A-109 

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 (N2O) emissions from stationary combustion were estimated using IPCC 

emission factors and methods. Estimates were obtained by multiplying emission factors—by sector and fuel type—by 

fossil fuel and wood consumption data. This “top-down” methodology is characterized by two basic steps, described 

below. Data are presented in Table A-81 through Table A-85. 

Step 1: Determine Energy Consumption by Sector and Fuel Type 

Energy consumption from stationary combustion activities was grouped by sector: industrial, commercial, 

residential, electric power, and U.S. territories. For CH4 and N2O, estimates were based upon consumption of coal, gas, 

oil, and wood. Energy consumption data for the United States were obtained from EIA’s Monthly Energy Review, 

January 2010 and Published Supplemental Tables on Petroleum Product detail (EIA 2011). Wood consumption data for 

the United States was obtained from EIA’s Annual Energy Review (EIA 2010). Because the United States does not 

include territories in its national energy statistics, fuel consumption data for territories were collected separately from the

EIA from Jacobs (2010).34 Fuel consumption for the industrial sector was adjusted to subtract out construction and 

agricultural use, which is reported under mobile sources.35 Construction and agricultural fuel use was obtained from EPA 

(2010). The energy consumption data by sector were then adjusted from higher to lower heating values by multiplying by 

0.9 for natural gas and wood and by 0.95 for coal and petroleum fuel. This is a simplified convention used by the 

International Energy Agency. Table A-81 provides annual energy consumption data for the years 1990 through 2009. 

Step 2: Determine the Amount of CH4 and N2O Emitted 

Activity data for each sector and fuel type were then multiplied by emission factors to obtain emission estimates. 

Emission factors for the residential, commercial, industrial, and electric power sectors were taken from the 2006 IPCC 

Guidelines for National Greenhouse Gas Inventories (IPCC 2006). These N2O emission factors by fuel type (consistent 

across sectors) were also assumed for U.S. territories. The CH4 emission factors by fuel type for U.S. territories were 

estimated based on the emission factor for the primary sector in which each fuel was combusted. Table A-82 provides 

emission factors used for each sector and fuel type. 

Estimates of NOx, CO, and NMVOC Emissions 

Emissions estimates for NOx, CO, and NMVOCs were obtained from preliminary data (EPA 2010b, EPA 2009) 

and disaggregated based on EPA (2003), which, in its final iteration, will be published on the National Emission Inventory 

(NEI) Air Pollutant Emission Trends web site. 

For indirect greenhouse gases, the major source categories included coal, fuel oil, natural gas, wood, other fuels 

(i.e., bagasse, liquefied petroleum gases, coke, coke oven gas, and others), and stationary internal combustion, which 

includes emissions from internal combustion engines not used in transportation. EPA periodically estimates emissions of 

NOx, CO, and NMVOCs by sector and fuel type using a "bottom-up" estimating procedure. In other words, the emissions 

were calculated either for individual sources (e.g., industrial boilers) or for many sources combined, using basic activity 

data (e.g., fuel consumption or deliveries, etc.) as indicators of emissions. The national activity data used to calculate the

individual categories were obtained from various sources. Depending upon the category, these activity data may include 

 

34 U.S. territories data also include combustion from mobile activities because data to allocate territories’ energy use were unavailable. 

For this reason, CH4 and N2O emissions from combustion by U.S. Territories are only included in the stationary combustion totals. 

35 Though emissions from construction and farm use occur due to both stationary and mobile sources, detailed data was not available to 

determine the magnitude from each. Currently, these emissions are assumed to be predominantly from mobile sources. 
A-110 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

fuel consumption or deliveries of fuel, tons of refuse burned, raw material processed, etc. Activity data were used in 

conjunction with emission factors that relate the quantity of emissions to the activity. Table A-83 through Table A-85 

present indirect greenhouse gas emission estimates for 1990 through 2008. 

The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2009) is 

represented by the following equation: 

Ep,s = As × EFp,s × (1 - Cp,s/100) 

Where, 

 E = Emissions 

 p = Pollutant 

 s = Source category 

 A = Activity level 

 EF = Emission factor 

 C = Percent control efficiency 

 

The EPA currently derives the overall emission control efficiency of a category from a variety of sources, 

including published reports, the 1985 National Acid Precipitation and Assessment Program (NAPAP) emissions inventory, 

and other EPA databases. The U.S. approach for estimating emissions of NOx, CO, and NMVOCs from stationary 

combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/IEA 1997). 
Table A-81: Fuel Consumption by Stationary Combustion for Calculating CH4 and N2O Emissions (TBtu) Fuel/End-Use Sector 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Coal 18,064 19,138 20,032 20,517 20,802 20,779 21,682 21,085 21,139 21,562 21,713 22,095 21,758 2

Residential 31 17 17 16 12 14 11 12 12 12 11 8 6 

Commercial 124 117 122 129 93 103 92 97 90 82 103 97 65 Industrial 1,640 1,527 1,455 1,457 1,471 1,373 1,349 1,358 1,244 1,249 1,262 1,219 1,188 Electric Power 16,261 17,466 18,429 18,905 19,216 19,279 20,220 19,614 19,783 20,185 20,305 20,737 20,462 2U.S. Territories 7 10 10 10 11 10 10 4 11 34 32 33 37 

Petroleum 6,749 5,425 5,933 5,879 5,697 5,844 6,047 6,657 5,978 6,630 6,881 6,812 6,413 

Residential 1,375 1,261 1,397 1,334 1,207 1,342 1,427 1,463 1,359 1,466 1,475 1,369 1,205 

Commercial 891 694 718 655 609 614 694 719 645 762 767 716 678 Industrial 2,818 2,254 2,566 2,519 2,129 2,216 2,310 2,567 2,456 2,576 2,774 2,869 3,261 

Electric Power 1,289 755 817 927 1,306 1,211 1,144 1,277 961 1,205 1,212 1,235 648 U.S. Territories 375 462 435 445 445 461 472 632 557 622 654 623 621 

Natural Gas 18,202 21,088 21,492 21,556 21,230 21,254 22,281 21,400 22,129 21,550 21,604 21,243 20,904 2

Residential 4,491 4,954 5,354 5,093 4,646 4,835 5,105 4,889 5,014 5,209 4,981 4,946 4,476 Commercial 2,682 3,096 3,226 3,285 3,083 3,115 3,252 3,097 3,225 3,261 3,201 3,073 2,902 Industrial 7,721 8,736 9,049 9,052 8,827 8,402 8,619 7,933 8,100 7,806 7,802 7,185 7,125 Electric Power 3,309 4,302 3,862 4,126 4,675 4,902 5,293 5,458 5,767 5,246 5,595 6,015 6,375 U.S. Territories 0 0 0 0 0 0 13 23 23 27 25 24 26 

Wood 2,216 2,370 2,437 2,371 2,184 2,214 2,262 2,006 1,995 2,002 2,121 2,136 2,109 

Residential 580 520 540 430 380 390 420 370 380 400 410 430 390 Commercial 66 72 76 73 64 67 71 67 69 71 70 70 65 Industrial 1,442 1,652 1,683 1,731 1,603 1,620 1,636 1,443 1,396 1,363 1,476 1,452 1,472 Electric Power 129 125 138 137 137 138 134 126 150 167 165 185 182 U.S. Territories NE NE NE NE NE NE NE NE NE NE NE NE NE 

NE (Not Estimated) Note: Totals may not sum due to independent rounding. 
A-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-82: CH4 and N2O Emission Factors by Fuel Type and Sector (g/GJ)36

Fuel/End-Use Sector CH4 N2O

Coal 

Residential 300 1.5

Commercial 10 1.5

Industrial 10 1.5

Electric Power 1 1.5

U.S. Territories 1 1.5

Petroleum 

Residential 10 0.6

Commercial 10 0.6

Industrial 3 0.6

Electric Power 3 0.6

U.S. Territories 5 0.6

Natural Gas 

Residential 5 0.1

Commercial 5 0.1

Industrial 1 0.1

Electric Power 1 0.1

U.S. Territories 1 0.1

Wood 

Residential 300 4.0

Commercial 300 4.0

Industrial 30 4.0

Electric Power 30 4.0

U.S. Territories NA NA

NA (Not Applicable) 

 

36 GJ (Gigajoule) = 109

 joules. One joule = 9.486×10-4 Btu 
Table A-83: NOx Emissions from Stationary Combustion (Gg) Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Electric Power 6,045 5,792 5,581 5,683 5,637 5,183 4,829 4,453 4,265 3,988 3,711 3,434 3,121 

Coal 5,119 5,061 5,079 5,118 4,932 4,437 4,130 3,802 3,634 3,398 3,162 2,926 2,659 Fuel Oil 200 87 107 131 202 179 147 149 142 133 124 114 104 Natural gas 513 510 248 277 329 393 376 325 310 290 270 250 227 Wood NA NA 5 6 24 33 36 37 36 33 31 29 26 Other Fuelsa NA NA NA NA NA NA NA NA NA NA NA NA NA Internal Combustion 213 134 142 150 149 141 140 140 143 134 124 115 105 

Industrial 2,559 2,650 2,666 2,614 2,570 2,283 2,278 2,296 1,699 1,638 1,577 1,515 1,520 

Coal 530 541 490 487 475 475 484 518 384 370 356 342 343 Fuel Oil 240 224 203 196 190 190 166 153 114 110 105 101 102 Natural gas 877 999 900 880 869 706 710 711 526 507 488 469 471 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA Other Fuelsa 119 111 109 103 104 100 109 116 86 82 79 76 77 Internal Combustion 792 774 965 948 932 813 809 798 591 569 548 527 528 

Commercial 671 607 734 539 510 483 507 428 438 456 473 490 486 

Coal 36 35 30 32 34 23 21 21 19 19 19 19 19 Fuel Oil 88 94 86 88 73 54 52 52 50 49 49 49 49 Natural gas 181 210 224 229 220 156 161 165 157 156 156 155 155 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA Other Fuelsa 366 269 394 191 183 249 273 189 213 231 249 267 263 

Residential 749 813 726 699 651 441 439 446 423 421 420 418 417 

Coalb 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 Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA Wood 42 44 27 27 27 25 21 22 21 21 21 20 20 Other Fuelsa 707 769 699 671 624 416 417 424 402 400 399 397 397 

Total 10,023 9,862 9,707 9,534 9,369 8,390 8,053 7,623 6,825 6,503 6,181 5,858 5,545 NA (Not Applicable) a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b). Note: Totals may not sum due to independent rounding. Table A-84: CO Emissions from Stationary Combustion (Gg) Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Electric Power 329 337 369 385 410 450 439 439 595 590 586 582 598 

Coal 213 227 228 233 220 187 221 220 298 296 293 292 300 

Fuel Oil 18 9 11 13 17 36 27 28 38 37 37 37 38 Natural gas 46 49 72 76 88 150 96 92 125 124 123 122 126 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA Other Fuelsa NA NA 7 8 30 24 31 32 44 43 43 43 44 Internal 

Combustion 52 52 52 54 55 52 63 67 91 90 89 89 91 

Industrial 797 958 1,078 1,054 1,044 1,100 1,106 1,137 1,149 1,115 1,080 1,045 1,064 

Coal 95 88 100 99 96 114 118 125 127 123 119 115 117 
A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Fuel Oil 67 64 49 47 46 54 48 45 46 44 43 42 42 

Natural gas 205 313 307 307 305 350 355 366 370 359 347 336 342 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA Other Fuelsa 253 270 316 302 303 286 300 321 325 315 305 296 301 Internal 

Combustion 177 222 305 299 294 296 285 279 282 274 265 257 261 

Commercial 205 211 122 126 122 151 151 154 177 173 169 166 166 

Coal 13 14 13 13 14 16 14 13 15 15 15 14 15 Fuel Oil 16 17 17 18 15 17 17 17 20 19 19 19 19 Natural gas 40 49 58 59 57 81 83 84 97 95 93 91 91 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA Other Fuelsa 136 132 34 36 36 36 36 38 44 43 42 41 42 

Residential 3,668 3,877 2,364 2,361 2,352 3,323 2,644 2,648 3,044 2,981 2,919 2,856 2,867 

Coalb 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 Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA Wood 3,430 3,629 2,133 2,133 2,133 3,094 2,416 2,424 2,787 2,730 2,672 2,615 2,624 

Other Fuelsa 238 248 231 229 220 229 228 224 257 252 246 241 242 

Total 5,000 5,383 3,934 3,926 3,928 5,024 4,340 4,377 4,965 4,860 4,753 4,649 4,695 

NA (Not Applicable) a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b). 

Note: Totals may not sum due to independent rounding. 

Table A-85: NMVOC Emissions from Stationary Combustion (Gg) 

Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 20Electric Power 43 40 44 47 51 49 56 55 44 44 44 44 44 

Coal 24 26 25 26 26 25 27 26 21 21 21 21 21 

Fuel Oil 5 2 3 4 5 4 4 4 4 4 4 3 4 Natural gas 2 2 7 7 9 9 12 12 10 10 10 10 10 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NOther Fuelsa NA NA + + 1 2 2 2 1 1 1 1 1 Internal 

Combustion 11 9 9 10 10 10 11 11 8 8 8 8 8 Industrial 165 187 163 160 159 156 157 160 138 132 126 121 120 1

Coal 7 5 6 6 6 9 9 10 9 9 8 8 8 Fuel Oil 11 11 8 7 7 10 9 9 7 7 7 6 6 Natural gas 52 66 54 54 54 52 53 54 47 45 43 41 41 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NOther Fuelsa 46 45 33 31 31 26 27 29 25 24 23 22 22 Internal 

Combustion 49 60 63 62 61 60 58 57 49 47 45 43 43 Commercial 18 21 22 22 21 25 28 29 61 53 45 33 36 

Coal 1 1 1 1 1 1 1 1 1 1 1 1 1 Fuel Oil 3 3 3 3 3 3 4 4 6 5 3 2 3 
Natural gas 7 10 13 13 12 11 14 14 23 18 14 9 12 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NOther Fuelsa 8 8 5 5 5 10 9 10 31 29 27 22 19 

Residential 686 725 788 788 786 815 837 837 1,341 1,066 792 519 719 9

Coalb NA NA NA NA NA NA NA NA NA NA NA NA NA NFuel Oilb NA NA NA NA NA NA NA NA NA NA NA NA NA NNatural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA NWood 651 688 756 757 756 794 809 809 1,298 1,032 767 502 695 8Other Fuelsa 35 37 33 32 30 21 27 27 43 35 26 17 23 

Total 912 973 1,018 1,017 1,016 1,045 1,077 1,081 1,585 1,296 1,008 716 918 1,1

NA (Not Applicable) + Does not exceed 1 Gg. a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b). 

Note: Totals may not sum due to independent rounding. 
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

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 GHG Emissions 

Estimating CO2 Emissions by Transportation Mode 

Transportation-related CO2 emissions, as presented in the Carbon Dioxide Emissions from Fossil Fuel 

Combustion section of the Energy chapter, were calculated using the methodology described in Annex 2.1. This section 

provides additional information on the data sources and approach used for each transportation fuel type. As noted in 

Annex 2.1, CO2 emissions estimates for the transportation sector were calculated directly for on-road diesel fuel and motor 

gasoline based on data sources for individual modes of transportation (considered a “bottom up” approach). For most 

other fuel and energy types (i.e., jet fuel, aviation gasoline, residual fuel oil, natural gas, LPG, and electricity), CO2

emissions were calculated based on transportation sector-wide fuel consumption estimates from the Energy Information 

Administration (EIA 2007b and EIA 2007 through 2009) and apportioned to individual modes (considered a “top down” 

approach). 

Based on interagency discussions between EPA, EIA, and FHWA beginning in 2005, it was agreed that use of 

“bottom up” data would be more accurate for diesel fuel and motor gasoline, based on the availability of reliable 

transportation-specific data sources. A “bottom up” diesel calculation was implemented in the 1990-2005 Inventory, and a 

bottom-up gasoline calculation was introduced in the 1990-2006 Inventory for the calculation of emissions from on-road 

vehicles. Motor gasoline and diesel consumption data for on-road vehicles come from FHWA’s Highway Statistics, Table 

VM-1 (FHWA 1996 through 2009), and are based on federal and state fuel tax records. These fuel consumption estimates 

were then combined with estimates of fuel shares by vehicle type from DOE’s Transportation Energy Data Book (DOE 

1993 through 2009) to develop an estimate of fuel consumption for each vehicle type (i.e., passenger cars, light-duty 

trucks, buses, medium- and heavy-duty trucks, motorcycles). The on-road gas and diesel fuel consumption estimates by 

vehicle type were then adjusted for each year so that the sum of gasoline and diesel fuel consumption across all vehicle 

categories matched the fuel consumption estimates in Highway Statistics’ Tables MF-21 and MF-27 (FHWA 1996 through 

2010). Table MF-21 provided fuel consumption estimates for the most current Inventory year; Table MF-27 provided fuel 

consumption estimates for years 1990-2008. This resulted in a final estimate of motor gasoline and diesel fuel use by 

vehicle type, consistent with the FHWA total for on-road motor gasoline and diesel fuel use. 

Estimates of diesel fuel consumption from rail were taken from the Association of American Railroads (AAR 

2008 through 2010) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2010 and 

APTA 2006) and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through 

2004) and Whorton (2006 through 2009) for Class II and III railroads, and DOE’s Transportation Energy Data Book

(DOE 1993 through 2010) for passenger rail. Estimates of diesel from ships and boats were taken from EIA’s Fuel Oil 

and Kerosene Sales (1991 through 2011). 

Since EIA’s total fuel consumption estimate for each fuel type is considered to be accurate at the national level, 

adjustments needed to be made in the estimates for other sectors to equal the EIA total. In the case of motor gasoline, 

estimates of fuel use by recreational boats come from EPA’s NONROAD Model (EPA 2010b), and these estimates along 

with those from other sectors (e.g., commercial sector, industrial sector) were adjusted. Similarly, to ensure consistency 

with EIA’s total diesel estimate for all sectors, the diesel consumption totals for the residential, commercial, and industrial

sectors were adjusted downward proportionately. 

As noted above, for fuels other than motor gasoline and diesel, EIA’s transportation sector total was apportioned 

to specific transportation sources. For jet fuel, estimates come from: DOT (1991 through 2010) and FAA (2011, 2006) for 

commercial aircraft, FAA (2009) for general aviation aircraft, and DESC (2008) for military aircraft. Estimates for 

biofuels, including ethanol and biodiesel were discussed separately and were not apportioned to specific transportation 

sources. Consumption estimates for biofuels were calculated based on data from the Energy Information Administration 

(EIA2010b). 

Table A-86 displays estimated fuel consumption by fuel and vehicle type. Table A-87 displays estimated energy 

consumption by fuel and vehicle type. The values in both of these tables correspond to the figures used to calculate CO2

emissions from transportation. Except as noted above, they are estimated based on EIA transportation sector energy 

estimates by fuel type, with activity data used to apportion consumption to the various modes of transport. For motor 

gasoline, the figures do not include ethanol blended with gasoline; although ethanol is included in FHWA’s totals for 

reported motor gasoline use. Ethanol is a biofuel and in order to be in line with IPCC methodological guidance and 
A-117 

UNFCCC reporting obligations, net carbon fluxes in biogenic carbon reservoirs in croplands are accounted for in the 

estimates for Land Use, Land-Use Change and Forestry chapter, not in Energy chapter totals. Ethanol and biodiesel 

consumption estimates are shown separately in Table A-88. 

Table A-86. Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified)a

Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009

Motor Gasolineb 110,440.9 118,216.0 129,101.8 137,294.2 132,944.7 132,543.8 127,524.7 127,012.2 

 Passenger Cars 69,762.4 67,948.0 72,859.7 76,059.6 71,645.8 70,548.0 67,022.7 66,945.0 

 Light-Duty Trucks 34,698.4 44,369.3 50,773.8 55,336.4 55,459.3 56,117.9 54,889.7 54,826.0 

 Motorcycles 194.0 200.3 209.8 183.4 212.2 230.9 241.5 241.2 

 Buses 38.9 41.7 43.6 41.9 41.7 43.6 42.8 38.9 

 Medium- and HeavyDuty Trucks 4,349.8 4,071.6 4,095.5 3,960.1 4,008.3 4,046.5 3,803.7 3,454.2 

 Recreational Boatsc 1,397.3 1,585.1 1,119.3 1,597.9 1,577.4 1,556.9 1,524.2 1,506.8 

Distillate Fuel Oil (Diesel 

Fuel) 25,631.2 31,604.6 39,241.4 44,659.2 45,847.6 46,432.3 43,236.5 39,243.4 

 Passenger Cars 771.2 764.9 356.1 414.0 403.2 399.7 382.4 380.2 

 Light-Duty Trucks 1,118.5 1,452.0 1,960.6 2,518.4 2,609.9 2,658.5 2,618.5 2,603.2 

 Buses 781.0 851.1 996.6 1,030.1 1,048.5 1,048.5 1,007.3 910.4 

 Medium- and HeavyDuty Trucks 18,574.2 23,240.7 30,179.9 35,160.0 36,078.5 36,665.4 34,706.1 31,368.6 

 Recreational Boats 190.4 228.1 265.6 305.4 313.4 321.4 87.8 337.5 

 Ships and Other Boats 735.3 1,204.3 1,376.8 785.3 729.5 799.8 218.4 118.8 

 Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,538.9 4,216.0 3,524.8 

Jet Fueld 18,349.7 17,850.3 20,491.9 19,919.9 17,386.1 17,305.8 15,906.2 14,239.3 

 Commercial Aircraft 14,103.4 14,795.9 17,352.8 16,532.7 14,065.0 14,163.9 12,533.8 11,425.1 

 General Aviation Aircraft 662.9 559.8 972.0 1,526.7 1,642.6 1,485.6 1,705.7 1,364.3 

 Military Aircraft 3,583.4 2,494.6 2,167.1 1,860.5 1,678.5 1,656.3 1,666.8 1,449.9 

Aviation Gasolined 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 

 General Aviation Aircraft 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 

Residual Fuel Oild, e 2,006.2 2,587.4 2,962.5 1,713.1 2,046.3 2,579.4 1,770.5 1,064.8 

 Ships and Other Boats 2,006.2 2,587.4 2,962.5 1,713.1 2,046.3 2,579.4 1,770.5 1,064.8 

Natural Gas d (million 

cubic feet) 0.7 0.7 0.7 0.6 0.6 0.6 0.7 0.7 

 Passenger Cars - + - - - - - -

 Light-Duty Trucks - + - - - - - -

 Buses - + + + + + + +

 Pipelines 0.7 0.7 0.6 0.6 0.6 0.6 0.7 0.6 

LPGd 265.2 205.5 138.4 308.7 320.0 256.7 459.5 487.5 

 Buses - 1.6 1.5 1.0 1.0 - - -

 Light-Duty Trucks 106.1 98.3 88.0 233.2 229.5 184.7 334.0 354.3 

 Medium- and HeavyDuty Trucks 159.1 105.6 48.8 74.6 89.5 72.0 125.5 133.2 

Electricityd

 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 8,172.6 7,699.6 7,688.5 

 Rail 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 8,172.6 7,699.6 7,688.5 a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection 

procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated 

to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as 

described below.

Fuel use by vehicle class (also in the VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and gasoline 

consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline were estimated to grow by the rate of 

growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel were estimated to fall by the same rate 

that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates, 

assuming no relative change in MPG between vehicle classes. b

 Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, LandUse Change and Forestry chapter. 

c

 Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline 

estimates. 

d

 Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes. e

 Fluctuations in reported fuel consumption may reflect data collection problems. 

+ Less than 0.05 million gallons or 0.05 million cubic feet 

- Unreported or zero 

Table A-87: Energy Consumption by Fuel and Vehicle Type (Tbtu) 

Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 
A-118 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Motor Gasolineb 13,813.0 14,678.5 16,014.8 16,729.6 16,516.8 16,470.1 15,843.4 15,779.7 

 Passenger Cars 8,725.3 8,436.9 9,038.1 9,268.0 8,901.1 8,766.4 8,326.8 8,317.1 

 Light-Duty Trucks 4,339.8 5,509.2 6,298.4 6,742.8 6,890.2 6,973.3 6,819.4 6,811.5 

 Motorcycles 24.3 24.9 26.0 22.8 26.4 28.7 30.0 30.0 

 Buses 4.9 5.2 5.4 5.1 5.2 5.4 5.3 4.8 

 Medium- and HeavyDuty Trucks 544.0 505.6 508.0 492.2 498.0 502.8 472.6 429.1 

 Recreational Boatsc 174.8 196.8 138.8 198.6 196.0 193.5 189.4 187.2 

Distillate Fuel Oil 

(Diesel Fuel) 3,554.8 4,383.3 5,442.4 6,193.8 6,358.6 6,439.7 5,996.5 5,442.7 

 Passenger Cars 107.0 106.1 49.4 57.4 55.9 55.4 53.0 52.7 

 Light-Duty Trucks 155.1 201.4 271.9 349.3 362.0 368.7 363.2 361.0 

 Buses 108.3 118.0 138.2 142.9 145.4 145.4 139.7 126.3 

 Medium- and HeavyDuty Trucks 2,576.1 3,223.3 4,185.7 4,876.4 5,003.7 5,085.1 4,813.4 4,350.5 

 Recreational Boats 26.4 31.6 36.8 42.4 43.5 44.6 12.2 46.8 

 Ships and Other Boats 102.0 167.0 190.9 108.9 101.2 110.9 30.3 16.5 

 Rail 480.0 535.8 569.4 616.6 646.9 629.5 584.7 488.9 

Jet Fuelc 2,477.2 2,409.8 2,766.4 2,689.2 2,347.1 2,336.3 2,147.3 1,922.3 

 Commercial Aircraft 1,904.0 1,997.4 2,342.6 2,231.9 1,898.8 1,912.1 1,692.1 1,542.4 

 General Aviation 

Aircraft 89.5 75.6 131.2 206.1 221.8 200.6 230.3 184.2 

 Military Aircraft 483.8 336.8 292.6 251.2 226.6 223.6 225.0 195.7 

Aviation Gasolined 45.0 39.6 36.3 35.4 33.4 31.6 28.3 26.6 

 General Aviation 

Aircraft 45.0 39.6 36.3 35.4 33.4 31.6 28.3 26.6 

Residual Fuel Oild, e 300.3 387.3 443.5 256.4 306.3 386.1 265.0 159.4 

 Ships and Other Boats 300.3 387.3 443.5 256.4 306.3 386.1 265.0 159.4 

Natural Gasd 679.9 724.0 672.0 623.9 625.0 665.4 694.5 684.2 

 Passenger Cars - 1.9 - - - - - - 

 Light-Duty Trucks - 0.1 - - - - - - 

 Buses - 1.0 8.3 15.8 15.5 18.6 20.7 20.8 

 Pipelines 679.9 721.0 663.7 608.1 609.4 646.8 673.8 663.5 

LPGd 22.9 17.7 11.9 28.2 27.5 21.9 39.4 41.2 

 Buses - 0.1 0.1 0.1 0.1 - - - 

 Light-Duty Trucks 9.2 8.5 7.6 21.3 19.7 15.8 28.6 30.0 

 Medium- and HeavyDuty Trucks 13.7 9.1 4.2 6.8 7.7 6.2 10.8 11.3 

Electricityd 16.2 17.0 18.4 25.6 25.1 27.9 26.1 26.1 

 Rail 16.2 17.0 18.4 25.6 25.1 27.9 26.1 26.1 

Total 20,909.3 22,657.1 25,405.6 26,582.1 26,239.8 26,379.1 25,040.5 24,082.3 a FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection 

procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated 

to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009, as 

described below. 

Fuel use by vehicle class (also in the VM-1 table) was not available from FHWA for 2009, but changes in overall diesel and gasoline 

consumption were released in Table MF21. Fuel use in vehicle classes that were predominantly gasoline were estimated to grow by the rate of 

growth for gasoline between 2008 and 2009. Fuel use in vehicle classes that were predominantly diesel were estimated to fall by the same rate 

that diesel fuel consumption fell overall in 2009. VMT was then distributed to vehicle classes based on these fuel consumption estimates, 

assuming no relative change in MPG between vehicle classes. b

 Figures do not include ethanol blended in motor gasoline. Net carbon fluxes associated with ethanol are accounted for in the Land Use, LandUse Change and Forestry chapter. 

c

 Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline 

estimates. 

d

 Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes e

 Fluctuations in reported fuel consumption may reflect data collection problems. 

- Unreported or zero 

Table A-88. Biofuel Consumption by Fuel Type (million gallons) 

Fuel Type 1990 1995 2000 2005 2006 2007 2008 2009 

Ethanol 717.5 1,335.8 1,601.7 3,888.0 5,245.0 6,611.6 9,331.5 10,432.2 

Biodiesel NA NA NA 137.5 394.6 542.3 484.9 514.0 
A-119 

Estimates of CH4 and N2O Emissions 

Mobile source emissions of greenhouse gases other than CO2 are reported by transport mode (e.g., road, rail, 

aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH4 and N2O were derived using a 

methodology similar to that outlined in the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997). 

Activity data were obtained from a number of U.S. government agencies and other publications. Depending on 

the category, these basic activity data included fuel consumption and vehicle miles traveled (VMT). These estimates were 

then multiplied by emission factors, expressed as grams per unit of fuel consumed or per vehicle mile. 

Methodology for On-Road Gasoline and Diesel Vehicles 

Step 1: Determine Vehicle Miles Traveled by Vehicle Type, Fuel Type, and Model Year 

VMT by vehicle type (e.g., passenger cars, light-duty trucks, medium- and heavy-duty trucks,37 buses, and 

motorcycles) were obtained from the Federal Highway Administration’s (FHWA) Highway Statistics (FHWA 1996 

through 2009). As these vehicle categories are not fuel-specific, VMT for each vehicle type was disaggregated by fuel 

type (gasoline, diesel) so that the appropriate emission factors could be applied. VMT from Highway Statistics Table VM1 (FHWA 1996 through 2009) was allocated to fuel types (gasoline, diesel, other) using historical estimates of fuel shares 

reported in the Appendix to the Transportation Energy Data Book (DOE 1993 through 2010). These fuel shares are drawn 

from various sources, including the Vehicle Inventory and Use Survey, the National Vehicle Population Profile, and the 

American Public Transportation Association. The fuel shares were first adjusted proportionately so that the gasoline and 

diesel shares for each vehicle type summed to 100 percent in order to develop an interim estimate of VMT for each 

vehicle/fuel type category that summed to the total national VMT estimate. VMT for alternative fuel vehicles (AFVs) was 

calculated separately, and the methodology is explained in the following section on AFVs. Estimates of VMT from AFVs 

were then subtracted from the appropriate interim VMT estimates to develop the final VMT estimates by vehicle/fuel type 

category.38 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 89 

and Table A- 90, respectively. 

Total VMT for each on-road category (i.e., gasoline passenger cars, light-duty gasoline trucks, heavy-duty 

gasoline vehicles, diesel passenger cars, light-duty diesel trucks, medium- and heavy-duty diesel vehicles, and 

motorcycles) were distributed across 31 model years shown for 2009 in Table A- 93. Distributions for 1990-2007 are 

presented in the Inventory Docket. This distribution was derived by weighting the appropriate age distribution of the U.S. 

vehicle fleet according to vehicle registrations by the average annual age-specific vehicle mileage accumulation of U.S. 

vehicles. Age distribution values were obtained from EPA’s MOBILE6 model for all years before 1999 (EPA 2000) and 

EPA’s MOVES model for years 1999 forward (EPA 2010a).39 Age-specific vehicle mileage accumulation was obtained 

from EPA’s MOBILE6 model (EPA 2000). 

Step 2: Allocate VMT Data to Control Technology Type 

VMT by vehicle type for each model year was distributed across various control technologies as shown in Table 

A- 97 through Table A- 100. The categories “EPA Tier 0” and “EPA Tier 1” were used instead of the early three-way 

catalyst and advanced three-way catalyst categories, respectively, as defined in the Revised 1996 IPCC Guidelines. EPA 

Tier 0, EPA Tier 1, EPA Tier 2, and LEV refer to U.S. emission regulations, rather than control technologies; however, 

each does correspond to particular combinations of control technologies and engine design. EPA Tier 2 and its 

predecessors EPA Tier 1 and Tier 0 apply to vehicles equipped with three-way catalysts. The introduction of “early threeway catalysts,” and “advanced three-way catalysts,” as described in the Revised 1996 IPCC Guidelines, roughly 

correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).40 EPA Tier 2 regulations affect 

vehicles produced starting in 2004 and are responsible for a noticeable decrease in N2O emissions compared EPA Tier 1 

emissions technology (EPA 1999b). 

 

37 Medium-duty trucks include vehicles with a gross vehicle weight rating (GVWR) of 8,500 to 14,000 lbs. while heavy-duty trucks include 

those with a GVWR of over 14,000 lbs.

38 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. 

39 Age distributions were held constant for the period 1990-1998, and reflect a 25-year vehicle age span. EPA (2010) provides a variable age 

distribution and 31-year vehicle age span beginning in year 1999.

40 For further description, see “Definitions of Emission Control Technologies and Standards” section of this annex.
A-120 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Control technology assignments for light and heavy-duty conventional fuel vehicles for model years 1972 (when 

regulations began to take effect) through 1995 were estimated in EPA (1998). Assignments for 1998 through 2007 were 

determined using confidential engine family sales data submitted to EPA (EPA 2007b). Vehicle classes and emission 

standard tiers to which each engine family was certified were taken from annual certification test results and data (EPA 

2007a). This information was used to determine the fraction of sales of each class of vehicle that met EPA Tier 0, EPA 

Tier 1, Tier 2, and LEV standards. Assignments for 1996 and 1997 were estimated based on the fact that EPA Tier 1 

standards for light-duty vehicles were fully phased in by 1996. Tier 2 began initial phase-in by 2004. 

Step 3: Determine CH4 and N2O Emission Factors by Vehicle, Fuel, and Control Technology Type 

Emission factors for gasoline and diesel on-road vehicles were developed by ICF (2004). These factors were 

based on EPA and CARB laboratory test results of different vehicle and control technology types. The EPA and CARB 

tests were designed following the Federal Test Procedure (FTP), which covers three separate driving segments, since 

vehicles emit varying amounts of GHGs depending on the driving segment. These driving segments are: (1) a transient 

driving cycle that includes cold start and running emissions, (2) a cycle that represents running emissions only, and (3) a 

transient driving cycle that includes hot start and running emissions. For each test run, a bag was affixed to the tailpipe of

the vehicle and the exhaust was collected; the content of this bag was later analyzed to determine quantities of gases 

present. The emission characteristics of segment 2 was used to define running emissions, and subtracted from the total 

FTP emissions to determine start emissions. These were then recombined based upon MOBILE6.2’s ratio of start to 

running emissions for each vehicle class to approximate average driving characteristics. 

Step 4: Determine the Amount of CH4 and N2O Emitted by Vehicle, Fuel, and Control Technology Type 

Emissions of CH4 and N2O were then calculated by multiplying total VMT by vehicle, fuel, and control 

technology type by the emission factors developed in Step 3. 

Methodology for Alternative Fuel Vehicles (AFVs) 

Step 1: Determine Vehicle Miles Traveled by Vehicle and Fuel Type 

VMT for alternative fuel and advanced technology vehicles were calculated from “VMT Projections for 

Alternative Fueled and Advanced Technology Vehicles through 2025” (Browning 2003). Alternative Fuels include 

Compressed Natural Gas (CNG), Liquid Natural Gas (LNG), Liquefied Petroleum Gas (LPG), Ethanol, Methanol, and 

Electric Vehicles (battery powered). Most of the vehicles that use these fuels run on an Internal Combustion Engine (ICE) 

powered by the alternative fuel, although many of the vehicles can run on either the alternative fuel or gasoline (or diesel), 

or some combination.41 The data obtained include vehicle fuel use and total number of vehicles in use from 1992 through 

2007. Because AFVs run on different fuel types, their fuel use characteristics are not directly comparable. Accordingly, 

fuel economy for each vehicle type is expressed in gasoline equivalent terms, i.e., how much gasoline contains the 

equivalent amount of energy as the alternative fuel. Energy economy ratios (the ratio of the gasoline equivalent fuel 

economy of a given technology to that of conventional gasoline or diesel vehicles) were taken from full fuel cycle studies 

done for the California Air Resources Board (Unnasch and Browning, Kassoy 2001). These ratios were used to estimate 

fuel economy in miles per gasoline gallon equivalent for each alternative fuel and vehicle type. Energy use per fuel type 

was then divided among the various weight categories and vehicle technologies that use that fuel. Total VMT per vehicle 

type for each calendar year was then determined by dividing the energy usage by the fuel economy. Note that for AFVs 

capable of running on both/either traditional and alternative fuels, the VMT given reflects only those miles driven that 

were powered by the alternative fuel, as explained in Browning (2003). VMT estimates for AFVs by vehicle category 

(passenger car, light-duty truck, heavy-duty vehicles) are shown in Table A- 91, while more detailed estimates of VMT by 

control technology are shown in Table A- 92. 

 

41 Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol or methanol; 

some vehicles are also designed to run on gasoline or an alternative fuel – either natural gas or LPG – but not at the same time, while other 

vehicles are designed to run on E85 (85% ethanol) or gasoline, or any mixture of the two. Heavy-duty vehicles are more likely to run on a 

combination of diesel fuel and either natural gas, LPG, ethanol, or methanol.
A-121 

Step 2: Determine CH4 and N2O Emission Factors by Vehicle and Alternative Fuel Type 

CH4 and N2O emission factors for alternative fuel vehicles (AFVs) are calculated according to studies by 

Argonne National Laboratory (2006) and Lipman & Delucchi (2002), and are reported in ICF (2006a). In these studies, 

N2O and CH4 emissions for AFVs were expressed as a multiplier corresponding to conventional vehicle counterpart 

emissions. Emission estimates in these studies represent the current AFV fleet and were compared against Tier 1 

emissions from light-duty gasoline vehicles to develop new multipliers. Alternative fuel heavy-duty vehicles were 

compared against gasoline heavy-duty vehicles as most alternative fuel heavy-duty vehicles use catalytic after treatment 

and perform more like gasoline vehicles than diesel vehicles. These emission factors are shown in Table A- 102. 

Step 3: Determine the Amount of CH4 and N2O Emitted by Vehicle and Fuel Type 

Emissions of CH4 and N2O were calculated by multiplying total VMT for each vehicle and fuel type (Step 1) by 

the appropriate emission factors (Step 2). 

Methodology for Non-Road Mobile Sources 

CH4 and N2O emissions from non-road mobile sources were estimated by applying emission factors to the 

amount of fuel consumed by mode and vehicle type. 

Activity data for non-road vehicles include annual fuel consumption statistics by transportation mode and fuel 

type, as shown in Table A- 96. Consumption data for ships and other boats (i.e., vessel bunkering) were obtained from 

DHS (2008) and EIA (1991 through 2011) for distillate fuel, and DHS (2008) and EIA (2007b) for residual fuel; marine 

transport fuel consumption data for U.S. territories (EIA 2008b, EIA 1991 through 2011) were added to domestic 

consumption, and this total was reduced by the amount of fuel used for international bunkers.42 Gasoline consumption by 

recreational boats was obtained from EPA’s NONROAD model (EPA 2010). Annual diesel consumption for Class I rail 

was obtained from the Association of American Railroads (AAR) (2008), diesel consumption from commuter rail was 

obtained from APTA (2007 through 2010) and Gaffney (2007), and consumption by Class II and III rail was provided by 

Benson (2002 through 2004) and Whorton (2006 through 2009). Diesel consumption by commuter and intercity rail was 

obtained from DOE (1993 through 2010). Data on the consumption of jet fuel and aviation gasoline in aircraft were 

obtained from EIA (2007b), as described in Annex 2.1: Methodology for Estimating Emissions of CO2 from Fossil Fuel 

Combustion, and were reduced by the amount allocated to international bunker fuels. Pipeline fuel consumption was 

obtained from EIA (2007 through 2010) (note: pipelines are a transportation source but are stationary, not mobile, 

sources). Data on fuel consumption by all non-transportation mobile sources43 were obtained from EPA’s NONROAD 

model (EPA 2010) and from FHWA (1996 through 2010) for gasoline consumption for trucks used off-road. 

Emissions of CH4 and N2O from non-road mobile sources were calculated by multiplying U.S. default emission 

factors in the Revised 1996 IPCC Guidelines (IPCC/UNEP/OECD/IEA 1997) by activity data for each source type (see 

Table A- 103). 

Estimates of NOx, CO, and NMVOC Emissions 

The emission estimates of NOx, CO, and NMVOCs from mobile combustion (transportation) were obtained from 

preliminary data (EPA 2010, EPA 2009), which, in final iteration, will be published on the EPA's National Emission 

Inventory (NEI) Air Pollutant Emission Trends web site. This EPA report provides emission estimates for these gases by 

fuel type using a procedure whereby emissions were calculated using basic activity data, such as amount of fuel delivered 

or miles traveled, as indicators of emissions. 

Table A- 104 through Table A- 106 provides complete emission estimates for 1990 through 2009. 

Table A- 89: Vehicle Miles Traveled for Gasoline On-Road Vehicles (109

 Miles)a

Year Passenger 

Cars 

Light-Duty 

Trucks 

Heavy-Duty 

Vehicles 

Motorcycles 

1990 1391.2 554.3 25.4 9.6 

1991 1341.7 627.2 25.0 9.2 

1992 1354.8 682.9 24.8 9.6 

 

42 See International Bunker Fuels section of the Energy Chapter.

43 “Non-transportation mobile sources” are defined as any vehicle or equipment not used on the traditional road system, but excluding aircraft, 

rail and watercraft. This category includes snowmobiles, golf carts, riding lawn mowers, agricultural equipment, and trucks used for off-road 

purposes, among others.
A-122 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

1993 1356.5 720.5 24.5 9.9 

1994 1387.5 738.8 25.0 10.2 

1995 1420.6 762.5 24.7 9.8 

1996 1454.7 788.0 24.0 9.9 

1997 1488.5 820.8 23.6 10.1 

1998 1536.6 836.8 23.6 10.3 

1999 1559.1 867.4 23.8 10.6 

2000 1591.5 886.7 23.6 10.5 

2001 1619.3 904.9 23.2 9.6 

2002 1649.2 925.8 23.1 9.6 

2003 1662.6 943.0 23.5 9.6 

2004 1690.2 984.2 23.8 10.1 

2005 1698.6 997.5 24.1 10.5 

2006 1680.8 1037.2 24.2 12.0 

2007 1662.7 1065.6 24.7 13.6 

2008 1,606.4 1,062.0 24.7 14.5 

2009 1,621.1 1,071.7 22.7 14.6 

Source: Derived from FHWA (1996 through 2009). 

a

 FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection 

procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated 

to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009 

Table A- 90: Vehicle Miles Traveled for Diesel On-Road Vehicles (109 Miles)a

Year Passenger 

Cars 

Light-Duty 

Trucks 

Heavy-Duty 

Vehicles 

1990 16.9 19.7 125.5 

1991 16.3 21.6 129.3 

1992 16.5 23.4 133.5 

1993 17.9 24.7 140.3 

1994 18.3 25.3 150.5 

1995 17.3 26.9 158.7 

1996 14.7 27.8 164.3 

1997 13.5 29.0 173.4 

1998 12.4 30.5 178.4 

1999 9.4 32.6 185.3 

2000 8.0 35.2 188.0 

2001 8.1 37.0 191.1 

2002 8.3 38.9 196.4 

2003 8.3 39.6 199.1 

2004 8.4 41.3 201.6 

2005 8.4 41.9 202.8 

2006 8.3 43.5 201.4 

2007 8.1 44.6 204.7 

2008 7.8 44.4 205.5 

2009 7.9 44.8 187.8 

Source: Derived from FHWA (1996 through 2009). 

a

 FHWA data on vehicle miles traveled from the VM-1 table were not available for 2009 due to a delay caused by changes in data collection 

procedures. Based on data from FHWA’s Traffic Volume Trends Program, the overall increase in VMT between 2008 and 2009 was estimated 

to be 0.2%. Total VMT was distributed among vehicle classes based on trends in fuel consumption by fuel type between 2008 and 2009 

Table A- 91: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (109 Miles) 

Year 

Passenger 

Cars 

Light-Duty 

Trucks 

Heavy-Duty 

Vehicles 

1990 0.2 0.7 1.1 

1991 0.2 0.6 1.0 

1992 0.3 0.6 0.9 

1993 0.3 0.6 1.3 

1994 0.3 0.5 1.2 

1995 0.4 0.6 1.2 

1996 0.5 0.7 1.2 

1997 0.6 0.9 1.3 
A-123 

1998 0.6 1.0 1.4 

1999 0.6 1.0 1.3 

2000 0.8 1.2 1.5 

2001 0.9 1.2 1.8 

2002 1.0 1.3 2.0 

2003 1.2 1.4 2.1 

2004 1.3 1.6 2.2 

2005 1.4 1.6 2.1 

2006 1.5 1.8 3.7 

2007 1.6 2.0 4.6 

2008 1.7 2.2 4.3 

2009 1.8 2.3 4.8 

Source: Derived from Browning (2003). 

Table A- 92: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles) 

Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Passenger Cars 206.3 400.6 788.1 1,362.2 1,481.2 1,606.3 1,662.3 1,813.0 

 Methanol-Flex Fuel ICE - 40.9 13.2 - - - - - 

 Ethanol-Flex Fuel ICE + 2.2 120.4 399.1 429.8 504.4 554.2 661.0 

 CNG ICE 10.6 28.0 68.9 118.8 125.0 129.2 129.7 135.3 

 CNG Bi-fuel 28.2 75.1 202.9 253.3 275.0 295.3 306.5 330.4 

 LPG ICE 20.6 40.3 41.9 31.3 24.9 18.5 16.5 11.8 

 LPG Bi-fuel 146.9 201.7 197.6 175.1 149.1 123.0 112.0 94.4 

 Biodiesel (BD20) - - 8.2 93.8 186.4 246.1 259.3 295.0 

 NEVs - 5.2 62.4 135.5 135.7 136.0 136.2 136.5 

 Electric Vehicle - 7.2 72.6 155.2 155.2 153.8 147.8 148.7 

Light-Duty Trucks 660.7 606.8 1,162.0 1,640.0 1,840.3 1,992.0 2,153.0 2,299.7 

 Ethanol-Flex Fuel ICE - 1.3 122.6 415.0 518.7 657.7 788.5 921.7 

 CNG ICE 10.9 29.6 145.9 163.8 169.3 175.4 181.8 186.3 

 CNG Bi-fuel 24.2 71.0 280.1 348.2 365.8 389.2 405.2 432.9 

 LPG ICE 56.9 48.5 58.4 57.3 54.6 38.4 36.1 32.9 

 LPG Bi-fuel 568.7 449.4 511.9 502.3 491.4 438.5 422.1 383.6 

 Biodiesel (BD20) - - 8.2 76.7 163.8 219.2 240.9 263.0 

 Electric Vehicle - 7.1 35.0 76.4 76.4 73.1 77.8 78.4 

Medium-Duty Trucks 508.0 458.4 629.6 831.4 866.4 866.9 921.0 924.0 

 CNG Bi-fuel 2.3 20.1 117.0 300.7 314.2 337.4 370.8 396.8 

 LPG ICE 24.3 20.0 29.7 36.8 33.8 31.3 30.9 31.0 

 LPG Bi-fuel 481.4 418.3 475.9 444.8 362.5 289.1 284.3 223.2 

 Biodiesel (BD20) - - 7.0 49.1 156.0 209.1 235.0 272.9 

Heavy-Duty Trucks 523.9 627.0 712.3 1,084.9 2,495.7 3,337.8 3,024.7 3,435.3 

 Neat Methanol ICE 3.0 7.8 - - - - - - 

 Neat Ethanol ICE - 2.0 0.1 - - - - - 

 CNG ICE 12.7 32.2 83.7 218.7 252.8 332.1 495.3 516.8 

 LPG ICE 36.3 46.3 48.3 69.0 65.5 65.3 64.8 64.8 

 LPG Bi-fuel 471.9 531.9 529.7 569.4 524.1 512.9 505.2 502.2 

 LNG - 6.9 22.2 117.3 126.5 141.8 158.1 174.2 

 Biodiesel (BD20) - - 28.3 110.4 1,526.8 2,285.7 1,801.4 2,177.3 

Buses 41.4 80.5 111.9 207.4 349.7 406.1 412.3 422.1 

 Neat Methanol ICE 1.9 3.7 - - - - - - 

 Neat Ethanol ICE 0.1 2.2 - - - - - - 

 CNG ICE 11.2 37.5 53.4 122.9 123.4 131.7 139.2 148.8 

 LPG ICE 28.2 30.9 35.6 34.6 35.1 33.5 33.7 33.9 

 LNG - 4.3 13.3 27.8 28.1 25.1 21.1 18.8 

 Biodiesel (BD20) - - 4.5 15.6 156.8 209.1 210.9 212.5 

 Electric - 2.0 5.1 6.5 6.2 6.3 6.7 7.3 

Total VMT 1,940.3 2,173.4 3,403.9 5,125.9 7,033.3 8,209.0 8,173.3 8,894.0 

Source: Derived from Browning (2003). 

Note: Throughout the rest of this Inventory, medium-duty trucks are grouped with heavy-duty trucks; they are reported separately here because 

these two categories may run on a slightly different range of fuel types. 

+ Less than 0.05 million vehicle miles traveled 

- Unreported or zero 
A-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 93: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,a 2009 

Vehicle Age LDGV LDGT HDGV LDDVb LDDT HDDV MC 

0 5.2% 4.0% 5.2% 4.6% 4.1% 5.6% 6.2% 

1 5.8% 5.0% 5.7% 5.2% 5.1% 6.2% 12.2% 

2 6.4% 7.4% 5.9% 5.8% 6.8% 6.5% 11.0% 

3 6.6% 7.5% 6.0% 5.9% 6.5% 7.9% 10.4% 

4 6.4% 7.9% 6.0% 5.7% 6.8% 7.6% 9.1% 

5 6.2% 7.6% 4.7% 5.5% 10.0% 6.0% 7.7% 

6 6.1% 7.1% 3.8% 5.5% 7.2% 4.6% 6.6% 

7 6.4% 6.7% 3.3% 5.7% 7.7% 4.2% 5.8% 

8 6.4% 6.3% 3.9% 5.7% 8.0% 4.6% 5.0% 

9 6.5% 5.8% 4.3% 0.0% 6.6% 5.5% 4.0% 

10 6.0% 5.0% 4.3% 0.0% 7.1% 5.8% 3.0% 

11 5.3% 4.5% 3.5% 0.0% 1.7% 4.5% 2.5% 

12 4.9% 3.8% 2.7% 0.0% 4.3% 3.5% 2.4% 

13 4.3% 3.4% 2.7% 0.0% 2.9% 3.3% 2.1% 

14 3.7% 2.9% 3.2% 0.0% 2.8% 3.7% 1.6% 

15 3.1% 2.6% 3.3% 0.0% 2.4% 3.2% 1.9% 

16 2.3% 2.1% 2.5% 0.0% 1.8% 2.4% 1.5% 

17 1.8% 1.6% 1.8% 0.0% 1.2% 1.6% 1.3% 

18 1.4% 1.3% 1.8% 0.0% 1.0% 1.6% 1.0% 

19 1.2% 1.3% 2.3% 0.0% 0.9% 1.8% 0.8% 

20 1.0% 1.1% 3.1% 0.0% 0.8% 2.0% 0.6% 

21 0.8% 1.1% 2.9% 0.0% 0.6% 1.7% 0.5% 

22 0.6% 0.9% 2.5% 0.0% 0.5% 1.3% 0.5% 

23 0.5% 0.8% 3.1% 0.0% 0.7% 1.4% 0.4% 

24 0.4% 0.7% 2.4% 1.0% 0.6% 1.0% 0.4% 

25 0.3% 0.5% 1.5% 1.1% 0.6% 0.6% 0.3% 

26 0.2% 0.3% 2.3% 1.2% 0.5% 0.6% 0.2% 

27 0.1% 0.2% 1.3% 1.6% 0.5% 0.3% 0.2% 

28 0.1% 0.2% 1.2% 1.6% 0.1% 0.3% 0.2% 

29 0.1% 0.2% 1.4% 0.8% 0.2% 0.3% 0.2% 

30 0.1% 0.2% 1.6% 0.6% 0.1% 0.3% 0.3% 

Total 100% 100% 100% 57% 100% 100% 100% 

Source: EPA (2010). 

a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV 

(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC 

(motorcycles). 

b According to EPA’s MOVES model, sales of diesel passenger cars in model years 9-23 was very small compared to total passenger car sales, so 

the calculated fraction of these vehicles in these model years was stored as zero. 

Table A- 94: Annual Average Vehicle Mileage Accumulation per Vehicle a (miles) 

Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MCb

0 14,910 19,906 20,218 14,910 26,371 28,787 4,786

1 14,174 18,707 18,935 14,174 24,137 26,304 4,475

2 13,475 17,559 17,100 13,475 22,095 24,038 4,164

3 12,810 16,462 16,611 12,810 20,228 21,968 3,853

4 12,178 15,413 15,560 12,178 18,521 20,078 3,543

5 11,577 14,411 14,576 11,577 16,960 18,351 3,232

6 11,006 13,454 13,655 11,006 15,533 16,775 2,921

7 10,463 12,541 12,793 10,463 14,227 15,334 2,611

8 9,947 11,671 11,987 9,947 13,032 14,019 2,300

9 9,456 10,843 11,231 9,456 11,939 12,817 1,989

10 8,989 10,055 10,524 8,989 10,939 11,719 1,678

11 8,546 9,306 9,863 8,546 10,024 10,716 1,368

12 8,124 8,597 9,243 8,124 9,186 9,799 1,368

13 7,723 7,925 8,662 7,723 8,420 8,962 1,368
A-125 

14 7,342 7,290 8,028 7,342 7,718 8,196 1,368

15 6,980 6,690 7,610 6,980 7,075 7,497 1,368

16 6,636 6,127 7,133 6,636 6,487 6,857 1,368

17 6,308 5,598 6,687 6,308 5,948 6,273 1,368

18 5,997 5,103 6,269 5,997 5,454 5,739 1,368

19 5,701 4,642 5,877 5,701 5,002 5,250 1,368

20 5,420 4,214 5,510 5,420 4,588 4,804 1,368

21 5,152 3,818 5,166 5,152 4,209 4,396 1,368

22 4,898 3,455 4,844 4,898 3,861 4,023 1,368

23 4,656 3,123 4,542 4,656 3,542 3,681 1,368

24 4,427 2,822 4,259 4,427 3,250 3,369 1,368

25 4,427 2,822 4,259 4,427 3,250 3,369 1,368

26 4,427 2,822 4,259 4,427 3,250 3,369 1,368

27 4,427 2,822 4,259 4,427 3,250 3,369 1,368

28 4,427 2,822 4,259 4,427 3,250 3,369 1,368

29 4,427 2,822 4,259 4,427 3,250 3,369 1,368

30 4,427 2,822 4,259 4,427 3,250 3,369 1,368

Source: EPA (2000). 

a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV 

(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC 

(motorcycles). 

b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and therefore 

are presented in aggregate. 

Table A- 95: VMT Distribution by Vehicle Age and Vehicle/Fuel Type, a 2009 

Vehicle Age LDGV LDGT HDGV LDDVb LDDT HDDV MC 

0 7.56% 6.52% 9.50% 10.78% 7.43% 10.67% 9.53% 

1 8.03% 7.67% 9.80% 11.44% 8.51% 10.80% 17.45% 

2 8.49% 10.68% 9.15% 12.11% 10.26% 10.30% 14.68% 

3 8.23% 10.14% 8.97% 11.74% 9.01% 11.49% 12.87% 

4 7.65% 9.95% 8.40% 10.90% 8.65% 10.06% 10.36% 

5 7.00% 8.98% 6.23% 9.98% 11.59% 7.30% 8.02% 

6 6.59% 7.87% 4.64% 9.39% 7.68% 5.15% 6.13% 

7 6.51% 6.85% 3.83% 9.28% 7.54% 4.27% 4.88% 

8 6.24% 6.07% 4.20% 8.90% 7.14% 4.27% 3.65% 

9 6.04% 5.20% 4.34% 0.00% 5.43% 4.69% 2.52% 

10 5.25% 4.14% 4.08% 0.00% 5.32% 4.46% 1.60% 

11 4.45% 3.40% 3.14% 0.00% 1.19% 3.18% 1.12% 

12 3.92% 2.65% 2.26% 0.00% 2.69% 2.27% 1.07% 

13 3.28% 2.20% 2.15% 0.00% 1.69% 1.95% 0.94% 

14 2.63% 1.71% 2.33% 0.00% 1.46% 2.00% 0.71% 

15 2.11% 1.42% 2.30% 0.00% 1.15% 1.57% 0.81% 

16 1.52% 1.05% 1.60% 0.00% 0.80% 1.08% 0.66% 

17 1.10% 0.75% 1.09% 0.00% 0.51% 0.68% 0.55% 

18 0.81% 0.54% 1.01% 0.00% 0.36% 0.60% 0.44% 

19 0.67% 0.49% 1.21% 0.00% 0.32% 0.64% 0.36% 

20 0.52% 0.40% 1.56% 0.00% 0.24% 0.64% 0.26% 

21 0.41% 0.33% 1.36% 0.00% 0.16% 0.50% 0.22% 

22 0.29% 0.26% 1.08% 0.00% 0.13% 0.36% 0.22% 

23 0.23% 0.20% 1.29% 0.00% 0.16% 0.33% 0.18% 

24 0.16% 0.15% 0.94% 0.69% 0.13% 0.22% 0.15% 

25 0.11% 0.12% 0.56% 0.78% 0.13% 0.14% 0.14% 

26 0.07% 0.08% 0.87% 0.86% 0.12% 0.13% 0.10% 

27 0.05% 0.05% 0.48% 1.11% 0.11% 0.07% 0.08% 

28 0.04% 0.04% 0.48% 1.12% 0.03% 0.06% 0.08% 

29 0.03% 0.04% 0.53% 0.54% 0.04% 0.06% 0.09% 

30 0.03% 0.05% 0.62% 0.38% 0.02% 0.06% 0.14% 

Total 100% 100% 100% 100% 100% 100% 100% 

Note: Estimated by weighting data in Table A- 93 by data in Table A- 94. a The following abbreviations correspond to vehicle types: LDGV (light-duty gasoline vehicles), LDGT (light-duty gasoline trucks), HDGV 

(heavy-duty gasoline vehicles), LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC 

(motorcycles). 

b According to EPA’s MOVES model, sales of diesel passenger cars in model years 9-23 was very small compared to total passenger car sales, so 

the calculated fraction of these vehicles in these model years was stored as zero. 
A-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 96: Fuel Consumption for Off-Road Sources by Fuel Type (million gallons) 

Vehicle Type/Year 1990 1995 2000 2005 2006 2007 2008 2009 

Aircrafta 18,723.9 18,179.6 20,793.7 20,214.1 17,664.4 17,568.6 16,141.6 14,460.2 

 Aviation Gasoline 374.2 329.3 301.9 294.2 278.3 262.8 235.3 221.0 

 Jet Fuel 18,349.7 17,850.3 20,491.9 19,919.9 17,386.1 17,305.8 15,906.2 14,239.3 

Ships and Other 

Boats 4,507.2 5,789.1 6,430.6 4,882.7 5,144.6 5,747.1 4,113.4 3,548.7 

 Diesel 1,043.1 1,545.7 1,750.1 1,471.4 1,410.5 1,490.7 675.7 825.7 

 Gasoline 1,403.4 1,597.3 1,652.9 1,629.9 1,619.6 1,609.6 1,599.8 1,590.7 

 Residual 2,060.7 2,646.1 3,027.5 1,781.4 2,114.5 2,646.8 1,837.9 1,132.3 

Construction/ 

Mining 

Equipmentb

 4,160.3 4,834.7 5,439.1 6,520.5 6,656.4 6,683.7 6,835.0 6,960.0 

 Diesel 3,674.4 4,386.9 5,094.9 5,823.4 5,968.2 6,112.9 6,257.7 6,402.5 

 Gasoline 485.9 447.8 344.2 697.1 688.3 570.8 577.3 557.5 

Agricultural 

Equipmentc

 3,133.7 3,698.3 3,874.5 4,715.0 4,947.8 4,861.5 4,516.6 4,640.9 

 Diesel 2,320.9 2,771.6 3,222.3 3,637.2 3,719.1 3,800.9 3,882.8 3,964.6 

 Gasoline 812.8 926.7 652.3 1,077.8 1,228.7 1,060.6 633.8 676.3 

Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,538.9 4,216.0 3,524.8 

 Diesel 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,538.9 4,216.0 3,524.8 

Otherd

 5,916.5 6,524.8 6,826.1 8,281.2 8,396.1 8,255.6 8,386.7 8,481.6 

 Diesel 1,423.3 1,720.0 2,016.0 2,340.3 2,405.4 2,470.6 2,535.8 2,601.0 

 Gasoline 4,493.2 4,804.7 4,810.2 5,940.9 5,990.7 5,785.0 5,850.9 5,880.7 

Total 39,902.2 42,890.0 47,470.0 49,059.5 47,473.8 47,655.5 44,209.2 41,616.4 

Sources: AAR (2008 through 2009), APTA (2007 through 2009), BEA (1991 through 2005), Benson (2002 through 2004), DHS (2008), DOC 

(1991 through 2008), DESC (2008), DOE (1993 through 2009), DOT (1991 through 2009), EIA (2002), EIA (2007b), EIA (2008), EIA (2007 

through 2008), EIA (1991 through 2011), EPA (2007b), FAA (200), FAA (2006), Gaffney (2007), and Whorton (2006 through 2009). 

a

 For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline. b

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport 

equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for 

commercial/industrial purposes. 

 

Table A- 97: Control Technology Assignments for Gasoline Passenger Cars (Percent of VMT) 

Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV EPA Tier 2

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 - - 60% 40% - -

1995 - - 20% 80% - -

1996 - - 1% 97% 2% -

1997 - - 0.5% 96.5% 3% -

1998 - - <1% 87% 13% -

1999 - - <1% 67% 33% -

2000 - - - 44% 56% -

2001 - - - 3% 97% -

2002 - - - 1% 99% -

2003 - - - <1% 87% 13%

2004 - - - <1% 41% 59%

2005 - - - - 38% 62%

2006 - - - - 18% 82%

2007 - - - - 4% 96%

2008 - - - - 2% 98%

2009 - - - - - 100%

Sources: EPA (1998), EPA (2007a), and EPA (2007b). 

Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex. 
A-127 

- Not applicable. 

Table A- 98: Control Technology Assignments for Gasoline Light-Duty Trucks (Percent of VMT)a

Model Years Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV b EPA Tier 2

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 - - - 80% 20% -

1999 - - - 57% 43% -

2000 - - - 65% 35% -

2001 - - - 1% 99% -

2002 - - - 10% 90% -

2003 - - - <1% 53% 47%

2004 - - - - 72% 28%

2005 - - - - 38% 62%

2006 - - - - 25% 75%

2007 - - - - 14% 86%

2008 - - - - - 100%

2009 - - - - - 100%

Sources: EPA (1998), EPA (2007a), and EPA (2007b). 

a

 Detailed descriptions of emissions control technologies are provided in the following section of this annex. b

 The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission reductions with certain 

types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can reduce the total number of LEVs they 

need to build to meet a specified emission average for all of their vehicles in a given model year.

- Not applicable. 

Table A- 99: Control Technology Assignments for Gasoline Heavy-Duty Vehicles (Percent of VMT)a

Model Years Uncontrolled Non-catalyst Oxidation EPA Tier 0 EPA Tier 1 LEV b EPA Tier 2

≤1981 100% - - - - - -

1982-1984 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 - - - - 96% 4% -

1999 - - - - 78% 22% -

2000 - - - - 54% 46% -

2001 - - - - 64% 36% -

2002 - - - - 69% 31% -

2003 - - - - 65% 30% 5%

2004 - - - - 5% 37% 59%

2005 - - - - - 23% 77%

2006 - - - - - 22% 78%

2007 - - - - - 21% 79%

2008 - - - - - 20% 80%

2009 - - - - - 19% 81%

Sources: EPA (1998), EPA (2007a), and EPA (2007b). 

a

 Detailed descriptions of emissions control technologies are provided in the following section of this annex. b

 The proportion of LEVs as a whole has decreased since 2000, as carmakers have been able to achieve greater emission reductions with certain 

types of LEVs, such as ULEVs. Because ULEVs emit about half the emissions of LEVs, a manufacturer can reduce the total number of LEVs 

they need to build to meet a specified emission average for all of their vehicles in a given model year. 
A-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

- Not applicable. 

Table A- 100: Control Technology Assignments for Diesel On-Road Vehicles and Motorcycles 

Vehicle Type/Control Technology Model Years 

Diesel Passenger Cars and Light-Duty Trucks 

Uncontrolled 1960-1982 

Moderate control 1983-1995 

Advanced control 1996-2009 

Diesel Medium- and Heavy-Duty Trucks and Buses 

Uncontrolled 1960-1990 

Moderate control 1991-2003 

Advanced control 2004-2006 

Aftertreatment 2007-2009 

Motorcycles 

Uncontrolled 1960-1995 

Non-catalyst controls 1996-2009 

Source: EPA (1998) and Browning (2005) 

Note: Detailed descriptions of emissions control technologies are provided in the following section of this annex. 

Table A- 101: Emission Factors for CH4 and N2O for On-Road Vehicles 

Vehicle Type/Control 

Technology 

N2O CH4

(g/mi) (g/mi) 

Gasoline Passenger Cars 

 EPA Tier 2 0.0036 0.0173 

 Low Emission Vehicles 0.0150 0.0105 

 EPA Tier 1a

 0.0429 0.0271 

 EPA Tier 0 a 0.0647 0.0704 

 Oxidation Catalyst 0.0504 0.1355 

 Non-Catalyst Control 0.0197 0.1696 

 Uncontrolled 0.0197 0.1780 

Gasoline Light-Duty Trucks 

 EPA Tier 2 0.0066 0.0163 

 Low Emission Vehicles 0.0157 0.0148 

 EPA Tier 1a

 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 2 0.0134 0.0333 

 Low Emission Vehicles 0.0320 0.0303 

 EPA Tier 1a

 0.1750 0.0655 

 EPA Tier 0a

 0.2135 0.2630 

 Oxidation Catalyst 0.1317 0.2356 

 Non-Catalyst Control 0.0473 0.4181 

 Uncontrolled 0.0497 0.4604 

Diesel Passenger Cars 

 Advanced 0.0010 0.0005 

 Moderate 0.0010 0.0005 

 Uncontrolled 0.0012 0.0006 

Diesel Light-Duty Trucks 

 Advanced 0.0015 0.0010 

 Moderate 0.0014 0.0009 

 Uncontrolled 0.0017 0.0011 

Diesel Medium- and Heavy-Duty 

Trucks and Buses 

 

 Aftertreatment 0.0048 0.0051 

 Advanced 0.0048 0.0051 

 Moderate 0.0048 0.0051 

 Uncontrolled 0.0048 0.0051 

Motorcycles 

 Non-Catalyst Control 0.0069 0.0672 

 Uncontrolled 0.0087 0.0899 

Source: ICF (2006b and 2004). 
A-129 

a The categories “EPA Tier 0” and “EPA Tier 1” were substituted for the early three-way catalyst and advanced three-way catalyst categories, 

respectively, as defined in the Revised 1996 IPCC Guidelines. Detailed descriptions of emissions control technologies are provided at the end of 

this annex. 

Table A- 102: Emission Factors for CH4 and N2O for Alternative Fuel Vehicles (g/mi) 

 N2O CH4

Light Duty Vehicles 

 Methanol 0.067 0.018 

 CNG 0.050 0.737 

 LPG 0.067 0.037 

 Ethanol 0.067 0.055 

 Biodiesel (BD20) 0.001 0.0005 

Medium- and Heavy-Duty Trucks 

 Methanol 0.175 0.066 

 CNG 0.175 1.966 

 LNG 0.175 1.966 

 LPG 0.175 0.066 

 Ethanol 0.175 0.197 

 Biodiesel (BD20) 0.005 0.005 

Buses 

 Methanol 0.175 0.066 

 CNG 0.175 1.966 

 Ethanol 0.175 0.197 

 Biodiesel (BD20) 0.005 0.005 

Source: Developed by ICF (2006a) using ANL (2006) and Lipman and Delucchi (2002). 

Table A- 103: Emission Factors for CH4 and N2O Emissions from Non-Road Mobile Combustion (g/kg fuel) 

Vehicle Type/Fuel Type N2O CH4

Ships and Boats 

Residual 0.16 0.03

Gasoline 0.08 0.23

Diesel 0.14 0.02

Rail 

Diesel 0.08 0.25

Agricultural Equipmenta

Gasoline 0.08 0.45

Diesel 0.08 0.45

Construction/Mining 

Equipmentc

Gasoline 0.08 0.18

Diesel 0.08 0.18

Other Non-Road 

All “Other” Categoriesc

 0.08 0.18

Aircraft 

Jet Fuel 0.10 0.087

Aviation Gasoline 0.04 2.64

Source: IPCC/UNEP/OECD/IEA (1997) and ICF (2009). 

a

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. b

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. c

 “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport 

equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for 

commercial/industrial purposes. 

Table A- 104: NOx Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Gasoline On-Road 5,746 4,560 3,812 3,102 2,897 2,693 2,488 2,212 

Passenger Cars 3,847 2,752 2,084 1,692 1,581 1,469 1,357 1,207 

Light-Duty Trucks 1,364 1,325 1,303 1,073 1,002 931 860 765 

Medium- and HeavyDuty Trucks and Buses 515 469 411 326 304 283 261 232 

Motorcycles 20 14 13 11 11 10 9 8 

Diesel On-Road 2,956 3,493 3,803 2,787 2,603 2,419 2,235 1,987 

Passenger Cars 39 19 7 5 5 4 4 3 

Light-Duty Trucks 20 12 6 5 4 4 4 3 
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Medium- and HeavyDuty Trucks and Buses 2,897 3,462 3,791 2,778 2,594 2,411 2,228 1,981 

Alternative Fuel OnRoada IE IE IE IE IE IE IE IE 

Non-Road 2,160 2,483 2,584 3,122 2,988 2,853 2,718 2,007 

Ships and Boats 402 488 506 646 618 590 562 415 

Rail 338 433 451 576 551 527 502 371 

Aircraftb 25 31 40 47 45 43 41 30 

Agricultural Equipmentc 437 478 484 565 540 516 491 363 

Construction/Mining 

Equipmentd

 641 697 697 811 776 741 706 521 

Othere

 318 357 407 478 457 437 416 307 

Total 10,862 10,536 10,199 9,012 8,488 7,965 7,441 6,206 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b

 Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. 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. 

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Table A- 105: CO Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Gasoline On-Road 98,328 74,673 60,657 43,374 40,492 37,610 34,727 34,199 

Passenger Cars 60,757 42,065 32,867 24,166 22,560 20,954 19,348 19,054 

Light-Duty Trucks 29,237 27,048 24,532 17,264 16,117 14,969 13,822 13,612 

Medium- and HeavyDuty Trucks and Buses 8,093 5,404 3,104 1,844 1,721 1,599 1,476 1,454 

Motorcycles 240 155 154 100 94 87 80 79 

Diesel On-Road 1,696 1,424 1,088 665 621 576 532 524 

Passenger Cars 35 18 7 4 4 4 4 4 

Light-Duty Trucks 22 16 6 4 4 3 3 3 

Medium- and HeavyDuty Trucks and Buses 1,639 1,391 1,075 656 613 569 525 517 

Alternative Fuel OnRoada IE IE IE IE IE IE IE IE 

Non-Road 19,337 21,533 21,814 18,652 17,859 17,067 16,274 8,633 

Ships and Boats 1,559 1,781 1,825 1,534 1,469 1,404 1,339 710 

Rail 85 93 90 75 72 69 66 35 

Aircraftb 217 224 245 196 187 179 171 91 

Agricultural Equipmentc

 581 628 626 520 498 476 454 241 

Construction/Mining 

Equipmentd

 1,090 1,132 1,047 872 835 798 761 404 

Othere 15,805 17,676 17,981 15,455 14,798 14,141 13,484 7,153 

Total 119,360 97,630 83,559 62,692 58,972 55,253 51,533 43,355 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b

 Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. 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. 

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Table A- 106: NMVOCs Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Gasoline On-Road 8,110 5,819 4,615 3,558 3,358 3,158 2,958 2,878 

Passenger Cars 5,120 3,394 2,610 1,987 1,875 1,764 1,652 1,607 

Light-Duty Trucks 2,374 2,019 1,750 1,382 1,304 1,226 1,149 1,118 

Medium- and HeavyDuty Trucks and Buses 575 382 232 171 162 152 142 138 
A-131 

Motorcycles 42 24 23 18 17 16 15 15 

Diesel On-Road 406 304 216 172 163 153 143 139 

Passenger Cars 16 8 3 3 2 2 2 2 

Light-Duty Trucks 14 9 4 3 3 3 3 2 

Medium- and HeavyDuty Trucks and Buses 377 286 209 167 157 148 139 135 

Alternative Fuel OnRoada IE IE IE IE IE IE IE IE 

Non-Road 2,415 2,622 2,399 2,600 2,516 2,430 2,346 1,134 

Ships and Boats 608 739 744 798 772 746 720 348 

Rail 33 36 35 39 37 36 35 17 

Aircraftb 28 28 24 21 20 20 19 9 

Agricultural Equipmentc

 85 86 76 79 77 74 71 35 

Construction/Mining 

Equipmentd

 149 152 130 137 132 128 123 60 

Othere

 1,512 1,580 1,390 1,527 1,477 1,427 1,378 666 

Total 10,932 8,745 7,229 6,330 6,037 5,742 5,447 4,151 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b

 Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. 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. 

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Definitions of Emission Control Technologies and Standards 

The N2O and CH4 emission factors used depend on the emission standards in place and the corresponding level 

of control technology for each vehicle type. Table A- 97 through Table A- 100 show the years in which these technologies 

or standards were in place and the penetration level for each vehicle type. These categories are defined below. 

Uncontrolled 

Vehicles manufactured prior to the implementation of pollution control technologies are designated as 

uncontrolled. Gasoline passenger cars and light-duty trucks (pre-1973), gasoline heavy-duty vehicles (pre-1984), diesel 

vehicles (pre-1983), and motorcycles (pre-1996) are assumed to have no control technologies in place. 

Gasoline Emission Controls 

Below are the control technologies and emissions standards applicable to gasoline vehicles. 

Non-catalyst 

These emission controls were common in gasoline passenger cars and light-duty gasoline trucks during model 

years (1973-1974) but phased out thereafter, in heavy-duty gasoline vehicles beginning in the mid-1980s, and in 

motorcycles beginning in 1996. This technology reduces hydrocarbon (HC) and carbon monoxide (CO) emissions 

through adjustments to ignition timing and air-fuel ratio, air injection into the exhaust manifold, and exhaust gas 

recirculation (EGR) valves, which also helps meet vehicle NOx standards. 

Oxidation Catalyst 

This control technology designation represents the introduction of the catalytic converter, and was the most 

common technology in gasoline passenger cars and light-duty gasoline trucks made from 1975 to 1980 (cars) and 1975 to 

1985 (trucks). This technology was also used in some heavy-duty gasoline vehicles between 1982 and 1997. The two-way 

catalytic converter oxidizes HC and CO, significantly reducing emissions over 80 percent beyond non-catalyst-system 

capacity. One reason unleaded gasoline was introduced in 1975 was due to the fact that oxidation catalysts cannot 

function properly with leaded gasoline. 
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

EPA Tier 0 

This emission standard from the Clean Air Act was met through the implementation of early "three-way" 

catalysts, therefore this technology was used in gasoline passenger cars and light-duty gasoline trucks sold beginning in 

the early 1980s, and remained common until 1994. This more sophisticated emission control system improves the 

efficiency of the catalyst by converting CO and HC to CO2 and H2O, reducing NOx to nitrogen and oxygen, and using an 

on-board diagnostic computer and oxygen sensor. In addition, this type of catalyst includes a fuel metering system 

(carburetor or fuel injection) with electronic "trim" (also known as a "closed-loop system"). New cars with three-way 

catalysts met the Clean Air Act's amended standards (enacted in 1977) of reducing HC to 0.41 g/mile by 1980, CO to 3.4 

g/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, and applied to light-duty gasoline vehicles beginning in 1994. These 

advanced emission control systems included advanced three-way catalysts, electronically controlled fuel injection and 

ignition timing, EGR, and air injection. 

EPA Tier 2 

This emission standard was specified in the 1990 amendments to the Clean Air Act, limiting passenger car NOx

emissions to 0.07 g/mi on average and aligning emissions standards for passenger cars and light-duty trucks. 

Manufacturers can meet this average emission level by producing vehicles in 11 emission “Bins”, the three highest of 

which expire in 2006. These new emission levels represent a 77 to 95% reduction in emissions from the EPA Tier 1 

standard set in 1994. Emission reductions were met through the use of more advanced emission control systems and lower 

sulfur fuels and are applied to vehicles beginning in 2004. These advanced emission control systems include improved 

combustion, advanced three-way catalysts, electronically controlled fuel injection and ignition timing, EGR, and air 

injection. 

Low Emission Vehicles (LEV) 

This emission standard requires a much higher emission control level than the Tier 1 standard. Applied to lightduty gasoline passenger cars and trucks beginning in small numbers in the mid-1990s, LEV includes multi-port fuel 

injection with adaptive learning, an advanced computer diagnostics systems and advanced and close coupled catalysts with 

secondary air injection. LEVs as defined here include transitional low-emission vehicles (TLEVs), low emission vehicles, 

ultra-low emission vehicles (ULEVs) and super ultra-low emission vehicles (SULEVs). In this analysis, all categories of 

LEVs are treated the same due to the fact that there are very limited CH4 or N2O emission factor data for LEVs to 

distinguish among the different types of vehicles. Zero emission vehicles (ZEVs) are incorporated into the alternative fuel 

and advanced technology vehicle assessments. 

Diesel Emission Controls 

Below are the two levels of emissions control for diesel vehicles. 

Moderate control 

Improved injection timing technology and combustion system design for light- and heavy-duty diesel vehicles 

(generally in place in model years 1983 to 1995) are considered moderate control technologies. These controls were 

implemented to meet emission standards for diesel trucks and buses adopted by the EPA in 1985 to be met in 1991 and 

1994. 
A-133 

Advanced control 

EGR and modern electronic control of the fuel injection system are designated as advanced control technologies. 

These technologies provide diesel vehicles with the level of emission control necessary to comply with standards in place 

from 1996 through 2006. 

Aftertreatment

Use of diesel particulate filters (DPFs), oxidation catalysts and NOx absorbers or selective catalytic reduction 

(SCR) systems are designated as aftertreatment control. These technologies provide diesel vehicles with a level of 

emission control necessary to comply with standards in place from 2007 on.

Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources 

This section of this Annex includes supplemental information on the contribution of transportation and other 

mobile sources to U.S. greenhouse gas emissions. In the main body of the Inventory report, emission estimates are 

generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate CO2, N2O, CH4, 

and HFC emissions. Although the inventory is not required to provide detail beyond what is contained in the body of this 

report, the IPCC allows presentation of additional data and detail on emission sources. The purpose of this sub-annex, 

within the annex that details the calculation methods and data used for non- CO2 calculations, is to provide all 

transportation estimates presented throughout the repot in one place. 

This section of this Annex reports total greenhouse gas emissions from transportation and other (nontransportation) mobile sources in CO2 equivalents, with information on the contribution by greenhouse gas and by mode, 

vehicle type, and fuel type. In order to calculate these figures, additional analyses were conducted to develop estimates of 

CO2 from non-transportation mobile sources (e.g., agricultural equipment, construction/mining equipment, recreational 

vehicles), and to provide more detailed breakdowns of emissions by source. 

Estimation of CO2 from Non-Transportation Mobile Sources 

The estimates of N2O and CH4 from fuel combustion presented in the Energy chapter of the inventory include 

both transportation sources and other mobile sources. Other mobile sources include construction/mining equipment, 

agricultural equipment, vehicles used off-road, and other sources that have utility associated with their movement but do 

not have a primary purpose of transporting people or goods (e.g., snowmobiles, riding lawnmowers, etc.). Estimates of 

CO2 from non-transportation mobile sources, based on EIA fuel consumption estimates, are included in the agricultural, 

industrial, and commercial sectors. In order to provide comparable information on transportation and mobile sources, 

Table A- 107 provides estimates of CO2 from these other mobile sources, developed from EPA’s NONROAD model and 

FHWA’s Highway Statistics. These other mobile source estimates were developed using the same fuel consumption data 

utilized in developing the N2O and CH4 estimates. 

Table A- 107: CO2 Emissions from Non-Transportation Mobile Sources (Tg CO2 Eq.) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Agricultural Equipmenta 31.0 36.6 38.8 46.8 49.0 48.4 45.4 46.7 

Construction/Mining 

Equipmentb

 42.0 48.9 55.3 65.9 67.3 67.8 69.3 70.6 

Other Sourcesc

 54.5 59.8 62.8 76.2 77.6 76.7 77.7 78.6 

Total 127.6 145.4 156.9 188.9 193.9 193.0 192.4 195.9 a

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. b

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. c

 “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport 

equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for 

commercial/industrial purposes. 

Estimation of HFC Emissions from Transportation Sources 

In addition to CO2, N2O and CH4 emissions, transportation sources also result in emissions of HFCs. HFCs are 

emitted to the atmosphere during equipment manufacture and operation (as a result of component failure, leaks, and 

purges), as well as at servicing and disposal events. There are three categories of transportation-related HFC emissions; 

Mobile AC represents the emissions from air conditioning units in passenger cars and light-duty trucks, Comfort Cooling 

represents the emissions from air conditioning units in passenger trains and buses, and Refrigerated Transport represents 

the emissions from units used to cool freight during transportation. 

Table A- 108 below presents these HFC emissions. Table A- 109 presents all transportation and mobile source 

greenhouse gas emissions, including HFC emissions. 
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 108: HFC Emissions from Transportation Sources 

Vehicle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Mobile AC - 16.7 46.0 59.4 58.3 54.7 50.7 45.9 

Passenger Cars - 10.1 24.3 28.4 27.1 24.6 22.1 19.3 

Light-Duty Trucks - 6.5 21.7 31.0 31.2 30.1 28.6 26.6 

Comfort Cooling for 

Trains and Buses - + 0.1 0.2 0.3 0.3 0.4 0.4 

School and Tour Buses - + 0.1 0.2 0.3 0.3 0.3 0.4 

Transit Buses - + + + + + + + 

Rail - - - + + + + + 

Refrigerated Transport - 2.3 9.6 13.2 13.6 13.8 13.8 13.9 

Medium- and Heavy-Duty 

Trucks - 1.7 7.4 11.1 11.4 11.5 11.6 11.6 

Rail - 0.5 2.0 2.1 2.2 2.2 2.2 2.2 

Ships and Other Boats - + 0.1 + + + + + 

Total - 19.0 55.7 72.9 72.2 68.8 64.9 60.2 

Note: Totals may not sum due to independent rounding. 

