A-125

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

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

residential, electric power, and U.S. territories. For CH4 and N2O from industrial, commercial, residential, and U.S. 

territories, estimates were based upon consumption of coal, gas, oil, and wood. Energy consumption and wood 

consumption data for the United States were obtained from EIA’s Monthly Energy Review, February 2014 and Published 

Supplemental Tables on Petroleum Product detail (EIA 2014). Because the United States does not include territories in its 

national energy statistics, fuel consumption data for territories were collected separately from the EIA’s International 

Energy Statistics (EIA 2013a).39 Fuel consumption for the industrial sector was adjusted to subtract out construction and 

agricultural use, which is reported under mobile sources.40 Construction and agricultural fuel use was obtained from EPA 

(2011). 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-84 provides annual energy consumption data for the years 1990 through 2012. 

In this inventory, the emission estimation methodology for the electric power sector was revised from Tier 1 to 

Tier 2 as fuel consumption by technology-type for the electricity generation sector was obtained from the Acid Rain 

Program Dataset (EPA 2013). This combustion technology-and fuel-use data was available by facility from 1996 to 2012. 

Since there was a difference between the EPA (2013) and EIA (2014) total energy consumption estimates, the remainder 

between total energy consumption using EPA (2013) and EIA (2014) was apportioned to each combustion technology 

type and fuel combination using a ratio of energy consumption by technology type from 1996 to 2012. 

Energy consumption estimates were not available from 1990 to 1995 in the EPA (2013) dataset, and as a result, 

consumption was calculated using total electric power consumption from EIA (2014) and the ratio of combustion 

technology and fuel types from EPA 2013. The consumption estimates from 1990 to 1995 were estimated by applying the 

1996 consumption ratio by combustion technology type to the total EIA consumption for each year from 1990 to 1995. 

Lastly, there were significant differences between wood biomass consumption in the electric power sector 

between the EPA (2013) and EIA (2014) datasets. The difference in wood biomass consumption in the electric power 

sector was distributed to the residential, commercial, and industrial sectors according to their percent share of wood 

biomass energy consumption calculated from EIA (2014).

Step 2: Determine the Amount of CH4 and N2O Emitted

Activity data for industrial, commercial, residential, and U.S. territories and fuel type for each of these sectors 

were then multiplied by default Tier 1 emission factors to obtain emission estimates. Emission factors for the residential, 

commercial, and industrial sectors were taken from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories

(IPCC 2006). These 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-85 provides emission factors used for each sector and fuel type. For 

the electric power sector, emissions were estimated by multiplying fossil fuel and wood consumption by technology- and 

fuel-specific Tier 2 IPCC emission factors shown in Table A-86. Emission factors were used from the 2006 IPCC 

Guidelines as the factors presented in this IPCC guidance were taken directly from U.S. EPA publications on emissions 

rates for combustion sources.

 

39 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.

40 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-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Estimates of NOx, CO, and NMVOC Emissions

Emissions estimates for NOx, CO, and NMVOCs were obtained from data published on the National Emission 

Inventory (NEI) Air Pollutant Emission Trends web site (EPA 2013a, b, EPA 2009), and disaggregated based on EPA 

(2003). . 

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

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

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

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

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

data (e.g., fuel consumption or deliveries, 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 

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

conjunction with emission factors that relate the quantity of emissions to the activity. 

The basic calculation procedure for most source categories presented in EPA (2003) and EPA (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).
A-127

Table A-84: 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 2007 2008 2009 2010 2011 2012

Coal 19,610 20,888 21,328 21,879 22,224 22,159 23,080 22,391 22,343 22,576 22,636 22,949 22,458 22,710 22,225 19,670 20,697 18,989 6,525

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

Commercial 124 117 122 129 93 103 92 97 90 82 103 97 65 70 81 73 70 62 44

Industrial 1,640 1,527 1,455 1,458 1,471 1,373 1,349 1,358 1,244 1,249 1,262 1,219 1,189 1,131 1,081 877 952 866 785

Electric Power 17,807 19,217 19,724 20,266 20,637 20,659 21,618 20,920 20,987 21,199 21,228 21,591 21,161 21,465 21,026 18,682 19,639 18,024 5,659

U.S. Territories 7 10 10 10 11 10 10 4 11 34 32 33 37 37 37 37 37 37 37

Petroleum 6,226 5,652 6,150 6,194 5,885 6,205 6,160 6,638 6,016 6,399 6,586 6,504 6,212 6,079 5,268 4,692 4,865 4,454 6,611

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 1,225 1,183 1,139 1,119 1,032 950

Commercial 891 694 718 655 609 614 694 719 645 762 767 716 678 681 650 675 651 635 517

Industrial 2,788 2,375 2,718 2,660 2,220 2,328 2,298 2,545 2,381 2,505 2,684 2,792 3,118 3,004 2,456 1,959 2,068 1,907 1,795

Electric Power 797 860 883 1,100 1,403 1,459 1,269 1,279 1,074 1,043 1,007 1,004 590 618 488 383 412 266 2,736

U.S. Territories 375 462 435 445 445 461 472 632 557 622 654 623 621 552 492 536 614 614 614

Natural Gas 17,266 19,337 20,233 20,131 19,840 19,778 20,919 20,224 20,908 20,894 21,152 20,938 20,626 22,019 22,286 21,952 22,913 23,115 31,423

Residential 4,491 4,954 5,354 5,093 4,646 4,835 5,105 4,889 4,995 5,209 4,981 4,946 4,476 4,835 5,010 4,883 4,878 4,805 4,242

Commercial 2,682 3,096 3,226 3,285 3,083 3,115 3,252 3,097 3,212 3,261 3,201 3,073 2,902 3,085 3,228 3,187 3,165 3,216 2,960

Industrial 7,716 8,723 9,020 9,033 8,826 8,425 8,656 7,949 8,086 7,845 7,914 7,330 7,323 7,521 7,571 7,125 7,683 7,783 8,201

Electric Power 2,376 2,564 2,632 2,720 3,285 3,403 3,894 4,266 4,591 4,551 5,032 5,565 5,899 6,550 6,447 6,730 7,159 7,194 15,994

U.S. Territories NE NE NE NE NE NE 13 23 23 27 25 24 26 27 29 27 28 27 27

Wood 2,216 2,370 2,437 2,371 2,184 2,214 2,262 2,006 1,995 2,002 2,121 2,137 2,099 2,089 2,059 1,931 1,981 2,010 2,001

Residential 614 547 571 455 404 414 444 393 409 434 442 468 413 456 508 545 482 489 417

Commercial 70 76 80 78 68 71 76 71 74 78 76 76 71 76 79 79 78 75 62

Industrial 1,526 1,739 1,779 1,831 1,704 1,720 1,731 1,533 1,503 1,480 1,592 1,581 1,599 1,534 1,446 1,284 1,395 1,422 1,318

Electric Power 7 8 8 8 8 9 11 9 9 10 11 11 17 23 27 23 25 24 204

U.S. Territories NE NE NE NE NE NE 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-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-85: CH4 and N2O Emission Factors by Fuel Type and Sector (g/GJ)41

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)

Table A-86: CH4 and N2O Emission Factors by Technology Type and Fuel Type for the Electric Power Sector(g/GJ) 42

Technology Configuration CH4 N2O

Liquid Fuels

Residual Fuel Oil/Shale Oil Boilers Normal Firing 0.8 0.3

Tangential Firing 0.8 0.3

Gas/Diesel Oil Boilers Normal Firing 0.9 0.4

Tangential Firing 0.9 0.4

Large Diesel Oil Engines >600 hp (447kW) 4 NA

Solid Fuels

Pulverized Bituminous Combination Boilers Dry Bottom, wall fired 0.7 0.5

Dry Bottom, tangentially fired 0.7 1.4

Wet bottom 0.9 1.4

Bituminous Spreader Stoker Boilers With and without re-injection 1 0.7

Bituminous Fluidized Bed Combustor Circulating Bed 1 61

Bubbling Bed 1 61

Bituminous Cyclone Furnace 0.2 0.6

Lignite Atmospheric Fluidized Bed NA 71

Natural Gas

Boilers 1 1

Gas-Fired Gas Turbines >3MW 4 1

Large Duel-Fuel Engines 258 NA

Combined Cycle 1 3

Peat

Peat Fluidized Bed Combustion Circulating Bed 3 7

Bubbling Bed 3 3

Biomass

Wood/Wood Waste Boilers 11 7

Wood Recovery Boilers 1 1

 

41 GJ (Gigajoule) = 109

joules. One joule = 9.486×10-4 Btu

42 GJ (Gigajoule) = 109

joules. One joule = 9.486×10-4 Btu
A-129

Table A-87: NOx Emissions from Stationary Combustion (Gg)

Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Electric Power 6,045 5,792 5,581 5,683 5,638 5,183 4,829 4,454 4,265 3,929 3,594 3,434 3,213 2,993 2,772 2,451 2,129 1,807 1,557 

Coal 5,119 5,061 5,079 5,118 4,932 4,437 4,130 3,802 3,634 3,348 3,062 2,926 2,738 2,550 2,362 2,088 1,814 1,540 1,327 

Fuel Oil 200 87 107 131 202 179 147 149 142 131 120 114 107 100 92 82 71 60 52 

Natural gas 513 510 248 277 329 393 376 325 310 286 261 250 234 218 202 178 155 131 113 

Wood NA NA 5 6 24 33 36 37 36 33 30 29 27 25 23 21 18 15 13 

Other Fuelsa NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Internal Combustion 213 134 142 150 149 141 140 140 143 132 121 115 108 100 93 82 71 61 52 

Industrial 2,559 2,650 2,667 2,613 2,570 2,283 2,278 2,296 1,699 1,636 1,573 1,506 1,390 1,274 1,158 1,131 1,105 1,078 1,078 

Coal 530 541 490 487 475 475 484 518 384 369 355 340 314 288 261 255 249 243 243 

Fuel Oil 240 224 203 196 190 190 166 153 114 109 105 101 93 85 77 76 74 72 72 

Natural gas 877 999 900 880 869 706 710 711 526 506 487 466 430 394 359 350 342 334 334 

Wood NA NA NA NA NA NA 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 70 64 58 57 56 54 54 

Internal Combustion 792 774 965 948 932 813 809 798 591 568 546 523 483 443 403 393 384 375 375 

Commercial 671 607 734 538 510 483 507 428 438 455 473 490 471 452 433 453 472 582 582 

Coal 36 35 30 32 34 23 21 21 19 19 19 19 18 17 15 15 15 15 15 

Fuel Oil 88 94 86 88 73 54 52 52 50 49 49 49 46 42 39 39 38 38 38 

Natural gas 181 210 224 229 220 156 161 165 157 156 156 155 145 134 124 122 121 119 119 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Fuelsa 366 269 394 190 184 249 273 189 212 230 249 267 263 258 254 276 299 410 410 

Residential 749 813 726 699 651 441 439 446 422 421 419 417 390 362 335 330 325 321 321 

Coalb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Fuel Oilb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Natural Gasb NA NA NA NA NA NA 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 19 18 16 16 16 16 16 

Other Fuelsa 707 769 699 671 624 416 417 424 402 400 399 397 371 345 318 314 309 305 305 

Total 10,023 9,862 9,708 9,534 9,369 8,390 8,053 7,623 6,825 6,442 6,059 5,847 5,464 5,081 4,698 4,365 4,031 3,787 3,538 

NA (Not Applicable)

a

“Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2009, 2010b, 2013).

b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2009, 2010b, 2013).

Note: Totals may not sum due to independent rounding.

Table A-88: CO Emissions from Stationary Combustion (Gg)

Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Electric Power 329 337 369 384 410 450 439 439 594 590 586 581 606 631 655 670 685 700 700 

Coal 213 227 228 233 220 187 221 220 298 296 293 291 304 316 328 336 343 351 351 

Fuel Oil 18 9 11 13 17 36 27 28 38 37 37 37 38 40 41 42 43 44 44 

Natural gas 46 49 72 76 88 151 96 92 125 124 123 122 127 132 137 141 144 147 147 

Wood NA NA NA NA NA NA 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 46 48 49 50 51 51 

Internal Combustion 52 52 52 54 55 52 63 67 91 90 89 89 93 96 100 102 105 107 107 

Industrial 797 958 1,078 1,055 1,044 1,100 1,106 1,137 1,150 1,114 1,079 1,042 965 888 811 820 829 839 839 

Coal 95 88 100 99 96 114 118 125 127 123 119 115 106 98 89 90 91 92 92 
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Fuel Oil 67 64 49 47 46 54 48 45 46 44 43 41 38 35 32 33 33 33 33 

Natural gas 205 313 307 307 305 350 355 366 370 359 347 335 311 286 261 264 267 270 270 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Fuelsa 253 270 316 302 303 286 300 321 325 315 305 295 273 251 229 232 235 237 237 

Internal Combustion 177 222 305 299 294 296 285 279 282 274 265 256 237 218 199 201 204 206 206 

Commercial 205 211 122 126 122 151 151 154 177 173 169 166 156 146 137 140 142 145 145 

Coal 13 14 13 13 14 16 14 13 15 15 15 14 14 13 12 12 12 13 13 

Fuel Oil 16 17 17 18 15 17 17 17 20 19 19 19 18 16 15 16 16 16 16 

Natural gas 40 49 58 59 57 81 83 84 97 95 93 91 86 80 75 77 78 80 80 

Wood NA NA NA NA NA NA 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 39 37 34 35 36 36 36 

Residential 3,668 3,877 2,364 2,362 2,353 3,323 2,644 2,648 3,044 2,981 2,919 2,855 2,689 2,523 2,356 2,406 2,455 2,504 2,504 

Coalb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Fuel Oilb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Wood 3,430 3,629 2,132 2,133 2,133 3,094 2,416 2,424 2,787 2,730 2,672 2,614 2,462 2,310 2,157 2,202 2,248 2,293 2,293 

Other Fuelsa 238 248 231 229 220 229 228 224 257 252 246 241 227 213 199 203 207 211 211 

Total 5,000 5,383 3,933 3,927 3,928 5,024 4,340 4,377 4,965 4,859 4,752 4,644 4,416 4,188 3,959 4,036 4,112 4,188 4,188 

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-89: NMVOC Emissions from Stationary Combustion (Gg)

Sector/Fuel Type 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Electric Power 43 40 44 47 51 49 56 55 45 44 44 44 42 41 39 38 37 36 36 

Coal 24 26 25 26 26 25 27 26 21 21 21 21 20 19 19 18 18 17 17 

Fuel Oil 5 2 3 4 5 4 4 4 4 4 3 3 3 3 3 3 3 3 3 

Natural gas 2 2 7 7 9 9 12 12 10 10 10 10 9 9 9 8 8 8 8 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Fuelsa NA NA + + 1 2 2 2 1 1 1 1 1 1 1 1 1 1 1 

Internal Combustion 11 9 9 10 10 10 11 10 9 8 8 8 8 8 7 7 7 7 7 

Industrial 165 187 163 160 159 156 157 159 138 132 126 120 112 104 97 97 98 98 98 

Coal 7 5 6 6 6 9 9 10 9 9 8 8 7 7 6 6 6 6 6 

Fuel Oil 11 11 8 7 7 10 9 9 7 7 7 6 6 6 5 5 5 5 5 

Natural gas 52 66 54 54 53 52 53 54 47 45 43 41 38 35 33 33 33 33 33 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Fuelsa 46 45 33 31 31 26 27 29 25 24 23 22 21 19 18 18 18 18 18 

Internal Combustion 49 60 63 62 61 60 58 57 49 47 45 43 40 37 35 35 35 35 35 

Commercial 18 21 22 22 21 25 28 29 61 53 45 33 34 35 36 38 40 45 45 

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 2 2 2 2 2 2 2 

Natural gas 7 10 13 13 12 11 14 14 23 18 14 9 8 7 6 7 7 7 7 

Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Other Fuelsa 8 8 5 5 5 10 9 10 31 29 27 22 24 26 28 30 31 35 35 
A-131

Residential 686 725 789 788 786 815 837 836 1,341 1,067 792 518 465 411 357 379 401 423 423 

Coalb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Fuel Oilb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

Wood 651 688 756 756 756 794 809 809 1,297 1,032 767 501 450 398 346 367 388 409 409 

Other Fuelsa 35 37 33 32 30 21 27 27 43 35 26 17 15 13 12 12 13 14 14 

Total 912 973 1,018 1,017 1,017 1,045 1,077 1,080 1,585 1,296 1,008 715 653 591 530 553 576 602 602 

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-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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 (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 2014 and EIA 2013a) and apportioned to individual modes (considered a “top down” approach). CO2 emissions from 

commercial jet fuel use are obtained directly from the Federal Aviation Administration (FAA 2014), while CO2 emissions 

from other aircraft jet fuel consumption is determined using a top down approach. 

