Annexes to the Inventory of U.S. GHG
Emissions and Sinks
The following nine annexes provide additional information related to the material presented in the main body of this
report as directed in the UNFCCC Guidelines on Reporting and Review (UNFCCC 2014). Annex I contains an analysis of the
key categories of emissions discussed in this report and a review of the methodology used to identify those key
categories. Annex 2 describes the methodologies used to estimate C02 emissions from fossil fuel combustion, the carbon
content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3
discusses the methodologies used for a number of individual source categories in greater detail than was presented in
the main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC
reference approach for estimating C02 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the
inclusion of an emission source or sink category and discusses some of the sources that are excluded from U.S.
estimates. Annex 6 provides a range of additional information that is relevant to the contents of this report. Annex 7
provides data on the uncertainty of the emission estimates included in this report. Annex 8 provides information on the
QA/QC methods and procedures used in the development of the Inventory, including responses to UNFCCC reviews.
Finally, Annex 9 provides an overview of GHGRP data use in the Inventory.
Table of Contents
Annexes to the Inventory of U.S. GHG Emissions and Sinks 1
ANNEX 1 Key Category Analysis 13
ANNEX 2 Methodology and Data for Estimating C02 Emissions from Fossil Fuel Combustion 36
2.1. Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion 36
2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 64
2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels 103
ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories 133
3.1. Methodology for Estimating Emissions of CH4, N20, and Indirect Greenhouse Gases from Stationary Combustion
133
3.2. Methodology for Estimating Emissions of CH4, N20, and Indirect Greenhouse Gases from Mobile Combustion and
Methodology for and Supplemental Information on Transportation-Related Greenhouse Gas Emissions 141
3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel Consumption 196
3.4. Methodology for Estimating CH4 Emissions from Coal Mining 202
3.5. Methodology for Estimating CH4, C02, and N20 Emissions from Petroleum Systems 210
3.6. Methodology for Estimating CH4, C02, and N20 Emissions from Natural Gas Systems 216
3.7. Methodology for Estimating C02, CH4, and N20 Emissions from the Incineration of Waste 225
3.8. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military 230
3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances 236
3.10. Methodology for Estimating CH4 Emissions from Enteric Fermentation 268
3.11. Methodology for Estimating CH4 and N20 Emissions from Manure Management 301
3.12. Methodologies for Estimating Soil Organic C Stock Changes, Soil N20 Emissions, and CH4 Emissions and from
Agricultural Lands (Cropland and Grassland) 343
3.13. Methodology for Estimating Net Carbon Stock Changes in Forest Ecosystems and Harvested Wood Products for
Forest Land Remaining Forest Land and Land Converted to Forest Land as well as Non-C02 Emissions from Forest
Fires 399
3.14. Methodology for Estimating CH4 Emissions from Landfills 439
ANNEX 4 IPCC Reference Approach for Estimating C02 Emissions from Fossil Fuel Combustion 463
ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Not Included 474
ANNEX 6 Additional Information 489
6.1. Global Warming Potential Values 489
6.2. Ozone Depleting Substance Emissions 498
6.3. Complete List of Source and Sink Categories 501
6.4. Constants, Units, and Conversions 503
6.5. Chemical Formulas 506
6.6. Greenhouse Gas Precursors Cross-Walk of National Emission Inventory (NEI) Categories to the National Inventory
Report (NIR) 509
ANNEX 7 Uncertainty 514
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7.1. Overview 514
7.2. Methodology and Results 515
7.3. Information on Uncertainty Analyses by Source and Sink Category 523
7.4. Reducing Uncertainty and Planned Improvements 523
ANNEX 8 QA/QC Procedures 527
8.1. Background 527
8.2. Purpose 527
8.3. Assessment Factors 529
8.4. Responses to Review Processes 531
ANNEX 9 Use of EPA Greenhouse Gas Reporting Program in Inventory 583
List of Tables, Figures, Boxes, and Equations
Tables
Table A-l: Key Categories for the United States (1990 and 2020) 14
Table A-2: U.S. Greenhouse Gas Inventory Source Categories without LULUCF 20
Table A-3: U.S. Greenhouse Gas Inventory Source Categories with LULUCF 26
Table A-4: 2020 Energy Consumption Data by Fuel Type (TBtu) and Adjusted Energy Consumption Data 41
Table A-5: 2020 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 42
Table A-6: 2019 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 43
Table A-7: 2018 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 44
Table A-8: 2017 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 45
Table A-9: 2016 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 46
Table A-10: 2015 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 47
Table A-ll: 2014 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 48
Table A-12: 2013 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 49
Table A-13: 2012 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 50
Table A-14: 2011 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 51
Table A-15: 2010 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 52
Table A-16: 2005 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 53
Table A-17: 2000 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 54
Table A-18: 1995 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 55
Table A-19: 1990 Energy Consumption Data and C02 Emissions from Fossil Fuel Combustion by Fuel Type 56
Table A-20: Unadjusted Non-Energy Fuel Consumption (TBtu) 57
Table A-21: International Bunker Fuel Consumption (TBtu) 57
Table A-22: C Content Coefficients by Year (MMT C/QBtu) 58
Table A-23: C02 Content Coefficients by Year (MMT C02/QBtu) 60
Table A-24: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours) 61
Table A-25: Electric Power Generation by Fuel Type (Percent) 62
Table A-26: Geothermal Net Generation by Geotype (Billion Kilowatt-Hours) 62
Table A-27: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank (MMT C/QBtu) (1990-2020) 67
Table A-28: Variability in Carbon Content Coefficients by Rank Across States (Kilograms C02 Per MMBtu) 69
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-29: Composition of Natural Gas (Percent) 71
Table A-30: Carbon Content of Pipeline-Quality Natural Gas by C02 and Heat Content (MMT C/QBtu) 71
Table A-31: Carbon Content Coefficients for Natural Gas (MMT Carbon/QBtu) 72
Table A-32: Carbon Content Coefficients and Underlying Data for Petroleum Products 77
Table A-33: Characteristics of Major Reformulated Fuel Additives 78
Table A-34: Physical Characteristics of Hydrocarbon Gas Liquids 85
Table A-35: Industrial Sector Consumption and Carbon Content Coefficients of Hydrocarbon Gas Liquids, 1990-2020... 86
Table A-36: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas 88
Table A-37: Characteristics of Non-hexane Special Naphthas 91
Table A-38: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990 - 2000 (MMT C/QBtu) 93
Table A-39: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (MMT C/QBtu) 95
Table A-40: Carbon Content Coefficients and Underlying Data for Petroleum Products 96
Table A-41: Physical Characteristics of Liquefied Petroleum Gases 97
Table A-42: Carbon Content Coefficients for Petroleum Products, 1990-2007 (MMT C/QBtu) 99
Table A-43: Fuel Types and Percent of C Stored for Non-Energy Uses 103
Table A-44: Net Exports of Petrochemical Feedstocks, 1990-2020 (MMT C02 Eq.) 105
Table A-45: C Stored and Emitted by Products from Feedstocks in 2020 (MMT C02 Eq.) 106
Table A-46:1998 TRI Releases by Disposal Location (kt C02 Eq.) 107
Table A-47: Industrial and Solvent NMVOC Emissions 108
Table A-48: Non-Combustion Carbon Monoxide Emissions 109
Table A-49: Assumed Composition of Combusted Hazardous Waste by Weight (Percent) 110
Table A-50: C02 Emitted from Hazardous Waste Incineration (MMT C02 Eq.) 110
Table A-51: Summary of 2018 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery (Trillion Btu) Ill
Table A-52: Carbon Emitted from Fuels Burned for Energy Recovery (MMT C02 Eq.) Ill
Table A-53: 2020 Plastic Resin Production (MMT dry weight) and C Stored (MMT C02 Eq.) 112
Table A-54: Assigned C Contents of Plastic Resins (% by weight) 113
Table A-55: Major Nylon Resins and their C Contents (% by weight) 113
Table A-56: 2002 Rubber Consumption (kt) and C Content (%) 114
Table A-57: 2020 Fiber Production (MMT), C Content (%), and C Stored (MMT C02 Eq.) 114
Table A-58: Active Ingredient Consumption in Pesticides (Million lbs.) and C Emitted and Stored (MMT C02 Eq.) in 2012
115
Table A-59: C Emitted from Utilization of Soaps, Shampoos, and Detergents (MMT C02 Eq.) 116
Table A-60: C Emitted from Utilization of Antifreeze and Deicers (MMT C02 Eq.) 116
Table A-61: C Emitted from Utilization of Food Additives (MMT C02 Eq.) 117
Table A-62: C Stored in Silicone Products (MMT C02 Eq.) 117
Table A-63: Commercial and Environmental Fate of Oil Lubricants (Percent) 122
Table A-64: Commercial and Environmental Fate of Grease Lubricants (Percent) 123
Table A-65: Emissive and Non-emissive (Storage) Fates of Waxes: Uses by Fate and Percent of Total Mass 124
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Table A-66: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total C Mass Stored 125
Table A-67: Fuel Consumption by Stationary Combustion for Calculating CH4 and N20 Emissions (TBtu) 135
Table A-68: CH4 and N20 Emission Factors by Fuel Type and Sector (g/GJ)a 136
Table A-69: CH4 and N20 Emission Factors by Technology Type and Fuel Type for the Electric Power Sector (g/GJ)3 136
Table A-70: NOx Emissions from Stationary Combustion (kt) 137
Table A-71: CO Emissions from Stationary Combustion (kt) 138
Table A-72: NMVOC Emissions from Stationary Combustion (kt) 139
Table A-73: Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified) 144
Table A-74: Energy Consumption by Fuel and Vehicle Type (TBtu) 145
Table A-75: Transportation Sector Biofuel Consumption by Fuel Type (million gallons) 147
Table A-76: Vehicle Miles Traveled for Gasoline On-Road Vehicles (billion miles) 152
Table A-77: Vehicle Miles Traveled for Diesel On-Road Vehicles (billion miles) 153
Table A-78: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (billion miles) 155
Table A-79: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles) 156
Table A-80: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,3 2020 158
Table A-81: Annual Average Vehicle Mileage Accumulation per Vehicles3 (miles) 158
Table A-82: VMT Distribution by Vehicle Age and Vehicle/Fuel Type,3 2020 159
Table A-83: Fuel Consumption for Non-Road Sources by Fuel Type (million gallons unless otherwise noted) 161
Table A-84: Emissions Control Technology Assignments for Gasoline Passenger Cars (Percent of VMT) 163
Table A-85: Emissions Control Technology Assignments for Gasoline Light-Duty Trucks (Percent of VMT)3 164
Table A-86: Emissions Control Technology Assignments for Gasoline Heavy-Duty Vehicles (Percent of VMT)3 165
Table A-87: Emissions Control Technology Assignments for Diesel On-Road Vehicles and Motorcycles 166
Table A-88: Emission Factors for CH4 and N20 for On-Road Vehicles 166
Table A-89: Emission Factors for N20 for Alternative Fuel Vehicles (g/mi) 168
Table A-90: Emission Factors for CH4 for Alternative Fuel Vehicles (g/mi) 169
Table A-91: Emission Factors for N20 Emissions from Non-Road Mobile Combustion (g/kg fuel) 170
Table A-92: Emission Factors for CH4 Emissions from Non-Road Mobile Combustion (g/kg fuel) 172
Table A-93: NOx Emissions from Mobile Combustion (kt) 174
Table A-94: CO Emissions from Mobile Combustion (kt) 175
Table A-95: NMVOCs Emissions from Mobile Combustion (kt) 176
Table A-96: C02 Emissions from Non-Transportation Mobile Sources (MMT C02 Eq.)3 180
Table A-97: HFC Emissions from Transportation Sources (MMT C02 Eq.) 182
Table A-98: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (MMT C02 Eq.) 186
Table A-99: Transportation and Mobile Source Emissions by Gas (MMT C02 Eq.) 189
Table A-100: Greenhouse Gas Emissions from Passenger Transportation (MMT C02 Eq.) 191
Table A-101: Greenhouse Gas Emissions from Domestic Freight Transportation (MMT C02 Eq.) 191
Table A-102: Commercial Aviation Fuel Burn for the United States and Territories 198
Table A-103: Mine-Specific Data Used to Estimate Ventilation Emissions 203
A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-104: Coal Basin Definitions by Basin and by State 205
Table A-105: Annual Coal Production (Thousand Short Tons) 207
Table A-106: Coal Underground, Surface, and Post-Mining CH4 Emission Factors (ft3 per Short Ton) 207
Table A-107: Underground Coal Mining CH4 Emissions (Billion Cubic Feet) 208
Table A-108: Total Coal Mining CH4 Emissions (Billion Cubic Feet) 208
Table A-109: Total Coal Mining CH4 Emissions by State (Million Cubic Feet) 208
Table A-110: Municipal Solid Waste Incinerated (MetricTons) 225
Table A-lll: Calculated Fossil C02 Content per Ton Waste Incinerated (kg C02/Short Ton Incinerated) 226
Table A-112: Elastomers Consumed in 2002 (kt) 226
Table A-113: Scrap Tire Constituents and C02 Emissions from Scrap Tire Incineration in 2020 227
Table A-114: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons) 232
Table A-115: Total U.S. Military Aviation Bunker Fuel (Million Gallons) 233
Table A-116: Total U.S. DoD Maritime Bunker Fuel (Million Gallons) 233
Table A-117: Aviation and Marine Carbon Contents (MMT Carbon/QBtu) and Fraction Oxidized 234
Table A-118: Annual Variable Carbon Content Coefficient for Jet Fuel (MMT Carbon/QBtu) 234
Table A-119: Annual Variable Carbon Content Coefficient for Distillate Fuel Oil (MMT Carbon/QBtu) 234
Table A-120: Total U.S. DoD C02 Emissions from Bunker Fuels (MMT C02 Eq.) 234
Table A-121: Refrigeration and Air-Conditioning Market Transition Assumptions 240
Table A-122: Refrigeration and Air-Conditioning Lifetime Assumptions 249
Table A-123: Aerosol Product Transition Assumptions 251
Table A-124: Solvent Market Transition Assumptions 252
Table A-125: Fire Extinguishing Market Transition Assumptions 254
Table A-126: Foam Blowing Market Transition Assumptions 257
Table A-127: Emission Profile for the Foam End-Uses 262
Table A-128: Sterilization Market Transition Assumptions 264
Table A-129: Banks of ODS and ODS Substitutes, 1990-2020 (MT) 265
Table A-130: 2020 Cattle Population Estimates, by Animal Type and State (1,000 head) 268
Table A-131: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2020 (1,000 head) 269
Table A-132: Cattle Population Categories Used for Estimating CH4 Emissions 270
Table A-133: Estimated Beef Cow Births by Month 271
Table A-134: Example of Monthly Average Populations from Calf Transition Matrix (1,000 head) 271
Table A-135: Example of Monthly Average Populations from Stocker Transition Matrix (1,000 head) 272
Table A-136: Typical Animal Mass (lbs) 273
Table A-137: Weight Gains that Vary by Year (lbs) 274
Table A-138: Feedlot Placements in the United States for 2020 (Number of animals placed/1,000 Head) 274
Table A-139: Estimates of Average Monthly Milk Production by Beef Cows (lbs/cow) 275
Table A-140: Dairy Lactation Rates by State (lbs/ year/cow) 275
Table A-141: Regions used for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle from 1990-2006 276
A-5
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Table A-142: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2020 276
Table A-143: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition Estimates ... 278
Table A-144: DE Values with Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 1990-
2006 280
Table A-145: DE Values and Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 2007-
2020 281
Table A-146: Foraging Animal DE (% of GE) and Ym Values for Each Region and Animal Type for 2007-2020 281
Table A-147: Regional DE (% of GE) and Ym Rates for Dairy and Feedlot Cattle by Animal Type for 2020 282
Table A-148: Calculated Annual GE by Animal Type and State, for 2020 (GJ) 284
Table A-149: Calculated Annual National Emission Factors for Cattle by Animal Type, for 2020 (kg CH4/head/year) 286
Table A-150: Emission Factors for Cattle by Animal Type and State, for 2020 (kg CH4/head/year) 287
Table A-151: Annex I Countries' Implied Emission Factors for Cattle by Year (kg CH4/head/year) 289
Table A-152: CH4 Emissions from Cattle (kt) 290
Table A-153: CH4 Emissions from Cattle (MMT C02 Eq.) 291
Table A-154: Emission Factors for Other Livestock (kg CH4/head/year) 291
Table A-155: CH4 Emissions from Enteric Fermentation (kt) 292
Table A-156: CH4 Emissions from Enteric Fermentation (MMT C02 Eq.) 292
Table A-157: CH4 Emissions from Enteric Fermentation from Cattle (metric tons), by State, for 2020 293
Table A-158: CH4 Emissions from Enteric Fermentation from Cattle (MMT C02 Eq.), by State, for 2020F 294
Table A-159: CH4 Emissions from Enteric Fermentation from Other Livestock (metric tons), by State, for 2020 296
Table A-160: CH4 Emissions from Enteric Fermentation from Other Livestock (MMT C02 Eq.), by State, for 2020F 297
Table A-161: Livestock Population (1,000 Head) 313
Table A-162: Waste Characteristics Data 314
Table A-163: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by year for Swine,
Poultry, Sheep, Goats, Horses, Mules and Asses, and Cattle Calves (kg/day/1000 kg animal mass) 315
Table A-164: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by State for Cattle (other
than Calves) and American Bison3 for 2020 (kg/animal/year) 317
Table A-165: 2020 Manure Distribution Among Waste Management Systems by Operation (Percent) 318
Table A-166: 2020 Manure Distribution Among Waste Management Systems by Operation (Percent) Continued 321
Table A-167: Manure Management System Descriptions 323
Table A-168: Methane Conversion Factors (percent) for Dry Systems 324
Table A-169: Methane Conversion Factors by State for Liquid Systems for 2020 (Percent) 324
Table A-170: Direct Nitrous Oxide Emission Factors (kg N20-N/kg N excreted) 325
Table A-171: Indirect Nitrous Oxide Loss Factors (Percent) 326
Table A-172: Total Methane Emissions from Livestock Manure Management (kt)a 327
Table A-173: Total Methane Emissions from Livestock Manure Management (MMT C02 Eq.)a 328
Table A-174: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (kt) 329
Table A-175: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (MMT C02 Eq.)330
Table A-176: Methane Emissions by State from Livestock Manure Management for 2020 (kt)a b 331
A-6 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-177: Methane Emissions by State from Livestock Manure Management for 2020 (MMT C02 Eq.)a 332
Table A-178: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2020
(kt) 334
Table A-179: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2020
(MMTC02 Eq.) 335
Table A-180: Total Cropland and Grassland Area Estimated with Tier 1/2 and 3 Inventory Approaches (Million Hectares)
346
Table A-181: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral Soil Organic C Approach
(Million Hectares) 347
Table A-182: Total Land Areas for Drained Organic Soils by Land Management Category and Climate Region (Million
Hectares) 350
Table A-183: Total Rice Harvested Area Estimated with Tier 1 and 3 Inventory Approaches (Million Hectares) 351
Table A-184: Sources of Soil Nitrogen (kt N) 357
Table A-185: U.S. Soil Groupings Based on the IPCC Categories and Dominant Taxonomic Soil, and Reference Carbon
Stocks (MetricTons C/ha) 364
Table A-186: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated
with Management Impacts on Mineral Soils 365
Table A-187: Rate and standard deviation for the Initial Increase and Subsequent Annual Mass Accumulation Rate (Mg
C/ha-yr) in Soil Organic C Following Wetland Restoration of Conservation Reserve Program 365
Table A-188: Direct Soil N20 Emissions from Mineral Soils in Cropland (MMT C02 Eq.) 366
Table A-189: Direct Soil N20 Emissions from Mineral Soils in Grassland (MMT C02 Eq.) 367
Table A-190: Annual Change in Soil Organic Carbon Stocks in Croplands (MMT C02 Eq./yr) 368
Table A-191: Annual Change in Soil Organic Carbon Stocks in Grasslands (MMT C02 Eq./yr) 370
Table A-192: Methane Emissions from Rice Cultivation (MMT C02 Eq.) 371
Table A-193: Direct Soil N20 Emissions from Drainage of Organic Soils (MMT C02 Eq.) 372
Table A-194: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC Default
Rates (Metric Ton C/ha-yr) 372
Table A-195: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Cropland (MMT C02 Eq.) 373
Table A-196: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in Grasslands (MMT C02 Eq.) 373
Table A-197: Indirect Soil N20 Emissions for Cropland from Volatilization and Atmospheric Deposition, and from
Leaching and Runoff (MMT C02 Eq.) 375
Table A-198: Indirect Soil N20 Emissions for Grassland from Volatilization and Atmospheric Deposition, and from
Leaching and Runoff (MMT C02 Eq.) 375
Table A-199: Total Soil N20 Emissions (Direct and Indirect), Soil Organic C Stock Changes and Rice CH4 Emissions from
Agricultural Lands by State in 2015 (MMT C02 Eq.) 377
Table A-200: Specific annual forest inventories by state used in development of forest C stock and stock change estimate
403
Table A-201: Coefficients for Estimating the Ratio of C Density of Understory Vegetation (above- and belowground, T
C/ha) by Region and Forest Type3 405
Table A-202: Ratio for Estimating Downed Dead Wood by Region and Forest Type 407
Table A-203: Coefficients for Estimating Logging Residue Component of Downed Dead Wood 408
Table A-204: Harvested Wood Products from Wood Harvested in the United States—Annual Additions of C to Stocks and
Total Stocks under the Production Approach 416
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Table A-205: Comparison of Net Annual Change in Harvested Wood Products C Stocks Using Alternative Accounting
Approaches (kt C02 Eq./year) 417
Table A-206: Harvested Wood Products Sectoral Background Data for LULUCF—United States 418
Table A-207: Half-life of Solidwood and Paper Products in End-Uses 419
Table A-208: Parameters Determining Decay of Wood and Paper in SWDS 420
Table A-209: Net C02 Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C02
Eq.) 420
Table A-210: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C)420
Table A-211: Forest area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT
C) 421
Table A-212: Forest Land Area Estimates and Differences Between Estimates in 6.1 Representation of the U.S. Land Base
(CRF Category 4.1) and 6.2 Forest Land Remaining Forest Land (CRF Category 4A1) (kha) 422
Table A-213: State-level Net C Flux from all Forest Pools in Forest Land Remaining Forest Land (MMT C) with Uncertainty
Range Relative to Flux Estimate, 2020 422
Table A-214: Annual change in Mineral Soil C stocks from U.S. agricultural soils that were estimated using a Tier 2
method (MMT C/year) 426
Table A-215: Total land areas (hectares) by land use/land use change subcategory for mineral soils between 1990 to
2015 426
Table A-216: Land Converted to Forest Land area estimates and differences between estimates in the Representation of
the U.S. Land Base (CRF Category 4.1) and Land Converted to Forest Land (CRF Category 4A1) (kha) 427
Table A-217: Areas (Hectares) and Corresponding Emissions (MMT/year) Associated with Past Forest Fires3 431
Table A-218: Equivalence Ratios, of CH4 and N20 to C02 432
Table A-219: Solid Waste in MSW and Industrial Waste Landfills Contributing to CH4 Emissions (MMT unless otherwise
noted) 443
Table A-220: Average Values for Rate Constant (k) by Precipitation Range (yr1) 446
Table A-221: Percent of U.S. Population within Precipitation Ranges by Decade (%) 446
Table A-222: Revised Waste-in-Place (WIP) for GHGRP Reporting and Non-reporting Landfills in 2016 451
Table A-223: Table HH-3 to Subpart HH of the EPA's Greenhouse Gas Reporting Program, Area Types Applicable to the
Calculation of Gas Collection Efficiency 452
Table A-224: Total Waste Disposed over 50 Years (1970-2020) for GHGRP Reporting and Non-reporting Landfills in 2020
454
Table A-225: Table HH-4 to Subpart HH of Part 98—Landfill Methane Oxidation Fractions 457
Table A-226: CH4 Emissions from Landfills (kt) 459
Table A-227: 2020 U.S. Energy Statistics (Physical Units) 467
Table A-228: 2020 Conversion Factors to Energy Units (Heat Equivalents) 468
Table A-229: 2020 Apparent Consumption of Fossil Fuels (TBtu) 469
Table A-230: 2020 Potential C02 Emissions 470
Table A-231: 2020 Non-Energy Carbon Stored in Products 471
Table A-232: 2020 Reference Approach C02 Emissions from Fossil Fuel Consumption (MMT C02 Eq.) 471
Table A-233: Fuel Consumption in the United States by Estimating Approach (TBtu)a 472
Table A-234: C02 Emissions from Fossil Fuel Combustion by Estimating Approach (MMT C02 Eq.)a 472
A-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A- 235:Summary of Sources and Sinks Not Included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks:
1990-2020 476
Table A-236: Summary of Geographic Completeness 487
Table A-237: IPCC AR4 Global Warming Potentials (GWP) and Atmospheric Lifetimes (Years) of Gases Used in this Report
490
Table A-238: Comparison of GWP values and Lifetimes Used in the AR4, AR5, and AR6C 492
Table A-239: Effects on U.S. Greenhouse Gas Emissions Using AR4, AR5, and AR6C GWP values (MMT C02 Eq.) 494
Table A-240: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4
GWP Values (MMT C02 Eq.) 495
Table A-241: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon Feedbacks Relative to AR4
GWP Values (Percent) 495
Table A-242: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values (MMT C02 Eq.) 496
Table A-243: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon Feedbacks3 Relative to AR4 GWP
Values (Percent) 496
Table A-244: Change in U.S. Greenhouse Gas Emissions Using AR6 Relative to AR4 GWP Values (MMT C02 Eq.) 497
Table A-245: Change in U.S. Greenhouse Gas Emissions Using AR6 Relative to AR4 GWP Values (Percent) 497
Table A-246:100-year Direct Global Warming Potentials for Select Ozone Depleting Substances 499
Table A-247: Emissions of Ozone Depleting Substances (kt) 499
Table A-248: Guide to Metric Unit Prefixes 503
Table A-249: Conversion Factors to Energy Units (Heat Equivalents) 505
Table A-250: Guide to Chemical Formulas 506
Table A-251: Cross-walk of NEI and NIR Categories for Greenhouse Gas Precursors 510
Table A-252: Summary Results of Source and Sink Category Uncertainty Analyses 516
Table A-253: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 1990 (MMT C02 Eq. and
Percent) 518
Table A-254: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 2020 (MMT C02 Eq. and
Percent) 519
Table A-255: Quantitative Assessment of Trend Uncertainty (MMT C02 Eq. and Percent) 521
Table A-256: Assessment Factors and Definitions 530
Table A-257: Response to UN Review of the 2020 Inventory Submission 533
Table A-258: Summary of EPA GHGRP Data Use in U.S. Inventory 585
Figures
Figure A-l: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute
Database 72
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon
Density 74
Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number 76
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2020 190
Figure A-5: Commercial Aviation Fuel Burn for the United States and Territories 198
Figure A-6: Locations of U.S. Coal Basins 206
A-9
-------
Figure A-7: GHG Emissions and Removals for Cropland & Grassland 377
Figure A-8: DayCent Model Flow Diagram 380
Figure A-9: Modeled versus measured net primary production 381
Figure A-10: Effect of Soil Temperature (a), Water-Filled Pore Space (b), and pH (c) on Nitrification Rates 384
Figure A-ll: Effect of Soil Nitrite Concentration (a), Heterotrophic Respiration Rates (b), and Water-Filled Pore Space (c)
on Denitrification Rates 385
Figure A-12: Comparisons of Results from DayCent Model and Measurements of Soil Organic C Stocks 387
Figure A-13: Comparison of Estimated Soil Organic C Stock Changes and Uncertainties using Tier 1 (IPCC 2006), Tier 2
(Ogle et al. 2003, 2006) and Tier 3 Methods 388
Figure A-14: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous Oxide Emissions 389
Figure A-15: Comparisons of Results from DayCent Model and Measurements of Soil Methane Emissions 390
Figure A-16: Flowchart of the inputs necessary in the estimation framework, including the methods for estimating
individual pools of forest C in the conterminous United States 400
Figure A-17: Annual FIA plots (remeasured and not remeasured) across the United States 402
Figure A-18: Landfill Gas Composition Over Time 439
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 440
Figure A-20: U.S. QA/QC Plan Summary 529
Boxes
Box A-l: Uses of Greenhouse Gas Reporting Program Data in Reporting Emissions from Industrial Sector Fossil Fuel Combustion
39
Box A-2: DayCent Model Simulation of N Gas losses and Nitrate Leaching 382
Box A-3: Comparison of Annual Waste Disposal Estimates Across Available Data Sources 443
BoxA-4: Reducing Uncertainty 523
Equations
Equation A-l: C Content for Coal by Consuming Sector 66
Equation A-2: C Content for Coal by Rank 68
Equation A-3: C Content of Pipeline and Flared Natural Gas 71
Equation A-4: C Content for a Petroleum-based Fuel 73
Equation A-5: C Content of Cruel Oil 93
Equation A-6: NEU Storage Factor Estimate for 2020 106
Equation A-7: NOx, CO, and NMVOC Emissions Estimates 134
Equation A-8: Calculation of Emissions from Refrigeration and Air-conditioning Equipment First-fill 238
Equation A-9: Calculation of Emissions from Refrigeration and Air-conditioning Equipment Serviced 238
Equation A-10: Calculation of Emissions from Refrigeration and Air-conditioning Equipment Disposed 239
Equation A-ll: Calculation of Total Emissions from Refrigeration and Air-conditioning Equipment 239
Equation A-12: Calculation of Emissions from Aerosols 249
Equation A-13: Calculation of Emissions from Solvents 252
Equation A-14: Calculation of Emissions from Fire Extinguishing 253
A-10 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Equation A-15: Calculation of Emissions from Foam Blowing Manufacturing 255
Equation A-16: Calculation of Emissions from Foam Blowing Lifetime Losses (Closed-cell Foams) 255
Equation A-17: Calculation of Emissions from Foam Blowing Disposal (Closed-cell Foams) 255
Equation A-18: Calculation of Emissions from Foam Blowing Post-disposal (Closed-cell Foams) 256
Equation A-19: Calculation of Total Emissions from Foam Blowing (Open-cell and Closed-cell Foams) 256
Equation A-20: Calculation of Total Emissions from Sterilization 262
Equation A-21: Calculation of Chemical Bank (All Sectors) 265
Equation A-22: Best Fit Curve for Estimating the Methane Conversion Rate for Dairy Cattle 277
Equation A-23: Scaling Factor for the Dairy Cattle Methane Conversion Rate 277
Equation A-24: Gross Energy Calculation for Enteric Fermentation 283
Equation A-25: Daily Emission Factor for Enteric Fermentation Based on Gross Energy Intake and Methane Conversion Factor
286
Equation A-26: Total Enteric Fermentation Emissions Calculated from Daily Emissions Rate and Population 290
Equation A- 27: VS Production for Cattle 303
Equation A-28: Nex Rates for Cattle 304
Equation A-29: Daily Nitrogen Intake for Cattle 304
Equation A-30: Nitrogen Retention from Milk and Body Weight for Cattle 304
Equation A-31: VS Proportion Available to Convert to CH4 Based on Temperature (van't Hoff-Arrhenius/factor) 307
Equation A-32: MCF for Anaerobic Lagoons and Liquid Systems 308
Equation A-33: VS Excreted for Animals Other Than Cattle 309
Equation A-34: VS Excreted for Cattle 309
Equation A-35: CH4 Emissions for All Animal Types 310
Equation A-36: CH4 Production from AD Systems 310
Equation A-37: CH4 Emissions from AD Systems 310
Equation A-38: Nex for Calves and Animal Types Other Than Cattle 311
Equation A-39: Nex from Cattle Other Than Calves 311
Equation A-40: Direct N20 emissions from All Animal Types 311
Equation A-41: Indirect N20 Emissions from All Animal Types 312
Equation A-42: Soil Nitrification Rate 382
Equation A-43: Soil Denitrification Rate 382
Equation A-44: Inflection Point Calculation 382
Equation A-45: Ratio of Nitrogen Gas (N2) to Nitrous Oxide 383
Equation A-46: Ratio of understory C density to live tree C density 404
Equation A-47: Understory C density 405
Equation A-48: C density of downed dead wood 407
Equation A-49: Logging residue C density 407
Equation A-50: Adjusted C density of downed dead wood 407
Equation A-51: Litter C density 409
Equation A-52: Total mass of mineral and organic soil C 409
A-11
-------
Equation A-53: Soil organic C at midpoint depth 410
Equation A-54: Total soil organic C density 410
Equation A-55: Predicted soil organic carbon 410
Equation A-56: Example age transition matrix 412
Equation A-57: C Stock Change 412
Equation A-58: Backcasting Age Class Distribution 413
Equation A-59: Age Transition Model 413
Equation A-60: Forest Area Change 413
Equation A-61: Land Use Change and Disturbance 413
Equation A-62: Variance of the C Stock Change 428
Equation A-63: Percent Modeling Error 428
Equation A-64: Uncertainty of C Stock Estimate at Time t 428
Equation A-65: Model-based Uncertainty of C Stock Change 428
Equation A-66: Total Uncertainty of C Stock Change 428
Equation A-67: Net Methane Emissions from Solid Waste 444
Equation A-68: Methane Generation from MSW Landfills 445
Equation A-69: Degradable Organic Carbon Fraction of Solid Waste 445
Equation A-70: Back-calculated Methane Oxidation 456
Equation A-71: Calculating C02 Equivalent Emissions 489
A-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
ANNEX 1 Key Category Analysis
The United States has identified national key categories based on the estimates presented in this report. The 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories (IPCC 2006)
describes a key category as a "[category] that is prioritized within the national inventory system because its estimate has
a significant influence on a country's total inventory of greenhouse gases in terms of the absolute level, the trend, or the
uncertainty in emissions and removals." By definition, key categories are sources or sinks that have the greatest
contribution to the absolute overall level of national emissions in any of the years covered by the time series. In addition,
when an entire time series of emission estimates is prepared, a determination of key categories must also account for
the influence of the trends of individual categories. Therefore, a trend assessment is conducted to identify source and
sink categories for that may not be large enough to be identified by the level assessment, but whose trend contributes
significantly to the overall Inventory trend (IPCC 2019). Finally, a qualitative evaluation of key categories should be
performed, in order to capture any key categories that were not identified in either of the quantitative analyses, but can
be considered key because of the unique country-specific estimation methods.
The methodology for conducting a key category analysis, as defined by Volume 1, Chapter 4 of the 2006 IPCC Guidelines
for National Greenhouse Gas Inventories (IPCC 2006), includes:
• Approach 1 (including both level and trend assessments);
• Approach 2 (including both level and trend assessments, and incorporating uncertainty analysis); and
• Qualitative approach.
This Annex presents an analysis of key categories, both for sources only and also for sources and sinks (i.e., including
Land Use, Land-Use Change and Forestry LULUCF); discusses Approach 1, Approach 2, and qualitative approaches used to
identify key categories for the United States; provides level and trend assessment equations; and provides a brief
evaluation of IPCC's quantitative methodologies for defining key categories. The UNFCCC common reporting format
(CRF) reporting software generates Table 7, which also identifies key categories using an Approach 1 analysis based on
the default disaggregation approach provided in Volume 1, Chapter 4, Table 4.1 of the 2006 IPCC Guidelines. The
disaggregation of categories presented in CRF Table 7 and this annex vary but the results of the key category analysis are
consistent. Consistent with the UNFCCC reporting guidelines, the United States key category analysis uses the IPCC
suggested aggregation level as the basis for the analysis, but in some cases the disaggregation does differ. Differences
arise from implementation of special considerations identified in Table 4.1. As stated in section 4.2 in Volume 1, Chapter
4 of the 2006 IPCC Guidelines, "...countries using Approach 2 will probably choose the same level of aggregation that was
used for the uncertainty analysis." In order to retain consistency in the categorization with the uncertainty analysis, the
aggregation level for this analysis (i.e. Approach 1, 2 etc.) does reflect some (e.g., for l.A.l, 3.A, 3.B) but not all special
considerations such as disaggregating for significant subcategories, fuel types, and/or carbon pools for the following
categories: Fuel Combustion Activities—Water-borne Navigation (l.A.3.d), Fuel Combustion Activities—Other Sectors
(1.A.4), Fugitive Emissions from Fuels -Oil (l.B.2.a) and Natural Gas (l.B.2.b), Petrochemical and Carbon Black
Production (2.B.8), Direct and Indirect N20 Emissions (3.D.1 and 3.D.2), land use categories (4.A, 4.B, 4.C, 4.D, 4.E, and
4.F), Solid Waste Disposal (5.A) and Wastewater (5.D). Most other differences stem from additional disaggregation to
subcategories consistent with the uncertainty analysis, including within Fuel Combustion Activities—Other Sectors
(l.A.4.a Commercial/Institutional and l.A.4.b Residential), Fossil Fuel Combustion—Non-Specified Stationary (l.A.5.a
Incineration of Waste, Non-Energy Use of Fossil Fuels, and U.S. Territories) and Mobile (l.A.5.b Military), Biomass
Burning (4.A(V) Forest Fires and 4.C(V) Grass Fires), and Biological Treatment of Solid Waste (5.B.1 Composting and 5.B.2
Anaerobic Digestion at Biogas Facilities). As EPA disaggregates the uncertainty analysis, it will reflect these special
considerations in aggregation levels of the key category analysis. Finally, in addition to conducting Approach 1 and 2 level
and trend assessments, a qualitative assessment of categories, as described in the 2006 IPCC Guidelines, was conducted
to capture any key categories that were not identified by either quantitative method. For this Inventory, no additional
categories were identified using criteria recommend by IPCC, but EPA continues to review its qualitative assessment on
an annual basis.
Table A-l 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 C02 Eq. emissions in 2020. 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
A-13
-------
Table A-l: Key Categories for the United States (1990 and 2020)
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
Energy
C02 Emissions from
Mobile Combustion:
C02
•
• •
•
•
• •
•
1,333.8
Road
C02 Emissions from
Stationary Combustion
co2
788.2
- Coal - Electricity
Generation
C02 Emissions from
Stationary Combustion
- Gas - Electricity
co2
•
• •
•
•
• •
•
634.3
Generation
C02 Emissions from
Stationary Combustion
co2
•
• •
•
•
• •
•
485.5
- Gas - Industrial
C02 Emissions from
Stationary Combustion
co2
•
• •
•
•
•
256.4
- Gas - Residential
C02 Emissions from
Stationary Combustion
co2
•
• •
•
•
• •
•
237.8
- Oil - Industrial
C02 Emissions from
Stationary Combustion
co2
•
• •
•
•
• •
173.9
- Gas - Commercial
C02 Emissions from
Mobile Combustion:
co2
•
• •
•
•
• •
•
121.3
Aviation
C02 Emissions from Non-
Energy Use of Fuels
co2
•
• •
•
•
• •
•
121.0
C02 Emissions from
Stationary Combustion
co2
•
• •
•
•
•
59.5
- Oil - Residential
A-14 Inventory of U.S. Greenhouse Gas
Emissions and Sinks: 1990-2020
-------
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
C02 Emissions from
Mobile Combustion:
C02
•
• •
•
57.1
Other3
C02 Emissions from
Stationary Combustion
C02
•
• •
•
51.6
- Oil - Commercial
C02 Emissions from
Stationary Combustion
C02
•
• •
•
•
• •
•
43.0
- Coal - Industrial
C02 Emissions from
Natural Gas Systems
C02
•
•
•
35.4
C02 Emissions from
Mobile Combustion:
C02
•
•
O
*—1
m
Railways
C02 Emissions from
Petroleum Systems
C02
•
• •
•
•
• •
•
30.2
C02 Emissions from
Mobile Combustion:
C02
•
• •
•
23.7
Marine
C02 Emissions from
Stationary Combustion
C02
•
•
16.9
- Oil - U.S. Territories
C02 Emissions from
Stationary Combustion
C02
16.2
- Oil - Electricity
Generation
C02 Emissions from
Mobile Combustion:
C02
•
•
5.2
Military
C02 Emissions from Coal
Mining
CO?
•
2.2
C02 Emissions from
Stationary Combustion
C02
•
•
•
1.4
- Coal - Commercial
Annex 1
A-15
-------
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
C02 Emissions from
Stationary Combustion
C02
•
•
-
- Coal - Residential
CH4 Emissions from
Natural Gas Systems
ch4
•
• •
•
•
• •
•
164.9
Fugitive Emissions from
ch4
41.2
Coal Mining
CH4 Emissions from
Petroleum Systems
ch4
•
• •
•
•
• •
•
40.2
CH4 Emissions from
Abandoned Oil and Gas
ch4
•
•
6.9
Wells
CH4 Emissions from
Stationary Combustion
ch4
•
• •
•
4.1
- Residential
N20 Emissions from
Stationary Combustion
n2o
15.2
- Coal - Electricity
Generation
N20 Emissions from
Mobile Combustion:
n2o
•
• •
•
•
9.8
Road
N20 Emissions from
Stationary Combustion
- Gas - Electricity
n2o
•
4.5
Generation
N20 Emissions from
Stationary Combustion
n2o
•
•
2.3
- Industrial
Industrial Processes and Product Use
C02 Emissions from
Cement Production
co2
•
• •
•
40.7
C02 Emissions from Iron
and Steel Production &
co2
•
• •
•
•
• •
•
37.7
A-16 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
Metallurgical Coke
Production
C02 Emissions from
Petrochemical
C02
•
• •
•
30.0
Production
Emissions from
Substitutes for Ozone
Depleting Substances:
HFCs, PFCs
•
• •
•
•
• •
•
137.7
Refrigeration and Air
conditioning
Emissions from
Substitutes for Ozone
Depleting Substances:
HFCs, PFCs
•
• •
•
•
• •
•
18.1
Aerosols
Emissions from
Substitutes for Ozone
Depleting Substances:
HFCs, PFCs
•
•
15.5
Foam Blowing Agents
SF6and CF4 Emissions
from Electrical
Transmission and
sf6, cf4
•
• •
•
•
•
3.8
Distribution
HFC-23 Emissions from
HCFC-22 Production
HFCs
•
• •
•
•
•
2.1
PFC Emissions from
Aluminum Production
PFCs
•
• •
•
1.7
Agriculture
C02 Emissions from
Liming
C02
•
•
2.4
CH4 Emissions from
Enteric Fermentation:
ch4
•
• •
•
•
• •
168.9
Cattle
CH4 Emissions from
Manure Management:
ch4
•
• •
•
•
•
•
33.5
Cattle
Annex 1
A-17
-------
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF LULUCF
LULUCF
(MMT COz Eq.)
CH4 Emissions from
Manure Management:
ch4
•
•
•
•
26.1
Other Livestock
CH4 Emissions from Rice
Cultivation
ch4
•
•
•
•
15.7
Direct N20 Emissions
from Agricultural Soil
n2o
•
•
•
•
271.7
Management
Indirect N20 Emissions
from Applied Nitrogen
n2o
•
•
•
• •
44.6
Waste
CH4 Emissions from
Commercial Landfills
ch4
•
• •
•
•
• •
•
94.2
CH4 Emissions from
Domestic Wastewater
ch4
•
11.8
Treatment
N20 Emissions from
Domestic Wastewater
n2o
•
•
•
•
•
23.0
Treatment
Land Use, Land-Use Change, and Forestry
Net C02 Emissions from
Land Converted to
co2
•
•
•
•
77.9
Settlements
Net C02 Emissions from
Land Converted to
co2
•
•
•
54.4
Cropland
Net C02 Emissions from
Grassland Remaining
co2
•
•
4.5
Grassland
Net C02 Emissions from
Cropland Remaining
co2
•
•
(23.3)
Cropland
Net C02 Emissions from
Land Converted to
co2
•
•
•
•
(24.1)
Grassland
A-18 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Approach 1
Approach 2
Level
Trend Level
Trend
Level
Trend
Level
Trend
CRF Source/Sink
Greenhouse
Without
Without With
With
Without
Without
With
With
2020 Emissions
Category
Gas
LULUCF
LULUCF LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
LULUCF
(MMT COz Eq.)
Net C02 Emissions from
Land Converted to
C02
•
•
(99.5)
Forest Land
Net C02 Emissions from
Settlements Remaining
co2
•
•
•
•
(126.1)
Settlements
Net C02 Emissions from
Forest Land Remaining
co2
•
•
•
•
(668.1)
Forest Land
CH4 Emissions from
Flooded Lands
Remaining Flooded
ch4
•
19.9
Lands
CH4 Emissions from
Forest Fires
ch4
•
•
13.6
N20 Emissions from
Forest Fires
n2o
•
•
11.7
Subtotal of Key Categories Without LULUCF1,
5,793.6
Total Gross Emissions Without LULUCF
5,981.4
Percent of Total Without LULUCF
97%
Subtotal of Key Categories With LULUCF'
5,013.7
Total Net Emissions With LULUCF
5,222.4
Percent of Total With LULUCF
96%
Note: Parentheses indicate negative values (or sequestration).
a Other includes emissions from pipelines.
b Subtotal includes key categories from Level Approach 1 Without LULUCF, Level Approach 2 Without LULUCF, Trend Approach 1 Without LULUCF, and Trend Approach 2
Without LULUCF.
c Subtotal includes key categories from Level Approach 1 With LULUCF, Level Approach 2 With LULUCF, Trend Approach 1 With LULUCF, and Trend Approach 2 With LULUCF.
Annex 1
A-19
-------
Table A-2 provides a complete listing of source categories by IPCC sector, along with notations on the criteria used in
identifying key categories, without LULUCF sources and sinks. Similarly, Table A-3 provides a complete listing of source
and sink categories by IPCC sector, along with notations on the criteria used in identifying key categories, including
LULUCF sources and sinks. The notations refer specifically to the year(s) in the Inventory time series (i.e., 1990 to 2020)
in which each source or sink category reached the threshold for being a key category based on either an Approach 1 or
Approach 2 level assessment.
Table A-2: U.S. Greenhouse Gas Inventory Source Categories without LULUCF
Level in
Greenhouse
1990 Emissions
2020 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
(MMT COz Eq.)
Category
Criteria3
year(s)b
Energy
l.A.3.b C02 Emissions from Mobile
Combustion: Road
C02
1,157.4
1,333.8
•
Li Ti L2 T2
1990, 2020
l.A.l C02 Emissions from Stationary
Combustion - Coal - Electricity
co2
1,546.5
788.2
•
Li Ti L2 T2
1990, 2020
Generation
l.A.l C02 Emissions from Stationary
Combustion - Gas - Electricity
co2
175.4
634.3
•
Li Ti L2 T2
1990, 2020
Generation
1.A.2 C02 Emissions from Stationary
Combustion - Gas - Industrial
co2
408.8
485.5
•
Li Ti L2 T2
1990, 2020
l.A.4.b C02 Emissions from
Stationary Combustion - Gas -
co2
237.8
256.4
•
Li Ti L2
1990, 2020
Residential
1.A.2 C02 Emissions from Stationary
Combustion - Oil - Industrial
co2
287.1
237.8
•
Li Ti L2 T2
1990, 2020
l.A.4.a C02 Emissions from
Stationary Combustion - Gas -
co2
142.0
173.9
•
Li Ti L2 T2
1990, 2020
Commercial
l.A.3.a C02 Emissions from Mobile
Combustion: Aviation
co2
187.2
121.3
•
Li Ti L2 T2
1990, 2020
1.A.5 C02 Emissions from Non-
Energy Use of Fuels
co2
112.2
121.0
•
Li Ti L2 T2
1990, 2020
l.A.4.b C02 Emissions from
Stationary Combustion - Oil -
co2
97.8
59.5
•
Li Ti L2 T2
1990, 2020i
Residential
l.A.3.e C02 Emissions from Mobile
Combustion: Otherb
co2
36.0
57.1
•
Li Ti
1990,, 2020,
l.A.4.a C02 Emissions from
Stationary Combustion - Oil -
co2
74.3
51.6
•
Li Ti
1990,, 2020,
Commercial
1.A.2 C02 Emissions from Stationary
Combustion - Coal - Industrial
co2
157.8
43.0
•
Li Ti L2 T2
1990, 2020
1.B.2 C02 Emissions from Natural
Gas Systems
co2
31.9
35.4
•
Li L2
1990, 2020
1.A.3.C C02 Emissions from Mobile
Combustion: Railways
co2
35.5
31.0
•
Li
1990,, 2020,
1.B.2 C02 Emissions from
Petroleum Systems
co2
9.6
30.2
•
Li Ti L2 T2
2020
l.A.3.d C02 Emissions from Mobile
Combustion: Marine
co2
39.3
23.7
•
Li Ti
1990,, 2020,
1.A.5 C02 Emissions from Stationary
Combustion - Oil - U.S. Territories
co2
21.2
16.9
•
Li
1990,, 2020,
A-20 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
CRF Source/Sink Category
Greenhouse 1990 Emissions 2020 Emissions Key ID
Gas (MMT C02 Eq.) (MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
l.A.l C02 Emissions from Stationary
Combustion - Oil - Electricity
Generation
5.C.1 C02 Emissions from
Incineration of Waste
l.A.5.b C02 Emissions from Mobile
Combustion: Military
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S.
Territories
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S.
Territories
l.B.l C02 Emissions from Coal
Mining
l.A.4.a C02 Emissions from
Stationary Combustion - Coal -
Commercial
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
1.B.2 C02 Emissions from
Abandoned Oil and Gas Wells
l.A.4.b C02 Emissions from
Stationary Combustion - Coal -
Residential
1.B.2 CH4 Emissions from Natural
Gas Systems
l.B.l Fugitive Emissions from Coal
Mining
1.B.2 CH4 Emissions from Petroleum
Systems
1.B.2 CH4 Emissions from
Abandoned Oil and Gas Wells
l.B.l Fugitive Emissions from
Abandoned Underground Coal
Mines
l.A.4.b CH4 Emissions from
Stationary Combustion -
Residential
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.4.a CH4 Emissions from
Stationary Combustion -
Commercial
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
l.A.3.e CH4 Emissions from Mobile
Combustion: Otherb
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
97.5
12.9
13.6
0.5
NO
4.6
12.0
0.5
+
3.0
195.5
96.5
47.8
6.5
7.2
5.2
1.8
1.1
0.1
0.8
5.2
16.2
13.1
5.2
3.1
2.6
2.2
1.4
0.4
+
0.0
164.9
41.2
40.2
6.9
5.8
4.1
1.4
1.2
1.1
1.0
0.8
Li Ti L2 T2
1990, 2020i
TiT2
Li Ti L2 T2
Li Ti L2 T2
Li Ti L2 T2
l2
l2 t2
1990, 2020
1990, 2020
1990, 2020
19902, 20 202
19902, 20 202
Annex 1
A-21
-------
CRF Source/Sink Category
Greenhouse
Gas
1990 Emissions
(MMT C02 Eq.)
2020 Emissions
(MMT C02 Eq.)
Key
Category
ID
Criteria3
Level in
which
year(s)b
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
5.B.2 CH4 Emissions from Anaerobic
Digestion at Biogas Facilities
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
5.C.1 CH4 Emissions from
Incineration of Waste
l.A.l N20 Emissions from
Stationary Combustion - Coal -
Electricity Generation
l.A.3.b N20 Emissions from Mobile
Combustion: Road
l.A.3.e N20 Emissions from Mobile
Combustion: Otherb
l.A.l N20 Emissions from
Stationary Combustion - Gas -
Electricity Generation
1.A.2 N20 Emissions from
Stationary Combustion -
Industrial
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from
Stationary Combustion -
Residential
5.C.1 N20 Emissions from
Incineration of Waste
l.A.4.a N20 Emissions from
Stationary Combustion -
Commercial
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
CH4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
0.4
0.3
0.1
0.1
20.1
37.7
4.7
0.3
3.1
1.7
1.0
0.5
0.4
0.3
0.3
0.4
0.2
0.2
0.1
15.2
9.8
6.1
4.5
2.3
1.1
0.8
0.4
0.3
0.2
0.2
U L2 T2 1990, 20202
U Ti 1990i
L2 t2
19902
A-22 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
CRF Source/Sink Category
Greenhouse 1990 Emissions
Gas (MMT C02 Eq.)
2020 Emissions Key ID
(MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
1.A.5 N20 Emissions from
Stationary Combustion - U.S.
Territories
1.B.2 N20 Emissions from
Petroleum Systems
l.A.l N20 Emissions from
Stationary Combustion - Wood -
Electricity Generation
1.B.2 N20 Emissions from Natural
Gas Systems
l.A.l N20 Emissions from
Stationary Combustion - Oil -
Electricity Generation
l.A.5.b N20 Emissions from Mobile
Combustion: Military
N20
n2o
n2o
n2o
n2o
n2o
0.1
0.1
0.1
Industrial Processes and Product Use
2.A.1 C02 Emissions from Cement
Production
2.C.1 C02 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
2.B.8 C02 Emissions from
Petrochemical Production
2.B.1 C02 Emissions from Ammonia
Production
2.A.2 C02 Emissions from Lime
Production
2.A.4 C02 Emissions from Other
Process Uses of Carbonates
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag
Purposes
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
2.A.3 C02 Emissions from Glass
Production
2.C.3 C02 Emissions from Aluminum
Production
2.B.7 C02 Emissions from Soda Ash
Production
2.C.2 C02 Emissions from Ferroalloy
Production
2.B.6 C02 Emissions from Titanium
Dioxide Production
2.C.6 C02 Emissions from Zinc
Production
2.B.10 C02 Emissions from
Phosphoric Acid Production
2.C.5 C02 Emissions from Lead
Production
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
C02
co2
co2
33.5
104.7
21.6
13.0
11.7
6.2
3.8
1.5
2.3
6.8
1.4
2.2
1.2
0.6
1.5
0.5
40.7
37.7
30.0
12.7
11.3
9.8
6.0
5.0
1.9
1.7
1.5
1.4
1.3
1.0
0.9
0.5
L, T, 1990-1, 2020i
L, T, L2 T2 1990, 2020
L, T, 1990i, 2020i
Annex 1
A-23
-------
CRF Source/Sink Category
Greenhouse 1990 Emissions 2020 Emissions Key ID
Gas (MMT C02 Eq.) (MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
2.B.5 C02 Emissions from Silicon
Carbide Production and
Consumption
2.C.4 C02 Emissions from
Magnesium Production and
Processing
2.B.8 CH4 Emissions from
Petrochemical Production
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
2.C.2 CH4 Emissions from Ferroalloy
Production
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
2.B.2 N20 Emissions from Nitric
Acid Production
2.B.3 N20 Emissions from Adipic
Acid Production
2.G N20 Emissions from Product
Uses
2.B.4 N20 Emissions from
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
2.E N20 Emissions from Electronics
Industry
2.F.1 Emissions from Substitutes for
Ozone Depleting Substances:
Refrigeration and Air conditioning
2.F.4 Emissions from Substitutes for
Ozone Depleting Substances:
Aerosols
2.F.2 Emissions from Substitutes for
Ozone Depleting Substances:
Foam Blowing Agents
2.F.3 Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
2.F.5 Emissions from Substitutes for
Ozone Depleting Substances:
Solvents
2.E PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
2.G SF6 and CF4 Emissions from
Electrical Transmission and
Distribution
2.B.9 HFC-23 Emissions from HCFC-
22 Production
2.C.3 PFC Emissions from Aluminum
Production
C02
C02
CH4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HiGWP
SF6, cf4
HFCs
PFCs
0.2
0.1
0.2
12.1
15.2
4.2
1.7
0.2
NO
NO
3.6
23.2
46.1
21.5
0.2
0.3
9.3
8.3
4.2
1.2
0.3
137.7
18.1
15.5
2.8
2.0
4.4
3.8
2.1
1.7
Li Ti L2 T2
Li Ti L2 T2
Ti
Li Ti T2
Li Ti T2
Li Ti
2020
2020
1990,
1990,
1990,
A-24 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Level in
Greenhouse
1990 Emissions
2020 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
(MMT C02 Eq.)
Category
Criteria3
year(s)b
2.C.4 SF6 Emissions from
Magnesium Production and
sf6
5.2
0.9
Processing
2.C.4 HFC-134a Emissions from
Magnesium Production and
HFCs
NO
0.1
Processing
Agriculture
3.H C02 Emissions from Urea
Fertilization
C02
2.4
5.3
3.G C02 Emissions from Liming
C02
4.7
2.4
•
T 2
3.A.1 CH4 Emissions from Enteric
Fermentation: Cattle
ch4
157.2
168.9
•
Li Ti L2 T2
1990, 2020
3.B.1 CH4 Emissions from Manure
Management: Cattle
ch4
15.9
33.5
•
Li Ti L2 T2
2020
3.B.4 CH4 Emissions from Manure
Management: Other Livestock
ch4
19.0
26.1
•
LiT2
1990,, 2020,
3.C CH4 Emissions from Rice
Cultivation
ch4
16.0
15.7
•
Li L2
19902, 20 20
3.A.4 CH4 Emissions from Enteric
ch4
6.3
6.2
Fermentation: Other Livestock
3.F CH4 Emissions from Field
Burning of Agricultural Residues
ch4
0.4
0.4
3.D.1 Direct N20 Emissions from
Agricultural Soil Management
n2o
272.6
271.7
•
Li L2
1990, 2020
3.D.2 Indirect N20 Emissions from
Applied Nitrogen
n2o
43.5
44.6
•
Li L2 T2
1990, 2020
3.B.1 N20 Emissions from Manure
Management: Cattle
n2o
11.1
15.5
3.B.4 N20 Emissions from Manure
n2o
4.2
Management: Other Livestock
Z.o
3.F N20 Emissions from Field
n2o
0.2
0.2
Burning of Agricultural Residues
Waste
5.A CH4 Emissions from Commercial
Landfills
ch4
165.7
94.2
•
Li Ti L2 T2
1990, 2020
5.A CH4 Emissions from Industrial
Landfills
ch4
10.9
15.1
5.D CH4 Emissions from Domestic
Wastewater T reatment
ch4
14.7
11.8
•
l2
19902
5.D CH4 Emissions from Industrial
Wastewater T reatment
ch4
5.6
6.4
5.B CH4 Emissions from Composting
ch4
0.4
2.3
5.D N20 Emissions from Domestic
Wastewater T reatment
n2o
16.2
23.0
•
Li L2 T2
19902, 20 20
5.B N20 Emissions from Composting
n2o
0.3
2.0
5.D N20 Emissions from Industrial
n2o
0.4
0.5
Wastewater T reatment
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NO (Not Occurring)
a If the source is a key category for both Liand L2 (as designated in the ID criteria column), it is a key category for both
assessments in the years provided unless noted by a subscript, in which case it is a key category for that assessment in that
year only (e.g., 19902 designates a category is key for the Approach 2 assessment only in 1990).
Annex 1
A-25
-------
b Other includes emissions from pipelines.
Note: LULUCF sources and sinks are not included in the analysis presented in this table. See Table A-3 for the results of the
analysis with LULUCF.
Table A-3: U.S. Greenhouse Gas Inventory Source Categories with LULUCF
Level in
Greenhouse
1990 Emissions
2020 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT C02 Eq.)
(MMT C02 Eq.)
Category
Criteria3
year(s)b
Energy
l.A.3.b C02 Emissions from Mobile
Combustion: Road
C02
1,157.4
1,333.8
•
Li Ti L2 T2
1990, 2020
l.A.l C02 Emissions from Stationary
Combustion - Coal - Electricity
C02
1,546.5
788.2
•
Li Ti L2 T2
1990, 2020
Generation
l.A.l C02 Emissions from Stationary
Combustion - Gas - Electricity
C02
175.4
634.3
•
Li Ti L2 T2
1990,, 2020
Generation
1.A.2 C02 Emissions from Stationary
Combustion - Gas - Industrial
C02
408.8
485.5
•
Li Ti L2 T2
1990, 2020,
l.A.4.b C02 Emissions from
Stationary Combustion - Gas -
C02
237.8
256.4
•
Li Ti L2
1990, 2020,
Residential
1.A.2 C02 Emissions from Stationary
Combustion - Oil - Industrial
C02
287.1
237.8
•
Li Ti L2 T2
1990, 2020
l.A.4.a C02 Emissions from
Stationary Combustion - Gas -
C02
142.0
173.9
•
Li Ti L2
1990, 2020
Commercial
l.A.3.a C02 Emissions from Mobile
Combustion: Aviation
C02
187.2
121.3
•
Li Ti L2 T2
1990, 2020
1.A.5 C02 Emissions from Non-
Energy Use of Fuels
C02
112.2
121.0
•
Li Ti L2 T2
1990, 2020
l.A.4.b C02 Emissions from
Stationary Combustion - Oil -
C02
97.8
59.5
•
Li Ti
1990,, 2020,
Residential
l.A.3.e C02 Emissions from Mobile
Combustion: Otherb
C02
36.0
57.1
•
Li Ti
1990,, 2020,
l.A.4.a C02 Emissions from
Stationary Combustion - Oil -
C02
74.3
51.6
•
Li Ti
1990,, 2020,
Commercial
1.A.2 C02 Emissions from Stationary
Combustion - Coal - Industrial
C02
157.8
43.0
•
Li Ti L2 T2
1990, 2020,
1.B.2 C02 Emissions from Natural
Gas Systems
C02
31.9
35.4
•
Li
1990,, 2020,
1.A.3.C C02 Emissions from Mobile
Combustion: Railways
C02
35.5
31.0
•
Li
1990,, 2020,
1.B.2 C02 Emissions from
Petroleum Systems
C02
9.6
30.2
•
Li Ti L2 T2
2020
l.A.3.d C02 Emissions from Mobile
Combustion: Marine
C02
39.3
23.7
•
Li Ti
1990,, 2020,
1.A.5 C02 Emissions from Stationary
Combustion - Oil - U.S. Territories
C02
21.2
16.9
•
Li
1990,, 2020,
l.A.l C02 Emissions from Stationary
Combustion - Oil - Electricity
C02
97.5
16.2
•
Li Ti T2
1990,, 2020,
Generation
A-26 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
CRF Source/Sink Category
Greenhouse 1990 Emissions 2020 Emissions Key ID
Gas (MMT C02 Eq.) (MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
5.C.1 C02 Emissions from
Incineration of Waste
l.A.5.b C02 Emissions from Mobile
Combustion: Military
1.A.5 C02 Emissions from Stationary
Combustion - Coal - U.S.
Territories
1.A.5 C02 Emissions from Stationary
Combustion - Gas - U.S.
Territories
l.B.l C02 Emissions from Coal
Mining
l.A.4.a C02 Emissions from
Stationary Combustion - Coal -
Commercial
l.A.l C02 Emissions from Stationary
Combustion - Geothermal Energy
1.B.2 C02 Emissions from
Abandoned Oil and Gas Wells
l.A.4.b C02 Emissions from
Stationary Combustion - Coal -
Residential
1.B.2 CH4 Emissions from Natural
Gas Systems
l.B.l Fugitive Emissions from Coal
Mining
1.B.2 CH4 Emissions from Petroleum
Systems
1.B.2 CH4 Emissions from
Abandoned Oil and Gas Wells
l.B.l Fugitive Emissions from
Abandoned Underground Coal
Mines
l.A.4.b CH4 Emissions from
Stationary Combustion -
Residential
1.A.2 CH4 Emissions from Stationary
Combustion - Industrial
l.A.4.a CH4 Emissions from
Stationary Combustion -
Commercial
l.A.l CH4 Emissions from Stationary
Combustion - Gas - Electricity
Generation
l.A.3.e CH4 Emissions from Mobile
Combustion: Otherb
l.A.3.b CH4 Emissions from Mobile
Combustion: Road
l.A.3.d CH4 Emissions from Mobile
Combustion: Marine
C02
C02
C02
C02
C02
C02
C02
C02
C02
CH4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
12.9
13.6
0.5
NO
4.6
12.0
0.5
+
3.0
195.5
96.5
47.8
6.5
7.2
5.2
1.8
1.1
0.1
0.8
5.2
0.4
13.1
5.2
3.1
2.6
2.2
1.4
0.4
+
0.0
164.9
41.2
40.2
6.9
5.8
4.1
1.4
1.2
1.1
1.0
0.8
0.4
Ti
Li Ti L2 T2
Li Ti L2 T2
Li Ti L2 T2
l2
l2 t2
1990, 2020
1990, 2020i
1990, 2020
19902,
20202
19902,
20202
Annex 1
A-27
-------
CRF Source/Sink Category
Greenhouse
Gas
1990 Emissions
(MMT C02 Eq.)
2020 Emissions
(MMT C02 Eq.)
Key
Category
ID
Criteria3
Level in
which
year(s)b
l.A.l CH4 Emissions from Stationary
Combustion - Coal - Electricity
Generation
5.B.2 CH4 Emissions from Anaerobic
Digestion at Biogas Facilities
1.A.3.C CH4 Emissions from Mobile
Combustion: Railways
1.A.5 CH4 Emissions from Stationary
Combustion - U.S. Territories
l.A.3.a CH4 Emissions from Mobile
Combustion: Aviation
l.A.l CH4 Emissions from Stationary
Combustion - Wood - Electricity
Generation
l.A.l CH4 Emissions from Stationary
Combustion - Oil - Electricity
Generation
l.A.5.b CH4 Emissions from Mobile
Combustion: Military
5.C.1 CH4 Emissions from
Incineration of Waste
l.A.l N20 Emissions from
Stationary Combustion - Coal -
Electricity Generation
l.A.3.b N20 Emissions from Mobile
Combustion: Road
l.A.3.e N20 Emissions from Mobile
Combustion: Otherb
l.A.l N20 Emissions from
Stationary Combustion - Gas -
Electricity Generation
1.A.2 N20 Emissions from
Stationary Combustion -
Industrial
l.A.3.a N20 Emissions from Mobile
Combustion: Aviation
l.A.4.b N20 Emissions from
Stationary Combustion -
Residential
5.C.1 N20 Emissions from
Incineration of Waste
l.A.4.a N20 Emissions from
Stationary Combustion -
Commercial
1.A.3.C N20 Emissions from Mobile
Combustion: Railways
l.A.3.d N20 Emissions from Mobile
Combustion: Marine
1.A.5 N20 Emissions from
Stationary Combustion - U.S.
Territories
CH4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
n2o
0.3
0.0
0.1
+
0.1
20.1
37.7
4.7
0.3
3.1
1.7
1.0
0.5
0.4
0.3
0.3
0.1
0.2
0.2
0.1
15.2
9.8
6.1
4.5
2.3
1.1
0.8
0.4
0.3
0.2
0.2
0.1
L, L2 T2 1990, 20202
Li T, T2
1990,
A-28 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Level in
Greenhouse 1990 Emissions 2020 Emissions Key ID which
CRF Source/Sink Category Gas (MMT C02 Eq.) (MMT C02 Eq.) Category Criteria3 year(s)b
1.B.2 N20 Emissions from
Petroleum Systems
N20
+
+
l.A.l N20 Emissions from
Stationary Combustion - Wood -
n2o
+
+
Electricity Generation
1.B.2 N20 Emissions from Natural
Gas Systems
n2o
+
+
l.A.l N20 Emissions from
Stationary Combustion - Oil -
n2o
0.1
+
Electricity Generation
l.A.5.b N20 Emissions from Mobile
n2o
Combustion: Military
+
+
Industrial Processes and Product Use
2.A.1 C02 Emissions from Cement
Production
C02
33.5
40.7
•
L, Ti 1990-I, 2020i
2.C.1 C02 Emissions from Iron and
Steel Production & Metallurgical
C02
104.7
37.7
•
U Ti L2 T2 1990, 2020i
Coke Production
2.B.8 C02 Emissions from
Petrochemical Production
C02
21.6
30.0
•
U Ti 1990,, 2020,
2.B.1 C02 Emissions from Ammonia
Production
C02
13.0
12.7
2.A.2 C02 Emissions from Lime
Production
C02
11.7
11.3
2.A.4 C02 Emissions from Other
Process Uses of Carbonates
C02
6.2
9.8
2.B.10 C02 Emissions from Urea
Consumption for Non-Ag
C02
3.8
6.0
Purposes
2.B.10 C02 Emissions from Carbon
Dioxide Consumption
C02
1.5
5.0
2.A.3 C02 Emissions from Glass
Production
C02
2.3
1.9
2.C.3 C02 Emissions from Aluminum
Production
co2
6.8
1.7
2.B.7 C02 Emissions from Soda Ash
Production
co2
1.4
1.5
2.C.2 C02 Emissions from Ferroalloy
co2
2.2
1.4
Production
2.B.6 C02 Emissions from Titanium
co2
1.2
1.3
Dioxide Production
2.C.6 C02 Emissions from Zinc
Production
co2
0.6
1.0
2.B.10 C02 Emissions from
Phosphoric Acid Production
co2
1.5
0.9
2.C.5 C02 Emissions from Lead
Production
co2
0.5
0.5
2.B.5 C02 Emissions from Silicon
Carbide Production and
co2
0.2
0.2
Consumption
Annex 1
A-29
-------
CRF Source/Sink Category
Greenhouse 1990 Emissions 2020 Emissions Key ID
Gas (MMT C02 Eq.) (MMT C02 Eq.) Category Criteria3
Level in
which
year(s)b
2.C.4 C02 Emissions from
Magnesium Production and
Processing
2.B.8 CH4 Emissions from
Petrochemical Production
2.B.5 CH4 Emissions from Silicon
Carbide Production and
Consumption
2.C.2 CH4 Emissions from Ferroalloy
Production
2.C.1 CH4 Emissions from Iron and
Steel Production & Metallurgical
Coke Production
2.B.2 N20 Emissions from Nitric
Acid Production
2.B.3 N20 Emissions from Adipic
Acid Production
2.G N20 Emissions from Product
Uses
2.B.4 N20 Emissions from
Caprolactam, Glyoxal, and
Glyoxylic Acid Production
2.E N20 Emissions from Electronics
Industry
2.F.1 Emissions from Substitutes for
Ozone Depleting Substances:
Refrigeration and Air conditioning
2.F.4 Emissions from Substitutes for
Ozone Depleting Substances:
Aerosols
2.F.2 Emissions from Substitutes for
Ozone Depleting Substances:
Foam Blowing Agents
2.F.3 Emissions from Substitutes for
Ozone Depleting Substances: Fire
Protection
2.F.5 Emissions from Substitutes for
Ozone Depleting Substances:
Solvents
2.E PFC, HFC, SF6, and NF3 Emissions
from Electronics Industry
2.G SF6 and CF4 Emissions from
Electrical Transmission and
Distribution
2.B.9 HFC-23 Emissions from HCFC-
22 Production
2.C.3 PFC Emissions from Aluminum
Production
2.C.4 SF6 Emissions from
Magnesium Production and
Processing
C02
CH4
ch4
ch4
ch4
n2o
n2o
n2o
n2o
n2o
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HFCs, PFCs
HiGWP
SF6, cf4
HFCs
PFCs
SF6
0.1
0.2
12.1
15.2
4.2
1.7
0.2
NO
NO
3.6
23.2
46.1
21.5
5.2
0.3
9.3
8.3
4.2
1.2
0.3
137.7
18.1
15.5
2.8
2.0
4.4
3.8
2.1
1.7
0.9
Li Ti L2 T2
Li Ti L2 T2
2020
2020
Ti
Li Ti T2
Li Ti T2
Li Ti
1990,
1990,
1990,
A-30 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Level in
Greenhouse
1990 Emissions
2020 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT COz Eq.)
(MMT C02 Eq.)
Category
Criteria3
year(s)b
2.C.4 HFC-134a Emissions from
Magnesium Production and
HFCs
NO
0.1
Processing
Agriculture
3.H C02 Emissions from Urea
Fertilization
C02
2.4
5.3
3.G C02 Emissions from Liming
C02
4.7
2.4
•
t2
3.A.1 CH4 Emissions from Enteric
Fermentation: Cattle
ch4
157.2
168.9
•
Li Ti L2
1990, 2020
3.B.1 CH4 Emissions from Manure
Management: Cattle
ch4
15.9
33.5
•
Li Ti T2
2020i
3.B.4 CH4 Emissions from Manure
Management: Other Livestock
ch4
19.0
26.1
•
Li Ti
1990,, 2020,
3.C CH4 Emissions from Rice
Cultivation
ch4
16.0
15.7
•
LiL2
19902, 20 20
3.A.4 CH4 Emissions from Enteric
Fermentation: Other Livestock
ch4
6.3
6.2
3.F CH4 Emissions from Field
ch4
0.4
0.4
Burning of Agricultural Residues
3.D.1 Direct N20 Emissions from
Agricultural Soil Management
n2o
272.6
271.7
•
LiL2
1990, 2020
3.D.2 Indirect N20 Emissions from
Applied Nitrogen
n2o
43.5
44.6
•
LiL2
1990, 2020
3.B.1 N20 Emissions from Manure
Management: Cattle
n2o
11.1
15.5
3.B.4 N20 Emissions from Manure
Management: Other Livestock
n2o
2.8
4.2
3.F N20 Emissions from Field
Burning of Agricultural Residues
n2o
0.2
0.2
Waste
5.A CH4 Emissions from Commercial
Landfills
ch4
165.7
94.2
•
Li T, L2 T2
1990, 2020
5.A CH4 Emissions from Industrial
Landfills
ch4
10.9
15.1
5.D CH4 Emissions from Domestic
Wastewater T reatment
ch4
14.7
11.8
5.D CH4 Emissions from Industrial
Wastewater T reatment
ch4
5.6
6.4
5.B CH4 Emissions from Composting
ch4
0.4
2.3
5.D N20 Emissions from Domestic
Wastewater T reatment
n2o
16.2
23.0
•
Li L2 T2
19902, 20 20
5.B N20 Emissions from Composting
n2o
0.3
2.0
5.D N20 Emissions from Industrial
Wastewater T reatment
n2o
0.4
0.5
Land Use, Land Use Change, and Forestry
4.E.2 Net C02 Emissions from Land
Converted to Settlements
co2
60.8
77.9
•
Li T, L2 T2
1990, 2020
4.B.2 Net C02 Emissions from Land
Converted to Cropland
co2
51.8
54.4
•
Li L2 T2
1990, 2020
4.C.1 Net C02 Emissions from
Grassland Remaining Grassland
co2
6.9
4.5
•
l2 t2
19902,
20202
Annex 1
A-31
-------
Level in
Greenhouse
1990 Emissions
2020 Emissions
Key
ID
which
CRF Source/Sink Category
Gas
(MMT COz Eq.)
(MMT C02 Eq.)
Category
Criteria3
year(s)b
4.D.2 Net C02 Emissions from Lands
Converted to Wetlands
C02
4.3
0.3
4.D.1 Net C02 Emissions from
(+)
Coastal Wetlands Remaining
C02
(+)
Coastal Wetlands
4.B.1 Net C02 Emissions from
Cropland Remaining Cropland
C02
(23.2)
(+)
•
LiL2
1990, 2020
4.C.2 Net C02 Emissions from Land
Converted to Grassland
C02
(+)
(+)
•
Li Ti L2 T2
2020
4.A.2 Net C02 Emissions from Land
Converted to Forest Land
C02
(+)
(+)
•
LiL2
1990, 2020
4.E.1 Net C02 Emissions from
(+)
(+)
Settlements Remaining
C02
•
Li Ti L2 T2
1990, 2020
Settlements
4.A.1 Net C02 Emissions from
(+)
(+)
Forest Land Remaining Forest
C02
•
Li T, L2 T2
1990, 2020
Land
4.D.1 CH4 Emissions from Flooded
Lands Remaining Flooded Lands
ch4
18.2
19.9
•
Li
2020i
4.A.1 CH4 Emissions from Forest
Fires
ch4
2.3
13.6
•
TiT2
4.D.1 CH4 Emissions from Coastal
Wetlands Remaining Coastal
ch4
3.7
3.8
Wetlands
4.C.1 CH4 Emissions from Grass
Fires
ch4
0.1
0.3
4.D.2 CH4 Emissions from Land
ch4
2.6
0.2
Converted to Flooded Lands
4.D.2 CH4 Emissions from Land
Converted to Coastal Wetlands
ch4
0.2
0.2
4.A.4 CH4 Emissions from Drained
Organic Soils
ch4
+
+
4.D.1 CH4 Emissions from Peatlands
Remaining Peatlands
ch4
+
+
4.A.1 N20 Emissions from Forest
Fires
n2o
1.8
11.7
•
TiT2
4.E.1 N20 Emissions from
n2o
2.0
2.5
Settlement Soils
4.A.1 N20 Emissions from Forest
Soils
n2o
0.1
0.5
4.C.1 N20 Emissions from Grass
Fires
n2o
0.1
0.3
4.D.1 N20 Emissions from Coastal
Wetlands Remaining Coastal
n2o
0.1
0.2
Wetlands
4.A.4 N20 Emissions from Drained
Organic Soils
n2o
0.1
0.1
4.D.1 N20 Emissions from Peatlands
Remaining Peatlands
n2o
+
+
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NO (Not Occurring)
A-32 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
a If the source is a key category for both Li and L2 (as designated in the ID criteria column), it is a key category for both
assessments in the years provided unless noted by a subscript, in which case it is a key category only for that assessment in
only that year (e.g., 19902 designates a category is key for the Approach 2 assessment only in 1990).
b Other includes emissions from pipelines.
Note: Parentheses indicate negative values (or sequestration).
Approach for Evaluation of Key Categories
Level Assessment
When using an Approach 1 for the level assessment, a predetermined cumulative emissions threshold is used to identify
key categories. When source and sink categories are sorted in order of decreasing absolute emissions, those that fall at
the top of the list and cumulatively account for 95 percent of emissions are considered key categories. The 95 percent
threshold in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) was designed to establish a
general level where the key category analysis covers approximately 90 percent of inventory uncertainty.
Including the Approach 2 provides additional insight into why certain source and sink categories are considered key, and
how to prioritize inventory improvements. In the Approach 2, the level assessment for each category from the Approach
1 is multiplied by its percent relative uncertainty. If the uncertainty reported is asymmetrical, the absolute value of the
larger uncertainty is used. When source and sink categories are sorted in decreasing order of this calculation, those that
fall at the top of the list and cumulatively account for 90 percent of emissions are considered key categories. The key
categories identified by the Approach 2 level assessment may differ from those identified by the Approach 1 assessment.
The final set of key categories includes all source and sink categories identified as key by either the Approach 1 or the
Approach 2 assessment, keeping in mind that the two assessments are not mutually exclusive. The uncertainty
associated with C02 from mobile combustion is applied to each mode's emission estimate. Note, an uncertainty analysis
was conducted for the C02 and N20 emissions from waste incineration, but has not yet been conducted for the CH4
emissions from waste incineration.
It is important to note that a key category analysis can be sensitive to the definitions of the source and sink categories. If
a large source or sink category is split into many subcategories, then the subcategories may have contributions to the
total inventory that are too small for those source categories to be considered key. Similarly, a collection of small, non-
key source categories adding up to less than 5 percent of total emissions could become key source categories if those
source categories were aggregated into a single source or sink category. The United States has attempted to define
source and sink categories by the conventions that would allow comparison with other international key category
analyses, while still maintaining the category definitions that constitute how the emissions estimates were calculated for
this report. As such, some of the category names used in the key category analysis may differ from the names used in the
main body of the report. Additionally, the United States accounts for some source categories, including fossil fuel
feedstocks, international bunkers, and emissions from U.S. Territories, that are derived from unique data sources using
country-specific methodologies.
Table KCA-1 through Table KCA-4 contain the 1990 and 2020 level assessments for both with and without LULUCF
sources and sinks, and contain further detail on where each source falls within the analysis. Approach 1 key categories
are shaded dark gray. Additional key categories identified by the Approach 2 assessment are shaded light gray. Tables
are available online at: https://www.epa.gov/ghgernissions/inventory-us-greenhouse-ga5-ernissions-and-sinks-199Q-
2020.
Trend Assessment
Approach 1 for trend assessment is defined as the product of the source or sink category level assessment and the
absolute difference between the source or sink category trend and the total trend. In turn, the source or sink category
trend is defined as the change in emissions from the base year to the current year, as a percentage of current year
emissions from that source or sink category. The total trend is the percentage change in total inventory emissions from
the base year to the current year.
Thus, the source or sink category trend assessment will be large if the source or sink category represents a large
percentage of emissions and/or has a trend that is quite different from the overall inventory trend. To determine key
categories, the trend assessments are sorted in descending order, so that the source or sink categories with the highest
trend assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all
categories that fall within that cumulative 95 percent are considered key categories.
Annex 1
A-33
-------
For Approach 2, the trend assessment for each category from Approach 1 is multiplied by its percent relative
uncertainty. If the uncertainty reported is asymmetrical, the larger uncertainty is used. When source and sink categories
are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively account for 90
percent of emissions are considered key categories. The key categories identified by the Approach 2 trend assessment
may differ from those identified by the Approach 1 assessment. The final set of key categories includes all source and
sink categories identified as key by either the Approach 1 or the Approach 2 assessment, keeping in mind that the two
assessments are not mutually exclusive.
Table KCA-5 through Table KCA-6 contain the trend assessments with and without LULUCF sources and sinks, and contain
further detail on where each source falls within the analysis. Approach 1 key categories are shaded dark gray. Additional
key categories identified by the Approach 2 assessment are shaded light gray. Tables are available online at:
https://www.epa.gov/ghgernissions/inventorv-us-greenhouse-gas-ernission5-and-sink5-199Q-202Q.
A-34 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
IPCC (2019) 2019 Refinement to the 2006IPCC Guidelines for National Greenhouse Gas Inventories. The
Intergovernmental Panel on Climate Change. [Buendia, E., Guendehou S., Limmeechokachai B., Pipatti R., Rojas Y.,
Sturgiss R., Tanabe K., Wirth T., (eds.)]. Cambridge University Press. In Press.
IPCC (2006) Volume 1, Chapter 4: Methodological Choice and Identification of Key Categories, 2006 IPCC Guidelines for
National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental
Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Negara, and K. Tanabe (eds.). Hayman, Kanagawa,
Japan.
Annex 1
A-35
-------
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
Carbon dioxide (C02) emissions from fossil fuel combustion were estimated using a "bottom-up" methodology
characterized by eight steps. These steps are described below.
Step 1: Determine Total Fuel Consumption by Fuel Type and Sector
The bottom-up methodology used by the United States for estimating C02 emissions from fossil fuel combustion is
conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change (IPCC) for
countries that intend to develop detailed, sector-based emission estimates in line with a Tier 2 method in the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006). Total consumption data and adjustments to
consumption are presented in Columns 2 through 13 of Table A-4.
Adjusted consumption data for years 1990,1995, 2000, 2005, and 2010 through 2020 are presented in columns 2
through 8 of Table A-5 through Table A-19 with totals by fuel type in column 8 and totals by end-use sector in the last
rows.55 Fuel consumption data for the bottom-up approach were obtained directly from the Energy Information
Administration (EIA) of the U.S. Department of Energy. These data were first gathered in physical units, and then
converted to their energy equivalents (see Annex 6.4 Constants, Units, and Conversions). The EIA data were collected
through a variety of consumption surveys at the point of delivery or use and qualified with survey data on fuel
production, imports, exports, and stock changes. Individual data elements were supplied by a variety of sources within
EIA. Most information was taken from published reports, although some data were drawn from unpublished energy
studies and databases maintained by EIA.
Energy use data were aggregated by sector (i.e., residential, commercial, industrial, transportation, electric power, and
U.S. Territories), primary fuel type (e.g., coal, natural gas, and petroleum), and secondary fuel type (e.g., motor gasoline,
distillate fuel). The 2020 total adjusted fossil energy consumption across all sectors, including U.S. Territories, and energy
types was 65,545.3 trillion British thermal units (TBtu), as indicated in the last entry of Column 13 in Table A-4. This total
excludes fuel used for non-energy purposes and fuel consumed as international bunkers, both of which were deducted in
earlier steps.
Electricity use information was allocated to each sector based on ElA's distribution of electricity retail sales to ultimate
customers (i.e., residential, commercial, industrial, and other). Because the "other" fuel use includes sales to both the
commercial and transportation sectors, ElA's limited transportation electricity use data were subtracted from "other"
electricity use and reported separately, and the remaining "other" electricity use was consequently combined with the
commercial electricity data. Further information on these electricity end uses is described in ElA's Monthly Energy
Review (EIA 2022). Within the transportation sector, electricity use from electric vehicle charging in commercial and
residential locations, not specifically reported by EIA, was calculated and re-allocated from the residential and
commercial sectors to the transportation sector, for the years 2010 to present. The methodology for estimating
electricity consumption by electric vehicles is outlined in Browning (2018).
There are also three basic differences between the consumption data presented in Table A-4 and Table A-5 through
Table A-19 and those recommended in the IPCC (2006) emission inventory methodology.
55 Adjusted consumption data for other years in the time series are available along with all other data tables for this report on U.S.
EPA's homepage at https://www.epa.gov/ghgemissions/inventory-us-greenhouse-gas-emissions-and-sinks.
A-36 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
First, consumption data in the U.S. Inventory are presented using higher heating values (HHV)56 rather than the lower
heating values (LHV)57 reflected in the IPCC (2006) emission inventory methodology. This convention is followed because
data obtained from EIA are based on HHV. Of note, however, is that EIA renewable energy statistics are often published
using LHV. The difference between the two conventions relates to the treatment of the heat energy that is consumed in
the process of evaporating the water contained in the fuel. The simplified convention used by the International Energy
Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and coal and by 0.9 for
natural gas.
Second, while ElA's energy use data for the United States includes only the 50 U.S. states and the District of Columbia,
the data reported to the United Nations Framework Convention on Climate Change (UNFCCC) are to include energy use
within U.S. Territories. Therefore, estimates for U.S. Territories58 were added to domestic consumption of fossil fuels.
Energy use data from U.S. Territories are presented in Column 7 of Table A-5 through Table A-19. It is reported
separately from domestic sectoral consumption, because it is collected separately by EIA with no sectoral disaggregation.
Third, there were a number of modifications made in this report that may cause consumption information herein to
differ from figures given in the cited literature. These are (1) the reallocation of select amounts of coking coal, petroleum
coke, natural gas, residual fuel oil, and other oil (>401 degrees Fahrenheit) for processes accounted for in the Industrial
Processes and Product Use chapter, (2) corrections for synthetic natural gas production, (3) subtraction of other fuels
used for non-energy purposes, and (4) subtraction of international bunker fuels. These adjustments are described in the
following steps.
Step 2: Subtract Uses Accounted for in the Industrial Processes 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 (>401 degrees Fahrenheit)—were reallocated to the Industrial
Processes and Product Use (IPPU) chapter, as these portions were consumed as raw materials during non-energy related
industrial processes. Emissions from these fuels used as raw materials are presented in the Industrial Processes and
Product Use chapter and are removed from the energy and non-energy use estimates within the Energy chapter.
• Coking coal is used as a raw material (specifically as a reducing agent) in the blast furnace process to
produce iron and steel, lead, and zinc and therefore is not used as a fuel for this process.
• Similarly, petroleum coke is used in multiple processes as a raw material and is thus not used as a fuel in
those applications. The processes in which petroleum coke is used include (1) ferroalloy production, (2)
aluminum production (for the production of C anodes and cathodes), (3) titanium dioxide production (in
the chloride process), (4) ammonia production, and (5) silicon carbide.
• Natural gas consumption is used as a feedstock for the production of ammonia.
• Residual fuel oil and other oil (>401 degrees Fahrenheit) are both used in the production of C black.
• Natural gas, distillate fuel, coal, and net imports of metallurgical coke are used to produce pig iron through
the reduction of iron ore in the production of iron and steel.
Examples of iron and steel production adjustments in allocating emissions in Energy and IPPU sectors:
The consumption of coking coal, natural gas, distillate fuel, and coal used in iron and steel production are adjusted within
the Energy chapter to avoid double counting of emissions from consumption of these fuels during activities in IPPU
related sectors. These fuels are adjusted based on activity data utilized in calculating emissions estimates within the Iron
and Steel Production section. Iron and steel production is an industrial process in which coal coke is used as a raw
material rather than as a fuel;59 as such, the total use of industrial coking coal, as reported by EIA, is adjusted downward
56 Also referred to as gross calorific values (GCV).
57 Also referred to as net calorific values (NCV).
58 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.
59 In addition to iron and steel, lead and zinc production are also industrial processes in which coal coke is used as a raw
material. Iron and steel, lead and zinc production accounts for the major portion of consumption of coal coke in the United
States.
Annex 2
A-37
-------
to account for this consumption within the iron and steel category. In this case, if the reported amount of coking coal
used in these processes is greater than the amount of coking coal consumption reported by the EIA, the excess amount
of coking coal used in these processes that is greater than the amount reported from consumption is subtracted from
the industrial other coal fuel type.
In 2020,14,414 thousand tons of coking coal were consumed,60 resulting in an Energy sector adjustment of 335 TBtu.
Natural gas, fuel oil, and coal are other fossil fuels also used in the production of iron and steel; therefore, the
consumption of these fuels in industrial processes is subtracted from the industrial fossil fuel combustion sector to
account for the amount of fuel used in the iron and steel calculation. In 2020, the iron and steel industry consumed
2,465 tons of coal (bituminous), 49,238 million ft3 of natural gas, and 2,321 thousand gallons of distillate fuel as fuel. This
resulted in Energy chapter adjustments of roughly 50 TBtu for coal, 44 TBtu for natural gas, and 0.3 TBtu for distillate
fuel. In addition, an additional 79 TBtu is adjusted to account for coking coal consumed for industrial processes other
than iron and steel, lead, and zinc production in 2020.
Step 3: Adjust for Conversion of Fossil Fuels and Exports
First, ethanol has been added to the motor gasoline stream for many years, but prior to 1993 this addition was not
captured in EIA motor gasoline statistics. Starting in 1993, ethanol was included in gasoline statistics. Carbon dioxide
emissions from ethanol added to motor gasoline 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, therefore, fuel
consumption estimates are adjusted to remove ethanol. Thus, motor gasoline consumption statistics given in this report
exclude ethanol and may be slightly lower than in EIA sources for finished gasoline that includes ethanol.
Second, EIA distillate fuel oil consumption statistics include "biodiesel" and "other renewable diesel fuel" consumption
starting in 2009. Carbon dioxide emissions from biodiesel and other renewable diesel added to diesel fuel 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, therefore, fuel consumption estimates are adjusted to remove biodiesel and
other renewable diesel fuel. Thus, distillate fuel oil consumption statistics for the transportation sector in this report may
be slightly lower than in EIA sources.
Third, a portion of industrial "other" coal that is accounted for in EIA coal combustion statistics is actually used to make
"synthetic natural gas" via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The plant
produces synthetic natural gas and byproduct C02. Since October 2000, a portion of the C02 produced by the coal
gasification plant has been exported to Canada by pipeline. The energy in this synthetic natural gas enters the natural gas
distribution stream, however it is accounted for in EIA coal combustion statistics.61 The exported C02 is not emitted to
the atmosphere in the United States, and therefore the energy associated with the amount of C02 exported is subtracted
from industrial other coal.
Step 4: Adjust Sectoral Allocation of Distillate Fuel Oil and Motor Gasoline
EPA conducted a separate bottom-up analysis of transportation fuel consumption based on data from the Federal
Highway Administration (FHWA). The FHWA data indicated that the amount of distillate and motor gasoline
consumption allocated to the transportation sector in the EIA statistics should be adjusted (FHWA 1996 through 2021).
Therefore, for the estimates presented in the U.S. Inventory, the transportation sector's distillate fuel and motor
gasoline consumption were 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.
Step 5: Subtract Consumption for Non-Energy Use
U.S. aggregate energy statistics include consumption of fossil fuels for non-energy purposes. Depending on the end-use,
non-energy uses of fossil fuels can result in long term storage of some or all of the C contained in the fuel. For example,
asphalt made from petroleum can sequester up to 100 percent of the C contained in the petroleum feedstock for
60 Coking coal includes non-imported coke consumption from the iron and steel, lead, and zinc industries.
61 To avoid double-counting, ElA's MER statistics account for supplemental gaseous fuels (including synthetic natural gas) in their
primary energy category (i.e., coal, petroleum, or biomass) (EIA 2021b).
A-38 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
extended periods of time. Other non-energy fossil fuel products, such as lubricants or plastics also store C, but can lose
or emit some of this C when they are used and/or burned as waste. As the emission pathways of C used for non-energy
purposes are vastly different than fuel combustion, these emissions are estimated separately in the Carbon Emitted in
Products from Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for non-
energy purposes, shown in Table A-20, was subtracted from total fuel consumption.
Step 6: Subtract Consumption of International Bunker Fuels
Emissions from international transport activities, or international bunker fuel consumption, are not included in national
totals and instead reported separately, as required by the IPCC (2006) and UNFCCC (2014) inventory reporting
guidelines. EIA energy statistics, however, include these bunker fuels jet fuel for aircraft, and distillate fuel oil and
residual fuel oil for marine shipping as part of fuel consumption by the transportation end-use sector. Therefore, the
amount of consumption for international bunker fuels was estimated and subtracted from total fuel consumption (see
Table A-21). Emissions from international bunker fuels have been estimated separately and not included in national
totals.62
Step 7: Determine the C Content of All Fuels
The C content of combusted fossil fuels was estimated by multiplying adjusted energy consumption (Columns 2 through
8 of Table A-5 through Table A-19) by fuel-specific C content coefficients (see Table A-22) that reflect the amount of C
per unit of energy in each fuel. The C content coefficients used in the Inventory were derived in part by EIA and EPA from
detailed fuel information and are similar to the C content coefficients contained in the IPCC's default methodology (IPCC
2006), with modifications reflecting fuel qualities specific to the United States.
For geothermal electricity production, C content was estimated by multiplying net generation for each geotype (see
Table A-26) by technology-specific C content coefficients (see Table A-22). For industrial energy and non-energy
hydrocarbon gas liquids (HGL)63 consumption, annually variable C contents were estimated by multiplying annual energy
and non-energy consumption for each HGL component (e.g., ethane, ethylene, propane, propylene) by its respective C
content coefficient (see Table A-22).
Step 8: Estimate CO2 Emissions
Actual C02 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas, geothermal)
and consuming sector (i.e., residential, commercial, industrial, transportation, electric power, and U.S. Territories).
Emission estimates are expressed in million metric tons of carbon dioxide equivalents (MMT C02 Eq.). To convert from C
content to C02 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent based on guidance
in IPCC (2006).
To determine total emissions by final end-use sector, emissions from electric power were distributed to each end-use
sector according to its share of aggregate electricity use (see Table A-24). This pro-rated approach to allocating emissions
from electric power may overestimate or underestimate emissions for particular sectors due to differences in the
average C content of fuel mixes burned to generate electricity.
To provide a more detailed accounting of emissions from transportation, fuel consumption data by vehicle type and
transportation mode were used to allocate emissions by fuel type calculated for the transportation end-use sector.
Additional information on the allocation is available in Annex 3.1.
Box A-l: Uses of Greenhouse Gas Reporting Program Data in Reporting Emissions from Industrial Sector Fossil
Fuel Combustion
As described in the calculation methodology, total fossil fuel consumption for each year is based on aggregated end-
use sector consumption published by the EIA. The availability of facility-level combustion emissions through EPA's
Greenhouse Gas Reporting Program (GHGRP) has provided an opportunity to better characterize the industrial
62 Refer to the International Bunker Fuels section of the Energy chapter and Annex 3.3 for a description of the methodology for
distinguishing between international and domestic fuel consumption.
63 EIA defines HGL as "a group of hydrocarbons including ethane, propane, normal butane, isobutane, and natural gasoline, and their
associated olefins, including ethylene, propylene, butylene, and isobutylene" (EIA 2021b).
Annex 2
A-39
-------
sector's energy consumption and emissions in the United States, through a disaggregation of ElA's industrial sector
fuel consumption data from select industries.
For EPA's GHGRP 2010 through 2020 reporting years, facility-level fossil fuel combustion emissions reported through
EPA's GHGRP were categorized and distributed to specific industry types by utilizing facility-reported NAICS codes (as
published by the U.S. Census Bureau). As noted previously in this report, the definitions and provisions for reporting
fuel types in EPA's GHGRP include some differences from the Inventory's use of EIA national fuel statistics to meet the
UNFCCC reporting guidelines. The IPCC has provided guidance on aligning facility-level reported fuels and fuel types
published in national energy statistics, which guided this exercise.64
As with previous Inventory reports, this year's effort represents an attempt to align, reconcile, and coordinate the
facility-level reporting of fossil fuel combustion emissions under EPA's GHGRP with the national-level approach
presented in this report. Consistent with recommendations for reporting the Inventory to the UNFCCC, progress was
made on certain fuel types for specific industries and has been included in the Common Reporting Format (CRF) tables
that are submitted to the UNFCCC along with this report.65 The efforts in reconciling fuels focus on standard, common
fuel types (e.g., natural gas, distillate fuel oil) where the fuels in ElA's national statistics aligned well with facility-level
GHGRP data. For these reasons, the current information presented in the CRF tables should be viewed as an initial
attempt at this exercise. Additional efforts will be made for future Inventory reports to improve the mapping of fuel
types, and examine ways to reconcile and coordinate any differences between facility-level data and national
statistics.
This year's analysis includes the full time series presented in the CRF tables. Analyses were conducted linking GHGRP
facility-level reporting with the information published by EIA in its MECS data in order to disaggregate the full 1990
through 2020 time series in the CRF tables. It is believed that the current analysis has led to improvements in the
presentation of data in the Inventory, but further work will be conducted, and future improvements will be realized in
subsequent Inventory reports. This includes incorporating the latest MECS data as it becomes available.
64 See Section 4 "Use of Facility-Level Data in Good Practice National Greenhouse Gas Inventories" of the IPCC meeting report, and
specifically the section on using facility-level data in conjunction with energy data, available at: http://www.ipcc-
nggip.iges.or.jp/public/tb/TFI Technical Bulletin l.pdf.
65 See http://www.epa.gov/climatechange/ghgemissions/usinventoryreport.htnil.
A-40 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-4: 2020 Energy Consumption Data by Fuel Type (TBtu) and Adjusted Energy Consumption Data
1 2 3 4 5 6 7 8 9 10 11 12 13
Total Consumption (TBtu)a
Adjustments (TBtu)b
Total Adjusted
Consumption
(TBtu)
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Bunker Fuel
Unadjusted NEU Consumption
Ind. Trans. Terr.
Total Coal
NO
14.5
536.7
NO
8,229.3
33.5
8,814.0
88.2
8,725.7
Residential Coal
NO
NO
NO
Commercial Coal
14.5
14.5
14.5
Industrial Other Coal
457.9
457.9
9.5
448.5
Transportation Coal
NO
NO
NO
Electric Power Coal
8,229.3
8,229.3
8,229.3
U.S. Territory Coal (bit)
33.5
33.5
33.5
Natural Gas
4,845.5
3,286.3
9,907.3
1,097.4
11,988.9
50.0
31,175.5
730.0
30,445.4
Total Petroleum
884.2
737.4
8,229.0
22,170.8
184.4
235.9
32,441.7
962.6
5,036.2 119.3 3.6
26,319.9
Asphalt & Road Oil
832.3
832.3
832.3
Aviation Gasoline
20.2
20.2
20.2
Distillate Fuel Oil
336.0
227.3
878.5
6,137.8
44.3
71.6
7,695.5
105.0
5.8
7,584.7
Jet Fuel
2,233.8
NA
34.6
2,268.4
563.7
1,704.7
Kerosene
11.9
2.0
1.6
0.5
16.0
16.0
LPG (Propane)
536.3
173.3
6.5
716.1
716.1
HGL
2,885.9
1.7
2,887.6
2,884.5
3.1
Lubricants
107.4
119.3
1.0
227.7
107.4 119.3 1.0
Motor Gasoline
332.9
241.3
13,259.6
74.1
13,907.9
13,907.9
Residual Fuel
1.7
393.6
52.7
49.8
497.8
294.0
203.8
Other Petroleum
AvGas Blend Components
(0.8)
(0.8)
(0.8)
Crude Oil
MoGas Blend Components
Misc. Products
170.7
2.6
173.3
170.7 2.6
Naphtha (<401 deg. F)
354.6
354.6
354.6
Other Oil (>401 deg. F)
217.0
217.0
217.0
Pentanes Plus
354.3
354.3
176.5
177.8
Petroleum Coke
0.1
495.2
87.4
582.7
46.2
536.5
Still Gas
1,404.8
1,404.8
145.4
1,259.4
Special Naphtha
86.6
86.6
86.6
Unfinished Oils
190.5
190.5
190.5
Waxes
9.2
9.2
9.2
Geothermal
54.2
54.2
54.2
Total (All Fuels)
5,729.7
4,038.2
18,673.0
23,268.2
20,456.8
319.4
72,485.3
962.6
5,854.5 119.3 3.6
65,545.3
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-41
-------
Table A-5: 2020 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
14.5
448.5
NO
8,229.3
33.5
8,725.7
NO
1.4
43.0
NO
788.2
3.1
835.6
Residential Coal
NO
NO
NO
NO
Commercial Coal
14.5
14.5
1.4
1.4
Industrial Other Coal
448.5
448.5
43.0
43.0
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
8,229.3
8,229.3
788.2
788.2
U.S. Territory Coal (bit)
33.5
33.5
3.1
3.1
Natural Gas
4,845.5
3,286.3
9,177.2
1,097.4
11,988.9
50.0
30,445.4
256.4
173.9
485.5
58.1
634.3
2.6
1,610.7
Total Petroleum
884.2
737.4
3,192.8
21,088.9
184.4
232.3
26,319.9
59.5
51.6
237.8
1,514.0
16.2
16.9
1,895.9
Asphalt & Road Oil
Aviation Gasoline
20.2
20.2
1.4
1.4
Distillate Fuel Oil
336.0
227.3
872.7
6,032.9
44.3
71.6
7,584.7
24.9
16.9
64.7
447.2
3.3
5.3
562.2
Jet Fuel
1,670.2
NA
34.6
1,704.7
120.6
NA
2.5
123.1
Kerosene
11.9
2.0
1.6
0.5
16.0
0.9
0.1
0.1
+
1.2
LPG (Propane)
536.3
173.3
6.5
716.1
33.7
10.9
0.4
45.0
HGL
1.4
1.7
3.1
0.1
0.1
0.2
Lubricants
Motor Gasoline
332.9
241.3
13,259.6
74.1
13,907.9
23.5
17.0
936.9
5.2
982.7
Residual Fuel
1.7
99.6
52.7
49.8
203.8
0.1
7.5
4.0
3.7
15.3
Other Petroleum
AvGas Blend Components
(0.8)
(0.8)
(0.1)
(0.1)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
177.8
177.8
11.9
11.9
Petroleum Coke
0.1
448.9
87.4
536.5
+
45.8
8.9
54.8
Still Gas
1,259.4
1,259.4
84.0
84.0
Special Naphtha
Unfinished Oils
190.5
190.5
14.2
14.2
Waxes
Geothermal
54.2
54.2
0.4
0.4
Total (All Fuels)
5,729.7
4,038.2
12,818.5
22,186.4
20,456.8
315.8
65,545.3
315.8
226.8
766.3
1,572.0
1,439.0
22.7
4,342.7
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-42 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-6: 2019 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
16.7
517.4
NO
10,181.3
38.6
10,754.0
NO
1.6
49.5
NO
973.5
3.6
1,028.2
Residential Coal
NO
NO
NO
NO
Commercial Coal
16.7
16.7
1.6
1.6
Industrial Other Coal
517.4
517.4
49.5
49.5
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
10,181.3
10,181.3
973.5
973.5
U.S. Territory Coal (bit)
38.6
38.6
3.6
3.6
Natural Gas
5,208.0
3,647.3
9,484.1
1,114.1
11,646.8
71.3
31,171.7
275.5
192.9
501.6
58.9
616.0
3.8
1,648.8
Total Petroleum
975.0
801.3
3,539.7
24,458.3
188.6
232.3
30,195.1
65.9
56.2
265.0
1,754.8
16.2
16.9
2,175.0
Asphalt & Road Oil
Aviation Gasoline
23.4
23.4
1.6
1.6
Distillate Fuel Oil
400.9
278.8
1,021.1
6,393.0
53.9
71.6
8,219.2
29.7
20.7
75.7
474.0
4.0
5.3
609.4
Jet Fuel
2,461.0
NA
34.6
2,495.5
177.7
NA
2.5
180.2
Kerosene
10.8
1.8
1.4
0.5
14.5
0.8
0.1
0.1
+
1.1
LPG (Propane)
563.4
182.0
6.9
752.3
35.4
11.4
0.4
47.3
HGL
51.1
1.7
52.8
3.3
0.1
3.4
Lubricants
Motor Gasoline
336.1
242.7
15,381.1
74.1
16,034.1
23.7
17.1
1,086.5
5.2
1,132.7
Residual Fuel
2.3
192.9
58.8
49.8
303.8
0.2
14.5
4.4
3.7
22.8
Other Petroleum
AvGas Blend Components
(1.2)
(1.2)
(0.1)
(0.1)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
167.9
167.9
11.2
11.2
Petroleum Coke
0.2
545.9
75.9
622.0
+
55.7
7.8
63.5
Still Gas
1,374.7
1,374.7
91.7
91.7
Special Naphtha
Unfinished Oils
135.9
135.9
10.1
10.1
Waxes
Geothermal
52.8
52.8
0.4
0.4
Total (All Fuels)
6,183.1
4,465.3
13,541.3
25,572.4
22,069.4
342.2
72,173.6
341.4
250.7
816.1
1,813.8
1,606.1
24.3
4,852.3
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-43
-------
Table A-7: 2018 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5 6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
18.7
569.0
NO 12,053.0
27.7
12,668.4
NO
1.8
54.4
NO
1,152.9
2.6
1,211.6
Residential Coal
NO
NO
NO
NO
Commercial Coal
18.7
18.7
1.8
1.8
Industrial Other Coal
569.0
569.0
54.4
54.4
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
12,053.0
12,053.0
1,152.9
1,152.9
U.S. Territory Coal (bit)
27.7
27.7
2.6
2.6
Natural Gas
5,174.4
3,638.3
9,334.9
962.2 10,912.1
62.4
30,084.3
273.8
192.5
494.0
50.9
577.4
3.3
1,592.0
Total Petroleum
945.7
734.6
3,550.7
24,556.0 260.4
270.9
30,318.3
64.4
51.5
265.7
1,761.8
22.2
19.6
2,185.3
Asphalt & Road Oil
Aviation Gasoline
22.4
22.4
1.5
1.5
Distillate Fuel Oil
431.0
274.3
1,058.1
6,427.8 80.6
85.4
8,357.2
32.0
20.3
78.5
476.6
6.0
6.3
619.7
Jet Fuel
2,385.1 NA
40.4
2,425.5
172.3
NA
2.9
175.2
Kerosene
8.2
1.3
1.6
0.4
11.6
0.6
0.1
0.1
+
0.9
LPG (Propane)
506.5
176.0
6.7
689.2
31.8
11.1
0.4
43.3
HGL
128.3
1.7
130.0
8.3
0.1
8.4
Lubricants
Motor Gasoline
279.5
205.6
15,527.5
119.1
16,131.8
19.7
14.5
1,097.0
8.4
1,139.7
Residual Fuel
3.1
186.5 78.3
23.8
291.7
0.2
14.0
5.9
1.8
21.9
Other Petroleum
AvGas Blend Components
(1.6)
(1.6)
(0.1)
(0.1)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
112.7
112.7
7.5
7.5
Petroleum Coke
0.4
569.8
101.5
671.6
+
58.2
10.4
68.6
Still Gas
1,445.3
1,445.3
96.4
96.4
Special Naphtha
Unfinished Oils
30.9
30.9
2.3
2.3
Waxes
Geothermal
54.5
54.5
0.4
0.4
Total (All Fuels)
6,120.1
4,391.6
13,454.6
25,518.2 23,279.9
361.1
73,125.5
338.2
245.8
814.1
1,812.8
1,752.9
25.5
4,989.3
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-8: 2017 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5 6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
20.7
614.1
NO 12,622.2
25.0
13,281.9
NO
2.0
58.7
NO
1,207.1
2.3
1,270.0
Residential Coal
NO
NO
NO
NO
Commercial Coal
20.7
20.7
2.0
2.0
Industrial Other Coal
614.1
614.1
58.7
58.7
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
12,622.2
12,622.2
1,207.1
1,207.1
U.S. Territory Coal (bit)
25.0
25.0
2.3
2.3
Natural Gas
4,563.5
3,272.9
8,872.4
798.6 9,555.2
48.1
27,110.6
241.5
173.2
469.5
42.3
505.6
2.5
1,434.6
Total Petroleum
766.1
808.9
3,515.7
24,215.2 217.7
285.2
29,808.8
51.9
56.8
262.2
1,737.7
18.9
20.6
2,148.3
Asphalt & Road Oil
Aviation Gasoline
20.9
20.9
1.4
1.4
Distillate Fuel Oil
327.0
244.1
905.2
6,287.9 54.7
68.8
7,887.7
24.2
18.1
67.1
465.9
4.1
5.1
584.4
Jet Fuel
2,377.2 NA
43.9
2,421.0
171.7
NA
3.2
174.9
Kerosene
8.4
1.2
1.1
0.4
11.2
0.6
0.1
0.1
+
0.8
LPG (Propane)
430.7
155.7
7.1
593.5
27.1
9.8
0.4
37.3
HGL
175.6
1.6
177.2
11.3
0.1
11.4
Lubricants
Motor Gasoline
403.7
295.7
15,302.8
129.1
16,131.3
28.5
20.9
1,081.8
9.1
1,140.4
Residual Fuel
3.8
2.8
219.3 65.8
41.3
333.0
0.3
0.2
16.5
4.9
3.1
25.0
Other Petroleum
AvGas Blend Components
(0.2)
(0.2)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
87.0
87.0
5.8
5.8
Petroleum Coke
0.5
553.0
97.2
650.8
0.1
56.5
9.9
66.4
Still Gas
1,419.0
1,419.0
94.7
94.7
Special Naphtha
Unfinished Oils
76.4
76.4
5.7
5.7
Waxes
Geothermal
54.3
54.3
0.4
0.4
Total (All Fuels)
5,329.6
4,102.4
13,002.2
25,013.8 22,449.5
358.2
70,255.7
293.4
232.0
790.4
1,780.0
1,732.0
25.5
4,853.3
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-45
-------
Table A-9: 2016 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5 6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans. Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
23.7
661.6
NO 12,996.4
35.5
13,717.2
NO
2.3
63.2
NO
1,242.0
3.3
1,310.7
Residential Coal
NO
NO
NO
NO
Commercial Coal
23.7
23.7
2.3
2.3
Industrial Other Coal
661.6
661.6
63.2
63.2
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
12,996.4
12,996.4
1,242.0
1,242.0
U.S. Territory Coal (bit)
35.5
35.5
3.3
3.3
Natural Gas
4,505.8
3,223.5
8,769.1
757.2 10,301.3
63.6
27,620.6
238.4
170.5
463.9
40.1
545.0
3.4
1,461.3
Total Petroleum
799.2
834.5
3,553.5
23,955.2 243.9
267.7
29,654.0
54.4
58.7
265.7
1,717.6
21.5
19.4
2,137.2
Asphalt & Road Oil
Aviation Gasoline
20.5
20.5
1.4
1.4
Distillate Fuel Oil
355.7
266.9
940.0
6,104.1 54.9
80.2
7,801.8
26.4
19.8
69.7
452.4
4.1
5.9
578.3
Jet Fuel
2,297.8 NA
29.6
2,327.4
166.0
NA
2.1
168.1
Kerosene
13.7
2.1
2.3
0.4
18.4
1.0
0.2
0.2
+
1.3
LPG (Propane)
429.9
150.0
7.6
587.5
27.0
9.4
0.5
36.9
HGL
227.0
6.3
233.3
14.6
0.4
15.0
Lubricants
Motor Gasoline
410.8
287.2
15,352.9
105.0
16,155.9
29.0
20.3
1,084.4
7.4
1,141.1
Residual Fuel
4.4
2.1
172.4 70.7
46.2
295.8
0.3
0.2
12.9
5.3
3.5
22.2
Other Petroleum
AvGas Blend Components
(0.3)
(0.3)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
56.5
56.5
3.8
3.8
Petroleum Coke
0.3
591.4
118.3
710.1
+
60.4
12.1
72.5
Still Gas
1,438.6
1,438.6
96.0
96.0
Special Naphtha
Unfinished Oils
8.6
8.6
0.6
0.6
Waxes
Geothermal
54.0
54.0
0.4
0.4
Total (All Fuels)
5,305.1
4,081.8
12,984.2
24,712.4 23,595.6
366.8
71,045.7
292.8
231.5
792.7
1,757.6
1,808.9
26.0
4,909.6
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and
international bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-10: 2015 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
31.1
733.9
NO
14,138.3
35.9
14,939.2
NO
3.0
70.0
NO
1,351.4
3.3
1,427.6
Residential Coal
NO
NO
NO
NO
Commercial Coal
31.1
31.1
3.0
3.0
Industrial Other Coal
733.9
733.9
70.0
70.0
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
14,138.3
14,138.3
1,351.4
1,351.4
U.S. Territory Coal (bit)
35.9
35.9
3.3
3.3
Natural Gas
4,776.9
3,315.6
8,678.5
744.8
9,926.5
57.4
27,499.8
252.7
175.4
459.1
39.4
525.2
3.0
1,454.9
Total Petroleum
939.0
937.9
3,580.1
23,419.0
276.0
303.8
29,455.8
64.6
66.2
268.2
1,678.8
23.7
22.1
2,123.6
Asphalt & Road Oil
Aviation Gasoline
21.1
21.1
1.5
1.5
Distillate Fuel Oil
483.1
315.5
1,018.1
6,154.7
70.4
78.8
8,120.6
35.8
23.4
75.5
456.3
5.2
5.8
602.1
Jet Fuel
2,180.9
NA
36.0
2,217.0
157.5
NA
2.6
160.1
Kerosene
10.1
1.4
1.7
0.1
13.4
0.7
0.1
0.1
+
1.0
LPG (Propane)
445.7
148.0
7.2
601.0
28.0
9.3
0.5
37.8
HGL
242.6
6.2
248.8
15.6
0.4
16.0
Lubricants
Motor Gasoline
468.6
321.4
14,998.5
113.0
15,901.4
33.1
22.7
1,058.8
8.0
1,122.5
Residual Fuel
4.0
56.6
93.9
69.6
224.0
0.3
4.2
7.0
5.2
16.8
Other Petroleum
AvGas Blend Components
(0.3)
(0.3)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
80.9
80.9
5.4
5.4
Petroleum Coke
0.5
600.8
111.7
713.0
0.1
61.3
11.4
72.8
Still Gas
1,332.9
1,332.9
88.9
88.9
Special Naphtha
Unfinished Oils
(17.8)
(17.8)
(1.3)
(1.3)
Waxes
Geothermal
54.3
54.3
0.4
0.4
Total (All Fuels)
5,715.9
4,284.7
12,992.5
24,163.8
24,395.0
397.2
71,949.1
317.3
244.6
797.3
1,718.2
1,900.6
28.4
5,006.5
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-47
-------
Table A-ll: 2014 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
40.2
833.0
NO
16,427.4
37.3
17,338.0
NO
3.8
79.2
NO
1,568.6
3.4
1,655.1
Residential Coal
NO
NO
NO
NO
Commercial Coal
40.2
40.2
3.8
3.8
Industrial Other Coal
833.0
833.0
79.2
79.2
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
16,427.4
16,427.4
1,568.6
1,568.6
U.S. Territory Coal (bit)
37.3
37.3
3.4
3.4
Natural Gas
5,242.5
3,571.9
8,817.9
759.7
8,361.7
60.6
26,814.3
277.7
189.2
467.0
40.2
442.9
3.2
1,420.2
Total Petroleum
1,003.3
558.1
3,568.0
23,254.3
295.5
295.7
28,974.9
68.9
39.4
268.3
1,667.1
25.3
21.4
2,090.4
Asphalt & Road Oil
Aviation Gasoline
21.7
21.7
1.5
1.5
Distillate Fuel Oil
500.0
334.4
1,273.3
5,991.8
82.2
65.6
8,247.3
37.1
24.8
94.4
444.5
6.1
4.9
611.8
Jet Fuel
2,053.3
NA
35.0
2,088.3
148.3
NA
2.5
150.8
Kerosene
13.7
2.0
2.8
0.1
18.7
1.0
0.1
0.2
+
1.4
LPG (Propane)
489.5
160.5
7.2
657.1
30.8
10.1
0.4
41.3
HGL
177.9
6.4
184.3
11.4
0.4
11.9
Lubricants
Motor Gasoline
52.7
205.6
15,103.0
126.7
15,488.0
3.7
14.5
1,066.6
8.9
1,093.8
Residual Fuel
7.9
77.4
95.1
61.9
242.4
0.6
5.8
7.1
4.7
18.2
Other Petroleum
AvGas Blend Components
(0.1)
(0.1)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
44.5
44.5
3.0
3.0
Petroleum Coke
0.5
592.1
118.2
710.8
0.1
60.5
12.1
72.6
Still Gas
1,352.4
1,352.4
90.2
90.2
Special Naphtha
Unfinished Oils
(80.6)
(80.6)
(6.0)
(6.0)
Waxes
Geothermal
54.2
54.2
0.4
0.4
Total (All Fuels)
6,245.7
4,170.3
13,218.9
24,014.0
25,138.7
393.7
73,181.3
346.5
232.4
814.6
1,707.3
2,037.2
28.1
5,166.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-12: 2013 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
41.4
836.8
NO
16,450.6
35.5
17,364.4
NO
3.9
79.5
NO
1,571.3
3.3
1,658.0
Residential Coal
NO
NO
NO
NO
Commercial Coal
41.4
41.4
3.9
3.9
Industrial Other Coal
836.8
836.8
79.5
79.5
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
16,450.6
16,450.6
1,571.3
1,571.3
U.S. Territory Coal (bit)
35.5
35.5
3.3
3.3
Natural Gas
5,022.9
3,379.8
8,512.5
887.3
8,376.3
58.2
26,237.0
266.4
179.2
451.4
47.0
444.2
3.1
1,391.3
Total Petroleum
916.9
580.6
4,075.5
22,604.2
255.2
298.8
28,731.1
62.8
41.1
303.5
1,622.1
22.4
21.7
2,073.5
Asphalt & Road Oil
Aviation Gasoline
22.4
22.4
1.5
1.5
Distillate Fuel Oil
445.1
311.2
1,138.8
5,795.2
55.4
71.9
7,817.7
33.0
23.1
84.5
429.9
4.1
5.3
579.9
Jet Fuel
2,036.1
NA
30.0
2,066.1
147.0
NA
2.2
149.2
Kerosene
8.3
1.0
1.5
0.1
10.9
0.6
0.1
0.1
+
0.8
LPG (Propane)
463.5
151.6
7.1
622.2
29.1
9.5
0.4
39.1
HGL
293.8
6.3
300.1
18.8
0.4
19.3
Lubricants
Motor Gasoline
92.1
606.2
14,542.0
117.5
15,357.8
6.5
42.9
1,028.0
8.3
1,085.7
Residual Fuel
24.4
201.4
77.2
72.9
375.9
1.8
15.1
5.8
5.5
28.2
Other Petroleum
AvGas Blend Components
(0.4)
(0.4)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
47.5
47.5
3.2
3.2
Petroleum Coke
0.4
600.9
122.5
723.7
+
61.4
12.5
73.9
Still Gas
1,370.6
1,370.6
91.4
91.4
Special Naphtha
Unfinished Oils
16.7
16.7
1.2
1.2
Waxes
Geothermal
53.8
53.8
0.4
0.4
Total (All Fuels)
5,939.8
4,001.8
13,424.8
23,491.5
25,135.8
392.5
72,386.3
329.1
224.2
834.4
1,669.1
2,038.3
28.1
5,123.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-49
-------
Table A-13: 2012 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
43.6
822.6
NO
15,821.2
35.7
16,723.1
NO
4.1
78.2
NO
1,511.7
3.3
1,597.3
Residential Coal
NO
NO
NO
NO
Commercial Coal
43.6
43.6
4.1
4.1
Industrial Other Coal
822.6
822.6
78.2
78.2
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
15,821.2
15,821.2
1,511.7
1,511.7
U.S. Territory Coal (bit)
35.7
35.7
3.3
3.3
Natural Gas
4,242.1
2,959.5
8,195.6
779.8
9,286.8
49.2
25,512.9
225.1
157.0
434.9
41.4
492.8
2.6
1,353.8
Total Petroleum
832.6
550.7
3,909.4
22,502.4
214.2
373.0
28,382.3
57.3
39.1
293.7
1,618.2
18.3
27.2
2,053.8
Asphalt & Road Oil
Aviation Gasoline
25.1
25.1
1.7
1.7
Distillate Fuel Oil
429.3
316.1
1,123.3
5,751.5
52.4
63.5
7,736.3
31.8
23.4
83.3
426.4
3.9
4.7
573.6
Jet Fuel
1,984.3
NA
39.1
2,023.3
143.3
NA
2.8
146.1
Kerosene
7.7
1.2
2.0
0.6
11.6
0.6
0.1
0.1
+
0.8
LPG (Propane)
395.6
135.5
7.1
538.2
24.9
8.5
0.4
33.8
HGL
280.1
11.1
291.2
18.0
0.7
18.7
Lubricants
Motor Gasoline
66.1
432.2
14,523.3
131.4
15,153.0
4.7
30.7
1,030.4
9.3
1,075.1
Residual Fuel
31.4
211.1
76.7
127.3
446.5
2.4
15.8
5.8
9.6
33.5
Other Petroleum
AvGas Blend Components
(+)
(+)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
42.5
42.5
2.8
2.8
Petroleum Coke
0.4
649.0
85.1
734.4
+
66.3
8.7
75.0
Still Gas
1,320.2
1,320.2
88.1
88.1
Special Naphtha
Unfinished Oils
60.1
60.1
4.5
4.5
Waxes
Geothermal
53.1
53.1
0.4
0.4
Total (All Fuels)
5,074.7
3,553.8
12,927.6
23,282.2
25,375.3
457.8
70,671.4
282.4
200.3
806.9
1,659.6
2,023.3
33.1
5,005.4
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-14: 2011 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
61.7
905.8
NO
18,035.2
37.3
19,040.0
NO
5.8
86.0
NO
1,722.4
3.4
1,817.6
Residential Coal
NO
NO
NO
NO
Commercial Coal
61.7
61.7
5.8
5.8
Industrial Other Coal
905.8
905.8
86.0
86.0
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
18,035.2
18,035.2
1,722.4
1,722.4
U.S. Territory Coal (bit)
37.3
37.3
3.4
3.4
Natural Gas
4,804.6
3,216.1
7,871.5
733.5
7,712.2
27.1
24,365.0
255.1
170.7
417.9
38.9
409.4
1.4
1,293.5
Total Petroleum
1,033.9
670.1
3,904.0
22,664.7
295.0
391.8
28,959.4
71.1
47.9
293.3
1,631.6
25.8
28.5
2,098.2
Asphalt & Road Oil
Aviation Gasoline
27.1
27.1
1.9
1.9
Distillate Fuel Oil
522.8
391.5
1,227.4
5,767.8
63.7
59.3
8,032.4
38.8
29.0
91.0
427.6
4.7
4.4
595.5
Jet Fuel
2,029.0
NA
47.1
2,076.1
146.5
NA
3.4
149.9
Kerosene
18.5
3.2
3.6
1.1
26.4
1.4
0.2
0.3
0.1
1.9
LPG (Propane)
492.6
142.5
7.3
642.4
31.0
9.0
0.5
40.4
HGL
159.6
9.0
168.6
10.3
0.6
10.8
Lubricants
Motor Gasoline
79.0
455.9
14,575.5
147.7
15,258.1
5.6
32.4
1,035.7
10.5
1,084.2
Residual Fuel
53.7
50.1
258.0
93.1
127.5
582.3
4.0
3.8
19.4
7.0
9.6
43.7
Other Petroleum
AvGas Blend Components
+
+
+
+
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
27.6
27.6
1.8
1.8
Petroleum Coke
0.2
600.3
138.3
738.8
+
61.3
14.1
75.4
Still Gas
1,323.4
1,323.4
88.3
88.3
Special Naphtha
Unfinished Oils
56.1
56.1
4.2
4.2
Waxes
Geothermal
52.3
52.3
0.4
0.4
Total (All Fuels)
5,838.5
3,947.9
12,681.2
23,398.2
26,094.7
456.1
72,416.6
326.2
224.5
797.1
1,670.5
2,158.1
33.4
5,209.8
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Annex 2
A-51
-------
Table A-15: 2010 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
NO
69.7
993.0
NO
19,133.5
34.7
20,230.8
NO
6.6
94.2
NO
1,827.2
3.2
1,931.2
Residential Coal
NO
NO
NO
NO
Commercial Coal
69.7
69.7
6.6
6.6
Industrial Other Coal
993.0
993.0
94.2
94.2
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
19,133.5
19,133.5
1,827.2
1,827.2
U.S. Territory Coal (bit)
34.7
34.7
3.2
3.2
Natural Gas
4,878.1
3,164.7
7,685.4
719.0
7,527.6
27.8
24,002.6
258.9
168.0
407.9
38.2
399.5
1.5
1,274.0
Total Petroleum
1,103.3
697.9
3,912.5
23,031.3
370.3
409.6
29,524.9
75.8
49.9
294.4
1,658.6
31.4
29.9
2,140.1
Asphalt & Road Oil
Aviation Gasoline
27.0
27.0
1.9
1.9
Distillate Fuel Oil
544.4
379.2
1,108.5
5,729.4
79.7
66.4
7,907.6
40.4
28.1
82.3
425.2
5.9
4.9
586.8
Jet Fuel
2,096.4
NA
36.6
2,133.0
151.4
NA
2.6
154.0
Kerosene
29.1
4.8
7.3
7.5
48.7
2.1
0.4
0.5
0.5
3.6
LPG (Propane)
529.8
140.0
7.5
677.3
33.3
8.8
0.5
42.6
HGL
148.0
17.6
165.5
9.5
1.1
10.7
Lubricants
Motor Gasoline
111.8
559.7
14,898.8
112.9
15,683.2
7.9
39.8
1,059.3
8.0
1,115.0
Residual Fuel
61.7
25.9
272.2
154.1
168.7
682.5
4.6
1.9
20.4
11.6
12.7
51.2
Other Petroleum
AvGas Blend Components
(0.2)
(0.2)
(+)
(+)
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
78.4
78.4
5.2
5.2
Petroleum Coke
0.3
633.0
136.6
770.0
+
64.6
13.9
78.6
Still Gas
1,324.0
1,324.0
88.3
88.3
Special Naphtha
Unfinished Oils
28.0
28.0
2.1
2.1
Waxes
Geothermal
51.9
51.9
0.4
0.4
Total (All Fuels)
5,981.4
3,932.2
12,590.9
23,750.2
27,083.3
472.1
73,810.2
334.8
224.5
796.4
1,696.8
2,258.6
34.6
5,345.7
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-16: 2005 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
8.4
97.0
1,246.0
NO
20,737.2
32.7
22,121.4
0.8
9.3
117.8
NO
1,982.8
3.0
2,113.7
Residential Coal
8.4
8.4
0.8
0.8
Commercial Coal
97.0
97.0
9.3
9.3
Industrial Other Coal
1,246.0
1,246.0
117.8
117.8
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
20,737.2
20,737.2
1,982.8
1,982.8
U.S. Territory Coal (bit)
32.7
32.7
3.0
3.0
Natural Gas
4,946.4
3,073.2
7,330.7
623.9
6,014.5
24.3
22,013.1
262.2
162.9
388.6
33.1
318.9
1.3
1,167.0
Total Petroleum
1,366.4
760.7
4,599.1
25,369.9
1,222.1
712.4
34,030.6
95.9
54.9
345.0
1,825.5
98.0
51.6
2,470.9
Asphalt & Road Oil
Aviation Gasoline
35.4
35.4
2.4
2.4
Distillate Fuel Oil
769.1
402.9
1,119.4
6,193.8
114.5
136.5
8,736.3
57.4
30.1
83.6
462.6
8.5
10.2
652.5
Jet Fuel
2,620.4
NA
65.5
2,685.9
189.2
NA
4.7
194.0
Kerosene
83.8
21.6
39.1
5.8
150.2
6.1
1.6
2.9
0.4
11.0
LPG (Propane)
513.5
131.6
28.2
673.3
32.3
8.3
1.8
42.3
HGL
281.9
73.6
355.4
18.1
4.7
22.8
Lubricants
Motor Gasoline
88.6
689.5
16,235.7
200.2
17,213.9
6.3
48.8
1,150.1
14.2
1,219.4
Residual Fuel
115.8
223.2
256.4
876.5
230.9
1,702.8
8.7
16.8
19.3
65.8
17.3
127.9
Other Petroleum
AvGas Blend Components
8.3
8.3
0.6
0.6
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
98.9
98.9
6.6
6.6
Petroleum Coke
0.3
706.6
231.1
938.0
+
72.1
23.6
95.8
Still Gas
1,429.4
1,429.4
95.4
95.4
Special Naphtha
Unfinished Oils
2.8
2.8
0.2
0.2
Waxes
Geothermal
50.1
50.1
0.5
0.5
Total (All Fuels)
6,321.2
3,930.9
13,175.8
25,993.8
28,024.0
769.4
78,215.1
358.9
227.1
851.5
1,858.6
2,400.1
55.9
5,752.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Annex 2
A-53
-------
Table A-17: 2000 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
11.4
91.9
1,361.6
NO
20,220.2
5.2
21,690.2
1.1
8.8
128.5
NO
1,926.4
0.5
2,065.2
Residential Coal
11.4
11.4
1.1
1.1
Commercial Coal
91.9
91.9
8.8
8.8
Industrial Other Coal
1,361.6
1,361.6
128.5
128.5
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
20,220.2
20,220.2
1,926.4
1,926.4
U.S. Territory Coal (bit)
5.2
5.2
0.5
0.5
Natural Gas
5,104.6
3,251.5
8,659.3
672.0
5,293.4
12.7
22,993.5
270.8
172.5
459.4
35.7
280.8
0.7
1,219.8
Total Petroleum
1,425.4
766.7
3,753.8
24,295.9
1,144.1
575.2
31,961.3
99.8
55.3
282.1
1,756.6
88.5
41.3
2,323.5
Asphalt & Road Oil
Aviation Gasoline
36.3
36.3
2.5
2.5
Distillate Fuel Oil
775.2
420.7
1,000.1
5,442.4
174.7
87.5
7,900.6
58.0
31.5
74.8
406.9
13.1
6.5
590.7
Jet Fuel
2,698.9
NA
68.6
2,767.5
194.9
NA
5.0
199.9
Kerosene
94.6
29.7
15.6
2.4
142.2
6.9
2.2
1.1
0.2
10.4
LPG (Propane)
555.6
150.6
11.9
718.1
34.9
9.5
0.7
45.1
HGL
393.8
91.8
485.6
25.2
5.9
31.1
Lubricants
Motor Gasoline
74.1
249.9
15,663.0
186.3
16,173.3
5.3
17.8
1,118.2
13.3
1,154.6
Residual Fuel
91.6
190.3
443.5
870.8
138.6
1,734.8
6.9
14.3
33.3
65.4
10.4
130.3
Other Petroleum
AvGas Blend Components
3.8
3.8
0.3
0.3
Crude Oil
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
172.9
172.9
11.6
11.6
Petroleum Coke
0.2
697.3
98.6
796.2
+
71.2
10.1
81.3
Still Gas
1,431.2
1,431.2
95.5
95.5
Special Naphtha
Unfinished Oils
(401.2)
(401.2)
(29.7)
(29.7)
Waxes
Geothermal
48.1
48.1
0.5
0.5
Total (All Fuels)
6,541.4
4,110.2
13,774.8
24,967.9
26,705.8
593.0
76,693.0
371.7
236.5
869.9
1,792.2
2,296.2
42.4
5,609.0
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-18: 1995 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
17.5
116.8
1,557.0
NO
17,466.3
4.7
19,162.2
1.7
11.2
147.2
NO
1,659.9
0.4
1,820.4
Residential Coal
17.5
17.5
1.7
1.7
Commercial Coal
116.8
116.8
11.2
11.2
Industrial Other Coal
1,557.0
1,557.0
147.2
147.2
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
17,466.3
17,466.3
1,659.9
1,659.9
U.S. Territory Coal (bit)
4.7
4.7
0.4
0.4
Natural Gas
4,954.2
3,096.0
8,725.9
724.0
4,302.0
21,802.0
262.8
164.2
462.8
38.4
228.2
1,156.4
Total Petroleum
1,259.3
724.1
3,752.0
21,528.1
754.5
323.7
28,341.7
88.7
52.4
279.6
1,542.3
58.7
23.3
2,045.0
Asphalt & Road Oil
Aviation Gasoline
39.6
39.6
2.7
2.7
Distillate Fuel Oil
789.7
418.0
964.1
4,383.3
108.0
62.5
6,725.6
58.4
30.9
71.3
324.2
8.0
4.6
497.4
Jet Fuel
2,427.1
NA
57.2
2,484.4
172.1
NA
4.1
176.2
Kerosene
74.3
22.1
15.4
2.0
113.9
5.4
1.6
1.1
0.1
8.3
LPG (Propane)
395.3
108.9
17.8
521.9
24.9
6.8
1.1
32.8
HGL
277.8
35.6
313.5
17.8
2.3
20.0
Lubricants
Motor Gasoline
33.5
370.5
14,273.1
84.5
14,761.5
2.4
26.3
1,013.1
6.0
1,047.7
Residual Fuel
141.5
284.7
387.3
566.0
81.9
1,461.3
10.6
21.4
29.1
42.5
6.1
109.7
Other Petroleum
AvGas Blend Components
5.3
5.3
0.4
0.4
Crude Oil
14.5
14.5
1.1
1.1
MoGas Blend Components
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
170.3
170.3
11.4
11.4
Petroleum Coke
0.1
600.7
80.6
681.4
+
61.3
8.2
69.6
Still Gas
1,369.5
1,369.5
91.4
91.4
Special Naphtha
Unfinished Oils
(320.9)
(320.9)
(23.8)
(23.8)
Waxes
Geothermal
45.6
45.6
0.4
0.4
Total (All Fuels)
6,231.0
3,936.9
14,034.9
22,252.1
22,568.4
328.4
69,351.5
353.1
227.8
889.7
1,580.7
1,947.2
23.7
5,022.1
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
Annex 2
A-55
-------
Table A-19: 1990 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Adjusted Consumption (TBtu)a
Emissions' (MMT CO2 Eq.) from Energy Use
Fuel Type
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Res.
Comm.
Ind.
Trans.
Elec.
Terr.
Total
Total Coal
31.1
124.5
1,668.2
NO
16,261.0
5.4
18,090.1
3.0
12.0
157.8
NO
1,546.5
0.5
1,719.8
Residential Coal
31.1
31.1
3.0
3.0
Commercial Coal
124.5
124.5
12.0
12.0
Industrial Other Coal
1,668.2
1,668.2
157.8
157.8
Transportation Coal
NO
NO
NO
NO
Electric Power Coal
16,261.0
16,261.0
1,546.5
1,546.5
U.S. Territory Coal (bit)
5.4
5.4
0.5
0.5
Natural Gas
4,486.6
2,679.6
7,712.8
679.2
3,308.5
18,866.7
237.8
142.0
408.8
36.0
175.4
1,000.0
Total Petroleum
1,375.8
1,022.6
3,846.2
19,974.7
1,289.4
294.8
27,803.5
97.8
74.3
287.1
1,432.9
97.5
21.2
2,010.9
Asphalt & Road Oil
Aviation Gasoline
45.0
45.0
3.1
3.1
Distillate Fuel Oil
959.3
525.5
1,098.3
3,554.8
96.5
56.4
6,290.8
70.9
38.9
81.2
262.9
7.1
4.2
465.2
Jet Fuel
2,587.7
NA
48.6
2,636.3
184.1
NA
3.5
187.5
Kerosene
63.9
11.8
12.3
2.0
90.0
4.7
0.9
0.9
0.1
6.6
LPG (Propane)
352.6
102.4
22.9
477.9
22.2
6.4
1.4
30.0
HGL
227.1
33.4
260.5
14.5
2.1
16.6
Lubricants
Motor Gasoline
153.0
254.8
13,464.1
75.9
13,947.7
10.9
18.1
958.9
5.4
993.3
Residual Fuel
229.8
364.1
300.3
1,162.6
78.5
2,135.3
17.3
27.3
22.6
87.3
5.9
160.3
Other Petroleum
AvGas Blend Components
0.2
0.2
+
+
Crude Oil
50.9
50.9
3.8
3.8
MoGas Blend Components
53.7
53.7
3.8
3.8
Misc. Products
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
126.1
126.1
8.4
8.4
Petroleum Coke
591.2
30.4
621.5
60.4
3.1
63.5
Still Gas
1,436.5
1,436.5
95.8
95.8
Special Naphtha
Unfinished Oils
(369.0)
(369.0)
(27.3)
(27.3)
Waxes
Geothermal
52.7
52.7
0.5
0.5
Total (All Fuels)
5,893.5
3,826.6
13,227.1
20,654.0
20,911.6
300.2
64,813.0
338.6
228.3
853.7
1,468.9
1,820.0
21.7
4,731.2
+ Does not exceed 0.05 TBtu or 0.05 MMT C02 Eq.
NO (Not Occurring)
NA (Not Available)
a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-20), and international
bunker fuel consumption (see Table A-21).
b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.
Note: Parentheses indicate negative values.
A-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-20: Unadjusted Non-Energy Fuel Consumption (TBtu)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Industry
4,644.2
5,154.8
5,575.9
5,486.8
4,735.1
4,663.1
4,599.2
4,829.2
4,724.9
5,022.6
5,106.3
5,378.9
5,789.3
5,880.6
5,854.5
Industrial Coking Coal
0.0
37.8
53.5
80.4
64.8
60.8
132.5
119.9
49.3
122.4
89.6
113.0
124.8
113.4
78.8
Industrial Other Coal
7.6
10.5
11.5
11.0
9.6
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
Natural Gas to Chemical
Plants, Other Uses
305.9
371.0
401.7
270.4
310.0
314.0
319.3
324.6
330.6
431.8
532.0
631.1
730.8
732.2
730.0
Asphalt & Road Oil
1,170.2
1,178.2
1,275.7
1,323.2
877.8
859.5
826.7
783.3
792.6
831.7
853.4
849.2
792.8
843.9
832.3
HGL
1,302.2
1,651.6
1,759.3
1,659.5
1,899.9
1,954.4
1,983.7
2,155.2
2,142.8
2,216.8
2,257.1
2,329.7
2,677.4
2,758.8
2,884.5
Lubricants
186.3
177.8
189.9
160.2
135.9
127.4
118.3
125.1
130.7
142.1
135.1
124.9
122.0
118.3
107.4
Pentanes Plus
125.2
169.0
171.6
98.1
77.7
27.3
42.2
47.1
44.2
80.2
56.1
86.4
111.8
166.6
176.5
Naphtha (<401 deg. F)
347.8
373.0
613.5
698.7
490.6
487.3
453.9
517.8
442.6
428.1
420.0
436.2
447.1
396.7
354.6
Other Oil (>401 deg. F)
753.9
801.0
722.2
708.0
452.5
388.5
287.2
223.9
247.2
229.0
222.5
262.9
239.1
234.1
217.0
Still Gas
36.7
47.9
17.0
67.7
147.8
163.6
160.6
166.7
164.5
162.2
166.1
163.8
166.9
158.7
145.4
Petroleum Coke
123.1
120.6
98.7
186.9
61.0
62.4
67.8
62.4
61.4
62.5
61.2
57.0
58.9
56.4
46.2
Special Naphtha
107.1
70.8
97.4
62.5
26.1
22.6
14.7
100.0
106.1
99.3
93.6
100.3
92.0
95.6
86.6
Other (Wax/Misc.)
Distillate Fuel Oil
7.0
8.0
11.7
16.0
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
5.8
Waxes
33.3
40.6
33.1
31.4
17.1
15.1
15.3
16.5
14.8
12.4
12.8
10.2
12.4
10.4
9.2
Miscellaneous Products
137.8
97.1
119.2
112.8
158.7
164.7
161.6
171.2
182.7
188.9
191.3
198.8
198.0
180.2
170.7
Transportation
176.0
167.9
179.4
151.3
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.0
131.3
119.3
Lubricants
176.0
167.9
179.4
151.3
154.8
148.4
135.4
143.4
149.4
162.8
154.4
142.0
137.0
131.3
119.3
U.S. Territories
50.8
55.4
140.8
114.9
27.4
14.6
17.6
10.5
10.7
10.3
10.5
3.5
3.6
3.6
3.6
Lubricants
0.7
2.0
3.1
4.6
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Other Petroleum (Misc.
Prod.)
50.1
53.4
137.7
110.3
26.4
13.6
16.6
9.5
9.6
9.3
9.5
2.4
2.5
2.6
2.6
Total
4,871.1
5,378.2
5,896.1
5,753.1
4,917.3
4,826.1
4,752.2
4,983.1
4,884.9
5,195.8
5,271.1
5,524.3
5,929.8
6,015.5
5,977.4
Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for
in the Industrial Processes and Product Use chapter.
Table A-21: International Bunker Fuel Consumption (TBtu)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Aviation Jet Fuel
541.8
705.1
881.5
854.4
866.4
920.8
917.2
932.5
988.8
1,023.3
1,052.1
1,104.2
1,147.7
1,147.0
563.7
Marine Residual Fuel Oil
715.7
523.2
444.1
581.0
619.8
518.4
459.5
379.8
369.2
406.8
450.7
445.3
417.6
336.2
294.0
Marine Distillate Fuel Oil
158.0
125.7
85.9
126.9
128.2
107.4
91.7
75.4
82.0
113.5
117.5
121.3
134.4
136.3
105.0
Total
1,415.5
1,354.0
1,411.4
1,562.3
1,614.4
1,546.6
1,468.3
1,387.7
1,440.0
1,543.6
1,620.3
1,670.8
1,699.7
1,619.5
962.6
Note: Further information on the calculation of international bunker fuel consumption of aviation jet fuel is provided in Annex 3.3 Methodology for Estimating Emissions from
Commercial Aircraft Jet Fuel Consumption.
Annex 2
A-57
-------
Table A-22: C Content Coefficients by Year (MMT C/QBtu)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
26.21
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
26.21
25.53
25.57
25.63
25.60
25.58
25.57
25.57
25.58
25.57
25.57
25.57
25.56
25.59
25.59
25.60
25.81
25.79
25.74
25.79
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
26.07
26.13
25.94
25.92
25.98
26.08
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
26.08
26.12
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
25.14
14.46
14.47
14.47
14.46
14.48
14.48
14.47
14.46
14.45
14.43
14.43
14.43
14.43
14.43
14.43
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
20.55
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
18.86
20.17
20.17
20.39
20.37
20.24
20.22
20.22
20.23
20.23
20.22
20.21
20.21
20.22
20.22
20.22
19.40
19.34
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.70
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
19.96
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.15
17.39
17.43
17.48
17.47
17.59
17.54
17.51
17.49
17.54
17.55
17.55
17.59
17.60
17.65
17.66
17.10
17.12
17.06
17.03
16.91
16.83
16.85
16.87
16.85
16.87
16.84
16.82
16.80
16.82
16.77
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
20.20
19.42
19.36
19.47
19.32
19.39
19.38
19.35
19.28
19.26
19.25
19.26
19.28
19.27
19.27
19.27
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
20.48
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
20.15
20.21
20.22
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
20.31
19.42
19.36
19.33
19.36
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
19.46
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
18.55
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
20.17
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
18.20
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
19.74
Coal
Residential Coal3
Commercial Coal
Industrial Coking Coal
Industrial Other Coal
Electric Power Coalc
U.S. Territory Coal (bit)
Natural Gas
Pipeline Natural Gasd
Petroleum
Asphalt & Road Oil
Aviation Gasoline
Distillate Fuel Oil No.
2b,d
Jet Fueld
Kerosene
LPG (Propane)
HGL (Energy Use)d
HGL (Non-Energy Use)d
Lubricants
Motor Gasolined
Residual Fuel Oil No.
6a,b
Other Petroleum
AvGas Blend
Components
Crude Oild
MoGas Blend
Components0'd
Misc. Productse
Other Petroleum
Liquids
Naphtha (<401 deg. F)
Other Oil (>401 deg. F)
Pentanes Plus
Petroleum Coke
Still Gas
Special Naphtha
A-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Unfinished Oilsd 20.15 20.21 20.22 20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31 20.31
Waxes
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
19.80
Geothermalf
Flash Steam
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
2.18
Dry Steam
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
3.22
Binary
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Binary/Flash Steam
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
a EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore, the
number cited here is developed from commercial/institutional consumption.
b Distillate fuel oil No. 2 and residual fuel oil No. 6 are the only fuel oils used in the C02 from fossil fuel combustion calculations.
c Content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of
heat released when a fuel is burned. Coal, crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and
hydrogen combine with oxygen in the air to produce C02 and water. Some of the energy released in burning goes into transforming the water into steam and is usually lost. The
amount of heat spent in transforming the water into steam is counted as part of gross heat content. Lower heating value (net heating value), in contrast, does not include the
heat spent in transforming the water into steam. Using a simplified methodology based on International Energy Agency defaults, higher heating value can be converted to
lower heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by 0.90. Carbon content coefficients are presented in higher
heating value because U.S. energy statistics are reported by higher heating value.
d C contents vary annually based on changes in fuel composition.
e The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do not contain carbon (EIA 2019).
f C contents based on geotype (i.e., flash steam and dry steam) were obtained from EPA's Emissions & Generation Resource Integrated Database (eGRID) 2019 Technical Support
Document (EPA 2020a). C contents were obtained in pounds C02/megawatt hour and were applied to net generation by geotype (in megawatt hours) from EIA (2022). C
contents were converted to MMT Carbon/QBtu in this table.
Source: Non-variable C coefficients from EIA (2009), EPA (2010), and EPA (2020). Coal C content coefficients calculated from USGS (1998), PSU (2010), Gunderson (2019), IGS
(2019), ISGS (2019), EIA (1990 through 2001), EIA (2001 through 2021a), and EIA (2001 through 2021b); pipeline natural gas C content coefficients calculated from EIA (2022)
and EPA (2010); petroleum carbon contents from EPA (2010), EIA (1994), EIA (2009), EPA (2020), and ICF (2020). See Annex 2.2 for information on how these C content
coefficients are calculated.
Annex 2
A-59
-------
Table A-23: CO2 Content Coefficients by Year (MMT CCh/QBtu)
Fuel Type
1990
1995
2000
2005
2010 2011 2012 2013 2014 2015 2016 2017
2018 2019
2020
Coal
Residential Coal3 96.02 95.79 95.33 95.47 94.42 94.65 94.90 94.98 94.91 95.27 95.38 95.65 95.68 95.72 96.10
Commercial Coal 96.02 95.79 95.33 95.47 94.42 94.65 94.90 94.98 94.91 95.27 95.38 95.65 95.68 95.72 96.10
Industrial Coking Coal 94.62 93.74 93.97 93.87 93.80 93.77 93.76 93.79 93.74 93.75 93.75 93.73 93.85 93.84 93.87
Industrial Other Coal 95.11 94.55 94.37 94.58 94.83 94.90 95.10 95.06 95.13 95.35 95.46 95.55 95.63 95.59 95.80
Electric Power Coalc 95.11 95.03 95.27 95.61 95.50 95.50 95.55 95.51 95.48 95.58 95.57 95.63 95.65 95.62 95.78
U.S. Territory Coal (bit) 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18 92.18
Natural Gas
Pipeline Natural Gasd 53.00 53.04 53.05 53.01 53.08 53.09 53.06 53.03 52.97 52.91 52.91 52.92 52.92 52.89 52.91
Petroleum
Asphalt & Road Oil 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36 75.36
Aviation Gasoline 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14 69.14
Distillate Fuel Oil No. 2bd 73.96 73.96 74.76 74.69 74.21 74.14 74.14 74.18 74.18 74.15 74.12 74.09 74.15 74.15 74.13
Jet Fueld 71.13 70.91 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22 72.22
Kerosene 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20 73.20
LPG (Propane) 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87 62.87
HGL (Energy Use)d 63.75 63.92 64.09 64.05 64.48 64.31 64.20 64.14 64.32 64.33 64.34 64.49 64.52 64.73 64.74
HGL (Non-Energy Use)d 62.69 62.77 62.55 62.44 62.00 61.72 61.78 61.85 61.77 61.87 61.74 61.68 61.60 61.68 61.47
Lubricants 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06 74.06
Motor Gasolined 71.22 70.99 71.39 70.84 71.10 71.06 70.95 70.69 70.62 70.58 70.62 70.69 70.66 70.66 70.66
Residual Fuel Oil No. 6a b 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09 75.09
Other Petroleum
AvGas Blend Components 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19 69.19
Crude Oild 73.87 74.09 74.13 74.49 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45
MoGas Blend
Components0'd 71.22 70.98 70.87 71.00 71.34 71.34 71.34 71.34 71.34 71.34 71.34 71.34 71.34 71.34 71.34
Misc. Productse 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
Other Petroleum Liquids 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33 73.33
Naphtha (<401 deg. F) 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02 68.02
Other Oil (>401 deg. F) 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96 73.96
Pentanes Plus 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88 66.88
Petroleum Coke 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11 102.11
Still Gas 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72 66.72
Special Naphtha 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37 72.37
A-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Unfinished Oilsd 73.87 74.09 74.13 74.49 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45 74.45
Waxes 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58 72.58
Geothermalf
Flash Steam 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98 7.98
Dry Steam 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81 11.81
a EIA discontinued collection of residential sector coal consumption data in 2008, because consumption of coal in the residential sector is extremely limited. Therefore, the
number cited here is developed from commercial/institutional consumption.
b Distillate fuel oil No. 2 and residual fuel oil No. 6 are the only fuel oils used in the C02 from fossil fuel combustion calculations.
c Content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of
heat released when a fuel is burned. Coal, crude oil, and natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and
hydrogen combine with oxygen in the air to produce C02 and water. Some of the energy released in burning goes into transforming the water into steam and is usually lost. The
amount of heat spent in transforming the water into steam is counted as part of gross heat content. Lower heating value (net heating value), in contrast, does not include the
heat spent in transforming the water into steam. Using a simplified methodology based on International Energy Agency defaults, higher heating value can be converted to
lower heating value for coal and petroleum products by multiplying by 0.95 and for natural gas by multiplying by 0.90. C02 content coefficients are presented in higher heating
value because U.S. energy statistics are reported by higher heating value.
d C contents vary annually based on changes in fuel composition.
e The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do not contain carbon (EIA 2019).
f C contents based on geotype (i.e., flash steam and dry steam) were obtained from EPA's Emissions & Generation Resource Integrated Database (eGRID) 2019 Technical Support
Document (EPA 2020a). C contents were obtained in pounds C02/megawatt hour and were applied to net generation by geotype (in megawatt hours) from EIA (2022). C02
contents for binary and binary/flash geotypes are zero and have been excluded from this table.
Notes: C02 content coefficients calculated based on C content coefficients in Table A-22. Coefficients assume 100% oxidation of C to C02. See Annex 2.2 for information on how
C content coefficients are calculated.
Table A-24: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours)
End-Use Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Residential
924
1,043
1,192
1,359
1,446
1,423
1,374
1,394
1,407
1,403
1,410
1,377
1,466
1,437
1,460
Commercial
838
953
1,159
1,275
1,330
1,328
1,327
1,337
1,352
1,361
1,367
1,353
1,381
1,360
1,287
Industrial
1,070
1,163
1,235
1,169
1,103
1,124
1,123
1,129
1,136
1,128
1,117
1,125
1,145
1,146
1,098
Transportation3
5
5
5
8
8
8
8
8
9
9
9
10
11
12
12
Total
2,837
3,164
3,592
3,811
3,887
3,883
3,832
3,868
3,903
3,900
3,902
3,864
4,003
3,954
3,857
a Includes electricity used for electric vehicle charging in the residential and commercial sectors.
Note: Does not include the U.S. Territories.
Source: Retail sales of electricity to end-users obtained from EIA (2022). Industrial electricity consumption includes direct use.
Annex 2
A-61
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Table A-25: Electric Power Generation by Fuel Type (Percent)
Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Coal
54.1%
52.7%
53.3%
51.1%
46.0%
43.5%
38.6%
40.2%
39.9%
34.2%
31.4%
30.9%
28.4%
24.2%
19.9%
Natural Gas
10.7%
13.1%
14.2%
17.5%
22.7%
23.5%
29.1%
26.4%
26.3%
31.6%
32.7%
30.9%
34.0%
37.3%
39.5%
Nuclear
19.9%
21.1%
20.7%
20.0%
20.3%
20.0%
19.8%
20.2%
20.3%
20.4%
20.6%
20.8%
20.1%
20.4%
20.5%
Renewables
11.3%
10.9%
8.8%
8.3%
10.0%
12.2%
11.9%
12.5%
12.8%
13.0%
14.7%
16.8%
16.8%
17.6%
19.5%
Petroleum
4.1%
2.1%
2.9%
3.0%
0.9%
0.7%
0.5%
0.6%
0.7%
0.7%
0.6%
0.5%
0.6%
0.4%
0.4%
Other Gases3
+%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
Net Electricity
Generation (Billion
kWh)b
2,905
3,197
3,643
3,902
3,971
3,947
3,888
3,901
3,936
3,917
3,917
3,877
4,017
3,963
3,849
+ Does not exceed 0.05 percent.
a Other gases include blast furnace gas, propane gas, and other manufactured and waste gases derived from fossil fuels.
b Represents net electricity generation from the electric power sector. Excludes net electricity generation from commercial and industrial combined-heat-and-power and
electricity-only plants. Net electricity generation differs from the total presented in Table A-24 (i.e., end-use consumption of electricity) due to electricity transmitted across
U.S. borders, as well as transmission and distribution losses.
Note: Does not include electricity generation from purchased steam as the fuel used to generate the steam cannot be determined. Does not include non-renewable waste (i.e.,
municipal solid waste from non-biogenic sources, and tire-derived fuels).
Source: EIA (2022).
Table A-26: Geothermal Net Generation by Geotype (Billion Kilowatt-Hours)
Geotype
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Binary
0.08
0.28
0.24
0.68
2.41
2.16
2.43
2.75
3.12
3.36
3.62
3.56
3.84
4.34
4.22
Flash Steam
6.15
7.63
7.43
7.93
6.83
7.17
7.02
7.03
6.92
7.00
6.65
6.69
6.39
5.92
6.05
Dry Steam
9.21
5.47
6.43
6.09
5.98
5.98
6.11
6.00
5.84
5.56
5.55
5.67
5.73
5.21
5.61
Total
15.43
13.38
14.09
14.69
15.22
15.32
15.56
15.77
15.88
15.92
15.83
15.93
15.97
15.47
15.89
Source: EIA (2021).
A-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
Browning, L. (2018) Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles.
Technical Memo, October 2018.
EIA (2022) Monthly Energy Review. February 2022, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2022/02).
EIA (2021) Form EIA-923 detailed data with previous form data (EIA-906/920), Energy Information Administration, U.S.
Department of Energy. Washington, DC. DOE/EIA. September 2021.
EIA (2001 through 2021a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA-0584.
EIA (2001 through 2021b) Annual Coal Distribution Report, Energy Information Administration, U.S. Department of
Energy. Washington, D.C. DOE/EIA.
EIA (2009) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2008).
EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, D.C. November 1994. DOE/EIA 0573.
EPA (2020) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel C02
Emission Factors - Memo.
EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
FHWA (1996 through 2021) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FHWA-PL-96-023-annual. Available online at:
http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
UNFCCC (2014) Report of the Conference of the Parties on its nineteenth session, held in Warsaw from 11 to 23
November 2013. (FCCC/CP/2013/10/Add.3). January 31, 2014. Available online at:
http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf#page=2.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
Annex 2
A-63
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2.2. Methodology for Estimating the Carbon Content of Fossil Fuels
This sub-annex presents the background and methodology for estimating the carbon (C) content of fossil fuels
combusted in the United States. The C content of a particular fossil fuel represents the maximum potential emissions to
the atmosphere if all C in the fuel is oxidized during combustion. The C content coefficients used in this report were
developed using methods first outlined in the U.S. Energy Information Administration's (EIA) Emissions of Greenhouse
Gases in the United States: 1987-1992 (1994) and were developed primarily by EIA. EPA has updated many of the C
content coefficients based on carbon dioxide (C02) emission factors developed for the Mandatory Reporting of
Greenhouse Gases Rule, signed in September 2009 (EPA 2009b, 2010). In addition, EPA has revised many of the C
content coefficients to vary annually across the time series to account for the annual variability in carbon content (or
composition) of each fuel type as it is consumed in the United States (ICF 2020; USGS 1998; PSU 2010; Gunderson 2019;
IGS 2019; ISGS 2019; Martel and Angello 1977; ASTM 1985; NIPER 1990 through 2009; Green & Perry ed. 2008;
Wauquier ed. 1995; EPA (2009b; 2010; 2013; 2020a); and EIA (1994; 2008a; 2009a; 2010; 2021b; 1990 through 2001;
2001 through 2021a; 2001 through 2021b)). This sub-annex presents a time-series analysis of changes in U.S. C content
coefficients for coal, petroleum products, and natural gas. A summary of C content coefficients used in this report
appears in Table A-22.
Though the methods for estimating C contents for coal, natural gas, and petroleum products differ in their details, they
each follow the same basic approach. First, because C coefficients are presented in terms of mass per unit energy (i.e.,
million metric tons C per quadrillion Btu or MMT C/QBtu), those fuels that are typically described in volumetric units (i.e.,
petroleum products and natural gas) are converted to units of mass using an estimated density. Second, C contents are
derived from fuel sample data, using descriptive statistics to estimate the C share of the fuel by weight. The heat content
of the fuel is then estimated based on the sample data, or where sample data are unavailable or unrepresentative, by
default values that reflect the characteristics of the fuel as defined by market requirements. A discussion of each fuel
appears below.
The C content of coal is described first; approximately one-fifth of all U.S. C emissions from fossil fuel combustion are
associated with coal consumption. The methods and sources for estimating the C content of natural gas are provided
next. Approximately one-third of U.S. greenhouse gas emissions from fossil fuel combustion are attributable to natural
gas consumption. Finally, this sub-annex examines C contents of petroleum products. U.S. energy use statistics account
for more than 20 different petroleum products.
A-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Coal
Although the IPCC (2006) guidelines provide C contents for coal according to rank, it was necessary to develop C content
coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the C
content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each
consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies
over time. A time series of C contents by coal rank and consuming sector appears in Table A-27.66
Methodology
The methodology for developing C contents for coal by consuming sector consists of four steps. An additional step has
been taken to calculate C contents by coal rank to facilitate comparison with IPCC default values.
Step 1: Determine Carbon Contents by Rank and by State of Origin
Carbon contents by rank and state of origin are estimated on the basis of 8,672 coal samples, 6,588 of which were
collected by the U.S. Geological Survey (USGS) (1998), 504 samples that come from the Pennsylvania State University
database (PSU 2010), and the remainder from individual State Geological Surveys. Samples obtained directly from
individual State Geological Surveys include 908 samples from the Montana Bureau of Mines & Geology (Gunderson
2019), 745 samples from the Indiana Geological Survey Coal Quality Database (IGS 2019), and 460 samples from the
Illinois State Geological Survey (ISGS 2019). Because the data obtained directly from the State Geological Surveys for
these three states included both samples collected by the USGS and additional samples, these data were used to
determine C content coefficients for these states instead of the USGS and Pennsylvania State University data.
These coal samples are classified according to rank and state of origin. For each rank in each state, the average heat
content and C content of the coal samples are calculated based on the proximate (heat) and ultimate (percent carbon)
analyses of the samples. Dividing the C content (reported in pounds of C02) by the heat content (reported in million Btu
or MMBtu) yields an average C content coefficient. This coefficient is then converted into units of MMT C/QBtu.
Step 2: Determine Weighted Average Carbon Content by State
Carbon contents by rank and origin calculated in Step 1 are then weighted by the annual share of state production
that was each rank. State production by rank is obtained from the EIA. This step yields a single carbon content per
state that varies annually based on production by coal type. However, most coal-producing states produce only
one rank of coal. For these states the weighted factor equals the carbon content calculated in Step 1 and is
constant across the time series.
Step 3: Allocate Sectoral Consumption by State of Origin
U.S. energy statistics67 through 2020 provide data on the origin of coal used in four areas: 1) the electric power industry,
2) industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use sectors.68 Because U.S.
energy statistics do not provide the distribution of coal rank consumed by each consuming sector, it is assumed that each
sector consumes a representative mixture of coal ranks from a particular state that matches the mixture of all coal
produced in that state during the year. Thus, the weighted state-level factor developed in Step 2 is applied.
Step 4: Weight Sectoral Carbon Contents to Reflect the Rank and State of Origin of Coal Consumed
Sectoral C contents are calculated by multiplying the share of coal purchased from each state by the state's weighted C
content estimated in Step 2. The resulting partial C contents are then totaled across all states to generate a national
sectoral C content.
66 For a comparison to earlier estimated carbon contents see Chronology and Explanation of Changes in Individual Carbon Content
Coefficients of Fossil Fuels near the end of this Annex.
67U.S. Energy Information Administration (EIA). Annual Coal Distribution Report (2001-2019b); Coal Industry Annual (1990-2001).
68 In 2008, EIA began collecting and reporting data on commercial and institutional coal consumption, rather than residential and
commercial consumption. Thus, the residential/commercial coal coefficient reported in Table A-22 for 2009 to the present represents
the mix of coal consumed by commercial and institutional users. Currently, only an extremely small amount of coal is consumed in the
U.S. residential sector.
Annex 2
A-65
-------
Equation A-l: C Content for Coal by Consuming Sector
Csector — Sstatel*Cstatel Sstate2*Cstate2 +.... + Sstate50*Cstate50
where,
Csector = The C content by consuming sector;
Sstate = The portion of consuming sector coal consumption attributed to production from a
given state;
Cstate = The estimated weighted C content of all ranks produced in a given state.
A-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-27: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank (MMT C/QBtu) (1990-2020)
Consuming Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electric Power
25.94
25.92
25.98
26.08
26.05
26.05
26.06
26.05
26.04
26.07
26.06
26.08
26.09
26.08
26.12
Industrial Coking
25.53
25.57
25.63
25.60
25.58
25.57
25.57
25.58
25.57
25.57
25.57
25.56
25.59
25.59
25.60
Other Industrial
25.81
25.79
25.74
25.79
25.86
25.88
25.94
25.93
25.95
26.00
26.03
26.06
26.08
26.07
26.13
Residential/ Commercial3
26.19
26.13
26.00
26.04
25.75
25.81
25.88
25.90
25.88
25.98
26.01
26.09
26.09
26.11
26.21
Coal Rankb
Anthracite
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
28.28
Bituminous
25.38
25.42
25.45
25.45
25.42
25.42
25.41
25.41
25.41
25.40
25.40
25.40
25.41
25.41
25.43
Sub-bituminous
26.46
26.47
26.46
26.48
26.47
26.49
26.49
26.49
26.49
26.49
26.49
26.20
26.49
26.49
26.49
Lignite
26.58
26.59
26.61
26.62
26.63
26.61
26.61
26.62
26.63
26.66
26.64
26.67
26.76
26.75
26.77
a In 2008, EIA began collecting consumption data for commercial and institutional consumption rather than commercial and residential consumption.
b Emission factors for coal rank are weighted based on production in each state.
Sources: C content coefficients calculated from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), EIA (1990 through 2001; 2001 through 2021a; 2001 through
2021b).
Annex 2
A-67
-------
Step 5: Develop National-Level Carbon Contents by Rank for Comparison to IPCC Defaults
Although not used to calculate emissions, national-level C contents by rank are more easily compared to C contents of
other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank developed
under Step 1 by overall coal production by state and rank. Each state-level C content by rank is multiplied by the share of
national production of that rank that each state represents. The resulting partial C contents are then summed across all
states to generate an overall C content for each rank.
Equation A-2: C Content for Coal by Rank
Data Sources
The ultimate analysis of coal samples was based on 8,672 coal samples, 6,588 of which are from USGS (1998), 504 from
the Pennsylvania State University Coal Database (PSU 2010), and the remainder from individual State Geological Surveys.
Samples obtained directly from individual State Geological Surveys include 908 samples from the Montana Bureau of
Mines & Geology (Gunderson 2019), 745 samples from the Indiana Geological Survey Coal Quality Database (IGS 2019),
and 460 samples from the Illinois State Geological Survey (ISGS 2019). Because the data obtained directly from the State
Geological Surveys for these three states included both samples collected by the USGS and additional samples, these
data were used to determine C content coefficients for these states instead of the USGS and Pennsylvania State
University data. Data contained in the USGS's CoalQual Database are derived primarily from samples taken between
1973 and 1989, and were largely reported in State Geological Surveys. Data in the PSU Coal Database are mainly from
samples collected by PSU since 1967 and are housed at the PSU Sample Bank. Additional samples that were not
contained in the USGS's CoalQual Database, many of which were more recent samples taken after 1989, were obtained
directly from the State Geological Surveys for Montana, Illinois, and Indiana. Whole-seam channel samples provided by
PSU, Illinois, and Indiana, and both whole-seam channel and drill core samples provided by Montana, were included in
the development of carbon factors.
Data on coal consumption by sector and state of origin, as well as coal production by state and rank, were obtained from
EIA. ElA's Annual Coal Report (EIA 2001 through 2021a) is the source for state coal production by rank from 2001 through
2020. In prior years, EIA reported this data in its Coal Industry Annual (EIA 1990 through 2001). Data for coal
consumption by state of origin and consuming sector for 2001 through 2020 was obtained from the ElA's Annual Coal
Distribution Report (EIA 2001 through 2021b). For 1990 through 2000, end-use data was obtained from the Coal Industry
Annual (EIA 1990 through 2001).
Uncertainty
Carbon contents vary considerably by state. Bituminous coal production and sub-bituminous coal production
represented 47.2 percent and 45.0 percent of total U.S. supply in 2020, respectively. Of the states that have been
producing bituminous coal since 1990, state average C content coefficients for bituminous coal vary from a low of 85.58
kg C02 per MMBtu in Texas to a high of 96.36 kg C02 per MMBtu in Arkansas. The next lowest average emission factor
for bituminous coal is found in Missouri (91.71 kg C02 per MMBtu). In 2020, Missouri production accounted for less than
0.1 percent of overall bituminous production. More than 50 percent of bituminous coal was produced in three states in
2020: West Virginia, Kentucky, and Pennsylvania, and this share has remained fairly constant since 1990. These three
states show a variation in C content for bituminous coals of +0.7 percent, based on more than 2,000 samples (see Table
A-28).
Similarly, the C content coefficients for sub-bituminous coal range from 91.29 kg C02 per MMBtu in Utah to 98.10 kg C02
per MMBtu in Alaska. However, Utah has no recorded production of sub-bituminous coal since 1990. Production of sub-
bituminous coal in Alaska has made up less than 0.7 percent of total sub-bituminous production since 1990, with even
this small share declining over time. Wyoming has represented between 75 percent and 90 percent of total sub-
bituminous coal production in the United States throughout the time series (1990 through 2020). Thus, the C content
coefficient for Wyoming (97.21 kg C02 per MMBtu), based on 503 samples, dominates the national average.
where,
The national C content by rank;
The portion of U.S. coal production of a given rank attributed to each state; and
The estimated C content of a given rank in each state.
A-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was +1.5 percent
from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and Pennsylvania
was +1.2 percent or less for each state. The large number of samples and the low variability within the sample set of the
states that represent the predominant source of supply of U.S. coal suggest that the uncertainty in this factor is very low,
on the order of +1.0 percent.
For comparison, J. Quick (2010) completed an analysis similar in methodology to that used here, in order to generate
national average C emission factors as well as county-level factors. This study's rank-based national average factors have
a maximum deviation from the factors developed in this Inventory report of 0.78 percent, which is for sub-bituminous
(range: -0.32 to +0.78 percent). This corroboration further supports the assertion of minimal uncertainty in the
application of the rank-based factors derived for the purposes of this Inventory.
Table A-28: Variability in Carbon Content Coefficients by Rank Across States (Kilograms CO2
Per MMBtu)
State
Number of Samples
Bituminous
Sub-bituminous
Anthracite
Lignite
Alabama
951
92.84
-
-
99.10
Alaska
91
98.32
98.09
-
98.65
Arizona
15
93.94
97.34
-
-
Arkansas
77
96.36
-
-
94.97
Colorado
317
94.37
96.52
-
101.10
Georgia
35
95.00
-
-
-
Idaho
1
-
94.90
-
-
Illinois
460
92.53
-
-
-
Indiana
745
92.30
-
-
-
Iowa
100
91.87
-
-
-
Kansas
29
90.91
-
-
-
Kentucky
897
92.61
-
-
-
Louisiana
1
-
-
-
96.01
Maryland
47
94.29
-
-
-
Massachusetts
3
-
-
114.82
-
Michigan
3
-
-
-
92.87
Mississippi
8
-
-
-
98.18
Missouri
111
91.71
-
-
-
Montana
908
96.01
96.61
-
98.34
Nebraska
6
103.59
-
-
-
Nevada
2
94.41
-
-
99.86
New Mexico
185
94.28
94.88
103.92
-
North Dakota
202
-
93.97
-
99.47
Ohio
674
91.84
-
-
-
Oklahoma
63
92.33
-
-
-
Pennsylvania
849
93.33
-
103.68
-
Tennessee
61
92.81
-
-
-
Texas
64
85.58
94.19
-
94.46
Utah
169
95.75
91.29
-
-
Virginia
465
93.51
-
98.54
-
Washington
18
94.53
97.35
102.53
106.55
West Virginia
612
93.84
-
-
-
Wyoming
503
94.80
97.21
-
-
U.S. Average
8,672
93.46
96.01
102.15
98.95
Note: Indicates no sample data available. Average is weighted by number of samples.
Sources: Calculated from USGS (1998) and PSU (2010), Gunderson (2019), IGS (2019), and ISGS (2019).
Annex 2
A-69
-------
Natural Gas
Natural gas is predominantly composed of methane (CH4), which is 75 percent C by weight and contains 14.2 MMT
C/QBtu (higher heating value), but it may also contain many other compounds that can lower or raise its overall C
content. These other compounds may be divided into two classes: (1) natural gas liquids (NGLs) and (2) non-hydrocarbon
gases. The most common NGLs are ethane (C2H6), propane (C3HS), butane (C4Hi0), and, to a lesser extent, pentane (C5Hi2)
and hexane (C6Hi4). Because the NGLs have more C atoms than CH4 (which has only one), their presence increases the
overall C content of natural gas. NGLs have a commercial value greater than that of CH4, and therefore are usually
separated from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a
petrochemical feedstock, propane and butane have diverse uses, and natural gasoline69 contributes to the
gasoline/naphtha "octane pool," used primarily to make motor gasoline.
Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as C02, nitrogen, helium and other
noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total, but
there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon gases
in raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial enough to
reduce the energy content of the gas below pipeline specifications (see Step 1, below). Otherwise, inert gases will usually
be left in the natural gas. Because the raw gas that is usually flared (see Step 2, below) contains NGLs and C02, it will
typically have a higher overall C content than gas that has been processed and moved to end-use customers via
transmission and distribution pipelines.
Methodology
The methodology for estimating the C contents of pipeline and flared natural gas can be described in five steps.
Step 1: Define pipeline-quality natural gas
In the United States, pipeline-quality natural gas is required to have an energy content greater than 970 Btu per cubic
foot, but less than 1,100 Btu per cubic foot. Hydrogen sulfide content must be negligible. Typical pipeline-quality natural
gas is about 95 percent CH4, 3 percent NGLs, and 2 percent non-hydrocarbon gases, of which approximately half is C02.
However, there remains a range of gas compositions that are consistent with pipeline specifications. The minimum C
content coefficient for natural gas would match that for pure CH4, which equates to an energy content of 1,005 Btu per
standard cubic foot. Gas compositions with higher or lower Btu content tend to have higher C emission factors, because
the "low" Btu gas has a higher content of inert gases (including C02 offset with more NGLs), while "high" Btu gas tends to
have more NGLs.
Step 2: Define flared gas
Every year, a certain amount of natural gas is flared in the United States. There are several reasons that gas is flared:
• There may be no market for some batches of natural gas, the amount may be too small or too variable, or the
quality might be too poor to justify treating the gas and transporting it to market (such is the case when gas
contains large shares of C02). Most natural gas that is flared for these reasons is "rich" associated gas, with
relatively high energy content, high NGL content, and a high C content.
• Gas treatment plants may flare substantial volumes of natural gas because of "process upsets," because the gas
is "off spec," or possibly as part of an emissions control system. Gas flared at processing plants may be of
variable quality.
Data on the energy content of flare gas, as reported by states to EIA, indicate an average energy content of 1,130 Btu per
standard cubic foot (EIA 1994). Flare gas may have an even higher energy content than reported by EIA since rich
associated gas can have energy contents as high as 1,300 to 1,400 Btu per cubic foot.
Step 3: Determine a relationship between carbon content and heat content
A relationship between C content and heat content may be used to develop a C content coefficient for natural gas
consumed in the United States. In 1994, EIA examined the composition (including C contents) of 6,743 samples of
69 A term used in the gas processing industry to refer to a mixture of liquid hydrocarbons (mostly pentanes and heavier hydrocarbons)
extracted from natural gas.
A-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
pipeline-quality natural gas from utilities and/or pipeline companies in 26 cities located in 19 states. To demonstrate that
these samples were representative of actual natural gas "as consumed" in the United States, their heat content was
compared to that of the national average. For the most recent year, the average heat content of natural gas consumed in
the United States was -1,036 Btu per cubic foot, and has varied by less than 1 percent (1,025 to 1,037 Btu per cubic foot)
over the past 10 years. Meanwhile, the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the
median heat content was 1,031 Btu per cubic foot. Thus, the average heat content of the sample set falls well within the
typical range of natural gas consumed in the United States, suggesting that these samples continue to be representative
of natural gas "as consumed" in the United States. The average and median composition of these samples appear in
Table A-29.
Table A-29: Composition of Natural Gas (Percent)
Compound
Average
Median
Methane
93.07
95.00
Ethane
3.21
2.79
Propane
0.59
0.48
Higher Hydrocarbons
0.32
0.30
Non-hydrocarbons
2.81
1.43
Higher Heating Value (Btu per cubic foot)
1,027
1,031
Source: Gas Technology Institute (1992).
Carbon contents were calculated for a series of sub-samples based on their C02 content and heat content. Carbon
contents were calculated for the groups of samples with less than 1.0 percent (n=5,181) and less than 1.5 percent C02
only (n=6,522) and those with less than 1.0 or 1.5 percent C02 and less than 1,050 Btu/cf (n=4,888 and 6,166,
respectively). These stratifications were chosen to exclude samples with C02 content and heat contents outside the
range of pipeline-quality natural gas. In addition, hexane was removed from the samples since it is usually stripped out of
raw natural gas before delivery because it is a valuable natural gas liquid used as a feedstock for gasoline. The average
carbon contents for the four separate sub-samples are shown below in Table A-30.
Table A-30: Carbon Content of Pipeline-Quality Natural Gas by CO2 and Heat Content (MMT
C/QBtu)
Sample Average Carbon Content
Full Sample 14.48
< 1.0% C02 14.43
< 1.5% C02 14.47
< 1.0 % C02 and <1,050 Btu/cf 14.42
< 1.5 % C02 and <1,050 Btu/cf 14.47
Source: EPA (2010).
Step 4: Apply carbon content coefficients developed in Step 3 to pipeline natural gas
A regression analysis was performed on the sub-samples in to further examine the relationship between carbon (C)
content and heat content (both on a per cubic foot basis). The regression used carbon content as the dependent variable
and heat content as the independent variable. The resulting R-squared values70 for each of the sub-samples ranged from
0.79 for samples with less than 1.5 percent C02 and under 1,050 Btu/cf to 0.91 for samples containing less than 1.0
percent C02 only. However, the sub-sample with less than 1.5 percent C02 and 1,050 Btu/cf was chosen as the
representative sample for two reasons. First, it most accurately reflects the range of C02 content and heat content of
pipeline quality natural gas. Secondly, the R-squared value, although it is the lowest of the sub-groups tested, remains
relatively high. This high R-squared indicates a low percentage of variation in C content as related to heat content. The
regression for this sub-sample resulted in the following equation:
Equation A-3: C Content of Pipeline and Flared Natural Gas
C Content = (0.011 x Heat Content) + 3.5341
70 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in the
independent variables.
Annex 2
A-71
-------
This equation was used to estimate the annual predicted carbon content of natural gas from 1990 to 2020 based on the
ElA's national average pipeline-quality gas heat content for each year (EIA 2022). The table of average C contents for
each year is shown below in Table A-31.
Fuel Type 1990
1995
2000
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Natural Gas 14.46
14.47
14.47
14.48 14.48 14.47 14.46 14.45 14.43 14.43 14.43 14.43 14.43 14.43
Source: Calculated from EPA (2010) and EIA (2022).
Figure A-l: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas
Technology Institute Database
10.0
15.5 -
^ S
•SS c
" o
cti
14.5 -
National Average
Source:
Energy,
140 1 1 1 1 1 1 1 1 1
970 990 1,010 1,030 1,050 1,070 1,090 1,110 1,130
Energy Conterrt (Btu per Cubic Foot)
EIA (1994) Energy Information Administration, Emissions of Greenhouse Gases in the United States 19S7-1992, U.S. Department of
Washington, DC, November, 1994, DOE/EIA0573, Appendix A
Natural gas suppliers may achieve the same overall energy content from a wide variety of methane, higher hydrocarbon,
and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu value. In fact,
the variation in C content for a single Btu value may be nearly as great as the variation for the whole sample. As a result,
while energy content has some predictive value, the specific energy content does not substantially improve the accuracy
of an estimated C content coefficient beyond the +5.0 percent offered with the knowledge that it is of pipeline-quality.
The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients tend to
have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure CH4. Samples with a greater
proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu per cubic
foot, along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have lower
energy content, but they usually contain C02 as one of the inert gases and, consequently, also tend to have higher
emission coefficients (see left side of Figure A-l).
For the full sample (n=6,743), the average C content of a cubic foot of gas was 14.48 MMT C/QBtu. Additionally, a
regression analysis using the full sample produced a predicted C content of 14.49 MMT C/QBtu based on a heat content
A-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
of 1,029 Btu/cf (the average heat content in the United States for the most recent year). However, these two values
include an upward influence on the resulting carbon content that is caused by inclusion in the sample set of the samples
that contain large amounts of inert carbon dioxide and those samples with more than 1,050 Btu per cubic foot that
contain an unusually large amount of NGLs. Because typical gas consumed in the United States does not contain such a
large amount of carbon dioxide or natural gas liquids, a C content of 14.43 MMT C/QBtu (see Table A-31), based on
samples with less than 1.5 percent carbon dioxide and less than 1,050 Btu per cubic foot, better represents the pipeline-
quality fuels typically consumed.
Furthermore, research was done on C02 emission factors for fuel gas used by upstream oil and gas producers in order to
determine whether a different C02 emission factor for fuel gas used in offshore oil and gas production than the emission
factor for the processed gas that enters the transmission, storage and distribution networks used in power and industrial
plants and by other users is warranted. It was determined that a different factor was not warranted as natural gas
carbon content is based on the heating value of the gas and EIA reports that the heat content of dry natural gas
produced (which is used in upstream oil and gas production) is the same value as natural gas consumed in downstream
operations (EIA 2022). Therefore, the same carbon factor is used for all natural gas consumption including upstream
operations.
Petroleum
There are four critical determinants of the C content coefficient for a petroleum-based fuel:
• The density of the fuel (e.g., the weight in kilograms of one barrel of fuel);
• The fraction by mass of the product that consists of hydrocarbons, and the fraction of non-
hydrocarbon impurities;
• The specific types of "families" of hydrocarbons that make up the hydrocarbon portion of the fuel;
and
• The heat content of the fuel.
Equation A-4: C Content for a Petroleum-based Fuel
where,
Cfuel — (Dfuel* SfUel) / Efuel
CfUei = The C content coefficient of the fuel
Dfuei = The density of the fuel
Sfuel = The share of the fuel that is C
Efuei = The heat content of the fuel
Most of the density, carbon share, or heat contents applied to calculate the carbon coefficients for petroleum products
that are described in this sub-Annex and applied to this emissions Inventory were updated in 2010 for the 1990 through
2008 Inventory report. These changes have been made where necessary to increase the accuracy of the underlying data
or to align the petroleum properties data used in this report with that developed for use in EPA's Mandatory Reporting
of Greenhouse Gases Rule (EPA 2009b).
Petroleum products vary between 5.6 degrees API gravity71 (dense products such as asphalt and road oil) and 247
degrees (ethane). This is a range in density of 60 to 150 kilograms per barrel, or +50 percent. The variation in C content,
however, is much smaller (+5 to 7 percent) for products produced by standard distillation refining: ethane is 80 percent
C by weight, while petroleum coke is 90 to 92 percent C. This tightly bound range of C contents can be explained by basic
petroleum chemistry (see below). Additional refining can increase carbon contents. Calcined coke, for example, is
formed by heat treating petroleum coke to about 1600 degrees Kelvin (calcining), to expel volatile materials and increase
71 API gravity is an arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the American
Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter the
compound. Light crude oils generally exceed 38 degrees API and heavy crude oils are all crude oils with an API gravity of 22 degrees or
below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. API gravity can be calculated with the following
formula: API Gravity = (141.5/Specific Gravity) - 131.5. Specific gravity is the density of a material relative to that of water. At standard
temperature and pressure, there are 62.36 pounds of water per cubic foot, or 8.337 pounds water per gallon.
Annex 2
A-73
-------
the percentage of elemental C, This product can contain as much as 97 to 99 percent carbon. Calcined coke is mainly
used in the aluminum and steel industry to produce C anodes.
Petroleum Chemistry
Crude oil and petroleum products are typically mixtures of several hundred distinct compounds, predominantly
hydrocarbons. All hydrocarbons contain hydrogen and C in various proportions. When crude oil is distilled into
petroleum products, it is sorted into fractions by the boiling temperature of these hundreds of organic compounds.
Boiling temperature is strongly correlated with the number of C atoms in each molecule. Petroleum products consisting
of relatively simple molecules and few C atoms have low boiling temperatures, while larger molecules with more C
atoms have higher boiling temperatures.
Products that boil off at higher temperatures are usually denser, which implies greater C content as well. Petroleum
products with higher C contents, in general, have lower energy content per unit mass and higher energy content per unit
volume than products with lower C contents. Empirical research led to the establishment of a set of quantitative
relationships between density, energy content per unit weight and volume, and C and hydrogen content.
Figure A-2 compares C content coefficients calculated on the basis of the derived formula with actual C content
coefficients for a range of crude oils, fuel oils, petroleum products, and pure hydrocarbons. The actual fuel samples were
drawn from the sources described below in the discussions of individual petroleum products.
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content
Coefficients and Hydrocarbon Density
24 -i
22 -
V. E
¦£ O
CD
¦!<
¦=¦ o
iS ,:j'
20 -
18
16
1 Refo r mate
¦ Lig ht Refo rrriate
Heavy Refo rrnate
¦Catalytic Naphthas
J-++
i-peritane
n-butane i-butane
+
¦ Fro pylere
-j- P ro pane
~l~ = F'araffi n Hyd ro carbo re
—1 1 1 1 1 1 1 1 1 1
15 30 45 60 75 90 105 120 135 150
Hydrocarbon Density (API Gravity)
Source: Carton content factors for paraffins are calcufeted based on the properties of hydrocarbons in V. Guthrie (ed.), Petroleum Products
Handbook (New York: McG™ Hill, 1960) p. S3. Carbon content factors from other petroleum products are drawn from sources described
be I™. Relationship between density ard emission factors based on the relationship between density and energy" content in U.S. Department of
Commerce, National Bureau of Standards, Thermal Properties of Petroleum Products, Miscellaneous Publication, No. 37 [Washington, DC.,
1929), pp. 16-21, arid relationship between e re rgy content a rd fuel composition inS. Ringeri, J. Lanum, and F.P. Miknls, "Calculating Heating
Values from the Elemental Composition of Fossil Fuels,' Fuel, Vol. 58 (January 1979), p.69.
The derived empirical relationship between C content per unit heat and density is based on the types of hydrocarbons
most frequently encountered. Petroleum fuels can vary from this relationship due to non-hydrocarbon impurities and
variations in molecular structure among classes of hydrocarbons. In the absence of more exact information, this
empirical relationship offers a good indication of C content.
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Non-hydrocarbon Impurities
Most fuels contain a certain share of non-hydrocarbon material. This is also primarily true of crude oils and fuel oils. The
most common impurity is sulfur, which typically accounts for between 0.5 and 4 percent of the mass of most crude oils,
and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain appreciable
quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and oxygen content
of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have much lower levels of
impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the residual oil fraction.
Light products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content of heavy fuel oils can
often be several percent lower than that of lighter fuels, due entirely to the presence of non-hydrocarbons.
Variations in Hydrocarbon Classes
Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density and C
content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in order to
give petroleum products distinct properties. The main classes of hydrocarbons are described below.
Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass. Paraffins are
straight-chain hydrocarbons with the general formula CnH2n+2. Paraffins include ethane (C2H6), propane (CsHs), butane
(C4H10), and octane (CsHis). As the chemical formula suggests, the C content of the paraffins increases with their C
number: ethane is 79.89 percent C by weight, octane 84.12 percent. As the size of paraffin molecules increases, the C
content approaches the limiting value of 85.7 percent asymptotical (see Figure A-3).
Cycloparaffins. Cycloparaffins are similar to paraffins, except that the C molecules form ring structures rather than
straight chains, and consequently require two fewer hydrogen molecules than paraffins. Cycloparaffins always have the
general formula CnH2n and are 85.63 percent C by mass, regardless of molecular size.
Olefins. Olefins are a very reactive and unstable form of paraffin: a straight chain with two carbon atoms double bonded
together (thus are unsaturated) compared to the carbon atoms in a paraffin (which are saturated with hydrogen). They
are never found in crude oil but are created in moderate quantities by the refining process. Gasoline, for example, may
contain between 2 and 20 percent olefins. They also have the general formula CnH2n, and hence are also always 85.63
percent C by weight. Propylene (C3H6), a common intermediate petrochemical product, is an olefin.
Aromatics. Aromatics are very reactive hydrocarbons that are relatively uncommon in crude oil (10 percent or less). Light
aromatics increase the octane level in gasoline, and consequently are deliberately created by catalytic reforming of
heavy naphtha. Aromatics also take the form of ring structures with some double bonds between C atoms. The most
common aromatics are benzene (C6H6), toluene (C7HS), and xylene (CsHi0). The general formula for aromatics is CnH2n-6.
Benzene is 92.26 percent C by mass, while xylene is 90.51 percent C by mass and toluene is 91.25 percent C by mass.
Unlike the other hydrocarbon families, the C content of aromatics declines asymptotically toward 85.7 percent with
increasing C number and density (see Figure A-3).
Polynuclear Aromatics. Polynuclear aromatics are large molecules with a multiple ring structure and few hydrogen
atoms, such as naphthalene (Ci0Hs and 93.71 percent C by mass) and anthracene (C14H10 and 97.7 percent C). They are
relatively rare but do appear in heavier petroleum products.
Figure A-3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules containing 2 to 4 C
atoms are all natural gas liquids; hydrocarbons with 5 to 10 C atoms are predominantly found in naphtha and gasoline;
and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to make diesel fuel,
kerosene and jet fuel. Larger molecules which can be vacuum distilled may be used as lubricants, waxes, and residual
fuel oil or cracked and blended into the gasoline or distillate pools.
Annex 2
A-75
-------
Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number
100
95 -
90
85 -
¦ Paraffins
t Cyclo paraffins
~ Arc rnatics
&
O
c
¦ii
2
-------
Table A-32: Carbon Content Coefficients and Underlying Data for Petroleum Products
Gross Heat of
Carbon Content
Combustion
Density
Percent
Fuel
(MMTC/QBtu)
(MMBtu/Barrel)
(API Gravity)
Carbon
Motor Gasoline
19.27
(See a)
(See a)
(See a)
LPG (Propane)
17.15
3.841
155.3
81.80
HGL (Energy Use)b
17.66
(See b)
(See b)
(See b)
HGL (Non-Energy Use)b
16.77
(See b)
(See b)
(See b)
Jet Fuel
19.70
5.670
42.0
86.30
Distillate Fuel No. 1
19.98
5.822
35.3
86.40
Distillate Fuel No. 2
20.22
(See c)
(See c)
(See c)
Distillate Fuel No. 4
20.47
6.135
23.2
86.47
Residual Fuel No. 5
19.89
5.879
33.0
85.67
Residual Fuel No. 6
20.48
6.287
15.5
84.67
Asphalt and Road Oil
20.55
6.636
5.6
83.47
Lubricants
20.20
6.065
25.7
85.80
Naphtha (< 400 deg. F)c
18.55
5.248
62.4
84.11
Other Oil (>400 deg. F)c
20.17
5.825
35.8
87.30
Aviation Gasoline
18.86
5.048
69.0
85.00
Kerosene
19.96
5.670
35.3
86.40
Petroleum Coke
27.85
6.024
-
92.28
Special Naphtha
19.74
5.248
52.0
84.75
Petroleum Waxes
19.80
5.537
43.3
85.30
Still Gas
18.20
6.000
-
77.70
Crude Oil
20.31
5.800
31.2
85.49
Unfinished Oils
20.31
5.825
31.2
85.49
Miscellaneous Products
0.00
5.796
31.2
0.00
Natural Gasoline
18.24
4.638
81.3
83.70
a Calculation of the carbon content coefficient for motor gasoline starting in 2009 uses separate higher heating values for
conventional and reformulated gasoline of 5.222 and 5.150, respectively (EIA 2009a). Densities and carbon shares (percent
carbon) are annually variable and separated by both fuel formulation and grade, see Motor Gasoline and Blending
Components, below, for details.
b HGL is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, and their associated
olefins: ethylene, propylene, isobutylene, and butylene, each with their own heat content, density, and C content, see Table A-
34.
c Petrochemical feedstocks have been split into naphthas and other oils for this Inventory report. Parameters presented are for
naphthas with a boiling temperature less than 400 degrees Fahrenheit. Other oils are petrochemical feedstocks with higher
boiling points. They are assumed to have the same characteristics as distillate fuel oil no. 2.
Note: Indicates no sample data available. For carbon content coefficients that are annually variable, 2020 values are shown.
Sources: EIA (1994); EIA (2009a); EPA (2020b); and EPA (2010).
Motor Gasoline and Motor Gasoline Blending Components
Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of additives,
blended to form a fuel suitable for use in spark-ignition engines.72 "Motor Gasoline" includes conventional gasoline; all
types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline.
Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly 21
percent of all U.S. C02 emissions. EIA collects consumption data (i.e., "petroleum products supplied" to end-users) for
several types of finished gasoline over the 1990 through 2020 time period: regular, mid-grade, and premium
72 Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification VV-G-1690C, is characterized as having a boiling
range of 122 degrees to 1S8 degrees Fahrenheit at the 10-percent recovery point to 36S degrees to 374 degrees Fahrenheit at the 90-
percent recovery point.
Annex 2
A-77
-------
conventional gasoline (all years) and regular, mid-grade, and premium reformulated gasoline (November 1994 to 2020).
Leaded and oxygenated gasoline are not separately included in the data used for this report.73
The American Society for Testing and Materials (ASTM) standards permit a broad range of densities for gasoline, ranging
from 50 to 70 degrees API gravity, or 111.52 to 112.65 kilograms per barrel (EIA 1994), which implies a range of possible
C and energy contents per barrel. The density of motor gasoline across grades and formulations for 1990-2008 is taken
from the National Institute of Petroleum and Energy Research. Values from 2008 have been used as a proxy for 2009
through 2020.
The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline phase-
out. In order to maintain the "anti-knock" quality and octane ratings of gasoline in the absence of lead, the portion of
aromatic hydrocarbons blended into gasoline through the refining process was increased. As discussed above, aromatic
hydrocarbons have a lower ratio of hydrogen to C than other hydrocarbons typically found in gasoline, and therefore
increase fuel density.
The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised oxygen
content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean Air Act
Amendments further changed the composition of gasoline consumed in the United States. Through 2005, methyl tertiary
butyl ether (MTBE), ethanol, ethyl tertiary butyl ether (ETBE), and tertiary amyl methyl ether (TAME) were added to
reformulated and sometimes to conventional gasoline to boost its oxygen content, reduce its toxics impacts and increase
its octane. The increased oxygen reduced the emissions of carbon monoxide and unburned hydrocarbons. These oxygen-
rich blending components are also much lower in C than standard gasoline. The average gallon of reformulated gasoline
consumed in 2005 contained over 10 percent MTBE and 0.6 percent TAME (by volume). The characteristics of
reformulated fuel additives appear in Table A-33.
Additive
Density (Degrees
API)
Carbon Share (Percent)
MTBE
58.6
68.13
ETBE
58.5
70.53
TAME
51.2
70.53
DIPE
62.7
70.53
Ethanol (100%)
45.8
52.14
Source: EPA (2009b).
Since 2005, due to concerns about the potential environmental consequences of the use of MTBE in fuels, there has
been a shift away from the addition of MTBE, TAME, ETBE, and DIPE and towards the use of ethanol as a fuel
oxygenate.74 Ethanol, also called ethyl alcohol, is an anhydrous alcohol with molecular formula C2H5OH. Ethanol has a
lower C share than other oxygenates, approximately 52 percent compared to about 70 percent for MTBE and TAME. The
density of ethanol was calculated by fitting density data at 10-degree intervals to a polynomial of order two and then
using the fit to interpolate the value of the density at 15 degrees Celsius. A common fuel mixture of 10 percent
denatured ethanol (denatured by 2 percent hydrocarbons) and 90 percent gasoline, known as E10, is widely used in the
United States and does not require any modification to vehicle engines or fuel systems. The federal Renewable Fuel
Standard (RFS) program requires a certain volume of renewable fuel, including ethanol, be blended into the national fuel
supply.75 Ethanol blends up to E85 (85 percent ethanol, 15 percent gasoline) are in use in the United States but can only
be used in specially designed vehicles called flexible fuel vehicles (FFVs). Most ethanol fuel in the United States is
produced using corn as feedstock,76 although production pathways utilizing agricultural waste, woody biomass and other
resources are in development.
73 Oxygenated gasoline volumes are included in the conventional gasoline data provided by EIA from 2007 onwards. Leaded gasoline
was included in total gasoline by EIA until October 1993.
74The annual motor gasoline carbon contents that are applied for this Inventory do not include the carbon contributed by the ethanol
contained in reformulated fuels. Ethanol is a biofuel, and net carbon fluxes from changes in biogenic carbon reservoirs in croplands are
accounted for in the estimates for Land Use, Land-Use Change and Forestry.
75 See https://www.epa.gov/renewable-fuel-standard-program.
76 See https://www.epa.gov/fuels-registration-reporting-and-compliance-help/public-data-renewable-fuel-standard.
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Methodology for Years 1990-1999:
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both standard
gasoline and reformulated gasoline were obtained from the EIA.
Step 2. Develop carbon content coefficients for each grade and type
Annual C content coefficients for each gasoline grade, type, and season are derived from four parameters for each
constituent of the finished gasoline blend: the volumetric share of each constituent,77 the density of the constituent,
share of the constituent78 that is C; and the energy content of a gallon of the relevant formulation of gasoline. The
percent by mass of each constituent of each gasoline type was calculated using percent by volume data from the
National Institute for Petroleum and Energy Research (NIPER) and the density of each constituent.
The ether additives listed in Table A-33 are accounted for in both reformulated fuels and conventional fuels, to the
extent that they were present in the fuel. From 2006 onward, reformulated fuel mass percentages are calculated from
their constituents, net of the share provided by ethanol. C content coefficients were then derived from the calculated
percent by mass values by weighting the C share of each constituent by its contribution to the total mass of the finished
motor gasoline product.
Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type
The C content for each grade, type, and season of fuel is multiplied by the share of annual consumption represented by
the grade and fuel type during the relevant time period. Individual coefficients are then summed and totaled to yield an
overall C content coefficient for each year.
Methodology for Years 2000-Present:
Step 1. Disaggregate U.S. gasoline consumption by grade and type
Separate monthly data for U.S. sales to end users of finished gasoline by product grade and season for both standard
gasoline and reformulated gasoline were obtained from the EIA. The EIA publishes prime supplier sales volumes of motor
gasoline by type (conventional, oxygenated, and reformulated) and by grade (regular, midgrade and premium) for each
month from 1983 to present (EIA 2021b). Gasoline sold in May through August was assumed to be summer grade,
gasoline sold in September was assumed to be half summer and half winter grade, and gasoline sold in other months
was assumed to be winter grade. The amount of ethanol within each gasoline is removed as ethanol is treated separately
in this Inventory.
Step 2. Develop carbon content coefficients for each grade and type
Fuel properties are gathered through the Alliance of North American Fuel Survey (NAFS) published by the Alliance of
Automobile Manufacturers (AAM), an association which is now part of the Alliance for Automotive Innovation. This fuel
survey includes measured properties of both regular and premium gasoline.
The carbon content are calculated according to ASTM D3343, Standard Test Method for the Estimation of Hydrogen
Content of Aviation Fuels, and ASTM D3338, Standard Test Method for the Net Heat of Combustion of Aviation Fuels,
respectively using fuel properties inputs from the NAFS for each year and season. .Historically, the carbon mass fraction
of the hydrocarbon fraction of fuels calculated according to ASTM D3343 applies to hydrocarbon containing fuels only
and is not applicable towards oxygenated fuel blends. However, recently EPA has proposed an amendment to 40 CFR
§600.113-12, containing equations allowing for the estimation of base fuel blendstock properties using the bulk
oxygenated fuel properties. This technique is applied in this Inventory for oxygenated gasoline calculations.
The fuels sampled in the NAFS by AAM are assumed to be representative of the seasonal fuels sold throughout the U.S.
"Calculations account for the properties of the individual constituents of gasoline, including, as applicable to the fuel grade and type:
aromatics (excluding benzene), olefins, benzene, saturates, MTBE, TAME, ETBE, DIPE and ethanol.
78Saturates are assumed to be octane and aromatics are assumed to be toluene.
Annex 2
A-79
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Data Sources
Data for the density of motor gasoline were derived from NIPER (1990 through 2009). Data on the characteristics of
reformulated gasoline, including C share, were also taken from NIPER (1990 through 2009) and Alliance of North
American Fuel Survey (NAFS) published by the Alliance of Automobile Manufacturers (AAM), an association which is now
part of the Alliance for Automotive Innovation.
Standard heat contents for motor gasoline of 5.222 MMBtu per barrel conventional gasoline and 5.150 MMBtu per
barrel reformulated gasoline79 were adopted from EIA (2009a).
Uncertainty
For 1990 through 1999, the uncertainty underlying the C content coefficients for motor gasoline has three underlying
sources: (1) the uncertainty in the averages published by NIPER, (2) uncertainty in the C shares assumed in the EPA's
analysis to be representative of the constituent hydrocarbon classes within gasoline (aromatics, olefins and saturates),
and (3) uncertainty in the heat contents applied. For 2000 through 2020, the uncertainty underlying the C content
coefficients for motor gasoline has two sources: (1) the uncertainty in the fuel properties gathered through the Alliance
of North American Fuel Survey (NAFS) to determine carbon content and (2) uncertainty in the heat contents applied.
For 1990 through 1999, a variable number of samples are used each year to determine the average percent by volume
share of each hydrocarbon within each grade, season and formulation of gasoline that were obtained from NIPER
through 1999. The total number of samples analyzed for each seasonal NIPER report varies from approximately 730 to
over 1,800 samples over the period from 1990 through 2009. The number of samples analyzed that underlie the
calculation of the average make-up of each seasonal formulation and grade varies from approximately 50 to over 400,
with the greatest number of samples each season being of conventional, regular or premium gasoline. Further, not all
sample data submitted to NIPER contains data for each of the properties, such that the number of samples underlying
each constituent average value for each season, grade and formulation may be variable within the single gasoline type
(e.g., of the 1,073 samples for which some data was obtained for gasoline sold in Winter 1995 through 1996, benzene
content was provided for all samples, while olefin, aromatic and saturate content was provided for just 736 of those
samples).
The distribution of sample origin collected for the NIPER report and the calculation of national averages are not reflective
of sales volumes. The publication of simple, rather than sales-weighted averages to represent national average values
increases the uncertainty in their application to the calculation of carbon content factors for the purposes of this
Inventory. Further, data for each sample is submitted voluntarily, which may also affect their representativeness.
Additionally, because the simple average constituent shares are calculated based upon data that have been
renormalized to account for the share of ethers and alcohols, total average volume shares may not equal 100 percent.
The simple average for each hydrocarbon constituent is contained within a range of values that are as wide as
-63.0/+74.5 percent of the mean across the Winter 2007 through 2008 and -51.3/+49.6 percent across the Summer 2008
samples of conventional, regular grade gasoline. However, these wide ranges exist for benzene, which generally
accounts for only 1 percent, by volume, of each gallon. In contrast, saturates, the class of hydrocarbon that contribute
the largest share, by volume, ranges only -6.5/+6.4 percent for the same set of winter samples and -8.8/+15.7 percent
for the summer samples.
Secondly, for 1991 through 2000, EPA's calculation of C content factors for each gasoline type includes the following
assumptions: for the purposes of assigning a carbon share to each compound in the blend, aromatic content (other than
benzene) is assumed to be toluene and saturated hydrocarbons are assumed to be octane. All olefins have the same
carbon share because they all have a molecular formula in the form CnH2n, so the C share applied to the olefin portion of
the total gasoline blend does not increase the level of uncertainty in the calculation. These assumptions are based upon
the use of octane and octane isomers as the primary saturates and toluene as the primary non-benzene aromatic in U.S.
motor gasoline blends. The octane rating of a particular blend is based upon the equivalent iso-octane to heptane ratio,
which is achieved through significant octane content relative to the other saturates. Aside from benzene, U.S. gasolines
will include toluene as a major aromatic component, so toluene may be assumed a reasonable representative of total
non-benzene aromatic content (EPA 2009a).
79 The reformulated gasoline heat content is applied to both reformulated blends containing ethers and those containing ethanol.
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For each hydrocarbon category, the assumed C content lies within a range of possible values for all such hydrocarbons.
Among saturated hydrocarbons, the C share of octane (84.12 percent) is at the high end of the range while ethane
represents the low end of the range (79.89 percent C). Total saturates constitute from 40 to 95 percent by volume of a
given gasoline blend. For aromatics, toluene (91.25 percent C) lies in the middle of the possible range. This range is
bounded by cumene (89.94 percent C) and naphthalene (93.71 percent C). Total aromatics may make up between 3 and
50 percent by volume of any given gasoline blend. The range of these potential values contributes to the uncertainty
surrounding the final calculated C factors.
However, as demonstrated above in Figure A-3, the amount of variation in C content of gasoline is restricted by the
compounds in the fuel to +4 percent. Further, despite variation in sampling survey response, sample size and annually
variable fuel formulation requirements, the observed variation in the annual weighted motor gasoline coefficients
estimated for this Inventory is +0.4 percent over 1990 through 1999.
For 2000 through 2020, the exact number of samples to determine measured fuel carbon content of both regular and
premium gasoline vary by year and location. Fuel samples are drawn from multiple retail locations in each of over 20 U.S.
cities for each biannual survey which occur in January and July. The fuel carbon content for gasoline was determined
separately for each city and season included for each year in the NAFS. These values were averaged by fuel Petroleum
Administration for Defense Districts (PADDs) to assure accurate representations for each distribution area, but the
number of samples used in the averages varies by fuel PADD. To determine annual national values for gasoline carbon
content, a weighted average was performed using the sales volumes for each season and PADD as published by the EIA.
Across the time-series, seasons, and gasoline types, the C share of gasoline ranges from 85.38 to 87.94 percent. The
range of these C shares contributes to the uncertainty surrounding the final calculated C contents.
Additionally, for 2000 through 2020, it is assumed the midgrade C content for gasoline is an average of Regular and
Premium gasoline, which may not be representative. Also, the method of calculation of the fuel properties of the
hydrocarbon fraction of the fuel from blended fuel properties was developed for Tier 3 certification test fuels, and not
commercial fuel blends as it is used for in this Inventory.
The third primary contributor to uncertainty across the entire time-series is the assumed heat content. The heat
contents are industry standards established many years ago. The heat contents are standard conversion factors used by
EIA to convert volumetric energy data to energy units. Because the heat contents of fuels change over time, without
necessarily and directly altering their volume, the conversion of known volumetric data to energy units may introduce
bias. Because gasoline is an oxygenated blend, the measured API gravity and the heating value calculated from ASTM
D3338 cannot be used so the yearly heating value as published by EIA and previously reported API gravities are used for
this purpose. A more precise approach to estimating emissions factors would be to calculate C content per unit of
volume, rather than per unit of energy. Adopting this approach, however, makes it difficult to compare U.S. C content
coefficients with those of other nations.
The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also changing.
However, that change within any season grade has been less than 1 percent over the decade. Of greater concern is the
use of a standardized heat content across grades that show a variation in density of +1.5 percent from the mean for
conventional gasoline and +1.0 percent for reformulated fuels.
Jet Fuel
Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the United
States: "naphtha-based" jet fuels and "kerosene-based" jet fuels. In 1989,13 percent of U.S. consumption was naphtha-
based fuel, with the remainder kerosene-based jet fuel. In 1993, the U.S. Department of Defense began a conversion
from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand for
reformulated motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel
represented less than one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in
this report prior to 1996 represents a consumption-weighted combination of the naphtha-based and kerosene-based
coefficients. From 1996 to 2020, only the kerosene-based portion of total consumption is considered significant.
Annex 2
A-81
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Methodology
Step 1. Estimate the carbon content for naphtha-based jet fuels
Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its characteristics are
limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the acceptable API
gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per barrel based on EIA
industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent (Martel and Angello
1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent.
Step 2. Estimate the carbon content for kerosene-based jet fuels
The density of kerosene-based jet fuels was estimated at 42 degrees API and the carbon share at 86.3 percent. The
density estimate was based on 38 fuel samples examined by NIPER. Carbon share was estimated on the basis of a
hydrogen content of 13.6 percent found in fuel samples taken in 1959 and reported by Martel and Angello, and on an
assumed sulfur content of 0.1 percent. The ElA's standard heat content of 5.670 MMBtu per barrel was adopted for
kerosene-based jet fuel.
Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type of fuel (1990-1995
only)
For years 1990 through 1995, the C content for each jet fuel type (naphtha-based, kerosene-based) is multiplied by the
share of overall consumption of that fuel type, as reported by EIA (2009a). Individual coefficients are then summed and
totaled to yield an overall C content coefficient. Only the kerosene-based C coefficient is reflected in the overall jet fuel
coefficient for 1996 through 2020.
Data Sources
Data on the C content of naphtha-based jet fuel was taken from C.R. Martel and L.C. Angello (1977). Data on the density
of naphtha-based jet fuel was taken from ASTM (1985). Standard heat contents for kerosene and naphtha-based jet fuels
were adopted from EIA (2009a). Data on the C content of kerosene-based jet fuel is based on C.R. Martel and L.C.
Angello (1977) and the density is derived from NIPER (1993).
Uncertainty
Variability in jet fuel is relatively small with the average C share of kerosene-based jet fuel varying by less than +1
percent and the density varying by +1 percent. This is because the ratio of fuel mass to useful energy must be tightly
bounded to maximize safety and range. There is more uncertainty associated with the density and C share of naphtha-
based jet fuel because sample data were unavailable and default values were used. This uncertainty has only a small
impact on the overall uncertainty of the C content coefficient for jet fuels, however, because naphtha-based jet fuel
represents a small and declining share of total jet fuel consumption in the United States and is treated as negligible when
calculating C content factors for 1996 onward.
Distillate Fuel
Distillate fuel is a general classification for diesel fuels and fuel oils. Products known as No. 1, No. 2, and No. 4 diesel fuel
are used in on-highway diesel engines, such as those in trucks and automobiles, as well as off-highway engines, such as
those in railroad locomotives and agricultural machinery. No. 1, No. 2, and No. 4 fuel oils are also used for space heating
and electric power generation.
Methodology
For this Inventory, separate C coefficients have been estimated for each of the three distillates, although the level of
aggregation of U.S. energy statistics requires that a single coefficient is used to represent all three grades in inventory
calculations. Distillate No. 2 is the representative grade applied to the distillate class for calculation purposes.
Coefficients developed for No. 1 and No. 4 distillate are provided for informational purposes. The C share for distillate
No. 1 and No. 4 is drawn from Perry's Chemical Engineers' Handbook, 8th Ed. (Green & Perry 2008). Each C share was
combined with individual heat contents of 5.822 and 6.135 MMBtu per barrel, respectively for distillates No. 1 and No. 4,
and densities of 35.3 and 23.2 degrees API to calculate C coefficients for each distillate type.
A-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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For 1990 to 1999, the C share for distillate No. 2 is drawn from Perry's Chemical Engineers' Handbook, 8th Ed. (Green &
Perry 2008) and each share was combined with the heat content of 5.825 MMBtu per barrel and density of 35.8 degrees
API to calculate C coefficients. For 2000 through 2020, the carbon content and net heating value of distillate No. 2, which
is used in this Inventory for all distillate consumption, is calculated according to ASTM D3343, Standard Test Method for
the Estimation of Hydrogen Content of Aviation Fuels, and ASTM D3338, Standard Test Method for the Net Heat of
Combustion of Aviation Fuels, using fuel properties inputs from the Alliance of North American Fuel Survey (NAFS) data
for each year and season. These methods use a correlation between the measured fuel distillation range, API gravity, and
aromatic content to estimate the hydrogen content and net heating value. Data Sources
For 2000 through 2020, fuel properties for distillate No. 2 were derived from diesel surveys taken by the Alliance of
Automobile Manufacturers, an association which is now part of the Alliance for Automotive Innovation. Prime supplier
sales volumes of diesel fuel for each month from 1983 to present are from EIA (2021b).
For previous years, the density of distillate fuel oil No. 2 is taken from Perry's Chemical Engineer's Handbook, 8th Ed.
(Green & Perry, ed. 2008), Table 24-6. Heat contents are adopted from EIA (2022), and carbon shares for distillates No. 2
are from Perry's Chemical Engineers' Handbook (Green & Perry, ed. 2008), Table 24-6.
Uncertainty
Across the time-series, the primary source of uncertainty for the estimated C content of distillate fuel is the selection of
No. 2 distillate as the typical distillate fuel oil or diesel fuel. No. 2 fuel oil is generally consumed for home heating. No. 1
distillate is generally less dense and if it is consumed in large portions for mobile sources, the application of the C
content estimated for No. 2 for this report is likely to be too high when applied to both No. 1 and No. 2 distillates. The
opposite is true of the application of a coefficient based upon the properties of No. 2 to the consumption of No. 4
distillate, which is of a significantly higher density and thus, has a higher C coefficient despite its lower C share. The
overall effect on uncertainty from applying a single factor will depend on the relative annual consumption of each
distillate.
For 1990 through 1999, the densities applied to the calculation of each carbon factor are an underlying a source of
uncertainty. The factor applied to all distillates in the Inventory estimates (that for No. 2 oil) is based on a sample size of
144. The uncertainty associated with the assumed density of distillate fuels is predominately a result of the use of No. 2
to represent all distillate consumption. There is also a small amount of uncertainty in the No. 2 distillate density itself.
This is due to the possible variation across seasonal diesel formulations and fuel grades and between stationary and
transport applications within the No. 2 distillate classification. The range of the density of the samples of No. 2 diesel
(regular grade, 15 ppm sulfur) is ± 2.5 percent from the mean, while the range in density across the small sample set of
No. 1 diesel is -2.1 to +1.6 percent of the mean. Samples from AAM (2009) of Premium No. 2 diesel (n=5) and higher
sulfur (500 ppm S) regular diesel (n=2), each have nominally higher average densities (+1.3 percent and +0.6 percent,
respectively) than do the low-sulfur regular diesel samples that underlie the density applied in this Inventory.
The use of the 144 AAM samples to define the density of No. 2 distillate (and those four samples used to define that of
No. 1 distillate) may introduce additional uncertainty because the samples were collected from just one season of on-
road fuel production (Winter 2008). Despite the limited sample frame, the average No. 2 density calculated from the
samples is applied to the calculation of a uniform C coefficient applicable for all years of the Inventory and for all types of
distillate consumption. The ASTM standards for each grade of diesel fuel oil do not include a required range in which the
density must lie, and the density (as well as heat content and carbon share) may vary according to the additives in each
seasonal blend and the sulfur content of each sub-grade.
However, previous studies also show relatively low variation in density across samples of No. 2 and across all distillates,
supporting the application of a single No. 2 density to all U.S. distillate consumption. The average density calculated from
samples analyzed by the EIA in 1994 (n=7) differs only very slightly from the value applied for the purposes of this
Inventory (-0.12 percent for No. 2 distillate). Further, the difference between the mean density applied to this Inventory
(No. 2 only) and that calculated from EIA samples of all distillates, regardless of grade, is also near zero (-0.06 percent,
based on n=14, of distillates No. 1, No. 2 and No. 4 combined).
A C share of 87.30 percent is applied to No. 2 distillate, while No. 1 and No. 4 have C shares estimated at 86.40 and 86.47
percent, respectively. Again, the application of parameters specific to No. 2 to the consumption of all three distillates
contributes to an increased level of uncertainty in the overall coefficient and emissions estimate and its broad
application. For comparison, four No. 1 fuel oil samples obtained by EIA (1994) contained an average of 86.19 percent C,
while seven samples No. 2 fuel oil from the same EIA analysis showed an average of 86.60 percent C. Additionally, three
Annex 2
A-83
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samples of No. 4 distillate indicate an average C share of 85.81 percent. The range of C share observed across the seven
No. 2 samples is 86.1 to 87.5 percent, and across all samples (all three grades, n=14) the range is 85.3 to 87.5 percent C.
There also exists an uncertainty of +1 percent in the share of C in No. 2 based on the limited sample size.
For 2000 through 2020, the exact number of samples to determine measured fuel carbon content of distillates vary by
year and location. As is the same for motor gasoline, fuel samples are drawn from multiple retail locations in each of
over 20 U.S. cities for each biannual survey which occur in January and July. The fuel carbon content for diesel fuel was
determined separately for each city and season included for each year in the NAFS. Diesel national fuel averages for
summer and winter are combined with sales volumes for each season to determine a national total. Across the time-
series and seasons, the C share of diesel ranges from 86.68 to 87.07 percent. The range of these C shares contributes to
the uncertainty surrounding the final calculated C contents.
Additionally, the two ASTM standard methods used for the calculation of carbon content and other properties, ASTM
D3343 and D3338, were developed specifically for aviation fuels and not motor vehicle fuels. However, the EPA and
other organizations regularly uses these methods for diesel fuel, and both are specified methods in Code of Federal
Regulations (CFR) fuel economy calculations.
Residual Fuel
Residual fuel is a general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after the
distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Residual fuel conforms to ASTM
Specifications D 396 and D 975 and Federal Specification VV-F-815C. No. 5, a residual fuel oil of medium viscosity, is also
known as Navy Special and is defined in Military Specification MIL-F-859E, including Amendment 2 (NATO Symbol F-770).
It is used in steam-powered vessels in government service and inshore power plants. No. 6 fuel oil includes Bunker C fuel
oil and is used for the production of electric power, space heating, vessel bunkering, and various industrial purposes.
In the United States, electric utilities purchase about one-third of the residual oil consumed. A somewhat larger share is
used for vessel bunkering, and the balance is used in the commercial and industrial sectors. The residual oil (defined as
No. 6 fuel oil) consumed by electric utilities has an energy content of 6.287 MMBtu per barrel (EIA 2008a) and an
average sulfur content of 1 percent (EIA 2001). This implies a density of about 17 degrees API.
Methodology
Because U.S. energy consumption statistics are available only as an aggregate of No. 5 and No. 6 residual oil, a single
coefficient must be used to represent the full residual fuel category. As in earlier editions of this report, residual fuel oil
has been defined as No. 6 fuel oil, due to the majority of residual consumed in the United States being No. 6. However,
for this report, a separate coefficient for fuel oil No. 5 has also been developed for informational purposes. Densities of
33.0 and 15.5 degrees API were adopted when developing the C content coefficients for Nos. 5 and 6, respectively
(Wauquier, J.-P., ed. 1995; Green & Perry, ed. 2008).
The estimated C share of fuel oil No. 5 is 85.67 percent, based on an average of 12 ultimate analyses of samples of fuel
oil (EIA 1994). An average share of C in No. 6 residual oil of 84.67 percent by mass was used, based on Perry's, 8th Ed.
(Green & Perry, ed. 2008).
Data Sources
Data on the C share and density of residual fuel oil No. 6 were obtained from Green & Perry, ed. (2008). Data on the C
share of fuel oil No. 5 was adopted from EIA (1994), and the density of No. 5 was obtained from Wauquier, J.-P., ed.
(1995). Heat contents for both No. 5 and No. 6 fuel oil are adopted from EPA (2009b).
Uncertainty
Beyond the application of a C factor based upon No. 6 oil to all residual oil consumption, the largest source of
uncertainty in estimating the C content of residual fuel centers on the estimates of density. Fuel oils are likely to differ
depending on the application of the fuel (i.e., power generation or as a marine vessel fuel). Slight differences between
the density of residual fuel used by utilities and that used in mobile applications are likely attributable to non-sulfur
impurities, which reduce the energy content of the fuel, but do not greatly affect the density of the product. Impurities
of several percent are commonly observed in residual oil. The extent of the presence of impurities has a greater effect on
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the uncertainty of C share estimation than it does on density. This is because these impurities do provide some Btu
content to the fuel, but they are absent of carbon. Fuel oils with significant sulfur, nitrogen and heavy metals contents
would have a different total carbon share than a fuel oil that is closer to pure hydrocarbon. This contributes to the
uncertainty of the estimation of an average C share and C coefficient for these varied fuels.
The 12 samples of residual oil (EIA 1994) cover a density range from 4.3 percent below to 8.2 percent above the mean
density. The observed range of C share in these samples is -2.5 to +1.8 percent of the mean. Overall, the uncertainty
associated with the C content of residual fuel is probably +1 percent.
Hydrocarbon Gas Liquids (HGL)
EIA identifies four categories of paraffinic hydrocarbons (i.e., ethane, propane, isobutane, and n-butane) and four
categories of olefinic hydrocarbons (i.e., ethylene, propylene, isobutylene, and butylene) as HGL. HGL also includes
pentanes plus, or natural gasoline, but this category is calculated separately from other HGL components in this report.
Because each of these compounds is a pure paraffinic or olefinic hydrocarbon, their C shares are easily derived by taking
into account the atomic weight of C (12.01) and the atomic weight of hydrogen (1.01). Thus, for example, the C share of
propane, CsHs, is 81.71 percent. The densities and heat contents of the compounds are also well known, allowing C
content coefficients to be calculated directly. Table A-34 summarizes the physical characteristic of HGL.
Table A-34: Physical Characteristics of Hydrocarbon Gas Liquids
Compound
Chemical
Formula
Density (Barrels
Per Metric Ton)
Carbon Content
(Percent)
Energy Content
(MMBtu/Barrel)
Carbon Content
Coefficient (MMT
C/QBtu)
Ethane
c2h6
11.55
80.00
2.783
16.25
Propane
CsHs
12.76
81.80
3.841
17.15
Isobutane
C4H10
11.42
82.80
4.183
17.71
n-butane
C4H10
10.98
82.30
4.354
17.66
Ethylene
C2H4
11.07
85.71
2.436
17.99
Propylene
c3H6
12.45
85.71
3.835
17.99
Isobutylene
C4Hs
10.68
85.71
4.355
18.78
Butylene
C4Hs
10.70
85.71
4.377
18.74
Source: Densities - CRC Handbook of Chemistry and Physics (2008/09) and EPA (2009c); Carbon Contents - derived from
the atomic weights of the elements EPA (2013); Energy Contents - EIA (2022). All values are for the compound in liquid
form. The density and energy content of ethane are for refrigerated ethane (-89 degrees C). Values for n-butane are for
pressurized butane (-25 degrees C).
Methodology
Step 1. Assign carbon content coefficients to each pure paraffinic compound
Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in the U.S.
energy statistics category, HGL.
Step 2. Weight individual HGL coefficients for share of fuel use consumption
A C content coefficient for HGL used as fuel is developed based on the consumption mix of the individual compounds
reported in U.S. energy statistics, excluding pentanes plus, which is calculated separately.
Step 3. Weight individual HGL coefficients for share of non-fuel use consumption
The mix of HGL consumed for non-fuel use differs significantly from the mix of HGL that is combusted. EIA (2022) states
that HGL consumption in the residential, commercial, and transportation sector is 100 percent propane, therefore a
constant, non-weighted propane C content coefficient is applied to HGL (LPG - Propane) in these sectors. While the
majority of HGL consumed for fuel use in the industrial sector is propane, ethane is the largest component of HGL used
for non-fuel applications. C content coefficients for HGL used for fuel use and non-fuel applications are developed based
on the consumption mix of the individual compounds reported in U.S. energy statistics.
Annex 2
A-85
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Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of
consumption
The changing shares of HGL fuel use and non-fuel use consumption appear below in Table A-35.
Data Sources
Data on C share was derived via calculations based on atomic weights of each element of the four individual compounds
densities are from the CRC Handbook of Chemistry and Physics, 89th Education. The energy content of each HGL is from
EIA (2022). HGL consumption was based on data obtained from EIA (2021a). Non-fuel use of HGL was obtained from EIA
(2021a).
Uncertainty
Because HGL consists of pure paraffinic and olefinic compounds whose density, heat content, and C share are physical
constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any
uncertainty is associated with the collection of data tabulating fuel- and non-fuel consumption in U.S. energy statistics.
This uncertainty is likely less than +3 percent.
Table A-35: Industrial Sector Consumption and Carbon Content Coefficients of Hydrocarbon
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Energy Consumption (QBtu)
Fuel Use
8.40
10.40
12.28
11.51
10.12
10.38
11.67
12.52
10.69
11.34
11.11
10.55
10.92
10.00
9.31
Ethane
0.33
0.26
0.73
0.34
0.03
0.03
0.04
0.04
0.04
0.04
0.04
0.05
0.05
0.06
0.06
Propane
6.60
7.94
7.03
7.09
5.17
5.51
6.74
7.36
5.62
6.11
5.84
5.32
5.56
4.65
4.42
Butane
0.0
0.0
0.73
0.51
0.50
0.33
0.37
0.57
0.57
0.62
0.41
0.19
0.17
0.25
(0.05)
Isobutane
0.0
0.0
0.61
0.14
0.12
0.15
0.19
0.28
0.29
0.46
0.60
0.65
0.75
0.88
1.00
Ethylene
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Propylene
1.47
2.20
3.15
3.41
4.28
4.34
4.32
4.29
4.17
4.13
4.23
4.32
4.36
4.18
3.90
Butylene
0.0
0.0
0.03
0.02
0.02
+
+
(0.02)
0.01
(0.03)
(0.01)
0.02
0.04
+
(0.02)
Isobutylene
0.0
0.0
+
+
+
0.01
+
(+)
(+)
(+)
+
+
(+)
+
(+)
Non-Fuel Use
1.21
1.56
1.68
1.59
1.80
1.85
1.88
2.04
2.03
2.10
2.13
2.19
2.51
2.58
2.69
Ethane
0.48
0.61
0.71
0.64
0.88
0.96
0.96
1.01
1.05
1.09
1.15
1.26
1.50
1.56
1.74
Propane
0.39
0.49
0.56
0.59
0.57
0.57
0.57
0.64
0.58
0.57
0.55
0.53
0.59
0.58
0.55
Butane
0.17
0.17
0.11
0.12
0.11
0.07
0.08
0.13
0.13
0.14
0.09
0.04
0.04
0.06
(0.01)
Isobutane
0.03
0.10
0.09
0.03
0.03
0.03
0.04
0.06
0.07
0.10
0.14
0.15
0.17
0.20
0.23
Ethylene
+
+
+
+
0.01
0.01
+
+
+
+
+
+
+
+
+
Propylene
0.14
0.17
0.20
0.21
0.20
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.21
0.20
0.18
Butylene
0.01
0.01
0.01
0.01
+
+
+
(+)
+
(0.01)
(+)
+
0.01
+
(+)
Isobutylene
+
+
+
+
+
+
+
(+)
(+)
(+)
+
+
(+)
+
(+)
Carbon Content (MMT C/QBtu)
Fuel Use
Non-Fuel Use
17.39
17.10
17.43
17.12
17.48
17.06
17.47
17.03
17.59
16.91
17.54
16.83
17.51
16.85
17.49
16.87
17.54
16.85
17.55
16.87
17.55
16.84
17.59
16.82
17.60
16.80
17.65
16.82
17.66
16.77
Notes: "+" indicates a value less than 0.01 QBtu. Parentheses indicate negative values.
Sources: Fuel use of HGL based on data from EIA (2021a). Non-fuel use of HGL from (EIA 2021a). Volumes converted using the
energy contents provided in Table A-34. C contents from EPA (2013).
Aviation Gasoline
Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile hydrocarbons
with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating engines.
Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation gas is a
relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing
approximately 0.1 percent of all consumption.
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The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content to a
maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must have
as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as high
anti-knock quality.
Methodology
A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of 5.048
MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon, based on
the relationship between heat content and density of petroleum liquids, as described in Thermal Properties of Petroleum
Products (DOC 1929). To estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5 percent iso-
octane, 9.0 percent toluene, and 3.5 percent xylene. The maximum allowable sulfur content in aviation gasoline is 0.05
percent, and the maximum allowable lead content is 0.1 percent. These amounts were judged negligible and excluded
for the purposes of this analysis. This yielded a C share of 85.00 percent and a C content coefficient of 18.86 MMT
C/QBtu.
Data Sources
Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2009a).
Uncertainty
The relationship used to calculate density from heat content has an accuracy of five percent at 1 atm. The uncertainty
associated with the C content coefficient for aviation gasoline is larger than that for other liquid petroleum products
examined because no ultimate analyses of samples are available. Given the requirements for safe operation of piston-
powered aircraft the composition of aviation gas is well bounded, and the uncertainty of the C content coefficient is
likely to be +5 percent.
Still Gas
Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a refinery.
The composition of still gas is highly variable, depending primarily on the nature of the refining process and secondarily
on the composition of the product being processed. Petroleum refineries produce still gas from many different
processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified
and sold as pipeline-quality natural gas. For the purposes of this Inventory, the coefficient derived here is only applied to
still gas that is consumed as a fuel. In general, still gas tends to include large amounts of free hydrogen and methane, as
well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous by-
products, it is difficult to determine what represents typical still gas.
Methodology
The properties of still gas used to calculate the carbon content are taken from the literature. The carbon share of still gas
was calculated from its net calorific value and carbon content from IPCC (2006). This calculation yields a carbon share of
77.7 percent. The density of still gas was estimated to be 0.1405 metric tons per barrel based on its heat content (EIA
2008a) and the relationship between heat content and density that is described by the U.S. Department of Commerce,
Bureau of Standards (DOC 1929).
Data Sources
The carbon share of still gas is calculated from data provided by IPCC (2006). Density is estimated at 0.1405 metric tons
per barrel, approximately 28.3 degrees API, based on the heat content of 6.00 MMbtu/barrel of still gas from EIA
(2009a).
Uncertainty
The EIA obtained data on four samples of still gas. Table A-36 below shows the composition of those samples.
Annex 2
A-87
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Table A-36: Composition, Energy Content, and Carbon Content Coefficient for Four Samples
of Still Gas
Sample
Hydrogen
Methane
Ethane
Propane
Btu Per Cubic
Carbon Content
(%)
(%)
(%)
(%)
Foot
(MMT C/QBtu)
One
12.7
28.1
17.1
11.9
1,388
17.51
Two
34.7
20.5
20.5
6.7
1,143
14.33
Three
72.0
12.8
10.3
3.8
672
10.23
Four
17.0
31.0
16.2
2.4
1,100
15.99
Sources: EIA (2008b).
Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly
uncertain. Gas streams with a large, free-hydrogen content are likely to be used as refinery or chemical feedstocks.
Therefore, the sample cited above with the very high H content of 72 percent (and the lowest calculated C content) is
less likely to be representative of the still gas streams to which the calculated coefficient is applied. The C content
coefficient used for this report is probably at the high end of the plausible range given that it is higher than the greatest
sample-based C content in Table A-36.
Asphalt
Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of carbon dioxide
emissions. It is derived from a class of hydrocarbons called "asphaltenes," which are abundant in some crude oils but not
in others. Asphaltenes have oxygen and nitrogen atoms bound into their molecular structure, so that they tend to have
lower C contents than do other hydrocarbons.
Methodology
Ultimate analyses of twelve samples of asphalts showed an average C content of 83.47 percent. The EIA standard Btu
content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables show a density
of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content coefficient of
20.55 MMT C/QBtu.
Data Sources
A standard heat content for asphalt was adopted from EIA (2009b). The density of asphalt was determined by the ASTM
(1985). C share is adopted from analyses in EIA (2008b).
Uncertainty
The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and sulfur,
with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for sulfur.
Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely to be
accurate to +5 percent.
Lubricants
Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing materials
used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be produced
either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder oil to those
used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range of product
compositions and end uses within this category.
Methodology
The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API, or 0.1428 metric tons per
barrel. Ultimate analysis of a single sample of motor oil yielded a C content of 85.80 percent. A standard heat content of
6.065 MMBtu per barrel was adopted from EIA. These factors produce a C content coefficient of 20.20 MMT C/QBtu.
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Data Sources
A standard heat content was adopted from the EIA (2009b). The carbon content of lubricants is adopted from ultimate
analysis of one sample of motor oil (EPA 2009a). The density of lubricating oils was determined by ASTM (1985).
Uncertainty
Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product compositions and
end uses in this category combined with an inability to establish the shares of the various products captured under this
category in U.S. energy statistics. Because lubricants may be produced from either the distillate or residual fractions
during refineries, the possible C content coefficients range from 19.89 MMT C/QBtu to 21.48 MMT C/QBtu or an
uncertainty band from -1.5 percent to +1.4 percent of the estimated value.
Petrochemical Feedstocks
U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling
temperature below 400 degrees Fahrenheit, generally called "naphtha," and those with a boiling temperature 401
degrees Fahrenheit and above, referred to as "other oils" for the purposes of this Inventory.
Methodology
The C content of these petrochemical feedstocks are estimated independently according to the following steps.
Step 1. Estimate the carbon content coefficient for naphtha
Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content and octane
rating), "straight-run" naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five samples of
naphtha were examined and showed an average C share of 84.11 percent. A density of 62.4 degrees API gravity was
taken from the Handbook of Petroleum Refining Processes, 3rd ed. (Meyers 2004). The standard EIA heat content of 5.248
MMBtu per barrel is used to estimate a C content coefficient of 18.55 MMT C/QBtu.
Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400
degrees Fahrenheit and above ("other oils")
The boiling temperature of this product places it into the "middle distillate" fraction in the refining process, and EIA
estimates that these petrochemical feedstocks have the same heat content as distillate fuel No. 2. Thus, the C content
coefficient of 20.17 MMT C/QBtu used for distillate fuel No. 2 is also adopted for this portion of the petrochemical
feedstocks category.
Data Sources
Naphthas: Data on the C content was taken from Unzelman (1992). Density is from Meyers (2004). A standard heat
content for naphthas was adopted from EIA (2009a). Other oils: See Distillate Fuel, Distillate No.2.
Uncertainty
Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity of the
purchaser, who are presumed to be a chemical company, or a petrochemical unit co-located on the refinery grounds.
Naphthas are defined, for the purposes of U.S. energy statistics, as those naphtha products destined for use as a
petrochemical feedstock. Because naphthas are also commonly used to produce motor gasoline, there exists a
considerable degree of uncertainty about the exact composition of petrochemical feedstocks.
Different naphthas are distinguished by their density and by the share of paraffins, isoparaffins, olefins, naphthenes and
aromatics contained in the oil. Naphtha from the same crude oil fraction may have vastly different properties depending
on the source of the crude. Two different samples of Egyptian crude, for example, produced two straight run naphthas
having naphthene and paraffin contents (percent volume) that differ by 18.1 and 17.5 percent, respectively (Matar and
Hatch 2000).
Naphthas are typically used either as a petrochemical feedstock or a gasoline feedstock, with lighter paraffinic naphthas
going to petrochemical production. Naphthas that are rich in aromatics and naphthenes tend to be reformed or blended
into gasoline. Thus, the product category encompasses a range of possible fuel compositions, creating a range of possible
C shares and densities. The uncertainty associated with the calculated C content of naphthas is primarily a function of
Annex 2
A-89
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the uncertainty that underlies the average carbon share calculation, which is based on a limited number of samples. Two
additional samples cited by the EIA (1994) have a range of 83.80 to 84.42 percent C.
The uncertainty of the C content for other oils is based upon the assumption of distillate oil No. 2 as a product
representative of the ill-defined classification of "other oils," and from the calculation of the C content of No. 2 itself (see
"Distillate Fuels," above). While No. 2 distillate is used as a proxy for "other oils" for the purposes of this Inventory's
carbon coefficient, important differences exist between these two petroleum products, contributing some uncertainty to
the cross-application. Other oils are defined herein as those "oils with a boiling range equal to or greater than 401
degrees F that are generally intended for use as a petrochemical feedstock and are not defined elsewhere." For
comparison, various material safety data sheets (MSDSs) published by producers of distillate No. 2 indicate a boiling
range for this product of 320 to 700 degrees Fahrenheit. The relatively open definition of the classification "other oils"
leaves room for potentially significant variation in the heating value, density and carbon share properties of each
feedstock oil having a boiling point above 400 degrees Fahrenheit, creating a large band of uncertainty beyond that
associated with the C factor for distillate No. 2.
Kerosene
A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use as a light
source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel. Kerosene is
generally comparable to No. 1 distillate oil.
Methodology
The average density and C share of kerosene are assumed to be the same as those for distillate No. 1 since the physical
characteristics of the products are very similar. Thus, a density of 35.3 degrees API and average C share of 86.40 percent
were applied to a standard heat content for distillate No. 1 of 5.825 MMBtu per barrel to yield a C content coefficient of
19.96 MMT C/QBtu.
Data Sources
A standard heat content for distillate No. 1 was adopted from EIA (2009a).
Uncertainty
Uncertainty in the estimated C content for kerosene is driven by the selection of distillate No. 1 as a proxy for kerosene.
If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 1.3 percent lower. If
kerosene is more aptly compared to No. 2 distillate oil, then the true C content coefficient is likely to be about 1.1
percent higher. While kerosene is a light petroleum distillate, like distillate No. 1, the two oil classes have some variation
in their properties. For example, the boiling range of kerosene is 250 to 550 degrees Fahrenheit, whereas No. 1 oils
typically boil over a range from 350 to 615 degrees Fahrenheit. The properties of individual kerosenes will vary with their
use and particular crude origin, as well. Both kerosene and fuel oil No. 1 are primarily composed of hydrocarbons having
9 to 16 carbon atoms per molecule. However, kerosene is a straight-run No. 1 fuel oil, additional cracking processes and
additives contribute to the range of possible fuels that make up the broader distillate No. 1 oil category.
Petroleum Coke
Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid, usually has a
C content greater than 90 percent, and is used as a boiler fuel and industrial raw material.
Methodology
Ultimate analyses of two samples of petroleum coke showed an average C share of 92.28 percent. The
density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu
assumed. Together, these factors produced an estimated C content coefficient of 27.85 MMT C/QBtu.
Data Sources
C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken from the
ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2009a).
ASTM standard
per barrel
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Uncertainty
The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two factors: the
use of only two samples to establish C contents and a standard heat content which may be too low. Together, these
uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent.
Special Naphtha
Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial hexane
and four classes of solvent: (1) Stoddard solvent, used in dry cleaning; (2) high flash point solvent, used as an industrial
paint because of its slow evaporative characteristics; (3) odorless solvent, most often used for residential paints; and (4)
high solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage,
requiring the development of multiple coefficients.
Methodology
The method for estimating the C content coefficient of special naphtha includes three steps.
Step 1. Estimate the carbon content coefficient for hexane
Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.63 percent C. Its density is 83.7
degrees API or 5.477 pounds per gallon and its derived C content coefficient is 21.40 MMT C/QBtu.
Step 2. Estimate the carbon contents of non-hexane special naphthas
The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion above).
The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point solvents
contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall 1977). These
assumptions, when combined with the relevant densities, yield the C content factors contained in Table A-37, below.
Table A-37: Characteristics of Non-hexane Special Naphthas
Aromatic Content
Density
Carbon Share
Carbon Content
Special Naphtha
(Percent)
(Degrees API)
(Percent Mass)
(MMT C/QBtu)
Odorless Solvent
1
55.0
84.51
19.41
Stoddard Solvent
15
47.9
84.44
20.11
High Flash Point
15
47.6
84.70
20.17
Mineral Spirits
30
43.6
85.83
20.99
Sources: EIA (2008b) and Boldt and Hall (1977).
Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha
EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are
weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the
remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions
coefficient for special naphthas is 19.74 MMT C/QBtu.
Data Sources
A standard heat content for special naphtha was adopted from EIA (2009a). Density and aromatic contents were
adopted from Boldt and Hall (1977).
Uncertainty
The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation of
overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of
odorless solvent and on the upper end by the C content of hexane. This implies an uncertainty band of -1.7 percent to
+8.4 percent.
Petroleum Waxes
The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at 77
degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline
Annex 2
A-91
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waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have
longer C chains and more variation in their chemical bonds than paraffin waxes.
Methodology
The method for estimating the C content coefficient for petroleum waxes includes three steps.
Step 1. Estimate the carbon content of paraffin waxes
For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic compounds with
a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is estimated at 45 degrees
API or 6.684 pounds per gallon.
Step 2. Estimate the carbon content of microcrystalline waxes
Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic compounds with a
chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is estimated at 36.7
degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products Handbook (Martin, S.W.
1960).
Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content
of paraffinic and microcrystalline waxes
A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption is 80
percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.30 percent, and the
weighted average density is 6.75 pounds per gallon. ElA's standard heat content for waxes is 5.537 MMBtu per barrel.
These inputs yield a C content coefficient for petroleum waxes of 19.80 MMT C/QBtu.
Data Sources
Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10 samples
found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2009a).
Uncertainty
Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and
microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to + 1 percent because of the
nearly uniform relationship between C and other elements in petroleum waxes broadly defined.
Crude Oil, Unfinished Oils, and Miscellaneous Products
U.S. energy statistics include several categories of petroleum products designed to ensure that reported refinery
accounts "balance" and cover any "loopholes" in the taxonomy of petroleum products. These categories include crude
oil, unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products account
for less than one percent of oil consumption, and unfinished oils are a balancing item that may show negative
consumption. For C accounting purposes, it was assumed that all unfinished oils have the same C content as crude oil.
The miscellaneous products category reported by EIA includes miscellaneous products that are not reported elsewhere
in the EIA data set. According to EIA recovered sulfur compounds from petroleum and natural gas processing, and
potentially carbon black feedstock could be reported in this category. Recovered sulfur has no carbon content and would
not be reported in the Inventory. Based on this information, the miscellaneous products category reported by EIA was
assumed to be mostly petroleum refinery sulfur compounds that do not contain carbon (EIA 2019). Therefore, the
carbon content for miscellaneous products was assumed to be zero across the time series.
Methodology
EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a method of
estimating C content based on this information, results of ultimate analyses of 182 crude oil samples were collected.
Within the sample set, C content ranged from 82 to 88 percent C, but almost all samples fell between 84 percent and 86
percent C. The density and sulfur content of the crude oil data were regressed on the C content, producing the following
equation:
A-92 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Equation A-5: C Content of Cruel Oil
Percent C = 76.99 + (10.19 x Specific Gravity) + (-0.76 x Sulfur Content)
Absent the term representing sulfur content, the equation had an R-squared of only 0.35.80 When C content was adjusted
to exclude sulfur, the R-squared value rose to 0.65. While sulfur is the most important non-hydrocarbon impurity,
nitrogen and oxygen can also be significant, but they do not seem to be correlated with either density or sulfur content.
Restating these results, density accounts for about 35 percent of the variation in C content, impurities account for about
30 percent of the variation, and the remaining 35 percent is accounted for by other factors, including (presumably) the
degree to which aromatics and polynuclear aromatics are present in the crude oil. Applying this equation to the 2008
crude oil quality data (30.21 degrees API and 1.47 percent sulfur) produces an estimated C content of 84.79 percent.
Applying the density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced
an emissions coefficient of 20.31 MMT C/QBtu.
Data Sources
Carbon content was derived from 182 crude oil samples, including 150 samples from U.S. National Research Council
(1927). A standard heat content for crude oil was adopted from EIA (2009a).
Uncertainty
The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be explained
by density and sulfur content. This variation is likely to alter the C content coefficient by +3 percent. Since unfinished oils
and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is likely to be
bounded by the range of petroleum products described in this chapter at +10 percent.
Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil
Fuels
The following section describes changes to carbon content coefficients of fossil fuels, organized by the calendar year in
which the update was implemented. Additional information on which Inventory year these changes appear is provided
within each section.
Coal
Original 1994 Analysis
A set of 5,426 coal samples from the EIA coal analysis file were used to develop C content estimates. The results from
that sample set appear below in Table A-38. The EIA Coal Analysis File was originally developed by the U.S. Bureau of
Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the United States.
Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in U.S. government
laboratories. The coefficients developed in 1994 were in use for the 1990 through 2000 Inventory and are provided in
Table A-38.
Table A-38: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
Consuming Sector
Electric Power
25.68
25.69
25.69
26.71
25.72
25.74
25.74
25.76
25.76
25.76
25.76
Industrial Coking
25.51
25.51
25.51
25.51
25.52
25.53
25.55
25.56
25.56
25.56
25.56
Other Industrial
25.58
25.59
25.62
25.61
25.63
25.63
25.61
25.63
25.63
25.63
25.63
Residential/Commercial
25.92
26.00
26.13
25.97
25.95
26.00
25.92
26.00
26.00
26.00
26.00
Coal Rank
80 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by variation in
the independent variables.
Annex 2
A-93
-------
Anthracite
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
28.13
Bituminous
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
25.37
Sub-bituminous
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
26.24
Lignite
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
26.62
Sources: Emission factors by consuming sector from B.D. Hong and E.R. Slatnick, "Carbon Dioxide Emission Factors for Coal,
"U.S. EIA, Quarterly Coal Report, January-March 1994 (Washington, DC, 1994); and emission factors by rank from Science
Applications International Corporation, Analysis of the Relationship Between Heat and Carbon Content of U.S. Fuels: Final Task
Report, Prepared for the U.S. EIA, Office of Coal, Nuclear, Electric and Alternative Fuels (Washington, DC 1992).
Subsequent Updates
In 2002 a database compiled by the U.S. Geological Survey (USGS), CoalQual 2.0 (1998), was adopted to update the
analysis. The updated sample set included 6,588 coal samples collected by the USGS and its state affiliates between 1973
and 1989. The decision to switch to the sample data contained in the USGS CoalQual database from the EIA database
was made because the samples contained in the USGS database were collected and analyzed more recently than those
obtained by EIA from the Bureau of Mines. The updated methodology first appeared in the 1990-2004 Inventory. The
methodology employed for these estimates has remained unchanged since 2002,81 however, the underlying coal data
sample set has been updated over the years to integrate new data sets as they became available.
In 2010 sample data from the Energy Institute at Pennsylvania State University (504 samples) were added to the 6,588
USGS samples to create a new database of 7,092 samples. The new coefficients developed in the 2010 update were first
implemented for the 1990 through 2008 Inventory.
In 2019 sample data from the Montana Bureau of Mines & Geology (908 samples), the Illinois State Geological Survey
Coal Quality Database (460 samples), and the Indiana Geological Survey Coal Quality Database (745 samples) were used
to calculate updated carbon contents by rank for Montana, Illinois, and Indiana. Combining revised carbon contents for
these three states with the carbon contents for all other states calculated from the USGS and Pennsylvania State
University samples yielded updated national average carbon contents by coal rank and end-use sector. The new
coefficients developed in the 2019 update were first implemented for the 1990 through 2017 Inventory.
In 2021, carbon content coefficients for industrial coking coal were updated to be annually variable to align with the
variability of other sectors and coal ranks. The new coefficients developed were first implemented for the 1990 through
2019 Inventory. See Table A-22 for the carbon content coefficients values used in this Inventory.
Natural Gas
Original 1994 Analysis
Prior to the 1990 through 2008 Inventory, descriptive statistics were used to stratify 6,743 samples of pipeline quality
natural gas by heat content and then to determine the average C content of natural gas at the national average heat
content (EIA 1994). The same coefficient was applied to all pipeline natural gas consumption for all years, because U.S.
energy statistics showed a range of national average heat contents of pipeline gas of only 1,025 to 1,031 Btu per cubic
foot (1 percent) from 1990 through 1994. A separate factor was developed in the same manner for all flared gas.
Previously, a weighted national average C content was calculated using the average C contents for each sub-sample of
gas that conformed with an individual state's typical cubic foot of natural gas since there is regional variation in energy
content. The result was a weighted national average of 14.47 MMT C/QBtu.
2010 and 2019 Updates
A revised analytical methodology introduced in 2010 underlies the natural gas C coefficients used in this report. This
methodology was first implemented in the 1990 through 2008 Inventory. The revised analysis conducted in 2010 used
the same set of samples, but utilized a regression equation, as described above, of sample-based heat content and
carbon content data in order to calculate annually variable national average C content coefficients based on annual
81 In 2009, the analysis of the USGS Coal Qual data was updated to make a technical correction that affected the value for
lignite and those sectors which consume lignite. The updated coefficients resulting from this correction were first implemented
for the 1990 through 2007 Inventory.
A-94 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
national average heat contents for pipeline natural gas and for flare gas. In addition, the revised analysis calculated an
average C content from all samples with less than 1.5 percent C02 and less than 1,050 Btu/cf (samples most closely
approximating the makeup of pipeline quality natural gas).
In 2019, this analysis was updated again to calculate annually variable national average C content coefficients for years
2009 through 2017 in the time series using heat contents published in EIA (2019). The resulting average was 14.43 MMT
C/QBtu, which is slightly less than the previous weighted national average of 14.47 MMT C/QBtu. The 2019 update was
first implemented in the 1990 through 2017 Inventory. The average C contents from the 1994 calculations are presented
in Table A-39 below for comparison.
Table A-39: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (MMT
C/QBtu)
Sample Average Carbon Content
GRI Full Sample 14.51
Greater than 1,000 Btu 14.47
1,025 to 1,035 Btu 14.45
975 to 1,000 Btu 14.73
1,000 to 1,025 Btu 14.43
1,025 to 1,050 Btu 14.47
1,050 to 1,075 Btu 14.58
1,075 to 1,100 Btu 14.65
Greater than 1,100 Btu 14.92
Weighted National Average 14.47
Source: EIA (1994).
Petroleum Products
2010 Update
All of the petroleum product C coefficients except that for Aviation Gasoline Blending Components were updated in 2010
for the 1990 through 2008 Inventory and held constant through the current Inventory. EPA updated these factors to
better align the fuel properties data that underlie the Inventory factors with those published in EPA's Mandatory
Reporting of Greenhouse Gases Rule (EPA 2009b), Suppliers of Petroleum Products (MM) and Stationary Combustion (C)
subparts. The coefficients that were applied in previous reports are provided in Table A-40 below. Specifically, each of
the coefficients used in this report have been calculated from updated density and C share data, largely adopted from
analyses undertaken for the Greenhouse Gas Reporting Rule (EPA 2009b). In some cases, the heat content applied to the
conversion to a carbon-per-unit-energy basis was also updated. Additionally, the category Misc. Products (U.S.
Territories), which is based upon the coefficients calculated for crude oil, was allowed to vary annually with the crude oil
coefficient. The petrochemical feedstock category was eliminated because the constituent products—naphthas and
other oils—are estimated independently. Further, although the level of aggregation of U.S. energy statistics currently
limits the application of coefficients for residual and distillate fuels to these two generic classifications, individual
coefficients for the five major types of fuel oil (Nos. 1, 2, 4, 5 and 6) were estimated and are presented in Table A-32
above. Each of the C coefficients applied in previous Inventories are provided below for comparison (Table A-40).
Annex 2
A-95
-------
Table A-40: Carbon Content Coefficients and Underlying Data for Petroleum Products
Carbon Content
Gross Heat of Combustion
Density
Fuel
(MMT C/QBtu)
(MMBtu/Barrel)
(API Gravity)
Percent Carbon
Motor Gasoline
19.27
5.220
59.1
86.60
LPG (Propane)
17.15
3.841
155.3
81.80
HGL (Energy Use)a
17.47
(See b)
(See b)
(See b)
HGL (Non-Energy Use)a
16.85
(See b)
(See b)
(See b)
Jet Fuel
19.33
5.670
42.0
86.30
Distillate Fuel
19.95
5.825
35.5
86.34
Residual Fuel
21.49
6.287
11.0
85.68
Asphalt and Road Oil
20.62
6.636
5.6
83.47
Lubricants
20.24
6.065
25.6
85.80
Petrochemical Feedstocks
19.37
5.248c
67.lc
84.11c
Aviation Gas
18.87
5.048
69.0
85.00
Kerosene
19.72
5.670
41.4
86.01
Petroleum Coke
27.85
6.024
-
92.28
Special Naphtha
19.86
5.248
51.2
84.76
Petroleum Waxes
19.81
5.537
43.3
85.29
Still Gas
17.51
6.000
-
-
Crude Oil
20.33
5.800
30.5
85.49
Unfinished Oils
20.33
5.825
30.5
85.49
Miscellaneous Products'1
0.00
0.00
30.5
85.49
Pentanes Plus
18.24
4.620
81.7
83.70
Natural Gasoline
18.24
4.620
81.7
83.70
Indicates no sample data available.
a HGL is a blend of multiple paraffinic and olefinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with
their own heat content, density and C content, see Table A-34.
b Heat, density, and percent carbon values are provided separately for ethane, and isobutene, butane, ethylene, isobutylene,
and butylene.
c Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical
feedstocks with higher boiling points are assumed to have the same characteristics as distillate fuel.
d The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do
not contain carbon (EIA 2019).
Sources: EIA (1994), EIA (2008a), EPA (2009c), EPA (2020b), ICF (2020).
Additional revisions to the Inventory's C coefficients since 1990 are detailed below.
Jet Fuel
1995 Update
Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71 MMT
C/QBtu to 19.33 MMT C/QBtu. This downward revision was the result of a shift in the sample set used from one
collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and
published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as
slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based
on an EIA standard and probably yields a downward bias in the revised C content coefficient. The coefficient revised in
1995 was first implemented in the 1990 through 2007 Inventory.
2010 Update
The coefficient was revised again for the 1990 through 2008 Inventory, returning to Martel and Angello and NIPER as the
source of the carbon share and density data, respectively, for kerosene-based fuels. This change was made in order to
align the coefficients used for this report with the values used in EPA's Mandatory Reporting of Greenhouse Gases Rule
(EPA 2009b). The return to the use of the Martel and Angello and NIPER coefficients was deemed more appropriate for
A-96 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
the Rule as it was considered a more conservative coefficient given the uncertainty and variability in coefficients across
the types of jet fuel in use in the United States.
Hydrocarbon Gas Liquids (HGL)
Summary of Previous Updates
The C content coefficient of HGL is updated annually to reflect changes in the consumption mix of the underlying
compounds: ethane; propane; isobutane; normal butane; ethylene; propylene; isobutylene; and butylene. According to
EIA, LPG is a subset of HGL, which include the paraffinic compounds: ethane; propane; isobutane; and normal butane. In
1994, EIA included pentanes plus—assumed to have the characteristics of hexane—in the mix of compounds broadly
described as LPG. In 1995, EIA removed pentanes plus from this fuel category. Because pentanes plus is relatively rich in
C per unit of energy, its removal from the consumption mix lowered the C content coefficient for LPG from 17.26 MMT
C/QBtu to 16.99 MMT C/QBtu. In 1998, EIA began separating LPG consumption into two categories: energy use and non-
fuel use and providing individual coefficients for each. Because LPG for fuel use typically contains higher proportions of
propane than LPG for non-fuel use, the C content coefficient for fuel use was 1.8 to 2.5 percent higher than the
coefficient for non-fuel use in previous inventories (see Table A-40).
However, in 2010 the assumptions that underlie the selection of density and heat content data for each pure LPG
compound were updated, leading to a significant revision of the assumed properties of ethane. In 2010, the physical
characteristics of ethane, which constitutes over 90 percent of LPG consumption for non-fuel uses, were updated to
reflect ethane that is in (refrigerated) liquid form. Previously, the share of ethane was included using the density and
energy content of gaseous ethane. Table A-41, below, compares the values applied for each of the compounds under the
two sets of coefficient calculations, those used in the 1990 through 2007 Inventory and those used in the 1990 through
2008 Inventory to the 1990 through 2018 Inventory. The C share of each pure compound was also updated by using
more precise values for each compound's molecular weight.
Due in large part to the revised assumptions for ethane, the weighted C content for non-fuel use was now higher than
that of the weighted coefficient for fuel use, which is dominated by the consumption of more dense propane. Under the
revised assumptions, each annual weighted coefficient for non-fuel LPG consumption is 1.2 to 1.7 percent higher each
year than is that for LPGs consumed for fuel (energy) uses.
Table A-41: Physical Characteristics of Liquefied Petroleum Gases
1990-2007
2010 Update
1990-2007
2010 Update
1990-2007
2010 Update
C Content
C Content
Chemical
Density
Density
Energy Content
Energy Content
Coefficient
Coefficient
Compound
Formula
(bbl / MT)
(bbl / MT)
(MMBtu/bbl)
(MMBtu/bbl)
(MMT C/QBtu)
(MMT C/QBtu)
Ethane
c2h6
16.88
11.55
2.916
3.082
16.25
17.16
Propane
C3Hs
12.44
12.76
3.824
3.836
17.20
16.76
Isobutane
C4H10
11.20
11.42
4.162
3.974
17.75
17.77
n-butane
C4H10
10.79
10.98
4.328
4.326
17.72
17.75
Sources: Updated: Densities - CRC Handbook of Chemistry and Physics, 89th Ed. (2008/09); Energy Contents - EPA (2009b). All
values are for the compound in liquid form. The density and energy content of ethane are for refrigerated ethane (-89 degrees
C). Values for n-butane are for pressurized butane (-25 degrees C). Values in previous editions of this Inventory: Gurthrie
(1960).
2021 Updates
In 2021, the coefficients were revised again. This update was made in order to align the coefficients used for this report
with the updated heat content values used in ElA's energy data statistics (EIA 2022; EIA 2021a). EIA states, "LPG is a
subset of HGL, which include the paraffinic compounds: ethane; propane; isobutane; and normal butane," therefore the
Inventory revised the fuel type classification of LPG to HGL to indicate this fuel types includes both paraffinic and olefinic
compounds. Furthermore, EIA (2020a) states that HGL consumption in the residential, commercial, and transportation
sectors is 100 percent propane. Therefore, a constant, non-weighted propane C content coefficient is applied to HGL
consumption in these sectors and is referred to as "LPG - Propane" throughout the Inventory.
The mix of HGL consumed for non-fuel use differs significantly from the mix of HGL that is combusted. C content
coefficients for HGL used for fuel use and non-fuel applications were developed based on the consumption mix of the
Annex 2
A-97
-------
individual compounds reported in U.S. energy statistics (EIA 2021a) for industrial fuel use and industrial non-fuel use
across the Inventory time series. The C content of each HGL was obtained from EPA (2013) and applied to the fuel use
and non-fuel use consumption of each compound. The carbon content coefficient for industrial fuel use and industrial
non-fuel use HGL was then calculated through a weighted average that accounts for the consumption proportion for
each paraffinic and olefinic compound and their associated C contents (ICF 2020).
Distillate Fuel
2021 Updates
The carbon content of diesel fuel is calculated according to ASTM D3343,82 Standard Test Method for the Estimation of
Hydrogen Content of Aviation Fuels using fuel properties inputs from the NAFS for each year and season. This method
uses a correlation between the measured fuel distillation range, API gravity, and aromatic content to estimate the
hydrogen content (Browning 2020).83
Motor Gasoline
Summary of Previous Updates
The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives in a
representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects of
the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change
resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in the
1990 through 1995 Inventory. In 2005 through 2006 reformulated fuels containing ethers began to be phased out
nationally. Ethanol was added to gasoline blends as a replacement oxygenate, leading to another shift in gasoline density
(see Table A-32), in the list and proportion of constituents that form the blend and in the blended C share based on those
constituents.
82 ASTM I nternational, ASTM D3343-16, Standard Test Method for Estimation of Hydrogen Content of Aviation Fuels,
https://www.astm.org/Staridards/D3343.htm.
83 As equations are based on assuming hydrocarbon containing fuels only, C % is 100 - H %.
A-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-42: Carbon Content Coefficients for Petroleum Products, 1990-2007 (MMT C/QBtu)
Fuel Type
1990
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
Petroleum
Asphalt and Road Oil
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
20.62
Aviation Gasoline
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
Distillate Fuel Oil
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
Jet Fuel3
19.40
19.34
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
19.33
Kerosene
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
19.72
LPG (energy use)a
17.21
17.20
17.20
17.18
17.23
17.25
17.20
17.21
17.20
17.21
17.20
17.19
17.19
17.18
LPG (non-energy use)a
16.83
16.87
16.86
16.88
16.88
16.84
16.81
16.83
16.82
16.84
16.81
16.81
16.78
16.76
Lubricants
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
20.24
Motor Gasoline3
19.41
19.38
19.36
19.35
19.33
19.33
19.34
19.34
19.35
19.33
19.33
19.33
19.33
19.33
Residual Fuel
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
21.49
Other Petroleum
AvGas Blend Components
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
18.87
MoGas Blend Components3
19.41
19.38
19.36
19.35
19.33
19.33
19.34
19.34
19.35
19.33
19.33
19.33
19.33
19.33
Crude Oil3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Misc. Products3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Misc. Products (Terr.)
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
20.00
Naphtha (<401 deg. F)
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
18.14
Other Oil (>401 deg. F)
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
19.95
Pentanes Plus
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
18.24
Petrochemical Feed.
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
19.37
Petroleum Coke
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
27.85
Still Gas
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
17.51
Special Naphtha
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
19.86
Unfinished Oils3
20.16
20.23
20.25
20.24
20.24
20.19
20.23
20.29
20.30
20.28
20.33
20.33
20.33
20.33
Waxes
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
Other Wax and Misc.
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
19.81
a C contents vary annually based on changes in fuel composition.
2021 Updates
The annual C content of gasoline over the time series of the Inventory was determined using a combination of two data sources (Browning 2020). The first is the
measured properties of both regular and premium gasoline from the Alliance of North American Fuel Survey (NAFS). The second is the prime supplier sales volumes of
motor gasoline by type and grade from the EIA.
Annex 2
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References
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API (1990 through 2008) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum Institute.
ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for Testing and
Materials. Philadelphia, PA.
Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American Society for Testing
and Materials, p. 30.
Browning, L. (2020) GHG Inventory EF Development Using Certification Data. Technical Memo, September 2020.
Chemical Rubber Company (CRC) (2008/2009), Handbook of Chemistry and Physics, 89th Ed., editor D. Lide, Cleveland,
OH: CRC Press.
DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of Standards.
Washington, D.C. pp. 16-21.
EIA (2022) Monthly Energy Review, February 2022, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2022/02).
EIA (2021a) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
EIA (2021b) Prime Supplier Sales Volume, U.S. Department of Energy, Washington, D.C. Available online at
https://www.eia.gov/dnav/pet/pet_cons_prim_dcu_nus_m.htm.
EIA (2001 through 2021a) Annual Coal Report, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA-0584.
EIA (2001 through 2021b) Annual Coal Distribution Report, U.S. Department of Energy, Energy Information
Administration. Washington, D.C. DOE/EIA.
EIA (2019) Personal communication between EIA and ICF on November 11, 2019.
EIA (2009a) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2008).
EIA (2009b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
EIA (2008a) Monthly Energy Review, September 2006 and Published Supplemental Tables on Petroleum Product detail.
Energy Information Administration, U.S. Department of Energy, Washington, D.C. DOE/EIA-0035(2007/9).
EIA (2008b) Documentation for Emissions of Greenhouse Gases in the United States 2006. DOE/EIA-0638(2006). October
2008.
EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. Washington, D.C.
August 2001. Available online at http://www.eia.doe.gov/cneaf/electricity/cq/cq_sum.html.
EIA (1990 through 2001) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration.
Washington, D.C. DOE/EIA 0584.
EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, U.S.
Department of Energy. Washington, D.C. November 1994. DOE/EIA 0573.
EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply Division
(unpublished manuscript, April 1993).
EPA (2020a) The Emissions & Generation Resource Integrated Database (eGRID) 2018 Technical Support Document.
Clean Air Markets Division, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2020b) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel
C02 Emission Factors - Memo.
A-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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EPA (2013) Memo: Table of Final 2013 Revisions to the Greenhouse Gas Reporting Rule, Amendments to Table C-l to 40
CFR Part 98, Subpart C: Table C—1 to Subpart C—Default C02 Emission Factors and High Heat Values for Various Types
of Fuel. Available online at: https://www.epa.gov/sites/production/files/2015-01/documents/memo-2013-technical-
revisions.pdf.
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2009a) "Industry Overview and Current Reporting Requirements for Petroleum Refining and Petroleum Imports,"
Petroleum Product Suppliers Technical Support Document for the Proposed Mandatory Reporting Rule. Office of Air and
Radiation. January 30, 2009.
EPA (2009b) Mandatory Reporting of Greenhouse Gases Rule. Federal Register Docket ID EPA-HQ-OAR-2008-0508-2278,
September 30, 2009.
EPA (2009c) Technical Support Document, Petroleum Products and Natural Gas Liquids: Definitions, Emission Factors,
Methods and Assumptions. Final Rule for Mandatory Reporting of Greenhouse Gases. September 15, 2009. Available
online at: https://www.epa.gov/sites/production/files/2015-07/documents/subpartmmproductdefinitions.pdf.
Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. Chowdiah, and
A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. GRI-92/0123.
March 1992.
Green & Perry, ed. (2008) Perry's Chemical Engineers' Handbook, 8th Ed. New York, NY, McGraw-Hill.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
Guthrie, V.B., ed. (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY, McGraw-
Hill.
Hadaller, O.J. and A.M. Momenthy (1990) The Characteristics of Future Fuels, Part 1, "Conventional Heat Fuels". Seattle,
WA, Boeing Corp. September 1990. pp. 46-50 (2006).
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
Intergovernmental Panel on Climate Change (IPCC) 2006 IPCC Guidelines for National Greenhouse Gas Inventories.
Prepared by the National Greenhouse Gas Inventories Programme (Japan, 2006).Matar, S. and L. Hatch (2000) Chemistry
of Petrochemical Processes, 2nd Ed. Gulf Publishing Company: Houston.
Martel, C.R., and L.C. Angello (1977) "Hydrogen Content as a Measure of the Combustion Performance of Hydrocarbon
Fuels," in Current Research in Petroleum Fuels, Volume I. New York, NY, MSS Information Company, p. 116.
Martin, S.W. (1960) "Petroleum Coke," in Virgil Guthrie (ed.), Petroleum Processing Handbook, New York, NY, McGraw-
Hill, pp. 14-15.
Meyers (2004) Handbook of Petroleum Refining Processes, 3rd ed., NY, NY: McGraw Hill.
National Institute for Petroleum and Energy Research (NIPER) (1990 through 2009) Motor Gasolines, Summer and Motor
Gasolines, Winter.
NIPER (1993) C. Dickson, Aviation Turbine Fuels, 1992, NIPER-179 PPS93/2 (Bartlesville, OK: National Institute for
Petroleum and Energy Research, March 1993).
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
Quick, Jeffrey (2010) "Carbon Dioxide Emission Factors for U.S. Coal by Origin and Destination," Environmental Science &
Technology, Forthcoming.
Annex 2
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U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and
Technology, New York, NY, McGraw-Hill.
Unzelman, G.H. (1992) "A Sticky Point for Refiners: FCC Gasoline and the Complex Model," Fuel Reformulation,
July/August 1992, p. 29.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
Wauquier, J., ed. (1995) Petroleum Refining, Crude Oil, Petroleum Products and Process Flowsheets (Editions Technip
Paris, 1995) pg. 225, Table 5.16.
A-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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2.3. Methodology for Estimating Carbon Emitted from Non-Energy
Uses of Fossil Fuels
Carbon (C) storage associated with the non-energy use of fossil fuels was calculated by multiplying each fuel's potential
emissions (i.e., each fuel's total C content) by a fuel-specific storage factor, as listed in Table A-43. The remaining C—i.e.,
that which is not stored—is emitted. This sub-annex explains the methods and data sources employed in developing the
storage factors for (1) petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer uses, hydrocarbon
gas liquids (HGL), pentanes plus, naphthas, other oils, still gas, special naphtha), (2) asphalt and road oil, (3) lubricants,
and (4) waxes. The storage factors84 for the remaining other (industrial coking coal, petroleum coke, distillate fuel oil, and
other petroleum) non-energy fuel uses are either based on values recommended for use by IPCC (2006), or when these
were not available, assumptions based on the potential fate of C in the respective non-energy use (NEU) products.
Table A-43: Fuel Types and Percent of C Stored for Non-Energy Uses
Sector/Fuel Type Storage Factor (%)
Industry
Industrial Coking Coal3 10%
Industrial Other Coalb 63%
Natural Gas to Chemical Plants'5 63%
Asphalt & Road Oil 100%
HGLb 63%
Lubricants 9%
Natural Gasolineb 63%
Naphtha (<401 deg. F)b 63%
Other Oil (>401 deg. F)b 63%
Still Gasb 63%
Petroleum Cokec 30%
Special Naphthab 63%
Distillate Fuel Oil 50%
Waxes 58%
Miscellaneous Products'1 0%
Transportation
Lubricants 9%
U.S. Territories
Lubricants 9%
Other Petroleum (Misc. Prod.) 10%
a Includes processes for which specific coking coal consumption and emission factor data are not available. Consumption of
coking coal for production of iron and steel is covered in the Industrial Processes and Product Use chapter.
b The storage factor listed is the value for 2020. As described in this annex, the factor varies over time.
c Assumes petroleum coke consumption is for pigments. Consumption of petroleum coke for production of primary aluminum
anodes, electric arc furnace anodes, titanium dioxide, ammonia, urea, and ferroalloys is covered in the Industrial Processes
and Product Use chapter.
d The miscellaneous products category reported by EIA is assumed to be mostly petroleum refinery sulfur compounds that do
not contain carbon (EIA 2020).
The following sections describe the non-energy uses in greater detail, outlining the methods employed and data used in
estimating each storage factor. Several of the fuel types tracked by EIA are used in organic chemical synthesis and in
other manufacturing processes and are referred to collectively as "petrochemical feedstocks." Because the methods and
data used to analyze them overlap, they are handled as a group and are discussed first. Discussions of the storage factors
for asphalt and road oil, lubricants, waxes, and other products follow.
84Throughout this section, references to "storage factors" represent the proportion of carbon stored.
Annex 2
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Petrochemical Feedstocks
Petrochemical feedstocks—industrial other coal, natural gas for non-fertilizer uses,85 HGL, natural gasoline, naphthas,
other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals and
products. Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a few
of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e.,
predominantly CH4) to heavier, more complex naphthas and other oils.86
After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted
approximately 262.3 MMT C02 Eq., or 75 percent, of the 350.5 MMT C02 Eq. of non-energy fuel consumption in 2020.
For 2020, the storage factor for the eight fuel categories was 63 percent. In other words, of the net consumption, 63
percent was destined for long-term storage in products—including products subsequently combusted for waste
disposal—while the remaining 37 percent was emitted to the atmosphere directly as C02 (e.g., through combustion of
industrial by-products) or indirectly as C02 precursors (e.g., through evaporative product use). The indirect emissions
include a variety of organic gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which
eventually oxidize into C02 in the atmosphere. The derivation of the storage factor is described in the following sections.
Methodology and Data Sources
The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C content for
the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of feedstocks. One
aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were grouped
because of the overlap of their derivative products. Due to the many reaction pathways involved in producing
petrochemical products (or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their
parent fuel feedstocks.
Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the major
categories of petrochemical feedstocks. ElA's Petroleum Supply Annual publication tracks imports and exports of
petrochemical feedstocks, including HGL,87 and naphthas (i.e., most of the large volume primary chemicals produced by
petroleum refineries). These imports and exports are already factored into the U.S. fuel consumption statistics. However,
EIA does not track imports and exports of chemical intermediates and products produced by the chemical industry (e.g.,
xylenes, vinyl chloride), which are derived from the primary chemicals produced by the refineries. These products
represent very large flows of C derived from fossil fuels (i.e., fossil C), so estimates of net flows not already considered in
ElA's dataset were developed for the entire time series from 1990 to 2020.
The approach to estimate imports and exports involves three steps, listed here and then described in more detail below:
Step 1. Identify commodities derived from petrochemical feedstocks and calculate net import/export for
each.
Step 2. Estimate the C content for each commodity.
Step 3. Sum the net C imports/exports across all commodities.
Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA) and U.S.
Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC). NPRA provided
a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile import-export
85 Natural gas used as a petrochemical feedstock includes use in production of methanol. The storage factor developed for
petrochemical feedstocks includes emissions from the use of products. Therefore, it is assumed that emissions from the combustion of
methanol used in biodiesel are captured here and not reported as part of biodiesel combustion emissions.
86 Naphthas are compounds distilled from petroleum containing 4 to 12 carbon atoms per molecule and having a boiling point less than
401 degrees Fahrenheit. "Other oils" are distillates containing 12 to 25 carbon atoms per molecule and having a boiling point greater
than 401 degrees Fahrenheit.
87 HGL (formerly referred to as liquefied petroleum gas, or LPG) are hydrocarbons that occur as gases at atmospheric pressure and as
liquids under higher pressures. HGLs include paraffins, such as ethane, propane, butanes, and pentanes plus, and HGLs include olefins,
such as ethylene, propylene, and butylene. Adjustments were made in the current Inventory report to HGL activity data, carbon
content coefficients, and heat contents HGL.
A-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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data for periodic reports issued to NPRA's membership on trade issues. Additional feedstock commodities were
identified by HTS code in the BoC data system and included in the net import/export analysis.
One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are fuels
and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRA-
supplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of
petrochemical feedstocks. Although MTBE and related ether imports are included in the published NPRA data, these
commodities are not included in the total net imports/exports calculated here, because it is assumed that they are fuel
additives and do not contribute to domestic petrochemical feedstocks. Net exports of MTBE and related ethers are also
not included in the totals, as these commodities are considered to be refinery products that are already accounted for in
the EIA data. Imports and exports of commodities for which production and consumption data are provided by EIA (e.g.,
butane, ethylene, and liquefied petroleum gases) are also not included in the totals, to avoid double-counting.
Another difficulty is that one must be careful to assure that there is not double-counting of imports and exports in the
data set. Other parts of the mass balance (described later) provide information on C flows, in some cases based on
production data and in other cases based on consumption data. Production data relates only to production within the
country; consumption data incorporates information on imports and exports as well as production. Because many
commodities are emissive in their use, but not necessarily their production, consumption data is appropriately used in
calculations for emissive fates. For purposes of developing an overall mass balance on U.S. non-energy uses of C, for
those materials that are non-emissive (e.g., plastics), production data is most applicable. And for purposes of adjusting
the mass balance to incorporate C flows associated with imports and exports, it was necessary to carefully review
whether or not the mass balance already incorporated cross-boundary flows (through the use of consumption data), and
to adjust the import/export balance accordingly.
The BoC trade statistics are publicly available88 and cover a complete time series from 1990 to 2020. These statistics
include information on imports and exports of thousands of commodities. After collecting information on annual flows of
the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each commodity from
its chemical formula. In cases where the imports and exports were expressed in units of volume, rather than mass, they
were converted to mass based on the commodities' densities.
Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in Table A-
44. As shown in the table, the United States has been a net exporter of chemical intermediates and products throughout
the 1990 to 2020 period.
Table A-44: Net Exports of Petrochemical Feedstocks, 1990-2020 (MMT CO2 Eq.)
1990 2005 2010 2016 2017 2018 2019 2020
Net Exports 1Z0 j 65 " 73 12/7 li!i 16^9 204 21.6
After adjusting for imports and exports, the C budget is adjusted for the quantity of C that is used in the Industrial
Processes and Product Use sector of the Inventory. Fossil fuels used for non-energy purposes in industrial processes—
and for which C emissions and storage have been characterized through mass balance calculations and/or emission
factors that directly link the non-energy use fossil fuel raw material and the industrial process product—are not included
in the non-energy use sector. These industrial processes (and their non-energy use fossil fuel raw materials) include iron
and steel (coal coke), primary aluminum (petroleum coke), titanium oxide (petroleum coke), ferroalloys (petroleum
coke), carbon black (petroleum coke and other oils), silicon carbide (petroleum coke), and ammonia and urea (petroleum
coke and natural gas).
For each year of the Inventory, the total C content of non-energy uses was calculated by starting with the EIA estimate of
non-energy use, and reducing it by the adjustment factor for net exports (see Table A-44) and non-energy use reported
in the Industrial Processes and Product Use (IPPU) sector to yield net domestic fuel consumption for non-energy. The
balance was apportioned to either stored C or emissive C, based on a storage factor.
The overall storage factor for the feedstocks was determined by developing a mass balance on the C in feedstocks, and
characterizing products, uses, and environmental releases as resulting in either storage or emissions. The total C in the
system was estimated by multiplying net domestic consumption for non-energy by the C content of each of the
feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, HGL, pentanes plus, naphthas, other oils, still gas,
88See the U.S. International Trade Commission (USITC) Trade Dataweb at http://dataweb.usitc.gov/.
Annex 2
A-105
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special naphtha). Carbon content values for the fuel feedstocks are discussed in the Estimating Emissions from Fossil Fuel
Combustion and Estimating the Carbon Content from Fossil Fuel Combustion Annexes.
Next, C pools and releases in a variety of industrial releases, energy recovery processes, and products were
characterized. The C fate categories are plastics, energy recovery, synthetic rubber, synthetic fibers, organic solvents, C
black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions,
hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and
deicers (glycols), and silicones.89
The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage factor is the
C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of stored C and
emitted C (i.e., the outputs of the system) exceeded total C consumption (i.e., the inputs to the system) for some years in
the time series. To address this mass imbalance, the storage factor was calculated as C storage divided by total C outputs
(rather than C storage divided by C inputs).
Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C
consumed in the United States insofar as emissions of C02from waste combustion are accounted for separately in the
Inventory and are discussed in the Incineration of Waste section of the Energy chapter.
The following sections provide details on the calculation steps, assumptions, and data sources employed in estimating
and classifying the C in each product and waste shown in Table A-45. Summing the C stored and dividing it by total C
outputs yields the overall storage factor, as shown in the following equation for 2020:
Equation A-6: NEU Storage Factor Estimate for 2020
Overall Storage Factor = C Stored / (C Stored + C Emitted + C Unaccounted for) =
164.2 MMT C02 Eq. / (164.2 + 63.2 + 34.8) MMT C02 Eq. = 63%
C Stored
C Emitted
Product/Waste Type
(MMT C02 Eq.)
(MMT C02 Eq.)
Industrial Releases
0.1
5.8
TRI Releases
0.1
1.0
Industrial VOCs
NA
3.4
Non-combustion CO
NA
0.5
Hazardous Waste Incineration
NA
0.9
Energy Recovery
NA
44.4
Products
164.1
13.1
Plastics
143.7
NA
Synthetic Rubber
12.9
NA
Antifreeze and Deicers
NA
1.0
Abraded Tire Rubber
NA
0.2
Food Additives
NA
1.0
Silicones
0.5
NA
Synthetic Fiber
6.8
NA
Pesticides
0.2
0.3
Soaps, Shampoos, Detergents
NA
5.0
Solvent VOCs
NA
5.6
Total 164.2 63.2
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
The C unaccounted for is the difference between the C accounted for (discussed below) and the total C in the Total U.S.
Petrochemical consumption, which are the potential carbon emissions from all energy consumption in Non-Energy Use.
89 For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water discharges
associated with production facilities. Similarly, VOC emissions are generally associated with production facilities. These emissions could
have been accounted for as part of the Waste chapter, but because they are not necessarily associated with waste management, they
were included here. Toxic releases are not a "product" category, but they are referred to as such for ease of discussion.
A-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each is
discussed below.
Industrial Releases
Industrial releases include toxic chemicals reported through the Toxics Release Inventory (TRI), industrial emissions of
volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions from
hazardous waste incineration.
TRI Releases
Fossil-derived C is found in many toxic substances released by industrial facilities. The TRI, maintained by EPA, tracks
these releases by chemical and environmental release medium (i.e., land, air, or water) on a biennial basis (EPA 2000b).
By examining the C contents and receiving media for the top 35 toxic chemicals released, which account for 90 percent
of the total mass of chemicals, the quantity of C stored and emitted in the form of toxic releases can be estimated.
The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular formula. The
TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further subdivided
into air emissions, surface water discharges, underground injection, and releases to land; the latter is further broken
down to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to "Other On-Site Land Disposal."90 The C released
in each disposal location is provided in Table A-46.
Each on-site classification was assigned a storage factor. A 100 percent storage factor was applied to disposition of C to
underground injection and to disposal to RCRA-permitted landfills, while the other disposition categories were assumed
to result in an ultimate fate of emission as C02 (i.e., a storage factor of zero was applied to these categories). The release
allocation is not reported for off-site releases; therefore, the approach was to develop a C-weighted average storage
factor for the on-site C and apply it to the off-site releases.
For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C content
value, based upon the top 35 chemicals' C contents, was applied. The storage and emission allocation for the remaining
10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals.
Data on TRI releases for the full 1990 through 2020 time series were not readily available. Since this category is small
(less than 1 MMT C emitted and stored), the 1998 value was applied for the entire time series.
Carbon Stored
Carbon Emitted
Disposal Location
(kt CO? Eq.)
(kt CO? Eq.)
Air Emissions
NA
924
Surface Water Discharges
NA
6.7
Underground Injection
89.4
NA
RCRA Subtitle C Landfill Disposal
1.4
NA
Other On-Site Land Releases
NA
15.9
Off-site Releases
6.4
36
Total
97.2
982.6
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions
Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained (EPA 2021a) and
disaggregated based on EPA (2003), which has been published on the National Emission Inventory (NEI) Air Pollutant
Emission Trends web site. The 1990 through 2020 Trends data include information on NMVOC emissions by end-use
category; some of these fall into the heading of "industrial releases" in Table A-45 above, and others are related to
90 Only the top nine chemicals had their land releases separated into RCRA Landfills and Other Land Disposal. For the remaining
chemicals, it was assumed that the ratio of disposal in these two categories was equal to the carbon-weighted average of the land
disposal fate of the top nine chemicals (i.e., 8 percent attributed to RCRA Landfills and 92 percent in the "Other" category).
Annex 2
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"product use;" for ease of discussion, both are covered here. The end-use categories that represent "Industrial NMVOC
Emissions" include some chemical and allied products, certain petroleum related industries, and other industrial
processes. NMVOC emissions from solvent utilization (product use) were considered to be a result of non-energy use of
petrochemical feedstocks. These categories were used to distinguish non-energy uses from energy uses; other categories
where VOCs could be emitted due to combustion of fossil fuels were excluded to avoid double counting.
Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs, assumptions
were made concerning the average C content of the NMVOCs for each category of emissions. The assumptions for
calculating the C fraction of industrial and solvent utilization emissions were made separately and differ significantly. For
industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect the C content of
an average volatile organic compound based on the list of the most abundant NMVOCs provided in the Trends Report.
The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85 percent based
upon C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents approximately 90
percent, depending upon substitution).
An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent emissions. The
dataset identifies solvent emissions by compound or compound category for six different solvent end-use categories:
degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes. The
percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual
compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent
emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a single individual
compound was selected to represent each category, and the C content of the category was estimated based on the C
content of the representative compound. The overall C content of the solvent evaporation emissions for 1998, estimated
to be 56 percent, is assumed to be constant across the entire time series.
The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-47 for 1990 through 2020.
Industrial NMVOC emissions in 2020 were 3.4 MMT C02 Eq. and solvent evaporation emissions in 2020 were 5.6 MMT
C02 Eq.
Table A-47: Industrial and Solvent NMVOC Emissions
1990 1995 2000 2005 2016 2017 2018 2019 2020
Industrial NMVOCsa
NMVOCs ('000 Short Tons) 1,279 1,358 802 825 1,277 1,206 1,206 1,206 1,206
Carbon Content (%) 85% 85% 85% 85% 85% 85% 85% 85% 85%
Carbon Emitted (MMT C02
Eq.) 3.6 3.8 2.3 2.3 3.6 3.4 3.4 3.4 3.4
Solvent Evaporation*1
Solvents ('000 Short Tons) 5,750 6,183 4,832 4,245 2,999 2,972 2,972 2,972 2,972
Carbon Content (%) 56% 56% 56% 56% 56% 56% 56% 56% 56%
Carbon Emitted (MMT C02
Eq.) 10.8 11.6 9.0 7.9 5.6 5.6 5.6 5.6 5.6
a Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes
categories.
b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other
industrial processes, and non-industrial processes.
Non-Combustion Carbon Monoxide Emissions
Carbon monoxide (CO) emissions data were also obtained from the NEI data (EPA 2021a) and disaggregated based on
EPA (2003). There are three categories of CO emissions in the report that are classified as process-related emissions not
related to fuel combustion. These include chemical and allied products manufacturing, metals processing, and other
industrial processes. Some of these CO emissions are accounted for in the Industrial Processes and Product Use section
of this report and are therefore not accounted for in this section. These include total C emissions from the primary
aluminum, titanium dioxide, iron and steel, and ferroalloys production processes. The total C (CO and C02) emissions
from oil and gas production, petroleum refining, and asphalt manufacturing are also accounted for elsewhere in this
Inventory. Biogenic emissions (e.g., pulp and paper process emissions) are accounted for in the Land Use, Land-Use
Change and Forestry chapter and excluded from calculation of CO emissions in this section. Those CO emissions that are
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not accounted for elsewhere are considered to be by-products of non-fuel use of feedstocks and are thus included in the
calculation of the petrochemical feedstocks storage factor. Table A-48 lists the CO emissions that remain after taking into
account the exclusions listed above.
Table A-48: Non-Combustion Carbon Monoxide Emissions
1990
1995
2000
2005
2016
2017
2018
2019
2020
CO Emissions ('000 Short Tons)
489
481
623
461
358
327
327
327
327
Carbon Emitted (MMT C02 Eq.)
0.7
0.7
0.9
0.7
0.5
0.5
0.5
0.5
0.5
Note: Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other
industrial processes categories.
Hazardous Waste Incineration
Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).91 Industrial wastes,
such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air pollution control,
are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or toxic according to
standardized tests or studies conducted by EPA.
Hazardous wastes must be treated prior to disposal according to the federal regulations established under the authority
of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for those
wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking,
combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management,
and boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the
hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy
recovery calculations described below, they are not included as part of hazardous waste incineration.
EPA's Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these reports
provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this
information in its Resource Conservation and Recovery Act (RCRA) Information system (EPA 2013a), formerly reported in
its Biennial Reporting System (BRS) database (EPA 2000a, 2009, 2015a, 2016a, 2018, 2021b). Combusted hazardous
wastes are identified based on EPA-defined management system types M041 through M049 (incineration). Combusted
quantities are grouped into four representative waste form categories based on the form codes reported in the BRS:
aqueous liquids, organic liquids and sludges, organic solids, and inorganic solids. To relate hazardous waste quantities to
C emissions, "fuel equivalent" factors were derived for hazardous waste by assuming that the hazardous wastes are
simple mixtures of a common fuel, water, and noncombustible ash. For liquids and sludges, crude oil is used as the fuel
equivalent and coal is used to represent solids.
Fuel equivalent factors were multiplied by the tons of waste incinerated to obtain the tons of fuel equivalent. Multiplying
the tons of fuel equivalent by the C content factors (discussed in the Estimating the Carbon Content from Fossil Fuel
Combustion Annex) yields tons of C emitted. Implied C content is calculated by dividing the tons of C emitted by the
associated tons of waste incinerated.
Waste quantity data for hazardous wastes were obtained from EPA's RCRA Information/BRS database for reporting years
1989, 1991, 1993, 1995, 1997, 1999, 2001, 2003, 2005, 2007, 2009, 2011, 2013, 2015, 2017, 2019 (EPA 2000a, 2009,
2013a, 2015a, 2016a, 2018, 2021b). Combusted waste quantities were obtained from Form GM (Generation and
Management) for wastes burned on site and Form WR (Wastes Received) for waste received from off-site for
combustion. For each of the waste types, assumptions were developed on average waste composition (see Table A-49).
Regulations require incinerators to achieve at least 99.99 percent destruction of organics; this formed the basis for
assuming the fraction of C oxidized. Emissions from hazardous waste incineration in 2020 were 0.9 MMT C02 Eq. Table A-
50 lists the C02 emissions from hazardous waste incineration.
91 [42 U.S.C. §6924, SDWA §3004]
Annex 2
A-109
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Table A-49: Assumed Composition of Combusted Hazardous Waste by Weight (Percent)
Waste Type Water (%) Noncombustibles (%) Fuel Equivalent (%)
Aqueous Waste 90 5 5
Organic Liquids and Sludges 40 20 40
Organic Solids 20 40 40
Inorganic Solids 20 70 10
Table A-50: CO2 Emitted from Hazardous Waste Incineration (MMT CO2 Eq.)
1990
1995
2000
2005
2016
2017
2018
2019
2020
C02 Emissions
1.1
1.7
1.4
1.5
0.9
0.9
0.9
0.9
0.9
Energy Recovery
The amount of feedstocks combusted for energy recovery was estimated from data included in ElA's Manufacturers
Energy Consumption Survey (MECS) for 1991, 1994,1998, 2002, 2006, 2010, 2014, and 2018 (EIA 1994; 1997; 2001;
2005; 2010; 2013b; 2017; 2021). Some fraction of the fossil C exiting refineries and designated for use for feedstock
purposes actually ends up being combusted for energy recovery (despite the designation of feedstocks as a "non-energy"
use) because the chemical reactions in which fuel feedstocks are used are not 100 percent efficient. These chemical
reactions may generate unreacted raw material feedstocks or generate by-products that have a high energy content. The
chemical industry and many downstream industries are energy-intensive and often have boilers or other energy recovery
units on-site, and thus these unreacted feedstocks or by-products are often combusted for energy recovery. Also, as
noted above in the section on hazardous waste incineration, regulations provide a strong incentive—and in some cases
require—burning of organic wastes generated from chemical production processes.
Information available from the MECS include data on the consumption for energy recovery of "other" fuels in the
petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors. These
"other" fuels include refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and
blast furnace gases; scrap tires; liquor or black liquor; woodchips and bark; and other uncharacterized fuels. Fuel use of
petroleum coke is included separately in the fuel use data provided annually by EIA, and energy recovery of coke oven
gas and blast furnace gas (i.e., by-products of the iron and steel production process) is addressed in the Iron and Steel
production section in the Industrial Processes and Product Use chapter. Consumption of refinery still gas in the refinery
sector is also included separately in the fuel use data from EIA. The combustion of scrap tires in cement kilns, lime kilns,
and electric arc furnaces is accounted for in the Waste Incineration chapter; data from the Rubber Manufacturers
Association (RMA 2009a) were used to subtract out energy recovery from scrap tires in these industries. Consumption of
net steam, assumed to be generated from fossil fuel combustion, is also included separately in the fuel use data from
EIA. Therefore, these categories of "other" fuels are addressed elsewhere in the Inventory and not considered as part of
the petrochemical feedstocks energy recovery analysis. Liquor or black liquor and woodchips and bark are assumed to be
biogenic fuels, in accordance with IPCC (2006), and therefore are not included in the Inventory. The remaining categories
of fuels, including waste gas; waste oils, tars, and related materials; and other uncharacterized fuels are assumed to be
petrochemical feedstocks burned for energy recovery (see Table A-51). The conversion factors listed in Annex 2.1 were
used to convert the Btu values for each fuel feedstock to MMT C02. Petrochemical feedstocks combusted for energy
recovery corresponded to 42.5 MMT C02 Eq. in 1991, 35.1 MMT C02 Eq. in 1994, 58.0 MMT C02 Eq. in 1998, 70.6 MMT
C02 Eq. in 2002, 74.7 MMT C02 Eq. in 2006, 41.3 MMT C02 Eq. in 2010, 45.6 MMT C02 Eq. in 2014, and 44.4 MT C02 Eq in
2018. Values for petrochemical feedstocks burned for energy recovery for years between 1991 and 1994, between 1994
and 1998, between 1998 and 2002, between 2002 and 2006, between 2007 and 2010, between 2011 and 2013, and
between 2015 and 2017 have been estimated by linear interpolation. The value for 1990 is assumed to be the same as
the value for 1991, and the values for 2019 and 2020 are assumed to be the same as the value for 2018 (Table A-52).
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Table A-51: Summary of 2018 MECS Data for Other Fuels Used in Manufacturing/Energy
Recovery (Trillion Btu)
Waste
Refinery Still
Net
Other
Subsectorand Industry
NAICS CODE
Waste Gasa
Oils/Tarsb
Gasc
Steamd
Fuelse
Printing and Related Support
323
0
0
0
0
0
Petroleum and Coal Products
324
0
2
1,394
191
76
Chemicals
325
402
6
0
310
116
Plastics and Rubber Products
326
0
0
0
0
0
Nonmetallic Mineral Products
327
0
9
0
0
18
Primary Metals
331
3
0
0
10
3
Fabricated Metal Products
332
0
0
0
0
2
Machinery
333
0
0
0
0
1
Computer and Electronic Products
334
0
0
0
0
0
Electrical Equip., Appliances,
Components
335
0
0
0
0
0
Transportation Equipment
336
1
0
0
1
5
Furniture and Related Products
337
0
0
0
0
5
Miscellaneous
339
0
0
0
0
1
Total (Trillion Btu)
406
17
1,394
511
227
Average C Content (MMT/QBtu)
18.14
20.62
17.51
0
19.37
Fraction Oxidized
1
1
1
0
1
Total C (MMT)
7.36
0.35
24.41
0.00
4.40
Total C (MMT) (ex. still gas from
refining)
7.36
0.35
0.00
0.00
4.40
NA (Not Applicable)
a C content: Waste Gas is assumed to be same as naphtha <401 deg. F.
b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil.
c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate.
e C content: "Other" is assumed to be the same as petrochemical feedstocks.
Table A-52: Carbon Emitted from Fuels Burned for Energy Recovery (MMT CO2 Eg.)
1990 1995 2000 2005 2016 2017 2018 2019 2020
C Emissions 42.5 40.8 I 64.3 73.7 45.0 44.7 44.4 44.4 44.4
Products
More C is found in products than in industrial releases or energy recovery. The principal types of products are plastics;
synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze and
deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases of
NMVOCs; the other product types are discussed below.
Plastics
Data on annual production of plastics through 2005 were taken from the American Plastics Council (APC), as published in
Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through direct
communication with the APC (APC 2000, 2001, 2003 through 2006; SPI 2000; Eldredge-Roebuck 2000). Data for 2006
through 2020 were taken directly or derived from the American Chemistry Council (ACC 2007 through 2021a
supplemented by Vallianos 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021). In 2009, the American
Chemistry Council consolidated the resin categories for which it reports plastics production. Production numbers in the
original categories were provided via personal correspondence for 2009, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
2018, 2019, and 2020 (Vallianos 2011; 2012; 2013; 2014; 2015; 2016; 2017; 2018; 2019; 2020; 2021). Production figures
Annex 2
A-111
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for the consolidated resin categories in 2010 were linearly interpolated from 2009 and 2011 data. Production was
organized by resin type (see Table A-53) and by year.
Several of the resin categories included production from Canada and/or Mexico, in addition to the U.S. values for part of
the time series. The production data for the affected resins and years were corrected using an economic adjustment
factor, based on the percent of North American production value in this industry sector accounted for by the United
States (Chemistry Industry Association of Canada 2021; Bank of Canada 2021). A C content was then assigned for each
resin. These C contents were based on molecular formulae and are listed in Table A-54 and Table A-55. In cases where
the resin type is generic, referring to a group of chemicals and not a single polymer (e.g., phenolic resins, other styrenic
resins), a representative compound was chosen. For other resins, a weighted C content of 75 percent was assumed (i.e.,
it was assumed that these resins had the same content as those for which a representative compound could be
assigned).
There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. As noted in
the chapter, an estimate of emissions related to the combustion of these plastics in the municipal solid waste stream can
be found in the Incineration of Waste section of the Energy chapter; those emissions are not incorporated in the mass
balance for feedstocks (described in this annex) to avoid double-counting.
2020 Production3
Carbon Stored
Resin Type
(MMT dry weight)
(MMT CO? Eq.)
Epoxy
0.2
0.7
Polyester
0.6
1.5
Urea
1.1
1.4
Melamine
0.1
0.1
Phenolic
1.6
4.4
Low-Density Polyethylene (LDPE)
3.3
10.4
Linear Low-Density Polyethylene (LLDPE)
9.7
30.6
High Density Polyethylene (HDPE)
9.7
30.4
Polypropylene (PP)
6.9
21.5
Acrylonitrile-butadiene-styrene (ABS)
0.5
1.5
Other Styrenicsb
0.5
1.7
Polystyrene (PS)
1.5
5.2
Nylon
0.4
1.0
Polyvinyl chloride (PVC)C
6.4
9.1
Thermoplastic Polyester
3.0
6.9
All Other (including Polyester (unsaturated))
6.3
17.4
Total 51.9 143.7
a Production estimates provided by the American Chemistry Council include Canadian production
for Urea, Melamine, Phenolic, LDPE, LLDPE, HDPE, PP, ABS, SAN, Other Styrenics, PS, Nylon, PVC,
and Thermoplastic Polyester, and Mexican production for PP, ABS, SAN, Other Styrenics, Nylon,
and Thermoplastic Polyester. Values have been adjusted to account just for U.S. production.
b Includes Styrene-acrylonitrile (SAN).
c Includes copolymers.
Note: Totals may not sum due to independent rounding.
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Table A-54: Assigned C Contents of Plastic Resins (% by weight)
Resin Type
C Content
Source of C Content Assumption
Epoxy
76%
Typical epoxy resin made from epichlorhydrin and bisphenol A
Polyester (Unsaturated)
63%
Poly (ethylene terephthalate) (PET)
Urea
34%
50% carbamal, 50% N-(hydroxymethyl) urea3
Melamine
29%
Trimethylol melamine3
Phenolic
77%
Phenol
Low-Density Polyethylene (LDPE)
86%
Polyethylene
Linear Low-Density Polyethylene (LLDPE)
86%
Polyethylene
High Density Polyethylene (HDPE)
86%
Polyethylene
Polypropylene (PP)
86%
Polypropylene
Acrylonitrile-Butadiene-Styrene (ABS)
85%
50% styrene, 25% acrylonitrile, 25% butadiene
Styrene-Acrylonitrile (SAN)
80%
50% styrene, 50% acrylonitrile
Other Styrenics
92%
Polystyrene
Polystyrene (PS)
92%
Polystyrene
Nylon
65%
Average of nylon resins (see Table A-55)
Polyvinyl Chloride (PVC)
38%
Polyvinyl chloride
Thermoplastic Polyester
63%
Polyethylene terephthalate
All Other
76%
Weighted average of other resin production
a Does not include alcoholic hydrogens.
Resin
C Content
Nylon 6
64%
Nylon 6,6
64%
Nylon 4
52%
Nylon 6,10
68%
Nylon 6,11
69%
Nylon 6,12
70%
Nylon 11
72%
Synthetic Rubber
Data on synthetic rubber in tires were derived from data on the scrap tire market and the composition of scrap tires
from the Rubber Manufacturers' Association (RMA). The market information is presented in the report 2019 U.S. Scrap
Tire Management Summary (RMA 2020), while the tire composition information is from the "Scrap Tires, Facts and
Figures" section of the organization's website (RMA 2009). Data on synthetic rubber in other products (durable goods,
nondurable goods, and containers and packaging) were obtained from EPA's Municipal Solid Waste in the United States
reports (1996 through 2003a, 2005, 2007b, 2008, 2009a, 2011a, 2013b, 2014, 2016b, 2019) and detailed unpublished
backup data for some years not shown in the Characterization of Municipal Solid Waste in the United States reports
(Schneider 2007). The abraded rubber from scrap passenger tires was assumed to be 2.5 pounds per scrap tire, while the
abraded rubber from scrap commercial tires was assumed to be 10 pounds per scrap tire. Data on abraded rubber
weight were obtained by calculating the average weight difference between new and scrap tires (RMA 2020). Import and
export data were obtained from the published by the U.S. International Trade Commission (U.S. International Trade
Commission 1990 through 2019).
A C content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic rubber) was
assigned based on the weighted average of C contents (based on molecular formula) by elastomer type consumed in
1998, 2001, and 2002 (see Table A-56). The 1998 consumption data were obtained from the International Institute of
Synthetic Rubber Producers (IISRP) press release Synthetic Rubber Use Growth to Continue Through 2004, Says IISRP and
RMA (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release, IISRP Forecasts
Moderate Growth in North America to 2007 (IISRP 2003).
Annex 2
A-113
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The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was
considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-
tire rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the
combustion of rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy
chapter.
Table A-56: 2002 Rubber Consumption (kt) and C Content (%)
Elastomer Type
2002 Consumption (kt)a
C Content
SBR Solid
768
91%
Polybutadiene
583
89%
Ethylene Propylene
301
86%
Polychloroprene
54
59%
NBR Solid
84
77%
Polyisoprene
58
88%
Others
367
88%
Weighted Average
NA
90%
Total
2,215
NA
NA (Not Applicable)
a Includes consumption in Canada.
Note: Totals may not sum due to independent rounding.
Synthetic Fibers
Annual synthetic fiber production data were obtained from the ACC, as published in the Guide to the Business of
Chemistry (ACC 2021b), and the Fiber Economics Bureau, as published in Chemical & Engineering News (FEB 2001, 2003,
2005, 2007, 2009, 2010, 2011, 2012, 2013). For acrylic fiber, the most recent data available were for 2012, so it was
assumed that the 2013, 2014, 2015, 2016, 2017, 2018, and 2019 consumption was equal to that of 2012. For polyester,
nylon, and olefin, the most recent data were for 2020. These data are organized by year and fiber type. For each fiber, a
C content was assigned based on molecular formula (see Table A-57). For polyester, the C content for poly (ethylene
terephthalate) (PET) was used as a representative compound. For nylon, the average C content of nylon 6 and nylon 6.6
was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such as acetate and rayon, have been
omitted from the synthetic fibers' C accounting displayed here because much of their C is of biogenic origin and carbon
fluxes from biogenic compounds are accounted for in the Land Use, Land-Use Change and Forestry chapter. These fibers
account for only 4 percent of overall fiber production by weight.
There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Note that emissions
related to the combustion of textiles in municipal solid waste are accounted for under the Incineration of Waste section
of the Energy chapter.
Production
C Stored
Fiber Type
(MMT)
C Content
(MMT CO? Eq.)
Polyester
1.0
63%
2.3
Nylon
0.5
64%
1.2
Olefin
1.0
86%
3.2
Acrylic
+
68%
0.1
Total
2.6
NA
6.8
+ Does not exceed 0.05 MMT.
NA (Not Applicable)
Note: Totals may not sum due to independent rounding.
Pesticides
Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, 2000/2001, 2006/2007, and
2008-2012 Pesticides Industry Sales and Usage Market Estimates (EPA 1998, 1999, 2002, 2004, 2011b, 2017) reports. The
most recent data available were for 2012, so it was assumed that the 2013 through 2020 consumption was equal to that
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of 2012. Active ingredient compound names and consumption weights were available for the top 25 agriculturally-used
pesticides and top 10 pesticides used in the home and garden and the industry/commercial/government categories. The
report provides a range of consumption for each active ingredient; the midpoint was used to represent actual
consumption. Each of these compounds was assigned a C content value based on molecular formula. If the compound
contained aromatic rings substituted with chlorine or other halogens, then the compound was considered persistent and
the C in the compound was assumed to be stored. All other pesticides were assumed to release their C to the
atmosphere. Over one-third of 2012 total pesticide active ingredient consumption was not specified by chemical type in
the Sales and Usage report (EPA 2017). This unspecified portion of the active ingredient consumption was treated as a
single chemical and assigned a C content and a storage factor based on the weighted average of the known chemicals'
values.
Table A-58: Active Ingredient Consumption in Pesticides (Million lbs.) and C Emitted and
Active Ingredient
C Emitted
C Stored
Pesticide Usea
(Million lbs.)
(MMT CO? Eq.)
(MMT CO? Eq.)
Agricultural Uses
606.0
0.2
0.1
Non-Agricultural Uses
58.0
+
+
Home & Garden
39.5
+
+
Industry/Gov't/Commercial
28.0
+
+
Other
342.0
0.1
0.1
Total
1,006.0
0.3
0.2
+ Does not exceed 0.05 MMT C02 Eq.
a 2012 estimates (EPA 2017).
Note: Totals may not sum due to independent rounding.
Soaps, Shampoos, and Detergents
Cleansers—soaps, shampoos, and detergents—are among the major consumer products that may contain fossil C. All of
the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was assumed to
be emitted. The first step in estimating C flows was to characterize the "ingredients" in a sample of cleansers. For this
analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo, laundry
detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption of
household personal cleansers were obtained from the U.S. Census Bureau 1992,1997, 2002, 2007, 2012, and 2017
Economic Census (U.S. Bureau of the Census 1994,1999, 2004, 2009, 2014, 2021). Production values, given in terms of
the value of shipments, for 1990 and 1991 were assumed to be the same as the 1992 value; consumption was
interpolated between 1992 and 1997, 1997 and 2002, 2002 and 2007, 2007 and 2012; 2012 and 2017; production for
2018 through 2020 was assumed to equal the 2017 value. Cleanser production values were adjusted by import and
export data to develop U.S. consumption estimates.
Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each
product's overall C content was then derived from the composition and contents of its ingredients. From these values
the mean C content for cleansers was calculated to be 21.9 percent.
The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds) and/or
terms of value (thousands of dollars) for eight specific categories, such as "household liquid laundry detergents, heavy
duty" and "household dry alkaline automatic dishwashing detergents." Additionally, the report provides dollar values for
the total consumption of "soaps, detergents, etc.—dry" and "soaps, detergents, etc.—liquid." The categories for which
both quantity and value data are available is a subset of total production. Those categories that presented both quantity
and value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were
extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid92
cleanser categories, respectively.
Next, the total tonnage of cleansers was calculated (wet and dry combined) for 1997. Multiplying the mean C content
(21.9 percent) by this value yielded an estimate of 4.6 MMT C02 Eq. in cleansers for 1997. For all subsequent years, it
92 A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers.
Annex 2
A-115
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was assumed that the ratio of value of shipments to total carbon content remained constant. For 1998 through 2020,
value of shipments was adjusted to 1997 dollars using the producer price index for soap and other detergent
manufacturing (Bureau of Labor Statistics 2021). The ratio of value of shipments to carbon content was then applied to
arrive at total carbon content of cleansers. Estimates are shown in Table A-59.
Table A-59: C Emitted from Utilization of Soaps, Shampoos, and Detergents (MMT CO2 Eq.)
1990 1995 2000 2005 2016 2017 2018 2019 2020
C Emissions 3.6 1 4.2 4.5 6.7 1 5.0 5.1 5.1 5.1 5.0
Antifreeze and Deicers
Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are used as
antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol compounds are
assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater treatment
process to C02 or to otherwise biodegrade to C02. Glycols are water soluble and degrade rapidly in the environment
(Howard 1993).
Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide to the
Business of Chemistry (ACC 2021b) and the EPA Chemical Data Access Tool (CDAT) (EPA 2014). Import and export data
were used to adjust annual production data to annual consumption data. The percentage of the annual consumption of
each glycol compound used for antifreeze and deicing applications was estimated from Chemical Profiles data published
from the Innovation Group website and from similar data published in the Chemical Market Reporter, which became I CIS
Chemical Business in 2005.93 Production data for propylene glycol, diethylene glycol, and triethylene glycol are no longer
reported in the Guide to the Business of Chemistry, so data from ICIS Chemical Business on total demand was used with
import and export data to estimate production of these chemicals. ICIS last reported total demand for propylene glycol
and diethylene glycol in 2006, and triethylene glycol demand in 2007. EPA reported total U.S. production of propylene
glycol, diethylene glycol, and triethylene glycol in 2012 in the CDAT (EPA 2014). Total demand for these compounds for
2012 was calculated from the 2012 production data using import and export data. Demand for propylene glycol and
diethylene glycol was interpolated for years between 2006 and 2012, and demand for triethylene glycol was interpolated
for years between 2007 and 2012, using the calculated 2012 total demand values for each compound and the most
recently reported total demand data from ICIS. Values for 2013, 2014, 2015, 2016, 2017, 2018, 2019, and 2020 for these
compounds were assumed to be the same as the 2012 values.
The glycol compounds consumed in antifreeze and deicing applications is assumed to be 100 percent emitted as C02.
Emissions of C02 from utilization of antifreeze and deicers are summarized in Table A-60.
Table A-60: C Emitted from Utilization of Antifreeze and Deicers (MMT CO2 Eq.)
1990 1995 2000 2005 2016 2017 2018 2019 2020
C Emissions 12 ~ 1A I 13 ~ 12 I To To Tl To To
Food Additives
Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring agents,
and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds. These
compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants
where they are degraded by the wastewater treatment processes to C02 or to otherwise biodegrade to C02. Certain food
additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic feedstocks. Food
additives that are derived from biogenic feedstocks are accounted for in the Land Use, Land-Use Change and Forestry
chapter.
Annual production data for food additive compounds were obtained from the Guide to the Business of Chemistry (ACC
2021b). Historical values for adipic acid, acetic acid, and maleic anhydride were adjusted according to the most recent
data in the 2020 Guide to the Business of Chemistry. Import and export data were used to adjust annual production data
to annual consumption data. The percentage of the annual consumption of food additive compounds was estimated
from Chemical Profiles data published on by the Innovation Group and from similar data published in the Chemical
93 See http://www.icis.com/honie/default.aspx.
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Market Reporter, which became ICIS Chemical Business in 2005.94 Production data for several food additive compounds
are no longer reported in the Guide to the Business of Chemistry, so data from ICIS Chemical Business on total demand
was used with import and export data to estimate production of these chemicals.
ICIS last reported total demand for glycerin and benzoic acid in 2007, and demand for propionic acid in 2008. Total
demand for dipropylene glycol was last reported by ICIS in 2004. ICIS last reported cresylic acid demand in 1999. EPA
reported total U.S. production of these compounds in 2012 in the CDAT (EPA 2014). Total demand for these compounds
for 2012 was calculated from the 2012 production data using import and export data. Demand for each of these
compounds was interpolated for years between the most recently reported total demand data from ICIS and 2012, using
the calculated 2012 total demand values for each compound. Values for 2013, 2014, 2015, 2016, 2017, 2018, 2019, and
2020 for these compounds were assumed to be the same as the 2012 values.
The consumption of synthetic food additives is assumed to be 100 percent emitted as C02. Emissions of C02 from
utilization of synthetic food additives are summarized in Table A-61.
Table A-61: C Emitted from Utilization of Food Additives (MMT CO2 Eq.)
1990
1995
2000
2005
2016
2017
2018
2019
2020
C Emissions 0.6
0.7
0.7
00
O
1.1
1.1
1.1
1.1
1.0
Silicones
Silicone compounds (e.g., polymethyl siloxane) are used as sealants and in manufactured products. Silicone compounds
are manufactured from petrochemical feedstocks including methyl chloride. It is assumed that petrochemical feedstocks
used to manufacture silicones are incorporated into the silicone products and not emitted as C02 in the manufacturing
process. It is also assumed that the C contained in the silicone products is stored, and not emitted as C02.
Import and export data were used to adjust annual production data to annual consumption data. The percentage of the
annual consumption of each silicone manufacturing compound was estimated from Chemical Profiles data published on
The Innovation Group website and from similar data published in the Chemical Market Reporter, which became ICIS
Chemical Business in 2005.95 ICIS last reported production of methyl chloride in 2007. EPA reported total U.S. production
of methyl chloride in 2012 in the CDAT (EPA 2014). Total consumption of methyl chloride for 2012 was calculated from
the 2012 production data using import and export data. Production of methyl chloride was interpolated for years
between 2007 and 2012, using the calculated 2012 total production value for methyl chloride and the most recently
reported total production data from ICIS. The production values for 2013, 2014, 2015, 2016, 2017, 2018 2019, and 2020
were assumed to be the same as the 2012 value.
The consumption of silicone manufacturing compounds is assumed to be 100 percent stored, and not emitted as C02.
Storage of silicone manufacturing compounds is summarized in Table A-62.
Table A-62: C Stored in Silicone Products (MMT CO2 Eq.)
1990
1995
2000
: 2005
2016
2017 2018
2019
2020
C Storage
0.3
0.4
0.4
0.4
0.5
LO
O
LO
O
0.5
0.5
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty surrounding
the estimates of the feedstocks C storage factor and the quantity of C emitted from feedstocks in 2020. The Tier 2
analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments
of uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty
estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for
production data (the majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20
percent in the underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of
imports and exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils
"See http://www.icis.com/honie/default.aspx.
"'See http://www.icis.com/home/default.aspx.
Annex 2
A-117
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(>401 degrees Fahrenheit) to reflect the additional uncertainty in the assignment of part of the production quantity to
industrial processes. A relatively narrow uniform distribution ±1 percent to ±15 percent, depending on the fuel type, was
applied to each C coefficient.
The Monte Carlo analysis produced a storage factor distribution with a standard deviation of 7 percent and the 95
percent confidence interval of 50 percent and 73 percent. This compares to the calculated Inventory estimate of 63
percent. The analysis produced a C emission distribution with a standard deviation of 28.9 MMT C02 Eq. and 95 percent
confidence limits of 56.0 and 159.4 MMT C02 Eq. This compares with a calculated Inventory estimate of 98.1 MMT C02
Eq.
The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as a result of
the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on an analysis
of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions (e.g., volatile
organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate processes, the
current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As the production
statistics that drive the storage factors are relatively well-characterized, this approach yields a result that is probably
biased toward understating uncertainty.
As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the characterization of C
flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other coal, natural gas for
non-fertilizer uses, HGL, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on assuming that the
ultimate fates of all of these fuel types—in terms of storage and emissions—are similar. In addition, there are
uncertainties associated with the simplifying assumptions made for each end use category C estimate. Generally, the
estimate for a product is subject to one or more of the following uncertainties:
• The value used for estimating the C content has been assumed or assigned based upon a representative
compound.
• The split between C storage and emission has been assumed based on an examination of the environmental
fate of the products in each end use category.
• Environmental fates leading to emissions are assumed to operate rapidly, i.e., emissions are assumed to occur
within one year of when the fossil C enters the non-energy mass balance. Some of the pathways that lead to
emissions as C02 may actually take place on a time-scale of several years or decades. By attributing the
emissions to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a
non-energy fuel use and thus starts being tracked by EIA), this approach has the effect of "front-end loading"
the emission profile.
Another cross-cutting source of uncertainty is that for several sources the amount of C stored or emitted was calculated
based on data for only a single year. This specific year may not be representative of storage for the entire Inventory
period. Sources of uncertainty associated with specific elements of the analysis are discussed below.
Import and export data for petrochemical feedstocks were obtained from EIA, the National Petroleum Refiners
Association, and the BoC for the major categories of petrochemical feedstocks (EIA 2001; NPRA 2001; and U.S. Bureau of
the Census 2017). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle
differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is
accurate and the approach used here may underestimate or overestimate net exports of C.
Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for energy
uses. However, for those fuels where IPCC storage factors are used, this "oxidation factor" may be inherent in the
storage factor applied when calculating emissions from non-energy consumption, which would result in a double-
counting of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore
application of oxidation factors to non-energy uses may result in a slight underestimation of C emissions from non-
energy uses.
The major uncertainty in using the TRI data is the possibility of double counting emissions that are already accounted for
in the NMVOC data (see above) and in the storage and emission assumptions used. The approach for predicting
environmental fate simplifies some complex processes, and the balance between storage and emissions is very sensitive
to the assumptions on fate. Extrapolating from known to unknown characteristics also introduces uncertainty. The two
extrapolations with the greatest uncertainty are: (1) that the release media and fate of the off-site releases were
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assumed to be the same as for on-site releases, and (2) that the C content of the least frequent 10 percent ofTRI
releases was assumed to be the same as for the chemicals comprising 90 percent of the releases. However, the
contribution of these chemicals to the overall estimate is small. The off-site releases only account for 3 percent of the
total releases, by weight, and, by definition, the less frequent compounds only account for 10 percent of the total
releases.
The principal sources of uncertainty in estimating C02 emissions from solvent evaporation and industrial NMVOC
emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial NMVOC
emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate or
overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific solvent
compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content for
industrial emissions has more uncertainty, however, as it is calculated from the average C content of an average volatile
organic compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a).
Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the composition of
combusted hazardous wastes, including the characterization that hazardous wastes are similar to mixtures of water,
noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C that enters
hazardous waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash—but given
that the destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99 percent, this is a
very minor source of uncertainty. C emission estimates from hazardous waste should be considered central value
estimates that are likely to be accurate to within +50 percent.
The amount of feedstocks combusted for energy recovery was estimated from data included in the Manufacturers
Energy Consumption Surveys (MECS) for 1991, 1994, 1998, 2002, 2006, 2010, 2014, and 2018 (EIA 1994, 1997, 2001,
2005, 2010, 2013b, 2017, 2021a). MECS is a comprehensive survey that is conducted every four years and intended to
represent U.S. industry as a whole, but because EIA does not receive data from all manufacturers (i.e., it is a sample
rather than a census), EIA must extrapolate from the sample. Also, the "other" fuels are identified in the MECS data in
broad categories, including refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke
oven and blast furnace gases; and other uncharacterized fuels. Moreover, the industries using these "other" fuels are
also identified only in broad categories, including the petroleum and coal products, chemicals, primary metals,
nonmetallic minerals, and other manufacturing sectors. The "other" fuel consumption data are reported in BTUs (energy
units) and there is uncertainty concerning the selection of a specific conversion factor for each broad "other" fuel
category to convert energy units to mass units. Taken as a whole, the estimate of energy recovery emissions probably
introduces more uncertainty than any other element of the non-energy analysis.
Uncertainty in the C storage estimate for plastics arises primarily from four factors. First, production of some plastic
resins is not tracked directly and must be estimated based on other market data. Second, the raw data on production for
several resins include Canadian and/or Mexican production and may overestimate the amount of plastic produced from
U.S. fuel feedstocks; this analysis includes adjustments to "back out" the Canadian and Mexican values, but these
adjustments are approximate. Third, the assumed C content values are estimates for representative compounds, and
thus do not account for the many formulations of resins available. This uncertainty is greater for resin categories that are
generic (e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g., polypropylene,
polyethylene). Fourth, the assumption that all of the C contained in plastics is stored ignores certain end uses (e.g.,
adhesives and coatings) where the resin may be released to the atmosphere; however, these end-uses are likely to be
small relative to use in plastics.
The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The value does
not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable goods, or
containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also uncertainties as to
the assignment of C content values; however, they are much smaller than in the case of plastics. There are probably
fewer variations in rubber formulations than in plastics, and the range of potential C content values is much narrower.
Lastly, assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or degradation
during product lifetimes. However, the proportion of the total C that is released to the atmosphere during use is
probably negligible.
A small degree of uncertainty arises from the assignment of C content values in textiles; however, the magnitude of this
uncertainty is less than that for plastics or rubber. Although there is considerable variation in final textile products, the
stock fiber formulations are standardized and proscribed explicitly by the Federal Trade Commission.
Annex 2
A-119
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For pesticides, the largest source of uncertainty involves the assumption that an active ingredient's C is either zero
percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-ingredient
molecular structure, and not on compound-specific environmental data. The mechanism by which a compound is bound
or released from soils is very complicated and can be affected by many variables, including the type of crop,
temperature, application method, and harvesting practice. Another smaller source of uncertainty arises from the C
content values applied to the unaccounted for portion of active ingredient. C contents vary widely among pesticides,
from 7 to 77 percent, and the remaining pesticides may have a chemical make-up that is very different from the 49
pesticides that have been examined. Additionally, pesticide consumption data were only available for 1987,1993,1995,
1997,1999, 2001, 2007, 2009, and 2012; the majority of the time series data were interpolated or held constant at the
latest (2012) value. Another source of uncertainty is that only the "active" ingredients of pesticides are considered in the
calculations; the "inactive" ingredients may also be derived from petrochemical feedstocks.
It is important to note that development of this uncertainty analysis is a multi-year process. The current feedstocks
analysis examines NEU fuels that end in storage fates. Thus, only C stored in pesticides, plastics, synthetic fibers,
synthetic rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in the
uncertainty estimate above. In the future this analysis will be expanded to include the uncertainty surrounding emitted
fates in addition to the storage fates. Estimates of variable uncertainty will also be refined where possible to include
fewer assumptions. With these major changes in future Inventories, the uncertainty estimate is expected to change, and
likely increase. An increase in the uncertainty estimate in the coming years will not indicate that the Inventory
calculations have become less certain, but rather that the methods for estimating uncertainty have become more
comprehensive; thus, potential future changes in the results of this analysis will reflect a change in the uncertainty
analysis, not a change in the Inventory quality.
Asphalt and Road Oil
Asphalt is one of the principal non-energy uses of fossil fuels. The term "asphalt" generally refers to a mixture of asphalt
cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this analysis,
"asphalt" is used interchangeably with asphalt cement, a residue of crude oil. Though minor amounts of C are emitted
during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below.
Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of asphalt
paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. HMA, which makes
up 90 percent of total asphalt paving (EPA 2001), contains asphalt cement mixed with an aggregate of rock materials.
Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha). Emulsified
asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt cement,
accounting for approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C in
asphalt roofing; it was assumed that it has the same fate as C in asphalt paving applications.
Methodology and Data Sources
A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type was
multiplied by consumption data for asphalt paving (EPA 2001) to estimate a weighted average C storage factor for
asphalt as a whole.
The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic compounds
[VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs], and phenol)
from HMA paving, using emission factors reported in EPA (2001) and total HMA production.96 The next step was to
estimate the C content of the organic emissions. This calculation was based on the C content of CO and phenol, and an
assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of (1) the
proportion of asphalt cement in asphalt paving, assumed to be 8 percent asphalt cement content based on EPA (2001),
and (2) the proportion of C in asphalt cement. For the latter factor, all paving types were characterized as having a mass
fraction of 85 percent C in asphalt cement, based on the assumption that asphalt is primarily composed of saturated
paraffinic hydrocarbons. By combining these estimates, the result is that over 99.5 percent of the C in asphalt cement
was retained (i.e., stored), and less than 0.5 percent was emitted.
96The emission factors are expressed as a function of asphalt paving tonnage (i.e., including the rock aggregate as well as the asphalt
cement).
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Cut-back asphalt is produced in three forms: rapid, medium, and slow cure. The production processes for all three forms
emit C primarily from the volatile petroleum distillate used in the process as a diluent to thin the asphalt cement so that
it can be applied more readily (EPA 2001).
A mass balance on C losses from asphalt was constructed by first estimating the amount of carbon emitted as VOCs.
Values for medium cure asphalt are used to represent all cut-back asphalt. The average weight of distillates used in
medium cure cut-back asphalt (35 percent) is multiplied by the loss rate (as emissions of VOCs) of 70 percent from the
Emissions Inventory Guidebook to arrive at an estimate that 25 percent of the diluent is emitted (Environment Canada
2006). Next, the fraction of C in the asphalt/ diluent mix that is emitted was estimated, assuming 85 percent C content;
this yields an overall storage factor of 93.5 percent for cut-back asphalt.
One caveat associated with this calculation is that it is possible that the carbon flows for asphalt and diluent (volatile
petroleum distillate) are accounted for separately in the EIA statistics on fossil fuel flows, and thus the mass balance
calculation may need to re-map the system boundaries to correctly account for carbon flows. EPA plans to re-evaluate
this calculation in the future.
It was assumed that there was no loss of C from emulsified asphalt (i.e., the storage factor is 100 percent) based on
personal communication with an expert from Akzo Nobel Coatings, Inc. (James 2000).
Data on asphalt and road oil consumption and C content factors were supplied by EIA. Hot mix asphalt production and
emissions factors, and the asphalt cement content of HMA were obtained from Hot Mix Asphalt Plants Emissions
Assessment Report from EPA's AP-42 (EPA 2001) publication. The consumption data for cut-back and emulsified asphalts
were taken from a Moulthrop, et al. study used as guidance for estimating air pollutant emissions from paving processes
(EllP 2001). "Asphalt Paving Operation" AP-42 (EPA 2001) provided the emissions source information used in the
calculation of the C storage factor for cut-back asphalt. The storage factor for emulsified asphalt was provided by Alan
James of Akzo Nobel Coatings, Inc. (James 2000).
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty surrounding
the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2020. The Tier 2 analysis was
performed to allow the specification of probability density functions for key variables, within a computational structure
that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty were not
available directly from the information sources for the activity variables; thus, uncertainty estimates were determined
using assumptions based on source category knowledge. Uncertainty estimates for asphalt production were assumed to
be ±20 percent, while the asphalt property variables were assumed to have narrower distributions. A narrow uniform
distribution, with maximum 5 percent uncertainty (± 5 percent) around the mean, was applied to the C content
coefficient.
The Monte Carlo analysis produced a tight distribution of storage factor values, with the 95 percent confidence interval
of 99 percent and 100 percent. This compares to the storage factor value used in the Inventory of 99.6 percent. The
analysis produced a C emission distribution with a standard deviation of 0.1 and 95 percent confidence limits of 0.1 MMT
C02 Eq. and 0.6 MMT C02 Eq. This compares to an Inventory calculated estimate of 0.3 MMT C02 Eq.
The principal source of uncertainty is that the available data are from short-term studies of emissions associated with the
production and application of asphalt. As a practical matter, the cement in asphalt deteriorates over time, contributing
to the need for periodic re-paving. Whether this deterioration is due to physical erosion of the cement and continued
storage of C in a refractory form or physicochemical degradation and eventual release of C02 is uncertain. Long-term
studies may reveal higher lifetime emissions rates associated with degradation.
Many of the values used in the analysis are also uncertain and are based on estimates and professional judgment. For
example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the range is variable—
from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly 2000). Over this
range, the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly, changes in the
assumed C content of asphalt cement would have only a minor effect.
The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994. More recent
trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in emulsified asphalt
use as a substitute. This change in trend would indicate an overestimate of emissions from asphalt.
Annex 2
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Future improvements to this uncertainty analysis, and to the overall estimation of a storage factor for asphalt, include
characterizing the long-term fate of asphalt.
Lubricants
Lubricants are used in industrial and transportation applications. They can be subdivided into oils and greases, which
differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate. According to
EIA (2021b), the C content from U.S. production of lubricants in 2020 was approximately 4.6 MMT C. Based on
apportioning oils and greases to various environmental fates, and characterizing those fates as resulting in either long-
term storage or emissions, the overall C storage factor was estimated to be 9.2 percent; thus, emissions in 2020 were
about 4.2 MMT C, or 15.3 MMT C02 Eq.
Methodology and Data Sources
For each lubricant category, a storage factor was derived by identifying disposal fates and applying assumptions as to the
disposition of the C for each practice. An overall lubricant C storage factor was calculated by taking a production-
weighted average of the oil and grease storage factors.
Oils
Regulation of used oil in the United States has changed dramatically over the past 20 years.97 The effect of these
regulations and policies has been to restrict landfilling and dumping, and to encourage collection of used oil. The
economics of the petroleum industry have generally not favored re-refining—instead, most of the used oil that has been
collected has been combusted.
Table A-63 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an estimate of the
proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is combustion, either
during initial use or after collection as used oil. Combustion results in 99 percent oxidation to C02 (ElIP 1999), with
correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm sewers, primarily
by "do-it-yourselfers" who change their own oil, is another fate that results in conversion to C02 given that the releases
are generally small and most of the oil is biodegraded (based on the observation that land farming—application to soil-
is one of the most frequently used methods for degrading refinery wastes). In the landfill environment, which tends to
be anaerobic within municipal landfills, it is assumed that 90 percent of the oil persists in an undegraded form, based on
analogy with the persistence of petroleum in native petroleum-bearing strata, which is also anaerobic. Re-refining adds a
recycling loop to the fate of oil. Re-refined oil was assumed to have a storage factor equal to the weighted average for
the other fates (i.e., after re-refining, the oil would have the same probability of combustion, landfilling, or dumping as
virgin oil), that is, it was assumed that about 97 percent of the C in re-refined oil is ultimately oxidized. Because of the
dominance of fates that result in eventual release as C02, only about 3 percent of the C in oil lubricants goes into long-
term storage.
Table A-63: Commercial and Environmental Fate of Oil Lubricants (Percent)
Fate of Oil
Portion of Total Oil
C Stored
Combusted During Use
20%
0.2%
Not Combusted During Use
80%
2.7%
Combusted as Used Oil3
64%
0.6%
Dumped on the ground or in storm sewers
6%
NA
Landfilled
2%
1.8%
Re-refined into lube oil base stock and other products
8%
0.2%
Weighted Average
NA
2.9%
NA (Not Applicable)
a For example, in boilers or space heaters.
97 For example, the U.S. EPA "RCRA (Resource Conservation and Recovery Act) On-line" web site (http://www.epa.gov/rcraonline/) has
over 50 entries on used oil regulation and policy for 1994 through 2000.
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Greases
Table A-64 provides analogous estimates for lubricant greases. Unlike oils, grease is generally not combusted during use,
and combustion for energy recovery and re-refining is thought to be negligible. Although little is known about the fate of
waste grease, it was assumed that 90 percent of the non-combusted portion is landfilled, and the remainder is dumped
onto the ground or storm sewers. Because much of the waste grease will be in containers that render it relatively
inaccessible to biodegradation, and because greases contain longer chain paraffins, which are more persistent than oils,
it was assumed that 90 percent and 50 percent of the C in landfilled and dumped grease, respectively, would be stored.
The overall storage factor is 82 percent for grease.
Portion of Total
Fate of Grease
Grease
C Stored
Combusted During Use
5%
0.1%
Not Combusted During Use
95%
81.7%
Landfilled
90%
77.0%
Dumped on the ground or in storm sewers
10%
4.8%
Weighted Average NA 81.8%
Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average for
lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S.
Census Bureau does maintain records of the value of production of lubricating oils and lubricating greases. These were
retrieved from the relevant industry series summaries from the 1997 Economic Census (U.S. Bureau of the Census 1999).
Assuming that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil,
8 percent grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an
overall storage factor of 9.2 percent.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty surrounding
the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from lubricants in 2020.
The Tier 2 analysis was performed to allow the specification of probability density functions for key variables, within a
computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments
of uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty
estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for oil and
grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty estimates
for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 6 percent
uncertainty (± 6 percent) around the mean, was applied to the lubricant C content coefficient.
The Monte Carlo analysis produced a storage factor distribution with the 95 percent confidence interval of 4 percent and
18 percent. This compares to the calculated Inventory estimate of 9.2 percent. The analysis produced a C emission
distribution approximating a normal curve with a standard deviation of 1.3 and 95 percent confidence limits of 12.7
MMT C02 Eq. and 17.8 MMT C02 Eq. This compares to an inventory-calculated estimate of 15.3 MMT C02 Eq.
The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use, post-use,
and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment. There is
no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate of the
commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but also
introduce uncertainty in the estimate.
The assumption that the mass of oil and grease can be divided according to their value also introduces uncertainty. Given
the large difference between the storage factors for oil and grease, changes in their share of total lubricant production
have a large effect on the weighted storage factor.
Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored include
further refinement of the uncertainty estimates for the individual activity variables.
Annex 2
A-123
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Waxes
Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature
increases. Most commercial waxes are produced from petroleum refining, though "mineral" waxes derived from animals,
plants, and lignite (coal) are also used. An analysis of wax end uses in the United States, and the fate of C in these uses,
suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored.
Methodology and Data Sources
The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year by end use
to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in packaging
materials, though this percentage has declined in recent years. The next highest wax end use, and fastest growing end
use, is candles, followed by construction materials and firelogs. Table A-65 categorizes some of the wax end uses, which
the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically modified wax
substances, and other miscellaneous wax uses (NPRA 2002).
Table A-65: Emissive and Non-emissive (Storage) Fates of Waxes: Uses by Fate and Percent
of Total Mass
Use
Emissive
Non-emissive
Packaging
6%
24%
Non-packaging
36%
34%
Candles
18%
2%
Construction Materials
4%
14%
Firelogs
7%
+
Cosmetics
1%
2%
Plastics
1%
2%
Tires/Rubber
1%
1%
Hot Melts
1%
1%
Chemically Modified
+
1%
Other
2%
9%
Total 42% 58%
+ Does not exceed 0.5 percent.
A C storage factor for each wax end use was estimated and then summed across all end uses to provide an overall C
storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax
products are assumed to have the same C content.
Table A-66 categorizes wax end uses identified by the NPRA and lists the estimated C storage factor of each end use.
A-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-66: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of
Total C Mass Stored
Percent of Total
Percent of C
Percent of Total
Use
Wax Mass
Stored
C Mass Stored
Packaging
30%
79%
24%
Non-Packaging
Candles
20%
10%
2%
Construction Materials
18%
79%
14%
Firelogs
7%
1%
+
Cosmetics
3%
79%
2%
Plastics
3%
79%
2%
Tires/Rubber
3%
47%
1%
Hot Melts
3%
50%
1%
Chemically Modified
1%
79%
1%
Other
12%
79%
9%
Total
100%
NA
58%
+ Does not exceed 0.5 percent.
NA (Not Applicable)
Notes: Totals may not sum due to independent rounding. Estimates of percent stored are based on ICF professional judgment.
Source mass percentages: NPRA (2002).
Emissive wax end-uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and explosives.
At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles combust during
use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90 percent of C
contained in candles is emitted as C02. In firelogs, petroleum wax is used as a binder and as a fuel, and is combusted
during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C contained in
hotmelts is assumed to be emitted as C02 as heat is applied to these products during use. It is estimated that 50 percent
of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30 percent
of annual wax production (NPRA 2002).
All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is
assumed to remain in the product (i.e., it is assumed that there are no emissions of C02 from wax during the production
of the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and
corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products
degrade rapidly in an aerobic environment, producing C02; however, because packaging products ultimately enter
landfills typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the
landfill.
In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as particle
board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product, which is
classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in landfills or
be combusted (EPA 2003).
The fate of wax used for packaging, in construction materials, and for most remaining end uses is ultimately to enter the
municipal solid waste (MSW) stream, where it is either combusted or sent to landfill for disposal. Most of the C
contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these
products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end-uses are
assigned a C storage factor of 79 percent.
Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately recycled,
landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of rubber
products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for waxes,
wax contained in tires and rubber products is assigned a C storage factor of 47 percent.
Annex 2
A-125
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Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty surrounding
the estimates of the wax C storage factor and the quantity of C emitted from wax in 2020. A Tier 2 analysis was
performed to allow the specification of probability density functions for key variables, within a computational structure
that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty were not
available directly from the information sources for the activity variables; thus, uncertainty estimates were determined
using assumptions based on source category knowledge. Uncertainty estimates for wax variables were assumed to have
a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied to total
consumption of waxes and the C content coefficients.
The Monte Carlo analysis produced a storage factor distribution, whose 95 percent confidence interval values fell within
the range of 47 percent and 68 percent. This compares to the calculated Inventory estimate of 57.8 percent. The analysis
produced an emission distribution, with the 95 percent confidence interval values of 0.2 MMT C02 Eq. and 0.6 MMT C02
Eq. This compares with a calculated Inventory estimate of 0.3 MMT C02 Eq., which falls within the range of 95 percent
confidence limits established by this quantitative uncertainty analysis. Uncertainty associated with the wax storage
factor is considerable due to several assumptions pertaining to wax imports/exports, consumption, and fates.
Miscellaneous Products
Miscellaneous products are defined by the U.S. Energy Information Administration as: "all finished [petroleum] products
not classified elsewhere, e.g., petrolatum; lube refining by-products (e.g., aromatic extracts and tars); absorption oils;
ram-jet fuel; petroleum rocket fuel; synthetic natural gas feedstocks; and specialty oils."
Methodology and Data Sources
The "miscellaneous products" category reported by EIA 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 compounds from petroleum
and natural gas processing, and potentially also carbon black feedstock could be reported in this category. Recovered
sulfur has no carbon content and would not be reported in the NEU calculation or elsewhere in the Inventory. Based on
this information, the miscellaneous products category reported by EIA was assumed to be mostly petroleum refinery
sulfur compounds that do not contain carbon (EIA 2019). Therefore, the carbon content for miscellaneous products was
updated to be zero across the time series in the previous Inventory. This resulted in recalculating historical emissions
from 1990 through 2018.
Other Non-Energy Uses
The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking coal and
distillate fuel oil, storage factors were taken from Marland and Rotty (1984). These factors are 0.1 and 0.5, respectively.
IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke and other petroleum),
and assumptions were made based on the potential fate of C in the respective NEUs. Specifically, the storage factor for
petroleum coke is 0.3, based on information from Huurman (2006) indicating that petroleum coke is used in the
Netherlands for production of pigments, with 30 percent being stored long-term. Carbon dioxide emissions from carbide
production are implicitly accounted for in the storage factor calculation for the non-energy use of petroleum coke. The
"other petroleum" category is reported by U.S. Territories and accounts mostly for the same products as miscellaneous
products, but probably also includes some asphalt, known to be non-emissive. The exact amount of asphalt or any of the
other miscellaneous products is confidential business information, but based on judgment, the storage factor for this
category was estimated at 0.1.
For all these fuel types, the overall methodology simply involves multiplying C content by a storage factor, yielding an
estimate of the mass of C stored. To provide a complete analysis of uncertainty for the entire NEU subcategory, the
uncertainty around the estimate of "other" NEUs was characterized, as discussed below.
Uncertainty
A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty surrounding
the weighted average of the remaining fuels' C storage factors and the total quantity of C emitted from these other fuels
A-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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in 2020. A Tier 2 analysis was performed to allow the specification of probability density functions for key variables,
within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert
judgments of uncertainty were not available directly from the information sources for some of the activity variables;
thus, uncertainty estimates were determined using assumptions based on source category knowledge. A uniform
distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to be
normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest uncertainty
range of 10 percent (± 10 percent) around the Inventory value, was applied to coking coal. C coefficients for distillate fuel
oil ranged from 18.5 to 21.1 MMT C/QBtu. The fuel-specific storage factors were assigned wide triangular distributions
indicating greater uncertainty.
The Monte Carlo analysis produced a storage factor distribution with 95 percent confidence limits of 7 percent and 83
percent. This compares to the Inventory calculation of weighted average (across the various fuels) storage factor of
about 11.5 percent. The analysis produced an emission distribution, with the 95 percent confidence limit of 1.4 MMT C02
Eq. and 8.1 MMT C02 Eq. This compares with the Inventory estimate of 7.0 MMT C02 Eq., which falls closer to the upper
boundary of the 95 percent confidence limit. The uncertainty analysis results are driven primarily by the very broad
uncertainty inputs for the storage factors.
Annex 2
A-127
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Annex 2
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Chemistry Council, November 1, 2017.
Vallianos, Jean (2016) Personal communication between Drew Stilson of ICF and Jean Vallianos of the American
Chemistry Council, November 17, 2016.
Vallianos, Jean (2015) Personal communication between Tyler Fitch of ICF International and Jean Vallianos of the
American Chemistry Council, December 20, 2015.
Vallianos, Jean (2014) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
American Chemistry Council, November 13, 2014.
Vallianos, Jean (2013) Personal communication between Sarah Biggar of ICF International and Jean Vallianos of the
American Chemistry Council, November 8, 2013.
Vallianos, Jean (2012) Personal communication between Ben Eskin of ICF International and Jean Vallianos of the
American Chemistry Council, September 14, 2012.
Vallianos, Jean (2011) Personal communication between Joe Indvik of ICF International and Jean Vallianos of the
American Chemistry Council, January 4, 2011.
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
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
Estimates of CH4 and N2O Emissions
Methane (CH4) and nitrous oxide (N20) emissions from stationary combustion were estimated using methods from the
Intergovernmental Panel on Climate Change (IPCC). Estimates were obtained by multiplying emission factors—by sector
and fuel type—by fossil fuel and wood consumption data. This "top-down" methodology is characterized by two basic
steps, described below. Data are presented in Table A-67 through Table A-72.
Step 1: Determine Energy Consumption by Sector and Fuel Type
Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial, residential,
electric power, and U.S. Territories. For CH4 and N20 emissions from industrial, commercial, residential, and U.S.
Territories, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption and wood
consumption data for the United States were obtained from the Energy Information Administration's (EIA) Monthly
Energy Review (EIA 2022a). Because the United States does not include U.S. Territories in its national energy statistics,
fuel consumption data for U.S. Territories were collected from ElA's International Energy Statistics database (EIA 2022b)
and Jacobs (2010).98 Fuel consumption for the industrial sector was adjusted to subtract out construction and agricultural
use, which is reported under mobile sources." Construction and agricultural fuel use was obtained from EPA (2021b) and
the Federal Highway Administration (FHWA) (1996 through 2021). The energy consumption data by sector were then
adjusted from higher to lower heating values by multiplying by 0.90 for natural gas and wood and by 0.95 for coal and
petroleum fuel. This is a simplified convention used by the International Energy Agency (IEA). Table A-67 provides annual
energy consumption data for the years 1990 through 2020.
In this Inventory, the energy consumption estimation methodology for the electric power sector used a Tier 2
methodology as fuel consumption by technology-type for the electric power sector was estimated based on the Acid
Rain Program Dataset (EPA 2022). Total fuel consumption in the electric power sector from EIA (2022a) was apportioned
to each combustion technology type and fuel combination using a ratio of fuel consumption by technology type derived
from EPA (2022) data. The combustion technology and fuel use data by facility obtained from EPA (2022) were only
available from 1996 to 2019, so the consumption estimates from 1990 to 1995 were estimated by applying the 1996
consumption ratio by combustion technology type from EPA (2022) to the total EIA (2022a) consumption for each year
from 1990 to 1995.
Step 2: Determine the Amount of CH4 and N20 Emitted
Activity data for industrial, commercial, residential, and U.S. Territories and fuel type for each of these sectors were then
multiplied by default Tier 1 emission factors to obtain emission estimates. Emission factors for the residential,
commercial, and industrial sectors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories
(IPCC 2006). These N20 emission factors by fuel type (equivalent across sectors) were also assumed for U.S. Territories.
The CH4 emission factors by fuel type for U.S. Territories were estimated based on the emission factor for the primary
sector in which each fuel was combusted. Table A-68 provides emission factors used for each sector and fuel type. For
the electric power sector, emissions were estimated by multiplying fossil fuel and wood consumption by technology- and
98 U.S. Territories data also include combustion from mobile activities because data to allocate U.S. Territories' energy use were
unavailable. For this reason, CH4 and N20 emissions from combustion by U.S. Territories are only included in the stationary combustion
totals.
99 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.
Annex 3
A-133
-------
fuel-specific Tier 2 IPCC emission factors shown in Table A-69. Emission factors were taken from U.S. EPA publications on
emissions rates for combustion sources, and EPA's Compilation of Air Pollutant Emission Factors, AP-42 (EPA 1997) for
combined cycle natural gas units. The EPA factors were in large part used in the 2006 IPCC Guidelines as the factors
presented.
Estimates of NOx, CO, and NMVOC Emissions
Emissions estimates for NOx, CO, and NMVOCs were obtained from data published on the National Emission Inventory
(NEI) Air Pollutant Emission Trends web site (EPA 2021a) and disaggregated based on EPA (2003).
For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels (i.e.,
bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which includes
emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of NOx, CO,
and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions were
calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity data
(e.g., fuel consumption or deliveries) as indicators of emissions. The national activity data used to calculate the individual
categories were obtained from various sources. Depending upon the category, these activity data may include fuel
consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in
conjunction with emission factors that relate the quantity of emissions to the activity.
The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2021a) is represented by
the following equation:
Equation A-7: NOx, CO, and NMVOC Emissions Estimates
Ep,s = As x EFp,s x (1 - Cp.s/100)
where,
E
= Emissions
P
= Pollutant
s
= Source category
A
= Activity level
EF
= Emission factor
C
= Percent control efficiency
EPA currently derives the overall emission control efficiency of a category from a variety of sources, including published
reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, and other EPA
databases. The U.S. approach for estimating emissions of NOx, CO, and NMVOCs from stationary combustion as
described above is similar to the methodology recommended by IPCC.
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-67: Fuel Consumption by Stationary Combustion for Calculating ChU and N2O Emissions (TBtu)
Fuel/End-Use
Sector
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Coal
19,637
20,912
23,088
22,966
20,731
19,536
16,940
17,833
17,799
15,446
14,268
13,770
13,156
11,127
9,117
Residential
31
17
11
8
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Commercial
124
117
92
97
70
62
44
41
40
31
24
21
19
17
15
Industrial
1,668
1,557
1,362
1,246
993
906
823
837
833
734
662
614
569
517
448
Electric Power
17,807
19,216
21,618
21,582
19,633
18,531
16,038
16,919
16,889
14,645
13,547
13,110
12,540
10,554
8,620
U.S. Territories3
5
5
5
33
35
37
36
36
37
36
35
25
28
39
33
Petroleum
6,089
5,624
6,492
6,739
5,029
4,779
4,457
4,549
4,137
4,613
4,267
4,036
4,143
4,113
3,670
Residential
1,376
1,259
1,425
1,366
1,103
1,034
833
917
1,003
939
799
766
946
975
884
Commercial
1,023
724
767
761
698
670
551
581
558
938
834
809
735
801
737
Industrial
2,599
2,457
2,456
2,896
2,406
2,410
2,413
2,568
2,124
2,260
2,206
2,104
2,099
2,062
1,792
Electric Power
797
860
1,269
1,003
412
273
288
185
157
173
159
71
93
42
24
U.S. Territories3
295
324
575
712
410
392
373
299
296
304
268
285
271
232
232
Natural Gas
17,255
19,340
20,923
20,937
22,913
23,315
24,605
25,130
25,924
26,536
26,565
26,137
28,957
29,964
29,247
Residential
4,487
4,954
5,105
4,946
4,878
4,805
4,242
5,023
5,242
4,777
4,506
4,563
5,174
5,208
4,846
Commercial
2,680
3,096
3,252
3,073
3,165
3,216
2,960
3,380
3,572
3,316
3,224
3,273
3,638
3,647
3,286
Industrial
7,713
8,726
8,659
7,331
7,685
7,871
8,196
8,513
8,818
8,679
8,769
8,872
9,335
9,484
9,177
Electric Power
2,376
2,564
3,894
5,562
7,157
7,396
9,158
8,156
8,231
9,707
10,003
9,381
10,747
11,553
11,888
U.S. Territories3
0
0
13
24
28
27
49
58
61
57
64
48
62
71
50
Wood
2,095
2,252
2,138
1,963
2,046
2,055
1,989
2,160
2,209
2,127
2,059
2,018
2,106
2,104
1,952
Residential
580
520
420
430
541
524
438
572
579
513
445
429
524
544
458
Commercial
66
72
71
70
72
69
61
70
76
79
84
84
84
84
83
Industrial
1,442
1,652
1,636
1,452
1,409
1,438
1,462
1,489
1,495
1,476
1,474
1,442
1,432
1,407
1,356
Electric Power
7
8
11
11
25
24
28
30
60
59
57
62
66
68
56
U.S. Territories
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE
NE (Not Estimated)
NO (Not Occurring)
a U.S. Territories coal is assumed to be primarily consumed in the electric power sector, natural gas in the industrial sector, and petroleum in the transportation sector.
Note: Totals may not sum due to independent rounding.
Annex 3
A-135
-------
Table A-68: Cm and N2O Emission Factors by Fuel Type and Sector (g/GJ)a
Fuel/End-Use Sector CH4 N20
Coal
Residential
Commercial
Industrial
U.S. Territories
Petroleum
Residential
Commercial
Industrial
U.S. Territories
Natural Gas
Residential
Commercial
Industrial
U.S. Territories
Wood
Residential
Commercial
Industrial
U.S. Territories
NA (Not Applicable)
a GJ (Gigajoule) = 109 joules. One joule = 9.486xl0 4 Btu.
Table A-69: Cm and N2O Emission Factors by Technology Type and Fuel Type for the Electric
Power Sector (g/GJ)a
Technology Configuration CH4 N2Q
Liquid Fuels
Residual Fuel Oil/Shale Oil Boilers Normal Firing 0.8 0.3
Tangential Firing 0.8 0.3
Gas/Diesel Oil Boilers Normal Firing 0.9 0.4
Tangential Firing 0.9 0.4
Large Diesel Oil Engines >600 hp (447kW) 4.0 NA
Solid Fuels
Pulverized Bituminous Combination Boilers Dry Bottom, wall fired 0.7 5.8
Dry Bottom, tangentially fired 0.7 1.4
Wet bottom 0.9 1.4
Bituminous Spreader Stoker Boilers With and without re-injection 1.0 0.7
Bituminous Fluidized Bed Combustor Circulating Bed 1.0 61
Bubbling Bed 1.0 61
Bituminous Cyclone Furnace 0.2 0.6
Lignite Atmospheric Fluidized Bed NA 71
Natural Gas
Boilers 1.0 0.3
Gas-Fired Gas Turbines >3MW 3.7 1.3
Large Dual-Fuel Engines 258 NA
Combined Cycle 3.7 1.3
Peat
Peat Fluidized Bed Combustion Circulating Bed 3.0 7.0
Bubbling Bed 3.0 3.0
Biomass
Wood/Wood Waste Boilers 11.0 7.0
Wood Recovery Boilers 1.0 1.0
NA (Not Applicable)
aIbid.
300 1.5
10 1.5
10 1.5
1 1.5
10 0.6
10 0.6
3 0.6
5 0.6
5 0.1
5 0.1
1 0.1
1 0.1
300 4.0
300 4.0
30 4.0
NA NA
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-70: NOx Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electric Power
6,045
5,792
4,829
3,434
2,226
1,893
1,779
1,666
1,603
1,419
1,234
1,049
987
859
733
Coal
5,119
5,061
4,130
2,926
1,896
1,613
1,516
1,419
1,366
1,209
1,051
894
841
732
624
Fuel Oil
200
87
147
114
74
63
59
55
53
47
41
35
33
29
24
Natural gas
513
510
376
250
162
138
129
121
117
103
90
76
72
62
53
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
36
29
19
16
15
14
13
12
10
9
8
7
6
Internal Combustion
213
134
140
115
75
63
60
56
54
48
41
35
33
29
25
Industrial
2,559
2,650
2,278
1,515
1,087
1,048
1,016
984
952
921
890
859
859
859
859
Coal
530
541
484
342
245
237
229
222
215
208
201
194
194
194
194
Fuel Oil
240
224
166
101
73
70
68
66
64
62
60
57
57
57
57
Natural gas
877
999
710
469
336
324
314
305
295
285
275
266
266
266
266
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
119
111
109
76
55
53
51
50
48
46
45
43
43
43
43
Internal Combustion
792
774
809
527
378
364
353
342
331
320
309
298
298
298
298
Commercial
671
607
507
490
456
548
535
521
448
444
440
537
537
537
537
Coal
36
35
21
19
15
15
14
14
14
14
13
13
13
13
13
Fuel Oil
88
94
52
49
38
37
37
37
36
35
34
33
33
33
33
Natural gas
181
210
161
155
120
118
117
116
115
112
108
105
105
105
105
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
366
269
273
267
284
378
366
354
283
283
284
386
386
386
386
Residential
749
813
439
418
324
318
315
312
310
301
292
283
283
283
283
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
42
44
21
20
16
16
15
15
15
15
14
14
14
14
14
Other Fuels3
707
769
417
398
308
302
300
297
295
286
278
269
269
269
269
Total
10,023
9,862
8,053 _
5,858
4,092
3,807
3,645
3,483
3,313
3,084
2,856
2,728
2,666
2,537
2,412
NA (Not Applicable)
a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2021a).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2021a).
Note: Totals may not sum due to independent rounding.
Annex 3
A-137
-------
Table A-71: CO Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electric Power
329
337
439
582
693
710
694
678
661
618
575
532
532
532
532
Coal
213
227
221
292
347
356
348
340
331
310
288
267
267
267
267
Fuel Oil
18
9
27
37
44
45
44
43
42
39
36
34
34
34
34
Natural gas
46
49
96
122
145
149
146
142
139
130
121
112
112
112
112
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
31
43
51
52
51
50
48
45
42
39
39
39
39
Internal Combustion
52
52
63
89
106
108
106
104
101
94
88
81
81
81
81
Industrial
797
958
1,106
1,045
853
872
861
851
840
806
771
736
736
736
736
Coal
95
88
118
115
94
96
95
94
93
89
85
81
81
81
81
Fuel Oil
67
64
48
42
34
35
34
34
33
32
31
29
29
29
29
Natural gas
205
313
355
336
274
281
277
274
270
259
248
237
237
237
237
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
253
270
300
295
241
247
244
241
238
228
218
208
208
208
208
Internal Combustion
177
222
285
257
209
214
212
209
206
198
189
181
181
181
181
Commercial
205
211
151
166
140
142
134
127
120
124
128
133
133
133
133
Coal
13
14
14
14
12
12
12
11
10
11
11
12
12
12
12
Fuel Oil
16
17
17
19
16
16
15
14
13
14
14
15
15
15
15
Natural gas
40
49
83
91
77
78
74
70
66
68
71
73
73
73
73
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
136
132
36
41
35
35
34
32
30
31
32
33
33
33
33
Residential
3,668
3,877
2,644
2,856
2,416
2,446
2,319
2,192
2,065
2,140
2,215
2,291
2,291
2,291
2,291
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
3,430
3,629
2,416
2,615
2,212
2,239
2,123
2,007
1,890
1,959
2,028
2,097
2,097
2,097
2,097
Other Fuels3
238
248
228
241
204
207
196
185
174
181
187
193
193
193
193
Total
5,000
5,383
4,340
4,648
4,103
4,170
4,009
3,847
3,686
3,688
3,690
3,691
3,691
3,691
3,691
NA (Not Applicable)
a Other Fuels include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2021a).
b Residential coal, fuel oil, and natural gas emissions are included in the Other Fuels category (EPA 2021a).
Note: Totals may not sum due to independent rounding.
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-72: NMVOC Emissions from Stationary Combustion (kt)
Sector/Fuel Type
1990
1995
2000
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Electric Power
43
40
56
44
38
37
36
35
34
33
31
29
29
29
29
Coal
24
26
27
21
18
18
17
17
16
16
15
14
14
14
14
Fuel Oil
5
2
4
3
3
3
3
3
3
3
2
2
2
2
2
Natural Gas
2
2
12
10
8
8
8
8
8
7
7
6
6
6
6
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
2
1
1
1
1
1
1
1
1
1
1
1
1
Internal Combustion
11
9
11
8
7
7
7
7
7
6
6
6
6
6
6
Industrial
165
187
157
120
100
101
101
100
99
100
101
101
101
101
101
Coal
7
5
9
8
7
7
7
7
7
7
7
7
7
7
7
Fuel Oil
11
11
9
6
5
5
5
5
5
5
5
5
5
5
5
Natural Gas
52
66
53
41
34
34
34
34
34
34
34
34
34
34
34
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
46
45
27
22
18
19
19
18
18
18
19
19
19
19
19
Internal Combustion
49
60
58
43
36
36
36
36
35
36
36
36
36
36
36
Commercial
10
14
304
188
145
152
141
130
119
118
117
116
116
116
116
Coal
1
1
1
1
+
+
+
+
+
+
+
+
+
+
+
Fuel Oil
3
3
4
2
2
2
2
2
1
1
1
1
1
1
1
Natural Gas
7
10
14
9
7
7
7
6
6
6
6
6
6
6
6
Wood
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Other Fuels3
NA
NA
285
177
136
143
132
122
111
111
110
109
109
109
109
Residential
686
725
837
518
399
419
389
358
327
324
322
319
319
319
319
Coalb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Fuel Oilb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Natural Gasb
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Wood
651
688
809
502
386
406
376
346
317
314
311
308
308
308
308
Other Fuels3
35
37
27
17
13
14
13
12
11
11
10
10
10
10
10
Total
904
966
1,353
871
681
710
667
623
580
575
570
565
565
565
565
+ Does not exceed 0.5 kt.
NA (Not Applicable)
a "Other Fuels" include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2021a).
b Residential coal, fuel oil, and natural gas emissions are included in the "Other Fuels" category (EPA 2021a).
Note: Totals may not sum due to independent rounding.
Annex 3
A-139
-------
References
EIA (2022a) Monthly Energy Review, February 2022, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2022/02).
EIA (2022b) International Energy Statistics 1980-2020. Energy Information Administration, U.S. Department of Energy.
Washington, D.C. Available online at: https://www.eia.gov/international/data/world.
EPA (2022) Acid Rain Program Dataset 1996-2020. Office of Air and Radiation, Office of Atmospheric Programs, U.S.
Environmental Protection Agency, Washington, D.C.
EPA (2021a) "Criteria pollutants National Tier 1 for 1970 - 2020." National Emissions Inventory (NEI) Air Pollutant
Emissions Trends Data. Office of Air Quality Planning and Standards, March 2021. Available online at:
https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data.
EPA (2021b) MOtor Vehicle Emissions Simulator (MOVES3). Office of Transportation and Air Quality, U.S. Environmental
Protection Agency, Washington, D.C. Available online at: https://www.epa.gov/moves.
EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data. Office of Air Pollution and the
Office of Air Quality Planning and Standards, U.S. Environmental Protection Agency. December 22, 2003.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Research Triangle Park, NC. October 1997.
FHWA (1996 through 2021) Highway Statistics. Federal Highway Administration, U.S. Department of Transportation,
Washington, D.C. Report FHWA-PL-96-023-annual. Available online at:
http://www.fhwa.dot.gov/policy/ohpi/hss/hsspubs.htm.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Jacobs, G. (2010) Personal communication. Gwendolyn Jacobs, Energy Information Administration and Rubaab Bhangu,
ICF International. U.S. Territories Fossil Fuel Consumption. Unpublished. U.S. Energy Information Administration.
Washington, D.C.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
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
Estimating CO2 Emissions by Transportation Mode
Transportation-related C02 emissions, as presented in the C02 Emissions from Fossil Fuel Combustion section of the
Energy chapter, were calculated using the methodology described in Annex 2.1. This section provides additional
information on the data sources and approach used for each transportation fuel type. As noted in Annex 2.1, C02
emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor gasoline
based on data sources for individual modes of transportation (considered a bottom-up approach). For most other fuel
and energy types (aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), C02 emissions were calculated
based on transportation sector-wide fuel consumption estimates from the Energy Information Administration (EIA 2021a
and EIA 2020d) and apportioned to individual modes (considered a "top down" approach). Carbon dioxide emissions
from commercial jet fuel use are obtained directly from the Federal Aviation Administration (FAA 2022), while C02
emissions from other aircraft jet fuel consumption is determined using a top-down approach.
Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of "bottom up"
data would be more accurate for diesel fuel and motor gasoline consumption in the transportation sector, based on the
availability of reliable data sources. A "bottom up" diesel calculation was first implemented in the 1990 through 2005
Inventory, and a bottom-up gasoline calculation was introduced in the 1990 through 2006 Inventory for the calculation
of emissions from on-road vehicles. Estimated motor gasoline and diesel consumption data for on-road vehicles by
vehicle type come from FHWA's Highway Statistics, Table VM-1 (FHWA 1996 through 2021),100 and are based on federal
and state fuel tax records. These fuel consumption estimates were then combined with estimates of fuel shares by
vehicle type from DOE's Transportation Energy Data Book Annex Tables A.l through A.6 (DOE 1993 through 2021) to
develop an estimate of fuel consumption for each vehicle type (i.e., passenger cars, light-duty trucks, buses, medium-
and heavy-duty trucks, motorcycles). The on-road gas and diesel fuel consumption estimates by vehicle type were then
adjusted for each year so that the sum of gasoline and diesel fuel consumption across all on-road vehicle categories
matched the fuel consumption estimates in Highway Statistics' Table MF-27 (FHWA 1996 through 2021). This resulted in
a final "bottom-up" estimate of motor gasoline and diesel fuel use by vehicle type, consistent with the FHWA total for
on-road motor gasoline and diesel fuel use.
A primary challenge to switching from a top-down approach to a bottom-up approach for the transportation sector
relates to potential incompatibilities with national energy statistics. From a multi-sector national standpoint, EIA
develops the most accurate estimate of total motor gasoline and diesel fuel supplied and consumed in the United States.
EIA then allocates this total fuel consumption to each major end-use sector (residential, commercial, industrial and
transportation) using data from the Fuel Oil and Kerosene Sales (FOKS) report for distillate fuel oil and FHWA for motor
gasoline. However, the "bottom-up" approach used for the on-road and non-road fuel consumption estimate, as
described above, is considered to be the most representative of the transportation sector's share of the EIA total
consumption. Therefore, for years in which there was a disparity between ElA's fuel allocation estimate for the
transportation sector and the "bottom-up" estimate, adjustments were made to other end-use sector fuel allocations
(residential, commercial and industrial) in order for the consumption of all sectors combined to equal the "top-down"
EIA value.
100 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 2008 Inventory and applied to the 2007 to 2020 time period. This resulted in large changes in VMT and fuel
consumption data by vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger
Cars" has been replaced by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4 Tire Vehicles" has been replaced by "Light-duty Vehicles,
Long Wheelbase." This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to
the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006
and 2007.
Annex 3
A-141
-------
In the case of motor gasoline, estimates of fuel use by recreational boats come from the Nonroad component of EPA's
MOVES3 model (EPA 2021a), and these estimates, along with those from other sectors (e.g., commercial sector,
industrial sector), were adjusted for years in which the bottom-up on-road motor gasoline consumption estimate
exceeded the EIA estimate for total gasoline consumption of all sectors. With respect to estimating C02 emissions from
the transportation sector, EPA's MOVES model is used only to estimate fuel use by recreational boats. Similarly, to
ensure consistency with ElA's total diesel estimate for all sectors, the diesel consumption totals for the residential,
commercial, and industrial sectors were adjusted proportionately.
Estimates of diesel fuel consumption from rail were taken from: the Association of American Railroads (AAR 2008
through 2021) for Class I railroads, the American PublicTransportation Association (APTA 2007 through 2021 and APTA
2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through
2004), Whorton (2006 through 2014), and Railinc (2014 through 2021) for Class II and III railroads, and the U.S.
Department of Energy's Transportation Energy Data Book (DOE 1993 through 2021) for passenger rail. Class II and III
railroad diesel consumption is estimated by applying the historical average fuel usage per carload factor to yearly
carloads.
Estimates of diesel fuel consumption from ships and boats were taken from ElA's Fuel Oil and Kerosene Sales (1991
through 2021).
As noted above, for fuels other than motor gasoline and diesel, ElA's transportation sector total was apportioned to
specific transportation sources. For jet fuel, estimates come from: FAA (2022) for domestic and international commercial
aircraft (2020 data was estimated using 2019-2020 trends from DOT BTS data for jet fuel consumption), and DLA Energy
(2021) for domestic and international military aircraft. General aviation jet fuel consumption is calculated as the
difference between total jet fuel consumption as reported by EIA and the total consumption from commercial and
military jet fuel consumption. Commercial jet fuel C02 estimates are obtained directly from the Federal Aviation
Administration (FAA 2022), while C02 emissions from domestic military and general aviation jet fuel consumption is
determined using a top-down approach. Domestic commercial jet fuel C02 from FAA is subtracted from total domestic
jet fuel C02 emissions, and this remaining value is apportioned among domestic military and domestic general aviation
based on their relative proportion of energy consumption. Estimates for biofuels, including ethanol and biodiesel, were
discussed separately in Section 3.2 Carbon Emitted from Non-Energy Uses of Fossil Fuels under the methodology for
Estimating C02 from Fossil Combustion, and in Section 3.11 Wood Biomass and Ethanol Consumption, and were not
apportioned to specific transportation sources. Consumption estimates for biofuels were calculated based on data from
the Energy Information Administration (EIA 2021a).
Table A-73 displays estimated fuel consumption by fuel and vehicle type. Table A-74 displays estimated energy
consumption by fuel and vehicle type. The values in both tables correspond to the figures used to calculate C02
emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy
estimates by fuel type, with activity data used to apportion fuel consumption to the various modes of transport. The
motor gasoline and diesel fuel consumption volumes published by EIA and FHWA include ethanol blended with gasoline
and biodiesel blended with diesel. Biofuels blended with conventional fuels were subtracted from these consumption
totals in order to be consistent with IPCC methodological guidance and UNFCCC reporting obligations, for which net
carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the estimates for Land Use, Land-Use
Change and Forestry chapter, not in Energy chapter totals. Ethanol fuel volumes were removed from motor gasoline
consumption estimates for years 1990 through 2020. Biodiesel fuel volumes were removed from diesel fuel consumption
volumes for years 2001 through 2020, as there was negligible use of biodiesel as a diesel blending competent prior to
2001. The subtraction or removal of biofuels blended into motor gasoline and diesel were conducted following the
methodology outlined in Step 2 ("Remove Biofuels from Petroleum") of the ElA's Monthly Energy Review (MER) Section
12 notes.
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
In order to remove the volume of biodiesel blended into diesel fuel, the 2009 to 2020 biodiesel and renewable diesel fuel
consumption estimates from EIA (2021a) were subtracted from the transportation sector's total diesel fuel consumption
volume (for both the "top-down" EIA and "bottom-up" FHWA estimates). To remove the ethanol blended into motor
gasoline, ethanol energy consumption data sourced from MER Table 10.2b - Renewable Energy Consumption: Industrial
and Transportation Sectors (EIA 2021a) were subtracted from the total EIA and FHWA transportation motor gasoline
energy consumption estimates. Total ethanol and biodiesel consumption estimates are shown separately in Table A-
75.101
101 Note that the refinery and blender net volume inputs of renewable diesel fuel sourced from ElA's Petroleum Supply Annual (PSA) differs
from the biodiesel volume presented in Table A-75. The PSA data is representative of the amount of biodiesel that refineries and blenders
added to diesel fuel to make low level biodiesel blends. This is the appropriate value to subtract from total diesel fuel volume, as it represents
the amount of biofuel blended into diesel to create low-level biodiesel blends. The biodiesel consumption value presented in Table A-73 is
representative of the total biodiesel consumed and includes biodiesel components in all types of fuel formulations, from low level (<5%) to high
level (6-20%, 100%) blends of biodiesel. This value is sourced from MER Table 10.4 and is calculated as biodiesel production plus biodiesel net
imports minus biodiesel stock exchange.
Annex 3
A-143
-------
Table A-73: Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified)
Fuel/Vehicle Type
1990
1995
2000
2010a
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Motor Gasolineb'c
107,651
114,119
125,232
119,829
117,229
116,810
116,960
121,472
120,631
123,482
123,079
124,886
123,709
106,645
Passenger Cars
67,846
65,554
70,380
81,012
80,445
80,326
80,369
82,325
82,532
83,979
83,898
85,236
84,497
68,286
Light-Duty Trucks
33,745
42,806
49,046
32,376
30,780
30,459
30,510
32,938
31,959
33,214
32,793
33,115
32,910
32,259
Motorcycles
189
193
203
400
390
447
426
425
413
430
421
427
407
367
Buses
38
40
42
80
78
90
93
101
99
99
107
116
110
93
Medium- and Heavy-Duty Trucks
4,230
3,928
3,956
4,646
4,267
4,245
4,341
4,486
4,432
4,556
4,648
4,775
4,564
4,513
Recreational Boatsd
1,604
1,598
1,606
1,315
1,270
1,243
1,220
1,196
1,197
1,205
1,211
1,218
1,220
1,126
Distillate Fuel Oil (Diesel Fuel)b
-------
Buses
Rail
+ + j + 10 10 6 7 8 8 15 20 32 39 41
4,751 4,975 5,382 7,712 7,672 7,320 7,625 7,758 7,637 7,497 7,523 7,665 7,632 6,548
+ Does not exceed 0.05 units (trillion cubic feet, million kilowatt-hours, or million gallons, as specified).
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2020 time period. These methodological changes include how on-road
vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class
between 2006 and 2007.
b Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land
Use, Land-Use Change and Forestry chapter. This table is calculated with the heat content for gasoline without ethanol (from Table A.l in the EIA Monthly Energy Review)
rather than the annually variable quantity-weighted heat content for gasoline with ethanol, which varies by year.
c 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 2021).
Data from Table VM-1 is used to estimate the share of consumption between each on-road vehicle class. 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 2021).
d Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
e Class II and Class III diesel consumption data for 2014-2020 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
f Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes. Transportation sector natural gas
and LPG consumption are based on data from EIA (2021a). In previous 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 2017) is now used to determine each vehicle class's share of the total natural gas and LPG consumption. These changes were first incorporated in the
2016 Inventory and apply to the 1990 through 2020 time period.
g Fluctuations in reported fuel consumption may reflect data collection problems.
h Million kilowatt-hours
' Electricity consumption by passenger cars, light-duty trucks (SUVs), and buses is based on plug-in electric vehicle sales data and engine efficiencies, as outlined in Browning
(2018a). In prior Inventory years, C02 emissions from electric vehicle charging were allocated to the residential and commercial sectors. They are now allocated to the
transportation sector. These changes were first incorporated in the 2017 Inventory and applied to the 2010 through 2020 time period.
Table A-74: Energy Consumption by Fuel and Vehicle Type (TBtu)
Fuel/Vehicle Type
1990
1995
2000
2010a
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Motor Gasolineb'c
13,464
14,273
15,663
14,899
14,576
14,523
14,542
15,103
14,999
15,353
15,303
15,528
15,381
13,260
Passenger Cars
8,486
8,199
8,803
10,073
10,002
9,987
9,993
10,236
10,261
10,441
10,431
10,597
10,505
8,490
Light-Duty Trucks
4,221
5,354
6,134
4,025
3,827
3,787
3,793
4,095
3,974
4,130
4,077
4,117
4,092
4,011
Motorcycles
24
24
25
50
49
56
53
53
51
53
52
53
51
46
Buses
5
5
5
10
10
11
12
13
12
12
13
15
14
12
Medium- and Heavy-
Duty Trucks
529
491
495
578
531
528
540
558
551
566
578
594
567
561
Recreational Boatsd
201
200
201
163
158
155
152
149
149
150
151
151
152
140
Distillate Fuel Oil (Diesel
Fuel)b'c
3,555
4,383
5,442
5,729
5,768
5,751
5,795
5,992
6,155
6,104
6,288
6,428
6,393
6,033
Passenger Cars
107
106
49
51
54
55
54
55
57
57
58
58
61
48
Light-Duty Trucks
155
201
272
170
174
174
172
185
186
189
189
190
198
188
Buses
108
118
138
183
194
207
207
225
229
224
240
256
256
210
Annex 3
A-145
-------
Medium- and Heavy-
Duty Trucks
2,576
3,223
4,186
4,653
4,619
4,634
4,675
4,831
4,934
4,963
5,124
5,239
5,253
5,031
Recreational Boats
37
37
37
36
35
35
34
34
36
36
37
38
39
35
Ships and Non-
Recreational Boats
91
161
190
112
149
115
117
100
177
147
135
126
101
103
Raile
480
536
569
525
542
531
536
560
535
487
504
520
486
418
Jet Fuelf
2,588
2,427
2,699
2,096
2,029
1,984
2,036
2,053
2,181
2,298
2,377
2,385
2,461
1,670
Commercial Aircraft
1,562
1,638
1,981
1,611
1,629
1,611
1,624
1,638
1,692
1,711
1,819
1,843
1,908
1,263
General Aviation
Aircraft
532
445
419
309
252
220
271
236
314
426
399
389
399
258
Military Aircraft
494
344
299
177
148
154
141
179
175
161
159
154
154
149
Aviation Gasoline'
45
40
36
27
27
25
22
22
21
20
21
22
23
20
General Aviation
Aircraft
45
40
36
27
27
25
22
22
21
20
21
22
23
20
Residual Fuel Oilf-B
300
387
443
272
258
211
201
77
57
172
219
186
193
100
Ships and Non-
Recreational Boats
300
387
443
272
258
211
201
77
57
172
219
186
193
100
Natural Gasf
679
724
672
719
734
780
887
760
745
757
799
962
1,114
1,097
Passenger Cars
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Light-Duty Trucks
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Medium- and Heavy-
+
+
+
+
1
1
1
1
1
1
1
Duty Trucks
+
+
+
Buses
+
+
3
15
15
15
15
15
17
16
18
18
18
19
Pipelines
679
724
668
703
718
765
872
744
727
740
780
943
1,095
1,077
LPGf
23
18
12
7
7
7
7
7
7
8
7
7
7
7
Passenger Cars
+
+
+
+
+
+
+
+
0
0
+
+
+
+
Light-Duty Trucks
3
2
2
2
1
1
1
2
1
1
1
1
1
1
Medium- and Heavy-
Duty Trucks
18
14
9
4
5
6
5
5
5
5
5
4
4
4
Buses
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Electricity11
16
17
18
26
27
26
28
29
30
31
33
37
40
39
Passenger Cars
+
+
+
0.1
0.3
0.7
1.5
2.5
3.6
4.8
6.2
9.1
11.9
13.6
Light-Duty Trucks
+
+
+
+
+
+
+
0.1
0.1
0.4
0.8
1.4
2.0
3.2
Buses
+
+
+
+
+
+
+
+
+
0.1
0.1
0.1
0.1
0.1
Rail
16
17
18
26
26
25
26
26
26
26
26
26
26
22
Total
20,760
22,269
24,986
23,777
23,425
23,308
23,519
24,043
24,194
24,743
25,047
25,555
25,612
22,226
+ Does not exceed 0.5 TBtu
a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007 to 2020 time period. These methodological changes include how on-road vehicles
are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006
and 2007.
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Figures do not include ethanol blended in motor gasoline or biodiesel blended into distillate fuel oil. Net carbon fluxes associated with ethanol are accounted for in the Land Use,
Land-Use Change and Forestry chapter.
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 2021). Data
from Table VM-1 is used to estimate the share of consumption between each on-road vehicle class. 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 2021).
Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates.
Class II and Class III diesel consumption data for 2014-2020 is estimated by applying the historical average fuel usage per carload factor to the annual number of carloads.
Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes. Transportation sector natural gas and LPG consumption
are based on data from EIA (2021a). In previous 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 2017) is now used to determine each vehicle class's share of the total natural gas and LPG consumption. These changes were first incorporated in the 2016 Inventory
and apply to the 1990-2020 time period.
Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and boats data is based on EIA (2021a).
Electricity consumption by passenger cars, light-duty trucks (SUVs), and buses is based on plug-in electric vehicle sales data and engine efficiencies, as outlined in Browning
(2018a). In Inventory years prior to 2017, C02 emissions from electric vehicle charging were allocated to the residential and commercial sectors. They are now allocated to the
transportation sector. These changes were first incorporated in the 2017 Inventory and apply to the 2010 through 2020 time period.
Table A-75: Transportation Sector Biofuel Consumption by Fuel Type (million gallons)
Fuel Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Ethanol
699
1,290
1,556
11,833
11,972
11,997
12,154
12,758
12,793
13,261
13,401
13,573
13,589
11,743
Biodiesel
NA
NA
NA
260
886
899
1,429
1,417
1,494
2,085
1,985
1,904
1,813
1,873
NA (Not Available)
Note: According to the MER, there was no biodiesel consumption prior to 2001.
Annex 3
A-147
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Estimates of CH4 and N2O Emissions
Mobile source emissions of greenhouse gases other than C02 are reported by transport mode (e.g., road, rail, aviation,
and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N20 were derived using a methodology
similar to that outlined in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006).
Activity data were obtained from a number of U.S. government agencies and other publications. Depending on the
category, these basic activity data included fuel consumption and vehicle miles traveled (VMT). These estimates were
then multiplied by emission factors, expressed as grams per unit of fuel consumed or per vehicle mile.
Methodology for On-Road Gasoline and Diesel Vehicles
Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year
VMT by vehicle type (e.g., passenger cars, light-duty trucks, medium- and heavy-duty trucks,102 buses, and motorcycles)
were obtained from the FHWA's Highway Statistics (FHWA 1996 through 2021).103 As these vehicle categories are not
fuel-specific, VMT for each vehicle type was disaggregated by fuel type (gasoline, diesel) so that the appropriate emission
factors could be applied. VMT from Highway Statistics Table VM-1 (FHWA 1996 through 2021) was allocated to fuel
types (gasoline, diesel, other) using historical estimates of fuel shares reported in the Appendix to the Transportation
Energy Data Book, Tables A.5 andA.6 (DOE 1993 through 2021). These fuel shares are drawn from various sources,
including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the American Public
Transportation Association. Fuel shares were first adjusted proportionately such that gasoline and diesel shares for each
vehicle/fuel type category equaled 100 percent of national VMT. VMT for alternative fuel vehicles (AFVs) was calculated
separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from AFVs were then
subtracted from the appropriate total VMT estimates to develop the final VMT estimates by vehicle/fuel type
category.104 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A-76
and Table A-77, respectively.
Total VMT for each on-road category (i.e., gasoline passenger cars, light-duty gasoline trucks, heavy-duty gasoline
vehicles, diesel passenger cars, light-duty diesel trucks, medium- and heavy-duty diesel trucks, heavy-duty diesel buses,
and motorcycles) were distributed across 30 model years shown for 2020 in Table A-78.
This distribution was derived by weighting the appropriate age distribution of the U.S. vehicle fleet according to vehicle
registrations by the average annual age-specific vehicle mileage accumulation of U.S. vehicles. Age distribution values
were obtained from EPA's MOBILE6 model for all years before 1999 (EPA 2000) and EPA's MOVES3 model for years 1999
forward (EPA 2021a).105 Age-specific vehicle mileage accumulations were also obtained from EPA's MOVES3 model (EPA
2021a).106
Step 2: Allocate VMT Data to Control Technology Type
VMT by vehicle type for each model year was distributed across various control technologies as shown in Table A-84
through Table A-87. The categories "EPA Tier 0" and "EPA Tier 1" were used instead of the early three-way catalyst and
102 Medium- and heavy-duty trucks correspond to FHWA's reporting categories of single-unit trucks and combination trucks. Single-unit trucks
are defined as single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating (GVWR) exceeding 10,000 lbs.
103 In 2011 FHWA changed its methods for estimated vehicle miles traveled (VMT) and related data. These methodological changes included
how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first
incorporated for the 1990 through 2008 Inventory and apply to the 2007 to 2020 time period. This resulted in large changes in VMT data by
vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category "Passenger Cars" has been replaced
by "Light-duty Vehicles-Short Wheelbase" and "Other 2 axle-4Tire Vehicles" has been replaced by "Light-duty Vehicles, Long Wheelbase." This
change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle
category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
104 In Inventories through 2002, gasoline-electric hybrid vehicles were considered part of an "alternative fuel and advanced technology"
category. However, vehicles are now only separated into gasoline, diesel, or alternative fuel categories, and gas-electric hybrids are now
considered within the gasoline vehicle category.
105 Age distributions were held constant for the period 1990 to 1998 and reflect a 25-year vehicle age span. EPA (2021) provides a variable age
distribution and 31-year vehicle age span beginning in year 1999.
106 The updated vehicle distribution and mileage accumulation rates by vintage obtained from the MOVES3 model resulted in a decrease in
emissions due to more miles driven by newer light-duty gasoline vehicles.
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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advanced three-way catalyst categories, respectively, as defined in the Revised 1996IPCC Guidelines. EPA Tier 0, EPA Tier
1, EPA Tier 2, and EPA Tier 3 refer to U.S. emission regulations and California Air Resources Board (CARB) LEV, CARB
LEVI I, and CARB LEVII refer to California emissions regulations, rather than control technologies; however, each does
correspond to particular combinations of control technologies and engine design. EPA Tier 2 and Tier 3 and its
predecessors EPA Tier 1 and Tier 0 as well as CARB LEV, LEVII, and LEVIN apply to vehicles equipped with three-way
catalysts. The introduction of "early three-way catalysts," and "advanced three-way catalysts," as described in the
Revised 1996 IPCC Guidelines, roughly correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA
1998).107 EPA Tier 2 regulations affect vehicles produced starting in 2004 and are responsible for a noticeable decrease in
N20 emissions compared EPA Tier 1 emissions technology (EPA 1999b). EPA Tier 3 regulations affect vehicles produced
starting in 2017 and are fully phased in by 2025. ARB LEVII regulations affect California vehicles produced starting in 2004
while ARB LEVIN affect California vehicles produced starting in 2015.
Emission control technology assignments for light- and heavy-duty conventional fuel vehicles for model years 1972
(when regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through 2020
were determined using confidential engine family sales data submitted to EPA (EPA 2021c). Vehicle classes and emission
standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA
2021d). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier 0, EPA Tier
1, EPA Tier 2, EPA Tier 3 and CARB LEV, CARB LEVII and CARB LEVII standards. Assignments for 1996 and 1997 were
estimated based on the fact that EPA Tier 1 standards for light-duty vehicles were fully phased in by 1996. Tier 2 began
initial phase-in by 2004. EPA Tier 3 began initial phase-in by 2017 and CARB LEV III standards began initial phase-in by
2015.
Step 3: Determine CH4 and N20 Emission Factors by Vehicle, Fuel, and Control Technology Type
Methane and N20 emission factors (in grams of CH4 and N20 per mile) for gasoline and diesel on-road vehicles utilizing
EPA Tier 2, EPA Tier 3, and CARB LEV, LEVII, and LEVIN technologies were developed by Browning (2019). These emission
factors were calculated based upon annual certification data submitted to EPA by vehicle manufacturers. Emission
factors for earlier standards and technologies were developed by ICF (2004) based on EPA, CARB and Environment and
Climate Change Canada laboratory test results of different vehicle and control technology types. The EPA, CARB and
Environment and Climate Change Canada tests were designed following the Federal Test Procedure (FTP). The procedure
covers three separate driving segments, since vehicles emit varying amounts of GHGs depending on the driving segment.
These driving segments are: (1) a transient driving cycle that includes cold start and running emissions, (2) a cycle that
represents running emissions only, and (3) a transient driving cycle that includes hot start and running emissions. For
each test run, a bag was affixed to the tailpipe of the vehicle and the exhaust was collected; the content of this bag was
later analyzed to determine quantities of gases present. The emission characteristics of driving Segment 2 was used to
define running emissions. Running emissions were subtracted from the total FTP emissions to determine start emissions.
These were then recombined based upon MOBILE6.2's ratio of start to running emissions for each vehicle class to
approximate average driving characteristics.
Step 4: Determine the Amount of CH4 and N20 Emitted by Vehicle, Fuel, and Control Technology Type
Emissions of CH4 and N20 were then calculated by multiplying total VMT by vehicle, fuel, and control technology type by
the emission factors developed in Step 3.
Methodology for Alternative Fuel Vehicles (AFVs)
Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type
VMT for alternative fuel and advanced technology vehicles were calculated from "Updated Methodology for Estimating
CH4 and N20 Emissions from Highway Vehicle Alternative Fuel Vehicles" (Browning 2017). Alternative Fuels include
Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol, Biodiesel,
Hydrogen and Electricity. Most of the vehicles that use these fuels run on an Internal Combustion Engine (ICE) powered
by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or diesel), or
107 Forfurther description, see "Definitions of Emission Control Technologies and Standards" section of this annex below.
Annex 3
A-149
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some combination.108 Except for electric vehicles and plug-in hybrid vehicles, the alternative fuel vehicle VMT were
calculated using the Energy Information Administration (EIA) Alternative Fuel Vehicle Data. The EIA data provides vehicle
counts and fuel use for fleet vehicles used by electricity providers, federal agencies, natural gas providers, propane
providers, state agencies and transit agencies, for calendar years 2003 through 2020. For 1992 to 2002, EIA Data Tables
were used to estimate fuel consumption and vehicle counts by vehicle type. These tables give total vehicle fuel use and
vehicle counts by fuel and calendar year for the United States over the period 1992 through 2010. Breakdowns by vehicle
type for 1992 through 2002 (both fuel consumed and vehicle counts) were assumed to be at the same ratio as for 2003
where data existed. For 1990,1991, 2018, 2019 and 2020, fuel consumed by alternative fuel and vehicle type were
extrapolated based on a regression analysis using the best curve fit based upon R2 using the nearest five years of data.