+ Less than 0.05 Tg CO2 Eq. 

- Unreported or zero 

Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Mode/Vehicle 

Type/Fuel Type 

Table A- 109 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 CO2 equivalents. In total, transportation and non-transportation mobile sources emitted 2,014.7 Tg CO2 Eq. in 2009, an 

increase of 20 percent from 1990. Transportation sources account for 1,816.9 Tg CO2 Eq. while non-transportation mobile 

sources account for 197.7 Tg CO2 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 CO2 emissions from 

transportation sources reported in the Carbon Dioxide Emissions from Fossil Fuel Combustion section, and CH4 emissions 

and N2O 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 Chapter 4; and estimates of CO2

emitted from non-transportation mobile sources reported in Table A- 107 above. 

Although all emissions reported here are based on estimates reported throughout this inventory, some additional 

calculations were performed in order to provide a detailed breakdown of emissions by mode and vehicle category. In the 

case of N2O and CH4, additional calculations were performed to develop emissions estimates by type of aircraft and type 

of heavy-duty vehicle (i.e., medium- and heavy-duty trucks or buses) to match the level of detail for CO2 emissions. N2O 

and CH4 estimates were developed for individual aircraft types by multiplying the emissions estimates for aircraft for each 

fuel type (jet fuel and aviation gasoline) by the portion of fuel used by each aircraft type (from FAA 2010, 2009, and 

2006). Similarly, N2O 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 2010). 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 CO2), Chapter 4, and Annex 3.8 (for HFCs), and earlier in this Annex (for CH4

and N2O). 

Transportation sources include on-road vehicles, aircraft, boats and ships, rail, and pipelines (note: pipelines are a 

transportation source but are stationary, not mobile sources). In addition, transportation-related greenhouse gas emissions 

also include HFC released from mobile air conditioners and refrigerated transportation, and the release of CO2 from 

lubricants (such as motor oil) used in transportation. Together, transportation sources were responsible for 1,816.9 Tg 

CO2 Eq. in 2009. 

On-road vehicles were responsible for about 77 percent of all transportation and non-transportation mobile GHG 

emissions in 2009. Although passenger cars make up the largest component of on-road vehicle greenhouse gas emissions, 

light-duty and medium- and heavy-duty trucks have been the primary sources of growth in on-road vehicle emissions. 

Between 1990 and 2009, greenhouse gas emissions from passenger cars decreased 5 percent, while emissions from lightduty trucks increased 64 percent, largely due to the increased use of sport-utility vehicles and other light-duty trucks. 

Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks increased 58 percent, reflecting the increased 

volume of total freight movement and an increasing share transported by trucks. 
A-135 

Greenhouse gas emissions from aircraft decreased 22 percent between 1990 and 2009. Emissions from military 

aircraft decreased 59 percent and commercial aircraft emissions rose 2 percent between 1990 and 2007 then dropped 19 

percent from 2007 to 2009. 

Non-transportation mobile sources, such as construction/mining equipment, agricultural equipment, and 

industrial/commercial equipment, emitted approximately 197.7 Tg CO2 Eq. in 2009. Together, these sources emitted more 

greenhouse gases than ships and boats, rail, and pipelines combined. Emissions from non-transportation mobile sources 

increased rapidly, growing approximately 54 percent between 1990 and 2009. CH4 and N2O emissions from these sources 

are included in the “Mobile Combustion” section and CO2 emissions are included in the relevant economic sectors. 

Contribution of Transportation and Mobile Sources to Greenhouse Gas Emissions, by Gas 

Table A- 110 presents estimates of greenhouse gas emissions from transportation and other mobile sources 

broken down by greenhouse gas. As this table shows, CO2 accounts for the vast majority of transportation greenhouse gas 

emissions (approximately 96 percent in 2009). Emissions of CO2 from transportation and mobile sources increased by 

300.2 Tg CO2 Eq. between 1990 and 2009. In contrast, the combined emissions of CH4 and N2O decreased by 22.7 Tg 

CO2 Eq. over the same period, due largely to the introduction of control technologies designed to reduce criteria pollutant 

emissions.44

 Meanwhile, HFC emissions from mobile air conditioners and refrigerated transport increased from virtually 

no emissions in 1990 to 60.2 Tg CO2 Eq. in 2009 as these chemicals were phased in as substitutes for ozone depleting 

substances. It should be noted, however, that the ozone depleting substances that HFCs replaced are also powerful 

greenhouse gases, but are not included in national greenhouse gas inventories due to their mandated phase out. 

Greenhouse Gas Emissions from Freight and Passenger Transportation 

Table A- 111 and Table A- 112present greenhouse gas estimates from transportation, broken down into the 

passenger and freight categories. Passenger modes include light-duty vehicles, buses, passenger rail, aircraft (general 

aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A- 111. 

Freight modes include medium- and heavy-duty trucks, freight rail, refrigerated transport, waterborne freight vessels, 

pipelines, and commercial aircraft and are illustrated in Table A- 112. Commercial aircraft do carry some freight, in 

addition to passengers, and for this Inventory, the emissions have been split between passenger and freight transportation. 

(In previous Inventories, all commercial aircraft emissions were considered passenger transportation.) The amount of 

commercial aircraft emissions to allocate to the passenger and freight categories was calculated using BTS data on freight 

shipped by commercial aircraft, and the total number of passengers enplaned. Each passenger was considered to weigh an 

average of 150 pounds, with a luggage weight of 50 pounds. The total freight weight and total passenger weight carried 

were used to determine percent shares which were used to split the total commercial aircraft emissions estimates. The 

remaining transportation and mobile emissions were from sources not considered to be either freight or passenger modes 

(e.g., construction/mining and agricultural equipment, lubricants). 

The estimates in these tables are derived from the estimates presented in Table A- 109. In addition, estimates of 

fuel consumption from DOE (1993 through 2010) were used to allocate rail emissions between passenger and freight 

categories. 

In 2009, passenger transportation modes emitted 1,322.1 Tg CO2 Eq., while freight transportation modes emitted 

475.3 Tg CO2 Eq. Since 1990, the rate of growth of greenhouse gas emissions from freight sources has been 7 percent 

higher than emissions from passenger sources, due largely to the rapid increase in emissions associated with medium- and 

heavy-duty trucks. 

Table A- 109: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (Tg CO2 Eq.) 

Mode / Vehicle 

Type / Fuel Type 1990 1995 2000 2005 2006 2007 2008 2009 

Percent 

Change 

1990-2009 

Transportation 

Totala

 1,548.3 1,698.5 1,935.8 2,022.2 1,999.0 2,008.9 1,895.4 1,816.9 17% 

On-Road Vehicles 1,235.2 1,371.3 1,575.1 1,682.9 1,679.7 1,682.1 1,603.8 1,557.4 26% 

Passenger Cars 657.4 646.0 695.3 709.5 682.9 672.0 632.5 627.4 -5% 

Gasoline 649.4 627.8 667.3 676.9 651.6 643.3 606.5 604.1 -7% 

Diesel 7.9 7.9 3.7 4.2 4.1 4.1 3.9 3.9 -51% 

AFVs + 0.1 + + + + + + 551% 

HFCs from + 10.1 24.3 28.4 27.1 24.6 22.1 19.3 NA 

 

44 The decline in CFC emissions is not captured in the official transportation estimates.
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Mobile AC 

Light-Duty 

Trucks 336.6 436.6 512.1 551.3 564.0 570.3 553.8 551.0 64% 

Gasoline 324.5 414.6 469.8 493.0 504.7 511.8 496.5 495.8 53% 

Diesel 11.5 14.9 20.1 25.9 26.8 27.3 26.9 26.7 133% 

AFVs 0.6 0.5 0.5 1.4 1.3 1.0 1.8 1.9 227% 

HFCs from 

Mobile AC + 6.5 21.7 31.0 31.2 30.1 28.6 26.6 NA 

Medium- and 

Heavy-Duty 

Trucks 231.1 277.8 354.6 408.4 418.6 425.2 403.1 365.6 58% 

Gasoline 39.5 36.8 37.0 35.8 36.3 36.8 34.4 31.1 -21% 

Diesel 190.7 238.6 309.9 361.0 370.4 376.4 356.3 322.0 69% 

AFVs 0.9 0.6 0.3 0.5 0.6 0.5 0.8 0.8 -6% 

HFCs from 

Refrigerated 

Transport + 1.7 7.4 11.1 11.4 11.5 11.6 11.6 NA 

Buses 8.4 9.2 11.2 12.0 12.3 12.5 12.2 11.2 34% 

Gasoline 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 -1% 

Diesel 8.0 8.7 10.2 10.6 10.8 10.8 10.3 9.3 17% 

AFVs + 0.1 0.5 0.9 0.8 1.0 1.1 1.1 40569% 

HFCs from 

Comfort 

Cooling + + 0.1 0.2 0.3 0.3 0.4 0.4 NA 

Motorcycles 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% 

Gasoline 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% 

Aircraft 181.2 175.4 204.4 198.7 173.6 172.7 158.7 142.1 -22% 

Commercial 

Aircraft 136.8 143.1 170.9 162.8 138.5 139.5 123.4 112.5 -18% 

Jet Fuel 136.8 143.1 170.9 162.8 138.5 139.5 123.4 112.5 -18% 

General Aviation 

Aircraft 9.6 8.2 12.1 17.5 18.5 16.9 18.8 15.3 59% 

Jet Fuel 6.4 5.4 9.6 15.0 16.2 14.6 16.8 13.4 109% 

Aviation Gasoline 3.2 2.8 2.6 2.5 2.4 2.2 2.0 1.9 -41% 

Military Aircraft 34.8 24.1 21.3 18.3 16.5 16.3 16.4 14.3 -59% 

Jet Fuel 34.8 24.1 21.3 18.3 16.5 16.3 16.4 14.3 -59% 

Ships and Boatsb 45.1 58.6 61.0 45.2 48.4 55.2 37.1 30.5 -32% 

Gasoline 12.6 14.1 10.0 14.2 14.1 14.0 13.6 13.5 7% 

Distillate Fuel 9.6 14.9 17.1 11.4 10.9 11.7 3.2 4.8 -50% 

Residual Fuel 22.9 29.5 33.8 19.6 23.4 29.5 20.2 12.2 -47% 

HFCs from 

Refrigerated 

Transport - + 0.1 + + + + + NA 

Rail 39.0 43.7 48.1 53.0 55.1 54.3 50.6 43.3 11% 

Distillate Fuel 35.8 40.0 42.5 46.0 48.3 47.0 43.6 36.5 2% 

Electricity 3.1 3.1 3.5 4.8 4.5 5.0 4.7 4.4 45% 

HFCs from 

Comfort Cooling + + + + + + + + NA 

HFCs from 

Refrigerated 

Transport + 0.5 2.0 2.1 2.2 2.2 2.2 2.2 NA 

Other Emissions 

from Rail 

Electricity Use 0.1 0.1 + 0.1 0.1 0.1 0.1 0.1 -9% 

Pipelinesc 36.0 38.2 35.2 32.3 32.3 34.3 35.7 35.2 -2% 

Natural Gas 36.0 38.2 35.2 32.3 32.3 34.3 35.7 35.2 -2% 

Other 

Transportation 11.8 11.3 12.1 10.2 9.9 10.2 9.5 8.5 -28% 

Lubricants 11.8 11.3 12.1 10.2 9.9 10.2 9.5 8.5 -28% 

NonTransportation 

Mobile Total 128.8 146.8 158.3 190.7 195.8 194.8 194.2 197.7 54% 

Agricultural 

Equipmentd 31.4 37.0 39.2 47.3 49.6 49.0 45.9 47.2 50% 
A-137 

Gasoline 7.3 8.3 5.8 9.6 11.0 9.6 5.7 6.1 -17% 

Diesel 24.1 28.7 33.4 37.7 38.6 39.4 40.3 41.1 71% 

Construction/ 

Mining 

Equipmente 42.4 49.4 55.8 66.5 67.9 68.4 69.9 71.2 68% 

Gasoline 4.4 4.0 3.1 6.2 6.2 5.1 5.2 5.0 14% 

Diesel 38.0 45.4 52.7 60.3 61.8 63.3 64.8 66.3 74% 

Other Equipmentf 55.0 60.4 63.4 76.9 78.3 77.4 78.4 79.3 44% 

Gasoline 40.3 42.6 42.5 52.7 53.4 51.8 52.2 52.4 30% 

Diesel 14.7 17.8 20.9 24.2 24.9 25.6 26.2 26.9 83% 

Transportation 

and NonTransportation 

Mobile Total 1,677.1 1,845.3 2,094.1 2,212.9 2,194.8 2,203.6 2,089.7 2,014.7 20% a

 Not including emissions from international bunker fuels. 

b

 Fluctuations in emission estimates reflect data collection problems. c

 Includes only CO2 from natural gas used to power pipelines; does not include emissions from electricity use or non-CO2 gases. d

 Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. e

 Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. f “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport 

equipment, commercial equipment, and industrial equipment, as well as fuel consumption from trucks that are used off-road for 

commercial/industrial purposes. 

+ Less than 0.05 Tg CO2 Eq. 

- Unreported or zero 

NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated. 

Table A- 110: Transportation and Mobile Source Emissions by Gas (Tg CO2 Eq.) 

Gas 1990 1995 2000 2005 2006 2007 2008 2009 

Percent 

Change 

1990-

2009 

CO2 1,628.4 1,767.9 1,981.9 2,100.4 2,086.5 2,102.2 1,996.5 1,928.6 18% 

N2O 43.9 54.0 53.2 36.9 33.6 30.3 26.1 23.9 -46% 

CH4 4.7 4.3 3.4 2.5 2.2 2.2 2.0 2.0 -58% 

HFC - 19.0 55.7 72.9 72.2 68.8 64.9 60.2 NA 

Total 1,677.0 1,845.2 2,094.1 2,212.8 2,194.7 2,203.5 2,089.6 2,014.6 20% 

- Unreported or zero 

NA = Not Applicable, as there were no HFC emissions allocated to the transport sector in 1990, and thus a growth rate cannot be calculated. 

Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2009 (Tg CO2 Eq.) 

Table A- 111: Greenhouse Gas Emissions from Passenger Transportation (Tg CO2 Eq.) 

Vehicle Type 1990 1995 2000 2005 2006 

 

2007 2008 2009 

Percent 

Change 

1990-

2009 

On-Road 

Vehicles 1,004.1 1,093.5 1,220.5 1,274.5 1,261.1 1,256.9 1,200.8 1,191.7 19% 

Passenger Cars 657.4 646.0 695.3 709.5 682.9 672.0 632.5 627.4 -5% 

Light-Duty 

Trucks 336.6 

 

436.6 512.1 551.3 564.0 

 

570.3 

 

553.8 551.0 64% 

Buses 8.4 9.2 11.2 12.0 12.3 12.5 12.2 11.2 34% 

Motorcycles 1.8 1.8 1.9 1.7 1.9 2.1 2.2 2.2 24% 

Aircraft 122.7 126.5 153.4 154.4 135.3 136.0 124.3 111.4 -9% 

General Aviation 9.6 8.2 12.1 17.5 18.5 16.9 18.8 15.3 59% 

Commercial 

Aircraft 113.1 118.3 141.3 136.8 116.7 119.2 105.5 96.1 -15% 

Recreational 

Boats 14.5 16.4 12.7 17.4 17.3 17.3 14.5 16.9 17% 

Passenger Rail 4.4 4.5 5.2 6.2 6.0 6.5 6.2 6.0 37% 
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Total 1,145.7 1,241.0 1,391.8 1,452.5 1,419.7 1,416.8 1,345.8 1,326.1 16% 

Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included 

in these estimates. 

Table A- 112: Greenhouse Gas Emissions from Domestic Freight Transportation (Tg CO2 Eq.) 

By Mode 1990 1995 2000 2005 2006 2007 2008 2009 

Percent 

Change 

1990-

2009 

Trucking 231.1 277.8 354.6 408.4 418.6 425.2 403.1 365.6 58% 

Freight Rail 34.5 39.1 42.8 46.7 49.0 47.8 44.5 37.2 8% 

Ships and Other Boats 30.6 42.2 48.3 27.9 31.1 37.9 22.6 13.5 -56% 

Pipelines 36.0 38.2 35.2 32.2 32.3 34.3 35.7 35.2 -2% 

Commercial Aircraft 23.7 24.8 29.6 26.0 21.8 20.3 18.0 16.4 -31% 

Total 356.0 422.1 510.5 541.1 552.8 565.5 523.7 467.9 31% 

Note: Data from DOE (1993 through 2010) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included 

in these estimates. 
A-139 

3.3. Methodology for Estimating CH4 Emissions from Coal Mining 

The methodology for estimating CH4 emissions from coal mining consists of two distinct steps. The first step 

addresses emissions from underground mines. For these mines, emissions are estimated on a mine-by-mine basis and then 

are summed to determine total emissions. The second step of the analysis involves estimating CH4 emissions for surface 

mines and post-mining activities. In contrast to the methodology for underground mines, which uses mine-specific data, 

the surface mine and post-mining activities analysis consists of multiplying basin-specific coal production by basinspecific emission factors. 

Step 1: Estimate CH4 Liberated and CH4 Emitted from Underground Mines 

Underground mines generate CH4 from ventilation systems and from degasification systems. Some mines 

recover and use CH4 generated from degasification systems, thereby reducing emissions to the atmosphere. Total CH4

emitted from underground mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from 

degasification systems, minus CH4 recovered and used. 

Step 1.1: Estimate CH4 Liberated from Ventilation Systems 

All coal mines with detectable CH4 emissions45 use ventilation systems to ensure that CH4 levels remain within 

safe concentrations. Many coal mines do not have detectable levels of CH4, while others emit several million cubic feet 

per day (MMCFD) from their ventilation systems. On a quarterly basis, the U.S. Mine Safety and Health Administration 

(MSHA) measures CH4 emissions levels at underground mines. MSHA maintains a database of measurement data from 

all underground mines with detectable levels of CH4 in their ventilation air. Based on the four quarterly measurements, 

MSHA estimates average daily CH4 liberated at each of the underground mines with detectable emissions. 

For the years 1990 through 1996, 1998 through 2006, 2008 and 2009, MSHA emissions data were obtained for a 

large but incomplete subset of all mines with detectable emissions. This subset includes mines emitting at least 0.1 

MMCFD for some years and at least 0.5 MMCFD for other years, as shown in Table A- 113. Well over 90 percent of all 

ventilation emissions were concentrated in these subsets. For 1997 and 2007, the complete MSHA databases for all 586 

mines (in 1997) and 730 mines (in 2007) with detectable CH4 emissions were obtained. These mines were assumed to 

account for 100 percent of CH4 liberated from underground mines. Using the complete database from 1997, the 

proportion of total emissions accounted for by mines emitting less than 0.1 MMCFD or 0.5 MMCFD was estimated (see 

Table A- 113). The proportion was then applied to the years 1990 through 2006 to account for the less than 5 percent of 

ventilation emissions coming from mines without MSHA data. 

For 1990 through 1999, average daily CH4 emissions were multiplied by the number of days in the year (i.e., coal 

mine assumed in operation for all four quarters) to determine the annual emissions for each mine. For 2000 through 2009, 

MSHA provided quarterly emissions. The average daily CH4 emissions were multiplied by the number of days 

corresponding to the number of quarters the mine vent was operating. For example, if the mine vent was operational in 

one out of the four quarters, the average daily CH4 emissions were multiplied by 92 days. Total ventilation emissions for a 

particular year were estimated by summing emissions from individual mines. 

 

45 MSHA records coal mine methane readings with concentrations of greater than 50 ppm (parts per million) methane. Readings below 

this threshold are considered non-detectable.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 113: Mine-Specific Data Used to Estimate Ventilation Emissions 

Year Individual Mine Data Used 

1990 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

1991 1990 Emissions Factors Used Instead of Mine-Specific Data 

1992 1990 Emissions Factors Used Instead of Mine-Specific Data 

1993 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

1994 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

1995 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)* 

1996 All Mines Emitting at Least 0.5 MMCFD (Assumed to Account for 94.1% of Total)* 

1997 All Mines with Detectable Emissions (Assumed to Account for 100% of Total) 

1998 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

1999 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2000 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2001 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2002 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2003 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2004 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2005 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

2006 

2007 

2008 

2009 

All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 97.8% of Total)* 

All Mines with Detectable Emissions (Assumed to Account for 100% of Total) 

All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)** 

All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)** 

* Factor derived from a complete set of individual mine data collected for 1997. 

** Factor derived from a complete set of individual mine data collected for 2007. 

Step 1.2: Estimate CH4 Liberated from Degasification Systems 

Coal mines use several different types of degasification systems to remove CH4, including vertical wells and 

horizontal boreholes to recover CH4 prior to mining of the coal seam. Gob wells and cross-measure boreholes recover 

CH4 from the overburden (i.e., GOB area) after mining of the seam (primarily in longwall mines). 

MSHA collects information about the presence and type of degasification systems in some mines, but does not 

collect quantitative data on the amount of CH4 liberated. Thus, the methodology estimated degasification emissions on a 

mine-by-mine basis based on other sources of available data. Many of the coal mines employing degasification systems 

have provided EPA with information regarding CH4 liberated from their degasification systems. For these mines, this 

reported information was used as the estimate. In other cases in which mines sell CH4 recovered from degasification 

systems to a pipeline, gas sales were used to estimate CH4 liberated from degasification systems (see Step 1.3). Finally, 

for those mines that do not sell CH4 to a pipeline and have not provided information to EPA, CH4 liberated from 

degasification systems was estimated based on the type of system employed. For example, for coal mines employing gob 

wells and horizontal boreholes, the methodology assumes that degasification emissions account for 40 percent of total CH4

liberated from the mine. 

Step 1.3: Estimate CH4 Recovered from Degasification Systems and Utilized (Emissions Avoided) 

In 2009, fourteen active coal mines had CH4 recovery and use projects. Thirteen mines sold the recovered CH4

to a pipeline, one used the CH4 on site to heat mine ventilation air, and one of the coal mines used CHB

4B

 to generate 

electricity. One of the mines that sold gas to a pipeline also used CHB

4 to fuel a thermal coal dryer. In order to calculate 

emissions avoided from pipeline sales, information was needed regarding the amount of gas recovered and the number of 

years in advance of mining that wells were drilled. Several state agencies provided gas sales data, which were used to 

estimate emissions avoided for these projects. Additionally, coal mine operators provided information on gas sales and/or 

the number of years in advance of mining. Emissions avoided were attributed to the year in which the coal seam was 

mined. For example, if a coal mine recovered and sold CH4 using a vertical well drilled five years in advance of mining, 

the emissions avoided associated with those gas sales (cumulative production) were attributed to the well at the time it was 

mined through (e.g., five years of gas production). Where individual well data is not available, estimated percentages of 

the operator’s annual gas sales within the field around the coal mine are attributed to emissions avoidance. For some 

mines, individual well data were used to assign gas sales to the appropriate emissions avoided year. In most cases, coal 

mine operators provided this information, which was then used to estimate emissions avoided for a particular year. 

Additionally, several state agencies provided production data for individual wells. 
A-141 

Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities 

Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining 

activities. For surface mines and post-mining activities, basin-specific coal production was multiplied by a basin-specific 

emission factor to determine CH4 emissions. 

Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data 

The first step in estimating CH4 emissions from surface mining and post-mining activities was to define the 

geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was 

conducted by coal basin as defined in Table A- 114, which presents coal basin definitions by basin and by state. 

The Energy Information Administration’s (EIA) Annual Coal Report includes state- and county-specific 

underground and surface coal production by year. To calculate production by basin, the state level data were grouped into 

coal basins using the basin definitions listed in Table A- 114. For two states⎯West Virginia and Kentucky⎯county-level 

production data was used for the basin assignments because coal production occurred from geologically distinct coal 

basins within these states. Table A- 115 presents the coal production data aggregated by basin. 

Step 2.2: Estimate Emissions Factors for Each Emissions Type 

Emission factors for surface mined coal were developed from the in situ CH4 content of the surface coal in each 

basin. Based on an analysis presented in EPA (1993), surface mining emission factors were estimated to be from 1 to 3 

times the average in situ CH4 content in the basin. For this analysis, the surface mining emission factor was determined to 

be twice the in situ CH4 content in the basin. Furthermore, the post-mining emission factors used were estimated to be 25 

to 40 percent of the average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was 

determined to be 32.5 percent of the in situ CH4 content in the basin. Table A- 116 presents the average in situ content for 

each basin, along with the resulting emission factor estimates. 

Step 2.3: Estimate CH4 Emitted 

The total amount of CH4 emitted was calculated by multiplying the coal production in each basin by the 

appropriate emission factors. 

Total annual CH4 emissions are equal to the sum of underground mine emissions plus surface mine emissions 

plus post-mining emissions. Table A- 117 and Table A- 118 present estimates of CH4 liberated, used, and emitted for 

1990 through 2008. Table A- 119 provides emissions by state. 

Table A- 114: Coal Basin Definitions by Basin and by State 

Basin States 

Northern Appalachian Basin Maryland, Ohio, Pennsylvania, West Virginia North 

Central Appalachian Basin Kentucky East, Tennessee, Virginia, West Virginia South 

Warrior Basin Alabama, Mississippi 

Illinois Basin Illinois, Indiana, Kentucky West 

South West and Rockies Basin Arizona, California, Colorado, New Mexico, Utah 

North Great Plains Basin Montana, North Dakota, Wyoming 

West Interior Basin Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas 

Northwest Basin Alaska, Washington 

State Basin 

Alabama Warrior Basin 

Alaska Northwest Basin 

Arizona South West and Rockies Basin 

Arkansas West Interior Basin 

California South West and Rockies Basin 

Colorado South West and Rockies Basin 

Illinois Illinois Basin 

Indiana Illinois Basin 

Iowa West Interior Basin 

Kansas West Interior Basin 

Kentucky East Central Appalachian Basin 

Kentucky West Illinois Basin 

Louisiana West Interior Basin 

Maryland Northern Appalachian Basin 
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

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 
Table A- 115: Annual Coal Production (Thousand Short Tons) 

Basin 1990 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Underground Coal 

Production 423,556 399,102 396,249 409,850 420,657 417,729 391,791 372,766 380,627 357,384 352,785 367,558 368,611 

N. Appalachia 103,865 100,122 98,103 106,729 112,135 116,718 107,575 105,374 107,025 98,643 98,369 106,915 111,151 Cent. Appalachia 198,412 170,893 166,495 171,845 177,720 171,279 157,058 150,584 152,457 137,224 130,724 128,559 123,083 

Warrior 17,531 14,471 17,605 18,217 18,505 17,316 14,799 15,895 15,172 14,916 15,375 16,114 13,295 

Illinois 69,167 69,050 69,009 67,046 64,728 64,463 63,529 53,720 54,364 54,016 51,780 56,320 59,180 S. West/Rockies 32,754 41,681 42,994 43,088 44,503 45,983 46,957 45,742 51,193 52,121 56,111 59,039 60,865 

N. Great Plains 1,722 2,738 2,018 2,788 2,854 1,723 1,673 1,210 - - 32 201 572 

West Interior 105 147 25 137 212 247 200 241 415.623 464 394 410 465 

Northwest - ---- --- --- - - 

Surface Coal 

Production 602,753 634,401 636,726 654,007 669,271 699,806 708,639 700,608 745,306 735,912 717,869 743,553 762,191 

N. Appalachia 60,761 44,960 39,372 39,788 40,179 41,043 33,928 34,908 35,334 30,088 27,370 28,174 28,873 Cent. Appalachia 94,343 106,129 106,250 108,869 113,275 108,345 107,507 110,479 116,983 111,340 99,419 103,968 112,222 

Warrior 11,413 8,795 7,036 6,420 5,963 5,697 4,723 4,252 4,796 6,320 8,437 9,742 11,599 

Illinois 72,000 51,868 40,376 44,754 46,862 45,715 40,474 33,631 40,894 39,380 36,675 34,016 33,702 

S. West/Rockies 43,863 49,119 46,643 43,814 48,374 49,635 50,349 49,587 52,180 50,006 41,237 42,558 42,756 

N. Great Plains 249,356 308,279 331,367 343,404 349,612 385,438 407,683 407,670 438,367 441,346 444,007 466,224 474,056 

West Interior 64,310 58,791 59,116 60,912 59,061 57,951 58,309 54,170 50,613 50,459 53,411 51,706 52,263 

Northwest 6,707 6,460 6,566 6,046 5,945 5,982 5,666 5,911 6,138 6,973 7,313 7,165 6,720 

Total Coal 

Production 931,068 937,594 937,115 965,131 988,783 1,013,828 998,310 973,765 1,021,446 991,838 971,297 1,008,000 1,025,864 

N. Appalachia 164,626 145,082 137,475 146,517 152,314 157,761 141,503 140,282 142,360 128,731 125,739 135,089 140,024 Cent. Appalachia 292,755 277,022 272,745 280,714 290,995 279,624 264,565 261,063 269,440 248,564 230,143 232,527 235,305 

Warrior 28,944 23,266 24,641 24,637 24,468 23,013 19,522 20,147 19,967 21,236 23,812 25,856 24,894 

Illinois 141,167 120,918 109,385 111,800 111,590 110,178 104,003 87,351 95,258 93,396 88,455 90,336 92,882 

S. West/Rockies 76,617 90,800 89,637 86,902 92,877 95,618 97,306 95,329 103,373 102,127 97,348 101,597 103,621 

N. Great Plains 251,078 311,017 333,385 346,192 352,466 387,161 409,356 408,880 438,367 441,346 444,039 466,425 474,628 

West Interior 64,415 58,938 59,141 61,049 59,273 58,198 58,509 54,411 51,028 50,923 53,805 52,116 52,728 

Northwest 6,707 6,460 6,566 6,046 5,945 5,982 5,666 5,911 6,138 6,973 7,313 7,165 6,720 

Source for 1990-2009 data: EIA (1990 through 2009), Annual Coal Report. U.S. Department of Energy, Washington, DC, Table 1. Note: Totals may not sum due to independent rounding. 
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 116: Coal Surface and Post-Mining CH4 Emission Factors (ft3

 per Short Ton) 

Basin 

Surface Average

in situ Content

Underground Average

In situ Content

Surface Mine 

Factors 

Post-Mining

Surface Factors

Post Mining

Underground

Northern Appalachia 59.5 138.4 119.0 19.3 45.0

Central Appalachia (WV) 24.9 136.8 49.8 8.1 44.5

Central Appalachia (VA) 24.9 399.1 49.8 8.1 129.7

Central Appalachia (E KY) 24.9 61.4 49.8 8.1 20.0

Warrior 30.7 266.7 61.4 10.0 86.7

Illinois 34.3 64.3 68.6 11.1 20.9

Rockies (Piceance Basin) 33.1 196.4 66.2 10.8 63.8

Rockies (Uinta Basin) 16.0 99.4 32.0 5.2 32.3

Rockies (San Juan Basin) 7.3 104.8 14.6 2.4 34.1

Rockies (Green River Basin) 33.1 247.2 66.2 10.8 80.3

Rockies (Raton Basin) 33.1 127.9 66.2 10.8 41.6

N. Great Plains (WY, MT) 20.0 15.8 40.0 6.5 5.1

N. Great Plains (ND) 5.6 15.8 11.2 1.8 5.1

West Interior (Forest City, Cherokee 

Basins) 

34.3 64.3 68.6 11.1 20.9

West Interior (Arkoma Basin) 74.5 331.2 149.0 24.2 107.6

West Interior (Gulf Coast Basin) 11.0 127.9 22.0 3.6 41.6

Northwest (AK) 16.0 160.0 32.0 1.8 52.0

Northwest (WA) 16.0 47.3 32.0 5.2 15.4

Sources: 1986 USBM Circular 9067, Results of the Direct Method Determination of the Gas Contents of U.S. Coal Basins, 1983 U.S. DOE 

Report (DOE/METC/83-76), Methane Recovery from Coalbeds: A Potential Energy Source, 1986-88 Gas Research Institute Topical Reports, A 

Geologic Assessment of Natural Gas from Coal Seams; Surface Mines Emissions Assessment, U.S. EPA Draft Report, November 2005. 
Table A- 117: Underground Coal Mining CH4 Emissions (Billion Cubic Feet) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 Ventilation Output 112 NA NA 95 96 100 90 96 94 92 87 84 79 76 83 Adjustment Factor for Mine 

Data* 97.8% NA NA 97.8% 97.8% 91.4% 91.4% 100.0% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 97.8% 97

Adjusted Ventilation Output 114 NA NA 97 98 109 99 96 96 94 89 86 80 77 84 Degasification System 

Liberated 54 NA NA 45 46 36 52 43 49 40 45 49 51 50 45 

Total Underground 

Liberated 168 164 162 142 144 146 150 139 146 134 135 135 131 127 130 

Recovered & Used (14) (15) (17) (23) (27) (30) (37) (28) (35) (31) (37) (41) (43) (38) (40) Total 154 150 145 120 117 116 113 111 110 103 98 95 88 89 90 

* Refer to Table A- 113. Note: Totals may not sum due to independent rounding. Table A- 118: Total Coal Mining CH4 Emissions (Billion Cubic Feet) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2Underground Mining 154 150 145 120 117 116 113 111 110 103 98 95 88 89 Surface Mining 30 28 28 28 29 28 29 30 31 31 30 33 32 31 Post-Mining (Underground) 19 18 18 16 17 17 18 18 18 17 17 17 16 16 Post-Mining (Surface) 5 5 5 4 5 5 5 5 5 5 5 5 5 5 Total 208 201 195 167 168 166 165 164 165 156 149 149 140 141 

Note: Totals may not sum due to independent rounding. Table A- 119: Total Coal Mining CH4 Emissions by State (Million Cubic Feet) 

State 1990 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2Alabama 32,272 29,630 33,735 29,556 26,426 26,440 25,702 23,342 21,896 18,686 19,288 18,246 15,Alaska 63 58 63 55 54 50 58 61 56 43 40 56 

Arizona 192 222 203 177 199 192 200 223 228 217 205 216 Arkansas 7 8 5 4 3 4 4 2 2 2 1 1 California 1 - - - - + + + + + + + Colorado 10,325 9,192 7,582 5,972 9,189 9,181 9,390 10,808 11,117 12,082 13,216 12,582 13,

Illinois 10,502 10,585 11,563 10,876 8,534 7,847 7,810 8,542 7,270 5,972 4,744 5,798 6,

Indiana 2,795 2,495 2,025 2,192 2,742 2,878 2,650 2,231 3,373 3,496 3,821 3,531 3,

Iowa 30 4 - - - + + + + + + + 

Kansas 57 23 23 19 29 27 33 16 14 16 12 6 Kentucky 10,956 11,259 10,269 8,987 10,451 10,005 9,561 9,105 9,363 8,464 8,028 7,926 7,

Louisiana 81 89 95 82 91 82 76 94 95 97 103 97 

Maryland 519 237 237 259 267 251 225 331 340 401 391 411 Mississippi - - - - - 0 1 57 43 165 264 256 Missouri 211 67 44 57 32 30 31 35 29 20 43 46 Montana 1,749 1,936 1,834 1,756 1,906 1,992 1,911 1,783 1,820 1,738 1,719 1,853 1,

Mew Mexico 451 679 586 408 459 489 497 464 630 1,280 1,864 2,052 3,North Dakota 380 420 392 389 385 389 405 407 397 401 401 390 

Ohio 5,065 4,583 4,189 4,068 4,349 4,350 3,914 3,519 3,619 2,831 2,649 3,183 3,

Oklahoma 285 359 323 286 385 395 469 454 620 660 620 849 
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Pennsylvania 22,735 24,024 25,611 26,440 30,026 30,888 24,867 24,830 22,252 19,668 20,281 20,020 18,

Tennessee 296 101 112 143 148 116 119 99 142 142 124 136 

Texas 1,426 1,339 1,347 1,411 1,364 1,345 1,357 1,240 1,152 1,157 1,215 1,173 1,

Utah 3,587 2,626 2,570 2,810 3,566 3,859 3,633 2,816 2,080 2,709 3,408 5,253 4,

Virginia 46,137 26,742 20,059 19,771 16,851 13,978 13,321 12,065 11,506 11,227 11,906 11,389 8,

Washington 186 182 181 170 167 173 153 159 172 217 232 210 

West Virginia 49,039 30,664 30,552 36,384 33,554 35,566 33,599 30,563 33,985 31,405 28,474 29,465 30,

Wyoming 8,496 10,912 12,185 12,838 12,994 14,549 15,607 15,725 17,147 17,352 17,497 18,435 18,Total 207,844 168,433 165,785 165,109 164,171 165,075 155,568 149,371 149,348 140,449 140,544 143,581 140,

- Zero Cubic Feet + Does not exceed 0.5 Million Cubic Feet Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining activities. The following states have n

mining and thus report no emissions as a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, Minnesota, NeHampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South Carolina, South Dakota, Vermont, and Wisconsin. 
A-147 

3.4. Methodology for Estimating CH4 and CO2 Emissions from Natural Gas 

Systems 

The following steps were used to estimate CH4 and non-energy CO2 emissions from natural gas systems. 

Step 1: Calculate Emission Estimates for Base Year 1992 Using EPA Adjusted GRI/EPA Study 

The first step in estimating CH4 and non-energy related (i.e., fugitive, vented and flared) CO2 emissions from 

natural gas systems was to develop a detailed base year estimate of emissions. The study by EPA/GRI (1996) divides the 

industry into four stages to construct a detailed emission inventory for the year 1992. These stages include: field 

production, processing, transmission and storage (i.e., both underground and liquefied gas storage), and distribution. This 

study produced emission factors and activity data for over 100 different emission sources within the natural gas system. 

Emissions for 1992 were estimated by multiplying activity levels by emission factors for each system component and then 

summing by stage. 

Since publication activity data for some of the components in the system have been updated based on publicly 

available data. For other sources where annual activity data are not available, a set of industry activity factor drivers was 

developed that can be used to update activity data. Table A-120 through Table A-123 display the 2009 activity levels, 

CH4 emission factors, and CH4 emissions for each stage. These data are shown to illustrate the kind of data used to 

calculate CH4 and non-energy CO2 emissions from all stages. Many emission factors determined by EPA/GRI (1996) 

were assumed to be representative of emissions from each source type over the period 1990 through 2009. 

However, several emission factors have been updated since publication of the EPA/GRI 1996 study. Notably, 

emission factors for gas well cleanups (EPA 2006a, HPDI 2009), condensate storage tanks (EPA 1999, HPDI 2009, TERC 

2009), and centrifugal compressors (EPA 2006b, WGC 2009) have been revised. Emissions for gas well completions and 

workovers (re-completions) with hydraulic fracturing (i.e. unconventional) (EPA 2004, 2007), which were not included in 

the EPA/GRI study, have also been added. The EPA/GRI study and Inventory did, however, include an estimate for well 

completions without hydraulic fracturing under the source category Completion Flaring.. The revised 2009 emission factor 

for centrifugal compressors will be reevaluated in the next Inventory cycle. 

For most sources, the CH4 emission factors were adjusted for CO2 content when estimating fugitive and vented 

non-energy CO2 emissions. In the case of non-energy CO2 emissions from flared sources, acid gas removal units and 

condensate tanks, specific industry data related to those sources was used to derive their respective emission factors. 

The activity levels and CH4 emission factors in Table A-120 are arranged into regions designated by the National 

Energy Modeling System (NEMS) for Oil and Gas Supply. NEMS for Oil and Gas Supply splits the continental United 

States into 6 regions: Northeast, Midcontinent, Rocky Mountain, South West, West Coast and Gulf Coast. GRI, however, 

does not evaluate activity data for each of these regions separately. The GRI national AF estimates were allocated to the 

NEMS oil and gas supply module regions using the NEMS regional gas well counts to national well count ratios. 

Step 2: Collect Aggregate Statistics on Main Driver Variables 

As detailed data on each of the over 100 sources were not available for the period 1990 through 2009, activity 

levels were estimated using aggregate statistics on key drivers, including: number of producing wells (EIA 2010a-b, New 

Mexico 2010a-b, Texas 2010a-b), number of gas plants (AGA 1991-1997; OGJ 1998-2010), number of shallow and deep 

offshore platforms (BOEMRE 2010a-d), miles of transmission pipeline (OPS 2010a), miles of distribution pipeline (OPS 

2010b), miles of distribution services (OPS 2010b), and energy consumption (EIA 2010c). Table A-124 provides the 

activity levels of some of the key drivers in the natural gas analysis. 

Step 3: Estimate CH4 Emissions for Each Year and Stage 

Emissions from each stage of the natural gas industry were estimated by multiplying the activity factors by the 

appropriate emission factors, summing all sources for each stage and then accounting for CH4 reductions reported to the 

Natural Gas STAR Program and CH4 reductions resulting from regulations such as National Emission Standards for 

Hazardous Air Pollutants (NESHAP) regulations. 

Industry partners report CH4 emission reductions by project to the Natural Gas STAR Program. The reductions 

are estimates using actual measurement data or equipment-specific emission factors. The reductions undergo quality 

assurance and quality control checks to identify errors, inconsistencies, or irregular data before being incorporated into the 

Inventory. These quality assurance and quality control checks include matching Natural Gas STAR reported reductions to 
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

specific inventory sources to make sure that a reported reduction for one source is not greater than the emission estimate 

for that source. Total emissions are estimated by adding the emission estimates from each stage. The base year of the 

inventory is 1992; therefore any reductions reported for 1992 or earlier are considered to be already included in the baseyear emission factors and are not subtracted from the inventory estimate. If the reported reduction occurred between 1990 

and 1992, then the reduction is added back into the estimate for the appropriate year(s). The reductions are also adjusted 

to remove the sunsetting time period which is relevant to Natural Gas STAR accounting but not the Inventory. For 

example, replacing a gas-assisted pump with an electric pump permanently reduces the vented methane emissions from 

that source, even after the Natural Gas STAR sunsetting period. CH4 emission reductions from the Natural Gas STAR 

Program beyond the efforts reflected in the 1992 base year are summarized in Table A-125. 

The 1990 Clean Air Act (CAA) sets the limits on the amount of hazardous air pollutants (HAPs) that can be 

emitted in the United States. The NESHAP regulations set the standards to limit emissions of HAPs. The emission sources 

are required to use the Maximum Achievable Control Technology, giving the operators flexibility to choose the type of 

control measure(s) to implement. In regards to the oil and natural gas industry, the NESHAP regulation addresses HAPs 

from the oil and natural gas production sectors and the natural gas transmission and storage sectors of the industry. 

Though the regulation deals specifically with HAPs reductions, methane emissions are also reduced. 

 The NESHAP regulation requires that glycol dehydration unit vents and storage tanks that have HAP emissions 

and exceed a gas throughput and liquids throughput value, respectively, be connected to a closed loop emission control 

system that reduces emissions by 95 percent. Also, gas processing plants exceeding the threshold natural gas throughput 

limit are required to routinely implement Leak Detection and Survey (LDAR) programs. The emissions reductions 

achieved as a result of NESHAP regulations were estimated using data provided in the Federal Register Background 

Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the 

enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control 

measures already in place that would be impacted by the regulation. The reductions are representative of the control 

measures in both the oil and natural gas industry. CH4 emission reductions from regulations, such as NESHAP, are 

summarized in Table A-126. 

Additionally, some states, such as Wyoming, may require that natural gas produced during well completions not 

be vented. In these regions emissions from natural gas well completions and re-completions are either recovered for sales 

or must be flared. The volume of gas recovered by bringing equipment to the wellsite for the treatment and injection of 

the produced completion gas into the sales pipeline is reported by Natural Gas STAR. The remaining volume of 

completion gas from states that do not allowing the venting of this gas is flared. 

Step 4: Estimate CO2 Emissions for Each Year and Stage 

The same procedure for estimating CH4 emissions holds true for estimating non-energy related CO2 emissions, 

except the emission estimates are not corrected for reductions due to the Natural Gas STAR program or other regulations. 

Produced natural gas is composed of primarily CH4, but as shown in Table A-131, the natural gas contains, in 

some cases, as much as 8 percent CO2. The same vented and fugitive emissions of natural gas that led to CH4 emissions 

also contain a certain volume of CO2. Accordingly, the CO2 emissions for each sector can be estimated using the same 

methane activity and emissions factors for these vented and fugitive sources. The primary difference is that EPA/GRI 

emission factors are adjusted for CO2 content in each sector. 

 Using the default CH4 content of produced natural gas from EPA/GRI (1996) of 78.8 percent, the corresponding 

amount of CO2 emissions from the production sector can be estimated. Each sector of the natural gas system has varying 

CO2 contents, similar to the way the CH4 content varies among the sectors. Table A-131 shows the CO2 content for the 

different well types in the production sector of the natural gas system. In the estimation of CO2 emissions from the 

production sector, the production sector CH4 emission factors were used as a basis to estimate CO2 emissions; however, 

they were converted to CO2 emission factors by multiplication by a conversion factor. This conversion factor is the ratio 

of CO2 content in the gas stream (in this case the production concentrations provided in Table A-131) to the corresponding 

CH4 content of the same gas stream. The three exceptions to this methodology are CO2 emissions from flares, acid gas 

removal units, and condensate tanks. In the case of flare emissions, a direct CO2 emission factor from EIA (1996) was 

used. This emission factor was applied to the portion of offshore gas that is not vented and all of the gas reported as 

vented and flared onshore by EIA. The amount of CO2 emissions from an acid gas unit in a processing plant is equal to 

the difference in CO2 concentrations between produced natural gas and pipeline quality gas applied to the throughput of 

the plant. This methodology was applied to the national gas throughput using national average CO2 concentrations in 

produced gas (3.45 percent) and transmission quality gas (1 percent). For condensate tanks, a series of E&P Tank (EPA 
A-149 

1999) simulations provide the total CO2 vented per barrel of condensate throughput from fixed roof tank flash gas for 

condensate gravities of API 45 degree and higher. The ratios of emissions to throughput were used to estimate the CO2

emission factor for condensate passing through fixed roof tanks. The detailed source emission estimates for CH4 and CO2

from the production sector are presented in Table A-127 and 

Table A-132, respectively. 

In the processing sector, the CO2 content of the natural gas remains the same as the CO2 content in the 

production sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only 

minimally treated after being produced and then transported to natural gas processing plants via gathering pipelines. The 

CO2 content in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline 

quality gas. The EPA/GRI study estimates the average CH4 content of natural gas in the processing sector to be 87 percent 

CH4. Consequently, the processing sector CH4 emission factors were proportioned to reflect the CO2 content of either 

produced natural gas or pipeline quality gas using the same methodology as the production sector. The detailed source 

emission estimates for CH4 and CO2 from the processing sector are presented in Table A-128 and Table A-133, 

respectively. 

For the transmission sector, CO2 content in natural gas transmission pipelines was estimated for the top twenty 

transmission pipeline companies in the United States (separate analyses identified the top twenty companies based on gas 

throughput and total pipeline miles). The weighted average CO2 content in the transmission pipeline quality gas in both 

cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the CO2

emissions for the transmission sector the CH4 emission factors were proportioned from the 93.4 percent CH4 reported in 

EPA/GRI (1996) to reflect the 1 percent CO2 content found in transmission quality natural gas. The detailed source 

emissions estimates for CH4 and CO2 for the transmission sector are presented in Table A-129 and Table A-134, 

respectively. 

The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the 

transmission sector. The CH4 content (93.4 percent) and CO2 content (1 percent) are identical due to the absence of any 

further treatment between sector boundaries. Thus, the CH4 emissions factors were converted to CO2 emission factors 

using the same methodology as discussed for the transmission sector. The detailed source emission estimates for CH4 and 

CO2 for the distribution sector are presented in Table A-130 and Table A-135, respectively. 

 Because Partners report only CH4 emission reductions to the Natural Gas STAR Program, there was no need to 

adjust for the Natural Gas STAR program in the CO2 emissions estimates for any of the sectors in the natural gas system. 

The impact of regulations, such as NESHAP, on CO2 emission reductions are not currently addressed in the CO2 emission 

estimates. 

Table A-120: 2009 Data and CH4 Emissions (Mg) for the Natural Gas Production Stage 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions (Mg)

 

North East 

Gas Wells 

NE - Associated Gas Wells1,2 46,914 wells NA NA 

NE - Non-associated Gas Wells (less 

Unconventional) 183,834 wells 7.59 scfd/well 9,804.22

NE - Unconventional Gas Wells3 0 wells NE scfd/well 0

Field Separation Equipment 

Heaters 368 heaters 15.13 scfd/heater 39.10

Separators 130,522 separators 0.96 scfd/sep 877.52

Dehydrators 1,213 dehydrators 23.15 scfd/dehy 197.33

Meters/Piping 9,230 meters 9.59 scfd/meter 622.25

Gathering Compressors 

Small Reciprocating Compressors 184 compressors 284.95 scfd/comp 368.26

Large Reciprocating Compressors 24 compressors 16,182 scfd/comp 2,730.18

Large Reciprocating Stations 3 stations 8,776.43 scfd/station 185.09

Pipeline Leaks 87,900 miles 56.57 scfd/mile 34,954.19

Drilling and Well Completion 

Completion Flaring 266 completions/yr 780 scf/comp 4.00

Unconventional Gas Well Completions 0 completions/yr 7,694,435 scf/comp 0

Well Drilling 8,170 Wells 2,706 scf/well 425.89

Normal Operations 

Pneumatic Device Vents 89,343 controllers 367 scfd/device 230,608.23

Chemical Injection Pumps 919 active pumps 264 scfd/pump 1,705.46
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions (Mg)

Kimray Pumps 354,971 MMscf/yr 1,056 scf/MMscf 7,217.80

Dehydrator Vents 398,396 MMscf/yr 293.3 scf/MMscf 2,250.58

Condensate Tank Vents 

Condensate Tanks without Control Devices 0 MMbbl/yr 21.87 scf/bbl 0

Condensate Tanks with Control Devices 0 MMbbl/yr 4.37 scf/bbl 0

Compressor Exhaust Vented 

Gas Engines 0 MMHPhr 0.26 scf/HPhr 0

Well Workovers 

Conventional Gas Wells 7,997 workovers/yr 2,612 scf/w.o. 402.26

Unconventional Gas Wells 0 workovers/yr 7,694,435 scf/w.o. 0

Well Clean Ups (LP Gas Wells) 75,923 LP Gas Wells 1,361,786 scfy/LP well 1,991,319.86

Blowdowns 

Vessel BD 132,103 vessels 83 scfy/vessel 211.21

Pipeline BD 87,900 miles (gathering) 329 scfy/mile 556.73

Compressor BD 184 compressors 4,016 scfy/comp 14.22

Compressor Starts 184 compressors 8,985 scfy/comp 31.81

Upsets 

Pressure Relief Valves 352,630 PRV 36 scfy/PRV 245.75

Mishaps 21,975 miles 712 scf/mile 301.34

Midcontinent 

Gas Wells 

MC - Associated Gas Wells1,2 72,935 wells NA NA 

MC - Non-associated Gas Wells 73,913 wells 7.54 scfd/well 3,918.89

MC - Unconventional Gas Wells 13,277 wells 4.71 scfd/well 439.78

Field Separation Equipment 

Heaters 35,399 heaters 15.08 scfd/heater 3,753.52

Separators 37,928 separators 0.95 scfd/sep 254.26

Dehydrators 12,518 dehydrators 96.70 scfd/dehy 8,510.31

Meters/Piping 119,028 meters 9.56 scfd/meter 8,001.30

Gathering Compressors 

Small Reciprocating Compressors 9,852 compressors 284.13 scfd/comp 19,679.53

Large Reciprocating Compressors 16 compressors 16,135 scfd/comp 1,814.87

Large Reciprocating Stations 2 stations 8,751.13 scfd/station 123.04

Pipeline Leaks 68,424 miles 56.40 scfd/mile 27,130.93

Drilling and Well Completion 

Completion Flaring 126 completions/yr 778 scf/comp 1.89

Unconventional Gas Well Completions 575 completions/yr 7,672,247 scf/comp 84,966.30

Well Drilling 3,875 wells 2,699 scf/well 201.41

Normal Operations 

Pneumatic Device Vents 135,232 controllers 366 scfd/device 348,046.02

Chemical Injection Pumps 12,381 active pumps 263 scfd/pump 22,905.82

Kimray Pumps 3,664,068 MMscf/yr 1,053 scf/MMscf 74,288.52

Dehydrator Vents 4,112,309 MMscf/yr 292.5 scf/MMscf 23,163.89

Condensate Tank Vents 

Condensate Tanks without Control Devices 13 MMbbl/yr 302.75 scf/bbl 75,803.69

Condensate Tanks with Control Devices 13 MMbbl/yr 60.55 scf/bbl 15,160.74

Compressor Exhaust Vented 

Gas Engines 16,304 MMHPhr 0.25 scf/HPhr 79,973.26

Well Workovers 

Conventional Gas Wells 3,793 workovers/yr 2,604 scf/w.o. 190.24

Unconventional Gas Wells 1,328 workovers/yr 7,672,247 scf/w.o. 196,190.87

Well Clean Ups (LP Gas Wells) 30,526 LP Gas Wells 711,385 scfy/LP well 418,245.79

Blowdowns 

Vessel BD 85,845 vessels 83 scfy/vessel 136.85

Pipeline BD 68,424 miles (gathering) 328 scfy/mile 432.13

Compressor BD 9,852 compressors 4,005 scfy/comp 759.96

Compressor Starts 9,852 compressors 8,960 scfy/comp 1,700.15

Upsets 

Pressure Relief Valves 167,248 PRV 36 scfy/PRV 116.22

Mishaps 17,106 miles 710 scf/mile 233.90

Rocky Mountain 

Gas Wells 
A-151 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions (Mg)

RM - Associated Gas Wells1,2 14,948 wells NA NA 

RM - Non-associated Gas Wells 75,418 wells 35.62 scfd/well 18,882.55

RM - Unconventional Gas Wells 23,417 wells 6.97 scfd/well 1,147.38

Field Separation Equipment 

Heaters 45,069 heaters 57.43 scfd/heater 18,196.66

Separators 49,319 separators 121.42 scfd/sep 42,097.53

Dehydrators 14,190 dehydrators 90.68 scfd/dehy 9,046.43

Meters/Piping 114,129 meters 52.65 scfd/meter 42,238.56

Gathering Compressors 

Small Reciprocating Compressors 10,971 compressors 266.44 scfd/comp 20,549.03

Large Reciprocating Compressors 32 compressors 15,131 scfd/comp 3,403.78

Large Reciprocating Stations 4 stations 8,206.34 scfd/station 230.76

Pipeline Leaks 125,062 miles 52.89 scfd/mile 46,501.51

Drilling and Well Completion 

Completion Flaring1 143 completions/yr 729 scf/comp 2.01

Unconventional Gas Well Completions 0 completions/yr 7,194,624 scf/comp 0

Well Drilling 4,393 wells 2,531 scf/well 214.10

Normal Operations 

Pneumatic Device Vents 144,695 controllers 343 scfd/device 349,219.02

Chemical Injection Pumps 17,593 active pumps 247 scfd/pump 30,521.75

Kimray Pumps 4,153,459 MMscf/yr 987 scf/MMscf 78,968.46

Dehydrator Vents 4,661,570 MMscf/yr 274.3 scf/MMscf 24,623.14

Condensate Tank Vents 

Condensate Tanks without Control Devices 12 MMbbl/yr 21.87 scf/bbl 5,054.59

Condensate Tanks with Control Devices 12 MMbbl/yr 4.37 scf/bbl 1,010.92

Compressor Exhaust Vented 

Gas Engines 18,481 MMHPhr 0.24 scf/HPhr 85,011.32

Well Workovers 

Conventional Gas Wells 4,299 workovers/yr 2,442 scf/w.o. 202.20

Unconventional Gas Wells 2,342 workovers/yr 7,194,624 scf/w.o. 324,485.75

Well Clean Ups (LP Gas Wells) 31,148 LP Gas Wells 710,583 scfy/LP well 426,283.58

Blowdowns 

Vessel BD 108,578 vessels 78 scfy/vessel 162.32

Pipeline BD 125,062 miles (gathering) 307 scfy/mile 740.65

Compressor BD 10,971 compressors 3,756 scfy/comp 793.54

Compressor Starts 10,971 compressors 8,402 scfy/comp 1,775.27

Upsets 

Pressure Relief Valves 189,586 PRV 34 scfy/PRV 123.54

Mishaps 31,266 miles 666 scf/mile 400.89

Coal Bed Methane 

 Powder River 28,577,932,572

gal produced 

water 2.002E-09

Gg/gallon water 

drainage 57,494.47

South West 

Gas Wells 

SW - Associated Gas Wells1,2 56,810 wells NA NA 

SW - Non-associated Gas Wells 27,802 wells 37.24 scfd/well 7,278.09

SW - Unconventional Gas Wells 13,740 wells 36.52 scfd/well 3,527.07

Field Separation Equipment 

Heaters 11,258 heaters 58.97 scfd/heater 4,667.26

Separators 23,347 separators 124.68 scfd/sep 20,462.48

Dehydrators 5,964 dehydrators 93.12 scfd/dehy 3,904.30

Meters/Piping 50,678 meters 54.06 scfd/meter 19,258.70

Gathering Compressors 

Small Reciprocating Compressors 5,650 compressors 273.59 scfd/comp 10,866.09

Large Reciprocating Compressors 16 compressors 15,536 scfd/comp 1,747.51 

Large Reciprocating Stations 2 stations 8,426.34 scfd/station 118.47 

Pipeline Leaks 60,894 miles 54.31 scfd/mile 23,249.07

Drilling and Well Completion 

Completion Flaring 60 completions/yr 749 scf/comp 0.87

Unconventional Gas Well Completions 3,594 completions/yr 7,387,499 scf/comp 511,365.92

Well Drilling 1,846 wells 2,598 scf/well 92.40

Normal Operations 

Pneumatic Device Vents 55,168 controllers 353 scfd/device 136,716.45

Chemical Injection Pumps 2,534 active pumps 253 scfd/pump 4,514.24
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions (Mg)

Kimray Pumps 1,745,768 MMscf/yr 1,014 scf/MMscf 34,081.57

Dehydrator Vents 1,959,335 MMscf/yr 281.6 scf/MMscf 10,626.97

Condensate Tank Vents 

Condensate Tanks without Control Devices 7 MMbbl/yr 302.75 scf/bbl 40,817.37

Condensate Tanks with Control Devices 7 MMbbl/yr 60.55 scf/bbl 8,163.47

Compressor Exhaust Vented 

Gas Engines 7,768 MMHPhr 0.25 scf/HPhr 36,689.57

Well Workovers 

Conventional Gas Wells 1,807 workovers/yr 2,507 scf/w.o. 87.27

Unconventional Gas Wells 1,374 workovers/yr 7,387,499 scf/w.o. 195,497.16

Well Clean Ups (LP Gas Wells) 11,482 LP Gas Wells 864,974 scfy/LP well 191,288.32

Blowdowns 

Vessel BD 40,569 vessels 80 scfy/vessel 62.27

Pipeline BD 60,894 miles (gathering) 316 scfy/mile 370.30

Compressor BD 5,650 compressors 3,856 scfy/comp 419.62

Compressor Starts 5,650 compressors 8,627 scfy/comp 938.74

Upsets 

Pressure Relief Valves 79,686 PRV 35 scfy/PRV 53.32

Mishaps 15,224 miles 684 scf/mile 200.44

West Coast 

Gas Wells 

WC - Associated Gas Wells1,2 24,548 wells NA NA 

WC - Non-associated Gas Wells 1,786 wells 42.49 scfd/well 533.50

WC - Unconventional Gas Wells3 0 wells NE 0

Field Separation Equipment 

Heaters 1,786 heaters 67.29 scfd/heater 844.89

Separators 1,304 separators 142.27 scfd/sep 1,303.93

Dehydrators 256 dehydrators 106.25 scfd/dehy 191.54

Meters/Piping 3,364 meters 61.68 scfd/meter 1,458.59

Gathering Compressors 

Small Reciprocating Compressors 2,074 compressors 312.19 scfd/comp 4,550.68

Large Reciprocating Compressors 8 compressors 17,728 scfd/comp 997.03

Large Reciprocating Stations 1 stations 9,615.15 scfd/station 67.59

Pipeline Leaks 16,367 miles 61.97 scfd/mile 7,130.46

Drilling and Well Completion 

Completion Flaring 3 completions/yr 855 scf/comp 0.04 

Unconventional Gas Well Completions 0 completions/yr 8,429,754 scf/comp 0

Well Drilling 79 wells 2,965 scf/well 4.53

Normal Operations 

Pneumatic Device Vents 1,790 controllers 402 scfd/device 5,060.58

Chemical Injection Pumps 1,213 active pumps 289 scfd/pump 2,465.10

Kimray Pumps 75,055 MMscf/yr 1,157 scf/MMscf 1,671.97

Dehydrator Vents 84,237 MMscf/yr 321.3 scf/MMscf 521.34

Condensate Tank Vents 

Condensate Tanks without Control Devices 0 MMbbl/yr 21.87 scf/bbl 0

Condensate Tanks with Control Devices 0 MMbbl/yr 4.37 scf/bbl 0

Compressor Exhaust Vented 

Gas Engines 334 MMHPhr 0.28 scf/HPhr 1,799.92

Well Workovers 

Conventional Gas Wells 78 workovers/yr 2,861 scf/w.o. 4.30

Unconventional Gas Wells 0 workovers/yr 8,429,754 scf/w.o. 0

Well Clean Ups (LP Gas Wells) 738 LP Gas Wells 1,491,925 scfy/LP well 21,195.07

Blowdowns 

Vessel BD 3,346 vessels 91 scfy/vessel 5.86

Pipeline BD 16,367 miles (gathering) 360 scfy/mile 113.57

Compressor BD 2,074 compressors 4,400 scfy/comp 175.73

Compressor Starts 2,074 compressors 9,844 scfy/comp 393.14

Upsets 

Pressure Relief Valves 3,426 PRV 40 scfy/PRV 2.62

Mishaps 4,092 miles 780 scf/mile 61.48

Gulf Coast 

Gas Wells 
A-153 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions (Mg)

GC - Associated Gas Wells1,2 27,134 wells NA NA 

GC - Non-associated Gas Wells 75,046 wells 40.97 scfd/well 21,613.40

GC - Unconventional Gas Wells3 0 wells NE scfd/well 0

Field Separation Equipment 

Heaters 16,810 heaters 64.88 scfd/heater 7,667.69

Separators 49,381 separators 137.17 scfd/sep 47,618.06

Dehydrators 10,775 dehydrators 102.45 scfd/dehy 7,760.09

Meters/Piping 87,505 meters 59.48 scfd/meter 36,586.28

Gathering Compressors 

Small Reciprocating Compressors 6,079 compressors 301.01 scfd/comp 12,863.02

Large Reciprocating Compressors 24 compressors 17,094 scfd/comp 2,884.00

Large Reciprocating Stations 3 stations 9,270.90 scfd/station 195.52

Pipeline Leaks 98,419 miles 59.75 scfd/mile 41,342.16

Drilling and Well Completion 

Completion Flaring 109 completions/yr 824 scf/comp 1.73

Unconventional Gas Well Completions 0 completions/yr 8,127,942 scf/comp 0

Well Drilling 3,335 wells 2,859 scf/well 183.66

Normal Operations 

Pneumatic Device Vents 52,157 controllers 388 scfd/device 142,210.39

Chemical Injection Pumps 2,477 active pumps 279 scfd/pump 4,853.92

Kimray Pumps 3,153,749 MMscf/yr 1,115 scf/MMscf 67,739.71

Dehydrator Vents 3,539,561 MMscf/yr 309.8 scf/MMscf 21,121.91

Condensate Tank Vents 

Condensate Tanks without Control Devices 33 MMbbl/yr 21.87 scf/bbl 13,900.13

Condensate Tanks with Control Devices 33 MMbbl/yr 4.37 scf/bbl 2,780.03

Compressor Exhaust Vented 

Gas Engines 14,033 MMHPhr 0.27 scf/HPhr 72,923.33

Well Workovers 

Conventional Gas Wells 3,265 workovers/yr 2,759 scf/w.o. 173.49

Unconventional Gas Wells 0 workovers/yr 8,127,942 scf/w.o. 0

Well Clean Ups (LP Gas Wells) 30,994 LP Gas Wells 2,522,975 scfy/LP well 1,506,084

Blowdowns 

Vessel BD 76,966 vessels 88 scfy/vessel 129.99

Pipeline BD 98,419 miles (gathering) 347 scfy/mile 658.48

Compressor BD 6,079 compressors 4,243 scfy/comp 496.73

Compressor Starts 6,079 compressors 9,492 scfy/comp 1,111.26

Upsets 

Pressure Relief Valves 143,954 PRV 38 scfy/PRV 105.98

Mishaps 24,605 miles 752 scf/mile 356.41

Coal Bed Methane 

 Black Warrior 5,026 wells 0.0023 Gg/well 11,693.03

Offshore Platforms 

Shallow water Gas Platforms (GoM and 

Pacific) 2,072

Shallow water 

gas platforms 19,178 scfd/platform 279,356

Deepwater Gas Platforms (GoM and 

Pacific) 40

Deepwater gas 

platforms 79,452 scfd/platform 22,389 1

 Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems model. Here the oil wells count is used as a driver 

only. 

2

 NA = not applicable; i.e. this data is not applicable for the Natural Gas systems model. 3

 NE = not estimate; some emission factors were not estimated because there is no corresponding activity for that region. 

Table A-121: 2009 Data and CH4 Emissions (Mg) for the Natural Gas Processing Stage 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions(Mg) 

Plants 578 plants 7,906 scfd/plant 32,124 

Recip. Compressors 4,876 compressors 11,196 scfd/comp 383,767 

Centrifugal Compressors (wet 

seals) 646 compressors 51,370 scfd/comp 233,371 

Centrifugal Compressors (dry 

seals) 140 compressors 25,189 scfd/comp 24,725 

Compressor Exhaust 

 Gas Engines 35,030 MMHPhr 0.24 scf/HPhr 161,923 

 Gas Turbines 41,535 MMHPhr 0.01 scf/HPhr 4,560 
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

 2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions(Mg) 

AGR Vents 293 AGR units 6,083 scfd/AGR 12,527 

Kimray Pumps 1,269,018 MMscf/yr 178 scf/MMscf 4,344 

Dehydrator Vents 11,432,591 MMscf/yr 122 scf/MMscf 26,764 

Pneumatic Devices 578 gas plants 164,721 scfy/plant 1,834 

Blowdowns/Venting 578 gas plants 4,060 Mscfy/plant 45,197 

Table A-122: 2009 Data and CH4 Emissions (Mg) for the Natural Gas Transmission Stage 

2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions(Mg) 

Pipeline Leaks 301,546 miles 1.55 Scfd/ mile 3,294 

Compressor Stations 

(Transmission) 

Station 1,790 Stations 8,778 Scfd/station 110,458 

Recip Compressor 7,197 Compressors 15,205 Scfd/ comp 769,264 

Centrifugal Compressor (wet 

seals) 667 Compressors 50,222 Scfd/ comp 235,445 

Centrifugal Compressor (dry seals) 55 Compressors 32,208 Scfd/ comp 12,436 

Compressor Stations (Storage) 

Station 392 Stations 21,507 Scfd/ comp 59,230 

Recip Compressor 1,152 Compressors 21,116 Scfd/ comp 171,007 

Centrifugal Compressor (wet 

seals) 84 Compressors 45,441 Scfd/ comp 26,819 

Centrifugal Compressor (dry seals) 29 Compressors 31,989 Scfd/ comp 6,532 

Wells (Storage) 18,267 Wells 115 Scfd/ comp 14,704 

M&R (Trans. Co. Interconnect) 2,684 Stations 3,984 scfd/station 75,180 

M& R (Farm Taps + Direct Sales) 79,592 Stations 31 scfd/station 17,457 

Dehydrator vents (Transmission) 1,141,012 MMscf/Year 94 scf/MMscf 2,060 

Dehydrator vents (Storage) 2,029,824 MMscf/Year 117 scf/MMscf 4,581 

Compressor Exhaust 

Engines (Transmission) 45,546 MMHPhr 0.24 scf/HPhr 210,531 

Turbines (Transmission) 10,868 MMHPhr 0.01 scf/HPhr 1,193 

Engines (Storage) 4,995 MMHPhr 0.24 scf/HPhr 23,091 

Turbines (Storage) 1,755 MMHPhr 0.01 scf/HPhr 193 

Generators (Engines) 2,229 MMHPhr 0.24 scf/HPhr 10,302 

Generators (Turbines) 26 MMHPhr 0.01 scf/HPhr 3 

Pneumatic Devices Trans + Stor 

Pneumatic Devices Trans 70,458 Devices 162,197 Scfy/device 220,104 

Pneumatic Devices Storage 15,421 Devices 162,197 Scfy/device 48,174 

Routine Maintenance/Upsets 

Pipeline venting 301,546 Miles 32 Mscfy/mile 183,816 

Station Venting Trans + Storage 

Station Venting Transmission 1,790 

Compressor 

Stations 4,359 Mscfy/station 150,278 

Station Venting Storage 392 

Compressor 

Stations 4,359 Mscfy/station 32,890 

LNG Storage 

LNG Stations 70 Stations 21,507 scfd/station 10,623 

LNG Reciprocating Compressors 270 Compressors 21,116 scfd/comp 40,147 

LNG Centrifugal Compressors 64 Compressors 30,573 scfd/comp 13,766 

LNG Compressor Exhaust 

LNG Engines 718 MMHPhr 0.24 scf/HPhr 3,320 

LNG Turbines 113 MMHPhr 0.01 scf/HPhr 12 

LNG Station Venting 70 Stations 4,359 Mscfy/station 5,899 

LNG Import Terminals 

LNG Stations 9 Stations 21,507 scfd/station 1,376 

LNG Reciprocating Compressors 44 Compressors 21,116 scfd/comp 6,561 

LNG Centrifugal Compressors 8 Compressors 30,573 scfd/comp 1,676 

LNG Compressor Exhaust 

LNG Engines 1,904 MMHPhr 0.24 scf/HPhr 8,802 

LNG Turbines 461 MMHPhr 0.01 scf/HPhr 51 
A-155 

2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions(Mg) 

LNG Station Venting 9 Stations 4,359 Mscfy/station 764 

Table A-123: 2009 Data and CH4 Emissions (Mg) for the Natural Gas Distribution Stage 

2009 EPA Inventory Values 

Activity Activity Data Emission Factor Emissions(Mg) 

Pipeline Leaks 

Mains—Cast Iron 35,429 miles 239 Mscf/mile-yr 162,880 

Mains—Unprotected steel 67,331 miles 110 Mscf/mile-yr 142,893 

Mains—Protected steel 484,337 miles 3 Mscf/mile-yr 28,609 

Mains—Plastic 621,404 miles 10 Mscf/mile-yr 118,605 

Services—Unprotected steel 5,218,497 services 2 Mscf/service 170,941 

Services Protected steel 15,389,666 services 0.2 Mscf/service 52,314 

Services—Plastic 42,601,520 services 0.01 Mscf/service 7,629 

Services—Copper 1,083,539 services 0.3 Mscf/service 5,308 

Meter/Regulator (City Gates) 

M&R >300 3,945 stations 180 scfh/station 119,685 

M&R 100-300 14,398 stations 96 scfh/station 232,233 

M&R <100 7,696 stations 4 scfh/station 5,596 

Reg >300 4,314 stations 162 scfh/station 117,825 

R-Vault >300 2,533 stations 1 scfh/station 556 

Reg 100-300 13,049 stations 41 scfh/station 89,166 

R-Vault 100-300 5,863 stations 0.2 scfh/station 178 

Reg 40-100 39,159 stations 1 scfh/station 6,871 

R-Vault 40-100 34,726 stations 0.1 scfh/station 507 

Reg <40 16,604 stations 0.1 scfh/station 373 

Customer Meters 

Residential 40,646,493 outdr meters 143 scfy/meter 112,160 

Commercial/Industry 4,121,452 meters 48 scfy/meter 3,802 

Routine Maintenance 

Pressure Relief Valve 

Releases 1,208,501 mile main 0.1 Mscf/mile 1,164 

Pipeline Blowdown 1,316,917 miles 0.1 Mscfy/mile 2,587 

Upsets 

Mishaps (Dig-ins) 1,316,917 miles 2 Mscfy/mile 40,328 
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-124: Key Activity Data Drivers 

Variable Units 1990 1992 1995 2000 2005 2006 Transmission Pipelines Length miles 291,990 291,468 296,947 298,957 300,663 300,458 Wells 

NE—Associated Gas Wells* # wells 68,261 67,489 66,102 58,671 46,471 47,034 NE—Non-associated Gas Wells* # wells 124,241 129,157 129,789 143,922 158,238 164,322 MC—Associated Gas Wells* # wells 64,379 70,640 72,483 67,880 65,652 65,903 MC—Non-associated Gas Wells* # wells 53,940 59,358 65,585 51,217 71,379 73,914 RM—Associated Gas Wells* # wells 13,749 14,142 13,745 12,328 13,327 13,437 RM—Non-associated Gas Wells* # wells 24,339 26,323 32,668 64,539 72,438 75,170 SW—Associated Gas Wells* # wells 69,339 68,130 59,954 54,830 55,502 54,550 SW—Non-associated Gas Wells* # wells 24,217 22,609 27,392 32,346 34,194 35,417 WC—Associated Gas Wells* # wells 20,672 19,819 19,109 20,494 21,562 22,189 WC—Non-associated Gas Wells* # wells 1,292 1,254 1,114 1,338 1,424 1,503 GC—Associated Gas Wells* # wells 36,279 35,376 34,792 32,497 28,090 27,319 GC—Non-associated Gas Wells* # wells 41,753 37,307 41,978 48,316 57,600 60,715 

Platforms

Gulf of Mexico and Pacific OCS Offshore Platforms # platforms 3,943 3,966 3,981 4,020 3,911 3,914 GoM and Pacific OCS Deep Water 

Platforms # platforms 17 19 23 38 59 67 

Gas Plants # gas plants 761 732 675 585 566 571 Distribution Services # of services 47,883,083 49,142,008 54,644,033 56,761,042 58,556,335 62,255,435 

Steel—Unprotected # of services 7,633,526 7,138,563 6,151,653 5,675,520 5,308,375 5,642,470 Steel—Protected # of services 19,781,581 19,742,086 21,002,455 17,855,560 15,883,423 15,732,037 Plastic # of services 18,879,865 20,692,674 26,044,545 31,795,871 36,152,277 39,632,313 Copper # of services 1,588,111 1,568,685 1,445,380 1,434,091 1,212,260 1,248,615 

Distribution Mains miles 944,157 888,925 1,001,706 1,048,485 1,093,909 1,209,419 

Cast Iron miles 58,292 52,917 50,625 44,750 37,371 36,977 Steel—Unprotected miles 108,941 99,619 94,058 82,800 69,291 71,738 Steel—Protected miles 465,538 469,106 503,288 471,510 461,459 481,811 Plastic miles 311,386 267,283 353,735 449,425 525,788 618,893 

* NEMS (National Energy Modeling System) projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomi

energy markets, resource availability and costs, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demograph
A-157 

Table A-125: CH4 Reductions Derived from the Natural Gas STAR Program (Gg) 

Process 1992* 1995 2000 2005 2006 2007 2008 2009 

Production 0 75 318 1,425 1,471 1,863 2,239 1,993 

Processing 0 5 23 129 140 139 145 83 

Transmission and Storage 0 121 258 500 480 454 440 367 

Distribution 0 19 27 35 48 35 28 41 

Total 0 220 626 2,090 2,138 2,492 2,852 2,484 

*Reductions are relative to 1992; therefore, there are zero reductions in 1992. 

Note: These reductions will not match the Natural Gas STAR program reductions. These numbers are adjusted for reductions prior to the 1992 

base year, and do not include a sunsetting period. Totals may not sum due to independent rounding. 

Table A-126: CH4 Reductions Derived from Regulations (Gg) 

Process 1990 1995 2000 2005 2006 2007 2008 2009

Production 9.8 24.1 303.6 278.4 1,572 783.5 966.5 733.7

Processing NA NA 12.9 12.1 12.4 13.0 13.7 14.2

Transmission and Storage NA NA NA NA NA NA NA NA

Distribution NA NA NA NA NA NA NA NA

Total 9.8 24.1 316.5 290.5 1,584 796.5 980.2 747.9

NA Not applicable 

Note: NESHAP regulations went into effect in 1999. Totals may not sum due to independent rounding. 

Table A-127: CH4 Emission Estimates from the Natural Gas Production Stage Excluding Reductions from the Natural Gas 

STAR Program and Regulations (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Normal Fugitives 

 Associated Gas Wells 

 Non-Associated Gas 

Wells (less 

conventional) 30.82 31.68 37.42 48.05 52.88 50.86 52.48 58.63 62.03

 Unconventional Gas 

Wells 0.01 0.02 0.04 0.26 0.53 1.32 1.53 1.67 1.59

 Field Separation 

Equipment 

 Heaters 12.57 13.28 16.48 23.56 26.90 28.01 28.36 33.09 35.17

 Separators 43.83 44.16 54.79 76.51 86.43 90.24 94.09 106.73 112.61

 Dehydrators 14.75 15.76 17.89 20.77 23.95 24.93 26.17 28.07 29.61

 Meter/ Piping 43.61 44.95 54.89 73.80 83.74 87.18 90.23 102.50 108.17

 Gathering Compressors 

 Small Reciprocating 

Comp. 30.22 32.15 38.27 45.57 53.85 56.10 58.49 65.19 68.88

 Large Reciprocating 

Comp. 7.50 8.56 9.80 11.70 12.60 12.59 12.71 13.58 13.58

 Large Reciprocating 

Stations 0.51 0.58 0.66 0.79 0.85 0.85 0.86 0.92 0.92

 Pipeline Leaks 98.03 102.22 114.49 133.58 148.45 152.97 160.02 172.60 180.31

Vented and Combusted 

 Drilling and Well 

Completion 

 Completion Flaring 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

 Unconventional Gas 

Well Completions 16.48 23.53 31.66 422.38 203.69 2,502.62 855.93 1,089.76 596.33

 Well Drilling 0.74 0.54 0.53 1.01 1.63 1.85 1.90 1.93 1.12

 Coal Bed Methane 

 Powder River 0.04 0.47 1.48 28.76 46.38 56.38 54.60 57.58 57.49

 Black Warrior 2.72 4.84 6.25 6.82 9.88 10.45 11.09 11.69 11.69

 Normal Operations 

 Pneumatic Device 

Vents 569.76 607.88 697.61 810.83 954.88 991.35 1,043.32 1,145.65 1,211.86

 Chemical Injection 

Pumps 26.89 29.38 35.04 43.44 51.88 53.93 54.93 62.93 66.97

 Kimray Pumps 131.82 140.65 159.45 185.01 213.32 222.04 233.36 250.28 263.97

 Dehydrator Vents 41.10 43.86 49.72 57.69 66.52 69.24 72.76 78.04 82.31

 Condensate Tank Vents 
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

 Condensate Tanks 

without Control 

Device 77.69 59.56 58.09 67.47 99.33 109.76 114.96 135.58 135.58

 Condensate Tanks 

with Control 

Device 15.54 11.91 11.62 13.49 19.87 21.95 22.99 27.12 27.12

 Compressor Exhaust 

Vented 

 Gas Engines 119.06 125.48 151.43 180.03 215.10 224.16 235.19 261.65 276.40

 Well Workovers 

 Conventional Gas 

Wells 0.56 0.58 0.64 0.73 0.84 0.87 0.94 1.00 1.06

 Unconventional Gas 

Wells 2.92 7.16 16.07 98.40 241.50 492.00 574.10 685.67 716.17

 Well Clean Ups (LP 

Gas Wells) 2,651.65 2,699.26 2,931.40 3,330.18 3,755.97 3,804.83 4,134.53 4,333.13 4,554.42

 Blowdowns 

 Vessel BD 0.36 0.38 0.42 0.49 0.56 0.58 0.62 0.67 0.71

 Pipeline BD 1.56 1.63 1.82 2.13 2.36 2.44 2.55 2.75 2.87

 Compressor BD 1.17 1.24 1.48 1.76 2.08 2.17 2.26 2.52 2.66

 Compressor Starts 2.61 2.78 3.31 3.94 4.65 4.85 5.05 5.63 5.95

 Upsets 

 Pressure Relief 

Valves 0.34 0.35 0.39 0.45 0.51 0.53 0.57 0.61 0.65

 Mishaps 0.85 0.88 0.99 1.15 1.28 1.32 1.38 1.49 1.55

Offshore 

 Offshore water Gas 

Platforms (GoM & 

Pacific) 290.64 298.00 307.56 324.06 321.98 321.51 305.25 298.98 279.36

 Deepwater Gas 

Platforms (GoM & 

Pacific) 5.21 5.94 7.40 12.81 20.43 23.20 21.10 21.78 22.39

Total 4,242 4,360 4,819 6,028 6,725 9,423 8,274 9,059 8,931

Note: Totals may not sum due to independent rounding. 

Table A-128: CH4 Emission Estimates from the Natural Gas Processing Plants Excluding Reductions from the Natural Gas 

STAR Program and Regulations (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009 

Normal Fugitives 

 Plants 42.30 40.68 37.52 32.51 31.46 31.74 31.90 32.07 32.12 

 Reciprocating 

Compressors 324.94 324.74 338.42 349.51 327.87 337.12 351.55 371.25 383.77 

 Centrifugal Compressors 

(wet seals) 240.29 240.15 248.60 251.32 229.24 229.92 230.99 232.45 233.37 

 Centrifugal 

Compressors 

 (dry seals) 0.00 0.00 0.81 3.50 6.48 9.50 14.21 20.64 24.73 

Vented and 

Combusted 

 Normal Operations 

 Compressor 

Exhaust 

 Gas Engines 137.10 137.02 142.79 147.47 138.34 142.24 148.33 156.64 161.92 

 Gas Turbines 3.86 3.86 4.02 4.15 3.90 4.01 4.18 4.41 4.56 

 AGR Vents 16.49 15.87 14.63 12.68 12.27 12.38 12.44 12.51 12.53 

 Kimray Pumps 3.68 3.68 3.83 3.96 3.71 3.82 3.98 4.20 4.34 

 Dehydrator Vents 22.66 22.65 23.60 24.38 22.87 23.51 24.52 25.89 26.76 

 Pneumatic Devices 2.41 2.32 2.14 1.86 1.80 1.81 1.82 1.83 1.83 

Routine Maintenance 

Blowdowns/Venting 59.51 57.24 52.78 45.74 44.26 44.65 44.88 45.12 45.20 

Total 853.24 848.20 869.16 877.08 822.18 840.69 868.81 907.01 931.14
A-159 

Note: Totals may not sum due to independent rounding. 