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

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

transportation-specific data sources. A “bottom up” diesel calculation was first 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 onroad vehicles. Estimated motor gasoline and diesel consumption data for on-road vehicles by vehicle type come from 

FHWA’s Highway Statistics, Table VM-1 (FHWA 1996 through 2014)

43, 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 2013) 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 MF21 and MF-27 (FHWA 1996 through 2014). Table MF-21 provided fuel consumption estimates for the most current 

Inventory year; Table MF-27 provided fuel consumption estimates for years 1990-2011. 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 2013) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2013 and 

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

2004) and Whorton (2006 through 2012) for Class II and III railroads, and DOE’s Transportation Energy Data Book

(DOE 1993 through 2013) for passenger rail. Estimates of diesel from ships and boats were taken from EIA’s Fuel Oil 

and Kerosene Sales (1991 through 2013). 

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 2013b), 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. 

 

43 In 2011 FHWA changed its methods for estimating vehicle miles traveled (VMT) and related data. These methodological changes 

included how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were 

first incorporated for the 2010 Inventory and apply to the 2007-12 time period. This resulted in large changes in VMT and fuel 

consumption data by vehicle class, thus leading to a shift in emissions among on-road vehicle classes. For example, the category 

“Passenger Cars” has been replaced by “Light-duty Vehicles-Short Wheelbase” and “Other 2 axle-4 Tire Vehicles” has been replaced by 

“Light-duty Vehicles, Long Wheelbase.” This change in vehicle classification has moved some smaller trucks and sport utility vehicles 

from the light truck category to the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in 

light-truck emissions between 2006 and 2007. 
A-133

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: FAA (2014) for domestic and international 

commercial aircraft, and DESC (2013) for domestic and international military aircraft. General aviation jet fuel 

consumption is calculated as the difference between total jet fuel consumption as reported by EIA and the total 

consumption from commercial and military jet fuel consumption. Commercial jet fuel CO2 estimates are obtained directly 

from the Federal Aviation Administration (FAA 2014), while CO2 emissions from domestic military and general aviation 

jet fuel consumption is determined using a top down approach. Domestic commercial jet fuels CO2 from FAA is 

subtracted from total domestic jet fuel CO2 emissions, and this remaining value is apportioned among domestic military 

and domestic general aviation based on their relative proportion of energy consumption. Estimates for biofuels, including 

ethanol and biodiesel were discussed separately in Chapter 3.2 under the methodology for Estimating CO2 from Fossil 

Combustion, and in Chapter 3.10 Wood Biomass and Ethanol Consumption and were not apportioned to specific 

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

Administration (EIA 2014).

Table A-90 displays estimated fuel consumption by fuel and vehicle type. Table A-91 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 for consistency with IPCC methodological guidance and 

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-92.
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-90.Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified)

Fuel/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007a 2008 2009 2010 2011 2012

Motor Gasolineb 110,441 118,217 129,102 130,582 133,257 133,900 135,708 134,659 132,947 132,546 127,528 127,322 126,922 124,430 124,081

 Passenger Cars 69,763 67,948 72,860 73,466 74,911 72,623 72,223 74,600 71,647 89,795 86,376 86,019 85,745 85,301 85,241

 Light-Duty Trucks 34,698 44,369 50,774 51,251 52,442 55,951 58,118 54,274 55,460 35,401 33,723 34,311 34,267 32,638 32,325

 Motorcycles 194 200 210 194 191 185 195 184 212 478 496 479 424 414 473 

 Buses 39 42 44 40 38 36 50 41 42 81 86 89 87 85 91 

 Medium- and HeavyDuty Trucks 4,350 4,072 4,096 3,990 4,038 3,479 3,510 3,962 4,008 5,233 5,322 4,918 4,918 4,525 4,493 

 Recreational Boatsc 1,397 1,585 1,119 1,641 1,637 1,626 1,612 1,599 1,577 1,557 1,524 1,506 1,481 1,468 1,459 

Distillate Fuel Oil 

(Diesel Fuel) 25,631 31,605 39,241 39,058 40,348 41,177 42,668 44,659 45,848 46,432 44,032 39,940 41,571 42,536 42,445 

 Passenger Cars 771 765 356 357 364 412 419 414 403 402 362 354 368 401 405 

 Light-Duty Trucks 1,119 1,452 1,961 2,029 2,133 2,652 2,822 2,518 2,610 1,326 1,182 1,181 1,228 1,283 1,284 

 Buses 781 851 997 906 860 930 1,316 1,030 1,049 1,547 1,477 1,374 1,370 1,479 1,585 

 Medium- and HeavyDuty Trucks 18,574 23,241 30,180 30,125 31,418 31,540 32,599 35,160 36,079 37,496 35,692 32,384 33,719 34,015 34,140 

Recreational Boats 190 228 266 274 282 289 297 305 313 321 329 337 345 353 361 

 Ships and Other Boats 735 1,204 1,377 1,248 1,202 1,178 807 785 729 800 773 775 733 1,005 746 

 Rail 3,461 3,864 4,106 4,119 4,089 4,176 4,407 4,446 4,665 4,539 4,216 3,535 3,807 3,999 3,923 

Jet Fueld 19,186 17,991 20,002 19,454 19,004 18,389 19,147 19,420 18,695 18,407 17,749 15,809 15,537 15,036 14,705 

 Commercial Aircraft 11,569 12,136 14,672 13,121 12,774 12,943 13,147 13,976 14,426 14,708 13,400 12,588 11,931 12,067 11,932 

 General Aviation 

Aircraft 4,034 3,361 3,163 3,975 4,119 3,323 3,815 3,583 2,590 2,043 2,682 1,787 2,322 1,895 1,659 

 Military Aircraft 3,583 2,495 2,167 2,359 2,110 2,123 2,185 1,860 1,679 1,656 1,667 1,434 1,283 1,074 1,114 

Aviation Gasolined 374 329 302 291 281 251 260 294 278 263 235 221 225 225 209 

 General Aviation 

Aircraft 374 329 302 291 281 251 260 294 278 263 235 221 225 225 209 

Residual Fuel Oild, e 2,006 2,587 2,963 1,066 1,522 662 1,245 1,713 2,046 2,579 1,812 1,241 1,818 1,723 1,410

 Ships and Other Boats 2,006 2,587 2,963 1,066 1,522 662 1,245 1,713 2,046 2,579 1,812 1,241 1,818 1,723 1,410

Natural Gas d(trillion 

cubic feet) 0.7 0.7 0.7 0.6 0.7 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.8

 Passenger Cars - - - - - - - - - - - - - - - 

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

 Buses + + + + + + + + + + + + + + + 

 Pipelines 0.7 0.7 0.6 0.6 0.7 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 

LPGd 265 206 138 159 166 207 222 327 320 257 468 331 348 390 401

 Buses - 1.6 1.5 0.3 0.6 0.7 0.7 1.0 1.0 - - - - - -

 Light-Duty Trucks 106 98 88 108 117 144 167 247 229 185 340 228 243 274 281

 Medium- and HeavyDuty Trucks 159 106 49 51 49 62 55 79 89 72 128 103 106 117 120

Electricityd,f 4,751 4,975 5,382 5,724 5,517 6,810 7,224 7,506 7,358 8,173 7,700 7,781 7,712 7,672 7,320 

 Rail 4,751 4,975 5,382 5,724 5,517 6,810 7,224 7,506 7,358 8,173 7,700 7,781 7,712 7,672 7,320 

a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how on-road vehicles are classified, moving from a system based 

on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007. 
A-135

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

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

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

e Fluctuations in reported fuel consumption may reflect data collection problems.

f Million Kilowatt-hours

+ Less than 0.05 million gallons or 0.05 trillion cubic feet

- Unreported or zero
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-91: Energy Consumption by Fuel and Vehicle Type (Tbtu)

Fuel/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007a 2008 2009 2010 2011 2012

Motor Gasolineb 13,813 14,679 16,015 16,198 16,524 16,600 16,850 16,730 16,517 16,470 15,844 15,818 15,769 15,459 15,419

 Passenger Cars 8,725 8,437 9,038 9,113 9,289 9,004 8,968 9,268 8,901 11,158 10,731 10,687 10,653 10,598 10,592

 Light-Duty Trucks 4,340 5,509 6,298 6,358 6,503 6,937 7,216 6,743 6,890 4,399 4,190 4,263 4,257 4,055 4,017

 Motorcycles 24 25 26 24 24 23 24 23 26 59 62 60 53 51 59

 Buses 5 5 5 5 5 4 6 5 5 10 11 11 11 11 11

 Medium- and HeavyDuty Trucks 544 506 508 495 501 431 436 492 498 650 661 611 611 562 558

 Recreational Boatsc 175 197 139 204 203 202 200 199 196 193 189 187 184 182 181

Distillate Fuel Oil 

(Diesel Fuel) 3,555 4,383 5,442 5,417 5,596 5,711 5,918 6,194 6,359 6,440 6,107 5,539 5,765 5,899 5,887

 Passenger Cars 107 106 49 50 51 57 58 57 56 56 50 49 51 56 56

 Light-Duty Trucks 155 201 272 281 296 368 391 349 362 184 164 164 170 178 178

 Buses 108 118 138 126 119 129 183 143 145 215 205 191 190 205 220

 Medium- and HeavyDuty Trucks 2,576 3,223 4,186 4,178 4,357 4,374 4,521 4,876 5,004 5,200 4,950 4,491 4,676 4,718 4,735

 Recreational Boats 26 32 37 38 39 40 41 42 43 45 46 47 48 49 50

 Ships and Other 

Boats 102 167 191 173 167 163 112 109 101 111 107 107 102 139 103

 Rail 480 536 569 571 567 579 611 617 647 630 585 490 528 555 544

Jet Fueld 2,590 2,429 2,700 2,626 2,565 2,482 2,585 2,622 2,524 2,485 2,396 2,134 2,097 2,030 1,985

 Commercial Aircraft 1,562 1,638 1,981 1,771 1,725 1,747 1,775 1,887 1,948 1,986 1,809 1,699 1,611 1,629 1,611

 General Aviation 

Aircraft 545 454 427 537 556 449 515 484 350 276 362 241 314 256 224

 Military Aircraft 484 337 293 318 285 287 295 251 227 224 225 194 173 145 150

Aviation Gasolined 45 40 36 35 34 30 31 35 33 32 28 27 27 27 25

 General Aviation 

Aircraft 45 40 36 35 34 30 31 35 33 32 28 27 27 27 25

Residual Fuel Oild, e 300 387 443 159 228 99 186 256 306 386 271 186 272 258 211

 Ships and Other 

Boats 300 387 443 159 228 99 186 256 306 386 271 186 272 258 211

Natural Gasd 680 724 672 658 699 627 602 624 625 663 692 715 719 734 777

 Passenger Cars - 2 - - - - - - - - - - - - - 

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

 Buses - 1 8 9 12 14 16 16 16 19 21 22 20 20 20

 Pipelines 680 721 664 649 687 614 586 608 609 645 672 693 699 713 757

LPGd 23 18 12 14 14 18 19 28 27 22 40 28 29 33 34

 Buses - - - - - - - - - - - - - - - 

 Light-Duty Trucks 9 8 8 9 10 12 14 21 20 16 29 19 21 23 24

 Medium- and HeavyDuty Trucks 14 9 4 4 4 5 5 7 8 6 11 9 9 10 10

Electricityd 16 17 18 20 19 23 25 26 25 28 26 27 26 26 25

 Rail 16 17 18 20 19 23 25 26 25 28 26 27 26 26 25

Total 21,022 22,676 25,339 25,127 25,679 25,592 26,216 26,515 26,417 26,526 25,405 24,474 24,705 24,466 24,362

a In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how on-road vehicles are classified, moving from a system based 

on body-type to one that is based on wheelbase. This resulted in large changes in fuel consumption data by vehicle class between 2006 and 2007. 
A-137

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

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

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

e Fluctuations in reported fuel consumption may reflect data collection problems. Residual fuel oil for ships and other boats data is based on EIA’s February 2013 Monthly Energy Review data.

- Unreported or zero

Table A-92. Biofuel Consumption by Fuel Type (million gallons)

Fuel Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Ethanol 712.6 1,327 1,591 1,661 1,977 2,690 3,377 3,862 5,210 6,567 9,269 10,543 12,282 12,326 12,316

Biodiesel NA NA NA 10 16 14 27 91 261 354 304 322 260 886 895 

NA (Not Available)
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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,44 buses, and 

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

through 2014).45 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 2014) 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 2013). 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 total VMT estimates to develop the final VMT estimates by vehicle/fuel type 

category.46 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 93

and Table A- 94, 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 2012 in Table A- 97. Distributions for 1990-2012 are 

presented in the Inventory Docket 47. 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 2009 forward (EPA 2013c).

48 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- 101

through Table A- 104. 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 

 

44 Medium- and heavy-duty trucks correspond to FHWA’s reporting categories of single-unit trucks and combination trucks. Single-unit trucks 

are defined as single frame trucks that have 2-axles and at least 6 tires or a gross vehicle weight rating (GVWR) exceeding 10,000 lbs.

45 In 2011 FHWA changed its methods for estimated vehicle miles traveled (VMT) and related data. These methodological changes included 

how vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. These changes were first incorporated 

for the 2010 Inventory and apply to the 2007-12 time period. This resulted in large changes in VMT data by vehicle class, thus leading to a shift 

in emissions among on-road vehicle classes. For example, the category “Passenger Cars” has been replaced by “Light-duty Vehicles-Short 

Wheelbase” and “Other 2 axle-4 Tire Vehicles” has been replaced by “Light-duty Vehicles, Long Wheelbase.” This change in vehicle 

classification has moved some smaller trucks and sport utility vehicles from the light truck category to the passenger vehicle category in this 

emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 2007.

46 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. 

47 Available on CD by request.

48 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.
A-139

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

Tier 1 and Tier 0 apply to vehicles equipped with three-way catalysts. The introduction of “early three-way catalysts,” and 

“advanced three-way catalysts,” as described in the Revised 1996 IPCC Guidelines, roughly correspond to the introduction 

of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).49 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).

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 utilizing Tier 2 and Low Emission Vehicle (LEV) 

technologies were developed by ICF (2006b); all other gasoline and diesel on-road vehicle emissions factors were 

developed by ICF (2004).These factors were based on EPA,CARB and Environment Canada laboratory test results of 

different vehicle and control technology types. The EPA, CARB and Environment Canada tests were designed following 

the Federal Test Procedure (FTP), which covers three separate driving segments, since vehicles emit varying amounts of 

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.50 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 

 

49 For further description, see “Definitions of Emission Control Technologies and Standards” section of this annex below.

50 Fuel types used in combination depend on the vehicle class. For light-duty vehicles, gasoline is generally blended with ethanol and diesel is 

blended with biodiesel; dual-fuel vehicles can run on gasoline or an alternative fuel – either natural gas or LPG – but not at the same time, while 

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

likely to run on diesel fuel, natural gas, or LPG,.
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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- 95, while more detailed estimates of VMT by 

control technology are shown in Table A- 96.

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- 106.

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- 100. Consumption data for ships and other boats (i.e., vessel bunkering) were obtained from 

DHS (2008) and EIA (1991 through 2013) for distillate fuel, and DHS (2008) and EIA (2013a) for residual fuel; marine 

transport fuel consumption data for U.S. territories (EIA 2008b) were added to domestic consumption, and this total was 

reduced by the amount of fuel used for international bunkers.

51

 Gasoline consumption by recreational boats was obtained 

from EPA’s NONROAD model (EPA 2013b). Annual diesel consumption for Class I rail was obtained from the 

Association of American Railroads (AAR 2008 through 2013), diesel consumption from commuter rail was obtained from 

APTA (2007 through 2013) and Gaffney (2007), and consumption by Class II and III rail was provided by Benson (2002 

through 2004) and Whorton (2006 through 2012).

52 Diesel consumption by commuter and intercity rail was obtained from 

DOE (1993 through 2013). Data on the consumption of jet fuel and aviation gasoline in aircraft were obtained from EIA 

(2014) and FAA (2014), 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 (DESC 2013 and FAA 2014). 

Pipeline fuel consumption was obtained from EIA (2007 through 2012) (note: pipelines are a transportation source but are 

stationary, not mobile, sources). Data on fuel consumption by all non-transportation mobile sources

53

were obtained from 

EPA’s NONROAD model (EPA 2013b) and from FHWA (1996 through 2014) 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- 107).

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), 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- 108 through Table A- 110 provides complete emission estimates for 1990 through 

2012.

 

51 See International Bunker Fuels section of the Energy Chapter.

52 Diesel consumption from Class II and Class III railroad were unavailable for 2012. Values are proxied from 2010, which is the last year the 

data was available.

