percent of crop area burned by crop type for each state. The average percentage of
crop area burned at the national scale is shown in Table 5-31. Data on fraction of crop area burned were only
available from McCarty (2010) for the years 2003 through 2007. For other years in the time series, the percent area
burned was set equal to the average over the five-year period from 2003 to 2007. Table 5-31 shows the resulting
percentage of crop residue burned at the national scale by crop type. State-level estimates are also available upon
request.
All residue: crop product mass ratios except sugarcane and cotton were obtained from Strehler and Stiitzle (1987).
The ratio for sugarcane is from Kinoshita (1988) and the ratio for cotton is from Huang et al. (2007). The residue:
crop ratio for lentils was assumed to be equal to the average of the values for peas and beans. Residue dry matter
fractions for all crops except soybeans, lentils, and cotton were obtained from Turn et al. (1997). Soybean and lentil
dry matter fractions were obtained from Strehler and Stiitzle (1987); the value for lentil residue was assumed to
equal the value for bean residue. 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), and 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). 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). See Table 5-32 for a summary of the crop-specific conversion factors.
Emission ratios and mole ratio conversion factors for all gases were based on the Revised 1996 IPCC Guidelines
(IPCC/UNEP/OECD/IEA 1997) (see Table 5-33).
Table 5-30: Agricultural Crop Production (kt of Product)
Crop
1990
2005
2011
2012
2013
2014
2015
Corn3
229,257
300,965
356,783
311,751
398,817
429,405
422,436
Cotton
4,446
6,811
5,607
5,967
5,647
5,934
5,575
Lentils
38
248
59
121
147
134
117
Rice
8,907
12,596
10,408
10,080
10,381
10,347
10,202
Soybeans
55,178
86,908
87,557
85,523
93,928
102,065
102,772
Sugarcane
31,827
32,496
16,795
16,555
16,129
17,136
18,336
Wheat
79,011
70,074
61,902
71,234
69,287
64,650
66,672
a Corn for grain (i.e., excludes corn for silage).
Table 5-31: U.S. Average Percent Crop Area Burned by Crop (Percent)
State
1990
2005
2011
2012
2013
2014
2015
Corn
+
+
+
+
+
+
+
Cotton
1%
1%
1%
1%
1%
1%
1%
Lentils
2%
+
+
+
+
+
+
Rice
9%
5%
7%
7%
7%
7%
7%
Soybeans
+
+
+
+
+
+
+
Sugarcane
10%
14%
53%
53%
52%
53%
54%
Wheat
2%
2%
3%
3%
3%
3%
2%
+ Does not exceed 0.5 percent.
18 Alaska and Hawaii were excluded.
19 McCarty (2009) also examined emissions from burning of Kentucky bluegrass and a general "other crops/fallow" category,
but USDA crop area and production data were insufficient to estimate emissions from these crops using the methodology
employed in the Inventory. McCarty (2009) estimates that approximately 18 percent of crop residue emissions result from
burning of the Kentucky bluegrass and "other crops" categories.
5-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Table 5-32: Key Assumptions for Estimating Emissions from Field Burning of Agricultural
2 Residues
Burning
Combustion
Residue: Crop
Dry Matter
Efficiency
Efficiency
Crop
Ratio
Fraction
C Fraction
N Fraction
(Fraction)
(Fraction)
Corn
1.0
0.91
0.448
0.006
0.93
0.88
Cotton
1.6
0.90
0.445
0.012
0.93
0.88
Lentils
2.0
0.85
0.450
0.023
0.93
0.88
Rice
1.4
0.91
0.381
0.007
0.93
0.88
Soybeans
2.1
0.87
0.450
0.023
0.93
0.88
Sugarcane
0.2
0.62
0.424
0.004
0.81
0.68
Wheat
1.3
0.93
0.443
0.006
0.93
0.88
3 Table 5-33: Greenhouse Gas Emission Ratios and Conversion Factors
Gas
Emission Ratio
Conversion Factor
CH4:C
0.005a
16/12
CO:C
0.060a
28/12
N20:N
0.007b
44/28
NOx:N
0.121b
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).
4 Uncertainty and Time-Series Consistency
5 The results of the Approach 2 Monte Carlo uncertainty analysis are summarized in Table 5-34. Methane emissions
6 from field burning of agricultural residues in 2015 were estimated to be between 0.17 and 0.39 MMT CO2 Eq. at a
7 95 percent confidence level. This indicates a range of 40 percent below and 41 percent above the 2015 emission
8 estimate of 0.3 MMT CO2 Eq. Nitrous oxide emissions were estimated to be between 0.07 and 0.13 MMT CO2 Eq.,
9 or approximately 29 percent below and 30 percent above the 2015 emission estimate of 0.1 MMT CO2 Eq.
10 Table 5-34: Approach 2 Quantitative Uncertainty Estimates for ChU and N2O Emissions from
11 Field Burning of Agricultural Residues (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Field Burning of Agricultural
Residues
CH4
0.3
0.17
0.39
-40%
41%
Field Burning of Agricultural
Residues
N2O
0.1
0.07
0.13
-29%
30%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
12 Due to data limitations, there are additional uncertainties in agricultural residue burning, particularly the omission of
13 burning associated with Kentucky bluegrass and "other crop" residues.
14 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
15 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
16 above.
Agriculture 5-43
-------
i QA/QC and Verification
2 A source-specific QA/QC plan for field burning of agricultural residues was implemented with Tier 1 analyses, and
3 no errors were found in this Inventory.
4 Recalculations Discussion
5 The crop area data were updated with the 2012 NRI survey (USDA-NRCS 2015). This change resulted in a
6 relatively small change in emissions, with CH4 and N20 emissions decreasing by 0.8 percent and 0.5 percent,
7 respectively.
8 Planned Improvements
9 A new method is in development that will directly link agricultural residue burning with the Tier 3 methods that are
10 used in several other source categories, including Agricultural Soil Management, Cropland Remaining Cropland,
11 and Land Converted to Cropland chapters of the Inventory. The method is based on the DAYCENT model, and
12 burning events will be simulated directly within the process-based model framework using information derived from
13 remote sensing fire products. This improvement will lead to greater consistency in the methods for these sources,
14 and better ensure mass balance of C and N in the Inventory analysis.
15
16
5-44 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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
Land Usi
Forestry
This chapter provides an assessment of the greenhouse gas fluxes resulting from land use and conversion of land-use
categories in the United States.1 The Intergovernmental Panel on Climate Change's 2006IPCC Guidelines for
National Greenhouse Gas Inventories (IPCC 2006) recommends reporting fluxes according to changes within and
conversions between certain land-use types termed: Forest Land, Cropland, Grassland, Settlements and Wetlands (as
well as Other Land).
The greenhouse gas flux from Forest Land Remaining Forest Land is reported for all forest ecosystem carbon (C)
stocks, harvested wood pools, non-carbon dioxide (non-CCh) emissions from forest fires, the application of synthetic
fertilizers to forest soils, and the draining of organic soils. Fluxes from Land Converted to Forest Land are included
for C stock changes from mineral soils, aboveground biomass, belowground biomass, dead wood, and litter.
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. The reported
greenhouse gas fluxes from these agricultural lands include changes in soil organic C stocks in mineral and organic
soils due to land use and management, and for Forest Land Converted to Cropland and Forest Land Converted to
Grassland, the changes in aboveground biomass, belowground biomass, dead wood, and litter C stocks are
reported.2 The greenhouse gas flux from Grassland Remaining Grassland also includes estimates of non-CCh
emissions from grassland fires.
Fluxes from Wetlands Remaining Wetlands include CO2, methane (CH4) and nitrous oxide (N2O) emissions from
managed peatlands, as well as C stock changes in coastal wetlands soils, CH4 emissions from vegetated coastal
wetlands, and N20 emissions from aquaculture in coastal wetlands. Estimates for Land Converted to Wetlands
include C stock changes and CH4 emissions from land converted to vegetated coastal wetlands.
Fluxes from Settlements Remaining Settlements include those from organic soils, urban trees, application of nitrogen
fertilizer to soils, and landfilled yard trimmings and food scraps. The reported greenhouse gas flux from Land
Converted to Settlements includes changes in organic C stocks in mineral and organic soils due to land use and
management, and for Forest Land Converted to Settlements, the changes in aboveground biomass, belowground
biomass, dead wood, and litter C stocks are also included.
The land use, land-use change, and forestry (LULUCF) sector in 2015 resulted in a net increase in C stocks (i.e., net
CO2 removals) of 386.8 MMT CO2 Eq. (105.5 MMT C).3 This represents an offset of approximately 5.9 percent of
1 The term "flux" is used to describe the net emissions of greenhouse gases accounting for both the emissions of CO2 to and the
removals of CO2 from the atmosphere. Removal of CO2 from the atmosphere is also referred to as "carbon sequestration."
2 Direct and indirect emissions of N2O from inputs of N to cropland and grassland soils are included in the Agriculture Chapter.
3 LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land
Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land
Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and
Land Converted to Settlements.
Land Use, Land-Use Change, and Forestry 6-1
-------
1 total (i.e., gross) greenhouse gas emissions in 2015. Emissions from LULUCF activities in 2015 are 20.4 MMT CO2
2 Eq. and represent 0.3 percent of total greenhouse gas emissions.4
3 Total C sequestration in the LULUCF sector decreased by approximately 16.0 percent between 1990 and 2015. This
4 decrease was primarily due to a decrease in the rate of net C accumulation in forests and an increase in emissions
5 from Land Converted to Grassland,5 Net C accumulation in Settlements Remaining Settlements increased from 1990
6 to 2015, while net C accumulation in Forest Land Remaining Forest Land, Land Converted to Forest Land,
1 Cropland Remaining Cropland, and Grassland Remaining Grassland slowed over this period. Net C accumulation
8 remained steady from 1990 to 2015 in Wetlands Remaining Wetlands and Land Converted to Wetlands. Emissions
9 from Land Converted to Cropland decreased during this period, while emissions from Land Converted to Grassland
10 and Land Converted to Settlements increased. The C stock change from LULUCF is summarized in Table 6-1.
11 Table 6-1: C Stock Change from Land Use, Land-Use Change, and Forestry (MMT CO2 Eq.)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Forest Land Remaining Forest Land
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
Changes in Forest Carbon Stocka
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
Land Converted to Forest Land
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Changes in Forest Carbon Stock3
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Cropland Remaining Cropland
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Changes in Agricultural Carbon Stockb'c
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Land Converted to Cropland
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Changes in Agricultural Carbon Stockb,c
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Grassland Remaining Grassland
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Changes in Agricultural Carbon Stockb-C
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Land Converted to Grassland
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Changes in Agricultural Carbon Stockb-C
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Wetlands Remaining Wetlands
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
Changes in Coastal Wetland Carbon Stock
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
Land Converted to Wetlands
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Changes in Coastal Wetland Carbon Stock
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Remaining Settlements
(86.2)
(91.4)
(98.7)
(99.2)
(99.8)
(101.2)
(102.1)
Changes in Settlement Soil Carbon Stock
0.1
0.5
1.3
1.3
1.3
1.3
1.4
Changes in Urban Tree Carbon Stock
(60.4)
(80.5)
(87.3)
(88.4)
(89.5)
(90.6)
(91.7)
Landfilled Yard Trimmings and Food
Scraps
(26.0)
(11.4)
(12.7)
(12.2)
(11.6)
(11.9)
(11.8)
Land Converted to Settlements
123.8
163.6
157.6
150.2
150.2
150.2
150.2
Changes in Settlement Soil Carbon Stock
123.8
163.6
157.6
150.2
150.2
150.2
150.2
LULUCF C Stock Change
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
b Estimates include C stock changes in all pools.
c Quality control uncovered errors in the estimate and uncertainty for 2013, 2014,2015, which will be updated following
public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland sections in
the LULUCF chapter of this report.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
12 Emissions from LULUCF activities are shown in Table 6-2. Lands undergoing peat extraction (i.e., Peatlands
13 Remaining Peatlands) resulted in CO2 emissions of 0.8 MMT CO2 Eq. (763 kt of CO2). Forest fires were the largest
14 source of CH4 emissions from LULUCF in 2015, totaling 7.3 MMT CO2 Eq. (292 kt of CH4). Coastal Wetlands
4 LULUCF emissions include the CO2, CELi, andN20 emissions from Peatlands Remaining Peatlands, CELi andN20 emissions
reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal Wetlands Remaining
Coastal Wetlands; CELi emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from Forest Soils and Settlement
Soils.
5 Carbon sequestration estimates are net figures. The C stock in a given pool fluctuates due to both gains and losses. When losses
exceed gains, the C stock decreases, and the pool acts as a source. When gains exceed losses, the C stock increases, and the pool
acts as a sink; also referred to as net C sequestration or removal.
6-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Remaining Coastal Wetlands resulted in CH4 emissions of 3.5 MMT CO2 Eq. (141 kt of CH4). Grassland fires
2 resulted in CH4 emissions of 0.4 MMT CO2 Eq. (16 kt of CH4). Peatlands Remaining Peatlands and Land
3 Converted to Wetlands resulted in CH4 emissions of less than 0.05 MMT CO2 Eq.
4 Forest fires were also the largest source of N20 emissions from LULUCF in 2015, totaling 4.8 MMT CO2 Eq. (16 kt
5 of N20). Nitrous oxide emissions from fertilizer application to settlement soils in 2015 totaled to 2.6 MMT CO2 Eq.
6 (9 kt of N20). This represents an increase of 81.5 percent since 1990. Additionally, the application of synthetic
7 fertilizers to forest soils in 2015 resulted in N20 emissions of 0.5 MMT CO2 Eq. (2 kt of N20). Nitrous oxide
8 emissions from fertilizer application to forest soils have increased by 455 percent since 1990, but still account for a
9 relatively small portion of overall emissions. Grassland fires resulted in N20 emissions of 0.4 MMT CO2 Eq. (1 kt
10 of N2O). Coastal Wetlands Remaining Coastal Wetlands resulted in N2O emissions of 0.1 MMT CO2 Eq. (0.5 kt of
11 N2O), and Peatlands Remaining Peatlands resulted in N2O emissions of less than 0.05 MMT CO2 Eq.
12 Emissions and removals from LULUCF are summarized in Table 6-3 by land-use and category, and Table 6-4 and
13 Table 6-5 by gas in MMT CO2 Eq. and kt, respectively.
14 Table 6-2: Emissions from Land Use, Land-Use Change, and Forestry by Gas (MMT CO2 Eq.)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
CO2
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
1.1
1.1
0.9
0.8
0.8
0.8
0.8
CH4
6.7
13.3
11.2
14.9
11.0
11.2
11.2
Forest Land Remaining Forest Land:
Non-CCh Emissions from Forest Fires
3.2
9.4
6.8
10.8
7.2
7.3
7.3
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Grassland Remaining Grassland:
Non-CCh Emissions from Grassland Fires
0.1
0.3
0.8
0.6
0.2
0.4
0.4
Land Converted to Wetlands: Land
Converted to Coastal Wetlands
+
+
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
N2O
3.8
9.6
8.6
11.0
8.1
8.4
8.4
Forest Land Remaining Forest Land:
Non-CCh Emissions from Forest Fires
2.1
6.2
4.5
7.1
4.7
4.8
4.8
Settlements Remaining Settlements:
N2O Fluxes from Settlement Soils3
1.4
2.5
2.6
2.7
2.6
2.6
2.6
Forest Land Remaining Forest Land:
N2O Fluxes from Forest Soilsb
0.1
0.5
0.5
0.5
0.5
0.5
0.5
Grassland Remaining Grassland:
Non-CCh Emissions from Grassland Fires
0.1
0.3
0.9
0.6
0.2
0.4
0.4
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
0.1
0.2
0.1
0.1
0.1
0.1
0.1
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
LULUCF Emissions
11.6
24.0
20.7
26.8
19.9
20.4
20.4
+ Does not exceed 0.05 MMT CO2 Eq.
a Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to
Settlements.
b Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to
Forest Land.
Note: Totals may not sum due to independent rounding.
15
16 Table 6-3: Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and
17 Forestry by Land Use and Land-Use Change Category (MMT CO2 Eq.)
Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Forest Land Remaining Forest Land
(693.0)
(649.3)
(659.0)
(649.4)
(659.3)
(657.6)
(654.5)
Changes in Forest Carbon Stock3
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
Land Use, Land-Use Change, and Forestry 6-3
-------
Non-CC>2 Emissions from Forest Fires
5.3
15.6
11.3
17.9
11.9
12.1
12.1
N2O Fluxes from Forest Soilsb
0.1
0.5
0.5
0.5
0.5
0.5
0.5
Land Converted to Forest Land:
Changes in Forest Carbon Stock3
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Cropland Remaining Cropland:
Changes in Agricultural Carbon Stockc,d
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Land Converted to Cropland:
Changes in Agricultural Carbon Stockc>d
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Grassland Remaining Grassland
(4.1)
6.2
(10.9)
(19.6)
8.1
8.6
8.1
Changes in Agricultural Carbon Stockc,d
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Non-CC>2 Emissions from Grassland Fires
0.2
0.7
1.7
1.2
0.4
0.8
0.8
Land Converted to Grassland:
Changes in Agricultural Carbon Stockc,d
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Wetlands Remaining Wetlands
(4.0)
(5.3)
(4.1)
(4.2)
(4.2)
(4.2)
(4.2)
Peatlands Remaining Peatlands
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Changes in Coastal Wetland Carbon Stock
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
CH4 Emissions from Coastal Wetlands
Remaining Coastal Wetlands
3.4
3.5
3.5
3.5
3.5
3.5
3.5
N2O Emissions from Coastal Wetlands
Remaining Coastal Wetlands
0.1
0.2
0.1
0.1
0.1
0.1
0.1
Land Converted to Wetlands
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Changes in Coastal Wetland Carbon Stock
(+)
(+)
(+)
(+)
(+)
(+)
(+)
CH4 Emissions from Land Converted to
Coastal Wetlands
+
+
+
+
+
+
+
Settlements Remaining Settlements
(84.8)
(88.9)
(96.0)
(96.5)
(97.2)
(98.6)
(99.5)
Changes in Settlement Soil Carbon Stock
0.1
0.5
1.3
1.3
1.3
1.3
1.4
Changes in Urban Tree Carbon Stock
(60.4)
(80.5)
(87.3)
(88.4)
(89.5)
(90.6)
(91.7)
N2O Fluxes from Settlement Soils6
1.4
2.5
2.6
2.7
2.6
2.6
2.6
Landfilled Yard Trimmings and Food
Scraps
(26.0)
(11.4)
(12.7)
(12.2)
(11.6)
(11.9)
(11.8)
Land Converted to Settlements:
Changes in Settlement Soil Carbon Stock
123.8
163.6
157.6
150.2
150.2
150.2
150.2
LULUCF Emissions'
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change®
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Total"
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
b Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to
Forest Land.
c Estimates include C stock changes in all pools.
d Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014,2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, said Land Converted to Grassland
sections in the LULUCF chapter of this report.
e Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to
Settlements.
f LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands, CH4 and N2O
emissions reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal
Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from
Forest Soils and Settlement Soils.
B LULUCF C Stock Change includes any C stock gains and losses from all land use and land use conversion categories.
h The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
1 Table 6-4: Emissions and Removals (Net Flux) from Land Use, Land-Use Change, and
2 Forestry by Gas (MMT CO2 Eq.)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Net CO2 Fluxa
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
Forest Land Remaining Forest Landb
(698.4)
I (665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
6-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Land Converted to Forest Land
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
Cropland Remaining Cropland0
(40.9)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
Land Converted to Cropland0
100.7
42.6
35.3
35.3
28.6
28.6
28.6
Grassland Remaining Grassland0
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
Land Converted to Grassland0
245.2
323.8
296.9
293.2
294.2
294.2
294.2
Wetlands Remaining Wetlands
(8.6)
(10.1)
(8.7)
(8.7)
(8.7)
(8.7)
(8.7)
Land Converted to Wetlands
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Remaining Settlements
(86.2)
(91.4)
(98.7)
(99.2)
(99.8)
(101.2)
(102.1)
Land Converted to Settlements
123.8
163.6
157.6
150.2
150.2
150.2
150.2
CO2
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
1.1
1.1
0.9
0.8
0.8
0.8
0.8
CH4
6.7
13.3
11.2
14.9
11.0
11.2
11.2
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
3.2
9.4
6.8
10.8
7.2
7.3
7.3
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
3.4
3.5
3.5
3.5
3.5
3.5
3.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.8
0.6
0.2
0.4
0.4
Land Converted to Wetlands: Land
Converted to Coastal Wetlands
+
+
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
N2O
3.8
9.6
8.6
11.0
8.1
8.4
8.4
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
2.1
6.2
4.5
7.1
4.7
4.8
4.8
Settlements Remaining Settlements:
N2O Fluxes from Settlement Soils'1
1.4
2.5
2.6
2.7
2.6
2.6
2.6
Forest Land Remaining Forest Land:
N2O Fluxes from Forest Soils6
0.1
0.5
0.5
0.5
0.5
0.5
0.5
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
0.1
0.3
0.9
0.6
0.2
0.4
0.4
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
0.1
0.2
0.1
0.1
0.1
0.1
0.1
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
LULUCF Emissions'
11.6
24.0
20.7
26.8
19.9
20.4
20.4
LULUCF C Stock Change3
(460.7)
(339.3)
(395.8)
(414.5)
(390.3)
(389.2)
(386.8)
LULUCF Sector Net Total"
(449.1)
(315.3)
(375.1)
(387.7)
(370.4)
(368.8)
(366.4)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land,
Land Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, Land Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements
Remaining Settlements, and Land Converted to Settlements.
b Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
c Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013, 2014, 2015, which will be
updated following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland
sections in the LULUCF chapter of this report.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to
Settlements.
e Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to
Fore st Land.
f LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands, CH4 and N2O emissions
reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires, and Coastal Wetlands
Remaining Coastal Wetlands; CH4 emissions from Land Converted to Coastal Wetlands; and N2O Fluxes from Forest Soils
and Settlement Soils.
B The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the atmosphere plus
removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
Land Use, Land-Use Change, and Forestry 6-5
-------
1 Table 6-5: Emissions and Removals (Flux) from Land Use, Land-Use Change, and Forestry by
2 Gas (kt)
Gas/Land-Use Category
1990
2005
2011
2012
2013
2014
2015
Net CO2 Flux3
(460,663)
(339,275)
(395,772)
(414,466)
(390,271)
(389,228)
(386,822)
Forest Land Remaining Forest Landb
(698,363)
(665,338)
(670,819)
(667,755)
(671,678)
(670,128)
(667,023)
Land Converted to Forest Land
(92,018)
(81,396)
(75,759)
(75,190)
(75,204)
(75,204)
(75,204)
Cropland Remaining Cropland0
(40,940)
(26,544)
(19,150)
(21,385)
(15,649)
(14,817)
(14,046)
Land Converted to Cropland0
100,719
42,649
35,349
35,271
28,560
28,575
28,566
Grassland Remaining Grassland0
(4,214)
5,492
(12,516)
(20,814)
7,734
7,814
7,251
Land Converted to Grassland0
245,238
323,786
296,862
293,161
294,221
294,224
294,226
Wetlands Remaining Wetlands
(8,616)
(10,074)
(8,698)
(8,700)
(8,691)
(8,685)
(8,676)
Land Converted to Wetlands
(19)
(15)
(24)
(24)
(24)
(24)
(24)
Settlements Remaining Settlements
(86,241)
(91,413)
(98,655)
(99,230)
(99,776)
(101,212)
(102,137)
Land Converted to Settlements
123,790
163,578
157,638
150,201
150,235
150,229
150,246
CO2
1,055
1,101
926
812
770
775
763
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
1,055
1,101
926
812
770
775
763
CH4
269
531
447
597
439
450
450
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
128
378
273
431
289
292
292
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
138
140
141
142
141
141
141
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
3
13
32
23
8
16
16
Land Converted to Wetlands: Land
Converted to Coastal Wetlands
1
+
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
N2O
13
32
29
37
27
28
28
Forest Land Remaining Forest Land:
Non-CC>2 Emissions from Forest Fires
7
21
15
24
16
16
16
Settlements Remaining Settlements:
N2O Fluxes from Settlement Soils'1
5
8
9
9
9
9
9
Forest Land Remaining Forest Land:
N2O Fluxes from Forest Soils6
+
2
2
2
2
2
2
Grassland Remaining Grassland:
Non-CC>2 Emissions from Grassland Fires
+
1
3
2
1
1
1
Wetlands Remaining Wetlands: Coastal
Wetlands Remaining Coastal Wetlands
+
1
+
+
+
+
+
Wetlands Remaining Wetlands: Peatlands
Remaining Peatlands
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt
a LULUCF C Stock Change is the net C stock change from the following categories: Forest Land Remaining Forest Land, Land
Converted to Forest Land, Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, Land
Converted to Grassland, Wetlands Remaining Wetlands, Land Converted to Wetlands, Settlements Remaining Settlements, and Land
Converted to Settlements.
b Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood products.
0 Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015, which will be updated
following public review. Corrected estimates are provided in footnotes of the emission summary tables for Cropland Remaining
Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland sections in the LULUCF
chapter of this report.
d Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land Converted to Settlements.
e Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land Converted to Forest
Land.
Box 6-1: Methodological Approach for Estimating and Reporting U.S. Emissions and Sink
4 In following the UNFCCC requirement under Article 4.1 to develop and submit national greenhouse gas emissions
5 inventories, the gross emissions total presented in this report for the United States excludes emissions and sinks
6-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
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21
22
23
24
25
26
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30
31
32
33
34
35
36
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39
from LULUCF. The net emissions total presented in this report for the United States includes emissions and sinks
from LULUCF. All emissions and sinks estimates are calculated using internationally-accepted methods provided
by the IPCC.6 Additionally, the calculated emissions and sinks in a given year for the United States are presented in
a common manner in line with the UNFCCC reporting guidelines for the reporting of inventories under this
international agreement.7 The use of consistent methods to calculate emissions and sinks by all nations providing
their inventories to the UNFCCC ensures that these reports are comparable. In this regard, U.S. emissions and sinks
reported in this Inventory report are comparable to emissions and sinks reported by other countries. The manner that
emissions and sinks are provided in this Inventory is one of many ways U.S. emissions and sinks could be
examined; this Inventory report presents emissions and sinks in a common format consistent with how countries are
to report inventories under the UNFCCC. The report itself follows this standardized format, and provides an
explanation of the IPCC methods used to calculate emissions and sinks, and the manner in which those calculations
are conducted.
6.1 Representation of the U.S. Land Base
A national land-use categorization system that is consistent and complete, both temporally and spatially, is needed in
order to assess land use and land-use change status and the associated greenhouse gas 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 (Table 6-6), (2) describe and apply a consistent set of definitions for land-use
categories over the entire national land base and time series (i.e., such that increases in the land areas within
particular land-use categories are balanced by decreases in the land areas of other categories unless the national land
base is changing) (Table 6-7), and (3) account for greenhouse gas fluxes on all managed lands. The IPCC (2006,
Vol. IV, Chapter 1) considers all anthropogenic greenhouse gas emissions and removals associated with land use
and management to occur on managed land, and all emissions and removals on managed land should be reported
based on this guidance (see IPCC 2010 for further discussion). Consequently, managed land serves as a proxy for
anthropogenic emissions and removals. This proxy is intended to provide a practical framework for conducting an
inventory, even though some of the greenhouse gas emissions and removals on managed land are influenced by
natural processes that may or may not be interacting with the anthropogenic drivers. Guidelines for factoring out
natural emissions and removals may be developed in the future, but currently the managed land proxy is considered
the most practical approach for conducting an inventory in this sector (IPCC 2010). This section of the Inventory has
been developed in order to comply with this guidance.
Three databases are used to track land management in the United States and are used as the basis to classify U.S.
land area into the thirty-six IPCC land-use and land-use change categories (Table 6-7) (IPCC 2006). The primary
databases are the U.S. Department of Agriculture (USDA) National Resources Inventory (NRI)8 and the USDA
Forest Service (USFS) Forest Inventory and Analysis (FIA)9 Database. The Multi-Resolution Land Characteristics
Consortium (MRLC) National Land Cover Dataset (NLCD)10 is also used to identify land uses in regions that were
not included in the NRI or FIA.
The total land area included in the U.S. Inventory is 936 million hectares across the 50 states.11 Approximately 890
million hectares of this land base is considered managed and 46 million hectares is unmanaged, which has not
6 See .
7 See .
8 NRI data are available at .
9 FIA data are available at .
10 NLCD data are available at and MRLC is a consortium of several U.S. government agencies.
11 The current land representation does not include areas from U.S. Territories, but there are planned improvements to include
these regions in future reports.
Land Use, Land-Use Change, and Forestry 6-7
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1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
changed by much over the time series of the Inventory (Table 6-7). In 2015, the United States had a total of 293
million hectares of managed Forest Land (2.4 percent increase since 1990), 163 million hectares of Cropland (6.6
percent decrease since 1990), 325 million hectares of managed Grassland (1.1 percent decrease since 1990), 42
million hectares of managed Wetlands (5.6 percent decrease since 1990), 43 million hectares of Settlements (29
percent increase since 1990), and 23 million hectares of managed Other Land (4 percent increase from 1990) (Table
6-7). Wetlands are not differentiated between managed and unmanaged, and are reported solely as managed12. In
addition, C stock changes are not currently estimated for the entire land base, which leads to discrepancies between
the managed land area data presented here and in the subsequent sections of the Inventory (e.g., Grassland
Remaining Grassland within interior Alaska).13 Planned improvements are under development to account for C
stock changes on all managed land (e.g., Grasslands and Forest Lands in Alaska) and ensure consistency between
the total area of managed land in the land-representation description and the remainder of the Inventory.
Dominant land uses vary by region, largely due to climate patterns, soil types, geology, proximity to coastal regions,
and historical settlement patterns (Table 6-6). Forest Land tends to be more common in the eastern states,
mountainous regions of the western United States and Alaska. Cropland is concentrated in the mid-continent region
of the United States, and Grassland is more common in the western United States and Alaska. Wetlands are fairly
ubiquitous throughout the United States, though they are more common in the upper Midwest and eastern portions
of the country. Settlements are more concentrated along the coastal margins and in the eastern states.
Table 6-6: Managed and Unmanaged Land Area by Land-Use Categories for All 50 States
(Thousands of Hectares)
Land-Use Categories
1990
2005
2011
2012
2013
2014
2015
Managed Lands
889,924
889,914
889,898
889,897
889,896
889,896
889,896
Forest Land
286,612
289,064
292,043
292,439
292,879
293,180
293,480
Croplands
174,510
165,599
163,264
163,040
163,040
163,040
163,040
Grasslands
328,520
328,863
326,446
325,955
325,601
325,300
324,998
Settlements
33,370
40,298
42,790
43,118
43,118
43,118
43,118
Wetlands
45,004
43,523
42,623
42,558
42,471
42,472
42,474
Other Land
21,908
22,567
22,732
22,787
22,787
22,787
22,787
Unmanaged Lands
46,272
46,282
46,298
46,299
46,300
46,300
46,300
Forest Land
9,515
8,474
8,586
8,593
8,601
8,601
8,601
Croplands
0
0
0
0
0
0
0
Grasslands
25,953
27,043
26,948
26,942
26,936
26,936
26,936
Settlements
0
0
0
0
0
0
0
Wetlands
0
0
0
0
0
0
0
Other Land
10,804
10,765
10,764
10,764
10,764
10,764
10,764
Total Land Areas
936,196
936,196
936,196
936,196
936,196
936,196
936,196
Forest Land
296,127
297,538
300,629
301,032
301,480
301,780
302,081
Croplands
174,510
165,599
163,264
163,040
163,040
163,040
163,040
Grasslands
354,473
355,906
353,394
352,897
352,537
352,235
351,933
Settlements
33,370
40,298
42,790
43,118
43,118
43,118
43,118
Wetlands
45,004
43,523
42,623
42,558
42,471
42,472
42,474
Other Land
32,713
33,332
33,496
33,551
33,551
33,551
33,551
12 According to the IPCC (2006), wetlands are 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 in the United States is difficult due to limited data availability. Wetlands are not characterized within the NRI with
information regarding water table management. As a result, all Wetlands are reported as managed. See the Planned
Improvements section of the Inventory for future refinements to the Wetland area estimates.
13 These "managed area" discrepancies also occur in the Common Reporting Format (CRF) tables submitted to the UNFCCC.
6-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 6-7: Land Use and Land-Use Change for the U.S. Managed Land Base for All 50 States
2 (Thousands of Hectares)
Land-Use & Land-Use
Change Categories3
1990
2005
2011
2012
2013
2014
2015
Total Forest Land
286,612
289,064
292,043
292,439
292,879
293,180
293,480
FF
285,369
288,011
291,048
291,458
291,897
292,193
292,493
CF
213
193
169
165
165
165
165
GF
909
692
682
676
677
678
678
WF
24
27
32
28
28
32
31
SF
13
15
15
17
17
17
17
OF
84
126
96
95
95
95
95
Total Cropland
174,510
165,599
163,264
163,040
163,040
163,040
163,040
CC
162,051
150,583
149,996
149,722
149,722
149,722
149,722
FC
286
94
56
60
60
60
60
GC
11,754
14,418
12,781
12,827
12,827
12,827
12,827
WC
150
176
127
128
128
128
128
SC
76
85
85
91
91
91
91
oc
192
243
218
213
213
213
213
Total Grassland
328,520
328,863
326,446
325,955
325,601
325,300
324,998
GG
318,373
306,412
304,473
304,078
303,724
303,422
303,120
FG
1,154
4,114
3,976
3,961
3,961
3,961
3,961
CG
8,309
16,825
16,572
16,555
16,555
16,555
16,555
WG
231
429
253
199
199
199
199
SG
53
106
111
114
114
114
114
OG
400
976
1,061
1,048
1,048
1,048
1,048
Total Wetlands
45,004
43,523
42,623
42,558
42,471
42,472
42,474
WW
44,249
42,138
41,396
41,358
41,270
41,271
41,273
FW
43
62
57
55
55
56
56
CW
214
378
340
346
346
346
346
GW
452
835
727
700
700
700
700
SW
5
0
1
1
1
1
1
OW
41
110
103
98
98
98
98
Total Settlements
33,370
40,298
42,790
43,118
43,118
43,118
43,118
SS
30,469
31,978
35,281
35,848
35,848
35,848
35,848
FS
342
445
437
418
418
418
418
CS
1,247
3,550
3,082
2,982
2,982
2,982
2,982
GS
1,250
4,102
3,774
3,653
3,653
3,653
3,653
WS
6
25
26
26
26
26
26
OS
58
199
189
190
190
190
190
Total Other Land
21,908
22,567
22,732
22,787
22,787
22,787
22,787
OO
21,000
20,728
20,786
20,809
20,809
20,809
20,809
FO
41
68
76
75
75
75
75
CO
300
613
678
679
679
679
679
GO
481
982
1,070
1,109
1,109
1,109
1,109
WO
82
168
108
102
102
102
102
SO
5
9
13
13
13
13
13
Grand Total
889,924
889,914
889,898
889,897
889,896
889,896
889,896
a The abbreviations are "F" for Forest Land, "C" for Cropland, "G" for Grassland, "W" for Wetlands, "S" for
Settlements, and "O" for Other Lands. Lands remaining in the same land-use category are identified with the
land-use abbreviation given twice (e.g., "FF" is, Forest Land Remaining Forest Land), and land-use change
categories are identified with the previous land use abbreviation followed by the new land-use abbreviation (e.g.,
"CF" is Cropland Converted to Forest Land).
Note: All land areas reported in this table are considered managed. A planned improvement is underway to deal
with an exception for Wetlands, which based on the definitions for the current U.S. Land Representation
Assessment includes both managed and unmanaged lands. U.S. Territories have not been classified into land uses
and are not included in the U.S. Land Representation Assessment. See the Planned Improvements section for
discussion on plans to include territories in future inventories. In addition, C stock changes are not currently
estimated for the entire land base, which leads to discrepancies between the managed land area data presented
here and in the subsequent sections of the Inventory.
Land Use, Land-Use Change, and Forestry 6-9
-------
1 Figure 6-1: Percent of Total Land Area for Each State in the General Land-Use Categories for
2 2015
Croplands Forest Lands
Other Lands
Wetlands
Settlements
rf
M\
f f
{ Yn
—
\ •. \ ) -yj
\ rvr • -,'1
J
"VJ,
-r~Y -\
\\
_ < 10
"10-30
| 30-50
| > 50
-] ?%jL
10 • 30
so
6-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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3
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5
6
7
8
9
10
11
12
13
14
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19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Methodology
IPCC Approaches for Representing Land Areas
IPCC (2006) describes three approaches for representing land areas. Approach 1 provides data on the total area for
each individual land-use category, but does not provide detailed information on changes of area between categories
and is not spatially explicit other than at the national or regional level. With Approach 1, total net conversions
between categories can be detected, but not the individual changes (i.e., additions and/or losses) between the land-
use categories that led to those net changes. Approach 2 introduces tracking of individual land-use changes between
the categories (e.g., Forest Land to Cropland, Cropland to Forest Land, and Grassland to Cropland), using survey
samples or other forms of data, but does not provide location data on all parcels of land. Approach 3 extends
Approach 2 by providing location data on all parcels of land, such as maps, along with the land-use history. The
three approaches are not presented as hierarchical tiers and are not mutually exclusive.
According to IPCC (2006), the approach or mix of approaches selected by an inventory agency should reflect
calculation needs and national circumstances. For this analysis, the NRI, FIA, and the NLCD have been combined to
provide a complete representation of land use for managed lands. These data sources are described in more detail
later in this section. NRI and FIA are Approach 2 data sources that do not provide spatially-explicit representations
of land use and land-use conversions, even though land use and land-use conversions are tracked explicitly at the
survey locations. NRI and FIA data are aggregated and used to develop a land-use conversion matrix for a political
or ecologically-defined region. NLCD is a spatially-explicit time series of land-cover data that is used to inform the
classification of land use, and is therefore Approach 3 data. Lands are treated as remaining in the same category
(e.g., Cropland Remaining Cropland) if a land-use change has not occurred in the last 20 years. Otherwise, the land
is classified in a land-use change category based on the current use and most recent use before conversion to the
current use (e.g., Cropland Converted to Forest Land).
Definitions of Land Use in the United States
Managed and Unmanaged Land
The United States definition of managed land is similar to the general definition of managed land provided by the
IPCC (2006), but with some additional elaboration to reflect national circumstances. Based on the following
definitions, most lands in the United States are classified as managed:
• Managed Land: Land is considered managed if direct human intervention has influenced its condition.
Direct intervention occurs mostly in areas accessible to human activity and includes altering or maintaining
the condition of the land to produce commercial or non-commercial products or services; to serve as
transportation corridors or locations for buildings, landfills, or other developed areas for commercial or
non-commercial purposes; to extract resources or facilitate acquisition of resources; or to provide social
functions for personal, community, or societal objectives where these areas are readily accessible to
society.14
• Unmanaged Land: All other land is considered unmanaged. Unmanaged land is largely comprised of areas
inaccessible to society due to the remoteness of the locations. Though these lands may be influenced
14 Wetlands are an exception to this general definition, because these lands, as specified by IPCC (2006), are only considered
managed if they are created through human activity, such as dam construction, or the water level is artificially altered by human
activity. Distinguishing between managed and unmanaged wetlands in the United States is difficult due to limited data
availability. Wetlands are not characterized within the NRI with information regarding water table management. Therefore,
unless wetlands are managed for cropland or grassland, it is not possible to know if they are artificially created or if the water
table is managed based on the use of NRI data. As a result, all Wetlands are reported as managed. See the Planned Improvements
section of the Inventory for future refinements to the Wetland area estimates.
Land Use, Land-Use Change, and Forestry 6-11
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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
31
32
33
34
35
36
37
38
39
indirectly by human actions such as atmospheric deposition of chemical species produced in industry or
CO2 fertilization, they are not influenced by a direct human intervention.15
In addition, land that is previously managed remains in the managed land base for 20 years before re-classifying the
land as unmanaged in order to account for legacy effects of management on C stocks.
Land-Use Categories
As with the definition of managed lands, IPCC (2006) provides general non-prescriptive definitions for the six main
land-use categories: Forest Land, Cropland, Grassland, Wetlands, Settlements and Other Land. In order to reflect
national circumstances, country-specific definitions have been developed, based predominantly on criteria used in
the land-use surveys for the United States. Specifically, the definition of Forest Land is based on the FIA definition
of forest,16 while definitions of Cropland, Grassland, and Settlements are based on the NRI.17 The definitions for
Other Land and Wetlands are based on the IPCC (2006) definitions for these categories.
• Forest Land: A land-use category that includes areas at least 120 feet (36.6 meters) wide and at least one
acre (0.4 hectare) in size with at least 10 percent cover (or equivalent stocking) by live trees including land
that formerly had such tree cover and that will be naturally or artificially regenerated. Trees are woody
plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches (7.6 centimeters)
in diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 m) at
maturity in situ. Forest Land includes all areas recently having such conditions and currently regenerating
or capable of attaining such condition in the near future. Forest Land also includes transition zones, such as
areas between forest and non-forest lands that have at least 10 percent cover (or equivalent stocking) with
live trees and forest areas adjacent to urban and built-up lands. Unimproved roads and trails, streams, and
clearings in forest areas are classified as forest if they are less than 120 feet (36.6 m) wide or an acre (0.4
ha) in size. However, land is not classified as Forest Land if completely surrounded by urban or developed
lands, even if the criteria are consistent with the tree area and cover requirements for Forest Land. These
areas are classified as Settlements. In addition, Forest Land does not include land that is predominantly
under an agricultural land use (Oswalt et al. 2014).
• Cropland. A land-use category that includes areas used for the production of adapted crops for harvest; this
category includes both cultivated and non-cultivated lands. Cultivated crops include row crops or close-
grown crops and also hay or pasture in rotation with cultivated crops. Non-cultivated cropland includes
continuous hay, perennial crops (e.g., orchards) and horticultural cropland. Cropland also includes land
with agroforestry, such as alley cropping and windbreaks,18 if the dominant use is crop production,
assuming the stand or woodlot does not meet the criteria for Forest Land. Lands in temporary fallow or
enrolled in conservation reserve programs (i.e., set-asides19) are also classified as Cropland, as long as
these areas do not meet the Forest Land criteria. Roads through Cropland, including interstate highways,
state highways, other paved roads, gravel roads, dirt roads, and railroads are excluded from Cropland area
estimates and are, instead, classified as Settlements.
• Grassland: A land-use category on which the plant cover is composed principally of grasses, grass-like
plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and browsing, and includes both
pastures and native rangelands. This includes areas where practices such as clearing, burning, chaining,
and/or chemicals are applied to maintain the grass vegetation. Grassland may have three or fewer years of
15 There are some areas, such as Forest Land and Grassland in Alaska that are classified as unmanaged land due to the
remoteness of their location.
16 See , page 22.
17 See .
18 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.
19 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.
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hay production20 that is otherwise pasture or rangelands. Savannas, deserts, and tundra are considered
Grassland.21 Drained wetlands are considered Grassland if the dominant vegetation meets the plant cover
criteria for Grassland. 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 silvopasture and
windbreaks, if the land is principally grasses, grass-like plants, forbs, and shrubs suitable for grazing and
browsing, and assuming the stand or woodlot does not meet the criteria for Forest Land. Roads through
Grassland, including interstate highways, state highways, other paved roads, gravel roads, dirt roads, and
railroads are excluded from Grassland and are, instead, classified as Settlements.
• Wetlands: A land-use category that includes land covered or saturated by water for all or part of the year, in
addition to the areas of lakes, reservoirs, and rivers. Managed Wetlands are those where the water level is
artificially changed, or were created by human activity. Certain areas that fall under the managed Wetlands
definition are included in other land uses based on the IPCC guidance, including Cropland (drained
wetlands for crop production and also systems that are flooded for most or just part of the year, such as rice
cultivation and cranberry production), Grassland (drained wetlands dominated by grass cover), Forest Land
(including drained or un-drained forested wetlands), and Settlements (drained wetlands in developed areas).
• Settlements: A land-use category representing developed areas consisting of units of 0.25 acres (0.1 ha) or
more that includes residential, industrial, commercial, and institutional land; construction sites; public
administrative sites; railroad yards; cemeteries; airports; golf courses; sanitary landfills; sewage treatment
plants; water control structures and spillways; parks within urban and built-up areas; and highways,
railroads, and other transportation facilities. Also included are tracts of less than 10 acres (4.05 ha) that may
meet the definitions for Forest Land, Cropland, Grassland, or Other Land but are completely surrounded by
urban or built-up land, and so are included in the Settlements category. Rural transportation corridors
located within other land uses (e.g., Forest Land, Cropland, and Grassland) are also included in
Settlements.
• Other Land: A land-use category that includes bare soil, rock, ice, and all land areas that do not fall into
any of the other five land-use categories. Following the guidance provided by the IPCC (2006), C stock
changes and non-CCh emissions are not estimated for Other Lands because these areas are largely devoid of
biomass, litter and soil C pools. However, C stock changes and non-C02 emissions are estimated for Land
Converted to Other Land during the first 20 years following conversion to account for legacy effects.
Land-Use Data Sources: Description and Application to U.S.
Land Area Classification
U.S. Land-Use Data Sources
The three main sources for land-use data in the United States are the NRI, FIA, and the NLCD (Table 6-8). These
data sources are combined to account for land use in all 50 states. FIA and NRI data are used when available for an
area because the surveys contain additional information on management, site conditions, crop types, biometric
measurements, and other data that is needed to estimate C stock changes, N20, and CH4 emissions on those lands. If
NRI and FIA data are not available for an area, however, then the NLCD product is used to represent the land use.
20 Areas with four or more years of continuous hay production are Cropland because the land is typically more intensively
managed with cultivation, greater amounts of inputs, and other practices.
21 2006 IPCC Guidelines do not include provisions to separate desert and tundra as land-use categories.
Land Use, Land-Use Change, and Forestry 6-13
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Table 6-8: Data Sources Used to Determine Land Use and Land Area for the Conterminous
United States, Hawaii, and Alaska
NRI FIA
NLCD
Forest Land
Conterminous United
States
Non-Federal
•
Federal
•
Hawaii
Non-Federal
•
Federal
•
Alaska
Non-Federal
•
Federal
•
Croplands, Grasslands, Other Lands, Settlements, and Wetlands
Conterminous United
States
Non-Federal
•
Federal
•
Hawaii
Non-Federal
•
Federal
•
Alaska
Non-Federal
•
•
Federal
•
•
National Resources Inventory
For the Inventory, the NRI is the official source of data for land use and land use change on non-federal lands in the
conterminous United States and Hawaii (except Forest Land), and is also used to determine the total land base for
the conterminous United States and Hawaii. The NRI is a statistically-based survey conducted by the USDA Natural
Resources Conservation Service and is designed to assess soil, water, and related environmental resources on non-
federal lands. The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the
basis of county and township boundaries defined by the United States Public Land Survey (Nusser and Goebel
1997). Within a primary sample unit (typically a 160 acre [64.75 ha] square quarter-section), three sample points are
selected according to a restricted randomization procedure. Each point in the survey is assigned an area weight
(expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). The NRI survey
utilizes data derived from remote sensing imagery and site visits in order to provide detailed information on land use
and management, particularly for Croplands and Grasslands (i.e., agricultural lands), and is used as the basis to
account for C stock changes in agricultural lands (except federal Grasslands). The NRI survey was conducted every
5 years between 1982 and 1997, but shifted to annualized data collection in 1998. The land use between five-year
periods from 1982 and 1997 are assumed to be the same for a five-year time period if the land use is the same at the
beginning and end of the five-year period (Note: most of the data has the same land use at the beginning and end of
the five-year periods). If the land use had changed during a five-year period, then the change is assigned at random
to one of the five years. For crop histories, years with missing data are estimated based on the sequence of crops
grown during years preceding and succeeding a missing year in the NRI history. This gap-filling approach allows
for development of a full time series of land-use data for non-federal lands in the conterminous United States and
Hawaii. This Inventory incorporates data through 2012 from the NRI. The land use patterns are assumed to remain
the same from 2012 through 2015 for this Inventory, but the time series will be updated when new data are released.
Forest Inventory and Analysis
The FIA program, conducted by the USFS, is another statistically-based survey for the conterminous United States,
and the official source of data on Forest Land area and management data for the Inventory. 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
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satellite imagery) primarily to classify land into forest or non-forest and to identify landscape patterns like
fragmentation and urbanization. Phase 2 is the collection of field data on a network of ground plots that enable
classification and summarization of area, tree, and other attributes associated with forest-land uses. Phase 3 plots are
a subset of Phase 2 plots where data on indicators of forest health are measured. Data from all three phases are also
used to estimate C stock changes for Forest Land. Historically, FIA inventory surveys have been conducted
periodically, with all plots in a state being measured at a frequency of every five to 14 years. A new national plot
design and annual sampling design was introduced by FIA about ten years ago. Most states, though, have only
recently been brought into this system. Annualized sampling means that a portion of plots throughout each state is
sampled each year, with the goal of measuring all plots once every five years. See Annex 3.13 to see the specific
survey data available by state. The most recent year of available data varies state by state (range of most recent data
is from 2012 through 2015; see Table A-255).
National Land Cover Dataset
While the NRI survey sample covers the conterminous United States and Hawaii, land use data are only collected on
non-federal lands. In addition, FIA only records data for forest land across the land base in the conterminous United
States and a portion of Alaska.22 Consequently, gaps exist in the land representation when the datasets are
combined, such as federal grassland operated by Bureau of Land Management (BLM), USD A, and National Park
Service, as well as Alaska.23 The NLCD is used as a supplementary database to account for land use on federal
lands in the conterminous United States and Hawaii, in addition to federal and non-federal lands in Alaska.
NLCD products provide land-cover for 1992, 2001, 2006, and 2011 in the conterminous United States (Homer et al.
2007), and also for Alaska in 2001 and 2011 and Hawaii in 2001. For the conterminous United States, the NLCD
data have been further processed to derive Land Cover Change Products for 2001, 2006, and 2011 (Fry et al. 2011;
Homer et al. 2007; Homer et al. 2015). A Land Cover Change Product is also available for Alaska from 2001 to
2011. A NLCD change product is not available for Hawaii because data are only available for one year, i.e., 2001.
The NLCD products are based primarily on Landsat Thematic Mapper imagery at a 30 meter resolution, and contain
21 categories of land-cover information, which have been aggregated into the 36 IPCC land-use categories for the
conterminous United States and Alaska, and into the six IPCC land-use categories for Hawaii.
The aggregated maps of IPCC land-use categories were used in combination with the NRI database to represent land
use and land-use change for federal lands, as well as federal and non-federal lands in Alaska. Specifically, NRI
survey locations designated as federal lands were assigned a land use/land use change category based on the NLCD
maps that had been aggregated into the IPCC categories. This analysis addressed shifts in land ownership across
years between federal or non-federal classes as represented in the NRI survey (i.e., the ownership is classified for
each survey location in the NRI). The sources of these additional data are discussed in subsequent sections of the
NIR.
Managed Land Designation
Lands are designated as managed in the United States based on the definition provided earlier in this section. In
order to apply the definition in an analysis of managed land, the following criteria are used:
• All Croplands and Settlements are designated as managed so only Grassland, Forest Land or Other
Lands may be designated as unmanaged land;
• All Forest Lands with active fire protection are considered managed;
• All Grassland is considered managed at a county scale if there are livestock in the county;24
• Other areas are considered managed if accessible based on the proximity to roads and other
transportation corridors, and/or infrastructure;
22 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.
23 The FIA and NRI survey programs also do not include U.S. Territories with the exception of non-federal lands in Puerto Rico,
which are included in the NRI survey. Furthermore, NLCD does not include coverage for all U.S. Territories.
24 Assuming all Grasslands are grazed in a county with even very small livestock populations is a conservative assumption about
human impacts on Grasslands. Currently, detailed information on grazing at sub-county scales is not available for the United
States to make a finer delineation of managed land.
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• Protected lands maintained for recreational and conservation purposes are considered managed (i.e.,
managed by public and private organizations);
• Lands with active and/or past resource extraction are considered managed; and
• Lands that were previously managed but subsequently classified as unmanaged, remain in the managed
land base for 20 years following the conversion to account for legacy effects of management on C
stocks.
The analysis of managed lands is conducted using a geographic information system. Lands that are used for crop
production or settlements are determined from the NLCD (Fry et al. 2011; Homer et al. 2007; Homer et al. 2015).
Forest Lands with active fire management are determined from maps of federal and state management plans from
the National Atlas (U.S. Department of Interior 2005) and Alaska Interagency Fire Management Council (1998). It
is noteworthy that all forest lands in the conterminous United States have active fire protection, and are therefore
designated as managed regardless of accessibility or other criteria. The designation of grasslands as managed is
based on livestock population data at the county scale from the USDA National Agricultural Statistics Service (U.S.
Department of Agriculture 2015). Accessibility is evaluated based on a 10-km buffer surrounding road and train
transportation networks using the ESRI Data and Maps product (ESRI2008), and a 10-km buffer surrounding
settlements using NLCD. Lands maintained for recreational purposes are determined from analysis of the Protected
Areas Database (U.S. Geological Survey 2012). The Protected Areas Database includes lands protected from
conversion of natural habitats to anthropogenic uses and describes the protection status of these lands. Lands are
considered managed that are protected from development if the regulations allow for extractive or recreational uses
or suppression of natural disturbance. Lands that are protected from development and not accessible to human
intervention, including no suppression of disturbances or extraction of resources, are not included in the managed
land base. Multiple data sources are used to determine lands with active resource extraction: Alaska Oil and Gas
Information System (Alaska Oil and Gas Conservation Commission 2009), Alaska Resource Data File (U.S.
Geological Survey 2012), Active Mines and Mineral Processing Plants (U.S. Geological Survey 2005), and Coal
Production and Preparation Report (U.S. Energy Information Administration 2011). A buffer of 3,300 and 4,000
meters is established around petroleum extraction and mine locations, respectively, to account for the footprint of
operation and impacts of activities on the surrounding landscape. The buffer size is based on visual analysis of
approximately 130 petroleum extraction sites and 223 mines. The resulting managed land area is overlaid on the
NLCD to estimate the area of managed land by land use for both federal and non-federal lands. The remaining land
represents the unmanaged land base. The resulting spatial product is used to identify NRI survey locations that are
considered managed and unmanaged for the conterminous United States and Hawaii, in addition to determining
which areas in the NLCD for Alaska are included in the managed land base.
Approach for Combining Data Sources
The managed land base in the United States has been classified into the 36 IPCC land-use/land-use conversion
categories using definitions developed to meet national circumstances, while adhering to IPCC (2006).25 In practice,
the land was initially classified into a variety of land-use categories within the NRI, FIA, and NLCD datasets, and
then aggregated into the 36 broad land use and land-use change categories identified in IPCC (2006). All three
datasets provide information on forest land areas in the conterminous United States, but the area data from FIA serve
as the official dataset for Forest Land.
Therefore, another step in the analysis is to address the inconsistencies in the representation of the Forest Land
among the three databases. NRI and FIA have different criteria for classifying Forest Land in addition to different
sampling designs, leading to discrepancies in the resulting estimates of Forest Land area on non-federal land in the
conterminous United States. Similarly, there are discrepancies between the NLCD and FIA data for defining and
classifying Forest Land on federal lands. Any change in Forest Land Area in the NRI and NLCD also requires a
corresponding change in other land use areas because of the dependence between the Forest Land area and the
amount of land designated as other land uses, such as the amount of Grassland, Cropland, and Wetlands (i.e., areas
for the individual land uses must sum to the total managed land area of the country).
FIA is the main database for forest statistics, and consequently, the NRI and NLCD are adjusted to achieve
consistency with FIA estimates of Forest Land in the conterminous United States. Adjustments are made in the
25 Definitions are provided in the previous section.
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Forest Land Remaining Forest Land, Land Converted to Forest Land, and Forest Land converted to other uses (i.e.,
Grassland, Cropland and Wetlands). All adjustments are made at the state scale to address the differences in Forest
Land definitions and the resulting discrepancies in areas among the land use and land-use change categories. There
are three steps in this process. The first step involves adjustments for Land Converted to Forest Land (Grassland,
Cropland, and Wetlands), followed by adjustments in Forest Land converted to another land use (i.e., Grassland,
Cropland, and Wetlands), and finally adjustments to Forest Land Remaining Forest Land.
In the first step, Land Converted to Forest Land in the NRI and NLCD are adjusted to match the state-level
estimates in the FIA data for non-federal and federal Land Converted to Forest Land, respectively. FIA data do not
provide specific land-use categories that are converted to Forest Land, but rather a sum of all Land Converted to
Forest Land. The NRI and NLCD provide information on specific land use conversions, such as Grassland
Converted to Forest Land. Therefore, adjustments at the state level to NRI and NLCD are made proportional to the
amount of land use change into Forest Land for the state, prior to any adjustments. For example, if 50 percent of
land use change to Forest Land is associated with Grassland Converted to Forest Land in a state according to NRI
or NLCD, then half of the discrepancy with FIA data in the area of Land Converted to Forest Land is addressed by
increasing or decreasing the area in Grassland Converted to Forest Land. Moreover, any increase or decrease in
Grassland Converted to Forest Land in NRI or NLCD is addressed by a corresponding change in the area of
Grassland Remaining Grassland, so that the total amount of managed area is not changed within an individual state.
In the second step, state-level areas are adjusted in the NRI and NLCD to address discrepancies with FIA data for
Forest Land converted to other uses. Similar to Land Converted to Forest Land, FIA does not provide information
on the specific land-use changes, and so areas associated with Forest Land conversion to other land uses in NRI and
NLCD are adjusted proportional to the amount area in each conversion class in these datasets.
In the final step, the area of Forest Land Remaining Forest Land in a given state according to the NRI and NLCD is
adjusted to match the FIA estimates for non-federal and federal land, respectively. It is assumed that the majority of
the discrepancy in Forest Land Remaining Forest Land is associated with an under- or over-prediction of Grassland
Remaining Grassland and Wetland Remaining Wetland in the NRI and NLCD. This step also assumes that there are
no changes in the land use conversion categories. Therefore, corresponding increases or decreases are made in the
area estimates of Grasslands Remaining Grasslands and Wetlands Remaining Wetlands from the NRI and NLCD.
This adjustment balances the change in Forest Land Remaining Forest Land area, which ensures no change in the
overall amount of managed land within an individual state. The adjustments are based on the proportion of land
within each of these land-use categories at the state level (i.e., a higher proportion of Grassland led to a larger
adjustment in Grassland area).
The modified NRI data are then aggregated to provide the land-use and land-use change data for non-federal lands
in the conterminous United States, and the modified NLCD data are aggregated to provide the land use and land-use
change data for federal lands. Data for all land uses in Hawaii are based on NRI for non-federal lands and on NLCD
for federal lands. Land use data in Alaska are based solely on the NLCD data (Table 6-8). The result is land use and
land-use change data for the conterminous United States, Hawaii, and Alaska.
A summary of the details on the approach used to combine data sources for each land use are described below.
• Forest Land: Land representation for both non-federal and federal forest lands in the conterminous United
States and coastal Alaska are based on the 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 in the conterminous United States. FIA does have
survey plots in coastal Alaska that are used to determine the C stock changes, and the associated area data
for this region are harmonized with the NLCD using the methods described above. However, interior
Alaska is not currently surveyed by FIA so forest land in this region is based on the 2001 and 2011 NLCD.
NRI is used in the current report to provide Forest Land areas on non-federal lands in Hawaii, and NLCD is
used for federal lands. FIA data will be collected in Hawaii in the future.
• Cropland: Cropland is classified using the NRI, which covers all non-federal lands within 49 states
(excluding Alaska), including state and local government-owned land as well as tribal lands. NRI is used as
the basis for both Cropland area data as well as to estimate soil C stocks and fluxes on Cropland. NLCD is
used to determine Cropland area and soil C stock changes on federal lands in the conterminous United
States and Hawaii. NLCD is also used to determine croplands in Alaska, but C stock changes are not
estimated for this region in the current Inventory.
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• Grassland: Grassland on non-federal lands is classified using the NRI within 49 states (excluding Alaska),
including state and local government-owned land as well as tribal lands. NRI is used as the basis for both
Grassland area data as well as to estimate soil C stocks and fluxes on Grassland. Grassland area and soil C
stock changes are determined using the classification provided in the NLCD for federal land within the
conterminous United States. NLCD is also used to estimate the areas of federal and non-federal grasslands
in Alaska, and the federal lands in Hawaii, but the current Inventory does not include C stock changes in
these areas.
• Wetlands: NRI captures wetlands on non-federal lands within 49 states (excluding Alaska), while the land
representation data for federal wetlands and wetlands in Alaska are based on the NLCD.26
• Settlements: NRI captures non-federal settlement area in 49 states (excluding Alaska). If areas of Forest
Land or Grassland under 10 acres (4.05 ha) are contained within settlements or urban areas, they are
classified as Settlements (urban) in the NRI database. If these parcels exceed the 10 acre (4.05 ha) threshold
and are Grassland, they will be classified as such by NRI. Regardless of size, a forested area is classified as
non-forest by FIA if it is located within an urban area. Land representation for settlements on federal lands
and in Alaska is based on the NLCD.
• Other Land: Any land that is not classified into one of the previous five land-use categories, is categorized
as Other Land using the NRI for non-federal areas in the conterminous United States and Hawaii and using
the NLCD for the federal lands in all regions of the United States and for non-federal lands in Alaska.
Some lands can be classified into one or more categories due to multiple uses that meet the criteria of more than one
definition. However, a ranking has been developed for assignment priority in these cases. The ranking process is
from highest to lowest priority, in the following manner:
Settlements > Cropland > Forest Land > Grassland > Wetlands > Other Land
Settlements are given the highest assignment priority because they are extremely heterogeneous with a mosaic of
patches that include buildings, infrastructure, and travel corridors, but also open grass areas, forest patches, riparian
areas, and gardens. The latter examples could be classified as Grassland, Forest Land, Wetlands, and Cropland,
respectively, but when located in close proximity to settlement areas, they tend to be managed in a unique manner
compared to non-settlement areas. Consequently, these areas are assigned to the Settlements land-use category.
Cropland is given the second assignment priority, because cropping practices tend to dominate management
activities on areas used to produce food, forage, or fiber. The consequence of this ranking is that crops in rotation
with pasture are classified as Cropland, and land with woody plant cover that is used to produce crops (e.g.,
orchards) is classified as Cropland, even though these areas may meet the definitions of Grassland or Forest Land,
respectively. Similarly, Wetlands are considered Croplands if they are used for crop production, such as rice or
cranberries. Forest Land occurs next in the priority assignment because traditional forestry practices tend to be the
focus of the management activity in areas with woody plant cover that are not croplands (e.g., orchards) or
settlements (e.g., housing subdivisions with significant tree cover). Grassland occurs next in the ranking, while
Wetlands then Other Land complete the list.
The assignment priority does not reflect the level of importance for reporting greenhouse gas emissions and
removals on managed land, but is intended to classify all areas into a discrete land use. Currently, the IPCC does not
make provisions in the guidelines for assigning land to multiple uses. For example, a wetland is classified as Forest
Land if the area has sufficient tree cover to meet the stocking and stand size requirements. Similarly, wetlands are
classified as Cropland if they are used for crop production, such as rice or cranberries, or as Grassland if they are
composed principally of grasses, grass-like plants (i.e., sedges and rushes), forbs, or shrubs suitable for grazing and
browsing. Regardless of the classification, emissions from these areas are included in the Inventory if the land is
considered managed, and therefore impacted by anthropogenic activity in accordance with the guidance provided by
the IPCC (2006).
26 This analysis does not distinguish between managed and unmanaged wetlands, which is a planned improvement for the
Inventory.
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28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
QA/QC and Verification
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 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. The U.S. Census Survey does not provide a time series of land-use
change data or land management information, which is needed for reporting greenhouse gas emissions from land use
and land use change. Regardless, the U.S. Census Survey does provide sufficient information to provide a check on
the Inventory data. The total difference between the U.S. Census Survey and the combined NRI, FIA, and NLCD
data is about 46 million hectares for the total U.S. land base of about 936 million hectares currently included in the
Inventory, or a 5 percent difference. Much of this difference is associated with open waters in coastal regions and
the Great Lakes, which is included in the TIGER Survey of the U.S. Census, but not included in the land
representation using the NRI, FIA and NLCD. There is only a 0.4 percent difference when open water in coastal
regions is removed from the TIGER data.
Recalculations Discussion
The land representation data in this Inventory changed slightly from the previous Inventory. Areas associated with
Forest Land changed due to updates in the FIA that also influenced the areas for Grasslands, Wetlands, and
Croplands through the process of harmonizing these datasets. FIA also provided area data for coastal Alaska that
was used in this year's analysis. In addition to the changes in the FIA data, a new NRI dataset was incorporated into
the current Inventory extending the time series from 2010 to 2012. The NRI program recalculated the previous time
series based on changes to the classification and imputation procedures for filling gaps. One of the key updates in
the recalculation was that the new (2012) NRI dataset had a slightly smaller land base than that reported in the 2010
NRI dataset, resulting in a reduction in the managed area by approximately 34,000 ha. Overall, the updates from
NRI and FIA led to a decrease in Forest Land area by 0.4 percent, a decrease in Cropland area by 0.3 percent, an
increase in Grassland area by 1.2 percent, an increase in Wetland area by 0.4 percent, a decrease in Settlements area
by 0.1 percent, and a decrease in Other Lands by 10 percent.
Planned Improvements
A key planned improvement for the Inventory is to fully incorporate area data by land-use type for U.S. Territories.
Fortunately, most of the managed land in the United States is included in the current land-use statistics, but a
complete accounting is a key goal for the near future. Preliminary land-use area data for U.S. Territories by land-use
category are provided in Box 6-2.
Box 6-2: Preliminary Estimates of Land Use in U.S. Territori
Several programs have developed land cover maps for U.S. Territories using remote sensing imagery, including the
Gap Analysis Program, Caribbean Land Cover project, National Land Cover Dataset, USFS Pacific Islands Imagery
Project, and the National Oceanic and Atmospheric Administration (NOAA) Coastal Change Analysis Program (C-
CAP). Land-cover data can be used to inform a land-use classification if there is a time series to evaluate the
dominate practices. For example, land that is principally used for timber production with tree cover over most of the
time series is classified as forest land even if there are a few years of grass dominance following timber harvest.
These products were reviewed and evaluated for use in the national Inventory as a step towards implementing a
planned improvement to include U.S. Territories in the land representation for the Inventory. Recommendations are
to use the NOAA C-CAP Regional Land Cover Database for the smaller island Territories (U.S. Virgin Islands,
Guam, Northern Marianas Islands, and American Samoa) because this program is ongoing and therefore will be
continually updated. The C-CAP product does not cover the entire territory of Puerto Rico so the NLCD was used
for this area. The final selection of a land-cover product for these territories is still under discussion. Results are
presented below (in hectares). The total land area of all U.S. Territories is 1.05 million hectares, representing 0.1
percent of the total land base for the United States.
Table 6-9: Total Land Area (Hectares) by Land-Use Category for U.S. Territories
Land Use, Land-Use Change, and Forestry 6-19
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33
Puerto Rico
U.S. Virgin
Islands
Guam
Northern
Marianas
Islands
American
Samoa
Total
Cropland
19,712
138
236
289
389
20,764
Forest Land
404,004
13,107
24,650
25,761
15,440
482,962
Grasslands
299,714
12,148
15,449
13,636
1,830
342,777
Other Land
5,502
1,006
1,141
5,186
298
13,133
Settlements
130,330
7,650
11,146
3,637
1,734
154,496
Wetlands
24,525
4,748
1,633
260
87
31,252
Total
883,788
38,796
54,255
48,769
19,777
1,045,385
As adopted by the UNFCCC, new guidance in the 2013 Supplement to the 2006 Guidelines for National Greenhouse
Gas Inventories: Wetlands will be implemented in the Inventory. As a first step in this development, greenhouse gas
emissions from coastal wetlands have been developed for this Inventory using the NOAA C-CAP land cover
product. The NOAA C-CAP product is not used directly in the land representation analysis, however, so a planned
improvement for the next Inventory is to reconcile the coastal wetlands data from the C-CAP product with the
wetlands area data provided in the NRI. Further implementation of the new guidance will have implications for the
classification of managed and unmanaged wetlands in the Inventory report, and more detailed wetlands datasets will
likely also be evaluated and integrated into the analysis.
NOAA C-CAP data for Hawaii were recently released for 2011, and will be used to analyze land use change for this
state in the near future. There are also other databases that may need to be reconciled with the NRI and NLCD
datasets, particularly for Settlements. Urban area estimates, used to produce C stock and flux estimates from urban
trees, are currently based on population data (1990, 2000, and 2010 U.S. Census data). Using the population
statistics, "urban clusters" are defined as areas with more than 500 people per square mile. The USFS is currently
moving ahead with an Urban Forest Inventory program so that urban forest area estimates will be consistent with
FIA forest area estimates outside of urban areas, which would be expected to reduce omissions and overlap of forest
area estimates along urban boundary areas.
6.2 Forest Land Remaining Forest Land
Changes in Forest Carbon Stocks (IPCC Source Category 4A1)
Delineation of Carbon Pools
For estimating carbon (C) stocks or stock change (flux), C in forest ecosystems can be divided into the following
five storage pools (IPCC 2006):
• Aboveground biomass, which includes all living biomass above the soil including stem, stump, branches,
bark, seeds, and foliage. This category includes live understory.
• Belowground biomass, which includes all living biomass of coarse living roots greater than 2 millimeters
(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 centimeters (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 belowground pools.
In addition, there are two harvested wood pools included when estimating C flux:
6-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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• Harvested wood products (HWP) in use.
• HWP in solid waste disposal sites (SWDS).
Forest Carbon Cycle
Carbon is continuously cycled among the previously defined C storage pools and the atmosphere as a result of
biogeochemical processes in forests (e.g., photosynthesis, respiration, decomposition, and disturbances such as fires
or pest outbreaks) and anthropogenic activities (e.g., harvesting, thinning, and replanting). As trees photosynthesize
and grow, C is removed from the atmosphere and stored in living tree biomass. As trees die and otherwise deposit
litter and debris on the forest floor, C is released to the atmosphere and is also transferred to the litter, dead wood
and soil pools by organisms that facilitate decomposition.
The net change in forest C is not equivalent to the net flux between forests and the atmosphere because timber
harvests do not cause an immediate flux of all harvested biomass C to the atmosphere. Instead, harvesting transfers a
portion of the C stored in wood to a "product pool." Once in a product pool, the C is emitted over time as CO2 in the
case of decomposition and as CO2, CH4, N20, CO, NOx when the wood product combusts. The rate of emission
varies considerably among different product pools. For example, if timber is harvested to produce energy,
combustion releases C immediately, and these emissions are reported for information purposes in the Energy sector
while the harvest (i.e., the associated reduction in forest C stocks) and subsequent combustion are implicitly
estimated in the Land Use, Land-Use Change, and Forestry (LULUCF) sector (i.e., the harvested timber does not
enter the HWP pools). Conversely, if timber is harvested and used as lumber in a house, it may be many decades or
even centuries before the lumber decays and C is released to the atmosphere. If wood products are disposed of in
SWDS, the C contained in the wood may be released many years or decades later, or may be stored almost
permanently in the SWDS. These latter fluxes, with the exception of CH4 from wood in SWDS which is included in
the Waste sector, are also estimated in the LULUCF sector.