Table A-129: CH4 Emission Estimates from the Natural Gas Transmission and Storage Excluding Reductions from the 

Natural Gas STAR Program and Regulations (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Fugitives 

 Pipelines Leaks 3.19 3.18 3.24 3.27 3.28 3.28 3.29 3.31 3.29

Compressor Stations 

(Transmission) 

 Station 106.96 106.77 108.77 109.51 110.13 110.06 110.32 111.13 110.46

 Recip Compressor 744.89 743.55 757.53 762.66 767.01 766.49 768.33 773.96 769.26

 Centrifugal Compressor 

(wet seals) 246.76 246.32 249.68 245.59 236.93 236.75 236.81 237.00 235.44

 Centrifugal Compressor 

(dry seals) 0.00 0.00 0.81 4.53 11.01 11.01 11.36 12.44 12.44

Compressor Stations 

(Storage) 

 Station 54.64 58.36 60.35 62.17 60.06 54.36 58.76 60.62 59.23

 Recip Compressor 157.80 168.48 174.27 179.62 173.53 157.05 169.67 175.16 171.01

 Centrifugal 

 Compressor (wet seals) 33.22 35.46 36.55 34.41 30.91 27.39 27.65 27.78 26.82

 Centrifugal Compressor 

(dry seals) 0.00 0.00 0.13 2.54 4.10 4.10 5.72 6.53 6.53

 Wells (Storage) 13.56 14.49 14.98 15.43 14.91 13.49 14.59 15.05 14.70

 M&R (Trans. Co. 

Interconnect) 72.80 72.67 74.03 74.53 74.96 74.91 75.09 75.64 75.18

 M&R (Farm Taps + 

Direct Sales) 16.90 16.87 17.19 17.31 17.41 17.39 17.44 17.56 17.46

Vented and Combusted 

Normal Operation 

 Dehydrator Vents 

(Transmission) 1.99 1.99 2.03 2.04 2.05 2.05 2.06 2.07 2.06

Dehydrator Vents 

(Storage) 4.23 4.51 4.67 4.81 4.65 4.20 4.54 4.69 4.58

Compressor Exhaust 

Engines (Transmission) 176.92 186.65 204.91 215.30 203.10 200.09 213.12 214.32 210.53

Turbines (Transmission) 1.00 1.06 1.16 1.22 1.15 1.13 1.21 1.21 1.19

Engines (Storage) 21.30 22.75 23.53 24.24 23.41 21.19 22.91 23.63 23.09

Turbines (Storage) 0.18 0.19 0.20 0.20 0.20 0.18 0.19 0.20 0.19

Generators (Engines) 8.66 9.13 10.03 10.54 9.94 9.79 10.43 10.49 10.30

Generators (Turbines) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Pneumatic Devices 

Trans+Stor 

Pneumatic Devices Trans 213.13 212.75 216.75 218.21 219.46 219.31 219.84 221.45 220.10

Pneumatic Devices 

Storage 44.44 47.46 49.09 50.56 48.85 44.21 47.79 49.31 48.17

Routine 

Maintenance/Upsets 

Pipeline Venting 177.99 177.67 181.01 182.24 183.28 183.15 183.59 184.94 183.82

Station venting 

Trans+Storage 

Station Venting 

Transmission 145.52 145.26 147.99 148.99 149.84 149.74 150.10 151.20 150.28

Station Venting Storage 30.34 32.41 33.51 34.52 33.35 30.18 32.63 33.66 32.89

LNG Storage 

LNG Stations 9.24 9.45 9.77 10.30 10.62 10.62 10.62 10.62 10.62

LNG Reciprocating 

Compressors 34.50 35.37 36.67 38.84 40.15 40.15 40.15 40.15 40.15

LNG Centrifugal 

Compressors 11.78 12.09 12.55 13.31 13.77 13.77 13.77 13.77 13.77

LNG Compressor Exhaust 

LNG Engines 3.21 3.23 3.26 3.30 3.32 3.32 3.32 3.32 3.32

LNG Turbines 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

LNG Station Venting 5.13 5.25 5.43 5.72 5.90 5.90 5.90 5.90 5.90
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

LNG Import Terminals 

LNG Stations 0.21 0.21 0.21 0.21 0.42 0.42 0.42 1.06 1.38

LNG Reciprocating 

Compressors 1.01 1.01 1.01 1.01 2.02 2.02 2.02 5.05 6.56

LNG Centrifugal 

Compressors 0.26 0.26 0.26 0.26 0.52 0.52 0.52 1.29 1.68

LNG Compressor Exhaust 

LNG Engines 1.74 0.96 0.49 4.41 12.18 11.28 14.81 6.91 8.80

LNG Turbines1 0.01 0.00 0.00 0.03 0.07 0.07 0.09 0.04 0.05

LNG Station Venting 0.12 0.12 0.12 0.12 0.24 0.24 0.24 0.59 0.76

Total 2,344 2,376 2,442 2,482 2,473 2,430 2,479 2,502 2,482 1

 Emissions are not actually 0, but too small to show at this level of precision. 

 Note: Totals may not sum due to independent rounding. 

Table A-130: CH4 Emission Estimates from the Natural Gas Distribution Stage Excluding Reductions from the Natural Gas STAR 

Program (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Pipeline Leaks 

 Mains—Cast Iron 267.99 243.28 232.74 205.73 171.81 170.00 173.18 167.63 162.88

 Mains—Unprotected steel 231.20 211.42 199.62 175.72 147.05 152.25 147.55 147.23 142.89

 Mains—Protected steel 27.50 27.71 29.73 27.85 27.26 28.46 28.93 28.32 28.61

 Mains—Plastic 59.43 51.02 67.52 85.78 100.36 118.13 114.82 115.16 118.61

 Services—Unprotected steel 250.05 233.84 201.51 185.91 173.89 184.83 178.49 176.51 170.94

 Services Protected steel 67.24 67.11 71.39 60.70 53.99 53.48 53.56 52.54 52.31

 Services—Plastic 3.38 3.71 4.66 5.69 6.47 7.10 7.36 7.44 7.63

 Services—Copper 7.78 7.68 7.08 7.02 5.94 6.12 6.01 5.59 5.31

Meter/Regulator (City Gates) 

 M&R >300 110.41 117.93 121.96 125.62 121.36 109.84 118.73 122.50 119.68

 M&R 100-300 214.25 228.82 236.64 243.76 235.49 213.13 230.38 237.70 232.23

 M&R <100 5.16 5.51 5.70 5.87 5.67 5.14 5.55 5.73 5.60

 Reg >300 108.70 116.09 120.06 123.67 119.48 108.13 116.88 120.60 117.83

 R-Vault >300 0.51 0.55 0.57 0.58 0.56 0.51 0.55 0.57 0.56

 Reg 100-300 82.26 87.86 90.86 93.59 90.42 81.83 88.45 91.26 89.17

 R-Vault 100-300 0.16 0.18 0.18 0.19 0.18 0.16 0.18 0.18 0.18

 Reg 40-100 6.34 6.77 7.00 7.21 6.97 6.31 6.82 7.03 6.87

 R-Vault 40-100 0.47 0.50 0.52 0.53 0.51 0.47 0.50 0.52 0.51

 Reg <40 0.34 0.37 0.38 0.39 0.38 0.34 0.37 0.38 0.37

Customer Meters 

 Residential 103.47 110.51 114.29 117.72 113.73 102.93 111.26 114.80 112.16

 Commercial/Industry 3.97 4.25 4.78 4.66 3.95 3.85 3.98 4.03 3.80

Routine Maintenance 

 Pressure Relief Valve Releases 0.91 0.86 0.96 1.01 1.05 1.16 1.15 1.14 1.16

 Pipeline Blowdown 2.39 2.55 2.64 2.72 2.62 2.37 2.57 2.65 2.59

Upsets 

 Mishaps (Dig-ins) 37.20 39.74 41.09 42.33 40.89 37.01 40.01 41.28 40.33

Total 1,591 1,568 1,562 1,524 1,430 1,394 1,437 1,451 1,422

Note: Totals may not sum due to independent rounding.

Table A-131: U.S. Production Sector CO2 Content in Natural Gas by NEMS Region and Natural Gas Well type 

 U.S. Region 

Well Types

North 

East 

Midcontinen

t Gulf Coast South West 

Rocky 

Mountain West Coast Lower-48 States 

Conventional 0.92% 0.79% 2.17% 3.81% 7.95% 0.16% 3.41% 

Un-conventional 7.42% 0.31% 0.23% NA 0.64% NA 4.83% 

All types 3.04% 0.79% 2.17% 3.81% 7.58% 0.16% 3.45% 
A-161 

Table A-132: CO2 Emission Estimates from the Natural Gas Production Stage (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Normal Fugitives 

 Gas Wells 

 Non-Associated Gas Wells 3.56 3.57 4.25 6.46 7.00 6.18 5.86 7.33 8.00 

 Unconventional Gas Wells2 0.00 0.00 0.00 0.01 0.01 0.03 0.03 0.03 0.03 

 Field Separation Equipment 

 Heaters 1.86 1.91 2.32 3.95 4.44 4.61 4.48 5.54 5.95 

 Separators 6.04 5.97 7.17 11.29 12.68 13.20 13.16 15.78 16.82 

 Dehydrators 1.50 1.54 1.73 2.58 2.83 2.94 2.93 3.38 3.60 

 Meter/ Piping 5.85 5.85 6.95 10.90 12.21 12.69 12.57 15.14 16.15 

 Gathering Compressors 

 Small Reciprocating Comp. 2.95 2.98 3.57 5.46 6.17 6.41 6.38 7.63 8.15 

 Large Reciprocating Comp. 0.69 0.91 1.02 1.33 1.40 1.40 1.40 1.62 1.62 

 Large Reciprocating 

Stations 0.05 0.06 0.07 0.09 0.09 0.09 0.09 0.11 0.11 

 Pipeline Leaks 10.75 10.85 11.99 16.04 17.64 18.21 18.25 20.80 21.94 

Vented and Combusted 

 Drilling and Well Completion 

 Completion Flaring2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 

 Unconventional Gas Well 

Completions 2.19 1.58 5.27 122.20 21.29 748.86 166.74 108.05 85.45

 Well Drilling 0.07 0.05 0.05 0.12 0.18 0.21 0.20 0.22 0.13 

 Coal Bed Methane 

 Powder River1 NE NE NE NE NE NE NE NE NE 

 Black Warrior1 NE NE NE NE NE NE NE NE NE 

 Normal Operations 

 Pneumatic Device Vents 58.49 60.24 69.38 101.86 114.87 119.29 118.75 139.81 149.18 

 Chemical Injection Pumps 2.95 3.11 3.75 6.33 7.18 7.45 7.20 8.94 9.61 

 Kimray Pumps 13.41 13.71 15.43 22.91 25.15 26.12 26.05 30.04 31.99 

 Dehydrator Vents 4.18 4.28 4.81 7.14 7.84 8.15 8.12 9.37 9.98 

 Condensate Tank Vents 

 Condensate Tanks without 

Control Device 10.25 9.43 8.79 9.34 10.25 11.26 11.17 11.90 11.90 

 Condensate Tanks with 

Control Device 2.05 1.89 1.76 1.87 2.05 2.25 2.23 2.38 2.38 

 Compressor Exhaust Vented 

 Gas Engines1 NE NE NE NE NE NE NE NE NE 

 Well Workovers 

 Gas Wells 0.06 0.06 0.06 0.08 0.09 0.10 0.10 0.11 0.12 

 Unconventional Gas Wells 0.47 0.84 2.35 22.89 43.99 118.88 135.55 146.36 145.24

 Well Clean Ups (LP Gas 

Wells) 251.41 252.88 273.66 339.56 375.89 367.69 377.12 413.61 439.94 

 Blowdowns 

 Vessel BD 0.04 0.04 0.04 0.06 0.06 0.07 0.07 0.08 0.08 

 Pipeline BD 0.17 0.17 0.19 0.26 0.28 0.29 0.29 0.33 0.35 

 Compressor BD 0.11 0.12 0.14 0.21 0.24 0.25 0.25 0.29 0.31 

 Compressor Starts 0.25 0.26 0.31 0.47 0.53 0.55 0.55 0.66 0.70 

 Upsets 

 Pressure Relief Valves 0.03 0.03 0.04 0.05 0.06 0.06 0.06 0.07 0.07 

 Mishaps 0.09 0.09 0.10 0.14 0.15 0.16 0.16 0.18 0.19 

 Flaring Emissions - Onshore 9,092.72 10,172.49 17,167.79 5,525.04 7,193.00 7,812.35 8,664.25 10,024.82 9,545.39 

Offshore 

 Offshore water Gas Platforms 

(GoM & Pacific) 1.48 1.51 1.56 1.64 1.63 1.63 1.55 1.52 1.42 

 Deepwater Gas Platforms 

(GoM & Pacific) 0.03 0.03 0.04 0.07 0.10 0.12 0.11 0.11 0.11 

 Flaring Emissions - Offshore 230.37 163.13 197.22 204.31 180.66 146.48 160.05 360.00 360.00 

Total 9,704 10,720 17,792 6,425 8,050 9,438 9,746 11,336 10,877 1

 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated. 2

 Emissions are not actually 0, but too small to show at this level of precision. 

 Note: Totals may not sum due to independent rounding. 
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-133: CO2 Emission Estimates from the Natural Gas Processing Plants (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Normal Fugitives 

 Plants – Before CO2 removal 2.56 2.46 2.27 1.97 1.90 1.92 1.93 1.94 1.94 

 Plants – After CO2 removal 0.57 0.55 0.50 0.44 0.42 0.43 0.43 0.43 0.43 

 Reciprocating Compressors – 

Before CO2 removal 19.67 19.66 20.49 21.16 19.85 20.41 21.28 22.47 23.23 

 Reciprocating Compressors – 

After CO2 removal 4.37 4.36 4.55 4.70 4.41 4.53 4.72 4.99 5.16 

 Centrifugal Compressors (wet 

seals) – Before CO2 removal 14.55 14.54 15.05 15.21 13.88 13.92 13.98 14.07 14.13 

 Centrifugal Compressors (wet 

seals) – After CO2 removal 3.23 3.23 3.34 3.38 3.08 3.09 3.10 3.12 3.14 

 Centrifugal Compressors (dry 

seals) – Before CO2 removal 0 0 0.05 0.21 0.39 0.58 0.86 1.25 1.50

 Centrifugal Compressors (dry 

seals) – After CO2 removal 0 0 0.01 0.05 0.09. 0.13 0.19 0.28 0.33

Vented and Combusted 

 Normal Operations 

 Compressor Exhaust 

 Gas Engines1 NE NE NE NE NE NE NE NE NE

 Gas Turbines1 NE NE NE NE NE NE NE NE NE

 AGR Vents 27,708 26,652 24,577 23,288 21,694 21,161 21,144 21,328 21,130 

 Kimray Pumps 0.39 0.39 0.41 0.42 0.40 0.41 0.43 0.45 0.46 

 Dehydrator Vents 2.42 2.42 2.52 2.61 2.45 2.51 2.62 2.77 2.86 

 Pneumatic Devices 0.29 0.27 0.25 0.22 0.21 0.21 0.22 0.22 0.22 

Routine Maintenance 

 Blowdowns/Venting 6.36 6.12 5.64 4.89 4.73 4.78 4.80 4.83 4.83 

Total 27,763 26,706 24,632 23,343 21,746 21,214 21,199 21,385 21,189 1 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated. 

Note: Totals may not sum due to independent rounding. 

Table A-134: CO2 Emission Estimates from the Natural Gas Transmission and Storage (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Fugitives 

 Pipelines Leaks 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.10 0.10 

 Compressor Stations 

(Transmission) 

 Station 3.09 3.08 3.14 3.16 3.18 3.17 3.18 3.21 3.19 

 Recip Compressor 21.49 21.45 21.85 22.00 22.12 22.11 22.16 22.33 22.19 

 Centrifugal Compressor (wet 

seals) 7.12 7.11 7.20 7.08 6.83 6.83 6.83 6.84 6.79 

 Centrifugal Compressor (dry 

seals) 0 0 0.02 0.13 0.32 0.32 0.33 0.36 0.36

 Compressor Stations (Storage) 

 Station 1.58 1.68 1.74 1.79 1.73 1.57 1.69 1.75 1.71 

 Recip Compressor 4.55 4.86 5.03 5.18 5.01 4.53 4.89 5.05 4.93 

 Centrifugal Compressor (wet 

seals) 0.96 1.02 1.05 0.99 0.89 0.79 0.80 0.80 0.77 

 Centrifugal Compressor (dry 

seals) 0 0 0.00 0.07 0.12 0.12 0.17 0.19 0.19

 Wells (Storage) 0.39 0.42 0.43 0.45 0.43 0.39 0.42 0.43 0.42 

 M&R (Trans. Co. Interconnect) 2.10 2.10 2.14 2.15 2.16 2.16 2.17 2.18 2.17 

 M&R (Farm Taps + Direct Sales) 0.49 0.49 0.50 0.50 0.50 0.50 0.50 0.51 0.50 

Vented and Combusted 

 Normal Operation 

 Dehydrator Vents 

(Transmission) 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 

 Dehydrator Vents (Storage) 0.12 0.13 0.13 0.14 0.13 0.12 0.13 0.14 0.13 

 Compressor Exhaust 

 Engines (Transmission)1 NE NE NE NE NE NE NE NE NE 

 Turbines (Transmission)1 NE NE NE NE NE NE NE NE NE 
A-163 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

 Engines (Storage)1 NE NE NE NE NE NE NE NE NE 

 Turbines (Storage)1 NE NE NE NE NE NE NE NE NE 

 Generators (Engines)1 NE NE NE NE NE NE NE NE NE 

 Generators (Turbines)1 NE NE NE NE NE NE NE NE NE 

 Pneumatic Devices Trans+Stor 

 Pneumatic Devices Trans 6.15 6.14 6.25 6.29 6.33 6.33 6.34 6.39 6.35 

 Pneumatic Devices Storage 1.28 1.37 1.42 1.46 1.41 1.28 1.38 1.42 1.39 

 Routine Maintenance/Upsets 

 Pipeline Venting 5.13 5.13 5.22 5.26 5.29 5.28 5.30 5.33 5.30 

 Station venting Trans+Storage 

 Station Venting Transmission 4.20 4.19 4.27 4.30 4.32 4.32 4.33 4.36 4.33 

 Station Venting Storage 0.88 0.93 0.97 1.00 0.96 0.87 0.94 0.97 0.95 

LNG Storage 

 LNG Stations 0.31 0.32 0.33 0.35 0.36 0.36 0.36 0.36 0.36 

 LNG Reciprocating Compressors 1.16 1.18 1.23 1.30 1.34 1.34 1.34 1.34 1.34 

 LNG Centrifugal Compressors 0.39 0.41 0.42 0.45 0.46 0.46 0.46 0.46 0.46 

 LNG Compressor Exhaust 

 LNG Engines1 NE NE NE NE NE NE NE NE NE 

 LNG Turbines1 NE NE NE NE NE NE NE NE NE 

 LNG Station Venting 0.17 0.18 0.18 0.19 0.20 0.20 0.20 0.20 0.20 

LNG Import Terminals 

 LNG Stations 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.04 0.05 

 LNG Reciprocating Compressors 0.03 0.03 0.03 0.03 0.07 0.07 0.07 0.17 0.22 

 LNG Centrifugal Compressors 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.04 0.06 

 LNG Compressor Exhaust 

 LNG Engines1 NE NE NE NE NE NE NE NE NE 

 LNG Turbines1 NE NE NE NE NE NE NE NE NE 

 LNG Station Venting2 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.02 0.03 

Total 61.75 62.37 63.73 64.44 64.36 63.31 64.18 65.04 64.55 1 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.

2 Emissions are not actually 0, but too small to show at this level of precision. 

 Note: Totals may not sum due to independent rounding. 

Table A-135: CO2 Emission Estimates from the Natural Gas Distribution Stage (Gg) 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

Pipeline Leaks 

 Mains—Cast Iron 7.73 7.02 6.71 5.93 4.96 4.90 5.00 4.84 4.70 

 Mains—Unprotected steel 6.67 6.10 5.76 5.07 4.24 4.39 4.26 4.25 4.12 

 Mains—Protected steel 0.79 0.80 0.86 0.80 0.79 0.82 0.83 0.82 0.83 

 Mains—Plastic 1.71 1.47 1.95 2.47 2.89 3.41 3.31 3.32 3.42 

Total Pipeline Miles 

 Services—Unprotected steel 7.21 6.75 5.81 5.36 5.02 5.33 5.15 5.09 4.93 

 Services Protected steel 1.94 1.94 2.06 1.75 1.56 1.54 1.54 1.52 1.51 

 Services—Plastic 0.10 0.11 0.13 0.16 0.19 0.20 0.21 0.21 0.22 

 Services—Copper 0.22 0.22 0.20 0.20 0.17 0.18 0.17 0.16 0.15 

Meter/Regulator (City 

Gates) 

 M&R >300 3.18 3.40 3.52 3.62 3.50 3.17 3.42 3.53 3.45 

 M&R 100-300 6.18 6.60 6.83 7.03 6.79 6.15 6.65 6.86 6.70 

 M&R <100 0.15 0.16 0.16 0.17 0.16 0.15 0.16 0.17 0.16 

 Reg >300 3.14 3.35 3.46 3.57 3.45 3.12 3.37 3.48 3.40

 R-Vault >300 0.01 0.02 0.02 0.02 0.02 0.01 0.02 0.02 0.02 

 Reg 100-300 2.37 2.53 2.62 2.70 2.61 2.36 2.55 2.63 2.57 

 R-Vault 100-300 0.00 0.01 0.01 0.01 0.01 0.00 0.01 0.01 0.01 

 Reg 40-100 0.18 0.20 0.20 0.21 0.20 0.18 0.20 0.20 0.20 

 R-Vault 40-100 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 

 Reg <40 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

Customer Meters 

 Residential 2.98 3.19 3.30 3.40 3.28 2.97 3.21 3.31 3.24 

 Commercial/Industry 0.11 0.12 0.14 0.13 0.11 0.11 0.11 0.12 0.11 

Routine Maintenance 
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Activity 1990 1992 1995 2000 2005 2006 2007 2008 2009

 Pressure Relief Valve 

Releases 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 

 Pipeline Blowdown 0.07 0.07 0.08 0.08 0.08 0.07 0.07 0.08 0.07 

Upsets 

 Mishaps (Dig-ins) 1.07 1.15 1.19 1.22 1.18 1.07 1.15 1.19 1.16 

Total 45.90 45.24 45.05 43.97 41.25 40.20 41.46 41.85 41.03 1

 Emissions are not actually 0, but too small to show at this level of precision. 

 Note: Totals may not sum due to independent rounding. 
A-165 

3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum 

Systems 

The methodology for estimating CH4 and non-combustion CO2 emissions from petroleum systems is based on 

the 1999 EPA draft report, Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999) and the study, Methane 

Emissions from the U.S. Petroleum Industry (EPA/GRI 1996). Sixty-four activities that emit CH4 and thirty activities that 

emit non-combustion CO2 from petroleum systems were examined from these reports. Most of the activities analyzed 

involve crude oil production field operations, which accounted for about 98 percent of total oil industry CH4 emissions. 

Crude transportation and refining accounted for the remaining CH4 emissions of less than one half and about one and a 

half percent, respectively. Non-combustion CO2 emissions were analyzed for production operations and asphalt blowing 

in refining operations. Non-combustion CO2 emissions from transportation operations are not included because they are 

negligible. The following steps were taken to estimate CH4 and CO2 emissions from petroleum systems. 

Step 1: Determine Emission Factors for all Activities 

The CH4 emission factors for the majority of the activities for 1995 are taken from the 1999 EPA draft report, 

which contained the most recent and comprehensive determination of CH4 emission factors for the 64 CH4-emitting 

activities in the oil industry at that time. Emission factors for pneumatic devices in the production sector were recalculated

in 2002 using emissions data in the EPA/GRI 1996c study. The gas engine emission factor is taken from the EPA/GRI 

1996b study. The oil tank venting emission factor is taken from the API E&P Tank Calc weighted average for API gravity 

less than 45 API degrees with the distribution of gravities taken from a sample of production data from the HPDI database. 

Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the Gulf-wide Offshore 

Activity Data System (GOADS) report (EPA 2005, BOEMRE 2004). The emission factors determined for 1995 were 

assumed to be representative of emissions from each source type over the period 1990 through 2009. Therefore, the same 

emission factors are used for each year throughout this period. 

The CO2 emission factors were derived from the corresponding source CH4 emission factors. The amount of CO2

in the crude oil stream changes as it passes through various equipment in petroleum production operations. As a result, 

four distinct stages/streams with varying CO2 contents exist. The four streams that are used to estimate the emissions 

factors are the associated gas stream separated from crude oil, hydrocarbons flashed out from crude oil (such as in storage 

tanks), whole crude oil itself when it leaks downstream, and gas emissions from offshore oil platforms. The standard 

approach used to estimate CO2 emission factors was to use the existing CH4 emissions factors and multiply them by a 

conversion factor, which is the ratio of CO2 content to methane content for the particular stream. Ratios of CO2 to CH4

volume in emissions are presented in Table A- 5. The two exceptions are the emissions factor for storage tanks, which is 

estimated using API E&P Tank Calc simulation runs of tank emissions for crude oil of different gravities less than 45 API 

degrees; and the emissions factor for uncontrolled asphalt blowing, which is estimated using the data and methods 

provided by API (2009). 

Step 2: Determine Activity Levels for Each Year 

Activity levels change from year to year. Some factors change in proportion to crude oil rates: production, 

transportation, refinery runs. Some change in proportion to the number of facilities: oil wells, petroleum refineries. Some 

factors change proportional to both the rate and number of facilities. 

For most sources, activity levels found in the EPA/GRI 1996 for the 1995 base year are extrapolated to other 

years using publicly-available data sources. For the remaining sources, the activity levels are obtained directly from 

publicly available data and are not extrapolated from the 1995 base year. 

For both sets of data, a determination was made on a case-by-case basis as to which measure of petroleum 

industry activity best reflects the change in annual activity. Publicly-reported data from the Bureau of Ocean Energy 

Management, Regulation and Enforcement (BOEMRE), Energy Information Administration (EIA), American Petroleum 

Institute (API), the Oil & Gas Journal (O&GJ), the Interstate Oil and Gas Compact Commission (IOGCC), and the U.S 

Army Corps of Engineers (USACE) were used to extrapolate the activity levels from the base year to each year between 

1990 and 2009. Data used include total domestic crude oil production, number of domestic crude oil wells, total imports 

and exports of crude oil, total petroleum refinery crude runs, and number of oil-producing offshore platforms. The activity 

data for the total crude transported in the transportation sector is not available. In this case, all the crude oil that was 

transported was assumed to go to refineries. Therefore, the activity data for the refining sector was used also for the 

transportation sector. In the few cases where no data was located, oil industry data based on expert judgment was used. In 

the case of non-combustion CO2 emission sources, the activity factors remain the same as for CH4 emission sources. 
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Step 3: Estimate Methane and Carbon Dioxide Emissions for Each Activity for Each Year 

Annual CH4 emissions from each of the 64 petroleum system activities and CO2 emissions from the 30 petroleum 

system activities were estimated by multiplying the activity data for each year by the corresponding emission factor. 

These annual emissions for each activity were then summed to estimate the total annual CH4 and CO2 emissions, 

respectively. Table A- 140 provides the CO2 content in natural gas for equipment in different crude streams to estimate 

CO2 emission factors using CH4 emission factors. 

Table A-136, Table A-137, Table A-138, and Table A-141 provide 2009 activity factors, emission factors, and 

emission estimates and Table A-139 and Table A-142 provide a summary of emission estimates for the years 1990, 1995, 

2000, and 2005 through 2009. Table A- 140 provides the CO2 content in natural gas for equipment in different crude 

streams to estimate CO2 emission factors using CH4 emission factors. 

Table A-136: 2009 CH4 Emissions from Petroleum Production Field Operations 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 67.625 1,300

Oil Tanks 7.39 scf of CH4/bbl crude 1,490 MMbbl/yr (non stripper 

wells) 

11.016 211.8

Pneumatic Devices, High Bleed 330 scfd CH4/device 139,797 No. of high-bleed 

devices 

16.859 324.2

Pneumatic Devices, Low Bleed 52 scfd CH4/device 259,624 No. of low-bleed 

devices 

4.928 94.76

Chemical Injection Pumps 248 scfd CH4/pump 28,166 No. of pumps 2.550 49.04

Vessel Blowdowns 78 scfy CH4/vessel 182,451 No. of vessels 0.014 0.274

Compressor Blowdowns 3,775 scf/yr of CH4/compressor 2,473 No. of compressors 0.009 0.179

Compressor Starts 8,443 scf/yr. of CH4/compressor 2,473 No. of compressors 0.021 0.401

Stripper wells 2,345 scf/yr of CH4/stripper well 317,230 No. of stripper wells 

vented 

0.744 14.31

Well Completion Venting 733 scf/completion 11,804 Oil well completions 0.009 0.166

Well Workovers 96 scf CH4/workover 39,450 Oil well workovers 0.004 0.073

Pipeline Pigging 2.40 scfd of CH4/pig station 0 No. of crude pig 

stations 

- -

Offshore Platforms, Shallow water 

Oil, fugitive, vented and combusted 

54,795 scfd CH4/platform 1,441 No. of shallow water oil 

platforms 

28.823 554.3

Offshore Platforms, Deepwater oil, 

fugitive, vented and combusted 

260,274 scfd CH4/platform 28 No. of deep water oil 

platforms 

2.649 50.93

Fugitive Emissions 2.539 49

Oil Wellheads (heavy crude) 0.13 scfd/well 14,718 No. of hvy. crude wells 0.001 0.013

Oil Wellheads (light crude) 16.6 scfd/well 194,052 No. of lt. crude wells 1.178 22.65

Separators (heavy crude) 0.15 scfd CH4/separator 10,740 No. of hvy. crude seps. 0.001 0.012

Separators (light crude) 14 scfd CH4/separator 97,745 No. of lt. crude seps. 0.494 9.504

Heater/Treaters (light crude) 19 scfd CH4/heater 73,966 No. of heater treaters 0.518 9.965

Headers (heavy crude)1 0.08 scfd CH4/header 13,721 No. of hvy. crude hdrs. 0.000 0.007

Headers (light crude) 11 scfd CH4/header 42,536 No. of lt. crude hdrs. 0.169 3.241

Floating Roof Tanks 338,306 scf CH4/floating roof 

tank/yr. 

24 No. of floating roof 

tanks 

0.008 0.159

Compressors 100 scfd CH4/compressor 2,473 No. of compressors 0.090 1.736

Large Compressors 16,360 scfd CH4/compressor 0 No. of large comprs. - -

Sales Areas 41 scf CH4/loading 1,630,377 Loadings/year 0.066 1.271

Pipelines NE scfd of CH4/mile of 

pipeline 

11,249 Miles of gathering line NE NE

Well Drilling NE scfd of CH4/oil well 

drilled 

13,255 No. of oil wells drilled NE NE

Battery Pumps 0.24 scfd of CH4/pump 157,800 No. of battery pumps 0.014 0.266

Combustion Emissions 4.7926 92.22

Gas Engines 0.24 scf CH4/HP-hr 15,577 MMHP-hr 3.739 71.89

Heaters 0.52 scf CH4/bbl 1956.8 MMbbl/yr 1.020 19.61

Well Drilling 2,453 scf CH4/well drilled 13,255 Oil wells drilled 0.033 0.625
A-167 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Flares 20 scf CH4/Mcf flared 242,355 Mcf flared/yr 0.005 0.932

Process Upset Emissions 0.116 2.233

Pressure Relief Valves 35 scf/yr/PR valve 163,449 No. of PR valves 0.006 0.109

Well Blowouts Onshore 2.5 MMscf/blowout 44.2 No. of blowouts/yr 0.110 2.124

Total 75.08 1,444

- Zero Emissions 

1

 Emissions are not actually 0, but too small to show at this level of precision. 

 NE: Not estimated for lack of data 

Table A-137: 2009 CH4 Emissions from Petroleum Transportation 

Activity/Equipment 

Emission 

Factor Units 

Activity

Factor Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 0.209 4.024

Tanks 0.021 scf CH4/yr/bbl of crude delivered to 

refineries 

5,233 MMbbl crude feed/yr 

0.108 2.073

Truck Loading 0.520 scf CH4/yr/bbl of crude transported by 

truck 

67.8 MMbbl trans. by 

truck 0.035 0.678

Marine Loading 2.544 scf CH4/1000 gal. crude marine loadings 19,609,664 1,000 gal./yr loaded 0.050 0.959

Rail Loading 0.520 scf CH4/yr/bbl of crude transported by 

rail 

4.2 MMbbl Crude by 

rail/yr 0.002 0.042

Pump Station 

Maintenance1 

36.80 scf CH4/station/yr 496 No. of pump stations 

0.000 0.000

Pipeline Pigging 39 scfd of CH4/pig station 992 No. of pig stations 0.014 0.271

Fugitive Emissions 0.050 0.958

Pump Stations 25 scf CH4/mile/yr. 49,585 No. of miles of crude 

p/l 

0.001 0.024

Pipelines NE scf CH4/bbl crude transported by 

pipeline 

6,431 MMbbl crude piped NE NE

Floating Roof Tanks 58,965 scf CH4/floating roof tank/yr. 824 No. of floating roof 

tanks 

0.049 0.934

Combustion Emissions NE NE

Pump Engine Drivers 0.24 scf CH4/hp-hr NE No. of hp-hrs NE NE

Heaters 0.521 scf CH4/bbl burned NE No. of bbl Burned NE NE

Total 0.259 4.982 1

 Emissions are not actually 0, but too small to show at this level of precision. 

 NE: Not estimated for lack of data 

Table A-138: 2009 CH4 Emissions from Petroleum Refining 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 1.065 20.48

 Tanks 20.6 scf CH4/Mbbl 1,839 Mbbl/calendar day heavy 

crude feed 0.014 0.266

 System Blowdowns 137 scf CH4/Mbbl 14,336 Mbbl/calendar day refinery 

feed 0.716 13.77

 Asphalt Blowing 2,555 scf CH4/Mbbl 359 Mbbl/calendar day 

production 0.335 6.445

Fugitive Emissions 0.088 1.702

 Fuel Gas System 439 Mscf CH4/refinery/yr 150 Refineries 0.066 1.266

 Floating Roof Tanks1 587 scf CH4/floating roof 

tank/yr. 

 767 No. of floating roof tanks 0.000

0.009

 Wastewater Treating 1.88 scf CH4/Mbbl 14,336 Mbbl/calendar day refinery 

feed 

0.010

0.189

 Cooling Towers 2.36 scf CH4/Mbbl 14,336 Mbbl/calendar day refinery 

feed 

0.012

0.237

Combustion Emissions 0.090 1.723

 Atmospheric Distillation 3.61 scf CH4/Mbbl 14,659 Mbbl/calendar day refinery 

feed 0.019 0.371

 Vacuum Distillation 3.61 scf CH4/Mbbl 6,503 Mbbl/calendar day feed 0.009 0.165
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

 Thermal Operations 6.01 scf CH4/Mbbl 2,034 Mbbl/calendar day feed 0.004 0.086

 Catalytic Cracking 5.17 scf CH4/Mbbl 4,694 Mbbl/calendar day feed 0.009 0.170

 Catalytic Reforming 7.22 scf CH4/Mbbl 2,970 Mbbl/calendar day feed 0.008 0.150

 Catalytic Hydrocracking 7.22 scf CH4/Mbbl 1,393 Mbbl/calendar day feed 0.004 0.071

 Hydrorefining 2.17 scf CH4/Mbbl 2,067 Mbbl/calendar day feed 0.002 0.031

 Hydrotreating 6.50 scf CH4/Mbbl 9,480 Mbbl/calendar day feed 0.022 0.432

 Alkylation/Polymerization 12.6 scf CH4/Mbbl 1,027 Mbbl/calendar day feed 0.005 0.091

 Aromatics/Isomeration 1.80 scf CH4/Mbbl 956 Mbbl/calendar day feed 0.001 0.012

 Lube Oil Processing 0.00 scf CH4/Mbbl 165 Mbbl/calendar day feed 0.000 0.000

 Engines 0.006 scf CH4/hp-hr 1,128 MMhp-hr/yr 0.006 0.124

 Flares 0.189 scf CH4/Mbbl 14,336 Mbbl/calendar day refinery 

feed 0.001 0.019

Total 1.243 23.90

1

 Emissions are not actually 0, but too small to show at this level of precision. 

Table A-139: Summary of CH4 Emissions from Petroleum Systems (Gg) 

Activity 1990 1995 2000 2005 2006 2007 2008 2009 

Production Field 

Operations 1,653 1,557 1,468 1,366 1,365 1,396 1,409 1,444 

Pneumatic device venting 489 463 428 397 396 398 416 419 

Tank venting 250 226 214 187 188 192 189 212 

Combustion & process 

upsets 88 82 76 71 71 72 75 94 

Misc. venting & fugitives 799 762 727 691 693 714 707 696 

Wellhead fugitives 26 25 22 19 17 20 23 23 

Crude Oil Transportation 7 6 5 5 5 5 5 5 

Refining 25 26 28 28 28 27 25 24 

Total 1,685 1,589 1,501 1,398 1,398 1,427 1,439 1,473 

Note: Totals may not sum due to independent rounding. 

Table A-140: Ratios of CO2 to CH4 Volume in Emissions from Petroleum Production Field Operations 

Whole Crude, 

Post-Separator Associated Gas Tank Flash Gas Offshore 

Ratio %CO2 / %CH4 0.052 0.020 0.017 0.004 

Table A-141: 2009 CO2 Emissions from Petroleum Production Field Operations and Petroleum Refining 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 5.972 316.5

Oil Tanks 3.53 scf of CO2/bbl crude 1,490 MMbbl/yr (non stripper 

wells) 

5.256 278.6

Pneumatic Devices, High Bleed 6.704 scfd CO2/device 139,797 No. of high-bleed 

devices 

0.342 18.13

Pneumatic Devices, Low Bleed 1.055 scfd CO2/device 259,624 No. of low-bleed devices 0.100 5.299

Chemical Injection Pumps 5.033 scfd CO2/pump 28,166 No. of pumps 0.052 2.742

Vessel Blowdowns1 1.583 scfy CO2/vessel 182,451 No. of vessels 0.000 0.015

Compressor Blowdowns1 77 scf/yr of CO2/compressor 2,473 No. of compressors 0.000 0.010

Compressor Starts1 171 scf/yr. of CO2/compressor 2,473 No. of compressors 0.000 0.022

Stripper wells 48 scf/yr of CO2/stripper well 317,230 No. of stripper wells 

vented 

0.015 0.800

Well Completion Venting1 14.87 scf/completion 11,804 Oil well completions 0.000 0.009

Well Workovers1 1.95 scf CO2/workover 39,450 Oil well workovers 0.000 0.004

Pipeline Pigging NE scfd of CO2/pig station NE No. of crude pig stations NE NE

Offshore Platforms, Shallow water 

Oil, fugitive, vented and combusted 

 358 scfd CO2/platform 1,441 No. of shallow water oil 

platforms 

0.188 9.984

Offshore Platforms, Deepwater oil, 1,701 scfd CO2/platform 28 No. of deep water oil 0.017 0.917
A-169 

Activity/Equipment 

2009 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

fugitive, vented and combusted platforms 

Fugitive Emissions 0.052 2.776

Oil Wellheads (heavy crude)1 0.003 scfd/well 14,718 No. of hvy. crude wells 0.000 0.001

Oil Wellheads (light crude) 0.337 scfd/well 194,052 No. of lt. crude wells 0.024 1.267

Separators (heavy crude)1 0.003 scfd CO2/separator 10,740 No. of hvy. crude seps. 0.000 0.001

Separators (light crude) 0.281 scfd CO2/separator 97,745 No. of lt. crude seps. 0.010 0.531

Heater/Treaters (light crude) 0.319 scfd CO2/heater 73,966 No. of heater treaters 0.009 0.456

Headers (heavy crude)1 0.002 scfd CO2/header 13,721 No. of hvy. crude hdrs. 0.000 0.000

Headers (light crude) 0.220 scfd CO2/header 42,536 No. of lt. crude hdrs. 0.003 0.181

Floating Roof Tanks1 17,490 scf CO2/floating roof 

tank/yr. 

24 No. of floating roof tanks 0.000 0.023

Compressors 2.029 scfd CO2/compressor 2,473 No. of compressors 0.002 0.097

Large Compressors 332 scfd CO2/compressor 0 No. of large comprs. 0.000 0.000

Sales Areas 2.096 scf CO2/loading 1,630,377 Loadings/year 0.003 0.181

Pipelines NE scfd of CO2/mile of 

pipeline 

11,249 Miles of gathering line NE NE

Well Drilling NE scfd of CO2/oil well 

drilled 

13,255 No. of oil wells drilled NE NE

Battery Pumps 0.012 scfd of CO2/pump 157,800 No. of battery pumps 0.001 0.038

Process Upset Emissions 0.003 0.134

Pressure Relief Valves1 1.794 scf/yr/PR valve 163,499 No. of PR valves 0.000 0.016

Well Blowouts Onshore 0.051 MMscf/blowout 44 No. of blowouts/yr 0.002 0.119

Refining Emissions 2.720 144.2

 Asphalt Blowing2 20,736 scf CO2/Mbbl 359 Mbbl/calendar day 

production 

2.720 144.2

Total 8.747 463.6 1

 Emissions are not actually 0, but too small to show at this level of precision. 2 Asphalt Blowing emissions are the only significant vented emissions from the refining sector; other sources are too small to show at this level of 

precision. 

 NE: Not estimated for lack of data 

Energy use CO2 emissions not estimated to avoid double counting with fossil fuel combustion 

Table A-142: Summary of CO2 Emissions from Petroleum Systems (Gg) 

Activity 1990 1995 2000 2005 2006 2007 2008 2009 

Production Field 

Operations 376 341 323 285 285 292 288 319 

Pneumatic device venting 27 26 24 22 22 22 23 23 

Tank venting 328 296 281 246 246 252 247 278 

Misc. venting & fugitives 18 18 17 16 16 16 16 16 

Wellhead fugitives 1 1 1 111 1 1 

Refining 

Asphalt Blowing 180 187 211 205 203 182 165 144 

Total 555 528 534 490 488 474 453 463 
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

3.6. Methodology for Estimating CO2, N2O and CH4 Emissions from the 

Incineration of Waste 

Emissions of CO2 from the incineration of waste include CO2 generated by the incineration of plastics, synthetic 

rubber and synthetic fibers in municipal solid waste (MSW), and incineration of tires (which are composed in part of 

synthetic rubber and C black) in a variety of other combustion facilities (e.g., cement kilns). Incineration of waste also 

results in emissions of N2O and CH4. The methodology for calculating emissions from each of these waste incineration 

sources is described in this Annex. 

CO2 from Plastics Incineration 

In the Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures

reports (EPA 1999 through 2003, 2005 through 2011), the flows of plastics in the U.S. waste stream are reported for seven 

resin categories. For 2009, the quantity generated, recovered, and discarded for each resin is shown in Table A-143. The 

data set for 1990 through 2009 is incomplete, and several assumptions were employed to bridge the data gaps. The EPA 

reports do not provide estimates for individual materials landfilled and incinerated, although they do provide such an 

estimate for the waste stream as a whole. To estimate the quantity of plastics landfilled and incinerated, total discards 

were apportioned based on the proportions of landfilling and incineration for the entire U.S. waste stream for each year in 

the time series according to Biocycle’s State of Garbage in America (van Haaren et al. 2010). For those years when 

distribution by resin category was not reported (1990 through 1994), total values were apportioned according to 1995 (the 

closest year) distribution ratios. Generation and recovery figures for 2002 and 2004 were linearly interpolated between 

surrounding years’ data. 

Table A-143: 2008 Plastics in the Municipal Solid Waste Stream by Resin (Gg) 

Waste Pathway PET HDPE PVC

LDPE/

LLDPE PP PS Other Total

Generation 3,202 4,727 1,016 5,715 5,017 2,241 5,144 27,261

Recovery 662 535 0 290 45 18 372 1,923

Discard 2,540 4,191 1,016 5,425 4,971 2,223 4,772 25,338

Landfill 2,316 3,822 926 4,947 4,533 2,027 4,351 23,104

Combustion 224 369 90 478 438 196 421 2,234

Recovery* 21% 11% 0% 5% 1% 1% 7% 7%

Discard* 79% 89% 100% 95% 99% 99% 93% 93%

 Landfill* 72% 81% 91% 87% 90% 90% 85% 85%

Combustion* 7% 8% 9% 8% 9% 9% 8% 8%

*As a percent of waste generation. 

Note: Totals may not sum due to independent rounding. Abbreviations: PET (polyethylene terephthalate), HDPE (high density polyethylene), 

PVC (polyvinyl chloride), LDPE/LLDPE (linear low density polyethylene), PP (polypropylene), PS (polystyrene). 

Fossil fuel-based CO2 emissions were calculated as the product of plastic combusted, C content, and fraction 

oxidized (see Table A-144). The C content of each of the six types of plastics is listed, with the value for “other plastics” 

assumed equal to the weighted average of the six categories. The fraction oxidized was assumed to be 98 percent. 

Table A-144: 2008 Plastics Incinerated (Gg), Carbon Content (%), Fraction Oxidized (%) and Carbon Incinerated (Gg) 

Factor PET HDPE PVC 

LDPE/

LLDPE PP PS Othera Total 

Quantity Combusted 224 369 90 478 438 196 421 2,234 

Carbon Content of Resin 63% 86% 38% 86% 86% 92% 66% - 

Fraction Oxidized 98% 98% 98% 98% 98% 98% 98% - 

Carbon in Resin Combusted 137 310 34 402 368 177 271 1,700 

Emissions (Tg CO2 Eq.) 0.5 1.1 0.1 1.5 1.3 0.6 1.0 6.2 a

 Weighted average of other plastics produced. 

Note: Totals may not sum due to independent rounding. 

CO2 from Incineration of Synthetic Rubber and Carbon Black in Tires 

Emissions from tire incineration require two pieces of information: the amount of tires incinerated and the C 

content of the tires. Scrap Tire Markets in the United States: 9th Biennial Report (RMA 2009a) reports that 2,484.4 
A-171 

thousand of the 4,105.8 thousand tons of scrap tires generated in 2007 (approximately 61 percent of generation) were used 

for fuel purposes. Using RMA’s estimates of average tire composition and weight, the mass of synthetic rubber and C 

black in scrap tires was determined: 

• Synthetic rubber in tires was estimated to be 90 percent C by weight, based on the weighted average C contents 

of the major elastomers used in new tire consumption.46 Table A- 145 shows consumption and C content of 

elastomers used for tires and other products in 2002, the most recent year for which data are available. 

• C black is 100 percent C (Aslett Rubber Inc. n.d.). 

Multiplying (a) the mass of scrap tires incinerated by (b) the total C content of the synthetic rubber and C black 

portions of scrap tires and (c) by a 98 percent oxidation factor yielded CO2 emissions, as shown in Table A- 146. The 

disposal rate of rubber in tires (0.5 Tg C/yr) is smaller than the consumption rate for tires based on summing the 

elastomers listed in Table A- 145 (1.3 Tg/yr); this is due to the fact that much of the rubber is lost through tire wear during

the product’s lifetime and may also reflect the lag time between consumption and disposal of tires. Tire production and 

fuel use for 1990 through 2007 were taken from RMA 2006 and RMA 2009a; when data were not reported, they were 

linearly interpolated between bracketing years’ data or, for the ends of time series, set equal to the closest year with 

reported data. 

RMA 2009a changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap tire. 

As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert the 

previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006, 

2009b). 

Table A- 145: Elastomers Consumed in 2002 (Gg) 

Elastomer Consumed

Carbon 

Content Carbon Equivalent 

Styrene butadiene rubber solid 768 91% 700 

For Tires 660 91% 602 

For Other Products* 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 - 1,950 

 For Tires 1,307 - 1,174 

*Used to calculate C content of non-tire rubber products in municipal solid waste. 

- Not applicable 

Note: Totals may not sum due to independent rounding. 

 

46 The carbon content of tires (1,158 Gg C) divided by the mass of rubber in tires (1,285 Gg) equals 90 percent.
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 146: Scrap Tire Constituents and CO2 Emissions from Scrap Tire Incineration in 2007 

Material 

Weight of Material 

(Tg) 

Fraction 

Oxidized Carbon Content 

Emissions (Tg 

CO2 Eq.) 

Synthetic Rubber 0.5 98% 90% 1.8 

Carbon Black 0.6 98% 100% 2.3 

Total 1.1 - - 4.1 

- Not applicable 

CO2 from Incineration of Synthetic Rubber in Municipal Solid Waste 

Similar to the methodology for scrap tires, CO2 emissions from synthetic rubber in MSW were estimated by 

multiplying the amount of rubber incinerated by an average rubber C content. The amount of rubber discarded in the 

MSW stream was estimated from generation and recycling data47 provided in the Municipal Solid Waste Generation, 

Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2011) 

and unpublished backup data (Schneider 2007). The reports divide rubber found in MSW into three product categories: 

other durables (not including tires), non-durables (which includes clothing and footwear and other non-durables), and 

containers and packaging. EPA (2011) did not report rubber found in the product category “containers and packaging;” 

however, containers and packaging from miscellaneous material types were reported for 2009. As a result, EPA assumes 

that rubber containers and packaging are reported under the “miscellaneous” category; and therefore, the quantity reported 

for 2009 was set equal to the quantity reported for 2008. Since there was negligible recovery for these product types, all 

the waste generated is considered to be discarded. Similar to the plastics method, discards were apportioned into 

landfilling and incineration based on their relative proportions, for each year, for the entire U.S. waste stream. The report 

aggregates rubber and leather in the MSW stream; an assumed synthetic rubber content of 70% was assigned to each 

product type, as shown in Table A-147.48 A C content of 85 percent was assigned to synthetic rubber for all product types 

(based on the weighted average C content of rubber consumed for non-tire uses), and a 98 percent fraction oxidized was 

assumed. 

Table A-147: Rubber and Leather in Municipal Solid Waste in 2009 

Product Type 

Incinerated 

(Gg) 

Synthetic 

Rubber (%) 

Carbon Content 

(%) 

Fraction 

Oxidized (%) 

Emissions 

(Tg CO2 Eq.) 

Durables (not Tires) 271 70% 85% 98% 0.8

Non-Durables 85 - - - 0.3

Clothing and Footwear 63 70% 85% 98% 0.2 

Other Non-Durables 22 70% 85% 98% 0.1 

Containers and Packaging 2 70% 85% 98% +

Total 358 -- - 1.1 

+ Less than 0.05 Tg CO2 Eq. 

- Not applicable. 

CO2 from Incineration of Synthetic Fibers 

CO2 emissions from synthetic fibers were estimated as the product of the amount of synthetic fiber discarded 

annually and the average C content of synthetic fiber. Fiber in the MSW stream was estimated from data provided in the 

Municipal Solid Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 

through 2003, 2005 through 2011) for textiles. Production data for the synthetic fibers was based on data from the 

American Chemical Society (FEB 2009). The amount of synthetic fiber in MSW was estimated by subtracting (a) the 

amount recovered from (b) the waste generated (see Table A-148). As with the other materials in the MSW stream, 

discards were apportioned based on the annually variable proportions of landfilling and incineration for the entire U.S. 

waste stream as found in van Haaren et al. (2010). It was assumed that approximately 55 percent of the fiber was synthetic 

in origin, based on information received from the Fiber Economics Bureau (DeZan 2000). An average C content of 70 

percent was assigned to synthetic fiber using the production-weighted average of the C contents of the four major fiber 

 

47 Discards = Generation minus recycling. 

48 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-173 

types (polyester, nylon, olefin, and acrylic) produced in 1999 (see Table A-149). The equation relating CO2 emissions to 

the amount of textiles combusted is shown below. 

CO2 Emissions from the Incineration of Synthetic Fibers = Annual Textile Incineration (Gg) ×

(Percent of Total Fiber that is Synthetic) × (Average C Content of Synthetic Fiber) ×

(44g CO2/12 g C) 

Table A-148: Synthetic Textiles in MSW (Gg) 

Year Generation Recovery Discards Incineration 

1990 2,884 328 2,557 332 

1995 3,674 447 3,227 442 

1996 3,832 472 3,361 467 

1997 4,090 526 3,564 458 

1998 4,269 556 3,713 407 

1999 4,498 611 3,887 406 

2000 4,686 655 4,031 415 

2001 4,870 715 4,155 432 

2002 5,123 750 4,373 459 

2003 5,297 774 4,522 472 

2004 5,451 884 4,567 473 

2005 5,649 918 4,731 473 

2006 5,893 933 4,959 479 

2007 5,927 953 4,974 460 

2008 6,170 948 5,222 460 

2009 6,319 943 5376 474 

Table A-149: Synthetic Fiber Production in 1999 

Fiber Production (Tg) Carbon Content 

Polyester 1.8 63% 

Nylon 1.2 64% 

Olefin 1.4 86% 

Acrylic 0.1 68% 

Total 4.5 70% 

N2O and CH4 from Incineration of Waste 

Estimates of N2O emissions from the incineration of waste in the United States are based on the methodology 

outlined in the EPA’s Compilation of Air Pollutant Emission Factors (EPA 1995) and presented in the Municipal Solid 

Waste Generation, Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 

2005 through 2011) and unpublished backup data (Schneider 2007). According to this methodology, emissions of N2O 

from waste incineration are the product of the mass of waste incinerated, an emission factor of N2O emitted per unit mass 

of waste incinerated, and an N2O emissions control removal efficiency. The mass of waste incinerated was derived from 

the information published in BioCycle (van Haaren et al. 2010). For waste incineration in the United States, an emission 

factor of 50 g N2O/metric ton MSW based on the 2006 IPCC Guidelines and an estimated emissions control removal 

efficiency of zero percent were used (IPCC 2006). It was assumed that all MSW incinerators in the United States use 

continuously-fed stoker technology (Bahor 2009, 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 Guidelines for National Greenhouse Gas Inventories (IPCC 2006). According to this 

methodology, emissions of CH4 from waste incineration are the product of the mass of waste incinerated and an emission 

factor of CH4 emitted per unit mass of waste incinerated. Similar to the N2O emissions methodology, the mass of waste 

incinerated was derived from the information published in Biocycle (van Haaren et al. 2010). For waste incineration in the 

United States, an emission factor of 0.20 kg CH4/Gg MSW was used based on the 2006 IPCC Guidelines and assuming 

that all MSW incinerators in the United States use continuously-fed stoker technology (Bahor 2009, ERC 2009). No 

information was available on the mass of waste incinerated from Biocycle in 2009, so the value was assumed to remain 

constant at the 2008 level. 

Despite the differences in methodology and data sources, the two series of references (EPA’s and BioCycle’s) 

provide estimates of total solid waste incinerated that are relatively consistent (see Table A-150). 
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-150: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons) 

Year EPA BioCycle 

1990 28,939,680 30,632,057 

1995 32,241,888 29,639,040 

1996 32,740,848 29,707,171 

1997 33,294,240 27,798,368 

1998 31,216,752 25,489,893 

1999 30,881,088 24,296,249 

2000 30,599,856 25,974,978 

2001 30,481,920 25,942,036a

2002 30,255,120 25,802,917 

2003 30,028,320 25,930,542b

2004 28,585,872 26,037,823 

2005 28,685,664 25,973,520c

2006 28,985,040 25,853,401 

2007 29,003,184 24,788,539d

2008 28,622,160 23,674,017 

2009 26,317,872 NA 

NA (Not Available) 

a

 Interpolated between 2000 and 2002 values. 

b

 Interpolated between 2002 and 2004 values. 

c

 Interpolated between 2004 and 2006 values. 

d

 Interpolated between 2006 and 2008 values 

References 

ArSova, Ljupka, Rob van Haaren, Nora Goldstein, Scott M. Kaufman, and Nickolas J. Themelis (2008). “16th Annual 

BioCycle Nationwide Survey: The State of Garbage in America” Biocycle, JG Press, Emmaus, PA. December. 

Astlett Rubber Inc. (n.d.) Material Safety Data Sheet, Carbon Black. Available online at: 

<http://www.astlettrubber.com/pdf/specialty/msds/carbonblack.pdf>. Accessed 5 November 2009. 

Bahor, B (2009) Covanta Energy’s public review comments re: Draft Inventory of U.S. Greenhouse Gas Emissions and 

Sinks: 1990-2007. Submitted via email on April 9, 2009 to Leif Hockstad, U.S. EPA. 

DeZan, D.(2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF 

Consulting. 4 August 2000. 

Energy Recovery Council (2009). “2007 Directory of Waste-to-Energy Plants in the United States,” accessed September 

29, 2009. 

EPA (2005 through 20011) Municipal Solid Waste in the United States: Facts and Figures. Office of Solid Waste and 

Emergency Response, U.S. Environmental Protection Agency. Washington, DC. Available online at 

<http://www.epa.gov/epaoswer/non-hw/muncpl/msw99.htm>. 

EPA (1999 through 2003) Characterization of Municipal Solid Waste in the United States: Source Data Update. Office of 

Solid Waste, U.S. Environmental Protection Agency. Washington, DC. 

EPA (1995). Compilation of Air Pollutant Emission Factors, AP-42. Fifth Edition, Vol. I: Stationary Point and Area 

Sources, Introduction. Office of Air Quality Planning and Standards. Research Triangle Park, NC. October. 

FEB (2009) Fiber Economics Bureau, as cited in C&EN (2009) Chemical Output Slipped In Most Regions Chemical & 

Engineering News, American Chemical Society, 6 July. Available online at <http://www.cen-online.org>. 

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. 

RMA (2009a) Scrap Tire Markets in the United States: 9th Biennial Report. Rubber Manufacturers Association. 

Washington, DC. May 2009. 
A-175 

RMA (2009b) “Scrap Tire Markets: Facts and Figures – Scrap Tire Characteristics.” Available online at: 

<http://www.rma.org/scrap_tires/scrap_tire_markets/scrap_tire_characteristics/> Accessed 17 September 2009. 

RMA (2002 through 2006) U.S. Scrap Tire Markets. Rubber Manufacturers Association. Washington, DC. Available 

online at: <https://www.rma.org/publications/scrap_tires/index.cfm?CategoryID=614>. 

Schneider, S. (2007) E-mail communication Shelly Schneider, Franklin Associates to Sarah Shapiro, ICF International., A 

Division of ERG. January 10, 2007. 

STMC (1990 through 1997) Scrap Tire Use/Disposal Study. Rubber Manufacturers Association: Scrap Tire Management 

Council. Available online at: <https://www.rma.org/publications/scrap_tires/index.cfm?CategoryID=614>.van 

Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010. 

Volume 51, Number 10, pg. 16-23. 
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

3.7. Methodology for Estimating Emissions from International Bunker Fuels 

used by the U.S. Military 

Bunker fuel emissions estimates for the Department of Defense (DoD) are developed using data generated by the 

Defense Energy Support Center (DESC) for aviation and naval fuels. The DESC of the Defense Logistics Agency (DLA) 

prepared a special report based on data in the Fuels Automated System (FAS), a database that recently replaced the 

Defense Fuels Automated Management System (DFAMS). Data for intermediate fuel oil, however, currently remains in 

the original DFAMS database. DFAMS/FAS contains data for 1995 through 2009, but the data set was not complete for 

years prior to 1995. Fuel quantities for 1990 to 1994 were estimated based on a back-calculation of the 1995 DFAMS 

values using DLA aviation and marine fuel procurement data. The back-calculation was refined in 1999 to better account 

for the jet fuel conversion from JP4 to JP8 that occurred within DoD between 1992 and 1995. 

Step 1: Omit Extra-Territorial Fuel Deliveries 

Beginning with the complete FAS data set for each year, the first step in the development of DoD-related 

emissions from international bunker fuels was to identify data that would be representative of international bunker fuel 

consumption as that term is defined by decisions of the UNFCCC (i.e., fuel sold to a vessel, aircraft, or installation within 

the United States or its territories and used in international maritime or aviation transport). Therefore, fuel data were 

categorized by the location of fuel delivery in order to identify and omit all international fuel transactions/deliveries (i.e., 

sales abroad). 

Step 2: Allocate JP-8 between Aviation and Land-based Vehicles 

As a result of DoD49 and NATO50 policies on implementing the Single Fuel For the Battlefield concept, DoD 

activities have been increasingly replacing diesel fuel with JP8 (a type of jet fuel) in compression ignition and turbine 

engines in land-based equipment. Based on this concept and examination of all data describing jet fuel used in land-based 

vehicles, it was determined that a portion of JP8 consumption should be attributed to ground vehicle use. Based on 

available Service data and expert judgment, it was determined that a small fraction of the total JP8 use should be 

reallocated from the aviation subtotal to a new land-based jet fuel category for 1997 and subsequent years. The amount of 

JP8 reallocated was determined to be between 1.78 and 2.7 times the amount of diesel fuel used, depending on the Service. 

As a result of this reallocation, the JP8 use reported for aviation will be reduced and the total fuel use for land-based 

equipment will increase. DoD’s total fuel use will not change. 

Table A-151 displays DoD’s consumption of fuels that remain at the completion of Step 1, summarized by fuel 

type. Table A-151 reflects the adjustments for jet fuel used in land-based equipment, as described above. 

Step 3: Omit Land-Based Fuels 

Navy and Air Force land-based fuels (i.e., fuel not used by ships or aircraft) were also omitted for the purpose of 

calculating international bunker fuels. The remaining fuels, listed below, were considered potential DoD international 

bunker fuels. 

• Marine: naval distillate fuel (F76), marine gas oil (MGO), and intermediate fuel oil (IFO). 

• Aviation: jet fuels (JP8, JP5, JP4, JAA, JA1, and JAB). 

Step 4: Omit Fuel Transactions Received by Military Services that are not Considered to be International 

Bunker Fuels 

Next, the records were sorted by Military Service. The following assumptions were used regarding bunker fuel 

use by Service, leaving only the Navy and Air Force as users of military international bunker fuels. 

 

49 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Directive 4140.25, DoD Management Policy for Energy Commodities and 

Related Services, 1999.

50 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipments Using Compression Ignition or Turbine 

Engines, 1987.
A-177 

• Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential 

international bunker fuel. Fuel consumed in international aviation or marine transport was included in the 

bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a 

country’s borders was not considered a bunker fuel. 

• Based on discussions with the Army staff, only an extremely small percentage of Army aviation emissions, 

and none of its watercraft emissions, qualified as bunker fuel emissions. The magnitude of these emissions 

was judged to be insignificant when compared to Air Force and Navy emissions. Based on this, Army 

bunker fuel emissions were assumed to be zero. 

• Marine Corps aircraft operating while embarked consumed fuel reported as delivered to the Navy. Bunker 

fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel estimates. 

Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero. 

• Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased 

fuel from DESC were assumed to be zero. 

Step 5: Determine Bunker Fuel Percentages 

Next it was necessary to determine what percent of the marine and aviation fuels were used as international 

bunker fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States and 

end in a foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations 

principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights). 

For the Air Force, a bunker fuel weighted average was calculated based on flying hours by major command. International 

flights were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In 

addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel per hour of 

flight than most tactical aircraft. The Air Force bunker fuel percentage was determined to be 13.2 percent. This 

percentage was multiplied by total annual Air Force aviation fuel delivered for U.S. activities, producing an estimate for 

international bunker fuel consumed by the Air Force. The Naval Aviation bunker fuel percentage of total fuel was 

calculated using flying hour data from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year 

1998, and estimates of bunker fuel percent of flights provided by the fleet. The Naval Aviation bunker fuel percentage, 

determined to be 40.4 percent, was multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding 

total Navy aviation bunker fuel consumed. 

For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels. In 2000, the 

Navy reported that 79 percent of vessel operations were underway, while the remaining 21 percent of operations occurred 

in port (i.e., pierside). Therefore, the Navy maritime bunker fuel percentage was determined to be 79 percent. The 

percentage of time underway may vary from year-to-year. For example, for years prior to 2000, the bunker fuel 

percentage was 87 percent. Table A-152 and Table A-153 display DoD bunker fuel use totals for the Navy and Air Force. 

Step 6: Calculate Emissions from International Bunker Fuels 

Bunker fuel totals were multiplied by appropriate emission factors to determine GHG emissions. CO2 emissions 

from Aviation Bunkers and distillate Marine Bunkers are the total of military aviation and marine bunker fuels, 

respectively. 

 The rows labeled “U.S. Military” and “U.S. Military Naval Fuels” in the tables in the International Bunker Fuels 

section of the Energy Chapter were based on the totals provided in Table A-152 and Table A-153, below. CO2 emissions 

from aviation bunkers and distillate marine bunkers presented in Table A-156, and are based on emissions from fuels 

tallied in Table A-152 and Table A-153. 
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-151: Transportation Fuels from Domestic Fuel Deliveriesa (Million Gallons) 

Vehicle Type/Fuel 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Aviation 4,598.4 3,099.9 2,941.9 2,685.6 2,741.4 2,635.2 2,664.4 2,900.6 2,609.8 2,615.0 2,703.1 2,338.1  Total Jet Fuels 4,598.4 3,099.9 2,941.9 2,685.6 2,741.4 2,635.2 2,664.4 2,900.6 2,609.6 2,614.9 2,703.1 2,338.0 

 JP8 285.7 2,182.8 2,253.1 2,072.0 2,122.5 2,066.5 2,122.7 2,326.2 2,091.4 2,094.3 2,126.2 1,838.8  JP5 1,025.4 691.2 615.8 552.8 515.6 505.5 472.1 503.2 442.2 409.1 433.7 421.6 

 Other Jet Fuels 3,287.3 225.9 72.9 60.9 103.3 63.3 69.6 71.2 76.1 111.4 143.2 77.6 

 Aviation Gasoline + + + + + + + + 0.1 0.1 + 0.1 

Marine 686.8 438.9 493.3 639.8 674.2 598.9 454.4 418.4 455.8 609.1 704.5 604.9 

 Middle Distillate (MGO) + + 38.5 47.5 51.1 49.2 48.3 33.0 41.2 88.1 71.2 54.0  Naval Distillate (F76) 686.8 438.9 449.0 583.4 608.4 542.9 398.0 369.1 395.1 460.9 583.5 525.9  Intermediate Fuel Oil (IFO)b + + 5.9 9.0 14.7 6.7 8.1 16.3 19.5 60.2 49.9 25.0 

Other c 717.1 310.9 276.9 263.3 256.8 256.0 248.2 109.8 211.1 221.2 170.9 205.6 

 Diesel 93.0 119.9 126.1 132.6 139.5 146.8 126.6 26.6 57.7 60.8 46.4 56.8  Gasoline 624.1 191.1 150.8 119.0 93.9 74.1 74.8 24.7 27.5 26.5 19.4 24.3  Jet Fuel d + + + 11.7 23.4 35.0 46.7 58.4 125.9 133.9 105.1 124.4 

Total (Including Bunkers) 6,002.4 3,849.8 3,712.1 3,588.8 3,672.4 3,490.1 3,367.0 3,428.8 3,276.7 3,445.3 3,578.5 3,148.6 

Note: Totals may not sum due to independent rounding. a Includes fuel consumption in the United States and U.S. Territories. b Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command. c Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the military Services for 1990 and 1996. The 1991 through 1995 data points were i

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 g

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 h

consumption in land-based vehicles. Data sets for other diesel and gasoline consumed by the military in 2000 were estimated based on ground fuels consumption trends. that was more consistent with expected consumption for 2000. In 2001, other gasoline and diesel fuel totals were generated by DESC. 

d The fraction of jet fuel consumed in land-based vehicles was estimated using Service data, DESC data, and expert judgment. 

+ Does not exceed 0.05 million gallons. 
Table A-152: Total U.S. Military Aviation Bunker Fuel (Million Gallons) Fuel Type/Service 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 JP8 56.7 300.4 308.8 292.0 306.4 301.4 307.6 341.2 309.5 305.1 309.8 285.6 262.5 249.1  Navy 56.7 38.3 39.8 46.9 53.8 55.5 53.4 73.8 86.6 76.3 79.2 70.9 64.7 62.7 

 Air Force + 262.2 269.0 245.1 252.6 245.9 254.2 267.4 222.9 228.7 230.6 214.7 197.8 186.5 JP5 370.5 249.8 219.4 194.2 184.4 175.4 160.3 169.7 158.3 146.1 157.9 160.6 125.0 144.5  Navy 365.3 246.3 216.1 191.2 181.4 170.6 155.6 163.7 153.0 141.3 153.8 156.9 122.8 141.8  Air Force 5.3 3.5 3.3 3.0 3.0 4.8 4.7 6.1 5.3 4.9 4.1 3.7 2.3 2.7 

JP4 420.8 21.5 1.1 0.1 + + + + + + + + + + 

 Navy + + + + + + + + + + + + + + 

 Air Force 420.8 21.5 1.1 0.1 + + + + + + + + + + JAA 13.7 9.2 10.3 9.4 10.8 10.8 12.5 12.6 13.7 21.7 30.0 15.5 11.7 15.6 

 Navy 8.5 5.7 6.6 5.9 6.6 6.3 7.9 8.0 9.8 15.5 21.5 11.6 9.1 11.7  Air Force 5.3 3.5 3.7 3.5 4.2 4.5 4.5 4.6 3.8 6.2 8.6 3.9 2.6 3.9 

JA1 + + + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.1  Navy + + + + + + + + + + + + + 0.1  Air Force + + + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.0 JAB + + + + + + + + + + + + + +  Navy + + + + + + + + + + + + + +  Air Force + + + + + + + + + + + + + + Navy Subtotal 430.5 290.2 262.5 244.0 241.8 232.4 216.9 245.5 249.4 233.1 254.4 239.4 196.6 216.3 

Air Force Subtotal 431.3 290.7 277.0 251.7 259.9 255.2 263.5 278.1 232.7 239.9 243.7 222.9 203.1 194.0 

Total 861.8 580.9 539.5 495.6 501.7 487.5 480.4 523.6 482.1 473.0 498.1 462.3 399.7 410.3 

+ Does not exceed 0.05 million gallons. Note: Totals may not sum due to independent rounding. Table A-153: Total U.S. DoD Maritime Bunker Fuel (Million Gallons) Marine Distillates 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2Navy—MGO + + 30.3 35.6 31.9 39.7 23.8 22.5 27.1 63.7 56.2 38.0 33.0 31.6 4Navy—F76 522.4 333.8 331.9 441.7 474.2 466.0 298.6 282.6 305.6 347.8 434.4 413.1 355.9 404.1 37

Navy—IFO + + 4.6 7.1 11.6 5.3 6.4 12.9 15.4 47.5 39.4 19.7 25.4 8.8 1

Total 522.4 333.8 366.8 484.3 517.7 511.0 328.8 318.0 348.2 459.0 530.0 470.7 414.3 444.4 43

+ Does not exceed 0.005 million gallons. Note: Totals may not sum due to independent rounding. 
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-154: Aviation and Marine Carbon Contents (Tg Carbon/QBtu) and Fraction Oxidized 

Mode (Fuel) Carbon Content 

Coefficient 

Fraction 

Oxidized

Aviation (Jet Fuel) Variable 1.00

Marine (Distillate) 20.17 1.00

Marine (Residual) 20.48 1.00

Source: EPA (2010) and IPCC (2006) 

Table A-155: Annual Variable Carbon Content Coefficient for Jet Fuel (Tg Carbon/QBtu) 

Fuel 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Jet Fuel 19.40 19.34 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70 19.70

Source: EPA (2010) 

Table A-156: Total U.S. DoD CO2 Emissions from Bunker Fuels (Tg CO2 Eq.) 

Mode 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Aviation 8.1 5.5 5.2 4.8 4.9 4.7 4.7 5.1 4.7 4.6 4.8 4.5 3.9 4.0 3.8 3.6

Marine 5.4 3.4 3.8 5.0 5.3 5.2 3.4 3.3 3.6 4.7 5.4 4.8 4.2 4.6 4.5 3.9

Total 13.4 9.0 9.0 9.8 10.2 10.0 8.0 8.3 8.3 9.3 10.3 9.3 8.1 8.5 8.2 7.5

Note: Totals may not sum due to independent rounding. 
A-181 

3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of 

Ozone Depleting Substances 

Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a 

country-specific modeling approach. The Vintaging Model was developed as a tool for estimating the annual chemical 

emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal 

Protocol and the United States’ Clean Air Act Amendments of 1990, the domestic U.S. consumption of ODS—

chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs)—

has been drastically reduced, forcing these industrial sectors to transition to more ozone friendly chemicals. As these 

industries have moved toward ODS alternatives such as hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs), the 

Vintaging Model has evolved into a tool for estimating the rise in consumption and emissions of these alternatives, and the 

decline of ODS consumption and emissions. 

The Vintaging Model estimates emissions from five ODS substitute end-use sectors: air-conditioning and 

refrigeration, foams, aerosols, solvents, and fire-extinguishing. Within these sectors, there are 59 independently modeled 

end-uses. The model requires information on the market growth for each of the end-uses, as well as a history of the 

market transition from ODS to alternatives, as well as the characteristics of each end-use such as market size or charge 

sizes and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to 

each of its substitutes. 

The model, named for its method of tracking the emissions of annual “vintages” of new equipment that enter into 

service, is a “bottom-up” model. It models the consumption of chemicals based on estimates of the quantity of equipment 

or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain 

the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of 

the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a “business-asusual” baseline case, and does not incorporate measures to reduce or eliminate the emissions of these gases other than 

those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying annual leak rates, 

service emission rates, and disposal emission rates to each population of equipment. By aggregating the emission and 

consumption output from the different end-uses, the model produces estimates of total annual use and emissions of each 

chemical. 

The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System 

maintained by the Stratospheric Protection Division and information from submissions to EPA under the Significant New 

Alternatives Policy (SNAP) program. Published sources include documents prepared by the United Nations Environment 

Programme (UNEP) Technical Options Committees, reports from the Alternative Fluorocarbons Environmental 

Acceptability Study (AFEAS), and conference proceedings from the International Conferences on Ozone Protection 

Technologies and Earth Technologies Forums. EPA also coordinates extensively with numerous trade associations and 

individual companies. For example, the Alliance for Responsible Atmospheric Policy; the Air-Conditioning, Heating and 

Refrigeration Institute; the Association of Home Appliance Manufacturers; the American Automobile Manufacturers 

Association; and many of their member companies have provided valuable information over the years. In some instances 

the unpublished information that the EPA uses in the model is classified as Confidential Business Information (CBI). The 

annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be inferred. Full public 

disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has been entrusted to the 

EPA. 

The following sections discuss the emission equations used in the Vintaging Model for each broad end-use 

category. These equations are applied separately for each chemical used within each of the different end-uses. In the 

majority of these end-uses, more than one ODS substitute chemical is used. 

In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market. 

Estimates of the consumption of ODS alternatives can be inferred by determining the transition path of each regulated 

ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding which 

alternatives have been used, and what fraction of the ODS market in each end-use has been captured by a given 

alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated 

for each chemical used within each end-use. 

Methodology 

The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps: 
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken 

from published sources and industry experts. 

2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed 

characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate 

the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this 

simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to 

comply with the ODS phase-out. 

3. Estimate emissions of the ODS substitutes. The chemical use is estimated from the amount of 

substitutes that are required each year for the manufacture, installation, use, or servicing of products. The emissions are 

estimated from the emission profile for each vintage of equipment or product in each end-use. By aggregating the 

emissions from each vintage, a time profile of emissions from each end-use is developed. 

Each set of end-uses is discussed in more detail in the following sections. 

 Refrigeration and Air-Conditioning 

For refrigeration and air conditioning products, emission calculations are split into two categories: emissions 

during equipment lifetime, which arise from annual leakage and service losses, and disposal emissions, which occur at the 

time of discard. Two separate steps are required to calculate the lifetime emissions from leakage and service, and the 

emissions resulting from disposal of the equipment. For any given year, these lifetime emissions (for existing equipment) 

and disposal emissions (from discarded equipment) are summed to calculate the total emissions from refrigeration and airconditioning. As new technologies replace older ones, it is generally assumed that there are improvements in their leak, 

service, and disposal emission rates. 

Step 1: Calculate lifetime emissions 

Emissions from any piece of equipment include both the amount of chemical leaked during equipment operation 

and the amount emitted during service. Emissions from leakage and servicing can be expressed as follows: 

 Esj = (la + ls) × Σ Qcj-i+1 for i = 1→k

Where: 

Es = Emissions from Equipment Serviced. Emissions in year j from normal leakage and servicing (including 

recharging) of equipment. 

la = Annual Leak Rate. Average annual leak rate during normal equipment operation (expressed as a 

percentage of total chemical charge). 

ls = Service Leak Rate. Average leakage during equipment servicing (expressed as a percentage of total 

chemical charge). 

Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new 

equipment in a given year by weight. 

i = Counter, runs from 1 to lifetime (k).

j = Year of emission. 

k = Lifetime. The average lifetime of the equipment. 

Step 2: Calculate disposal emissions 

The disposal emission equations assume that a certain percentage of the chemical charge will be emitted to the 

atmosphere when that vintage is discarded. Disposal emissions are thus a function of the quantity of chemical contained 

in the retiring equipment fleet and the proportion of chemical released at disposal: 

 Edj = Qcj-k+1 × [1 – (rm × rc)] 

Where: 

Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of equipment. 
A-183 

Qc = Quantity of Chemical in New Equipment. Total amount of a specific chemical used to charge new 

equipment in year j-k+1, by weight. 

rm = Chemical Remaining. Amount of chemical remaining in equipment at the time of disposal (expressed as 

a percentage of total chemical charge). 

rc = Chemical Recovery Rate. Amount of chemical that is recovered just prior to disposal (expressed as a 

percentage of chemical remaining at disposal (rm)). 

j = Year of emission. 

k = Lifetime. The average lifetime of the equipment.

Step 3: Calculate total emissions 

Finally, lifetime and disposal emissions are summed to provide an estimate of total emissions. 

 Ej = Esj + Edj

Where: 

E = Total Emissions. Emissions from refrigeration and air conditioning equipment in year j.

Es = Emissions from Equipment Serviced. Emissions in year j from leakage and servicing (including 

recharging) of equipment. 

Ed = Emissions from Equipment Disposed. Emissions in year j from the disposal of equipment. 

j = Year of emission. 

Assumptions 

The assumptions used by the Vintaging Model to trace the transition of each type of equipment away from ODS 

are presented in Table A- 157, below. As new technologies replace older ones, it is generally assumed that there are 

improvements in their leak, service, and disposal emission rates. Additionally, the market for each equipment type is 

assumed to grow independently, according to annual growth rates. 
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 157: Refrigeration and Air-Conditioning Market Transition Assumptions 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

Centrifugal Chillers 

CFC-11 HCFC-123 1993 1993 45% Unknown  HCFC-22 1991 1993 16% HFC-134a 2000 2010 100% None  HFC-134a 1992 1993 39% None CFC-12 HFC-134a 1992 1994 53% None  HCFC-22 1991 1994 16% HFC-134a 2000 2010 100% None  HCFC-123 1993 1994 31% Unknown R-500 HFC-134a 1992 1994 53% None  HCFC-22 1991 1994 16% HFC-134a 2000 2010 100% None  HCFC-123 1993 1994 31% Unknown CFC-114 HFC-236fa 1993 1994 100% HFC-236fa 1996 1996 100% HFC-134a 1998 2

Cold Storage 

CFC-12 HCFC-22 1990 1993 65% R-404A 1996 2010 75% None  R-507 1996 2010 25% None  R-404A 1994 1996 26% None  R-507 1994 1996 9% None HCFC-22 HCFC-22 1992 1993 100% R-404A 1996 2009 8% None  R-507 1996 2009 3% None  R-404A 2009 2010 68% None  R-507 2009 2010 23% None R-502 HCFC-22 1990 1993 40% R-404A 1996 2010 38% None  R-507 1996 2010 12% None 

NonODP/GWP 1996 2010 50% None 

 R-404A 1993 1996 45% None  R-507 1994 1996 15% None 

Commercial Unitary Air Conditioners (Large) 

HCFC-22 HCFC-22 1992 1993 100% R-410A 2001 2005 5% None  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  R-410A 2001 2005 18% None  R-410A 2006 2009 8% None  R-410A 2009 2010 71% None 

Dehumidifiers 

HCFC-22 HFC-134a 1997 1997 89% None 
Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

 R-410A 2007 2010 11% None 

Ice Makers 

CFC-12 HFC-134a 1993 1995 100% None 

Industrial Process Refrigeration 

CFC-11 HCFC-123 1992 1994 70% Unknown  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  R-404A 1995 2010 50% None  R-410A 1999 2010 20% None  R-507 1995 2010 15% None  HCFC-123 1992 1994 35% Unknown  HFC-134a 1992 1994 50% None  R-401A 1995 1996 5% HFC-134a 1997 2000 100% None HCFC-22 HCFC-22 1992 1993 100% HFC-134a 1995 2009 2% None  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% None 

Mobile Air Conditioners (Light Duty Trucks) 

CFC-12 HFC-134a 1993 1994 100% None 

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 

Mobile Air Conditioners (Transit Buses)

HCFC-22 HFC-134a 1995 2009 100% None 

Mobile Air Conditioners (Trains)

HCFC-22 HFC-134a 

R-407C 

2002 2002 

2009 2009 

50% 50% 

None None 

Packaged Terminal Air Conditioners and Heat Pumps 

HCFC-22 R-410A 2006 2009 10% None  R-410A 2009 2010 90% None 

Positive Displacement Chillers 

HCFC-22 HCFC-22 1996 1996 100% HFC-134a 2000 2009 9% R-407C 2010 2 R-410A 2010 2 R-407C 2000 2009 1% None 
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

 HFC-134a 2009 2010 81% R-407C 2010 2 R-410A 2010 2 R-407C 2009 2010 9% None CFC-12 HCFC-22 1993 1993 100% HFC-134a 2000 2009 9% R-407C 2010 2 R-410A 2010 2 R-407C 2000 2009 1% None  HFC-134a 2009 2010 81% R-407C 2010 2 R-410A 2010 2 R-407C 2009 2010 9% None 

Refrigerated Appliances 

CFC-12 HFC-134a 1994 1995 100% HFC-134a 2005 2005 100% None 

Residential Unitary Air Conditioners 

HCFC-22 HCFC-22 2006 2006 70% R-410A 2007 2010 29% None  R-407C 2010 2010 14% R-410A 2011 2 R-410A 2010 2010 57% 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) 

CFC-12 R-502 1988 1990 100% HCFC-22 1991 1993 100% R-404A 1995 2 R-507 1995 2 R-404A 2000 2 R-507 2000 2 R-404A 2005 2 R-507 2005 2R-502 HCFC-22 1990 1993 100% R-404A 1995 2000 17.5% None  R-507 1995 2000 7.5% None  R-404A 2000 2005 31.5% None  R-507 2000 2005 13.5% None  R-404A 2005 2010 18% None  R-507 2005 2010 12% None 

Retail Food (Medium) 

HCFC-22 R-404A 1995 2000 17.5% None  R-507 1995 2000 7.5% None  R-404A 2000 2005 31.5% None  R-507 2000 2005 13.5% None  R-404A 2005 2010 18% None  R-507 2005 2010 12% None 

Retail Food (Small) 

CFC-12 HCFC-22 1990 1993 90% HFC-134a 1993 1995 90% CO2 2010 2
Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

 R-404A 2000 2009 7.5% None  R-507 2000 2009 2.5% None  R-404A 1993 1996 7.5% None  R-507 1993 1996 2.5% None 

Transport Refrigeration 

CFC-12 HFC-134a 1993 1995 98% None  HCFC-22 1993 1995 2% HFC-134a 1995 1999 100% None R-502 HFC-134a 1993 1995 55% None  R-404A 1993 1995 45% None 

Water-Source and Ground-Source Heat Pumps 

HCFC-22 R-407C 2000 2006 5% None  R-410A 2000 2006 5% None  HFC-134a 2000 2009 2% None  R-407C 2006 2009 2.5% None  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-407C 2003 2009 3% None  R-407C 2009 2010 35% None  R-410A 2003 2009 7% None  R-410A 2009 2010 55% None 
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 158 presents the average equipment lifetimes and annual HFC emission rates (for servicing and leaks) 

for each end-use assumed by the Vintaging Model. 