53 “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-141

Table A- 93: Vehicle Miles Traveled for Gasoline On-Road Vehicles (109 Miles)

Year Passenger 

Cars

Light-Duty 

Trucks

Heavy-Duty 

Vehicles

Motorcycles

1990 1,391.2 554.3 25.4 9.6

1991 1,341.7 627.2 25.0 9.2

1992 1,354.8 682.9 24.8 9.6

1993 1,356.5 720.5 24.5 9.9

1994 1,387.5 738.8 25.0 10.2

1995 1,420.6 762.5 24.7 9.8

1996 1,454.7 788.0 24.0 9.9

1997 1,488.5 820.8 23.6 10.1

1998 1,536.6 836.8 23.6 10.3

1999 1,559.1 867.4 23.8 10.6

2000 1,591.5 886.7 23.6 10.5

2001 1,619.3 904.9 23.2 9.6

2002 1,649.2 925.8 23.1 9.6

2003 1,662.6 943.0 23.5 9.6

2004 1,690.2 984.2 23.9 10.1

2005 1,698.8 997.8 24.2 10.5

2006 1,681.0 1,037.5 24.4 12.0

2007a 2,092.8 561.5 33.7 21.4

2008 2,013.6 579.5 34.5 20.8

2009 2,004.6 591.1 32.1 20.8

2010 2,014.6 595.7 31.9 18.5

2011 2,034.6 576.9 29.7 18.5

2012 2,051.4 573.4 29.9 21.3

Source: Derived from FHWA (1996 through 2014).

a

In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how 

on-road vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in VMT data by 

vehicle class between 2006 and 2007

Table A- 94: Vehicle Miles Traveled for Diesel On-Road Vehicles (109 Miles)

Year Passenger 

Cars

Light-Duty 

Trucks

Heavy-Duty 

Vehiclesb

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 39.7 199.1

2004 8.4 41.3 201.6

2005 8.4 41.9 202.6

2006 8.3 43.5 201.0

2007 10.3 23.4 280.2

2008 9.9 24.2 286.4

2009 9.8 24.7 265.8

2010 9.9 24.9 264.3

2011 10.0 24.2 247.5

2012 10.1 24.1 249.0

Source: Derived from FHWA (1996 through 2014). 

a

In 2011, FHWA changed its methodology for Table VM-1, which impacts estimates for the 2007-2012 time period. These methodological changes include how 

on-road vehicles are classified, moving from a system based on body-type to one that is based on wheelbase. This resulted in large changes in VMT data by 

vehicle class between 2006 and 2007. 

b Heavy-Duty Vehicles includes Medium-Duty Trucks, Heavy-Duty Trucks, and Buses
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 95: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (109 Miles)

Year

Passenger 

Cars Light-Duty Trucks Heavy-Duty Vehiclesa

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

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 1.2 1.4 2.7

2006 1.3 1.5 3.9

2007 1.3 1.6 4.8

2008 1.3 1.8 4.6

2009 1.2 1.8 4.8

2010 1.3 2.1 4.1

2011 1.6 3.1 4.2

2012 1.8 3.6 4.2

Source: Derived from Browning (2003). 

aHeavy Duty-Vehicles includes medium-duty trucks, heavy-duty trucks, and buses. 
A-143

Table A- 96: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles)

Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005a 2006 2007 2008 2009 2010 2011 2012

Passenger Cars 206.3 400.6 788.1 922.0 1,042.4 1,172.9 1,273.0 1,243.1 1,296.4 1,295.4 1,268.7 1,242.1 1,252.2 1,604.1 1,849.4

 Methanol-Flex Fuel ICE + 40.9 13.2 10.1 7.8 6.4 3.6 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

 Ethanol-Flex Fuel ICE + 2.2 120.4 147.9 189.1 271.2 311.5 391.7 412.8 448.6 459.9 488.4 546.0 569.3 725.3

 CNG ICE 10.6 28.0 68.9 78.1 83.0 85.0 112.1 62.6 62.5 58.4 53.0 47.3 50.4 53.4 54.8

 CNG Bi-fuel 28.2 75.1 202.9 236.6 267.2 283.5 274.1 187.6 193.8 180.0 170.9 159.7 163.2 166.0 172.7

 LPG ICE 20.6 40.3 41.9 45.0 48.8 43.1 37.4 40.7 37.3 24.6 25.9 14.2 11.9 11.9 11.9

 LPG Bi-fuel 146.9 201.7 197.6 224.8 237.9 221.9 199.4 207.1 186.4 121.4 112.0 88.7 83.1 83.0 83.4

 Biodiesel (BD20) + + 8.2 8.3 13.4 13.6 62.3 125.1 186.4 246.1 235.8 236.6 189.9 187.4 185.8

 NEVs + 5.2 62.4 88.4 98.8 114.4 124.8 104.2 99.2 99.4 99.1 97.1 97.7 97.5 98.1

 Electric Vehicle + 7.2 72.6 82.8 96.3 133.8 147.8 124.2 118.1 116.9 112.0 110.0 109.8 296.5 374.3

Light-Duty Trucks 660.7 606.8 1,162.0 1,235.1 1,344.2 1,447.5 1,567.3 1,401.0 1,545.6 1,648.5 1,779.6 1,806.9 2,096.3 3,113.9 3,602.1

 Ethanol-Flex Fuel ICE + 1.3 122.6 150.1 179.1 279.1 353.7 420.8 532.4 702.4 864.0 1,021.1 1,363.4 2,378.8 2,864.4

 CNG ICE 10.9 29.6 145.9 145.7 153.4 158.0 162.2 65.5 65.5 64.7 63.0 58.2 60.7 66.1 72.0

 CNG Bi-fuel 24.2 71.0 280.1 280.1 301.2 313.4 330.0 171.4 178.6 175.4 169.6 157.5 161.6 165.2 171.6

 LPG ICE 56.9 48.5 58.4 64.4 68.2 64.9 61.5 60.1 60.1 43.8 43.8 33.0 30.3 30.2 28.8

 LPG Bi-fuel 568.7 449.4 511.9 522.9 557.1 541.8 525.6 513.2 486.1 383.7 361.4 259.7 239.9 238.9 234.8

 Biodiesel (BD20) + + 8.2 8.4 17.1 16.9 55.9 109.4 163.8 219.2 219.0 219.8 176.5 170.7 166.1

 Electric Vehicle + 7.1 35.0 63.4 68.2 73.4 78.3 60.1 58.4 58.0 57.5 55.8 61.9 61.8 62.1

Medium-Duty Trucks 508.0 458.4 629.6 862.5 977.7 899.7 856.6 802.6 628.8 693.1 716.3 629.8 578.6 578.7 573.3

 CNG Bi-fuel 2.3 20.1 117.0 203.2 228.2 245.3 241.9 146.7 112.5 154.4 188.3 178.8 187.6 198.9 206.1

 LPG ICE 24.3 20.0 29.7 41.9 48.3 43.9 40.7 40.5 33.8 31.3 35.0 31.0 29.9 30.5 30.0

 LPG Bi-fuel 481.4 418.3 475.9 609.7 671.8 585.0 535.7 517.2 326.5 298.2 289.0 215.2 211.7 212.6 212.3

 Biodiesel (BD20) + + 7.0 7.6 29.4 25.6 38.2 98.2 156.0 209.1 204.0 204.8 149.4 136.7 124.9

Heavy-Duty Trucks 523.9 627.0 712.3 820.5 845.2 1,041.2 1,093.9 1,644.2 2,867.9 3,627.7 3,416.3 3,639.5 3,083.2 3,107.6 3,141.5

 Neat Methanol ICE 3.0 7.8 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

 Neat Ethanol ICE + 2.0 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

 CNG ICE 12.7 32.2 83.7 149.0 146.3 183.6 187.7 408.0 491.8 557.2 647.6 843.7 900.0 959.6 1,011.7

 LPG ICE 36.3 46.3 48.3 57.1 60.9 73.4 70.8 69.0 81.9 78.4 76.9 74.7 74.4 75.0 72.0

 LPG Bi-fuel 471.9 531.9 529.7 558.0 548.5 650.2 626.7 499.2 530.0 522.5 516.8 513.4 515.2 515.0 507.2

 LNG + 6.9 22.2 26.9 28.6 56.8 108.1 115.9 123.2 127.5 129.5 131.9 136.6 139.8 143.3

 Biodiesel (BD20) + + 28.3 29.5 60.9 77.1 100.6 552.2 1,641.1 2,342.2 2,045.4 2,075.8 1,456.9 1,418.3 1,407.2

Buses 41.4 80.5 111.9 133.1 140.3 139.0 229.3 250.8 406.5 468.7 481.7 500.4 493.8 493.6 513.2

 Neat Methanol ICE 1.9 3.7 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

 Neat Ethanol ICE 0.1 2.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

 CNG ICE 11.2 37.5 53.4 65.0 65.5 64.3 145.2 160.0 165.1 179.8 202.5 220.2 238.0 254.7 273.2

 LPG ICE 28.2 30.9 35.6 36.9 36.4 34.1 38.9 30.3 28.9 28.9 28.7 28.4 28.4 28.3 27.7

 LNG + 4.3 13.3 21.0 22.3 23.6 25.7 28.6 30.0 33.5 37.6 37.8 38.1 38.2 38.5

 Biodiesel (BD20) + + 4.5 4.7 9.9 10.5 13.1 15.6 165.1 209.1 194.0 194.8 170.0 152.9 151.6

 Electric + 2.0 5.1 5.6 6.3 6.6 6.4 16.2 17.3 17.1 18.4 18.5 18.5 18.7 21.3

Total VMT 1,940.3 2,173.4 3,403.9 3,973.1 4,349.9 4,700.4 5,020.2 5,341.7 6,745.2 7,733.3 7,662.6 7,818.6 7,503.9 8,897.8 9,679.5
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.

a In 2011, EIA changed its reporting methodology for 2005-2010 data. EIA provided more detail on alternative fuel vehicle use by vehicle class. The fuel use breakdown by vehicle class had previously been based on 

estimates of the distribution of fuel use by vehicle class. The new data from EIA allowed actual data to be used for fuel use, and resulted in greater share of heavy-duty AFV VMT estimated for 2005-2010. The source of this 

data is the U.S. Energy Information Administration, Office of Energy Consumption and Efficiency Statistics and the DOE/GSA Federal Automotive Statistical Tool (FAST).

+ Less than 0.05 million vehicle miles traveled

Table A- 97: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,a2012

Vehicle Age LDGV LDGT HDGV LDDVb LDDT HDDV MC

0 7.7% 6.2% 6.0% 10.1% 6.3% 6.2% 11.0%

1 7.0% 6.0% 5.6% 9.3% 6.1% 5.8% 9.0%

2 6.2% 5.4% 5.2% 8.1% 5.5% 5.4% 6.8%

3 5.1% 3.8% 5.0% 6.8% 3.8% 5.3% 4.8%

4 5.7% 4.7% 5.4% 7.5% 4.8% 5.8% 9.0%

5 6.3% 6.8% 5.5% 8.3% 6.2% 5.9% 8.1%

6 6.3% 6.8% 5.5% 8.3% 5.8% 7.1% 7.7%

7 6.1% 7.0% 5.3% 8.0% 6.0% 6.6% 6.7%

8 5.8% 6.7% 4.2% 7.6% 8.6% 5.2% 5.7%

9 5.6% 6.1% 3.3% 7.4% 6.1% 4.0% 4.8%

10 5.7% 5.6% 2.9% 7.6% 6.4% 3.6% 4.3%

11 5.7% 5.2% 3.3% 7.5% 6.5% 3.9% 3.6%

12 5.5% 4.8% 3.6% 0.0% 5.3% 4.5% 2.9%

13 4.5% 4.0% 3.6% 0.0% 5.6% 4.7% 2.2%

14 3.5% 3.5% 2.9% 0.0% 1.3% 3.6% 1.9%

15 2.9% 2.8% 2.2% 0.0% 3.2% 2.8% 1.8%

16 2.4% 2.5% 2.4% 0.0% 2.1% 2.6% 1.6%

17 1.9% 2.1% 2.8% 0.0% 2.0% 2.8% 1.2%

18 1.5% 1.8% 2.7% 0.0% 1.7% 2.4% 1.4%

19 1.1% 1.5% 2.0% 0.0% 1.2% 1.8% 1.1%

20 0.8% 1.1% 1.5% 0.0% 0.9% 1.2% 0.9%

21 0.6% 0.9% 1.5% 0.0% 0.7% 1.1% 0.7%

22 0.5% 0.9% 1.8% 0.0% 0.6% 1.3% 0.6%

23 0.4% 0.8% 2.5% 0.0% 0.5% 1.4% 0.4%

24 0.4% 0.7% 2.3% 0.0% 0.4% 1.2% 0.4%

25 0.3% 0.6% 2.0% 0.0% 0.3% 0.9% 0.4%

26 0.2% 0.5% 2.6% 0.0% 0.4% 0.9% 0.3%

27 0.2% 0.4% 2.0% 0.6% 0.4% 0.7% 0.3%

28 0.1% 0.3% 1.2% 0.7% 0.4% 0.4% 0.2%

29 0.1% 0.2% 1.8% 0.8% 0.3% 0.4% 0.2%

30 0.1% 0.2% 1.4% 1.5% 0.4% 0.3% 0.2%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

Source: EPA (2013c).

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).
A-145

b According to EPA’s MOVES model, sales of diesel passenger cars 12-26 years of age was very small compared to total passenger car sales, so the calculated fraction of these vehicles was stored as zero.

Table A- 98: Annual Average Vehicle Mileage Accumulation per Vehiclea(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

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.
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 99: VMT Distribution by Vehicle Age and Vehicle/Fuel Type, a 2012

Vehicle Age LDGV LDGT HDGV LDDVb LDDT HDDV MC

0 11.10% 10.47% 10.91% 13.33% 11.90% 12.17% 17.67%

1 9.64% 9.52% 9.55% 11.58% 10.54% 10.46% 13.48%

2 8.03% 8.14% 7.93% 9.65% 8.79% 8.90% 9.51%

3 6.36% 5.30% 7.40% 7.64% 5.59% 7.91% 6.17%

4 6.71% 6.15% 7.56% 8.05% 6.34% 7.93% 10.67%

5 7.02% 8.40% 7.17% 8.43% 7.53% 7.41% 8.80%

6 6.72% 7.81% 6.65% 8.07% 6.50% 8.10% 7.53%

7 6.16% 7.50% 6.10% 7.39% 6.14% 6.95% 5.90%

8 5.55% 6.61% 4.48% 6.67% 8.07% 4.99% 4.41%

9 5.14% 5.65% 3.30% 6.17% 5.25% 3.49% 3.22%

10 4.99% 4.80% 2.70% 5.99% 5.06% 2.86% 2.42%

11 4.70% 4.15% 2.93% 5.64% 4.70% 2.83% 1.67%

12 4.29% 3.48% 2.96% 0.00% 3.52% 3.05% 1.34%

13 3.32% 2.71% 2.76% 0.00% 3.39% 2.87% 1.01%

14 2.46% 2.15% 2.08% 0.00% 0.74% 2.03% 0.86%

15 1.93% 1.62% 1.50% 0.00% 1.62% 1.43% 0.82%

16 1.51% 1.29% 1.53% 0.00% 0.99% 1.22% 0.72%

17 1.15% 0.98% 1.65% 0.00% 0.84% 1.22% 0.54%

18 0.88% 0.80% 1.53% 0.00% 0.65% 0.95% 0.63%

19 0.61% 0.58% 1.04% 0.00% 0.45% 0.64% 0.51%

20 0.43% 0.40% 0.75% 0.00% 0.28% 0.40% 0.43%

21 0.31% 0.29% 0.70% 0.00% 0.20% 0.35% 0.34%

22 0.26% 0.25% 0.77% 0.00% 0.17% 0.37% 0.27%

23 0.20% 0.21% 1.00% 0.00% 0.13% 0.36% 0.20%

24 0.15% 0.17% 0.88% 0.00% 0.09% 0.28% 0.17%

25 0.11% 0.14% 0.77% 0.00% 0.07% 0.22% 0.17%

26 0.09% 0.12% 0.98% 0.00% 0.10% 0.22% 0.14%

27 0.07% 0.11% 0.75% 0.25% 0.09% 0.16% 0.12%

28 0.05% 0.08% 0.44% 0.28% 0.09% 0.10% 0.11%

29 0.03% 0.05% 0.68% 0.31% 0.08% 0.09% 0.08%

30 0.03% 0.05% 0.55% 0.58% 0.10% 0.07% 0.09%

Total 100.00% 100.00% 100.00% 100.00% 100.00% 100.00% 100.00%

Note: Estimated by weighting data in Table A- 97 by data in Table A- 98.