Net Change in Carbon Stocks within Forest Land of the United States
This section describes the general method for quantifying the net changes in C stocks in the five forest C pools and
two harvested wood pools. The underlying methodology for determining C stock and stock-change relies on data
from the Forest Inventory and Analysis (FIA) program within the USD A Forest Service. The annual forest inventory
system is implemented across all U.S. forest lands within the conterminous 48 states, but at this time does not
include interior Alaska, Hawaii, and U.S. Territories. The methods for estimation and monitoring are continuously
improved and these improvements are reflected in the C estimates (Domke et al. 2016; Domke et al. In press). First,
the total C stocks are estimated for each pool, next the net changes in C stocks for each pool are estimated, and then
the changes in stocks are summed for all pools to estimate total net flux. The focus on C implies that all C-based
greenhouse gases are included, and the focus on stock change suggests that specific ecosystem fluxes do not need to
be separately itemized in this report. Changes in C stocks from disturbances, such as forest fires or harvesting, are
included in the net changes. For instance, an inventory conducted after fire counts only the trees that are left.
Therefore, changes in C stocks from natural disturbances, such as wildfires, pest outbreaks, and storms, are included
in the forest inventory approach; however, they are highly variable from year to year. The IPCC (2006) recommends
estimating changes in C stocks from forest lands according to several land-use types and conversions, specifically
Forest Land Remaining Forest Land and Land Converted to Forest Land, with the former being lands that have
been forest lands for 20 years or longer and the latter being lands that have been classified as forest lands for less
than 20 years. The methods and data used to delineate forest C stock changes by these two categories continue to
improve and in order to facilitate this delineation, a combination of modeling approaches for carbon estimation were
used this year in the U.S.
Forest Area in the United States
Approximately 33 percent of the U.S. land area is estimated to be forested in 2015 based on the U.S. definition of
forest land as provided in the Section 6.1 Representation of the U.S. Land Base. The most recent forest inventories
from each of the conterminous 48 states (USDA Forest Service 2016a, 2016b) comprise an estimated 266 million
hectares of forest land that are considered managed and are included in this Inventory. An additional 6.2 million
hectares of forest land in southeast and south central coastal Alaska are inventoried and are also included here. Some
differences exist in forest land area estimates from the latest update to the Resources Planning Act (RPA)
Land Use, Land-Use Change, and Forestry 6-21
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33
Assessment (Oswalt et al. 2014) and the forest land area estimates included in this report, which are based on the
most recent annual inventory data available for all states (USDA Forest Service 2016b). Sufficient annual inventory
data are not yet available for Hawaii and interior Alaska, but estimates of these areas are included in Oswalt et al.
(2014). Updated survey data for central and western forest land in both Oklahoma and Texas have only recently
become available, and these forests contribute to overall C stocks reported below. While Hawaii and U.S. Territories
have relatively small areas of forest land and thus may not substantially influence the overall C budget for forest
land, these regions will be added to the forest C estimates as sufficient data become available. Agroforestry systems
that meet the definition of forest land are also not currently included in this Inventory since they are not explicitly
inventoried by either the FIA program or the Natural Resources Inventory (NRI)27 of the USDA Natural Resources
Conservation Service (Perry et al. 2005).
An estimated 77 percent (211 million hectares) of U.S. forests in southeast and southcentral coastal Alaska and the
conterminous United States are classified as timberland, meaning they meet minimum levels of productivity and
have not been removed from production. Approximately ten percent of southeast and southcentral coastal Alaska
forest land and 80 percent of forest land in the conterminous United States are classified as timberland. Of the
remaining non-timberland, 30 million hectares are reserved forest lands (withdrawnby law from management for
production of wood products) and 69 million hectares are lower productivity forest lands (Oswalt et al. 2014).
Historically, the timberlands in the conterminous 48 states have been more frequently or intensively surveyed than
the forest land removed from production because it does not meet the minimum level of productivity.
Since the late 1980s, forest land area in southeast and southcentral coastal Alaska and the conterminous U.S. has
increased by about 14 million hectares (Oswalt et al. 2014) with the southern region of the U.S. containing the most
forest land (Figure 6-2). A substantial portion of this accrued forest land is from the conversion of abandoned
croplands to forest (e.g., Woodall et al. 2015b). Current trends in the estimated forest land area in the conterminous
U.S. and the portion of southeast and south central coastal Alaska represented here show an average annual rate of
increase of 0.1 percent. In addition to the increase in forest area, the major influences to the net C flux from forest
land across the 1990 to 2015 time series are management activities and the ongoing impacts of previous land-use
conversions. These activities affect the net flux of C by altering the amount of C stored in forest ecosystems and also
the area converted to forest land. For example, intensified management of forests that leads to an increased rate of
growth of aboveground biomass (and possible changes to the other C pools) may increase the eventual biomass
density of the forest, thereby increasing the uptake and storage of C in the aboveground biomass pool.28 Though
harvesting forests removes much of the C in aboveground biomass (and possibly changes C density in other pools),
on average, the estimated volume of annual net growth in the conterminous U.S. states is about double the volume
of annual removals on timberlands (Oswalt et al. 2014). The net effects of forest management and changes in Forest
Land Remaining Forest Land are captured in the estimates of C stocks and fluxes presented in this section.
27 The Natural Resources Inventory of the USDA Natural Resources Conservation Service is described in Section 6.1 —
Representation of the U.S. Land Base.
28 The term "biomass density" refers to the mass of live vegetation per unit area. It is usually measured on a dry-weight basis.
Dry biomass is assumed to be 50 percent C by weight.
6-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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10
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17
18
19
20
Figure 6-2: Changes in Forest Area by Region for Forest Land Remaining Forest Land in the
conterminous United States and coastal Alaska (1990-2015, Million Hectares)
275-,
(/)
(D
te 225-1
o
tu
.c
c
0
1 175-1
TO
(1)
03
•+—»
(/)
2 125-
75
¦ South
North
Rocky
Mountain
Pacific
Coast
| i i i i | i i i i | i i i i | i i i i | i i i i | i
1990 1995 2000 2005 2010 2015
Year
Rocky
Mountain
Pacific
North
South
Forest Carbon Stocks and Stock Change
In the U.S., forest management practices, the regeneration of forest areas cleared more than 20 years prior to the
reporting year, and timber harvesting have resulted in net uptake (i.e., net sequestration) of C each year from 1990
through 2015. The rate of forest clearing in the 17th centuiy following European settlement had slowed by the late
19th century. Through the later part of the 20th centuiy many areas of previously forested land in the U.S. were
allowed to revert to forests or were actively reforested. The impacts of these land-use changes still influence C
fluxes from these forest lands. More recently, the 1970s and 1980s saw a resurgence of federally-sponsored forest
management programs (e.g., the Forestry Incentive Program) and soil conservation programs (e.g., the Conservation
Reserve Program), which have focused on tree planting, improving timber management activities, combating soil
erosion and converting marginal cropland to forests. In addition to forest regeneration and management, forest
harvests have also affected net C fluxes. Because most of the timber harvested from U.S. forest land 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 these long-term storage pools rather than being released rapidly
to the atmosphere (Skog 2008). With sustainable harvesting practices and regeneration of these forested lands, along
with continued input of harvested products into the HWP pool, C stocks in the Forest Land Remaining Forest Land
category are likely to continue to increase in the near term, though possibly at a lower rate. Changes in C stocks in
the forest and harvested wood pools associated with Forest Land Remaining Forest Land were estimated to result in
Land Use, Land-Use Change, and Forestry 6-23
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1 net sequestration of 667.0 MMT CChEq. (181.9 MMT C) in 2015 (Table 6-10 and Table 6-11).29 Overall, estimates
2 of average C density in forest ecosystems (including all pools) remained stable at approximately 0.0002 MMT C ha"
3 1 from 1990 to 2015 (Table 6-12). The stable forest ecosystem C density when combined with increasing forest area
4 results in net C accumulation over time. These increases may be influenced in some regions by reductions in C
5 density or forest land area due to natural disturbances (e.g., wildfire, weather, insects/disease). Aboveground live
6 biomass is responsible for the majority of net sequestration among all forest ecosystem pools (Figure 6-4).
7 The estimated net sequestration of C inHWP was 95.9 MMT CO2 Eq. (26.1 MMT C) in 2015 (Table 6-10 and
8 Table 6-11). The majority of this sequestration, 64.4 MMT CO2 Eq. (17.6 MMT C), was from wood and paper in
9 SWDS. Products in use were an estimated 31.4 MMT CO2 Eq. (8.6 MMT C) in 2015.
10 Table 6-10: Net CO2 Flux from Forest Pools in Forest Land Remaining Forest Land and
11 Harvested Wood Pools (MMT CO2 Eq.)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Forest
(574.7)
(557.3)
(605.0)
(598.5)
(596.1)
(593.7)
(571.1)
Aboveground Biomass
(327.9)
(314.4)
(337.2)
(331.5)
(329.6)
(327.7)
(310.0)
Belowground Biomass
(70.0)
(66.6)
(71.0)
(69.7)
(69.2)
(68.7)
(64.6)
Dead Wood
(33.5)
(40.3)
(48.5)
(49.1)
(49.2)
(49.2)
(43.7)
Litter
(17.0)
(14.3)
(16.5)
(16.3)
(16.3)
(16.3)
(15.2)
Soil (Mineral)
(126.1)
(121.7)
(131.9)
(132.0)
(131.9)
(131.9)
(137.6)
Soil (Organic)3
(0.1)
+
0.1
0.1
0.1
0.1
0.1
Harvested Wood
(123.8)
(108.0)
(65.7)
(69.2)
(75.6)
(76.4)
(95.9)
Products in Use
(54.8)
(44.6)
(3.9)
(7.0)
(13.0)
(13.7)
(31.4)
SWDS
(69.0)
(63.5)
(61.8)
(62.2)
(62.6)
(62.7)
(64.4)
Total Net Flux
(698.4)
(665.3)
(670.8)
(667.8)
(671.7)
(670.1)
(667.0)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
aThese estimates do not include drained organic soils. See Table 6-21 and Table 6-22 for emissions from
drainage of organic soils from Forest Land Remaining Forest Land and Land Converted to Forest Land.
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. Harvested wood estimates are based on results from annual
surveys and models. Totals may not sum due to independent rounding.
12 Table 6-11: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and
13 Harvested Wood Pools (MMT C)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Forest
(156.7)
(152.0)
(165.0)
(163.2)
(162.6)
(161.9)
(155.7)
Aboveground Biomass
(89.4)
(85.7)
(92.0)
(90.4)
(89.9)
(89.4)
(84.6)
Belowground Biomass
(19.1)
(18.2)
(19.4)
(19.0)
(18.9)
(18.7)
(17.6)
Dead Wood
(9.1)
(11.0)
(13.2)
(13.4)
(13.4)
(13.4)
(11.9)
Litter
(4.6)
(3.9)
(4.5)
(4.4)
(4.4)
(4.4)
(4.1)
Soil (Mineral)
(34.4)
(33.2)
(36.0)
(36.0)
(36.0)
(36.0)
(37.5)
Soil (Organic)3
+
+
+
+
+
+
+
Harvested Wood
(33.8)
(29.5)
(17.9)
(18.9)
(20.6)
(20.8)
(26.1)
Products in Use
(14.9)
(12.2)
(1.1)
(1.9)
(3.5)
(3.7)
(8.6)
SWDS
(18.8)
(17.3)
(16.9)
(17.0)
(17.1)
(17.1)
(17.6)
Total Net Flux
(190.5)
(181.5)
(183.0)
(182.1)
(183.2)
(182.8)
(181.9)
+ Absolute value does not exceed 0.05 MMT C
aThese estimates do not include drained organic soils. See Tables 6.20 and 6.21 for emission from drainage of
organic soils from Forest Land Remaining Forest Land and Land Converted to Forest Land.
29 It is important to note that litter and soil C stock and stock change estimates reported in the 1990-2014 Inventory were
inadvertently compiled using English units resulting in estimates that were 2.2417 times larger than they should have been for the
Forest Land Remaining Forest Land category. Please see the Recalculations Discussion and the QA/QC and Verifications
sections for additional details.
6-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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.
1 Stock estimates for forest and harvested wood C storage pools are presented in Table 6-12. Together, the estimated
2 aboveground biomass and soil C pools account for a large proportion of total forest C stocks. Note that the forest
3 land area estimates in Table 6-12 do not precisely match those in Section 6.1 Representation of the U.S. Land Base
4 for Forest Land Remaining Forest Land. This is because the forest land area estimates in Table 6-12 only include
5 managed forest land in the conterminous 48 states and southeast and south central coastal Alaska (which is the
6 current area encompassed by FIA survey data, approximately 6.2 million ha) while the area estimates in Section 6.1
7 include all managed forest land in Alaska (approximately 25.9 million ha with approximately 19.7 million ha in
8 interior Alaska which is not currently included in this Inventory) and Hawaii.
9 Table 6-12: Forest Area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and
10 Harvested Wood Pools (MMT C)
1990
20(15
2011
2012
2013
2014
2015
2016
Forest Area (1000 ha)
262,119
267,479
270,654
271,064
271,512
271,812
272,113
272,260
Carbon Pools (MMT C)
Forest
46,967
49,223
50,166
50,331
50,494
50,657
50,819
50,975
Aboveground Biomass
11,889
13,122
13,650
13,742
13,833
13,922
14,012
14,096
Belowground Biomass
2,439
2,700
2,812
2,831
2,850
2,869
2,888
2,905
Dead Wood
2,262
2,424
2,494
2,507
2,521
2,534
2,548
2,560
Litter
2,568
2,630
2,654
2,659
2,663
2,668
2,672
2,676
Soil (Mineral)
27,456
27,994
28,204
28,240
28,276
28,312
28,348
28,385
Soil (Organic)3
352
352
352
352
352
352
352
352
Harvested Wood
1,895
2,353
2,481
2,498
2,517
2,538
2,559
2,585
Products in Use
1,249
1,447
1,473
1,474
1,476
1,479
1,483
1,492
SWDS
646
906
1,008
1,025
1,042
1,059
1,076
1,093
Total C Stock
48,862
51,576
52,647
52,830
53,012
53,195
53,378
53,560
a These estimates do not include drained organic soils. See Table 6-21 and Table 6-22 for emissions from drainage of organic soils
from Forest Land Remaining Forest Land and Land Converted to Forest Land.
Note: Forest area and C stock estimates include all Forest Land Remaining Forest Land in the conterminous 48 states and
southeast and south central coastal Alaska (6.2 million ha), which is the current area encompassed by FIA survey data. Forest C
stocks do not include forest stocks in U.S. Territories, Hawaii, a large portion of interior Alaska (19.7 million ha), or trees on
non-forest land (e.g., urban trees, agroforestry systems). The forest area estimates in this table do not match those in Section 6.1
Representation of the U.S. Land Base, which includes all managed forest land in Alaska and Hawaii. Harvested wood product
stocks include exports, even if the logs are processed in other countries, and exclude imports. Harvested wood estimates are based
on results from annual surveys and models. Totals may not sum due to independent rounding. Population estimates compiled
using FIA data 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 2015 requires estimates of C stocks for 2015 and 2016.
11
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22
Figure 6-3: Estimated Net Annual Changes in C Stocks for All C Pools in Forest Land
Remaining Forest Land in the Conterminous U.S. and Coastal Alaska (MMT C per Year)
20-,
h -120-
-160-
-200-
-220—1
i I | I I I i | I i
2000 2005
Year
i I i i i i |
2010 2015
All forest pools
Aboveground biomass
• Belowground biomass
Dead wood
• Litter
Soil (mineral)
¦ Soil (organic)
• Harvested Wood
Products in use
Solid waste disposal sites
Total net change
Box 6-3: CO2 Emissions from Forest Fires
As stated previously, the forest inventory approach implicitly includes all C losses due to disturbances such as forest
fires, because only C remaining in the forest is estimated. Net C stock change is estimated by subtracting
consecutive C stock estimates. A forest fire disturbance removes C from the forest. The inventory data on which net
C stock estimates are based already reflect this C loss. Therefore, estimates of net annual changes in C stocks for
U.S. forest land already includes CO2 emissions from forest fires occurring in the conterminous states as well as the
portion of managed forest lands in Alaska that are captured in this Inventory. Because it is of interest to quantify the
magnitude of CO2 emissions from fire disturbance, these separate estimates are liiglilighted here. Note that these
CO2 estimates are based 011 the same methodology as applied for the non-CO: greenhouse gas emissions from forest
fires that are also quantified in a separate section below as required by IPCC Guidance and UNFCCC Reporting
Requirements.
The IPCC (2006) methodology and a combination of U.S.-specific data on annual area burned and potential fuel
availability together with default combustion factors were employed to estimate CO2 emissions from forest fires.
CO2 emissions for wildfires in the conterminous 48 states and in Alaska as well as prescribed fires in 2015 were
estimated to be 96.3 MMT CO2 per year (Table 6-13). This quantity is an embedded component of the net annual
forest C stock change estimates provided previously (i.e.. Table 6-11), but this separate approach to estimate
emissions is necessary in order to associate a portion of emissions, including estimates of CH4 and N20, with fire.
See the discussion in Annex 3.13 for more details 011 this methodology. Note that the estimates for Alaska provided
6-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 in Table 6-13 include all managed forest land in the state and are not limited to the subset with permanent inventory
2 plots on managed lands as specified elsewhere in this chapter (i.e., Table 6-11).
3 Table 6-13: Estimates of CO2 (MMT per Year) Emissions from Forest Fires in the
4 Conterminous 48 States and Alaska3
CO2 emitted from
Wildfires in the
CO2 emitted from
CO2 emitted from
Conterminous 48
Wildfires in Alaska
Prescribed Fires
Total CO2 emitted
Year
States (MMT yr1)
(MMTyr1)
(MMTyr1)
(MMTyr1)
1990
22.7
19.5
0.2
42.4
2005
43.5
80.1
1.3
124.9
2011
81.3
3.6
6.0
90.9
2012
138.0
2.7
3.0
143.6
2013
68.0
22.3
5.5
95.7
2014
85.3
4.9
6.1
96.3
2015b
85.3
4.9
6.1
96.3
a These emissions have already been included in the estimates of net annual changes in C stocks, which include the
amount sequestered minus any emissions, including the assumption that combusted wood may continue to decay
through time.
b The data for 2015 were incomplete when these estimates were summarized; therefore 2014, the most recent
available estimate, is applied to 2015.
5
6 Methodology and Data Sources
7 The methodology described herein is consistent with IPCC (2006). Forest ecosystem C stocks and net annual C
8 stock change were determined according to the stock-difference method, which involved applying C estimation
9 factors to annual forest inventories across time to obtain C stocks and then subtracting between the years to obtain
10 the stock change. Harvested wood C estimates were based on factors such as the allocation of wood to various
11 primary and end-use products as well as half-life (the time at which half of the amount placed in use will have been
12 discarded from use) and expected disposition (e.g., product pool, SWDS, combustion). An overview of the different
13 methodologies and data sources used to estimate the C in forest ecosystems or harvested wood products is provided
14 here. See Annex 3.13 for details and additional information related to the methods and data.
15 Forest Ecosystem Carbon from Forest Inventory
16 The U.S. applied the compilation approach used in the 1990 to 2014 Inventory and described in Woodall et al.
17 (2015a) for this Inventory which removes the older periodic inventory data, which may be inconsistent with annual
18 inventory data, from the estimation procedures and enables the delineation of forest C accumulation by forest
19 growth, land use change, and natural disturbances such as fire. Development will continue on a system that
20 attributes changes in forest C to disturbances and delineates Land Converted to Forest Land from Forest Land
21 Remaining Forest Land. As part of this development, C pool science will continue and will be expanded to include
22 C stock transfers from forest land to other land uses, and include techniques to better identify land use change (see
23 the Planned Improvements section below).
24 Unfortunately, the annual inventory system does not extend into the 1990s, necessitating the adoption of a system to
25 "backcast" the annual C estimates. To facilitate the backcasting of the U.S. annual forest inventory C estimates, the
26 estimation system used in this Inventory is comprised of a forest dynamics module (age transition matrices) and a
27 land use dynamics module (land area transition matrices). The forest dynamics module assesses forest sequestration,
28 forest aging, and disturbance effects (e.g., disturbances such as wind, fire, and floods identified by foresters on
29 inventory plots). The land use dynamics module assesses C stock transfers associated with afforestation and
30 deforestation (Woodall et al. 2015b). Both modules are developed from land use area statistics and C stock change
31 or C stock transfer by age class. The required inputs are estimated from more than 625,000 forest and non-forest
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observations recorded in the FIA national database (U.S. Forest Service 2016a, b, c). Model predictions prior to the
annual inventory period are constructed from the estimation system using the annual estimates. The estimation
system is driven by the annual forest inventory system conducted by the FIA program (Frayer and Furnival 1999;
Bechtold and Patterson 2005; USDA Forest Service 2016d, 2016a). The FIA program relies on a rotating panel
statistical design with a sampling intensity of one 674.5 m2 ground plot per 2,403 ha of land and water area. A five-
panel design, with 20 percent of the field plots typically measured each year within a state, is used in the eastern
United States and a ten-panel design, with 10 percent of the field plots measured each year within a state, is used in
the western United States. The interpenetrating hexagonal design across the U.S. landscape enables the sampling of
plots at various intensities in a spatially and temporally unbiased manner. Typically, tree and site attributes are
measured with higher sample intensity while other ecosystem attributes such as downed dead wood are sampled
during summer months at lower intensities. The first step in incorporating FIA data into the estimation system is to
identify annual inventory datasets by state. Inventories include data collected on permanent inventory plots on forest
lands and were organized as separate datasets, each representing a complete inventory, or survey, of an individual
state at a specified time. Many of the annual inventories reported for states are represented as "moving window"
averages, which mean that a portion—but not all—of the previous year's inventory is updated each year (USDA
Forest Service 2016d). Forest C estimates are organized according to these state surveys, and the frequency of
surveys varies by state.
Using this FIA data, separate estimates were prepared for the five C storage pools identified by IPCC (2006) and
described above. All estimates were based on data collected from the extensive array of permanent, annual forest
inventory plots and associated models (e.g., live tree belowground biomass) in the U.S. (USDA Forest Service
2016b, 2016c). Carbon conversion factors were applied at the disaggregated level of each inventory plot and then
appropriately expanded to population estimates.
Carbon in Biomass
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at breast
height (dbh) of at least 2.54 cm at 1.37 m above the litter. Separate estimates were made for above- and
belowground biomass components. If inventory plots included data on individual trees, aboveground and
belowground (coarse roots) tree C was based on Woodall et al. (201 la), which is also known as the component ratio
method (CRM), and is a function of tree volume, species, and diameter. An additional component of foliage, which
was not explicitly included in Woodall et al. (201 la), was added to each tree following the same CRM method.
Understory vegetation is a minor component of biomass, which is defined in the FIA program as all biomass of
undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm dbh. For this Inventory, it was
assumed that 10 percent of total understory C mass is belowground (Smith et al. 2006). Estimates of C density were
based on information in Birdsey (1996) and biomass estimates from Jenkins et al. (2003). Understory biomass
represented over one percent of C in biomass, but its contribution rarely exceeded 2 percent of the total carbon
stocks or stock changes across all forest ecosystem C pools each year.
Carbon in Dead Organic Matter
Dead organic matter was initially calculated as three separate pools—standing dead trees, downed dead wood, and
litter—with C stocks estimated from sample data or from models as described below. The standing dead tree C pool
includes aboveground and belowground (coarse root) biomass for trees of at least 12.7 cm dbh. Calculations
followed the basic method applied to live trees (Woodall et al. 201 la) with additional modifications to account for
decay and structural loss (Domke et al. 2011; Harmon et al. 2011). Downed dead wood estimates are based on
measurement of a subset of FIA plots for downed dead wood (Domke et al. 2013; Woodall and Monleon 2008;
Woodall et al. 2013). Downed dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect
intersection, that are not attached to live or standing dead trees. This includes stumps and roots of harvested trees.
To facilitate the downscaling of downed dead wood C estimates from the state-wide population estimates to
individual plots, downed dead wood models specific to regions and forest types within each region are used. Litter C
is the pool of organic C (also known as duff, humus, and fine woody debris) above the mineral soil and includes
woody fragments with diameters of up to 7.5 cm. A subset of FIA plots are measured for litter C. A modeling
approach, using litter C measurements from FIA plots (Domke et al. 2016) was used to estimate litter C for every
FIA plot used in the estimation framework.
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Carbon in Forest Soil
Soil carbon is the largest terrestrial C sink with much of that C in forest ecosystems. The FIA program has been
consistently measuring soil attributes as part of the annual inventory since 2001 and has amassed an extensive
inventory of soil measurement data on forest land in the conterminous U.S. and coastal Alaska (O'Neill et al. 2005).
Observations of mineral and organic soil C on forest land from the FIA program and the International Soil Carbon
Monitoring Network were used to develop and implement a modeling approach that enabled the prediction of
mineral and organic soil C to a depth of 100 cm from empirical measurements to a depth of 20 cm and included site-
, stand-, and climate-specific variables that yield predictions of soil C stocks specific to forest land in the United
States (Domke et al. In press). This new approach allowed for separation of mineral and organic soils also referred
to as Histosols for the first time in the Forest Land Remaining Forest Land category. Note that mineral and organic
soil C is reported to a depth of 100 cm for Forest Land Remaining Forest Land to remain consistent with past
reporting in this category, however for consistency across land-use categories mineral soil C is reported to a depth of
30 cm in Section 6.3 Land Converted to Forest Land. Estimates of C from organic soils in this section (Table 6-10,
Table 6-11, and Table 6-12) do not include emissions from drained organic soils. Estimates of emissions from
drainage of organic soils from Forest Land Remaining Forest Land and Land Converted to Forest Land can be
found in the Drained Organic Soils section below (Table 6-21 and Table 6-22).
Harvested Wood Carbon
Estimates of the HWP contribution to forest C sinks and emissions (hereafter called "HWP contribution") were
based on methods described in Skog (2008) using the WOODCARB II model. These methods are based on IPCC
(2006) guidance for estimating the HWP contribution. IPCC (2006) provides methods that allow for reporting of
HWP contribution using one of several different methodological approaches: Production, stock change and
atmospheric flow, as well as a default method that assumes there is no change in HWP C stocks (see Annex 3.13 for
more details about each approach). The U.S. uses the production approach to report HWP contribution. Under the
production approach, C in exported wood was estimated as if it remains in the U.S., and C in imported wood was not
included in the estimates. Though reported U.S. HWP estimates are based on the production approach, estimates
resulting from use of the two alternative approaches, the stock change and atmospheric flow approaches, are also
presented for comparison (see Annex 3.13). Annual estimates of change were calculated by tracking the annual
estimated 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 SWDS. Emissions from HWP associated with wood biomass
energy are not included in this section of the Inventory—a net of zero sequestration and emissions as they are a part
of the Energy sector reporting (see Chapter 3).
Solidwood products include lumber and panels. End-use categories for solidwood include single and multifamily
housing, alteration and repair of housing, and other end-uses. There is one product category and one end-use
category for paper. Additions to and removals from pools were tracked beginning in 1900, with the exception that
additions of softwood lumber to housing, which began in 1800. Solidwood and paper product production and trade
data were taken from USDA Forest Service and other sources (Hair and Ulrich 1963; Hair 1958; USDC Bureau of
Census 1976; Ulrich 1985, 1989; Steer 1948; AF&PA 2006a, 2006b; Howard 2003, 2007, 2016, In preparation).
Estimates for disposal of products reflected the change over time in the fraction of products discarded to SWDS (as
opposed to burning or recycling) and the fraction of SWDS that were in sanitary landfills versus dumps.
There are five annual HWP variables that were used in varying combinations to estimate HWP contribution using
any one of the three main approaches listed above. These are:
(IA) annual change of C in wood and paper products in use in the U.S.,
(IB) annual change of C in wood and paper products in SWDS in the U.S.,
(2A) annual change of C in wood and paper products in use in the U.S. and other countries where the wood
came from trees harvested in the U.S.,
(2B) annual change of C in wood and paper products in SWDS in the U.S. and other countries where the
wood came from trees harvested in the U.S.,
(3) C in imports of wood, pulp, and paper to the U.S.,
(4) C in exports of wood, pulp and paper from the U.S., and
Land Use, Land-Use Change, and Forestry 6-29
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1 (5) C in annual harvest of wood from forests in the U.S.
2 The sum of variables 2 A and 2B yielded the estimate for HWP contribution under the production estimation
3 approach. A key assumption for estimating these variables was that products exported from the U.S. and held in
4 pools in other countries have the same half-lives for products in use, the same percentage of discarded products
5 going to SWDS, and the same decay rates in SWDS as they would in the U.S.
6 Uncertainty and Time-Series Consistency
7 A quantitative uncertainty analysis placed bounds on current flux for forest ecosystems through a combination of
8 sample-based and model-based approaches to uncertainty for forest ecosystem CO2 flux (IPCC Approach 1). A
9 Monte Carlo Stochastic Simulation of the Methods described above and probabilistic sampling of C conversion
10 factors were used to determine the HWP uncertainty (IPCC Approach 2). See Annex 3.13 for additional information.
11 The 2015 net annual change for forest C stocks was estimated to be between -898.5 and -397.2 MMT CO2 Eq.
12 around a central estimate of -666.9 MMT CO2 Eq. at a 95 percent confidence level. This includes a range of -820.3
13 to -321.9 MMT CO2 Eq. around a central estimate of -571.1 MMT CO2 Eq. forforest ecosystems and -115.1 to -
14 76.6 MMT CO2 Eq. around a central estimate of -95.9 MMT CO2 Eq. for HWP.
15 Table 6-14: Quantitative Uncertainty Estimates for Net CO2 Flux from Forest Land
16 Remaining Forest Land: Changes in Forest C Stocks (MMT CO2 Eq. and Percent)
Source
Gas
2015 Flux Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Flux Estimate
(MMT CO2 Eq.) (%)
Lower
Upper
Lower Upper
Bound
Bound
Bound Bound
Forest C Pools3
CO2
(571.1)
(820.3)
(321.9)
-43.6% 43.6%
Harvested Wood Products'5
CO2
(95.9)
(115.1)
(76.6)
-20.1% 20.1%
Total Forest
CO2
(667.0)
(916.9)
(417.0)
-37.5% 37.5%
a Range of flux estimates predicted through a combination of sample based and model based uncertainty for a 95 percent
confidence interval, IPCC Approach 1.
b Range of flux estimates predicted by Monte Carlo stochastic simulation for a 95 percent confidence interval, IPCC
Approach 2.
Note: Parentheses indicate negative values or net sequestration.
17 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
18 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
19 above.
20 QA/QC and Verification
21 As discussed above, the FIA program has conducted consistent forest surveys based on extensive statistically-based
22 sampling of most of the forest land in the conterminous United States, dating back to 1952. The FIA program
23 includes numerous quality assurance and quality control (QA/QC) procedures, including calibration among field
24 crews, duplicate surveys of some plots, and systematic checking of recorded data. Because of the statistically-based
25 sampling, the large number of survey plots, and the quality of the data, the survey databases developed by the FIA
26 program form a strong foundation for C stock estimates. Field sampling protocols, summary data, and detailed
27 inventory databases are archived and are publicly available on the Internet (USDA Forest Service 2016d).
28 General quality control procedures were used in performing calculations to estimate C stocks based on survey data.
29 For example, the C datasets, which include inventory variables such as areas and volumes, were compared to
30 standard inventory summaries such as the forest resource statistics of Oswalt et al. (2014) or selected population
31 estimates generated from the FIA database, which are available at an FIA internet site (USDA Forest Service
32 2016b). Agreement between the C datasets and the original inventories is important to verily accuracy of the data
33 used. Estimates were also compiled using an FIADB-to-C calculator (Smith et al. 2010) and compared with
34 estimates compiled using the current estimation system. During the implementation of this additional QA step in
35 preparation of this Inventory, a mistake in the unit conversion code used to compile plot-level estimates of C stocks
36 and stock changes from the FIADB was discovered. Specifically, the litter and soil carbon stock and stock change
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estimates reported in the 1990 to 2014 Inventory were inadvertently compiled using English units resulting in
estimates that were 2.2417 times larger than they should have been for the Forest Land Remaining Forest Land
category. This mistake was not caught last year since the soil carbon model and the estimation system used to
compile estimates for the U.S. were both being used for the first time with no similar estimates (e.g., national-level
population estimates using similar data) available for comparison. This mistake has been corrected in this Inventory.
Finally, C stock estimates for this Inventory 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). As previously mentioned, the litter and soil C stock and stock change estimates from the 1990 to
2014 Inventory must be divided by 2.2417 to put them in the correct units for comparison with estimates in the
current Inventory.
Estimates of the HWP variables and the HWP contribution under the production estimation approach use data from
U.S. Census and USDA Forest Service surveys of production and trade 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, 2016, In preparation). Factors to convert wood and paper to units of C are based on estimates by industry and
Forest Service published sources (see Annex 3.13 and Table A-240 and A-241). 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 used in the Waste sector 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 U.S. and, to a lesser
degree, reduce uncertainty in estimates of annual change in C in products made from wood harvested in the U.S. In
addition, WOODCARB II landfill decay rates have been validated by ensuring that estimates of CH4 emissions from
landfills based on EPA (2006) data are reasonable in comparison to CH4 estimates based on WOODCARB II
landfill decay rates.
Recalculations Discussion
Forest ecosystem stock and stock-change estimates differ from previous Inventory reports in two primary ways.
First, a different estimation system was used in this Inventory and the previous (1990 to 2014) Inventory (Woodall
et al. 2015a). The major differences between the estimation system used in the last two Inventory reports and past
estimation approaches is the sole use of annual FIA data and the back-casting of forest C stocks across the 1990s
based on forest C stock density and land use change information obtained from the nationally consistent annual
forest inventory coupled with in situ observations of non-tree C pools such as soils, dead wood, and litter in the
1990-2014 Inventory and this Inventory. The use of this estimation framework has enabled the creation of the two
land use sections for forest C stocks: Forest Land Remaining Forest Land and Land Converted to Forest Land. In
prior Inventory reports (e.g., the 1990 to 2013 Inventory), the C stock changes from Land Converted to Forest Land
were a part of the Forest Land Remaining Forest Land section and it was not possible to disaggregate the estimates
with the methodology applied at that time. A second major change in the 1990-2014 Inventory submission was the
adoption of a new approach to estimate forest soil C, the largest C stock in the United States. However, the litter and
soil C stock and stock change estimates reported in the 1990 to 2014 Inventory were inadvertently compiled using
English units resulting in estimates that were 2.2 times larger than they should have been for the Forest Land
Remaining Forest Land category. This mistake was not caught during compilation of the previous Inventory report
since the soil C model and the estimation system used to compile estimates for the United States were both being
used for the first time with no similar (e.g., national-level population estimates using similar data) estimates
available for comparison. In addition to these major changes, the refined land representation analysis described in
Section 6.1 Representation of the U.S. Land Base re-classified some of the forest land in south central and
southeastern coastal Alaska as unmanaged; this is in contrast to past assumptions where forest lands included in the
FIA database were always considered part of the "managed" land base. Therefore, the C stock and flux estimates for
southeast and south central coastal Alaska, as included here, reflect that adjustment, which effectively reduces the
managed forest area by approximately 5 percent.
In addition to the creation of explicit estimates of removals and emissions from Forest Land Remaining Forest Land
and Land Converted to Forest Land, the estimation system used in the current Inventory and the previous (1990 to
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2014) Inventory eliminated the use of periodic data (which may be inconsistent with annual inventory data) and
contributed to a data artifact in prior estimates of emissions/removals from 1990 to the present. In the previous
Inventory reports (i.e., prior to the 1990 to 2014 Inventory), there was a reduction in net sequestration from 1995 to
2000 followed by an increase in net sequestration from 2000 to 2004. This artifact, resulting from comparing
inconsistent inventories of the 1980s through 1990s to the nationally consistent inventories of the 2000s has been
removed in the last two Inventory reports.
Emissions from drained organic soil within Forest Land Remaining Forest Land and Land Converted to Forest
Land are reported for the first time in this Inventory. These estimates of drained organic soils on forest land are
identified separately from other forest soils largely because mineralization of the exposed or partially dried organic
material results in continuous CO2 emissions (IPCC 2006). This distinction merits the separate estimates provided
here according to IPCC (2006) and primarily the 2013 Supplement to the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories: Wetlands (IPCC 2013).
Estimated annual net additions to HWP C stocks increased slightly between 2014 and 2015 but decreased overall
dating back to 2008 due to revised data for the solidwood products in use category. The decline in net additions to
HWP C stocks continued through 2009 from the recent high point in 2006. This is due to sharp declines in U.S.
production of solidwood and paper products in 2007 and 2008 primarily due to the decline in housing construction.
The low level of gross additions to solidwood and paper products in use in 2009 and 2010 were exceeded by
discards from uses. The result is a net reduction in the amount of HWP C that is held in products in use during this
time period and ultimately this category became a net source. Since the recent recession in 2009 the products in use
have not recovered while additions to the SWDS have remained relatively stable.
Planned Improvements
Reliable estimates of forest C stocks and changes across the diverse ecosystems of the U.S. require a high level of
investment in both annual monitoring and associated analytical techniques. Development of improved
monitoring/reporting techniques is a continuous process that occurs simultaneously with annual Inventory
submissions. Planned improvements can be broadly assigned to the following categories: development of a robust
estimation and reporting system, individual C pool estimation, coordination with other land-use categories, and
annual inventory data incorporation.
As this is only the second Inventory submission to delineate C change by Forest Land Remaining Forest Land and
Land Converted to Forest Land, there are many improvements that are still necessary. Since the estimation approach
used this year operates at the regional scale for the U.S., research is underway to leverage auxiliary information (i.e.,
remotely sensed information) to operate at finer spatial scales. As in past submissions, deforestation is implicitly
included in the report given the design of the annual forest inventory system, but not explicitly estimated. The
transparency and repeatability of estimation and reporting systems will be improved through the dissemination of
open source code (e.g., R programming language) in concert with the public availability of the annual forest
inventory data (USDA Forest Service 2016b). Also, several FIA database processes are being institutionalized to
increase efficiency and QA/QC in reporting and further improve transparency, completeness, consistency, accuracy,
and availability of data used in reporting. Finally, a Tier 1 approach was used to estimate uncertainty associated with
C stock changes in the Forest Land Remaining Forest Land category in this report. There is research underway
investigating more robust approaches to total uncertainty (Clough et al. 2016) which will be considered in future
Inventory reports.
The modeling framework used to estimate downed dead wood within the dead wood C pool will be updated similar
to the litter (Domke et al. 2016) and soil C pools (Domke et al. In press). Finally, components of other pools, such as
C in belowground biomass (Russell et al. 2015) and understory vegetation (Russell et al. 2014; Johnson et al. In
review), are being explored but may require additional investment in field inventories before improvements can be
realized with Inventory submissions.
The foundation of forest C estimation and reporting is the annual forest inventory system. The ongoing annual
surveys by the FIA program are expected to improve the accuracy and precision of forest C estimates as new state
surveys become available (USDA Forest Service 2016b), particularly in western states. Hawaii and U.S. Territories
will be included when appropriate forest C data are available (as of April 30, 2016, only a small number of plots
from Hawaii were available from the annualized sampling design). Forest lands in interior Alaska are now included
in the annual forest inventory, however alternative methods of estimating C stock change will need to be explored as
6-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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43
it will take several years to re-measure newly established plots. To that end, research is underway to incorporate all
FIA plot information (both annual and periodic data) and the dense time-series of remotely sensed data in a design-
based, model-assisted format for estimating greenhouse gas emissions and removals as well as change detection and
attribution across the entire reporting period and all managed forest land in the U.S. Leveraging this auxiliary
information will aid not only the interior Alaska effort but the entire inventory system. In addition to fully
inventorying all managed forest land in the U.S., the more intensive sampling of fine woody debris, litter, and SOC
on a subset of FIA plots continues and will substantially improve resolution of C pools (i.e., greater sample
intensity; Westfall et al. 2013) as this information becomes available (Woodall et al. 2011b). Increased sample
intensity of some C pools and using annualized sampling data as it becomes available for those states currently not
reporting are planned for future submissions. The FIA sampling frame extends beyond the forest land use category
(e.g., woodlands and urban areas) with inventory-relevant information for these lands which will likely become
increasingly available in coming years.
Box 6-4: Preliminary Estimates of Historical Carbon Stock Change and Methane Emissions from Managed Land
in Alaska (Represents Mean for Years 2000 to 2009)
Starting in the 1990s, a forest inventory of south central and southeastern coastal (SCSE) Alaska was initiated
following the same approach applied in the conterminous U.S. These data have been used to compile Forest Land
estimates for SCSE Alaska in the U.S. Inventory since 2008. However, there still remain vast expanses of Alaska
that are in the U.S. managed land base (See Section 0) where forest inventories have only recently been established
and thus are not included as part of the greenhouse gas flux reporting in this Inventory. In addition, this Inventory
does not report on Grasslands in Alaska due to lack of land use and management data. Recognizing the need to
report on these emissions and removals, efforts have been initiated to apply a combination of approaches that will
eventually lead to complete reporting for all managed land in Alaska. The most promising near-term option for
Forest Lands that would meet the minimum UNFCCC reporting requirements is application of the IPCC Tier 1
Gain-Loss Method. Work is also underway to utilize forest inventory plots in combination with remote sensing to
estimate C stock changes. This work was initiated as a pilot study and lias now moved fully operational with the
annual forest inventory in interior Alaska underway. Full implementation of either of these approaches for reporting
in the Inventory is several years in the future.
In order to provide some insight into the greenhouse gas flux in Alaska, preliminary C stock change and CH4
emissions for Alaska have been developed using data from a recently completed USGS effort overlaid on the
Alaskan managed land base to provide a preliminary assessment of the mean historical anthropogenic greenhouse
gas flux between 2000 and 2009.
The assessment by the USGS, in collaboration with USD A Forest Service and the University of Alaska in Fairbanks,
estimated Alaska C stock changes and CH4 emissions using an approach that couples modeling, remote sensing
analysis, literature and database review (Zhu and McGuire, eds, 2016). Annual variation of soil and vegetation C
stocks and associated CO2 and CH4 fluxes, in both upland and wetland ecosystems in Alaska, were analyzed from
1950 to 2009, using this USGS modeling framework.
Results of the assessment include C stocks and fluxes from vegetation and soil organic C pools, and CH4 fluxes.
Vegetation C pools included aboveground and belowground biomass. The soil C pool included dead woody debris
and C stored in organic and mineral horizons. Carbon dioxide fluxes from vegetation net primary productivity, soil
heterotrophic respiration wildfire emissions and harvest were estimated. Methane fluxes included biogenic and
pyrogenic sources. The results of this USGS analysis (i.e., mean values for 2000 to 2009-time period) overlaid on
the Alaskan managed land base are presented in Table 6-15.
Table 6-15: Mean C Stocks, CO2 and ChU Fluxes in Alaska between 2000 and 2009
Land Use: C Pool
Area (1,000 ha)a
C stock (MMT C)
CO2 Flux (Change in
C stocks)
(MMT CO2)
Eq./Year)b
CH4 Flux
(MMT CO2
Eq./Year)
Forest Land
39,917
15,226
44.86
1.675
Aboveground Biomass
Belowground Biomass
Soilc
-
2,130
532
12,563
4.03
40.83
-
Grassland"1
34,844
18,856
(30.60)
0.102
Land Use, Land-Use Change, and Forestry 6-33
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Aboveground
Vegetation
Belowground
Vegetation
Soil"
-
315
178
18,363
(5.83)
(24.77)
-
Wetland
12,346
3,927
17.52
23.170
Aboveground
Vegetation
Belowground
Vegetation
Soil0
-
264
176
3,487
1.12
16.41
-
Total
87,107
38,008
31.80
24.947
a The USGS assessment did not include the Aleutian Islands, Saint Lawrence Island, glacier, bare ground or urban areas,
therefore the area data does not match up precisely with the Land Representation analysis in this Inventory (see Section 6.1 for
more details).
b This assessment considers carbon exported out of the ecosystem from harvesting as a loss, it does not include the
contribution to the harvested wood products pool.
c Soil pool includes dead woody debris and C stored in organic and mineral horizons.
d Grassland also includes heath and shrubland
Note: Parentheses indicate net sequestration.
1
2 Non-COz Emissions from Forest Fires
3 Emissions of non-C02 gases from forest fires were estimated using U.S.-specific data for annual area of forest
4 burned and potential fuel availability as well as the default IPCC (2006) emissions and combustion factors applied to
5 the IPCC methodology. In 2015, emissions from this source were estimated to be 7.3 MMT CO2 Eq. of CH4 and 4.8
6 MMT CO2 Eq. of N20 (Table 6-16; kt units provided in Table 6-17). The estimates of non-CCh emissions from
7 forest fires include wildfires and prescribed fires in the conterminous 48 states and Alaska.
8 Table 6-16: N011-CO2 Emissions from Forest Fires (MMT CO2 Eq.)a
Gas
1990
2005
2011
2012
2013
2014
2015b
CH4
3.2
9.4
6.8
10.8
7.2
7.3
7.3
n2o
2.1
6.2
4.5
7.1
4.7
4.8
4.8
Total
5.3
15.6
11.3
17.9
11.9
12.1
12.1
a These estimates include Non-CC>2 Emissions from Forest Fires on Forest Land Remaining
Forest Land and Land Converted to Forest Land.
bThe data for 2015 were incomplete when these estimates were developed, therefore 2014, the
most recent available estimate, is applied to 2015.
9 Table 6-17: N011-CO2 Emissions from Forest Fires (kt)a
Gas
1990
2005
2011
2012
2013
2014
2015"
CH4
128
378
273
431
289
292
292
N2O
7
21
15
24
16
16
16
CO
2,832
8,486
6,136
9,815
6,655
6,642
6,642
NOx
80
239
172
276
185
188
188
a These estimates include Non-CC>2 Emissions from Forest Fires on Forest Land Remaining
Forest Land and Land Converted to Forest Land.
bThe data for 2015 were incomplete when these estimates were summarized, therefore 2014,
the most recent available estimate, is applied to 2015.
6-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Methodology
2 N011-CO2 emissions from forest fires—primarily CH4 and N20 emissions—were calculated following IPCC (2006)
3 methodology, which included a combination of U.S. specific data on area burned and potential fuel available for
4 combustion along with IPCC default combustion and emission factors. The estimates were calculated according to
5 Equation 2.27 of IPCC (2006, Volume 4, Chapter 2), which is:
6 Emissions = Area burned x Fuel available x Combustion factor x Emission factor x 10-3
7 where area burned data are based on Monitoring Trends in Burn Severity (MTBS) data summaries (MTBS 2015),
8 fuel estimates are based on current C density estimates obtained from the latest FIA data for each state, and
9 combustion and emission factors are from IPCC (2006, Volume 4, Chapter 2). See Annex 3.13 for further details.
10 Uncertainty and Time-Series Consistency
11 In order to quantify the uncertainties for non-CCh emissions from wildfires and prescribed burns, a Monte Carlo
12 (IPCC Approach 2) sampling approach was employed to propagate uncertainty based on the model and data applied
13 for U.S. forest land. See IPCC (2006) and Annex 3.13 forthe quantities and assumptions employed to define and
14 propagate uncertainty. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 6-18.
15 Table 6-18: Quantitative Uncertainty Estimates of Non-C02 Emissions from Forest Fires
16 (MMT CO2 Eq. and Percent)3
Source
Gas
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimateb
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Non-CC>2 Emissions from
Forest Fires
CH4
7.3
2.7
19.1
-63%
161%
Non-CC>2 Emissions from
Forest Fires
N2O
4.8
1.9
12.3
-60%
157%
a These estimates include N011-CO2 Emissions from Forest Fires on Forest Land Remaining Forest Land and Land
Converted to Forest Land.
b Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
17 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
18 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
19 above.
20 QA/QC and Verification
21 Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
22 control measures for forest fires included checking input data, documentation, and calculations to ensure data were
23 properly handled through the inventory process. Further, the set of fire emissions estimates using MODIS imagery
24 and post-fire observations developed for Alaska by Veraverbeke et al. (2015) (Annex 3.13, Table A-247 were
25 compared to the estimates of CO2 and C emissions from forest fires in Alaska (Table 6-13 and Annex 3.13 Table A-
26 248). These alternate sources of data for annual areas burned and possible fuel availability in Alaska were found to
27 be similar to the data used here. The QA/QC procedures did not reveal any inaccuracies or incorrect input values.
28 Recalculations Discussion
29 The current non-CCh emissions estimates are based on the calculation described above and in IPCC (2006), which is
30 a very similar approach to the basic calculation of previous Inventory reports. However, some of the data
31 summarized and applied to the calculations are very different for the current Inventory. The use of the MTBS data
32 summaries is the most prominent example. Annual burned areas on managed forest lands were identified according
33 to Ruefenacht et al. (2008) and Ogle et al. (In preparation). The other change with the estimates provided in this
34 Inventory is in the use of the underlying plot level C densities based on forest inventory plots. Although the base
Land Use, Land-Use Change, and Forestry 6-35
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1 data are similar to past years, the current uncertainty estimates are based on an assumption that plot-to-plot
2 variability is a greater influence on uncertainty than the uncertainty in the forest-inventory to C conversion factors
3 (as employed for uncertainty in the past) .See Annex 3.13 for additional details.
4 Planned Improvements
5 Possible future improvements within the context of this same IPCC (2006) methodology are most likely to involve
6 greater specificity by fire or groups of fires and less reliance on wide regional values or IPCC defaults. Spatially
7 relating potential fuel availability to more localized forest structure is the best example of this. An additional
8 improvement would be the use of combustion factors that are more locally appropriate for the type, location, and
9 intensity of fire, which are currently unused information provided with the MTBS data summaries. All planned
10 improvements depend on future availability of appropriate U.S.-specific data.
11 N20 Emissions from N Additions to Forest Soils
12 Anthropogenic management can influence the N cycles in several ways and lead to higher N20 emissions, such as
13 fertilization, planting N-fixing species, and drainage of organic soils. This Inventory addresses the impact of N
14 fertilization management on soil N20 emissions, but may be extended to include other management impacts in
15 future inventories. Of the synthetic nitrogen (N) fertilizers applied to soils in the U.S., no more than one percent is
16 applied to forest soils and most of those additions occur in the industrial forests of the southeastern U.S. Application
17 rates are similar to those occurring on cropland soils, but in any given year, only a small proportion of total forested
18 land receives N fertilizer. This is because forests are typically fertilized only twice during their approximately 40-
19 year growth cycle (once at planting and once midway through their life cycle). While the rate of N fertilizer
20 application for the area of forests that receives N fertilizer in any given year is relatively high, the annual application
21 rate is quite low over the entire forestland area.
22 N additions to forest soils result in direct and indirect N20 emissions. Direct emissions occur on-site due to the N
23 additions. Indirect emissions result from fertilizer N that is transformed and transported to another location in a form
24 other than N20 (i.e., ammonia [NH3] and nitrogen oxide [NOx] through volatilization, and nitrate [NO3 ] through
25 leaching and runoff), and later converted into N20 at the off-site location. These indirect emissions are assigned to
26 forest land because the management activity leading to the emissions occurred on forest land.
27 Direct soil N20 emissions from Forest Land Remaining Forest Land and Land Converted to Forest Land in 2015
28 were 0.3 MMT C02 Eq. (1 kt), and the indirect emissions were 0.1 MMT C02 Eq. (0.4 kt). Total emissions for 2015
29 were 0.5 MMT C02 Eq. (2 kt) and have increased by 455 percent from 1990 to 2015. Increasing emissions over the
30 time series is a result of an increasing area of N fertilized pine plantations in the southeastern U.S. and Douglas-fir
31 timberland in western Washington and Oregon. Total forest soil N20 emissions are summarized in Table 6-19.
32 Table 6-19: N2O Emissions from N Additions to Soils3'b (MMT CO2 Eq. and kt N2O)
1990
2005
2011
2012
2013
2014
2015
Direct N2O Fluxes from Forest Soils
MMT CO2 Eq.
0.1
(
0.3
0.3
0.3
0.3
0.3
ktN20
+
I
1
1
1
1
1
Indirect N2O Fluxes from Forest Soils
MMT CO2 Eq.
0.0
O.I
0.1
0.1
0.1
0.1
0.1
ktN20
+
+
+
+
+
+
Total
MMT CO2 Eq.
0.1
0.5
0.5
0.5
0.5
0.5
0.5
kt N2O
+
2
2
2
2
2
2
+ Does not exceed 0.05 MMT CO2 Eq. or 0.5 kt.
aThis table includes estimates from Forest Land Remaining Forest Land mdLand Converted to
Forest Land
bN20 from drained organic soils from Forest Land Remaining Forest Land and Land Converted to
Forest Land are reported in that subsection in this inventory.
Note: Totals may not sum due to independent rounding.
6-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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16
17
18
19
20
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22
23
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26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
Methodology
The IPCC Tier 1 approach is used to estimate N2O emissions from soils within Forest Land Remaining Forest Land
and Land Converted to Forest Land. According to U.S. Forest Service statistics for 1996 (USDA Forest Service
2001), approximately 75 percent of trees planted are for timber, and about 60 percent of national total harvested
forest area is in the southeastern U.S. Although southeastern pine plantations represent the majority of fertilized
forests in the U.S., this Inventory also accounts for N fertilizer application to commercial Douglas-fir stands in
western Oregon and Washington. For the Southeast, estimates of direct and indirect N20 emissions from fertilizer
applications to forests are based on the area of pine plantations receiving fertilizer in the southeastern U.S and
estimated application rates (Albaugh et al. 2007; Fox et al. 2007). Not accounting for fertilizer applied to non-pine
plantations is justified because fertilization is routine for pine forests but rare for hardwoods (Binkley et al. 1995).
For each year, the area of pine receiving N fertilizer is multiplied by the weighted average of the reported range of N
fertilization rates (121 lbs. N per acre). Area data for pine plantations receiving fertilizer in the Southeast are not
available for 2005 through 2015, so data from 2004 are used for these years. For commercial forests in Oregon and
Washington, only fertilizer applied to Douglas-fir is addressed in this Inventory because the vast majority
(approximately 95 percent) of the total fertilizer applied to forests in this region is applied to Douglas-fir (Briggs
2007). Estimates of total Douglas-fir area and the portion of fertilized area are multiplied to obtain annual area
estimates of fertilized Douglas-fir stands. Similar to the Southeast, data are not available for 2005 through 2015, so
data from 2004 are used for these years. The annual area estimates are multiplied by the typical rate used in this
region (200 lbs. N per acre) to estimate total N applied (Briggs 2007), and the total N applied to forests is multiplied
by the IPCC (2006) default emission factor of one percent to estimate direct N20 emissions.
For indirect emissions, the volatilization and leaching/runoff N fractions for forest land are calculated by
multiplying the amount of fertilizer by the IPCC default factors of 10 percent and 30 percent, respectively as well as
from the quantities of fertilizer describes above for direct emissions. The amount of N volatilized is multiplied by
the IPCC default factor of one percent for the portion of volatilized N that is converted to N20 off-site. The amount
of N leached/runoff is multiplied by the IPCC default factor of 0.075 percent for the portion of leached/runoff N that
is converted to N20 off-site The resulting estimates are summed to obtain total indirect emissions.
Uncertainty and Time-Series Consistency
The amount of N20 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 N20
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 or other possible N addition, as mentioned above, are
not estimated, as well as other anthropogenic impacts on the N cycle such as drainage of organic soils. However, the
total quantity of organic N inputs to soils is included in Section 5 A Agricultural Soil Management and Section 6.10
Settlements Remaining Settlements.
Uncertainties exist in the fertilization rates, annual area of forest lands receiving fertilizer, and the emission factors.
Fertilization rates are assigned a default level30 of uncertainty at ±50 percent, and area receiving fertilizer is
assigned a ±20 percent according to expert knowledge (Binkley 2004). The uncertainty ranges around the 2005
activity data and emission factor input variables are directly applied to the 2015 emission estimates. IPCC (2006)
provided estimates for the uncertainty associated with direct and indirect N20 emission factor for synthetic N
fertilizer application to soils.
Uncertainty is quantified using simple error propagation methods (IPCC 2006). The results of the quantitative
uncertainty analysis are summarized in Table 6-20. Direct N20 fluxes from soils in 2015 are estimated to be
between 0.1 and 1.1 MMT C02 Eq. at a 95 percent confidence level. This indicates a range of 59 percent below and
211 percent above the 2015 emission estimate of 0.3 MMT C02 Eq. Indirect N20 emissions in 2015 are between
30 Uncertainty is unknown for the fertilization rates so a conservative value of ±50 percent is used in the analysis.
Land Use, Land-Use Change, and Forestry 6-37
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1 0.02 and 0.4 MMT CO2 Eq., ranging from 86 percent below to 238 percent above the 2015 emission estimate of 0.1
2 MMT C02 Eq.
3 Table 6-20: Quantitative Uncertainty Estimates of N2O Fluxes from Soils in Forest Land
4 Remaining Forest Land and Land Con verted to Forest Land (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Forest Land Remaining Forest
Lower
Upper
Lower
Upper
Land
Bound
Bound
Bound
Bound
Direct N2O Fluxes from Soils
N2O
0.3
0.1
1.1
-59%
211%
Indirect N2O Fluxes from Soils
N2O
0.1
+
0.4
-86%
238%
+ Does not exceed 0.05 MMT CO2 Eq.
5 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
6 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
7 above.
8 QA/QC and Verification
9 The spreadsheet tab containing N fertilizer applied to forests and calculations for N20 and uncertainty ranges are
10 checked and verified.
11 Planned Improvements
12 Additional data will be compiled to update estimates of forest areas receiving N fertilizer as new reports are made
13 available. Another improvement is to further disaggregate emissions by state for southeastern pine plantations and
14 northwestern Douglas-fir forests to estimate soil N20 emission. This improvement is contingent on the availability
15 of state-level N fertilization data for forest land.
16 Drained Organic Soils
17 Emissions from drained organic soils on forest land are reported in the Inventory for the first time. While the
18 Inventory text includes drained organic soils, the net greenhouse gas emissions total and LULUCF sector total
19 presented in this Inventory do not include emissions from drained organic soils. Emissions from drained organic
20 soils will be included in net emissions and LULUCF totals in the final 1990 to 2015 Inventory report.
21 Drained organic soils on forest land are identified separately from other forest soils largely because mineralization
22 of the exposed or partially dried organic material results in continuous CO2 and N20 emissions (IPCC 2006). This
23 distinction merits the separate estimates provided here according to IPCC (2006) and primarily the new guidance in
24 the 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories: Wetlands (IPCC
25 2014).
26 Organic soils are identified on the basis of thickness of organic horizon and percent organic matter. All organic soils
27 are assumed to have originally been wet, and drained organic soils are further characterized by drainage or the
28 process of artificially lowering the soil water table, which exposes the organic material to drying and the associated
29 emissions described in this section. The land base considered here is drained inland organic soils that are coincident
30 with forest area as identified by the forest inventory of the USDA Forest Service (USDA Forest Service 2016).
31 The estimated area of drained organic soils on forest land is 70,849 ha and did not change over the time series based
32 on the data used to compile the estimates in this Inventory. These estimates are based on permanent plot locations of
33 the forest inventory (USDA Forest Service 2016) coincident with mapped organic soil locations (STATSG02 2016),
34 which identifies forest land on organic soils. Forest sites that are drained are not explicitly identified in the data, but
6-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 for this estimate, planted forest stands on sites identified as mesic or xeric (which are identified in USD A Forest
2 Service 2016) are labeled "drained organic soil" sites.
3 Land use, region, and climate are broad determinants of emissions as are more site specific factors such as nutrient
4 status, drainage level, exposure, or disturbance. Current data are limited in spatial precision and thus lack site
5 specific details. At the same time, corresponding emissions factor data specific to U.S. forests are similarly lacking.
6 Tier 1 estimates are provided here following IPCC (2014). Total annual emissions on forest land with drained
7 organic soils in 2015 are estimated as 0.9 MMT CO2 Eq. per year (Table 6-21) with uncertainty (as the 95 percent
8 confidence interval) at 38 percent (Table 6-24).
9 The Tier 1 methodology provides methods to estimate C emission as CO2 from three pathways: direct emissions
10 primarily from mineralization; indirect, or off-site, emissions associated with dissolved organic carbon releasing
11 CO2 from drainage waters; and emissions from (peat) fires on organic soils. Data about forest fires specifically
12 located on drained organic soils are not currently available; as a result, no corresponding estimate is provided here.
13 Non-C02 emissions provided here include CH4 and N20. Methane emissions generally associated with anoxic
14 conditions do occur from the drained land surface but the majority of these emissions originate from ditches
15 constructed to facilitate drainage at these sites. Emission of N20 can be significant from these drained organic soils
16 in contrast to the very low emissions from wet organic soils.
17 Table 6-21: Estimated CO2 and N011-CO2 Emissions on Drained Organic Forest Soils3 (MMT
18 COz Eq.)
Source
191)0
2005
2011
2012
2013
2014
2015
CO2-C, Direct
0.7
0.7
0.7
0.7
0.7
0.7
0.7
CO2-C, Dissolved
Organic C
0.1
0.1
0.1
0.1
0.1
0.1
0.1
CH4
+
+
+
+
+
+
+
N2O
0.1
0.1
0.1
0.1
0. 1
0.1
0.1
Total
0.9
0.9
0.9
0.9
0.9
0.9
0.9
+ Does not exceed 0.05 MMT CO2 Eq.
aThis table includes estimates from Forest Land Remaining Forest Land rndLand Converted to Forest
Land.
19 Table 6-22: Estimated CO2-C (MMT C) and Non-C02 Emissions on Drained Organic Forest
20 Soils® (kt)
Source
1990
2005
2011
2012
2013
2014
2015
CO2-C, Direct
0.2
0.2
0.2
0.2
0.2
0.2
0.2
CO2-C, Dissolved
Organic C
+
+
+
+
+
+
+
CH4
0.6
0.6
0.6
0.6
0.6
0.6
0.6
N2O
0.3
0.3
0.3
0.3
0.3
0.3
0.3
+ Does not exceed 0.05 MMT C
aThis table includes estimates from Forest Land Remaining Forest Land rndLand Converted to Forest
Land.
21 Methodology
22 The Tier 1 methods for estimating emissions from drained inland organic soils on forest lands follow IPCC (2006),
23 with extensive updates and additional material presented in the 2013 Supplement to the 2006 IPCC Guidelines for
24 National Greenhouse Gas Inventories: Wetlands (IPCC 2014). With the exception of quantifying area of forest on
25 drained organic soils, which is user-supplied, all quantities necessary for Tier 1 estimates are provided in Chapter 2,
26 Drained Inland Organic Soils of IPCC (2014).
27 Estimated area of drained organic soils on forest land is 70,849 ha based on analysis of the permanent forest
28 inventory of the USDA Forest Service and did not change over the time series (2016, data downloaded 14 June
29 2016). The current (i.e., most-recent) plot data per state were used in a spatial overlay with the STATSG02 (2016)
30 data, and forest plots coincident with the soil order histosol were selected as having organic soils. Information
Land Use, Land-Use Change, and Forestry 6-39
-------
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
31
32
33
34
35
36
37
38
specific to identifying "drained organic" are not in the inventory data so an indirect approach was employed here.
Specifically, artificially regenerated forest stands (inventory field STDORGCD=l) on mesic or xeric sites (inventory
field 11
-------
1 Forest Land Remaining Forest Land and Land Converted to Forest Land were estimated to be between 0.5 and 1.2
2 MMT CO2 Eq. around a central estimate of 0.9 MMT CO2 Eq. at a 95 percent confidence level.
3 Table 6-24: Quantitative Uncertainty Estimates for Annual CO2 and Non-C02 Emissions on
4 Drained Organic Forest Soils (MMT CO2 Eq. and Percent)3
2015 Emission
Source Estimate Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
CO2, direct
0.7
0.4
0.9
-39%
39%
CO2, dissolved organic C
0.1
+
0.1
-56%
56%
CH4
+
+
+
-76%
76%
N2O
0.1
+
0.2
-124%
124%
Total
0.9
0.5
1.2
-38%
38%
+ Does not exceed 0.05 MMT CO2 Eq.
a Range of flux estimates predicted through a combination of sample based and IPCC defaults for a 95 percent
confidence interval, IPCC Approach 1.
5 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
6 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
7 above.
8 QA/QC and Verification
9 IPCC (2014) guidance cautions of a possibility of double counting some of these emissions. Specifically, the off-site
10 emissions of dissolved organic C from drainage waters may be double counted if soil C stock and change is based
11 on sampling and this C is captured in that sampling. Additionally, some of the non-CCh emissions maybe be
12 included in either the Wetlands or sections on N20 emissions from managed soils. These paths to double counting
13 emissions are unlikely here because these issues are taken into consideration when developing the estimates and this
14 chapter is the only section directly including such emissions on forest land.
15 Planned Improvements
16 Additional data will be compiled to update estimates of forest areas on drained organic soils as new reports are made
17 available and new geospatial products become available.
is 6.3 Land Converted to Forest Land (IPCC
19 Source Category 4A2)
20 The C stock change estimates for Land Converted to Forest Land that are provided in this Inventory include all
21 forest land in an inventory year that had been in another land use(s) during the previous 20 years31 (USDA NRCS
22 2012). For example, cropland or grassland converted to forest land during the past 20 years would be reported in this
23 category. Converted lands are in this category for 20 years as recommended in the 2006 IPCC Guidelines (IPCC
24 2006), after which they are classified as Forest Land Remaining Forest Land. Estimates of C stock changes from
25 mineral soils are included in Land Converted to Forest Land following Ogle et al (2003, 2006) and IPCC (2006).
26 Carbon stock changes for the other pools (i.e., aboveground and belowground biomass, dead wood, and litter), as
31 The 2009 USDA National Resources Inventory (NRI) land-use survey points were classified according to land-use history
records starting in 1982 when the NRI survey began. Consequently, the classifications from 1990 to 2001 were based on less than
20 years. Furthermore, the FIA data used to compile estimates of carbon sequestration in this section are based on 5- to 10-yr
remeasurements so the exact conversion period was limited to the remeasured data over the time series.
Land Use, Land-Use Change, and Forestry 6-41
-------
1 recommended for inclusion by IPCC (2006) are included for the Land Converted to Forest Land category for the
2 first time in this Inventory.
3 Area of Land Converted to Forest in the United States
4 Land conversion to and from forests has occurred regularly throughout U.S. history. The 1970s and 1980s saw a
5 resurgence of federally-sponsored forest management programs (e.g., the Forestry Incentive Program) and soil
6 conservation programs (e.g., the Conservation Reserve Program), which have focused on tree planting, improving
7 timber management activities, combating soil erosion, and converting marginal cropland to forests. Recent analyses
8 suggest that net accumulation of forest area continues in areas of the U. S., in particular the northeastern U.S.
9 (Woodall et al. 2015b). Specifically, the annual conversion of land from other land-use categories (i.e., Cropland,
10 Grassland, Wetlands, Settlements, and Other Lands) to Forest Land resulted in a fairly continuous net annual
11 accretion of Forest Land area from 1990 to the present at an average rate of 1 million ha year1.