For the current Inventory, counts of electric vehicles (EVs) and plug-in hybrid-electric vehicles (PHEVs) were taken from
data compiled by the Hybridcars.com from 2010 to 2018 (Hybridcars.com, 2019). For 2019 and 2020, EV and PHEV sales
were taken from Wards Intelligence U.S. Light Vehicle Sales Report (Wards Intelligence, 2021). EVs were divided into cars
and trucks using vehicle type information from fueleconomy.gov publications (EPA 2010-2020). Fuel use per vehicle for
personal EVs and PHEVs were calculated from fuel economies listed in the fueleconomy.gov publications times average
light duty car and truck mileage accumulation rates determined from MOVES3. PHEV VMT was divided into gasoline and
electric VMT using the Society of Automotive Engineers Utility Factor Standard J2841 (SAE 2010).
Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly, fuel
economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the equivalent
amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel economy of a
given technology to that of conventional gasoline or diesel vehicles) were taken from the Argonne National Laboratory's
GREET2021 model (ANL 2021). These ratios were used to estimate fuel economy in miles per gasoline gallon equivalent
for each alternative fuel and vehicle type. Energy use per fuel type was then divided among the various weight
categories and vehicle technologies that use that fuel. Total VMT per vehicle type for each calendar year was then
determined by dividing the energy usage by the fuel economy. Note that for AFVs capable of running on both/either
traditional and alternative fuels, the VMT given reflects only those miles driven that were powered by the alternative
fuel, as explained in Browning (2017). Note that AFV VMT in 2020 was adjusted to account for the impacts of COVID-19
pandemic related declines in travel. AFV VMT was adjusted based on the EIA trend in gasoline and diesel consumption
for transportation between 2019 and 2020. The EIA data show that gasoline use was reduced 13.9 percent and diesel
was reduced 7.7 percent from 2019. These reductions were applied to the AFV VMT 2020 estimate to reduce light duty
AFV VMT by 13.9 percent and heavy duty AFV VMT by 7.7 percent. VMT estimates for AFVs by vehicle category
(passenger car, light-duty truck, medium-duty and heavy-duty vehicles) are shown in Table A-78, while more detailed
estimates of VMT by control technology are shown in Table A-79.
Step 2: Determine CH4 and N20 Emission Factors by Vehicle and Alternative Fuel Type
Methane and N20 emission factors for alternative fuel vehicles (AFVs) were calculated using Argonne National
Laboratory's GREET model (ANL 2021) and are reported in Browning (2018). These emission factors are shown in Table
A-89 and Table A-90.
Step 3: Determine the Amount of CH4 and N20 Emitted by Vehicle and Fuel Type
Emissions of CH4 and N20 were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by the
appropriate emission factors (Step 2).
Methodology for Non-Road Mobile Sources
Methane and N20 emissions from non-road mobile sources were estimated by applying emission factors to the amount
of fuel consumed by mode and vehicle type.
Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel type, as
shown in Table A-83. Consumption data for ships and boats (i.e., vessel bunkering) were obtained from DHS (2008) and
EIA (1991 through 2021) for distillate fuel, and DHS (2008) and EIA (2021a) for residual fuel; marine transport fuel
108 Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol and diesel is
blended with biodiesel; dual-fuel vehicles can run on gasoline or an alternative fuel - either natural gas or LPG - but not at the same time, while
flex-fuel vehicles are designed to run on E85 (85 percent ethanol) or gasoline, or any mixture of the two in between. Heavy-duty vehicles are
more likely to run on diesel fuel, natural gas, or LPG.
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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consumption data for U.S. Territories (EIA 2017) were added to domestic consumption, and this total was reduced by the
amount of fuel used for international bunkers.109 Fuel consumption data and emissions for ships and non-recreational
boats are not further disaggregated by vessel type or vocation. Gasoline consumption by recreational boats was
obtained from the Nonroad component of EPA's MOVES3 model (EPA 2021a). Annual diesel consumption for Class I rail
was obtained from the Association of American Railroads (AAR 2008 through 2021), diesel consumption from commuter
rail was obtained from APTA (2007 through 2021) and Gaffney (2007), and consumption by Class II and III rail was
provided by Benson (2002 through 2004) and Whorton (2006 through 2014).110 It is estimated that an average of 41
gallons of diesel consumption per Class II and III carload originated from 2000-2009 based on carload data reported from
AAR (2008 through 2021) and fuel consumption data provided by Whorton, D. (2006 through 2014). Class II and Class III
diesel consumption for 2014-2020 is estimated by multiplying this average historical fuel usage per carload factor by the
number of shortline carloads originated each year (Raillnc 2014 through 2020). Diesel consumption by commuter and
intercity rail was obtained from DOE (1993 through 2021). Data for 2020 was estimated by applying a 17 percent
reduction factor to the 2019 fuel consumption, to account for the COVID-19 pandemic and associated restrictions. The
reduction factor was derived by comparing the "fuel, power, and utilities" expenses from 2019 and 2020 for National
Railroad Passenger Corporation and Subsidiaries (Amtrak 2021). Data on the consumption of jet fuel and aviation
gasoline in aircraft were obtained from EIA (2021a) and FAA (2022), as described in Annex 2.1: Methodology for
Estimating Emissions of C02 from Fossil Fuel Combustion, and were reduced by the amount allocated to international
bunker fuels (DLA 2021 and FAA 2022). Pipeline fuel consumption was obtained from EIA (2007 through 2021) (note:
pipelines are a transportation source but are stationary, not mobile sources). Data on fuel consumption by non-
transportation mobile sources were obtained from the Nonroad component of EPA's MOVES3 model (EPA 2021a) for
gasoline and diesel powered equipment, and from FHWA (1996 through 2021) for gasoline consumption by off-road
trucks used in the agriculture, industrial, commercial, and construction sectors.'"Specifically, this Inventory uses FHWA's
Agriculture, Construction, and Commercial/Industrial MF-24 fuel volumes along with the MOVES-Nonroad model
gasoline volumes to estimate non-road mobile source CH4 and N20 emissions for these categories. For agriculture, the
MF-24 gasoline volume is used directly because it includes both off-road trucks and equipment. For construction and
commercial/industrial gasoline estimates, the 2014 and older MF-24 volumes represented off-road trucks only;
therefore, the MOVES-Nonroad gasoline volumes for construction and commercial/industrial are added to the respective
categories in the Inventory. Beginning in 2015, this addition is no longer necessary since the FHWA updated its method
for estimating on-road and non-road gasoline consumption. Among the method updates, FHWA now incorporates
MOVES-Nonroad equipment gasoline volumes in the construction and commercial/industrial categories.
Since the nonroad component of EPA's MOVES3 model does not account for the COVID-19 pandemic and associated
restrictions, fuel consumption for non-transportation mobile sources for 2020 were developed by adjusting 2019
consumption. Sector specific adjustments were applied to the 2019 consumption for agricultural equipment (-1.6
percent) and airport equipment (-38 percent). An adjustment factor for agricultural equipment was derived using
employment data from the Bureau of Labor and Statistics (BLS 2022). An adjustment factor for airport equipment was
derived based on the decline in commercial aviation fuel consumption. For all other nonroad equipment sectors, a 7.7
percent reduction factor was applied to 2019 values. This is based on the reduction in transportation diesel consumption
from 2019 to 2020 (EIA 2021a).
Emissions of CH4 and N20 from non-road mobile sources were calculated using the updated 2006 IPCC Tier 3 guidance
and estimates of activity from EPA's MOVES3 model. CH4 and N20 emission factors were calculated from engine
certification data by engine and fuel type and weighted by activity estimates calculated by MOVES3 to determine overall
emission factors in grams per kg of fuel consumed by fuel type (Browning 2020).
Estimates of NOx, CO, and NMVOC Emissions
The emission estimates of NOx, CO, and NMVOCs from mobile combustion (transportation) were obtained from EPA's
National Emission Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2021). This EPA report provides emission
estimates for these gases by fuel type using a procedure whereby emissions were calculated using basic activity data,
109 See International Bunker Fuels section of the Energy chapter.
110 Diesel consumption from Class II and Class III railroad were unavailable for 2014-2017. Diesel consumption data for 2014-2017 is estimated
by applying the historical average fuel usage per carload factor to the annual number of carloads.
111 "Non-transportation mobile sources" are defined as any vehicle or equipment not used on the traditional road system, but excluding
aircraft, rail and watercraft. This category includes snowmobiles, golf carts, riding lawn mowers, agricultural equipment, and trucks used for off-
road purposes, among others.
Annex 3
A-151
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such as amount of fuel delivered or miles traveled, as indicators of emissions. Emissions for heavy-duty diesel trucks and
heavy-duty diesel buses were calculated by distributing the total heavy-duty diesel vehicle emissions in the ratio of VMT
for each individual category. Table A-93 through Table A-95 provides complete emission estimates for 1990 through
2020.
Table A-76: Vehicle Miles Traveled for Gasoline On-Road Vehicles (billion miles)
Year
Passenger
Carsb
Light-Duty
Trucksb
Heavy-Duty
Vehiclesa'b
Motorcyclesb
1990
1,391.4
554.8
25.9
9.6
1991
1,341.9
627.8
25.4
9.2
1992
1,355.1
683.4
25.2
9.6
1993
1,356.8
721.0
24.9
9.9
1994
1,387.8
739.2
25.3
10.2
1995
1,421.0
763.0
25.1
9.8
1996
1,455.1
788.6
24.5
9.9
1997
1,489.0
821.7
24.1
10.1
1998
1,537.2
837.7
24.1
10.3
1999
1,559.6
868.3
24.3
10.6
2000
1,592.2
887.7
24.2
10.5
2001
1,620.1
906.0
24.0
9.6
2002
1,650.0
926.9
23.9
9.6
2003
1,663.6
944.2
24.3
9.6
2004
1,691.2
985.5
24.6
10.1
2005
1,699.7
998.9
24.8
10.5
2006
1,681.9
1,038.6
24.8
12.0
2007
2,093.7
562.8
34.2
21.4
2008
2,014.5
580.9
35.0
20.8
2009
2,005.5
592.5
32.5
20.8
2010
2,015.4
597.4
32.3
18.5
2011
2,035.7
579.6
30.2
18.5
2012
2,051.8
576.8
30.5
21.4
2013
2,062.5
578.7
31.2
20.4
2014
2,059.3
612.5
31.7
20.0
2015
2,133.7
606.1
31.8
19.6
2016
2,176.3
630.9
32.7
20.4
2017
2,203.8
629.2
33.8
20.1
2018
2,212.7
636.5
34.7
20.1
2019
2,230.9
641.1
34.2
19.7
2020
1,874.9
642.9
34.2
17.6
a Heavy-Duty Vehicles includes Medium-Duty Trucks, Heavy-Duty Trucks,
and Buses.
b In 2011, FHWA changed its methodology for Table VM-1, which impacts
estimates for the 2007 to 2020 time period. These methodological
changes include how on-road vehicles are classified, moving from a
system based on body-type to one that is based on wheelbase. This
resulted in large changes in VMT data by vehicle class between 2006
and 2007.
Notes: In 2015, EIA changed its methods for estimating AFV fuel
consumption. These methodological changes included how vehicle
counts are estimated, moving from estimates based on modeling to one
that is based on survey data. EIA now publishes data about fuel use and
number of vehicles for only four types of AFV fleets: federal
government, state government, transit agencies, and fuel providers.
These changes were first incorporated in the 1990 through 2014
Inventory and apply to the 1990 through 2020 time period. This
resulted in large reductions in AFV VMT, thus leading to a shift in VMT
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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to conventional on-road vehicle classes. Gasoline and diesel highway
vehicle mileage are based on data from FHWA Highway Statistics Table
VM-1 (FHWA 1996 through 2021). These mileage 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
2019).
Source: Derived from FHWA (1996 through 2021), DOE (1990 through
2021), Browning (2018a), and Browning (2017).
Table A-77: Vehicle Miles Traveled for Diesel On-Road Vehicles (billion miles)
Passenger Light-Duty Heavy-Duty Heavy-Duty
Year Carsb Trucksb Vehiclesab Busesb
1990
16.9
19.7
120.3
5.5
1991
16.3
21.6
124.1
5.5
1992
16.5
23.4
128.2
5.5
1993
17.9
24.7
134.9
5.9
1994
18.3
25.3
144.8
6.1
1995
17.3
26.9
153.0
6.1
1996
14.7
27.8
158.4
6.3
1997
13.5
29.0
167.3
6.6
1998
12.4
30.5
172.2
6.7
1999
9.4
32.6
178.3
7.4
2000
8.0
35.2
181.2
7.3
2001
8.1
37.0
184.8
6.8
2002
8.3
38.9
190.3
6.6
2003
8.4
39.7
193.2
6.5
2004
8.5
41.4
195.7
6.5
2005
8.5
41.8
196.8
6.7
2006
8.4
43.2
195.9
6.5
2007
10.5
23.1
268.0
13.9
2008
10.1
23.9
274.1
14.1
2009
10.0
24.4
254.0
13.7
2010
10.1
24.7
252.8
13.1
2011
10.1
22.7
232.6
13.1
2012
10.2
22.4
234.0
14.0
2013
10.1
21.6
236.3
14.3
2014
10.1
23.0
239.9
15.1
2015
10.4
22.5
240.0
15.3
2016
10.5
22.4
243.7
15.4
2017
10.7
22.7
252.7
16.2
2018
10.8
23.2
259.6
17.2
2019
10.9
23.6
255.6
16.9
2020
9.1
23.7
257.9
13.6
Annex 3
A-153
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a Heavy-Duty Vehicles includes Medium-Duty Trucks and Heavy-Duty Trucks.
b In 2011, FHWA changed its methodology for Table VM-1, which impacts
estimates for the 2007 to 2020 time period. These methodological changes
include how on-road vehicles are classified, moving from a system based on
body-type to one that is based on wheelbase. This resulted in large changes
in VMT data by vehicle class between 2006 and 2007.
Notes: Gasoline and diesel highway vehicle mileage are based on data from
FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2021). These
mileage 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 2021).
Sources: Derived from FHWA (1996 through 2021), DOE (1993 through 2021),
and Browning (2017), Browning (2018a).
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-78: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (billion miles)
Passenger
Light-Duty
Heavy-Duty
Year
Cars
Trucks
Vehicles3
1990
0.0
0.1
0.3
1991
0.0
0.1
0.3
1992
0.0
0.1
0.3
1993
0.0
0.1
0.4
1994
0.0
0.1
0.3
1995
0.0
0.1
0.3
1996
0.0
0.1
0.4
1997
0.0
0.1
0.4
1998
0.0
0.1
0.4
1999
0.0
0.1
0.4
2000
0.1
0.1
0.5
2001
0.1
0.2
0.6
2002
0.2
0.2
0.7
2003
0.1
0.3
0.7
2004
0.2
0.3
0.8
2005
0.2
0.4
1.2
2006
0.2
0.6
2.1
2007
0.2
0.8
2.6
2008
0.2
0.6
2.3
2009
0.2
0.7
2.4
2010
0.2
0.6
2.1
2011
0.5
1.8
5.4
2012
0.9
2.0
5.5
2013
1.8
3.0
8.4
2014
2.7
3.0
8.4
2015
3.8
3.3
9.0
2016
4.9
4.7
12.4
2017
6.2
4.8
12.0
2018
8.9
5.0
11.7
2019
12.1
5.4
11.3
2020
12.2
6.0
11.4
a Heavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and
buses.
Sources: Derived from Browning (2017), Browning (2018a), and EIA (2021).
Notes: In 2017, estimates of alternative fuel vehicle mileage for the last ten
years were revised to reflect updates made to EIA data on alternative fuel
use and vehicle counts. These changes were incorporated into this year's
Inventory and apply to the 2005 to 2020 time period.
Annex 3
A-155
-------
Table A-79: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles)
Vehicle Type/Year
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
»ht-Duty Cars
3.5
13.2
88.0
228.2
527.2
905.6
1,778.7
2,691.0
3,785.3
4,943.0
6,165.2
8,873.4
12,146.9
12,153.7
Methanol-Flex
Fuel ICE
+
0.1
+
+
+
+
+
+
+
+
+
+
+
+
Ethanol-Flex Fuel
ICE
+
0.3
18.5
108.7
105.4
132.6
154.4
120.5
104.5
117.7
81.8
80.2
69.1
61.0
CNG ICE
+
+
4.9
9.6
10.1
10.2
10.9
10.3
10.3
11.8
11.0
11.8
12.2
10.6
CNG Bi-fuel
+
0.2
15.9
7.0
6.3
3.9
3.0
2.2
1.6
1.3
1.4
1.6
2.1
1.8
LPG ICE
1.0
1.1
1.1
+
0.1
+
0.2
3.0
15.0
5.8
1.7
0.9
0.5
0.4
LPG Bi-fuel
2.5
2.7
2.6
1.1
0.3
0.2
0.2
0.1
0.1
0.1
+
+
+
+
Biodiesel (BD100)
+
+
1.4
36.9
145.9
156.0
257.7
270.8
349.1
444.8
393.6
351.7
327.1
334.6
NEVs
+
8.6
42.4
61.6
102.9
98.9
103.8
113.2
124.3
83.8
89.9
86.5
83.5
76.9
Electric Vehicle
+
0.3
1.2
1.3
107.9
265.2
771.0
1,438.4
2,232.9
2,976.8
3,868.1
6,092.8
8,979.3
9,189.5
SI PHEV-
Electricity
+
+
+
2.0
48.4
238.5
477.4
732.5
947.5
1,300.7
1,717.4
2,247.5
2,672.8
2,478.5
Fuel Cell
Hydrogen
+
+
+
+
+
+
+
+
+
0.2
0.3
0.4
0.5
0.4
Light-Duty Trucks
68.7
77.7
149.8
601.5
1,821.1
2,034.3
3,006.2
3,005.6
3,256.8
4,694.5
4,834.7
5,035.7
5,392.1
6,040.4
Ethanol-Flex Fuel
ICE
+
0.3
19.1
113.5
127.6
167.4
198.2
194.7
203.0
259.4
387.7
380.3
476.1
420.3
CNG ICE
+
+
4.6
7.4
7.9
8.0
7.7
6.6
5.7
4.9
7.5
5.4
6.6
5.8
CNG Bi-fuel
+
0.4
38.6
17.7
17.2
13.7
14.9
18.0
18.9
24.2
22.0
25.8
27.0
23.6
LPG ICE
19.9
22.1
22.6
9.0
9.0
5.5
5.9
6.8
7.0
6.8
7.3
7.7
7.6
6.7
LPG Bi-fuel
48.9
54.3
55.5
22.2
11.9
4.7
5.8
21.7
00
00
6.6
7.9
8.9
8.4
7.4
LNG
+
+
0.1
+
+
+
+
+
+
+
0.1
0.1
0.1
0.1
Biodiesel (BD100)
+
+
6.4
428.1
1,644.3
1,829.7
2,756.9
2,726.8
2,972.0
4,079.3
3,772.7
3,543.0
3,360.4
3,351.8
Electric Vehicle
+
0.6
3.0
3.5
3.2
5.2
16.9
30.5
33.2
268.0
526.2
851.2
1,189.8
1,818.9
SI PHEV-
Electricity
+
+
+
+
+
+
+
0.4
8.1
45.3
103.3
213.4
316.1
405.8
Fuel Cell
Hydrogen
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Medium-Duty
Trucks
192.3
206.6
217.8
464.3
1,487.1
1,526.5
2,393.1
2,457.4
2,618.2
3,715.1
3,557.1
3,448.4
3,342.4
3,387.2
CNG ICE
+
+
0.7
4.7
6.4
7.7
8.1
9.2
10.4
11.2
12.6
13.6
15.1
14.1
CNG Bi-fuel
+
0.1
6.9
5.3
5.3
6.1
6.4
8.9
9.8
11.0
12.0
13.6
14.7
13.8
LPG ICE
162.3
174.3
171.3
24.6
23.2
22.1
20.5
20.0
15.9
14.4
13.4
11.0
9.0
8.4
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
LPG Bi-fuel
30.0
32.3
31.7
6.6
6.1
8.3
9.1
11.9
9.1
11.2
12.2
12.7
13.2
12.3
LNG
+
+
+
+
+
+
0.1
+
0.1
0.1
0.2
0.3
0.3
0.3
Biodiesel (BD100)
+
+
7.2
423.1
1,446.2
1,482.2
2,348.9
2,407.3
2,572.9
3,667.3
3,506.6
3,397.1
3,290.1
3,338.3
Heavy-Duty Trucks
90.8
94.8
111.7
993.3
3,235.4
3,219.9
5,149.7
5,137.7
5,460.0
7,670.2
7,408.3
7,177.5
6,957.3
7,040.2
Neat Methanol
ICE
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Neat Ethanol ICE
+
+
+
3.5
5.5
00
00
12.2
14.6
19.7
23.3
10.8
7.1
8.0
7.5
CNG ICE
+
+
0.8
3.3
3.4
3.8
4.6
5.0
7.1
9.1
8.2
10.2
11.2
10.5
LPG ICE
85.3
89.0
85.4
32.1
33.8
21.9
21.5
17.5
16.4
15.0
13.3
11.2
9.5
8.9
LPG Bi-fuel
5.5
5.7
5.5
4.2
6.3
4.9
5.3
2.3
2.2
2.2
2.2
2.1
1.9
1.8
LNG
+
+
+
1.4
1.6
1.5
1.4
1.8
1.9
1.6
1.5
1.4
1.3
1.2
Biodiesel (BD100)
+
+
20.0
948.6
3,185.0
3,179.1
5,104.8
5,096.5
5,412.8
7,619.0
7,372.3
7,145.5
6,925.4
7,010.5
Buses
16.4
34.4
132.0
642.4
733.4
734.9
839.3
856.5
944.8
1,006.4
1,057.6
1,054.8
1,069.1
1,019.9
Neat Methanol
ICE
5.2
9.2
+
+
+
+
+
+
+
+
+
+
+
+
Neat Ethanol ICE
+
4.2
0.1
+
+
0.1
0.1
2.3
3.1
1.2
0.8
0.4
0.2
0.2
CNG ICE
+
1.0
93.8
554.2
535.3
526.4
543.3
541.8
607.3
562.5
622.0
630.9
657.0
608.4
LPG ICE
10.9
11.4
11.0
6.3
3.5
3.3
3.5
3.8
2.8
3.8
4.5
4.2
4.6
4.2
LNG
0.3
7.5
20.9
33.7
33.2
34.4
24.6
31.8
31.3
15.0
9.2
5.9
3.0
2.7
Biodiesel (BD100)
+
+
1.0
43.7
156.6
167.4
264.5
272.9
295.5
415.8
410.8
397.4
384.7
386.3
Electric
+
1.1
5.2
4.5
4.5
3.0
3.1
3.6
3.9
7.2
9.2
14.7
18.2
16.8
Fuel Cell
Hydrogen
+
+
+
+
0.3
0.3
0.3
0.3
0.7
0.9
1.1
1.2
1.4
1.3
Total VMT
371.9
426.7
699.3
2,929.7
7,804.2
8,421.2
13,166.9
14,148.2
16,065.1
22,029.3
23,022.9
25,589.9
28,907.8
29,641.4
+ Does not exceed 0.05 million vehicle miles traveled.
Sources: Derived from Browning (2017), Browning (2018a), and EIA (2021).
Notes: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty trucks; they are reported separately here because these two categories may run
on a slightly different range of fuel types. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to reflect updates made to EIA data on
alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to 2020 time period.
Annex 3 A-157
-------
Table A-80: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,3 2020
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
HDDB
0
6.0%
5.8%
5.1%
5.0%
9.9%
5.9%
6.0%
5.7%
1
6.0%
5.9%
5.2%
2.9%
8.7%
6.1%
6.0%
5.9%
2
6.0%
5.8%
4.9%
1.0%
7.2%
5.7%
6.0%
5.6%
3
5.2%
7.4%
5.3%
0.2%
7.6%
6.2%
4.4%
8.1%
4
5.9%
7.0%
5.0%
1.0%
6.4%
5.9%
4.1%
7.6%
5
6.2%
6.4%
4.9%
22.4%
5.1%
6.2%
3.9%
7.0%
6
6.3%
5.6%
4.4%
14.0%
3.5%
5.6%
3.7%
6.6%
7
5.8%
4.4%
2.8%
11.7%
2.6%
3.5%
3.1%
3.7%
8
5.2%
3.7%
3.5%
9.6%
3.1%
4.2%
3.2%
3.6%
9
3.9%
3.7%
2.6%
6.8%
2.8%
2.8%
2.2%
3.3%
10
4.0%
2.9%
1.4%
6.2%
1.2%
1.6%
1.7%
3.5%
11
3.5%
2.1%
2.0%
4.0%
1.1%
2.0%
3.8%
4.0%
12
4.4%
3.6%
3.7%
0.4%
3.4%
3.3%
4.7%
3.9%
13
4.6%
3.7%
2.8%
0.3%
3.1%
5.1%
5.7%
3.5%
14
4.0%
3.6%
4.0%
4.1%
4.8%
4.7%
5.6%
3.5%
15
3.5%
3.6%
3.2%
2.6%
3.8%
4.1%
5.0%
2.6%
16
2.9%
3.5%
2.8%
1.4%
4.1%
2.7%
4.1%
2.8%
17
2.6%
3.1%
2.4%
1.6%
3.4%
2.5%
4.4%
2.5%
18
2.2%
2.9%
2.3%
1.5%
2.9%
2.1%
3.5%
2.5%
19
1.8%
2.4%
2.7%
0.9%
2.8%
2.7%
2.9%
2.8%
20
1.6%
2.2%
2.8%
0.7%
1.9%
3.1%
2.3%
2.7%
21
1.2%
1.9%
4.2%
0.3%
2.2%
2.2%
1.7%
1.5%
22
1.0%
1.5%
2.1%
0.3%
0.6%
1.4%
1.3%
1.3%
23
0.8%
1.3%
2.3%
0.1%
1.7%
1.3%
1.0%
1.1%
24
0.6%
0.9%
1.6%
0.1%
1.1%
1.2%
0.9%
0.9%
25
0.6%
0.9%
2.2%
0.1%
1.0%
1.3%
0.8%
0.8%
26
0.4%
0.7%
1.3%
0.0%
0.7%
1.0%
0.6%
0.5%
27
0.3%
0.5%
1.1%
0.0%
0.6%
0.7%
0.6%
0.5%
28
0.3%
0.4%
1.0%
0.0%
0.4%
0.5%
0.4%
0.4%
29
0.2%
0.3%
0.9%
0.1%
0.3%
0.5%
0.3%
0.4%
30
3.0%
2.3%
9.8%
0.5%
2.1%
3.9%
5.9%
1.2%
Total
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
100.0%
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), MC (motorcycles) and HDDB (heavy-duty diesel buses).
Note: This year's Inventory includes updated vehicle population data based on the MOVES3 Model.
Source: EPA (2021a)
Table A-81: Annual Average Vehicle Mileage Accumulation per Vehicles3 (miles)
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MCb
HDDB
0
14,378
16,252
20,153
14,378
16,252
44,728
9,371
24,727
1
14,106
15,946
20,080
14,106
15,946
45,692
5,004
23,925
2
13,811
15,601
19,977
13,811
15,601
45,575
3,786
23,181
3
13,495
15,224
22,664
13,495
15,224
47,435
3,130
22,275
4
13,163
14,818
21,299
13,163
14,818
45,931
2,708
21,888
5
12,814
14,386
19,921
12,814
14,386
47,665
2,408
20,603
6
12,453
13,932
18,647
12,453
13,932
43,838
2,184
20,027
7
12,080
13,461
16,425
12,080
13,461
44,919
2,005
19,876
8
11,698
12,977
16,140
11,698
12,977
37,523
1,856
18,969
9
11,309
12,484
14,046
11,309
12,484
30,064
1,734
17,312
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
10
10,916
11,986
14,763
10,916
11,986
33,491
1,631
18,507
11
10,521
11,487
12,512
10,521
11,487
30,280
1,537
17,077
12
10,126
10,991
10,877
10,126
10,991
16,963
1,462
15,997
13
9,733
10,503
9,120
9,733
10,503
22,707
1,387
16,144
14
9,345
10,027
7,894
9,345
10,027
16,990
1,321
16,226
15
8,963
9,566
6,841
8,963
9,566
14,740
1,265
14,332
16
8,590
9,125
5,502
8,590
9,125
10,722
1,218
13,620
17
8,228
8,708
5,359
8,228
8,708
10,185
1,171
15,064
18
7,880
8,319
4,998
7,880
8,319
8,413
1,125
13,654
19
7,546
7,963
4,667
7,546
7,963
8,895
1,087
13,313
20
7,231
7,643
4,326
7,231
7,643
9,514
1,050
13,832
21
6,937
7,364
3,946
6,937
7,364
9,259
1,021
13,887
22
6,664
7,128
3,659
6,664
7,128
9,245
993
12,835
23
6,416
6,943
3,551
6,416
6,943
7,077
937
12,418
24
6,194
6,809
3,211
6,194
6,809
7,136
881
11,994
25
6,002
6,731
2,957
6,002
6,731
5,735
825
11,296
26
5,840
6,717
2,904
5,840
6,717
5,294
759
12,421
27
5,712
6,717
2,451
5,712
6,717
4,587
703
11,083
28
5,620
6,717
2,223
5,620
6,717
3,750
665
9,619
29
5,565
6,717
1,819
5,565
6,717
2,705
619
8,662
30
5,565
6,717
937
5,565
6,717
1,186
572
10,838
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), MC (motorcycles) and HDDB (heavy-duty diesel buses).
b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel
characteristics, and therefore are presented in aggregate.
Source: EPA (2021a).
Table A-82: VMT Distribution by Vehicle Age and Vehicle/Fuel Type,3 2020
Vehicle Age
LDGV
LDGT
HDGV
LDDV
LDDT
HDDV
MC
HDDB
0
7.78%
7.76%
9.05%
6.18%
13.02%
8.97%
24.05%
20.99%
1
7.62%
7.68%
9.15%
3.54%
11.21%
9.46%
12.87%
11.67%
2
7.50%
7.46%
8.61%
1.12%
9.11%
8.92%
9.62%
8.34%
3
6.35%
9.18%
10.51%
0.23%
9.30%
9.96%
5.86%
10.02%
4
6.92%
8.49%
9.29%
1.16%
7.65%
9.25%
4.77%
8.12%
5
7.12%
7.56%
8.49%
24.46%
5.92%
10.13%
4.00%
6.66%
6
7.05%
6.37%
7.20%
14.88%
3.92%
8.33%
3.40%
5.67%
7
6.32%
4.86%
3.97%
12.04%
2.82%
5.43%
2.64%
2.91%
8
5.43%
3.99%
4.96%
9.62%
3.28%
5.39%
2.55%
2.60%
9
3.96%
3.80%
3.19%
6.55%
2.80%
2.88%
1.64%
2.26%
10
3.88%
2.86%
1.79%
5.77%
1.14%
1.81%
1.19%
2.27%
11
3.32%
2.02%
2.17%
3.62%
1.05%
2.07%
2.48%
2.45%
12
4.02%
3.23%
3.55%
0.39%
3.01%
1.90%
2.91%
2.24%
13
4.04%
3.17%
2.23%
0.26%
2.62%
3.92%
3.40%
1.94%
14
3.33%
2.96%
2.74%
3.23%
3.86%
2.73%
3.14%
1.84%
15
2.85%
2.85%
1.92%
1.95%
2.97%
2.05%
2.71%
1.31%
16
2.24%
2.63%
1.34%
1.05%
3.05%
1.00%
2.12%
1.32%
17
1.92%
2.20%
1.12%
1.11%
2.41%
0.85%
2.19%
1.14%
18
1.53%
1.95%
1.02%
0.99%
1.92%
0.60%
1.69%
1.10%
19
1.20%
1.57%
1.08%
0.56%
1.82%
0.81%
1.36%
1.20%
20
1.05%
1.40%
1.05%
0.45%
1.19%
0.99%
1.04%
1.10%
21
0.77%
1.14%
1.47%
0.20%
1.28%
0.70%
0.76%
0.62%
22
0.59%
0.87%
0.66%
0.18%
0.33%
0.44%
0.55%
0.50%
23
0.47%
0.74%
0.72%
0.06%
0.93%
0.32%
0.42%
0.40%
Annex 3
A-159
-------
24
0.33%
0.51%
0.45%
0.06%
0.60%
0.29%
0.35%
0.30%
25
0.30%
0.48%
0.57%
0.04%
0.53%
0.26%
0.27%
0.27%
26
0.21%
0.40%
0.33%
0.00%
0.38%
0.18%
0.21%
0.15%
27
0.16%
0.28%
0.23%
0.02%
0.33%
0.11%
0.17%
0.14%
28
0.13%
0.20%
0.19%
0.02%
0.22%
0.07%
0.12%
0.11%
29
0.10%
0.17%
0.15%
0.04%
0.17%
0.05%
0.09%
0.10%
30
1.50%
1.25%
0.80%
0.22%
1.16%
0.16%
1.44%
0.28%
Total
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
100.00%
a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks),
HDGV (heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty
diesel vehicles), MC (motorcycles) and HDDB (heavy-duty diesel buses).
Note: Estimated by weighting data in Table A-81. This year's Inventory includes updated vehicle population data based on the
MOVES3 model that affects this distribution.
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-83: Fuel Consumption for Non-Road Sources by Fuel Type (million gallons unless otherwise noted)
Vehicle Type/Year
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Aircraft3
19,542
18,308
20,294
15,754
15,255
14,907
15,268
15,390
16,331
17,191
17,783
17,854
18,424
12,540
Aviation Gasoline
374
329
302
225
225
209
186
181
176
170
174
186
195
168
Jet Fuel
19,168
17,979
19,992
15,529
15,030
14,698
15,082
15,210
16,155
17,021
17,609
17,667
18,230
12,372
Commercial Aviationb
11,569
12,136
14,672
11,931
12,067
11,932
12,031
12,131
12,534
12,674
13,475
13,650
14,132
9,358
Ships and Boats
4,826
5,932
6,544
4,693
4,833
4,239
4,175
3,191
3,652
4,235
4,469
4,190
4,053
3,326
Diesel
1,156
1,661
1,882
1,361
1,641
1,389
1,414
1,284
1,881
1,680
1,593
1,525
1,342
1,342
Gasoline
1,611
1,626
1,636
1,446
1,401
1,372
1,349
1,323
1,325
1,335
1,344
1,352
1,355
1,251
Residual
2,060
2,646
3,027
1,886
1,791
1,477
1,413
584
445
1,219
1,532
1,313
1,356
733
Construction/Mining
Equipment11
Diesel
4,317
4,718
5,181
5,727
5,650
5,533
5,447
5,313
5,200
5,483
5,978
6,262
6,464
5,966
Gasoline
472
437
357
678
634
651
1,100
710
367
375
375
385
387
389
CNG (million cubic feet)
5,082
5,463
6,032
6,219
6,121
5,957
5,802
5,598
5,430
5,629
6,018
6,204
6,321
5,834
LPG
22
24
27
26
25
24
24
23
22
23
25
26
27
25
Agricultural Equipment11
Diesel
3,514
3,400
3,278
3,942
3,876
3,932
3,900
3,925
3,862
3,760
3,728
3,732
3,742
3,682
Gasoline
813
927
652
692
799
875
655
644
159
168
168
160
129
135
CNG (million cubic feet)
1,758
1,712
1,678
1,647
1,600
1,611
1,588
1,590
1,561
1,517
1,503
1,502
1,507
1,483
LPG
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Rail
3,461
3,864
4,106
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,832
3,936
3,696
3,203
Diesel
3,461
3,864
4,106
3,807
3,999
3,921
4,025
4,201
4,020
3,715
3,832
3,936
3,696
3,203
Othere
Diesel
2,095
2,071
2,047
2,450
2,523
2,639
2,725
2,811
2,832
2,851
2,919
3,027
3,110
2,849
Gasoline
4,371
4,482
4,673
5,525
5,344
5,189
5,201
5,281
5,083
5,137
5,178
5,238
5,287
5,041
CNG (million cubic feet)
20,894
22,584
25,035
29,891
32,035
35,085
37,436
39,705
38,069
37,709
38,674
40,390
41,474
38,280
LPG
1,412
1,809
2,191
2,165
2,168
2,181
2,213
2,248
2,279
2,316
2,408
2,526
2,616
2,415
Total (gallons)
44,845
45,972
49,351
45,459
45,106
44,092
44,734
43,737
43,808
45,254
46,864
47,335
47,936
39,571
Total (million cubic feet)
27,735
29,759
32,745
37,757
39,755
42,653
44,826
46,893
45,060
44,854
46,194
48,097
49,301
45,597
a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline.
b Commercial aviation, as modeled in FAA's AEDT, consists of passenger aircraft, cargo, and other chartered flights.
c Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
d Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Annex 3
A-161
-------
Sources: AAR (2008 through 2021), APTA (2007 through 2021), BEA (2018), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2021), DLA (2021), DOE (1993 through
2021), DOT (1991 through 2021), EIA (2002), EIA (2007b), EIA (2021a), EIA (2007 through 2021), EIA (1991 through 2021), EPA (2021), FAA (2022), Gaffney (2007), and Whorton
(2006 through 2014).
Note: This year's Inventory uses the Nonroad component of MOVES3 for years 1999 through 2020.
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-84: Emissions Control Technology Assignments for Gasoline Passenger Cars
(Percent of VMT)
Model
Non-
Years
catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEV
CARB LEV 2 EPA Tier 2
CARB LEV 3 EPA Tier 3
1973-1974
100%
-
-
-
-
-
-
-
1975
20%
80%
-
-
-
-
-
-
1976-1977
15%
85%
-
-
-
-
-
-
1978-1979
10%
90%
-
-
-
-
-
-
1980
5%
88%
7%
-
-
-
-
-
1981
-
15%
85%
-
-
-
-
-
1982
-
14%
86%
-
-
-
-
-
1983
-
12%
88%
-
-
-
-
-
1984-1993
-
-
100%
-
-
-
-
-
1994
-
-
80%
20%
-
-
-
-
1995
-
-
60%
40%
-
-
-
-
1996
-
-
40%
54%
6%
-
-
-
1997
-
-
20%
68%
12%
-
-
-
1998
-
-
<1%
82%
18%
-
-
-
1999
-
-
<1%
67%
33%
-
-
-
2000
-
-
-
44%
56%
-
-
-
2001
-
-
-
3%
97%
-
-
-
2002
-
-
-
1%
99%
-
-
-
2003
-
-
-
<1%
85%
2%
12%
-
2004
-
-
-
<1%
24%
16%
60%
-
2005
-
-
-
-
13%
27%
60%
-
2006
-
-
-
-
18%
35%
47%
-
2007
-
-
-
-
4%
43%
53%
-
2008
-
-
-
-
2%
42%
56%
-
2009
-
-
-
-
<1%
43%
57%
-
2010
-
-
-
-
-
44%
56%
-
2011
-
-
-
-
-
42%
58%
-
2012
-
-
-
-
-
41%
59%
-
2013
-
-
-
-
-
40%
60%
-
2014
-
-
-
-
-
37%
62%
1%
2015
-
-
-
-
-
33%
56%
11% <1%
2016
-
-
-
-
-
25%
50%
18% 6%
2017
-
-
-
-
-
14%
0%
29% 56%
2018
-
-
-
-
-
7%
0%
42% 52%
2019
-
-
-
-
-
3%
0%
44% 53%
2020
-
-
-
-
-
0%
0%
50% 50%
- (Not Applicable)
Note: Detailed descriptions of emissions control technologies are provided in the following section of this Annex. In 2016,
historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First several light-
duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales
data. Second, which emission standards each vehicle type was assumed to have met were re-examined using confidential sales
data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now classified as gasoline
vehicles across the entire time series.
Sources: EPA (1998), EPA (2021d), and EPA (2021c).
Annex 3
A-163
-------
Table A-85: Emissions Control Technology Assignments for Gasoline Light-Duty Trucks
(Percent of VMT)a
Model
Non-
Years
catalyst
Oxidation
EPA Tier 0
EPA Tier 1
CARB LEVb CARB LEV 2
EPA Tier 2 CARB LEV 3
EPA Tier 3
1973-1974
100%
-
-
-
-
-
-
-
-
1975
30%
70%
-
-
-
-
-
-
-
1976
20%
80%
-
-
-
-
-
-
-
1977-1978
25%
75%
-
-
-
-
-
-
-
1979-1980
20%
80%
-
-
-
-
-
-
-
1981
-
95%
5%
-
-
-
-
-
-
1982
-
90%
10%
-
-
-
-
-
-
1983
-
80%
20%
-
-
-
-
-
-
1984
-
70%
30%
-
-
-
-
-
-
1985
-
60%
40%
-
-
-
-
-
-
1986
-
50%
50%
-
-
-
-
-
-
1987-1993
-
5%
95%
-
-
-
-
-
-
1994
-
-
60%
40%
-
-
-
-
-
1995
-
-
20%
80%
-
-
-
-
-
1996
-
-
-
100%
-
-
-
-
-
1997
-
-
-
100%
-
-
-
-
-
1998
-
-
-
87%
13%
-
-
-
-
1999
-
-
-
61%
39%
-
-
-
-
2000
-
-
-
63%
37%
-
-
-
-
2001
-
-
-
24%
76%
-
-
-
-
2002
-
-
-
31%
69%
-
-
-
-
2003
-
-
-
25%
69%
-
6%
-
-
2004
-
-
-
1%
26%
8%
65%
-
-
2005
-
-
-
-
17%
17%
66%
-
-
2006
-
-
-
-
24%
22%
54%
-
-
2007
-
-
-
-
14%
25%
61%
-
-
2008
-
-
-
-
<1%
34%
66%
-
-
2009
-
-
-
-
-
34%
66%
-
-
2010
-
-
-
-
-
30%
70%
-
-
2011
-
-
-
-
-
27%
73%
-
-
2012
-
-
-
-
-
24%
76%
-
-
2013
-
-
-
-
-
31%
69%
-
-
2014
-
-
-
-
-
26%
73%
1%
-
2015
-
-
-
-
-
22%
72%
6%
-
2016
-
-
-
-
-
20%
62%
16%
2%
2017
-
-
-
-
-
9%
14%
28%
48%
2018
-
-
-
-
-
7%
-
38%
55%
2019
-
-
-
-
-
3%
0%
44%
53%
2020
-
-
-
-
-
-
-
50%
50%
- (Not Applicable)
a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission
reductions with certain types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can
reduce the total number of LEVs they need to build to meet a specified emission average for all of their vehicles in a given
model year.
Notes: In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments.
First several light-duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and
confidential sales data. Second, which emission standards each vehicle type was assumed to have met were re-examined using
confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative
fueled vehicles and therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now
classified as gasoline vehicles across the entire time series.
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Sources: EPA (1998), EPA (2021d), and EPA (2021c).
Table A-86: Emissions Control Technology Assignments for Gasoline Heavy-Duty Vehicles
(Percent of VMT)a
Model Non-
Years Uncontrolled catalyst Oxidation EPA Tier 0 EPA Tier 1 CARB LEV b CARB LEV 2 EPA Tier 2 CARB LEV 3 EPA Tier 3
<1980
100%
1981-1984
95%
5%
-
-
1985-1986
95%
5%
-
-
1987
70%
15%
15%
-
1988-1989
60%
25%
15%
-
1990-1995
45%
30%
25%
-
1996
-
25%
10%
65%
1997
-
10%
5%
85%
1998
-
-
-
100%
1999
-
-
-
98%
2000
-
-
-
93%
2001
-
-
-
78%
2002
-
-
-
94%
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
85%
2%
7%
22%
6%
14%
33%
15%
50%
27%
46%
45%
24%
7%
17%
17%
19%
31%
24%
8%
13%
10%
1%
67%
85%
50%
73%
54%
55%
76%
93%
83%
83%
81%
64%
10%
8%
5%
21%
39%
35%
40%
48%
44%
45%
52%
50%
52%
"-"(Not Applicable)
a Detailed descriptions of emissions control technologies are provided in the following section of this Annex.
b The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission
reductions with certain types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can
reduce the total number of LEVs they need to build to meet a specified emission average for all of their vehicles in a given
model year.
Notes: In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments.
First several light-duty trucks were re-characterized as heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and
confidential sales data. Second, which emission standards each vehicle type was assumed to have met were re-examined using
confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative
fueled vehicles and therefore were not included in the engine technology breakouts. For this Inventory, HEVs are now
classified as gasoline vehicles across the entire time series.
Sources: EPA (1998), EPA (2021d), and EPA (2021c).
Annex 3
A-165
-------
Table A-87: Emissions Control Technology Assignments for Diesel On-Road Vehicles and
Motorcycles
Vehicle Type/Control Technology Model Years
Diesel Passenger Cars and Light-Duty Trucks
Uncontrolled 1960-1982
Moderate control 1983-1995
Advanced control 1996-2006
Aftertreatment 2007-2020
Diesel Medium- and Heavy-Duty Trucks and Buses
Uncontrolled 1960-1989
Moderate control 1990-2003
Advanced control 2004-2006
Aftertreatment 2007-2020
Motorcycles
Uncontrolled 1960-1995
Non-catalyst controls 1996-2005
Advanced 2006-2020
Note: Detailed descriptions of emissions control technologies are provided
in the following section of this Annex.
Source: EPA (1998) and Browning (2005).
Table A-88: Emission Factors for ChU and N2O for On-Road Vehicles
n2o
ch4
Vehicle Type/Control Technology
(g/mi)
(g/mi)
Gasoline Passenger Cars
EPA Tier 3
0.0015
0.0055
ARB LEVIN
0.0012
0.0045
EPA Tier 2
0.0048
0.0072
ARB LEV II
0.0043
0.0070
ARB LEV
0.0205
0.0100
EPA Tier la
0.0429
0.0271
EPA Tier 0a
0.0647
0.0704
Oxidation Catalyst
0.0504
0.1355
Non-Catalyst Control
0.0197
0.1696
Uncontrolled
0.0197
0.1780
Gasoline Light-Duty Trucks
EPA Tier 3
0.0012
0.0092
ARB LEVIN
0.0012
0.0065
EPA Tier 2
0.0025
0.0100
ARB LEV II
0.0057
0.0084
ARB LEV
0.0223
0.0148
EPA Tier la
0.0871
0.0452
EPA Tier 0a
0.1056
0.0776
Oxidation Catalyst
0.0639
0.1516
Non-Catalyst Control
0.0218
0.1908
Uncontrolled
0.0220
0.2024
Gasoline Heavy-Duty Vehicles
EPA Tier 3
0.0063
0.0252
ARB LEVIN
0.0136
0.0411
EPA Tier 2
0.0015
0.0297
ARB LEV II
0.0049
0.0391
ARB LEV
0.0466
0.0300
EPA Tier la
0.1750
0.0655
EPA Tier 0a
0.2135
0.2630
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Oxidation Catalyst
Non-Catalyst Control
Uncontrolled
Diesel Passenger Cars
Aftertreatment
Advanced
Moderate
Uncontrolled
Diesel Light-Duty Trucks
Aftertreatment
Advanced
Moderate
Uncontrolled
Diesel Medium- and Heavy-Duty
Trucks and Buses
Aftertreatment
Advanced
Moderate
Uncontrolled
Motorcycles
Advanced
Non-Catalyst Control
Uncontrolled
0.0083
0.0000
0.0083
0.0431
0.0048
0.0048
0.0048
0.0214
0.0014
0.0014
0.0017
0.1317
0.0473
0.0497
0.0192
0.0010
0.0010
0.0012
0.0302
0.0005
0.0005
0.0006
0.0290
0.0009
0.0009
0.0011
0.2356
0.4181
0.4604
0.0095
0.0051
0.0051
0.0051
0.0070
0.0000
0.0070
a The categories "EPA Tier 0" and "EPA Tier 1" were substituted for the
early three-way catalyst and advanced three-way catalyst categories,
respectively, as defined in the 2006 IPCC Guidelines. Detailed descriptions
of emissions control technologies are provided at the end of this Annex.
Source: ICF (2006b and 2017a).
Annex 3 A-167
-------
Table A-89: Emission Factors for N2O for Alternative Fuel Vehicles (g/mi)
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Light-Duty Cars
Methanol-Flex Fuel ICE
0.035
0.035
0.034
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
Ethanol-Flex Fuel ICE
0.035
0.035
0.034
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
CNG ICE
0.021
0.021
0.027
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
CNG Bi-fuel
0.021
0.021
0.027
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
LPG ICE
0.021
0.021
0.027
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
LPG Bi-fuel
0.021
0.021
0.027
0.008
0.008
0.008
0.008
0.008
0.008
0.007
0.006
0.005
0.005
0.004
Biodiesel (BD100)
0.001
0.001
0.001
0.001
0.004
0.008
0.012
0.015
0.019
0.019
0.019
0.019
0.019
0.019
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.068
0.069
0.072
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
CNG ICE
0.041
0.041
0.058
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
CNG Bi-fuel
0.041
0.041
0.058
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
LPG ICE
0.041
0.041
0.058
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
LPG Bi-fuel
0.041
0.041
0.058
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
LNG
0.041
0.041
0.058
0.016
0.016
0.016
0.016
0.016
0.016
0.014
0.011
0.009
0.007
0.005
Biodiesel (BD100)
0.001
0.001
0.001
0.001
0.005
0.009
0.013
0.017
0.021
0.021
0.021
0.021
0.021
0.021
Medium Duty Trucks
CNG ICE
0.002
0.002
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
CNG Bi-fuel
0.002
0.002
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
0.055
0.055
0.069
0.034
0.034
0.034
0.034
0.034
0.034
0.031
0.027
0.024
0.021
0.018
LPG Bi-fuel
0.055
0.055
0.069
0.034
0.034
0.034
0.034
0.034
0.034
0.031
0.027
0.024
0.021
0.018
LNG
0.002
0.002
0.003
0.003
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
Biodiesel (BD100)
0.002
0.002
0.003
0.003
0.011
0.019
0.027
0.035
0.043
0.043
0.043
0.043
0.043
0.043
Heavy-Duty Trucks
Neat Methanol ICE
0.040
0.040
0.049
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
Neat Ethanol ICE
0.040
0.040
0.049
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
0.028
CNG ICE
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
LPG ICE
0.045
0.045
0.049
0.026
0.026
0.026
0.026
0.026
0.026
0.022
0.018
0.014
0.010
0.007
LPG Bi-fuel
1.229
0.045
0.049
0.026
0.026
0.026
0.026
0.026
0.026
0.022
0.018
0.014
0.010
0.007
LNG
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.000
0.000
0.000
Biodiesel (BD100)
0.002
0.002
0.002
0.002
0.010
0.018
0.027
0.035
0.043
0.043
0.043
0.043
0.043
0.043
Buses
Neat Methanol ICE
0.045
0.045
0.058
0.032
0.032
0.032
0.032
0.032
0.032
0.035
0.038
0.041
0.044
0.047
Neat Ethanol ICE
0.045
0.045
0.058
0.032
0.032
0.032
0.032
0.032
0.032
0.035
0.038
0.041
0.044
0.047
CNG ICE
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
LPG ICE
0.051
0.051
0.058
0.030
0.028
0.025
0.022
0.020
0.017
0.016
0.015
0.014
0.013
0.011
LNG
0.002
0.002
0.002
0.002
0.002
0.002
0.001
0.001
0.001
0.001
0.001
0.001
0.001
0.001
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Biodiesel (BD100) O.OQ2 0.002 0.002 0.002 0.011 0.019 0.027 0.035 0.043 0.043 0.043 0.043 0.043 0.043
Note: When driven in all-electric mode, plug-in electric vehicles have zero tailpipe emissions. Therefore, emissions factors for battery electric vehicle (BEVs) and the electric
portion of plug-in hybrid electric vehicles (PHEVs) are not included in this table.
Source: Developed by ICF (Browning 2017) using ANL (2021).
Table A-90: Emission Factors for CH4 for Alternative Fuel Vehicles (g/mi)
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Light-Duty Cars
Methanol-Flex Fuel ICE
0.034
0.034
0.019
0.015
0.014
0.013
0.011
0.010
0.009
0.010
0.011
0.012
0.013
0.015
Ethanol-Flex Fuel ICE
0.034
0.034
0.019
0.015
0.014
0.013
0.011
0.010
0.009
0.010
0.011
0.012
0.013
0.015
CNG ICE
0.489
0.489
0.249
0.153
0.139
0.126
0.113
0.100
0.086
0.098
0.110
0.122
0.134
0.146
CNG Bi-fuel
0.489
0.489
0.249
0.153
0.139
0.126
0.113
0.100
0.086
0.098
0.110
0.122
0.134
0.146
LPG ICE
0.049
0.049
0.025
0.015
0.014
0.013
0.011
0.010
0.009
0.010
0.011
0.012
0.013
0.015
LPG Bi-fuel
0.049
0.049
0.025
0.015
0.014
0.013
0.011
0.010
0.009
0.010
0.011
0.012
0.013
0.015
Biodiesel (BD100)
0.002
0.002
0.002
0.001
0.007
0.013
0.018
0.024
0.030
0.019
0.030
0.030
0.030
0.030
Light-Duty Trucks
Ethanol-Flex Fuel ICE
0.051
0.051
0.053
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.014
0.015
0.015
0.016
CNG ICE
0.728
0.725
0.709
0.332
0.292
0.251
0.210
0.170
0.129
0.135
0.140
0.146
0.152
0.158
CNG Bi-fuel
0.728
0.725
0.709
0.332
0.292
0.251
0.210
0.170
0.129
0.135
0.140
0.146
0.152
0.158
LPG ICE
0.073
0.072
0.071
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.014
0.015
0.015
0.016
LPG Bi-fuel
0.073
0.072
0.071
0.033
0.029
0.025
0.021
0.017
0.013
0.013
0.014
0.015
0.015
0.016
LNG
0.728
0.725
0.709
0.332
0.292
0.251
0.210
0.170
0.129
0.135
0.140
0.146
0.152
0.158
Biodiesel (BD100)
0.005
0.005
0.005
0.001
0.007
0.012
0.018
0.023
0.029
0.029
0.029
0.029
0.029
0.029
Medium Duty Trucks
CNG ICE
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
3.726
3.251
2.777
2.303
1.829
CNG Bi-fuel
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
3.726
3.251
2.777
2.303
1.829
LPG ICE
0.262
0.262
0.248
0.021
0.020
0.018
0.017
0.016
0.014
0.013
0.012
0.011
0.010
0.009
LPG Bi-fuel
0.262
0.262
0.248
0.021
0.020
0.018
0.017
0.016
0.014
0.013
0.012
0.011
0.010
0.009
LNG
6.800
6.800
6.800
6.800
6.280
5.760
5.240
4.720
4.200
3.726
3.251
2.777
2.303
1.829
Biodiesel (BD100)
0.004
0.004
0.004
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
0.009
0.009
Heavy-Duty Trucks
Neat Methanol ICE
0.296
0.296
0.095
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
0.075
0.075
Neat Ethanol ICE
0.296
0.296
0.095
0.151
0.136
0.120
0.105
0.090
0.075
0.075
0.075
0.075
0.075
0.075
CNG ICE
4.100
4.100
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.144
2.589
2.033
1.477
0.921
LPG ICE
0.158
0.158
0.149
0.013
0.013
0.013
0.013
0.013
0.013
0.011
0.009
0.007
0.005
0.003
LPG Bi-fuel
0.158
0.158
0.149
0.013
0.013
0.013
0.013
0.013
0.013
0.011
0.009
0.007
0.005
0.003
LNG
4.100
4.100
4.100
4.100
4.020
3.940
3.860
3.780
3.700
3.144
2.589
2.033
1.477
0.921
Biodiesel (BD100)
0.012
0.012
0.005
0.005
0.006
0.007
0.007
0.008
0.009
0.009
0.009
0.009
0.009
0.009
Buses
Neat Methanol ICE
0.086
0.086
0.067
0.075
0.067
0.060
0.052
0.045
0.037
0.050
0.063
0.076
0.089
0.102
Annex 3
A-169
-------
Neat Ethanol ICE
0.086
0.086
0.067
0.075
0.067
0.060
0.052
0.045
0.037
0.050
0.063
0.076
0.089
0.102
CNG ICE
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
8.557
7.115
5.672
4.230
2.787
LPG ICE
0.725
0.725
0.686
0.058
0.053
0.048
0.044
0.039
0.034
0.029
0.025
0.020
0.015
0.010
LNG
18.800
18.800
18.800
18.800
17.040
15.280
13.520
11.760
10.000
8.557
7.115
5.672
4.230
2.787
Biodiesel (BD100)
0.004
0.004
0.003
0.002
0.004
0.005
0.006
0.008
0.009
0.009
0.009
0.009
0.009
0.009
Note: When driven in all-electric mode, plug-in electric vehicles have zero tailpipe emissions. Therefore, emissions factors for battery electric vehicle (BEVs) and the electric
portion of plug-in hybrid electric vehicles (PHEVs) are not included in this table.
Source: Developed by ICF (Browning 2017) using ANL (2021).
Table A-91: Emission Factors for N20 Emissions from Non-Road Mobile Combustion (g/kg fuel)
1990
1995
2000
2010 2011
2012
2013
2014 2015
2016
2017
2018
2019
2020
Ships and Boats
Residual Fuel Oil
Gasoline
2 Stroke
4 Stroke
Distillate Fuel Oil
Rail
Diesel
Aircraft
Jet Fuel
Aviation Gasoline
Agricultural
Equipment3
Gasoline-Equipment
2 Stroke
4 Stroke
Gasoline-Off-road
Trucks
Diesel-Equipment
Diesel-Off-Road
Trucks
CNG
LPG
Construction/Mining
Equipment'5
Gasoline-Equipment
2 Stroke
4 Stroke
0.09
0.021
0.002
0.054
0.08
0.10
0.04
0.103
0.355
0.36
0.336
0.174
0.061
0.389
0.028
0.408
0.09
0.021
0.002
0.054
0.08
0.10
0.04
0.110
0.360
0.36
0.336
0.174
0.061
0.389
0.029
0.430
0.09
0.021
0.002
0.054
0.08
0.10
0.04
0.118
0.365
0.36
0.336
0.174
0.061
0.389
0.030
0.450
0.09
0.024
0.002
0.054
0.08
0.10
0.04
0.170
0.409
0.41
0.336
0.174
0.074
0.437
0.042
0.516
0.09
0.025
0.002
0.054
0.08
0.10
0.04
0.170
0.411
0.41
0.336
0.174
0.074
0.440
0.042
0.519
0.09
0.025
0.002
0.054
0.08
0.10
0.04
0.170
0.415
0.42
0.336
0.174
0.075
0.444
0.042
0.521
0.09
0.026
0.002
0.054
0.08
0.10
0.04
0.170
0.417
0.42
0.336
0.174
0.075
0.446
0.042
0.523
0.09
0.026
0.002
0.054
0.08
0.10
0.04
0.170
0.420
0.42
0.336
0.174
0.076
0.449
0.042
0.524
0.09
0.026
0.002
0.054
0.08
0.10
0.04
0.170
0.422
0.42
0.336
0.174
0.076
0.451
0.042
0.525
0.09
0.027
0.002
0.054
0.08
0.10
0.04
0.170
0.423
0.42
0.336
0.174
0.076
0.452
0.042
0.526
0.09
0.027
0.002
0.054
0.08
0.10
0.04
0.170
0.425
0.43
0.336
0.174
0.076
0.454
0.042
0.527
0.09
0.027
0.002
0.054
0.08
0.10
0.04
0.170
0.427
0.43
0.336
0.174
0.076
0.456
0.042
0.527
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.170
0.429
0.43
0.336
0.174
0.076
0.458
0.042
0.528
0.09
0.027
0.003
0.054
0.08
0.10
0.04
0.170
0.431
0.43
0.336
0.174
0.076
0.460
0.042
0.528
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Gasoline-Off-road
Trucks
0.41
0.43
0.45
0.52
0.52
Diesel-Equipment
0.295
0.295
0.295
0.294
0.295
Diesel-Off-Road
Trucks
0.174
0.174
0.174
0.174
0.174
CNG
0.367
0.367
0.367
0.391
0.395
LPG
0.197
0.197
0.197
0.223
0.226
Lawn and Garden Equipment
Gasoline-Residential
2 Stroke
0.107
0.113
0.120
0.171
0.171
4 Stroke
0.519
0.545
0.578
0.684
0.688
Gasoline-
Commercial
2 Stroke
0.071
0.075
0.079
0.110
0.110
4 Stroke
0.409
0.444
0.476
0.530
0.531
Diesel-Residential
Diesel-Commercial
0.167
0.159
0.153
0.153
0.153
LPG
0.245
0.245
0.245
0.291
0.297
Airport Equipment
Gasoline
4 Stroke
0.299
0.309
0.316
0.372
0.376
Diesel
0.364
0.364
0.364
0.364
0.364
LPG
0.346
0.346
0.346
0.414
0.421
Industrial/Commercial
Equipment
Gasoline
2 Stroke
0.107
0.116
0.123
0.177
0.177
4 Stroke
0.425
0.450
0.473
0.542
0.545
Diesel
0.183
0.182
0.180
0.187
0.188
CNG
0.034
0.032
0.031
0.040
0.041
LPG
0.250
0.250
0.250
0.291
0.297
Logging Equipment
Gasoline
2 Stroke
-
-
-
-
-
4 Stroke
0.579
0.591
0.604
0.672
0.678
Diesel
0.398
0.398
0.398
0.398
0.398
Railroad Equipment
Gasoline
4 Stroke
0.498
0.527
0.555
0.643
0.645
Annex 3
0.52
0.52
0.52
0.52
0.53
0.53
0.53
0.53
0.53
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.295
0.174
0.174
0.174
0.174
0.174
0.174
0.174
0.174
0.174
0.398
0.402
0.405
0.409
0.416
0.424
0.431
0.437
0.442
0.229
0.231
0.233
0.235
0.237
0.239
0.240
0.242
0.243
0.172
0.172
0.172
0.172
0.172
0.172
0.172
0.172
0.172
0.690
0.692
0.693
0.694
0.695
0.695
0.695
0.696
0.696
0.110
0.110
0.110
0.110
0.110
0.110
0.110
0.110
0.110
0.532
0.533
0.534
0.534
0.534
0.535
0.535
0.535
0.535
0.153
0.153
0.153
0.153
0.153
0.153
0.153
0.153
0.153
0.300
0.302
0.303
0.304
0.305
0.306
0.306
0.306
0.306
0.378
0.380
0.381
0.382
0.382
0.383
0.383
0.383
0.383
0.364
0.364
0.364
0.364
0.364
0.364
0.364
0.364
0.364
0.424
0.427
0.429
0.430
0.431
0.431
0.432
0.432
0.432
0.177
0.178
0.178
0.178
0.178
0.178
0.178
0.178
0.178
0.548
0.550
0.551
0.552
0.553
0.553
0.552
0.551
0.551
0.190
0.191
0.192
0.190
0.190
0.189
0.189
0.189
0.189
0.043
0.044
0.044
0.044
0.043
0.043
0.043
0.043
0.043
0.303
0.305
0.307
0.308
0.309
0.310
0.311
0.311
0.311
0.688
0.699
0.709
0.719
0.725
0.730
0.733
0.735
0.736
0.398
0.398
0.398
0.398
0.398
0.398
0.398
0.398
0.398
0.646
0.647
0.648
0.649
0.649
0.650
0.650
0.650
0.650
A-171
-------
Diesel
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
0.297
LPG
0.005
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
0.004
Recreational
Equipment
Gasoline
2 Stroke
0.034
0.034
0.034
0.035
0.035
0.036
0.036
0.037
0.037
0.037
0.038
0.038
0.039
0.039
4 Stroke
0.487
0.496
0.503
0.534
0.535
0.535
0.536
0.536
0.536
0.536
0.536
0.536
0.537
0.537
Diesel
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
0.207
LPG
0.255
0.255
0.255
0.270
0.272
0.275
0.277
0.279
0.281
0.284
0.286
0.288
0.290
0.293
- Not applicable
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
Source: IPCC (2006) and Browning, L (2018b), EPA (2021a).
Table A-92: Emission Factors for Cm Emissions from Non-Road Mobile Combustion (g/kg fuel)
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Ships and Boats
Residual Fuel Oil
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
0.31
Gasoline
2 Stroke
1.255
1.259
1.270
1.465
1.489
1.514
1.536
1.557
1.578
1.597
1.615
1.629
1.642
1.652
4 Stroke
0.717
0.720
0.725
0.760
0.763
0.768
0.773
0.777
0.783
0.788
0.793
0.797
0.801
0.805
Distillate Fuel Oil
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
2.008
Rail
Diesel
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
Aircraft
Jet Fuelc
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Aviation Gasoline
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
2.64
Agricultural Equipment3
Gasoline-Equipment
2 Stroke
1.500
1.612
1.720
2.480
2.480
2.480
2.480
2.480
2.480
2.480
2.480
2.480
2.480
2.480
4 Stroke
0.570
0.577
0.586
0.656
0.660
0.666
0.670
0.674
0.677
0.679
0.682
0.686
0.689
0.692
Gasoline-Off-road Trucks
0.570
0.577
0.586
0.656
0.660
0.666
0.670
0.674
0.677
0.679
0.682
0.686
0.689
0.692
Diesel-Equipment
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
0.397
Diesel-Off-Road Trucks
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
CNG
1.391
1.391
1.391
1.676
1.698
1.710
1.719
1.726
1.731
1.734
1.736
1.736
1.736
1.736
LPG
0.135
0.135
0.135
0.152
0.153
0.154
0.155
0.156
0.157
0.157
0.158
0.158
0.159
0.160
Construction/Mining
Equipment'5
Gasoline-Equipment
2 Stroke
1.868
1.939
1.997
2.857 2.858 2.858 2.858 2.858 2.858 2.858 2.858 2.858 2.858 2.858
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
4 Stroke
0.789
0.832
0.871
0.999
1.005
Gasoline-Off-road Trucks
0.789
0.832
0.871
0.999
1.005
Diesel-Equipment
0.317
0.317
0.317
0.316
0.316
Diesel-Off-Road Trucks
0.286
0.286
0.286
0.286
0.286
CNG
1.322
1.322
1.322
1.409
1.422
LPG
0.233
0.233
0.233
0.264
0.267
Lawn and Garden
Equipment
Gasoline-Residential
2 Stroke
1.49
1.57
1.67
2.36
2.37
4 Stroke
0.80
0.84
0.89
1.06
1.06
Gasoline-Commercial
2 Stroke
1.69
1.78
1.86
2.61
2.61
4 Stroke
0.82
0.89
0.96
1.06
1.07
Diesel-Residential
Diesel-Commercial
0.24
0.22
0.21
0.21
0.21
LPG
0.16
0.16
0.16
0.19
0.20
Airport Equipment
Gasoline
4 Stroke
0.29
0.30
0.30
0.36
0.36
Diesel
0.59
0.59
0.59
0.59
0.59
LPG
0.14
0.14
0.14
0.16
0.17
Industrial/Commercial
Equipment
Gasoline
2 Stroke
1.54
1.67
1.77
2.55
2.55
4 Stroke
0.76
0.80
0.84
0.97
0.97
Diesel
0.12
0.11
0.11
0.13
0.13
CNG
2.33
2.39
2.42
2.82
2.84
LPG
0.17
0.17
0.17
0.20
0.21
Logging Equipment
Gasoline
2 Stroke
2.29
2.36
2.42
3.47
3.47
4 Stroke
0.91
0.93
0.95
1.07
1.08
Diesel
0.15
0.15
0.15
0.15
0.15
Railroad Equipment
Gasoline
4 Stroke
0.90
0.94
0.99
1.15
1.15
Diesel
0.12
0.12
0.12
0.13
0.12
Annex 3
1.009
1.011
1.013
1.015
1.017
1.019
1.020
1.021
1.022
1.009
1.011
1.013
1.015
1.017
1.019
1.020
1.021
1.022
0.316
0.316
0.316
0.316
0.316
0.317
0.317
0.317
0.317
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
0.286
1.434
1.447
1.459
1.473
1.499
1.529
1.554
1.574
1.595
0.271
0.273
0.276
0.278
0.280
0.283
0.285
0.286
0.287
2.38
2.38
2.38
2.38
2.38
2.38
2.38
2.38
2.38
1.07
1.07
1.07
1.07
1.07
1.07
1.08
1.08
1.08
2.61
2.61
2.61
2.61
2.61
2.61
2.61
2.61
2.61
1.07
1.07
1.07
1.07
1.07
1.07
1.07
1.07
1.07
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.36
0.36
0.36
0.37
0.37
0.37
0.37
0.37
0.37
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.59
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.17
2.55
2.55
2.55
2.55
2.55
2.55
2.55
2.55
2.55
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.98
0.98
0.14
0.14
0.14
0.14
0.14
0.13
0.13
0.13
0.13
2.84
2.84
2.83
2.85
2.87
2.88
2.88
2.89
2.90
0.21
0.21
0.21
0.21
0.21
0.22
0.22
0.22
0.22
3.47
3.47
3.47
3.47
3.47
3.47
3.47
3.47
3.47
1.10
1.11
1.13
1.14
1.14
1.15
1.15
1.16
1.16
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
1.15
1.15
1.16
1.16
1.16
1.16
1.16
1.16
1.16
0.12
0.12
0.13
0.12
0.12
0.12
0.12
0.12
0.12
A-173
-------
LPG
0.78
0.79
0.79
0.87
0.89
0.90
0.92
0.93
0.94
0.94
0.96
0.96
0.97
0.97
Recreational Equipment
Gasoline
2 Stroke
5.17
5.21
5.25
5.55
5.62
5.70
5.78
5.86
5.94
6.02
6.10
6.18
6.24
6.31
4 Stroke
0.93
0.95
0.96
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
1.03
Diesel
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
0.23
LPG
0.18
0.18
0.18
0.19
0.19
0.20
0.20
0.20
0.20
0.20
0.20
0.21
0.21
0.21
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c Emissions of CH4 from jet fuels have been zeroed out across the time series. Recent research indicates that modern aircraft jet engines are typically net consumers of
methane (Santoni et al., 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines consumer methane. Over the range of
engine operating modes, aircraft engines are net consumers of methane on average. Based on this data, CH4 emissions factors for jet aircraft were changed to zero to reflect
the latest emissions testing data.
Sources: IPCC (2006) and Browning, L (2018b), EPA (2021a).
Table A-93: NQX Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Gasoline On-Road
5,746
4,560
3,812
2,724
2,805
2,647
2,489
2,332
2,122
1,751
1,670
1,498
1,325
1,153
Passenger Cars
3,847
2,752
2,084
1,486
1,530
1,444
1,358
1,272
1,158
955
911
817
723
629
Light-Duty Trucks
1,364
1,325
1,303
942
970
915
861
806
734
605
578
518
458
399
Medium- and Heavy-Duty
Trucks and Buses
515
469
411
286
294
278
261
245
223
184
175
157
139
121
Motorcycles
20
14
13
10
10
10
9
9
8
6
6
6
5
4
Diesel On-Road
2,956
3,493
3,803
2,448
2,520
2,379
2,237
2,095
1,907
1,573
1,501
1,346
1,191
1,036
Passenger Cars
39
19
7
4
4
4
4
4
3
3
3
2
2
2
Light-Duty Trucks
20
12
6
4
4
4
4
3
3
3
2
2
2
2
Medium- and Heavy-Duty
Trucks
2,771
3,328
3,644
2,321
2,381
2,240
2,106
1,968
1,790
1,478
1,409
1,261
1,116
981
Medium - and Heavy-Duty
Buses
126
133
146
119
131
131
123
120
110
89
86
80
71
52
Alternative Fuel On-Roada
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
2,160
2,483
2,584
2,118
1,968
1,883
1,797
1,712
1,605
1,416
1,348
1,310
1,272
1,233
Ships and Boats
402
488
506
438
407
389
372
354
332
293
279
271
263
255
Rail
338
433
451
391
363
348
332
316
296
261
249
242
235
228
Aircraft15
25
31
40
32
29
28
27
26
24
21
20
20
19
18
Agricultural Equipment0
437
478
484
383
356
340
325
309
290
256
244
237
230
223
Construction/Mining
Equipment
641
697
697
550
511
489
467
445
417
368
350
340
330
320
Othere
318
357
407
324
301
288
275
262
246
217
206
200
195
189
Total
10,862
10,536
10,199
7,290
7,294
6,909
6,523
6,138
5,634
4,739
4,519
4,153
3,788
3,422
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
IE (Included Elsewhere)
a N0X emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES3 is a change that affects the emissions time series. Totals may not sum
due to independent rounding.
Table A-94: CO Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Gasoline On-Road
98,328
74,673
60,657
25,235
24,442
23,573
22,704
21,834
20,864
17,995
17,435
16,446
15,458
14,469
Passenger Cars
60,757
42,065
32,867
14,060
13,618
13,134
12,649
12,165
11,625
10,026
9,714
9,163
8,612
8,062
Light-Duty Trucks
29,237
27,048
24,532
10,044
9,729
9,383
9,037
8,690
8,304
7,162
6,940
6,546
6,153
5,759
Medium- and Heavy-
Duty Trucks and Buses
8,093
5,404
3,104
1,073
1,039
1,002
965
928
887
765
741
699
657
615
Motorcycles
240
155
154
58
57
55
53
51
48
42
40
38
36
33
Diesel On-Road
1,696
1,424
1,088
387
375
361
348
335
320
276
267
252
237
222
Passenger Cars
35
18
7
3
3
2
2
2
2
2
2
2
2
1
Light-Duty Trucks
22
16
6
2
2
2
2
2
2
2
2
2
1
1
Medium- and Heavy-
Duty Trucks
1,567
1,337
1,034
363
350
337
324
311
297
257
249
234
220
208
Medium- and Heavy-
Duty Buses
71
54
41
19
19
20
19
19
18
15
15
15
14
11
Alternative Fuel On-
Road3
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
19,337
21,533
21,814
13,853
13,488
12,981
12,474
11,966
11,451
10,518
10,240
10,236
10,231
10,227
Ships and Boats
1,559
1,781
1,825
1,140
1,109
1,068
1,026
984
942
865
842
842
842
841
Rail
85
93
90
56
54
52
50
48
46
42
41
41
41
41
Aircraft15
217
224
245
145
141
136
131
125
120
110
107
107
107
107
Agricultural Equipment0
581
628
626
386
376
362
348
334
319
293
286
286
285
285
Construction/Mining
Equipment
1,090
1,132
1,047
648
631
607
583
560
535
492
479
479
478
478
Other0
15,805
17,676
17,981
11,479
11,176
10,756
10,335
9,915
9,488
8,715
8,485
8,481
8,477
8,473
Total
119,360
97,630
83,559
39,475
38,305
36,915
35,525
34,135
32,635
28,789
27,942
26,934
25,926
24,918
IE (Included Elsewhere)
a CO emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
Annex 3
A-175
-------
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES3 is a change that affects the emissions time series. Totals may not sum
due to independent rounding.
Table A-95: NMVOCs Emissions from Mobile Combustion (kt)
Fuel Type/Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Gasoline On-Road
8,110
5,819
4,615
2,393
2,485
2,342
2,200
2,058
1,929
1,626
1,570
1,444
1,318
1,191
Passenger Cars
5,120
3,394
2,610
1,336
1,388
1,308
1,229
1,149
1,077
908
877
806
736
665
Light-Duty Trucks
2,374
2,019
1,750
929
965
910
854
799
749
631
610
561
512
463
Medium- and Heavy-
Duty Trucks and Buses
575
382
232
115
120
113
106
99
93
78
76
69
63
57
Motorcycles
42
24
23
12
13
12
11
11
10
8
8
7
7
6
Diesel On-Road
406
304
216
116
120
113
106
100
93
79
76
70
64
58
Passenger Cars
16
8
3
2
2
2
2
1
1
1
1
1
1
1
Light-Duty Trucks
14
9
4
2
2
2
2
2
2
1
1
1
1
1
Medium- and Heavy-
Duty Trucks
360
275
201
107
110
104
97
91
85
72
69
64
58
53
Medium-and Heavy-
Duty Buses
16
11
8
5
6
6
6
6
5
4
4
4
4
3
Alternative Fuel On-
Road3
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
IE
Non-Road
2,415
2,622
2,398
2,082
1,957
1,837
1,717
1,597
1,435
1,168
1,082
1,041
1,001
960
Ships and Boats
608
739
744
639
600
564
527
490
440
358
332
320
307
295
Rail
33
36
35
31
29
27
26
24
21
17
16
15
15
14
Aircraft15
28
28
24
17
16
15
14
13
12
9
9
8
8
8
Agricultural Equipment0
85
86
76
63
60
56
52
49
44
36
33
32
30
29
Construction/Mining
Equipment
149
152
130
109
103
96
90
84
75
61
57
55
53
50
Othere
1,512
1,580
1,390
1,223
1,149
1,079
1,008
938
843
686
636
612
588
564
Total
10,932
8,745
7,230
4,591
4,562
4,293
4,023
3,754
3,458
2,873
2,728
2,555
2,382
2,209
IE (Included Elsewhere)
a NMVOC emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road.
b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions.
c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
e "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Notes: The source of this data is the National Emissions Inventory. Updates to estimates from MOVES3 is a change that affects the emissions time series. Totals may not sum
due to independent rounding.
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Definitions of Emission Control Technologies and Standards
The N20 and CH4 emission factors used depend on the emission standards in place and the corresponding level of control
technology for each vehicle type. Table A-84 through Table A-87 show the years in which these technologies or standards
were in place and the penetration level for each vehicle type. These categories are defined below and were compiled
from EPA (1993, 1994a, 1994b, 1998, 1999) and IPCC/UNEP/OECD/IEA (1997).
Uncontrolled
Vehicles manufactured prior to the implementation of pollution control technologies are designated as uncontrolled.
Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel vehicles (pre-
1983), and motorcycles (pre-1996) are assumed to have no control technologies in place.
Gasoline Emission Controls
Below are the control technologies and emissions standards applicable to gasoline vehicles.
Non-catalyst
These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model years
(1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in motorcycles
beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions through adjustments
to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas recirculation (EGR) valves,
which also helps meet vehicle NOx standards.
Oxidation Catalyst
This control technology designation represents the introduction of the catalytic converter, which was the most common
technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to 1985
(trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way
catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system
capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot
function properly with leaded gasoline.
EPA Tier 0
This emission standard from the Clean Air Act was met through the implementation of early "three-way" catalysts, a
technology used in gasoline passenger cars and light-duty gasoline trucks beginning in the early 1980s which remained
common until 1994. This more sophisticated emission control system improves the efficiency of the catalyst by
converting CO and HC to C02 and H20, reducing NOx to nitrogen and oxygen, and using an on-board diagnostic computer
and oxygen sensor. In addition, this type of catalyst includes a fuel metering system (carburetor or fuel injection) with
electronic "trim" (also known as a "closed-loop system"). New cars with three-way catalysts met the Clean Air Act's
amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 g/mile by 1981 and NOx to 1.0
g/mile by 1981.
EPA Tier 1
This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOx emissions
to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent reduction from
the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for NOx, and 0.25
to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use of more
advanced emission control systems applied to light-duty gasoline vehicles beginning in 1994. These advanced emission
control systems included advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR,
and air injection.
EPA Tier 2
This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx emissions
to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks. Manufacturers can
Annex 3
A-177
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meet this average emission level by producing vehicles in 11 emission "Bins," the three highest of which expired in 2006.
These emission standards represent a 77 to 95 percent reduction in emissions from the EPA Tier 1 standard set in 1994.
Emission reductions were met through the use of more advanced emission control systems and lower sulfur fuels and
applied to vehicles beginning in 2004. These advanced emission control systems include improved combustion, advanced
three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air injection.
EPA Tier 3
These standards begin in 2017 and will fully phase-in by 2025, although some Tier 3-compliant vehicles were produced
prior to 2017. This emission standard reduces both tailpipe and evaporative emissions from passenger cars, light-duty
trucks, medium-duty passenger vehicles, and some heavy-duty vehicles. It is combined with a gasoline sulfur standard
that will enable more stringent vehicle emissions standards and will make emissions control systems more effective.
CARB Low Emission Vehicles (LEV)
This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to light-duty
gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel injection
with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts with
secondary air injection. LEVs as defined here include transitional low-emission vehicles (TLEVs), low emission vehicles,
ultra-low emission vehicles (ULEVs). In this analysis, all categories of LEVs are treated the same due to the fact that there
are very limited CH4 or N20 emission factor data for LEVs to distinguish among the different types of vehicles. Zero
emission vehicles (ZEVs) are incorporated into the alternative fuel and advanced technology vehicle assessments.
CARB LEVII
This emission standard builds upon ARB's LEV emission standards. They represent a significant strengthening of the
emission standards and require light trucks under 8500 lbs gross vehicle weight meet passenger car standards. It also
introduces a super ultra-low vehicle (SULEV) emission standard. The LEVII standards decreased emission requirements
for LEV and ULEV vehicles as well as increasing the useful life of the vehicle to 150,000. These standards began with 2004
vehicles. In this analysis, all categories of LEVI Is are treated the same due to the fact that there are very limited CH4 or
N20 emission factor data for LEVIIs to distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are
incorporated into the alternative fuel and advanced technology vehicle assessments.
CARB LEVIII
These standards begin in 2015 and are fully phased in by 2025, although some LEVIII-compliant vehicles were produced
prior to 2017. LEVIII set new vehicle emissions standards and lower the sulfur content of gasoline, considering the vehicle
and its fuel as an integrated system. These new tailpipe standards apply to all light-duty vehicles, medium duty and some
heavy-duty vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel and advanced technology
vehicle assessments.
Diesel Emission Controls
Below are the three levels of emissions control for diesel vehicles.
Moderate control
Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles (generally
in place in model years 1983 to 1995) are considered moderate control technologies. These controls were implemented
to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and 1994.
Advanced control
EGR and modern electronic control of the fuel injection system are designated as advanced control technologies. These
technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place from
1996 through 2006.
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Aftertreatment
Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction (SCR)
systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of emission
control necessary to comply with standards in place from 2007 on.
Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources
This section of this Annex includes supplemental information on the contribution of transportation and other mobile
sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are generally
presented by greenhouse gas, with separate discussions of the methodologies used to estimate C02, N20, CH4, and HFC
emissions. Although the Inventory is not required to provide detail beyond what is contained in the body of this report,
the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex, within the
Annex that details the calculation methods and data used for non-C02 calculations, is to consolidate all transportation
estimates presented throughout the report.
This section of this Annex reports total greenhouse gas emissions from transportation and other (non-transportation)
mobile sources in C02 equivalents, with information on the contribution by greenhouse gas and by mode, vehicle type,
and fuel type. Additional analyses were conducted to develop estimates of C02 from non-transportation mobile sources
(e.g., agricultural equipment, construction/mining equipment, recreational vehicles), and to provide more detailed
breakdowns of emissions by source.
Estimation of C02 from Non-Transportation Mobile Sources
The estimates of N20 and CH4 from fuel combustion presented in the Energy chapter of the Inventory include both
transportation sources and other mobile sources. Other mobile sources include construction/mining equipment,
agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but
do not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates
of C02 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the industrial
and commercial sectors of the Inventory. In order to provide comparable information on transportation and mobile
sources, Table A-96 provides estimates of C02 from these other mobile sources, developed from the Nonroad
component of EPA's MOVES3 model, and FHWA's Highway Statistics. These other mobile source estimates were
developed using the same fuel consumption data utilized in developing the N20 and CH4 estimates (see Table A-83). Note
that the method used to estimate fuel consumption volumes for C02 emissions from non-transportation mobile sources
for the supplemental information presented in Table A-96, Table A-98, and Table A-99 differs from the method used to
estimate fuel consumption volumes for C02 in the industrial and commercial sectors in this Inventory, which include C02
emissions from all non-transportation mobile sources (see Section 3.1 for a discussion of that methodology).
Annex 3
A-179
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Table A-96: CO2 Emissions from Non-Transportation Mobile Sources (MMT CO2 Eq.)a
Fuel Type/ Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Agricultural Equipment3
43.4
43.1
39.9
46.6
46.8
48.0
45.8
45.9
41.1
40.2
39.8
39.8
39.7
39.1
Construction/Mining
Equipment15
48.9
52.7
57.4
65.3
64.0
62.9
65.9
61.1
57.0
60.0
65.1
68.2
70.3
65.1
Other Sources0
69.6
72.2
76.3
86.6
85.8
85.9
87.0
88.8
87.4
88.3
89.9
92.3
94.1
88.0
Total
161.9
168.0
173.6
198.4
196.6
196.8
198.7
195.9
185.6
188.4
194.8
200.3
204.1
192.2
a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
c "Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial
equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for commercial/industrial purposes.
Notes: The method used to estimate C02 emissions in this supplementary information table differs from the method used to estimate C02 in the industrial and commercial
sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for estimating C02 emissions from fossil
fuel combustion in this Inventory). The current Inventory uses the Nonroad component of MOVES3 for years 1999 through 2020.
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Estimation of HFC Emissions from Transportation Sources
In addition to C02, N20 and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are emitted to
the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and purges), as
well as at servicing and disposal events. There are three categories of transportation-related HFC emissions: Mobile air-
conditioning represents the emissions from air conditioning units in passenger cars, light-duty trucks, and heavy-duty
vehicles; Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses; and
Refrigerated Transport represents the emissions from units used to cool freight during transportation.
Table A-97 below presents these HFC emissions. Table A-98 presents all transportation and mobile source greenhouse
gas emissions, including HFC emissions.
Annex 3
A-181
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Table A-97: HFC Emissions from Transportation Sources (MMT CO2 Eg.)
Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Mobile AC
+
19.4
55.2
64.7
58.6
52.7
46.7
43.4
40.5
36.9
33.3
31.0
28.8
26.6
Passenger Cars
+
11.2
28.0
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
9.3
8.3
Light-Duty Trucks
+
7.8
25.6
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
16.9
15.6
Heavy-Duty Vehicles
+
0.5
1.6
3.1
3.0
2.9
2.9
2.9
2.8
2.7
2.6
2.6
2.6
2.7
Comfort Cooling for Trains and
Buses
+
+
0.1
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
0.5
School and Tour Buses
+
+
0.1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Transit Buses
+
+
+
+
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Rail
+
+
+
+
+
+
+
+
+
+
+
+
0.1
0.1
Refrigerated Transport
+
0.2
0.8
2.9
3.4
3.9
4.4
4.9
5.4
5.9
6.4
6.9
7.4
7.9
Medium- and Heavy-Duty Trucks
+
0.1
0.4
1.6
1.8
2.1
2.3
2.5
2.7
2.9
3.1
3.3
3.5
3.7
Rail
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Ships and Boats
+
+
0.3
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
3.9
4.2
Total
+
19.6
56.2
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
36.7
35.0
+ Does not exceed 0.05 MMT C02 Eq.
Note: Totals may not sum due to independent rounding.
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by
Mode/Vehicle Type/Fuel Type
Table A-98 presents estimates of greenhouse gas emissions from an expanded analysis including all transportation and
additional mobile sources, as well as emissions from electricity generation by the consuming category, in C02
equivalents. In total, transportation and non-transportation mobile sources emitted 1,831.7 MMT C02 Eq. in 2020, an
increase of 8 percent from 1990.112 Transportation sources account for 1,632.4 MMT C02 Eq. while non-transportation
mobile sources account for 199.3 MMT C02 Eq. These estimates include HFC emissions for mobile AC, comfort cooling
for trains and buses, and refrigerated transport. These estimates were generated using the estimates of C02 emissions
from transportation sources reported in Section 3.1 C02 Emissions from Fossil Fuel Combustion, and CH4 emissions and
N20 emissions reported in the Mobile Combustion section of the Energy chapter; information on HFCs from mobile air
conditioners, comfort cooling for trains and buses, and refrigerated transportation from the Substitution of Ozone
Depleting Substances section of the IPPU chapter; and estimates of C02 emitted from non-transportation mobile sources
reported in Table A-96 above.
Although all emissions reported here are based on estimates reported throughout this Inventory, some additional
calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle category. In the
case of N20 and CH4, additional calculations were performed to develop emission estimates by type of aircraft and type
of heavy-duty vehicle (i.e., medium- and heavy-duty trucks or buses) to match the level of detail for C02 emissions. N20
estimates for both jet fuel and aviation gasoline, and CH4 estimates for aviation gasoline were developed for individual
aircraft types by multiplying the emissions estimates for each fuel type (jet fuel and aviation gasoline) by the portion of
fuel used by each aircraft type (from FAA 2022 and DLA 2021). Emissions of CH4 from jet fuels are no longer considered
to be emitted from aircraft gas turbine engines burning jet fuel A at higher power settings. This update applies to the
entire time series.113 Recent research indicates that modern aircraft jet engines are typically net consumers of methane
(Santoni et al. 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines
consume methane. Over the range of engine operating modes, aircraft engines are net consumers of methane on
average. Based on this data, CH4 emission factors for jet aircraft were reported as zero to reflect the latest emissions
testing data.
Similarly, N20 and CH4 estimates were developed for medium- and heavy-duty trucks and buses by multiplying the
emission estimates for heavy-duty vehicles for each fuel type (gasoline, diesel) from the Mobile Combustion section in
the Energy chapter, by the portion of fuel used by each vehicle type (from DOE 1993 through 2021). Carbon dioxide
emissions from non-transportation mobile sources are calculated using data from the Nonroad component of EPA's
MOVES3 model (EPA 2021a). Otherwise, the table and figure are drawn directly from emission estimates presented
elsewhere in the Inventory, and are dependent on the methodologies presented in Annex 2.1 (for C02), Chapter 4, and
Annex 3.9 (for HFCs), and earlier in this Annex (for CH4 and N20).
Transportation sources include on-road vehicles, aircraft, boats and ships, rail, and pipelines (note: pipelines are a
transportation source but are stationary, not mobile, emissions sources). In addition, transportation-related greenhouse
gas emissions also include HFC released from mobile air-conditioners and refrigerated transport, and the release of C02
from lubricants (such as motor oil) used in transportation. Together, transportation sources were responsible for 1,632.4
MMTC02 Eq. in 2020.
On-road vehicles were responsible for about 75 percent of all transportation and non-transportation mobile greenhouse
gas emissions in 2020. Although passenger cars make up the largest component of on-road vehicle greenhouse gas
112 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan,
Turbojet and Turboprop Engines," EPA-420-R-09-901, May 27, 2009 (see https://www.epa.gov/regulations-emissions~vehicles-
and-engines/organic-gas-speciation-profile-aircraft).
113 In 2011 FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007 to 2010. The
old approach to vehicle classification was based on body type and split passenger vehicles into "Passenger Cars" and "Other 2
Axle 4-Tire Vehicles." The new approach is a vehicle classification system based on wheelbase. Vehicles with a wheelbase less
than or equal to 121 inches are counted as "Light-duty Vehicles -Short Wheelbase." Passenger vehicles with a wheelbase
greater than 121 inches are counted as "Light-duty Vehicles - Long Wheelbase." This change in vehicle classification has moved
some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle category in this Inventory.
These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.
Annex 3
A-183
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emissions, medium- and heavy-duty trucks have been the primary sources of growth in on-road vehicle emissions.
Greenhouse gas emissions from passenger cars increased by 19 percent between 1990 and 2019, followed by a decline
of 19 percent between 2019 and 2020. Greenhouse gas emissions from light duty trucks decreased by one percent
between 1990 and 2019, followed by a decline of 2 percent between 2019 and 2020. Overall, between 1990 and 2020,
greenhouse gas emissions from passenger cars and light-duty trucks decreased by 3 percent. Meanwhile, greenhouse gas
emissions from medium- and heavy-duty trucks increased 84 percent between 1990 and 2020, reflecting the increased
volume of total freight movement and an increasing share transported by trucks.
Greenhouse gas emissions from aircraft decreased four percent between 1990 and 2019, followed by a decline of 32
percent between 2019 and 2020. Emissions from military aircraft decreased 66 percent between 1990 and 2019,
followed by another 12 percent decline from 2019 to 2020. Commercial aircraft emissions rose 27 percent between 1990
and 2007, dropped 4 percent from 2007 to 2019, and then dropped 32 percent from 2019 to 2020, a reduction by
approximately 17 percent between 1990 and 2020.
Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and
industrial/commercial equipment, emitted approximately 199.3 MMT C02 Eq. in 2020. Together, these sources emitted
more greenhouse gases than ships and boats, and rail combined. Emissions from non-transportation mobile sources
increased, growing approximately 19 percent between 1990 and 2020. Methane and N20 emissions from these sources
are included in the "Mobile Combustion" section and C02 emissions are included in the relevant economic sectors.
Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas
Table A-99 presents estimates of greenhouse gas emissions from transportation and other mobile sources broken down
by greenhouse gas. As this table shows, C02 accounts for the vast majority of transportation greenhouse gas emissions
(approximately 97 percent in 2020). Emissions of C02 from transportation and mobile sources increased by 131.3 MMT
C02 Eq. between 1990 and 2020. In contrast, the combined emissions of CH4 and N20 decreased by 31.5 MMT C02 Eq.
over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant
emissions.114Meanwhile, HFC emissions from mobile air-conditioners and refrigerated transport increased from virtually
no emissions in 1990 to 35 MMT C02 Eq. in 2020 as these chemicals were phased in as substitutes for ozone depleting
substances. It should be noted, however, that the ozone depleting substances that HFCs replaced are also powerful
greenhouse gases, but are not included in national greenhouse gas inventories per UNFCCC reporting requirements.
Greenhouse Gas Emissions from Freight and Passenger Transportation
Table A-100 and Table A-101 present greenhouse gas estimates from transportation, broken down into the passenger
and freight categories. Passenger modes include light-duty vehicles, buses, passenger rail, aircraft (general aviation and
commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A-100. Freight modes
include medium- and heavy-duty trucks, freight rail, refrigerated transport, waterborne freight vessels, pipelines, and
commercial aircraft and are illustrated in Table A-101. Commercial aircraft do carry some freight, in addition to
passengers, and emissions have been split between passenger and freight transportation. The amount of commercial
aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on freight shipped by
commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to weigh an average
of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger weight carried were
used to determine percent shares which were used to split the total commercial aircraft emission estimates. The
remaining transportation and mobile emissions were from sources not considered to be either freight or passenger
modes (e.g., construction/mining and agricultural equipment, lubricants).
The estimates in these tables are derived from the estimates presented in Table A-98. In addition, estimates of fuel
consumption from DOE (1993 through 2021) were used to allocate rail emissions between passenger and freight
categories.
In 2020, passenger transportation modes emitted 1,070.3 MMT C02 Eq., while freight transportation modes emitted
540.6 MMT C02 Eq. Between 1990 and 2020, the percentage growth of greenhouse gas emissions from freight sources
was 55 percent. Emissions from passenger sources grew by 13 percent from 1990 to 2019, followed by a decline of 16
114 The decline in CFC emissions is not captured in the official transportation estimates.
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
percent from 2019 to 2020, an overall reduction of 5 percent from 1990 to 2020. This difference in growth is due largely
to the rapid increase in emissions associated with medium- and heavy-duty trucks.
Annex 3
A-185
-------
Table A-98: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (MMT CO2 Eg.)
Percent
Mode / Vehicle Type / Change
Fuel Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1990-2020
Transportation Total3
1,529.6
1,670.2
1,917.1
1,806.9
1,773.0
1,753.0
1,755.9
1,790.0
1,797.8
1,832.4
1,849.6
1,879.5
1,879.1
1,632.4
7%
On-Road Vehicles
1,206.8
1,342.0
1,557.8
1,515.5
1,483.6
1,472.7
1,465.9
1,514.0
1,510.1
1,533.0
1,538.8
1,561.4
1,551.7
1,377.6
14%
Passenger Cars
639.6
629.9
685.8
762.7
753.3
746.2
739.2
753.0
752.6
763.2
760.6
770.2
763.1
617.7
-3%
Gasolineb
631.7
610.8
654.1
731.4
725.3
721.4
717.7
732.6
733.1
745.1
744.0
754.3
747.8
604.1
-4%
Dieselb
7.9
7.9
3.7
3.8
4.1
4.1
4.1
4.2
4.3
4.3
4.3
4.4
4.6
3.6
-55%
AFVsc
+
+
+
+
0.1
0.1
0.2
0.4
0.6
0.7
0.8
1.2
1.4
1.6
NA
HFCs from Mobile AC
0.0
11.2
28.0
27.5
23.9
20.6
17.2
15.8
14.7
13.2
11.4
10.4
9.3
8.3
NA
Light-Duty Trucks
326.7
425.2
506.7
339.6
322.6
316.0
312.1
331.9
320.9
330.0
324.3
325.6
323.7
315.8
-3%
Gasolineb
315.1
402.4
460.7
292.7
277.9
273.8
272.6
293.2
283.9
294.6
290.7
293.0
291.6
285.7
-9%
Dieselb
11.5
14.9
20.3
12.6
13.0
13.0
12.8
13.9
13.9
14.1
14.1
14.2
14.8
14.1
23%
AFVsc
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.2
0.3
0.3
0.5
141%
HFCs from Mobile AC
0.0
7.8
25.6
34.1
31.6
29.2
26.5
24.7
23.0
21.1
19.2
18.1
16.9
15.6
NA
Medium- and Heavy-
Duty Trucks
230.3
275.9
352.3
393.5
387.5
388.7
393.0
406.1
413.4
416.8
429.7
440.0
439.5
422.8
84%
Gasolineb
38.5
35.8
36.4
42.0
38.5
38.1
38.8
39.9
39.3
40.4
41.2
42.2
40.3
39.9
4%
Dieselb
190.7
238.6
313.2
346.6
343.8
345.2
348.6
360.5
368.1
370.3
382.4
391.5
392.6
376.1
97%
AFVsc
1.2
0.9
0.6
0.3
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
0.4
-62%
HFCs from Refrigerated
Transport and Mobile
ACe
0.0
0.6
2.0
4.7
4.8
5.0
5.2
5.3
5.5
5.5
5.7
5.9
6.1
6.3
NA
Buses
8.5
9.2
11.1
16.0
16.6
17.7
17.8
19.2
19.5
19.0
20.5
21.8
21.7
18.0
113%
Gasolineb
0.3
0.4
0.4
0.7
0.7
0.8
0.8
0.9
0.9
0.9
0.9
1.1
1.0
0.9
148%
Dieselb
8.0
8.7
10.3
13.6
14.4
15.4
15.4
16.8
17.1
16.7
17.9
19.3
19.1
15.7
95%
AFVsc
0.1
0.1
0.3
1.2
1.1
1.0
1.1
1.0
1.1
1.0
1.1
1.1
1.1
1.0
1002%
HFCs from Comfort
Cooling
0.0
+
0.1
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
NA
Motorcycles
1.7
1.8
1.9
3.6
3.6
4.1
3.9
3.9
3.7
3.9
3.8
3.9
3.7
3.3
93%
Gasolineb
1.7
1.8
1.9
3.6
3.6
4.1
3.9
3.9
3.7
3.9
3.8
3.9
3.7
3.3
93%
Aircraft
189.0
176.5
199.3
154.7
149.8
146.4
150.0
151.2
160.5
168.9
174.7
175.4
181.0
123.2
-35%
General Aviation Aircraft
42.0
35.2
35.3
26.4
22.2
19.6
23.3
20.5
26.5
34.8
33.0
32.4
33.4
20.2
-52%
Jet Fuel'
38.8
32.4
32.8
24.4
20.3
17.8
21.7
19.0
25.0
33.3
31.5
30.9
31.7
18.8
-52%
Aviation Gasoline
3.2
2.8
2.6
1.9
1.9
1.8
1.6
1.5
1.5
1.5
1.5
1.6
1.7
1.4
-55%
Commercial Aircraft
110.9
116.3
140.6
114.3
115.6
114.3
115.4
116.3
120.1
121.5
129.2
130.8
135.4
92.1
-17%
Jet Fuel'
110.9
116.3
140.6
114.3
115.6
114.3
115.4
116.3
120.1
121.5
129.2
130.8
135.4
92.1
-17%
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Military Aircraft
36.1
25.0
23.3
14.0
11.9
12.5
11.3
14.4
13.9
12.6
12.6
12.2
12.2
10.8
-70%
Jet Fuel'
36.1
25.0
23.3
14.0
11.9
12.5
11.3
14.4
13.9
12.6
12.6
12.2
12.2
10.8
-70%
Ships and Boatsd
47.0
58.8
65.8
44.9
46.4
40.3
39.7
29.0
33.8
40.7
43.8
41.1
40.0
32.3
-31%
Gasoline
14.4
14.3
14.5
11.8
11.4
11.1
10.9
10.6
10.7
10.7
10.8
10.9
10.9
10.0
-30%
Distillate Fuel
9.7
15.0
17.4
11.3
14.0
11.4
11.5
10.2
16.2
13.9
13.1
12.5
10.6
10.5
8%
Residual Fuele
22.8
29.4
33.7
20.7
19.6
16.0
15.3
5.9
4.3
13.1
16.7
14.2
14.6
7.6
-67%
HFCs from Refrigerated
Transport6
+
+
0.3
1.2
1.5
1.7
2.0
2.3
2.6
2.9
3.3
3.6
3.9
4.2
NA
Rail
39.0
43.2
46.6
44.0
45.1
43.9
44.4
46.3
44.0
40.2
41.4
42.5
39.7
34.2
-12%
Distillate Fuel'
37.3
41.3
44.4
40.4
41.6
40.8
41.2
43.0
41.2
37.7
39.0
40.1
36.4
31.3
-13%
Electricity
3.1
3.1
3.5
4.5
4.3
3.9
4.1
4.1
3.8
3.5
3.4
3.4
3.1
2.7
-12%
Other Emissions from
Rail Electricity Use®
0.1
0.1
+
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
8%
HFCs from Comfort
Cooling
0.0
+
+
+
+
+
+
+
+
+
+
+
0.1
0.1
NA
HFCs from Refrigerated
Transport6
0.0
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NA
Pipelines'1
36.0
38.4
35.5
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.9
57.9
57.1
59%
Natural Gas
36.0
38.4
35.5
37.3
38.1
40.6
46.2
39.4
38.5
39.2
41.3
49.9
57.9
57.1
59%
Other Transportation
11.8
11.3
12.1
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.2
8.8
8.0
-32%
Lubricants
11.8
11.3
12.1
10.4
10.0
9.1
9.6
10.0
11.0
10.4
9.6
9.2
8.8
8.0
-32%
Non-Transportation
Mobile'Total
167.3
173.7
179.5
205.9
204.0
204.2
206.2
203.3
192.5
195.4
202.0
207.6
211.5
199.3
19%
Agricultural Equipment1''
44.9
44.6
41.2
48.3
48.5
49.7
47.4
47.6
42.6
41.5
41.2
41.2
41.0
40.4
-10%
Gasoline
7.5
8.5
6.0
6.2
7.2
7.8
5.8
5.7
1.4
1.5
1.5
1.4
1.2
1.2
-84%
Diesel
37.3
36.1
35.1
42.0
41.2
41.8
41.5
41.7
41.1
40.0
39.6
39.7
39.8
39.1
5%
CNG
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
-15%
LPG
+
+
+
+
+
+
+
+
+
+
+
+
+
+
-10%
Construction/Mining
Equipment'^
50.3
54.2
59.0
67.3
66.0
64.9
68.0
63.1
58.7
61.8
67.0
70.2
72.4
67.1
33%
Gasoline
4.4
4.0
3.3
6.2
5.8
5.9
9.9
6.4
3.3
3.4
3.4
3.5
3.5
3.5
-20%
Diesel
45.5
49.8
55.2
60.6
59.7
58.5
57.6
56.2
55.0
57.9
63.2
66.2
68.3
63.1
39%
CNG
0.3
0.3
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.3
17%
LPG
0.1
0.1
0.2
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.2
0.2
0.1
11%
Other Equipment''1
72.1
74.8
79.3
90.4
89.6
89.6
90.8
92.7
91.2
92.1
93.8
96.3
98.1
91.8
27%
Gasoline
40.7
41.2
43.3
50.2
48.5
47.0
46.9
47.6
45.8
46.3
46.7
47.2
47.6
45.4
12%
Diesel
21.9
21.7
21.6
25.7
26.5
27.7
28.6
29.5
29.7
29.9
30.6
31.7
32.6
29.9
36%
CNG
1.2
1.3
1.4
1.7
1.8
2.0
2.1
2.2
2.2
2.1
2.2
2.3
2.4
2.2
85%
LPG
8.3
10.7
12.9
12.8
12.9
12.9
13.1
13.3
13.5
13.7
14.3
15.0
15.5
14.3
72%
Annex 3 A-187
-------
Transportation and Non-
Transportation Mobile
Total1 1,696.9 1,843.9 2,096.6 2,012.8 1,977.0 1,957.2 1,962.1 1,993.3 1,990.4 2,027.8 2,051.6 2,087.1 2,092.7 1,831.7 8%
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable), as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
a Not including emissions from international bunker fuels.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2021). Data from Table VM-1 are used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4
and N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2020).
These fuel consumption and 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
2021).
c In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to reflect updates made to EIA data on alternative fuel use and vehicle counts. These
changes were incorporated into this year's Inventory and apply to the 2005 to 2020 time period.
d Fluctuations in emission estimates reflect data collection problems. Note that CH4 and N20 from U.S. Territories are included in this value, but not C02 emissions from U.S.
Territories, which are estimated separately in the section on U.S. Territories.
e Domestic residual fuel for ships and boats is estimated by taking the total amount of residual fuel and subtracting out an estimate of international bunker fuel use.
f Class II and Class III diesel consumption data for 2014 to 2017 is not available. Diesel consumption data for 2014-2017 is estimated by applying the historical average fuel usage
per carload factor to the annual number of carloads.
s 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).
h Includes only C02 from natural gas used to power natural gas pipelines; does not include emissions from electricity use or non-C02 gases.
' Note that the method used to estimate C02 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to
estimate C02 in the industrial and commercial sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the
methodology for estimating C02 emissions from fossil fuel combustion in this Inventory).
> Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture.
k Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction.
1 "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.
Notes: Increases to CH4 and N20 emissions from mobile combustion relative to previous Inventories are largely due to updates made to the Motor Vehicle Emissions Simulator
(MOVES3) model that is used to estimate on-road gasoline vehicle distribution and mileage across the time series, as well as non-transportation mobile fuel consumption. See
Section 3.1 "CH4 and N20 from Mobile Combustion" for more detail. This year's Inventory uses the Nonroad component of MOVES3 for years 1999 through 2020. In 2016,
historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as heavy-duty
vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met were re-
examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and therefore not
included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-99: Transportation and Mobile Source Emissions by Gas (MMT CO2 Eg.)
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Percent
Change
1990-2020
C02a
1,645.7
1,763.0
1,981.3
1,910.1
1,881.5
1,869.4
1,881.7
1,917.7
1,919.0
1,960.6
1,988.7
2,026.9
2,031.4
1,777.0
8%
n2o
44.6
55.0
54.1
31.1
29.7
27.4
25.7
23.9
22.1
21.1
20.1
19.2
20.0
17.4
-61%
ch4
6.5
6.2
4.9
3.4
3.3
3.1
3.0
2.8
2.7
2.6
2.6
2.5
2.5
2.2
-66%
HFC
+
19.6
56.2
68.1
62.4
57.1
51.6
48.8
46.3
43.3
40.1
38.5
36.7
35.0
NA
Totalb
1,696.8
1,843.8
2,096.5
2,012.7
1,976.9
1,957.1
1,962.0
1,993.2
1,990.1
2,027.6
2,051.5
2,087.0
2,090.5
1,831.6
8%
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable), as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated.
a The method used to estimate C02 emissions from non-transportation mobile sources in this supplementary information table differs from the method used to estimate C02 in
the industrial and commercial sectors in the Inventory, which include C02 emissions from all non-transportation mobile sources (see Section 3.1 for the methodology for
estimating C02 emissions from fossil fuel combustion in this Inventory).
b Total excludes other emissions from electricity generation and CH4 and N20 emissions from electric rail.
Notes: Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table
MF-21, MF-27 and VM-1 (FHWA 1996 through 2021). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile
CH4 and N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through
2021). These fuel consumption and 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 2021).
In 2016, historical confidential vehicle sales data was re-evaluated to determine the engine technology assignments. First several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
Annex 3
A-189
-------
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2020
o
u
2,500
2,000
1,500
1,000
500
Passenger Cars/Motorcycles
I Medium- and Heavy-Duty Trucks and Buses
I Boats/Ships, Rail, and Pipelines
I Non-Transportation Mobile Sources
I Light-Duty Trucks
Aircraft
Mobile AC, Refrig. Transport, Lubricants
o^Ho^-io^-irMcn^-Lntor--.oo(T>o
cncncncncncncncncncnoooooooooo^H^H^H^H^H^H^H'H'H'HCN
cncncncncncncncncncnooooooooooooooooooooo
1^—1^—1^—1^—1^—1^—1^—1^—1^—IfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfMfN
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-100: Greenhouse Gas Emissions from Passenger Transportation (MMT CO2 Eg.)
Percent
Change
Vehicle Type
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
1990-2020
On-Road Vehiclesa'b
976.5
1,066.1
1,205.5
1,121.9
1,096.1
1,084.0
1,072.9
1,107.9
1,096.7
1,116.2
1,109.1
1,121.4
1,112.2
954.8
-2%
Passenger Cars
639.6
629.9
685.8
762.7
753.3
746.2
739.2
753.0
752.6
763.2
760.6
770.2
763.1
617.7
-3%
Light-Duty Trucks
326.7
425.2
506.7
339.6
322.6
316.0
312.1
331.9
320.9
330.0
324.3
325.6
323.7
315.8
-3%
Buses
8.5
9.2
11.1
16.0
16.7
17.7
17.8
19.2
19.5
19.0
20.5
21.8
21.7
18.0
113%
Motorcycles
1.7
1.8
1.9
3.6
3.6
4.1
3.9
3.9
3.7
3.9
3.8
3.9
3.7
3.3
93%
Aircraft
133.7
131.4
151.6
124.4
121.8
118.2
122.8
120.6
130.1
139.5
143.7
144.6
149.4
99.2
-26%
General Aviation
42.0
35.2
35.3
26.4
22.2
19.6
23.3
20.5
26.5
34.8
33.0
32.4
33.4
20.2
Figure
Commercial
Aircraft
91.7
96.2
116.3
98.0
99.6
98.6
99.5
100.0
103.6
104.7
110.7
112.1
116.1
79.0
-14%
Recreational Boats
17.2
17.1
17.3
14.5
14.0
13.7
13.4
13.2
13.3
13.4
13.6
13.7
13.7
12.7
-26%
Passenger Rail
4.4
4.5
5.2
6.2
6.0
5.5
5.8
5.7
5.4
5.2
5.1
4.4
4.2
3.6
-18%
Total
1,131.8
1,219.0
1,379.6
1,267.0
1,237.9
1,221.4
1,214.8
1,247.4
1,245.5
1,274.3
1,271.5
1,284.1
1,279.6
1,070.3
-5%
a The current Inventory includes updated vehicle population data based on the MOVES3 Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2021). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and
N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2021).
These fuel consumption and 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
2021).
Notes: Data from DOE (1993 through 2021) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. This year's
Inventory uses the Nonroad component of MOVES3 for years 1999 through 2020. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to
reflect updates made to EIA data on alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to 2020 time
period.
In 2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
Table A-101: Greenhouse Gas Emissions from Domestic Freight Transportation (MMT CO2 Eg.)
Percent
By Mode
1990
1995
2000
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Change
1990-2020
Trucking3'15
230.3
275.4
350.7
390.5
384.5
385.8
390.1
403.2
410.5
414.1
427.1
437.4
436.8
420.1
82%
Freight Rail
34.5
38.6
41.4
37.8
39.1
38.3
38.6
40.5
38.6
35.0
36.2
38.0
35.4
30.6
-11%
Ships and Non-
Recreational Boats
29.8
41.7
48.5
30.5
32.4
26.6
26.3
15.9
20.5
27.3
30.3
27.4
26.3
19.7
-34%
Annex 3
A-191
-------
Pipelines0
Commercial Aircraft
36.0
19.2
38.4
20.1
35.5
24.3
37.3 38.1 40.6 46.2 39.4 38.5 39.2 41.3 49.9 57.9 57.1
16.3 16.0 15.8 15.9 16.2 16.5 16.8 18.4 18.7 19.3 13.2
59%
-31%
Total
349.8
414.3
500.4
512.4 510.2 507.0 517.1 515.2 524.5 532.3 553.3 571.4 575.8 540.6
55%
a The current Inventory includes updated vehicle population data based on the MOVES3 Model.
b Gasoline and diesel highway vehicle fuel consumption estimates used to develop C02 estimates in this Inventory are based on data from FHWA Highway Statistics Table MF-21,
MF-27 and VM-1 (FHWA 1996 through 2021). Data from Table VM-1 is used to estimate the share of fuel consumption between each on-road vehicle class. For mobile CH4 and
N20 emissions estimates, gasoline and diesel highway vehicle mileage estimates are based on data from FHWA Highway Statistics Table VM-1 (FHWA 1996 through 2021).
These fuel consumption and 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
c Pipelines reflect C02 emissions from natural gas powered pipelines transporting natural gas.
Notes: Data from DOE (1993 through 2021) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. This year's
Inventory uses the Nonroad component of MOVES3 for years 1999 through 2020. In 2017, estimates of alternative fuel vehicle mileage for the last ten years were revised to
reflect updates made to EIA data on alternative fuel use and vehicle counts. These changes were incorporated into this year's Inventory and apply to the 2005 to 2020 time
In 2016, historical confidential vehicle sales data were re-evaluated to determine the engine technology assignments. First, several light-duty trucks were re-characterized as
heavy-duty vehicles based upon gross vehicle weight rating (GVWR) and confidential sales data. Second, the emission standards each vehicle type was assumed to have met
were re-examined using confidential sales data. Also, in previous Inventories, non-plug-in hybrid electric vehicles (HEVs) were considered alternative fueled vehicles and
therefore not included in the engine technology breakouts. For this Inventory, HEVs are classified as gasoline vehicles across the entire time series.
2021).
period.
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
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Browning, L. (2018a) Updated Methodology for Estimating Electricity Use from Highway Plug-In Electric Vehicles.
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Annex 3
A-193
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Washington, D.C. DOE/EIA-0035 (2022/02).
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Greenhouse Gas Emissions and Sinks, 1990-1996. Office of Mobile Sources, Assessment and Modeling Division, U.S.
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emissions estimates from the Aviation Environmental Design Tool (AEDT). March 2022.
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Administration, Publication Number FHWA-PL-17-012. Available online at:
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Gaffney, J. (2007) Email Communication. John Gaffney, American PublicTransportation Association and Joe Aamidor, ICF
International. December 17, 2007.
HybridCars.com (2019) Monthly Plug-In Electric Vehicle Sales Dashboard, 2010-2018. Available online at
https://www.hybridcars.com/december-2017-dashboard/.
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.)]. Hayama, Kanagawa, Japan.
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Environmental Protection Agency. February 2004.
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Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011 Aug
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Annex 3
A-195
-------
3.3. Methodology for Estimating Emissions from Commercial Aircraft
Jet Fuel Consumption
IPCCTier 3B Method: Commercial aircraft jet fuel burn and carbon dioxide (C02) emissions estimates were developed by
the U.S. Federal Aviation Administration (FAA) using radar-informed data from the FAA Enhanced Traffic Management
System (ETMS) for 2000 through 2019 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 for National Greenhouse Gas Inventories.
International Bunkers: The IPCC guidelines define international aviation (International Bunkers) as emissions from flights
that depart from one country and arrive in a different country. Bunker fuel emissions estimates for commercial aircraft
were developed for this report for 2000 through 2020 using the same radar-informed data modeled with AEDT. Since
this process builds estimates from flight-specific information, the emissions estimates for commercial aircraft can include
emissions associated with the U.S. Territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin Islands, Wake Island,
and other U.S. Pacific Islands). However, to allow for the alignment of emissions estimates for commercial aircraft with
other data that is provided without the U.S. Territories, this annex includes emissions estimates for commercial aircraft
both with and without the U.S. Territories included.
Time Series and Analysis Update: The FAA incrementally improves the consistency, robustness, and fidelity of the C02
emissions modeling for commercial aircraft, which is the basis of the Tier3B inventories presented in this report. While
the FAA does not anticipate significant changes to the AEDT model in the future, recommended improvements are
limited by budget and time constraints, as well as data availability. For instance, previous reports included reported
annual C02 emission estimates for 2000 through 2005 that were modeled using the FAA's System for assessing Aviation's
Global Emissions (SAGE). That tool and its capabilities were significantly improved after it was incorporated and evolved
into AEDT. For this report, the AEDT model was used to generate annual C02 emission estimates for 2000, 2005, 2010,
2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019 and 2020 only. The reported annual C02 emissions values for 2001
through 2004 were estimated from the previously reported SAGE data. Likewise, C02 emissions values for 2006 through
2009 were estimated by interpolation to preserve trends from past reports.
Commercial aircraft radar data sets are not available for years prior to 2000. Instead, the FAA applied a Tier3B
methodology by developing Official Airline Guide (OAG) schedule-informed estimates modeled with AEDT and great
circle trajectories for 1990, 2000 and 2010. The ratios between the OAG schedule-informed and the radar-informed
inventories for the years 2000 and 2010 were applied to the 1990 OAG scheduled-informed inventory to generate the
best possible C02 inventory estimate for commercial aircraft in 1990. The resultant 1990 C02 inventory served as the
reference for generating additional 1995-1999 emissions estimates, which were established using previously available
trends. International consumption estimates for 1991-1999 and domestic consumption estimates for 1991-1994 are
calculated using fuel consumption estimates from the Bureau of Transportation Statistics (DOT 1991 through 2013),
adjusted based on the ratio of DOT to AEDT data.
Notes on the 1990 C02 Emissions Inventory for Commercial Aircraft: There are uncertainties associated with the
modeled 1990 data that do not exist for the modeled 2000 to 2020 data. Radar-based data is not available for 1990. The
OAG schedule information generally includes fewer carriers than radar information, and this will result in a different fleet
mix, and in turn, different C02 emissions than would be quantified using a radar-based data set. For this reason, the FAA
adjusted the OAG-informed schedule for 1990 with a ratio based on radar-informed information. In addition, radar
trajectories are also generally longer than great circle trajectories. While the 1990 fuel burn data was adjusted to address
these differences, it inherently adds greater uncertainty to the revised 1990 commercial aircraft C02 emissions as
compared to data from 2000 forward. Also, the revised 1990 C02 emissions inventory now reflects only commercial
aircraft jet fuel consumption, while previous reports may have aggregated jet fuel sales data from non-commercial
aircraft into this category. Thus, it would be inappropriate to compare 1990 to future years for other than qualitative
purposes.
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
The 1990 commercial aircraft C02 emissions inventory is approximately 18 percent lower than the 2020 C02 emissions
inventory. It is important to note that the distance flown increased by more than 63 percent over this 31- year period
and that fuel burn and aviation activity trends over the past two decades indicate significant improvements in
commercial aviation's ability to provide increased service levels while using less fuel.61
Methane Emissions: Contributions of methane (CH4) emissions from commercial aircraft are reported as zero. Years of
scientific measurement campaigns conducted at the exhaust exit plane of commercial aircraft gas turbine engines have
repeatedly indicated that CH4 emissions are consumed over the full mission flight envelope (Aircraft Emissions of
Methane and Nitrous Oxide during the Alternative Aviation Fuel Experiment, Santoni et al., Environ. Sci. Technol., 2011,
45, 7075-7082). As a result, the U.S. Environmental Protection Agency published that "...methane is no longer considered
to be an emission from aircraft gas turbine engines burning Jet A at higher power settings and is, in fact, consumed in net
at these higher powers."62 In accordance with the following statements in the 2006 IPCC Guidelines (IPCC 2006), the FAA
does not calculate CH4 emissions for either the domestic or international bunker commercial aircraft jet fuel emissions
inventories. "Methane (CH4) may be emitted by gas turbines during idle and by older technology engines, but recent data
suggest that little or no CH4 is emitted by modern engines." "Current scientific understanding does not allow other gases
(e.g., N20 and CH4) to be included in calculation of cruise emissions." (IPCC 1999)
Results: For each inventory calendar year the graph and table below include four jet fuel burn values. These values are
comprised of domestic and international fuel burn totals for the U.S. 50 States and the U.S. 50 States + Territories. Data
are presented for domestic defined as jet fuel burn from any commercial aircraft flight departing and landing in the U.S.
50 States and for the U.S. 50 States + Territories. The data presented as international is respective of the two different
domestic definitions, and represents flights departing from the specified domestic area and landing anywhere in the
world outside of that area.
Note that the graph and table present more fuel burn for the international U.S. 50 States + Territories than for the
international U.S. 50 States. This is because the flights between the 50 states and U.S. Territories are "international"
when only the 50 states are defined as domestic, but they are "domestic" for the U.S. 50 States + Territories definition.
Additional information on the AEDT modeling process is available at:
http://www.faa.gov/about/office org/headquarters offices/apl/research/models/.
Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan,
Turbojet and Turboprop Engines, EPA-420-R-09-901, May 27, 2009, http://www.epa.gov/otaq/aviation.htm.
Annex 3
A-197
-------
Figure A-5: Commercial Aviation Fuel Burn for the United States and Territories
5.00E+10
Commercial Aviation Fuel Burn
for the United States and Territories
no
^ 3.00E+10
O
$ A ^ ^
000
itttt14
ft
15
CO
2.50E+10
2.00E+10
0
Giaiaitji(jiaiai(TiO)OiooooooooooHHHHHHHHriHiN
a»cr>cncT50^a^cncno^a^ooooooooooooooooooooo
HHHrlHrlrlrlHH(N(M(N(NfMrM(M(N(NfMfM(N(N(NN(NMfMfM(N(N
Note: Hollow markers are estimates from data generated by prior tools and methods.
1990 is estimated using non-radar methods.
O States+Territories (Domestic) nStates+Territories (International) A States (Domestic) OStates (International)
Table A-102: Commercial Aviation Fuel Burn for the United States and Territories
Fuel
Fuel
Distance
Burn (M
Burn
co2
Year
Region
Flown (nmi)
Gallon)
(TBtu)
Fuel Burn (Kg)
(MMT)
1990
Domestic U.S. 50 States and U.S. Territories
4,057,195,988
11,568
1,562
34,820,800,463
109.9
International U.S. 50 States and U.S. Territories
599,486,893
3,155
426
9,497,397,919
30.0
Domestic U.S. 50 States
3,984,482,217
11,287
1,524
33,972,832,399
107.2
International U.S. 50 States
617,671,849
3,228
436
9,714,974,766
30.7
1995a
Domestic U.S. 50 States and U.S. Territories
N/A
12,136
1,638
36,528,990,675
115.2
1996a
Domestic U.S. 50 States and U.S. Territories
N/A
12,492
1,686
37,600,624,534
118.6
1997a
Domestic U.S. 50 States and U.S. Territories
N/A
12,937
1,747
38,940,896,854
122.9
1998a
Domestic U.S. 50 States and U.S. Territories
N/A
12,601
1,701
37,930,582,643
119.7
1999a
Domestic U.S. 50 States and U.S. Territories
N/A
13,726
1,853
41,314,843,250
130.3
2000
Domestic U.S. 50 States and U.S. Territories
5,994,679,944
14,672
1,981
44,161,841,348
139.3
International U.S. 50 States and U.S. Territories
1,309,565,963
6,040
815
18,181,535,058
57.4
Domestic U.S. 50 States
5,891,481,028
14,349
1,937
43,191,000,202
136.3
Internationa! U.S. 50 States
1,331,784,289
6,117
826
18,412,169,613
58.1
2001a
Domestic U.S. 50 States and U.S. Territories
5,360,977,447
13,121
1,771
39,493,457,147
124,6
International U.S. 50 States and U.S. Territories
1,171,130,679
5,402
729
16,259,550,186
51.3
Domestic U.S. 50 States
5,268,687,772
12,832
1,732
38,625,244,409
121.9
International U.S. 50 States
1,191,000,288
5,470
739
16,465,804,174
51.9
2002a
Domestic U.S. 50 States and U.S. Territories
5,219,345,344
12,774
1,725
38,450,076,259
121.3
International U.S. 50 States and U.S. Territories
1,140,190,481
5,259
710
15,829,987,794
49.9
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Domestic U.S. 50 States
5,129,493,877
12,493
1,687
37,604,800,905
118.6
International U.S. 50 States
1,159,535,153
5,326
719
16,030,792,741
50.6
2003a
Domestic U.S. 50 States and U.S. Territories
5,288,138,079
12,942
1,747
38,956,861,262
122.9
International U.S. 50 States and U.S. Territories
1,155,218,577
5,328
719
16,038,632,384
50.6
Domestic U.S. 50 States
5,197,102,340
12,658
1,709
38,100,444,893
120.2
International U.S. 50 States
1,174,818,219
5,396
728
16,242,084,008
51.2
2004a
Domestic U.S. 50 States and U.S. Territories
5,371,498,689
13,146
1,775
39,570,965,441
124.8
International U.S. 50 States and U.S. Territories
1,173,429,093
5,412
731
16,291,460,535
51.4
Domestic U.S. 50 States
5,279,027,890
12,857
1,736
38,701,048,784
122.1
International U.S. 50 States
1,193,337,698
5,481
740
16,498,119,309
52.1
2005
Domestic U.S. 50 States and U.S. Territories
6,476,007,697
13,976
1,887
42,067,562,737
132.7
International U.S. 50 States and U.S. Territories
1,373,543,928
5,858
791
17,633,508,081
55.6
Domestic U.S. 50 States
6,370,544,998
13,654
1,843
41,098,359,387
129.7
International U.S. 50 States
1,397,051,323
5,936
801
17,868,972,965
56.4
2006a
Domestic U.S. 50 States and U.S. Territories
5,894,323,482
14,426
1,948
43,422,531,461
137.0
International U.S. 50 States and U.S. Territories
1,287,642,623
5,939
802
17,877,159,421
56.4
Domestic U.S. 50 States
5,792,852,211
14,109
1,905
42,467,943,091
134.0
International U.S. 50 States
1,309,488,994
6,015
812
18,103,932,940
57.1
2007a
Domestic U.S. 50 States and U.S. Territories
6,009,247,818
14,707
1,986
44,269,160,525
139.7
International U.S. 50 States and U.S. Territories
1,312,748,383
6,055
817
18,225,718,619
57.5
Domestic U.S. 50 States
5,905,798,114
14,384
1,942
43,295,960,105
136.6
International U.S. 50 States
1,335,020,703
6,132
828
18,456,913,646
58.2
2008a
Domestic U.S. 50 States and U.S. Territories
5,475,092,456
13,400
1,809
40,334,124,033
127.3
International U.S. 50 States and U.S. Territories
1,196,059,638
5,517
745
16,605,654,741
52.4
Domestic U.S. 50 States
5,380,838,282
13,105
1,769
39,447,430,318
124.5
International U.S. 50 States
1,216,352,196
5,587
754
16,816,299,099
53.1
2009a
Domestic U.S. 50 States and U.S. Territories
5,143,268,671
12,588
1,699
37,889,631,668
119.5
International U.S. 50 States and U.S. Territories
1,123,571,175
5,182
700
15,599,251,424
49.2
Domestic U.S. 50 States
5,054,726,871
12,311
1,662
37,056,676,966
116.9
International U.S. 50 States
1,142,633,881
5,248
709
15,797,129,457
49.8
2010
Domestic U.S. 50 States and U.S. Territories
5,652,264,576
11,931
1,611
35,912,723,830
113.3
International U.S. 50 States and U.S. Territories
1,474,839,733
6,044
816
18,192,953,916
57.4
Domestic U.S. 50 States
5,554,043,585
11,667
1,575
35,116,863,245
110.8
International U.S. 50 States
1,497,606,695
6,113
825
18,398,996,825
58.0
2011
Domestic U.S. 50 States and U.S. Territories
5,767,378,664
12,067
1,629
36,321,170,730
114.6
International U.S. 50 States and U.S. Territories
1,576,982,962
6,496
877
19,551,631,939
61.7
Domestic U.S. 50 States
5,673,689,481
11,823
1,596
35,588,754,827
112.3
International U.S. 50 States
1,596,797,398
6,554
885
19,727,043,614
62.2
2012
Domestic U.S. 50 States and U.S. Territories
5,735,605,432
11,932
1,611
35,915,745,616
113.3
International U.S. 50 States and U.S. Territories
1,619,012,587
6,464
873
19,457,378,739
61.4
Domestic U.S. 50 States
5,636,910,529
11,672
1,576
35,132,961,140
110.8
International U.S. 50 States
1,637,917,110
6,507
879
19,587,140,347
61.8
2013
Domestic U.S. 50 States and U.S. Territories
5,808,034,123
12,031
1,624
36,212,974,471
114.3
International U.S. 50 States and U.S. Territories
1,641,151,400
6,611
892
19,898,871,458
62.8
Domestic U.S. 50 States
5,708,807,315
11,780
1,590
35,458,690,595
111.9
International U.S. 50 States
1,661,167,498
6,657
899
20,036,865,038
63.2
2014
Domestic U.S. 50 States and U.S. Territories
5,825,999,388
12,131
1,638
36,514,970,659
115.2
International U.S. 50 States and U.S. Territories
1,724,559,209
6,980
942
21,008,818,741
66.3
Domestic U.S. 50 States
5,725,819,482
11,882
1,604
35,764,791,774
112.8
International U.S. 50 States
1,745,315,059
7,027
949
21,152,418,387
66.7
2015
Domestic U.S. 50 States and U.S. Territories
5,900,440,363
12,534
1,692
37,727,860,796
119.0
International U.S. 50 States and U.S. Territories
1,757,724,661
7,227
976
21,752,301,359
68.6
Domestic U.S. 50 States
5,801,594,806
12,291
1,659
36,997,658,406
116.7
International U.S. 50 States
1,793,787,700
7,310
987
22,002,733,062
69.4
2016
Domestic U.S. 50 States and U.S. Territories
5,929,429,373
12,674
1,711
38,148,578,811
120.4
Annex 3
A-199
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International U.S. 50 States and U.S. Territories
1,817,739,570
7,453
1,006
22,434,619,940
70.8
Domestic U.S. 50 States
5,827,141,640
12,422
1,677
37,391,339,601
118.0
International U.S. 50 States
1,839,651,091
7,504
1,013
22,588,366,704
71.3
2017
Domestic U.S. 50 States and U.S. Territories
6,264,650,997
13,475
1,819
40,560,206,261
128.0
International U.S. 50 States and U.S. Territories
1,944,104,275
7,841
1,059
23,602,935,694
74.5
Domestic U.S. 50 States
6,214,083,068
13,358
1,803
40,207,759,885
126.9
International U.S. 50 States
1,912,096,739
7,755
1,047
23,343,627,689
73.6
2018
Domestic U.S. 50 States and U.S. Territories
6,408,870,104
13,650
1,843
41,085,494,597
129.6
International U.S. 50 States and U.S. Territories
2,037,055,865
8,402
1,134
25,291,329,878
79.8
Domestic U.S. 50 States
6,318,774,158
13,425
1,812
40,410,478,534
127.5
International U.S. 50 States
2,066,756,708
8,254
1,114
24,843,232,462
78.4
2019
Domestic U.S. 50 States and U.S. Territories
6,721,417,987
14,397
1,944
43,334,968,184
136.7
International U.S. 50 States and U.S. Territories
1,980,425,952
7,908
1,068
23,803,403,228
75.1
Domestic U.S. 50 States
6,617,074,577
14,131
1,908
42,535,165,758
134.2
International U.S. 50 States
2,008,158,986
7,973
1,076
23,997,773,004
75.7
2020
Domestic U.S. 50 States and U.S. Territories
4,391,123,811
9,613
1,298
28,934,254,672
91.3
International U.S. 50 States and U.S. Territories
910,801,671
3,863
521
11,626,780,467
36.7
Domestic U.S. 50 States
4,297,034,877
9,358
1,263
28,167,145,166
88.9
International U.S. 50 States
944,600,496
3,954
534
11,900,792,661
37.5
NA (Not Applicable)
a Estimates for these years were derived from previously reported tools and methods.
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas
Inventories Programme, The Intergovernmental Panel on Climate Change. [H.S. Eggleston, L. Buendia, K. Miwa, T.
Ngara, and K. Tanabe (eds.)]. Hayama, Kanagawa, Japan.
IPCC (1999) Aviation and the Global Atmosphere. Intergovernmental Panel on Climate Change. [J.E. Penner, et al.
(eds.)]. Cambridge University Press. Cambridge, United Kingdom.
Santoni, G., B. Lee, E. Wood, S. Herndon, R. Miake-Lye, S Wofsy, J. McManus, D. Nelson, M. Zahniser (2011) Aircraft
emissions of methane and nitrous oxide during the alternative aviation fuel experiment. Environ Sci Technol. 2011
Aug 15; 45(16):7075-82.
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3.4. Methodology for Estimating CH4 Emissions from Coal Mining
EPA uses an IPCC Tier 3 method for estimating CH4 emissions from underground mining and an IPCC Tier 2 method for
estimating CH4 emissions from surface mining and post-mining activities (for both coal production from underground
mines and surface mines). 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 emissions from surface mines and post-mining activities. This step does not use mine-specific
data; rather, it consists of multiplying coal-basin-specific coal production by coal-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 degasification systems. Some mines recover and use the
generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground mines equals the
CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus CH4 recovered and
used.
Step 1.1: Estimate CH4 Liberated from Ventilation Systems
All coal mines with detectable CH4 emissions use ventilation systems to ensure that CH4 levels remain within safe
concentrations. Many coal mines do not have detectable levels of CH4; others emit several million cubic feet per day
(MMCFD) from their ventilation systems. On a quarterly basis, the U.S. Mine Safety and Health Administration (MSHA)
measures CH4 concentration levels at underground mines. MSHA maintains a database of measurement data from all
underground mines with detectable levels of CH4 in their ventilation air (MSHA 2021).125 Based on quarterly
measurements, MSHA estimates average daily CH4 liberated at each of these underground mines.
For 1990 through 1999, average daily CH4 emissions from MSHA were multiplied by the number of days in the year (i.e.,
coal mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For 2000 through
2020, the average daily CH4 emission rate for each mine is determined using the CH4 total for all data measurement
events conducted during the calendar year and total duration of all data measurement events (in days). The calculated
average daily CH4 emissions were then multiplied by 365 days to estimate annual ventilation emissions.
Total ventilation emissions for a particular year are estimated by summing emissions from individual mines.
Since 2011, the nation's "gassiest" underground coal mines—those that liberate more than 36,500,000 cubic feet of CH4
per year (about 17,525 MT C02 Eq.)—have been required to report to EPA's GHGRP (EPA 2021).126 Mines that report to
EPA's GHGRP must report quarterly measurements of CH4 emissions from ventilation systems; 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 U.S. with detectable CH4 concentrations.
Since 2013, ventilation emission estimates have been calculated based on both EPA's GHGRP127 data submitted by
underground mines, and on mine-specific CH4 measurement data obtained directly from MSHA for the remaining mines.
The MSHA measurement data are used to determine the average daily emission rate for all mines in the reporting year.
125 MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane.
Readings below this threshold are considered non-detectable.
126 Underground coal mines report to EPA under subpart FF of EPA's GHGRP (40 CFR part 98). In 2020, 71 underground coal
mines reported to the program.
127 In implementing improvements and integrating data from EPA's GHGRP, the EPA followed the latest guidance from the IPCC
on the use of facility-level data in national inventories (IPCC 2011).
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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The CH4 liberated from ventilation systems is estimated by summing the emissions from the mines reporting to EPA's
GHGRP and emissions based on MSHA measurements for the remaining mines not reporting to EPA's GHGRP.
Table A-103: Mine-Specific Data Used to Estimate Ventilation Emissions
Year Individual Mine Data Used
1990 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
1991 1990 Emission Factors Used Instead of Mine-Specific Data
1992 1990 Emission Factors Used Instead of Mine-Specific Data
1993 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
1994 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
1995 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)a
1996 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)a
1997 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
1998 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
1999 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2000 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2001 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2002 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2003 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2004 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2005 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)3
2006 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)a
2007 All Mines with Detectable Emissions (Assumed to Account for 100% of Total)
2008 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b
2009 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b
2010 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b
2011 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b
2012 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)b
2013 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2014 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2015 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2016 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2017 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2018 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2019 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
2020 All Mines with Detectable Emissions and GHGRP reported data (Assumed to account for 100% of Total)
a Factor derived from a complete set of individual mine data collected for 1997.
b Factor derived from a complete set of individual mine data collected for 2007.
Step 1.2: Estimate CH4 Liberated from Degasification Systems
Coal mines use several types of degasification systems to remove CH4, including pre-mining vertical and horizontal wells
(to recover CH4 before mining) and post-mining vertical wells and horizontal boreholes (to recover CH4 during mining of
the coal seam). Post-mining gob wells and cross-measure boreholes recover CH4 from the overburden (i.e., gob area)
after mining of the seam (primarily in longwall mines).
Twenty mines employed degasification systems in 2020, and 19 of these mines reported the CH4 liberated through these
systems to the EPA's GHGRP (EPA 2021). Thirteen of the 20 mines with degasification systems had operational CH4
recovery and use projects, and the other seven reported emitting CH4 from degasification systems to the atmosphere.
Several of the mines venting CH4 from degasification systems use a small portion of the gas to fuel gob well blowers or
compressors in remote locations where electricity is not available. However, this CH4 use is not considered to be a formal
recovery and use project.
Degasification information reported to EPA's GHGRP by underground coal mines is the primary source of data used to
develop estimates of CH4 liberated from degasification systems. Data reported to EPA's GHGRP were used exclusively to
estimate CH4 liberated from degasification systems at 15 of the 20 mines that used degasification systems in 2020.
A-203
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Degasification volumes for the life of mined-through, pre-mining wells are attributed to the mine as emissions in the year
in which the well is mined through.128 EPA's GHGRP does not require gas production from virgin coal seams (coalbed
methane) to be reported by coal mines under Subpart FF. Most pre-mining wells drilled from the surface are considered
coalbed methane wells and are reported under another subpart of the program (Subpart W, "Petroleum and Natural Gas
Systems"). As a result, for the five mines with degasification systems that include pre-mining wells that were mined
through in 2020, EPA's GHGRP information was supplemented with historical data from state gas well production
databases and mine-specific information regarding the dates on which pre-mining wells were mined through (GSA 2021;
DMME 2021; WVGES 2021; JWR 2010; El Paso 2009; ERG 2021). For pre-mining wells, the cumulative CH4 production
from the well is totaled using gas sales data and is considered liberated from the mine's degasification system the year in
which the well is mined through.
Reports to EPA's GHGRP with CH4 liberated from degasification systems are reviewed for errors in reporting. For some
mines, GHGRP data are corrected for the Inventory based on expert judgment. Common errors include reporting CH4
liberated as CH4 destroyed and vice versa. Other errors include reporting CH4 destroyed without reporting any CH4
liberated by degasification systems. In the rare cases where GHGRP data are inaccurate and gas sales data are
unavailable, estimates of CH4 liberated are based on historical CH4 liberation rates.
Step 1.3: Estimate CH4 Recovered from Ventilation and Degasification Systems, and Utilized or
Destroyed (Emissions Avoided)
There were 13 active coal mines with operational CH4 recovery and use projects in 2020, including one mine that had
two recovery and use projects. Thirteen of these projects involved degasification systems, in place at twelve mines, and
one involved ventilation air methane (VAM). Eleven of these mines sold the recovered CH4 to a pipeline, including one
mine that used CH4 to fuel a thermal coal dryer. One mine used CH4 to heat mine ventilation air (data was unavailable for
estimating CH4 recovery at this mine). One mine destroyed the recovered CH4 (VAM) using Regenerative Thermal
Oxidation (RTO) without energy recovery.
The CH4 recovered and used (or destroyed) at the thirteen coal mines described above were estimated using the
following methods:
• EPA's GHGRP data was exclusively used to estimate the CH4 recovered and used from seven mines that
deployed degasification systems in 2020. Based on weekly measurements of gas flow and CH4
concentrations, the GHGRP summary data for degasification destruction at each mine were added
together to estimate the CH4 recovered and used from degasification systems.
• State sales data were used to estimate CH4 recovered and used from the remaining five mines that
deployed degasification systems in 2020 (DMME 2021; GSA 2021). These five mines intersected pre-mining
wells in 2020. Supplemental information was used for these mines because estimating CH4 recovery and
use from pre-mining wells requires additional data (data not reported under Subpart FF of EPA's GHGRP;
see discussion in step 1.2 above) to account for the emissions avoided prior to the well being mined
through. The 2020 data came from state gas production databases (DMME 2021; GSA 2021; WVGES 2021),
as well as mine-specific information on the timing of mined-through, pre-mining wells (JWR 2010; El Paso
2009, ERG 2019-2021). For pre-mining wells, the cumulative CH4 production from the wells was totaled
using gas sales data, and was considered to be CH4 recovered and used from the mine's degasification
system in the year in which the well was mined through.
• For the single mine that employed VAM for CH4 recovery and use, the estimates of CH4 recovered and used
were obtained from the mine's offset verification statement (OVS) submitted to the California Air
Resources Board (CARB) (McElroy OVS 2021).
Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities
Mine-specific data are not available for estimating CH4 emissions from surface coal mines or for post-mining activities.
For surface mines, basin-specific coal production data obtained from the Energy Information Administration's Annual
Coal Report are multiplied by basin-specific gas contents and a 150 percent emission factor (to account for CH4from
over- and under-burden) to estimate CH4 emissions (King 1994; Saghafi 2013). For post-mining activities, basin-specific
128 A well is "mined through" when coal mining development or the working face intersects the borehole or well.
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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coal production data are multiplied by basin-specific gas contents and a mid-range 32.5 percent emission factor
accounting for CH4 desorption during coal transportation and storage (Creedy 1993). Basin-specific in situ gas content
data were compiled from AAPG (1984) and USBM (1986). Beginning in 2006, revised data on in situ CH4 content and
emission factors have been used (EPA 1996, 2005).
Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data
The first step in estimating CH4 emissions from surface mining and post-mining activities is to define the geographic
resolution of the analysis and to collect coal production data at that level of resolution. The analysis is conducted by coal
basin as defined in Table A-104, which presents coal basin definitions by basin and by state.
The Energy Information Administration's Annual Coal Report (EIA 2021) includes state- and county-specific underground
and surface coal production by year. To calculate production by basin, the state-level data are grouped into coal basins
using the basin definitions listed in Table A-104. For two states—West Virginia and Kentucky—county-level production
data are used for the basin assignments because coal production occurred in geologically distinct coal basins within
these states. Table A-105 presents the coal production data aggregated by basin.
Step 2.2: Estimate Emission Factors for Each Emissions Type
Emission factors for surface-mined coal were developed from the in situ CH4 content of the surface coal in each basin.
Based on analyses conducted in Canada and Australia on coals similar to those present in the United States (King 1994;
Saghafi 2013), the surface mining emission factor used was conservatively estimated to be 150 percent of the in situ CH4
content of the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent of the
average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5
percent of the in situ CH4 content in the basin. Table A-106 presents the average in situ content for each basin, along
with the resulting emission factor estimates.
Step 2.3: Estimate CH4 Emitted
The total amount of CH4 emitted from surface mines and post-mining activities is calculated by multiplying the coal
production in each basin by the appropriate emission factors.
Table A-104 lists each of the major coal mine basins in the United States and the states in which they are located. As
shown in Figure A-6, several coal basins span several states. Table A-105 shows annual underground, surface, and total
coal production (in short tons) for each coal basin. Table A-106 shows the surface, post-surface, and post-underground
emission factors used for estimating CH4 emissions for each of the categories. For underground mines, Table A-107
presents annual estimates of CH4 emissions for ventilation and degasification systems, and CH4 recovered and used.
Table A-108 presents annual estimates of total CH4 emissions from underground, post-underground, surface, and post-
surface activities.
Table A-104: Coal Basin Definitions by Basin and by State
Basin
States
Northern Appalachian Basin
Maryland, Ohio, Pennsylvania, West Virginia North
Central Appalachian Basin
Kentucky East, Tennessee, Virginia, West Virginia South
Warrior Basin
Alabama, Mississippi
Illinois Basin
Illinois, Indiana, Kentucky West
South West and Rockies Basin
Arizona, California, Colorado, New Mexico, Utah
North Great Plains Basin
Montana, North Dakota, Wyoming
West Interior Basin
Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas
Northwest Basin
Alaska, Washington
State
Basin
Alabama
Warrior Basin
Alaska
Northwest Basin
Arizona
South West and Rockies Basin
Arkansas
West Interior Basin
California
South West and Rockies Basin
Colorado
South West and Rockies Basin
Illinois
Illinois Basin
Indiana
Illinois Basin
Iowa
West Interior Basin
A-205
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Kansas
West Interior Basin
Kentucky (east)
Central Appalachian Basin
Kentucky (west)
Illinois Basin
Louisiana
West Interior Basin
Maryland
Northern Appalachian Basin
Mississippi
Warrior Basin
Missouri
West Interior Basin
Montana
North Great Plains Basin
New Mexico
South West and Rockies Basin
North Dakota
North Great Plains Basin
Ohio
Northern Appalachian Basin
Oklahoma
West Interior Basin
Pennsylvania
Northern Appalachian Basin
Tennessee
Central Appalachian Basin
Texas
West Interior Basin
Utah
South West and Rockies Basin
Virginia
Central Appalachian Basin
Washington
Northwest Basin
West Virginia South
Central Appalachian Basin
West Virginia North
Northern Appalachian Basin
Wyoming
North Great Plains Basin
Figure A-6: Locations of U.S. Coal Basins
Coaibed Methane Fields, Lower 48 States
North Central
Coal Region
Williston
Basin
Powder River
WBasin
Wind River Basins
Wyoming j
OvarthrusU I -
Michigan
I Basin
Northern
AppaJachian
¦SWColbrado
Coal Area
CtrerokeelRatform
San Juah
Basin
Miles
Black Warrior
Basin
Southwestern
Coal Region
Coaibed Methane Fields
Coal Basins, Regions & Fields
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-105: Annual Coal Production (Thousand Short Tons)
Basin
1990
2005
2016
2017
2018
2019
2020
Underground Coal Production
423,556
368,612
252,106
273,129
275,361
267,373
195,528
N. Appalachia
103,865
111,151
94,685
97,741
97,070
97,905
71,998
Cent. Appalachia
198,412
123,082
39,800
46,053
45,306
39,957
30,249
Warrior
17,531
13,295
7,434
10,491
12,199
11,980
10,451
Illinois
69,167
59,180
76,578
80,855
85,416
81,061
54,334
S. West/Rockies
32,754
60,866
26,413
30,047
25,387
27,257
20,049
N. Great Plains
1,722
572
6,776
7,600
9,777
9,213
8,447
West Interior
105
465
420
343
206
0
0
Northwest
0
0
0
0
0
0
0
Surface Coal Production
602,753
762,190
475,407
500,782
480,080
438,445
339,450
N. Appalachia
60,761
28,873
8,739
9,396
9,219
8,476
6,215
Cent. Appalachia
94,343
112,222
26,759
31,796
33,799
32,742
17,921
Warrior
11,413
11,599
5,079
4,974
5,523
4,841
4,288
Illinois
72,000
33,703
21,707
22,427
21,405
18,591
13,098
S. West/Rockies
43,863
42,756
18,951
19,390
19,599
18,394
13,420
N. Great Plains
249,356
474,056
350,898
372,874
362,664
329,164
262,968
West Interior
64,310
52,262
42,342
38,966
26,969
25,261
20,519
Northwest
6,707
6,720
932
959
902
975
1,021
Total Coal Production
1,026,309
1,130,802
727,514
773,911
755,442
705,818
534,978
N. Appalachia
164,626
140,023
103,424
107,137
106,289
106,381
78,213
Cent. Appalachia
292,755
235,305
66,558
77,848
79,105
72,700
48,170
Warrior
28,944
24,894
12,513
15,464
17,723
16,822
14,739
Illinois
141,167
92,883
98,285
103,282
106,821
99,652
67,432
S. West/Rockies
76,617
103,622
45,364
49,437
44,987
45,652
33,469
N. Great Plains
251,078
474,629
357,675
380,474
372,441
338,376
271,415
West Interior
64,415
52,727
42,763
39,309
27,175
25,261
20,519
Northwest
6,707
6,720
932
959
902
975
1,021
Note: Totals may not sum due to independent rounding.
Table A-106: Coal Underground, Surface, and Post-Mining ChU Emission Factors (ft3 per
Short Ton)
Surface
Underground
Post-Mining
Average
Average
Surface Mine Post-Mining 1
Underground
Basin
In Situ Content In Situ Content
Factors Surface Factors
Factors
Northern Appalachia
59.5
138.4
89.3
19.3
45.0
Central Appalachia (WV)
24.9
136.8
37.4
8.1
44.5
Central Appalachia (VA)
24.9
399.1
37.4
8.1
129.7
Central Appalachia (E KY)
24.9
61.4
37.4
8.1
20.0
Warrior
30.7
266.7
46.1
10.0
86.7
Illinois
34.3
64.3
51.5
11.1
20.9
Rockies (Piceance Basin)
33.1
196.4
49.7
10.8
63.8
Rockies (Uinta Basin)
16.0
99.4
24.0
5.2
32.3
Rockies (San Juan Basin)
7.3
104.8
11.0
2.4
34.1
Rockies (Green River Basin)
33.1
247.2
49.7
10.8
80.3
Rockies (Raton Basin)
33.1
127.9
49.7
10.8
41.6
N. Great Plains (WY, MT)
20.0
15.8
30.0
6.5
5.1
N. Great Plains (ND)
5.6
15.8
8.4
1.8
5.1
West Interior (Forest City, Cherokee Basins)
34.3
64.3
51.5
11.1
20.9
West Interior (Arkoma Basin)
74.5
331.2
111.8
24.2
107.6
West Interior (Gulf Coast Basin)
11.0
127.9
16.5
3.6
41.6
Northwest (AK)
16.0
160.0
24.0
5.2
52.0
Northwest (WA)
16.0
47.3
24.0
5.2
15.4
Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins-, U.S.
DOE Report DOE/METC/83-76, Methane Recovery from Coalbeds: A Potential Energy Source; 1986-1988 Gas Research
Institute Topical Report, A Geologic Assessment of Natural Gas from Coal Seams; 2005 U.S. EPA Draft Report, Surface Mines
Emissions Assessment.
Annex 3
A-207
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Table A-107: Underground Coal Mining Cm Emissions (Billion Cubic Feet)
Activity
1990
2005
2016 2017
2018
2019 2020
Ventilation Output
112
75
76 78
73
62
60
Adjustment Factor for Mine Data
98%
98%
100% 100%
100%
100% 100%
Adjusted Ventilation Output
114
77
76 78
73
62
60
Degasification System Liberated
54
47
42 42
47
42
37
Total Underground Liberated
168
124
118 121
120
104
97
Recovered & Used
(14)
(37)
(34) (36)
(39)
(33)
(32)
Total
154
87
85 84
81
72
65
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
fable A-108: Total Coal Mining ChU Emissions (Billion Cubic Feet)
Activity
1990
2005
2016 2017
2018
2019 2020
Underground Mining
154
87
85 84
81
72
65
Surface Mining
22 1
25
14 15
15
13
10
Post-Mining (Underground)
19
16
10 11
11
11
8
Post-Mining (Surface]
5
5
3 3
3
3
2
Total
200
133
112 114
110
98
86
\lote: Totals may not sum due to independent rounding.
fable A-109: Total Coal Mining ChU Emissions by State (Million Cubic Feet)
State
1990
2005
2016
2017
2018
2019
2020
Alabama
32,097
15,789
10,752
11,044
12,119
9,494
9,767
Alaska
50
42
27
28
26
28
30
Arizona
151
161
72
83
87
51
0
Arkansas
5
+
247
770
71
0
0
California
1
0
0
0
0
0
0
Colorado
10,187
13,441
2,272
1,940
1,616
1,730
1,380
Illinois
10,180
6,488
11,034
8,513
6,530
5,661
4,100
Indiana
2,232
3,303
6,713
6,036
6,729
6,807
6,067
Iowa
24
0
0
0
0
0
0
Kansas
45
11
2
0
0
0
0
Kentucky
10,018
6,898
4,880
4,636
4,636
2,264
1,765
Louisiana
64
84
56
42
129
36
14
Maryland
474
361
131
152
113
119
92
Mississippi
0
199
161
146
165
151
145
Missouri
166
37
15
15
16
12
10
Montana
1,373
1,468
1,004
1,102
1,172
1,038
775
New Mexico
363
2,926
1,954
1,728
1,360
1,446
723
North Dakota
299
306
287
294
303
276
270
Ohio
4,406
3,120
1,998
1,473
1,342
1,283
793
Oklahoma
226
825
867
2,407
2,317
116
367
Pennsylvania
21,864
18,605
17,932
19,662
20,695
23,528
18,931
Tennessee
276
115
27
14
23
17
7
Texas
1,119
922
783
730
498
468
395
Utah
3,587
4,787
788
678
629
811
845
Virginia
46,041
8,649
6,692
7,663
7,051
6,959
6,726
Washington
146
154
0
0
0
0
0
West Virginia
48,335
29,745
32,309
33,122
28,686
25,711
24,253
Wyoming
6,671
14,745
10,812
11,497
13,201
10,409
8,099
Total
200,399
133,182
111,815
113,777
109,515
98,416
85,555
+ Does not exceed 0.5 million cubic feet.
Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining
activities. The totals include CH4 liberated, minus CH4 recovered and used (i.e., representing total "net" emissions). The following
states have neither underground nor surface mining and thus report no emissions as a result of coal mining: Connecticut,
Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, Minnesota, Nebraska, Nevada, New Hampshire,
New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota, Vermont, and Wisconsin.
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
AAPG (1984) Coalbed Methane Resources of the United States. AAPG Studies in Geology Series #17.
Creedy, D.P. (1993) Chemosphere. Vol. 26, pp. 419-440.
DMME (2021) DGO Data Information System. Department of Mines, Minerals and Energy of Virginia. Available online at
https://www.dmme.virginia.gov/dgoinquiry/frmmain.aspx.
EIA (2021) Annual Coal Report 2020. Table 1. Energy Information Administration, U.S. Department of Energy.
El Paso (2009) Shoal Creek Mine Plan, El Paso Exploration & Production.
EPA (2021) Greenhouse Gas Reporting Program (GHGRP), Data for reporting year 2020, Subpart FF: Underground Coal
Mines. Available online at http://www.epa.gov/ghgreporting/ghgdata/reported/coalmines.html.
EPA (2005) Surface Mines Emissions Assessment. Draft. U.S. Environmental Protection Agency.
EPA (1996) Evaluation and Analysis of Gas Content and Coal Properties of Major Coal Bearing Regions of the United
States. U.S. Environmental Protection Agency. EPA/600/R-96-065.
ERG (2019-2021) Correspondence between ERG and Buchanan Mine.
Geological Survey of Alabama State Oil and Gas Board (GSA) (2021) Well Records Database. Available online at
http://www.gsa.state.al.us/ogb/database.aspx.
IPCC (2011) Use of Models and Facility-Level Data in Greenhouse Gas Inventories. Report of IPCC Expert Meeting on Use
of Models and Measurements in Greenhouse Gas Inventories 9-11 August 2010, Sydney, Australia. Eds: Eggleston H.S.,
Srivastava N., Tanabe K., Baasansuren J., Fukuda M. IGES.
JWR (2010) No. 4 & 7 Mines General Area Maps. Walter Energy: Jim Walter Resources.
King, B. (1994) Management of Methane Emissions from Coal Mines: Environmental, Engineering, Economic and
Institutional Implication of Options, Neil and Gunter Ltd., Halifax, March 1994.
McElroy OVS (2021) Marshall County VAM Abatement Project Offset Verification Statement submitted to California Air
Resources Board, August 2021.
MSHA (2021) Data Transparency at MSHA. Mine Safety and Health Administration. Available online at
http://www.msha.gov/.
Mutmansky, Jan M., and Yanbei Wang (2000) Analysis of Potential Errors in Determination of Coal Mine Annual Methane
Emissions. Department of Energy and Geo-Environmental Engineering, Pennsylvania State University. University Park,
PA.
Saghafi, Abouna (2013) Estimation of fugitive emissions from open cut coal mining and measurable gas content, 13th
Coal Operators' Conference, University of Wollongong, The Australian Institute of Mining and Metallurgy & Mine
Managers Association of Australia, 2013, 306-313.
USBM (1986) Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins. Circular 9067, U.S.
Bureau of Mines.
West Virginia Geological & Economic Survey (WVGES) (2021) Oil & Gas Production Data. Available online at
http://www.wvgs.wvnet.edu/www/datastat/datastat.htm.
Annex 3
A-209
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3.5. Methodology for Estimating CH4, CO2, and N2O Emissions from
Petroleum Systems
For details on the emissions, emission factors, activity data, data sources, and methodologies for each year from 1990 to
2020 please see the spreadsheet file annexes for the current (i.e., 1990 to 2020) Inventory, available at
https://www.epa.gov/ghgernissions/stakeholder-proces5-natural-gas-and-petroleurn-svsterns-199Q-2Q20-inventory.
As described in the main body text on Petroleum Systems, the Inventory methodology involves the calculation of CH4,
C02, and N20 emissions for approximately 100 emissions sources, and then the summation of emissions for each
petroleum systems segment. The approach for calculating emissions for petroleum systems generally involves the
application of emission factors to activity data.
Emission Factors
Table 3.5-2, Table 3.5-7, and Table 3.5-10 show CH4, C02, and N20 emissions, respectively, for all sources in Petroleum
Systems, for all time series years. Table 3.5-3, Table 3.5-8, and Table 3.5-11 show the CH4, C02, and N20 average emission
factors, respectively, for all sources in Petroleum Systems, for all time series years. These emission factors are calculated
by dividing net emissions by activity. Therefore, in a given year, these emission factors reflect the estimated contribution
from controlled and uncontrolled fractions of the source population.
Additional detail on the basis for emission factors used across the time series is provided in Table 3.5-4, Table 3.5-9,
Table 3.5-12, and below.
In addition to the Greenhouse Gas Reporting Program (GHGRP), key references for emission factors for CH4 and non-
combustion-related C02 emissions from the U.S. petroleum industry include a 1999 EPA/Radian report Methane
Emissions from the U.S. Petroleum Industry (EPA/Radian 1999), which contained the most recent and comprehensive
determination of CH4 emission factors for CH4-emitting activities in the oil industry at that time, a 1999 EPA/ICF draft
report Estimates of Methane Emissions from the U.S. Oil Industry (EPA/ICF 1999) which is largely based on the 1999
EPA/Radian report, and a detailed study by the Gas Research Institute and EPA Methane Emissions from the Natural Gas
Industry (EPA/GRI 1996). These studies still represent best available data in many cases—in particular, for the early years
of the time series.
Data from studies and EPA's GHGRP (EPA 2021a) allows for emission factors to be calculated that account for adoption of
control technologies and emission reduction practices. For several sources, EPA has developed control category-specific
emission factors from recent data that are used over the time series (paired with control category-specific activity data
that fluctuates to reflect control adoption over time). For oil well completions with hydraulic fracturing, controlled and
uncontrolled emission factors were developed using GHGRP data. For associated gas, separate emission estimates are
developed from GHGRP data for venting and flaring. For oil tanks, emissions estimates were developed for large and
small tanks with flaring or VRU control, without control devices, and with upstream malfunctioning separator dump
valves. For pneumatic controllers, separate estimates are developed for low bleed, high bleed, and intermittent
controllers. For chemical injection pumps, the estimate is calculated with an emission factor developed with GHGRP
data, which is based on the previous GRI/EPA factor but takes into account operating hours. Some sources in Petroleum
Systems that use methodologies based on GHGRP data use a basin-level aggregation approach, wherein EPA calculates
basin-specific emissions and/or activity factors for basins that contribute at least 10 percent of total annual emissions (on
a C02 Eq. basis) from the source in any year—and combines all other basins into one grouping. This methodology is
applied for associated gas venting and flaring and miscellaneous production flaring. Produced Water CH4 estimates are
calculated using annual produced water quantities (Enverus Drillinglnfo 2021 and EPA 2021b and an emission factor from
EPA's Nonpoint Oil and Gas Emission Estimation Tool (EPA 2017b).
For the refining segment, EPA has directly used the GHGRP data for all emission sources for recent years (2010 forward)
(EPA 2021a) and developed source level throughput-based emission factors from GHGRP data to estimate emissions in
earlier time series years (1990-2009). For some sources within refineries, EPA continues to apply the historical emission
factors for all time series years. All refineries have been required to report CH4, C02, and N20 emissions to GHGRP for all
major activities since 2010. The national totals of these emissions for each activity were used for the 2010 to 2020
emissions. The national emission totals for each activity were divided by refinery feed rates for those four Inventory
years (2010-2013) 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 2015b).
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Offshore emissions are taken from analysis of the Gulfwide Emission Inventory Studies and GHGRP data (BOEM 2021a-d;
EPA 2021a; EPA 2020). Emission factors are calculated for offshore facilities located in the Gulf of Mexico, Pacific, and
Alaska regions.
When a C02-specific emission factor is not available for a source, the C02 emission factors were derived from the
corresponding source CH4 emission factors. The amount of C02 in the crude oil stream changes as it passes through
various equipment in petroleum production operations. As a result, four distinct stages/streams with varying C02
contents exist. The four streams that are used to estimate the emissions factors are the associated gas stream separated
from crude oil, hydrocarbons flashed out from crude oil (such as in storage tanks), whole crude oil itself when it leaks
downstream, and gas emissions from offshore oil platforms. For this approach, C02 emission factors are estimated by
multiplying the existing CH4emissions factors by a conversion factor, which is the ratio of C02 content to methane
content for the particular stream. Ratios of C02 to CH4 volume in emissions are presented in Table 3.5-1.
N20 emission factors were calculated using GHGRP data. For each flaring emission source calculation methodology that
uses GHGRP data, the existing source-specific methodology was applied to calculate N20 emission factors.
Activity Data
Table 3.5-5 shows the activity data for all sources in Petroleum Systems, for all time series years. Additional detail on the
basis for activity data used across the time series is provided in Table 3.5-6, and below.
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. Where appropriate, the activity data were calculated from related statistics using ratios
developed based on EPA 1996, and/or GHGRP data. For major equipment (equipment leak categories), pneumatic
controllers, and chemical injection pumps, GHGRP Subpart W data were used to develop activity factors (i.e., count per
well) that are applied to calculated activity in recent years; to populate earlier years of the time series, linear
interpolation is used to connect GHGRP-based estimates with existing estimates in years 1990 to 1995. In other cases,
the activity data were held constant from 1990 through 2014 based on EPA (1999). Lastly, the previous year's data were
used when data for the current year were unavailable. For offshore production in the GOM, the number of active major
and minor complexes are used as activity data. For offshore production in the Pacific and Alaska region, the activity data
are region-specific production. The activity data for the total crude transported in the transportation segment are not
available, therefore the activity data for the refining sector (i.e., refinery feed in 1000 bbl/year) was also used for the
transportation sector, applying an assumption that all crude transported is received at refineries. In the few cases where
no data were located, oil industry data based on expert judgment were used. In the case of non-combustion C02 and N20
emission sources, the activity factors are the same as for CH4emission sources. In some instances, where recent time
series data (e.g., year 2020) are not yet available, year 2019 or prior data were used as proxy.
Methodology for well counts and events
EPA used Drillinglnfo and Prism, production databases maintained by Enverus Inc. (Enverus Drillinglnfo 2021), covering
U.S. oil and natural gas wells to populate time series activity data for active oil wells, oil wells drilled, and oil well
completions and workovers with hydraulic fracturing. For more information on Enverus data processing, please see
Annex 3.6 Methodology for Estimating CH4, C02, and N20 from Natural Gas Systems.
Reductions data: Federal regulations
Regulatory actions reducing emissions in the current Inventory include the New Source Performance Standards (NSPS)
and National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents in the
production segment.
The Inventory reflects the NSPS for oil and gas through the use of a net factor approach that captures shifts to lower
emitting technologies required by the regulation. Examples include separating oil well completions and workovers with
hydraulic fracturing into four categories and developing control technology-specific methane emission factors and year-
specific activity data for each category; establishing control category-specific emission factors and associated year-
specific activity data for oil tanks; and calculating year-specific activity data for pneumatic controller bleed categories.
In regard to the oil and natural gas industry, the NESHAP regulation addresses HAPs from the oil and natural gas
production sectors and the natural gas transmission and storage sectors of the industry. Though the regulation deals
specifically with HAPs reductions, methane emissions are also incidentally reduced.
Annex 3
A-211
-------
NESHAP driven reductions from storage tanks are estimated with net emission methodologies that take into account
controls implemented due to regulations.
Methane, Carbon Dioxide, and Nitrous Oxide Emissions by Emission Source for Each Year
Annual CH4, C02, and N20 emissions for each source were calculated by multiplying the activity data for each year by the
corresponding emission factor. These annual emissions for each activity were then summed to estimate the total annual
CH4, C02, and N20 emissions, respectively. Emissions at a segment level are shown in Table 3.5-2, Table 3.5-7, and Table
3.5-10.
Refer to the 1990-2020 Inventory section at https://www.epa.gov/ghgernissions/natural-ga5-and-petroleurn-systerns for
the following data tables, in spreadsheet format:
• Table 3.5-1: Ratios of C02 to CH4 Volume in Emissions from Petroleum Production Field Operations
• Table 3.5-2: CH4 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
• Table 3.5-3: Average CH4 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
• Table 3.5-4: CH4 Emission Factors for Petroleum Systems, Data Sources/Methodology
• Table 3.5-5: Activity Data for Petroleum Systems Sources, for All Years
• Table 3.5-6: Activity Data for Petroleum Systems, Data Sources/Methodology
• Table 3.5-7: C02 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
• Table 3.5-8: Average C02 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
• Table 3.5-9: C02 Emission Factors for Petroleum Systems, Data Sources/Methodology
• Table 3.5-10: N20 Emissions (kt) for Petroleum Systems, by Segment and Source, for All Years
• Table 3.5-11: Average N20 Emission Factors (kg/unit activity) for Petroleum Systems Sources, for All Years
• Table 3.5-12: N20 Emission Factors for Petroleum Systems, Data Sources/Methodology
• Table 3.5-13: Annex 3.5 Electronic Tables - References
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
AL/OGB (2021) Oil and Gas Production. Alabama Oil and Gas Board. Available online at:
http://www.gsa.state.al.us/ogb/production.
AOGCC (2021a) List of wells. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
http://aogweb.state.ak.us/DataMiner3/Forms/WellList.aspx.
AOGCC (2021b) Oil and Gas Production. Alaska Oil and Gas Conservation Commission (AOGCC). Available online at:
http://aogweb.state.ak.us/DataMiner3/Forms/Production.aspx?.
API (1989) Aboveground Storage Tank Survey report prepared by Entropy Limited for American Petroleum Institute, April
1989.
API (1992) Global Emissions of Methane from Petroleum Sources. American Petroleum Institute, Health and
Environmental Affairs Department, Report No. DR140, February 1992.
API (1995) API 4615: Emission Factors For Oil and Gas Production Operations. American Petroleum Institute.
Washington, DC.
API (1996) API 4638: Calculation Workbook For Oil And Gas Production Equipment Fugitive Emissions. American
Petroleum Institute. Washington, DC.
API (2000) API 4697: Production Tank Emissions Model - A Program For Estimating Emissions From Hydrocarbon
Production Tanks - E&P Tank Version 2.0. American Petroleum Institute. Washington, DC.
API (2003) Basic Petroleum Data Book, 1990-2003. American Petroleum Institute. Washington, DC.
BOEM (2005) Year 2005 Gulfwide Emission Inventory Study. Each GEI study is available online:
https://www.boem.gov/Gulfwide-Offshore-Activity-Data-System-GOADS/.
BOEM (2008) Year 2008 Gulfwide Emission Inventory Study. Each GEI study is available online:
https://www.boem.gov/Gulfwide-Offshore-Activity-Data-System-GOADS/.
BOEM (2011) Year 2011 Gulfwide Emission Inventory Study. Each GEI study is available online:
https://www.boem.gov/Gulfwide-Offshore-Activity-Data-System-GOADS/.
BOEM (2014) Year 2014 Gulfwide Emission Inventory Study. Each GEI study is available online:
https://www.boem.gov/Gulfwide-Offshore-Activity-Data-System-GOADS/.
BOEM (2017) Year 2017 Gulfwide Emission Inventory Study. Each GEI study is available online:
https://www.boem.gov/Gulfwide-Offshore-Activity-Data-System-GOADS/.
BOEM (2021a) BOEM Platform Structures Online Query. Available online at:
https://www.data.boem.gov/Platform/PlatformStructures/Default.aspx.
BOEM (2021b) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1947 to 2020. Download
"Production Data" online at: https://www.data.boem.gov/Main/RawData.aspx.
BOEM (2021c) BOEM Oil and Gas Operations Reports - Part A (OGOR-A). Production Data for 1996 to 2020. Available
online at: https://www.data.boem.gov/Main/OGOR-A.aspx.
BOEM (2021d) BOEM Oil and Gas Operations Reports - Part B (OGOR-B). Flaring volumes for 1996 to 2020. Available
online at: https://www.data.boem.gov/Main/OGOR-B.aspx.
CA/DOC (2021) Annual Oil and Gas Reports for 1990-2020. State Oil and Gas Supervisor, California Department of
Conservation. Available online at:
https://www.conservation.ca.gov/calgem/pubs_stats/annual_reports/Pages/annual_reports.aspx.
CAPP (1992) Canadian Association of Petroleum Producers (CAPP), A Detailed Inventory of CH4 and VOC Emissions from
Upstream Oil & Gas Operations in Alberta. March 1992.
Enverus Drillinglnfo (2021) August 2021 Download. Dl Desktop® Enverus Drillinglnfo, Inc.
EIA (2021a) Monthly Energy Review, 1995-2020 editions. Energy Information Administration, U.S. Department of Energy.
Washington, DC. Available online at: http://www.eia.gov/totalenergy/data/monthly/index.cfm.
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EIA (2021b) Petroleum Supply Annual. 2001-2020 editions. U.S Department of Energy Washington, DC. Available online
at: http://www.eia.gov/petroleum/supply/annual/volumel/.
EIA (2021c) Refinery Capacity Report, 2005-2020 editions. Energy Information Administration, U.S. Department of
Energy. Washington, DC. Available online at: http://www.eia.gov/petroleum/refinerycapacity/.
EIA (2021d) 1981-Current: Energy Information Administration estimates published in the Petroleum Supply Annual and
Petroleum Supply Monthly reports. Energy Information Administration, U.S. Department of Energy. Washington, DC.
Available online at: https://www.eia.gov/dnav/pet/pet_crd_crpdn_adc_mbbl_a.htm.
EPA (1997) Compilation of Air Pollutant Emission Factors, AP-42. Office of Air Quality Planning and Standards, U.S.
Environmental Protection Agency. Research Triangle Park, NC. October 1997.
EPA (2015a) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-inventory-updates-1990-2013-inventory-
published.
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Refineries Emissions Estimate.
Available online at: https://www.epa.gov/ghgemissions/additional-information-oil-and-gas-estimates-1990-2013-ghg-
inventory-published-april.
EPA (2016a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: https://www.epa.gov/ghgemissions/additional-information-oil-and-gas-
estimates-1990-2014-ghg-inventory-published-april.
EPA (2017a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2015-ghg.
EPA (2017b) 2017 Nonpoint Oil and Gas Emission Estimation Tool, Version 1.2. Prepared for U.S. Environmental
Protection Agency by Eastern Research Group, Inc. (ERG). October 2019.
EPA (2018a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions Under
Consideration. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2016-ghg.
EPA (2018b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-Specific
Emissions and Activity Factors. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems-ghg-inventory-additional-information-1990-2016-ghg.
EPA (2018c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to C02 Emissions Estimation
Methodologies. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2016-ghg.
EPA (2019a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates Considered for 2019 and
Future GHGIs. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2020) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2018: Update for Offshore Production Emissions.
Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2021a) Greenhouse Gas Reporting Program. U.S. Environmental Protection Agency. Data reported as of August 7,
2021.
EPA (2021b) Preliminary state-level produced water data for IL, IN, KS, OK, PA, and WV from EPA's Draft 2020 National
Emissions Inventory. U.S. Environmental Protection Agency. Data obtained via email in November 2021.
EPA (2021c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Produced Water Emissions.
Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M., T. Shires, J. Wessels, and R.
Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, Air Pollution Prevention and
Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
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EPA/ICF (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF International.
Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.
EPA/Radian (1999) Methane Emissions from the U.S. Petroleum Industry. Prepared by Radian International. U.S.
Environmental Protection Agency. February 1999.
LA/DNR (2021) Production Data. Louisiana Department of Natural Resources. Available online at:
http://www.dnr.louisiana.gov/index.cfm?md=pagebuilder&tmp=home&pid=206.
OGJ (2021) Special Report: Pipeline Economics, 2005-2021 Editions. Oil & Gas Journal, PennWell Corporation, Tulsa, OK.
Available online at: http://www.ogj.com/.
Radian/API (1992) "Global Emissions of Methane from Petroleum Sources." American Petroleum Institute, Health and
Environmental Affairs Department, Report No. DR140, February 1992.
TRC (2021) Oil & Gas Production Data Query. Texas Railroad Commission. Available online at:
http://webapps.rrc.state.tx.us/PDO/generalReportAction.do.
WCUS (2021) Waterborne Commerce of the United States, Part 5: National Summaries, 2000-2019 Editions. United
States Army Corps of Engineers. Washington, DC, June 4, 2021. Available online at:
http://www.navigationdatacenter.us/wcsc/wcscparts.htm.
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3.6. Methodology for Estimating CH4, CO2, and N2O Emissions from
Natural Gas Systems
For details on the emissions, emission factors, activity data, data sources, and methodologies for each year from 1990 to
2020 please see the spreadsheet file annexes for the current (i.e., 1990 to 2020) Inventory, available at
https://www.epa.gov/ghgernissions/stakeholder-proces5-natural-gas-and-petroleurn-svsterns-199Q-2Q20-inventory.
As described in the main body text on Natural Gas Systems, the Inventory methodology involves the calculation of CH4,
C02, and N20 emissions for over 100 emissions sources, and the summation of emissions for each natural gas segment.
The approach for calculating emissions for natural gas systems generally involves the application of emission factors to
activity data. For many sources, the approach uses technology-specific emission factors or emission factors that vary
over time and take into account changes to technologies and practices, which are used to calculate net emissions
directly. For others, the approach uses what are considered "potential methane factors" and reduction data to calculate
net emissions.
Emission Factors
Table 3.6-1, Table 3.6-10, and Table 3.6-14 show CH4, C02, and N20 emissions, respectively, for all sources in Natural Gas
Systems, for all time series years. Table 3.6-2, Table 3.6-12, and Table 3.6-15 show the CH4, C02, and N20 average
emission factors, respectively, for all sources in Natural Gas Systems, for all time series years. These emission factors are
calculated by dividing net emissions by activity. Therefore, in a given year, these emission factors reflect the estimated
contribution from controlled and uncontrolled fractions of the source population and any source-specific reductions (see
below section "Reductions Data"); additionally, for sources based on the GRI/EPA study, the values take into account
methane compositions from GTI 2001 adjusted year to year using gross production for National Energy Modeling System
(NEMS) oil and gas supply module regions from the EIA. These adjusted region-specific annual CH4 compositions are
presented in Table 3.6-3 (for general sources), Table 3.6-4 (for gas wells without hydraulic fracturing), and Table 3.6-5
(for gas wells with hydraulic fracturing).
Additional detail on the basis for the CH4, C02, and N20 emission factors used across the time series is provided in Table
3.6-6, Table 3.6-13, Table 3.6-16, and below.
Key references for emission factors for CH4 and non-combustion-related C02 emissions from the U.S. natural gas industry
include the 1996 Gas Research Institute (GRI) and EPA study (GRI/EPA 1996), the Greenhouse Gas Reporting Program
(GHGRP) (EPA 2021c), and others.
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 for base year 1992. Since the time of this study, practices and
technologies have changed. This study still represents best available data in many cases—in particular, for early years of
the time series.
Data from studies and EPA's GHGRP (EPA 2021c) allow for emission factors to be calculated that account for adoption of
control technologies and emission reduction practices. For some sources, EPA has developed control category-specific
emission factors from recent data that are used over the time series (paired with control category-specific activity data
that fluctuates to reflect control adoption over time). In other cases, EPA retains emission factors from the EPA/GRI
study for early time series years (1990 to 1992), applies updated emission factors in recent years (e.g., 2011 forward),
and uses interpolation to calculate emission factors for intermediate years. For some sources, EPA continues to apply the
EPA/GRI emission factors for all time series years, and accounts for emission reductions through data reported to Gas
STAR or estimated based on regulations (see below section "Reductions Data"). For the following sources in the
exploration and production segments, EPA has used GHGRP data to calculate net emission factors and establish source
type and/or control type subcategories:
• For gas well completions and workovers with hydraulic fracturing, separate emissions estimates were
developed for hydraulically fractured completions and workovers that vent, flared hydraulic fracturing
completions and workovers, hydraulic fracturing completions and workovers with reduced emissions
completions (RECs), and hydraulic fracturing completions and workovers with RECs that flare.
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• For gas well completions without hydraulic fracturing, separate emissions estimates were developed for
completions that vent and completions that flare.
• For liquids unloading, separate emissions estimates were developed for wells with plunger lifts and wells
without plunger lifts.
• For condensate tanks, emissions estimates were developed for large and small tanks with flaring or vapor
recovery control (VRU) control, without control devices, and with upstream malfunctioning separator
dump valves.
• For pneumatic controllers, separate estimates are developed for low bleed, high bleed, and intermittent
controllers.
• Chemical injection pumps estimates are calculated with an emission factor developed with GHGRP data,
which is based on the previous GRI/EPA factor but takes into account operating hours.
For most sources in the processing, transmission and storage, and distribution segments, net emission factors have been
developed for application in recent years of the time series, while the existing emission factors are applied in early time
series years.
When a C02-specific emission factor is not available for a source, the C02 emission factors were derived from the
corresponding source CH4 emission factors using default gas composition data. C02 emission factors are estimated by
multiplying the CH4emission factors by the ratio of the C02-to-CH4 gas content. This approach is applied for certain
sources in the natural gas production, gas processing (only for early time series years), transmission and storage, and
distribution segments. The default gas composition data are specific to segment and are provided in Table 3.6-11. The
default values were derived from GRI/EPA (1996), EIA (1994), and GTI (2001).
N20 emission factors were calculated using GHGRP data. For each flaring emission source calculation methodology that
uses GHGRP data, the source-specific methodology used to estimate C02 was applied to calculate N20 emission factors.
1990-2020 Inventory updates to emission factors
Summary information for emission factors for sources with revisions in this year's Inventory is below. The details are
presented in memoranda,67 Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2020: Update for Natural Gas
Anomalous Leak Events( EPA 2022a) and, Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2020: Update for
Post-Meter Emissions (EPA 2022b), as well as the "Recalculations Discussion" section of the main body text.
EPA added well blowout emissions into the Inventory for three discrete well blowout events for this Inventory. The well
blowouts occurred in Ohio in 2018 and in Texas and Louisiana in 2019.
The Inventory was updated to include an estimate for post-meter emissions. Post-meter emission factors are presented
in the 2019 Refinement to the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National
Greenhouse Gas Inventories under natural gas systems (IPCC 2019). Post-meter emission sources include certain leak
emissions from residential and commercial appliances, industrial facilities and power plants, and natural gas fueled
vehicles.
Activity Data
Table 3.6-7 shows the activity data for all sources in Natural Gas Systems, for all time series years. Additional detail on
the basis for activity data used across the time series is provided in Table 3.6-8, and below.
For a few sources, recent direct activity data were not available. For these sources, either 2019 data were used as proxy
for 2020 data or a set of industry activity data drivers was developed and was used to update activity data. Key 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.
Methodology for well counts and events
67 Stakeholder materials including EPA memoranda for the Inventory are available at
https://www.epa.gov/ehgemissions/natural-gas-and-petroleum-systems.
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EPA used Drillinglnfo and Prism datasets from Enverus (Enverus 2021), covering U.S. oil and natural gas wells to populate
time series activity data for active gas wells, gas wells drilled, and gas well completions and workovers with hydraulic
fracturing (for 1990 to 2010). EPA queried the Enverus datasets for relevant data on an individual well basis—including
location, natural gas and liquids (i.e., oil and condensate) production by year, drill type (e.g., horizontal or vertical), and
date of completion or first production. Non-associated gas wells were classified as any well that had non-zero gas
production in a given year, and with a gas-to-oil ratio (GOR) of greater than 100 mcf/bbl in that year. Oil wells were
classified as any well that had non-zero liquids production in a given year, and with a GOR of less than or equal to 100
mcf/bbl in that year. Gas wells with hydraulic fracturing were assumed to be the subset of the non-associated gas wells
that had fracking fluid data within Enverus or were horizontally drilled and/or located in an unconventional formation
(i.e., shale, tight sands, or coalbed). Unconventional formations were identified based on well basin, reservoir, and field
data reported in the Enverus datasets referenced against a formation type crosswalk developed by EIA (EIA 2012a).
For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas wells with
hydraulic fracturing that had a date of completion or first production in the specified year. To calculate workovers for all
time series years, EPA applied a refracture rate of 1 percent (i.e., 1 percent of all wells with hydraulic fracturing are
assumed to be refractured in a given year) to the total counts of wells with hydraulic fracturing from the Enverus
datasets. For 2011 forward, EPA used GHGRP data for the total number of well completions. The GHGRP data represents
a subset of the national completions, due to the reporting threshold, and therefore using this data without scaling it up
to national level results in an underestimate. However, because EPA's GHGRP counts of completions were higher than
national counts of completions (estimated using the Enverus datasets), EPA directly used the GHGRP data to estimate
national activity for years 2011 forward.
EPA calculated the percentage of gas well completions and workovers with hydraulic fracturing in each of the four
control categories using year-specific GHGRP data (applying year 2011 factors to earlier years). EPA assumed no REC use
from 1990 through 2000, used a REC use percentage calculated from GHGRP data for 2011 forward, and then used linear
interpolation between the 2000 and 2011 percentages. For flaring, EPA used an assumption of 10 percent (the average
of the percent of completions and workovers that were flared in 2011 through 2013 GHGRP data) flaring from 1990
through 2010 to recognize that some flaring has occurred over that time period. For 2011 forward, EPA used a flaring
percentage calculated from GHGRP data.
Reductions Data
As described under "Emission Factors" above, some sources in Natural Gas Systems rely on CH4 emission factors
developed from the 1996 EPA/GRI study. Application of these emission factors across the time series represents
potential emissions and does not take into account any use of technologies or practices that reduce emissions. To take
into account use of such technologies for emission sources that use potential factors, data were collected on relevant
voluntary and regulatory reductions.
Voluntary and regulatory emission reductions by segment, for all time series years, are included in Table 3.6-1.
Reductions by emission source, for all time series years, are shown in Table 3.6-9.
Voluntary reductions
Voluntary reductions included in the Inventory were those reported to Gas STAR and Methane Challenge for activities
such as replacing gas engines with electric compressor drivers and installing automated air-to-fuel ratio controls for
engines.
The latest reported data for each program were paired with sources in the Inventory that use potential emissions
approaches and incorporated into the estimates (e.g., gas engines). Reductions data are only included in the Inventory if
the emission source uses "potential" emission factors, and for Natural Gas STAR reductions, short-term emission
reductions are assigned to the reported year only, while long-term emission reductions are assigned to the reported year
and every subsequent year in the time series. See Recalculations Discussion for more information.
Federal regulations
Regulatory actions reducing emissions in the current Inventory include the New Source Performance Standards (NSPS)
and National Emission Standards for Hazardous Air Pollutants (NESHAP) regulations for dehydrator vents in the
production segment.
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The Inventory reflects the NSPS for oil and gas through the use of a net factor approach that captures shifts to lower
emitting technologies required by the regulation. Examples include separating gas well completions and workovers with
hydraulic fracturing into four categories and developing control technology-specific methane emission factors and year-
specific activity data for each category; establishing control category-specific emission factors and associated year-
specific activity data for condensate tanks; calculating year-specific activity data for pneumatic controller bleed
categories; and estimating year-specific activity data for wet versus dry seal centrifugal compressors.
In regards to the oil and natural gas industry, the NESHAP regulation addresses HAPs from the oil and natural gas
production segments and the natural gas transmission and storage segments of the industry. Though the regulation
deals specifically with HAPs reductions, methane emissions are also incidentally reduced.
The NESHAP regulation requires that glycol dehydration unit vents that have HAP emissions and exceed a gas
throughput threshold be connected to a closed loop emission control system that reduces emissions by 95 percent. The
emissions reductions achieved as a result of NESHAP regulations for glycol dehydrators in the production segment were
calculated using data provided in the Federal Register Background Information Document (BID) for this regulation. The
BID provides the levels of control measures in place before the enactment of regulation. The emissions reductions were
estimated by analyzing the portion of the industry without control measures already in place that would be impacted by
the regulation.
NESHAP-driven reductions from storage tanks and from dehydrators in the processing segment are estimated with net
emission methodologies that take into account controls implemented due to regulations.
Methane, Carbon Dioxide, and Nitrous Oxide Emissions by Emission Source for Each Year
Annual CH4, C02, and N20 emissions for each source were estimated by multiplying the activity data for each year by the
corresponding emission factor. These annual emissions for each activity were then summed to estimate the total annual
CH4, C02, and N20 emissions, respectively. As a final step for CH4 emissions, any relevant reductions data from each
segment is summed for each year and deducted from the total calculated emissions in that segment to estimate net CH4
emissions for the Inventory. CH4 potential emissions, reductions, and net emissions at a segment level are shown in
Table 3.6-1. C02 emissions by segment and source are summarized in Table 3.6-10. N20 emissions by segment and
source are summarized in Table 3.6-14.
Refer to the 1990-2019 Inventory section at https://www.epa.gov/ghgernissions/natural-ga5-and-petroleurn-systerns for
the following data tables, in spreadsheet format:
• Table 3.6-1: CH4 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years. Emissions
presented here are net and include GasSTAR or Methane Challenge reductions.
• Table 3.6-2: Average CH4 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
• Table 3.6-3: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (General Sources)
• Table 3.6-4: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells Without Hydraulic
Fracturing)
• Table 3.6-5: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells With Hydraulic
Fracturing)
• Table 3.6-6: CH4 Emission Factors for Natural Gas Systems, Data Sources/Methodology
• Table 3.6-7: Activity Data for Natural Gas Systems Sources, for All Years
• Table 3.6-8: Activity Data for Natural Gas Systems, Data Sources/Methodology
• Table 3.6-9: Voluntary and Regulatory CH4 Reductions for Natural Gas Systems (kt)
• Table 3.6-10: C02 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
• Table 3.6-11: Default Gas Content by Segment, for All Years
• Table 3.6-12: Average C02 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
• Table 3.6-13: C02 Emission Factors for Natural Gas Systems, Data Sources/Methodology
• Table 3.6-14: N20 Emissions (kt) for Natural Gas Systems, by Segment and Source, for All Years
• Table 3.6-15: Average N20 Emission Factors (kg/unit activity) for Natural Gas Systems Sources, for All Years
• Table 3.6-16: N20 Emission Factors for Natural Gas Systems, Data Sources/Methodology
• Annex 3.6-17: Electronic Tables - References
Annex 3
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EPA (2015a) Inventory of U.S. GHG Emissions and Sinks 1990-2013: Revision to Well Counts Data. Available online at:
https://www.epa.gov/sites/production/files/2015-12/documents/revision-data-source-well-counts-4-10-2015.pdf.
EPA (2015b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2013: Update to Offshore Oil and Gas Platforms
Emissions Estimate. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-updates-1990-2013-inventory-published.
EPA (2016a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2014-ghg.
EPA (2016b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Gathering and
Boosting Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2014-ghg.
EPA (2016c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Transmission
and Storage Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2014-ghg.
EPA (2016d) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2014: Revisions to Natural Gas Distribution
Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-inventory-
additional-information-1990-2014-ghg.
Annex 3
A-221
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EPA (2017a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas and Petroleum
Production Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2015-ghg.
EPA (2017b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Revisions to Natural Gas Processing
Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-inventory-
additional-information-1990-2015-ghg.
EPA (2017c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2015: Incorporating an Estimate for the Aliso
Canyon Leak. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2015-ghg.
EPA (2018a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Additional Revisions Under
Consideration. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2016-ghg.
EPA (2018b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to Create Year-Specific
Emissions and Activity Factors. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems-ghg-inventory-additional-information-1990-2016-ghg.
EPA (2018c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2016: Revisions to C02 Emissions Estimation
Methodologies. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems-ghg-
inventory-additional-information-1990-2016-ghg.
EPA (2019a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates to Natural Gas Gathering &
Boosting Pipeline Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-
systems.
EPA (2019b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Updates to Liquefied Natural Gas
Segment. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2019c) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2017: Other Updates Considered for 2019 and
Future GHGIs. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2020a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990- 2018: Updates for Offshore Production
Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2020b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2018: Updates for Natural Gas Gathering &
Boosting Station Emissions. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2021a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Natural Gas Customer Meter
Emissions (Customer Meters memo). Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-
petroleum-systems.
EPA (2021b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Update for Produced Water Emissions
(Produced Water memo). Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
EPA (2021c) Greenhouse Gas Reporting Program- Subpart W - Petroleum and Natural Gas Systems. Environmental
Protection Agency. Data reported as of August 7, 2021.
EPA (2021d) National CNG vehicle counts from M0VES3. Available online at https://www.epa.gov/moves/latest-version-
motor-vehicle-emission-simulator-moves. Accessed August 2021.
EPA (2021e) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2020: Updates under Consideration for
Anomalous Events including Well Blowout and Well Release Emissions. Available online at:
https://www.epa.gov/ghgemissions/stakeholder-webinar-sept-2021-natural-gas-petroleum-systems-ghg-inventory.
EPA (2021f) Preliminary state-level-produced water data for IL, IN, KS, OK, and PA from EPA's Draft 2020 Emissions
Inventory. U.S. Environmental Protection Agency. Data obtained via email in November 2021.
EPA (2022a) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2020: Update for Natural Gas Anomalous Leak
Events. Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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EPA (2022b) Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2020: Update for Post-Meter Emissionss.
Available online at: https://www.epa.gov/ghgemissions/natural-gas-and-petroleum-systems.
FERC (2017) North American LNG Terminals. Federal Energy Regulatory Commission, Washington, DC. Available online
at: https://www.ferc.gov/industries/gas/indus-act/lng/lng-existing.pdf.
Fischer, et al. (2018). Marc L. Fischer, Wanyu R. Chan, Woody Delp, Seongeun Jeong, Vi Rapp, Zhimin Zhu. An Estimate of
Natural Gas Methane Emissions from California Homes. Environmental Science & Technology 2018, 52 (17), 10205-
10213. https://pubs.acs.org/doi/10.1021/acs.est.8b03217.
GRI/EPA (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M., T. Shires, J. Wessels, and R.
Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, Air Pollution Prevention and
Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.
GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. GRI-
01/0136.
GTI (2009) Gas Technology Institute and Innovative Environmental Solutions, Field Measurement Program to Improve
Uncertainties for Key Greenhouse Gas Emission Factors for Distribution Sources, November 2009. GTI Project Number
20497. OTD Project Number 7.7.b.
GTI (2019) Gas Technology Institute and US Department of Energy, Classification of Methane Emissions from Industrial
Meters, Vintage vs Modern Plastic Pipe, and Plastic-lined Steel and Cast-Iron Pipe. June 2019. GTI Project Number 22070.
DOE project Number ED-FE0029061.
ICF (1997) "Additional Changes to Activity Factors for Portions of the Gas Industry."September 18,1997.
ICF (2008) "Natural Gas Model Activity Factor Basis Change."January 7, 2008.
ICF (2010) "Emissions from Centrifugal Compressors." December, 2010.
IPCC (2019) 2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Calvo Buendia, E.,
Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize, S., Osako, A., Pyrozhenko, Y., Shermanau, P. and Federici, S.
(eds). Published: IPCC, Switzerland.
LA/DNR (2021) Production Data. Louisiana Department of Natural Resources. Available online at:
http://www.dnr.louisiana.gov/index.cfm?md=pagebuilder&tmp=home&pid=206.
Lamb, et al. (2015) Direct Measurements Show Decreasing Methane Emissions from Natural Gas Local Distribution
Systems in the United States. Environmental Science & Technology, Vol. 49 5161-5169.
Marchese, et al. (2015) Methane Emissions from United States Natural Gas Gathering and Processing. Environmental
Science and Technology, Vol. 49 10718-10727.
OGJ (1997-2014) "Worldwide Gas Processing." Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available online at:
http://www.ogj.com/.
PHMSA (2021a) "Annual Report Mileage for Natural Gas Transmission and Gathering Systems." Pipeline and Hazardous
Materials Safety Administration, U.S. Department of Transportation, Washington, DC. Available online at:
http://phmsa.dot.gov/pipeline/library/data-stats.
PHMSA (2021b) "Annual Report Mileage for Natural Gas Distribution Systems." Pipeline and Hazardous Materials Safety
Administration, U.S. Department of Transportation, Washington, DC. Available online at:
http://phmsa.dot.gov/pipeline/library/data-stats.
PHMSA (2021c) LNG Annual Data, Pipeline and Hazardous Materials Safety Administration (PHMSA), Washington, DC.
Available online at: https://www.phmsa.dot.gov/pipeline/liquified-natural-gas/lng-data-and-maps.
Radian/API (1992) "Global Emissions of Methane from Petroleum Sources." American Petroleum Institute, Health and
Environmental Affairs Department, Report No. DR140, February 1992.
TRC (2021) Oil & Gas Production Data Query. Texas Railroad Commission. Available online at:
http://webapps.rrc.state.tx.us/PDO/generalReportAction.do.
Annex 3
A-223
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U.S. Census Bureau (2021). American Housing Survey, U.S. Census Bureau. Available online at
https://www.census.gov/programs-surveys/ahs/data.html. Accessed August 2021.
Zimmerle, et al. (2015) "Methane Emissions from the Natural Gas Transmission and Storage System in the United
States." Environmental Science and Technology, Vol. 49 9374-9383.
Zimmerle, et al. (2019) "Characterization of Methane Emissions from Gathering Compressor Stations." Available at
https://mountainscholar.org/handle/10217/195489. October 2019.
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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3.7. Methodology for Estimating CO2, CH4, and N2O Emissions from
the Incineration of Waste
Emissions of C02 from the incineration of waste include C02 generated by the incineration of plastics, synthetic rubber
and synthetic fibers in municipal solid waste (MSW), which, in the United States, tends to occur at waste-to-energy
facilities or industrial facilities, and the incineration of tires (which are composed in part of synthetic rubber and C black)
in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also results in emissions of CH4 and
N20. The emission estimates are calculated for all MSW sources on a mass-basis based on the data available, with the
emissions from the incineration of tires calculated separately. The methodology for calculating emissions from waste
incineration sources is described in this Annex.
Municipal Solid Waste Incineration
To determine both C02 and non-C02 emissions from the incineration of waste, the tonnage of waste incinerated and an
estimated emissions factor are needed. Emission estimates from the incineration of tires are discussed separately. Data
for total waste incinerated, excluding tires, was derived from BioCycle (van Haaren et al. 2010), EPA Facts and Figures
Report, Energy Recovery Council (ERC 2018), EPA's Greenhouse Gas Reporting Program (GHGRP) (EPA 2020b), and the
U.S. Energy Information Administration (EIA 2019). Multiple sources were used to ensure a complete, quality dataset, as
each source encompasses a different timeframe.
EPA's Greenhouse Gas Reporting Program (GHGRP) collects data from facilities on methane (CH4) and nitrous oxide (N20)
emissions by fuel type under Subpart C. From these reported emissions for MSW fuel, EPA back-calculated the tonnage
of waste incinerated using GHGRP default emission factors for CH4 and N20 for 2011 through 2020.
EPA Facts and Figures Reports detail materials combusted with energy recovery in the municipal waste stream. This
tonnage is estimated as a percentage of total MSW after recycling and composting. These data exclude major appliances,
tires and lead-acid batteries, and food. Waste-to-energy data is reported to EIA and available at the plant level. Biogenic
and non-biogenic waste incinerated tonnage are both reported on a monthly and annual basis starting in 2006 (EIA
2019). The sum total is used in the following calculations. Similarly, ERC's 2018 Directory of Waste and Energy Facilities
reports throughput data in tons of MSW for waste-to-energy facilities operating in the United States. Both Biocycle and
ERC data include the tons of tires incinerated in their raw data reporting. To determine total MSW incinerated using
these data, tire incineration tonnage is subtracted.
EPA determined the MSW incineration tonnages based on data availability and accuracy throughout the time series, and
the two estimates were averaged together and converted to MSW tonnage.
• 1990-2006: MSW incineration tonnages are from BioCycle incineration data. Tire incineration data from RMA
are removed to arrive at MSW incinerated without tires.
• 2006-2010: MSW incineration tonnages are an average of BioCycle (with RMA tire data tonnage removed), U.S.
EPA Facts and Figures, EIA, and Energy Recovery Council data (with RMA tire data tonnage removed).
• 2011-2020: MSW incineration tonnages are from EPA's GHGRP data.
Table A-110 provides the estimated tons of MSW incinerated including and excluding tires.
Table A-110: Municipal Solid Waste Incinerated (Metric Tons)
Waste Incinerated Waste Incinerated
Year (excluding tires) (including tires)
1990 33,344,839 33,766,239
2005 26,486,414 28,631,054
2015 29,053,560 30,976,230
2016 29,704,817 31,534,322
2017 28,574,258 30,310,598
2018 29,162,364 30,853,949
Annex 3
A-225
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2019 28,174,311 29,821,141
202 0 27,586,271 29,233,101
Sources: BioCycle, EPA Facts and Figures, ERC, GHGRP, EIA,
RMA.
CO2 Emissions from MSW Excluding Scrap Tires
Fossil C02 emission factors were calculated from EPA's GHGRP data for non-biogenic sources. MSW tonnage using
GHGRP data, excluding tires, was calculated following the method outlined previously. Dividing fossil C02 emissions from
GHGRP FLIGHT data for facilities classified as MSW combustors by the estimated tonnage from those facilities yielded an
annual C02 emission factor. Note the MSW tonnage calculated for facilities characterized as MSW combustors is smaller
than the total MSW tonnage back calculated from emissions by fuel type data. This indicates MSW could be co-fired at
facilities whose main purpose is not waste combustion alone. As this data was only available following 2011, the C02
emission factor was proxied using an average of the C02 emission factors from years 2011 through 2020.
Finally, C02 emissions were calculated by multiplying the annual tonnage estimates, excluding tires, by the calculated
emissions factor. Calculated fossil C02 emission factors are shown in Table A-lll.
Table A-lll: Calculated Fossil CO2 Content per Ton Waste Incinerated (kg C02/Short Ton
Incinerated)
1990
2005
2016
2017
2018
2019
2020
C02 Emission Factors
367
367
381
360
361
363
377
CO2 from Incineration of Synthetic Rubber and Carbon Black in Tires
Calculating emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C
content of the tires. "2019 U.S. Scrap Tire Management Summary" (RMA 2020) reports that 1,646.8 thousand of the
3,241 thousand tons of scrap tires generated in 2019 (approximately 51 percent of generation) were used for fuel
purposes. 2020 values are proxied from 2019 data. Using RMA's estimates of average tire composition and weight, the
mass of synthetic rubber and C black in scrap tires was determined:
• Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C
contents of the major elastomers used in new tire consumption.68 Table A-112 shows consumption and C
content of elastomers used for tires and other products in 2002, the most recent year for which data are
available.
• C black is 100 percent C (Aslett Rubber Inc. n.d.).
Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of scrap
tires, and then by a 98 percent oxidation factor, yields C02 emissions, as shown in Table A-113. The disposal rate of
rubber in tires (0.3 MMT C/year) is smaller than the consumption rate for tires based on summing the elastomers listed
in Table A-112 (1.3 MMT/year); this is due to the fact that much of the rubber is lost through tire wear during the
product's lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and fuel
use for 1990 through 2019 were taken from RMA 2006; RMA 2009; RMA 2011; RMA 2014a; RMA 2016; RMA 2018; RMA
2020. For years where data were not reported, data were linearly interpolated or, for the ends of time series, set equal
to the closest year with reported data.
In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap tire. As a
result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert the
previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through RMA
2006; RMA 2014b; RMA 2016; RMA 2018; RMA 2020).
Table A-112: Elastomers Consumed in 2002 (kt)
Elastomer Consumed Carbon Content Carbon Equivalent
Styrene butadiene rubber solid 768 91% 700
For Tires 660 91% 602
68 The carbon content of tires (1,174 kt C) divided by the mass of rubber in tires (1,307 kt) equals 90 percent.
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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For Other Products3
108
91%
98
Polybutadiene
583
89%
518
For Tires
408
89%
363
For Other Products
175
89%
155
Ethylene Propylene
301
86%
258
For Tires
6
86%
5
For Other Products
295
86%
253
Polychloroprene
54
59%
32
For Tires
0
59%
0
For Other Products
54
59%
32
Nitrile butadiene rubber solid
84
77%
65
For Tires
1
77%
1
For Other Products
83
77%
64
Polyisoprene
58
88%
51
For Tires
48
88%
42
For Other Products
10
88%
9
Others
367
88%
323
For Tires
184
88%
161
For Other Products
184
88%
161
Total
2,215
NA
1,950
For Tires
1,307
NA
1,174
NA (Not Applicable)
a Used to calculate C content of non-tire rubber products in municipal solid waste.
Note: Totals may not sum due to independent rounding.
Table A-113: Scrap Tire Constituents and CO2 Emissions from Scrap Tire Incineration in
2020
Weight of Material
Emissions (MMT
Material
(MMT)
Fraction Oxidized
Carbon Content
C02 Eq.)
Synthetic Rubber
0.3
98%
90%
1.2
Carbon Black
0.4
98%
100%
1.5
Total
0.7
NA
NA
2.6
NA (Not Applicable)
CH4 and N20 from Incineration of Waste
Estimates of N20 emissions from the incineration of waste in the United States are based on the methodology outlined in
the EPA's Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid Waste
Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005
through 2014), Advancing Sustainable Materials Management: Facts and Figures: Assessing Trends in Material
Generation, Recycling and Disposal in the United States (EPA 2015; EPA 2016; EPA 2018; EPA 2019; EPA 2020a) and
unpublished backup data (Schneider 2007). According to this methodology, emissions of N20 from waste incineration are
the product of the mass of waste incinerated, an emission factor of N20 emitted per unit mass of waste incinerated, and
an N20 emissions control removal efficiency. The tonnage of MSW waste derived as described previously, including tires,
is used in this calculation. An emission factor of 50 g N20/metric ton MSW based on the 2006 IPCC Guidelines and an
estimated emissions control removal efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW
incinerators in the United States use continuously-fed stoker technology (Bahor 2009; ERC 2009).
Estimates of CH4 emissions from the incineration of waste in the United States are based on the methodology outlined in
IPCC's 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this methodology,
emissions of CH4 from waste incineration are the product of the mass of waste incinerated and an emission factor of CH4
emitted per unit mass of waste incinerated. Similar to the N20 emissions methodology, the mass of waste incinerated
including tires was derived following the methods previously outlined. An emission factor of 0.20 kg CH4/kt MSW was
used based on the 2006 IPCC Guidelines and assuming that all MSW incinerators in the United States use continuously-
fed stoker technology (Bahor 2009; ERC 2009).
Annex 3 A-227
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International, January 10, 2007.
Shin, D. (2014) Generation and Disposition of Municipal Solid Waste (MSW) in the United States-A National Survey.
Thesis. Columbia University, Department of Earth and Environmental Engineering, January 3, 2014.
Simmons, et al. (2006) "15th Nationwide Survey of Municipal Solid Waste Management in the United States: The State of
Garbage in America" BioCycle, JG Press, Emmaus, PA. April 2006.
Themelis and Shin (2014) U.S. Survey of Generation and Disposition of Municipal Solid Waste. Waste Management.
Columbia University. January 2014. http://www.seas.columbia.edu/earth/wtert/sofos/Dolly_Shin_Thesis.pdf.
van Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010.
Volume 51, Number 10, pg. 16-23.
Annex 3
A-229
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3.8. Methodology for Estimating Emissions from International
Bunker Fuels used by the U.S. Military
Bunker fuel emissions estimates for the Department of Defense (DoD) were developed using data generated by the
Defense Logistics Agency Energy (DLA Energy) for aviation and naval fuels. DLA Energy prepared a special report based
on data in the Fuels Automated System (FAS) for calendar year 2020 fuel sales in the Continental United States
(CONUS).69 The following steps outline the methodology used for estimating emissions from international bunker fuels
used by the U.S. Military.
Step 1: Omit Extra-Territorial Fuel Deliveries
Beginning with the complete FAS data set for each year, the first step in quantifying DoD-related emissions from
international bunker fuels was to identify data that would be representative of international bunker fuel consumption as
defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within the United States or its
territories and used in international maritime or aviation transport). Therefore, fuel data were categorized by the
location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e., sales abroad).
Step 2: Allocate Jet Fuel between Aviation and Land-based Vehicles
As a result of DoD70 and NATO71 policies on implementing the Single Fuel For the Battlefield concept, DoD activities have
been increasingly replacing diesel fuel with jet fuel in compression ignition and turbine engines of land-based equipment.
Based on this concept and examination of all data describing jet fuel used in land-based vehicles, it was determined that
a portion of jet fuel consumption should be attributed to ground vehicle use. Based on available Military Service data
and expert judgment, a small fraction of jet fuel use (i.e., between 1.78 and 2.7 times the quantity of diesel fuel used,
depending on the Service) was reallocated from the aviation subtotal to a new land-based jet fuel category for 1997 and
subsequent years. As a result of this reallocation, the jet fuel use reported for aviation was reduced and the fuel use for
land-based equipment increased. DoD's total fuel use did not change. DoD has been undergoing a transition from JP-8
jet fuel to commercial specification Jet A fuel with additives (JAA) for non-naval aviation and ground assets. To account
for this transition jet fuel used for ground-based vehicles was reallocated from JP8 prior to 2014 and from JAA in 2014
and subsequent years. The transition was completed in 2016.
Table A-114 displays DoD's consumption of transportation fuels, summarized by fuel type, that remain at the completion
of Step 1, and reflects the adjustments for jet fuel used in land-based equipment, as described above.
Step 3: Omit Land-Based Fuels
Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were omitted for the purpose of calculating
international bunker fuels. The remaining fuels, listed below, were considered potential DoD international bunker fuels.
• Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB).
• Marine: naval distillate fuel (F76), marine gas oil (MGO), and intermediate fuel oil (IFO).
Step 4: Omit Fuel Transactions Received by Military Services that are not considered to be
International Bunker Fuels
Only Navy and Air Force were deemed to be users of military international bunker fuels after sorting the data by Military
Service and applying the following assumptions regarding fuel use by Service.
69 FAS contains data for 1995 through 2019, but the dataset was not complete for years prior to 1995. Using DLA aviation and
marine fuel procurement data, fuel quantities from 1990 to 1994 were estimated based on a back-calculation of the 1995 data
in the legacy database, the Defense Fuels Automated Management System (DFAMS). The back-calculation was refined in 1999
to better account for the jet fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995.
70 DoD Directive 4140.25-M-V1, Fuel Standardization and Cataloging, 2013; DoD Instruction 4140.25, DoD Management Policy
for Energy Commodities and Related Services, 2015.
71 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipment Using Compression Ignition or Turbine
Engines, 2012.
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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• Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential
international bunker fuel. Fuel consumed in international aviation or marine transport was included in the
bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a
country's borders was not considered a bunker fuel.
• Based on previous discussions with the Army staff, only an extremely small percentage of Army aviation
emissions, and none of Army watercraft emissions, qualified as bunker fuel emissions. The magnitude of
these emissions was judged to be insignificant when compared to Air Force and Navy emissions. Based on
this research, Army bunker fuel emissions were assumed to be zero.
• Marine Corps aircraft operating while embarked consumed fuel that was reported as delivered to the Navy.
Bunker fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel
estimates. Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero.
• Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased
fuel from DLA Energy were assumed to be zero.
Step 5: Determine Bunker Fuel Percentages
It was necessary to determine what percent of the aviation and marine fuels were used as international bunker fuels.
Military aviation bunkers include international operations (i.e., sorties that originate in the United States and end in a
foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations
principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights).
Methods for quantifying aviation and marine bunker fuel percentages are described below.
• Aviation: The Air Force Aviation bunker fuel percentage was determined to be 13.2 percent. A bunker fuel
weighted average was calculated based on flying hours by major command. International flights were
weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In
addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel
per hour of flight than most tactical aircraft. This percentage was multiplied by total annual Air Force
aviation fuel delivered for U.S. activities, producing an estimate for international bunker fuel consumed by
the Air Force.
The Naval Aviation bunker fuel percentage was calculated to be 40.4 percent by using flying hour data from
Chief of Naval Operations Flying Hour Projection System Budget for fiscal year 1998 and estimates of bunker
fuel percent of flights provided by the fleet. This Naval Aviation bunker fuel percentage was then multiplied
by total annual Navy aviation fuel delivered for U.S. activities, yielding total Navy aviation bunker fuel
consumed.
• Marine: For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels.
The Navy maritime bunker fuel percentage was determined to be 79 percent because the Navy reported that
79 percent of vessel operations were underway, while the remaining 21 percent of operations occurred in
port (i.e., pierside) in the year 2000.72
Table A-115 and Table A-116 display DoD bunker fuel use totals for the Navy and Air Force.
Step 6: Calculate Emissions from International Bunker Fuels
Bunker fuel totals were multiplied by appropriate emission factors to determine greenhouse gas (GHG) emissions. C02
emissions from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels,
respectively.
The rows labeled "U.S. Military" and "U.S. Military Naval Fuels" in the tables in the International Bunker Fuels section of
the Energy chapter were based on the totals provided in Table A-115 and Table A-116, below. C02 emissions from
aviation bunkers and distillate marine bunkers are presented in Table A-120, and are based on emissions from fuels
tallied in Table A-115 and Table A-116.
72 Note that 79 percent is used because it is based on Navy data, but the percentage of time underway may vary from year-to-
year depending on vessel operations. For example, for years prior to 2000, the bunker fuel percentage was 87 percent.
Annex 3
A-231
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Table A-114: Transportation Fuels from Domestic Fuel Deliveries3 (Million Gallons)
Vehicle
Type/Fuel
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Aviation
4,598.4
3,099.9
2,664.4
2,338.1
1,663.9
1,663.7
1,558.0
1,537.7
1,482.2
1,487.6
1,435.7
Total Jet Fuels
4,598.4
3,099.9
2,664.4
2,338.0
1,663.7
1,663.5
1,557.7
1,537.5
1,481.9
1,487.4
1,435.5
JP8
285.7
2,182.8
2,122.7
1,838.8
1,100.1
126.6
(9.5)
(11.4)
1.9
4.7
(4.4)
JP5
1,025.4
691.2
472.1
421.6
399.3
316.4
320.4
316.3
304.1
314.4
309.0
Other Jet Fuels
3,287.3
225.9
69.6
77.6
164.3
1,220.5
1,246.9
1,232.7
1,175.9
1,168.2
1,130.9
Aviation
Gasoline
+
+
+
0.1
0.2
0.3
0.3
0.2
0.3
0.2
0.2
Marine
686.8
438.9
454.4
604.9
578.8
421.7
412.4
395.2
370.9
365.4
384.1
Middle Distillate
(MGO)
0.0
0.0
48.3
54.0
48.4
56.0
23.1
24.4
19.9
23.2
26.1
Naval Distillate
(F76)
686.8
438.9
398.0
525.9
513.7
363.3
389.1
370.8
351.0
342.2
358.0
Intermediate
Fuel Oil (IFO)b
0.0
0.0
8.1
25.0
16.7
2.4
0.1
0.0
0.0
0.0
0.0
Other0
717.1
310.9
248.2
205.6
224.0
181.1
178.3
165.8
170.4
161.4
130.3
Diesel
93.0
119.9
126.6
56.8
64.1
54.8
54.7
50.4
51.8
48.7
39.2
Gasoline
624.1
191.1
74.8
24.3
25.5
16.2
15.9
15.6
14.7
14.9
12.5
Jet Fueld
0.0
0.0
46.7
124.4
134.4
110.1
107.6
99.9
104.0
97.7
78.6
Total (Including
Bunkers)
6,002.4
3,849.8
3,367.0
3,148.6
2,466.7
2,266.5
2,148.7
2,098.7
2,023.4
2,014.3
1,950.1
+ Indicates value does not exceed 0.05 million gallons.
a Includes fuel distributed in the United States and U.S. Territories.
b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.
c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the Military Services for 1990 and 1996. The 1991 through 1995 data points were
interpolated from the Service inventory data. The 1997 through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors
used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel estimates from 1997 through 2000 were reduced according to the
estimated consumption of jet fuel that is assumed to have replaced the diesel fuel consumption in land-based vehicles. Datasets for other diesel and gasoline consumed by the
military in 2000 were estimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000.
Since 2001, other gasoline and diesel fuel totals were generated by DLA Energy.
d The fraction of jet fuel consumed in land-based vehicles was estimated based on DLA Energy data as well as Military Service and expert judgment.
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent returned products.
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-115: Total U.S. Military Aviation Bunker Fuel (Million Gallons)
Fuel Type/Service
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Jet Fuels
JP8
56.7
300.4
307.6
285.6
182.5
17.2
2.4
2.5
2.9
1.2
0.6
Navy
56.7
38.3
53.4
70.9
60.8
0.8
5.5
6.4
4.8
2.5
2.8
Air Force
+
262.2
254.2
214.7
121.7
16.4
(3.1)
(3.9)
(1.9)
(1.3)
(2.2)
JP5
370.5
249.8
160.3
160.6
152.5
124.1
126.1
124.7
120.1
123.9
122.0
Navy
365.3
246.3
155.6
156.9
149.7
122.6
124.7
123.4
118.9
122.5
120.7
Air Force
5.3
3.5
4.7
3.7
2.8
1.5
1.4
1.3
1.2
1.4
1.2
JP4
420.8
21.5
+
+
0.1
0.0
0.0
0.0
0.0
0.0
0.0
Navy
+
+
0.0
+
+
0.0
0.0
0.0
0.0
0.0
0.0
Air Force
420.8
21.5
+
+
0.1
0.0
0.0
0.0
0.0
0.0
0.0
JAA
13.7
9.2
12.5
15.5
31.4
199.8
203.7
198.9
191.8
192.5
185.2
Navy
8.5
5.7
7.9
11.6
13.7
71.7
72.9
67.8
68.1
71.2
66.1
Air Force
5.3
3.5
4.5
3.9
17.7
128.1
130.8
131.1
123.7
121.4
119.1
JA1
+
+
+
0.5
0.3
0.3
0.5
0.2
0.5
0.3
0.3
Navy
+
+
+
+
0.1
+
0.1
(+)
+
+
(+)
Air Force
+
+
+
0.5
0.1
0.3
0.5
0.2
0.5
0.3
0.3
JAB
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Navy
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Air Force
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
NO
Navy Subtotal
430.5
290.2
216.9
239.4
224.4
195.0
203.2
197.5
191.8
196.1
189.6
Air Force Subtotal
431.3
290.7
263.5
222.9
142.4
146.4
129.5
128.8
123.5
121.8
118.5
Total
861.8
580.9
480.4
462.3
366.7
341.4
332.8
326.3
315.3
317.9
308.1
+ Does not exceed 0.05 million gallons.
NO (Not Occurring)
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values. The negative values in this table represent
returned products.
Table A-116: Total U.S. DoD Maritime Bunker Fuel (Million Gallons)
Marine Distillates
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Navy - MGO
0.0
0.0
23.8
38.0
32.9
37.8
5.7
13.2
8.5
10.6
13.5
Navy - F76
522.4
333.8
298.6
413.1
402.2
286.7
307.8
293.3
276.9
270.0
282.6
Navy - IFO
0.0
0.0
6.4
19.7
12.9
1.9
+
0.0
0.0
0.0
0.0
Total
522.4
333.8
328.8
470.7
448.0
326.3
313.6
306.5
285.4
280.6
296.1
+ Does not exceed 0.05 million gallons.
Note: Totals may not sum due to independent rounding.
Annex 3
A-233
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Table A-117: Aviation and Marine Carbon Contents (MMT Carbon/QBtu) and Fraction
Oxidized
Carbon Content
Fraction
Mode (Fuel)
Coefficient
Oxidized
Aviation (Jet Fuel)
Variable
1.00
Marine (Distillate)
Variable
1.00
Marine (Residual)
20.48
1.00
Source: EPA (2010) and IPCC (2006).
Table A-118: Annual Variable Carbon Content Coefficient for Jet Fuel (MMT Carbon/QBtu)
Fuel 1990
1995 2000 2005 2010 2015 2016 2017
2018
2019
2020
Jet Fuel 19.40
4 19.34 19.70 19.70 19.70 i 19.70 19.70 19.70
19.70
19.70
19.70
Source: EPA (2010).
Table A-119: Annual Variable Carbon Content Coefficient for Distillate Fuel Oil (MMT
Carbon/QBtu)
Fuel 1990
1995 2000 2005 2010 2015 2016 2017
2018
2019
2020
Distillate Fuel
Oil 20.17
, 20.17 20.39 , ; 20.37 , ; 20.24 .. 1 20.22 20.21 20.21
20.22
20.22
20.22
Source: EPA (2020).
Table A-120: Total U.S. DoD CO2 Emissions from Bunker Fuels (MMT CO2 Eq.)
Mode 1990
1995 2000 2005 2010 2015 2016 2017
2018
2019
2020
Aviation 8.2
Marine 5.4
5.7 4.8 4.6 3.6 3.4 3.3 3.3
3.4 3.4 4.9 4.6 3.4 3.2 3.1
3.2
2.9
3.2
2.9
3.1
3.0
Total 13.6
9.1 8.2 9.5 8.3 6.8 6.6 6.4
6.1
6.1
6.1
Note: Totals may not sum due to independent rounding.
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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References
DLA Energy (2021) Unpublished data from the Defense Fuels Automated Management System (DFAMS). Defense Energy
Support Center, Defense Logistics Agency, U.S. Department of Defense. Washington, D.C.
EPA (2010) Carbon Content Coefficients Developed for EPA's Inventory of Greenhouse Gases and Sinks. Office of Air and
Radiation, Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
EPA (2020) EPA Inventory of U.S. Greenhouse Gas Emissions and Sinks 1990-2019: Updated Gasoline and Diesel Fuel C02
Emission Factors - Memo.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change. H.S. Eggleston, L. Buendia, K. Miwa, T. Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
Annex 3
A-235
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3.9. Methodology for Estimating HFC and PFC Emissions from
Substitution of Ozone Depleting Substances
Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a country-
specific modeling approach. The Vintaging Model73 was developed as a tool for estimating the annual chemical emissions
from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal Protocol and
the United States Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—chlorofluorocarbons
(CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—has been drastically
reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these industries have moved
toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the Vintaging Model has
evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the decline of ODS
consumption and emissions.
The Vintaging Model estimates emissions from five ODS substitute (i.e., HFC-emitting) end-use sectors: refrigeration and
air-conditioning, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 78 independently
modeled end-uses. The model requires information on the market growth for each of the end-uses, a history of the
market transition from ODS to alternatives, and the characteristics of each end-use such as market size or charge sizes
and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to each
of its substitutes.
The model, named for its method of tracking the emissions of annual "vintages" of new equipment that enter into
service, is a "bottom-up" model. It models the consumption of chemicals based on estimates of the quantity of
equipment or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture
and/or maintain the equipment. The Vintaging Model makes use of this market information to build an inventory of the
in-use stocks of the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a
"business-as-usual" baseline case and does not incorporate measures to reduce or eliminate the emissions of these
gases other than those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying
annual leak rates, service emission rates, and disposal emission rates to each population of equipment. By aggregating
the emission and consumption output from the different end-uses, the model produces estimates of total annual use
and emissions of each chemical.
The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System
maintained by the Stratospheric Protection Division, the Greenhouse Gas Reporting Program maintained by the Climate
Change Division, and information from submissions to EPA under the Significant New Alternatives Policy (SNAP)
program. Published sources include documents prepared by the United Nations Environment Programme (UNEP)
Technical Options Committees, reports from the Alternative Fluorocarbons Environmental Acceptability Study (AFEAS),
and conference proceedings from the International Conferences on Ozone Protection Technologies and Earth
Technologies Forums. EPA also coordinates extensively with numerous trade associations and individual companies. For
example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and Refrigeration Institute; the
Association of Home Appliance Manufacturers; the American Automobile Manufacturers Association; and many of their
member companies have provided valuable information over the years.
In some instances, the unpublished information that the EPA uses in the model is classified as Confidential Business
Information (CBI). The annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be
inferred. Full public disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has
been entrusted to the EPA. In addition, emissions of certain gases (including HFC-152a, HFC-227ea, HFC-245fa, HFC
365mfc, HFC-43-10mee, HCFO-1233zd(E), HFO-1234yf, HFO-1234ze(E), HFO-1336mzz(Z), C4Fi0, and PFC/PFPEs, the latter
being a proxy for a diverse collection of PFCs and perfluoropolyethers (PFPEs) employed for solvent applications) are
marked as confidential because they are produced or imported by a small number of chemical providers and in such
small quantities or for such discrete applications that reporting national data would effectively be reporting the chemical
provider's output, which is considered confidential business information. These gases are modeled individually in the
Vintaging Model, but are aggregated and reported as an unspecified mix of HFCs and PFCs.
73 Vintaging Model version VM 10 file_v5.1_3.23.22 was used for all Inventory estimates.
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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The Vintaging Model is regularly updated to incorporate up-to-date market information, including equipment stock
estimates, leak rates, and sector transitions. In addition, comparisons against published emission and consumption
sources are performed when available. Independent peer reviews of the Vintaging Model are periodically performed,
including one conducted in 2017 (EPA, 2018), to confirm Vintaging Model estimates and identify updates.
The following sections discuss the emission equations used in the Vintaging Model for each broad end-use category.
These equations are applied separately for each chemical used within each of the different end-uses. In the majority of
these end-uses, more than one ODS substitute chemical is used.
In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market.
Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated
ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which
alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given
alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated
for each chemical used within each end-use.
Methodology
The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:
1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken from published
sources and industry experts.
2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed characterizations of
the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate the
implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this
simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to
comply with the ODS phase-out and other regulations.
3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of substitutes that are
required each year for the manufacture, installation, use, or servicing of products. The emissions are estimated from the
emission profile for each vintage of equipment or product in each end-use. By aggregating the emissions from each
vintage, a time profile of emissions from each end-use is developed.
Each set of end-uses is discussed in more detail in the following sections.
Refrigeration and Air-Conditioning
For refrigeration and air conditioning products, emission calculations are split into three categories: emissions at first-fill,
which arise during manufacture or installation, emissions during equipment lifetime, which arise from annual leakage
and service losses, and disposal emissions, which occur at the time of discard. This methodology is consistent to the 2006
Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas Inventories, where the total
refrigerant emissions from Ref/AC equipment is the sum of first-fill emissions, annual operational and servicing
emissions, and disposal emissions under the Tier 2a emission factor approach (IPCC 2006). Three separate steps are
required to calculate the lifetime emissions from installation, leakage and service, and the emissions resulting from
disposal of the equipment. The model assumes that equipment is serviced annually so that the amount equivalent to
average annual emissions for each product (and hence for the total of what was added to the bank in a previous year in
equipment that has not yet reached end-of-life) is replaced/applied to the starting charge size (or chemical bank). For
any given year, these first-fill emissions (for new equipment), lifetime emissions (for existing equipment), and disposal
emissions (from discarded equipment) are summed to calculate the total emissions from refrigeration and air-
conditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak,
service, and disposal emission rates.
At disposal, refrigerant that is recovered from discarded equipment is assumed to be reused to the extent necessary in
the following calendar year. The Vintaging Model does not make any explicit assumption whether recovered refrigerant
is reused as-is (allowed under U.S. regulations if the refrigerant is reused in the same owner's equipment), recycled
(commonly practiced even when re-used directly), or reclaimed (brought to new refrigerant purity standards and
available to be sold on the open market).
Annex 3
A-237
-------
Step 1: Calculate first-fill emissions
The first-fill emission equation assumes that a certain percentage of the chemical charge will be emitted to the
atmosphere when the equipment is charged with refrigerant during manufacture or installation. First-fill emissions are
considered for all Ref/AC equipment that are charged with refrigerant within the United States, including those which
are produced for export, and excluding those that are imported pre-charged. First-fill emissions are thus a function of the
quantity of chemical contained in new equipment and the proportion of equipment that are filled with refrigerant in the
United States:
Equation A-8: Calculation of Emissions from Refrigeration and Air-conditioning Equipment
First-fill
Efi = Qq x |f x Aj
where:
Ef = Emissions from Equipment First-fill. Emissions in year j from filling new equipment.
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
charge new equipment in year j, by weight.
If = First-fill Leak Rate. Average leak rate during installation or manufacture of new
equipment (expressed as a percentage of total chemical charge).
A = Applicability of First-fill Leak Rate. Percentage of new equipment that are filled with
refrigerant in the United States in year j.
j = Year of emission.
Step 2: Calculate lifetime emissions
Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation and the
amount emitted during service. Emissions from leakage and servicing can be expressed as follows:
Equation A-9: Calculation of Emissions from Refrigeration and Air-conditioning Equipment
Serviced
ESj = (la +ls)xl QcH+1 for i= l^k
where:
Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and
servicing (including recharging) of equipment.
Ia = Annual Leak Rate. Average annual leak rate during normal equipment operation
(expressed as a percentage of total chemical charge).
Is = Service Leak Rate. Average leakage during equipment servicing (expressed as a
percentage of total chemical charge).
Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
charge new equipment in a given year by weight.
, = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Step 3: Calculate disposal emissions
The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the
atmosphere when that vintage is discarded, while remaining refrigerant is assumed to be recovered and reused. Disposal
emissions are thus a function of the quantity of chemical contained in the retiring equipment fleet and the proportion of
chemical released at disposal:
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Equation A-10: Calculation of Emissions from Refrigeration and Air-conditioning Equipment
Disposed
Edj = QCj-k-n x [1 - (rm x rc)]
where:
Ed
Qc
rm
rc
J
Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
Quantity of Chemical in New Equipment. Total amount of a specific chemical used to
charge new equipment in year j-k+1, by weight.
Chemical Remaining. Amount of chemical remaining in equipment at the time of
disposal (expressed as a percentage of total chemical charge).
Chemical Recovery Rate. Amount of chemical that is recovered just prior to disposal
(expressed as a percentage of chemical remaining at disposal (rm)).
Year of emission.
Lifetime. The average lifetime of the equipment.
Step 4: Calculate total emissions
Finally, first-fill, lifetime, and disposal emissions are summed to provide an estimate of total emissions.
Equation A-ll: Calculation of Total Emissions from Refrigeration and Air-conditioning
Equipment
Ej = Efi + ESj + Edj
where:
E
Ef
Es
Ed
Total Emissions. Emissions from refrigeration and air conditioning equipment in year j.
Emissions from first Equipment Fill. Emissions in year j from filling new equipment.
Emissions from Equipment Serviced. Emissions in year j from leakage and servicing
(including recharging) of equipment.
Emissions from Equipment Disposed. Emissions in year j from the disposal of
equipment.
Year of emission.
Assumptions
The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS are
presented in Table A-121, below. As new technologies replace older ones, it is generally assumed that there are
improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is
assumed to grow independently, according to annual growth rates.
Annex 3
A-239
-------
Table A-121: Refrigeration and Air-Conditioning Market Transition Assumptions
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Centrifugal Chillers
HCFO-
CFC-11
HCFC-123
1993
1993
45%
1233zd(E)
2016
2016
1%
None
1.6%
R-514A
2017
2017
1%
None
HCFO-
1233zd(E)
2017
2020
49%
None
R-514A
2018
2020
49%
None
HCFC-22
1991
1993
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
HFC-134a
1992
1993
39%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
CFC-12
HFC-134a
1992
1994
53%
R-450A
2017
2017
1%
None
1.5%
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HCFC-22
1991
1994
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
HCFO-
HCFC-123
1993
1994
31%
1233zd(E)
2016
2016
1%
None
R-514A
2017
2017
1%
None
HCFO-
1233zd(E)
2017
2020
49%
None
R-514A
2018
2020
49%
None
R-500
HFC-134a
1992
1994
53%
R-450A
2017
2017
1%
None
1.5%
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HCFC-22
1991
1994
16%
HFC-134a
2000
2010
100%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
A-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-
I-2020
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-450A
2018
2024
49%
R-513A
2018
2024
49%
HCFO-
HCFC-123
1993
1994
31%
1233zd(E)
R-514A
HCFO-
1233zd(E)
R-514A
2016
2017
2017
2018
2016
2017
2020
2020
1%
1%
49%
49%
None
None
None
None
CFC-114
HFC-236fa
1993
1996
100%
HFC-134a
1998
2009
100%
None
1.4%
Cold Storage
CFC-12
HCFC-22
1990
1993
65%
R-404A
1996
2010
75%
R-407F
2017
2023
100%
3.1%
R-507
1996
2010
25%
R-407F
2017
2023
100%
R-404A
1994
1996
26%
R-407F
2017
2023
100%
None
R-507
1994
1996
9%
R-407F
2017
2023
100%
None
HCFC-22
HCFC-22
1992
1993
100%
R-404A
1996
2009
8%
R-407F
2017
2023
100%
3.0%
R-507
1996
2009
3%
R-407F
2017
2023
100%
R-404A
2009
2010
68%
R-407F
2017
2023
100%
R-507
2009
2010
23%
R-407F
2017
2023
100%
R-502
HCFC-22
1990
1993
40%
R-404A
1996
2010
38%
R-407F
2017
2023
100%
2.6%
R-507
1996
2010
12%
R-407F
2017
2023
100%
Non-
ODP/GWP
1996
2010
50%
None
R-404A
1993
1996
45%
R-407F
2017
2023
100%
None
R-507
1994
1996
15%
R-407F
2017
2023
100%
None
Commercial Unitary Air Conditioners (Large)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
2001
2005
5%
None
1.6%
R-407C
2006
2009
1%
None
R-410A
2006
2009
9%
None
R-407C
2009
2010
5%
None
R-410A
2009
2010
81%
None
Commercial Unitary Air Conditioners (Small)
HCFC-22
HCFC-22
1992
1993
100%
R-410A
1996
2000
3%
None
1.9%
R-410A
2001
2005
18%
None
R-410A
2006
2009
8%
None
Annex 3
A-241
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-410A
2009
2010
71%
None
Dehumidifiers
HCFC-22
HFC-134a
1997
1997
89%
None
1.3%
R-410A
2007
2010
11%
None
Ice Makers
CFC-12
HFC-134a
1993
1995
27%
None
2.1%
R-404A
1993
1995
73%
R-410A
2013
2019
32%
None
Industrial Process Refrigeration
HCFO-
CFC-11
HCFC-123
1992
1994
70%
1233zd(E)
2016
2016
2%
None
3.2%
HCFO-
1233zd(E)
2017
2020
98%
None
HFC-134a
1992
1994
15%
None
HCFC-22
1991
1994
15%
HFC-134a
1995
2010
100%
None
CFC-12
HCFC-22
1991
1994
10%
HFC-134a
1995
2010
15%
None
3.1%
R-404A
1995
2010
50%
None
R-410A
1999
2010
20%
None
R-507
1995
2010
15%
None
HCFO-
HCFC-123
1992
1994
35%
1233zd(E)
2016
2016
2%
None
HCFO-
1233zd(E)
2017
2020
98%
None
HFC-134a
1992
1994
50%
None
R-401A
1995
1996
5%
HFC-134a
1997
2000
100%
None
HCFC-22
HFC-134a
1995
2009
2%
None
3.0%
R-404A
1995
2009
5%
None
R-410A
1999
2009
2%
None
R-507
1995
2009
2%
None
HFC-134a
2009
2010
14%
None
R-404A
2009
2010
45%
None
R-410A
2009
2010
18%
None
R-507
2009
2010
14%
None
Mobile Air Conditioners
Passenger Cars)
CFC-12
HFC-134a
1992
1994
100%
HFO-1234yf
2012
2015
1%
None
0.3%
HFO-1234yf
2016
2021
99%
None
A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Mobile Air Conditioners
Light Duty Trucks)
CFC-12
HFC-134a
1993
1994
100%
HFO-1234yf
HFO-1234yf
2012
2016
2015
2021
1%
99%
None
None
1.4%
Mobile Air Conditioners
Heavy Duty Vehicles)
CFC-12
HFC-134a
1993
1994
100%
None
1
| 0.8%
Mobile Air Conditioners
School and Tour Buses)
CFC-12
HCFC-22
HFC-134a
1994
1994
1995
1997
0.5%
99.5%
HFC-134a
None
2006
2007
100%
None
0.3%
Mobile Air Conditioners
Transit Buses)
HCFC-22
HFC-134a
1995
2009
100%
None
1
| 0.3%
Mobile Air Conditioners
Trains)
HCFC-22
HFC-134a
R-407C
2002
2002
2009
2009
50%
50%
None
None
0.3%
Packaged Terminal Air Conditioners and Heat Pumps
HCFC-22
R-410A
2006
2009
10%
None
3.0%
R-410A
2009
2010
90%
None
Positive Displacement Chillers (Reciprocating and Screw)
CFC-12
HCFC-22C
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
2.5%
R-410A
2010
2020
40%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
R-407C
2000
2009
1%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
None
None
None
None
HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-450A
R-513A
R-450A
R-513A
2017
2017
2018
2018
2017
2017
2024
2024
1%
1%
49%
49%
R-410A
2010
2020
40%
R-450A
R-513A
2017
2017
2017
2017
1%
1%
Annex 3
A-243
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-407C
2009
2010
9%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
2.5%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-410A
2010
2020
40%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-407C
2000
2009
1%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-410A
2010
2020
40%
R-450A
2017
2017
1%
R-513A
2017
2017
1%
R-450A
2018
2024
49%
R-513A
2018
2024
49%
R-407C
2009
2010
9%
R-450A
2017
2017
1%
None
R-513A
2017
2017
1%
None
R-450A
2018
2024
49%
None
R-513A
2018
2024
49%
None
Positive Displacement Chillers (Scroll)
HCFC-22
HFC-134a
2000
2009
9%
R-407C
2010
2020
60%
R-452B
2024
2024
100%
2.5%
R-410A
2010
2020
40%
R-452B
2024
2024
100%
R-407C
2000
2009
1%
R-452B
2024
2024
100%
None
HFC-134a
2009
2010
81%
R-407C
2010
2020
60%
R-452B
2024
2024
100%
R-410A
2010
2020
40%
R-452B
2024
2024
100%
R-407C
2009
2010
9%
R-452B
2024
2024
100%
None
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Refrigerated Appliances
Non-
CFC-12
HFC-134a
1994
1995
100%
ODP/GWP
2019
2021
86%
None
1.7%
R-450A
2021
2021
7%
None
R-513A
2021
2021
7%
None
Refrigerated Food Processing and Dispensing Equipment
CFC-12
HCFC-22
1990
1994
100%
HFC-134a
1995
1998
70%
None
2.1%
R-404A
1995
1998
30%
R-448A
2021
2021
50%
R-449A
2021
2021
50%
Residential Unitary Air Conditioners
HCFC-22
HCFC-22
2006
2006
70%
R-410A
2007
2010
29%
None
2.6%
R-410A
2010
2010
71%
None
R-410A
2000
2005
5%
R-410A
2006
2006
100%
None
R-410A
2000
2006
5%
None
R-410A
2006
2006
20%
None
Retail Food (Large; Technology Transition)
DXd
DX
2001
2006
67.5%
DX
2006
2015
62%
None
1.7%
DRe
2000
2015
23%
None
SLSf
2000
2015
15%
None
DR
2000
2006
22.5%
None
SLS
2000
2006
10%
None
Retail Food (Large; Refrigerant Transition)
CFC-12
R-404A
1995
2000
17.5%
R-404A
2000
2000
3.3%
R-407A
2017
2017
100%
1.7%
R-502S
R-407A
2011
2015
63.3%
None
R-407A
2017
2017
33.3%
None
R-507
1995
2000
7.5%
R-404A
2006
2010
71%
R-407A
2017
2017
100%
R-407A
2006
2010
30%
None
HCFC-22
1995
2000
75%
R-404A
2006
2010
13.3%
R-407A
2011
2015
100%
R-407A
2001
2005
1.3%
None
R-404A
2001
2005
12%
R-407A
2017
2017
100%
R-507
2001
2005
6.7%
R-407A
2011
2015
100%
R-404A
2006
2010
34%
R-407A
2011
2015
100%
R-404A
2006
2010
7.3%
R-407A
2017
2017
100%
R-407A
2006
2010
25.3%
None
Retail Food (Large Condensing Units)
HCFC-22
R-402A
1995
2005
5%
R-404A
2006
2006
100%
R-407A
2018
2018
100%
1.5%
Annex 3
A-245
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-404A
1995
2005
25%
R-407A
2018
2018
100%
None
R-507
1995
2005
10%
R-407A
2018
2018
100%
None
R-404A
2008
2010
45%
R-407A
2018
2018
100%
None
R-507
2008
2010
15%
R-407A
2018
2018
100%
None
Retail Food (Small Condensing Units)
HCFC-22
R-401A
1995
2005
6%
HFC-134a
2006
2006
100%
None
1.6%
R-402A
1995
2005
4%
HFC-134a
2006
2006
100%
None
HFC-134a
1993
2005
30%
None
R-404A
1995
2005
30%
R-407A
2018
2018
100%
R-404A
2008
2010
30%
R-407A
2018
2018
100%
Retail Food (Small)
CFC-12
HCFC-22
1990
1993
91%
HFC-134a
1993
1995
91%
C02
2012
2015
1%
2.2%
Non-
ODP/GWP
2012
2015
3.7%
Non-
ODP/GWP
2014
2019
31%
Non-
ODP/GWP
2016
2016
17.3%
R-450A
2016
2020
23%
R-513A
2016
2020
23%
Non-
HFC-134a
2000
2009
9%
ODP/GWP
2014
2019
30%
R-450A
2016
2020
35%
R-513A
2016
2020
35%
Non-
R-404A
1990
1993
9%
ODP/GWP
2016
2016
30%
None
R-448A
2019
2020
35%
None
R-449A
2019
2020
35%
None
Transport Refrigeration
Road Transport)
CFC-12
HFC-134a
1993
1995
10%
None
5.5%
R-404A
1993
1995
60%
R-452A
2017
2021
5%
R-452A
2021
2030
95%
HCFC-22
1993
1995
30%
R-410A
2000
2003
5%
None
R-404A
2006
2010
95%
R-452A
2017
2021
5%
R-452A
2021
2030
95%
Transport Refrigeration
Intermodal Containers)
CFC-12
HFC-134a
1993
1993
60%
C02
2017
2021
5%
None
7.3%
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-404A
1993
1993
5%
C02
2017
2021
5%
None
HCFC-22
1993
1993
35%
HFC-134a
2000
2010
100%
C02
2017
2021
5%
Transport Refrigeration
Merchant Fishing Transport)
HCFC-22
HFC-134a
1993
1995
10%
None
5.7%
R-507
1994
1995
10%
None
R-404A
1993
1995
10%
None
HCFC-22
1993
1995
70%
R-407C
2000
2005
3%
R-410A
2005
2007
100%
R-507
2006
2010
49%
None
R-404A
2006
2010
49%
None
Transport Refrigeration
Reefer Ships)
HCFC-22
HFC-134a
1993
1995
3.3%
None
4.2%
R-507
1994
1995
3.3%
None
R-404A
1993
1995
3.3%
None
HCFC-22
1993
1995
90%
HFC-134a
2006
2010
25%
None
R-507
2006
2010
25%
None
R-404A
2006
2010
25%
None
R-407C
2006
2010
25%
None
Transport Refrigeration
Vintage Rail Transport)
CFC-12
HCFC-22
1993
1995
100%
HFC-134a
1996
2000
100%
None
| -100%
Transport Refrigeration
Modern Rail Transport)
HFC-134a
R-404A
1999
1999
50%
None
0.3%
HFC-134a
2005
2005
50%
None
Vending Machines
CFC-12
HFC-134a
1995
1998
90%
C02
2012
2012
1%
Propane
100%
2019
2019
-0.03%
Propane
2013
2017
39%
None
Propane
2014
2014
1%
None
Propane
2019
2019
49%
None
R-450A
2019
2019
5%
None
R-513A
2019
2019
5%
None
R-404A
1995
1998
10%
R-450A
2019
2019
50%
None
R-513A
2019
2019
50%
None
Water-Source and Ground-Source Heat Pumps
HCFC-22
R-407C
2000
2006
5%
None
1.3%
R-410A
2000
2006
5%
None
HFC-134a
2000
2009
2%
None
R-407C
2006
2009
2.5%
None
Annex 3
A-247
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration in
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
R-410A
2006
2009
4.5%
None
HFC-134a
2009
2010
18%
None
R-407C
2009
2010
22.5%
None
R-410A
2009
2010
40.5%
None
Window Units
HCFC-22
R-410A
2008
2009
10%
HFC-32
2015
2019
50%
None
2.6%
R-410A
2009
2010
90%
HFC-32
2015
2019
50%
None
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original ODS
or the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c The CFC-12 reciprocating chillers market for new systems transitioned to HCFC-22 overnight in 1993. This transition is not shown in the table in order to provide the HFC transitions
in greater detail.
d DX refers to direct expansion systems where the compressors are mounted together in a rack and share suction and discharge refrigeration lines that run throughout the store,
feeding refrigerant to the display cases in the sales area.
e DR refers to distributed refrigeration systems that consist of multiple smaller units that are located close to the display cases that they serve such as on the roof above the cases,
behind a nearby wall, or on top of or next to the case in the sales area.
f SLS refers to secondary loop systems wherein a secondary fluid such as glycol or carbon dioxide is cooled by the primary refrigerant in the machine room and then pumped
throughout the store to remove heat from the display equipment,
s The CFC-12 large retail food market for new systems transitioned to R-502 from 1988 to 1990, and subsequently transitioned to HCFC-22 from 1990 to 1993. These transitions are
not shown in the table in order to provide the HFC transitions in greater detail.
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-122 presents the average equipment lifetimes and annual HFC emission rates (for first-fill, servicing, leaks, and
disposal) for each end-use assumed by the Vintaging Model.
Table A-122: Refrigeration and Air-Conditioning Lifetime Assumptions
HFC Emission Rates
HFC Emission Rates
HFC Emission Rates
End-Use
Lifetime
(First-fill)3
(Servicing and Leaks)
(Disposal)b
(Years)
(%)
(%)
(%)
Centrifugal Chillers
20-27
0.2-0.5
2.0-10.9
10
Cold Storage
20-25
1
15.0
10
Commercial Unitary A/C
15
0.5-1
7.9-8.6
18-40
Condensing Units (Medium Retail Food)
10-20
0.5-3
8-15
10-20
Dehumidifiers
11
0.5-1
0.5
50
Ice Makers
8
0.5-2
3.0
49
Industrial Process Refrigeration
25
1
3.6-12.3
10
Large Retail Food
18
2
17-33
10
Mobile Air Conditioners
5-16
LO
0
1
-------
Transition Assumptions
Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital medical
devices and specialty consumer products, are presented in Table A-123.
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-123: Aerosol Product Transition Assumptions
Growth
Primary Substitute
Secondary Substitute
Tertiary Substitute
Rateb
Initial
Date of Full
Maximum
Date of Full
Maximum
Date of Full
Maximum
Market
Name of
Start
Penetration in
Market
Name of
Start
Penetration in
Market
Name of
Start
Penetration in
Market
Segment
Substitute
Date
New Equipment3
Penetration
Substitute
Date
New Equipment3
Penetration
Substitute
Date
New Equipment3
Penetration
MDIs
CFC Mixc
HFC-134a
1997
1997
6%
None
3.8%
Non-
ODP/GWP
1998
2007
7%
None
CFC Mix3
2000
2000
87%
HFC-134a
2001
2011
28%
Non-
2012
2018
64%
ODP/GWP
HFC-227ea
2015
2015
1%
Non-
2001
2014
67%
ODP/GWP
None
HFC-227ea
2007
2013
5%
Non-
2015
2018
44%
ODP/GWP
Consumer Aerosols (Non-MDIs)
NAd
HFC-152a
1990
1991
50%
None
4.2%
HFC-134a
1995
1995
50%
HFC-152a
1997
1998
44%
None
HFC-152a
2001
2005
38%
None
HFO-
1234ze(E)
2016
2018
16%
None
Technical Aerosols (Non-MDIs
CFC-12
HCFC-142b
1994
1994
10%
HFC-152a
2001
2010
90%
None
4.2%
HFC-134a
2001
2010
10%
None
Non-
ODP/GWP
1994
1994
5%
None
HFO-
HCFC-22
1994
1994
50%
HFC-134a
2001
2010
100%
1234ze(E)
2012
2016
10%
HFC-152a
1994
1994
10%
None
HFC-134a
1994
1994
25%
None
3 Transitions between the start year and date of full penetration in new products are assumed to be linear so that in total 100% of the market is assigned to the original ODS or
the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
CCFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs from 1993 to 2008. It is assumed that
CFC mix was stockpiled in the United States and used in new products through 2013.
d Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that is now using HFC propellants is
modeled.
Annex 3
A-251
-------
Solvents
ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need to be
cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving the
item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid
phase and are not emitted as gas. Thus, emissions are considered "incomplete," and are a fixed percentage of the
amount of solvent consumed in a year. The solvent is assumed to be recycled or continuously reused through a distilling
and cleaning process until it is eventually almost entirely emitted. The remainder of the consumed solvent is assumed to
be entrained in sludge or wastes and disposed of by incineration or other destruction technologies without being
released to the atmosphere (U.S. EPA 2004). The following equation calculates emissions from solvent applications.
Equation A-13: Calculation of Emissions from Solvents
Ej = I x Qq
where:
E
I
Qc
j
Transition Assumptions
The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals cleaning,
precision cleaning, and adhesives, coatings and inks, are presented in Table A-124.
Emissions. Total emissions of a specific chemical in year j from use in solvent
applications, by weight.
Percent Leakage. The percentage of the total chemical that is leaked to the
atmosphere, assumed to be 90 percent.
Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent
applications in the year j, by weight.
Year of emission.
Table A-124: Solvent Market Transition Assumptions
Primary Substitute
Secondary Substitute
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Adhesives
CH3CCI3 || Non-ODP/GWP
1994
1995
100%
None
| 2.0%
Electronics
CFC-113
Semi-Aqueous
1994
1995
52%
None
2.0%
HCFC-225ca/cb
1994
1995
0.2%
Unknown
HFC-43-10mee
1995
1996
0.7%
None
HFE-7100
1994
1995
0.7%
None
nPB
1992
1996
5%
None
Methyl Siloxanes
1992
1996
0.8%
None
No-Clean
1992
2013c
40%
None
CH3CCI3
Non-ODP/GWP
1996
1997
99.8%
None
2.0%
Non-
PFC/PFPE
1996
1997
0.2%
ODP/GWP
2000
2003
90%
Non-
ODP/GWP
2005
2009
10%
Metals
CH3CCI3
Non-ODP/GWP
1992
1996
100%
None
2.0%
CFC-113
Non-ODP/GWP
1992
2013c
100%
None
2.0%
CCI4
Non-ODP/GWP
1992
1996
100%
None
2.0%
Precision
CH3CCI3 | Non-ODP/GWP
1995
1996
99.3%
None
2.0%
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Primary Substitute
Secondary Substitute
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
HFC-43-10mee
1995
1996
0.6%
None
Non-
PFC/PFPE
1995
1996
0.1%
ODP/GWP
Non-
ODP/GWP
2000
2005
2003
2009
90%
10%
CFC-113
Non-ODP/GWP
Methyl Siloxanes
HCFC-225ca/cb
HFE-7100
1995
1995
1995
1995
2013c
1996
1996
1996
90%
6%
1%
3%
None
Unknown
None
2.0%
a Transitions between the start year and date of full penetration in new equipment or chemical supply are assumed to be linear
so that in total 100 percent of the market is assigned to the original ODS or the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
transition assumed to be completed in 2013 to mimic CFC-113 stockpile use.
Note: Non-ODP/GWP includes chemicals with zero ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-
kind alternatives such as "no clean" technologies.
Fire Extinguishing
ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held "streaming" applications
as well as in built-up "flooding" equipment similar to water sprinkler systems. Although these systems are generally built
to be leak-tight, some leaks do occur and emissions occur when the agent is released. Total emissions from fire
extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in operation at a given
time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate for an average
equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are assumed to be 3.5
percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of chemical is
assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a single year,
accounting for all fire protection equipment in operation in that year. The model assumes that equipment is serviced
annually so that the amount equivalent to average annual emissions for each product (and hence for the total of what
was added to the bank in a previous year in equipment that has not yet reached end-of-life) is replaced/applied to the
starting charge size (or chemical bank). Each fire protection agent is modeled separately. In the Vintaging Model,
streaming applications have a 24-year lifetime and flooding applications have a 33-year lifetime. At end-of-life, remaining
agent is recovered from equipment being disposed and is reused.
Equation A-14: Calculation of Emissions from Fire Extinguishing
Ej = r x Z Qcj-m for i=l^k
where:
E = Emissions. Total emissions of a specific chemical in year j for fire extinguishing
equipment, by weight.
r = Percent Released. The percentage of the total chemical in operation that is released to
the atmosphere.
Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire
extinguishing equipment in a given year,y-/+l, by weight.
/' = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of the equipment.
Annex 3
A-253
-------
Transition Assumptions
Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A-125.
Table A-125: Fire Extinguishing Market Transition Assumptions
Primary Substitute
Secondary Substitute
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
Penetration
Rateb
Flooding Agents
Halon-
1301
Halon-1301c
1994
1994
4%
Unknown
2.2%
HFC-23
1994
1999
0.2%
None
HFC-227ea
1994
1999
50.2%
FK-5-1-12
2003
2020
35%
HFC-125
2001
2012
10%
Non-
ODP/GWP
2005
2020
13%
Non-ODP/GWP
1994
1994
22%
FK-5-1-12
2003
2020
7%
Non-ODP/GWP
1995
2003
7%
None
C02
1998
2006
7%
None
C4F10
1994
1999
0.5%
FK-5-1-12
2003
2003
100%
HFC-125
1997
2006
9.1%
FK-5-1-12
2003
2020
35%
Non-
ODP/GWP
2005
2020
10%
Non-
ODP/GWP
2005
2019
3%
Streaming Agents
Halon-
1211
Halon-1211c
HFC-236fa
Halotron
1992
1997
1994
1992
1999
1995
5%
3%
0.1%
Unknown
None
Unknown
Non-
3.0%
Halotron
1996
2000
5.4%
ODP/GWP
2020
2020
56%
Non-ODP/GWP
1993
1994
56%
None
Non-ODP/GWP
1995
2024
20%
None
Non-ODP/GWP
1999
2018
10%
None
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100
percent of the market is assigned to the original ODS or the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled
with stockpiled or recovered supplies.
Foam Blowing
ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels around
refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate,
increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell
or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime
of the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use.
Step 1: Calculate manufacturing emissions (open-cell and closed-cell foams)
Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following
equation. Manufacturing emissions are considered for all foam equipment that are filled with foam within the United
States, including those which are produced for export, and excluding those that are imported pre-filled.
Equation A-15: Calculation of Emissions from Foam Blowing Manufacturing
Enrtj = Im x Qq
where:
Enrtj = Emissions from manufacturing. Total emissions of a specific chemical in year j due to
manufacturing losses, by weight.
Im = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For
open-cell foams, Im is 100%.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-
cell foams in a given year.
j = Year of emission.
Step 2: Calculate lifetime emissions (closed-cell foams)
Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as presented
in the following equation.
Equation A-16: Calculation of Emissions from Foam Blowing Lifetime Losses (Closed-cell
Foams)
EUj = lu x Z Qcj-m for i=l^k
where:
EUj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to
lifetime losses during use, by weight.
lu = Leak Rate. Percent of original blowing agent emitted each year during lifetime use.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-
cell foams in a given year.
/' = Counter, runs from 1 to lifetime (k).
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Step 3: Calculate disposal emissions (closed-cell foams)
Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following equation.
Equation A-17: Calculation of Emissions from Foam Blowing Disposal (Closed-cell Foams)
Edj = Id x Qcj-k
where:
Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by
weight.
Id = Loss Rate. Percent of original blowing agent emitted at disposal.
Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-
cell foams in a given year.
j = Year of emission.
k = Lifetime. The average lifetime of foam product.
Annex 3
A-255
-------
Step 4: Calculate post-disposal emissions (closed-cell foams)
Post-disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while the
disposed foam is in a landfill. Currently, five foam types are assumed to have post-disposal emissions.
Equation A-18: Calculation of Emissions from Foam Blowing Post-disposal (Closed-cell
Foams)
Epj = Ip x Z QCj-m for m=k^k + 26
where:
EPi
= Emissions from post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
Ip
= Leak Rate. Percent of original blowing agent emitted post disposal.
Qc
= Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-
cell foams in a given year.
k
= Lifetime. The average lifetime of foam product.
m
= Counter. Runs from lifetime (k) to (k+26).
j
= Year of emission.
Step 5: Calculate total emissions (open-cell and closed-cell foams)
To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as presented
in the following equation.
Equation A-19: Calculation of Total Emissions from Foam Blowing (Open-cell and Closed-cell
Foams)
Ej = Errij + EUj+ Edj + Epj
where:
Enrtj
Euj
Edj
EPi
Assumptions
The Vintaging Model contains thirteen foam types, whose transition assumptions away from ODS and growth rates are
presented in Table A-126. The emission profiles of these thirteen foam types are shown in Table A-127.
= Total Emissions. Total emissions of a specific chemical in year j, by weight.
= Emissions from manufacturing. Total emissions of a specific chemical in year j due to
manufacturing losses, by weight.
= Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to
lifetime losses during use, by weight.
= Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by
weight.
= Emissions from post disposal. Total post-disposal emissions of a specific chemical in
year j, by weight.
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-126: Foam Blowing Market Transition Assumptions
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Maximum
Date of Full
Initial
Penetration
Maximum
Penetration
Market
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
Vending Machine Foam
CFC-11
HCFC-141b
1993
1995
100%
HFC-245fa
2001
2004
100%
Non-ODP/GWP
2004
2006
45%
-0.03%
Non-ODP/GWP
2007
2009
5%
Non-ODP/GWP
2007
2009
25%
Non-ODP/GWP
2010
2010
10%
Non-ODP/GWP
2017
2017
2%
Non-ODP/GWP
2017
2017
8%
Stand-alone Equipment Foam
CFC-11
HCFC-141b
1990
1995
40%
HFC-245fa
HFC-134a
Non-
ODP/GWP
2003
2003
2003
2005
2005
2005
80%
40%
40%
HCFO-1233zd(E)
None
None
2019
2020
25%
2.2%
HCFC-22
1990
1995
56%
HFC-134a
Non-
ODP/GWP
2004
2004
2008
2008
46%
54%
Non-ODP/GWP
HCFO-1233zd(E)
None
2010
2019
2018
2020
32%
36%
Ice Machine Foam
CFC-11
HCFC-141b
1989
1996
40%
C02
2002
2003
69%
None
2.1%
HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%
HCFC-142b
1989
1996
8%
C02
2002
2003
69%
None
HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%
HCFC-22
1989
1996
52%
C02
2002
2003
69%
None
HFC-134a
2002
2003
31%
C02
HCFO-1233zd(E)
2017
2017
2020
2020
47%
20%
Refrigerated Food Processing and Dispensing Equipment Foam
CFC-11
HCFC-22
1989
1997
100%
HFC-134a
Non-
ODP/GWP
2004
2009
2004
2008
2010
2008
75%
20%
25%
Non-ODP/GWP
HCFO-1233zd(E)
HFO-1234ze
None
2015
2020
2020
2021
2021
2021
30%
3%
3%
2.1%
Small Walk-in Cooler Foam
CFC-11
HCFC-141b
1990
1995
50%
HFC-245fa
2001
2003
100%
None
1.6%
HCFC-22
1990
1995
50%
HFC-134a
2000
2001
10%
None
Annex 3
A-257
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Maximum
Date of Full
Initial
Penetration
Maximum
Penetration
Market
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
HFC-245fa
HFC-134a
2009
2009
2010
2010
50%
40%
HCFO-1233zd(E)
None
2020
2020
20%
Large Walk-in Cooler Foam
CFC-11
HCFC-141b
1990
1995
50%
HFC-245fa
2001
2003
100%
None
1.5%
HCFC-22
1990
1995
50%
HFC-134a
2000
2001
10%
None
HFC-245fa
2009
2010
50%
HCFO-1233zd(E)
2020
2020
20%
HFC-134a
2009
2010
40%
None
Display Case Foam
CFC-11
HCFC-141b
1991
1992
50%
HFC-245fa
2003
2003
100%
None
1.7%
HCFC-142b
1991
1992
50%
HFC-245fa
2004
2004
100%
None
CFC-12
HCFC-22
1991
1993
100%
HFC-134a
2003
2007
100%
HCFO-1233zd(E)
2015
2020
60%
Road Transport Foam
CFC-11
HCFC-141b
1989
1996
19%
HCFC-22
C02
Non-
ODP/GWP
1999
1999
1999
2001
2001
2001
37%
11%
53%
HFC-245fa
None
None
2005
2007
100%
5.5%
HCFC-22
1989
1996
81%
HFC-134a
2005
2007
37%
None
HFC-245fa
2005
2007
63%
HCFO-1233zd(E)
2020
2020
76%
Intermodal Container Foam
CFC-11
HCFC-141b
1989
1996
19%
HCFC-22
C02
Non-
ODP/GWP
1999
1999
1999
2001
2001
2001
37%
11%
53%
HFC-245fa
None
None
2005
2007
100%
7.3%
HCFC-22
1989
1996
81%
HFC-134a
2005
2007
37%
None
HFC-245fa
2005
2007
63%
HCFO-1233zd(E)
2020
2020
76%
Flexible PU Foam: Integral Skin Foam
HCFC-141bc
HFC-134a
C02
1996
1996
2000
2000
50%
50%
HFC-245fa
Non-
ODP/GWP
None
2003
2003
2010
2010
96%
4%
HCFO-1233zd(E)
Non-ODP/GWP
HFO-1336mzz(Z)
None
2017
2017
2017
2017
2017
2017
83%e
6%
10%
2.0%
Flexible PU Foam: Slabstock Foam,
Moulded Foam
CFC-11
Non-
ODP/GWP
1992
1992
100%
None
2.0%
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Initial
Market
Segment
Primary Substitute
Secondary Substitute
Tertiary Substitute
Growth
Rateb
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetration
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetrati
on
Name of
Substitute
Start
Date
Date of Full
Penetration
in New
Equipment3
Maximum
Market
Penetration
Phenolic Foam
Non-
CFC-11
HCFC-141b
1989
1990
100%
ODP/GWP
1992
1992
100%
None
2.0%
Polyolefin Foam
Non-
CFC-114
HFC-152a
1989
1993
10%
ODP/GWP
Non-
2005
2010
100%
None
2.0%
HCFC-142b
1989
1993
90%
ODP/GWP
1994
1996
100%
None
PU and PIR Rigid: Boardstock
Non-
CFC-11
HCFC-141b
1993
1996
100%
ODP/GWP
2000
2003
100%
None
4.8%
PU Rigid: Domestic Refrigerator and Freezer Insulation
CFC-11
HCFC-141b
1993
1995
100%
HFC-134a
1996
2001
7%
Non-ODP/GWP
2002
2003
100%
0.8%
HFC-245fa
2001
2003
50%
Non-ODP/GWP
HCFO-1233zd(E)
2015
2015
2020
2020
50%
50%
HFC-245fa
2006
2009
10%
Non-ODP/GWP
HCFO-1233zd(E)
2015
2015
2020
2020
50%
50%
Non-
ODP/GWP
2002
2005
10%
None
Non-
ODP/GWP
2006
2009
3%
None
Non-
ODP/GWP
2009
2014
20%
None
PU Rigid: One Component Foam
HCFC-
142b/22
Non-
CFC-12
Blend
1989
1996
70%
ODP/GWP
2009
2010
80%
None
4.0%
HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%
HFC-152a
2009
2010
10%
None
Non-
HCFC-22
1989
1996
30%
ODP/GWP
2009
2010
80%
None
HFC-134a
2009
2010
10%
HFO-1234ze(E)
2018
2020
100%
HFC-152a
2009
2010
10%
None
PU Rigid: Other: Slabstock Foam
CFC-11
HCFC-141b
1989
1996
100%
C02
1999
2003
45%
None
2.0%
Annex 3
A-259
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Maximum
Date of Full
Initial
Penetration
Maximum
Penetration
Market
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
Non-
ODP/GWP
HCFC-22
2001
2003
2003
2003
45%
10%
None
Non-ODP/GWP
2009
2010
100%
PU Rigid: Sandwich Panels: Continuous and Discontinuous
HCFC-141bd
HCFC-22
HCFC-
HFC-
22/Water
245fa/C02
Blend
2001
2003
20%
Blend
Non-
ODP/GWP
2009
2009
2010
2010
50%
50%
HCFO-1233zd(E)
None
2015
2020
100%
HFC-
245fa/C02
HCFO-
Blend
2002
2004
20%
1233zd(E)
2015
2020
100%
None
Non-
ODP/GWP
2001
2004
40%
None
Non-
HFC-134a
2002
2004
20%
ODP/GWP
2015
2020
100%
None
HFC-
245fa/C02
HCFO-
Blend
2009
2010
40%
1233zd(E)
2015
2020
100%
None
Non-
ODP/GWP
2009
2010
20%
None
C02
2009
2010
20%
None
Non-
HFC-134a
2009
2010
20%
ODP/GWP
2015
2020
100%
None
PU Rigid: High Pressure Two-Component Spray Foam
CFC-11
HCFC-141b
1989
1996
100%
HFC-245fa
HFC-
245fa/C02
2002
2003
C
HFO-1336mzz(Z)
HFO-
1336mzz(Z)/C02
2016
2020
100%
0.8%
Blend
2002
2003
C
Blend
2016
2020
100%
HFC-
227ea/HFC-
365mfc
Blend
2002
2003
C
HCFO-1233zd(E)
2016
2020
100%
PU Rigid: Low Pressure Two-Component Spray Foam
CFC-12
HCFC-22
1989
1996
100%
HFC-245fa
2002
2003
15%
HCFO-1233zd(E)
2017
2021
100%
0.8%
HFC-134a
2002
2003
85%
HFO-1234ze
2017
2021
100%
XPS: Boardstock Foam
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Maximum
Date of Full
Initial
Penetration
Maximum
Penetration
Market
Penetration
Maximum
Market
Name of
Start
in New
Market
Name of
Start
in New
Penetrati
Name of
Start
in New
Market
Growth
Segment
Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment3
on
Substitute
Date
Equipment3
Penetration
Rateb
HCFC-
142b/22
CFC-12
Blend
1989
1994
10%
HFC-134a
HFC-152a
C02
Non-
ODP/GWP
2009
2009
2009
2009
2010
2010
2010
2010
70%
10%
10%
10%
Non-ODP/GWP
None
None
None
2021
2021
100%
2.5%
HCFC-142b
1989
1994
90%
HFC-134a
HFC-152a
C02
Non-
ODP/GWP
2009
2009
2009
2009
2010
2010
2010
2010
70%
10%
10%
10%
Non-ODP/GWP
None
None
None
2021
2021
100%
XPS: Sheet Foam
CFC-12
C02
Non-
1989
1994
1%
None
2.0%
ODP/GWP
1989
1994
99%
C02
HFC-152a
1995
1995
1999
1999
9%
10%
None
None
C (Confidential)
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original ODS or
the various ODS substitutes.
b Growth Rate is the average annual growth rate for individual market sectors from the base year to 2030.
c CFC-11 was the initial blowing agent used for through 1989. This transition is not shown in the table in order to provide the HFC transitions in greater detail.
d The CFC-11 PU Rigid: Sandwich Panels: Continuous and Discontinuous market for new systems transitioned to 82 percent HCFC-141b and 18 percent HCFC-22 from 1989 to 1996. These
transitions are not shown in the table in order to provide the HFC transitions in greater detail.
e A linear transition to HFO-1336mzz(Z) from the HCFO-1233zd(E) market is assumed to take place beginning in 2020 and reaching 88 percent of the market by 2030. This transition is not
shown in the table.
Annex 3
A-261
-------
Table A-127: Emission Profile for the Foam End-Uses
Loss at Annual Leakage
Manufacturing Leakage Rate Lifetime Loss at Post-life Total3
Foam End-Use (%) (%) (years) Disposal (%) Loss (%) (%)
Flexible PU Foam: Slabstock Foam,
Moulded Foam
Vending Machine Foam
Stand-alone Equipment Foam
Ice Machine Foam
Refrigerated Food Processing and
Dispensing Equipment Foam
Small Walk-in Cooler Foam
Large Walk-in Cooler Foam
CFC-11 Display Case Foam
CFC-12 Display Case Foam
Road Transport Foam
Intermodal Container Foam
Rigid PU: High Pressure Two-
Component Spray Foam
Rigid PU: Low Pressure Two-
Component Spray Foam
Rigid PU: Slabstock and Other3
Phenolic Foam
Polyolefin Foam
Rigid PU: One Component Foam
XPS: Sheet Foam
XPS: Boardstock Foam
Flexible PU Foam: Integral Skin Foam
Rigid PU: Domestic Refrigerator and
Freezer Insulation (HFC-134a)a
Rigid PU: Domestic Refrigerator and
Freezer Insulation (all others)3
PU and PIR Rigid: Boardstock3
PU Sandwich Panels: Continuous and
Discontinuous3
PIR (Polyisocyanurate)
PU (Polyurethane)
XPS (Extruded Polystyrene)
3 Total emissions from foam end-uses are assumed to be 100 percent. In the Rigid PU: Slabstock and Other, Rigid PU Domestic
Refrigerator and Freezer Insulation, PU and PIR Boardstock, and PU Sandwich Panels end-uses, the source of emission rates
and lifetimes did not yield 100 percent emissions; the remainder is assumed to be emitted post-disposal.
Sterilization
Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a blend of 12
percent ethylene oxide (EtO) and 88 percent CFC-12, known as "12/88." In that blend, ethylene oxide sterilizes the
equipment and CFC-12 is a diluent solvent to form a non-flammable blend. The sterilization sector is modeled as a single
end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to be
emitted in that year, as shown in the following equation.
Equation A-20: Calculation of Total Emissions from Sterilization
Ej = Qq
where:
E = Emissions. Total emissions of a specific chemical in year j from use in sterilization
equipment, by weight.
100 0 1
4 0.25 10
4 0.25 10
4 0.25 8
4 0.25 10
4 0.25 20
4 0.25 20
4 0.25 18
4 0.25 18
4 0.25 12
4 0.25 15
15 1.5 50
15 1.5 50
20 1 15
28 0.875 32
40 3 20
95 2.5 2
50 25 2
25 0.75 25
95 2.5 2
6.5 0.5 14
3.75 0.25 14
10 1 40
15 0.5 75
0 0 100
93.5 0 100
93.5 0 100
94.0 0 100
100
93.5 0
91.0 0 100
91.0 0 100
91.5 0 100
91.5 0 100
93.0 0 100
92.3 0 100
10.0 0 100
10.0 0 100
22.5 1.5 57.5
44.0 0 100
0 0 100
0 0 100
0 0 100
56.25 0 100
0 0 100
37.2 2.0 50.7
39.9 2.0 47.15
22.5 1.5 72.5
22.5 1.25 75
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization
equipment in year j, by weight.
j = Year of emission.
Assumptions
The Vintaging Model contains one sterilization end-use, whose transition assumptions away from ODS and growth rates
are presented in Table A-128.
Annex 3
A-263
-------
Table A-128: Sterilization Market Transition Assumptions
Primary Substitute
Secondary Substitute
Tertiary Substitute
Date of Full
Date of Full
Date of Full
Initial
Penetration
Maximum
Penetration
Maximum
Penetration
Maximum
Market
Start
in New
Market
Name of
Start
in New
Market
Name of
Start
in New
Market
Growth
Segment
Name of Substitute
Date
Equipment3
Penetration
Substitute
Date
Equipment
Penetration
Substitute
Date
Equipment
Penetration
Rate
12/88
EtO
1994
1995
95%
None
2.0%
Non-ODP/GWP
1994
1995
0.8%
None
HCFC-124/EtO Blend
1993
1994
1.4%
Non-ODP/GWP
2015
2015
100%
None
HCFC-22/HCFC-124/EtO Blend
1993
1994
3.1%
Non-ODP/GWP
2010
2010
100%
None
a Transitions between the start year and date of full penetration in new equipment are assumed to be linear so that in total 100 percent of the market is assigned to the original
ODS or the various ODS substitutes.
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Model Output
By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions for ODS
and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis, summed
across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in metric tons
and in million metric tons of C02 equivalent (MMT C02 Eq.). The conversion of metric tons of chemical to MMT C02 Eq. is
accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical.
Throughout its development, the Vintaging Model has undergone annual modifications. As new or more accurate
information becomes available, the model is adjusted in such a way that both past and future emission estimates are
often altered.
Bank of ODS and ODS Substitutes
The bank of an ODS or an ODS substitute is "the cumulative difference between the chemical that has been consumed in
an application or sub-application and that which has already been released" (IPCC 2006). For any given year, the bank is
equal to the previous year's bank, less the chemical in equipment disposed of during the year, plus chemical in new
equipment entering the market during that year, less the amount emitted but not replaced, plus the amount added to
replace chemical emitted prior to the given year, as shown in the following equation:
Equation A-21: Calculation of Chemical Bank (All Sectors)
Bq = Bq-rQdj+Qpj-Ee+Qr
where:
Bq = Bank of Chemical. Total bank of a specific chemical in year j, by weight.
Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in
equipment disposed of in year j, by weight.
QPi = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical that
is entering the market in year j, by weight.
Ee = Emissions of Chemical Not Replaced. Total quantity of a specific chemical that is
emitted during year j but is not replaced in that year. The Vintaging Model assumes all
chemical emitted from refrigeration, air conditioning and fire extinguishing equipment
is replaced in the year it is emitted, hence this term is zero for all sectors except foam
blowing.
Qr = Chemical Replacing Previous Year's Emissions. Total quantity of a specific chemical that
is used to replace emissions that occurred prior to year j. The Vintaging Model assumes
all chemical emitted from refrigeration, air conditioning and fire extinguishing
equipment is replaced in the year it is emitted, hence this term is zero for all sectors.
/' = Year of emission.
Table A-129 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990 to 2020.
Table A-129: Banks of ODS and ODS Substitutes, 1990-2020 (MT)
Year
CFC
HCFC
HFC
1990
728,543
183,887
872
1995
772,295
421,473
50,353
2000
631,209
825,536
189,407
2001
601,421
894,966
218,596
2002
575,846
951,093
250,994
2003
550,694
994,708
292,765
2004
525,108
1,038,943
336,286
2005
494,543
1,085,234
382,483
2006
463,002
1,127,294
434,296
Annex 3
A-265
-------
2007
434,022
1,157,165
487,736
2008
410,180
1,172,953
537,785
2009
395,734
1,164,422
592,571
2010
380,423
1,132,048
663,418
2011
366,697
1,091,759
736,835
2012
354,333
1,048,642
812,324
2013
344,105
999,771
890,318
2014
335,150
950,640
969,769
2015
327,483
902,731
1,044,824
2016
320,990
853,551
1,117,540
2017
314,786
805,000
1,182,625
2018
311,138
752,987
1,244,651
2019
309,227
699,130
1,297,920
2020
307,434
641,743
1,342,850
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
U.S. EPA (2018) EPA's Vintaging Model of ODS Substitutes: A Summary of the 2017 Peer Review. Office of Air and
Radiation. Document Number EPA-400-F-18-001. Available online at: https://www.epa.gov/sites/production/files/2018-
09/documents/epas-vintaging-model-of-ods-substitutes-peer-review-factsheet.pdf.
U.S. EPA (2004) The U.S. Solvent Cleaning Industry and the Transition to Non Ozone Depleting Substances. September
2004. Available online at: https://www.epa.gov/sites/production/files/2014-
11/documents/epasolventmarketreport.pdf.
Data are also taken from various government sources, including rulemaking analyses from the U.S. Department of Energy
and from the Motor Vehicle Emission Simulator (MOVES) model from EPA's Office of Transportation and Air Quality.
Annex 3
A-267
-------
3.10. Methodology for Estimating CH4 Emissions from Enteric
Fermentation
Methane emissions from enteric fermentation were estimated for seven livestock categories: cattle, horses, sheep,
swine, goats, American bison, and the non-horse equines (mules and asses). Emissions from cattle represent the majority
of U.S. emissions from enteric fermentation; consequently, a more detailed IPCC Tier 2 methodology was used to
estimate emissions from cattle. The IPCC Tier 1 methodology was used to estimate emissions for the other types of
livestock, including horses, goats, sheep, swine, American bison, and mules and asses (IPCC 2006).
Estimate Methane Emissions from Cattle
This section describes the process used to estimate CH4 emissions from enteric fermentation from cattle using the Cattle
Enteric Fermentation Model (CEFM). The CEFM was developed based on recommendations provided in the 2006 IPCC
Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and uses information on population, energy
requirements, digestible energy, and CH4 conversion rates to estimate CH4 emissions.82 The emission methodology
consists of the following three steps: (1) characterize the cattle population to account for animal population categories
with different emission profiles; (2) characterize cattle diets to generate information needed to estimate emission
factors; and (3) estimate emissions using these data and the IPCC Tier 2 equations.
Step 1: Characterize U.S. Cattle Population
The CEFM's state-level cattle population estimates are based on data obtained from the U.S. Department of Agriculture's
(USDA) National Agricultural Statistics Service Quick Stats database (USDA 2021a). State-level cattle population estimates
are shown by animal type for 2020 in Table A-130. A national-level summary of the annual average populations upon
which all livestock-related emissions are based is provided in Table A-131. Cattle populations used in the Enteric
Fermentation source category for the 1990 to 2020 Inventory were estimated using the cattle transition matrix in the
CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from
birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while taking into
account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates. The use of
supplemental USDA data and the cattle transition matrix in the CEFM results in cattle population estimates for this
sector differing slightly from the January 1 or July 1 USDA point estimates and the cattle population data obtained from
the Food and Agriculture Organization of the United Nations (FAO).
Table A-130: 2020 Cattle Population Estimates, by Animal Type and State (1,000 head)
Dairy
Dairy
Beef
Beef
Repl.
Repl.
Repl.
Repl.
Heif.
Heif.
Heif.
Heif.
Dairy
Dairy
7-11
12-23
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Alabama
2
4
1
1
50
347
696
28
67
21
21
7
Alaska
0
0
0
0
4
4
8
0
1
0
0
0
Arizona
101
196
33
80
20
97
194
6
14
126
7
270
Arkansas
3
5
1
2
62
456
915
35
84
53
33
14
California
891
1,725
221
531
60
327
655
28
67
285
98
551
Colorado
98
189
33
80
50
385
771
36
87
358
266
1,130
Conn.
10
20
3
7
1
3
6
0
1
1
1
0
Delaware
2
4
1
1
0
1
2
0
0
1
0
0
Florida
60
116
10
24
60
451
904
31
73
11
12
4
Georgia
42
81
9
21
32
259
519
21
50
20
14
5
Hawaii
0
1
0
1
4
38
75
3
6
5
2
1
Idaho
331
640
96
231
40
244
490
31
73
138
93
314
Illinois
42
82
13
31
20
189
378
14
34
110
42
248
82 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Indiana
91
176
22
52
16
97
194
10
23
50
21
106
Iowa
111
215
33
80
60
452
905
34
81
597
280
1,327
Kansas
86
167
41
98
85
720
1,443
59
140
956
723
2,607
Kentucky
25
49
12
28
70
509
1,021
29
70
99
51
17
Louisiana
5
10
1
2
30
227
455
19
46
11
9
3
Maine
14
28
4
10
2
5
11
1
2
2
2
1
Maryland
22
42
8
18
4
23
47
3
6
5
4
7
Mass.
5
10
2
5
1
3
6
0
1
1
0
0
Michigan
221
427
51
122
15
46
93
5
13
80
15
165
Minn.
230
445
68
164
30
182
365
22
53
239
75
412
Miss.
4
8
1
3
39
240
482
21
50
27
20
7
Missouri
40
77
10
24
120
1,039
2,083
79
188
188
103
110
Montana
6
12
1
3
105
712
1,428
92
218
115
107
49
Nebraska
30
58
9
21
120
959
1,922
87
207
1,094
700
2,694
Nevada
16
31
3
7
15
124
249
10
25
18
15
3
N.Hamp.
6
11
2
5
1
2
4
0
1
1
0
0
N.Jersey
2
5
1
2
1
5
9
1
1
1
1
0
N.Mexico
170
330
41
98
30
239
480
19
45
46
37
14
New York
323
625
100
241
20
52
105
9
22
25
19
23
N.Car.
21
41
5
13
31
184
369
15
35
18
11
5
N.Dakota
8
15
2
6
70
496
995
43
102
126
107
47
Ohio
130
252
35
84
30
149
298
16
39
101
30
169
Oklahoma
21
41
6
14
170
1,052
2,109
87
207
464
247
346
Oregon
66
127
20
49
40
266
533
25
59
67
56
96
Penn
248
480
80
192
20
110
220
14
34
62
26
102
R.Island
0
1
0
0
0
1
1
0
0
0
0
0
S.Car.
6
11
1
3
13
89
179
6
15
6
5
2
S.Dakota
66
127
13
31
110
890
1,783
85
202
326
215
447
Tenn.
16
31
7
17
60
453
909
29
70
71
40
18
Texas
300
580
82
196
350
2,280
4,570
188
448
1,218
723
2,998
Utah
50
97
15
35
25
179
358
20
48
37
28
21
Vermont
64
124
16
38
3
6
13
1
3
2
3
1
Virginia
38
74
10
23
40
312
626
22
52
76
30
19
Wash.
146
282
37
89
20
114
228
13
32
94
63
243
W.Virg.
3
6
1
2
15
99
199
8
19
20
10
4
Wisconsin
651
1,260
201
482
30
155
310
21
50
156
23
255
Wyoming
3
6
1
3
45
361
724
38
90
71
59
71
Table A-131: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2020
(1,000 head)
Livestock Type 1990 1995 2000 2005 2010 2016 2017 2018 2019 2020
Dairy
Dairy Calves (0-6
months)
Dairy Cows
Dairy Replacements 7-
11 months
Dairy Replacements 12-
23 months
Beef
Beef Calves (0-6 months)
Bulls
Beef Cows
5,369
10,015
1,214
2,915
16,909
2,160
32,455
5,091
9,482
1,216
2,892
18,177
2,385
35,190
4,951
9,183
1,196
2,812
17,431
2,293
33,575
4,628 4,666 4,760 4,797 4,833 4,834 4,825
9,004 9,087 9,312 9,369 9,432 9,353 9,343
1,257 1,351 1,414 1,416 1,400 1,391 1,364
2,905 3,194 3,371 3,342 3,341 3,304 3,273
16,918 16,067 15,546 15,931 16,221 15,892 15,635
2,214 2,190 2,137 2,244 2,252 2,253 2,237
32,674 31,440 30,164 31,171 31,466 31,691 31,339
Annex 3
A-269
-------
Beef Replacements 7-11
months
1,269
1,493
1,313
1,363
1,238
1,514
1,479
1,420
1,380
1,366
Beef Replacements 12-
23 months
2,967
3,637
3,097
3,171
3,050
3,575
3,594
3,444
3,321
3,254
Steer Stockers
10,321
11,716
8,724
8,185
8,234
8,133
7,904
7,633
7,745
7,600
Heifer Stockers
5,946
6,699
5,371
5,015
5,061
4,802
4,717
4,595
4,500
4,447
Feedlot Cattle
9,549
11,064
13,006
12,652
13,204
13,451
14,346
14,690
14,917
14,935
The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an estimate of
the population age and weight structure by cattle type on a monthly basis.83 Since cattle often do not remain in a single
population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and emission profiles
vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation model
to obtain more accurate emission estimates than would be available from annual point estimates of population (such as
available from USDA statistics) and weight for each cattle type.
The transition matrix tracks both dairy and beef populations, and divides the populations into males and females, and
subdivides the population further into specific cattle groupings for calves, replacements, stockers, feedlot, and mature
animals. The matrix is based primarily on two types of data: population statistics and weight statistics (including target
weights, slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of
animals over time by month. The matrix also relies on supplementary data, such as feedlot placement statistics,
slaughter statistics, death rates, and calving rates, described in further detail below.
The basic method for tracking population of animals per category is based on the number of births (or graduates) into
the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category
(such as stockers to feedlot placements).
Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This level of
detail accounts for the variability in CH4 emissions associated with each life stage. Given that a stage can last less than
one year (e.g., calves are usually weaned between 4 and 6 months of age), each is modeled on a per-month basis. The
type of cattle also influences CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition
matrix for each of three separate lifecycle phases, 1) calves, 2) replacements and stockers, and 3) feedlot animals. In
addition, the number of mature cows and bulls are tabulated for both dairy and beef stock. The transition matrix
estimates total monthly populations for all cattle subtypes. These populations are then reallocated to the state level
based on the percent of the cattle type reported in each state in the January 1 USDA data. Each lifecycle is discussed
separately below, and the categories tracked are listed in Table A-132.
Table A-132: Cattle Population Categories Used for Estimating ChU Emissions
Dairy Cattle Beef Cattle
Calves Calves
Heifer Replacements Heifer Replacements
Cows Heifer and Steer Stockers
Animals in Feedlots (Heifers & Steer)
Cows
Bulls3
a Bulls (beef and dairy) are accounted for in a single category.
The key variables tracked for each of these cattle population categories are as follows:
Calves. Although enteric emissions are only calculated for 4- to 6-month old calves, it is necessary to calculate
populations from birth as emissions from manure management require total calf populations and the estimates of
populations for older cattle rely on the available supply of calves from birth. The number of animals born on a monthly
basis was used to initiate monthly cohorts and to determine population age structure. The number of calves born each
month was obtained by multiplying annual births by the percentage of births per month. Annual birth information for
83 Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the
January estimates downloaded from USDA (2021).
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
each year was taken from USDA (2021). For dairy cows, the number of births is assumed to be distributed equally
throughout the year (approximately 8.3 percent per month) while beef births are distributed according to Table A-133,
based on approximations from the National Animal Health Monitoring System (NAHMS) (USDA/APHIS/VS 1998,1994,
1993). To determine whether calves were born to dairy or beef cows, the dairy cow calving rate (USDA/APHIS/VS 2002,
USDA/APHIS/VS 1996) was multiplied by the total dairy cow population to determine the number of births attributable
to dairy cows, with the remainder assumed to be attributable to beef cows. Total annual calf births are obtained from
USDA and distributed into monthly cohorts by cattle type (beef or dairy). Calf growth is modeled by month, based on
estimated monthly weight gain for each cohort (approximately 61 pounds per month). The total calf population is
modified through time to account for veal calf slaughter at 4 months and a calf death loss of 0.35 percent annually
(distributed across age cohorts up to 6 months of age). An example of a transition matrix for calves is shown in Table A-
134. Note that 1- to 6-month old calves in January of each year have been tracked through the model based on births
and death loss from the previous year.
Table A-133: Estimated Beef Cow Births by Month
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
7%
15%
28%
22%
9%
3%
2%
2%
3%
4%
3%
3%
Table A-134: Example of Monthly Average Populations from Calf Transition Matrix (1,000
head)
Age (month)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
6
1,163
1,154
1,378
1,618
1,552
1,541
2,515
4,711
8,199
6,637
3,089
1,542
5
1,155
1,379
1,619
1,553
1,541
2,516
4,712
8,202
6,640
3,091
1,544
1,151
4
1,426
1,660
1,598
1,580
2,556
4,754
8,243
6,688
3,135
1,588
1,194
1,184
3
1,662
1,599
1,581
2,557
4,755
8,246
6,690
3,136
1,588
1,194
1,185
1,459
2
1,600
1,582
2,558
4,757
8,249
6,693
3,138
1,589
1,195
1,186
1,460
1,698
1
1,584
2,560
4,760
8,253
6,695
3,139
1,590
1,195
1,186
1,461
1,699
1,635
0
2,562
4,763
8,257
6,698
3,140
1,590
1,196
1,187
1,462
1,700
1,636
1,618
Note: The cohort starting at age 0 months on January 1 is tracked in order to illustrate how a single cohort moves through
the transition matrix. Each month, the cohort reflects the decreases in population due to the estimated 0.35 percent
annual death loss, and between months 4 and 5, a more significant loss is seen than in other months due to estimated
veal slaughter.
Replacements and Stockers. At 7 months of age, calves "graduate" and are separated into the applicable cattle types:
replacements (cattle raised to give birth), or stockers (cattle held for conditioning and growing on grass or other forage
diets). First the number of replacements required for beef and dairy cattle are calculated based on estimated death
losses and population changes between beginning and end of year population estimates. Based on the USDA estimates
for "replacement beef heifers" and "replacement dairy heifers," the transition matrix for the replacements is back-
calculated from the known animal totals from USDA, and the number of calves needed to fill that requirement for each
month is subtracted from the known supply of female calves. All female calves remaining after those needed for beef
and dairy replacements are removed and become "stockers" that can be placed in feedlots (along with all male calves).
During the stocker phase, animals are subtracted out of the transition matrix for placement into feedlots based on
feedlot placement statistics from USDA (2021).
The data and calculations that occur for the stocker category include matrices that estimate the population of
backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of
year populations in January and model the progression of each cohort. The age structure of the January population is
based on estimated births by month from the previous two years, although in order to balance the population properly,
an adjustment is added that slightly reduces population percentages in the older populations. The populations are
modified through addition of graduating calves (added in month 7, bottom row of Table A-135) and subtraction through
death loss and animals placed in feedlots. Eventually, an entire cohort population of stockers may reach zero, indicating
that the complete cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown
in Table A-135.
Annex 3
A-271
-------
Table A-135: Example of Monthly Average Populations from Stocker Transition Matrix (1,000
head)
Age (month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
23
22
21
20
19
18
17
16
15
14
13
12
11
10
9
8
7
185
320
260
123
63
48
47
58
67
65
64
982
1,814
3,133
2,545
1,200
180
146
69
35
27
27
33
38
36
36
845
1,602
2,770
2,255
1,062
3,381
2,951
104
49
25
19
19
23
27
26
25
599
1,478
2,556
2,056
945
2,502
800
664
794
37
19
14
14
17
20
19
19
452
1,172
2,309
1,858
855
2,241
15
12
11
14
16
16
15
363
977
1,921
1,639
755
1,872
484
482
956
385
335
557
1,160
9
9
11
13
13
13
295
828
1,619
1,378
629
1,512
9
11
10
10
241
709
1,380
1,172
534
1,117
277
189
341
759
1,109
196
610
1,179
1,000
456
862
214
138
184
420
658
1,100
47
46
76
231
372
649
1,876
6
6
6
133
472
900
759
348
603
3
3
68
331
615
514
237
340
47
46
57
89
209
371
1,503
3,666
1
17
218
387
318
149
129
47
46
57
66
81
185
1,292
3,247
6,504
0
181
313
254
120
61
47
46
57
66
63
80
1,135
2,786
5,984
5,243
47
46
57
66
63
63
1,016
2,445
5,299
4,877
2,353
Note: The cohort starting at age 7 months on January 1 is tracked in order to illustrate how a single cohort moves through
the transition matrix. Each month, the cohort reflects the decreases in population due to the estimated 0.35 percent
annual death loss and loss due to placement in feedlots (the latter resulting in the majority of the loss from the matrix).
In order to ensure a balanced population of both stockers and placements, additional data tables are utilized in the
stocker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the
total number of animals within the population that are available to be placed in feedlots and the actual feedlot
placement statistics provided by USDA (2021). In cases where there are discrepancies between the USDA estimated
placements by weight class and the calculated animals available by weight, the model pulls available stockers from one
higher weight category if available. If there are still not enough animals to fulfill requirements the model pulls animals
from one lower weight category. In the current time series, this method was able to ensure that total placement data
matched USDA estimates, and no shortfalls have occurred.
In addition, average weights were tracked for each monthly age group using starting weight and monthly weight gain
estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target
weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be
88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 pounds. Other reported target
weights were available for 12-, 15-, 24-, and 36-month-old animals, depending on the animal type. Beef cow mature
weight was taken from measurements provided by a major British Bos taurus breed (Enns 2008) and increased during
the time series through 2007.84 Bull mature weight was calculated as 1.5 times the beef cow mature weight (Doren et al.
1989). Beef replacement weight was calculated as 70 percent of mature weight at 15 months and 85 percent of mature
weight at 24 months. As dairy weights are not a trait that is typically tracked, mature weight for dairy cows was
estimated at 1,500 pounds for all years, based on a personal communication with Kris Johnson (2010) and an estimate
from Holstein Association USA (2010).85 Dairy replacement weight at 15 months was assumed to be 875 pounds and
1,300 pounds at 24 months. Live slaughter weights were estimated from dressed slaughter weight (USDA 2021a) divided
by 0.63. This ratio represents the dressed weight (i.e., weight of the carcass after removal of the internal organs), to the
live weight (i.e., weight taken immediately before slaughter). The annual typical animal mass for each livestock type are
presented in Table A-136.
84 Mature beef weight is held constant after 2007 but future inventory submissions will incorporate known trends through 2007 and
extrapolate to future years, as noted in the Planned Improvements section of 5.1 Enteric Fermentation.
85 Mature dairy weight is based solely on Holstein weight, so could be higher than the national average. Future Inventory submissions
will consider other dairy breeds, as noted in the Planned Improvements section of 5.1 Enteric Fermentation.
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Weight gain for stocker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from average
daily estimates from Lippke et al. (2000), Pinchack et al. (2004), Platter et al. (2003), and Skogerboe et al. (2000) for
2000. Interim years were calculated linearly, as shown in Table A-137, and weight gain was held constant starting in
2000. Table A-137 provides weight gains that vary by year in the CEFM.
Table A-136: Typical Animal Mass (lbs)
Year/Cattle
Dairy
Dairy
Beef
Beef
Steer
Heifer
Steer
Heifer
Type
Calves
Cows3
Replacements'1
Bulls3
Cows3
Replacements'1
Stockersb
Stockersb Feedlotb
Feedlotb
1990
269
1499
899
1830
1220
819
691
651
923
845
1991
270
1499
897
1836
1224
821
694
656
933
855
1992
269
1499
897
1893
1262
840
714
673
936
864
1993
270
1499
898
1918
1279
852
721
683
929
863
1994
270
1499
897
1918
1279
853
720
688
943
875
1995
270
1499
897
1921
1281
857
735
700
947
879
1996
269
1499
898
1926
1284
858
739
707
939
878
1997
270
1499
899
1927
1285
860
736
707
938
876
1998
270
1499
896
1942
1295
865
736
709
956
892
1999
270
1499
899
1936
1291
861
730
708
959
894
2000
270
1499
896
1906
1271
849
719
702
960
898
2001
270
1499
897
1906
1271
850
725
707
963
900
2002
270
1499
896
1912
1275
851
725
707
981
915
2003
270
1499
899
1960
1307
871
718
701
972
904
2004
270
1499
896
1983
1322
877
719
702
966
904
2005
270
1499
894
1989
1326
879
717
706
974
917
2006
270
1499
897
2010
1340
889
724
712
983
925
2007
270
1499
896
2020
1347
894
720
706
991
928
2008
270
1499
897
2020
1347
894
720
704
999
938
2009
270
1499
895
2020
1347
894
730
715
1007
947
2010
270
1499
897
2020
1347
896
726
713
996
937
2011
270
1499
897
2020
1347
891
721
712
989
932
2012
270
1499
899
2020
1347
892
714
706
1003
945
2013
270
1499
898
2020
1347
892
718
709
1016
958
2014
270
1499
895
2020
1347
888
720
713
1021
960
2015
270
1499
896
2020
1347
890
717
714
1037
982
2016
270
1499
898
2020
1347
892
721
718
1047
991
2017
270
1499
896
2020
1347
894
714
709
1037
977
2018
270
1499
898
2020
1347
894
708
701
1030
972
2019
270
1499
897
2020
1347
893
710
698
1032
972
2020
271
1499
899
2020
1347
893
711
699
1046
984
a Input into the model.
b Annual average calculated in model based on age distribution.
Annex 3
A-273
-------
Table A-137: Weight Gains that Vary by Year (lbs)
Year/Cattle Type
Steer Stockers to 12
months(lbs/day)
Steer Stockers to 24
months (lbs/day)
Heifer Stockers to 12
months(lbs/day)
Heifer Stockers to 24
months(lbs/day)
1990
1.53
1.23
1.23
1.08
1991
1.56
1.29
1.29
1.15
1992
1.59
1.35
1.35
1.23
1993
1.62
1.41
1.41
1.30
1994
1.65
1.47
1.47
1.38
1995
1.68
1.53
1.53
1.45
1996
1.71
1.59
1.59
1.53
1997
1.74
1.65
1.65
1.60
1998
1.77
1.71
1.71
1.68
1999
1.80
1.77
1.77
1.75
2000-onwards
1.83
1.83
1.83
1.83
Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al.
(2003), Skogerboe et al. (2000).
Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the stocker
population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number of
animals from the stocker cohorts into the feedlot populations to match the reported placement data. After animals are
placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become acclimated
to the new feed type (e.g., more grain than forage, along with new dietary supplements), during this time weight gain is
estimated to be 2.7 to 3 pounds per day (Johnson 1999). Animals are then switched to a finishing diet (concentrated,
high energy) for a period of time before they are slaughtered. Weight gain during finishing diets is estimated to be 2.9 to
3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the start weight (i.e.,
placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target weight (as
determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are tracked for
inclusion in the following year's emission and population counts. For 1990 to 1995, only the total placement data were
available, therefore placements for each weight category (categories displayed in Table A-138) for those years are based
on the average of monthly placements from the 1996 to 1998 reported figures. Placement data is available by weight
class for all years from 1996 onward. Table A-138 provides a summary of the reported feedlot placement statistics for
2020.
Table A-138: Feedlot Placements in the United States for 2020 (Number of animals
placed/1,000 Head)
Weight
Placed When:
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
< 600 lbs
390
340
310
295
380
430
420
405
445
570
520
460
600-700 lbs
455
320
220
180
310
310
315
335
360
495
460
435
700-800 lbs
535
465
410
315
485
360
435
470
500
465
400
425
>800 lbs
575
591
617
642
877
698
723
847
922
662
523
524
Total
1,955
1,716
1,557
1,432
2,052
1,798
1,893
2,057
2,227
2,192
1,903
1,844
Note: Totals may not sum due to independent rounding.
Source: USDA (2021).
Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for particular
animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed on the
percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4 emissions. A
weighted average percent of pregnant cows each month was estimated using information on births by month and
average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was estimated
using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This process results
in a range of weighted monthly lactation estimates expressed as pounds per animal per month. The monthly estimates
for daily milk production by beef cows are shown in Table A-139. Annual estimates for dairy cows were taken from USDA
milk production statistics. Dairy lactation estimates for 1990 through 2020 are shown in Table A-140. Beef and dairy cow
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
and bull populations are assumed to remain relatively static throughout the year, as large fluctuations in population size
are assumed to not occur. These estimates are taken from the USDA beginning and end of year population datasets.
Table A-139: Estimates of Average Monthly Milk Production by Beef Cows (lbs/cow)
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
Beef Cow Milk Production
(lbs/head)
3.3
5.1
8.7
12.0
13.6
13.3
11.7
9.3
6.9
4.4
3.0
2.8
Table A-140: Dairy Lactation Rates by State (lbs/ year/cow)
State/Year
1990
2000
2005
2010
2016
2017
2018
2019
2020
Alabama
12,214
13,920
14,000
14,182
13,429
15,000
14,600
12,000
15,000
Alaska
13,300
14,500
12,273
11,833
11,667
9,667
9,333
4,455
5,333
Arizona
17,500
21,820
22,679
23,452
24,065
24,197
23,909
24,096
24,568
Arkansas
11,841
12,436
13,545
12,750
13,667
13,333
12,333
13,400
12,800
California
18,456
21,130
21,404
23,025
22,968
22,755
23,301
23,533
23,987
Colorado
17,182
21,618
22,577
23,664
25,654
25,858
25,892
25,844
26,142
Connecticut
15,606
17,778
19,200
19,158
21,526
22,105
22,474
22,526
23,053
Delaware
13,667
14,747
16,622
16,981
19,100
18,560
19,063
17,976
18,553
Florida
14,033
15,688
16,591
18,711
20,293
20,129
19,833
20,224
20,257
Georgia
12,973
16,284
17,259
17,658
21,786
21,905
21,277
21,598
21,877
Hawaii
13,604
14,358
12,889
13,316
14,542
16,913
16,950
4,455
5,333
Idaho
16,475
20,816
22,332
22,647
24,647
24,388
24,870
25,011
25,180
Illinois
14,707
17,450
18,827
18,400
20,340
20,742
20,867
20,810
21,530
Indiana
14,590
16,568
20,295
20,094
22,527
22,754
22,754
22,899
23,661
Iowa
15,118
18,298
20,641
20,676
23,634
23,757
23,955
24,271
24,651
Kansas
12,576
16,923
20,505
20,983
22,801
23,020
23,321
23,429
23,694
Kentucky
10,947
12,841
12,896
14,769
18,052
18,607
18,345
18,840
19,542
Louisiana
11,605
12,034
12,400
11,750
14,167
13,417
13,818
13,500
13,400
Maine
14,619
17,128
18,030
18,344
21,000
21,000
20,600
21,414
21,963
Maryland
13,461
16,083
16,099
18,537
20,021
19,917
20,556
19,535
20,905
Massachusetts
14,871
17,091
17,059
17,286
18,417
17,583
18,364
19,300
19,900
Michigan
15,394
19,017
21,635
23,277
25,957
26,302
26,409
26,725
27,170
Minnesota
14,127
17,777
18,091
19,366
20,967
21,544
21,784
22,147
22,705
Mississippi
12,081
15,028
15,280
13,118
14,300
15,222
14,333
15,750
16,375
Missouri
13,632
14,662
16,026
14,596
14,847
14,600
14,386
14,103
14,276
Montana
13,542
17,789
19,579
20,643
21,071
22,154
22,833
21,583
21,167
Nebraska
13,866
16,513
17,950
19,797
23,317
24,067
24,000
24,293
24,746
Nevada
16,400
19,000
21,680
23,714
22,000
22,156
22,938
23,091
24,677
New Hampshire
15,100
17,333
18,875
19,600
20,500
21,000
20,750
21,727
21,273
New Jersey
13,538
15,250
16,000
17,500
17,429
19,833
18,333
20,000
19,800
New Mexico
18,815
20,944
21,192
24,551
24,479
24,960
25,106
25,113
24,755
New York
14,658
17,378
18,639
20,807
23,834
23,925
23,888
24,118
24,500
North Carolina
15,220
16,746
18,741
19,682
20,978
21,156
21,295
21,476
21,829
North Dakota
12,624
14,292
14,182
18,286
21,500
21,563
22,267
21,733
21,867
Ohio
13,767
17,027
17,567
19,446
21,140
21,284
21,359
21,614
22,118
Oklahoma
12,327
14,440
16,480
17,125
18,703
18,200
18,125
17,829
17,452
Oregon
16,273
18,222
18,876
20,331
20,744
20,395
20,577
20,913
21,032
Pennsylvania
14,726
18,081
18,722
19,847
20,439
20,749
20,534
20,629
21,320
Rhode Island
14,250
15,667
17,000
17,727
17,625
16,250
16,429
17,667
21,800
South Carolina
12,771
16,087
16,000
17,875
16,667
16,533
17,286
17,167
18,900
South Dakota
12,257
15,516
17,741
20,478
22,139
22,376
22,364
22,480
23,111
Tennessee
11,825
14,789
15,743
16,346
16,571
17,325
17,135
17,219
18,067
Texas
14,350
16,503
19,646
21,375
22,585
23,406
23,948
24,513
24,926
Utah
15,838
17,573
18,875
21,898
22,989
23,073
23,220
23,061
23,198
Vermont
14,528
17,199
18,469
18,537
20,977
21,155
21,126
21,405
21,328
Annex 3
A-275
-------
Virginia
14,213
15,833
16,990
18,095
19,144
19,954
19,699
19,867
20,293
Washington
18,532
22,644
23,270
23,514
24,094
23,836
24,318
24,225
24,346
West Virginia
11,250
15,588
14,923
15,700
14,889
15,875
15,857
15,000
14,833
Wisconsin
13,973
17,306
18,500
20,630
23,542
23,735
24,002
24,123
24,408
Wyoming
12,337
13,571
14,878
20,067
23,300
23,033
23,700
24,433
25,173
Source: USDA(2021).
Step 2: Characterize U.S. Cattle Population Diets
To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and CH4
conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population categories,
data were collected on diets considered representative of different regions. For both grazing animals and animals being
fed mixed rations, representative regional diets were estimated using information collected from state livestock
specialists, the USDA, expert opinion, and other literature sources. The designated regions for this analysis for dairy
cattle for all years and foraging beef cattle from 1990 through 2006 are shown in Table A-141. For foraging beef cattle
from 2007 onwards, the regional designations were revised based on data available from the NAHMS 2007 through 2008
survey on cow-calf system management practices (USDA:APHIS:VS 2010) and are shown in Table A-142. The data for
each of the diets (e.g., proportions of different feed constituents, such as hay or grains) were used to determine feed
chemical composition for use in estimating DE and Ym for each animal type.
Table A-141: Regions used for Characterizing the Diets of Dairy Cattle (all years) and
West California
Northern
Midwestern Northeast
Southcentral
Southeast
Great Plains
Alaska California
Colorado
Illinois Connecticut
Arkansas
Alabama
Arizona
Kansas
Indiana Delaware
Louisiana
Florida
Hawaii
Montana
Iowa Maine
Oklahoma
Georgia
Idaho
Nebraska
Michigan Maryland
Texas
Kentucky
Nevada
North Dakota
Minnesota Massachusetts
Mississippi
New Mexico
South Dakota
Missouri New
North Carolina
Oregon
Wyoming
Ohio Hampshire
South Carolina
Utah
Wisconsin New Jersey
Tennessee
Washington
New York
Virginia
Pennsylvania
Rhode Island
Vermont
West Virginia
Source: USDA (1996).
Table A-142: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2020
West
Central
Northeast
Southeast
Alaska
Illinois
Connecticut
Alabama
Arizona
Indiana
Delaware
Arkansas
California
Iowa
Maine
Florida
Colorado
Kansas
Maryland
Georgia
Hawaii
Michigan
Massachusetts
Kentucky
Idaho
Minnesota
New Hampshire
Louisiana
Montana
Missouri
New Jersey
Mississippi
Nevada
Nebraska
New York
North Carolina
New Mexico
North Dakota
Pennsylvania
Oklahoma
Oregon
Ohio
Rhode Island
South Carolina
Utah
South Dakota
Vermont
Tennessee
Washington
Wisconsin
West Virginia
Texas
Wyoming
Virginia
Note: States in bold represent a change in region from the 1990 to 2006 assessment.
Source: Based on data from USDA:APHIS:VS (2010).
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle and 6.5+1.0
percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different regions and animal
types in the United States, DE and Ym values unique to the United States were developed for dairy and beef cattle.
Digestible energy and Ym values were estimated across the time series for each cattle population category based on
physiological modeling, published values, and/or expert opinion.
For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input required by the
models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final weight, age at
start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data used to simulate
ruminant digestion is provided for a particular animal that is then used to represent a group of animals with similar
characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999), which represents
physiological processes in the ruminant animals, as well as diet characteristics from USDA (1996). The Donovan and
Baldwin model is able to account for differing diets (i.e., grain-based or forage-based), so that Ym values for the variable
feeding characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review of dairy diets
was conducted and nearly 250 diets were analyzed from 1990 through 2009 across 23 states—the review indicated
highly variable diets, both temporally and spatially. Kebreab et al. (2008) conducted an evaluation of models and found
that the COWPOLL model was the best model for estimating Ymfor dairy, so COWPOLL was used to determine the Ym
value associated with each of the evaluated diets. The statistical analysis of the resulting Ym estimates showed a
downward trend in predicting Ym, which inventory team experts modeled using the following best-fit non-liner curve:
Equation A-22: Best Fit Curve for Estimating the Methane Conversion Rate for Dairy Cattle
The team determined that the most comprehensive approach to estimating annual, region-specific Ym values was to use
the 1990 baseline Ym values derived from Donovan and Baldwin and then scale these Ym values for each year beyond
1990 with a factor based on this function. The scaling factor is the ratio of the Ym value for the year in question to the
1990 baseline Ym value. The scaling factor for each year was multiplied by the baseline Ym value. The resulting Ym
equation (incorporating both Donovan and Baldwin (1999) and COWPOLL) is shown below (and described in ERG 2016):
Equation A-23: Scaling Factor for the Dairy Cattle Methane Conversion Rate
DE values for dairy cows were estimated from the literature search based on the annual trends observed in the data
collection effort. The regional variability observed in the literature search was not statistically significant, and therefore
DE was not varied by region, but did vary over time, and was grouped by the following years 1990 through 1993,1994
through 1998, 1999 through 2003, 2004 through 2006, 2007, and 2008 onwards.
Considerably less data was available for dairy heifers and dairy calves. Therefore, for dairy heifers assumptions were
based on the relationship of the collected data in the literature on dairy heifers to the data on dairy cow diets. From this
relationship, DE was estimated as the mature cow DE minus three percent, and Ym was estimated as that of the mature
dairy cow plus 0.1 percent.
To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted DE
values for a combination of forage and supplemental diets. The forage portion makes up an estimated 85 to 95 percent
of grazing beef cattle diets, and there is considerable variation of both forage type and quality across the United States.
Currently there is no comprehensive survey of this data, so for this analysis two regional DE values were developed to
account for the generally lower forage quality in the "West" region of the United States versus all other regions in Table
A-141 (California, Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-142(Central,
Northeast, and Southeast). For all non-western grazing cattle, the forage DE was an average of the estimated seasonal
values for grass pasture diets for a calculated DE of 64.2 percent. For foraging cattle in the west, the forage DE was
calculated as the seasonal average for grass pasture, meadow and range diets, for a calculated DE of 61.3 percent. The
assumed specific components of each of the broad forage types, along with their corresponding DE value and the
calculated regional DE values can be found in Table A-143. In addition, beef cattle are assumed to be fed a supplemental
diet, consequently, two sets of supplemental diets were developed, one for 1990 through 2006 (Donovan 1999) and one
for 2007 onwards (Preston 2010, Archibeque 2011, USDA:APHIS:VS 2010) as shown in Table A-144 and Table A-145 along
with the percent of each total diet that is assumed to be made up of the supplemental portion. By weighting the
Annex 3 A-277
Ym = 4.52e'>'ea:r-198oJ
-------
calculated DE values from the forage and supplemental diets, the DE values for the composite diet were calculated.86
These values are used for steer and heifer stockers and beef replacements. Finally, for mature beef cows and bulls, the
DE value was adjusted downward by two percent to reflect the lower digestibility diets of mature cattle based on
Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). The Ym values and
the resulting final weighted DE values by region for 2007 onwards are shown in Table A-146.
For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted based on new
and improved nutritional information and availability of feed types. Feedlot diets are assumed to not differ significantly
by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym values for 1990
were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were linearly extrapolated
based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were estimated using the
MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed compositions from Galyean
and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. In addition, feedlot
animals are assumed to spend the first 25 days in the feedlot on a "step-up" diet to become accustomed to the higher
quality feedlot diets. The step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE and Ym
values.
For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months assumed to be
25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6 months. The portion
of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For calves, the DE for the
remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both beef and dairy are
calculated separately). The Ym for beef calves is also assumed to be similar to that of beef replacement heifers (6.5
percent), as literature does not provide an alternative Ym for use in beef calves. For dairy calves, the Ym is assumed to be
7.8 percent at 4 months, 8.03 percent at 5 months, and 8.27 percent at 6 months based on estimates provided by Soliva
(2006) for Ym at 4 and 7 months of age and a linear interpolation for 5 and 6 months.
Table A-147 shows the regional DE and Ym for U.S. cattle in each region for 2020.
Table A-143: Feed Components and Digestible Energy Values Incorporated into Forage Diet
Composition Estimates
ai aj aj ^ =
.—£ £ £ « a> n
tn >- tn c .>¦ 2? ai.E'
•ft ® <¦ 3 3 < 5
o a. m a. £ a. -> -> ^ E o„o
s° u>cu>iu> ® " " i « a> -n -a
£v u!=u!Eu!= M M M an 2 m X c X
... (0 & 10 3 (0(0 C £ £ £ Q. £ gi 'C qj
Pnratro Tx/no » ^ (/) ^ V) ^ il (0 (0 (0 (0 S
Bahiagrass Paspalum notatum, fresh 61.38 x
Bermudagrass Cynodon dactylon, fresh 66.29 x
Bremudagrass, Coastal Cynodon
dactylon, fresh 65.53 x
Bluegrass, Canada Poa compressa, fresh,
early vegetative 73.99 x
Bluegrass, Kentucky Poa pratensis,
fresh, early vegetative 75.62 x
Bluegrass, Kentucky Poa pratensis,
fresh, mature 59.00 x x
Bluestem Andropagon spp, fresh, early
vegetative 73.17 x
Bluestem Andropagon spp, fresh,
mature 56.82 x x x x x
Brome Bromus spp, fresh, early
vegetative 78.57 x
Brome, Smooth Bromus inermis, fresh,
early vegetative 75.71 x
86 For example, the West has a forage DE of 61.3 which makes up 90 percent of the diet and a supplemented diet DE of 67.4 percent
was used for 10 percent of the diet, for a total weighted DE of 61.9 percent, as shown in Table A-146.
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
0)
0)
0)
LU
O
o
3
4-*
VI
to
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txo
3
4-*
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to
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0)
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to
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LU
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txO txO U) U) £» buOnC-
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i-orage lype Q cd.cd.cd. cc cc oc cg in oc S t/>
Brome, Smooth Bromus inermis, fresh,
mature 57.58 x x x
Buffalograss, Buchloe dactyloides, fresh 64.02 x x
Clover, Alsike Trifolium hybridum, fresh,
early vegetative 70.62 x
Clover, Ladino Trifolium repens, fresh,
early vegetative 73.22 x
Clover, Red Trifolium pratense, fresh,
early bloom 71.27 x
Clover, Red Trifolium pratense, fresh,
full bloom 67.44 x x
Corn, Dent Yellow Zea mays indentata,
aerial part without ears, without
husks, sun-cured, (stover)(straw) 55.28 x
Dropseed, Sand Sporobolus
cryptandrus, fresh, stem cured 64.69 x x x x
Fescue Festuca spp, hay, sun-cured,
early vegetative 67.39 x
Fescue Festuca spp, hay, sun-cured,
early bloom 53.57 x
Grama Bouteloua spp, fresh, early
vegetative 67.02 x
Grama Bouteloua spp, fresh, mature 63.38 x x x
Millet, Foxtail Setaria italica, fresh 68.20 x x
Napiergrass Pennisetum purpureum,
fresh, late bloom 57.24 x x
Needleandthread Stipa comata, fresh,
stem cured 60.36 x x x
Orchardgrass Dactylis glomerata, fresh,
early vegetative 75.54 x
Orchardgrass Dactylis glomerata, fresh,
midbloom 60.13 x
Pearlmillet Pennisetum glaucum, fresh 68.04 x
Prairie plants, Midwest, hay, sun-cured 55.53 x
Rape Brassica napus, fresh, early bloom 80.88 x
Rye Secale cereale, fresh 71.83 x
Ryegrass, Perennial Lolium perenne,
fresh 73.68 x
Saltgrass Distichlis spp, fresh, post ripe 58.06 x x
Sorghum, Sudangrass Sorghum bicolor
sudanense, fresh, early vegetative 73.27 x
Squirreltail Stanion spp, fresh, stem-
cured 62.00 x x
Summercypress, Gray Kochia vestita,
fresh, stem-cured 65.11 x x x
Timothy Phleum pratense, fresh, late
vegetative 73.12 x
Timothy Phleum pratense, fresh,
midbloom 66.87 x
Annex 3
A-279
-------
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Table A-145: DE Values and Representative Regional Diets for the Supplemental Diet of
Grazing Beef Cattle for 2007-2020
Feed
Source of DE
Unweighted
(NRC1984)
DE (% of GE)
West3
Central3
Northeast3
Southeast3
Alfalfa Hay
Table 8,
feed #006
61.79
65%
30%
12%
Bermuda
Table 8,
feed #030
66.29
20%
Bermuda Hay
Table 8,
feed #031
50.79
20%
Corn
Table 8,
feed #089
88.85
10%
15%
13%
10%
Corn Silage
Table 8,
feed #095
72.88
35%
20%
Grass Hay
Table 8,
feed #126, 170, 274
58.37
10%
Orchard
Table 8,
feed #147
60.13
30%
Protein supplement
(West)
Table 8,
feed #082, 134, 225b
81.01
10%
Protein Supplement
(Central and Northeast) Table 8,
feed #082, 134, 225b
80.76
10%
10%
Protein Supplement
(Southeast)
Table 8,
feed #082,134,101b
77.89
10%
Sorghum
Table 8,
feed #211
84.23
5%
10%
Timothy Hay
Table 8,
feed #244
60.51
45%
Wheat Middlings
Table 8,
feed #257
68.09
5%
Wheat
Table 8,
feed #259
87.95
5%
Weighted Supplement DE
67.4
73.1
68.9
66.6
Percent of Diet that is Supplement
10%
15%
5%
15%
a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the
table above.
b Not in equal proportions.
Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).
Table A-146: Foraging Animal DE (% of GE) and Ym Values for Each Region and Animal Type
for 2007-2020
Animal Type
Data
West3
Central
Northeast
Southeast
Beef Repl. Heifers
DEb
61.9
65.6
64.5
64.6
Y c
i m
6.5%
6.5%
6.5%
6.5%
Beef Calves (4-6 mo)
DE
61.9
65.6
64.5
64.6
Ym
6.5%
6.5%
6.5%
6.5%
Steer Stockers
DE
61.9
65.6
64.5
64.6
Ym
6.5%
6.5%
6.5%
6.5%
Heifer Stockers
DE
61.9
65.6
64.5
64.6
Ym
6.5%
6.5%
6.5%
6.5%
Beef Cows
DE
59.9
63.6
62.5
62.6
Ym
6.5%
6.5%
6.5%
6.5%
Bulls
DE
59.9
63.6
62.5
62.6
Ym
6.5%
6.5%
6.5%
6.5%
a Note that emissions are currently calculated on a state-by-state basis, but diets are
applied by the regions shown in the table above. To see the regional designation per
state, please see Table A-142.
b DE is the digestible energy in units of percent of GE (MJ/Day).
c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
Annex 3
A-281
-------
Table A-147: Regional DE (% of GE) and Ym Rates for Dairy and Feedlot Cattle by Animal
Type for 2020
Animal Type
Data
California3
West
Northern
Great Plains
Southcentral
Northeast
Midwest
Southeast
Dairy Repl. Heifers
DEb
63.7
63.7
63.7
63.7
63.7
63.7
63.7
Y c
i m
5.5%
5.5%
5.2%
5.9%
5.8%
5.2%
6.4%
Dairy Calves (4-6
mo)
DE
63.7
63.7
63.7
63.7
63.7
63.7
63.7
Ym
8.0%
8.0%
8.0%
8.0%
8.0%
8.0%
8.0%
Dairy Cows
DE
66.7
66.7
66.7
66.7
66.7
66.7
66.7
Ym
5.4%
5.4%
5.1%
5.8%
5.7%
5.1%
6.3%
Steer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5
Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
Heifer Feedlot
DE
82.5
82.5
82.5
82.5
82.5
82.5
82.5
Ym
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
3.9%
a Emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-141 by the regions shown in the
table above. To see the regional designation for foraging cattle per state, please see Table A-141.
b DE is the digestible energy in units of percent of GE (MJ/Day).
c Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
Step 3: Estimate CH4 Emissions from Cattle
Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC (2006)
equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily
emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate.
The necessary data values for each state and animal type include:
• Body Weight (kg)
• Weight Gain (kg/day)
• Net Energy for Activity (Ca, MJ/day)87
• Standard Reference Weight (kg)88
• Milk Production (kg/day)
• Milk Fat (percent of fat in milk)89
• Pregnancy (percent of population that is pregnant)
• DE (percent of GE intake digestible)
• Ym (the fraction of GE converted to CH4)
• Population
Step 3a: Determine Gross Energy, GE
As shown in the following equation, GE is derived based on the net energy estimates and the feed characteristics. Only
variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require the NEi factor). All
net energy equations are provided in IPCC (2006). Calculated GE values for 2020 are shown by state and animal type in
Table A-148.
87 Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain
grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the
year (IPCC 2006).
88 Standard Reference Weight is the mature weight of a female animal of the animal type being estimated, used in the model to
account for breed potential.
89 Average milk fat varies by year and is derived from USDA's Economic Research Service Dairy Data set (USDA 2021b).
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Equation A-24: Gross Energy Calculation for Enteric Fermentation
GE =
fNEm + NEa + NEt + NEworl +NEp] f NE„ \
REM
DE%
100
where,
GE = Gross energy (MJ/day)
NEm = Net energy required by the animal for maintenance (MJ/day)
NEa = Net energy for animal activity (MJ/day)
NEi = Net energy for lactation (MJ/day)
NEwork = Net energy for work (MJ/day)
NEP = Net energy required for pregnancy (MJ/day)
REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed
NEg = Net energy needed for growth (MJ/day)
REG = Ratio of net energy available for growth in a diet to digestible energy consumed
DE = Digestible energy expressed as a percent of gross energy (percent)
Annex 3
A-283
-------
Table A-148: Calculated Annual GE by Animal Type and State, for 2020 (GJ)
Dairy Dairy Beef Beef
Replace- Replace- Replace- Replace-
ment ment ment ment
Dairy
Dairy
Heifers 7-11
Heifers 12-
Beef
Heifers 7-11
Heifers 12-
Steer
Heifer
State
Calves
Cows
Months
23 Months
Bulls
Calves
Beef Cows
Months
23 Months
Stockers
Stockers
Feedlot
Alabama
18
488
27
97
4,166
3,121
56,024
1,431
3,950
1,024
1,055
320
Alaska
1
18
1
5
347
37
662
16
43
15
10
4
Arizona
870
30,802
1,537
5,573
1,779
939
16,671
324
890
6,735
388
12,037
Arkansas
22
552
40
145
5,165
4,104
73,653
1,804
4,979
2,616
1,679
666
California
7,661
265,333
10,160
36,828
5,336
3,171
56,285
1,557
4,274
15,185
5,428
25,489
Colorado
839
30,890
1,537
5,573
4,447
3,732
66,253
2,011
5,520
19,104
14,733
52,866
Conn.
87
2,947
127
460
42
25
444
24
67
34
36
11
Delaware
17
506
24
87
33
10
178
8
23
59
12
11
Florida
515
16,574
468
1,696
4,999
4,054
72,767
1,563
4,315
569
600
180
Georgia
360
12,097
401
1,454
2,666
2,328
41,777
1,082
2,987
978
720
263
Hawaii
3
41
13
48
356
365
6,471
143
392
245
129
54
Idaho
2,842
102,143
4,412
15,991
3,558
2,372
42,106
1,686
4,630
7,348
5,170
14,633
Illinois
364
11,892
602
2,181
1,629
1,652
29,746
703
1,944
5,324
2,104
11,328
Indiana
782
27,022
1,003
3,634
1,303
848
15,266
480
1,328
2,396
1,052
4,956
Iowa
955
33,860
1,537
5,573
4,887
3,954
71,217
1,699
4,698
28,841
14,029
60,891
Kansas
742
25,662
1,872
6,784
6,923
6,305
113,554
2,930
8,099
46,145
36,241
121,781
Kentucky
218
6,861
535
1,938
5,832
4,579
82,185
1,503
4,149
4,892
2,639
802
Louisiana
44
1,128
40
145
2,499
2,041
36,625
986
2,722
569
480
162
Maine
124
4,109
187
678
125
49
888
42
117
114
84
31
Maryland
187
5,986
348
1,260
292
211
3,794
133
366
262
192
283
Mass.
44
1,385
87
315
84
25
444
24
67
46
24
11
Michigan
1,896
71,540
2,340
8,480
1,222
406
7,318
270
745
3,882
748
7,788
Minn.
1,976
66,624
3,142
11,388
2,443
1,595
28,723
1,113
3,078
11,536
3,741
18,881
Miss.
36
1,019
67
242
3,249
2,162
38,799
1,082
2,987
1,320
1,031
363
Missouri
342
8,938
468
1,696
9,774
9,101
163,917
3,926
10,853
9,096
5,144
5,192
Montana
53
1,723
67
242
9,339
6,913
122,710
5,059
13,890
6,123
5,945
2,360
Nebraska
258
9,156
401
1,454
9,774
8,398
151,248
4,336
11,987
52,801
35,072
122,725
Nevada
138
4,885
134
485
1,334
1,205
21,397
571
1,567
980
853
142
N. Hamp.
49
1,584
87
315
42
18
323
16
43
34
17
8
N. Jersey
21
649
41
150
84
42
751
27
73
46
29
12
N. Mexico
1,466
52,107
1,872
6,784
2,668
2,324
41,247
1,038
2,849
2,449
2,068
648
New York
2,776
98,052
4,612
16,718
1,671
472
8,475
483
1,332
1,255
962
1,038
N. Car.
182
6,115
241
872
2,583
1,655
29,703
758
2,091
910
552
227
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Dairy
Dairy
Beef
Beef
Replace-
Replace-
Replace-
Replace-
ment
ment
ment
ment
Dairy
Dairy
Heifers 7-11
Heifers 12-
Beef
Heifers 7-11
Heifers 12-
Steer
Heifer
State
Calves
Cows
Months
23 Months
Bulls
Calves
Beef Cows
Months
23 Months
Stockers
Stockers
Feedlot
N. Dakota
67
2,196
107
388
5,701
4,347
78,299
2,133
5,896
6,101
5,378
1,982
Ohio
1,119
37,139
1,604
5,815
2,443
1,302
23,450
820
2,268
4,881
1,520
8,024
Oklahoma
182
5,289
267
969
14,163
9,458
169,764
4,450
12,281
22,980
12,713
16,049
Oregon
564
18,166
936
3,392
3,558
2,580
45,802
1,362
3,740
3,551
3,102
4,484
Penn.
2,132
69,210
3,676
13,326
1,671
990
17,758
724
1,997
3,081
1,323
4,720
R. Island
3
88
7
24
8
5
97
6
17
11
5
3
S. Car.
49
1,512
53
194
1,083
803
14,409
313
863
273
264
83
S. Dakota
564
19,220
602
2,181
8,959
7,790
140,310
4,219
11,663
15,751
10,755
20,769
Tenn.
138
4,158
334
1,211
4,999
4,077
73,170
1,503
4,149
3,527
2,039
863
Texas
2,576
92,122
3,743
13,568
29,160
20,495
367,861
9,621
26,553
60,293
37,179
140,662
Utah
431
14,714
668
2,423
2,224
1,733
30,764
1,103
3,027
1,959
1,551
944
Vermont
551
17,883
735
2,665
251
58
1,049
54
150
91
132
34
Virginia
329
10,584
441
1,599
3,333
2,807
50,390
1,118
3,087
3,754
1,535
944
Wash.
1,252
44,068
1,698
6,154
1,779
1,104
19,592
739
2,030
5,021
3,515
11,328
W. Virg.
27
710
40
145
1,253
895
16,063
410
1,132
1,004
529
189
Wisconsin
5,596
197,212
9,225
33,436
2,443
1,354
24,395
1,055
2,916
7,543
1,169
11,328
Wyoming
27
957
53
194
4,002
3,505
62,214
2,076
5,698
3,796
3,257
3,304
Annex 3
A-285
-------
Step 3b: Determine Emission Factor
The daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym) for each
category. This relationship is shown in the following equation:
Equation A-25: Daily Emission Factor for Enteric Fermentation Based on Gross Energy Intake
and Methane Conversion Factor
GEx Y
DayEmit = —
55.65
where,
DayEmit = Emission factor (kg CH4/head/day)
GE = Gross energy intake (MJ/head/day)
Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%)
55.65 = A factor for the energy content of methane (MJ/kg CH4)
The daily emission factors were estimated for each animal type and state. Calculated annual national emission factors
are shown by animal type in Table A-149. State-level emission factors are shown by animal type for 2020 in Table A-150.
Table A-149: Calculated Annual National Emission Factors for Cattle by Animal Type, for
2020 (kg CH4/head/year)
Cattle Type
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Dairy
Calves
12
12
12
12
12
12
12
12
12
12
Cows
121
122
129
130
138
144
145
147
148
150
Replacements
7-11 months
48
46
46
45
46
46
46
46
45
45
Replacements
12-23 months
73
69
70
67
69
69
69
69
69
69
Beef
Calves
11
11
11
11
11
11
11
11
11
11
Bulls
91
94
94
97
98
98
98
98
98
98
Cows
88
91
90
93
94
94
94
94
94
95
Replacements
7-11 months
54
57
56
59
60
60
60
60
60
60
Replacements
12-23 months
63
66
66
68
70
70
70
70
70
70
Steer Stockers
55
57
58
58
58
58
58
58
58
58
Heifer Stockers
52
56
60
60
60
60
60
60
60
60
Feedlot Cattle
39
38
39
39
42
44
43
43
43
43
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the
number of days in a year).
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-150: Emission Factors for Cattle by Animal Type and State, for 2020 (kg ClWhead/year)
State
Dairy
Calves
Dairy
Cows
Dairy
Replacement
Heifers 7-11
Months
Dairy
Replacement
Heifers 12-23
Months
Bulls
Beef
Calves
Beef
Cows
Beef
Replacement
Heifers 7-11
Months
Beef
Replacement
Heifers 12-23
Months
Steer
Stockers
Heifer
Stockers
Feedlot
Alabama
12
138
53
80
97
10
94
60
69
58
60
35
Alaska
12
57
45
69
104
11
100
64
74
62
65
32
Arizona
12
153
45
69
104
11
100
64
74
62
65
33
Arkansas
12
115
49
74
97
10
94
60
69
58
60
35
California
12
150
45
69
104
11
100
64
74
62
65
34
Colorado
12
150
43
65
104
11
100
64
74
62
65
34
Conn.
12
156
48
73
98
11
94
60
69
58
60
35
Delaware
12
137
48
73
98
11
94
60
69
58
60
35
Florida
12
162
53
80
97
10
94
60
69
58
60
36
Georgia
12
169
53
80
97
10
94
60
69
58
60
35
Hawaii
12
57
45
69
104
11
100
64
74
62
65
35
Idaho
12
155
45
69
104
11
100
64
74
62
65
34
Illinois
12
133
43
65
95
10
92
58
68
56
59
33
Indiana
12
141
43
65
95
10
92
58
68
56
59
34
Iowa
12
145
43
65
95
10
92
58
68
56
59
34
Kansas
12
141
43
65
95
10
92
58
68
56
59
34
Kentucky
12
158
53
80
97
10
94
60
69
58
60
34
Louisiana
12
118
49
74
97
10
94
60
69
58
60
34
Maine
12
151
48
73
98
11
94
60
69
58
60
36
Maryland
12
147
48
73
98
11
94
60
69
58
60
30
Mass.
12
143
48
73
98
11
94
60
69
58
60
35
Michigan
12
154
43
65
95
10
92
58
68
56
59
34
Minn.
12
138
43
65
95
10
92
58
68
56
59
33
Miss.
12
144
53
80
97
10
94
60
69
58
60
35
Missouri
12
107
43
65
95
10
92
58
68
56
59
34
Montana
12
132
43
65
104
11
100
64
74
62
65
35
Nebraska
12
145
43
65
95
10
92
58
68
56
59
33
Nevada
12
153
45
69
104
11
100
64
74
62
65
31
N. Hamp.
12
148
48
73
98
11
94
60
69
58
60
35
N. Jersey
12
142
48
73
98
11
94
60
69
58
60
35
N. Mexico
12
154
45
69
104
11
100
64
74
62
65
34
New York
12
162
48
73
98
11
94
60
69
58
60
34
N. Car.
12
169
53
80
97
10
94
60
69
58
60
36
N. Dakota
12
135
43
65
95
10
92
58
68
56
59
31
Annex 3
A-287
-------
Ohio
12
136
43
65
95
10
92
58
68
56
59
35
Oklahoma
12
135
49
74
97
10
94
60
69
58
60
34
Oregon
12
139
45
69
104
11
100
64
74
62
65
34
Penn.
12
148
48
73
98
11
94
60
69
58
60
34
R. Island
12
150
48
73
98
11
94
60
69
58
60
36
S. Car.
12
156
53
80
97
10
94
60
69
58
60
36
S. Dakota
12
139
43
65
95
10
92
58
68
56
59
34
Tenn.
12
152
53
80
97
10
94
60
69
58
60
36
Texas
12
166
49
74
97
10
94
60
69
58
60
34
Utah
12
148
45
69
104
11
100
64
74
62
65
33
Vermont
12
149
48
73
98
11
94
60
69
58
60
34
Virginia
12
162
53
80
97
10
94
60
69
58
60
36
Wash.
12
152
45
69
104
11
100
64
74
62
65
34
W. Virg.
12
122
48
73
98
11
94
60
69
58
60
34
Wisconsin
12
144
43
65
95
10
92
58
68
56
59
32
Wyoming
12
147
43
65
104
11
100
64
74
62
65
34
Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year).
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
For quality assurance purposes, U.S. emission factors for each animal type were compared to estimates provided by the
other Annex I member countries of the United Nations Framework Convention on Climate Change (UNFCCC) (the most
recently available summarized results for Annex I countries are through 2012 only). Results, presented in Table A-151,
indicate that U.S. emission factors are comparable to those of other Annex I countries. Results in Table A-151 are
presented along with Tier I emission factors provided by IPCC (2006). Throughout the time series, beef cattle in the
United States generally emit more enteric CH4 per head than other Annex I member countries, while dairy cattle in the
United States generally emit comparable enteric CH4 per head.
Table A-151: Annex I Countries' Implied Emission Factors for Cattle by Year (kg
cm/headn
fear)90
Dairy Cattle
Beef Cattle
Mean of Implied Emission
United States
Mean of Implied Emission
United States Implied
Factors for Annex 1 countries
Implied Emission
Factors for Annex 1 countries
Year
Emission Factor
(excluding U.S.)
Factor
(excluding U.S.)
1990
105
96
71
53
1991
105
97
71
53
1992
105
96
72
54
1993
104
97
72
54
1994
104
98
72
54
1995
104
98
72
54
1996
104
99
72
54
1997
104
100
72
54
1998
104
101
73
55
1999
108
102
72
55
2000
109
103
72
55
2001
108
104
72
55
2002
109
105
72
55
2003
109
106
73
55
2004
107
107
74
55
2005
108
109
74
55
2006
108
110
74
55
2007
112
111
74
55
2008
112
112
75
55
2009
112
112
75
56
2010
113
113
74
55
2011
113
113
74
55
2012
115
112
74
51
2013
115
NA
74
NA
2014
116
NA
74
NA
2015
115
NA
74
NA
2016
116
NA
74
NA
2017
117
NA
74
NA
2018
118
NA
74
NA
2019
119
NA
74
NA
2020
121
NA
74
NA
Tier 1 EFs For North America, from IPCC
(2006)
121
53
NA (Not Applicable)
90 Excluding calves.
Annex 3
A-289
-------
Step 3c: Estimate Total Emissions
Emissions were summed for each month and for each state population category using the daily emission factor for a
representative animal and the number of animals in the category. The following equation was used:
Equation A-26: Total Enteric Fermentation Emissions Calculated from Daily Emissions Rate
and Population
Emissionsstate = DayEmitstate x Days/Month x SubPopstate
where,
Emissionsstate = Emissions for state during the month (kg CH4)
DayEmitstate = Emission factor for the subcategory and state (kg CH4/head/day)
Days/Month = Number of days in the month
SubPopstate = Number of animals in the subcategory and state during the month
This process was repeated for each month, and the monthly totals for each state subcategory were summed to achieve
an emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The
estimates for each of the 10 subcategories of cattle are listed in Table A-152. The emissions for each subcategory were
then aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year.
Table A-152: Cm Emissions from Cattle (kt)
Cattle Type
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Dairy
1,547
1,471
1,492
1,473
1,594
1,700
1,715
1,737
1,732
1,744
Calves (4-6 months)
62
59
59
54
57
58
58
59
59
59
Cows
1,214
1,156
1,182
1,167
1,253
1,345
1,363
1,385
1,383
1,398
Replacements 7-11
months
58
56
55
56
62
65
65
64
63
62
Replacements 12-23
months
212
201
196
196
222
232
230
230
227
225
Beef
4,742
5,396
5,050
4,986
4,963
4,905
5,033
5,042
5,062
5,013
Bulls
196
225
215
214
215
210
220
221
221
219
Calves (4-6 months)
182
193
186
179
169
164
168
171
168
165
Cows
2,862
3,199
3,037
3,035
2,955
2,843
2,941
2,972
2,994
2,963
Replacements 7-11
months
69
85
74
80
75
91
89
86
83
82
Replacements 12-23
months
188
241
204
217
213
250
251
240
232
227
Steer Stockers
563
662
509
473
476
471
458
442
449
440
Heifer Stockers
306
375
323
299
302
288
284
277
271
267
Feedlot Cattle
375
416
502
488
560
587
621
633
644
649
Total
6,289
6,866
6,541
6,460
6,557
6,604
6,748
6,779
6,794
6,757
Note: Totals may not sum due to independent rounding.
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-153: Cm Emissions from Cattle (MMT CO2 Eg.)
Cattle Type
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Dairy
38.7
36.8
37.3
36.8
39.8
42.5
42.9
43.4
43.3
43.6
Calves (4-6 months)
1.5
1.5
1.5
1.4
1.4
1.4
1.5
1.5
1.5
1.5
Cows
30.4
28.9
29.5
29.2
31.3
33.6
34.1
34.6
34.6
34.9
Replacements 7-11
months
1.5
1.4
1.4
1.4
1.6
1.6
1.6
1.6
1.6
1.6
Replacements 12-23
months
5.3
5.0
4.9
4.9
5.5
5.8
5.7
5.7
5.7
5.6
Beef
118.5
134.9
126.2
124.7
124.1
122.6
125.8
126.0
126.5
125.3
Bulls
4.9
5.6
5.4
5.4
5.4
5.2
5.5
5.5
5.5
5.5
Calves (4-6 months)
4.6
4.8
4.7
4.5
4.2
4.1
4.2
4.3
4.2
4.1
Cows
71.6
80.0
75.9
75.9
73.9
71.1
73.5
74.3
74.9
74.1
Replacements 7-11
months
1.7
2.1
1.9
2.0
1.9
2.3
2.2
2.1
2.1
2.1
Replacements 12-23
months
4.7
6.0
5.1
5.4
5.3
6.2
6.3
6.0
5.8
5.7
Steer Stockers
14.1
16.6
12.7
11.8
11.9
11.8
11.5
11.0
11.2
11.0
Heifer Stockers
7.7
9.4
8.1
7.5
7.5
7.2
7.1
6.9
6.8
6.7
Feedlot Cattle
9.4
10.4
12.5
12.2
14.0
14.7
15.5
15.8
16.1
16.2
Total
157.2
171.7
163.5
161.5
163.9
165.1
168.7
169.5
169.8
168.9
Note: Totals may not sum due to independent rounding.
Emission Estimates from Other Livestock
"Other livestock" include horses, sheep, swine, goats, American bison, and mules and asses. All livestock population
data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture (USDA)
National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2021a) or the Census of Agriculture
(USDA 2019). The Manure Management Annex discusses the methods for obtaining annual average populations and
disaggregating into state data where needed and provides the resulting population data for the other livestock that were
used for estimating all livestock-related emissions (see Table A-155).For each animal category, the USDA publishes
monthly, annual, or multi-year livestock population and production estimates. American bison estimates prior to 2002
were estimated using data from the National Bison Association (1999).
Methane emissions from swine, horses, mules and asses were estimated by multiplying national population estimates by
the default IPCC emission factor (IPCC 2006). For sheep and goats, default national emission factors were updated to
reflect revisions made in the 2019 Refinement to the 2006 IPCC Guidelines. For American bison the emission factor for
buffalo (IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006) and 1,130
pounds (513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for
determining emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-154
shows the emission factors used for these other livestock. National enteric fermentation emissions from all livestock
types are shown in Table A-155 and Table A-156. Enteric fermentation emissions from most livestock types, broken
down by state, for 2020 are shown in Table A-157 through Table A-160. Livestock populations are shown in Table A-130.
Table A-154: Emission Factors for Other Livestock (kg ChU/head/year)
Livestock Type Emission Factor
Swine 1.5
Horses 18
Sheep 9
Goats 9
American Bison 82.2
Mules and Asses 10.0
Source: IPCC (2006), IPCC (2019), except
American Bison, as described in text.
Annex 3
A-291
-------
Table A-155: Cm Emissions from Enteric Fermentation (kt)
Livestock
Type
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Beef Cattle
4,742
5,396
5,050
4,986
4,963
4,905
5,033
5,042
5,062
5,013
Dairy Cattle
1,547
1,471
1,492
1,473
1,594
1,700
1,715
1,737
1,732
1,744
Swine
81
88
88
92
97
105
108
110
115
116
Horses
40
47
61
70
68
54
51
48
46
43
Sheep
102
81
63
55
51
48
47
47
47
47
Goats
23
21
22
26
26
24
24
24
25
25
American
Bison
4
9
16
17
15
15
15
15
16
16
Mules and
Asses
1
1
1
2
3
3
3
3
3
3
Total
6,539
7,114
6,793
6,722
6,816
6,853
6,998
7,028
7,046
7,007
Note: Totals may not sum due to independent rounding.
Table A-156: ChU Emissions from Enteric Fermentation (MMT CO2 Eq.)
Livestock
Type
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Beef Cattle
118.5
134.9
126.2
124.7
124.1
122.6
125.8
126.0
126.5
125.3
Dairy Cattle
38.7
36.8
37.3
36.8
39.8
42.5
42.9
43.4
43.3
43.6
Swine
2.0
2.2
2.2
2.3
2.4
2.6
2.7
2.8
2.9
2.9
Horses
1.0
1.2
1.5
1.7
1.7
1.4
1.3
1.2
1.1
1.1
Sheep
2.6
2.0
1.6
1.4
1.3
1.2
1.2
1.2
1.2
1.2
Goats
0.6
0.5
0.5
0.7
0.6
0.6
0.6
0.6
0.6
0.6
American
Bison
0.1
0.2
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Mules and
Asses
+
+
+
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Total
163.5
177.8
169.8
168.0
170.4
171.3
174.9
175.7
176.1
175.2
+ Does not exceed 0.05 MMT C02 Eq.
Note: Totals may not sum due to independent rounding.
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-157: Cm Emissions from Enteric Fermentation from Cattle (metric tons), by State, for 2020
Dairy
Beef
Beef
Replace-
Dairy
Replace
Replace
ment
Replace-
-ment
-ment
Heifers
ment
Heifers
Heifers
Dairy
Dairy
7-11
Heifers 12-
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
23 Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Total
Alabama
25
552
31
111
4,866
3,646
65,437
1,672
4,613
1,196
1,233
287
83,668
Alaska
2
17
1
5
405
44
773
18
50
17
12
4
1,348
Arizona
1,242
29,953
1,523
5,519
2,078
1,097
19,472
379
1,040
7,867
453
11,756
82,378
Arkansas
32
577
43
154
6,033
4,793
86,027
2,107
5,815
3,056
1,961
608
111,207
California
10,934
258,013
10,063
36,474
6,233
3,704
65,742
1,818
4,992
17,736
6,340
23,957
446,006
Colorado
1,198
28,417
1,442
5,227
5,194
4,360
77,385
2,349
6,448
22,314
17,209
49,093
220,634
Conn.
124
3,034
133
482
49
29
519
28
78
40
42
10
4,568
Delaware
24
521
25
91
39
12
207
10
27
69
14
10
1,050
Florida
735
18,753
538
1,949
5,839
4,735
84,993
1,826
5,040
664
700
160
125,933
Georgia
513
13,687
461
1,671
3,114
2,719
48,796
1,264
3,489
1,143
840
235
77,933
Hawaii
4
40
13
48
416
426
7,558
167
458
286
151
49
9,615
Idaho
4,057
99,325
4,369
15,837
4,155
2,771
49,181
1,970
5,408
8,582
6,038
13,632
215,325
Illinois
520
10,940
564
2,045
1,903
1,929
34,744
821
2,270
6,219
2,458
10,770
75,183
Indiana
1,116
24,859
940
3,409
1,522
990
17,831
561
1,551
2,799
1,229
4,618
61,425
Iowa
1,363
31,149
1,442
5,227
5,708
4,619
83,183
1,985
5,487
33,686
16,386
57,650
247,883
Kansas
1,059
23,608
1,755
6,363
8,086
7,364
132,633
3,422
9,460
53,898
42,330
113,201
403,179
Kentucky
311
7,763
615
2,228
6,812
5,348
95,994
1,756
4,846
5,714
3,082
746
135,213
Louisiana
63
1,178
43
154
2,919
2,383
42,779
1,152
3,179
664
560
150
55,226
Maine
177
4,232
196
711
146
58
1,037
49
136
133
98
27
7,001
Maryland
266
6,165
364
1,320
342
247
4,431
155
428
307
225
297
14,546
Mass.
63
1,427
91
330
98
29
519
28
78
53
28
10
2,753
Michigan
2,706
65,813
2,194
7,954
1,427
475
8,548
315
870
4,535
874
7,186
102,897
Minn.
2,821
61,290
2,947
10,681
2,854
1,863
33,549
1,300
3,595
13,475
4,370
17,883
156,626
Miss.
51
1,153
77
278
3,795
2,525
45,317
1,264
3,489
1,541
1,205
325
61,021
Missouri
488
8,222
439
1,591
11,416
10,630
191,458
4,585
12,676
10,624
6,008
4,790
262,928
Montana
76
1,585
63
227
10,908
8,075
143,327
5,910
16,224
7,152
6,944
2,123
202,613
Nebraska
368
8,423
376
1,363
11,416
9,809
176,660
5,064
14,001
61,672
40,964
117,089
447,205
Nevada
196
4,751
132
480
1,558
1,408
24,992
667
1,830
1,144
996
143
38,298
N. Hamp.
70
1,631
91
330
49
21
377
18
51
40
20
7
2,704
N. Jersey
30
669
43
157
98
49
877
31
86
53
34
10
2,136
N. Mexico
2,092
50,670
1,854
6,719
3,117
2,714
48,177
1,212
3,328
2,861
2,415
611
125,769
New York
3,961
100,975
4,833
17,517
1,952
552
9,900
564
1,555
1,466
1,124
978
145,376
Annex 3
A-293
-------
N. Car.
260
6,919
277
1,003
3,017
1,933
34,693
885
2,442
1,063
644
201
53,337
N. Dakota
95
2,020
100
364
6,659
5,078
91,455
2,491
6,887
7,126
6,281
2,037
130,593
Ohio
1,597
34,165
1,505
5,454
2,854
1,521
27,391
958
2,649
5,701
1,775
7,358
92,927
Oklahoma
260
5,525
284
1,030
16,543
11,048
198,286
5,197
14,344
26,840
14,849
15,016
309,222
Oregon
805
17,665
927
3,359
4,155
3,014
53,497
1,591
4,368
4,148
3,623
4,173
101,325
Penn.
3,042
71,273
3,852
13,963
1,952
1,156
20,742
845
2,333
3,599
1,546
4,446
128,749
R. Island
4
90
7
25
10
6
113
7
19
13
6
2
303
S. Car.
70
1,711
61
223
1,265
938
16,829
365
1,008
319
308
72
23,169
S. Dakota
805
17,681
564
2,045
10,465
9,099
163,884
4,927
13,622
18,398
12,562
19,411
273,465
Tenn.
196
4,705
384
1,392
5,839
4,762
85,463
1,756
4,846
4,119
2,381
763
116,607
Texas
3,676
96,227
3,977
14,417
34,059
23,939
429,667
11,237
31,014
70,423
43,426
130,184
892,247
Utah
615
14,308
662
2,400
2,597
2,024
35,932
1,288
3,536
2,289
1,811
919
68,381
Vermont
786
18,416
770
2,793
293
68
1,226
63
175
107
155
32
24,884
Virginia
469
11,975
507
1,838
3,892
3,279
58,856
1,306
3,605
4,385
1,793
839
92,746
Wash.
1,787
42,853
1,682
6,095
2,078
1,289
22,884
864
2,371
5,864
4,106
10,570
102,443
W. Virg.
38
731
42
152
1,464
1,046
18,762
479
1,322
1,173
618
178
26,005
Wisconsin
7,986
181,424
8,652
31,360
2,854
1,582
28,493
1,232
3,406
8,810
1,365
11,070
288,234
Wyoming
38
881
50
182
4,675
4,094
72,667
2,424
6,656
4,434
3,804
3,062
102,967
Table A-158:
CH4 Emissions from Enteric Fermentation from Cattle (MMT CO2 Eq.), by State, for 2020
Dairy
Beef
Beef
Replace-
Dairy
Replace
Replace-
ment
Replace-
-ment
ment
Heifers
ment
Heifers
Heifers
Dairy
Dairy
7-11
Heifers 12-
Beef
Beef
7-11
12-23
Steer
Heifer
State
Calves
Cows
Months
23 Months
Bulls
Calves
Cows
Months
Months
Stockers
Stockers
Feedlot
Total
Alabama
0.001
0.014
0.001
0.003
0.122
0.091
1.636
0.042
0.115
0.030
0.031
0.007
2.092
Alaska
0.000
0.000
0.000
0.000
0.010
0.001
0.019
0.000
0.001
0.000
0.000
0.000
0.034
Arizona
0.031
0.749
0.038
0.138
0.052
0.027
0.487
0.009
0.026
0.197
0.011
0.294
2.059
Arkansas
0.001
0.014
0.001
0.004
0.151
0.120
2.151
0.053
0.145
0.076
0.049
0.015
2.780
California
0.273
6.450
0.252
0.912
0.156
0.093
1.644
0.045
0.125
0.443
0.159
0.599
11.150
Colorado
0.030
0.710
0.036
0.131
0.130
0.109
1.935
0.059
0.161
0.558
0.430
1.227
5.516
Conn.
0.003
0.076
0.003
0.012
0.001
0.001
0.013
0.001
0.002
0.001
0.001
0.000
0.114
Delaware
0.001
0.013
0.001
0.002
0.001
0.000
0.005
0.000
0.001
0.002
0.000
0.000
0.026
Florida
0.018
0.469
0.013
0.049
0.146
0.118
2.125
0.046
0.126
0.017
0.018
0.004
3.148
Georgia
0.013
0.342
0.012
0.042
0.078
0.068
1.220
0.032
0.087
0.029
0.021
0.006
1.948
Hawaii
0.000
0.001
0.000
0.001
0.010
0.011
0.189
0.004
0.011
0.007
0.004
0.001
0.240
Idaho
0.101
2.483
0.109
0.396
0.104
0.069
1.230
0.049
0.135
0.215
0.151
0.341
5.383
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Illinois
0.013
0.274
0.014
0.051
0.048
Indiana
0.028
0.621
0.024
0.085
0.038
Iowa
0.034
0.779
0.036
0.131
0.143
Kansas
0.026
0.590
0.044
0.159
0.202
Kentucky
0.008
0.194
0.015
0.056
0.170
Louisiana
0.002
0.029
0.001
0.004
0.073
Maine
0.004
0.106
0.005
0.018
0.004
Maryland
0.007
0.154
0.009
0.033
0.009
Mass.
0.002
0.036
0.002
0.008
0.002
Michigan
0.068
1.645
0.055
0.199
0.036
Minn.
0.071
1.532
0.074
0.267
0.071
Miss.
0.001
0.029
0.002
0.007
0.095
Missouri
0.012
0.206
0.011
0.040
0.285
Montana
0.002
0.040
0.002
0.006
0.273
Nebraska
0.009
0.211
0.009
0.034
0.285
Nevada
0.005
0.119
0.003
0.012
0.039
N. Hamp.
0.002
0.041
0.002
0.008
0.001
N.Jersey
0.001
0.017
0.001
0.004
0.002
N. Mexico
0.052
1.267
0.046
0.168
0.078
New York
0.099
2.524
0.121
0.438
0.049
N. Car.
0.006
0.173
0.007
0.025
0.075
N. Dakota
0.002
0.050
0.003
0.009
0.166
Ohio
0.040
0.854
0.038
0.136
0.071
Oklahoma
0.006
0.138
0.007
0.026
0.414
Oregon
0.020
0.442
0.023
0.084
0.104
Penn.
0.076
1.782
0.096
0.349
0.049
R. Island
0.000
0.002
0.000
0.001
0.000
S. Car.
0.002
0.043
0.002
0.006
0.032
S. Dakota
0.020
0.442
0.014
0.051
0.262
Tenn.
0.005
0.118
0.010
0.035
0.146
Texas
0.092
2.406
0.099
0.360
0.851
Utah
0.015
0.358
0.017
0.060
0.065
Vermont
0.020
0.460
0.019
0.070
0.007
Virginia
0.012
0.299
0.013
0.046
0.097
Wash.
0.045
1.071
0.042
0.152
0.052
W. Virg.
0.001
0.018
0.001
0.004
0.037
Wisconsin
0.200
4.536
0.216
0.784
0.071
Wyoming
0.001
0.022
0.001
0.005
0.117
Annex 3
0.048
0.869
0.021
0.057
0.155
0.061
0.269
1.880
0.025
0.446
0.014
0.039
0.070
0.031
0.115
1.536
0.115
2.080
0.050
0.137
0.842
0.410
1.441
6.197
0.184
3.316
0.086
0.237
1.347
1.058
2.830
10.079
0.134
2.400
0.044
0.121
0.143
0.077
0.019
3.380
0.060
1.069
0.029
0.079
0.017
0.014
0.004
1.381
0.001
0.026
0.001
0.003
0.003
0.002
0.001
0.175
0.006
0.111
0.004
0.011
0.008
0.006
0.007
0.364
0.001
0.013
0.001
0.002
0.001
0.001
0.000
0.069
0.012
0.214
0.008
0.022
0.113
0.022
0.180
2.572
0.047
0.839
0.033
0.090
0.337
0.109
0.447
3.916
0.063
1.133
0.032
0.087
0.039
0.030
0.008
1.526
0.266
4.786
0.115
0.317
0.266
0.150
0.120
6.573
0.202
3.583
0.148
0.406
0.179
0.174
0.053
5.065
0.245
4.416
0.127
0.350
1.542
1.024
2.927
11.180
0.035
0.625
0.017
0.046
0.029
0.025
0.004
0.957
0.001
0.009
0.000
0.001
0.001
0.000
0.000
0.068
0.001
0.022
0.001
0.002
0.001
0.001
0.000
0.053
0.068
1.204
0.030
0.083
0.072
0.060
0.015
3.144
0.014
0.247
0.014
0.039
0.037
0.028
0.024
3.634
0.048
0.867
0.022
0.061
0.027
0.016
0.005
1.333
0.127
2.286
0.062
0.172
0.178
0.157
0.051
3.265
0.038
0.685
0.024
0.066
0.143
0.044
0.184
2.323
0.276
4.957
0.130
0.359
0.671
0.371
0.375
7.731
0.075
1.337
0.040
0.109
0.104
0.091
0.104
2.533
0.029
0.519
0.021
0.058
0.090
0.039
0.111
3.219
0.000
0.003
0.000
0.000
0.000
0.000
0.000
0.008
0.023
0.421
0.009
0.025
0.008
0.008
0.002
0.579
0.227
4.097
0.123
0.341
0.460
0.314
0.485
6.837
0.119
2.137
0.044
0.121
0.103
0.060
0.019
2.915
0.598
10.742
0.281
0.775
1.761
1.086
3.255
22.306
0.051
0.898
0.032
0.088
0.057
0.045
0.023
1.710
0.002
0.031
0.002
0.004
0.003
0.004
0.001
0.622
0.082
1.471
0.033
0.090
0.110
0.045
0.021
2.319
0.032
0.572
0.022
0.059
0.147
0.103
0.264
2.561
0.026
0.469
0.012
0.033
0.029
0.015
0.004
0.650
0.040
0.712
0.031
0.085
0.220
0.034
0.277
7.206
0.102
1.817
0.061
0.166
0.111
0.095
0.077
2.574
A-295
-------
Table A-159: ChU Emissions from Enteric Fermentation from Other Livestock (metric tons),
by State, for 2020
State
Swine
Horses
Sheep
Goats
American
Bison
Mules and
Asses
Total
Alabama
18
744
164
454
8
109
1,496
Alaska
3
25
6
8
121
0
163
Arizona
212
1,252
945
492
8
26
2,935
Arkansas
189
645
150
303
11
74
1,373
California
149
1,329
5,130
1,164
111
53
7,936
Colorado
1,009
1,618
3,825
516
933
60
7,960
Connecticut
6
140
47
56
39
10
297
Delaware
11
54
12
11
15
1
103
Florida
20
1,303
163
600
5
116
2,206
Georgia
57
731
165
623
6
110
1,692
Hawaii
15
76
181
163
7
3
445
Idaho
48
732
2,070
310
2,275
27
5,462
Illinois
8,006
574
495
352
59
46
9,531
Indiana
6,488
1,194
513
384
42
43
8,664
Iowa
36,825
792
1,359
869
223
33
40,101
Kansas
3,158
730
657
476
426
43
5,489
Kentucky
690
1,912
558
515
185
120
3,981
Louisiana
9
612
87
171
6
63
949
Maine
8
117
105
50
18
4
301
Maryland
28
486
155
141
4
20
834
Massachusetts
14
200
103
63
1
13
394
Michigan
1,853
899
765
275
264
38
4,093
Minnesota
13,688
633
1,035
341
228
33
15,958
Mississippi
165
532
111
309
20
83
1,219
Missouri
5,644
1,197
900
547
59
112
8,458
Montana
308
1,211
1,800
150
1,796
32
5,297
Nebraska
5,625
705
702
279
2,547
18
9,875
Nevada
4
164
585
77
0
5
834
New Hampshire
6
104
64
35
25
5
238
New Jersey
11
374
118
112
3
15
634
New Mexico
2
701
855
337
396
26
2,316
New York
104
1,002
783
242
94
29
2,254
North Carolina
13,838
755
270
473
19
116
15,470
North Dakota
215
358
675
70
1,123
9
2,450
Ohio
4,069
1,566
1,134
580
88
81
7,517
Oklahoma
3,229
1,899
468
912
70
173
6,751
Oregon
14
1,083
1,485
474
179
41
3,276
Pennsylvania
2,096
1,249
864
487
100
96
4,892
Rhode Island
2
31
14
9
-
1
58
South Carolina
28F2
649
84
377
3
63
1,458
South Dakota
3,053
773
2,250
168
2,291
21
8,555
Tennessee
390
1,461
441
914
29
194
3,429
Texas
1,613
5,249
6,615
7,319
772
927
22,494
Utah
1,493
888
2,565
200
82
14
5,241
Vermont
6
119
135
84
14
0
359
Virginia
465
951
657
430
45
81
2,629
Washington
26
820
450
277
81
31
1,684
West Virginia
5
390
297
234
9
39
974
Wisconsin
600
1,039
729
1,116
566
37
4,088
Wyoming
143
822
3,060
154
811
34
5,024
Indicates there are no emissions, as there is no significant population of this animal type.
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-160: ChU Emissions from Enteric Fermentation from Other Livestock (MMT CO2 Eq.),
by State, for 2020
American Mules and
State
Swine
Horses
Sheep
Goats
Bison
Asses
Total
Alabama
0.0005
0.0186
0.0041
0.0114
0.0002
0.0027
0.0374
Alaska
0.0001
0.0006
0.0001
0.0002
0.0030
0.0000
0.0041
Arizona
0.0053
0.0313
0.0236
0.0123
0.0002
0.0006
0.0734
Arkansas
0.0047
0.0161
0.0038
0.0076
0.0003
0.0018
0.0343
California
0.0037
0.0332
0.1283
0.0291
0.0028
0.0013
0.1984
Colorado
0.0252
0.0404
0.0956
0.0129
0.0233
0.0015
0.1990
Connecticut
0.0002
0.0035
0.0012
0.0014
0.0010
0.0002
0.0074
Delaware
0.0003
0.0013
0.0003
0.0003
0.0004
0.0000
0.0026
Florida
0.0005
0.0326
0.0041
0.0150
0.0001
0.0029
0.0551
Georgia
0.0014
0.0183
0.0041
0.0156
0.0001
0.0028
0.0423
Hawaii
0.0004
0.0019
0.0045
0.0041
0.0002
0.0001
0.0111
Idaho
0.0012
0.0183
0.0518
0.0077
0.0569
0.0007
0.1366
Illinois
0.2002
0.0144
0.0124
0.0088
0.0015
0.0011
0.2383
Indiana
0.1622
0.0298
0.0128
0.0096
0.0010
0.0011
0.2166
Iowa
0.9206
0.0198
0.0340
0.0217
0.0056
0.0008
1.0025
Kansas
0.0789
0.0182
0.0164
0.0119
0.0106
0.0011
0.1372
Kentucky
0.0173
0.0478
0.0140
0.0129
0.0046
0.0030
0.0995
Louisiana
0.0002
0.0153
0.0022
0.0043
0.0002
0.0016
0.0237
Maine
0.0002
0.0029
0.0026
0.0013
0.0005
0.0001
0.0075
Maryland
0.0007
0.0122
0.0039
0.0035
0.0001
0.0005
0.0209
Massachusetts
0.0003
0.0050
0.0026
0.0016
0.0000
0.0003
0.0098
Michigan
0.0463
0.0225
0.0191
0.0069
0.0066
0.0010
0.1023
Minnesota
0.3422
0.0158
0.0259
0.0085
0.0057
0.0008
0.3989
Mississippi
0.0041
0.0133
0.0028
0.0077
0.0005
0.0021
0.0305
Missouri
0.1411
0.0299
0.0225
0.0137
0.0015
0.0028
0.2115
Montana
0.0077
0.0303
0.0450
0.0038
0.0449
0.0008
0.1324
Nebraska
0.1406
0.0176
0.0176
0.0070
0.0637
0.0004
0.2469
Nevada
0.0001
0.0041
0.0146
0.0019
0.0000
0.0001
0.0209
New Hampshire
0.0001
0.0026
0.0016
0.0009
0.0006
0.0001
0.0060
New Jersey
0.0003
0.0094
0.0030
0.0028
0.0001
0.0004
0.0158
New Mexico
0.0000
0.0175
0.0214
0.0084
0.0099
0.0006
0.0579
New York
0.0026
0.0250
0.0196
0.0061
0.0024
0.0007
0.0564
North Carolina
0.3459
0.0189
0.0068
0.0118
0.0005
0.0029
0.3867
North Dakota
0.0054
0.0090
0.0169
0.0017
0.0281
0.0002
0.0613
Ohio
0.1017
0.0392
0.0284
0.0145
0.0022
0.0020
0.1879
Oklahoma
0.0807
0.0475
0.0117
0.0228
0.0017
0.0043
0.1688
Oregon
0.0003
0.0271
0.0371
0.0119
0.0045
0.0010
0.0819
Pennsylvania
0.0524
0.0312
0.0216
0.0122
0.0025
0.0024
0.1223
Rhode Island
0.0001
0.0008
0.0003
0.0002
-
0.0000
0.0014
South Carolina
0.0071
0.0162
0.0021
0.0094
0.0001
0.0016
0.0364
South Dakota
0.0763
0.0193
0.0563
0.0042
0.0573
0.0005
0.2139
Tennessee
0.0098
0.0365
0.0110
0.0229
0.0007
0.0048
0.0857
Texas
0.0403
0.1312
0.1654
0.1830
0.0193
0.0232
0.5624
Utah
0.0373
0.0222
0.0641
0.0050
0.0020
0.0003
0.1310
Vermont
0.0002
0.0030
0.0034
0.0021
0.0004
0.0000
0.0090
Virginia
0.0116
0.0238
0.0164
0.0108
0.0011
0.0020
0.0657
Washington
0.0006
0.0205
0.0113
0.0069
0.0020
0.0008
0.0421
West Virginia
0.0001
0.0098
0.0074
0.0058
0.0002
0.0010
0.0243
Wisconsin
0.0150
0.0260
0.0182
0.0279
0.0142
0.0009
0.1022
Wyoming
0.0036
0.0206
0.0765
0.0039
0.0203
0.0009
0.1256
Indicates there are no emissions, as there is no significant population of this animal type.
Annex 3
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A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Archibeque, S. (2011) Personal Communication. Shawn Archibeque, Colorado State University, Fort Collins, Colorado and
staff at ICF International.
Crutzen, P.J., I. Aselmann, and W. Seiler (1986) Methane Production by Domestic Animals, Wild Ruminants, Other
Herbivores, Fauna, and Humans. Tellus, 38B:271-284.
Donovan, K. (1999) Personal Communication. Kacey Donovan, University of California at Davis and staff at ICF
International.
Doren, P.E., J. F. Baker, C. R. Long and T. C. Cartwright (1989) Estimating Parameters of Growth Curves of Bulls, J Animal
Science 67:1432-1445.
Enns, M. (2008) Personal Communication. Dr. Mark Enns, Colorado State University and staff at ICF International.
ERG (2016) Development of Methane Conversion Rate Scaling Factor and Diet-Related Inputs to the Cattle Enteric
Fermentation Model for Dairy Cows, Dairy Heifers, and Feedlot Animals. ERG, Lexington, MA. December 2016.
Galyean and Gleghorn (2001) Summary of the 2000 Texas Tech University Consulting Nutritionist Survey. Texas Tech
University. Available online at http://www.depts.ttu.edu/afs/burnett_center/progress_reports/bcl2.pdf. June 2009.
Holstein Association (2010) History of the Holstein Breed (website). Available online at
http://www.holsteinusa.com/holstein_breed/breedhistory.html. Accessed September 2010.
ICF (2006) Cattle Enteric Fermentation Model: Model Documentation. Prepared by ICF International for the
Environmental Protection Agency. June 2006.
ICF (2003) Uncertainty Analysis of 2001 Inventory Estimates of Methane Emissions from Livestock Enteric Fermentation
in the U.S. Memorandum from ICF International to the Environmental Protection Agency. May 2003.
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Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.
Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom 996 pp.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
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Johnson, D. (2002) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and ICF International.
Johnson, D. (1999) Personal Communication. Don Johnson, Colorado State University, Fort Collins, and David Conneely,
ICF International.
Johnson, K. (2010) Personal Communication. Kris Johnson, Washington State University, Pullman, and ICF International.
Kebreab E., K. A. Johnson, S. L. Archibeque, D. Pape, and T. Wirth (2008) Model for estimating enteric methane emissions
from United States dairy and feedlot cattle. J. Anim. Sci. 86: 2738-2748.
Lippke, H., T. D. Forbes, and W. C. Ellis. (2000) Effect of supplements on growth and forage intake by stocker steers
grazing wheat pasture. J. Anim. Sci. 78:1625-1635.
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http://www.bisoncentral.com/about-bison/handling-and-carcass-info. Accessed August 16, 2011.
National Bison Association (1999) Total Bison Population—1999. Report provided during personal email communication
with Dave Carter, Executive Director, National Bison Association July 19, 2011.
NRC (1999) 1996 Beef NRC: Appendix Table 22. National Research Council.
NRC (1984) Nutrient requirements for beef cattle (6th Ed.). National Academy Press, Washington, D.C.
Annex 3
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Pinchak, W.E., D. R. Tolleson, M. McCloy, L. J. Hunt, R. J. Gill, R. J. Ansley, and S. J. Bevers (2004) Morbidity effects on
productivity and profitability of stocker cattle grazing in the southern plains. J. Anim. Sci. 82:2773-2779.
Platter, W. J., J. D. Tatum, K. E. Belk, J. A. Scanga, and G. C. Smith (2003) Effects of repetitive use of hormonal implants on
beef carcass quality, tenderness, and consumer ratings of beef palatability. J. Anim. Sci. 81:984-996.
Preston, R.L. (2010) What's The Feed Composition Value of That Cattle Feed? Beef Magazine, March 1, 2010. Available
at: http://beefmagazine.com/nutrition/feed-composition-tables/feed-composition-value-cattle--0301.
Skogerboe, T. L., L. Thompson, J. M. Cunningham, A. C. Brake, V. K. Karle (2000) The effectiveness of a single dose of
doramectin pour-on in the control of gastrointestinal nematodes in yearling stocker cattle. Vet. Parasitology 87:173-181.
Soliva, C.R. (2006) Report to the attention of IPCC about the data set and calculation method used to estimate methane
formation from enteric fermentation of agricultural livestock population and manure management in Swiss agriculture.
On behalf of the Federal Office for the Environment (FOEN), Berne, Switzerland.
U.S. Department of Agriculture (USDA) (2021a) Quick Stats: Agricultural Statistics Database. National Agriculture
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USDA (2021b) Economic Research Service Dairy Data. Available online at: https://www.ers.usda.gov/data-
products/dairy-data/. Accessed May 2021.
USDA (2019) 1987, 1992, 1997, 2002, 2007, 2012, and 2017 Census of Agriculture. National Agriculture Statistics Service,
U.S. Department of Agriculture. Washington, D.C. Available online at: https://www.nass.usda.gov/AgCensus/index.php.
May 2019.
USDA (1996) Beef Cow/Calf Health and Productivity Audit (CHAPA): Forage Analyses from Cow/Calf Herds in 18 States.
National Agriculture Statistics Service, U.S. Department of Agriculture. Washington, D.C. Available online at
http://www.aphis.usda.gov/vs/ceah/cahm. March 1996.
USDA:APHIS:VS (2010) Beef 2007-08, Part V: Reference of Beef Cow-calf Management Practices in the United States,
2007-08. USDA-APHIS-VS, CEAH. Fort Collins, CO.
USDA:APHIS:VS (2002) Reference of 2002 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
Available online at http://www.aphis.usda.gov/vs/ceah/cahm.
USDA:APHIS:VS (1998) Beef'97, Parts l-IV. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available online at
http://www.aphis.usda.gOv/animal_health/nahms/beefcowcalf/index.shtml#beef97.
USDA:APHIS:VS (1996) Reference of 1996 Dairy Management Practices. USDA-APHIS-VS, CEAH. Fort Collins, CO.
Available online at http://www.aphis.usda.gov/vs/ceah/cahm.
USDA:APHIS:VS (1994) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. Available
online at http://www.aphis.usda.gov/vs/ceah/cahm.
USDA:APHIS:VS (1993) Beef Cow/Calf Health and Productivity Audit. USDA-APHIS-VS, CEAH. Fort Collins, CO. August
1993. Available online at http://www.aphis.usda.gov/vs/ceah/cahm.
Vasconcelos and Galyean (2007) Nutritional recommendations of feedlot consulting nutritionists: The 2007 Texas Tech
University Study. J. Anim. Sci. 85:2772-2781.
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3.11. Methodology for Estimating CH4 and N2O Emissions from
Manure Management137
The following steps were used to estimate methane (CH4) and nitrous oxide (N20) emissions from the management of
livestock manure for the years 1990 through 2020.
Step 1: Livestock Population Characterization Data
Annual animal population data for 1990 through 2020 for all livestock types, except American bison, goats, horses, mules
and asses were obtained from the USDA NASS. The population data used in the emissions calculations for cattle, swine,
and sheep were downloaded from the USDA NASS Quick Stats Database (USDA 2021). Poultry population data were
obtained from USDA NASS reports (USDA 1995a, 1995b, 1998, 1999, 2004a, 2004b, 2009a, 2009b, 2009c, 2009d, 2010a,
2010b, 2011a, 2011b, 2012a, 2012b, 2013a, 2013b, 2014, 2015, 2016, 2017, 2018a, 2018b, 2019a, 2019b, 2019c, 2021b,
and 2021c). Goat population data for 1992,1997, 2002, 2007, 2012, and 2017 were obtained from the Census of
Agriculture (USDA 2019d), as were horse, mule and ass population data for 1987,1992,1997, 2002, 2007, 2012, and
2017 and American bison population for 2002, 2007, 2012, and 2017. American bison population data for 1990-1999
were obtained from the National Bison Association (1999). Additional data sources used and adjustments to these data
sets are described below.
Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories from
January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the year,
sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered;
therefore, to develop the best estimate for the annual animal population, the populations and the individual
characteristics, such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle
Enteric Fermentation Model (CEFM—see section 5.1 Enteric Fermentation). For animals that have relatively static
populations throughout the year, such as mature cows and bulls, the January 1 values were used. For animals that have
fluctuating populations throughout the year, such as calves and growing heifers and steer, the populations are modeled
based on a transition matrix that uses annual population data from USDA along with USDA data on animal births,
placement into feedlots, and slaughter statistics.
Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under 23 kg),
market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over
(greater than 82 kg). The average of the quarterly data was used in the emission calculations. For states where only
December inventory is reported, the December data were used directly.
Sheep: The USDA provides total state-level data annually for lambs and sheep. Population distribution data for lambs
and sheep on feed are not available after 1993 (USDA 1994). The number of lambs and sheep on feed for 1994 through
2020 were calculated using the average of the percent of lambs and sheep on feed from 1990 through 1993. In addition,
all of the sheep and lambs "on feed" are not necessarily on "feedlots;" they may be on pasture/crop residue
supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop residue were provided
only for lamb in 1993. To calculate the populations of sheep and lambs in feedlots for all years, it was assumed that the
percentage of sheep and lambs on feed that are in feedlots versus pasture/crop residue is the same as that for lambs in
1993 (Anderson 2000).
Goats: Annual goat population data by state were available for 1992, 1997, 2002, 2007, 2012, and 2017 (USDA 2019d).
The data for 1992 were used for 1990 through 1992. Data for 1993 through 1996, 1998 through 2001, 2003 through
137 Note that direct N20 emissions from dung and urine spread onto fields either directly as daily spread or after it is removed
from manure management systems (e.g., lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or
paddock lands are accounted for and discussed in the Agricultural Soil Management source category within the Agriculture
sector. Indirect N20 emissions dung and urine spread onto fields after it is removed from manure management systems (e.g.,
lagoon, pit, etc.) and from livestock dung and urine deposited on pasture, range, or paddock lands are also included in the
Agricultural Soil Management source category. EPA is aware that there are minor differences in the PRP manure N data used in
Agricultural Soil Management and Manure Management across the time series which are reflected in CRF tables and will be
updated in the subsequent Inventory.
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2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the 1992, 1997, 2002, 2007, 2012, and
2017 Census data. Data for 2018 through 2020 were extrapolated based on 2017 Census data.
Horses: Annual horse population data by state were available for 1987,1992,1997, 2002, 2007, 2012, and 2017 (USDA
2019d). Data for 1990 through 1991, 1993 through 1996,1998 through 2001, 2003 through 2006, 2008 through 2011,
and 2013 through 2016 were interpolated based on the 1987, 1992, 1997, 2002, 2007, 2012, and 2017 Census data. Data
for 2018 through 2020 were extrapolated based on 2017 Census data.
Mules and Asses: Annual mule and ass (burro and donkey) population data by state were available for 1987,1992,1997,
2002, 2007, 2012, and 2017 (USDA 2019d). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003
through 2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the 1987, 1992, 1997, 2002,
2007, 2012, and 2017 Census data. Data for 2018 through 2020 were extrapolated based on 2017 Census data.
American Bison: Annual American bison population data by state were available for 2002, 2007, 2012, and 2017 (USDA
2019d). Data for 1990 through 1999 were obtained from the Bison Association (1999). Data for 2000, 2001, 2003
through 2006, 2008 through 2011, and 2013 through 2016 were interpolated based on the Bison Association and 2002,
2007, 2012, and 2017 Census data. Data for 2018 through 2020 were extrapolated based on 2017 Census data.
Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one year old),
other chickens, and production (slaughter) data for broilers and turkeys (USDA 1995a, 1995b, 1998,1999, 2004a, 2004b,
2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013a, 2013b, 2014a, 2014b, 2015a, 2015b,
2016a, 2016b, 2017a, 2017b, 2018a, 2018b, 2019b, 2019c, 2021b, and 2021c). All poultry population data were adjusted
to account for states that report non-disclosed populations to USDA NASS. The combined populations of the states
reporting non-disclosed populations are reported as "other" states. State populations for the non-disclosed states were
estimated by using Census of Agriculture data to provide a ratio of the non-disclosed state population to the "other"
states' total population (ERG 2021).
Because only production data are available for broilers and turkeys, population data are calculated by dividing the
number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal
communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4
to 5.5 over the course of the inventory (Lange 2000). For turkeys, the turnover rate ranges from 2.4 to 3.0. A summary of
the livestock population characterization data used to calculate CH4 and N20 emissions is presented in Table A-161.
Step 2: Waste Characteristics Data
Methane and N20 emissions calculations are based on the following animal characteristics for each relevant livestock
population:
• Volatile solids (VS) excretion rate;
• Maximum methane producing capacity (B0) for U.S. animal waste;
• Nitrogen excretion rate (Nex); and
• Typical animal mass (TAM).
Table A-162 presents a summary of the waste characteristics used in the emissions estimates. Published sources were
reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population data
discussed in Step 1. The USDA's Agricultural Waste Management Field Handbook (AWMFH; USDA 1996, 2008) is one of
the primary sources of waste characteristics for non-cattle animal groups. Data from the 1996 and 2008 USDA AWMFH
were used to estimate VS and Nex for most non-cattle animal groups across the time series of the Inventory, as shown in
Table A-163 (ERG 2010b and 2010c). The 1996 AWMFH data were based on measured values from U.S. farms; the 2008
AWMFH data were developed using the calculation method created by the American Society of Agricultural and
Biological Engineers (ASABE), which is based on U.S. animal dietary intake and performance measures. Since the values
from each of the two AWMFHs result from different estimation methods and reflect changes in animal genetics and
nutrition over time, both data sources were used to create a time series across the Inventory as neither value would be
appropriate to use across the entire span of Inventory years. Expert sources agreed interpolating the two data sources
across the time series would be appropriate as each methodology reflect the best available for that time period and the
more recent data may not appropriately reflect the historic time series (ERG 2010b). Although the AWMFH values are
lower than the IPCC (2006) values, these values are more appropriate for U.S. systems because they have been
calculated using U.S.-specific data. Animal-specific notes about VS and Nex are presented below:
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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• Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make
up this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of
breeding swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5
percent boars (Safley 2000). Differing trends in VS and Nex values are due to the updated Nex calculation
method from 2008 AWMFH. VS calculations did not follow the same procedure and were updated based
on a fixed ratio of VS to total solids and past ASABE standards (ERG 2010b).
• Poultry: Due to the change in USDA reporting of hens and pullets in 2005, new nitrogen and VS excretion
rates were calculated for the combined population of hens and pullets; a weighted average rate was
calculated based on hen and pullet population data from 1990 to 2004.
• Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents,
data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006IPCC
Guidelines were used as a supplement.
The method for calculating VS excretion and Nex for cattle (including American bison, beef and dairy cows, bulls, heifers,
and steers) is based on the relationship between animal performance characteristics such as diet, lactation, and weight
gain and energy utilization. The method used is outlined by IPCC (2019) Tier 2 methodology, and is modeled using the
CEFM as described in the enteric fermentation portion of the inventory (documented in Moffroid and Pape 2013) in
order to take advantage of the detailed diet and animal performance data assembled as part of the Tier II analysis for
cattle. For American bison, VS and Nex were assumed to be the same as beef NOF bulls.
The VS content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted as fecal
material; fecal material combined with urinary excretions constitutes manure. The CEFM uses the input of digestible
energy (DE) and the energy requirements of cattle to estimate gross energy (GE) intake and enteric CH4 emissions. GE
and DE are used to calculate the indigestible energy per animal as gross energy minus digestible energy plus the amount
of gross energy for urinary energy excretion per animal (2 or 4 percent). This value is then converted to VS production
per animal using the typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting
out the ash content of manure. The current equation recommended by the 2006 IPCC Guidelines is:
Equation A- 27: VS Production for Cattle
l-ASH
VS production (kg) = [(GE - DE) + (UE x GE)]
x -
18.45
where,
GE = Gross energy intake (MJ)
DE = Digestible energy (MJ)
(UE x GE) = Urinary energy expressed as fraction of GE, assumed to be 0.04 except for feedlots
which are reduced 0.02 as a result of the high grain content of their diet.
ASH = Ash content of manure calculated as a fraction of the dry matter feed intake
(assumed to be 0.08).
18.45 = Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is
relatively constant across a wide range of forage and grain-based feeds
commonly consumed by livestock.
Total nitrogen ingestion in cattle is determined by dietary protein intake. When feed intake of protein exceeds the
nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate the
nitrogen excreted by each animal type, the CEFM utilizes the energy balance calculations recommended by the 2006
IPCC Guidelines for gross energy and the energy required for growth along with inputs of weight gain, milk production,
and the percent of crude protein in the diets. The total nitrogen excreted is measured in the CEFM as nitrogen consumed
minus nitrogen retained by the animal for growth and in milk. The basic equation for calculating Nex is shown below,
followed by the equations for each of the constituent parts, based on the 10th Corrigenda for the 2006 IPCC Guidelines
(IPCC 2018).
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Equation A-28: Nex Rates for Cattle
where,
Nex(i)
Njntake(T)
Nretention(T)
Nex^ — Nintake x (l N,
retention _fract(T) >
The annual N intake per head of animal of species/category T (kg N animal1 yr1)
Fraction of annual N intake that is retained by animal
N intake is estimated as:
Equation A-29: Daily Nitrogen Intake for Cattle
intoke(T)
CP%
GE J 100
18.45 6.25
where,
Njntake(T)
GE
18.45
CP%
6.25
Daily N consumed per animal of category T (kg N animal1 day1)
Gross energy intake of the animal based on digestible energy, milk production,
pregnancy, current weight, mature weight, rate of weight gain, and IPCC
constants (MJ animal1 day1)
Conversion factor for dietary GE per kg of dry matter (MJ kg"1)
Percent crude protein in diet, input
Conversion from kg of dietary protein to kg of dietary N (kg feed protein per kg N)
The portion of consumed N that is retained as product equals the nitrogen in milk plus the nitrogen required for weight
gain. The N content of milk produced is calculated using milk production and percent protein, along with conversion
factors. The nitrogen retained in body weight gain by stockers, replacements, or feedlot animals is calculated using the
net energy for growth (NEg), weight gain (WG), and other conversion factors and constants. The equation matches the
10th Corrigenda to the 2006 IPCC Guidelines (IPCC 2018), and is as follows:
Equation A-30: Nitrogen Retention from Milk and Body Weight for Cattle
N
retention(T)
Milk x
/Milk PR%v
I 100 )
6.38
+
WG x
268
(703 X NEg"\
\ WG /
1000 X 6.25
where,
Nretention(T)
Milk
268
7.03
NEg
1,000
6.25
Milk PR%
6.38
WG
Daily N retained per animal of category T (kg N animal1 day1)
Milk production (kg animal1 day1)
Constant from 2019 IPCC Guidelines
Constant from 2019 IPCC Guidelines
Net energy for growth, calculated in livestock characterization, based on current
weight, mature weight, rate of weight gain, and IPCC constants, (MJ day1)
Conversion from grams to kilograms (g kg"1)
Conversion from kg dietary protein to kg dietary N (kg protein per kg N)
Percent of protein in milk (%)
Conversion from milk protein to milk N (kg protein per kg N)
Weight gain, as input into the CEFM transition matrix (kg day1)
The VS and N equations above were used to calculate VS and Nex rates for each state, animal type (heifers and steer on
feed, heifers and steer not on feed, bulls and American bison), and year. Table A-164 presents the state-specific VS and
Nex production rates used for cattle in 2020. As shown in Table A-164, the differences in the VS daily excretion and Nex
rate trends between dairy cattle animal types is due to milk production. Milk production by cow varies from state to
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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state and is used in calculating net energy for lactating, which is used to calculate VS and Nex for dairy cows. Milk
production is zero for dairy heifers (dairy heifers do not produce milk because they have not yet had a calf). Over time,
the differences in milk production are also a big driver for the higher variability of VS and Nex rates in dairy cows.
Step 3: Waste Management System Usage Data
Table A-165 and Table A-166 summarize 2020 manure distribution data among waste management systems (WMS) at
beef feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining
animal types (beef cattle not on feed, American bison, goats, horses, mules and asses and sheep) is managed on pasture,
range, or paddocks, on drylot, or with solids storage systems. Note that the Inventory WMS estimates are based on state
or regional WMS usage data and not built upon farm-level WMS estimates. Additional information on the development
of the manure distribution estimates for each animal type is presented below. Definitions of each WMS type are
presented in Table A-167.
Beef Cattle, Dairy Heifers and American Bison-. The beef feedlot and dairy heifer WMS data were developed using
regional information from EPA's Office of Water's engineering cost analyses conducted to support the development of
effluent limitations guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and
state contacts supporting this work and additional personal communication with the national USDA office to estimate
the percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots.
Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100 percent. In
addition, there is a small amount of manure contained in runoff, which may or may not be collected in runoff ponds.