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 12-26 years of age was very small compared to total passenger car sales, so the calculated fraction of these vehicles was stored as zero.
A-147

Table A-100: Fuel Consumption for Off-Road Sources by Fuel Type (million gallons)

Vehicle Type/Year 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Aircrafta 19,560 18,320 20,304 19,745 19,284 18,640 19,407 19,714 18,973 18,670 17,984 16,030 15,762 15,262 14,914

 Aviation Gasoline 374 329 302 291 281 251 260 294 278 263 235 221 225 225 209

 Jet Fuel 19,186 17,991 20,002 19,454 19,004 18,389 19,147 19,420 18,695 18,407 17,749 15,809 15,537 15,036 14,705

 Commercial 

Aviation 11,569 12,136 14,672 13,121 12,774 12,943 13,147 13,976 14,426 14,708 13,400 12,588 11,931 12,067 11,932

Ships and Other 

Boats 4,507 5,789 6,431 4,416 4,834 4,089 4,300 4,881 5,143 5,746 4,950 4,379 4,910 5,084 4,510

 Diesel 1,043 1,546 1,750 1,630 1,592 1,711 1,347 1,470 1,409 1,489 1,470 1,480 1,446 1,727 1,475

 Gasoline 1,403 1,597 1,653 1,655 1,654 1,648 1,640 1,630 1,620 1,610 1,600 1,591 1,578 1,567 1,557

 Residual 2,061 2,646 3,028 1,131 1,588 730 1,313 1,781 2,115 2,647 1,880 1,308 1,886 1,791 1,477

Construction/ 

Mining Equipmentb 4,160 4,835 5,439 5,897 6,067 6,248 6,428 6,520 6,656 6,684 6,835 6,960 7,204 7,307 7,473

 Diesel 3,674 4,387 5,095 5,241 5,386 5,532 5,678 5,823 5,968 6,113 6,258 6,403 6,547 6,693 6,839

 Gasoline 486 448 344 657 681 716 751 697 688 571 577 558 656 614 634

Agricultural 

Equipmentc 3,134 3,698 3,875 4,107 4,220 4,324 4,648 4,715 4,948 4,862 4,517 4,641 4,739 4,928 5,086

 Diesel 2,321 2,772 3,222 3,305 3,388 3,471 3,554 3,637 3,719 3,801 3,883 3,965 4,046 4,129 4,211

 Gasoline 813 927 652 802 832 853 1,094 1,078 1,229 1,061 634 676 692 799 875

Rail 3,461 3,864 4,106 4,119 4,089 4,176 4,407 4,446 4,665 4,539 4,216 3,535 3,807 3,999 3,923

 Diesel 3,461 3,864 4,106 4,119 4,089 4,176 4,407 4,446 4,665 4,539 4,216 3,535 3,807 3,999 3,923

Otherd 5,916 6,525 6,826 7,657 7,840 8,049 8,263 8,281 8,396 8,256 8,387 8,482 8,830 8,795 8,730

 Diesel 1,423 1,720 2,016 2,079 2,144 2,210 2,275 2,340 2,405 2,471 2,536 2,601 2,666 2,731 2,797

 Gasoline 4,493 4,805 4,810 5,578 5,696 5,840 5,988 5,941 5,991 5,785 5,851 5,881 6,164 6,063 5,933

Total 40,738 43,031 46,980 45,941 46,334 45,528 47,453 48,558 48,781 48,755 46,888 44,027 45,252 45,375 44,636

Sources: AAR (2008 through 2013), APTA (2007 through 2013), BEA (1991 through 2013), Benson (2002 through 2004), DHS (2008), DOC (1991 through 2013), DESC (2013), DOE (1993 through 2013), DOT (1991 through 2013), 

EIA (2002), EIA (2007b), EIA (2008), EIA (2007 through 2013), EIA (1991 through 2013), EPA (2013b), FAA (2014), Gaffney (2007), and Whorton (2006 through 2012).

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.

 
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-101: 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%

2009-12 - - - - - 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.

- Not applicable.

Table A-102: 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%

2008-2012 - - - - - 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.
A-149

Table A-103: 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 - - - - - 20% 80%

2007 - - - - - 10% 90%

2008-2012 - - - - - 0% 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 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. 

- Not applicable.

Table A-104: 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 1996-2012

Diesel Medium- and Heavy-Duty Trucks and Buses

Uncontrolled 1960-1990

Moderate control 1991-2003

Advanced control 2004-2006

Aftertreatment 2007-2012

Motorcycles

Uncontrolled 1960-1995

Non-catalyst controls 1996-2012

Source: EPA (1998) and Browning (2005)

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

Table A-105: 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
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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 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-106: 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-107: 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
A-151

Diesel 0.08 0.18

Other Non-Road

All “Other” Categoriesc 0.08 0.18

Aircraft

Jet Fuel 0.10 0.00

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.

d Emissions of CH4 from jet fuels have been zeroed out across the time series. Recent research indicates that modern aircraft jet engines are typically net 

consumers of methane (Santoni et al, 2011). Methane is emitted at low power and idle operation, but at higher power modes aircraft engines consumer 

methane. Over the range of engine operating modes, aircraft engines are net consumers of methane on average. Based on this data, methane emissions 

factors for jet aircraft were changed to zero in this year’s Inventory to reflect the latest emissions testing data.
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-108: NOx Emissions from Mobile Combustion (Gg)

Fuel Type/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gasoline On-Road 5,746 4,560 3,812 3,715 4,917 4,600 4,284 3,967 3,800 3,633 3,295 2,962 2,722 2,749 2,564 

Passenger Cars 3,847 2,752 2,084 2,027 2,683 2,510 2,337 2,164 2,073 1,982 1,798 1,616 1,485 1,500 1,399 

Light-Duty Trucks 1,364 1,325 1,303 1,285 1,700 1,591 1,481 1,372 1,314 1,257 1,139 1,024 941 951 887 

Medium- and Heavy-Duty Trucks and 

Buses 515 469 411 390 516 483 450 416 399 381 346 311 286 288 269 

Motorcycles 20 14 13 14 18 17 16 15 14 13 12 11 10 10 9 

Diesel On-Road 2,956 3,493 3,803 3,338 4,418 4,134 3,849 3,564 3,414 3,265 2,960 2,661 2,446 2,470 2,303 

Passenger Cars 39 19 7 6 8 7 7 6 6 6 5 5 4 4 4 

Light-Duty Trucks 20 12 6 5 7 7 6 6 6 5 5 4 4 4 4 

Medium- and Heavy-Duty Trucks and 

Buses 2,897 3,462 3,791 3,326 4,403 4,119 3,836 3,552 3,403 3,254 2,950 2,652 2,438 2,461 2,295 

Alternative Fuel On-Roada

IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE

Non-Road 2,160 2,483 2,584 2,643 3,095 2,969 2,844 2,719 2,479 2,240 2,225 2,186 2,139 1,995 1,865 

Ships and Boats 402 488 506 544 640 614 588 562 513 463 460 452 443 413 386 

Rail 338 433 451 485 571 548 525 502 458 414 411 403 395 368 344 

Aircraftb 25 31 40 39 46 44 43 41 37 33 33 33 32 30 28 

Agricultural Equipmentc 437 478 484 480 560 537 514 492 448 405 402 395 387 361 337 

Construction/Mining Equipmentd 641 697 697 690 804 771 739 706 644 582 578 568 556 518 484 

Othere 318 357 407 406 474 454 435 416 379 343 341 335 327 305 285 

Total 10,862 10,536 10,199 9,696 12,430 11,703 10,977 10,250 9,694 9,138 8,481 7,809 7,307 7,214 6,732 

aNOx 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-109: CO Emissions from Mobile Combustion (Gg)

Fuel Type/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012f

Gasoline On-Road 98,328 74,673 60,657 56,716 45,690 43,102 40,513 37,925 35,485 33,046 29,418 24,554 25,294 23,793 23,793 

Passenger Cars 60,757 42,065 32,867 31,600 25,456 24,014 22,572 21,130 19,771 18,412 16,390 13,680 14,093 13,256 13,256 

Light-Duty Trucks 29,237 27,048 24,532 22,574 18,186 17,155 16,125 15,095 14,124 13,153 11,709 9,773 10,068 9,470 9,470 

Medium- and Heavy-Duty 

Trucks and Buses 8,093 5,404 3,104 2,411 1,942 1,832 1,722 1,612 1,509 1,405 1,251 1,044 1,075 1,011 1,011 

Motorcycles 240 155 154 131 106 100 94 88 82 77 68 57 59 55 55 

Diesel On-Road 1,696 1,424 1,088 869 700 661 621 581 544 506 451 376 388 365 365 

Passenger Cars 35 18 7 6 5 4 4 4 4 3 3 3 3 2 2 

Light-Duty Trucks 22 16 6 5 4 4 4 4 3 3 3 2 2 2 2 

Medium- and Heavy-Duty 

Trucks and Buses 1,639 1,391 1,075 858 691 652 613 574 537 500 445 371 383 360 360 

Alternative Fuel On-Roada

IE IE IE IE IE IE IE IE IE IE IE IE IE IE IE

Non-Road 19,337 21,533 21,814 22,266 20,187 19,976 19,766 19,556 18,181 16,807 16,134 14,290 13,786 13,329 13,329 

Ships and Boats 1,559 1,781 1,825 1,831 1,660 1,643 1,626 1,609 1,496 1,382 1,327 1,175 1,134 1,096 1,096 

Rail 85 93 90 90 81 81 80 79 73 68 65 58 56 54 54 

Aircraftb 217 224 245 233 212 209 207 205 191 176 169 150 145 140 140 

Agricultural Equipmentc 581 628 626 621 563 557 551 546 507 469 450 399 385 372 372 
A-153

Construction/Mining 

Equipmentd 1,090 1,132 1,047 1,041 944 934 924 914 850 786 754 668 645 623 623 

Othere 15,805 17,676 17,981 18,449 16,726 16,552 16,377 16,203 15,064 13,926 13,368 11,840 11,423 11,044 11,044 

Total 119,360 97,630 83,559 79,851 66,577 63,739 60,900 58,062 54,211 50,359 46,003 39,219 39,468 37,486 37,486 

aNOx 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.

f Criteria Air Pollutant emissions for 2012 were unavailable. Values from 2011 are used as proxy estimates.

Note: Totals may not sum due to independent rounding.

IE = Included Elsewhere

Table A-110: NMVOCs Emissions from Mobile Combustion (Gg)

Fuel Type/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gasoline On-Road 8,110 5,819 4,615 4,285 3,437 3,274 3,111 2,948 2,970 2,993 2,623 2,383 2,393 2,076 1,967 

Passenger Cars 5,120 3,394 2,610 2,393 1,919 1,828 1,737 1,646 1,659 1,671 1,465 1,331 1,336 1,159 1,098 

Light-Duty Trucks 2,374 2,019 1,750 1,664 1,335 1,271 1,208 1,145 1,153 1,162 1,019 926 929 806 764 

Medium- and Heavy-Duty 

Trucks and Buses 575 382 232 206 165 157 150 142 143 144 126 115 115 100 95 

Motorcycles 42 24 23 22 18 17 16 15 15 15 13 12 12 11 10 

Diesel On-Road 406 304 216 207 166 158 151 143 144 145 127 115 116 100 95 

Passenger Cars 16 8 3 3 2 2 2 2 2 2 2 2 2 1 1 

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

Medium- and Heavy-Duty 

Trucks and Buses 377 286 209 201 161 153 146 138 139 140 123 112 112 97 92 

Alternative Fuel On-RoadaIE IE IE IE IE IE IE IE IE IE IE IE IE IE IE

Non-Road 2,415 2,622 2,398 2,379 2,774 2,708 2,642 2,576 2,469 2,361 2,309 2,153 2,087 1,941 1,863 

Ships and Boats 608 739 744 730 851 831 811 791 757 724 708 661 640 596 572 

Rail 33 36 35 35 41 40 39 38 37 35 34 32 31 29 28 

Aircraftb 28 28 24 19 22 22 21 21 20 19 19 17 17 16 15 

Agricultural Equipmentc 85 86 76 72 84 82 80 78 75 72 70 66 64 59 57 

Construction/Mining 

Equipmentd 149 152 130 125 146 142 139 135 130 124 121 113 110 102 98 

Othere 1,512 1,580 1,390 1,397 1,629 1,590 1,552 1,513 1,450 1,387 1,356 1,264 1,225 1,140 1,094 

Total 10,932 8,745 7,230 6,872 6,377 6,140 5,903 5,667 5,583 5,498 5,059 4,652 4,596 4,118 3,925 

aNOx 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.

f Note: Totals may not sum due to independent rounding.

IE = Included Elsewhere
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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- 101 through Table A- 104 show the years in which these 

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

and were compiled from EPA (1993, 1994a, 1994b, 1998, 1999a) and IPCC/UNEP/OECD/IEA (1997).

Uncontrolled

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

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

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

Gasoline Emission Controls

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

Non-catalyst

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

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

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

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

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

Oxidation Catalyst 

This control technology designation represents the introduction of the catalytic converter, 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.

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 
A-155

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

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

sulfur fuels and 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 three levels of emissions control for diesel vehicles.

Moderate control

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

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

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

1994.

Advanced control

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

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

from 1996 through 2006.

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 report 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, 
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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

Table A- 111 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.
A-157

Table A-111: CO2 Emissions from Non-Transportation Mobile Sources (Tg CO2 Eq.) 

Fuel Type/Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Agricultural Equipmenta 31.0 36.6 38.8 41.0 42.1 43.1 46.1 46.8 49.0 48.4 45.4 46.7 47.6 49.4 51.0 

Construction/Mining Equipmentb 42.0 48.9 55.3 59.5 61.2 63.0 64.9 65.9 67.3 67.8 69.3 70.6 73.0 74.1 75.8 

Other Sourcesc 54.5 59.8 62.8 70.2 72.0 73.9 76.0 76.2 77.6 76.7 77.7 78.6 81.8 81.6 81.1 

Total 127.6 145.4 156.9 170.7 175.3 180.0 187.0 188.9 193.9 193.0 192.4 195.9 202.4 205.1 207.8 

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 transportationrelated 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- 112 below presents these HFC emissions. Table A- 113 presents all transportation and mobile source greenhouse gas emissions, including HFC 

emissions.

Table A-112: HFC Emissions from Transportation Sources

Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Mobile AC - 17.2 48.7 53.7 56.9 59.4 62.0 64.3 66.3 68.0 69.4 69.2 66.9 62.5 58.5 

Passenger Cars - 10.1 25.6 27.9 29.3 30.0 30.8 31.4 32.2 32.6 32.9 32.1 30.4 27.6 25.2 

Light-Duty Trucks - 7.1 23.1 25.8 27.6 29.4 31.2 32.8 34.2 35.4 36.4 37.0 36.5 34.9 33.3 

Comfort Cooling for Trains and Buses - + 0.1 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 

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

Transit Buses - + + + + + + + + + + + + + + 

Rail - + + + + + + + + + + + + + + 

Refrigerated Transport - 2.3 9.6 10.5 11.2 12.0 12.8 13.2 13.6 13.8 13.8 13.9 13.9 14.0 14.0 

Medium- and Heavy-Duty Trucks - 1.7 7.4 8.1 8.7 10.0 10.7 11.1 11.4 11.5 11.6 11.6 11.6 11.7 11.7 

Rail - 0.5 2.0 2.2 2.4 1.9 2.1 2.1 2.2 2.2 2.2 2.2 2.3 2.3 2.3 

Ships and Other Boats - + 0.1 0.1 0.2 + + + + + + + + + + 

Total - 19.6 58.4 64.3 68.3 71.6 75.0 77.8 80.2 82.1 83.6 83.5 81.3 76.9 72.9 

Note: Totals may not sum due to independent rounding. 

+ Less than 0.05 Tg CO2 Eq. 

- Unreported or zero
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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

Type

Table A- 113 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 2,050.7 Tg CO2 Eq. in 2012, an 

increase of 22 percent from 1990. Transportation sources account for 1,841.0 Tg CO2 Eq. while non-transportation mobile 

sources account for 209.8 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- 109 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 

estimates for jet fuel and aviation gasoline and CH4 estimates for aviation gasoline 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 2014). Emissions of CH4 from jet fuels are no longer considered to be emitted 

across the time series from aircraft gas turbine engines burning jet fuel A at higher power settings.54 Recent research 

indicates that modern aircraft jet engines are typically net consumers of methane (Santoni et al, 2011). Methane is emitted 

at low power and idle operation, but at higher power modes aircraft engines consumer methane. Over the range of engine 

operating modes, aircraft engines are net consumers of methane on average. Based on this data, methane emissions factors 

for jet aircraft were reported as zero to reflect the latest emissions testing data.

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 2013). 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 1,841.0 Tg 

CO2 Eq. in 2012.

On-road vehicles were responsible for about 76 percent of all transportation and non-transportation mobile GHG 

emissions in 2012. 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 2012, greenhouse gas emissions from passenger cars increased by 21 percent, while emissions from 

light-duty trucks increased by one percent.

55 Meanwhile, greenhouse gas emissions from medium- and heavy-duty trucks 

increased 75 percent between 1990 and 2012, reflecting the increased volume of total freight movement and an increasing 

share transported by trucks.

 

54 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, Turbojet and 

Turboprop Engines,” EPA-420-R-09-901, May 27, 2009 (see http://www.epa.gov/otaq/regs/nonroad/aviation/420r09901.pdf>

55 In 2011 FHWA changed how they defined vehicle types for the purposes of reporting VMT for the years 2007-2010. The old approach to 

vehicle classification was based on body type and split passenger vehicles into “Passenger Cars” and “Other 2 Axle 4-Tire Vehicles”. The new 

approach is a vehicle classification system based on wheelbase. Vehicles with a wheelbase less than or equal to 121 inches are counted as 

“Light-duty Vehicles –Short Wheelbase”. Passenger vehicles with a Wheelbase greater than 121 inches are counted as “Light-duty Vehicles - 

Long Wheelbase”. This change in vehicle classification has moved some smaller trucks and sport utility vehicles from the light truck category to 

the passenger vehicle category in this emission inventory. These changes are reflected in a large drop in light-truck emissions between 2006 and 

2007.
A-159

Greenhouse gas emissions from aircraft decreased 23 percent between 1990 and 2012. Emissions from military 

aircraft decreased 65 percent between 1990 and 2012. Commercial aircraft emissions rose 27 percent between 1990 and 

2007 then dropped 19 percent from 2007 to 2012, a change of approximately 3.1 percent between 1990 and 2012.