12 Since 1990, the conversion of grassland to forest land resulted in the largest source of C sequestration, accounting
13 for approximately 67 percent of the sequestration in the Land Converted to Forest Land category in 2015. However,
14 estimated gains have decreased over the time series due to less Grassland conversion into the Forest Land category
15 in recent years (see Table 6-25). The net flux of C from all forest pool stock changes in 2015 was -75.2 MMT CO2
16 Eq. (-20.5 MMT C) (Table 6-25 and Table 6-26). Note that soil C in this Inventory report has historically been
17 reported to a depth of 100 cm in the Forest Land Remaining Forest Land category (Domke et al. In press) while
18 other land-use categories report soil C to a depth of 20 or 30 cm. To ensure consistency in the Land Converted to
19 Forest Land category where C stock transfers occur between land-use categories, all soil C estimates are based on
20 methods from Ogle et al. (2003, 2006) and IPCC (2006), which are also used in Cropland, Grasslands and
21 Settlements land use categories of this Inventory.
22 Table 6-25: Net CO2 Flux from Forest C Pools in Land Converted to Forest Land by Land Use
23 Change Category (MMT CO2 Eq.)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Cropland Converted to Forest Land
(16.0)
(13.8)
(12.2)
(11.8)
(11.8)
(11.8)
(11.8)
Aboveground Biomass
(6.4)
(5.5)
(5.0)
(4.8)
(4.8)
(4.8)
(4.8)
Belowground Biomass
(0.5)
(0.4)
(0.4)
(0.4)
(0.4)
(0.4)
(0.4)
Dead Wood
(3.3)
(2.8)
(2.5)
(2.5)
(2.5)
(2.5)
(2.5)
Litter
(5.8)
(5.0)
(4.3)
(4.1)
(4.1)
(4.1)
(4.1)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Grassland Converted to Forest Land
(63.6)
(51.1)
(50.5)
(50.2)
(50.2)
(50.2)
(50.2)
Aboveground Biomass
(31.5)
(25.0)
(25.6)
(25.5)
(25.5)
(25.5)
(25.5)
Belowground Biomass
7.6
6.3
5.9
5.9
5.9
5.9
5.9
Dead Wood
(14.6)
(11.9)
(11.5)
(11.4)
(11.4)
(11.4)
(11.4)
Litter
(25.0)
(20.3)
(19.3)
(19.1)
(19.1)
(19.1)
(19.1)
Mineral Soil
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Other Land Converted to Forest Land
(9.0)
(12.5)
(9.2)
(9.1)
(9.1)
(9.1)
(9.1)
Aboveground Biomass
(3.8)
(5.4)
(4.2)
(4.2)
(4.2)
(4.2)
(4.2)
Belowground Biomass
(0.7)
(1.0)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
Dead Wood
(1.4)
(2.0)
(1.5)
(1.5)
(1.5)
(1.5)
(1.5)
Litter
(3.0)
(4.2)
(2.7)
(2.7)
(2.7)
(2.7)
(2.7)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Converted to Forest Land
(1.3)
(1.5)
(1.6)
(1.8)
(1.8)
(1.8)
(1.8)
Aboveground Biomass
(0.6)
(0.7)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
Belowground Biomass
(0.1)
(0.1)
(0.1)
(0.2)
(0.2)
(0.2)
(0.2)
Dead Wood
(0.2)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
Litter
(0.4)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
(0.5)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Wetlands Converted to Forest Land
(2.2)
(2.5)
(2.2)
(2.2)
(2.2)
(2.2)
(2.2)
Aboveground Biomass
(1.0)
(1.1)
(1.0)
(1.0)
(1.0)
(1.0)
(1.0)
Belowground Biomass
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Dead Wood
(0.3)
(0.4)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
Litter
(0.7)
(0.8)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
6-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Total Aboveground Biomass Flux
(43.3)
(37.7)
(36.5)
(36.3)
(36.3)
(36.3)
(36.3)
Total Belowground Biomass Flux
6.1
4.5
4.4
4.4
4.4
4.4
4.4
Total Dead Wood Flux
(19.8)
(17.3)
(16.1)
(15.9)
(15.9)
(15.9)
(15.9)
Total Litter Flux
(34.8)
(30.8)
(27.4)
(27.2)
(27.2)
(27.2)
(27.2)
Total Mineral Soil Flux
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Total Flux
(92.0)
(81.4)
(75.8)
(75.2)
(75.2)
(75.2)
(75.2)
+ Absolute value does not exceed 0.05 MMT CO2 Eq.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
1
2 Table 6-26: Net C Flux from Forest C Pools in Land Converted to Forest Land by Land Use
3 Change Category (MMT C)
Soil Type
1990
2005
2011
2012
2013
2014
2015
Cropland Converted to Forest Land
(4.4)
(3.8)
(3.3)
(3.2)
(3.2)
(3.2)
(3.2)
Aboveground Biomass
(1.7)
(1.5)
(1.4)
(1.3)
(1.3)
(1.3)
(1.3)
Belowground Biomass
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Dead Wood
(0.9)
(0.8)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
Litter
(1.6)
(1.4)
(1.2)
(1.1)
(1.1)
(1.1)
(1.1)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Grassland Converted to Forest Land
(17.3)
(13.9)
(13.8)
(13.7)
(13.7)
(13.7)
(13.7)
Aboveground Biomass
(8.6)
(6.8)
(7.0)
(7.0)
(7.0)
(7.0)
(7.0)
Belowground Biomass
2.1
1.7
1.6
1.6
1.6
1.6
1.6
Dead Wood
(4.0)
(3.2)
(3.1)
(3.1)
(3.1)
(3.1)
(3.1)
Litter
(6.8)
(5.5)
(5.3)
(5.2)
(5.2)
(5.2)
(5.2)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Other Land Converted to Forest Land
(2.4)
(3.4)
(2.5)
(2.5)
(2.5)
(2.5)
(2.5)
Aboveground Biomass
(1.0)
(1.5)
(1.1)
(1.1)
(1.1)
(1.1)
(1.1)
Belowground Biomass
(0.2)
(0.3)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Dead Wood
(0.4)
(0.5)
(0.4)
(0.4)
(0.4)
(0.4)
(0.4)
Litter
(0.8)
(1.1)
(0.7)
(0.7)
(0.7)
(0.7)
(0.7)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Settlements Converted to Forest Land
(0.4)
(0.4)
(0.4)
(0.5)
(0.5)
(0.5)
(0.5)
Aboveground Biomass
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Belowground Biomass
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
(0.0)
Dead Wood
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Litter
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Wetlands Converted to Forest Land
(0.6)
(0.7)
(0.6)
(0.6)
(0.6)
(0.6)
(0.6)
Aboveground Biomass
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
Belowground Biomass
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Dead Wood
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Litter
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Mineral Soil
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Total Aboveground Biomass Flux
(11.8)
(10.3)
(9.9)
(10.0)
(9.9)
(9.9)
(9.9)
Total Belowground Biomass Flux
1.7
1.2
1.2
1.2
1.2
1.2
1.2
Total Dead Wood Flux
(5.4)
(4.7)
(4.3)
(4.4)
(4.3)
(4.3)
(4.3)
Total Litter Flux
(9.5)
(8.4)
(7.4)
(7.5)
(7.4)
(7.4)
(7.4)
Total Mineral Soil Flux
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Total Flux
(25.1)
(22.2)
(20.7)
(20.5)
(20.5)
(20.5)
(20.5)
+ Absolute value does not exceed 0.05 MMT C.
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
4 Methodology
5 The following section includes a description of the methodology used to estimate stock changes in all forest C pools
6 for Land Converted to Forest Land. Forest Inventory and Analysis data and IPCC (2006) defaults for reference C
7 stocks were used to compile separate estimates for the five C storage pools. Estimates for Aboveground and
Land Use, Land-Use Change, and Forestry 6-43
-------
1 Belowground Biomass, Dead Wood and Litter were based on data collected from the extensive array of permanent,
2 annual forest inventory plots and associated models (e.g., live tree belowground biomass estimates) in the U.S.
3 (USDA Forest Service 2015b, 2015c). Carbon conversion factors were applied at the disaggregated level of each
4 inventory plot and then appropriately expanded to population estimates. To ensure consistency in the Land
5 Converted to Forest Land category where C stock transfers occur between land-use categories, all soil estimates are
6 based on methods from Ogle et al. (2003, 2006) and IPCC (2006).
7 Carbon in Biomass
8 Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at breast
9 height (dbh) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates were made for above and
10 belowground biomass components. If inventory plots included data on individual trees, above- and belowground
11 tree C was based on Woodall et al. (201 la), which is also known as the component ratio method (CRM), and is a
12 function of volume, species, and diameter. An additional component of foliage, which was not explicitly included in
13 Woodall et al. (201 la), was added to each tree following the same CRM method.
14 Understory vegetation is a minor component of biomass and is defined as all biomass of undergrowth plants in a
15 forest, including woody shrubs and trees less than 2.54 cm dbh. For this Inventory, it was assumed that 10 percent of
16 total understory C mass is belowground (Smith et al. 2006). Estimates of C density were based on information in
17 Birdsey (1996) and biomass estimates from Jenkins et al. (2003). Understory biomass represented over one percent
18 of C in biomass, but its contribution rarely exceeded 2 percent of the total.
19 Carbon in Dead Organic Matter
20 Dead organic matter was initially calculated as three separate pools—standing dead trees, downed dead wood, and
21 litter—with C stocks estimated from sample data or from models. The standing dead tree C pool includes
22 aboveground and belowground (coarse root) biomass for trees of at least 12.7 cm dbh. Calculations followed the
23 basic method applied to live trees (Woodall et al. 201 la) with additional modifications to account for decay and
24 structural loss (Domke et al. 2011; Harmon et al. 2011). Downed dead wood estimates are based on measurement of
25 a subset of FIA plots for downed dead wood (Domke et al. 2013; Woodall and Monleon 2008; Woodall et al. 2013).
26 Downed dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are
27 not attached to live or standing dead trees. This includes stumps and roots of harvested trees. To facilitate the
28 downscaling of downed dead wood C estimates from the state-wide population estimates to individual plots, downed
29 dead wood models specific to regions and forest types within each region are used. Litter C is the pool of organic C
30 (also known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with
31 diameters of up to 7.5 cm. A subset of FIA plots are measured for litter C. A modeling approach, using litter C
32 measurements from FIA plots (Domke et al. 2016) was used to estimate litter C for every FIA plot used in the
33 estimation framework.
34 Mineral Soil Carbon Stock Changes
35 A Tier 2 method is applied to estimate mineral soil C stock changes for Land Converted to Forest Land (Ogle et al.
36 2003, 2006; IPCC 2006). For this method, land is stratified by climate, soil types, land use, and land management
37 activity, and then assigned reference carbon levels and factors for the forest land and the previous land use. The
38 difference between the stocks is reported as the stock change under the assumption that the change occurs over 20
39 years. Reference C stocks have been estimated from data in the National Soil Survey Characterization Database
40 (USDA-NRCS 1997), and U.S.-specific stock change factors have been derived from published literature (Ogle et
41 al. 2003, 2006). Land use and land use change patterns are determined from a combination of the Forest Inventory
42 and Analysis Dataset (FIA), the 2010 National Resources Inventory (NRI) (USDA-NRCS 2013), and National Land
43 Cover Dataset (NLCD) (Homer et al. 2007). See Annex 3.12 for more information about this method (Methodology
44 for Estimating N20 Emissions, CH4 Emissions and Soil Organic C Stock Changes from Agricultural Soil
45 Management).
46 Uncertainty and Time-Series Consistency
47 A quantitative uncertainty analysis placed bounds on the flux estimates for Land Converted to Forest Land through
48 a combination of sample-based and model-based approaches to uncertainty for forest ecosystem CO2 Eq. flux (IPCC
6-44 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1
2
3
4
5
6
7
8
9
10
11
12
13
14
Approach 1). Uncertainty estimates for forest pool C stock changes were developed using the same methodologies
as described in the Forest Land Remaining Forest Land section for aboveground and biomass ground biomass, dead
wood, and litter. The exception was when IPCC default estimates were used for reference C stocks in certain
conversion categories (i.e., Cropland Converted to Forest Land and Grassland Converted to Forest Land). In those
cases, the uncertainties associated with the IPCC (2006) defaults were included in the uncertainty calculations. IPCC
Approach 2 was used for mineral soils and is described in the Cropland Remaining Cropland section.
Uncertainty estimates are presented in Table 6-27 for each land conversion category and C pool. Uncertainty
estimates were obtained using a combination of sample-based and model-based approaches for all non-soil C pools
(IPCC Approach 1) and a Monte Carlo approach (IPCC Approach 2) was used for mineral soil. Uncertainty
estimates were combined using the error propagation model (IPCC Approach 1). The combined uncertainty for all C
stocks in Land Converted to Forest Land ranged from 10 percent below to 11 percent above the 2015 stock change
estimate of -75.2 MMT CO2 Eq.
Table 6-27: Quantitative Uncertainty Estimates for Forest C Pool Stock Changes (MMT CO2
Eq. per Year) in 2015 from Land Converted to Forest Land by Land Use Change
2015 Flux
Source Estimate Uncertainty Range Relative to Flux Range3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Cropland Converted to Forest Land
(11.8)
(13.5)
(8.4)
-14%
28%
Aboveground Biomass
(4.8)
(6.4)
(3.3)
-32%
32%
Belowground Biomass
(0.4)
(0.6)
(0.1)
-76%
76%
Dead Wood
(2.5)
(2.9)
(2.0)
-19%
19%
Litter
(4.1)
(4.6)
(3.7)
-12%
12%
Mineral Soils
+
(+)
0.1
-250%
244%
Grassland Converted to Forest Land
(50.0)
(57.6)
(50.0)
-15%
8%
Aboveground Biomass
(25.5)
(31.8)
(19.2)
-25%
25%
Belowground Biomass
5.9
4.0
7.8
-31%
31%
Dead Wood
(11.4)
(14.0)
(8.8)
-23%
23%
Litter
(19.1)
(21.7)
(16.6)
-14%
14%
Mineral Soils
0.1
(0.1)
0.3
-250%
244%
Other Lands Converted to Forest Land
(9.1)
(10.2)
(8.0)
-12%
12%
Aboveground Biomass
(4.2)
(5.1)
(3.2)
-23%
23%
Belowground Biomass
(0.8)
(1.0)
(0.6)
-25%
25%
Dead Wood
(1.5)
(1.8)
(1.1)
-24%
24%
Litter
(2.7)
(3.1)
(2.4)
-13%
13%
Mineral Soils
+
(+)
+
-250%
244%
Settlements Converted to Forest Land
(1.8)
(2.0)
(1.5)
-13%
13%
Aboveground Biomass
(0.8)
(1.0)
(0.6)
-25%
25%
Belowground Biomass
(0.2)
(0.2)
(0.1)
-27%
27%
Dead Wood
(0.3)
(0.3)
(0.2)
-24%
24%
Litter
(0.5)
(0.6)
(0.4)
-14%
14%
Mineral Soils
+
(+)
+
-250%
244%
Wetlands Converted to Forest Land
(2.2)
(2.5)
(2.0)
-11%
11%
Aboveground Biomass
(1.0)
(1.2)
(0.8)
-20%
20%
Belowground Biomass
(0.2)
(0.2)
(0.1)
-22%
22%
Dead Wood
(0.3)
(0.4)
(0.3)
-27%
21%
Litter
(0.7)
(0.8)
(0.6)
-13%
13%
Mineral Soils
+
(+)
+
-250%
244%
Total: Aboveground Biomass
(36.3)
(42.9)
(29.3)
-18%
19%
Total: Belowground Biomass
4.4
2.5
6.4
-43%
44%
Total: Dead Wood
(15.9)
(18.6)
(13.2)
-17%
17%
Total: Litter
(27.2)
(29.8)
(24.4)
-10%
10%
Total: Mineral Soils
0.1
(0.1)
0.4
-175%
171%
Total: Lands Converted to Forest Lands
(75.2)
(82.7)
(66.6)
-10%
11%
+ Does not exceed 0.05 MMT CO2 Eq.
a Range of flux estimate for 95 percent confidence interval
Note: Parentheses indicate net sequestration.
Land Use, Land-Use Change, and Forestry 6-45
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In the case of mineral soil estimates which were reported in the 1990 to 2014 Inventory, methodological
recalculations were applied to the entire time-series to ensure time-series consistency from 1990 through 2015.
Details on the emission trends through time are described in more detail in the Methodology section above.
QA/QC and Verification
See QA/QC and Verification section under Forest land Remaining Forest Land and Cropland Remaining Cropland.
Recalculations Discussion
This is the second U.S. Inventory report to include a Land Converted to Forest Land section containing specific soil
C stock change estimates and the first Inventory report to include all C pools for Land Converted to Forest Land. In
prior Inventory reports (e.g., EPA 2015), the C stock changes from Land Converted to Forest Land were apart of
the Forest Land Remaining Forest Land estimates. See the Recalculations section in Forest Land Remaining Forest
Land for a detailed explanation on overall changes resulting from implementing a different methodological approach
in the current Inventory report. These changes, particularly the inclusion of biomass, dead wood and litter in the
estimates, resulted in an average annual increase in sequestration of 89.9 MMT CO2 Eq. relative to the previous
Inventory.
Planned Improvements
A different estimation approach (Woodall et al. 2015a) was used for the forest land category beginning with the
1990 to 2014 Inventory with the specific intent of separating Forest Land Remaining Forest Land and Land
Converted to Forest Land. While this new approach led to improvements (e.g., disaggregation of forest land area
between the land-use categories), there are many improvements still necessary to fully incorporate all C pool
estimates and all land-use categories over the entire time series. First, research, in coordination with the other land-
use categories, into the length of time that forest land remains in the Land Converted to Forest Land category will be
undertaken and a mechanism to account for emissions and removals for all IPCC pools in this conversion category
will be developed. Second, soil C has historically been reported to a depth of 100 cm in the Forest Land Remaining
Forest Land category (Domke et al. In press) while other land-use categories (e.g., Grasslands and Croplands) report
soil carbon to a depth of 20 or 30 cm. To ensure greater consistency in the Land Converted to Forest Land category
where C stock transfers occur between land-use categories, all mineral soil estimates in the Land Converted to
Forest Land category in this Inventory are based on methods from Ogle et al. (2003, 2006) and IPCC (2006).
Methods have recently been developed (Domke et al. In press) to estimate soil C to depths of 20, 30, and 100 cm in
the Forest Land category using in situ measurements from the Forest Inventory and Analysis program within the
USD A Forest Service and the International Soil Carbon Network. In subsequent Inventories, a common reporting
depth will be defined for all land conversion categories and Domke et al. (In press) will be used in the Forest Land
Remaining Forest Land and Land Converted to Forest Land categories to ensure consistent reporting across all
forest land. Third, due to 5 to 10 year remeasurement periods within the FIA program and limited land use change
information available over the entire time-series, estimates presented in this section may not reflect the entire 20-
year conversion. Work is underway to integrate the dense time-series of remotely sensed data into a new estimation
system which will facilitate land conversion estimation over the entire time series.
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6.4 Cropland Remaining Cropland (IPCC Source
Category 4B1)
Mineral and Organic Soil Carbon Stock Changes
Carbon (C) in cropland ecosystems occurs in biomass, dead organic matter, and soils. However, C storage in
cropland biomass and dead organic matter is relatively ephemeral, with the exception of C stored in perennial
woody crop biomass, such as citrus groves and apple orchards. Within soils, C is found in organic and inorganic
forms of C, but soil organic C (SOC) is the main source and sink for atmospheric CO2 in most soils. IPCC (2006)
recommends reporting changes in SOC stocks due to agricultural land-use and management activities on both
mineral and organic soils.32
Well-drained mineral soils typically contain from 1 to 6 percent organic C by weight, whereas mineral soils with
high water tables for substantial periods during the year may contain significantly more C (NRCS 1999). Conversion
of mineral soils from their native state to agricultural land uses can cause up to half of the SOC to be lost to the
atmosphere due to enhanced microbial decomposition. The rate and ultimate magnitude of C loss depends on
subsequent management practices, climate and soil type (Ogle et al. 2005). Agricultural practices, such as clearing,
drainage, tillage, planting, grazing, crop residue management, fertilization, and flooding, can modify both organic
matter inputs and decomposition, and thereby result in a net C stock change (Parton et al. 1987; Paustian et al.
1997a; Conant et al. 2001; Ogle et al. 2005). Eventually, the soil can reach a new equilibrium that reflects a balance
between C inputs (e.g., decayed plant matter, roots, and organic amendments such as manure and crop residues) and
C loss through microbial decomposition of organic matter (Paustian et al. 1997b).
Organic soils, also referred to as Histosols, include all soils with more than 12 to 20 percent organic C by weight,
depending on clay content (NRCS 1999; Brady and Weil 1999). The organic layer of these soils can be very deep
(i.e., several meters), and form under inundated conditions that results in minimal decomposition of plant residues.
When organic soils are prepared for crop production, they are drained and tilled, leading to aeration of the soil that
accelerates both the decomposition rate and CO2 emissions.33 Due to the depth and richness of the organic layers, C
loss from drained organic soils can continue over long periods of time, which varies depending on climate and
composition (i.e., decomposability) of the organic matter (Armentano and Menges 1986). Due to deeper drainage
and more intensive management practices, the use of organic soils for annual crop production (and also settlements)
leads to higher C loss rates than drainage of organic soils in grassland or forests (IPCC 2006).
Cropland Remaining Cropland includes all cropland in an Inventory year that has been cropland for a continuous
time period of at least 20 years according to the 2012 United States Department of Agriculture (USD A) National
Resources Inventory (NRI) land-use survey for non-federal lands (USDA-NRCS 2015) or according to the National
Land Cover Dataset for federal lands (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015). Cropland includes all
land used to produce food and fiber, in addition to forage that is harvested and used as feed (e.g., hay and silage),
and cropland that has been enrolled in the Conservation Reserve Program (CRP) (i.e., considered reserve cropland).
Cropland in Alaska is not included in the Inventory, but is a relatively small amount of U.S. cropland area
(approximately 28,700 hectares). Some miscellaneous croplands are also not included in the Inventory due to limited
understanding of greenhouse gas emissions from these management systems (e.g., aquaculture). This leads to a
small discrepancy between the total amount of managed area in Cropland Remaining Cropland (see Section 6.1
Representation of the U.S. Land Base) and the cropland area included in the Inventory analysis (1.2 to 1.6 million
hectares or 0.8 percent of the total cropland areas in the United States between 1990 and 2015). Improvements are
underway to include croplands in Alaska as part of future C inventories.
32 Carbon dioxide emissions associated with liming and urea application are also estimated but are included in the Agriculture
chapter of the report.
33 N2O emissions from soils are included in the Agricultural Soil Management section.
Land Use, Land-Use Change, and Forestry 6-47
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1 Carbon dioxide emissions and removals34 due to changes in mineral soil C stocks are estimated using a Tier 3
2 method for the majority of annual crops (Ogle et al. 2010). A Tier 2 IPCC method is used for the remaining crops
3 not included in the Tier 3 method (see Methodology section for a list of crops in the Tier 2 and 3 methods) (Ogle et
4 al. 2003, 2006). In addition, a Tier 2 method is used for very gravelly, cobbly, or shaley soils (i.e., classified as soils
5 that have greater than 35 percent of soil volume comprised of gravel, cobbles, or shale) regardless of crop, and for
6 additional changes in mineral soil C stocks that are not addressed with the Tier 3 approach (i.e., change in C stocks
7 after 2010 due to CRP enrollment). Emissions from organic soils are estimated using a Tier 2 IPCC method.
8 Land-use and land management of mineral soils are the largest contributor to total net C stock change, especially in
9 the early part of the time series (see Table 6-28 and Table 6-29). (Note: Estimates after 2012 are based on NRI data
10 from 2012 and therefore do not fully reflect changes occurring in the latter part of the time series). In 2015, mineral
11 soils are estimated to sequester 42.1 MMT C02Eq. from the atmosphere (11.5 MMT C).35 This rate of C storage in
12 mineral soils represents about a 41 percent decrease in the rate since the initial reporting year of 1990. Carbon
13 dioxide emissions from organic soils are 28.0 MMT CO2 Eq. (7.6 MMT C) in 2015, which is a 7 percent decrease
14 compared to 1990. In total, United States agricultural soils in Cropland Remaining Cropland sequestered
15 approximately 14.0 MMT CO2 Eq. (3.8 MMT C) in 2015.
16 Table 6-28: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland (MMT
17 COz Eq.)
Soil Type
J'J'JO
2005
2011
2012
2013 a
2014 a
2015 a
Mineral Soils
(71.2)
(56.2) -
(47.1)
(49.5)
(43.7)
(42.9)
(42.1)
Organic Soils
29.7 ^
27.9
28.1
28.1
28.1
28.0
Total Net Flux
(40.'J)
(26.5)
(19.1)
(21.4)
(15.6)
(14.8)
(14.0)
a Quality control uncovered errors in the estimates of mineral soils and the total net flux for
2013,2014 and 2015, which will be updated following public review. The corrected mineral
soil estimates are (47.6), (46.8), and (46.0) MMT CO2 Eq., respectively for 2013, 2014,2015,
and the total net flux is (19.6), (18.7) and (18.0) MMT CO2 Eq., respectively for the three
years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully
reflect changes occurring in the latter part of the time series. Totals may not sum due to
independent rounding. Parentheses indicate net sequestration.
18 Table 6-29: Net CO2 Flux from Soil C Stock Changes in Cropland Remaining Cropland [WW
19 C)
Soil Type
1990
2005
2011
2012
2013a
2014 a
2015 a
Mineral Soils
(19.4)
(15.3)
(12.8)
(13.5)
(11.9)
(11.7)
(11.5)
Organic Soils
8.3
8.1
7.6
7.7
7.7
7.7
7.6
Total Net Flux
(11.2)
(7.2)
(5.2)
(5.8)
(4.3)
(4.0)
(3.8)
a Quality control uncovered errors in the estimates of mineral soils and the total net flux for
2013,2014 and 2015, which will be updated following public review. The corrected mineral
soil estimates are (13.0), (12.8) and (12.6) MMT C, respectively for 2013, 2014 and 2015, and
the total net flux is (5.3), (5.1) and (4.9) MMT C, respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully
reflect changes occurring in the latter part of the time series. Totals may not sum due to
independent rounding. Parentheses indicate net sequestration.
20 Soil C stocks increase on Cropland Remaining Cropland across the entire time series, largely driven by the nearly
21 10 million hectares of land currently enrolled in CRP (i.e., set-aside program), as well as from increased hay
22 production, adoption of conservation tillage (i.e., reduced- and no-till practices), and intensification of crop
34 Removals occur through uptake of CO2 into crop and forage biomass that is later incorporated into soil C pools.
35 Quality control uncovered errors in the mineral soil and total net flux estimates for 2015, which will be updated following
public review. Based on the revision, soil C stocks increased by 46.0 MMT CO2 Eq. (12.6 MMT C) in 2015. The total net flux
implies C sequestration of 18.0 MMT CO2 Eq. (4.9 MMT C). The corrected overall trend is a decrease in soil C stock change by
56 percent since the initial reporting year in 1990.
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production by limiting the use of bare-summer fallow in semi-arid regions. However, there is a decline in the net
amount of carbon sequestration (i.e.. 2015 is 41 percent less than 1990), and this decline is due to less annual
cropland enrolled in the CRP36 that was initiated in 1985. For example, over 1.8 million hectares, which had been
enrolled in the CRP, were returned to agricultural production during the last 3 years resulting in a loss of soil C. Soil
C losses from drainage of organic soils are relatively stable across the time series with a small decline associated
with the land base declining by 7 percent for Cropland Remaining Cropland on organic soils since 1990.
The spatial variability in the 2015 annual C stock changes are displayed in Figure 6-4 and Figure 6-5 for mineral and
organic soils, respectively. Isolated areas with high rates of C accumulation occur throughout the agricultural land
base in the United States, but there are more concentrated areas with gains in the northern Great Plains, which has
high rates of CRP enrollment. High rates of net C accumulation in mineral soils also occurred in the Corn Belt
region, which is the region with the largest amounts of conservation tillage, along with moderate rates of CRP
enrollment. The regions with the highest rates of emissions from drainage of organic soils occur in the Southeastern
Coastal Region (particularly Florida), upper Midwest and Northeast surrounding the Great Lakes, and isolated areas
along the Pacific Coast (particularly California), which coincides with the largest concentrations of organic soils in
the United States that are used for agricultural production.
Figure 6-4: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management
within States, 2015, Cropland Remaining Cropland
MT C02 ha1 yr1
¦ -4 to -2 ~ 2 to 4
~ -2 to -1 ¦ > 4
~ -1 to 1
36 The Conservation Reserve Program (CRP) is a land conservation program administered by the Farm Service Agency (FSA).
In exchange for a yearly rental payment, farmers enrolled in the program agree to remove environmentally sensitive land from
agricultural production and plant species that will improve environmental health and quality. Contracts for land enrolled in CRP
are 10 to 15 years in length. The long-term goal of the program is to re-establish valuable land cover to help improve water
quality, prevent soil erosion, and reduce loss of wildlife habitat.
Land Use, Land-Use Change, and Forestry 6-49
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1 Figure 6-5: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management
2 within States, 2015, Cropland Remaining Cropland
5 Methodology
6 The following section includes a description of the methodology used to estimate changes in soil C stocks for
7 Cropland Remaining Cropland, including (1) agricultural land-use and management activities on mineral soils; and
8 (2) agricultural land-use and management activities on organic soils.
9 Soil C stock changes on non-federal lands are estimated for Cropland Remaining Cropland (as well as agricultural
10 land falling into the IPCC categories Land Converted to Cropland, Grassland Remaining Grassland, and Land
11 Converted to Grassland) according to land-use histories recorded in the USDA NRI survey (USDA-NRCS 2015).
12 The NRI is a statistically-based sample of all non-federal land, and includes approximately 609,211 survey locations
13 in agricultural land for the conterminous United States and Hawaii. Each survey location is associated with an
14 "expansion factor" that allows scaling of C stock changes from NRI survey locations to the entire country (i.e., each
15 expansion factor represents the amount of area with the same land-use/management history as the sample point).
16 Land-use and some management information (e.g., crop type, soil attributes, and irrigation) were collected for each
17 NRI point on a 5-year cycle beginning from 1982 through 1997. For cropland, data had been collected for 4 out of 5
18 years during each survey cycle (i.e., 1979 through 1982, 1984 through 1987, 1989 through 1992, and 1994 through
19 1997). In 1998, the NRI program began collecting annual data, and the annual data are currently available through
20 2012 (USDA-NRCS 2015). NRI survey locations are classified as Cropland Remaining Cropland in a given year
21 between 1990 and 2012 if the land use had been cropland for a continuous time period of at least 20 years. NRI
22 survey locations are classified according to land-use histories starting in 1979, and consequently the classifications
23 are based on less than 20 years from 1990 to 1998. This may have led to an overestimation of Cropland Remaining
24 Cropland in the early part of the time series to the extent that some areas are converted to cropland between 1971
25 and 1978.
26 Mineral Soil Carbon Stock Changes
27 An IPCC Tier 3 model-based approach (Ogle et al. 2010) is applied to estimate C stock changes for mineral soils on
28 the majority of land that is used to produce annual crops in the United States. These crops include alfalfa hay.
6-50 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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¦ 30 to 40
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barley, corn, cotton, dry beans, grass hay, grass-clover hay, lentils, oats, onions, peanuts, peas, potatoes, rice,
sorghum, soybeans, sugar beets, sunflowers, tobacco, tomatoes, and wheat, but is not applied to estimate C stock
changes from other crops or rotations with other crops. The model-based approach uses the DAYCENT
biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011) to estimate soil C stock changes and soil
nitrous oxide (N20) emissions from agricultural soil management. Carbon and N dynamics are linked in plant-soil
systems through the biogeochemical processes of microbial decomposition and plant production (McGill and Cole
1981). Coupling the two source categories (i.e., agricultural soil C and N20) in a single inventory analysis ensures
that there is a consistent treatment of the processes and interactions between C and N cycling in soils.
The remaining crops on mineral soils are estimated using an IPCC Tier 2 method (Ogle et al. 2003), including some
vegetables, tobacco, perennial/horticultural crops, and crops that are rotated with these crops. The Tier 2 method is
also used for very gravelly, cobbly, or shaley soils (greater than 35 percent by volume), and soil C stock changes on
federal croplands. Mineral SOC stocks are estimated using a Tier 2 method for these areas because the DAYCENT
model, which is used for the Tier 3 method, has not been fully tested for estimating C stock changes associated with
these crops and rotations, as well as cobbly, gravelly, or shaley soils. In addition, there is insufficient information to
simulate croplands on federal lands using DAYCENT. The Tier 2 method is also used to estimate additional soil C
stock changes on lands enrolled in CRP after 2012, which is the last year of data in the NRI time series, using
aggregated data on CRP enrollment compiled by the USDA Farm Services Agency.
Further elaboration on the methodology and data used to estimate stock changes from mineral soils are described
below and in Annex 3.12.
Tier 3 Approach
Mineral SOC stocks and stock changes are estimated using the DAYCENT biogeochemical37 model (Parton et al.
1998; Del Grosso et al. 2001, 2011), which is able to simulate cycling of C, N and other nutrients in cropland,
grassland, forest, and savanna ecosystems. The DAYCENT model utilizes the soil C modeling framework
developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to
simulate dynamics at a daily time-step. The modeling approach uses daily weather data as an input, along with
information about soil physical properties. Input data on land use and management are specified at a daily resolution
and include land-use type, crop/forage type, and management activities (e.g., planting, harvesting, fertilization,
manure amendments, tillage, irrigation, and grazing). The model simulates net primary productivity (NPP) using the
NASA-CASA production algorithm MODIS Enhanced Vegetation Index (EVI) products, MOD13Q1 and
MYD13Q1, for most croplands38 (Potter et al. 1993, 2007). The model also simulates soil temperature, and water
dynamics, in addition to turnover, stabilization, and mineralization of soil organic matter C and nutrients (N, P, K,
S). This method is more accurate than the Tier 1 and 2 approaches provided by the IPCC (2006) because the
simulation model treats changes as continuous over time as opposed to the simplified discrete changes represented
in the default method (see Box 6-5 for additional information).
A Tier 3 model-based approach is used to estimate soil C stock changes on the majority of agricultural land on
mineral soils. This approach results in a more complete and accurate accounting of soil C stock changes and entails
several fundamental differences from the IPCC Tier 1 or 2 methods, as described below.
(1) The IPCC Tier 1 and 2 methods are simplified and classify land areas into discrete categories based on
highly aggregated information about climate (six regions), soil (seven types), and management (eleven
management systems) in the United States. In contrast, the Tier 3 model incorporates the same variables
(i.e., climate, soils, and management systems) with considerably more detail both temporally and spatially,
and captures multi-dimensional interactions through the more complex model structure.
37 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
38 NPP is estimated with the NASA-CASA algorithm for most of the cropland that is used to produce major commodity crops in
the central United States from 2000 to 2012. Other regions and years prior to 2000 are simulated with a method that incorporates
water, temperature and moisture stress on crop production (see Metherell et al. 1993), but does not incorporate the additional
information about crop condition provided with remote sensing data.
Land Use, Land-Use Change, and Forestry 6-51
pproach for Soil C Stocks Compared to Tier 1 or 2 Approach
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(2) The IPCC Tier 1 and 2 methods have a simplified spatial resolution in which data are aggregated to soil
types in climate regions, of which there about 30 of combinations in the United States. In contrast, the Tier
3 model simulates soil C dynamics at more than 300,000 individual NRI survey locations in individual
fields.
(3) The IPCC Tier 1 and 2 methods use simplified equilibrium step changes for changes in C emissions. In
contrast, the Tier 3 approach simulates a continuous time period. More specifically, the DAYCENT model
(i.e., daily time-step version of the Century model) simulates soil C dynamics (and CO2 emissions and
uptake) on a daily time step based on C emissions and removals from plant production and decomposition
processes. These changes in soil C stocks are influenced by multiple sources that affect primary production
and decomposition, including changes in land use and management, weather variability and secondary
feedbacks between management activities, climate, and soils.
Historical land-use patterns and irrigation histories are simulated with DAYCENT based on the 2012 USDA NRI
survey (USDA-NRCS 2015). Additional sources of activity data are used to supplement the land-use information
from the NRI. The Conservation Technology Information Center (CTIC 2004) provided annual data on tillage
activity at the county level for the conterminous United States between 1989 and 2004, and these data are adjusted
for long-term adoption of no-till agriculture (Towery 2001). No-till adoption is assumed to remain constant from
2005 through 2015 due to lack of data, but there is a planned improvement to update the tillage histories with a
dataset that was recently released by the USDA (Conservation Effects Assessment Program Data, See Planned
Improvements section). Information on fertilizer use and rates by crop type for different regions of the United States
are obtained primarily from the USDA Economic Research Service. The data collection program was known as the
Cropping Practices Surveys through 1995 (USDA-ERS 1997), and then became the Agricultural Resource
Management Surveys (ARMS) (USDA-ERS 2015). Additional data are compiled through other sources particularly
the National Agricultural Statistics Service (NASS 1992, 1999, 2004). Frequency and rates of manure application to
cropland for 1997 are 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 are
used to adjust the area amended with manure (see Annex 3.12 for further details). Greater availability of managed
manure N relative to 1997 is assumed to increase the area amended with manure, while reduced availability of
manure N relative to 1997 is assumed to reduce the amended area. Data on the county-level N available for
application are estimated for managed systems based on the total amount of N excreted in manure minus N losses
during storage and transport, and include the addition of N from bedding materials. Nitrogen losses include direct
N20 emissions, volatilization of ammonia and NOx, N runoff and leaching, and the N in poultry manure used as a
feed supplement. More information on livestock manure production is available in Section 5.2 - Manure
Management and Annex 3.11.
Daily weather data are another input to the model simulations. These data are based on a 4 kilometer gridded
product from the PRISM Climate Group (2015). Soil attributes are obtained from the Soil Survey Geographic
Database (SSURGO) (Soil Survey Staff 2016). The C dynamics at each NRI point are simulated 100 times as part of
the uncertainty analysis, yielding a total of over 18 million simulation runs for the analysis. Uncertainty in the C
stock estimates from DAYCENT associated with parameterization and model algorithms are adjusted using a
structural uncertainty estimator accounting for uncertainty in model algorithms and parameter values (Ogle et al.
2007, 2010). Carbon stocks and 95 percent confidence intervals are estimated for each year between 1990 and 2012
using the NRI survey data, which is available through 2012. C stock change rates from 2013 to 2015 are assumed to
be similar to 2012 for this Inventory.39 However, the areas may have changed through the process in which the NRI
survey data are reconciled with the Forest Inventory and Analysis (FIA) survey data and the National Land Cover
Dataset (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015). This process ensures that the areas of Forest Land
Remaining Forest Land and Land Converted to Forest Land are consistent in all three datasets, and leads to some
modification of other lands use areas to ensure the total land area of the United States does not change. For example,
if the FIA estimate less Cropland Converted to Forest Land than the NRI, then the amount of area for this land use
conversion is reduced in the NRI dataset and re-classified as Cropland Remaining Cropland (See Section 0,
39 Note: CRP enrollment is modified from 2013 to 2015 as described in the section on the Tier 2 Approach.
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1 Representation of the U.S. Land Base for more information). Future Inventories will be updated with new NRI
2 activity data when the data are made available, and the time series from 2013 to 2015 will be recalculated.
3 Tier 2 Approach
4 In the IPCC Tier 2 method, data on climate, soil types, land-use, and land management activity are used to classify
5 land area and apply appropriate soil C stock change factors (Ogle et al. 2003, 2006). Reference C stocks are
6 estimated using the National Soil Survey Characterization Database (NRCS 1997) with cultivated cropland as the
7 reference condition, rather than native vegetation as used in IPCC (2006). Soil measurements under agricultural
8 management are much more common and easily identified in the National Soil Survey Characterization Database
9 (NRCS 1997) than are soils under a native condition, and therefore cultivated cropland provided a more robust
10 sample for estimating the reference condition. U.S.-specific C stock change factors are derived from published
11 literature to determine the impact of management practices on SOC storage (Ogle et al. 2003, Ogle et al. 2006). The
12 factors include changes in tillage, cropping rotations, intensification, and land-use change between cultivated and
13 uncultivated conditions. U.S. factors associated with organic matter amendments are not estimated due to an
14 insufficient number of studies in the United States to analyze the impacts. Instead, factors from IPCC (2006) are
15 used to estimate the effect of those activities.
16 Climate zones in the United States are classified using mean precipitation and temperature (1950 to 2000) variables
17 from the WorldClim data set (Hijmans et al. 2005) and potential evapotranspiration data from the Consortium for
18 Spatial Information (CGIAR-CSI) (Zomer et al. 2008, 2007) (Figure A-15). IPCC climate zones are then assigned to
19 NRI point locations.
20 Activity data are primarily based on the historical land-use/management patterns recorded in the 2012 NRI (USDA-
21 NRCS 2015). Each NRI point is classified by land use, soil type, climate region, and management condition. Survey
22 locations on federal lands are included in the NRI, but land use and cropping history are not compiled at these
23 locations in the survey program (i.e., NRI is restricted to data collection on non-federal lands). Land-use patterns at
24 the NRI survey locations on federal lands are based on the National Land Cover Database (NLCD) (Fry et al. 2011;
25 Homer et al. 2007; Homer et al. 2015). Classification of cropland area by tillage practice is based on data from the
26 Conservation Technology Information Center (CTIC 2004; Towery 2001) as described in the Tier 3 approach above.
27 Activity data on wetland restoration of Conservation Reserve Program land are obtained from Euliss and Gleason
28 (2002). Manure N amendments over the inventory time period are based on application rates and areas amended
29 with manure N from Edmonds et al. (2003), in addition to the managed manure production data discussed in the
30 methodology subsection for the Tier 3 approach.
31 Utilizing information from these data sources, SOC stocks for mineral soils are estimated 50,000 times for each year
32 in the time series, using a Monte Carlo stochastic simulation approach and probability distribution functions for
33 U.S.-specific stock change factors, reference C stocks, and land-use activity data (Ogle et al. 2002; Ogle et al. 2003;
34 Ogle et al. 2006). The annual C stock change rates from 2013 through 2015 for the Tier 2 method are assumed to be
35 similar to 2012, but the areas may be adjusted through the process of reconciling NRI and NLCD with the FIA data
36 (See section on the Tier 3 Approach for more information). As with the Tier 3 method, future Inventories will be
37 updated with new NRI activity data when the data are made available, and the time series will be recalculated (see
38 Planned Improvements section).
39 Additional Mineral Soil C Stock Change
40 Annual C stock change estimates for mineral soils between 2013 and 2015 are adjusted to account for additional C
41 stock changes associated with gains or losses in soil C after 2012 due to changes in CRP enrollment (USDA-FSA
42 2015). The change in enrollment relative to 2012 is based on data from USDA-FSA (2015) for 2012 through 2015.
43 The differences in mineral soil areas are multiplied by 0.5 metric tons C per hectare per year to estimate the net
44 effect on soil C stocks. The stock change rate is based on country-specific factors and the IPCC default method (see
45 Annex 3.12 for further discussion).
46 Organic Soil Carbon Stock Changes
47 Annual C emissions from drained organic soils in Cropland Remaining Cropland are estimated using the Tier 2
48 method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) rather than default IPCC rates.
49 The final estimates included a measure of uncertainty as determined from the Monte Carlo Stochastic Simulation
Land Use, Land-Use Change, and Forestry 6-53
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with 50,000 iterations. Emissions are based on the annual data for drained organic soils from 1990 to 2012 for
Cropland Remaining Cropland areas in the 2012 NRI (USDA-NRCS 2015). The annual emission rates estimated for
2012 are applied to 2013 through 2015, but the areas may be adjusted through the process of reconciling NRI and
NLCD with the FIA data. (See section on the Tier 3 Approach for more information.) Future Inventories will be
updated with new NRI activity data for 2012 through 2015 when the data are made available, and the time series
will be recalculated (see Planned Improvements section).
Uncertainty and Time-Series Consistency
Uncertainty associated with the Cropland Remaining Cropland land-use category is addressed for changes in
agricultural soil C stocks (including both mineral and organic soils). Uncertainty estimates are presented in Table
6-30 for each subsource (mineral soil C stocks and organic soil C stocks) and the methods that are used in the
Inventory analyses (i.e., Tier 2 and Tier 3). Uncertainty for the Tier 2 and 3 approaches is derived using a Monte
Carlo approach (see Annex 3.12 for further discussion), but the C stock changes from the individual Tier 2 and 3
approaches are combined using the simple error propagation method provided by the IPCC (2006). The combined
uncertainty is calculated by taking the square root of the sum of the squares of the standard deviations of the
uncertain quantities. The combined uncertainty for soil C stocks in Cropland Remaining Cropland ranged from 230
percent below to 237 percent above the 2015 stock change estimate of -14.0 MMT CO2 Eq.40
Table 6-30: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Cropland Remaining Cropland (MMT CO2 Eq. and Percent)
Source
2015 Flux
Estimate3
Uncertainty Range Relative to Flux Estimatea'b
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
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
(42.7)
(2.7)
3.3
(73.5)
(4.2)
1.6
(11.9)
(1.4)
4.9
-72%
-55%
-50%
72%
48%
50%
to 2003)
Organic Soil C Stocks: Cropland Remaining
Cropland, Tier 2 Inventory Methodology
28.0
18.4
40.4
-34%
44%
Combined Uncertainty for Flux associated
with Agricultural Soil Carbon Stock
Change in Cropland Remaining Cropland
(14.0)
(46.4)
19.2
-230%
237%
a Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review.
The corrected Tier 3 mineral soil C stock change is (46.6) MMT CO2 Eq., with an uncertainty ranging from -78 percent to
+78 percent. The corrected combined flux estimate for 2015 is (18.0) MMT CO2 Eq., with an uncertainty ranging from -210
percent to 214 percent.
b Range of C stock change estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Notes: Parentheses indicate net sequestration.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Uncertainty is also associated with lack of reporting of agricultural woody biomass and dead organic matter C stock
changes. The IPCC (2006) does not recommend reporting of annual crop biomass in Cropland Remaining Cropland
because all of the biomass senesces each year and so there is no long term storage of C in this pool. For woody
40 Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review.
Based on the revision, soil C stocks increased by 18.0 MMT CO2 Eq. in 2015, with an uncertainty ranging from -210 percent
below to 214 percent above the estimate.
6-54 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 plants, biomass C stock changes are likely minor in perennial crops, such as orchards and nut plantations. There will
2 be some removal and replanting of tree crops each year, but the net effect on biomass C stock changes is probably
3 minor because the overall area is relatively constant across time series. In contrast, agroforestry practices, such as
4 shelterbelts, riparian forests and intercropping with trees, may be significantly changing biomass C stocks over the
5 Inventory times series, at least in some regions of the United States, but there are currently no datasets to evaluate
6 the trends. Changes in litter C stocks are also assumed to be negligible in croplands over annual time frames,
7 although there are certainly significant changes at sub-annual time scales across seasons. However, this trend may
8 change in the future, particularly if crop residue becomes a viable feedstock for bioenergy production.
9 QA/QC and Verification
10 Quality control measures included checking input data, model scripts, and results to ensure data are properly
11 handled throughout the inventory process. Inventory reporting forms and text are reviewed and revised as needed to
12 correct transcription errors. Results from the DAYCENT model are compared to field measurements, and a
13 statistical relationship has been developed to assess uncertainties in the predictive capability of the model. The
14 comparisons include 92 long-term experiments, representing about 908 combinations of management treatments
15 across all of the sites (see Ogle et al. 2007 and Annex 3.12 for more information).
16 Recalculations Discussion
17 Methodological recalculations in the current Inventory are associated with the following improvements: (1) driving
18 the DAYCENT simulations with updated input data for land management from the National Resources Inventory
19 from 1979 through 2012; (2) increasing the number of experimental study sites used to quantify model uncertainty;
20 (3) DAYCENT model development to improve the simulation of soil temperature; and (4) improvements in the
21 cropping and land use histories that are simulated in DAYCENT between 1950 and 1979 to reduce the amount of
22 grassland converted into cropland when the NRI histories begin in 1979 (Note the histories generate initial values
23 for the model state variables, including the initial soil organic C stock values; more detail is provide in Annex 3.12).
24 As a result of these improvements, SOC stocks increased by an average of 7.5 MMT CO2 Eq. across the time series,
25 which is an 18 percent increase in the reported soil C stock changes compared to the previous Inventory.41 The
26 largest driver of this change is associated with corrective actions taken to more accurately represent the land use
27 histories prior to 1979.
28 Planned Improvements
29 There are several planned improvements underway. The DAYCENT model will be refined to simulate soil organic
30 C stock changes to a depth of at least 30 cm. Improvements are also underway to more accurately simulate plant
31 production. Crop parameters associated with temperature effects on plant production will be further improved in
32 DAYCENT with additional model calibration. Senescence events following grain filling in crops, such as wheat, are
33 being modified based on recent model algorithm development, and will be incorporated. Experimental study sites
34 will continue to be added for quantifying model structural uncertainty.
35 There is an effort underway to update the time series of management data with information from the USDA-NRCS
36 Conservation Effects Assessment Program (CEAP). This improvement will fill several gaps in the management data
37 including more specific data on fertilizer rates, updated tillage practices, and more information on planting and
38 harvesting dates for crops.
39 Improvements are underway to simulate crop residue burning in the DAYCENT model based on the amount of crop
40 residues burned according to the data that is used in the Field Burning of Agricultural Residues source category (see
41 Section 5.7). This improvement will more accurately represent the C inputs to the soil that are associated with
42 residue burning.
41 Quality control uncovered errors in the estimate and uncertainty for 2013, 2014,2015, which will be updated following public
review. These corrections impact the comparison between the prior and current Inventories in the Recalculation Discussion,
which will also be updated after public review.
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The inventory will include an analysis of C stock changes in Alaska for cropland and managed grassland, using the
Tier 2 method for mineral and organic soils that is described earlier in this section. This analysis will initially focus
on land use change, which typically has a larger impact on soil C stock changes, but will be further refined over time
to incorporate more of the management data.
All of these improvements are expected to be completed for the 1990 through 2017 Inventory (2019 submission to
the UNFCCC). However, the time line may be extended if there are insufficient resources to fund all or part of these
planned improvements.
6.5 Land Converted to Cropland (IPCC Source
Category 4B2)
Land Converted to Cropland includes all cropland in an Inventory year that had been in another land use(s) during
the previous 20 years (USDA-NRCS 2015), and used to produce food or fiber, or forage that is harvested and used
as feed (e.g., hay and silage). For example, grassland or forestland converted to cropland during the past 20 years
would be reported in this category. Recently converted lands are retained in this category for 20 years as
recommended by IPCC (2006). This Inventory includes all croplands in the conterminous United States and Hawaii,
but does not include a minor amount of Land Converted to Cropland in Alaska. Some miscellaneous croplands are
also not included in the Inventory due to limited understanding of greenhouse gas dynamics in management systems
(e.g., aquaculture) or climate zones (e.g., boreal climates). Consequently, there is a discrepancy between the total
amount of managed area in Land Converted to Cropland (see Section 6.1 Representation of the U.S. Land Base) and
the cropland area included in the Inventory. Improvements are underway to include croplands in Alaska and
miscellaneous crops in future C inventories.
Land use change can lead to large losses of C to the atmosphere, particularly conversions from forest land
(Houghton et al. 1983). Moreover, conversion of forest to another land use (i.e., deforestation) is one of the largest
anthropogenic sources of emissions to the atmosphere globally (Schimel 1995), although this source may be
declining according to a recent assessment (Tubiello et al. 2015).
The 2006 IPCC Guidelines recommend reporting changes in biomass, dead organic matter and soil organic carbon
(SOC)42 stocks with land use change. All SOC stock changes are estimated and reported for Land Converted to
Cropland, but for belowground biomass, dead wood and litter pools reporting is limited to Forest Land Converted to
Cropland.^
Loss of aboveground woody biomass C from Forest Land Converted to Cropland is the largest contributor to C loss
throughout the time series, accounting for approximately 66 percent of the total emissions (Table 6-31 and Table
6-32). Grassland Converted to Cropland is the largest source of emissions associated with soil C pools across the
time-series (accounting for approximately 93 percent of the average loss of soil C) because the area of Grassland
Converted to Cropland is significantly higher than for other land use conversions to cropland. The net change in
total C stocks for 2015 led to CO2 emissions to the atmosphere of 28.6 MMT CO2 Eq. (7.8 MMT C), including 10.3
MMT CO2 Eq. (2.8 MMT C) from aboveground biomass C losses, 2.3 MMT CO2 Eq. (0.6 MMT C) from
belowground biomass C losses, 1.8 MMT CO2 Eq. (0.5 MMT C) from dead wood C losses, 1.7 MMT CO2 Eq. (0.5
MMT C) from litter C losses, 8.9 MMT CO2 Eq. (2.4 MMT C) from mineral soils and 3.7 MMT CO2 Eq. (1.0 MMT
42 Carbon dioxide emissions associated with liming and urea application are also estimated but are included in the Agriculture
chapter of the report..
43 Changes in biomass C stocks are not currently reported for other land use conversions (other than forest land) to cropland, but
this is a planned improvement for a future inventory. Note: changes in dead organic matter are assumed to negligible for other
land use conversions (i.e., other than forest land) to cropland based on the Tier 1 method in IPCC (2006).
6-56 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 C) from drainage and cultivation of organic soils.44 Emissions in 2015 are 72 percent lower than the emissions in the
2 initial reporting year of 1990, largely due to a reduction in the area of Forest Land Converted to Cropland.
3 Table 6-31: Net CO2 Flux from Soil C Stock Changes in Land Converted to Croplandby Land
4 Use Change Category (MMT CO2 Eq.)
5
1990
2005
2011
2012
2013a
2014 a
2015 a
Grassland Converted to Cropland
Mineral Soils
21.9
13.9
16.0
15.1
8.4
8.4
8.4
Organic Soils
2.5
3.3
3.0
3.0
3.0
3.0
3.0
Forest Land Converted to
Cropland
Aboveground Live Biomass
46.7
15.3
9.8
10.3
10.3
10.3
10.3
Belowground Live Biomass
10.5
3.4
2.2
2.3
2.3
2.3
2.3
Dead Wood
9.1
2.8
1.7
1.8
1.8
1.8
1.8
Litter
8.6
2.7
1.6
1.7
1.7
1.7
1.7
Mineral Soils
0.2
0.1
0.1
0.1
0.1
0.1
0.1
Organic Soils
0.1
I +
+
+
+
+
+
Other Lands Converted to
Cropland
Mineral Soils
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Organic Soils
0.1
0.1
0.0
0.0
0.0
0.0
0.0
Settlements Converted to
Cropland
Mineral Soils
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Organic Soils
+
+ *V
0.1
0.1
0.1
0.1
0.1
Wetlands Converted to Cropland
Mineral Soils
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Organic Soils
0.6
0.7
0.5
0.5
0.5
0.5
0.5
Aboveground Live Biomass
46.7
15.3
9.8
10.3
10.3
10.3
10.3
Belowground Live Biomass
10.5
3.4
2.2
2.3
2.3
2.3
2.3
Dead Wood
9.1
2.S
1.7
1.8
1.8
1.8
1.8
Litter
8.6
2.7
1.6
1.7
1.7
1.7
1.7
Total Mineral Soil Flux
22.5
14.4
16.5
15.6
8.9
8.9
8.9
Total Organic Soil Flux
3.4
4.2
3.6
3.7
3.7
3.7
3.7
Total Net Flux
100.7
42.6
35.3
35.3
28.6
28.6
28.6
+ Does not exceed 0.05 MMT CO2 Eq.
a Quality control uncovered errors in the estimates for 2013, 2014 and 2015 for mineral soils in Grassland
Converted to Cropland, Total Mineral Soil Flux and the Total Net Flux, which will be updated following
public review. The corrected mineral soil estimates for Grassland Converted to Cropland are 15.1, 15.1, and
15.1 MMT CO2 Eq., respectively for 2013, 2014, 2015; the total mineral net flux is 15.6, 15.6, and 15.6
MMT CO2 Eq., respectively for the three years; and the total net flux for Land Converted to Cropland is
35.3, 35.3, and 35.3 MMT CO2 Eq., respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect changes
occurring in the latter part of the time series. Totals may not sum due to independent rounding.
6 Table 6-32: Net CO2 Flux from Soil C Stock Changes in Land Converted to Cropland [WW C)
1990
2005
2011
2012
2013 a
2014 a
2015 a
Grassland Converted to Cropland
Mineral Soils
6.0
3.8
4.4
4.1
2.3
2.3
2.3
Organic Soils
0.7
0.9
0.8
0.8
0.8
0.8
0.8
Forest Land Converted to
Cropland
44 Quality control uncovered errors in the mineral soil C and total net flux estimates for 2015, which will be updated following
public review. Based on the revision, mineral soil C stocks decreased by 15.1 MMT CO2 Eq. (4.1 MMT C) in 2015. The total net
flux is a loss of 35.3 MMT CO2 Eq. (9.6 MMT C) from Land Converted to Cropland. The corrected overall trend is a decrease in
C stock change by 64 percent since the initial reporting year in 1990.
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20
Aboveground Live Biomass
12.7
4.2
2.7
2.8
2.8
2.8
2.8
Belowground Live Biomass
2.9
0.9
0.6
0.6
0.6
0.6
0.6
Dead Wood
2.5
0.8
0.5
0.5
0.5
0.5
0.5
Litter
2.4
o.-
0.4
0.5
0.5
0.5
0.5
Mineral Soils
0.1
+
+
+
+
+
Organic Soils
+
+
+
+
+
+
Other Lands Converted to
Cropland
Mineral Soils
+
o.i
0.1
0.1
0.1
0.1
0.1
Organic Soils
+
0.0
0.0
0.0
0.0
0.0
Settlements Converted to
Cropland
Mineral Soils
+
+
+
+
+
+
Organic Soils
+
+
+
+
+
+
Wetlands Converted to Cropland
Mineral Soils
+
+
+
+
+
+
Organic Soils
0.2
0.2
0.1
0.1
0.1
0.1
0.1
Aboveground Live Biomass
12.7
4.2
2.7
2.8
2.8
2.8
2.8
Belowground Live Biomass
2.9
0.')
0.6
0.6
0.6
0.6
0.6
Dead Wood
2.5
0.S
0.5
0.5
0.5
0.5
0.5
Litter
2.4
0.7
0.4
0.5
0.5
0.5
0.5
Total Mineral Soil Flux
6.1
3.')
4.5
4.2
2.4
2.4
2.4
Total Organic Soil Flux
0.9
1.1
1.0
1.0
1.0
1.0
1.0
Total Net Flux
27.5
11.6
9.6
9.6
7.8
7.8
7.8
+ Does not exceed 0.05 MMT C
a Quality control uncovered errors in the estimates for 2013, 2014 and 2015 for mineral soils in Grassland
Converted to Cropland, Total Mineral Soil Flux and the Total Net Flux, which will be updated following
public review. The corrected mineral soil estimates for Grassland Converted to Cropland are 4.1, 4.1, and
4.1 MMT C, respectively for 2013, 2014,2015; the total mineral net flux is 4.2, 4.2, and 4.2 MMT C,
respectively for the three years; and the total net flux for Land Converted to Cropland is 9.6, 9.6, and 9.6
MMT C, respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect changes
occurring in the latter part of the time series. Totals may not sum due to independent rounding.
Methodology
The following section includes a description of the methodology used to estimate changes in C stocks for Land
Converted to Cropland, including: (1) loss of aboveground and belowground biomass, dead wood and litter C with
conversion of forest lands to cropland; (2) agricultural land-use and management activities on mineral soils; and (3)
agricultural land-use and management activities on organic soils.
Biomass, Dead Biomass and Litter Carbon Stock Changes
A Tier 2 method is applied to estimate aboveground biomass C stock changes for Forest Land Converted to
Cropland. For this method, forest land conversions to croplands were identified in each state and C density estimates
were compiled by state for aboveground biomass, belowground biomass, dead wood, and litter for croplands
(assumed to be zero since no reference biomass C density estimates exist) and forest land use categories. The
difference between the stocks is reported as the stock change under the assumption that the change occurred in the
year of the conversion. Reference C density estimates (i.e., aboveground biomass, belowground biomass, dead
wood, and litter) for the forest land use have been estimated from data in the Forest Inventory and Analysis (FIA)
program within the USDA Forest Service (USDA Forest Service 2015). If FIA plots include data on individual trees,
aboveground and belowground C density estimates are based on Woodall et al. (2011). Aboveground and
belowground biomass estimates also include live understory which is a minor component of biomass defined as all
biomass of undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm dbh. For this
Inventory, it was assumed that 10 percent of total understory C mass is belowground (Smith et al. 2006). Estimates
of C density are based on information in Birdsey (1996) and biomass estimates from Jenkins et al. (2003). If FIA
plots include data on standing dead trees, standing dead tree C density is estimated following the basic method
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38
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44
45
applied to live trees (Woodall et al. 2011) with additional modifications to account for decay and structural loss
(Domke et al. 2011; Harmon et al. 2011). If FIA plots include data on downed dead wood, downed dead wood C
density is estimated based on measurements of a subset of FIA plots for downed dead wood (Domke et al. 2013;
Woodall and Monleon 2008). Downed dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at
transect intersection, that are not attached to live or standing dead trees. This includes stumps and roots of harvested
trees. To facilitate the downscaling of downed dead wood C estimates from the state-wide population estimates to
individual plots, downed dead wood models specific to regions and forest types within each region are used. Litter C
is the pool of organic C (also known as duff, humus, and fine woody debris) above the mineral soil and includes
woody fragments with diameters of up to 7.5 cm. A subset of FIA plots are measured for litter C. If FIA plots
include litter material, a modeling approach using litter C measurements from FIA plots is used to estimate litter C
density (Domke et al. 2016). See Annex 3.13 for more information about reference C density estimates for forest
land.
Soil Carbon Stock Changes
SOC stock changes are estimated for Land Converted to Cropland according to land-use histories recorded in the
2012 USDA NRI survey for non-federal lands (USDA-NRCS 2015). Land-use and some management information
(e.g., crop type, soil attributes, and irrigation) had been collected for each NRI point on a 5-year cycle beginning in
1982. In 1998, the NRI program began collecting annual data, which are currently available through 2012 (USDA-
NRCS 2015). NRI survey locations are classified as Land Converted to Cropland in a given year between 1990 and
2012 if the land use is cropland but had been another use during the previous 20 years. NRI survey locations are
classified according to land-use histories starting in 1979, and consequently the classifications are based on less than
20 years from 1990 to 1998, which may have led to an underestimation of Land Converted to Cropland in the early
part of the time series to the extent that some areas are converted to cropland from 1971 to 1978. For federal lands,
the land use history is derived from land cover changes in the National Land Cover Dataset (Homer et al. 2007; Fry
etal. 2011; Homer etal. 2015).
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach (Ogle et al. 2010) is applied to estimate C stock changes for mineral soils on
the majority of land that is used to produce annual crops in the United States. These crops include alfalfa hay,
barley, corn, cotton, dry beans, grass hay, grass-clover hay, lentils, oats, onions, peanuts, peas, potatoes, rice,
sorghum, soybeans, sugar beets, sunflowers, tobacco, tomatoes, and wheat. SOC stock changes on the remaining
soils are estimated with the IPCC Tier 2 method (Ogle et al. 2003), including land used to produce some vegetables
and perennial/horticultural crops and crops rotated with these crops; land on very gravelly, cobbly, or shaley soils
(greater than 35 percent by volume); and land converted from another land use or federal ownership.45
Tier 3 Approach. For the Tier 3 method, mineral SOC stocks and stock changes are estimated using the DAYCENT
biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011). The DAYCENT model utilizes the soil C
modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has
been refined to simulate dynamics at a daily time-step. National estimates are obtained by using the model to
simulate historical land-use change patterns as recorded in the USDA NRI (USDA-NRCS 2015). Carbon stocks and
95 percent confidence intervals are estimated for each year between 1990 and 2012, and C stock changes from 2012
to 2015 are assumed to be similar to 2012. Future inventories will be updated with new activity data when the data
are made available, and the time series will be recalculated (See Planned Improvements section in Cropland
Remaining Cropland). See the Cropland Remaining Cropland section for additional discussion of the Tier 3
methodology for mineral soils.
Tier 2 Approach. For the mineral soils not included in the Tier 3 analysis, SOC stock changes are estimated using a
Tier 2 Approach for Land Converted to Cropland as described in the Tier 2 Approach for mineral soils in the
Cropland Remaining Cropland section.
45 Federal land is not a land use, but rather an ownership designation that is treated as grassland for purposes of these
calculations. The specific land use on federal lands is not identified in the NRI survey (USDA-NRCS 2015).
Land Use, Land-Use Change, and Forestry 6-59
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21
22
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Cropland are estimated using the Tier 2
method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland
Remaining Cropland section for organic soils.
Uncertainty and Time-Series Consistency
The uncertainty analysis for biomass, dead wood and litter C losses with Forest Land Converted to Cropland is
conducted in the same way as the uncertainty assessment for forest ecosystem C flux in the Forest Land Remaining
Forest Land category. Sample and model-based error are combined using simple error propagation methods
provided by the IPCC (2006). For additional details see the Uncertainty Analysis in Annex 3.13. The uncertainty
analyses for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies are based on a Monte Carlo
approach that is described for Cropland Remaining Cropland. The uncertainty for annual C emission estimates from
drained organic soils in Land Converted to Cropland is estimated using a Monte Carlo approach, which is also
described in the Cropland Remaining Cropland section.
Uncertainty estimates are presented in Table 6-33 for each subsource (i.e., biomass C stocks, dead wood C stocks,
litter C stocks, mineral soil C stocks and organic soil C stocks) and the method applied in the Inventory analysis
(i.e., Tier 2 and Tier 3). Uncertainty estimates from the Tier 2 and 3 approaches are combined using the simple error
propagation methods provided by the 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 total C stocks in Land Converted to
Cropland ranged from 38 percent below to 42 percent above the 2015 stock change estimate of 28.6 MMT CO2
Eq.46
Table 6-33: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Land Converted to Cropland (MMT CO2 Eq. and Percent)
2015 Flux Estimate3 Uncertainty Range Relative to Flux Estimatea'b
(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
Lower
Upper
Lower
Upper
Bound
Bound
Bound
Bound
Grassland Converted to Cropland
11.4
0.8
22.0
-93%
93%
Mineral Soil C Stocks: Tier 3
6.7
(3.5)
16.8
-152%
152%
Mineral Soil C Stocks: Tier 2
1.7
+
2.6
-100%
55%
Organic Soil C Stocks: Tier 2
3.0
5.5
+
-83%
99%
Forest Land Converted to Cropland
16.1
14.3
17.9
-11%
11%
Aboveground Live Biomass
10.3
8.6
12.1
-17%
17%
Belowground Live Biomass
2.3
2.0
2.5
-12%
12%
Dead Wood
1.8
1.6
1.9
-11%
11%
Litter
1.7
1.6
1.8
-4%
4%
Mineral Soil C Stocks: Tier 2
0.1
+
0.1
-100%
55%
Organic Soil C Stocks: Tier 2
+
+
+
-103%
100%
Other Lands Converted to Cropland
0.2
+
0.4
-100%
69%
Mineral Soil C Stocks: Tier 2
0.2
+
0.3
-100%
55%
Organic Soil C Stocks: Tier 2
0.0
0.0
0.1
0%
0%
Settlements Converted to Cropland
0.2
0.1
0.6
-71%
241%
Mineral Soil C Stocks: Tier 2
0.1
+
0.1
-100%
55%
Organic Soil C Stocks: Tier 2
0.1
0.2
0.5
-101%
487%
Wetlands Converted to Croplands
0.7
0.3
5.6
-50%
763%
Mineral Soil C Stocks: Tier 2
0.1
+
0.2
-98%
55%
Organic Soil C Stocks: Tier 2
0.5
0.8
5.5
-57%
939%
Total: Land Converted to Cropland
28.6
17.8
40.4
-38%
42%
Aboveground Live Biomass
10.3
8.6
12.1
-17%
17%
46 Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review. The
corrected total stock declined by 35.3 MMT CO2 Eq. in 2015, with an uncertainty ranging from -34 percent below to 36 percent
above the estimate.
6-60 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Belowground Live Biomass
2.3
2.0
2.5
-12%
12%
Dead Wood
1.8
1.6
1.9
-11%
11%
Litter
1.7
1.6
1.8
-4%
4%
Mineral Soil C Stocks: Tier 3
6.7
(3.5)
16.8
-152%
152%
Mineral Soil C Stocks: Tier 2
2.2
0.5
3.2
-78%
43%
Organic Soil C Stocks: Tier 2
3.7
1.1
9.5
-69%
160%
+ Does not exceed 0.05 MMT CO2 Eq.
a Quality control uncovered errors in the 2015 estimates for mineral soils in Grassland Converted to Cropland for Tier 3, Total
Grassland Converted to Cropland, Total Mineral Soil Flux for Tier 3, and the Total Net Flux for Land Converted Cropland,
which will be updated following public review. The corrected estimate for mineral soils in Grassland Converted to Cropland
for Tier 3 is 13.4 MMT CO2 Eq. for 2015 with an uncertainty ranging from -84 percent to 84 percent; Grassland Converted to
Cropland is 18.1 MMT CO2 Eq. for 2015 with an uncertainty ranging from -65 percent to 65 percent; the total Tier 3 mineral
stock change is also 13.4 MMT CO2 Eq. with uncertainty ranging from -84 percent to 84 percent; and the total net flux for
Land Converted to Cropland is 35.3 MMT CO2 Eq. with uncertainty ranging from -34 percent to 36 percent.
b Range of C stock change estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or net sequestration.
1 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
2 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
3 above.
4 Uncertainty is also associated with lack of reporting of agricultural biomass and dead organic matter C stock
5 changes. Biomass C stock changes are likely minor in perennial crops, such as orchards and nut plantations, given
6 the small amount of change in land used to produce these commodities in the United States. In contrast, agroforestry
7 practices, such as shelterbelts, riparian forests and intercropping with trees, may have led to significant changes in
8 biomass C stocks, at least in some regions of the United States. However, there are currently no datasets to evaluate
9 the trends. Changes in dead organic matter C stocks are assumed to be negligible with conversion of land to
10 croplands with the exception of forest lands, which are included in this analysis. This assumption will be further
11 explored in a future analysis.
12 QA/QC and Verification
13 See the QA/QC and Verification section in Cropland Remaining Cropland.
14 Recalculations Discussion
15 Methodological recalculations in the current Inventory are associated with the following improvements: (1) driving
16 the DAYCENT simulations with updated input data for land use and management from the National Resources
17 Inventory extending the time series through 2012; (2) modifying the number of experimental study sites used to
18 quantify model uncertainty; (3) DAYCENT model development to improve the simulation of soil temperature; (4)
19 improvements in the cropping and land use histories that are simulated in DAYCENT between 1950 and 1979 that
20 generate initial values for the model state variables, including the initial soil organic C stock values; and (5)
21 incorporating belowground biomass, dead wood and litter C stock losses with Forest Land Converted to Cropland.
22 As a result of these improvements to the Inventory, Land Converted to Cropland have a larger reported loss of C,
23 estimated at 19.1 MMT CO2 Eq. over the time series.47 This represents a 45 percent increase in the losses of carbon
24 with Land Converted to Cropland compared to the previous Inventory, and is largely driven by reporting
25 belowground biomass, dead wood and litter C loss from Forest Land Converted to Croplands.
47 Quality control uncovered errors in the estimate and uncertainty for 2013, 2014,2015, which will be updated following public
review. These corrections impact the comparison between the prior and current Inventories in the Recalculation Discussion,
which will also be updated after public review.
Land Use, Land-Use Change, and Forestry 6-61
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34
Planned Improvements
Soil C stock changes with land use conversion from forest land to cropland are undergoing further evaluation to
ensure consistency in the time series. Different methods are used to estimate soil C stock changes in forest land and
croplands, and while the areas have been reconciled between these land uses, there has been limited evaluation of
the consistency in C stock changes with conversion from forest land to cropland. Additional planned improvements
are discussed in the Cropland Remaining Cropland section.
6.6 Grassland Remaining Grassland (IPCC
Source Category 4C1)
Soil Carbon Stock Changes (IPCC Source Category 3B3a)
Grassland Remaining Grassland includes all grassland in an Inventory year that had been grassland for a continuous
time period of at least 20 years (USDA-NRCS 2015). Grassland includes pasture and rangeland that are primarily,
but not exclusively used for livestock grazing. Rangelands are typically extensive areas of native grassland that are
not intensively managed, while pastures are typically seeded grassland (possibly following tree removal) that may
also have additional management, such as irrigation or interseeding of legumes. This Inventory includes all
privately-owned and federal grasslands in the conterminous United States and Hawaii, but does not include
approximately 50 million hectares of Grassland Remaining Grassland in Alaska. This leads to a discrepancy with
the total amount of managed area in Grassland Remaining Grassland (see Section 6.1 Representation of the U.S.
Land Base) and the grassland area included in the Inventory analysis (IPCC Source Category 4C1—Section 6.6).
Background on agricultural soil carbon (C) stock changes is provided in Section 6.4, Cropland Remaining
Cropland, and will only be summarized here. Soils are the largest pool of C in agricultural land, and also have the
greatest potential for longer-term storage or release of C. Biomass and dead organic matter C pools are relatively
small and ephemeral compared to the soil C pool, with the exception of C stored in tree and shrub biomass that
occurs in grasslands. The 2006 IPCC Guidelines (IPCC 2006) recommend reporting changes in soil organic C
(SOC) stocks due to (1) agricultural land-use and management activities on mineral soils, and (2) agricultural land-
use and management activities on organic soils.48
In Grassland Remaining Grassland, there has been considerable variation in soil C stocks between 1990 and 2015.