Using EPA and USDA data and expert opinions (documented in ERG 2000a), the runoff from feedlots was calculated by
region in Calculations: Percent Distribution of Manure for Waste Management Systems and was used to estimate the
percentage of manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3 percent
(ERG 2000a). The percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent.
The remaining population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock
systems, which are utilized for the majority of the population of beef cattle in the country. American bison WMS data
were assumed to be the same as beef cattle NOF.
Dairy Cows: The WMS data for dairy cows were developed using state and regional data from the Census of Agriculture,
EPA's Office of Water, USDA, and the expert sources noted below. Farm-size distribution data are reported in the 1992,
1997, 2002, 2007, 2012, and 2017 Census of Agriculture (USDA 2019d). It was assumed that the Census data provided for
1992 were the same as that for 1990 and 1991, and data provided for 2017 were the same as that for 2018. Data for
1993 through 1996, 1998 through 2001, and 2003 through 2006, 2008 through 2011, and 2013 through 2016 were
interpolated using the 1992,1997, 2002, 2007, 2012, and 2017 Census data. The percent of waste by system was
estimated using the USDA data broken out by geographic region and farm size.
For 1990 through 1996 the following methodology and sources were used to estimate dairy WMS:
Based on EPA site visits and the expert opinion of state contacts, manure from dairy cows at medium (200 through 700
head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In
addition, they may have a solids separator in place prior to their storage component. Estimates of the percent of farms
that use each type of system (by geographic region) were developed by EPA's Office of Water and were used to estimate
the percent of waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage
(separated solids) (EPA 2002b).
Manure management system data for small (fewer than 200 head) dairies were obtained at the regional level from
USDA's Animal and Plant Health Inspection Service (APHIS)'s National Animal Health Monitoring System (Ott 2000).
These data are based on a statistical sample of farms in the 20 U.S. states with the most dairy cows. Small operations are
more likely to use liquid/slurry and solid storage management systems than anaerobic lagoon systems. The reported
manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earth-basin, and aerated lagoon),
anaerobic lagoon, and solid storage (includes manure pack, outside storage, and inside storage).
Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from
personal communications with personnel from several organizations. These organizations include state NRCS offices,
state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000, Stettler
2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure
management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992,1997, 2002, 2007,
Annex 3
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2012, and 2017 (USDA 2019d) were used in conjunction with the state data obtained from personal communications to
determine regional percentages of total dairy cattle and dairy waste that are managed using these systems.
Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies reported using
more than one type of manure management system. Due to limitations in how USDA APHIS collects the manure
management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure
is typically partitioned to use only one manure management system, rather than transferred between several different
systems. Emissions estimates are only calculated for the final manure management system used for each portion of
manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total
of 100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep
pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54
percent (i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55
percent divided by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).
Starting in 2016, EPA estimates dairy WMS based on 2016 USDA Economic Research Service (ERS) Agricultural
Resource Management Survey (ARMS) data. These data were obtained from surveys of nationally representative
dairy producers. WMS data for 2016 were assumed the same for 2017 through 2020. WMS for 1997 through 2015
were interpolated between the data sources used for the 1990-1997 dairy WMS (noted above) and the 2016 ARMs
data (ERG 2019).
Finally, the percentage of manure managed with anaerobic digestion (AD) systems with methane capture and
combustion was added to the WMS distributions at the state-level. AD system data were obtained from EPA's AgSTAR
Program's project database (EPA 2021). This database includes basic information for AD systems in the United States,
based on publicly available data and data submitted by farm operators, project developers, financiers, and others
involved in the development of farm AD projects.
Swine: The regional distribution of manure managed in each WMS was estimated using data from a 1995 USDA APHIS
survey, EPA's Office of Water site visits, and 2009 USDA ERS ARMS data (Bush 1998, ERG 2000a, ERG 2018). The USDA
APHIS data are based on a statistical sample of farms in the 16 U.S. states with the most hogs. The ERS ARMS data are
based on surveys of nationally representative swine producers. Prior to 2009, operations with less than 200 head were
assumed to use pasture, range, or paddock systems and swine operations with greater than 200 head were assigned
WMS as obtained from USDA APHIS (Bush 1998). WMS data for 2009 were obtained from USDA ERS ARMS; WMS data
for 2010 through 2018 were assumed to be the same as 2009 (ERG 2018). The percent of waste managed in each system
was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data
reported in the 1992,1997, 2002, 2007, 2012, and 2017 Census of Agriculture (USDA 2019d) were used to determine the
percentage of all swine utilizing the various manure management systems. It was assumed that the swine farm size data
provided for 1992 were the same as that for 1990 and 1991. Data for 1993 through 1996, 1998 through 2001, 2003
through 2006, and 2008 through 2011, and 2013 through 2016 were interpolated using the 1992, 1997, 2002, 2007,
2012, and 2017 Census data.
Some swine operations reported using more than one management system; therefore, the total percent of systems
reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the
manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is
handled in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to
another, which could result in increased emissions, so reported systems data were normalized to 100 percent for
incorporation into the WMS distribution, using the same method as described above for dairy operations. As with dairy,
AD WMS were added to the state-level WMS distribution based on data from EPA's AgSTAR database (EPA 2021).
Sheep: WMS data for sheep were obtained from USDA NASS sheep report for years 1990 through 1993 (USDA 1994).
Data for 2001 are obtained from USDA APHIS's national sheep report (USDA, APHIS 2003). The USDA APHIS data are
based on a statistical sampled of farms in the 22 U.S. states with the most sheep. The data for years 1994-2000 are
calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for years 2002 and
beyond are assumed to be the same as 2001. Based on expert opinion, it was assumed that all sheep manure not
deposited in feedlots was deposited on pasture, range, or paddock lands (Anderson 2000).
Goats, Horses, and Mules and Asses: WMS data for 1990 to 2020 were obtained from Appendix H of Global Methane
Emissions from Livestock and Poultry Manure (EPA 1992). This report presents state WMS usage in percentages for the
major animal types in the United States, based on information obtained from extension service personnel in each state.
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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It was assumed that all manure not deposited in pasture, range, or paddock lands was managed in dry systems. For
mules and asses, the WMS was assumed to be the same as horses.
Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: WMS data for 1992
were obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). These data were also
used to represent 1990 and 1991. The percentage of layer operations using a shallow pit flush house with anaerobic
lagoon or high-rise house without bedding was obtained for 1999 from a United Egg Producers voluntary survey (UEP
1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX, and
WA) with USDA data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and 1999 is
proportionally distributed among those years of the inventory. It was also assumed that system usage in 2000 through
2020 was equal to that estimated for 1999. Data collected for EPA's Office of Water, including information collected
during site visits (EPA 2002b), were used to estimate the distribution of waste by management system and animal type.
As with dairy and swine, using information about AD WMS from EPA's AgSTAR database (EPA 2021), AD was added to
the WMS distribution for poultry operations.
Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office of Air
and Radiation's Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one
percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed
to be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems were used
to update the WMS distributions based on information from EPA's AgSTAR database (EPA 2021).
Step 4: Emission Factor Calculations
Methane conversion factors (MCFs) and N20 emission factors (EFs) used in the emission calculations were determined
using the methodologies presented below.
Methane Conversion Factors (MCFs)
Climate-based IPCC default MCFs (IPCC 2006; 2019) were used for all dry systems; these factors are presented in Table A-
168. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems.
For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual average
temperature data. The average county and state temperature data were obtained from the National Climate Data Center
(NOAA 2021) and each state and year in the inventory was assigned a climate classification of cool, temperate or warm.
Although there are some specific locations in the United States that may be included in the warm climate category, no
aggregated state-level annual average temperatures are included in this category. In addition, some counties in a
particular state may be included in the cool climate category, although the aggregated state-level annual average
temperature may be included in the temperate category. Although considering the temperatures at a state level instead
of a county level may be causing some specific locations to be classified into an inappropriate climate category, using the
state level annual average temperature provides an estimate that is appropriate for calculating the national average.
For anaerobic lagoons and other liquid systems, a climate-based approach based on the van't Hoff-Arrhenius equation
was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for long-term
retention time. This approach is consistent with the IPCC (2006) guidelines. The van't Hoff-Arrhenius equation, with a
base temperature of 30°C, is shown in the following equation (Safley and Westerman 1990):
Equation A-31: VS Proportion Available to Convert to ChU Based on Temperature (van't Hoff-
Arrhenius /factor)
where,
/
van't Hoff-Arrhenius/factor, the proportion of VS that are biologically available
for conversion to CH4 based on the temperature of the system
303.15K
Ambient temperature (K) for climate zone (in this case, a weighted value for each state)
Activation energy constant (15,175 cal/mol)
Ideal gas constant (1.987 cal/K mol)
Ti
T2
E
R
Annex 3
A-307
-------
For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow, dairy
heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where the
specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in each
county). County population data were calculated from state-level population data from NASS and county-state
distribution data from the 1992, 1997, 2002, 2007, 2012, and 2017 Census data (USDA 2019d). County population
distribution data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993
through 1996 were interpolated based on 1992 and 1997 data; county population distribution data for 1998 through
2001 were interpolated based on 1997 and 2002 data; county population distribution data for 2003 through 2006 were
interpolated based on 2002 and 2007 data; county population distribution data for 2008 through 2011 were interpolated
based on 2007 and 2012 data; county population distribution data for 2013 through 2016 were interpolated based on
2012 and 2017 data; county population distributions for 2018 through 2020 were assumed to be the same as 2017.
Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:
• The weighted-average temperature for a state is calculated using the county population estimates and average
monthly temperature in each county. Monthly temperatures are used to calculate a monthly van't Hoff-
Arrhenius/factor, using the equation presented above. A minimum temperature of 5°C is used for uncovered
anaerobic lagoons and 7.5°C is used for liquid/slurry and deep pit systems due to the biological activity in the
lagoon which keeps the temperature above freezing.
• Monthly production of VS added to the system is estimated based on the animal type, number of animals
present, and the volatile solids excretion rate of the animals.
• For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This
factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from
anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents management and design
factors which cause a system to operate at a less than optimal level.
• For all systems other than anaerobic lagoons, the amount of VS available for conversion to CH4 each month is
assumed to be equal to the amount of VS produced during the month (from Step 3). For anaerobic lagoons, the
amount of VS available also includes VS that may remain in the system from previous months.
• The amount of VS consumed during the month is equal to the amount available for conversion multiplied by
the/factor.
• For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount
available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out
once per year, occurring in the month of October.
• The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by
B0.
The annual MCF is then calculated as:
Equation A-32: MCF for Anaerobic Lagoons and Liquid Systems
MCP CH-t generated mmial
mmial VS produced „IslxB0
where,
MCF annual = Methane conversion factor
VS produced annual = Volatile solids excreted annually
B0 = Maximum CH4 producing potential of the waste
In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from the
previous year are available in the lagoon system in the next year. For example, the VS from October, November, and
December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006.
Following this procedure, the resulting MCF for lagoons accounts for temperature variation throughout the year, residual
VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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CH4. It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation
doesn't reflect the VS carry-over.
The liquid system MCFs are presented in Table A-169 by state, WMS, and animal group for 2020.
Nitrous Oxide Emission Factors
Direct N20 EFs for manure management systems (kg N20-N/kg excreted N) were set equal to the most recent default
IPCC factors (IPCC 2006), presented in Table A-170.
Indirect N20 EFs account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and NOx (Fracgas) and
runoff/leaching (Fracrunoff/ieach). IPCC default indirect N20 EFs were used to estimate indirect N20 emissions. These factors
are 0.010 kg N20-N/kg N for volatilization and 0.0075 kg N20/kg N for runoff/leaching.
Country-specific estimates of N losses were developed for Fracgas and Fracrunoff/ieachfor the United States. The vast
majority of volatilization losses are NH3. Although there are also some small losses of NOx, no quantified estimates were
available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. Therefore,
Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA's National Emission Inventory -
Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracmnoff/ieach, data from EPA's Office of
Water were used that estimate the amount of runoff from beef, dairy, and heifer operations in five geographic regions of
the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, WMS, and region.
Nitrogen losses from leaching are believed to be small in comparison to the runoff losses and there are a lack of data to
quantify these losses. Therefore, leaching losses were assumed to be zero and Fracrunoff/ieach was set equal to the runoff
loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A-171.
Step 5: CH4 Emission Calculations
To calculate CH4 emissions for animals other than cattle, first the amount of VS excreted in manure that is managed in
each WMS was estimated:
Equation A-33: VS Excreted for Animals Other Than Cattle
TAM
VS excreted state_AmnBl WMS = Population state_Amlml x x VS x WMS x 365.25
where,
VS excreted state, Animai.wMs = Amount of VS excreted in manure managed in each WMS for each
animal type (kg/yr)
Population state. Animal = Annual average state animal population by animal type (head)
TAM = Typical animal mass (kg)
VS = Volatile solids production rate (kg VS/1000 kg animal mass/day)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
365.25 = Days per year
Using the CEFM VS data for cattle, the amount of VS excreted in manure that is managed in each WMS was estimated
using the following equation:
Equation A-34: VS Excreted for Cattle
VS excretedstate, Animal, WMS = Populationstate, Animal x VS x WMS
where,
VS excreted state, Animai.wMs = Amount of VS excreted in manure managed in each WMS for each
animal type (kg/yr)
Population state. Animal = Annual average state animal population by animal type (head)
VS = Volatile solids production rate (kg VS/animal/year)
WMS = Distribution of manure by WMS for each animal type in a state (percent)
For all animals, the estimated amount of VS excreted into a WMS was used to calculate CH4 emissions using the following
equation:
Annex 3
A-309
-------
Equation A-35: ChU Emissions for All Animal Types
CH4 = X(VSexcreted xB.xMCFx0.662)
State. Animal. WMS
where,
CH4 = CH4 emissions (kg CH4/yr)
VS excreted wms, state = Amount of VS excreted in manure managed in each WMS (kg/yr)
B0 = Maximum CH4 producing capacity (m3 CH4/kg VS)
MCF animal, state, wms = MCF for the animal group, state and WMS (percent)
0.662 = Density of methane at 25° C (kg CH4/m3CH4)
A calculation was developed to estimate the amount of CH4 emitted from AD systems utilizing CH4 capture and
combustion technology. First, AD systems were assumed to produce 90 percent of B0 of the manure. This value is applied
for all climate regions and AD system types. However, this is a conservative assumption as the actual amount of CH4
produced by each AD system is very variable and will change based on operational and climate conditions and an
assumption of 90 percent is likely overestimating CH4 production from some systems and underestimating CH4
production in other systems. The CH4 production of AD systems is calculated using the equation below:
Equation A-36: ChU Production from AD Systems
TAM
CH4 ProductionADADSystem = ProductionADADSystem x ^ nnn x 75 x B0 x 0.662 x 365.25 x 0.90
where,
1000
CH4 Production ADAdsystem = CH4 production from a particular AD system, (kg/yr)
Population AD state = Number of animals on a particular AD system
VS = Volatile solids production rate (kg VS/1000 kg animal mass-day)
TAM = Typical Animal Mass (kg/head)
B0 = Maximum CH4 producing capacity (CH4 m3/kg VS)
0.662 = Density of CH4 at 25° C (kg CH4/m3 CH4)
365.25 = Days/year
0.90 = CH4 production factor for AD systems
The total amount of CH4 produced by AD is calculated only as a means to estimate the emissions from AD; i.e., only the
estimated amount of CH4 actually entering the atmosphere from AD is reported in the inventory. The emissions to the
atmosphere from AD are a result of leakage from the system (e.g., from the cover, piping, tank, etc.) and incomplete
combustion and are calculated using the collection efficiency (CE) and destruction efficiency (DE) of the AD system. The
three primary types of AD systems in the United States are covered lagoons, complete mix and plug flow systems. The CE
of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD systems was
assumed to be 99 percent (EPA 2008). The CH4 DE from flaring or burning in an engine was assumed to be 98 percent;
therefore, the amount of CH4 that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The
amount of CH4 produced by systems with AD was calculated with the following equation:
Equation A-37: ChU Emissions from AD Systems
^[CH 4 Production AD ADsystem x CE ADsystem x (l - DE)] >
+ [CH 4 Production ADADsystemx (l - CE ADsystem)]
CH 4 Emissions AD = £
St at e, Ani mal, AD Sy s t ems
where,
CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr)
CH4 Production ADAdsystem = CH4 production from a particular AD system, (kg/yr)
CEad system = Collection efficiency of the AD system, varies by AD system type
DE = Destruction efficiency of the AD system, 0.98 for all systems
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Step 6: N2O Emission Calculations
Total N20 emissions from manure management systems were calculated by summing direct and indirect N20 emissions.
The first step in estimating direct and indirect N20 emissions was calculating the amount of N excreted in manure and
managed in each WMS. For calves and animals other than cattle the following equation was used:
Equation A-38: Nex for Calves and Animal Types Other Than Cattle
N excreted ^ = PopulationState>Animal xWMS
TAM
' 1000
x Nex x 365.25
where,
N excreted
State, Animal, WMS
Population state
WMS
TAM
Nex
365.25
Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Annual average state animal population by animal type (head)
Distribution of manure by waste management system for each animal
type in a state (percent)
Typical animal mass (kg)
Nitrogen excretion rate (kg N/1000 kg animal mass/day)
Days per year
Using the CEFM Nex data for cattle other than calves, the amount of N excreted was calculated using the following
equation:
Equation A-39: Nex from Cattle Other Than Calves
N excreted
State, Animal,WMS
= Population
State, Animal
x WMS x Nex
where,
N excreted
State, Animal, WMS
Population state
WMS
Nex
Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Annual average state animal population by animal type (head)
Distribution of manure by waste management system for each animal
type in a state (percent)
Nitrogen excretion rate (kg N/animal/year)
For all animals, direct N20 emissions were calculated as follows:
Equation A-40: Direct N2O emissions from All Animal Types
Direct N20= % N excreted state>Anllral>WMS x EFWMS
State, Animal,WMS \
44
28
where,
Direct N20
N eXCreted state. Animal, WMS
EFwms
44/28
Direct N20 emissions (kg N20/yr)
Amount of N excreted in manure managed in each WMS for each
animal type (kg/yr)
Direct N20 emission factor from IPCC guidelines (kg N20-N /kg N)
Conversion factor of N20-N to N20
Indirect N20 emissions were calculated for all animals with the following equation:
Annex 3
A-311
-------
Equation A-41: Indirect N2O Emissions from All Animal Types
where,
Indirect N2O = X
State, Animal WMS
Indirect N20
N excreted State, Animal, WMS
Fracgas,wMs
Fr3Crunoff/leach,WMS
EF volatilization
EF runoff/leach
44/28
N excreted StaIC Animal, wms
Frac
gas, mis
loo
xEF,
voiatlza tton
44
28
N excreted
Frac,
State, Animal
runoffleach. WMS
Too
xEE
nmoofpleach '
44
'28
Indirect N20 emissions (kg N20/yr)
Amount of N excreted in manure managed in each WMS for each animal
type (kg/yr)
Nitrogen lost through volatilization in each WMS
Nitrogen lost through runoff and leaching in each WMS (data were not
available for leaching so the value reflects only runoff)
Emission factor for volatilization (0.010 kg N20-N/kg N)
Emission factor for runoff/leaching (0.0075 kg N20-N/kg N)
Conversion factor of N20-N to N20
Emission estimates of CH4 and N20 by animal type are presented for all years of the inventory in Table A-172 and Table
A-174 respectively. Emission estimates for 2020 are presented by animal type and state in Table A-176 and Table A-178
respectively.
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-161: Livestock Population (1,000 Head)
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Dairy Cattle
19,512
18,681
17,793
18,297
18,442
18,587
18,505
18,517
18,812
18,857
18,923
19,006
18,849
18,804
Dairy Cows
10,015
9,482
9,004
9,087
9,156
9,236
9,221
9,209
9,312
9,312
9,369
9,432
9,353
9,343
Dairy Heifer
4,129
4,108
4,162
4,545
4,577
4,581
4,523
4,571
4,727
4,785
4,757
4,741
4,677
4,637
Dairy Calves
5,369
5,091
4,628
4,666
4,709
4,770
4,761
4,737
4,774
4,760
4,797
4,833
4,818
4,825
Swine3
53,941
58,899
61,073
64,723
65,572
66,363
65,437
64,195
68,178
70,065
72,125
73,430
76,898
77,267
Market <50 lb.
18,359
19,656
20,228
19,067
19,285
19,472
19,002
18,939
19,843
20,572
20,973
21,359
22,278
22,047
Market 50-119 lb.
11,734
12,836
13,519
16,645
16,904
17,140
16,834
16,559
17,577
18,175
18,767
19,039
20,195
20,153
Market 120-179 lb.
9,440
10,545
11,336
12,377
12,514
12,714
12,674
12,281
13,225
13,575
13,982
14,311
14,852
15,143
Market >180 lb.
7,510
8,937
9,997
10,856
11,078
11,199
11,116
10,525
11,555
11,714
12,282
12,418
13,138
13,604
Breeding
6,899
6,926
5,993
5,778
5,791
5,839
5,812
5,892
5,978
6,030
6,122
6,303
6,435
6,321
Beef Cattleb
81,576
90,361
82,193
80,484
78,937
76,858
76,010
74,966
76,149
79,323
81,385
81,722
82,049
80,812
Feedlot Steers
6,357
7,233
8,116
8,584
8,771
8,586
8,613
8,696
8,594
9,017
9,560
9,605
9,706
9,685
Feedlot Heifers
3,192
3,831
4,536
4,620
4,830
4,742
4,655
4,518
4,334
4,433
4,786
5,085
5,210
5,250
NOF Bulls
2,160
2,385
2,214
2,190
2,165
2,100
2,074
2,038
2,109
2,137
2,244
2,252
2,253
2,237
Beef Calves
16,909
18,177
16,918
16,067
15,817
15,288
14,805
14,737
14,998
15,546
15,931
16,221
16,146
15,635
NOF Heifers
10,182
11,829
9,550
9,349
8,874
8,687
8,780
8,730
9,291
9,892
9,790
9,460
9,257
9,066
NOF Steers
10,321
11,716
8,185
8,234
7,568
7,173
7,451
7,291
7,491
8,133
7,904
7,633
7,786
7,600
NOF Cows
32,455
35,190
32,674
31,440
30,913
30,282
29,631
28,956
29,332
30,164
31,171
31,466
31,691
31,339
Sheep
11,358
8,989
6,135
5,620
5,470
5,375
5,360
5,235
5,270
5,295
5,270
5,265
5,230
5,200
Sheep On Feed
1,180
1,769
2,976
2,784
2,691
2,669
2,658
2,588
2,587
2,624
2,618
2,623
2,616
2,611
Sheep NOF
10,178
7,220
3,159
2,836
2,779
2,706
2,702
2,647
2,683
2,671
2,652
2,642
2,614
2,589
Goats
2,516
2,357
2,897
2,829
2,725
2,622
2,637
2,652
2,668
2,683
2,699
2,714
2,729
2,14S
Poultry0
1,537,074
1,826,977
2,150,410
2,104,335
2,095,951
2,168,697
2,106,502
2,116,333
2,134,445
2,173,216
2,214,462
2,256,552
2,276,951
2,269,691
Hens >1 yr.
273,467
299,071
348,203
341,884
338,944
346,965
361,403
370,637
351,656
377,299
388,006
402,536
403,102
391,010
Pullets
73,167
81,369
96,809
105,738
102,233
104,460
106,646
106,490
118,114
112,061
117,173
124,729
121,971
119,898
Chickens
6,545
7,637
8,289
7,390
6,922
6,827
6,853
6,403
7,211
6,759
6,859
6,626
7,130
7,371
Broilers
1,066,209
1,331,940
1,613,091
1,567,927
1,565,018
1,625,945
1,551,600
1,553,636
1,579,764
1,595,764
1,620,691
1,643,327
1,668,582
1,676,745
Turkeys
117,685
106,960
84,018
81,396
82,833
84,500
80,000
79,167
77,700
81,333
81,733
79,333
76,167
74,667
Horses
2,212
2,632
3,875
3,784
3,703
3,621
3,467
3,312
3,157
3,002
2,847
2,692
2,538
2,383
Mules and Asses
63
101
212
289
291
293
298
303
308
313
318
323
328
333
American Bison
47
104
212
177
169
162
166
171
175
179
184
188
193
197
a Prior to 2008, the Market <50 lbs category was <60 lbs and the Market 50-119 lbs category was Market 60-119 lbs; USDA updated the categories to be more consistent with international
animal categories.
b NOF - Not on Feed
c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
Source(s): See Step 1: Livestock Population Characterization Data.
Note: Totals may not sum due to independent rounding.
Annex 3
A-313
-------
Table A-162: Wasl
e Characteristics Data
Maximum Methane Generation
Typical Animal Mass, TAM
Total Nitrogen Excreted, Nexa
Potential, B0
Volatile Solids Excreted, VSa
Value
Value
(m3 Cm/kg VS
Animal Group
(kg)
Source
Value
Source
added)
Source
Value
Source
Dairy Cows
680
CEFM
Table A-164
CEFM
0.24
Morris 1976
Table A-164
CEFM
Dairy Heifers
406-408
CEFM
Table A-164
CEFM
0.17
Bryant et al. 1976
Table A-164
CEFM
Feedlot Steers
419-457
CEFM
Table A-164
CEFM
0.33
Hashimoto 1981
Table A-164
CEFM
Feedlot Heifers
384-430
CEFM
Table A-164
CEFM
0.33
Hashimoto 1981
Table A-164
CEFM
NOF Bulls
831-917
CEFM
Table A-164
CEFM
0.17
Hashimoto 1981
Table A-164
CEFM
NOF Calves
118
ERG 2003b
Table A-163
USDA 1996, 2008
0.17
Hashimoto 1981
Table A-163
USDA 1996, 2008
NOF Heifers
296-407
CEFM
Table A-164
CEFM
0.17
Hashimoto 1981
Table A-164
CEFM
NOF Steers
314-335
CEFM
Table A-164
CEFM
0.17
Hashimoto 1981
Table A-164
CEFM
NOF Cows
554-611
CEFM
Table A-164
CEFM
0.17
Hashimoto 1981
Table A-164
CEFM
American Bison
578.5
Meagher 1986
Table A-164
CEFM
0.17
Hashimoto 1981
Table A-164
CEFM
Market Swine <50 lbs.
13
ERG 2010a
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Market Swine <60 lbs.
16
Safley 2000
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Market Swine 50-119 lbs.
39
ERG 2010a
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Market Swine 60-119 lbs.
41
Safley 2000
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Market Swine 120-179 lbs.
68
Safley 2000
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Market Swine >180 lbs.
91
Safley 2000
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
Breeding Swine
198
Safley 2000
Table A-163
USDA 1996, 2008
0.48
Hashimoto 1984
Table A-163
USDA 1996, 2008
ASAE 1998, USDA
ASAE 1998, USDA
Feedlot Sheep
25
EPA 1992
Table A-163
2008
0.36
EPA 1992
Table A-163
2008
ASAE 1998, USDA
ASAE 1998, USDA
NOF Sheep
80
EPA 1992
Table A-163
2008
0.19
EPA 1992
Table A-163
2008
Goats
64
ASAE1998
Table A-163
ASAE1998
0.17
EPA 1992
Table A-163
ASAE 1998
ASAE 1998, USDA
ASAE 1998, USDA
Horses
450
ASAE1998
Table A-163
2008
0.33
EPA 1992
Table A-163
2008
Mules and Asses
130
IPCC 2006
Table A-163
IPCC 2006
0.33
EPA 1992
Table A-163
IPCC 2006
Hens >/= 1 yr
1.8
ASAE1998
Table A-163
USDA 1996, 2008
0.39
Hill 1982
Table A-163
USDA 1996, 2008
Pullets
1.8
ASAE1998
Table A-163
USDA 1996, 2008
0.39
Hill 1982
Table A-163
USDA 1996, 2008
Other Chickens
1.8
ASAE1998
Table A-163
USDA 1996, 2008
0.39
Hill 1982
Table A-163
USDA 1996, 2008
Broilers
0.9
ASAE1998
Table A-163
USDA 1996, 2008
0.36
Hill 1984
Table A-163
USDA 1996, 2008
Turkeys
6.8
ASAE1998
Table A-163
USDA 1996, 2008
0.36
Hill 1984
Table A-163
USDA 1996, 2008
a Nex and VS values vary by year; Table A-164 shows state-level values for 2020 only.
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-163: Estimated Volatile Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by year for Swine, Poultry,
Sheep, Goats, Horses, Mules and Asses, and Cattle Calves (kg/day/1000 kg animal mass)
Animal Type 1990 1995 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
VS
Swine, Market
<50 lbs.
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
8.8
Swine, Market
50-119 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
120-179 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Market
>180 lbs.
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
5.4
Swine, Breeding
2.6
2.6
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
2.7
NOF Cattle Calves
6.4
6.4
7.4
7.5
7.6
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
7.7
Sheep
9.2
9.2
8.6
8.5
8.4
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
8.3
Goats
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
9.5
Hens >lyr.
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Pullets
10.1
10.1
10.1
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
10.2
Chickens
10.8
10.8
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
11.0
Broilers
15.0
15.0
16.5
16.7
16.8
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
17.0
Turkeys
9.7
9.7
8.8
8.7
8.6
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
8.5
Horses
10.0
10.0
7.3
6.9
6.5
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
6.1
Mules and Asses
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
7.2
Nex
Swine, Market
<50 lbs.
0.60
0.60
0.84
0.87
0.89
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
0.92
Swine, Market
50-119 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
120-179 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Market
>180 lbs.
0.42
0.42
0.51
0.52
0.53
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
0.54
Swine, Breeding
0.24
0.24
0.21
0.21
0.21
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
0.20
NOF Cattle Calves
0.30
0.30
0.41
0.43
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Sheep
0.42
0.42
0.44
0.44
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Goats
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
0.45
Hens >lyr.
0.70
0.70
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
Pullets
0.70
0.70
0.77
0.77
0.78
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
0.79
Chickens
0.83
0.83
1.03
1.06
1.08
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
1.10
Broilers
1.10
1.10
1.00
0.98
0.97
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
0.96
Annex 3 A-315
-------
Animal Type
1990
1995
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Turkeys
0.74
0.74
0.65
0.64
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
0.63
Horses
0.30
0.30
0.26
0.26
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
0.25
Mules and Asses
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
0.30
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-164: Estimated Volatile
Calves) and American Bison3 for
Solids (VS) and Total Nitrogen Excreted (Nex) Production Rates by State for Cattle (other
2020 (kg/animal/year)
than
Volatile Solids
Nitrogen Excreted
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
State
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Alabama
2,267
1,255
1,663
1,099
973
689
671
1,721
1,721
136
69
73
50
41
58
60
83
83
Alaska
1,099
1,255
1,890
1,263
1,119
689
672
1,956
1,956
84
69
59
41
33
59
60
69
69
Arizona
2,923
1,255
1,890
1,251
1,119
689
672
1,956
1,956
162
69
59
41
33
59
60
69
69
Arkansas
2,054
1,255
1,663
1,095
973
689
671
1,721
1,721
125
69
73
50
41
58
60
83
83
California
2,861
1,255
1,890
1,222
1,119
689
671
1,956
1,956
159
69
59
39
33
59
60
69
69
Colorado
3,040
1,255
1,890
1,201
1,119
689
671
1,956
1,956
167
69
59
38
33
59
60
69
69
Connecticut
2,810
1,255
1,672
1,092
979
689
671
1,731
1,731
157
69
74
50
42
58
60
84
84
Delaware
2,476
1,255
1,672
1,093
979
689
671
1,731
1,731
143
69
74
50
42
58
60
84
84
Florida
2,657
1,255
1,663
1,107
973
689
671
1,721
1,721
152
69
73
51
41
58
60
83
83
Georgia
2,778
1,255
1,663
1,100
973
689
671
1,721
1,721
158
69
73
50
41
58
60
83
83
Hawaii
1,099
1,255
1,890
1,257
1,119
689
671
1,956
1,956
84
69
59
41
33
58
60
69
69
Idaho
2,968
1,255
1,890
1,226
1,119
689
671
1,956
1,956
164
69
59
39
33
59
60
69
69
Illinois
2,697
1,255
1,587
1,016
926
689
671
1,643
1,643
152
69
75
50
43
59
60
85
85
Indiana
2,856
1,255
1,587
1,023
926
689
671
1,643
1,643
159
69
75
50
43
59
60
85
85
Iowa
2,929
1,255
1,587
991
926
689
671
1,643
1,643
162
69
75
48
43
59
60
85
85
Kansas
2,858
1,255
1,587
983
926
689
671
1,643
1,643
159
69
75
47
43
59
60
85
85
Kentucky
2,604
1,255
1,663
1,081
973
689
671
1,721
1,721
150
69
73
49
41
59
60
83
83
Louisiana
2,098
1,255
1,663
1,104
973
689
671
1,721
1,721
127
69
73
51
41
59
60
83
83
Maine
2,730
1,255
1,672
1,085
979
689
670
1,731
1,731
154
69
74
50
42
58
60
84
84
Maryland
2,651
1,255
1,672
1,092
979
689
673
1,731
1,731
150
69
74
50
42
59
61
84
84
Massachusetts
2,576
1,255
1,672
1,100
979
689
671
1,731
1,731
147
69
74
51
42
58
60
84
84
Michigan
3,116
1,255
1,587
1,017
926
689
671
1,643
1,643
170
69
75
50
43
59
60
85
85
Minnesota
2,785
1,255
1,587
1,013
926
689
671
1,643
1,643
156
69
75
49
43
59
60
85
85
Mississippi
2,369
1,255
1,663
1,094
973
689
671
1,721
1,721
140
69
73
50
41
58
60
83
83
Missouri
2,159
1,255
1,587
1,035
926
689
671
1,643
1,643
129
69
75
51
43
59
60
85
85
Montana
2,670
1,255
1,890
1,252
1,119
689
671
1,956
1,956
151
69
59
41
33
58
60
69
69
Nebraska
2,936
1,255
1,587
991
926
689
671
1,643
1,643
163
69
75
48
43
59
60
85
85
Nevada
2,931
1,255
1,890
1,246
1,119
689
672
1,956
1,956
162
69
59
40
33
59
61
69
69
New Hampshire
2,678
1,255
1,672
1,099
979
689
671
1,731
1,731
152
69
74
51
42
58
60
84
84
New Jersey
2,569
1,255
1,672
1,099
979
689
671
1,731
1,731
147
69
74
51
42
58
60
84
84
New Mexico
2,937
1,255
1,890
1,238
1,119
689
671
1,956
1,956
163
69
59
40
33
59
60
69
69
New York
2,918
1,255
1,672
1,085
979
689
671
1,731
1,731
162
69
74
50
42
59
60
84
84
North Carolina
2,774
1,255
1,663
1,099
973
689
670
1,721
1,721
158
69
73
50
41
58
60
83
83
North Dakota
2,722
1,255
1,587
1,020
926
689
672
1,643
1,643
153
69
75
50
43
59
61
85
85
Ohio
2,741
1,255
1,587
1,027
926
689
671
1,643
1,643
154
69
75
51
43
58
60
85
85
Annex 3
A-317
-------
Volatile Solids
Nitrogen Excreted
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Beef
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
Dairy
Dairy
NOF
NOF
NOF
OF
OF
NOF
American
State
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Cow
Heifers
Cow
Heifers
Steer
Heifers
Steer
Bull
Bison
Oklahoma
2,399
1,255
1,663
1,069
973
689
671
1,721
1,721
140
69
73
48
41
59
60
83
83
Oregon
2,660
1,255
1,890
1,235
1,119
689
671
1,956
1,956
151
69
59
40
33
59
60
69
69
Pennsylvania
2,682
1,255
1,672
1,087
979
689
671
1,731
1,731
152
69
74
50
42
59
60
84
84
Rhode Island
2,717
1,255
1,672
1,104
979
689
670
1,731
1,731
153
69
74
51
42
58
60
84
84
South Carolina
2,557
1,255
1,663
1,097
973
689
670
1,721
1,721
148
69
73
50
41
58
60
83
83
South Dakota
2,815
1,255
1,587
1,020
926
689
671
1,643
1,643
157
69
75
50
43
59
60
85
85
Tennessee
2,495
1,255
1,663
1,087
973
689
670
1,721
1,721
146
69
73
50
41
58
60
83
83
Texas
2,954
1,255
1,663
1,061
973
689
671
1,721
1,721
164
69
73
48
41
59
60
83
83
Utah
2,821
1,255
1,890
1,248
1,119
689
672
1,956
1,956
158
69
59
40
33
59
60
69
69
Vermont
2,682
1,255
1,672
1,080
979
689
671
1,731
1,731
152
69
74
50
42
59
60
84
84
Virginia
2,660
1,255
1,663
1,087
973
689
670
1,721
1,721
153
69
73
50
41
58
60
83
83
Washington
2,907
1,255
1,890
1,213
1,119
689
671
1,956
1,956
161
69
59
39
33
59
60
69
69
West Virginia
2,200
1,255
1,672
1,095
979
689
671
1,731
1,731
131
69
74
51
42
59
60
84
84
Wisconsin
2,911
1,255
1,587
1,038
926
689
672
1,643
1,643
162
69
75
51
43
59
60
85
85
Wyoming
2,968
1,255
1,890
1,245
1,119
689
671
1,956
1,956
164
69
59
40
33
59
60
69
69
a Beef NOF Bull values were used for American bison Nex and VS.
Source: CEFM.
Table A-165: 2020 Manure Distribution Among Waste Management Systems by Operation (Percent)
Beef Feedlots
Beef Not on Feed
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
State
Dry Lot
Liquid/
Slurryb
Pasture, Range,
Paddock
Pasture, Range,
Paddock
Daily
Spread
Dry Solid
Lot Storage
Liquid/Anaerobic
Slurry Lagoon
Deep
Pit
Daily
Spreadb
Dry
Lotb
Liquid/ Pasture, Range,
Slurryb Paddockb
Alabama
100
1
100
48
0
0
14
2
22
14
17
38
0
45
Alaska
100
1
100
25
12
0
26
5
9
22
6
90
1
4
Arizona
100
100
10
0
11
42
6
30
2
10
90
0
0
Arkansas
100
1
100
47
0
0
13
3
23
14
15
28
0
57
California
100
1
100
5
0
3
26
3
54
9
11
88
1
1
Colorado
100
100
11
0
11
41
5
30
2
1
98
0
1
Connecticut
100
1
100
15
3
0
16
6
33
28
43
51
0
6
Delaware
100
1
100
14
2
0
18
7
29
31
44
50
0
6
Florida
100
1
100
48
0
0
7
0
40
4
22
61
1
17
Georgia
100
1
100
48
0
0
9
1
36
6
18
42
0
40
Hawaii
100
1
100
4
0
4
27
2
54
9
0
99
1
1
Idaho
100
100
5
0
3
26
2
53
10
1
99
0
0
Illinois
100
1
100
24
0
0
23
3
33
18
8
87
0
5
Indiana
100
1
100
21
0
0
21
2
41
16
13
79
0
8
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Beef Feedlots
Beef Not on Feed
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
State
Dry Lot
Liquid/
Slurryb
Pasture, Range,
Paddock
Pasture, Range,
Paddock
Daily
Spread
Dry Solid
Lot Storage
Liquid/Anaerobic
Slurry Lagoon
Deep
Pit
Daily
Spreadb
Dry
Lotb
Liquid/ Pasture, Range,
Slurryb Paddockb
Iowa
100
1
100
20
0
0
21
3
41
16
10
83
0
6
Kansas
100
1
100
14
0
0
16
1
55
13
5
92
0
3
Kentucky
100
1
100
51
0
0
14
2
23
11
14
24
0
61
Louisiana
100
1
100
48
0
0
13
3
23
12
14
26
0
60
Maine
100
1
100
18
4
0
16
5
30
28
45
48
0
7
Maryland
100
1
100
21
4
0
16
6
23
29
44
49
0
7
Massachusetts
100
1
100
25
5
0
17
6
17
30
45
47
0
7
Michigan
100
1
100
11
3
0
22
6
36
22
6
91
0
3
Minnesota
100
1
100
16
6
0
24
6
26
23
10
84
0
6
Mississippi
100
1
100
50
0
0
14
2
23
11
15
28
0
57
Missouri
100
1
100
29
0
0
25
2
26
17
14
77
0
8
Montana
100
100
19
0
0
21
4
38
18
4
93
0
3
Nebraska
100
1
100
15
0
0
18
2
50
15
6
90
0
4
Nevada
100
100
11
0
0
14
2
61
13
0
99
0
0
New Hampshire
100
1
100
21
4
0
17
5
22
31
44
49
0
7
New Jersey
100
1
100
27
5
0
16
6
16
29
45
47
0
8
New Mexico
100
100
10
0
11
42
6
30
2
10
90
0
0
New York
100
1
100
14
3
0
15
5
38
25
45
48
0
7
North Carolina
100
1
100
48
0
0
10
2
31
9
15
31
0
54
North Dakota
100
1
100
18
0
0
19
3
44
16
11
83
0
6
Ohio
100
1
100
24
0
0
23
2
35
17
14
78
0
8
Oklahoma
100
100
11
0
8
41
5
23
12
6
94
0
0
Oregon
100
1
100
9
0
3
24
4
50
11
0
80
1
20
Pennsylvania
100
1
100
27
6
0
16
5
18
29
47
44
0
9
Rhode Island
100
1
100
29
6
0
17
5
14
30
47
44
0
9
South Carolina
100
1
100
45
0
0
10
2
33
11
15
31
0
54
South Dakota
100
1
100
14
0
0
16
2
54
14
8
87
0
5
Tennessee
100
1
100
48
0
0
12
2
26
11
15
26
0
59
Texas
100
100
11
0
10
41
5
30
3
8
92
0
0
Utah
100
100
12
0
9
40
5
28
7
1
98
0
1
Vermont
100
1
100
14
3
0
16
5
36
26
44
49
0
7
Virginia
100
1
100
49
0
0
12
2
26
11
15
28
0
57
Washington
100
1
100
8
0
3
25
3
51
10
0
83
1
17
West Virginia
100
1
100
29
6
0
17
5
13
30
45
48
0
7
Wisconsin
100
1
100
15
5
0
24
6
27
23
12
82
0
7
Wyoming
100
0
100
16
0
0
18
2
49
15
12
81
0
7
Annex 3
A-319
-------
Beef Feedlots
Beef Not on Feed
Operations
Dairy Cow Farms3
Dairy Heifer Facilities
State
Liquid/
Dry Lot Slurryb
Pasture, Range,
Paddock
Pasture, Range,
Paddock
Daily Dry Solid Liquid/Anaerobic
Spread Lot Storage Slurry Lagoon
Deep
Pit
Daily
Spreadb
Dry Liquid/ Pasture, Range,
Lotb Slurryb Paddock'5
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA's AgSTAR Program.
b Deep pit systems are their own manure management systems in the U.S. but are included under Liquid Systems in the UNFCCC CRF tables due to lack of a separate allocation
for those systems within the tables. For Dairy Cows, solid storage and dry lot systems calculated separately in Table A-165, but are reported as "NE" in the UNFCCC CRF tables
due to lack of a separate allocation for those systems within the tables.
Source(s): See Step 3: Waste Management System Usage Data.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-166: 2020 Manure Distribution Among Waste Management Systems by Operation (Percent) Continued
Broiler and Turkey
Swine Operations3
Layer Operations
Operations
Sheep
Pasture,
Poultry
Pasture,
Pasture,
Range,
Solid
Liquid/
Anaerobic
Deep Pit (<1
Anaerobic
without
Range,
Poultry with
Range,
State
Paddock
Storage
Slurry
Lagoon
Deep Pit
month)
Lagoon
Litter
Paddock
Litter
Dry Lot
Paddock
Alabama
15
0
29
30
12
14
42
58
1
99
95
5
Alaska
57
0
3
2
34
4
25
75
1
99
31
69
Arizona
19
0
28
29
11
13
60
40
1
99
28
72
Arkansas
6
0
60
26
5
2
0
100
1
99
83
18
California
15
0
28
29
13
14
12
88
1
99
31
69
Colorado
2
0
53
0
23
22
60
40
1
99
28
72
Connecticut
66
0
2
2
26
4
5
95
1
99
95
5
Delaware
29
0
4
5
56
5
5
95
1
99
95
5
Florida
53
0
20
14
9
5
42
58
1
99
95
5
Georgia
13
0
56
28
3
1
42
58
1
99
95
5
Hawaii
42
0
22
18
11
7
25
75
1
99
31
69
Idaho
16
0
16
3
57
8
60
40
1
99
28
72
Illinois
2
0
15
7
71
5
2
98
1
99
83
18
Indiana
1
0
3
12
78
7
0
100
1
99
83
18
Iowa
1
0
10
4
80
5
0
100
1
99
83
18
Kansas
1
0
13
35
21
30
2
98
1
99
83
18
Kentucky
8
0
19
21
31
21
5
95
1
99
95
5
Louisiana
67
0
17
9
6
2
60
40
1
99
83
18
Maine
74
0
2
1
20
4
5
95
1
99
95
5
Maryland
37
0
10
2
44
6
5
95
1
99
95
5
Massachusetts
60
0
2
2
31
4
5
95
1
99
95
5
Michigan
3
0
12
6
69
9
2
98
1
99
83
18
Minnesota
1
0
3
2
88
5
0
100
1
99
83
18
Mississippi
2
0
31
36
13
18
60
40
1
99
95
5
Missouri
2
0
16
33
34
15
0
100
1
99
83
18
Montana
3
0
21
2
64
9
60
40
1
99
28
72
Nebraska
2
0
9
22
49
19
2
98
1
99
83
18
Nevada
12
0
29
32
12
15
0
100
1
99
28
72
New
Hampshire
65
0
2
2
27
4
5
95
1
99
95
5
New Jersey
54
0
3
3
36
4
5
95
1
99
95
5
New Mexico
67
0
17
9
6
2
60
40
1
99
28
72
New York
41
0
6
3
44
5
5
95
1
99
95
5
North Carolina
1
0
33
49
1
16
42
58
1
99
95
5
Annex 3
A-321
-------
Broiler and Turkey
Swine Operations3
Layer Operations
Operations
Sheep
Pasture,
Poultry
Pasture,
Pasture,
Range,
Solid
Liquid/
Anaerobic
Deep Pit (<1
Anaerobic
without
Range,
Poultry with
Range,
State
Paddock
Storage
Slurry
Lagoon
Deep Pit
month)
Lagoon
Litter
Paddock
Litter
Dry Lot
Paddock
North Dakota
2
0
21
2
65
9
2
98
1
99
83
18
Ohio
1
0
10
9
67
13
0
100
1
99
95
5
Oklahoma
1
0
11
53
3
32
60
40
1
99
83
18
Oregon
51
0
20
15
9
5
25
75
1
99
31
69
Pennsylvania
1
0
8
5
77
9
0
100
1
99
95
5
Rhode Island
64
0
2
2
28
4
5
95
1
99
95
5
South Carolina
6
0
30
34
13
16
60
40
1
99
95
5
South Dakota
1
0
17
11
57
14
2
98
1
99
83
18
Tennessee
7
0
30
33
13
16
5
95
1
99
95
5
Texas
6
0
31
34
13
17
12
88
1
99
28
72
Utah
1
0
22
2
65
9
60
40
1
99
28
72
Vermont
69
0
2
1
24
4
5
95
1
99
95
5
Virginia
6
0
14
29
15
35
5
95
1
99
95
5
Washington
35
0
12
2
45
7
12
88
1
99
31
69
West Virginia
82
0
1
0
13
3
5
95
1
99
95
5
Wisconsin
15
0
23
1
57
4
2
98
1
99
83
18
Wyoming
3
0
21
2
64
9
60
40
1
99
28
72
a In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data
from EPA's AgSTAR Program. Deep pit systems are their own manure management systems in the U.S. but are included under
Liquid Systems in the UNFCCC CRF tables due to lack of a separate allocation for those systems within the tables.
b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot
and runoff collection pond), the percent of manure that generates emissions is greater than 100 percent.
Source(s): See Step 3: Waste Management System Usage Data.
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-167: Manure Management System Descriptions
Manure Management System Description3
Pasture, Range, Paddock
The manure from pasture and range grazing animals is allowed to lie as is and is not managed.
Methane emissions are accounted for under Manure Management, but the N20 emissions
from manure deposited on PRP are included under the Agricultural Soil Management
category.
Daily Spread
Manure is routinely removed from a confinement facility and is applied to cropland or pasture
within 24 hours of excretion. Methane and indirect N20 emissions are accounted for under
Manure Management. Direct N20 emissions from land application are covered under the
Agricultural Soil Management category.
Solid Storage
The storage of manure, typically for a period of several months, in unconfined piles or stacks.
Manure is able to be stacked due to the presence of a sufficient amount of bedding material
or loss of moisture by evaporation.
Dry Lot
A paved or unpaved open confinement area without any significant vegetative cover where
accumulating manure may be removed periodically. Dry lots are most typically found in dry
climates but also are used in humid climates.
Liquid/ Slurry
Manure is stored as excreted or with some minimal addition of water to facilitate handling
and is stored in either tanks or earthen ponds, usually for periods less than one year.
Anaerobic Lagoon
Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and
storage. Lagoon supernatant is usually used to remove manure from the associated
confinement facilities to the lagoon. Anaerobic lagoons are designed with varying lengths of
storage (up to a year or greater), depending on the climate region, the VS loading rate, and
other operational factors. Anaerobic lagoons accumulate sludge over time, diminishing
treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is
typically applied to agricultural lands. The water from the lagoon may be recycled as flush
water or used to irrigate and fertilize fields. Lagoons are sometimes used in combination with
a solids separator, typically for dairy waste. Solids separators help control the buildup of
nondegradable material such as straw or other bedding materials.
Anaerobic Digester
Animal excreta with or without straw are collected and anaerobically digested in a large
containment vessel (complete mix or plug flow digester) or covered lagoon. Digesters are
designed and operated for waste stabilization by the microbial reduction of complex organic
compounds to C02 and CH4, which is captured and flared or used as a fuel.
Deep Pit
Collection and storage of manure usually with little or no added water typically below a
slatted floor in an enclosed animal confinement facility. Typical storage periods range from 5
to 12 months, after which manure is removed from the pit and transferred to a treatment
system or applied to land.
Poultry with Litter
Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw,
peanut hulls, or other products, depending on availability. The bedding absorbs moisture and
dilutes the manure produced by the birds. Litter is typically cleaned out completely once a
year. These manure systems are typically used for all poultry breeder flocks and for the
production of meat type chickens (broilers) and other fowl.
Poultry without Litter
In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no
bedding to absorb moisture. The ventilation system dries the manure as it is stored. When
Annex 3
A-323
-------
designed and operated properly, this high-rise system is a form of passive windrow
composting.
a Manure management system descriptions and the classification of manure as managed or unmanaged are based on the 2006
IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and Other Land Use, Chapter 10:
Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development Document for the Final
Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated
Animal Feeding Operations (EPA-821-R-03-001, December 2002).
Waste Management System Cool Climate MCF Temperate Climate MCF
Warm Climate MCF
Aerobic Treatment
0
0
0
Anaerobic Digester
0
0
0
Cattle Deep Litter (<1 month)
2.75
6.5
18
Cattle Deep Litter (>1 month)
20
39
67.5
Composting - In Vessel
0.5
0.5
0.5
Composting - Static Pile
1
2
2.5
Composting-Extensive/ Passive
1
2.5
Composting-lntensive
0.5
1.5
Daily Spread
0.1
0.5
1
Dry Lot
1
1.5
2
Fuel
10
10
10
Pasture
0.47
0.47
0.47
Poultry with bedding
1.5
1.5
1.5
Poultry without bedding
1.5
1.5
1.5
Solid Storage
2
5
Source: IPCC (2019).
Table A-169: Methane Conversion Factors by State for Liquid
Systems for
2020 (Percent)
Dairy
Swine
Beef
Poultry
Liquid/Slurry
Anaerobic Liquid/Slurry
Anaerobic
and Pit
Anaerobic
State
Lagoon and Deep Pit
Lagoon
Storage
Liquid/Slurry
Lagoon
Alabama
76 40
76
40
42
76
Alaska
49 15
49
15
15
49
Arizona
81 64
79
51
48
77
Arkansas
75 35
76
37
36
75
California
75 34
75
34
45
76
Colorado
66 23
70
25
25
66
Connecticut
71 26
71
26
27
71
Delaware
75 34
75
34
33
75
Florida
78 60
78
58
54
78
Georgia
76 43
76
41
49
76
Hawaii
77 60
77
60
60
77
Idaho
68 24
64
21
21
64
Illinois
72 29
72
29
28
73
Indiana
71 27
71
27
28
71
Iowa
70 26
70
26
26
70
Kansas
74 32
74
32
32
74
Kentucky
74 32
74
33
32
74
Louisiana
78 50
78
48
51
77
Maine
64 21
64
22
21
65
Maryland
74 31
75
33
32
74
Massachusetts
69 25
70
26
26
70
Michigan
68 24
69
25
25
68
Minnesota
68 24
68
25
24
67
Mississippi
77 44
76
42
47
77
Missouri
74 32
73
30
31
74
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Dairy
Swine
Beef
Poultry
Liquid/Slurry
Anaerobic Liquid/Slurry
Anaerobic
and Pit
Anaerobic
State
Lagoon and Deep Pit
Lagoon
Storage
Liquid/Slurry
Lagoon
Montana
60
19
63
20
20
62
Nebraska
72
28
72
28
27
72
Nevada
72
27
72
27
24
74
New Hampshire
65
22
66
23
22
66
New Jersey
73
30
74
30
29
73
New Mexico
75
34
72
29
32
73
New York
67
23
68
24
24
68
North Carolina
74
34
76
40
34
74
North Dakota
66
23
66
22
23
66
Ohio
71
27
71
28
28
71
Oklahoma
76
38
75
36
36
76
Oregon
65
22
64
21
22
64
Pennsylvania
71
27
71
27
28
72
Rhode Island
71
27
71
27
27
71
South Carolina
76
41
76
42
39
76
South Dakota
69
25
70
26
25
69
Tennessee
74
33
75
36
34
74
Texas
77
41
77
44
41
78
Utah
68
23
66
22
24
68
Vermont
64
21
64
21
21
64
Virginia
72
29
75
35
29
73
Washington
64
21
64
21
22
65
West Virginia
71
27
71
27
27
71
Wisconsin
67
23
68
24
24
68
Wyoming
62
20
64
21
22
63
Note: MCFs developed using Tier 2 methods described in 2006IPCC Guidelines, Section 10.4.2.
Table A-170: Direct Nitrous Oxide Emission Factors (kg NzO-N/kg N excreted)
Waste Management System
Direct N20 Emission Factor
Aerobic Treatment (forced aeration)
0.005
Aerobic Treatment (natural aeration)
0.01
Anaerobic Digester
0
Anaerobic Lagoon
0
Cattle Deep Bed (active mix)
0.07
Cattle Deep Bed (no mix)
0.01
Compostingjn vessel
0.006
Compostingjntensive
0.1
Composting_passive
0.01
Composting_static
0.006
Daily Spread
0
Pit Storage
0.002
Dry Lot
0.02
Fuel
0
Liquid/Slurry
0.005
Pasture
0
Poultry with bedding
0.001
Poultry without bedding
0.001
Solid Storage
0.005
Source: IPCC (2006).
Annex 3
A-325
-------
Table A-171: Indirect Nitrous Oxide Loss Factors (Percent)
Runoff/Leaching Nitrogen Lossa
Waste Management
Volatilization
Mid-
Animal Type
System
Nitrogen Loss
Central
Pacific
Atlantic
Midwest
South
Beef Cattle
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Beef Cattle
Liquid/Slurry
26
0
0
0
0
0
Beef Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Anaerobic Lagoon
43
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Daily Spread
10
0
0
0
0
0
Dairy Cattle
Deep Pit
24
0
0
0
0
0
Dairy Cattle
Dry Lot
15
0.6
2
1.8
0.9
2.2
Dairy Cattle
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Dairy Cattle
Pasture
0
0
0
0
0
0
Dairy Cattle
Solid Storage
27
0.2
0
0
0
0
American Bison
Pasture
0
0
0
0
0
0
Goats
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Goats
Pasture
0
0
0
0
0
0
Horses
Dry Lot
23
0
0
0
0
0
Horses
Pasture
0
0
0
0
0
0
Mules and Asses
Dry Lot
23
0
0
0
0
0
Mules and Asses
Pasture
0
0
0
0
0
0
Poultry
Anaerobic Lagoon
54
0.2
0.8
0.7
0.4
0.9
Poultry
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Poultry
Pasture
0
0
0
0
0
0
Poultry
Poultry with bedding
26
0
0
0
0
0
Poultry
Poultry without bedding
34
0
0
0
0
0
Poultry
Solid Storage
8
0
0
0
0
0
Sheep
Dry Lot
23
1.1
3.9
3.6
1.9
4.3
Sheep
Pasture
0
0
0
0
0
0
Swine
Anaerobic Lagoon
58
0.2
0.8
0.7
0.4
0.9
Swine
Deep Pit
34
0
0
0
0
0
Swine
Liquid/Slurry
26
0.2
0.8
0.7
0.4
0.9
Swine
Pasture
0
0
0
0
0
0
Swine
Solid Storage
45
0
0
0
0
0
a Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff. Source:
EPA (2002b, 2005).
A-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-172: Total Methane Emissions from Livestock Manure Management (kt)a
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Dairy Cattle
572
665
943
1,091
1,114
1,158
1,140
1,159
1,203
1,232
1,248
1,278
1,237
1,269
Dairy Cows
564
657
935
1,082
1,106
1,149
1,131
1,150
1,193
1,223
1,239
1,269
1,228
1,260
Dairy Heifer
7
7
7
8
8
9
8
8
9
9
9
9
8
9
Dairy Calves
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Swine
621
762
812
797
791
821
755
718
807
846
840
882
891
895
Market Swine
482
607
665
657
653
678
623
585
665
699
697
730
740
748
Market <50 lbs.
101
121
128
95
94
98
88
86
95
101
100
105
106
103
Market 50-119 lbs.
101
123
131
144
142
149
136
130
145
154
153
160
163
163
Market 120-179 lbs.
136
170
184
188
185
193
179
169
192
203
200
211
211
215
Market >180 lbs.
144
193
221
229
231
238
219
200
232
241
244
254
260
267
Breeding Swine
139
155
147
140
138
143
133
133
143
146
143
152
151
147
Beef Cattle
63
69
67
67
67
66
65
64
65
68
70
70
71
71
Feed lot Steers
14
14
15
16
17
16
16
16
16
17
18
18
18
19
Feed lot Heifers
7
8
9
9
9
9
9
9
9
9
9
10
10
10
NOF Bulls
2
2
2
2
2
2
2
2
2
2
2
2
2
2
Beef Calves
2
3
3
3
3
3
3
3
3
3
3
3
3
3
NOF Heifers
5
6
5
5
5
5
5
5
5
6
6
5
5
5
NOF Steers
5
6
4
4
4
4
4
4
4
4
4
4
4
4
NOF Cows
27
30
28
28
27
27
26
26
26
27
28
28
28
28
Sheep
3
3
2
2
2
2
2
2
2
2
2
2
2
2
Goats
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Poultry
131
128
130
129
127
128
128
130
134
134
136
139
144
142
Hens >1 yr.
73
69
66
64
64
63
65
66
68
67
69
70
73
72
Total Pullets
25
22
22
24
23
24
24
24
26
26
26
28
29
28
Chickens
4
4
3
3
3
3
3
3
3
3
3
3
3
3
Broilers
19
23
31
31
31
32
31
31
31
32
32
33
33
33
Turkeys
10
g
6
6
6
6
6
6
6
6
6
6
6
6
Horses
4
5
5
4
4
4
4
4
4
3
3
3
3
3
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Does not exceed 0.5 kt.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
Annex 3
A-327
-------
Table A-173: Total Methane Emissions from Livestock Manure Management (MMT CO2 Eq.)a
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Dairy Cattle
14.3
16.6
23.6
27.3
27.9
29.0
28.5
29.0
30.1
30.8
31.2
32.0
30.9
31.7
Dairy Cows
14.1
16.4
23.4
27.1
27.6
28.7
28.3
28.7
29.8
30.6
31.0
31.7
30.7
31.5
Dairy Heifer
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Dairy Calves
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Swine
15.5
19.0
20.3
19.9
19.8
20.5
18.9
18.0
20.2
21.1
21.0
22.0
22.3
22.4
Market Swine
12.0
15.2
16.6
16.4
16.3
16.9
15.6
14.6
16.6
17.5
17.4
18.2
18.5
18.7
Market <50 lbs.
2.5
3.0
3.2
2.4
2.3
2.4
2.2
2.1
2.4
2.5
2.5
2.6
2.6
2.6
Market 50-119 lbs.
2.5
3.1
3.3
3.6
3.6
3.7
3.4
3.2
3.6
3.9
3.8
4.0
4.1
4.1
Market 120-179 lbs.
3.4
4.2
4.6
4.7
4.6
4.8
4.5
4.2
4.8
5.1
5.0
5.3
5.3
5.4
Market >180 lbs.
3.6
4.8
5.5
5.7
5.8
5.9
5.5
5.0
5.8
6.0
6.1
6.3
6.5
6.7
Breeding Swine
3.5
3.9
3.7
3.5
3.5
3.6
3.3
3.3
3.6
3.7
3.6
3.8
3.8
3.7
Beef Cattle
1.6
1.7
1.7
1.7
1.7
1.6
1.6
1.6
1.6
1.7
1.7
1.8
1.8
1.8
Feed lot Steers
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
Feed lot Heifers
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
NOF Bulls
+
0.1
0.1
0.1
0.1
+
+
+
+
+
0.1
0.1
0.1
0.1
Beef Calves
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NOF Heifers
0.1
0.2
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NOF Steers
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
NOF Cows
0.7
0.8
0.7
0.7
0.7
0.7
0.7
0.6
0.6
0.7
0.7
0.7
0.7
0.7
Sheep
0.1
0.1
0.1
0.1
+
0.1
+
+
+
+
+
+
+
+
Goats
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Poultry
3.3
3.2
3.2
3.2
3.2
3.2
3.2
3.2
3.4
3.3
3.4
3.5
3.6
3.5
Hens >1 yr.
1.8
1.7
1.7
1.6
1.6
1.6
1.6
1.7
1.7
1.7
1.7
1.8
1.8
1.8
Total Pullets
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.7
0.6
0.6
0.7
0.7
0.7
Chickens
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Broilers
0.5
0.6
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
Turkeys
0.3
0.2
0.2
0.2
0.2
0.2
0.1
0.1
0.1
0.2
0.2
0.1
0.1
0.1
Horses
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+ Does not exceed 0.05 MMT C02 Eq.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-174: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (kt)
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Dairy Cattle
17.5
18.0
18.1
18.8
19.0
19.3
19.3
19.4
19.9
20.2
20.3
20.6
20.5
20.6
Dairy Cows
10.4
10.5
10.3
10.5
10.7
10.9
10.9
11.0
11.2
11.3
11.6
11.8
11.8
11.9
Dairy Heifer
7.1
7.5
7.8
8.3
8.4
8.5
8.3
8.4
8.7
8.8
8.8
8.8
8.7
8.7
Dairy Calves
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Swine
4.0
4.5
5.5
5.8
5.9
6.0
6.0
5.8
6.2
6.3
6.6
6.7
7.0
7.1
Market Swine
3.0
3.5
4.6
5.1
5.2
5.2
5.2
5.0
5.4
5.6
5.7
5.8
6.1
6.2
Market <50 lbs.
0.6
0.6
0.9
0.7
0.8
0.8
0.7
0.7
0.8
0.8
0.8
0.8
0.9
0.9
Market 50-119 lbs.
0.6
0.7
0.9
1.1
1.2
1.2
1.2
1.1
1.2
1.2
1.3
1.3
1.4
1.4
Market 120-179 lbs.
0.9
1.0
1.3
1.5
1.5
1.5
1.5
1.5
1.6
1.6
1.7
1.7
1.8
1.8
Market >180 lbs.
0.9
1.1
1.5
1.7
1.8
1.8
1.8
1.7
1.8
1.9
2.0
2.0
2.1
2.2
Breeding Swine
1.0
1.1
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.8
0.9
0.8
Beef Cattle
19.8
21.8
24.0
25.3
25.9
25.8
26.0
26.0
26.8
28.4
29.9
30.5
31.0
31.4
Feedlot Steers
13.4
14.4
15.5
16.6
16.9
16.7
17.0
17.3
17.9
19.1
20.1
20.1
20.3
20.5
Feedlot Heifers
6.4
7.4
8.5
8.7
9.1
9.0
9.0
8.7
8.9
9.2
9.8
10.4
10.7
10.9
Sheep
+
0.7
1.2
1.1
1.1
1.1
1.1
1.0
1.0
1.0
1.0
1.0
1.0
1.0
Goats
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Poultry
4.7
5.1
5.4
5.2
5.2
5.3
5.2
5.2
5.2
5.4
5.5
5.6
5.6
5.5
Hens >1 yr.
1.0
1.0
1.3
1.3
1.3
1.3
1.3
1.4
1.3
1.4
1.4
1.5
1.5
1.4
Total Pullets
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Chickens
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Broilers
2.2
2.7
3.0
2.8
2.8
2.9
2.7
2.7
2.8
2.8
2.9
2.9
2.9
3.0
Turkeys
1.2
1.1
0.8
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.7
0.6
Horses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Note: American bison are maintained entirely on pasture, range, and paddock. Emissions from manure deposited on pasture are included in the Agricultural Soils
Management sector.
+ Does not exceed 0.05 kt.
NA (Not Applicable)
Annex 3
A-329
-------
Table A-175: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (MMT CO2 Eg.)
Animal Type
1990
1995
2005
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
Dairy Cattle
5.2
5.4
5.4
5.6
5.7
5.8
5.7
5.8
5.9
6.0
6.1
6.1
6.1
6.1
Dairy Cows
3.1
3.1
3.1
3.1
3.2
3.2
3.3
3.3
3.3
3.4
3.4
3.5
3.5
3.6
Dairy Heifer
2.1
2.2
2.3
2.5
2.5
2.5
2.5
2.5
2.6
2.6
2.6
2.6
2.6
2.6
Dairy Calves
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
Swine
1.2
1.4
1.6
1.7
1.8
1.8
1.8
1.7
1.8
1.9
2.0
2.0
2.1
2.1
Market Swine
0.9
1.0
1.4
1.5
1.5
1.6
1.5
1.5
1.6
1.7
1.7
1.7
1.8
1.9
Market <50 lbs.
0.2
0.2
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.3
0.3
Market 50-119 lbs.
0.2
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
Market 120-179 lbs.
0.3
0.3
0.4
0.4
0.4
0.5
0.5
0.4
0.5
0.5
0.5
0.5
0.5
0.5
Market >180 lbs.
0.3
0.3
0.5
0.5
0.5
0.5
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.6
Breeding Swine
0.3
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.3
0.2
Beef Cattle
5.9
6.5
7.2
7.6
7.7
7.7
7.7
7.8
8.0
8.5
8.9
9.1
9.2
9.4
Feedlot Steers
4.0
4.3
4.6
4.9
5.0
5.0
5.1
5.1
5.3
5.7
6.0
6.0
6.1
6.1
Feedlot Heifers
1.9
2.2
2.5
2.6
2.7
2.7
2.7
2.6
2.6
2.8
2.9
3.1
3.2
3.2
Sheep
0.1
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
0.3
Goats
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Poultry
1.4
1.5
1.6
1.5
1.5
1.6
1.6
1.6
1.6
1.6
1.6
1.7
1.7
1.7
Hens >1 yr.
0.3
0.3
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.4
Total Pullets
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Chickens
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Broilers
0.6
0.8
0.9
0.8
0.8
0.9
0.8
0.8
0.8
0.8
0.9
0.9
0.9
0.9
Turkeys
0.4
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Horses
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Mules and Asses
+
+
+
+
+
+
+
+
+
+
+
+
+
+
American Bison
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
NA
+ Does not exceed 0.05 MMT C02 Eq.
NA (Not Applicable)
Note: American bison are maintained entirely on pasture, range, and paddock. Emissions from manure deposited on pasture are included in the Agricultural Soils
Management sector.
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-176: Methane Emissions by State from Livestock Manure Management for 2020 (kt)a'b
Beef Not
Mules
Beef on
on
Dairy
Dairy
Swine-
Swine—
and
American
State
Feedlots
Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0200
0.7965
0.3505
0.0053
0.1637
0.1129
10.4743
4.3320
0.0094
0.0124
0.0070
0.0500
0.0045
0.0001
16.3386
Alaska
0.0001
0.0134
0.0048
0.0002
0.0035
0.0019
0.0034
+
+
0.0002
0.0001
0.0016
+
0.0015
0.0307
Arizona
0.6770
0.3419
22.8918
0.2719
2.0783
0.4671
1.7746
0.0001
0.0001
0.0373
0.0076
0.0842
0.0011
0.0001
28.6329
Arkansas
0.0407
1.0569
0.3915
0.0078
0.6583
1.1685
0.7033
3.7865
0.7699
0.0104
0.0047
0.0434
0.0031
0.0001
8.6449
California
1.7011
1.2240
317.5608
1.9510
1.2489
0.2325
3.0869
1.0859
0.1987
0.2117
0.0179
0.0894
0.0022
0.0014
328.6123
Colorado
1.8399
1.3662
20.5639
0.1717
3.3477
2.4368
5.0004
0.0022
0.0002
0.1233
0.0073
0.1009
0.0023
0.0117
34.9746
Connecticut
0.0004
0.0093
2.6819
0.0161
0.0111
0.0031
0.1870
0.0006
0.0001
0.0024
0.0008
0.0087
0.0004
0.0004
2.9224
Delaware
0.0005
0.0039
0.5256
0.0032
0.0267
0.0412
0.1952
0.9036
+
0.0006
0.0002
0.0034
+
0.0002
1.7043
Florida
0.0120
1.0126
14.6866
0.1027
0.0640
0.0750
8.6839
0.2352
0.0002
0.0123
0.0092
0.0876
0.0048
0.0001
24.9863
Georgia
0.0172
0.5983
10.7010
0.0811
0.3867
0.2860
17.6410
4.7800
0.0001
0.0125
0.0096
0.0491
0.0046
0.0001
34.5671
Hawaii
0.0038
0.0961
0.0608
0.0029
0.0827
0.0712
0.1161
0.0003
+
0.0075
0.0025
0.0051
0.0001
0.0001
0.4493
Idaho
0.5043
0.8411
104.1449
0.4940
0.1164
0.0820
0.7030
0.0002
0.0002
0.0667
0.0044
0.0457
0.0010
0.0286
107.0327
Illinois
0.4288
0.4774
10.1097
0.0721
45.8138
11.3238
0.3932
0.0048
0.0276
0.0234
0.0050
0.0358
0.0018
0.0006
68.7179
Indiana
0.1833
0.2630
15.2407
0.1191
43.4748
5.0620
1.1515
0.1651
0.4967
0.0243
0.0055
0.0744
0.0017
0.0004
66.2625
Iowa
2.2725
1.4102
33.6507
0.1814
208.2500
16.9126
1.5481
0.0838
0.2906
0.0644
0.0124
0.0494
0.0013
0.0024
264.7296
Kansas
4.6023
2.3741
34.8392
0.2275
31.1798
5.1008
0.1116
0.0009
0.0065
0.0311
0.0068
0.0455
0.0017
0.0045
78.5322
Kentucky
0.0335
1.1926
4.3549
0.0703
5.2372
1.1027
0.5727
1.0786
0.0064
0.0284
0.0073
0.1192
0.0046
0.0020
13.8105
Louisiana
0.0111
0.5177
0.8810
0.0084
0.0260
0.0238
2.1662
0.6894
+
0.0060
0.0026
0.0412
0.0026
0.0001
4.3761
Maine
0.0011
0.0183
3.0868
0.0229
0.0072
0.0047
0.1569
0.0011
0.0003
0.0053
0.0007
0.0073
0.0002
0.0002
3.3130
Maryland
0.0133
0.0626
5.0245
0.0455
0.1147
0.0410
0.3400
1.0364
0.0018
0.0079
0.0020
0.0303
0.0008
+
6.7208
Massachusetts
0.0004
0.0088
0.4016
0.0109
0.0238
0.0144
0.0138
0.0004
0.0007
0.0053
0.0009
0.0125
0.0005
+
0.4940
Michigan
0.2810
0.1952
64.0066
0.2743
9.5044
2.0089
0.9041
0.0391
0.1292
0.0362
0.0039
0.0560
0.0015
0.0028
77.4431
Minnesota
0.6987
0.6022
42.4605
0.3681
64.9038
8.7130
0.2654
0.2085
0.9935
0.0490
0.0048
0.0395
0.0013
0.0024
119.3106
Mississippi
0.0233
0.5691
0.6051
0.0136
0.6375
1.5494
7.6405
2.6468
+
0.0084
0.0047
0.0358
0.0034
0.0002
13.7379
Missouri
0.1934
2.3367
6.9035
0.0567
38.0145
12.4191
0.4875
1.0537
0.4222
0.0426
0.0078
0.0746
0.0043
0.0006
62.0172
Montana
0.0782
2.0063
1.2224
0.0074
0.8760
0.5279
0.9409
0.0021
0.0007
0.0580
0.0021
0.0755
0.0012
0.0226
5.8214
Nebraska
4.6448
2.9480
11.0344
0.0478
36.9683
10.0594
0.4945
0.0298
0.0094
0.0333
0.0040
0.0439
0.0007
0.0269
66.3452
Nevada
New
Hampshire
0.0054
0.3331
6.9107
0.0151
0.0349
0.0024
0.0007
+
+
0.0189
0.0011
0.0102
0.0002
+
7.3326
0.0003
0.0065
1.0599
0.0107
0.0107
0.0034
0.0334
0.0005
0.0001
0.0032
0.0005
0.0065
0.0002
0.0003
1.1362
New Jersey
0.0005
0.0122
0.4385
0.0054
0.0369
0.0096
0.2483
0.0005
0.0005
0.0060
0.0016
0.0233
0.0006
+
0.7840
New Mexico
0.0235
0.6760
39.5733
0.2157
0.0045
0.0034
0.1582
0.0001
0.0004
0.0276
0.0048
0.0437
0.0010
0.0050
40.7372
New York
0.0413
0.2191
85.7945
0.5724
0.2911
0.0792
0.6045
0.0115
0.0075
0.0399
0.0034
0.0625
0.0011
0.0010
87.7289
North Carolina
0.0126
0.4336
4.9719
0.0447
139.9367
30.2352
13.0597
3.4677
0.7451
0.0205
0.0073
0.0507
0.0048
0.0002
192.9906
North Dakota
0.0788
1.1825
2.2172
0.0125
0.6373
0.5206
0.0218
0.0001
0.0194
0.0320
0.0010
0.0223
0.0003
0.0119
4.7577
Annex 3
A-331
-------
Beef Not Mules
Beef on
on
Dairy
Dairy
Swine-
Swine—
and
American
State
Feedlots
Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Ohio
0.2922
0.4214
31.2611
0.1905
25.2366
4.1074
1.1404
0.3903
0.1490
0.0577
0.0083
0.0977
0.0031
0.0009
63.3566
Oklahoma
0.8408
2.7431
4.0010
0.0451
26.9750
16.1538
2.9354
0.7384
0.0094
0.0322
0.0140
0.1277
0.0072
0.0008
54.6239
Oregon
0.1828
0.7628
8.4436
0.1194
0.0396
0.0215
0.8190
0.0649
0.0003
0.0492
0.0067
0.0675
0.0016
0.0023
10.5811
Pennsylvania
0.1933
0.3500
42.9951
0.4681
11.1671
2.6555
0.9321
0.7976
0.1664
0.0440
0.0069
0.0779
0.0037
0.0011
59.8588
Rhode Island
0.0001
0.0017
0.0506
0.0008
0.0045
0.0013
0.0076
+
0.0003
0.0007
0.0001
0.0020
+
+
0.0698
South Carolina
0.0049
0.2037
1.3733
0.0107
3.3287
0.2440
4.0249
0.8784
0.2537
0.0064
0.0058
0.0436
0.0026
+
10.3808
South Dakota
0.7623
2.1900
23.0733
0.0709
15.4018
5.3918
0.1619
0.0005
0.1118
0.1066
0.0024
0.0482
0.0008
0.0242
47.3463
Tennessee
0.0478
1.0531
3.0003
0.0617
3.8577
0.7254
0.2834
0.6760
0.0002
0.0334
0.0140
0.0982
0.0080
0.0003
9.8595
Texas
7.3762
6.1803
75.7010
0.6354
14.3426
4.1507
5.6932
2.5341
0.0444
0.2612
0.1123
0.3528
0.0384
0.0085
117.4312
Utah
0.0343
0.5086
9.3209
0.0747
6.5296
1.2363
4.6238
0.0002
0.1156
0.0827
0.0028
0.0553
0.0005
0.0010
22.5865
Vermont
0.0013
0.0311
11.8076
0.0899
0.0073
0.0047
0.0164
0.0012
0.0002
0.0069
0.0012
0.0074
+
0.0002
11.9754
Virginia
0.0370
0.7472
7.0694
0.0569
4.8912
0.1107
0.3329
0.9963
0.3974
0.0334
0.0061
0.0593
0.0031
0.0005
14.7415
Washington
0.4654
0.4196
43.3138
0.2149
0.0620
0.0278
1.2900
0.0952
0.0002
0.0149
0.0039
0.0511
0.0012
0.0010
45.9612
West Virginia
0.0077
0.2398
0.4100
0.0051
0.0036
0.0033
0.1874
0.2532
0.0969
0.0151
0.0033
0.0243
0.0015
0.0001
1.2513
Wisconsin
0.4310
0.5563
127.6907
1.0769
2.2296
0.7664
0.4981
0.1984
0.0801
0.0345
0.0159
0.0648
0.0014
0.0060
133.6501
Wyoming
0.1136
0.9987
0.9744
0.0059
0.1711
0.5222
0.0325
0.0001
+
0.0986
0.0022
0.0513
0.0013
0.0102
2.9821
+ Does not exceed 0.00005 kt.
a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
b Beef Not on Feed includes calves.
Table A-177: Methane Emissions by State from Livestock Manure Management for 2020 (MMT CO2 Eq.)a
Beef Not Mules
Beef on on Dairy Dairy Swine— Swine— and American
State
Feedlots
Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0005
0.0199
0.0088
0.0001
0.0041
0.0028
0.2619
0.1083
0.0002
0.0003
0.0002
0.0013
0.0001
+
0.4085
Alaska
+
0.0003
0.0001
+
0.0001
+
0.0001
+
+
+
+
+
+
+
0.0008
Arizona
0.0169
0.0085
0.5723
0.0068
0.0520
0.0117
0.0444
+
+
0.0009
0.0002
0.0021
+
+
0.7158
Arkansas
0.0010
0.0264
0.0098
0.0002
0.0165
0.0292
0.0176
0.0947
0.0192
0.0003
0.0001
0.0011
0.0001
+
0.2161
California
0.0425
0.0306
7.9390
0.0488
0.0312
0.0058
0.0772
0.0271
0.0050
0.0053
0.0004
0.0022
0.0001
+
8.2153
Colorado
0.0460
0.0342
0.5141
0.0043
0.0837
0.0609
0.1250
0.0001
+
0.0031
0.0002
0.0025
0.0001
0.0003
0.8744
Connecticut
+
0.0002
0.0670
0.0004
0.0003
0.0001
0.0047
+
+
0.0001
+
0.0002
+
+
0.0731
Delaware
+
0.0001
0.0131
0.0001
0.0007
0.0010
0.0049
0.0226
+
+
+
0.0001
+
+
0.0426
Florida
0.0003
0.0253
0.3672
0.0026
0.0016
0.0019
0.2171
0.0059
+
0.0003
0.0002
0.0022
0.0001
+
0.6247
Georgia
0.0004
0.0150
0.2675
0.0020
0.0097
0.0072
0.4410
0.1195
+
0.0003
0.0002
0.0012
0.0001
+
0.8642
Hawaii
0.0001
0.0024
0.0015
0.0001
0.0021
0.0018
0.0029
+
+
0.0002
0.0001
0.0001
+
+
0.0112
Idaho
0.0126
0.0210
2.6036
0.0123
0.0029
0.0021
0.0176
+
+
0.0017
0.0001
0.0011
+
0.0007
2.6758
Illinois
0.0107
0.0119
0.2527
0.0018
1.1453
0.2831
0.0098
0.0001
0.0007
0.0006
0.0001
0.0009
+
+
1.7179
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Beef Not Mules
Beef on
on
Dairy
Dairy
Swine-
Swine—
and
American
State
Feedlots
Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Indiana
0.0046
0.0066
0.3810
0.0030
1.0869
0.1265
0.0288
0.0041
0.0124
0.0006
0.0001
0.0019
+
+
1.6566
Iowa
0.0568
0.0353
0.8413
0.0045
5.2062
0.4228
0.0387
0.0021
0.0073
0.0016
0.0003
0.0012
+
0.0001
6.6182
Kansas
0.1151
0.0594
0.8710
0.0057
0.7795
0.1275
0.0028
+
0.0002
0.0008
0.0002
0.0011
+
0.0001
1.9633
Kentucky
0.0008
0.0298
0.1089
0.0018
0.1309
0.0276
0.0143
0.0270
0.0002
0.0007
0.0002
0.0030
0.0001
0.0001
0.3453
Louisiana
0.0003
0.0129
0.0220
0.0002
0.0006
0.0006
0.0542
0.0172
+
0.0002
0.0001
0.0010
0.0001
+
0.1094
Maine
+
0.0005
0.0772
0.0006
0.0002
0.0001
0.0039
+
+
0.0001
+
0.0002
+
+
0.0828
Maryland
0.0003
0.0016
0.1256
0.0011
0.0029
0.0010
0.0085
0.0259
+
0.0002
0.0001
0.0008
+
+
0.1680
Massachusetts
+
0.0002
0.0100
0.0003
0.0006
0.0004
0.0003
+
+
0.0001
+
0.0003
+
+
0.0123
Michigan
0.0070
0.0049
1.6002
0.0069
0.2376
0.0502
0.0226
0.0010
0.0032
0.0009
0.0001
0.0014
+
0.0001
1.9361
Minnesota
0.0175
0.0151
1.0615
0.0092
1.6226
0.2178
0.0066
0.0052
0.0248
0.0012
0.0001
0.0010
+
0.0001
2.9828
Mississippi
0.0006
0.0142
0.0151
0.0003
0.0159
0.0387
0.1910
0.0662
+
0.0002
0.0001
0.0009
0.0001
+
0.3434
Missouri
0.0048
0.0584
0.1726
0.0014
0.9504
0.3105
0.0122
0.0263
0.0106
0.0011
0.0002
0.0019
0.0001
+
1.5504
Montana
0.0020
0.0502
0.0306
0.0002
0.0219
0.0132
0.0235
0.0001
+
0.0015
0.0001
0.0019
+
0.0006
0.1455
Nebraska
0.1161
0.0737
0.2759
0.0012
0.9242
0.2515
0.0124
0.0007
0.0002
0.0008
0.0001
0.0011
+
0.0007
1.6586
Nevada
New
Hampshire
0.0001
0.0083
0.1728
0.0004
0.0009
0.0001
+
+
+
0.0005
+
0.0003
+
+
0.1833
+
0.0002
0.0265
0.0003
0.0003
0.0001
0.0008
+
+
0.0001
+
0.0002
+
+
0.0284
New Jersey
+
0.0003
0.0110
0.0001
0.0009
0.0002
0.0062
+
+
0.0002
+
0.0006
+
+
0.0196
New Mexico
0.0006
0.0169
0.9893
0.0054
0.0001
0.0001
0.0040
+
+
0.0007
0.0001
0.0011
+
0.0001
1.0184
New York
0.0010
0.0055
2.1449
0.0143
0.0073
0.0020
0.0151
0.0003
0.0002
0.0010
0.0001
0.0016
+
+
2.1932
North Carolina
0.0003
0.0108
0.1243
0.0011
3.4984
0.7559
0.3265
0.0867
0.0186
0.0005
0.0002
0.0013
0.0001
+
4.8248
North Dakota
0.0020
0.0296
0.0554
0.0003
0.0159
0.0130
0.0005
+
0.0005
0.0008
+
0.0006
+
0.0003
0.1189
Ohio
0.0073
0.0105
0.7815
0.0048
0.6309
0.1027
0.0285
0.0098
0.0037
0.0014
0.0002
0.0024
0.0001
+
1.5839
Oklahoma
0.0210
0.0686
0.1000
0.0011
0.6744
0.4038
0.0734
0.0185
0.0002
0.0008
0.0003
0.0032
0.0002
+
1.3656
Oregon
0.0046
0.0191
0.2111
0.0030
0.0010
0.0005
0.0205
0.0016
+
0.0012
0.0002
0.0017
+
0.0001
0.2645
Pennsylvania
0.0048
0.0087
1.0749
0.0117
0.2792
0.0664
0.0233
0.0199
0.0042
0.0011
0.0002
0.0019
0.0001
+
1.4965
Rhode Island
+
+
0.0013
+
0.0001
+
0.0002
+
+
+
+
+
+
+
0.0017
South Carolina
0.0001
0.0051
0.0343
0.0003
0.0832
0.0061
0.1006
0.0220
0.0063
0.0002
0.0001
0.0011
0.0001
+
0.2595
South Dakota
0.0191
0.0547
0.5768
0.0018
0.3850
0.1348
0.0040
+
0.0028
0.0027
0.0001
0.0012
+
0.0006
1.1837
Tennessee
0.0012
0.0263
0.0750
0.0015
0.0964
0.0181
0.0071
0.0169
+
0.0008
0.0004
0.0025
0.0002
+
0.2465
Texas
0.1844
0.1545
1.8925
0.0159
0.3586
0.1038
0.1423
0.0634
0.0011
0.0065
0.0028
0.0088
0.0010
0.0002
2.9358
Utah
0.0009
0.0127
0.2330
0.0019
0.1632
0.0309
0.1156
+
0.0029
0.0021
0.0001
0.0014
+
+
0.5647
Vermont
+
0.0008
0.2952
0.0022
0.0002
0.0001
0.0004
+
+
0.0002
+
0.0002
+
+
0.2994
Virginia
0.0009
0.0187
0.1767
0.0014
0.1223
0.0028
0.0083
0.0249
0.0099
0.0008
0.0002
0.0015
0.0001
+
0.3685
Washington
0.0116
0.0105
1.0828
0.0054
0.0015
0.0007
0.0323
0.0024
+
0.0004
0.0001
0.0013
+
+
1.1490
West Virginia
0.0002
0.0060
0.0103
0.0001
0.0001
0.0001
0.0047
0.0063
0.0024
0.0004
0.0001
0.0006
+
+
0.0313
Annex 3
A-333
-------
Beef Not
Mules
Beef on
on
Dairy
Dairy
Swine-
Swine—
and American
State
Feedlots
Feedb
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Wisconsin
0.0108
0.0139
3.1923
0.0269
0.0557
0.0192
0.0125
0.0050
0.0020
0.0009
0.0004
0.0016
+
0.0001
3.3413
Wyoming
0.0028
0.0250
0.0244
0.0001
0.0043
0.0131
0.0008
+
+
0.0025
0.0001
0.0013
+
0.0003
0.0746
+ Does not exceed 0.00005 MMT C02 Eq.
a Accounts for CH
4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.
Table A-178:
Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2020 (kt)
Beef
Beef
Mules
Feedlot-
Feedlot-
Dairy
Dairy
Swine-
Swine-
and
American
State
Heifer
Steers
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0048
0.0092
0.0024
0.0018
0.0010
0.0005
0.0750
0.3859
0.0011
0.0070
0.0015
0.0047
0.0004
NA
0.4952
Alaska
0.0001
0.0001
0.0002
0.0002
0.0001
+
0.0001
+
+
0.0001
+
0.0002
+
NA
0.0009
Arizona
0.1978
0.3714
0.3650
0.2414
0.0107
0.0018
0.0089
+
+
0.0116
0.0016
0.0079
0.0001
NA
1.2182
Arkansas
0.0103
0.0195
0.0028
0.0020
0.0051
0.0065
0.0989
0.3373
0.0898
0.0055
0.0010
0.0040
0.0003
NA
0.5830
California
0.4053
0.7660
2.4408
1.5752
0.0081
0.0011
0.0637
0.0967
0.0232
0.0710
0.0039
0.0083
0.0002
NA
5.4636
Colorado
0.8186
1.5503
0.3585
0.2620
0.0439
0.0234
0.0295
0.0002
+
0.0469
0.0017
0.0101
0.0002
NA
3.1453
Connecticut
0.0002
0.0003
0.0209
0.0118
0.0001
+
0.0079
0.0001
+
0.0020
0.0002
0.0009
+
NA
0.0444
Delaware
0.0002
0.0003
0.0039
0.0022
0.0002
0.0002
0.0079
0.0805
+
0.0005
+
0.0003
+
NA
0.0963
Florida
0.0027
0.0051
0.0701
0.0508
0.0003
0.0003
0.0595
0.0210
+
0.0069
0.0020
0.0082
0.0005
NA
0.2274
Georgia
0.0039
0.0075
0.0522
0.0300
0.0026
0.0014
0.1264
0.4258
+
0.0070
0.0021
0.0046
0.0004
NA
0.6640
Hawaii
0.0008
0.0016
0.0005
0.0023
0.0004
0.0003
0.0013
+
+
0.0025
0.0005
0.0005
+
NA
0.0107
Idaho
0.2274
0.4305
0.9278
0.7572
0.0015
0.0008
0.0043
+
+
0.0254
0.0010
0.0046
0.0001
NA
2.3807
Illinois
0.1810
0.3413
0.0821
0.0913
0.4136
0.0753
0.0282
0.0004
0.0032
0.0180
0.0012
0.0036
0.0002
NA
1.2393
Indiana
0.0773
0.1463
0.1855
0.1381
0.3676
0.0316
0.1599
0.0147
0.0580
0.0187
0.0013
0.0075
0.0002
NA
1.2066
Iowa
0.9680
1.8270
0.2338
0.2238
2.0970
0.1255
0.2150
0.0075
0.0339
0.0495
0.0029
0.0050
0.0001
NA
5.7888
Kansas
1.8942
3.5869
0.1810
0.2994
0.1949
0.0235
0.0079
0.0001
0.0008
0.0239
0.0016
0.0046
0.0002
NA
6.2189
Kentucky
0.0126
0.0238
0.0310
0.0234
0.0369
0.0057
0.0318
0.0961
0.0007
0.0236
0.0017
0.0120
0.0005
NA
0.2998
Louisiana
0.0025
0.0048
0.0056
0.0019
0.0002
0.0001
0.0111
0.0614
+
0.0032
0.0006
0.0038
0.0003
NA
0.0954
Maine
0.0004
0.0009
0.0282
0.0164
0.0001
+
0.0071
0.0001
+
0.0044
0.0002
0.0007
+
NA
0.0586
Maryland
0.0051
0.0095
0.0405
0.0310
0.0010
0.0003
0.0139
0.0923
0.0002
0.0066
0.0005
0.0031
0.0001
NA
0.2038
Massachusetts
0.0002
0.0003
0.0089
0.0075
0.0002
0.0001
0.0006
+
0.0001
0.0044
0.0002
0.0013
0.0001
NA
0.0238
Michigan
0.1201
0.2277
0.5404
0.3692
0.0979
0.0152
0.0667
0.0035
0.0151
0.0279
0.0009
0.0056
0.0002
NA
1.4904
Minnesota
0.3003
0.5667
0.4866
0.4608
0.6868
0.0678
0.0369
0.0186
0.1159
0.0377
0.0011
0.0040
0.0001
NA
2.7834
Mississippi
0.0054
0.0104
0.0048
0.0034
0.0039
0.0070
0.0396
0.2353
+
0.0047
0.0010
0.0033
0.0003
NA
0.3191
Missouri
0.0800
0.1518
0.0624
0.0633
0.2608
0.0624
0.0679
0.0939
0.0493
0.0328
0.0018
0.0075
0.0004
NA
0.9344
Montana
0.0352
0.0670
0.0124
0.0108
0.0122
0.0054
0.0059
0.0002
0.0001
0.0220
0.0005
0.0076
0.0001
NA
0.1794
Nebraska
1.9687
3.7109
0.0643
0.0631
0.2806
0.0562
0.0357
0.0027
0.0011
0.0256
0.0009
0.0044
0.0001
NA
6.2142
Nevada
0.0024
0.0045
0.0347
0.0230
0.0003
+
0.0001
+
+
0.0072
0.0003
0.0010
+
NA
0.0735
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
New
Hampshire
0.0001
0.0002
0.0107
0.0078
0.0001
+
0.0015
+
+
0.0027
0.0001
0.0007
+
NA
0.0240
New Jersey
0.0002
0.0003
0.0041
0.0036
0.0003
0.0001
0.0103
+
0.0001
0.0050
0.0004
0.0023
0.0001
NA
0.0267
New Mexico
0.0102
0.0193
0.6182
0.2938
+
+
0.0008
+
+
0.0105
0.0011
0.0044
0.0001
NA
0.9586
New York
0.0165
0.0312
0.6869
0.4038
0.0031
0.0006
0.0265
0.0010
0.0009
0.0332
0.0008
0.0063
0.0001
NA
1.2108
North Carolina
0.0034
0.0064
0.0273
0.0131
0.7898
0.1254
0.0943
0.3089
0.0869
0.0114
0.0016
0.0047
0.0005
NA
1.4737
North Dakota
0.0347
0.0646
0.0156
0.0154
0.0080
0.0048
0.0016
+
0.0023
0.0246
0.0002
0.0022
+
NA
0.1741
Ohio
0.1228
0.2331
0.2539
0.2201
0.2269
0.0272
0.1550
0.0348
0.0174
0.0477
0.0019
0.0098
0.0003
NA
1.3509
Oklahoma
0.2507
0.4742
0.0612
0.0435
0.1393
0.0612
0.0152
0.0658
0.0011
0.0170
0.0030
0.0119
0.0007
NA
1.1448
Oregon
0.0705
0.1334
0.1600
0.1304
0.0004
0.0001
0.0105
0.0058
+
0.0205
0.0016
0.0068
0.0002
NA
0.5402
Pennsylvania
0.0751
0.1419
0.4242
0.2978
0.1093
0.0191
0.1295
0.0710
0.0194
0.0366
0.0016
0.0078
0.0004
NA
1.3338
Rhode Island
+
0.0001
0.0005
0.0005
+
+
0.0003
+
+
0.0006
+
0.0002
+
NA
0.0024
South Carolina
0.0012
0.0023
0.0071
0.0029
0.0201
0.0011
0.0219
0.0782
0.0296
0.0036
0.0013
0.0041
0.0002
NA
0.1736
South Dakota
0.3251
0.6151
0.1369
0.0916
0.1473
0.0379
0.0119
+
0.0130
0.0820
0.0006
0.0048
0.0001
NA
1.4663
Tennessee
0.0127
0.0243
0.0194
0.0157
0.0242
0.0034
0.0118
0.0602
+
0.0187
0.0030
0.0092
0.0008
NA
0.2034
Texas
2.1694
4.1111
1.0727
0.5995
0.0903
0.0192
0.1145
0.2257
0.0052
0.0810
0.0241
0.0329
0.0037
NA
8.5494
Utah
0.0155
0.0290
0.1655
0.1134
0.0809
0.0113
0.0264
+
0.0135
0.0314
0.0007
0.0056
0.0001
NA
0.4932
Vermont
0.0005
0.0010
0.1283
0.0654
0.0001
+
0.0008
0.0001
+
0.0057
0.0003
0.0007
+
NA
0.2030
Virginia
0.0140
0.0268
0.0484
0.0219
0.0303
0.0005
0.0139
0.0888
0.0464
0.0278
0.0014
0.0060
0.0003
NA
0.3265
Washington
0.1786
0.3380
0.3906
0.2472
0.0008
0.0003
0.0303
0.0085
+
0.0062
0.0009
0.0051
0.0001
NA
1.2066
West Virginia
0.0030
0.0057
0.0045
0.0035
+
+
0.0081
0.0226
0.0113
0.0126
0.0008
0.0024
0.0002
NA
0.0746
Wisconsin
0.1869
0.3509
1.4467
1.3162
0.0270
0.0068
0.0368
0.0177
0.0093
0.0266
0.0037
0.0065
0.0001
NA
3.4351
Wyoming
0.0510
0.0967
0.0067
0.0075
0.0025
0.0054
0.0002
+
+
0.0375
0.0005
0.0052
0.0001
NA
0.4952
+ Does not exceed 0.00005 kt.
Table A-179: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2020 (MMT CO2
Eg.)
Beef
Beef
Mules
Feedlot-
Feedlot-
Dairy
Dairy
Swine-
Swine-
and
American
State
Heifer
Steers
Cow
Heifer
Market
Breeding
Layer
Broiler
Turkey
Sheep
Goats
Horses
Asses
Bison
Total
Alabama
0.0014
0.0027
0.0007
0.0005
0.0003
0.0001
0.0223
0.1150
0.0003
0.0021
0.0004
0.0014
0.0001
NA
0.1476
Alaska
+
+
+
0.0001
+
+
+
+
+
+
+
+
+
NA
0.0003
Arizona
0.0590
0.1107
0.1088
0.0720
0.0032
0.0005
0.0027
+
+
0.0034
0.0005
0.0023
+
NA
0.3630
Arkansas
0.0031
0.0058
0.0008
0.0006
0.0015
0.0019
0.0295
0.1005
0.0268
0.0016
0.0003
0.0012
0.0001
NA
0.1737
California
0.1208
0.2283
0.7274
0.4694
0.0024
0.0003
0.0190
0.0288
0.0069
0.0211
0.0012
0.0025
0.0001
NA
1.6281
Colorado
0.2439
0.4620
0.1068
0.0781
0.0131
0.0070
0.0088
0.0001
+
0.0140
0.0005
0.0030
0.0001
NA
0.9373
Connecticut
+
0.0001
0.0062
0.0035
+
+
0.0024
+
+
0.0006
0.0001
0.0003
+
NA
0.0132
Delaware
+
0.0001
0.0012
0.0007
0.0001
0.0001
0.0024
0.0240
+
0.0001
+
0.0001
+
NA
0.0287
Florida
0.0008
0.0015
0.0209
0.0152
0.0001
0.0001
0.0177
0.0062
+
0.0021
0.0006
0.0024
0.0001
NA
0.0678
Georgia
0.0012
0.0022
0.0155
0.0089
0.0008
0.0004
0.0377
0.1269
+
0.0021
0.0006
0.0014
0.0001
NA
0.1979
Annex 3
A-335
-------
Hawaii
Idaho
Illinois
Indiana
Iowa
Kansas
Kentucky
Louisiana
Maine
Maryland
Massachusetts
Michigan
Minnesota
Mississippi
Missouri
Montana
Nebraska
Nevada
New
Hampshire
New Jersey
New Mexico
New York
North Carolina
North Dakota
Ohio
Oklahoma
Oregon
Pennsylvania
Rhode Island
South Carolina
South Dakota
Tennessee
Texas
Utah
Vermont
Virginia
Washington
West Virginia
Wisconsin
0.0002
0.0678
0.0539
0.0230
0.2885
0.5645
0.0037
0.0008
0.0001
0.0015
+
0.0358
0.0895
0.0016
0.0239
0.0105
0.5867
0.0007
0.0001
0.0030
0.0049
0.0010
0.0103
0.0366
0.0747
0.0210
0.0224
+
0.0004
0.0969
0.0038
0.6465
0.0046
0.0002
0.0042
0.0532
0.0009
0.0557
0.0005
0.1283
0.1017
0.0436
0.5444
1.0689
0.0071
0.0014
0.0003
0.0028
0.0001
0.0678
0.1689
0.0031
0.0452
0.0200
1.1058
0.0014
0.0001
0.0001
0.0058
0.0093
0.0019
0.0193
0.0695
0.1413
0.0398
0.0423
+
0.0007
0.1833
0.0072
1.2251
0.0087
0.0003
0.0080
0.1007
0.0017
0.1046
0.0002
0.2765
0.0245
0.0553
0.0697
0.0539
0.0092
0.0017
0.0084
0.0121
0.0027
0.1610
0.1450
0.0014
0.0186
0.0037
0.0192
0.0103
0.0032
0.0012
0.1842
0.2047
0.0081
0.0047
0.0757
0.0183
0.0477
0.1264
0.0002
0.0021
0.0408
0.0058
0.3197
0.0493
0.0382
0.0144
0.1164
0.0013
0.4311
0.0007
0.2256
0.0272
0.0411
0.0667
0.0892
0.0070
0.0006
0.0049
0.0092
0.0022
0.1100
0.1373
0.0010
0.0188
0.0032
0.0188
0.0069
0.0023
0.0011
0.0876
0.1203
0.0039
0.0046
0.0656
0.0130
0.0389
0.0887
0.0002
0.0009
0.0273
0.0047
0.1787
0.0338
0.0195
0.0065
0.0737
0.0010
0.3922
0.0001
0.0005
0.1232
0.1096
0.6249
0.0581
0.0110
+
+
0.0003
0.0001
0.0292
0.2047
0.0012
0.0777
0.0036
0.0836
0.0001
0.0001
+
0.0009
0.2354
0.0024
0.0676
0.0415
0.0001
0.0326
+
0.0060
0.0439
0.0072
0.0269
0.0241
+
0.0090
0.0002
+
0.0080
0.0001
0.0002
0.0224
0.0094
0.0374
0.0070
0.0017
+
+
0.0001
+
0.0045
0.0202
0.0021
0.0186
0.0016
0.0167
+
+
+
+
0.0002
0.0374
0.0014
0.0081
0.0182
+
0.0057
+
0.0003
0.0113
0.0010
0.0057
0.0034
+
0.0002
0.0001
+
0.0020
0.0004
0.0013
0.0084
0.0476
0.0641
0.0024
0.0095
0.0033
0.0021
0.0041
0.0002
0.0199
0.0110
0.0118
0.0202
0.0018
0.0106
0.0004
0.0031
0.0003
0.0079
0.0281
0.0005
0.0462
0.0045
0.0031
0.0386
0.0001
0.0065
0.0035
0.0035
0.0341
0.0079
0.0002
0.0042
0.0090
0.0024
0.0110
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
+
+
0.0007
0.0002
0.0001
+
NA
0.0032
+
+
0.0076
0.0003
0.0014
+
NA
0.7094
0.0001
0.0010
0.0054
0.0003
0.0011
0.0001
NA
0.3693
0.0044
0.0173
0.0056
0.0004
0.0022
0.0001
NA
0.3596
0.0022
0.0101
0.0148
0.0009
0.0015
+
NA
1.7251
+
0.0002
0.0071
0.0005
0.0014
0.0001
NA
1.8532
0.0286
0.0002
0.0070
0.0005
0.0036
0.0001
NA
0.0893
0.0183
+
0.0010
0.0002
0.0011
0.0001
NA
0.0284
+
+
0.0013
+
0.0002
+
NA
0.0175
0.0275
0.0001
0.0020
0.0001
0.0009
+
NA
0.0607
+
+
0.0013
0.0001
0.0004
+
NA
0.0071
0.0010
0.0045
0.0083
0.0003
0.0017
+
NA
0.4441
0.0055
0.0345
0.0112
0.0003
0.0012
+
NA
0.8295
0.0701
+
0.0014
0.0003
0.0010
0.0001
NA
0.0951
0.0280
0.0147
0.0098
0.0005
0.0022
0.0001
NA
0.2784
0.0001
+
0.0066
0.0001
0.0023
+
NA
0.0535
0.0008
0.0003
0.0076
0.0003
0.0013
+
NA
1.8518
+
+
0.0021
0.0001
0.0003
+
NA
0.0219
+
+
0.0008
+
0.0002
+
NA
0.0071
+
+
0.0015
0.0001
0.0007
+
NA
0.0079
+
+
0.0031
0.0003
0.0013
+
NA
0.2857
0.0003
0.0003
0.0099
0.0002
0.0019
+
NA
0.3608
0.0921
0.0259
0.0034
0.0005
0.0014
0.0001
NA
0.4392
+
0.0007
0.0073
0.0001
0.0007
+
NA
0.0519
0.0104
0.0052
0.0142
0.0006
0.0029
0.0001
NA
0.4026
0.0196
0.0003
0.0051
0.0009
0.0035
0.0002
NA
0.3412
0.0017
+
0.0061
0.0005
0.0020
+
NA
0.1610
0.0212
0.0058
0.0109
0.0005
0.0023
0.0001
NA
0.3975
+
+
0.0002
+
0.0001
+
NA
0.0007
0.0233
0.0088
0.0011
0.0004
0.0012
0.0001
NA
0.0517
+
0.0039
0.0244
0.0002
0.0014
+
NA
0.4370
0.0179
+
0.0056
0.0009
0.0027
0.0002
NA
0.0606
0.0673
0.0015
0.0241
0.0072
0.0098
0.0011
NA
2.5477
+
0.0040
0.0094
0.0002
0.0017
+
NA
0.1470
+
+
0.0017
0.0001
0.0002
+
NA
0.0605
0.0264
0.0138
0.0083
0.0004
0.0018
0.0001
NA
0.0973
0.0025
+
0.0019
0.0003
0.0015
+
NA
0.3596
0.0067
0.0034
0.0037
0.0002
0.0007
+
NA
0.0222
0.0053
0.0028
0.0079
0.0011
0.0019
+
NA
1.0237
-------
Wyoming 0.0152 0.0288 0.0020 0.0022 0.0007 0.0016 0.0001
+ Does not exceed 0.00005 MMT C02 Eq.
Annex 3
0.0112 0.0002 0.0015 + NA 0.0636
A-337
-------
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A-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
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Annex 3
A-341
-------
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A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
3.12. Methodologies for Estimating Soil Organic C Stock Changes,
Soil N2O Emissions, and CH4 Emissions and from Agricultural
Lands (Cropland and Grassland)
This annex provides a detailed description of Tier 1, 2, and 3 methods that are used to estimate soil organic C stock
changes for Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land
Converted to Grassland; direct N20 emissions from cropland and grassland soils; indirect N20 emissions associated with
volatilization, leaching, and runoff of N from croplands and grasslands; and CH4 emissions from rice cultivation.
Nitrous oxide (N20) is produced in soils through the microbial processes of nitrification and denitrification.138
Management influences these processes by modifying the availability of mineral nitrogen (N), which is a key control on
the N20 emissions rates (Mosier et al. 1998; Paustian et al. 2016). Emissions can occur directly in the soil where the N is
made available or can be transported to another location following volatilization, leaching, or runoff, and then converted
into N20. Management practices influence soil organic C stocks in agricultural soils by modifying crop and forage
production and microbial decomposition (Paustian et al. 1997, Paustian et al. 2016). CH4 emissions from rice cultivation
occur under flooded conditions through the process of methanogenesis, and is influenced by water management
practices, organic amendments and cultivar choice (Sanchis et al. 2014). This annex provides the underlying
methodologies for these three emission sources because there is considerable overlap in the methods with the majority
of emissions are estimated using the DayCent ecosystem simulation model.
A combination of Tier 1, 2, and 3 approaches are used to estimate soil organic C stock changes, direct and indirect soil
N20 emissions and CH4 emissions from rice cultivation in agricultural croplands and grasslands. The methodologies used
to estimate soil organic C stock changes include:
1) A Tier 3 method using the DayCent ecosystem model to estimate soil organic C stock changes in mineral soils on
non-federal lands that have less than 35 percent coarse fragments by volume and are used to produce alfalfa
hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts, potatoes, rice, sorghum, soybeans, sugar
beets, sunflowers, tobacco, and wheat, as well as non-federal grasslands and land use change between
grassland and cropland (with the crops listed above and less than 35 percent coarse fragments);
2) Tier 2 methods with country-specific factors for estimating mineral soil organic C stock changes for mineral soils
that are very gravelly, cobbly, or shaley (greater than 35 percent coarse fragments by volume), are used to
produce crops or have land use changes to cropland and grassland (other than the conversions between
cropland and grassland that are not simulated with DayCent);
3) Tier 2 methods with country-specific factors for estimating mineral soil organic C stock changes on federal
lands;
4) Tier 2 methods with country-specific factors for estimating losses of C from organic soils that are drained for
agricultural production; and
5) Tier 2 methods for estimating additional changes in mineral soil organic C stocks due to additions of biosolids
(i.e., treated sewage sludge) to soils.
The methodologies used to estimate soil N20 emissions include:
1) A Tier 3 method using the DayCent ecosystem model to estimate direct emissions from mineral soils that have
less than 35 percent coarse fragments by volume and are used to produce alfalfa hay, barley, corn, cotton,
grass hay, grass-clover hay, oats, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tobacco
and wheat, as well as non-federal grasslands and land use change between grassland and cropland (with the
crops listed above and less than 35 percent coarse fragments);
138 Nitrification and denitrification are driven by the activity of microorganisms in soils. Nitrification is the aerobic microbial
oxidation of ammonium (NH4+) to nitrate (N03 ), and denitrification is the anaerobic microbial reduction of nitrate to N2. Nitrous
oxide is a gaseous intermediate product in the reaction sequence of nitrification and denitrification.
Annex 3
A-343
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2) A combination of the Tier 1 and 3 methods to estimate indirect N20 emissions associated with management of
cropland and grassland simulated with DayCent;
3) A Tier 1 method to estimate direct and indirect N20 emissions from mineral soils that are not simulated with
DayCent, including very gravelly, cobbly, or shaley soils (greater than 35 percent coarse fragments by volume);
mineral soils with less than 35 percent coarse fragments that are used to produce crops that are not simulated
by DayCent; crops that are rotated with the crops that are not simulated with DayCent; Pasture/Range/Paddock
(PRP) manure N deposited on federal grasslands; and land application of biosolids (i.e., treated sewage sludge)
to soils; and
4) A Tier 1 method to estimate direct N20 emissions due to partial or complete drainage of organic soils in
croplands and grasslands.
The methodologies used to estimate soil CH4 emissions from rice cultivation include:
1) A Tier 3 method using the DayCent ecosystem model to estimate CH4 emissions from mineral soils that have less
than 35 percent coarse fragments by volume and rice grown continuously or in rotation with crops that are
simulated with DayCent, including alfalfa hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts,
potatoes, sorghum, soybeans, sugar beets, sunflowers, tobacco, and wheat; and
2) A Tier 1 method to estimate CH4 emissions from all other soils used to produce rice that are not estimated with
the Tier 3 method, including rice grown on organic soils (i.e., Histosols), mineral soils with very gravelly, cobbly,
or shaley soils (greater than 35 percent coarse fragments by volume), and rice grown in rotation with crops that
are not simulated by DayCent.
As described above, the Inventory uses a Tier 3 approach to estimate C stock changes, direct soil N20 emissions, and CH4
emissions from rice cultivation for most agricultural lands. This approach has the following advantages over the IPCC Tier
1 or 2 approaches:
1) It utilizes actual weather data at sub-county scales enabling quantification of inter-annual variability in N20
emissions and C stock changes at finer spatial scales, as opposed to a single emission factor for the entire
country for soil N20 or a broad climate region classification for soil organic C stock changes;
2) The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C and N
dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;
3) The simulation approach provides a more detailed representation of management influences and their
interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods;
4) The legacy effects of past management can be addressed with the Tier 3 approach such as land use change
from decades prior to the inventory time period that can have ongoing effects on soil organic C stocks, and the
ongoing effects of N fertilization that may continue to stimulate N20 emissions in years after the application;
and
5) Soil N20 and CH4 emissions, and C stock changes are estimated on a more continuous, daily basis as a function
of the interaction of climate, soil, and land management, compared with the linear rate changes that are
estimated with the Tier 1 and 2 methods.
More information is provided about the model structure and evaluation of the Tier 3 method at the end of this Annex
(See section Tier 3 Model Description, Parameterization and Evaluation).
Splicing methods are used to fill gaps in the time series for the emission sources and are not described in this annex.
Specifically, the splicing methods are applied when there are gaps in the activity data at the end of the time series and
the Tier 1, 2 and 3 methods cannot be applied. The splicing methods are described in the main chapters, particularly Box
6-4 in the Cropland Remaining Cropland section and Box 5-4 in the Agricultural Soil Management section.
Inventory Compilation Steps
There are five steps involved in this inventory to estimate the following sources: a) soil organic C stock changes for
Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land Converted to
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Grassland; b) direct N20 emissions from cropland and grassland soils; indirect N20 emissions from volatilization, leaching,
and runoff from croplands and grasslands; and c) CH4 emissions from rice cultivation. First, the activity data are compiled
from a combination of land-use, livestock, crop, and grassland management surveys, as well as expert knowledge. In the
second, third, and fourth steps, soil organic C stock changes, direct and indirect N20 emissions, and CH4 emissions are
estimated using Tier 1, 2 and 3 methods. In the fifth step, total emissions are calculated by summing all components for
soil organic C stock changes, N20 emissions and CH4 emissions. The remainder of this annex describes the methods
underlying each step.
Step 1: Derive Activity Data
This step describes how the activity data are derived to estimate soil organic C stock changes, direct and indirect N20
emissions, and CH4 emissions from rice cultivation. The activity data requirements include: (1) land base and history data,
139
(2) crop-specific mineral N fertilizer rates and timing, (3) crop-specific manure amendment N rates and timing, (4)
other N inputs, (5) tillage practices, (6) cover crop management, (7) planting and harvesting dates for crops, (8) irrigation
140
data, (9) Enhanced Vegetation Index (EVI), (10) daily weather data, and (11) edaphic characteristics.
Step la: Activity Data for the Agricultural Land Base and Histories
The U.S. Department of Agriculture's 2015 National Resources Inventory (NRI) (USDA-NRCS 2018a) provides the basis for
identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland Remaining Cropland,
Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to Grassland.141 The NRI program have
data available from 1979 through 2015 (USDA-NRCS 2018a). The time series will be extended as new data are released
by the USDA NRI program.
The NRI has a stratified multi-stage sampling design, where primary sample units are stratified on the basis of county and
township boundaries defined by the U.S. Public Land Survey (Nusser and Goebel 1997). Within a primary sample unit,
typically a 160-acre (64.75 ha) square quarter-section, three sample locations are selected according to a restricted
randomization procedure. Each sample location in the survey is assigned an area weight (expansion factor) (Nusser and
Goebel 1997). In principle, the expansion factors represent the amount of area with the land use and land use change
history that is the same as the survey location. The NRI uses a sampling approach, and therefore there is some
uncertainty associated with scaling the survey location data to a region or the country using the expansion factors. In
general, the uncertainty declines at larger scales because of a larger sample size, such as states compared to smaller
county units. An extensive amount of soils, land-use, and land management data have been collected through the survey
(Nusser et al. 1998).142 Primary sources for data include aerial photography as well as field visits and county office
records.
The NRI survey provides crop data for most years between 1979 and 2015, with the exception of 1983,1988, and 1993.
These years are gap-filled using an automated set of rules so that cropping sequences are filled with the most likely crop
type given the historical cropping pattern at each NRI survey location. Grassland data are reported on 5-year increments
prior to 1998, but it is assumed that the land use is also grassland between the years of data collection (see Easter et al.
2008 for more information).
NRI survey locations are included in the land base for the agricultural soil organic C and N20 emissions inventories if they
are identified as cropland or grassland143 between 1990 and 2015 (See Section 6.1 Representation of the U.S. Land Base
for more information about areas in each land use and land use change category).144 NRI survey locations on federal
lands are not sampled by the USDA NRI program. The land use at the survey locations in federal lands is determined from
139 No data are currently available at the national scale to distinguish the type of fertilizer applied or timing of applications rates. It is a
planned improvement to address variation in these practices in future inventories, such as application of enhanced efficiency
fertilizers.
140 Edaphic characteristics include such factors as soil texture and pH.
141 Note that the Inventory does not include estimates of N20 emissions for federal grasslands with the exception of soil N20 from PRP
manure N, i.e., manure deposited directly onto pasture, range or paddock by grazing livestock.
142 In the current Inventory, NRI data only provide land use and management statistics through 2015. More recent data will be
incorporated in the future to extend the time series of activity data.
143 Includes only non-federal lands because federal lands are not classified into land uses as part of the NRI survey (i.e., they are only
designated as federal lands).
144 Land use for 2016 to 2020 is not compiled, but will be updated with a new release of the NRI data.
Annex 3
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the National Land Cover Dataset (NLCD) (Yang et al. 2018), and included in the agricultural land base if the land uses are
cropland and/or grassland. The NRI data are harmonized with the Forest Inventory and Analysis Dataset, and in this
process, the land use and land use change data are modified to address differences in Forest Land Remaining Forest
Land, Land Converted to Forest Land and Forest Land converted to other land uses between the two national surveys
(See Section 6.1 for more information on the U.S. land representation). Through this process, 524,991 survey locations in
this NRI are designated as agricultural land in the conterminous United States and Hawaii.
For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland, Grassland
Remaining Grassland, and Land Converted to Grassland. Land parcels under crop management in a specific year are
classified as Cropland Remaining Cropland if the parcel has been used as cropland for at least 20 years.145 Similarly, land
parcels under grassland management in a specific year of the inventory are classified as Grassland Remaining Grassland
if they have been designated as grassland for at least 20 years. Otherwise, land parcels are classified as Land Converted
to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands are
retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland) for 20
years as recommended by the IPCC (2006). Lands converted into Cropland and Grassland are further subdivided into the
specific land use conversions (e.g., Forest Land Converted to Cropland).
The Tier 3 method using the DayCent model is applied to estimate soil organic C stock changes, CH4, and N20 emissions
for 349,464 NRI survey locations that occur on mineral soils. Parcels of land that are not simulated with DayCent are
allocated to the Tier 2 approach for estimating soil organic C stock change, and a Tier 1 method (IPCC 2006) to estimate
soil N20 emissions146 and to estimate CH4 emissions from rice cultivation (Table A-180).
The land base for the Tier 1 and 2 methods includes 175,527 survey locations, and is comprised of (1) land parcels
occurring on organic soils; (2) land parcels that include non-agricultural uses such as forest or settlements in one or more
years of the inventory; (3) land parcels on mineral soils that are very gravelly, cobbly, or shaley (i.e., classified as soils
that have greater than 35 percent of soil volume comprised of gravel, cobbles, or shale); or (4) land parcels that are used
to produce some of the vegetable crops and perennial/horticultural crops, which are either grown continuously or in
rotation with other crops. DayCent has not been fully tested or developed to simulate biogeochemical processes in soils
used to produce some annual (e.g., lettuce), horticultural (e.g., flowers), or perennial (e.g., vineyards, orchards) crops
and agricultural use of organic soils. In addition, DayCent has not been adequately tested for soils with a high gravel,
cobble, or shale content.
Table A-180: Total Cropland and Grassland Area Estimated with Tier 1/2 and 3 Inventory
Approaches (Million Hectares)
Land Areas (million ha)
Mineral
Organic
Year
Tier 1/2
Tier 3
Total
Tier 1/2
Total147
1990
152.22
307.63
459.85
1.39
461.24
1991
151.49
307.89
459.37
1.38
460.75
1992
150.83
308.07
458.90
1.38
460.28
1993
149.84
308.47
458.31
1.38
459.69
1994
149.04
308.87
457.91
1.38
459.29
1995
147.92
309.28
457.20
1.37
458.57
1996
146.90
309.75
456.65
1.36
458.01
1997
145.69
310.19
455.88
1.35
457.23
1998
144.67
310.63
455.31
1.35
456.65
145 NRI points are classified according to land-use history records starting in 1979 when the NRI survey began, and consequently the
classifications are based on less than 20 years from 1990 to 1998.
146 The Tier 1 method for soil N20 does not require land area data with the exception of emissions from drainage and cultivation
of organic soils, so in practice the Tier 1 method is only dependent on the amount of N input to mineral soils and not the actual
land area.
147 The current Inventory includes estimation of greenhouse gas emissions and removals from all privately-owned and federal
grasslands and croplands in the conterminous United States and Hawaii, but does not include the croplands and grasslands in
Alaska. This leads to a discrepancy between the total area in this table, which is included in the estimation, compared to the
total managed land area in Section 6.1 Representation of the U.S. Land Base. See Planned Improvement sections in Cropland
Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland and Land Converted to Grassland for more
information about filling these gaps in the future so that emissions and removals will be estimated for all managed land.
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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1999
143.71
311.10
454.81
1.35
456.16
2000
142.98
311.38
454.36
1.35
455.71
2001
142.49
311.82
454.31
1.34
455.66
2002
141.78
312.09
453.87
1.35
455.22
2003
141.15
312.00
453.16
1.32
454.48
2004
140.65
311.92
452.57
1.34
453.90
2005
140.12
311.81
451.93
1.34
453.27
2006
139.57
311.77
451.34
1.33
452.68
2007
139.04
311.74
450.78
1.32
452.10
2008
138.71
311.60
450.31
1.32
451.63
2009
138.36
311.54
449.89
1.32
451.21
2010
138.05
311.43
449.48
1.32
450.80
2011
137.65
311.41
449.06
1.32
450.38
2012
137.28
311.33
448.61
1.32
449.93
2013
136.99
311.12
448.10
1.32
449.42
2014
136.75
310.79
447.54
1.31
448.86
2015
136.38
310.66
447.04
1.30
448.34
Note: In the current Inventory, land use and management data have
been incorporated through 2015.
Additional data will be incorporated in the future to extend the time
series of the land use data.
NRI survey locations on mineral soils are classified into specific crop categories, continuous pasture/rangeland, and other
non-agricultural uses for the Tier 2 inventory analysis for soil organic C (Table A-181). NRI locations are assigned to IPCC
input categories (low, medium, high, and high with organic amendments) according to the classification provided in IPCC
(2006). For croplands on federal lands, information on specific crop systems is not available, so all croplands are assumed
to be medium input. In addition, NRI differentiates between improved and unimproved grassland, where improvements
include irrigation and interseeding of legumes. Grasslands on federal lands (as identified with the NLCD) are classified
according to rangeland condition (nominal, moderately degraded and severely degraded) in areas where information is
available. For lands managed for livestock grazing by the Bureau of Land Management (BLM), IPCC rangeland condition
classes are interpreted at the state-level from the Rangeland Inventory, Monitoring and Evaluation Report (BLM 2014). In
order to estimate uncertainties, probability distribution functions (PDFs) for the NRI land-use data are based on replicate
weights that allow for proper variance estimates that correctly account for the complex sampling design. In particular,
the variance estimates and resulting PDFs correctly account for spatial or temporal dependencies. For example,
dependencies in land use result from the likelihood that current use is correlated with past use. These dependencies
occur because as an area of a land use/management category increases, the area of another land use/management
category must decline.
Table A-181: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral
Soil Organic C Approach (Million Hectares)
Land Areas (million hectares)
Land-Use/Management
System 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
Cropland Systems 33.47 33.18 32.87 32.36 31.86 31.39 30.96 30.49 29.69 29.17 28.78 28.44 28.13
Conservation Reserve
Program 2.74 3.15 3.08 2.91 2.67 2.59 2.46 2.45 1.96 2.12 1.86 1.99 1.73
High Input Cropping
Systems, Full Tillage 2.41 2.21 2.20 2.11 2.28 2.26 2.10 1.99 1.94 1.93 1.97 1.78 1.58
High Input Cropping
Systems, Reduced
Tillage 0.57 0.50 0.50 0.50 0.54 0.52 0.50 0.48 0.49 0.49 0.50 0.49 0.45
High Input Cropping
Systems, No Tillage 0.41 0.37 0.37 0.37 0.38 0.36 0.45 0.43 0.44 0.45 0.45 0.52 0.51
High Input Cropping
Systems with Manure,
Full Tillage 0.67 0.64 0.61 0.59 0.55 0.52 0.51 0.49 0.47 0.43 0.40 0.34 0.32
Annex 3
A-347
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High Input Cropping
Systems with Manure,
Reduced Tillage
0.18
0.17
0.16
0.16
0.16
0.15
0.14
0.14
0.13
0.13
0.12
0.12
0.11
High Input Cropping
Systems with Manure,
No Tillage
0.22
0.20
0.19
0.19
0.19
0.18
0.17
0.16
0.15
0.15
0.14
0.17
0.17
Medium Input Cropping
Systems, Full Tillage
7.03
7.02
6.78
6.57
6.49
6.26
6.32
5.97
5.65
5.47
5.54
4.29
4.03
Medium Input Cropping
Systems, Reduced
Tillage
1.71
1.66
1.62
1.58
1.58
1.53
1.53
1.49
1.40
1.37
1.42
1.68
1.69
Medium Input Cropping
Systems, No Tillage
1.85
1.71
1.68
1.63
1.62
1.60
1.58
1.52
1.45
1.41
1.44
2.33
2.35
Low Input Cropping
Systems, Full Tillage
9.46
9.31
9.31
9.34
9.30
9.40
9.14
9.17
9.30
9.13
9.08
8.21
8.25
Low Input Cropping
Systems, Reduced
Tillage
1.06
1.04
1.04
1.05
1.05
1.07
1.08
1.07
1.11
1.05
1.04
1.11
1.11
Low Input Cropping
Systems, No Tillage
0.68
0.73
0.73
0.74
0.73
0.72
0.90
0.90
0.92
0.86
0.89
1.53
1.52
Hay with Legumes or
Irrigation
1.67
1.67
1.69
1.64
1.50
1.44
1.35
1.38
1.31
1.25
1.14
1.04
1.20
Hay with Legumes or
Irrigation and Manure
0.50
0.49
0.50
0.51
0.48
0.45
0.43
0.47
0.46
0.44
0.41
0.42
0.54
Hay, Unimproved
0.01
0.01
0.02
0.02
0.02
0.02
0.00
0.01
0.07
0.05
0.01
0.03
0.04
Pasture with Legumes
or Irrigation in Rotation
0.02
0.01
0.02
0.01
0.01
0.01
0.01
0.01
0.04
0.03
0.01
0.02
0.02
Pasture with Legumes
or Irrigation and
Manure, in Rotation
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Rice
0.04
0.05
0.04
0.04
0.05
0.06
0.05
0.05
0.05
0.05
0.06
0.07
0.08
Perennials
2.24
2.24
2.31
2.36
2.28
2.25
2.24
2.32
2.38
2.37
2.31
2.28
2.42
Grassland Systems
118.68
118.22
117.88
117.40
117.11
116.46
115.87
115.14
114.93
114.47
114.13
113.98
113.57
Pasture with Legumes
or Irrigation
3.62
3.47
3.28
3.25
3.27
3.14
2.83
2.41
2.51
2.46
2.26
2.17
2.08
Pasture with Legumes
or Irrigation and
Manure
0.17
0.16
0.15
0.15
0.15
0.15
0.15
0.14
0.14
0.14
0.12
0.11
0.11
Rangelands and
Unimproved Pasture
82.27
81.87
81.82
81.68
81.42
80.82
79.85
79.64
78.94
78.42
78.83
78.54
79.53
Rangelands and
Unimproved Pasture,
Moderately Degraded
23.62
23.78
23.91
23.79
23.84
23.95
24.43
24.30
25.08
25.11
24.46
24.70
23.63
Rangelands and
Unimproved Pasture,
Severely Degraded
9.01
8.93
8.72
8.53
8.43
8.41
8.60
8.65
8.25
8.34
8.46
8.46
8.22
Total
152.15
151.40
150.75
149.76
148.97
147.85
146.83
145.63
144.61
143.64
142.91
142.42
141.70
Land-
Use/Management
System
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Cropland Systems
27.88 27.55 27.39 :
27.16
26.99
26.83
26.62
26.51
26.33
26.29
26.24
26.16
25.96
Conservation Reserve
Program
1.60
1.50
1.52
1.42
1.38
1.30
1.35
1.26
1.89
0.92
1.43
0.90
0.73
High Input Cropping
Systems, Full Tillage
1.59
1.59
1.60
1.37
1.34
1.37
1.42
1.44
1.30
1.24
1.18
1.14
1.06
A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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High Input Cropping
Systems, Reduced
Tillage
0.47
0.47
0.47
0.49
0.49
0.52
0.53
0.53
0.57
0.55
0.52
0.52
0.50
High Input Cropping
Systems, No Tillage
0.48
0.50
0.50
0.59
0.61
0.63
0.65
0.63
0.72
0.73
0.71
0.71
0.67
High Input Cropping
Systems with Manure,
Full Tillage
0.30
0.29
0.29
0.24
0.26
0.27
0.26
0.27
0.25
0.26
0.28
0.27
0.26
High Input Cropping
Systems with Manure,
Reduced Tillage
0.11
0.11
0.11
0.13
0.14
0.13
0.14
0.14
0.17
0.18
0.19
0.18
0.18
High Input Cropping
Systems with Manure,
No Tillage
0.18
0.17
0.17
0.17
0.18
0.18
0.18
0.18
0.19
0.19
0.20
0.20
0.20
Medium Input
Cropping Systems, Full
Tillage
3.98
3.99
3.82
3.50
3.58
3.55
3.49
3.49
3.16
3.39
3.19
3.41
3.26
Medium Input
Cropping Systems,
Reduced Tillage
1.72
1.75
1.71
1.83
1.85
1.85
1.78
1.78
1.87
2.04
1.93
2.10
2.07
Medium Input
Cropping Systems, No
Tillage
2.41
2.40
2.39
2.53
2.57
2.58
2.49
2.49
2.39
2.77
2.49
2.83
2.79
Low Input Cropping
Systems, Full Tillage
8.26
8.11
8.13
7.93
7.83
7.78
7.75
7.72
7.46
7.54
7.52
7.46
7.60
Low Input Cropping
Systems, Reduced
Tillage
1.06
1.01
1.01
1.08
1.02
1.00
1.00
1.01
1.00
1.04
1.04
0.97
1.01
Low Input Cropping
Systems, No Tillage
1.45
1.36
1.38
1.67
1.59
1.56
1.54
1.55
1.39
1.45
1.45
1.34
1.42
Hay with Legumes or
Irrigation
1.18
1.16
1.18
1.16
1.14
1.11
1.06
1.02
0.98
0.99
1.02
1.02
1.02
Hay with Legumes or
Irrigation and Manure
0.52
0.54
0.50
0.49
0.48
0.47
0.46
0.45
0.43
0.43
0.47
0.47
0.48
Hay, Unimproved
0.04
0.05
0.04
0.02
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.00
0.00
Pasture with Legumes
or Irrigation in
Rotation
0.03
0.03
0.03
0.01
0.02
0.02
0.03
0.02
0.01
0.01
0.01
0.00
0.00
Pasture with Legumes
or Irrigation and
Manure, in Rotation
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Rice
0.06
0.06
0.04
0.04
0.04
0.04
0.03
0.04
0.03
0.03
0.03
0.03
0.03
Perennials
2.43
2.46
2.49
2.46
2.44
2.46
2.44
2.47
2.50
2.53
2.55
2.59
2.65
Grassland Systems
113.20
113.04
112.67
112.34
111.96
111.80
111.65
111.45
111.22
110.90
110.66
110.50
110.29
Pasture with Legumes
or Irrigation
2.01
2.05
1.97
1.91
1.86
1.84
1.85
1.80
1.79
1.71
1.61
1.64
1.59
Pasture with Legumes
or Irrigation and
Manure
0.11
0.11
0.11
0.10
0.09
0.08
0.08
0.08
0.07
0.07
0.07
0.07
0.07
Rangelands and
Unimproved Pasture
79.60
78.73
78.47
78.36
78.00
77.90
77.74
77.75
77.73
77.46
77.40
77.04
77.37
Rangelands and
Unimproved Pasture,
Moderately Degraded
23.19
23.22
23.25
23.15
23.25
23.24
23.25
23.17
23.06
22.89
22.80
22.61
22.51
Rangelands and
Unimproved Pasture,
Severely Degraded
8.28
8.93
8.87
8.82
8.76
8.74
8.71
8.65
8.57
8.77
8.79
9.14
8.74
Total 141.08 140.59 140.05 139.50 138.95 138.63 138.27 137.96 137.55 137.19 136.90 136.66 136.25
Annex 3
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Note: In the current Inventory, land use and management data have been incorporated through 2015. Additional data will be
incorporated in the future to extend the time series for the land use and management data.
Organic soils are categorized into land-use systems based on drainage (IPCC 2006) (Table A-182). Undrained soils are
treated as having no loss of organic C or soil N20 emissions. Drained soils are subdivided into those used for cultivated
cropland, which are assumed to have high drainage and relatively large losses of C, and those used for managed pasture,
which are assumed to have less drainage with smaller losses of C. N20 emissions are assumed to be similar for both
drained croplands and grasslands.
Table A-182: Total Land Areas for Drained Organic Soils by Land Management Category and
Climate Region (Million Hectares)
IPCC Land-Use Category
for Organic Soils
Land Areas (million ha)
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Cold Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.59 0.58 0.59 0.59 0.59 0.59 0.59 0.60 0.60 0.60 0.59 0.59 0.59 0.59
0.34 0.34 0.35 0.35 0.35 0.35 0.34 0.34 0.34 0.34 0.34 0.35 0.35 0.35
0.04 0.05 0.04 0.04 0.03 0.03 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.02
0.97 0.97 0.98 0.98 0.98 0.98 0.97 0.97 0.97 0.97 0.97 0.97 0.96 0.96
Warm Temperate
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.16
0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.08 0.09 0.09 0.09 0.09 0.09 0.09
0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.01 0.00 0.01 0.00 0.00
0.25 0.25 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.25 0.25 0.25 0.25
Sub-Tropical
Cultivated Cropland
(high drainage)
Managed Pasture
(low drainage)
Undrained
Total
0.24 0.24 0.24 0.25 0.25 0.25 0.26 0.26 0.26 0.17 0.17 0.29 0.28 0.28
0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.10 0.10 0.09
0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.10 0.10 0.00 0.01 0.00
0.37 0.37 0.37 0.37 0.37 0.38 0.38 0.38 0.38 0.38 0.38 0.39 0.39 0.37
IPCC Land-Use Category
Land Areas (million ha)
for Organic Soils
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
Cold Temperate
Cultivated Cropland
(high drainage)
0.59
0.59
0.59
0.59
0.59
0.58
0.58
0.58
0.59
0.60
0.60
0.60
Managed Pasture
(low drainage)
0.37
0.37
0.37
0.37
0.37
0.38
0.38
0.38
0.38
0.38
0.38
0.38
Undrained
0.02
0.03
0.03
0.02
0.03
0.03
0.03
0.03
0.02
0.02
0.01
0.01
Total
0.98
0.98
0.98
0.98
0.99
0.99
0.99
0.99
0.99
0.99
0.99
1.00
Warm Temperate
Cultivated Cropland
(high drainage)
0.16
0.16
0.16
0.16
0.17
0.17
0.17
0.17
0.17
0.17
0.17
0.17
Managed Pasture
(low drainage)
0.09
0.10
0.09
0.10
0.09
0.09
0.10
0.10
0.10
0.10
0.10
0.10
Undrained
0.00
0.00
0.01
0.00
0.00
0.00
0.00
0.00
0.01
0.00
0.00
0.00
Total
0.26
0.26
0.26
0.26
0.26
0.26
0.27
0.27
0.27
0.28
0.28
0.28
Sub-Tropical
Cultivated Cropland
(high drainage)
0.27
0.27
0.27
0.26
0.26
0.26
0.26
0.26
0.26
0.24
0.26
0.25
Managed Pasture
(low drainage)
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
0.09
Undrained
0.01
0.01
0.01
0.01
0.01
0.00
0.00
0.01
0.01
0.03
0.01
0.01
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Total
0.37 0.37 0.37 0.36 0.36 0.36 0.36 0.36 0.36 0.35 0.36 0.35
Note: In the current Inventory, land use and management data have been incorporated through 2015. Additional data will be
incorporated in the future to extend the time series for the land use and management data.
The harvested area for rice cultivation is estimated from the NRI based on survey locations classified as flooded rice
(Table A-183). Ratoon crops occur in the Southeastern United States with a second season of rice during the year,
including Louisiana (LSU 2015 for years 2000 through 2015) and Texas (TAMU 2015 for years 1993 through 2015),
averaging 32 percent and 48 percent of rice acres planted, respectively. Florida also has a large fraction of area with
ratoon crops (45 percent), but ratoon cropping is uncommon in Arkansas occurring on a relatively small fraction of fields
estimated at about 1 percent. No data are available for ratoon crops in Missouri or Mississippi, and so the amount of
ratooning is assumed similar to Arkansas. Ratoon rice crops are not grown in California.
Table A-183: Total Rice Harvested Area Estimated with Tier 1 and 3 Inventory Approaches
(Million Hectares)
Land Areas (Million Hectares)
Year
Tier 1
Tier 3
Total
1990
0.21
1.50
1.71
1991
0.21
1.54
1.74
1992
0.22
1.65
1.87
1993
0.22
1.58
1.80
1994
0.23
1.51
1.74
1995
0.21
1.53
1.74
1996
0.22
1.52
1.74
1997
0.20
1.47
1.67
1998
0.25
1.46
1.70
1999
0.38
1.43
1.81
2000
0.42
1.48
1.90
2001
0.24
1.39
1.63
2002
0.23
1.57
1.80
2003
0.21
1.42
1.63
2004
0.21
1.50
1.71
2005
0.21
1.58
1.79
2006
0.17
1.27
1.44
2007
0.18
1.38
1.56
2008
0.15
1.28
1.44
2009
0.21
1.52
1.73
2010
0.20
1.57
1.77
2011
0.17
1.24
1.41
2012
0.22
1.18
1.40
2013
0.16
1.26
1.42
2014
0.24
1.39
1.63
2015
0.17
1.45
1.62
Note: In the current Inventory, land use and
management data have been incorporated
through 2015.
Additional data will be incorporated in the
future to extend the time series of the land
use and management data.
Step lb: Obtain Management Activity Data to estimate Soil Organic C Stock Changes, N20 and CH4
Emissions from Mineral Soils
The USDA-NRCS Conservation Effects and Assessment Project (CEAP) provides data on a variety of cropland management
activities, and is used to inform the inventory analysis about tillage practices, mineral fertilization, manure amendments,
cover cropping management, as well as planting and harvest dates (USDA-NRCS 2018b; USDA-NRCS 2012). CEAP data are
collected at a subset of NRI survey locations, and currently provide management information from approximately 2002
Annex 3
A-351
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to 2006. Respondents provide detailed information about management practices at the NRI survey locations, such as
time of planting and harvest; amount, type and time of fertilization; implement type and timing of soil cultivation events;
and type and timing of cover crop planting and termination practices.
These data are combined with other datasets in an imputation analysis that extends the time series from 1980 to 2015.
The imputation analysis is comprised of three steps: a) determine the trends in management activity across the time
series by combining information from several datasets (discussed below); b) use an artificial neural network to
determine the likely management practice at a given NRI survey location (Cheng and Titterington 1994); and c) assign
management practices from the CEAP survey to the specific NRI locations using a predictive mean matching method that
is adapted to reflect the trending information (Little 1988, van Buuren 2012). The artificial neural network is a machine
learning method that approximates nonlinear functions of inputs and searches through a large class of models to impute
an initial value for management practices at specific NRI survey locations. The predictive mean matching method
identifies the most similar management activity recorded in the CEAP survey that matches the prediction from the
artificial neural network. The matching ensures that imputed management activities are realistic for each NRI survey
location, and not odd or physically unrealizable results that could be generated by the artificial neural network. The final
imputation product includes six complete imputations of the management activity data in order to adequately capture
the uncertainty. The sections below provide additional information for each of the management practices.
Synthetic and Manure N Fertilizer Applications: Data on synthetic mineral N fertilizer rates are imputed based on crop-
specific fertilizer rates in the USDA-NRCS CEAP product and fertilizer trends based on USDA-Economic Research Service
(ERS) data. The ERS crop management data had been collected as part of Cropping Practices Surveys through 1995
(USDA-ERS 1997), and are now compiled as part of Agricultural Resource Management Surveys (ARMS) starting in 1996
(USDA-ERS 2018). In these surveys, data on inorganic N fertilization rates are collected for crops in the high production
states and for a subset of low production states. Additional data on fertilization practices are compiled from other
sources, particularly the National Agricultural Statistics Service (USDA-NASS 1992,1999, 2004). These data are used to
build a time series of mineral fertilizer application rates for specific crops and states for 1980 to 2015, to the extent that
data are available. These data are then used to inform the imputation product in combination with the USDA CEAP
survey, as described previously. The donor survey data from CEAP contain both mineral fertilizer rates and manure
amendment rates, so that the selection of a donor via predictive mean matching yields the joint imputation of both
mineral and manure amendment rates. This approach captures the relationship between mineral fertilization and
manure amendment practices for US croplands based directly on the observed patterns in the CEAP survey data.
Fertilizer sales data are used to check and adjust synthetic mineral fertilizer amounts that are simulated with DayCent.
The total amount of synthetic fertilizer used on-farms (cropland and grazing land application) has been estimated by the
USGS from 1990 through 2012 on a county scale from fertilizer sales data (Brakebill and Gronberg 2017). For 2013
through 2015, county-level fertilizer used on-farms is adjusted based on annual fluctuations in total U.S. fertilizer sales
(AAPFCO 2013 through 2017).148 The resulting data are used to check the simulated synthetic fertilizer inputs in the
DayCent simulations at the state scale. Specifically, the simulated amounts of mineral fertilizer application for each state
and year are compared to the sales data. If the simulated amounts exceed the sales data in a year, then the simulated
N20 emissions are reduced based on the amount of simulated fertilizer that exceeded the sales data relative to the total
application of fertilizer in the DayCent simulations for the state. For example, if the simulated amount exceeded the sales
data by 3 percent, then the emissions associated with synthetic mineral fertilization149 is reduced by 3 percent (the same
adjustments are also made for leaching and volatilization losses of N that are used to estimate indirect N20 emissions).
This method ensures that the simulated amount of mineral fertilization using bottom-up data from the ARMS and CEAP
surveys are adjusted so that they do not exceed the sales data. The bottom-up data from CEAP and ARMS will be further
investigated in the future to evaluate the discrepancies with the sales data, and potentially improve these datasets to
attain greater consistency.
Similar to synthetic mineral fertilization in DayCent, total amount of manure available for application to soils is used to
check and adjust the simulated amounts of manure application to soils in the DayCent simulations. The available
manure is estimated using methods described in the Manure Management section (Section 5.2) and annex (Annex 3.11),
148 The fertilizer consumption data in AAPFCO are recorded in "fertilizer year" totals (i.e., July to June), but are converted to
calendar year totals. This is done by assuming that approximately 35 percent of fertilizer usage occurred from July to December
and 65 percent from January to June (TVA 1992b).
149 See Step 2A for the approach that is used to disaggregate N20 emissions from DayCent into the sources of N inputs (e.g.,
mineral fertilizer inputs).
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
and it is assumed that all available manure is applied to soils in cropland and grazing lands. If the amount of manure
amendments in DayCent simulations exceeded the available manure for application to soils, the amount of N20
emissions is reduced based on the amount of over-application in the simulations. For example, if the simulated amount
exceeded the available amount by 2 percent, then the emissions associated with manure N inputs are reduced by 2
percent (the same adjustments is also made for leaching and volatilization losses of N that are used to estimate indirect
N20 emissions). This method ensures that the simulated amount of manure amendments using bottom-up data from the
CEAP survey are adjusted so that they do not exceed the amount of manure available for application to soils. The
bottom-up data from CEAP will be further investigated in the future to evaluate the discrepancies with the manure
availability data, and potentially improve these datasets to attain greater consistency.
The resulting amounts of synthetic and manure fertilizer application data are found in Table A-184.
Simulations are also conducted for the time period prior to 1980 in order to initialize the DayCent model (see Step 2a),
and crop-specific regional fertilizer rates prior to 1980 are based largely on extrapolation/interpolation of mineral
fertilizer and manure amendment rates from the years with available data. There is little or no data available for some
states, so a geographic regional mean is used to simulate fertilization rates (e.g., no data are available for Alabama
during the 1970s for corn fertilization rates so mean values from the southeastern United States are used to simulate
fertilization to corn fields in this state).
PRP Manure N: Another key source of N for grasslands is PRP manure N (i.e., manure deposited by grazing livestock on
pasture, range or paddock). The total amount of PRP manure N is estimated using methods described in the Manure
Management section (Section 5.2) and annex (Annex 3.10). Nitrogen from PRP animal waste deposited on non-federal
grasslands in a county is generated by multiplying the total PRP N (based on animal type and population data in a county)
by the fraction of non-federal grassland area in the county. PRP manure N input rates for the Tier 3 DayCent simulations
are estimated by dividing the total PRP manure N amount by the land area associated with non-federal grasslands in the
county from the NRI survey data. During the simulations, the PRP N input is subdivided equally between urine and solid
manure (i.e., 50:50 split), and C is also added with the solids using C:N ratios estimated from livestock-specific data on
manure chemical content in the Agricultural Waste Management Field Handbook (USDA-NRCS 1996). Total PRP manure
N added to soils is found in Table A-184.
Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter are included as
sources of N to the soil, and these sources of N are estimated in the DayCent simulations as a function of vegetation
type, weather, and soil properties. That is, the model accounts for the contribution of N from crop residues to the soil
profile based on simulating the growth of the crop and senescence. This includes the total N inputs of above- and below-
ground N and fixed N in residues that are not harvested or burned (DayCent simulations assume that 3 percent of non-
150
harvested above ground residues for crops are burned), and the resulting amounts can be found in Table A-184.
Other N Inputs: Other N inputs are estimated within the DayCent simulation, and thus input data are not required,
including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the atmosphere.
Mineralization of soil organic matter will also include the effect of land use change on this process as recommended by
the IPCC (2006). The influence of additional inputs of N are estimated in the simulations so that there is full estimation of
all emissions from managed lands, as recommended by the IPCC (2006). The simulated N input from soil organic matter
mineralization and asymbiotic N fixation are provided in Table A-184.
Tillage Practices: Tillage practices are grouped into three categories: full, reduced, and no-tillage. Full tillage is defined as
multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface
residue coverage. This definition corresponds to the intensive tillage and "reduced" tillage systems as defined by CTIC
(2004). No-till is defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-till is
applied to all crops in the rotation. The remainder of the cultivated area is classified as reduced tillage, including mulch
tillage and ridge tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used
for different crops, rotations, and regions are derived from the 1995 Cropping Practices Survey by the Economic
Research Service (USDA-ERS 1997).
Tillage practices are estimated for each cropping system based on data from the Conservation Technology Information
Center for 1980 through 2004 (CTIC 2004), USDA-NRCS CEAP survey for 2000 through 2005 (USDA-NRCS 2018b), and
150 Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural Residues
source category (Section 5.5).
Annex 3
A-353
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USDA ARMS surveys for 2002 through 2015 (Claasen et al. 2018). CTIC compiles data on cropland area under tillage
management classes by major crop species and year for each county. The CTIC and ARMS surveys involve aggregate area,
and therefore they do not fully characterize tillage practices as they are applied within a management sequence (e.g.,
crop rotation). This is particularly true for area estimates of cropland under no-till. These estimates include a relatively
high proportion of "intermittent" no-till, where no-till in one year may be followed by tillage in a subsequent year,
leading to no-till practices that are not continuous in time. Estimates of the area under continuous no-till are provided by
experts at CTIC to account for intermittent tillage activity and its impact on soil organic C (Towery 2001).
Tillage data are further processed to impute a tillage management system for each NRI survey location over the time
series from 1980 to 2015. First, we impute a tillage management system for every NRI survey location in the "base block"
of 2001-2005 by forming imputation classes consisting of all NRI survey locations within the same CEAP region, crop
group, and soil texture class. Within one imputation class, NRI locations with missing tillage systems are assigned the
tillage system of a randomly-selected CEAP donor. Once the base block is imputed, tillage systems for remaining five-
year time blocks are imputed forward and backward in time using trending information obtained from CTIC and ARMS,
described above. The trending information from one-time block to the next is reflected in the imputations by first
constructing the 3x3 transition probability matrix, M, between the two blocks. Let a denote the vector of proportions in
the current time block (already imputed) and let b denote the vector of desired proportions in the target time block (to
be imputed) based on the trending information. The rows of M correspond to the tillage type (no-till, reduced till, or
conventional till) in the target time block and the columns of M correspond to the tillage type in the current time block.
The elements of M are constrained so that (a) each column is a probability distribution (all elements between 0 and 1
and column sums to 1); (b) Ma=b; and (c) the diagonal elements of M are as large as possible. The last constraint implies
as much temporal continuity as possible at a location, subject to overall trends. The solution for M is obtained by a
mathematical optimization technique known as linear programming. Once M is obtained, it is used for imputing the
tillage system as follows: determine the column that corresponds to the tillage system (imputed or real) of the current
block, and use the probabilities in that column to randomly select the tillage system for the target block. Repeat the
construction of M and the imputation block by block forward in time and backward in time.
Cover Crops-. Cover crop data are based on USDA CEAP data (USDA-NRCS 2018b) and information from 2011 to 2016 in
the USDA Census of Agriculture (USDA-NASS 2012, 2017). It is assumed that cover cropping was minimal prior to 1990
and the rates increased over the decade to the levels of cover crop management derived from the CEAP survey. Cover
crops in the "base block" of 2001-2005 are determined from the imputation for planting date (cover crops are assigned
based on recipients with donor that had a cover crop in the USDA CEAP survey). For 1996-2000, we randomly remove
cover crop from locations so that remaining cover crop area is about one-half of the 2001-2005 cover crop area. For
1991-1995, we randomly remove a subset of the remaining area each year until no cover crops are remaining in 1990.
For the blocks 2006-2010, 2011-2015, and 2016-2020, we add (or possibly delete, if cover crops declined in a region)
cover crops at random to match the trending information from USDA Census of Agriculture (USDA-NASS 2012, 2017).
Irrigation: NRI (USDA-NRCS 2018a) differentiates between irrigated and non-irrigated land, but does not provide more
detailed information on the type and intensity of irrigation. Hence, irrigation is modeled by assuming that water is
applied to the level of field capacity on the day after the soil at an NRI survey location drains to 60 percent of field
capacity in the DayCent model simulation.
Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded weather data
from the PRISM Climate Group (2018). Computer-generated weather data are used to drive the DayCent model
simulations because weather station data do not exist near all NRI points. The PRISM product uses interpolation
algorithms to derive weather patterns for areas between the existing network of weather stations (Daly et al. 1998).
PRISM weather data are available for the United States from 1981 through 2015 at a 4 km resolution. Each NRI survey
location is assigned the PRISM weather data for the grid cell containing the survey location.
Enhanced Vegetation Index: The Enhanced Vegetation Index (EVI) from the MODIS vegetation products, (MOD13Q1 and
MYD13Q1) is an input to DayCent for estimating net primary production using the NASA-CASA production algorithm
(Potter et al. 1993, 2007). MODIS imagery is collected on a nominal 8 day-time frequency when combining the two
products. A best approximation of the daily time series of EVI data is derived using a smoothing process based on the
Savitzky-Golay Filter (Savitzky and Golay 1964) after pre-screening for outliers and for cloud-free, high quality data as
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identified in the MODIS data product quality layer. The NASA-CASA production algorithm is only used for the following
crops: corn, soybeans, sorghum, cotton, wheat, and other close-grown crops such as barley and oats.151
The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land uses and crop types
at this resolution, which is problematic for estimating NPP associated with a specific crop at an NRI survey location.
Therefore, a threshold of 90 percent purity in an individual pixel is the cutoff for estimating NPP using the EVI data
derived from the imagery (i.e., pixels with less than 90 percent purity for a crop are assumed to generate bias in the
resulting NPP estimates). The USDA-NASS Crop Data Layer (CDL) (Johnson and Mueller 2010) is used to determine the
purity levels of the EVI data. CDL data have a 30 to 58 m spatial resolution, depending on the year. The level of purity for
individual pixels in the MODIS EVI products is determined by aggregating the crop cover data in CDL to the 250 m
resolution of the EVI data. In this step, the percent cover of individual crops is determined for the 250 m EVI pixels. Pixels
that do not meet a 90 percent purity level for any crop are eliminated from the dataset. CDL does not provide full
coverage for crops across the conterminous United States until 2009 so it is not possible to evaluate purity for the entire
cropland area prior to 2009. The nearest pixel with at least 90 percent purity for a crop is assigned to the NRI survey
location based on a 10 km buffer surrounding the survey location. EVI data are not assigned to a survey location if there
are no pixels with at least 90 percent purity within the 10 km buffer. In these cases, production is simulated with a single
value for the maximum daily NPP, which is reduced if there is water, temperature or nutrient stress affecting plant
growth.
Water Management for Rice Cultivation: Rice crop production in the United States is mostly managed with continuous
flooding, but does include a minor amount of land with mid-season drainage or alternate wet-dry periods (Hardke 2015;
UCCE 2015; Hollier 1999; Way et al. 2014). However, continuous flooding is applied to all rice cultivation areas in the
inventory because water management data are not available. Winter flooding is another key practice associated with
water management in rice fields. Winter flooding occurs on 34 percent of rice fields in California (Miller et al. 2010;
Fleskes et al. 2005), and approximately 21 percent of the fields in Arkansas (Wilson and Branson 2005 and 2006; Wilson
and Runsick 2007 and 2008; Wilson et al. 2009 and 2010; Hardke and Wilson 2013 and 2014; Hardke 2015). No data are
available on winter flooding for Texas, Louisiana, Florida, Missouri, or Mississippi. For these states, the average amount
of flooding is assumed to be similar to Arkansas. In addition, the amount of winter flooding is assumed to be relatively
constant over the Inventory time period.
Organic Amendments for Rice Cultivation: Rice straw is not typically harvested from fields in the United States. The C
input from rice straw is simulated directly within the DayCent model for the Tier 3 method. For the Tier 1 method,
residues are assumed to be left on the field for more than 30 days prior to cultivation and flooding for the next crop,
with the exception of ratoon crops, which are assumed to have residues on the field for less than 30 days prior to the
second crop in the season. To estimate the amount of rice straw, crop yield data (except rice in Florida) are compiled
from USDA NASS QuickStats (USDA 2015). Rice yield data are not collected by USDA for Florida, and so are derived based
on NRI crop areas and average primary and ratoon rice yields from Deren (2002). Relative proportions of ratoon crops
are derived from information in several publications (Schueneman 1997,1999, 2000, 2001; Deren 2002; Kirstein 2003,
2004, 2006; Cantens 2004, 2005; Gonzalez 2007 through 2014). The yields are multiplied by residue: crop product ratios
from Strehler and Stutzle (1987) to estimate rice straw input amounts for the Tier 1 method.
Soil Properties: Soil texture and drainage capacity (i.e., hydric vs. non-hydric soil characterization) are the main soil
variables used as inputs to the DayCent model. Texture is one of the main controls on soil C turnover and stabilization in
the model, which uses particle size fractions of sand (50-2,000 p.m), silt (2-50 p.m), and clay (<2 p.m) as inputs. Hydric
condition in soils are associated with poor drainage, and hence prone to have a high-water table for part of the year in
their native (pre-cultivation) condition. Non-hydric soils are moderately to well-drained.152 Poorly drained soils can be
subject to anaerobic (lack of oxygen) conditions if water inputs (precipitation and irrigation) exceed water losses from
drainage and evapotranspiration. Depending on moisture conditions, hydric soils can range from fully aerobic to
completely anaerobic, varying over the year. Decomposition rates are modified according to a linear function that varies
from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic conditions (default parameters in DayCent).153
Other soil characteristics needed in the simulation, such as field capacity and wilting-point water contents, are estimated
151 Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
152 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable.
153 Hydric soils are primarily subject to anaerobic conditions outside the plant growing season, such as late winter or early spring prior
to planting. Soils that are flooded during much of the year are typically classified as organic soils (e.g., peat), which are not simulated
with the DayCent model.
Annex 3
A-355
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from soil texture data using a standardized hydraulic properties calculator (Saxton et al. 1986). Soil input data are
derived from Soil Survey Geographic Database (SSURGO) (Soil Survey Staff 2019). The data are based on field
measurements collected as part of soil survey and mapping. Each NRI survey location is assigned the dominant soil
component in the polygon containing the point from the SSURGO data product.
Step lc: Obtain Additional Management Activity Data for the Tier 1 Method to estimate Soil N20
Emissions from Mineral Soils
Synthetic N Fertilizer: A process-of-elimination approach is used to estimate synthetic N fertilizer additions to crops in
the Tier 1 method. The total amount of synthetic fertilizer used on-farms has been estimated using USGS and AAPFCO
datasets, as discussed in Step lb (Brakebill and Gronberg 2017; AAPFCO 2013 through 2021). The amount of N applied to
crops in the Tier 1 method (i.e., not simulated by DayCent) is assumed to be the remainder of the fertilizer that is used
on farms after subtracting the amount applied to crops and non-federal grasslands simulated by DayCent. The
differences are aggregated to the national level, and PDFs are derived based on uncertainties in the amount of N applied
to crops and non-federal grasslands for the Tier 3 method. Total fertilizer application to crops in the Tier 1 method is
found in Table A-184.
Managed Livestock Manure and Other Organic Fertilizers: Managed manure N that is not applied to crops and grassland
simulated by DayCent is assumed to be applied to other crops that are included in the Tier 1 method. The total amount
of manure available for application to soils has been estimated with methods described in the Manure Management
section (Section 5.2) and annex (Annex 3.10). Managed manure N applied to croplands for the Tier 1 method is
calculated using a process of elimination approach. Specifically, the amount of managed manure N that is amended to
soils in the DayCent model simulations is subtracted from total managed manure N available for application to soils. The
difference is assumed to be applied to croplands that are not included in the DayCent model simulations. The fate of
manure available for application to soils is summarized in Table A-184.
Estimates of total national annual N additions from other commercial organic fertilizers are derived from organic
fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 through 2021).154 Commercial organic fertilizers include dried
blood, tankage, compost, and other organic materials, which are recorded in mass units of fertilizer. These data are
converted to mass units of N by multiplying the consumption values by the average organic fertilizer N content of
commercial organic fertilizers, which range between 2.3 to 4.2 percent across the time series (TVA 1991 through 1994;
AAPFCO 1995 through 2021). There is potential for double-counting N applications to soils for dried manure and
biosolids (i.e., treated sewage sludge) that are included as commercial fertilizers because these N inputs are already
addressed in the manure dataset (See Manure Management Section 5.2 and Annex 3.10) and biosolids (See Biosolids
below) that are estimated for this Inventory. Therefore, the amounts of dried manure and biosolids in other commercial
organic fertilizer, which are provided in the reports155 (TVA 1991 through 1994; AAPFCO 1995 through 2021), are
subtracted from the total commercial organic fertilizer before estimating emissions. The PDFs are derived for the
organic fertilizer applications assuming a default ±50 percent uncertainty. Annual consumption of other organic
fertilizers is presented in Table A-184.
PRP Manure N: Soil N20 emissions from PRP manure N deposited on federal grasslands are estimated with a Tier 1
method. PRP manure N data are derived using methods described in the Manure Management section (Section 5.2) and
Annex 3.11. PRP N deposited on federal grasslands is calculated using a process of elimination approach. Specifically, the
amount of PRP N included in the DayCent model simulations of non-federal grasslands is subtracted from total PRP N.
This difference was assumed to be deposited on federal grasslands. The total PRP manure N added to soils is found in
Table A-184.
Biosolids (i.e., Treated Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage in public
or private wastewater treatment works and are typically used as a soil amendment, or are sent to waste disposal
facilities, such as landfills. In this Inventory, all biosolids that are amended to agricultural soils are assumed to be applied
154 Similar to the data for synthetic fertilizers described above, the organic fertilizer consumption data are recorded in "fertilizer
year" totals, (i.e., July to June), but are converted to calendar year totals. This is done by assuming that approximately 35
percent of fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b).
155 The amount of reported dried manure and biosolids in other organic fertilizers must be converted into units of N. While the
amounts of dried manure and biosolids are provided in each report (TVA 1991 through 1994; AAPFCO 1995 through 2017), the
N contents of dried manure and biosolids are only provided in AAPFCO (2000). The values are 0.5 and 6.0 percent, respectively,
for dried manure and biosolids.
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to grasslands. Estimates of the amounts of biosolids N applied to agricultural lands are derived from national data on
biosolids generation, disposition, and N content. Total biosolids generation data for 1990 through 2004, in dry mass
units, are obtained from AAPFCO (1995 through 2004). Values for 2005 through 2020 are not available so a "least
squares line" statistical extrapolation using the previous 16 years of data to impute an approximate value. The total
sludge generation estimates are then converted to units of N by applying an average N content (the N content of
biosolids used in estimating the total N applied from biosolids is assumed to be 3.9 percent) (AAPFCO 2000), and
disaggregated into use and disposal practices using historical data in EPA (1993) and NEBRA (2007). The use and disposal
practices are agricultural land application, other land application, surface disposal, incineration, landfilling, ocean
dumping (ended in 1992), and other disposal methods. The resulting estimates of biosolids N applied to agricultural land
are used to estimate N20 emissions from agricultural soil management; the estimates of biosolids N applied to other land
and surface-disposed are used in estimating N20 fluxes from soils in Settlements Remaining Settlements (see section 6.9
of the Land Use, Land-Use Change, and Forestry chapter). Biosolids disposal data are provided in Table A-184.
Residue N Inputs'. Soil N20 emissions for residue N inputs from croplands that are not simulated by DayCent are
estimated with a Tier 1 method. Annual crop production statistics for all major commodity and specialty crops are taken
from U.S. Department of Agriculture crop production reports (USDA-NASS 2019). Total production for each crop is
converted to tons of dry matter product using the residue dry matter fractions. Dry matter yield is then converted to
tons of above- and below-ground biomass N. Above-ground biomass is calculated by using linear equations to estimate
above-ground biomass given dry matter crop yields, and below-ground biomass is calculated by multiplying above-
ground biomass by the below-to-above-ground biomass ratio. N inputs are estimated by multiplying above- and below-
ground biomass by respective N concentrations and by the portion of cropland that is not simulated by DayCent. All
ratios and equations used to calculate residue N inputs are from IPCC (2006) and Williams (2006). PDFs are derived
assuming a ±50 percent uncertainty in the yield estimates (USDA-NASS does not provide uncertainty), along with
uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of below-ground to
above-ground biomass, and residue N fractions. The resulting annual residue N inputs are presented in Table A-184.
Table A-184: Sources of Soil Nitrogen (kt N)
N Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
1.
Synthetic Fertilizer N: Cropland
9,810
9,999
10,079
9,969
11,126
10,300
10,871
10,852
10,815
10,970
2.
Synthetic Fertilizer N: Grassland
13
12
24
56
42
12
10
19
78
19
3.
Managed Manure N: Cropland
2,448
2481
2,489
2,475
2,537
2,570
2,563
2,580
2,599
2,603
4.
Managed Manure N: Grassland
+
1
1
2
1
+
+
2
1
1
5.
Pasture, Range, & Paddock Manure N
4,084
4,091
4,251
4,341
4,414
4,515
4,482
4,380
4,337
4,275
6.
N from Crop Residue Decomposition3
6,875
7,091
6,693
7,047
6,789
7,255
6,977
6,842
6,881
7,739
7.
N from Grass Residue Decomposition3
12,374
12,298
12,623
12,757
12,217
12,937
12,551
12,644
11,960
13,366
8.
Min. SOM / Asymbiotic N-Fixation:
Cropland15
11,344
10,931
10,686
12,089
10,722
11,596
11,000
11,219
12,605
11,296
9.
Min. SOM / Asymbiotic N-Fixation:
Grassland15
16,445
17,261
17,389
17,205
16,020
17,028
16,820
17,824
17,363
16,807
10.
Treated Sewage Sludge N: Grassland
52
55
58
62
65
68
72
75
78
81
11.
Other Organic Amendments: Cropland0
4
8
6
5
8
10
13
14
12
11
Total 63,449 64,277 64,299 66,008 63,941 66,291 65,358 66,450 66,729 67,167
N Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
1.
Synthetic Fertilizer N: Cropland
10,792
10,061
10,542
10,602
11,324
10,723
10,454
11,493
10,932
9,941
2.
Synthetic Fertilizer N: Grassland
24
30
27
24
44
18
19
15
22
18
3.
Managed Manure N: Cropland
2,635
2,620
2,653
2,660
2,583
2,614
2,689
2,710
2,684
2,659
4.
Managed Manure N: Grassland
1
2
+
1
+
1
1
+
1
+
5.
Pasture, Range, & Paddock Manure N
4,182
4,178
4,186
4,191
4,144
4,195
4,248
4,139
4,099
4,066
6.
N from Crop Residue Decomposition3
7,428
7,336
7,262
7,504
7,171
7,337
7,375
7,141
7,255
7,442
7.
8.
N from Grass Residue Decomposition3
Min. SOM / Asymbiotic N-Fixation:
12,532
12,936
12,677
13,040
12,243
13,092
12,689
13,178
13,034
12,571
Cropland15
11,414
11,821
11,284
11,433
12,839
11,494
11,346
11,961
12,054
12,484
9.
Min. SOM / Asymbiotic N-Fixation:
Grassland15
15,687
16,599
16,475
16,991
19,099
17,701
16,934
18,549
17,474
18,120
10.
Treated Sewage Sludge N: Grassland
84
86
89
91
94
98
101
104
107
110
11.
Other Organic Amendments: Cropland0
9
7
8
8
9
10
12
15
12
10
Annex 3
A-357
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Total
64,787 65,678 65,201 66,546 69,550 67,283 65,869 69,306 67,675 67,420
N Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
1.
Synthetic Fertilizer N: Cropland
10,784
11,261
11,906
11,905
11,706
11,486
11,985
11,915
12,157
12,204
2.
Synthetic Fertilizer N: Grassland
11
12
13
11
12
14
12
12
12
12
3.
Managed Manure N: Cropland
2,647
2,673
2,697
2,676
2,666
2,727
2,791
2,862
2,904
2,937
4.
Managed Manure N: Grassland
+
1
1
1
+
1
+
+
+
+
5.
Pasture, Range, & Paddock Manure N
4,015
3,919
3,832
3,791
3,730
3,809
3,938
4,005
4,002
4,007
6.
N from Crop Residue Decomposition3
7,887
7,676
7,448
7,359
7,621
7,231
7,507
7,493
7,696
7,666
7.
N from Grass Residue Decomposition3
12,910
12,499
13,091
12,107
12,211
11,769
12,202
12,091
12,233
12,689
8.
Min. SOM / Asymbiotic N-Fixation:
Cropland15
13,366
11,272
10,216
12,694
13,536
14,311
11,733
11,761
12,129
12,257
9.
Min. SOM / Asymbiotic N-Fixation:
Grassland15
18,527
16,127
15,341
18,472
18,501
19,041
16,733
16,581
16,776
17,401
10.
Treated Sewage Sludge N: Grassland
113
116
119
122
124
127
130
133
136
139
11.
Other Organic Amendments: Cropland0
10
12
13
13
11
12
13
12
11
10
fotal
70,270
65,567
64,677
69,151
70,118
70,529
67,044
68,866
68,057
69,422
N Source
2020
1.
Synthetic Fertilizer N: Cropland
11,610
2.
Synthetic Fertilizer N: Grassland
11
3.
Managed Manure N: Cropland
2,955
4.
Managed Manure N: Grassland
+
5.
Pasture, Range, & Paddock Manure N
3,947
6.
N from Crop Residue Decomposition3
7,421
7.
N from Grass Residue Decomposition3
11,205
8.
Min. SOM / Asymbiotic N-Fixation:
Cropland15
11,632
9.
Min. SOM / Asymbiotic N-Fixation:
Grassland15
15,366
10.
Treated Sewage Sludge N: Grassland
142
11.
Other Organic Amendments: Cropland0
10
Total
64,300
+ Does not exceed 0.5 kt
a Residue N inputs include unharvested fixed N from legumes as well as crop and grass residue N.
b Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
c Includes dried blood, tankage, compost, other. Excludes dried manure and bio-solids (i.e., treated sewage sludge) used as
commercial fertilizer to avoid double counting.
Note: For most activity sources data were not available after 2015 and emissions were estimated with a
data splicing method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be
applied in a future Inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
Step Id: Obtain Additional Management Activity Data for Tier 2 Method to estimate Soil Organic C
Stock Changes in Mineral Soils
Biosolids (i.e., Treated Sewage Sludge) Amendments: Biosolids are generated from the treatment of raw sewage in public
or private wastewater treatment facilities and are typically used as soil amendments or are sent for waste disposal to
landfills. In this Inventory, all biosolids that are amended to agricultural soils are assumed to be applied to grasslands.
See section on biosolids in Step lc for more information about the methods used to derive biosolid N estimates. The
total amount of biosolid N is given in Table A-184. Biosolid N is assumed to be applied at the assimilative capacity
provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and removed at harvest
representing the recommended application rate for manure amendments. In this Inventory, all biosolids are applied to
grasslands, and the rates based on crop nutrient uptake may not be representative of a biosolid amendments to
grasslands. However, there are no data available on N amendments that are specific to grasslands (Future Inventories
A-358 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
will incorporate new information when it is available). This capacity varies from year to year, because it is based on
specific crop yields during the respective year (Kellogg et al. 2000). Total biosolid N available for application is divided by
the assimilative capacity to estimate the total land area over which biosolids have been applied. The resulting estimates
are used for the estimation of soil organic C stock changes associated with application of biosolids.
Wetland Reserve: Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern Prairie
Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service (USFWS 2010). The
area of restored wetlands is estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide
reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to
estimate uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for the uncertainty analysis.
Step le: Additional Activity Data for Indirect N20 Emissions
A portion of the N that is applied as synthetic fertilizer, livestock manure, biosolids (i.e., treated sewage sludge), and
other organic amendments volatilizes as NH3 and NOx. In turn, the volatilized N is eventually returned to soils through
atmospheric deposition, thereby increasing mineral N availability and enhancing N20 production. Additional N is lost
from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N
losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N20. However, N
leaching is assumed to be an insignificant source of indirect N20 in cropland and grassland systems where the amount of
precipitation plus irrigation does not exceed 80 percent of the potential evapotranspiration. These areas are typically
semi-arid to arid regions in the Western United States, and nitrate leaching to groundwater is a relatively uncommon
event. Moreover IPCC (2006) recommends limiting the amount of nitrate leaching assumed to be a source of indirect
N20 emissions based on precipitation, irrigation and potential evapotranspiration.
The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, biosolids N, and
other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral soils, and may
be found in Table A-184.
Using the DayCent model and N sources contributing to indirect emissions described in IPCC (2006) guidelines,
volatilization and leaching/surface run-off of N from soils is estimated in the simulations for crops and non-federal
grasslands in the Tier 3 method. DayCent simulates the processes leading to these losses of N based on environmental
conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation, harvest), soil N
availability, and has been shown to represent observed leaching patterns (Del Grosso et al. 2005, 2008a; David et al.
2009). Note that the DayCent model accounts for losses of N from all anthropogenic activity, not just the inputs of N
from mineral fertilization and organic amendments156, which are addressed in the Tier 1 methodology. In addition,
DayCent is a mass balance model and ensures that are all N inputs are tracked through the flows in the ecosystem with
no double counting of losses. Similar to cropland, the N available for producing indirect emissions resulting from
grassland management as well as PRP manure is also estimated by DayCent. Consistent with the IPCC guidelines (2006),
indirect emissions are not estimated for leaching and runoff of N in arid regions. Arid regions in the United States occur
in areas where the precipitation water input does not exceed 80 percent of the potential evapotranspiration (Note:
Irrigated systems are always assumed to have leaching of N even in drier climates). Volatilized losses of N are summed
for each day in the annual cycle to provide an estimate of the amount of N subject to indirect N20 emissions. In addition,
the daily losses of N through leaching and runoff in overland flow are summed for the annual cycle. Uncertainty in the
estimates is derived from the measured of variability in the fertilizer and organic amendment activity data, in addition to
uncertainty in the DayCent model predictions.
The Tier 1 method is used to estimate N losses from mineral soils due to volatilization and leaching/runoff for crops,
applications of biosolids, and PRP manure on federal grasslands, which are not simulated by DayCent. To estimate
volatilized N losses, the amount of synthetic fertilizers, manure, biosolids, and other organic N inputs are multiplied by
the fraction subject to gaseous losses using the respective default values of 0.1 kg N/kg N added as mineral fertilizers and
0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N ranges from 0.03-0.3 kg NH3-N+NOx-N/kg N
for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of
N are estimated by summing the N additions from synthetic and other organic fertilizers, manure, biosolids, and above-
156 The amount of volatilization and leaching are reduced if the simulated amount of synthetic mineral fertilization in DayCent
exceeds the amount mineral fertilizer sales, or the simulated amount of manure application in DayCent exceeds the manure
available for applications to soils. See subsection on Synthetic and Manure N Fertilizer Applications in Step lb for more
information.
Annex 3
A-359
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and below-ground crop residues, and then multiplied by the default fraction subject to leaching/runoff losses of 0.3 kg
N/kg N applied, with an uncertainty from 0.1-0.8 kg N03-N/kg N (IPCC 2006). However, N leaching is assumed to be an
insignificant source of indirect N20 emissions if the amount of precipitation did not exceed 80 percent of the potential
evapotranspiration, consistent with the Tier 3 method (Note: Irrigated systems are always assumed to have leaching of N
even in drier climates). PDFs are derived for each of the N inputs in the same manner as direct N20 emissions, discussed
in Steps la and lc.
Volatilized N is summed for losses from croplands and grasslands. Similarly, the annual amounts of N lost from soil
profiles through leaching and surface runoff are summed to obtain the total losses for this pathway.
Step 2: Estimate GHG Emissions and Stocks Changes for Mineral Soils: Soil Organic C Stock
Changes, Direct N2O Emissions, and CH4 Emissions from Rice Cultivation
In this step, soil organic C stock changes, N20 emissions, and CH4 emissions from rice cultivation are estimated for
cropland and non-federal grasslands. Three methods are used to estimate soil organic C stock changes, direct N20
emissions from mineral soils, and CH4 emissions from rice cultivation. The DayCent process-based model is used for the
croplands and non-federal grasslands included in the Tier 3 method. A Tier 2 method is used to estimate soil organic C
stock changes for crop types, grasslands (i.e., federal grasslands) and soil types that are not simulated by DayCent and
land use change other than conversions between cropland and grassland. A Tier 1 methodology is used to estimate N20
emissions from crops that are not simulated by DayCent, PRP manure N deposition on federal grasslands, and CH4
emissions from rice cultivation.
Step 2a: Estimate Soil Organic C Stock Changes, Soil N20 Emissions, and CH4 emissions for Crops and
Non-Federal Grassland with the Tier 3 DayCent Model
Crops that are simulated with DayCent include alfalfa hay, barley, corn, cotton, grass hay, grass-clover hay, oats, peanuts,
potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tobacco, and wheat, which combined represent
approximately 85 percent of total cropland in the United States. The DayCent simulations also include all non-federal
grasslands in the United States.
The methodology description is divided into two sub-steps. First, the DayCent model is used to establish the initial
conditions and C stocks for 1979, which is the first year of the NRI survey. In the second sub-step, DayCent is used to
simulate changes in soil organic C stocks, direct soil N20 emissions, leaching and volatilization losses of N contributing to
indirect N20 emissions, and CH4 emissions from rice cultivation based on the land-use and management histories
recorded in the NRI (USDA-NRCS 2018a).
Simulate Initial Conditions (Pre-NRI Conditions): The purpose of the DayCent model initialization is to estimate the most
accurate stock for the pre-NRI history, and the distribution of organic C among the pools represented in the model (e.g.,
Structural, Metabolic, Active, Slow, and Passive). Each pool has a different turnover rate (representing the
heterogeneous nature of soil organic matter), and the amount of C in each pool at any point in time influences the
forward trajectory of the total soil organic C storage. There is currently no national set of soil C measurements
subdivided by the pools that can be used for establishing initial conditions in the model. Sensitivity analysis of the soil
organic C algorithms showed that the rate of change of soil organic matter is relatively insensitive to the amount of total
soil organic C but is highly sensitive to the relative distribution of C among different pools (Parton et al. 1987). By
simulating the historical land use prior to the inventory period, initial pool distributions are estimated in an unbiased
way.
The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation,
historical climate data based on the PRISM product (PRISM Climate Group 2018), and the soil characteristics for the NRI
survey locations. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United
States. The model simulates 5,000 years in the pre-settlement era in order to achieve a steady-state condition.
The second step is to simulate the period of time from European settlement and expansion of agriculture to the
beginning of the NRI survey, representing the influence of historic land-use change and management, particularly the
conversion of native vegetation to agricultural uses. This encompasses a varying time period from land conversion
(depending on historical settlement patterns) to 1979. The information on historical cropping practices used for DayCent
simulations has been gathered from a variety of sources, ranging from the historical accounts of farming practices
reported in the literature (e.g., Miner 1998) to national level databases (e.g., NASS 2004). A detailed description of the
A-360 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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data sources and assumptions used in constructing the base history scenarios of agricultural practices can be found in
Williams and Paustian (2005).
NRI History Simulations: After model initialization, DayCent is used to simulate the NRI land use and management
histories from 1979 through 2015. The simulations address the influence of soil management on direct soil N20
emissions, soil organic C stock changes, and losses of N from the profile through leaching/runoff and volatilization. The
NRI histories identify the land use and land use change histories for the NRI survey locations, as well as cropping patterns
and irrigation history (see Step la for description of the NRI data). The input data for the model simulations also include
the PRISM weather dataset and SSURGO soils data, synthetic N fertilizer rates, managed manure amendments to
cropland and grassland, manure deposition on grasslands (i.e., PRP), tillage histories, cover crop usage, and EVI data (See
Step lb for description of the inputs). There are six DayCent simulations for each NRI survey location based on the
imputation product in order to capture the uncertainty in the management activity data derived by combining data from
CEAP, ARMS, Census of Agriculture and CTIC surveys. See Step lb for more information. The simulation system
incorporates a dedicated MySQL database server and a parallel processing computer cluster. Input/output operations
are managed by a set of run executive programs.
Evaluating uncertainty is an integral part of the analysis and includes three components: (1) uncertainty in the
management activity data inputs (input uncertainty); (2) uncertainty in the model formulation and parameterization
(structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling uncertainty) (Ogle et
al. 2010; Del Grosso et al. 2010). For the first component, the uncertainty is based on the six imputations underlying the
data product combining CEAP, ARMS, Census of Agriculture and CTIC survey data. See Step lb for discussion about the
imputation product. The second component deals with uncertainty inherent in model formulation and parameterization.
This component is the largest source of uncertainty in the Tier 3 model-based inventory analysis, accounting for more
than 80 percent of the overall uncertainty in the final estimates (Ogle et al. 2010; Del Grosso et al. 2010). An empirically-
based procedure is applied to develop a structural uncertainty estimator from the relationship between modeled results
and field measurements from agricultural experiments (Ogle et al. 2007). For soil organic C, the DayCent model is
evaluated with measurements from 72 long-term experiment sites and 142 NRI soil monitoring network sites (Spencer et
al. 2011) with 948 observations across all of the sites that represent a variety of management conditions (e.g., variation
in crop rotation, tillage, fertilization rates, and manure amendments). There are 41 experimental sites available with over
200 treatment observations to evaluate structural uncertainty in the N20 emission predictions from DayCent (Del Grosso
et al. 2010). There are 17 long-term experiments with data on CH4 emissions from rice cultivation, representing 238
combinations of management treatments. The inputs to the model are essentially known in the simulations for the long-
term experiments, and, therefore, the analysis is designed to evaluate uncertainties associated with the model structure
(i.e., model algorithms and parameterization). However, additional uncertainty is introduced with the measurements
from the NRI soil monitoring network because the management data are represented by the six imputations. Therefore,
we statistically analyzed the results and quantified uncertainty for each imputation separately for soil organic C.
The empirical relationship between field measurements and modeled soil organic C stocks, soil N20 emissions and CH4
emissions are statistically analyzed using linear-mixed effect modeling techniques. The modeled stocks and emissions are
treated as a fixed effect in the statistical models. The resulting relationships are used to make an adjustment to modeled
values if there are biases due to significant mismatches between the modeled and measured values. Several other
variables are tested in these models including soil characteristics, geographic location (i.e., state), and management
practices (e.g., tillage practices, fertilizer rates, rice production with and without winter flooding). Random effects are
included in all of these models to capture the dependence in time series and data collected from the same site, which
are needed to estimate appropriate standard deviations for parameter coefficients. See the Tier 3 Model Description,
Parameterization and Evaluation Section for more information about model evaluation, including graphs illustrating the
relationships between modeled and measured values.
The third element is the uncertainty associated with scaling the DayCent results for each NRI survey location to the
entire land base, using the expansion factors and replicate weights provided with the NRI dataset. The expansion factors
represent the number of hectares associated with the land-use and management history for a particular survey location.
The scaling uncertainty is due to the complex sampling design that selects the locations for NRI, and this uncertainty is
properly reflected in the replicate weights for the expansion factor. Briefly, each set of replicate weights is used to
compute one weighted estimate. The empirical variation across the weighted estimates from all replicates is an estimate
of the theoretical scaling uncertainty due to the complex sampling design.
A Monte Carlo approach is used to propagate uncertainty from the three components through the analysis with 1,000
iterations for each NRI survey location. In each iteration, there is a random selection of management activity data from
Annex 3
A-361
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the imputation product; a random draw of parameter values for the uncertainty estimator (Ogle et al. 2010); and a
random draw of a set of replicate weights to scale the emissions and stock changes from the individual NRI survey
locations to the entire domain of the inventory analysis. Note that parameter values for the statistical equations (i.e.,
fixed effects) are selected from their joint probability distribution, as well as random error associated with the time
series and data collected from the same site, and the residual/unexplained error. The randomly selected parameter
values for soil organic C, N20 and CH4 emissions and associated management information are then used as input into the
linear mixed-effect model, and adjusted values are computed for each C stock change, N20 and CH4 emissions estimate.
After completing the Monte Carlo stochastic simulation, the median of the final distribution from the 1,000 replicates is
used as the estimate of total emissions or soil organic C stock changes, and a 95 percent confidence interval is based on
2.5 and 97.5 percentile values.
In DayCent, the model cannot distinguish among the original sources of N after the mineral N enters the soil pools, and
therefore it is not possible to determine which management activity led to specific N20 emissions. This means, for
example, that N20 emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs,
such as crop residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each NRI
survey location, the N inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006),
including synthetic mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing
crops). The percentage of N input for anthropogenic practices is divided by the total N input, and this proportion is used
to determine the amount of N20 emissions assigned to each of the practices. For example, if 70 percent of the mineral N
made available in the soil is due to synthetic mineral fertilization, then 70 percent of the N20 emissions are assigned to
this practice.
A portion of soil N20 emissions is reported under "other N inputs," which includes mineralization due to decomposition
of soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Mineralization of soil organic
matter is significant source of N, but is typically less than half of the amount of N made available in cropland soils
compared to application of synthetic fertilizers and manure amendments, along with symbiotic fixation. Mineralization
of soil organic matter accounts for the majority of available N in grassland soils. Asymbiotic N fixation by soil bacteria is a
minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems. Accounting for the influence
of "other N inputs" is necessary because the processes leading to these inputs of N are influenced by management.
This attribution of N20 emissions to the individual N inputs to the soils is required for reporting emissions based on
UNFCCC reporting guidelines. However, this method is a simplification of reality to allow partitioning of N20 emissions,
as it assumes that all N inputs have an identical chance of being converted to N20. It is important to realize that sources
such as synthetic fertilization may have a larger impact on N20 emissions than would be suggested by the associated
level of N input for this source (Delgado et al. 2009). Further research will be needed to improve upon this attribution
method, however.
For the land base that is simulated with the DayCent model, direct soil N20 emissions are provided in Table A-188 and
Table A-189, soil organic C stock changes are provided in Table A-190, and rice cultivation CH4 emissions in Table A-192.
Step 2b: Soil N20 Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1
Approach
To estimate direct N20 emissions from N additions to crops in the Tier 1 method, the amount of N in applied synthetic
fertilizer, manure, and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to N
inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of 0.01 kg
N20-N/kg N (IPCC 2006). The uncertainty is determined based on simple error propagation methods (IPCC 2006). The
uncertainty in the default emission factor ranges from 0.3-3.0 kg N20-N/kg N (IPCC 2006). For flooded rice soils, the IPCC
157
default emission factor is 0.003 kg N20-N/kg N and the uncertainty range is 0.000-0.006 kg N20-N/kg N (IPCC 2006).
Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations used to
calculate residue N additions from above- and below-ground biomass dry matter and N concentration to derive overall
uncertainty.
157 Due to lack of data, uncertainties are not addressed for managed manure N production, PRP manure N production, other
commercial organic fertilizer amendments, indirect losses of N in the DayCent simulations, and biosolids (i.e., treated sewage sludge),
but these sources of uncertainty will be included in future Inventories.
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The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal lands (i.e., PRP
manure N), and from biosolids (i.e., treated sewage sludge) application to grasslands. These two sources of N inputs to
soils are multiplied by the IPCC (2006) default emission factors (0.01 kg N20-N/kg N for sludge and horse, sheep, and
goat manure, and 0.02 kg N20-N/kg N for cattle, swine, and poultry manure) to estimate N20 emissions. The uncertainty
is determined based on the simple error propagation methods provided by the IPCC (2006) with uncertainty in the
default emission factor ranging from 0.007 to 0.06 kg N20-N/kg N (IPCC 2006).
The results for direct soil N20 emissions using the Tier 1 method are provided in Table A-188 and Table A-189.
Step 2c: Soil CH4 Emissions from Agricultural Lands Approximated with the Tier 1 Approach
To estimate CH4emissions from rice cultivation for the Tier 1 method, an adjusted daily emission factor is calculated
using the default baseline emission factor of 1.30 kg CH4 ha 1 d 1 (ranging 0.8-2.2 kg CH4 ha 1 d"1) multiplied by a scaling
factor for the cultivation water regime, pre-cultivation water regime and a scaling factor for organic amendments (IPCC
2006). The water regime during cultivation is continuously flooded for rice production in the United States and so the
scaling factor is always 1 (ranging from 0.79 to 1.26). The pre-season water regime varies based on the proportion of
land with winter flooding; land that does not have winter flooding is assigned a value of 0.68 (ranging from 0.58 to 0.80)
and areas with winter flooding are assigned a value of 1 (ranging from 0.88 to 1.14). Organic amendments are estimated
based on the amount of rice straw and multiplied by 1 (ranging 0.97 to 1.04) for straw incorporated greater than 30 days
before cultivation, and by 0.29 (0.2 to 0.4) for straw incorporated greater than 30 days before cultivation. The adjusted
daily emission factor is multiplied by the cultivation period and harvested area to estimate the total CH4 emissions. The
uncertainty is propagated through the calculation using an Approach 2 method with a Monte Carlo analysis (IPCC 2006),
combining uncertainties associated with the adjusted daily emission factor and the harvested areas derived from the
USDA NRI survey data.
The results for rice CH4 emissions using the Tier 1 method are provided in Table A-192.
Step 2d: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the
Tier 2 Approach
Mineral soil organic C stock values are derived for crop rotations that were not simulated by DayCent and land converted
from non-agricultural land uses to cropland or grassland from 1990 through 2015, based on the land-use and
management activity data in conjunction with appropriate reference C stocks, land-use change, management, input, and
wetland restoration factors. Each quantity in the inventory calculations has uncertainty that is quantified in PDFs,
including the land use and management activity data based on the six imputations in the data product combining CEAP,
ARMS, Census of Agriculture, and CTIC data (See Step lb for more information); reference C stocks and stock change
factors; and the replicated weights form the NRI survey. A Monte Carlo Analysis is used to quantify uncertainty in soil
organic C stock changes for the inventory period based on random selection of values from each of these sources of
uncertainty. Input values are randomly selected from PDFs in an iterative process to estimate soil organic C change for
1,000 iterations in the analysis.
Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. conditions are estimated
from published studies (Ogle et al. 2003; Ogle et al. 2006). The numerical factors quantify the impact of changing land
use and management on soil organic C storage in mineral soils, including tillage practices, cropping rotation or
intensification, and land conversions between cultivated and native conditions (including set-asides in the Conservation
Reserve Program). Studies from the United States and Canada are used in this analysis under the assumption that they
would best represent management impacts for the Inventory.
The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon average annual
temperature, average annual precipitation, and the length of the dry season (IPCC 2006). Seven of these climate zones
occur in the conterminous United States and Hawaii (Eve et al. 2001). Climate zones are classified using mean annual
precipitation and temperature (1950-2000) data from the WorldClim data set (Hijmans et al. 2005) and potential
evapotranspiration data from the Consortium for Spatial Information (CGIAR-CSI) (Zomer et al. 2008; Zomer et al. 2007).
Soils are classified into one of seven classes based upon texture, morphology, and ability to store organic matter (IPCC
2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, representing
estimates from conventionally managed cropland, are computed for each of the mineral soil types across the various
climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 1997)
(Table A-185). These stocks are used in conjunction with management factors to estimate the change in soil organic C
Annex 3
A-363
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stocks that result from management and land-use activity. PDFs, which represent the variability in the stock estimates,
are constructed as normal densities based on the mean and variance from the pedon data. Pedon locations are clumped
in various parts of the country, which reduces the statistical independence of individual pedon estimates. To account for
this lack of independence, samples from each climate by soil zone are tested for spatial autocorrelation using the
Moran's I test, and variance terms are inflated by 10 percent for all zones with significant p-values.
Table A-185: U.S. Soil Groupings Based on the IPCC Categories and Dominant Taxonomic
Soil, and Reference Carbon Stocks (Metric Tons C/ha)
Reference Carbon Stock in Climate Regions
Cold
Cold
Warm
Warm
Sub-
Sub-
IPCC Inventory
USDA Taxonomic Soil
Temperate,
Temperate,
Temperate,
Temperate,
Tropical,
Tropical,
Soil Categories
Orders
Dry
Moist
Dry
Moist
Dry
Moist
High Clay Activity Vertisols, Mollisols,
Mineral Soils
Inceptisols, Aridisols, and
high base status Alfisols
42 (n = 133)
65 (n = 526)
37 (n
= 203)
51 (n
= 424)
42 (n = 26)
57 (n = 12)
Low Clay Activity
Ultisols, Oxisols, acidic
Mineral Soils
Alfisols, and many Entisols
45 (n = 37)
52 (n = 113)
25 (
n = 86)
40 (n
= 300)
39 (n = 13)
47 (n = 7)
Sandy Soils
Any soils with greater than
70 percent sand and less
than 8 percent clay (often
Entisols)
24 (n = 5)
40 (n = 43)
16 (
n = 19)
30 (n
= 102)
33 (n = 186)
50 (n = 18)
Volcanic Soils
Andisols
124 (n = 12)
114 (n = 2)
124 (
n = 12)
124(i
n = 12)
124 (n = 12)
128 (n = 9)
Spodic Soils
Spodosols
86 (n=20)
74 (n = 13)
86
o
-------
Table A-186: Soil Organic Carbon Stock Change Factors for the United States and the IPCC
Default Values Associated with Management Impacts on Mineral Soils
IPCC
default
Warm Moist
Climate
U.S. Factor
Warm Dry
Climate
Cool Moist
Climate
Cool Dry
Climate
Land-Use Change Factors
Cultivated3 1
General Unculta'b (n=251) 1.4
Set-Asidea (n=142) 1.25
Improved Grassland Factors
Medium Input 1.1
High Input NA
Wetland Rice Production Factorb 1.1
Tillage Factors
Conv. Till 1
Red. Till (n=93) 1.05
No-till (n=212) 1.1
Cropland Input Factors
Low (n=85) 0.9
Medium 1
High (n=22) 1.1
High with amendment11 1.2
1.42±0.06
1.31±0.06
1.14±0.06
1.11±0.04
1.1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
1.37±0.05
1.26±0.04
1.14±0.06
1.11±0.04
1.1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
1.24±0.06
1.14±0.06
1.14±0.06
1.11±0.04
1.1
1.08±0.03
1.13±0.02
0.94±0.01
1
1.07±0.02
1.38±0.06
1.20±0.06
1.10±0.05
1.14±0.06
1.11±0.04
1.1
1.01±0.03
1.05±0.03
0.94±0.01
1
1.07±0.02
1.34±0.08
Note: The "n" values refer to sample size.
a Factors in the IPCC documentation (IPCC 2006) are converted to represent changes in soil organic C storage from a cultivated
condition rather than a native condition.
b U.S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few
studies addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United
States, or the impact of wetland rice production in the US. Factors provided in IPCC (2006) are used as the best estimates of
these impacts.