Table A- 158. Refrigeration and Air-conditioning Lifetime Assumptions 

End-Use Lifetime HFC Emission Rates 

 (Years) (%) 

Centrifugal Chillers 20 – 27 2.0 – 10.9 

Cold Storage 20 – 25 15.0 

Commercial Unitary A/C 15 7.9 – 8.6 

Dehumidifiers 11 0.5 

Ice Makers 20 3.0 

Industrial Process Refrigeration 25 3.6 – 12.3 

Mobile Air Conditioners 5 –12 2.3 – 18.0 

Positive Displacement Chillers 20 0.5 – 1.5 

PTAC/PTHP 12 3.9 

Retail Food 15 – 20 7.8 – 29.9 

Refrigerated Appliances 14 0.6 

Residential Unitary A/C 15 7.2 – 8.3 

Transport Refrigeration 12 20.6 – 27.9 

Water & Ground Source Heat Pumps 20 3.9 

Window Units 12 0.6 

Aerosols 

ODSs, HFCs and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle 

releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are 

modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols 

was banned in 1978, and many products transitioned to hydrocarbons or “not-in-kind” technologies, such as solid 

deodorants and finger-pump hair sprays. However, MDIs can continue to use CFCs as propellants because their use has 

been deemed essential. Essential use exemptions granted to the United States under the Montreal Protocol for CFC use in 

MDIs are limited to the treatment of asthma and chronic obstructive pulmonary disease. 

All HFCs and PFCs used in aerosols are assumed to be emitted in the year of manufacture. Since there is 

currently no aerosol recycling, it is assumed that all of the annual production of aerosol propellants is released to the 

atmosphere. The following equation describes the emissions from the aerosols sector. 

 Ej = Qcj

Where: 

E = Emissions. Total emissions of a specific chemical in year j from use in aerosol products, by weight. 

Qc = Quantity of Chemical. Total quantity of a specific chemical contained in aerosol products sold in year 

j, by weight. 

j = Year of emission. 

Transition Assumptions 

Transition assumptions and growth rates for those items that use ODSs or HFCs as propellants, including vital 

medical devices and specialty consumer products, are presented in Table A- 159. 
A-189 

Table A- 159. Aerosol Product Transition Assumptions 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Growth 

Rate 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

MDIs 

CFC Mix* HFC-134a 1997 1997 6% None 0.8% 

 Non-ODP/GWP 1998 2007 7% None 

CFC Mix* 2000 2000 87% HFC-134a 2002 2002 34% 

HFC-134a 2003 2009 47% 

HFC-227ea 2006 2009 5% 

HFC-134a 2010 2011 6% 

HFC-227ea 2010 2011 1% 

HFC-134a 2011 2012 3% 

HFC-227ea 2011 2012 0.3% 

HFC-134a 2014 2014 3% 

HFC-227ea 2014 2014 0.3% 

Consumer Aerosols (Non-MDIs) 

NA** HFC-152a 1990 1991 50% None 2.0% 

 HFC-134a 1995 1995 50% HFC-152a 1997 1998 44% 

 HFC-152a 2001 2005 36% 

*CFC Mix consists of CFC-11, CFC-12 and CFC-114 and represents the weighted average of several CFCs consumed for essential use in MDIs 

from 1993 to 2008. 

**Consumer Aerosols transitioned away from ODS prior to 1985, the year in which the Vintaging Model begins. The portion of the market that 

is now using HFC propellants is modeled. 

Solvents 

ODSs, HFCs, PFCs and other chemicals are used as solvents to clean items. For example, electronics may need 

to be cleaned after production to remove any manufacturing process oils or residues left. Solvents are applied by moving 

the item to be cleaned within a bath or stream of the solvent. Generally, most solvents are assumed to remain in the liquid 

phase and are not emitted as gas. Thus, emissions are considered “incomplete,” and are a fixed percentage of the amount 

of solvent consumed in a year. The remainder of the consumed solvent is assumed to be reused or disposed without being 

released to the atmosphere. The following equation calculates emissions from solvent applications. 

 Ej = l × Qcj

Where: 

E = Emissions. Total emissions of a specific chemical in year j from use in solvent applications, by weight. 

l = Percent Leakage. The percentage of the total chemical that is leaked to the atmosphere, assumed to be 

90 percent. 

Qc = Quantity of Chemical. Total quantity of a specific chemical sold for use in solvent applications in the 

year j, by weight. 

j = Year of emission. 

Transition Assumptions 

The transition assumptions and growth rates used within the Vintaging Model for electronics cleaning, metals 

cleaning, precision cleaning, and adhesives, coatings and inks, are presented in Table A- 160. 
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 160. Solvent Market Transition Assumptions 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Growth 

Name of Rate 

Substitute 

Start 

Date 

Date of Full 

Penetration in 

New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

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-4310mee 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 1996 40% None 

CH3CCI3 Non-ODP/GWP 1996 1997 99.8% None 2.0% 

 PFC/PFPE 1996 1997 0.2% Non-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 1996 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% 

 HFC-4310mee 1995 1996 0.6% None 

 PFC/PFPE 1995 1996 0.1% Non-ODP/GWP 2000 2003 90% 

 Non-ODP/GWP 2005 2009 10% 

CFC-113 Non-ODP/GWP 1995 1996 96% None 2.0% 

 HCFC-225ca/cb 1995 1996 1% Unknown 

 HFE-7100 1995 1996 3% None 

Non-ODP/GWP includes chemicals with 0 ODP and low GWP, such as hydrocarbons and ammonia, as well as not-in-kind alternatives such as 

“no clean” technologies. 

Fire Extinguishing 

ODSs, HFCs, PFCs and other chemicals are used as fire-extinguishing agents, in both hand-held “streaming” 

applications as well as in built-up “flooding” equipment similar to water sprinkler systems. Although these systems are 

generally built to be leak-tight, some leaks do occur and of course emissions occur when the agent is released. Total 

emissions from fire extinguishing are assumed, in aggregate, to equal a percentage of the total quantity of chemical in 

operation at a given time. For modeling purposes, it is assumed that fire extinguishing equipment leaks at a constant rate 

for an average equipment lifetime, as shown in the equation below. In streaming systems, non-halon emissions are 

assumed to be 3.5 percent of all chemical in use in each year, while in flooding systems 2.5 percent of the installed base of 

chemical is assumed to leak annually. Halon systems are assumed to leak at higher rates. The equation is applied for a 

single year, accounting for all fire protection equipment in operation in that year. Each fire protection agent is modeled 

separately. In the Vintaging Model, streaming applications have a 12-year lifetime and flooding applications have a 20-

year lifetime. 

 Ej = r × Σ Qcj-i+1 for i=1→k

Where: 

E = Emissions. Total emissions of a specific chemical in year j for streaming fire extinguishing equipment, 

by weight. 

r = Percent Released. The percentage of the total chemical in operation that is released to the atmosphere. 

Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire extinguishing equipment in 

a given year, j-i+1, by weight. 

i = Counter, runs from 1 to lifetime (k).

j = Year of emission. 
A-191 

k = Lifetime. The average lifetime of the equipment. 

Transition Assumptions 

Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A- 161. 

Table A- 161. Fire Extinguishing Market Transition Assumptions 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Growth 

Rate 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Flooding Agents 

Halon-1301 Halon-1301* 1994 1994 4% Unknown 2.2% 

 HFC-23 1994 1999 0.2% None 

 HFC-227ea 1994 1999 18% FK-5-1-12 2003 2010 10% 

 HFC-125 2001 2008 10% 

 Non-ODP/GWP 1994 1994 46% FK-5-1-12 2003 2010 7% 

 Non-ODP/GWP 1995 2034 10% None 

 Non-ODP/GWP 1998 2027 10% None 

 C4F10 1994 1999 1% FK-5-1-12 2003 2003 100% 

 HFC-125 1997 2006 11% None 

Streaming Agents 

Halon-1211 Halon-1211* 1992 1992 5% Unknown 3.0% 

 HFC-236fa 1997 1999 3% None 

 Halotron 1994 1997 4% Non-ODP/GWP 2015 2015 25% 

 HFC-236fa 2015 2015 75% 

 Non-ODP/GWP 1993 1994 58% None 

 Non-ODP/GWP 1995 2024 20% None 

 Non-ODP/GWP 1999 2018 10% None 

*Despite the 1994 consumption ban, a small percentage of new halon systems are assumed to continue to be built and filled with stockpiled or 

recovered supplies. 

Foam Blowing 

ODSs, HFCs, and other chemicals are used to produce foams, including such items as the foam insulation panels 

around refrigerators, insulation sprayed on buildings, etc. The chemical is used to create pockets of gas within a substrate, 

increasing the insulating properties of the item. Foams are given emission profiles depending on the foam type (open cell 

or closed cell). Open cell foams are assumed to be 100 percent emissive in the year of manufacture. Closed cell foams 

are assumed to emit a portion of their total HFC content upon manufacture, a portion at a constant rate over the lifetime of 

the foam, a portion at disposal, and a portion after disposal; these portions vary by end-use. 

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. 

 Emj = lm × Qcj

Where: 

Emj = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing 

losses, by weight. 

lm = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For open-cell foams, 

lm is 100%. 

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year. 

j = Year of emission. 
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Step 2: Calculate lifetime emissions (closed-cell foams) 

Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as 

presented in the following equation. 

Euj = lu × Σ Qcj-i+1 for i=1→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. 

i = Counter, runs from 1 to lifetime (k).

j = Year of emission. 

k = Lifetime. The average lifetime of foam product. 

Step 3: Calculate disposal emissions (closed-cell foams) 

Disposal emissions occur in the year the foam is disposed, and are calculated as presented in the following 

equation. 

Edj = ld × Qcj-k

Where: 

Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight. 

ld = Loss Rate. Percent of original blowing agent emitted at disposal. 

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year. 

j = Year of emission. 

k = Lifetime. The average lifetime of foam product. 

Step 4: Calculate post-disposal emissions (closed-cell foams) 

Post-Disposal emissions occur in the years after the foam is disposed; for example, emissions might occur while 

the disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane 

foam used as domestic refrigerator and freezer insulation, which is expected to continue to emit for 26 years post-disposal, 

calculated as presented in the following equation. 

Epj = lp × Σ Qcj-m for m=k→k + 32 

Where: 

Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight. 

lp = Leak Rate. Percent of original blowing agent emitted post disposal. 

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year. 

k = Lifetime. The average lifetime of foam product. 

m = Counter. Runs from lifetime (k) to (k+26). 

j = Year of emission. 
A-193 

Step 5: Calculate total emissions (open-cell and closed-cell foams) 

To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as 

presented in the following equation. 

Ej = Emj + Euj + Edj + Epj 

Where: 

Ej = Total Emissions. Total emissions of a specific chemical in year j, by weight. 

Em = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing 

losses, by weight. 

Euj = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses 

during use, by weight. 

Edj = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight. 

Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight. 

Assumptions 

The Vintaging Model contains 13 foam types, whose transition assumptions away from ODS and growth rates 

are presented in Table A- 162. The emission profiles of these 13 foam types are shown in Table A- 163. 
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 162. Foam Blowing Market Transition Assumptions 

Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

Commercial Refrigeration Foam 

CFC-11 HCFC-141b 1989 1996 40% HFC-245fa 2002 2003 80% None  Non-ODP/GWP 2002 2003 20% None  HCFC-142b 1989 1996 8% Non-ODP/GWP 2009 2010 80% None  HFC-245fa 2009 2010 20% None  HCFC-22 1989 1996 52% Non-ODP/GWP 2009 2010 80% None  HFC-245fa 2009 2010 20% None 

Flexible PU Foam: Integral Skin Foam 

CFC-11 HCFC-141b 1989 1990 100% HFC-134a 1993 1996 25% None  HFC-134a 1994 1996 25% None  CO2 1993 1996 25% None  CO2 1994 1996 25% None 

Flexible PU Foam: Slabstock Foam, Moulded Foam 

CFC-11 Non-ODP/GWP 1992 1992 100% None 

Phenolic Foam 

CFC-11 HCFC-141b 1989 1990 100% Non-ODP/GWP 1992 1992 100% None 

Polyolefin Foam 

CFC-114 HFC-152a 1989 1993 10% Non-ODP/GWP 2005 2010 100% None  HCFC-142b 1989 1993 90% Non-ODP/GWP 1994 1996 100% None 

PU and PIR Rigid: Boardstock 

CFC-11 HCFC-141b 1993 1996 100% Non-ODP/GWP 2000 2003 95% None 

HC/HFC-245fa Blend 2000 2003 5% None 

PU Rigid: Domestic Refrigerator and Freezer Insulation 

CFC-11 HCFC-141b 1993 1995 100% HFC-134a 1996 2001 7% Non-ODP/GWP 2002 2 HFC-245fa 2001 2003 50% Non-ODP/GWP 2015 2

HFC-245fa 2006 2009 10% Non-ODP/GWP 2015 2

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 

CFC-12 HCFC-142b/22 

Blend 1989 1996 70% Non-ODP/GWP 2009 2010 80% None 

 HFC-134a 2009 2010 10% None  HFC-152a 2009 2010 10% None 
Initial 

Market 

Segment 

Primary Substitute Secondary Substitute Tertiary Sub

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date of Full 

Penetration 

in New 

Equipment 

Maximum 

Market 

Penetration 

Name of 

Substitute 

Start 

Date 

Date 

Penet

N

Equ

 HCFC-22 1989 1996 30% Non-ODP/GWP 2009 2010 80% None  HFC-134a 2009 2010 10% None  HFC-152a 2009 2010 10% None 

PU Rigid: Other: Slabstock Foam 

CFC-11 HCFC-141b 1989 1996 100% CO2 1999 2003 45% None  Non-ODP/GWP 2001 2003 45% None  HCFC-22 2003 2003 10% Non-ODP/GWP 2009 2

PU Rigid: Sandwich Panels: Continuous and Discontinuous 

CFC-11 HCFC-141b 1989 1996 82% 

HCFC-22/Water 

Blend 2001 2003 20% HFC Blend 2009 2

 Non-ODP/GWP 2009 2

HFC-245fa/CO2

Blend 2002 2004 20% None 

 Non-ODP/GWP 2001 2004 40% None  HFC-134a 2002 2004 20% None  HCFC-22 1989 1996 18% 

HFC-245fa/CO2

Blend 2009 2010 40% None 

 Non-ODP/GWP 2009 2010 20% None  CO2 2009 2010 20% None  HFC-134a 2009 2010 20% None 

PU Rigid: Spray Foam 

CFC-11 HCFC-141b 1989 1996 100% HFC-245fa 2002 2003 30% None 

HFC-245fa/CO2

Blend 2002 2003 60% None 

 Non-ODP/GWP 2001 2003 10% None 

XPS: Boardstock Foam 

CFC-12 

HCFC-142b/22 

Blend 1989 1994 10% HFC-134a 2009 2010 70% None 

 HFC-152a 2009 2010 10% None  CO2 2009 2010 10% None  Non-ODP/GWP 2009 2010 10% None  HCFC-142b 1989 1994 90% HFC-134a 2009 2010 70% None  HFC-152a 2009 2010 10% None  CO2 2009 2010 10% None  Non-ODP/GWP 2009 2010 10% None 

XPS: Sheet Foam 

CFC-12 CO2 1989 1994 1% None  Non-ODP/GWP 1989 1994 99% CO2 1995 1999 9% None  HFC-152a 1995 1999 10% None 
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 163. Emission profile for the foam end-uses 

Foam End-Use 

Loss at 

Manufacturing (%)

Annual 

Leakage Rate 

(%) 

Leakage 

Lifetime 

(years) 

Loss at 

Disposal (%) 

Total* 

(%) 

Flexible PU Foam: Slabstock Foam, Moulded Foam 100 0 1 0 100 

Commercial Refrigeration 6 0.25 15 90.25 100 

Rigid PU: Spray Foam 15 1.5 56 1 100 

Rigid PU: Slabstock and Other 37.5 0.75 15 51.25 100 

Phenolic Foam 23 0.875 32 49 100 

Polyolefin Foam 95 2.5 2 0 100 

Rigid PU: One Component Foam 100 0 1 0 100 

XPS: Sheet Foam* 40 2 25 0 90 

XPS: Boardstock Foam 25 0.75 50 37.5 100 

Flexible PU Foam: Integral Skin Foam 95 2.5 2 0 100 

Rigid PU: Domestic Refrigerator and Freezer 

Insulation* 4 0.25 14 40.0 47.5 

PU and PIR Rigid: Boardstock 6 1 50 44 100 

PU Sandwich Panels: Continuous and Discontinuous 5.5 0.5 50 69.5 100 

PIR (Polyisicyanurate) 

PU (Polyurethane) 

XPS (Extruded Polystyrene) 

*In general, total emissions from foam end-uses are assumed to be 100 percent, although work is underway to investigate that assumption. In the 

XPS Sheet/Insulation Board end-use, the source of emission rates and lifetimes did not yield 100 percent emission; it is unclear at this time 

whether that was intentional. In the Rigid PU Appliance Foam end-use, the source of emission rates and lifetimes did not yield 100 percent 

emission; the remainder is anticipated to be emitted at a rate of 2.0%/year post-disposal for the next 26 years. 

Sterilization 

Sterilants kill microorganisms on medical equipment and devices. The principal ODS used in this sector was a 

blend of 12% ethylene oxide (EtO) and 88% CFC-12, known as “12/88.” In that blend, ethylene oxide sterilizes the 

equipment and CFC-12 is a dilutent solvent to form a non-flammable blend. The sterilization sector is modeled as a single 

end-use. For sterilization applications, all chemicals that are used in the equipment in any given year are assumed to be 

emitted in that year, as shown in the following equation. 

 Ej = Qcj

Where: 

E = Emissions. Total emissions of a specific chemical in year j from use in sterilization equipment, by 

weight. 

Qc = Quantity of Chemical. Total quantity of a specific chemical used in sterilization equipment in year j, by 

weight. 

j = Year of emission. 

Assumptions 

The Vintaging Model contains 1 sterilization end-use, whose transition assumptions away from ODS and growth 

rates are presented in Table A- 164. 

Table A- 164. Sterilization Market Transition Assumptions 

Initial Market 

Segment 

Primary Substitute Growth 

Name of Substitute Start Rate 

Date 

Date of Full 

Penetration in 

New Equipment 

Maximum 

Market 

Penetration 

12/88 EtO 1994 1995 95% 2.0% 

 Non-ODP/GWP 1994 1995 1% 

 HCFC/EtO Blends 1993 1994 4% 
A-197 

Model Output 

By repeating these calculations for each year, the Vintaging Model creates annual profiles of use and emissions 

for ODS and ODS substitutes. The results can be shown for each year in two ways: 1) on a chemical-by-chemical basis, 

summed across the end-uses, or 2) on an end-use or sector basis. Values for use and emissions are calculated both in 

metric tons and in teragrams of CO2 equivalents (Tg CO2 Eq.). The conversion of metric tons of chemical to Tg CO2 Eq. 

is accomplished through a linear scaling of tonnage by the global warming potential (GWP) of each chemical. 

Throughout its development, the Vintaging Model has undergone annual modifications. As new or more 

accurate information becomes available, the model is adjusted in such a way that both past and future emission estimates 

are often altered. 

Bank of ODS and ODS Substitutes 

The bank of an ODS or an ODS substitute is “the cumulative difference between the chemical that has been 

consumed in an application or sub-application and that which has already been released” (IPCC 2006). For any given 

year, the bank is equal to the previous year’s bank, less the chemical in equipment disposed of during the year, plus 

chemical in new equipment entering the market during that year, less the amount emitted but not replaced, plus the amount 

added to replace chemical emitted prior to the given year, as shown in the following equation: 

 Bcj = Bcj-1-Qdj+Qpj+Ee-Qr 

Where: 

Bcj = Bank of Chemical. Total bank of a specific chemical in year j, by weight. 

Qdj = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in equipment 

disposed of in year j, by weight. 

Qpj = 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. 

j = Year of emission. 

Table A- 165 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990-2009. 
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 165. Banks of ODS and ODS Substitutes, 1990-2009 (MT) 

CFC HCFC HFC 

1990 653,338 235,625 868 

 

1995 757,485 454,719 50,761 

2000 628,584 878,343 181,693 

2001 602,580 947,180 210,207 

2002 578,017 999,467 241,960 

2003 554,078 1,033,925 280,053 

2004 531,714 1,067,106 318,505 

2005 513,896 1,103,085 354,544 

2006 500,430 1,135,099 390,237 

2007 489,582 1,160,421 425,719 

2008 482,541 1,174,094 457,860 

2009 479,890 1,165,278 498,695 

References 

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories 

Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. 

Tanabe (eds.). Hayama, Kanagawa, Japan. 
A-199 

3.9. Methodology for Estimating CH4 Emissions from Enteric Fermentation 

Methane emissions from enteric fermentation were estimated for five livestock categories: cattle, horses, sheep, 

swine, and goats. Emissions from cattle represent the majority of U.S. emissions from enteric fermentation; consequently, 

the more detailed IPCC Tier 2 methodology was used to estimate emissions from all cattle (except for bulls). The IPCC 

Tier 1 methodology was used to estimate emissions from bulls and the other types of livestock. 

Estimate Methane Emissions from Cattle 

This section describes the process used to estimate methane emissions from cattle enteric fermentation using the 

Cattle Enteric Fermentation Model (CEFM).51 The CEFM, based on recommendations provided in 

IPCC/UNEP/OECD/IEA (1997), IPCC (2000) and IPCC (2006), uses information on population, energy requirements, 

digestible energy, and methane conversion rates to estimate methane emissions.52 The emission methodology consists of 

the following three steps: (1) characterize the cattle population to account for animal population categories with different 

emission profiles; (2) characterize cattle diets to generate information needed to estimate emission factors; and (3) estimate 

emissions using these data and the IPCC Tier 2 equations. 

Step 1: Characterize U.S. Cattle Population 

The national cattle population estimates in the inventory submission are based on data obtained from the U.S. 

Department of Agriculture’s (USDA) National Agricultural Statistics Service Quick Stats database (USDA 2010). A 

summary of the annual average populations upon which all livestock-related emissions are based is provided in Table A166. Cattle populations used in the Enteric Fermentation sector were estimated using the cattle transition matrix in the 

CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from 

birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while taking into 

account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates. The use of 

supplemental USDA data and the cattle transition matrix in the CEFM results in the cattle population estimates for this 

sector differing slightly from the January 1 or July 1 USDA point estimates and the cattle population data obtained from 

the Food and Agriculture Organization of the United Nations (FAO). 

Table A-166: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2009 

Livestock Type 1990 1995 2000 2005 2006 2007 2008 2009

Calves 0-6 months 22,561 23,499 22,569 21,678 21,621 21,483 21,155 20,940

Dairy 

Dairy Cows 10,015 9,482 9,183 9,004 9,104 9,145 9,257 9,333

Dairy Replacements 7-11 months 1,214 1,216 1,196 1,257 1,277 1,299 1,304 1,325

Dairy Replacements 12-23 months 2,915 2,892 2,812 2,905 3,017 3,043 3,097 3,101

Beef 

Bulls 2,160 2,385 2,293 2,214 2,258 2,214 2,207 2,184

Beef Cows 32,455 35,190 33,575 32,674 32,703 32,644 32,435 31,712

Beef Replacements 7-11 months 1,269 1,493 1,313 1,363 1,380 1,349 1,312 1,287

Beef Replacements 12-23 months 2,967 3,637 3,097 3,171 3,294 3,276 3,169 3,098

Steer Stockers 10,321 11,716 8,724 8,185 8,248 8,302 8,233 8,501

Heifer Stockers 5,946 6,699 5,371 5,015 5,041 4,966 4,868 5,051

Feedlot Cattle 9,549 11,064 13,006 12,652 13,526 13,404 13,070 12,964

Note: Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the January 

estimates downloaded from USDA (2010). 

The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an 

estimate of the population age and weight structure by cattle type on a monthly basis. Since cattle often do not remain in a 

single population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and emission 

 

51 Emissions from bulls are estimated in the CEFM using a Tier 1 approach based on published population statistics and national average 

emission factors because the variation in diets and within year population is assumed to be minimal. The IPCC recommends the use of a 

methane conversion factor of zero for calves, because they consume mainly milk, therefore this results in no methane emissions from 

calves through 6 months.

52 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation 

model to obtain more accurate emission estimates than would be available from annual point estimates of population (such 

as available from USDA statistics) and weight for each cattle type. 

The transition matrix tracks both dairy and beef populations, and divides the populations into males and females, 

and subdivides the population further into specific cattle groupings for replacement, stocker, feedlot, and mature animals. 

The matrix is based primarily on two types of data: population statistics and weight statistics (including target weights, 

slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of animals over 

time by month. The matrix also relies on supplementary data, such as feedlot placement statistics, slaughter statistics, 

death rates, and calving rates. 

The basic method for tracking population of animals per category is based on the number of births (or graduates) 

into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category 

(such as 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., beef calves are weaned at 7 months), each is modeled on a per-month basis. The type of cattle also 

impacts CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition matrix for each of three 

separate lifecycle phases, 1) calves, 2) replacements and stockers, and 3) feedlot animals. In addition, the number of 

mature cows is tracked for both dairy and beef stock. Each lifecycle is discussed separately below, and the categories 

tracked are listed in Table A-167. 

Table A-167: Cattle Population Categories Used for Estimating CH4 Emissions 

Dairy Cattle Beef Cattle 

Calves Calves 

Heifer Replacements Heifer Replacements 

Cows Heifer and Steer Stockers 

 Animals in Feedlots (Heifers & Steers) 

 Cows 

 Bulls* 

* Bulls (beef and dairy) are accounted for in a single category. 

The key variables tracked for each of these cattle population categories (except bulls) are as follows: 

Calves. The number of animals born on a monthly basis was used to initiate monthly cohorts and to determine 

population age structure. The number of calves born each month was obtained by multiplying annual births by the 

percentage of births by month. Annual birth information for each year was taken from USDA (2010). For dairy cows, the 

number of births is assumed to be distributed equally throughout the year (approximately 8.3 percent per month), beef 

births are distributed according to Table A-168, based on estimates from the National Animal Health Monitoring System 

(NAHMS) (USDA/APHIS/VS 1998, 1994, 1993). To determine whether calves were born to dairy or beef cows, the dairy 

cow calving rate (USDA/APHIS/VS 2002, USDA/APHIS/VS 1996) was multiplied by the total dairy cow population to 

determine the number of births attributable to dairy cows, with the remainder assumed to be attributable to beef cows. 

Total annual calf births are obtained from USDA, and distributed into monthly cohorts by cattle type (beef or dairy). Calf 

growth is modeled by month, based on estimated monthly weight gain for each cohort (approximatey 61 pounds per 

month). Total calf population is modified through time to account for veal calf slaughter at 4 months and a calf death loss 

of 0.35 percent annually (distributed across age cohorts up to six months of age). An example of a transition matrix for 

calves is shown in Table A-169. Note that calves age one through six months available in January have been tracked 

through the model based on births and death loss from the previous year. 

Table A-168: Estimated Beef Cow Births by Month 

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

7% 15% 28% 22% 9% 3% 2% 2% 3% 4% 3% 3% 
A-201 

Table A-169: Example of Monthly Average Populations from Calf Transition Matrix 

Replacements and Stockers. At seven months of age, calves ‘graduate’ and are separated into the applicable 

cattle types. First the number of replacements required for beef and dairy cattle are calculated based on estimated death 

losses and population changes between beginning and end of year population estimates. All steer, and remaining heifers 

(after subtracting required replacements), are considered ‘stockers,’ that is backgrounding animals that are eligible for 

placement into feedlots as they reach the appropriate weight class. During the stocker phase animals are subtracted out of 

the transition matrix for placement into feedlots based on feedlot placement statistics from USDA (2010). 

The data and calculations that occur for the 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 (month 7, bottom row of Table A-170) and subtraction through death loss and 

animals placed in feedlots. Eventually, an entire cohort population of stockers may be zero, indicating that the complete 

cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown in Table A-170. 

Table A-170: Example of Monthly Average Populations from Stocker Transition Matrix 

Age 

(month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

23 202 214 126 47 20 12 10 11 9 7 5 51 

22 353 174 59 23 15 12 13 13 9 7 103 311 

21 286 81 29 17 15 15 15 12 9 153 421 559 

20 133 40 22 17 18 18 14 12 197 526 765 457 

19 66 30 22 21 21 17 14 273 620 965 629 212 

18 49 30 27 25 20 17 321 779 1,141 795 291 305 

17 49 37 31 24 20 379 882 1,442 942 368 546 49 

16 60 43 30 24 453 1,004 1,636 1,193 435 779 49 49 

15 71 41 30 531 1,160 1,867 1,355 551 985 49 49 61 

14 68 41 669 1,324 2,162 1,547 625 1,337 49 49 61 71 

13 67 918 1,607 2,471 1,796 716 1,669 49 49 61 71 68 

12 1,076 1,774 2,813 2,017 857 2,242 282 49 61 71 68 67 

11 2,027 3,105 2,297 962 2,765 365 179 82 91 76 72 1,094 

10 3,532 2,542 1,082 3,229 507 253 209 261 213 128 1,151 2,683 

9 2,860 1,204 3,588 607 451 425 504 464 429 1,353 2,916 6,195 

8 1,328 4,083 770 617 664 915 782 831 1,682 3,596 6,832 5,605 

7 4,450 878 871 1,060 1,295 1,232 1,230 2,114 4,188 7,484 6,012 2,665 

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 (2010). In cases where there are discrepancies between the USDA estimated placements by 

weight class and the calculated animals available by weight, the model pulls available stockers from one higher weight 

category if available. If there are still not enough animals to fulfill requirements the model pulls animals from the next 

lower category. Note that in the current time series, this method was able to ensure that total placement data matched 

USDA estimates, and no shortfalls have occurred. 

In addition, average weights were tracked for each monthly age group using starting weight and monthly weight 

gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target 

weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be 

88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 lbs. Other reported target weights 

Age 

(month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

6 1,207 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 

5 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 

4 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 

3 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 

2 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 

1 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 

0 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 1,661 
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight was 

taken from measurements provided by a major British Bos taurus breed (Enns 2008). Beef replacement weight was 

calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at 24 months. As dairy weights 

are not a trait that is typically tracked, mature weight for dairy cows was estimated at 1,500 for all years, based on a 

personal communication with Kris Johnson (2010) and an estimate from Holstein Association USA (2010). Dairy 

replacement at 15 months was assumed to be 875 lbs and replacement at 24 months is 1,300 lbs. Live slaughter weights 

were derived from dressed slaughter weight data for each year (USDA 2010). The annual typical animal mass for each 

livestock type are presented in Table A- 171 

Weight gain for 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 through 2007. Interim years were calculated linearly, as shown in Table A-172, and weight gain was held constant 

starting in 2008. Live slaughter weight was estimated as dressed weight divided by 0.63. This ratio represents the dressed 

weight (i.e. weight of the carcass after removal of the internal organs), to the live weight (i.e. weight taken immediately 

before slaughter). Table A-172 provides weights and weight gains that vary by year in the CEFM. 

Table A- 171: Typical Animal Mass that Varies by Year (lbs) 

Year/Cattle 

Type 

Dairy 

Cows 

Dairy 

Replacements 

Beef 

Cows 

Beef 

Replacements 

Steer 

Stockers 

Heifer 

Stockers 

Steer 

Feedlot 

Heifer 

Feedlot 

1990 1,500 900 1,221 820 692 652 923 846 

1991 1,500 898 1,225 822 695 656 975 867 

1992 1,500 897 1,263 841 714 673 984 878 

1993 1,500 899 1,280 852 721 683 930 864 

1994 1,500 898 1,280 854 721 689 944 876 

1995 1,500 898 1,282 858 735 701 947 880 

1996 1,500 898 1,285 859 739 707 940 878 

1997 1,500 900 1,286 861 737 708 939 877 

1998 1,500 897 1,296 866 736 710 957 892 

1999 1,500 899 1,292 862 731 709 960 895 

2000 1,500 897 1,272 849 720 702 961 899 

2001 1,500 898 1,272 850 726 707 963 901 

2002 1,500 897 1,276 852 726 708 982 915 

2003 1,500 900 1,308 872 719 702 973 905 

2004 1,500 897 1,323 878 719 702 967 905 

2005 1,500 895 1,327 880 718 706 975 917 

2006 1,500 898 1,341 890 725 713 984 925 

2007 1,500 897 1,348 895 721 707 992 928 

2008 1,500 898 1,348 895 721 705 1,000 939 

2009 1,500 897 1,348 895 731 715 1,007 948 
A-203 

Table A-172: 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 1.83 1.83 1.83 1.83 

2001 1.83 1.83 1.83 1.83 

2002 1.83 1.83 1.83 1.83 

2003 1.83 1.83 1.83 1.83 

2004 1.83 1.83 1.83 1.83 

2005 1.83 1.83 1.83 1.83 

2006 1.83 1.83 1.83 1.83 

2007 1.83 1.83 1.83 1.83 

2008 1.83 1.83 1.83 1.83 

2009 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, during this time weight gain is estimated to be 2.8 to 3 pounds per day (Johnson 1999). Animals are 

then switched to a finishing diet for a period of time before they are slaughtered. Weight gain during finishing diets is 

estimated to be 3 to 3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the 

start weight (i.e., placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target 

weight (as determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are 

tracked for inclusion in the following year’s emission and population counts. For 1990 to 1995, only the total placement 

data were available, therefore placements for each weight category (displayed in Table A-173) for those years are based on 

the average of monthly placements from the 1996 to 1998 reported figures. Placement data is available by weight class for 

all years from 1996 onward. Table A-173 provides a summary of the reported feedlot placement statistics for 2009. 

Table A-173: Feedlot Placements in the United States for 2009 (Number of animals placed in Thousand Head)

Weight 

Placed When: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

< 600 lbs 380 320 305 355 395 315 455 435 490 615 510 430 

600 – 700 lbs 505 385 340 315 305 290 365 395 450 645 565 420 

700 – 800 lbs 553 538 593 405 433 371 458 514 593 579 394 401 

> 800 lbs 420 435 570 525 505 415 585 775 855 635 375 295 

Total 1,858 1,678 1,808 1,600 1,638 1,391 1,863 2,119 2,388 2,474 1,844 1,546 

Source: USDA (2010). 

Note: Totals may not sum due to independent rounding. 

Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for 

particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed 

on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4

emissions. A weighted average percent of pregnant cows each month was estimated using information on births by month 

and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was 

estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This 

process results in a range of weighted monthly lactation estimates expressed as lbs/animal/month. The monthly estimates 

from January to December are 3.3, 5.1, 8.7, 12.0, 13.6, 13.3, 11.7, 9.3, 6.9, 4.4, 3.0, and 2.8 lbs milk/animal/day. Annual 

estimates for dairy cattle were taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 

2009 are shown in 
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-174. Beef and dairy cow and bull populations are assumed to remain relatively static throughout the 

year, as large fluctuations in population size are assumed to not occur. These estimates are taken from the USDA 

beginning and end of year population datasets. 

Table A-174: Dairy Lactation by State (lbs/ year/cow)* 

State/Year 1990 1995 2000 2005 2006 2007 2008 2009 

Alabama 12,214 14,176 13,920 14,000 14,500 15,154 15,333 14,909 

Alaska 13,300 17,000 14,500 12,273 12,250 14,667 12,000 10,000 

Arizona 17,500 19,735 21,820 22,679 22,855 23,260 23,382 23,028 

Arkansas 11,841 12,150 12,436 13,545 12,900 12,941 12,400 12,615 

California 18,456 19,573 21,130 21,404 21,815 22,440 22,344 22,000 

Colorado 17,182 18,687 21,618 22,577 23,155 22,932 22,930 23,089 

Connecticut 15,606 16,438 17,778 19,200 19,316 19,211 19,158 18,684 

Delaware 13,667 14,500 14,747 16,622 16,286 16,618 16,923 17,000 

Florida 14,033 14,698 15,688 16,591 16,447 16,832 17,167 18,061 

Georgia 12,973 15,550 16,284 17,259 18,234 18,169 17,829 18,600 

Hawaii 13,604 13,654 14,358 12,889 13,256 12,241 10,882 14,200 

Idaho 16,475 18,147 20,816 22,332 22,346 22,513 22,432 22,091 

Illinois 14,707 15,887 17,450 18,827 19,252 18,612 18,569 18,873 

Indiana 14,590 15,375 16,568 20,295 19,861 20,307 19,683 20,137 

Iowa 15,118 16,124 18,298 20,641 20,127 20,085 19,995 20,367 

Kansas 12,576 14,390 16,923 20,505 20,938 19,882 20,641 21,085 

Kentucky 10,947 12,469 12,841 12,896 13,296 13,889 13,444 14,190 

Louisiana 11,605 11,908 12,034 12,400 12,375 12,034 12,269 11,870 

Maine 14,619 16,025 17,128 18,030 17,938 17,788 18,273 18,061 

Maryland 13,461 14,725 16,083 16,099 17,281 18,121 18,375 18,255 

Massachusetts 14,871 16,000 17,091 17,059 17,375 17,000 16,933 17,571 

Michigan 15,394 17,071 19,017 21,635 22,234 22,761 22,180 22,445 

Minnesota 14,127 15,894 17,777 18,091 18,598 18,817 18,927 19,230 

Mississippi 12,081 12,909 15,028 15,280 14,957 15,429 14,550 13,889 

Missouri 13,632 14,158 14,662 16,026 16,000 14,982 14,682 14,654 

Montana 13,542 15,000 17,789 19,579 18,632 18,500 18,412 19,933 

Nebraska 13,866 14,797 16,513 17,950 18,328 18,220 18,672 19,721 

Nevada 16,400 18,128 19,000 21,680 20,148 20,481 20,704 21,821 

New Hampshire 15,100 16,300 17,333 18,875 19,533 19,333 19,933 19,533 

New Jersey 13,538 13,913 15,250 16,000 16,182 16,800 16,900 17,889 

New Mexico 18,815 18,969 20,944 21,192 21,853 21,958 23,269 24,320 

New York 14,658 16,501 17,378 18,639 18,879 19,303 19,859 20,071 

North Carolina 15,220 16,314 16,746 18,741 18,510 19,188 18,979 19,644 

North Dakota 12,624 13,094 14,292 14,182 14,688 15,310 16,077 16,739 

Ohio 13,767 15,917 17,027 17,567 17,737 18,109 18,321 18,744 

Oklahoma 12,327 13,611 14,440 16,480 16,630 16,580 16,578 16,983 

Oregon 16,273 17,289 18,222 18,876 19,000 19,417 19,772 19,719 

Pennsylvania 14,726 16,492 18,081 18,722 19,390 19,422 19,262 19,360 

Rhode Island 14,250 14,773 15,667 17,000 17,273 16,455 18,091 17,818 

South Carolina 12,771 14,481 16,087 16,000 16,294 17,889 17,889 18,765 

South Dakota 12,257 13,398 15,516 17,741 18,580 19,306 19,956 20,128 

Tennessee 11,825 13,740 14,789 15,743 15,657 15,857 16,068 16,232 

Texas 14,350 15,244 16,503 19,646 19,501 18,982 20,134 20,898 

Utah 15,838 16,739 17,573 18,875 20,314 20,376 20,894 20,988 

Vermont 14,528 16,210 17,199 18,469 18,383 18,079 18,400 18,289 

Virginia 14,213 15,116 15,833 16,990 17,363 17,530 17,612 18,083 

Washington 18,532 20,091 22,644 23,270 23,055 23,239 23,344 23,171 

West Virginia 11,250 12,667 15,588 14,923 15,385 15,000 15,083 14,727 

Wisconsin 13,973 15,397 17,306 18,500 18,824 19,310 19,546 20,079 

Wyoming 12,337 13,197 13,571 14,878 17,612 18,831 19,386 18,811 

Source: USDA (2010). 

* Beef lactation data were developed using the methodology described in Step 1. 
A-205 

Step 2: Characterize U.S. Cattle Population Diets 

To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and 

CH4 conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population categories, 

data were collected on diets considered representative of different regions. For both grazing animals and animals being 

fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists 

and from USDA (1996). The data for each of the diets (e.g., proportions of different feed constituents, such as hay or 

grains) were used to determine feed chemical composition for use in estimating digestible energy and Ym for each animal 

type. 

DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle 

and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different 

regions and animal types in the United States, digestible energy and Ym values unique to the United States53 were 

developed. Digestible energy and Ym values were estimated for each cattle population category, for each year in the time 

series based on physiological modeling, published values, and/or expert opinion. 

For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input 

required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final 

weight, age at start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data used to

simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals with 

similar characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999) that 

represents physiological processes in the ruminant animals and diet characteristics from USDA (1996). The Donovan and 

Baldwin model accounts for differing diets (i.e., grain-based or forage-based), so that Ym values for the variable feeding 

characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review of dairy diets was 

conducted and nearly 250 diets were analyzed from 1990 through 2008 across 23 states. Kebreab et al (2008). conducted 

an evaluation of models and found that the COWPOLL model was the best model for estimating Ym for dairy. Therefore, 

the COWPOLL model was used to estimate Ym values for each of the diets. Due to the variability associated with cattle 

diets, a function based on the national trend observed from the analysis of the diary diets was used to calculate 1991 and 

beyond values based on the regional 1990 Ym values from Donovan and Baldwin.. The resulting scaling factor is shown 

below: 

( )⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

− = 1980

1.22 (1990) Year

Ym Ym EXP ( )⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

1990 −1980

1.22 / EXP

DE values for dairy cows were estimated from the literature search based on the on annual trends observed in the 

recent data collection effort. The regional variability observed in the literature search was not statistically significant, and 

therefore DE was not varied by region. 

Considerably less data was available for dairy heifers, so assumptions were based on the relationship of the 

collected data literature on dairy heifers to the data on dairy cow diets. From this relationship, DE was estimated as the 

mature cow DE minus three percent, and Ym was estimated as that of the mature dairy cow plus 0.1 percent. 

To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted 

DE values for a combination of forage and supplemental diets. Where DE values were not available for specific feed 

types, total digestible nutrients (TDN) as a percent of dry matter (DM) intake was used as a proxy for DE, as listed in NRC 

(2000). Forage diets make up the majority of beef cattle diets, and two separate regional DE values were used to account 

for the generally lower forage quality in the western United States. For all non-western beef cattle, the forage DE was an 

average of the seasonal values for Grass Pasture diets listed in Appendix Table 1 of the NRC (2000). This resulted in a 

DE of 64.7 percent for the forage portion of the diet for all beef cattle in regions other than the west. For beef cattle in the 

west, the forage DE was calculated as the seasonal average for meadow and range diets listed in Appendix Table 1 of the 

NRC (2000). This resulted in a DE for the forage portion of the western region diet of 58.5 percent. In addition, it was 

assumed that each region fed a supplemental diet, as shown in Table A- 175. By weighting the calculated DE values from 

the forage and supplemental diets, the DE values for the composite diet were calculated.54 The percent of each diet that is 

assumed to be supplemental and the forage DE values used for each region, and the resulting weighted DE values are 

 

53 In some cases, the Ym values used for this analysis extend beyond the range provided by the IPCC. However, EPA believes that these 

values are representative for the U.S. due to research conducted to characterize the diets of U.S. cattle and assess the Ym values 

associated with different animal performance and feed characteristics in the United States.

54 For example, in California the forage DE of 64.7 was used for 95 percent of the grazing cattle diet and a supplemented diet DE of 69.3 

percent was used for five percent of the diet, for a total weighted DE of 64.9 percent, as shown in Table A-176. 
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

shown in Table A-176. These values are used for steer and heifer stockers and beef replacements. Finally, for beef cows, 

the DE value was adjusted downward by two percent to reflect the lower digestibility diets of the mature beef cow based 

on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). 

For feedlot animals, DE and Ym values for 1990 were taken from Johnson (1999). . In the CEFM, values for 

1991 through 1999 were linearly extrapolated based on values for 1990 and 2000. DE and Ym values for 2000 onwards 

were estimated using the MOLLY model as described in Kebreab et al (2008). In addition, feedlot animals are assumed 

to spend the first 25 days in the feedlot on a “step-up” diet to become accustomed to the higher quality feedlot diets. The 

step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE and Ym values. 

Table A-177 shows the regional DE and Ym for U.S. cattle in each region for 2009. 

Table A- 175: DE Values and Representative Regional Diets (Percent of Diet for each Region) for the Supplemental Diet of 

Grazing Beef Cattle 

Source of representative regional diets: Donovan (1999). 

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. The Western 

region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, 

ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the 

Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, 

and FL. 

Table A-176: Percent of Each Diet that is Supplemental and the Resulting DE Values for Each Region 

Regiona Percent 

Supplement Percent Forage Forage DE Used Calculated Weighted Average DE 

West 10 90 59 59 

Northeast 15 85 65 65 

Southcentral 10 90 65 64 

Midwest 15 85 65 65 

Northern Great Plains 15 85 65 66 

Southeast 5 95 65 64 

California 5 95 65 65 

Sources: Percent of total diet that is supplemental diet, Donovan (1999); Forage DE, NRC (2000). 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. The Western 

region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, 

ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the 

Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, 

and FL. 

Feed 

Source of TDN 

(NRC 2000) 

Unweighted

TDN 

or DE California a West

Northern

Great 

Plains Southcentral Northeast Midwest Southeast

Alfalfa Hay Table 11-1, feed #4 59.6% 65% 30% 30% 29% 12% 30% 

Barley Table 11-1, feed #12 86.3% 10% 15% 

Bermuda Table 11-1, feed #17 48.5% 35% 

Bermuda Hay Table 11-1, feed #17 48.5% 40% 

Corn Table 11-1, feed #38 88.1% 10% 10% 25% 11% 13% 13% 

Corn Silage Table 11-1, feed #39 71.2% 25% 20% 20% 

Cotton Seed Meal Table 11-1, feed #42 74.4% 7% 

Grass Hay Table 1a, feed #129, 

147, 148 53.7% 40% 30% 

Orchard Table 11-1, feed #61 53.5% 40% 

Soybean Meal 

Supplement Table 11-1, feed #70 83.1% 5% 5% 5% 

Sorghum Table 11-1, feed #67 81.3% 20% 

Soybean Hulls Table 11-1, feed #69 76.4% 7% 

Timothy Hay Table 11-1, feed #77 55.5% 50% 

Whole Cotton 

Seed Table 11-1, feed #41 89.2% 5% 5% 

Wheat Middlings Table 1a, feed #433 83.0% 15% 13% 

Wheat Table 11-1, feed #83 87.2% 10% 

Weighted Total 69% 65% 74% 62% 65% 65% 59% 
A-207 

Table A-177: Regional Digestible Energy (DE) and CH4 Conversion Rates (Ym) for Cattle in 2009 

Animal Type Data California c West Northern 

Great Plains Southcentral Northeast Midwest Southeast 

Beef Repl. Heif. DEa 65 59 66 64 65 65 64 

 Ym

b

 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Dairy Repl. Heif. DE 64 64 64 64 64 64 64 

 Ym 6.0% 6.0% 5.7% 6.5% 6.4% 5.7% 7.0% 

Steer Stockers DE 65 59 66 64 65 65 64 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Heifer Stockers DE 65 59 66 64 65 65 64 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Steer Feedlot DE 83 83 83 83 83 83 83 

 Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 

Heifer Feedlot DE 83 83 83 83 83 83 83 

 Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 

Beef Cows DE 63 57 64 62 63 63 62 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Dairy Cows DE 67 67 67 67 67 67 67 

 Ym 5.9% 5.9% 5.6% 6.4% 6.3% 5.6% 6.9% 

Steer Step-Up DE 72 72 72 72 72 72 72 

 Ym 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 

Heifer Step-Up DE 72 72 72 72 72 72 72 

 Ym 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% 5.2% a

 Digestible Energy in units of percent of GE (MJ/Day). b Methane Conversion Rate is the fraction of GE in feed converted to methane. c Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western 

region includes AK, WA, OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, 

ME, and WV; the Southcentral region includes AR, LA, OK, and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the 

Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern region includes VA, NC, KY, TN, MS, AL, GA, SC, 

and FL. 

Step 3: Estimate CH4 Emissions from Cattle 

Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the IPCC (2006) 

equations, b) determine an emission factor using the GE values and other factors, and c) sum the daily emissions for each 

animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. The necessary data 

values for each state and animal type include: 

• Body Weight (kg) 

• Weight Gain (kg/day) 

• Net Energy for Activity (Ca, MJ/day)55

• Standard Reference Weight (kg)56

• Milk Production (kg/day) 

• Milk Fat (percent of fat in milk = 4) 

• Pregnancy (percent of population that is pregnant) 

• DE (percent of gross energy intake digestible) 

• Ym (the fraction of gross energy converted to CH4) 

• Population 

Step 3a: Determine Gross Energy, GE 

As shown in the following equation, gross energy (GE) is derived based on the net energy estimates and the feed 

characteristics. Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require 

the NEl factor). All net energy equations are provided in IPCC (2006). 

 

55 Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain 

grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the 

year. 

56 Standard Reference Weight is the mature weight of a female animal of the animal type being estimated, used in the model to account 

for breed potential.
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

⎥

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ + + + +

=

100

DE%

REG

NE

REM

NE NE NE NE NE

GE

m a l work p g

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) 

NEl = Net energy for lactation (MJ/day) 

NEwork = Net energy for work (MJ/day) 

NEp = Net energy required for pregnancy (MJ/day) 

REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed 

NEg = Net energy needed for growth (MJ/day) 

REG = Ratio of net energy available for growth in a diet to digestible energy consumed 

DE = Digestible energy expressed as a percent of gross energy (percent) 

Step 3b: Determine Emission Factor 

The emission factor (DayEmit) was determined using the gross energy value and the methane conversion factor 

(Ym) for each category. This relationship is shown in the following equation: 

55.65

GE × Ym DayEmit =

Where, 

DayEmit = Emission factor (kg CH4/head/day) 

GE = Gross energy intake (MJ/head/day) 

Ym = CH4 conversion rate, which is the fraction of gross energy in feed converted to CH4 (%) 

55.65 = A factor for the energy content of methane (MJ/kg CH4) 

The daily emission factors were estimated for each animal type and state, calculated national emission factors are 

shown by animal type in Table A-178. 

Table A-178: Calculated National Emission Factors for Cattle by Animal Type (kg CH4/head/year) 

Cattle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Dairy 

Cows 124 125 132 133 134 139 139 140 

Replacements 7-11 

months 48 46 46 45 45 46 46 46 

Replacements 12-23 

months 73 69 70 67 67 70 69 70 

Beef 

Bulls 53 53 53 53 53 53 53 53 

Cows 89 92 91 94 94 94 94 94 

Replacements 7-11 

months 54 57 57 59 60 60 60 60 

Replacements 12-23 

months 63 66 66 68 69 69 69 69 

Steer Stockers 55 56 58 58 58 58 57 57 

Heifer Stockers 51 56 60 59 59 59 59 59 

Feedlot Cattle 39 38 39 39 39 42 42 43 

Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year). 
A-209 

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: 

Emissionsstate = DayEmitstate × Days/Month × SubPopstate 

Where, 

DayEmitState = The emission factor for the subcategory and state (kg CH4/head/day) 

Days/Month = The number of days in the month 

SubPopState = The number of animals in the subcategory and state during the month 

This process was repeated for each month, and the totals for each state subcategory were summed to achieve an 

emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The 

estimates for each of the 10 subcategories of cattle are listed in Table A-179. The emissions for each subcategory were 

then summed to estimate total emissions from beef cattle and dairy cattle for the entire year. 

Table A-179: CH4 Emissions from Cattle (Gg) 

Cattle Type 1990 1995 2000 2005 2006 2007 2008 2009 

Dairy 1,513 1,440 1,460 1,449 1,479 1,544 1,564 1,581 

Cows 1,242 1,183 1,209 1,197 1,219 1,271 1,289 1,304 

Replacements 7-11 months 58 56 55 56 57 60 60 61 

Replacements 12-23 

months 212 201 196 196 203 213 214 216 

Beef 4,502 5,128 4,790 4,731 4,803 4,837 4,796 4,742 

Bulls 114 126 122 117 120 117 117 116 

Cows 2,887 3,223 3,059 3,056 3,079 3,083 3,065 2,995 

Replacements 7-11 months 69 85 74 80 82 81 79 77 

Replacements 12-23 

months 188 241 204 217 228 228 220 215 

Steer Stockers 563 661 508 472 475 478 473 489 

Heifer Stockers 305 374 322 298 298 294 288 298 

Feedlot Cattle 375 416 502 489 521 556 554 553 

Total 6,015 6,567 6,251 6,180 6,282 6,381 6,374 6,323 

Notes: Totals may not sum due to independent rounding. Because calves younger than 7 months consume mainly milk the IPCC recommends the 

use of methane conversion factor of zero, resulting in no methane emissions from this subcategory of cattle. 

Emission Estimates from Other Livestock 

All livestock population data, except for horses, were taken from the U.S. Department of Agriculture (USDA) 

National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2009). Table A- 183of the Manure 

Management Annex shows the population data for all livestock species that were used for estimating all livestock-related 

emissions. For each animal category, the USDA publishes monthly, annual, and multi-year livestock population and 

production estimates. All data were downloaded from the USDA-NASS agricultural database (USDA 2010). The Food 

and Agriculture Organization (FAO) publishes horse population data. These data were accessed from the FAOSTAT 

database (FAO 2010). Methane emissions from sheep, goats, swine, and horses were estimated by multiplying published 

national population estimates by the IPCC emission factor for each year. Table A-180 shows the emission factors used for 

these other livestock. 

Enteric fermentation emissions from all livestock types are shown in Table A-181 and Table A-182. 

Table A-180: Emission Factors for Other Livestock (kg CH4/head/year) 

Livestock Type Emission Factor 

Sheep 8 

Goats 5 

Horses 18 

Swine 1.5 

Source: IPCC (2006) 
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-181: CH4 Emissions from Enteric Fermentation (Tg CO2 Eq.) 

Livestock Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Beef Cattle 94.5 107.7 100.6 99.9 100.0 100.0 98.3 99.3 100.9 101.6 100.7 99.6

Dairy Cattle 31.8 30.2 30.7 30.5 30.6 28.4 29.9 30.4 31.1 32.4 32.9 33.2

Horses 1.9 1.9 2.0 2.1 2.3 2.6 3.0 3.5 3.6 3.6 3.6 3.6

Sheep 1.9 1.5 1.2 1.2 1.1 1.1 1.0 1.0 1.0 1.0 1.0 1.0

Swine 1.7 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.1 2.1 2.1

Goats 0.3 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Total 132.1 143.5 136.5 135.7 136.1 134.4 134.4 136.5 138.8 141.0 140.6 139.8

Note: Totals may not sum due to independent rounding. 

Table A-182: CH4 Emissions from Enteric Fermentation (Gg) 

Livestock 

Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 

Beef Cattle 4,502 5,128 4,790 4,757 4,761 4,762 4,680 4,731 4,803 4,837 4,796 4,742

Dairy Cattle 1,513 1,440 1,460 1,453 1,457 1,354 1,422 1,449 1,479 1,544 1,564 1,581

Horses 91 92 94 99 108 126 144 166 171 171 171 171

Sheep 91 72 56 55 53 51 49 49 50 49 48 46

Swine 81 88 88 88 90 90 91 92 93 98 101 99

Goats 13 12 12 12 13 13 14 15 15 16 16 16

Total 6,290 6,832 6,502 6,464 6,481 6,396 6,400 6,500 6,611 6,715 6,696 6,655

Note: Totals may not sum due to independent rounding. 
A-211 

3.10. Methodology for Estimating CH4 and N2O Emissions from Manure 

Management 

The following steps were used to estimate methane (CH4) and nitrous oxide (N2O) emissions from the 

management of livestock manure. Nitrous oxide emissions associated with pasture, range, or paddock systems and daily 

spread systems are included in the emission estimates for Agricultural Soil Management (see the Agricultural Soils 

Management Annex).

Step 1: Livestock Population Characterization Data 

Annual animal population data for 1990 through 2009 for all livestock types, except horses and goats were 

obtained from the USDA National Agricultural Statistics Service (NASS). The population data used in the emissions 

calculations for cattle, swine, and sheep were downloaded from the USDA NASS Population Estimates Database (USDA 

2009a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998a, 1999, 2004a, 

2004b, 2010a, and 2010b). Horse population data were obtained from the Food and Agriculture Organization (FAO) 

FAOSTAT database (FAO 2010). Goat population data for 1992, 1997, 2002, and 2007 were obtained from the Census of 

Agriculture (USDA 2009a). Additional data sources used and adjustments to these data sets are described below. 

Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories 

from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the 

year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered; 

therefore, to develop the best estimate for the annual animal population, the populations and the individual characteristics, 

such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle Enteric 

Fermentation Model (CEFM). For animals that have relatively static populations throughout the year, such as mature 

cows and bulls, the January 1 values were used. For animals that have flucatuating populations throughout the year, such 

as calves and growing heifers and steer, the populations are modeled based on a transition matrix that uses annual 

population data from USDA along with USDA data on animal births, placement into feedlots, and slaughter statistics. 

Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under 

23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over 

(greater than 82 kg). The average of the quarterly data was used in the emissions calculations. For states where only 

December inventory is reported, the December data were used directly. 

Sheep: Population data for lamb and sheep on feed are not available after 1993 (USDA 1994). The number of 

lamb and sheep on feed for 1994 through 2009 were calculated using the average of the percent of lamb and sheep on feed 

from 1990 through 1993. In addition, all of the sheep and lamb “on feed” are not necessarily on “feedlots;” they may be 

on pasture/crop residue supplemented by feed. Data for those animals on feed that are in feedlots versus pasture/crop 

residue were provided only for lamb in 1993. To calculate the populations of sheep and lamb in feedlots for all years, it 

was assumed that the percentage of sheep and lamb on feed that are in feedlots versus pasture/crop residue is the same as 

that for lambs in 1993 (Anderson 2000). 

Goats: Annual goat population data by state were available for 1992, 1997, 2002, and 2007 (USDA 2009a). The 

data for 1992 were used for 1990 through 1992 and the data for 2007 were used for 2007 through 2009. Data for 1993 

through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated based on the 1992, 1997, and 2002 Census 

data. 

Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one 

year old), broilers, other chickens, and turkeys (USDA 1995a, 1995b, 1998a, 1999, 2004a, 2004b, 2010a, and 2010b). The 

annual population data for boilers and turkeys were adjusted for the turnover (i.e., slaughter) rate (Lange 2000). All 

poultry population data were adjusted to account for states that report non-disclosed populations to USDA NASS. The 

combined populations of the states reporting non-disclosed populations are reported as “other” states. State populations 

for the non-disclosed states were estimated by equally distributing the population attributed to “other” states to each of the 

non-disclosed states. 

Horses: The FAO publishes annual total U.S. horse population, which were accessed from the FAOSTAT 

database (FAO 2010). State horse population data were estimated using state population distributions from the 1992, 

1997, and 2002 Census of Agriculture and the FAO national population data. A summary of the livestock population 

characterization data used to calculate CH4 and N2O emissions is presented in Table A- 183. 
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Step 2: Waste Characteristics Data 

Methane and N2O emissions calculations are based on the following animal characteristics for each relevant 

livestock population: 

• Volatile solids (VS) excretion rate; 

• Maximum methane producing capacity (Bo) for U.S. animal waste; 

• Nitrogen excretion rate (Nex); and 

• Typical animal mass (TAM). 

Table A- 184 presents a summary of the waste characteristics used in the emissions estimates. Published sources 

were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population 

data discussed in Step 1. The USDA’s Agricultural Waste Management Field Handbook (AWMFH; USDA 1996a, 2008) 

is one of the primary sources of waste characteristics. Data from the 1996 and 2008 USDA AWMFH were used to 

estimate VS and Nex for most animal groups across the time series of the inventory, as shown in Table A-3. In some 

cases, data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) were used to supplement 

the USDA data. The VS and Nex data for breeding swine are from a combination of the types of animals that make up this 

animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding swine is typically 

broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars (Safley 2000). Due to the change 

in USDA reporting of hens and pullets, new nitrogen and VS excretion rates were calculated for the combined population 

of hens and pullets; a weighted average rate was calculated based on hen and pullet population data from 1990 to 2004. 

The method for calculating VS excretion and Nex from beef and dairy cows, heifers, and steers is based on the 

relationship between animal performance characteristics such as diet, lactation, and weight gain and energy utilization. 

The method used is outlined by the IPCC Tier II methodology, and is modeled in the enteric fermentation portion of the 

inventory in order to take advantage of the detailed diet and animal performance data assembled as part of the Tier II 

analysis for cattle. 

Volatile solids content of manure is the fraction of the diet consumed by cattle that is not digested and thus 

excreted as fecal material; fecal material combined with urinary excretions constitutes manure. The enteric fermentation 

model requires the estimation of gross energy intake and its fractional digestibility to estimate enteric CH4 emissions (see 

the Enteric Fermentation Annex for details on the enteric energy model). These two inputs are used to calculate the 

indigestible energy per animal unit as gross energy minus digestible energy plus the amount of gross energy for urinary 

energy excretion per animal unit (2 or 4 percent). This value is then converted to VS production per animal unit using the 

typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting out the ash content of 

manure. The current equation recommended by the 2006 IPCC Guidelines is: 

[ ] ( )( ) 18.45

1 VS production (kg) GE - DE UE GE − ASH = + × ×

Where, 

GE = Gross energy intake (MJ) 

DE = Digestible energy (MJ) 

(UE × GE) = Urinary energy expressed as fraction of GE, assumed to be 0.04 except for 

feedlots which are reduced 0.02 as a result of the high grain content of their diet. 

ASH = Ash content of manure calculated as a fraction of the dry matter feed intake 

(assumed to be 0.08). 

18.45 = Conversion factor for dietary GE per kg of dry matter (MJ per kg). This value is 

relatively constant across a wide range of forage and grain-based feeds 

commonly consumed by livestock. 

Nitrogen uptake in cattle is carried out through dietary protein intake. However, when feed intake of protein 

exceeds the nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate 

the nitrogen excreted by each animal type, the cattle enteric fermentation model (CEFM) utilizes the energy balance 

calculations recommended by the IPCC (2006) for gross energy and the energy required for growth along with inputs of 

weight gain, milk production, and the percent of crude protein in the diets. The total nitrogen excreted is measured in the 

CEFM as nitrogen consumed minus nitrogen retained by the animal for growth and in milk. The basic equation for 

calculating Nex is shown below, followed by the equations for each of the constituent parts. 
A-213 

( ) Nexcreted = Nconsumed − Ngrowth + N milk

Where, 

N excreted = Daily N excreted per animal, kg per animal per day. 

N consumed = Daily N intake per animal, kg per animal per day 

N growth = Nitrogen retained by the animal for growth, kg per animal per day 

N milk = Nitrogen retained in milk, kg per animal per day 

The equation for nitrogen consumed is based on the 2006 IPCC Guidelines, and is estimated as: 

⎥

⎥

⎥

⎥

⎦

⎤

⎢

⎢

⎢

⎢

⎣

⎡

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

= 6.25

100

%

*

18.45

CP

GE Nconsumed

Where: 

N consumed = Daily N intake per animal, kg per animal per day 

GE = Gross energy intake, as calculated in the CEFM, MJ per animal per day 

18.45 = Conversion factor for dietary GE per kg of dry matter, MJ per kg. 

CP% = Percent crude protein in diet, input into the CEFM 

6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N 

The portion of consumed nitrogen that is retained as product equals the nitrogen required for weight gain plus 

that in milk. The nitrogen retained in body weight gain by 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 

current 2006 IPCC Guidelines recommendations, and is as follows: 

( )

6.25

1000

7.03*

* 268

⎭

⎬

⎫

⎩

⎨

⎧

⎥

⎦

⎤ ⎢

⎣

⎡ −

=

WG

NEg WG

Ngrowth

Where, 

N growth = Nitrogen retained by the animal for growth, kg per animal per day 

WG = Daily weight gain of the animal, kg per day 

268 = Constant from 2006 IPCC Guidelines 

7.03 = Constant from 2006 IPCC Guidelines 

NEg = Net energy required for growth, MJ per animal per day 

1,000 = Conversion from grams to kilograms 

6.25 = Conversion from kg of dietary protein to kg of dietary N, kg feed per kg N 

The N content of milk produced also currently matches the 2006 IPCC Guidelines. Milk nitrogen retained as 

product is calculated using the following equation: 

6.38

100

% * ⎟

⎠

⎞ ⎜

⎝

⎛

=

pr milk

N milk
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Where, 

 

N milk = Nitrogen retained in milk, kg per animal per day 

milk = Milk production, kg per day 

pr% = Percent protein in milk, estimated from the fat content as 1.9 + 0.4 * %Fat 

100 = Conversion from percent to value (e.g., 4% to 0.04) 

6.38 = Conversion from kg Protein to kg N 

The VS and N equations above were used to calculate VS and Nex rates for each state, cattle type, and year. 

Table A- 186 presents the state-specific VS and Nex production rates used for cattle in 2009. 

Step 3: Waste Management System Usage Data 

Table A- 187 summarizes 2009 manure distribution data among waste management systems (WMS) at beef 

feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining 

animal types (beef cattle not on feed, sheep, horses, and goats) is managed on pasture, range, or paddocks, on drylot, or 

with solids storage systems. Additional information on the development of the manure distribution estimates for each 

animal type is presented below. Definitions of each WMS type are presented in Table A- 188. 

Beef Cattle and Dairy Heifers: The beef feedlot and dairy heifer WMS data were developed using information 

from EPA's Office of Water's engineering cost analyses conducted to support the development of effluent limitations 

guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and state contacts 

supporting this work and additional contacts with the national USDA office to estimate the percent of beef steers and 

heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots. Therefore, for these animal 

groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition, there is a small amount of 

manure contained in runoff, which may or may not be collected in runoff ponds. The runoff from feedlots was calculated 

by region in Calculations: Percent Distribution of Manure for Waste Management Systems (ERG 2000b) and was used to 

estimate the percentage of manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3 

percent. The percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent. The 

remaining population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock systems, 

which are utilized for the majority of the population of beef cattle in the country. 

Dairy Cows: The WMS data for dairy cows were developed using data from the Census of Agriculture, EPA’s 

Office of Water, USDA, and expert sources. Farm-size distribution data are reported in the 1992, 1997, and 2002 Census 

of Agriculture (USDA 2009a). It was assumed that the data provided for 1992 were the same as that for 1990 and 1991, 

and data provided for 2002 were the same as that for 2003 through 2009. Data for 1993 through 1996 and 1998 through 

2001 were extrapolated using the 1992, 1997, and 2002 data. The percent of waste by system was estimated using the 

USDA data broken out by geographic region and farm size. 

Based on EPA site visits and state contacts, manure from dairy cows at medium (200 through 700 head) and 

large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In addition, they 

may have a solids separator in place prior to their storage component. Estimates of the percent of farms that use each type 

of system (by geographic region) were developed by EPA's Office of Water, and were used to estimate the percent of 

waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage (separated solids) 

(EPA 2002b). Manure management system data for small (fewer than 200 head) dairies were obtained from USDA 

(2000a). These operations are more likely to use liquid/slurry and solid storage management systems than anaerobic 

lagoon systems. The reported manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earthbasin, and aerated lagoon), anaerobic lagoon, and solid storage (includes manure pack, outside storage, and inside storage). 

Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from 

personal communications with personnel from several organizations. These organizations include state NRCS offices, 

state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000, 

Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure 

management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, and 2002 

(USDA 2009a) were used in conjunction with the state data obtained from personal communications to determine regional 

percentages of total dairy cattle and dairy waste that are managed using these systems. These percentages were applied to 

the total annual dairy cow and heifer state population data for 1990 through 2009, which were obtained from the USDA 

NASS (USDA 2009b). 
A-215 

Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies 

reported using more than one type of manure management system. Due to limitations in how USDA reports the manure 

management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure is 

typically partitioned to use only one manure management system, rather than transferred between several different 

systems. Emissions estimates are only calculated for the final manure management system used for each portion of 

manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total of 

100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep 

pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent 

(i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided 

by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a). 

Swine: The distribution of manure managed in each WMS was estimated using data from a USDA report and 

EPA’s Office of Water site visits (USDA 1998, ERG 2000a). For operations with less than 200 head, manure 

management system data were obtained from USDA (USDA 1998). It was assumed that those operations use pasture, 

range, or paddock systems. For swine operations with greater than 200 head, the percent of waste managed in each system 

was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data 

reported in the 1992, 1997, and 2002 Census of Agriculture (USDA 2009a) were used to determine the percentage of all 

swine utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992 

were the same as that for 1990 and 1991, and data provided for 2002 were the same as that for 2003 through 2009. Data 

for 1993 through 1996 and 1998 through 2001 were extrapolated using the 1992, 1997, and 2002 data. The reported 

manure management systems were deep pit, liquid/slurry (includes above- and below-ground slurry), anaerobic lagoon, 

and solid storage (includes solids separated from liquids). 

Some swine operations reported using more than one management system; therefore, the total percent of systems 

reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the 

manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled 

in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another, 

which could result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into 

the WMS distribution, using the same method as described above for dairy operations. 

Sheep: Waste management system data for sheep were obtained from USDA NASS sheep report for years 1990 

through 1993 (USDA 1994). Data for 2001 are obtained from USDA APHIS sheep report (USDA 2003). The data for 

years 1994-2000 are calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for 

years 2002 and beyond are assumed to be the same as 2001. It was assumed that all sheep manure not deposited in feedlots 

was deposited on pasture, range, or paddock lands (Anderson 2000). 

Goats and Horses: Waste management system data for 1990 to 2009 were obtained from Appendix H of Global 

Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that all manure not deposited in 

pasture, range, or paddock lands was managed in dry systems. 

Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: Waste 

management system data for 1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure

(EPA 1992). These data were also used to represent 1990 and 1991. The percentage of layer operations using a shallow 

pit flush house with anaerobic lagoon or high-rise house without bedding was obtained for 1999 from a United Egg 

Producers voluntary survey (UEP 1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, 

IN, MN, MO, NC, NE, OH, PA, TX, and WA) with USDA data (USDA 2000b). It was assumed that the change in 

system usage between 1990 and 1999 is proportionally distributed among those years of the inventory. It was assumed 

that system usage in 2000 through 2009 was equal to that estimated for 1999. Data collected for EPA's Office of Water, 

including information collected during site visits (EPA 2002b), were used to estimate the distribution of waste by 

management system and animal type. 

Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from Global 

Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one percent of poultry waste is 

deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed to be deposited in operations 

with bedding management.

Step 4: Emission Factor Calculations 

Methane conversion factors (MCFs) and N2O emission factors (EFs) used in the emission calculations were 

determined using the methodologies presented below. 
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Methane Conversion Factors (MCFs) 

Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in 

Table A- 189. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems. 

For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual 

average temperature data. Each state and year in the inventory was assigned a climate classification of cool, temperate or 

warm. 

For anaerobic lagoons and other liquid systems a climate-based approach based on the van’t Hoff-Arrhenius 

equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for longterm retention time. This approach is consistent with the recently revised guidelines from IPCC (IPCC 2006). The van’t 

Hoff-Arrhenius equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 

1990): 

Where, 

T1 = 303.15K 

T2 = Ambient temperature (K) for climate zone (in this case, a weighted value for each state) 

E = Activation energy constant (15,175 cal/mol) 

R = Ideal gas constant (1.987 cal/K mol) 

The factor f represents the proportion of VS that are biologically available for conversion to CH4 based on the 

temperature of the system. For those animal populations using liquid manure management systems or manure runoff 

ponds (i.e., dairy cow, dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on 

the counties where the specific animal population resides (i.e., the temperatures were weighted based on the percent of 

animals located in each county). The average county and state temperature data were obtained from the National Climate 

Data Center (NOAA 2009). County population data were calculated from state-level population data from NASS and 

county-state distribution data from the 1992, 1997, and 2002 Census data (USDA 2009a). County population distribution 

data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996 

were extrapolated based on 1992 and 1997 data; county population data for 1998 through 2001 were extrapolated based on 

1997 and 2002 data; and county population data for 2003 to 2009 were assumed to be the same as 2002. 

Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory: 

• The weighted-average temperature for a state is calculated using the population estimates and average monthly 

temperature in each county. 

• Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius “f” factor, using the equation 

presented above. A minimum temperature of 5°C is used for uncovered anaerobic lagoons and 7.5°C is used for 

liquid/slurry and deep pit systems. 

• Monthly production of VS added to the system is estimated based on the number of animals present. 

• For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This 

factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from 

anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents a variety of factors that 

may affect methane production in lagoon systems. 

• The amount of VS available for conversion to CH4 is assumed to be equal to the amount of VS produced during 

the month (from Step 3). For anaerobic lagoons, the amount of VS available also includes VS that may remain 

in the system from previous months. 

• The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the 

“f” factor. 

⎥

⎦

⎤ ⎢

⎣

⎡ − =

1 2

2 1 ( ) exp RTT

E T T f
A-217 

• For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount 

available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once 

per year, occurring after the month of September. 

• The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by 

the maximum CH4 potential of the waste (Bo). 

The annual MCF is then calculated as: 

annual o

annual 4

annual VSproduced B

CH generated MCF

× =

Where, 

MCF annual = Methane conversion factor

VS produced annual = Volatile solids excreted annually 

Bo = Maximum CH4 producing potential of the waste 

In order to account for the carry-over of VS from one year to the next, it is assumed that a portion of the VS from 

the previous year are available in the lagoon system in the next year. For example, the VS from October, November, and 

December of 2005 are available in the lagoon system starting January of 2006 in the MCF calculation for lagoons in 2006. 

Following this procedure, the resulting MCF for lagoons accounts for temperature variation throughout the year, residual 

VS in a system (carry-over), and management and design practices that may reduce the VS available for conversion to 

CH4. It is assumed that liquid-slurry systems have a retention time less than 30 days, so the liquid-slurry MCF calculation 

doesn’t reflect the VS carry-over. 

The liquid system MCFs are presented in Table A-8 by state, WMS, and animal group for 2009. 

Nitrous Oxide Emission Factors 

Direct N2O emission factors for manure management systems (kg N2O-N/kg excreted N) were set equal to the 

most recent default IPCC factors (IPCC 2006), presented in Table A- 191. 

Indirect N2O emission factors account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and 

NOX (Fracgas) and runoff/leaching (Fracrunoff/leach). IPCC default indirect N2O emission factors were used to estimate 

indirect N2O emissions. These factors are 0.010 kg N2O-N/kg N for volatilization and 0.0075 kg N2O/kg N for 

runoff/leaching. 

EPA developed country-specific estimates of nitrogen losses for Fracgas and Fracrunoff/leach for the U.S. The vast 

majority of volatilization losses are NH3. Although there are also some small losses of NOX, no quantified estimates were 

available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. Therefore, 

Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA’s National Emission Inventory 

- Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracrunoff/leach, EPA used data from 

EPA’s Office of Water that estimate the amount of runoff from beef, dairy, and heifer operations in five geographic 

regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, WMS, and 

region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses; therefore, Fracrunoff/leach 

was set equal to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A192. 

Step 5: CH4 and N2O Emission Calculations 

To calculate methane emissions for animals other than cattle, EPA first estimated the amount of volatile solids 

excreted in manure that is managed in each WMS: 

VS WMS 365.25

1000

TAM VS excreted State, Animal, WMS = Population State, Animal × × × ×

Where, 

VS excreted State, Animal, WMS = Amount of VS excreted in manure managed in each WMS for each 

animal type (kg/yr) 
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

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 was calculated using the following equation: 

VS excretedState, Animal, WMS = Population State, Animal × VS× WMS

Where, 

VS excreted State, Animal, WMS = Amount of VS excreted in manure managed in each WMS for each 

animal type (kg/yr) 

Population State, Animal = Annual average state animal population by animal type (head) 

VS = Volatile solids production rate (kg VS/animal/year) 

WMS = Distribution of manure by WMS for each animal type in a state (percent) 

For all animals, the estimated amount of VS was used to calculate methane emissions using the following 

equation: 

= ∑( × × × )

State, Animal, WMS

CH4 VS excreted State, Animal, WMS Bo MCF 0.662

Where, 

CH4 = CH4 emissions (kg CH4/yr) 

VS excreted WMS, State = Amount of VS excreted in manure managed in each WMS (kg/yr) 

Bo = Maximum CH4 producing capacity (m3

 CH4/kg VS) 

MCF animal, state, WMS = MCF for the animal group, state and WMS (percent) 

0.662 = Density of methane at 25o C (kg CH4/m3 CH4) 

EPA developed a calculation to estimate the amount of CH4 emitted from anaerobic digestion (AD) systems 

utilizing CH4 capture and combustion technology. First, EPA assumed that AD systems produce 90 percent of the 

maximum CH4 producing capacity. This value is applied for all climate regions and AD system types. However, EPA 

realizes that the actual amount of CH4 produced by each AD system is very variable and will change based on operational 

and climate conditions and an assumption of 90 percent is likely overestimating CH4 production from some systems and 

underestimating CH4 production in other systems. The CH4 production of AD systems is calculated using the equation 

below: 

VS B 0.662 365.25 0.90

1000

TAM CH4 Production ADADSystem = Population ADADSystem × × × o × × ×

Where, 

CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr) 

Population AD state = Number of animals on a particular AD system 

VS = Volatile solids production rate (kg VS/1000 kg animal mass-day) 

TAM = Typical Animal Mass (kg/head) 

Bo = Maximum CH4 producing capacity (CH4 m3

/kg VS) 

0.662 = Density of methane at 25o

 C (kg CH4/m3 CH4) 

365.25 = Days/year 

0.90 = CH4 production factor for AD systems 

 

 Next, EPA considered the collection efficiency (CE) and destruction efficiency (DE) of the AD system. The CE 

of covered lagoon systems was assumed to be 75 percent, and the CE of complete mix and plug flow AD systems was 

assumed to be 99 percent (EPA 2008). The CH4 DE from flaring or burning in an engine was assumed to be 98 percent; 
A-219 

therefore, the amount of CH4 that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The 

amount of CH4 produced by systems with anaerobic digestion was calculated with the following equation: 

[ ( )]

[ ] ( ) ∑ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛

+ ×

× × − =

State, Animal, AD Systems 4 AD system AD system

4 AD system AD system

4 CH Production AD 1-CE

CH Production AD CE 1 DE

CH Emissions AD

Where: 

CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr) 

CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr) 

CEAD system = Collection efficiency of the AD system, varies by AD system type 

DE = Destruction efficiency of the AD system, 0.98 for all systems 

In addition to methane emissions, EPA also estimated total N2O emissions from manure management systems. 

Total N2O emissions were calculated by summing direct and indirect N2O emissions. The first step in estimating direct 

and indirect N2O emissions was calculating the amount of nitrogen excreted in manure and managed in each WMS for 

animals other than cattle using the following equation: 

Nex 365.25

1000

TAM N excreted State, Animal, WMS = Population State, Animal × WMS× × ×

Where, 

N excreted State, Animal, WMS = Amount of N excreted in manure managed in each WMS for each 

animal type (kg/yr) 

Population state = Annual average state animal population by animal type (head) 

WMS = Distribution of manure by waste management system for each animal 

type in a state (percent) 

TAM = Typical animal mass (kg) 

Nex = Total Kjeldahl nitrogen excretion rate (kg N/1000 kg animal mass/day) 

365.25 = Days per year 

 Using the CEFM Nex data for cattle, the amount of N excreted was calculated using the following equation: 

N excreted Population WMS Nex State, Animal, WMS = State, Animal × ×

Where, 

N excreted State, Animal, WMS = Amount of N excreted in manure managed in each WMS for each 

animal type (kg/yr) 

Population state = Annual average state animal population by animal type (head) 

WMS = Distribution of manure by waste management system for each animal 

type in a state (percent) 

Nex = Total Kjeldahl nitrogen excretion rate (kg N/animal/year) 

For all animals, direct N2O emissions were calculated as follows: 

∑ ⎟

⎠

⎞ ⎜

⎝

⎛ = × × State, Animal, WMS

State, Animal, WMS WMS 2

28

44 Direct N O N excreted EF

Where, 

Direct N2O = Direct N2O emissions (kg N2O/yr) 

N excreted State, Animal, WMS = Amount of N excreted in manure managed in each WMS for each 

animal type (kg/yr) 

EFWMS = Direct N2O emission factor from IPCC guidelines (kg N2O-N /kg N) 

44/28 = Conversion factor of N2O-N to N2O 
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Indirect N2O emissions were calculated for all animals with the following equation: 

∑

⎟

⎟

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎜

⎜

⎝

⎛

⎥

⎦

⎤ ⎢

⎣

⎡ × × ×

+

⎥

⎥

⎦

⎤

⎢

⎢

⎣

⎡ × × ×

=

State, Animal, WMS

runnoff/leach

runoff/leach, WMS

State, Animal, WMS

volatilization

gas, WMS

State, Animal, WMS

2

28

44 EF

100

Frac

N excreted

28

44 EF

100

Frac

N excreted

Indirect N O

Where, 

Indirect N2O = Indirect N2O emissions (kg N2O/yr) 

N excreted State, Animal, WMS = Amount of N excreted in manure managed in each WMS for each 

animal type (kg/yr) 

Fracgas,WMS = Nitrogen lost through volatilization in each WMS 

Fracrunoff/leach,WMS = Nitrogen lost through runoff and leaching in each WMS; data were not 

available for leaching so the value reflects only runoff

EFvolatilization = Emission factor for volatilization (0.010 kg N2O-N/kg N) 

 EFrunoff/leach = Emission factor for runoff/leaching (0.0075 kg N2O-N/kg N) 

44/28 = Conversion factor of N2O-N to N2O 

Emission estimates of CH4 and N2O by animal type are presented for all years of the inventory in Table A- 193 

and Table A- 194, respectively. Emission estimates for 2009 are presented by animal type and state in Table A- 195 and 

Table A- 196, respectively. 
Table A- 183: Livestock Population (1,000 Head) 

Animal Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 Dairy Cattle 14,144 13,590 13,191 13,217 13,165 13,215 13,021 13,165 13,398  Dairy Cows 10,015 9,482 9,183 9,172 9,106 9,142 8,988 9,004 9,104 

 Dairy Heifer 4,129 4,108 4,008 4,045 4,060 4,073 4,033 4,162 4,294 Swine1 53,941 58,899 58,864 58,913 60,028 59,827 60,735 61,073 61,887 

 Market <50 lb. 18,359 19,656 19,574 19,659 19,863 19,929 20,222 20,228 20,514 

 Market 50-119 lb. 11,734 12,836 12,926 12,900 13,284 13,138 13,400 13,519 13,727  Market 120-179 lb. 9,440 10,545 10,748 10,708 11,013 11,050 11,227 11,336 11,443  Market >180 lb. 7,510 8,937 9,385 9,465 9,738 9,701 9,922 9,997 10,113 

 Breeding 6,899 6,926 6,231 6,181 6,129 6,011 5,963 5,993 6,090 Beef Cattle2 87,228 95,683 89,948 89,118 89,102 88,232 86,441 86,954 88,070 

 Feedlot Steers 6,357 7,233 8,304 7,932 8,116 8,416 8,018 8,116 8,724 

 Feedlot Heifers 3,192 3,831 4,702 4,569 4,557 4,676 4,521 4,536 4,801  NOF Bulls 2,160 2,385 2,293 2,274 2,244 2,248 2,201 2,214 2,258  NOF Calves 22,561 23,499 22,569 22,389 22,325 21,997 21,781 21,678 21,621 

 NOF Heifers 10,182 11,829 9,781 9,832 9,843 9,564 9,321 9,550 9,716 

 NOF Steers 10,321 11,716 8,724 8,724 8,883 8,347 8,067 8,185 8,248  NOF Cows 32,455 35,190 33,575 33,398 33,134 32,983 32,531 32,674 32,703 

Sheep 11,358 8,989 7,036 6,908 6,623 6,321 6,105 6,135 6,230 

 Sheep On Feed 1,180 1,771 2,963 3,256 3,143 3,049 2,943 2,982 3,043 

 Sheep NOF 10,178 7,218 4,073 3,652 3,480 3,272 3,162 3,153 3,187 Goats 2,516 2,357 2,419 2,475 2,530 2,652 2,774 2,897 3,019 

Poultry3 1,537,074 1,826,977 2,033,123 2,060,398 2,097,691 2,085,268 2,130,877 2,150,410 2,154,236 

 Hens >1 yr. 273,467 299,071 333,593 340,317 340,209 340,979 343,922 348,203 349,888 

 Pullets 73,167 81,369 95,159 95,656 95,289 100,346 101,429 96,809 96,596 

 Chickens 6,545 7,637 8,088 8,126 8,353 8,439 8,248 8,289 7,938 

 Broilers 1,066,209 1,331,940 1,506,127 1,525,413 1,562,015 1,544,155 1,589,209 1,613,091 1,612,327 

 Turkeys 117,685 106,960 90,155 90,887 91,826 91,349 88,069 84,018 87,487 

Horses 5,069 5,130 5,240 5,500 6,000 7,000 8,000 9,200 9,500 

Note: Totals may not sum due to independent rounding. 1 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 

categories. 