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

industrial/commercial equipment, emitted approximately 209.8 Tg CO2 Eq. in 2012. Together, these sources emitted more 

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

rapidly, growing approximately 63 percent between 1990 and 2012. 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- 114 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 2012). Emissions of CO2 from transportation and mobile sources increased by 

323.1 Tg CO2 Eq. between 1990 and 2012. In contrast, the combined emissions of CH4 and N2O decreased by 30.4 Tg 

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

emissions.

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

no emissions in 1990 to 72.9 Tg CO2 Eq. in 2012 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- 115 and Table A- 116 present greenhouse gas estimates from transportation, broken down into the 

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

aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A- 115. 

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- 116. 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- 113. In addition, estimates of 

fuel consumption from DOE (1993 through 2013) were used to allocate rail emissions between passenger and freight 

categories.

In 2012, passenger transportation modes emitted 1,296.0Tg CO2 Eq., while freight transportation modes emitted 

524.5 Tg CO2 Eq. Between 1990 and 2012, the percentage growth of greenhouse gas emissions from freight sources was 

49 percent, while emissions from passenger sources grew by 12 percent. This difference in growth is due largely to the 

rapid increase in emissions associated with medium- and heavy-duty trucks.

 

56 The decline in CFC emissions is not captured in the official transportation estimates.
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-113: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (Tg CO2 Eq.)

Mode / Vehicle Type / 

Fuel Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Percent

Change 

1990-2012

Transportation Totala 1,556.3 1700.4 1933.6 1920.2 1961.0 1953.7 2002.0 2022.0 2019.8 2031.5 1939.9 1866.9 1880.9 1856.4 1841.0 18%

On-Road Vehicles 1,235.2 1371.9 1577.8 1589.5 1628.4 1641.1 1677.5 1687.7 1687.7 1694.1 1621.6 1581.4 1587.9 1564.6 1558.4 26%

Passenger Cars 657.4 646.0 696.6 703.2 716.9 695.6 693.1 712.6 688.0 855.4 817.9 811.5 805.8 798.0 793.8 21%

Gasoline 649.4 627.9 667.3 671.6 683.9 661.3 658.0 676.9 651.6 818.6 781.2 775.7 771.7 766.2 764.4 18%

Diesel 7.9 7.9 3.7 3.7 3.7 4.2 4.3 4.2 4.1 4.1 3.7 3.6 3.8 4.1 4.2 -47%

AFVs + 0.1 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 421%

HFCs from Mobile AC + 10.1 25.6 27.9 29.3 30.0 30.8 31.4 32.2 32.6 32.9 32.1 30.4 27.6 25.2 NA

Light-Duty Trucks 336.6 437.1 513.5 520.4 531.6 567.1 589.9 553.1 566.9 371.7 354.8 359.9 359.1 343.1 338.4 1%

Gasoline 324.5 414.6 469.8 473.2 481.4 509.7 528.9 493.0 504.7 321.7 304.4 309.5 308.6 293.5 290.3 -11%

Diesel 11.5 14.9 20.1 20.8 21.9 27.2 29.0 25.9 26.8 13.6 12.1 12.1 12.6 13.2 13.2 15%

AFVs 0.6 0.5 0.5 0.6 0.7 0.8 0.9 1.3 1.2 1.0 1.8 1.2 1.3 1.5 1.5 164%

HFCs from Mobile AC + 7.1 23.1 25.8 27.6 29.4 31.2 32.8 34.2 35.4 36.4 37.0 36.5 34.9 33.3 NA

Medium- and HeavyDuty Trucks 231.1 277.8 354.6 353.8 368.1 365.9 377.7 408.4 418.6 444.7 427.0 389.2 402.9 402.4 403.4 75%

Gasoline 39.5 36.8 37.0 36.1 36.5 31.6 31.9 35.8 36.3 47.6 48.1 44.3 44.2 40.7 40.3 2%

Diesel 190.7 238.6 309.9 309.3 322.6 323.8 334.7 361.0 370.4 385.0 366.5 332.6 346.3 349.3 350.6 84%

AFVs 0.9 0.6 0.3 0.4 0.4 0.5 0.4 0.5 0.6 0.5 0.8 0.7 0.7 0.8 0.8 -12%

HFCs from 

Refrigerated 

Transport + 1.7 7.4 8.1 8.7 10.0 10.7 11.1 11.4 11.5 11.6 11.6 11.6 11.7 11.7 NA

Buses 8.4 9.2 11.2 10.3 10.0 10.8 15.0 12.1 12.3 18.0 17.4 16.5 16.3 17.5 18.6 122%

Gasoline 0.4 0.4 0.4 0.4 0.3 0.3 0.5 0.4 0.4 0.7 0.8 0.8 0.8 0.8 0.8 131%

Diesel 8.0 8.7 10.2 9.3 8.8 9.5 13.5 10.6 10.8 15.9 15.2 14.1 14.1 15.2 16.3 103%

AFVs + 0.1 0.5 0.5 0.6 0.7 0.9 0.9 0.8 1.0 1.1 1.2 1.1 1.1 1.1 40484%

HFCs from Comfort 

Cooling + 0.0 0.1 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.4 0.4 0.4 0.4 0.4 NA

Motorcycles 1.8 1.8 1.9 1.7 1.7 1.7 1.8 1.7 1.9 4.3 4.5 4.3 3.8 3.7 4.3 141%

Gasoline 1.8 1.8 1.9 1.7 1.7 1.7 1.8 1.7 1.9 4.3 4.5 4.3 3.8 3.7 4.3 141%

Aircraft 189.2 176.7 199.4 194.0 189.4 183.1 190.7 193.7 186.4 183.4 176.7 157.5 154.8 149.9 146.5 -23%

General Aviation 

Aircraft 43.0 35.8 35.9 43.7 45.1 36.9 41.9 40.1 30.1 24.4 30.5 21.2 26.7 22.5 19.9 -54%

Jet Fuel 39.8 33.0 33.4 41.2 42.7 34.7 39.7 37.6 27.7 22.2 28.5 19.4 24.8 20.6 18.2 -54%

Aviation Gasoline 3.2 2.8 2.6 2.5 2.4 2.1 2.2 2.5 2.4 2.2 2.0 1.9 1.9 1.9 1.8 -44%

Commercial Aircraft 110.9 116.4 140.7 125.8 122.5 124.1 126.0 134.0 138.3 141.0 128.5 120.7 114.4 115.7 114.4 3%

Jet Fuel 110.9 116.4 140.7 125.8 122.5 124.1 126.0 134.0 138.3 141.0 128.5 120.7 114.4 115.7 114.4 3%

Military Aircraft 35.3 24.5 22.9 24.5 21.9 22.2 22.7 19.5 18.0 18.0 17.7 15.6 13.7 11.7 12.2 -65%

Jet Fuel 35.3 24.5 22.9 24.5 21.9 22.2 22.7 19.5 18.0 18.0 17.7 15.6 13.7 11.7 12.2 -65%

Ships and Boatsb 45.1 58.6 61.0 42.7 47.6 37.3 40.1 45.2 48.4 55.2 45.9 39.3 45.3 47.0 40.8 -10%

Gasoline 12.6 14.1 10.0 14.6 14.6 14.4 14.4 14.2 14.1 14.0 13.6 13.5 13.3 13.1 13.1 4%

Distillate Fuel 9.6 14.9 17.1 15.8 15.4 15.3 11.5 11.4 10.9 11.7 11.5 11.6 11.3 14.2 11.6 20%

Residual Fuel 22.9 29.5 33.8 12.2 17.4 7.6 14.2 19.6 23.4 29.5 20.7 14.2 20.8 19.7 16.1 -30%

HFCs from Refrigerated 

Transport + 0.0 0.1 0.1 0.2 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NA

Rail 39.0 43.7 48.1 48.6 48.2 49.5 52.3 53.0 55.1 54.4 50.7 43.4 46.3 48.0 46.9 20%

Distillate Fuel 35.8 40.0 42.5 42.6 42.3 43.2 45.6 46.0 48.3 47.0 43.6 36.6 39.4 41.4 40.6 13%

Electricity 3.1 3.1 3.5 3.7 3.5 4.3 4.6 4.8 4.6 5.1 4.7 4.5 4.5 4.3 3.9 27%
A-161

Other Emissions from 

Rail Electricity Use 0.1 0.1 + + + + 0.1 0.1 0.1 0.1 + + + + + -31%

HFCs from Comfort 

Cooling + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 NA

HFCs from Refrigerated 

Transport + 0.5 2.0 2.2 2.4 1.9 2.1 2.1 2.2 2.2 2.2 2.2 2.3 2.3 2.3 NA

Pipelinesc 36.0 38.2 35.2 34.4 36.4 32.5 31.1 32.2 32.3 34.2 35.6 36.7 37.1 37.8 40.1 11%

Natural Gas 36.0 38.2 35.2 34.4 36.4 32.5 31.1 32.2 32.3 34.2 35.6 36.7 37.1 37.8 40.1 11%

Other Transportation 11.8 11.3 12.1 11.1 10.9 10.1 10.2 10.2 9.9 10.2 9.5 8.5 9.5 9.0 8.3 -30%

Lubricants 11.8 11.3 12.1 11.1 10.9 10.1 10.2 10.2 9.9 10.2 9.5 8.5 9.5 9.0 8.3 -30%

Non-Transportation 

Mobile Total 128.8 146.8 158.3 172.3 177.0 181.7 188.7 190.7 195.8 194.8 194.2 197.7 204.3 207.0 209.8 63%

Agricultural Equipmentd 31.4 37.0 39.2 41.4 42.5 43.6 46.6 47.3 49.6 49.0 45.9 47.2 48.2 50.0 51.5 64%

Gasoline 7.3 8.3 5.8 7.1 7.4 7.6 9.8 9.6 11.0 9.6 5.7 6.1 6.2 7.2 7.8 7%

Diesel 24.1 28.7 33.4 34.3 35.1 36.0 36.8 37.7 38.6 39.4 40.3 41.1 41.9 42.8 43.7 81%

Construction/ Mining 

Equipmente 42.4 49.4 55.8 60.1 61.8 63.6 65.4 66.5 67.9 68.4 69.9 71.2 73.6 74.8 76.4 80%

Gasoline 4.4 4.0 3.1 5.8 6.1 6.4 6.7 6.2 6.2 5.1 5.2 5.0 5.9 5.5 5.7 30%

Diesel 38.0 45.4 52.7 54.2 55.7 57.2 58.8 60.3 61.8 63.3 64.8 66.3 67.8 69.3 70.8 86%

Other Equipmentf 55.0 60.4 63.4 70.9 72.7 74.5 76.7 76.9 78.3 77.4 78.4 79.3 82.5 82.3 81.8 49%

Gasoline 40.3 42.6 42.5 49.3 50.5 51.7 53.1 52.7 53.4 51.8 52.2 52.4 54.9 54.0 52.9 31%

Diesel 14.7 17.8 20.9 21.5 22.2 22.9 23.5 24.2 24.9 25.6 26.2 26.9 27.6 28.3 28.9 97%

Transportation and NonTransportation Mobile 

Total 1,685.1 1847.1 2091.9 2092.5 2137.9 2135.4 2190.7 2212.7 2215.6 2226.3 2134.2 2064.7 2085.2 2063.4 2050.7 22%

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 natural gas 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-114: Transportation and Mobile Source Emissions by Gas (Tg CO2 Eq.)

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Percent Change 

1990-2012

CO2 1,636.4 1,769.3 1,977.1 1,974.6 2,020.3 2,018.2 2,072.9 2,095.6 2,099.3 2,112.9 2,023.1 1,956.6 1,981.4 1,966.3 1,959.5 20%

N2O 44.0 54.0 53.1 50.4 46.4 42.8 40.2 36.9 33.8 29.0 25.5 22.7 20.7 18.5 16.5 -62%

CH4 4.6 4.2 3.3 3.2 2.8 2.6 2.5 2.4 2.3 2.1 1.9 1.8 1.8 1.7 1.7 -63%

HFC + 19.6 58.4 64.3 68.3 71.6 75.0 77.8 80.2 82.1 83.6 83.5 81.3 76.9 72.9 N/A

Total 1,685.0 1,847.0 2,091.9 2,092.5 2,137.9 2,135.3 2,190.6 2,212.6 2,215.5 2,226.2 2,134.1 2,064.6 2,085.2 2,063.4 2,050.7 22%

- 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.
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2012 (Tg CO2 Eq.) 

Table A-115: Greenhouse Gas Emissions from Passenger Transportation (Tg CO2 Eq.)

Vehicle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Percent Change 

1990-2012

On-Road Vehicles 1,004.1 1,094.1 1,223.2 1,235.7 1,260.2 1,275.2 1,299.9 1,279.4 1,269.1 1,249.4 1,194.5 1,192.3 1,185.1 1,162.2 1,155.1 15%

Passenger Cars 657.4 646.0 696.6 703.2 716.9 695.6 693.1 712.6 688.0 855.4 817.9 811.5 805.8 798.0 793.8 21%

Light-Duty Trucks 336.6 437.1 513.5 520.4 531.6 567.1 589.9 553.1 566.9 371.7 354.8 359.9 359.1 343.1 338.4 1%

Buses 8.4 9.2 11.2 10.3 10.0 10.8 15.0 12.1 12.3 18.0 17.4 16.5 16.3 17.5 18.6 122%

Motorcycles 1.8 1.8 1.9 1.7 1.7 1.7 1.8 1.7 1.9 4.3 4.5 4.3 3.8 3.7 4.3 141%

Aircraft 134.7 132.0 152.2 147.7 146.4 139.5 146.8 152.7 146.6 144.9 141.0 125.2 124.8 122.2 118.6 -12%

General Aviation 43.0 35.8 35.9 43.7 45.1 36.9 41.9 40.1 30.1 24.4 30.5 21.2 26.7 22.5 19.9 -54%

Commercial Aircraft 91.7 96.2 116.3 104.0 101.3 102.6 104.9 112.6 116.5 120.5 110.5 104.0 98.1 99.6 98.6 8%

Recreational Boats 14.5 16.4 12.7 17.4 17.4 17.4 17.4 17.4 17.3 17.3 17.0 16.9 16.8 16.8 16.8 15%

Passenger Rail 4.4 4.5 5.2 5.4 5.1 5.8 6.0 6.2 6.0 6.6 6.3 6.2 6.2 6.0 5.6 27%

Total 1,157.6 1,247.0 1,393.3 1,406.2 1,429.1 1,437.9 1,470.1 1,455.7 1,439.1 1,418.2 1,358.8 1,340.6 1,332.9 1,307.1 1,296.0 12%
A-163

Note: Data from DOE (1993 through 2013) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. 

Table A-116: Greenhouse Gas Emissions from Domestic Freight Transportation (Tg CO2 Eq.)

By Mode 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

% Change 

1990-2012

Trucking 231.1 277.8 354.6 353.8 368.1 365.9 377.7 408.4 418.6 444.7 427.0 389.2 402.9 402.4 403.4 75%

Freight Rail 34.5 39.1 42.8 43.1 43.1 43.7 46.2 46.7 49.0 47.8 44.4 37.2 40.0 42.0 41.2 19%

Ships and Other Boats 30.6 42.2 48.3 25.4 30.1 19.9 22.7 27.9 31.1 37.9 28.9 22.4 28.5 30.3 24.0 -22%

Pipelinesa 36.0 38.2 35.2 34.4 36.4 32.5 31.1 32.2 32.3 34.2 35.6 36.7 37.1 37.8 40.1 11%

Commercial Aircraft 19.2 20.1 24.4 21.8 21.2 21.5 21.1 21.4 21.8 20.5 18.0 16.7 16.3 16.1 15.8 -18%

Total 351.5 417.4 505.3 478.5 499.0 483.4 498.8 536.5 552.8 585.0 553.9 502.2 524.8 528.5 524.5 49%

a Pipelines reflect CO2 emissions from natural gas powered pipelines transporting natural gas

Note: Data from DOE (1993 through 2013) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates.
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.3. Methodology for Estimating Emissions from Commercial Aircraft Jet Fuel 

Consumption

IPCC Tier 3B Method: Commercial aircraft jet fuel burn and carbon dioxide (CO2) emissions estimates were 

developed by the U.S. Federal Aviation Administration (FAA) using radar-informed data from the FAA Enhanced Traffic 

Management System (ETMS) for 2000 through 2012 as modeled with the Aviation Environmental Design Tool (AEDT). 