Wetland restoration management also influences soil organic C storage in mineral soils, because restoration leads to
higher water tables and inundation of the soil for at least part of the year. A stock change factor is estimated assessing
the difference in soil organic C storage between restored and unrestored wetlands enrolled in the Conservation Reserve
Program (Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the first 10 years
(Table A-187). A PDF with a normal density is constructed from these data based on results from a linear regression
model. Following the initial increase of C, natural erosion and deposition leads to additional accretion of C in these
wetlands. The mass accumulation rate of organic C is estimated using annual sedimentation rates (cm/yr) in combination
with percent organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002). Procedures for calculation of mass
accumulation rate are described in Dean and Gorham (1998); the resulting rate and standard deviation are used to
construct a PDF with a normal density (Table A-187).
Table A-187: Rate and standard deviation for the Initial Increase and Subsequent Annual
Mass Accumulation Rate (Mg C/ha-yr) in Soil Organic C Following Wetland Restoration of
Variable
Value
Factor (Initial Increase—First 10 Years)
1.22±0.18
Mass Accumulation (After Initial 10 Years)
0.79±0.05
Note: Mass accumulation rate represents additional gains in C for mineral soils
after the first 10 years (Euliss and Gleason 2002).
Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology, annual changes in
mineral soil organic C are calculated by subtracting the beginning stock from the ending stock and then dividing by 20.159
For this analysis, stocks are estimated for each year and difference between years is the stock change. From the final
159 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period.
Annex 3
A-365
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distribution of 1,000 values, the median is used as the estimate of soil organic C stock change and a 95 percent
confidence interval is generated based on the simulated values at the 2.5 and 97.5 percentiles in the distribution.
Soil organic C stock changes using the Tier 2 method are provided in Table A-190 and Table A-191.
Step 2e: Estimate Additional Changes in Soil Organic C Stocks Due to Biosolids (i.e., Treated Sewage
Sludge) Amendments
There are two additional land use and management activities occurring on mineral soils of U.S. agricultural lands that are
not estimated in Steps 2a and 2b. The first activity involves the application of biosolids to agricultural lands. Minimal data
exist on where and how much biosolids are applied to U.S. agricultural soils. However, national estimates of mineral soil
land area receiving biosolids can be approximated based on biosolids N production data, and the assumption that
amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). In this Inventory, it is
assumed that biosolids for agricultural land application to soils is only used as an amendment in grassland. The impact of
organic amendments on soil organic C is calculated as 0.38 metric tonnes C/ha-yr. This rate is based on the IPCC default
method and country-specific factors, by calculating the effect of converting nominal, medium-input grassland to high
input improved grassland. The assumptions for this estimation are as follows: a) the reference C stock is 50 metric
tonnes C/ha, which represents a mid-range value of reference C stocks for the cropland soils in the United States,160 b)
the land use factor for grassland of 1.4 and 1.11 for high input improved grassland are representative of typical
conditions, and c) the change in stocks are occurring over a 20 year (default value) time period (i.e., [50 x 1.4 x 1.11 - 50
x 1.4] / 20 = 0.38). A ±50 percent uncertainty is attached to these estimates due to limited information on application
and the rate of change in soil organic C stock change with amendments of biosolids.
The influence of biosolids (i.e., treated sewage sludge) on soil organic C stocks is provided in Table A-191.
Table A-188: Direct Soil N2O Emissions from Mineral Soils in Cropland (MMT CO2 Eq.)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Mineral Soil Emission
181.9
173.4
169.6
187.3
182.0
179.8
187.5
178.6
176.3
178.4
Tier 3 Cropland
164.8
157.3
152.6
170.5
163.7
161.3
168.8
160.0
158.3
160.5
Inorganic N Fertilizer Application
54.1
53.1
52.9
54.6
59.8
52.9
59.0
55.9
53.2
55.2
Managed Manure Additions
5.4
5.3
5.2
5.4
5.3
4.9
5.4
5.0
4.7
4.7
Crop Residue N
35.5
35.3
32.5
36.8
34.2
36.2
36.7
33.9
31.8
37.6
Min. SOM / Asymbiotic N-Fixationa
69.7
63.5
61.9
73.6
64.4
67.3
67.7
65.2
68.7
62.9
Tier 1 Cropland
17.1
16.2
17.0
16.8
18.3
18.4
18.7
18.6
18.0
17.9
Inorganic N Fertilizer Application
4.7
4.0
4.4
4.7
5.4
5.5
6.0
5.8
4.9
4.9
Managed Manure Additions
7.3
7.3
7.5
7.4
7.8
8.1
7.9
8.0
8.1
8.2
Other Organic Amendments'5
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.1
Crop Residue N
5.1
4.8
5.0
4.7
5.1
4.7
4.8
4.7
4.8
4.7
Implied Emission Factor for Croplandsc(kt
N2Q-N/kt N) 0.013 0.012 0.012 0.013 0.013 0.012 0.013 0.012 0.012 0.012
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Mineral Soil Emission
173.0
182.4
182.6
183.7
183.4
179.9
175.4
181.2
179.0
180.4
Tier 3 Cropland
155.1
164.5
164.0
164.1
163.0
160.8
156.2
161.9
159.8
163.0
Inorganic N Fertilizer Application
53.2
53.0
55.1
54.2
53.8
54.0
51.7
56.7
53.6
51.1
Managed Manure Additions
4.6
4.7
4.8
4.6
4.5
4.6
4.7
4.8
4.6
4.8
Crop Residue N
35.0
37.4
37.4
38.3
33.9
36.4
36.1
34.2
34.8
36.5
Min. SOM / Asymbiotic N-Fixationa
62.2
69.4
66.6
67.0
70.8
65.8
63.8
66.3
66.8
70.6
Tier 1 Cropland
18.0
17.9
18.7
19.6
20.4
19.1
19.1
19.3
19.2
17.4
Inorganic N Fertilizer Application
4.8
4.8
5.7
6.3
7.3
6.0
5.9
6.0
5.9
4.3
Managed Manure Additions
8.4
8.5
8.6
8.7
8.4
8.4
8.7
8.7
8.7
8.4
Other Organic Amendments'5
0.0
0.0
0.0
0.0
0.0
0.0
0.1
0.1
0.1
0.0
Crop Residue N
4.7
4.5
4.3
4.5
4.8
4.6
4.5
4.6
4.6
4.6
Implied Emission Factor for Croplandsc(kt
N2Q-N/kt N) 0.012 0.013 0.130 0.012 0.012 0.012 0.012 0.012 0.012 0.012
160 Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
A-366 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Mineral Soil Emission
182.2
180.3
172.7
193.7
203.4
196.0
187.4
187.0
191.6
192.9
Tier 3 Cropland
163.3
161.2
154.4
174.0
184.0
171.7
164.3
164.7
169.9
171.7
Inorganic N Fertilizer Application
50.7
55.2
58.2
59.4
60.6
52.3
57.1
57.2
59.0
59.6
Managed Manure Additions
4.6
5.1
5.0
5.4
5.3
4.1
5.1
5.1
5.2
5.3
Crop Residue N
36.8
37.8
35.5
36.5
38.7
35.0
36.3
36.4
37.5
37.9
Min. SOM / Asymbiotic N-Fixationa
71.2
63.1
55.6
72.6
79.3
80.4
65.9
66.1
68.1
66.9
Tier 1 Cropland
18.9
19.1
18.3
19.8
19.4
24.4
23.1
22.2
21.7
21.2
Inorganic N Fertilizer Application
5.9
6.5
5.7
7.1
6.4
10.2
8.6
8.1
7.8
7.5
Managed Manure Additions
8.3
8.3
8.3
8.1
8.3
9.4
9.6
9.3
9.1
9.0
Other Organic Amendments'5
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
Crop Residue N
4.6
4.3
4.3
4.5
4.7
4.7
4.9
4.8
4.8
4.8
Implied Emission Factor for Croplandsc(kt
N20-N/kt N)
0.011
0.012
0.012
0.012
0.012
0.012
0.012
0.012
0.012
0.012
Land Use Change Category 2020
Total Cropland Mineral Soil Emission
183.8
Tier 3 Cropland
162.9
Inorganic N Fertilizer Application
56.6
Managed Manure Additions
5.0
Crop Residue N
36.0
Min. SOM / Asymbiotic N-Fixationa
65.3
Tier 1 Cropland
20.8
Inorganic N Fertilizer Application
7.2
Managed Manure Additions
8.8
Other Organic Amendments'5
0.0
Crop Residue N
4.7
Implied Emission Factor for Croplandsc(kt
N2Q-N/kt N) 0.012
a Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
b Includes dried blood, tankage, compost, other. Excludes dried manure and bio-solids (i.e., treated
sewage sludge) used as commercial fertilizer to avoid double counting.
c Annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by
total activity data for N applied.
Note: For most activity sources data were not available after 2015 and emissions were estimated with a
data splicing method. Additional activity data will be collected and the Tier 1, 2 and 3 methods will be
applied in a future Inventory to recalculate the part of the time series that is estimated with the data
splicing methods.
Table A-189: Direct Soil N2O Emissions from Mineral Soils in Grassland (MMT CO2 Eq.)
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Mineral Soil Emission
84.1
84.2
83.3
84.5
80.5
83.6
85.7
86.5
87.6
82.3
Tier 3 Grassland
77.1
77.4
76.3
77.6
73.6
76.7
79.1
80.2
81.2
76.2
Inorganic N Fertilizer Application
0.0
0.0
0.1
0.1
0.1
0.0
0.0
0.0
0.2
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
7.7
7.6
7.7
8.1
8.4
8.4
00
00
8.3
8.7
8.0
Grass Residue N
29.8
29.0
28.8
29.5
28.2
29.5
30.0
29.8
29.5
30.2
Min. SOM / Asymbiotic N-Fixationa
39.6
40.7
39.7
39.8
36.9
38.8
40.2
42.0
42.8
38.0
Tier 1 Grassland
6.9
6.8
7.0
6.9
6.9
6.8
6.6
6.3
6.3
6.1
Pasture, Range, & Paddock N Deposition
6.7
6.5
6.7
6.6
6.6
6.5
6.3
6.0
6.0
5.7
Treated Sewage Sludge Additions
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.4
0.4
0.4
Implied Emission Factor for Grassland15 (kt
N20-N/kt N)
0.006
0.005
0.005
0.005
0.005
0.005
0.006
0.005
0.006
0.005
Annex 3
A-367
-------
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Mineral Soil Emission
77.0
83.3
83.9
83.5
91.8
86.6
85.2
87.4
84.8
88.5
Tier 3 Grassland
71.0
77.4
78.1
77.7
86.1
80.9
79.4
81.8
79.2
83.0
Inorganic N Fertilizer Application
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
0.1
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
7.8
8.3
8.4
8.1
8.4
8.1
8.4
8.1
8.0
8.4
Grass Residue N
28.0
30.2
30.3
30.2
30.3
30.9
30.4
30.6
30.4
30.5
Min. SOM / Asymbiotic N-Fixationa
35.0
38.8
39.3
39.3
47.2
41.8
40.6
43.1
40.8
44.0
Tier 1 Grassland
6.0
5.9
5.8
5.8
5.8
5.8
5.8
5.5
5.6
5.5
Pasture, Range, & Paddock N Deposition
5.6
5.5
5.4
5.3
5.3
5.3
5.3
5.0
5.1
5.0
Treated Sewage Sludge Additions
0.4
0.4
0.4
0.4
0.4
0.5
0.5
0.5
0.5
0.5
Implied Emission Factor for Grassland15 (kt
N2Q-N/kt N) 0.005 0.005 0.006 0.005 0.006 0.005 0.006 0.005 0.005 0.006
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Mineral Soil Emission
90.7
81.2
76.5
92.3
93.6
93.3
88.5
87.8
88.8
91.9
Tier 3 Grassland
85.2
75.7
71.1
87.0
88.3
88.0
83.1
82.4
83.3
86.4
Inorganic N Fertilizer Application
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Managed Manure Additions
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Pasture, Range, & Paddock N Deposition
8.2
7.9
7.2
8.0
8.1
8.2
8.2
8.1
8.2
8.5
Grass Residue N
31.6
29.6
29.4
31.3
31.9
30.5
31.6
31.3
31.7
32.8
Min. SOM / Asymbiotic N-Fixationa
45.3
38.2
34.5
47.7
48.3
49.3
43.3
42.9
43.4
45.0
Tier 1 Grassland
5.5
5.4
5.4
5.4
5.3
5.3
5.4
5.4
5.4
5.5
Pasture, Range, & Paddock N Deposition
5.0
4.9
4.9
4.8
4.7
4.7
4.7
4.8
4.8
4.8
Treated Sewage Sludge Additions
0.5
0.5
0.6
0.6
0.6
0.6
0.6
0.6
0.6
0.7
Implied Emission Factor for Grassland15 (kt
N2Q-N/kt N) 0.006 0.005 0.005 0.006 0.006 0.006 0.006 0.006 0.006 0.006
Land Use Change Category 2020
Total Grassland Mineral Soil Emission
81.8
Tier 3 Grassland
76.3
Inorganic N Fertilizer Application
0.0
Managed Manure Additions
0.0
Pasture, Range, & Paddock N Deposition
7.5
Grass Residue N
29.0
Min. SOM / Asymbiotic N-Fixationa
39.8
Tier 1 Grassland
5.5
Pasture, Range, & Paddock N Deposition
4.8
Treated Sewage Sludge Additions
0.7
Implied Emission Factor for Grassland15 (kt
N20-N/kt N)
0.006
a Mineralization of soil organic matter and the asymbiotic fixation of nitrogen gas.
b Annual Implied Emission Factor (kt N20-N/kt N) is calculated by dividing total estimated emissions by total activity data for N
applied.
Note: For most activity sources data were not available after 2015 and emissions were estimated with a data splicing method.
Additional activity data will be collected and the Tier 1, 2 and 3 methods will be applied in a future Inventory to recalculate the
part of the time series that is estimated with the data splicing methods.
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Soil Organic C Stock Change
-55.8
-60.3
-56.4
-43.4
-51.0
-39.6
-55.9
-44.5
-38.6
-40.9
Cropland Remaining Cropland (CRC)
-58.2
-63.3
-60.0
-45.8
-53.5
-46.1
-61.4
-53.1
-43.5
-46.0
Tier 2
-0.6
-1.5
-1.6
-1.4
-0.4
-0.6
-0.5
-1.8
-0.7
-1.9
Tier 3
-57.6
-61.7
-58.4
-44.4
-53.1
-45.5
-60.8
-51.3
-42.9
-44.1
Grassland Converted to Cropland (GCC)
4.1
4.9
5.8
4.7
4.8
8.9
8.0
11.3
7.6
7.9
A-368 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Tier 2
3.9
4.2
4.0
4.0
4.3
4.7
5.0
5.0
5.1
5.0
Tier 3
0.2
0.7
1.8
0.7
0.6
4.2
2.9
6.3
2.5
2.9
Forest Converted to Cropland (FCC) (Tier 2
Only)
0.4
0.4
0.4
0.4
0.4
0.4
0.4
0.3
0.3
0.3
Other Lands Converted to Cropland (OCC)
(Tier 2 Only)
-2.3
-2.4
-2.5
-2.7
-2.9
-2.9
-3.0
-3.1
-3.1
-3.2
Settlements Converted to Cropland (SCC)
(Tier 2 Only)
-0.1
-0.1
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Wetlands Converted to Cropland (WCC) (Tier
2 Only)
0.3
0.3
0.2
0.3
0.3
0.3
0.3
0.3
0.3
0.3
Land Use Change Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Soil Organic C Stock Change
-47.0
-56.6
-63.6
-55.8
-58.6
-61.1
-58.3
-61.3
-52.7
-43.0
Cropland Remaining Cropland (CRC)
-51.6
-60.7
-65.4
-57.8
-59.9
-62.4
-58.5
-61.8
-55.4
-46.2
Tier 2
-0.9
-3.9
-5.6
-5.1
-4.9
-5.0
-4.5
-4.9
-4.7
-5.1
Tier 3
-50.7
-56.8
-59.8
-52.7
-55.0
-57.4
-53.9
-56.9
-50.7
-41.1
Grassland Converted to Cropland (GCC)
7.8
7.4
4.9
4.8
4.0
4.0
2.8
2.9
5.0
5.3
Tier 2
5.2
5.2
5.0
4.6
4.8
4.8
4.7
4.7
4.5
4.5
Tier 3
2.6
2.2
-0.1
0.2
-0.7
-0.8
-1.9
-1.8
0.4
0.8
Forest Converted to Cropland (FCC) (Tier 2
Only)
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.1
Other Lands Converted to Cropland (OCC)
(Tier 2 Only)
-3.6
-3.6
-3.4
-3.2
-3.1
-2.9
-2.9
-2.7
-2.5
-2.4
Settlements Converted to Cropland (SCC)
(Tier 2 Only)
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.1
Wetlands Converted to Cropland (WCC) (Tier
2 Only)
0.4
0.3
0.4
0.4
0.3
0.3
0.3
0.3
0.3
0.2
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Soil Organic C Stock Change
-46.5
-62.7
-56.2
-43.5
-40.3
-39.9
-51.0
-51.7
-46.3
-44.4
Cropland Remaining Cropland (CRC)
-51.0
-64.1
-58.7
-46.6
-44.7
-44.9
-54.3
-55.1
-49.4
-47.4
Tier 2
-4.6
-5.2
-3.6
-5.6
-5.5
-6.2
-5.7
-5.4
-5.9
-5.9
Tier 3
-46.4
-58.9
-55.1
-41.0
-39.2
-38.8
-48.6
-49.6
-43.5
-41.5
Grassland Converted to Cropland (GCC)
6.7
3.7
4.5
5.2
6.2
6.9
5.2
5.4
5.1
5.1
Tier 2
4.5
4.6
4.7
4.4
4.3
4.2
4.2
4.3
4.3
4.3
Tier 3
2.2
-0.9
-0.1
0.8
1.9
2.7
1.0
1.1
0.9
0.8
Forest Converted to Cropland (FCC) (Tier 2
Only)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Other Lands Converted to Cropland (OCC)
(Tier 2 Only)
-2.4
-2.4
-2.3
-2.3
-2.0
-2.0
-2.1
-2.2
-2.2
-2.3
Settlements Converted to Cropland (SCC)
(Tier 2 Only)
-0.1
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
-0.2
Wetlands Converted to Cropland (WCC) (Tier
2 Only)
0.2
0.2
0.3
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Land Use Change Category 2020
Total Cropland Soil Organic C Stock Change -56.2
Cropland Remaining Cropland (CRC) -51.0
Tier 2 -4.9
Tier 3 -51.4
Grassland Converted to Cropland (GCC) 5.5
Tier 2 4.4
Tier 3 1.1
Forest Converted to Cropland (FCC) (Tier 2
Only) 0.1
Other Lands Converted to Cropland (OCC)
(Tier 2 Only) -2.3
Annex 3
A-369
-------
Settlements Converted to Cropland (SCC)
(Tier 2 Only) -0.2
Wetlands Converted to Cropland (WCC) (Tier
2 Only) 0.3
Land Use Change Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Soil Organic C Stock Change
-25.6
-25.4
-23.5
-23.9
-42.7
-37.0
-41.7
-39.4
-52.0
-52.0
Grassland Remaining Grassland (GRG)
-2.2
-2.1
-0.5
2.3
-10.7
-2.5
-3.5
0.5
-5.5
-1.3
Tier 2
-0.2
-0.5
-1.1
-1.4
-1.5
-1.4
-0.7
-0.7
-1.5
-1.3
Tier 3
-1.4
-0.9
1.3
4.4
-8.5
-0.4
-2.0
2.1
-3.1
0.9
Treated Sewage Sludge Additions
-0.6
-0.6
-0.7
-0.7
-0.7
-0.8
-0.8
-0.9
-0.9
-0.9
Cropland Converted to Grassland (CCG)
-18.9
-18.7
-18.3
-18.5
-19.8
-19.8
-20.5
-20.1
-24.0
-24.7
Tier 2
-4.0
-3.9
-3.9
-4.3
-4.9
-4.8
-4.8
-4.8
-5.6
-5.9
Tier 3
-15.0
-14.8
-14.4
-14.2
-15.0
-14.9
-15.7
-15.3
-18.3
-18.8
Forest Converted to Grassland (FCG) (Tier 2
Only)
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
Other Lands Converted to Grassland (OCG)
(Tier 2 Only)
-4.2
-4.3
-4.5
-7.2
-11.4
-14.0
-16.7
-18.8
-21.4
-24.7
Settlements Converted to Grassland (SCG)
(Tier 2 Only)
-0.2
-0.2
-0.2
-0.3
-0.5
-0.7
-0.8
-0.9
-1.0
-1.2
Wetlands Converted to Grassland (WCG)
(Tier 2 Only)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Land Use Change Category 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Total Grassland Soil Organic C Stock Change
-69.9
-61.9
-63.7
-64.8
-58.7
-57.4
-71.2
-55.9
-59.9
-58.6
Grassland Remaining Grassland (GRG)
-13.9
-2.5
-4.0
-5.7
0.0
0.8
-12.0
2.2
-5.0
-3.9
Tier 2
-1.4
-1.5
-2.6
-2.6
-0.9
-1.1
-1.3
-1.4
-1.4
-1.6
Tier 3
-11.5
0.0
-0.4
-2.0
1.9
3.0
-9.6
4.8
-2.3
-1.0
Treated Sewage Sludge Additions
-1.0
-1.0
-1.0
-1.0
-1.1
-1.1
-1.2
-1.2
-1.2
-1.3
Cropland Converted to Grassland (CCG)
-26.4
-26.4
-26.8
-26.1
-25.7
-25.0
-26.0
-24.9
-21.7
-21.5
Tier 2
-6.1
-6.3
-6.2
-5.9
-5.8
-5.6
-5.4
-5.2
-5.0
-4.7
Tier 3
-20.3
-20.2
-20.6
-20.1
-19.9
-19.4
-20.6
-19.8
-16.7
-16.8
Forest Converted to Grassland (FCG) (Tier 2
Only)
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
-0.1
Other Lands Converted to Grassland (OCG)
(Tier 2 Only)
-28.3
-31.4
-31.4
-31.6
-31.5
-31.7
-31.6
-31.7
-31.7
-31.8
Settlements Converted to Grassland (SCG)
(Tier 2 Only)
-1.3
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
-1.4
Wetlands Converted to Grassland (WCG)
(Tier 2 Only)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Land Use Change Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Soil Organic C Stock Change
-43.0
-45.0
-58.1
-41.8
-32.5
-36.8
-42.3
-41.1
-40.4
-36.7
Grassland Remaining Grassland (GRG)
10.6
7.9
-6.3
6.4
10.0
4.0
0.1
1.4
1.8
4.6
Tier 2
-1.6
-1.5
-0.6
-0.2
1.1
0.1
-0.8
-0.9
-0.9
-0.9
Tier 3
13.5
10.8
-4.3
8.0
10.3
5.4
2.3
2.5
2.9
5.7
Treated Sewage Sludge Additions
-1.3
-1.3
-1.4
-1.4
-1.4
-1.5
-1.5
-0.2
-0.2
-0.2
Cropland Converted to Grassland (CCG)
-20.3
-19.4
-18.3
-17.5
-15.9
-16.9
-19.1
-19.4
-19.3
-18.7
Tier 2
-4.6
-4.6
-4.5
-4.1
-3.5
-3.4
-3.5
-3.6
-3.7
-3.7
Tier 3
-15.7
-14.8
-13.8
-13.3
-12.4
-13.4
-15.6
-15.8
-15.6
-15.0
Forest Converted to Grassland (FCG) (Tier 2
Only)
-0.1
-0.1
-0.1
-0.1
0.0
-0.1
-0.0
-0.0
-0.1
-0.1
Other Lands Converted to Grassland (OCG)
(Tier 2 Only)
-31.8
-32.1
-32.0
-29.5
-25.6
-22.9
-22.3
-22.2
-21.9
-21.6
A-370 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Settlements Converted to Grassland (SCG)
(Tier 2 Only) -1.4 -1.4 -1.4 -1.3 -1.1 -1.0 -1.0 -1.0 -0.9 -0.9
Wetlands Converted to Grassland (WCG)
(Tier 2 Only) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Land Use Change Category 2020
Total Grassland Soil Organic C Stock Change -47.2
Grassland Remaining Grassland (GRG) -3.3
Tier 2 -0.9
Tier 3 -2.3
Treated Sewage Sludge Additions -0.2
Cropland Converted to Grassland (CCG) -21.0
Tier 2 -3.8
Tier 3 -17.2
Forest Converted to Grassland (FCG) (Tier 2
Only) -0.0
Other Lands Converted to Grassland (OCG)
(Tier 2 Only) -21.9
Settlements Converted to Grassland (SCG)
(Tier 2 Only) -1.0
Wetlands Converted to Grassland (WCG)
(Tier 2 Only) 0.0
Table A-192: Methane Emissions from Rice Cultivation (MMT CO2 Eq.)
Approach 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Total Rice Methane Emission 16.0 16.1 16.1 17.1 15.7 16.5 16.7 15.4 17.1 17.7
Tier 1 2.2 2.3 2.4 2.4 2.5 2.3 2.4 2.3 2.7 4.2
Tier 3 13.8 13.9 13.8 14.7 13.2 14.2 14.3 13.1 14.4 13.5
Approach 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009
Total Rice Methane Emission 19.0 15.4 17.7 14.7 15.6 18.0 14.7 15.9 14.1 16.2
Tier 1 4.4 2.8 2.5 2.4 2.4 2.2 1.9 2.2 1.8 2.5
Tier 3 14.6 12.6 15.2 12.3 13.2 15.8 12.8 13.8 12.2 13.7
Approach 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Total Rice Methane Emission 18.9 15.3 15.2 13.8 15.4 16.2 15.8 14.9 15.6 15.1
Tier 1 2.4 2.1 2.8 2.1 3.4 2.4 2.4 2.5 2.5 2.5
Tier 3 16.5 13.2 12.4 11.7 12.0 13.8 13.4 12.4 13.1 12.5
Approach 2020
Total Rice Methane Emission 15.7
Tier 1 2.5
Tier 3 13.2
Note: Estimates after 2015 are based on a data splicing method (See Rice Cultivation section for more information). The Tier 1
and 3 methods will be applied in a future inventory to recalculate the part of the time series that is estimated with data
splicing.
Step 3: Estimate Soil Organic C Stock Changes and Direct N2O Emissions from Organic Soils
In this step, soil organic C losses and N20 emissions are estimated for organic soils that are drained for agricultural
production.
Annex 3
A-371
-------
Step 3a: Direct N20 Emissions Due to Drainage of Organic Soils in Cropland and Grassland
To estimate annual N20 emissions from drainage of organic soils in cropland and grassland, the area of drained organic
soils in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default emission factor for
temperate soils and the corresponding area in sub-tropical regions is multiplied by the average (12 kg N20-N/ha
cultivated) of IPCC (2006) default emission factors for temperate (8 kg N20-N/ha cultivated) and tropical (16 kg N20-N/ha
cultivated) organic soils. The uncertainty is determined based on simple error propagation methods (IPCC 2006),
including uncertainty in the default emission factor ranging from 2-24 kg N20-N/ha (IPCC 2006). Table A-193 lists the
direct N20 emissions associated with drainage of organic soils in cropland and grassland.
Table A-193: Direct Soil N2O Emissions from Drainage of Organic Soils (MMT CO2 Eg.)
Land Use
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Organic Soil Emissions
6.3
6.2
6.2
6.3
6.3
6.3
6.3
6.2
6.2
6.2
Cropland
3.8
3.8
3.7
3.7
3.7
3.8
3.8
3.7
3.7
3.7
Grassland
2.5
2.5
2.5
2.5
2.6
2.5
2.5
2.5
2.5
2.5
Land Use
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Organic Soil Emission
6.2
6.2
6.2
6.1
6.1
6.1
6.1
6.0
6.0
6.0
Cropland
3.7
3.8
3.8
3.7
3.7
3.7
3.7
3.6
3.6
3.5
Grassland
2.5
2.4
2.4
2.3
2.4
2.4
2.4
2.4
2.4
2.5
Land Use
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Organic Soil Emission
6.0
6.0
6.0
5.9
5.9
5.9
5.9
5.9
5.9
5.9
Cropland
3.5
3.5
3.5
3.5
3.4
3.4
3.4
3.4
3.4
3.4
Grassland
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
2.5
Land Use
2020
Total Organic Soil Emission
Cropland
Grassland
5.9
3.4
2.5
Step 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland
Change in soil organic C stocks due to drainage of organic soils in cropland and grassland are estimated annually from
1990 through 2015, based on the land-use and management activity data in conjunction with appropriate emission
factors. The activity data are based on annual data from 1990 through 2015 from the NRI. Organic soil emission factors
that are representative of U.S. conditions have been estimated from published studies (Ogle et al. 2003), based on
subsidence studies in the United States and Canada (Table A-194). PDFs are constructed as normal densities based on
the mean C loss rates and associated variances. Input values are randomly selected from PDFs in a Monte Carlo analysis
to estimate soil organic C change for 1,000 iterations and produce a 95 percent confidence interval for the inventory
results. Losses of soil organic C from drainage of cropland and grassland soils are provided in Table A-195 for croplands
and Table A-196 for grasslands.
Table A-194: Carbon Loss Rates for Organic Soils Under Agricultural Management in the
——v -¦ ¦ V ~.
Cropland
Grassland
Region
IPCC
U.S. Revised
IPCC
U.S. Revised
Cold Temperate, Dry & Cold Temperate, Moist
1
11.2±2.5
0.25
2.8±0.5a
Warm Temperate, Dry & Warm Temperate, Moist
10
14.0±2.5
2.5
3.5±0.8a
Sub-Tropical, Dry & Sub-Tropical, Moist
1
14.3±2.5
0.25
2.8±0.5a
s There are not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25
percent of the values for cropland, which is an assumption that is used for the IPCC default organic soil C losses on grassland.
A-372 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-195: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in
Cropland (MMT CCh Eg.)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Soil Organic C Stock Change
38.6
38.0
38.1
38.3
38.5
38.6
38.5
38.5
38.5
32.9
Cropland Remaining Cropland (CRC)
35.0
34.2
34.5
34.2
34.2
34.1
33.9
34.0
33.6
28.0
Grassland Converted to Cropland (GCC)
2.7
2.8
2.8
3.1
3.2
3.5
3.5
3.4
3.8
3.8
Forest Converted to Cropland (FCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Other Lands Converted to Cropland (OCC)
0.2
0.2
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.6
0.6
0.6
0.7
0.8
0.9
0.9
0.9
0.9
0.9
Land Use Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Soil Organic C Stock Change
32.5
39.0
38.8
38.6
38.1
37.7
37.5
36.7
36.4
36.0
Cropland Remaining Cropland (CRC)
27.9
33.5
33.5
33.7
33.8
33.4
33.2
32.6
32.4
32.2
Grassland Converted to Cropland (GCC)
3.6
4.5
4.5
4.1
3.6
3.5
3.5
3.3
3.4
3.1
Forest Converted to Cropland (FCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.0
0.0
0.0
Other Lands Converted to Cropland (OCC)
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.7
0.7
0.6
0.5
0.6
0.6
0.6
0.6
0.6
0.5
Land Use Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Soil Organic C Stock Change
36.1
36.1
36.2
35.3
36.3
35.8
35.2
36.5
36.5
36.6
Cropland Remaining Cropland (CRC)
32.3
32.4
32.3
31.3
32.5
32.1
31.6
32.8
32.8
32.9
Grassland Converted to Cropland (GCC)
3.1
3.1
3.4
3.5
3.4
3.3
3.3
3.3
3.3
3.3
Forest Converted to Cropland (FCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Other Lands Converted to Cropland (OCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Settlements Converted to Cropland (SCC)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Cropland (WCC)
0.6
0.6
0.5
0.5
0.3
0.3
0.3
0.3
0.4
0.4
Land Use Category 2020
Total Cropland Soil Organic C Stock Change
36.6
Cropland Remaining Cropland (CRC)
32.9
Grassland Converted to Cropland (GCC)
3.3
Forest Converted to Cropland (FCC)
0.0
Other Lands Converted to Cropland (OCC)
0.0
Settlements Converted to Cropland (SCC)
0.0
Wetlands Converted to Cropland (WCC)
0.4
Table A-196: Soil Organic Carbon Stock Changes due to Drainage of Organic Soils in
Grasslands (MMT CO2 Eg.)
Land Use Category
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Soil Organic C Stock Change
7.1
7.0
7.1
7.1
7.2
7.1
7.0
7.0
7.0
7.1
Grassland Remaining Grassland (GRG)
6.3
6.2
6.2
6.1
6.1
6.0
6.0
5.9
5.7
5.7
Cropland Converted to Grassland (CCG)
0.6
0.6
0.7
0.8
0.9
0.9
0.8
0.8
1.0
1.0
Forest Converted to Grassland (FCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.1
Other Lands Converted to Grassland (OCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Settlements Converted to Grassland (SCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Grassland (WCG)
0.1
0.1
0.1
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Land Use Category
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Soil Organic C Stock Change
7.1
7.0
7.1
6.9
7.1
7.1
7.1
7.1
7.1
7.3
Grassland Remaining Grassland (GRG)
5.6
5.3
5.3
5.2
5.2
5.2
5.2
5.2
5.3
5.3
Cropland Converted to Grassland (CCG)
1.1
1.2
1.4
1.3
1.5
1.5
1.4
1.4
1.3
1.5
Annex 3
A-373
-------
Forest Converted to Grassland (FCG) 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2
Other Lands Converted to Grassland (OCG) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Settlements Converted to Grassland (SCG) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0
Wetlands Converted to Grassland (WCG) 0.3 0.3 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3
Land Use Category
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Grassland Soil Organic C Stock Change
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.3
7.2
7.2
Grassland Remaining Grassland (GRG)
5.3
5.3
5.3
5.3
5.5
5.4
5.4
5.4
5.4
5.4
Cropland Converted to Grassland (CCG)
1.5
1.4
1.4
1.4
1.3
1.4
1.4
1.4
1.3
1.3
Forest Converted to Grassland (FCG)
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
0.2
Other Lands Converted to Grassland (OCG)
0.0
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
0.1
Settlements Converted to Grassland (SCG)
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
0.0
Wetlands Converted to Grassland (WCG)
0.3
0.3
0.4
0.3
0.3
0.3
0.3
0.2
0.2
0.2
Land Use Category 2020
Total Grassland Soil Organic C Stock Change 7.2
Grassland Remaining Grassland (GRG) 5.4
Cropland Converted to Grassland (CCG) 1.3
Forest Converted to Grassland (FCG) 0.2
Other Lands Converted to Grassland (OCG) 0.1
Settlements Converted to Grassland (SCG) 0.0
Wetlands Converted to Grassland (WCG) X2
Step 4: Estimate Indirect Soil N2O Emissions for Croplands and Grasslands
In this step, soil N20 emissions are estimated for the two indirect emission pathways (N20 emissions due to volatilization,
and N20 emissions due to leaching and runoff of N), which are summed to yield total indirect N20 emissions from
croplands and grasslands.
Step 4a: Indirect Soil N20 Emissions Due to Volatilization
Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP manure, are
calculated according to the amount of mineral N that is volatilized from the soil profile and later emitted as soil N20
following atmospheric deposition. See Step le for additional information about the methods used to compute N losses
due to volatilization (i.e., DayCent is used to generate the amount of N volatilized). The estimated N volatilized is
multiplied by the IPCC default emission factor of 0.01 kg N20-N/kg N (IPCC 2006) to estimate total indirect soil N20
emissions from volatilization. The uncertainty is estimated using simple error propagation methods (IPCC 2006), by
combining uncertainties in the amount of N volatilized, with uncertainty in the default emission factor ranging from
0.002-0.05 kg N20-N/kg N (IPCC 2006). This approach is consistent with the 2006 IPCC Guidelines. Additionally, please
see the following peer-reviewed publications on the use of DayCent for estimating soil N20 emissions that speak to
scientific basis of the model: Del Grosso et al. (2001; 2005; 2008b; 2010; 2011), Delgado et al. (2009) and Scheer et al.
(2013). The estimates and implied emission factors are provided in Table A-188 for cropland and in Table A-189 for
grassland.
Step 4b: Indirect Soil N20 Emissions Due to Leaching and Runoff
The amounts of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N
mineralization, asymbiotic fixation that is transported from the soil profile in water flows are used to calculate indirect
emissions from leaching of mineral N from soils and losses in runoff associated with overland flow. See Step le for
additional information about the methods used to compute N losses from soils due to leaching and runoff in overland
water flows. The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by the
IPCC default emission factor of 0.0075 kg N20-N/kg N (IPCC 2006) to estimate emissions for this source. The uncertainty
is estimated based on simple error propagation methods (IPCC 2006), including uncertainty in the default emission factor
ranging from 0.0005 to 0.025 kg N20-N/kg N (IPCC 2006). The emission estimates are provided in Table A-197 and Table
A-198 including the implied Tier 3 emission factors.
A-374 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-197: Indirect Soil N2O Emissions for Cropland from Volatilization and Atmospheric
Deposition, and from Leaching and Runoff (MMT CO2 Eg.)
Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
34.3
6.5
27.8
31.5
6.3
25.3
33.7
6.1
27.7
37.9
6.4
31.5
29.3
6.6
22.7
34.1
6.7
27.4
33.7
6.7
27.0
32.2
6.7
25.5
36.3
6.9
29.3
32.7
6.9
25.8
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
30.1
7.0
23.1
35.0
7.0
28.0
32.1
7.2
24.8
33.3
7.2
26.1
36.6
7.4
29.2
31.6
7.3
24.4
33.1
7.2
25.8
35.1
7.2
27.9
36.6
7.2
29.3
35.8
7.1
28.7
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Source
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
Total Cropland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
36.1
7.6
28.5
35.2
7.3
28.0
28.6
6.9
21.7
37.9
7.7
30.2
37.6
8.0
29.6
42.7
8.5
34.2
38.9
8.1
30.8
37.4
7.9
29.5
42.3
8.0
34.4
43.8
7.9
35.9
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Source
2020
Total Cropland Indirect Emissions
35.4
Volatilization & Atmospheric Deposition
7.6
Leaching & Runoff
27.8
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100
0.0075
Note: Estimates after 2015 are based on a data splicing
method (See the Agricultural Soil Management section
for more information). The Tier 1 and 3 methods will be
applied in a future inventory to recalculate the part of
the time series that is estimated with the data splicing
methods.
Table A-198: Indirect Soil N2O Emissions for Grassland from Volatilization and Atmospheric
Deposition, and from Leaching and Runoff (MMT CO2 Eg.)
Source
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
Total Grassland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
9.2
3.6
5.6
9.2
3.6
5.6
9.5
3.6
5.9
9.8
3.5
6.3
9.1
3.5
5.6
9.4
3.6
5.8
9.2
3.6
5.6
9.5
3.6
5.9
10.6
3.7
6.9
9.4
3.5
5.9
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Source
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
Total Grassland Indirect Emissions
Volatilization & Atmospheric Deposition
Leaching & Runoff
8.2
3.2
5.0
10.0
3.5
6.5
9.6
3.6
6.0
9.2
3.6
5.6
10.6
3.8
6.8
9.3
3.7
5.6
9.3
3.7
5.6
10.1
3.6
6.5
10.0
3.5
6.5
10.4
3.6
6.8
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Annex 3
A-375
-------
Source
2010
2011
2012
2013
2014
2015
2016
2017
2018 2019
Total Grassland Indirect Emissions
9.9
9.6
8.9
10.3
9.4
10.9
10.0
10.0
10.1 10.5
Volatilization & Atmospheric Deposition
3.7
3.3
3.3
3.7
3.8
3.7
3.5
3.6
36 3.7
Leaching & Runoff
6.2
6.3
5.6
6.6
5.6
7.2
6.5
6.4
cn
Ln
cn
CO
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100 0.0100
0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075 0.0075
Source
2020
Total Grassland Indirect Emissions
9.2
Volatilization & Atmospheric Deposition
3.5
Leaching & Runoff
5.7
Volatilization Implied Emission Factor
Leaching & Runoff Implied Emission Factor
0.0100
0.0075
Note: Estimates after 2015 are based on a data splicing
method (See the Agricultural Soil Management section
for more information). The Tier 1 and 3 methods will be
applied in a future Inventory to recalculate the part of
the time series that is estimated with the data splicing
methods.
Step 5: Estimate Total Emissions for U.S. Agricultural Soils
Total N20 emissions are estimated by summing total direct and indirect emissions for croplands and grasslands (both
organic and mineral soils). Total soil organic C stock changes for cropland (Cropland Remaining Cropland and Land
Converted to Cropland) and grassland (Grassland Remaining Grassland and Land Converted to Grassland) are summed to
determine the total change in soil organic C stocks (both organic and mineral soils). Total rice CH4 emissions are
estimated by summing results from the Tier 1 and 3 methods. The results are provided in Figure A-7. In general, N20
emissions from agricultural soil management have been increasing slightly from 1990 to 2020, while CH4 emissions from
rice cultivation have been relatively stable. Agricultural soil organic C stocks have increased for most years in croplands
and grasslands leading to sequestration of C in soils, with larger increases in grassland soils.
A-376 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Figure A-7: GHG Emissions and Removals for Cropland & Grassland
„n_ Cropland SOC ______ Cropland Soil N20
Grassland SOC — — — Rice Cultivation CH.
Grassland Soil N20
"T 250 -
cr
LU
-100 -
—i 1 1 1 1 1 1—
1990 1995 2000 2005 2010 2015 2020
Years
Direct and indirect simulated emissions of soil N20 vary regionally in croplands and grasslands as a function of N input,
other management practices, weather, and soil type. The highest total N20 emissions for 2015161 occur in Iowa, Illinois,
Kansas, Minnesota, Missouri, Montana, Nebraska, South Dakota, and Texas (Table A-199). These areas are used to grow
corn or have extensive areas of grazing with large amounts of PRP manure N inputs. Note that there are other
management practices, such as fertilizer formulation (Halvorson et al. 2013), that influence emissions but are not
represented in the model simulations. The states with largest increases in soil organic C stocks in 2015 include Illinois,
Iowa, Missouri, Nebraska, North Dakota (Table A-199). These states tend to have larger amounts of land conversion to
grassland and/or more conservation practices such as enrollment in Conservation Reserve Program or adoption of
conservation tillage. For rice cultivation, the states with highest CH4 emissions are Arkansas, California, Louisiana and
Texas (Table A-199). These states also have the largest areas of rice cultivation, and Louisiana and Texas have a relatively
large proportion of fields with a second ratoon crop each year. Ratoon crops extend the period of flooding, and with the
residues left from the initial rice crop, there are additional CH4 emissions compared to non-ratoon rice management
systems.
Table A-199: Total Soil N2O Emissions (Direct and Indirect), Soil Organic C Stock Changes
and Rice CH4 Emissions from Agricultural Lands by State in 2015 (MMT CO2 Eg.)
N?Q Emissions3 Soil Organic C Stock Change Rice Total
State Croplands Grasslands Croplands Grasslands CH,; Emissions
AL 1.34 1.15 -0.39 -1.00 0.00 1.10
AR 5.30 1.37 -0.65 -0.72 6.39 11.69
AZ 0.24 3.82 0.16 -0.27 0.00 3.95
CA 1.08 2.07 0.45 -3.57 4.14 4.17
CO 3.38 4.37 0.06 -2.24 0.00 5.57
161 The emissions data at the state scale is available for 1990 to 2015, but data splicing methods have been applied at national
scales to estimate emissions for most emission sub-source categories for 2016 to 2020. Therefore, the final year of emissions
data at the state scale is 2015.
Annex 3
A-377
-------
CT
0.06
0.02
-0.05
-0.05
0.00
-0.02
DE
0.17
0.02
-0.04
-0.03
0.00
0.12
FL
0.25
1.68
11.88
0.16
0.00
13.97
GA
1.83
0.82
0.35
-0.55
0.00
2.45
Hlb
NE
NE
0.29
0.53
0.00
0.82
IA
21.23
2.14
-3.83
-1.15
0.00
18.39
ID
2.04
1.01
-0.25
-2.05
0.00
0.76
IL
18.43
0.93
-6.23
-0.65
0.00
12.48
IN
9.02
0.61
0.51
-0.52
0.00
9.63
KS
16.28
4.98
-0.77
-1.30
0.00
19.19
KY
3.66
2.28
-0.30
-0.76
0.00
4.88
LA
3.32
0.92
-0.85
-0.55
2.57
5.41
MA
0.08
0.03
0.21
-0.02
0.00
0.30
MD
0.73
0.16
-0.04
-0.11
0.00
0.74
ME
0.16
0.07
-0.12
0.02
0.00
0.13
Ml
3.73
0.70
2.50
-0.25
0.00
6.68
MN
13.26
1.39
5.75
1.18
0.01
21.60
MO
10.71
3.48
-2.93
-0.85
0.00
10.41
MS
3.50
0.84
-1.04
-0.73
1.00
3.57
MT
6.43
6.74
-1.52
1.27
0.00
12.91
NC
2.09
0.60
1.95
-0.63
0.00
4.01
ND
7.80
2.04
-3.12
-1.70
0.00
5.02
NE
13.18
4.94
-2.87
-1.15
0.00
14.10
NH
0.06
0.03
-0.04
0.01
0.00
0.05
NJ
0.14
0.04
-0.01
-0.07
0.00
0.11
NM
0.55
6.63
0.02
2.95
0.00
10.16
NV
0.20
1.10
-0.03
-1.37
0.00
-0.10
NY
2.27
1.04
-0.91
-0.13
0.00
2.28
OH
7.25
0.72
-1.79
-0.84
0.00
5.34
OK
4.56
5.26
0.55
-1.39
0.00
8.98
OR
0.96
1.11
-0.07
-1.65
0.00
0.35
PA
2.70
0.67
-1.33
-0.77
0.00
1.27
Rl
0.01
0.01
0.02
-0.01
0.00
0.03
SC
1.09
0.37
-0.18
-0.37
0.00
0.90
SD
10.84
4.66
-1.99
-0.89
0.00
12.62
TN
2.60
1.67
-0.63
-0.60
0.00
3.04
TX
13.66
16.72
2.10
-1.11
1.43
32.80
UT
0.60
1.26
0.22
-3.72
0.00
-1.65
VA
1.43
1.26
-0.73
-0.42
0.00
1.54
VT
0.35
0.16
-0.11
0.01
0.00
0.42
WA
1.69
0.70
-0.03
0.01
0.00
2.37
Wl
5.98
1.18
2.18
0.24
0.00
9.58
WV
0.24
0.48
-0.30
-0.29
0.00
0.12
WY
0.77
3.79
-0.22
0.03
0.00
4.38
a This table only includes N20 emissions estimated by DayCent using the Tier 3 method.
b N20 emissions are not reported for Hawaii except from cropland organic soils, which
are estimated with the Tier 1 method and therefore not included in this table.
Tier 3 Model Description, Parameterization and Evaluation
The DayCent ecosystem model (Parton et al. 1998; Del Grosso et al. 2001, 2011) simulates biogeochemical C and N fluxes
between the atmosphere, vegetation, and soil. The model is consistent with the approaches laid out in the 2006IPCC
Guidelines but provides a more complete estimation of soil organic C stock changes, CH4 and N20 emissions than IPCC
Tier 1 or 2 methods by accounting for a broader suite of environmental drivers that influence emissions and C stock
changes. These drivers include soil characteristics, weather patterns, crop and forage characteristics, and management
practices. The DayCent model utilizes the soil C modeling framework developed in the Century model (Parton et al. 1987,
1988,1994; Metherell et al. 1993), but has been refined to simulate dynamics at a daily time-step. Carbon and N
dynamics are linked in plant-soil systems through biogeochemical processes of microbial decomposition and plant
A-378 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
production (McGill and Cole 1981). Coupling the three source categories (i.e., agricultural soil organic C, rice CH4 and soil
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, and ensuring conservation of mass. For example, plant growth is controlled by nutrient
availability, water, and temperature stress. Plant growth, along with residue management, determines C inputs to soils
and influences C stock changes. Removal of soil mineral N by microbial organisms influences the amount of production
and C inputs, while plant uptake of N influence availability of N for microbial processes of nitrification and denitrification
that generate N20 emissions. Nutrient supply is a function of external nutrient additions as well as litter and soil organic
matter (SOM) decomposition rates, and increasing decomposition can lead to a reduction in soil organic C stocks due to
microbial respiration, and greater N20 emissions by enhancing mineral N availability in soils.
The DayCent process-based simulation model (daily time-step version of the Century model) has been selected for the
Tier 3 approach based on the following criteria:
1) The model has been developed in the United States and extensively tested for U.S. conditions (e.g., Parton et al.
1987,1993). In addition, the model has been widely used by researchers and agencies in many other parts of
the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India, Jordan,
Kenya, Mexico), soil N20 emissions (e.g., Canada, China, Ireland, New Zealand) (Abdalla et al. 2010; Li et al.
2005; Smith et al. 2008; Stehfest and Muller 2004; Cheng et al. 2014), and CH4 emissions (Cheng et al. 2013).
2) The model is designed to simulate management practices that influence soil C dynamics, CH4 emissions and
direct N20 emissions, with the exception of cultivated organic soils; cobbly, gravelly, or shaley soils; and crops
that have not been parameterized for DayCent simulations (e.g., some vegetables, perennial/horticultural
crops, and crops that are rotated with these crops). For these latter cases, an IPCC Tier 2 method has been used
to estimate soil organic C stock changes, and IPCC Tier 1 method is used to estimate CH4 and N20 emissions.
The model can also be used to estimate the amount of nitrate leaching and runoff, as well as volatilization of
ammonia and nitrogen oxides, which are subject to indirect N20 emissions.
3) Much of the data needed for the model is available from existing national databases. The exceptions are
management of federal grasslands and amendments of biosolids (i.e., treated sewage sludge) to soils, which
are not known at a sufficient resolution or detail to use the Tier 3 model. Soil N20 emissions and C stock
changes associated with these practices are addressed with Tier 1 and 2 methods, respectively.
DayCent Model Description
Key processes simulated by DayCent include (1) plant growth; (2) organic matter formation and decomposition; (3) soil
water and temperature regimes by layer; (4) nitrification and denitrification processes; and (5) methanogenesis (Figure
A-8). Each submodel is described below.
1) The plant-growth submodel simulates C assimilation through photosynthesis; N uptake; dry matter production;
partitioning of C within the crop or forage; senescence; and mortality. The primary function of the growth submodel
is to estimate the amount, type, and timing of organic matter inputs to soil, and to represent the influence of the
plant on soil water, temperature, and N balance. Yield and removal of harvested biomass are also simulated.
Separate submodels are designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and woody
vegetation (i.e., trees and scrub). Maximum daily net primary production (NPP) is estimated using the NASA-CASA
production algorithm (Potter et al.1993, 2007) and MODIS Enhanced Vegetation Index (EVI) products, MOD13Q1
and MYD13Q1. The NASA-CASA production algorithm is only used for the following major crops: corn, soybeans,
sorghum, cotton, wheat, and other close-grown crops such as barley and oats.162 Other regions and crops are
simulated with a single value for the maximum daily NPP, instead of the more dynamic NASA-CASA algorithm. The
maximum daily NPP rate is modified by air temperature and available water to capture temperature and moisture
stress. If the NASA-CASA algorithm is not used in the simulation, then production is further subject to nutrient
limitations (i.e., nitrogen). Model evaluation has shown that the NASA-CASA algorithm improves the precision of
162 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method in the
future.
Annex 3
A-379
-------
NPP estimates by using the EVI products to inform the production model. The r2 is 83 percent for the NASA-CASA
algorithm and 64 percent for the single parameter value approach. See Figure A-9.
Figure A-8: DayCent Model Flow Diagram
Plant
Production
Submodel
f(TEMP)
rcwFPS)
f(SOLAR)
EVI/PRDX
Biomass
SOM
Submodel
f(Lignin:IV>
CO,.
f(TEXT)
f(MOIST)
f(TEMP)
f(Kp)
Active
E9(
SOM
co2,
Slow
SOM
C02JSmin
f(STORMf,
Passive
SOM
CO,,Nmin
V
Dissolved Organic C, Dissolved Organic N, Mineral N
2) Dynamics of soil organic C and N (Figure A-8) are simulated for the surface and belowground litter pools and soil
organic matter in the top 30 cm of the soil profile; mineral N dynamics are simulated through the whole soil profile.
Organic C and N stocks are represented by two plant litter pools (metabolic and structural) and three soil organic
matter (SOM) pools (active, slow, and passive). The metabolic litter pool represents the easily decomposable
constituents of plant residues, while the structural litter pool is composed of more recalcitrant, ligno-cellulose plant
materials. The three SOM pools represent a gradient in decomposability, from active SOM (representing microbial
biomass and associated metabolites) having a rapid turnover (months to years), to passive SOM (representing highly
processed, humified, condensed decomposition products), which is highly recalcitrant, with mean residence times
on the order of several hundred years. The slow pool represents decomposition products of intermediate stability,
having a mean residence time on the order of decades and is the fraction that tends to be influenced the most by
land use and management activity. Soil texture influences turnover rates of the slow and passive pools. The clay and
silt-sized mineral fraction of the soil provides physical protection from microbial decomposition, leading to
enhanced SOM stabilization in finely textured soils. Soil temperature and moisture, tillage disturbance, aeration, and
other factors influence decomposition and loss of C from the soil organic matter pools.
3) The soil-water submodel simulates water flows and changes in soil water availability, which influences both plant
growth, decomposition and nutrient cycling. Soil moisture content is simulated through a multi-layer profile based
on precipitation, snow accumulation and melting, interception, soil and canopy evaporation, transpiration, soil
water movement, runoff, and drainage.
A-380 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Figure A-9: Modeled versus measured net primary production
Yield Carbon from Published Data (g m"2)
Yield Carbon from Published Data (g m"2)
Part a) presents results of the NASA-CASA algorithm (i2 = 83°/
-------
Box A-2: DayCent Model Simulation of N Gas losses and Nitrate Leaching
The DayCent model simulates the two biogeochemical processes, nitrification and denitrification, that result in N20
and NOx emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm
of soil (where nitrification mostly occurs) while denitrification is calculated for the entire soil profile (accounting for
denitrification near the surface and subsurface as nitrate leaches through the profile). The equations and key
parameters controlling N20 emissions from nitrification and denitrification are described below.
Nitrification is controlled by soil ammonium (NH4+) concentration, temperature (t), Water Filled Pore Space (WFPS)
and pH according to the following equation:
Equation A-42: Soil Nitrification Rate
Nit = NH4+ x Kmax x F(t) x F(WFPS) x F(pH)
where,
Nit
the
nh4+
the
Kmax —
the
F(t)
the
F(WFPS) =
the
F(pH)
the
; = 0.10/day)
The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N20.
The model assumes that denitrification rates are controlled by the availability of soil N03" (electron acceptor), labile C
compounds (electron donor) and oxygen (competing electron acceptor). Heterotrophic soil respiration is used as a
proxy for labile C availability, while oxygen availability is a function of soil physical properties that influence gas
diffusivity, soil WFPS, and oxygen demand. The model selects the minimum of the N03" and C02 functions to establish
a maximum potential denitrification rate. These rates vary for particular levels of electron acceptor and C substrate,
and account for limitations of oxygen availability to estimate daily denitrification rates according to the following
equation:
Equation A-43: Soil Denitrification Rate
where,
Den = min[F(C02), F(N03)] x F(WFPS)
Den = the soil denitrification rate (|_ig N/g soil/day)
F(NOs) = a function relating N gas flux to nitrate levels Figure A-lla)
F(C02) = a function relating N gas flux to soil respiration (Figure A-llb)
F(WFPS) = a dimensionless multiplier (Figure A-llc)
The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (DFc):
Equation A-44: Inflection Point Calculation
x inflection = 0.90 - M(C02)
where,
M = a multiplier that is a function of DFc- In technical terms, the inflection point is the domain
where either F(WFPS) is not differentiable or its derivative is 0. In this case, the inflection
point can be interpreted as the WFPS value at which denitrification reaches half of its
maximum rate.
A-382 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Respiration has a much stronger effect on the water curve in clay soils with low DFc than in loam or sandy soils with
high DFc (Figure A-lOb). The model assumes that microsites in fine-textured soils can become anaerobic at relatively
low water contents when oxygen demand is high. After calculating total N gas flux, the ratio of N2/N20 is estimated so
that total N gas emissions can be partitioned between N20 and N2:
Equation A-45: Ratio of Nitrogen Gas (N2) to Nitrous Oxide
Rn2/n20= Fr(N0s/C02) X Fr(WFPS).
where,
Rn2/n2o = the ratio of N2/N20
Fr(N03/C02) = a function estimating the impact of the availability of electron donor relative to substrate
Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N20.
For Fr(N03/C02), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be in the
form of N20. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2.
After calculating and summing N20 emissions from nitrification and dentification, NOx emissions are calculated using
a N0x/N20 ratio function based on soil gas diffusivity. The N0x/N20 ratio is high (maximum of about 17) when soil gas
diffusivity is high and decreases to a minimum of approximately 0.28 as diffusivity decreases.
Ammonia volatilization is simulated less mechanistically than the other N gas losses. A soil texture specific portion of
N excreted from animals ranging from 15-30 percent is assumed to be volatilized with more volatilization as soil
texture becomes coarser. In addition, a plant specific portion ranging from 2-15 % of harvested or senesced biomass N
is assumed to be volatilized.
A portion of the nitrate is assumed to be dissolved and flows with water between soil layers during saturated and
unsaturated water movement. The portion of nitrate that flows from the upper layer to the lower layer increases with
increasing sand content and with water flow volume so most movement occurs during saturated flow events
triggered by precipitation or irrigation. The amount of nitrate leaching for estimating indirect N20 emissions is based
on the nitrate that flows through the entire profile in the model simulation. In addition to sand content, leaching rates
are influenced by soil depth, plant N demand, precipitation event size, and other factors.
Annex 3
A-383
-------
Figure A-10: Effect of Soil Temperature (a), Water-Filled Pore Space (b), and pH (c) on
Nitrification Rates
Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates
A-384 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Figure A-ll: Effect of Soil Nitrite Concentration (a), Heterotrophic Respiration Rates (b),
and Water-Filled Pore Space (c) on Denitrification Rates
Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates
NQ pg N/g soil
CQng C/g soil/day
WFPS%
Hot moments, or pulses, of N20 emissions can occur during freeze-thaw events in soils of cold climates, and these events
can contribute a substantial portion of annual emissions in northern temperate and boreal regions (Butterbach-Bahl et
al. 2017). A recent analysis suggests that not accounting for these events could lead to under-estimation of global
agricultural N20 emissions by 17-28 percent (Wagner-Riddle et al. 2017). The mechanisms responsible for this
phenomenon are not entirely understood but the general hypotheses include accumulation of substrates while the soil is
frozen that drives denitrification as the soil thaws; impacts on soil gas diffusivity and 02 availability in pores during
Annex 3
A-385
-------
freeze-thaw events that influence denitrification rates; and differing temperature sensitives of the enzymatic processes
that control the amounts of N2 and N20 gases released during denitrification (Congreves et al. 2018). The denitrification
routine in DayCent was amended so that periods of thawing of frozen soils in the 2-5 cm layer during the late
winter/spring will trigger a hot moment or pulse of N20 emissions. Specifically, the soil water content and microbial
respiration controls on denitrification are relaxed for approximately 3 days upon melting and N20 from denitrification is
amplified by an amount proportional to cumulative freezing degree days during the winter season. DayCent was
evaluated using annual high frequency N20 data collected at research sites in eastern and western Canada (Wagner-
Riddle et al. 2017). The results showed less bias with a better match to observed patterns of late winter/spring emissions
than the previous version of the DayCent model (Del Grosso et al. 2020).
DayCent Model Parameterization and Evaluation
DayCent has been widely applied and calibrated over the years through manual parameterization (e.g., Parton et al.
1998; Del Grosso et al. 2001). However, manual approaches to parametrization do not necessarily provide the best
calibration for a process-based model, and so there is an effort underway to re-parameterize DayCent with Bayesian
calibration methods. There are three steps to this calibration method: a) conduct a sensitivity analysis to identify the
most influential parameters, b) conduct the Bayesian calibration with the most sensitive parameters, and 3) evaluate the
results with independent data. First, the framework uses a global sensitivity analysis to evaluate the importance of
parameters given their full parameter space and potential interactions with other parameters (Saltelli et al. 2008). This
approach is considered more robust for ranking parameter importance rather than a local sensitivity analysis that
focuses on the effect of varying one parameter, generally within a small area of the overall parameter space. The Sobol
method is used to conduct the global sensitivity analysis (Sobol 2001), which is appropriate for the complexity in the
DayCent model (Saltelli 2002). Second, the model is calibrated using Bayesian logic with the Sampling Importance
Resampling (SIR) method (Rubin 1987, Rubin 1988). A set of prior parameter distributions are developed based on the
knowledge of the inventory compilers and information in the published literature. The model is then applied in a Monte
Carlo analysis by randomly selecting values from the prior parameter distributions using a Latin Hypercube Sampling
(LHS) approach. The LHS approach for selecting parameters allows for values that are used in the simulations to be
distributed throughout the entire domain of the prior parameter distributions. The posterior distribution is
approximated from the results generated by the Monte Carlo analysis using a likelihood function and weighting
parameters based on the level of mismatch between modeled and measured emissions or soil organic C stock changes. If
the data are informative, the likelihood will update the prior parameter distribution based on the weighting and lead to
more resolved joint posterior parameter distribution. Third, the model is applied to simulate experimental sites that are
not used in the Bayesian calibration, and the results are evaluated relative to the model application with the prior
parameter distributions. If the model has been improved through the calibration process, then the results should have
less bias and/or variance than the model application with the prior parameter distributions.
This Bayesian calibration model framework has initially been applied to calibrate DayCent for modeling soil organic C
stock changes to a 30 cm depth (Gurung et al. 2020). The analysis reduced uncertainty in model predictions by a factor of
6.6. See Gurung et al. (2020) for more detail about this application. We anticipate expanding the calibration to other
model processes in the near future, and eventually using the joint posterior parameter distribution to quantify
uncertainty in model predictions. In this Inventory, the maximum a posterior value for each parameter from the
posterior distribution has been used to simulate soil organic C stock changes.
DayCent has been applied to sites that are independent from model calibration to evaluate the effectiveness of the
model for estimating greenhouse gas emissions and soil organic C stock changes in the United States inventory.
Moreover, these analyses are used to quantify uncertainty with an empirical approach as discussed in Step 2a of this
annex (Ogle et al. 2007). Comparison of model results and plot level data show that DayCent simulates soil organic
matter levels with reasonable accuracy. The model was tested and shown to capture the general trends in C storage
across 948 observations from 72 long-term experiment sites and 142 NRI soil monitoring network sites (Spencer et al.
2011) (Figure A-12). Some bias and imprecision occur in predictions of soil organic C, which is reflected in the uncertainty
associated with DayCent model results. Regardless, the Tier 3 approach has considerably less uncertainty than Tier 1 and
2 methods (Del Grosso et al. 2010; Figure A-13).
A-386 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Figure A-12: Comparisons of Results from DayCent Model and Measurements of Soil Organic
C Stocks
Cropland
y = x
Grassland
y = x
••
10
Ln Modeled SOC Stock
(g C m"2)
Annex 3
A-387
-------
Figure A-13: Comparison of Estimated Soil Organic C Stock Changes and Uncertainties using
Tier 1 (IPCC 2006), Tier 2 (Ogle et al. 2003, 2006) and Tier 3 Methods
T 80 -
L_
>
CM
0
o
ci 60
H
CD
CI
c
™ 40
O
x.
o
o
CO 20 -
O
o
w
0 -
Similarly, DayCent model results have been compared to trace gas N20 fluxes for a number of native and managed
systems from 41 experimental sites with over 200 treatment observations (Del Grosso et al. 2001, 2005, 2010) (Figure A-
14). In general, the model simulates accurate emissions, but some bias and imprecision does occur in predictions, which
is reflected in the uncertainty associated with DayCent model results. Comparisons with measured data showed that
DayCent estimated N20 emissions more accurately and precisely than the IPCC Tier 1 methodology (IPCC 2006) with
higher r2 values and a fitted line closer to a perfect 1:1 relationship between measured and modeled N20 emissions (Del
Grosso et al. 2005, 2008b). This is not surprising, since DayCent includes site-specific factors (climate, soil properties, and
previous management) that influence N20 emissions. Furthermore, DayCent also simulated N03" leaching (root mean
square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 69 percent) (Del
Grosso et al. 2005). Volatilization of N gases that contribute to indirect soil N20 emissions is the only component that has
not been thoroughly tested, which is due to a lack of measurement data.
DayCent predictions of soil CH4 emissions have also been compared to experimental measurements from sites in
California, Texas, Arkansas, and Louisiana (Figure A-15). There are 17 long-term experiments with data on CH4 emissions
from rice cultivation, representing 238 treatment observations. In general, the model estimates CH4emissions with no
apparent bias, but there is a lack of precision, which is addressed in the uncertainty analysis.
Tier 1
Tier 2
Tier 3
A-388 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Figure A-14: Comparisons of Results from DayCent Model and Measurements of Soil Nitrous
Oxide Emissions
0 -
Corn without Freeze-Thaw Effect • •• y=x •'
* *•*
•• • /
/.«• • •• •
# •? ••
Com with Freeze-Thaw Effect y = x
• * •*«
• • V&*?
.
* V • •
M,- .
" *
• -
# -
•
Other Crops without Freeze-Thaw Effect y - x ..
. ••
Other Crops with Freeze-Thaw Effect y = * , ¦"
1 •
•
*. ..
• •
: *
••
• * •
#
y = x •'
Grassland . *
•
• • •
• • *
•2 -1 0 1 2 3 4 5
Ln Modeled N2O Emissions
(g N2O-N ha"1 day"1)
* * *"
•
TO
"~
V 2
ro
1
Z
DJ3 0
-2-10123
Ln Modeled N2O Emissions
(g N2O-N ha"1 day"1)
Annex 3
A-389
-------
Figure A-15: Comparisons of Results from DayCent Model and Measurements of Soil
Methane Emissions
Ln Modeled CH4
(mg CH. m"2d"1)
A-390 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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3.13. Methodology for Estimating Net Carbon Stock Changes in
Forest Ecosystems and Harvested Wood Products for Forest
Land Remaining Forest Land and Land Converted to Forest
Land as well as Non-C02 Emissions from Forest Fires
This sub-annex expands on the methodology used to estimate net changes in carbon (C) stocks in forest ecosystems and
harvested wood products for Forest Land Remaining Forest Land and Land Converted to Forest Land as well as non-C02
emissions from forest fires. Full details of the C conversion factors and procedures may be found in the cited references.
For details on the methods used to estimate changes in mineral soil C stocks in the Land Converted to Forest Land section
please refer to Annex 3.12.
Carbon stocks and net stock change in forest ecosystems
The inventory-based methodologies for estimating forest C stocks are based on a combination of approaches (Woodall et
al 2015a) and are consistent with the IPCC (2003, 2006) stock-difference (used for the conterminous United States (U.S.))
and gain-loss (used for Alaska) methods. Estimates of ecosystem C are based on data from the network of annual
national forest inventory (NFI) plots established and measured by the Forest Inventory and Analysis (FIA) program within
the USDA Forest Service; either direct measurements or variables from the NFI are the basis for estimating metric tons of
C per hectare in forest ecosystem C pools (i.e., above- and belowground biomass, dead wood, litter, and soil carbon). For
the conterminous United States, plot-level estimates are used to inform land area (by use) and stand age transition
matrices across time which can be summed annually for an estimate of forest C stock change for Forest Land Remaining
Forest Land and Land Converted to Forest Land. A general description of the land use and stand age transition matrices
that are informed by the annual NFI of the United States and were used in the estimation framework to compile
estimates for the conterminous United States in this Inventory are described in Coulston et al. (2015). The annual NFI
data in the conterminous United States allows for empirical estimation of net change in forest ecosystem carbon stocks
within the estimation framework. In contrast, Wyoming and West Oklahoma have limited remeasurement data so
theoretical age transition matrices were developed (Figure A-16). The incorporation of all managed forest land in Alaska
was facilitated by an analysis to determine the managed land base in Alaska (Ogle et al. 2018), the expansion of the NFI
into interior Alaska beginning in 2014, and a myriad of publicly available data products that provided information
necessary for prediction of C stocks and fluxes on plots that have yet to be measured as part of the NFI.
The following subsections of this annex describe the estimation system used this year (Figure A-16) including the
methods for estimating individual pools of forest ecosystem C in addition to the approaches to informing land use and
stand age transitions.
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Figure A-16: Flowchart of the inputs necessary in the estimation framework, including the
methods for estimating individual pools of forest C in the conterminous United States
Conterminous US West Oklahoma and Wyoming
Note: An empirical age class transition matrix was used in every state in the conterminous United States with the exception of
west Oklahoma and Wyoming where a theoretical age class transition matrix was used due to a lack of remeasurements in the
annual NFI.
Forest Land Definition
The definition of forest land within the United States and used for this Inventory is defined in Oswalt et al. (2019) as
"Land at least 120 feet (37 meters) wide and at least 1 acre (0.4 hectare) in size with at least 10 percent cover (or
equivalent stocking) by live trees including land that formerly had such tree cover and that will be naturally or artificially
regenerated. Trees are woody plants having a more or less erect perennial stem(s) capable of achieving at least 3 inches
(7.6 cm) in diameter at breast height, or 5 inches (12.7 cm) diameter at root collar, and a height of 16.4 feet (5 meters) at
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maturity in situ. The definition here includes all areas recently having such conditions and currently regenerating or
capable of attaining such condition in the near future. Forest land also includes transition zones, such as areas between
forest and non-forest lands that have at least 10 percent cover (or equivalent stocking) with live trees and forest areas
adjacent to urban and built-up lands. Unimproved roads and trails, streams, and clearings in forest areas are classified as
forest if they are less than 120 feet (36.6 meters) wide or an acre (0.4 hectare) in size. Forest land does not include land
that is predominantly under agricultural or urban land use." Timberland is productive forest land, which is on unreserved
land and is producing or capable of producing crops of industrial wood. This is an important subclass of forest land
because timberland is the primary source of C incorporated into harvested wood products. Productivity for timberland is
at a minimum rate of 20 cubic feet per acre (1.4 cubic meters per hectare) per year of industrial wood (Woudenberg and
Farrenkopf 1995). There are about 208 million hectares of timberland in the conterminous United States, which
represents 67 percent of all forest lands over the same area (Oswalt et al. 2019).
Forest Inventory Data
The estimates of forest C stocks are based on data from the annual NFI. NFI data were obtained from the USDA Forest
Service, FIA Program (Frayer and Furnival 1999; USDA Forest Service 2022a; USDA Forest Service 2022b). NFI data
include remote sensing information and a collection of measurements in the field at sample locations called plots. Tree
measurements include diameter at breast height, tree height, species, and variables describing tree form and condition.
On a subset of plots, additional measurements or samples are taken on downed dead wood, litter, and soil variables. The
technical advances needed to estimate C stocks from these data are ongoing (Woodall et al. 2015a) with the latest
research incorporated on an annual basis (see Domke et al. 2016, Domke et. al. 2017). The field protocols are thoroughly
documented and available for download from the USDA Forest Service (2022c). Bechtold and Patterson (2005) provide
the estimation procedures for standard NFI results. The data are freely available for download at USDA Forest Service
(2011b) as the FIA Database (FIADB) Version 8.0 (USDA Forest Service 2022b; USDA Forest Service 2022c); these are the
primary sources of NFI data used to estimate forest C stocks. In addition to the field sampling component, fine-scale
remotely sensed imagery (National Agriculture Imagery Program; NAIP 2015; Woodall et al. 2015b) is used to assign the
land use at each sample location which has a nominal spatial resolution (raster cell size) of 1 m2. Prior to field
measurement of each year's collection of annual plots due for measurement (i.e., panel), each sample location in the
panel (i.e., systematic distribution of plots within each state each year) is photo-interpreted manually to classify the land
use. Annual NFI data are available for the temperate oceanic ecoregion of Alaska (southeast and south central) from
2004 to present as well as for interior Alaska from a pilot inventory in 2014 which became operational in 2016.
Agroforestry systems are not currently accounted for in the U.S. Inventory, since they are not explicitly inventoried by
either of the two primary national natural resource inventory programs: the FIA program of the USDA Forest Service and
the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry et al. 2005). The
majority of these tree-based practices do not meet the size and definitions for forests within each of these resource
inventories.
A national plot design and annualized sampling (USDA Forest Service 2022a) were introduced by FIA with most new
annual NFIs beginning after 1998. These are the only NFIs used in the compilation of estimates for this Inventory. These
NFIs involve the sampling of all forest land including reserved and lower productivity lands. All states with the exception
of Hawaii have annualized NFI data available with substantial remeasurement (with the exception of Wyoming and West
Oklahoma) in the conterminous United States (Figure A-17). Annualized sampling means that a spatially representative
portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to 10 years,
depending on the region of the U.S. The full unique set of data with all measured plots, such that each plot has been
measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable, unbiased
samples of forest inventory within the state, but with higher sampling uncertainty than the full cycle. After all plots have
been measured once, the sequence continues with remeasurement of the first year's plots, starting the next new cycle.
Most eastern states have completed three or four cycles of the annualized NFI, and most western states are on their
second annual cycle. Annually updated estimates of forest C stocks are affected by the redundancy in the data used to
generate the annual updates of C stock. For example, a typical annual inventory update for an eastern state will include
new data from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that
included in the previous year's annual update. The interpretation and use of the annual inventory data can affect trend
estimates of C stocks and stock changes (e.g., estimates based on 60 percent of an inventory cycle will be different than
estimates with a complete (100 percent) cycle). In general, the C stock and stock change estimates use annual NFI
summaries (updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update
(i.e., 2021) is the exception because it is included in stock change calculations in order to include the most recent
Annex 3
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available data for each state. The specific inventories used in this report are listed in Table A-200 and this list can be
compared with the full set of summaries available for download (USDA Forest Service 2022b).
Figure A-17: Annual FIA plots (remeasured and not remeasured) across the United States
Remeasured
Not Remeasured
Note: Due to the vast number of plots (where land use is measured even if no forest is present) they appear as spatially
contiguous when displayed at the scale and resolution presented in this figure.
It should be noted that as the FIA program explores expansion of its vegetation inventory beyond the forest land use to
other land uses (e.g., woodlands and urban areas) this will require that subsequent inventory observations will need to
be delineated between forest and other land uses as opposed to a strict forest land use inventory. The forest C estimates
provided here represent C stocks and stock change on managed forest lands (IPCC 2006, see Section 6.1 Representation
of the U.S. Land Base), which is how all forest lands are classified. In some cases, there are NFI plots that do not meet the
height component of the definition of forest land (Coulston et al. 2016). These plots are identified as "woodlands" (i.e.,
not forest land use) and were removed from the forest estimates and classified as grassland.163 Note that minor
differences (approximately 2 percent less forest land area in the CONUS) in identifying and classifying woodland as
"forest" versus "woodland" exist between the current Resources Planning Act Assessment (RPA) data (Oswalt et al.
2014) and the FIADB (USDA Forest Service 2015b) due to a refined modelling approach developed specifically for
Inventory reporting (Coulston et al. 2016). Plots in the coastal region of the conterminous United States were also
evaluated using the National Land Cover Database and the Coastal Change Analysis Program data products to ensure
that land areas were completely accounted for in this region and also that they were not included in both the Wetlands
category and the Forest Land category. This resulted in several NFI plots or subplots being removed from the Forest Land
compilation.
163 See the Grassland Remaining Grassland and Land Converted to Grassland sections for details.
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Table A-200: Specific annual forest inventories by state used in development of forest C
stock and stock change estimate
Remeasured Annual Plots
Split Annual Cycle Plots
Time 1 Year
Time 2 Year
State
Range
Range
State
Time 1 Year Range
Time 2 Year Range
Alabama
2006 - 2016
2013 - 2020
Oklahoma (West)
2010-2012
2013 - 2019
Arizona
2Q01 - 2009
2011 - 2019
HHyfmifg
/2QpQ
2011 - 2020
Arkansas
2007 - 2014
2014-2019
California
2001 - 2009
2011-2019
Alaska (Coastal)1
2004 - 2020
Colorado
2002 - 2009
2012 - 2019
Alaska (Interior)1
2014, 2016 - 2020
Connecticut
2008 - 2013
2013 - 2019
Delaware
2008 - 2013
2013 - 2019
Florida
2002 - 2012
2012 - 2017
Georgia
2010 - 2014
2015 - 2019
Idaho
2004 - 2009
2014-2019
Illinois
2007 - 2013
2012 - 2019
Indiana
2008 - 2013
2013 - 2019
Iowa
2008 - 2013
2013 - 2019
Kansas
2008 - 2013
2013 - 2019
Kentucky
2005 - 2012
2012 - 2017
Louisiana
2001 - 2012
2009 - 2018
Maine
2010 - 2014
2015 - 2019
Maryland
2008 - 2013
2013 - 2019
Massachusetts
2008 - 2013
2013 - 2019
Michigan
2008 - 2013
2013 - 2019
Minnesota
2010 - 2014
2015 - 2019
Mississippi
2006 - 2015
2014-2019
Missouri
2008 - 2013
2013 - 2019
Montana
2003 - 2009
2013 - 2019
Nebraska
2008 - 2013
2013 - 2019
Nevada
2004 - 2009
2014-2019
New Hampshire
2008 - 2013
2013 - 2019
New Jersey
2009 - 2014
2015 - 2019
New Mexico
2005 - 2009
2015 - 2019
New York
2008 - 2013
2013 - 2019
North Carolina
2003 - 2013
2011-2019
North Dakota
2008 - 2013
2013 - 2019
Ohio
2008 - 2013
2013 - 2019
2014 - 2018
Oregon
2001 - 2009
2011-2019
Pennsylvania
2008 - 2013
2013 - 2019
Rhode Island
2008 - 2013
2013 - 2019
South Carolina
2007 - 2014
2014-2019
South Dakota
2008 - 2013
2013 - 2019
Tennessee
2005 - 2012
2012 - 2017
Texas (East)
2009 - 2014
2015 - 2019
Texas (West)
2004 - 2012
2014-2017
Utah
2000 - 2009
2010-2019
Vermont
2008 - 2013
2013 - 2019
Virginia
2008 - 2015
2014-2019
Washington
2002 - 2009
2012 - 2019
Annex 3
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West Virginia
Wisconsin
2008 - 2013
2008 - 2013
2013 - 2019
2013 - 2019
1 Plots in Alaska have not been split but are included in this column to conserve space in the table.
Note: Remeasured annual plots represent a complete inventory cycle between measurements of the same plots while spilt
annual cycle plots represent a single inventory cycle of plots that are split where remeasurements have yet to occur.
Estimating Forest Inventory Plot-Level C-Density
For each inventory plot in each state, field data from the FIA program are used alone or in combination with auxiliary
information (e.g., climate, surficial geology, elevation) to predict C density for each forest ecosystem C pool (i.e.,
aboveground and belowground biomass, dead wood, litter, SOC). In the past, most of the conversion factors and models
used for inventory-based forest C estimates (Smith et al. 2010; Heath et al. 2011) were initially developed as an
extension of the forest C simulation model FORCARB (Heath et al. 2010). The conversion factors and model coefficients
were usually categorized by region and forest type. Thus, region and type are specifically defined for each set of
estimates. More recently, the coarse approaches of the past have been updated with empirical information regarding C
variables for individual forest C pools such as dead wood and litter (e.g., Domke et al. 2013 and Domke et al. 2016).
Factors are applied to the forest inventory data at the scale of NFI plots which are a systematic sample of all forest
attributes and land uses within each state. The results are estimates of C density (T per hectare) for each forest
ecosystem C pool. Carbon density for live trees, standing dead trees, understory vegetation, downed dead wood, litter,
and soil organic matter are estimated. All non-soil C pools except litter and downed dead wood can be separated into
aboveground and belowground components. The live tree and understory C pools are combined into the aboveground
and belowground biomass pools in this Inventory. Similarly, standing dead trees and downed dead wood are pooled as
dead wood in this Inventory. C stocks and fluxes for Forest Land Remaining Forest Land and Land Converted to Forest
Land are reported in forest ecosystem C pools following IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at diameter
breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above- and
below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et al.
(2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter, and,
in some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing deductions)
provided in the tree table of the FIADB is the principal input to the CRM biomass calculation for each tree (Woodall et al.
2011). The estimated volumes of wood and bark are converted to biomass based on the density of each. Additional
components of the trees such as tops, branches, and coarse roots, are estimated according to adjusted component
estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of sound volume in
the FIADB, and CRM biomass estimates follow a separate process (see Woodall et al. 2011 for details). An additional
component of foliage, which was not explicitly included in Woodall et al. (2011), was added to each tree following the
same CRM method. Carbon is estimated by multiplying the estimated oven-dry biomass by a C fraction of 0.5 because
biomass is 50 percent of dry weight (USDA Forest Service 2022d). Further discussion and example calculations are
provided in Woodall et al. (2011) and Domke et al. (2012).
Understory vegetation
Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is defined as all
biomass of undergrowth plants in a forest, including woody shrubs and trees less than 2.54 cm d.b.h. In this Inventory, it
is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is near the lower
range of temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: ratios are
likely to be lower for light-limited understory vegetation as compared with larger trees, and a greater proportion of all
root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots. These were
fit to the model:
Equation A-46: Ratio of understory C density to live tree C density
Ratj0 — g(A - B x ln(live tree C density))
(i)
A-404 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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In this model, the ratio is the ratio of understory C density (T C/ha) to live tree C density (above- and below-ground)
according to Jenkins et al. (2003) and expressed in T C/ha. An additional coefficient is provided as a maximum ratio; that
is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum ratio. A
full set of coefficients are in Table A-201. Regions and forest types are the same classifications described in Smith et al.
(2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is:
Equation A-47: Understory C density
Understory (T C/ha) = (live tree C density) x e<°-S55"103 x ln
-------
Red Alder 2.094 1.230 4.745
Western Hemlock 2.081 1.218 4.693
Nonstocked 4.401 4.401 4.589
Douglas-fir
2.342
1.360
4.731
Fir-Spruce
2.129
1.315
4.749
Hardwoods
1.860
1.110
4.745
RMN
Lodgepole Pine
2.571
1.500
4.773
Other Conifer
2.614
1.518
4.821
Pinyon-Juniper
2.708
2.708
4.820
Ponderosa Pine
2.099
1.344
4.776
Nonstocked
4.430
4.430
4.773
Douglas-fir
5.145
2.232
4.829
Fir-Spruce
2.861
1.568
4.822
Hardwoods
1.858
1.110
4.745
RMS
Lodgepole Pine
3.305
1.737
4.797
Other Conifer
2.134
1.382
4.821
Pinyon-Juniper
2.757
2.757
4.820
Ponderosa Pine
3.214
1.732
4.820
Nonstocked
4.243
4.243
4.797
Bottomland Hardwood
0.917
1.109
1.842
Misc. Conifer
1.601
1.129
4.191
Natural Pine
2.166
1.260
4.161
SC
Oak-Pine
1.903
1.190
4.173
Planted Pine
1.489
1.037
4.124
Upland Hardwood
2.089
1.235
4.170
Nonstocked
4.044
4.044
4.170
Bottomland Hardwood
0.834
1.089
1.842
Misc. Conifer
1.601
1.129
4.191
Natural Pine
1.752
1.155
4.178
SE
Oak-Pine
1.642
1.117
4.195
Planted Pine
1.470
1.036
4.141
Upland Hardwood
1.903
1.191
4.182
Nonstocked
4.033
4.033
4.182
a Prediction of ratio of understory C to live tree C is based on the model: Ratio=exp(A - B x ln(tree_carbon_tph)),
where "ratio" is the ratio of understory C density to live tree (above-and below- ground) C density, and
"tree_carbon_density" is live tree (above-and below- ground) C density in T C/ha. Note that this ratio is multiplied
by tree C density on each plot to produce understory vegetation.
b Regions and types as defined in Smith et al. (2003).
c Maximum ratio: any estimate predicted from the model that is greater than the maximum ratio is set equal to the
maximum ratio.
Dead Wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011; Woodall et al.
2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that
can substantially affect C mass are decay, which affects density and thus specific C fraction (Domke et al. 2011; Harmon
et al. 2011), and structural loss such as branches and bark (Domke et al. 2011). A C fraction of 0.5 is used for standing
dead trees (USDA forest Service 2022d).
Downed dead wood, inclusive of logging residue, are sampled on a subset of NFI plots. Despite a reduced sample
intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et al.
2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined
as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing
dead trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree
biomass were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for
downed dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of
ratios is provided in Table A-202. An additional component of downed dead wood is a regional average estimate of
A-406 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
logging residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at
age zero and first order decay; initial densities and decay coefficients are provided in Table A-203. These amounts are
added to explicitly account for downed dead wood following harvest. The sum of these two components are then
adjusted by the ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013). An
example of this 3-part calculation for downed dead wood in a 25-year-old naturally regenerated loblolly pine forest with
82.99 T C/ha in live trees (Jenkins et al. 2003) in Louisiana is as follows:
First, an initial estimate from live tree C density and Table A-202 (SC, Natural Pine)
Equation A-48: C density of downed dead wood
C density = 82.99 x 0.068 = 5.67 (T C/ha)
Second, an average logging residue from age and Table A-202 (SC, softwood)
Equation A-49: Logging residue C density
C density = 5.5 x e(-25/17.9) = 1.37 (T C/ha)
Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886
Equation A-50: Adjusted C density of downed dead wood
C density = (5.67 + 1.37) x 0.886 = 6.24 (T C/ha)
Table A-202: Ratio for Estimating Downed Dead Wood by Region and Forest Type
Region3
Forest type3
Ratiob
Aspen-Birch
0.078
MBB/Other Hardwood
0.071
Oak-Hickory
0.068
NE
Oak-Pine
0.061
Other Pine
0.065
Spruce-Fir
0.092
White-Red-Jack Pine
0.055
Nonstocked
0.019
Aspen-Birch
0.081
Lowland Hardwood
0.061
Maple-Beech-Birch
0.076
NLS
Oak-Hickory
0.077
Pine
0.072
Spruce-Fir
0.087
Nonstocked
0.027
Conifer
0.073
Lowland Hardwood
0.069
NPS
Maple-Beech-Birch
0.063
Oak-Hickory
0.068
Oak-Pine
0.069
Nonstocked
0.026
Douglas-fir
0.091
Fir-Spruce
0.109
Hardwoods
0.042
PSW
Other Conifer
0.100
Pinyon-Juniper
0.031
Redwood
0.108
Nonstocked
0.022
Douglas-fir
0.103
Fir-Spruce
0.106
PWE
Hardwoods
0.027
Lodgepole Pine
0.093
Pinyon-Juniper
0.032
Ponderosa Pine 0.103
Annex 3
A-407
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Nonstocked
0.024
Douglas-fir
0.100
Fir-Spruce
0.090
Other Conifer
0.073
PWW
Other Hardwoods
0.062
Red Alder
0.095
Western Hemlock
0.099
Nonstocked
0.020
Douglas-fir
0.062
Fir-Spruce
0.100
Hardwoods
0.112
RMN
Lodgepole Pine
0.058
Other Conifer
0.060
Pinyon-Juniper
0.030
Ponderosa Pine
0.087
Nonstocked
0.018
Douglas-fir
0.077
Fir-Spruce
0.079
Hardwoods
0.064
RMS
Lodgepole Pine
0.098
Other Conifer
0.060
Pinyon-Juniper
0.030
Ponderosa Pine
0.082
Nonstocked
0.020
Bottomland Hardwood
0.063
Misc. Conifer
0.068
Natural Pine
0.068
SC
Oak-Pine
0.072
Planted Pine
0.077
Upland Hardwood
0.067
Nonstocked
0.013
Bottomland Hardwood
0.064
Misc. Conifer
0.081
Natural Pine
0.081
SE
Oak-Pine
0.063
Planted Pine
0.075
Upland Hardwood
0.059
Nonstocked
0.012
a Regions and types as defined in Smith et al. (2003).
b The ratio is multiplied by the live tree C density on a plot to
produce downed dead wood C density (T C/ha).
Table A-203: Coefficients for Estimating Logging Residue Component of Downed Dead Wood
Forest Type
Groupb (softwood/
Initial C
Region3
hardwood)
Density (T/ha)
Decay Coefficient
Alaska
hardwood
6.9
12.1
Alaska
softwood
8.6
32.3
NE
hardwood
13.9
12.1
NE
softwood
12.1
17.9
NLS
hardwood
9.1
12.1
NLS
softwood
7.2
17.9
NPS
hardwood
9.6
12.1
NPS
softwood
6.4
17.9
PSW
hardwood
9.8
12.1
PSW
softwood
17.5
32.3
PWE
hardwood
3.3
12.1
PWE
softwood
9.5
32.3
A-408 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
PWW
hardwood
18.1
12.1
PWW
softwood
23.6
32.3
RMN
hardwood
7.2
43.5
RMN
softwood
9.0
18.1
RMS
hardwood
5.1
43.5
RMS
softwood
3.7
18.1
SC
hardwood
4.2
8.9
SC
softwood
5.5
17.9
SE
hardwood
6.4
8.9
SE
softwood
7.3
17.9
a Regions are defined in Smith et al. (2003) with the addition of coastal
Alaska.
b Forest types are according to majority hardwood or softwood species.
Litter carbon
Carbon in the litter layer is currently sampled on a subset of the NFI plots. Litter C is the pool of organic C (including
material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with
diameters of up to 7.5 cm. Because litter attributes are only collected on a subset of NFI plots, a model (3) was
developed to predict C density based on plot/site variables for plots that lacked litter information (Domke et al. 2016):
Equation A-51: Litter C density
P(FFCFull)=f(lat, Ion, elev, fortypgrp, above, ppt, tmax, gmi) + u (3)
Where lat = latitude, lort = longitude, elev = elevation, fortypgrp = forest type group, above = aboveground live tree C
(trees > 2.54 cm dbh), ppt = mean annual precipitation, tmax = average maximum temperature, gmi = the ratio of
precipitation to potential evapotranspiration, u = the uncertainty in the prediction resulting from the sample-based
estimates of the model parameters and observed residual variability around this prediction.
Due to data limitations in certain regions and inventory periods a series of reduced non-parametric models, which did
not include climate variables, were used rather than replacing missing variables with imputation techniques. Database
records used to compile estimates for this report were grouped by variable availability and the approaches described
herein were applied. Litter C predictions are expressed as density (T ha-1).
Soil organic carbon
This section provides a summary of the methodology used to predict SOC for this report. A complete description of the
approach is in Domke et al. (2017). The data used to develop the modeling framework to predict SOC on forest land
came from the NFI and the International Soil Carbon Network. Since 2001, the FIA program has collected soil samples on
every 16th base intensity plot (approximately every 2,428 ha) distributed approximately every 38,848 ha, where at least
one forested condition exists (Woodall et al. 2010). On fully forested plots, mineral and organic soils were sampled
adjacent to subplots 2 by taking a single core at each location from two layers: 0 to 10.16 cm and 10.16 to 20.32 cm. The
texture of each soil layer was estimated in the field, and physical and chemical properties were determined in the
laboratory (U.S. Forest Service 2011). For this analysis, estimates of SOC from the NFI were calculated following O'Neill et
al. (2005):
Equation A-52: Total mass of mineral and organic soil C
Y SOC = ( ' ¦ HD ¦ I ¦ ucf (4)
L-t FIA_TOTAL i i i J 1 '
where,
Zsoc = total mass (Mg C ha-1) of the mineral and organic soil C over all /th layers,
FIA_TOTAL
C. = percent organic C in the /'th layer,
BDt = bulk density calculated as weight per unit volume of soil (g-cm-3) at the /'th soil layer,
t. = thickness (cm) of the /'th soil layer (either 0 to 10.16 cm or 10.16 to 20.32 cm), and
ucf = unit conversion factor (100).
The SOCfia_total estimates from each plot were assigned by forest condition on each plot, resulting in 3,667 profiles with
SOC layer observations at 0 to 10.16 and 10.16 to 20.32 cm depths. Since the United States has historically reported SOC
Annex 3
A-409
-------
estimates to a depth of 100 cm (Heath et al. 2011, USEPA 2015), International Soil Carbon Monitoring Network (ISCN)
data from forests in the United States were harmonized with the FIA soil layer observations to develop model functions
of SOC by soil order to a depth of 100 cm. All observations used from the ISCN were contributed by the Natural
Resources Conservation Service. A total of 16,504 soil layers from 2,037 profiles were used from ISCN land uses defined
as deciduous, evergreen, or mixed forest. The FIA-ISCN harmonized dataset used for model selection and prediction
included a total of 5,704 profiles with 23,838 layer observations at depths ranging from 0 to 1,148 cm. The modeling
framework developed to predict SOC for this report was built around strategic-level forest and soil inventory information
and auxiliary variables available for all FIA plots in the United States. The first phase of the new estimation approach
involved fitting models using the midpoint of each soil layer from the harmonized dataset and SOC estimates at those
midpoints. Several linear and nonlinear models were evaluated, and a log-log model provided the optimal fit to the
harmonized data:
Equation A-53: Soil organic C at midpoint depth
log 10SOCt = / + log ! „ Depth (5)
where,
log 10SOCi = SOC density (Mg C ha-1 cm depth-1) at the midpoint depth,
I = intercept,
log l0 Depth = profile midpoint depth (cm).
The model was validated by partitioning the complete harmonized dataset multiple times into training and testing
groups and then repeating this step for each soil order to evaluate model performance by soil order. Extra sum of
squares F tests were used to evaluate whether there were statistically significant differences between the model
coefficients from the model fit to the complete harmonized dataset and models fit to subsets of the data by soil order.
Model coefficients for each soil order were used to predict SOC for the 20.32 to 100 cm layer for all FIA plots with soil
profile observations. Next, the SOC layer observations from the FIA and predictions over the 100 cm profile for each FIA
plot were summed:
Equation A-54: Total soil organic C density
SOC, 00 = SOCFIATOTAL + SOC20100 (6)
where,
SOC100 = total estimated SOC density from 0-100 cm for each forest condition with a soil
sample in the FIA database,
SOC as previously defined in model (4), SOC20-100
^>^^FIA_TOTAL r / v It
= predicted SOC from 20.32 to 100 cm from model (5).
In the second phase of the modeling framework, SOC10o estimates for FIA plots were used to predict SOC for plots lacking
SOC10o estimates using a non-parametric model, this particular machine learning tool used bootstrap aggregating (i.e.,
bagging) to develop models to improve prediction (Breimen 2001). It also relies on random variable selection to develop
a forest of uncorrelated regression trees. These trees recognize the relationship between a dependent variable, in this
case SOCW0, and a set of predictor variables. All relevant predictor variables—those that may influence the formation,
accumulation, and loss of SOC—from annual inventories collected on all base intensity plots and auxiliary climate, soil,
and topographic variables obtained from the PRISM climate group (Northwest Alliance 2015), Natural Resources
Conservation Service (NRCS 2015), and U.S. Geological Survey (Danielson and Gesch 2011), respectively, were included in
the analysis. Due to regional differences in sampling protocols, many of the predictor variables included in the variable
selection process were not available for all base intensity plots. To avoid problems with data limitations, pruning was
used to reduce the models to the minimum number of relevant predictors (including both continuous and categorical
variables) without substantial loss in explanatory power or increase in root mean squared error (RMSE). The general
form of the full non-parametric models were:
Equation A-55: Predicted soil organic carbon
P(SOC) = /(lat, Ion, elev,fortypgrp, ppt,t max, g mi, order, surf geo) (7)
A-410 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
where,
P(SOC)
lat
= predicted soil organic carbon per hectare to a depth of 100 cm
= latitude,
= longitude,
= elevation,
= forest type group,
= mean annual precipitation,
= average maximum temperature,
= the ratio of precipitation to potential evapotranspiration,
= soil order,
= surficial geological description.
Ion
elev
fortypgrp
ppt
nnax
gmi
order
surfgeo
Compilation of population estimates using NFI plot data
Methods for the conterminous United States
The estimation framework is fundamentally driven by the annual NFI. Unfortunately, the annual NFI does not extend to
1990 and the periodic data from the NFI are not consistent (e.g., different plot design) with the annual NFI necessitating
the adoption of a system to predict the annual C parameters back to 1990. To facilitate the C prediction parameters, the
estimation framework is comprised of a forest dynamics module (age transition matrices) and a land use dynamics
module (land area transition matrices). The forest dynamics module assesses forest uptake, forest aging, and disturbance
effects (i.e., disturbances such as wind, fire, and floods identified by foresters on inventory plots). The land use dynamics
module assesses C stock transfers associated with afforestation and deforestation (e.g., Woodall et al. 2015b). Both
modules are developed from land use area statistics and C stock change or C stock transfer by age class. The required
inputs are estimated from more than 625,000 forest and nonforest observations in the NFI database (U.S. Forest Service
2022a-c). Model predictions for before or after the annual NFI period are constructed from the estimation framework
using only the annual observations. This modeling framework includes opportunities for user-defined scenarios to
evaluate the impacts of land use change and disturbance rates on future C stocks and stock changes. As annual NFIs have
largely completed at least one cycle and been remeasured, age and area transition matrices can be empirically informed.
In contrast, as annual inventories in west Oklahoma and Wyoming are still undergoing their first complete cycle they are
still in the process of being remeasured, and as a result theoretical transition matrices need to be developed.
Wear and Coulston (2015) and Coulston et al. (2015) provide the framework for the model. The overall objective is to
estimate unmeasured historical changes and future changes in forest C parameters consistent with annual NFI estimates.
For most regions, forest conditions are observed at time t0 and at a subsequent time ti=t0+s, where s is the time step
(time measured in years) and is indexed by discrete (5 year) forest age classes. The inventory from t0 is then predicted
back to the year 1990 and projected from ti to 2020. This prediction approach requires simulating changes in the age-
class distribution resulting from forest aging and disturbance events and then applying C density estimates for each age
class. For all states in the conterminous United States (except for Wyoming and west Oklahoma) age class transition
matrices are estimated from observed changes in age classes between t0 and ti. In west Oklahoma and Wyoming only
one inventory was available (t0) so transition matrices were obtained from theory but informed by the condition of the
observed inventory to predict from t0 to 1990 and predict from t0 to 2020.
Theoretical Age Transition Matrices
Without any mortality-inducing disturbance, a projection of forest conditions would proceed by increasing all forest ages
by the length of the time step until all forest resided in a terminal age class where the forest is retained indefinitely (this
is by assumption, where forest C per unit area reaches a stable maximum). For the most basic case, disturbances (e.g.,
wildfire or timber harvesting) can reset some of the forest to the first age class. Disturbance can also alter the age class
in more subtle ways. If a portion of trees in a multiple-age forest dies, the trees comprising the average age calculation
change, thereby shifting the average age higher or lower (generally by one age class).
With n age classes, the age transition matrix (T) is an n x n matrix, and each element (Tqr) defines the proportion of
forest area in class q transitioning to class r during the time step (s). The values of the elements of T depend on a number
Annex 3
A-411
-------
of factors, including forest disturbances such as harvests, fire, storms, and the value of s, especially relative to the span
of the age classes. For example, holding area fixed, allowing for no mortality, defining the time step s equivalent to the
span of age classes, and defining five age classes results in:
Equation A-56: Example age transition matrix
(°
0
0
0
1
0
0
0
0
0
1
0
0
0
0
0
1
0
0
\o
0
0
1
lj
where all forest area progresses to the next age class and forests within the terminal age class are retained forever. With
this version of T, after five time steps all forests would be in the terminal age class. Relaxing these assumptions changes
the structure of T. If all disturbances, including harvesting and fire, that result in stand regeneration are accounted for
and stochastic elements in forest aging are allowed, T defines a traditional Lefkovitch matrix population model (e.g.,
Caswell 2001) and becomes:
T =
1 t2
0
0
d-3
0
£3 — d3
£3
0
C^4
0
0
1 - t4 - d4
d-5 \
0
0
0
1 - dj
(9)
where tq is the proportion of forest of age class q transitioning to age class q+1, dq is the proportion of age class q that
experiences a stand-replacing disturbance, and (1 — tq — dq) is the proportion retained within age class q (Tqr).
Projections and Backcast for West Oklahoma and Wyoming
Projections of forest C in west Oklahoma and Wyoming are based on a life stage model:
Equation A-57: C Stock Change
ACt = Ct+m - Ct = (FtT - Ft) ¦ Den + Lt ¦ Den (10)
In this framework T is an age transition matrix that shifts the age distribution of the forest F. The difference in forest area
by age class between time t and t+s is FtT-Ft. This quantity is multiplied by C density by age class (Den) to estimate C
stock change of forest remaining forest between t and t+s. Land use change is accounted for by the addition of Lt-Den,
where Lt identifies the age distribution of net land shifts into or out of forests. A query of the forest inventory databases
provides estimates of F and Den, while inventory observations and modeling assumptions are used to estimate T. By
expanding Den to a matrix of C contained in all the constituent pools of forest carbon, projections for all pools are
generated.
Land use change is incorporated as a 1 x n vector L, with positive entries indicating increased forest area and negative
entries indicating loss of forest area, which provides insights of net change only. Implementing a forest area change
requires some information and assumptions about the distribution of the change across age classes (the n dimension of
L). In the eastern states, projections are based on the projection of observed gross area changes by age class. In western
states, total forest area changes are applied using rules. When net gains are positive, the area is added to the youngest
forest age class; when negative, area is subtracted from all age classes in proportion to the area in each age class
category.
Backcasting forest C inventories generally involve the same concepts as forecasting. An initial age class distribution is
shifted at regular time steps backwards through time, using a transition matrix (B):
A-412 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Equation A-58: Backcasting Age Class Distribution
= Ft ' B
(11)
B is constructed based on similar logic used for creating T. The matrix cannot simply be derived as the inverse of T
(Ft_s = FtT_1) because of the accumulating final age class (i.e., T does not contain enough information to determine
the proportion of the final age class derived from the n-1 age class and the proportion that is retained in age class n from
the previous time step).164 However, B can be constructed using observed changes from the inventory and assumptions
about transition/accumulation including nonstationary elements of the transition model:
Equation A-59: Age Transition Model
A
B =
7 d.
b2
0
0
0
*->q
di
l-b2
b3
0
0
d-2
0
l-b3
b4
0
^3
0
0
1 - b4
br
0
0
0
1-b,
(12)
Forest area changes need to be accounted for in the backcasts as well:
Equation A-60: Forest Area Change
Ft-s = FtB — Lt
Where Lt is the forest area change between ti and t0 as previously defined.
(13)
In west Oklahoma and Wyoming, the theoretical life-stage models described by matrices (9) and (10) were applied. The
disturbance factors (d) in both T and B are obtained from the current NFI by assuming that the area of forest in age class
1 resulted from disturbance in the previous period, the area in age class 2 resulted from disturbance in the period before
that, and so on. The source of disturbed forest was assumed to be proportional to the area of forest in each age class.
For projections (T), the average of implied disturbance for the previous two periods was applied. For the backcast (B),
the disturbance frequencies implied by the age class distribution for each time step are moved. For areas with empirical
transition matrices, change in forest area (Lt) was backcasted/projected using the change in forest area observed for the
period t0 to ti.
Projections and Backcast for CONUS (excluding west Oklahoma and Wyoming)
For all states in the conterminous United States (with the exception of west Oklahoma and Wyoming) remeasured plots
were available. When remeasured data are available, the previously described approach is extended to estimate change
more directly; in this case ACt=Ft-6C, where AC is net stock change by pool within the analysis area, F is as previously
defined, and 6C is an n x cp matrix of per unit area forest C stock change per year by pool (cp) arrayed by forest age class.
Inter-period forest C dynamics are previously described, and the age transition matrix (T) is estimated from the observed
data directly. Forest C change at the end of the next period is defined as: ACt+s = Ft-T-6C. Land use change and
disturbances such as cutting, fire, weather, insects, and diseases were incorporated by generalizing to account for the
change vectors and undisturbed forest remaining as undisturbed forest:
Equation A-61: Land Use Change and Disturbance
AQ+s = ^\Atd ¦ Td ¦ SCd)
(14)
dEL
164 Simulation experiments show that a population that evolves as a function of T can be precisely predicted using T1. However,
applying the inverse to a population that is not consistent with the long-run outcomes of the transition model can result in
predictions of negative areas within some stage age classes.
Annex 3
A-413
-------
Where Atd = area by age class of each mutually exclusive land category in L which includes d disturbances at time t.
L = (FF, NFF, FNF, Fcut, Ffire, Fweather, Fid) where FF=undisturbed forest remaining as undisturbed forest,
NFF=nonforest to forest conversion, FNF=forest to nonforest conversion, Fcut=cut forest remaining as forest, Ffire=forest
remaining as forest disturbed by fire, Fweather=forest remaining as forest disturbed by weather, and Fid=forest
remaining as forest disturbed by insects and diseases. In the case of land transfers (FNF and NFF), Td is an n x n identity
matrix and 5Cd is a C stock transfer rate by age. Paired measurements for all plots in the inventory provide direct
estimates of all elements of SC,Td, and Atd matrices.
Predictions are developed by specifying either Ft+s or At+sd for either a future or a past state. To move the system
forward, T is specified so that the age transition probabilities are set up as the probability between a time 0 and a time 1
transition. To move the system backward, T is replaced by B so that the age transition probabilities are for transitions
from time 1 to time 0. Forecasts were developed by assuming the observed land use transitions and disturbance rates
would continue for the next 5 years. Prediction moving back in time were developed using a Markov Chain process for
land use transitions, observed disturbance rates for fire, weather, and insects. Historical forest cutting was incorporated
by using the relationship between the area of forest cutting estimated from the inventory plots and the volume of
roundwood production from the Timber Products Output program (U.S. Forest Service 2022d). This relationship allowed
for the modification of Fcut such that it followed trends described by Oswalt et al. (2014).
Methods for Alaska
Inventory and sampling
The NFI has been measuring plots in southeast and southcentral coastal Alaska as part of the annual NFI since 2004. In
2014, a pilot inventory was established in the Tanana Valley State Forest and Tetlin National Wildlife Refuge in Interior
Alaska. This pilot inventory was a collaboration between the USDA Forest Service, FIA program, the National
Aeronautical and Space Administration, and many other federal, state, and local partners. This effort resulted in the
establishment of 98 field plots which were measured during the summer of 2014 and integrated with NASA's Goddard
LiDAR/Hyperspectral/Thermal (G-LiHT) imaging system. Given the remote nature of Interior Alaska forest, the NFI plots
in the pilot campaign were sampled at a lower intensity than base NFI plots (1 plot per 2403 ha) in the CONUS and
coastal Alaska. Several plot-level protocols were also adapted to accommodate the unique conditions of forests in this
region (see Pattison et al. 2018 for details on plot design and sampling protocols). The pilot field campaign became
operational in 2016 and plots measured on a 1/5 intensity (1 plot per 12013 ha) from 2014, 2016 to 2020 from the
Interior Alaska NFI were used (n = 898) with base-intensity annual NFI plots from coastal AK (n = 2975) in this analysis.
A spatially balanced sampling design was used to identify field sample locations across all of Alaska following standard
FIA procedures with a tessellation of hexagons and one sample plot selected per hexagon -1/5 intensity in interior
Alaska and base-intensity in coastal Alaska (Bechtold and Patterson 2005). The sampling locations were classified as
forest or non-forest using the NLCD from 2001 and 2011. It is important to note that this is different from how NFI plots
are classified into land cover and land use categories in the CONUS where high resolution areal imagery is used. Since the
fine-scale remotely sensed imagery (National Agriculture Imagery Program; NAIP 2015) used in the conterminous United
States were not available for AK and given that the NLCD has been used to classify land use categories in Alaska in the
Representation of the U.S. Land Base in this Inventory, the NLCD was the most consistent and credible option for
classification. Next, the forest land was further classified as managed or unmanaged following the definition in the
Representation of the U.S. Land Base and using similar procedures (see Ogle et al. 2018 for details on the managed land
layer for the United States).
While only a subset of the total NFI sample was available at the time of this Inventory, all NFI plot locations within the
sampling frame were used in this analysis. Auxiliary climate, soil, structural, disturbance, and topographic variables were
harmonized with each plot location and year of occurrence (if relevant and available) over the entire time series (1990 to
2020).
Prediction
The harmonized data were used to predict plot-level parameters using non-parametric random forests (RF) for
regression, a machine learning tool that uses bootstrap aggregating (i.e., bagging) to develop models to improve
prediction (Breiman 2001). Random forests also relies on random variable selection to develop a forest of uncorrelated
regression trees. These trees uncover the relationship between a dependent variable (e.g., live aboveground biomass
carbon) and a set of predictor variables. The RF analysis included predictor variables (n > 100) that may influence carbon
A-414 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
stocks within each forest ecosystem pool at each plot location over the entire time series. To avoid problems with data
limitations over the time series, variable pruning was used to reduce the RF models to the minimum number of relevant
predictors without substantial loss in explanatory power or increase in root mean squared error (RMSE; see Domke et al.
2017, Domke et al. In prep for more information). The harmonized dataset used to develop the RF models for each plot-
level parameter were partitioned 10 times into training (70 percent) and testing (30 percent) groups and the results were
evaluated graphically and with a variety of statistical metrics including Spearman's rank correlation, equivalence tests
(Wellek 2003), as well as RMSE. All analyses were conducted using R statistical software (R Core Team 2018).
The RF predictions of carbon stocks for the year 2016 were used as a baseline for plots that have not yet been measured.
Next, simple linear regression was used to predict average annual gains/losses by forest ecosystem carbon pool using the
chronosequence of plot measurements available at the time of this Inventory. These predicted gains/losses were applied
over the time series from the year of measurement or the 2016 base year in the case of plots that have not yet been
measured. Since the RF predictions of carbon stocks and the predicted gains/losses were obtained from empirical
measurements on NFI plots that may have been disturbed at some point over the time series, the predictions inherently
incorporate gains/losses associated with natural disturbance and harvesting. That said, there was no evidence of fire
disturbance on the plots that have been measured to date. To account for carbon losses associated with fire, carbon
stock predictions for plots that have not been measured but were within a fire perimeter, using the same geospatial
layers described in the Emissions from Forest Fires section, during the Inventory period were adjusted to account for
area burned (see Table A-214) and the IPCC (Table 2.6, IPCC 2006) default combustion factor for boreal forests was
applied to all live, dead, and litter biomass carbon stocks in the year of the disturbance. The plot-level predictions in each
year were then multiplied by the area they represent within the sampling frame to compile population estimates over
the time series for this Inventory.
Forest Land Remaining Forest Land Area Estimates
Forest land area estimates in section 6.2 Forest Land Remaining Forest Land (CRF Category 4A1) of this Inventory are
compiled using NFI data. Forest Land area estimates obtained from these data are also used as part of section 6.1
Representation of the U.S. Land Base (CRF Category 4.1). The Forest Land area estimates in section 6.2 do not include
Hawaii as insufficient data is available from the NFI to compile area estimates over the entire time series. The National
Land Cover Dataset is used in addition to NFI estimates in section 6.2 Representation of the U.S. Land Base and Forest
Land in Hawaii are included in that section. This results in small differences in the managed Forest Land area in sections
6.1 and 6.2 of this Inventory (Table A-212). There are also other factors contributing to the small differences such as
harmonization of aspatial and spatial data across all land use categories in section 6.1 over the entire Inventory time
series.
Carbon in Harvested Wood Products
Estimates of the Harvested Wood Product (HWP) contribution to forest C sinks and emissions (hereafter called "HWP
Contribution") are based on methods described in Skog (2008) using the WOODCARB II model and the U.S. forest
products module (Ince et al. 2011). These methods are based on IPCC (2006) guidance for estimating HWP C. The 2006
IPCC Guidelines provide methods that allow Parties to report HWP Contribution using one of several different accounting
approaches: production, stock change, and atmospheric flow, as well as a default method. The various approaches are
described below. The approaches differ in how HWP Contribution is allocated based on production or consumption as
well as what processes (atmospheric fluxes or stock changes) are emphasized.
• Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool, but
attributes both to the producing country.
• Stock-change approach: Accounts for changes in the product pool within the boundaries of the consuming
country.
• Atmospheric-flow approach: Accounts for net emissions or removals of C to and from the atmosphere within
national boundaries. Carbon removal due to forest growth is accounted for in the producing country while C
emissions to the atmosphere from oxidation of wood products are accounted for in the consuming country.
• Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption be
justified if this is how a Party is choosing to report.
Annex 3
A-415
-------
The United States uses the production accounting approach (as in previous years) to report HWP Contribution
(Table A-204) but estimates for all three approaches are provides in Table A-205. Annual estimates of change are
calculated by tracking the additions to and removals from the pool of products held in end uses (i.e., products in
use such as housing or publications) and the pool of products held in solid waste disposal sites (SWDS).
Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and atmospheric
flow approaches for imports and exports are provided in Table A-206. The HWP variables estimated are:
(IA) Annual change of C in wood and paper products in use in the United States,
(IB) Annual change of C in wood and paper products in SWDS in the United States,
(2A) Annual change of C in wood and paper products in use in the United States and other countries where the
wood came from trees harvested in the United States,
(2B) Annual change of C in wood and paper products in SWDS in the United States and other countries where the
wood came from trees harvested in the United States,
(3) Carbon in imports of wood, pulp, and paper to the United States,
(4) Carbon in exports of wood, pulp, and paper from the United States, and
(5) Carbon in annual harvest of wood from forests in the United States. The sum of these variables yield estimates
for HWP contribution under the production accounting approach.
Table A-204: Harvested Wood Products from Wood Harvested in the United States—Annual
Additions of C to Stocks and Total Stocks under the Production Approach
Year
Net C additions per year (MMT C per year)
Total C stocks (MMT C)
Total
Products in use
Products in SWDS
Total
Total
Total
Products in use
Products in SWDS
1990
(33.8)
(14.9)
(18.8)
1,895
1,249
646
1991
(33.8)
(16.3)
(17.4)
1,929
1,264
665
1992
(32.9)
(15.0)
(17.9)
1,963
1,280
683
1993
(33.4)
(15.9)
(17.5)
1,996
1,295
701
1994
(32.3)
(15.1)
(17.2)
2,029
1,311
718
1995
(30.6)
(14.1)
(16.5)
2,061
1,326
735
1996
(32.0)
(14.7)
(17.3)
2,092
1,340
752
1997
(31.1)
(13.4)
(17.7)
2,124
1,355
769
1998
(32.5)
(14.1)
(18.4)
2,155
1,368
787
1999
(30.8)
(12.8)
(18.0)
2,188
1,382
805
2000
(25.5)
(8.7)
(16.8)
2,218
1,395
823
2001
(26.8)
(9.6)
(17.2)
2,244
1,404
840
2002
(25.6)
(9.4)
(16.2)
2,271
1,413
857
2003
(28.4)
(12.1)
(16.3)
2,296
1,423
873
2004
(28.7)
(12.4)
(16.4)
2,325
1,435
890
2005
(28.9)
(11.6)
(17.3)
2,353
1,447
906
2006
(27.3)
(10.0)
(17.4)
2,382
1,459
923
2007
(20.8)
(3.7)
(17.1)
2,410
1,469
941
2008
(14.9)
1.8
(16.7)
2,430
1,473
958
2009
(16.6)
(0.0)
(16.6)
2,445
1,471
974
2010
(18.8)
(2.0)
(16.8)
2,462
1,471
991
2011
(19.4)
(2.4)
(17.0)
2,481
1,473
1,008
2012
(20.9)
(3.7)
(17.1)
2,500
1,475
1,025
2013
(22.6)
(5.3)
(17.3)
2,521
1,479
1,042
2014
(23.4)
(6.1)
(17.4)
2,543
1,484
1,059
2015
(24.9)
(7.4)
(17.5)
2,567
1,490
1,076
2016
(25.9)
(8.3)
(17.7)
2,592
1,498
1,094
2017
(27.3)
(9.5)
(17.8)
2,618
1,506
1,112
2018
(25.7)
(7.9)
(17.8)
2,645
1,515
1,129
2019
(24.2)
(6.6)
(17.6)
2,671
1,523
1,147
2020
(22.8)
(5.5)
(17.3)
2,695
1,530
1,165
A-416 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Table A-205: Comparison of Net Annual Change in Harvested Wood Products C Stocks Using
Alternative Accounting Approaches (kt CO2 Eg ./year)
HWP Contribution to LULUCF Emissions/ Removals (MMT C02 Eq.)
Stock-Change
Atmospheric Flow
Production
Inventory Year
Approach
Approach
Approach
1990
(116.6)
(131.4)
(123.8)
1991
(120.2)
(131.6)
(123.8)
1992
(127.1)
(127.8)
(120.7)
1993
(130.3)
(129.9)
(122.5)
1994
(126.0)
(128.0)
(118.4)
1995
(122.3)
(122.5)
(112.2)
1996
(131.3)
(127.4)
(117.3)
1997
(137.2)
(122.8)
(114.2)
1998
(147.1)
(127.2)
(119.0)
1999
(141.2)
(120.2)
(112.9)
2000
(125.0)
(100.3)
(93.4)
2001
(130.7)
(103.3)
(98.2)
2002
(125.8)
(98.5)
(93.7)
2003
(143.2)
(107.9)
(104.1)
2004
(142.1)
(109.7)
(105.4)
2005
(136.4)
(112.0)
(106.0)
2006
(113.5)
(109.9)
(100.3)
2007
(72.1)
(88.3)
(76.1)
2008
(41.9)
(70.1)
(54.5)
2009
(48.3)
(79.9)
(60.8)
2010
(51.5)
(92.3)
(69.1)
2011
(59.1)
(95.2)
(71.0)
2012
(72.4)
(103.0)
(76.5)
2013
(85.9)
(109.5)
(82.7)
2014
(92.8)
(113.2)
(85.9)
2015
(104.5)
(119.0)
(91.5)
2016
(109.0)
(122.7)
(95.1)
2017
(113.5)
(128.6)
(100.2)
2018
(111.2)
(124.4)
(94.1)
2019
(108.9)
(116.3)
(88.8)
2020
(121.3)
(122.9)
(83.6)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
Annex 3
A-417
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Table A-206: Harvested Wood Products Sectoral Background Data for LULUCF—United States
1A
IB
2A
2B
3
4
5
6
7
8
Inventory year
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
Annual
HWP
Change in
Change in
Change in
Change in
Imports of
Exports of
Domestic
release of C to
release of C to
Contribution to
stock of
stock of HWP
stock of
stock of
wood, and
wood, and
Harvest
the
the
AFOLU C02
HWP in use
in SWDS from
HWP in use
HWP in
paper
paper
atmosphere
atmosphere
emissions/
from
consumption
produced
SWDS
products plus
products plus
from HWP
from HWP
removals
consumption
from
produced
wood fuel.
wood fuel.
consumption
(including
domestic
from
pulp.
pulp.
(from
firewood)
harvest
domestic
recovered
recovered
fuelwood and
where wood
harvest
paper.
paper.
products in
came from
roundwood/
roundwood/
use and
domestic
chips
chips
products in
harvest (from
SWDS)
products in
use and
products in
SWDS)
ACHWP IU
ACHWP
AC HWP IU
ACHWP
PIM
PEX
H
•fCHWP DC
•fCHWP DH
DC
SWDS DC
DH
SWDS DH
MMTC/yr
MMT C02/yr
1990
13.2
18.6
14.9
18.8
11.6
15.6
144.4
108.6
110.7
(123.8)
1995
17.0
16.3
14.1
16.5
16.7
16.7
134.5
101.1
103.9
(112.2)
2000
16.5
17.6
8.7
16.8
22.1
15.3
127.9
100.5
102.4
(93.4)
2005
18.7
18.6
11.6
17.3
25.5
18.8
120.1
89.6
91.2
(106.0)
2010
(2.1)
16.1
2.0
16.8
13.9
25.0
102.7
77.5
83.9
(69.1)
2016
12.6
17.2
8.3
17.7
18.1
21.8
123.9
90.5
98.0
(95.1)
2017
13.6
17.4
9.5
17.8
18.0
22.2
126.5
91.4
99.2
(100.2)
2018
12.8
17.5
7.9
17.8
15.7
19.3
125.8
91.9
100.2
(94.1)
2019
12.2
17.5
6.6
17.6
15.9
17.9
123.8
92.1
99.6
(88.8)
2020
15.3
17.8
5.5
17.3
16.1
16.6
121.8
88.3
99.0
(83.6)
Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).
A-418 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Annual estimates of variables 1A, IB, 2A and 2B were calculated by tracking the additions to and removals from the pool
of products held in end uses (e.g., products in uses such as housing or publications) and the pool of products held in
SWDS. In the case of variables 2A and 2B, the pools include products exported and held in other countries and the pools
in the United States exclude products made from wood harvested in other countries. Solidwood products added to pools
include lumber and panels. End-use categories for solidwood include single and multifamily housing, alteration and
repair of housing, and other end uses. There is one product category and one end-use category for paper. Additions to
and removals from pools are tracked beginning in 1900, with the exception that additions of softwood lumber to housing
begins in 1800. Solidwood and paper product production and trade data are from USDA Forest Service and other sources
(Hair and Ulrich 1963; Hair 1958; USDC Bureau of Census 1976; Ulrich, 1985, 1989; Steer 1948; AF&PA 2006a, 2006b;
Howard 2003, 2007; Howard and Jones 2016; Howard and Liang 2019' AF&PA 2021; FAO 2021).
The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order
(exponential) decay curves with given half-lives (time at which half of amount placed in use will have been discarded
from use). Half-lives for products in use, determined after calibration of the model to meet two criteria, are shown in
Table A-207. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to
match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second
criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA
estimates of discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 1A, and
to a lesser extent variable 2A. The calibration also determines the amounts going to SWDS. In addition, WOODCARB II
landfill decay rates have been validated by making sure that estimates of methane emissions from landfills based on EPA
data are reasonable in comparison to methane estimates based on WOODCARB II landfill decay rates.
Decay parameters for products in SWDS are shown in Table A-208. Estimates of IB and 2B also reflect the change over
time in the fraction of products discarded to SWDS (versus burning or recycling) and the fraction of SWDS that are
sanitary landfills versus dumps.
Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key
assumption for estimating these variables is that products exported from the United States and held in pools in other
countries have the same half-lives for products in use, the same percentage of discarded products going to SWDS, and
the same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in
Table A-204. The decline in net additions to HWP C stocks continued through 2009 from the recent high point in 2006.
This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to the decline in
housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was exceeded by
discards from uses. The result is a net reduction in the amount of HWP C that is held in products in use during 2009. For
2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have remained
relatively stable. Overall, there were net C additions to HWP in use and in landfills combined.
A key assumption for estimating these variables is that products exported from the United States and held in pools in
other countries have the same half-lives for products in use, the same percentage of discarded products going to SWDS,
and the same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in
Land Converted to Forest Land - Soil C Methods.
Parameter
Value
Units
Half-life of wood in single family housing 1920 and
before
78.0
Years
Half-life of wood in single family housing 1920-1939
78.0
Years
Half-life of wood in single family housing 1940-1959
80.0
Years
Half-life of wood in single family housing 1960-1979
81.9
Years
Half-life of wood in single family housing 1980 +
83.9
Years
Ratio of multifamily half-life to single family half life
0.61
Ratio of repair and alterations half-life to single family
half-life
0.30
Half-life for other solidwood product in end uses
38.0
Years
Half-life of paper in end uses
2.54
Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the U.S." Forest Products Journal 58:56-72.
Annex 3
A-419
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Table A-208: Parameters Determining Decay of Wood and Paper in SWDS
Parameter
Value
Units
Percentage of wood and paper in dumps that is subject to decay
100
Percent
Percentage of wood in landfills that is subject to decay
23
Percent
Percentage of paper in landfills that is subject to decay
56
Percent
Half-life of wood in landfills / dumps (portion subject to decay)
29
Years
Half-life of paper in landfills/ dumps (portion subject to decay)
14.5
Years
Source: Skog, K.E. (2008) "Sequestration of C in harvested wood products for the U.S." Forest Products Journal 58:56-72.
Table A-209: Net CO2 Flux from Forest Pools in Forest Land Remaining Forest Land and
Harvested Wood Pools (MMT CO2 Eg.)
Carbon Pool
1990
1995
2000
2005
2010
2016
2017
2018
2019
Total Net Flux
2020
Forest (650.2) (646.0) (622.7) (581.2) (606.7) (630.4) (588.1) (583.0) (546.0) (584.4)
Aboveground Biomass (462.5) (450.9) (435.4) (416.3) (421.4) (432.7) (407.7) (406.6) (393.1) (398.7)
Belowground Biomass (94.2) (91.6) (88.3) (84.2) (84.7) (86.3) (80.9) (80.8) (78.1) (79.1)
Dead Wood (96.8) (98.7) (98.5) (96.8) (100.1) (106.4) (99.8) (102.0) (97.0) (101.5)
Litter 0.6 (7.0) (1.6) 16.0 0.8 (3.1) (1.9) 1.3 22.8 (1.9)
Soil (Mineral) 3.0 2.2 0.9 (0.3) (1.9) (5.6) (1.1) 4.1 (0.6) (4.1)
Soil (Organic) (0.9) (0.8) (0.5) (0.3) (0.1) 3.0 2.5 0.3 (0.7) 0.2
Drained Organic Soil3 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8
Harvested Wood (123.8) (112.2) (93.4) (106.0) (69.1) (95.1) (100.2) (94.1) (88.8) (83.6)
Products in Use (54.8) (51.7) (31.9) (42.6) (7.4) (30.4) (34.9) (29.0) (24.4) (20.0)
SWDS (69.0) (60.5) (61.5) (63.4) (61.7) (64.8) (65.3) (65.1) (64.5) (63.6)
(774.0) (758.2) (716.2) (687.3) (675.7) (725.6) (688.3) (677.1) (634.8) (668.1)
a These estimates include C stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land
Converted to Forest Land.
Note: Parentheses indicate negative values.
Table A-210: Net C Flux from Forest Pools in Forest Land Remaining Forest Land and
Harvested Wood Pools (MMT C)
Carbon Pool 1990 1995 2000 2005 2010 2016 2017 2018 2019 2020
Forest (177.3) (176.2) (169.8) (158.5) (165.5) (171.9) (160.4) (159.0) (148.9) (159.4)
Aboveground Biomass (126.1) (123.0) (118.7) (113.5) (114.9) (118.0) (111.2) (110.9) (107.2) (108.7)
Belowground Biomass (25.7) (25.0) (24.1) (23.0) (23.1) (23.5) (22.1) (22.0) (21.3) (21.6)
Dead Wood (26.4) (26.9) (26.9) (26.4) (27.3) (29.0) (27.2) (27.8) (26.5) (27.7)
Litter 0.2 (1.9) (0.4) 4.4 0.2 (0.9) (0.5) 0.3 6.2 (0.5)
Soil (Mineral) 0.8 0.6 0.2 (0.1) (0.5) (1.5) (0.3) 1.1 (0.2) (1.1)
Soil (Organic) (0.3) (0.2) (0.1) (0.1) (0.0) 0.8 0.7 0.1 (0.2) 0.1
Drained Organic Soil3 0.21 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2
Harvested Wood (33.8) (30.6) (25.5) (28.9) (18.8) (25.9) (27.3) (25.7) (24.2) (22.8)
Products in Use (14.9) (14.1) (8.7) (11.6) (2.0) (8.3) (9.5) (7.9) (6.6) (5.5)
SWDS (18.8) (16.5) (16.8) (17.3) (16.8) (17.7) (17.8) (17.8) (17.6) (17.3)
Total Net Flux (211.1) (206.8) (195.3) (187.4) (184.3) (197.9) (187.7) (184.7) (173.1) (182.2)
a These estimates include C stock changes from drained organic soils from both Forest Land Remaining Forest Land and Land
Converted to Forest Land.
Note: Parentheses indicate negative values.
A-420 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-211: Forest area (1,000 ha) and C Stocks in Forest Land Remaining Forest Land and Harvested Wood Pools (MMT C)
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
2021
Forest Area (1000 ha)
282,585
282,621
282,575
282,250
282,243
282,344
282,352
282,312
282,177
282,061
281,951
Carbon Pools
Forest
53,148
54,039
54,909
55,721
56,538
57,515
57,687
57,848
58,007
58,156
58,316
Aboveground Biomass
12,062
12,687
13,294
13,874
14,445
15,132
15,250
15,361
15,472
15,579
15,688
Belowground Biomass
2,375
2,502
2,625
2,743
2,858
2,996
3,019
3,041
3,064
3,085
3,106
Dead Wood
2,060
2,194
2,328
2,460
2,595
2,758
2,787
2,814
2,842
2,868
2,896
Litter
3,838
3,845
3,852
3,834
3,829
3,814
3,815
3,816
3,815
3,809
3,810
Soil (Mineral)
25,458
25,454
25,452
25,452
25,453
25,457
25,458
25,458
25,457
25,457
25,459
Soil (Organic)
7,355
7,357
7,358
7,358
7,358
7,358
7,357
7,357
7,357
7,357
7,357
Harvested Wood
1,895
2,061
2,218
2,353
2,462
2,592
2,618
2,645
2,671
2,695
2,718
Products in Use
1,249
1,326
1,395
1,447
1,471
1,498
1,506
1,515
1,523
1,530
1,536
SWDS
646
735
823
906
991
1,094
1,112
1,129
1,147
1,165
1,182
Total Stock
55,043
56,101
57,128
58,074
59,000
60,107
60,305
60,493
60,678
60,851
61,034
Annex 3
A-421
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Table A-212: Forest Land Area Estimates and Differences Between Estimates in 6.1
Representation of the U.S. Land Base (CRF Category 4.1) and 6.2 Forest Land Remaining
Forest Land (CRF Category 4A1) (kha)
Difference between Forest
Land Areas (managed) - 6.1
Year
Forest Land (managed) - 6.1
Representation of the U.S.
Land Base
Forest Land (managed) -
6.2 Forest Land Remaining
Forest Land
and Forest Land Remaining
Forest Land - 6.2 area
estimates
1990
280,393
282,585
-2,192
1995
280,414
282,621
-2,207
2000
280,518
282,575
-2,057
2005
280,207
282,250
-2,043
2010
280,369
282,243
-1,874
2016
280,528
282,344
-1,815
2017
280,529
282,352
-1,972
2018
280,380
282,312
-2,037
2019
280,274
282,177
-1,903
2020
280,274
282,061
-1,786
Table A-213: State-level Net C Flux from all Forest Pools in Forest Land Remaining Forest
Land (MMT C) with Uncertainty Range Relative to Flux Estimate, 2020
Stock
Lower
Upper
State
Change
Lower Bound
Bound (%)
Upper Bound
Bound (%)
Alabama
(13.2)
(15.0)
14%
(11.3)
14%
Alaska
(4.8)
(11.9)
-147%
2.3
147%
Arizona
0.6
0.2
-62%
1.0
62%
Arkansas
(8.1)
(9.6)
-19%
(6.5)
19%
California
(7.0)
(15.0)
-114%
1.0
114%
Colorado
3.1
(5.4)
-271%
11.7
271%
Connecticut
(0.9)
(1.2)
-32%
(0.6)
32%
Delaware
(0.1)
(0.1)
-57%
(0.0)
57%
Florida
(5.1)
(5.8)
-13%
(4.5)
13%
Georgia
(8.2)
(8.7)
-6%
(7.7)
6%
Idaho
1.0
(2.5)
-351%
4.5
351%
Illinois
(1.2)
(2.2)
-84%
(0.2)
84%
Indiana
(1.4)
(3.0)
-113%
0.2
113%
Iowa
(0.7)
(1.0)
-44%
(0.4)
44%
Kansas
(0.6)
(1.0)
-70%
(0.2)
70%
Kentucky
(4.7)
(6.3)
-33%
(3.2)
33%
Louisiana
(7.2)
(7.7)
-7%
(6.7)
7%
Maine
(2.6)
(5.6)
-118%
0.5
118%
Maryland
(1.1)
(1.6)
-47%
(0.6)
47%
Massachusetts
(1.2)
(1.6)
-30%
(0.9)
30%
Michigan
(3.9)
(7.5)
-92%
(0.3)
92%
Minnesota
(3.7)
(6.0)
-63%
(1.4)
63%
Mississippi
(15.6)
(18.4)
-18%
(12.7)
18%
Missouri
(2.9)
(5.5)
-89%
(0.3)
89%
Montana
2.7
(5.3)
-292%
10.7
292%
Nebraska
(0.2)
(0.2)
-33%
(0.1)
33%
Nevada
0.0
(0.2)
-1059%
0.3
1059%
New Hampshire
(1.4)
(2.0)
-42%
(0.8)
42%
New Jersey
(0.6)
(0.7)
-16%
(0.5)
16%
New Mexico
1.1
(0.8)
-171%
3.0
171%
New York
(6.5)
(8.8)
-36%
(4.2)
36%
North Carolina
(8.2)
(9.5)
-15%
(6.9)
15%
A-422 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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North Dakota
(0.0)
(0.1)
-299%
0.1
299%
Ohio
(1.6)
(3.7)
-126%
0.4
126%
Oklahoma
(1.8)
(2.4)
-35%
(1.1)
35%
Oregon
(9.8)
(11.9)
-22%
(7.7)
22%
Pennsylvania
(5.2)
(9.7)
-85%
(0.8)
85%
Rhode Island
(0.1)
(0.2)
-172%
0.1
172%
South Carolina
(3.4)
(4.0)
-16%
(2.9)
16%
South Dakota
0.1
(0.2)
-317%
0.4
317%
Tennessee
(6.3)
(7.8)
-24%
(4.8)
24%
Texas
(5.9)
(6.4)
-9%
(5.4)
9%
Utah
0.9
(0.5)
-151%
2.3
151%
Vermont
(1.6)
(2.3)
-45%
(0.9)
45%
Virginia
(11.0)
(13.7)
-25%
(8.2)
25%
Washington
(4.6)
(9.2)
-101%
0.1
101%
West Virginia
(4.0)
(5.7)
-43%
(2.3)
43%
Wisconsin
(4.4)
(4.8)
-11%
(3.9)
11%
Wyoming
1.3
0.7
-48%
2.0
48%
49 States
(159.6)
(179.5)
-12%
(139.7)
12%
Note: Parentheses indicate negative values.
Land Converted to Forest Land
The following section includes a description of the methodology used to estimate stock changes in all forest C pools for
Land Converted to Forest Land. Forest Inventory and Analysis data and IPCC (2006) defaults for reference C stocks were
used to compile separate estimates for the five C storage pools within an age class transition matrix for the 20-year
conversion period (where possible). The 2015 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 the age class transition matrix are based on 5- to 10-yr remeasurements so the exact
conversion period was limited to the remeasured data over the time series. Estimates for aboveground and belowground
biomass, dead wood and litter 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 United States (USDA Forest Service
2022b, 2022c). Carbon conversion factors were applied at the disaggregated level of each inventory plot and then
appropriately expanded to population estimates. To ensure consistency in the Land Converted to Forest Land category
where C stock transfers occur between land-use categories, all soil estimates are based on methods from Ogle et al.
(2003, 2006) and IPCC (2006).
Live tree C pools
Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at diameter
breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above- and
below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et al.
(2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter, and,
in some regions, tree height and site quality. The estimated sound volume (i.e., after rotten/missing deductions)
provided in the tree table of the FIADB is the principal input to the CRM biomass calculation for each tree (Woodall et al.
2011). The estimated volumes of wood and bark are converted to biomass based on the density of each. Additional
components of the trees such as tops, branches, and coarse roots, are estimated according to adjusted component
estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm do not have estimates of sound volume in
the FIADB, and CRM biomass estimates follow a separate process (see Woodall et al. 2011 for details). An additional
component of foliage, which was not explicitly included in Woodall et al. (2011), was added to each tree following the
same CRM method. Carbon is estimated by multiplying the estimated oven-dry biomass by a C constant of 0.5 because
biomass is 50 percent of dry weight (USDA Forest Service 2022d). Further discussion and example calculations are
provided in Woodall et al. 2011 and Domke et al. 2012.
Annex 3
A-423
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Understory vegetation
Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is defined as all
biomass of undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this Inventory, it
is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is near the lower
range of temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: ratios are
likely to be lower for light-limited understory vegetation as compared with larger trees, and a greater proportion of all
root mass will be less than 2 mm diameter.
Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots. See model
(1) in the Forest Land Remaining Forest Land section of the Annex.
In this model, the ratio is the ratio of understory C density (T C/ha) to live tree C density (above- and below-ground)
according to Jenkins et al. (2003) and expressed in T C/ha. An additional coefficient is provided as a maximum ratio; that
is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum ratio. A
full set of coefficients are in Table A-201. Regions and forest types are the same classifications described in Smith et al.
(2003). An example calculation for understory C in aspen-birch forests in the Northeast is provided in the Forest Land
Remaining Forest Land section of the Annex.
This calculation is followed by three possible modifications. First, the maximum value for the ratio is set to 2.02 (see
value in column "maximum ratio"); this also applies to stands with zero tree C, which is undefined in the above model.
Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked (i.e., currently lacking tree cover but still in
the forest land use) and pinyon/juniper forest types (see Table A-201) are set to coefficient A, which is a C density (T
C/ha) for these types only.
Dead wood
The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011; Woodall et al.
2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm
d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications
to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that
can significantly affect C mass are decay, which affects density and thus specific C content (Domke et al. 2011; Harmon et
al. 2011), and structural loss such as branches and bark (Domke et al. 2011). Dry weight to C mass conversion is by
multiplying by 0.5 (USDA Forest Service 2022d).
Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced sample
intensity, a single down woody material population estimate (Woodall et al. 2010; Domke et al. 2013; Woodall et al.
2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined
as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing
dead trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree
biomass were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for
downed dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of
ratios is provided in Table A-202. An additional component of downed dead wood is a regional average estimate of
logging residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at
age zero and first order decay; initial densities and decay coefficients are provided in Table A-203. These amounts are
added to explicitly account for downed dead wood following harvest. The sum of these two components are then
adjusted by the ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013).
Litter carbon
Carbon in the litter layer is currently sampled on a subset of the FIA plots. Litter C is the pool of organic C (including
material known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with
diameters of up to 7.5 cm. Because litter attributes are only collected on a subset of FIA plots, a model was developed to
predict C density based on plot/site attributes for plots that lacked litter information (Domke et al. 2016).
As the litter, or forest floor, estimates are an entirely new model this year, a more detailed overview of the methods is
provided here. The first step in model development was to evaluate all relevant variables—those that may influence the
formation, accumulation, and decay of forest floor organic matter—from annual inventories collected on FIADB plots
(P2) using all available estimates of forest floor C (n = 4,530) from the P3 plots (hereafter referred to as the research
dataset) compiled from 2000 through 2014 (Domke et al. 2016).
A-424 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Random forest, a machine learning tool (Domke et al. 2016), was used to evaluate the importance of all relevant forest
floor C predictors available from P2 plots in the research dataset. Given many of the variables were not available due to
regional differences in sampling protocols during periodic inventories, the objective was to reduce the random forest
regression model to the minimum number of relevant predictors without substantial loss in explanatory power. The
model (3) and parameters are described in the Forest Land Remaining Forest Land section of the Annex.
Due to data limitation in certain regions and inventory periods a series of reduced random forest regression models were
used rather than replacing missing variables with imputation techniques in random forest. Database records used to
compile estimates for this report were grouped by variable availability and the approaches described herein were
applied to replace forest floor model predictions from Smith and Heath (2002). Forest floor C predictions are expressed
in T*ha-1.
A Tier 2 method is applied to estimate soil C stock changes for Land Converted to Forest Land (Ogle et al. 2003, 2006;
IPCC 2006). For this method, land is stratified by climate, soil types, land-use, and land management activity, and then
assigned reference C levels and factors for the forest land and the previous land use. The difference between the stocks
is reported as the stock change under the assumption that the change occurs over 20 years. Reference C stocks have
been estimated from data in the National Soil Survey Characterization Database (USDA-NRCS 1997), and U.S.-specific
stock change factors have been derived from published literature (Ogle et al. 2003; Ogle et al. 2006). Land use and land
use change patterns are determined from a combination of the Forest Inventory and Analysis Dataset (FIA), the 2015
National Resources Inventory (NRI) (USDA-NRCS 2018), and National Land Cover Dataset (NLCD) (Yang et al. 2018). See
Annex 3.12 for more information about this method (Methodology for Estimating N20 Emissions, CH4 Emissions and Soil
Organic C Stock Changes from Agricultural Soil Management).
Table A-214 summarizes the annual change in mineral soil C stocks from U.S. soils that were estimated using a Tier 2
method (MMT C/year). The range is a 95 percent confidence interval estimated from the standard deviation of the NRI
sampling error and uncertainty associated with the 1000 Monte Carlo simulations (See Annex 3.12). Table A-215
summarizes the total land areas by land use/land use change subcategory that were used to estimate soil C stock
changes for mineral soils between 1990 and 2015.
Land Converted to Forest Land Area Estimates
Forest land area estimates in section 6.3 Land Converted to Forest Land (CRF Category 4A2) of this Inventory are
compiled using NFI data. Forest Land area estimates obtained from these data are also used as part of section 6.1
Representation of the U.S. Land Base (CRF Category 4.1). The Forest Land area estimates in section 6.3 do not include
Hawaii as insufficient data is available from the NFI to compile area estimates over the entire time series. The National
Land Cover Dataset is used in addition to NFI estimates in section 6.1 Representation of the U.S. Land Base and Forest
Land in Hawaii is included in that section. This results in small differences in the managed Forest Land area in sections 6.1
and 6.3 of this Inventory (Table A-216). There are also other factors contributing to the small differences in area such as
harmonization of aspatial and spatial data across all land use categories in section 6.1 over the entire Inventory time
series.
Annex 3
A-425
-------
Table A-214: Annual change in Mineral Soil C stocks from U.S. agricultural soils that were estimated using a Tier 2 method (MMT
Category
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
0.08
0.07
0.07
0.07
0.06
0.06
0.06
0.06
0.06
0.06
Cropland Converted to
(0.03 to
(0.03 to
(0.02 to
(0.02 to
(0.01 to
(-0.02 to
(-0.02 to
(-0.02 to
(-0.02 to
(-0.02 to
Forest Land
0.13)
0.12)
0.12)
0.13)
0.11)
0.13)
0.13)
0.13)
0.13)
0.13)
-0.05
-0.05
-0.07
-0.08
-0.08
-0.08
-0.08
-0.07
-0.07
-0.07
Grassland Converted to
(-0.08 to -
(-0.1 to-
(-0.12 to-
(-0.14 to -
(-0.15 to-
(-0.18 to
(-0.17 to
(-0.17 to
(-0.17 to
(-0.17 to
Forest Land
0.01)
0.01)
0.01)
0.02)
0.02)
0.02)
0.02)
0.02)
0.03)
0.03)
0.17
0.22
0.24
0.30
0.32
0.31
0.31
0.31
0.31
0.31
Other Lands Converted to
(0.13 to
(0.14 to
(0.17 to
(0.22 to
(0.22 to
(0.13 to
(0.12 to
(0.12 to
(0.11 to
(0.11 to
Forest Land
0.21)
0.25)
0.29)
0.36)
0.38)
0.5)
0.5)
0.51)
0.51)
0.52)
0.01
0.01
0.01
0.01
0.01
0.02
0.02
0.02
0.02
0.02
Settlements Converted to
(0 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
(0.01 to
Forest Land
0.02)
0.01)
0.01)
0.01)
0.01)
0.02)
0.02)
0.02)
0.02)
0.02)
Wetlands Converted to
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
Forest Land
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
(0 to 0)
Total Lands Converted to
Forest Lands
0.22
0.25
0.26
0.30
0.31
0.31
0.31
0.31
0.31
0.31
Note: The range is a 95 percent confidence interval from 50,000 simulations (Ogle et al. 2003, 2006).
Table A-215: Total land areas (hectares) by land use/land use change subcategory for mineral soils between 1990 to 2015
Conversion Land Areas (Hectares xlO6) 1990 1995 2000 2005 2007 2008 2009 2010 2011 2012 2013 2014 2015
Cropland Converted to Forest Land 0.17 0.16 0.17 0.16 0.16 0.15 0.15 0.15 0.15 0.14 0.14 0.14 0.14
Grassland Converted to Forest Land 0.75 0.81 0.80 0.81 0.82 0.84 0.84 0.84 0.83 0.84 0.84 0.83 0.80
Other Lands Converted to Forest Land 0.05 0.06 0.07 0.08 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09
Settlements Converted to Forest Land 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.02 0.02
Wetlands Converted to Forest Land 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Total Lands Converted to Forest Lands
0.99 1.06 1.05 1.08 1.09 1.11 1.11 1.10 1.10 1.10 1.10 1.09 1.06
Note: Estimated with a Tier 2 approach and based on analysis of USDA National Resources Inventory data (USDA-NRCS 2018).
A-426 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-216: Land Converted to Forest Land area estimates and differences between estimates in the Representation of the U.S.
Land Base (CRF Category 4.1) and Land Converted to Forest Land (CRF Category 4A1) (kha)
Other Settleme-
Cropland
Grassland
Lands
Other
nts
Settleme-
Wetlands
Converted
Cropland
Converted
Grassland
Converted
Lands
Converted
nts
Converted
Wetlands
to Forest
Converted
to Forest
Converted
to Forest
Converted
to Forest
Converted
to Forest
Converted
Land - 6.1
to Forest
Land - 6.1
to Forest
Land - 6.1
to Forest
Land - 6.1
to Forest
Land - 6.1
to Forest
Represen-
Land - 6.3
Represen-
Land - 6.3
Represen-
Land - 6.3
Represen-
Land - 6.3
Represen-
Land - 6.3
tation of
Land
tation of
Land
tation of
Land
tation of
Land
tation of
Land
the U.S.
Converted
the U.S.
Converted
the U.S.
Converted
the U.S.
Converted
the U.S.
Converted
Land Base
to Forest
Land Base
to Forest
Land Base
to Forest
Land Base
to Forest
Land Base
to Forest
(CRF
Land (CRF
Difference
(CRF
Land (CRF
Difference
(CRF
Land (CRF
Difference
(CRF
Land (CRF
Difference
(CRF
Land (CRF
Difference
Category
Category
between
Category
Category
between
Category
Category
between
Category
Category
between
Category
Category
between
Year
4.1)
4A2)
estimates
4.1)
4A2)
estimates
4.1)
4A2)
estimates
4.1)
4A2)
estimates
4.1)
4A2)
estimates
Total
1990
169
314
(145)
919
471
449
50
112
(62)
12
170
(158)
77
33
44
128
1995
170
314
(144)
1,077
481
596
66
115
(48)
20
171
(151)
28
33
(5)
248
2000
176
336
(160)
1,129
498
631
74
119
(46)
23
179
(156)
27
35
(7)
262
2005
167
315
(148)
1,162
490
672
93
117
(24)
24
169
(146)
28
33
(4)
350
2010
152
325
(173)
1,195
515
679
100
123
(24)
24
176
(152)
28
34
(5)
325
2015
139
323
(184)
1,125
514
611
100
120
(20)
27
175
(148)
25
33
(9)
251
2016
134
315
(181)
989
508
481
93
120
(27)
26
174
(148)
25
32
(7)
118
2017
135
315
(179)
992
500
492
93
101
(8)
26
172
(146)
25
32
(7)
152
2018
135
314
(178)
992
473
519
93
52
41
26
174
(148)
25
31
(6)
228
2019
135
319
(183)
992
472
520
93
52
41
26
178
(152)
25
31
(6)
219
2020
135
319
(183)
992
472
520
93
52
41
26
178
(152)
25
31
(6)
219
Annex 3
A-427
-------
Uncertainty Analysis
The uncertainty analyses for total net flux of forest C (see Table 6-14 in the FLRFL section) are consistent with the IPCC-
recommended Tier 1 methodology (IPCC 2006). Specifically, they are considered approach 1 (propagation of error
[Section 3.2.3.1]) (IPCC 2006). To better understand the effects of covariance, the contributions of sampling error and
modeling error were parsed out. In addition, separate analyses were produced for forest ecosystem and HWP flux.
Estimates of forest C stocks in the United States are based on C estimates assigned to each of several thousand inventory
plots from a regular grid. Uncertainty in these estimates and uncertainty associated with change estimates arise from
many sources including sampling error and modeling error. Here EPA focuses on these two types of error but
acknowledge several other sources of error are present in the overall stock and stock change estimates. In terms of
sampling-based uncertainty, design-based estimators described by Bechtold and Patterson (2005) were used to quantify
the variance of C stock estimates. In this section EPA denotes the estimate of C stock at time t as Ct and the variances of
the estimate of C stock for time t as Var(Ct). These calculations follow Bechtold and Patterson (2005). The variance of
stock change is then:
Equation A-62: Variance of the C Stock Change
Var(Ct2-Ctl)=Var(Ct2)+Var(Ctl)-2-Cov(Ct2,Ctl) (15)
The uncertainty of a stock estimate associated with sampling error is U(Ct)s= Var(Ct)0.5. The uncertainty of a stock
changes estimate associated with sampling error is U(AC)s=Var(Ct2-Ctl)0.5.
Model-based uncertainty is important because the pool-level C models have error. The total modeling mean-squared
error (MSEm) is approximately 1,622 (Mg/ha)2. The percent modeling error at time t is
Equation A-63: Percent Modeling Error
%U(Ct)m =100-MSEm/dt (16)
Where dt is the total C stock density at time t calculated as Ct/At where At is the forest area at time t.
The uncertainty of Ct from modeling error is
Equation A-64: Uncertainty of C Stock Estimate at Time t
U(Ct)m=Ct-%U(Ct)m/100 (17)
The model-based uncertainty with respect to stock change is then
Equation A-65: Model-based Uncertainty of C Stock Change
U(AC)m=( U(Ctl)m + U(Ct2)m - 2-Cov(U(Ctlm,Ct2m)))0.5 (18)
The sampling and model-based uncertainty are combined for an estimate of total uncertainty. We considered these
sources of uncertainty independent and combined as follows for stock change (AC):
Equation A-66: Total Uncertainty of C Stock Change
U(AC)=( U(AC)m2+ U(AC)s2)0.5 and the 95 percent confidence bounds was +- 2- U(AC) (19)
The mean square error (MSE) of pool models was (MSE, [Mg C/ha]2): soil C (1143.0), litter (78.0), live tree (259.6), dead
trees (101.5), understory (0.9), down dead wood (38.9), total MSE (1,621.9).
Numerous assumptions were adopted for creation of the forest ecosystem uncertainty estimates. Potential pool error
correlations were ignored. Given the magnitude of the MSE for soil, including correlation among pool error would not
appreciably change the modeling error contribution. Modeling error correlation between time 1 and time 2 was assumed
to be 1. Because the MSE was fixed over time EPA assumed a linear relationship dependent on either the measurements
at two points in time or an interpolation of measurements to arrive at annual flux estimates. Error associated with
interpolation to arrive at annual flux is not included.
Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling is the basis
for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the production
approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated the effect of
uncertainty in 13 sources, including production and trade data and parameters used to make the estimate. Uncertain
data and parameters include data on production and trade and factors to convert them to C, the census-based estimate
A-428 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
of C in housing in 2001, the EPA estimate of wood and paper discarded to SWDS for 1990 to 2000, the limits on decay of
wood and paper in SWDS, the decay rate (half-life) of wood and paper in SWDS, the proportion of products produced in
the United States made with wood harvested in the United States, and the rate of storage of wood and paper C in other
countries that came from U.S. harvest, compared to storage in the United States.
The uncertainty about HWP and forest ecosystem net C flux were combined and assumed to be additive. Typically, when
propagating error from two estimates the variances of the estimates are additive. However, the uncertainty around the
HWP flux was approximated using a Monte Carlo approach which resulted in the lack of a variance estimate for HWP C
flux. Therefore, EPA considered the uncertainty additive between the HWP sequestration and the Forest Land Remaining
Forest Land sequestration. Further, EPA assumed there was no covariance between the two estimates which is plausible
as the observations used to construct each estimate are independent.
Emissions from Forest Fires
C02 Emissions from Forest Fires
As stated in other sections, the forest inventory approach implicitly accounts for C02 emissions due to disturbances. Net
C stock change is estimated from successive C stock estimates. A disturbance, such as a forest fire, removes C from the
forest. The inventory data, on which net C stock estimates are based, already reflects the C loss from such disturbances
because only C remaining in the forest is estimated. Estimating the C02 emissions from a disturbance such as fire and
adding those emissions to the net C02 change in forests would result in double-counting the loss from fire because the
inventory data already reflect the loss. There is interest, however, in the size of the C02, CH4, and N20 emissions from
disturbances such as fire.
Estimates of historic forest fires and associated emissions (i.e., starting from 1990) provided with this report represent a
change in methodology from recent years, which is in response to reviewer suggestions. Past reports were modeled after
Tier 1 methodology with country-specific factors replacing the Tier 1 defaults where more specific local data were
available. This year's estimates are based on a system of published country specific models to simulate fire emissions.
Estimated annual emissions (C02 and non-C02) from forest fires over the interval from 1990 to the current inventory are
calculated consistent with IPCC (2006) methodology; this includes U.S.-specific data and models on area, fuel,
consumption, and emission. Area of forest burned is based on annual area of forest coincident with fires according to
annual datasets from MonitoringTrends in Burn Severity (MTBS perimeters, Eidenshink et al. 2007) or MODIS burned
area mapping (MODIS MCD64A1, Giglio et al. 2018). Annual estimates were calculated by the Wildland Fire Emissions
Inventory System (WFEIS, French et al. 2011, 2014). The WFEIS calculator111 was used to provide annual emissions
estimates by state and year. Note that N20 emissions are not included in WFEIS calculations; emissions provided here are
based on the average N20 to C02 ratio of 0.000166 following Larkin et al. (2014).
Forest area within the full burn areas defined by MTBS or MODIS were determined following two approaches. A fuels
model within WFEIS, North American Wildland Fuels Database (NAWFD, Prichard et al. 2019), delineates fuelbed classes,
and forest classifications within each fire identified forest land per fire. Additionally, the National Land Cover (NLCD)
images that include forest transition classes (Homer et al. 2015; Yang et al. 2018) identified forest land on spatial
features for individual MTBS and MODIS burned areas. The MTBS data do not include fires smaller than approximately
400 or 200 ha for the western or eastern U.S., respectively. Fire areas and emissions for Alaska are reduced to only
include managed land (Ogle et al. 2018).
Emissions from prescribed fires on forest land contribute to total annual emissions from forest fires. The MTBS records
identify fire origin, including many prescribed fires, but a large number of records include unknown fire origins.
Additionally, the minimum size thresholds for MTBS reporting are likely to exclude many of these controlled burns. This
report does not include separate emission estimates for prescribed fires (a change from recent annual reports) because
reporting unknown proportions are likely biased and do not provide usable information.
111 See https://wfeis.mtri.org/calculator.
Annex 3
A-429
-------
However, statistics for prescribed fires, but without separate forest classification, are available for the U.S; for example
see the National Interagency Fire Center112 or annual reports by the National Association of State Foresters and the
Coalition of Prescribed Fire Councils.113
The MTBS data available for this report (MTBS 2021) included fires through 2018 with only a partial set of the 2019 fires
included with the data. The MODIS-based records include 2001 through 2020. Emissions reported here originate from
MTBS data for the 1990 to 2018 interval, and the 2019 and 2020 emissions are based on MODIS burned areas. All other
parts of calculations - fuels, fire characteristics, and emissions-are via WFEIS and therefore identical throughout the
1990 to 2020 interval.
Current uncertainty estimates provided with emissions are based on two aspects of the calculations—identification of
forest land within fire perimeters and variability in modeled fuel loading. Uncertainty in the MTBS or MODIS data are not
currently addressed. Similarly, uncertainty in other parts of the WFEIS system, such as the Consume model (Prichard et
al. 2014), are not a part of uncertainty quantified here. Planned improvements for future analyses are to incorporate
preliminary WFEIS uncertainty analyses (Prichard et al. 2019, Kennedy et al. 2020) in reported forest fire emissions. The
two approaches for determining area of forest within fire perimeters (described above) identified differences, which
were summarized annually by state and incorporated as uniform distributions of uncertainty about area and thus
emissions. Variability in fuel loading modeled from the North American Wildland Fuels Database (NAWFD, Prichard et al.
2019) is available through additional calculation and download of the WFEIS calculator114 as emissions based on the 25th,
50th, or 75th percentiles of fuel, which were resampled for this component of uncertainty. A simple Monte Carlo
(Approach 2) method was employed to propagate uncertainty by state by year to county-wide totals. For additional
details and analysis see Smith et al. (in preparation).
112 See https://www.nifc.Eov/fire-informatiori/statistics.
113 See http://www.prescribedfire.net/.
114 See https://wfeis.mtri.org/calculator.
A-430 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-217: Areas (Hectares) and Corresponding Emissions (MMT/year) Associated with Past Forest Fires3
1990
1995
2000
2005
2010
2016
2017
2018
2019
2020
Conterminous 48
States
Forest area burned (1000 ha)
C emitted (MMT/yr)
119.8
3.5
98.8
2.4
796.3
21.2
379.7
8.0
396.0
6.1
778.4
20.1
1301.2
44.8
928.6
30.8
324.7
7.1
1781.9
67.8
C02 emitted (MMT/yr)
11.2
8.6
75.6
33.9
29.1
73.1
154.8
108.5
27.1
236.8
Forest area burned (1000 ha)
246.9
5.7
135.0
1074.8
159.2
69.2
105.7
90.5
646.3
5.2
Alaska
C emitted (MMT/yr)
12.2
0.2
4.5
34.9
2.3
3.1
5.3
2.8
20.6
0.2
C02 emitted (MMT/yr)
25.9
0.4
10.3
93.5
10.2
5.7
10.0
6.7
55.8
0.4
CH4 emitted (kt/yr)
92.2
14.7
182.9
259.6
48.6
154.5
381.1
249.4
44.6
545.2
All States
N20 emitted (kt/yr)
6.2
1.5
14.3
21.1
6.5
13.1
27.4
19.1
4.5
39.3
(CONUS+Alaska)
CO emitted (kt/yr)
2589.0
366.3
4296.5
7283.8
1334.0
3774.5
8590.9
5457.3
1095.3
11739.4
NOx emitted (kt/yr)
46.7
11.5
84.5
120.1
32.6
87.2
166.9
119.5
29.6
224.2
a These emissions have already been accounted for in the estimates of net annual changes in C stocks, which accounts for the amount sequestered minus any emissions,
including the assumption that combusted wood may continue to decay through time.
Annex 3
A-431
-------
Table A-218: Equivalence Ratios, of CH4 and N2O to CO2
Equivalence Ratios3
CH4 to C02 25
N20 to C02 298
a Source: IPCC(2007)
Non-C02 Emissions from Forest Fires
Emissions of non-C02 gases (CH4, N20, CO, and NOx), with methane (CH4) and nitrous oxide (N20) expressed as C02 Eq.
(Table A-218) are estimated using the same methodology described above for estimating C02 emissions from forest fires.
These estimates of non-C02 emissions associated with forest fires (Table A-217), as provided with this year's report also
represent a change from recent years in that they follow identical fire emissions methods as described above for C02
emissions. Similarly, estimated uncertainty follows methods described in the previous section.
A-432 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
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3.14. Methodology for Estimating CH4 Emissions from Landfills
A combination of Tier 2 and 3 approaches are used to calculate emissions from MSW Landfills. A Tier 2 approach is used
to calculate emissions for industrial waste landfills.
Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in solid
125
waste. By volume, landfill gas is about half CH4 and half C02. The amount and rate of CH4 generation depends upon the
quantity and composition of the landfilled material, as well as the surrounding landfill environment. Not all CH4
generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared or utilized for
energy, thus oxidizing the CH4 to C02 during combustion. Of the remaining CH4, a portion oxidizes to C02 as it travels
through the top layer of the landfill cover. In general, landfill-related C02 emissions are of biogenic origin and primarily
result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.
Figure A-18 illustrates how landfill gas composition varies over time after waste is disposed in an MSW landfill when
bacterial populations decompose the waste in different, often concurrent phases of waste decomposition (ATSDR 2001).
Gas is generated at a stable rate in Phase IV for approximately 20 years and may be generated for 50 or more years after
waste is placed in the landfill depending on management practices and waste composition (ASTDR 2001).
Figure A-18:
Source: ASTDR (2001)
Methane emissions from landfills are estimated using two primary methods. The first method uses the first order decay
(FOD) 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
second method used to calculate CH4 emissions from landfills, also called the back-calculation method, is based off
125 Typically, landfill gas also contains small amounts of nitrogen, oxygen, and hydrogen, less than 1 percent nonmethane volatile
organic compounds (NMVOCs), and trace amounts of inorganic compounds.
Landfill Gas Composition Over Time
Aerobic Anaerobic
Phase I
Phase IV
Metha noge nic,
Steady
2-5%
Time After Placement
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directly measured amounts of recovered CH4 from the landfill gas and is expressed by Equation HH-8 in CFR Part 98.343
of the EPA's Greenhouse Gas Reporting Program (GHGRP).
The current Inventory methodology uses both methods to estimate CH4 emissions across the time series. The 1990 to
2015 Inventory was the first Inventory to incorporate directly reported GHGRP net CH4 emissions data for landfills. In
previous Inventories, only the first order decay method was used. EPA's GHGRP requires landfills meeting or exceeding a
threshold of 25,000 metric tons (MT) 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's GHGRP provides a consistent methodology, a broader range of values for the
oxidation factor, and allows for facility-specific annual waste disposal data to be used, thus these data are considered
Tier 3 (highest quality data) under the 2006IPCC Guidelines. Using EPA's GHGRP data was a significant methodological
change and required a merging of the GHGRP methodology with the Inventory methodology used in previous years to
ensure time-series consistency.
Figure A-19 presents the CH4 emissions process—from waste generation to emissions—in graphical format. A detailed
discussion of the steps taken to compile the 1990 to 2020 Inventory are presented in the remainder of this Annex.
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste
a MSW waste generation is not calculated because annual quantities of waste landfilled are available through secondary
sources as described in figure note b.
b Quantities of MSW landfilled for 1940 through 1988 are based on EPA 1988 and EPA 1993; 1989 through 2004 are based on
BioCycle 2010; 2005 through 2020 are incorporated through the directly reported emissions from MSW landfills to the
Greenhouse Gas Reporting Program. Quantities of industrial waste landfilled are estimated using a disposal factor and
industrial production data sourced from Lockwood Post's Directory and the USDA.
c The 2006 IPCC Guidelines - First Order Decay (FOD) Model is used for industrial waste landfills.
d Two different methodologies are used in the time series for MSW landfills. For 1990 to 2004, the 2006 IPCC Guidelines - FOD
Model is used. For 2005 to 2020, directly reported net CH4 emissions from the GHGRP for 2010 to the current Inventory year
are used with the addition of a scale-up factor applied to each year's emissions. The scale-up factor accounts for emissions
from landfills that do not report to the GHGRP. A scale-up factor of 9 percent is applied to 2005-2016 and a scale-up factor of
11 percent is applied to 2017-2020. The GHGRP emissions from 2010 to the current Inventory year are also used to backcast
emissions for 2005 to 2009 to merge the FOD methodology with the GHGRP methodology for time series consistency.
Additional details on how the scale-up factor was developed and the backcasting approach are included in Step 4 of this
Annex chapter.
e Methane recovery from industrial waste landfills is not incorporated into the Inventory because it does not appear to be a
common practice according to the GHGRP dataset.
f Methane recovery data are pulled from four recovery databases: EIA 2007, flare vendor database, the landfill gas-to-energy
database, and EPA (GHGRP) 2015(a). These databases are used to estimate national recovery for the Inventory between 1990
to 2009. CH4 recovery estimates between 2010 to the current inventory year are calculated from GHGRP recovery amounts
with a scale-up factor applied as explained in Step 3 of this Annex chapter.
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g For years 1990 to 2004, the total CH4 generated from MSW landfills and industrial waste landfills are summed. For years
2005 to 2020, MSW landfill CH4 generated is back-calculated from the annual net CH4 emissions, recovery, and oxidation; CH4
generation from industrial waste landfills are summed with the back-calculated MSW landfills CH4 generation amounts.
h An oxidation factor of 10 percent is applied to all CH4 generated in years 1990 to 2004 (2006 IPCC Guidelines; Mancinelli and
McKay 1985; Czepiel et al 1996). For years 2005 to 2020, directly reported CH4 emissions from the GHGRP are used for MSW
landfills. Various oxidation factor percentages are included in the GHGRP dataset (0,10, 25, and 35) with an average percent
of 0.14 effectively applied between 2005 to 2009, 0.18 between 2010 to 2016, and 0.21 between 2017 to 2020.
Step 1: Estimate Annual Quantities of Solid Waste Placed in MSW Landfills for 1940 to the
Present Year
Total national annual waste generation and disposal data back to 1940 are directly used to estimate CH4 emissions for
the 1990 to 2009 Inventory time series. The waste generation and disposal estimates are also made for the rest of the
Inventory time series (i.e., 2010 to the current Inventory year) for informational purposes; these data however do not
inform the annual CH4 emission estimates for this portion of the time series. The specific steps are described below (in
sections la and lb), followed by a summary of a comparative analysis of datasets that contain or are used to estimate
annual waste disposal (in Box A-3). Step 2 describes how the estimated annual quantities of waste landfilled are used to
estimate annual CH4 generation between 1990 to 2009, and the methodology used to estimate CH4 generation for 2010
to the current Inventory year.
Step la. Historical Estimates: 1940 to 1988
Historical waste data, preferably from 50 years prior to the first year of the inventory time series (i.e. since 1940 because
the time series begins in 1990), are required for the FOD model to estimate CH4 generation for the Inventory time series
(IPCC 2006). States and local municipalities across the United States do not consistently track and report quantities of
MSW generated or collected for management, nor do they report end-of-life disposal methods to a centralized system.
Therefore, national MSW landfill waste generation and disposal data are obtained from secondary data sources or
estimated via proxy data.
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.
Step lb. Inventory Time Series Estimates: 1990 to the Current Inventory Year
For 1989 to 2008, estimates of the annual quantity of MSW generated were developed from a survey of state agencies
as reported in the State of Garbage (SOG) in America surveys (BioCycle 2001, 2004, 2006, 2010), adjusted to include U.S.
Territories.126 The SOG surveys collected data from state agencies and then applied 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 2006; Shin 2014). This approach assumes that all waste management methods are tracked and
reported to state agencies. Survey respondents were 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 surveys 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.
While non-municipal solid wastes may have been disposed of in MSW landfills, they were not the primary type of waste
material disposed and are typically inert. In last survey (BioCycle 2010), state agencies were asked to provide MSW-only
data. Where this was not possible, they were asked to provide comments to better understand the data being reported.
Methodological changes have occurred over the time frame the SOG surveys have been published, which directly
126 Since the SOG survey does not include U.S. Territories, waste landfilled in U.S. Territories was estimated using population data for
the U.S. Territories (U.S. Census Bureau 2020) and the per capita rate for waste landfilled from BioCycle (2010).
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impacted the fluctuating trends observed in the waste disposal data and emission estimates from 1990 to 2004 (RTI
2013).
The SOG survey is voluntary and not all states provided data in each survey year. To estimate waste generation for states
that did not provide data in any given reporting year, one of the following methods was used (RTI 2013):
• For years when a state-specific waste generation rate was available from the previous SOG reporting year
submission, the state-specific waste generation rate for that state was used.
- or -
• For years where a state-specific waste generation rate was not available from the previous SOG reporting year
submission, the waste amount is generated using the national average waste generation rate. In other words,
Waste Generated = Reporting Year U.S. Population x the National Average Waste Generation Rate
o The National Average Waste Generation Rate is determined by dividing the total reported waste
generated across the reporting states by the total population for reporting states.
o This waste generation rate may be above or below the waste generation rate for the non-reporting
states and contributes to the overall uncertainty of the annual total waste generation amounts used in
the model.
Use of these methods to estimate solid waste generated by states is a key aspect of how the SOG data was manipulated
and why the results differ for total solid waste generated as presented in the SOG reports and in the Inventory. In the
early years (2002 data in particular), SOG made no attempt to fill gaps for non-survey responses. For the 2004 data, the
SOG team used proxy data (mainly from the Waste Business Journal [WBJ]) to fill gaps for non-reporting states and
survey responses.
Although some fluctuation in waste generation data reported by states to the SOG survey is expected, for some states,
the year-to-year fluctuations are quite significant (>20 percent increase or decrease in some case) (RTI 2013). The SOG
survey reports for these years do not provide additional explanation for these fluctuations and the source data are not
available for further assessment. Although exact reasons for the large fluctuations are difficult to obtain without direct
communication with states, staff from the SOG team that were contacted speculated that significant fluctuations are
present because the particular state could not gather complete information for waste generation (i.e., they are missing
part of recycled and composted waste data) during a given reporting year. In addition, SOG team staff speculated that
some states may have included C&D and industrial wastes in their previous MSW generation submissions but made
efforts to exclude that (and other non-MSW categories) in more recent reports (RTI 2013).
The SOG surveys provide state-specific landfill waste generation data used in the Inventory for select years -1989 to
2000, 2002, 2004, 2006, and 2008. In-between year waste generation is interpolated using the prior and next SOG report
data. For example, waste generated in 2003 = (waste generation in 2002 + waste generation in 2004)/2.
For the Inventory year 2010 and later, EREF's 2016 report entitled, MSW Management in the United States, is used as
the primary data source because BioCycle ceased preparing the SOG surveys. EREF (2016) includes state-specific landfill
MSW generation and disposal data for 2010 and 2013 using a similar methodology as the SOG surveys. Waste generation
data were interpolated for 2009, the year in-between the 2008 SOG survey data and the 2010 EREF data. Waste
generation data were also extrapolated for 2011 and 2012 using the EREF data for 2010 and 2013. Waste generation
data for 2014 and the current year were extrapolated based on the EREF 2013 data and population increases from the
U.S. Census (U.S. Census Bureau 2020). No data source on annual waste generation by state or nationally (similar to an
SOG or EREF report) has been published since EREF (2016).
For each year in the time series, estimates of the quantity of waste landfilled are determined by applying a waste
disposal factor to the total amount of waste generated. A waste disposal factor was determined for each year a SOG
survey was published and is the ratio of the total amount of waste landfilled to the total amount of waste generated. The
waste disposal factor is interpolated for the years in between the SOG surveys and EREF report and extrapolated for
years after the last year of EREF data (i.e., 2013). The waste disposal factor has ranged from approximately 77 percent in
1990 to 65.3 percent from 2015 to 2020.
Table A-219 shows estimates of MSW generated and landfilled, and industrial waste landfilled. A description of the data
sources used to estimate industrial waste landfilled is included in Step 7. Estimates for MSW generated and landfilled are
presented for various years after 2004 for informational purposes only. As described in Step 4, after 2004, the Inventory
A-442 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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methodology relies on the GHGRP net reported CH4 emissions data, replacing the need for the now discontinued SOG
surveys and intermittent EREF estimates.
Table A-219: Solid Waste in MSW and Industrial Waste Landfills Contributing to ChU
Emissions (MMT unless otherwise noted)
1990
2005
2016
2017
2018
2019
2020
Total MSW Generated3
270
368
326
327
329
331
332
Percent of MSW Landfilled
77%
64%
65%
65%
65%
65%
65%
Total MSW Landfilled
205
234
210
212
213
214
214
MSW last 30 years'5
4,876
5,992
6,480
6,501
6,520
6,537
6,547
MSW since 1940c
6,808
9,925
12,297
12,509
12,721
12,935
13,149
Total Industrial Waste Production
Data
185
218
211
208
210
209
210
Pulp and Paper Sectord
116
135
125
121
123
122
121
Food and Beverage Sector6
69
83
86
87
87
88
88
Percent Total Industrial Waste
Landfilled
5%
5%
5%
5%
5%
5%
5%
Total Industrial Waste Landfilled
9.7
10.9
10.3
10.3
10.5
10.4
10.3
Pulp and Paper Sectord
6.5
6.9
6.1
6.1
6.2
6.0
5.9
Food and Beverage Sectore
3.3
4.0
4.2
4.3
4.3
4.4
4.4
a This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the CH4
generation. Values are based on EPA (1993) for years 1940 to years 1988 (not presented in table), BioCycle 2001, 2004, 2006,
and 2010 for years 1989 to 2009 (1981 to 2004, and 2006 to 2011 are not presented in table). Values for years 2010 to 2019
are based on EREF (2016) and annual population data from the U.S. Census Bureau (2020).
b This estimate is the cumulative amount of waste that has been placed in landfills for the 30 years prior to the year indicated
and is the sum of the annual disposal rates used in the first order decay model. Values are based on EPA 1993; BioCycle 2001,
2004, 2006, and 2010; EREF 2016; and extrapolated data based on annual population increases (U.S. Census Bureau 2020).
c This estimate represents the cumulative amount of waste that has been placed in landfills since 1940 to the year indicated and
is the sum of the annual disposal rates used in the first order decay model. Values are based on EPA 1993; BioCycle 2001,
2004, 2006, and 2010; EREF 2016; and extrapolated data based on annual population increases (U.S. Census Bureau 2020).
d A disposal factor of 0.050 MT/MT of product is applied to total pulp and paper production data to estimate the annual
amount landfilled. See Step 7 for the references and rationale for this method. The same disposal factor is applied to every
year of the time series. Production data from 1990 and 2001 are from Lockwood-Post's Directory (2002). Production data from
2002 to 2020 are from the FAOStat database.127
e A disposal factor of 0.046 MT/MT of product is applied to total food production data to estimate the annual amount landfilled.
See Step 7 for the references and rationale for this method. The same disposal factor is applied to every year of the time
series. Food production values for 1990 to 2020 are from ERG (2021 and FAO (2021)).128
Box A-3: Comparison of Annual Waste Disposal Estimates Across Available Data Sources
In 2020, EPA compared the available data on estimates of total waste generated and landfilled as presented in Table
A-219 for the years 2017 and 2018 and found inconsistencies between the estimates of MSW landfilled between the
data sources. Data sources directly compared include the EREF-extrapolated estimate for 2017 and 2018 to the
Advancing Sustainable Materials Management: Facts and Figures report (EPA (2020) Advancing Sustainable Materials
Management: Facts and Figures 2018. November 2020). These inconsistencies are expected, as the data sources use
two different methodologies to estimate MSW landfilled. While there are differences in the methods used between
these data sources, the uncertainty factors for MSW Landfills are intended to account for these variabilities in the
emission estimates for 1990 to 2004.
The EREF-extrapolated estimate of total MSW landfilled for 2017 and 2018 is based on a bottom-up approach using
information at the facility-level to estimate MSW for the sector as a whole, while the Facts and Figures report uses a
top-down (materials flow mass balance) approach to estimate the same quantity. The materials flow methodology is
127 Available at: http://faostat3.fao.ore/home/index.htm IffDOWNLOAD. Accessed on June 18, 2021.
128 2020 USDA-NASS Ag QuickStats. Available at: http://quickstats.nass.usda.gov.
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generally based on production data for each material at the state- (recycling, composting) or national- (waste
generation) level. Discarded or landfilled material is Subtitle D waste only and assumed to be the calculated difference
between generation and recovery through recycling and composting, other food management (e.g., anaerobic
digestion), and combustion (EPA 2020). Subtitle D wastes do not include construction and demolition waste, for
example, which many GHGRP-reporting facilities accept and include in their greenhouse gas reports.
As a quality check, EPA also compared the MSW landfilled estimates from the EREF-extrapolated data, the Facts and
Figures report, and the estimated waste disposed by facilities reporting to EPA's GHGRP under Subpart HH (MSW
Landfills) for 2017 and 2018 plus an 11 percent scale-up factor to account for landfills that do not report to Subpart
HH.
On average, the EREF-extrapolated value was 39 percent less than GHGRP-based estimated waste disposal amount for
the year 2017 and 41 percent less than GHGRP-based estimated waste disposal amount for the year 2018 (including a
scale-up factor of 11 percent for 2017 and 2018).
The difference between the EREF-extrapolated and GHGRP-based estimates are largely assumed to be due to the
difference in estimated number of facilities included in the respective sources, and because the EREF 2013 waste
landfilled estimate was extrapolated to 2018 based on population growth. In 2013, EREF estimated 1,540 landfills
(data collected from state agencies, individual facilities for Hawaii and Florida, and estimated using population-based
estimates for Alaska, Idaho and Wyoming). In 2018, the GHGRP-based estimate includes 2,111 total facilities,
including 1,136 facilities reporting to the GHGRP, and 975 assumed or confirmed operational MSW landfills identified
through WBJ 2016 and LMOP 2020 that do not report to the GHGRP.
Estimates of MSW landfilled from the Facts and Figures report for the year 2017 and 2018 were, on average, 61
percent less than the GHGRP + scale-up factor waste quantity (including a scale-up factor of 11 percent and
subtracting 23 percent estimate of construction and demolition waste for both years).
While this 61 percent difference is large, it is not unexpected given the Facts and Figures top-down methodology and
focus on MSW (i.e., non-MSW streams are purposely excluded). The GHGRP uses a facility-specific, bottom-up
approach to estimating emissions while the Facts and Figures report uses a top-down approach which incorporates
many assumptions regarding production, import and export values, and estimated product life are built into the MSW
generation and landfill disposal estimate at the national level. The Facts and Figures report also specifically omits
certain types of waste that are explicitly included in the GHGRP reports, such as construction and demolition waste,
industrial waste, biosolids (sludges), agricultural waste, and other inert wastes (EPA 2020). Construction and
demolition waste that was reported under the GHGRP were excluded to the extent possible, but because the GHGRP
facilities typically report a default waste composition, some construction and demolition waste may still be included
in what is assumed to be the MSW quantity. Additionally, the amount of biosolids (sludges) and other non-MSW
streams could not reliably be estimated and excluded from the GHGRP data and may also be contributing to the
percent difference.
Step 2: Estimate CH4 Generation at MSW Landfills
Step 2a. CH4 Generation at MSW Landfills for 1990 to 2009
The FOD method is exclusively used for 1990 to 2009. For the FOD method, methane generation is based on nationwide
MSW generation data, to which a national average disposal factor is applied; it is not landfill-specific.
The FOD method is presented below and is similar to Equation HH-6 in CFR Part 98.343 for MSW landfills, and Equation
TT-6 in CFR Part 98.463 for industrial waste landfills.
Equation A-67: Net Methane Emissions from Solid Waste
ch4 .Solid Waste = [GcH4,MSW — R] - Ox
where,
CH4,soiid waste = Net CH4 emissions from solid waste
Gch4,msw = CH4 generation from MSW or industrial waste landfills
R = CH4 recovered and combusted
Ox = CH4 oxidized from MSW or industrial waste landfills before release to the atmosphere
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The input parameters needed for the FOD model equations are the mass of waste disposed each year (discussed under
Step 1), degradable organic carbon (DOC) as a function of methane generation potential (Lo), and the decay rate
constant (k). The equation below provides additional detail on the activity data and emission factors used in the CH4,msw
equation presented above to calculate CH4 generation.
Equation A-68: Methane Generation from MSW Landfills
CH4,msw = X Lo X X (e-fc(T-*-i) _ e-*a-*))}]
where,
CH4,msw = Total CH4 generated from MSW or industrial waste landfills
T = Reporting year for which emissions are calculated
x = Year in which waste was disposed
S = Start year of calculation
Wx = Quantity of waste disposed of in the landfill in a given year
L0 = Methane generation potential (100 m3 CH4/Mg waste; EPA 1998, 2008)
16/12 = conversion factor from CH4 to C
k = Decay rate constant (yr1, see Table A-220)
The DOC is determined from the CH4 generation potential (L0 in m3 CH4/Mg waste) as shown in the following equation:
Equation A-69: Degradable Organic Carbon Fraction of Solid Waste
DOC = [Lo x 6.74 x 10 4] -f [Fx 16/12 x DOCf x MCF]
where,
DOC = degradable organic carbon (fraction, kt C/kt waste),
L0 = CH4 generation potential (100 m3 CH4/Mg waste; EPA 1998, 2008),
6.74 x 10-4 = CH4 density (Mg/m3),
F = fraction of CH4 by volume in generated landfill gas (equal to 0.5)
16/12 = molecular weight ratio CH4/C,
DOCf = fraction of DOC that can decompose in the anaerobic conditions in the landfill (fraction equal
to 0.5 for MSW), and
MCF = methane correction factor for year of disposal (fraction equal to 1 for anaerobic managed
sites).
DOC values can be derived for individual landfills if a good understanding of the waste composition over time is known. A
default DOC value is used in the Inventory because waste composition data are not regularly collected for all landfills
nationwide. When estimating CH4 generation for the years 1990 to 2009, a default DOC value is used. This DOC value is
calculated from a national CH4 generation potential129 of 100 m3 CH4/Mg waste (EPA 2008) as described below.
The DOC value used in the CH4 generation estimates from MSW landfills for 1990 to 2009 is 0.2028, which is based on
the CH4 generation potential of 100 m3 CH4/Mg waste (EPA 1998; EPA 2008). After EPA developed the L0 value, RTI
analyzed data from a set of 52 representative landfills across the United States in different precipitation ranges to
evaluate L0, and ultimately the national DOC value. The 2004 Chartwell Municipal Solid Waste Facility Directory
confirmed that each of the 52 landfills chosen accepted or accepts both MSW and construction and demolition (C&D)
waste (Chartwell 2004; RTI 2009). The values for L0 were evaluated from landfill gas recovery data for this set of 52
landfills, which resulted in a best fit value for L0 of 99 m3/Mg of waste (RTI 2004). This value compares favorably with a
range of 50 to 162 (midrange of 106) m3/Mg presented by Peer, Thorneloe, and Epperson (1993); a range of 87 to 91
129 Methane generation potential (L0) varies with the amount of organic content of the waste material. A higher L0 occurs with a higher
content of organic waste.
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m3/Mg from a detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA
1998); and a value of 100 m3/Mg recommended in EPA's compilation of emission factors (EPA 1998; EPA 2008; based on
data from 21 landfills). Based on the results from these studies, a value of 100 m3/Mg appears to be a reasonable best
estimate to use in the FOD model for the national inventory for years 1990 through 2009, and is the value used to derive
the DOC value of 0.2028.
In 2004, the FOD model was also applied to the gas recovery data for the 52 landfills to calculate a decay rate constant
(k) directly for L0 = 100 m3/Mg. The decay rate constant was found to increase with annual average precipitation;
consequently, average values of k were developed for three precipitation ranges, shown in Table A-220 and
recommended in EPA's compilation of emission factors (EPA 2008).
Table A-220: Average Values for Rate Constant (k) by Precipitation Range (yr"1)
Precipitation range (inches/year) k (yr1)
<20 0.020
20-40 0.038
>40 0.057
These values for k show reasonable agreement with the results of other studies. For example, EPA's compilation of
emission factors (EPA 1998; EPA 2008) recommends a value of 0.02 yr1 for arid areas (less than 25 inches/year of
precipitation) and 0.04 yr^for non-arid areas. The SWANA (1998) study of 18 landfills reported a range in values of k
from 0.03 to 0.06 yr1 based on CH4 recovery data collected generally in the time frame of 1986 to 1995.
Using data collected primarily for the year 2000, the distribution of waste-in-place versus precipitation was developed
from over 400 landfills (RTI 2004). A distribution was also developed for population versus precipitation for comparison.
The two distributions were very similar and indicated that population in areas or regions with a given precipitation range
was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using U.S. Census data and
rainfall data, the distributions of population versus rainfall were developed for each Census decade from 1950 through
2010. The distributions showed that the U.S. population has shifted to more arid areas over the past several decades.
Consequently, the population distribution was used to apportion the waste landfilled in each decade according to the
precipitation ranges developed for k, as shown in Table A-221.
Precipitation Range (inches/year)
1950
1960
1970
1980
1990
2000
<20
10
13
14
16
19
20
20-40
40
39
37
36
34
33
>40
50
48
48
48
48
48
Note: The precipitation range data are no longer used in the IPCC waste model (i.e., the FOD method) for 2010
and later years. Totals may not add to 100% due to independent rounding.
Source: Years 1950 through 2000 are from RTI (2004) using population data from the U.S. Census Bureau and
precipitation data from the National Climatic Data Center's National Oceanic and Atmospheric Administration.
The 2006 IPCC Guidelines also require annual proportions of waste disposed of in managed landfills versus unmanaged
and uncategorized sites prior to 1980. Based on the historical data presented by Mintz et al. (2003), a timeline was
developed for the transition from the use of unmanaged and uncategorized sites for solid waste disposed to the use of
managed landfills. Based on this timeline, it was estimated that 6 percent of the waste that was land disposed in 1940
was disposed of in managed landfills and 94 percent was managed in uncategorized sites. The uncategorized sites
represent those sites where not enough information was available to assign a percentage to unmanaged shallow versus
unmanaged deep solid waste disposal sites. Between 1940 and 1980, the fraction of waste that was land disposed
transitioned towards managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For
wastes disposed of in the uncategorized sites, a methane correction factor (MCF) of 0.6 was used based on the
recommended IPCC default value for uncharacterized land disposal (IPCC 2006). The recommended IPCC default value
for the MCF for managed landfills of 1 (IPCC 2006) has been used for the managed landfills for the years where the first
order decay methodology was used (i.e., 1990 to 2009).
Step 2b. CH4 Generation at MSW Landfills for 2010 to Present
A different methodology is used to estimate CH4 generation at MSW landfills between 2010 to 2020. Recent inventories
prior to the 1990-2020 Inventory did not separately present CH4 generation, CH4 recovery, or CH4 oxidation from MSW
A-446 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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landfills after 2005 because the methodology switched to using the directly reported net CH4 emissions plus a scale-up
factor (discussed in Step 4) between 2005 to the current Inventory year. In response to various queries and comments,
estimates for CH4 generation, CH4 recovery, and CH4 oxidation have been added to the 1990 to 2020 Inventory and will
be updated annually. The methodology developed to estimate CH4 generation between 2010 to 2020 is described below.
Step 3: Estimate CH4 Emissions Avoided from MSW Landfills
Between 1990 to 2009, the estimated landfill gas recovered per year (R) at MSW landfills is based on a combination of
four databases that include recovery from flares and/or landfill gas-to-energy projects:
• a database developed by the Energy Information Administration (EIA) for the voluntary reporting of
greenhouse gases (EIA 2007),
• a database of LFGE projects that is primarily based on information compiled by EPA LMOP (EPA 2016),
• the flare vendor database (contains updated sales data collected from vendors of flaring equipment), and the
130
• EPA's GHGRP MSW landfills database (EPA 2015a).
Between 2010 and 2020, the estimated R at MSW landfills is calculated using directly reported annual quantities of R
from EPA's GHGRP (EPA 2021a) plus a scale-up factor to account for recovery from MSW landfills that may not be
reporting to the GHGRP. The development of the scale-up factor is detailed under Step 4a. A scale-up factor of 9 percent
and 11 percent is applied to the total R from EPA's GHGRP from 2010 to 2016 and 2017 to 2020, respectively. In 2021,
the Inventory team compared the total R from EPA's GHGRP and EPA's LMOP 2021 database (EPA 2021b); total R
between the two databases were within a reasonable range, but higher in the LMOP 2021 database. The GHGRP data
consist of mandatory, annually updated facility-specific data, while the LMOP database includes the GHGRP data in
addition to voluntary, intermittent facility-specific data for facilities that do not report to the GHGRP.
Step 3a: Estimate CH4 Emissions Avoided Through Landfill Gas-to-Energy (LFGE) and Flaring Projects
for 1990 to 2009
The quantity of CH4 avoided due to LFGE systems was estimated based on information from three sources: (1) a database
developed by the EIA for the voluntary reporting of greenhouse gases (EIA 2007); (2) a database compiled by LMOP and
referred to as the LFGE database for the purposes of this inventory (EPA 2016); and (3) the GHGRP MSW landfills dataset
(EPA 2015a).
The EIA database includes location information for landfills with LFGE projects, estimates of CH4 reductions, descriptions
of the projects, and information on the methodology used to determine the CH4 reductions. In general, the CH4
reductions for each reporting year were based on the measured amount of landfill gas collected and the percent CH4 in
the gas.
For the LFGE database, data on landfill gas flow and energy generation (i.e., MW capacity) were used to estimate the
total direct CH4 emissions avoided due to the LFGE project.
The GHGRP MSW landfills database contains the most detailed data on landfills that reported under EPA's GHGRP for
years 2010 through 2015, however the amount of CH4 recovered is not specifically allocated to a flare versus a LFGE
project. The allocation into flares or LFGE was performed by matching landfills to the EIA and LMOP databases for LFGE
projects and to the flare database for flares. Detailed information on the landfill name, owner or operator, city, and state
are available for both the EIA and LFGE databases; consequently, it was straightforward to identify landfills that were in
both databases against those in EPA's GHGRP MSW landfills database. The EPA's GHGRP MSW landfills database was first
introduced as a source for recovery data for the 1990 to 2013 Inventory. The GHGRP MSW landfills database contains
facility-reported data that undergoes rigorous verification and is considered to contain the least uncertain data of the
four databases. However, this database only contains a portion of the landfills in the United States (although,
presumably the highest emitters since only those landfills that meet the methane generation threshold must report) and
only contains data from 2010 and later. For landfills in this database, methane recovery data reported data for 2010 and
130 The 2015 GHGRP dataset is used in the GHGRP MSW landfills dataset described in Step 3a. This database is no longer updated
because the methodology has changed such that the directly reported net methane emissions are used. The GHGRP dataset is
available through Envirofacts http://www.epa.gov/enviro/facts/ghg/search.htnil.