These changes are driven by variability in weather patterns and associated interaction with land management
activity. Moreover, changes are small on a per hectare rate across the time series even in the years with a larger total
change in stocks. Land use and management generally increased soil C in mineral soils for Grassland Remaining
Grassland between 1990 and 2012, after which the trend is reversed to a small decline in soil C. In contrast, organic
soils lose a relatively constant amount of C annually from 1990 through 2015. In 2015, soil C stocks decreased by
7.3 MMT CO2 Eq. (2.0 MMT C), with a small loss of 1.7 MMT CO2 Eq. (0.5 MMT C)49 in mineral soils, but a loss
of 5.5 MMT CO2 Eq. (1.5 MMT C) from organic soils (Table 6-34 and Table 6-35). The overall trend represents a
272 percent decline in the soil C stock change rate from a gain to a loss of soil C.
48 CO2 emissions associated with liming and urea fertilization are also estimated but included in the Agriculture chapter of the
report.
49 Quality control uncovered errors in the mineral soil and total net flux estimates for 2015, which will be updated following
public review. Based on the revision, soil C stocks increased by 26.4 MMT CO2 Eq. (7.2 MMT C) in 2015. The total net flux
implies C sequestration of 20.9 MMT CO2 Eq. (5.7 MMT C). The corrected overall trend is an increase in soil C stock change by
395 percent since the initial reporting year in 1990.
6-62 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 6-34: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland(MMT
2 COz Eq.)
Soil Type
1990
2005
2011
2012
2013 a
2014 a
2015 a
Mineral Soils
(11.4) i
(0.5)
(18.1)
(26.3)
2.2
2.3
1.7
Organic Soils
7.2 !
6.0
5.6
5.5
5.5
5.5
5.5
Total Net Flux
(4.2)
5.5
(12.5)
(20.8)
7.7
7.8
7.3
a Quality control uncovered errors in the estimates of mineral soils and the total net flux for 2013,
2014 and 2015, which will be updated following public review. The corrected mineral soil
estimates are (26.0), (25.9), and (26.4) MMT CO2 Eq., respectively for 2013,2014, 2015, and the
total net flux is (20.5), (20.4) and (20.9) MMT CO2 Eq., respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect
changes occurring in the latter part of the time series. Totals may not sum due to independent
rounding. Parentheses indicate net sequestration.
3 Table 6-35: Net CO2 Flux from Soil C Stock Changes in Grassland Remaining Grassland (MMT
4 C)
Soil Type
1990
2005
2011
2012
2013 a
2014 a
2015 a
Mineral Soils
(3.1)
(0.1)
(4.9)
(7.2)
0.6
0.6
0.5
Organic Soils
2.0
1.6
1.5
1.5
1.5
1.5
1.5
Total Net Flux
(1.1)
1.5
(3.4)
(5.7)
2.1
2.1
2.0
a Quality control uncovered errors in the estimates of mineral soils and the total net flux for 2013,
2014 and 2015, which will be updated following public review. The corrected mineral soil
estimates are (7.1), (7.1) and (7.2) MMT C, respectively for 2013, 2014 and 2015, and the total net
flux is (5.6), (5.6) and (5.7) MMT C, respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect
changes occurring in the latter part of the time series. Totals may not sum due to independent
rounding. Parentheses indicate net sequestration.
5 The spatial variability in the 2015 annual CO2 flux associated with mineral soils is displayed in Figure 6-6 and
6 organic soils in Figure 6-7. Although relatively small on a per-hectare basis, grassland soils gained C in isolated
7 areas throughout the country, with a larger concentration of grasslands sequestering soil C in Iowa. For organic
8 soils, the regions with the highest rates of emissions coincide with the largest concentrations of organic soils used
9 for managed grassland, including the Southeastern Coastal Region (particularly Florida), upper Midwest and
10 Northeast, and a few isolated areas along the Pacific Coast.
Land Use, Land-Use Change, and Forestry 6-63
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1
2
Figure 6-6: Total Net Annual CO2 Flux for Mineral Soils under Agricultural Management
within States, 2015, Grassland Remaining Grassland
MT C02 ha1 yr1
¦ < -4 ~ 1 to 2
¦ -4 to -2 ~ 2 to 4
~ -2 to -1 ¦ > 4
~ -1 to 1
4
5 Figure 6-7: Total Net Annual CO2 Flux for Organic Soils under Agricultural Management
6 within States, 2015, Grassland Remaining Grassland
MT C02 ha1 yr1
~ < 10
~ 10 to 20
~ 20 to 30
¦ 30 to 40
¦ > 40
6-64 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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46
47
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C stocks for
Grassland Remaining Grassland, including: (1) agricultural land-use and management activities on mineral soils;
and (2) agricultural land-use and management activities on organic soils. Further elaboration on the methodologies
and data used to estimate stock changes from mineral and organic soils are provided in the Cropland Remaining
Cropland section and Annex 3.12.
Soil C stock changes are estimated for Grassland Remaining Grassland on non-federal lands according to land use
histories recorded in the 2012 USDANRI survey (USDA-NRCS 2015). Land-use and some management
information (e.g., grass type, soil attributes, and irrigation) were originally collected for each NRI survey location on
a 5-year cycle beginning in 1982. In 1998, the NRI program began collecting annual data, and the annual data are
currently available through 2012 (USDA-NRCS 2015). NRI survey locations are classified as Grassland Remaining
Grassland in a given year between 1990 and 2012 if the land use had been grassland for 20 years. NRI survey
locations are classified according to land-use histories starting in 1979, and consequently the classifications are
based on less than 20 years from 1990 to 1998. This may have led to an overestimation of Grassland Remaining
Grassland in the early part of the time series to the extent that some areas are converted to grassland prior between
1971 and 1978. For federal lands, the land use history is derived from land cover changes in the National Land
Cover Dataset (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015).
Mineral Soil Carbon Stock Changes
An IPCC Tier 3 model-based approach (Ogle et al. 2010) is applied to estimate C stock changes for most mineral
soils in Grassland Remaining Grassland. The C stock changes for the remaining soils are estimated with an IPCC
Tier 2 method (Ogle et al. 2003), including gravelly, cobbly, or shaley soils (greater than 35 percent by volume) and
additional stock changes associated with sewage sludge amendments.
Tier 3 Approach
Mineral SOC stocks and stock changes for Grassland Remaining Grassland are estimated using the DAYCENT
biogeochemical50 model (Partonet al. 1998; Del Grosso et al. 2001, 2011), as described in Cropland Remaining
Cropland. The DAYCENT model utilizes the soil C modeling framework developed in the Century model (Parton et
al. 1987, 1988, 1994; Metherell et al. 1993), but has been refined to simulate dynamics at a daily time-step.
Historical land-use patterns and irrigation histories are simulated with DAYCENT based on the 2012 USDA NRI
survey (USDA-NRCS 2015). Frequency and rates of manure application to grassland during 1997 are estimated
from data compiled by the USDA Natural Resources Conservation Service (NRCS) (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 are used to adjust the area amended
with manure (see Cropland Remaining Cropland section and Annex 3.12 for further details). Greater availability of
managed manure nitrogen (N) relative to 1997 is, thus, assumed to increase the area amended with manure, while
reduced availability of manure N relative to 1997 is assumed to reduce the amended area.
The amount of manure produced by each livestock type is calculated for managed and unmanaged waste
management systems based on methods described in Section 5.2 - Manure Management and Annex 3.11. Manure N
deposition from grazing animals (i.e., PRP manure) is an input to the DAYCENT model, and the remainder is
deposited on federal lands (i.e., the amount that is not included in DAYCENT simulations is assumed to be applied
on federal grasslands). Carbon stocks and 95 percent confidence intervals are estimated for each year between 1990
and 2012 using the NRI survey data, which is available through 2012. C stock change rates from 2013 to 2015 are
assumed to be similar to 2012 for this Inventory, but the areas may have changed through the process in which the
NRI survey data are reconciled with the Forest Inventory and Analysis (FIA) survey data and the National Land
Cover Dataset (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015). This process ensures that the areas of Forest
Land Remaining Forest Land and Land Converted to Forest Land are consistent in all three datasets, and leads to
some modification of other lands use areas to ensure the total land area does not change. For example, if the FIA
estimate less Grassland Converted to Forest Land than the NRI, then the amount of area for this land use conversion
50 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
Land Use, Land-Use Change, and Forestry 6-65
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1 is reduced in the NRI dataset and re-classified as Grassland Remaining Grassland (See Section 6.1, Representation
2 of the U.S. Land Base for more information). Future Inventories will be updated with new NRI activity data when
3 the data are made available, and the time series from 2013 to 2015 will be recalculated. See the Cropland Remaining
4 Cropland section for additional discussion of the Tier 3 methodology for mineral soils.
5 Tier 2 Approach
6 The Tier 2 approach is based on the same methods described in the Tier 2 portion of Cropland Remaining Cropland
7 section for mineral soils, with the exception of the land use and management data that are used in the Inventory for
8 federal grasslands. The NRI (USDA-NRCS 2015) provides land use and management histories for all non-federal
9 lands, and is the basis for the Tier 2 analysis for these areas. However, NRI does not provide land use information
10 on federal lands. The land use data for federal lands is based on the National Land Cover Database (NLCD) (Fry et
11 al. 2011; Homer et al. 2007; Homer et al. 2015). In addition, the Bureau of Land Management (BLM) manages
12 some of the federal grasslands, and has compiled information on grassland condition through the BLM Rangeland
13 Inventory (BLM 2014). To estimate soil C stock changes from federal grasslands, rangeland conditions in the BLM
14 data are aligned with IPCC grassland management categories of nominal, moderately degraded, and severely
15 degraded in order to apply the appropriate emission factors. Further elaboration on the Tier 2 methodology and data
16 used to estimate C stock changes from mineral soils are described in Annex 3.12.
17 Additional Mineral C Stock Change Calculations
18 A Tier 2 method is used to adjust annual C stock change estimates for mineral soils between 1990 and 2015 to
19 account for additional C stock changes associated with sewage sludge amendments. Estimates of the amounts of
20 sewage sludge N applied to agricultural land are derived from national data on sewage sludge generation,
21 disposition, and N content. Although sewage sludge can be added to land managed for other land uses, it is assumed
22 that agricultural amendments only occur in Grassland Remaining Grassland. Cropland is not likely to be amended
23 with sewage sludge due to the high metal content and other pollutants in human waste. Total sewage sludge
24 generation data for 1988, 1996, and 1998, in dry mass units, are obtained from EPA (1999) and estimates for 2004
25 are obtained from an independent national biosolids survey (NEBRA 2007). These values are linearly interpolated to
26 estimate values for the intervening years, and linearly extrapolated to estimate values for years since 2004. N
27 application rates from Kellogg et al. (2000) are used to determine the amount of area receiving sludge amendments.
28 The soil C storage rate is estimated at 0.38 metric tons C per hectare per year for sewage sludge amendments to
29 grassland as described above. The stock change rate is based on country-specific factors and the IPCC default
3 0 method (see Annex 3.12 for further discussion).
31 Organic Soil Carbon Stock Changes
32 Annual C emissions from drained organic soils in Grassland Remaining Grassland are estimated using the Tier 2
33 method provided in IPCC (2006), which utilizes U.S.-specific C loss rates (Ogle et al. 2003) rather than default
34 IPCC rates. For more information, see the Cropland Remaining Cropland section for organic soils.
35 Uncertainty and Time-Series Consistency
36 Uncertainty analysis for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies are based on a
37 Monte Carlo approach that is described in the Cropland Remaining Cropland section. The uncertainty for annual C
38 emission estimates from drained organic soils in Grassland Remaining Grassland is estimated using a Monte Carlo
39 approach, which is also described in the Cropland Remaining Cropland section.
40 Uncertainty estimates are presented in Table 6-36 for each subsource (i.e., mineral soil C stocks and organic soil C
41 stocks) and the method applied in the inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty estimates from the Tier
42 2 and 3 approaches are combined using the simple error propagation methods provided by the IPCC (2006), i.e., by
43 taking the square root of the sum of the squares of the standard deviations of the uncertain quantities. The combined
44 uncertainty for soil C stocks in Grassland Remaining Grassland ranges from -461 percent below to 465 percent
6-66 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 above the 2015 stock change estimate of 7.3 MMT CO2 Eq.51 The large relative uncertainty is due to the almost zero
2 level of change in soil C for 2015, particularly in the land base for Grassland Remaining Grassland included in the
3 Tier 2 analysis.
4 Table 6-36: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes Occurring
5 Within Grassland Remaining Grassland (MMT CO2 Eq. and Percent)
2015 Flux Uncertainty Range Relative to Flux Estimatea'b
Source Estimate3 (MMT CO2 Eq.) (%)
(MMT CO2 Eq.)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Mineral Soil C Stocks Grassland Remaining
Grassland, Tier 3 Methodology
3.9
(28.0)
35.8
-823%
823%
Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology
(0.7)
(10.3)
9.6
-1,387%
1,490%
Mineral Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology (Change in
Soil C due to Sewage Sludge Amendments)
(1.5)
(2.2)
(0.7)
-50%
50%
Organic Soil C Stocks: Grassland Remaining
Grassland, Tier 2 Methodology
5.5
3.0
8.9
-46%
61%
Combined Uncertainty for Flux Associated
with Agricultural Soil Carbon Stock
Change in Grassland Remaining Grassland
7.3
(26.2)
41.0
-461%
465%
a Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review.
The corrected Tier 3 mineral soil C stock change is (24.2) MMT CO2 Eq., with an uncertainty ranging from -150 percent to
150 percent. The corrected combined flux estimate for 2015 is (20.9) MMT CO2 Eq., with an uncertainty ranging from -180
percent to 182 percent.
b Range of C stock change estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or net sequestration.
6 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
7 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
8 above.
9 Uncertainty is also associated with a lack of reporting on biomass and litter C stock changes and non-CCh
10 greenhouse gas emissions from grassland fires. Biomass C stock changes may be significant for managed grasslands
11 with woody encroachment despite not having attained enough tree cover to be considered forest lands. In addition,
12 changes in dead organic matter C stocks are assumed to be negligible in grasslands over annual time frames,
13 although there are certainly significant changes at sub-annual time scales across seasons.
14 QA/QC and Verification
15 See the QA/QC and Verification section in Cropland Remaining Cropland.
16 Recalculations Discussion
17 Methodological recalculations in the current Inventory are associated with the following improvements, including
18 (1) driving the DAYCENT simulations with updated input data for land use and management from the National
19 Resources Inventory from 1979 through 2012; (2) increasing the number of experimental study sites used to
20 quantify model uncertainty; (3) DAYCENT model development to improve the simulation of soil temperature; and
51 Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review.
Based on the revision, soil C stocks increased by 20.9 MMT CO2 Eq. in 2015, with an uncertainty ranging from -180 percent
below to 182 percent above the estimate.
Land Use, Land-Use Change, and Forestry 6-67
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1
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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
31
32
33
34
35
36
37
38
39
(4) improvements in the cropping and land use histories that are simulated in DAYCENT between 1950 and 1979
that generate initial values for the model state variables, including the initial soil organic C stock values. The
differences in SOC stock changes with the recalculations are highly variable across the time series, with an increase
in some years and a decrease in other years. On average, the SOC stock changes decreased by 0.8 MMT CO2 Eq.
compared to the previous Inventory, but with the large variability, there is an 83 percent decrease on average in the
reported soil C stock changes.52
Planned Improvements
Grasslands in Alaska are not currently included in the Inventory. This is a significant planned improvement and
estimates are expected to be available for the 1990 through 2017 Inventory (2019 submission to the UNFCCC).
Another key planned improvement is to estimate woody biomass C stock changes for grasslands (See Box 6-6). For
information about other improvements, see the Planned Improvements section in Cropland Remaining Cropland.
IL
-------
ch4
0.1
0.3
0.8
0.6
0.2
0.4
0.4
n2o
0.1
0.3
0.9
0.6
0.2
0.4
0.4
Total Net Flux
0.2
0.7
1.7
1.2
0.4
0.8
0.8
Notes: Estimate for 2015 is based on the 2014 data, and therefore may not fully reflect
changes occurring in the last year of the time series. Burned area in 2015 was assumed to be
the same as 2014 because MODIS data were not available and processed in time for this
Inventory. Totals may not sum due to independent rounding.
1 Table 6-38: ChU, N2O, CO, and NOx Emissions from Biomass Burning in Grassland (kt)
2
1990
2005
2011
2012
2013
2014
2015
ch4
3
13
32
23
8
16
16
n2o
+
1
3
2
1
1
1
CO
84
358
894
657
217
442
442
NOx
5
21
54
39
13
27
27
+ Does not exceed 0.5 kt
Notes: Estimate for 2015 is based on the 2014 data, and therefore may not fully reflect
changes occurring in the last year of the time series. Burned area in 2015 was assumed to be
the same as 2014 because MODIS data were not available and processed in time for this
Inventory. Totals may not sum due to independent rounding.
3 Methodology
4 The following section includes a description of the methodology used to estimate non-CCh greenhouse gas
5 emissions from biomass burning in grassland, including (1) determination of the land base that is classified as
6 managed grassland; (2) assessment of managed grassland area that is burned each year, and (3) estimation of
7 emissions resulting from the fires. For this Inventory, the IPCC Tier 1 method is applied to estimate non-C02
8 greenhouse gas emissions from biomass burning in grassland (IPCC 2006).
9 The land area designated as managed grassland is based primarily on the 2012 National Resources Inventory (NRI)
10 (Nusser and Goebel 1997; USDA 2015). NRI has survey locations across the entire United States, but does not
11 classify land use on federally-owned areas. These survey locations are designated as grassland using land cover data
12 from the National Land Cover Dataset (NLCD) (Fry et al. 2011; Homer et al. 2007; Homer et al. 2015) (see Section
13 0 Representation of the U.S. Land Base).
14 The area of biomass burning in grasslands (Grassland Remaining Grassland and Land Converted to Grassland) is
15 determined using 30-m fire data from the Monitoring Trends in Burn Severity (MTBS) program for 1990 through
16 2014.54 NRI survey locations on grasslands are designated as burned in a year if there is a fire within a 500 m of the
17 survey point. The area of biomass burning is estimated from the NRI spatial weights and aggregated to the country
18 (Table 6-39).
19 Table 6-39: Thousands of Grassland Hectares Burned Annually
Year
Thousand Hectares
1990
317
2005
1.343
2011
3,356
2012
2,464
2013
815
2014
1,659
2015
1,659
Notes: Burned area in 2015
was assumed to be the same as
2014 because MTBS data were
54 See .
Land Use, Land-Use Change, and Forestry 6-69
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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
31
not available and processed in
time for this Inventory. The
burned area will be updated in
the next Inventory.
To estimate the amount of combusted biomass, the total area of grassland burned is multiplied by the IPCC default
factor for grassland biomass (4.1 tonnes dry matter per ha) (IPCC 2006). A combustion factor of 1 is assumed in this
Inventory, and the resulting biomass estimate is multiplied by the IPCC default grassland emission factors for CH4
(2.3 g CH4 per kg dry matter), N20 (0.21 g CH4 per kg dry matter), CO (65 g CH4 per kg dry matter) and NOx (3.9 g
CH4 per kg dry matter) (IPCC 2006). The Tier 1 analysis is implemented in the Agriculture and Land Use National
Greenhouse Gas Inventory (ALU) software (Ogle et al. 2016).55
Uncertainty and Time-Series Consistency
The results of the Approach 2 Monte Carlo uncertainty analysis are summarized in Table 6-40. Methane emissions
from Biomass Burning in Grassland for 2015 is estimated to be between 0 and 1.2 MMT CO2 Eq. at a 95 percent
confidence level. This indicates a range of 100 percent below and 209 percent above the 2015 emission estimate of
0.4 MMT CO2 Eq. Nitrous oxide emissions are estimated to be between 0 and 1.4 MMT CO2 Eq., or approximately
100 percent below and 229 percent above the 2015 emission estimate of 0.4 MMT CO2 Eq.
Table 6-40: Uncertainty Estimates for Non-C02 Greenhouse Gas Emissions from Biomass
Burning in Grassland (MMT CO2 Eg. and Percent)
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
Source
Gas
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Grassland Burning
Grassland Burning
CH4
N2O
0.4
0.4
0.0 1.2
0.0 1.4
-100% 209%
-100% 229%
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: Quality control measures are still underway and uncertainty estimates will be finalized after the public review.
Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
Uncertainty is also associated with lack of reporting of emissions from biomass burning in grassland of Alaska.
Grassland burning emissions could be relatively large in this region of the United States, and therefore extending
this analysis to include Alaska is a planned improvement for the Inventory. There is also uncertainty due to lack of
reporting combustion of woody biomass, and this is another planned improvement.
QA/QC and Verification
Quality control measures included checking input data, model scripts, and results to ensure data are properly
handled throughout the inventory process. Inventory reporting forms and text are reviewed and revised as needed to
correct transcription errors. Quality control identified problems with cell references in the spreadsheets, which have
been corrected.
Planned Improvements
Two planned improvements have been identified for this source category, including a) incorporation of country-
specific grassland biomass factors, and b) extending the analysis to include Alaska. In the current Inventory,
biomass factors are based on a global default for grasslands that is provided by the IPCC (2006). There is
considerable variation in grassland biomass, however, which would affect the amount of biomass combusted in a
55 See .
6-70 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 fire. Alaska has an extensive area of grassland and includes tundra vegetation, although some of the areas are not
2 managed. There has been an increase in fire frequency in boreal forest of the region (Chapin et al. 2008), and this
3 may have led to an increase in burning of neighboring grassland areas. There is also an effort under development to
4 incorporate grassland fires into DAYCENT model simulations. Both improvements are expected to reduce
5 uncertainty and lead to more accurate estimates of non-CCh greenhouse gas emissions from grassland burning.
6 6.7 Land Converted to Grassland (IPCC Source
7 Category 4C2)
8 Land Converted to Grassland includes all grassland in an Inventory year that had been in another land use(s) during
9 the previous 20 years (USDA-NRCS 2015).56 For example, cropland or forest land converted to grassland during
10 the past 20 years would be reported in this category. Recently-converted lands are retained in this category for 20
11 years as recommended by IPCC (2006). Grassland includes pasture and rangeland that are used primarily but not
12 exclusively for livestock grazing. Rangelands are typically extensive areas of native grassland that are not
13 intensively managed, while pastures are typically seeded grassland (possibly following tree removal) that may also
14 have additional management, such as irrigation or interseeding of legumes. This Inventory includes all grasslands in
15 the conterminous United States and Hawaii, but does not include Land Converted to Grassland in Alaska.
16 Consequently there is a discrepancy between the total amount of managed area for Land Converted to Grassland
17 (see Section 6.1 Representation of the U.S. Land Base) and the grassland area included in the inventory analysis
18 (IPCC Source Category 4C2—Section 6.7).
19 Land-use change can lead to large losses of C to the atmosphere, particularly conversions from forest land
20 (Houghton et al. 1983). Moreover, conversion of forest to another land use (i.e., deforestation) is one of the largest
21 anthropogenic sources of emissions to the atmosphere globally (Schimel 1995), although this source may be
22 declining according to a recent assessment (Tubiello et al. 2015).
23 IPCC (2006) recommends reporting changes in biomass, dead organic matter, and soil organic C (SOC) stocks due
24 to land use change.57 All soil C stock changes are estimated and reported for Land Converted to Grassland, but
25 there is limited reporting of other pools in this Inventory. Loss of aboveground and belowground biomass, dead
26 wood and litter C from Forest Land Converted to Grassland is reported, but biomass and dead organic matter C
27 stock changes are not estimated for other land use conversions to grassland.58
28 Land use and management of mineral soils in Land Converted to Grassland led to an increase in soil C stocks
29 between 1990 and 2015 (see Table 6-41 and Table 6-42). The average soil C stock change for mineral soils between
30 1990 and 2015 sequestered 11.3 MMT CChEq. from the atmosphere (3.1 MMT C).59 In contrast, over the same
31 period, drainage of organic soils for grassland management led to CO2 emissions to the atmosphere of 1.3 MMT
32 CO2 Eq. (0.4 MMT C). In addition, aboveground biomass, belowground biomass, dead wood and litter C losses
33 from Forest Land Converted to Grassland led to CO2 emissions to the atmosphere of 184.9, 24.0, 46.3 and 50.0
34 MMT CO2 Eq. (50.4, 6.5, 12.6 and 13.6 MMT C), respectively, in 2015. The total net C stock change in 2015 for
56 NRI survey locations are classified according to land-use histories starting in 1979, and consequently the classifications are
based on less than 20 years from 1990 to 2001. This may have led to an underestimation of Land Converted to Grassland in the
early part of the time series to the extent that some areas are converted to grassland between 1971 and 1978.
57 CO2 emissions associated with liming and urea fertilization are also estimated but included in the Agriculture chapter of the
report.
58 Changes in biomass C stocks are not currently reported for other conversions to grassland (other than forest land), but this is a
planned improvement for a future inventory. Note: changes in dead organic matter are assumed to negligible for other land use
conversions (i.e., other than forest land) to grassland based on the Tier 1 method in IPCC (2006).
59 Quality control uncovered errors in the mineral soil C and total net flux estimates for 2015, which will be updated following
public review. Based on the revisions, mineral soil C stocks increased by 13.6 MMT CO2 Eq. (3.7 MMT C) in 2015. The total
net flux represents a loss of 293.2 MMT CO2 Eq. (80.0 MMT C) from Land Converted to Grassland.
Land Use, Land-Use Change, and Forestry 6-71
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1 Land Converted to Grassland is estimated as a loss of 294.2 MMT CO2 Eq. (80.2 MMT C), which is a 20 percent
2 increase in emissions compared to the emissions in the initial reporting year of 1990.
3 Table 6-41: Net CO2 Flux from Soil and Biomass C Stock Changes for Land Converted to
4 Grassland (MMT CO2 Eq.)
1990
2005
2011
2012
2013 a
2014 a
2015 a
Cropland Converted to Grassland
Mineral Soils
(8.0)
(12.7)
(12.2)
(12.4)
(11.3)
(11.3)
(11.3)
Organic Soils
0.5
1.1
1.1
1.1
1.1
1.1
1.1
Forest Land Converted to
Grassland
Aboveground Live Biomass
159.0
205.2
187.3
184.9
184.9
184.9
184.9
Belowground Live Biomass
19.6
26.7
24.3
24.0
24.0
24.0
24.0
Dead Wood
36.2
50.5
46.7
46.3
46.3
46.3
46.3
Litter
39.4
54.7
50.4
50.0
50.0
50.0
50.0
Mineral Soils
(0.8)
(0.5)
(0.3)
(0.3)
(0.3)
(0.3)
(0.3)
Organic Soils
+
0.1
0.1
0.1
0.1
0.1
0.1
Other Lands Converted Grassland
Mineral Soils
(0.5)
(1.1)
(0.8)
(0.8)
(0.8)
(0.8)
(0.8)
Organic Soils
+
+
0.1
0.1
0.1
0.1
0.1
Settlements Converted Grassland
Mineral Soils
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Organic Soils
+
+
+
+
+
+
+
Wetlands Converted Grassland
Mineral Soils
(0.3)
(0.4)
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
Organic Soils
0.1
0.3
0.3
0.3
0.3
0.3
0.3
Aboveground Live Biomass
159.0
205.2
187.3
184.9
184.9
184.9
184.9
Belowground Live Biomass
19.6
26.7
24.3
24.0
24.0
24.0
24.0
Dead Wood
36.2
50.5
46.7
46.3
46.3
46.3
46.3
Litter
39.4
54.7
50.4
50.0
50.0
50.0
50.0
Total Mineral Soil Flux
(9.7)
(14.8)
(13.5)
(13.6)
(12.6)
(12.6)
(12.6)
Total Organic Soil Flux
0.7
1.5
1.6
1.6
1.6
1.6
1.6
Total Net Flux
245.2
323.8
296.9
293.2
294.2
294.2
294.2
+ Does not exceed 0.05 MMT CO2 Eq.
a Quality control uncovered errors in the estimates for 2013, 2014 and 2015 for mineral soils in Cropland
Converted to Grassland, Total Mineral Soil Flux and the Total Net Flux, which will be updated following
public review. The corrected mineral soil estimates for Cropland Converted to Grassland are (12.4), (12.4) and
(12.4) MMT CO2 Eq., respectively for 2013,2014,2015; the total mineral net flux is (13.6). (13.6), and (13.6)
MMT CO2 Eq., respectively for the three years; and the total net flux for Land Converted to Grassland is
293.2, 293.2 and 293.2 MMT CO2 Eq., respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect changes
occurring in the latter part of the time series. Totals may not sum due to independent rounding. Parentheses
indicate net sequestration.
5 Table 6-42: Net CO2 Flux from Soil and Biomass C Stock Changes for Land Converted to
6 Grassland (MMT C)
1990
2005
2011
2012
2013 a
2014 a
2015 a
Cropland Converted to Grassland
Mineral Soils
(2.2)
(3.5)
(3.3)
(3.4)
(3.1)
(3.1)
(3.1)
Organic Soils
0.1
0.3
0.3
0.3
0.3
0.3
0.3
Forest Land Converted to
Grassland
Aboveground Live Biomass
43.4
56.0
51.1
50.4
50.4
50.4
50.4
Belowground Live Biomass
5.3
7.3
6.6
6.5
6.5
6.5
6.5
Dead Wood
9.9
13.8
12.7
12.6
12.6
12.6
12.6
Litter
10.8
14.9
13.7
13.6
13.6
13.6
13.6
Mineral Soils
(0.2)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
(0.1)
Organic Soils
+
+
+
+
+
+
+
6-72 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
Other Lands Converted Grassland
Mineral Soils (0.1) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2)
Organic Soils
+
+
+
+
+
+
+
Settlements Converted Grassland
Mineral Soils
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Organic Soils
+
+
+
+
+
+
+
Wetlands Converted Grassland
Mineral Soils
(0.1)
(0.1)
(+)
(+)
(+)
(+)
(+)
Organic Soils
+
0.1
0.1
0.1
0.1
0.1
0.1
Aboveground Live Biomass
44.7
56.0
51.1
50.4
50.4
50.4
50.4
Belowground Live Biomass
5.3
7.3
6.6
6.5
6.5
6.5
6.5
Dead Wood
9.9
13.8
12.7
12.6
12.6
12.6
12.6
Litter
10.8
14.9
13.7
13.6
13.6
13.6
13.6
Total Mineral Soil Flux
(2.6)
(4.0)
(3.7)
(3.7)
(3.4)
(3.4)
(3.4)
Total Organic Soil Flux
0.2
0.4
0.4
0.4
0.4
0.4
0.4
Total Net Flux
66.9
88.3
81.0
80.0
80.2
80.2
80.2
+ Absolute value does not exceed 0.05 MMT C
a Quality control uncovered errors in the estimates for 2013, 2014 and 2015 for mineral soils in Cropland
Converted to Grassland, Total Mineral Soil Flux and the Total Net Flux, which will be updated following public
review. The corrected mineral soil estimates for Cropland Converted to Grassland are (3.4), (3.4) and (3.4)
MMT C, respectively for 2013,2014, 2015; the total mineral net flux is (3.7), (3.7) and (3.7) MMT C,
respectively for the three years; and the total net flux for Land Converted to Grassland is 80.0, 80.0 and 80.0
MMT C, respectively for the three years.
Notes: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully reflect changes
occurring in the latter part of the time series. Totals may not sum due to independent rounding. Parentheses
indicate net sequestration.
Methodology
The following section includes a description of the methodology used to estimate changes inbiomass and soil C
stocks for Land Converted to Grassland, including: (1) loss of aboveground and belowground biomass, dead wood
and litter C with conversion of forest lands to grassland; (2) agricultural land-use and management activities on
mineral soils; and (3) agricultural land-use and management activities on organic soils.
Biomass, Dead Biomass and Litter Carbon Stock Changes
A Tier 2 method is applied to estimate aboveground biomass C stock changes for Forest Land Converted to
Grassland. For this method, forest land conversions to grasslands were identified in each state and C density
estimates were compiled by state for aboveground biomass, belowground biomass, dead wood, and litter for
grasslands (assumed to be zero since no reference biomass C density estimates exist) and forest land use categories.
The difference between the stocks is reported as the stock change under the assumption that the change occurred in
the year of the conversion. Reference C density estimates (i.e., aboveground biomass, belowground biomass, dead
wood, and litter) for the forest land use have been estimated from data in the Forest Inventory and Analysis (FIA)
program within the USDA Forest Service (USDA Forest Service 2015). If FIA plots include data on individual trees,
aboveground and belowground C density estimates are based on Woodall et al. (2011). Aboveground and
belowground biomass estimates also include live understory which is a minor component of biomass defined as all
biomass of undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm dbh. For this
Inventory, it was assumed that 10 percent of total understory C mass is belowground (Smith et al. 2006). Estimates
of C density are based on information in Birdsey (1996) and biomass estimates from Jenkins et al. (2003). If FIA
plots include data on standing dead trees, standing dead tree C density is estimated following the basic method
applied to live trees (Woodall et al. 2011) with additional modifications to account for decay and structural loss
(Domke et al. 2011; Harmon et al. 2011). If FIA plots include data on downed dead wood, downed dead wood C
density is estimated based on measurements of a subset of FIA plots for downed dead wood (Domke et al. 2013;
Woodall and Monleon 2008). Downed dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at
transect intersection, that are not attached to live or standing dead trees. This includes stumps and roots of harvested
trees. To facilitate the downscaling of downed dead wood C estimates from the state-wide population estimates to
individual plots, downed dead wood models specific to regions and forest types within each region are used. Litter C
Land Use, Land-Use Change, and Forestry 6-73
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1 is the pool of organic C (also known as duff, humus, and fine woody debris) above the mineral soil and includes
2 woody fragments with diameters of up to 7.5 cm. A subset of FIA plots are measured for litter C. If FIA plots
3 include litter material, a modeling approach using litter C measurements from FIA plots is used to estimate litter C
4 density (Domke et al. 2016). See Annex 3.13 for more information about reference C density estimates forforest
5 land.
6 Soi I Ca rbon Stock Cha nges
7 Soil C stock changes are estimated for Land Converted to Grassland according to land-use histories recorded in the
8 2012 USDA NRI survey for non-federal lands (USDA-NRCS 2015). Land use and some management information
9 (e.g., crop type, soil attributes, and irrigation) were originally collected for each NRI survey locations on a 5-year
10 cycle beginning in 1982 In 1998, the NRI Program began collecting annual data, and the annual data are currently
11 available through 2012 (USDA-NRCS 2015). NRI survey locations are classified as Land Converted to Grassland
12 in a given year between 1990 and 2012 if the land use is grassland but had been classified as another use during the
13 previous 20 years. NRI survey locations are classified according to land-use histories starting in 1979, and
14 consequently the classifications are based on less than 20 years from 1990 to 1998. This may have led to an
15 underestimation of Land Converted to Grassland in the early part of the time series to the extent that some areas are
16 converted to grassland between 1971 and 1978. For federal lands, the land use history is derived from land cover
17 changes in the National Land Cover Dataset (Homer et al. 2007; Fry et al. 2011; Homer et al. 2015).
18 Mineral Soil Carbon Stock Changes
19 An IPCC Tier 3 model-based approach (Ogle et al. 2010) is applied to estimate C stock changes for Land Converted
20 to Grassland on most mineral soils. C stock changes on the remaining soils are estimated with an IPCC Tier 2
21 approach (Ogle et al. 2003), including prior cropland used to produce vegetables, tobacco, and
22 perennial/horticultural crops; land areas with very gravelly, cobbly, or shaley soils (greater than 35 percent by
23 volume); and land converted to grassland from another land use other than cropland.
24 Tier 3 Approach. Mineral SOC stocks and stock changes are estimated using the DAYCENT biogeochemical60
25 model (Parton et al. 1998; Del Grosso et al. 2001, 2011). The DAYCENT model utilizes the soil C modeling
26 framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), but has been
27 refined to simulate dynamics at a daily time-step. Historical land-use patterns and irrigation histories are simulated
28 with DAYCENT based on the 2012 USDA NRI survey (USDA-NRCS 2015). C stocks and 95 percent confidence
29 intervals are estimated for each year between 1990 and 2012, but C stock changes from 2013 to 2015 are assumed to
30 be similar to 2012. Future inventories will be updated with new activity data when the data are made available, and
31 the time series will be recalculated (See Planned Improvements section in Cropland Remaining Cropland). See the
32 Cropland Remaining Cropland section and Annex 3.12 for additional discussion of the Tier 3 methodology for
33 mineral soils.
34 Tier 2 Approach. For the mineral soils not included in the Tier 3 analysis, SOC stock changes are estimated using a
35 Tier 2 Approach for Land Converted to Grassland as described in the Tier 2 Approach for mineral soils in the
36 Grassland Remaining Grassland section.
37 Organic Soil Carbon Stock Changes
38 Annual C emissions from drained organic soils in Land Converted to Grassland are estimated using the Tier 2
39 method provided in IPCC (2006), with U.S.-specific C loss rates (Ogle et al. 2003) as described in the Cropland
40 Remaining Cropland section for organic soils.
41 Uncertainty and Time-Series Consistency
42 The uncertainty analysis for biomass, dead wood and litter C losses with Forest Land Converted to Grassland is
43 conducted in the same way as the uncertainty assessment for forest ecosystem C flux in the Forest Land Remaining
60 Biogeochemical cycles are the flow of chemical elements and compounds between living organisms and the physical
environment.
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10
11
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13
14
Forest Land category. Sample and model-based error are combined using simple error propagation methods
provided by the IPCC (2006). For additional details see the Uncertainty Analysis in Annex 3.13. The uncertainty
analyses for mineral soil C stock changes using the Tier 3 and Tier 2 methodologies are based on a Monte Carlo
approach that is described in the Cropland Remaining Cropland section. The uncertainty for annual C emission
estimates from drained organic soils in Land Converted to Grassland is estimated using a Monte Carlo approach,
which is also described in the Cropland Remaining Cropland section.
Uncertainty estimates are presented in Table 6-43 for each subsource (i.e., biomass C stocks, mineral soil C stocks
and organic soil C stocks) and the method applied in the inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty
estimates from the Tier 2 and 3 approaches are combined using the simple error propagation methods provided by
the 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 total C stocks in Land Converted to Grassland ranges from 20 percent
below to 20 percent above the 2015 stock change estimate of 294.2 MMT CO2 Eq.61
Table 6-43: Approach 2 Quantitative Uncertainty Estimates for Soil C Stock Changes
occurring within Land Con verted to Grassland (MMT CO2 Eq. and Percent)
2015 Flux Estimate3 Uncertainty Range Relative to Flux Estimatea'b
(MMT CO2 Eq.) (MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Cropland Converted to Grassland
(10.2)
(18.9)
(1.5)
-86%
86%
Mineral Soil C Stocks: Tier 3
(10.2)
(18.9)
(1.5)
-86%
86%
Mineral Soil C Stocks: Tier 2
(1.2)
(1.7)
(0.6)
-50%
45%
Organic Soil C Stocks: Tier 2
1.1
0.6
1.9
-49%
68%
Forest Land Converted to Grassland
305.0
246.5
363.6
-19%
19%
Aboveground Live Biomass
184.9
130.4
239.4
-29%
29%
Belowground Live Biomass
24.0
8.1
39.9
-66%
66%
Dead Wood
46.3
33.5
59.0
-28%
28%
Litter
50.0
43.5
56.5
-13%
13%
Mineral Soil C Stocks: Tier 2
(0.3)
(1.0)
0.5
-307%
295%
Organic Soil C Stocks: Tier 2
0.1
+
0.2
-57%
82%
Other Lands Converted to Grassland
(0.7)
(1.1)
(0.4)
-54%
49%
Mineral Soil C Stocks: Tier 2
(0.8)
(1.2)
(0.4)
-50%
45%
Organic Soil C Stocks: Tier 2
0.1
+
0.1
-71%
107%
Settlements Converted to Grassland
(0.1)
(0.1)
(+)
-63%
62%
Mineral Soil C Stocks: Tier 2
(0.1)
(0.1)
(+)
-50%
45%
Organic Soil C Stocks: Tier 2
+
+
+
-75%
127%
Wetlands Converted to Grasslands
0.2
0.1
0.4
-72%
92%
Mineral Soil C Stocks: Tier 2
(0.1)
(0.2)
(0.1)
-58%
53%
Organic Soil C Stocks: Tier 2
0.3
0.2
0.5
-43%
58%
Total: Land Converted to Grassland
294.2
235.0
353.4
-20%
20%
Aboveground Live Biomass
184.9
130.4
239.4
-29%
29%
Belowground Live Biomass
24.0
8.1
39.9
-66%
66%
Dead Wood
46.3
33.5
59.0
-28%
28%
Litter
50.0
43.5
56.5
-13%
13%
Mineral Soil C Stocks: Tier 3
(10.2)
(18.9)
(1.5)
-86%
86%
Mineral Soil C Stocks: Tier 2
(2.4)
(3.5)
(1.4)
-44%
41%
Organic Soil C Stocks: Tier 2
1.6
1.1
2.4
-35%
49%
61 Quality control uncovered errors in the estimate and uncertainty for 2015, which will be updated following public review. The
corrected total stock declined by 293.2 MMT CO2 Eq. in 2015, with an uncertainty ranging from -20 percent below to 20 percent
above the estimate.
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+ Absolute value does not exceed 0.05 MMT CO2 Eq.
a Quality control uncovered errors in the 2015 estimates for mineral soils in Cropland Converted to Grassland for Tier 3,
Total Cropland Converted to Grassland, Total Mineral Soil Flux for Tier 3, and the Total Net Flux for Land Converted
Grassland, which will be updated following public review. The corrected estimate for mineral soils in Cropland Converted
to Grassland for Tier 3 is (11.2) MMT CO2 Eq. for 2015 with an uncertainty ranging from -74 percent to 74 percent;
Cropland Converted to Grassland is (11.2) MMT CO2 Eq. for 2015 with an uncertainty ranging from -74 percent to 74
percent; the total Tier 3 mineral stock change is also (11,2) MMT CO2 Eq. with uncertainty ranging from -74 percent to 74
percent; and the total net flux for Land Converted to Grassland is 293 MMT CO2 Eq. with uncertainty ranging from -20
percent to 20 percent.
b Range of C stock change estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values or net sequestration.
1 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
2 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
3 above.
4 Uncertainty is also associated with a lack of reporting on biomass and dead organic matter C stock changes for Land
5 Converted to Grassland with the exception of forest land conversion. Biomass C stock changes may be significant
6 for managed grasslands with woody encroachment despite not having attained enough tree cover to be considered
7 forest lands. Changes in dead organic matter C stocks are assumed to be negligible with conversion of land to
8 grasslands with the exception of forest lands, which are included in this analysis. This assumption will be further
9 explored in a future Inventory.
10 QA/QC and Verification
11 See the QA/QC and Verification section in Cropland Remaining Cropland.
12 Recalculations Discussion
13 Methodological recalculations in the current Inventory are associated with the following improvements, including:
14 (1) driving the DAYCENT simulations with updated input data for land use and management from the National
15 Resources Inventory extending the time series through 2012; (2) modifying the number of experimental study sites
16 used to quantify model uncertainty; (3) DAYCENT model development to improve the simulation of soil
17 temperature; (4) improvements in the cropping and land use histories that are simulated in DAYCENT between
18 1950 and 1979 that generate initial values for the model state variables, including the initial soil organic C stock
19 values; and (5) incorporating belowground biomass, dead wood and litter C stock losses for Forest Land Converted
20 to Grassland. As a result of these improvements to the Inventory, changes in stocks declined, relative to the previous
21 report, by an average of 272.9 MMT CO2 Eq. annually over the time series. This represents a 673 percent increase in
22 the losses of carbon from Land Converted to Grassland compared to the previous Inventory.62 This change is due to
23 a larger amount of aboveground biomass C that is lost from Forest Land Converted to Grasslands, in addition to
24 inclusion of belowground biomass, dead wood and litter C stock changes in this Inventory.
25 Planned Improvements
26 Soil C stock changes with land use conversion from forest land to grassland are undergoing further evaluation to
27 ensure consistency in the time series. Different methods are used to estimate soil C stock changes in forest land and
28 grasslands, and while the areas have been reconciled between these land uses, there has been limited evaluation of
29 the consistency in C stock changes with conversion from forest land to grassland. In addition, biomass C stock
30 changes will be estimated for Cropland Converted to Grassland, and other land use conversions to grassland, to the
31 extent that data are available. One additional planned improvement for the Land Converted to Grassland category is
62 Quality control uncovered errors in the estimate and uncertainty for 2013, 2014,2015, which will be updated following public
review. These corrections impact the comparison between the prior and current Inventories in the Recalculation Discussion,
which will also be updated after public review.
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1 to develop an inventory of C stock changes for grasslands in Alaska. For information about other improvements, see
2 the Planned Improvements section in Cropland Remaining Cropland and Grassland Remaining Grassland.
3 6.8 Wetlands Remaining Wetlands (IPCC
4 Source Category 4D1)
5 Wetlands Remaining Wetlands includes all wetland in an Inventory year that had been classified as wetland for the
6 previous 20 years, and in this Inventory includes Peatlands and Coastal Wetlands.
7 Peatlands Remaining Peatlands
8 Emissions from Managed Peatlands
9 Managed peatlands are peatlands which have been cleared and drained for the production of peat. The production
10 cycle of a managed peatland has three phases: land conversion in preparation for peat extraction (e.g., clearing
11 surface biomass, draining), extraction (which results in the emissions reported under Peatlands Remaining
12 Peatlands), and abandonment, restoration, or conversion of the land to another use.
13 CO2 emissions from the removal of biomass and the decay of drained peat constitute the major greenhouse gas flux
14 from managed peatlands. Managed peatlands may also emit CH4 and N20. The natural production of CH4 is largely
15 reduced but not entirely shut down when peatlands are drained in preparation for peat extraction (Strack et al. 2004
16 as cited in the 2006 IPCC Guidelines). Drained land surface and ditch networks contribute to the CH4 flux in
17 peatlands managed for peat extraction. Methane emissions were considered insignificant under IPCC Tier 1
18 methodology (IPCC 2006), but are included in the emissions estimates for Peatlands Remaining Peatlands
19 consistent with the 2013 Supplement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories:
20 Wetlands (IPCC 2013). Nitrous oxide emissions from managed peatlands depend on site fertility. In addition,
21 abandoned and restored peatlands continue to release greenhouse gas emissions. Although methodologies are
22 provided for rewetted organic soils (which includes rewetted/restored peatlands) in IPCC (2013) guidelines,
23 information on the areal extent of rewetted/restored peatlands in the United States is currently unavailable. This
24 Inventory estimates CO2, N20, and CH4 emissions from peatlands managed for peat extraction in accordance with
25 IPCC (2006 and 2013) guidelines.
26 CO2, N2O, and CH4 Emissions from Peatlands Remaining Peatlands
27 IPCC (2013) recommends reporting CO2, N20, and CH4 emissions from lands undergoing active peat extraction
28 (i.e., Peatlands Remaining Peatlands) as part of the estimate for emissions from managed wetlands. Peatlands occur
29 where plant biomass has sunk to the bottom of water bodies and water-logged areas and exhausted the oxygen
30 supply below the water surface during the course of decay. Due to these anaerobic conditions, much of the plant
31 matter does not decompose but instead forms layers of peat over decades and centuries. In the United States, peat is
32 extracted for horticulture and landscaping growing media, and for a wide variety of industrial, personal care, and
33 other products. It has not been used for fuel in the United States for many decades. Peat is harvested from two types
34 of peat deposits in the United States: sphagnum bogs in northern states (e.g., Minnesota) and wetlands in states
35 further south (e.g., Florida). The peat from sphagnum bogs in northern states, which is nutrient poor, is generally
36 corrected for acidity and mixed with fertilizer. Production from more southerly states is relatively coarse (i.e.,
37 fibrous) but nutrient rich.
38 IPCC (2006 and 2013) recommend considering both on-site and off-site emissions when estimating CO2 emissions
39 from Peatlands Remaining Peatlands using the Tier 1 approach. Current methodologies estimate only on-site N2O
40 and CH4 emissions, since off-site N20 estimates are complicated by the risk of double-counting emissions from
41 nitrogen fertilizers added to horticultural peat, and off-site CH4 emissions are not relevant given the non-energy uses
42 of peat, so methodologies are not provided in IPCC (2013) guidelines. On-site emissions from managed peatlands
43 occur as the land is cleared of vegetation and the underlying peat is exposed to sun and weather. As this occurs,
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some peat deposit is lost and CO2 is emitted from the oxidation of the peat. Since N20 emissions from saturated
ecosystems tend to be low unless there is an exogenous source of nitrogen, N20 emissions from drained peatlands
are dependent on nitrogen mineralization and therefore on soil fertility. Peatlands located on highly fertile soils
contain significant amounts of organic nitrogen in inactive form. Draining land in preparation for peat extraction
allows bacteria to convert the nitrogen into nitrates which leach to the surface where they are reduced to N20, and
contributes to the activity of methanogens and methanotrophs (Blodau 2002; Treat et al. 2007 as cited in IPCC
2013). Drainage ditches, which are constructed to drain the land in preparation for peat extraction, also contribute to
the flux of CH4 through in situ production and lateral transfer of CH4 from the organic soil matrix (IPCC 2013).
Off-site CO2 emissions from managed peatlands occur from waterborne carbon losses and the horticultural and
landscaping use of peat. Dissolved organic carbon from water drained off peatlands reacts within aquatic ecosystems
and is converted to CO2, then emitted to the atmosphere (Billet et al. 2004 as cited in IPCC 2013). During the
horticultural and landscaping use of peat, nutrient-poor (but fertilizer-enriched) peat tends to be used in bedding
plants and in greenhouse and plant nursery production, whereas nutrient-rich (but relatively coarse) peat is used
directly in landscaping, athletic fields, golf courses, and plant nurseries. Most (nearly 94 percent) of the CO2
emissions from peat occur off-site, as the peat is processed and sold to firms which, in the United States, use it
predominantly for the aforementioned horticultural and landscaping purposes.
Total emissions from Peatlands Remaining Peatlands were estimated to be 0.8 MMT CO2 Eq. in 2015 (see Table
6-44) comprising 0.8 MMT C02 Eq. (763 kt) of C02, 0.001 MMT C02 Eq. (0.002 kt) of N20, and 0.004 MMT C02
Eq. (0.16 kt) of CH4. Total emissions in 2015 were about 2 percent less than total emissions in 2014. Peat
production in Alaska in 2015 was not reported in Alaska's Mineral Industry 2014 report. However, peat production
reported in the lower 48 states in 2015 was two percent less than in 2014, resulting in a decrease in total emissions
forthe 48 states and Alaska from Peatlands Remaining Peatlands in 2015 compared to 2014.
Total emissions from Peatlands Remaining Peatlands have fluctuated between 0.8 and 1.3 MMT CO2 Eq. across the
time series with a decreasing trend from 1990 until 1993, followed by an increasing trend until reaching peak
emissions in 2000. After 2000, emissions generally decreased until 2006 and then increased until 2009. The trend
reversed in 2009 and total emissions have generally decreased between 2009 and 2015. Carbon dioxide emissions
from Peatlands Remaining Peatlands have fluctuated between 0.8 and 1.3 MMT CO2 across the time series, and
these emissions drive the trends in total emissions. Methane and N20 emissions remained close to zero across the
time series. Nitrous oxide emissions showed a decreasing trend from 1990 until 1995, followed by an increasing
trend through 2001. Nitrous oxide emissions decreased between 2001 and 2006, followed by a leveling off between
2008 and 2010, and a general decline between 2011 and 2015. Methane emissions decreased from 1990 until 1995,
followed by an increasing trend through 2000, a period of fluctuation through 2010, and a general decline between
2010 and 2015.
Table 6-44: Emissions from Peatlands Remaining Peatlands (MMT CO2 Eq.)
Gas
1990
2005
2011
2012
2013
2014
2015
CO2
1.1
1.1
0.9
0.8
0.8
0.8
0.8
Off-site
1.0
1.0
0.9
0.8
0.7
0.7
0.7
On-site
0.1
0.1
0.1
0.1
+
0.1
+
N2O (On-site)
+
+
+
+
+
+
+
CH4 (On-site)
+
+
+
+
+
+
+
Total
1.1
1.1
0.9
0.8
0.8
0.8
0.8
+ Does not exceed 0.05 MMT CO2 Eq.
Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which
does not take into account imports, exports, and stockpiles (i.e., apparent consumption). Off-site
N2O emissions are not estimated to avoid double-counting N2O emitted from the fertilizer that the
peat is mixed with prior to horticultural use (see IPCC 2006). Totals may not sum due to
independent rounding.
Table 6-45: Emissions from Peatlands Remaining Peatlands (kt)
Gas 1990 2005 2011 2012 2013 2014 2015
CO2 1,055 1,101 926 812 770 775 763
Off-site 985 1,030 866 760 720 725 713
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36
37
38
39
On-site 70 71 60 53 50 50 49
N2O (On-site) + + + + + +
CH4 (On-site) + + + + + +
+ Does not exceed 0.5 kt
Note: These numbers are based on U.S. production data in accordance with Tier 1 guidelines, which does
not take into account imports, exports, and stockpiles (i.e., apparent consumption). Off-site N2O
emissions are not estimated to avoid double-counting N2O emitted from the fertilizer that the peat is
mixed with prior to horticultural use (see IPCC 2006). Totals may not sum due to independent rounding.
Methodology
Off-Site CO2 Emissions
Carbon dioxide emissions from domestic peat production were estimated using a Tier 1 methodology consistent with
IPCC (2006). Off-site CO2 emissions from Peatlands Remaining Peat lands were calculated by apportioning the
annual weight of peat produced in the United States (Table 6-46) into peat extracted from nutrient-rich deposits and
peat extracted from nutrient-poor deposits using annual percentage-by-weight figures. These nutrient-rich and
nutrient-poor production values were then multiplied by the appropriate default C fraction conversion factor taken
from IPCC (2006) in order to obtain off-site emission estimates. For the lower 48 states, both annual percentages of
peat type by weight and domestic peat production data were sourced from estimates and industry statistics provided
in the Minerals Yearbook and Mineral Commodity Summaries from the U.S. Geological Survey (USGS 1995
through 2015; USGS 2016). To develop these data, the U.S. Geological Survey (USGS; U.S. Bureau of Mines prior
to 1997) obtained production and use information by surveying domestic peat producers. On average, about 75
percent of the peat operations respond to the survey; and USGS estimates data for non-respondents on the basis of
prior-year production levels (Apodaca 2011).
The Alaska estimates rely on reported peat production from the annual Alaska's Mineral Industry reports (DGGS
1993 through 2015). Similar to the U.S. Geological Survey, the Alaska Department of Natural Resources, Division
of Geological & Geophysical Surveys (DGGS) solicits voluntary reporting of peat production from producers for the
Alaska's Mineral Industry report. However, the report does not estimate production for the non-reporting producers,
resulting in larger inter-annual variation in reported peat production from Alaska depending on the number of
producers who report in a given year (Szumigala 2011). In addition, in both the lower 48 states and Alaska, large
variations in peat production can also result from variations in precipitation and the subsequent changes in moisture
conditions, since unusually wet years can hamper peat production. The methodology estimates Alaska emissions
separately from lower 48 emissions because the state conducts its own mineral survey and reports peat production
by volume, rather than by weight (Table 6-47). However, volume production data were used to calculate off-site
CO2 emissions from Alaska applying the same methodology but with volume-specific C fraction conversion factors
from IPCC (2006).63 Peat production was not reported for 2015 in A laska 's Mineral Industry 2014 report (DGGS
2015); and reliable data are not available beyond 2012, so Alaska's peat production in 2013, 2014, and 2015
(reported in cubic yards) was assumed to be equal to the 2012 value.
Consistent with IPCC (2013) guidelines, off-site CO2 emissions from dissolved organic carbon were estimated based
on the total area of peatlands managed for peat extraction, which is calculated from production data using the
methodology described in the On-Site C02 Emissions section below. CO2 emissions from dissolved organic C were
estimated by multiplying the area of peatlands by the default emissions factor for dissolved organic C provided in
IPCC (2013).
The apparent consumption of peat, which includes production plus imports minus exports plus the decrease in
stockpiles, in the United States is over times the amount of domestic peat production. However, consistent with the
Tier 1 method whereby only domestic peat production is accounted for when estimating off-site emissions, off-site
CO2 emissions from the use of peat not produced within the United States are not included in the Inventory. The
United States has largely imported peat from Canada for horticultural purposes; from 2011 to 2014, imports of
sphagnum moss (nutrient-poor) peat from Canada represented 97 percent of total U.S. peat imports (USGS 2016).
63 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).
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1 Most peat produced in the United States is reed-sedge peat, generally from southern states, which is classified as
2 nutrient rich by IPCC (2006). Higher-tier calculations of CO2 emissions from apparent consumption would involve
3 consideration of the percentages of peat types stockpiled (nutrient rich versus nutrient poor) as well as the
4 percentages of peat types imported and exported.
5 Table 6-46: Peat Production of Lower 48 States (kt)
Type of Deposit
1990
2005
2011
2012
2013
2014
2015
Nutrient-Rich
595.1
657.6
511.2
409.9
418.5
416.5
409.4
Nutrient-Poor
55.4
27.4
56.8
78.1
46.5
51.5
50.6
Total Production
692.0
685.0
568.0
488.0
465.0
468.0
460.0
Sources: United States Geological Survey (USGS) (1991-2015)Minerals Yearbook: Peat (1994-2014);
United States Geological Survey (USGS) (2016) Mineral Commodity Summaries: Peat (2016).
6 Table 6-47: Peat Production of Alaska (Thousand Cubic Meters)
1990
2005
2011
2012
2013
2014
2015
Total Production
49.7
47.8
61.5
93.1
93.1
93.1
93.1
Sources: Division of Geological & Geophysical Surveys (DGGS), Alaska Department of Natural Resources
(1997-2015)/4/aste's Mineral Industry Report (1997-2014).
1 On-site CO2 Emissions
8 IPCC (2006) suggests basing the calculation of on-site emission estimates on the area of peatlands managed for peat
9 extraction differentiated by the nutrient type of the deposit (rich versus poor). Information on the area of land
10 managed for peat extraction is currently not available for the United States, but in accordance with IPCC (2006), an
11 average production rate for the industry was applied to derive an area estimate. In a mature industrialized peat
12 industry, such as exists in the United States and Canada, the vacuum method can extract up to 100 metric tons per
13 hectare per year (Cleary et al. 2005 as cited in IPCC 2006).64 The area of land managed for peat extraction in the
14 lower 48 states of the United States was estimated using nutrient-rich and nutrient-poor production data and the
15 assumption that 100 metric tons of peat are extracted from a single hectare in a single year. The annual land area
16 estimates were then multiplied by the IPCC (2013) default emission factor in order to calculate on-site CO2 emission
17 estimates. Production data are not available by weight for Alaska. In order to calculate on-site emissions resulting
18 from Peatlands Remaining Peatlands in Alaska, the production data by volume were converted to weight using
19 annual average bulk peat density values, and then converted to land area estimates using the same assumption that a
20 single hectare yields 100 metric tons. The IPCC (2006) on-site emissions equation also includes a term which
21 accounts for emissions resulting from the change in C stocks that occurs during the clearing of vegetation prior to
22 peat extraction. Area data on land undergoing conversion to peatlands for peat extraction is also unavailable for the
23 United States. However, USGS records show that the number of active operations in the United States has been
24 declining since 1990; therefore, it seems reasonable to assume that no new areas are being cleared of vegetation for
25 managed peat extraction. Other changes in C stocks in living biomass on managed peatlands are also assumed to be
26 zero under the Tier 1 methodology (IPCC 2006 and 2013).
27 On-site N2O Emissions
28 IPCC (2006) suggests basing the calculation of on-site N20 emission estimates on the area of nutrient-rich peatlands
29 managed for peat extraction. These area data are not available directly for the United States, but the on-site CO2
30 emissions methodology above details the calculation of area data from production data. In order to estimate N20
31 emissions, the area of nutrient rich Peatlands Remaining Peatlands was multiplied by the appropriate default
32 emission factor taken from IPCC (2013).
64 The vacuum method is one type of extraction that annually "mills" or breaks up the surface of the peat into particles, which
then dry during the summer months. The air-dried peat particles are then collected by vacuum harvesters and transported from
the area to stockpiles (IPCC 2006).
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On-site CH4 Emissions
IPCC (2013) also suggests basing the calculation of on-site CH4 emission estimates on the total area of peatlands
managed for peat extraction. Area data is derived using the calculation from production data described in the On-site
C02 Emissions section above. In order to estimate CH4 emissions from drained land surface, the area of Peatlands
Remaining Peatlands was multiplied by the emission factor for direct CH4 emissions taken from IPCC (2013). In
order to estimate CH4 emissions from drainage ditches, the total area of peatland was multiplied by the default
fraction of peatland area that contains drainage ditches, and the appropriate emission factor taken from IPCC (2013).
Uncertainty and Time-Series Consistency
A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the uncertainty of CO2, CH4, and N20
emissions from Peatlands Remaining Peatlands, using the following assumptions:
• The uncertainty associated with peat production data was estimated to be ± 25 percent (Apodaca 2008) and
assumed to be normally distributed.
• The uncertainty associated with peat production data stems from the fact that the USGS receives data from
the smaller peat producers but estimates production from some larger peat distributors. The peat type
production percentages were assumed to have the same uncertainty values and distribution as the peat
production data (i.e., ± 25 percent with a normal distribution).
• The uncertainty associated with the reported production data for Alaska was assumed to be the same as for
the lower 48 states, or ± 25 percent with a normal distribution. It should be noted that the DGGS estimates
that around half of producers do not respond to their survey with peat production data; therefore, the
production numbers reported are likely to underestimate Alaska peat production (Szumigala 2008).
• The uncertainty associated with the average bulk density values was estimated to be ± 25 percent with a
normal distribution (Apodaca 2008).
• IPCC (2006 and 2013) gives uncertainty values for the emissions factors for the area of peat deposits
managed for peat extraction based on the range of underlying data used to determine the emission factors.
The uncertainty associated with the emission factors was assumed to be triangularly distributed.
• The uncertainty values surrounding the C fractions were based on IPCC (2006) and the uncertainty was
assumed to be uniformly distributed.
• The uncertainty values associated with the fraction of peatland covered by ditches was assumed to be ± 100
percent with a normal distribution based on the assumption that greater than 10 percent coverage, the upper
uncertainty bound, is not typical of drained organic soils outside of The Netherlands (IPCC 2013).
The results of the Approach 2 quantitative uncertainty analysis are summarized in Table 6-48. CO2 emissions from
Peatlands Remaining Peatlands in 2015 were estimated to be between 0.6 and 0.9 MMT CO2 Eq. at the 95 percent
confidence level. This indicates a range of 16 percent below to 16 percent above the 2015 emission estimate of 0.8
MMT CO2 Eq. Methane emissions from Peatlands Remaining Peatlands in 2015 were estimated to be between
0.002 and 0.007 MMT CO2 Eq. This indicates a range of 58 percent below to 78 percent above the 2015 emission
estimate of 0.004 MMT CO2 Eq. Nitrous oxide emissions from Peatlands Remaining Peatlands in 2015 were
estimated to be between 0.0003 and 0.0009 MMT CO2 Eq. at the 95 percent confidence level. This indicates a range
of 53 percent below to 53 percent above the 2015 emission estimate of 0.0006 MMT CO2 Eq.
Table 6-48: Approach 2 Quantitative Uncertainty Estimates for CO2, Cm, and N2O Emissions
from Peatlands Remaining Peatlands (MMT CO2 Eq. and Percent)
2015 Emission
Source
Gas
Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Peatlands Remaining Peatlands
CO2
0.8
0.6
0.9
-16%
16%
Peatlands Remaining Peatlands
CH4
+
+
+
-58%
78%
Peatlands Remaining Peatlands
N2O
+
+
+
-53%
53%
+ Does not exceed 0.05 MMT CO2 Eq.
Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Land Use, Land-Use Change, and Forestry 6-81
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1 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
2 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
3 above.
4 QA/QC and Verification
5 A QA/QC analysis was performed to review input data and calculations, and no issues were identified. In addition,
6 the emission trends were analyzed to ensure they reflected activity data trends.
7 Recalculations Discussion
8 The emissions estimates for Peatlands Remaining Peatlands were updated for 2015 using the Peat section of the
9 Mineral Commodity Summaries 2016. The new edition provided 2015 data and updated 2014 data for the lower 48
10 states. Although Alaska peat production data for 2015 was unavailable, 2014 data was recently published in the
11 Alaska's Mineral Industry 2014 report. However, the reported values represented an apparent 98 percent decrease in
12 production since 2012. Due to the uncertainty of the most recent data, 2013, 2014, and 2015 values were assumed to
13 be equal to the 2012 value. If updated data are available for the next inventory cycle, this will result in a
14 recalculation in the next Inventory report.
15 Planned Improvements
16 In order to further improve estimates of CO2, N2O, and CH4 emissions from Peatlands Remaining Peatlands, future
17 efforts will investigate if data sources exist for determining the quantity of peat harvested per hectare and the total
18 area undergoing peat extraction.
19 Efforts will also be made to find a new source for Alaska peat production. The current source has not been reliably
20 updated since 2012 and future publication of these data may discontinue.
21 The 2013 Supplement to the 20061PCC Guidelines for National Greenhouse Gas Inventories: Wetlands describes
22 inventory methodologies for various wetland source categories. In the 1990-2013 Inventory, EPA began including
23 updated methods for Peatlands Remaining Peatlands to align them with the 20131PCC Supplement. For future
24 inventories, EPA will determine if additional updates are needed to further address the 20131PCC Supplement for
25 Peatlands Remaining Peatlands.
26 The 20061PCC Guidelines do not cover all wetland types; they are restricted to peatlands drained and managed for
27 peat extraction, conversion to flooded lands, and some guidance for drained organic soils. They also do not cover all
28 of the significant activities occurring on wetlands (e.g., rewetting of peatlands). Since this inventory only includes
29 Peatlands Remaining Peatlands, additional wetland types and activities found in the 20131PCC Supplement will be
30 reviewed to determine if they apply to the United States. For those that do, available data will be investigated to
31 allow for the estimation of greenhouse gas fluxes in future inventory years.
32 Coastal Wetlands Remaining Coastal Wetlands
33 The Inventory recognizes Wetlands as a "land-use that includes land covered or saturated for all or part of the year,
34 in addition to areas of lakes, reservoirs and rivers." Consistent with ecological definitions of wetlands,65 the United
35 States has historically included under the category of Wetlands those coastal shallow water areas of estuaries and
36 bays that fall in the Land Representation.
37 Additional guidance on quantifying greenhouse gas emissions and removals on Coastal Wetlands is provided in the
38 2013 Supplement to the 20061PCC Guidelines for National GHG Inventories: Wetlands (Wetlands Supplement),
39 which recognizes the particular importance of vascular plants in sequestering CO2 from the atmosphere and building
40 soil carbon stocks. Thus, the Wetlands Supplement provides specific guidance on quantifying emissions on organic
65 See
6-82 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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and mineral soils that are covered or saturated for part of the year by tidal freshwater, brackish or saline water and
are vegetated by vascular plants and may extend seaward to the maximum depth of vascular plant vegetation.
The United States recognizes both Vegetated Wetlands and Unvegetated Open Water as Coastal Wetlands. Per
guidance provided by the Wetlands Supplement sequestration of carbon into biomass and soils carbon pools is
recognized only in Vegetated Coastal Wetlands and not to occur in Unvegetated Open Water Coastal Wetlands. The
United States takes the additional step of recognizing that stock losses occur when Vegetated Coastal Wetlands are
converted to Unvegetated Coastal Wetlands.
This Inventory includes all privately-owned and federal coastal wetlands along the oceanic shores on the
conterminous U.S., but does not include Coastal Wetlands Remaining Coastal Wetlands in Alaska or Hawaii.
Seagrasses are not currently included within the Inventory due to insufficient data on distribution, change through
time and carbon (C) stocks or C stock changes as a result of anthropogenic influence.
Under the Coastal Wetlands Remaining Coastal Wetlands category, the following emissions and removals are
quantified in this chapter.
1) C stock changes and CH4 emissions on Vegetated Coastal Wetlands Remaining Vegetated Coastal
Wetlands.
2) C changes with conversion of Vegetated Coastal Wetlands to Unvegetated Open Water Coastal Wetlands.
3) C stock changes with conversion of Unvegetated Open Water Coastal Wetlands to Vegetated Coastal
Wetlands.
4) N20 emissions from aquaculture.
Vegetated coastal wetlands hold C in all five C pools (i.e., aboveground, belowground, dead organic matter [DOM;
dead wood and litter], and soil) though typically soil C and, to a lesser extent aboveground- and belowground-
biomass, are the dominant pools, depending on wetland type (i.e., forested vs. marsh). Vegetated Coastal Wetlands
are net accumulators of soil C as soils C accumulates under anaerobic soil conditions. Emissions from soil C and
biomass stocks occur when Vegetated Coastal Wetlands are converted to Unvegetated Open Water Coastal
Wetlands (i.e., when managed Vegetated Coastal Wetlands are lost due to subsidence), but are still recognized as
Coastal Wetlands in this Inventory. These C emissions resulting from conversion to Unvegetated Open Water
Coastal Wetlands, can cause the release of many years of accumulated soil C. Conversion of Unvegetated Open
Water Coastal Wetlands to Vegetated Coastal Wetlands initiates the re-building of soil C stocks within soils and
biomass. In application of 2013IPCC Wetlands Supplement methodologies for CH4 emissions, coastal wetlands in
salinity conditions less than half that of sea water are sources of CH4 and are a result of slow decomposition of
organic matter under freshwater, anaerobic conditions. Conversion of Vegetated Coastal Wetlands to or from
Unvegetated Open Water Coastal Wetlands do not result in a change in salinity condition and are assumed to have
no impact on CH4 emissions. 2013 IPCC Wetlands Supplement guidance provides methodologies to estimate nitrous
oxide emissions on coastal wetlands occur due Aquaculture. While N20 emissions can occur due to anthropogenic N
loading from the watershed and atmospheric deposition, these emissions are not reported. The N20 emissions from
Aquaculture result from the N derived from consumption of the applied food stock which is then excreted as N load
available for conversion to N20.
The Wetlands Supplement provides procedures for estimating CO2 emissions and removals and CH4 emissions from
mangroves, tidal marshes and seagrasses. Depending upon their height and area, emissions and removals from
managed mangroves may be reported under the Forest Land category or under Coastal Wetlands. All non-drained,
intact coastal marshes are intended to be reported under Coastal Wetlands
Because of human use and level of regulatory oversight, all coastal wetlands within the conterminous U.S. are
within the managed land area described in Section 6.1, and as such all estimates of CO2 emissions and removals, and
emissions of CH4, and N20 from aquaculture are included in this Inventory. At the present stage of Inventory
development, Coastal Wetlands are not explicitly shown in the Land Representation analysis while work continues
harmonizing data from NOAA's Coastal Change Analysis Program66 with NRI data used to compile the Land
Representation. However, a check was undertaken to confirm that Coastal Wetlands recognized by C-CAP
represented a subset of Wetlands recognized by the NRI for marine coastal states.