This bottom-up approach is built from modeling dynamic aircraft performance for each flight occurring within an 

individual calendar year. The analysis incorporates data on the aircraft type, date, flight identifier, departure time, arrival 

time, departure airport, arrival airport, ground delay at each airport, and real-world flight trajectories. To generate results 

for a given flight within AEDT, the radar-informed aircraft data is correlated with engine and aircraft performance data to 

calculate fuel burn and exhaust emissions. Information on exhaust emissions for in-production aircraft engines comes 

from the International Civil Aviation Organization (ICAO) Aircraft Engine Emissions Databank (EDB). This bottom-up 

approach is in accordance with the Tier 3B method from the 2006 IPCC Guidelines for National Greenhouse Gas 

Inventories.

International Bunkers: The IPCC guidelines define international aviation (International Bunkers) as emissions 

from flights that depart from one country and arrive in a different country. Bunker fuel emissions estimates for 

commercial aircraft were developed for this report for 2000 through 2012 using the same radar-informed data modeled 

with AEDT. Since this process builds estimates from flight-specific information, the emissions estimates for commercial 

aircraft can include emissions associated with the U.S. territories (i.e., American Samoa, Guam, Puerto Rico, U.S. Virgin 

Islands, Wake Island, and other U.S. Pacific Islands). However, to allow for the alignment of emissions estimates for 

commercial aircraft with other data that is provided without the U.S. territories, this annex includes emissions estimates

for commercial aircraft both with and without the U.S. territories included.

Time Series and Analysis Update: The FAA incrementally improves the consistency, robustness, and fidelity 

of the CO2 emissions modeling for commercial aircraft, which is the basis of the Tier3B inventories presented in this 

report. While the FAA does not anticipate significant changes to the AEDT model in the future, recommended 

improvements are limited by budget and time constraints, as well as data availability. For instance, previous reports 

included reported annual CO2 emission estimates for 2000 through 2005 that were modeled using the FAA’s System for 

assessing Aviation’s Global Emissions (SAGE). That tool and its capabilities were significantly improved after it was

incorporated and evolved into AEDT. For this report, the AEDT model was used to generate annual CO2 emission 

estimates for 2000, 2005, 2010, 2011 and 2012 only. The reported annual CO2 emissions values for 2001 through 2004 

were estimated from the previously reported SAGE data. Likewise, CO2 emissions values for 2006 through 2009 were 

estimated by interpolation to preserve trends from past reports.

The radar-informed method is not possible for 1990 through 1999 because radar data sets are not available for 

years prior to 2000. Instead, the FAA applied a Tier3B methodology by developing Official Airline Guide (OAG) 

schedule-informed estimates modeled with AEDT and great circle trajectories for 1990, 2000 and 2010. The ratios 

between the OAG schedule-informed and the radar-informed inventories for the years 2000 and 2010 were applied to the 

1990 OAG scheduled-informed inventory to generate the best possible CO2 inventory estimate for commercial aircraft in 

1990. The resultant 1990 CO2 inventory served as the reference for generating the additional 1991-1999 emissions 

estimates, which were established using previously available trends.

Notes on Revised 1990 CO2 Emissions Inventory for Commercial Aircraft: In 2013, the 1990 inventory was 

revised to achieve time series consistency. The observed change in 1990 emissions when compared to previous GHG 

inventory reports (EPA GHG Sources and Sinks Report circa 2000, 2002, 2007, 2010) was purely due to using a 

Tier3B methodology, and not reflective of revised industry performance and should not be used to infer or evaluate such 

performance.

To achieve time series consistency, the 1990 jet fuel burn was modeled with the latest AEDT version using great 

circle trajectories and OAG schedule information. There are uncertainties associated with the modeled 1990 data that do 

not exist for the modeled 2000 to 2012 data. Radar-based data is not available for 1990. The OAG schedule information 

generally includes fewer carriers than radar information, and this will result in a different fleet mix, and in turn, different 

CO2 emissions than would be quantified using a radar-based data set. For this reason, the FAA adjusted the OAGinformed schedule for 1990 with a ratio based on radar-informed information. In addition, radar trajectories are also 

generally longer than great circle trajectories. The revised 1990 CO2 emissions inventory now reflects only commercial 

aircraft jet fuel consumption, while previous reports may have aggregated jet fuel sales data from non-commercial aircraft 

into this category. Thus, it would be inappropriate to compare 1990 to future years for other than qualitative purposes. 
A-165

The revised 1990 commercial aircraft CO2 emissions inventory is approximately 3 percent lower than the 2012

CO2 emissions inventory. It is important to note that the distance flown increased 41percent over this twenty-three-year 

period and that fuel burn and aviation activity trends over the past two decades indicate significant improvements in 

commercial aviation’s ability to provide increased service levels while using less fuel.57

Methane Emissions: Contributions of methane (CH4) emissions from commercial aircraft are reported as zero. 

Years of scientific measurement campaigns conducted at the exhaust exit plane of commercial aircraft gas turbine engines 

have repeatedly indicated that CH4 emissions are consumed over the full mission flight envelope (Aircraft Emissions of 

Methane and Nitrous Oxide during the Alternative Aviation Fuel Experiment, Santoni et al., Environ. Sci. Technol., 2011, 

45, 7075-7082). As a result, the U.S. Environmental Protection Agency published that “...methane is no longer 

considered to be an emission from aircraft gas turbine engines burning Jet A at higher power settings and is, in fact, 

consumed in net at these higher powers.”

58 In accordance with the following statements in the 2006 IPCC Guidelines 

(IPCC 2006), the FAA does not calculate CH4 emissions for either the domestic or international bunker commercial 

aircraft jet fuel emissions inventories. “Methane (CH4) may be emitted by gas turbines during idle and by older 

technology engines, but recent data suggest that little or no CH4 is emitted by modern engines.” “Current scientific 

understanding does not allow other gases (e.g., N2O and CH4) to be included in calculation of cruise emissions.” (IPCC 

1999).

Results: The graph and table below, four jet fuel burn values are reported for each calendar year. These values 

are comprised of domestic and international fuel burn totals for the US 50 States and the US 50 States + Territories. Data 

are presented for domestic defined as jet fuel burn from any commercial aircraft flight departing and landing in the US 50 

States and for the US 50 States + Territories. The data presented as international is respective of the two different 

domestic definitions, and represents flights departing from the specified domestic area and landing anywhere in the world 

outside of that area.

Note that the graph and table present less fuel burn for the international US 50 States + Territories than for the 

international US 50 States. This is because the flights between the 50 states and US Territories are “international” when 

only the 50 states are defined as domestic, but they are “domestic” for the US 50 States + Territories definition.

 

57 Additional information on the AEDT modeling process is available at:

http://www.faa.gov/about/office_org/headquarters_offices/apl/research/models/

58 Recommended Best Practice for Quantifying Speciated Organic Gas Emissions from Aircraft Equipped with Turbofan, 

Turbojet and Turboprop Engines, EPA-420-R-09-901, May 27, 2009, http://www.epa.gov/otaq/aviation.htm. 
A-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-5: Commerical Aviation Fuel Burn for the United States and Terrirtories 

Note: Hollow markers are estimates from data generated by prior tools and methods. 

 1990 is estimated using non-radar methods. 

Table A- 117: Commercial Aviation Fuel Burn for the United States and Territories 

Year Region

Distance Flown Fuel Burn 

(M Gallon)

Fuel Burn 

(Tbtu) Fuel Burn (Kg) CO2 (Tg)

1990

Domestic US 50 States and US Territories 4,057,195,988 11,568 1,562 34,820,800,463 109.9

International US 50 States and US Territories 599,486,893 3,155 426 9,497,397,919 30.0

Domestic US 50 States 3,984,482,217 11,287 1,524 33,972,832,399 107.2

International US 50 States 617,671,849 3,228 436 9,714,974,766 30.7

1995 Domestic US 50 States and US Territories N/A 12,136 1,638 36,528,990,675 115.2

1996 Domestic US 50 States and US Territories N/A 12,492 1,686 37,600,624,534 118.6

1997 Domestic US 50 States and US Territories N/A 12,937 1,747 38,940,896,854 122.9

1998 Domestic US 50 States and US Territories N/A 12,601 1,701 37,930,582,643 119.7

1999 Domestic US 50 States and US Territories N/A 13,726 1,853 41,314,843,250 130.3

2000

Domestic US 50 States and US Territories 5,994,679,944 14,672 1,981 44,161,841,348 139.3

International US 50 States and US Territories 1,309,565,963 6,040 815 18,181,535,058 57.4

Domestic US 50 States 5,891,481,028 14,349 1,937 43,191,000,202 136.3

International US 50 States 1,331,784,289 6,117 826 18,412,169,613 58.1

2001

Domestic US 50 States and US Territories 5,360,977,447 13,121 1,771 39,493,457,147 124.6

International US 50 States and US Territories 1,171,130,679 5,402 729 16,259,550,186 51.3

Domestic US 50 States 5,268,687,772 12,832 1,732 38,625,244,409 121.9

International US 50 States 1,191,000,288 5,470 739 16,465,804,174 51.9

2002

Domestic US 50 States and US Territories 5,219,345,344 12,774 1,725 38,450,076,259 121.3

International US 50 States and US Territories 1,140,190,481 5,259 710 15,829,987,794 49.9

Domestic US 50 States 5,129,493,877 12,493 1,687 37,604,800,905 118.6

International US 50 States 1,159,535,153 5,326 719 16,030,792,741 50.6

2003 Domestic US 50 States and US Territories 5,288,138,079 12,942 1,747 38,956,861,262 122.9

0.00E+00

5.00E+09

1.00E+10

1.50E+10

2.00E+10

2.50E+10

3.00E+10

3.50E+10

4.00E+10

4.50E+10

5.00E+10

Fuel Burn (Kg)

States+Territories (Domestic) States+Territories (International) States (Domestic) States (International)
A-167

International US 50 States and US Territories 1,155,218,577 5,328 719 16,038,632,384 50.6

Domestic US 50 States 5,197,102,340 12,658 1,709 38,100,444,893 120.2

International US 50 States 1,174,818,219 5,396 728 16,242,084,008 51.2

2004

Domestic US 50 States and US Territories 5,371,498,689 13,146 1,775 39,570,965,441 124.8

International US 50 States and US Territories 1,173,429,093 5,412 731 16,291,460,535 51.4

Domestic US 50 States 5,279,027,890 12,857 1,736 38,701,048,784 122.1

International US 50 States 1,193,337,698 5,481 740 16,498,119,309 52.1

2005

Domestic US 50 States and US Territories 6,476,007,697 13,976 1,887 42,067,562,737 132.7

International US 50 States and US Territories 1,373,543,928 5,858 791 17,633,508,081 55.6

Domestic US 50 States 6,370,544,998 13,654 1,843 41,098,359,387 129.7

International US 50 States 1,397,051,323 5,936 801 17,868,972,965 56.4

2006

Domestic US 50 States and US Territories 5,894,323,482 14,426 1,948 43,422,531,461 137.0

International US 50 States and US Territories 1,287,642,623 5,939 802 17,877,159,421 56.4

Domestic US 50 States 5,792,852,211 14,109 1,905 42,467,943,091 134.0

International US 50 States 1,309,488,994 6,015 812 18,103,932,940 57.1

2007

Domestic US 50 States and US Territories 6,009,247,818 14,707 1,986 44,269,160,525 139.7

International US 50 States and US Territories 1,312,748,383 6,055 817 18,225,718,619 57.5

Domestic US 50 States 5,905,798,114 14,384 1,942 43,295,960,105 136.6

International US 50 States 1,335,020,703 6,132 828 18,456,913,646 58.2

2008

Domestic US 50 States and US Territories 5,475,092,456 13,400 1,809 40,334,124,033 127.3

International US 50 States and US Territories 1,196,059,638 5,517 745 16,605,654,741 52.4

Domestic US 50 States 5,380,838,282 13,105 1,769 39,447,430,318 124.5

International US 50 States 1,216,352,196 5,587 754 16,816,299,099 53.1

2009

Domestic US 50 States and US Territories 5,143,268,671 12,588 1,699 37,889,631,668 119.5

International US 50 States and US Territories 1,123,571,175 5,182 700 15,599,251,424 49.2

Domestic US 50 States 5,054,726,871 12,311 1,662 37,056,676,966 116.9

International US 50 States 1,142,633,881 5,248 709 15,797,129,457 49.8

2010

Domestic US 50 States and US Territories 5,652,264,576 11,931 1,611 35,912,723,830 113.3

International US 50 States and US Territories 1,474,839,733 6,044 816 18,192,953,916 57.4

Domestic US 50 States 5,554,043,585 11,667 1,575 35,116,863,245 110.8

International US 50 States 1,497,606,695 6,113 825 18,398,996,825 58.0

2011

Domestic US 50 States and US Territories 5,767,378,664 12,067 1,629 36,321,170,730 114.6

International US 50 States and US Territories 1,576,982,962 6,496 877 19,551,631,939 61.7

Domestic US 50 States 5,673,689,481 11,823 1,596 35,588,754,827 112.3

International US 50 States 1,596,797,398 6,554 885 19,727,043,614 62.2

2012

Domestic US 50 States and US Territories 5,735,605,432 11,932 1,611 35,915,745,616 113.3

International US 50 States and US Territories 1,619,012,587 6,464 873 19,457,378,739 61.4

Domestic US 50 States 5,636,910,529 11,672 1,576 35,132,961,140 110.8

International US 50 States 1,637,917,110 6,507 879 19,587,140,347 61.8

*Estimates for these years were derived from previously reported tools and methods
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.4. Methodology for Estimating CH4 Emissions from Coal Mining 

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

estimate emissions from underground mines. There are two sources of underground mine emissions: ventilation systems 

and degasification systems. These emissions are estimated on a mine-by-mine basis and then are summed to determine 

total emissions. The second step of the analysis involves estimating CH4 emissions from surface mines and post-mining 

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

estimating emissions from surface mines and post-mining activities consists of multiplying basin-specific coal production 

by basin-specific gas content and an emission factor.

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

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

recover and use the generated CH4, thereby reducing emissions to the atmosphere. Total CH4 emitted from underground 

mines equals the CH4 liberated from ventilation systems, plus the CH4 liberated from degasification systems, minus CH4

recovered and used.

Step 1.1: Estimate CH4 Liberated from Ventilation Systems

All coal mines with detectable CH4 emissions59 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 (MSHA 2013). 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, and 2008 through 2012, 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-118. Well over 90 

percent of all ventilation emissions were concentrated in these subsets of approximately 125-150 mines. 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-118). 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 2011, 

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. 

Since 2009, two coal mines have destroyed a portion of their CH4 emissions from ventilation systems using

thermal oxidation technology. The amount of methane destroyed through these two projects was determined through 

publicly-available emission reduction project information (CAR 2013). 

 

59 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-169

Table A-118: 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

2010 

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)**

All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**

 2011 All Mines Emitting at Least 0.1 MMCFD (Assumed to Account for 98.96% of Total)**

 2012 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, degasification emissions were estimated on a mine-bymine basis based on other sources of available data. For those mines that sell CH4 recovered from degasification systems 

to a pipeline, gas sales records were used to estimate CH4 liberated from degasification systems (see Step 1.3). For those 

mines venting CH4 from degasification wells, data reported by mines to EPA’s Greenhouse Gas Reporting Program 

(GHGRP) were used. Since 2011, EPA’s GHGRP has required underground coal mines liberating greater than 36,500,000 

actual cubic feet of CH4 per year (about 14,700 metric tons CO2 Eq.) to report their emissions directly to EPA. 

Step 1.3: Estimate CH4 Recovered from Degasification Systems and Utilized (Emissions Avoided)

In 2012, sixteen active coal mines had CH4 recovery and use projects, of which fourteen mines sold the 

recovered CH4 to a pipeline. One of the mines that sold gas to a pipeline also used CH4 to fuel a thermal coal dryer. One 

mine used recovered CH4 for electrical power generation, and another mine used recovered CH4 to heat mine ventilation 

air. For mines that utilize CH4 on-site, either the GHGRP (EPA 2013) or project-specific information (CAR 2013) was 

used to estimate CH4 liberated from degasification systems.

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. Alabama and West Virginia state 

agencies provided gas sales data (GSA 2013; WVGES 2013), which were used to estimate emissions avoided for these 

projects. Additionally, coal mine operators provided information on eligible pre-drainage wells drilled in advance of 

mining (JWR 2010, 2013). 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). The coal mine operators with the largest CH4 recovery and use projects 

provided this information (Consol 2013; JWR 2010, 2013), which was then used to estimate emissions avoided for a 

particular year. 
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities

Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining 

activities. For surface mines, basin-specific coal production was multiplied by basin-specific gas contents and a 150 

percent emission factor (to account for CH4 from over- and under-burden) to estimate CH4 emissions. This emission factor 

was revised downward in 2012 from 200 percent based on more recent studies (King 1994; Saghafi 2013). The 150 

percent emission factor was applied to all inventory years since 1990, retroactively. For post-mining activities, basinspecific coal production was multiplied by basin-specific gas contents and a 32.5 percent emission factor accounting for 

CH4 desorption during coal transportation and storage (Creedy 1993).

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-119, which presents coal basin definitions by basin and by state.