Annex 3
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later were linearly backcasted to 1990, or the date the landfill gas collection system at a facility began operation,
whichever is earliest.
A destruction efficiency of 99 percent was applied to amounts of CH4 recovered to estimate CH4 emissions avoided for all
recovery databases. This value for destruction efficiency was selected based on the range of efficiencies (86 to 99+
percent) recommended for flares in EPA's AP-42 Compilation of Air Pollutant Emission Factors, Draft Chapter 2.4, Table
2.4-3 (EPA 2008). A typical value of 97.7 percent was presented for the non-methane components (i.e., volatile organic
compounds and non-methane organic compounds) in test results (EPA 2008). An arithmetic average of 98.3 percent and
a median value of 99 percent are derived from the test results presented in EPA 2008. Thus, a value of 99 percent for the
destruction efficiency of flares has been used in Inventory methodology. Other data sources supporting a 99 percent
destruction efficiency include those used to establish New Source Performance Standards (NSPS) for landfills and in
recommendations for closed flares used in the EPA's LMOP.
The same landfill may be included one or more times across these four databases before RTI data cleaning. 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, the GHGRP > EIA >
LFGE > flare vendor database.
If a landfill in the GHGRP MSW landfills database was also in the EIA, LFGE, and/or flare vendor database, the avoided
emissions were only based on EPA's GHGRP MSW landfills database to avoid counting the recovery amounts multiple
times across the different databases. In other words, the CH4 recovery from the same landfill was not included in the
total recovery from the EIA, LFGE, or flare vendor databases. While the GHGRP contains facility-reported information on
MSW Landfills starting in the year 2010, EPA has backcasted GHGRP emissions to the year 2005 in order to merge the
two methodologies (more information provided in Steps 4a and 4b). Prior to 2005, if a landfill in EPA's GHGRP was also in
the LFGE or EIA databases, the landfill gas project information, specifically the project start year, from either the LFGE 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 LFGE database under a project that started in 2002 that is
still operational, the CH4 recovery data in the GHGRP database for that facility was backcasted to the year 2002 only.
If a landfill in the EIA database was also in the LFGE 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. The EIA database only
includes facility-reported data through 2006; the amount of CH4 recovered in this database for years 2007 and later were
assumed to be the same as in 2006. Nearly all (93 percent) of landfills in the EIA database also report to EPA's GHGRP.
If both the flare data and LFGE recovery data were available for any of the remaining landfills (i.e., not in the EIA or EPA's
GHGRP databases), then the CH4 recovered were based on the LFGE data, which provides reported landfill-specific data
on gas flow for direct use projects and project capacity (i.e., megawatts) for electricity projects. The LFGE 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. Given that each LFGE project is likely to also have a flare, double counting reductions from flares and
LFGE projects in the LFGE database was avoided by subtracting emission reductions associated with LFGE projects for
which a flare had not been identified from the emission reductions associated with flares (referred to as the flare
correction factor).
Step 3b: Estimate CH4 Emissions Avoided Through Flaring for the Flare Database for 1990 to 2009
To avoid double counting, flares associated with landfills in EPA's GHGRP, EIA and LFGE databases were not included in
the total quantity of CH4 recovery from the flare vendor database. As with the LFGE projects, reductions from flaring
landfill gas in the EIA database were based on measuring the volume of gas collected and the percent of CH4 in the gas.
The information provided by the flare vendors included information on the number of flares, flare design flow rates or
flare dimensions, year of installation, and generally the city and state location of the landfill. When a range of design
flare flow rates was provided by the flare vendor, the median landfill gas flow rate was used to estimate CH4 recovered
from each remaining flare (i.e., for each flare not associated with a landfill in the EIA, EPA's GHGRP, or LFGE databases).
Several vendors have provided information on the size of the flare rather than the flare design gas flow rate for most
years of the Inventory. Flares sales data has not been obtained since the 1990 to 2015 Inventory year, when the net CH4
emission directly reported to EPA's GHGRP began to be used to estimate emission from MSW landfills.
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To estimate a median flare gas flow rate for flares associated with these vendors, the size of the flare was matched with
the size and corresponding flow rates provided by other vendors. Some flare vendors reported the maximum capacity of
the flare. An analysis of flare capacity versus measured CH4 flow rates from the EIA database showed that the flares
operated at 51 percent of capacity when averaged over the time series and at 72 percent of capacity for the highest flow
rate for a given year. For those cases when the flare vendor supplied maximum capacity, the actual flow was estimated
as 50 percent of capacity. Total CH4 avoided through flaring from the flare vendor database was estimated by summing
the estimates of CH4 recovered by each flare for each year.
Step 3c: Correct Overestimation of CH4 Emissions Avoided Through Flaring for 1990 to 2009
If comprehensive data on flares were available, each LFGE project in EPA's GHGRP, EIA, and LFGE databases would have
an identified flare because it is assumed that most LFGE projects have flares. However, given that the flare vendor
database only covers approximately 50 to 75 percent of the flare population, an associated flare was not identified for all
LFGE projects. These LFGE projects likely have flares, yet flares were unable to be identified for one of two reasons: 1)
inadequate identifier information in the flare vendor data, or 2) a lack of the flare in the flare vendor database. For those
projects for which a flare was not identified due to inadequate information, CH4 avoided would be overestimated, as
both the CH4 avoided from flaring and the LFGE project would be counted. To avoid overestimating emissions avoided
from flaring, the CH4 avoided from LFGE projects with no identified flares was determined and the flaring estimate from
the flare vendor database was reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis.
This step likely underestimates CH4 avoided due to flaring but was applied to be conservative in the estimates of CH4
emissions avoided.
Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990 to 2009
and 1990 to 2014 inventory years to reduce the total number of flares in the flare vendor database that were not
matched to landfills and/or LFGE projects in the EIA and LFGE databases. Each flare in the flare vendor database not
associated with a LFGE project in the EIA, LFGE, or EPA's GHGRP databases was investigated to determine if it could be
matched. For some unmatched flares, the location information was missing or incorrectly transferred to the flare vendor
database and was corrected during the review. In other instances, the landfill names were slightly different between
what the flare vendor provided, and the actual landfill name as listed in the EIA, LFGE and EPA's GHGRP databases. The
remaining flares did not have adequate information through the name, location, or owner to identify it to a landfill in any
of the recovery databases or through an Internet search; it is these flares that are included in the flare correction factor
for the current inventory year.
A large majority of the unmatched flares are associated with landfills in the LFGE database that are currently flaring but
are also considering LFGE. These landfills projects considering a LFGE project are labeled as candidate, potential, or
construction in the LFGE database. The flare vendor database was improved in the 1990 to 2009 inventory year to match
flares with operational, shutdown as well as candidate, potential, and construction LFGE projects, thereby reducing the
total number of unidentified flares in the flare vendor database, all of which are used in the flare correction factor. The
results of this effort significantly decreased the number of flares used in the flare correction factor, and consequently,
increased recovered flare emissions, and decreased net emissions from landfills for the 1990 through 2009 Inventory.
The revised state-by-state flare correction factors were applied to the entire Inventory time series (RTI 2010).
Step 4: Estimate CH4 Emissions from MSW Landfills for 1990 to 2009
Methane emissions from MSW Landfills between 1990 and 2004 are estimated by subtracting the total annual amount
of CH4 recovered from the estimated CH4 generation (see Equation A-67).
Methane emissions from MSW Landfills between 2005 to 2009 are estimated via a different methodology as described in
the remainder of this step. During preparation of the 1990 to 2015 Inventory, EPA engaged with stakeholders both
within and outside of the landfill industry on the methodology used in the Inventory, the data submitted by facilities
under EPA's GHGRP Subpart HH for MSW Landfills, and the application of this information as direct inputs to the MSW
landfill methane emissions estimates in the 1990 to 2015 Inventory. Based on discussions with stakeholders, EPA
developed several options for improving the Inventory through methodological changes and moved forward with using
the directly reported net GHGRP methane emissions from 2010 to 2015 for MSW landfills in the 1990 to 2015 Inventory.
The Inventory methodology now uses directly reported net CH4 emissions for the 2010 to 2020 reporting years from
EPA's GHGRP to backcast emissions for 2005 to 2009. The emissions for 2005 to 2009 are recalculated each year the
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Inventory is published to account for the additional year of reported data and any revisions that facilities make to past
GHGRP reports. When EPA verifies the greenhouse gas reports, comparisons are made with data submitted in earlier
reporting years and errors may be identified in these earlier year reports. Facility representatives may submit revised
reports for any reporting year in order to correct these errors. Facilities reporting to EPA's GHGRP that do not have
landfill gas collection and control systems use the FOD method. Facilities with landfill gas collection and control must use
both the FOD method and a back-calculation approach. The back-calculation approach starts with the amount of CH4
recovered and works back through the system to account for gas not collected by the landfill gas collection and control
system (i.e., the collection efficiency).
Including the GHGRP net emissions data was a significant methodological change from the FOD method previously
described in Steps 1 to 3 and only covered a portion of the Inventory time series. Therefore, EPA needed to merge the
previous method with the new (GHGRP) dataset to create a continuous time series and avoid any gaps or jumps in.
estimated emissions in the year the GHGRP net emissions are first included (i.e., 2010).
To accomplish this, EPA backcasted GHGRP net emissions to 2005 to 2009 and added a scale-up factor to account for
emissions from landfills that do not report to the GHGRP. A description of how the scale-up factor was determined and
why the GHGRP emissions were backcasted are included below as Step 4a and Step 4b, respectively. The methodology
described in this section was determined based on the good practice guidance in Volume 1: Chapter 5 Time Series
Consistency of the 2006IPCC Guidelines. Additional details including other options considered are included in RTI (2017a)
and RTI (2018).
Step 4a: Developing and Applying the Scale-up Factor for MSW Landfills for 2005 to 2009
Landfills that do not meet the reporting threshold are not required to report to the GHGRP. As a result, the GHGRP
dataset is only partially complete when considering the universe of MSW landfills. In theory, national emissions from
MSW landfills equals the emissions from landfills that report to the GHGRP plus emissions from landfills that do not
report to the GHGRP. Therefore, for completeness, a scale-up factor had to be developed to estimate the amount of
emissions from the landfills that do not report to the GHGRP. A scale-up factor of 9 percent is applied annually to the net
GHGRP CH4 emissions between 2005 to 2016.
To develop the 9 percent scale-up factor, EPA completed four main steps:
1. EPA determined the number of landfills that do not report to the GHGRP (hereafter referred to as the non-
reporting landfills). Source databases included the LMOP database 2017 (EPA 2017) and the WBJ Directory
2016 (WBJ 2016). This step identified 1,544 landfills that accepted MSW between 1940 and 2016 and had
never reported to the GHGRP. These landfills and the data collected were compiled into the 2016 Non-
Reporting Landfills Database.
2. EPA estimated annual waste disposed and the total waste-in-place (WIP) at each non-reporting landfill as of
2016. Both databases include critical details about individual landfills to estimate annual methane emissions,
including the year waste was first accepted, the year the landfill closed (as applicable), and the estimated
amount of waste disposed. But not all details are included for all landfills. A total of 969 of the 1,544 landfills
(63 percent) contained the critical information necessary to estimate WIP.
a. For 234 non-reporting landfills, there was not enough information in the source databases to
estimate WIP.
b. For 341 of the non-reporting landfills, WIP could be estimated with assumptions that either (i)
"forced" the year that waste was first accepted as 30 years prior to the landfill closure year (if a
closure date was included); or (ii) "forced" a closure year of 2016 if the landfill was known to be
closed and a closure year was not included in the source database.
c. The database was reviewed by industry and staff from LMOP at this stage to help fill data gaps and
rectify discrepancies between individual landfills across the source databases, which improved the
WIP estimates by landfill and overall.
3. EPA summed the total WIP for the non-reporting landfills. Using the assumptions mentioned above, the total
WIP in 2016 across the non-reporting landfills was approximately 0.922 billion MT.
4. EPA calculated the scale-up factor (9 percent) by dividing the non-reporting landfills WIP (0.92 billion MT) by
the sum of the GHGRP WIP and the non-reporting landfills WIP (10.0 billion MT).
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Table A-222: Revised Waste-in-Place (WIP) for GHGRP Reporting and Non-reporting
Landfills in 2016
Category
Estimated WIP
(billion metric tons)
Percentage
Non-reporting
9 percent
facilities
0.92
(the applied scale-up factor)
GHGRP facilities
9.1
91 percent
Total
10.0
100 percent
Note: The scale-up factor is applied in each year the GHGRP reported emissions
are used in the Inventory.
Step 4b: Backcasting GHGRP Emissions for MSW Landfills for 2005 to 2009 to Ensure Time Series
Consistency
Regarding the time series and as stated in 2006IPCC Guidelines Volume 1: Chapter 5 Time Series Consistency (IPCC 2006),
"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 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" (IPCC 2006). Chapter 5 however, does not recommend backcasting
emissions to 1990 with a limited set of data and instead provides guidance on techniques to splice, or join
methodologies together. One of those techniques is referred to as the overlap technique. The overlap technique is
recommended when new data becomes available for multiple years, which was the case with the GHGRP data, where
directly reported net CH4 emissions data became available for more than 1,200 MSW landfills beginning in 2010. The
GHGRP emissions data had to be merged with emissions from the FOD method to avoid a drastic change in emissions in
2010, when the datasets were combined. EPA also had to consider that according to IPCC's good practice, efforts should
be made to reduce uncertainty in Inventory calculations and that, when compared to the GHGRP data, the FOD method
presents greater uncertainty.
In evaluating the best way to combine the two datasets, EPA considered either using (1) the FOD method from 1990 to
2009, or (2) using the FOD method for a portion of that time series and backcasting the GHGRP emissions data to a year
where emissions from the two methodologies aligned. Plotting the backcasted GHGRP emissions against the emissions
estimates from the FOD method showed an alignment of the data in 2004 and later years which facilitated the use of the
overlap technique while also reducing uncertainty. Therefore, EPA decided to backcast the GHGRP emissions from 2009
to 2005 only, to merge the datasets and adhere to the IPCC good practice guidance.
EPA used the Excel Forecast function to backcast net methane emissions using the GHGRP data. The forecast function is
used to predict a future value by using existing values, but EPA has applied it to predict previous values. Although it is not
ideal, it allowed for expeditious implementation. In the forecast function, the known values are existing x-values and y-
values (i.e., the years and data for the GHGRP, 2010 to 2015). The unknown y-values are the years to be estimated (i.e.,
all years prior to 2009). The following Excel formula was used: =FORECAST(year to backcast, GHGRP data for 2010 to
2015, years 2010 to 2015). The forecast function is a linear regression; thus, it will not account for annual fluctuations in
CH4 emissions when used for multiple years.
An important factor in this approach is that the backcasted emissions for 2005 to 2009 are subject to change with each
Inventory because the GHGRP dataset may change as facilities revise their annual reports. The revisions are generally
minor considering the entire GHGRP dataset and EPA has not determined any revisions to the backcasting approach or
scale-up factor are necessary to date.
Step 5: Estimate CH4 Emissions from MSW Landfills for 2010 to 2016
CH4 emissions directly reported to EPA's GHGRP are used for 2010 to 2016. Inherent in these direct emissions are the use
of various GHGRP default emission factors such as the gas collection and control system collection efficiencies (where
applicable), decay rate (k), and degradable organic carbon (DOC).
Facilities reporting to Subpart HH of the GHGRP can report their k and DOC values under one of three waste type
options: (1) Bulk waste option, where all waste is accounted for within one bulk k and DOC value; (2) Modified bulk
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waste option, where waste disposed of at the landfill can be binned into bulk MSW excluding inerts and construction and
demolition waste, construction and demolition waste, and inerts; and (3) Waste Composition option, where waste
disposed of can be delineated into specific waste streams (i.e., food waste, garden waste, textiles, etc.) OR where
facilities report a known quantity of inert waste and consider the remaining waste as bulk MSW (using the same k and
DOC value for MSW as the bulk waste option).
The GHGRP requires facilities with a gas collection and control system to report their emissions using both a forward-
estimating (i.e., using a first order decay approach, accounting for soil oxidation) and a back-calculating (i.e., using
methane recovery and collection efficiency data, accounting for soil oxidation) method as described in Chapter 7 of this
Inventory. To determine collection efficiency, facilities are required to report the amount of waste-in-place (surface area
and soil depth) at their landfill as categorized by one of five area types (see Table A-223).
Table A-223: Table HH-3 to Subpart HH of the EPA's Greenhouse Gas Reporting Program,
Area Types Applicable to the Calculation of Gas Collection Efficiency
Description Landfill Gas Collection Efficiency
Al: Area with no waste-in-place Not applicable, do not use this area in the calculation
A2: Area without active gas collection, regardless of cover
CE2: O/o
type
A3: Area with daily soil cover and active gas collection CE3: 60%
A4: Area with an intermediate soil cover, or a final soil cover
not meeting the criteria for A5 below, and active gas CE4: 75%
collection
A5: Area with a final soil cover of 3 feet or thicker of clay or
final cover (as approved by the relevant agency) and/or CE5: 95%
geomembrane cover system and active gas collection
Weighted average collection efficiency for landfills:
. . „ . , . ..... CEavel = (A2*CE2) + A3*CE3 + A4*CE4 + A5*CE5) / (A2 + A3
Area weighted average collection efficiency for landfills +A4 + A5)
If facilities are unable to bin their waste into these area types, they are instructed to use 0.75, or 75 percent as a default
value. In the EPA's original rulemaking for the GHGRP, the EPA proposed this default collection efficiency of 75 percent
because it was determined to be a reasonable central-tendency default considering the availability of data such as
surface monitoring under the EPA's New Source Performance Standards for MSW Landfills (40 CFR Part 60 Subpart
WWW), which suggested that gas collection efficiencies generally range from 60 to 95 percent. This 75 percent default
gas collection efficiency value only applies to areas at the landfill that are under gas collection and control; for areas of
the landfill that are not under gas collection and control, a gas collection efficiency of 0 percent is applied.
The 9 percent scale-up factor is applied to the net annual emissions reported to the GHGRP for 2010 to 2016 as is done
for 2005 to 2009 because the GHGRP does not capture emissions from all landfills in the United States.
Step 6: Estimate CH4 Emissions from MSW Landfills for 2017 to 2020
The same methodology described in Step 5 is used to estimate CH4 emissions from MSW Landfills for 2017 to 2020,
except the scale-up factor applied is different (11 percent instead of 9 percent). The scale-up factor was initially
developed to use the GHGRP reported data and account for the remaining subset of landfills that are not required to
report to the GHGRP. The EPA acknowledges there are uncertainties associated with the 9 percent scale-up factor and
underlying landfill-specific data used to develop the Non-Reporting Landfills database. Specifically, the GHGRP allows
facilities to off-ramp (i.e., stop reporting to the GHGRP) after meeting certain criteria; therefore, the number of facilities
and WIP reported under the GHGRP will vary year to year. Nearly 200 facilities have off-ramped from the GHGRP to date,
which means there is now more WIP for non-reporting landfills than there was in the 2016 scale-up factor analysis.
Reassessment of the scale-up factor at regular intervals to account for changes in the GHGRP dataset and LMOP
database is considered good practice and was therefore included in the Planned Improvements section for a previous
(1990 to 2018) Inventory.
The methodology used to revise the scale-up factor largely followed that to develop the 2016 Non-Reporting Landfills
Database, as summarized below, except that the scale-up factor is now a time-based threshold considering total waste
disposed in the 50 years prior to 2020 (i.e., between 1970 to 2020) instead of total waste-in-place for all non-reporting
landfills. This methodological change was made in response to reviewer comments on the 1990 to 2019 Inventory. Both
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a 30-year and a 50-year time-based threshold were evaluated for the scale-up factor under the knowledge that peak
production of landfill gas typically occurs within 5 to 7 years after wastes are first disposed, almost all gas is produced
within 20-30 years after waste is disposed, and small quantities of gas may continue to be emitted from a landfill for 50
or more years (ASTDR, 2001). EPA decided to use the 50-year threshold for the scale-up factor applied between 2017 to
2020 for three reasons: (1) because 50 years aligns with the IPCC recommendation of using 50 years of historical waste
disposal data in the FOD model to estimate CH4 generation; (2) expert knowledge that MSW landfills can generate CH4
for up to 50 years (ASTDR, 2001); and (3) because the Non-Reporting Landfills Database cannot estimate waste disposal
for several hundred landfills where not enough data are available. The 50-year threshold for the scale-up factor is a
conservative approach considering the number of assumptions and missing data in the Non-Reporting Landfills
Database.
Details on the revised 2020 scale-up factor are included in RTI (2021) and the general methodology is summarized in the
remainder of this Step.
1. EPA streamlined the layout of the 2016 Non-Reporting Landfills Database to remove extraneous columns,
clearly present the landfill-specific data from the main sources (i.e., the 2017 LMOP Database [EPA 2017] and
the WBJ Directory 2016 [WBJ 2016]), and the calculation columns that yield the start year, closure year, and
WIP data used to estimate the total WIP at all non-reporting landfills. The database is hereafter referred to as
the 2018 Non-Reporting Landfills Database.
2. EPA added in new or updated data for existing non-reporting landfills and added in entries for new non-
reporting landfills.
a. Added the 194 landfills that have off-ramped from the GHGRP as of 2020 (EPA 2021a) into the Non-
Reporting Landfills Database.
b. Cross-referenced and updated the 2017 LMOP Database (EPA 2017) information with the 2021 LMOP
Database (EPA 2021b) information. Approximately 217 new cases or updated information from the
2021 LMOP Database were added or revised.
c. These revisions increased the count of non-reporting landfills from 1,544 landfills to 1,672 landfills, a
net increase of 128 landfills from the 2016 Non-Reporting Landfills Database; however, only 1,069
landfills had enough information for the scale-up factor calculations
3. EPA conducted additional quality control checks on calculations in the 2016 Non-Reporting Landfills Database
and rectified identified errors, which resulted in an increase of 38,498,070 MT of waste from the 2016 Non-
Reporting Landfills Database.
a. A formula error was identified that under-estimated the WIP for landfills with a permitted end year
after 2016, especially for those landfills that had reported closure dates in 2030 or later. For example,
if the start year was 1980 and the permitted closure year was 2040, the formula was estimating 50
years when, for the purposes of this exercise, the number of years should have been 36 years. Dividing
the WIP by 60 years results in a lower annual waste disposal value than dividing the WIP by 36 years
(2016-1980). The methodology calculates an annual disposal rate for each landfill and then applies the
annual disposal rate to 2016 minus the start year.
b. The WIP data year was not pulled from the 2017 LMOP Database and it was assumed the WIP data
were from 2016 unless otherwise noted. The WIP year is now included in the 2018 Non-Reporting
Landfills Database. The WBJ Directory does not present the year the WIP data are from, thus we
assumed each data point was from 2016. These assumptions underestimate the amount of WIP for a
large majority of the landfills where the WIP data year is not reported.
4. EPA estimated annual waste disposed at each non-reporting landfill as of 2020. Where available, the databases
include details about individual landfills, including the year waste was first accepted, the year the landfill closed
(as applicable), and the estimated amount of waste disposed. When enough data were available, EPA
estimated total WIP by calculating an annual waste disposal rate and multiplied that by the number of
operating years up to the closure year, or 2018 (if the landfill was known or assumed to be open). EPA used a
tiered methodology when a landfill with critical information was included in more than one database:
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Tier 1: If the landfill has off-ramped from the GHGRP, use the Subpart HH WIP value (and update to
include assumed waste disposed between the year the landfill off-ramped to 2020, if operational
during that time frame).
Tier 2: If the landfill is in the 2021 LMOP Database, use the 2021 LMOP WIP value.
Tier 3: Otherwise, EPA used the average of the estimated WIP value that was forced or provided
from the 2016 Non-reporting Landfill Database industry and LMOP reviewers.
5. Annual waste disposal was then calculated by dividing the total WIP by the number of operational years for
each landfill between 1970 to 2020 (i.e., 50 years).
a. A total of 1,352 of the 1,672 landfills (approximately 81 percent) contained enough critical information
necessary to estimate the 2020 WIP (i.e., first year of operation, either total WIP or annual waste
disposal data, and either an indication the landfill was still operating or the closure date). It is important
to note that the WIP and annual waste disposal data are estimates. The quality of the source data for
WIP and annual waste disposed have not been individually verified by the EPA team. In the case of the
GHGRP data, the annual waste disposal quantities are either estimates using defined methodologies
or actual waste disposed from tipping receipts. In general, most landfills have relied on tipping receipts
for the past decade, meaning that annual waste disposed several decades ago are estimates.
b. For 593 of the 1,672 landfills (35 percent), WIP could be estimated with assumptions that either (i)
"forced" the year that waste was first accepted as 30 years prior to the landfill closure year (if a closure
year was included); or (ii) forced a closure year of 2018 if the landfill was known or thought to be open
and a closure year was not included in the source database. These are the same general assumptions
applied in the 2016 Non-Reporting Landfills Database.
6. For 321 of the 1,672 landfills (19 percent), there was not enough information in the source databases to
estimate WIP, thus no WIP data was calculated for these facilities, which underestimates the total WIP and
total waste disposed between 1970 to 2020 for the non-reporting landfills. EPA summed the total waste
disposed for the 50-year threshold (1970 to 2020) for the non-reporting landfills, yielding 1.33 billion MT.
7. EPA calculated the scale-up factor (11 percent) by dividing the waste disposed by non-reporting landfills (1.33
billion MT) by the sum of the reporting landfills' waste disposed and the total of both categories (12.3 billion
MT).
Table A-224: Total Waste Disposed over 50 Years (1970-2020) for GHGRP Reporting and
Non-reporting Landfills in 2020
Category
Estimated Waste
Disposed
(billion metric tons)
Percentage
Non-reporting
11 percent
facilities
1.33
(the applied scale-up factor)
GHGRP facilities
11.0
89 percent
Total
12.33
100 percent
An 11 percent scale-up factor is applied annually for 2017 to 2020 because the GHGRP does not capture emissions from
all landfills in the United States. In future inventories, the scale-up factor will be reassessed annually to include additional
facilities that off-ramp from the GHGRP, revisions to the LMOP Database, and adjust the start and end years for a 50-
year threshold.
Step 7: Estimate CH4 Generation at Industrial Waste Landfills for 1990 to the Current
Inventory Year
A Tier 2 approach (IPCC 2006) is used to estimate annual emissions from industrial waste landfills. A tailored IPCC waste
model, based on the FOD method and country-specific defaults, is exclusively used for 1990 to 2020. For the FOD
method, methane generation is based on nationwide industrial production data from two major sectors - pulp and
paper, and food and beverage manufacturing - to which a national average disposal factor is applied, separately for each
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sector. The same disposal factor and emission factors are applied to every year in the time series. The methodology is
not Tier 3 (i.e., it is not landfill-specific) because data for individual landfills are limited. Table A-219 presents the amount
of industrial production data and estimated amount of industrial waste landfilled for select years.
The FOD method is presented is presented in Equation A-67 and is similar to Equation HH-6 in CFR Part 98.343 for MSW
landfills, and Equation TT-6 in CFR Part 98.463 for industrial waste landfills.
Industrial waste landfills receive waste from factories, processing plants, and other manufacturing activities. In national
inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven
percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). In 2005, the
methodology was updated and improved by using activity factors (industrial production levels) to estimate the amount
of industrial waste landfilled each year, and by applying the FOD model to estimate CH4 generation. A nationwide survey
of industrial waste landfills found that most of the organic waste placed in industrial waste landfills originated from two
sectors: food processing (meat, vegetables, fruits) and pulp and paper (EPA 1993). Data for annual nationwide
production for the food and beverage processing and pulp and paper sectors were taken from industry and government
sources for recent years and estimates were developed for production for the earlier years for which data were not
available.
For the pulp and paper sector, production data published by the Lockwood-Post's Directory were used for years 1990 to
2001 and production data published by the Food and Agriculture Organization were used for years 2002 through 2020.
An extrapolation based on U.S. real gross domestic product was used for years 1940 through 1964.
For the food and beverage processing sector, production data were obtained from the U.S. Department of Agriculture for
the years 1990 through 2020 (ERG 2021). An extrapolation based on U.S. population was used for the years 1940
through 1989.
In addition to production data for the pulp and paper and food processing sectors, the following inputs are needed to
use the FOD model for estimating CH4 generation from industrial waste landfills: 1) quantity of waste that is disposed in
industrial waste landfills (as a function of production), 2) CH4 generation potential (L0) from which a DOC value can be
calculated, and 3) the decay rate constant (k).
Research into waste generation and disposal in landfills for the pulp and paper sector indicated that the quantity of
waste landfilled was about 0.050 MT/MT of product compared to 0.046 MT/MT product for the food processing sector
(RTI 2006). These emission factors were applied to estimates of annual production to estimate annual waste disposal in
industrial waste landfills (see Table A-219 for select years). Estimates for DOC were derived from available data (EPA,
2015b; Heath et al., 2010; NCASI, 2005; Kraft and Orender, 1993; NCASI 2008; Flores et al. 1999 as documented in RTI
2015a). The DOC value for industrial pulp and paper waste is estimated at 0.15 (L0 of 49 m3/MT); the DOC value for
industrial food waste is estimated as 0.26 (L0 of 128 m3/MT) (RTI 2015; RTI 2014). Estimates for k were taken from the
default values in the 2006IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is
0.19 yr1, and the value given for paper waste is 0.06 yr1.
A literature review was conducted for the 1990 to 2010 and 1990 to 2014 inventory years with the intent of updating
values for L0 (specifically DOC) and k in the pulp and paper sector (RTI 2014). Where pulp and paper mill wastewater
treatment residuals or sludge are the primary constituents of pulp and paper waste landfilled, values for k available in
131
the literature range from 0.01/yr to 0.1/yr, while values for L0 range from 50 m3/Mt to 200 m3/Mt. Values for these
factors are highly variable and are dependent on the soil moisture content, which is generally related to rainfall amounts.
At this time, sufficient data were available through EPA's GHGRP to warrant a change to the L0 (DOC) from 99 to 49
m3/MT, but sufficient data were not obtained to warrant a change to k. EPA will consider an update to the k values for
the pulp and paper sector as new data arises and will work with stakeholders to gather data and other feedback on
potential changes to these values.
As with MSW landfills, a similar trend in disposal practices from unmanaged landfills, or open dumps to managed
landfills was expected for industrial waste landfills; therefore, the same timeline that was developed for MSW landfills
was applied to the industrial landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of
131 Sources reviewed included Heath et al. 2010; Miner 2008; Skog 2008; Upton et al. 2008; Barlaz 2006; Sonne 2006; NCASI
2005; Barlaz 1998; and Skog and Nicholson 2000.
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waste that was land disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to 100
percent managed landfills in 1980 and on. For wastes disposed of in unmanaged sites, an MCF of 0.6 was used and for
wastes disposed of in managed landfills, an MCF of 1 was used, based on the recommended IPCC default values (IPCC
2006).
The parameters discussed above were used in the integrated form of the FOD model to estimate CH4 generation from
industrial waste landfills.
Step 8: Estimate CH4 Oxidation from MSW and Industrial Waste Landfills
Step 8a: Estimate CH4 Oxidation from Industrial Waste Landfills for 1990 to Present
A portion of the CH4 escaping from a landfill oxidizes to C02 in the top layer of the soil. The amount of oxidation depends
upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed that of the CH4
generated, minus the amount of gas recovered for flaring or LFGE projects, 10 percent was oxidized in the soil (Jensen
and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The literature was reviewed in 2011 (RTI 2011) and
2017 (RTI 2017b) to provide recommendations for the most appropriate oxidation rate assumptions. It was found that
oxidation values are highly variable and range from zero to over 100 percent (i.e., the landfill is considered to be an
atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric methane down through the cover).
There is considerable uncertainty and variability surrounding estimates of the rate of oxidation because oxidation is
difficult to measure and varies considerably with the presence of a gas collection system, thickness and type of the cover
material, size and area of the landfill, climate, and the presence of cracks and/or fissures in the cover material through
which methane can escape. IPCC (2006) notes that test results from field and laboratory studies may lead to over-
estimations of oxidation in landfill cover soils because they largely determine oxidation using uniform and homogeneous
soil layers. In addition, several studies note that gas escapes more readily through the side slopes of a landfill as
compared to moving through the cover thus complicating the correlation between oxidation and cover type or gas
recovery.
An oxidation factor of 0.10 (IPCC 2006) is applied for industrial waste landfills for the entire time series.
Step 8b: Estimate CH4 Oxidation from MSW Landfills for 1990 to 2004
An oxidation factor of 0.10 (IPCC 2006) is applied for MSW Landfills between 1990 to 2004. A variety of oxidation factors
(0.0, 0.10, 0.25, or 0.35) are applied for MSW landfills between 2005 to 2009 as described below. The oxidation factors
applied for MSW landfills are based on IPCC 2006 (0.10) and scientific literature reviewed for the development of the
GHGRP regulations (40 CFR Part 98). An annual weighted average of facility-reported oxidation factors from the GHGRP
dataset are applied between 2005 to 2020. Between 2005 to 2009, the annual weighted average oxidation factor ranges
from 11 percent to 15 percent. Between 2010 to 2016, the annual weighted average oxidation factor ranges from 17 to
21 percent; and from 2017 to 2020, the annual weighted average oxidation factor ranges from 21 to 22 percent (EPA
2021a).
The annual amount of CH4 oxidized is calculated for 1990 to 2004 by applying the 10 percent oxidation factor to the sum
of CH4 generation minus recovery as presented in Equation A-67. The annual amount of CH4 oxidized is calculated for
2005 to present by solving for oxidation in Equation A-67 when CH4 generation, R, and the net CH4 emission values are
known. In other words, when solving Equation A-70 below:
Equation A-70: Back-calculated Methane Oxidation
Ox = - (GcH4,MSW + R - CH4 .Solid Waste)
where,
Ox = CH4 oxidized from MSW landfills before release to the atmosphere
CH4,soiid waste = Net CH4 emissions from MSW landfills
Gch4,msw = CH4 generation from MSW landfills
R = CH4 recovered and combusted from MSW landfills.
The remainder of this step provides supporting documentation on the oxidation factors applied for MSW Landfills.
MSW landfills with landfill gas collection systems are generally designed and managed better to improve gas recovery.
More recent research (2006 to 2012) than IPCC (2006) on landfill cover methane oxidation has relied on stable isotope
techniques that may provide a more reliable measure of oxidation. Results from this recent research consistently point
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to higher cover soil methane oxidation rates than the IPCC (2006) default of 10 percent. A continued effort will be made
to review the peer-reviewed literature to better understand how climate, cover type, and gas recovery influence the rate
of oxidation at active and closed landfills. At this time, the IPCC recommended oxidation factor of 10 percent will
continue to be used for all landfills for the years 1990 to 2004 and for industrial waste landfills for the full time series.
For years 2005 to 2020, net CH4 emissions from MSW landfills as directly reported to EPA's GHGRP, which include the
adjustment for oxidation, are used. Subpart HH of the GHGRP includes default values for oxidation which are dependent
on the mass flow rate of CH4 per unit at the bottom of the surface soil prior to any oxidation, also known as methane flux
rate. The oxidation factors included in the GHGRP (0, 0.10, 0.25, 0.35) are based on published, peer-reviewed literature
and facility data provided through external stakeholder engagement. The EPA concluded, during review of both the
literature and facility-reported emissions data, that simply revising the IPCC's Tier 1 oxidation default of 10 percent to a
new singular default oxidation value would not take into account the key variable - methane flux rate - entering the
surface soil layer. More information regarding analysis of methane oxidation fractions can be found in the
memorandums entitled "Review of Oxidation Studies and Associated Cover Depth in the Peer Reviewed Literature", June
17, 2015 (RTI 2015b). More information about the landfill specific conditions required to use higher oxidation factors can
be found in Table HH-4 of 40 CFR Part 98, Subpart HH, as shown below.
Table A-225: Table HH-4 to Subpart HH of Part 98—Landfill Methane Oxidation Fractions
Use this landfill
methane oxidation
Under these conditions: fraction:
I. For all reporting years prior to the 2013 reporting year
CI: For all landfills regardless of cover type or methane flux 0.10
II. For the 2013 reporting year and all subsequent years
C2: For landfills that have a geomembrane (synthetic) cover or other non-soil barrier
meeting the definition of final cover with less than 12 inches of cover soil for greater
than 50% of the landfill area containing waste 0.10
C3: For landfills that do not meet the conditions in C2 above and for which you elect not to
determine methane flux 0.10
C4: For landfills that do not meet the conditions in C2 or C3 above and that do not have
final cover, or intermediate or interim cover3 for greater than 50% of the landfill area
containing waste 0.10
C5: For landfills that do not meet the conditions in C2 or C3 above and that have final
cover, or intermediate or interim cover3 for greater than 50% of the landfill area
containing waste and for which the methane flux rateb is less than 10 grams per square
meter per day (g/m2/d) 0.35
C6: For landfills that do not meet the conditions in C2 or C3 above and that have final cover
or intermediate or interim cover3 for greater than 50% of the landfill area containing
waste and for which the methane flux rateb is 10 to 70 g/m2/d 0.25
C7: For landfills that do not meet the conditions in C2 or C3 above and that have final cover
or intermediate or interim cover3 for greater than 50% of the landfill area containing
waste and for which the methane flux rateb is greater than 70 g/m2/d 0.10
3 Where a landfill is in a state that does not have an intermediate or interim cover requirement, the landfill must have
soil cover of 12 inches or greater in order to use an oxidation fraction of 0.25 or 0.35.
b Methane flux rate (in grams per square meter per day; g/m2/d) is the mass flow rate of methane per unit area at the
bottom of the surface soil prior to any oxidation and is calculated as follows:
Annex 3
A-457
-------
For Equation HH-5 of this subpart, or for Equation TT-6 of subpart TT of this part,
MF == K /SAnca
For Equation HH-6 of this subpart,
MF=K.xfo„u ->;R„ | /SArca
f *>
<10™, -zx
1
For Equations HH-7 of this subpart.
MF=Kk|
t V I..-
For Equation flH-t of this subpart.
A
Area
MF = K x
1
CE
1
/SArea
The EPA's GHGRP also requires landfills to report the type of cover material used at their landfill as: organic cover, clay
cover, sand cover, and/or other soil mixtures.
Step 9: Estimate Total Net CH4 Emissions for the Inventory
For 1990 to 2004, total net CH4 emissions were calculated by adding emissions from MSW and industrial landfills, and
subtracting CH4 recovered and oxidized, as shown in Table A-226. A different methodology is applied for 2005 to 2020
where directly reported net CH4 emissions to EPA's GHGRP plus a scale-up factor to account for landfills that do not
report to the GHGRP was applied. For 2005 to 2009, the directly reported GHGRP net emissions from 2010 to 2018 were
used to backcast emissions for 2005 to 2009. Note that the emissions values for 2005 to 2009 are recalculated for each
Inventory and are subject to change if facilities reporting to the GHGRP revise their annual greenhouse gas reports for
any year. A 9 percent scale-up factor was applied annually to the net CH4 reported to the GHGRP for 2005 to 2016, and
an 11 percent scale-up factor was applied to the net CH4 reported to the GHGRP for 2016 to 2020.
A-458 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-226: Cm Emissions from Landfills (kt)
1990
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
MSW CH4 Generation
8,214
10,845
11,037
11,245
11,447
11,642
11,809
11,430
11,742
11,563
11,458
11,213
11,321
11,672
11,878
12,186
12,193
Industrial CH4
Generation
484
638
641
645
650
655
658
659
660
663
664
665
666
667
668
669
672
MSW CH4 Recovered
(851)
(5,301)
(5,850)
(6,070)
(6,281)
(6,514)
(6,516)
(6,559)
(6,815)
(6,813)
(6,699)
(6,537)
(6,637)
(6,884)
(6,965)
(7,182)
(7,362)
MSW CH4 Oxidized
(736)
(856)
(588)
(664)
(743)
(794)
(921)
(848)
(857)
(827)
(852)
(828)
(965)
(1,020)
(1,046)
(1,061)
(1,063)
Industrial CH4Oxidized
(48)
(64)
(64)
(64)
(65)
(66)
(66)
(66)
(66)
(66)
(66)
(66)
(67)
(67)
(67)
(67)
(67)
MSW Net CH4 Emissions
6,627
4,687
4,599
4,511
4,422
4,334
4,372
4,023
4,070
3,924
3,907
3,848
3,719
3,768
3,867
3,943
3,768
Industrial Net CH4
Emissions
436
575
577
580
585
590
592
593
594
596
597
598
599
600
601
602
605
Net CH4 Emissions3
7,063
5,262
5,176
5,091
5,007
4,924
4,964
4,616
4,664
4,520
4,504
4,447
4,318
4,368
4,467
4,545
4,373
Note: Parentheses indicate negative values.
a MSW Net CH4 emissions for years 2010 to 2020 are directly reported CH4 emissions to the EPA's GHGRP for MSW landfills and are backcasted to estimate
emissions for 2005 to 2009. A scale-up factor of 9 percent of each year's emissions from 2005 to 2016, and a scale-up factor of 11 percent of each year's
emissions from 2017 to 2020 is applied to account for landfills that do not report annual methane emissions to the GHGRP. Emissions for years 1990 to 2004
are calculated by the FOD methodology.
Annex 3
A-459
-------
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A-462 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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ANNEX 4 IPCC Reference Approach for
Estimating C02 Emissions from Fossil Fuel
Combustion
It is possible to estimate carbon dioxide (C02) emissions from fossil fuel consumption using alternative methodologies
and different data sources than those described in Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil
Fuel Combustion. For example, the United Nations Framework Convention on Climate Change (UNFCCC) reporting
guidelines request that countries, in addition to their "bottom-up" sectoral methodology, complete a "top-down"
Reference Approach for estimating C02 emissions from fossil fuel combustion. Volume 2: Energy, Chapter 6: Reference
Approach of the 2006 Intergovernmental Panel on Climate Change (IPCC) Guidelines for National Greenhouse Gas
Inventories (IPCC 2006) states, "comparability between the sectoral and reference approaches continues to allow a
country to produce a second independent estimate of C02 emissions from fuel combustion with limited additional effort
and data requirements." This reference method estimates fossil fuel consumption by adjusting national aggregate fuel
production data for imports, exports, and stock changes rather than relying on end-user consumption surveys. The basic
principle is that once carbon (C)-based fuels are brought into a national economy, they are either saved in some way
(e.g., stored in products, kept in fuel stocks, or left unoxidized in ash) or combusted, and therefore the C in them is
oxidized and released into the atmosphere. Accounting for actual consumption of fuels at the sectoral or sub-national
level is not required. The following discussion provides the detailed calculations for estimating C02 emissions from fossil
fuel combustion from the United States using the IPCC-recommended Reference Approach.
Step 1: Collect and Assemble Data in Proper Format
To ensure the comparability of national inventories, the IPCC has recommended that countries report energy data using
the International Energy Agency (IEA) reporting convention. National energy statistics were collected in physical units
from several Energy Information Administration (EIA) documents in order to obtain the necessary data on production,
imports, exports, and stock changes.
It was necessary to modify these data to generate more accurate apparent consumption estimates of these fuels. The
first modification adjusts for consumption of fossil fuel feedstocks accounted for in the Industrial Processes and Product
Use chapter, which include the following: unspecified coal for coal coke used in iron and steel production; natural gas,
distillate fuel, and coal used in iron and steel production; natural gas used for ammonia production; petroleum coke used
in the production of aluminum, ferroalloys, titanium dioxide, ammonia, and silicon carbide; and other oil and residual
fuel oil used in the manufacture of C black. The second modification adjusts for the inclusion of biofuels in motor fuel
statistics. 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). The third modification adjusts for consumption of bunker
fuels, which refer to quantities of fuels used for international transportation estimated separately from U.S. totals. The
fourth modification consists of the addition of U.S. Territories data that are typically excluded from the national
aggregate energy statistics. The territories include Puerto Rico, U.S. Virgin Islands, Guam, American Samoa, Wake Island,
and U.S. Pacific Islands. These data, as well as the production, import, export, and stock change statistics, are presented
in Table A-227. Furthermore, waste fuels (e.g., MSW combustion) is not captured as part of the reference approach.
Therefore, waste fuels are not used in the comparison between the sectoral and reference approaches in order to
improve consistency between the reference and sectoral approaches in terms of estimation coverage.
The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of C02 emissions, fuel
statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics are
typically provided in physical units (as in Table A-227 for 2020), they were converted to units of energy before C02
emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion factors
provided by EIA. These factors and their data sources are displayed in Table A-228. The resulting fuel type-specific energy
data for 2020 are provided in Table A-229.
Annex 4
A-463
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Step 2: Estimate Apparent Fuel Consumption
The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the country. This
requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes. In this way, C
enters an economy through energy production and imports (and decreases in fuel stocks) and is transferred out of the
country through exports (and increases in fuel stocks). Thus, apparent consumption of primary fuels (including crude oil,
natural gas liquids, anthracite, bituminous, subbituminous and lignite coal, and natural gas) can be calculated as follows:
Apparent Consumption = Production + Imports - Exports - Stock Change
Flows of secondary fuels (e.g., gasoline, residual fuel, coke) should be added to primary apparent consumption. The
production of secondary fuels, however, should be ignored in the calculations of apparent consumption since the C
contained in these fuels is already accounted for in the supply of primary fuels from which they were derived (e.g., the
estimate for apparent consumption of crude oil already contains the C from which gasoline would be refined). Flows of
secondary fuels should therefore be calculated as follows:
Secondary Consumption = Imports - Exports - Stock Change
Note that this calculation can result in negative numbers for apparent consumption of secondary fuels. This result is
perfectly acceptable since it merely indicates a net export or stock increase in the country of that fuel when domestic
production is not considered.
Next, the apparent consumption and secondary consumption need to be adjusted for feedstock uses of fuels accounted
for in the Industrial Processes and Product Use chapter, international bunker fuels, and U.S. territory fuel consumption.
Bunker fuels and feedstocks accounted for in the Industrial Processes and Product Use chapter are subtracted from
these estimates, while fuel consumption in U.S. Territories is added.
The IPCC Reference Approach calls for estimating apparent fuel consumption before converting to a common energy
unit. However, certain primary fuels in the United States (e.g., natural gas and steam coal) have separate conversion
factors for production, imports, exports, and stock changes. In these cases, it is not appropriate to multiply apparent
consumption by a single conversion factor since each of its components has different heat contents. Therefore, United
States fuel statistics were converted to their heat equivalents before estimating apparent consumption. Results are
provided in Table A-228.
Step 3: Estimate Carbon Emissions
Once apparent consumption is estimated, the remaining calculations are similar to those for the "bottom-up" Sectoral
Approach (see Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion). Potential C02
emissions were estimated using fuel-specific C coefficients (see Table A-229).176 The C in products from non-energy uses
of fossil fuels (e.g., plastics or asphalt) that is stored was then estimated and subtracted (see Table A-230). This step
differs from the Sectoral Approach in that emissions from both fuel combustion and non-energy uses are accounted for
directly in the Reference Approach. As a result, the Reference Approach emission estimates are comparable to those of
the Sectoral Approach, with the exception that the NEU source category emissions are included in the Reference
Approach and reported separately in the Sectoral Approach.177 Finally, to obtain actual C02 emissions, net emissions
were adjusted for any C that remained unoxidized as a result of incomplete combustion (e.g., C contained in ash or soot).
The fraction oxidized was assumed to be 100 percent for petroleum, coal, and natural gas based on guidance in IPCC
(2006) (see Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil Fuel Combustion).
176 Carbon coefficients from EIA were used wherever possible. Because EIA did not provide coefficients for coal, the IPCC-
recommended emission factors were used in the top-down calculations for these fuels. See notes in Table A-230 for more
specific source information.
177 The emission scope of the reference and the sectoral approaches is the same since C emissions from NEU (i.e. C not
excluded) are included in both approaches, the energy consumption covered by the sectoral approach includes both fuel
consumption and NEU, which is reported under category 1.A.5 other, hence the scope of energy consumption under the
sectoral approach is comparable with that under the reference approach without excluding NEU. To the extent it is indicated
that NEU emissions are subtracted under the sectoral approach, it means that they are reported separately, not that they are
not covered by the sectoral approach.
A-464 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Step 4: Convert to C02 Emissions
Because the 2006IPCC Guidelines recommend that countries report greenhouse gas emissions on a full molecular weight
basis, the final step in estimating C02 emissions from fossil fuel consumption was converting from units of C to units of
C02. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of C02 to C (44/12) to obtain total C02
emitted from fossil fuel combustion in million metric tons (MMT). The results are contained in Table A-230.
Comparison Between Sectoral and Reference Approaches
These two alternative approaches can both produce reliable estimates that are comparable within a few percent. Note
that the reference approach includes emissions from non-energy uses. Therefore, these totals should be compared to
the aggregation of fuel use and emission totals from Annex 2.1 Methodology for Estimating Emissions of C02 from Fossil
Fuel Combustion and Annex 2.3 Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels. These
two sections together are henceforth referred to as the Sectoral Approach. Other than this distinction, the major
difference between methodologies employed by each approach lies in the energy data used to derive C emissions (i.e.,
the actual surveyed consumption for the Sectoral Approach versus apparent consumption derived for the Reference
Approach). In theory, both approaches should yield identical results. In practice, however, slight discrepancies occur. An
examination of past Common Reporting Format (CRF) table submissions during UNFCCC reviews has highlighted the
need to further investigate these discrepancies. The investigation found that the most recent (two to three) inventory
years tend to have larger differences in consumption and emissions estimates occurring earlier in the time series. This is
a result of annual energy consumption data revisions in the EIA energy statistics, and the revisions have the greatest
impact on the most recent few years of inventory estimates. As a result, the differences between the Sectoral and
Reference Approach decrease and are resolved over time. For the United States, these differences are discussed below.
Differences in Total Amount of Energy Consumed
Table A-233 summarizes the differences between the Reference and Sectoral Approaches in estimating total energy
consumption in the United States. Although theoretically the two methods should arrive at the same estimate for U.S.
energy consumption, the Reference Approach provides an energy consumption total that is 1.2 percent lower than the
Sectoral Approach for 2020. The greatest differences lie in lower estimates for petroleum and coal consumption for the
Reference Approach (2.5 percent and 2.4 percent, respectively) and higher estimates for natural gas consumption for the
Reference Approach (0.6 percent).
There are several potential sources for the discrepancies in consumption estimates:
• Product Definitions. The fuel categories in the Reference Approach are different from those used in the
Sectoral Approach, particularly for petroleum. For example, the Reference Approach estimates apparent
consumption for crude oil. Crude oil is not typically consumed directly but refined into other products. As a
result, the United States does not focus on estimating the energy content of the various grades of crude
oil, but rather estimating the energy content of the various products resulting from crude oil refining. The
United States does not believe that estimating apparent consumption for crude oil, and the resulting
energy content of the crude oil, is the most reliable method for the United States to estimate its energy
consumption. Additionally, the accounting of pentanes plus as a part of HGL is different between the
approaches. The United States reports consumption of all HGL components (i.e., ethane, propane,
isobutane, normal butane, ethylene, propylene, isobutylene, butylene, and pentanes plus) for both
approaches, but in the Sectoral Approach, pentanes plus is accounted for separately from other HGL
components whereas it is included in HGL in the Reference Approach. Other differences in product
definitions include using sector-specific coal statistics in the Sectoral Approach (i.e., residential,
commercial, industrial coking, industrial other, and transportation coal), while the Reference Approach
characterizes coal by rank (e.g., anthracite, bituminous).
• Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for
categories such as "crude oil" where the key statistics are derived from thousands of producers in the
United States and abroad. Furthermore, Hydrocarbon Gas Liquids (HGL) is a blend of multiple paraffinic
hydrocarbons: ethane, propane, isobutane, and normal butane, and their associated olefins: ethylene,
propylene, isobutylene, and butylene, each with their own heat content. HGL also includes pentanes plus.
The heat content for HGL varies annually depending upon the components of the blend.
• Possible Inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection
efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the
Annex 4
A-465
-------
United States believes that its emphasis on collection of detailed energy consumption data is a more
accurate methodology for the United States to obtain reliable energy data. Therefore, top-down statistics
used in the Reference Approach may not be as accurately collected as bottom-up statistics applied to the
Sectoral Approach.
• Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral
Approach uses reported consumption estimates. While these numbers should be equal, there always
seems to be a slight difference that is often accounted for in energy statistics as a "balancing item."
Differences in Estimated CO2 Emissions
Given these differences in energy consumption data, the next step for each methodology involved estimating emissions
of C02. Table A-234 summarizes the differences between the two methods in estimated C emissions.
As mentioned above, for 2020, the Reference Approach resulted in a 1.2 percent lower estimate of energy consumption
in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference Approach was 0.3
percent higher. Estimates of natural gas and petroleum emissions from the Reference Approach are higher (0.8 percent
and 1.2 percent respectively), and coal emission estimates are lower (2.6 percent) than the Sectoral Approach. Potential
reasons for these differences may include:
• Product Definitions. Coal data are aggregated differently in each methodology, as noted above. The format
used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach.
Also, the Reference Approach relies on a "crude oil" category for determining petroleum-related
emissions. Given the many sources of crude oil in the United States, it is not an easy matter to track
potential differences in C content between many different sources of crude; particularly since information
on the C content of crude oil is not regularly collected.
• Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by
IPCC (2006), while the Sectoral Approach uses annually updated category-specific coefficients by sector
that are likely to be more accurate. Also, as noted above, the C coefficient for crude oil is more uncertain
than that for specific secondary petroleum products, given the many sources and grades of crude oil
consumed in the United States.
Although the two approaches produce similar results, the United States believes that the "bottom-up" Sectoral Approach
provides a more accurate assessment of C02 emissions at the fuel level. This improvement in accuracy is largely a result
of the data collection techniques used in the United States, where there has been more emphasis on obtaining the
detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy flow data used
in the Reference Approach. The United States believes that it is valuable to understand both methods.
A-466 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-227: 2020 U.S. Energy Statistics (Physical Units)
Stock
U.S.
Fuel Category (Units)
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Thousand Short Tons)
Anthracite Coal
2,372
[1]
[1]
[1]
Bituminous Coal
237,916
[1]
[1]
[1]
Sub-bituminous Coal
245,781
[1]
[1]
[1]
367
Lignite
49,365
[1]
[1]
[1]
1,221
Coke
162
683
440
Unspecified Coal
5,137
69,067
(3,616)
16,432
1,500
Gaseous Fuels
Natural Gas (Million Cubic Feet)
Still Gas (Thousand Barrels)
33,389,498
2,551,342
0
5,283,607
0
179,766
0
402,211
48,258
Liquid Fuels (Thousand Barrels)
Crude Oil
4,129,563
2,150,267
1,173,342
55,818
HGL
1,893,894
58,380
761,581
16,472
449
Other Liquids
0
388,466
149,742
(20,369)
Motor Gasoline
(29,607)
38,758
264,282
(731)
207,745
14,671
Aviation Gasoline
253
0
(31)
Kerosene
351
2,203
(135)
81
Jet Fuel
54,787
35,296
(1,840)
99,222
6,096
Distillate Fuel
79,789
434,353
21,105
47
18,019
12,285
Residual Fuel
60,922
54,101
(347)
7,000
46,761
7,925
Naphtha for petrochemical feedstocks
5,535
0
(123)
Petroleum Coke
2,940
190,054
(1,356)
9,375
Other Oil for petrochemical feedstocks
1,255
0
(55)
1,240
Special Naphthas
4,643
0
(166)
Lubricants
13,207
34,406
(2,984)
172
Waxes
1,760
1,642
(171)
Asphalt/Road Oil
16,563
8,773
(311)
Misc. Products
14
477
(101)
449
[1] Included in Unspecified Coal
Note: Parentheses indicate negative values.
Sources: Solid and Gas Fuels: EIA (2021 and 2022a); Liquid Fuels: EIA (2022b).
Annex 4
A-467
-------
Table A-228: 2020 Conversion Factors to Energy Units (Heat Equivalents)
Stock
U.S.
Fuel Category (Units) Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Solid Fuels (Million Btu/ShortTon) Anthracite Coal
22.57
Bituminous Coal
23.89
Sub-bituminous Coal
17.14
25.77
Lignite
12.87
12.87
Coke
20.35
24.97
20.35
Unspecified
25.00
25.97
20.86
23.47
25.14
Gaseous Fuels Natural Gas (BTU/Cubic Foot)
1,037
1,025
1,009
1,037
1,037
1,037
Still Gas (Million Btu/Barrel)
6.00
6.00
6.00
6.00
6.00
Liquid Fuels (Million Btu/Barrel) Crude Oil
5.69
6.07
5.71
5.71
5.71
5.71
HGL
4.19
4.19
4.19
4.19
4.19
4.19
Other Liquids
5.83
5.83
5.83
5.83
5.83
5.83
Motor Gasoline
5.05
5.05
5.05
5.05
5.05
5.05
5.05
Aviation Gasoline
5.05
5.05
5.05
5.05
5.05
Kerosene
5.67
5.67
5.67
5.67
5.67
Jet Fuela
5.67
5.67
5.67
5.68
5.67
Distillate Fuel
5.83
5.83
5.83
5.83
5.83
5.83
Residual Oil
6.29
6.29
6.29
6.29
6.29
6.29
Naphtha for petrochemical feedstocks
5.25
5.25
5.25
5.25
5.25
Petroleum Coke
6.02
6.02
6.02
6.02
6.02
6.02
Other Oil for petrochemical feedstocks
5.83
5.83
5.83
5.83
5.83
5.83
Special Naphthas
5.25
5.25
5.25
5.25
5.25
Lubricants
6.07
6.07
6.07
6.07
6.07
Waxes
5.54
5.54
5.54
5.54
5.54
Asphalt/Road Oil
6.64
6.64
6.64
6.64
6.64
Misc. Products
5.80
5.80
5.80
5.80
5.80
a Jet fuel used in bunkers has a different heating value based on data specific to that source.
Sources: Coal and lignite production: EIA (1992); Coke, Natural Gas Crude Oil, HGL, and Motor Gasoline: EIA (2022a); Unspecified Solid Fuels: EIA (2011).
A-468 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-229: 2020 Apparent Consumption of Fossil Fuels (TBtu)
Stock
U.S.
Apparent
Fuel Category
Fuel Type
Production
Imports
Exports
Change
Adjustment
Bunkers
Territories
Consumption
Solid Fuels
Anthracite Coal
Bituminous Coal
Sub-bituminous Coal
Lignite
Coke
53.6
5,683.8
4,212.7
635.1
3.3
17.1
9.0
9.5
15.7
-
53.6
5,683.8
4,203.2
619.4
(22.7)
Unspecified
-
128.4
1,793.8
(75.4)
385.6
37.7
(1,937.8)
Gaseous Fuels
Natural Gas
Still Gas
34,624.9
2,615.1
5,331.2
186.4
417.1
50.0
31,355.4
Liquid Fuels
Crude Oil
23,501.3
13,043.5
6,698.6
318.7
-
-
29,527.6
HGL
7,932.2
244.5
3,189.7
69.0
-
1.9
4,919.9
Other Liquids
-
2,262.8
872.2
(118.6)
-
-
1,509.2
Motor Gasoline
(149.6)
195.8
1,335.2
(3.7)
-
74.1
(1,211.1)
Aviation Gasoline
-
1.3
-
(0.2)
-
-
1.4
Kerosene
-
2.0
12.5
(0.8)
-
0.5
(9.3)
Jet Fuel
-
310.6
200.1
(10.4)
563.7
34.6
(408.2)
Distillate Fuel
-
464.8
2,530.1
122.9
0.3
105.0
71.6
(2,221.9)
Residual Oil
-
383.0
340.1
(2.2)
44.0
294.0
49.8
(243.1)
Naphtha for petrochemical feedstocks
-
29.0
-
(0.6)
-
-
29.7
Petroleum Coke
-
17.7
1,144.9
(8.2)
56.5
-
-
(1,175.5)
Other Oil for petrochemical feedstocks
-
7.3
-
(0.3)
7.2
-
-
0.4
Special Naphthas
-
24.4
-
(0.9)
-
-
25.2
Lubricants
-
80.1
208.7
(18.1)
-
1.0
(109.4)
Waxes
-
9.7
9.1
(0.9)
-
-
1.6
Asphalt/Road Oil
-
109.9
58.2
(2.1)
-
-
53.8
Misc. Products
-
0.1
2.8
(0.6)
-
2.6
0.5
Total
76,494.1
19,933.5
23,744.2
463.0
935.8
962.6
323.8
70,645.7
Notes: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Annex 4
A-469
-------
Table A-230: 2020 Potential CO2 Emissions
Apparent Consumption
Carbon Coefficients
Potential Emissions
Fuel Category Fuel Type
(QBtu)
(MMT Carbon/QBtu)
(MMT CO? Eq.)
Solid Fuels Anthracite Coal
0.05
28.28
5.6
Bituminous Coal
5.68
25.43
530.0
Sub-bituminous Coal
4.20
26.49
408.2
Lignite
0.62
26.77
60.8
Coke
(0.02)
25.60
(2.1)
Unspecified
(1.94)
25.34
(180.0)
Gaseous Fuels Natural Gas
31.36
14.43
1,658.9
Still Gas
0.00
18.20
0.0
Liquid Fuels Crude Oil
29.53
20.31
2,198.4
HGL
4.92
18.53
334.4
Other Liquids
1.51
20.31
112.4
Motor Gasoline
(1.21)
19.27
(85.6)
Aviation Gasoline
+
18.86
0.1
Kerosene
(0.01)
19.96
(0.7)
Jet Fuel
(0.41)
19.70
(29.5)
Distillate Fuel
(2.22)
20.22
(164.7)
Residual Oil
(0.24)
20.48
(18.3)
Naphtha for petrochemical feedstocks
0.03
18.55
2.0
Petroleum Coke
(1.18)
27.85
(120.0)
Other Oil for petrochemical feedstocks
+
20.17
+
Special Naphthas
0.03
19.74
1.8
Lubricants
(0.11)
20.20
(8.1)
Waxes
+
19.80
0.1
Asphalt/Road Oil
0.05
20.55
4.1
Misc. Products
+
0.00
0.0
Total
4,707.7
+ Does not exceed 0.005 QBtu or 0.05 MMT C02 Eq.
Note: Totals may not sum due to independent rounding. Parentheses indicate negative values.
Sources: C content coefficients by coal rank from USGS (1998), PSU (2010), Gunderson (2019), IGS (2019), ISGS (2019), and EIA (2021); natural gas C
content coefficients from EPA (2010) and EIA (2022a); unspecified solid fuel and liquid fuel C content coefficients from EPA (2010) and ICF (2020).
A-470 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-231: 2020 Non-Energy Carbon Stored in Products
Consumption
Carbon
Carbon
for Non-
Coefficients
Content
Carbon
Energy Use
(MMT
(MMT
Fraction Stored (MMT
Fuel Type
(TBtu)
Carbon/QBtu)
Carbon)
Sequestered
C02 Eq.)
Coal
78.8
25.60
2.02
0.10
1.3
Natural Gas
663.0
14.43
9.57
0.63
22.0
Asphalt & Road Oil
832.3
20.55
17.11
1.00
62.4
HGL
2,656.5
16.82
44.68
0.63
102.6
Lubricants
227.7
20.20
4.60
0.09
1.5
Pentanes Plus
163.6
18.24
2.98
0.63
6.9
Petrochemical Feedstocks
[1]
[1]
[1]
[1]
28.9
Petroleum Coke
0.0
27.85
0.00
0.30
0.0
Special Naphtha
80.7
19.74
1.59
0.63
3.7
Waxes/Misc.
[1]
[1]
[1]
[1]
0.6
Misc. U.S. Territories Petroleum
[1]
[1]
[1]
[1]
0.0
Total 229.9
[1] Values for Misc. U.S. Territories Petroleum, Petrochemical Feedstocks, and Waxes/Misc. are not shown because
these categories are aggregates of numerous smaller components.
Note: Totals may not sum due to independent rounding.
Potential
Carbon
Net
Fraction
Total
Fuel Category
Emissions
Sequestered
Emissions
Oxidized
Emissions
Coal
822.4
1.3
821.1
100.0%
821.1
Petroleum
2,226.5
206.6
2,019.9
100.0%
2,019.9
Natural Gas
1,658.9
22.0
1,636.9
100.0%
1,636.9
Total
4,707.7
229.9
4,477.8
4,477.8
Note: Totals may not sum due to independent rounding.
Annex 4
A-471
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Table A-233: Fuel Consumption in the United States by Estimating Approach (TBtiQa
Approach
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Sectoral
69,631
74,684
82,541
83,918
78,676
77,091
76,263
75,726
79,001
78,136
71,468
Coal
18,098
19,210
21,755
22,213
20,305
15,071
13,816
13,404
12,803
10,877
8,814
Natural Gas
19,173
22,173
23,395
22,283
24,313
27,932
28,153
27,742
30,815
31,904
31,175
Petroleum
32,361
33,301
37,391
39,422
34,058
34,088
34,294
34,580
35,383
35,356
31,479
Reference (Apparent)
68,794
74,087
81,934
83,867
78,081
76,276
75,407
75,268
78,260
77,316
70,646
Coal
17,598
18,591
20,964
22,013
19,659
14,826
13,580
13,137
12,568
10,698
8,600
Natural Gas
19,280
22,277
23,487
22,350
24,409
28,011
28,236
27,862
30,945
32,072
31,355
Petroleum
31,916
33,218
37,482
39,504
34,013
33,439
33,591
34,269
34,747
34,546
30,691
Difference
-1.2%
-0.8%
-0.7%
-0.1%
-0.8%
-1.1%
-1.1%
-0.6%
-0.9%
-1.1%
-1.2%
Coal
-2.8%
-3.2%
-3.6%
-0.9%
-3.2%
-1.6%
-1.7%
-2.0%
-1.8%
-1.6%
-2.4%
Natural Gas
0.6%
0.5%
0.4%
0.3%
0.4%
0.3%
0.3%
0.4%
0.4%
0.5%
0.6%
Petroleum
-1.4%
-0.2%
0.2%
0.2%
! -0.1%
-1.9%
-2.1%
-0.9%
-1.8%
-2.3%
-2.5%
a Includes U.S. Territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
Table A-234: CO2 Emissions from Fossil Fuel Combustion by Estimating Approach (MMT CO2 Eq.)a
Approach
1990
1995
2000
2005
2010
2015
2016
2017
2018
2019
2020
Sectoral
4,843
5,142
5,737
5,881
5,442
5,114
5,009
4,966
5,118
4,979
4,463
Coal
1,720
i,8;
2,070
2,121
1,937
1,438
1,319
1,280
1,223
1,038
843
Natural Gas
1,007
1,164
1,228
1,172
1,279
1,462
1,470
1,446
1,606
1,662
1,624
Petroleum
2,116
2,154
2,439
2,587
2,226
2,214
2,220
2,240
2,290
2,278
1,997
Reference (Apparent)
4,806
5,144
5,714
5,924
5,437
5,119
5,014
4,996
5,141
5,005
4,478
Coal
1,655
1,756
1,987
2,089
1,870
1,413
1,291
1,244
1,197
1,021
821
Natural Gas
1,014
1,171
1,233
1,176
1,285
1,467
1,476
1,454
1,615
1,674
1,637
Petroleum
2,137
2,216
2,494
2,659
2,282
2,238
2,248
2,297
2,329
2,310
2,020
Difference
-0.8%
+%
-0.4%
0.7%
-0.1%
0.1%
0.1%
0.6%
0.5%
0.5%
0.3%
Coal
-3.8%
-3.7%
-4.0%
-1.5%
-3.4%
-1.8%
-2.1%
-2.8%
-2.1%
-1.6%
-2.6%
Natural Gas
0.7%
0.6%
0.5%
0.3%
0.5%
0.3%
0.4%
0.6%
0.6%
0.7%
0.8%
Petroleum
1.0%
2.9%
2.3%
2.8%
2.5%
1.1%
1.2%
2.5%
1.7%
1.4%
1.2%
+ Does not exceed 0.05 percent.
a Includes U.S. Territories. Does not include international bunker fuels.
Note: Totals may not sum due to independent rounding.
A-472 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
EIA (2022a) Monthly Energy Review, February 2022, Energy Information Administration, U.S. Department of Energy.
Washington, D.C. DOE/EIA-0035(2022/02).
EIA (2022b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, D.C.,
Volume I. DOE/EIA-0340.
EIA (2021) Annual Coal Report 2020, Energy Information Administration, U.S. Department of Energy. Washington, D.C.
DOE/EIA-0584(2020).
EIA (2011) Annual Energy Review, Energy Information Administration, U.S. Department of Energy, Washington, D.C.
DOE/EIA-0384(2011).
EIA (1992) Coal and lignite production. EIA State Energy Data Report 1992, Energy Information Administration, U.S.
Department of Energy, Washington, DC.
EPA (2010) Carbon Content Coefficients Developed for EPA's Mandatory Reporting Rule. Office of Air and Radiation,
Office of Atmospheric Programs, U.S. Environmental Protection Agency, Washington, D.C.
Gunderson, J. (2019) Montana Coal Sample Database. Data received 28 February 2019 from Jay Gunderson, Montana
Bureau of Mines & Geology.
ICF (2020) Potential Improvements to Energy Sector Hydrocarbon Gas Liquid Carbon Content Coefficients. Memorandum
from ICF to Vincent Camobreco, U.S. Environmental Protection Agency. December 7, 2020.
Illinois State Geological Survey (ISGS) (2019) Illinois Coal Quality Database, Illinois State Geological Survey.
Indiana Geological Survey (IGS) (2019) Indiana Coal Quality Database 2018, Indiana Geological Survey.
IPCC (2006) 2006IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas
Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES, Japan.
Pennsylvania State University (PSU) (2010) Coal Sample Bank and Database. Data received by SAIC 18 February 2010
from Gareth Mitchell, The Energy Institute, Pennsylvania State University.
USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey.
Annex 5
A-473
-------
ANNEX 5 Assessment of the Sources and
Sinks of Greenhouse Gas Emissions Not
Included
Although this report is intended to be a comprehensive assessment of anthropogenic124sources and sinks of greenhouse
gas emissions for the United States, certain sources and/or sinks have been identified which are not included in the
estimates presented for various reasons. Before discussing these sources and sinks, it is important to note that processes
or activities that are not anthropogenic in origin or do not result in a net source or sink of greenhouse gas emissions are
intentionally excluded from a national inventory of anthropogenic greenhouse gas emissions, in line with guidance from
the IPCC in their guidelines for national inventories.
The anthropogenic source and sink category of greenhouse gas emissions described in this annex are not included in the
U.S. national inventory estimates. The reasons for not including that source in the national greenhouse gas Inventory
include one or more of the following:
• Emissions and/or removals do not occur within the United States.
• A methodology for estimating emissions and/or removals from a source and/or sink does not currently
exist.
• Though an estimating method has been developed, adequate data are not currently available to estimate
emissions and/or removals.
• Emissions are determined to be insignificant in terms of overall national emissions, as defined per UNFCCC
reporting guidelines, based on available data or a preliminary assessment of significance. Further, data
collection to estimate emissions and/or removals would require disproportionate amount of effort (e.g.,
dependent on additional resources and impact improvements to key categories, etc.).
In general, data availability remains the primary constraint for estimating and including the emissions and removals from
source and sink categories that do occur within the United States and are not estimated, as discussed further below.
Methods to estimate emissions and removals from these categories are available in the 2006 IPCC Guidelines and or its
supplements and refinements. Many of these categories are insignificant in terms of overall national emissions based on
available proxy information, qualitative information on activity levels per national circumstances, and/or expert
judgment, and not including them introduces a very minor bias.
Reporting of inventories to the UNFCCC under Decision 24/CP.19 states that "Where methodological or data gaps in
inventories exist, information on these gaps should be presented in a transparent manner." Furthermore, these
reporting guidelines allow a country to indicate if a disproportionate amount of effort would be required to collect data
for a gas from a specific category that would be insignificant in terms of the overall level and trend in national
emissions.125 Specifically, where the notation key "NE," meaning not estimated, is used in the Common Reporting Format
(CRF)126 tables that accompany this Inventory report submission to the UNFCCC, countries are required to further
describe why such emissions or removals have not been estimated (UNFCCC 2013).
Based on the latest UNFCCC reporting guidance, the United States is providing more information on the significance of
these excluded categories below and aims to update information on the significance to the extent feasible during each
annual compilation cycle. Data availability may impact the feasibility of undertaking a quantitative significance
124The term "anthropogenic," in this context, refers to greenhouse gas emissions and removals that are a direct result of
human activities or are the result of natural processes that have been affected by human activities (2006 IPCC Guidelines for
National Greenhouse Gas Inventories).
125 Paragraph 37(b) of Decision 24/CP.19 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties
included in Annex I to the Convention." See MtQiZMfcccJntZresMJ^^
126See http://unfccc.int/national reports/annex i ghg inventories/reporting requirements/items/2759.php.
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assessment. The United States is continually working to improve the understanding of such sources or sinks and seeking
to find the data required to estimate related emissions, prioritizing efforts and resources for significant categories. As
such improvements are implemented, new emission and removal categories will be quantified and included in the
Inventory to enhance completeness of the Inventory. The full list of sources and sink categories not estimated, along
with explanations for their exclusion, is provided in Table 9 of the CRF submission.
Source and Sink Categories Not Estimated
This section provides additional information on the reasons each category was not estimated, arranged by sector and
source or sink category. A summary of these exclusions, including the estimated level of emissions where feasible, is
included in Table A-235. Per 37(b) of the UNFCCC Reporting Guidelines Decision 24/CP.19, considering overall level and
trend of U.S. emissions, the threshold for significance for estimating emissions from a specific category is 500 kt C02 Eq.
Collectively, per paragraph 37(b) of the UNFCCC Reporting Guidelines noted above, these exclusions should not exceed
0.1 percent of gross emissions, or 5.98 MMT C02 Eq. (5,981 kt C02 Eq.). While it is not possible to proxy all categories due
to the availability of data and the disproportionate efforts to collect data necessary to estimate emissions and/or
removals, categories for which proxies have been estimated total 3.6 MMT C02 Eq. (3,609 kt C02 Eq).
Annex 5
A-475
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Table A-235: Summary of Sources and Sinks Not Included in the Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-
2020
CRF Category
Number
Source/Sink Category
Gas(es)
Reason for Exclusion
Estimated 2020
Emissions
(kt C02 Eq.)
Energy
l.A Fossil Fuel Combustion
1.A.3 Transport
l.A.3.a
Domestic Aviation-Biomass
N20
Prior to 2011, no biobased jet fuel was assumed to be used for domestic aviation.
After 2011 several airlines performed commercial passenger flights with biofuel
blends and have offtake agreements with biofuel suppliers. Furthermore, biofuel
jet fuel can qualify under the U.S. Renewable Fuel Standard (RFS) program. The
RFS is a national policy that requires a certain volume of renewable fuel to replace
or reduce the quantity of petroleum-based transportation fuel, heating oil or jet
fuel. An analysis was conducted based on the total volume of biofuel jet fuel
produced in 2020 under the RFS program. Emissions of N20 were estimated based
on the factors for jet fuel combustion. As for jet fuel use in commercial aircraft,
contributions of methane (CH4) emissions are reported as zero.
0.4
l.A.3.b.iv
Motorcycles-Biomass
CH4 and N20
Emissions from ethanol mixed with gasoline in low blends are included in the on-
road gasoline emissions for motorcycles. If there is any use of high blend ethanol
fuel in motorcycles, it is considered insignificant. The percent of VMT from high
ethanol blends in light duty gas vehicles (flex fuel vehicles) is less than 1 percent. If
the same percentage is applied to motorcycle VMT with assumed flex fuel CH4 and
N20 emission factors, it results in estimated emissions of 0.0015 kt C02 Eq.
0.0015
1.A.3.C
Railways-Biomass
CH4 and N20
There are no readily available data sources to estimate the use of biofuel in
railways. Railways represent about 6 percent of all diesel fuel use. An assumption
can be made that railways consume that same percentage of biofuels (6 percent of
all biodiesel). Based on that assumption for biofuel use and applying fossil fuel CH4
and N20 factors results in estimated emissions of 12.9 kt C02 Eq. per year.
12.9
l.A.3.d
Domestic Navigation-Biomass
CH4 and N20
There are no readily available data sources to estimate the use of biofuel in
domestic navigation. Domestic navigation represents about 3 percent of all diesel
fuel use and about 1 percent of all gasoline fuel use. An assumption can be made
that domestic navigation consumes that same percentage of biofuels (3 percent of
all biodiesel and 1 percent of all ethanol use). Based on that assumption for biofuel
use and applying fossil fuel CH4 and N20 factors results in estimated emissions of
39.0 kt C02 Eq. per year.
39.0
l.A.3.d
Domestic Navigation-
Gaseous Fuels
C02
Emissions from gaseous fuel use in domestic navigation are not currently
estimated. Gaseous fuels are used in liquid natural gas (LNG) tankers and are being
NE
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demonstrated in a small number of other ships. Data are not available to
characterize these uses currently.
l.A.3.e Other Transportation
l.A.3.e.i Pipeline Transport—Liquid
Fuels
C02, CH4 and
N20
Use of liquid fuels to power pipeline pumps is uncommon, but has occurred. Data
for fuel used in various activities including pipelines are based on survey data
conducted by the U.S. Energy Information Association (EIA). From January 1983
through December 2009, EIA Survey data included information on liquid fuel used
to power pipelines reported in terms of crude oil product supplied. Reporting of
crude oil used for this purpose was discontinued after December 2009. Beginning
with data for January 2010, product supplied for pipeline fuel is assumed to equal
zero. 1997 was the last year of data reported on pipeline fuel. Taking the data
reported for 1997 of 797,000 barrels of crude oil and using conversion factors of
5.8 MMBtu/bbl and 20.21 MMT C/Qbtu results in estimated emissions of 342.6 kt
C02.
342.6
l.A.3.e.i Pipeline Transport—Gaseous
Fuels
C02, CH4 and
N20
C02 emissions from gaseous fuels used as pipeline transport fuel are estimated in
the Inventory, however CH4 and N20 emissions from gaseous pipeline fuel use
have not been estimated. The C02 / non-C02 emissions split for other natural gas
combustion can be used to estimate emissions. Based on that analysis, non-C02
emissions represent approximately 0.43 percent of C02 emissions from all natural
gas combustion. If that percentage is applied to C02 emissions from natural gas
use as pipeline fuel, it results in an emissions estimate of 179.6 kt C02 Eq. in 2017.
179.6
l.A.3.e.ii Non-Transportation Mobile-
Biomass
CH4 and N20
There are no readily available data sources to estimate the use of biofuel in non-
transportation mobile sources. These sources represent about 21 percent of all
diesel fuel use and about 4 percent of all gasoline fuel use. An assumption can be
made that these sources consume that same percentage of biofuels (21 percent of
all biodiesel and 4 percent of all ethanol use). Based on that assumption for biofuel
use and applying fossil fuel CH4 and N20 factors results in estimated emissions of
256.4 kt C02 Eq. per year.
256.4
l.A.5.a Other Stationary
l.A.5.a Incineration of Waste: Medical
Waste Incineration
C02
The category l.A.5.a Other Stationary sources not specified elsewhere includes
emissions from waste incineration of the municipal waste stream and waste tires.
The category also includes emissions from non-energy uses of fuels which includes
an energy recovery component that includes emissions from waste gas, waste oils,
tars, and related materials from the industrial sector. While this is not a
comprehensive inclusion of hazardous industrial waste, it does capture a subset.
A portion of hazardous industrial waste not captured is from medical waste.
However, a conservative analysis was conducted based on a study of
hospital/medical/infectious waste incinerator (HMIWI) facilities in the United
333
Annex 5
A-477
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States127 showing that medical waste incineration emissions could be considered
insignificant. The analysis was based on assuming the total amount of annual
waste throughput was of fossil origin and an assumption of 68.9 percent carbon
composition of the waste. It was determined that annual greenhouse gas
emissions for medical waste incineration are approximately 333 kt C02 Eq. per
year.
l.A.5.a Stationary Fuel Combustion:
Biomass in U.S. Territories
CH4 and N20 Data are not available to estimate emissions from biomass in U.S. Territories.
However, biomass consumption is likely small in comparison with other fuel types.
An estimate of non-C02 emissions from biomass fuels used in Territories can be
made based on assuming the same ratio of domestic biomass non-C02 emissions
to fossil fuel C02 emissions. Non-Territories data indicate that biomass non-C02
emissions represents 0.2 percent of fossil fuel combustion C02 emissions. Applying
this same percentage to proxy U.S. Territories fossil fuel combustion C02 emissions
results in estimated emissions of 74.8 kt C02 Eq. from biomass in U.S. Territories.
74.8
l.B Fugitive Emissions from Fuels
l.B.l-Solid Fuels
l.B.l.a.l.ii, Fugitive Emissions from Coal
l.B.l.a.2.ii Mining Related to Post-Mining
Activities
C02
A preliminary analysis by EPA determined that fugitive C02 emissions for post-
mining activities related to underground coal mining and surface coal mining are
negligible.
EPA calculated the ratio of underground post-mining CH4 emissions to net
underground CH4 emissions (0.12). This ratio was then applied to the net
underground C02 emissions to estimate underground post-mining C02 emissions.
The underground post-mining C02 emissions were estimated to be 236 kt for 2020.
Similarly, surface post-mining C02 emissions were estimated by multiplying the
ratio of surface post-mining CH4 and surface CH4 emissions (0.22) with surface C02
estimates. The surface post-mining C02 emissions were estimated to be 54 kt.
Total C02 emissions from post-mining activities (underground and surface) were
estimated to be 290 kt for 2020.
Note, fugitive C02 emissions from active underground and surface coal mining are
reported based on methods in the IPCC 2019 Refinement. Neither the 2006 IPCC
Guidelines nor the IPCC 2019 Refinement provide any method for estimating
fugitive C02 emissions from post-mining activities (see section 3.4 of Chapter 3 of
the Inventory).
290
l.B.l.a.l.ii! Fugitive Emissions from
Abandoned Underground Coal
Mines
C02
A preliminary analysis by EPA determined that C02 emissions for abandoned
underground coal mining activities are negligible. EPA notes that neither the 2006
IPCC Guidelines nor the IPCC 2019 Refinement provide any method for estimating
93
127 RTI (2009). Updated Hospital/Medical/lnfectious Waste Incinerator (HMIWI) Inventory Database.
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fugitive C02 emissions from Abandoned Underground Coal Mines. The analysis was
based on gas composition data from two abandoned underground mines in two
different states.128 An average ratio of C02 to CH4 composition in mine gas was
derived for abandoned mines. This ratio was applied as a percentage (1.5 percent)
to CH4 emission estimates to derive an estimate of C02 emissions for abandoned
mines. Applying a C02 emission rate as a percentage of CH4 emissions for
abandoned coal mines results in a national emission estimate below 93 kt C02 Eq.
per year. Future inventories may quantify these emissions, if it is deemed it will
not require a disproportionate amount of effort.
Industrial Processes and Product Use
2.A Mineral Industry
2.A.4.a Other Process Uses of C02 Data are not currently available to estimate emissions from this source. During the 1,160
Carbonates: Ceramics expert review process for compilation of the current Inventory, EPA sought expert
solicitation on data for carbonate consumption in the ceramics industry but has
yet to identify data sources to apply IPCC methods to proxy emissions and assess
significance.
The 2006 IPCC Guidelines specify that activity data should consist of national
production data for bricks and roof tiles, vitrified clay pipes, and refractory
products or the total quantity of carbonates used in ceramics production, which is
not currently available. To assess the significance of emissions from ceramics, EPA
used data on clay sold or used in the U.S. in lieu of activity data listed above and
approximated carbonate use for ceramics production (USGS 2020 Minerals
Commodity Summaries for Clay) in 2019 to be 2.86 million metric tons, based on
2006 IPCC Guidelines defaults of carbonate content for clay (10 percent) and loss
factor (1.1). Using a Tier 1 method and default mix of 85 percent limestone and 15
percent dolomite, national emissions from ceramics production were then
calculated to be 1.16 million metric tons of C02 (or 1,160 kt C02 Eq.) for 2019,
which exceeds the category-1 eve I threshold for significance of 500 kt C02 Eq. This
estimate does not include emissions from the calcination of other raw materials
for ceramics production, including shale, limestone, dolomite, and witherite, and it
may include some limestone and dolomite emissions already reported under Other
Process Uses of Carbonates. Further research is needed to identify the portion of
clay used for ceramics production, as clay has other uses in addition to ceramics
(e.g., drilling mud, pet waste absorbents, paper coating and filling, paint, catalysts).
EPA plans to include emissions from use of carbonates for ceramic production as a
medium-term improvement.
128 Ibid.
Annex 5
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2.A.4.C Other Process Uses of C02 Data are not currently available to estimate emissions from this source. During the NE
Carbonates: Non-metallurgical Expert Review process for compilation of the current Inventory, EPA sought expert
Magnesium Production solicitation on data for non-metallurgical magnesium production but has yet to
identify data sources to apply IPCC methods to proxy emissions and assess
significance.
2.B. Chemical Industry
2.B.4.b Glyoxal Production C02 and N20 Glyoxal production data are not readily available to apply Tier 1 methods and 71
estimate emissions from this source. EPA continues to conduct basic outreach to
relevant trade associations and review EPA and other potential databases that may
contain the necessary data. Glyoxal production is believed to have taken place
earlier in the time series, and it is unknown whether production is still occurring in
the United States. To assess the significance of emissions from glyoxal production,
EPA used limited data on the range of domestic production and imports (U.S. EPA
ChemView for data submitted under TSCA in 2023 and 2016) and assumptions that
half of the amount was domestically produced, liquid-phase oxidation of
acetaldehyde with nitric acid process accounts for 20 percent of total glyoxal
production (Teles et al 2015), and N20 control equipment have an efficiency of 80
percent, to estimate process emissions of 71,000 mt C02 Eq. or 71 kt C02 Eq. per
year in recent years, which does not exceed the category-level threshold for
significance of 500 kt C02 Eq. Any further progress on outreach will be included in
next (i.e., 1990 through 2021) Inventory report.
2.B.4.C Glyoxylic Acid Production C02 and N20 Data on national glyoxylic acid production data are currently not available to NE
estimate emissions from this source using IPCC methods and then assess
significance. EPA is conducting basic outreach to relevant trade associations
reviewing EPA and other potential databases that may contain the necessary data.
Outreach this year did not identify potential data sources. Research suggests that
glyoxylic acid may not be produced in the U.S. at levels that would exceed the
category-level threshold for significance of 500 kt C02 Eq. Any further progress on
outreach will be included in next (i.e., 1990 through 2021) Inventory report.
2.B.5.b Calcium Carbide CH4 Data are not currently available to estimate CH4 emissions from this source. It is 1.1
difficult to obtain production data because there is currently only one U.S.
producer of calcium carbide. This information is not collected by USGS, the agency
that collects information on silicon carbide. One other facility is believed to have
been in operation during portions of the time series and ceased operations in
2014. During the Expert Review process for compilation of the current Inventory,
EPA sought expert solicitation on production data for this source but has yet to
identify data sources. Using data reported to GHGRP and an estimated amount of
calcium carbide produced, CH4 emissions from calcium carbide production for
2020 are estimated at 1,075 mt C02 Eq. (43 mt CH4) or 1.075 kt C02 Eq. which
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does not exceed the category-level threshold for significance of 500 kt C02 Eq.
2.B.8.d Petrochemical and Carbon Black C02 recovery EPA's GHGRP has data starting in reporting year 2010 on the amount of C02 NE
Production captured, including at petrochemical facilities and ethylene oxide processes. Due
to schedule and resource constraints, data on C02 sequestration have not been
compiled and need to be reviewed to better understand available data to estimate
the fate of these captured emissions. Any C02 potentially captured from
petrochemical facilities is currently assumed to be released.
2.C. Metal Industry
2.C.1.C
Iron and Steel Production:
Direct Reduced Iron (DRI)
Production
ch4
Data on fuel consumption used in the production of DRI are not readily available to
apply the IPCC default Tier 1 CH4emission factor; however, an assumed emission
factor can be developed based on the default energy consumption of 12.5 GJ
natural gas per metric ton of DRI produced. This assumption and annual DRI
production in metric tons results in CH4 emissions of 0.74 kt C02. Eq.
0.74
2.E Electronics Industry
2.E.2
Fluorinated Gas Emissions
from Electronics Industry: TFT
Flat Panel Displays
HFCs, PFCs,
SF6, and NF3
In addition to requiring reporting of emissions from semiconductor manufacturing,
micro-electro-mechanical systems (MEMs), and photovoltaic cells, EPA's GHGRP
requires the reporting of emissions from the manufacture of flat panel displays.
However, no flat panel displays manufacturing facilities have ever reported to
EPA's GHGRP, indicating that there are no facilities in the United States that have
exceeded the GHGRP's applicability threshold for display manufacturers since
2010. The available information on this sector indicates that these emissions are
well below the significance threshold.129 Per this published literature, the United
States has never been a significant display manufacturer aside from a small
amount of manufacturing in the 1990s, but not mass production.
NE
2.G Other
2.G.2
Other Product Manufacture
and Use: SF6 and PFCs from
Other Product Use
sf6
Emissions of SF6 occur from particle accelerators and military applications, and
emissions of PFCs and other F-GHGs occur from military applications such as use of
fluorinated heat transfer fluids (HTFs). Emissions from some particle accelerators
and from military applications are reported by the U.S. government to the Federal
Energy Management Program along with emissions of other fluorinated
700
greenhouse gases (e.g., HFCs from mobile and stationary air conditioning) under
the categories "Fugitive Fluorinated Gases and Other Fugitive Emissions" and
"Industrial Process Emissions." Analysis of the underlying data for 2018 indicated
"fugitive" emissions of SF6 of approximately 600 kt C02 Eq. from the U.S.
government as a whole, and "process" emissions of SF6 of approximately 100 kt
C02 Eq. (Emissions of SF6 that are known to be accounted for elsewhere, such as
129 The Display Industry: Fast to Grow, Slow to Change Article in Information Display 28(5):18-21 ¦ May 2012 with 4. DOI: 10.1002/j.2637-496X.2012.tb00504.x The Display
Industry: Fast to Grow, Slow to Change. Available online at: http://archive.informationdisplay.org/id-archive/2012/may-iune/display-marketplace-the-display-industry-fast-to.
Annex 5
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under Electrical Transmission and Distribution, have been excluded from these
totals.) The sources of the "fugitive" emissions of SF6 were not identified, but the
source of the vast majority of "process" emissions of SF6 was particle accelerators.
Note, fugitive emissions of approximately 200 kt C02 Eq. of compounds that are
commonly used as fluorinated HTFs (HFEs and fully fluorinated compounds, likely
perfluoroamines, perfluoromorpholines, and/or PFPMIEs) were also reported. Per
paragraph 33 of the UN reporting guidelines, such "additional GHGs" should be
reported separately from national totals so are not considered in estimate of 2019
emissions. EPA still plans to contact reporting agencies to better understand the
sources of the emissions and the estimation methods used by reporters, which
may equate emissions to consumption and therefore over- or underestimate some
emissions, depending on the circumstances. This step will help EPA improve its
assessment of significance and prioritize incorporating estimates in future
Inventory submissions, but has been postponed due to focus on new EPA
programs to improve data collection on HFCs (e.g., implementation of regulations
phasing down production and consumption of HFCs).
Agriculture
3.A Livestock
3.A.4 Enteric Fermentation: Camels CH4 Enteric fermentation emissions from camels are not estimated because there is no 2.8
significant population of camels in the United States. Due to limited data
availability (no population data are available from the USDA Agricultural Census),
the estimates are based on use of IPCC defaults and population data from Baum,
Doug (2010).130 Based on this source, a Tier 1 estimate of enteric fermentation CH4
emissions from camels results in a value of approximately 2.8 kt C02 Eq. per year
from 1990 to 2020. See Chapter 5.1 for more information.
3.A.4 Enteric Fermentation: Poultry CH4 No IPCC method has been developed for determining enteric fermentation CH4 No method
emissions from poultry. See Chapter 5.1. provided in 2006
IPCC Guidelines
Manure management emissions from camels are not estimated because there is 0.1
no significant population of camels in the United States.131 Due to limited data
availability and disproportionate effort to collect time-series data (i.e., no
population data is available from the Agricultural Census), this estimate is based on
population data from Baum, Doug (2010).132 Based on this source, a Tier 1
3.B.1.4, Manure Management: Camels CH4andN20
3.B.2
130 The status of the camel in the United States of America. Available online at: https://www.soas.ac.uk/camelconference2011/file84331.pdf.
131 Paragraph 37(b) of Decision 24/CP.19 "Revision of the UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention." See
http://unfccc.int/resource/docs/2013/copl9/eng/10a03.pdf.
132 The status of the camel in the United States of America. Available online at: https://www.soas.ac.uk/camelconference2011/file84331.pdf.
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estimate of manure management CH4 and N20 emissions from camels results in a
value of approximately 0.14 kt C02 Eq. per year from 1990 to 2020. See Chapter
5.2 for more information.
3.F Field Burning of Agricultural Residues
3.F.1.4, Sugarcane
3.F.4
CH4 and N20
Currently available data did not allow for identification of burning of sugarcane.
Based on prior analysis, EPA estimates that sugarcane emissions may range from
less than 10.4 to 61.2 kt C02 Eq. (0.42 kt CH4 to 2.45 kt CH4), and less than 11.4 kt
C02 Eq. (0.04 kt N20), across the 1990 to 2016 time series. The estimate for 2016
(37.8 kt C02 Eq.) is the most recent estimate available and can be used as a proxy
for 2020. See the Planned Improvements section in Chapter 5.7 Field Burning of
Agricultural Residues for more information.
37.8
Land Use, Land-Use Change, and Forestry
4.A Forest Land
4.A(II) Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02and CH4
Not required based on the 2006IPCC Guidelines. Emissions from this source may
be estimated in future Inventories using guidance from the 2013 Wetlands
Supplement when data necessary for classifying the area of rewetted organic and
mineral soils become available.
NE, encouraged
not required
reporting
4.A.1 Forest Land Remaining Forest Land
4.A.1 N mineralization/
immobilization
N20
Direct N20 emissions from N mineralization/immobilization associated with loss or
gain of soil organic matter resulting from change of land use or management of
mineral soils will be estimated in a future Inventory. They are not estimated
currently because resources have limited EPA's ability to use the available data on
soil carbon stock changes on forest lands to estimate these emissions.
NE
4.B Cropland
4.B(II) Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02 and CH4
Not required based on the 2006 IPCC Guidelines. Emissions from this source may
be estimated in future Inventories using guidance from the 2013 Wetlands
Supplement when data necessary for classifying the area of rewetted organic and
mineral soils become available, except for CH4 emissions from drainage and
rewetting for rice cultivation.
NE, encouraged
not required
reporting
4.B.1 Cropland Remaining Cropland
4.B.1 Carbon Stock Change in Living
Biomass and Dead Organic
Matter
C02
Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to cropland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.B.1(V) Biomass Burning—Controlled
Burning
C02
Emissions of C02 from biomass burning on Croplands Remaining Cropland are only
relevant for perennial biomass and as noted under 4.B.1 above. EPA does not
NE
Annex 5
A-483
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currently include carbon stock change for perennial biomass on Cropland
Remaining Cropland. The C02 emissions from controlled burning of crop biomass
are not estimated for annual crops as they are part of the annual cycle of C and not
considered net emissions. Methane and N20 emissions are included under 3.F
Field Burning of Agricultural Residues.
4.B.1(V)
Biomass Burning—Wildfires
C02, CH4, and
N20
Emissions from wildfires are not estimated because the activity data on fire area
and fuel load, particularly for perennial vegetation, are not available to apply IPCC
methods.
NE
4.B.2 Land Converted to Cropland
4.B.2
Carbon Stock Change in
Perennial Living Biomass and
Dead Organic Matter
C02
Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to cropland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.B.2(V)
Biomass Burning—Wildfires
and Controlled Burning
C02
Emissions of C02 from biomass burning on Land Converted to Cropland are only
relevant for perennial biomass and as noted under 4.B.2 above EPA does not
currently include carbon stock change for perennial biomass on Land Converted to
Cropland. Emissions from wildfires are not estimated because the activity data on
fire area and fuel load, particularly for perennial vegetation, are not available.
NE
4.C Grassland
4.C(II)
Emissions and Removals from
Rewetting of Organic and
Mineral Soils
C02and CH4
Not required based on the 2006 IPCC Guidelines. Emissions from this source may
be estimated in future Inventories using guidance from the 2013 Wetlands
Supplement when data necessary for classifying the area of rewetted organic and
mineral soils become available.
NE, encouraged
not required
reporting
4.C.2 Land Converted to Grassland
4.C.2
Carbon Stock Change in Living
Biomass and Dead Organic
Matter
C02
Carbon stock change in living biomass and dead organic matter are not estimated,
other than for forest land converted to grassland, because data are currently not
available. The impact of management on perennial biomass C is currently under
investigation for agroforestry management and will be included in a future
Inventory if stock changes are significant and activity data can be compiled for this
source.
NE
4.D Wetlands
4. D(l I)
Flooded Lands and Peat
Extraction Lands: Emissions
and Removals from Drainage
and Rewetting and Other
C02, CH4, and
N20
Data are currently not available to apply IPCC methods and estimate emissions
from rewetting of peat extraction lands and flooded lands.
NE
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Management of Organic and
Mineral Soils
4.D.1 Wetlands Remaining Wetlands
4.D.1(V) Biomass Burning: Controlled
Burning, Wildfires
C02, CH4, and
N20
Data are not currently available to apply IPCC methods to estimate emissions from
biomass burning in Wetlands.
NE
4.D.2 Land Converted to Wetlands
4.D.2(V) Biomass Burning: Controlled
Burning, Wildfires
C02, CH4, and
N20
Data are not currently available to apply IPCC methods to estimate emissions from
biomass burning in Wetlands.
NE
4.E Settlements
4.E(V) Biomass Burning in
Settlements
C02, CH4, and
N20
Data are currently not available to apply IPCC methods to estimate emissions from
biomass burning in Settlements.
NE
4.E.1 Settlements Remaining Settlements
4.E.1 Settlements Remaining
Settlements
ch4
Data are not currently available to apply IPCC methods to estimate CH4 emissions
in Settlements.
NE
4.E.1 Direct N20 Emissions from N
Mineralization/Immobilization
(Mineral Soils)
n2o
Activity data are not available on N20 emissions from nitrogen
mineralization/immobilization in Settlements Remaining Settlements and Land
Converted to Settlements as a result of soil organic carbon stock losses from land
use conversion and management.
NE
4.E.2 Land Converted to Settlements
4.E.2 Direct N20 Emissions from N
Mineralization/Immobilization
n2o
Activity data are not available on N20 emissions from nitrogen
mineralization/immobilization in Settlements Remaining Settlements and Land
Converted to Settlements as a result of soil organic carbon stock losses from land
use conversion and management.
NE
4.F Other Land
4.F(V) Carbon Stock Change, Biomass
Burning
C02, CH4, and
N20
While the United States is conducting research to track carbon pools for other
land, it is unable to estimate C02, CH4 and N20 emissions for other land or land
converted to other land. See section 6.13 of the NIR.
NE
Waste
5.A.1 Solid Waste Disposal
5.A.l.a Managed Waste Disposal Sites-
Anaerobic
ch4
The amount of CH4 flared and the amount of CH4 for energy recovery is not
estimated for the years 2005 through 2020 in the time series. A methodological
change was made for 2005 to the current Inventory year to use the directly
reported net CH4 emissions from the EPA's GHGRP versus estimate CH4 generation
and recovery. See the Methodology explanation in Section 7.1.
NE
5.B Biological Treatment of Solid Waste
5.B.l.a Composting- Municipal Solid
Waste
Recovered CH4
and N20
CH4 and N20 emissions from combustion of the recovered gas at composting sites
are very small "so good practice in the Waste Sector does not require their
estimation." (IPCC 2006, Volume 5, Chapter 4, pp. 4.5). EPA will periodically assess
NE
Annex 5
A-485
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trends and based on significance consider reflecting as data become available and
prioritize with other improvements to make best use of available resources.
Estimating emissions at this time, given the likely significance, would require a
disproportionate amount of effort, so this will be considered for future Inventories
based on trends and available data.
5.C Waste Incineration
5.C.1 Waste Incineration
CH4 and N20
from
incineration of
sewage sludge
Based on data on the amount of sewage sludge incinerated and assumed emission
factors for N20 and CH4 from EPA's GHGRP for biomass solids, emissions were
estimated to be approximately 9 kt C02 Eq. per year. Approximated emissions
associated with sewage sludge incineration are considered insignificant for the
purposes of inventory reporting under the UNFCCC.
9
5.D Wastewater Treatment
5.D.2 Industrial Wastewater
ch4
Emissions associated with sludge generated from the treatment of industrial
wastewater is not included because the likely level of emissions is insignificant and
because quantitative activity data on who operates anaerobic sludge digesters is
unavailable. It would require a disproportionate amount of effort to collect this
data, and more recent methodological work also suggests this is the case (i.e.,
Table 6.3 (Updated) in the IPCC 2019 Refinement only identifies CH4 emissions
5
from anaerobic digestion of sludge as a source of emissions to be reported in the
Wastewater sector [note that N20 is noted as "not significant" in Table 6.8A]).
Methane emissions from the wastewater treatment category are not considered a
key source category (see Annex 1, Table A-l). In addition, the United States
continues to review the six industries included in the wastewater sector to
determine if activity data are sufficient to include methane emissions from
anaerobic digestion of sludge. The United States has worked first with the pulp
and paper industry to confirm that virtually no pulp and paper mills operate
anaerobic sludge digesters and will continue to identify stakeholders in the
remaining five industries to confirm sludge management techniques. The United
States notes that methane emissions associated with anaerobic digestion of
ethanol waste (a combination of process wastewater and solids) is already
included in the Inventory and is not considered sludge management.
The United States believes the likely level of emissions associated with anaerobic
digestion of industrial wastewater sludge is less than 5 kt C02 Eq., which is
considered insignificant for the purposes of inventory reporting under the
UNFCCC.
NE (Not Estimated), indicating also it is not possible to derive a likely level of emissions and/or removals or quantified estimate due to lack of approximated activity data and/or
in some cases also default emission factors but a method is available in the 2006 IPCC Guidelines.
A-486 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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While summarized below in Table A-236, information on coverage of activities within the United States, the District of Columbia, and U.S. Territories is provided in the
sectoral chapters with details in the category-specific estimate discussions as relevant. U.S. Territories include American Samoa, Guam, Puerto Rico, U.S. Virgin Islands,
Commonwealth of Northern Mariana Islands, and other minor outlying Pacific Islands which have no permanent population and are inhabited by military and/or
scientific purposes.187 As part of continuous improvement efforts, EPA reviews coverage on an ongoing basis to ensure emission and removal categories are included
across all geographic areas including U.S. Territories where they are occurring.
Table A-236: Summary of Geographic Completeness
CRF Sector
Geographic Completeness
Energy
Includes emissions from all 50 states, including Hawaii and Alaska, and the District of Columbia. Emissions are also included
from U.S. Territories to the extent they are known to occur (e.g., coal mining does not occur in U.S. Territories). For some
sources there is a lack of detailed information on U.S. Territories, including non-C02 emissions, so emissions estimates may
not be available at same levels of disaggregation those covering the states and District of Columbia.
Industrial Processes and Product
Use
Includes emissions from all 50 states, including Hawaii and Alaska, as well as from the District of Columbia and U.S.
Territories to the extent to which industries are occurring. While most IPPU sources do not occur in U.S. Territories (e.g.,
electronics manufacturing does not occur in U.S. Territories), they are estimated and accounted for where they are known
to occur (e.g., substitutes from ozone depleting substance substitutes, cement production, lime production, and electrical
transmission and distribution).
Agriculture
Emissions reported in the Agriculture chapter include those from all states; however, for Hawaii and Alaska some
agricultural practices that can increase nitrogen availability in the soil, and thus cause N20 emissions, are not included (i.e.,
for field burning of agricultural residues, agricultural soil management). In addition, U.S. Territories and the District of
Columbia are not estimated due to incomplete data, with the exception of Urea Fertilization in Puerto Rico. Emissions
currently not estimated for U.S. Territories have not been approximated for significance. Other minor outlying U.S.
territories in the Pacific Islands have no permanent populations (e.g., Baker Island) and therefore EPA assumes no
agriculture activities are occurring.
Land Use, Land Use Change and
Forestry
Emissions and removals reported in the LULUCF chapter include those from all states, however, for Hawaii and Alaska
some emissions and removals from land use and land use change are not included. Specifically, for Alaska carbon stock
changes from coastal wetlands, cropland and lands converted to cropland, grasslands and lands converted to grassland,
settlements and lands converted to settlements, N20 from settlement soils, non-C02 emission from grassfires are not
estimated. For Hawaii, estimates of C02, CH4, N20 from peatlands are not estimated. See chapter sections on Uncertainty
and Planned Improvements for more details. In addition, U.S. Territories are not included (see Box 6). Emissions currently
not estimated for U.S. Territories have not yet been approximated for significance.
Waste
Emissions reported in the Waste chapter for landfills, wastewater treatment, and anaerobic digestion at biogas facilities
include those from all 50 states, including Hawaii and Alaska, the District of Columbia, as well as from U.S. Territories.
Emissions from landfills include modern, managed sites in most U.S. Territories except for outlying Pacific Islands.
Emissions from domestic wastewater treatment include most U.S. Territories except for outlying Pacific Islands. Those
emissions are likely insignificant as those outlying Pacific Islands (e.g., Baker Island) have no permanent population. No
187 More information is available at: https://www.usgs.gov/faqs/how-are-us-states-territories-and-commonwealths-designated-geographic-names-information-system.
Annex 5
A-487
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industrial wastewater treatment emissions are estimated for U.S. Territories, due to lack of data availability. However,
industrial wastewater treatment emissions are not expected for outlying Pacific Islands and assumed to be small for other
U.S. Territories. Emissions for composting include all 50 states, including Hawaii and Alaska, but not U.S. Territories.
Composting emissions from U.S. Territories are assumed to be small and have not yet been approximated. Similarly, EPA is
not aware of any anerobic digestion at biogas facilities in U.S. Territories but will review this on an ongoing basis to include
these emissions if they are occurring.
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ANNEX 6 Additional Information
6.1. Global Warming Potential Values
Global warming potential (GWP) is intended as a quantified measure of the globally averaged relative radiative forcing
impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcing-both direct and indirect effects-
integrated over a specific period of time from the emission of a unit mass of gas relative to some reference gas (IPCC
2007). Carbon dioxide (C02) was chosen as this reference gas. Direct effects occur when the gas itself is a greenhouse
gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas or gases
that are greenhouse gases, or when a gas influences other radiatively important processes such as the atmospheric
lifetimes of other gases. The relationship between kilotons (kt) of a gas and million metric tons of C02 equivalents (MMT
C02 Eq.) can be expressed as follows:
Equation A-71: Calculating CO2 Equivalent Emissions
MMT CO2 Eq. = (kt of gas) x (GWP ) x
where,
MMTCO2 Eq.
kt
GWP
MMT
Million metric tons of C02 equivalent
kilotons (equivalent to a thousand metric tons)
Global warming potential
Million metric tons
GWP values allow policy makers to compare the impacts of emissions and reductions of different gases. According to
the IPCC, GWP values typically have an uncertainty of+40 percent, though some GWP values have larger uncertainty
than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the countries
who are Parties to the United Nations Framework Convention on Climate Change (UNFCCC) have agreed to use
consistent GWP values from the IPCC Fourth Assessment Report (AR4), based upon a 100-year time horizon, although
other time horizon values are available (see Table A-237). While this Inventory uses agreed-upon GWP values according
to the specific reporting requirements of the UNFCCC, described below, unweighted gas emissions and sinks in kilotons
(kt) are provided in the Trends chapter of this report (Table 2-2) and users of the Inventory can apply different metrics
and different time horizons to compare the impacts of different greenhouse gases.
...the global warming potential values used by Parties included in Annex I to the Convention (Annex /
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...188
Greenhouse gases with relatively long atmospheric lifetimes (e.g., C02, CH4, N20, HFCs, PFCs, SF6, and NF3) tend to be
evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined.
However, short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect greenhouse
gases (e.g., NOx and NMVOCs), and tropospheric aerosols (e.g., S02 products and black carbon) vary spatially, and
consequently it is difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to
188 United Nations Framework Convention on Climate Change; http://unfccc.int/resource/docs/2Q13/copl9/erig/lQaQ3.pdf; 31
January 2014; Report of the Conference of the Parties at its nineteenth session; held in Warsaw from 11 to 23 November 2013;
Addendum; Part two: Action taken by the Conference of the Parties at its nineteenth session; Decision 24/CP.19; Revision of the
UNFCCC reporting guidelines on annual inventories for Parties included in Annex I to the Convention; p. 2. (UNFCCC 2014).
Annex 6
A-489
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these gases that are short-lived and spatially inhomogeneous in the atmosphere. See Annex 6.2 for a discussion of
GWPs for ozone depleting substances.
Table A-237: IPCC AR4 Global Warming Potentials (GWP) and Atmospheric Lifetimes
(Years) of Gases Used in this Report
Gas
Atmospheric Lifetime
100-year GWPa
20-year GWP
500-year GWP
Carbon dioxide (C02)
See footnote15
1
1
1
Methane (CH4)C
12d
25
72
7.6
Nitrous oxide (N20)
114d
298
289
153
HFC-23
270
14,800
12,000
12,200
HFC-32
4.9
675
2,330
205
HFC-41
2.4
92
323
28
HFC-125
29
3,500
6,350
1,100
HFC-134a
14
1,430
3,830
435
HFC-143a
52
4,470
5,890
1,590
HFC-152a
1.4
124
437
38
HFC-227ea
34.2
3,220
5,310
1,040
HFC-236fa
240
9,810
8,100
7,660
HFC-43-10mee
15.9
1,640
4,140
500
HFC-245fa
7.6
1,030
3,380
314
HFC-365mfc
8.6
794
2,520
241
CF4
50,000d
7,390
5,210
11,200
c2f6
10,000
12,200
8,630
18,200
CsFs
2,600
8,830
6,310
12,500
C4F60
1.1
0.003
NA
NA
c-C5Fs0
31
1.97
7.0
NA
C4F10
2,600
8,860
6,330
12,500
c-C4Fs
3,200
10,300
7,310
14,700
C5Fi2
4,100
9,160
6,510
13,300
C6F14
3,200
9,300
6,600
13,300
SFe
3,200
22,800
16,300
32,600
NFs
740
17,200
12,300
20,700
NA (Not Available)
a GWP values used in this report are calculated over 100-year time horizon.
b For a given amount of C02 emitted, some fraction of the atmospheric increase in concentration is quickly absorbed by
the oceans and terrestrial vegetation, some fraction of the atmospheric increase will only slowly decrease over a
number of years, and a small portion of the increase will remain for many centuries or more.
c The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone
and stratospheric water vapor. The indirect effect due to the production of C02 is not included.
d Methane and N20 have chemical feedback systems that can alter the length of the atmospheric response, in these
cases, global mean atmospheric lifetime (LT) is given first, followed by perturbation time (PT), but only the
perturbation time is listed here and not the atmospheric residence time.
e See Table A-l of FR 40 CFR Part 98.
Source: IPCC (2007)
The IPCC published its Fifth Assessment Report (AR5) in 2013 and its Sixth Assessment Report (AR6) in 2021, providing
the most current and comprehensive scientific assessments of climate change (IPCC 2013; IPCC 2021). Within this report,
the GWP values were revised relative to the IPCC's Fifth Assessment Report (AR5) (IPCC 2013). Although the AR4 GWP
values are used throughout this Inventory report in line with UNFCCC inventory reporting guidelines, it is informative to
review the changes to the 100-year GWP values and the impact they have on the total GWP-weighted emissions of the
United States. All GWP values use C02 as a reference gas; a change in the radiative efficiency of C02 thus impacts the
GWP of all other greenhouse gases. Since the Second Assessment Report (SAR) and Third Assessment Report (TAR), the
IPCC has applied an improved calculation of C02 radiative forcing and an improved C02 response function. The GWP
values are drawn from IPCC (2007), with updates for those cases where new laboratory or radiative transfer results have
been published. Additionally, the atmospheric lifetimes of some gases have been recalculated, and updated background
concentrations were used. Table A-238 shows how the GWP values of the other gases relative to C02 tend to be larger in
A-490 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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AR4, AR5, and AR6 because the revised radiative forcing of C02 is lower than in earlier assessments, taking into account
revisions in lifetimes. Comparisons of GWP values are based on the 100-year time horizon required for UNFCCC
inventory reporting. However, there were some instances in which other variables, such as the radiative efficiency or the
chemical lifetime, were altered that resulted in further increases or decreases in particular GWP values in AR5 and AR6,
including addressing inconsistencies with incorporating climate carbon feedbacks. In addition, the values for radiative
forcing and lifetimes have been calculated for a variety of halocarbons. Updates in some well-mixed HFC compounds
(including HFC-23, HFC-32, HFC-134a, and HFC-227ea) for AR4 result from investigation into radiative efficiencies in these
compounds, with some GWP values changing by up to 40 percent; with this change, the uncertainties associated with
these well-mixed HFCs are thought to be approximately 12 percent.
It should be noted that the use of IPCC AR4 GWP values for the current Inventory applies across the entire time series of
the Inventory (i.e., from 1990 to 2020). As such, GWP comparisons throughout this chapter are presented relative to AR4
GWPs. Updated reporting guidelines under the Paris Agreement which require the United States and other countries to
shift to use of IPCC Fifth Assessment Report (AR5) (IPCC 2013) 100-year GWP values (without feedbacks) take effect for
national inventory reporting in 2024.189
189 See https://unfccc.int/process-and-meetings/transparencv-and-reporting/reporting-and-review-under-the-paris-agreement.
Annex 6
A-491
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Table A-238: Comparison of GWP values and Lifetimes Used in the AR4, AR5, and AR6C
Lifetime (years)
GWP (100 year)
Difference in GWP (Relative to AR4)
AR5 with
AR5 with
AR5 with
Gas
AR4
AR5
AR6
AR4
AR5a
feedbacks'1
AR6C
AR5a
AR5 (%)
feedbacks'1 feedbacks'1 (%)
AR6
AR6 (%)
Carbon dioxide (C02)
d
e
e
1
1
1
1
NC
NC
NC
NC
NC
NC
Methane (CH4)f
8.7/126
12.4
11.8
25
28
34
27
3
12%
9
36%
2
12%
Nitrous oxide (N20)
120/1148
121
109
298
265
298
273
(33)
-11%
0
0%
(25)
-8%
Hydrofluorocarbons
HFC-23
270
222
228
14,800
12,400
13,856
14,600
(2,400)
-16%
(944)
-6%
(200)
-1%
HFC-32
4.9
5.2
5.4
675
677
817
771
2
+%
142
21%
96
14%
HFC-41
NA
2.8
2.8
NA
116
141
135
NA
NA
NA
NA
43
47%
HFC-125
29
28.2
30
3,500
3,170
3,691
3,740
(330)
-9%
191
5%
240
7%
HFC-134a
14
13.4
14
1,430
1,300
1,549
1,530
(130)
-9%
119
8%
100
7%
HFC-143a
52
47.1
51
4,470
4,800
5,508
5,810
330
7%
1,038
23%
1,340
32%
HFC-152a
1.4
1.5
1.6
124
138
167
164
14
11%
43
35%
40
32%
HFC-227ea
34.2
38.9
36
3,220
3,350
3,860
3,600
130
4%
640
20%
380
12%
HFC-236fa
240
242
213
9,810
8,060
8,998
8,690
(1,750)
-18%
(812)
-8%
(1,120)
-11%
HFC-245fa
7.6
7.7
7.9
1,030
858
1,032
962
(172)
-17%
2
+%
(68)
-7%
HFC-365mfc
8.6
8.7
8.9
794
804
966
914
10
1%
172
22%
120
15%
HFC-43-10mee
15.9
16.1
17
1,640
1,650
1,952
1,600
10
1%
312
19%
(40)
-2%
Fully Fluorinated Species
sf6
3,200
3,200 About 1,000
22,800
23,500
26,087
25,200
700
3%
3,287
14%
2,400
11%
cf4
50,000
50,000
50,000
7,390
6,630
7,349
7,380
(760)
-10%
(41)
-1%
(10)
-+%
c2f6
10,000
10,000
10,000
12,200
11,100
12,340
12,400
(1,100)
-9%
140
1%
200
2%
CsFs
2,600
2,600
2,600
8,830
8,900
9,878
9,290
70
1%
1,048
12%
460
5%
C4F10
2,600
2,600
2,600
8,860
9,200
10,213
10,000
340
4%
1,353
15%
1,140
13%
c-C4Fs
3,200
3,200
3,200
10,300
9,540
10,592
10,200
(760)
-7%
292
3%
(100)
-1%
c-CsFs
NA
31
NA
NA
2.0
NA
NA
NA
NA
NA
NA
NA
NA
C5F12
4,100
4,100
4,100
9,160
8,550
9,484
9,220
(610)
-7%
324
4%
60
1%
C6F14
3,200
3,100
3,100
9,300
7,910
8,780
8,620
(1,390)
-15%
(520)
-6%
(680)
-7%
C4F6
1.1
NA
NA
0.003
NA
NA
NA
NA
NA
NA
NA
NA
NA
C4FsO
NA
NA
3,000
NA
NA
NA
13,900
NA
NA
NA
NA
NA
NA
nf3
740
500
569
17,200
16,100
17,885
17,400
(1,100)
-6%
685
4%
200
1%
+ Does not exceed 0.5 percent.
NC (No Change)
NA (Not Applicable)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report. See footnote e for more information on GWPs for methane of
fossil origin.
A-492 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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b The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02 gases in order to be consistent with the approach used in calculating
the C02 lifetime.
cThe 100-year GWPs from AR6 are prepublication values based on the Working Group 1 report published in August 2021. As the report is finalized for full publication, in the final
editing process, these values may be updated in corrigenda and EPA will update this analysis to reflect the final value.
d For a given amount of C02 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. See footnote e for more
information on GWPs for methane of fossil origin.
e No single lifetime can be determined for C02 (see IPCC 2007).
f The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. Additionally, the AR5
reported separate values for fossil versus biospheric methane in order to account for the C02 oxidation product. The GWP associated with methane of fossil origin is not shown
in this table. Per AR5, the GWP for methane of fossil origin is 30 versus 28 using methodology most consistent with AR4. If using methodology to include climate carbon
feedbacks, per the AR5 report, the value is higher by 2 for GWP for methane of fossil origin, so would be 36 versus 34.
s Methane and N20 have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean residence time is given first, followed by
perturbation time.
Note: Parentheses indicate negative values.
Source: IPCC (2021), IPCC (2013), IPCC (2007).
Annex 6
A-493
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The choice of GWP values between the AR4, AR5, and AR6 with or without climate-carbon feedbacks has an impact on
both the overall emissions estimated by the Inventory, as well as the trend in emissions over time. To summarize, Table
A-239 shows the overall trend in U.S. greenhouse gas emissions, by gas, from 1990 through 2020 using the four GWP
sets. The table also presents the impact of AR5 and AR6 100-year GWP values with or without feedbacks on the total
emissions for 1990 and for 2020.
Table A-239: Effects on U.S. Greenhouse Gas Emissions Using AR4, AR5, and AR6C GWP
values (MMT CO2 Eg.)
Difference in Emissions Between 1990
Gas
and 2020 (Relative to 1990)
Revisions to Annual Emission Estimates (Relative to AR4)
AR5a
AR5b
AR6
AR5a
AR5b
AR6
AR4
AR5a
AR5b
AR6C
1990
2020
C02
(406.8)
(406.8)
(406.8)
(406.8)
NC
NC
NC
NC
NC
NC
ch4
(130.4)
(146.0)
(177.3)
(140.8)
93.7
281.1
62.5
78.1
234.2
52.0
n2o
(24.4)
(21.7)
(24.4)
(22.4)
(49.9)
NC
(37.8)
(47.2)
NC
NC
HFCs, PFCs, SF6,
89.5
87.7
107.7
79.9
and NFs
(9.0)
1.3
1.9
(10.9)
19.5
(7.6)
Total
(472.1)
(486.9)
(500.8)
(490.1)
34.8
282.4
26.6
20.0
253.7
44.4
Percent Change
-7.3%
-7.5%
-7.4%
-7.6%
0.5%
4.4%
0.4%
0.3%
4.2%
0.1%
NC (No Change)
a The GWP values 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-C02 gases in order to be
consistent with the approach used in calculating the C02 lifetime. Additionally, for methane the AR5 reported separate values
for fossil versus biogenic methane in order to account for the C02 oxidation product and that is not shown on this table. See
footnotes to Table A-237.
cThe 100-year GWPs from AR6 are prepublication values based on the Working Group 1 report published in August 2021. As
the report is finalized for full publication, in the final editing process, these values may be updated in corrigenda and EPA will
update this analysis to reflect the final value.
Notes: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values.
Table A-240 and Table A-241 show the comparison of emission estimates using AR5 GWP values relative to AR4 GWP
values without climate-carbon feedbacks for the non-C02 gases, on an emissions and percent change basis. Table A-242
and Table A-243 show the comparison of emission estimates using AR5 GWP values with climate-carbon feedbacks. The
use of AR5 GWP values without climate-carbon feedbacks™ results in an increase in emissions of CH4 and SF6 relative to
AR4 GWP values, but a decrease in emissions of other gases. The use of AR5 GWP values with climate-carbon feedbacks
does not impact C02 and N20 emissions; however, it results in an increase in emissions of CH4, SF6, and NF3 relative to
AR4 GWP values, and has mixed impacts on emissions of other gases. Overall, these comparisons of AR4 and AR5 GWP
values do not have a significant effect on calculated U.S. emissions, resulting in an increase in emissions of less than 1
percent using AR5 GWP values, or approximately 4 percent when using AR5 GWP values with climate-carbon feedbacks.
The percent change in emissions is equal to the percent change in the GWP for each gas; however, in cases where
multiple gases are emitted in varying amounts the percent change is variable over the years, such as with Substitution of
Ozone Depleting Substances.
Table A-244 and Table A-245 show the comparison of emission estimates using AR6 GWP values relative to AR4 GWP
values for the non-C02 gases, on an emissions and percent change basis. When the GWP values from the AR6 are applied
to the emission estimates presented in this report, total emissions for the year 2020 are 5,990.0 MMT C02 Eq., as
compared to the official emission estimate of 5,981.4 MMT C02 Eq. using AR4 GWP values (i.e., the use of AR6 GWPs
results in a O.lpercent increase relative to emissions estimated using AR4 GWPs). As with the comparison of AR4 and
AR5 GWP values presented above, the percent change in emissions is equal to the percent change in the GWP for each
gas or varies by year based on the mix of gases (i.e., HFCs and PFCs).
190 The IPCC AR5 report provides additional information on emission metrics. See https://www.ipcc.ch/pdf/assessment-
report/arS/wgl/WGlARS ChapterOS FlNAL.pdf.
A-494 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-240: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon
Feedbacks Relative to AR4 GWP Values (MMT CO2 Eg.)
Gas
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
93.7
83.7
78.9
79.7
80.5
80.3
78.1
n2o
(49.9)
(50.2)
(49.7)
(49.2)
(50.7)
(50.6)
(47.2)
HFCs
(7.5)
(10.9)
(9.8)
(10.2)
(10.1)
(10.5)
(10.6)
PFCs
(2.4)
(0.6)
(0.4)
(0.4)
(0.5)
(0.4)
(0.4)
sf6
0.9
0.4
0.2
0.2
0.2
0.2
0.2
nf3
(+)
(+)
(+)
(+)
(+)
(+)
(+)
Total
34.8
22.2
19.1
19.9
19.4
18.9
20.0
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
The AR5 report has also calculated GWP values (shown in Table A-238) where climate-carbon feedbacks
have been included for the non-C02 gases in order to be consistent with the approach used in calculating
the C02 lifetime. Additionally, for methane the AR5 reported separate values for fossil versus biogenic
methane in order to account for the C02 oxidation product and that is not shown on this table. See
footnotes to Table A-237.
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding.
Parentheses indicate negative values.
Table A-241: Change in U.S. Greenhouse Gas Emissions Using AR5a without Climate-Carbon
Feedbacks Relative to AR4 GWP Values (Percent)
Gas/Source
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
12.0%
n2o
(11%)
(11%)
(11%)
(11%)
(11%)
(11%)
(11%)
sf6
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
3.1%
NF3
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
(6.4%)
HFCs
(16.1%)
(8.6%)
(5.8%)
(6.0%)
(5.9%)
(6.0%)
(5.9%)
PFCs
(10.0%)
(9.6%)
(9.5%)
(9.5%)
(9.6%)
(9.6%)
(9.7%)
Total
0.5%
0.3%
0.3%
0.3%
0.3%
0.3%
0.3%
NC (No Change)
a The GWP values presented here are the ones most consistent with the methodology used in the AR4 report.
The AR5 report has also calculated GWP values (shown in Table A-238) where climate-carbon feedbacks have
been included for the non-C02 gases in order to be consistent with the approach used in calculating the C02
lifetime. Additionally, the AR5 reported separate values for fossil versus biogenic methane in order to
account for the C02 oxidation product.
b HFC-23 emitted.
c Emissions from HFC-23, CF4, C2F6, C3Fs, C4FS, C4F6, CH2F2, CH3F, CH2FCF3, C2H2F4 and C5FS,, as well as other
HFCs and PFCs used as heat transfer fluids.
d Zero change in beginning of time series since emissions were zero.
e PFC emissions from CF4 and C2F6.
f PFC emissions from CF4.
Note: Total emissions presented without LULUCF. Parentheses indicate negative values. Totals may not sum
due to independent rounding.
Annex 6
A-495
-------
Table A-242: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon
Feedbacks3 Relative to AR4 GWP Values (MMT CO2 Eg.)
Gas
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
281.1
251.1
236.7
239.0
241.6
240.8
234.2
n2o
NC
NC
NC
NC
NC
NC
NC
HFCs
(2.9)
9.4
17.9
17.7
17.9
18.3
18.7
PFCs
+
+
+
+
+
+
+
sf6
4.2
1.7
0.9
0.8
0.8
0.8
0.8
nf3
+
+
+
+
+
+
+
Total
282.4
262.2
255.6
257.6
260.4
259.9
253.7
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02
gases in order to be consistent with the approach used in calculating the C02 lifetime. Additionally, for
methane the AR5 reported separate values for fossil versus biogenic methane in order to account for the
C02 oxidation product and that is not shown on this table. See footnotes to Table A-237.
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding.
Parentheses indicate negative values.
Table A-243: Change in U.S. Greenhouse Gas Emissions Using AR5 with Climate-Carbon
Feedbacks3 Relative to AR4 GWP Values (Percent)
Gas/Source
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
36.0%
n2o
NC
NC
NC
NC
NC
NC
NC
sf6
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
14.4%
NFs
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
4.0%
HFCs
(6.2%)
7.4%
10.6%
10.3%
10.5%
10.4%
10.5%
PFCs
0.2%
0.5%
0.7%
0.7%
0.6%
0.5%
0.5%
Total
4.4%
3.5%
3.9%
4.0%
3.9%
4.0%
4.2%
NC (No Change)
a The GWP values presented here from the AR5 report include climate-carbon feedbacks for the non-C02
gases in order to be consistent with the approach used in calculating the C02 lifetime. Additionally, for
methane the AR5 reported separate values for fossil versus biogenic methane in order to account for the
C02 oxidation product and that is not shown on this table. See footnotes to Table A-237.
b HFC-23 emitted.
c Emissions from HFC-23, CF4, C2F6, C3Fs, C4FS, C4F6, CH2F2, CH3F, CH2FCF3, C2H2F4 and C5FS,, as well as other
HFCs and PFCs used as heat transfer fluids.
d Zero change in beginning of time series since emissions were zero.
e PFC emissions from CF4 and C2F6.
f PFC emissions from CF4.
Notes: Total emissions presented without LULUCF. Parentheses indicate negative values. Excludes Sinks.
A-496 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-244: Change in U.S. Greenhouse Gas Emissions Using AR6 Relative to AR4 GWP
Values (MMT CCh Eg.)
Gas
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
62.5
55.8
52.6
53.1
53.7
53.5
52.0
n2o
(37.8)
(38.0)
(37.7)
(37.3)
(38.4)
(38.3)
(35.7)
HFCs
(0.6)
(7.9)
(9.3)
(9.4)
(9.5)
(9.9)
(10.2)
PFCs
(0.5)
(0.1)
1.4
1.6
1.8
1.8
2.0
sf6
3.0
1.2
0.6
0.6
0.6
0.6
0.6
nf3
+
+
+
+
+
+
+
Total
26.6
10.9
7.7
8.6
8.2
7.7
8.6
+ Absolute value does not exceed 0.05 MMT C02 Eq.
NC (No Change)
Notes: Total emissions presented without LULUCF. Totals may not sum due to independent rounding. Parentheses indicate
negative values.
Table A-245: Change in U.S. Greenhouse Gas Emissions Using AR6 Relative to AR4 GWP
Gas/Source
1990
2005
2016
2017
2018
2019
2020
C02
NC
NC
NC
NC
NC
NC
NC
ch4
8.0%
8.0%
8.0%
8.0%
8.0%
8.0%
8.0%
n2o
(8%)
(8%)
(8%)
(8%)
(8%)
(8%)
(8%)
sf6
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
10.5%
nf3
1.2%
1.2%
1.2%
1.2%
1.2%
1.2%
1.2%
HFCs
(1.3%)
(6.2%)
(5.5%)
(5.5%)
(5.6%)
(5.6%)
(5.7%)
PFCs
(2.0%)
(2.2%)
32.0%
37.7%
38.0%
38.9%
44.4%
Total
0.4%
0.1%
0.1%
0.1%
0.1%
0.1%
0.1%
NC (No Change)
+ Does not exceed 0.05 percent.
a HFC-23 emitted.
b Emissions from HFC-23, CF4, C2F6, CsFs, C4FS, C4F6, CH2F2, CH3F, CH2FCF3, C2H2F4 and C5Fs,, as well as other
HFCs and PFCs used as heat transfer fluids.
c Zero change in beginning of time series since emissions were zero.
d PFC emissions from CF4 and C2F6.
e PFC emissions from CF4.
Notes: Total emissions presented without LULUCF. Parentheses indicate negative values. Excludes Sinks.
Annex 6
A-497
-------
6.2. Ozone Depleting Substance Emissions
Ozone is present in both the stratosphere,191 where it shields the earth from harmful levels of ultraviolet radiation, and
at lower concentrations in the troposphere,192 where it is the main component of anthropogenic photochemical "smog."
Chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs),
along with certain other chlorine and bromine containing compounds, have been found to deplete the ozone levels in
the stratosphere. These compounds are commonly referred to as ozone depleting substances (ODSs). If left unchecked,
stratospheric ozone depletion could result in a dangerous increase of ultraviolet radiation reaching the earth's surface. In
1987, nations around the world signed the Montreal Protocol on Substances that Deplete the Ozone Layer. This
landmark agreement created an international framework for limiting, and ultimately eliminating, the production of most
ozone depleting substances. ODSs have historically been used in a variety of industrial applications, including
refrigeration and air conditioning, foam blowing, fire extinguishing, sterilization, solvent cleaning, and as an aerosol
propellant.
In the United States, the Clean Air Act Amendments of 1990 provide the legal instrument for implementation of the
Montreal Protocol controls. The Clean Air Act classifies ozone depleting substances as either Class I or Class II, depending
upon the ozone depletion potential (ODP) of the compound.193 The production of CFCs, halons, carbon tetrachloride, and
methyl chloroform—all Class I substances—has already ended in the United States. However, large amounts of these
chemicals remain in existing equipment,194 and stockpiles of the ODSs, as well as material recovered from equipment
being decommissioned, are used for maintaining the existing equipment. As a result, emissions of Class I compounds will
continue, albeit generally in decreasing amounts, for many more years. Class II designated substances, all of which are
HCFCs, have been, or are being, phased out at later dates than Class I compounds because they have lower ODPs. These
compounds served as interim replacements for Class I compounds in many industrial applications. The use and emissions
of HCFCs in the United States is anticipated to continue for several decades as equipment that use Class II substances are
retired from use. Under current Montreal Protocol controls, however, the production for domestic use of all HCFCs as an
ODS substitute in the United States must end by the year 2030.
In addition to contributing to ozone depletion, CFCs, halons, carbon tetrachloride, methyl chloroform, and HCFCs are also
potent greenhouse gases. However, the depletion of the ozone layer has a cooling effect on the climate that counteracts
the direct warming from tropospheric emissions of ODSs. Stratospheric ozone influences the earth's radiative balance by
absorption and emission of longwave radiation from the troposphere as well as absorption of shortwave radiation from
the sun; overall, stratospheric ozone has a warming effect.
The IPCC has prepared both direct GWP values and net (combined direct warming and indirect cooling) GWP ranges for
some of the most common ozone depleting substances (IPCC 2007). Table A-246 presents direct GWP values for ozone
depleting substances. Ozone depleting substances directly absorb infrared radiation and contribute to positive radiative
forcing; however, their effect as ozone-depleters also leads to a negative radiative forcing because ozone itself is a
potent greenhouse gas. There is considerable uncertainty regarding this indirect effect; direct GWP values are shown,
but AR4 does provide a range of net GWP values for ozone depleting substances. The 2006 IPCC Guidelines and the
UNFCCC do not include reporting instructions for estimating emissions of ODSs because their use is being phased out
under the Montreal Protocol (see note below Table A-246). The effects of these compounds on radiative forcing are not
addressed in this report.
191 The stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of
atmospheric ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in
a region commonly called the ozone layer.
192 The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial
regions (i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in
the atmosphere and is the site for weather processes including most of the water vapor and clouds.
193 Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other designated substances
that deplete stratospheric ozone but which have an ODP of less than 0.2 are Class II.
194 Older refrigeration and air-conditioning equipment, fire extinguishing systems, and foam products blown with CFCs/HCFCs
may still contain Class I ODS.
A-498 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-246: 100-year Direct Global Warming Potentials for Select Ozone Depleting
Substances
Gas
Direct GWP
CFC-11
4,750
CFC-12
10,900
CFC-113
6,130
HCFC-22
1,810
HCFC-123
77
HCFC-124
609
HCFC-141b
725
HCFC-142b
2,310
CH3CCI3
146
CCU
1,400
CHsBr
5
Halon-1211
1,890
Halon-1301
7,140
Note: Because these compounds have been shown to
deplete stratospheric ozone, they are typically referred to
as ODSs. However, they are also potent greenhouse
gases. Recognizing the harmful effects of these
compounds on the ozone layer, in 1987 many
governments signed the Montreal Protocol on Substances
that Deplete the Ozone Layer to limit the production and
importation of a number of CFCs and other halogenated
compounds. The United States furthered its commitment
to phase-out ODSs by signing and ratifying the
Copenhagen Amendments to the Montreal Protocol in
1992. Under these amendments, the United States
committed to ending the production and importation of
halons by 1994, and CFCs by 1996, and HCFCs by 2030.
Source: IPCC(2007).
Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting
substances, the United States believes that the inventory presents a more complete picture of climate impacts when EPA
includes these compounds. Emission estimates for several ozone depleting substances are provided in Table A-247.
Table A-247: Emissions of Ozone Depleting Substances (kt)
Compound
1990
2005
2016
2017
2018
2019
2020
Class 1
CFC-11
29
12
6
6
6
6
6
CFC-12
136
23
3
2
1
1
+
CFC-113
59
17
0
0
0
0
0
CFC-114
4
1
0
0
0
0
0
CFC-115
8
2
+
+
+
+
+
Carbon
Tetrachloride
4
0
0
0
0
0
0
Methyl Chloroform
223
0
0
0
0
0
0
Halon-1211
2
2
1
1
1
1
1
Halon-1301
2
+
+
+
+
+
+
Class II
HCFC-22
31
74
54
51
47
43
40
HCFC-123
0
1
1
1
1
1
1
HCFC-124
0
2
+
+
+
+
+
HCFC-141b
1
4
8
7
7
7
7
HCFC-142b
1
4
3
3
4
5
5
HCFC-225ca/cb
0
+
+
+
+
+
+
Annex 6
A-499
-------
+ Does not exceed 0.5 kt.
Methodology and Data Sources
Emissions of ozone depleting substances were estimated using the EPA's Vintaging Model. The model, named for its
method of tracking the emissions of annual "vintages" of new equipment that enter into service, is a "bottom-up"
model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold,
serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the
equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the
equipment in each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and
disposal emission rates to each population of equipment. By aggregating the emission and consumption output from the
different end-uses, the model produces estimates of total annual use and emissions of each chemical. Please see Annex
3.9, Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances, of this
Inventory for a more detailed discussion of the Vintaging Model.
Uncertainty Assessment
Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics, and
end-use emissions profiles that are used by these models. Please see the Substitution of Ozone Depleting Substances
section of this report for a more detailed description of the uncertainties that exist in the Vintaging Model.
A-500 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
6.3. Complete List of Source and Sink Categories
Chapter/Source/Sink
Gas(es)
Energy
Fossil Fuel Combustion
Non-Energy Use of Fossil Fuels
Stationary Combustion (excluding C02)
Mobile Combustion (excluding C02)
Coal Mining
Abandoned Underground Coal Mines
Petroleum Systems
Natural Gas Systems
Abandoned Oil and Gas Wells
Incineration of Waste
Industrial Processes and Product Use
Cement Production
Lime Production
Glass Production
Other Process Uses of Carbonates
Ammonia Production
Urea Consumption for Non-Agricultural Purposes
Nitric Acid Production
Adipic Acid Production
Caprolactam, Glyoxal, and Glyoxylic Production
Carbide Production and Consumption
Titanium Dioxide Production
Soda Ash Production
Petrochemical Production
HCFC-22 Production
Carbon Dioxide Consumption
Phosphoric Acid Production
Iron and Steel Production & Metallurgical Coke Production
Ferroalloy Production
Aluminum Production
Magnesium Production and Processing
Lead Production
Zinc Production
Electronics Industry
Substitution of Ozone Depleting Substances
Electrical Transmission and Distributing
N20 from Product Uses
Agriculture
Enteric Fermentation
Manure Management
Rice Cultivation
Liming
Urea Fertilization
Field Burning of Agricultural Residues
Agricultural Soil Management
Land Use, Land-Use Change, and Forestryc
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
C02
C02
cm, N20
ch4, n2o
C02, CH4
ch4
co2, ch4, n2o
co2, ch4, n2o
co2, ch4
co2,ch4,n2o
co2
co2
co2
co2
co2
co2
n2o
n2o
n2o
co2, ch4
co2
co2
co2, ch4
HFC-23
co2
co2
co2, ch4
co2, ch4
co2, cf4, c2f6
C02, HFCs, SF6
co2
co2
N20, HFCs, PFCs,a SF6, NF3
HFCs, PFCsb
SF6, cf4
n2o
ch4
ch4, n2o
ch4
C02
co2
ch4, n2o
n2o
co2, ch4, n2o
co2
co2
co2
co2, ch4, n2o
co2
co2, ch4, n2o
co2, ch4
Annex 6
A-501
-------
Settlements Remaining Settlements
Land Converted to Settlements
Waste
Landfills
Wastewater Treatment
Composting
Anaerobic Digestion at Biogas Facilities
C02, N20
C02
CH4
ch4, n2o
ch4, n2o
ch4
a Includes HFC-23, CF4, C2F6, CsFs, C4FS, C4F6, C5Fs, CH2F2, CH3F, CH2FCF3, and C2H2F4, as well as a mix other HFCs and
PFCs used as heat transfer fluids.
b Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-
365mfc, HFC-4310mee, HCFO-1233zd(E), HFO-1234yf, HFO-1234ze, HFO-1336mzz(Z), C4Fi0, and PFC/PFPEs.
cThe LULUCF Sector includes CH4 and N20 emissions to the atmosphere and net carbon stock changes. The term
"flux" is used to describe the exchange of C02 to and from the atmosphere, with net flux being either positive or
negative depending on the overall balance. Removal and long-term storage of C02 from the atmosphere is also
referred to as "carbon sequestration."
A-502 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
6.4. Constants, Units, and Conversions
Metric Prefixes
Although most activity data for the United States is gathered in customary U.S. units, these units are converted into
metric units per international reporting guidelines. Table A-248 provides a guide for determining the magnitude of
metric units.
Table A-248: Guide to Metric Unit Prefixes
Prefix/Symbol
Factor
atto (a)
CO
o
T—1
femto (f)
10"15
pico (p)
10-12
nano (n)
10"9
micro (p)
10"6
milli (m)
10"3
centi (c)
10"2
deci (d)
10"1
deca (da)
10
hecto (h)
102
kilo (k)
103
mega (M)
106
giga (G)
109
tera (T)
1012
peta (P)
1015
exa (E)
101S
Unit Conversions
1 kilogram =
1 pound =
1 short ton =
1 metric ton =
2.205 pounds
0.454 kilograms
2,000 pounds =
1,000 kilograms =
0.9072 metric tons
1.1023 short tons
1 cubic meter =
1 cubic foot =
1 U.S. gallon =
1 barrel (bbl) =
1 barrel (bbl) =
1 liter
35.315 cubic feet
0.02832 cubic meters
3.785412 liters
0.159 cubic meters
42 U.S. gallons
0.001 cubic meters
1 foot
1 meter
1 mile
1 kilometer
0.3048 meters
3.28 feet
1.609 kilometers
0.621 miles
1 acre = 43,560 square feet = 0.4047 hectares
1 square mile = 2.589988 square kilometers
4,047 square meters
Degrees Celsius= (Degrees Fahrenheit - 32)*5/9
Annex 6
A-503
-------
Degrees Kelvin = Degrees Celsius + 273.15
Density Conversions195
Methane 1 cubic meter = 0.67606 kilograms
Carbon dioxide 1 cubic meter = 1.85387 kilograms
Natural gas liquids
1
metric
ton =
11.6 barrels =
1,844.2 liters
Unfinished oils
1
metric
ton =
7.46 barrels =
1,186.04 liters
Alcohol
1
metric
ton =
7.94 barrels =
1,262.36 liters
Liquefied petroleum gas
1
metric
ton =
11.6 barrels =
1,844.2 liters
Aviation gasoline
1
metric
ton =
8.9 barrels =
1,415.0 liters
Naphtha jet fuel
1
metric
ton =
8.27 barrels =
1,314.82 liters
Kerosene jet fuel
1
metric
ton =
7.93 barrels =
1,260.72 liters
Motor gasoline
1
metric
ton =
8.53 barrels =
1,356.16 liters
Kerosene
1
metric
ton =
7.73 barrels =
1,228.97 liters
Naphtha
1
metric
ton =
8.22 barrels =
1,306.87 liters
Distillate
1
metric
ton =
7.46 barrels =
1,186.04 liters
Residual oil
1
metric
ton =
6.66 barrels =
1,058.85 liters
Lubricants
1
metric
ton =
7.06 barrels =
1,122.45 liters
Bitumen
1
metric
ton =
6.06 barrels =
963.46 liters
Waxes
1
metric
ton =
7.87 barrels =
1,251.23 liters
Petroleum coke
1
metric
ton =
5.51 barrels =
876.02 liters
Petrochemical feedstocks
1
metric
ton =
7.46 barrels =
1,186.04 liters
Special naphtha
1
metric
ton =
8.53 barrels =
1,356.16 liters
Miscellaneous products
1
metric
ton =
8.00 barrels =
1,271.90 liters
Energy Conversions
Converting Various Energy Units to Joules
The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule is the energy
required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) joules. A British thermal
unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the temperature of one pound of
water one degree Fahrenheit at or near 39.2 degrees Fahrenheit.
2.388xlOn calories
23.88 metric tons of crude oil equivalent
1 TJ =
947.8 million Btus
277,800 kilowatt-hours
Converting Various Physical Units to Energy Units
Data on the production and consumption of fuels are first gathered in physical units. These units must be converted to
their energy equivalents. The conversion factors in Table A-249 can be used as default factors, if local data are not
available. See Appendix A of ElA's Monthly Energy Review, November 2021 (EIA 2021) for more detailed information on
the energy content of various fuels.
195 Reference: EIA (2007)
A-504 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Table A-249: Conversion Factors to Energy Units (Heat Equivalents)
Fuel Type (Units)
Factor
Solid Fuels (Million Btu/Short ton)
Anthracite coal
22.57
Bituminous coal
23.89
Sub-bituminous coal
17.14
Lignite
12.87
Coal Coke
24.80
Natural Gas (Btu/Cubic foot)
1,037
Liquid Fuels (Million Btu/Barrel)
Motor gasoline
5.052
Aviation gasoline
5.048
Kerosene
5.670
Jet fuel, kerosene-type
5.670
Distillate fuel
5.825
Residual oil
6.287
Naphtha for petrochemicals
5.248
Petroleum coke
6.024
Other oil for petrochemicals
5.825
Special naphthas
5.248
Lubricants
6.065
Waxes
5.537
Asphalt
6.636
Still gas
6.000
Misc. products
5.796
Note: For petroleum and natural gas, Monthly
Energy Review, February 2022 (EIA 2022).
For coal ranks, State Energy Data Report
1992 (EIA 1993). All values are given in
higher heating values (gross calorific values).
Annex 6
A-505
-------
6.5. Chemical Formulas
Table A-250: Guide to Chemical Formulas
Symbol Name
Al Aluminum
AI2O3 Aluminum oxide
Br Bromine
C Carbon
CH4 Methane
C2H6 Ethane
CsHs Propane
CF4 Perfluoromethane
C2F6 Perfluoroethane, hexafluoroethane
c-CbF6 Perfluorocyclopropane
C3Fs Perfluoropropane
C4F6 Hexafluoro-l,3-butadiene
c-C4Fs Perfluorocyclobutane
C4FsO Octafluorotetrahydrofuran
C4F10 Perfluorobutane
c-C5Fs Perfluorocyclopentene
C5F12 Perfluoropentane
C6Fi4 Perfluorohexane
CFsl Trifluoroiodomethane
CFCIs Trichlorofluoromethane (CFC-11)
CF2CI2 Dichlorodifluoromethane (CFC-12)
CFsCI Chlorotrifluoromethane (CFC-13)
C2FbCIs Trichlorotrifluoroethane (CFC-113)*
CCI3CF3 CFC-113a*
C2F4CI2 Dichlorotetrafluoroethane (CFC-114)
C2F5CI Chloropentafluoroethane (CFC-115)
CHCI2F HCFC-21
CHF2CI Chlorodifluoromethane (HCFC-22)
C2F3HCI2 HCFC-123
C2F4HCI HCFC-124
C2FH3CI2 HCFC-141b
C2H3F2CI HCFC-142b
CF3CF2CHCI2 HCFC-225ca
CCIF2CF2CHCIF HCFC-225cb
CCI4 Carbon tetrachloride
CHCICCI2 Trichloroethylene
CCI2CCI2 Perchloroethylene, tetrachloroethene
CH3CI Methylchloride
CH3CCI3 Methylchloroform
CH2CI2 Methylenechloride
CHCI3 Chloroform, trichloromethane
CHF3 HFC-23
CH2F2 HFC-32
CH3F HFC-41
C2HF5 HFC-125
C2H2F4 HFC-134
CH2FCF3 HFC-134a
C2H3F3 HFC-143*
C2H3F3 HFC-143a*
CH2FCH2F HFC-152*
C2H4F2 HFC-152a*
A-506 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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CH3CH2F
HFC-161
C3HF7
HFC-227ea
CF3CF2CH2F
HFC-236cb
CF3CHFCHF2
HFC-236ea
C3H2F6
HFC-236fa
C3H3F5
HFC-245ca
CHF2CH2CF3
HFC-245fa
CF3CH2CF2CH3
HFC-365mfc
c5H2F10
HFC-43-10mee
CF30CHF2
HFE-125
CF2HOCF2H
HFE-134
CH30CF3
HFE-143a
CF3CHFOCF3
HFE-227ea
CF3CHCIOCHF2
HCFE-235da2
CF3CHFOCHF2
HFE-236ea2
CF3CH20CF3
HFE-236fa
CF3CF20CH3
HFE-245cb2
CHF2CH20CF3
HFE-245fal
CF3CH20CHF2
HFE-245fa2
CHF2CF20CH3
HFE-254cb2
CF3CH20CH3
HFE-263fb2
CF3CF20CF2CHF2
HFE-329mcc2
CF3CF20CH2CF3
HFE-338mcf2
CF3CF2CF20CH3
HFE-347mcc3
CF3CF20CH2CHF2
HFE-347mcf2
CF3CHFCF20CH3
HFE-356mec3
CHF2CF2CF20CH3
HFE-356pcc3
CHF2CF20CH2CHF2
HFE-356pcf2
CHF2CF2CH20CHF2
HFE-356pcf3
CF3CF2CH20CH3
HFE-365mcf3
CHF2CF20CH2CH3
HFE-374pcf2
C4F90CH3
HFE-7100
C4F90C2H5
HFE-7200
CH2CFCF3
HFO-1234yf
CHFCHCFs
HFO-1234ze(E)
CF3CHCHCF3
HFO-1336mzz(Z)
C3H2CIF3
HCFO-1233zd(E)
CHF20CF20C2F40CHF2
H-Galden 1040x
CHF20CF20CHF2
HG-10
CHF20CF2CF20CHF2
HG-01
CH30CH3
Dimethyl ether
CH2Br2
Dibromomethane
CH2BrCI
Dibromochloromethane
CHBr3
Tribromomethane
CHBrF2
Bromodifluoromethane
CH3Br
Methylbromide
CF2BrCI
Bromodichloromethane (Halon 1211)
CF3Br(CBrF3)
Bromotrifluoromethane (Halon 1301)
CF3I
FIC-1311
CO
Carbon monoxide
C02
Carbon dioxide
CaC03
Calcium carbonate, Limestone
CaMg(C03)2
Dolomite
CaO
Calcium oxide, Lime
CI
atomic Chlorine
F
Fluorine
Annex 6
A-507
-------
Fe
Iron
Fe203
Ferric oxide
FeSi
Ferrosilicon
GaAs
Gallium arsenide
H, H2
atomic Hydrogen, molecular Hydrogen
h2o
Water
h2o2
Hydrogen peroxide
OH
Hydroxyl
N, N2
atomic Nitrogen, molecular Nitrogen
nh3
Ammonia
nh4+
Ammonium ion
HNOs
Nitric acid
MgO
Magnesium oxide
NFs
Nitrogen trifluoride
N20
Nitrous oxide
NO
Nitric oxide
no2
Nitrogen dioxide
NO3
Nitrate radical
NOx
Nitrogen oxides
Na
Sodium
Na2C03
Sodium carbonate, soda ash
Na3AIF6
Synthetic cryolite
0, 02
atomic Oxygen, molecular Oxygen
03
Ozone
S
atomic Sulfur
h2so4
Sulfuric acid
sf6
Sulfur hexafluoride
SF5CF3
Trifluoromethylsulphur pentafluoride
S02
Sulfur dioxide
Si
Silicon
Sic
Silicon carbide
Si02
Quartz
* Distinct isomers.
A-508 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
6.6. Greenhouse Gas Precursors Cross-Walk of National Emission
Inventory (NEI) Categories to the National Inventory Report (NIR)
Emissions of precursor gases (CO, NOx, NMVOC, and S02) occur in all sectors and are summarized in Chapter 2, Section
2.3, presented in sectoral chapters of this Inventory. Emissions of these gases are provided by EPA's National Emissions
Inventory (NEI). The categories used in the NEI vary from those presented in this Inventory and included in IPCC
guidelines. Table A-251 below indicates how NEI Tier 1/Tier 2 categories were recategorized from NEI source categories
to those more closely aligned with National Inventory Report (NIR) categories and CRF categories, based on EPA (2022)
and detailed mapping of categories between this Inventory and the NEI. Precursor emissions from Agriculture and
LULUCF categories are estimated separately and therefore are not taken from EPA (2021); see Sections 5.7, 6.2, and 6.6.
Annex 6
A-509
-------
Table A-251: Cross-walk of NEI and NIR Categories for Greenhouse Gas Precursors
NEI Category (Tier 1)
NEI Category (Tier 2)
NIR Chapter
NIR Category
CRF Category
Fuel Combustion Electric
Coal
Energy
Fossil Fuel
Combustion - Electric Power
l.A.l.a Public Electricity and Heat
Utility
Sector
Production
Fuel Combustion Electric
Gas
Energy
Fossil Fuel
Combustion - Electric Power
l.A.l.a Public Electricity and Heat
Utility
Sector
Production
Fuel Combustion Electric
Internal Combustion
Energy
Fossil Fuel
Combustion - Electric Power
l.A.l.a Public Electricity and Heat
Utility
Sector
Production
Fuel Combustion Electric
Oil
Energy
Fossil Fuel
Combustion - Electric Power
l.A.l.a Public Electricity and Heat
Utility
Sector
Production
Fuel Combustion Electric
Other
Energy
Fossil Fuel
Combustion - Electric Power
l.A.l.a Public Electricity and Heat
Utility
Sector
Production
Fuel Combustion Industrial
Coal
Energy
Fossil Fuel
Combustion - Industrial
l.A.2.g Other (please specify)
Fuel Combustion Industrial
Gas
Energy
Fossil Fuel
Combustion - Industrial
l.A.2.g Other (please specify)
Fuel Combustion Industrial
Internal Combustion
Energy
Fossil Fuel
Combustion - Industrial
l.A.2.g Other (please specify)
Fuel Combustion Industrial
Oil
Energy
Fossil Fuel
Combustion - Industrial
l.A.2.g Other (please specify)
Fuel Combustion Industrial
Other
Energy
Fossil Fuel
Combustion - Industrial
l.A.2.g Other (please specify)
Fuel Combustion Other
Commercial/Institutional Coal
Energy
Fossil Fuel
Combustion - Commercial
l.A.4.a Commercial/Institutional
Fuel Combustion Other
Commercial/Institutional Gas
Energy
Fossil Fuel
Combustion - Commercial
l.A.4.a Commercial/Institutional
Fuel Combustion Other
Commercial/Institutional Oil
Energy
Fossil Fuel
Combustion - Commercial
l.A.4.a Commercial/Institutional
Fuel Combustion Other
Misc. Fuel Combustion (Except
Residential)
Energy
Fossil Fuel
Combustion - Commercial
l.A.4.a Commercial/Institutional
Fuel Combustion Other
Residential Other
Energy
Fossil Fuel
Combustion - Residential
l.A.4.b Residential
Fuel Combustion Other
Residential Wood
Energy
Fossil Fuel
Combustion - Residential
l.A.4.b Residential
Petroleum and Related
Asphalt Manufacturing
Energy
Other Energy
l.B.2.d Other
Industries
Petroleum and Related
Oil & Gas Production
Energy
Petroleum and Natural Gas Systems
l.B.2.d Other
Industries
Petroleum and Related
Petroleum Refineries & Related
Energy
Petroleum and Natural Gas Systems
l.B.2.d Other
Industries
Industries
Highway Vehicles
Compressed Natural Gas (CNG)
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.b Road Transportation
Highway Vehicles
Diesel Fuel
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.b Road Transportation
Highway Vehicles
Electricity
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.b Road Transportation
Highway Vehicles
Ethanol (E-85)
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.b Road Transportation
Highway Vehicles
Gasoline
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.b Road Transportation
Off-Highway
Aircraft
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.a Domestic Aviation
Off-Highway
Marine Vessels
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.d Domestic Navigation
Off-Highway
Non-Road Diesel
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.e Other Transportation
Off-Highway
Non-Road Gasoline
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.e Other Transportation
Off-Highway
Other
Energy
Fossil Fuel
Combustion - Transportation
l.A.3.e Other Transportation
Off-Highway
Railroads
Energy
Fossil Fuel
Combustion - Transportation
1.A.3.C Railways
A-510 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Chemical and Allied Product
Agricultural Chemical
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Manufacturing
Chemical and Allied Product
Inorganic Chemical
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Manufacturing
Chemical and Allied Product
Paint, Varnish, Lacquer, Enamel
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Manufacturing
Chemical and Allied Product
Pharmaceutical Manufacturing
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Chemical and Allied Product
Organic Chemical Manufacturing
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Chemical and Allied Product
Other Chemical Manufacturing
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Chemical and Allied Product
Polymer & Resin Manufacturing
IPPU
Chemical Industry
2.B.10 Other - Other non-specified
Manufacturing
Metals Processing
Ferrous Metals Processing
IPPU
Metal Industry
2.C.7 Other - Other non-specified
Metals Processing
Metals Processing NEC
IPPU
Metal Industry
2.C.7 Other - Other non-specified
Metals Processing
Non-Ferrous Metals Processing
IPPU
Metal Industry
2.C.7 Other - Other non-specified
Storage & Transport
Bulk Materials Storage
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Bulk Materials Transport
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Bulk Terminals & Plants
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Inorganic Chemical Storage
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Inorganic Chemical Transport
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Petroleum & Petroleum Product
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage
Storage & Transport
Petroleum & Petroleum Product
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Transport
Storage & Transport
Service Stations: Breathing &
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Emptying
Storage & Transport
Service Stations: Stage I
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Storage & Transport
Service Stations: Stage II
IPPU
Other Industrial Processes
2.H.3 Other-Storage and Transport
Solvent Utilization
Degreasing
IPPU
Other Industrial Processes
2.G.4 Other - Degreasing and Dry Cleaning
Solvent Utilization
Dry Cleaning
IPPU
Other Industrial Processes
2.G.4 Other - Degreasing and Dry Cleaning
Solvent Utilization
Graphic Arts
IPPU
Other Industrial Processes
2.G.4 Other-Graphic Arts
Solvent Utilization
Nonindustrial
IPPU
Other Industrial Processes
2.G.4 Other - Nonindustrial
Solvent Utilization
Solvent Utilization NEC
IPPU
Other Industrial Processes
2.G.4 Other - Other non-specified
Solvent Utilization
Other Industrial
IPPU
Other Industrial Processes
2.G.4 Other - Other non-specified
Solvent Utilization
Surface Coating
IPPU
Other Industrial Processes
2.G.4 Other - Surface Coating
Other Industrial Processes
Agriculture, Food, & Kindred
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Products
Other Industrial Processes
Construction
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Other Industrial Processes
Electronic Equipment
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Other Industrial Processes
Machinery Products
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Annex 6
A-511
-------
Other Industrial Processes Mineral Products IPPU Mineral Industry 2.H.3 Other - Other Industrial Processes
Other Industrial Processes Miscellaneous Industrial IPPU Other Industrial Processes 2.H.3 Other - Other Industrial Processes
Processes
Other Industrial Processes Rubber & Miscellaneous Plastic IPPU Other Industrial Processes 2.H.3 Other - Other Industrial Processes
Products
Other Industrial Processes
Textiles, Leather, & Apparel
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Products
Other Industrial Processes
Transportation Equipment
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Other Industrial Processes
Wood, Pulp & Paper, &
IPPU
Other Industrial Processes
2.H.3 Other - Other Industrial Processes
Publishing Products
Miscellaneous
Agriculture & Forestry
IPPU
NA
NA
Miscellaneous
Health Services
IPPU
Miscellaneous
2.H.3 Other - Other Industrial Processes
Miscellaneous
Catastrophic/Accidental
IPPU
Miscellaneous
2.H.3 Other - Other Industrial Processes
Releases
Miscellaneous
Other
IPPU
Miscellaneous
2.H.3 Other - Other Industrial Processes
Miscellaneous
Other Combustion
IPPU
Miscellaneous; NAa
2.H.3 Other - Other Industrial Processes
Miscellaneous
Other Fugitive Dust
IPPU
Miscellaneous
2.H.3 Other - Other Industrial Processes
Miscellaneous
Repair Shops
IPPU
Miscellaneous
2.H.3 Other - Other Industrial Processes
Waste Disposal and Recycling
Incineration
Energy
Incineration of Waste
l.A.5.a Stationary
Waste Disposal and Recycling
Open Burning
Energy
Incineration of Waste
l.A.5.a Stationary
Waste Disposal and Recycling
Landfills
Waste
Landfills
5.A.1 Managed Waste Disposal Sites
Waste Disposal and Recycling
POTW
Waste
Wastewater Treatment
5.D.1 Domestic Wastewater
Waste Disposal and Recycling
Industrial Waste Water
Waste
Wastewater Treatment
5.D.2 Industrial Wastewater
Waste Disposal and Recycling
TSDF
Waste
Miscellaneous
5.E Other-Other non-specified
Waste Disposal and Recycling
Other
Waste
Miscellaneous
5.E Other-Other non-specified
Natural Resources
Biogenic
NA
NA
NA
Natural Resources
Geogenic
NA
NA
NA
Natural Resources
Miscellaneous
NA
NA
NA
Wildfires
NA
NAb
NA
NA (Not Applicable)
a Miscellaneous - Other Combustion emissions from Structural Fires and other sources are allocated to the IPPU miscellaneous NIR category. Miscellaneous - Other Combustion
emissions from agricultural fires, forest wildfires, and prescribed burning are not from the NEI and calculated separately in the NIR. Miscellaneous - Other Combustion
emissions from Slash burning (logging) are not included in the NIR.
b Wildfire emissions are not from the NEI and calculated separately in the NIR.
A-512 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
References
EIA (2022) Monthly Energy Review, February 2022, Energy Information Administration, U.S. Department of Energy,
Washington, D.C. DOE/EIA-0035(2022/02).
EIA (2007) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of Integrated Analysis and
Forecasting, Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE-EIA-0573
(2006).
EIA (1993) State Energy Data Report 1992, DOE/EIA-O214(93), Energy Information Administration, U.S. Department
of Energy. Washington, DC. December.
EPA (2022) "Crosswalk of Precursor Gas Categories." U.S. Environmental Protection Agency. April 6, 2022.
EPA (2021) "Criteria pollutants National Tier 1 for 1970 - 2020." National Emissions Inventory (NEI) Air Pollutant
Emissions Trends Data. Office of Air Quality Planning and Standards, March 2021. Available online at:
https://www.epa.gov/air-ernissions-inventories/air-pollutant-ernission5-trends-data.
IPCC (2021) Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change. [Masson-Delmotte, V., P. Zhai, A. Pirani, S.
L. Connors, C. Pean, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M. I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R.
Matthews, T. K. Maycock, T. Waterfield, O. Yelekgi, R. Yu and B. Zhou (eds.)]. Cambridge University Press. In Press.
IPCC (2013) Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth
Assessment Report of the Intergovernmental Panel on Climate Change. [Stocker, T.F., D. Qin, G.-K. Plattner, M.
Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press,
Cambridge, United Kingdom and New York, NY, USA, 1535 pp.
IPCC (2007) Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth
Assessment Report of the Intergovernmental Panel on Climate Change. S. Solomon, D. Qin, M. Manning, Z. Chen,
M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.). Cambridge University Press. Cambridge, United Kingdom
996 pp.
IPCC (1996) Climate Change 1995: The Science of Climate Change. Intergovernmental Panel on Climate Change,
J.T.Houghton, L.G. Meira Filho, B.A. Callander, N. Harris, A. Kattenberg, and K. Maskell (eds.). Cambridge University
Press. Cambridge, United Kingdom.
Annex 6
A-513
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ANNEX 7 Uncertainty
The annual U.S. Inventory presents the best effort to produce emission estimates for greenhouse gas source and sink
categories in the United States. These estimates were generated according to the UNFCCC reporting guidelines, following
the recommendations set forth in the 2006IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2006) and
2019 Refinement to the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (IPCC 2019). This Annex provides
an overview of the overall uncertainty analysis conducted to support the U.S. Inventory, including the sources of
uncertainty characterized throughout the Inventory associated with various source categories (including emissions and
sinks), and the methods used to collect, quantify, and present this uncertainty information. An Addendum to Annex 7 is
provided separately which includes additional information related to the uncertainty characteristics of input variables
used in the development of the overall uncertainty estimates reported in Section 1.7 of the Inventory report.
7.1. Overview
The uncertainty analysis conducted in support of the Inventory (1) determines the quantitative uncertainty associated
with the emission source and sink estimates presented in the main body of this report, (2) evaluates the relative
contribution of the input parameters to the uncertainty associated with each source or sink category estimate and in the
overall inventory and (3) estimates the uncertainty in the overall emissions for the latest year, the base year and in the
emissions trend. Note, overall uncertainty estimates in the Inventory capture quantifiable uncertainties in the input
activity and emission factors data, but do not account for the potential of additional sources of uncertainty such as
modeling uncertainties, measurement errors, and misreporting or misclassification. Thus, the U.S. Inventory uncertainty
analysis helps inform and prioritize improvements for source and sink categories estimation process which are discussed
in the "Planned Improvements" sections of each source or sink category's discussion within the main body of the report.
For each source or sink category, the uncertainty analysis highlights opportunities for changes to data measurement,
data collection, and calculation methodologies to reduce uncertainties.
For some category estimates, such as C02 emissions from energy-related combustion activities, the impact of
uncertainties on overall emission estimates is relatively small. For some other limited categories of emissions,
uncertainties could have a larger impact on the estimates presented (i.e., storage factors of non-energy uses of fossil
fuels). In all source and sink category chapters, the inventory emission estimates include "Uncertainty and Time-Series
Consistency" sections that consider both quantitative and qualitative assessments of uncertainty, considering factors
consistent with good practices noted in Volume 1, Chapter 3 of the 2006 IPCC Guidelines (e.g., completeness of data,
representativeness of data and models, sampling errors, measurement errors). The two major types of uncertainty
associated with these emission estimates are (1) model uncertainty, which arises when the emission and/or removal
estimation models used in developing the Inventory estimates do not fully and accurately characterize the respective
emission and/or removal processes (due to a lack of technical details or other resources), and (2) parameter uncertainty,
which arises due to potential bias or a lack of accurate, complete, representative, or precise input data such as emission
factors and activity data and inherent variability.
The uncertainty associated with emission (or removal) estimation models can be partially analyzed by comparing the
model emission (or removal) results with those of other models developed to characterize the same emission (or
removal) process, after taking into account differences in their conceptual framework, capabilities, data, and underlying
assumptions. However, in many cases it would be very difficult—if not impossible—to use this approach to quantify the
model uncertainty associated with the emission estimates in this report, primarily because most categories only have a
single model that has been developed to estimate emissions. Therefore, model uncertainty was not quantified in this
report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the individual source or sink
category description and inventory estimation methodology.
Parameter uncertainty encompasses several causes such as lack of completeness, lack of data or representative data,
sampling error, random or systematic measurement error, or misreporting or misclassification. Uncertainties associated
with input emission parameters have been quantified for all of the emission sources and sinks included in the U.S.
Inventory totals. Given the very low emissions for these source categories, uncertainty estimates were not derived.
A-514 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
7.2. Methodology and Results
The United States has developed both a quality assurance and quality control (QA/QC) and uncertainty management
plan (EPA 2002). Like the QA/QC plan, the uncertainty management plan is part of a continually evolving process. The
uncertainty management plan provides for a quantitative assessment of the Inventory analysis itself, thereby
contributing to continuing efforts to understand both what causes uncertainty and how to improve Inventory and
accuracy. Although the plan provides both general and specific guidelines for implementing a quantitative uncertainty
analysis, its components are intended to evolve over time, consistent with the inventory estimation process. The U.S.
plan includes procedures and guidelines, and forms and templates, for developing quantitative assessments of
uncertainty in the national Inventory estimates (EPA 2002). For the 1990 through 2020 Inventory, EPA has used the
uncertainty management plan as well as the methodology presented in the 2006IPCC Guidelines and 2019 Refinement.
The 2006 IPCC Guidelines and 2019 Refinement recommend two methods—Approach 1 and Approach 2—for developing
quantitative estimates of uncertainty associated with individual categories and the overall Inventory estimates. The
United States is continuing efforts to develop quantitative estimates of uncertainty for all source categories using
Approach 2. In following the UNFCCC requirement under Article 4.1, emissions from International Bunker Fuels, Wood
Biomass and Biofuel Consumption, and Indirect Greenhouse Gas Emissions are not included in the total emissions
estimated for the U.S. Inventory; therefore, no quantitative uncertainty estimates have been developed for these
categories.196 C02 Emissions from Biomass and Biofuel Consumption are accounted for implicitly in the Land Use, Land-
Use Change and Forestry (LULUCF) chapter through the calculation of changes in carbon stocks. The Energy sector
provides an estimate of C02 emissions from Biomass and Biofuel Consumption as a memo item for informational
purposes, consistent with the UNFCCC reporting requirements.
Approach 1 and Approach 2 Methods
The Approach 1 method for estimating uncertainty is based on the propagation of errors, as shown in Eq. 3.1 and Eq. 3.2
of the 2006 IPCC Guidelines and 2019 Refinement. These equations combine the random component of uncertainty
associated with the activity data and the emission (or the other) factors. Inherent in employing the Approach 1 method
are the assumptions that, for each source and sink category, (i) both the uncertainties in the activity data and the
emission factor values are approximately normally distributed, (ii) the coefficient of variation (i.e., the ratio of the
standard deviation to the mean) associated with each input variable is less than 30 percent, and (iii) the input variables
within and across sub- source categories are not correlated (i.e., value of each variable is independent of the values of
other variables).
The Approach 2 method is preferred if (i) the uncertainty associated with the input variables is large (i.e., >30 percent),
(ii) the distributions of uncertainties in the underlying the input variables are not normal (e.g., non-gaussian), (iii) the
estimates of uncertainty associated with the input variables are correlated, and/or if (iv) a complex estimation
methodology and/or several input variables are used to characterize the emission (or removal) process. Due to the input
parameters and estimation methodologies used in the Inventory, the uncertainties are assessed using the Approach 2
method for all categories where sufficient and reliable data are available to characterize the uncertainty of the input
variables.
The Approach 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the Monte Carlo
method). Under this method, emission (or removal) estimates for a particular source (or sink) category are estimated by
randomly selecting values of emission factors, activity data, and other estimation parameters according to their
individual Probability Density Functions (PDFs). This process is repeated many times using computer software, in order to
build up the probability density function, which is then used to estimate the final uncertainty values of the overall
emission (or removal) estimates for that source (or sink). For most categories, the Monte Carlo approach is implemented
using commercially available simulation software such as Palisade's @RISK Microsoft Excel add-in.
196 However, because the input variables that determine the emissions from the Fossil Fuel Combustion and the International
Bunker Fuels source categories are correlated, uncertainty associated with the activity variables in the International Bunker
Fuels was taken into account in estimating the uncertainty associated with the Fossil Fuel Combustion.
Annex 7
A-515
-------
Characterization of Uncertainty in Input Variables
Both Approach 1 and Approach 2 uncertainty analyses require that all the input variables have defined PDFs. In the
absence of sufficient data measurements, data samples, or expert judgments that determined otherwise, the PDFs
incorporated in the current source or sink category uncertainty analyses were limited to normal, lognormal, uniform,
triangular, pert, and beta distributions. The choice among these six PDFs depended largely on the observed or measured
data and expert judgment. If no additional uncertainty information is available than the previous year's Inventory
uncertainty data is used. Input variables with asymmetrical PDFs shift the overall output which can lead to asymmetrical
bounds for a source (or sink) category and in turn, for the overall Inventory uncertainty analysis.
Individual Source and Sink Category Inventory Uncertainty Estimates
The body of this report provides an overview of the input parameters and sources of uncertainty for each source and
sink category. Table A-252 summarizes results based on assessments of source and sink category-level uncertainty. The
table presents base year (1990) and current year (2020) emissions for each source and sink category. The combined
uncertainty (at the 95 percent confidence interval) for each source and category is expressed as the percentage above
and below the total 2020 emissions estimated for each source and sink category. Uncertainty in the trend of each source
and sink category is described subsequently in this Appendix.
Table A-252: Summary Results of Source and Sink Category Uncertainty Analyses
Base Year
Source or Sink Category
Emissions3
2020 Emissions'1
2020 Uncertainty11
MMT CO? Eq.
MMT CO? Eq.
Lower
Bound
Upper
Bound
co2
5,122.5
4,715.7
-3%
3%
Fossil Fuel Combustion
4,731.2
4,342.7
-2%
4%
Non-Energy Use of Fuels
112.2
121.0
-37%
49%
Cement Production
33.5
40.7
-6%
6%
Iron and Steel Production & Metallurgical Coke
104.7
37.7
-17%
17%
Natural Gas Systems
31.9
35.4
-16%
19%
Petroleum Systems
9.6
30.2
-22%
26%
Petrochemical Production
21.6
30.0
-5%
6%
Incineration of Waste
12.9
13.1
-17%
17%
Ammonia Production
13.0
12.7
-10%
11%
Lime Production
11.7
11.3
-2%
2%
Other Process Uses of Carbonates
6.2
9.8
-19%
29%
Urea Consumption for Non-Agricultural Purposes
3.8
6.0
-14%
14%
Urea Fertilization
2.4
5.3
-43%
3%
Carbon Dioxide Consumption
1.5
5.0
-5%
5%
Liming
4.7
2.4
-111%
98%
Coal Mining
4.6
2.2
-68%
76%
Glass Production
2.3
1.9
-2%
2%
Aluminum Production
6.8
1.7
-2%
2%
Soda Ash Production
1.4
1.5
-9%
8%
Ferroalloy Production
2.2
1.4
-13%
13%
Titanium Dioxide Production
1.2
1.3
-13%
13%
Zinc Production
0.6
1.0
-19%
20%
Phosphoric Acid Production
1.5
0.9
-18%
20%
Lead Production
0.5
0.5
-15%
16%
Carbide Production and Consumption
0.2
0.2
-9%
9%
Abandoned Oil and Gas Wells
+
+
-83%
197%
Magnesium Production and Processing
0.1
+
-4%
4%
Wood Biomass, Ethanol, and Biodiesel Consumption
219.4
291.6
NE
NE
International Bunker Fuelsd
103.6
69.6
NE
NE
ch4
780.8
650.4
-10%
10%
Enteric Fermentation
163.5
175.2
-11%
18%
Natural Gas Systems
195.5
164.9
-18%
18%
Landfills
176.6
109.3
-23%
22%
A-516 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Manure Management
34.8
59.6
-18%
20%
Coal Mining
96.5
41.2
-9%
17%
Petroleum Systems
47.8
40.2
-28%
32%
Wastewater T reatment
20.3
18.3
-35%
23%
Rice Cultivation
16.0
15.7
-75%
75%
Stationary Combustion
8.6
7.9
-34%
125%
Abandoned Oil and Gas Wells
6.5
6.9
-83%
197%
Abandoned Underground Coal Mines
7.2
5.8
-22%
20%
Composting
0.4
2.3
-58%
58%
Mobile Combustion
6.5
2.2
-8%
24%
Field Burning of Agricultural Residues
0.4
0.4
-18%
18%
Petrochemical Production
0.2
0.3
-57%
46%
Anaerobic Digestion at Biogas Facilities
+
0.2
-54%
54%
Carbide Production and Consumption
+
+
-9%
9%
Ferroalloy Production
+
+
-12%
13%
Iron and Steel Production & Metallurgical Coke
+
+
-21%
23%
Anaerobic Digestion at Biogas Facilities
+
+
NE
NE
International Bunker Fuels'1
0.2
0.1
NE
NE
n2o
450.5
426.1
-21%
27%
Agricultural Soil Management
316.0
316.2
-27%
26%
Stationary Combustion
25.1
23.2
-24%
51%
Manure Management
13.9
19.7
-16%
24%
Mobile Combustion
44.6
17.4
-8%
19%
Wastewater T reatment
16.6
23.5
-35%
194%
Nitric Acid Production
12.1
9.3
-5%
5%
AdipicAcid Production
15.2
8.3
-5%
5%
N20 from Product Uses
4.2
4.2
-24%
24%
Composting
0.3
2.0
-58%
58%
Caprolactam, Glyoxal, and Glyoxylic Acid Production
1.7
1.2
-31%
32%
Incineration of Waste
0.5
0.4
-53%
162%
Electronics Industry
+
0.3
-10%
11%
Field Burning of Agricultural Residues
0.2
0.2
-17%
17%
Petroleum Systems
+
+
-22%
26%
Natural Gas Systems
+
+
-16%
19%
International Bunker Fuels'1
0.9
0.6
NE
NE
HFCs, PFCs, SF6 and NF3
99.7
189.2
-8%
8%
Substitution of Ozone Depleting Substances
0.2
176.3
-3%
14%
Electronics Industry
3.6
4.4
-6%
7%
Electrical Transmission and Distribution
23.2
3.8
-16%
18%
HCFC-22 Production
46.1
2.1
-7%
10%
Aluminum Production
21.5
1.7
-6%
7%
Magnesium Production and Processing
5.2
0.9
-9%
9%
Total Gross Emissions0
6,453.5
5,981.4
-2%
5%
LULUCF Emissions'
31.4
53.2
-17%
18%
LULUCF Carbon Stock Change Flux5
(892.0)
(812.2)
32%
-20%
LULUCF Sector NetTotalh
(860.6)
(758.9)
35%
-22%
Net Emissions (Sources and Sinks)8
5,592.8
5,222.4
-5%
6%
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
+ Does not exceed 0.05 MMT C02 Eq. or 0.5 percent.
NE (Not Estimated)
a Base Year is 1990 for all sources.
bThe uncertainty estimates correspond to a 95 percent confidence interval, with the lower bound corresponding to 2.5th
percentile and the upper bound corresponding to 97.5th percentile.
c Emissions from Wood Biomass and Biofuel Consumption are not included in the energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
e Totals exclude emissions for which uncertainty was not quantified.
Annex 7
A-517
-------
f LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands, Land Converted to Flooded Land, and Flooded Land Remaining Flooded Land; and N20 emissions from
Forest Soils and Settlement Soils.
g LULUCF Carbon 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. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
hThe LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
Overall (Aggregate) Inventory Level Uncertainty Estimates
The overall level uncertainty estimate for the U.S. Inventory was developed using the IPCC Approach 2 uncertainty
estimation methodology for 1990 and 2020. The overall Inventory uncertainty estimates were estimated by combining
the Monte Carlo simulation output data for each emission source or sink category (as described above) across all sources
and categories as a function of gas. If such detailed output data were not available for a particular source or sink
category, individual PDFs were assigned based on the most detailed data available from the category-specific
quantitative uncertainty analysis. The overall Inventory uncertainty was then derived through the resulting PDF of the
combined emissions data.
For select categories such as composting, several LULUCF source categories, and parts of Agricultural Soil Management
source categories, Approach 1 uncertainty results were used in the overall uncertainty analysis. However, for all other
emission sources, Approach 2 uncertainty results were used in the overall uncertainty estimation.
The overall uncertainty model results indicate that the 1990 U.S. greenhouse gas emissions are estimated to be within
the range of approximately 6,330.2 to 6,761.5 MMT C02 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of-2 percent to 5 percent with respect to the total U.S. greenhouse gas emission estimate of
approximately 6,453.5 MMT C02 Eq. The uncertainty interval associated with total C02 emissions, ranges from -2 percent
to 5 percent of total C02 emissions estimated. The results indicate that the uncertainty associated with the inventory
estimate of the total CH4 emissions ranges from -8 percent to 12 percent, uncertainty associated with the total inventory
N20 emission estimate ranges from -19 percent to 28 percent, and uncertainty associated with fluorinated greenhouse
gas (F-GHG) emissions ranges from -9 percent to 13 percent. When the LULUCF sector is included in the analysis, the
uncertainty is estimated to be -5 to 6 percent of Net Emissions (sources and sinks) in 1990. The uncertainties presented
are quantifiable uncertainties in the input activity and emission factors data, not uncertainties in the models, data
representativeness, measurement errors, or misreporting or misclassification of data.
Table A-253: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 1990 (MMT CO2
Eq. and Percent)
1990
Emission Standard
Estimate Uncertainty Range Relative to Emission Estimate3 Mean'5 Deviation'5
Gas (MMTC02
Eq.) (MMTC02Eq.) (%) (MMT C02 Eq.)
Lower
Upper
Lower
Upper
Boundc
Boundc
Bound
Bound
co2
5,122.5
5,017.3
5,357.6
-2%
5%
5,186.5
88.0
CH4d
780.8
720.1
871.5
-8%
12%
794.9
38.8
N2Od
450.5
365.6
574.9
-19%
28%
457.8
54.1
PFCs, HFCs, SF6, and NF3d
99.7
90.2
112.5
-9%
13%
100.4
5.6
Total Gross Emissions
6,453.5
6,330.2
6,761.5
-2%
5%
6,539.5
110.6
LULUCF Emissions6
31.4
29.3
33.8
-7%
8%
31.5
1.1
LULUCF Carbon Stock Change Fluxf
(892.0)
(1,183.9)
(709.3)
33%
-20%
(944.1)
119.3
LULUCF Sector Net Totals
(860.6)
(1,152.7)
(677.7)
34%
-21%
(912.6)
119.3
Net Emissions (Sources and Sinks)
5,592.8
5,306.8
5,953.6
-5%
6%
5,626.9
163.9
A-518 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
a The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5th percentile and the upper bound corresponding to 97.5th percentile.
b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation of the simulated values from the mean.
c 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.
d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N20, and high GWP
gases used in the inventory emission calculations for 1990.
e LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands, Land Converted to Flooded Land, and Flooded Land Remaining Flooded Land; and N20 emissions from
Forest Soils and Settlement Soils.
f LULUCF Carbon 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. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
s The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
The overall uncertainty model results indicate that the 2020 U.S. greenhouse gas emissions are estimated to be within
the range of approximately 5,863.8 to 6,253.0 MMT C02 Eq., reflecting a relative 95 percent confidence interval
uncertainty range of-2 percent to 5 percent with respect to the total gross U.S. greenhouse gas emission estimate of
approximately 5,981.4 MMT C02 Eq. The uncertainty interval associated with total C02 emissions, which constitute about
79 percent of the total U.S. greenhouse gas emissions in 2020, ranges from -2 percent to 4 percent of total C02 emissions
estimated. The results indicate that the uncertainty associated with the inventory estimate of the total CH4 emissions
ranges from -8 percent to 11 percent, uncertainty associated with the total inventory N20 emission estimate ranges from
-20 percent to 29 percent, and uncertainty associated with fluorinated greenhouse gas (F-GHG) emissions ranges from -3
percent to 13 percent. When the LULUCF sector is included in the analysis, the uncertainty is estimated to be -5 to 6
percent of Net Emissions (sources and sinks) in 2020.
A summary of the overall quantitative uncertainty estimates is shown below.
Table A-254: Quantitative Uncertainty Assessment of Overall National Inventory Emissions for 2020 (MMT CO2
Eq. and Percent)
2020
Emission
Estimate Uncertainty Range Relative to Emission Estimate3
Gas (MMTC02
Eq.) (MMTC02Eq.) (%)
Lower
Upper
Lower
Upper
Boundc
Boundc
Bound
Bound
co2
4,715.7
4,610.6
4,908.0
-2%
4%
4,759.8
76.4
CH4d
650.4
595.9
723.6
-8%
11%
659.7
32.6
N2Od
426.1
342.4
551.1
-20%
29%
436.1
53.3
PFC, HFC, SFe, and NF3d
189.2
182.6
213.7
-3%
13%
198.2
7.9
Total Gross Emissions
5,981.4
5,863.8
6,253.0
-2%
5%
6,053.7
98.2
LULUCF Emissions6
53.2
44.4
62.9
-17%
18%
53.5
4.9
LULUCF Carbon Stock Change Fluxf
(812.2)
(1,075.7)
(647.8)
32%
-20%
(860.2)
109.4
LULUCF Sector Net Totals
(758.9)
(1,023.2)
(594.5)
35%
-22%
(806.7)
109.6
Net Emissions (Sources and Sinks)
5,222.4
4,956.9
5,540.9
-5%
6%
5,247.0
148.1
Standard
Meanb Deviation'5
(MMT C02 Eq.)
Annex 7
A-519
-------
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with
LULUCF.
a The lower and upper bounds for emission estimates correspond to a 95 percent confidence interval, with the lower bound
corresponding to 2.5th percentile and the upper bound corresponding to 97.5th percentile.
b Mean value indicates the arithmetic average of the simulated emission estimates; standard deviation indicates the extent of
deviation of the simulated values from the mean.
c 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.
d The overall uncertainty estimates did not take into account the uncertainty in the GWP values for CH4, N20, and high GWP
gases used in the inventory emission calculations for 2020.
e LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands, Land Converted to Flooded Land, and Flooded Land Remaining Flooded Land; and N20 emissions from
Forest Soils and Settlement Soils.
f LULUCF Carbon 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. Since the resulting flux is negative the signs of the resulting lower and upper
bounds are reversed.
s The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
Trend Uncertainty
In addition to the estimates of uncertainty associated with the current and base year emission estimates, this Annex also
presents the estimates of trend uncertainty. The 2006IPCC Guidelines define trend as the difference in emissions
between the base year (i.e., 1990) and the current year (i.e., 2020) Inventory estimates. However, for purposes of
understanding the concept of trend uncertainty, the emission trend is defined in this Inventory as the percentage change
in the emissions (or removal) estimated for the current year, relative to the emission (or removal) estimated for the base
year. The uncertainty associated with this emission trend is referred to as trend uncertainty.
Under the Approach 1 method, there are two types of uncertainty to consider when estimating the trend uncertainty in
an individual source or sink category. As described in the 2006 IPCC Guidelines, correlated (Type A) uncertainties are
estimated by comparing the change in emissions trend given a 1 percent change in both base (i.e., 1990) and current
emissions (i.e., 2020), while uncorrelated or random errors in the emissions trend (Type B) are estimated by comparing
the change in emissions trend given a 1 percent change in only the current year emissions. When combined, both types
of uncertainty capture the sensitivity in trend emission estimates to sources of uncertainty that are correlated between
the base and current year (Type A), as well as the random component of uncertainty in the emission estimates (Type B).
Under the Approach 2 method, the trend uncertainty is estimated using the Monte Carlo Stochastic Simulation
technique. As described in the 2006 IPCC Guidelines, this Approach follows four steps. First, the PDFs for emission
factors, activity data, and other input estimation parameters are determined for both the current and base year. For
purposes of this Inventory, due to data limitations, for some categories where uncertainty assessments for 1990 are
undergoing updates for future reports but were not ready to incorporate for this submission, a simple approach has
been adopted, under which the base year source or sink category emissions are assumed to exhibit the same uncertainty
characteristics as the current year emissions (or removals). Source and sink category-specific PDFs for base year
estimates were developed using current year (i.e., 2020) uncertainty output data. These were adjusted to account for
differences in magnitude between the two years' inventory estimates. The second and third steps follow the Monte
Carlo approach described previously to calculate repeated emission estimates for each source and sink category in the
base and current years according to the input data PDFs. The overall Inventory trend uncertainty estimate was
developed by combining all source and sink category-specific trend uncertainty estimates. These trend uncertainty
estimates represent the 95 percent confidence interval of the estimated percent change in emissions between 1990 and
2020 and are shown in Table A-255.
A-520 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-255: Quantitative Assessment of Trend Uncertainty (MMT CO2 Eq. and Percent)
Gas/Source
Base Year
Emissions3
2020
Emissions
Emissions
Trend
Trend Range
b
(MMT CO
2 Eq).)
(%)
(%)
Lower
Bound
Upper
Bound
co2
5,122.5
4,715.7
-8%
-12%
-4%
Fossil Fuel Combustion
4,731.2
4,342.7
-8%
-13%
-4%
Non-Energy Use of Fuels
112.2
121.0
8%
-39%
80%
Cement Production
33.5
40.7
22%
7%
40%
Iron and Steel Production & Metallurgical Coke
Production
104.7
37.7
-64%
-72%
-55%
Natural Gas Systems
31.9
35.4
11%
-14%
42%
Petroleum Systems
9.6
30.2
214%
115%
357%
Petrochemical Production
21.6
30.0
39%
28%
51%
Incineration of Waste
12.9
13.1
2%
-20%
30%
Ammonia Production
13.0
12.7
-3%
-18%
19%
Lime Production
11.7
11.3
-3%
-6%
-1%
Other Process Uses of Carbonates
6.2
9.8
57%
24%
113%
Urea Consumption for Non-Agricultural
Purposes
3.8
6.0
58%
27%
97%
Urea Fertilization
2.4
5.3
118%
23%
281%
Carbon Dioxide Consumption
1.5
5.0
238%
196%
288%
Liming
4.7
2.4
-49%
-601%
523%
Coal Mining
4.6
2.2
-53%
-87%
68%
Glass Production
2.3
1.9
-19%
-22%
-16%
Aluminum Production
6.8
1.7
-74%
-75%
-73%
Soda Ash Production
1.4
1.5
2%
-10%
16%
Ferroalloy Production
2.2
1.4
-36%
-46%
-24%
Titanium Dioxide Production
1.2
1.3
12%
-7%
34%
Zinc Production
0.6
1.0
60%
22%
109%
Phosphoric Acid Production
1.5
0.9
-39%
-55%
-17%
Lead Production
0.5
0.5
-4%
-22%
18%
Carbide Production and Consumption
0.2
0.2
-37%
-48%
-21%
Abandoned Oil and Gas Wells
0.0
+
9%
-1494%
1331%
Magnesium Production and Processing
0.1
+
-99%
-99%
-99%
Wood Biomass and Biofuel Consumptionc
219.4
291.6
33%
NE
NE
International Bunker Fuels'1
103.6
69.6
-33%
NE
NE
ch4
780.8
650.4
-17%
-28%
-5%
Enteric Fermentation
163.5
175.2
7%
-21%
45%
Natural Gas Systems
195.5
164.9
-16%
-35%
9%
Landfills
176.6
109.3
-38%
-56%
-12%
Manure Management
34.8
59.6
71%
9%
168%
Coal Mining
96.5
41.2
-57%
-64%
-49%
Petroleum Systems
47.8
40.2
-16%
-46%
33%
Wastewater T reatment
20.3
18.3
-10%
-46%
30%
Rice Cultivation
16.0
15.7
-2%
-528%
905%
Stationary Combustion
8.6
7.9
-8%
-66%
156%
Abandoned Oil and Gas Wells
6.5
6.9
6%
-87%
752%
Abandoned Underground Coal Mines
7.2
5.8
-20%
-43%
13%
Composting
0.4
2.3
498%
161%
1255%
Mobile Combustion
6.5
2.2
-66%
-70%
-58%
Field Burning of Agricultural Residues
0.4
0.4
14%
-22%
66%
Petrochemical Production
0.2
0.3
43%
-43%
251%
Anaerobic Digestion at Biogas Facilities
+
0.2
952%
349%
2386%
Carbide Production and Consumption
+
+
-46%
-57%
-30%
Annex 7
A-521
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Ferroalloy Production
+
+
-43%
-52%
-33%
Iron and Steel Production & Metallurgical Coke
+
+
-69%
-77%
-59%
Production
Incineration of Waste
+
+
-13%
NE
NE
International Bunker Fuels'1
0.2
0.1
-54%
NE
NE
n2o
450.5
426.1
-5%
-31%
32%
Agricultural Soil Management
316.0
316.2
0%
-36%
58%
Stationary Combustion
25.1
23.2
-7%
-48%
71%
Manure Management
13.9
19.7
41%
-7%
115%
Mobile Combustion
44.6
17.4
-61%
-70%
-43%
Wastewater T reatment
16.6
23.5
42%
-55%
268%
Nitric Acid Production
12.1
9.3
-23%
-29%
-18%
AdipicAcid Production
15.2
8.3
-45%
-48%
-42%
N20 from Product Uses
4.2
4.2
0%
-25%
28%
Composting
0.3
2.0
498%
167%
1249%
Caprolactam, Glyoxal, and Glyoxylic Acid
1.7
1.2
-28%
-54%
15%
Production
Incineration of Waste
0.5
0.4
-13%
-76%
203%
Electronics Industry
+
0.3
730%
454%
1518%
Field Burning of Agricultural Residues
0.2
0.2
15%
-20%
65%
Petroleum Systems
+
+
153%
50%
329%
Natural Gas Systems
+
+
105%
40%
197%
International Bunker Fuels'1
0.9
0.6
-30%
NE
NE
HFCs, PFCs, SF6, and NF3
99.7
189.2
90%
73%
125%
Substitution of Ozone Depleting Substances
0.2
176.3
77486%
29230%
797019%
Electronics Industry
3.6
4.4
25%
10%
42%
Electrical Transmission and Distribution
23.2
3.8
-84%
-89%
-75%
HCFC-22 Production
46.1
2.1
-95%
-96%
-95%
Aluminum Production
21.5
1.7
-92%
-93%
-92%
Magnesium Production and Processing
5.2
0.9
-82%
-85%
-78%
Total Gross Emissions0
6,453.5
5,981.4
-7%
-12%
-3%
LULUCF Emissions'
31.4
53.2
70%
39%
103%
LULUCF Carbon Stock Change Flux®
(892.0)
(812.2)
-9%
-37%
30%
LULUCF Sector Net Totalh
(860.6)
(758.9)
-12%
-40%
28%
Net Emissions (Sources and Sinks)8
5,592.8
5,222.4
-7%
-14%
1%
Notes: Totals may not sum due to independent rounding. Parentheses indicate net sequestration. Total emissions (excluding
emissions for which uncertainty was not quantified) are presented without LULUCF. Net emissions are presented with LULUCF.