2 NOF = Not on Feed 3 Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months. 
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 184: Waste Characteristics Data 

Animal Group TAM (kg) 

TAM

 Source

Total Kjeldahl 

Nitrogen, Nex

(kg/day per 

1,000 kg mass)

Nex

Source

Maximum 

Methane 

Generation

Potential, Bo

(m3 CH4/kg VS 

added)

Bo

 Source

Volatile 

(kg/day p

Dairy Cows 680 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.24 Morris 1976

T

Dairy Heifers 406-408 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.17 Bryant et. al. 1976

T

Feedlot Steers 419-457 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.33 Hashimoto 1981

T

Feedlot Heifers 384-430 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.33 Hashimoto 1981

T

NOF Bulls 750 Shuyler 2000 Table A- 186 USDA 1996a 0.17 Hashimoto 1981 Ta

NOF Calves 118 USDA 1996a Table A- 186 USDA 1996a, 2008 0.17 Hashimoto 1981 TaNOF Heifers 296-406 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.17 Hashimoto 1981

T

NOF Steers 314-335 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.17 Hashimoto 1981

T

NOF Cows 554-611 Enteric 

Fermentation Table A- 171

Table A- 185

Moffroid and Pape, 2010 0.17 Hashimoto 1981

T

Market Swine <50 lbs. 13 ERG 2010a Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 Ta

Market Swine <60 lbs. 16 Safley 2000 Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaMarket Swine 50-119 lbs. 39 ERG 2010a Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaMarket Swine 60-119 lbs. 41 Safley 2000 Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaMarket Swine 120-179 lbs. 68 Safley 2000 Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaMarket Swine >180 lbs. 91 Safley 2000 Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaBreeding Swine 198 Safley 2000 Table A- 186 USDA 1996a, 2008 0.48 Hashimoto 1984 TaFeedlot Sheep 25 EPA 1992 Table A- 186 ASAE 1998, USDA 2008 0.36 EPA 1992 Ta

NOF Sheep 80 EPA 1992 Table A- 186 ASAE 1998, USDA 2008 0.19 EPA 1992 TaGoats 64 ASAE 1999 Table A- 186 ASAE 1998 0.17 EPA 1992 Ta

Horses 450 ASAE 1999 Table A- 186 ASAE 1998, USDA 2008 0.33 EPA 1992 Ta

Hens >/= 1 yr 1.8 ASAE 1999 Table A- 186 USDA 1996a, 2008 0.39 Hill 1982 Ta

Pullets 1.8 ASAE 1999 Table A- 186 USDA 1996a, 2008 0.39 Hill 1982 TaOther Chickens 1.8 ASAE 1999 Table A- 186 USDA 1996a, 2008 0.39 Hill 1982 Ta
Broilers 0.9 ASAE 1999 Table A- 186 USDA 1996a, 2008 0.36 Hill 1984 Ta

Turkeys 6.8 ASAE 1999 Table A- 186 USDA 1996a, 2008 0.36 Hill 1984 Ta
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 185: Estimated Volatile Solids and Nitrogen Excreted Production Rate by year for Animals Other Than Cattle (kg/day/1000 kg animal 

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 VS  Market Swine 

<50 lb. 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 

 Market Swine 50-

119 lb. 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 

 Market Swine 

120-179 lb. 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 

 Market Swine 

>180 lb. 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 

 Breeding Swine 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.611 2.623 2.634 2.645 2.656 2.668 2.679 2.69  NOF Bulls 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 6.04 

 NOF Calves 6.41 6.41 6.41 6.41 6.41 6.41 6.41 6.5175 6.625 6.733 6.84 6.948 7.055 7.163 7.27  Sheep 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.11 9.02 8.93 8.84 8.75 8.66 

 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  Hens >1 yr. 10.091 10.091 10.091 10.091 10.091 10.091 10.091 10.097 10.103 10.109 10.115 10.121 10.1265 10.132 10.138 

 Pullets 10.091 10.091 10.091 10.091 10.091 10.091 10.091 10.097 10.103 10.109 10.115 10.121 10.1265 10.132 10.138 

 Chickens 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.817 10.833 10.85 10.867 10.883 10.9 10.917 10.933  Broilers 15 15 15 15 15 15 15 15.167 15.333 15.5 15.667 15.833 16 16.167 16.333  Turkeys 9.7 9.7 9.7 9.7 9.7 9.7 9.7 9.596 9.492 9.388 9.283 9.179 9.075 8.971 8.867 

 Horses 10 10 10 10 10 10 10 10 10 9.61 9.22 8.83 8.44 8.05 7.66 

Nex 

 Market Swine 

<50 lb. 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.627 0.653 0.68 0.707 0.733 0.76 0.787 0.813 

 Market Swine 

50-119 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 

 Market Swine 

120-179 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 

 Market Swine 

>180 lb. 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 

 Breeding Swine 0.235 0.235 0.235 0.235 0.235 0.235 0.235 0.232 0.230 0.227 0.224 0.221 0.219 0.216 0.213 

 NOF Bulls 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 0.31 

 NOF Calves 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.313 0.325 0.338 0.35 0.363 0.375 0.388 0.4  Sheep 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.423 0.426 0.429 0.432 0.435 0.438 

 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  Hens >1 yr. 0.695 0.695 0.695 0.695 0.695 0.695 0.695 0.703 0.711 0.719 0.727 0.735 0.743 0.750 0.758 

 Pullets 0.695 0.695 0.695 0.695 0.695 0.695 0.695 0.703 0.711 0.719 0.727 0.735 0.743 0.750 0.758 

 Chickens 0.83 0.83 0.83 0.83 0.83 0.83 0.83 0.853 0.875 0.898 0.92 0.943 0.965 0.988 1.01 

 Broilers 1.1 1.1 1.1 1.1 1.1 1.1 1.1 1.088 1.077 1.065 1.053 1.042 1.03 1.018 1.007 

 Turkeys 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.730 0.721 0.711 0.702 0.692 0.683 0.673 0.663 

 Horses 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.295 0.289 0.284 0.278 0.273 0.267 
A-225 

Table A- 186: Estimated Volatile Solids and Nitrogen Excreted Production Rate by State for Cattle for 2009 

(kg/animal/year) 

 Volatile Solids Nitrogen Excreted 

State Dairy Cow

Dairy 

Heifers

Beef NOF 

Cow

Beef NOF 

Heifers

Beef NOF 

Steer

Beef OF 

Heifers

Beef OF 

Steer

Dairy 

Cow

Dairy 

Heifers 

Beef 

NOF 

Cow 

Beef 

NOF 

Heifers

Beef 

NOF 

Steer

Beef 

OF 

Heifers

Beef 

OF 

Steer

Alabama 2,267.41 1,251.47 1,680.43 1,103.73 979.63 673.81 656.14 135.78 68.85 70.16 48.18 40.04 53.69 55.01

Alaska 1,846.07 1,251.47 2,158.74 1,475.35 1,287.98 758.17 723.58 115.86 68.85 84.14 60.31 49.59 61.06 61.25

Arizona 2,819.52 1,251.47 2,158.74 1,440.02 1,287.98 674.59 656.77 157.68 68.85 84.14 58.35 49.59 53.76 55.07

Arkansas 2,046.05 1,251.47 1,675.36 1,093.22 976.43 731.19 702.01 124.61 68.85 72.93 49.63 41.72 58.70 59.26

California 2,723.70 1,251.47 1,638.85 1,042.04 953.48 688.37 667.78 152.93 68.85 71.23 46.75 40.63 54.96 56.09

Colorado 2,824.09 1,251.47 1,546.94 951.51 896.01 685.35 665.37 157.87 68.85 71.65 44.97 40.74 54.70 55.87

Connecticut 2,494.94 1,251.47 1,652.50 1,093.00 962.06 674.37 656.59 143.73 68.85 68.93 47.85 39.24 53.74 55.06

Delaware 2,369.10 1,251.47 1,652.50 1,062.76 962.06 740.93 709.80 138.33 68.85 68.93 45.95 39.24 59.55 59.98

Florida 2,502.90 1,251.47 1,680.43 1,117.18 979.63 723.70 696.02 145.89 68.85 70.16 49.02 40.04 58.05 58.70

Georgia 2,543.18 1,251.47 1,680.43 1,103.56 979.63 706.46 682.24 147.62 68.85 70.16 48.17 40.04 56.54 57.43

Hawaii 2,159.89 1,251.47 2,158.74 1,450.79 1,287.98 706.46 682.24 129.34 68.85 84.14 58.95 49.59 56.54 57.43

Idaho 2,749.48 1,251.47 2,158.74 1,392.62 1,287.98 679.06 660.34 154.67 68.85 84.14 55.73 49.59 54.15 55.40

Illinois 2,509.01 1,251.47 1,653.05 1,037.84 962.40 664.58 648.76 144.34 68.85 70.73 45.55 40.34 52.88 54.33

Indiana 2,603.48 1,251.47 1,653.05 1,050.80 962.40 661.25 646.10 148.40 68.85 70.73 46.38 40.34 52.59 54.09

Iowa 2,620.71 1,251.47 1,653.05 1,022.85 962.40 675.86 657.78 149.14 68.85 70.73 44.59 40.34 53.87 55.17

Kansas 2,674.30 1,251.47 1,546.94 945.66 896.01 684.40 664.60 151.44 68.85 71.65 44.57 40.74 54.61 55.80

Kentucky 2,213.71 1,251.47 1,680.43 1,092.37 979.63 731.19 702.01 133.47 68.85 70.16 47.47 40.04 58.70 59.26

Louisiana 1,990.33 1,251.47 1,675.36 1,112.51 976.43 714.03 688.29 122.21 68.85 72.93 50.88 41.72 57.20 57.99

Maine 2,448.34 1,251.47 1,652.50 1,080.42 962.06 692.36 670.97 141.73 68.85 68.93 47.06 39.24 55.31 56.39

Maryland 2,462.83 1,251.47 1,652.50 1,066.48 962.06 641.27 630.13 142.36 68.85 68.93 46.18 39.24 50.85 52.61

Massachusetts 2,411.79 1,251.47 1,652.50 1,080.42 962.06 706.46 682.24 140.16 68.85 68.93 47.06 39.24 56.54 57.43

Michigan 2,775.94 1,251.47 1,653.05 1,045.14 962.40 674.88 656.99 155.80 68.85 70.73 46.02 40.34 53.78 55.09

Minnesota 2,535.74 1,251.47 1,653.05 1,047.02 962.40 690.51 669.49 145.49 68.85 70.73 46.14 40.34 55.15 56.25

Mississippi 2,191.18 1,251.47 1,680.43 1,103.12 979.63 680.89 661.80 132.50 68.85 70.16 48.14 40.04 54.31 55.54

Missouri 2,193.82 1,251.47 1,653.05 1,067.85 962.40 681.24 662.08 130.80 68.85 70.73 47.47 40.34 54.34 55.56

Montana 2,588.27 1,251.47 1,546.94 1,003.22 896.01 657.92 643.44 147.74 68.85 71.65 48.53 40.74 52.30 53.84

Nebraska 2,572.43 1,251.47 1,546.94 950.97 896.01 680.84 661.76 147.06 68.85 71.65 44.93 40.74 54.30 55.53

Nevada 2,729.35 1,251.47 2,158.74 1,428.01 1,287.98 691.23 670.06 153.80 68.85 84.14 57.69 49.59 55.21 56.30

New 

Hampshire 2,558.38 1,251.47 1,652.50 1,073.63 962.06 706.46 682.24 146.46 68.85 68.93 46.63 39.24 56.54 57.43

New Jersey 2,435.51 1,251.47 1,652.50 1,068.91 962.06 745.24 713.24 141.18 68.85 68.93 46.34 39.24 59.93 60.30

New Mexico 2,916.03 1,251.47 2,158.74 1,417.32 1,287.98 667.61 651.18 161.82 68.85 84.14 57.10 49.59 53.15 54.56

New York 2,598.56 1,251.47 1,652.50 1,050.97 962.06 675.38 657.39 148.18 68.85 68.93 45.21 39.24 53.83 55.13

North 

Carolina 2,621.22 1,251.47 1,680.43 1,103.07 979.63 731.19 702.01 150.97 68.85 70.16 48.14 40.04 58.70 59.26

North Dakota 2,349.60 1,251.47 1,546.94 985.19 896.01 671.24 654.09 137.49 68.85 71.65 47.29 40.74 53.46 54.83

Ohio 2,499.38 1,251.47 1,653.05 1,061.17 962.40 668.17 651.63 143.92 68.85 70.73 47.04 40.34 53.20 54.60

Oklahoma 2,372.40 1,251.47 1,675.36 1,066.91 976.43 672.96 655.46 138.62 68.85 72.93 47.94 41.72 53.61 54.95

Oregon 2,572.28 1,251.47 2,158.74 1,414.00 1,287.98 671.24 654.09 147.06 68.85 84.14 56.91 49.59 53.46 54.83

Pennsylvania 2,545.40 1,251.47 1,652.50 1,050.97 962.06 671.24 654.09 145.90 68.85 68.93 45.21 39.24 53.46 54.83

Rhode Island 2,430.23 1,251.47 1,652.50 1,095.96 962.06 675.44 657.44 140.95 68.85 68.93 48.03 39.24 53.83 55.14

South 

Carolina 2,555.49 1,251.47 1,680.43 1,107.64 979.63 688.56 667.94 148.15 68.85 70.16 48.42 40.04 54.98 56.11

South Dakota 2,602.79 1,251.47 1,546.94 970.02 896.01 674.32 656.55 148.37 68.85 71.65 46.25 40.74 53.73 55.05

Tennessee 2,366.26 1,251.47 1,680.43 1,100.05 979.63 671.24 654.09 140.02 68.85 70.16 47.95 40.04 53.46 54.83

Texas 2,664.94 1,251.47 1,675.36 1,058.67 976.43 678.95 660.25 151.19 68.85 72.93 47.41 41.72 54.14 55.40

Utah 2,667.08 1,251.47 2,158.74 1,424.78 1,287.98 719.20 692.43 151.13 68.85 84.14 57.51 49.59 57.66 58.37

Vermont 2,465.40 1,251.47 1,652.50 1,055.51 962.06 727.14 698.78 142.47 68.85 68.93 45.49 39.24 58.35 58.96

Virginia 2,504.58 1,251.47 1,680.43 1,097.87 979.63 675.38 657.39 145.96 68.85 70.16 47.81 40.04 53.83 55.13

Washington 2,830.17 1,251.47 2,158.74 1,401.02 1,287.98 674.99 657.08 158.13 68.85 84.14 56.19 49.59 53.79 55.10

West Virginia 2,199.28 1,251.47 1,652.50 1,071.08 962.06 617.96 611.49 131.04 68.85 68.93 46.47 39.24 48.81 50.89

Wisconsin 2,599.14 1,251.47 1,653.05 1,067.78 962.40 676.24 658.08 148.21 68.85 70.73 47.46 40.34 53.90 55.19

Wyoming 2,504.44 1,251.47 1,546.94 989.11 896.01 671.24 654.09 144.14 68.85 71.65 47.56 40.74 53.46 54.83

Source: Moffroid and Pape, 2010. 
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 187: 2009 Manure Distribution Among Waste Management Systems by Operation (Percent) 

 Beef Feedlots Dairies1 Dairy Heifer Facilities Swine Operations1 State Dry Lot2 Liquid/ Slurry2 Pasture Daily Spread Solid Storage Liquid/ Slurry Anaerobic Lagoon Deep Pit Daily Spread2 Dry Lot2 Liquid/ Slurry2 Pasture2 Pasture Solid Storage Liquid/ Slurry Anaerobic Lagoon 

Alabama 100 1.3 45 17 11 10 17 0 17 38 0 45 5 4 8 51 Alaska 100 1.3 4 6 28 24 30 8 6 90 1 4 53 2 13 10 Arizona 100 0.4 0 10 9 20 61 0 10 90 0 0 14 3 5 50 Arkansas 100 1.3 57 15 11 7 10 1 15 28 0 57 4 4 12 46 California 100 1.3 1 11 9 21 58 0 11 88 1 1 13 3 8 47 Colorado 100 0.4 1 1 12 24 62 1 1 98 0 1 2 5 26 17 Connecticut 100 1 6 43 17 20 12 2 43 51 0 6 57 2 12 9 Delaware 100 1 6 44 19 19 10 2 44 50 0 6 12 4 24 18 Florida 100 1.3 17 22 8 15 39 0 22 61 1 17 71 1 8 6 Georgia 100 1.3 40 18 10 11 21 0 18 42 0 40 8 4 10 47 Hawaii 100 1.3 1 0 11 21 67 0 0 99 1 1 23 3 18 21 Idaho 100 0.4 0 1 12 23 63 1 1 99 0 0 46 3 15 10 Illinois 100 0.6 5 8 43 26 13 5 8 87 0 5 2 5 28 15 Indiana 100 0.6 8 13 35 24 16 3 13 79 0 8 3 5 28 15 Iowa 100 0.6 6 10 41 25 14 4 10 83 0 6 1 4 12 48 Kansas 100 0.6 3 5 28 33 29 3 5 92 0 3 2 5 28 13 Kentucky 100 1 61 14 14 6 2 2 14 24 0 61 5 4 12 45 Louisiana 100 1.3 60 14 10 6 9 1 14 26 0 60 54 2 13 10 Maine 100 1 7 45 20 17 9 2 45 48 0 7 73 1 7 6 Maryland 100 1 7 44 23 15 8 3 44 49 0 7 21 4 21 16 Massachusetts 100 1 7 45 24 15 6 3 45 47 0 7 31 3 19 14 Michigan 100 0.6 3 6 32 33 22 4 6 91 0 3 5 5 25 17 Minnesota 100 0.6 6 10 44 24 12 5 10 84 0 6 2 5 26 18 Mississippi 100 1.3 57 15 10 7 10 0 15 28 0 57 2 4 6 57 Missouri 100 0.6 8 14 48 18 6 5 14 77 0 8 3 5 28 14 Montana 100 0.4 3 4 25 26 36 6 4 93 0 3 6 5 25 17 Nebraska 100 0.6 4 6 35 30 21 4 6 90 0 4 3 5 28 15 Nevada 100 0.4 0 0 11 24 64 0 0 99 0 0 35 2 4 39 New Hampshire 100 1 7 44 21 16 9 3 44 49 0 7 57 2 12 9 New Jersey 100 1 8 45 24 14 6 3 45 47 0 8 31 3 19 14 New Mexico 100 0.4 0 10 9 19 61 0 10 90 0 0 92 0 2 2 New York 100 1 7 45 20 16 10 2 45 48 0 7 20 4 21 15 North Carolina 100 1 54 15 12 11 6 1 15 31 0 54 0 4 6 58 North Dakota 100 0.6 6 11 45 22 12 4 11 83 0 6 9 5 24 17 Ohio 100 0.6 8 14 41 23 11 4 14 78 0 8 7 4 27 15 Oklahoma 100 0.4 0 6 25 23 40 6 6 94 0 0 1 4 6 58 Oregon 100 1.3 20 0 13 21 44 2 0 80 1 20 58 2 12 9 Pennsylvania 100 1 9 47 25 12 5 2 47 44 0 9 5 5 25 18 
 Beef Feedlots Dairies1 Dairy Heifer Facilities Swine Operations1 State Dry Lot2 Liquid/ Slurry2 Pasture Daily Spread Solid Storage Liquid/ Slurry Anaerobic Lagoon Deep Pit Daily Spread2 Dry Lot2 Liquid/ Slurry2 Pasture2 Pasture Solid Storage Liquid/ Slurry Anaerobic Lagoon 

Rhode Island 100 1 9 47 26 12 4 3 47 44 0 9 56 2 12 9 South Carolina 100 1.3 54 15 8 9 13 0 15 31 0 54 6 4 9 50 South Dakota 100 0.6 5 8 38 28 18 4 8 87 0 5 3 5 26 18 Tennessee 100 1 59 15 12 9 4 2 15 26 0 59 10 4 12 41 Texas 100 0.4 0 8 13 24 53 2 8 92 0 0 7 3 6 54 Utah 100 0.4 1 1 17 26 51 3 1 98 0 1 1 6 26 17 Vermont 100 1 7 44 19 17 10 2 44 49 0 7 86 1 4 3 Virginia 100 1 57 15 12 9 4 2 15 28 0 57 3 4 7 55 Washington 100 1.3 17 0 11 22 49 1 0 83 1 17 37 3 17 12 West Virginia 100 1 7 45 23 16 7 3 45 48 0 7 58 2 11 8 Wisconsin 100 0.6 7 12 42 24 12 4 12 82 0 7 13 4 24 17 Wyoming 100 0.4 7 12 22 23 30 6 12 81 0 7 3 5 26 17 

1 In the methane inventory for manure management, the percent of dairy cows and swine with anaerobic digestion systems is estimated using data from EPA’s AgSTAR 2 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 per

emissions is greater than 100 percent. 
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 188: Manure Management System Descriptions 

Manure Management System Description1

Pasture The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. N2O 

emissions from deposited manure are covered under the N2O from Agricultural Soils category. 

Daily Spread Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 

24 hours of excretion. N2O emissions during storage and treatment are assumed to be zero. N2O 

emissions from land application are covered under the Agricultural Soils category. 

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 volatile solids loading rate, and other operational factors. 

Anaerobic lagoons accumulate sludge over time, diminishing treatment capacity. Lagoons must be 

cleaned out once every 5 to 15 years, and the sludge is typically applied to agricultural lands. The 

water from the lagoon may be recycled as flush water or used to irrigate and fertilize fields. Lagoons 

are sometimes used in combination with a solids separator, typically for dairy waste. Solids 

separators help control the buildup of nondegradable material such as straw or other bedding 

materials. 

Anaerobic Digester Animal excreta with or without straw are collected and anaerobically digested in a large containment 

vessel or covered lagoon. Digesters are designed and operated for waste stabilization by the 

microbial reduction of complex organic compounds to CO2 and CH4, which is captured and flared or 

used as a fuel. 

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 designed 

and operated properly, this high-rise system is a form of passive windrow composting. 

1

 Manure management system descriptions are from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, 

Forestry and Other Land Use, Chapter 10: Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development 

Document for the Final Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for 

Concentrated Animal Feeding Operations (EPA-821-R-03-001, December 2002). 

Table A- 189: Methane Conversion Factors (percent) for Dry Systems 

Waste Management System Cool Climate MCF Temperate Climate MCF Warm Climate MCF 

Aerobic Treatment 0 0 0

Cattle Deep Litter (<1 month) 0.03 0.03 0.3 

Cattle Deep Litter (>1 month) 0.21 0.44 0.76

Composting - In Vessel 0.005 0.005 0.005 

Composting - Static Pile 0.005 0.005 0.005 

Composting-Extensive/ Passive 0.005 0.01 0.015 

Composting-Intensive 0.005 0.01 0.015 

Daily Spread 0.001 0.005 0.01 

Dry Lot 0.01 0.015 0.05 

Fuel 0.1 0.1 0.1
A-229 

Waste Management System Cool Climate MCF Temperate Climate MCF Warm Climate MCF 

Pasture 0.01 0.015 0.02 

Poultry with bedding 0.015 0.015 0.015 

Poultry without bedding 0.015 0.015 0.015 

Solid Storage 0.02 0.04 0.05 

Table A- 190: Methane Conversion Factors by State for Liquid Systems for 2009 (percent) 

State Dairy Swine Beef Poultry 

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit

Liquid/Slurry Anaerobic 

Lagoon 

Alabama 0.76 0.39 0.76 0.38 0.40 0.76 

Alaska 0.46 0.14 0.46 0.14 0.14 0.46 

Arizona 0.80 0.62 0.75 0.37 0.48 0.76 

Arkansas 0.74 0.34 0.75 0.36 0.34 0.74 

California 0.75 0.35 0.74 0.32 0.42 0.75 

Colorado 0.63 0.21 0.67 0.24 0.22 0.63 

Connecticut 0.68 0.24 0.68 0.24 0.25 0.69 

Delaware 0.74 0.31 0.74 0.31 0.31 0.74 

Florida 0.78 0.52 0.78 0.50 0.51 0.78 

Georgia 0.76 0.39 0.76 0.38 0.38 0.75 

Hawaii 0.77 0.58 0.77 0.58 0.58 0.77 

Idaho 0.67 0.23 0.65 0.22 0.22 0.65 

Illinois 0.71 0.28 0.71 0.28 0.27 0.71 

Indiana 0.70 0.27 0.71 0.27 0.27 0.71 

Iowa 0.68 0.24 0.69 0.25 0.25 0.69 

Kansas 0.73 0.30 0.73 0.30 0.30 0.73 

Kentucky 0.74 0.31 0.74 0.31 0.31 0.74 

Louisiana 0.77 0.46 0.77 0.46 0.47 0.77 

Maine 0.63 0.20 0.63 0.20 0.20 0.63 

Maryland 0.72 0.29 0.73 0.30 0.30 0.73 

Massachusetts 0.67 0.23 0.68 0.24 0.24 0.67 

Michigan 0.67 0.23 0.68 0.24 0.23 0.68 

Minnesota 0.66 0.23 0.67 0.23 0.23 0.66 

Mississippi 0.76 0.41 0.76 0.39 0.41 0.76 

Missouri 0.73 0.30 0.72 0.30 0.30 0.73 

Montana 0.59 0.19 0.61 0.20 0.20 0.61 

Nebraska 0.70 0.26 0.70 0.26 0.25 0.70 

Nevada 0.71 0.26 0.71 0.27 0.26 0.71 

New Hampshire 0.63 0.20 0.65 0.22 0.20 0.64 

New Jersey 0.72 0.28 0.72 0.29 0.28 0.72 

New Mexico 0.72 0.30 0.71 0.28 0.28 0.68 

New York 0.65 0.22 0.66 0.22 0.22 0.67 

North Carolina 0.73 0.31 0.75 0.37 0.32 0.73 

North Dakota 0.65 0.21 0.65 0.21 0.21 0.64 

Ohio 0.70 0.26 0.71 0.27 0.26 0.71 

Oklahoma 0.75 0.37 0.74 0.35 0.34 0.75 

Oregon 0.62 0.20 0.63 0.21 0.22 0.64 

Pennsylvania 0.69 0.25 0.70 0.26 0.26 0.71 

Rhode Island 0.69 0.26 0.69 0.26 0.26 0.69 

South Carolina 0.76 0.38 0.76 0.39 0.39 0.76 

South Dakota 0.68 0.24 0.68 0.24 0.24 0.68 

Tennessee 0.74 0.31 0.75 0.34 0.32 0.74 

Texas 0.76 0.44 0.76 0.43 0.37 0.77 

Utah 0.67 0.23 0.66 0.22 0.23 0.66 

Vermont 0.63 0.21 0.63 0.21 0.21 0.63 

Virginia 0.71 0.28 0.73 0.31 0.28 0.72 

Washington 0.62 0.20 0.64 0.21 0.22 0.63 

West Virginia 0.69 0.26 0.70 0.26 0.26 0.69 

Wisconsin 0.66 0.22 0.67 0.23 0.23 0.67 

Wyoming 0.59 0.18 0.64 0.21 0.21 0.62 
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 191: Direct Nitrous Oxide Emission Factors for 2009 (kg N2O-N/kg Kjdl N) 

Waste Management System Direct N2O 

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

Composting_in vessel 0.006

Composting_intensive 0.1

Composting_passive 0.01

Composting_static 0.006

Daily Spread 0

Deep Pit 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

Table A- 192: Indirect Nitrous Oxide Loss Factors (percent) 

Animal Type 

Waste Management 

System Volatilization 

Nitrogen Loss

Runoff/Leaching Nitrogen Loss1

Central Pacific

MidAtlantic 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

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

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

1

 Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff. 
Table A- 193: Methane Emissions from Livestock Manure Management (Gg) a

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 200Dairy Cattle 599 615 598 623 663 693 702 734 781 854 900 960 997 856 96 Dairy Cows 592 608 591 616 656 686 695 727 774 846 893 952 990 850 96 Dairy Heifer 7 7 7 7 7 7 7 7 7 7 7 7 7 6 Swine 624 676 639 680 741 764 730 783 892 849 834 854 879 857 85 Market Swine 484 524 500 534 585 608 582 626 720 692 680 696 720 703 70 Market <50 lbs. 102 110 104 109 119 121 116 125 141 133 131 134 137 135 13 Market 50-119 lbs. 101 111 105 110 120 124 117 127 144 138 136 138 144 140 14 Market 120-179 lbs. 136 147 140 151 164 170 163 175 201 193 189 192 199 196 19 Market >180 lbs. 145 156 152 165 182 194 185 198 235 229 225 232 240 233 23 Breeding Swine 140 152 139 146 156 155 148 157 172 157 155 158 158 154 15Beef Cattle 128 128 131 131 137 140 139 136 139 139 133 135 133 133 13 Feedlot Steers 14 14 14 13 14 14 14 13 13 14 15 15 15 16 1 Feedlot Heifers 7 7 7 7 8 8 8 8 8 8 9 9 9 9  NOF Bulls 5 5 5 5 5 5 5 5 5 5 5 5 5 5  NOF Calves 8 8 8 8 9 9 8 8 9 9 9 9 9 9  NOF Heifers 12 12 13 14 14 15 15 14 14 14 13 13 13 13 1 NOF Steers 12 12 13 14 13 14 14 13 13 12 11 11 11 10 1 NOF Cows 69 69 70 71 74 76 76 74 76 76 71 73 71 71 7Sheep 7 7 7 6 6 5 5 5 5 4 4 4 4 4 Goats 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Poultry 131 132 127 131 131 128 126 128 130 126 127 131 130 130 12 Hens >1 yr. 73 72 70 73 72 69 68 67 70 66 67 70 67 68 6 Total Pullets 25 26 23 23 23 22 21 23 23 21 22 22 22 22 2 Chickens 4 4 4 4 4 4 3 3 4 4 3 3 4 4  Broilers 19 20 20 21 22 23 24 25 26 27 28 28 29 29 3 Turkeys 10 10 10 10 9 9 9 9 8 7 7 7 7 7 Horses 22 22 21 21 21 21 21 21 22 21 20 20 21 24 2

a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters. 

Table A- 194: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (Gg) 

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 200Dairy Cattle 17.1 17.01 17.03 17.27 17.4 17.7 17.7 17.9 18.0 17.6 17.9 18.2 18.5 16.5 17. Dairy Cows 10.0 10.07 9.97 10.05 10.1 10.3 10.3 10.4 10.5 10.2 10.5 10.5 10.6 9.6 10.

 Dairy Heifer 7.0 6.93 7.06 7.22 7.3 7.4 7.4 7.4 7.5 7.4 7.5 7.7 7.8 7.0 7.Swine 4.0 4.18 4.33 4.37 4.6 4.5 4.4 4.7 5.1 5.0 5.0 5.1 5.3 5.4 5. Market Swine 3.0 3.11 3.26 3.29 3.5 3.5 3.3 3.6 4.0 4.1 4.1 4.2 4.4 4.5 4. Market <50 lbs. 0.6 0.59 0.61 0.60 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0. Market 50-119 lbs. 0.6 0.67 0.70 0.70 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0. Market 120-179 lbs. 0.9 0.90 0.94 0.95 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.2 1.2 1.3 1. Market >180 lbs. 0.9 0.95 1.02 1.04 1.1 1.1 1.1 1.2 1.3 1.3 1.3 1.4 1.5 1.5 1. Breeding Swine 1.0 1.07 1.08 1.07 1.1 1.1 1.0 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.Beef Cattle 19.8 20.33 20.15 19.10 21.0 21.8 21.5 21.5 21.7 24.1 25.0 24.1 24.8 25.1 23.

 Feedlot Steers 13.4 13.67 13.56 12.83 13.9 14.4 14.0 13.9 14.1 15.5 16.1 15.4 16.1 16.3 15.
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

 Feedlot Heifers 6.4 6.65 6.60 6.27 7.0 7.4 7.4 7.6 7.6 8.5 8.9 8.7 8.8 8.8 8.Sheep 0.4 0.41 0.44 0.44 0.6 0.7 0.8 0.9 0.9 1.0 1.1 1.2 1.2 1.2 1.Goats 0.1 0.07 0.07 0.07 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.

Poultry 4.7 4.79 4.88 4.96 5.1 5.1 5.3 5.3 5.3 5.3 5.3 5.3 5.4 5.3 5.

 Hens >1 yr. 1.0 1.02 1.02 1.03 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.

 Total Pullets 0.3 0.29 0.29 0.29 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0. Chickens 0.0 0.03 0.03 0.03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.

 Broilers 2.2 2.26 2.36 2.46 2.6 2.7 2.8 2.8 2.9 2.9 2.9 2.9 3.0 2.9 2.

 Turkeys 1.2 1.19 1.18 1.14 1.1 1.1 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.

Horses 0.7 0.70 0.71 0.71 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.9 1.
A-233 

Table A- 195: Methane Emissions by State from Livestock Manure Management for 2009(Gg) a

State 

Beef on 

Feedlots 

Beef Not 

on Feed Dairy Cow 

Dairy 

Heifer

Swine—

Market

Swine—

Breedin

g Layer Broiler Turkey Sheep Goats Horses

Alabama 0.0091 2.5130 0.7878 0.0133 1.8870 0.5402 8.8601 3.6406 0.0528 0.0090 0.0302 0.6750

Alaska 0.0001 0.0214 0.0336 0.0003 0.0036 0.0019 0.1801 0.0000 0.0526 0.0060 0.0001 0.0096

Arizona 0.8202 1.3914 52.2827 0.1192 2.2931 0.6103 0.6572 0.0000 0.0528 0.1057 0.0158 0.5327

Arkansas 0.0042 2.3427 0.4741 0.0095 1.5543 2.1073 0.5189 3.8171 0.7252 0.0090 0.0190 0.6119

California 1.3793 3.5354 401.7280 2.0424 1.5433 0.2113 4.2273 0.2654 0.3751 0.4652 0.0490 1.4005

Colorado 1.5272 2.2547 25.9551 0.0901 4.7356 2.7422 3.1055 + 0.0526 0.1927 0.0122 0.6150

Connecticut 0.0002 0.0214 0.9206 0.0186 0.0086 0.0054 0.2664 0.2645 0.0526 0.0038 0.0011 0.0595

Delaware 0.0002 0.0127 0.3593 0.0041 0.0533 0.0427 0.0727 0.8381 0.0526 0.0060 0.0009 0.0205

Florida 0.0049 3.3731 17.9106 0.0998 0.0643 0.0530 6.8326 0.1526 0.0528 0.0090 0.0216 0.9338

Georgia 0.0078 2.0242 6.0643 0.0614 2.6177 1.0343 15.2216 4.8018 0.0528 0.0090 0.0315 0.5944

Hawaii 0.0014 0.3871 0.3521 0.0029 0.1295 0.0854 0.1325 + 0.0528 0.0090 0.0034 0.0507

Idaho 0.3405 1.7547 113.6925 0.4155 0.1240 0.0714 0.5614 + 0.0526 0.0987 0.0044 0.3824

Illinois 0.2797 1.1587 7.1004 0.0951 36.9889 9.7843 0.2512 + 0.0526 0.0273 0.0084 0.4106

Indiana 0.1859 0.6572 9.9651 0.1057 31.3953 5.4684 0.8165 0.2645 0.3739 0.0235 0.0118 0.4193

Iowa 2.0285 3.3553 15.5070 0.2062 264.5844 28.7171 1.7071 0.2645 0.0526 0.0940 0.0140 0.3719

Kansas 3.8479 5.2668 17.1442 0.1058 18.8129 3.5728 0.0416 + 0.0526 0.0376 0.0124 0.4644

Kentucky 0.0190 2.8498 1.5234 0.0881 5.0055 1.0488 0.5920 1.1114 0.0526 0.0188 0.0245 0.9063

Louisiana 0.0043 1.8598 0.8950 0.0194 0.0565 0.0306 2.1219 0.2654 0.0528 0.0090 0.0081 0.4687

Maine 0.0003 0.0387 1.3237 0.0296 0.0078 0.0058 0.3816 + 0.0526 0.0038 0.0015 0.0628

Maryland 0.0202 0.1159 2.5013 0.0453 0.2579 0.0736 0.2180 1.0567 0.0526 0.0060 0.0042 0.1588

Massachusetts 0.0002 0.0237 0.4876 0.0117 0.0643 0.0141 0.0106 + 0.0526 0.0038 0.0021 0.1062

Michigan 0.2544 0.4613 34.7198 0.2332 8.3149 1.9980 0.6120 0.2645 0.0526 0.0367 0.0070 0.5225

Minnesota 0.4411 1.5054 28.1062 0.4485 58.7581 10.6826 0.3644 0.1622 0.0526 0.0658 0.0092 0.4657

Mississippi 0.0088 1.8861 0.8255 0.0190 5.8277 1.2846 8.2188 2.8818 0.0528 0.0090 0.0115 0.5058

Missouri 0.0965 5.1365 4.7540 0.0650 25.0029 7.4122 0.2482 0.2645 0.4611 0.0390 0.0241 0.7706

Montana 0.0645 3.4419 1.6729 0.0149 1.1172 0.3543 0.3486 + 0.0526 0.1198 0.0031 0.5437

Nebraska 3.9357 5.7938 5.8908 0.0319 25.5224 7.3081 0.5819 0.2645 0.0526 0.0334 0.0086 0.3390

Nevada 0.0092 0.7279 6.3173 0.0153 0.0285 0.0121 0.0214 + 0.0526 0.0315 0.0030 0.0950

New Hampshire 0.0001 0.0181 0.6294 0.0123 0.0107 0.0034 0.0659 + 0.0526 0.0038 0.0010 0.0511

New Jersey 0.0003 0.0283 0.3514 0.0087 0.0531 0.0160 0.0715 + 0.0526 0.0060 0.0027 0.1549

New Mexico 0.2468 1.6482 77.4893 0.2155 0.0015 0.0010 0.5926 + 0.0526 0.0564 0.0089 0.2770

New York 0.0481 0.3778 27.1686 0.5322 0.4586 0.1625 0.4220 0.2645 0.0526 0.0291 0.0100 0.4391

North Carolina 0.0038 1.0137 1.7797 0.0354 151.1504 35.4253 11.0133 2.7590 0.8878 0.0197 0.0369 0.6074

North Dakota 0.1065 2.1409 1.3253 0.0313 0.8294 0.6165 0.0390 + 0.0526 0.0413 0.0011 0.2312

Ohio 0.3035 0.9541 16.8066 0.1837 16.4061 3.1993 0.9148 0.2042 0.1296 0.0611 0.0174 0.6158

Oklahoma 0.5381 6.0089 9.8078 0.0684 32.2081 14.2156 3.5948 0.8181 0.0526 0.0376 0.0313 0.8553

Oregon 0.1395 1.7579 13.5532 0.0933 0.0571 0.0239 0.8226 0.2645 0.0526 0.1034 0.0095 0.4620

Pennsylvania 0.1272 0.6633 15.4864 0.4585 10.2107 1.8110 0.7260 0.5557 0.2243 0.0470 0.0148 0.6010

Rhode Island + 0.0042 0.0310 0.0009 0.0049 0.0039 0.0696 + 0.0526 0.0038 0.0002 0.0180

South Carolina 0.0025 0.7246 1.0262 0.0186 4.2497 0.4742 5.0711 0.8637 0.2976 0.0090 0.0163 0.3354

South Dakota 0.6037 4.0757 7.4168 0.0632 10.1931 2.8293 0.1373 + 0.1122 0.1433 0.0027 0.3628

Tennessee 0.0071 2.4632 1.5864 0.0796 2.6957 0.4871 0.2492 0.6867 0.0526 0.0160 0.0327 0.7329

Texas 6.2856 22.3466 93.4873 0.5075 14.7215 2.4491 4.3726 2.4289 0.0528 0.6132 0.4273 3.4006

Utah 0.0392 1.1290 14.7238 0.0679 4.0454 1.0264 2.7412 + 0.0798 0.1363 0.0043 0.3088

Vermont 0.0005 0.0547 5.2580 0.1005 0.0032 0.0018 0.0188 + 0.0526 0.0038 0.0016 0.0686

Virginia 0.0505 1.6854 2.7183 0.0776 5.7130 0.8152 0.4380 0.8717 0.4237 0.0352 0.0158 0.4668

Washington 0.2798 1.0249 37.9185 0.1870 0.0989 0.0570 1.1358 0.2645 0.0526 0.0249 0.0082 0.4636

West Virginia 0.0142 0.5231 0.3899 0.0051 0.0223 0.0137 0.0971 0.2994 0.0823 0.0155 0.0070 0.1949

Wisconsin 0.3705 1.1936 70.4229 1.0204 2.4065 0.8365 0.2969 0.1658 0.0526 0.0399 0.0140 0.6197

Wyoming 0.1025 1.6831 0.6483 0.0075 0.2398 0.2827 0.0085 + 0.0526 0.1973 0.0021 0.4158

+ Emission estimate is less than 0.00005 Gg. 

a

 Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters. 
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 196: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2009

(Gg) 

State 

Beef 

FeedlotHeifer 

Beef 

FeedlotSteers Dairy 

Cow 

Dairy 

Heifer

Swine—

Market

Swine—

Breeding Layer Broiler Turkey Sheep Goats Horses

Alabama 0.0021 0.0040 0.0060 0.0045 0.0099 0.0021 0.0639 0.3221 0.0061 0.0049 0.0024 0.0232

Alaska + + 0.0006 0.0004 0.0001 + 0.0031 + 0.0061 0.0016 + 0.0005

Arizona 0.2208 0.4235 0.2285 0.1056 0.0116 0.0024 0.0034 + 0.0061 0.0165 0.0012 0.0183

Arkansas 0.0014 0.0026 0.0050 0.0033 0.0088 0.0087 0.0736 0.3377 0.0840 0.0042 0.0015 0.0210

California 0.3129 0.5977 2.1407 1.6247 0.0085 0.0008 0.0873 0.0235 0.0435 0.0822 0.0039 0.0481

Colorado 0.6402 1.2240 0.1751 0.1381 0.0493 0.0212 0.0192 + 0.0061 0.0452 0.0014 0.0317

Connecticut 0.0001 0.0001 0.0149 0.0133 0.0001 + 0.0116 0.0235 0.0061 0.0030 0.0001 0.0031

Delaware 0.0001 0.0002 0.0049 0.0027 0.0004 0.0003 0.0030 0.0744 0.0061 0.0049 0.0001 0.0011

Florida 0.0011 0.0020 0.0924 0.0506 0.0004 0.0002 0.0469 0.0135 0.0061 0.0049 0.0017 0.0321

Georgia 0.0018 0.0035 0.0434 0.0228 0.0141 0.0041 0.1106 0.4248 0.0061 0.0049 0.0025 0.0204

Hawaii 0.0003 0.0006 0.0018 0.0023 0.0007 0.0003 0.0014 + 0.0061 0.0016 0.0003 0.0017

Idaho 0.1429 0.2737 0.7334 0.6375 0.0013 0.0005 0.0034 + 0.0061 0.0232 0.0005 0.0197

Illinois 0.1096 0.2108 0.1408 0.1205 0.3498 0.0678 0.0181 + 0.0061 0.0190 0.0010 0.0212

Indiana 0.0727 0.1399 0.2096 0.1218 0.3033 0.0388 0.1134 0.0235 0.0435 0.0164 0.0014 0.0216

Iowa 0.8062 1.5456 0.2924 0.2542 1.7628 0.1407 0.2370 0.0235 0.0061 0.0656 0.0017 0.0192

Kansas 1.4902 2.8499 0.1782 0.1401 0.1705 0.0239 0.0030 + 0.0061 0.0262 0.0015 0.0239

Kentucky 0.0068 0.0129 0.0316 0.0288 0.0299 0.0046 0.0246 0.0987 0.0061 0.0152 0.0029 0.0467

Louisiana 0.0010 0.0018 0.0081 0.0043 0.0003 0.0001 0.0111 0.0235 0.0061 0.0042 0.0006 0.0161

Maine 0.0001 0.0002 0.0244 0.0204 0.0001 + 0.0176 + 0.0061 0.0030 0.0002 0.0032

Maryland 0.0071 0.0137 0.0427 0.0300 0.0022 0.0005 0.0091 0.0938 0.0061 0.0049 0.0005 0.0082

Massachusetts 0.0001 0.0001 0.0107 0.0078 0.0006 0.0001 0.0005 + 0.0061 0.0030 0.0002 0.0055

Michigan 0.1022 0.1959 0.5270 0.3146 0.0858 0.0152 0.0453 0.0235 0.0061 0.0256 0.0008 0.0269

Minnesota 0.1778 0.3394 0.6319 0.5617 0.6070 0.0812 0.0506 0.0144 0.0061 0.0459 0.0011 0.0240

Mississippi 0.0020 0.0039 0.0071 0.0047 0.0291 0.0047 0.0430 0.2550 0.0061 0.0049 0.0009 0.0174

Missouri 0.0375 0.0718 0.1234 0.0724 0.2284 0.0499 0.0346 0.0235 0.0536 0.0272 0.0029 0.0397

Montana 0.0270 0.0520 0.0213 0.0218 0.0134 0.0031 0.0022 + 0.0061 0.0281 0.0004 0.0280

Nebraska 1.5630 2.9921 0.0829 0.0423 0.2541 0.0536 0.0425 0.0235 0.0061 0.0233 0.0010 0.0175

Nevada 0.0038 0.0073 0.0368 0.0233 0.0002 0.0001 0.0030 + 0.0061 0.0074 0.0004 0.0049

New Hampshire + 0.0001 0.0117 0.0087 0.0001 + 0.0030 + 0.0061 0.0030 0.0001 0.0026

New Jersey 0.0001 0.0002 0.0070 0.0055 0.0005 0.0001 0.0030 + 0.0061 0.0049 0.0003 0.0080

New Mexico 0.1006 0.1934 0.4144 0.2955 + + 0.0034 + 0.0061 0.0132 0.0011 0.0143

New York 0.0181 0.0347 0.4805 0.3629 0.0049 0.0013 0.0186 0.0235 0.0061 0.0236 0.0012 0.0226

North Carolina 0.0014 0.0026 0.0228 0.0144 0.7694 0.1326 0.0811 0.2441 0.1029 0.0107 0.0029 0.0209

North Dakota 0.0431 0.0827 0.0314 0.0387 0.0092 0.0050 0.0030 + 0.0061 0.0289 0.0001 0.0119

Ohio 0.1194 0.2294 0.3446 0.2115 0.1571 0.0225 0.1270 0.0181 0.0151 0.0493 0.0021 0.0317

Oklahoma 0.2153 0.4131 0.0760 0.0657 0.1696 0.0546 0.0190 0.0726 0.0061 0.0262 0.0037 0.0441

Oregon 0.0497 0.0954 0.1223 0.1036 0.0006 0.0002 0.0106 0.0235 0.0061 0.0274 0.0011 0.0238

Pennsylvania 0.0465 0.0892 0.3984 0.2817 0.0966 0.0127 0.1009 0.0493 0.0261 0.0381 0.0018 0.0310

Rhode Island + + 0.0008 0.0005 + + 0.0030 + 0.0061 0.0030 + 0.0009

South Carolina 0.0006 0.0011 0.0076 0.0051 0.0220 0.0018 0.0265 0.0764 0.0345 0.0049 0.0013 0.0115

South Dakota 0.2413 0.4627 0.1309 0.0819 0.1030 0.0210 0.0101 + 0.0130 0.1001 0.0003 0.0187

Tennessee 0.0025 0.0048 0.0238 0.0279 0.0157 0.0021 0.0105 0.0610 0.0061 0.0130 0.0039 0.0378

Texas 1.7394 3.3315 0.5319 0.4742 0.0833 0.0102 0.0892 0.2149 0.0061 0.0959 0.0337 0.1168

Utah 0.0165 0.0313 0.1145 0.1031 0.0599 0.0109 0.0163 + 0.0093 0.0320 0.0005 0.0159

Vermont 0.0002 0.0003 0.1044 0.0703 + + 0.0009 + 0.0061 0.0030 0.0002 0.0035

Virginia 0.0181 0.0347 0.0430 0.0295 0.0317 0.0033 0.0186 0.0774 0.0493 0.0286 0.0019 0.0241

Washington 0.1000 0.1917 0.2872 0.2164 0.0011 0.0005 0.0274 0.0235 0.0061 0.0066 0.0010 0.0239

West Virginia 0.0051 0.0099 0.0077 0.0034 0.0002 0.0001 0.0043 0.0266 0.0096 0.0126 0.0008 0.0100

Wisconsin 0.1489 0.2855 1.6751 1.2450 0.0246 0.0063 0.0221 0.0147 0.0061 0.0279 0.0017 0.0319

Wyoming 0.0429 0.0824 0.0079 0.0094 0.0045 0.0039 0.0001 + 0.0061 0.0463 0.0002 0.0214

+ Emission estimate is less than 0.00005 Gg. 
A-235 

3.11. Methodology for Estimating N2O Emissions from Agricultural Soil 

Management 

Nitrous oxide emissions from agricultural soils result from the interaction of the natural processes of 

denitrification and nitrification with management practices that add or release mineral nitrogen (N) in the soil profile. 

Emissions can occur directly in the soil where the N is made available or can be transported to another location following 

volatilization, leaching, or runoff, and then converted into N2O. 

A combination of Tier 1 and Tier 3 approaches was used to estimate direct and indirect N2O emissions from 

agricultural soils. The process-based biogeochemical model DAYCENT (a Tier 3 approach) was used to estimate N2O 

emissions resulting from croplands on mineral soils that were used to produce major crops, while the IPCC (2006) Tier 1 

methodology was applied to estimate N2O emissions for non-major crop types on mineral soils. The Tier 1 method was 

also used to estimate direct N2O emissions due to drainage and cultivation of organic cropland soils. Direct N2O 

emissions from grasslands were estimated by using a combination of DAYCENT and IPCC (2006) Tier 1 methods. A 

combination of DAYCENT and Tier 1 methods was also used to estimate indirect emissions from all managed lands (i.e., 

croplands, grasslands, forest lands, and settlements). Specifically, the amount of N volatilized from soils, as well as 

leaching or transport of nitrate (NO3

-

) off-site in surface runoff waters was computed by DAYCENT for the direct 

emission analyses, while IPCC default factors were used to estimate N transport for the analyses using the Tier 1 

methodology. The indirect N2O emissions resulting from off-site transport of N were then computed using the IPCC 

(2006) Tier 1 default emission factor. Overall, the Tier 3 approach is used to estimate approximately 85 percent of direct 

soil emissions and 70 percent of total soil N2O emissions associated with agricultural soil management in the United 

States. 

DAYCENT (Del Grosso et al. 2001, Parton et al. 1998) simulates biogeochemical N fluxes between the 

atmosphere, vegetation, and soil, allowing for a more complete estimation of N2O emissions than IPCC Tier 1 methods by 

accounting for the influence of environmental conditions including soil characteristics and weather patterns, specific crop 

and forage qualities that influence the N cycle, and management practices at a daily time step. For example, plant growth 

is controlled by nutrient availability, water, and temperature stress; moreover, growth removes mineral N from the soil 

before it can potentially be converted into N2O. Nutrient supply is a function of external nutrient additions as well as litter 

and soil organic matter (SOM) decomposition rates, and increasing decomposition can lead to greater N2O emissions by 

enhancing mineral N availability in soils. In this model-based assessment framework, daily maximum/minimum 

temperature and precipitation, timing and description of management events (e.g., fertilization, tillage, harvest), and soil 

texture data are model inputs to DAYCENT, which form the basis to simulate key processes and generate robust estimates 

of N2O emissions from soils. Key processes simulated within sub-models of DAYCENT include plant production, organic 

matter formation and decomposition, soil water and soil temperature regimes by layer, and nitrification and denitrification 

processes (Figure A- 7). Comparison of model results and plot level data show that DAYCENT reliably simulates crop 

yields, soil organic matter levels, and trace gas fluxes for a number of native and managed systems (Del Grosso et al. 

2001, 2005). Comparisons with measured data showed that DAYCENT estimated emissions more accurately and 

precisely than the IPCC Tier 1 methodology (Figure A- 8). The linear regression of simulated vs. measured emissions for 

DAYCENT had higher r2

 and a fitted line closer to a perfect 1:1 relationship between measured and modeled N2O 

emissions (Del Grosso et al. 2005, 2008). This is not surprising, since DAYCENT includes site-specific factors (climate, 

soil properties, and previous management) that influence N2O emissions. Furthermore, DAYCENT also simulated NO3

-

leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 

69 percent) (Del Grosso et al. 2005). Thus, the Tier 3 approach has reduced uncertainties in the agricultural soil 

management section relative to earlier Inventory years where the IPCC Tier 1 method was used. The latest operational 

version of DAYCENT has several improvements, including (1) elimination of the influence of labile (i.e., easily 

decomposable by microbes) C availability on surface litter denitrification rates, (2) incorporation of precipitation events on 

surface litter denitrification, and (3) having the wettest soil layer within the rooting zone control plant transpiration. 

[Begin Text Box] 

Box 1. DAYCENT Model Simulation of Nitrification and Denitrification 

The DAYCENT model simulates the two biogeochemical processes, nitrification and denitrification, that result 

in N2O emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm of 

soil, while denitrification is calculated for the entire soil profile. The equations and key parameters controlling N2O 

emissions from nitrification and denitrification are described below. 
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Nitrification is controlled by soil ammonium (NH4

+

) concentration, water filled pore space (WFPS), temperature 

(t), and pH according to the following equation: 

Nit = NH4 × Kmax × F(t) × F(WFPS) × F(pH) 

where, 

Nit = the soil nitrification rate (g N/m2

/day) 

NH4 = the model-derived soil ammonium concentration (g N/m2

) 

Kmax = the maximum fraction of NH4

+

 nitrified (Kmax = 0.10/day) 

F(t) = the effect of soil temperature on nitrification (Figure A- 5a) 

F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A- 5b) 

F(pH) = the effect of soil pH on nitrification (Figure A- 5c) 

The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O. 

N2O emissions from denitrification are a function of soil NO3

-

 concentration, WFPS, heterotrophic (i.e., 

microbial) respiration, and texture. Denitrification is calculated for each soil layer in the profile, and N2O emissions from 

each layer are summed to obtain total soil emissions. The model assumes that denitrification rates are controlled by the 

availability of soil NO3

-

 (electron acceptor), labile C compounds (electron donor) and oxygen (competing electron 

acceptor). Heterotrophic soil respiration is used as a proxy for labile C availability, while oxygen availability is a function 

of soil physical properties that influence gas diffusivity, soil WFPS, and oxygen demand. The model selects the minimum 

of the NO3

-

 and CO2 functions to establish a maximum potential denitrification rate for particular levels of electron 

acceptor and C substrate and accounts for limitations of oxygen availability to estimate daily denitrification rates 

according to the following equation: 

Den = min[F(CO2), F(NO3)] × F(WFPS) 

where, 

Den = the soil denitrification rate (μg N/g soil/day) 

F(CO2) = a function relating N gas flux to soil respiration (Figure A- 6a) 

F(NO3) = a function relating N gas flux to nitrate levels (Figure A- 5b) 

F(WFPS) = a dimensionless multiplier (Figure A- 6c). 

The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (DFC): 

x inflection = 0.90 - M(CO2) 

where, 

M = a multiplier that is a function of DFC. 

Respiration has a much stronger effect on the water curve in clay soils with low DFC than in loam or sandy soils 

with high DFC (Figure A- 6c). The model assumes that microsites in fine-textured soils can become anaerobic at relatively 

low water contents when oxygen demand is high. 

After calculating total N gas flux, the ratio of N2/N2O is estimated so that total N gas emissions can be 

partitioned between N2O and N2: 

RN2/N2O = Fr(NO3/CO2) × Fr(WFPS). 

where, 

RN2/N2O = the ratio of N2/N2O 

Fr(NO3/CO2) = a function estimating the impact of the availability of electron donor relative to substrate 

Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N2O. 

For Fr(NO3/CO2), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be 

in the form of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2. 

[End Box] 
A-237 

Figure A- 5: Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates 

Figure A- 6: Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on 

Denitrification Rates 

There are five steps in estimating direct N2O emissions from cropland and grassland soils, and indirect N2O 

emissions from volatilization, leaching, and runoff from all managed lands (i.e., croplands, grasslands, forest lands, and 

settlements). First, the activity data are derived from a combination of land-use, livestock, crop, and grassland 

management surveys, as well as expert knowledge. In the second, third, and fourth steps, direct and indirect N2O 

emissions are estimated using DAYCENT and/or the Tier 1 method. In the fifth step, total emissions are computed by 

summing all components. The remainder of this annex describes the methods underlying each step. 

Step 1: Derive Activity Data 

The activity data requirements vary for major crops, non-major crops, grasslands, organic cropland soils, 

settlements and forest lands. Activity data were derived for direct and indirect N2O emission calculations as described 

below. 

Step 1a: Activity Data for Direct Emissions from Crop Production on Mineral Soils 

Nitrous oxide emissions from mineral cropland soils include emissions from both major and non-major cropping 

systems and were estimated using a Tier 3 and Tier 1 approach, respectively. 

Major Crop Types: Tier 3 DAYCENT Simulations 

The activity data requirements for estimating N2O emissions from major crop types (corn, soybeans, wheat, 

alfalfa hay, other hay, sorghum, and cotton) include the following: (1) crop-specific mineral N fertilizer rates and timing, 

(2) crop-specific manure amendment N rates and timing, (3) other N inputs, (4) crop-specific land management 

information, (5) native vegetation, (6) daily weather data for every county, (7) sub-county-level soil texture data, and (8) 

county-level crop areas. The United States was divided into 63 agricultural regions based on common cropping practices 

as defined by McCarl et al. (1993), and data were assembled and provided as inputs to the DAYCENT biogeochemical 

ecosystem model. 

Synthetic N Fertilizer Application: Data on N fertilizer rates were obtained primarily from the U.S. Department 

of Agriculture–Economic Research Service 1995 Cropping Practices Survey (ERS 1997). In this survey, data on 

inorganic N fertilization rates were collected for major crops (corn, cotton, soybeans, and wheat) in the high production 

states during 1995. It is assumed that the fertilization rates have not changed much during the Inventory reporting period, 

which is confirmed by the sales data showing relatively minor change in the amount of fertilizer sold for on-farm use 

across the time series of this Inventory (Ruddy et al. 2006). The trend and therefore the rates and uncertainties reflected in

the 1995 survey data are considered representative for 1990 through 2008 (trends will be re-evaluated when new 

fertilization data are released by U.S. Department of Agriculture). Note that all wheat data were combined into one 

category and assumed to represent small grains in aggregate. Estimates for sorghum fertilizer rates were derived from 

corn fertilizer rates using a ratio of national average corn fertilizer rates to national average sorghum fertilizer rates, which 

were derived from additional publications (NASS 1992, 1999, 2004; ERS 1988; Grant and Krenz 1985; USDA 1954, 

1957, 1966). Alfalfa hay is assumed to not be fertilized, but grass hay is fertilized according to rates from published farm 

enterprise budgets (NRIAI 2003). 

The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records 

deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for 

each region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state with 

a minimum of 15 data points for irrigated and rainfed, respectively. If a state was not surveyed for a particular crop or if 

fewer than 15 data points existed for one of the categories, then data were aggregated to U.S. Department of Agriculture 

Farm Production Regions in order to estimate a mean and standard deviation for fertilization rates (Farm Production 

Regions are groups of states in the United States with similar agricultural commodities). If Farm Production Region data 

were not available, crop data were aggregated to the entire United States (all major states surveyed) to estimate a mean and 

standard deviation for a particular crop in a state lacking sufficient data. Standard deviations for fertilizer rates were used 
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

to construct probability distribution functions (PDFs) with log-normal densities in order to address uncertainties in 

application rates (see Step 2a for discussion of uncertainty methods). Total fertilizer application data are found in Table 

A- 197. 

Simulations were conducted for the period prior to 1990 in order to initialize the DAYCENT model (see Step 

2a), and estimates for crop-specific regional fertilizer rates prior to 1990 were based largely on extrapolation/interpolation 

of fertilizer rates from the years with available data. For crops in some agricultural regions, little or no data were 

available, and, therefore, a geographic regional mean was used to simulate N fertilization rates (e.g., no data were 

available from Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the 

southeastern United States were used to simulate fertilization to corn fields in this state). 

Managed Livestock Manure57 N Amendment Rates and Timing: County-level manure addition estimates have 

been derived from manure N addition rates developed by the Natural Resources Conservation Service (NRCS) (Edmonds 

et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, NRCS has coupled 

estimates of manure N produced with estimates of manure N recoverability by animal waste management system to 

produce county-level estimates of manure N applied to cropland and pasture. Edmonds et al. (2003) defined a hierarchy 

that included 24 crops, cropland used as pasture, and permanent pasture. They estimated the area amended with manure 

and application rates in 1997 for both manure-producing farms and manure-receiving farms within a county and for two 

scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation 

(Edmonds et al. 2003). The goal of nutrient management plans is to apply manure nutrients at a rate meeting plant 

demand, thus limiting leaching losses of nutrients to groundwater and waterways. For DAYCENT simulations, the 

baseline scenario estimates have been used as the basis for manure amendment applications under the assumption that 

Comprehensive Nutrient Management Plans have not been fully implemented. This is a conservative assumption because 

it allows for higher leaching rates due to some over-application of manure to soils. The rates for manure-producing farms 

and manure-receiving farms have been area-weighted and combined to produce a single county-level estimate for the 

amount of land amended with manure and the manure N application rate for each crop in each county. Several of the 

crops in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all 

small grain crops have been combined into one category. In order to address uncertainty in these data, uniform probability 

distributions were constructed based on the proportion of land receiving manure versus the amount not receiving manure 

for each crop type and pasture. For example, if 20 percent of land producing corn in a county was amended with manure, 

randomly drawing a value equal to or greater than 0 and less than 20 would lead to a simulation with a manure 

amendment, while drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the 

simulation (see Step 2a for further discussion of uncertainty methods). 

Edmonds et al. (2003) only provides manure application rate data for 1997, but the amount of managed manure 

available for soil application changes annually, so the area amended with manure was adjusted relative to 1997 to account 

for all the manure available for application in other years. Specifically, the manure N available for application in other 

years was divided by the manure N available in 1997. If the ratio was greater than 1, there was more manure N available 

in that county relative to the amount in 1997, and so it was assumed a larger area was amended with manure. In contrast, 

ratios less than one implied less area was amended with manure because there was a lower amount available in the year 

compared to 1997. The amendment area in each county for 1997 was multiplied by the ratio to reflect the impact of 

manure N availability on the area amended. The amount of managed manure N available for application to soils was 

calculated by determining the populations of animals that were on feedlots or otherwise housed in order to collect and 

manage the manure, as described in the Manure Management section (Section 6.2) and annex (Annex 3.10). 

To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003), carbonnitrogen (C:N) ratios for livestock-specific manure types were adapted from the Agricultural Waste Management Field 

Handbook (USDA 1996), On-Farm Composting Handbook (NRAES 1992), and recoverability factors provided by 

Edmonds et al (2003). The C:N ratios were applied to county-level estimates of manure N excreted by animal type and 

management system to produce a weighted county average C:N ratio for manure amendments. The average C:N ratio was 

used to determine the associated C input for crop amendments derived from Edmonds et al. (2003). 

To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a cropland 

soil, crop-specific reduction factors were derived from mineral fertilization data for land amended with manure versus land 

 

57 For purposes of the Inventory, total livestock manure is divided into two general categories: (1) managed manure, and (2) unmanaged 

manure. Managed manure includes manure that is stored in manure management systems such as pits and lagoons, as well as manure

applied to soils through daily spread manure operations. Unmanaged manure encompasses all manure deposited on soils by animals on 

PRP. 
A-239 

not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). Mineral N fertilization rates were 

reduced for crops receiving manure N based on a fraction of the amount of manure N applied, depending on the crop and 

whether it was irrigated or rainfed. The reduction factors were randomly selected from PDFs with normal densities in 

order to address uncertainties in the dependence between manure amendments and mineral fertilizer application. 

Manure N that was not applied to major crops and grassland was assumed to be applied to non-major crop types. 

The fate of manure N is summarized in Table A- 198. 

Residue N Inputs: Residue N input is estimated as part of the DAYCENT simulation and is not an input to the 

model. Unlike the Tier 1 approach, N inputs from crop residues are not considered activity data in the DAYCENT 

simulations because N availability from this source is simulated by the model based on N uptake during crop growth 

according to environmental and management conditions, such as temperature, precipitation, and edaphic (i.e., soil) 

characteristics, in combination with the harvest practices. That is, while the model accounts for the contribution of N from 

crop residues to the soil profile and subsequent N2O emissions, this source of mineral soil N is not activity data in the 

sense that it is not a model input. Similarly, N from mineralization of soil organic matter and asymbiotic N fixation are 

also simulated by the model. The simulated total N inputs of above- and below-ground residue N and fixed N that was not 

harvested and not burned (the DAYCENT simulations assumed that 3 percent of non-harvested above ground residues for 

grain crops were burned) are provided in Table A- 199. 

Other N Inputs: Other N inputs are estimated within the DAYCENT simulation, and thus input data are not 

required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the 

atmosphere. The influence of additional inputs of N are estimated in the simulations so that there is full accounting of all 

emissions from managed lands, as recommended by IPCC (2006). The simulated total N inputs from other sources are 

provided in Table A- 199. 

Crop Rotation and Land Management Information: Data were obtained on specific timing and type of 

cultivation, timing of planting/harvest, and crop rotation schedules for the 63 agricultural regions (Hurd 1930, 1929, Latta 

1938, Iowa State College Staff Members 1946, Bogue 1963, Hurt 1994, USDA 2000a, 2000b, CTIC 1998, Piper et al. 

1924, Hardies and Hume 1927, Holmes 1902, 1929, Spillman 1902, 1905, 1907, 1908, Chilcott 1910, Smith 1911, Kezer 

ca 1917, Hargreaves 1993, ERS 2002, Warren 1911, Langston et al. 1922, Russell et al. 1922, Elliott and Tapp 1928, 

Elliott 1933, Ellsworth 1929, Garey 1929, Holmes 1929, Hodges et al. 1930, Bonnen and Elliott 1931, Brenner et al. 2002, 

2001, Smith et al. 2002). As with N fertilizer and manure additions, data were not complete, so regional averages were 

used to fill spatial gaps in the datasets and interpolation/extrapolation was used to fill temporal gaps. The amount of 

agricultural residue burning was based on state inventory data (ILENR 1993, Oregon Department of Energy 1995, Noller 

1996, Wisconsin Department of Natural Resources 1993, and Cibrowski 1996). 

Native Vegetation by County: Pre-agricultural land cover for each county was designated according to the 

potential native vegetation used in the Vegetation-Ecosystem Modeling and Analysis Project (VEMAP 1995), which was 

based on the Kuchler (1964) Potential Vegetation Map for the conterminous United States. 

Daily Weather Data by County: Daily maximum/minimum temperature and precipitation data were obtained 

from the DAYMET model, which generates daily surface precipitation, temperature, and other meteorological data at 1 

km2 resolution driven by weather station observations and an elevation model (Thornton et al. 2000 and 1997, Thornton 

and Running 1999, DAYMET no date). It is necessary to use computer-generated weather data because weather station 

data do not exist in each county, and moreover weather station data are for a point in space, while the DAYMET uses this 

information with interpolation algorithms to derive weather patterns for areas between these stations. DAYMET weather 

data are available for the United States at 1 km2

 resolution for 1980 through 2003. For each county, DAYMET weather 

data were selected from the 1 km2

 cell that occurred in agricultural lands according the National Land Cover Dataset 

(Vogelman et al. 2001). The grid cells formed the basis for county-scale PDFs based on the frequency of cells with same 

weather patterns. Separate PDFs were developed for cropland, pasture/hay land, and rangeland. A weather record was 

then randomly selected from the PDFs in each iteration of the Monte Carlo analysis to represent variation in precipitation 

and temperature at the county scale. Weather data were randomly selected from the previous 23 years to represent 2004 

through 2009, accounting for uncertainty in the weather during the years that have no data. The time series will be 

updated when new weather data are available. 

Soil Properties by County: Soil texture data required by DAYCENT were obtained from STATSGO (Soil Survey 

Staff 2005). Observed data for soil hydraulic properties needed for model inputs were not available, so they were 

calculated from STATSGO (Soil Survey Staff 2005) texture class and Saxton et al.’s (1986) hydraulic properties 

calculator. Similar to the weather data, soil types within the STATSGO map that occurred in agricultural lands according 

to the National Land Cover Dataset (Vogelman et al. 2001) were used to form a county-scale PDF. Specifically, the PDFs 

were an area-weighted proportion for the extent of overlap between STATSGO map units and agricultural land. Separate 

PDFs were developed for cropland, pasture/hay land, and rangeland. Individual soil types were randomly selected from 
A-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

the PDFs during each iteration of the Monte Carlo analysis to represent variation in soil texture and depth at the county 

scale. 

 Crop Areas by County: County-level total crop area data were downloaded from the NASS web site for the 

years 1990 through 2009 (USDA 2010a, 2010b), and these data formed the basis to scale emissions from individual crop 

types to an entire county. 

Non-Major Crop Types: Tier 1 Method 

The activity data required for calculating emissions from non-major crop types include: (1) the amount of 

mineral N in synthetic fertilizers that are applied annually, (2) managed manure N, (3) the amount of N in other 

commercial organic fertilizers and (4) the amount of N in the above- and below-ground residue retained on and in soils of 

all non-major crops. 

Application of Synthetic Commercial Fertilizers: A process-of-elimination approach was used to estimate 

synthetic N fertilizer additions to non-major crop types. The total amount of fertilizer used on farms has been estimated 

by the USGS from 1990-2001 on a county scale from fertilizer sales data (Ruddy et al. 2006). For 2002-2009, countylevel fertilizer used on farms was adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 

through 2009). In addition, fertilizer application data are available for major crops and grasslands (discussed in Step 1 

sections for Major Crops and Grasslands). Thus, the amount of N applied to non-major crops was assumed to be the 

remainder of the fertilizer used on farms after subtracting the amount applied to major crops and grasslands. The 

differences were aggregated to the state level and PDFs were derived based on uncertainties in the amount of N applied to 

major crops and grasslands. Total fertilizer application is found in Table A- 200. 

Manure and Other Commercial Organic Fertilizers:58 Manure N applied to non-major crops was estimated 

using the activity data described for major crops (Table A- 198). Estimates of total national annual N additions from other 

commercial organic fertilizers were derived from organic fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 

through 2009). AAPFCO fertilizer data were not yet available for 2009, so 2008 values were used as a placeholder until 

data become available. Commercial organic fertilizers include dried blood, tankage, compost, and other; dried manure and 

sewage sludge that are used as commercial fertilizer were subtracted from totals to avoid double counting. The dried 

manure N is counted with the non-commercial manure applications, and sewage sludge is assumed to be applied only to 

grasslands. The organic fertilizer data, which are recorded in mass units of fertilizer, had to be converted to mass units of 

N by multiplying the consumption values by the average organic fertilizer N contents provided in the annual fertilizer 

publications. These N contents are weighted average values, and vary from year to year (ranging from 2.3 percent to 3.9 

percent over the period 1990 through 2008). The fertilizer consumption data are recorded in “fertilizer year” totals, (i.e., 

July to June), but were converted to calendar year totals. This was done by assuming that approximately 35 percent of 

fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b). July to December 

values were not available for calendar year 2008 so a “least squares line” statistical extrapolation using the previous 14 

years of data was used to arrive at an approximate value. PDFs were derived for the organic fertilizer applications 

assuming a default ±50 percent uncertainty. Annual consumption of other organic fertilizers is presented in Table A- 201. 

Retention of Crop Residue: Annual crop yield (metric tons per hectare) and area harvested (hectare) statistics for 

non-major N-fixing crops, including bean and pulse crops, were taken from U.S. Department of Agriculture crop 

production reports (USDA 1994, 1998, 2003, 2005, 2006, 2008, 2009, 2010a,b). Crop yield per hectare and area planted 

were multiplied to determine total crop yield for each crop, which was then converted to tons of dry matter product using 

the residue dry matter fractions shown in 

Table A- 202. Dry matter yield was then converted to tons of above- and below-ground biomass N. Aboveground biomass was calculated by using linear equations to estimate above-ground biomass given dry matter crop yields, 

and below-ground biomass was calculated by multiplying above-ground biomass by the below-to-above-ground biomass 

ratio. N inputs were estimated by multiplying above- and below-ground biomass by respective N concentrations. All 

ratios and equations used to calculate residue N inputs (shown in Table A- 203) are from IPCC (2006) and Williams 

(2006). PDFs were derived assuming a ±50 percent uncertainty in the yield estimates (NASS does not provide 

uncertainty), along with uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of 

 

58 Other commercial organic fertilizers include, dried blood, dried manure, tankage, compost, sewage, and other minor organic fertilizer 

types, but manure and sewage sludge have been excluded in order to avoid double-counting and ensure consistency across the Inventory 

as these inputs are calculated using alternative data sources and methods. 
A-241 

below-ground to above-ground biomass, and residue N fractions. The resulting annual biomass N inputs are presented in 

Table A- 203. 

Step 1b: Activity Data for Direct Emissions from Drainage and Cultivation of Organic Cropland Soils 

Tier 1 Method 

Estimates and associated uncertainty for the area of drained and cultivated organic cropland soils in 1992 and 

1997 were obtained from the U.S. Department of Agriculture 1997 National Resources Inventory (USDA 2000a, as 

extracted by Eve 2001, and revised by Ogle 2002).59 These areas were grouped by broad climatic region60 using 

temperature and precipitation estimates from Daly et al. (1994, 1998) and then further aggregated to derive total land in 

temperate and sub-tropical regions. Areas for 1992 were assumed to represent 1990 through 1992 and areas for 1997 were 

assumed to represent 1993 through 2009 (Table A- 204). 

Step 1c: Activity Data for Direct Emissions from Grassland Management 

N2O emissions from non-federal grasslands were estimated using DAYCENT. DAYCENT simulations 

addressed the influence of legume seeding, managed manure N amendments, unmanaged manure N excreted by livestock 

and deposited directly onto pasture, range, and paddock (PRP) soils, and synthetic fertilizer applications. N2O emissions 

from PRP manure N deposition on federal grasslands and sewage sludge amendments to agricultural soils were addressed 

using the Tier 1 method. 

Tier 3 DAYCENT Simulations 

Activity data for DAYCENT simulations of grasslands (i.e., climate, soils, and N inputs) were based on the same 

sources as those used for major crop types described in Step 1a. In addition to the data sources used for major crops, 

county-level area data on non-federal pasture and rangeland (i.e., mostly privately-owned) were needed for U.S. 

grasslands. This information was based on U.S. Land Representation Analysis for Land Use, Land Use Change and 

Forestry sector (See Section 7.1), and included data compiled from the U.S. Department of Agriculture National 

Resources Inventory (USDA 2000a, Nusser and Goebel 1997, http://www.ncgc.nrcs.usda.gov/products/nri/index.htm), the 

USDA Forest Service (USFS) Forest Inventory and Analysis Database (FIA, http://fia.fs.fed .us/tools-data/data) and the 

U.S. Geological Survey (USGS) National Land Cover Dataset (NLCD, Vogelman et al. 2001, http://www.mrlc.gov). 

Grassland on non-federal lands is classified using the NRI and grassland on federal lands is classified using the NLCD. 

Grassland area data from the NRI and NLCD were adjusted to achieve consistency with FIA estimates of Forest Land. 

Another key source of N for grasslands is PRP manure N deposition. Activity data for PRP manure N excretion from dairy 

cattle, beef cattle, swine, sheep, goats, poultry, and horses were derived from multiple sources as described in the Manure 

Management section (Section 6.2) and Annex 3.10. The amount of PRP manure N deposited on non-federal grasslands 

(non-federal grasslands are mostly under private ownership) was based on the relative proportion of federal and nonfederal grasslands in each county based on the U.S. Land Representation Analysis (See Section 7.1) . For example, if 75 

percent of the grasslands in a county were non-federal then 75 percent of PRP manure N was assumed to be deposited on 

non-federal grasslands. 

Nitrogen fixation by legumes, and N residue inputs from senesced grass litter were included as sources of N to 

the soil, and were estimated in the DAYCENT simulations as a function of vegetation type, weather, and soil properties. 

Similar to the methodology for major crops, “other N inputs” were simulated within the DAYCENT model framework in 

order to estimate all greenhouse gas emissions from managed lands (IPCC 2006), including mineralization from 

decomposition of soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Decomposition 

rates are a function of litter quality and quantity, soil texture, water content and temperature, and other factors. Total 

annual amounts of PRP manure N, mineral N fertilizer application, manure N amendments, forage legume N and “other N 

inputs” can be found in Table A- 205. 

 

59 These areas do not include Alaska, but Alaska’s cropland area accounts for less than 0.1 percent of total U.S. cropland area, so this 

omission is not significant. 

60 The climatic regions were: (1) cold temperate, dry, (2) cold temperate, moist, (3) sub-tropical, dry, (4) sub-tropical, moist, (5) warm 

temperate, dry, and (6) warm temperate, moist. 
A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Tier 1 Method: Additional Direct Soil N2O Emissions 

The Tier 1 method was used to estimate emissions from PRP manure that were not simulated with DAYCENT, 

in addition to emissions due to sewage sludge amendments to agricultural soils. 

PRP Manure: PRP manure N data were derived using methods described in the Manure Management section 

(Section 6.2) and Annex 3.10. The amount of PRP manure N deposited on federal grasslands was based on the relative 

proportion of federal to non-federal grassland area in each county. As discussed in the Tier 3 DAYCENT Simulations 

section, the area data were based on the U.S. Department of Agriculture’s National Resources Inventory (USDA 2000a) 

and the National Land Cover Dataset (Vogelman et al. 2001), respectively, and were reconciled with the Forest Inventory 

and Analysis dataset in order to produce the U.S. Land Representation (See Section 7.1). Soil N2O emissions from the 

proportion of PRP manure N deposited on federal grasslands were estimated with the Tier 1 method. 

Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or private 

wastewater treatment works and is typically used as a soil amendment or is sent to waste disposal facilities such as 

landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to grasslands. 

Estimates of the amounts of sewage sludge N applied to agricultural lands were derived from national data on sewage 

sludge generation, disposition, and N content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass 

units, were obtained from EPA (1999) and estimates for 2004 were obtained from an independent national biosolids 

survey (NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years. Sewage 

sludge generation data are not available after 2004 (Bastian 2007), so the 1990 through 2004 data were linearly 

extrapolated for the most recent years. The total sludge generation estimates were then converted to units of N by 

applying an average N content of 3.9 percent (McFarland 2001), and disaggregated into use and disposal practices using 

historical data in EPA (1993) and NEBRA (2007). The use and disposal practices were agricultural land application, other 

land application, surface disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The 

resulting estimates of sewage sludge N applied to agricultural land were used here; the estimates of sewage sludge N 

applied to other land and surface-disposed were used in estimating N2O fluxes from soils in Settlements Remaining 

Settlements (see section 7.5 of the Land Use, Land-Use Change, and Forestry chapter). Sewage sludge disposal data are 

provided in Table A- 206. 

Step 1d: Activity Data for Indirect N2O Emissions from Managed Soils of all Land-Use Types and Managed 

Manure Systems 

Volatilization of N that was applied or deposited as synthetic fertilizer, livestock manure, sewage sludge, and 

other organic amendments leads to emissions of NH3 and NOx to the atmosphere. In turn, this N is returned to soils 

through atmospheric deposition, thereby increasing mineral N availability and enhancing N2O production. Additional N is 

lost from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N 

losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N2O. However, N 

leaching was assumed to be an insignificant source of indirect N2O in cropland and grassland systems where the amount of 

precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. These areas are typically semiarid to arid, and nitrate leaching to groundwater is a relatively uncommon event; moreover IPCC (2006) recommends 

limiting the amount of nitrate leaching assumed to be a source of indirect N2O emissions based on precipitation, irrigation 

and potential evapotranspiration. 