66 See
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Emissions and Removals from Vegetated Coastal Wetlands
Remaining Vegetated Coastal Wetlands
The conterminous U.S. hosts 2.9 million hectares of intertidal Vegetated Coastal Wetlands Remaining Vegetated
Coastal Wetlands comprised of tidally influenced palustrine emergent marsh (599,005 ha), palustrine scrub shrub
(137,590 ha) and estuarine emergent marsh (1,853,863 ha), estuarine scrub shrub (96, 998 ha) and estuarine forest
(191,473 ha). Mangroves fall under both estuarine forest and estuarine scrub shrub categories depending upon
height. Dwarf mangroves, found in Texas, do not attain the height status to be recognized as Forest Land, and are
therefore always classified within Vegetated Coastal Wetlands. Vegetated Coastal Wetlands Remaining Vegetated
Coastal Wetlands are found in cold temperate (52,403 ha), warm temperate (890,458 ha), subtropical (1,879,314 ha)
and Mediterranean (56,755 ha) climate zones.
Soils are the largest pool of C in Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands reflecting
long-term removal of atmospheric CO2 by vegetation and transfer into the soil pool in the form of decaying organic
matter. Emissions of soil C are not assumed to occur in coastal wetlands that remain vegetated. In this Inventory,
only C stock changes within soils are reported as insufficient data exists on C stock changes in biomass, DOM and
litter. Methane emissions from decomposition of organic matter in anaerobic conditions are significant at salinity
less than half that of sea water. Mineral and organic soils are not differentiated in terms of C removals or CH4
emissions.
Table 6-49 through Table 6-52 below summarize nationally aggregated soil C stock emissions and removals and
CH4 emissions on Vegetated Coastal Wetlands. Intact Vegetated Coastal Wetlands Remaining Vegetated Coastal
Wetlands hold a large stock of C (here estimated to be 870 MMT C (3,190 MMT CO2 Eq.) within the top 1 meter of
soil to which C is accumulated each year at a rate of 12.2 MMT CO2 Eq. Methane emissions of 3.5 of MMT CO2
Eq. offset C removals resulting in an annual net C removal rate of 8.7 MMT CO2 Eq. Due to federal regulatory
protection, loss of Vegetated Coastal Wetland area slowed considerably in the 1970s and currently rates of C stock
change and CH4 emissions are relatively constant over time. Losses of Vegetated Coastal Wetlands to Unvegetated
Open Water Coastal Wetlands (described later in this chapter) and to other land uses do occur, which because of the
depth to which soil C stocks are impacted, do have a significant impact on the net emissions and removals on
Coastal Wetlands.
Table 6-49: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands
Remaining Vegetated Coastal Wetlands (MMT CO2 Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
Net Flux
(12.1)
1 (12.2)
, (12.2)
(12.2)
(12.2)
(12.2)
(12.2)
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated from the trend based on Coastal Change
Analysis Program (C-CAP) data and therefore may not fully reflect changes occurring in the latter
part of the time series. Mineral and organic soils are not differentiated in terms of C removals.
Quality control measures are still underway and estimates will be finalized after public review.
Table 6-50: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands
Remaining Vegetated Coastal Wetlands (MMT C)
Year
1990
2005
2011
2012
2013
2014
2015
Net Flux
(3.3)
(3.3)
(3.3)
(3.3)
(3.3)
(3.3)
(3.3)
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated from the trend based on C-CAP data and
therefore may not fully reflect changes occurring in the latter part of the time series. Mineral and
organic soils are not differentiated in terms of C removals. Quality control measures are still
underway and estimates will be finalized after public review.
6-84 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 6-51: Net CH4 Flux from Vegetated Coastal Wetlands Remaining Vegetated Coastal
2 Wetlands (MMT COz Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
Net Flux
3.4 ;
3.5
3.5
3.5
3.5
3.5
3.5
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not
fully reflect changes occurring in the latter part of the time series. Mineral and organic soils are not
differentiated in terms of methane emissions. Quality control measures are still underway and
estimates will be finalized after public review.
3 Table 6-52: Net CH4 Flux from Vegetated Coastal Wetlands Remaining Vegetated Coastal
4 Wetlands [VX. CH4)
Year
1990
2005
2011
2012
2013
2014
2015
Net Flux
138
140
141
141
141
141
141
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not
fully reflect changes occurring in the latter part of the time series. Mineral and organic soils are not
differentiated in terms of methane emissions. Quality control measures are still underway and
estimates will be finalized after public review.
5 Methodology
6 The following section includes a brief description of the methodology used to estimate changes in soil C stocks and
7 emissions of CH4 for Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands.
8 Soil Carbon Stock Changes
9 Soil C removals are estimated for Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands for both
10 mineral and organic soils on wetlands below the elevation of high tides (taken to be mean high water spring tide
11 elevation) and as far seawards as the extent of intertidal vascular plants within the U.S. Land Representation
12 according to the national LiDAR dataset, the national network of tide gauges and land use histories recorded in the
13 1996, 2001, 2005 and 2010 NOAA C-CAP surveys.67 Federal and non-federal lands are represented. Trends in land
14 cover change are extrapolated to 1990 and 2015 from these datasets. Based upon NOAA C-CAP, coastal wetlands
15 are subdivided into freshwater (Palustrine) and saline (Estuarine) classes and further subdivided into emergent
16 marsh, scrub shrub and forest classes.68 Soil C stock changes, stratified by climate zones and wetland classes, are
17 derived from a synthesis of peer-reviewed literature (Mangrove pool and removals data: Cahoon & Lynch
18 unpublished data; Lynch 1989; Callaway et al. 1997; Chen & Twilley 1999; McKee & Faulkner 2000; Ross et al.
19 2000; Chmura et al. 2003; Perry & Mendelssohn 2009; Castaneda-Moya et al. 2013; Henry & Twilley 2013;
20 Doughty et al. 2015; Marchio et al. 2016. Tidal marsh pool and removals data: Anisfeld unpublished data; Cahoon
21 unpublished data; Cahoon & Lynch unpublished data; Chmura unpublished data; McCaffrey & Thomson 1980;
22 Hatton 1981; Callaway et al. 1987; Craft et al. 1988; Cahoon & Turner 1989; Patrick & DeLaune 1990; Kearney
23 & Stevenson 1991 ;Cahoon et al. 1996; Callaway et al. 1997; Roman et al. 1997; Bryant & Chabrek 1998; Orson et
24 al. 1998; Markewich et al. 1998; Anisfeld et al. 1999; Connor et al. 2001; Choi & Wang 2001; Chmura et al. 2003,
25 Hussein et al. 2004; Craft 2007; Miller et al. 2008; Drexler et al. 2009; Perry & Mendelssohn 2009; Loomis &
26 Craft 2010; EPA 's NWCA 2011; Callaway et al. 2012; Henry & Twilley 2013; Weston et al. 2014). To estimate soil
27 C stock changes no differentiation is made between organic and mineral soils.
28 Tier 2 level estimates of soil C removal associated with annual soil C accumulation from managed Vegetated
29 Coastal Wetlands Remaining Vegetated Coastal Wetlands were developed with country-specific soil C removal
30 factors multiplied by activity data of land area for Vegetated Coastal Wetlands Remaining Vegetated Coastal
67 See
68 See
Land Use, Land-Use Change, and Forestry 6-85
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Wetlands. The methodology follows Eq. 4.7, Chapter 4 of the Wetlands Supplement, and is applied to the area of
Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands on an annual basis. Emission factors were
developed from literature references that provided soil C removal factors disaggregated by climate region,
vegetation type by salinity range (estuarine or palustrine) as identified using NOAA C-CAP as described above.
Quantification of regional coastal wetland above and belowground biomass C stock changes for woody and
perennial herbaceous vegetation, DOM [dead wood and litter] C stocks are in development and are not presented
this year, though will be included in future reports.
Soil Methane Emissions
Tier 1 estimates of CH4 emissions for Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands are
derived from the same wetland map used to in the analysis of wetland soil C fluxes, produced from C-CAP, LiDAR
and tidal data, in combination with default CH4 emission factors provided in Table 4.14 of the Wetlands Supplement.
The methodology follows Eq. 4.9, Chapter 4 of the Wetlands Supplement, and applied to the area of Vegetated
Coastal Wetlands Remaining Vegetated Coastal Wetlands on an annual basis. The AR4 global warming potential
factor of 25 was used in converting CH4 to CO2 Eq. values.
Uncertainty and Time-Series Consistency
Underlying uncertainties in estimates of soil C stock changes and CH4 include error in uncertainties associated with
Tier 2 literature values of soil C stocks and CH4 flux, assumptions that underlie the methodological approaches
applied and uncertainties linked to interpretation of remote sensing data. Uncertainty specific to Vegetated Coastal
Wetlands Remaining Vegetated Coastal Wetlands include differentiation of palustrine and estuarine community
classes, which determines the soil C stock and CH4 flux applied. Soil C stocks and CH4 fluxes applied are
determined from vegetation community classes across the coastal zone and identified by NOAA C-CAP.
Community classes are further subcategorized by climate zones and growth form (forest, shrub-scrub, marsh).
Uncertainties for soil C stock data for all subcategories are not available and thus assumptions were applied using
expert judgement about the most appropriate assignment of a soil C stock to a disaggregation of a community class.
Because mean soil C stocks for each available community class are in a fairly narrow range, the same overall
uncertainty was assigned to each (i.e., applying approach for asymmetrical errors, where the largest uncertainty for
any one soil C stock referenced using published literature values for a community class; uncertainty approaches
provide that if multiple values are available for a single parameter, the highest uncertainty value should be applied to
the propagation of errors; IPCC 2000). Uncertainties for CH4 flux are the Tier 1 default values reported in the
Wetlands Supplement. Overall uncertainty of the NOAA C-CAP remote sensing product is 15 percent. This is in the
range of remote sensing methods (±10-15 percent; IPCC 2003). However, there is significant uncertainty in salinity
ranges for tidal and non-tidal estuarine wetlands and activity data used to apply CH4 flux emission factors
(delineation of an 18 ppt boundary) will need significant improvement to reduce uncertainties.
Table 6-53: Approach 1 Quantitative Uncertainty Estimates for C Stock Changes occurring
within Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands (MMT CO2 Eq.
and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower Upper
Bound Bound
Combined Uncertainty for Flux Associated
with Wetlands Soil C Stock Change in
Vegetated Coastal Wetlands Remaining
Vegetated Coastal Wetlands
(12.2)
(15.8)
(8.6)
-29.5% 29.5%
Notes: Parentheses indicate net sequestration. Quality control measures are still underway and estimates will be finalized
after public review.
6-86 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Table 6-54: Approach 1 Quantitative Uncertainty Estimates for ChU Emissions occurring
2 within Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands (MMT CO2 Eq.
3 and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Combined Uncertainty for Flux Associated
with CH4 emissions in Vegetated Coastal
Wetlands Remaining Vegetated Coastal
Wetlands
3.5
2.5
4.5
-29.8%
29.8%
Note: Quality control measures are still underway and estimates will be finalized after public review.
4 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
5 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
6 above.
7 QA/QC and Verification
8 NOAA provided National LiDAR Dataset, tide data, and C-CAP land cover and land cover change mapping, all of
9 which are subject to agency internal QA/QC assessment. Acceptance of final datasets into archive and dissemination
10 are contingent upon the product compilation are compliant with mandatory QA/QC requirements (McCombs, et al.,
11 2016). QA/QC and verification of soil C stock dataset has been provided by the Smithsonian Environmental
12 Research Center and Coastal Wetland Inventory team leads who reviewed summary tables against reviewed sources.
13 Land cover estimates were assessed to ensure that the total land area did not change over the time series in which the
14 inventory was developed, and verified by a second QA team. A team of two evaluated and verified there were no
15 computational errors within the calculation worksheets. Soil C stock, emissions/removals data where based upon
16 peer-reviewed literature and CH4 emission factors derived from the IPCC Wetlands Supplement.
17 Planned Improvements
18 A USGS/ NASA Carbon Monitoring System investigation is in progress to establish a U.S. country-specific
19 database of soil C stock, wetland biomass and CH4 emissions. Refined error analysis combining land cover change
20 and C stock estimates will be provided. Through this work a model is in development to represent changes in soil C
21 stocks. This research effort is due to be complete November 2017, with the potential to include the results from the
22 new model in the 1990 to 2016 Inventory (2018 submission) or the 1990 to 2017 Inventory (2019 submission).
23 Emissions from Vegetated Cor*^ WrH € diverted to
24 Unvegetated Open Water Coastal Wetlands
25 Conversion of intact Vegetated Coastal Wetlands to Unvegetated Open Water Coastal Wetlands is a source of
26 emissions from both soil and biomass C stocks. It is estimated that 8,428 ha of Vegetated Coastal Wetlands were
27 converted to Unvegetated Open Water Coastal Wetlands in 2015. The Mississippi Delta represents more than 40
28 percent of the total coastal wetland of the U. S, and over 90 percent of the conversion of Vegetated Coastal Wetlands
29 to Unvegetated Open Water Coastal Wetlands. The drivers of coastal wetlands loss include legacy human impacts
30 on sediment supply through rerouting river flow, direct impacts of channel cutting on hydrology, salinity and
31 sediment delivery and accelerated subsidence from aquafer extraction. Each of these drivers directly contributes to
32 wetland erosion and subsidence, while also reducing the resilience of the wetland to build with sea level rise or
33 recover from hurricane disturbance. Over recent decades the rate of Mississippi Delta wetland loss has slowed,
34 though episodic mobilization of sediment occurs during hurricane events (Couvillion et al., 2011; Couvillion et al.,
35 2016). The most recent land cover analysis recorded by the C-CAP surveys 2005-2010 coincides with two such
36 events, hurricanes Katrina and Rita both in 2005.
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Shallow nearshore open water within the U.S. Land Representation is recognized as falling under the Wetlands
category within the U.S. Inventory. Changes in biomass are not presented this year but will be in the future (see
Planned Improvements). While high resolution mapping of coastal wetlands provides data to support Tier 2
approaches for tracking land cover change, the depth to which sediment is lost is less clear. This Inventory adopts
the Tier 1 methodological guidance from the Wetlands Supplement for estimating emissions following the
methodology for excavation (see Methodology section, below) when Vegetated Coastal Wetlands are converted to
Unvegetated Open Water Coastal Wetlands, assuming aim depth of disturbed soil. This 1 m depth of disturbance is
consistent with estimates of wetland C loss provided in the literature (Crooks, et al., 2009; Couvillon et al., 2011;
Delaune and White, 2012; IPCC 2013). A Tier 1 assumption is also adopted that all mobilized C is immediately
returned to the atmosphere (as assumed for terrestrial land use categories), rather than redeposited in long-term C
storage. The science is currently under evaluation to adopt more refined emissions factors for mobilized coastal
wetland C based upon the geomorphic setting of the depositional environment.
Table 6-55: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands
Converted to Unvegetated Open Water Coastal Wetlands (MMT CO2 Eq.)
Year
19'JO
2005
2011
2012
2013
2014
2015
Net Soil Flux
2.1
3.5
3.5
3.5
3.5
3.5
Note: Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore
may not fully reflect changes occurring in the latter part of the time series. Mineral and Organic
Soils are not differentiated in terms of C removals. Quality control measures are still underway and
estimates will be finalized after public review.
Table 6-56: Net CO2 Flux from Soil C Stock Changes in Vegetated Coastal Wetlands
Converted to Unvegetated Open Water Coastal Wetlands {MM1C)
Year
1990
2005
2011
2012
2013
2014
2015
Net Soil Flux
l.u
0.6
1.0
1.0
1.0
1.0
1.0
Note: Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore
may not fully reflect changes occurring in the latter part of the time series. Mineral and Organic
Soils are not differentiated in terms of C removals. Quality control measures are still underway and
estimates will be finalized after public review.
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C stocks for
Vegetated Coastal Wetlands Converted to Unvegetated Open Water Coastal Wetlands.
Soil Carbon Stock Changes
Soil C stock changes are estimated for Vegetated Coastal Wetlands Converted to Unvegetated Open Water Coastal
Wetlands on lands below the elevation of high tides (taken to be mean high water spring tide elevation) within the
U.S. Land Representation according to the national LiDAR dataset, the national network of tide gauges and land use
histories recorded in the 1996, 2001, 2005 and 2010 NOAA C-CAP surveys. Federal and non-federal lands are
represented. Trends in land cover change are extrapolated to 1990 and 2015 from these datasets. C-CAP provides
peer reviewed country-specific mapping to support IPCC Approach 3 quantification of coastal wetland distribution,
including conversion to and from open water. Country-specific soil C stocks for mineral and organic soils, stratified
by climate zones and wetland classes, are derived from a synthesis of peer-reviewed literature (Mangrove pool and
removals data: Cahoon & Lynch unpublished data; Lynch 1989; Callaway et al. 1997; Chen & Twilley 1999;
McKee & Faulkner 2000; Ross et al. 2000; Chmura et al. 2003; Perry & Mendelssohn 2009; Castaneda-Moya et al.
2013; Henry & Twilley 2013; Doughty et al. 2015; Marchio et al. 2016. Tidal marsh pool and removals data:
Anisfeld unpublished data; Cahoon unpublished data; Cahoon & Lynch unpublished data; Chmura unpublished
data; McCaffrey & Thomson 1980; Hatton 1981; Callaway et al. 1987; Craft et al. 1988; Cahoon & Turner 1989;
Patrick & DeLaune 1990; Kearney & Stevenson 1991 ;Cahoon et al. 1996; Callaway et al. 1997; Roman et al. 1997;
Bryant & Chabrek 1998; Orson et al. 1998; Markewich et al. 1998; Anisfeld et al. 1999; Connor et al. 2001; Choi
& Wang 2001; Chmura et al. 2003, Hussein et al. 2004; Craft 2007; Miller et al. 2008; Drexler et al. 2009; Perry
& Mendelssohn 2009; Loomis & Craft 2010; EPA 's NWCA 2011; Callaway et al. 2012; Henry & Twilley 2013;
6-88 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Weston et al. 2014). For soil C stock change no differentiation is made between organic and mineral soils. Following
2 the Tier 1 approach for estimating CO2 emissions with extraction provided within the Wetlands Supplement, soil C
3 loss with conversion of Vegetated Coastal Wetlands to Unvegetated Open Water Coastal Wetlands is assumed to
4 affect soil C stock to one-meter depth with all emissions occurring in the year of wetland conversion, and multiplied
5 by activity data of land area for management coastal wetlands. The methodology follows Eq. 4.6. Quantification of
6 regional coastal wetland biomass stock changes for conversion of Vegetated Coastal Wetlands to Unvegetated Open
7 Water Coastal Wetlands are in development and are not presented this year, though will be included in future
8 reports.
9 Soil Methane Emissions
10 A Tier 1 assumption has been applied that salinity conditions are unchanged and hence methane emissions are
11 assumed to be zero with conversion of Vegetated Coastal Wetlands to Unvegetated Open Water Coastal Wetlands.
12 Uncertainty and Time-Series Consistency
13 Underlying uncertainties in estimates of soil C stock changes associated with Tier 2 literature values of soil C
14 stocks, assumptions that underlie the methodological approaches applied and uncertainties linked to interpretation of
15 remote sensing data are included in this uncertainty assessment. Uncertainty specific to coastal wetlands include
16 differentiation of palustrine and estuarine community classes, which determines the soil C stock applied. Soil C
17 stocks applied are determined from vegetation community classes across the coastal zone and identified by NOAA
18 C-CAP. Community classes are further subcategorized by climate zones and growth form (forest, shrub-scrub,
19 marsh). Soil C stock data for all subcategories are not available and thus assumptions were applied using expert
20 judgement about the most appropriate assignment of a soil C stock to a disaggregation of a community class.
21 Because mean soil C stocks for each available community class are in a fairly narrow range, the same overall
22 uncertainty was assigned to each (i.e., applying approach for asymmetrical errors, where the largest uncertainty for
23 any one soil C stock referenced using published literature values for a community class; uncertainty approaches
24 provide that if multiple values are available for a single parameter, the highest uncertainty value should be applied to
25 the propagation of errors; IPCC 2000). Uncertainties for CH4 flux are the Tier 1 default values reported in the
26 Wetlands Supplement. Overall uncertainty of the NOAA C-CAP remote sensing product is 15 percent. This is in the
27 range of remote sensing methods (±10-15 percent; IPCC 2003).
28 Table 6-57: Approach 1 Quantitative Uncertainty Estimates for Net CO2 Flux occurring
29 within Vegetated Coastal Wetlands Converted to Unvegetated Open Water Coastal Wetlands
30 (MMT CO2 Eq. and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Combined Uncertainty for Flux Associated
with Soil C Stock Change in Vegetated
Coastal Wetlands Converted to Unvegetated
3.5
2.1
5.0
-41.7%
41.7%
Open Water Coastal Wetlands
Note: Quality control measures are still underway and estimates will be finalized after public review.
31 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
32 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
33 above.
34 The C-CAP dataset, consisting of a time series of four time intervals, each five years in length, and two major
35 hurricanes striking the Mississippi Delta in the most recent time interval (2006 to 2010), creates a challenge in
36 utilizing it to represent the annual rate of wetland loss and for extrapolation to 1990 and 2015. Uncertainty in the
37 defining the long term trend will be improved with release of the 2015 survey, expected in 2018 to 2019.
38 More detailed research is in development that provides a longer term assessment and more highly refined rates of
39 wetlands loss across the Mississippi Delta (e.g., Couvillion et al, 2016), which could provide a more refined regional
40 Approach 2-3 for assessing wetland loss and support the national scale assessment provided by CCAP.
Land Use, Land-Use Change, and Forestry 6-89
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1 Based upon the IPCC Tier 1 methodological guidance for estimating emissions with excavation in coastal wetlands,
2 it has been assumed that a 1 meter column of soil has been remobilized with erosion and the C released immediately.
3 This depth of disturbance is a simplifying assumption that is commonly applied in the scientific literature to gain a
4 first order estimate of scale of emissions (e.g., Delaune and White, 2012). It is also a simplifying assumption that all
5 that C is released back to the atmosphere immediately and future development of Tier 2 estimate may refine the
6 emissions both in terms of scale and rate. Given that erosion has been ongoing for multiple decades the assumption
7 that the C eroded is released to the atmosphere the year of erosion is a reasonable simplification that could be further
8 refined.
9 QA/QC and Verification
10 NOAA provided data (i.e., National LiDAR Dataset, NOS Tide Data, and C-CAP land cover and land cover change
11 mapping) undergo internal agency QA/QC procedures. Acceptance of final datasets into archive and dissemination
12 are contingent upon assurance that the data product is compliant with mandatory NOAA QA/QC requirements
13 (McCombs et al., 2016). QA/QC and Verification of the soil C stock dataset has been provided by the Smithsonian
14 Environmental Research Center and by the Coastal Wetlands project team leads who reviewed produced summary
15 tables against primary scientific literature. Land cover estimates were assessed to ensure that the total land area did
16 not change over the time series in which the inventory was developed, and verified by a second QA team. A team of
17 two evaluated and verified there were no computational errors within the calculation worksheets. Two
18 biogeochemists at the USGS also members of the NASA Carbon Monitoring System Science Team, corroborated
19 the assumption that where salinities are unchanged CH4 emissions are constant with conversion of Unvegetated
20 Open Water Coastal Wetlands to Vegetated Coastal Wetlands.
21 Planned Improvements
22 A refined uncertainty analysis and efforts to improve times series consistency is planned for the 1990 to 2016
23 Inventory (2018 submission) or the 1990 to 2017 Inventory (2019 submission). An approach for calculating the
24 fraction of remobilized coastal wetland soil C returned to the atmosphere is currently under review and may be
25 included in future reports. Research by USGS is investigating higher resolution mapping approaches to quantify
26 conversion of coastal wetlands is also underway. Such approaches may form the basis of an Approach 3 land
27 representation assessment in future years.
28 Removals from Unvegetated Open Water Coastal Wetlands
29 Converted to Vegetated Coastal Wetlands
30 Open Water within the Land Representation is recognized as Wetlands within the Inventory. The appearance of
31 vegetated tidal wetlands on lands previously recognized as open water reflects either the building of new vegetated
32 marsh through sediment accumulation or the transition from other lands uses through an intermediary open water
33 stage as flooding intolerant plants are displaced and then replaced by wetland plants. Biomass and soil C
34 accumulation on Unvegetated Open Water Coastal Wetlands Converted to Vegetated Coastal Wetlands begins with
35 vegetation establishment.
36 Within the U.S., conversion of Unvegetated Open Water Coastal Wetlands to Vegetated Coastal Wetlands is
37 predominantly due to engineered activities, which include active restoration of wetlands (e.g., wetlands restoration
38 in SanFrancisco Bay), dam removals or other means to reconnect sediment supply to the nearshore (e.g.,
39 Atchafalaya Delta, Louisiana, Couvillion et al., 2011). Wetlands restoration projects have been ongoing in the U.S.
40 since the 1970s. Early projects were small, a few hectares in size. By the 1990s, restoration projects, each hundreds
41 of hectares in size, were becoming common in major estuaries. In a number of coastal areas e.g., San Francisco Bay,
42 Puget Sound, Mississippi Delta and south Florida, restoration activities are in planning and implementation phases,
43 each with the goal of recovering tens of thousands of hectares of wetlands.
44 During wetland restoration, Unvegetated Open Water Coastal Wetland is a common intermediary phase bridging
45 land use transitions from Cropland or Grassland to Vegetated Coastal Wetlands. The time period of open water may
46 last from five to 20 years depending upon the conditions. The conversion of these other land uses to Unvegetated
47 Open Water Coastal Wetland will result in reestablishment of wetland biomass and soil C sequestration and may
6-90 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 result in cessation of emissions from drained organic soil. Only changes in soil C stocks are reported in the
2 Inventory at this time, but improvements are being evaluated to include changes from other C pools.
3 Table 6-58: Net CO2 Flux from Soil C Stock Changes from Unvegetated Open Water Coastal
4 Wetlands Con verted to Vegetated Coastal Wetlands (MMT CO2 Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
Net Soil Flux
(0.01)
< (0.004) ...
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not fully
reflect changes occurring in the latter part of the time series. Mineral and Organic Soils are not
differentiated in terms of C removals.
5 Table 6-59: Net CO2 Flux from Soil C Stock Changes from Unvegetated Open Water Coastal
6 Wetlands Con verted to Vegetated Coastal Wetlands (M MT C)
Year
1990
2005
2011
2012
2013
2014
2015
Net Soil Flux
(0.002)
i (0.001)
(0.002)
(0.002)
(0.002)
(0.002)
(0.002)
Note: Parentheses indicate net sequestration.
Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not fully
reflect changes occurring in the latter part of the time series. Mineral and Organic Soils are not
differentiated in terms of C removals.
7 Methodology
8 The following section includes a brief description of the methodology used to estimate changes in soil C stocks and
9 CH4 emissions for Unvegetated Open Water Coastal Wetlands Converted to Vegetated Coastal Wetlands.
10 Soil Carbon Stock Change
11 Soil C removals are estimated for Unvegetated Open Water Coastal Wetlands Converted to Vegetated Coastal
12 Wetlands on lands below the elevation of high tides (taken to be mean high water spring tide elevation) within the
13 U.S. Land Representation according to the national LiDAR dataset, the national network of tide gauges and land use
14 histories recorded in the 1996, 2001, 2005 and 2010 NOAA C-CAP surveys. Federal and non-federal lands are
15 represented. Trends in land cover change are extrapolated to 1990 and 2015 from these datasets. C-CAP provides
16 peer reviewed Tier 2 level mapping of coastal wetland distribution, including conversion to and from open water.
17 Country-specific soil C stock change associated with soil C accretion, stratified by climate zones and wetland
18 classes, are derived from a synthesis of peer-reviewed literature (Mangrove pool and removals data: Cahoon &
19 Lynch unpublished data; Lynch 1989; Callaway et al. 1997; Chen & Twilley 1999; McKee & Faulkner 2000; Ross
20 et al. 2000; Chmura et al. 2003; Perry & Mendelssohn 2009; Castaneda-Moya et al. 2013; Henry & Twilley 2013;
21 Doughty et al. 2015; Marchio et al. 2016. Tidal marsh pool and removals data: Anisfeld unpublished data; Cahoon
22 unpublished data; Cahoon & Lynch unpublished data; Chmura unpublished data; McCaffrey & Thomson 1980;
23 Hatton 1981; Callaway et al. 1987; Craft et al. 1988; Cahoon & Turner 1989; Patrick & DeLaune 1990; Kearney
24 & Stevenson 1991 ;Cahoon et al. 1996; Callaway et al. 1997; Roman et al. 1997; Bryant & Chabrek 1998; Orson et
25 al. 1998; Markewich et al. 1998; Anisfeld et al. 1999; Connor et al. 2001; Choi & Wang 2001; Chmura et al. 2003,
26 Hussein et al. 2004; Craft 2007; Miller et al. 2008; Drexler et al. 2009; Perry & Mendelssohn 2009; Loomis &
27 Craft 2010; EPA 's NWCA 2011; Callaway et al. 2012; Henry & Twilley 2013; Weston et al. 2014).). Soil C
28 removals are stratified based upon wetland class (Estuarine, Palustrine) and subclass, (Emergent Marsh, Scrub
29 Shrub). For soil C stock change no differentiation is made for soil type.
30 Tier 2 level estimates of CO2 removals associated with annual soil C accumulation in managed Vegetated Coastal
31 Wetlands were developed using country-specific soil C removal factors multiplied by activity data on land area for
32 management coastal wetlands. The methodology follows Eq. 4.7, Chapter 4 of the Wetlands Supplement, and
33 applied to the area of managed Vegetated Coastal Wetlands on an annual basis. Emission factors were developed
34 from literature references that provided soil C removal factors disaggregated by climate region and vegetation type
35 by salinity range (estuarine or palustrine) as identified using NOAA C-CAP as described above. Quantification of
Land Use, Land-Use Change, and Forestry 6-91
-------
1 regional coastal wetland biomass C stock changes for perennial vegetation are in development and are not presented
2 this year, though will be included in future reports.
3 Soil Methane Emissions
4 A Tier 1 assumption has been applied that salinity conditions are unchanged and hence methane emissions are
5 assumed to be zero with conversion of Vegetated Open Water Coastal Wetlands to Vegetated Coastal Wetlands.
6 Uncertainty and Time-Series Consistency
7 Underlying uncertainties in estimates of soil C stock changes and methane emissions include error in uncertainties
8 associated with Tier 2 literature values of soil C stocks and methane flux and assumptions that underlie the
9 methodological approaches applied and uncertainties linked to interpretation of remote sensing data. Uncertainty
10 specific to coastal wetlands include differentiation of palustrine and estuarine community classes which determines
11 the soil C stock and methane flux applied. Soil C stocks and methane fluxes applied are determined from vegetation
12 community classes across the coastal zone and identified by NOAA C-CAP. Community classes are further
13 subcategorized by climate zones and growth form (forest, shrub-scrub, marsh). Soil C stock data for all
14 subcategories are not available and thus assumptions were applied using expert judgement about the most
15 appropriate assignment of a soil C stock to a disaggregation of a community class. Because mean soil C stocks for
16 each available community class are in a fairly narrow range, the same overall uncertainty was applied to each (i.e.,
17 applying approach for asymmetrical errors, where the largest uncertainty for any one soil C stock referenced using
18 published literature values for a community class; uncertainty approaches provide that if multiple values are
19 available for a single parameter, the highest uncertainty value should be applied to the propagation of errors; IPCC
20 2000). Uncertainties for CH4 flux are the Tier 1 default values reported in the Wetlands Supplement. Overall
21 uncertainty of the NOAA C-CAP remote sensing product is 15 percent. This is in the range of remote sensing
22 methods (±10-15 percent; IPCC 2003). Uncertainties for methane flux include the Tier 1 default values reported in
23 the Wetlands Supplement along with the overall uncertainty of the NOAA C-CAP remote sensing product, which is
24 estimated at 15 percent. This is in the typical range of remote sensing methods (±10-15; GPG LULUCF, Chapter 3).
25 However, there is significant uncertainty in salinity ranges for tidal and non-tidal estuarine wetlands and activity
26 data used to develop the methane flux (delineation of an 18 ppt boundary) and will need significant improvement to
27 reduce uncertainties.
28 Table 6-60: Approach 1 Quantitative Uncertainty Estimates for C Stock Changes occurring
29 within Unvegetated Open Water Coastal Wetlands Converted to Vegetated Coastal Wetlands
30 (MMT CO2 Eq. and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Combined Uncertainty for Flux Associated
with Wetlands Soil C Stock Change in
Unvegetated Open Water Coastal Wetlands
Converted to Vegetated Coastal Wetlands
(0.009)
(0.012)
(0.006)
-29.5%
29.5%
Notes: Parentheses indicate net sequestration. Quality control measures are still underway and estimates will be finalized
after public review.
31 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
32 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
33 above.
34 QA/QC and Verification
35 NOAA provided data (i.e., National LiDAR Dataset, NOS Tide Data, and C-CAP land cover and land cover change
36 mapping) undergo internal agency QA/QC assessment procedures. Acceptance of final datasets into the archive for
37 dissemination are contingent upon assurance that the product is compliant with mandatory NOAA QA/QC
38 requirements (McCombs et al., 2016). QA/QC and Verification of soil C stock dataset has been provided by the
6-92 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 Smithsonian Environmental Research Center and Coastal Wetlands project team leads who reviewed produced
2 summary tables against primary scientific literature. Land cover estimates were assessed to ensure that the total land
3 area did not change over the time series in which the inventory was developed, and verified by a second QA team. A
4 team of two evaluated and verified there were no computational errors within calculation worksheets. Two
5 biogeochemists at the USGS, also members of the NASA Carbon Monitoring System Science Team, corroborated
6 the simplifying assumption that where salinities are unchanged CH4 emissions are constant with conversion of
7 Unvegetated Open Water Coastal Wetlands to Vegetated Coastal Wetlands.
8 Planned Improvements
9 A USGS/ NASA Carbon Monitoring System Carbon is in progress to establish a U.S. country-specific database of
10 published measurement data quantifying soil C stock, wetland biomass and methane emissions. Refined error
11 analysis combining land cover change and soil and biomass C stock estimates will be provided. Under this
12 investigation a model is in development to represent changes in soil C stocks. This investigation is to be completed
13 by November 2017 and may be included in either the 1990 to 2016 Inventory (2018 submission) or the 1990 to 2017
14 Inventory (2019 submission).
15 N20 Emissions from Aquaculture in Coastal Wetlands
16 Shrimp and fish cultivation in coastal areas increases nitrogen loads resulting in direct emissions of N20. Nitrous
17 oxide is generated and emitted as a by-product of the conversion of ammonia (contained in fish urea) to nitrate
18 through nitrification and nitrate to N2 gas through denitrification (Hu et al., 2012). Nitrous oxide emissions can be
19 readily estimated from data on fish production (IPCC 2013 Wetlands Supplement).
20 Overall, aquaculture production in the U.S. has fluctuated slightly from year to year though it is essentially at a
21 similar level since 2011 as in baseline year of 1990. Data for 2015 are not yet available and emissions have been
22 held constant with 2014 at 0.14 MMT CO2 Eq).
23 Table 6-61: Net N2O Flux from Aquaculture in Coastal Wetlands (MMT CO2 Eq.)
Year
1990
2005
2011
2012
2013
2014
2015
Flux
0.13
0.18
0.14
0.14
0.14
0.14
0.14
Note: Estimates are derived fromNOAA Fisheries, Fisheries Statistics Division. All reported
aquaculture production for coastal areas is included in calculation with the exception of clams,
mussels and oysters, for which no applied food stock is assumed. Data for 2015 has yet to be
published, value held constant with recent years.
24 Table 6-62: Net N2O Flux from Aquaculture in Coastal Wetlands (kt N2O)
Year
1990
2005
2011
2012
2013
2014
2015
Flux
0.44 '
0.59
0.47
0.46
0.48
0.47
0.47
Note: Estimates are derived fromNOAA Fisheries, Fisheries Statistics Division. All reported
aquaculture production in coastal areas is included in calculation with the exception of clams,
mussels and oysters, for which no applied food stock is assumed. Data for 2015 has yet to be
published, value held constant with recent years.
25 Methodology
26 The methodology to estimate N20 emissions from Aquaculture in Coastal Wetlands follows guidance in the 2013
27 IPCC Wetlands Supplement applying country-specific fisheries production data and the IPCC Tier 1 default
28 emission factor.
Land Use, Land-Use Change, and Forestry 6-93
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1 Each year NO AA Fisheries document the status of U.S. marine fisheries in the annual report of Fisheries of the
2 United States, from which activity data for this analysis is derived.69 The fisheries report has been produced in
3 various forms for more than 100 years, primarily at the national level, on U.S. recreational catch and commercial
4 fisheries landings and values. In addition, data are reported on U.S. aquaculture production, the U.S. seafood
5 processing industry, imports and exports of fish-related products, and domestic supply and per capita consumption
6 of fisheries products. Within the aquaculture chapter mass of production for Catfish, Striped bass, Tilapia, Trout,
7 Crawfish, Salmon and Shrimp are reported. While some of these fisheries are produced on land and some in open
8 water cages, all have data on the quantity of food stock produced, which is the activity data that is applied to the
9 IPCC Tier 1 default emissions factor to estimate emissions of N20 from aquaculture. It is not apparent from the data
10 as to the extent of aquaculture occurring above the extent of high tides on river floodplains. While some aquaculture
11 likely occurs on coastal lowland floodplains this is likely a minor component of tidal aquaculture production at
12 because of the need a regular source of water for pond flushing. The estimation of N20 emissions from aquaculture
13 is not sensitive to salinity using IPCC approaches and as such the location of aquaculture ponds on the landscape
14 does not influence the calculations.
15 Other open water shellfisheries for which no food stock is provided, and thus no additional N inputs, are not
16 applicable for estimating N20 emissions (e.g., Clams, Mussels and Oysters) have not been included in the analysis.
17 The IPCC Tier 1 default emissions factor of 0.00169 kg N20-N per kg of fish produced (95 percent confidence
18 interval - 0,0038) is applied to the activity data to calculate total N20 emissions. The AR4 global warming potential
19 value of 298 is applied in deriving C02 Eq. values from N20 emissions.
20 Uncertainty and Time-Series Consistency
21 Uncertainty estimates are based upon the Tier 1 default 95 percent confidence interval provided within the Wetlands
22 Supplement for N20 emissions. Uncertainties in N20 emissions from aquaculture are based on expert judgement for
23 the NOAA Fisheries of the United States fisheries production data (± 100 percent) multiplied by default uncertainty
24 level for N20 emissions found in Table 4.15, chapter 4 of the Wetlands Supplement. Given the overestimate of
25 fisheries production from coastal wetland areas due to the inclusion of fish production in non-coastal wetland areas,
26 this is a reasonable initial first approximation for an uncertainty range.
27 Table 6-63: Approach 1 Quantitative Uncertainty Estimates for N2O Emissions for
28 Aquaculture Production in Coastal Wetlands (MMT CO2 Eq. and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Emissions Estimate3
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower Upper
Bound Bound
Combined Uncertainty for Flux Associated
with N2O Emissions for Aquaculture
Production in Coastal Wetlands
0.14
(0.02)
0.30
-116% 116%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
29 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
30 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
31 above.
32 QA/QC and Verification
33 NOAA provide internal QA/QC review of reported fisheries data. The Coastal Wetlands Inventory team consulted
34 with the Coordinating Lead Authors of the Coastal Wetlands chapter, IPCC 2013 Wetlands Supplement on which
35 fisheries production to include in reporting. It was concluded that N20 emissions estimates should be applied to any
36 fish production to which food supplement is supplied be they pond or open water and that salinity conditions was
37 not a determining factor in N20 emissions.
69 See
6-94 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1
2
6.9 Land Converted to Wetlands (IPCC Source
Category 4D2)
3 Emissions and Removals from Land Converted to Vegetated
4 Coastal Wetlands
5 Land Converted to Vegetated Coastal Wetlands occur as a result of inundation of unprotected low-lying coastal
6 areas with gradual sea level rise, flooding of previously drained land behind hydrological barriers, and through
7 active restoration and creation of coastal wetlands through removal of hydrological barriers. All other land
8 categories are identified has having some area converting to Vegetated Coastal Wetlands. Between 1990 and 2015
9 the rate of annual transition for Land Converted to Vegetated Coastal Wetlands ranged from 2,619 ha/year to 5,316
10 ha/year. Conversion rates were higher during the period 2010 through 2015 than during the earlier part of the time
11 series, driven an increase in the extent unvegetated lands bear ground in Other Land Converted to Wetlands.
12 However, at the present stage of Inventory development, Coastal Wetlands are not explicitly shown in the Land
13 Representation analysis while work continues harmonizing data from NOAA's Coastal Change Analysis Program70
14 with NRI data used to compile the Land Representation. As a QC step a check was undertaken to confirm that
15 Coastal Wetlands recognized by C-CAP represented a subset of Wetlands recognized by the NRI for marine coastal
16 states. Delineating Vegetated Coastal Wetlands from ephemerally flooded upland Grasslands represents a particular
17 challenge in remote sensing. Moreover, at the boundary between wetlands and uplands, which may be gradual on
18 low lying coastlines, the presence of wetlands may be ephemeral depending upon whether and climate cycles and as
19 such the emissions and removals will also vary over such time frames.
20 Following conversion to Vegetated Coastal Wetlands there are increases in biomass and soil C storage. Additionally,
21 at salinities less than half that of seawater the transition from upland dry soils to wetland soils results in CH4
22 emissions. In this Inventory analysis, soil C stock changes and CH4 emissions are quantified. Estimates of biomass C
23 stock changes will be included in subsequent reports. Estimates of emissions and removals are based on emission
24 factor data that have been applied to estimate changes in soil C stock for Land Converted to Vegetated Coastal
25 Wetlands.
26 Table 6-64: Net CO2 Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal
27 Wetlands (MMT COz Eq.)
Year
1990
2005
2010
2011
2012
2013
2014
2015
Net Soil Flux
(0.02) ;
(0.01)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
(0.02)
Note: Parentheses indicate net sequestration. Estimates prior to 1996 and after 2010 are extrapolated based on
C-CAP data and therefore may not fully reflect changes occurring in the latter part of the time series. Mineral
and Organic Soils are not differentiated in terms of C removals.
28 Table 6-65: Net CO2 Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal
29 Wetlands (MMT C)
Year
1990
2005
2010
2011
2012
2013
2014
2015
Net Soil Flux
(0.01) •
(0.00)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
(0.01)
Note: Parentheses indicate net sequestration. Estimates prior to 1996 and after 2010 are extrapolated based on
C-CAP data and therefore may not fully reflect changes occurring in the latter part of the time series. Mineral
and Organic Soils are not differentiated in terms of C removals.
70 See .
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22
23
24
25
26
27
28
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30
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32
33
Table 6-66: Net CH4 Flux in Land Converted to Vegetated Coastal Wetlands (MMX CO2 Eq.)
Soil Type
1990
2005
2010
2011
2012
2013
2014
2015
Net Flux
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
Note: Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not
fully reflect changes occurring in the latter part of the time series. Mineral and Organic Soils are not
differentiated in terms of methane emissions.
Table 6-67: Net ChU Flux from Soil C Stock Changes in Land Converted to Vegetated Coastal
Wetlands (kt CH4)
Soil Type
19')0
2005
2010
2011
2012
2013
2014
2015
Net Flux
0.57
0.48
0.57
0.48
0.48
0.48
0.48
0.48
Note: Estimates prior to 1996 and after 2010 are extrapolated based on C-CAP data and therefore may not
fully reflect changes occurring in the latter part of the time series. Mineral and Organic Soils are not
differentiated in terms of methane emissions.
Methodology
The following section includes a brief description of the methodology used to estimate changes in soil C removals
and CH4 emissions for Land Converted to Vegetated Coastal Wetlands.
Soil Carbon Stock Changes
Soil C removals are estimated for Land Converted to Vegetated Coastal Wetlands for land below the elevation of
high tides (taken to be mean high water spring tide elevation) and as far seawards as the extent of intertidal vascular
plants within the U.S. Land Representation according to the national LiDAR dataset, the national network of tide
gauges and land use histories recorded in the 1996, 2001, 2005 and 2010 NOAA C-CAP surveys.71 As noted above,
the NOAA C-CAP dataset has yet to be harmonized with the NRI dataset from which the Land Representation is
derived. Federal and non-federal lands are represented. Trends in land cover change are extrapolated to 1990 and
2015 from these datasets. Based upon NOAA C-CAP, wetlands are subdivided into freshwater (Palustrine) and
saline (Estuarine) classes and further subdivided into Emergent marsh, scrub shrub and forest classes. Soil C stock
changes, stratified by climate zones and wetland classes, are derived from a synthesis of peer-reviewed literature
(Mangrove pool and removals data: Cahoon & Lynch unpublished data; Lynch 1989; Callaway et al. 1997; Chen &
Twilley 1999; McKee & Faulkner 2000; Ross et al. 2000; Chmura et al. 2003; Perry & Mendelssohn 2009;
Castaneda-Moya et al. 2013; Henry & Twilley 2013; Doughty et al. 2015; Marchio et al. 2016. Tidal marsh pool
and removals data: Anisfeld unpublished data; Cahoon unpublished data; Cahoon & Lynch unpublished data;
Chmura unpublished data; McCaffrey & Thomson 1980; Hatton 1981; Callaway et al. 1987; Craft et al. 1988;
Cahoon & Turner 1989; Patrick & DeLaune 1990; Kearney & Stevenson 1991 ;Cahoon et al. 1996; Callaway et al.
1997; Roman et al. 1997; Bryant & Chabrek 1998; Orson et al. 1998; Markewich et al. 1998; Anisfeld et al. 1999;
Connor et al. 2001; Choi & Wang 2001; Chmura et al. 2003, Hussein et al. 2004; Craft 2007; Miller et al. 2008;
Drexler et al. 2009; Perry & Mendelssohn 2009; Loomis & Craft 2010; EPA's NWCA 2011; Callaway et al. 2012;
Henry & Twilley 2013; Weston et al. 2014). To estimate soil C stock changes no differentiation is made for soil
type.
Tier 2 level estimates of soil C removal associated with annual soil C accumulation from Land Converted to
Vegetated Coastal Wetlands were developed using country-specific soil C removal factors multiplied by activity
data of land area for Land Converted to Vegetated Coastal Wetlands. The methodology follows Eq. 4.7, Chapter 4
of the Wetlands Supplement, and applied to the area of Land Converted to Vegetated Coastal Wetlands on an annual
basis. Emission factors were developed from literature references that provided soil C removal factors disaggregated
by climate region, vegetation type by salinity range (estuarine or palustrine) as identified using NOAA C-CAP as
71 See .
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1 described above. Quantification of regional coastal wetland biomass C stock changes for perennial vegetation are in
2 development and are not presented this year, though will be included in future reports.
3 Soil Methane Emissions
4 Tier 1 estimates of CH4 emissions for Vegetated Coastal Wetlands Remaining Vegetated Coastal Wetlands are
5 derived from the same wetland map used to in the analysis of wetland soil C fluxes, produced from C-CAP, LiD AR
6 and tidal data, in combination with default CH4 emission factors provided in Table 4.14 of the Wetlands Supplement.
1 The methodology follows Eq. 4.9, Chapter 4 of the Wetlands Supplement, and is applied to the total area of Land
8 Converted to Vegetated Coastal Wetlands on an annual basis. The AR4 global warming potential factor of 25 was
9 used in converting CH4 to CO2 Eq. values.
10 Uncertainty and Time-Series Consistency
11 Underlying uncertainties in estimates of soil C removal factors and CH4 include error in uncertainties associated
12 with Tier 2 literature values of soil C removal estimates and CH4 flux, assumptions that underlie the methodological
13 approaches applied and uncertainties linked to interpretation of remote sensing data.
14 Uncertainty specific to coastal wetlands include differentiation of palustrine and estuarine community classes which
15 determines the soil C removal and CH4 flux applied. Soil C removal and CH4 fluxes applied are determined from
16 vegetation community classes across the coastal zone and identified by NOAA C-CAP. Community classes are
17 further subcategorized by climate zones and growth form (forest, shrub-scrub, marsh). Soil C removal data for all
18 subcategories are not available and thus assumptions were applied using expert judgement about the most
19 appropriate assignment of a soil C removal factor to a disaggregation of a community class. Because mean soil C
20 removal for each available community class are in a fairly narrow range, the same overall uncertainty was assigned
21 to each, (i.e., applying approach for asymmetrical errors, where the largest uncertainty for any one soil C stock
22 referenced using published literature values for a community class; uncertainty approaches provide that if multiple
23 values are available for a single parameter, the highest uncertainty value should be applied to the propagation of
24 errors; IPCC 2000). Uncertainties for CH4 flux are the Tier 1 default values reported in the Wetlands Supplement.
25 Overall uncertainty of the NOAA C-CAP remote sensing product is 15 percent. This is in the range of remote
26 sensing methods (±10-15 percent; IPCC 2003). However, there is significant uncertainty in salinity ranges for tidal
27 and non-tidal estuarine wetlands and activity data used to estimate the apply CH4 flux (e.g., delineation of an 18 ppt
28 boundary) which will need significant improvement to reduce uncertainties.
29 Table 6-68: Approach 1 Quantitative Uncertainty Estimates for Net CO2 Flux Changes
30 occurring within Land Converted to Vegetated Coastal Wetlands (MMT CO2 Eq. and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate3
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Combined Uncertainty for Flux Associated
with Land Converted to Vegetated Coastal
Wetlands
(0.02)
(0.03)
(0.02)
-29.5%
29.5%
a Range of flux estimates based on error propagation at 95 percent confidence interval.
Notes: Parentheses indicate negative values or net sequestration. Quality control measures are still underway and estimates
will be finalized after public review.
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Table 6-69: Approach 1 Quantitative Uncertainty Estimates for ChU Emissions occurring
within Land Converted to Vegetated Coastal Wetlands (MMT CO2 Eg. and Percent)
2015 Flux Estimate
Uncertainty Range Relative to Flux Estimate3
Source
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Combined Uncertainty for Flux Associated
with Land Converted to Vegetated Coastal
Wetlands
0.01
0.01
0.02
-29.8%
29.8%
a Range of flux estimates based on error propagation at 95 percent confidence interval.
Note: Quality control measures are still underway and estimates will be finalized after public review.
3 Conversion of Vegetated Lands to Vegetated Coastal Wetlands is a particular challenge for recognition by remote
4 sensing. Methodological recalculations were applied to the entire time-series to ensure time-series consistency from
5 1990 through 2015. Details on the emission trends through time are described in more detail in the Methodology
6 section, above.
7 QA/QC and Verification
8 NOAA provided National LiDAR Dataset, tide data, and C-CAP land cover and land cover change mapping, all of
9 which are subject to agency internal QA/QC assessment. Acceptance of final datasets into archive and dissemination
10 are contingent upon the product compilation are compliant with mandatory QA/QC requirements (McCombs, et al.,
11 2016). QA/QC and verification of soil C stock dataset has been provided by the Smithsonian Environmental
12 Research Center and Coastal Wetland Inventory team leads who reviewed summary tables against reviewed sources.
13 Land cover estimates were assessed to ensure that the total land area did not change over the time series in which the
14 inventory was developed, and verified by a second QA team. A team of two evaluated and verified there were no
15 computational errors within the calculation worksheets. Soil C stock, emissions/removals data where based upon
16 peer-reviewed literature and CH4 emission factors derived from the IPCC Wetlands Supplement.
17 Planned Improvements
18 A USGS/ NASA Carbon Monitoring System investigation is in progress to establish a U.S. country-specific
19 database of soil C stocks, wetland biomass and CH4 emissions. Refined error analysis combining land cover change
20 and C stock estimates will be provided. Under this investigation, a model is in development to represent changes in
21 soil C stocks. This investigation is due to be completed by November 2017. Future improvements will thus include
22 estimates of biomass C stock change with Land Converted to Vegetated Coastal Wetlands.
23 6.10 Settlements Remaining Settlements
24 Soil Carbon Stock Changes (IPCC Source Category 3B5a)
25 Drainage of organic soils is common when wetland areas have been developed for settlements. Organic soils, also
26 referred to as Histosols, include all soils with more than 12 to 20 percent organic C by weight, depending on clay
27 content (NRCS 1999, Brady and Weil 1999). The organic layer of these soils can be very deep (i.e., several meters),
28 and form under inundated conditions that results in minimal decomposition of plant residues. Drainage of organic
29 soils leads to aeration of the soil that accelerates decomposition rate and CO2 emissions.72 Due to the depth and
30 richness of the organic layers, C loss from drained organic soils can continue over long periods of time, which varies
31 depending on climate and composition (i.e., decomposability) of the organic matter (Armentano and Menges 1986).
72 N2O emissions from soils are included in the N2O Fluxes from Settlement Soils section.
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1 The United States does not estimate changes in soil organic C stocks for mineral soils on Settlements Remaining
2 Settlements, which is consistent with the assumption of the Tier 1 method in the IPCC guidelines (2006). This
3 assumption may be evaluated in the future if funding and resources are available to conduct an analysis of soil C
4 stock changes in mineral soils of Settlements Remaining Settlements.
5 Settlements Remaining Settlements includes all areas that have been settlements for a continuous time period of at
6 least 20 years according to the 2012 United States Department of Agriculture (USD A) National Resources Inventory
7 (NRI) (USDA-NRCS 20 1 5)73 or according to the National Land Cover Dataset for federal lands (Homer et al. 2007;
8 Fry et al. 2011; Homer et al. 2015). The Inventory includes settlements on privately-owned lands in the
9 conterminous United States and Hawaii. Alaska and the small amount of settlements on federal lands are not
10 included in this Inventory even though these areas are part of the U.S. managed land base. This leads to a
11 discrepancy with the total amount of managed area in Settlements Remaining Settlements (see Section 6.1
12 Representation of the U.S. Land Base) and the settlements area included in the Inventory analysis. There is a
13 planned improvement to include settlements on organic soils in these areas as part of a future Inventory.
14 CO2 emissions from drained organic soils in settlements are 1.4 MMT CO2 Eq. (0.4 MMT C) in 2015. Although the
15 flux is relatively small, the amount has increased by over 850 percent since 1990.
16 Table 6-70: Net CO2 Flux from Soil C Stock Changes in Settlements Remaining Settlements
17 (MMT COz Eq.)
Soil Type
1990
2005
2011
2012
2013
2014
2015
Organic Soils
0.1
:5 0.5
1.3
1.3
1.3
1.3
1.4
Note: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully
reflect changes occurring in the latter part of the time series.
18 Table 6-71: Net CO2 Flux from Soil C Stock Changes in Settlements Remaining Settlements
19 (MMT C)
Soil Type
1990
2005
2011
2012
2013
2014
2015
Organic Soils
+
0.1
0.3
0.4
0.4
0.4
0.4
+ Does not exceed 0.05 MMT C
Note: Estimates after 2012 are based on NRI data from 2012 and therefore may not fully
reflect changes occurring in the latter part of the time series.
20 Methodology
21 The IPCC Tier 2 method is used to estimate soil organic C stock changes for organic soils in Settlements Remaining
22 Settlements (IPCC 2006). The Tier 1 method assumes that soil organic C stocks in mineral soils are not changing
23 (i.e., C inputs are equal to C outputs). The Tier 2 method assumes that organic soils are losing C at a rate similar to
24 croplands, and therefore uses the country-specific values for cropland (Ogle et al. 2003). The following section
25 includes a description of the methodology, including (1) determination of the land base that is classified as
26 settlements; and (2) estimation of emissions from drained organic soils.
27 The land area designated as settlements is based primarily on the 2012 National Resources Inventory (NRI) (USDA
28 2015) with additional information form the National Land Cover Dataset (NLCD) (Fry et al. 2011; Homer et al.
29 2007; Homer et al. 2015). It is assumed that all settlement area on organic soils is drained, and those areas are
30 provided in Table 6-72 (See Section 0, Representation of the U.S. Land Base for more information). The area of
31 drained organic soils in Settlements Remaining Settlements is estimated from the NRI spatial weights and aggregated
32 to the country (Table 6-72). The area of land on organic soils in Settlements Remaining Settlements has increased
33 from 3 thousand hectares in 1990 to over 28 thousand hectares in 2015.
73 NRI survey locations are classified according to land-use histories starting in 1979, and consequently the classifications are
based on less than 20 years from 1990 to 1998. This may have led to an overestimation of Settlements Remaining Settlements in
the early part of the time series to the extent that some areas are converted to settlements between 1971 and 1978.
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Table 6-72: Thousands of Hectares of Drained Organic Soils in Settlements Remaining
Settlements
Year
Area
(Thousand Hectares)
1990
3
2005
10
2011
26
2012
28
2013
28
2014
28
2015
28
Notes: Estimates after 2012 are
based on NRI data from 2012 and
therefore may not fully reflect
changes occurring in the latter part
of the time series.
To estimate CO2 emissions from drained organic soils, the total area of organic soils in Settlements Remaining
Settlements is multiplied by the country-specific emission factors for Cropland Remaining Cropland under the
assumption that there is deep drainage of the soils. The emission factors are 11.2 MMT C per ha in cool temperate
regions, 14.0 MMT C per ha in warm temperate regions, and 11.2 MMT C per ha in subtropical regions (See Annex
3.12 for more information).
Uncertainty and Time-Series Consistency
The results of the Approach 2 Monte Carlo uncertainty analysis are summarized in Table 6-73. Soil C losses from
drained organic soils in Settlements Remaining Settlements for 2015 are estimated to be between 0.7 and 2.3 MMT
CO2 Eq. at a 95 percent confidence level. This indicates a range of 47 percent below and 67 percent above the 2015
emission estimate of 1.4 MMT CO2 Eq.
Table 6-73: Uncertainty Estimates for CO2 Emissions from Drained Organic Soils in
Settlements Remaining Settlements (MMT CO2 Eg. and Percent)
2015 Emission
Source
Gas
Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Organic Soils
CO2
1.4
0.7 2.3 -47% 67%
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 2015. 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 are properly
handled throughout the inventory process. Inventory reporting forms and text are reviewed and revised as needed to
correct transcription errors.
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Planned Improvements
This source will be extended to include CO2 emissions from drainage of organic soils in settlements of Alaska and
federal lands in order to provide a complete inventory of emissions for this category.
Changes in Carban Vcocks in Urban Trees (IPCC Source
Category 4E1)
Urban forests constitute a significant portion of the total U.S. tree canopy cover (Dwyer et al. 2000). Urban areas
(cities, towns, and villages) are estimated to cover over 3 percent of the United States (U.S. Census Bureau 2012).
With an average tree canopy cover of 35 percent, urban areas account for approximately 5 percent of total tree cover
in the continental United States (Nowak and Greenfield 2012). Trees in urban areas of the United States were
estimated to account for an average annual net sequestration of 77.0 MMT CO2 Eq. (21.0 MMT C) over the period
from 1990 through 2015. Net C flux from urban trees in 2015 was estimated to be -91.7 MMT CO2 Eq. (-25.0 MMT
C). Annual estimates of CO2 flux (Table 6-74) were developed based on periodic (1990, 2000, and 2010) U.S.
Census data on urbanized area. The estimate of urbanized area is smaller than the area categorized as Settlements in
the Representation of the U.S. Land Base developed for this report: over the 1990 through 2015 time series the
Census urban area totaled, on average, about 63 percent of the Settlements area.
In 2015, Census urban area totaled about 68 percent of the total area defined as Settlements. Census area data are
preferentially used to develop C flux estimates for this source category since these data are more applicable for use
with the available peer-reviewed data on urban tree canopy cover and urban tree C sequestration. Annual
sequestration increased by 52 percent between 1990 and 2015 due to increases in urban land area. Data on C storage
and urban tree coverage were collected since the early 1990s and have been applied to the entire time series in this
report. As a result, the estimates presented in this chapter are not truly representative of changes in C stocks in urban
trees for Settlements areas, but are representative of changes in C stocks in urban trees for Census urban area. The
method used in this report does not attempt to scale these estimates to the Settlements area. Therefore, the estimates
presented in this chapter are likely an underestimate of the true changes in C stocks in urban trees in all Settlements
areas—i.e., the changes in C stocks in urban trees presented in this chapter are a subset of the changes in C stocks in
urban trees in all Settlements areas.
Urban trees often grow faster than forest trees because of the relatively open structure of the urban forest (Nowak
and Crane 2002). Because tree density in urban areas is typically much lower than in forested areas, the C storage
per hectare of land is in fact smaller for urban areas than for forest areas. To quantify the C stored in urban trees, the
methodology used here requires analysis per unit area of tree cover, rather than per unit of total land area (as is done
for Forestlands). Expressed in this way per unit of tree cover, areas covered by urban trees actually have a greater C
density than do forested areas (Nowak and Crane 2002). Expressed per unit of land area, however, the situation is
the opposite: because tree density is so much lower in urban areas, these areas have a smaller C density per unit land
area than forest areas.
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Table 6-74: Net C Flux from Urban Trees (MMT CO2 Eq. and MMT C)
Year MMT CO2 Eg. MMT C
1000
(60.4)
(16.5)
2005
(80.5)
(22.0)
2011
(87.3)
(23.8)
2012
(88.4)
(24.1)
2013
(80.5)
(24.4)
2014
(00.6)
(24.7)
2015
(91.7)
(25.0)
Note: Parentheses indicate net
sequestration.
Methodology
Methods for quantifying urban tree biomass, C sequestration, and C emissions from tree mortality and
decomposition were taken directly from Nowak et al. (2013), Nowak and Crane (2002), and Nowak (1994). In
general, the methodology used by Nowak et al. (2013) to estimate net C sequestration in urban trees followed three
steps, each of which is explained further in the paragraphs below. First, field data from cities and states were used to
estimate C in urban tree biomass from field data on measured tree dimensions. Second, estimates of annual tree
growth and biomass increment were generated from published literature and adjusted for tree condition, crown
competition, and growing season to generate estimates of gross C sequestration in urban trees for all 50 states and
the District of Columbia. Third, estimates of C emissions due to mortality and decomposition were subtracted from
gross C sequestration values to derive estimates of net C sequestration.
For this Inventory report, net C sequestration estimates for all 50 states and the District of Columbia, that were
generated using the Nowak et al. (2013) methodology and expressed in units of C sequestered per unit area of tree
cover, were then used to estimate urban tree C sequestration in the United States. To accomplish this, we used urban
area estimates from U.S. Census data together with urban tree cover percentage estimates for each state and the
District of Columbia from remote sensing data, an approach consistent with Nowak et al. (2013).
This approach is also consistent with the default IPCC Gain-Loss methodology in IPCC (2006), although sufficient
field data are not yet available to separately determine interannual gains and losses in C stocks in the living biomass
of urban trees. Instead, the methodology applied here uses estimates of net C sequestration based on modeled
estimates of decomposition, as given by Nowak et al. (2013).
The first step in the methodology is to estimate C in urban tree biomass. To develop urban tree carbon estimates
Nowak et al. (2013) and previously published research (Nowak and Crane 2002; and Nowak 1994, 2007b, and
2009) collected field measurements in a number of U.S. cities between 1989 and 2012. For a random sample of trees
in representative cities, tree data were collected regarding stem diameter, tree height, crown height and crown width,
tree location, species, and canopy condition. The data for each tree were converted into total dry-weight biomass
estimates using allometric equations, a root-to-shoot ratio to convert aboveground biomass estimates to whole tree
biomass, and wood moisture content. Total dry weight biomass was converted to C by dividing by two (50 percent
carbon content). An adjustment factor of 0.8 was used for open grown trees 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). Carbon storage estimates for deciduous trees include only C stored in wood. Estimated C storage was divided
by tree cover in the area to estimate carbon storage per square meter of tree cover. The second step in the
methodology is to estimate rates of tree growth for urban trees in the United States. In the Nowak et al. (2013)
methodology that is applied here, growth rates were standardized for open-grown trees in areas with 153 days of
frost free length based on measured data on tree growth. These growth rates were then adjusted to local tree
conditions based on length of frost free season, crown competition (as crown competition increased, growth rates
decreased), and tree condition (as tree condition decreased, growth rates decreased). 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 tree were then scaled up to city estimates using tree population information. The
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area of assessment for each city or state was defined by its political boundaries; parks and other forested urban areas
were thus included in sequestration estimates (Nowak 2011).
Most of the field data used to develop the methodology of Nowak et al. (2013) were analyzed using the U.S. Forest
Service's i-Tree Eco model (formerly Urban Forest Effects (UFORE) model). The i-Tree Eco computer model uses
standardized field data from randomly located plots, along with local hourly 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. The model was used with field data from randomly sampled plots in each city or urban areas
in states to quantify the characteristics of the urban forest (Nowak et al. 2013).
Where gross C sequestration accounts for all carbon sequestered, net C sequestration for urban trees takes into
account C emissions associated with tree death and removals. In the third step in the methodology developed by
Nowak et al. (2002; 2013), estimates of net C emissions from urban trees were derived by applying estimates of
annual mortality based on tree condition, and assumptions about whether dead trees were removed from the site.
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 plot were then scaled up
to city estimates using tree population information.
The data for all 50 states and the District of Columbia are described in Nowak et al. (2013) and reproduced in Table
6-75, which builds upon previous research, including: Nowak and Crane (2002), Nowak et al. (2007), Nowak and
Greenfield (2012), and references cited therein. The full methodology development is described in the underlying
literature, and key details and assumptions were made as follows. The allometric equations applied to the field data
for the Nowak methodology 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 or
botanical relative was used. The adjustment (0.8) to account for less live tree biomass in open-grown urban trees
was based on information in Nowak (1994). Measured tree growth rates for street (Frelich 1992; Fleming 1988;
Nowak 1994), park (deVries 1987), and forest (Smith and Shifley 1984) trees were standardized to an average
length of growing season (153 frost free days) and adjusted for site competition and tree condition. Standardized
growth rates of trees of the same species or genus were then compared to determine the average difference between
standardized street tree growth and standardized park and forest growth rates. Crown light exposure (CLE)
measurements (number of sides and/or top of tree exposed to sunlight) were used to represent forest, park, and open
(street) tree growth conditions. Local tree base growth rates (BG) were then calculated as the average standardized
growth rate for open-grown trees multiplied by the number of frost free days divided by 153. Growth rates were then
adjusted for CLE. The CLE adjusted growth rate was then adjusted based on tree health and tree condition to
determine the final growth rate. Assumptions for which dead trees would be removed versus left standing were
developed specific to each land use and were based on expert judgment of the authors. Decomposition rates were
based on literature estimates (Nowak et al. 2013).
Estimates of gross and net sequestration rates for each of the 50 states and the District of Columbia (Table 6-75)
were compiled in units of C sequestration per unit area of tree canopy cover. These rates were used in conjunction
with estimates of state urban area and urban tree cover data (Nowak and Greenfield 2012) to calculate each state's
annual net C sequestration by urban trees. This method was described in Nowak et al. (2013) and has been modified
here to incorporate U.S. Census data.
Specifically, urban area estimates were based on 1990, 2000, and 2010 U.S. Census data. The 1990 U.S. Census
defined urban land as "urbanized areas," which included land with a population density greater than 1,000 people
per square mile, and adjacent "urban places," which had predefined political boundaries and a population total
greater than 2,500. In 2000, the U.S. Census replaced the "urban places" category with a new category of urban land
called an "urban cluster," which included areas with more than 500 people per square mile. In 2010, the Census
updated its definitions to have "urban areas" encompassing Census tract delineated cities with 50,000 or more
people, and "urban clusters" containing Census tract delineated locations with between 2,500 and 50,000 people.
Urban land area increased by approximately 23 percent from 1990 to 2000 and 14 percent from 2000 to 2010;
Nowak et al. (2005) estimate that the changes in the definition of urban land are responsible for approximately 20
percent of the total reported increase in urban land area from 1990 to 2000. Under all Census (i.e., 1990, 2000, and
2010) definitions, the urban category encompasses most cities, towns, and villages (i.e., it includes both urban and
suburban areas). Settlements area, as assessed in the Representation of the U.S. Land Base developed for this report,
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encompassed all developed parcels greater than 0.1 hectares in size, including rural transportation corridors, and as
previously mentioned represents a larger area than the Census-derived urban area estimates. However, the smaller,
Census-derived urban area estimates were deemed to be more suitable for estimating national urban tree cover given
the data available in the peer-reviewed literature (i.e., the data set available is consistent with Census urban rather
than Settlements areas), and the recognized overlap in the changes in C stocks between urban forest and non-urban
forest (see Planned Improvements below). U.S. Census urban area data is reported as a series of continuous blocks
of urban area in each state. The blocks or urban area were summed to create each state's urban area estimate.
Net annual C sequestration estimates were derived for all 50 states and the District of Columbia by multiplying the
gross annual emission estimates by 0.74, the standard ratio for net/gross sequestration set out in Table 3 of Nowak et
al. (2013) (unless data existed for both gross and net sequestration for the state in Table 2 of Nowak et. al. (2013), in
which case they were divided to get a state-specific ratio). The gross and net annual C sequestration values for each
state were multiplied by each state's area of tree cover, which was the product of the state's urban/community area
as defined in the U.S. Census (2012) and the state's urban/community tree cover percentage. The urban/community
tree cover percentage estimates for all 50 states were obtained from Nowak and Greenfield (2012). The
urban/community tree cover percentage estimate for the District of Columbia was obtained from Nowak et al.