The Energy Information Administration’s (EIA) Annual Coal Report (2013) 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-119. For two states—West Virginia and Kentucky—county-level 

production data was used for the basin assignments because coal production occurred in geologically distinct coal basins 

within these states. Table A-120 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 analyses conducted in Canada and Australia on coals similar to those present in the U.S. (King 1994; 

Saghafi 2013), the surface mining emission factor used was conservatively estimated to be 150 percent of the in situ CH4

content of the basin. Furthermore, the post-mining emission factors used were estimated to be 25 to 40 percent of the 

average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was determined to be 32.5 

percent of the in situ CH4 content in the basin. Table A-121 presents the average in situ content for each basin, along with 

the resulting emission factor estimates.

Step 2.3: Estimate CH4 Emitted

The total amount of CH4 emitted from surface mines and post-mining activities was calculated by multiplying the 

coal production in each basin by the appropriate emission factors.

Table A-119 lists each of the major coal mine basins in the U.S. and the states in which they are located. As 

shown in Figure A-6, several coal basins span several states. Table A-120 shows annual underground, surface, and total 

coal production (in short tons) for each coal basin. Table A-121 shows the surface, post-surface, and post-underground 

emission factors used for estimating CH4 emissions for each of the categories. Table A-122 presents annual estimates of 

CH4 emissions for ventilation and degasification systems, and CH4 used and emitted by underground coal mines. Table A123 presents annual estimates of total CH4 emissions from underground, post-underground, surface, and post-surface 

activities. Table A-124 provides the total net CH4 emissions by state.

Table A-119: 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
A-171

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

Mississippi Warrior Basin

Missouri West Interior Basin

Montana North Great Plains Basin

New Mexico South West and Rockies Basin

North Dakota North Great Plains Basin

Ohio Northern Appalachian Basin

Oklahoma West Interior Basin

Pennsylvania. Northern Appalachian Basin

Tennessee Central Appalachian Basin

Texas West Interior Basin

Utah South West and Rockies Basin

Virginia Central Appalachian Basin

Washington Northwest Basin

West Virginia South Central Appalachian Basin

West Virginia North Northern Appalachian Basin

Wyoming North Great Plains Basin

Figure A-6: Locations of U.S Coal Basins
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-120: Annual Coal Production (Thousand Short Tons)

Basin 1990 2005 2008 2009 2010 2011 2012

Underground 

Coal 

Production 423,556 368,611 357,074 332,061 337,155 345,607 342,387

N. Appalachia 103,865 111,151 105,228 99,629 103,109 105,752 103,408

Cent. Appalachia 198,412 123,083 114,998 98,689 96,354 94,034 78,067

Warrior 17,531 13,295 12,281 11,505 12,513 10,879 12,570

Illinois 69,167 59,180 64,609 67,186 72,178 81,089 92,500

S. West/Rockies 32,754 60,865 55,781 50,416 44,368 45,139 45,052

N. Great Plains 1,722 572 3,669 4,248 8,208 8,179 10,345

West Interior 105 465 508 388 425 535 445

Northwest + + + + + + +

Surface Coal 

Production 602,753 762,191 813,321 740,175 764,709 754,871 672,748

N. Appalachia 60,761 28,873 30,413 26,552 26,082 26,382 21,411

Cent. Appalachia 94,343 112,222 118,962 97,778 89,788 90,778 69,721

Warrior 11,413 11,599 11,172 10,731 11,406 10,939 9,705

Illinois 72,000 33,702 34,266 34,837 32,911 34,943 34,771

S. West/Rockies 43,863 42,756 34,283 32,167 28,889 31,432 30,475

N. Great Plains 249,356 474,056 538,387 496,290 507,995 502,734 455,320

West Interior 64,310 52,263 44,361 39,960 46,136 55,514 49,293

Northwest 6,707 6,720 1,477 1,860 2,151 2,149 2,052

Total Coal 

Production 1,026,309 1,130,802 1,170,395 1,072,236 1,101,864 1,100,478 1,015,135

N. Appalachia 164,626 140,024 135,641 126,181 129,191 132,134 124,819

Cent. Appalachia 292,755 235,305 233,960 196,467 186,142 184,812 147,788

Warrior 28,944 24,894 23,453 22,236 23,919 21,818 22,275

Illinois 141,167 92,882 98,875 102,023 105,089 116,032 127,271

S. West/Rockies 76,617 103,621 90,064 82,583 73,257 76,571 75,527

N. Great Plains 251,078 474,628 542,056 500,538 516,203 510,913 465,665

West Interior 64,415 52,728 44,869 40,348 46,561 56,049 49,738

Northwest 6,707 6,720 1,477 1,860 2,151 2,149 2,052

Source for 1990-2012 data: EIA (1990 through 2012), Annual Coal Report. U.S. Department of Energy, Washington, DC, Table 1. Source for 2012 data: 

spreadsheet for the 2012 Annual Coal Report.

Note: Totals may not sum due to independent rounding.
A-173

Table A-121: Coal Underground, 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-122: Underground Coal Mining CH4 Emissions (Billion Cubic Feet)

Activity 1990 2005 2008 2009 2010 2011 2012

Ventilation Output 112 75 100 114 117 97 90

Adjustment Factor for Mine Data* 97.8% 97.8% 99.0% 99.0% 99% 99% 99%

Adjusted Ventilation Output 114 77 101 115 118 98 91

Degasification System Liberated 54 48 49 49 58 48 45

Total Underground Liberated 168 124 150 163 177 147 137

Recovered & Used (14) (37) (40) (40) (49) (42) (38)

Total 154 87 110 123 128 104 98

* Refer to Table A-118.

Note: Totals may not sum due to independent rounding

Table A-123: Total Coal Mining CH4 Emissions (Billion Cubic Feet)

Activity 1990 2005 2008 2009 2010 2011 2012

Underground Mining 154 87 110 123 128 104 98

Surface Mining 22 25 27 24 24 24 21

Post-Mining (Underground) 19 16 15 14 14 14 14

Post-Mining (Surface) 5 5 6 5 5 5 5

Total 200 132 157 166 171 148 138

Note: Totals may not sum due to independent rounding.

Table A-124: Total Coal Mining CH4 Emissions by State (Million Cubic Feet)

State 1990 2005 2008 2009 2010 2011 2012

Alabama 32,097 15,789 20,992 22,119 21,377 18,530 18,129

Alaska 50 42 43 54 63 63 60

Arizona 151 161 107 100 103 108 100

Arkansas 5 + 237 119 130 348 391

California 1 + - - - - -

Colorado 10,187 13,441 12,871 13,999 16,470 11,187 9,305

Illinois 10,180 6,488 7,568 7,231 8,622 7,579 9,763

Indiana 2,232 3,303 5,047 5,763 5,938 6,203 7,374

Iowa 24 + - - - - -

Kansas 45 11 14 12 8 2 1

Kentucky 10,018 6,898 9,986 12,035 12,303 10,592 7,993

Louisiana 64 84 77 73 79 168 80

Maryland 474 361 263 219 238 263 197
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Mississippi - 199 159 193 224 154 165

Missouri 166 3 15 28 29 29 26

Montana 1,373 1,468 1,629 1,417 1,495 1,445 1,160

New Mexico 363 2,926 3,411 3,836 3,956 4,187 2,148

North Dakota 299 306 303 306 296 289 281

Ohio 4,406 3,120 3,686 4,443 3,614 3,909 3,389

Oklahoma 226 825 932 624 436 360 499

Pennsylvania 21,864 17,904 20,684 22,939 23,372 17,708 17,773

Tennessee 276 115 86 69 67 60 35

Texas 1,119 922 783 704 823 922 887

Utah 3,587 4,787 5,524 5,449 5,628 3,651 3,624

Virginia 46,041 8,649 9,223 8,042 9,061 8,526 6,516

Washington 146 154 - - - - -

West Virginia 48,335 29,745 36,421 40,452 40,638 35,709 33,608

Wyoming 6,671 14,745 16,959 15,627 16,032 15,916 14,507

Total 200,399 132,481 157,112 165,854 171,000 147,908 138,012

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 postmining activities. The following states have neither underground nor surface mining and thus report no emissions as a 

result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii, Idaho, Maine, Massachusetts, Michigan, 

Minnesota, Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, South 

Carolina, South Dakota, Vermont, and Wisconsin.
A-175

3.5. Methodology for Estimating CH4 and CO2 Emissions from Natural Gas Systems

As described in the main body text on Natural Gas Systems, the GHG Inventory methodology involves the 

calculation of CH4 and CO2 emissions for over 100 emissions sources, and then the summation of emissions for each 

natural gas sector stage. 

 

Step 1: Calculate Potential Methane 

Potential Methane Factors

The primary basis for potential CH4 factors and emission factors for non-combustion-related CO2 emissions from 

the U.S. natural gas industry is a detailed study by the Gas Research Institute and EPA (EPA/GRI 1996). The EPA/GRI 

study developed over 80 CH4 emission factors to characterize emissions from the various components within the operating 

stages of the U.S. natural gas system. Since the time of this study, practices and technologies have changed. While this 

study still represents best available data in many cases, using these emission factors alone to represent actual emissions 

without adjusting for emissions controls would in many cases overestimate emissions. For this reason, “potential 

methane” are calculated using the data, and then more recent data on voluntary and regulatory emission reduction 

activities (step 3) is deducted to calculate actual emissions. See main body text on Natural Gas Systems for more 

information.

For certain CH4 emissions sources, new data and information allows for net emissions to be calculated directly: 

gas well completions and workovers with hydraulic fracturing, liquids unloading, condensate storage tanks, and 

centrifugal compressors. For these sources, EPA developed emissions factors that directly reflect the use of control 

technologies. For gas well completions and workovers with hydraulic fracturing, separate emissions estimates were 

developed for hydraulically fractured completions and workovers that vent, flared hydraulic fracturing completions and 

workovers, hydraulic fracturing completions and workovers with RECs, and hydraulic fracturing completions and 

workovers with RECs that flare. For liquids unloading, separate emissions estimates were developed for wells with 

plunger lifts, and wells without plunger lifts. Likewise, for condensate tanks, emissions estimates were developed for tanks 

with and without control devices. Finally, for centrifugal compressors, separate emissions estimates were developed for 

compressors with wet and dry seals. 

For potential CH4 factors and emission factors used in the Inventory, see Table A-125 to Table A-130. Methane 

compositions from GTI 2001 are adjusted year to year using gross production for National Energy Modeling System 

(NEMS) oil and gas supply module regions from the EIA. These adjusted region-specific annual CH4 compositions are 

presented in Table A-131 (for general sources), Table A-132 (for gas wells without hydraulic fracturing), and Table A-133

(for gas wells with hydraulic fracturing). Therefore, emission factors may vary from year to year due to slight changes in 

the CH4 composition between each NEMS oil and gas supply module region. 

1990-2012 Inventory updates to potential emission factors and emission factors

The current Inventory includes an update to emission factors for gas well completions and workovers with 

hydraulic fracturing. Technology- specific national emission factors were developed based on 2011 and 2012 GHGRP 

data. The emission factors used for gas well completions and workovers with hydraulic fracturing are not potential factors, 

but are factors for actual emissions because control technologies are taken into account through the use of separate 

emission factors for each of the aforementioned categories. The updated factors are included in Table A-125. 

Activity Data

Activity data were taken from the following sources: DrillingInfo, Inc (DrillingInfo 2014); American Gas 

Association (AGA 1991–1998); Bureau of Ocean Energy Management, Regulation and Enforcement (previous Minerals 

and Management Service) (BOEMRE 2011a, 2011b, 2011c, 2011d); Monthly Energy Review (EIA 2012f, 2012g, 2012h, 

2011a, 2011b, 2011c, 2011d); Natural Gas Liquids Reserves Report (EIA 2005); Natural Gas Monthly (EIA 2012c, 2012d,

2012e, 2013a, 2013b, 2013c); the Natural Gas STAR Program annual emissions savings (EPA 2012a, 2013c); Oil and Gas 

Journal (OGJ 1997–2013); Pipeline and Hazardous Materials Safety Administration (PHMSA 2013); Federal Energy 

Regulatory Commission (FERC 2011); GHGRP data for natural gas systems (40 CFR 98, subpart W); and other Energy 

Information Administration publications (EIA 2001, 2004, 2010, 2011, 2012i, 2014). Data for estimating emissions from 

hydrocarbon production tanks were incorporated (EPA 1999). Coalbed CH4 well activity factors were taken from the 

Wyoming Oil and Gas Conservation Commission (Wyoming 2013) and the Alabama State Oil and Gas Board (Alabama 

2013). Activity data are presented in Table A-125 through Table A-130.
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

For many sources, recent direct activity data were not available. For these sources, either 2011 data was used as

proxy for 2012 data or a set of industry activity data drivers was developed and was used to update activity data. Drivers 

include statistics on gas production, number of wells, system throughput, miles of various kinds of pipe, and other 

statistics that characterize the changes in the U.S. natural gas system infrastructure and operations. For example, recent 

data on various types of field separation equipment in the production stage (i.e., heaters, separators, and dehydrators) were

unavailable. EPA determined that each of these types of field separation equipment relate to the number of non-associated 

gas wells. Using the number of each type of field separation equipment estimated by GRI/EPA in 1992, and the number 

of non-associated gas wells in 1992, EPA developed a factor that is used to estimate the number of each type of field 

separation equipment throughout the time series. The key activity drivers are presented in Table A-134.

EPA used DI Desktop, a production database maintained by DrillingInfo, Inc. (DrillingInfo) (DrillingInfo 2014), 

covering U.S. oil and natural gas wells to populate activity data for associated gas wells, non-associated gas wells, gas 

wells with hydraulic fracturing, and completions with hydraulic fracturing. EPA queried DI Desktop for relevant data on 

an individual well basis—including location, natural gas and liquids (i.e., oil and condensate) production by year, drill 

type (e.g., horizontal or vertical), and date of completion or first production. Associated gas wells were identified as any 

well within DI Desktop that EPA classified as producing “oil” or “oil and gas” based on the production type description 

and that had non-zero natural gas and liquids production. Non-associated gas wells were identified as any well that met 

either of the following criteria: (1) classified as “oil” or “oil and gas” producing and had zero liquids production, but nonzero natural gas production; or (2) classified as “gas” producing and had non-zero gas production. Both oil and condensate 

are included in the liquids production data in DI Desktop; therefore, the count of associated gas wells may include wells 

that produce gas and condensate only. Gas wells with hydraulic fracturing were assumed to be the subset of the nonassociated gas wells that were horizontally drilled and/or located in an unconventional formation (i.e., shale, tight sands, or 

coalbed). Unconventional formations were identified based on well basin, reservoir, and field data reported in DI Desktop 

referenced against a formation type crosswalk developed by EIA (EIA 2012a). 

For 1990 through 2010, gas well completions with hydraulic fracturing were identified as a subset of the gas 

wells with hydraulic fracturing that had a date of completion or first production in the specified year. To calculate 

workovers for 1990 through 2010, EPA applied a refracture rate of 1 percent (i.e. 1 percent of all wells with hydraulic 

fracturing are assumed to be refractured in a given year) to the total counts of wells with hydraulic fracturing from the 

DrillingInfo data. For 2011 and 2012, EPA used GHGRP data for the total number of well completions and workovers. 

The GHGRP data represents a subset of the national completions and workovers, due to the reporting threshold, and 

therefore using this data without scaling it up to national level results in an underestimate. However, because EPA’s 

GHGRP counts of completions and workovers were higher than national counts of completions and workovers, obtained 

using DIDesktop data, EPA directly used the GHGPR data for completions and workovers for 2011 and 2012. 

The methodological update for gas well completions and workovers with hydraulic fracturing required updated 

activity data on RECs use and flaring for use with the new emission factors. EPA calculated the percentage of gas well 

completions and workovers with hydraulic fracturing in the each of the four categories using 2011 Subpart W data. EPA 

assumed 0 percent RECs use from 1990 through 2000, used GHGRP RECs percentage for 2011 and 2012, and then used 

linear interpolation between the 2000 and 2011 percentages. For flaring, EPA used an assumption of 10 percent (the 

average of the percent of completions and workovers that were flared in 2011 and 2012 GHGRP data) flaring from 1990-

2010 to recognize that some flaring has occurred over that time period. For 2011 and 2012, EPA used the GHGRP data on 

flaring. 

Step 2: Compile Reductions Data

The emissions calculated in Step 1 above represent expected emissions from an activity in the absence of 

emissions controls (with the exceptions of gas well completions and workovers with hydraulic fracturing, liquids 

unloading, centrifugal compressors, and condensate tanks, as noted above), and do not take into account any use of 

technologies or practices that reduce emissions. To take into account use of such technologies, data were collected on 

voluntary and regulatory reductions. Voluntary reductions included in the Inventory were those reported to Gas STAR for 

activities such as replacing a high bleed pneumatic device with a low bleed device and replacing wet seals with dry seals at 

reciprocating compressors. Regulatory actions reducing emissions include NESHAP regulations for dehydrator vents and 

condensate tanks. 

Voluntary reductions. Industry partners report CH4 emission reductions by project to the Natural Gas STAR 

Program. The reductions from the implementation of specific technologies and practices (e.g., vapor recovery units, 

centrifugal compressors are calculated by the reporting partners using actual measurement data or equipment-specific 

emission factors. Natural Gas STAR Partners do not report reductions when they are required due to regulation. 