The activity data for synthetic fertilizer, livestock manure, other organic amendments, residue N inputs, sewage 

sludge N, and other N inputs are the same as those used in the calculation of direct emissions from agricultural mineral 

soils, and may be found in Table A- 197 through Table A- 201, Table A- 203, and Table A- 206. The activity data for 

computing direct and indirect N2O emissions from settlements and forest lands are described in the Land Use, Land-Use 

Change, and Forestry chapter. 

Using the DAYCENT model, volatilization and leaching/surface run-off of N from soils was computed internally 

for major crop types and non-federal grasslands. DAYCENT simulates the processes leading to these losses of N based on 

environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation, 

harvest), and soil N availability. Note that the DAYCENT method accounts for losses of N from all anthropogenic 

activity, not just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1 

methodology. Similarly, the N available for producing indirect emissions resulting from grassland management as well as 

deposited PRP manure was also estimated by DAYCENT. Estimated leaching losses of N from DAYCENT were not used 

in the indirect N2O calculation if the amount of precipitation plus irrigation did not exceed 80 percent of the potential 

evapotranspiration. Volatilized losses of N were summed for each day in the annual cycle to provide an estimate of the 
A-243 

amount of N subject to indirect N2O emissions. In addition, the daily losses of N through leaching and runoff in overland 

flow were summed for the annual cycle. Uncertainty in the estimates was derived from uncertainties in the activity data 

for the N inputs (i.e., fertilizer and organic amendments; see Step 1a for further information) 

The Tier 1 method was used to estimate N losses from mineral soils due to volatilization and leaching/runoff for 

non-major crop types, forestland, settlements, sewage sludge applications, and PRP manure on federal grasslands not 

accounted for by DAYCENT simulations. To estimate volatilized losses, synthetic fertilizers, manure, sewage sludge, and 

other organic N inputs were multiplied by the fraction subject to gaseous losses using the respective default values of 0.1 

kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N 

ranged from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic 

amendments (IPCC 2006). Leaching/runoff losses of N were estimated by summing the N additions from synthetic and 

other organic fertilizers, manure, sewage sludge, and above- and below-ground crop residues, and then multiplying by the 

default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty from 0.1–0.8 kg NO3-N/kg 

N (IPCC 2006). However, N leaching was assumed to be an insignificant source of indirect N2O emissions if the amount 

of precipitation plus irrigation did not exceed 80 percent of the potential evapotranspiration. PDFs were derived for each 

of the N inputs in the same manner as direct N2O emissions, discussed in Steps 1a and 1c. 

Volatilized N was summed for losses from major crop types, minor crop types, grasslands, settlements, and 

forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching and surface runoff were summed 

to obtain the total losses for this pathway. 

Step 2: Estimate Direct N2O Emissions from Cropland Soils 

In this step, N2O emissions were calculated for major crop types and non-major crop types on mineral soils, in 

addition to emissions associated with drainage and cultivation of organic soils. 

Step 2a: Direct N2O Emissions from Cropland Mineral Soils 

Two methods were used to estimate direct N2O emissions from N additions and crop production on mineral soils. 

The DAYCENT ecosystem model was used to estimate emissions from major crop types, while the Tier 1 methodology 

was used to estimate emissions from crops considered non-major types, which are grown on a considerably smaller 

portion of land than the major types. 

Major Crops: Tier 3 DAYCENT Simulations 

Simulations were performed over three major time periods for each county in the United States using the 

DAYCENT model. The first time period was used for simulation of native vegetation up to date of cultivation in the 

county (1 A.D. to plow out). Plow out was assumed to occur between 1600 and 1850, depending on the state in which the 

county lies. Simulation of at least 1600 years of native vegetation was needed to initialize soil organic matter (SOM) 

pools in the model. The second time period of the simulation started at plowout and represents historical agricultural 

practices up to the modern period (plow out to 1970). Simulation of the historical cropping period was needed to establish 

modern day SOM levels, which is important because N2O emissions are sensitive to the amount of SOM. Lastly, 

simulations were performed for the modern agricultural period (1971 through 2009). 

Corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton are defined as major crops and were 

simulated in every county where they were grown. These crops represent approximately 90 percent of total principal 

cropland in the United States as defined by the U.S. Department of Agriculture (USDA 2003). Overall, the DAYCENT 

simulations included approximately 86 percent of total cropland area. For rotations that include a cycle that repeats every 

two or more years (e.g., corn/soybeans, wheat/corn/fallow), different simulations were performed where each phase of the 

rotation was simulated every year. For example, 3 rotations were simulated in regions where wheat/corn/fallow cropping 

is a dominant rotation—one with wheat grown the first year, a second with corn the first year and a third with fallow the 

first year. This ensured that each crop was represented during each year in one of the three simulations. In cases where 

the same crop was grown in the same year in two or more distinct rotations for a region, N2O emissions were averaged 

across the different rotations to obtain a value for that crop. Emissions from cultivated fallow land were also included. 

Fallow area was assumed to be equal to winter wheat area in regions where winter wheat/fallow rotations are the dominant 

land management for winter wheat. 

The simulations reported here assumed conventional tillage cultivation, gradual improvement of cultivars, and 

gradual increases in fertilizer application until 1989. Note that there is a planned improvement to incorporate use of 

conservation tillage in the United States into this Inventory. The productivity of cultivars (cultivated varieties) has 
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

steadily improved over the last century and therefore it is unrealistic to assume that modern varieties of crops, such as 

corn, are identical to the popular varieties grown in 1900 in terms of yield potential, N demand, etc. Realistic simulations 

of historical land management and vegetation type are important because they influence present day soil C and N levels, 

which influence present-day N cycling and associated N2O emissions. 

Uncertainty estimation was an integral part of this analysis; uncertainty in the input data for the county-scale 

simulations and structural uncertainty associated with the DAYCENT model predictions were both addressed (Del Grosso 

et al. 2010). In the first step, a Monte Carlo Analysis was used to propagate input data uncertainty through the modeling 

process. Thus, input data were randomly selected from PDFs for weather records, soil type, mineral N fertilization rate, 

and organic amendments. See Step 1a for additional discussion about the PDFs. After selecting a set of inputs for a 

county, DAYCENT was used to simulate each crop and then the process was repeated until 100 iterations were completed. 

Due to the computationally intensive requirements for DAYCENT, it was not possible to simulate every county with the 

Monte Carlo Analysis. Two counties were selected from each of the 63 agricultural regions, and additional counties were 

added based on the variance in N2O emissions across regions from the past year’s Inventory, using a Neyman allocation 

(Cochran 1977). A Neyman allocation is based on the variance in N2O emissions across the 63 regions; regions with 

larger variances were allocated a larger number of counties for the Monte Carlo Analysis. A total of 300 counties were 

included in the Monte Carlo Analysis, which is approximately 10 percent of all counties with agricultural land. In 

addition, all counties were simulated once based on the dominant conditions from the PDFs (i.e., most common soil type, 

weather condition, manure amendment, and mineral fertilizer rate). 

In the second step of the uncertainty analysis, a structural uncertainty estimator was developed to account for 

uncertainty inherent in model formulation and parameterization using an empirically-based procedure described by Ogle et 

al. (2007). The procedure is based on developing a statistical relationship between modeled results and field 

measurements. Specifically, DAYCENT was used to simulate 11 agricultural experiments with 108 treatments, and the 

results were analyzed using a linear-mixed effect model in which measurements were statistically modeled as a function of 

simulated emissions. Simulated DAYCENT emissions were a highly significant predictor of the measurements, with a pvalue of <0.01. Several other variables were tested in the statistical model to evaluate if DAYCENT exhibited bias under 

certain conditions related to climate, soil types, and management practices. The type of crop or grassland was significant 

at an alpha level of 0.05, demonstrating that DAYCENT tended to over-estimate emissions for small grains systems and 

grassland, but was accurate in predicting the N2O emissions for other crops. Random effects were included in the model 

to capture the dependence in time series and data collected from the same site, which were needed to estimate appropriate 

standard deviations for parameter coefficients. 

The structural uncertainty estimator accounted for bias and prediction error in the DAYCENT model results, as 

well as random error associated with fine-scale emission predictions in counties over a time series from 1990 to 2009. To 

apply the uncertainty estimator, DAYCENT N2O emission estimates were used as an input to the linear mixed effect 

model after randomly selecting statistical parameter coefficients from their joint probability distribution, in addition to 

random draws from PDFs representing the uncertainty due to site, site by year random effects and the residual error from 

the linear-mixed effect model (Del Grosso et al. 2010). 

In DAYCENT, once N enters the plant/soil system, the model cannot distinguish among the original sources of 

the N to determine which management activity led to specific N2O emissions. This means, for example, that N2O 

emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop 

residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each crop in a county, 

the N inputs in a simulation were determined for anthropogenic practices discussed in IPCC (2006), including synthetic 

mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage 

of N input for anthropogenic practices was divided by the total N input, and this proportion was used to determine the 

amount of N2O emissions assigned to each of the practices.61 For example, if 70 percent of the mineral N made available 

in the soil was due to mineral fertilization, then 70 percent of the N2O emissions were assigned to this practice. The 

remainder of soil N2O emissions is reported under “other N inputs,” which includes mineralization due to decomposition 

of soil organic matter and litter, as well as asymbiotic fixation of mineral N in soils from the atmosphere. Asymbiotic N 

fixation by soil bacteria is a minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems. 

Mineralization of soil organic matter is a more significant source of N, but is still typically less than half of the amount of

N made available in the soil compared to fertilization, manure amendments, and symbiotic fixation. Accounting for the 

 

61 This method is a simplification of reality to allow partitioning of N2O emissions, as it assumes that all N inputs have an identical 

chance of being converted to N2O. This is unlikely to be the case, but DAYCENT does not track N2O emissions by source of mineral N 

so this approximation is the only approach that can be used for partitioning N2O emissions by source of N input. Moreover, this 

approach is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for most N sources (e.g., PRP). 
A-245 

influence of “other N inputs” was necessary in order to meet the recommendation of reporting all emissions from managed 

lands (IPCC 2006). While this method allows for attribution of N2O emissions to the individual N inputs to the soils, it is 

important to realize that sources such as synthetic fertilization may have a larger impact on N2O emissions than would be 

suggested by the associated level of N input for this source (Delgado et al. 2009). Further research will be needed to 

improve upon this attribution method, however. 

The final N2O emission estimate was determined by summing the estimates from the single simulation conducted 

in each county for the dominant condition to the 63 regions. Estimates were then adjusted to account for the difference 

between the emissions estimated in the Monte Carlo analysis and the dominant condition simulation on a region-by-region 

basis (i.e., if the Monte Carlo mean was slightly higher than the dominant condition among the counties included in the 

Monte Carlo analysis, the total emission estimate for the region would be raised by the difference) (Del Grosso et al. 

2010). In turn, regional values were summed to produce the national total. The uncertainty was based on the variance in 

simulated N2O emissions for the iterations in the Monte Carlo Analysis and the variance associated with difference 

between the means from the Monte Carlo Analysis and the simulated N2O emissions for the dominant condition, expressed 

as a 95 percent confidence interval (Del Grosso et al. 2010). 

Non-Major Crops: Tier 1 Method 

To estimate direct N2O emissions from N additions to non-major crops, the amount of N in applied synthetic 

fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) was added to N 

inputs from crop residues, and the resulting annual totals were multiplied by the IPCC default emission factor of 0.01 kg 

N2O-N/kg N (IPCC 2006). The uncertainty was determined based on simple error propagation methods (IPCC 2006). 

The uncertainty in the default emission factor ranged from 0.3–3.0 kg N2O-N/kg N (IPCC 2006). Uncertainty in activity 

data is ± 20 percent for fertilizer additions (Mosier 2004).62 Uncertainties in the emission factor and fertilizer additions 

were combined with uncertainty in the equations used to calculate residue N additions from above- and below-ground 

biomass dry matter and N concentration to derive overall uncertainty. 

Step 2b: Direct N2O Emissions Due to Drainage and Cultivation of Organic Cropland Soils 

To estimate annual N2O emissions from drainage and cultivation of organic soils, the area of cultivated organic 

soils in temperate regions was multiplied by the IPCC (2006) default emission factor for temperate soils and the 

corresponding area in sub-tropical regions was multiplied by the average (12 kg N2O-N/ha cultivated) of IPCC (2006) 

default emission factors for temperate (8 kg N2O-N/ha cultivated) and tropical (16 kg N2O-N/ha cultivated) organic soils. 

The uncertainty was determined based on simple error propagation methods (IPCC 2006), including uncertainty in the 

default emission factor ranging from 2–24 kg N2O-N/ha (IPCC 2006). 

Step 2c: Estimate Total Direct N2O Emissions from Cropland Soils 

In this step, total direct N2O emissions from cropland soils are calculated by summing direct emissions on 

mineral soils with emissions resulting from the drainage and cultivation of organic soils (i.e., histosols) (Table A- 207). 

Uncertainties were combined using the simple error propagation method (IPCC 2006). 

Step 3: Estimate Direct N2O Emissions from Grasslands 

DAYCENT was used to estimate direct N2O emissions from soils in non-federal grasslands (pastures and 

rangeland), and the Tier 1 method was used for federal grasslands. Managed pastures were simulated with DAYCENT by 

assuming that the vegetation mix includes forage legumes and grasses, and that grazing intensity was moderate to heavy. 

Rangelands were simulated without forage legumes and grazing intensity was assumed to be light to moderate. The 

methodology used to conduct the DAYCENT simulations of grasslands was similar to that for major crop types described 

above in Step 2a, including the analysis addressing uncertainty in the model inputs and model structure. Carbon and 

nitrogen additions to grasslands from grazing animals were obtained from county level animal excretion data and area data 

for federal and non-federal grasslands, as described in Step1c. 

 

62 Note that due to lack of data, uncertainties in managed manure N production, PRP manure N production, other commercial organic

fertilizer amendments, indirect losses of N in the DAYCENT simulations, and sewage sludge amendments to soils are currently treated 

as certain; these sources of uncertainty will be included in future Inventories. 
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

The Tier 1 method was used to estimate emissions from N excreted by livestock on federal lands (i.e., PRP 

manure N). The Tier 1 method was also used to estimate emissions from sewage sludge application to grasslands. These 

two sources of N inputs to soils were multiplied by the IPCC (2006) default emission factors (0.01 kg N2O-N/kg N from 

sludge and horse, sheep, and goat manure, and 0.02 kg N2O-N/kg N from cattle, swine, and poultry manure) to estimate 

N2O emissions. This emission estimate was summed with the DAYCENT simulated emissions to provide the national 

total for direct N2O losses from grasslands (Table A- 208). The uncertainty was determined based on the Tier 1 error 

propagation methods provided by the IPCC (2006) with uncertainty in the default emission factor ranging from 0.007 to 

0.06 kg N2O-N/kg N (IPCC 2006). 

Step 4: Estimate Indirect N2O Emissions for All Land-Use Types 

In this step, N2O emissions were calculated for the two indirect emission pathways (N2O emissions due to 

volatilization, and N2O emissions due to leaching and runoff of N), which were then summed to yield total indirect N2O 

emissions from croplands, grasslands, forest lands, and settlements. 

Step 4a: Indirect Emissions Due to Volatilization 

Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP 

manure, were calculated according to the amount of mineral N that was transported in gaseous forms from the soil profile 

and later emitted as soil N2O following atmospheric deposition. See Step 1d for additional information about the methods 

used to compute N losses due to volatilization. The estimated N volatilized for all land-use and livestock activities was 

multiplied by the IPCC default emission factor of 0.01 kg N2O-N/kg N (IPCC 2006) to compute total N2O emissions from 

volatilization. The resulting estimates are provided in Table A- 209. The uncertainty was determined using simple error 

propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the 

default emission factor ranging from 0.002–0.05 kg N2O-N/kg N (IPCC 2006). 

Step 4b: Indirect Emissions Due to Leaching and Runoff 

The amount of mineral N (i.e., synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N 

mineralization, asymbiotic fixation) that was transported from the soil profile in aqueous form was used to calculate 

indirect emissions from (1) leaching of mineral N from soils and (2) losses in runoff of water associated with overland 

flow. See Step 1d for additional information about the methods used to compute N losses from soils due to leaching and 

runoff in overland water flows. 

The total amount of N transported from soil profiles through leaching and surface runoff was multiplied by the 

IPCC default emission factor of 0.0075 kg N2O-N/kg N (IPCC 2006) to estimate emissions for this source. The resulting 

emission estimates are provided in Table A- 210. The uncertainty was determined based on simple error propagation 

methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg N2O-N/kg N 

(IPCC 2006). 

Step 5: Estimate Total N2O Emissions for U.S. Soils 

Total emissions were estimated by adding total direct emissions (from major crop types and non-major crop 

types on mineral cropland soils, drainage and cultivation of organic soils, and grassland management) to indirect 

emissions for all land use and management activities. U.S. national estimates for this source category are provided in 

Table A- 210. Uncertainties in the final estimate were combined using simple error propagation methods (IPCC 2006), 

and expressed as a 95 percent confidence interval. 

Direct and indirect emissions of soil N2O vary regionally in both croplands and grasslands as a function of N 

inputs, weather, and soil type. A little more than half of the total N2O emissions from major crops occur in Iowa, Illinois, 

Nebraska, Minnesota, Texas, Kansas and Indiana where N inputs associated with corn rotations are high or where large 

land areas are cropped (Table A- 211). On a per area unit basis, direct N2O emissions are also high in many of the 

Mississippi River Basin states where there are also high N input to corn and soybean crops (Figure A- 9). Emissions are 

also high in some western and New England states. Only a small portion of the land in these regions is used for crop 

production, but management and conditions lead to higher emissions on a per unit area basis than other regions. For 

example, emissions are high in California, Arizona, and other western states due to intensive irrigation management 

systems. For some New England states, emissions are high on a per unit area because subsurface soil layers remain frozen 
A-247 

when surface soil layers thaw in spring. This creates saturated conditions near the surface that facilitate denitrification and

N2O emissions. Indirect emissions tend to be high on an area basis in the central and eastern United States because 

relatively high rainfall facilitates N losses from leaching and runoff and in some western states where irrigation can 

contribute to leaching and runoff (Figure A- 10). 

Direct and indirect emissions from grasslands are typically lower than those from croplands (Table A- 211, 

Figure A- 11, and Figure A- 12) because N inputs tend to be lower, particularly from synthetic fertilizer. Texas was by far 

the highest emitter for this category, followed by Nebraska, Montana, Oklahoma, New Mexico Colorado and South 

Dakota. On a per area unit basis, emissions are lower in the western United States because grasslands in the East and 

Central regoins are more intensively managed (legume seeding, fertilization) while western rangelands receive few, if any, 

N inputs. Also, rainfall is limited in most of the western United States, and grasslands are not typically irrigated so 

minimal leaching and runoff of N occurs in these grasslands. 

Figure A- 7: DAYCENT Model Flow Diagram 

Figure A- 8: Comparisons of Results from DAYCENT Model and IPCC Tier 1 Method with Measurements of Soil N2O Emissions 

Figure A- 9: Major Crops, Average Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, 1990-2009 (Metric 

Tons CO2 Eq./ha/year) 

Figure A- 10: Major Crops, Average Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the DAYCENT Model, 

1990-2009 (kg N/ha/year) 

Figure A- 11: Grasslands, Average Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, 1990-2009 (Metric 

Tons CO2 Eq./ha/year) 

Figure A- 12: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the DAYCENT Model, 

1990-2009 (kg N/ha/year) 
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 197: Synthetic Fertilizer N Added to Major Crops (Gg N) 

 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 

 Fertilizer N 7,468 7,307 7,915 7,705 7,641 7,412 7,575 7,450 7,547 7,370 7,355 7,000 6,855 

Table A- 198: Fate of Livestock Manure Nitrogen (Gg N) 

Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20Managed Manure N Applied 

to Major Crops and Grasslandsa,b 993 968 1,038 1,063 1,116 1,103 1,099 1,086 1,164 1,079 1,077 1,036 8

Managed Manure N Applied 

to Non-Major Cropsb 1,473 1,618 1,568 1,563 1,519 1,561 1,587 1,583 1,585 1,549 1,598 1,687 1,8

Managed Manure N Applied 

to Grasslands 446 515 514 503 542 478 463 466 470 470 472 475 4

Pasture, Range, & Paddock 

Manure N 4,083 4,458 4,412 4,296 4,237 4,158 4,056 4,043 4,049 4,072 4,077 4,157 4,2

Total 6,994 7,559 7,532 7,426 7,414 7,300 7,204 7,177 7,268 7,171 7,223 7,355 7,4

a Accounts for N volatilized and leached/runoff during treatment, storage and transport before soil application. b Includes managed manure and daily spread manure amendments c Totals may not sum exactly due to rounding. Table A- 199: Crop Residue N and Other N Inputs to Major Crops as Simulated by DAYCENT (Gg N) Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Residue Na 2,982 3,305 3,322 3,329 3,099 3,659 3,450 3,151 3,253 3,407 3,231 3,041 3,186 Mineralization & 

Asymbiotic Fixation 12,406 12,010 12,590 12,811 14,166 12,926 13,548 13,520 13,238 13,216 13,361 12,740 12,800 

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N. Table A- 200: Synthetic Fertilizer N Added to Non-Major Crops (Gg N) 

 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 

 Fertilizer N 1,076 1,884 1,326 1,638 1,669 1,841 1,546 1,571 1,540 1,838 1,905 2,155 2,039 Table A- 201: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N) 

 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 

 Other Commercial Organic Fertilizer Na 4 10 13 14 12 11 9 7 8 8 9 10 a Includes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double counting. 
Table A- 202: Key Assumptions for Production of Non-Major Crops and Retention of Crop Residues 

Crop 

Dry Matter 

Fraction of 

Harvested 

Product 

Above-ground Residue Ratio of 

Below-ground 

Residue to 

Above-ground 

Biomass 

Residue N Fraction 

Slope Intercept Above-ground Below-ground 

Peanuts for Nuts 0.94 1.07 1.54 0.20 0.016 0.014 

Dry Edible Beans 0.90 0.36 0.68 0.19 0.010 0.010 Dry Edible Peas 0.91 1.13 0.85 0.19 0.008 0.008 Austrian Winter 

Peas 0.91 1.13 0.85 0.19 0.008 0.008 

Lentils 0.91 1.13 0.85 0.19 0.008 0.008 Wrinkled Seed Peas 0.91 1.13 0.85 0.19 0.008 0.008 Barley 0.89 0.98 0.59 0.22 0.007 0.014 Oats 0.89 0.91 0.89 0.25 0.007 0.008 Rye 0.88 1.09 0.88 0.22 0.005 0.011 Millet 0.90 1.43 0.14 0.22 0.007 0.009 Rice 0.89 0.95 2.46 0.16 0.007 0.009 

Table A- 203: Nitrogen in Crop Residues Retained on Soils Producing Non-Major Crops (Gg N) Crop 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 200Peanuts for Nuts 64 63 65 64 68 66 61 71 61 69 71 77 6Dry Edible Beans 16 16 15 16 16 16 15 13 16 14 13 15 1

Dry Edible Peas 9 11 9 11 11 10 10 10 10 11 14 15 Austrian Winter Peas 8 8 8 8 8 8 8 8 8 8 8 8 Lentils 9 9 9 9 9 9 10 10 9 9 10 11 1Wrinkled Seed Peas 9 9 8 8 8 8 8 8 8 8 9 8 Barley 112 96 105 96 94 74 86 68 63 76 76 59 5Oats 55 29 28 30 30 27 28 24 23 27 23 23 2Rye 9 9 9 8 9 9 9 8 8 9 8 8 Millet 7 7 7 7 7 7 3 7 2 5 6 5 Rice 80 87 86 91 91 99 94 103 101 97 109 106 9Total 378 343 349 348 352 336 331 330 311 333 347 336 29
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 204: Drained and Cultivated Organic Soil Area (Thousand Hectares) 

Year Temperate Area Sub-Tropical Area

1990 444 194 

1995 450 196 1996 450 196 1997 450 196 1998 450 196 1999 450 196 2000 450 196 2001 450 196 2002 450 196 2003 450 196 2004 450 196 2005 450 196 2006 450 196 2007 450 196 2008 450 196 2009 450 196 

Table A- 205: Synthetic Fertilizer N, PRP Manure N, Organic Manure N Amendment, Forage Legume N, and Other N Inputs Simulated with the DAY

N Source 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 200Fertilizer N 9,678 9,662 10,245 9,973 9,891 9,628 9,747 9,620 9,723 9,553 9,536 9,213 9,08PRP Manure N 3,718 4,065 4,021 3,906 3,856 3,787 3,697 3,688 3,701 3,728 3,731 3,806 3,85

Managed Manure 1,438 1,483 1,552 1,566 1,658 1,581 1,561 1,552 1,634 1,549 1,549 1,511 1,32Residue Na 11,164 11,560 11,054 11,069 10,667 12,264 11,056 11,078 10,631 11,234 10,855 10,731 10,84Mineralization & 

Asymbiotic Fixation 23,864 23,237 23,649 24,172 25,721 23,703 24,190 24,412 23,731 23,942 24,175 23,635 23,54

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N. 

Table A- 206: Sewage Sludge Nitrogen by Disposal Practice (Gg N) 

Disposal Practice 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2Applied to Agricultural Soils 52 69 72 75 78 81 30 86 89 91 94 98 Other Land Application 25 28 29 29 29 30 30 30 30 30 30 31 

Surface Disposal 20 16 15 14 13 12 10 9 8 6 5 5 Total 98 113 116 118 121 122 124 125 127 128 130 134 

Note: Totals may not sum due to independent rounding. 
Table A- 207: Direct N2O Emissions from Cropland Soils (Tg CO2 Eq.) Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2Mineral Soils 100.1 107.1 115.5 112.9 116.5 111.1 112.7 120.1 112.5 109.7 116.2 115.2 1Major Crops 85.8 88.3 99.6 95.6 99.2 92.9 95.7 103.1 95.7 91.5 97.4 94.8 

Synthetic Fertilizer 27.1 27.4 31.7 29.6 29.4 27.4 28.4 30.4 28.8 27.0 28.7 28.1 Managed Manure 3.6 3.6 4.2 4.1 4.1 4.0 4.0 4.4 4.3 3.9 4.1 4.1 

Residue Na 10.6 12.4 13.5 12.8 11.6 13.7 12.7 12.7 12.4 12.4 12.6 12.1 

Mineralization and Asymbiotic 

Fixation 44.6 44.8 50.2 49.0 54.0 47.7 50.6 55.5 50.2 48.3 52.1 50.5 

Non-Major Crops 14.3 18.8 15.9 17.4 17.3 18.3 16.9 17.0 16.8 18.2 18.8 20.4 

Synthetic Fertilizer 5.2 9.2 6.5 8.0 8.1 9.0 7.5 7.7 7.5 9.0 9.3 10.5 

Managed Manure and Other 

Organic Commercial Fertilizer 7.2 7.9 7.7 7.7 7.5 7.7 7.8 7.7 7.8 7.6 7.8 8.3 

Residue N 1.8 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.5 1.6 1.7 1.6 

Organic Soils 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 Total* 102.9 110.0 118.4 115.8 119.4 114.0 115.6 123.0 115.4 112.6 119.1 118.1 1

+ Less than 0.05 Tg CO2 Eq. a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.

Table A- 208: Direct N2O Emissions from Grasslands (Tg CO2 Eq.) 

 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 20DAYCENT 47.1 45.2 49.4 46.2 47.3 42.9 43.3 47.3 47.5 42.6 44.5 45.6 44

Synthetic Fertilizer 3.9 4.1 4.5 4.1 4.2 3.7 3.9 4.1 4.3 3.8 4.0 4.1 4

PRP Manure 4.6 4.7 5.2 4.7 4.6 4.4 4.5 4.7 4.7 4.4 4.6 4.9 4Managed Manure 1.5 1.8 2.0 1.8 1.8 1.5 1.6 1.7 1.8 1.5 1.6 1.6 Residue Na 15.6 14.6 15.5 14.6 14.5 14.9 13.8 15.5 15.3 13.9 14.2 14.6 14Mineralization and 

Asymbiotic Fixation 21.5 20.0 22.3 21.2 22.2 18.4 19.5 21.2 21.3 19.0 20.1 20.4 19

Tier 1 3.7 4.1 4.1 4.0 4.0 3.9 3.8 3.7 3.7 3.7 3.7 3.8 3

PRP Manure 3.5 3.7 3.7 3.7 3.6 3.5 3.4 3.3 3.3 3.2 3.3 3.3 3Sewage Sludge 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0

Total 50.9 49.2 53.5 50.3 51.3 46.8 47.1 51.0 51.2 46.3 48.2 49.4 48

+ Less than 0.05 Tg CO2 Eq. a Residue N inputs include unharvested fixed N from legumes as well as crop residue N. 
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A- 209: Indirect N2O Emissions (Tg CO2 Eq.) 

Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2Volatilization and Atm. Deposition 16.9 18.2 17.6 17.9 17.8 17.8 17.6 17.6 17.5 17.6 17.8 18.2 

Croplands 11.6 12.8 12.4 12.6 12.4 12.6 12.7 12.5 12.5 12.5 12.8 13.1 

Settlements 0.1 0.2 0.2 0.2 0.2 0.2 0.1 0.2 0.2 0.2 0.2 0.2 

Forest Land + + + + + + + + + + + + 

Grasslands 5.1 5.2 5.0 5.1 5.2 5.0 4.7 4.9 4.7 4.8 4.8 4.8 

Surface Leaching & Run-off 27.1 30.3 29.8 25.8 29.1 24.5 26.5 28.8 24.8 25.9 26.7 25.7 

Croplands 25.8 29.1 28.1 24.4 27.5 23.0 25.0 27.2 23.2 24.0 24.9 23.7 Settlements 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 

Forest Land + + 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Grasslands 1.0 0.8 1.4 0.9 1.3 1.0 1.2 1.1 1.2 1.4 1.3 1.5 

Total 44.0 48.5 47.4 43.7 46.9 42.3 44.1 46.3 42.3 43.5 44.5 43.9 

+ Less than 0.05 Tg CO2 Eq. Table A- 210: Total N2O Emissions from Agricultural Soil Management (Tg CO2 Eq.) Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2Total Direct 153.8 159.2 171.9 166.1 170.7 160.9 162.6 174.0 166.6 158.9 167.3 167.5 1Direct Emissions from Mineral 

Cropland Soils 100.1 107.1 115.5 112.9 116.5 111.1 112.7 120.1 112.5 109.7 116.2 115.2 1

Synthetic Fertilizer 32.3 36.6 38.2 37.6 37.5 36.4 36.0 38.1 36.3 35.9 37.9 38.6 

Organic Amendmenta 10.8 11.6 11.9 11.8 11.6 11.7 11.8 12.2 12.1 11.5 12.0 12.3 Residue Nb 12.4 14.0 15.2 14.5 13.4 15.3 14.3 14.3 13.9 14.0 14.3 13.7 Mineralization and Asymbiotic Fixation 44.6 44.8 50.2 49.0 54.0 47.7 50.6 55.5 50.2 48.3 52.1 50.5 

Direct Emissions from Drained 

Organic Cropland Soils 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 

Direct Emissions from Grasslands* 50.9 49.2 53.5 50.3 51.3 46.8 47.1 51.0 51.2 46.3 48.2 49.4 

Synthetic Mineral Fertilizer 3.9 4.1 4.5 4.1 4.2 3.7 3.9 4.1 4.3 3.8 4.0 4.1 

PRP Manure* 8.1 8.4 8.9 8.4 8.2 7.9 7.9 8.1 8.0 7.6 7.8 8.2 Managed Manure 1.5 1.8 2.0 1.8 1.8 1.5 1.6 1.7 1.8 1.5 1.6 1.6 Sewage Sludge 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 Residueb 15.6 14.6 15.5 14.6 14.5 14.9 13.8 15.5 15.3 13.9 14.2 14.6 

Mineralization and Asymbiotic Fixation 21.5 20.0 22.3 21.2 22.2 18.4 19.5 21.2 21.3 19.0 20.1 20.4 

Total Indirect 44.0 48.5 47.4 43.7 46.9 42.3 44.1 46.3 42.3 43.5 44.5 43.9 

 Volatilization 16.9 18.2 17.6 17.9 17.8 17.8 17.6 17.6 17.5 17.6 17.8 18.2  Leaching/Runoff 27.1 30.3 29.8 25.8 29.1 24.5 26.5 28.8 24.8 25.9 26.7 25.7 

Total Emissions 197.8 207.6 219.3 209.7 217.6 203.2 206.8 220.4 208.8 202.4 211.8 211.3 2

+ Less than 0.05 Tg CO2 Eq. a Organic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e., dried blood, tankage, compost, and b Residue N inputs include unharvested fixed N from legumes as well as crop residue N. 
A-253

Table A- 211: Total 2009 N2O Emissions (Direct and Indirect) from Agricultural Soil Management by State (Tg CO2 Eq.) 

State Croplands1 Grasslands2 Settlements3 Forest Lands4 Total 

Lower 

Bound 

Upper 

Bound 

AL 0.67 0.71 0.03 n.e. 1.42 0.87 2.51 

AR 3.07 0.76 0.02 n.e. 3.85 2.47 7.37 

AZ 0.91 0.93 0.02 n.e. 1.86 0.95 4.58 

CA 6.96 1.80 0.19 n.e. 8.95 5.73 17.46 

CO 1.23 2.46 0.02 n.e. 3.71 2.36 6.14 

CT 0.57 0.03 0.02 n.e. 0.62 0.36 1.58 

DE 0.77 0.01 0.01 n.e. 0.78 0.45 2.38 

FL 2.02 1.03 0.22 n.e. 3.27 2.28 5.58 

GA 1.22 0.36 0.01 n.e. 1.59 1.02 3.23 

HI5 0.00 n.e. n.e. n.e. 1.59 1.02 3.23 

IA 15.79 0.85 0.07 n.e. 16.71 11.99 33.98 

ID 2.19 1.57 0.02 n.e. 3.78 2.34 7.60 

IL 11.86 0.37 0.12 n.e. 12.34 8.81 19.28 

IN 7.55 0.30 0.07 n.e. 7.92 5.05 12.74 

KS 5.66 2.21 0.07 n.e. 7.94 5.58 12.16 

KY 2.00 1.14 0.03 n.e. 3.17 1.91 5.69 

LA 1.58 0.54 0.04 n.e. 2.16 1.65 3.44 

MA 0.13 0.02 0.04 n.e. 0.19 0.13 0.77 

MD 1.03 0.09 0.05 n.e. 1.18 0.30 3.27 

ME 0.22 0.02 0.01 n.e. 0.25 0.17 1.36 

MI 4.48 0.37 0.08 n.e. 4.92 3.50 8.35 

MN 9.71 0.80 0.03 n.e. 10.54 7.38 16.41 

MO 6.07 1.82 0.07 n.e. 7.96 5.34 12.65 

MS 2.44 0.42 0.03 n.e. 2.90 1.81 6.33 

MT 2.01 3.09 0.01 n.e. 5.11 3.21 8.22 

NC 2.38 0.65 0.05 n.e. 3.08 2.14 6.28 

ND 5.46 1.17 0.03 n.e. 6.66 4.46 10.67 

NE 6.38 2.98 0.07 n.e. 9.43 6.72 14.63 

NH 0.06 0.01 0.01 n.e. 0.08 0.06 0.41 

NJ 1.61 0.02 0.07 n.e. 1.71 0.34 5.10 

NM 0.54 2.91 0.01 n.e. 3.46 1.66 6.09 

NV 0.31 0.61 0.01 n.e. 0.93 0.04 9.01 

NY 3.19 0.33 0.07 n.e. 3.60 2.35 7.02 

OH 7.37 0.49 0.11 n.e. 7.97 5.67 12.54 

OK 1.21 3.02 0.03 n.e. 4.27 1.97 7.83 

OR 1.12 1.43 0.01 n.e. 2.56 1.60 5.42 

PA 3.05 0.12 0.06 n.e. 3.24 1.77 7.60 

RI 0.02 0.00 0.01 n.e. 0.03 0.02 0.58 

SC 0.66 0.15 0.03 n.e. 0.85 0.16 2.10 

SD 3.25 2.53 0.02 n.e. 5.79 3.97 9.35 

TN 1.60 0.77 0.05 n.e. 2.42 1.46 6.22 

TX 5.32 8.93 0.09 n.e. 14.34 8.26 22.86 

UT 0.64 0.92 0.01 n.e. 1.57 1.06 3.24 

VA 4.26 0.84 0.06 n.e. 5.16 3.34 12.17 

VT 0.46 0.06 0.00 n.e. 0.52 0.37 1.11 

WA 2.28 1.00 0.03 n.e. 3.31 1.24 7.93 

WI 6.64 0.77 0.04 n.e. 7.45 5.23 12.13 

WV 0.44 0.21 0.01 n.e. 0.66 0.36 1.62 

WY 0.51 1.95 0.01 n.e. 2.47 1.69 4.02 1

 Emissions from non-manure organic N inputs for minor crops were not estimated (n.e.) at the state level. 2

 Emissions from sewage sludge applied to grasslands and were not estimated (n.e.) at the state level 3

 Emissions from sewage sludge applied to settlements were not estimated (n.e.) at the state level. 4

 Forestland emissions were not estimated (n.e.) at the state level. 

5

 N2O emissions are not reported for Hawaii except from cropland organic soils. 
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

3.12. Methodology for Estimating Net Carbon Stock Changes in Forest 

Lands Remaining Forest Lands 

This sub-annex expands on the methodology used to calculate net changes in carbon (C) stocks in forest 

ecosystems and in harvested wood products. Some of the details of C conversion factors and procedures for calculating 

net CO2 flux for forests are provided below; full details of selected topics may be found in the cited references.

Carbon Stocks and Net Changes in Forest Ecosystem Carbon Stocks 

At least two forest inventories exist for most forest land in the United States. C stocks are estimated based on 

data from each inventory, at the level of permanent inventory plots. C per hectare (for a sample location) is multiplied by 

the total number of hectares that the plot represents, and then totals are summed for an area of interest, such as the state of

Maine. Net annual C stock changes are calculated by taking the difference between the inventories and dividing by the 

number of years between the inventories for a selected state or sub-state area.

Forest inventory data 

The estimates of forest C stocks are based on data derived from forest inventory surveys. Forest inventory data 

were obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Frayer and Furnival 1999). 

FIA data include remote sensing information and collection of measurements in the field at sample locations called plots. 

Tree measurements include diameter and species. On a subset of plots, additional measurements or samples are taken of 

down dead wood, litter, and soil C; however, these are not yet available nationwide for C estimation. The field protocols 

are thoroughly documented and available for download from the USDA Forest Service (2010c). The inventory was 

designed for timber volume estimation rather than C stock estimation, so most C pools are not measured directly or are 

sampled only on a subset of plots. Bechtold and Patterson (2005) provide the estimation procedures for standard forest 

inventory results. The data are freely available for download at USDA Forest Service (2010b) as the Forest Inventory and 

Analysis Database (FIADB) Version 4.0; these data are the primary sources of forest inventory used to estimate forest C 

stocks. 

Forest surveys have begun in the U.S. territories and in Hawaii. Meanwhile this inventory assumes that these 

areas account for a net C change of zero. Survey data are available for the temperate oceanic ecoregion of Alaska 

(southeast and south central). Inventory data are publicly available for 6.1 million hectares of forest land, and these 

inventoried lands, comprising 12% of the total forest land in Alaska, contribute to the forest carbon stocks presented here. 

Agroforestry systems are also not currently accounted for in the U.S. Inventory, since they are not explicitly 

inventoried by either of the two primary national natural resource inventory programs: the FIA program of the USDA 

Forest Service and the National Resources Inventory (NRI) of the USDA Natural Resources Conservation Service (Perry 

et al. 2005). The majority of these tree-based practices do not meet the size and definitions for forests within each of these

resource inventories. The size characteristics that exclude them from inventories also allow these systems to provide their 

many services without taking the land out of agricultural production, making them an appealing C sequestration option. 

Agroforestry in the United States has been defined as “intensive land-use management that optimizes the benefits 

(physical, biological, ecological, economic, social) from bio-physical interactions created when trees and/or shrubs are 

deliberately combined with crops and/or livestock” (Gold et al. 2000). In the United States, there are six categories of 

agroforestry practices: riparian forest buffers, windbreaks, alley cropping, silvopasture, forest farming and special 

applications.63 These practices are used to address many issues facing agricultural lands, such as economic diversification, 

habitat fragmentation, and water quality. While providing these services and regardless of intent, these tree-based 

plantings will also reduce atmospheric CO2. This occurs directly through CO2 sequestration into woody biomass, and 

indirectly through enhancement of agricultural production, trapping wind-blown and surface runoff sediments, and/or 

reducing CO2 emissions through fuel-use savings (Quam et al. 1992). The effects of these individual practices can 

potentially be quite large when taken into account within a whole-farm or within an aggregating larger entity (i.e., statelevel) (Quam et al. 1992, Schoeneberger 2006). One estimate of the sequestration potential through agroforestry practices 

in the United States is 90.3 Mt C/year by 2025 (Nair and Nair 2003). 

 

63 More information on agroforestry practices can be found online at <http://www.unl.edu/nac>. 
A-255

Summing state-level C stocks to calculate United States net C flux in forest ecosystems 

The overall approach for determining forest C stocks and stock change is based on methodology and algorithms 

coded into the computer tool described in Smith et al. (2010). It focuses on estimating forest C stocks based on data from 

two or more forest surveys conducted several years apart for each state or sub-state. There are generally two or more 

surveys available for download for each state. C stocks are calculated separately for each state based on available 

inventories conducted since 1990 and for the inventory closest to, but prior to, 1990 if such data are available and 

consistent with these methods. This approach ensures that the period 1990 (the base year) to present can be adequately 

represented. Surveys conducted prior to and in the early to mid 1990s focused on land capable of supporting timber 

production (timberland).64 As a result, information on less productive forest land or lands reserved from harvest was 

limited. Inventory field crews periodically measured all the plots in a state at a frequency of every 5 to 14 years. 

Generally, forests in states with fast-growing (and therefore rapidly changing) forests tended to be surveyed more often 

than states with slower-growing (and therefore slowly changing) forests. Older surveys for some states, particularly in the 

West, also have National Forest System (NFS) lands or reserved lands that were surveyed at different times than 

productive, privately-owned forest land in the state. Periodic data for each state thus became available at irregular 

intervals and determining the year of data collection associated with the survey can sometimes be difficult. 

Table A-212: Source of Unique Forest Inventory and Average Year of Field Survey Used to Estimate Statewide Carbon 

Stocks 

State/Substatea

Source of Inventory Data, 

Report/Inventory Yearb

Average Year Assigned 

to Inventoryc

Alabama FIADB 4.0, 1982 1982 

FIADB 4.0, 1990 1990 

FIADB 4.0, 2000 1999 

FIADB 4.0, 2005 2003 

Alaska, non-reserved Southcentral FIADB 4.0, 2009 2007 

Alaska, non-reserved Southeast FIADB 4.0, 2009 2007 

Alaska, reserved Southcentral FIADB 4.0, 2009 2006 

Alaska, reserved Southeast FIADB 4.0, 2009 2006 

Arizona, NFS non-woodlands 1987 RPA 1985 

FIADB 4.0, 1999 1996 

FIADB 4.0, 2009 2005 

Arizona, NFS woodlands 1987 RPA 1984 

FIADB 4.0, 1999 1996 

FIADB 4.0, 2009 2005 

Arizona, non-NFS non-woodlands FIADB 4.0, 1985 1986 

FIADB 4.0, 1999 1996 

FIADB 4.0, 2009 2006 

Arizona, non-NFS woodlands FIADB 4.0, 1999 1990 

FIADB 4.0, 2009 2006 

Arkansas FIADB 4.0, 1988 1988 

FIADB 4.0, 1995 1996 

 

64 Forest land is defined as land at least 120 feet wide and 1 acre in size with at least 10 percent cover (or equivalent stocking by 

live trees of any size, including land that formerly had such tree cover and that will be naturally or artificially regenerated. . 

Forest land includes transition zones, such as areas between forest and nonforest lands that have at least 10 percent cover (or

equivalent stocking) with live trees and forest areas adjacent to urban and built-up lands. Roadside, streamside, and shelterbelt 

strips of trees must have a crown width of at least 120 feet and continuous length of at least 363 feet to qualify as forest land. 

Unimproved roads and trails, streams, and clearings in forest areas are classified as forest if they are less than 120 feet wide or an 

acre in size. Tree-covered areas in agricultural production settings, such as fruit orchards, or tree-covered areas in urban settings, 

such as city parks, are not considered forest land. (Smith et al. 2009) Timberland is the most productive type of forest land, which 

is on unreserved land and is producing or capable of producing crops of industrial wood. Productivity is at a minimum rate of 20 

cubic feet of industrial wood per acre per year (Woudenberg and Farrenkopf 1995). There are about 203 million hectares of 

timberland in the conterminous United States, which represents 81 percent of all forest lands over the same area (Smith et al. 

2009). 
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

FIADB 4.0, 2005 2003 

FIADB 4.0, 2009 2007 

California, NFS IDB, 1990s 1997 

FIADB 4.0, 2009 2006 

California, non-NFS IDB, 1990s 1993 

FIADB 4.0, 2009 2006 

Colorado, NFS non-woodlands 1997 RPA 1981 

FIADB 4.0, 2009 2006 

Colorado, NFS woodlands 1997 RPA 1975 

FIADB 4.0, 1984 1997 

FIADB 4.0, 2009 2006 

Colorado, non-NFS non-woodlands Westwide, 1983 1980 

FIADB 4.0, 2009 2006 

Colorado, non-NFS woodlands Westwide, 1983 1983 

FIADB 4.0, 2009 2006 

Connecticut FIADB 4.0, 1985 1985 

FIADB 4.0, 1998 1998 

FIADB 4.0, 2007 2006 

Delaware FIADB 4.0, 1986 1986 

FIADB 4.0, 1999 1999 

FIADB 4.0, 2008 2007 

Florida FIADB 4.0, 1987 1987 

FIADB 4.0, 1995 1995 

FIADB 4.0, 2007 2005 

Georgia FIADB 4.0, 1989 1989 

FIADB 4.0, 1997 1997 

FIADB 4.0, 2004 2001 

FIADB 4.0, 2008 2006 

Idaho, Caribou-Targhee NF Westwide, 1991 1992 

FIADB 4.0, 2009 2007 

Idaho, Kootenai NF 1987 RPA 1988 

FIADB 4.0, 1991 1995 

FIADB 4.0, 2009 2007 

Idaho, non-NFS non-woodlands FIADB 4.0, 1991 1990 

FIADB 4.0, 2009 2007 

Idaho, non-NFS woodlands FIADB 4.0, 1991 1982 

FIADB 4.0, 2009 2007 

Idaho, other NFS Westwide, 1991 1988 

FIADB 4.0, 1991 2000 

FIADB 4.0, 2009 2007 

Idaho, Payette NF 1987 RPA 1982 

FIADB 4.0, 2009 2007 

Idaho, Salmon-Challis NF 1987 RPA 1978 

FIADB 4.0, 2009 2007 

Idaho, Sawtooth NF Westwide, 1991 1983 

FIADB 4.0, 1991 1996 

FIADB 4.0, 2009 2007 

Illinois FIADB 4.0, 1985 1985 

FIADB 4.0, 1998 1998 

FIADB 4.0, 2005 2004 
A-257

FIADB 4.0, 2008 2006 

Indiana FIADB 4.0, 1986 1986 

FIADB 4.0, 1998 1998 

FIADB 4.0, 2003 2001 

FIADB 4.0, 2008 2007 

Iowa FIADB 4.0, 1990 1990 

FIADB 4.0, 2003 2002 

FIADB 4.0, 2008 2006 

Kansas FIADB 4.0, 1981 1981 

FIADB 4.0, 1994 1994 

FIADB 4.0, 2005 2003 

FIADB 4.0, 2008 2007 

Kentucky FIADB 4.0, 1988 1987 

FIADB 4.0, 2004 2002 

FIADB 4.0, 2007 2005 

Louisiana FIADB 4.0, 1984 1984 

FIADB 4.0, 1991 1991 

FIADB 4.0, 2005 2004 

Maine Eastwide, 1982 1983 

FIADB 4.0, 1995 1995 

FIADB 4.0, 2003 2002 

FIADB 4.0, 2008 2007 

Maryland FIADB 4.0, 1986 1986 

FIADB 4.0, 1999 2000 

FIADB 4.0, 2008 2007 

Massachusetts FIADB 4.0, 1985 1985 

FIADB 4.0, 1998 1998 

FIADB 4.0, 2007 2006 

Michigan FIADB 4.0, 1980 1980 

FIADB 4.0, 1993 1993 

FIADB 4.0, 2004 2003 

FIADB 4.0, 2008 2006 

Minnesota FIADB 4.0, 1990 1989 

FIADB 4.0, 2003 2001 

FIADB 4.0, 2008 2006 

Mississippi FIADB 4.0, 1987 1987 

FIADB 4.0, 1994 1994 

FIADB 4.0, 2006 2007 

Missouri FIADB 4.0, 1989 1988 

FIADB 4.0, 2003 2002 

FIADB 4.0, 2008 2006 

Montana, NFS 1987 RPA 1988 

FIADB 4.0, 1989 1996 

FIADB 4.0, 2009 2007 

Montana, non-NFS non-reserved FIADB 4.0, 1989 1989 

FIADB 4.0, 2009 2006 

Montana, non-NFS reserved 1997 RPA 1990 

FIADB 4.0, 2009 2007 

Nebraska FIADB 4.0, 1983 1983 

FIADB 4.0, 1994 1995 
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

FIADB 4.0, 2005 2004 

FIADB 4.0, 2008 2006 

Nevada, NFS non-woodlands 1987 RPA 1974 

FIADB 4.0, 1989 1997 

FIADB 4.0, 2005 2005 

Nevada, NFS woodlands 1987 RPA 1978 

FIADB 4.0, 1989 1997 

FIADB 4.0, 2005 2005 

Nevada, non-NFS non-woodlands 1997 RPA 1985 

FIADB 4.0, 2005 2005 

Nevada, non-NFS woodlands FIADB 4.0, 1989 1980 

FIADB 4.0, 2005 2005 

New Hampshire FIADB 4.0, 1983 1983 

FIADB 4.0, 1997 1997 

FIADB 4.0, 2007 2005 

New Jersey FIADB 4.0, 1987 1987 

FIADB 4.0, 1999 1999 

FIADB 4.0, 2008 2007 

New Mexico, NFS non-woodlands 1987 RPA 1986 

FIADB 4.0, 1999 1997 

New Mexico, NFS woodlands 1987 RPA 1986 

FIADB 4.0, 1999 1997 

New Mexico, non-NFS non-woodlands FIADB 4.0, 1987 1987 

FIADB 4.0, 1999 1999 

New Mexico, non-NFS woodlands FIADB 4.0, 1999 1989 

New York, non-reserved Eastwide, 1980 1981 

FIADB 4.0, 1993 1993 

FIADB 4.0, 2007 2005 

New York, reserved 1987 RPA 1988 

FIADB 4.0, 2007 2005 

North Carolina FIADB 4.0, 1984 1984 

FIADB 4.0, 1990 1990 

FIADB 4.0, 2002 2001 

FIADB 4.0, 2007 2006 

North Dakota FIADB 4.0, 1980 1979 

FIADB 4.0, 1995 1995 

FIADB 4.0, 2005 2003 

FIADB 4.0, 2008 2007 

Ohio FIADB 4.0, 1991 1991 

FIADB 4.0, 2006 2005 

Oklahoma, Central & West FIADB 4.0, 1989 1989 

Oklahoma, East FIADB 4.0, 1986 1986 

FIADB 4.0, 1993 1993 

FIADB 4.0, 2008 2008 

Oregon, NFS East IDB, 1990s 1995 

FIADB 4.0, 2009 2006 

Oregon, NFS West IDB, 1990s 1996 

FIADB 4.0, 2009 2006 

Oregon, non-NFS East Westwide, 1992 1991 

IDB, 1990s 1999 
A-259

FIADB 4.0, 2009 2005 

Oregon, non-NFS West Westwide, 1992 1989 

IDB, 1990s 1997 

FIADB 4.0, 2009 2006 

Pennsylvania FIADB 4.0, 1989 1990 

FIADB 4.0, 2004 2003 

FIADB 4.0, 2008 2007 

Rhode Island FIADB 4.0, 1985 1985 

FIADB 4.0, 1998 1999 

FIADB 4.0, 2007 2006 

South Carolina FIADB 4.0, 1986 1986 

FIADB 4.0, 1993 1993 

FIADB 4.0, 2001 2001 

FIADB 4.0, 2006 2005 

South Dakota, NFS 1997 RPA 1986 

FIADB 4.0, 1995 1999 

FIADB 4.0, 2005 2004 

FIADB 4.0, 2008 2006 

South Dakota, non-NFS 1987 RPA 1986 

FIADB 4.0, 1995 1995 

FIADB 4.0, 2005 2004 

FIADB 4.0, 2008 2007 

Tennessee FIADB 4.0, 1989 1989 

FIADB 4.0, 1999 1998 

FIADB 4.0, 2004 2003 

FIADB 4.0, 2007 2006 

Texas, Central & West FIADB 4.0, 2007 2006 

Texas, East FIADB 4.0, 1986 1986 

FIADB 4.0, 1992 1992 

FIADB 4.0, 2003 2003 

FIADB 4.0, 2008 2006 

Utah, non-woodlands FIADB 4.0, 1993 1993 

FIADB 4.0, 2009 2005 

Utah, woodlands FIADB 4.0, 1993 1994 

FIADB 4.0, 2009 2005 

Vermont FIADB 4.0, 1983 1983 

FIADB 4.0, 1997 1997 

FIADB 4.0, 2007 2006 

Virginia FIADB 4.0, 1985 1985 

FIADB 4.0, 1992 1991 

FIADB 4.0, 2001 2000 

FIADB 4.0, 2007 2005 

Washington, NFS East IDB, 1990s 1996 

FIADB 4.0, 2009 2006 

Washington, NFS West IDB, 1990s 1996 

FIADB 4.0, 2009 2006 

Washington, non-NFS East IDB, 1990s 1992 

FIADB 4.0, 2009 2006 

Washington, non-NFS West IDB, 1990s 1990 

FIADB 4.0, 2009 2006 
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

West Virginia FIADB 4.0, 1989 1988 

FIADB 4.0, 2000 2001 

FIADB 4.0, 2008 2007 

Wisconsin FIADB 4.0, 1983 1982 

FIADB 4.0, 1996 1995 

FIADB 4.0, 2004 2002 

FIADB 4.0, 2008 2006 

Wyoming, NFS 1997 RPA 1982 

FIADB 4.0, 2000 2000 

Wyoming, non-NFS non-reserved nonwoodlands FIADB 4.0, 1984 1984 

FIADB 4.0, 2000 2002 

Wyoming, non-NFS non-reserved woodlands FIADB 4.0, 1984 1984 

FIADB 4.0, 2000 2002 

Wyoming, non-NFS reserved 1997 RPA 1985 

FIADB 4.0, 2000 2000 a

 Substate areas (Smith et al. 2010) include National Forests (NFS), all forest ownerships except National Forest (non-NFS), woodlands (forest 

land dominated by woodland species, such as pinyon and juniper, where stocking cannot be determined (USDA Forest Service 2010c)), nonwoodlands (used for clarity to emphasize that woodlands are classified separately), reserved (forest land withdrawn from timber utilization 

through statute, administrative regulation, or designation, Smith et al. (2009)), and non-reserved (forest land that is not reserved, used for clarity). 

Some National Forests are listed individually by name, e.g., Payette NF. Oregon and Washington were divided into eastern and western forests 

(east or west of the crest of the Cascade Mountains). Oklahoma and Texas are divided into East versus Central & West according to forest 

inventroy survey units (USDA Forest Service 2010d). Alaska is represented by a portion of forest land, in the southcentral and southeast part of 

the state. 

b

 FIADB 4.0 is the current, publicly available, format of FIA inventory data, and these files were downloaded from the Internet September 12, 

2010 (USDA Forest Service 2010b). IDB (Integrated Database) data are a compilation of periodic inventory data from the 1990s for California, 

Oregon, and Washington (Waddell and Hiserote 2005). Eastwide (Hansen et al. 1992) and Westwide (Woudenberg and Farrenkopf 1995) 

inventory data are formats that predate the FIADB data. RPA data are periodic national summaries. The year is the nominal, or reporting, year 

associated with each dataset. 

c Average year is based on average measurement year of forest land survey plots and rounded to the nearest integer year. 

A new national plot design and annualized sampling (USDA Forest Service 2010a) was introduced by FIA with 

most new surveys beginning after 1998. These surveys include sampling of all forest land including reserved and lower 

productivity lands. Most states have annualized inventory data available as of September 2010. Annualized sampling 

means that a portion of plots throughout the state is sampled each year, with the goal of measuring all plots once every 5 to 

10 years, depending on the region of the United States. The full unique set of data with all measured plots, such that each 

plot has been measured one time, is called a cycle. Sampling is designed such that partial inventory cycles provide usable, 

unbiased samples of forest inventory, but with higher sampling errors than the full cycle. After all plots have been 

measured once, the sequence continues with remeasurement of the first year’s plots, starting the next new cycle. Most 

Eastern states have completed the first full cycle of the annualized inventories and are providing annual updates to the 

state’s forest inventory with each year’s remeasurements, such that one plot’s measurements are included in subsequent 

year’s annual updates. Thus, annually updated estimates of forest C stocks are accurate, but estimates of stock change 

cannot utilize the annually updated inventory measurements directly, as there is redundancy in the data used to generate 

the annual updates of C stock. For example, a typical annual inventory update for an eastern state will include new data 

from remeasurement on 20 percent of plots; data from the remaining 80 percent of plots is identical to that included in the 

previous year’s annual update. The interpretation and use of the sequence of annual inventory updates can affect trends in 

annualized stock and stock change. In general, the C stock and stock change calculations use annual inventory summaries 

(updates) with unique sets of plot-level data (that is, without redundant sets); the most-recent annual update is the 

exception because it is included in stock change calculations if at least half of the plots in a state include new 

measurements. Table A-212 lists the specific surveys used in this report, and this list can be compared with the full set of 

summaries available for download (USDA Forest Service 2010b). 

For each pool in each state in each year, C stocks are estimated by linear interpolation between survey years. 

Similarly, fluxes, or net stock changes, are estimated for each pool in each state by dividing the difference between two 

successive stocks by the number of intervening years between surveys. Thus, the number of separate stock change 

estimates for each state or sub-state is one less than the number of available inventories. Annual estimates of stock and net 

change since the most recent survey are based on linear extrapolation. C stock and flux estimates for each pool are 

summed over all forest land in all states as identified in Table A-212 to form estimates for the United States. Summed net 

annual stock change and stock are presented in Table A-213 and Table A-214, respectively. Table A-214 also provides an 
A-261

estimate of forest area based on the interpolation and extrapolation procedure described above. Estimated net stock 

change of non-soil forest ecosystem carbon for 2009 for each of the states is shown in Table A-215, which also includes 

estimated forest area and total non-soil forest carbon stock. The state-level forest areas and carbon stocks are from the 

most recent inventory available (USDA Forest Service 2010a), and the estimate for net stock change is the mean of the 

2000 through 2009 estimates from the carbon calculator (Smith et al. 2010). 
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Table A-213: Net Annual Changes in Carbon Stocks (Tg C yr-1) in Forest and Harvested Wood Pools, 1990–2009 

Carbon Pool 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Forest (149.8) (151.4) (149.8) (139.5) (147.1) (148.4) (130.3) (128.6) (102.8) (76.9) (72.4) (113.0) (162.3) (210.3) (219.6) (219.9) (2 Live, aboveground (98.2) (99.9) (100.5) (97.4) (101.5) (102.8) (92.7) (95.8) (84.7) (78.3) (78.3) (89.3) (103.0) (118.9) (122.1) (122.1) ( Live, belowground (19.3) (19.7) (19.8) (19.3) (20.1) (20.5) (18.4) (19.0) (16.9) (15.7) (15.7) (17.9) (20.5) (23.5) (24.1) (24.1) 

 Dead Wood (8.6) (8.6) (8.3) (7.6) (7.7) (7.7) (7.1) (6.2) (4.8) (4.0) (3.5) (4.0) (5.2) (7.6) (8.1) (8.4)  Litter (8.8) (8.8) (9.0) (8.1) (7.6) (6.0) (5.1) (5.1) (0.5) 4.9 7.5 1.8 (5.3) (9.9) (11.4) (11.4)  Soil Organic Carbon (14.9) (14.4) (12.1) (7.2) (10.2) (11.4) (7.1) (2.5) 4.1 16.2 17.6 (3.7) (28.3) (50.3) (53.8) (53.8) 

Harvested Wood (35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1) (32.5) (30.8) (25.5) (26.8) (25.9) (28.7) (28.7)  Products in Use (17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.6) (9.7) (12.4) (12.4)  SWDS (18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16.3) (16.3) 

Total Net Flux (185.7) (185.1) (183.6) (172.4) (180.5) (180.7) (160.9) (160.7) (133.9) (109.4) (103.2) (138.5) (189.1) (236.1) (248.3) (248.6) (2

Table A-214: Carbon Stocks (Tg C) in Forest and Harvested Wood Pools, 1990–2010 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Forest Area (1000 ha) 269,137 269,764 270,395 270,992 271,535 272,071 272,602 273,081 273,553 273,950 274,183 274,386 274,717 275,234 275,976 276,769 2Carbon Pool Forest 42,783 42,933 43,085 43,234 43,374 43,521 43,669 43,800 43,928 44,031 44,108 44,180 44,293 44,456 44,666 44,886 

Live, 

aboveground 15,072 15,171 15,271 15,371 15,468 15,570 15,673 15,765 15,861 15,946 16,024 16,102 16,192 16,295 16,414 16,536 

Live, 

belowground 2,995 3,014 3,034 3,053 3,073 3,093 3,113 3,132 3,151 3,168 3,183 3,199 3,217 3,237 3,261 3,285 

Dead Wood 2,960 2,969 2,978 2,986 2,994 3,001 3,009 3,016 3,022 3,027 3,031 3,035 3,039 3,044 3,052 3,060 Litter 4,791 4,799 4,808 4,817 4,825 4,833 4,839 4,844 4,849 4,850 4,845 4,837 4,835 4,841 4,851 4,862 Soil 

Organic Carbon 16,965 16,980 16,995 17,007 17,014 17,024 17,035 17,043 17,045 17,041 17,025 17,007 17,011 17,039 17,089 17,143 

Harvested Wood 1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124 2,155 2,187 2,218 2,244 2,271 2,296 2,325 

Products in 

Use 1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413 1,423 1,436 

SWDS 628 646 665 683 701 718 735 752 769 787 805 823 840 857 873 890 

Total Carbon Stock 44,643 44,828 45,014 45,197 45,370 45,550 45,731 45,892 46,052 46,186 46,296 46,399 46,537 46,726 46,962 47,211 
A-263

Table A-215: State-level forest area, carbon stock, and net annual stock change. Estimates are forest ecosystem carbon 

and do not include harvested wood 

State 

Mean year of 

field data 

collection 

Forest area 

(1000 ha) 

Nonsoil C 

stock (Mt 

C) 

Mean net annual 

nonsoil stock 

change 2000–2009 

(Mt C/yr) 

Alabama 2006 9,219 682 (1.2) 

Alaska 2007 6,132 1,217 0.0 

Arizona 2006 7,572 436 2.0 

Arkansas 2007 7,560 644 (3.6) 

California 2006 13,333 2,165 (5.2) 

Colorado 2006 9,294 915 (5.2) 

Connecticut 2007 698 89 0.7 

Delaware 2007 143 18 (0.1) 

Florida 2005 6,838 471 (5.0) 

Georgia 2006 10,046 802 (7.7) 

Idaho 2007 8,656 1,053 2.1 

Illinois 2006 1,942 215 (5.3) 

Indiana 2007 1,920 225 (3.7) 

Iowa 2006 1,227 113 (2.8) 

Kansas 2007 902 74 (1.5) 

Kentucky 2005 5,006 502 (5.3) 

Louisiana 2004 5,722 447 0.1 

Maine 2007 7,146 741 (1.9) 

Maryland 2007 1,009 131 (1.2) 

Massachusetts 2007 1,222 166 (0.4) 

Michigan 2006 8,021 826 (6.5) 

Minnesota 2006 6,876 536 (4.1) 

Mississippi 2007 7,941 610 (6.2) 

Missouri 2006 6,231 605 (10.5) 

Montana 2007 10,356 1,132 (4.2) 

Nebraska 2006 538 44 (0.6) 

Nevada 2005 4,520 247 (0.8) 

New Hampshire 2006 1,944 254 (0.5) 

New Jersey 2007 810 86 (0.0) 

New Mexico 1994 6,753 484 (3.8) 

New York 2006 7,669 943 (6.3) 

North Carolina 2006 7,521 727 (6.8) 

North Dakota 2007 289 20 0.2 

Ohio 2007 3,255 353 (3.0) 

Oklahoma 2003 3,235 237 (0.8) 

Oregon 2006 12,163 2,007 (8.2) 

Pennsylvania 2007 6,739 804 (7.0) 

Rhode Island 2007 141 17 (0.0) 

South Carolina 2006 5,239 440 (5.7) 

South Dakota 2007 753 51 (0.9) 

Tennessee 2006 5,659 576 (3.8) 

Texas 2006 24,363 977 (2.4) 

Utah 2005 7,396 510 (3.4) 

Vermont 2007 1,857 250 (0.3) 

Virginia 2006 6,400 683 (3.4) 

Washington 2006 9,060 1,711 (10.0) 

West Virginia 2007 4,846 592 (4.6) 

Wisconsin 2006 6,757 620 (6.0) 

Wyoming 2001 4,633 468 (1.1) 

Table A-216 shows average C density values for forest ecosystem C pools according to region and forest types 

based on forest lands in this Inventory. These values were calculated by applying plot-level C estimation procedures as 

described below to the most recent inventory per state as available 12 September 2010 (USDA 2010b). C density values 
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

reflect the most recent survey for each state as available in the FIADB, not potential maximum C storage. C densities are 

affected by the distribution of stand sizes within a forest type, which can range from regenerating to mature stands. A 

large proportion of young stands in a particular forest type are likely to reduce the regional average for C density. 

Table A-216: Average carbon density (Mg C/ha) by carbon pool and forest area (1000 ha) according to region and forest 

type, based on the most recent inventory survey available for each state from FIA, corresponding to an average year of 

2006 

Region 

(States) 

Forest Types 

Aboveground 

Biomass 

Belowground 

Biomass 

Dead 

Wood Litter 

Soil 

Organic 

Carbon 

Forest 

Area 

 Carbon Density (Mg C/ha) (1,000 ha) 

Northeast 

(CT,DE,MA,MD,ME,NH,NJ,NY,OH,PA,RI,VT,WV) 

White/Red/Jack Pine 91.6 19.0 11.9 13.7 78.1 1,584 

Spruce/Fir 52.7 11.2 13.3 30.7 98.0 2,970 

Oak/Pine 77.0 15.2 9.8 27.4 66.9 1,234 

Oak/Hickory 82.5 15.6 11.1 8.1 53.1 13,007 

Elm/Ash/Cottonwood 59.3 11.3 9.9 6.9 111.7 1,450 

Maple/Beech/Birch 77.9 15.0 13.3 27.1 69.6 13,673 

Aspen/Birch 48.1 9.5 9.3 8.6 87.4 1,704 

Minor Types and Nonstocked 48.2 9.5 10.3 10.9 73.7 1,855 

All 74.5 14.4 11.9 17.8 69.0 37,478 

Northern Lake States 

(MI,MN,WI) 

White/Red/Jack Pine 54.1 11.3 9.0 12.3 120.8 1,821 

Spruce/Fir 40.5 8.6 8.6 33.1 261.8 3,213 

Oak/Hickory 70.4 13.3 10.8 7.9 97.1 3,815 

Elm/Ash/Cottonwood 52.4 10.0 9.2 7.5 179.9 2,118 

Maple/Beech/Birch 74.1 14.2 11.5 27.3 134.3 4,301 

Aspen/Birch 40.9 7.9 9.5 8.3 146.1 5,272 

Minor Types and Nonstocked 34.5 6.8 8.8 18.0 122.6 1,113 

All 54.5 10.7 9.9 16.4 152.3 21,654 

Northern Prairie States 

(IA,IL,IN,KS,MO,ND,NE,SD) 

Ponderosa Pine 40.3 8.5 7.1 14.3 48.5 576 

Oak/Pine 52.0 10.1 7.5 25.5 40.1 551 

Oak/Hickory 69.8 13.2 9.7 7.7 49.4 9,570 

Elm/Ash/Cottonwood 74.7 14.0 11.8 6.8 83.2 1,874 

Minor Types and Nonstocked 41.6 8.1 8.3 17.9 60.3 1,231 

All 66.0 12.5 9.7 9.5 54.5 13,803 

Ponderosa Pine 40.3 8.5 7.1 14.3 48.5 576 

South Central 

(AL,AR,KY,LA,MS,OK,TN,TX) 40.6 8.3 5.5 10.9 55.5 1,198 

Loblolly/Shortleaf Pine 45.8 9.4 7.0 9.6 41.9 13,256 

Pinyon/Juniper 16.4 3.2 1.9 12.2 37.7 3,894 

Oak/Pine 48.1 9.4 6.6 9.3 41.7 5,115 

Oak/Hickory 58.5 11.0 6.8 6.4 38.6 24,619 

Oak/Gum/Cypress 76.2 14.5 9.2 6.5 52.8 5,131 

Elm/Ash/Cottonwood 51.9 9.8 7.1 5.9 49.9 3,441 

Woodland Hardwoods 10.3 1.7 0.9 5.0 65.0 8,977 

Minor Types and Nonstocked 32.3 6.2 5.8 7.1 54.3 4,271 

All 45.9 8.8 5.9 7.4 45.5 68,704 

Loblolly/Shortleaf Pine 45.8 9.4 7.0 9.6 41.9 13,256 

Southeast 

(FL,GA,NC,SC,VA) 

Longleaf/Slash Pine 34.7 7.1 6.2 9.7 110.0 4,139 

Loblolly/Shortleaf Pine 48.6 10.0 8.5 9.6 72.9 9,137 

Oak/Pine 52.6 10.3 6.7 9.3 61.4 4,054 

Oak/Hickory 73.1 13.8 8.9 6.4 45.3 12,014 

Oak/Gum/Cypress 74.2 14.3 9.7 6.5 158.0 4,551 

Elm/Ash/Cottonwood 57.9 10.9 9.7 5.6 95.7 760 

Minor Types and Nonstocked 45.7 8.7 9.0 5.8 107.0 1,389 

All 59.0 11.5 8.4 7.9 79.2 36,044 

Coastal Alaska 

(approximately 12 percent of 
A-265

forest land in Alaska) 

Spruce/Fir 24.2 5.0 5.6 33.8 62.1 367 

Fir/Spruce/Mountain 

Hemlock 96.0 20.3 26.8 43.2 62.1 2,233 

Hemlock/Sitka Spruce 142.2 30.0 37.5 50.5 116.3 2,754 

Aspen/Birch 42.0 8.0 9.1 10.6 42.5 310 

Minor Types and Nonstocked 40.0 7.8 10.7 19.5 75.7 469 

All 105.5 22.2 28.2 42.5 86.5 6,132 

Spruce/Fir 24.2 5.0 5.6 33.8 62.1 367 

Pacific Northwest, Westside 

(Western OR and WA) 

Douglas-fir 146.2 30.7 39.0 32.0 94.8 5,956 

Fir/Spruce/Mountain Hemlock 150.1 31.7 41.6 38.3 62.1 1,187 

Hemlock/Sitka Spruce 174.7 36.8 50.0 37.8 116.3 1,566 

Alder/Maple 84.8 16.6 24.2 7.6 115.2 1,189 

Minor Types and Nonstocked 68.9 13.7 18.0 13.5 85.9 1,216 

All 135.6 28.3 37.0 28.8 95.5 11,114 

Pacific Northwest, Eastside 

(Eastern OR and WA) 

Douglas-fir 77.5 16.2 19.5 36.3 94.8 2,089 

Ponderosa Pine 48.8 10.2 11.2 22.5 50.7 2,742 

Fir/Spruce/Mountain Hemlock 95.3 20.1 27.8 37.9 62.1 1,781 

Lodgepole Pine 41.2 8.7 12.0 21.1 52.0 1,041 

Western Larch 63.5 13.3 19.2 35.7 45.1 204 

Other Western Softwoods 15.7 3.0 4.5 36.2 78.8 1,252 

Minor Types and Nonstocked 36.0 7.1 16.1 25.1 81.5 999 

All 57.1 11.9 15.8 30.1 68.4 10,109 

Pacific Southwest 

(CA) 

Pinyon/Juniper 24.1 4.7 2.3 21.1 26.3 742 

Douglas-fir 162.8 33.8 37.3 35.7 40.1 442 

Ponderosa Pine 63.7 13.2 15.0 22.4 41.3 899 

Fir/Spruce/Mountain Hemlock 158.0 33.4 45.0 38.3 51.9 824 

Redwood 219.6 45.9 48.1 60.5 53.8 299 

Other Western Softwoods 33.7 6.6 7.6 37.5 49.8 806 

California Mixed Conifer 127.8 26.8 32.8 37.9 49.8 3,159 

Western Oak 67.0 12.8 7.9 29.7 27.6 3,791 

Tanoak/Laurel 132.0 26.0 20.1 28.0 27.6 830 

Minor Types and Nonstocked 57.8 11.7 17.7 25.2 37.0 1,540 

All 92.0 18.7 20.0 31.9 38.6 13,333 

Rocky Mountain, North 

(ID,MT) 

Douglas-fir 72.8 15.4 14.5 37.0 38.8 5,587 

Ponderosa Pine 40.0 8.3 8.2 22.9 34.3 1,865 

Fir/Spruce/Mountain Hemlock 67.6 14.3 22.1 37.4 44.1 4,471 

Lodgepole Pine 50.3 10.7 10.9 23.1 37.2 2,761 

Western Larch 60.1 12.7 15.4 36.3 34.2 492 

Other Western Softwoods 44.8 9.4 6.9 39.3 31.4 649 

Aspen/Birch 31.8 6.0 12.5 26.8 56.6 533 

Minor Types and Nonstocked 28.4 5.7 17.0 22.5 42.9 2,655 

Rocky Mountain, South 

(AZ,CO,NM,NV,UT,WY) 

Pinyon/Juniper 22.9 4.7 1.0 21.1 19.7 18,738 

Douglas-fir 74.7 15.9 16.9 38.1 30.9 1,797 

Ponderosa Pine 47.3 10.0 8.2 23.6 24.1 3,570 

Fir/Spruce/Mountain Hemlock 79.6 16.9 22.9 38.8 31.5 4,262 

Lodgepole Pine 52.0 11.1 13.0 24.0 27.0 2,024 

Aspen/Birch 56.9 11.0 13.2 28.5 58.8 2,555 

Woodland Hardwoods 19.0 3.6 4.6 28.2 25.9 4,135 

Minor Types and Nonstocked 18.7 3.6 7.9 22.6 25.4 3,088 

All 36.3 7.5 6.9 25.4 25.8 40,168 

United States (forest land 

included in Inventory) 60.0 11.9 11.1 17.6 62.0 277,552 

Note: The forest area values in this table do not equal the forest area values reported in Table A-214, because the forest area values in this table 

are estimated using the most recent dataset per state, with an average year of 2006. The time series of forest area values reported in Table A-214, 
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

in contrast, is constructed following the CCT methods used to construct the carbon stock series. The forest area values reported in Table A-214 

and Table A-216 would only be identical if all states were measured simultaneously or they all had identical rates of change. 

The Inventory is derived primarily from the current FIADB 4.0 data (USDA Forest Service 2010b), but it also 

draws on older FIA survey data where necessary. The Resources Planning Act Assessment (RPA) database, which 

includes periodic summaries of state inventories, is one example. Information about the RPA data is available on the 

Internet (USDA Forest Service 2010a, see Program Features), and compilations of analytical estimates based on these 

databases are found in Waddell et al. (1989) and Smith et al. (2001). The basic difference between the RPA database and 

the FIADB is that the FIADB includes some informative additional details such as individual-tree data. Having only plotlevel information (such as volume per hectare) limits the conversion to biomass. This does not constitute a substantial 

difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et al. 2004). In the past, FIA

made their data available in tree-level Eastwide (Hansen et al. 1992) or Westwide (Woudenberg and Farrenkopf 1995) 

formats, which included inventories for Eastern and Western states, respectively. The current Inventory estimates rely in 

part on older tree-level data that are not available on the current FIADB site. The Integrated Database (IDB) is a 

compilation of periodic forest inventory data from the 1990s for California, Oregon, and Washington (Waddell and 

Hiserote 2005). These data were identified by Heath et al. (submitted) as the most appropriate non-FIADB sources for 

these states. 

An historical focus of the FIA program was to provide information on timber resources of the United States. For 

this reason, prior to 1998, some forest land, which were less productive or reserved (i.e., land where harvesting was 

prohibited by law), were less intensively surveyed. This generally meant that on these less productive lands, forest type 

and area were identified but data were not collected on individual tree measurements. The practical effect that this 

evolution in inventories has had on estimating forest C stocks from 1990 through the present is that some older surveys of 

lands do not have the stand-level values for merchantable volume of wood or stand age. Any data gaps identified in the 

surveys taken before 1998 were filled by assigning average C densities calculated from the more complete, later 

inventories from the respective states. The overall effect of this necessary approach to generate estimates for C stock is 

that no net change in C density occurs on those lands with gaps in past surveys.

Estimating C stocks from forest inventory data 

For each inventory summary in each state, data are converted to C units or augmented by other ecological data. 

This collection of conversion factors and models used for inventory-based forest carbon estimates (Smith et al. 2010, 

Heath et al. in press) was intitially developed as an offshoot of the forest carbon simulation model FORCARB (Heath et 

al. 2010) and is incorporated into a number of applications (Birdsey and Heath 1995, Birdsey and Heath 2001, Heath et al. 

2003, Smith et al. 2004, Smith et al. 2007, Hoover and Rebain 2008). The conversion factors and model coefficients are 

usually categorized by region, and forest type. Classifications for both region and forest type are subject to change 

depending on the particular coefficient set. Thus, region and type are specifically defined for each set of estimates. 

Factors are applied to the survey data at the scale of FIA inventory plots. The results are estimates of C density (Mg per 

hectare) for the various forest pools. C density for live trees, standing dead trees, understory vegetation, down dead wood, 

litter, and soil organic matter are estimated. All non-soil pools except litter can be separated into aboveground and 

belowground components. The live tree and understory C pools are pooled as biomass in this inventory. Similarly, 

standing dead trees and down dead wood are pooled as dead wood in this inventory. C stocks and fluxes for Forest Land 

Remaining Forest Land are reported in pools following IPCC (2003). 

Live tree C pools 

The tree C pools include aboveground and belowground (coarse root) C mass of live trees. Separate estimates 

are made for full-tree and aboveground-only biomass to estimate the belowground component. Most tree C estimates are 

based on Jenkins et al. (2003) and are functions of species groups and diameter. For example, the equation for estimating 

aboveground biomass for a live tree of a species in the aspen/alder/cottonwood/willow group is: 

Biomass (kg dry weight) = e(-2.2094 + 2.3867 × ln(diameter))

Diameter is cm at diameter breast height (d.b.h.), which is measured at 1.37 m above the forest floor. C is 

calculated by multiplying biomass by 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 1997). A 

full set of coefficients can be found in Jenkins et al. (2003; Table 4). Belowground root biomass is estimated as a ratio of 

roots to total aboveground biomass. The equation for ratio of root biomass of a live tree in the 

aspen/alder/cottonwood/willow group is: 

Ratio = e(-1.6911 + 0.8160/diameter)
A-267

Belowground biomass is calculated by multiplying the ratio by total aboveground biomass. A full set of 

coefficients can be found in Jenkins et al. (2003; Table 6). The C per tree is summed for each plot, and multiplied by the 

appropriate expansion factors to obtain a C stock estimate for the plot. 

Some inventory data do not provide measurements of individual trees; tree C in these plots is estimated from 

plot-level growing stock volume of live trees and equations given in Table A-217 and Table A-218. These equations are 

updates of those in Smith et al. (2003), modified to reduce error and correspond to common forest types defined by 

inventories. Separate estimates are made for whole-tree and aboveground-only biomass based on forest type group and 

region. The belowground portion is determined as the difference between the two estimates. C density is estimated based 

on the growing stock volume of the plot, where growing stock includes live trees of commercial species meeting specified 

standards. Only trees 12.7 cm (5 inches) d.b.h. and larger are included in growing stock volume. The full sets of 

coefficients are in Table A-217 and Table A-218. For example, the total C in tree biomass per hectare of aspen-birch in 

the North averages 8.1 Mg C/ha if growing-stock volume is zero. If growing-stock volume is greater than zero, the 

estimate is in two parts. Average C density of non-growing-stock trees (sapling and cull trees) is 14.3 Mg C/ha, and the 

equation for C in growing-stock trees is: 

Growing-stock trees (Mg C/ha) = e(-0.337 + ln(volume) × 0.933)

Units for volume are m3

/ha. 

Table A-217: Coefficients for estimating carbon density of live trees (above- and below-ground, MgC/ha) by region and 

type for plot-level data such as RPA dataa

Regionb Forest type groupc

Carbon density, 

if Growing Stock 

Volume (GSV) = 0 

C density for nonGrowing Stock 

(GS), if GSV > 0 

Coefficient 

A Coefficient B 

North 

Aspen/Birch 8.138 14.335 -0.337 0.933 

Elm/Ash/Cottonwood 16.187 18.707 -0.206 0.920 

Maple/Beech/Birch 6.938 17.054 -0.170 0.925 

Oak/Hickory 13.083 15.914 -0.079 0.932 

Hardwood minor types 10.376 14.127 0.002 0.890 

Oak/Pine 4.079 15.473 -0.146 0.908 

Ponderosa Pine & Exotic 

Softwood 2.595 6.895 -0.074 0.886 

Loblolly/Shortleaf Pine 6.277 9.766 -0.415 0.943 

Spruce/Fir 6.424 16.903 -0.487 0.947 

White/Red/Jack Pine 3.908 12.117 -0.349 0.924 

Softwood minor types 6.277 17.234 -0.380 0.970 

Non-stocked 1.054 1.238 -0.174 0.866 

Pacific 

Coast 

Alder/Maple 8.425 4.444 0.056 0.828 

Other Western Hardwoods 8.425 10.483 0.041 0.864 

Tanoak/Laurel 8.425 10.203 -0.167 0.917 

Western Oak 8.425 7.400 0.344 0.850 

Hardwood minor types 8.425 4.802 0.333 0.770 

California Mixed Conifer 10.102 4.727 0.137 0.843 

Douglas-fir 2.752 4.961 0.180 0.834 

Fir/Spruce/Mt. Hemlock 10.102 6.462 0.171 0.834 

Hemlock/Sitka Spruce 10.102 8.034 0.085 0.830 

Lodgepole Pine 10.102 5.733 -0.129 0.857 

Pinyon/Juniper 22.552 5.065 -0.070 0.842 

Ponderosa Pine 10.102 2.262 0.145 0.813 

Western Larch 10.102 5.254 -0.264 0.853 

Softwood minor types 10.102 6.771 0.466 0.783 

RPA Western Hardwoods 8.425 7.460 0.302 0.831 

Non-stocked 0.880 0.300 0.049 0.806 

Rocky 

Mountain 

Aspen/Birch 4.594 9.516 0.324 0.792 

Harwood minor types 4.866 11.844 0.266 0.814 

Douglas-fir 1.987 5.363 0.331 0.825 

Fir/Spruce/Mt. Hemlock 1.987 6.693 0.065 0.825 

Lodgepole Pine 1.080 8.051 0.003 0.804 

Other Western Softwoods 1.987 12.217 0.361 0.796 

Ponderosa Pine 1.987 5.574 0.382 0.771 

Softwood minor types 1.987 5.496 -0.152 0.836 

RPA Western Hardwood 13.714 11.678 0.246 0.807 

Pinyon/Juniper 22.927 23.301 0.254 0.794 
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

West. Oak/Other West. 