(2013). The urban area estimates were taken from the 2010 U.S. Census (2012). The equation, used to calculate the
summed carbon sequestration amounts, can be written as follows:
Net annual C sequestration = Gross sequestration rate x Net to Gross sequestration ratio x Urban Area x
% Tree Cover
Table 6-75: Annual C Sequestration (Metric Tons C/Year), Tree Cover (Percent), and Annual
C Sequestration per Area of Tree Cover (kg C/m2-yr) for 50 states plus the District of
Columbia (2015)
Gross Annual
Net Annual
Net: Gross
Sequestration
Sequestration
Annual
Gross Annual
Net Annual
Tree
per Area of
per Area of
Sequestration
State
Sequestration
Sequestration
Cover
Tree Cover
Tree Cover
Ratio
Alabama
1,186,389
877,928
55.2
0.343
0.254
0.74
Alaska
44,669
33,055
39.8
0.168
0.124
0.74
Arizona
393,844
291,445
17.6
0.354
0.262
0.74
Arkansas
431,702
319,459
42.3
0.331
0.245
0.74
California
2,112,897
1,563,544
25.1
0.389
0.288
0.74
Colorado
156,207
115,593
18.5
0.197
0.146
0.74
Connecticut
773,253
572,207
67.4
0.239
0.177
0.74
Delaware
139,198
103,006
35.0
0.335
0.248
0.74
DC
14,560
11,570
35.0
0.263
0.209
0.79
Florida
3,478,878
2,574,369
35.5
0.475
0.352
0.74
Georgia
2,632,675
1,948,179
54.1
0.353
0.261
0.74
Hawaii
248,700
184,038
39.9
0.581
0.430
0.74
Idaho
25,970
19,218
10.0
0.184
0.136
0.74
Illinois
766,689
567,350
25.4
0.283
0.209
0.74
Indiana
410,635
379,697
23.7
0.250
0.231
0.92
Iowa
120,611
89,252
19.0
0.240
0.178
0.74
Kansas
188,038
146,325
25.0
0.283
0.220
0.78
Kentucky
246,818
182,646
22.1
0.286
0.212
0.74
Louisiana
760,473
562,750
34.9
0.397
0.294
0.74
Maine
108,201
80,069
52.3
0.221
0.164
0.74
Maryland
603,569
446,641
34.3
0.323
0.239
0.74
Massachusetts
1,317,294
974,798
65.1
0.254
0.188
0.74
Michigan
744,415
550,867
35.0
0.220
0.163
0.74
Minnesota
356,705
263,962
34.0
0.229
0.169
0.74
Mississippi
501,688
371,249
47.3
0.344
0.255
0.74
Missouri
504,245
373,141
31.5
0.285
0.211
0.74
Montana
54,573
40,384
36.3
0.184
0.136
0.74
Nebraska
51,538
43,492
15.0
0.238
0.201
0.84
Nevada
45,246
33,482
9.6
0.207
0.153
0.74
New Hampshire
253,439
187,545
66.0
0.217
0.161
0.74
New Jersey
1,205,107
891,779
53.3
0.294
0.218
0.74
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New Mexico
70,608
52,250
12.0
0.263
0.195
0.74
New York
1,099,935
813,952
42.6
0.240
0.178
0.74
North Carolina
2,119,981
1,568,786
51.1
0.312
0.231
0.74
North Dakota
15,233
7,238
13.0
0.223
0.106
0.48
Ohio
935,554
692,310
31.5
0.248
0.184
0.74
Oklahoma
370,059
273,844
31.2
0.332
0.246
0.74
Oregon
262,861
194,517
36.6
0.242
0.179
0.74
Pennsylvania
1,276,092
944,308
41.0
0.244
0.181
0.74
Rhode Island
137,300
101,602
51.0
0.258
0.191
0.74
South Carolina
1,129,970
836,178
48.9
0.338
0.250
0.74
South Dakota
21,844
18,943
14.0
0.236
0.205
0.87
Tennessee
1,079,558
965,252
43.8
0.303
0.271
0.89
Texas
2,856,332
2,113,685
31.4
0.368
0.272
0.74
Utah
93,759
69,381
16.4
0.215
0.159
0.74
Vermont
46,801
34,633
53.0
0.213
0.158
0.74
Virginia
848,272
627,721
39.8
0.293
0.217
0.74
Washington
576,566
426,659
34.6
0.258
0.191
0.74
West Virginia
258,258
191,111
61.0
0.241
0.178
0.74
Wisconsin
368,715
272,849
31.8
0.225
0.167
0.74
Wyoming
19,442
14,387
19.9
0.182
0.135
0.74
Total
33,465,363
24,018,645
Uncertainty and Time-Series Consistency
Uncertainty associated with changes in C stocks in urban trees includes the uncertainty associated with urban area,
percent urban tree coverage, and estimates of gross and net C sequestration for each of the 50 states and the District
of Columbia. A 10 percent uncertainty was associated with urban area estimates based on expert judgment.
Uncertainty associated with estimates of percent urban tree coverage for each of the 50 states was based on standard
error estimates reported by Nowak and Greenfield (2012). Uncertainty associated with estimate of percent urban tree
coverage for the District of Columbia was based on the standard error estimate reported by Nowak et al. (2013).
Uncertainty associated with estimates of gross and net C sequestration for each of the 50 states and the District of
Columbia was based on standard error estimates for each of the state-level sequestration estimates reported by
Nowak et al. (2013). These estimates are based on field data collected in each of the 50 states and the District of
Columbia, and uncertainty in these estimates increases as they are scaled up to the national level.
Additional uncertainty is associated with the biomass equations, conversion factors, and decomposition assumptions
used to calculate C sequestration and emission estimates (Nowak et al. 2002). These results also exclude changes in
soil C stocks, and there is some overlap between the urban tree C estimates and the forest tree C estimates as
detailed in Nowak et al. (2013). Due to data limitations, urban soil flux is not quantified as part of this analysis,
while reconciliation of urban tree and forest tree estimates will be addressed through the land-representation effort
described in the Planned Improvements section of this chapter.
A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the
sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table
6-76. The net C flux from changes in C stocks in urban trees in 2015 was estimated to be between -135.3 and -47.3
MMT CO2 Eq. at a 95 percent confidence level. This indicates a range of 47 percent more sequestration to 48
percent less sequestration than the 2015 flux estimate of -91.7 MMT CO2 Eq.
Table 6-76: Approach 2 Quantitative Uncertainty Estimates for Net C Flux from Changes in C
Stocks in Urban Trees (MMT CO2 Eq. and Percent)
Source
Gas
2015 Flux Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Flux Estimate3
(MMT CO2 Eq.) (%)
Lower
Bound
Upper
Bound
Lower Upper
Bound Bound
Changes in C Stocks in
Urban Trees
CO2
(91.7)
(135.3)
(47.3)
-47% 48%
Note: Parentheses indicate negative values or net sequestration.
a Range of flux estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
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Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
above.
QA/QC and Verification
Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
control measures for urban trees included checking input data, documentation, and calculations to ensure data were
properly handled through the inventory process. Errors that were found during this process were corrected as
necessary.
Planned Improvements
A consistent representation of the managed land base in the United States is discussed in Section 6.1 Representation
of the U.S. Land Base, and discusses a planned improvement by the USD A Forest Service to reconcile the overlap
between urban forest and non-urban forest greenhouse gas inventories. Because some plots defined as "forest" in the
Forest Inventory and Analysis (FIA) program of the USD A Forest Service actually fall within the boundaries of the
areas also defined as Census urban, there may be "double-counting" of these land areas in estimates of C stocks and
fluxes for this report. Specifically, Nowak et al. (2013) estimates that 1.5 percent of forest plots measured by the
FIA program fall within land designated as Census urban, suggesting that approximately 1.5 percent of the C
reported in the Forest source category might also be counted in the Urban Trees source category.
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 the
areas of land included in the Settlements land use category and Census-defined urban areas, and would have to
separately characterize sequestration on non-urban Settlements land.
To provide more accurate emissions estimates in the urban forest greenhouse gas inventories, the following actions
will be taken:
a) Development of a national definition of "settlements". Settlements are defined as including "all developed
land, including transportation infrastructure and human settlements of any size, unless they are already
included under other categories. This should be consistent with the selection of national definitions". In the
U.S., different types of classifications can be used to determine settlements e.g., Census urban, Census
urban/community, National Land Cover Dataset, and National Resources Inventory. A combination of
these data will be used to encompass settlement areas and improve consistency with the Representation of
the U.S Land Base chapter;
b) For settlement areas, estimates of land area will be obtained for 1990, 2000 and 2010 and projections
developed for annual growth during the 2010 to 2020 period;
c) 2,500 random points will be laid on aerial images using Google Earth imagery to estimate tree cover in the
settlement areas circa 1990, 2000 and 2010. Trends in tree cover change will be used to estimate tree cover
in settlement between 2010 and 2020;
d) Photo interpretation of settlement tree cover will be updated bi-annually to update tree cover estimates and
trends;
e) A review of recent literature will be performed to update C storage, sequestration and net-to-gross
sequestration rates per unit tree cover.
f) C rates per unit tree cover will be applied to tree cover estimates within estimated settlement areas annually
to estimate past and current C values.
g) Settlement areas will updated approximately every 10 years based on updated data from the US Census and
NLCD developed land.
NzO Fluxes from Settlement Soils (IPCC Source Category 4E1)
Of the synthetic N fertilizers applied to soils in the United States, approximately 3.1 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 N20 emissions per unit
area. In addition to synthetic N fertilizers, a portion of surface applied sewage sludge is applied to settlement areas,
6-106 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 and drained organic soils (i.e., soils with high organic matter content, known as Histosols) also contribute to
2 emissions of soil N20.
3 N additions to soils result in direct and indirect N20 emissions. Direct emissions occur on-site due to the N
4 additions. Indirect emissions result from fertilizer and sludge N that is transformed and transported to another
5 location in a form other than N20 (ammonia [NH3] and nitrogen oxide [NOx] volatilization, nitrate [NO3 ] leaching
6 and runoff), and later converted into N20 at the off-site location. The indirect emissions are assigned to settlements
7 because the management activity leading to the emissions occurred in settlements.
8 Total N20 emissions from Settlements Remaining Settlements74 are 2.6 MMT C02 Eq. (9 kt of N20) in 2015. There
9 is an overall increase of 81 percent from 1990 to 2015 due to an expanding settlement area leading to more synthetic
10 N fertilizer applications. Interannual variability in these emissions is directly attributable to interannual variability in
11 total synthetic fertilizer consumption, area of drained organic soils, and sewage sludge applications in the United
12 States. Emissions from this source are summarized in Table 6-77.
13 Table 6-77: N2O Fluxes from Soils in Settlements Remaining Settlements (MMT CO2 Eq. and
14 kt N2O)
1990
2005
2011
2012
2013
2014
2015
MMT CO2 Eq.
Direct N2O Fluxes from Soils
1.1
1.9
2.0
2.1
2.0
2.0
2.0
Synthetic Fertilizers
0.8
1.6
1.7
1.7
1.7
1.7
1.7
Sewage Sludge
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Drained Organic Soils
0.1
0.1
0.2
0.2
0.2
0.2
0.2
Indirect N2O Fluxes from
Soils
0.4
0.6
0.6
0.6
0.6
0.6
0.6
Total
1.4
2.5
2.6
2.7
2.6
2.6
2.6
ktN20
Direct N2O Fluxes from Soils
4
6
7
7
7
7
7
Synthetic Fertilizers
3
5
6
6
6
6
6
Sewage Sludge
1
1
1
1
1
1
1
Drained Organic Soils
+
1
1
1
1
1
1
Indirect N2O Fluxes from
Soils
1
2
2
2
2
2
2
Total
5
8
9
9
9
9
9
+ Does not exceed 0.5 kt
Note: Totals may not sum due to independent rounding.
Estimates of Soil N2O for Settlements Remaining Settlements include emissions from Land Converted to
Settlements because it was not possible to separate the activity data.
15 Methodology
16 For settlement soils, the IPCC Tier 1 approach is used to estimate soil N20 emissions from synthetic N fertilizer,
17 sewage sludge additions, and drained organic soils. Estimates of direct N20 emissions from soils in settlements are
18 based on the amount of N in synthetic commercial fertilizers applied to settlement soils, the amount of N in sewage
19 sludge applied to non-agricultural land and surface disposal (see Annex 3.12 for a detailed discussion of the
20 methodology for estimating sewage sludge application), and the area of drained organic soils used for settlements.
21 Nitrogen applications to settlement soils are estimated using data compiled by the USGS (Ruddy et al. 2006). The
22 USGS estimated on-farm and non-farm fertilizer use is based on sales records at the county level from 1982 through
23 2001 (Ruddy et al. 2006). Non-farm N fertilizer is assumed to be applied to settlements and forest lands; values for
24 2002 through 2015 are based on 2001 values adjusted for annual total N fertilizer sales in the United States because
25 there is no new activity data on application after 2001. Settlement application is calculated by subtracting forest
74 Estimates of Soil N2O for Settlements Remaining Settlements include emissions from Land Converted to Settlements because it
was not possible to separate the activity data.
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application from total non-farm fertilizer use. Sewage sludge applications are derived from national data on sewage
sludge generation, disposition, and N content (see Annex 3.12 for further detail). The total amount of N resulting
from these sources is multiplied by the IPCC default emission factor for applied N (one percent) to estimate direct
N20 emissions (IPCC 2006). The IPCC (2006) Tier 1 method is used to estimate direct N20 emissions due to
drainage of organic soils in settlements at the national scale. Estimates of the total area of drained organic soils are
obtained from the 2012 NRI (USDA-NRCS 2015) using soils data from the Soil Survey Geographic Database
(SSURGO) (Soil Survey Staff 2011). To estimate annual emissions, the total area is multiplied by the IPCC default
emission factor for temperate regions (IPCC 2006). This Inventory does not include soil N20 emissions from
drainage of organic soils in Alaska and federal lands, although this is a planned improvement for a future Inventory.
For indirect emissions, the total N applied from fertilizer and sludge is multiplied by the IPCC default factors of 10
percent for volatilization and 30 percent for leaching/runoff to calculate the amount of N volatilized and the amount
of N leached/runoff The amount of N volatilized is multiplied by the IPCC default factor of one percent for the
portion of volatilized N that is converted to N20 off-site and the amount of N leached/runoff is multiplied by the
IPCC default factor of 0.075 percent for the portion of leached/runoff N that is converted to N20 off-site. The
resulting estimates are summed to obtain total indirect emissions.
Uncertainty and Time-Series Consistency
The amount of N20 emitted from settlements depends not only on N inputs and fertilized area as well as drained
organic soils, 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 N20 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 is assigned a default level of ±50 percent.75 Uncertainty in drained organic
soils is based on the estimated variance from the NRI survey (USDA-NRCS 2015). Uncertainty in the amounts of
sewage sludge applied to non-agricultural lands and used in surface disposal is 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. In addition, the uncertainty ranges around 2005 activity data and emission factor input variables are
directly applied to the 2015 emission estimates. Uncertainty in the direct and indirect emission factors is provided by
IPCC (2006).
Uncertainty is quantified using simple error propagation methods (IPCC 2006), and the results are summarized in
Table 6-78. Direct N20 emissions from soils in Settlements Remaining Settlements in 2015 are estimated to be
between 1.0 and 5.2 MMT C02 Eq. at a 95 percent confidence level. This indicates a range of 49 percent below to
163 percent above the 2015 emission estimate of 2.0 MMT C02 Eq. Indirect N20 emissions in 2015 are between 0.1
and 1.9 MMT C02 Eq., ranging from a -85 percent to 212 percent around the estimate of 0.6 MMT C02 Eq.
Table 6-78: Quantitative Uncertainty Estimates of N2O Emissions from Soils in Settlements
Remaining Settlements (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emissions
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.) (%)
Settlements Remaining
Settlements
Lower Upper
Bound Bound
Lower Upper
Bound Bound
Direct N2O Fluxes from Soils
N2O
2.0
1.0 5.2
-49% 163%
Indirect N2O Fluxes from Soils
N2O
0.6
0.1 1.9
-85% 212%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Note: These estimates include direct and indirect N2O emissions from N fertilizer additions to both Settlements Remaining
Settlements and from Land Converted to Settlements.
75 No uncertainty is provided with the USGS fertilizer consumption data (Ruddy et al. 2006) so a conservative ±50 percent is
used in the analysis.
6-108 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
2 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
3 above.
4 QA/QC and Verification
5 The spreadsheet containing fertilizer, drainage of organic soils, and sewage sludge applied to settlements and
6 calculations for N20 and uncertainty ranges have been checked and verified.
7 Recalculations Discussion
8 Methodological recalculations in the current Inventory are associated with accounting for emissions from drained
9 organic soils in settlements, which were not included in previous inventories. The change resulted in a relatively
10 minor increase emissions on average across the time series by 0.13 MMT CO2 Eq., which is an 8 percent increase in
11 the reported emissions compared to the previous Inventory.
12 Planned Improvements
13 This source will be extended to include soil N20 emissions from drainage of organic soils in settlements of Alaska
14 and federal lands in order to provide a complete inventory of emissions for this category.
15 Changes in Yard Trimming and Food Scrap Carbon Stocks in
16 Landfills
17 In the United States, yard trimmings (i.e., grass clippings, leaves, and branches) and food scraps account for a
18 significant portion of the municipal waste stream, and a large fraction of the collected yard trimmings and food
19 scraps are put in landfills. Carbon (C) contained in landfilled yard trimmings and food scraps can be stored for very
20 long periods.
21 Carbon-storage estimates within the Inventory are associated with particular land uses. For example, harvested wood
22 products are reported under Forest Land Remaining Forest Land because these wood products originated from the
23 forest ecosystem. Similarly, C stock changes in yard trimmings and food scraps are reported under Settlements
24 Remaining Settlements because the bulk of the C, which comes from yard trimmings, originates from settlement
25 areas. While the majority of food scraps originate from cropland and grassland, this Inventory has chosen to report
26 these with the yard trimmings in the Settlements Remaining Settlements section. Additionally, landfills are
27 considered part of the managed land base under settlements (see Section 6.1 Representation of the U.S. Land Base),
28 and reporting these C stock changes that occur entirely within landfills fits most appropriately within the Settlements
29 Remaining Settlements section.
30 Both the amount of yard trimmings collected annually and the fraction that is landfilled have declined over the last
31 decade. In 1990, over 53 million metric tons (wet weight) of yard trimmings and food scraps were generated (i.e.,
32 put at the curb for collection to be taken to disposal sites or to composting facilities) (EPA 2016). Since then,
33 programs banning or discouraging yard trimmings disposal have led to an increase in backyard composting and the
34 use of mulching mowers, and a consequent 2.3 percent decrease in the tonnage of yard trimmings generated (i.e.,
35 collected for composting or disposal in landfills). At the same time, an increase in the number of municipal
36 composting facilities has reduced the proportion of collected yard trimmings that are discarded in landfills—from 72
37 percent in 1990 to 31 percent in 2015. The net effect of the reduction in generation and the increase in composting is
38 a 57 percent decrease in the quantity of yard trimmings disposed of in landfills since 1990.
39 Food scrap generation has grown by 61 percent since 1990, and while the proportion of total food scraps generated
40 that are eventually discarded in landfills has decreased slightly, from 82 percent in 1990 to 76 percent in 2015, the
41 tonnage disposed of in landfills has increased considerably (by 50 percent) due to the increase in food scrap
42 generation. Although the total tonnage of food scraps disposed of in landfills has increased from 1990 to 2015, the
43 difference in the amount of food scraps added from one year to the next generally decreased, and consequently the
Land Use, Land-Use Change, and Forestry 6-109
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1 annual carbon stock net changes from food scraps have generally decreased as well (as shown in Table 6-79 and
2 Table 6-80). As described in the Methodology section, the carbon stocks are modeled using data on the amount of
3 food scraps landfilled since 1960. These food scraps decompose over time, producing CH4 and CO2. Decomposition
4 happens at a higher rate initially, then decreases. As decomposition decreases, the carbon stock becomes more
5 stable. Because the cumulative carbon stock left in the landfill from previous years is (1) not decomposing as much
6 as the carbon introduced from food scraps in a single more recent year; and (2) is much larger than the carbon
7 introduced from food scraps in a single more recent year, the total carbon stock in the landfill is primarily driven by
8 the more stable 'older' carbon stock, thus resulting in less annual change in later years."
9 Overall, the decrease in the landfill disposal rate of yard trimmings has more than compensated for the increase in
10 food scrap disposal in landfills, and the net result is a decrease in annual net change landfill C storage from 26.0
11 MMT C02 Eq. (7.1 MMT C) in 1990 to 11.8 MMT C02 Eq. (3.2 MMT C) in 2015 (Table 6-79 and Table 6-80).
12 Table 6-79: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
13 (MMT COz Eq.)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Yard Trimmings
(21.0)
(7.4)
(9.2)
(9.1)
(8.4)
(8.3)
(8.3)
Grass
(1.8)
(0.6)
(0.9)
(0.9)
(0.8)
(0.8)
(0.8)
Leaves
(9.0)
(3.4)
(4.2)
(4.1)
(3.9)
(3.8)
(3.8)
Branches
(10.2)
(3.4)
(4.1)
(4.1)
(3.8)
(3.7)
(3.7)
Food Scraps
(5.0)
(4.0)
(3.5)
(3.1)
(3.2)
(3.6)
(3.4)
Total Net Flux
(26.0)
(11.4)
(12.7)
(12.2)
(11.6)
(11.9)
(11.8)
Note: Totals may not sum due to independent rounding. Parentheses indicate net sequestration.
14 Table 6-80: Net Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills
15 (MMT C)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Yard Trimmings
(5.7)
(2.0)
(2.5)
(2.5)
(2.3)
(2.3)
(2.3)
Grass
(0.5)
(0.2) /¦
(0.2)
(0.2)
(0.2)
(0.2)
(0.2)
Leaves
(2.5)
(0.9) /.
(1.1)
(1.1)
(1.1)
(1.0)
(1.0)
Branches
(2.8)
(0.9)
(1.1)
(1.1)
(1.0)
(1.0)
(1.0)
Food Scraps
(1.4)
(1.1)
(1.0)
(0.9)
(0.9)
(1.0)
(0.9)
Total Net Flux
(7.1)
(3.1)
(3.5)
(3.3)
(3.2)
(3.3)
(3.2)
Note: Totals may not sum due to independent rounding. Parentheses indicate net
sequestration.
16 Methodology
17 When wastes of biogenic origin (such as yard trimmings and food scraps) are landfilled and do not completely
18 decompose, the C that remains is effectively removed from the C cycle. Empirical evidence indicates that yard
19 trimmings and food scraps do not completely decompose in landfills (Barlaz 1998, 2005, 2008; De la Cruz and
20 Barlaz 2010), and thus the stock of C in landfills can increase, with the net effect being a net atmospheric removal of
21 C. Estimates of net C flux resulting from landfilled yard trimmings and food scraps were developed by estimating
22 the change in landfilled C stocks between inventory years, based on methodologies presented for the Land Use,
23 Land-Use Change, and Forestry sector in IPCC (2003) and the 2006 LPPC Guidelines for National Greenhouse Gas
24 Lnventories. Carbon stock estimates were calculated by determining the mass of landfilled C resulting from yard
25 trimmings and food scraps discarded in a given year; adding the accumulated landfilled C from previous years; and
26 subtracting the mass of C that was landfilled in previous years and has since decomposed.
27 To determine the total landfilled C stocks for a given year, the following were estimated: (1) The composition of the
28 yard trimmings; (2) the mass of yard trimmings and food scraps discarded in landfills; (3) the C storage factor of the
29 landfilled yard trimmings and food scraps; and (4) the rate of decomposition of the degradable C. The composition
30 of yard trimmings was assumed to be 30 percent grass clippings, 40 percent leaves, and 30 percent branches on a
31 wet weight basis (Oshins and Block 2000). The yard trimmings were subdivided, because each component has its
32 own unique adjusted C storage factor (i.e., moisture content and C content) and rate of decomposition. The mass of
6-110 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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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
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
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 Advancing Sustainable Materials Management: Facts and Figures 2014 (EPA 2016),
which provides data for 1960, 1970, 1980, 1990, 2000, 2005, 2009 and 2011 through 2013. To provide data for
some of the missing years, detailed backup data were obtained from historical data tables that EPA developed for
1960 through 2013 (EPA 2015). Remaining years in the time series for which data were not provided were estimated
using linear interpolation. Data for 2015 are not yet available, so they were set equal to 2014 values. The EPA
(2016) report and historical data tables (EPA 2015) do not subdivide the discards (i.e., total generated minus
composted) of individual materials into masses landfilled and combusted, although it provides a mass of overall
waste stream discards managed in landfills76 and combustors with energy recovery (i.e., ranging from 67 percent
and 33 percent, respectively, in 1960 to 92 percent and 8 percent, respectively, in 1985); it is assumed that the
proportion of each individual material (food scraps, grass, leaves, branches) that is landfilled is the same as the
proportion across the overall waste stream.
The amount of C disposed of in landfills each year, starting in 1960, was estimated by converting the discarded
landfilled yard trimmings and food scraps from a wet weight to a dry weight basis, and then multiplying by the
initial (i.e., pre-decomposition) C content (as a fraction of dry weight). The dry weight of landfilled material was
calculated using dry weight to wet weight ratios (Tchobanoglous et al. 1993, cited by Barlaz 1998) and the initial C
contents and the C storage factors were determined by Barlaz (1998, 2005, 2008) (Table 6-81).
The amount of C remaining in the landfill for each subsequent year was tracked based on a simple model of C fate.
As demonstrated by Barlaz (1998, 2005, 2008), a portion of the initial C resists decomposition and is essentially
persistent in the landfill environment. Barlaz (1998, 2005, 2008) conducted a series of experiments designed to
measure biodegradation of yard trimmings, food scraps, and other materials, in conditions designed to promote
decomposition (i.e., by providing ample moisture and nutrients). After measuring the initial C content, the materials
were placed in sealed containers along with methanogenic microbes from a landfill. Once decomposition was
complete, the yard trimmings and food scraps were re-analyzed for C content; the C remaining in the solid sample
can be expressed as a proportion of the initial C (shown in the row labeled "C Storage Factor, Proportion of Initial C
Stored (%)" in Table 6-81).
The modeling approach applied to simulate U.S. landfill C flows builds on the findings of Barlaz (1998, 2005,
2008). The proportion of C stored is assumed to persist in landfills. The remaining portion is assumed to degrade
over time, resulting in emissions of CH4 and CO2. (The CH4 emissions resulting from decomposition of yard
trimmings and food scraps are accounted for in the Waste chapter.) The degradable portion of the C is assumed to
decay according to first-order kinetics. The decay rates for each of the materials are shown in Table 6-81.
The first-order decay rates, k. for each refuse type 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 calculated so that the weighted average decay rate for all components is equal to the EPA AP-
42 default decay rate (0.04) for mixed MSW for regions that receive more than 25 inches of rain annually (EPA
1995). Because AP-42 values were developed using landfill data from approximately 1990, 1990 waste composition
for the United States fromEPA's Characterization of Municipal Solid Waste in the United States: 1990 Update was
used to calculate f. 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 located, including
dry conditions (less than 25 inches of rain annually, A'=0.02) and bioreactor landfill conditions (moisture is
controlled for rapid decomposition, k= 0.12). As in the Landfills section of the Inventory (Section 7.1), which
estimates CH4 emissions, the overall MSW decay rate is estimated 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
76 EPA (2016 and 2015) reports discards in two categories: "combustion with energy recovery" and "landfill, other disposal,"
which includes combustion without energy recovery. For years in which there is data from previous EPA reports on combustion
without energy recovery, EPA assumes these estimates are still applicable. For 2000 to present, EPA assumes that any
combustion of MSW that occurs includes energy recovery, so all discards to "landfill, other disposal" are assumed to go to
landfills.
Land Use, Land-Use Change, and Forestry 6-111
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10
11
12
13
14
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17
18
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20
21
22
23
24
25
26
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32
33
34
35
36
37
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39
40
41
42
43
44
45
46
of rain per year, and (3) greater than 40 inches of rain per year. These correspond to overall MSW decay rates of
0.020, 0.038, and 0.057 year-1, respectively.
De la Cruz and Barlaz (2010) calculate component-specific decay rates corresponding to the first value (0.020
year1), but not for the other two overall MSW decay rates. To maintain consistency between landfill methodologies
across the Inventory, the correction factors (J) were developed for decay rates of 0.038 and 0.057 year1 through
linear interpolation. A weighted national average component-specific decay rate was calculated by assuming that
waste generation is proportional to population (the same assumption used in the landfill methane emission estimate),
based on population data from the 2000 U.S. Census. The component-specific decay rates are shown in Table 6-81.
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 Equation 1:
t
LFCi,t = £ Wi,n x (1 - MG) x ICCjx {[CSi x ICG[ + [(1 - (CS, x ICG)) x
where,
t = Year for which C stocks are being estimated (year),
i = Waste type for which C stocks are being estimated (grass, leaves, branches, food scraps),
LFCit = Stock of C in landfills in year I. for waste i (metric tons),
Il 'u, = Mass of waste /' disposed of in landfills in year n (metric tons, wet weight),
n = Year in which the waste was disposed of (year, where 1960
-------
C Storage Factor, Proportion of Initial C
Stored (%) 53 85 77 16
Initial C Content (%) 45 46 49 51
Decay Rate (year'1) 0.323 0.185 0.016 0.156
1 Table 6-82: C Stocks in Yard Trimmings and Food Scraps in Landfills (MMT C)
Carbon Pool
1990
2005
2011
2012
2013
2014
2015
Yard Trimmings
155.8
202.9
216.1
218.6
220.9
223.1
225.4
Branches
14.5
18.1
19.3
19.5
19.7
19.9
20.2
Leaves
66.7
87.3
93.4
94.5
95.5
96.6
97.6
Grass
74.6
97.5
103.4
104.5
105.6
106.6
107.6
Food Scraps
17.6
32.8
38.9
39.8
40.7
41.6
42.6
Total Carbon Stocks
173.5
235.6
255.0
258.3
261.5
264.8
268.0
Note: Totals may not sum due to independent rounding.
2 Uncertainty and Time-Series Consistency
3 The uncertainty analysis for landfilled yard trimmings and food scraps includes an evaluation of the effects of
4 uncertainty for the following data and factors: disposal in landfills per year (tons of C), initial C content, moisture
5 content, decay rate, and proportion of C stored. The C storage landfill estimates are also a function of the
6 composition of the yard trimmings (i.e., the proportions of grass, leaves and branches in the yard trimmings
7 mixture). There are respective uncertainties associated with each of these factors.
8 A Monte Carlo (Approach 2) uncertainty analysis was applied to estimate the overall uncertainty of the
9 sequestration estimate. The results of the Approach 2 quantitative uncertainty analysis are summarized in Table
10 6-83. Total yard trimmings and food scraps CO2 flux in 2015 was estimated to be between -18.3 and -4.6 MMT CO2
11 Eq. at a 95 percent confidence level (or 19 of 20 Monte Carlo stochastic simulations). This indicates a range of 56
12 percent below to 61 percent above the 2015 flux estimate of -11.8 MMT CO2 Eq.
13 Table 6-83: Approach 2 Quantitative Uncertainty Estimates for CO2 Flux from Yard
14 Trimmings and Food Scraps in Landfills (MMT CO2 Eq. and Percent)
2015 Flux
Source
Gas
Estimate
Uncertainty Range Relative to Flux Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower Upper
Lower Upper
Bound Bound
Bound Bound
Yard Trimmings and Food
Scraps
CO2
(11.8)
(18.3) (4.6)
-56% 61%
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.
15 Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
16 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
17 above.
is QA/QC and Verification
19 Tier 1 and Tier 2 QA/QC activities were conducted consistent with the U.S. QA/QC plan. Source-specific quality
20 control measures for Landfilled Yard Trimming and Food Scraps included checking that input data were properly
21 transposed within the spreadsheet, checking calculations were correct, and confirming that all activity data and
22 calculations documentation was complete and updated to ensure data were properly handled through the inventory
23 process.
24 Order of magnitude checks and checks of time-series consistency were performed to ensure data were updated
25 correctly and any changes in emissions estimates were reasonable and reflected changes in activity data. An annual
Land Use, Land-Use Change, and Forestry 6-113
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1 change trend analysis was also conducted to ensure the validity of the emissions estimates. Errors that were found
2 during this process were corrected as necessary.
3 Recalculations Discussion
4 As noted in the Methodology section, activity data for this category are obtained from the Advancing Sustainable
5 Materials Management: Facts and Figures report. The current Inventory has been revised to reflect updated data in
6 the most recent report. The recalculations based on these updates resulted in the following changes for this category:
7 a 0.3 percent decrease in sequestration in 2012, a 0.6 decrease in sequestration in 2013, and a 3.1 percent increase in
8 sequestration in 2014.
9 Planned Improvements
10 Future work is planned to evaluate the consistency between the estimates of C storage described in this chapter and
11 the estimates of landfill CH4 emissions described in the Waste chapter. For example, the Waste chapter does not
12 distinguish landfill CH4 emissions from yard trimmings and food scraps separately from landfill CH4 emissions from
13 total bulk (i.e., municipal solid) waste, which includes yard trimmings and food scraps.
14 In addition, EPA will evaluate additional data from recent peer-reviewed literature that may modify the default C
15 storage factors, initial C contents, and decay rates for yard trimmings and food scraps in landfills. Based upon this
16 evaluation, changes may be made to the default values. EPA will also investigate updating the weighted national
17 average component-specific decay rate using new U.S. Census data, if any are available.
18 EPA will also evaluate the yard waste composition to determine if changes need to be made based on changes in
19 residential practices, EPA will conduct a review of available literature to determine if there are changes in the
20 allocation of yard trimmings. For example, leaving grass clippings in place is becoming a more common practice,
21 thus reducing the percentage of grass clippings in yard trimmings disposed in landfills.
22 6.11 Land Converted to Settlements (IPCC
23 Source Category 3B5b)
24 Land Converted to Settlements includes all settlements in an Inventory year that had been in another land use(s)
25 during the previous 20 years (USDA-NRCS 2015).77 For example, cropland, grassland or forest land converted to
26 settlements during the past 20 years would be reported in this category. Recently-converted lands are retained in this
27 category for 20 years as recommended by IPCC (2006). This Inventory includes all settlements in the conterminous
28 U.S. and Hawaii, but does not include settlements in Alaska. Areas of drained organic soils on settlements in federal
29 lands are also not included in this Inventory. Consequently, there is a discrepancy between the total amount of
30 managed area for Land Converted to Settlements (see Section 0—Representation of the U.S. Land Base) and the
31 settlements area included in the inventory analysis.
32 Land use change can lead to large losses of carbon (C) to the atmosphere, particularly conversions from forest land
33 (Houghton et al. 1983). Moreover, conversion of forest to another land use (i.e., deforestation) is one of the largest
34 anthropogenic sources of emissions to the atmosphere globally (Schimel 1995), although this source may be
35 declining according to a recent assessment (Tubiello et al. 2015).
36 IPCC (2006) recommends reporting changes inbiomass, dead organic matter, and soil organic C (SOC) stocks due
37 to land use change.78 All soil C stock changes are estimated and reported for Land Converted to Settlements, but
77 NRI survey locations are classified according to land-use histories starting in 1979, and consequently the classifications are
based on less than 20 years from 1990 to 2001. This may have led to an underestimation of Land Converted to Settlements in the
early part of the time series to the extent that some areas are converted to settlements from 1971 to 1978.
78 CO2 emissions associated with liming and urea fertilization are also estimated but included in the Agriculture chapter of the
report.
6-114 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 there is limited reporting of other pools in this Inventory. Loss of aboveground and belowground biomass, dead
2 wood and litter C are reported for Forest Land Converted to Settlements, but not for other land use conversions to
3 settlements.
4 Forest Land Converted to Settlements from 1990 to 2015 led to losses of aboveground and belowground biomass,
5 dead wood and litter C losses that averaged 89.0, 18.2, 15.6, and 15.6 MMT CO2 Eq. per year (24.3, 5.0, 4.3, and 4.2
6 MMT C per year). Mineral and organic soils also lost an average of 20.2 and 2.0 MMT CO2 Eq. per year (5.5 and
7 0.6 MMT C per year) between 1990 and 2015. The total net flux is 150.2 MMT CO2 Eq. in 2015, which is a 21
8 percent increase in CO2 emissions compared to the emissions in the initial reporting year of 1990.
9 Table 6-84: Net CO2 Flux from Soil, Dead Organic Matter and Biomass C Stock Changes for
10 Land Converted to Settlements (MMT CO2 Eq.)
1990
2005
2011
2012
2013
2014
2015
Cropland Converted to
Settlements
4.1
11.9
10.5
10.3
10.3
10.3
10.3
Mineral Soils
3.5
10.7
9.6
9.4
9.4
9.4
9.4
Organic Soils
0.6
L2
0.9
0.9
0.9
0.9
0.9
Forest Land Converted to
Settlements
115.6
137.5
133.7
126.8
126.8
126.8
126.8
Aboveground Live Biomass
72.4
87.7
87.0
82.6
82.6
82.6
82.6
Belowground Live Biomass
15.0
17.9
17.5
16.6
16.6
16.6
16.6
Dead Wood
13.8
15.4
13.5
12.6
12.6
12.6
12.6
Litter
13.4
15.2
14.4
13.7
13.7
13.7
13.7
Mineral Soils
0.9
1.3
1.3
1.3
1.3
1.3
1.3
Organic Soils
+
+
+
+
+
+
+
Grassland Converted
Settlements
4.0
13.5
12.6
12.4
12.4
12.4
12.4
Mineral Soils
3.5
12.3
11.7
11.5
11.5
11.5
11.5
Organic Soils
0.5
1.2
0.9
0.8
0.8
0.8
0.8
Other Lands Converted to
Settlements
0.2
0.7
0.7
0.7
0.7
0.7
0.7
Mineral Soils
0.2
0.6
0.6
0.6
0.6
0.6
0.6
Organic Soils
+
0.1
0.1
0.1
0.1
0.1
0.1
Wetlands Converted to
Settlements
+
0.1
0.1
0.1
0.1
0.1
0.1
Mineral Soils
+
0.1
0.1
0.1
0.1
0.1
0.1
Organic Soils
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Total Aboveground Biomass
Flux
72.4
87.7
87.0
82.6
82.6
82.6
82.6
Total Belowground Biomass
Flux
15.0
17.9
17.5
16.6
16.6
16.6
16.6
Total Dead Wood Flux
13.8
15.4
13.5
12.6
12.6
12.6
12.6
Total Litter Flux
13.4
15.2
14.4
13.7
13.7
13.7
13.7
Total Mineral Soil Flux
8.0
24.9
23.3
22.9
22.9
22.9
22.9
Total Organic Soil Flux
1.1
2.5
1.9
1.9
1.8
1.8
1.9
Total Net Flux
123.8
163.6
157.6
150.2
150.2
150.2
150.2
+ Does not exceed 0.05 MMT CO2 Eq.
Notes: Totals may not sum due to independent rounding. Estimates after 2012 for mineral and organic soils are based on
NRI data from 2012 and therefore may not fully reflect changes occurring in the latter part of the time series.
11
12 Table 6-85: Net CO2 Flux from Soil, Dead Organic Matter and Biomass C Stock Changes for
13 Land Con verted to Settlements ( M MT C)
1990
2005
2011
2012
2013
2014
2015
Cropland Converted to
Settlements
1.1
3.2
2.9
2.8
2.8
2.8
2.8
Mineral Soils
0.9
2.9
2.6
2.6
2.6
2.6
2.6
Organic Soils
0.2
0.3
0.3
0.2
0.2
0.2
0.2
Land Use, Land-Use Change, and Forestry 6-115
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3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
Forest Land Converted to
Settlements
31.5
37.5
36.5
34.6
34.6
34.6
34.6
Aboveground Live Biomass
19.8
23.9
23.7
22.5
22.5
22.5
22.5
Belowground Live Biomass
4.1
4.9
4.8
4.5
4.5
4.5
4.5
Dead Wood
3.8
4.2
3.7
3.4
3.4
3.4
3.4
Litter
3.7
4.1
3.9
3.7
3.7
3.7
3.7
Mineral Soils
0.3
0.4
0.4
0.4
0.4
0.4
0.4
Organic Soils
+
+
+
+
+
+
+
Grassland Converted
Settlements
1.1
3.7
3.4
3.4
3.4
3.4
3.4
Mineral Soils
0.9
3.4
3.2
3.1
3.1
3.1
3.1
Organic Soils
0.1
0.3
0.2
0.2
0.2
0.2
0.2
Other Lands Converted to
Settlements
+
0.2
0.2
0.2
0.2
0.2
0.2
Mineral Soils
+
0.2
0.2
0.2
0.2
0.2
0.2
Organic Soils
+
+
+
+
+
+
+
Wetlands Converted to
Settlements
+
+
+
+
+
+
+
Mineral Soils
+
+
+
+
+
+
+
Organic Soils
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Total Aboveground Biomass
Flux
19.8
23.9
23.7
22.5
22.5
22.5
22.5
Total Belowground Biomass Flux
4.1
4.9
4.8
4.5
4.5
4.5
4.5
Total Dead Wood Flux
3.8
4.2
3.7
3.4
3.4
3.4
3.4
Total Litter Flux
3.7
4.1
3.9
3.7
3.7
3.7
3.7
Total Mineral Soil Flux
2.2
6.8
6.4
6.2
6.3
6.3
6.3
Total Organic Soil Flux
0.3
0.7
0.5
0.5
0.5
0.5
0.5
Total Net Flux
33.8
44.6
43.0
41.0
41.0
41.0
41.0
+ Does not exceed 0.05 MMT C
Notes: Totals may not sum due to independent rounding. Estimates after 2012 for mineral and organic soils are based on
NRI data from 2012 and therefore may not fully reflect changes occurring in the latter part of the time series.
Methodology
The following section includes a description of the methodology used to estimate C stock changes for Land
Converted to Settlements, including (1) loss of aboveground and belowground biomass, dead wood and litter C with
conversion of forest lands to settlements, as well as (2) the impact of land use conversions to settlements on mineral
and organic soils.
Biomass, Dead Biomass, and Litter Carbon Stock Changes
A Tier 2 method is applied to estimate aboveground biomass C stock changes for Forest Land Converted to
Settlements. For this method, forest land conversions to settlements were identified in each state and C density
estimates were compiled by state for aboveground biomass, belowground biomass, dead wood, and litter for
settlements (assumed to be zero since no reference biomass C density estimates exist) and forest land use categories.
The difference between the stocks is reported as the stock change under the assumption that the change occurred in
the year of the conversion. Reference C density estimates (i.e., aboveground biomass, belowground biomass, dead
wood, and litter) for forest lands have been estimated from data in the Forest Inventory and Analysis (FIA) program
within the USDA Forest Service (USDA Forest Service 2015). If FIA plots include data on individual trees,
aboveground and belowground C density estimates are based on Woodall et al. (2011). Aboveground and
belowground biomass estimates also include live understory which is a minor component of biomass defined as all
biomass of undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm dbh. For this
Inventory, it was assumed that 10 percent of total understory C mass is belowground (Smith et al. 2006). Estimates
of C density are based on information in Birdsey (1996) and biomass estimates from Jenkins et al. (2003). If FIA
plots include data on standing dead trees, standing dead tree C density is estimated following the basic method
applied to live trees (Woodall et al. 2011) with additional modifications to account for decay and structural loss
(Domke et al. 2011; Harmon et al. 2011). If FIA plots include data on downed dead wood, downed dead wood C
density is estimated based on measurements of a subset of FIA plots for downed dead wood (Domke et al. 2013;
6-116 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Woodall and Monleon 2008). Downed dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at
transect intersection, that are not attached to live or standing dead trees. This includes stumps and roots of harvested
trees. To facilitate the downscaling of downed dead wood C estimates from the state-wide population estimates to
individual plots, downed dead wood models specific to regions and forest types within each region are used. Litter C
is the pool of organic C (also known as duff, humus, and fine woody debris) above the mineral soil and includes
woody fragments with diameters of up to 7.5 cm. A subset of FIA plots are measured for litter C. If FIA plots
include litter material, a modeling approach using litter C measurements from FIA plots is used to estimate litter C
density (Domke et al. 2016). See Annex 3.13 for more information about reference C density estimates for forest
land.
Soil Carbon Stock Changes
Soil C stock changes are estimated for Land Converted to Settlements according to land-use histories recorded in the
2012 USDA NRI survey for non-federal lands (USDA-NRCS 2015). Land use and some management information
were originally collected for each NRI survey locations on a 5-year cycle beginning in 1982. In 1998, the NRI
program began collecting annual data, and the annual data are currently available through 2012 (USDA-NRCS
2015). However, this Inventory only uses NRI data through 2012 because newer data were not available. NRI
survey locations are classified as Land Converted to Settlements in a given year between 1990 and 2012 if the land
use is settlements but had been classified as another use during the previous 20 years. NRI survey locations are
classified according to land-use histories starting in 1979, and consequently the classifications are based on less than
20 years from 1990 to 1998. This may have led to an underestimation of Land Converted to Settlements in the early
part of the time series to the extent that some areas are converted to grassland between 1971 and 1978. For federal
lands, the land use history is derived from land cover changes in the National Land Cover Dataset (Homer et al.
2007; Fry et al. 2011; Homer et al. 2015).
Mineral Soil Carbon Stock Changes
An IPCC Tier 2 method (Ogle et al. 2003) is applied to estimate C stock changes for Land Converted to Settlements
on mineral soils. Data on climate, soil types, land-use, and land management activity are used to classify land area
and apply appropriate stock change factors (Ogle et al. 2003, 2006). Reference C stocks are estimated using the
National Soil Survey Characterization Database (NRCS 1997) with cultivated cropland as the reference condition,
rather than native vegetation as used in IPCC (2006). Soil measurements under agricultural management are much
more common and easily identified in the National Soil Survey Characterization Database (NRCS 1997) than are
soils under a native condition, and therefore cultivated cropland provide a more robust sample for estimating the
reference condition. United States-specific C stock change factors are derived from published literature to determine
the impact of management practices on SOC storage (Ogle et al. 2003, Ogle et al. 2006). However, there are
insufficient data to estimate a set of land use, management, and input factors for settlements. Moreover, the 2012
NRI survey data (USDA-NRCS 2015) do not provide the information needed to assign different land use
subcategories to settlements, such as turf grass and impervious surfaces, which is needed to apply the Tier 1 factors
from the IPCC guidelines (2006). Therefore, the United States has adopted a land use factor of 0.7 to represent the
loss of carbon with conversion to settlements, which is similar to the estimated losses with conversion to cropland.
More specific factor values can be derived in future inventories as data become available. See Annex 3.12 for
additional discussion of the Tier 2 methodology for mineral soils.
Organic Soil Carbon Stock Changes
Annual C emissions from drained organic soils in Land Converted to Settlements are estimated using the Tier 2
method provided in IPCC (2006). The Tier 2 method assumes that organic soils are losing C at a rate similar to
croplands, and therefore uses the country-specific values for cropland (Ogle et al. 2003). To estimate CO2 emissions,
the total area of organic soils in Land Converted to Settlements is multiplied by the Tier 2 emission factor, which is
11.2 MMT C per ha in cool temperate regions, 14.0 MMT C per ha in warm temperate regions and 11.2 MMT C per
ha in subtropical regions (See Annex 3.12 for more information).
Land Use, Land-Use Change, and Forestry 6-117
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1 Uncertainty and Time-Series Consistency
2 The uncertainty analysis for C losses with Forest Land Converted to Settlements is conducted in the same way as the
3 uncertainty assessment for forest ecosystem C flux in the Forest Land Remaining Forest Land category. Sample and
4 model-based error are combined using simple error propagation methods provided by the IPCC (2006). For
5 additional details see the Uncertainty Analysis in Annex 3.13. The uncertainty analysis for mineral soil C stock
6 changes and annual C emission estimates from drained organic soils in Land Converted to Settlements is estimated
7 using a Monte Carlo approach, which is also described in the Cropland Remaining Cropland section.
8 Uncertainty estimates are presented in Table 6-86 for each subsource (i.e., biomass C stocks, mineral soil C stocks
9 and organic soil C stocks) and the method applied in the inventory analysis (i.e., Tier 2 and Tier 3). Uncertainty
10 estimates from the Tier 2 and 3 approaches are combined using the simple error propagation methods provided by
11 the IPCC (2006), i.e., by taking the square root of the sum of the squares of the standard deviations of the uncertain
12 quantities. The combined uncertainty for total C stocks in Land Converted to Settlements ranges from 4 percent
13 below to 4 percent above the 2015 stock change estimate of 150.2 MMT CO2 Eq.
14 Table 6-86: Approach 2 Quantitative Uncertainty Estimates for C Stock Changes occurring
15 within Land Converted to Settlements (MMT CO2 Eq. and Percent)
2015 Flux Estimate Uncertainty Range Relative to Flux Estimate3
Source (MMT CO2 Eq.) (MMT CO2 Eq.) (%^
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Cropland Converted to Settlements
10.3
9.3
11.5
-10%
11%
Mineral Soil C Stocks
9.4
8.5
10.4
-10%
10%
Organic Soil C Stocks
0.9
0.5
1.5
-45%
62%
Forest Land Converted to Settlements
126.8
121.3
132.2
-4%
4%
Aboveground Biomass C Stocks
82.6
78.0
87.2
-6%
6%
Belowground Biomass C Stocks
16.6
14.5
18.6
-12%
12%
Dead Wood
12.6
10.8
14.4
-14%
14%
Litter
13.7
12.5
14.9
-9%
9%
Mineral Soil C Stocks
1.3
1.2
1.4
-10%
10%
Organic Soil C Stocks
+
+
+
-60%
85%
Grassland Converted to Settlements
12.4
11.2
13.7
-10%
11%
Mineral Soil C Stocks
11.5
10.4
12.8
-10%
10%
Organic Soil C Stocks
0.8
0.5
1.4
-47%
64%
Other Lands Converted to Settlements
0.7
0.6
0.9
-17%
33%
Mineral Soil C Stocks
0.6
0.5
0.7
-10%
10%
Organic Soil C Stocks
0.1
+
0.3
-98%
208%
Wetlands Converted to Settlements
0.1
0.1
0.1
-10%
10%
Mineral Soil C Stocks
0.1
0.1
0.1
-10%
10%
Organic Soil C Stocks
0.0
0.0
0.0
0%
0%
Total: Land Converted to Settlements
150.2
144.5
156.0
-4%
4%
Aboveground Biomass C Stocks
82.6
78.0
87.2
-6%
6%
Belowground Biomass C Stocks
16.6
14.5
18.6
-12%
12%
Dead Wood
12.6
10.8
14.4
-14%
14%
Litter
13.7
12.5
14.9
-9%
9%
Mineral Soil C Stocks
22.9
21.5
24.5
-6%
7%
Organic Soil C Stocks
1.9
1.0
3.1
-46%
65%
+ Does not exceed 0.05 MMT CO2 Eq.
a Range of C stock change estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
16 Methodological recalculations were applied to the entire time-series to ensure time-series consistency from 1990
17 through 2015. Details on the emission trends through time are described in more detail in the Methodology section,
18 above.
6-118 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 QA/QC and Verification
2 Quality control measures included checking input data, model scripts, and results to ensure data are properly
3 handled throughout the inventory process. Inventory reporting forms and text are reviewed and revised as needed to
4 correct transcription errors.
5 Planned Improvements
6 A planned improvement for the Land Converted to Settlements category is to develop an inventory of C stock
7 changes in Alaska. This includes C stock changes for biomass, dead organic matter and soils. There are also plans to
8 extend the Inventory to included C losses associated with drained organic soils in settlements occurring on federal
9 lands.
10 6.12 Other Land Remaining Other Land
11 (IPCC Source Category 4F1)
12 Land use is constantly occurring, and areas under a number of differing land-use types remain in their respective
13 land-use type each year, just as other land can remain as other land. While the magnitude of Other Land Remaining
14 Other Land is known (see Table 6-7), research is ongoing to track C pools in this land use. Until such time that
15 reliable and comprehensive estimates of C for Other Land Remaining Other Land can be produced, it is not possible
16 to estimate CO2, CH4 or N2O fluxes on Other Land Remaining Other Land at this time.
17 6.13 Land Converted to Other Land (IPCC
is Source Category 4F2)
19 Land-use change is constantly occurring, and areas under a number of differing land-use types are converted to other
20 land each year, just as other land is converted to other uses. While the magnitude of these area changes is known
21 (see Table 6-7), research is ongoing to track C across Other Land Remaining Other Land and Land Converted to
22 Other Land. Until such time that reliable and comprehensive estimates of C across these land-use and land-use
23 change categories can be produced, it is not possible to separate CO2, CH4 or N20 fluxes on Land Converted to
24 Other Land from fluxes on Other Land Remaining Other Land at this time.
25
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7. Waste
Waste management and treatment activities are sources of greenhouse gas emissions (see Figure 7-1). Landfills
accounted for approximately 17.7 percent of total U.S. anthropogenic methane (CH4) emissions in 2015, the third
largest contribution of any CH4 source in the United States. Additionally, wastewater treatment and composting of
organic waste accounted for approximately 2.3 percent and 0.3 percent of U.S. CH4 emissions, respectively. Nitrous
oxide (N20) emissions from the discharge of wastewater treatment effluents into aquatic enviromnents were
estimated, as were N20 emissions from the treatment process itself. Nitrous oxide emissions from composting were
also estimated. Together, these waste activities account for 2.1 percent of total U.S. N20 emissions. Nitrogen oxides
(NOx), carbon monoxide (CO), and non-CH4 volatile organic compounds (NMVOCs) are emitted by waste
activities, and are addressed separately at the end of this chapter. A summary of greenhouse gas emissions from the
Waste chapter is presented in Table 7-1 and Table 7-2.
Figure 7-1: 2015 Waste Chapter Greenhouse Gas Sources (MMT CO2 Eq.)
Landfills
Wastewater Treatment
Composting
116
Waste as a Portion of all Emissions
2.1%
MMT CO* Eq
Overall, in 2015, waste activities generated emissions of 139.4 MMT CO2 Eq., or 2.1 percent of total U.S.
greenhouse gas emissions.
Table 7-1: Emissions from Waste (MMT CO2 Eq.)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CH4
195.6
152.1
136.2
137.9
133.7
133.5
132.6
Landfills
179.6
134.3
119.0
120.8
116.7
116.6
115.7
Wastewater Treatment
15.7
16.0
15.3
15.1
14.9
14.8
14.8
Composting
0.4
1.9
1.9
1.9
2.0
2.1
2.1
N2O
3.7
6.1
6.4
6.6
6.7
6.8
6.9
Wastewater Treatment
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Composting
0.3
1.7
1.7
1.7
1.8
1.9
1.9
Total
199.3
158.2
142.6
144.4
140.4
140.2
139.4
Note: Totals may not sum due to independent rounding.
Waste 7-1
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1 Table 7-2: Emissions from Waste (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
CH4
7,825
6,085
5,448
5,516
5,347
5,338
5,303
Landfills
7,182
5,372
4,760
4,834
4,669
4,663
4,628
Wastewater Treatment
627
639
613
604
597
592
591
Composting
15
75
75
77
81
84
84
N2O
12
20
22
22
23
23
23
Wastewater Treatment
11
15
16
16
16
16
17
Composting
1
6
6
6
6
6
6
Note: Totals may not sum due to independent rounding.
2 Carbon dioxide (CO2), CH4, and N20 emissions from the incineration of waste are accounted for in the Energy
3 sector rather than in the Waste sector because almost all incineration of municipal solid waste (MSW) in the United
4 States occurs at waste-to-energy facilities where useful energy is recovered. Similarly, the Energy sector also
5 includes an estimate of emissions from burning waste tires and hazardous industrial waste, because virtually all of
6 the combustion occurs in industrial and utility boilers that recover energy. The incineration of waste in the United
7 States in 2015 resulted in 11.0 MMT CO2 Eq. emissions, more than half of which is attributable to the combustion
8 of plastics. For more details on emissions from the incineration of waste, see Section 7.4.
9
10
Box 7-1: Waste Data from the Greenhouse Gas Reporting Program
On October 30, 2009, the U.S. EPA published a rule for the mandatory reporting of greenhouse gases from large
greenhouse gas emissions sources in the United States. Implementation of 40 CFR Part 98 is referred to as EPA's
Greenhouse Gas Reporting Program (GHGRP). 40 CFR Part 98 applies to direct greenhouse gas emitters, fossil
fuel suppliers, industrial gas suppliers, and facilities that inject CO2 underground for sequestration or other
reasons and requires reporting by 41 industrial categories. Reporting is at the facility level, except for certain
suppliers of fossil fuels and industrial greenhouse gases. In general, the threshold for reporting is 25,000 metric
tons or more of CO2 Eq. per year.
EPA's GHGRP dataset and the data presented in this Inventory report are complementary and, as indicated in the
respective planned improvements sections for source categories in this chapter, EPA is analyzing how to use
facility-level GHGRP data to improve the national estimates presented in this Inventory. Most methodologies
used in EPA's GHGRP are consistent with IPCC, though for EPA's GHGRP, facilities collect detailed
information specific to their operations according to detailed measurement standards. This may differ with the
more aggregated data collected for the Inventory to estimate total, national U.S. emissions. It should be noted that
the definitions for source categories in EPA's GHGRP may differ from those used in this Inventory in meeting
the UNFCCC reporting guidelines. In line with the UNFCCC reporting guidelines, the Inventory report is a
comprehensive accounting of all emissions from source categories identified in the 2006 IPCC Guidelines (IPCC
2006). Further information on the reporting categorizations in EPA's GHGRP and specific data caveats
associated with monitoring methods in EPA's GHGRP lias been provided on the EPA's GHGRP website.1
EPA presents the data collected by EPA's GHGRP through a data publication tool that allows data to be viewed
in several formats including maps, tables, charts and graphs for individual facilities or groups of facilities.2
1 See
.
2 See .
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7.1 Landfills (IPCC Source Category 5A1)
In the United States, solid waste is managed by landfilling, recovery through recycling or composting, and
combustion through waste-to-energy facilities (see Box 7-3). Disposing of solid waste in modern, managed landfills
is the most commonly used waste management technique in the United States. More information on how solid waste
data are collected and managed in the United States is provided in Box 7-2. The municipal solid waste (MSW) and
industrial waste landfills referred to in this section are all modern landfills that must comply with a variety of
regulations as discussed in Box 7-4. Disposing of waste in illegal dumping sites is not considered to have occurred
in years later than 1980 and these sites are not considered to contribute to net emissions in this section for the
timeframe of 1990 to the current Inventory year. MSW landfills, or sanitary landfills, are sites where MSW is
managed to prevent or minimize health, safety, and environmental impacts. Waste is deposited in different cells and
covered daily with soil; many have environmental monitoring systems to track performance, collect leachate, and
collect landfill gas. Industrial waste landfills are constructed in a similar way as MSW landfills, but accept waste
produced by industrial activity, such as factories, mills, and mines.
After being placed in a landfill, organic waste (such as paper, food scraps, and yard trimmings) is initially
decomposed by aerobic bacteria. After the oxygen has been depleted, the remaining waste is available for
consumption by anaerobic bacteria, which break down organic matter into substances such as cellulose, amino acids,
and sugars. These substances are further broken down through fermentation into gases and short-chain organic
compounds that form the substrates for the growth of methanogenic bacteria. These methane (CH4) producing
anaerobic bacteria convert the fermentation products into stabilized organic materials and biogas consisting of
approximately 50 percent biogenic carbon dioxide (CO2) and 50 percent CH4, by volume. Landfill biogas also
contains trace amounts of non-methane organic compounds (NMOC) and volatile organic compounds (VOC) that
either result from decomposition by-products or volatilization of biodegradable wastes (EPA 2008).
Methane and CO2 are the primary constituents of landfill gas generation and emissions. However, the 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines set an international convention to not report
biogenic CO2 released due to landfill decomposition in the Waste sector (IPCC 2006). Carbon dioxide emissions
from landfills are estimated and reported under the Land Use, Land-Use Change, and Forestry (LULUCF) sector.
Additionally, emissions of NMOC and VOC are not estimated because they are considered to be emitted in trace
amounts. Nitrous oxide (N20) emissions from the disposal and application of sewage sludge on landfills are also not
explicitly modeled as part of greenhouse gas emissions from landfills. Nitrous oxide emissions from sewage sludge
applied to landfills as a daily cover or for disposal are expected to be relatively small because the microbial
environment in an anaerobic landfill is not very conducive to the nitrification and denitrification processes that result
in N2O emissions. Furthermore, the 2006 IPCC Guidelines did not include a methodology for estimating N2O
emissions from solid waste disposal sites "because they are not significant." Therefore, only CH4 generation and
emissions are estimated for landfills under the Waste sector.
Methane generation and emissions from landfills are a function of several factors, including: (1) the total amount of
waste-in-place, which is the total waste landfilled annually over the operational lifetime of a landfill; (2) the
characteristics of the landfill receiving waste (e.g., composition of waste-in-place, size, climate, cover material); (3)
the amount of CH4 that is recovered and either flared or used for energy purposes; and (4) the amount of CH4
oxidized as the landfill gas - that is not collected by a gas collection system - passes through the cover material into
the atmosphere. Each landfill has unique characteristics, but all managed landfills employ similar operating
practices, including the application of a daily and intermediate cover material over the waste being disposed of in the
landfill to prevent odor and reduce risks to public health. Based on recent literature, the specific type of cover
material used can affect the rate of oxidation of landfill gas (RTI2011). The most commonly used cover materials
are soil, clay, and sand. Some states also permit the use of green waste, tarps, waste derived materials, sewage
sludge or biosolids, and contaminated soil as a daily cover. Methane production typically begins within the first year
after the waste is disposed of in a landfill and will continue for 10 to 60 years or longer as the degradable waste
decomposes over time.
In 2015, landfill CH4 emissions were approximately 115.7 MMT CO2 Eq. (4,628 kt), representing the third largest
source of CH4 emissions in the United States, behind natural gas systems and enteric fermentation. Emissions from
MSW landfills accounted for approximately 95 percent of total landfill emissions, while industrial landfills
accounted for the remainder. Estimates of operational MSW landfills in the United States have ranged from 1,900 to
Waste 7-3
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2,000 facilities (EPA 2016a; EPA 2016b; WBJ 2010). More recently, the Environment Research & Education
Foundation conducted a nationwide analysis of MSW management, and counted 1,540 operational MSW landfills in
2013 (EREF 2016). Conversely, there are approximately 3,200 MSW landfills in the United States that have been
closed since 1980 (for which a closure date is known) (EPA 2016a; WBJ 2010). While the number of active MSW
landfills has decreased significantly over the past 20 years, from approximately 6,326 in 1990 to 1,540 in the 2013,
the average landfill size has increased (EREF 2016; EPA 2016b; BioCycle 2010). While the exact number of active
and closed industrial waste landfills exist in the United States, the number of them is relatively low compared to
MSW landfills. The Waste Business Journal database (WBJ 2010) includes a total for 1,305 landfills accepting
industrial and construction and demolition debris for 2010 (WBJ 2010). Only 176 facilities with industrial waste
landfills met the reporting threshold under Subpart TT (Industrial Waste Landfills) of EPA's Greenhouse Gas
Reporting Program (GHGRP), indicating that there may be several hundreds of industrial waste landfills that are not
required to report under EPA's GHGRP.
The annual amount of MSW generated and subsequently disposed in MSW landfills varies annually and depends on
several factors (e.g., the economy, consumer patterns, recycling and composting programs, inclusion in a garbage
collection service). The estimated annual quantity of waste placed in MSW landfills increased 10 percent from
approximately 205.1 MMT in 1990 to 226.4 MMT in 2000 and then decreased by 11 percent to 203.4 MMT in 2015
(see Annex 3.14). The total amount of MSW generated is expected to increase as the U.S. population continues to
grow, but the percentage of waste landfilled may decline due to increased recycling and composting practices. The
estimated quantity of waste placed in industrial waste landfills (from the pulp and paper, and food processing
sectors) has remained relatively steady since 1990, ranging from 9.7 MMT in 1990 to 10.5 MMT in 2015.
Net CH4 emissions from MSW landfills have decreased since 1990. In 1990, approximately 0.7 MMT of CH4 were
recovered and combusted from landfills (see Table 7-4), while in 2015, approximately 7.4 MMT of CH4 were
recovered and combusted, representing an average annual increase in the quantity of CH4 recovered and combusted
at MSW landfills from 1990 to 2015 of 11 percent (see Annex 3.14). The decreasing trend since the 1990s can be
mostly attributed to increased use of gas collection and control systems, and a reduction of decomposable materials
(i.e., paper and paperboard, food scraps, and yard trimmings) discarded in MSW landfills over the time series. The
quantity of recovered CH4 that is collected and either flared or used for energy purposes at MSW landfills has
continually increased as a result of 1996 federal regulations that require large MSW landfills to collect and combust
landfill gas (see 40 CFR Part 60, Subpart Cc 2005 and 40 CFR Part 60, Subpart WWW 2005). Voluntary programs
that encourage CH4 recovery and beneficial reuse, such as EPA's Landfill Methane Outreach Program (LMOP) and
federal and state incentives that promote renewable energy (e.g., tax credits, low interest loans, and Renewable
Portfolio Standards), have also contributed to increased interest in landfill gas collection and control.
In 2015, an estimated 11 new landfill gas-to-energy (LFGTE) projects (EPA 2016a) began operation. While the
amount of landfill gas collected and combusted continues to increase, 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.
Landfill gas collection and control is not accounted for at industrial waste landfills in this chapter (see the
Methodology discussion for more information).
Table 7-3: ChU Emissions from Landfills (MMT CO2 Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
MSW CH4 Generation
205.3
-
-
-
-
-
-
Industrial CH4 Generation
12.1
15.9
16.4
16.5
16.5
16.6
16.6
MSW CH4 Recovered
(17.9)
-
-
-
-
-
-
MSW CH4 Oxidized
(18.7)
-
-
-
-
-
-
Industrial CH4 Oxidized
(1.2)
(1.6)
(1.6)
(1.6)
(1.7)
(1.7)
(1.7)
MSW net CH4 Emissions
(GHGRP)
-
120.0
104.2
106.0
101.9
101.7
100.8
Total
179.6
134.3
119.0
120.8
116.7
116.6
115.7
Notes: Totals may not sum due to independent rounding. For years 1990 to 2004, the Inventory methodology
uses the first order decay methodology. A methodological change occurs in year 2005. For years 2005 to 2015,
net CH4 emissions from EPA's GHGRP data are used. These data incorporate CH4 recovered and oxidized.
Parentheses indicate negative values.
7-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Table 7-4: ChU Emissions from Landfills (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
MSW CH4 Generation
8,214
-
-
-
-
-
-
Industrial CH4 Generation
484
636
657
659
661
662
662
MSW CH4 Recovered
(718)
-
-
-
-
-
-
MSW CH4 Oxidized
(750)
-
-
-
-
-
-
Industrial CH4 Oxidized
MSW net CH4 Emissions
(48)
(64)
(66)
(66)
(66)
(66)
(66)
(GHGRP)
-
4,800
4,169
4,241
4,074
4,067
4,032
Total
7,182
5,372
4,760
4,834
4,669
4,663
4,628
Notes: Totals may not sum due to independent rounding. For years 1990 to 2004, the Inventory methodology
uses the first order decay methodology. A methodological change occurs in year 2005. For years 2005 to 2015,
net CH4 emissions from EPA's GHGRP data are used. These data incorporate CH4 recovered and oxidized.
Parentheses indicate negative values.
Methodology
Methodology Applied for MSW Landfills
Methane emissions from landfills can be estimated using two primary methods. The first method uses the first order
decay model as described by the 2006IPCC Guidelines to estimate CH4 generation. The amount of CH4 recovered
and combusted from MSW landfills is subtracted from the CH4 generation, and is then adjusted with an oxidation
factor. The oxidation factor represents the amount of CH4 in a landfill that is oxidized to CO2 as it passes through
the landfill cover (e.g., soil, clay, geomembrane, alternative daily cover). This method is presented below, and is
similar to Equation HH-5 in CFR Part 98.343 for MSW landfills, and Equation TT-6 in CFR Part 98.463 for
industrial waste landfills.
CH4,Solid Waste = [CH4.MSW + CH4,Ind — R] — Ox
where,
CH4, solid waste = Net CH4 emissions from solid waste
CH4,msw = CH4 generation from MSW landfills
CH4jnd = CH4 generation from industrial landfills
R = CH4 recovered and combusted (only for MSW landfills)
Ox = CH4 oxidized from MSW and industrial waste landfills before release to the atmosphere
The second method used to calculate CH4 emissions from landfills, also called the back calculation method, is based
off of directly measured amounts of recovered CH4 from the landfill gas and is expressed below and by Equation
HH-8 in CFR Part 98.343. The two parts of the equation consider the portion of CH4 in the landfill gas is not
collected by the landfill gas collection system; and the portion that is collected. First, the recovered CH4 is adjusted
with the collection efficiency of the gas collection and control system and the fraction of hours the recovery system
operated in the calendar year. This quantity represents the amount of CH4 in the landfill gas that is not captured by
the collection system; it is then adjusted for oxidation. The second portion of the equation adjusts the portion of CH4
in the collected landfill gas with the efficiency of the destruction device(s), and the fraction of hours the destruction
device(s) operated during the year.
CH4,soHd Waste = - r) x(l - OX) + R x (l - (DE x fDest))
where,
R = Quantity of recovered CH4 from Equation HH-4 of the GHGRP
CE = Collection efficiency estimated at the landfill, taking into account system coverage,
operation, and cover system materials from Table HH-3 of the GHGRP. If area by soil
cover type information is not available, the default value of 0.75 should be used, (percent)
fREc = fraction of hours the recovery system was operating (percent)
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OX = oxidation factor (percent)
DE = destruction efficiency (percent)
fix,,i = fraction of hours the destruction device was operating (fraction)
The current Inventory uses both methods to estimate CH4 emissions across the time series. In previous Inventory
reports, only the first order decay method was used. Methodological changes have been made to the current
Inventory to incorporate higher tier data (i.e., CH4 emissions directly reported to EPA's GHGRP), which cannot be
directly applied to earlier years in the time series without significant bias. The overlap technique, as described in the
Methodological Recalculations section of this Inventory, and in the Time Series Consistency chapter of the 2006
IPCC Guidelines, was used to merge the higher tier data with the previously used method.
The first order decay method is exclusively used for 1990 to 2004. The CH4 generation is based on nationwide
MSW generation data, to which a national average disposal factor is applied; it is not landfill-specific. The amount
of CH4 recovered, however, is landfill-specific, but only for MSW landfills due to a lack of data specific to
industrial waste landfills. A combination of both methods are used for the rest of the time series (i.e., 2005 to 2015)
in this Inventory. Specifically, directly reported CH4 emissions from EPA's GHGRP are used for years they are
available (i.e., 2010 to 2015). Landfills reporting to EPA's GHGRP without gas collection and control apply the first
order decay method, while the majority of landfills with landfill gas collection and control apply the back-
calculation method. The directly reported GHGRP emissions data were used to back-cast CH4 emissions for 2005 to
2010. An overview of the data sources and methodology used to calculate CH4 generation and recovery is provided
below, while a more detailed description of the methodology used to estimate CH4 emissions from landfills can be
found in Annex 3.14.
Description of the First Order Decay Methodology for MSW Landfills
States and local municipalities across the United States do not consistently track and report quantities of MSW
generated or collected for management, nor are end-of-life disposal methods reported to a centralized system.
Therefore, national MSW landfill waste generation and disposal data are obtained from secondary data, specifically
the State of Garbage (SOG) surveys, published approximately every two years, with the most recent publication date
of 2014. The SOG survey was the only continually updated nationwide survey of waste disposed in landfills in the
United States and was the primary data source with which to estimate nationwide CH4 generation from MSW
landfills. Now, EPA's GHGRP waste disposal data and MSW management data published by EREF are available.
The SOG surveys use the principles of mass balance where all MSW generated is equal to the amount of MSW
landfilled, combusted in waste-to-energy plants, composted, and/or recycled (BioCycle 2010; Shin 2014). This
approach assumes that all waste management methods are tracked and reported to state agencies. Survey
respondents are asked to provide a breakdown of MSW generated and managed by landfilling, recycling,
composting, and combustion (in waste-to-energy facilities) in actual tonnages as opposed to reporting a percent
generated under each waste disposal option. The data reported through the survey have typically been adjusted to
exclude non-MSW materials (e.g., industrial and agricultural wastes, construction and demolition debris, automobile
scrap, and sludge from wastewater treatment plants) that may be included in survey responses. In the most recent
survey, state agencies were asked to provide already filtered, MSW-only data. Where this was not possible, they
were asked to provide comments to better understand the data being reported. All state disposal data are adjusted for
imports and exports across state lines where imported waste is included in a particular state's total while exported
waste is not. Methodological changes have occurred over the time frame the SOG survey has been published, and
this has affected the fluctuating trends observed in the data (RTI2013).