+ Does not exceed 0.05 MMT C02 Eq. or 0.5 percent.
NE (Not Estimated)
a Base Year is 1990 for all sources.
bThe trend range represents a 95 percent confidence interval for the emission trend, with the lower bound corresponding to
2.5th percentile value and the upper bound corresponding to 97.5th percentile value.
c Emissions from Wood Biomass and Biofuel Consumption are not included specifically in the energy sector totals.
d Emissions from International Bunker Fuels are not included in the totals.
e Totals exclude emissions for which uncertainty was not quantified.
f LULUCF emissions include the CH4 and N20 emissions reported for Peatlands Remaining Peatlands, Forest Fires, Drained
Organic Soils, Grassland Fires, and Coastal Wetlands Remaining Coastal Wetlands; CH4 emissions from Land Converted to
Coastal Wetlands, Land Converted to Flooded Land, and Flooded Land Remaining Flooded Land; and N20 emissions from
Forest Soils and Settlement Soils.
g LULUCF Carbon 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.
' The LULUCF Sector Net Total is the net sum of all CH4 and N20 emissions to the atmosphere plus net carbon stock changes.
A-522 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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7.3. Information on Uncertainty Analyses by Source and Sink Category
The quantitative uncertainty estimates associated with each emission and removal category are reported within sectoral
chapters of this Inventory following the discussions of inventory estimates and their estimation methodology. To better
understand the uncertainty analysis details, refer to the respective chapters and Uncertainty and Time-Series
Consistency sections in the body of this report. EPA provides additional documentation on uncertainty information
consistent with the guidance presented in Table 3.3 in Vol. 1, Chapter 3 of the 2006IPCC Guidelines for National
Greenhouse Gas Inventories (IPCC 2006) in an Uncertainty Addendum. Due to the number of detailed tables, it is not
published with the Inventory but is available upon request. EPA plans to publish this in a more easily accessible format
with future reports (e.g., the 2023 or 2024 Inventory reports). All uncertainty estimates are reported relative to the
current Inventory estimates for the 95 percent confidence interval, unless otherwise specified.
7.4. Reducing Uncertainty and Planned Improvements
The U.S. has implemented many improvements over the last several years that have reduced uncertainties across the
source and sink categories. These improvements largely result from new data sources that provide more accurate data
and/or increased data coverage, as well as methodological improvements, as described below.
BoxA-4: Reducing Uncertainty
The 2006 IPCC Guidelines provides the following guidance for ways to reduce Inventory uncertainty and improve the
quality of an Inventory and its uncertainty estimates.
• Improving conceptualization. Improving the inclusiveness of the structural assumptions chosen can reduce
uncertainties. An example is better treatment of seasonality effects that leads to more accurate annual
estimates of emissions or removals for the Agriculture, Land Use, Land Use Change and Forestry sector.
• Improving models. Improving the model structure and parameterization can lead to better understanding
and characterization of the systematic and random errors, as well as reductions in these causes of
uncertainty.
• Improving representativeness. This may involve stratification or other sampling strategies. For example,
continuous emissions monitoring systems (CEMS) can be used to reduce uncertainty for some sources and
gases as long as the representativeness is guaranteed. CEMS produces representative data at the facilities
where it is used, but in order to be representative of an entire source category, CEMS data must be available
for a random sample or an entire set of individual facilities that comprise the category. When using CEMS
both concentration and flow will vary, requiring simultaneous sampling of both attributes.
• Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of
greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile
sources, could not be developed at this time either because data are incomplete or because methodologies
do not exist for estimating emissions from these source categories. See Annex 5 of this report for a
discussion of the sources of greenhouse gas emissions and sinks excluded from this report. Consistent with
IPCC good practice principles, EPA continues efforts to estimate emissions and sinks from excluded emission
and removal sources occurring in U.S. and developing uncertainty estimates for all source and sink
categories for which emissions and removals are estimated.
• Collecting more measured data. Uncertainty associated with bias and random sampling error can be
reducing by increasing the sample size and filling in data gaps. This applies to both measurements and
surveys.
• Using more precise measurement methods. Measurement error can be reduced by using more precise
measurement methods, avoiding simplifying assumption, and ensuring that measurement technologies are
appropriately used and calibrated.
• Eliminating known risk of bias. This is achieved by ensuring instrumentation is properly positioned and
Annex 7
A-523
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calibrated, models or other estimation procedures are appropriate and representative, and by applying
expert judgements in a systematic way.
Improving state of knowledge. Improve the understanding of categories and processes leading to emissions
and removals, which can help to discover and correct for problems in incompleteness. It is Good Practice to
continuously improve emissions and removal estimates based on new knowledge.
The following sections describe the ongoing and planned Inventory and Uncertainty analysis improvements in the
context of these specific areas.
Recent and Ongoing Improvements
To collect more measured data, improve representativeness, and use more precise measurement methods, several source
categories in the Inventory now use the U.S. EPA's Greenhouse Gas Reporting Program (GHGRP) data, which improves
Inventory emission (or sink) estimation methods by allowing the incorporation of country-specific data rather than using
default IPCC estimates. EPA's GHGRP relies on facility-level data reported from large facilities emitting over 25,000
metric tons of C02 equivalent each year. The reported GHGRP data undergo a multi-step verification process, including
automated data checks to ensure consistency, comparison against expected ranged for similar facilities and industries,
and statistical analysis. See Annex 9 for more information on use of GHGRP data in the Inventory.
In addition to improving Inventory input data and methodologies, the use of EPA's GHGRP data also reduces uncertainty
in select Inventory emission categories. For example, replacing highly uncertain emission factor estimates with GHGRP
data for the Coal Mining category reduced the 95 percent uncertainty bounds for methane emissions from this category
from -15 percent to 18 percent in the 1990 to 2011 inventory down to -9 percent to 17 percent in the current (1990 to
2020) Inventory. Methane emission estimates from MSW landfills were also revised with GHGRP data, which resulted in
methodological and data quality improvements that also reduced the 95 percent uncertainty bounds for this category
compared to the prior use of default emission factors with larger assumed uncertainties.
Additional ongoing improvements to the U.S. Inventory uncertainty analyses for select categories will help to eliminate
known risk of bias, improve models, and advance thestate of knowledge, which may lead to further Inventory and
uncertainty analysis improvements in other areas including improved conceptualization and data representativeness.
Finally, ongoing improvements include review of documentation of source-specific input data and references, PDF
distributions, and Monte Carlo analysis results through the implementation of standardized source-specific uncertainty
reporting and documentation templates. Ongoing improvements to the overall Inventory Uncertainty Analysis
documentation will additionally ensure consistency with IPCC Good Practice and increase the transparency of the overall
analysis.
Planned Improvements
EPA continuously seeks new knowledge to improve the Inventory emissions and removal estimates. With available
resources, planned future improvements to the Inventory and Uncertainty Analysis are prioritized by focusing
improvements on categories identified in the Key Category Analysis (Chapter 1.5), or by quantitatively comparing the
relative contributions of uncertainties from various input parameters (e.g., activity data and emission factors) to the total
uncertainty levels within a source or sink category. Quantifying the sensitivity of the overall Inventory uncertainty
bounds to the uncertainty within each source or sink category can also prioritize future Inventory updates.
As described in Chapter 1.5, Key Categories in the current (1990 to 2020) Inventory include (but are not limited to)
categories that fall under Fossil Fuel Combustion (Chapter 3.1), Petroleum and Natural Gas Systems (Chapter 3.6 and
3.7), Industrial Processes and Product Use (Chapter 3), and Agriculture (Chapter 4). Planned improvements for these key
categories largely include the incorporation of more accurate and/or representative input parameters. For example, as
described in Chapter 3.1, planned inventory improvements for emissions from fossil fuel combustion categories include
efforts to assess the incorporation of more measured input activity data (e.g., GHGRP data, domestic marine activity) and
other input parameters (e.g., updated carbon factors for petroleum fuels, emission factors for non-road equipment,
etc.). Similarly, Chapters 3.6 and 3.7 discuss plans to continue stakeholder engagement to assess the potential for
incorporating new input data (e.g., from peer-reviewed publications, industry studies, etc.), updating methods for select
sources (e.g., Offshore Production, unassigned high-emitters), or including new sources (e.g., anomalous leak events)
A-524 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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within the Petroleum and Natural Gas System categories. Categories within the IPPU sector (Chapter 4) also discuss plans
to assess the future incorporation of additional facility-level GHGRP data, improve emission models (e.g., for ozone
depleting substance substitutes) and the methodological descriptions in the Inventory report. Similar to other categories,
planned improvements to Agricultural emissions from Manure Management and Enteric Fermentation include the
incorporation of new, more accurate and representative data, updates to emission models and conceptualization
(including moving to Tier 2 methods for all sources), as well as revised uncertainty estimates to the account for recent
updates. Details describing the planned improvements for these and nearly all other individual source and sink
categories are included in the category-specific Chapters of this report.
Implementation of these planned improvements will occur on an ongoing basis as new information becomes available.
Improvements are prioritized to make best use of available resources, including efforts to improve the accuracy of
emission factors, collect more detailed and representative activity data, as well as provide better estimates of input
parameter uncertainty. For example, further research is needed in some cases to improve the accuracy of emission
factors, including those currently applied to CH4 and N20 emissions from manure management. Lastly, for many
individual source categories, further research is also needed to characterize the PDFs of their input parameters more
accurately (e.g., emission factors and activity data). This might involve using measured or published statistics or
implementing a rigorous protocol to elicit expert judgment, if published or measured data are not available. Continued
efforts in these areas will reduce Inventory uncertainty and increase the completeness, accuracy, and transparency of
the category-specific and overall Inventory estimates.
Additional planned improvements for the overall Inventory uncertainty analysis include improving the presentation of
uncertainties in a format consistent with suggested tables in Volume 1, Chapter 3 of the 2006IPCC Guidelines. As
resources permit, in particular for key categories, improvements include reviewing and updating the existing uncertainty
models for the base year. This process would improve the base year and trend uncertainty analyses but may not
eliminate every simplifying assumptions described above due to limited data availability in the base year.
Annex 7
A-525
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References
IPCC (2019) 2019 Refinement to the 2006IPCC Guidelines for National Greenhouse Gas Inventories. Calvo Buendia, 3 E.,
Tanabe, K., Kranjc, A., Baasansuren, J., Fukuda, M., Ngarize S., Osako, A., Pyrozhenko, Y., Shermanau, P. and 4 Federici, S.
(eds). Published: IPCC, Switzerland.
IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories
Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K.
Tanabe (eds.). Hayama, Kanagawa, Japan.
EPA (2002) Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas 2
Inventory: Procedures Manual for Quality Assurance/Quality Control and Uncertainty Analysis, U.S. Greenhouse 3 Gas
Inventory Program, U.S. Environmental Protection Agency, Office of Atmospheric Programs, EPA 430-R-02- 4 007B, June
2002.
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ANNEX 8 QA/QC Procedures
8.1. Background
The purpose of this annex is to describe the Quality Assurance/Quality Control (QA/QC) procedures and information
quality considerations that are used throughout the process of creating and compiling the Inventory of U.S. Greenhouse
Gas Emissions and Sinks. This includes the evaluation of the quality and relevance of data and models used as inputs into
the Inventory; proper management, incorporation, and aggregation of data; and review of the numbers and estimates to
ensure that they are as accurate and transparent as possible. Quality control—in the form of both good practices (such
as documentation procedures) and checks on whether good practices and procedures are being followed—is applied at
every stage of inventory development and document preparation. In addition, quality assurance occurs at two stages —
an expert review and a public review. While both phases can significantly contribute to the quality of the Inventory, 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. As described in respective source category text, comments received
from these reviews may also result in updates or changes to continue to improve inventory quality.
8.2. Purpose
The Quality Assurance/Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas Inventory (QA/QC
Management Plan) guides the process of ensuring the quality of the Inventory. The QA/QC Management Plan describes
data and methodology checks, develops processes governing peer review and public comments, and provides guidance
on conducting an analysis of the uncertainty surrounding the emission estimates. The QA/QC Management Plan
procedures also stress continual improvement, providing for corrective actions that are designed to improve the
inventory estimates over time.
Key attributes of the QA/QC Management Plan are summarized in Figure A-20. 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 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 QA good practice
recommended in the 2006IPCC Guidelines (IPCC 2006).
• Quality Control: application of General (Tier 1) and Category-specific (Tier 2) quality controls and checks, as
recommended by 2006 IPCC Guidelines (IPCC 2006), along with consideration of secondary data and category-
specific checks (additional Tier 2 QC) in parallel, and coordination with the uncertainty assessment; the
development of protocols and templates, which provide for more structured communication and integration
with the suppliers of secondary information.
• Record Keeping: provisions to track which procedures have been followed, the results of the QA/QC process,
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, focusing on key categories.
• 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 to
reflect new information that becomes available as the program develops, methods are improved, or additional
Annex 8
A-527
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supporting documents become necessary. Further information on verification will be included in future
submissions.
In addition, based on the national QA/QC Management Plan for the Inventory, source and sink-specific QA/QC plans
have been developed for a number of sources and sinks. These plans follow the procedures outlined in the national
QA/QC plan, but tailor the procedures to the specific text and spreadsheets of the individual sources. For each
greenhouse gas emissions source or sink included in this Inventory, minimum general QA/QC analysis consistent with
Vol. 1, Chapter 6 of the 2006IPCC Guidelines has been undertaken. Where QA/QC activities for a particular source or sink
category go beyond the general level, and include category-specific checks, further explanation is provided within the
respective 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 or sink category
text. For transparency, responses to public and expert review comments are also posted on the EPA website with the
final report.
A-528 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Figure A-20: U.S. QA/QC Plan Summary
Data
Data
Calculating
Gathering
¦
^Documentation
¦
k Emissions
• Obtain data in
K
Contact reports
n Clearly label
electronic
for non-electronic
parameters, units.
format (if
communications
and conversion
possible)
•
Provide cell
factors
• Review
references for
• Review spreadsheet
spreadsheet
primary data
integrity
construction
elements
o Equations
o Avoid
•
Obtain copies of
o Units
hardwiring
all data sources
o Inputs and
o Use data
•
List and location
outputs
validation
of any
• Develop automated
4—1
V)
>V
o Protect cells
working/external
checkers for:
IB
• Develop
spreadsheets
o Input ranges
c
<
automatic
•
Document
o Calculations
checkers for:
assumptions
o Emission
o
4-*
o Outliers,
•
Complete QA/QC
aggregation
c
c
values, or
•
CRF and summary
checks
missing data
o Variable
types match
values
o Time series
consistency
• Maintain
tracking tab for
statu 5 of
gathering
efforts
tab links
• Check input
•
Check citations in
¦ Reproduce
data for
spreadsheet and
calculations
transcription
text for accuracy
• Review time
errors
and style
series consistency
• Inspect
•
Check reference
• Review changes
4—1
to
automatic
docket for new
in
(0
checkers
citations
data/consistency
c
<
¦ Identify
•
Review
with, IPCC
u
spreadsheet
documentation
methodology
o
modifications
that could
provide
additional
QA/QC checks
•
•
for any data /
methodology
changes
Complete QA/QC
checklists
CRF and summary
tab links
Cross-Cutting
Coordination
• Common starting
versions for each
inventory year
• Utilize
unalterable
summary and
CRF tab for each
source
spreadsheet for
linking to a
master summary
spreadsheet
• Follow strict
version control
procedures
• Document
QA/QC
procedures
8.3. Assessment Factors
The Inventory of U.S. Greenhouse Gas Emissions and Sinks development process follows guidance outlined in EPA's
Guidelines for Ensuring and Maximizing the Quality, ObjectivityUtility, and Integrity of Information Disseminated by the
Environmental Protection Agency143 and A Summary of General Assessment Factors for Evaluating the Quality of Scientific
143 EPA report #260R-02-008, October 2002, Available online at http://www.epa.gov/qualitv/guideliries-ensuring-and-
maximizing-aualitv-oblectivitv-utilitv-and-integritv-information.
Annex 8
A-529
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and Technical Information.144 This includes evaluating the data and models used as inputs into the Inventory against the
five general assessment factors: soundness, applicability and utility, clarity and completeness, uncertainty and variability,
evaluation and review. Table A-256 defines each factor and explains how it was considered during the process of
creating the current Inventory.
Table A-256: Assessment Factors and Definitions
General
Assessment Factor
Definition
How the Factor was Considered
Soundness (AF1)
The extent to which the
scientific and technical
procedures, measures,
methods or models employed
to generate the information are
reasonable for, and consistent
with their intended application.
The underlying data, methodologies, and models used to
generate the Inventory of U.S. Greenhouse Gas Emissions and
Sinks are reasonable for and consistent with their intended
application, to provide information regarding all sources and
sinks of greenhouse gases in the United States for the
Inventory year, as required per UNFCCC Annex I country
reporting requirements.
The U.S. emissions calculations follow the 2006 IPCC
Guidelines developed specifically for UNFCCC inventory
reporting. They are based on the best available, peer-
reviewed scientific information, and have been used by the
international community for over 25 years. When possible,
Tier 2 and Tier 3 methodologies from the 2006 IPCC Guidelines
are applied to calculate U.S. emissions more accurately.
Applicability and Utility
(AF2)
The extent to which the
information is relevant for the
Agency's intended use.
The Inventory's underlying data, methodology, and models
are relevant for their intended application because they
generate the sector-specific greenhouse gas emissions trends
necessary for assessing and understanding all sources and
sinks of greenhouse gases in the United States for the
Inventory year. They are relevant for communicating U.S.
emissions information to domestic audiences, and they are
consistent with the 2006 IPCC Guidelines developed
specifically for UNFCCC reporting purposes of international
greenhouse gas inventories.
Clarity and
Completeness (AF3)
The degree of clarity and
completeness with which the
data, assumptions, methods,
quality assurance, sponsoring
organizations and analyzes
employed to generate the
information are documented.
The methodological and calculation approaches applied to
generate the Inventory of U.S. Greenhouse Gas Emissions and
Sinks are extensively documented in the 2006 IPCC Guidelines.
The Inventory report describes its adherence to the 2006 IPCC
Guidelines, and the U.S. Government agencies provide data to
implement the 2006 IPCC Guidelines approaches. Any changes
made to calculations, due to updated data and methods, are
explained and documented in the report consistent with
UNFCCC reporting guidelines.
Uncertainty and
Variability (AF4)
The extent to which the
variability and uncertainty
(quantitative and qualitative) in
the information or in the
procedures, measures,
methods or models are
evaluated and characterized.
The evaluation of uncertainties for underlying data is
documented in the Annex 7 Uncertainty to the Inventory of
U.S. Greenhouse Gas Emissions and Sinks. In accordance with
the 2006 IPCC Guidelines, the uncertainty associated with the
Inventory's underlying input data was evaluated by running a
Monte Carlo uncertainty analysis on most source and/or
category emissions data to produce a 95 percent confidence
interval for the annual greenhouse gas emissions for that
source and/or sink. The error propagation approach is used to
144 EPA report #100/B-03/001, June 2003, Available online at http://www.epa.gov/risk/euidance-evaluatine-and-documentine-
qualitv-existing-scientific-and-technical-information. and Addendum to: A Summary of General Assessment Factors for
Evaluating the Quality of Scientific and Technical Information, December 2012, Available online at
http://www.epa.gov/risk/summarv-general-assessment-factors-evaluatine-quality-scientific-and-technical-information.
A-530 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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quantify uncertainties for some categories that are not
significant contributors to emissions across the time series. To
develop overall uncertainty estimates, the Monte Carlo
simulation output data for each emission source and/or sink
category uncertainty analysis were combined by type of gas,
and the probability distributions were fitted to the combined
simulation output data where such simulated output data
were available.
Evaluation and Review
(AF5)
The extent of independent
verification, validation and peer
review of the information or of
the procedures, measures,
methods or models.
The majority of the underlying methodology, calculations, and
models used to generate the Inventory of U.S. Greenhouse
Gas Emissions and Sinks have been independently verified and
peer reviewed as part of their publication in the 2006IPCC
Guidelines and the 2019 Refinement. In cases where the
methodology differs slightly from the 2006 IPCC Guidelines,
these were independently verified and validated by technical
experts during the annual expert review phase of the
Inventory development process.
For the data used in calculating greenhouse gas emissions for
each source, multiple levels of evaluation and review occur.
Data are compared to results from previous years, and
calculations and equations are continually evaluated and
updated as appropriate. Throughout the process, inventory
data and methodological improvements are planned and
incorporated.
The Inventory undergoes annual cycles of expert and public
review before publication. This process ensures that both
experts and the general public can review each category of
emissions and sinks and have an extended opportunity to
provide feedback on the methodologies used, calculations,
data sources, and presentation of information.
8.4. Responses to Review Processes
EPA is continually working to improve transparency, accuracy, completeness, comparability, and consistency of emission
estimates in the Inventory in response to the feedback received during the Expert, Public, and UNFCCC Review periods,
as well as supplemental stakeholder outreach efforts. For instance, as mentioned in the Planned Improvements section
of the Petroleum and Natural Gas Systems source categories (Section 3.6 and 3.7), EPA has engaged in stakeholder
outreach to increase the transparency in the Inventory methodology and to identify supplemental data sources that can
lead to methodological improvements. During the annual preparation of the Inventory of U.S. Greenhouse Gas Emissions
and Sinks, in considering and prioritizing improvements, EPA reviews the significance of the source and sink category
(i.e., key categories), along with QC, QA, and uncertainty assessments. Identified planned improvements to methods
(including data, emissions factors, and other key parameters), along with QA/QC and uncertainty assessments are
documented within each source and sink category to complement the Recalculations and Improvements chapter.
Additionally, the Executive Summary also highlights key changes in methodologies from previous Inventory reports.
As noted in the previous section, for transparency, responses to comments received while developing the annual
estimates from Public Review and Expert Review are posted on the EPA website with the final Inventory.145
As noted above in section 8.2, the expert technical review conducted by the UNFCCC supplements these QA processes.
This review by an international expert review team (ERT) occurs after submission of the final report to the UNFCCC and
assesses consistency with UNFCCC reporting guidelines. More information on the UNFCCC reporting guidelines and the
review process can be found here:
145 See https://www.epa.gov/eheemissions/inventorv-us-greenhouse-gas-emissions-and-sinks.
Annex 8
A-531
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• UNFCCC Reporting Guidelines for annual national greenhouse gas inventories146
• UNFCCC Review Process and Guidelines for annual national greenhouse gas inventories147
• Inventory Review reports of annual submissions (latest reviews).148
Table A-257 includes responses to findings from the latest UNFCCC expert review to facilitate future reviews. The
most recent review was conducted the week of November 2-7, 2020 and focused on the annual Inventory submitted in
April 2020.
146 Available online at: https://unfccc.int/resource/docs/2Q13/copl9/eng/lQaQ3.pdf#page=2.
147 Available online at: https://unfccc.int/resource/docs/2014/cop20/eng/10a03.pdf#page=3.
148 Available online at: https://unfccc.int/process/transparencv-and-reporting/reporting~and-review-under-the-
convention/greenhouse-gas-inventories-annex-i-parties/inventorv-review-reports-2019.
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Table A-257: Response to UN Review of the 2020 Inventory Submission
ID#
Issue Classification
Recommendation Made in Previous Review Report Including ERT
Assessment and Rationale
Response on Status of Issue
General
G.l
Annual submission
(G.l, 2019)
G.l, 2018)
(G.l, 2016)
(G.l, 2015)
(9, 2013)
(8, 2012)
Completeness
Improve the completeness of the inventory, in particular for those
categories for which there are methodologies in the 2006 IPCC
Guidelines. Addressing. The United States improved the completeness of
the inventory. The Party still reports "NE" for a number of categories (see
annex II for a list of the completeness issues identified by the ERT). The
ERT noted that the Party's planned improvements include incorporating
some of these categories into future submissions and/or providing
additional information on the likely level of emissions and removals in
annex 5 to the NIR (see also ID# G.2 below).
The United States is still addressing this issue and notes planned
improvements include incorporating these categories into future
submissions and/or providing additional information on the likely level
of emissions and removals in Annex 5 to the National Inventory Report
(NIR). EPA has approximated significance of additional categories for
some categories, per ongoing research into available data and also
included some categories previously not estimated (e.g., Flooded Lands
Remaining Flooded Lands and Lands Converted to Flooded Lands).
Remaining improvements will be made over time as data becomes
available and prioritized with other improvements to make best use of
available resources.
G.2
Annual submission
(G.2, 2019)
Completeness
The United States reported in the NIR (annex 5, table A-247, p.A-416) a
summary of sources and sinks not included in the inventory. This table
covers both sources and sinks for which methodologies are provided in
the 2006 IPCC Guidelines and those without methodologies. The ERT
commends the Party for the transparency provided by the table but
notes that a numerical value was not provided in the "Estimated 2017
emissions" column for all sources and sinks that occur in the United
States and for which there are methodologies in the 2006 IPCC
Guidelines. During the review, the Party stated that, in some cases,
approximated AD are currently unavailable to derive a likely level of
emissions or removals. Further, the effort to develop a proxy estimate is
better invested in developing estimates to include in the inventory itself
as part of ongoing planned improvements. The ERT acknowledges the
point made by the Party but notes that in accordance with paragraph
37(b) of the UNFCCC Annex 1 inventory reporting guidelines, Parties
should provide justifications for exclusions in terms of the likely level of
emissions for all mandatory sources and sinks considered insignificant
and the total national aggregate of estimated emissions for all gases and
categories considered insignificant shall remain below 0.1 per cent of
national total GHG emissions. The ERT recommends that the United
States provide a justification in the NIR, based on the likely level of
emissions as per paragraph 37(b) of the UNFCCC Annex 1 inventory
reporting guidelines, for all sources and sinks that occur but are
considered insignificant and excluded from the inventory and for which
there are methodologies provided in the 2006 IPCC Guidelines. The ERT
The United States is still addressing this issue and notes that planned
improvements include incorporating these categories into future
submissions and/or providing additional information on the likely level
of emissions and removals in Annex 5 to the NIR. These improvements
will be made over time as data becomes available and prioritized with
other improvements to make best use of available resources. Annex 5 of
the current (i.e., 2022) submission does include updates to both
quantitative and qualitative assessments of significance for some
categories.
Annex 8
A-533
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recommends that the Party provide in its next NIR evidence that the total
national aggregate of estimated emissions for all mandatory gases and
categories considered insignificant remains below 0.1 per cent of
national total GHG emissions.
Energy
E.l
1. General (energy
sector) - gaseous fuels-
C02 and CH4
(E.2, 2019)
(E.18, 2018)
Convention reporting
adherence
Addressing. Examine if the uncertainty analysis needs to be updated to
reflect the findings of the research on the natural gas combustion and
document the findings in future submissions. The uncertainty analysis is
provided in the NIR (pp.3-35-3-37) for C02 from fossil fuel combustion,
with supporting information given in annexes 2.2 and 7. The Party
explains in the NIR that the uncertainty estimates are not affected by the
updates to the carbon content of natural gas in the 2019 submission, and
that the general findings regarding the carbon content of fuels given in
NIR annex 2.2 (pp.A-103-A-106) still apply for natural gas without
updating. The uncertainty range reported in the 2020 submission for C02
emissions from natural gas combustion was in the 2019 inventory
submission with the exception of United States territories, where the
lower bound differs by 1 percentage point (from -13 per cent in the 2019
submission to -12 per cent in the 2020 submission). During the review,
the Party clarified that this was attributable to statistical variations in the
approach used (Monte Carlo analysis). The ERT considers that this issue
has not been fully addressed because no specific information has been
documented to demonstrate that the impact of updates to the carbon
content of natural gas on the uncertainty analysis is negligible.
This issue was addressed in the previous (i.e., 2021) submission. The
2021 NIR and current submission include specific information to
demonstrate that the impact of updates to the carbon content of
natural gas on the uncertainty analysis is negligible. See the 2021 NIR
Section 3.1 pp. 3-36: "For the United States, however, the impact of
these uncertainties on overall C02 emission estimates is believed to be
relatively small. See, for example, Marland and Pippin (1990). See also
Annex 2.2 for a discussion of uncertainties associated with fuel carbon
contents. Recent updates to carbon factors for natural gas and coal
utilized the same approach as previous Inventories with updated recent
data, therefore, the uncertainty estimates around carbon contents of
the different fuels as outlined in Annex 2.2 were not impacted and the
historic uncertainty ranges still apply."
E.2
1. General (energy
sector) - gaseous fuels-
C02 and CH4
(E.2, 2020)
(E.3, 2019)
(E.18, 2018)
Transparency
Addressing. Research C02 EF data for fuel gas used by upstream oil and
gas producers, and natural gas that has been processed and injected into
downstream distribution networks, in order to determine whether a
different C02 EF for fuel gas used in offshore oil and gas production than
the C02 EF for the processed gas that enters the transmission, storage
and distribution networks used in power and industrial plants and by
other users is warranted and whether it can be determined; and
document the findings of the research on the C02 EFs in the NIR. During
the review, the Party noted that, as reported in the NIR (section 3, p.3-36
and annex 2.2), the annual natural gas carbon content was updated
across the time series to reflect annual heat content data for natural gas
obtained from EIA. The C02 EF was based on the heat content of natural
gas. EIA also reports the heat content of natural gas produced as the
same value as natural gas consumed, meaning that the same EF would be
used in both upstream and downstream operations. However, the Party
did not document the findings of this research on C02 EFs in the NIR.
This issue was addressed in the previous (i.e., 2021) submission. The
2021 NIR documents research on why a separate C02 emission factor
(EF) for fuel gas used by upstream oil and gas producers is not needed.
See the 2021 NIR Annex Section 2.2 pp. A-96: "Furthermore, research
was done on C02 emission factors for fuel gas used by upstream oil and
gas producers in order to determine whether a different C02 emission
factor for fuel gas used in offshore oil and gas production than the
emission factor for the processed gas that enters the transmission,
storage and distribution networks used in power and industrial plants
and by other users is warranted. It was determined that a different
factor was not warranted as natural gas carbon content is based on the
heating value of the gas and EIA reports that the heat content of dry
natural gas produced (which is used in upstream oil and gas production)
is the same value as natural gas consumed in downstream operations
(EIA 2020a). Therefore, the same carbon factor is used for all natural gas
consumption including upstream operations. This language was retained
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in the 2022 NIR submission."
E.3
Fuel combustion -
reference approach - all
fuels-C02
(E.3, 2020)
(E.3, 2019)
(E.3, 2018)
(E.5, 2016)
(E.5, 2015)
(32, 2013)
(41, 2012)
Transparency
Addressing. Provide a more transparent clarification of how the
difference in emissions between the reference and the sectoral approach
is determined and which fuels are subtracted as NEU and feedstocks. For
the reference approach, the values reported in CRF table l.A(c) for
apparent energy consumption and apparent energy consumption
excluding NEU were the same for the entire time series. The Party
explained in the NIR (p.3-38) that emissions from carbon that was not
stored during NEU of fuels are subtracted under the sectoral approach
and reported separately but are not subtracted under the reference
approach. Thus, emission estimates under the reference approach are
comparable to those under the sectoral approach, except that the
emissions from NEU of fuels are included in the reference approach. The
ERT noted that a similar explanation was included in annex 4 to the NIR
(p.A-482). During the review, the Party confirmed that (1) the emission
scope of the reference and the sectoral approaches is the same since
carbon emissions from NEU (i.e. carbon not excluded) are included in
both approaches, except for other fossil fuels (see ID# E.25 in table 5); (2)
the energy consumption covered by the sectoral approach includes both
fuel consumption and NEU, which is reported under category 1.A.5 other,
hence the scope of energy consumption under the sectoral approach is
comparable with that under the reference approach without excluding
NEU; and (3) where it is indicated that NEU emissions are subtracted
under the sectoral approach, it means that they are reported separately,
not that they are not covered by the sectoral approach. The ERT
considers that it would be useful to include this explanation in the NIR of
future inventory submissions.
This issue was addressed in the previous (i.e., 2021) submission. The
United States refers the ERT to the 2021 NIR (annex 4, starting on pp. A-
470) describing the different treatments of NEU under the reference
and sectoral approaches. Further clarification is in the 2021 NIR Chapter
3 (pp. 3-39) and additional language is included in the 2021 submission
to address this issue; see Annex 4 pp. A-471 under Step 3 of the
Reference Approach description: "As a result, the Reference Approach
emission estimates are comparable to those of the Sectoral Approach,
with the exception that the NEU source category emissions are included
in the Reference Approach and reported separately in the Sectoral
Approach." Also, footnote 139 (pp. A-471): "The emission scope of the
reference and the sectoral approaches is the same since C emissions
from NEU (i.e., C not excluded) are included in both approaches, the
energy consumption covered by the sectoral approach includes both
fuel consumption and NEU, which is reported under category 1.A.5
other, hence the scope of energy consumption under the sectoral
approach is comparable with that under the reference approach
without excluding NEU. To the extent it is indicated that NEU emissions
are subtracted under the sectoral approach, it means that they are
reported separately, not that they are not covered by the sectoral
approach."
E.4
Feedstocks, reductants
and other NEU of fuels -
all fuels - C02
(E.5, 2019)
(E.4, 2018)
(E.7, 2016)
(E.7, 2015)
(38, 2013)
(47, 2012)
Comparability
Not resolved. Report only emissions from fuels combusted for the use of
energy under fuel combustion, and reallocate the relevant emissions
currently reported under the subcategory NEU (other) and part of the
fuel used under the subcategory United States territories (other).
Emissions from NEU of lubricants and waxes and other (e.g., asphalt and
road oil), which should be reported under CRF category 2.D, were still
reported under fuel combustion under category 1.A.5 and combined with
emissions from NEU of other fuels (see ID# E.3 above), and as "IE" under
the IPPU sector. Like in the 2019 submission, the Party indicated in the
NIR (p.3-54, box 3-5) that these emissions cannot be reallocated to IPPU
owing to national circumstances, in particular where a carbon balance
calculation was performed on the basis of the aggregated amount of
fossil fuels used for NEU, and that artificial adjustments to reallocate
emissions could lead to transparency issues. The ERT noted that a similar
The United States reiterates that it uses a country-specific methodology
for non-energy use of fuels in line with para. 10, Decision 24/CP.19 to
most accurately portray U.S. emissions from NEU.
The United States has improved the explanation of its country-specific
approach to the allocation of NEU of fuels in the introduction of the
IPPU Chapter 4 and Annex 2 of the 2021 NIR.
The United States continues to evaluate ways to update this approach,
including reallocation of lubricant non-combustion emissions and will
provide more clarification as applicable in the future NIRs (i.e., 2023
submission).
Annex 8
A-535
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explanation was provided in the IPPU section of the NIR (p.4-6), where it
is stated that artificial adjustments would result in the carbon emissions
for lubricants, waxes, asphalt and road oil being reported under the IPPU
sector, while carbon storage for those subcategories would be reported
under the energy sector. The ERT noted that the carbon balance
approaches for most petrochemical products were provided in NIR annex
2.3 (pp.A-141-A-157). Taking lubricants as an example, the ERT remarked
that, according to the information provided in the NIR (pp.A-152-A-154),
92 per cent of lubricants are categorized as lubricant oils and the
remaining 8 per cent as lubricant greases. Annex 2.3 to the NIR also
provides information on the commercial and environmental fate of oil
lubricant (table A-85) and grease lubricant (table A-86), with information
on the percentage combusted during use and not combusted during use.
The ERT is of the view that emissions relevant to lubricant use could be
allocated consistently with the 2006 IPCC Guidelines by using the existing
statistical information and assumptions mentioned above without raising
transparency concerns. While reallocating the small portion of emissions
associated with non-combustion use to the IPPU sector may not improve
the overall accuracy of the inventory, it would improve its comparability
with the inventories of other Annex 1 Parties (see ID# 1.18 below).
E.5
Feedstocks, reductants
and other NEU of fuels -
C02
(E.6, 2019)
(E.19, 2018)
Accuracy
Addressing. Continue to research the data for the emissions from NEU of
fuels reported under the energy and IPPU sectors mass-balance method
used across petrochemical production to estimate C02 emissions from
NEU of fuels and the method based on process emissions reported under
facility- level reporting used to estimate emissions from feedstock
consumption under IPPU, and further clarify the country-specific
approach used in the NIR consistently with paragraph 10 of the UNFCCC
Annex 1 inventory reporting guidelines. The Party reported in its NIR (p.4-
58) that some degree of double counting may occur between C02
emissions from NEU of fuels in the energy sector and C02 process
emissions from petrochemical production in the IPPU sector, but that
data integration is not feasible as feedstock data from EIA used to
estimate NEU of fuels were aggregated by fuel type, rather than
disaggregated by both fuel type and individual IPPU industries. The Party
noted in the NIR (footnote 65 on p.3-48) and further clarified during the
review that this is not considered to be a significant issue since NEU
industrial release data (e.g., the Toxics Release Inventory) include
different categories of sources to those included under the IPPU sector,
and the NEU estimates account for roughly 20 per cent of the emissions
captured in the IPPU sector. During the review, the Party further clarified
that, for 2018, carbon emissions from industrial releases from NEU of
This issue was addressed in the current (i.e., 2022) submission. See, for
example, the 2022 NIR Section 3.2 for the following discussion: "It is
important to ensure no double counting of emissions between fuel
combustion, non-energy use of fuels and industrial process emissions.
For petrochemical feedstock production, our review of the categories
suggests this is not a significant issue since the non-energy use industrial
release data includes different categories of sources and sectors than
those included in the Industrial Processes and Product Use (IPPU)
emissions category for petrochemicals. Further data integration is not
available at his time because feedstock data from the EIA used to
estimate non-energy uses of fuels are aggregated by fuel type, rather
than disaggregated by both fuel type and particular industries. Also,
GHGRP-reported data on quantities of fuel consumed as feedstocks by
petrochemical producers is unable to be used due to the data failing
GHGRP CBI aggregation criteria. "
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fuels, reported as 6,500 kt C02 in table A-67 of annex 2.3 to the NIR (p.A-
136), represent 21.8 per cent of the emissions from petrochemical
production (29,700 kt C02 eq) reported under the IPPU sector, as shown
in NIR table 4-46 (p.4-59) and CRF table 2(I).A-H (sheet 1) for category
2.B.8. However, the ERT considers that the Party has not yet fully
addressed the recommendation, in particular the potential issue related
to possible double counting, which the Party considers not to be
significant, by describing how the country-specific approach is better able
to reflect the Party's national situation and how these methodologies are
compatible with the 2006 IPCC Guidelines (see ID#s E.4 above and 1.12
below).
E.6
International aviation -
liquid fuels-C02, CH4
and N20
(E.6, 2019)
(E.5, 2018)
(E.6, 2016)
(E.6, 2015)
(35, 2013)
Transparency
Addressing. Harmonize and reconcile the data between the reference
and the sectoral approach for the reporting of jet kerosene consumption
between CRF tables l.A(b) and l.D or furnish an adequate explanation of
inconsistencies, where appropriate. There are still inconsistencies in the
reporting of jet kerosene consumption as international bunker fuel
between CRF tables l.A(b) and l.D (e.g., 198.85 Mbbl (approx.
1,207,361.48 TJ) and 1,209,889.16 TJ for 2018, respectively). An
explanation was provided in footnote 6 to table A-244 of NIR annex 4
(p.4-487), indicating that jet kerosene used in international aviation has a
different NCV based on data specific to that source. The Party clarified
during the review that physical values of jet kerosene consumption are
converted on the basis of a combined calorific value across all sources of
jet fuel (export, import and stock change, as shown in CRF table l.A(b)),
which may result in inconsistency with jet fuel data for international
aviation (as shown in CRF table l.D). The Party further clarified that the
value in CRF table l.D is based on bunkers only (198.85 Mbbl and heating
content of 6,084.42 TJ/Mbbl) while the values in table l.A(b) are based
on apparent consumption, including imports, exports and so on, and
average heating value (-227.08 Mbbl and 6071.71 TJ/Mbbl). The ERT is of
the view that the amount of jet fuel used as international bunker fuel
should be reported as a single value that is consistent across the
approaches used in the inventory reporting. In this regard, the ERT
considers that the footnote and the additional information provided do
not fully explain the inconsistencies between CRF tables l.A(b) and l.D.
The ERT believes it is necessary to provide in the NIR the reason why
different heating values are applied to jet kerosene in CRF tables l.A(b)
and l.D to resolve this issue.
This issue was addressed in the current (i.e., 2022) submission. See the
2022 NIR Annex 4, Footnote 6 to Table A-229 for the following
discussion: "Jet fuel used in bunkers has a different heating value based
on data specific to that source." Values in CRF Table l.A(b) and l.D
match for residual and distillate fuels for international bunker
consumption. For jet fuel, there is a small discrepancy because of the
difference in granularity of data. In the Sectoral Approach, jet fuels are
broken out by different types with varying densities used to calculate
consumption. In the Reference Approach, only one heat content is used
to calculate consumption for all jet fuel from bunker fuels.
Annex 8
A-537
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E.7
l.A Fuel combustion -
sectoral approach -
biomass - CH4 and N20
(E.9, 2019)
(E.20, 2018)
Completeness
Not resolved. Advance the research on CH4 and N20 emissions from the
combustion of landfill gas, sewage gas and other biogas in order to
review data sources for biogas, review the reporting of non-C02
emissions in the waste sector and assess the need to add new estimates.
The NIR did not contain information on any such research. In addition, in
the 2020 inventory submission, the amount of CH4 recovered for energy
use for subcategory 5.A.l.a anaerobic (managed waste disposal sites)
was reported in CRF table 5.A as numerical values for 1990-2004 and as
"NE" for 2005-2018, and in the 2018 inventory submission as "IE" for
2005-2016. During the review, the Party clarified that it is conducting
research on the sources of data on biogas use and biogas combustion for
energy purposes to confirm whether or not these emissions are reported
elsewhere, and that updates to CH4 and N20 emissions from the
combustion of landfill gas, sewage gas and other biogas will be made, as
needed, and described in future inventory submissions (see ID# W.9
below).
The United States is still investigating sources of data on biogas use and
combustion for energy and confirming whether these emissions are not
reported elsewhere. Updates will be implemented as needed and
described in future submissions.
E.8
l.A.2.g Other
(manufacturing
industries and
construction) - liquid
fuels - C02, CH4 and N20
(E.12, 2019)
(E.22, 2018)
Transparency
Addressing. Document the impacts of the new model and the validity of
the outputs and transparently document the recalculations in the NIR
when the latest version of the model (MOVES2014b) is incorporated in
the inventory. The MOVES2014b model has been incorporated in
inventory development since the 2019 inventory submission, in which
the impact of the recalculation on CH4 and N20 emissions was explained
without any reference to C02 emissions. According to the information
provided in the 2020 NIR (p.3-36), no particular recalculation was
performed for non-road mobile machinery. In addition, no
documentation on the validity of the outputs of the model was included
in the NIR. During the review, the Party emphasized that (1) the use of
the MOVES2014b model was limited primarily to the estimation of CH4
and N20 emissions from non-transportation mobile sources; (2) the
model was also used to generate vehicle age distributions that were used
to estimate CH4 and N20 emissions from transportation sources; (3) it
plans to incrementally improve the discussion of the validity of the
MOVES2014b model in future inventory submissions; and (4) the model
was not used to derive C02 emissions from non-road mobile machinery,
which were calculated using fuel consumption data from EIA and were
included under the industrial and commercial categories of the inventory,
so any recalculations performed using the MOVES2014b model will not
impact the estimated C02 emissions from non-transportation mobile
sources. The ERT considers that this issue has not yet been fully resolved
as the NIR does not indicate that the recalculation using the
MOVES2014b model had no impact on C02 emissions from non-road
See explanation included in the current (i.e., 2022) submission in
Section 3.1 (CH4 and N20 from Mobile Combustion) of Chapter 3 and
Annex 3.2. The use of the MOVES model in the development of the
Inventory is limited primarily to the estimation of CH4 and N20
emissions from non-transportation mobile sources. The model is also
used to generate vehicle age distributions and mileage accumulations
that are used to estimate CH4 and N20 emissions from Transportation
sources.
The United States plans to incrementally improve the discussion of the
validity of the MOVES model in future submissions.
A-538 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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mobile machinery, and the NIR could provide more information on
specific assumptions that were made and modifications to the
MOVES2014b model (see ID# E.14 below).
E.9
l.A.2.g Other
(manufacturing
industries and
construction) - liquid
fuels - C02, CH4 and N20
(E.12,2019)
(E.23, 2018)
Comparability
Not resolved. Research whether data are available to accurately
reallocate emissions from fuel use by agricultural mobile machinery from
subcategory l.A.2.g to l.A.4.c.ii and fuel use for fishing vessels to
l.A.4.c.iii in order to improve the comparability of the submission and
ensure that emissions of all gases from a given source are reported under
the same IPCC category. If data are not available to accurately reallocate
emissions to the different categories, clarify, in the NIR, the country-
specific approach taken consistently with paragraph 10 of the UNFCCC
Annex 1 inventory reporting guidelines. The NIR did not state that such
data are not available or clarify the use of the country-specific approach.
The Party stated during the review that it is researching and comparing
various AD sources, in addition to updating the MOVES model inputs (see
ID# E.12 above). This will include researching the availability of data for
addressing the allocation of emissions from fuel use by agricultural
mobile machinery from subcategory l.A.2.g (other) to l.A.4.c.ii (off-road
vehicles and other machinery) and fuel use for fishing vessels to l.A.4.c.iii
(fishing).
The United States is researching the availability of data for addressing
the allocation of emissions from fuel use by agricultural mobile
machinery from subcategory l.A.2.g (other) to l.A.4.c.ii (off-road
vehicles and other machinery).
The United States has researched data on allocating emissions and fuel
use for fishing vessels to category l.A.4.c.iii (fishing) and determined
that the information is not available. The activity data (AD) on marine
fuel use is not specified in terms of type of vessel and includes
recreational vehicles as well as cargo and passenger carrying, military
(i.e., U.S. Navy), fishing, and miscellaneous support ships (e.g.,
tugboats). More information stating the data is not available is found in
the latest submission. See Annex 3.2 of the 2022 NIR.
E.10
l.A.2.g Other
(manufacturing
industries and
construction) - liquid
fuels - C02, CH4 and N20
(E.14, 2019)
(E.24, 2018)
Accuracy
Addressing. Research data by non-road mobile machinery vehicle type
across the different data sets, including the Federal Highway
Administration and MOVES model outputs, to determine the optimum
AD estimate for each subsource under non-road mobile machinery, and
improve inventory accuracy, as necessary, including for C02, CH4 and
N20 emissions from industrial, commercial, agricultural machinery and
fishing vessels. According to the NIR (p.3-40), EPA tested an alternative
approach for disaggregating gasoline between road and non-road use. It
used on-road fuel consumption output from the MOVES2014b model to
determine the percentage of the Federal Highway Administration
consumption data totals that are attributable to highway transportation
sources, and then applied this to the EIA total data to determine gasoline
consumption from highway transportation sources, such that the
remainder could be defined as industrial and commercial consumption
and allocated to non-road mobile machinery. However, as the results of
the test revealed differences between fuel consumption data from the
MOVES2014b model and those from the Federal Highway
Administration, no changes were made to the methodology for
estimating motor gasoline consumption for non-road mobile sources. The
ERT considers that this issue has not been fully addressed as the
optimum AD were not determined for each subsource under non-road
The United States notes that information on AD used to calculate non-
road mobile source emissions is discussed in the NIR Section 3.1 and
Annex 3.2. The language from the 2020 NIR specified in the issue
rationale in terms of testing an alternative approach was in reference to
a specific backcasting methodology used to address a time series
inconsistency. As noted, that test determined that no changes were
needed to the current approach and the AD being used were
appropriate. The United Stated is therefore unsure of the basis of this
issue in the UNFCCC reporting guidelines and 2006 IPCC Guidelines and
requests clarification on how optimum AD has not been determined.
Annex 8
A-539
-------
mobile machinery.
E.ll
1.A.3 Transport - liquid
fuels - C02, CH4 and N20
(E.15, 2019)
(E.25, 2018)
Accuracy
Addressing. Advance the research in order to implement as soon as
practicable the following improvements indicated during the review:
Updating on-road diesel CH4 and n20 EFs;
Developing improved methodology and data sources to estimate
emissions from class II and III (short-line and regional) rail locomotives;
Applying a consistent methodology over time to estimate vehicle miles
travelled for on-road vehicles by vehicle type, defined by wheel base;
Including ongoing research and documentation of minor emissions
sources currently not included in the inventory, such as urea use in
trucks, bio jet fuel, and compressed natural gas or liquefied petroleum
gas use in shipping.
(a) Resolved. For the 2020 inventory submission, the Party updated the
CH4 and N20 EFs for diesel oil for subcategory l.A.3.b road transportation
for years after 2006. For example, the CH4 EF for diesel oil for 2017 was
updated from 0.24 kg/TJ in the 2019 inventory submission to 0.53 kg/TJ
in the 2020 inventory submission. The Party explained in the NIR (p.3-46)
that CH4 and N20 EFs for on-road gasoline and diesel oil vehicles were
developed on the basis of annual certification data compiled by EPA
instead of regression analyses (for N20) or the ratio of non-methane
organic gas emission standards (for CH4). It remarked during the review
that certification data containing CH4 and N20 emission information for
the period preceding 2006 were not available;
(b) Resolved. It also explained in the NIR (p.3-46) that the methodology
for estimating fuel consumption and emissions from class II and III rail
locomotives was updated to use surrogate carload data reported by the
company Railinc for 2014 onward, as 2014 is the last year for which the
Party was able to receive class II and III fuel consumption data from the
American Short Line and Regional Railroad Association;
(c) Not resolved. During the review, the Party confirmed that it will apply
a more consistent methodology over time to estimate vehicle miles
travelled for on-road vehicles by vehicle type;
(d) Not resolved. The ERT noted that the emissions from urea use for
non-agricultural purposes presented on page 4-32 of the NIR did not
contain any specific information on trucks. It also noted that, according
to annex 5 to the NIR (p.A-493), N20 emissions from biomass fuel use in
Items (a) and (b) were addressed in the 2020 submission as noted by
the ERT.
For item (d), the United States notes that urea use in trucks is captured
under Urea Consumption for Non-Agricultural Purposes. For example,
see pg. 4-32 of the 2020 NIR that indicates "In addition, urea is used for
abating nitrogen oxide (NOx) emissions from coal-fired power plants and
diesel transportation motors." Emissions from urea use in trucks is
specifically captured under this source. Furthermore, in the current (i.e.,
2022) NIR the United States has updated the estimate for non-C02
emissions from bio-jet fuel and found them to be insignificant. See
Annex 5 of the 2022 NIR.
Additional research (i.e., on issue c) and improvements will be
undertaken in stages over future submissions, pending data availability.
A-540 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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domestic aviation were not estimated as they are considered
insignificant. During the review, the Party confirmed that it will include
research results and document minor emissions sources not currently
included in the inventory in stages over the 2021 and 2022 inventory
submissions, pending data availability.
E.12
l.A.3.b Road
transportation - liquid
fuels-C02
(E.16, 2019)
(E.26, 2018)
Accuracy
Not resolved. Review and update the time series of diesel and gasoline
C02 EFs, including, where necessary, the data on fuel densities and
carbon share by fuel grade, and report on progress, or document in the
NIR that the EFs applied are accurate and representative of emissions
across the time series, and update the uncertainty analysis as needed to
reflect the findings of the research. The ERT noted that the Party did not
revise the C02 EFs for diesel oil and gasoline for subcategory l.A.3.b road
transportation in the 2020 inventory submission and continued to use
constant values for the EFs for gasoline (67.62 t C02/TJ) for 2008-2017
(the EFs vary between 70.68 and 71.55 t C02/TJ for other years) and for
diesel (70.10 t C02/TJ) for the entire time series, without justifying the
accuracy of the EFs. During the review, the Party clarified that it is in the
process of updating the time series of diesel oil and gasoline C02 EFs, and
that additional considerations identified by expert input during the 2020
inventory compilation cycle had the update. The Party expected to
address this issue in the 2021 inventory submission.
This issue was addressed in the current submission (i.e., 2022
submission). The update of the time series of diesel and gasoline was
implemented in the previous (i.e., April 2021) NIR submission. See the
Recalculations discussion in the Energy Chapter on page 3-40 in the
submission available online on UNFCCC website
httpsi//unfccc.int/documents/272415 or on EPA's website at
htt ds://www.eDa.gov/ghgemissions/inventorv-us-ereenhouse-eas-
emissions-and-sinks-1990-2019.
E.13
l.A.3.b Road
transportation - liquid
fuels-CO2
(E.17, 2019)
(E.27, 2018)
Completeness
Addressing. Either present information in the NIR to justify the omission
of any fossil carbon component in the C02 EF for biofuel use (e.g. fatty
acid methyl ester use) or update the inventory estimates to account for
emissions from the fossil carbon component of biofuels and explain the
estimations in the NIR. The inventory was not updated to account for
possible emissions from the fossil carbon component of biofuels. The
Party explained in footnote 97 to page 3-114 of the NIR that C02
emissions from biodiesel do not include emissions associated with the
carbon contained in methanol used in the process of combustion, as
emissions from methanol use in combustion are assumed to be
accounted for under NEU. It also explained in a footnote to page A-134 of
NIR annex 2.3 that natural gas used as a petrochemical feedstock
includes use in production of methanol and that, as a result, the carbon
storage factor developed for natural gas as petrochemical feedstocks (65
per cent stored and 35 per cent emitted for 2018) takes into
consideration the emissions from the use of the resulting products,
including methanol. However, the ERT noted that table A-67 of NIR annex
2.3 (p.A-136) shows the carbon stored and emitted by products obtained
from petrochemical feedstock for 2018 but provides no specific
information on methanol, which is one of the products obtained from
In addition to the existing documentation described in the NIR (footnote
91 and footnote 85 in Annex 2.3), the United States will continue to
examine ways to incorporate information into Table A-67 of NIR Annex
2.3 to further clarify uses of methanol as part of petrochemical
feedstocks.
Annex 8
A-541
-------
natural gas. During the review, the Party clarified that it will examine
ways to incorporate more information into table A-67 of NIR annex 2.3 to
further clarify uses of petrochemical feedstocks. The ERT considers that
the issue of possible underestimation has not been fully addressed, since
emissions from methanol combustion, which is assumed to be included
under NEU (CRF category 1.A.5 other), are not transparently estimated
and reported.
E.14
l.A.3.b Road
transportation -liquid
fuels-CH4 and N20
(E.18, 2019)
(E.28, 2018)
Convention reporting
adherence
Addressing. Include descriptions of the MOVES model used to estimate
CH4 and n20 emissions from road transportation and the 2016 GREET
model used to generate EF inputs for alternative fuel vehicles, and
information to verify that the models have been tested and calibrated to
be representative of the United States fleet, fuels, driving conditions,
road types and vehicle types. The Party reported in the NIR (p.3-44) that
CH4 and N20 EFs for alternatively fuelled vehicles were developed on the
basis of the 2018 GREET model and provided a related reference in annex
3.2 (p.A-219) (Argonne National Laboratory, 2018). It also provided a
reference for the MOVES model in annex 3.2 (p.A-220). During the
review, the Party reiterated its plans to incrementally improve discussion
of the validity of the MOVES and GREET models in future inventory
submissions. In relation to the list of provisional main findings, the Party
provided an additional document (EPA, 2020) showing that the CH4 and
N20 EFs for on-highway gasoline and diesel vehicles generated by
MOVES2014b were reviewed by experts in October 2019. The ERT
considers that this issue has not been fully addressed as no reference to
the expert review of EFs was included in NIR.
The United States plans to incrementally improve the discussion of the
validity of the MOVES model in future submissions.
E.15
l.A.5.b Mobile - solid
and gaseous fuels, and
biomass use - C02, CH4
and N20
(E.21, 2019)
(E.31, 2018)
Transparency
Addressing. The Party reported C02, CH4 and N20 emissions from solid
and gaseous fuel and biomass use in l.A.5.b (other mobile (military)) as
"NA".
The Party reported in CRF table l.A(a) (sheet 4) "NO" for consumption of
solid and gaseous fuels and biomass for C02, CH4 and N20 emissions for
subcategory l.A.5.b other - mobile (military) for the whole time series,
but "NA" for other fossil fuels.
This issue was addressed in the current submission, see CRF
Tablel.A(a)s4 in the 2022 Inventory Submission, the C02, CH4 and N20
emissions from solid, gaseous, biomass and other fossil fuels use in
l.A.5.b (other mobile (military)) are all reported as NO.
E.17
1.B.2.C Venting and
flaring-C02 and CH4
(E.23, 2019)
(E.16, 2018)
(E.20, 2016)
(E.20, 2015)
Addressing. Enhance transparency in reporting CH4 emissions from
petroleum systems from venting and flaring, in accordance with the
UNFCCC Annex 1 inventory reporting guidelines. The Party still reported
"IE" for C02 and CH4 emissions from venting and flaring in CRF table
1.B.2 and did not provide any specific information on venting and flaring
in the NIR. During the review, the Party reiterated the clarification and
response provided during previous reviews, namely that providing an
The United States reiterates its previous clarification and response
provided during previous reviews. Language was added to the NIR,
noting "The United States reports data to the UNFCCC using this
Inventory report along with Common Reporting Format (CRF) tables.
This note is provided for those reviewing the CRF tables: The notation
key "IE" is used for C02 and CH4 emissions from venting and flaring in
CRF table l.B.2. Disaggregating flaring and venting estimates across the
A-542 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Transparency
estimate of disaggregated flaring and venting emissions would involve
the application of many assumptions, which would result in inconsistent
reporting and, potentially, decreased transparency.
The Party also clarified during the review that there were inconsistencies
in data availability across segments (such as gathering) within oil and gas
activities systems and noted that EF data available for activities that
cover flaring (such as heavy fuel oil well completions with flaring) include
emissions from multiple sources (flaring, venting and leaks).
Inventory would involve the application of assumptions and could result
in inconsistent reporting and, potentially, decreased transparency. Data
availability varies across segments within oil and gas activities systems,
and emission factor data available for activities that include flaring can
include emissions from multiple sources (flaring, venting and leaks)."
This language can be found on page 3-76 and 3-94 and 3-95 of the 2021
NIR and the same language is also included in in Chapter 3, Sections 3.6
and 3.7 of the current submission (2022 NIR).
E.18
l.C C02 transport and
storage - C02
(E.25, 2019)
Transparency
Not Resolved. Report on the progress on the research to enable
estimation of emissions for category l.C.2, and provide a description of
emission pathways associated with EOR and CCS processes for all
relevant categories, including how leakage from C02 geological storage
formations is assessed for both EOR and CCS projects. No progress was
reported in the NIR, and C02 emissions for subcategories l.C.2.a injection
and l.C.2.b storage were reported as "IE" for all years of the time series
in the 2019 and 2020 inventory submissions. During the review, the Party
clarified that it will continue to review new data available from the
GHGRP and other sources of information for consideration in updating
emission estimates and allocations from category l.C.l transport of C02
and subcategories l.C.2.a injection and l.C.2.b storage. The Party
indicated that it will provide an update, as appropriate, in future
inventory submissions on recalculations and planned improvements,
where feasible.
The United States continues to review new data from its GHGRP and
other sources for consideration in updating emissions estimates from
transport of C02 (category l.C.l), injection (category l.C.2.a), and
storage (category l.C.2.b). The Party will provide an update as
appropriate in future submissions in recalculations and, where feasible
in planned improvements.
This improvement will be made over time as data becomes available
and prioritized with other improvements to make best use of available
resources.
E.19
l.C C02 transport and
storage - C02
(E.26, 2019)
Comparability
Not resolved. Report on the progress on the research to enable
estimation of emissions for category l.C.2, and provide a description of
emission pathways associated with EOR and CCS processes for all
relevant categories, including how leakage from C02 geological storage
formations is assessed for both EOR and CCS projects. The total amount
of C02 captured for storage was reported as "NA" for all years of the time
series in the 2019 and 2020 inventory submissions. During the review,
the Party clarified that it will review and correct notation key use as
appropriate in a future inventory submission.
This issue has been addressed in the latest submission. The United
States reviewed and corrected the notation keys reported under l.C.2
as appropriate.
E.20
l.C C02 transport and
storage - C02
Comparability
(E.26, 2019)
Not resolved. Report the total amounts of C02 injected at storage sites
and the total leakage from transport, injection and storage as "IE". C02
emissions for the total amounts of C02 injected at storage sites and total
leakage from transport, injection and storage were reported as "NA" for
all years of the time series in the 2019 and 2020 inventory submissions.
During the review, the Party clarified that it will review and correct
notation key use as appropriate in a future inventory submission.
This issue has been addressed in the current (i.e., 2022) submission.
The United States reported the total amounts of C02 injected at storage
sites and the total leakage from transport, injection and storage as "IE".
Annex 8
A-543
-------
E.21
Fuel combustion -
reference approach -
gaseous and liquid fuels -
C02
Convention Reporting
Adherence
The Party provided an explanation in annex 4 to the NIR of the
comparison between the reference approach and the sectoral approach.
The energy data presented in NIR table A-249 (pp.A-490-A-491) for fuel
consumption under the reference approach match the data presented in
CRF table l.A(c); however, the energy data reported under the sectoral
approach do not match those presented in CRF table l.A(c) for natural
gas, petroleum and total values (excluding other fossil fuels). For
example, NIR table A-249 shows natural gas consumption of 30,788 TBtu
for 2018 under the sectoral approach, equal to 34,483.2 PJ, whereas a
value of 32,630.1 PJ is given in CRF table l.A(c). During the review, the
Party clarified that the natural gas data presented in NIR table A-249
include natural gas for combustion and NEU, and that the gaseous fuels
data in CRF table l.A(c) are derived from CRF table l.A(a) and include
natural gas for combustion and NEU as well as still gas for NEU, which is
included as a gaseous fuel as opposed to a liquid fuel. The ERT
recommends that the Party consistently treat still gas as liquid fuel under
the sectoral and reference approaches to improve consistency between
CRF tables l.A(a), l.A(b), l.A(c) and the NIR table that compares fuel
consumption under the two approaches (see also ID# E.22 below).
The United States reports Still Gas under petroleum in the NIR because
it is a petroleum product. However, still gas is physically a gas, consisting
primary of methane and ethane, and some hydrogen and other trace
gases. Therefore, the United States will continue to report still gas as a
gaseous fuel in CRF. The most recent submission also lists still gas as a
gaseous fuel in the NIR. See Tables A-228 through A-231 in the current
2022 NIR.
E.22
Fuel combustion -
reference approach- all
fuels-C02
Comparability
The Party reported the quantity of carbon stored (carbon excluded) in
CRF table l.A(b) and the quantity of carbon excluded from the reference
approach in CRF table l.A(d). The ERT notes that the total carbon stored
in liquid, solid and gaseous fuels for 2018 (60,469.88 kt C) is exactly the
same in both tables, but that the disaggregated values are drastically
different. For example, carbon stored in liquid, solid and gaseous fuels
are reported as 57,034.45, 562.68 and 2,872.72 kt C, respectively, in CRF
table l.A(b) but as 38,903.00,16,784.93 and 4,781.96 kt C, respectively,
in CRF table l.A(d). During the review, the Party clarified that the data in
CRF table l.A(d) were taken from the reference approach but
recharacterized to reflect the Party's fuel categories, as explained in NIR
annex 4 (p.A-483). It also clarified that asphalt and road oil are treated as
a solid fuel, and still gas is treated as a gaseous fuel (see ID# E.21 above,
under both the reference and the sectoral approach. The ERT is of the
view that treating asphalt and road oil as a solid fuel is not in accordance
with the 2006 IPCC Guidelines (vol. 2, table 1.1). To improve consistency
between CRF tables l.A(b) and l.A(d) and compliance with the 2006 IPCC
Guidelines, the ERT recommends that the Party consistently categorize
asphalt and road oil as liquid fuels under both the reference and sectoral
approaches.
The United States has updated the CRF in the current (i.e., 2022)
submission so that Asphalt and Road Oil are reported as a liquid fuel in
Tables l.A9(b) and l.A(d) for consistency with how it is reported in the
NIR.
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E.23
Feedstocks, reductants
and other NEU of fuels-
all fuels - C02
Convention Reporting
Adherence
The ERT noted that the Party reported C02 emissions from NEU of fuels
under category l.A.5.a in CRF table l.A(a)s4 and only reported them for
certain years (1990, 2005 and 2014-2018) in NIR table 3-20 (p.3-48). The
data from the two sources are different; for example, the NIR and CRF
table l.A(a)s4 report 129.5 and 136.4 Mt C02, respectively, for 2018.
During the review, the Party clarified that, in CRF table l.A(a)s4, category
l.A.5.a covers incineration of waste, United States territories and NEU.
Emissions from NEU listed in CRF table l.A(a)s4 do not include NEU of
lubricants and other petroleum in United States territories (i.e. American
Samoa, Guam, Puerto Rico, the United States Virgin Islands, Wake Island
and other United States Pacific islands); these emissions are allocated to
territories together with other emissions in United States territories. For
example, for 2018, the total emissions from NEU of lubricants and other
petroleum in United States territories stood at 136.4 Mt C02 (i.e., 5.1 Mt
C02 (NIR table 3-22, p.3-20) plus 129.5 Mt C02 (CRF table l.A(a)s4)), as
reported in NIR table 3-20. The ERT concluded that the NIR and CRF
tables do not transparently explain what is included under category
l.A.5.a. The ERT recommends that the Party reconcile the emission data
on NEU of fuel reported in the NIR and CRF table l.A(a)s4 by either
reallocating NEU of lubricants and other petroleum in United States
territories to NEU in CRF table l.A(a)s4 or adding a footnote to NIR table
3-20 to explain how the data reported in that table differ from those
presented in CRF tablel.A(a)s4.
This issue has been addressed in the current (i.e., 2022) submission. A
footnote was added to Table 3-20 in the NIR explaining the differences.
E.24
Feedstocks, reductants
and other NEU of fuels-
solid fuels-CO2
Transparency
Whereas the Party reports in the NIR (p.3-50; annex 2.3, pp.A-133 and A-
156) that storage factors, including those for industrial coking coal and
distillate fuel oil (0.1 and 0.5, respectively), were taken from the 2006
IPCC Guidelines, which in turn draw on data from Marland and Rotty
(1984), the ERT understands that the 2006 IPCC Guidelines do not
provide storage factors for NEU of fuels. During the review, the Party
clarified that the storage factors for industrial coking coal and distillate
fuel oil were taken from the Revised 1996 IPCC Guidelines but primarily
from Marland and Rotty (1984). The ERT recommends that in future
submissions the Party include the correct reference, that is to the
Revised 1996 IPCC Guidelines rather than the 2006 IPCC Guidelines, for
storage factors for industrial coking coal and distillate fuel oil, together
with a justification of their applicability
This issue has been addressed in the current 2022 NIR submission. The
reference has been changed to the original source of the data Marland
and Rotty (1984). Annex 2.3 provides the justification for use of these
factors.
Annex 8
A-545
-------
E.25
Fuel combustion -
reference approach -
other fossil fuels -C02,
CH4 and N20
Consistency
Data on the non-biomass portion of waste, reported to IEA for all years,
are missing from CRF table l.A(b). In the 2020 submission, the ERT notes
that the AD and emissions for other fossil fuels are reported under CRF
categories l.A.l.a (public electricity and heat production) and l.A.5.a
(incineration of waste) under the sectoral approach, but as "NA" in CRF
tables lA(b) and lA(c) under the reference approach, for the whole time
series. During the review, the Party clarified that comparisons of energy
use and C02 values between the sectoral and reference approaches
concern only fossil fuel sources (coal, natural gas and petroleum) and
exclude waste fuels for reasons of consistency, as shown in table A-250
(NIR annex 4, p.A-491). The ERT recommends that the Party either take
into account other fossil fuels under the reference approach when
completing CRF table l.A(b) or document that waste fuels are not used in
the comparison between the sectoral and reference approaches in order
to improve consistency between the reference and sectoral approaches
in terms of estimation coverage, and amend the reference approach
column in CRF table l.A(c) as needed.
This issue has been addressed in the current 2022 NIR submission.
Language was added to Annex 4 of the NIR to indicate that waste fuels
are not used in the comparison between the sectoral and reference
approaches in order to improve consistency between the reference and
sectoral approaches in terms of estimation coverage.
E.26
Fuel combustion -
reference approach -LPG
-C02
Comparability
The ERT noted that data on LPG production, trade and stock changes
reported under NGL in CRF table l.A(b) seem to be different to those
reported to IEA. For example, apparent consumption of NGL for 2017 is
reported in the CRF table as 3,634,913 TJ (gross calorific value),
equivalent to 3,453,168 TJ (NCV), but to IEA as 4,669,988 TJ (NCV), while
LPG is reported as "NA" in the CRF table and as -1,238,360 TJ (NCV) to
IEA. All headings for LPG are reported as "NA" except for "C stored" for
the whole time series in CRF table l.A(b). During the review, the Party
clarified that LPG is a fuel category under the sectoral approach while
NGL is not. LPG statistics reported under the sectoral approach consist of
both NGL and LPG (as explained briefly in NIR annex 4, p.A-483), while
under the reference approach, LPG falls under NGL and liquefied refinery
gases, whose carbon content is based on the EF for LPG reported under
the sectoral approach. The Party believes that this is the most accurate
approach for calculating emissions under both the sectoral and reference
approaches. The ERT recommends that the Party either estimate NGL
and LPG consistently between the reference and sectoral approaches or
explain in the NIR why covering different fuels under the reference
approach applying a different list of fuels than that used for the sectoral
approach is the most accurate way to estimate emissions under both
approaches, and change the notation key reported for LPG in CRF table
l.A(b) from "NA" to "IE".
The discussion in Annex 4 of the NIR has been updated to further clarify
differences in the fuel definitions in the reference and sectoral
approach. LPG as a category is no longer used; it was replaced with
Hydrocarbon Gas Liquids (HGL). The following language was included
"Additionally, the accounting of pentanes plus as a part of HGL is
different between the approaches. The United States reports
consumption of all HGL components (i.e., ethane, propane, isobutane,
normal butane, ethylene, propylene, isobutylene, butylene, and
pentanes plus) for both approaches, but in the Sectoral Approach,
pentanes plus is accounted for separately from other HGL components
whereas it is included in HGL in the Reference Approach."
Furthermore, the notation key reported for LPG in CRF table l.A(b) has
been changed from "NA" to "IE".
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E.27
l.A.2.g Other
(manufacturing
industries and
construction)- all fuels -
C02, CH4 and N20
Transparency
The ERT noted that, in the recalculation performed for subcategory
l.A.2.g (other) in the 2020 submission, the values reported for fuel
consumption and C02 emissions were reduced by more than 20 per cent
for the whole time
series, whereas those reported for CH4 and N20 emissions were reduced
by only 5-6 and 2-3 per cent, respectively. It also noted that fuel
distribution among categories changed significantly in the 2020
submission compared with the 2019 submission. For example, for 2017,
fuel consumption increased by 2,838,783.55 TJ under category l.A.l and
decreased by 2,930,213.62 TJ under category 1.A.2 and by 293,474,205
TJ under subcategory l.A.2.g. According to the explanation provided in
the NIR (pp.3-38-3-39), EIA updated the data for LPG consumption in
economic sectors and revised sector allocations for propane and total
LPG for 2010-2017, and for natural gas, distillate fuel oil and kerosene
for 2017, without providing any explanation for the significant changes
noted by the ERT. The discussion in the NIR (pp.3-38-3-39) of the impact
of the recalculation on overall emissions similarly fails to broach these
changes. During the review, the Party noted that, in addition to the
reallocation of liquid fuels, as reported in the NIR (box 3-4, p.3-34), the
values reported in the CRF tables for petroleum refining (subcategory
l.A.l.b) and manufacture of solid fuels (subcategory l.A.l.c) were
corrected to include part of the total fuel consumption when calculating
energy use under subcategory l.A.2.g. That correction accounted for
most of the revisions in energy use between categories l.A.l and 1.A.2
for 2017. The Party explained that biomass energy use under category
1.A.2 and related non-C02 emissions are not disaggregated to
subcategories (i.e., l.A.2.a-f) and are reported only under subcategory
l.A.2.g, whereas biomass consumption remains unchanged in the 2020
submission. It noted that since the majority of non- C02 emissions are
driven by biomass combustion, the adjustment made to fossil energy use
and C02 emissions did not have as significant an impact on non-C02
emissions. The ERT recommends that the Party provide information in
the NIR on the recalculation of emission estimates and clearly indicate
the reason for any changes and corrections compared with previous
submissions.
The United States has provided information in the NIR on the
recalculation of emission estimates and clearly indicated the reason for
any changes and corrections compared with previous submissions. See,
for example, the recalculation discussions in Section 3.1 of the Energy
chapter of the NIR.
E.29
1.A.3 Transport- all fuels
-C02, CH4 and N20
Transparency
In CRF summary table 3, the United States reported on its use of a
combination of default and higher-tier methods and a mix of default and
country-specific EFs for estimating GHG emissions for subcategory 1.A.3,
which was identified as a key category in NIR annex 1 (p.A-3). However,
the NIR did not contain an explanation for every instance of the default
method and parameters being used to estimate emissions for key
This issue was addressed in the previous (i.e., 2021) submission. See
Section 3.1, pp. 3-46 of the 2021 NIR which states that "The non-road
mobile category for CH4 and N20 includes ships and boats, aircraft,
locomotives and off-road sources (e.g., construction or agricultural
equipment). For non-road sources, fuel-based emission factors are
applied to data on fuel consumption, following the IPCC Tier 1 approach,
Annex 8
A-547
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categories. The ERT noted that this is not in accordance with paragraphs
11 and 50(c) of the UNFCCC Annex 1 inventory reporting guidelines, which
state that the Party should make every effort to use a method
recommended in the 2006 IPCC Guidelines or otherwise shall explain in
its annual GHG inventory submission why it was unable to implement a
recommended method in accordance with the decision trees in the 2006
IPCC Guidelines. During the review, the Party clarified that the use of
default methods for gases for subcategories within the key categories
(1.A.3) estimating CH4 and N20 emissions from off-road transport
(category 1.A.3) could be enhanced. The ERT noted that the reasons for
the Party's inability to implement higher-tier methods for this category
were not transparently described in the NIR. In response, the Party
explained why it had been unable to implement higher-tier methods for
estimating CH4 and N20 emissions from off-road transport (category
1.A.3). The ERT recommends that the United States include the
explanation shared with the ERT during the review in its NIR describing
why it was unable to implement a recommended method in accordance
with the decision trees in the 2006 IPCC Guidelines, as outlined in
paragraphs 11 and 50(c) of the UNFCCC Annex 1 inventory reporting
guidelines, where default methods and emission parameters were used
for estimating GHG emissions and removals for categories identified as
key, particularly for category 1.A.3 (CH4 and N20 for off-road sources),
which includes ships and boats, aircraft, locomotives and off-road
sources (i.e. construction or agricultural equipment).
for locomotives, aircraft, ships and boats. The Tier 2 approach would
require separate fuel-based emissions factors by technology for which
data are not available. For some of the non-road categories, 2-stroke
and 4-stroke technologies are broken out and have separate emission
factors; those cases could be considered a Tier 2 approach."
E.30
l.A.5.a Stationary-other
fossil fuels -C02, CH4 and
N20
Accuracy
According to the NIR (p.3-56; table 3-27, p.3-57), the amount of waste
incinerated for 2012-2018 is assumed to be equal to the amount for
2011, and waste discarded for 2014-2018 is constant. This results in a
constant ratio of incinerated waste to total waste for 2014-2018 (7.6 per
cent). The ERT notes that according to historical data on MSW generation
in the United States for 2000-2018 published on the OECD website
(https://data.oecd.orB/waste/municipal-waste.htm), 265.2 Mt waste was
generated in 2018, whereas according to the NIR (table 3-27) this figure
is 273.1 Mt. It also notes that the OECD data are comparable to those
used for estimating emissions from waste incineration, as reported in the
NIR, and do not show how much of the waste is incinerated. During the
review, the Party acknowledged that the reporting of constant values for
waste incineration
for years after 2011 is an issue and stated that it has drawn up an
improvement plan to investigate additional sources of MSW data (NIR
p.3-58), including data on how much waste is incinerated, and will
include the results in a future submission. The ERT recommends that the
This issue has been addressed in the current (i.e., 2022) submission.
The methodology for waste incineration was updated for the 2022
submission. See the NIR Energy chapter Section 3.3 for a discussion of
the updated methodology.
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Party use updated data to estimate GHG emissions from waste
incineration, including by updating the amount of waste generated and
the ratio of incineration for the latest year of the time series, and
examine the applicability of data from the OECD website and other
sources.
IPPU
1.3
2.A.4 Other process uses
of carbonates - C02
(1.3, 2019)
(1.5, 2018)
(1.17, 2016)
(1.17, 2015)
Completeness
Addressing. Conduct further research and consultation with industry,
state- level regulators and/or statistical agencies to access additional AD
and EFs and/or to seek verification of the current method and
assumptions for estimating emissions from ceramics, non-metallurgical
magnesium production and from other limestone and dolomite use; and
report on progress in the NIR. The Party reported C02 emissions from
other limestone and dolomite use under category 2.A.4.d (other) in NIR
section 4.4 and CRF table2(l).A-Hsl, but "NE" for categories 2.A.4.a
(ceramics) and 2.A.4.C (non-metallurgical magnesium production) in CRF
table 2(I).A-Hsl. The Party reported its progress and the status of this
issue in the NIR (p.4-27). During the review, the Party clarified that there
is no reportable progress in identifying data for the estimation of
emissions based on further outreach and that efforts continue under the
current cycle (see NIR annex 5, p.A-495).
See Annex 5 of the current (i.e., 2022) NIR. Using recently identified
surrogate data in place of activity data as identified in the 2006 IPCC
Guidelines, the United States assessed that national emissions from
ceramics production will exceed the category-level threshold for
significance of 500 kt. EPA is still assessing if emissions are already
reflected in other process uses of carbonates. The United States has
made no reportable progress in identifying data to estimate emissions
for non-metallurgical magnesium production based on further outreach.
Efforts will continue with next Inventory cycle.
1.4
2.B.1 Ammonia
production - C02
(1.4, 2019)
(1.7, 2018)
(1.19, 2016)
(1.19, 2015)
Comparability
Not resolved. Allocate emissions from all fossil fuel uses (i.e. fuel and
feedstock use) for ammonia production under subcategory 2.B.1 of the
IPPU sector in accordance with the 2006 IPCC Guidelines. The Party
reported C02 emissions from fossil fuel use as fuel for energy use for
ammonia production under the energy sector (NIR p.4-27). During the
review, the Party clarified that its planned improvements (NIR p.4-31)
include assessing anticipated new data for updating EFs to include both
fuel and feedstock C02 emissions and to improve consistency with the
2006 IPCC Guidelines (vol. 3, chap. 3.2). The Party indicated that this is a
long-term improvement to be included in the 2024 or 2025 submission at
the earliest. Until these additional data are available and have been
assessed as indicated in the NIR, consistently with the UNFCCC Annex 1
inventory reporting guidelines, the United States has provided an
explanation on the use of a country-specific or national method as noted
in the NIR (p.4-29).
The United States reiterates that it currently uses a country-specific
methodology for ammonia production emissions consistent with para.
10, Decision 24/CP.19 to most accurately portray U.S. emissions from
ammonia production.
See the NIR IPPU chapter Section 4.5 for the discussion of the country-
specific methodology. C02 emissions from production of synthetic
ammonia from natural gas feedstock are estimated using a country-
specific approach modified from the 2006 IPCC Guidelines (IPCC 2006)
Tier 1 and 2 methods. In the country-specific approach, to avoid double
counting, emissions are not based on total fuel requirement per the
2006 IPCC Guidelines due to data disaggregation limitations of energy
statistics provided by the EIA. A country-specific emission factor is
developed and applied to national ammonia production to estimate
emissions from feedstock consumption, excluding consumption of fuel
for energy purposes to avoid double counting and compatibility with
methods in 2006 IPCC Guidelines.
The United States will continue to review the use of GHGRP data to
better understand energy use for ammonia production and any
information will be included as appropriate in future submissions.
Annex 8
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1.6
2.B.2 Nitric Acid
production - N20
(1.25, 2019)
Transparency
Not resolved. Include in the NIR an explanation of the trends observed
for N20 emissions and AD for nitric acid production. The observed trends
in N20 emissions and AD for nitric acid production for 2014-2016 were
not explained in the NIR. During the review, the Party clarified that work
is ongoing to update trend explanations in the 2021 submission.
This issue has been addressed in the current April 2022 submission. See
the NIR IPPU chapter Section 4.7 for an expanded discussion on
observed trends in emissions and nitric acid production.
1.8
2.B.4 Caprolactam,
glyoxal and glyoxylic acid
production - N20
(1.7, 2019)
(1.31, 2018)
Completeness
Not resolved. Gather the necessary data and report N20 emissions from
glyoxal and glyoxylic acid production. The Party reported AD and N20
emissions from glyoxal and glyoxylic acid production as "NE" in CRF table
2(I).A-Hsl. During the review, the Party clarified that potential data
sources for glyoxal and glyoxylic acid were being investigated on the basis
of ongoing research. It stated that progress on AD gathering and N20
estimates will be included in the 2022 or 2023 submission. If production
of glyoxal and/or glyoxylic acid is found to not occur in the United States,
then the notation key will be revised from "NE" to "NO".
See Annex 5 of the current (2022) NIR. EPA has identified potential data
sources for glyoxal, and glyoxylic acid based on ongoing research efforts.
Using limited data on the range of domestic production and import of
glyoxal, EPA estimates that emissions from glyoxal production do not
exceed the category-level threshold for significance of 500 kt in recent
years. Research suggests that glyoxylic acid may not be produced in the
United States at levels that would exceed the category-level threshold
for significance of 500 kt. EPA hopes to report more progress in the next
(i.e., April 2023) submission, but anticipates the earliest reflection of
this data, if useful, would be the April 2024 submission as additional
historical data to develop the time series has not been identified.
1.9
2.B.5 Carbide production
-C02
(1.8, 2019)
(1.32, 2018)
Comparability
Addressing. Allocate C02 emissions from production of calcium carbide to
the IPPU sector in line with the 2006 IPCC Guidelines or provide clarity in
the NIR as to the country-specific approach taken. The Party reported
C02 emissions from coke use for calcium carbide production under the
energy sector, with an appropriate explanation in the NIR and the correct
notation key ("IE") in CRF table (l).A-H. During the review, the Party
clarified that there are no AD for calculating C02 emissions from calcium
carbide production under the IPPU sector. The ERT noted that, according
to annex 5 to the NIR (pp.A-495-A-496), EPA has initiated research to
obtain data from the limited production facilities in the United States
(fewer than five). During the expert review of the inventory compilation,
EPA sought input on production data for C02 emissions from calcium
carbide production but was unable to identify data sources for applying
tier 1 methods.
The United States reiterates that a country-specific approach was taken
for C02 emissions from production of calcium carbide. Footnote 15 in
the 2022 NIR (pp. 4-19) indicates calcium carbide is produced from
quicklime and petroleum coke. Any emissions from quicklime
production are included in lime production emissions (Section 4.2).
Furthermore, Section 4.10 (pp. 4-48) in the 2020 NIR indicates that C02
(from petroleum coke used in calcium carbide production) is implicitly
accounted for in the storage factor calculation for the non-energy use of
petroleum coke in the Energy chapter. Table A-65 on pp. A-133 of the
2020 NIR Annexes indicates a storage factor of 30 percent for petroleum
coke used in non-energy uses. This indicates effectively that 70 percent
of any C02 emissions associated with petroleum coke used in calcium
carbide production is released and accounted for under NEU emissions
in the Inventory. There is no way to disaggregate and report emissions
specifically associated with petroleum coke used in calcium carbide
production (as is done for silicon carbide) since production data are not
available for calcium carbide to estimate emissions directly.
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1.11
2.B.8 Petrochemical and
carbon black production
-CH4 and N20
(1.9, 2019)
(1.10, 2018)
(1.22, 2016)
(1.22, 2015)
Completeness
Not resolved. Progress with plans to analyse new data reported by
facilities (i.e. GHGRP data) and include emissions from combustion and
flaring from installations not currently included in the inventory. The
Party stated in the NIR (p.4-63) that CH4 emissions from ethylene
production reported under the GHGRP have not been included as this
would result in double counting of carbon (i.e. all carbon in the CH4
emissions would also be included in the C02 emissions from ethylene
processing units, which are subset of facilities reporting under the
GHGRP use alternative methods to the carbon balance approach). During
the review, the Party clarified that EPA continues to assess the GHGRP
data to determine how best to disaggregate and incorporate them into
the inventory.
The United States also points to Section 4.13 of the 2022 NIR in the
QA/QC and Verification discussion, that "The CH4 emissions from
ethylene production under the GHGRP have not been included in this
chapter because this approach double counts carbon (i.e., all of the
carbon in the CH4 emissions is also included in the C02 emissions from
the ethylene process units)." So, it is not just an issue that the flaring
emissions are small but that the carbon at least is already included in
C02 emission estimates. The United States continues to assess its
GHGRP data for ways to better disaggregate the data and incorporate it
into the Inventory and any information will be included as appropriate
in future submissions.
1.12
2.B.8 Petrochemical and
carbon black production
-C02 and CH4
(1.10 2019)
(1.12, 2018)
(1.25, 2016)
(1.25, 2015)
Comparability
Addressing. Develop a methodology that is consistent with the 2006
IPCC Guidelines as soon as is practicable, allocating relevant fuel and
feedstock emissions within the IPPU sector. The ERT considers that the
recommendation has not been addressed because the C02 emissions for
category 2.B.8 were not fully allocated to the IPPU sector. As with ID# E.5
above, the Party will resolve this issue by describing how the country-
specific approach is better able to reflect its national situation and
providing a description of how these methodologies are compatible with
the 2006 IPCC Guidelines.
The United States reiterates that it uses an approach for calculating
emissions associated with petrochemical and carbon black production
that is consistent with the 2006 IPCC Guidelines.
Per question E.5, the issue of potential double counting is discussed in
the current 2022 submission. See Section 4.13 of the 2022 NIR for the
following discussion: "It is important to ensure no double counting of
emissions between fuel combustion, non-energy use of fuels and
industrial process emissions. For petrochemical feedstock production,
our review of the categories suggests this is not a significant issue since
the non-energy use industrial release data includes different categories
of sources and sectors than those included in the IPPU emissions
category for petrochemicals. As noted previously in the methodology
section, data integration is not available at his time because feedstock
data from the EIA used to estimate non-energy uses of fuels are
aggregated by fuel type, rather than disaggregated by both fuel type
and particular industries. Also, GHGRP-reported data on quantities of
fuel consumed as feedstocks by petrochemical producers is unable to be
used due to the data failing GHGRP CBI aggregation criteria."
Annex 8
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1.16
2.C.1 Iron and steel
production - C02
(1.14, 2019)
(1.17, 2018)
(1.28, 2016)
(1.28, 2015)
Transparency
Addressing. Explain the allocation of the emissions from coke production
and iron and steel production across both the energy and IPPU sectors,
including the amount of carbon stored in the products of iron and steel
production (this could be done, for example, through the provision of a
quantitative summary of the carbon balance that the Party uses to
compile and quality check the inventory estimates). The Party explained
in NIR section 4.16 and annex 2 the allocation of the C02 emissions from
iron and steel production across both the IPPU and energy sectors. In its
clarifications on the list of provisional main findings, the Party indicated
that factors are reported transparently in the NIR (p.4-80), including the
material carbon contents for metallurgical coke production (NIR table 4-
66) and the production and consumption data for the calculation of C02
emissions from metallurgical coke production (NIR tables 4-67 and 4-68).
However, the ERT noted that the United States did not confirm its
allocation of C02 emissions from coke production through a fully
transparent tracking of carbon flows as per the previous
recommendation. The ERT considers that the recommendation has not
yet been fully addressed because the Party did not confirm the allocation
of C02 emissions from coke production by providing a fully transparent
tracking of carbon flows.
The United States reiterates that the Party has transparently reported in
its NIR. See the 2022 NIR Annex 2.1 for how emissions and carbon
stored from iron and steel production have been allocated between the
energy and IPPU sectors.
The Party has also documented emission factors used in the iron and
steel and coke production emissions estimates. See for example Table
4-66 on pp. 4-80, Table 4-69 on pp. 4-81 and Tables 4-70 and 4-71 on
pp. 4-82 of the 2020 NIR.
The United States will continue to review ways to improve the
presentation of data and any updates will be included as appropriate in
future submissions.
1.17
2.C.4 Magnesium
production - SF6
(1.15, 2019)
(1.35, 2018)
Consistency
Addressing. Investigate the reasons for the SF6IEF increase between
2009 and 2011 and report in the NIR on the outcome of the investigation
and on any recalculations of AD, IEF or emissions resulting from those
investigations. The Party did not report in the NIR the outcomes of any
such investigation or the reasons for the increase in the SF6 IEF between
2009 and 2011. During the review, the Party clarified that the increase in
SF6 emissions between 2010 and 2011 was attributable partially to one
facility anomalously reporting high emissions for 2011 and partially to
increased production. It also stated that the 2021 NIR will include a
discussion on the trends in the SF6 IEF. The ERT noted that the SF6
emissions for 2009-2011 were revised in the previous submission and
approved by the ERT, and that there have been no new recalculations
since the previous submission. The ERT considers that the
recommendation has not yet been fully addressed because the Party did
not include in the NIR an explanation of the outstanding trends on the IEF
for magnesium production.
Adjustments to the activity data are discussed in the recalculation
sections of Section 4.20 in the 2019 and 2020 NIRs. The 2021 NIR
included a discussion on the trends in the SF6 IEF. The revised activity
data more accurately reflects the change in production that occurred
during the recession. The large increase in SF6 emissions from 2010 to
2011 is due in part to 1 facility reporting anomalously high emissions in
2011 and also partially due to increased production.
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1.18
2.D Non-energy products
from fuels and solvent
use - C02
(1.16, 2019)
(1.36, 2018)
Comparability
Not resolved. Estimate separately C02 emissions from lubricants and
paraffin wax use and report them under category 2.D. The Party reported
C02 emissions from paraffin wax as "IE" under category 2.D (non-energy
products from fuels and solvent use). The ERT noted that AD on the use
of waxes are available for the Party, for example, in NIR table 3-22 (pp.3-
49 and 3-50). The ERT is of the view that emissions from wax use could
be determined on the basis of the statistical information and
assumptions provided in the NIR and reported under category 2.D.
As per ID # above E.4, the United States reiterates that it uses a country-
specific methodology for non-energy use of fuels in line with para. 10,
Decision 24/CP.19 to most accurately portray U.S. emissions from NEU.
The United States has improved the explanation of its country-specific
approach to the allocation of NEU of fuels in the introduction of the
IPPU chapter 4 and Annex 2 of the 2021 NIR.
The United States continues to evaluate ways to update this approach,
including reallocation of lubricant non-combustion emissions and will
provides more clarification as applicable in future Inventory NIRs (i.e.,
2023 submission).
1.23
2.G.2 SF6 and PFCs from
other product use - SF6
(1.22, 2019)
(1.37, 2018)
Completeness
Addressing. Investigate possible SF6 emissions from airborne warning and
control systems, particle accelerators and radars and include them in the
next submission, providing a description of the identified sources, the SF6
emissions from them for the entire time series, a methodology
description and an uncertainty analysis, in accordance with the 2006 IPCC
Guidelines (vol. 2, chap. 8, pp.8.23-8.25 and 8.26-8.30). The Party
reported SF6 emissions for category 2.G.2 as "NE" and PFC emissions as
"NA" in CRF table 2(11). It clarified in NIR annex 5 (p.A-496) that emissions
from some particle accelerators and from military applications are
reported by the Government to the Federal Energy Management
Program. The updated analysis of the underlying data for 2018 identified
fugitive SF6 emissions of pproximately 600 kt C02 eq. The Party noted
that the sources of the identified emissions are probably particle
accelerators and compounds commonly used as fluorinated heat transfer
fluid (NIR p.A-496). According to NIR annex 5 (p. A-496), EPA plans to
contact reporting agencies to better understand the sources of the
emissions and the estimation methods used by reporters. The ERT
considers that the recommendation has not yet been resolved because
the identified emissions of SF6 and PFCs for category 2.G.2 were not
reported in the CRF tables.
See Annex 5 of the NIR. EPA's analysis of reported data is ongoing, and
EPA is continuing to review the available reported data and the
methods used to estimate emissions.
1.26
2.A.1 Cement production
-C02
Accuracy
The United States reported in the NIR (p.4-10) that it used the tier 2
method from the 2006 IPCC Guidelines for estimating C02 emissions for
the key category 2.A.1 cement production. The ERT noted that non-
carbonate sources of CaO in clinker production were not taken into
consideration, as stated in the NIR (p.4-11), whereas it is good practice
under the chosen tier 2 method to identify non-carbonate sources, for
example slag, fly ash and so on, and exclude them from CaO content in
clinker (2006 IPCC Guidelines, vol. 3, chap. 2, pp.2.12 and 2.14). During
the review, the Party confirmed that non-carbonate sources of CaO were
not included in the estimates and informed the ERT about a planned
The United States continues to review data from GHGRP and other
sources on CaO content of clinker and inputs of non-carbonate CaO for
consideration in order to estimate a country-specific C02 emission
factor for clinker. An update will be provided, as appropriate, in future
submissions.
Annex 8
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improvement involving the identification of non-carbonate raw materials
used in clinker production. The ERT noted that the estimates of C02
emissions for category 2.A.1 cement production may be not accurate
because non-carbonate sources of CaO were not included in the
estimates, which is not in compliance with the Party's chosen tier 2
method from the 2006 IPCC Guidelines. The ERT recommends that the
Party identify the amount of non-carbonate sources of CaO used in
cement production (category 2.A.1) by fully implementing the planned
improvement related to the use of non-carbonate raw materials in
clinker production, and revise estimates of C02 emissions in accordance
with the tier 2 method from the 2006 IPCC Guidelines by correcting the
amount of CaO from non-carbonate sources if data of noncarbonate CaO
sources are available.
1.27
2.A.3 Glass production -
C02
Transparency
The Party used the tier 3 method from the 2006 IPCC Guidelines (vol. 2,
chap. 2.4, p.2.28) for estimating C02 emissions from glass production on
the basis of carbonates used, including limestone, dolomite and soda ash
(NIR p.4-20). According to the NIR (section 4.3), AD on carbonate use can
be obtained directly from national statistics and are not consistent across
the time series. For example, dolomite consumption is reported as 541 kt
for 2005 but as 0 kt for 2014-2018 (NIR table 4-12, pp.4-20-4-21). During
the review, the Party clarified that updating the AD for glass production is
a priority among its planned improvements. In its clarifications to the
ERT, the Party reiterated information in the NIR that may impact data
consistency, such as withheld data. The ERT recommends that the Party
explain transparently in the NIR the reasons for the dramatic reduction in
reported dolomite use for glass production, from 541 kt for 2005 to 0 kt
for 2014-2018, and ensure that all major carbonates (limestone,
dolomite and soda ash) are estimated for the whole inventory period.
This issue has been addressed in the latest submission. New AD on
dolomite is consistent across the time series. See the current 2022 NIR
IPPU chapter Section 4.3 for a discussion on new AD from GHGRP used
for 2010-2020 and a revised methodology for 1990-2009 to address
time-series consistency.
1.28
2.B.7 Soda ash
production - C02
Transparency
The Party reported in NIR table 4-44 (p.4-56) the soda ash production AD
used for estimating C02 emissions. However, the ERT noted that
according to the NIR (p.4-55), the EF for C02 emissions was applied for
trona consumption (0.09741 C02/t trona) but not for soda ash
production. During the review, the Party clarified that the data provided
in NIR table 4-44 correspond not to soda ash production but to trona
consumption. The ERT also noted that the AD description provided in CRF
table 2(I).A-Hsl was also not clearly related to trona consumption and
still described AD as "soda ash production". The ERT recommends that
the Party correct the table heading for the AD from "soda ash
production" to "trona consumption" in the NIR and clarify the AD
description in CRF table 2(I).A-Hsl.
This issue was addressed in the April 2021 submission. See the previous
2021 NIR IPPU chapter Section 4.12 p. 4-58, table 4-44 for the revised
title: Trona Ore Use (kt) and the footnote clarifying that trona ore use is
assumed to be equal to trona ore production.
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1.29
2.B.10 Other (chemical
industry) - N20
Comparability
The Party reported C02 emissions from SiC consumption under category
2.B.10 in CRF table 2(I).A-Hsl (e.g. some 97.41 kt C02 in 2018). During the
review, the Party clarified that these emissions stem from the use of SiC
in non-abrasive applications, which include steel smelting and other end-
uses, where SiC is heated to a sufficiently high temperature that carbon is
oxidized and released as C02. The ERT agreed with the provided
explanation but noted that emissive sources of SiC are not transparently
described in the NIR. It also noted that emissions from SiC use were
reported in the NIR (section 4.10) as a sum total that also included
emissions from SiC production. The ERT recommends that the Party
clarify the emissive non-abrasive applications of SiC, document why these
emissions are not reported elsewhere (e.g. category 2.C.1) and
separately report in the NIR C02 emissions from SiC production and SiC
use.
See the 2022 NIR IPPU chapter Section 4.10 for clarification on why
emissive non-abrasive applications of SiC are reported here and not
elsewhere. See also Tables 4-36 and 4-37 which show emissions by SiC
production and consumption.
1.30
2.C.1 Iron and steel
production - C02
Accuracy
The Party included coke breeze production in the estimates of C02
emissions from coke production (NIR pp.4-79-4-80). The amount of coke
breeze produced was approximated using a production factor of 0.075 t
coke breeze/t coking coal consumed (NIR p.4-79) because actual data
were not available. However, the ERT noted that actual data on coke
breeze production in the United States can be obtained from EIA
quarterly coal reports. The ERT compared the estimated data on coke
breeze production used in the GHG inventory (1,248 kt coke breeze for
2018) with the EIA statistics (636 kt coke breeze for 2018) and concluded
that coke breeze production was potentially overestimated in the
inventory. The overestimation of coke breeze production could lead to an
underestimation of emissions because the emissions are estimated using
the carbon balance method, where the carbon content of products (coke
and coke breeze) is subtracted from the carbon inputs (coking coal).
During the review, the Party acknowledged the difference between the
EIA statistics and the data used for estimating Commissions. In its
clarifications on the list of provisional main findings, the Party indicated
that: (a) Industry data more accurately represent coke output data in
relation to the other industry data used (data on coke production output
are linked to other sources of iron and steel production emissions,
including sinter production, where coke breeze is often used, and non-
energy use of energy where coal tar is utilized); (b) Use of industry data
allows for a consistent approach across the different emission categories;
(c) Overall, there is no underestimation or overestimation of C02
emissions because all carbon associated with the coal used to make the
coke is eventually accounted for, either in the coke production process or
where the coke is eventually used, and a consistent approach is used to
The United States notes that the methodology used to calculate coke
production emissions is described in Section 4.17 of the 2022 NIR. See
for example Tables 4-67 and 4-68 on pp. 4-88. The Party continues to
assess EIA data on coke breeze production and the impact of this
change on emission estimates. The Party will provide an update as
appropriate in future submissions.
Annex 8
A-555
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track the carbon throughout (see ID# 1.31 below). The ERT recommends
that the Party revise estimates of C02 emissions from coke production
taking into account national statistics on coke breeze production, for
example from EIA quarterly coal reports, or demonstrate in the NIR that
C02 emissions from coke production were not underestimated by using
industry data on coke breeze production instead of EIA statistics, and
explain how there is a consistent approach used to track carbon
throughout the calculations.
1.31
2.C.1 Iron and steel
production - C02
Accuracy
The Party reported coke consumption for pig iron production in NIR table
4-72 (p.4-83) (e.g. 7,618 kt for 2018) and carbon content in the coke used
in estimates in NIR table 4-69 (p.4-81) (0.83 t C/t coke). During the
review, the Party clarified that data on coke consumption are reported in
t dry coke according to the data source (American Iron and Steel Institute
annual statistical report). The ERT noted that the chosen carbon content
of coke does not correspond to the coke consumption units because the
expected value of carbon content for dry coke is significantly higher (e.g.
according to the C02 Emissions Data Collection User Guide (version 7) of
the World Steel Association, the carbon content of dry coke is
approximately 0.891 C/t dry coke or 3.257 t C02/t dry coke). The ERT
concluded that C02 emissions for category 2.C.1 iron production were
probably underestimated because the carbon content of coke chosen for
estimates was incorrect. In the estimation of the ERT, the missing
emissions might account for 1,675.96 kt C02 for 2018 for iron production,
but emissions would be overestimated by the same amount for coke
production. During the review, the Party explained that underestimated
emissions from coke consumption were included in other parts of the
inventory. However, the ERT was unable to confirm this because the
Party did not provide the initial sources of data used in estimates. The
ERT recommends that the Party specify in the NIR the units of coke
consumption and coke production (t coke or t dry coke) and provide
supporting data sources, and revise estimates of C02 emissions as
needed from pig iron production and coke production by applying a
carbon content value for coke that corresponds to the AD for coke
production or consumption.
The United States uses the carbon content for coke as provided in the
2006 IPCC Guidelines, Volume 3, Table 4.3 on p. 4-27 for a Tier 2
methodology. EPA asked the data provider of coke consumption for pig
iron production for information on carbon content for this AD and will
continue to assess available resources. As noted in the NIR, the United
States utilizes a country-specific approach based on Tier 2
methodologies. See the 2022 NIR submission, IPPU chapter Section 4.17
for additional clarification that the units for coke consumed for pig iron
production are consistent with the units for the carbon content of coke.
1.32
2.C.1 Iron and steel
production - C02
Accuracy
The Party estimated that the carbon content of pellets, sinter and natural
ore used in pig iron production is equal to the carbon content of direct
reduced iron (2 per cent) (NIR p.4-84). During the review, the Party did
not provide any relevant sources to justify the chosen carbon content
value for pellets, sinter and natural ore. In its clarifications on the list of
provisional main findings, the Party indicated that, given the lack of
default carbon content values for pellets, sinter and natural ore, it
The United States reiterates the previous clarification and response
provided during the previous review. In the absence of a default carbon
content value from the 2006 IPCC Guidelines and the 2019 Refinement
for pellet, sinter, or natural ore consumed for pig iron production, the
United States uses a country-specific approach based on Tier 2
methodologies. EPA assumes that pellets, sinter, and natural ore used
as an input for pig iron production have the same carbon content as
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adopted a country-specific approach to determine these values, as
documented in the NIR (table 4-69, p.4-81). It added that, although iron
and steel is a key category, any updates to estimates for subcategories
resulting from updates to the carbon content of pellets, sinter and
natural ore are unlikely to lead to a significant recalculation of total
emissions for iron and steel. Noting that the carbon content of pellets,
sinter and natural ore is likely to be significantly lower than 2 per cent,
the ERT concluded that the related C02 emissions might not be accurate.
Moreover, the failure of the Party to provide any justification for its
chosen carbon content value for pellets, sinter and natural ore is not in
compliance with paragraph 50(a) of the UNFCCC Annex 1 inventory
reporting guidelines. The ERT recommends that the Party justify its
chosen carbon content value of 2 per cent for pellets, sinter and natural
ore by indicating that it used a country-specific approach of assuming the
same carbon content as direct reduced iron (2 per cent), with
confirmation by the references to the relevant data sources in the NIR, or
otherwise revise the emission estimates for iron and steel production
(category 2.C.1) by updating the carbon content value for pellets, sinter
and natural ore used in pig iron production on the basis of relevant data
sources.
direct reduced iron (2 percent). See the 2022 NIR submission, IPPU
chapter Section 4.17 for this clarification on this country-specific
approach. Current QC and outreach do not indicate that this approach
needs to be changed.
1.33
2.C.1 Iron and steel
production - C02
Accuracy
The Party included in its estimates of C02 emissions from iron and steel
production (category 2.C.1) flux consumption for electric arc furnace
steel and basic oxygen furnace steel production (NIR table 4-72, p.483).
According to the NIR (p.4-81), the amount of flux used in pig iron
production was deducted from other process uses of carbonates (CRF
source category 2.A.4) to avoid double counting. During the review, the
Party explained that data for flux consumption in both basic oxygen
furnace and electric arc furnace steel production were obtained from
American Iron and Steel Institute annual statistical reports. In its
clarifications on the list of provisional main findings, the Party indicated
that the flux consumption data provided by the American Iron and Steel
Institute include all flux types, including limestone, lime and fluorspar,
and that it only accounts for the use of fluxes containing carbon
(limestone and dolomite) in iron and steel sector emissions, since the
emissions associated with other fluxes are reported for their individual
sectors (e.g. lime production). The ERT recommends that the Party
transparently describe in the NIR the type of fluxes used in iron and steel
production and ensure that only C02 emissions from the emissive source
of fluxes are reported under category 2.C.1 and consumption of
carbonates under category 2.A.4 is adjusted to subtract emissive sources
accounted for elsewhere but not by subtracting non-carbonate fluxes.
The United States reiterates the previous clarification and response
provided during the previous review. The current 2022 NIR submission
clarifies in the IPPU chapter Section 4.17 that the United States includes
only carbon-containing fluxes (I.e., limestone and dolomite) in emissions
calculations from electric arc furnace and basic oxygen furnace steel
production.
Annex 8
A-557
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Agriculture
A.l
3. General (agriculture) -
CH4 and N20
(A.25, 2019)
Completeness
Not resolved. Include in the NIR (e.g. in annex 5) an indication of the
sources and categories not estimated for Hawaii and Alaska. If the
emissions are insignificant, the ERT recommends that the Party justify
their exclusion on the basis of the likely level of emissions in accordance
with paragraph 37(b) of the UNFCCC Annex 1 inventory reporting
guidelines. The Party reported in its NIR (pp.5-44 and 5-54) that the
current inventory includes N20 emissions from mineral fertilizer and Nex
on pasture, range and paddock in Alaska and Hawaii and drained organic
soils in Hawaii, but excludes CH4 and N20 emissions from field burning of
agricultural residues in those States. During the review, the Party clarified
that work is under way to assemble these data for Alaska and Hawaii for
inclusion in either the 2021 or 2022 NIR.
Work is ongoing to assemble this data for Alaska and Hawaii for
inclusion in the NIR. This will be provided at the earliest in the 2024
submission.
A.2
3. General (agriculture) -
CH4 and N20
(A.26, 2019)
Consistency
Not resolved. Explore the use of alternative data sources to derive AD for
the years of the time series where no DAYCENT data are available (2013-
2017), and if alternative data sets are not available, the ERT recommends
that the Party use proxy data or extrapolation methods to derive AD. The
Party reported in its NIR that surrogate data, trend analysis and statistical
approaches were used to estimate CH4 emissions from rice cultivation for
2016-2018 (p.5-24), N20 emissions from managed soils for 2016-2018
(p.5-36) and C02 emissions from field biomass burning for 2015-2018
(p.5-36). However, the ERT noted that the AD reported in CRF tables 3.C
for 2015-2018 and 3.F for 2014-2018 are simply the same figures. During
the review, the Party clarified that it will continue to seek out alternative
data sources to derive the inventory estimates for the portion of the time
series not covered by the National Resources Inventory. It noted that this
is a medium- to long-term update.
The United States will continue to seek out alternative data sources to
drive the Inventory estimates for the portion of the time series not
covered by the NRI. This is a medium- to long-term update.
A.3
3.A Enteric fermentation
-ch4
(A.2, 2019)
(A.16, 2018)
Convention reporting
adherence
Not resolved. Undertake a quantitative uncertainty assessment in
conjunction with future planned methodological updates. The Party
reported the same uncertainty range in its NIR (p.5-8) as in previous
submissions (i.e. a range of 11 per cent below to 18 per cent above the
2018 emission estimates). The ERT noted that the last quantitative
uncertainty analysis for CH4 emissions from enteric fermentation was
undertaken for the 2003 GHG inventory submission. During the review,
the Party reiterated its previous response, namely that updates will be
accounted for in methodological refinements planned for future
submissions.
The United States reiterates its previous response that updates will be
considered with methodological refinements planned and underway in
future submissions.
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A.4
3.A.1 Cattle-CH4
(A.6, 2019)
(A.20, 2018)
Accuracy
Not resolved. Update regional diet characterization data used in the
estimation of CH4 emissions from cattle in order to more accurately
reflect the differences in diets across farms and states. The Party
reported regional digestible energy intake, which is expressed in
percentage of GE, and average CH4conversion rate data in NIR tables A-
172 and A-173 and GE by animal type and state in table A-174 of NIR
annex 3.10. These data are the same as those reported in the previous
submission. In the footnotes to these tables it is indicated that they will
be updated for the entire time series in the next inventory submission.
During the review, the Party informed the ERT that work is under way to
address this issue by the 2022 submission at the earliest and that, since
the 2021 NIR will focus on the improvement, rather than the running, of
the Cattle Enteric Fermentation Model, updated values will not be
available until the 2022 NIR, when the model is next run.
Work is underway to address this in future submissions; the earliest will
be the next (i.e., 2023) submission.
A.7
3.A.1 Cattle-CH4
(A.4, 2019)
(A.18, 2018)
Accuracy
Not resolved. Improve the accuracy of the milk fat percentage, for
example by investigating the possibility of using additional data sources
for information on milk fat percentage values, such as creameries and
agricultural extension services. The Party reported in its NIR (p.5-9) that,
according to information obtained through recent improvements, the 4
per cent value is still representative of milk fat for 2018. During the
review, the Party informed the ERT that it had obtained a source for milk
fat percentages and expected to include these new values in the 2022
submission. The ERT commends the efforts made by the Party but
considers that the issue remains unresolved as the milk fat value has not
been updated as recommended.
The United States considers this issue resolved. Updated milk fat
percentages are included in the current submission. These values
ranged from 3.7 percent to 4.1 percent across the time series and are
more representative of U.S. livestock industry.
A.8
3.A.1 Cattle-CH4
(A.5, 2019)
(A.19, 2018)
Accuracy
Addressing. Investigate the possibility of using additional data sources
(e.g. farm extension services) to derive country-specific information on
calf births from dairy cows throughout the year and report on the results
of this investigation in the NIR. The Party reported in NIR annex 3.10 (p.A-
301) that the number of births is assumed to be distributed equally
throughout the year for calf births from dairy cows but noted in the
planned improvements section (p.5-9) that it is seeking data for births by
month. During the review, the Party informed the ERT that work is under
way to identify sources of data. It noted that this is a long-term
improvement and will be included in the 2023 submission at the earliest.
To date, the primary data source identified did not provide monthly
data on calf births. This is a longer-term improvement and the earliest
this could be incorporated would be the 2024 submission.
A.9
3.A.2 Sheep-CH4
(A.7, 2019)
(A.21, 2018)
Accuracy
Not resolved. Update the sheep population distribution as data
availability allows, focusing resources as appropriate, in line with the
2006 IPCC Guidelines. The Party reported in NIR annex 3.11 (p.A-326)
that population distribution data for lamb and sheep on feed are not
available for after 1993. During the review, the Party informed the ERT
It should be noted that the animal population distribution data used to
calculate Enteric Fermentation emissions (A.21, 2018 ERT issue) for
sheep were taken from the U.S. Department of Agriculture (USDA)
National Agricultural Statistics Service (NASS) agricultural statistics
database (USDA 2021a) or the Census of Agriculture (USDA 2019) and
Annex 8
A-559
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that it expects to include updated sheep EFs and populations in the 2021
and 2022 submissions, respectively.
updated on an annual basis. For sheep and goats, default national
emission factors were updated in the 2021 submission to reflect
revisions made in the 2019 IPCC Refinement to the 2006 IPCC Guidelines
and improve the accuracy of emissions.
EPA understands from exchange with ERT that the issue is manure
management waste management distribution systems for sheep. The
last year of available waste management distribution data for sheep is
2001. As described in the Annex 3.11, due to lack of additional data,
data for years 2002 and beyond are assumed to be the same as 2001.
Based on expert opinion cited, it was assumed that all sheep manure
not deposited in feedlots was deposited on pasture, range, or paddock
lands.
A. 10
3.B Manure management
-ch4
(A.ll, 2019)
(A.25, 2018)
Convention reporting
adherence
Not resolved. Update the quantitative uncertainty assessment. The Party
reported in its NIR (p.5-16) that the quantitative uncertainty analysis for
CH4and N20 emissions from manure management was performed in
2002 using approach 2 from the 2006 IPCC Guidelines, and that the
uncertainty estimates were applied directly to the values for 2018.
During the review, the Party reiterated its previous response, namely
that the updates will be accounted for in the methodological refinements
planned for future submissions.
The United States reiterates its previous response that updates will be
considered with methodological refinements planned and underway in
future submissions.
A.ll
3.B Manure management
-CH4and N20
(A.12, 2019)
(A.5, 2018)
(A.14, 2016)
(A.14, 2015)
Accuracy
Addressing. Obtain updated MMS data and estimate emissions using the
updated MMS usage data; if this is not possible, report on progress in the
effort to update the MMS data. The Party reported in NIR annex 3.11
updated MMS data for dairy cows (p.A-330), swine (p.A-331) and poultry
(p.A-332); however, data for other livestock types, such as sheep, have
not been updated since 2001. During the review, the Party informed the
ERT that it will report on further progress in the 2021 submission.
The United States considers this issue to be resolved as the 2020 and
2021 NIR submissions have reported on progress to update MMS data.
Efforts are underway with support from the USDA to update waste
management system data in the Inventory.
A.12
3.B Manure management
-N20
(A.14, 2019)
(A.26, 2018)
Accuracy
Addressing. Investigate other potential data sources of animal MMS data,
such as extension services (i.e. agricultural advisory services). The Party
reported in its NIR (p.5-18) that waste management system distribution
data for dairy cows were updated using data from the 2016 Agricultural
Resource Management Survey of dairy producers, and anaerobic
digestion data were updated for swine, dairy cows and poultry using data
from the EPA AgSTAR Program. The Party also reported that it is
continuing to investigate new sources of MMS data. During the review,
the Party informed the ERT that further progress on animal MMS data
will be reported in the 2021 submission. The ERT commends the Party's
progress but considers that the recommendation has not yet been fully
addressed; for example, the MMS distribution data for sheep have not
Please see response to A.ll; work is ongoing to obtain and incorporate
updated data.
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been updated since 2001 (NIR annex 3.11, p.A-332) (see ID# A.11 above).
A.13
3.B.1 Cattle-CH4
(A.16, 2019)
(A.7, 2018)
(A.15, 2016)
(A.15, 2015)
Transparency
Addressing. If not using a more disaggregated livestock categorization in
estimating emissions, use option A in reporting data and emissions for
cattle in the CRF tables; if applying option C, report the values for
population size, allocation by climate region to cool and temperate
regions, typical animal mass, volatile solid daily excretion and CH4
producing potential for all other cattle subcategories of option C in CRF
tables 3.B(a)sl and 3.B(a)s2. The Party applied option C and
disaggregated data on cattle characterization reported in CRF table
3.B(a)sl, such as livestock population, typical animal mass, volatile solid
daily excretion and CH4 producing potential. Data on population size in
CRF table 3.B(a)sl and MMS in CRF table 3.B(a)s2 are still reported
according to dairy and non-dairy cattle, rather than according to
disaggregated information on population allocations to climate regions
and usage of MMS. During the review, the Party reiterated its previous
response, namely that updates will be accounted for in methodological
refinements planned for future submissions. The Party is still
investigating the possibility of reporting disaggregated climate
parameters in the CRF tables.
The United States reiterates its previous response that updates will be
considered with methodological refinements planned and underway in
future submissions. The United States is still investigating the possibility
of reporting disaggregated climate parameters in the CRF Reporter.
A.15
3.B.1 Cattle-N20
(A.29, 2019)
Transparency
Not resolved. Report the correct Nex values for beef calves, dairy calves
and beef replacements in CRF table 3.B(b) so that they reflect the true
average Nex rate. Discrepancies persist in the reported total N excreted
and the results calculated by multiplying population by Nex rate for dairy
cows, beef calves and dairy calves in CRF table 3.B(b). During the review,
the Party indicated that it is currently investigating the possibility of
providing disaggregated Nex rates for these cattle types in its 2022
submission.
CRF reported Nex rates are average N excretion rates for all U.S. states.
For cattle, the United States calculates the N excreted for each state
using a state-specific N excretion rate factor and then combines all
states to calculate and report the total national N excreted value shown
in the CRF table. The total reported N excreted by MMS type and total N
excreted reported in the CRF tables reflect the actual totals calculated.
Reporting a different value for Nex rates other than the weighted values
currently reported would not accurately reflect the information used in
calculating emissions. Therefore, the United States does not believe it is
appropriate to report a different, average value just to ensure values N
excretion values align.
A.16
3.B.1 Cattle-N20
(A.30, 2019)
Transparency
Not resolved. Replace the Nex rates for dairy cattle and non-dairy cattle
with "IE" and explain in the documentation box of CRF table 3.B(b) that
the Nex rates are reported against individual livestock classes. The Party
continued to report "IE" for the Nex rate for heifer stockers and beef
replacements in CRF table 3.B(b) in its 2020 submission. During the
review, the Party indicated that it is currently investigating the possibility
of updating disaggregated Nex rates for these cattle types in its 2022
submission. The ERT considers that the recommendation has not yet
The United States is currently investigating the possibility of providing
the Nex values for these disaggregated cattle types in a future
Inventory. The earliest we could disaggregate Nex rates by cattle type is
the 2024 submission.
Annex 8
A-561
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been addressed.
A.17
3.B.1 Sheep-CH4 and N20
(A.31, 2019)
Transparency
Not resolved. Include information on MMS distribution for sheep in NIR
table A-189. The Party did not report MMS distribution for sheep in NIR
table A-189 (annex 3.11, pp.A-346-A-347). During the review, the Party
informed the ERT that it is currently working on including these values in
the 2022 submission.
This issue has been resolved in the current (i.e., 2022) submission).
A.18
3.D Direct and indirect
N20 emissions from
agricultural soils - N20
(A.19, 2019)
(A.30, 2018)
Completeness
Not resolved. Include all N20 emissions from the States of Alaska and
Hawaii in the emissions reported under this category or clearly outline in
the improvement plan steps for including those emissions in the
inventory. The Party reported that N20 emissions from the States of
Alaska and Hawaii are not included in the current inventory for
agricultural soil management, with the exception of N20 emissions from
drained organic soils in cropland and grassland for Hawaii and synthetic
fertilizer and pasture, range and paddock N amendments for grassland in
Alaska and Hawaii. This issue is identified in the Party's planned
improvements in its NIR (p.5-45). During the review, the Party informed
the ERT that work is under way to assemble these data for inclusion in
the agricultural soil N20 estimates by either the 2021 or 2022 submission.
Work is underway to assemble this data for inclusion in the Agricultural
Soils N20 estimates. This will be provided in the 2024 submission at
earliest.
A.19
3.D Direct and indirect
N20 emissions from
agricultural soils - N20
(A.20, 2019)
(A.32, 2018)
Transparency
Not resolved. Provide additional information in the NIR on the quantities
and N content of commercial organic amendments (e.g. biosolids, dried
blood and compost) applied to agricultural soils. The Party did not report
additional information on the N content of commercial organic
amendments included in the NIR (section 5.4). During the review, the
Party informed the ERT that it will include this information in a future
inventory if the unique N content of each of the non-commercial organic
amendments can be found.
This has been resolved with the previous 2021 submission; see page 5-
40.
A.20
3.D Direct and indirect
N20 emissions from
agricultural soils - N20
(A.32, 2019)
Convention reporting
adherence
Not resolved. Correct the text in its NIR to reflect the actual method
applied, namely that N20 emissions from tobacco crops are estimated
using the DAYCENT model (tier 3 method). The Party reported in its NIR
(p.5-36) both that DAYCENT is used and that it is not used to estimate
N20 emissions from tobacco. During the review, the Party indicated that
this issue will be addressed in the 2021 submission.
This has been resolved with the previous 2021 submission.
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A.23
3.D.a.3 Urine and dung
deposited by grazing
animals - N20
(A.41, 2019)
Transparency
Not resolved. Include in the NIR the information provided to the ERT
explaining the approach used to allocate N deposited in urine and dung
to each county and how the DAYCENT model uses these data in the
estimation of N20 emissions. The Party did not include in its NIR
information on the approach used to allocate N deposited in urine and
dung to each county and how the DAYCENT model uses these data in the
estimation of N20 emissions. During the review, the Party informed the
ERT that it planned to include an additional explanation on the approach
used to allocate N deposited in the 2021 submission.
This has been resolved with the previous 2021 submission; see page A-
366.
A.24
3.D.b Indirect N20
emissions from managed
soils - N20
(A.24, 2019)
(A.12, 2018)
(A.18, 2016)
(A.18, 2015)
Transparency
Addressing. Provide an explanation of how the methodology and the
DAYCENT model used to estimate N volatilized and N loss are both
compatible with the 2006 IPCC Guidelines and based on science. The ERT
was unable to identify any additional explanation in the NIR on how the
methodology and the DAYCENT model used to estimate N volatilized and
N loss are both compatible with the 2006 IPCC Guidelines and based on
science in its NIR. During the review, the Party informed the ERT that
additional information will be added to the NIR for either the 2021 or
2022 submission.
Information has been updated in the recent submission and is
transparently reported in Chapter 5 and Annex 3.12 of the NIR, which
provides detailed information on how DayCent is used to generate the
amount of N volatilized and how this is used in combination with IPCC
defaults to estimate emissions of indirect N20. This information is
consistent with the 2006 IPCC Guidelines. In addition, following peer-
reviewed publications are provided in the NIR on the use of DayCent for
estimating soil N20 emissions that speak to scientific basis of the model.
These papers are referenced in Chapter 10 and Annex 3.12.
Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005)
"DAYCENT Model Analysis of Past and Contemporary Soil N20 and Net
Greenhouse Gas Flux for Major Crops in the USA." Soil Tillage and
Research, 83: 9-24. doi: 10.1016/j.still.2005.02.007.
Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010)
"Estimating Uncertainty in N20 Emissions from U.S. Cropland Soils."
Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.
Del Grosso, S.J., W.J. Parton, C.A. Keough, and M. Reyes-Fox. (2011)
Special features of the DAYCENT modeling package and additional
procedures for parameterization, calibration, validation, and
applications, in Methods of Introducing System Models into Agricultural
Research, L.R. Ahuja and Liwang Ma, editors, p. 155-176, American
Society of Agronomy, Crop Science Society of America, Soil Science
Society of America, Madison, Wl. USA.
Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S.
Ojima, and D.S. Schimel (2001) "Simulated Interaction of Carbon
Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model." In
Schaffer, M., L. Ma, S. Hansen, (eds.). Modeling Carbon and Nitrogen
Dynamics for Soil Management. CRC Press. Boca Raton, Florida. 303-
332.
Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating
Annex 8
A-563
-------
agricultural nitrous oxide emissions. EOS 89, 529-530.
Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) "15N isotopic crop
residue cycling studies and modeling suggest that IPCC methodologies
to assess residue contributions to N20-N emissions should be
reevaluated." Nutrient Cycling in Agroecosystems, DOI 10.1007/sl0705-
009-9300-9.
Scheer, C., S.J. Del Grosso, W.J. Parton, D.W. Rowlings, P.R. Grace (2013)
Modeling Nitrous Oxide Emissions from Irrigated Agriculture: Testing
DAYCENT with High Frequency Measurements, Ecological Applications,
in press. Available online at: http://dx.doi.Org/10.1890/13-0570.l.
A.25
3. General (agriculture) -
CH4and N20
Transparency
The GE values reported in NIR table A-174 (pp.A-313-A-314) for each
subcategory differ significantly among States. For example, the annual GE
for dairy cows is reported as 29 MJ/1,000 head in Alaska and 262,323
MJ/1,000 head in California. During the review, the Party clarified that
the values reported in NIR table A-174 represent total GE for each animal
type in each State rather than on a per-head basis. The ERT recommends
that the Party correct the unit in the title of NIR table A-174 from
"MJ/1,000 head" to "MJ/head".
This has been resolved with the previous 2021 submission.
A.26
3. General (agriculture) -
N20
Convention Reporting
Adherence
The ERT noted that Nex on pasture, range and paddock for 2018 was
reported in CRF table 3.D as 3,569,237,661.43 kg N/year, while total Nex
on pasture, range and paddock for cattle, sheep, swine and other
livestock for 2018 was reported in CRF table 3.B(b) as 4,036,707,495.09
kg N/year. It also noted that N data reported by the Party for pasture,
range and paddock manure used in agricultural soil management and
manure management are inconsistent between these CRF tables for
1997-2018. The ERT acknowledges that the Party noted this discrepancy
in the NIR (annex 3.11, p.A-326, footnote 93). The ERT recommends that
the Party report the same values for Nex on pasture, range and paddock
in CRF tables 3.B(b) and 3.D.
The United States does not consider this to be an issue. This was clearly
described in footnote 93 (page A-326) in Annex 3.11 of the 2020
submission and resolved with the 2021 submission.
A.27
3.D.a.2 Organic N
fertilizers - N20
Convention Reporting
Adherence
The ERT considers that the average N content of biosolids of 69 per cent
reported by the Party in the NIR (annex 3.12, p.A-377) is too high
according to common scientific knowledge on the N content ratio of
organic material.
During the review, the Party clarified that the reported percentage was a
typographical error and that the N content of biosolids used in estimating
the total applied N from biosolids is assumed to be 3.9 per cent. The
error has no impact on the estimated emissions. The ERT recommends
that the Party correct the reported percentage for the average N content
This issue has been addressed in the current (i.e., 2022) submission.
A-564 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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of biosolids.
A.29
3.F Field burning of
agricultural residues -
CH4 and N20
Transparency
The ERT noted that the equation in the NIR (p.5-53) applied to calculate
carbon or N released from biomass burning is incorrect. During the
review, the Party stated that this typographical error in the equation
would be corrected in the next inventory report and noted that carbon or
N released from biomass burning was calculated using a country-specific
approach based on the equation from the Revised 1996 IPCC Guidelines
(vol. 3, p.4.82), as the Party clearly described in box 5-6 of the NIR. The
Party noted that the calculation was performed according to the correct
equation so will not require any recalculations. The ERT recommends
that the Party correctly report the equation used to calculate carbon or N
released from biomass burning.
The United States considers this issue as resolved. The equation for
biomass burning was updated in the previous 2021 submission.
A.30
3.H Urea application-
C02
Accuracy
The Party reported in its NIR (chap. 4.6, pp.4-32-4-35) that C02 emissions
from the application of urea to agricultural soils were estimated using the
Monte Carlo analysis, with an EF uncertainty range of 50 to 100 per cent
of emissions and a triangular distribution. During the review, the Party
explained that it applied a probabilistic Monte Carlo analysis based on
the methods described in the 2006 IPCC Guidelines (vol. 1, chap. 3). It
added that the result was based on the posterior distribution of the
analysis, with the mode as the estimated highest probability value, and
the confidence interval provided by distribution percentiles of 2.5 and
97.5. The ERT noted that the 2006 IPCC Guidelines (vol. 1, chap. 3)
provide guidance on how to use the Monte Carlo analysis for combining
uncertainties, not for reporting emission estimates. Moreover, the
country-specific EFs were not justified in the light of specific national
circumstances or well documented in the NIR. The ERT recommends that
the Party demonstrate that the country specific EFs are appropriate for
its specific national circumstances and are more accurate than the
default data provided in the 2006 IPCC Guidelines, or otherwise apply the
IPCC default value (0.2 t C02-C/t urea) for this category.
The United States considers this issue as resolved. Please see the
updated description for Urea Fertilization included in the previous 2021
submission (see page 5-50, QA/QC and Verification, and Recalculations
Discussion).
LULUCF
L.l
4. General (LULUCF)-
C02, CH4 and N20
(L.l, 2019)
(L.2, 2018)
(L.2, 2016)
(L.2, 2015)
Addressing. Conclude the technical work under way to be able to provide
estimates for the carbon stock changes in the living biomass and DOM
pools for each conversion category from forest land to any other land use
for each year based on a reliable land-use change matrix, and report on
the achievements made. The United States reported carbon losses in the
living biomass and DOM pools for categories 4.B.2.1 (forest land
converted to cropland), 4.C.2.1 (forest land converted to grassland) and
The United States does not currently include estimates for the
categories of Forest Land Converted to Other Land. These categories
will be included in a future Inventory submission and will contain the
estimates of carbon stock loss as a result of converting forest to these
lands.
The United States does not currently include estimates for the
Annex 8
A-565
-------
(81, 2013)
Completeness
4.E.2.1 (forest land converted to settlements) and in the living biomass
pool only for category 4.D.2.3.1 (forest land converted to other wetlands)
for the first time for 2018. Categories 4.D.2.2.1 (forest land converted to
flooded land) and 4.F.2.1 (forest land converted to other land) are still
reported as "NE" or "NA" in its CRF table 4.F. During the review, the Party
clarified that it does not currently include estimates for the categories
forest land converted to other land or flooded land, or land converted to
flooded land. These categories will be included in a future inventory
submission and will contain the estimates of carbon stock loss as a result
of converting forest land to these lands mentioned above. With respect
to flooded lands, the United States plans to include the flooded land
categories when it applies the updated guidance on flooded lands from
the 2019 Refinement to the 2006 IPCC Guidelines for National
Greenhouse Gas Inventories. The ERT considers that the
recommendation has not yet been fully addressed because the Party did
not include carbon stock change estimates for living biomass and DOM
for all managed lands in the inventory.
categories of Flooded Land/Land Converted to Flooded Land or Other
Land/Land Converted to Other Land. With respect to flooded lands, the
United States is planning to include these when it applies the updated
guidance on flooded lands from the 2019 Refinement to the 2006 IPCC
Guidelines. However, it will take several years to disaggregate the
carbon stock changes from lands converted to flooded lands by the
individual land use categories. Overall, this should be a very minor
category as most flooded lands in the United States were created well
before 1990.
L.2
4. General (LULUCF)-
C02, CH4 and N20
(L.2, 2019)
(L.3, 2018)
(L.3, 2016)
(L.3, 2015)
(82, 2013)
(97, 2012)
Completeness
Addressing. Include all managed United States lands in the inventory;
improve the consistency of the time series of national areas; and report
on the achievements made. The land-use matrix of CRF table 4.1 and the
land representation tables in the NIR (tables 6-6 and 6-7, pp.6-10-6-11)
include all areas of managed and unmanaged land in the United States
except for United States territories. During previous reviews, the Party
clarified that it plans to include these territories in future submissions,
including preliminary land-use information for the United States
territories in NIR table 6-9. In addition, the "total area" columns of CRF
background tables 4.A, 4.B, 4.C, 4.D, 4.E and 4.F do not include managed
land areas where emissions or removals do not occur. Instead, the
different coverage of the reported area is highlighted in a documentation
box for some of the CRF background tables. During the review, the Party
explained that it has included further information in the NIR to explain
the deviations. NIR tables 6-33 and 6-37 demonstrate that the area of
managed land left out for categories 4.B.1 and 4.B.2 is greater than 1 kha,
while NIR tables 6-41 and 6-49 show the deviations for categories 4.C.1
and 4.C.2, respectively, resulting from not including managed grassland
in Alaska. Similarly, deviations between the areas given in CRF tables 4.1
and 4.A are documented in NIR annex 3.13 tables A-231 and A-233. The
ERT considers that the recommendation has not yet been fully addressed
because the Party did not include all managed lands in the inventory.
See the following tables included in 2022 NIR:
Table 6-31: Area of Managed Land in Cropland Remaining Cropland that
is not included in the current Inventory (Thousand Hectares)
Table 6-35: Area of Managed Land in Land Converted to Cropland that
is not included in the current Inventory (Thousand Hectares)
Table 6-39: Area of Managed Land in Grassland Remaining Grassland in
Alaska that is not included in the current Inventory (Thousand Hectares)
Table 6-47: Area of Managed Land in Land Converted to Grassland in
Alaska that is not included in the current Inventory (Thousand Hectares)
Annex Table A-213: Forest Land Area Estimates and Differences
Between Estimates in 6.1 Representation of the U.S. Land Base (CRF
Category 4.1) and 6.2 Forest Land Remaining Forest Land (CRF Category
4A1) (kha)
Annex Table A-217: Land Converted to Forest Land area estimates and
differences between estimates in the Representation of the U.S. Land
Base (CRF Category 4.1) and Land Converted to Forest Land (CRF
Category 4A1) (kha)
A-566 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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L.3
4. General (LULUCF)-
C02, CH4 and N20
(L.3, 2019)
(L.36, 2018)
Convention reporting
adherence
Not resolved. Until the Party is able to report anthropogenic emissions
and removals from the entire national managed land area, report non-
estimated managed land as a subdivision in the relevant CRF tables (i.e.
tables 4.A, 4.B, 4.C, 4.D and 4.E), so that the managed land area for each
land category reported in CRF table 4.1 corresponds with that reported
for the same category in CRF tables 4.A, 4.B, 4.C, 4.D and 4.E. In CRF table
4.1 the United States reported for the first time areas for forest land
(unmanaged), grassland (unmanaged) and wetlands (unmanaged) for the
whole time series. The Party did not report non-estimated managed land
as a subdivision in CRF tables 4.A, 4.B, 4.C, 4.D and 4.E (see ID# L.2
above). During the review, the Party clarified that it is considering
reporting insignificant emissions as "NE" and justifying their exclusion in
accordance with paragraph 37(b) of the UNFCCC Annex 1 inventory
reporting guidelines. In its clarifications on the list of provisional main
findings, the Party indicated that it reports areas for managed lands that
are not included in the estimates of: (a) CRF table 4.A in NIR annex 3.13,
page 442, table A-231; and NIR table A-233, page 447; (b) CRF table 4.B in
NIR chapter 6.4, page 65, table 6-33; and NIR chapter 6.5, page 71, table
6-37; (c) CRF table 4.C in NIR chapter 6.6, page 79, table 6-41; and NIR
chapter 6.7, page 90, table 6-49; (d) CRF table 4.D - work is under way to
include information on additional wetlands such as flooded lands. The
coastal wetlands estimates are assumed to include all managed coastal
wetlands, but the area data are not linked to the land representation (see
pp.6-98-6-99 of the NIR for more information); (e) CRF table 4.E for
drained organic soils in NIR chapter 6.10, page 118, table 6-78; and NIR
chapter 6.11, page 142, table 6-93. Explanations were also included in
the documentation boxes of the CRF tables. The ERT considers that the
recommendation has not yet been fully addressed because the Party did
not report managed lands that have not been estimated as a subdivision
in CRF tables 4.A, 4.B, 4.C, 4.D and 4.E.
The United States will consider this suggestion for the 2023 or 2024 NIR
and CRF submission (i.e., use of notation key NE).
L.4
4. General (LULUCF)-
C02, CH4 and N20
(L.41, 2019)
Transparency
Report in the NIR preliminary emission or removal estimates for the land
areas of the United States territories reported as a preliminary result of
the planned improvement carried out in the Party's inventory. The Party
reported preliminary land-use data for United States territories but did
not report any preliminary emission or removal estimates for these land
areas. During the review, the Party clarified that work to improve the
land representation and tracking of managed and unmanaged land will
be initiated in 2021 with a view to updating NIR chapter 6 for the 2022 or
2023 submission. The improvement is expected to have been fully
implemented by the 2024 submission.
Work is still underway to develop the activity data needed to estimate
emissions and removals from U.S. Territories.
Annex 8
A-567
-------
L.5
Land representation -
C02, CH4 and N20
(L.4, 2019)
(L.7, 2018)
(L.21, 2016)
Consistency
Not resolved. Resolve the inconsistencies in land-use areas in the time
series reported in the CRF tables. The discrepancy between land-use
areas in the time series reported in CRF table 4.1, where the final area at
the end of a given year is not the same as the initial area of the
subsequent year, remains unresolved. For example, the final area
reported for category 4.1.1 forest land remaining forest land
(unmanaged) for 2017 is 281,651.72 kha, while the total initial area
reported for 2018 is 281,563.37 kha. During previous reviews, the Party
explained that the land-use areas in CRF table 4.1 were entered in
accordance with the IPCC definitions of remaining land (land that remains
subject to the same use for 20 years) and converted land (cumulative
area of conversion over the past 20 years) and also stated that the
heading of CRF table 4.1 can be understood to allow it to be compiled in
accordance with the IPCC definition (namely, using the 20-year
conversion). The ERT considers that the Party should bear in mind that
the CRF tables are designed to be presented as an inventory of emissions
for individual years, with a separate set of tables for each year. The land
transition matrix in CRF table 4.1, once published, is designed to show
the changes that have occurred that year between land uses, not
between land conversion categories. This approach helps to ensure
transparency, as it prevents the duplication of information on land areas
within an accounting category provided in CRF tables 4.A-4.F. For
example, where a Party converts 100 kha from grassland to settlements
each year under a default IPCC method, CRF table 4.1 would show for any
given year the movement of 100 kha from grassland under initial use and
to settlements under final use. By contrast, CRF table 4.E would show
2,000 kha under land converted to settlements to represent 20 years of
cumulative conversions for which emissions are calculated in relation to
land-use changes over time. CRF tables 4.1 and 4.E would be deemed
consistent where the total area of settlements is the same. This is in
accordance with the 2006 IPCC Guidelines (vol. 4), which state that
Parties should retain land in a conversion category for the conversion
period (CRF tables 4.A-F) while transparently reporting on the new
transitions for each year (CRF table 4.1). Further information on the
compilation of land transition matrices can be found in the 2006 IPCC
Guidelines (vol. 4, chap. 3.3), along with examples of final matrices (vol.
4, chap. 3.3, tables 3.5 and 3.6).
See explanation included in NIR Chapter 6 Section 6.1 and
documentation box in CRF Table 4.A.
L.6
Land representation -
C02, CH4 and N20
(L.42, 2019)
Not resolved. Include the land-use changes that occurred during the
periods 1971-1978 for land converted to cropland, grassland and
settlements, and 1971- 1981 for land converted to forest land, in order
to ensure that the areas of land converted categories for all inventory
Work is still underway with the goal of reporting in the 2023 or 2024
submission.
A-568 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Accuracy
years since 1990 contain the accumulated total of the land-use changes
over the past 20 years. The Party did not report the complete time series
for the land-use transition categories mentioned in the recommendation.
During the review, the Party explained that it will improve the
transparency of the reporting in the 2021 submission and that it plans to
report in the 2023 and 2024 submissions improvements to land
representation that will allow for tracking additional land-use
conversions.
L.7
Land representation -
C02, CH4 and N20
(L.43, 2019)
Accuracy
Addressing. Revise the area of unmanaged grassland for Alaska and
report on the changes in the NIR. During the previous review, the United
States informed the ERT that the area of unmanaged grassland in Alaska
had been overestimated and would be revised. The current ERT noted
that no land-use transitions were reported between managed and
unmanaged grassland (CRF table 4.1). During the review, the Party
clarified that areas of managed and unmanaged grassland were
recalculated on the basis of updated underlying data sources and that
the recalculation resulted in decreased areas of unmanaged grassland.
However, the Party reported in NIR table 6-41 that 50,040 kha of
managed grassland in Alaska is not yet included in the inventory. As a
result, the ERT considers that the recommendation has not yet been fully
addressed.
Work is still underway to reconcile the area of managed grassland in
Alaska and the area estimated in the Inventory. This will be updated for
the 2023 or 2024 submission.
L.8
Land representation -
C02, CH4 and N20
(L.43, 2019)
Transparency
Not resolved. Increase the transparency regarding the approach to
classifying managed and unmanaged land and include a specific example
of the change from managed land to unmanaged land in the NIR because
this type of land-use change is not common in the inventory reporting of
other Parties. The NIR does not include an explanation of the Party's
approach to classifying managed and unmanaged land or include an
example of the change from managed to unmanaged land.
The Land Representation chapter of the NIR provides detailed
information on the definition of managed and unmanaged land, the
sources of land-use data, the criteria used to designate managed lands
(with lands not designated as managed being unmanaged lands) and the
approach for combining the land-use data sets. We are unaware of a
reporting specific example of the change from managed to unmanaged
land and appreciate clarity on the basis for this reporting. A multi-year
effort to improve on the land representation, including the use of
additional datasets, is underway and will improve on the transparency
of the methods. While this effort will be ongoing for years to come, the
initial updates should be completed by the 2023 or 2024 submissions.
L.9
Land representation -
C02, CH4 and N20
(L.6, 2019)
(L.9, 2018)
(L.23,2016)
(L.22, 2015)
Transparency
Addressing. When providing detailed information in the NIR on how the
different data sources were harmonized, provide explicit information on
how the model ensures consistent integration of the three data sources,
for example by including a visual flow chart of data processing during the
harmonization process. Three sets of land-use data are used: NRI,
Forestry Inventory and Analysis and NLCD (see also ID# L.10 below). The
Party explains in the NIR (pp.6-20-6-24) how different land data sources
are used and harmonized to classify national land data into IPCC land-use
See section "Approach for Combining Data Sources" in Chapter 6 of the
current (2022) NIR submission. In addition, the United States will be
modifying its approach for developing the land representation over the
next several years and will update the NIR throughout this process.
Annex 8
A-569
-------
categories. During the review, it also explained that it will modify its
approach to developing land representation over the next few years and
will update its NIR accordingly. The ERT considers that the
recommendation has not yet been fully addressed because explicit
information on how the three data sources are consistently integrated
was not provided.
L.ll
4.A Forest land - C02
(L.10, 2019)
(L.39, 2018)
Convention reporting
adherence
Addressing. Report up-to-date information on the verification of the
outputs of the model used to estimate SOC changes in mineral soils, for
example, at the level of annual fluxes in single specific sites
representative of the variability of the population or, as done for the
DAYCENT model for agricultural soils (NIR figure A-12), at the level of the
total cumulated (across the time series and the entire territory modelled)
net flux. No information is provided in the NIR on verification of forest
soil estimation by model, despite a background research paper on the
soil estimation approach being cited in annex 3 to the NIR (p.A-361).
During the review, the Party explained that it expects to report this
information in the 2022 or 2023 submission.
Additional detail will be included in Annex 3.13 in a future submission—
e.g., tables by broad forest types and average C stock per unit area, and
stock changes. The discussion on uncertainty will also be expanded to
discuss issue of consistency in soil depth across land use categories. We
will also provide data on plot level soil carbon. We anticipate reporting
this information in the next (2023) submission at the earliest.
L.13
4.A Forest land - C02 and
N20
(L.13, 2019)
(L.42, 2018)
Transparency
Addressing. Calculate the carbon stock change in each carbon pool at the
level of each single plot and then aggregate the results at the state and
national level, and explain any recalculations in the NIR. During the
previous review, the Party provided additional information on the
methodology in response to a question raised by the ERT about double
counting of carbon. The previous ERT considered that the methodology
for calculating carbon stock change on forest land was appropriately
applied taking into account the information provided by the Party.
However, it noted that the information provided in the NIR did not
demonstrate that the stock-difference method for forest land was
applied at each land-use category level. During the most recent review,
the Party explained that it will provide the requisite information in the
NIR of its next submission. The ERT considers that the recommendation
has not yet been fully addressed because the Party did not update the
NIR information demonstrating that the stock-difference method for
forest land was applied at each land-use category level.
The United States provided this supplemental information in the Annex
3.13 to the 2021 NIR.
L.14
4.A.1 Forest land
remaining forest land-
C02
(L.14, 2019)
(L.13, 2018)
(L.26, 2016)
Not resolved. Provide in an annex to the NIR detailed tables on average
carbon fluxes by region and type (e.g., the region and forest type
classifications described in Smith et al. (2006) and used for estimating
downed deadwood and understory, which might better reflect the
diversity of forest types and age classes). The United States did not
provide tables with average carbon fluxes disaggregated by region, state
or forest type. During the review, the Party explained that this
We are still unsure on the reporting requirement and basis in
methodological guidance that requires providing detailed tables on
average carbon fluxes by region.
A-570 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
-------
Transparency
information will be included in the 2021 or 2022 submission.
L.15
4.B Cropland - C02
(L.16, 2019)
(L.18, 2018)
(L.14, 2016)
(L.14, 2015)
(93, 2013)
(107, 2012)
Completeness
Not resolved. Estimate the carbon stock changes in living biomass in
perennial crops for all years in the time series. The United States did not
report biomass stock changes in perennial cropland (for either cropland
remaining cropland or land converted to cropland). The ERT considers
that, if no information is available other than the time series of areas
covered by perennial crops reported in the national statistics on
agriculture, the Party should consider using this information and the tier
1 methodology from the 2006 IPCC Guidelines (vol. 4, chap. 5) to prepare
a time series of estimates of biomass changes in perennial crops. The
carbon stock dynamic of the perennial cropland area in 1989 can be
assumed to be at equilibrium and can be modelled for 1990 onward on
the basis of the ageing of trees and changes in the area planted. The
issue applies to both cropland remaining cropland and land converted to
cropland. During the review, the Party explained that this information
will be included in the 2022 submission.
This work is underway and will be included in the next (2023)
submission at the earliest.
L.17
4.B.2.2 Grassland
converted to cropland-
C02
(L.46, 2018)
Completeness
Not resolved. Estimate biomass carbon stock changes using the IPCC
default method and factors or, where available, country-specific methods
and factors, and reportthe estimations in the NIR. The Party did not
provide estimates and "NE" was reported for carbon stock changes in
biomass in grassland converted to cropland in CRF table 4.B. During the
review, the Party explained that it is working to address completeness
over time as improved data become available and to prioritize the work
in line with other improvements to make best use of available resources.
This work is underway and will be included in the next (2023)
submission at the earliest.
L.18
4.B Cropland
4.C Grassland - C02 and
N20
(L.19, 2019)
(L.47, 2018)
Convention reporting
adherence
Not resolved. Verify the model's output for the entire time series from
1990 onward and for all applicable land categories (e.g. by verifying the
model's output for each land-use category, or for the total of the land-
use categories, or for any subaggregation, as long as the total estimate of
all land-use categories modelled is verified) and report on the verification
and the results in the NIR. The Party reported the same verification in the
NIR as in the previous submission; that is, comparing SOC changes with
lower tiers (figure A-13). Therefore, the concern of previous ERTs
regarding coverage of land categories (i.e. that the output of the
DAYCENT model was verified for carbon stock change in cropland
remaining cropland, but not for other land-use categories and gases) has
not been addressed. During the review, the Party explained that it still
plans to improve the documentation on the model and refine the
calibration used for the model, and to implement an additional
verification, alongside ongoing methodological refinements for
As noted to the prior ERT, efforts to improve the documentation and
calibration are ongoing as well as implementation of additional
verification, in step with ongoing methodological refinements for
estimating soil carbon, soil N20 and soil CH4. This will be addressed in
the next (2023) submission at the earliest.
Annex 8
A-571
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estimating soil carbon, soil N20 and soil CH4. It noted that this issue will
be addressed in the 2021 and 2022 submissions. In its clarifications on
the list of provisional main findings, the Party indicated that it has
provided documentation on the model's prediction capability forSOC on
grassland and cropland (see NIR annex 3.12, p.A-405, figure A-12); the
output of the model is also shown for N20 and CH4 (figures A-14-A-15);
and these comparisons lend credibility to the ability of DAYCENT to
predict emissions and removals for these gases. The Party indicated that
it has allocated available resources to other improvements instead of
conducting a tier 1 analysis, which would effectively entail compiling the
inventory twice, and that it will work towards making this addition to the
1990-2020 inventory for reporting in 2022. The ERT considers that the
recommendation has not yet been addressed because the Party has not
verified the model's output for the entire time series from 1990 onward.
L.20
4.C Grassland - C02
(L.21, 2019)
(L.49, 2018)
Transparency
Not resolved. Report woody grassland as a subdivision of the grassland
category, estimate accordingly the area and carbon stock change for all
carbon pools of woody grassland within the category grassland remaining
grassland and within all land-use categories of conversion from and to
grassland, and report the estimations in the NIR. The Party did not
estimate carbon stock changes on woody grassland. Further, the Party
has removed from the NIR (box 6-6, p.6-71, of the 2019 NIR) an
explanation on grassland woody biomass analysis and a reference to its
plans to include the woody grassland subcategory in its reporting. The
Party explained during the review that while it intends to include this
subcategory in the 2021 submission, owing to administrative delays it
may have to include it in the 2022 submission instead. In its clarifications
on the list of provisional main findings, the Party indicated that it reports
all carbon stock pools for woodland that occur on grassland (i.e. land that
does not meet the definition of forest land). It acknowledges that there
may be some woody grassland which is not included and is reviewing the
data with a view to making the relevant refinements in the future. The
ERT considers that the recommendation has not yet been addressed
because the Party did not report emissions and uptake under the woody
grassland subcategory in CRF table 4.C.
The United States reports carbon stock changes for all pools for a
subcomponent of grasslands referred to as woodlands. Woodlands are
former forest lands that no longer meet the definition of forest lands
and are now classified in the grassland category. Because these
woodlands were formerly part of the forest land category, data are
collected on woody/perennial biomass and these data are used to
report on the carbon stock changes. For other grasslands not part of the
woodlands, we do not have woody/perennial biomass data and are not
able to report at this time. The United States is assessing how to
assemble perennial biomass data for these other grasslands for future
reporting. The earliest this would occur is the next (2023) submission.
L.22
4.C.2.2 Cropland
converted to grassland-
C02
(L.24, 2019)
(L.51, 2018)
Not resolved. Estimate biomass carbon stock change using the IPCC
default method and factors or, where available, country-specific methods
or factors, and explain the estimations in the NIR. The Party did not
provide estimates and reported "NE" for carbon stock changes in
biomass on cropland converted to grassland. The Party explained during
the review that while it intends to include carbon stock changes in
biomass on cropland converted to grassland in the 2021 submission,
This work is underway and will be included in the next (2023)
submission at the earliest.
A-572 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Completeness
owing to administrative delays it may have to include it in the 2022
submission instead.
L.23
4.D.1 Wetlands
remaining wetlands -
C02, CH4, and n20
(L.25, 2019)
(L.25, 2018)
(L.34, 2016)
(L.27, 2015)
Transparency
Addressing. Noting the need to determine the quantity of peat harvested
per ha and the total area undergoing peat extraction, provide the
respective AD and lEFs for the on-site CH4 and n20 emission estimates in
CRF table 4(11) for organic soils under peat extraction. The Party explained
in the NIR (p.6-91) that it used the total peat extraction area as AD for
on-site CH4 emissions and the nutrient-rich peat production area as AD
for on-site n20 emissions. However, these AD were not included in CRF
table 4(11). In a documentation box to CRF table 4(11), the Party explains
that, since different areas are used to estimate CH4 and n20 emissions, it
is not possible to provide the AD and IEF for both gases on the same row.
The ERT suggests that the Party report the area for CH4 emissions and the
values for CH4 and n20 emissions and explain the resulting n20 IEF value.
Documentation on our approach was provided in the documentation
box in CRF Table 4(11) of the previous (2021) and current (2022)
submission.
L.24
4.D.2.2 Land converted
to flooded land - C02
(L.26, 2019)
(L.53, 2018)
Completeness
Not resolved. Estimate carbon stock change in flooded land using the
2006 IPCC Guidelines (vol. 4, chap. 7) default method and factors or,
where available, country-specific methods or factors, and explain the
estimations in the NIR. Carbon stock changes in all carbon pools for land
Carbon stock changes in all carbon pools for land converted to flooded
land are reported as "NE" for the whole time series. During the review,
the Party explained that improvements in this regard are planned for the
2022 submission. (See also ID# L.l above for the case of forest land
converted to flooded land.)
This is addressed in the current submission for 2022.
L.25
4.D.2.3 Land converted
to wetlands-C02
(L.27, 2019)
(L.54, 2018)
Completeness
Not resolved. Estimate biomass and DOM carbon stock changes for forest
land converted to other wetlands as planned for the 2020 submission,
and explain the estimations in the NIR. The Party has reported carbon
stock changes in living biomass for land converted to other wetlands
(category 4.D.2.3) as numerical values since the 2019 submission, as
opposed to "NE" in the 2018 submission. However, it reported carbon
stock changes in DOM for category 4.D.2.3 as "NE" in the 2018, 2019 and
2020 submissions. During the review, the Party explained that it plans to
make improvements in this regard for future inventory submissions.
Work is planned to report on this information in a future submission.
Annex 8
A-573
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L.27
4.E Settlements - C02
(L.29, 2019)
(L.27, 2018)
(L.15, 2016)
(L.15, 2015)
(94, 2013)
Accuracy
Addressing. Eliminate the overlap between the urban forest inventory
and the forest inventory. The Party updated the tree cover area in
settlements (urban forest area) in the 2020 submission and indicated in
the NIR that it plans to address the overlap between the forest and urban
forest inventories (under planned improvements in settlements, p.6-
126). The Party explained in the NIR that there may be a minor overlap
between the forest and urban forest inventories and that this will be
addressed when new NLCD data become available. It added during the
review that it plans to take steps over the next few years to develop
spatially explicit and spatially continuous representations of land to
eliminate such overlaps and to enable the production of better
settlement area estimates.
This overlap is still being investigated with new NLCD data. EPA
anticipates reporting an updated status of this consideration in the next
(i.e., 2023) submission.
L.28
4.E.1 Settlements
remaining settlements-
C02
(L.30, 2019)
(L.55, 2018)
Comparability
Not resolved. Remove the reporting of the carbon stock change
associated with yard trimmings and food scraps from under the
settlements category and allocate it to the category other under the
relevant sector. The Party continues to report carbon stock changes
associated with yard trimmings and food scraps under the settlements
category instead of category 4.H (other). During the review, the Party
indicated that this reallocation will be addressed in the 2022 submission.
The Party could see the issue will be resolved by reporting emissions
from landfilled yard trimmings and food scraps under category 4.H
(other), applying a country-specific method or under category 4.G (HWP)
as an additional "other" HWP pool in solid waste disposal sites while
continuing to ensure that the methods used are consistent with the
waste sector reporting as per the 2006 IPCC Guidelines (vol. 4, chap.
12.2.1, and vol. 5, chap. 3.4).
Carbon stock estimates are reported as negative "Emissions" under 4.H.
The estimates for landfilled yard trimmings and food scraps are estimates
of changes in carbon stock, rather than emissions. Carbon stock change is
not included as a measure for 4.H Other category. Carbon storage
estimates within the Inventory are associated with particular land uses.
For example, harvested wood products are reported under Forest Land
Remaining Forest Land because these wood products originated from the
forest ecosystem. Similarly, C stock changes in yard trimmings and food
scraps are reported under Settlements Remaining Settlements because
the bulk of the C, which comes from yard trimmings, originates from
settlement areas. While the majority of food scraps originate from
cropland and grassland, in this Inventory they are reported with the yard
trimmings in the Settlements Remaining Settlements section. Additionally,
landfills are considered part of the managed land base under settlements
(see Section 6.1 Representation of the U.S. Land Base), and reporting
these C stock changes that occur entirely within landfills fits most
appropriately within the Settlements Remaining Settlements section given
these U.S.-specific circumstances and country approach, and therefore
reported under4.E.l.
L.29
4.E.1 Settlements
remaining settlements -
C02
(L.31, 2019)
(L.55, 2018)
Comparability
Not resolved. Report information on the long- term stored carbon stock
of yard trimmings and food scraps, as well as on its annual changes, in
the memo item in CRF table 5. The Party did not report in the memo item
in CRF table 5 on the long-term storage of carbon in waste disposal sites
or on the annual change in total long-term carbon storage. During the
review, the Party indicated that this will be addressed in the 2021 or
2022 submission. The ERT considers that the recommendation has not
yet been addressed because the Party did not report on the long-term
storage of carbon in waste disposal sites in the memo item in CRF table 5.
This has been updated in the current CRF submission; see Table 5 of the
2022 CRF submission.
A-574 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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L.30
Cropland converted to
settlements
Grassland converted to
settlements- C02
(L.32, 2019)
(L.56, 2018)
Completeness
Not resolved. Estimate biomass carbon stock change for cropland
converted to settlements (category 4.E.2.2) and grassland converted to
settlements (category 4.E.2.3) using the IPCC default method and factors
(2006 IPCC Guidelines, vol. 4, chap. 8) or, where available, country-
specific methods or factors, and explain the estimations in the NIR. The
Party did not estimate carbon stock changes in biomass for cropland
converted to settlements and grassland converted to settlements. During
the review, the Party explained that it plans to report this information in
the 2022 submission.
Work is planned to report on this information in a future submission.
L.31
4.F.2 Land converted to
other land - C02
(L.33, 2019)
(L.57, 2018)
Completeness
Not resolved. Report estimates of carbon stock change for land
converted to other land using the IPCC default method and factors (2006
IPCC Guidelines, vol. 4, chap. 9) or, where available, country-specific
methods or factors, and explain the estimations in the NIR. The Party
reported all carbon stock changes in all carbon pools under category
4.F.2 as "NA" (previously "NE"). During the review, the Party explained
that it was unable to report the required information under this category
but plans to do so in a future submission. It also explained that the
notation key was mistakenly changed to "NA" and will be changed back
to "NE" in the next submission. (See also ID# L.l above for the issue of
forest land converted to other land.)
Work is planned to report on this information in a future submission.
L.32
4.G HWP-C02
(L.34, 2019)
(L.58, 2018)
Transparency
Not resolved. Complete CRF table 4.Gs2 with aggregated values in t
carbon for each of the three HWP subcategories (solid wood, paper and
paperboard, and other) and report in the NIR a table with all
subcategories used by the model to calculate the HWP contribution as
well as the conversion factors to carbon weight applied for each
subcategory. The United States did not complete CRF table 4.Gs2 and
reported only the values of paper and paperboard for 1990-2018. It
reported "IE" for sawnwood and wood panels. During the review, the
Party explained that it is working towards improving the reporting of
HWP in its 2021 submission.
Work is planned to improve reporting of HWP in the CRF Reporter for the
2023 or 2024 submission.
L.34
4.H Other (LULUCF)-CH4
(L.36, 2019)
(L.60, 2018)
Transparency
Not resolved. Report the complete calculation of the decay rates applied
to yard trimmings and food scraps as well as information on the impact
that the calculation has on the CH4 emission rates applied to other MSW.
While the decay rates are properly explained (see ID# L.33 above), there
is still a transparency issue between the LULUCF and waste sectors. The
CH4 emissions from yard trimmings and food scraps are reported in the
waste sector as part of total CH4 emissions from MSW. As disaggregated
CH4 emissions from yard trimmings and food scraps are not reported in
the waste sector (NIR p.6-135), it is not possible to check the relationship
or consistency between carbon storage and the CH4 emissions from yard
This issue was resolved with 2020 submission. Discussion of decay rates
begins at the end of page 6-131 in the NIR (2020 submission).
Annex 8
A-575
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trimmings and food scraps. In the NIR, the Party explains that there are
no plans to disaggregate these waste components in the data in the
waste sector, which will hamper the separate reporting of CH4 emission
from yard trimmings and food scraps. During the review, the Party stated
that it considers this issue to have been resolved. However, the ERT is of
the opinion that, while it may be difficult to provide evidence of
consistency between sectoral methods, the Party should at least
demonstrate that the methods used are not inconsistent. This could be
done by showing that carbon losses resulting from the decay of yard
trimmings and food scraps as calculated under LULUCF are in keeping
with the waste sector estimates of CH4 emitted from landfills.
Alternatively, the Party could perform a model calculation of CH4
emissions from the yard trimming and food scraps carbon pool in landfills
(see also ID# L.29 above) and compare the results with the waste sector
CH4 estimates. The ERT considers that the recommendation has not yet
been fully addressed because the Party did not explain in the NIR how
the decay of yard trimmings and food scraps reported in CRF table 4.E
(recommended to be moved to category 4.H, see ID# L.28 above) is
consistent with the emissions of CH4 from landfills reported in the waste
sector.
L.35
4.A Forest land 4(ll)
Emissions and removals
from drainage and
rewetting and other
management of
organic/mineral soils —
C02, CH4 and N20
(L.44, 2019)
Transparency
Not resolved. Provide information regarding which emissions or removals
are estimated under carbon stock change in forest organic soils (category
4.A) and drained forest organic soils (category 4(H)) and how it avoids
double counting of emissions between the two sources in the NIR and in
the relevant documentation boxes of CRF tables 4.A and 4(11). No
information is provided either in the NIR or in the documentation boxes
of CRF tables 4.A or 4(11) on the avoidance of double counting. During the
review, the Party clarified that it plans to report this information in a
future submission.
Carbon stock change from drained organic soils are reported under the
Forest Ecosystem stock changes. See footnote "a" in Table 6-11: "These
estimates include carbon stock changes from drained organic soils from
both Forest Land Remaining Forest Land and Land Converted to Forest
Land. See the section below on C02, CH4, and N20 Emissions from Drained
Organic Soils for the methodology used to estimate the C flux from
drained organic soils. Also, see Table 6-22 and Table 6-23 for greenhouse
gas emissions from non-C02 gas changes from drainage of organic soils
from Forest Land Remaining Forest Land and Land Converted to Forest
Land."
L.37
4(111) Direct n20
emissions from N
mineralization/
immobilization - n20
(L.37, 2019)
(L.61, 2018)
Completeness
Not resolved. Estimate N20 emissions associated with the mineralization
of the N content of SOC losses in mineral soils for forest land, wetlands,
settlements and other land, as well as for their conversion to and from
cropland and grassland, using the IPCC default method and factors (2006
IPCC Guidelines, vol. 4, chap. 11) or, where available, country-specific
methods or factors, and report the estimations in CRF table 4(111) and the
NIR. Direct N20 emissions associated with the mineralization of the N
content of SOC losses in mineral soils are not estimated. During the
review, the Party informed the ERT that work is under way to enable all
land categories to be reported in future submissions. The ERT considers
that the recommendation has not yet been addressed because the Party
Work is underway to report these emissions for all land categories in
future submissions.
A-576 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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did not provide data on N20 emissions associated with mineralization of
N as a result of SOC losses in mineral soils.
L.38
4(IV) Indirect N20
emissions from managed
soils - N20
(L.38, 2019)
(L.62, 2018)
Completeness
Not resolved- Estimate indirect n20 emissions associated with the
mineralization of the N content of SOC losses in mineral soils for forest
land, wetlands, settlements and other land and report them in CRF table
4(IV), and explain the estimations in the NIR. No indirect N20 emissions
associated with organic matter are reported. During the review, the Party
clarified that work is under way to report these emissions for all land
categories in future submissions.
Work is underway to report these emissions for all land categories in
future submissions.
L.39
4(V) Biomass burning-
C02, CH4 and N20
(L.39, 2019)
(L.35, 2018)
(L.42, 2016)
(L.33, 2015)
Completeness
Not resolved. Noting that CH4 and N20 emissions from forest fires are key
categories, estimate CH4 and N20 emissions from biomass burning for
land converted to forest land, land converted to wetlands, cropland,
grassland and settlements; and populate CRF table 4(V). While CH4 and
N20 emissions from biomass burning for forest land and grassland are
estimated, all burning is reported under forest land remaining forest land
and grassland remaining grassland. The Party explained that it is
currently unable to separately report the emissions from land converted
to forest land and land converted to grassland but will continue to
explore ways of doing so. Biomass burning from wildfires on cropland
and biomass burning on wetlands and settlements were not estimated
owing to a lack of data.
As noted in our original response, we are unable to report on these
emissions at the level of land use conversion, but will continue to explore
approaches for doing this in future Inventories.
L.40
4.F Other land-C02, CH4
and N20
Comparability
The Party reported "NA" for all entries in CRF table 4.F (other land) owing
to a lack of data. It explained in the NIR (chaps. 6.12-6.13, pp.6-142-6-
143) that, while it is conducting research to track carbon pools for other
land, it is unable to estimate C02, CH4 and N20 emissions for other land
or land converted to other land. The ERT notes that, according to the
UNFCCC Annex 1 inventory reporting guidelines, categories that are not
estimated should be reported as "NE" where emissions or uptake can be
expected. During the review, the Party stated that it will report the
correct notation key in its next submission. It added that, while it is not
currently developing estimates for other lands, it will aim to complete
CRF table 4.F with the information available. The ERT recommends that
the Party report numerical values in CRF table 4.F for managed areas of
other land and "NE" for carbon pools for which numerical values cannot
be reported, or otherwise develop an assumption for carbon pools being
in equilibrium.
The notation keys for Table 4.F have been changed to NE for the current
submission. Area estimates will be provided in future submissions.
L.41
4.G HWP-C02
Transparency
According to the NIR (p.6-35), the Party reports HWP using the
production approach. Data for HWP are reported in CRF table 4.G (a
separate issue regarding this reporting is detailed under ID# L.32 in table
The United States is unsure of the basis of this recommendation in the
UNFCCC reporting guidelines and 2006IPCC Guidelines as they do not
specify where HWP should be presented in the report; therefore, HWP is
Annex 8
A-577
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3). The ERT noted that the value for carbon stock change in forest land
remaining forest land presented in NIR tables 6-1, 6-3, 6-4 and 6-5 (-
663.2 Mt C02 eq) differs from the value reported in CRF table 4.1 (-565.2
Mt C02 eq). In a footnote to NIR tables 6-1 and 6-3 (but not to NIR tables
6-4 and 6-5), the Party explains that this figure also includes the uptake
of carbon in HWP. This is contrary to reporting conventions, according to
which HWP should be reported under category 4.G (including HWP in
solid waste disposal sites) and not under forest land remaining forest
land (category 4.A.1). The ERT considers that reporting HWP as a
separate concept rather than as a subcategory of forest land is
important, as HWP can sometimes fall under other land uses, such as
forest converted to grassland, or former perennial horticulture on
cropland. The same rationale is behind the recommendation to report
the carbon balance of yard trimmings and food scraps under other
(category 4.H) rather than as a sub-component of settlements (category
4.E) (see ID# L.28 in table 3). The ERT recommends that the Party clearly
differentiate between HWP and forest carbon stock changes in the NIR
and ensure consistent reporting between the CRF and NIR tables.
included within the forest chapter of the NIR because that is the source of
wood that goes into the HWP estimates, but HWP estimates and methods
are presented and documented separately. See the section on Harvest
Wood Carbon (pp. 6-35 of the NIR). In the CRF submission, all HWP
emissions are reported under 4.G.
Waste
W.l
5. General (waste) - C02,
CH4 and N20
(W.l, 2019)
(W.l, 2018)
(W.9, 2016)
(W.9, 2015)
Transparency
Not resolved. Provide background information that is consistent with the
data actually used for the emission estimates, including the waste
management practices. The United States reported in the NIR (annex
3.14, table A-236) the total amount of MSW generated and landfilled
based on research by EPA, BioCycle and the Environmental Research and
Education Foundation. However, the trend in the amount of MSW
landfilled differs with the decreasing trend of CH4 emissions from
landfilled MSW for 1990-2018 (NIR tables 7-3-7-4). In addition, the ratio
of landfilled MSW to total MSW generated for 2017 is reported as 65 per
cent in NIR table A-236 but as 52.1 per cent in NIR box 7-4 (p.7-16). In its
clarifications on the list of provisional main findings, the Party indicated
that an explanation for these differences is provided in the NIR (annex
3.14, page A-463). However, the ERT considers that this explanation is
narrative rather than quantitative, and that the Party should provide an
analysis of the discrepancies and the data used forthe emission
estimates, such as waste composition data, DOC in MSW and background
information on MSW streams, like the waste stream analysis by waste
type provided in the 2006 IPCC Guidelines (vol. 5, chap. 2, box 2.1) (see
also ID# W.3 below).
Additional information and an explanation of differences has been added
in recent NIRs to explain different data sources and also estimation
methods over the time series.
In the current (i.e., April 2022) submission, the trends in amount of MSW
waste generated, waste landfilled, and resulting CH4 emissions are
explained in Section 7.1, pp. 7-6. The differences noted in the two ratios
of MSW landfilled to MSW generated are due to the two data sources and
methods used by these reports. As explained in Box 7-3, the SOG and
EREF data are used in the MSW methodology, while data from EPA Facts
and Figures is presented in Box 7-4 to show trends of waste management
in the United States for illustrative purposes. The discussion on the
quantitative differences between these two data sources was added to
Annex 3.14, Box A-3 (on p. A-451) of the April 2021 NIR submission and is
retained in the current submission; see Annex 3.14.
It is unclear that information outlined in Chapter 2 is required for
reporting, as it is an example and as noted in the example itself depends
on available data and national circumstances. The example in Chapter 2 is
not consistent with our available data. Noting Section 3.8 of Volume 5 of
the 2006 IPCC Guidelines does not suggest including such an analysis. We
are unsure of how this issue can be resolved in light of data sources and
methodological refinements in recent years to incorporate facility-level
A-578 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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GHGRP data.
W.8
5.A.1 Managed waste
disposal sites-CH4
(W.15, 2019)
Transparency
Addressing. Include information to justify the oxidation factor used,
including references and supporting data relevant to national
circumstances as well as an uncertainty analysis for the oxidation factor
applied in the estimation. The United States provided information in the
NIR (pp.A-473-474) to justify the use of a country-specific oxidation
factor greater than the default value of 0.1. During the review, the Party
explained that it is planning to include additional detail in the discussion
of the uncertainty analysis. This reporting is planned for the 2021
submission.
Addressed in current NIR submission Section 7.1 Uncertainty and Annex
3.14, Figure A-19.
W.9
5.A.1.a Anaerobic - CH4
(W.7, 2019)
(W.16, 2018)
Comparability
Addressing. Estimate and report the amounts of CH4 flared and CH4 for
energy recovery for anaerobic waste disposal sites, but, until that is
possible, report them as "NE" instead of "IE" in CRF table 5.A. The United
States reported the amount of CH4 flared and used for energy recovery as
"NE" in CRF table 5.A. During the previous review, the Party explained its
use of directly reported GHGRP net emissions and noted that facilities
were not required to report separately the total amounts of CH4
recovered for energy and CH4 flared. However, the ERT notes that the
EPA Landfill Methane Outreach Program provides information on the
amount of landfill gas collected and flared. It also notes that the 2006
IPCC Guidelines (vol. 5, chap. 3, p.3.18) state that if recovered gas is used
for energy, then the resulting GHG emissions should be reported under
the energy sector. Therefore, the Party should report the amount of CH4
for energy recovery in CRF table 5.A and include a corresponding
explanation in the NIR, taking into account the good practice outlines in
the 2006 IPCC Guidelines.
This issue was addressed in the 2020 submission. See CRF Tables 5.A and
Table 9 of the 2020 submission and NIR Annex 5. CH4 has been reported
as NE. Per engagement with the reporting community, future technical
corrections to EPA's GHGRP may allow for reporters to indicate volumes of
gas sent to flaring and to energy projects. Reporting of this information by
facilities would allow EPA to report separate amounts for CH4 flared and
CH4 for energy recovery. The timing for such updates has not been
proposed and the initial data reported will only reflect information for the
latest year of time series and will require some effort to develop time
series information to include in the national Inventory submission.
W.10
5.A.l.a Anaerobic - CH4
(W.8, 2019)
(W.7, 2018)
(W.12, 2016)
(W.ll, 2015)
Accuracy
Addressing. Obtain up-to-date data on the type and fractions of organic
waste placed in industrial waste landfills; and revise the CH4 estimates for
all major industrial waste landfills. The United States provided
information in the NIR (p.7-10) on an EPA analysis to validate the
assumption that most of the organic waste which would result in CH4
emissions is disposed of at pulp-, paper- and food-processing facilities (54
per cent) and food manufacturing facilities (7 per cent). However, the
ERT believes that the Party should consider including other industries
(e.g. metal foundries, petroleum refineries and chemical manufacturing
facilities) as recommended in the 2016 review report
(FCCC/ARR/2016/USA, ID# W.12). According to the NIR (p.7-15), EPA
plans to investigate the prevalence of food-related waste deposited in
industrial waste landfills and will record the findings from this exercise in
Progress was included in 2021 submission NIR Section 7.1. Work is still in
progress to finalize a memorandum summarizing literature search and
data availability.
Annex 8
A-579
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a memorandum and implement during the following inventory cycle any
warranted changes to the methodology or assumptions for industrial
waste landfills. The ERT welcomes the Party's provision of this
information on the estimation of CH4 emissions from industrial waste
landfill.
W.ll
5.B.2 Anaerobic digestion
at biogas facilities-CH4
(W.19, 2019)
(W.8, 2018)
(W.14, 2016)
(W.13, 2015)
Transparency
Not resolved. Estimate and report CH4 emissions from unintentional
leakages using the default value of 5 per cent provided in the 2006 IPCC
Guidelines. During the review, the Party explained that unintentional
leakages of CH4 emissions from anaerobic digestion of organic waste, as
described in the 2006 IPCC Guidelines (vol. 5, chap. 4.1), will be reported
in the 2021 submission, as indicated in the NIR (p.7-39).
The United State has included estimates from anaerobic digestion at
biogas facilities in the April 2021 submission. See Section 7.4 of the Waste
Chapter of the current NIR submission.
W.13
5.C.1 Waste incineration
- C02, CH4 and N20
(W.13, 2019)
(W.10, 2018)
(W.15, 2016)
(W.14, 2015)
Transparency
Not resolved. Provide in the NIR consistent information on the data that
are used for the estimation of emissions from waste incineration (e.g. on
the percentage of waste incinerated in 2013 reported in figure 7-2 and
tables 3-26 and A- 272 of the 2016 NIR). Inconsistencies still exist in the
combustion ratio of MSW between NIR figure 7-3 (12.7 per cent) and NIR
table 3-27 (7.6 per cent). During the review, the United States explained
that the percentage of waste incineration shown in figure 7-3 comes
from a different source than that used for table 3-27 and does not
represent the data used in the analysis for estimating emissions from
waste incineration. However, the ERT considers that this inconsistency
should be clearly explained in the NIR or NIR figure 7-3 should be
removed.
For the current April 2022 submission the United States has updated the
approach to calculating emissions from waste incineration. See Sections
3.3 and Annex 3.7 of the 2022 NIR. The updated approach does not rely
on the combustion ratio of MSW but rather the tons of MSW combusted
and emission factors. The tons of MSW combusted comes from multiple
sources including the data discusses in Section 7.1 but also other sources
including EPA's GHGRP. The data used for MSW incineration emissions is
not inconsistent with the data used to develop landfill emissions.
W.15
5.D.2 Industrial
wastewater-CH4
(W.13, 2019)
(W.14, 2018)
(W.5, 2016)
(W.5, 2015)
(105, 2013)
Completeness
Not resolved. Include information on the non-estimation of CH4
emissions from sludge under industrial wastewater. The Party did not
include information on emissions from sludge in the NIR. During the
review, the Party explained that sludge removed from industrial
wastewater is not estimated owing to insufficient data and that an
explanation will be added in annex 5 to the next submission in line with
paragraph 37(b) of the UNFCCC Annex 1 inventory reporting guidelines.
The United States has included an explanation in Annex 5 of the previous
and current submissions, including a quantified estimate of methane
emissions from sludge from industrial wastewater treatment
demonstrating insignificance of these emissions.
A-580 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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W.16
5.C.1 Waste incineration
-C02
Accuracy
The Party reported in the CRF tables C02 emissions from waste
incineration (category 5.C) as "IE" and stated in the NIR (pp.3-55 and 7-
39) that C02 emissions from incineration of plastics, synthetic rubber,
synthetic fibres and carbon black in scrap tyres are accounted for under
category 1.A.5 (fuel combustion - other) instead of category 5.C (waste
incineration). During the review, the Party explained that C02 emissions
from waste nappies and waste fossil oil are included under the NEU
emission estimates. The Party also explained that C02 emissions from
paper and cardboard waste are not estimated because paper waste was
assumed to have 0 per cent fossil carbon content. The default range of
fossil carbon fraction in the 2006 IPCC Guidelines is 0-5 per cent, and the
default value is 1 per cent (vol. 5, chap. 2, table 2.4, p.2.14). The Party
informed the ERT that it applies a country-specific parameter of 0 per
cent fossil carbon content in paper waste based on the approach from
the EPA Reduction Model (WARM). The Party noted that it could refer to
the Waste Reduction Model in a future submission. The ERT recommends
that the United States provide an explanation for reporting 0 per cent
fossil carbon content in paper waste as a country-specific parameter as
well as the reference on which the parameter is based.
For the April 2022 submission the United States has updated the approach
to calculating emissions from waste incineration. See Sections 3.3 and
Annex 3.7 of the 2022 NIR. The updated approach uses a country-specific
emission factor for C02 emissions from MSW combustion. The C02 factor
is based on measured C02 emissions divided by the amount of MSW
combusted. Therefore, the factor would take into account any C in the
MSW including from waste nappies, fossil oil, paper, etc.
W.17
5.C.1 Waste incineration
- CH4 and N20
Completeness
The ERT noted there were approximately 170 sewage sludge incineration
plants in operation in the United States in the early 1990s according to
the EPA website (https://www.epa.gov/sites/production/files/2020-
10/documents/c02s02.pdf) and that Cm and N?0 emissions from
incineration of sewage sludge may not be reported in the national
inventory, as the emissions reported under category 5.C.1 (waste
incineration - biogenic - MSW) are reported as "IE". During the review,
the Party explained that CH4 and N20 emissions from incineration of
wastewater treatment plant sludge are likely estimated as emissions
from MSW even though wastewater treatment plant sludge is not
officially categorized as MSW, or that emissions could be considered
insignificant given the increasing regulatory pressure on sludge
incineration. However, the ERT cannot be assured that CH4 and N20
emissions are accurately estimated in line with the 2006 IPCC Guidelines
because AD or emission estimates are not clearly shown in the NIR. It
notes that the 2006 IPCC Guidelines (vol. 5, chap. 5, table 5.6) provide a
default N20 EF for sewage sludge of 900 g N20/t waste (wet weight) and
the default N20 EF for MSW of 50-60 g N20/t waste (wet weight), but
could not assess whether these emissions are included in the inventory
on the basis of the information provided in the NIR and during the review
week. The ERT recommends that the United States estimate CH4 and N20
emissions from incineration of sewage sludge at wastewater treatment
The United States considered the potential emissions associated with
sewage sludge incineration and concluded they are insignificant. Based on
data on the amount of sewage sludge incinerated and assumed emission
factors for N20 and CH4 from our GHGRP for biomass solids, emissions
were estimated to be approximately 9 kt C02 Eq. per year.
Annex 8
A-581
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plants in the country and either include estimates or otherwise provide
an explanation in the NIR demonstrating that these emissions are already
included in the inventory estimation.
A-582 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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ANNEX 9 Use of EPA Greenhouse Gas
Reporting Program in Inventory
This Annex provides background information on the Greenhouse Gas Reporting Program (GHGRP) and its relationship to
this Inventory. The U.S. Environmental Protection Agency (EPA) tracks U.S. greenhouse gas emissions through two
complementary programs: the Inventory (estimates in this report), and the GHGRP. The Inventory provides a
comprehensive accounting of all emissions from source categories identified in the 2006IPCC Guidelines needed to
understand the United States' total net greenhouse gas emissions in line with the UNFCCC reporting guidelines, while the
GHGRP provides bottom-up detailed information that helps improve understanding of the sources and types of
greenhouse gas emissions at individual facilities and suppliers. The GHGRP provides facility-level greenhouse gas data
from major industrial sources across the United States; it does not provide full coverage of total annual U.S. greenhouse
gas emissions (e.g., the GHGRP excludes emissions from the agricultural, land use, and forestry sectors).
On October 30, 2009, the EPA published a regulation requiring annual reporting of greenhouse gas data from large
facilities203 in the United States. The program implementing the regulation, codified at 40 CFR Part 98, is referred to as
EPA's Greenhouse Gas Reporting Program (GHGRP). The GHGRP covers sources or suppliers in 41 industrial categories
("Subparts"204), including direct greenhouse gas emitters,205 fossil fuel suppliers, industrial gas suppliers, and facilities
that inject carbon dioxide (C02) underground for sequestration or other reasons.206 In general, the threshold for
reporting is 25,000 metric tons or more of C02 Eq. per year.207
Facilities in most source categories subject to the GHGRP began collecting data in 2010 while additional types of
industrial operations began collecting data in 2011. Currently, more than 8,000 facilities and suppliers are required to
report their data annually. Facilities calculate their emissions using methodologies that are specified at 40 CFR Part 98.
and they report their data to EPA using the electronic Greenhouse Gas Reporting Tool (e-GGRT). Annual reports covering
emissions from the prior calendar year are due by March 31st of each year. EPA verifies reported data through a multi-
step process to identify potential errors and ensure that data submitted to EPA are accurate, complete, and consistent.
All reports submitted to EPA are evaluated by electronic validation and verification checks, including industry-specific
checks. If potential errors are identified, EPA will notify the reporter, who can resolve the issue either by providing an
acceptable response describing why the flagged issue is not an error or by correcting the flagged issue and resubmitting
their annual greenhouse gas report.208
The reported data are made available to the public each fall. EPA presents the data collected by its GHGRP in a number
of ways, such as through a data publication tool known as the Facility Level Information on GHGs Tool (FLIGHT). FLIGHT
allows data to be viewed in several formats including maps, tables, charts and graphs for individual facilities or groups of
facilities.209 More information on EPA's GHGRP can be found at https://www.epa.gov/ghgreporting.
203 Annual reporting is at the facility level, except for certain suppliers of fossil fuels and industrial greenhouse gases (i.e.,
reporting at the corporate level).
204 See https://www.epa.gov/ehereportine/resources-subpart-ehe-reportine.
205 Data reporting by affected facilities includes the reporting of emissions from fuel combustion at that affected facility.
206 See https://www.epa.eov/ghereportine/resources-subpart-ehe-reportine and http://ghgdata.epa.gov/ehgp/main.do.
207 For some industrial categories ("Subparts") under the GHGRP, facilities must report if their combined emissions from
stationary fuel combustion and all applicable source categories are above a given threshold (e.g., 25,000 metric tons C02 Eq. or
more per year or another industry-specific threshold). For other source categories, new facilities must report regardless of their
quantity of annual emissions. These categories include, for example, cement production (Subpart H) and aluminum production
(Subpart F). However, any facility regardless of threshold can cease reporting if its emissions fall below 25,000 metric tons C02
Eq. for five years or below 15,000 metric tons C02 Eq for three years, and it informs EPA of its intention to cease reporting and
the reason(s) for any reduction in emissions. See 40 CFR 98.2(a), 98.2(i), and Tables A-3, A-4, and A-4 for more information.
208 See GHGRP Verification Fact Sheet https://www.epa.gov/sites/production/files/2Q15-
07/documents/ehgrp verification factsheet.pdf.
209 See Mtei/gMMi;eMr£QV-
Annex 8
A-583
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The GHGRP dataset is an important resource for the Inventory. EPA uses GHGRP data in a number of categories to
improve the national estimates, consistent with IPCC guidance, as summarized in Table A-258 below. Methodologies
used in the GHGRP are consistent with methods in 2006 IPCC Guidelines, in particular "higher tier" methods which
include collecting facility or plant-specific measurements. The GHGRP provides not only annual emissions information for
reporting facilities and suppliers, but also other annual information, such as activity data and emission factors that can
be used to 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, along with enhancing application of QA/QC procedures and assessment of uncertainties. Consistent with
considerations outlined in the Technical Bulletin 1 on Use of Facility-Specific Data in National Greenhouse Gas Inventories
from the IPCC Task Force on National Greenhouse Gas Inventories (IPCC 2011),210 EPA has paid particular attention both
to ensuring completeness in national coverage of emission estimates over time and to ensuring time-series consistency
by recalculating emissions for 1990 to 2010/2011 when incorporating GHGRP data into source category estimates.211
These issues are discussed further in the chapters where source category emissions estimates use GHGRP data. Source
category definitions are also considered in order to ensure completeness when using GHGRP data. For certain source
categories in the Industrial Processes and Product Use chapter, EPA has relied on data values that have been calculated
by aggregating 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 only publishing data values that meet these aggregation criteria.212 Specific uses of aggregated facility-
level data that are CBI are described in the respective methodological sections in Chapter 4 of the Inventory. Beyond the
current uses, EPA continues to analyze the GHGRP data on an annual basis to identify other source categories where it
could be further integrated in future editions of this report (see the Planned Improvement sections of those specific
source categories for details).
210 IPCC Task Force on National Greenhouse Gas Inventories (TFI) (2011). Technical Bulletin 1: Use of Facility-Specific Data
National Greenhouse Gas Inventories. Available at https://www.ipcc-negip.iges.or.jp/public/tb/TFl Technical Bulletin l.pdf.
211 See http://www.ipcc-nggip.iges.or.jp/public/tb/TFI Technical Bulletin l.pdf.
212U.S. EPA Greenhouse Gas Reporting Program. Confidential Business Information GHG Reporting. See
http://www.epa.gov/ghgreporting/confidential-business-information-ghg-reporting.
A-584 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Table A-258: Summary of EPA GHGRP Data Use in U.S. Inventory
Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold213
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
214
Energy Sector
Fossil Fuel Combustion:
Industrial Sector
C - General Stationary
Fuel Combustion
Sources
2010
Y
•
Section 3.1
and Box 3-4
Coal Mining:
Underground Mines
FF - Underground Coal
Mines
2011
Y
•
•
3.4
Petroleum Systems
W- Petroleum and
Natural Gas Systems;
Y- Petroleum
Refineries
2010, 2011
Y, N
•
•
•
•
3.6
Natural Gas Systems
W- Petroleum and
Natural Gas Systems
2011
Y
•
•
•
3.7
Waste Incineration
C - General Stationary
Fuel Combustion
Sources
2010
Y
•
3.3
Industrial Processes and Product Use Sector
Cement Production
H - Cement Production
2010
N
•
•
4.1
Lime Production
S - Lime Production
2010
N
•
4.2
Glass Production
N - Glass Production
2010
Y
•
4.3
213 Y=25,000 MTC02 Eq., or industry-specific threshold other than 25,000 MTC02 Eq.; N = all facilities in industry category must report regardless of annual emissions. Information on
industry-specific threshold and implications of the reporting threshold or lack of threshold in estimating national greenhouse gas emissions is discussed in the respective source category
methodology sections.
214 Consistent with IPCC good practices, QA/QC using GHGRP may not be appropriate if this is the primary data source for estimating emissions. Depending on use, other data sets may be
more appropriate for QA/QC of Inventory estimates.
Annex 9
A-585
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Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold213
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
214
Urea Consumption
from Non-Agricultural
Use
G - Ammonia
Manufacturing
2010
N
•
4.6
Nitric Acid Production
V-Nitric Acid
Production
2010
N
•
•
•
4.7
Adipic Acid Production
E-Adipic Acid
Production
2010
N
•
4.8
Petrochemical
Production
X- Petrochemical
Production
2010
N
•
•
•
4.13
HCFC-22 Production
0 - HCFC-22 Production
and HFC-23 Destruction
2010
Y
•
4.14
Carbon Dioxide
Consumption
PP-Suppliers of
Carbon Dioxide
2010
Y
•
4.15
Aluminum Production
F-Aluminum
Production
2010
N
•
4.19
Magnesium Production
and Processing
T- Magnesium
Production
2011
Y
•
4.20
Lead Production
R - Lead Production
2010
Y
•
4.21
Electronics Industry
I - Electronics
Manufacturing
2011
Y
•
4.23
A-586 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2020
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Inventory Category
GHGRP Industry
Subpart
Initial Calendar
Year of Reporting
under GHGRP
Reporting
Threshold213
Type of GHGRP Data Use
National
Inventory
Report (NIR)
Section with
details on
data use
Emissions
or Quantity
Supplied
Emission
Factor (EF)
Activity
Data (AD)
QA/QC
214
Substitution of ODS
00 - Suppliers of
Industrial Gases;
QQ- Imports and
Exports of Equipment
Pre-charged with
Fluorinated GHGs or
Containing Fluorinated
GHGs in Closed-cell
Foams
2010, 2011
N
(producers)
Y (all others)
•
4.24
Electrical Transmission
and Distribution
DD - Use of Electric
Transmission and
Distribution Equipment;
SS - Manufacture of
Electric Transmission
and Distribution
Equipment
2011
Y
•
•
•
4.25
Waste Sector
MSW Landfills
HH - Municipal Solid
Waste Landfills
2010
Y
•
•
•
7.1
Industrial Landfills
TT - Industrial Waste
Landfills
2011
Y
•
7.1
Industrial Wastewater
II - Industrial
Wastewater Treatment
2011
Y
•
7.2
Annex 9
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