Hardwoods 14.441 18.544 0.215 0.796 

Non-stocked 1.111 0.568 0.257 0.732 

South 

Elm/Ash/Cottonwood 12.841 21.633 -0.144 0.896 

Oak/Gum/Cypress 7.176 23.919 -0.216 0.907 

Oak/Hickory 14.594 20.007 -0.031 0.886 

Hardwood minor types 47.316 40.194 -0.442 0.960 

Oak/Pine 4.106 17.933 -0.086 0.858 

Loblolly/Shortleaf Pine 3.892 12.466 0.206 0.773 

Longleaf/Slash Pine 4.441 8.694 0.110 0.772 

Softwood minor types 7.161 20.189 -0.085 0.868 

Non-stocked 0.467 0.943 0.019 0.734 a

 Prediction of C in growing-stock trees is based on exp(A + B*ln(growing stock volume)). b

 Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast 

(CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, 

VA). 

c

 Forest type groups are identified in FIADB and RPA data (Smith et al. (2001, USDA Forest Service 2010d). 

Table A-218: Coefficients for estimating carbon density of live trees (aboveground only, MgC/ha) by region and type for 

plot-level data such as RPA dataa

Regionb Forest type groupc

Carbon density, 

if Growing Stock 

Volume (GSV)=0 

C density for nonGS, if GSV > 0 Coefficient 

A 

Coefficient 

B 

North 

Aspen/Birch 6.697 11.880 -0.521 0.934 

Elm/Ash/Cottonwood 13.585 15.653 -0.387 0.922 

Maple/Beech/Birch 5.762 14.219 -0.352 0.926 

Oak/Hickory 10.960 13.306 -0.260 0.933 

Hardwood minor types 8.647 11.796 -0.166 0.888 

Oak/Pine 3.368 12.881 -0.335 0.909 

Ponderosa Pine & Exotic Softwood 2.116 5.671 -0.269 0.886 

Loblolly/Shortleaf Pine 5.098 8.070 -0.620 0.946 

Spruce/Fir 5.206 13.833 -0.684 0.948 

White/Red/Jack Pine 3.174 10.010 -0.548 0.926 

Softwood minor types 5.098 14.246 -0.570 0.971 

Non-stocked 0.880 1.032 -0.357 0.866 

Pacific 

Coast 

Alder/Maple 7.006 3.676 -0.138 0.830 

Other Western Hardwoods 7.006 8.709 -0.154 0.867 

Tanoak/Laurel 7.006 8.469 -0.355 0.918 

Western Oak 7.006 6.163 0.167 0.850 

Hardwood minor types 7.006 3.974 0.136 0.773 

California Mixed Conifer 8.309 3.883 -0.061 0.844 

Douglas-fir 2.235 4.072 -0.017 0.835 

Fir/Spruce/Mt. Hemlock 8.309 5.285 -0.027 0.835 

Hemlock/Sitka Spruce 8.309 6.586 -0.113 0.831 

Lodgepole Pine 8.309 4.674 -0.327 0.858 

Pinyon/Juniper 18.583 4.170 -0.263 0.842 

Ponderosa Pine 8.309 1.849 -0.053 0.814 

Western Larch 8.309 4.282 -0.461 0.853 

Softwood minor types 8.309 5.563 0.267 0.784 

RPA Western Hardwoods 7.006 6.202 0.119 0.831 

Non-stocked 0.724 0.247 -0.146 0.808 

Rocky 

Mountain 

Aspen/Birch 3.798 7.914 0.139 0.793 

Harwood minor types 4.027 9.936 0.084 0.815 

Douglas-fir 1.616 4.388 0.134 0.826 

Fir/Spruce/Mt. Hemlock 1.616 5.466 -0.133 0.826 

Lodgepole Pine 0.871 6.571 -0.195 0.805 

Other Western Softwoods 1.616 10.031 0.165 0.797 

Ponderosa Pine 1.616 4.569 0.185 0.772 

Softwood minor types 1.616 4.473 -0.350 0.837 

RPA Western Hardwood 11.341 9.704 0.054 0.809 

Pinyon/Juniper 18.867 19.173 0.059 0.794 

West. Oak/Other West. Hardwoods 11.942 15.353 0.021 0.796 

Non-stocked 0.916 0.466 0.061 0.733 

South 

Elm/Ash/Cottonwood 10.749 18.129 -0.323 0.897 

Oak/Gum/Cypress 5.987 20.004 -0.400 0.909 

Oak/Hickory 12.223 16.731 -0.215 0.888 
A-269

Hardwood minor types 39.737 33.739 -0.631 0.964 

Oak/Pine 3.394 14.923 -0.277 0.859 

Loblolly/Shortleaf Pine 3.172 10.288 0.012 0.773 

Longleaf/Slash Pine 3.634 7.176 -0.088 0.773 

Softwood minor types 5.893 16.751 -0.280 0.869 

Non-stocked 0.388 0.788 -0.171 0.735 a Prediction of aboveground C in growing-stock trees is based on exp(A + B*ln(growing stock volume). b

 Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast 

(CA, OR, WA); Rocky Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, 

VA). 

c

 Forest type groups are identified in FIADB and RPA data (Smith et al. 2001, USDA Forest Service 2010d).

Understory vegetation 

Understory vegetation is a minor component of biomass. Understory vegetation is defined as all biomass of 

undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this inventory, it is assumed 

that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is near the lower range of 

temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: ratios are likely to 

be lower for light-limited understory vegetation as compared with larger trees, and a greater proportion of all root mass 

will be less than 2 mm diameter. 

Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots. 

These were fit to the equation: 

Ratio = e(A - B × ln(live tree C density)

In this equation, “ratio” is the ratio of understory C density (Mg C/ha) to live tree C density (above- and belowground) in Mg C/ha. An additional coefficient is provided as a maximum ratio; that is, any estimate predicted from the 

equation that is greater than the maximum ratio is set equal to the maximum ratio. A full set of coefficients is in Table A219. Regions and forest types are the same classifications described in Smith et al. (2003). As an example, the basic 

calculation for understory C in aspen-birch forests in the Northeast is: 

Understory (Mg C/ha) = (live tree C density) × e(0.855 – 1.03 × ln(tree C density)

This calculation is followed by three possible modifications. First, the maximum value for the ratio is set to 2.02 

(see value in column “maximum ratio”); this also applies to stands with zero tree C, which is undefined in the above 

equation. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked and pinyon/juniper stands are set 

to coefficient A, which is a carbon density (Mg C/ha) for these types only. 

Table A-219: Coefficients for estimating the ratio of carbon density of understory vegetation (above- and belowground, 

MgC/ha)a

 by region and forest type. The ratio is multiplied by tree carbon density on each plot to produce understory 

vegetation 

Regionb Forest Typeb A B Maximum 

ratioc

NE 

Aspen-Birch 0.855 1.032 2.023 

MBB/Other Hardwood 0.892 1.079 2.076 

Oak-Hickory 0.842 1.053 2.057 

Oak-Pine 1.960 1.235 4.203 

Other Pine 2.149 1.268 4.191 

Spruce-Fir 0.825 1.121 2.140 

White-Red-Jack Pine 1.000 1.116 2.098 

Nonstocked 2.020 2.020 2.060 

NLS 

Aspen-Birch 0.777 1.018 2.023 

Lowland Hardwood 0.650 0.997 2.037 

Maple-Beech-Birch 0.863 1.120 2.129 

Oak-Hickory 0.965 1.091 2.072 

Pine 0.740 1.014 2.046 

Spruce-Fir 1.656 1.318 2.136 

Nonstocked 1.928 1.928 2.117 

NPS 

Conifer 1.189 1.190 2.114 

Lowland Hardwood 1.370 1.177 2.055 

Maple-Beech-Birch 1.126 1.201 2.130 

Oak-Hickory 1.139 1.138 2.072 

Oak-Pine 2.014 1.215 4.185 
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Nonstocked 2.052 2.052 2.072 

PSW 

Douglas-fir 2.084 1.201 4.626 

Fir-Spruce 1.983 1.268 4.806 

Hardwoods 1.571 1.038 4.745 

Other Conifer 4.032 1.785 4.768 

Pinyon-Juniper 4.430 4.430 4.820 

Redwood 2.513 1.312 4.698 

Nonstocked 4.431 4.431 4.626 

PWE 

Douglas-fir 1.544 1.064 4.626 

Fir-Spruce 1.583 1.156 4.806 

Hardwoods 1.900 1.133 4.745 

Lodgepole Pine 1.790 1.257 4.823 

Pinyon-Juniper 2.708 2.708 4.820 

Ponderosa Pine 1.768 1.213 4.768 

Nonstocked 4.315 4.315 4.626 

PWW 

Douglas-fir 1.727 1.108 4.609 

Fir-Spruce 1.770 1.164 4.807 

Other Conifer 2.874 1.534 4.768 

Other Hardwoods 2.157 1.220 4.745 

Red Alder 2.094 1.230 4.745 

Western Hemlock 2.081 1.218 4.693 

Nonstocked 4.401 4.401 4.589 

RMN 

Douglas-fir 2.342 1.360 4.731 

Fir-Spruce 2.129 1.315 4.749 

Hardwoods 1.860 1.110 4.745 

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 

RMS 

Douglas-fir 5.145 2.232 4.829 

Fir-Spruce 2.861 1.568 4.822 

Hardwoods 1.858 1.110 4.745 

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 

SC 

Bottomland Hardwood 0.917 1.109 1.842 

Misc. Conifer 1.601 1.129 4.191 

Natural Pine 2.166 1.260 4.161 

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 

SE 

Bottomland Hardwood 0.834 1.089 1.842 

Misc. Conifer 1.601 1.129 4.191 

Natural Pine 1.752 1.155 4.178 

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 equation: Ratio=exp(A-B*ln(tree_carbon_tph)), where “ratio” is the ratio of 

understory C density to live tree (above-and below- ground) C density, and “tree_carbon_density” is live tree (above-and below- ground) C 

density in Mg C/ha. 

b

 Regions and types as defined in Smith et al. (2003) c

Maximum ratio: any estimate predicted from the equation that is greater than the maximum ratio is set equal to the maximum ratio. 

Dead Wood 

The standing dead tree C pools include aboveground and belowground (coarse root) mass. Estimates for 

standing dead tree C are not based on newly available FIA standing dead tree data because updated methodology is not yet 

available for integrating dead wood carbon estimates based on both older (which do not have consistent measurements of 

dead wood) and newer surveys. Instead, the estimates are based on a ratio of growing stock volume of live trees, stratified 

by region and forest type groups, applied at the FIA plot-level. The standing dead tree equations estimate mass; they are 
A-271

converted to C mass by multiplying by 0.5. An example calculation for standing dead tree C in aspen-birch forests in the 

Northeast is: 

Dry weight of dead trees (Mg/ha) = 1.0 × (growing stock volume)0.499

It is multiplied by 0.5 to obtain Mg C/ha. All coefficients are provided in Table A-220. Note that nonstocked 

stands are assigned a constant C density (the value of Coefficient A). 

Down dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are 

not attached to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. Ratio estimates 

of down dead wood to live tree biomass were developed using FORCARB2 simulations and applied at the plot level 

(Smith et al. 2004). Estimates for down dead wood correspond to the region and forest type classifications described in 

Smith et al. (2003). A full set of ratios is provided in Table A-221. An additional component of down dead wood is a 

regional average estimate of logging residue based on Smith et al. (2006) applied at the plot level. These are based on a 

regional average C density at age zero and first order decay; initial densities and decay coefficients are provided in Table 

A-222. These amounts are added to explicitly account for down dead wood following harvest. In practice, this 

modification resulted in minor changes to the estimates. Example calculations of the two components for down dead 

wood in 25-year-old aspen-birch forests in the Northeast are: 

C density (Mg C/ha) = (live tree C density, above- and below-ground) × (0.078) = 7.8% of live tree C 

Conversion to C units is not necessary because the live tree value is already in terms of C. 

C density additional for logging residue (Mg C/ha) = 13.9 × e(-25/12.1) = 1.8 (Mg C/ha) 

Table A-220: Coefficients for estimating standing dead tree carbon (MgC/ha) by region and forest type groupa

Regionb Forest type groupc Coefficient A Coefficient B 

MTN 

Douglas-fir 3.935 0.312

Fir/Spruce/Mt. Hemlock 4.550 0.358

Hemlock/Sitka Spruce 1.000 0.569

Lodgepole Pine 1.177 0.501

Ponderosa Pine 1.000 0.455

Nonstocked 12.855 -- 

MTS 

Douglas-fir 2.200 0.460

Fir/Spruce/Mt. Hemlock 6.923 0.293

Lodgepole Pine 1.177 0.501

Ponderosa Pine 1.944 0.292

Nonstocked 4.232 -- 

NC 

Aspen/Birch 1.962 0.400

Elm/Ash/Cottonwood 3.755 0.253

Maple/Beech/Birch 3.442 0.219

Planted Pine 1.000 0.298

Oak/Hickory 2.949 0.236

Oak/Pine 1.364 0.394

Spruce/Fir 1.320 0.472

White/Red/Jack Pine 2.844 0.266

Nonstocked 2.634 -- 

NE 

Aspen/Birch 1.000 0.499

Elm/Ash/Cottonwood 4.992 0.134

Maple/Beech/Birch 3.041 0.306

Oak/Hickory 3.332 0.191

Oak/Pine 1.725 0.311

Spruce/Fir 5.893 0.190

White/Red/Jack Pine 2.841 0.254

Nonstocked 2.876 -- 

PC 

California Mixed Conifer 1.000 0.608

Douglas-fir 1.237 0.559

Douglas-fir Planted 10.145 0.112

Fir/Spruce/Mt. Hemlock 4.235 0.415

Hemlock/Sitka Spruce 1.546 0.562

Redwood 5.385 0.287

Nonstocked 7.377 -- 

SC Elm/Ash/Cottonwood 2.393 0.284

Loblolly/Shortleaf Pine 1.203 0.271
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Loblolly/Shortleaf Planted 1.000 0.138

Oak/Gum/Cypress 4.234 0.121

Oak/Hickory 2.396 0.186

Oak/Pine 1.133 0.337

Nonstocked 0.286 -- 

SE 

Elm/Ash/Cottonwood 1.358 0.476 

Loblolly/Shortleaf Pine 1.000 0.324 

Loblolly/Shortleaf Planted 1.000 0.265 

Oak/Gum/Cypress 1.770 0.329 

Oak/Hickory 2.256 0.257 

Oak/Pine 1.000 0.351 

Nonstocked 0.349 -- 

South Longleaf/Slash Pine 1.000 0.184 

Longleaf/Slash Planted 1.000 0.106 

West 

Alder/Maple 2.190 0.466 

Aspen/Birch 3.062 0.376 

Pinyon/Juniper 3.163 0.100 

Tanoak/Laurel 1.000 0.593 

Western Hardwood/Woodlands 5.595 0.181 

Western Larch 2.049 0.449 

Western Oak 1.996 0.348 a

Standing dead tree C is based on the equation: mass (Mg/ha) = A* (live-tree growing stock volume)^B. Note that nonstocked stands are assigned 

a constant C density (the value listed under coefficient A). Note that the standing dead tree equations are for biomass. To convert to C mass, 

multiply by 0.5. 

b

Regions are PC (CA,OR-West,WA-West), MTN (OR-East,WA-East,ID,MT), MTS (AZ,CO,NM,NV,UT,WY), West (regions PC, MTN, and 

MTS), NC (IA, IL, IN, KS, MI, MN, MO, ND, NE, SD, WI), NE (CT, DE, MA, MD, ME, NH, NJ, NY, OH, PA, RI, VT, WV), SC (AL, AR, 

KY, LA, MS, OK, TN, TX), SE (FL, GA, NC, SC, VA), and South (regions SC and SE). 

c

Forest types are described in FIADB documentation (USDA Forest Service, 2010d). Minor forest types within a region that are not explicitly 

defined/listed in the table of coefficients are assigned to a similar hardwood or softwood forest type. 

Table A-221: Ratio for estimating down dead wood by region and forest type. The ratio is multiplied by the live tree carbon 

density on a plot to produce down dead wood carbon density (MgC/ha) 

Regiona Forest typea Ratio Region 

(cont’d) Forest type (cont’d) Ratio (cont’d) 

NE 

Aspen-Birch 0.078 

PWW 

Douglas-fir 0.100 

MBB/Other Hardwood 0.071 Fir-Spruce 0.090 

Oak-Hickory 0.068 Other Conifer 0.073 

Oak-Pine 0.061 Other Hardwoods 0.062 

Other Pine 0.065 Red Alder 0.095 

Spruce-Fir 0.092 Western Hemlock 0.099 

White-Red-Jack Pine 0.055 Nonstocked 0.020 

Nonstocked 0.019 

RMN 

Douglas-fir 0.062 

NLS 

Aspen-Birch 0.081 Fir-Spruce 0.100 

Lowland Hardwood 0.061 Hardwoods 0.112 

Maple-Beech-Birch 0.076 Lodgepole Pine 0.058 

Oak-Hickory 0.077 Other Conifer 0.060 

Pine 0.072 Pinyon-Juniper 0.030 

Spruce-Fir 0.087 Ponderosa Pine 0.087 

Nonstocked 0.027 Nonstocked 0.018 

NPS 

Conifer 0.073 

RMS 

Douglas-fir 0.077 

Lowland Hardwood 0.069 Fir-Spruce 0.079 

Maple-Beech-Birch 0.063 Hardwoods 0.064 

Oak-Hickory 0.068 Lodgepole Pine 0.098 

Oak-Pine 0.069 Other Conifer 0.060 

Nonstocked 0.026 Pinyon-Juniper 0.030 

PSW 

Douglas-fir 0.091 Ponderosa Pine 0.082 

Fir-Spruce 0.109 Nonstocked 0.020 

Hardwoods 0.042 

SC 

Bottomland Hardwood 0.063 

Other Conifer 0.100 Misc. Conifer 0.068 

Pinyon-Juniper 0.031 Natural Pine 0.068 

Redwood 0.108 Oak-Pine 0.072 

Nonstocked 0.022 Planted Pine 0.077 

PWE 

Douglas-fir 0.103 Upland Hardwood 0.067 

Fir-Spruce 0.106 Nonstocked 0.013 

Hardwoods 0.027 SE Bottomland Hardwood 0.064 
A-273

Lodgepole Pine 0.093 Misc. Conifer 0.081 

Pinyon-Juniper 0.032 Natural Pine 0.081 

Ponderosa Pine 0.103 Oak-Pine 0.063 

Nonstocked 0.024 Planted Pine 0.075 

 Upland Hardwood 0.059 

 Nonstocked 0.012 

a

 Regions and types as defined in Smith et al. (2003). 

Table A-222: Coefficients for estimating logging residue component of down dead wood. 

Regiona

Forest Type Groupb

(softwood/hardwood) 

Initial Carbon 

Density 

(Mg/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 

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 

C of the litter layer is currently sampled on a subset of the FIA plots. However, these data are not yet available 

electronically for general application to all inventories in Table A-1. Litter C is the pool of organic C (including material 

known as duff, humus, and fine woody debris) above the mineral soil and includes woody fragments with diameters of up 

to 7.5 cm. Estimates therefore continue to be based on equations of Smith and Heath (2002) and applied at the plot level. 

The equations describe processes for decay or loss of forest floor following harvest and the net accumulation of new forest 

floor material following stand growth. For example, total forest floor C at a given number of years after a clearcut harvest 

for aspen-birch forests in the North is: 

Total forest floor C (Mg C/ha) = (18.4×years)/(53.7+years) + 10.2× e(-years/9.2)

See Table 4 of Smith and Heath (2002) for the complete set of coefficients. Note that these are direct estimates 

of C density; the 0.5 conversion does not apply to litter.

Soil organic carbon 

Soil organic carbon (SOC) is currently sampled to a 20 cm depth on subsets of FIA plots, however, these data are 

not available for the entire United States. Thus, estimates of SOC are based on the national STATSGO spatial database 

(USDA 1991), and the general approach described by Amichev and Galbraith (2004). In their procedure, SOC was 

calculated for the conterminous United States using the STATSGO database, and data gaps were filled by representative 

values from similar soils. Links to region and forest type groups were developed with the assistance of the USDA Forest 

Service FIA Geospatial Service Center by overlaying FIA forest inventory plots on the soil C map. The average SOC 

densities are provided in Table A-216. 
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2009 

Carbon in Harvested Wood Products 

Estimates of the harvested wood product (HWP) contribution to forest C sinks and emissions (hereafter called 

“HWP Contribution”) are based on methods described in Skog (2008) using the WOODCARB II model. These methods 

are based on IPCC (2006) guidance for estimating HWP carbon. The 2006 IPCC Guidelines provide methods that allow 

Parties to report HWP Contribution using one of several different accounting approaches: production, stock change, and 

atmospheric flow, as well as a default method. The various approaches are described below. The approaches differ in 

how HWP Contribution is allocated based on production or consumption as well as what processes (atmospheric fluxes or 

stock changes) are emphasized. 

• Production approach: Accounts for the net changes in carbon stocks in forests and in the wood products 

pool, but attributes both to the producing country. 

• Stock change approach: Accounts for changes in the product pool within the boundaries of the consuming 

country. 

• Atmospheric flow approach: Accounts for net emissions or removals of carbon to and from the atmosphere 

within national boundaries. C removal due to forest growth is accounted for in the producing country while 

C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming country. 

• Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption 

be justified if this is how a Party is choosing to report. 

The United States uses the production accounting approach (as in previous years) to report HWP Contribution (Table A223). Though reported U.S. HWP estimates are based on the production approach, estimates resulting from use of the two 

alternative approaches—the stock change and atmospheric flow approaches—are also presented for comparison (see Table 

A-224). Annual estimates of change are calculated by tracking the additions to and removals from the pool of products 

held in end uses (i.e., products in use such as housing or publications) and the pool of products held in solid waste disposal 

sites (SWDS). 

Estimates of five HWP variables that can be used to calculate HWP contribution for the stock change and 

atmospheric flow approaches for imports and exports are provided in Table A-223. The HWP variables estimated are: 

(1A) annual change of C in wood and paper products in use in the United States, 

(1B) annual change of C in wood and paper products in SWDS in the United States, 

(2A) annual change of C in wood and paper products in use in the United States and other countries where the 

wood came from trees harvested in the United States, 

(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where the 

wood came from trees harvested in the United States, 

(3) C in imports of wood, pulp, and paper to the United States, 

(4) C in exports of wood, pulp and paper from the United States, and 

(5) C in annual harvest of wood from forests in the United States. 
Table A-223: Harvested wood products from wood harvested in United States—Annual additions of carbon to stocks and total stocks 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 

Net carbon additions per year (Tg C per year) Total Harvested 

wood carbon (35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1) (32.5) (30.8) (25.5) (26.8) (25.9) (28.7) (28.7) 

Products in use (17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.6) (9.7) (12.4) (12.4) 

Solid wood 

products (14.4) (11.9) (12.6) (12.2) (12.1) (11.2) (11.5) (11.8) (11.4) (12.1) (11.9) (10.1) (10.7) (10.1) (11.6) (11.9) 

Paper products (3.3) (3.1) (3.7) (2.8) (3.8) (3.8) (2.6) (3.0) (2.0) (2.0) (1.0) 1.4 1.1 0.4 (0.8) (0.5) Products in 

SWDS (18.3) (18.8) (17.4) (17.9) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16.3) (16.3) 

Solid wood 

products (9.9) (11.1) (9.5) (9.7) (9.8) (10.7) (10.6) (10.3) (10.2) (10.6) (10.7) (10.7) (11.1) (11.1) (11.3) (11.5) 

Paper products (8.3) (7.7) (7.9) (8.3) (7.7) (6.5) (6.0) (6.9) (7.5) (7.8) (7.3) (6.0) (6.1) (5.1) (5.0) (4.8) Total Carbon stocks (Tg C)Total Harvested wood 

carbon 1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124 2,155 2,187 2,218 2,244 2,271 2,296 2,325 

Products in use 1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413 1,423 1,436 

Products in SWDS 628 646 665 683 701 718 735 752 769 787 805 823 840 857 873 890 

Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere). 

Table A-224: Comparison of Net Annual Change in Harvested Wood Products Carbon Stocks Using Alternative Accounting Approaches 

HWP Contribution to LULUCF Emissions/ removals (Tg CO2 Eq.) 

Inventory Year 

Stock Change 

Approach 

Atmospheric Flow 

Approach 

Production 

Approach 

1990 (129.6) (138.4) (131.8) 

1991 (116.3) (131.4) (123.8) 1992 (120.0) (131.6) (123.8) 1993 (126.8) (127.8) (120.7) 1994 (130.0) (129.9) (122.5) 1995 (126.0) (128.0) (118.4) 1996 (122.3) (122.5) (112.2) 1997 (131.4) (127.4) (117.3) 1998 (139.8) (122.7) (114.1) 1999 (149.4) (127.3) (119.1) 2000 (143.2) (120.3) (112.9) 2001 (128.3) (100.3) (93.4) 2002 (135.6) (103.1) (98.2) 2003 (134.6) (99.2) (94.8) 2004 (163.0) (109.1) (105.3) 2005 (161.4) (109.0) (105.4) 2006 (138.6) (114.2) (108.6) 2007 (115.4) (112.1) (103.0) 
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

2008 (78.4) (94.3) (82.1) 2009 (41.9) (68.3) (54.3) Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere). 

Table A-225: Harvested Wood Products Sectoral Background Data for LULUCF—United States (production approach) 

 1A 1B 2A 2B 3 4 5 6 

Inventory 

year 

Annual 

Change in 

stock of HWP 

in use from 

consumption 

Annual 

Change in 

stock of HWP 

in SWDS from 

consumption 

Annual 

Change in 

stock of 

HWP in 

use 

produced 

from 

domestic 

harvest 

Annual 

Change in 

stock of HWP 

in SWDS 

produced 

from 

domestic 

harvest 

Annual 

Imports of 

wood, and 

paper 

products + 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips 

Annual 

Exports of 

wood, and 

paper 

products + 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips 

Annual 

Domestic 

Harvest 

Annual release 

of carbon to 

the 

atmosphere 

from HWP 

consumption 

(from 

fuelwood & 

products in 

use and 

products in 

SWDS) 

Annu

of car

atmosp

HWP firewo

wood c

domest

(from p

use and 

i

∆

CHWP IU DC

∆

CHWP SWDS DC

∆C HWP IU 

DH

∆

CHWP SWDS 

DH

 PIM PEX H ↑CHWP DC 

1990 17,044 18,308 17,659 18,278 12,680 15,078 142,297 104,547 1991 13,129 18,602 14,940 18,812 11,552 15,667 144,435 108,588 1992 15,718 17,006 16,334 17,427 12,856 16,032 139,389 103,489 1993 16,957 17,627 14,971 17,949 14,512 14,788 134,554 99,694 1994 18,221 17,221 15,930 17,479 15,685 15,665 134,750 99,328 1995 17,307 17,051 15,065 17,229 16,712 17,266 137,027 102,115 1996 17,018 16,348 14,092 16,513 16,691 16,733 134,477 101,069 1997 18,756 17,090 14,740 17,263 17,983 16,877 135,439 100,699 1998 20,311 17,818 13,376 17,735 19,722 15,057 135,021 101,558 1999 22,035 18,714 14,123 18,353 21,266 15,245 134,939 100,211 2000 20,491 18,560 12,828 17,962 22,426 16,185 134,458 101,648 2001 17,295 17,691 8,711 16,774 22,975 15,336 128,621 101,274 2002 18,629 18,357 9,570 17,207 24,604 15,744 127,567 99,441 2003 19,180 17,532 9,676 16,186 25,962 16,303 124,949 97,896 2004 26,384 18,077 12,429 16,298 31,650 16,948 130,460 100,700 2005 25,777 18,249 12,394 16,347 31,714 17,423 131,711 101,976 

 2006 19,010 18,780 12,308 17,302 25,485 18,836 127,064 95,922 

2007 12,999 18,497 10,673 17,409 21,603 20,670 120,922 90,360 2008 3,589 17,786 5,203 17,188 16,822 21,156 108,339 82,630 2009 (5,401) 16,825 (1,857) 16,652 13,281 20,485 95,130 76,501 

Note: ↑C HWP DC = H + PIM – PEX - 

∆C HWP IU DC - 

∆C HWP SWDS DC AND ↑C HWP DH = H - 

∆C HWP IU DH - 

∆C HWP SWDS DH . Parentheses indicate net C sequestration (i.e., a n

atmosphere).
A-277

Annual estimates of variables 1A, 1B, 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). 

The rate of removals from products in use and the rate of decay of products in SWDS are specified by first order 

(exponential) decay curves with given half-lives (time at which half of amount placed in use will have been discarded 

from use). Half-lives for products in use, determined after calibration of the model to meet two criteria, are shown in 

Table A-226. The first criterion is that the WOODCARB II model estimate of C in houses standing in 2001 needed to 

match an independent estimate of C in housing based on U.S. Census and USDA Forest Service survey data. The second 

criterion is that the WOODCARB II model estimate of wood and paper being discarded to SWDS needed to match EPA 

estimates of discards over the period 1990 to 2000. This calibration strongly influences the estimate of variable 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-227. Estimates of 1B and 2B also reflect the 

change over time in the fraction of products discarded to SWDS (versus burning or recycling) and the fraction of SWDS 

that are sanitary landfills versus dumps. 

Variables 2A and 2B are used to estimate HWP contribution under the production accounting approach. A key 

assumption for estimating these variables is that products exported from the United States and held in pools in other 

countries have the same half lives for products in use, the same percentage of discarded products going to SWDS, and the 

same decay rates in SWDS. Summaries of net fluxes and stocks for harvested wood in products and SWDS are in Table 

A-213 and Table A-214. The decline in net additions to HWP carbon stocks continued though 2009 from the recent high 

point in 2006. This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to 

the decline in housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was 

exceeded by discards from uses. The result is a net reduction in the amount of HWP carbon that is held in products in use 

during 2009. For 2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have 

remained relatively stable. Overall, there were net carbon additions to HWP in use and in landfills combined. 

Table A-226: Half-life of solidwood and paper products in end uses 

Parameter Value Units 

Half life of wood in single family housing 1920 and before 78.0 Years 

Half life of wood in single family housing 1920–1939 78.0 Years 

Half life of wood in single family housing 1940–1959 80.0 Years 

Half life of wood in single family housing 1960–1979 81.9 Years 

Half life of wood in single family housing 1980 + 83.9 Years 

Ratio of multifamily half live to single family half life 0.61 

Ratio of repair and alterations half life to single family half life 0.30 

Half life for other solidwood product in end uses 38.0 Years 

Half life of paper in end uses 2.54 Years 

Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72. 

Table A-227: Parameters determining decay of wood and paper in SWDS 

Parameter Value Units 

Percentage of wood and paper in dumps that is subject to decay 100% 

Percentage of wood in landfills that is subject to decay 23% 

Percentage of paper in landfills that is subject to decay 56% 

Half life of wood in landfills / dumps (portion subject to decay) 29 Years 

Half life of paper in landfills/ dumps (portion subject to decay) 14.5 Years 

Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72 
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Uncertainty Analysis 

The uncertainty analyses for total net flux of forest C (see uncertainty table in LULUCF chapter) are consistent 

with the IPCC-recommended Tier 2 methodology (IPCC 2006). Separate analyses are produced for forest ecosystem and 

HWP flux. The uncertainty estimates are from Monte Carlo simulations of the respective models and input data. Methods 

generally follow those described in Heath and Smith (2000), Smith and Heath (2000), and Skog et al. (2004). 

Uncertainties surrounding input data or model processes are quantified as probability distribution functions (PDFs), so that 

a series of sample values can be randomly selected from the distributions. Model simulations are repeated a large number 

of times to numerically simulate the effect of the random PDF selections on estimated total C flux. The separate results 

from the ecosystem and HWP simulations are pooled for total uncertainty (see uncertainty table in LULUCF chapter). 

Uncertainty surrounding current net C flux in forest ecosystems is based on the value for 2007 as obtained from 

the Monte Carlo simulation. C stocks are based on forest condition level (plot-level) calculations, and, therefore, 

uncertainty analysis starts probabilistic sampling at the plot level. Uncertainty surrounding C density (Mg/ha) is defined 

for each of six C pools for each inventory plot. Live trees are generally assigned normal PDFs, which are defined 

according to variability information in Jenkins et al. (2003) and the species and number of trees measured on each FIA 

plot. Plot-level live tree C estimates from RPA data are based on volume; these PDFs also include an additional level of 

uncertainty based on their respective regression equations. Similarly, the normally-distributed PDFs for standing dead 

trees are based on both volume regression and the individual-tree uncertainties related to the Jenkins et al. (2003) based 

estimates. Definitions of these normal distributions, which centered on expected values, depend on region, type, and 

specific plot information. Where data gaps—tree or volume—are identified for older inventory data, corresponding 

averages from later data are applied for live and standing dead tree C densities. Uniform PDFs with a range of ±90 

percent of the average are used for these plots. 

Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of 

information available about these estimates. Down dead wood, understory, and litter are assigned triangular distributions 

with the mean at the expected value for each plot and the minimum and mode at 10 percent of the expected value. The use 

of these PDFs skewed to the right reflects the assumption that a small proportion of plots will have relatively high C 

densities. Joint sampling of PDFs is specified for two pairs of samples: understory PDF sampling is slightly negatively 

correlated with live tree PDF sampling, and down dead wood sampling is slightly positively correlated with live tree 

sampling. This also reflects the structure of the estimates, which are dependent on live tree C. Soil organic C is defined as

a uniform PDF at ±50 percent of the mean. Sub-state or state total carbon stocks associated with each survey are the 

cumulative sum of random samples from the plot-level PDFs, which are then appropriately expanded to population 

estimates. These expected values for each carbon pool include uncertainty associated with sampling, which is also 

incorporated in the Monte Carlo simulation. Sampling errors are determined according to methods described for the 

FIADB (Bechtold and Patterson 2005), are normally distributed, and are assigned a slight positive correlation between 

successive surveys for Monte Carlo sampling. More recent annual inventories are assigned higher sampling correlation 

between successive surveys based on the proportion of plot data jointly included in each. Errors for older inventory data 

are not available, and these surveys are assigned values consistent with those obtained from the FIADB. 

Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube sampling 

is the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution under the 

production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided that evaluated the 

effect of uncertainty in 13 sources, including production and trade data and parameters used to make the estimate. 

Uncertain data and parameters include data on production and trade and factors to convert them to C, the Census-based 

estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to 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 United States harvest, compared to storage in the United States. 

A total of ten thousand samples are drawn from the PDF input to separately determine uncertainties about forest 

ecosystem and HWP flux before they are combined for a quantitative estimate of total forest carbon uncertainty (see 

uncertainty table in LULUCF chapter). Again this year, true Monte Carlo sampling is used for the forest ecosystem 

estimates (in contrast to Latin hypercube sampling, which was used in some previous estimates), and a part of the QA/QC 

process includes verifying that the PDFs are adequately sampled. 
A-279

Emissions from fires 

CO2

As stated in other sections, the forest inventory approach implicitly accounts for emissions due to disturbances. 

Net C stock change is estimated by subtracting consecutive C stock estimates. A disturbance removes C from the forest. 

The inventory data, on which net C stock estimates are based, already reflects the C loss because only C remaining in the 

forest is estimated. Estimating the CO2 emissions from a disturbance such as fire and adding those emissions to the net 

CO2 change in forests would result in double-counting the loss from fire because the inventory data already reflect the 

loss. There is interest, however, in the size of the CO2 emissions from disturbances such as fire. The IPCC (2003) 

methodology and IPCC (2006) default combustion factor for wildfire were employed to estimate emissions from forest 

fires. 

The same methodology was used to estimate emissions from both wildfires and prescribed fires occurring in the 

lower 48 states. Wildfire area statistics are available, but they include non-forest land, such as shrublands and grasslands. 

It was thus necessary to develop a rudimentary estimate of the percent of area burned in forest by multiplying the reported 

area burned by a ratio of total forest land area to the total area considered to be under protection from fire. Data on total 

area of forest land were obtained from FIA (USDA Forest Service 2010b). Data on “total area considered to be under 

protection from fire” were available at the state level and obtained for the year 1990 from 1984-1990 Wildfire Statistics 

prepared by the U.S. Forest Service (USDA Forest Service 1992). Data for years 1998, 2002, 2004, and 2006 were 

obtained from the National Association of State Foresters (NASF 2008, 2007a, 2007b, 2007c). For states where data were 

available for all four years, the 1990 value was assumed for years 1990 to 1994, values for 1998 were assumed for years 

1995 to 1998, values for 2002 were assumed for years 1999 to 2002, values for 2004 were assumed for years 2003 and 

2004, and values for 2006 were assumed for years 2005 to 2009. For states where data were available for all years except 

2002, 2004 data were assumed for years 1999 to 2004. For states where data were available for all years except 2004, 

2006 data were assumed for 2003 through 2008. For years where data were available for all years except 2006, 2004 data 

were assumed for years 2003 to 2008. Since no 1998 value was available for Alaska, the 1990 value was assumed for 

years 1990 to 1997, the 2002 value was assumed for years 1998 to 2002, and the 2004 value was assumed for years 2003 

and 2005 to 2009. Data for 1990, 1998, and 2006 were available for New Mexico, so the 1990 value was assumed for 

years 1990-1995, while the 1998 value was assumed for year 1996 through 2001, and 2006 data were assumed for all 

remaining years. Data for 1990, 1998, and 2002 were available for Illinois, so the 1990 value was assumed for years 

1990-1995, while the 1998 value was assumed for years 1995 through 2001, and the 2002 value was assumed for all 

remaining years. Total forestland area for the lower 48 states was divided by total area considered to be under protection 

from wildfire for the lower 48 states across the 1990 to 2009 time series to create ratios that were then applied to reported 

area burned to estimate the area of forestland burned for the lower 48 states. The ratio was applied to area burned from 

wildland fires and prescribed fires occurring in the lower 48 states. Reported area burned data for prescribed fires was 

available from 1998 to 2009 from the National Interagency Fire Center (NIFC 2010). Data for the year 1998 was assumed 

for years 1990 to 1997. 

Forest area burned data for Alaska are from the Alaska Department of Natural Resources (Alaska Department of 

Natural Resources 2008) or the Alaska Interagency Coordination Center (Alaska Interagency Coordination Center 2010). 

Data are acres of land which experienced fire activity on forest service land. Based on personal communication with 

USFS, forest areas under the protection of USFS serve as a proxy for coastal areas, which is where the majority of forest 

fires in Alaska occur (Heath 2008). According to expert judgment, the coastal area of Alaska included in this Inventory is 

mostly temperate rainforest and, therefore, there is little call for prescribed burns (Smith 2008a). It was, thus, assumed 

that reported area burned for prescribed fires covers only prescribed fires in the lower 48 states. 

The average C density in the lower 48 states for aboveground biomass C, dead wood C, and litter layer is 91 

Mg/ha, according to data from FIA. A default value of 0.45 from IPCC (2006) was assumed for the amount of biomass 

burned by wildfire (combustion factor value). Thus, approximately 41.0 Mg C/ha is estimated to be emitted by wildfire. 

For Alaska, an average C density of 179 Mg/ha was used based on data from FIA; this translates into 80.6 Mg C/ha 

emitted. Based on data from the U.S. Forest Service, a value of 30 Mg/ha was used as the average C density for 

prescribed fires, where the average C density is adjusted to reflect the fuel load included in dead wood and litter only and 

the thought that prescribed fires only occur in the lower 48 states (Smith 2008a). Thus, prescribed fires are estimated to 

emit 13.5 Mg C/ha. Estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting 

estimates are displayed in Table A-228. C estimates were multiplied by 92.8 percent to account for the proportion of 

carbon emitted as CO2 and by 3.67 to yield CO2 units. Total CO2 emissions for wildfires and prescribed fires in the lower 

48 states and wildfires in Alaska in 2009 were estimated to be 127.0 Tg/yr. 
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Table A-228: Areas (hectares) from wildfire statistics and corresponding estimates of carbon and CO2 (Tg/yr) emissions 

for wildfires and prescribed fires in the lower 48 states and wildfires in Alaska1

 Lower 48 States Alaska 

 Wildfires Prescribed Fires Wildfires 

Year 

Reported 

area burned2

(ha) 

Forest area 

burned3

 (ha) 

Carbon 

emitted 

(Tg/yr) 

CO2

emitted 

(Tg/yr) 

Reported 

area 

burned2

(ha) 

Forest 

area 

burned3

(ha) 

Carbon 

emitted 

(Tg/yr) 

CO2

emitted 

(Tg/yr) 

Forest 

area 

burned4

(acres) 

Forest 

area 

burned

(ha) 

Carbon 

emitted 

(Tg/yr) 

CO2

emitted 

(Tg/yr) 

1990 579,589 302,372 12 42 355,432 185,429 3 9 8 3 0.0003 0.0009

1991 486,807 254,618 10 35 355,432 185,904 3 9 557 225 0.0182 0.0618

1992 785,892 412,111 17 57 355,432 186,384 3 9 47 19 0.0015 0.0052

1993 438,865 230,704 9 32 355,432 186,845 3 9 110 45 0.0036 0.0122

1994 1,540,987 811,927 33 113 355,432 187,273 3 9 23 9 0.0007 0.0025

1995 727,051 418,819 17 58 355,432 204,747 3 9 7 3 0.0002 0.0008

1996 2,212,309 1,313,377 54 183 355,432 211,008 3 10 103 42 0.0034 0.0115

1997 335,914 199,827 8 28 355,432 211,437 3 10 33 13 0.0011 0.0036

1998 489,246 291,647 12 41 355,432 211,878 3 10 2 1 0.0001 0.0002

1999 1,869,918 1,123,023 46 156 806,780 484,530 7 22 7 3 0.0002 0.0007

2000 2,685,981 1,615,551 66 225 77,789 46,788 1 2 1 1 0.0000 0.0001

2001 1,356,830 817,280 33 114 667,428 402,022 5 18 2,078 841 0.0677 0.2305

2002 2,023,976 1,217,243 50 170 1,086,503 653,436 9 30 28 11 0.0009 0.0031

2003 1,358,986 718,172 29 100 1,147,695 606,513 8 28 17 7 0.0006 0.0019

2004 637,258 342,999 14 48 996,453 536,332 7 25 23 9 0.0008 0.0026

2005 1,629,067 940,471 39 131 934,965 539,761 7 25 353 143 0.0115 0.0392

2006 3,888,011 2,250,696 92 314 1,100,966 637,328 9 29 8 3 0.0003 0.0009

2007 3,512,122 2,038,630 83 284 1,274,383 739,723 10 34 2 1 0.0001 0.0002

2008 2,099,842 1,212,446 50 169 783,068 452,142 6 21 1 0 0.0000 0.0001

2009 1,201,996 711,766 29 99 1,024,306 606,547 8 28 22 9 0.0007 0.0024 1

 Note that these emissions have already been accounted for in the estimates of net annual changes in carbon stocks, which accounts for the 

amount sequestered minus any emissions, including the assumption that combusted wood may continue to decay through time. 2

 National Interagency Fire Center (2010). 

3 Ratios calculated using forest land area estimates from FIA (USDA Forest Service 2010b) and wildland area under protection estimates from 

USDA Forest Service (1992) and the National Association of State Foresters (2007). 4

 1990–2007 Alaskan forest fires data are from the Alaska Department of Natural Resources (2008). 2008 data are from Alaska Interagency 

Coordination Center (2009). 

Non-CO2 

Emissions of non-CO2 gases from forest fires were estimated using the default IPCC (2003) methodology, IPCC 

(2006) emission ratios, and default IPCC (2006) combustion factor for wildfires. Emissions estimates for CH4 and N2O 

are calculated by multiplying the total estimated CO2 emitted from forest burned by gas-specific emissions ratios and 

conversion factors. The equations used are: 

CH4 Emissions = (CO2 released) × 92.8% × (44/12) × (CH4 to CO2 emission ratio) 

N2O Emissions = (CO2 released) × 92.8% × (44/12) × (N2O to CO2 emission ratio) 

The resulting estimates are presented in Table A-229. 

Table A-229: Estimated carbon released and estimates of non-CO2 emissions (Tg/yr) for U.S. forests1

Year C emitted (Tg/yr) CH4 emitted 

(Tg/yr) 

N2O 

(Tg/yr) 

1990 14.886 0.152 0.008 

1991 12.954 0.132 0.007 

1992 19.394 0.198 0.011 

1993 11.973 0.122 0.007 

1994 35.777 0.365 0.020 

1995 19.915 0.203 0.011 

1996 56.635 0.577 0.032 

1997 11.038 0.113 0.006 

1998 14.803 0.151 0.008 

1999 52.529 0.535 0.030 

2000 66.789 0.681 0.038 

2001 38.963 0.397 0.022 

2002 58.668 0.598 0.033 

2003 37.598 0.383 0.021 
A-281

2004 21.287 0.217 0.012 

2005 45.811 0.467 0.026 

2006 100.770 1.027 0.057 

2007 93.468 0.953 0.053 

2008 55.754 0.568 0.031 

2009 37.336 0.381 0.021 1 Calculated based on C emission estimates in Table A-228 and default factors in IPCC (2003, 2006)

References 

AF&PA. 2006a (and earlier). Statistical roundup. (Monthly). Washington, DC: American Forest & Paper 

Association. 

AF&PA. 2006b (and earlier). Statistics of paper, paperboard and wood pulp. Washington, DC: American Forest & 

Paper Association. 

Alaska Department of Natural Resources (2008) Division of Forestry. “Fire Statistics.” Available online at 

<http://forestry.alaska.gov/firestats/> October 2008. 

Alaska Interagency Coordination Center (AICC) (2010). “2009 Fire Season Statistics.” Available online at <

http://fire.ak.blm.gov/content/aicc/stats/2009.pdf> 

Amichev, B. Y. and J. M. Galbraith (2004) “A Revised Methodology for Estimation of Forest Soil Carbon from 

Spatial Soils and Forest Inventory Data Sets.” Environmental Management 33(Suppl. 1):S74-S86. 

Bechtold, W.A.; Patterson, P.L. 2005. The enhanced forest inventory and analysis program—national sampling 

design and estimation procedures. Gen. Tech. Rep. SRS-80. Asheville, NC: US Department of Agriculture Forest 

Service, Southern Research Station. 85 p. 

Birdsey, R.A., and L.S. Heath (1995) “Carbon Changes in U.S. Forests.” In Productivity of America’s Forests and 

Climate Change. Gen. Tech. Rep. RM-271. Rocky Mountain Forest and Range Experiment Station, Forest 

Service, U.S. Department of Agriculture. Fort Collins, CO, 56-70. 

Birdsey, R. (1996) “Carbon Storage for Major Forest Types and Regions in the Conterminous United States.” In 

R.N. Sampson and D. Hair, (eds); Forest and Global Change, Volume 2: Forest Management Opportunities for 

Mitigating Carbon Emissions. American Forests. Washington, DC, 1-26 and 261-379 (appendices 262 and 263). 

Birdsey, R., and L. S. Heath (2001) “Forest Inventory Data, Models, and Assumptions for Monitoring Carbon Flux.” 

In Soil Carbon Sequestration and the Greenhouse Effect. Soil Science Society of America. Madison, WI, 125-

135. 

Frayer, W.E., and G.M. Furnival (1999) “Forest Survey Sampling Designs: A History.” Journal of Forestry 97(12): 

4-10. 

Gold, M. A., W. J. Rietveld, H. E. Garrett, and R. F. Fisher. (2000) Agroforestry Nomenclature, Concepts, and 

Practices for the United States. In H.E. Garrett, W.J. Rietveld, and R.F. Fisher, editors. North American 

Agroforestry: An Integrated Science and Practice. ASA. Madison, Wisconsin, 63-77. 

Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products – 1963. U.S. Department of 

Agriculture Forest Service, Misc Publication No. 953. Washington, DC. 

Hair, D. (1958) “Historical forestry statistics of the United States.” Statistical Bull. 228. U.S. Department of 

Agriculture Forest Service, Washington, DC. 

Hansen, M.H., T Frieswyk, J.F. Glover, J.F. Kelly (1992) The Eastwide Forest Inventory Data Base: Users Manual. 

General Technical Report NC-151. U.S. Department of Agriculture, Forest Service, North Central Forest 

Experiment Station. St. Paul, MN. 

Heath, L.S., J.E., Smith, and R.A. Birdsey (2003) Carbon Trends in U. S. Forestlands: A Context for the Role of 

Soils in Forest Carbon Sequestration. In J. M. Kimble, L. S. Heath, R. A. Birdsey, and R. Lal, editors. The 

Potential of U. S. Forest Soils to Sequester Carbon and Mitigate the Greenhouse Effect. Lewis Publishers (CRC 

Press). Boca Raton, FL, 35-45. 

Heath, L. S., and J.E. Smith (2000) “Soil Carbon Accounting and Assumptions for Forestry and Forest-related 
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Land Use Change.” In The Impact of Climate Change on America’s Forests. Joyce, L.A., and Birdsey, R.A. Gen. 

Tech. Rep. RMRS-59. Rocky Mountain Research Station, Forest Service, U.S. Department of Agriculture. Fort 

Collins, CO, 89-101. 

Heath, L.S., M.C. Nichols, J.E. Smith, and J.R. Mills. 2010. FORCARB2: An updated version of the U.S. Forest 

Carbon Budget Model. Gen. Tech. Rep. NRS-67.USDA Forest Service, Northern Research Station, Newtown 

Square, PA. 52 p. [CD-ROM]. 

Heath, L.S., J.E. Smith, K.E. Skog, D. Nowak, and C. Woodall. In press. Managed forest carbon estimates for the 

U.S. Greenhouse Gas Inventory, 1990-2008. Journal of Forestry. 

Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, and K. L. Waddell (submitted). Carbon stocks on forested 

lands of the United States, with emphasis on USDA Forest Service ownership.Hoover, C., and S. Rebain. 2008. 

The Kane Experimental Forest carbon inventory: Carbon reporting with FVS. Pages 17-22 in R.N. Havis and 

N.L. Crookston, comps. 2008. Third Forest Vegetation Simulator Conference; 2007 February 13-15; Fort Collins, 

CO. Proceedings RMRS-P-54. USDA Forest Service, Rocky Mountain Research Station, Fort Collins, Colorado. 

Howard, James L. (2003) U.S. timber production, trade, consumption, and price statistics 1965 to 2002. Res. Pap. 

FPL-RP-615. Madison, WI: USDA, Forest Service, Forest Products Laboratory. Available online at 

<http://www.fpl.fs.fed.us/documnts/fplrp/fplrp615/fplrp615.pdf>. 

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas 

Inventories Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, 

T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan. 

IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. The Intergovernmental 

Panel on Climate Change, National Greenhouse Gas Inventories Programme, J. Penman, et al., eds. August 13, 

2004. Available online at <http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm>. 

IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories. 

Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for 

Economic Co-Operation and Development, International Energy Agency. Paris, France. 

Jenkins, J,C., D.C. Chojnacky, L.S. Heath, and R.A. Birdsey (2003) “National-scale biomass estimators for United 

States tree species.” Forest Science 49(1):12-35. 

Nair, P.K.R. and V.D. Nair. (2003) Carbon storage in North American Agroforestry systems. In Kimble J., Heath 

L.S., Birdsey R.A., Lal R., editors. The potential of U.S. forest soils to sequester carbon and mitigate the 

greenhouse effect. CRC Press. Boca Raton, FL, 333–346. 

National Interagency Fire Center (2010) “Fire Information—Wildland Fire Statistics. Total Wildland Fires and 

Acres (1960-2009).” Available online at <http://www.nifc.gov/fire_info/fires_acres.htm> November 2010. 

National Association of State Foresters (2008) State Forestry Statistics 2006 Report. Available online at <

http://www.stateforesters.org/files/2006%20State%20Forestry%20Statistics-Web-Final.pdf> October 2008. 

National Association of State Foresters (2007a) State Forestry Statistics 1998 Report. Available online at 

<http://www.stateforesters.org/statistics/FY98_Statistics/Resource%20Base.htm> March 2008. 

National Association of State Foresters (2007b) State Forestry Statistics 2002 Report. Available online at 

<http://www.stateforesters.org/statistics/FY02_Statistics/2002%20Stat%20Resource%20Base.pdf> March 2008. 

National Association of State Foresters (2007c) State Forestry Statistics 2004 Report. Available online at 

<http://www.stateforesters.org/statistics/FY04_Statistics/FY2004Statistics.pdf> March 2008. 

Perry, C.H., C.W. Woodall, and M. Schoeneberger (2005) Inventorying trees in agricultural landscapes: towards an 

accounting of “working trees”. In: “Moving Agroforestry into the Mainstream.” Proc. 9th N. Am. Agroforestry 

Conf., Brooks, K.N. and Ffolliott, P.F. (eds). 12-15 June 2005, Rochester, MN [CD-ROM]. Dept. of Forest 

Resources, Univ. Minnesota, St. Paul, MN, 12 p. Available online at <http://cinram.umn.edu/afta2005/> (verified 

23 Sept 2006). 

Quam, V.C., J. Gardner, J.R. Brandle, and T.K. Boes (1992) Windbreaks in Sustainable Agricultural Systems. EC91-1772. University of Nebraska Extension. Lincoln, NE. 
A-283

Schoeneberger, P.J. (2006) Landscape Classification. U.S. Department of Agriculture. Lincoln, NE. 

Skog, K.E., K. Pingoud, and J.E. Smith (2004) “A method countries can use to estimate changes in carbon stored in 

harvested wood products and the uncertainty of such estimates.” Environmental Management 33(Suppl. 1):S65-

S73. 

Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products 

Journal 58:56-72. 

Smith, J. (2008a) E-mail correspondence between Jean Kim, ICF, and Jim Smith, U.S. Forest Service, December 3, 

2008. 

Smith, J. and L.S. Heath (2000) Considerations for interpreting probabilistic estimates of uncertainty of forest 

carbon. In Joyce, L.A., Birdsey, R., editors. The impact of climate change on America's forests: a technical 

document supporting the 2000 USDA Forest Service RPA Assessment. Gen. Tech. Rep. RMRS-GTR-59. U.S. 

Department of Agriculture, Forest Service, Rocky Mountain Research Station. Fort Collins, CO, 102-111. 

Smith, J.E., and L.S. Heath (2002) “A model of forest floor carbon mass for United States forest types.” Res. Paper 

NE-722. USDA Forest Service, Northeastern Research Station, Newtown Square, PA. 

Smith, W. B., J. S. Vissage, D. R. Darr, and R. M. Sheffield (2001) Forest Resources of the United States, 1997. 

General Technical Report NC-219, U.S. Department of Agriculture Forest Service, North Central Research 

Station, St. Paul, MN. 

Smith, J. E., L. S. Heath, and J. C. Jenkins (2003) Forest Volume-to-Biomass Models and Estimates of Mass for Live 

and Standing Dead Trees of U.S. Forests. General Technical Report NE-298, USDA Forest Service, Northeastern 

Research Station, Newtown Square, PA. 

Smith, J. E., L. S. Heath, and P. B. Woodbury (2004) “How to estimate forest carbon for large areas from inventory 

data.” Journal of Forestry 102:25-31. 

Smith, J.E., L.S. Heath, K.E. Skog, R.A. Birdsey. (2006) Methods for calculating forest ecosystem and harvested 

carbon with standard estimates for forest types of the United States. Gen. Tech. Rep. NE-343. U.S. Department of 

Agriculture, Forest Service, Northeastern Research Station. Newtown Square, PA. 

Smith, J.E., L.S. Heath, and M.C. Nichols (2007). U.S. Forest Carbon Calculation Tool User’s Guide: Forestland 

Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13, U.S. Department of 

Agriculture Forest Service, Northern Research Station. 

Smith, W. B., P. D. Miles, C. H. Perry, and S. A. Pugh (2009) Forest Resources of the United States, 2007. General 

Technical Report WO-78, U.S. Department of Agriculture Forest Service, Washington Office. 

Smith, J.E., L.S. Heath, and M.C. Nichols (2010). U.S. Forest Carbon Calculation Tool User’s Guide: Forestland 

Carbon Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of 

Agriculture Forest Service, Northern Research Station. 

Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture 

Forest Service. Washington, DC. 

Ulrich, Alice (1985) U.S. Timber Production, Trade, Consumption, and Price Statistics 1950-1985. Misc. Pub. 

1453, U.S. Department of Agriculture Forest Service. Washington, DC. 

Ulrich, A.H. (1989) U.S. Timber Production, Trade, Consumption, and Price Statistics, 1950-1987. USDA 

Miscellaneous Publication No. 1471, U.S. Department of Agriculture Forest Service. Washington, DC, 77. 

USDC Bureau of Census (1976) Historical Statistics of the United States, Colonial Times to 1970, Vol. 1. 

Washington, DC. 

USDA Forest Service (1992) “1984-1990 Wildfire Statistics.” Prepared by State and Private Forestry Fire and 

Aviation Management Staff. Facsimile from Helene Cleveland, USDA Forest Service, to ICF International. 

January 30, 2008. 

USDA Forest Service (2010a) Forest Inventory and Analysis National Program: User Information. U.S. Department 

of Agriculture Forest Service. Washington, DC. Available online at <http://fia.fs.fed.us/tools-
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

data/docs/default.asp>. Accessed 07 October 2010. 

USDA Forest Service. (2010b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department 

of Agriculture Forest Service. Washington, DC. Available online at <http://199.128.173.17/fiadb4-

downloads/datamart.html>. Accessed 07 October 2010. 

USDA Forest Service. (2010c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods 

and Procedures. U.S. Department of Agriculture Forest Service. Washington, DC. Available online at 

<http://www.fia.fs.fed.us/library/field-guides-methods-proc/>. Accessed 07 October 2010. 

USDA Forest Service (2010d) Forest Inventory and Analysis National Program, FIA library: Database 

Documentation. U.S. Department of Agriculture, Forest Service, Washington Office. Available online at < 

http://www.fia.fs.fed.us/library/database-documentation/ >. Accessed 07 October 2010. 

USDA (1991) State Soil Geographic (STATSG0) Data Base Data use information. Miscellaneous Publication 

Number 1492, National Soil Survey Center, Natural Resources Conservation Service, U.S. Department of 

Agriculture, Fort Worth, TX. 

Waddell, K. L., D. D. Oswald, and D. S. Powell. (1989) Forest statistics of the United States, 1987. Resource 

Bulletin PNW-168. U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station. Portland, 

OR. 

Waddell, K., and B. Hiserote. 2005. The PNW-FIA Integrated Database User Guide: A database of forest inventory 

information for California, Oregon, and Washington. Forest Inventory and Analysis Program, Pacific Northwest 

Research Station, Portland, Oregon, USA. 

Woudenberg, S.W. and T.O. Farrenkopf (1995) The Westwide forest inventory data base: user’s manual. General 

Technical Report INT-GTR-317. U.S. Department of Agriculture Forest Service, Intermountain Research Station. 

Ogden, UT. 
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3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral 

and Organic Soils on Cropland and Grassland 

This sub-annex describes the methodologies used to calculate annual carbon (C) stock changes from mineral and 

organic soils under agricultural management, including Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland. Three types of methodologies were applied: (1) a 

Tier 3 approach, employing the Century simulation model, (2) Tier 2 methods with country-specific stock change and 

emission factors; and (3) Tier 2 methods for estimating additional changes in mineral soil C stocks due to sewage sludge 

additions to soils and enrollment changes in the Conservation Reserve Program (CRP) after 2003. 

The Inventory uses a Tier 3 approach to estimate soil C stock changes for the majority of agricultural lands. This 

approach has several advantages over the IPCC Tier 1 or 2 approaches: 

• It utilizes actual weather data at county scales, rather than a broad climate region classification, enabling 

quantification of inter-annual variability in C fluxes at finer spatial scales; 

• The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C 

dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology; 

• The simulation approach provides a more detailed representation of management influences and their 

interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods; and 

• Soil C changes are estimated on a more continuous basis (monthly) as a function of the interaction of 

climate, soil, and land management, compared with the linear change between the start and end of the 

inventory that is used with the Tier 1 and 2 methods. 

The Century model was chosen as an appropriate tool for a Tier 3 approach based on several criteria: 

• The model was developed in the United States and has been extensively tested and verified for U.S. 

conditions. In addition, the model has been widely used by researchers and agencies in many other parts of 

the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India, 

Jordan, Kenya, Mexico). 

• The model is capable of simulating cropland, grassland, forest, and savanna ecosystems, and land-use 

transitions between these different land uses. It is, thus, well suited to model land-use change effects. 

• The model was designed to simulate all major types of management practices that influence soil C 

dynamics, with the exception of cultivated organic soils and a few crops that have not been parameterized 

for Century simulations (e.g., rice, perennial/horticultural crops, and tobacco). For these latter cases, an 

IPCC Tier 2 method has been used. 

• Much of the data needed for the model was obtainable from existing national databases. The exceptions are 

CRP enrollment after 2003 and sewage sludge amendments to soils, which are not known at a sufficient 

resolution to use the Tier 3 model. Soil C stock changes associated with these practices are addressed with a 

Tier 2 method. 

Century Model Description 

The Century model simulates C (and also N, P, and S) dynamics, soil temperature, and water dynamics for 

cropland, grassland, forest, and savanna (mixed forest-grassland) systems. For this analysis, only C and N dynamics have 

been included for several reasons: to simplify the analysis and reduce data requirements, and because P and S interactions 

are less important as determinants of land-use- and management-induced changes in soil C stocks for U.S. agricultural 

systems. 

The model has four main components: (1) soil organic matter and nutrient dynamics; (2) plant growth processes; 

(3) water and temperature dynamics; and (4) management practices. The model was designed to work with readily 

available input data: monthly weather data (e.g., temperature and precipitation); soil physical properties (e.g., soil texture,

drainage condition, rooting depth); and information about land use/land cover (e.g., vegetation attributes) and management 

activities (see below). The model operates on a monthly time step (with weekly time steps used for soil water dynamics). 

Dynamics of organic C and N (Figure A-13) are simulated for the surface and subsurface litter pools and the top 

20 cm of the soil profile; mineral N dynamics are simulated through the whole soil profile. Organic C and N stocks are 

represented by two plant litter pools (labelled metabolic and structural) and three soil organic matter (SOM) pools 

(labelled active, slow, and passive). The metabolic litter pool represents the easily decomposable constituents of plant 

residues, while the structural litter pool is composed of more recalcitrant, ligno-cellulose plant materials. The three SOM 

pools represent a gradient in decomposability, from active SOM (representing microbial biomass and associated 
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

metabolites) having a rapid turnover (months to years), to passive SOM (representing highly processed, humified, 

condensed decomposition products), which is highly recalcitrant, with mean residence times on the order of several 

hundred years. The slow pool represents decomposition products of intermediate stability, having a mean residence time 

on the order of decades and is the fraction that tends to change the most in terms of C content in response to changes in 

land use and management. Soil texture influences turnover rates of the slow and passive pools The clay and silt-sized 

mineral fraction of the soil provides physical protection from microbial attack, leading to slower decomposition and 

greater SOM stabilization in finely textured soils. Soil temperature and moisture, tillage disturbance, aeration, and other 

factors influence the decomposition and loss of C from the soil organic matter pools. 

Figure A-13: Flow diagram of Carbon submodel (A) and Nitrogen submodel (B) 

The plant-growth submodel simulates C assimilation through photosynthesis, N uptake, dry matter production, 

partitioning of C within the crop or forage, senescence, and mortality. The primary function of the growth submodel is to 

estimate the amount, type, and timing of organic matter inputs to soil and to represent the influence of the plant on soil 

water, temperature, and N balance. Yield and removal of harvested biomass are also simulated. Separate submodels are 

designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and woody vegetation (i.e., trees and scrub). 

Only the herbaceous plant submodel is currently used in the Inventory. Maximum monthly net primary production (NPP) 

rate (a parameter of crop and forage species/variety, specified in the model input files) is modified by air temperature and 

available water to estimate a potential monthly NPP, which is then further subject to nutrient limitations in order to 

estimate actual NPP and biomass allocation. 

The soil-water balance submodel calculates water balance components and changes in soil water availability, 

which influences both plant growth and decomposition/nutrient cycling processes. The moisture content of soils are 

simulated through a multi-layer profile based on precipitation, snow accumulation and melting, interception, soil and 

canopy evaporation, transpiration, soil water movement, runoff, and drainage. 

The final main component of the model is the management submodel, which includes options for specifying crop 

type, crop sequence (e.g., rotation), tillage, fertilization, organic matter addition (e.g., manure amendments), harvest (with 

variable residue removal), drainage, irrigation, burning, and grazing intensity. An input “schedule” file is used to simulate 

the timing of management activities and temporal trends; schedules can be organized into discrete time blocks to define a 

repeated sequence of events (e.g., a crop rotation or a frequency of disturbance such as a burning cycle for perennial 

grassland). Management options can be specified for any month of a year within a scheduling block, where management 

codes point to operation-specific parameter files (referred to as *.100 files), which contain the information used to 

simulate management effects within the model process algorithms. User-specified management activities can be defined 

by adding to or editing the contents of the *.100 files. Additional details of the model formulation are given in Parton et 

al. (1987, 1988, 1994) and Metherell et al. (1993), and archived copies of the model source code are available. 

The model has been tested for application in U.S. agricultural lands and has been shown to capture the general 

trends in C storage across approximately 870 field plots from 47 experimental sites (Figure A-14). Some biases and 

imprecision were found in predictions of soil organic C, which is reflected in the uncertainty associated with Century 

model results as described in Step 2b of this sub-annex. Additional discussion is provided in Ogle et al. (2007, 2010) 

Figure A-14: Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century 

Model 

IPCC Tier 2 Method Description 

The IPCC Tier 2 method has been developed to estimate C stock changes and CO2 fluxes between soils and the 

atmosphere based on land-use and management activity (IPCC 2003, 2006; Ogle et al. 2003). For mineral soils (i.e., all 

soil orders from the USDA taxonomic classification except Histosols), the Tier 2 method uses reference C values to 

establish baseline C stocks that are modified based on agricultural activities using land-use change, tillage, and input 

factors. The standard IPCC approach was modified to use agricultural SOC stocks as the reference condition, rather than 

uncultivated soils under native vegetation. This modification was needed because soil measurements under agricultural 
A-287

management are much more common and easily identified in the National Soil Survey Characterization Database (NRCS 

1997). Measurements of soils under native vegetation are uncommon in the major agricultural regions of the United States 

because most of the area has been converted into cropland. In addition, country-specific factors were derived for land-use 

change, tillage, and input factors. 

Organic soils used for agricultural production are treated in a separate calculation. These soils are made up of 

deep (greater than 30 cm) layers of organic material that can decompose at a steady rate over several decades following 

drainage for crop production or grazing (IPCC 2006). The IPCC approach uses an emission factor to estimate annual 

losses of CO2 from cultivated organic soils, rather than an explicit stock change approach. 

Methodological Steps for Derivation of Soil Organic C Stock Change Estimates 

The inventory of soil C stock changes in U.S. agricultural land combines Tier 2 and 3 approaches. A simulationbased Tier 3 approach was used to estimate soil C changes for most agricultural land (approximately 90 percent of total 

cropland and grassland) comprising the dominant cropping and grazing systems in the United States, for which the model 

has been well-tested. Estimates for the remaining area, comprising less-common crop systems (e.g., horticultural, 

vegetable, tobacco, rice), land converted between non-agricultural and agricultural uses, and all agricultural land occurring 

on drained organic soils, were developed using the Tier 2 approach. Tier 2 methods were also used to estimate additional 

changes in mineral soil C stocks due to sewage sludge additions to soils, and enrollment changes in the Conservation 

Reserve Program after 2003. Most of the activity data sources were common to the Tier 2 and Tier 3 approaches, and, 

hence, they are described in an integrated manner below. Additional activity data required for the methods are described 

in adjoining sections, followed by the computation steps. 

Step 1: Derive Activity Data 

Activity data were compiled for the Tier 3 Century biogeochemical model and Tier 2 IPCC methods, including 

climate data, soil characteristics, and land-use/management activity data. The first step was to obtain landuse/management activity data, and determine the land base for areas under agricultural management. The areas modeled 

with Century and those estimated with the Tier 2 IPCC method were also subdivided. Finally, additional data, specific to 

each method, were collected on other key management activities (e.g., tillage management, fertilizer and manure addition 

rates) and environmental conditions (e.g., climate and soil characteristics). 

Step 1a: Determine the Land Base and Classify Management Systems 

Land Base—The National Resources Inventory (NRI) provided the basis for identifying the U.S. agricultural 

land base on non-federal lands, and classifying parcels into Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland (USDA-NRCS 2000). Note that the Inventory does 

not include estimates of C stock changes for grasslands and a minor amout of croplands on federal lands, even though 

these areas are part of the managed land base for the United States. C stock changes on federal croplands and grasslands 

will be further evaluated and included in future inventories. The NRI has a stratified multi-stage sampling design, where 

primary sample units are stratified on the basis of county and township boundaries defined by the U.S. Public Land Survey 

(Nusser and Goebel 1997). Within a primary sample unit, typically a 160-acre (64.75 ha) square quarter-section, three 

sample points are selected according to a restricted randomization procedure. Each point in the survey is assigned an area 

weight (expansion factor) based on other known areas and land-use information (Nusser and Goebel 1997). In principle, 

the expansion factors represent the amount of area with the land use and land use change history that is the same as the 

point location. It is important to note that the NRI is a sampling of land use, and therefore there is some uncertainty 

associated with scaling the point data to a region or the country using the expansion factors. In general, those uncertainties

decline at larger scales, such as states compared to smaller county units, because of a larger sample size. An extensive 

amount of soils, land-use, and land management data have been collected through the survey, which occurs every five 

years (Nusser et al. 1998).65 Primary sources for data include aerial photography and remote sensing imagery as well as 

field visits and county office records. The annual NRI data product provides crop data for most years between 1979 and 

2003, with the exception of 1983, 1988, and 1993. These years were gap-filled using an automated set of rules so that 

cropping sequences were filled with the most likely crop type given the historical cropping pattern at each NRI point 

location. Grassland data were reported on 5-year increments prior to 1998, but it was assumed that the land use was also 

grassland between the years of data collection (see Easter et al. 2008 for more information). 

 

65 In the current Inventory, NRI data only provide land-use and management statistics through 2003, but additional data will be 

incorporated in the future to extend the time series of land use and management data. 
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

NRI points were included in the land base for the agricultural soil C inventory if they were identified as cropland 

or grassland66 between 1990 and 2003 (Table A-230). The most recent national-level data available for NRI were for 

2003; and so the designation for 2003 was extended to 2009 in order to provide C stock changes over the entire time 

series. An additional modification was made to the time series from 2004 to 2009 for Grassland Remaining Grassland and 

Land Converted to Grassland associated with the modification of NRI data with the Forest Inventory and Analysis 

Dataset. Overall, more than 260,000 NRI points were included in the inventory calculations, and the total agricultural land 

base varied from 370 to 367 million hectares from 1990 through 2009. Each NRI point represents a specific land parcel 

based upon the weighted expansion factors. 

For each year, land parcels were subdivided into Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a 

specific year were classified as Cropland Remaining Cropland if they had been cropland for at least 20 years. Similarly 

land parcels under grassland management in a specific year of the inventory were classified as Grassland Remaining 

Grassland if they had been designated as grassland for at least 20 years.67 Otherwise, land parcels were classified as Land 

Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands 

are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland) for 20 

years as recommended by the IPCC guidelines (IPCC 2006). 

Table A-230: Total Land Areas for the Agricultural Soil C Inventory, Subdivided by Land Use Categories (Million Hectares) 

Land Areas (106

 ha) 

Category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 

Mineral Soils 

Cropland Remaining Cropland 166.38 166.14 165.89 163.32 162.27 161.95 161.61 161.26 158.40 158.67

Land Converted to Cropland 14.01 14.24 14.41 14.23 15.71 15.88 16.24 16.37 17.79 17.43

Grassland Remaining Grassland 176.03 175.67 175.38 172.75 171.52 171.43 171.20 171.12 169.79 169.70

Land Converted to Grassland 10.10 10.23 10.44 9.94 10.51 10.77 10.91 11.27 13.73 13.90

Non-Agricultural Usesa

 2.46 2.46 2.46 8.08 8.08 8.08 8.08 8.08 8.08 8.08

Organic Soils 

Cropland Remaining Cropland 0.70 0.70 0.70 0.72 0.72 0.72 0.72 0.72 0.72 0.72

Land Converted to Cropland 0.07 0.07 0.07 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Grassland Remaining Grassland 0.50 0.50 0.50 0.48 0.48 0.48 0.48 0.48 0.48 0.48

Land Converted to Grassland 0.04 0.04 0.04 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Non-Agricultural Usesa

 0.04 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Total 370.32 370.10 369.93 369.66 369.43 369.45 369.40 369.44 369.13 369.12

Category 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 

Mineral Soils 

Cropland Remaining Cropland 158.83 158.87 159.56 160.72 160.72 160.72 160.72 160.72 160.72 160.72

Land Converted to Cropland 17.09 16.83 16.09 14.87 14.87 14.87 14.87 14.87 14.87 14.87

Grassland Remaining Grassland 169.65 169.50 169.97 170.26 170.04 169.78 169.52 169.25 168.99 168.73

Land Converted to Grassland 14.24 14.64 14.28 13.98 13.91 13.83 13.74 13.66 13.58 13.50

Non-Agricultural Usesa

 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08 8.08

Organic Soils 

Cropland Remaining Cropland 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72 0.72

Land Converted to Cropland 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Grassland Remaining Grassland 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48 0.48

Land Converted to Grassland 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Non-Agricultural Usesa

 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.05

Total 369.24 369.27 369.32 369.25 368.95 368.62 368.27 367.93 367.58 367.23 a

 The non-agricultural uses were converted to or from cropland or grassland between 1990 and 2003. 

 Subdivide Land Base for Tier 2 and 3 Inventory Approaches–The Tier 3 method based on application of the 

Century model was used to model NRI points on most mineral soils. Parcels of land that were not simulated with Century 

were allocated to the Tier 2 approach, including (1) land parcels occurring on organic soils; (2) land parcels that included 

non-agricultural uses such as forest and federal lands in one or more years of the inventory;68 (3) land parcels on mineral 

 

66 Includes non-federal lands only, because federal lands are not classified into land uses as part of the NRI survey (i.e, they are only 

designated as federal lands). 

67 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and consequently the 

classifcations were based on less than 20 years from 1990 to 2001. 
A-289

soils that were very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent of soil volume 

comprised of gravel, cobbles, or shale); or (4) land parcels that were used to produce vegetables, perennial/horticultural 

crops, tobacco or rice, which was either grown continuously or in rotation with other crops. Century has not been fully 

tested for non-major crops, horticultural or perennial crops, rice and agricultural use of organic soils. In addition, Century 

has not been adequately tested for soils with a high gravel, cobble, or shale content, or fully tested for the transitions 

between agricultural and non-agricultural uses. 

Table A-231: Total Land Area Estimated with Tier 2a

 and 3 Inventory Approaches (Million Hectares) 

Land Areas (106

 ha) 

Year Tier 2* Tier 3 Total 

1990 47.27 323.05 370.32 

1991 47.27 322.83 370.10 

1992 47.27 322.66 369.93 

1993 47.25 322.41 369.66 

1994 47.25 322.17 369.43 

1995 47.25 322.19 369.45 

1996 47.25 322.14 369.40 

1997 47.25 322.19 369.44 

1998 47.25 321.88 369.13 

1999 47.25 321.87 369.12 

2000 47.25 321.98 369.24 

2001 47.25 322.01 369.27 

2002 47.25 322.07 369.32 

2003 47.25 321.99 369.25 

2004 47.25 321.70 368.95 

2005 47.25 321.36 368.62 

2006 47.25 321.02 368.27 

2007 47.25 320.67 367.93 

2008 47.25 320.32 367.58 

2009 47.25 319.98 367.23 

a

 Land use data for 1998-2003 are based on the Revised 1997 NRI data product for the Tier 2 method. Consequently, area data estimates in this 

table are not used for the Tier 2 portion of the Inventory. 

Management System Classification—NRI points on mineral soils were classified into specific crop rotations, 

continuous pasture/rangeland, and other non-agricultural uses for the Tier 2 inventory analysis based on the survey data 

(Table A-232). NRI points were assigned to IPCC input categories (low, medium, high, and high with organic 

amendments) according to the classification provided in IPCC (2006). In addition, NRI differentiates between improved 

and unimproved grassland, where improvements include irrigation and interseeding of legumes. In order to estimate 

uncertainties, PDFs for the NRI land-use data were constructed as multivariate normal based on the total area estimates for 

each land-use/management category and associated covariance matrix. Through this approach, dependencies in land use 

were taken into account resulting from the likelihood that current use is correlated with past use. 

For the Tier 3 inventory estimates, the actual cropping and grassland histories were simulated with the Century 

model so it was not necessary to classify NRI points into management systems. Uncertainty in the areas associated with 

each management system was determined from the estimated sampling variance from the NRI survey (Nusser and Goebel 

1997). See Step 2b for additional discussion. 

Table A-232: Total Land Areas by Land-Use and Management System for the Tier 2 Approach (Million Hectares) 

 Land Areas (106 ha) 

Land-Use/Management System 1990-92 (Tier 2) 1993-2009 (Tier 2) 

Cropland Systems 31.53 29.25 

Irrigated Crops 7.27 6.91 

Continuous Row Crops 4.12 3.63 

Continuous Small Grains 1.25 1.04 

Continuous Row Crops and Small Grains 2.30 1.95 

Row Crops in Rotation with Hay and/or Pasture 0.30 0.23 

Small Grains in Rotation with Hay and/or Pasture 0.06 0.06 

Row Crops and Small Grains in Rotation with Hay and/or Pasture 0.03 0.04 

 

68 Federal land is treated as forest or nominal grassland for purposes of these calculations, although the specific use is not identified in 

the NRI survey (USDA-NRCS 2000). Future inventories will include C estimation for the disaggegrated land use and land use change 

categories on federal lands. 
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Vegetable Crops 2.90 3.16 

Low Residue Annual Crops (e.g., Tobacco or Cotton) 0.87 1.03 

Small Grains with Fallow 2.01 1.31 

Row Crops and Small Grains with Fallow 1.72 1.80 

Row Crops with Fallow 0.52 0.34 

Miscellaneous Crop Rotations 0.54 0.43 

Continuous Rice 0.34 0.31 

Rice in Rotation with other crops 1.78 1.91 

Continuous Perennial or Horticultural Crops 2.57 2.50 

Continuous Hay 0.59 0.50 

Continuous Hay with Legumes or Irrigation 1.31 1.12 

CRP 1.03 0.96 

Aquaculture 0.01 0.01 

Grassland Systems 12.02 8.68 

Rangeland 5.98 5.16 

Continuous Pasture 3.76 2.49 

Continuous Pasture with Legumes or Irrigation (i.e., improved) 2.25 1.03 

CRP 0.02 0.00 

Non-Agricultural Systems 2.46 8.08 

Forest 1.53 3.95 

Federal 0.01 0.05 

Water 0.11 0.25 

Settlements 0.04 2.46 

Miscellaneous 0.77 1.36 

Total 46.01 46.01 

Organic soils are also categorized into land-use systems based on drainage (IPCC 2006). Undrained soils are 

treated as having no loss of organic C. Drained soils are subdivided into those used for cultivated cropland, which are 

assumed to have high drainage and greater losses of C, and those used for managed pasture, which are assumed to have 

less drainage and smaller losses of C. Overall, the area of organic soils drained for cropland and grassland has remained 

relatively stable since 1992 (see Table A-233). 

Table A-233: Total Land Areas for Drained Organic Soils By Land Management Category and Climate Region (Million 

Hectares) 

 Land Areas (106 ha) 

 Cold Temperate Warm Temperate Tropical 

IPCC Land-Use Category for Organic Soils 1992 1997 1992 1997 1992 1997 

Undrained 0.07 0.06 0.0020 0.0017 0.12 0.09 

Managed Pasture (Low Drainage) 0.42 0.42 0.0136 0.0119 0.07 0.08 

Cultivated Cropland (High Drainage) 0.33 0.34 0.0971 0.0974 0.19 0.20 

Other Land Usesa 0.02 0.01 0.0002 0.0017 0.00 0.02 

Total 0.84 0.84 0.11 0.11 0.39 0.39 

a

Urban, water, and miscellaneous non-cropland, which are part of the agricultural land base because these areas were converted from or into 

agricultural land uses during the 1990s. 

Step 1b: Obtain Additional Management Activity Data for the Tier 3 Century Model 

Tillage Practices—Tillage practices were estimated for each cropping system based on data compiled by the 

Conservation Technology Information Center (CTIC 1998). CTIC compiles data on cropland area under five tillage 

classes by major crop species and year for each county. Because the surveys involve county-level aggregate area, they do 

not fully characterize tillage practices as they are applied within a management sequence (e.g., crop rotation). This is 

particularly true for area estimates of cropland under no-till, which include a relatively high proportion of “intermittent” 

no-till, where no-till in one year may be followed by tillage in a subsequent year. For example, a common practice in 

maize-soybean rotations is to use tillage in the maize crop while no-till is used for soybean, such that no-till practices are 

not continuous in time. Estimates of the area under continuous no-till were provided by experts at CTIC to account for 

intermittent tillage activity and its impact on soil C (Towery 2001). 

Tillage practices were grouped into 3 categories: full, reduced, and no-tillage. Full tillage was defined as multiple 

tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface residue 

coverage. This definition corresponds to the intensive tillage and “reduced” tillage systems as defined by CTIC (1998). 

No-till was defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-till is applied 

to all crops in the rotation. Reduced tillage made up the remainder of the cultivated area, including mulch tillage and ridge 
A-291

tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for different 

crops, rotations, and regions to represent the three tillage classes were derived from the 1995 Cropping Practices Survey 

by the Economic Research Service (ERS 1997). 