The SOG survey is voluntary and not all states provide data for each survey year. Where no waste generation data
are provided by a state in the SOG survey, the amount generated is estimated by multiplying the waste per capita
from a previous SOG survey by that particular state's population. If that particular state did not report any waste
generation data in the previous SOG survey, the average nationwide waste per capita rate for the current SOG
survey is multiplied by that particular state's population. The quantities of waste generated across all states are
summed and that value is then used as the nationwide quantity of waste generated in a given reporting year.
State-specific landfill MSW generation data and a national average disposal factor for 1989 through 2008 were
obtained from the SOG survey every two years (i.e., 2002, 2004, 2006, and 2008 as published in BioCycle 2006,
and 2008 as published in BioCycle 2010). The most recent SOG survey provides data for 2011 (Shin 2014). The
EREF published a report on MSW Management in the United States that includes state-specific landfill MSW
7-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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generation and disposal data for 2010 and 2013 using a similar methodology as the SOG surveys (EREF 2016).
State-specific landfill waste generation data for the years in-between the SOG surveys and EREF report (e.g., 2001,
2003, 2005, 2007, and 2009) were either interpolated or extrapolated based on the SOG or EREF data and the U.S.
Census population data. In the current Inventory methodology, the MSW generation and disposal data are no longer
used to estimate CH4 emissions for the years 2005 to 2015 because EPA's GHGRP emissions data are now used for
those years. The MSW generation and disposal data for these years are still useful for examining general trends in
MSW management in the United States.
Estimates of the quantity of waste landfilled from 1989 to 2004 are determined by applying an average national
waste disposal factor to the total amount of waste generated (i.e., the SOG data). A waste disposal factor is
determined for each year an SOG survey is published and equals the ratio of the total amount of waste landfilled in
the United States to the total amount of waste generated in the United States. The waste disposal factor is
interpolated or extrapolated for the years in-between the SOG surveys, as is done for the amount of waste generated
for a given survey year.
The IPCC methodology recommends at least 50 years of waste disposal data in order to estimate CH4 emissions.
Estimates of the annual quantity of waste landfilled for 1960 through 1988 were obtained from EPA's
Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress (EPA 1993) and an
extensive landfill survey by the EPA's Office of Solid Waste in 1986 (EPA 1988). Although waste placed in
landfills in the 1940s and 1950s contributes very little to current CH4 generation, estimates for those years were
included in the FOD model for completeness in accounting for CH4 generation rates and are based on the population
in those years and the per capita rate for land disposal for the 1960s. For calculations in the current Inventory,
wastes landfilled prior to 1980 were broken into two groups: wastes disposed in landfills (Methane Conversion
Factor, MCF, of 1) and those disposed in dumps (MCF of 0.6). All calculations after 1980 assume waste is disposed
in managed, modern landfills. See Annex 3.14 for more details.
Methane recovery is currently only accounted for at MSW landfills. The estimated landfill gas recovered per year
(R) at MSW landfills was based on a combination of four databases and including recovery from flares and/or
landfill gas-to-energy projects:
• EPA's GHGRP dataset for MSW landfills (EPA 2015a);
• A database developed by the Energy Information Administration (EIA) for the voluntary reporting of
greenhouse gases (EIA 2007);
• A database of LFGTE projects that is primarily based on information compiled by the EPA LMOP (EPA
2016a); and
• The flare vendor database (contains updated sales data collected from vendors of flaring equipment).
The same landfill may be included one or more times across these four databases. To avoid double- or triple-
counting CH4 recovery, the landfills across each database were compared and duplicates identified. A hierarchy of
recovery data is used based on the certainty of the data in each database. In summary, EPA's GHGRP > EIA >
LFGTE > flare vendor database. The rationale for this hierarchy is described below.
EPA's GHGRP MSW landfills database was first introduced as a data source for the 1990 to 2013 Inventory. EPA's
GHGRP MSW landfills database contains facility-reported data that undergoes rigorous verification, thus it is
considered to contain the least uncertain data of the four CH4 recovery databases. However, as mentioned earlier,
this database is unique in that it only contains a portion of the landfills in the United States (although, presumably
the highest emitters since only those landfills that meet a certain CH4 generation threshold must report) and only
contains data for 2010 and later. Directly reported values for CH4 recovery to EPA's GHGRP are available for years
2010 through 2014. In the current Inventory methodology, methane recovery for 1990 to 2004 for facilities reporting
to EPA's GHGRP has been estimated using the directly reported emissions for those facilities from 2010 to 2015,
and an Excel forecasting function so that the GHGRP data source can be applied to earlier years in the time series.
Directly reported net CH4 emissions from EPA's GHGRP are used for 2010 to 2015, and back-casted from 2009 to
2005. Prior to 2005, if a landfill in EPA's GHGRP was also in the LFGTE or EIA databases, the landfill gas project
information, specifically the project start year, from either the LFGTE or EIA databases was used as the cutoff year
for the estimated CH4 recovery in the GHGRP database. For example, if a landfill reporting under EPA's GHGRP
was also included in the LFGTE database under a project that started in 2002 that is still operational, the CH4
recovery data in the GHGRP database for that facility was back-calculated to the year 2002 only. This method,
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although somewhat uncertain, can be refined in future Inventory reports after further investigating the landfill gas
project start years for landfills in the GHGRP database.
If a landfill in EPA's GHGRP MSW landfills database was also in the EIA, LFGTE, and/or flare vendor database,
the avoided emissions were only based on EPA's GHGRP MSW landfills database to avoid double or triple
counting the recovery amounts. In other words, the recovery from the same landfill was not included in the total
recovery from the EIA, LFGTE, or flare vendor databases.
If a landfill in the EIA database was also in the LFGTE and/or the flare vendor database, the CH4 recovery was
based on the EIA data because landfill owners or operators directly reported the amount of CH4 recovered using gas
flow concentration and measurements, and because the reporting accounted for changes over time. However, as the
EIA database only includes facility-reported data through 2006, the amount of CH4 recovered for years 2007 and
later were assumed to be the same as in 2006 for landfills that are in the EIA database, but not in the GHGRP or
LFGTE databases. This quantity likely underestimates flaring because the EIA database does not have information
on all flares in operation for the years after 2006. However, nearly all (93 percent) of landfills in the EIA database
also report to EPA's GHGRP, which means that only seven percent of landfills in the EIA database are counted in
the total recovery.
If both the flare data and LFGTE recovery data were available for any of the remaining landfills (i.e., not in the EIA
or GHGRP databases), then the avoided emissions were based on the LFGTE data, which provides reported landfill-
specific data on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects. The
LFGTE database is based on the most recent EPA LMOP database (published annually). The remaining portion of
avoided emissions is calculated by the flare vendor database, which estimates CH4 combusted by flares using the
midpoint of a flare's reported capacity. New flare vendor sales data were unable to be obtained for the current
Inventory year. Given that each LFGTE project is likely to also have a flare, double counting reductions from flares
and LFGTE projects in the LFGTE database was avoided by subtracting emission reductions associated with
LFGTE projects for which a flare had not been identified from the emission reductions associated with flares
(referred to as the flare correction factor). A further explanation of the methodology used to estimate the landfill gas
recovered can be found in Annex 3.14.
The destruction efficiencies reported through EPA's GHGRP were applied to the landfills in the GHGRP MSW
landfills database. The median value of the reported destruction efficiencies was 99 percent for all reporting years
(2010 through 2015). A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4
emissions avoided due to the combusting of CH4 in destruction devices (i.e., flares) in the EIA, LFGTE, and flare
vendor databases. The 99 percent destruction efficiency value selected was based on the range of efficiencies (86 to
greater than 99 percent) recommended for flares in EPA's AP-42 Compilation of Air Pollutant Emission Factors,
Draft Section 2.4, Table 2.4-3 (EPA 2008). A typical value of 97.7 percent was presented for the non-CH4
components (i.e., volatile organic compounds and non-methane organic compounds) in test results (EPA 2008). An
arithmetic average of 98.3 percent and a median value of 99 percent are derived from the test results presented in
EPA (2008). Thus, a value of 99 percent for the destruction efficiency of flares has been used in the Inventory
methodology. Other data sources supporting a 99 percent destruction efficiency include those used to establish New
Source Performance Standards (NSPS) for landfills and in recommendations for shutdown flares used by the EPA
LMOP.
The amount of CH4 oxidized by the landfill cover at both municipal and industrial waste landfills was assumed to be
10 percent of the CH4 generated that is not recovered (IPCC 2006; Mancinelli and McKay 1985; Czepiel et al. 1996)
for the years 1990 to 2004. For years 2005 to 2015, the current Inventory methodology uses directly reported net
CH4 emissions from EPA's GHGRP, or back-casted emissions based of the directly reported data. EPA's GHGRP
data allows facilities to apply a range of oxidation factors (0.0, 0.10, 0.25, or 0.35) based on the calculated CH4 flux
at the landfill.
For the years 1990 to 2004, net CH4 emissions are calculated by subtracting the CH4 recovered and CH4 oxidized
from CH4 generated at municipal and industrial waste landfills. For the years 2005 and onward, the same
methodology may be used, or the back-calculation approach may be used (Equation HH-8 in CFR Part 98.343, as
described above). The back-calculation approach starts with the amount of CH4 recovered and works back through
the system to account for the amount of gas not collected by the landfill gas collection and control system (i.e., the
collection efficiency). An oxidation factor (0.0, 0.10, 0.25, or 0.35) is applied to the amount of CH4 recovered
divided by the collection efficiency, subtracted from the amount of CH4 recovered.
7-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Description of the GHGRP Data for MSW Landfills
Directly reported CH4 emissions, or back-casted emissions based off EPA's GHGRP dataset were applied for years
2005 to 2015. Under the GHGRP methodology, the first order decay model methodology, adjusted for oxidation is
applied to estimate CH4 generation for landfills without landfill gas collection and control. Landfills with gas
collection and control are required to estimate CH4 emissions two ways; one that is based off of the first order decay
methodology, and a second that is based off of directly measured amounts of recovered landfill gas (Equation HH-8
in CFRPart 98.343, as described above). The GHGRP details allowable methodologies for monitoring quantities of
recovered CH4 from the landfill gas, and the EPA verifies all annual greenhouse gas reports.
Description of the First Order Decay Methodology for Industrial Waste Landfills
Emissions from industrial waste landfills were estimated from industrial production data from 2014 extrapolated to
2015 (ERG 2016), waste disposal factors, and the FOD model. The Inventory methodology assumes over 99 percent
of the organic waste placed in industrial waste landfills originates from the food processing (meat, vegetables, fruits)
and pulp and paper sectors (EPA 1993), thus estimates of industrial landfill emissions focused on these two sectors.
There are currently no data sources that track and report the amount and type of waste disposed of in the universe of
industrial waste landfills in the United States. EPA's GHGRP provides some insight into waste disposal in industrial
waste landfills and supports the focus of the Inventory on the two selected sectors, but is not comprehensive.
Therefore, the amount of waste landfilled is assumed to be a fraction of production that is held constant over the
time series as explained in Annex 3.14. The composition of waste disposed of in industrial waste landfills is
expected to be more consistent in terms of composition and quantity than that disposed of in MSW landfills.
Data collected through EPA's GHGRP for industrial waste landfills (Subpart TT) show that only two of the 176
facilities, or 1 percent of facilities, have active gas collection systems (EPA 2015a). EPA's GHGRP is not a national
database and comprehensive data regarding gas collection systems have not been published for industrial waste
landfills. Assumptions regarding a percentage of landfill gas collection systems, or a total annual amount of landfill
gas collected for the non-reporting industrial waste landfills have not been made for the Inventory methodology.
Uncertainty and Time-Series Consistency - TO BE UPDATED
FOR FINAL INVENTORY REPORT
Several types of uncertainty are associated with the estimates of CH4 emissions from MSW and industrial waste
landfills when the first order decay model is applied. The approach used in the MSW emission estimates assumes
that the CH4 generation potential (L0) and the rate of decay that produces CH4from MSW, as determined from
several studies of CH4 recovery at MSW landfills, are representative of conditions at U.S. MSW landfills. When this
top-down approach is applied at the nationwide level, the uncertainties are assumed to be less than when applying
this approach to individual landfills and then aggregating the results to the national level. In other words, the first
order decay methodology as applied in this Inventory is not facility-specific modeling and while this approach may
over- or under-estimate CH4 generation at some landfills if used at the facility-level, the end result is expected to
balance out because it is being applied nationwide. There is also a high degree of uncertainty and variability
associated with the FOD model, particularly when a homogeneous waste composition and hypothetical
decomposition rates are applied to heterogeneous landfills (IPCC 2006).
The lack of landfill-specific information regarding the number and type of industrial waste landfills in the United
States is a primary uncertainty with respect to the industrial waste generation and emissions estimates. The approach
used here assumes that the majority (99 percent) of industrial waste disposed of in industrial waste landfills consists
of waste from the pulp and paper and food processing sectors. However, because waste generation and disposal data
are not available in an existing data source for all U.S. industrial waste landfills, a straight disposal factor is applied
over the entire time series to the amount of waste generated to determine the amounts disposed. Industrial waste
facilities reporting under EPA's GHGRP do report detailed waste stream information, and these data have been used
to improve, for example, the DOC value used in the Inventory methodology for the pulp and paper sector.
Aside from the uncertainty in estimating landfill CH4 generation uncertainty also exists in the estimates of the
landfill gas oxidized. A constant oxidation factor of 10 percent as recommended by the IPCC for managed landfills
is used for both MSW and industrial waste landfills regardless of climate, the type of cover material, and/or presence
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i»f ;i u;is collodion s\ sieni The number of published field siudies nic;isuriim ihe r;itc of o\id;ilion h;is increased
s 111~> s i; 11 i 11; 111 \ since I he 2nnt- II'< '<' < ini,!i./iih> were published ;uid. ;is discussed in I he I'olcuihil Iniprineuieuis
secliou. efforis ;ue bcnm m;ide lo re\ ie\\ I he hier;iiure ;iud re\ ise l Ins \;ilue bused on receui. pccr-rc\ icwcd siudies
\uoiher smuificmil source of uiiceri;iiui\ lies w uh ihe esiiui;iies of CI I rcco\ ered b\ fhirnm ;uid u;is-io-eneru\
projects ;ii \IS\V hiudfills The (il l( ikl' \ISW hiudfills d;il;ib;ise w;is ;idded ;is ;i fourih reco\er\ d;il;ib;ise simlnm
w nil I he I iwn throimh 2d I ' lu\ eiilorv report kcl> um ou multiple d;il;ib;ises for ;i complele picture niirodiices
iiiiceri;iiul\ bec;iuse I lie co\ crime ;uid ch;ir;iclerisiics of e;ich d;il;ib;ise differs, w Inch iucre;ises I lie ch;i lice of double
couuliim ;i\ oided emissions \ddiliou;ill\. I he melhodolous ;uid ;issuniptious ih;il uo uilo e;ich d;it;ib;isc differ for
c.vimplc. lhe Hire d;il;ib;ise nssunies (lie niidpoiui of e;ich fhire c;ip;icil> ;il llie lime il is sold ;iud installed ;il ;i
hiudlill. mi re;i 111\. llic fhire ui;i> be ;icliie\ um ;i liiuher c;ip;icil\. in w Inch c;ise I he llnrc d;il;ib;ise would
iiuderesiim;iie Ihe miiouui of CI I rcco\crcd
The I .l ( illd;il;ib;ise is upd;iled ;iuuu;ill\ The fl;ire d;il;ib;ise is populated In ihe \ ohiul;ir\ shmum of ll;ire s;iles
d;il;i In select \eudors ;uid is uoi ;ible lo be obi;iiued ;imiii;ill\. w Inch likel\ uuderesiininies reco\er\ for kiiidlills uoi
included in ihe iliree oilier rcco\cr\ d;il;ib;ises used In ihe lu\euior\ I lie U \ d;il;ib;ise h;is uoi been updnled since
2i)()(i ;uid h;is. for ihe niosi p;ul. been replaced In ihe (il l(iklJ \IS\V hiudfills d;il;ib;ise To ;i\ old double couuliim
mid lo use ihe mosi rclc\;uil esiim;iie ol'CI I rcco\cr\ for;i m\cn Imidlill. ;i lner;irchic;il ;ippro;ich is used mnoim ihe
four d;il;ib;ises (il l( iKP d;il;i ;ire ui\ eu precedence becnuse ( 11 reco\ er\ is direclK reporied b\ hiudfills ;md
uuderuoes ;i riuorous \ erificnlion process, ihe U \ d;il;i ;irc m\eu second prionis bccmisc l;icilil\ d;il;i were tlireell\
reporied. ihe IT'CIT d;il;i ;ire ui\e11 third prionis becniise ( II reco\er\ isesiini;iied from l;icihi\-reporied IT'CIT
s\ siem ch;ir;iclerisiics: ;uid ihe fl;irc d;il;i ;ire m\ eu fourih priorils bccuusc I Ins d;ii;ib;ise coul;iius mini m;il
iiiforuKiiiou nboul ihe fl;ire. uo siie-specilic opcmiiim ch;ir;iclerisiics. mid includes sm;iller hiudfills uoi included mi
ihe oilier iliree d;il;ib;ises i Uroiisieiu el ;il 2d 121 I'lie co\ crime pro\ ided ;icross ihe d;il;ib;ises most 11kel\ represeuis
ihe complete uui\erse of hiudl ill CI I u;is rcco\cr\. howe\er. ihe number of unique hiudfills between ihe four
d;il;ib;ises does differ
The ll'CC delimit \ ;ilue of Id percent for uuccri;iiiii\ mi rcco\ er\ esiini;ites w;is used for iwo of the lour rcco\ cr\
d;il;ib;ises mi the uuceri;iiul\ ;ui;il> sis w here uiclcriim of hiudl ill u;is w;is mi phicc (for ;iboul (>4 percent of the CI I
esiini;iled lo be rcco\crcdi. This Id percent uiiceri;iiiit\ f;iclor;ipphes to ihe l.l'(i IT d;il;ib;ise. 12 percent lo the U \
d;il;ib;ise. mid I percent for ihe (il l( ikl' \ISW hiudfills d;il;iscl bec;iuse oil he suppornim iiilorimiiou pro\ ided ;uid
riuorous \ erific;iliou process, for ll;iriim w iilioui metered reco\er\ d;il;i (Ihe Ihire d;il;ib;isei. ;i much liiuher
iiiiceri;iiut\ \;ilue of 5d pcrccul is used I lie conipouudiim uuceri;iililies ;issoci;iled with the four d;il;ib;ises in
;iddilioii lo the iiiicerl;iiulies ;issoci;iled w itli the l'( )l) model ;md ;iuuu;il w;isie disposal i|ii;uitilies lends to 1 he hi rue
upper mid lower bounds lor \IS\V hiudfills preseuied mi T;ible ~-5 ludusiri;il w;iste hiudfills ;irc show u with ;i lower
nnme of iiiiccri;iiul\ due lo ihe sninller iiuniber of d;il;i sources mid ;issoci;ilcd uiiceri;iiui\ ui\ ol\ ed I'or e\;imple.
iliree d;il;i sources me used lo ucucmic I he ;iuuu;il i|ii;iutiiies of \1S\V w;isie disposed o\ er I he I'Hd io curreiii \e;ir
limelrmiie. while iudusiri;il w;iste hiudfills rcl> oil iwo d;il;i sources.
There is less uuccrimun mi ihe (il l( ikl' d;il;i because ilns nielhodolous is l;icilit\-specific. uses direclK measured
CI I rcco\ er\ d;il;i i w lieu ;ipplic;iblei. ;iud nllows for ;i \ ;iriel\ of hiudlill u;is collection efficiencies, destruction
efficiencies, mid oro\id;itioii fuclors to be used
The resulls ofllie 2'inf- ll'< '<' < ini, h-lines \ppro;ich 2 c|ii;i ill i l;il i\ e iiuceri;i i ut\ ;iu;il\ sis ;ire sunuii;iri/ed mi T;iblc ~-5
Table 7-5: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from Landfills
(MMT CO2 Eq. and Percent)
2015 riniissiiui
Si hi in- (i;is l'.slim;ili' I niiThiiim Riiii^i' Ki-hiliu- in l!niissiiin llslim.ik-'1
(MMT CO: iMMT CO; l!(|.) ("¦»)
I.I HUT I |)|KT
I.IHUT
I |>|KT
lion ml liiniiid
lillllll(l
lilllllHl
L;mhII1IIs
CII4
MSW
CI 11
Industrial
CI 11
¦' Range of emu
ision estimates predicted by M
onto Carlo Stochastic Simulation I'oi
' a l)5 percent con
lidence interval.
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QA/QC and Verification
A Quality Assurance/Quality Control (QA/QC) analysis is performed each Inventory year. QA/QC checks are
performed for the transcription of the published data set used to populate the Inventory data set, including the
published GHGRP, LFGTE, and flare databases. While preparing the Inventory, QA/QC checks are not performed
on the data itself against primary data used. EPA verifies annual reports from Subpart HH through a multi-step
process (e.g., combination of electronic checks and manual reviews) to identify potential errors and ensure that data
submitted to EPA are accurate, complete, and consistent.3 A primary focus of the QA/QC checks in past Inventory
reports was to ensure that CH4 recovery estimates were not double-counted and that all LFGTE projects and flares
were included in the respective project databases. QA/QC checks performed in the past for the recovery databases
were not performed in this Inventory because new data were not added to the recovery databases for 2015.
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
Four major methodological recalculations were performed for the current Inventory.
• First, net CH4 emissions as directly reported to subpart HH of EPA's GHGRP were used for 2010 to 2015.
• Second, a 12.5 percent scale-up factor was applied to the subpart HH data to account for emission from
MSW landfills that are not required to report under subpart HH.
• Third, the net CH4 emissions from 2010 to 2015 from subpart HH were used to estimate, or back-cast, net
CH4 emissions for 2005 to 2009.
• Fourth, the previously used method, which relies on the first order decay model, was applied with revised
MSW generation data for years 1990 to 2004.
A detailed description of these methodological changes are included below.
Using directly reported net CH4 emissions from the GHGRP. The EPA has relied on a top-down approach to
calculate CH4 generation for MSW landfills in previous Inventory reports. The SOG survey has been used in
previous Inventories, but is no longer being published as routinely as it has been in the past. Therefore, EPA
investigated whether a bottom-up (or landfill-specific) approach could be used in future Inventories by either
supplementing the GHGRP annual waste disposal data with other relevant datasets (e.g., LMOP, state data) to
provide the annual waste disposal data needed for the FOD model; or, using directly reported net CH4 emissions
from EPA's GHGRP. EPA's GHGRP requires landfills meeting or exceeding a threshold of 25,000 metric tons of
CH4 generation per year to report a variety of facility-specific information, including historical and current waste
disposal quantities by year, CH4 generation, gas collection system details, CH4 recovery, and CH4 emissions. EPA
decided upon using the directly reported net CH4 emissions data for the years the data are available (i.e., 2010 to
2015). These data are considered to be Tier 3 data (the highest quality) under the 2006 IPCC Guidelines, and
undergo an extensive QA/QC review and verification process by EPA. Additionally, these data incorporate
oxidation factors that align with recent literature. The Inventory still applies an oxidation factor of 0.10 and a DOC
value of 0.2028 to the bulk MSW disposed in landfills for the years 1990 to 2004.
Applying a scale-up factor to the GHGRP data. The landfills reporting to EPA's GHGRP are considered the
largest emitters, but not all landfills are required to report. When this dataset is supplemented with others, such as
the EPA LMOP data and the Waste Business Journal data, a complete data set of the annual quantity of waste
landfilled may be represented. EPA is continuing to investigate the number of non-reporting landfills to the GHGRP
and the total annual quantities of CH4 emissions from these non-reporting landfills. For this Inventory, EPA has
applied a scale-up factor of 12.5 percent to the GHGRP net CH4 emissions to account for the non-reporting landfills.
This scale-up factor may be revised in future years after a thorough review of available data for the non-reporting
landfills is completed.
3 See .
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Back-casting net CH4 emissions from the GHGRP. The EPA also investigated various back-casting approaches to
estimate CH4 emissions throughout the entire time series (back to 1990) while relying solely on EPA's GHGRP
emissions data. Back-casting this far back with a limited set of data is not recommended in Volume 1: Chapter 5 of
the 2006IPCC Guidelines, which provides best practices for time series consistency when implementing
methodological changes and refinements. Plotting the GHGRP back-casted emissions against the emissions
estimates from the previously used method showed an alignment of the data in 2004 and later years. The 2006 IPCC
Guidelines recommend using a splicing technique if the data overlap for a period of years as the data do with the
revised methodology. Therefore, EPA decided to back-cast the GHGRP emissions from 2009 to 2005 only, while
also applying the 12.5 percent scale-up factor to the back-casted GHGRP data.
Recalculations to the MSW generation and disposal data and CH4 generation estimates. The revised
methodology relies on the previous methodology for the years 1990 to 2004, whereby a disposal factor is applied to
nationwide, annual MSW generation amounts. The MSW generation data were modified from the previous
Inventory for years 1990 to 2013 to reflect recently published data (i.e., EREF 2016), and to align with how MSW
quantities are applied under Subpart HH of the GHGRP to estimate CH4 generation. Revisions were made to the
SOG survey data applied by the Inventory to exclude construction and demolition (C&D) waste and inerts from the
annual quantities of MSW generated used in the first order decay model. Years that EPA has "hard" data for MSW
generation include 2002, 2004, 2006, 2008, 2010, and 2013. EPA used MSW generation data and population
changes for those years to extrapolate MSW generation for years 1990 and 2001. EPA used the 2002 and 2004 data
to interpolate MSW generation for 2003.
Merging methodologies for time series consistency. Volume 1: Chapter 5 of the 2006 IPCC Guidelines provides
guidance on good practices for time series consistency. As stated in this chapter, "the time series is a central
component of the greenhouse gas inventory because it provides information on historical emissions trends and tracks
the effects of strategies to reduce emissions at the national level. All emissions estimates in a time series should be
estimated consistently, which means that as far as possible, the time series should be calculated using the same
method and data sources in all years." This chapter also provides guidance on techniques to splice, or join
methodologies together. EPA's GHGRP data are considered higher tier data compared to the national MSW
generation estimates, and a new methodology was required to apply the GHGRP data to the Inventory because it is
only available for a portion of the time series.
The overlap technique is an example of a splicing technique. Other examples of splicing techniques include
surrogate data, interpolation, and extrapolation. The overlap technique can be used when new data become available
that cannot be applied to earlier years in the time series (IPCC 2006). EPA developed a time series based on the
relationship (or overlap) observed between the two methods (the previous method and the new method) during the
years when both methods align and can be used. The previously used method in this instance is based on the first
order decay model and national MSW generation estimates. The new method refers to EPA's GHGRP data (for
years 2010 to 2015, and back-casted estimates for years 1990 to 2009). Figure 7-2 shows how the revised Inventory
methodology compares to back-casting the directly reported GHGRP data for MSW landfills. EPA decided to apply
the previously used method for the earlier years in the time series (i.e., 1990 to 2004), and the new method for later
years (i.e., 2005 to 2015) for time series consistency. Figure 7-3 compares the previously used Inventory
methodology to the revised Inventory methodology. The CH4 emissions estimates from the previously used method
and the new method compare relatively well across the time series.
7-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 Figure 7-2: Comparison of the Revised Inventory Methodology to EPA's GHGRP Subpart HH
2 Emissions
8.00
.00
.00
.00
.00
o
ro 3.00
-!=
2
2.00
1.00
0.00
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Revised Inventory (MMT) — — Modified GHGRP HH (MMT)
3
4 Note: Emissions were back-casted from 2009 to 1990, and directly reported for 2010 to 2015.
5 Figure 7-3: Comparison of the 1990-2014 Inventory Methodology to the Revised Inventory
6 Methodology
7
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2OO0 2001 2002 2O03 2004 2O05 2O06 2007 2008 2009 2010 2011 2012 2013 2014 2015
199O-2014 Inventory (MMT)
Ra/ised Inventory (MMT)
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Planned Improvements
The EPA will continue to investigate the annual waste disposal quantity for landfills not reporting to EPA's GHGRP
to develop a more precise scale-up factor to apply to the GHGRP data. The LMOP database, WBJ database, and
other datasets will be reviewed against the GHGRP waste disposal data. Within the GHGRP data, the previous years
of waste disposal reported to EPA's GHGRP by facilities will be reviewed and used in the first order decay model
methodology to estimate CH4 emissions and review against the emissions estimates calculated by the new Inventory
methodology. EPA will also investigate options to adjust the oxidation factor from 10 percent currently used, to
another value such as those included in EPA's GHGRP.
Box 7-2: Nationwide Municipal Solid Waste Data Sourc
I
Municipal solid waste generated in the United States can be managed through landfilling, recycling, composting,
and combustion with energy recovery. There are three main sources for nationwide solid waste management data in
the United States:
• The BioCycle and Earth Engineering Center of Columbia University's State of Garbage (SOG) in America
surveys [no longer published];
• The EPA's Advancing Sustainable Materials Management reports; and
• The Environmental Research & Education Foundation's (EREF) Municipal Solid Waste Generation in the
United States reports.
The SOG surveys and, now EREF, collect state-reported data on the amount of waste generated and the amount of
waste managed via different management options: landfilling, recycling, composting, and combustion. The survey
asks for actual tonnages instead of percentages in each waste category (e.g., residential, commercial, industrial,
construction and demolition, organics, tires) for each waste management option. If such a breakdown is not
available, the survey asks for total tons landfilled. The data are adjusted for imports and exports across state lines so
that the principles of mass balance are adhered to, whereby the amount of waste managed does not exceed the
amount of waste generated. The SOG and EREF reports present survey data aggregated to the state level.
The EPA Advancing Sustainable Materials Management reports use a materials flow methodology, which relies
heavily on a mass balance approach. Data are gathered from industry associations, key businesses, similar industry
sources, and government agencies (e.g., the Department of Commerce and the U.S. Census Bureau) and are used to
estimate tons of materials and products generated, recycled, or discarded nationwide. The amount of MSW
generated is estimated by adjusting the imports and exports of produced materials to other countries. MSW that is
not recycled, composted, or combusted is assumed to be landfilled. The data presented in the report are nationwide
totals.
The SOG surveys have now been replaced by the EREF reports, and are the preferred data source for estimating
waste generation and disposal amounts over the EPA Sustainable Materials Management reports in the Inventory
because they are considered a more objective, numbers-based analysis of solid waste management in the United
States. However, the EPA Sustainable Materials Management reports are useful when investigating waste
management trends at the nationwide level and for typical waste composition data, which the SOG and EREF
surveys do not request.
In this Inventory, emissions from solid waste management are presented separately by waste management option,
except for recycling of waste materials. Emissions from recycling are attributed to the stationary combustion of
fossil fuels that may be used to power on-site recycling machinery, and are presented in the stationary combustion
chapter in the Energy sector, although the emissions estimates are not called out separately. Emissions from solid
waste disposal in landfills and the composting of solid waste materials are presented in the Landfills and
Composting chapters in the Waste sector of this report. In the United States, almost all incineration of MSW occurs
at waste-to-energy (WTE) facilities or industrial facilities where useful energy is recovered, and thus emissions from
waste incineration are accounted for in the Incineration of Waste chapter of the Energy sector of this report.
7-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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Box 7-3: Overview of Municipal Solid Waste Management
2 As shown in Figure 7-4 and Figure 7-5, landfilling of MSW is currently and has been the most common waste
3 management practice. A large portion of materials in the waste stream are recovered for recycling and composting,
4 which is becoming an increasingly prevalent trend throughout the country. Materials that are composted and
5 recycled would have normally been disposed of in a landfill.
6 Figure 7-4: Management of Municipal Solid Waste in the United States, 2014
Landfil led
52%
Recycled
26%
Composted
9%
MSW to WTE
13%
Source: EPA (2016b).
Figure 7-5: MSW Management Trends from 1990 to 2014
160
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10
11
*
* \S
* «.. Landfilling
^ ^ ^
Recycling
Combustion
with Energy
Recovery
Composting
OTHlNrOrfLTilflr^COCTl
cricricricricricricricricricri
o*-Hr\im<3-u">^£>r^oo
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Table 7-6 presents a typical composition of waste disposed of at a typical MSW landfill in the United States over
time. It is important to note that the actual composition of waste entering each landfill will vary from that presented
in Table 7-6. Understanding how the waste composition changes over time, specifically for the degradable waste
types, is important for estimating greenhouse gas emissions. For certain degradable waste types (i.e., paper and
paperboard), the amounts discarded have decreased over time due to an increase in waste recovery, including
recycling and composting (see Table 7-6 and Figure 7-6) do not reflect the impact of backyard composting on yard
trimming generation and recovery estimates. The recovery of food trimmings has been consistently low. Increased
recovery of degradable materials reduces the CH4 generation potential and CH4 emissions from landfills.
Table 7-6: Materials Discarded3 in the Municipal Waste Stream by Waste Type from 1990 to
2014 (Percent)
Waste Type
1990
2005
2010
2011
2012
2013
2014
Paper and Paperboard
30.0%
24.1%
15.1%
16.6%
13.4%
13.9%
13.4%
Glass
6.0%
5.7%
4.8%
5.7%
4.7%
4.8%
4.8%
Metals
7.2%
7.8%
8.4%
10.0%
8.4%
8.8%
8.8%
Plastics
9.5%
16.0%
16.8%
20.1%
16.6%
17.0%
17.3%
Rubber and Leather
3.2%
2.8%
3.0%
4.3%
2.9%
2.9%
2.9%
Textiles
2.9%
5.2%
6.1%
7.6%
6.5%
6.8%
7.2%
Wood
6.9%
7.4%
7.7%
9.2%
7.5%
7.4%
7.6%
Otherb
1.4%
1.8%
1.9%
2.3%
1.8%
1.8%
1.8%
Food Scraps
13.6%
18.2%
19.7%
24.1%
19.2%
19.4%
20.2%
Yard Trimmings
17.6%
6.9%
8.0%
9.9%
7.9%
7.5%
7.4%
Miscellaneous Inorganic
Wastes
1.7%
2.1%
2.3%
2.4%
2.4%
2.4%
2.2%
a Discards after materials and compost recovery. In this table, discards include combustion with energy recovery. Does not
include construction & demolition debris, industrial process wastes, or certain other wastes.
b Includes electrolytes in batteries and fluff pulp, feces, and urine in disposable diapers. Details may not add to totals due to
rounding.
Figure 7-6: Percent of Recovered Degradable Materials from 1990 to 2014 (Percent)
80%
Paper and Paperboard
Food Scraps
Yard Trimmings
70%
60%
50%
40%
30%
20%
10%
0%
o
in
H (N fO
(JJ (Ji r-. oo (T> o
(Jl Q) Q) Ql Ql O
*Ho
OOOOOOOOOr—i
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Box 7-4: Description of a Modern, Managed Landfi
Modern, managed landfills are well-engineered facilities that are located, designed, operated, and monitored to
ensure compliance with federal, state, and tribal regulations. Municipal solid waste (MSW) landfills must be
designed to protect the environment from contaminants which may be present in the solid waste stream.
Additionally, many new landfills collect and destroy landfill gas through flares or landfill gas-to-energy projects.
Requirements for affected MSW landfills may include:
• Siting requirements to protect sensitive areas (e.g., airports, floodplains, wetlands, fault areas, seismic impact
zones, and unstable areas);
• Design requirements for new landfills to ensure that Maximum Contaminant Levels (MCLs) will not be
exceeded in the uppermost aquifer (e.g., composite liners and leachate collection systems);
• Leachate collection and removal systems;
• Operating practices (e.g., daily and intermediate cover, receipt of regulated hazardous wastes, use of landfill
cover material, access options to prevent illegal dumping, use of a collection system to prevent stormwater
run-on/run-off, record-keeping);
• Air monitoring requirements (explosive gases);
• Groundwater monitoring requirements;
• Closure and post-closure care requirements (e.g., final cover construction); and
• Corrective action provisions.
Specific federal regulations that affected MSW landfills must comply with include the 40 CFR Part 258 (Subtitle D
of RCRA), or equivalent state regulations and the New Source Performance Standards (NSPS) 40 CFR Part 60
Subpart WWW. Additionally, state and tribal requirements may exist.4
7.2 Wastewater Treatment (IPCC Source
Category 5D)
Wastewater treatment processes can produce anthropogenic methane (CH4) and nitrous oxide (N20) emissions.
Wastewater from domestic and industrial sources is treated to remove soluble organic matter, suspended solids,
pathogenic organisms, and chemical contaminants.5 Treatment may either occur on site, most commonly through
septic systems or package plants, or off site at centralized treatment systems. In the United States, approximately 19
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 2013). Centralized wastewater treatment systems may include a variety of
processes, ranging from lagooning to advanced tertiary treatment technology for removing nutrients. Some
wastewater may also be treated through the use of constructed (or semi-natural) wetland systems, though it is much
less common in the United States (ERG 2016). Constructed wetlands may be used as the primary method of
wastewater treatment, or as a tertiary treatment step following settling and biological treatment. Constructed
wetlands develop natural processes that involve vegetation, soil, and associated microbial assemblages to trap and
treat incoming contaminants (IPCC 2014).
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,
4 For more information regarding federal MSW landfill regulations, see
.
5 Throughout the Inventory, emissions from domestic wastewater also include any commercial and industrial wastewater
collected and co-treated with domestic wastewater.
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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 N20 may also result from the
treatment of domestic wastewater during both nitrification and denitrification of the nitrogen (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). Nitrous oxide can be an intermediate product of both
processes, but has typically been associated with denitrification. Recent research suggests that higher emissions of
N20 may in fact originate from nitrification (Ahn et al. 2010). Other more recent research suggests that N20 may
also result from other types of wastewater treatment operations (Chandran 2012).
The principal factor in determining the CH4 generation potential of wastewater is the amount of degradable organic
material in the wastewater. Common parameters used to measure the organic component of the wastewater are the
biochemical oxygen demand (BOD) and chemical oxygen demand (COD). Under the same conditions, wastewater
with higher COD (or BOD) concentrations will generally yield more CH4 than wastewater with lower COD (or
BOD) concentrations. BOD represents the amount of oxygen that would be required to completely 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). The BOD value is most
commonly expressed in milligrams of oxygen consumed per liter of sample during 5 days of incubation at 20
degrees C, or BOD5. Because BOD is an aerobic parameter, it is preferable to use COD to estimate CH4 production.
The principal factor in determining the N20 generation potential of wastewater is the amount of N in the wastewater.
The variability of N in the influent to the treatment system, as well as the operating conditions of the treatment
system itself, also impact the N20 generation potential.
In 2015, CH4 emissions from domestic wastewater treatment were 9.0 MMT C02 Eq. (359 kt CH4). Emissions
remained fairly steady from 1990 through 1997, but have decreased since that time due to decreasing percentages of
wastewater being treated in anaerobic systems, including reduced use of on-site septic systems and central anaerobic
treatment systems (EPA 1992, 1996, 2000, and 2004; U.S. Census 2013). In 2015, CH4 emissions from industrial
wastewater treatment were estimated to be 5.8 MMT C02 Eq. (231 kt CH4). Industrial emission sources have
generally increased across the time series through 1999 and then fluctuated up and down with production changes
associated with the treatment of wastewater from the pulp and paper manufacturing, meat and poultry processing,
fruit and vegetable processing, starch-based ethanol production, and petroleum refining industries. Table 7-7 and
Table 7-8 provide CH4 and N20 emission estimates from domestic and industrial wastewater treatment.
With respect to N20, the United States identifies two distinct sources for N20 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 2015 emissions of N20 from centralized
wastewater treatment processes and from effluent were estimated to be 0.3 MMT C02 Eq. (1.2 kt N20) and 4.6
MMT C02 Eq. (15.5 kt N20), respectively. Total N20 emissions from domestic wastewater were estimated to be 5.0
MMT C02 Eq. (16.7 kt N20). Nitrous oxide emissions from wastewater treatment processes gradually increased
across the time series as a result of increasing U.S. population and protein consumption. N20 emissions are not
estimated from industrial wastewater treatment because there is no IPCC methodology provided or industrial
wastewater emission factors available.
Table 7-7: ChU and N2O Emissions from Domestic and Industrial Wastewater Treatment
(MMT COz Eq.)
Activity
1990
2005
2011
2012
2013
2014
2015
CH4
15.7
16.0
15.3
15.1
14.9
14.8
14.8
Domestic
10.5
10.1
9.5
9.3
9.1
9.1
9.0
Industrial3
5.1
5.9
5.9
5.8
5.8
5.7
5.8
N2O
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Domestic
3.4
4.4
4.8
4.8
4.9
4.9
5.0
Total
19.1
20.4
20.1
19.9
19.8
19.7
19.8
a Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit
and vegetable processing, starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
7-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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30
31
32
33
34
35
36
Table 7-8: ChU and N2O Emissions from Domestic and Industrial Wastewater Treatment (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
CH4
627
639
613
604
596
592
591
Domestic
422
404
379
372
365
365
359
Industrial3
205
235
234
232
231
227
231
N2O
11
15
16
16
16
16
17
Domestic
11
15
16
16
16
16
17
a Industrial activity includes the pulp and paper manufacturing, meat and poultry processing, fruit and
vegetable processing, starch-based ethanol production, and petroleum refining industries.
Note: Totals may not sum due to independent rounding.
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 and facultative lagoons), anaerobic systems (anaerobic lagoons and anaerobic reactors), and
from anaerobic digesters when the captured biogas is not completely combusted. The methodological equations are:
Emissions from Septic Systems = A
= USpop X (% onsite) X (EFseptic) X 1/109 X 365.25
Emissions from Centrally Treated Aerobic Systems (other than Constructed Wetlands) + Emissions from
Centrally Treated Aerobic Systems (Constructed Wetlands Only) + Emissions from Centrally Treated Aerobic
Systems (Constructed Wetlands used as Tertiary Treatment) = B
where,
Emissions from Centrally Treated Aerobic Systems (other than Constructed Wetlands)
= [(% collected) x (total BODs produced) x (% aerobicoTcw) x (% aerobic w/out primary) + (% collected) x
(total BODs produced) x (% aerobicoTcw) x (% aerobic w/primary) x (1-% BOD removed in prim, treat.)] x
(% operations not well managed) x (B0) x (MCF-aerobic_not_well_man)
Emissions from Centrally Treated Aerobic Systems (Constructed Wetlands Only)
= [(% collected) x (total BODs produced) x (%aerobiccw)] x (B0) x (MCF-constructed wetlands)
Emissions from Centrally Treated Aerobic Systems (Constructed Wetlands used as Tertiary Treatment)
= [(POTW_flow_CW) X (BODcwjnf) X 3.79] X 1/106 X 365.25
Emissions from Centrally Treated Anaerobic Systems = C
= {[(% collected) x (total BODs produced) x (% anaerobic) x (% anaerobic w/out primary)] + [(%
collected) x (total BODs produced) x (% anaerobic) x (% anaerobic w/primary) x (1-% BOD removed in
prim, treat.)]} x (B0) x (MCF-anaerobic)
Emissions from Anaerobic Digesters = D
= [(POTW_flow_AD) x (digester gas)/(per capita flow)] x conversion to m3 x (FRAC_CH4) x 365.25 x
(density ofCH4) x (1-DE) x 1/109
Total Domestic CH4 Emissions from Wastewater (kt) = A+ B + C + D
where,
USpop = U.S. population
% onsite = Flow to septic systems / total flow
% collected = Flow to POTWs / total flow
Waste 7-19
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33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
% aerobicoTcw
% aerobiccw
% anaerobic
% aerobic w/out primary
% aerobic w/primary
% BOD removed in prim, treat.
% operations not well managed
% anaerobic w/out primary
% anaerobic w/primary
EFSEPTIC
Total BOD5 produced
BODcw.inf
Bo
1/106
365.25
3.79
MCF-aerobicnotwellman.
MCF-anaerobic
MCF-constructed wetlands
DE
POTWflowCW
POTWflowAD
digester gas
100
0.0283
FRAC_CH4
662
1/109
Emissions from Septic Systems:
= Flow to aerobic systems, other than wetlands only / total flow to
POTWs
= Flow to aerobic systems, constructed wetlands used as sole treatment /
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
= Percent of BOD removed in primary treatment
= Percent of aerobic systems that are not well managed and in which
some anaerobic degradation occurs
= Percent of anaerobic systems that do not employ primary treatment
= Percent of anaerobic systems that employ primary treatment
= Methane emission factor - septic systems
= kg BOD/capita/day x U.S. population x 365.25 days/yr
= BOD concentration in wastewater entering the constructed wetland
= Maximum CH4-producing capacity for domestic wastewater
= Conversion factor, kg to kt
= Days in a year
= Conversion factor, liters to gallons
= CH4 correction factor for aerobic systems that are not well managed
= CH4 correction factor for anaerobic systems
= CH4 correction factor for surface flow constructed wetlands
= CH4 destruction efficiency from flaring or burning in engine
= Wastewater flow to POTWs that use constructed wetlands as tertiary
treatment (MGD)
= Wastewater influent flow to POTWs that have anaerobic digesters
(MGD)
= Cubic feet of digester gas produced per person per day
= Wastewater flow to POTW (gallons/person/day)
= Conversion factor, ft3 to m3
= Proportion of CH4 in biogas
= Density of CH4 (g CH4/m3 CH4)
= Conversion factor, g to kt
Methane emissions from septic systems were estimated by multiplying the U.S. population by the percent of
wastewater treated in septic systems (about 19 percent) and an emission factor (10.7 g CH4/capita/day) (Leverenz et
al. 2000), and then converting the result to kt/year. U.S. population data were taken from the U.S. Census Bureau
International Database (U.S. Census 2016) and include the populations of the United States, American Samoa,
Guam, Northern Mariana Islands, Puerto Rico, and the Virgin Islands. Table 7-9 presents U.S. population for 1990
through 2015.
Emissions from Centrally Treated Aerobic and Anaerobic Systems:
Methane emissions from POTWs were estimated by multiplying the total BOD5 produced in the United States by the
percent of wastewater treated centrally (about 81 percent) (EPA 1992, 1996, 2000, 2004), the relative percentage of
wastewater treated by aerobic and anaerobic systems (other than constructed wetlands), the relative percentage of
wastewater facilities with primary treatment, the percentage of BOD5 treated after primary treatment (67.5 percent,
32.5 percent removed in primary treatment) (Metcalf & Eddy 2003), the maximum CH4-producing capacity of
domestic wastewater (B0, 0.6 kg CH4/kg BOD) (IPCC 2006), and the relative methane conversion factors (MCF) for
well-managed aerobic (zero) (IPCC 2006), not well managed aerobic (0.3,) (IPCC 2006), and anaerobic (0.8) (IPCC
2006) systems.
Table 7-9 presents total BOD5 produced for 1990 through 2015. The proportions of domestic wastewater treated
onsite versus at centralized treatment plants were based on data from the 1989, 1991, 1993, 1995, 1997, 1999, 2001,
2003, 2005, 2007, 2009, 2011, and 2013 American Housing Surveys conducted by the U.S. Census Bureau (U.S.
Census 2013), with data for intervening years obtained by linear interpolation and data for 2014 and 2015 were
7-20 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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forecasted using 1990 to 2013 and 1990 to 2014 data, respectively. 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 Watersheds Needs Survey (EPA 1992, 1996, 2000, and 2004). Data for intervening years were obtained by
linear interpolation and the years 2004 through 2014 were forecasted from the rest of the time series. The percent of
wastewater flow to aerobic systems that use only constructed wetlands and wastewater flow to POTWs that use
constructed wetlands as tertiary treatment were obtained from the 1992, 1996, 2000, 2004, 2008, and 2012 Clean
Watersheds Needs Survey (EPA 1992, 1996, 2000, 2004, 2008b, and 2012). Data for intervening years were
obtained by linear interpolation and the years 2013 through 2015 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 & Eddy (2003). The B0 value, as well as the MCFs for anaerobic and
aerobic not well managed centralized treatment systems, were taken from IPCC (2006), while the CH4 emission
factor used for septic systems was taken from Leverenz et al. (2010).
For constructed wetlands, an MCF of 0.4 was used, which is the IPCC suggested MCF for surface flow wetlands.
This is the most conservative factor for constructed wetlands and was recommended by IPCC (2014) when the type
of constructed wetland is not known. A BOD concentration of 30 mg/L was used for wastewater entering
constructed wetlands used as tertiary treatment based on United States secondary treatment standards for POTWs.
These standards are based on plants generally utilizing simple settling and biological treatment (EPA 2013).
All aerobic systems are assumed to be well-managed as there are currently no data available to quantify the number
of systems that are not well-managed. In addition, methane emissions were calculated for systems that treat
wastewater with constructed wetlands and systems that use constructed wetlands as tertiary treatment; however,
constructed wetlands are a relatively small portion of wastewater treated centrally (<0.1 percent). Methane
emissions were estimated using the MCF for surface flow constructed wetlands (0.4). A BOD5 concentration
consistent with secondary treatment standards for POTWs in the United States (30 mg/L) (EPA 2013) was used to
account for emissions from constructed wetlands used as tertiary treatment. Methane emissions from anaerobic
digesters were estimated by multiplying the amount of biogas generated by wastewater sludge treated in anaerobic
digesters by the proportion of CH4 in digester biogas (0.65), the density of CH4 (662 g CH4/m3 CH4) (EPA 1993a),
and the destruction efficiency associated with burning the biogas in an energy/thermal device (0.99 for enclosed
flares).
Table 7-10 presents domestic wastewater CH4 emissions for both septic and centralized systems in 2015.
Emissions from Anaerobic Digesters:
Total CH4 emissions from anaerobic digesters were estimated by multiplying the wastewater influent flow to
POTWs with anaerobic digesters, the cubic feet of digester gas generated per person per day, the fraction of CH4 in
biogas, the density of CH4, one minus the destruction efficiency from flaring or burning in engine and then
converting the results to kt/year.
The CH4 destruction efficiency for methane recovered from sludge digestion operations, 99 percent, was selected
based on the range of efficiencies (98 to 100 percent) recommended for flares in AP-42 Compilation of Air Pollutant
Emission Factors, Chapter 2.4 (EPA 1998), efficiencies used to establish New Source Performance Standards
(NSPS) for landfills, along with data from CAR (2011), Sullivan (2007), Sullivan (2010), and UNFCCC (2012). The
cubic feet of digester gas produced per person per day (1.0 ft3/person/day) and the proportion of CH4 in biogas
(0.65) come from Metcalf & Eddy (2014). The wastewater flow to a POTW (100 gal/person/day) was taken from
the Great Lakes-Upper Mississippi River Board of State and Provincial Public Health and Environmental Managers,
Recommended Standards for Wastewater Facilities (Ten-State Standards) (2004).
Table 7-9: U.S. Population (Millions) and Domestic Wastewater BODs Produced (kt)
Year
Population
BODs
1990
253
8,333
2005
300
9,853
2011
316
10,381
2012
318
10,459
Waste 7-21
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2013
321
10,536
2014
323
10,613
2015
325
10,695
Sources: U.S. Census Bureau (2016);
Metcalf& Eddy (2003).
1 Table 7-10: Domestic Wastewater ChU Emissions from Septic and Centralized Systems
2 (2015, MMT CO2 Eq. and Percent)
CH4 Emissions (MMT CO2 Eq.)
% of Domestic Wastewater CH4
Septic Systems
5.9
65.8%
Centrally-Treated Aerobic Systems
0.1
1.1%
Centrally-Treated Anaerobic Systems
2.8
30.9%
Anaerobic Digesters
0.2
2.3%
Total
9.0
100%
Note: Totals may not sum due to independent rounding.
3 Industrial Wastewater CH4 Emission Estimates
4 Methane emission estimates from industrial wastewater were developed according to the methodology described in
5 IPCC (2006). Industry categories that are likely to produce significant CH4 emissions from wastewater treatment
6 were identified and included in the Inventory. The main criteria used to identify these industries are whether they
7 generate high volumes of wastewater, whether there is a high organic wastewater load, and whether the wastewater
8 is treated using methods that result in CH4 emissions. The top five industries that meet these criteria are pulp and
9 paper manufacturing; meat and poultry processing; vegetables, fruits, and juices processing; starch-based ethanol
10 production; and petroleum refining. Wastewater treatment emissions for these sectors for 2015 are displayed in
11 Table 7-11 below. Table 7-12 contains production data for these industries.
12 Table 7-11: Industrial Wastewater ChU Emissions by Sector (2015, MMT CO2 Eq. and
13 Percent)
CH4 Emissions (MMT CO2 Eq.) % of Industrial Wastewater CH4
Meat & Poultry
4.4
76%
Pulp & Paper
1.0
17%
Fruit & Vegetables
0.1
3%
Petroleum Refineries
0.1
2%
Ethanol Refineries
0.1
2%
Total
5.8
100%
Note: Totals may not sum due to independent rounding.
14 Table 7-12: U.S. Pulp and Paper, Meat, Poultry, Vegetables, Fruits and Juices, Ethanol, and
15 Petroleum Refining Production (MMT)
Year
Pulp and Paper3
Meat
(Live Weight
Killed)
Poultry
(Live Weight
Killed)
Vegetables, Fruits
and Juices
Ethanol
Petroleum
Refining
1990
128.9
27.3
14.6
38.7
2.5
702.4
2005
138.5
31.4
25.1
42.9
1 1.7
818.6
2011
126.1
33.8
26.2
44.3
41.6
858.8
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2012 124.4 33.8 26.1 45.6 39.5 856.1
2013 122.8 33.6 26.5 45.1 39.7 878.7
2014 120.9 32.2 26.9 45.8 42.8 903.9
201 5 1271 32^8 277 448 442 914.9
aPulp and paper production is the sum of woodpulp production plus paper and paperboard production.
Sources: Lockwood-Post (2002); FAO (2016); USDA (2016a); Cooper (2016); EIA (2016).
Methane emissions from these categories were estimated by multiplying the annual product output by the average
outflow, the organics loading (in COD) in the outflow, the maximum CH4 producing potential of industrial
wastewater (B0), and the percentage of organic loading assumed to degrade anaerobically in a given treatment
system (MCF). Ratios of BOD: COD in various industrial wastewaters were obtained from EPA (1997a) and used to
estimate COD loadings. The B0 value used for all industries is the IPCC default value of 0.25 kg CH4/kg COD
(IPCC 2006).
For each industry, the percent of plants in the industry that treat wastewater on site, the percent of plants that have a
primary treatment step prior to biological treatment, and the percent of plants that treat wastewater anaerobically
were defined. The percent of wastewater treated anaerobically onsite (TA) was estimated for both primary treatment
(%TAP) and secondary treatment (%TAS). For plants that have primary treatment in place, an estimate of COD that
is removed prior to wastewater treatment in the anaerobic treatment units was incorporated. The values used in the
%TA calculations are presented in Table 7-13 below.
The methodological equations are:
CH4 (industrial wastewater) = [P x W x COD x %TAP xB0x MCF] + [P x W x COD x %TAS xB0x MCF]
o/0TAp = [%Plants0 x %WWa,P x %CODP]
o/0TAs = [%Plantsa x %WWa,s x %CODs] + [%Plantst x %WWa,t x %CODs]
where,
CH4 (industrial wastewater) = Total CH4 emissions from industrial wastewater (kg/year)
P = Industry output (metric tons/year)
W = Wastewater generated (m3/metric ton of product)
COD = Organics loading in wastewater (kg/m3)
%TAP = Percent of wastewater treated anaerobically on site in primary treatment
%TAs = Percent of wastewater treated anaerobically on site in secondary treatment
%Plants0 = Percent of plants with onsite treatment
%WWa,P = Percent of wastewater treated anaerobically in primary treatment
%CODp = Percent of COD entering primary treatment
%Plantsa = Percent of plants with anaerobic secondary treatment
%Plantst = Percent of plants with other secondary treatment
%WW:ls = Percent of wastewater treated anaerobically in anaerobic secondary treatment
%WWa,t = Percent of wastewater treated anaerobically in other secondary treatment
%CODs = Percent of COD entering secondary treatment
B0 = Maximum CH4 producing potential of industrial wastewater (kg CH4/kg
COD)
MCF = CH4 correction factor, indicating the extent to which the organic content
(measured as COD) degrades anaerobically
Alternate methodological equations for calculating %TA were used for secondary treatment in the pulp and paper
industry to account for aerobic systems with anaerobic portions. These equations are:
%TAa = [%Plantsa x %WWa,s x %CODs] + [%Plants,t x %WWa,t x CODs]
%TAa,t = [%Plantsa,t x %WWa,s x %CODs]
where,
%TAa = Percent of wastewater treated anaerobically on site in secondary treatment
%TAa,t = Percent of wastewater treated in aerobic systems with anaerobic portions on
site in secondary treatment
Waste 7-23
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%Plants:i = Percent of plants with anaerobic secondary treatment
%Plantsa,t = Percent of plants with partially anaerobic secondary treatment
%WWa,s = Percent of wastewater treated anaerobically in anaerobic secondary treatment
%WWa,t = Percent of wastewater treated anaerobically in other secondary treatment
%CODs = Percent of COD entering secondary treatment
As described below, the values presented in Table 7-13 were used in the emission calculations and are described in
detail in ERG (2008), ERG (2013a), and ERG (2013b).
Table 7-13: Variables Used to Calculate Percent Wastewater Treated Anaerobically by
Industry (Percent)
Industry
Variable
Pulp
and
Paper
Meat
Processing
Poultry
Processing
Fruit/
Vegetable
Processing
Ethanol
Production
-Wet Mill
Ethanol
Production
- Dry Mill
Petroleum
Refining
%TAP
0
0
0
0
0
0
0
%TAS
0
33
25
4.2
33.3
75
23.6
%TAa
2.2
0
0
0
0
0
0
%TAa,t
11.8
0
0
0
0
0
0
%Plants0
0
100
100
11
100
100
100
%PlantSa
5
33
25
5.5
33.3
75
23.6
%PlantSa,t
28
0
0
0
0
0
0
%Plantst
35
67
75
5.5
66.7
25
0
%WWa,p
0
0
0
0
0
0
0
%WWa,s
100
100
100
100
100
100
100
%WWa,t
0
0
0
0
0
0
0
%CODp
100
100
100
100
100
100
100
%CODs
42
100
100
77
100
100
100
Note: Due to differences in data availability and methodology, zero values in the table may be used for calculation
purposes only.
Sources: ERG (2008); ERG (2013a); and ERG (2013b).
Pulp and Paper. Wastewater treatment for the pulp and paper industry typically includes neutralization, screening,
sedimentation, and flotation/hydrocycloning to remove solids (World Bank 1999; Nemerow and Dasgupta 1991).
Secondary treatment (storage, settling, and biological treatment) mainly consists of lagooning. In determining the
percent that degrades anaerobically, both primary and secondary treatment were considered. In the United States,
primary treatment is focused on solids removal, equalization, neutralization, and color reduction (EPA 1993b). 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
1993b). However, because the vast majority of primary treatment operations at U.S. pulp and paper mills use
mechanical clarifiers, and less than 10 percent of pulp and paper wastewater is managed in primary settling ponds
that are not expected to have anaerobic conditions, negligible emissions are assumed to occur during primary
treatment.
Approximately 42 percent of the BOD passes on to secondary treatment, which consists of activated sludge, aerated
stabilization basins, or non-aerated stabilization basins. Based onEPA's OAQPS Pulp and Paper Sector Survey, 5.3
percent of pulp and paper mills reported using anaerobic secondary treatment for wastewater and/or pulp
condensates (ERG 2013a). Twenty-eight percent of mills also reported the use of quiescent settling ponds. Using
engineering judgment, these systems were determined to be aerobic with possible anaerobic portions. For the truly
anaerobic systems, an MCF of 0.8 is used, as these are typically deep stabilization basins. For the partially anaerobic
systems, an MCF of 0.2 is used, which is the IPCC suggested MCF for shallow lagoons.
A time series of CH4 emissions for 1990 through 2001 was developed based on production figures reported in the
Lockwood-Post Directory (Lockwood-Post 2002). Data from the Food and Agricultural Organization of the United
Nations (FAO) database FAOSTAT were used for 2002 through 2015 (FAO 2016). The overall wastewater outflow
varies based on a time series outlined in ERG (2013a) to reflect historical and current industry wastewater flow, and
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the average BOD concentrations in raw wastewater was estimated to be 0.4 gram BOD/liter (EPA 1997b; EPA
1993b; World Bank 1999). The COD:BOD ratio used to convert the organic loading to COD for pulp and paper
mills was 2 (EPA 1997a).
Meat and Poultry Processing. The meat and poultry processing industry makes extensive use of anaerobic lagoons
in sequence with screening, fat traps, and dissolved air flotation when treating wastewater on site. About 33 percent
of meat processing operations (EPA 2002) and 25 percent of poultry processing operations (U.S. Poultry 2006)
perform on-site treatment in anaerobic lagoons. The IPCC default B0 of 0.25 kg CH4/kg COD and default MCF of
0.8 for anaerobic lagoons were used to estimate the CH4 produced from these on-site treatment systems. Production
data, in carcass weight and live weight killed for the meat and poultry industry, were obtained from the USD A
Agricultural Statistics Database and the Agricultural Statistics Annual Reports (USD A 2016a). Data collected by
EPA's Office of Water provided estimates for wastewater flows into anaerobic lagoons: 5.3 and 12.5 m3/metric ton
for meat and poultry production (live weight killed), respectively (EPA 2002). The loadings are 2.8 and 1.5 g
BOD/liter for meat and poultry, respectively. The COD:BOD ratio used to convert the organic loading to COD for
both meat and poultry facilities was 3 (EPA 1997a).
Vegetables, Fruits, and Juices Processing. Treatment of wastewater from fruits, vegetables, and juices processing
includes screening, coagulation/settling, and biological treatment (lagooning). The flows are frequently seasonal,
and robust treatment systems are preferred for on-site treatment. Effluent is suitable for discharge to the sewer. This
industry is likely to use lagoons intended for aerobic operation, but the large seasonal loadings may develop limited
anaerobic zones. In addition, some anaerobic lagoons may also be used (Nemerow and Dasgupta 1991).
Consequently, 4.2 percent of these wastewater organics are assumed to degrade anaerobically. The IPCC default B0
of 0.25 kg CH4/kg COD and default MCF of 0.8 for anaerobic treatment were used to estimate the CH4 produced
from these on-site treatment systems. The USDA National Agricultural Statistics Service (USDA 2016a) provided
production data for potatoes, other vegetables, citrus fruit, non-citrus fruit, and grapes processed for wine. Outflow
and BOD data, presented in Table 7-14, were obtained from EPA (1974) for potato, citrus fruit, and apple
processing, and from EPA (1975) for all other commodities. The COD:BOD ratio used to convert the organic
loading to COD for all fruit, vegetable, and juice facilities was 1.5 (EPA 1997a).
Table 7-14: Wastewater Flow (m3/ton) and BOD Production (g/L) for U.S. Vegetables,
Fruits, and Juices Production
Commodity
Wastewater Outflow (m3/ton)
BOD (g/L)
Vegetables
Potatoes
10.27
1.765
Other Vegetables
8.60
0.784
Fruit
Apples
3.66
1.371
Citrus Fruits
10.11
0.317
Non-citrus Fruits
12.42
1.204
Grapes (for wine)
2.78
1.831
Sources: EPA (1974); EPA (1975).
Ethanol Production. Ethanol, or ethyl alcohol, is produced primarily for use as a fuel component, but is also used in
industrial applications and in the manufacture of beverage alcohol. Ethanol can be produced from the fermentation
of sugar-based feedstocks (e.g., molasses and beets), starch- or grain-based feedstocks (e.g., corn, sorghum, and
beverage waste), and cellulosic biomass feedstocks (e.g., agricultural wastes, wood, and bagasse). Ethanol can also
be produced synthetically from ethylene or hydrogen and carbon monoxide. However, synthetic ethanol comprises
only about 2 percent of ethanol production, and although the U.S. Department of Energy (DOE) predicts cellulosic
ethanol to greatly increase in the coming years, currently it is only in an experimental stage in the United States.
Currently, ethanol is mostly made from sugar and starch crops, but with advances in technology, cellulosic biomass
is increasingly used as ethanol feedstock (DOE 2013).
Ethanol is produced from corn (or other starch-based feedstocks) primarily by two methods: wet milling and dry
milling. Historically, the majority of ethanol was produced by the wet milling process, but now the majority is
produced by the dry milling process. The dry milling process is cheaper to implement, and has become more
efficient in recent years (Rendleman and Shapouri 2007). The wastewater generated at ethanol production facilities
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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.
Methane 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; Ruocco 2006b; Merrick 1998; Donovan 1996; NRBP 2001). COD concentrations were also found
to be about 3 g/L (Ruocco 2006a; Merrick 1998; White and Johnson 2003). One hundred percent of plants were
estimated to have onsite wastewater treatment, and the variables used to calculate percent wastewater treated
anaerobically are presented in Table 7-13. A default MCF of 0.8 for anaerobic treatment was used to estimate the
CH4 produced from these on-site treatment systems. The amount of CH4 recovered through the use of
biomethanators was estimated, and a 99 percent destruction efficiency was used. Biomethanators are anaerobic
reactors that use microorganisms under anaerobic conditions to reduce COD and organic acids and recover biogas
from wastewater (ERG 2006). Methane emissions were then estimated as follows:
Methane = [Production x Flow x COD x 3.785 x ([%Plants0 x %WWa,P x %CODP] + [%Plantsa x %WWa,s x
%CODs] + [%Plantst x %WWa,t x %CODs]) xB0x MCF x % Not Recovered] + [Production x Flow x 3.785 x
COD x ([%PlantSo x %WWa,P x %CODP] + [%Plantsa x %WWa,s x %CODs] + [%Plantst x %WWa,t x %CODs])
x Bo x MCF x (% Recovered) x (1-DE)] x 1/109
where,
Production
= Gallons ethanol produced (wet milling or dry milling)
Flow
= Gallons wastewater generated per gallon ethanol produced
COD
= COD concentration in influent (g/1)
3.785
= Conversion factor, gallons to liters
%Plants0
= Percent of plants with onsite treatment
%wwa,P
= Percent of wastewater treated anaerobically in primary treatment
%CODp
= Percent of COD entering primary treatment
%Plants„
= Percent of plants with anaerobic secondary treatment
%Plantst
= Percent of plants with other secondary treatment
%wwa,s
= Percent of wastewater treated anaerobically in anaerobic secondary treatment
%WWa,t
= Percent of wastewater treated anaerobically in other secondary treatment
%CODs
= Percent of COD entering secondary treatment
Bo
= Maximum methane producing capacity (g CH4/g COD)
MCF
= Methane conversion factor
% 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
1/109
= Conversion factor, g to kt
A time series of CH4 emissions for 1990 through 2015 was developed based on production data from the Renewable
Fuels Association (Cooper 2016).
Petroleum Refining. Petroleum refining wastewater treatment operations have the potential to produce CH4
emissions from anaerobic wastewater treatment. EPA's Office of Air and Radiation performed an Information
Collection Request (ICR) for petroleum refineries in 2011.6 Of the responding facilities, 23.6 percent reported using
non-aerated surface impoundments or other biological treatment units, both of which have the potential to lead to
anaerobic conditions (ERG 2013b). In addition, the wastewater generation rate was determined to be 26.4 gallons
6 See .
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per barrel of finished product (ERG 2013b). An average COD value in the wastewater was estimated at 0.45 kg/m3
(Benyahia et al. 2006). A default MCF of 0.3 was used for partially aerobic systems.
The equation used to calculate CH4 generation at petroleum refining wastewater treatment systems is presented
below:
Methane = Flow x COD x %TA xB0x MCF
where,
Flow = Annual flow treated through anaerobic treatment system (m3/year)
COD = COD loading in wastewater entering anaerobic treatment system (kg/m3)
%TA = Percent of wastewater treated anaerobically on site
B0 = Maximum methane producing potential of industrial wastewater (kg CH4/kg COD)
MCF = Methane conversion factor
A time series of CH4 emissions for 1990 through 2015 was developed based on production data from the Energy
Information Administration (EIA 2016).
Domestic Wastewater N2O Emission Estimates
Nitrous oxide emissions from domestic wastewater (wastewater treatment) were estimated using the IPCC (2006)
methodology and supplemented with IPCC (2014) methodology to include constructed wetland emissions, including
calculations that take into account N removal with sewage sludge, non-consumption and industrial/commercial
wastewater N, and emissions from advanced and constructed wetlands at 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 discharged into aquatic environments as effluent 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 that data is adjusted using a factor to account for the fraction
of protein actually consumed.
• Small amounts of gaseous nitrogen oxides are formed as byproducts in the conversion of nitrate to N gas in
anoxic biological treatment systems. Approximately 7 g N20 is generated per capita per year if wastewater
treatment includes intentional nitrification and denitrification (Scheehle and Doom 2001). Analysis of the use
of treatment systems in the United States that include denitrification has shown a significant increase in the
time period between 2004 and 2012, from serving populations totaling 2.4 million people to 21.3 million
people (EPA 2004 and EPA 2012). This is consistent with efforts throughout the United States to improve
nutrient removal at centralized treatment systems in response to specific water quality concerns. Based on an
emission factor of 7 g per capita per year, approximately 21.2 metric tons of additional N20 may have been
emitted via denitrification in 2004, while about 186 metric tons may have been emitted via denitrification in
both 2008 and 2012. 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 or denitrification are assumed to generate 3.2 g N20 per capita per year.
• Constructed wetlands may be used as the sole treatment unit at a centralized wastewater treatment plant or
may serve as tertiary treatment after simple settling and biological treatment. Emissions from all constructed
wetland systems were included in the estimates of emissions from centralized wastewater treatment plant
processes and effluent from these plants. The emission factor of 0.0013 kg N20-N/kg N produced for
constructed wetlands is from IPCC (2014).
• N20 emissions from wastewater treatment plants are estimated, and as such, the N associated with these
emissions is subtracted from the amount of N estimated to be discharged into aquatic environments as
effluent, consistent with the IPCC methodology.
Nitrous oxide emissions from domestic wastewater were estimated using the following methodology:
NzOtotal = NzOplant + NzOeffluent
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NzOplant = NzOnit/denit + N20woutnit/denit+ NzOcwonly + N20cwtertiary
N20nit/denit= [(USpopnd) X EF2 X Find-com] X 1/109
N20woutnit/denit = {[(USpop X WWTP) - USpopnd - USpopcw] X 106 X Find-com X EFi} X 1/109
N20cwonly = {[(USpopcw X 106 X Protein X Fnpr X Fnon-con X Find-com) X EF4] X 44/28} X 1/106
N2O CW TERTIARY — {[(New,inf x POTW_flow_CW x 3.79 x 365.25) x EF4] x 44/28} x 1/106
N20effluent = [(USpop X WWTP X Protein X Fnpr X Fnon-con X Find-com) - Nsludge - (N20plant X 106 X 28/44)] X
EFs X 44/28 X 1/106
where,
N20TOTAL
= Annual emissions of N20 (kt)
N20PLANT
= N20 emissions from centralized wastewater treatment plants (kt)
N20NIT/DENIT
= N20 emissions from centralized wastewater treatment plants with
nitrification/denitrification (kt)
N2OWOUT NIT/DENIT
= N20 emissions from centralized wastewater treatment plants without
nitrification/denitrification (kt)
N2OCW ONLY
= N20 emissions from centralized wastewater treatment plants with constructed
wetlands only (kt)
N2OCW TERTIARY
= N20 emissions from centralized wastewater treatment plants with constructed
wetlands used as tertiary treatment (kt)
N2OEFFLUENT
= N20 emissions from wastewater effluent discharged to aquatic environments (kt)
USpop
= U.S. population
USpopnd
= U.S. population that is served by biological denitrification
USpopcw
= U.S. population that is served by only constructed wetland systems
WWTP
= Fraction of population using WWTP (as opposed to septic systems)
POTWflowCW
= Wastewater flow to POTWs that use constructed wetlands as tertiary treatment
(MGD)
EFi
= Emission factor -plants without intentional nitrification or denitrification
EF2
= Emission factor - plant with intentional denitrification
Protein
= Annual per capita protein consumption (kg/person/year)
New,INF
= Influent nitrogen concentration to constructed wetlands used as tertiary treatment
(mg/L)
Fnpr
= Fraction of N in protein (kg N/kg protein)
Fnon-con
= Factor for non-consumed protein added to wastewater
Find-com
= Factor for industrial and commercial co-discharged protein into the sewer
Nsludge
= N removed with sludge, kg N/yr
ef3
= Emission factor (kg N20 -N/kg sewage-N produced) - from effluent
ef4
= Emission factor (kg N20 -N/kg N produced) - constructed wetlands
3.79
= Conversion factor, liters to gallons
44/28
= Molecular weight ratio of N20 to N2
1/106
= Conversion factor, kg to Gg
1/109
= Conversion factor, g to Gg
U.S. population data were taken from the U.S. Census Bureau International Database (U.S. Census 2016) and
include the populations of the United States, American Samoa, Guam, Northern Mariana Islands, Puerto Rico, and
the Virgin Islands. The fraction of the U.S. population using wastewater treatment plants is based on data from the
1989, 1991, 1993, 1995, 1997, 1999, 2001,2003,2005,2007, 2009, 2011, and 2013 American Housing Survey
(U.S. Census 2013). Data for intervening years were obtained by linear interpolation and data from 2014 and 2015
were forecasted using 1990 to 2013 and 1990 to 2014 data, respectively. The emission factor (EFi) used to estimate
emissions from wastewater treatment for other plants was taken from IPCC (2006), while the emission factor (EF2)
used to estimate emissions from wastewater treatment for plants with intentional denitrification was taken from
Scheehle and Doom (2001). The emission factor (EF4) used to estimate emissions from surface flow constructed
wetlands (0.0013 kg N20 -N/kg N produced) was taken from IPCC (2014). Data on annual per capita protein intake
were provided by the U.S. Department of Agriculture Economic Research Service (USDA 2016b). Protein
consumption data for 2011 through 2015 were extrapolated from data for 1990 through 2010. An emission factor to
7-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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estimate emissions from effluent (EF3) has not been specifically estimated for the United States, thus the default
IPCC value (0.005 kg N20-N/kg sewage-N produced) was applied (IPCC 2006). The fraction of N in protein (0.16
kg N/kg protein) was also obtained from IPCC (2006). The factor for non-consumed protein (1.2) and the factor for
industrial and commercial co-discharged protein (1.25) were obtained from IPCC (2006). The amount of nitrogen
removed by denitrification systems was taken from EPA (2008a), while the population served by denitrification
systems was estimated from Clean Watersheds Needs Survey (EPA 1992, 1996, 2000, 2004, 2008b, and 2012).
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 2015 were forecasted from the rest of the time
series. The influent nitrogen concentration to constructed wetlands used as tertiary treatment (25 mg/L) was
obtained from Metcalf & Eddy (2014). 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 (EPA 1993b; Beecher et al. 2007; McFarland 2001; EPA
1999). In 2015, 292 kt N was removed with sludge. Table 7-15 presents the data for U.S. population, population
served by biological denitrification, population served by wastewater treatment plants, available protein, protein
consumed, and nitrogen removed with sludge.
Table 7-15: U.S. Population (Millions), Population Served by Biological Denitrification
(Millions), Fraction of Population Served by Wastewater Treatment (Percent), Available
Protein (kg/Person-Year), Protein Consumed (kg/Person-Year), and Nitrogen Removed with
Sludge (kt-N/Year)
Year Population Populations WWTP Population Available Protein Protein Consumed N Removed
1990 253 2.0 75.6 43.1 33.2 214.2
2005 300 7.1 78.8 44.9 34.7 261.1
2011 316 21.3 80.6 45.0 34.7 279.5
2012 318 21.3 81.0 45.1 34.7 282.6
2013 321 19.8 81.4 45.1 34.8 285.6
2014 323 20.8 81.1 45.2 34.8 288.7
2015 325 21.8 81.4 45.2 34.9 291.8
Sources: Population: U.S. Census (2016); Population^: EPA (1992), EPA (1996), EPA (2000), EPA (2004), EPA (2008b), EPA
(2012); WWTP Population: U.S. Census (2013); Available Protein: USDA (2016b); N Removed: Beecher et al. (2007),
McFarland (2001), EPA (1999), EPA (1993c).
Uncertainty and lime-Serfi insistency
The overall uncertainty associated with both the 2015 CH4 and N20 emission estimates from wastewater treatment
and discharge was calculated using the 2006IPCC Guidelines Approach 2 methodology (IPCC 2006). Uncertainty
associated with the parameters used to estimate CH4 emissions include that of numerous input variables used to
model emissions from domestic wastewater, and wastewater from pulp and paper manufacturing, meat and poultry
processing, fruits and vegetable processing, ethanol production, and petroleum refining. Uncertainty associated with
the parameters used to estimate N20 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. Uncertainty associated with constructed wetlands parameters including U.S. population served by
constructed wetlands, and emission and conversion factors are from IPCC (2014), whereas uncertainty associated
with POTW flow to constructed wetlands and influent BOD and nitrogen concentrations were based on expert
judgment.
The results of this Approach 2 quantitative uncertainty analysis are summarized in Table 7-16. Methane emissions
from wastewater treatment were estimated to be between 10.9 and 18.0 MMT C02 Eq. at the 95 percent confidence
level (or in 19 out of 20 Monte Carlo Stochastic Simulations). This indicates a range of approximately 26 percent
below to 22 percent above the 2015 emissions estimate of 14.8 MMT CO2 Eq. Nitrous oxide emissions from
wastewater treatment were estimated to be between 1.2 and 10.3 MMT CO2 Eq., which indicates a range of
approximately 75 percent below to 107 percent above the 2015 emissions estimate of 5.0 MMT CO2 Eq.
Waste 7-29
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Table 7-16: Approach 2 Quantitative Uncertainty Estimates for ChU Emissions from
Wastewater Treatment (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
Uncertainty Range Relative to Emission Estimate3
(MMT CO2 Eq.)
(MMT CO2 Eq.)
(%)
Lower
Bound
Upper
Bound
Lower
Bound
Upper
Bound
Wastewater Treatment
CH4
14.8
10.9
18.0
-26%
+22%
Domestic
ch4
9.0
6.7
11.4
-25%
+27%
Industrial
ch4
5.8
3.0
8.3
-48%
+44%
Wastewater Treatment
n2o
5.0
1.2
10.3
-75%
+107%
a Range of emission estimates predicted by Monte Carlo Stochastic Simulation for a 95 percent confidence interval.
Methodological recalculations were applied to the entire time series to ensure time-series consistency from 1990
through 2015. Details on the emission trends through time are described in more detail in the Methodology section.
QA/QC and Verification
A QA/QC analysis was performed on activity data, documentation, and emission calculations. This effort included a
Tier 1 analysis, including the following checks:
• Checked for transcription errors in data input;
• Ensured references were specified for all activity data used in the calculations;
• Checked a sample of each emission calculation used for the source category;
• Checked that parameter and emission units were correctly recorded and that appropriate conversion factors
were used;
• Checked for temporal consistency in time series input data for each portion of the source category;
• Confirmed that estimates were calculated and reported for all portions of the source category and for all years;
• Investigated data gaps that affected emissions estimates trends; and
• Compared estimates to previous estimates to identify significant changes.
All transcription errors identified were corrected. The QA/QC analysis did not reveal any systemic inaccuracies or
incorrect input values.
Recalculations Discussion
EPA concluded its investigation of constructed and semi-natural treatment wetlands and incorporated emissions
estimates from these wastewater treatment scenarios for both methane and nitrous oxide into the Inventory. Flow to
constructed wetlands and constructed wetlands used as tertiary treatment were determined with data available from
Clean Watersheds Needs Survey (EPA 1992, 1996, 2000, 2004, 2008b, and 2012). Emissions and conversion factors
as well as methodology associated with constructed wetlands were taken from IPCC (2014). For CH4 emissions, the
BOD concentration entering constructed wetlands used as tertiary treatment for the United States was set equal to
POTW secondary treatment standards (EPA 2013). For N20 emissions, the nitrogen concentration entering
constructed wetlands used as tertiary treatment for the United States was conservatively estimated to be 25 mg/L
(Metcalf & Eddy 2014). The inclusion of estimates for emissions from constructed wetlands resulted in minimal
changes to overall methane and nitrous emissions from domestic wastewater for the entire time series. In addition,
an analysis of 2008 and 2012 CWNS provided updated values for both the population associated with facilities with
denitrification processes and the total wastewater flow to POTWs (EPA 2008b and 2012). Data for intervening years
were obtained by linear interpolation and the years 2013 through 2015 were forecasted from the rest of the time
series. This changed resulted in updated values for both the population served by biological denitrification and total
wastewater flow for 2005 through 2014.
The calculation of the amount of N20 emitted from wastewater effluent was updated to properly back-calculate and
subtract out nitrogen associated with N20 emissions from centralized treatment plants.
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Planned Improvements
Data collected under the EPA's Greenhouse Gas Reporting Program Subpart II, Industrial Wastewater Treatment
(GHGRP) is being investigated for use in improving the emission estimates for the industrial wastewater category.
Because reporting data from EPA's GHGRP are not available for all Inventory years, ensuring time series
consistency has been a priority. In addition, the representativeness of GHGRP reporters has been investigated to
determine if moving to a facility-level implementation of GHGRP data is warranted, or whether the GHGRP data
will allow update of activity data for certain industry sectors, such as use of biogas recovery systems or update of
waste characterization data. Since EPA's GHGRP only includes reporters that have met the reporting threshold, and
because it is not currently possible to review whether reporters represent the majority of U.S. production, GHGRP
data are not believed to be sufficiently representative to move toward facility-level estimates in the Inventory.
However, EPA's GHGRP data continues to be evaluated for improvements to activity data, and in verifying
methodologies currently in use in the Inventory to estimate emissions (ERG 2014a, 2016). In implementing any
improvements and integration of data from EPA's GHGRP, the latest guidance from IPCC will be followed.7
In addition, reports continue to be investigated which could inform potential updates to the Inventory based on
international research. The Global Water Research Coalition (GWRC 2011) report was previously evaluated, which
included results of studies from Australia, France, the Netherlands and the US. Since each dataset was taken from a
variety of wastewater treatment plant types using different methodologies and protocols, it was not representative
enough to include in the Inventory (ERG 2014b). In addition to this report, wastewater inventory submissions from
other countries have been evaluated to determine if there are any emission factors, specific methodologies, or
additional industries that could be used to inform the U.S. inventory calculations. Although no comparable data have
been found, investigations into other countries' Inventory reports continues for investigating potential improvements
to the Inventory.
Currently, for domestic wastewater, it is assumed that all aerobic wastewater treatment systems are well-managed
and produce no CH4 and that all anaerobic systems have an MCF of 0.8. Efforts to obtain better data reflecting
emissions from various types of municipal treatment systems are currently being pursued by researchers, including
the Water Environment Research Federation (WERF). This research includes data on emissions from partially
anaerobic treatment systems which have been reviewed, but the emissions were too variable and the sample size too
small to include in the Inventory at this time (Willis et al. 2013). In addition, information on flare efficiencies were
reviewed, but they were not suitable for use in updating the Inventory because the flares used in the study are likely
not comparable to those used at wastewater treatment plants (ERG 2014b). The status of this and similar research
continues to be monitored for potential inclusion in the Inventory in the future.
For industrial wastewater emissions, we are working with the National Council of Air and Stream Improvement
(NCASI) to determine if there are sufficient data available to update the estimates of organic loading in pulp and
paper wastewaters treated on site. These data include the estimates of wastewater generated per unit of production,
the BOD and/or COD concentration of these wastewaters, and the industry-level production basis used in the
Inventory. Data on the industry-level production basis to date has been received and will be incorporated, but in
order to incorporate that data, the production basis in relation to the wastewater generation rate and the organic
content of the wastewater needs to be evaluated to ensure it is incorporated correctly into the Inventory.
Breweries are also being evaluated as sources of industrial wastewater emissions to determine the scale of methane
quantities produced. A benchmarking study will be available in the near future which could improve preliminary
brewery estimates and fill in current data gaps for potential inclusion in future inventories.
The inclusion of wastewater treatment emissions from dairy products processing into Inventory estimates is being
investigated, and will continue focusing on contacts in industry groups, such as the National Milk Producers
Federation, to determine if there are readily available data on a national scale that could facilitate calculation of
national emission estimates from this industry.
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
7 IPCC guidance for models and facility-level data, see .
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come from the 1992, 1996, 2000, and 2004 CWNS. The question of whether activity data for wastewater treatment
systems are sufficient across the time series to further differentiate aerobic systems with the potential to generate
small amounts of CH4 (aerobic lagoons) versus other types of aerobic systems, and to differentiate between
anaerobic systems to allow for the use of different MCFs for different types of anaerobic treatment systems,
continues to be explored. A methodology was developed to use the 2008 and 2012 CWNS data for wastewater
treated in denitrification systems, and in future years of the Inventory it may be possible to utilize these years of the
CWNS to update the aerobic/anaerobic data. Additional information and other data continue to be evaluated to
update future years of the Inventory, including anaerobic digester data compiled by the North East Biosolids and
Residuals Association (NEBRA) in collaboration with several other entities. While NEBRA is no longer involved in
the project, the Water Environment Federation (WEF) now hosts and manages the dataset which has been relocated
to www.wef.org/biosolids. Water Environment Federation (WEF) biosolid data continues to be evaluated as a
potential source of digester, sludge, and biogas data from POTWs.
Previously, new measurement data from WERF were used to develop a U.S.-specific emission factor for CH4
emissions from septic systems and incorporated into the Inventory emissions calculation. Due to the high
uncertainty of the measurements forN20 from septic systems, estimates of N20 emissions were not included.
Appropriate emission factors for septic system N20 emissions will continue to be investigated as the data collected
by WERF indicate that septic systems are a source of N20 emissions.
In addition, the estimate of N entering municipal treatment systems is under review. The factor that accounts for
non-sewage N in wastewater (bath, laundry, kitchen, industrial components) has a high uncertainty. Obtaining data
on the changes in average influent N concentrations to centralized treatment systems over the time series would
improve the estimate of total N entering the system, which would reduce or eliminate the need for other factors for
non-consumed protein or industrial flow. The dataset previously provided by the National Association of Clean
Water Agencies (NACWA) was reviewed to determine if it was representative of the larger population of
centralized treatment plants for potential inclusion into the Inventory. However, this limited dataset was not
representative of the number of systems by state or the service populations served in the United States, and therefore
could not be incorporated into the Inventory methodology. Additional data sources will continue to be researched
with the goal of improving the uncertainty of the estimate of N entering municipal treatment systems. Unfortunately,
NACWA's suggestion of using National Pollution Discharge Elimination System (NPDES) permit data to estimate
nitrogen loading rates is not feasible. Not every POTW is required to measure for N so the database is not a
complete source. Typically, only those POTWs that are required to reduce nutrients would be monitored, so the
database may reflect lower N effluent loadings than that typical throughout the United States.
Sources of data for development of a country-specific methodology for N20 emissions associated with on-site
industrial wastewater treatment operations continue to be investigated, including the appropriateness of using
IPCC's default factor for domestic wastewater (0.005 kg N20-N/kg N).
The value used for N content of sludge also continues to be investigated. This value is driving the N20 emissions for
wastewater treatment and is static over the time series. To date, new data have not been identified that would be able
to establish a time series for this value. The amount of sludge produced and sludge disposal practices will also be
investigated. In addition, based on UNFCCC review comments, the transparency of the fate of sludge produced in
wastewater treatment will continue to be improved.
7.3 Composting (IPCC Source Category 5B1)
Composting of organic waste, such as food waste, garden (yard) and park waste, and wastewater treatment sludge
and/or biosolids, is common in the United States. Advantages of composting include reduced volume of the waste,
stabilization of the waste, and destruction of pathogens in the waste. The end products of composting, depending on
its quality, can be recycled as a fertilizer and soil amendment, or be disposed of 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 (C02). Methane (CH4) is formed in anaerobic sections of the compost, which are
created when there is excessive moisture or inadequate aeration (or mixing) of the compost pile. This CH4 is then
oxidized to a large extent in the aerobic sections of the compost. The estimated CH4 released into the atmosphere
ranges from less than 1 percent to a few percent of the initial C content in the material (IPCC 2006). Depending on
7-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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1 how well the compost pile is managed, nitrous oxide (N20) emissions can be produced. The formation of N20
2 depends on the initial nitrogen content of the material and is mostly due to nitrogen oxide (NOx) denitrification
3 during the later composting stages. Emissions vary and range from less than 0.5 percent to 5 percent of the initial
4 nitrogen content of the material (IPCC 2006). Animal manures are typically expected to generate more N20 than, for
5 example, yard waste, however data are limited.
6 From 1990 to 2015, the amount of waste composted in the United States has increased from 3,810 kt to 21,052 kt.
7 The amount composted in 2015 is at an all-time high for the Inventory time series (see Table 7-19). Over the past
8 decade, the amount of waste composted has fluctuated. A peak of 20,049 kt composted was observed in 2008,
9 followed by a steep drop the following year to 18,824 kt composted, presumably driven by the economic crisis.
10 Since then, the amount of waste composted has gradually increased, and when comparing 2009 to 2015, a 12 percent
11 increase in waste composted is observed. Emissions of CH4 and N20 from composting from 2009 to 2015 have
12 increased by the same percentage. In 2015, CH4 emissions from composting (see Table 7-17 and Table 7-18) were
13 2.1 MMT C02 Eq. (84.2 kt), and N20 emissions from composting were 1.9 MMT C02 Eq. (6.3 kt). The wastes
14 composted primarily include yard trimmings (grass, leaves, and tree and brush trimmings) and food scraps from the
15 residential and commercial sectors (such as grocery stores; restaurants; and school, business, and factory cafeterias).
16 The composted waste quantities reported here do not include backyard composting or agricultural composing.
17 The growth in composting since the 1990s and specifically over the past decade is attributable primarily to three
18 factors: (1) the enactment of legislation by state and local governments that discouraged the disposal of yard
19 trimmings in landfills, (2) yard trimming collection and yard trimming drop off sites provided by local solid waste
20 management districts/divisions, and (3) an increased awareness of the environmental benefits of composting. Most
21 bans on the disposal of yard trimmings were initiated in the early 1990's by state or local governments (US
22 Composting Council 2010). By 2010, 25 states, representing about 50 percent of the nation's population, had
23 enacted such legislation (BioCycle 2010). An additional 16 states are known to have commercial-scale composting
24 facilities (Shin 2014). In the past 5 years, the amount of waste composted has gradually increased from 20.2 million
25 tons in 2010 to 23.2 million tons in 2015 (see Table 7-19).
26 Table 7-17: ChU and N2O Emissions from Composting (MMT CO2 Eq.)
Activity 1990 2005 2011 2012 2013 2014 2015
(II: 0.4 1.9 1.9 1.9 2.0 2.1 2.1
N2O 03 1.7 1 1.7 1.7 1.8 1.9 1.9
Total 0.7 3.6 3.5 3.7 3.9 4.0 4.0
27
28 Table 7-18: ChU and N2O Emissions from Composting (kt)
Activity 1990 2005 2011 2012 2013 2014 2015
CH4 15.2 74.6 74.6 77.4 81.4 83.5 84.2
N2O 1.1 5.6 5.6 5.8 6.1 6.3 6.3
29 Methodology
30 Methane and N20 emissions from composting depend on factors such as the type of waste composted, the amount
31 and type of supporting material (such as wood chips and peat) used, temperature, moisture content (e.g., wet and
32 fluid versus dry and crumbly), and aeration during the composting process.
33 The emissions shown in Table 7-17 and Table 7-18 were estimated using the IPCC default (Tier 1) methodology
34 (IPCC 2006), which is the product of an emission factor and the mass of organic waste composted (note: no CH4
35 recovery is expected to occur at composting operations in the emission estimates presented):
36 Ej =M -x EFj
37 where,
38 Ei = CH4 or N20 emissions from composting, kt CH4 or N20,
39 M mass of organic waste composted in kt,
Waste 7-33
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1 EF, = emission factor for composting, 4 t CH 4/kt of waste treated (wet basis) and 0.3
2 t N20/kt of waste treated (wet basis) (IPCC 2006), and
3 i = designates either CH4 or N20.
4 Estimates of the quantity of waste composted (M) are presented in Table 7-19 for select years. Estimates of the
5 quantity composted for 1990, 2005, 2010, and 2012 to 2014 were taken from EPA's Advancing Sustainable
6 Materials Management: Facts and Figures 2014 (EPA 2016); the estimate of the quantity composted for 2011 was
7 taken from EP A's Municipal Solid Waste In The United States: 2012 Facts and Figures (EPA 2014); estimates of
8 the quantity composted for 2015 were extrapolated using the 2014 quantity composted and a ratio of the U.S.
9 population growth between 2014 and 2015 (U.S. Census Bureau 2016).
10 Table 7-19: U.S. Waste Composted (kt)
Activity
1990
2005
2011
2012
2013
2014
2015
Waste Composted
3,810
18,643
18,661
19,351
20,358
20,884
21,052
11 Uncertainty and Time-Series Consistency
12 The estimated uncertainty from the 2006 IPCC Guidelines is ±50 percent for the Approach 1 methodology.
13 Emissions from composting in 2015 were estimated to be between 2.0 and 6.0 MMT CO2 Eq., which indicates a
14 range of 50 percent below to 50 percent above the actual 2015 emission estimate of 4.0 MMT CO2 Eq. (see Table
15 7-20).
16 Table 7-20: Approach 1 Quantitative Uncertainty Estimates for Emissions from Composting
17 (MMT CO2 Eq. and Percent)
Source
Gas
2015 Emission Estimate
(MMT CO2 Eq.)
Uncertainty Range Relative to Emission Estimate
(MMT CO2 Eq.) (%)
Lower Upper Lower Upper
Bound Bound Bound Bound
Composting
CH4, N2O
4.0
2.0 6.0 -50% +50%
is QA/QC and Verification
19 A QA/QC analysis was performed for data gathering and input, documentation, and calculation. A primary focus of
20 the QA/QC checks was to ensure that the amount of waste composted annually was correct according to the latest
21 EP A Advancing Sustainable Materials Management: Facts and Figures report.
22 Recalculations Discussion
23 No recalculations were made in this Inventory year.
24 Planned Improvements
25 For future Inventories, additional efforts will be made to improve the estimates of CH4 and N20 emissions from
26 composting. For example, a literature search on emission factors and composting systems and management
27 techniques has been completed and will be documented for the next Inventory year. The purpose of this literature
28 review was to compile all published emission factors specific to various composting systems and composted
29 materials. This information will be used to determine whether the emission factors used in the current methodology
30 should be revised, or expanded to account for geographical differences and/or differences in composting systems
31 used. For example, outdoor composting processes in arid regions typically require the addition of moisture
32 compared to similar composting processes in wetter climates. Additionally, composting systems that primarily
33 compost food waste may generate CH4 at different rates than those that compost yard trimmings because the food
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1 waste may have a higher moisture content and more readily degradable material. Further cooperation with
2 estimating emissions in cooperation with the LULUCF Other section will also be investigated.
3 7.4 Waste Incineration (IPCC Source Category
4 5C1)
5 As stated earlier in this chapter, carbon dioxide (CO2), nitrous oxide (N20), and methane (CH4) emissions from the
6 incineration of waste are accounted for in the Energy sector rather than in the Waste sector because almost all
7 incineration of municipal solid waste (MSW) in the United States occurs at waste-to-energy facilities where useful
8 energy is recovered. Similarly, the Energy sector also includes an estimate of emissions from burning waste tires and
9 hazardous industrial waste, because virtually all of the combustion occurs in industrial and utility boilers that
10 recover energy. The incineration of waste in the United States in 2015 resulted in 11.0 MMT CO2 Eq., over half of
11 which (5.9 MMT CO2 Eq.) is attributable to the combustion of plastics. For more details on emissions from the
12 incineration of waste, see Section 3.3 of the Energy chapter.
13 Additional sources of emissions from waste incineration include non-hazardous industrial waste incineration and
14 medical waste incineration. As described in Annex 5 of this report, data are not readily available for these sources
15 and emission estimates are not provided. An analysis of the likely level of emissions was conducted based on a 2009
16 study of hospital/ medical/ infectious waste incinerator (HMIWI) facilities in the United States (RTI 2009). Based
17 on that study's information of waste throughput and an analysis of the fossil-based composition of the waste, it was
18 determined that annual greenhouse gas emissions for medical waste incineration would be below 500 kt CO2 Eq. per
19 year and considered insignificant for the purposes of Inventory reporting under the UNFCCC. More information on
20 this analysis is provided in Annex 5.
21 7.5 Waste Sources of Indirect Greenhouse
22 Gases
23 In addition to the main greenhouse gases addressed above, waste generating and handling processes are also sources
24 of indirect greenhouse gas emissions. Total emissions of nitrogen oxides (NOx), carbon monoxide (CO), and non-
25 CH4 volatile organic compounds (NMVOCs) from waste sources for the years 1990 through 2015 are provided in
26 Table 7-21.
27 Table 7-21: Emissions of NOx, CO, and NMVOC from Waste (kt)
Gas/Source
1990
2005
2011
2012
2013
2014
2015
NOx
+
2
1
2
2
2
2
Landfills
+
2
1
2
2
2
2
Wastewater Treatment
+
0
0
0
0
0
0
Miscellaneous3
+
0
0
0
0
0
0
CO
1
7
5
6
8
9
9
Landfills
1
6
4
6
7
8
8
Wastewater Treatment
+
+
+
+
1
1
1
Miscellaneous3
+
0
0
0
0
0
0
NMVOCs
673
114
38
45
51
57
57
Wastewater Treatment
57
49
17
19
22
25
25
Miscellaneous3
557
43
15
17
19
22
22
Landfills
58
22
7
8
10
11
11
+ Does not exceed 0.5 kt.
Waste 7-35
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1
2
3
4
5
6
7
8
9
10
11
12
13
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.
Methodology
Emission estimates for 1990 through 2015 were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2016), and disaggregated based on EPA (2003). Emission
estimates for 2012 and 2013 for non-electric generating units (EGU) are held constant from 2011 in EPA (2016).
Emission estimates for 2012 and 2013 for non-mobile sources are recalculated emissions by interpolation from 2015
in EPA (2016). Emission estimates of these gases were provided by sector, using a "top down" estimating
procedure—emissions were calculated either for individual sources or for many sources combined, using basic
activity data (e.g., the amount of raw material processed) as an indicator of emissions. National activity data were
collected for individual categories from various agencies. Depending on the category, these basic activity data may
include data on production, fuel deliveries, raw material processed, etc.
Uncertainty and Time-Series Consistency
No quantitative estimates of uncertainty were calculated for this source category. Methodological recalculations
were applied to the entire time-series to ensure time-series consistency from 1990 through 2015. Details on the
emission trends through time are described in more detail in the Methodology section, above.
7-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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i Ihl HE* I
2 The United States does not report any greenhouse gas emissions under the Intergovernmental Panel on Climate
3 Change (IPCC) "Other" sector.
Other 8-1
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2
3
4
5
6
7
8
9
10
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12
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32
33
34
35
36
37
38
39
40
41
9. Recalculations and Improvements
Each year, emission and sink estimates are recalculated and revised for all years in the Inventory of U.S. Greenhouse
Gas Emissions and Sinks, as attempts are made to improve both the analyses themselves, through the use of better
methods or data, and the overall usefulness of the report. In this effort, the United States follows the 2006IPCC
Guidelines (IPCC 2006), which states, "Both methodological changes and refinements over time are an essential
part of improving inventory quality. It is good practice to change or refine methods when available data have
changed; the previously used method is not consistent with the IPCC guidelines for that category; a category has
become key; the previously used method is insufficient to reflect mitigation activities in a transparent manner; the
capacity for inventory preparation has increased; new inventory methods become available; and for correction of
errors."
The results of all methodological changes and historical data updates made in the current Inventory report are
presented in this section; detailed descriptions of each recalculation are contained within each source's description
found in this report, if applicable. Table 9-1 summarizes the quantitative effect of these changes on U.S. greenhouse
gas emissions and sinks and Table 9-2 summarizes the quantitative effect on annual net CO2 fluxes, both relative to
the previously published U.S. Inventory (i.e., the 1990 through 2015 report). These tables present the magnitude of
these changes in units of million metric tons of carbon dioxide equivalent (MMT CO2 Eq.).
The Recalculations Discussion section of each source's description in the respective chapter of this Inventory
presents the details of each recalculation. In general, when methodological changes have been implemented, the
entire time series (i.e., 1990 through 2015) has been recalculated to reflect the change, per IPCC (2006). Changes in
historical data are generally the result of changes in statistical data supplied by other agencies.
The following ten emission sources and sinks underwent some of the most significant methodological and historical
data changes. These emission sources consider only methodological and historical data changes. A brief summary of
the recalculations and/or improvements undertaken is provided for each of the ten sources.
• Land Converted to Grassland - Changes in Agricultural Soil Carbon Stocks (CO2). Methodological
recalculations in the current Inventory are associated with the following improvements, including: (1) driving
the DAYCENT simulations with updated input data for land use and management from the National Resources
Inventory extending the time series through 2012; (2) modifying the number of experimental study sites used to
quantify model uncertainty; (3) DAYCENT model development to improve the simulation of soil temperature;
(4) improvements in the cropping and land use histories that are simulated in DAYCENT between 1950 and
1979 that generate initial values for the model state variables, including the initial soil organic C stock values;
and (5) incorporating belowground biomass, dead wood and litter C stock losses for Forest Land Converted to
Grassland. As a result of these improvements to the Inventory, changes in stocks declined, relative to the
previous report, by an average of 272.9 MMT CO2 Eq. annually over the time series. This represents a 673
percent increase in the losses of carbon from Land Converted to Grassland compared to the previous Inventory.
This change is due to a larger amount of aboveground biomass C that is lost from Forest Land Converted to
Grasslands, in addition to inclusion of belowground biomass, dead wood and litter C stock changes in this
Inventory.
• Land Converted to Forest Land - This is the second U.S. Inventory report to include a Land Converted to
Forest Land section containing specific soil C stock change estimates and the first Inventory report to include
all C pools for Land Converted to Forest Land. In prior Inventory reports (e.g., EPA 2015), the C stock changes
from Land Converted to Forest Land were a part of the Forest Land Remaining Forest Land estimates. See the
Recalculations and Improvements 9-1
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2
3
4
5
6
7
8
9
10
11
12
13
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38
39
40
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42
43
44
45
46
47
48
49
50
51
52
53
54
Recalculations section in Forest Land Remaining Forest Land for a detailed explanation on overall changes
resulting from implementing a different methodological approach in the current Inventory report. These
changes, particularly the inclusion of bio mass, dead wood and litter in the estimates resulted in an average
annual increase in sequestration of 89.9 MMT CO2 Eq. relative to the previous Inventory.
• Agricultural Soil Management (N2O). Methodological recalculations in the current Inventory are associated
with the following improvements: (1) driving the DAYCENT simulations with updated input data for land
management from the National Resources Inventory extending the time series through 2012; (2) modifying the
number of experimental study sites used to quantify model uncertainty for direct N20 emissions; (3)
DAYCENT model development to improve the simulation of soil temperature; (4) improvements in the
cropping and land use histories that are simulated in DAYCENT between 1950 and 1979 that generate initial
values for the model state variables, such as initial soil organic C stock values; and (5) implementing a more
robust set of model output variables that enabled a more accurate and detailed accounting of N from synthetic
fertilizers, managed manure, and PRP manure applied to grasslands. These changes resulted in a decrease in
emissions of approximately 14.4 percent on average relative to the previous Inventory and an increase in the
upper bound of the 95 percent confidence interval for direct N20 emissions from 24 to 31 percent. The
differences in emissions and uncertainty are mainly due to increasing the number of study sites used to quantify
model uncertainty. These changes resulted in an average annual increase in emissions of 44.5 MMT CO2 Eq.
relative to the previous Inventory.
• Forest Land Remaining Forest Land - Changes in Forest Carbon Stock (CO2 sink). Forest ecosystem stock and
stock-change estimates differ from previous Inventory reports in two primary ways. First, a different estimation
system was used in this Inventory and the 1990-2014 report (Woodall et al. 2015a). The major differences
between the estimation system used in the last two Inventory reports and past estimation approaches is the sole
use of annual FIA data and the back-casting of forest C stocks across the 1990s based on forest C stock density
and land use change information obtained from the nationally consistent annual forest inventory coupled with in
situ observations of non-tree C pools such as soils, dead wood, and litter in the 1990-2014 Inventory and this
Inventory. The use of this estimation framework has enabled the creation of the two land use sections for forest
C stocks: Forest Land Remaining Forest Land and Land Converted to Forest Land. In prior Inventory reports
(e.g., the 1990-2013 Inventory), the C stock changes from Land Converted to Forest Land were a part of the
Forest Land Remaining Forest Land section and it was not possible to disaggregate the estimates with the
methodology applied at that time. A second major change in the 1990-2014 Inventory submission was the
adoption of a new approach to estimate forest soil C, the largest C stock in the United States. However, the litter
and soil C stock and stock change estimates reported in the 1990-2014 Inventory were inadvertently compiled
using English units resulting in estimates that were 2.2 times larger than they should have been for the Forest
Land Remaining Forest Land category. This mistake was not caught during compilation of the previous
Inventory report since the soil C model and the estimation system used to compile estimates for the United
States were both being used for the first time with no similar (e.g., national-level population estimates using
similar data) estimates available for comparison. In addition to these major changes, the refined land
representation analysis described in Section 6.1 Representation of the U.S. Land Base re-classified some of the
forest land in south central and southeastern coastal Alaska as unmanaged; this is in contrast to past
assumptions where forest lands included in the FIA database were always considered part of the "managed"
land base. Therefore, the C stock and flux estimates for southeast and south central coastal Alaska, as included
here, reflect that adjustment, which effectively reduces the managed forest area by approximately 5 percent.
In addition to the creation of explicit estimates of removals and emissions from Forest Land Remaining Forest
Land and Land Converted to Forest Land, the estimation system used in the current Inventory and the 1990-
2014 Inventory eliminated the use of periodic data (which may be inconsistent with annual inventory data) and
contributed to a data artifact in prior estimates of emissions/removals from 1990 to the present. In the previous
Inventory reports (i.e., prior to the 1990-2014 Inventory), there was a reduction in net sequestration from 1995
to 2000 followed by an increase in net sequestration from 2000 to 2004. This artifact, resulting from comparing
inconsistent inventories of the 1980s through 1990s to the nationally consistent inventories of the 2000s has
been removed in the last two Inventory reports. All these changes resulted in an average annual increase in
sequestration of 39.9 MMT CO2 Eq. relative to the previous Inventory.
• Land Converted to Cropland - Changes in Agricultural Carbon Stocks (CO2). Methodological recalculations in
the current Inventory are associated with the following improvements: (1) driving the DAYCENT simulations
9-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
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3
4
5
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7
8
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53
with updated input data for land use and management from the National Resources Inventory extending the time
series through 2012; (2) modifying the number of experimental study sites used to quantify model uncertainty;
(3) DAYCENT model development to improve the simulation of soil temperature; (4) improvements in the
cropping and land use histories that are simulated in DAYCENT between 1950 and 1979 that generate initial
values for the model state variables, including the initial soil organic C stock values; and (5) incorporating
belowground biomass, dead wood and litter C stock losses with Forest Land Converted to Cropland. As a result
of these improvements to the Inventory, Land Converted to Cropland have a larger reported loss of C, estimated
at 19.1 MMT CO2 Eq. over the time series. This represents a 45 percent increase in the losses of carbon with
Land Converted to Cropland compared to the previous Inventory, and is largely driven by reporting
belowground biomass, dead wood and litter C loss from Forest Land Converted to Croplands.
• Natural Gas Systems (CH4). EPA received information and data related to the emission estimates through the
Inventory preparation process, previous Inventories' formal public notice periods, GHGRP data, and new
studies. EPA carefully evaluated relevant information available, and made several updates to this review draft,
including revisions to production segment activity and emissions data, gathering and boosting facility
emissions, and processing segment activity and emissions data. In January 2017, EPA released draft memos that
discussed the changes under consideration and requested stakeholder feedback on those changes. In this public
review draft of the 1990-2015 Inventory, EPA has selected from the options presented in the 2017 Production
and Processing memos to develop emission estimates. The impact of all revisions to natural gas systems is an
average annual decrease in emissions of 15.3 MMT CO2 Eq. relative to the previous Inventory.
• Landfills (CH4). Four major methodological recalculations were performed for the current Inventory. First, net
CH4 emissions as directly reported to subpart HH of EPA's GHGRP were used for 2010 to 2015. Second, a 12.5
percent scale up factor was applied to the subpart HH data to account for emissions from MSW landfills that are
not required to report under subpart HH. Third, the net CH4 emissions from 2010 to 2015 from subpart HH
were used to estimate, or back-cast, net CH4 emissions for 2005 to 2009. Fourth, the previously used method,
which relies on the first order decay model, was applied with revised MSW generation data for years 1990 to
2004. The overall impact to the Inventory from these changes resulted in an average increase of nearly 7 percent
across the time series. These changes resulted in an average annual decrease in emissions of 10.2 MMT CO2
Eq. relative to the previous Inventory.
• Cropland Remaining Cropland - Changes in Agricultural Carbon Stocks (CO2 sink). Methodological
recalculations in the current Inventory are associated with the following improvements: (1) driving the
DAYCENT simulations with updated input data for land management from the National Resources Inventory
from 1979 through 2012; (2) increasing the number of experimental study sites used to quantify model
uncertainty; (3) DAYCENT model development to improve the simulation of soil temperature; and (4)
improvements in the cropping and land use histories that are simulated in DAYCENT between 1950 and 1979
to reduce the amount of grassland converted into cropland when the NRI histories begin in 1979 (Note the
histories generate initial values for the model state variables, including the initial soil organic C stock values;
more detail is provide in Annex 3.12). These changes in SOC stocks resulted in an average annual decrease in
sequestration of 7.5 MMT CO2 Eq. relative to the previous Inventory.
• Petroleum Systems (CH4). The EPA received information and data related to the emission estimates through the
Inventory preparation process, previous Inventories' formal public notice periods, EPA's Greenhouse Gas
Reporting Program (GHGRP) data, and new studies. The EPA carefully evaluated relevant information
available, and made revisions to the production segment methodology this public review draft of the Inventory
including revised well count, equipment count, and pneumatic controller activity data, and revised activity and
emissions data for tanks and associated gas venting and flaring. While the recalculations resulted in a decrease
in calculated emissions in recent years (e.g., a 35 percent decrease in the 2014 estimate), over the full time
series, the changes resulted in an average annual increase in emissions of 3.2 MMT CO2 Eq. relative to the
previous Inventory. The recalculations resulted in increases in the emission estimate in early years of the time
series, primarily due to recalculations related to associated gas venting and flaring, and decreases in the
emission estimate in later years of the time series, primarily due to recalculations for pneumatic controllers.
• Rice Cultivation (CIh). Methodological recalculations in the current Inventory are associated with the following
improvements: (1) DAYCENT model development to improve the simulation of soil temperature; (2)
improvements in the cropping and land use histories that are simulated in DAYCENT between 1950 and 1979,
Recalculations and Improvements 9-3
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1 which generate initial values for the state variables in the model and (3) driving the DAYCENT simulations
2 with updated input data for land use and management from the National Resources Inventory, which revised the
3 time series from 1990 through 2012. These changes resulted in an increase in emissions of approximately 25
4 percent on average relative to the previous Inventory and an increase in uncertainty from confidence interval
5 with a lower bound and upper bound of 17 percent to a confidence interval with an upper and lower bound of 28
6 percent. These changes resulted in an average annual increase in emissions of 2.9 MMT CO2 Eq. relative to the
7 previous Inventory.
8 Table 9-1: Revisions to U.S. Greenhouse Gas Emissions (MMT CO2 Eq.)
Average
Gas/Source
1990
2005
2011
2012
2013
2014
Annual
Change
co2
6.3
6.9
8.0
10.3
9.5
5.8
7.1
Fossil Fuel Combustion
M
NC
+
NC
+
(6.1)
(0.2)
Electricity Generation
M
NC
NC
NC
NC
(1.3)
(0.1)
Transportation
M
NC
+
NC
+
(7.2)
(0.3)
Industrial
NC
NC
1.6
NC
+
2.5
0.2
Residential
N(
NC
(1.3)
NC
NC
0.3
+
Commercial
N<
NC
(0.4)
NC
NC
(0.5)
+
U.S. Territories
N<
NC
NC
NC
NC
0.2
+
Non-Energy Use of Fuels
(0.5)
(0.5)
+
(0.1)
0.3
2.9
(0.2)
Natural Gas Systems
N<
NC
NC
NC
NC
NC
NC
Cement Production
N<
NC
NC
+
+
+
+
Time Production
+* /
+
+
0.1
+
0.1
+
Other Process Uses of Carbonates
N<
NC
NC
NC
NC
(0.3)
+
Glass Production
N<
NC
NC
NC
NC
+
+
Soda Ash Production and Consumption
N<
NC
NC
NC
NC
NC
NC
Carbon Dioxide Consumption
N<
NC
NC
NC
NC
NC
NC
Incineration of Waste
+ /
+
+
+
1.0
1.2
0.1
Titanium Dioxide Production
N<
NC
NC
NC
NC
(0.1)
+
Aluminum Production
N<
NC
NC
NC
NC
NC
NC
Iron and Steel Production & Metallurgical Coke
Production
+ /
+
+
+
+
2.1
0.1
Ferroalloy Production
N<
NC
NC
NC
NC
NC
NC
Ammonia Production
N<
NC
NC
NC
NC
0.2
+
Urea Consumption for Non-Agricultural Purposes
N<
NC
+
+
(0.2)
(2.6)
(0.1)
Phosphoric Acid Production
N<
NC
+
+
+
(0.1)
+
Petrochemical Production
(0.3)
(0.4)
+
+
+
+
(0.3)
Silicon Carbide Production and Consumption
N<
NC
NC
NC
NC
+
+
Tead Production
N<
NC
NC
NC
NC
+
+
Zinc Production
N<
NC
NC
NC
NC
+
+
Petroleum Systems
N<
NC
NC
NC
NC
NC
NC
Magnesium Production and Processing
N<
NC
NC
NC
NC
+
+
Timing
N<
NC
NC
NC
+
(0.5)
+
Urea Fertilization
N<
NC
+
+
0.2
0.3
+
Biomass - Wood"
AT
NC
NC
NC
NC
NC
NC
International Bunker Fuels"
NC
NC
NC
NC
NC
NC
NC
Biomass - Ethanol"
AT
NC
NC
NC
NC
NC
NC
cm"
12.2
(31.9)
(44.1)
(47.2)
(61.9)
(71.5)
(18.9)
Stationary Combustion
N<
NC
+
NC
+
+
+
Mobile Combustion
+ /
0.1
+
+
+
+
+
Coal Mining
N<
NC
NC
NC
NC
(2.7)
(0.1)
Abandoned Underground Coal Mines
N<
NC
NC
NC
NC
NC
NC
Natural Gas Systems
(10.2)
(15.2)
(16.4)
(17.3)
(17.6)
(15.3)
(15.2)
Petroleum Systems
19.-
(0.8)
(6.2)
(10.0)
(18.1)
(23.2)
3.2
Petrochemical Production
N(
NC
NC
NC
NC
NC
NC
Silicon Carbide Production and Consumption
N(
NC
NC
NC
NC
NC
NC
Iron and Steel Production & Metallurgical Coke
Production
N<
NC
NC
NC
NC
NC
NC
Ferroalloy Production
N<
NC
NC
NC
NC
NC
NC
9-4 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
Enteric Fermentation
N(
NC
+
NC
+
(0.1)
+
Manure Management
N(
NC
1.5
1.9
1.9
1.7
0.4
Rice Cultivation
2.9
3.7
2.3
(0.6)
(0.6)
(0.5)
2.9
Field Burning of Agricultural Residues
+ /
+
+
+
+
+
+
Landfills
N(
(19.7)
(25.3)
(21.4)
(27.6)
(31.4)
(10.2)
Wastewater Treatment
+ /
0.1
0.1
0.1
0.1
0.1
0.1
Composting
N(
NC
NC
NC
NC
+
+
Incineration of Waste
N(
NC
NC
NC
NC
NC
NC
International Bunker Fuels"
NC
NC
NC
NC
NC
NC
NC
N2Ob
(46.7)
(35.9)
(52.5)
(68.6)
(67.8)
(68.0)
(43.9)
Stationary Combustion
N(
NC
+
NC
+
+
+
Mobile Combustion
+ /
1.4
0.4
0.4
0.3
0.3
0.5
Adipic Acid Production
N(
NC
+
+
+
NC
+
Nitric Acid Production
+ /
+
+
+
+
+
+
Manure Management
N(
NC
+
+
+
+
+
Agricultural Soil Management
(46.7)
(37.3)
(53.0)
(69.0)
(68.1)
(68.4)
(44.5)
Field Burning of Agricultural Residues
-
+
+
+
+
+
+
Wastewater Treatment
+ /
+
0.1
0.1
0.1
0.1
+
N2O from Product Uses
N(
NC
NC
NC
NC
NC
NC
Incineration of Waste
N(
NC
NC
NC
NC
NC
NC
Composting
N(
NC
NC
NC
NC
+
+
Semiconductor Manufacture
N(
NC
+
+
+
+
+
International Bunker Fuels"
NC
NC
NC
NC
NC
NC
NC
HFCs, PFCs, SF6 and NF3
(2.3)
(2.3)
(0.9)
(0.7)
(0.9)
(0.5)
(1.6)
HFCs
+
0.1
0.1
0.1
0.1
+
+
Substitution of Ozone Depleting Substances
+ /
0.1
0.1
0.1
0.1
0.1
+
HCFC-22 Production
N(
NC
NC
NC
NC
NC
NC
Semiconductor Manufacture
N(
+
+
+
+
+
+
Magnesium Production and Processing
N(
NC
NC
NC
NC
(0.1)
+
PFCs
NC
+
(0.1)
+
(0.1)
0.2
+
Aluminum Production
N(
NC
NC
NC
NC
NC
NC
Semiconductor Manufacture
N(
+
(0.1)
+
(0.1)
0.2
+
SF«
(2.3)
(2.3)
(0.8)
(0.8)
(0.8)
(0.8)
(1.7)
Electrical Transmission and Distribution
(2.3)
(2.3)
(0.8)
(0.8)
(0.8)
(0.8)
(1.7)
Semiconductor Manufacture
N(
+
+
+
+
+
+
Magnesium Production and Processing
N(
NC
NC
NC
NC
+
+
NF3
NC
+
+
+
+
+
+
Semiconductor Manufacture
N(
+
+
+
+
+
+
Net Change in Total Emissions
(30.4)
(63.2)
(89.4) (106.2) (121.0) (134.2)
Percent Change
-0.5°/«
-0.9%
-1.3%
-1.6%
-1.8%
-2.0%
Note: Net change in total emissions presented without LULUCF.
NC (No Change)
+ Absolute value does not exceed 0.05 MMT CO2 Eq. or 0.05 percent.
a Not included in emissions total.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
2 Table 9-2: Revisions to U.S. Greenhouse Gas Emissions and Removals (Net Flux) from Land
3 Use, Land-Use Change, and Forestry (MMT CO2 Eg.)
Average
Annual
Land Use Category
1990
2005
2011
2012
2013
2014
Change
Forest Land Remaining Forest Land
25.0
25.7
66.2
67.6
67.2
72.1
39.7
Changes in Forest Carbon Stocka
25.2
26.5
65.9
68.1
67.4
72.2
39.9
Non-CC>2 Emissions from Forest Fires
(0.2)
(0.9)
0.3
(0.4)
(0.2)
(0.1)
(0.3)
N2O Fluxes from Forest Soilsb
NC
NC
NC
NC
NC
NC
NC
Land Converted to Forest Land
(91.3)
(80.6)
(75.4)
(74.8)
(74.9)
(74.9)
(89.9)
Changes in Forest Carbon Stock
(91.3)
(80.6)
(75.4)
(74.8)
(74.9)
(74.9)
(89.9)
Cropland Remaining Cropland
(13.7)
(20.3)
(14.6)
(20.4)
(14.6)
(15.0)
(15.3)
Changes in Agricultural Carbon Stockc>d
(6.6)
(12.4)
(6.7)
(10.2)
(6.4)
(6.4)
(7.5)
Recalculations and Improvements 9-5
-------
Land Converted to Cropland
35.0
10.5
13.7
13.2
6.5
6.5
19.1
Changes in Agricultural Carbon Stockc-d
35.0
10.5
13.7
13.2
6.5
6.5
19.1
Grassland Remaining Grassland
N.N
9.4
(14.0)
(23.2)
4.4
4.9
1.4
Changes in Agricultural Carbon Stockc,d
8.7
8.7
(15.6)
(24.4)
4.0
4.0
0.8
Non-CC>2 Emissions from Grass Fires
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Land Converted to Grassland
206.2
2N0.7
257.0
252. N
253. N
253. N
272.9
Changes in Agricultural Carbon Stockc-d
206.2
280.7
257.0
252.8
253.8
253.8
272.9
Wetlands Remaining Wetlands
(5.0)
(6.4)
(5.0)
(5.0)
(5.0)
(5.1)
(5.0)
Peatlands Remaining Peatlands
M
NC
NC
NC
NC
(0.1)
+
Changes in Coastal Wetland Carbon Stock
NC*
NC*
NC*
NC*
NC*
NC*
NC*
CH4 Emissions from Coastal Wetlands
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Remaining Coastal Wetlands
N2O Emissions from Coastal Wetlands
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Remaining Coastal Wetlands
Land Converted to Wetlands
NC-
NC*
NC*
NC*
NC*
NC*
NC*
Changes in Coastal Wetland Carbon Stock
NC*
NC*
NC*
NC*
NC*
NC*
NC*
CH4 Emissions from Land Converted to
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Coastal Wetlands
Settlements Remaining Settlements
0.2
0.6
1.4
1.6
1.6
1.1
0.6
Changes in Settlement Soil Carbon Stock
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Changes in Urban Tree Carbon Stock
M
NC
NC
NC
NC
NC
NC
N2O Fluxes from Settlement Soils6
0.I
0.1
0.1
0.2
0.2
0.2
0.1
Landfilled Yard Trimmings and Food
NC
+
+
+
0.1
(0.4)
+
Scraps
Land Converted to Settlements
NC*
NC*
NC*
NC*
NC*
NC*
NC*
Changes in Settlement Soil Carbon Stock
NC*
NC*
NC*
NC*
NC*
NC*
NC*
LULUCF Emissions'
(3.5)
(4-2)
(2.2)
(5.6)
(4.2)
(4.2)
Net Change in LULUCF C Stock Change®
292.3
3N7.4
3N9.1
367.6
393.4
397. N
LULUCF Sector Net Total"
2NN.9
3N3.2
3N6.9
362.0
3N9.2
393.7
Percent Change
39.1%
54.9%
50.N%
4N.3%
51.2%
51.6%
NC (No Change)
+ Absolute value does not exceed 0.05 MMT CO2 Eq. or 0.05 percent.
* Indicates a new source for the current Inventory year.
a Includes the effects of net additions to stocks of carbon stored in forest ecosystem pools and harvested wood
products.
b Estimates include emissions from N fertilizer additions on both Forest Land Remaining Forest Land and Land
Converted to Forest Land, but not from land-use conversion.
c Estimates include C stock changes in all pools.
d Quality control uncovered errors in the estimate and uncertainty of soil C stock changes for 2013,2014,2015,
which will be updated following public review. Corrected estimates are provided in footnotes of the emission
summary tables for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining
Grassland, and Land Converted to Grassland sections in the LULUCF chapter of this report.
e Estimates include emissions from N fertilizer additions on both Settlements Remaining Settlements and Land
Converted to Settlements.
f LULUCF emissions include the CO2, CH4, and N2O emissions from Peatlands Remaining Peatlands; CH4 and
N2O emissions reported for Non-CC>2 Emissions from Forest Fires, Non-CC>2 Emissions from Grassland Fires,
and Coastal Wetlands Remaining Coastal Wetlands', CH4 emissions from Land Converted to Coastal Wetlands',
and N2O Fluxes from Forest Soils and Settlement Soils.
g LULUCF C Stock Change includes any C stock gains and losses from all land use and land use conversion
categories.
h The LULUCF Sector Net Total is the net sum of all emissions (i.e., sources) of greenhouse gases to the
atmosphere plus removals of CO2 (i.e., sinks or negative emissions) from the atmosphere.
Notes: Numbers in parentheses indicate an increase in C sequestration. Totals may not sum due to independent
rounding.
9-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
1 10. References
2 Executive Summary
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4 "real" GDP, 1929-2015. Bureau of Economic Analysis (BEA), U.S. Department of Commerce, Washington, D.C.
5 Available online at: .
6 Carbon Dioxide Information Analysis Center (CDIAC) (2016) Recent Greenhouse Gas Concentrations. April 2016.
7 Available online at: .
8 EIA (2016) Electricity Generation. Monthly Energy Review, December 2016. Energy Information Administration,
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10 EIA (2016) Electricity in the United States. Electricity Explained. Energy Information Administration, U.S.
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12 .
13 EPA (2016a) 1970-2015 Average annual emissions, all criteria pollutants in MS Excel. National Emissions
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10-2 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2015
-------
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32 Energy
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-------
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32 Available online at: .
33 U.S. Census Bureau (2008) Current Industrial Reports Fertilizer Materials and Related Products: 2007 Summary.
34 Available online at: .
35 U.S. Census Bureau (2007) Current Industrial Reports Fertilizer Materials and Related Products: 2006 Summary.
36 Available online at: .
37 U.S. Census Bureau (2006) Current Industrial Reports Fertilizer Materials and Related Products: 2005 Summary.
38 Available online at: .
39 U.S. Census Bureau (2004, 2005) Current Industrial Reports Fertilizer Materials and Related Products: Fourth
40 Quarter Report Summary. Available online at: .
41 U.S. Census Bureau (1998 through 2003) Current Industrial Reports Fertilizer Materials and Related Products:
42 Annual Reports Summary. Available online at: .
43 U.S. Census Bureau (1991 through 1994) Current Industrial Reports Fertilizer Materials Annual Report. Report No.
44 MQ28B. U.S. Census Bureau, Washington, D.C.
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4 .
5 USGS (2014) 2012Minerals Yearbook: Nitrogen [Advance Release]. September 2014. Available online at:
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7 USGS (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at:
8 .
9 Urea Consumptive \^wU;ncultural Purposes
10 EFMA (2000) Best Available Techniques for Pollution Prevention and Control in the European Fertilizer Industry.
11 Booklet No. 5 of 8: Production of Urea and Urea Ammonium Nitrate.
12 IPCC (2006) 20061PCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
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17 U.S. Census Bureau (2001 through 2011) Current Industrial Reports Fertilizer Materials and Related Products:
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19 U.S. Department of Agriculture (2012) Economic Research Service Data Sets, Data Sets, U.S. Fertilizer
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23 2.5.0. Available online at: . August 2002.
24 United States Geological Survey (USGS) (2014 through 2016) Minerals Yearbook: Nitrogen [Advance Release],
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26 USGS (1994 through 2009) Minerals Yearbook: Nitrogen. Available online at:
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28 Nitri 1 i Km
29 Climate Action Reserve (CAR) (2013) Project Report. Available online at:
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32 EPA (2016) Greenhouse Gas Reporting Program (GHGRP). Aggregation of Reported Facility Level Data under
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34 Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
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36 September, 2010.
37 EPA (2012) Memorandum from Mausami Desai, U.S. EPA to Mr. Bill Herz, The Fertilizer Institute. November 26,
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24 Adipic Acid Production
25 ACC (2016) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
26 C&EN (1995) "Production of Top 50 Chemicals Increased Substantially in 1994." Chemical & Engineering News,
27 73(15): 17. April 10, 1995.
28 C&EN (1994) "Top 50 Chemicals Production Rose Modestly Last Year." Chemical & Engineering News,
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30 C&EN (1993) "Top 50 Chemicals Production Recovered Last Year." Chemical & Engineering News, 71(15): 11.
31 April 12, 1993.
32 C&EN (1992) "Production of Top 50 Chemicals Stagnates in 1991." Chemical & Engineering News, 70(15): 17.
33 April 13, 1992.
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36 CW (2005) "Product Focus: Adipic Acid." Chemical Week. May 4, 2005.
37 CW (1999) "Product Focus: Adipic Acid/Adiponitrile." Chemical Week, p. 31. March 10, 1999.
38 Desai (201 la) Personal communication. Mausami Desai, U.S. Environmental Protection Agency and Roy Nobel,
3 9 Ascend Performance Materials, October 18, 2011.
40 Desai (201 lb) Personal communication. Mausami Desai, U.S. Environmental Protection Agency with Steve Zuiss of
41 Invista, November 18, 2011.
42 Desai (2010) Personal communication. Mausami Desai, U.S. Environmental Protection Agency with Steve Zuiss of
43 Invista, October 15, 2010.
44 EPA (2014 through 2016) Greenhouse Gas Reporting Program. Full Subpart E, O, S-CEMS, BB, CC, LL Data Set
45 (XLS)(Adipic Acid Tab). Office of Air and Radiation, Office of Atmospheric Programs, U.S. Environmental
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3 EPA (2012) Analysis of Greenhouse Gas Reporting Program data - Subpart E (Adipic Acid), Office of Air and
4 Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
5 ICIS (2007) "Adipic Acid." ICIS Chemical Business Americas. July 9, 2007.
6 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
7 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
8 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
9 Reimer, R.A., Slaten, C.S., Seapan, M., Koch, T.A. and Triner, V.G. (1999) "Implementation of Technologies for
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11 Non-C02 Greenhouse Gases (NCGG-2), Noordwijkerhout, The Netherlands, 8-10 Sept. 1999, Ed. J. van Ham el al.,
12 Kluwer Academic Publishers, Dordrecht, pp. 347-358.
13 SEI (2010) Industrial N2O Projects Under the CDM: Adipic Acid-A Case for Carbon Leakage? Stockholm
14 Environment Institute Working Paper WP-US-1006. October 9, 2010.
15 Thiemens, M.H., and W.C. Trogler (1991) "Nylon production; an unknown source of atmospheric nitrous oxide."
16 Science 251:932-934.
17 VA DEQ (2010) Personal communication. Stanley Faggert, Virginia Department of Environmental Quality and
18 Joseph Herr, ICF International. March 12, 2010.
19 VA DEQ (2009) Personal communication. Stanley Faggert, Virginia Department of Environmental Quality and
20 Joseph Herr, ICF International. October 26, 2009.
21 VA DEQ (2006) Virginia Title V Operating Permit. Honeywell International Inc. Hopewell Plant. Virginia
22 Department of Environmental Quality. Permit No. PRO50232. Effective January 1, 2007.
23 Silicon Carbide Production
24 IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
25 Assessment Report of the Intergovernmental Panel on Climate Change. [S. Solomon, D. Qin, M. Manning, Z. Chen,
26 M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press. Cambridge, United
27 Kingdom 996 pp.
28 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
29 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
30 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
31 U.S. Census Bureau (2005 through 2016) U.S. International Trade Commission (USITC) Trade DataWeb. Available
32 online at: .
33 United States Geological Survey (USGS) (2016) Minerals Industry Surveys: Abrasives (Manufactured) in Third and
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37 Reston, VA. Available online at: .
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39 Available online at: .
40 Titanium Dioxide Production
41 Gambogi, J. (2002) Telephone communication. Joseph Gambogi, Commodity Specialist, U.S. Geological Survey
42 and Philip Groth, ICF International. November 2002.
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1 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
2 Inventories Programme, Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
3 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
4 United States Geological Survey (USGS) (1991 through 2015b) Minerals Yearbook: Titanium. U.S. Geological
5 Survey, Reston, VA.
6 USGS (2016) 2016Mineral Commodity Summary: Titanium and Titanium Dioxide. U.S. Geological Survey,
7 Reston, Va. January, 2016.
8 USGS (2015a) 2015 Mineral Commodity Summary: Titanium and Titanium Dioxide. U.S. Geological Survey,
9 Reston, VA. January, 2015.
10 Soda Ash Production and Consumption
11 Kostick, D. S. (2012) Personal communication. Dennis S. Kostick of U.S. Department of the Interior - U.S.
12 Geological Survey, Soda Ash Commodity Specialist with Gopi Manne and Bryan Lange of Eastern Research Group,
13 Inc. October 2012.
14 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
15 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
16 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
17 United States Geological Survey (USGS) (2016) Mineral Industry Surveys: Soda Ash in November 2016. U.S.
18 Geological Survey, Reston, VA. January, 2017.
19 USGS (2015a) Mineral Industry Surveys: Soda Ash in July 2015. U.S. Geological Survey, Reston, VA. September,
20 2015.
21 USGS (1994 through 2015b) Minerals Yearbook: Soda Ash Annual Report. U.S. Geological Survey, Reston, VA.
22 USGS (1995) Trona Resources in the Green River Basin, Southwest Wyoming. U.S. Department of the Interior, U.S.
23 Geological Survey. Open-File Report 95-476. Wiig, Stephen, Grundy, W.D., Dyni, JohnR.
24 Petrochemical Production
25 ACC (2016) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
26 ACC (2014a) U.S. Chemical Industry Statistical Handbook. American Chemistry Council, Arlington, VA.
27 ACC (2014b) Business of Chemistry (Annual Data). American Chemistry Council, Arlington, VA.
28 ACC (2002, 2003, 2005 through 2011) Guide to the Business of Chemistry. American Chemistry Council,
29 Arlington, VA.
30 AN (2014) About Acrylonitrile: Production. AN Group, Washington, D.C. Available online at:
31
32 EPA Greenhouse Gas Reporting Program (2016) Aggregation of Reported Facility Level Data under Subpart X -
33 National Petrochemical Production for Calendar Years 2010 through 2015. Office of Air and Radiation, Office of
34 Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
35 EPA Greenhouse Gas Reporting Program (2014) Aggregation of Reported Facility Level Data under Subpart X -
36 National Petrochemical Production for Calendar Years 2010 through 2013. Office of Air and Radiation, Office of
37 Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
38 EPA (2008) Technical Support Document for the Petrochemical Production Sector: Proposed Rule for Mandatory
39 Reporting of Greenhouse Gases. U.S. Environmental Protection Agency. September 2008.
40 EPA (2000) Economic Impact Analysis for the Proposed Carbon Black Manufacturing NESHAP, U.S.
41 Environmental Protection Agency. Research Triangle Park, NC. EPA-452/D-00-003. May 2000.
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2 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
3 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
4 Jordan J. (2011) Personal communication Jim Jordan of Jordan Associates on behalf of the Methanol Institute and
5 Pier LaFarge, ICF International. October 18, 2011.
6 Johnson G. L. (2005 through 2010) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the
7 International Carbon Black Association (ICBA) and Caroline Cochran, ICF International. September 2010.
8 Johnson G. L. (2003) Personal communication. Greg Johnson of Liskow & Lewis, on behalf of the International
9 Carbon Black Association (ICBA) and Caren Mintz, ICF International November 2003.
10 HCFC-22 Production - TO BE UPDATED FOR FINAL INVENTORY
11 REPORT
12 ARAP (2010) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
13 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. September 10, 2010.
14 ARAP (2009) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
15 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. September 21, 2009.
16 ARAP (2008) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
17 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. October 17, 2008.
18 ARAP (2007) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
19 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. October 2, 2007.
20 ARAP (2006) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
21 Atmospheric Policy to Sally Rand of the U.S. Enviromnental Protection Agency. July 11, 2006.
22 ARAP (2005) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
23 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. August 9, 2005.
24 ARAP (2004) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
25 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. June 3, 2004.
26 ARAP (2003) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
27 Atmospheric Policy to Sally Rand of the U.S. Enviromnental Protection Agency. August 18, 2003.
28 ARAP (2002) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
29 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. August 7, 2002.
30 ARAP (2001) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
31 Atmospheric Policy to Deborah Ottinger of the U.S. Enviromnental Protection Agency. August 6, 2001.
32 ARAP (2000) Electronic mail communication from Dave Stirpe, Executive Director, Alliance for Responsible
33 Atmospheric Policy to Sally Rand of the U.S. Enviromnental Protection Agency. August 13, 2000.
34 ARAP (1999) Facsimile from Dave Stirpe, Executive Director, Alliance for Responsible Atmospheric Policy to
35 Deborah Ottinger Schaefer of the U.S. Enviromnental Protection Agency. September 23, 1999.
36 ARAP (1997) Letter from Dave Stirpe, Director, Alliance for Responsible Atmospheric Policy to Elizabeth Dutrow
37 of the U.S. Enviromnental Protection Agency. December 23, 1997.
38 IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
39 Assessment Report of the Intergovernmental Panel on Climate Change. [S. Solomon, D. Qin, M. Manning, Z. Chen
40 M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press. Cambridge, United
41 Kingdom 996 pp.
42 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
43 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
44 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
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13 Allis, R. et al. (2000) Natural COj Reserwirs on the Colorado Plateau and Southern Rocky Mountains: Candidates
14 for C02 Sequestration. Utah Geological Survey and Utah Energy and Geoscience Institute. Salt Lake City, Utah.
15 ARI (1990 through 2010) CO2 Use in Enhanced Oil Recovery. Deliverable to ICF International under Task Order
16 102, July 15, 2011.
17 ARI (2007) COj-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
18 DOE/EPA Workshop on the Economic and Enviromnental Implications of Global Energy Transitions." Washington,
19 D.C.April 20-21, 2007.
20 ARI (2006) COj-EOR: An Enabling Bridge for the Oil Transition. Presented at "Modeling the Oil Transition—a
21 DOE/EPA Workshop on the Economic and Enviromnental Implications of Global Energy Transitions." Washington,
22 D.C.April 20-21, 2006.
23 Broadhead (2003) Personal communication. Ron Broadhead, Principal Senior Petroleum Geologist and Adjunct
24 faculty. Earth and Enviromnental Sciences Department, New Mexico Bureau of Geology and Mineral Resources,
25 and Robin Pestrusak, ICF International. September 5, 2003.
26 COGCC (1999 through 2009) Monthly CO2 Produced by County. Available online at:
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28 2014.
29 Denbury Resources Inc. (2002 through 2010) Annual Report: 2001 through 2009, Form 10-K. Available online at:
30 .
31 Accessed September 2014.
32 EPA Greenhouse Gas Reporting Program (2016). Aggregation of Reported Facility Level Data under Subpart PP -
33 National Level CO2 Transferred for Food & Beverage Applications for Calendar Years 2010 through 2014. Office
34 of Air and Radiation, Office of Atmospheric Programs, U.S. Enviromnental Protection Agency, Washington, D.C.
35 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
36 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
37 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
38 New Mexico Bureau of Geology and Mineral Resources (2006) Natural Accumulations of Carbon Dioxide in New
39 Mexico and Adjacent Parts of Colorado and Arizona: Commercial Accumulation of CO2. Available online at:
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41 Phosphoric Acid Production
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43 the European Fertilizer Industry. Booklet 4 of 8. European Fertilizer Manufacturers Association. Available online
44 at: .
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1 FIPR (2003a) "Analyses of Some Phosphate Rocks." Facsimile Gary Albarelli, the Florida Institute of Phosphate
2 Research, Bartow, Florida, to Robert Lanza, ICF International. July 29, 2003.
3 FIPR (2003b) Florida Institute of Phosphate Research. Personal communication. Mr. Michael Lloyd, Laboratory
4 Manager, FIPR, Bartow, Florida, to Mr. Robert Lanza, ICF International. August 2003.
5 NCDENR (2013) North Carolina Department of Environment and Natural Resources, Title V Air Permit Review for
6 PCS Phosphate Company, Inc. - Aurora. Available online at:
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8 United States Geological Survey (USGS) (2016) Mineral Commodity Summaries: Phosphate Rock 2016. January
9 2016. U.S. Geological Survey, Reston, VA. Available online at:
10 .
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12 VA.
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14 Reston, VA. Available online at: .
16 USGS (2012) Personal communication between Stephen Jasinski (USGS) and Mausami Desai (EPA) on October 12,
17 2012.
is Iron and Steel Production and Metallurgical Coke Production
19 AISI (2004 through 2016a) Annual Statistical Report, American Iron and Steel Institute, Washington, D.C.
20 AISI (2006 through 2016b) Personal communication, Mausami Desai, U.S. EPA, and American Iron and Steel
21 Institute, December 2016.
22 AISI (2008) Personal communication, Mausami Desai, U.S. EPA, and Bruce Steiner, Technical Consultant with the
23 American Iron and Steel Institute, October 2008.
24 Carroll (2016) Personal communication, Mausami Desai, U.S. EPA, and Colin P. Carroll, Director of Environment,
25 Health and Safety, American Iron and Steel Institute, December 2016.
26 DOE (2000) Energy and Environmental Profile of the U.S. Iron and Steel Industry. Office of Industrial
27 Technologies, U.S. Department of Energy. August 2000. DOE/EE-0229.EIA
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35 U.S. Department of Energy, Washington, D.C.
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37 Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
38 Fenton (2015) Personal communication. Michael Fenton, Commodity Specialist, U.S. Geological Survey and Marty
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40 IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
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4 U.S. Geological Survey, Reston, VA.
5 Ferroalloy Production
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7 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
8 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
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21 Aluminum Production
22 EPA (2016) Greenhouse Gas Reporting Program (GHGRP). Envirofacts, Subpart: F Aluminum Production.
23 Available online at: .
24 IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
25 Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
26 Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
27 USAA (2016b) U.S. Primary Aluminum Production: Report for August 2016. U.S. Aluminum Association,
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29 USAA (2016a) U.S. Primary Aluminum Production: Report for February 2016. U.S. Aluminum Association,
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