Tillage data were further processed to construct probability distribution functions (PDFs) using CTIC tillage 

data. Transitions between tillage systems were based on observed county-level changes in the frequency distribution of 

the area under full, reduced, and no-till from the 1980s through 2004. Generally, the fraction of full tillage decreased 

during this time span, with concomitant increases in reduced till and no-till management. Transitions that were modeled 

and applied to NRI points occurring within a county were full tillage to reduced and no-till, and reduced tillage to no-till. 

The remaining amount of cropland was assumed to have no change in tillage (e.g., full tillage remained in full tillage). 

Transition matrices were constructed from CTIC data to represent tillage changes for three time periods, 1980-1989, 1990-

1999, 2000-2009. Areas in each of the three tillage classes—full till (FT), reduced till (RT), no-till (NT)—in 1989 (the 

first year the CTIC data were available) were used for the first time period, data from 1997 were used for the second time 

period, and data from 2004 were used for the last time period. Percentage areas of cropland in each county were 

calculated for each possible transition (e.g., FT→FT, FT→RT, FT→NT, RT→RT, RT→NT) to obtain a probability for 

each tillage transition at an NRI point. Since continuous NT constituted < 1 percent of total cropland prior to 1990, there 

were no transitions for NT→FT or NT→NT. Uniform probability distributions were established for each tillage scenario 

in the county. For example, a particular crop rotation had 80 percent chance of remaining in full tillage over the two 

decades, a 15 percent chance of a transition from full to reduced tillage and a 5 percent chance of a transition from full to 

no-till. The uniform distribution was subdivided into three segments with random draws in the Monte Carlo simulation 

(discussed in Step 2b) leading to full tillage over the entire time period if the value was greater than or equal to 0 and less

than 80, a transition from full to reduced till if the random draw was equal to or greater than 80 and less than 95, or a 

transition from full to no-till if the draw was greater than or equal to 95. See step 2b for additional discussion of the 

uncertainty analysis. 

Mineral Fertilizer Application—Data on nitrogen fertilizer rates were obtained primarily from USDA’s 

Economic Research Service’s 1995 Cropping Practices Survey (ERS 1997). In this survey, data on inorganic nitrogen 

fertilization rates were collected for major crops (corn, cotton, soybeans, potatoes, winter wheat, durum wheat, and other 

spring wheat) in the key crop producing states. Note that all wheat data were combined into one category and assumed to 

represent small grains in general. Estimates for sorghum fertilizer rates were derived from corn rates using a ratio of 

national average corn fertilizer rates to national average sorghum fertilizer rates derived from additional publications 

(NASS 2004, 1999, 1992; ERS 1988; Grant and Krenz 1985; USDA 1954, 1957, 1966). 

The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records 

deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for 

a region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state at the 

finest resolution available. State-level data were produced for surveyed states if a minimum of 15 data points existed for 

each of the two categories (irrigated and rainfed). If a state was not surveyed for a particular crop or if fewer than 15 data

points existed for one of the categories, then data at the Farm Production Region level were substituted. If Farm 

Production Region data were not available, then U.S.-level estimates (all major states surveyed) were used in the 

simulation for that particular crop in the state lacking sufficient data. Note that standard deviations for fertilizer rates on 

log scale were used to construct PDFs on a log-normal scale, in order to address uncertainties in application rates (see Step 

2b for discussion of uncertainty methods). 

Manure Application—County-level manure N addition estimates were obtained from the Natural Resources 

Conservation Service (Edmonds et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of 

Agriculture, NRCS has coupled estimates of manure nitrogen produced with estimates of manure nitrogen recoverability 

by animal waste management system to produce county-level estimates of manure nitrogen applied to cropland and 

pasture. Edmonds et al. (2003) defined a hierarchy of land use systems to which manure is applied, that included 24 crops, 

cropland used as pasture, and permanent pasture. They estimated the area amended with manure and manure nitrogen 

application rates in 1997 for both manure-producing farms and manure-receiving farms within a county, for two 

scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation. 

The application rates for the baseline scenario were used in the inventory under the assumption that Comprehensive 

Nutrient Management Plans have not been fully implemented. 

In order to derive estimates of manure application rates over time, the availability of managed manure N for 

application to soils (which are available annually) was used to adjust the amount of area amended with manure on a county 

scale (Note: Edmonds et al. (2003) only provide information on application rates for 1997). Specifically, the estimated 

available managed manure N in another year was divided by the managed manure N available in 1997. The amendment 

area in a county for 1997 was then multiplied by the ratio to reflect the probability of manure amendments based on the 

variation in available manure N across time. If more managed manure N was available in a given year for a county 
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

relative to the amount available in 1997 (ratio > 1), it was assumed that there was a higher probability of a manure 

amendment. In contrast, if less managed manure N was available (ratio < 1), the probability of an amendment declined in 

comparison to 1997. A detailed description of the derivation of the managed manure N availability data is provided in the 

Manure Management section (Section 6.2) and Annex (Annex 3.10). Managed manure N availability in the 1980s was 

based on USDA estimates (Kellogg et al. 2000) after adjusting for relative differences in manure N production between 

the USDA dataset and estimates derived from the method described in Annex 3.10. Unmanaged manure classified as 

pasture/range/paddock manure was assumed to have negligible impacts on soil C stocks because of the tradeoff between 

reduced litterfall C versus C ingested by livestock and deposited on soils in manure. 

For Century simulations, the amended areas were averaged for three time periods (1980-1989, 1990-1999, and 

2000-2009) similar to the tillage transitions. Rates for manure-producing farms and manure-receiving farms have been 

area-weighted and combined to produce a manure nitrogen application rate for each crop in a county. Several of the crops 

in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all small 

grain crops have been combined into one category. In order to address uncertainty, uniform probability distributions were 

constructed based on the proportion of land receiving manure versus the amount not receiving manure for each crop type 

and pasture. For example, if the 20 percent of land producing corn in a county was amended with manure, randomly 

drawing a value equal to or greater than 0 and less than 20 would lead to simulation with a manure amendment, while 

drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2b 

for further discussion of uncertainty methods). 

To estimate the C inputs associated with the manure N application rates (from Edmonds et al. 2003), C:N ratios 

for various manure types (based on animal species and manure management system) were estimated from data in the 

Agricultural Waste Management Field Handbook (USDA 1996) and the On-Farm Composting Handbook (NRAES 1992). 

Weighted county-average C:N ratios for total manure applied were then calculated based on the C:N ratio and the manure 

N production rate for each manure type reported in the county. Manure C addition rates were then calculated by 

multiplying the county-average manure C:N ratio by the manure N application rates. 

To account for the common practice of reducing inorganic nitrogen fertilizer inputs when manure is added to a 

cropland soil, a set of crop-specific reduction factors were derived from mineral fertilization data for land amended with 

manure versus land not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). In the 

simulations, mineral N fertilization rates were reduced for crops receiving manure nitrogen based on a fraction of the 

amount of manure nitrogen applied, depending on the crop and whether it was irrigated or a rainfed system. The reduction 

factors were selected from PDFs with normal densities in order to address uncertainties in this dependence between 

manure amendments and mineral fertilizer application. 

Irrigation—NRI differentiates between irrigated and non-irrigated land but does not provide more detailed 

information on the type and intensity of irrigation. Hence, irrigation was modeled by assuming that applied water was 

sufficient to meet full crop demand (i.e., irrigation plus precipitation equaled potential evapotranspiration during the 

growing season). 

Step 1c—Obtain Additional Management Activity Data for Tier 2 IPCC Method 

Tillage Practices—PDFs were constructed for the CTIC tillage data, as bivariate normal on a log-ratio scale to 

reflect negative dependence among tillage classes. This structure ensured that simulated tillage percentages were nonnegative and summed to 100 percent. CTIC data do not differentiate between continuous and intermittent use of notillage, which is important for estimating SOC storage. Thus, regionally based estimates for continuous no-tillage (defined 

as 5 or more years of continuous use) were modified based on consultation with CTIC experts, as discussed in Step 1a 

(downward adjustment of total no-tillage acres reported, Towery 2001). 

Manure Amendments—Manure management is also a key practice in agricultural lands, with organic 

amendments leading to significant increases in SOC storage. USDA provides information on the amount of land amended 

with manure for 1997 based on manure production data and field-scale surveys detailing application rates that had been 

collected in the Census of Agriculture (Edmonds et al. 2003). Similar to the Century model discussion in Step1b, the 

amount of land receiving manure was based on the estimates provided by Edmonds et al. (2003), as a proportion of crop 

and grassland amended with manure within individual climate regions. The resulting proportions were used to re-classify 

a portion of crop and grassland into a new management category. Specifically, a portion of medium input cropping 

systems was re-classified as high input, and a portion of the high input systems was re-classified as high input with 

amendment. In grassland systems, the estimated proportions for land amended with manure were used to re-classify a 

portion of nominally-managed grassland as improved, and a portion of improved grassland as improved with high input. 

These classification approaches are consistent with the IPCC inventory methodology (IPCC 2003, 2006). Uncertainties in 
A-293

the amount of land amended with manure were based on the sample variance at the climate region scale, assuming normal 

density PDFs (i.e., variance of the climate region estimates, which were derived from county-scale proportions). 

Wetland Reserve—Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern 

Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service. The area 

of restored wetlands was estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide 

reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to 

estimate uncertainty. Consequently, a ±50 percent range was used to construct the PDFs for the uncertainty analysis. 

Step 1d—Obtain Management Activity Data to Compute Additional Changes in Soil Organic C Stocks in 

Mineral Soils Due to Sewage Sludge Applications and CRP Enrollment after 2003 

Two additional influences on soil organic C stocks in mineral soils were estimated using a Tier 2 method, 

including: sewage sludge additions to agricultural soils and changes in enrollment for the Conservation Reserve Program 

after 2003. 

Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass units, were obtained from an EPA 

report (EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey (NEBRA 2007). 

These values were linearly interpolated to estimate values for the intervening years. Sewage sludge generation data are 

not available for 2005 onwards (Bastian 2007), so the 1990 through 2004 data were linearly extrapolated for the most 

recent years. The total sludge generation estimates were then converted to units of N by applying an average N content of 

3.9 percent (McFarland 2001), and disaggregated into use and disposal practices using historical data in EPA (1993) and 

NEBRA (2007). The use and disposal practices were agricultural land application, other land application, surface disposal, 

incineration, landfilling, ocean dumping (ended in 1992), and other disposal. Sewage sludge N was assumed to be applied 

at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and 

removed at harvest, representing the recommended application rate for manure amendments. This capacity varies from 

year to year, because it is based on specific crop yields during the respective year (Kellogg et al. 2000). Total sewage 

sludge N available for application was divided by the assimilative capacity to estimate the total land area over which 

sewage sludge had been applied. The resulting estimates were used for the estimation of soil C stock change. 

The change in enrollment for the Conservation Reserve Program after 2003 was based on the amount of land 

under active contracts from 2004 through 2009 relative to 2003 (USDA-FSA 2010). 

Step 1e: Obtain Climate and Soils Data 

Tier 3 Century Model—Monthly weather data (temperature and precipitation) from the PRISM database 

(Parameter-elevation Regressions on Independent Slopes Model) (Daly et al. 1994) were used as an input to the Century 

model simulations for the period 1895 through 2003. PRISM is based on observed weather data from the National 

Weather Service network database and statistical models for interpolation and orographic corrections. The primary 

database consists of approximately 4×4 km grid cells. These data were averaged (weighted by area) for each county in the 

United States, so that counties are the finest spatial scale represented in the Century simulations. 

Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) were the main soil 

variables used as input to the Century model. Other soil characteristics needed in the simulation, such as field capacity 

and wilting-point water contents, were estimated from soil texture data using pedo-transfer functions available in the 

model. Soil input data are derived from the NRI database, which contain descriptions for the soil type at each NRI point 

(used to specify land-use and management time series-see below). The data are based on field measurements collected as 

part of soil survey and mapping. Soils are classified according to “soil-series,” which is the most detailed taxonomic level 

used for soil mapping in the United States. Surface soil texture and hydric condition were obtained from the soil attribute 

table in the NRI database. Texture is one of the main controls on soil C turnover and stabilization in the Century model, 

which uses particle size fractions of sand (50-2,000 μm), silt (2-50 μm), and clay (< 2 μm) as inputs. NRI points were 

assigned to one of twelve texture classes for the simulations. Hydric condition specifies whether soils are poorly-drained, 

and hence prone to water-logging, or moderately to well-drained (non-hydric), in their native (pre-cultivation) condition.69 

Poorly drained soils can be subject to anaerobic (lack of oxygen) conditions if water inputs (precipitation and irrigation) 

exceed water losses from drainage and evapotranspiration. Depending on moisture conditions, hydric soils can range from 

being fully aerobic to completely anaerobic, varying over the year. Decomposition rates are modified according to a linear 

 

69 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable. 
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

function that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic conditions (default 

parameters in Century).70 

IPCC Tier 2 Method—The IPCC inventory methodology for agricultural soils divides climate into eight distinct 

zones based upon average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006) 

(Table A-234). Six of these climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001). 

Table A-234: Characteristics of the IPCC Climate Zones that Occur in the United States 

Climate Zone 

Annual Average 

Temperature ( ̊C)

Average Annual Precipitation 

(mm)

Length of Dry Season 

(months)

Cold Temperate, Dry < 10 < Potential Evapotranspiration NA

Cold Temperate, Moist < 10 ≥ Potential Evapotranspiration NA

Warm Temperate, Dry 10 – 20 < 600 NA

Warm Temperate, Moist 10 – 20 ≥ Potential Evapotranspiration NA

Sub-Tropical, Dry* > 20 < 1,000 Usually long

Sub-Tropical, Moist (w/short dry season)a

 > 20 1,000 – 2,000 < 5 a

 The climate characteristics listed in the table for these zones are those that correspond to the tropical dry and tropical moist zones of the IPCC. 

They have been renamed “sub-tropical” here. 

Mean climate (1961-1990) variables from the PRISM data set (Daly et al. 1994) were used to classify climate 

zones. Mean annual precipitation and annual temperature data were averaged (weighted by area) for each of the 4×4 km 

grid cells occurring within a MLRA region. These averages were used to assign a climate zone to each MLRA according 

to the IPCC climate classification (Figure A-15). MLRAs represent geographic units with relatively similar soils, climate, 

water resources, and land uses; and there are approximately 180 MLRAs in the United States (NRCS 1981). 

Figure A-15: Major Land Resource Areas by IPCC Climate Zone 

Soils were classified into one of seven classes based upon texture, morphology, and ability to store organic 

matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, 

representing estimates from conventionally managed cropland, were computed for each of the mineral soil types across the 

various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 

1997) (Table A-235). These stocks are used in conjunction with management factors to compute the change in SOC 

stocks that result from management and land-use activity. PDFs, which represent the variability in the stock estimates, 

were constructed as normal densities based on the mean and variance from the pedon data. Pedon locations were clumped 

in various parts of the country, which reduces the statistical independence of individual pedon estimates. To account for 

this lack of independence, samples from each climate by soil zone were tested for spatial autocorrelation using the 

Moran’s I test, and variance terms were inflated by 10 percent for all zones with significant p-values. 

Table A-235: U.S. Soil Groupings Based on the IPCC Categories and Dominant Taxonomic Soil, and Reference Carbon 

Stocks (Metric Tons C/ha) 

IPCC Inventory 

Soil Categories 

USDA Taxonomic Soil 

Orders 

Reference Carbon Stock in Climate Regions 

Cold 

Temperate, 

Dry

Cold 

Temperate, 

Moist

Warm 

Temperate, 

Dry

Warm 

Temperate, 

Moist

SubTropical, 

Dry 

SubTropical,

Moist

High Clay Activity 

Mineral Soils 

Vertisols, Mollisols, 

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 

Mineral Soils 

Ultisols, Oxisols, acidic 

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 (n = 12) 124 (n = 12) 128 (n = 9)

Spodic Soils Spodosols 86 (n=20) 74 (n = 13) 86 (n=20) 107 (n = 7) 86 (n=20) 86 (n=20)

Aquic Soils Soils with Aquic suborder 86 (n = 4) 89 (n = 161) 48 (n = 26) 51 (n = 300) 63 (n = 503) 48 (n = 12)

 

70 Hydric soils are primarily subject to anaerobic conditions outside the plant growing season (i.e., in the absence of active plant water 

uptake). Soils that are water-logged during much of the year are typically classified as organic soils (e.g., peat), which are not simulated 

with the Century model. 
A-295

Organic Soilsa Histosols NA NA NA NA NA NA a

 C stocks are not needed for organic soils. 

Notes: C stocks are for the top 30 cm of the soil profile, and were estimated from pedon data available in the National Soil Survey 

Characterization database (NRCS 1997); sample size provided in parentheses (i.e., ‘n’ values refer to sample size). 

Step 2: Estimate Organic C Stock Changes for Agricultural Lands on Mineral Soils Simulated with the 

Tier 3 Century Model 

This methodology description is divided into two sub-steps. First, the model was used to establish the initial 

conditions and C stocks for 1979, which was the last year before the NRI survey was initiated. In the second sub-step, 

Century was used to estimate changes in soil organic C stocks based on the land-use and management histories recorded in 

the NRI (USDA-NRCS 2000), including the reporting period starting in 1990. 

Step 2a: Simulate Initial Conditions (Pre-NRI Conditions) 

Century model initialization involves two steps, with the goal of estimating the most accurate stock for the preNRI history, and the distribution of organic C among the pools represented in the model (e.g., Structural, Metabolic, 

Active, Slow, Passive). Each pool has a different turnover rate (representing the heterogeneous nature of soil organic 

matter), and the amount of C in each pool at any point in time influences the forward trajectory of the total soil organic C 

storage. There is currently no national set of soil C measurements that can be used for establishing initial conditions in the

model. Sensitivity analysis of the Century model showed that the rate of change of soil organic matter is relatively 

insensitive to the amount of total soil organic C but is highly sensitive to the relative distribution of C among different 

pools (Parton et al. 1987). By simulating the historical land use prior to the inventory period, initial pool distributions are 

estimated in an unbiased way. 

The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation, 

with long-term mean climate based on 30-yr averages of the PRISM data (1960-1990), and the soil physical attributes for 

the NRI points. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United States. 

The model was run for 7,000 years to represent a pre-settlement era and achieve a steady-state condition. 

The second step is to run the model for the period of time from settlement to the beginning of the NRI survey, 

representing the influence of historic land-use change and management, particularly the conversion of native vegetation to 

agricultural uses. This encompasses a varying time period from land conversion (depending on historical settlement 

patterns) to 1979. The information on historical cropping practices used for Century simulations was gathered from a 

variety of sources, ranging from the historical accounts of farming practices reported in the literature (e.g., Miner 1998) to 

national level databases (e.g., NASS 2004). A detailed description of the data sources and assumptions used in 

constructing the base history scenarios of agricultural practices can be found in Williams and Paustian (2005). 

Step 2b—Estimate Soil Organic C Stock Changes and Uncertainties 

After estimating model initialization, the model is used to simulate the NRI land use and management histories 

from 1979 through 2003.71 The simulation system incorporates a dedicated MySQL database server and a 24-node 

parallel processing computer cluster. Input/output operations are managed by a set of run executive programs written in 

PERL. The assessment framework for this analysis is illustrated in Figure A-16. 

Figure A-16: Uncertainty in Data Inputs 

Evaluating uncertainty was an integral part of the analysis, and included three components: (1) uncertainty in the 

main activity data inputs affecting soil C balance (input uncertainty); (2) uncertainty in the model formulation and 

parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling 

uncertainty) (Ogle et al. 2010). For component 1, input uncertainty was evaluated for fertilization management, manure 

applications, and tillage, which are the primary management activity data that were supplemental to the NRI observations 

and have significant influence on soil C dynamics. As described in Step 1b, PDFs were derived from surveys at the 

county scale in most cases. To represent uncertainty in these inputs, a Monte-Carlo Analysis was used with 100 iterations 

for each NRI cluster-point in which random draws were made from PDFs for fertilizer, manure application, and tillage. 

 

71 The estimated soil C stock change in 2003 is currently assumed to represent the changes between 2004 and 2009. New estimates will 

be available in the future to extend the time series of land use and management data. 
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

As described above, an adjustment factor was also selected from PDFs with normal densities to represent the dependence 

between manure amendments and N fertilizer application rates. The total number of Century simulations was over 12 

million for the Monte Carlo Analysis with 100 iterations. 

The second component dealt with uncertainty inherent in model formulation and parameterization. An 

empirically-based procedure was employed to develop a structural uncertainty estimator from the relationship between 

modeled results and field measurements from agricultural experiments (Ogle et al. 2007). The Century model was 

initialized for 45 long-term field experiments with over 800 treatments in which soil C was measured under a variety of 

management conditions (e.g., variation in crop rotation, tillage, fertilization rates, manure amendments). These studies 

were obtained from an extensive search of published studies. All studies located in North America that met minimum 

criteria of having sufficient site-level information and experimental designs were used, including C stock estimates, 

texture data, experimental designs with control plots, and land-use and management records for the experimental time 

period and pre-experiment condition. The inputs to the model were essentially known in the simulations for the long-term 

experiments, and, therefore, the analysis was designed to evaluate uncertainties associated with the model structure (i.e., 

model algorithms and parameterization). 

The relationship between modeled soil C stocks and field measurements was statistically analyzed using linearmixed effect modeling techniques. Additional fixed effects were included in the mixed effect model if they explained 

significant variation in the relationship between modeled and measured stocks (i.e., if they met an alpha level of 0.05 for 

significance). Several variables were tested including: land-use class; type of tillage; cropping system; geographic 

location; climate; soil texture; time since the management change; original land cover (i.e., forest or grassland); grain 

harvest as predicted by the model compared to the experimental values; and variation in fertilizer and residue 

management. The final model included variables for organic matter amendments, fertilizer rates, inclusion of hay/pasture 

in cropping rotations, use of no-till, and inclusion of bare fallow in the rotation, which were significant at an alpha level of 

0.05. These fixed effects were used to make an adjustment to modeled values due to biases that were creating significant 

mismatches between the modeled and measured stock values. Random effects captured the statistical dependence (i.e., the 

data are not fully independent) in time series and data collected from the same long-term experimental site. Accounting 

for this statistical dependency is needed to estimate appropriate standard deviations for parameter coefficients. 

A Monte Carlo approach was used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for the 

statistical equation (i.e., fixed effects) were selected from their joint probability distribution, as well as random error 

associated with fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixedeffect model. The stock estimate and associated management information was then used as input into the equation, and 

adjusted stock values were computed for each C stock estimate produced in the evaluation of input uncertainty for 

Cropland Remaining Cropland (Component 1 of the uncertainty analysis). Note that the uncertainty estimator needs 

further development for application to Grassland Remaining Grassland and the land-use change categories. This 

development is a planned improvement for the soil C inventory. The variance of the adjusted C stock estimates were 

computed from the 100 simulated values from the Monte Carlo analysis. 

The third element was the uncertainty associated with scaling the Century results for each NRI point to the entire 

land base, using the expansion factors provided with the NRI database. The expansion factors represent the number of 

hectares associated with the land-use and management history for a particular point. This uncertainty was determined by 

computing the variances of the expanded estimates, accounting for the two-stage sampling design of the NRI. 

For the land base that was simulated with the Century model, soil organic C stocks ranged from losses of 4.25 Tg 

CO2 Eq. to gains of 68.05 Tg CO2 Eq. annually, depending on the land-use/land-use change category and inventory time 

period. Estimates and uncertainties are provided in Table A-236. 

Table A-236: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with 

the Tier 3 Century Model-Based Approach (Tg CO2 Eq.) 

Year 

Cropland Remaining 

Cropland 

Land Converted to 

Cropland 

Grassland Remaining 

Grassland Land Converted to Grassland 

Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI 

1990 (55.19) (111.72) to 1.33 (4.43) (5.06) to (3.80) (55.10) (57.18) to 

(53.01)

(15.75) (18.43) to (13.07) 

1991 (57.50) (90.49) to (24.51) (4.30) (4.93) to (3.68) (28.04) 

(29.87) to 

(26.21) (15.06) (17.58) to (12.54) 

1992 (68.05) (99.81) to (36.28) (4.75) (5.38) to (4.13) (9.84) (10.82) to (8.85) (13.83) (16.23) to (11.44) 

1993 (64.25) (95.93) to (32.56) (8.66) (9.34) to (7.98) (1.48) (3.29) to 0.33 (13.52) (16.09) to (10.94) 

1994 (62.38) (94.48) to (30.29) (12.61) 

(13.42) to 

(11.80) (66.74) 

(68.28) to 

(65.20) (17.87) (20.62) to (15.12) 

1995 (47.58) (83.91) to (11.25) (3.84) (4.51) to (3.17) (29.45) (30.78) to 

(28.13) 

(18.21) (20.80) to (15.61) 
A-297

1996 (54.99) (83.65) to (26.32) (4.55) (5.24) to (3.85) 4.25 3.00 to 5.50 (15.19) (17.78) to (12.60) 

1997 (54.08) (81.53) to (26.63) (4.21) (4.94) to (3.48) (20.44) (21.73) to 

(19.14) 

(19.90) (22.64) to (17.16) 

1998 (44.34) (77.06) to (11.61) (10.77) (11.60) to (9.94) 1.46 2.49 to 0.43 (17.33) (20.33) to (14.33) 

1999 (29.72) (58.21) to (1.23) (3.30) (4.02) to (2.59) (18.92) 

(19.78) to 

(18.06) (23.95) (26.64) to (21.26) 

2000 (54.83) (85.86) to (23.80) (4.41) (5.15) to (3.67) (55.15) 

(56.04) to 

(54.26) (23.11) (26.26) to (19.97) 

2001 (37.57) (68.05) to (7.08) (2.53) (3.24) to (1.83) (27.34) 

(28.17) to 

(26.51) (24.03) (27.01) to (21.05) 

2002 (36.14) (67.43) to (4.85) (1.97) (2.68) to (1.25) (46.81) 

(47.62) to 

(46.00) (22.72) (25.70) to (19.73) 

2003 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.65) 

(12.50) to 

(10.79) (20.69) (23.50) to (17.89) 

2004 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.49) 

(12.34) to 

(10.64) (20.51) (23.31) to (17.70) 

2005 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.32) 

(12.17) to 

(10.47) (20.31) (23.11) to (17.50) 

2006 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (11.14) 

(11.99) to 

(10.29) (20.10) (22.90) to (17.30) 

2007 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.97) 

(11.82) to 

(10.12) (19.90) (22.70) to (17.09) 

2008 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.79) (11.65) to (9.94) (19.69) (22.49) to (16.89) 

2009 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (10.62) (11.47) to (9.77) (19.48) (22.29) to (16.68) 

Note: Does not include the change in storage resulting from the annual application of sewage sludge, or the additional Conservation Reserve 

Program enrollment. 

Step 3: Estimate C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 

Approach, in Addition to CO2 Emissions from Agricultural Lands on Drained Organic Soils 

Mineral and organic soil calculations were made for each climate by soil zone across the United States. Mineral 

stock values were derived for non-major crop rotations and land converted from non-agricultural uses to cropland in 1982, 

1992, and 1997 based on the land-use and management activity data in conjunction with appropriate reference C stocks, 

land-use change, tillage, input, and wetland restoration factors. C losses from organic soils were computed based on 1992 

and 1997 land use and management in conjunction with the appropriate C loss rate. Each input to the inventory 

calculations for the Tier 2 approach had some level of uncertainty that was quantified in PDFs, including the land-use and 

management activity data, reference C stocks, and management factors. A Monte Carlo Analysis was used to quantify 

uncertainty in SOC change for the inventory period based on uncertainty in the inputs. Input values were randomly 

selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent confidence 

interval for the inventory results. 

Step 3a: Derive Mineral Soil Stock Change and Organic Soil Emission Factors 

Stock change factors representative of U.S. conditions were estimated from published studies (Ogle et al. 2003, 

Ogle et al. 2006). The numerical factors quantify the impact of changing land use and management on SOC storage in 

mineral soils, including tillage practices, cropping rotation or intensification, and land conversions between cultivated and 

native conditions (including set-asides in the Conservation Reserve Program), as well as the net loss of SOC from organic 

soils attributed to agricultural production on drained soils. Studies from the United States and Canada were used in this 

analysis under the assumption that they would best represent management impacts for the Inventory. 

For mineral soils, studies had to report SOC stocks (or information to compute stocks), depth of sampling, and 

the number of years since a management change to be included in the analysis. The data were analyzed using linear 

mixed-effect modeling, accounting for both fixed and random effects. Fixed effects included depth, number of years since 

a management change, climate, and the type of management change (e.g., reduced tillage vs. no-till). For depth 

increments, the data were not aggregated for the C stock measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 

10-30 cm) was included as a separate point in the dataset. Similarly, time series data were not aggregated in these 

datasets. Consequently, random effects were needed to account for the dependence in time series data and the dependence 

among data points representing different depth increments from the same study. Factors were estimated for the effect of 

management practices at 20 years for the top 30 cm of the soil (Table A-237). Variance was calculated for each of the 

U.S. factor values, and used to construct PDFs with a normal density. In the IPCC method, specific factor values are 

given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which 
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

influences the C balance of soils. Specifically, higher stocks are associated with increased productivity and C inputs 

(relative to native grassland) on improved grassland with both medium and high input.72 Organic amendments in annual 

cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in rice cultivation are 

associated with reduced decomposition due to periodic flooding. There were insufficient field studies to derive factor 

values for these systems from the published literature, and, thus, estimates from IPCC (2003) were used under the 

assumption that they would best approximate the impacts, given the lack of sufficient data to derive U.S.-specific factors. 

A measure of uncertainty was provided for these factors in IPCC (2003), which was used to construct PDFs. 

Table A-237: Soil Organic Carbon Stock Change Factors for the United States and the IPCC Default Values Associated with 

Management Impacts on Mineral Soils 

 U.S. Factor 

IPCC 

default 

Warm Moist 

Climate 

Warm Dry 

Climate 

Cool Moist 

Climate 

Cool Dry 

Climate 

Land-Use Change Factors 

 Cultivateda 1 1 1 1 1 

 General Uncult.a,b (n=251) 1.4 1.42±0.06 1.37±0.05 1.24±0.06 1.20±0.06 

 Set-Asidea (n=142) 1.25 1.31±0.06 1.26±0.04 1.14±0.06 1.10±0.05 

Improved Grassland Factorsc

 

 Medium Input 1.1 1.14±0.06 1.14±0.06 1.14±0.06 1.14±0.06 

 High Input Na 1.11±0.04 1.11±0.04 1.11±0.04 1.11±0.04 

Wetland Rice Production Factorc

 1.1 1.1 1.1 1.1 1.1 

Tillage Factors 

 Conv. Till 1 1 1 1 1 

 Red. Till (n=93) 1.05 1.08±0.03 1.01±0.03 1.08±0.03 1.01±0.03 

 No-till (n=212) 1.1 1.13±0.02 1.05±0.03 1.13±0.02 1.05±0.03 

Cropland Input Factors 

 Low (n=85) 0.9 0.94±0.01 0.94±0.01 0.94±0.01 0.94±0.01 

 Medium 1 1 1 1 1 

 High (n=22) 1.1 1.07±0.02 1.07±0.02 1.07±0.02 1.07±0.02 

 High with amendmentc

 1.2 1.38±0.06 1.34±0.08 1.38±0.06 1.34±0.08 

Note: The “n” values refer to sample size. 

a Factors in the IPCC documentation (IPCC 2006) were converted to represent changes in SOC storage from a cultivated condition rather than a 

native condition. 

b Default factor was higher for aquic soils at 1.7. The U.S. analysis showed no significant differences between aquic and non-aquic soils, so a 

single U.S. factor was estimated for all soil types. 

c U.S.-specific factors were not estimated for land improvements, rice production, or high input with amendment because of few studies 

addressing the impact of legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland 

rice production in the US. Factors provided in IPCC (2003) were used as the best estimates of these impacts. 

Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to 

higher water tables and inundation of the soil for at least part of the year. A stock change factor was estimated assessing 

the difference in SOC storage between restored and unrestored wetlands enrolled in the Conservation Reserve Program 

(Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the first 10 years (Table A238). A PDF with a normal density was constructed from these data based on results from a linear regression model. 

Following the initial increase of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The 

mass accumulation rate of organic C was estimated using annual sedimentation rates (cm/yr) in combination with percent 

organic C, and soil bulk density (g/cm3

) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate 

are described in Dean and Gorham (1998); the resulting rate and variance were used to construct a PDF with a normal 

density (Table A-238). 

Table A-238: Factor Estimate for the Initial and Subsequent Increase in Organic Soil C Following Wetland Restoration of 

Conservation Reserve Program 

Variable Value 

Factor (Initial Increase—First 10 Years) 1.22±0.18

Mass Accumulation (After Initial 10 Years) 0.79±0.05 Mg C/ha-yr

Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002). 

In addition, C loss rates were estimated for cultivated organic soils based on subsidence studies in the United 

States and Canada (Table A-239). PDFs were constructed as normal densities based on the mean C loss rates and 

associated variances. 

 

72 Improved grasslands are identified in the 1997 National Resources Inventory as grasslands that were irrigated or seeded with legumes, 

in addition to those reclassified as improved with manure amendments. 
A-299

Table A-239: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC Default 

Rates (Metric Ton C/ha-yr) 

 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 20 14.0±3.3 5 3.5±0.8a

a There were not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values 

for cropland, which was an assumption used for the IPCC default organic soil C losses on grassland. 

Step 3b: Estimate Annual Changes in Mineral Soil Organic C Stocks and CO2 Emissions from Organic Soils 

In accordance with IPCC methodology, annual changes in mineral soil C were calculated by subtracting the 

beginning stock from the ending stock and then dividing by 20.73 For this analysis, the base inventory estimate for 1990 

through 1992 is the annual average of 1992 stock minus the 1982 stock. The annual average change between 1993 and 

2009 is the difference between the 1997 and 1992 C stocks. Using the Monte Carlo approach, SOC stock change for 

mineral soils was estimated 50,000 times between 1982 and 1992, and between 1992 and 1997. From the final 

distribution of 50,000 values, a 95 percent confidence interval was generated based on the simulated values at the 2.5 and 

97.5 percentiles in the distribution (Ogle et al. 2003). 

For organic soils, annual losses of CO2 were estimated for 1992 and 1997 by applying the Monte Carlo approach 

to 1992 and 1997 land-use data in the United States. The results for 1992 were applied to the years 1990 through 1992, 

and the results for 1997 were applied to the years 1993 through 2009. 

 Mineral soils for the land base estimated with the Tier 2 approach accumulated about 1.7 to 3.0 Tg CO2 Eq. 

annually in Cropland Remaining Cropland, while mineral soils in Land Converted to Cropland lost C at a rate of about 

4.1 to 4.2 Tg CO2 Eq. annually. Minerals soils in Grassland Remaining Grassland had small gains of about 0.2 to 0.3 Tg 

CO2 Eq. annually and sequestered from 4.6 to 5.0 Tg CO2 Eq. annually in Land Converted to Grassland. Organic soils lost 

about 27.4 to 27.7 Tg CO2 Eq. annually in Cropland Remaining Cropland and 2.4 to 2.6 Tg CO2 Eq. annually in Land 

Converted to Cropland, as well as an additional 3.7 to 3.9 Tg CO2 Eq. annually in Grassland Remaining Grassland (Table 

A-240) and 0.5 to 0.9 Tg CO2 Eq. annually in Land Converted to Grassland. Estimates and uncertainties are provided in 

Table A-240. 

Table A-240: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Estimated with 

the Tier 2 Analysis using U.S. Factor Values, Reference Carbon Stocks, and Carbon Loss Rates (Tg CO2 Eq./yr) 

Year 

Cropland Remaining 

Cropland 

Land Converted to 

Cropland 

Grassland Remaining 

Grassland* 

Land Converted to 

Grassland* 

Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI 

Mineral Soils 

 1990-1992 (1.65) (2.6) to 5.8 4.18 2.5 to 6.0 (0.33) (0.6) to (0.1) (4.55) (6.5) to (2.7) 

 1993-2009 (3.01) (6.9) to 0.8 4.14 2.5 to 6.0 (0.15) (0.4) to 0.04 (4.99) (7.2) to (2.9) 

Organic 

Soils 

 

 1990-1992 27.43 18.3 to 39.4 2.42 1.4 to 3.8 3.85 1.97 to 6.4 0.47 0.22 to 0.8 

 1993-2009 27.68 

18.5 to 

39.5 2.64 1.5 to 4.0 3.69 1.9 to 6.1 0.88 0.4 to 1.5 *

 Preliminary estimates that will be finalized after public review period following completion of quality control measures. 

Step 4: Compute Additional Changes in Soil Organic C Stocks Due to Organic Amendments and CRP 

Enrollment after 2003 

There are two additional land-use and management activities in U.S. agricultural lands that were not estimated in 

Steps 2 and 3. The first activity involved the application of sewage sludge to agricultural lands. Minimal data exist on 

where and how much sewage sludge is applied to U.S. agricultural soils, but national estimates of mineral soil land area 

receiving sewage sludge can be approximated based on sewage sludge N production data, and the assumption that 

amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). It was assumed that 

sewage sludge for agricultural land application was applied to grassland because of the high heavy metal content and other 

pollutants found in human waste, which limits its application to crops. The impact of organic amendments on SOC was 

 

73 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period. 
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

calculated as 0.38 metric tonnes C/ha-yr. This rate is based on the IPCC default method and country-specific factors (see 

Table A- 241), by calculating the effect of converting nominal, medium-input grassland to high input improved grassland 

(assuming a reference C stock of 50 metric tonnes C/ha, which represents a mid-range value for the dominant cropland 

soils in the United States, the land use factor for grassland (1.4) and the country-specific factor of 1.11 for high input 

improved grassland, with the change in stocks occurring over a 20 year (default value) time period; i.e., [50 × 1.4 × 1.11 – 

50 × 1.4] / 20 = 0.38). From 1990 through 2009, sewage sludge applications in agricultural lands increased SOC storage 

from 0.6 to 1.3 Tg CO2 Eq./year (Table A- 241). A nominal ±50 percent uncertainty was attached to these estimates due 

to limited information on application and the rate of change in soil C stock change with sewage sludge amendments. 

The second activity was the change in enrollment for the Conservation Reserve Program after 2003 for mineral 

soils. Relative to the enrollment in 2003, the total area in the Conservation Reserve Program decreased from 2004 to 

2009, leading to a reduction in enrollment of 0.16 million ha over the five-year period (USDA-FSA 2009). An average 

annual change in SOC of 0.5 metric tonnes C/ha-yr was used to estimate the effect of the enrollment changes. This rate is 

based on the IPCC default method and country-specific factors (see Table A-237) by calculating the impact of setting 

aside a medium input cropping system in the Conservation Reserve Program (assuming a reference C stock of 50 metric 

tonnes C/ha, which represents a mid-range value for the dominant cropland soils in the United States and the average 

country-specific factor of 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 yr 

(default value) time period; i.e., [50 × 1.2 – 50] / 20 = 0.5). While increases in enrollment from 2004 to 2009 generated 

additional accumulation of CO2 Eq. annually, reductions in enrollment in 2009 caused emissions of 0.29 Tg CO2 Eq 

(Table A-242). A nominal ±50 percent uncertainty was also attached to these estimates due to limited information about 

the enrollment trends at subregional scales, which creates uncertainty in the rate of the soil C stock change (stock change 

factors for set-aside lands vary by climate region). 

Step 5: Compute Net CO2 Emissions and Removals from Agricultural Lands 

The sum of total CO2 emissions and removals from the Tier 3 Century Model Approach (Step 2), Tier 2 IPCC 

Methods (Step 3) and additional land-use and management considerations (Step 4) are presented in Table A-242. Overall, 

there was a net accumulation of 99.2 Tg CO2 Eq. in 1990 for agricultural soils, and this rate had decreased by the end of 

the reporting period in 2009 to 43.4 Tg CO2 Eq. 

The total stock change (as seen in the Land Use, Land-Use Change, and Forestry chapter) as well as per hectare 

rate of change varies among the states (Figure A-17 and Figure A-18). On a per hectare basis, the highest rates of C 

accumulation occurred in the Northeast, Midwest, northern Great Plains, and Northwest. The states with highest total 

amounts of C sequestration were Iowa, Illinois, Missouri, Montana, Oklahoma, North Dakota, and South Dakota (Table 

A- 243). For organic soils, emission rates were highest in the regions that contain the majority of the drained organic 

soils, including California, Florida, Michigan, Minnesota, and New York. On a per hectare basis, the emission rate 

patterns were very similar to the total emissions in each state, with the highest rates in those regions with warmer climates 

and a larger proportion of the drained organic soil managed for crop production. 

Figure A-17: Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2009 

Figure A-18: Net C Stock Change, per Hectare, for Organic Soils Under Agricultural Management, 2009 
Table A- 241: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of Sewage Sludge to Mineral So

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 

Sewage Sludge N Applied 

to Agricultural Land (Mg N)a 52,198 55,658 59,250 62,977 65,966 69,001 72,081 75,195 78,353 80,932 83,523 86,124 88,736 91,358 93,991 

Assimilative Capacity (Mg 

N/ha)b 0.120 0.120 0.120 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 

Area covered by Available 

Sewage Sludge N (ha)c 434,985 463,816 493,746 516,202 540,707 565,583 590,828 616,357 642,240 663,381 684,612 705,932 727,341 748,836 770,418 

Average Annual Rate of C 

storage (Mg C/ha-yr)d 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 

Contribution to Soil C 

(TgCO2/yr)e,f (0.61) (0.65) (0.69) (0.72) (0.75) (0.79) (0.82) (0.86) (0.89) (0.92) (0.95) (0.98) (1.01) (1.04) (1.07) 

Values in parentheses indicate net C storage. a N applied to soils described in Step 1d. b Assimilative Capacity is the national average amount of manure-derived N that can be applied on cropland without buildup of nutrients in the soil (Kellogg et al., 2000). c Area covered by sewage sludge N available for application to soils is the available N applied at the assimilative capacity rate. The 1992 assimilative capacity rate was applied to 199

applied to 1993-2009. 

d Annual rate of C storage based on national average increase in C storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr) e Contribution to Soil C is estimated as the product of the area covered by the available sewage sludge N and the average annual C storage attributed to an organic matter amendment. f Some small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands. 

Table A-242: Annual Soil C Stock Change in Cropland Remaining Cropland (CRC), Land Converted to Cropland (LCC), Grassland Remaining 

Converted to Grassland (LCG), in U.S. Agricultural Soils (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Net emissions based on Tier 3 Century-based analysis (Step 2)  CRC (55.2) (57.5) (68.0) (64.2) (62.4) (47.6) (55.0) (54.1) (44.3) (29.7) (54.8) (37.6) (36.1) (42.3) (42.3) (42.3) (42.3)  LCC (4.4) (4.3) (4.8) (8.7) (12.6) (3.8) (4.5) (4.2) (10.8) (3.3) (4.4) (2.5) (2.0) (0.8) (0.8) (0.8) (0.8)  GRG (55.1) (28.0) (9.8) (1.5) (66.7) (29.5) 4.2 (20.4) 1.5 (18.9) (55.2) (27.3) (46.8) (11.7) (11.5) (11.3) (11.1) 

 LCG (15.8) (15.1) (13.8) (13.5) (17.9) (18.2) (15.2) (19.9) (17.3) (24.0) (23.1) (24.0) (22.7) (20.7) (20.5) (20.3) (20.1) 

Net emissions based on the IPCC Tier 2 analysis (Step 3) 

Mineral Soils 

CRC (1.6) (1.6) (1.6) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) 

LCC 4.2 4.2 4.2 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 

GRG (0.3) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) LCG (4.5) (4.5) (4.5) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) 

Organic Soils 

CRC 27.4 27.4 27.4 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 LCC 2.4 2.4 2.4 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 GRG 3.9 3.9 3.9 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 LCG 0.5 0.5 0.5 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 

Additional changes in net emissions from mineral soils based on application of sewage sludge to agricultural land (Step 4) 

GRG (0.6) (0.6) (0.7) (0.7) (0.8) (0.8) (0.8) (0.9) (0.9) (0.9) (1.0) (1.0) (1.0) (1.0) (1.1) (1.1) (1.2) 

Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4) 

CRC - - - - - - - - - - - - - - (0.4) (0.6) (1.4) 

Total Stock Changes by Land Use/Land-Use Change Category (Step 5) 
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

CRC (29.4) (31.7) (42.3) (39.6) (37.7) (22.9) (30.3) (29.4) (19.7) (5.1) (30.2) (12.9) (11.5) (17.7) (18.1) (18.3) (19.1) 

LCC 2.2 2.3 1.8 (1.9) (5.8) 2.9 2.2 2.6 (4.0) 3.5 2.4 4.2 4.8 5.9 5.9 5.9 5.9 GRG (52.2) (25.2) (7.0) 1.3 (64.0) (26.7) 7.0 (17.8) 4.1 (16.3) (52.6) (24.8) (44.3) (9.2) (9.0) (8.9) (8.8) LCG (19.8) (19.1) (17.9) (17.6) (22.0) (22.3) (19.3) (24.0) (21.4) (28.0) (27.2) (28.1) (26.8) (24.8) (24.6) (24.4) (24.2) 

Total * (99.2) (73.7) (65.3) (57.8) (129.5) (69.0) (40.4) (68.6) (41.0) (46.0) (107.6) (61.6) (77.8) (45.7) (45.8) (45.6) (46.1) 
A-303

Table A- 243: Soil C Stock Change for Mineral and Organic Soils during 2009 within individual states (Tg CO2 Eq.)) 

State Mineral Soil Organic Soil Total 

AL (0.45) - (0.45) 

AR (1.09) - (1.09) 

AZ (0.80) - (0. 80) 

CA (0.16) 2.29 2.14 

CO (0.54) 0.00 (0.54) 

CT (0.06) - (0.06) 

DE 0.02 - 0.02 

FL 0.26 10.84 11.10 

GA (0.21) - (0.21) 

HI (0.02) 0.25 0.24 

IA (4.67) 0.75 (3.92) 

ID (1.53) 0.11 (1.43) 

IL (5.16) 0.54 (4.61) 

IN (1.72) 2.93 1.21 

KS (2.34) - (2.34) 

KY (1.90) - (1.90) 

LA (0.93) 0.07 (0.86) 

MA (0.02) 0.03 0.01 

MD (0.19) 0.03 (0.16) 

ME (0.17) - (0.17) 

MI (2.28) 2.72 0.43 

MN (2.57) 7.30 4.73 

MO (9.54) - (9.54) 

MS (1.19) 0.00 (1.18) 

MT (6.10) 0.11 (5.99) 

NC (0.35) 2.25 1.90 

ND (6.03) - (6.03) 

NE (2.03) - (2.03) 

NH (0.04) 0.01 (0.03) 

NJ (0.09) 0.01 (0.08) 

NM (1.18) - (1.18) 

NV (0.23) 0.00 (0.23) 

NY (1.83) 0.61 (1.23) 

OH (2.91) 0.42 (2.49) 

OK (6.48) - (6.48) 

OR (2.13) 0.12 (2.00) 

PA (1.85) 0.01 (1.84) 

RI (0.01) 0.00 (0.00) 

SC 0.05 0.04 0.09 

SD (5.86) - (5.86) 

TN (1.85) - (1.85) 

TX 5.46 - 5.46 

UT (0.02) - (0.02) 

VA (0.50) 0.02 (0.48) 

VT (0.28) 0.00 (0.27) 

WA (2.19) 0.26 (1.93) 

WI (2.48) 2.88 0.41 

WV (0.41) - (0.41) 

WY 0.17 0.01 0.18 

Note: Parentheses indicate net C accumulation. Estimates do not include soil C stock change associated with CRP enrollment after 2003 or 

sewage sludge application to soils, which were only estimated at the national scale. The sum of state results will not match the national results 

because state results are generated in a separate programming package, the sewage sludge and CRP enrollment after 2003 are not included, and 

differences arise due to rounding of values in this table. 
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

3.14. Methodology for Estimating CH4 Emissions from Landfills 

Landfill gas is a mixture of substances generated when bacteria decompose the organic materials contained in 

solid waste. By volume, landfill gas is about half CH4 and half CO2.

74 The amount and rate of CH4 generation depends 

upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment. 

Not all CH4 generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared 

or utilized for energy, thus oxidizing to CO2 during combustion. Of the remaining CH4, a portion oxidizes to CO2 as it 

travels through the top layer of the landfill cover. In general, landfill-related CO2 emissions are of biogenic origin and 

primarily result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.75To 

estimate the amount of CH4 produced in a landfill in a given year, information is needed on the type and quantity of waste 

in the landfill, as well as the landfill characteristics (e.g., size, aridity, waste density). This information is not available for 

the majority landfills in the United States. Consequently, to estimate CH4 generation, a methodology was developed based 

on the quantity of waste placed in landfills nationwide each year, the first order decay model, and model parameters from 

the analysis of measured CH4 generation rates for U.S. landfills with gas recovery systems. 

From various studies and surveys of the generation and disposal of solid waste, estimates of the amount of waste 

placed in MSW and industrial landfills were developed. A database of measured CH4 generation rates at landfills with gas 

recovery systems was compiled and analyzed. The results of this analysis and other studies were used to develop an 

estimate of the CH4 generation potential for use in the first order decay model. In addition, the analysis and other studies 

provided estimates of the CH4 generation rate constant as a function of precipitation. The first order decay model was 

applied to annual waste disposal estimates for each year and for three ranges of precipitation to estimate CH4 generation 

rates nationwide for the years of interest. Based on the organic content of industrial wastes and the estimates of the 

fraction of these wastes sent to industrial landfills, CH4 emissions from industrial landfills were also estimated using the 

first order decay model. Total CH4 emissions were estimated by adding the CH4 from MSW and industrial landfills and 

subtracting the amounts recovered for energy or flaring and the amount oxidized in the soil. The steps taken to estimate 

CH4 emissions from U.S. landfills for the years 1990 through 2009 are discussed in greater detail below. 

Figure A-19 presents the CH4 emissions process—from waste generation to emissions—in graphical format. 

Step 1: Estimate Annual Quantities of Solid Waste Placed in Landfills 

For 1989 to 2009, estimates of the annual quantity of waste placed in MSW landfills were developed from a 

survey of State agencies as reported in BioCycle’s State of Garbage in America (BioCycle 2008), adjusted to include U.S. 

territories.76 The BioCycle survey is the only continually updated nationwide survey of waste disposed in landfills in the 

United States. 

Table A-244 shows estimates of waste quantities contributing to CH4 emissions. The table shows BioCycle 

estimates of total waste landfilled each year from 1990 through 2000, 2002, 2004, and 2006 adjusted for U.S. territories. 

A linear interpolation was used for 2001, 2003, 2005, 2007, 2008, and 2009 because there are no BioCycle surveys for 

those years. The next BioCycle survey will be published in 2011 representing 2008 data, at which time, the waste 

landfilled for 2007, 2008, and 2009 will be updated. 

Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 

Table A-244: Solid Waste in MSW Landfills Contributing to CH4 Emissions (Tg unless otherwise noted) 

Description 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Total Waste Generateda 271 302 377 416 455 462 470 459 448 452 456 460

Percent of Wastes Landfilleda 77% 63% 61% 63% 66% 65% 64% 64% 65% 65% 65% 65%

 

74 Typically, landfill gas also contains small amounts of nitrogen, oxygen, and hydrogen, less than 1 percent nonmethane volatile

organic compounds (NMVOCs), and trace amounts of inorganic compounds. 

75 See Box 8-1 “Biogenic Emissions and Sinks of Carbon” in the Waste chapter for additional background on how biogenic emissions of 

landfill CO2 are addressed in the U.S. Inventory. 

76 Since the BioCycle survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using population data for 

the U.S territories (U.S. Census Bureau 2010) and the per capita rate for waste landfilled from BioCycle (2008). 
A-305

Total Wastes Landfilleda 209 190 230 263 298 300 301 285 289 291 294 297

Waste in Place (30 years)b

 4,674 5,075 5,400 5,488 5,608 5,759 5,909 6,058 6,198 6,328 6,458 6,587

Waste Contributing to 

Emissionsc

 6,815 7,796 8,828 9,092 9,390 9,690 9,991 10,286 10,574 10,866 11,160 11,457 a

 Source: BioCycle (2008), adjusted for missing U.S. territories using U.S. Census Bureau (2010) population data and per capita disposal rate 

from BioCycle. The data, originally reported in short tons, are converted to metric tons. Estimates shown for 2001 and 2003 are based on an 

interpolation because there were no surveys in 2001 and 2003; estimates shown for 2005, 2007, 2008, and 2009 based on the increase in 

population. 

b

 This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the CH4 generation. Values 

are based on EPA (1993). 

c

 This estimate represents the cumulative amount of waste that has been placed in landfills from 1940 to the year indicated and is the sum of the 

annual disposal rates used in the first order decay model. Values are based on EPA (1993). 

Estimates of the annual quantity of waste placed in landfills from 1960 through 1988 were developed from 

EPA’s 1993 Report to Congress (EPA 1993) and a 1986 survey of MSW landfills (EPA 1988). Based on the national 

survey and estimates of the growth of commercial, residential and other wastes, the annual quantity of waste placed in 

landfills averaged 127 million metric tons in the 1960s, 154 million metric tons in the 1970s, 190 million metric tons in the 

1990s, and 285 million metric tons in the 2000’s. Estimates of waste placed in landfills in the 1940s and 1950s were 

developed based on U.S. population for each year and the per capital disposal rates from the 1960s. 

Step 2: Estimate CH4 Generation at Municipal Solid Waste Landfills 

The CH4 generation was estimated from the integrated form of the first order decay (FOD) model using the 

procedures and spreadsheets from IPCC (2006) for estimating CH4 emissions from solid waste disposal. The form of the 

FOD model that was applied incorporates a time delay of 6 months after waste disposal before the generation of CH4

begins. 

The input parameters needed for the FOD model equations are the mass of waste disposed each year, which was 

discussed in the previous section, degradable organic carbon (DOC), and the decay rate constant (k). The DOC is 

determined from the CH4 generation potential (L0 in m3

 CH4/Mg waste), which is discussed in more detail in subsequent 

paragraphs, and the following equation: 

DOC = [L0 × 6.74 × 10-4] ÷ [F × 16/12 × DOCf × MCF] 

Where, 

DOC = degradable organic carbon (fraction, Gg C/Gg waste), 

L0 = CH4 generation potential (m3

 CH4/Mg waste), 

6.74 × 10-4 = CH4 density (Mg/m3), 

F = fraction of CH4 by volume in generated landfill gas (equal to 0.5) 

16/12 = molecular weight ratio CH4/C, 

DOCf = fraction of DOC that can decompose in the anaerobic conditions in the landfill (fraction equal 

to 0.5 for MSW), and 

MCF = methane correction factor for year of disposal (fraction equal to 1 for anaerobic managed sites). 

The DOC value used in the CH4 generation estimates from MSW landfills is 0.203 based on the CH4 generation 

potential of 100 m3 CH4/Mg waste as described below. Data from a set of 52 representative landfills across the U.S. in 

different precipitation ranges were chosen to evaluate Lo, and ultimately the country-specific DOC value. The 2004 

Chartwell Municipal Solid Waste Facility Directory confirmed that each of the 52 landfills chosen accepted or accepts 

both MSW and construction and demolition (C&D) waste (Chartwell 2004; RTI 2009). 

The methane generation potential (Lo) varies with the amount of organic content of the waste material. A higher 

Lo occurrs with a higher content of organic waste. Waste composition data is not collected for all landfills nationwide; thus 

a default value must be used. Values for Lo were evaluated from landfill gas recovery data for this set of 52 landfills, 

which resulted in a best fit value for Lo of 99 m3/Mg of waste (RTI 2004). This value compares favorably with a range of 

50 to 162 (midrange of 106) m3

/Mg presented by Peer, Thorneloe, and Epperson (1993); a range of 87 to 91 m3/Mg from a 

detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America (SWANA 1998); and a value 

of 100 m3

/Mg recommended in EPA’s compilation of emission factors (EPA 1998; EPA 2008) based on data from 21 

landfills. Based on the results from these studies, a value of 100 m3

/Mg appears to be a reasonable best estimate to use in 

the FOD model for the national inventory. 
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

The FOD model was applied to the gas recovery data for the 52 landfills to calculate the rate constant (k) directly 

for L0 = 100 m3/Mg. The rate constant was found to increase with annual average precipitation; consequently, average 

values of k were developed for three ranges of precipitation, shown in Table A- 245 and recommended in EPA’s 

compilation of emission factors (EPA 2008). 

Table A- 245. Average Values for Rate Constant (k) by Precipitation Range (yr-1) 

Precipitation range (inches/year) k (yr-1) 

<20 0.020 

20-40 0.038 

>40 0.057 

These values for k show reasonable agreement with the results of other studies. For example, EPA’s compilation 

of emission factors (EPA 1998; EPA, 2008) recommends a value of 0.02 yr-1 for arid areas (less than 20 inches/year of 

precipitation) and 0.04 yr-1 for non-arid areas. The SWANA study of 18 landfills reported a range in values of k from 0.03 

to 0.06 yr-1 based on CH4 recovery data collected generally in the time frame of 1986 to 1995. 

Using data collected primarily for the year 2000, the distribution of waste in place versus precipitation was 

developed from over 400 landfills (RTI 2004). A distribution was also developed for population vs. precipitation for 

comparison. The two distributions were very similar and indicated that population in areas or regions with a given 

precipitation range was a reasonable proxy for waste landfilled in regions with the same range of precipitation. Using U.S. 

Census data and rainfall data, the distributions of population versus rainfall were developed for each Census decade from 

1950 through 2000. The distributions showed that the U.S. population has shifted to more arid areas over the past several 

decades. Consequently, the population distribution was used to apportion the waste landfilled in each decade according to 

the precipitation ranges developed for k, as shown in Table A-246. 

Table A-246. Percent of U.S. Population within Precipitation Ranges (%) 

Precipitation Range (inches/year) 1950 1960 1970 1980 1990 2000 

<20 11 13 14 16 19 20 

20-40 40 39 38 36 34 33 

>40 49 48 48 48 47 47 

Source: RTI (2004) using population data from the U.S. Bureau of Census and precipitation data from the National Climatic Data Center’s 

National Oceanic and Atmospheric Administration. 

In developing the Inventory, the proportion of waste disposed of in managed landfills versus open dumps prior to 

1980 was re-evaluated. Based on the historical data presented by Mintz et al. (2003), a timeline was developed for the 

transition from the use of open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, it 

was estimated that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94 

percent was managed in open dumps. Between 1940 and 1980, the fraction of waste land disposed transitioned towards 

managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For wastes disposed of in 

dumps, a methane correction factor (MCF) of 0.6 was used based on the recommended IPCC default value for 

uncharacterized land disposal (IPCC 2006); this MCF is equivalent to assuming 50 percent of the open dumps are deep 

and 50 percent are shallow. The recommended IPCC default value for the MCF for managed landfills of 1 was used for 

the managed landfills (IPCC 2006). 

Step 3: Estimate CH4 Generation at Industrial Landfills 

Industrial landfills receive waste from factories, processing plants, and other manufacturing activities. In 

national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven 

percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). For the 1990 

through 2007 and current inventories, the methodology was updated and improved by using activity factors (industrial 

production levels) to estimate the amount of industrial waste landfilled each year and by applying the FOD model to 

estimate CH4 generation. A nationwide survey of industrial waste landfills found that over 99 percent of the organic waste 

placed in industrial landfills originated from two industries: food processing (meat, vegetables, fruits) and pulp and paper 

(EPA 1993). Data for annual nationwide production for the food processing and pulp and paper industries were taken 

from industry and government sources for recent years; estimates were developed for production for the earlier years for 

which data were not available. For the pulp and paper industry, production data published by the Lockwood-Post’s 

Directory (ERG 2010) and U.S. Department of Agriculture (2010) were the primary sources for years 1965 through 2009. 

An extrapolation based on U.S. real gross domestic product was used for years 1940 through 1964. For the food 

processing industry, production levels were obtained or developed from the U.S. Department of Agriculture (2010) for the 
A-307

years 1990 through 2009 (ERG 2010). An extrapolation based on U.S. population was used for the years 1940 through 

1989. 

In addition to production data for the pulp and paper and food processing industries, the following inputs were 

needed to use the FOD model for estimating CH4 generation from industrial landfills: 1) quantity of waste that is disposed 

in industrial landfills (as a function of production), 2) CH4 generation potential (L0) or DOC, and 3) FOD decay constant 

(k). Research into waste generation and disposal in landfills for the pulp and paper industry indicated that the quantity of 

waste landfilled was about 0.050 Mg/Mg of product compared to 0.046 Mg/Mg product for the food processing industry 

(Weitz and Bahner 2006). These factors were applied to estimates of annual production to estimate annual waste disposal 

in landfills. Estimates for DOC were derived from available data (Kraft and Orender, 1993; NCASI 2008; Flores et al. 

1999). The DOC value for industrial pulp and paper waste is estimated as 0.20 (Lo of 99 m3

/Mg); the DOC value for 

industrial food waste is estimated as 0.26 (Lo of 128 m3/Mg) (Coburn 2008). Estimates for k were taken from the default 

values in the 2006 IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is 0.19 

yr-1, and the value given for paper waste is 0.06 yr-1. A literature review was conducted for the current inventory year with 

the intent of updating values for Lo and k in the pulp and paper industry. Insufficient data was obtained to warrant a 

change for the current inventory year, but ongoing efforts may result in a U.S. industry-specific value rather than a default 

IPCC value in future inventory years. 

As with MSW landfills, a similar trend in disposal practices from open dumps to managed landfills was expected 

for industrial landfills; therefore, the same time line that was developed for MSW landfills was applied to the industrial 

landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste land disposed transitioned 

from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed landfills in 1980 and on. 

For wastes disposed of in dumps, an MCF of 0.6 was used and for wastes disposed of in managed landfills, an MCF of 1 

was used, based on the recommended IPCC default values (IPCC 2006). 

The parameters discussed above were used in the integrated form of the FOD model to estimate CH4 generation 

from industrial landfills. 

Step 4: Estimate CH4 Emissions Avoided 

The estimate of CH4 emissions avoided (e.g., combusted) was based on landfill-specific data on landfill gas-toenergy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4

emissions avoided. The value for efficiency was selected based on the range of efficiencies (98 to 100 percent) 

recommended for flares in EPA’s “AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4," efficiencies used 

to establish new source performance standards (NSPS) for landfills, and in recommendations for closed flares used in the 

Landfill Methane Outreach Program (LMOP). 

Step 4a: Estimate CH4 Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects 

The quantity of CH4 avoided due to LFGTE systems was estimated based on information from two sources: (1) 

a database maintained by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases 

(EIA 2007) and (2) a database compiled by LMOP (EPA 2009). The EIA database included location information for 

landfills with LFGTE projects, estimates of CH4 reductions, descriptions of the projects, and information on the 

methodology used to determine the CH4 reductions. Generally the CH4 reductions for each reporting year were based on 

the measured amount of landfill gas collected and the percent CH4 in the gas. For the LMOP database, data on landfill gas 

flow and energy generation (i.e., MW capacity) were used to estimate the total direct CH4 emissions avoided due to the 

LFGTE project. Detailed information on the landfill name, owner or operator, city, and state were available for both the 

EIA and LMOP databases; consequently, it was straightforward to identify landfills that were in both databases. The EIA 

database was given priority because reductions were reported for each year and were based on direct measurements. 

Landfills in the LMOP database that were also in the EIA database were dropped to avoid double counting. 

Step 4b: Estimate CH4 Emissions Avoided Through Flaring 

The quantity of CH4 flared was based on data from the EIA database and on information provided by flaring 

equipment vendors. To avoid double-counting, flares associated with landfills in the EIA and LMOP databases were 

excluded from the flare vendor database. As with the LFGTE projects, reductions from flaring landfill gas in the EIA 

database were based on measuring the volume of gas collected and the percent of CH4 in the gas. The information 

provided by the flare vendors included information on the number of flares, flare design flow rates or flare dimensions, 

year of installation, and generally the city and state location of the landfill. When a range of design flare flow rates was 

provided by the flare vendor, the median landfill gas flow rate was used to estimate CH4 recovered from each remaining 
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

flare (i.e., for each flare not associated with a landfill in the EIA or LMOP databases). Several vendors provided 

information on the size of the flare rather than the flare design gas flow rate. To estimate a median flare gas flow rate for 

flares associated with these vendors, the size of the flare was matched with the size and corresponding flow rates provided 

by other vendors. Some flare vendors reported the maximum capacity of the flare. An analysis of flare capacity versus 

measured CH4 flow rates from the EIA database showed that the flares operated at 51 percent of capacity when averaged 

over the time series and at 72 percent of capacity for the highest flow rate for a given year. For those cases when the flare 

vendor supplied maximum capacity, the actual flow was estimated as 50 percent of capacity. Total CH4 avoided through 

flaring from the flare vendor database was estimated by summing the estimates of CH4 recovered by each flare for each 

year. 

Step 4c: Reduce CH4 Emissions Avoided Through Flaring 

As mentioned in Step 4b, flares in the flare vendor database associated with landfills in the EIA and LMOP 

databases were excluded from the flare reduction estimates in the flare vendor database. If comprehensive data on flares 

were available, each LFGTE project in the EIA and LMOP databases would have an identified flare because it is assumed 

that most LFGTE projects have flares. However, given that the flare vendor data only covers approximately 50 to 75 

percent of the flare population, an associated flare was not identified for all LFGTE projects. These LFGTE projects 

likely have flares, yet flares were unable to be identified for one of two reasons: 1) inadequate identifier information in the

flare vendor data; or 2) a lack of the flare in the flare vendor database. For those projects for which a flare was not 

identified due to inadequate information, CH4 avoided would be overestimated, as both the CH4 avoided from flaring and 

the LFGTE project would be counted. To avoid overestimating emissions avoided from flaring, the CH4 avoided from 

LFGTE projects with no identified flares was determined and the flaring estimate from the flare vendor database was 

reduced by this quantity (referred to as a flare correction factor) on a state-by-state basis. This step likely underestimates

CH4 avoided due to flaring but was applied to be conservative in the estimates of CH4 emissions avoided. 

Additional effort was undertaken to improve the methodology behind the flare correction factor for the 1990-

2009 Inventory to reduce the total number of flares in the flare vendor database that were not matched (512) to landfills 

and/or LFGTE projects in the EIA and LMOP databases. Each flare in the flare vendor database not associated with a 

LFGTE project in the EIA or LMOP databases was investigated to determine if it could be matched to either a landfill in 

the EIA database or a LFGTE project in the LMOP database. For some unmatched flares, the location information was 

missing or incorrectly transferred to the flare vendor database. In other instances, the landfill names were slightly different 

between what the flare vendor provided and the actual landfill name as listed in the EIA and LMOP databases. 

It was found that a large majority of the unmatched flares are associated with landfills in LMOP that are 

currently flaring, but are also considering LFGTE. These landfills projects considering a LFGTE project are labeled as 

candidate, potential, or construction in the LMOP database. The flare vendor database was improved to match flares with 

operational, shutdown as well as candidate, potential, and construction LFGTE projects, thereby reducing the total number 

of unidentified flares in the flare vendor database, all of which are used in the flare correction factor. The results of this

effort significantly decreased the number of flares used in the flare correction factor, and consequently, increased 

recovered flare emissions, and decreased net emissions from landfills for the 1990-2009 Inventory. The revised state-bystate flare correction factors were applied to the entire 1990 to 2009 time series for the current Inventory year. 

Step 5: Estimate CH4 Oxidation 

A portion of the CH4 escaping from a landfill oxidizes to CO2 in the top layer of the soil. The amount of 

oxidation depends upon the characteristics of the soil and the environment. For purposes of this analysis, it was assumed 

that of the CH4 generated, minus the amount of gas recovered for flaring or LFGTE projects, 10 percent was oxidized in 

the soil (Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The factor of 10 percent is consistent 

with the value recommended in the 2006 IPCC revised guidelines for managed and covered landfills, and was therefore 

applied to the estimates of CH4 generation minus recovery for both MSW and industrial landfills 

Literature reviews were conducted in 2006 and 2010 to provide recommendations for the most appropriate 

oxidation rate assumptions. It was found that oxidation values are highly variable and range from zero to over 100 percent 

(i.e., the landfill is considered to be an atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric

metane down through the cover). There is considerable uncertainty and variability surrounding estimates of oxidation 

because it is difficult to measure and varies considerably with the thickness and type of the cover material, climate, and the 

presence of cracks/fissures in the cover material through which methane can escape. IPCC (2006) notes that test results 

from field and laboratory studies may lead to over-estimations of oxidation in landfill cover soils, because they largely 

determine oxidation using uniform and homogeneous soil layers. In addition, a number of studies note that gas escapes 
A-309

more readily through the side slopes of a landfill as compared to moving through the cover thus complicating the 

correlation between oxidation and cover type or gas recovery. 

Spokas et al (2005), in particular, helps to illustrate expected patterns (e.g., seasonality of generation, 

effectiveness of gas recovery) associated with landfill methane production and flux through the cover system. This study 

also highlights the large variability in oxidation between and within sites and ultimately reports oxidation ranging between 

4-50 percent. All but one of the test sites had an active gas recovery system in place. For landfills with gas collection 

systems, there have been studies to show that gas recovery increases oxidation because it slows the flux of methane 

through the cover system. Although this may be true, there does not appear to be enough data to support the premise that 

landfills with gas recovery systems increase oxidation. This is demonstrated by the Spokas et al (2005) data where the 

oxidation rates were about the same for a landfill site with and without gas recovery. However, the site also had a thin 

temporary cover so the oxidation would be less than a site with a final cover system. Based on this and other studies, there 

does not appear to be adequate justification for increasing the default oxidation value for landfills with gas recovery. 

Changing the oxidation rate from 10 percent to 20 percent has a minimal impact on the overall inventory results. 

Changing the oxidation rate from 10 percent to 60 percent has a more significant impact on the overall inventory results, 

lowering the overall inventory by as much as 15 to 20 percent. 

The current default oxidation factor of 10 percent is recommended for all landfills in the Inventory until more 

reliable, peer-reviewed data is available about the influence of climate, cover type, and gas recovery is better understood. 

Step 6: Estimate Total CH4 Emissions 

Total CH4 emissions were calculated by adding emissions from MSW and industrial landfills, and subtracting 

CH4 recovered and oxidized, as shown in Table A- 247. 
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Table A- 247: CH4 Emissions from Landfills (Gg) 

Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 20MSW Generation 8,219 9,132 9,854 10,068 10,367 10,754 11,127 11,486 11,813 12,1Industrial Generation 549 611 682 695 710 710 716 724 727 7

Potential Emissions 8,768 9,742 10,536 10,763 11,069 11,464 11,842 12,210 12,540 12,8

Landfill Gas-to-Energy (649) (1,070) (2,352) (2,548) (2,589) (2,563) (2,673) (2,691) (2,807) (3,03Flare (321) (1,298) (2,276) (2,491) (2,738) (2,886) (3,364) (3,566) (3,820) (3,9

Emissions Avoided (970) (2,368) (4,628) (5,039) (5,326) (5,448) (6,037) (6,257) (6,628) (6,95

Oxidation at MSW Landfills (725) (676) (523) (503) (504) (531) (509) (523) (518) (5Oxidation at Industrial Landfills (55) (61) (68) (70) (70) (71) (72) (73) (73) (7

Net Emissions 7,018 6,637 5,317 5,152 5,169 5,414 5,224 5,358 5,321 5,2

Note: Totals may not sum exactly to the last significant figure due to rounding. Note: MSW generation in  represents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation. Note: Parentheses denote negative values. 
A-311

References 

BioCycle (2008) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N. 

Themelis. BioCycle. December 2008. Available online at < http://www.jgpress.com/archives/_free/001782.html> 

Czepiel, P., B. Mosher, P. Crill, and R. Harriss (1996) “Quantifying the Effect of Oxidation on Landfill Methane 

Emissions.” Journal of Geophysical Research, 101(D11):16721-16730. 

Coburn, J. (2008) “Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food 

Industry.” Memorandum to M. Weitz (EPA), October 13, 2008. 

EIA (2007) Voluntary Greenhouse Gas Reports for EIA Form 1605B (Reporting Year 2006). Available online at 

<ftp://ftp.eia.doe.gov/pub/oiaf/1605/cdrom/>. 

Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group. 

EPA (1993) Anthropogenic Methane Emissions in the United States, Estimates for 1990: Report to Congress, U.S. 

Environmental Protection Agency, Office of Air and Radiation. Washington, DC. EPA/430-R-93-003. April 1993. 

EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste 

Landfills. November 1998. 

EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency. 

Washington, DC. EPA/530-SW-88-011. September 1988. 

EPA (2008). Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste 

Landfills. October 2008. 

EPA (2009) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2009. 

ERG (2010). Production Data Supplied by ERG for 1990-2009 for Pulp and Paper, Fruits and Vegetables, and Meat. 

July. 

Flores, R.A., C.W. Shanklin, M. Loza-Garay, S.H. Wie (1999) “Quantification and Characterization of Food Processing 

Wastes/Residues.” Compost Science & Utilization, 7(1): 63-71. 

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories 

Programme, The Intergovernmental Panel on Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. 

Tanabe (eds.). Hayama, Kanagawa, Japan. 

Jensen, J.E.F., and R. Pipatti (2002) “CH4 Emissions from Solid Waste Disposal.” Background paper for the Good 

Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories. 

Kraft, D.L. and H.C. Orender (1993) “Considerations for Using Sludge as a Fuel.” Tappi Journal, 76(3): 175-183. 

Mancinelli, R. and C. McKay (1985) “Methane-Oxidizing Bacteria in Sanitary Landfills.” Proc. First Symposium on 

Biotechnical Advances in Processing Municipal Wastes for Fuels and Chemicals, Minneapolis, MN, 437-450. 

August. 

Mintz C., R. Freed, and M. Walsh (2003) “Timeline of Anaerobic Land Disposal of Solid Waste.” Memorandum to T. 

Wirth (EPA) and K. Skog (USDA), December 31, 2003. 

National Council for Air and Stream Improvement, Inc. (NCASI) (2008) “Calculations Documenting the Greenhouse Gas 

Emissions from the Pulp and Paper Industry.” Memorandum to R. Nicholson (RTI), May 21, 2008. 

Peer, R., S. Thorneloe, and D. Epperson (1993) “A Comparison of Methods for Estimating Global Methane Emissions 

from Landfills.” Chemosphere, 26(1-4):387-400. 

RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills. 

Memorandum to E. Scheehle (EPA), August 26, 2004. 

RTI (2009) GHG Inventory Improvement – Construction & Demolition Waste DOC and Lo Value. Memorandum to R. 

Schmeltz, April 15, 2010. 

Spokas, K.; Bogner, J.; Chanton, J.P.; Morcet, M.; Aran, C.; Graff, C.; Moreau-Le Golvan, Y.; and I. Hebe (2006). 

Methane Mass Balance at Three Landfill Sites: What is the Efficiency of Capture by Gas Collection Systems? Waste 

Management, 26(5): 516-525. 
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2009 

Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane 

Recovery. Publication No. GR-LG 0075. March 1998. 

U.S. Bureau of Census (2010) International Data Base. August 2010. Available online at 

<http://www.census.gov/ipc/www/idb/>. 

Weitz, K. and M. Bahner (2006) “Methane Emissions for Industrial Landfills.” Memorandum to M. Weitz (EPA), 

September 5, 2006. 
Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2009 (Tg CO2 Eq.) 

0 

500 

1,000 

1,500 

2,000 

2,500 

19901991199219931994199519961997199819992000200120022003200420052006200720082009

Tg CO2 Eq.

Passenger Cars/Motorcycles

Light-Duty Trucks

Medium- and Heavy-Duty Trucks

Aircraft

Boats/Ships, Rail, and Pipelines

Mobile AC, Refrig. Transport, Lubricants

Non-Transportation Mobile Sources
Section Name X-7

Figure A-5

Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60

F(temp)

Soil Temperature

0

0.2

0.4

0.6

0.8

1

1.2

F(WFPS)

0 0.2 0.4 0.6 0.8 1

WFPS

0

0.2

0.4

0.6

0.8

1

1.2

F(pH)

3 4 5 6 7 8 9 10

pH

fine

coarse
X-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Figure A-6

Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates

0

5

10

15

20

25

30

35

0 100 200 300 400

N +N 2 O μg N/g soil/day 2

NO3 μg N/g soil

0

5

10

15

20

25

30

35

40

N +N 2 O μg N/g soil/day 2

0 20 40 60 80 100

CO2 μg C/g soil/day

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

Relative Denitrification

WFPS %

 clay-low resp

clay-high resp

loam-low resp

loam-high resp
Figure A-7: DAYCENT model flow diagram 

DEAD PLANT

MATERIAL

METABOLIC

STRUCTURAL

PLANT 

COMPONENTS

LEAVES

 FINE ROOTS

BRANCHES

LARGE WOOD

LARGE

ROOTS

NO3

-

 NH4

+

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

0-15 cm

SOM

ACTIVE

0.5-1 yr

SLOW

10-50 yr

PASSIVE

1000-5000 yr

H2Osoil

Tsoil

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

N GAS

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

C:N

CO2

S,Rh

PPT,V,L

C:N

V

S

C:N

CO2

CO2

CH4

Uptake

Decomp

Rh S

Rh

S

S

NPP

DAYCENT MODEL

S=soil type

V=veg type

L=land use
y = 1.0765x - 0.6936

R2 = 0.9587

y = 0.1688x + 3.6132

R2 = 0.3931

0

5

10

15

20

25

30

35

40

0 10 20 30 40

N2O gN ha-1 day-1

N O gN ha 2 -1 day-1

DAYCENT

IPCC

1-1 line

Linear (DAYCENT)

Linear (IPCC)

Linear (1-1 line)

Figure A-8: Comparisons of Results from DAYCENT Model and IPCC Tier 1 

Method with Measurements of Soil N2O Emissions
Major Crops, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model,

 1990–2009 (Metric Tons CO2 Eq./ha/year)

Figure A-9

Figure A-5: Major Crops, Average Annual Direct N2O Emissions Estimated 

Using the DAYCENT Model, 1990-2009 (Mg CO2 eq/ha/yr)

Metric Tons CO2 Eq./ha/year

< 0.4

0.4 –0.5

0.5 –0.6

>1

0.8–1

0.7 – 0.8

0.6–0.7
Figure A-6: Major Crops, Average Annual N Losses Leading to Indirect N2O Emissions

Estimated Using the DAYCENT Model, 1990-2009 (kg N/ha/yr)

Major Crops, Average Annual N losses Leading to Indirect N2O Emissions Estimated Using the DAYCENT Model, 

1990-2009 (kg N/ha/year)

Figure A-10

kg N/ha/year

60 to 80

40 to 60

20 to 40

10 to 20

< 10

80 to 100

> 100
Figure A-7: Grasslands, Average Annual Direct N2O Emissions Estimated 

Using the DAYCENT Model, 1990-2009 (Mg CO2 eq/ha/yr)

Grasslands, Average Annual Direct N2O Emissions Estimated Using the DAYCENT Model,

 1990–2009 (Metric Tons CO2 Eq./ha/year)

Figure A-11

Metric Tons CO2 Eq./ha/year

< 0.1

0.1–0.2

0.2 –0.3

>0.6

0.5–0.6

0.4 –0.5

0.3 – 0.4
Figure A-8: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions

Estimated Using the DAYCENT Model, 1990-2009 (kg N/ha/yr)

Figure A-12

Grasslands, Average Annual N Losses Leading to Indirect N20 Emissions Estimated Using the DAYCENT Model, 

1990-2009 (kg N/ha/year)

kg N/ha/year

20 to 30

15 to 20

10 to 15

5 to 10

< 5

30 to 50

> 50
X-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Flow diagram of Carbon submodel (A) and Nitrogen submodel (B)

Figure A-13 'MPXEJBHSBNPG$TVCNPEFM	"
BOE/TVCNPEFM	#


'JHVSF

A

B
Section Name X-13

Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century Model

Figure A-14

30

50

70

90

30 50 70 90

Sqrt Modeled Soil Organic Carbon (g C m-2)

Sqrt Modeled Soil Organic Carbon (g C m-2)
X-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Major Land Resource Areas by IPCC Climate Zone

Figure A-15
Section Name X-15

Uncertainty in Data Inputs

Figure A-16

6ODFSUBJOUZJO%BUB*OQVUT

'JHVSF

6ODFSUBJOUZJOEBUBJOQVUT	JFGFSUJMJ[FS
NBOVSFBOEUJMMBHFQSBDUJDFT
BSFFTUJNBUFEVTJOHB.POUF$BSMPQSPDFEVSFXJUISBOEPNESBXTGSPNJOQVU

EBUBQSPCBCJMJUZEJTUSJCVUJPOT
GPSFBDI/3*QPJOUTJNVMBUFE.PEFMVODFSUBJOUZJTFTUJNBUFEUISPVHIBOFNQJSJDBMCBTFEBQQSPBDI6ODFSUBJOUZJOUIF

MBOESFQSFTFOUBUJPOPG/3*JTFTUJNBUFEGSPNUIFTUBUJTUJDTDPNQJMFEGSPNUIF/3*TVSWFZTUPEFUFSNJOFUIFMBOEBSFBFYQBOTJPOGBDUPST
XIJDIBSFVTFE

UPVQTDBMFEBUBUPUIFOBUJPOBMMFWFM
Figure A-17

Net C Stock Change, Per Hectare, For Mineral Soils

Under Agricultural Management, 2009

Note: Values greater than zero represent emissions, and values less than zero represent sequestration. Map accounts for fluxes associated with the 

Tier 2 and 3 inventory computations. See Methodology for additional details.

Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2009

Metric Tons CO2 Eq./ha/year

< -0.5

-0.5 to -0.25

-0.25 to -0.1

-0.1 to -0.05

-0.05 to 0

> 0
Net C Stock Changes, per Hectare, for Organic Soils Under Agricultural Management, 2009

Figure A-18

Note: Values greater than zero represent emissions.

Metric Tons CO2 Eq./year

0 to 0.1

0.1 to 0.25

0.25 to 0.5

0.5 to 1

> 1

No organic soils

Net C Stock Changes, Per Hectare, For Organic Soils

Under Agricultural Management, 2009
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 

a BioCycle 2006 for MSW and activity factors for industrial waste. b 1960 through 1988 based on EPA 1988 and EPA 1993; 1989 through 2006 based on BioCycle 2006. c 2006 IPCC Guidelines – First Order Decay Model. d EIA 2007 and flare vendor database. e EIA 2007 and EPA (LMOP) 2007. f 2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996

MSW and 

Industrial 

Waste 

Generateda

Total 

Methane 

Generated 

at 

Landfillsc

MSW and 

Industrial 

Waste 

Landfilledb

To

Met

Em

Nonrecovered 

Methane 

Generated 

at Landfills

Composted

Recycled

Incinerated

Flaredd Oxidized

Recovered 

for 

Energye

10%f