Therefore, the Inventory assumes there is no overlap between the reductions reported through Natural Gas STAR and 

reductions due to state regulations. The reductions undergo quality assurance and quality control checks to identify 
A-177

errors, inconsistencies, or irregular data before being incorporated into the Inventory. In general, the Inventory uses 

aggregated Gas STAR reductions by natural gas system stage (i.e., production, processing, transmission and storage, and 

distribution). However, aggregate emissions reductions data by Gas STAR technology are provided for several sources, as 

shown in Table A-135 of the Annex. For those sources, EPA has also used data on potential emissions, and the Gas STAR 

data on reductions, to calculate net emissions, as shown in Table A-141 of the Annex. Many of the activities reported to 

Gas Star are cross-cutting and apply to more than one emissions source and therefore cannot be assigned to one emissions 

source, but instead are included in the “other” category. For Inventory sources with emission factors that already take into 

account the use of control technologies (i.e., gas well completions and workovers with hydraulic fracturing, liquids 

unloading, and condensate storage tanks) Natural Gas STAR reported reductions for those activities are not incorporated 

into the Inventory, as this would double count reductions. CH4 emission reductions from the Natural Gas STAR Program 

are summarized in Table A-135. 

Federal regulations. The 1990 Clean Air Act (CAA) sets 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 (MACT), 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 

incidentally 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 threshold, 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 Repair (LDAR) programs. The emissions reductions 

achieved as a result of NESHAP regulations were calculated using data provided in the Federal Register Background 

Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the 

enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control 

measures already in place that would be impacted by the regulation. CH4 emission reductions from federal regulations, 

such as NESHAP, are summarized in Table A-136. In addition to the NESHAP applicable to natural gas, future 

Inventories will reflect the 2012 NSPS for oil and gas. By separating gas well completions and workovers with hydraulic 

fracturing into four categories and developing control technology-specific methane emission factors for each category, 

EPA is implicitly accounting for NSPS reductions from hydraulically fractured gas wells. The rule also has VOC 

reduction requirements for compressors, storage vessels, pneumatic controllers, and equipment leaks at processing plants, 

which will also impact CH4 emissions in future Inventories.

Step 3: Calculate Net Emissions 

For CH4, the reductions described above in Step 2 are summed and deducted from the potential CH4 emissions 

calculated in Step 1. These net emissions are reported in the Natural Gas Systems inventory text. 

The same procedure for estimating CH4 emissions holds true for estimating non-energy related CO2 emissions, 

except the emission estimates are not adjusted for reductions due to the Natural Gas STAR program or regulations. 

Produced natural gas is composed of primarily CH4, but as shown in Table A-142, the natural gas contains, in 

some cases, as much as 8 percent CO2. The same vented and fugitive natural gas that led to CH4 emissions also contains a 

certain volume of CO2. Accordingly, the CO2 emissions for each sector can be estimated using the same activity data for 

these vented and fugitive sources. The emission factors used to estimate CH4 were also used to calculate non-combustion 

CO2 emissions. The Gas Technology Institute’s (GTI, formerly GRI) Unconventional Natural Gas and Gas Composition 

Databases (GTI 2001) were used to adapt the CH4 emission factors into non-combustion related CO2 emission factors. 

Additional information about CO2 content in transmission quality natural gas was obtained from numerous U.S. 

transmission companies to help further develop the non-combustion CO2 emission factors. For the CO2 content used to 

develop CO2 emission factors from CH4 potential factors, see Table A-142. The detailed source emission estimates for 

CH4 and CO2 from the production sector are presented in Table A-137 and Table A-137, 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 CO2 emission factors were developed using CH4 emission factors, proportioned 
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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-138 and Table A-144, respectively. 

For the transmission sector, CO2 content in natural gas transmission pipelines was estimated for the top 20 

transmission pipeline companies in the United States (separate analyses identified the top 20 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-139 and Table A-145, 

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 to transmission segment 

contents 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-140 and Table A-146, 

respectively.

Three exceptions to this methodology are CO2 emissions from flares, CO2 from acid gas removal units, and CO2

from 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, including associated gas. 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). Data were unavailable to use 

annual values for CO2 concentration. For condensate tanks, a series of E&P Tank (EPA 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. 

Table A-125 through Table A-130 display the 2012 activity data, CH4 emission factors, and calculated potential 

CH4 emissions for each stage.

The tables provide references for emission factors and activity data in footnotes (i.e., lettered footnotes). The 

tables also provide information on which method was used for supplying activity data for 2012 (i.e., numbered footnotes).
A-179

Table A-125: 2012 Data and Calculated CH4 Potential Emissions (Mg) for the Natural Gas Production Stage, by NEMS Region

2012 EPA Inventory Values

Activity Activity Data Emission Factor (Potential)aa

Calculated 

Potential (Mg)bb

North East

Gas Wells

NE - Associated Gas Wellscc,dd 38,770 wellsa,1 NA NA

NE - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 112,607 wellsa,1 7.67 scfd/wellb

6,071.08 

NE - Gas Wells with Hydraulic Fracturing 46,367 wellsa,1 7.54 scfd/wellb 2,457.18 

Field Separation Equipment

Heaters 318 heatersb,2 15.38 scfd/heaterb 34.37 

Separators 112,872 separatorsb,2 0.97 scfd/separatorb 771.31 

Dehydrators 22,164 dehydrators b,2 23.53 scfd/dehydratorb 3,665.46 

Meters/Piping 7,910 meters c,2 9.75 scfd/meterb 542.00 

Gathering Compressors

Small Reciprocating Compressors 159 compressors b,2 289.63 scfd/compressorb 323.68 

Large Reciprocating Compressors 24 compressors b,2 16,447.52 scfd/compressorb 2,774.99 

Large Reciprocating Stations 3 stations b,2 8,920.47 scfd/stationb 188.13 

Pipeline Leaks 77,956 miles c,2 57.50 scfd/mileb 31,508.62 

Drilling, Well Completion, and Well Workover

Gas Well Completions without Hydraulic 

Fracturingee 268 completions/yr d,2 791 scf/completionb

4.08 

Gas Well Workovers without Hydraulic Fracturing 4,898 workovers/yr a,1 2,647 scf/workoverb 249.74 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

2,985 completions/yro

See Table A-126p 384 workovers/yr 68,559.0 o

Well Drilling 6,359 wellsf,1 2,751 scf/wellg 336.92 

Normal Operations

Pneumatic Device Vents 77,261 controllers b,2 373 scfd/deviceb 202,695.69 

Chemical Injection Pumps 795 active pumps b,2 268 scfd/pumpb 1,499.03 

Kimray Pumps 6,487,241 MMscf/yr b,2 1,073 scf/MMscfb 134,073.18 

Dehydrator Vents 7,280,854 MMscf/yr b,2 298 scf/MMscfb 41,805.34 

Condensate Tank Vents

Condensate Tanks without Control Devices 1 MMbbl/yr h,1 21.87 scf/bbli,ff 421.22 

Condensate Tanks with Control Devices 1 MMbbl/yr h,1 4.37 scf/bbli,ff 84.24 

Compressor Exhaust Vented

Gas Engines - MMHPhr b,2 0.26 scf/HPhrb - 

Liquids Unloading 

Liquids Unloading (with plunger lifts) 6,924 venting wellsa,j,2a,j,2 268,185 scfy/venting wellj,gg 35,764.16 

Liquids Unloading (without plunger lifts) 17,906 venting wellsa,j,2 141,646 scfy/venting wellj,gg

48,849.27 

Blowdowns

Vessel Blowdown 135,353 vessels b,2 84 scfy/vesselb 219.96 

Pipeline Blowdown 77,956 miles (gathering) c,2 334 scfy/mileb 501.85 

Compressor Blowdown 159 compressors b,2 4,082 scfy/compressorb 12.50 

Compressor Starts 159 compressors b,2 9,133 scfy/compressorb 27.96 

Upsets

Pressure Relief Valves 354,796 PRV b,2 36.78 scfy/PRVb 251.32 

Mishaps 19,489 miles c,2 724 scf/mileb 271.64 

Midcontinent

Gas Wells

MC - Associated Gas Wellscc,dd 27,470 wellsa,1 NA - 

MC - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 77,896 wellsa,1 7.45 scfd/wellb

4,080.19 

MC - Gas Wells with Hydraulic Fracturing 30,156 wellsa,1 8.35 scfd/wellb 1,770.61 

Field Separation Equipment

Heaters 43,869 heaters b,2 14.90 scfd/heaterb 4,595.66 

Separators 47,003 separators b,2 0.94 scfd/separatorb 311.30 
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Dehydrators 15,064 dehydrators b,2 95.54 scfd/dehydratorb 10,117.92 

Meters/Piping 143,186 meters c,2 9.45 scfd/meterb 9,509.40 

Gathering Compressors

Small Reciprocating Compressors 12,210 compressors b,2 280.71 scfd/compressorb 24,094.82 

Large Reciprocating Compressors 24 compressors b,2 15,941 scfd/compressorb 2,689.55 

Large Reciprocating Stations 3 stations b,2 8,646 scfd/stationb 182.34 

Pipeline Leaks 81,359 miles c,2 55.72 scfd/mileb 31,871.63 

Drilling, Well Completion, and Well Workover

Gas Well Completions without Hydraulic 

Fracturingee 182 completions/yr d,2 768 scf/completionb

2.70 

Gas Well Workovers without Hydraulic 

Fracturing 3,388 workovers/yr a,1 2,572 scf/workoverb

167.84 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

1,139 completions/yro

See Table A-126p 143 workovers/yr 26,118.0 o

Well Drilling 4,322 wells f,1 2,666 scf/wellg 221.95 

Normal Operations

Pneumatic Device Vents 167,589 controllers b,2 362 scfd/deviceb 426,133.39 

Chemical Injection Pumps 15,343 active pumps b,2 260 scfd/pumpb 28,044.95 

Kimray Pumps 4,409,271 MMscf/yr b,2 1,040 scf/MMscfb 88,321.70 

Dehydrator Vents 4,948,676 MMscf/yr b,2 288.9 scf/MMscfb 27,539.58 

Condensate Tank Vents

Condensate Tanks without Control Devices 19 MMbbl/yr h,1 302.75 scf/bbli,ff 107,874.48 

Condensate Tanks with Control Devices 19 MMbbl/yr h,1 60.55 scf/bbli,ff 21,574.90 

Compressor Exhaust Vented

Gas Engines 19,041 MMHPhr b,2 0.25 scf/HPhrb 92,278.23 

Liquids Unloading

Liquids Unloading (with plunger lifts) 2,516 venting wells a,j,2 1,140,052 scfy/venting wellj,gg 55,244.84 

Liquids Unloading (without plunger lifts) 4,469 venting wells a,j,2 190,179 scfy/venting wellj,gg

16,369.26 

Blowdowns

Vessel Blowdown 105,936 vessels b,2 82 scfy/vesselb 166.85 

Pipeline Blowdown 81,359 miles (gathering) c,2 324 scfy/mileb 507.64 

Compressor Blowdown 12,210 compressors b,2 3,957 scfy/compressorb 930.47 

Compressor Starts 12,210 compressors b,2 8,852 scfy/compressorb 2,081.60 

Upsets

Pressure Relief Valves 241,149 PRV b,2 36 scfy/PRVb 165.56 

Mishaps 20,340 miles c,2 701 scf/mileb 274.77 

Rocky Mountain

Gas Wells

RM - Associated Gas Wellscc,dd 32,598 wells a,1 NA - 

RM - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 9,665 wells a,1 35.05 scfd/wellb

2,381.29 

RM - Gas Wells with Hydraulic Fracturing 73,755 wells a,1 40.72 scfd/wellb 21,115.06 

Field Separation Equipment

Heaters 38,040 heaters b,2 56.73 scfd/heaterb 15,171.70 

Separators 41,627 separators b,2 120 scfd/separatorb 35,099.35 

Dehydrators 11,630 dehydrators b,2 89.58 scfd/dehydratorb 7,324.14 

Meters/Piping 97,399 meters c,2 52.01 scfd/meterb 35,608.50 

Gathering Compressors

 Small Reciprocating Compressors 9,260 compressors b,2 263.20 scfd/compressorb 17,133.02 

 Large Reciprocating Compressors 32 compressors b,2 14,947 scfd/compressorb 3,362.38 

 Large Reciprocating Stations 4 stations b,2 8,107 scfd/stationb 227.95 

 Pipeline Leaks 107,797 miles c,2 52.25 scfd/mileb 39,594.37 

Drilling, Well Completion, and Well Workover

 Gas Well Completions without Hydraulic 

Fracturingee 141 completions/yr d,2 705 scf/completionb

1.91 

 Gas Well Workovers without Hydraulic Fracturing 420 workovers/yr a,1 2,360 scf/workoverb 19.11 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

2,334 completions/yro

See Table A-126p 275 workovers/yr 53,087.0 o

 Well Drilling 3,337 wells f,1 2,500 scf/wellg 160.66 

Normal Operations

 Pneumatic Device Vents 122,127 controllers b,2 339 scfd/deviceb 291,165.83 

 Chemical Injection Pumps 14,849 active pumps b,2 244 scfd/pumpb 25,447.90 
A-181

 Kimray Pumps 3,404,114 MMscf/yr b,2 975 scf/MMscfb 63,934.16 

 Dehydrator Vents 3,820,555 MMscf/yr b,2 270.9 scf/MMscfb 19,935.30 

Condensate Tank Vents

 Condensate Tanks without Control Devices 5 MMbbl/yr h,1 21.87 scf/bbli,ff 1,895.47 

 Condensate Tanks with Control Devices 5 MMbbl/yr h,1 4.37 scf/bbli,ff 379.09 

Compressor Exhaust Vented

 Gas Engines 14,701 MMHPhr b,2 0.24 scf/HPhrb 66,798.21 

Liquids Unloading

Liquids Unloading (with plunger lifts) 10,741 venting wells a,j,2 119,523 scfy/venting wellj,gg 24,726.01 

Liquids Unloading (without plunger lifts) 1,267 venting wells a,j,2 1,998,082 scfy/venting wellj,gg 48,758.03 

Blowdowns

 Vessel Blowdown 91,296 vessels b,2 77 scfy/vesselb 134.82 

 Pipeline Blowdown 107,797 miles (gathering) c,2 304 scfy/mileb 630.64 

 Compressor Blowdown 9,260 compressors b,2 3,710 scfy/compressorb 661.63 

 Compressor Starts 9,260 compressors b,2 8,300 scfy/compressorb 1,480.15 

Upsets

 Pressure Relief Valves 186,176 PRV b,2 33 scfy/PRVb 119.84 

 Mishaps 26,949 miles c,2 658 scf/mileb 341.34 

Produced Water from Coal Bed Methane

 Powder River 20,602,022,700 

gallons produced 

water k,1 2.3E-09

Gg/gallon water 

drainage k

47,244.27 

South West

Gas Wells

 SW - Associated Gas Wellscc,dd 155,119 wells a,1 NA - 

 SW - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 13,860 wells a,1 37.24 scfd/wellb

3,628.27 

 SW - Gas Wells with Hydraulic Fracturing 27,627 wells a,1 37.24 scfd/wellb 7,232.19 

Field Separation Equipment

 Heaters 11,243 heaters b,2 58.97 scfd/heaterb 4,661.03 

 Separators 23,316 separators b,2 125 scfd/separatorb 20,435.18 

 Dehydrators 5,784 dehydrators b,2 93.11 scfd/dehydratorb 3,786.17 

 Meters/Piping 55,885 meters c,2 54.06 scfd/meterb 21,236.97 

Gathering Compressors

 Small Reciprocating Compressors 5,642 compressors b,2 274 scfd/compressorb 10,851.59 

 Large Reciprocating Compressors 16 compressors b,2 15,536 scfd/compressorb 1,747.50 

 Large Reciprocating Stations 2 stations b,2 8,426 scfd/stationb 118.47 

 Pipeline Leaks 60,832 miles c,2 54.31 scfd/mileb 23,225.26 

Drilling, Well Completion, and Well Workover

 Gas Well Completions without Hydraulic 

Fracturingee 70 completions/yr d,2 749 scf/completionb

1.01 

 Gas Well Workovers without Hydraulic Fracturing 603 workovers/yr a,1 2,507 scf/workoverb 29.12 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

649 completions/yro

See Table A-126p 116 workovers/yr 15,601.0 o

 Well Drilling 1,660 wells f,1 2,598 scf/wellg 83.05 

Normal Operations

 Pneumatic Device Vents 55,095 controllers b,2 353 scfd/deviceb 136,534.04 

 Chemical Injection Pumps 2,531 active pumps b,2 253 scfd/pumpb 4,508.22 

 Kimray Pumps 1,692,957 MMscf/yr b,2 1,014 scf/MMscfb 33,050.39 

 Dehydrator Vents 1,900,064 MMscf/yr b,2 282 scf/MMscfb 10,305.44 

Condensate Tank Vents

 Condensate Tanks without Control Devices 10 MMbbl/yr h,1 303 scf/bbli,ff 55,395.00 

 Condensate Tanks with Control Devices 10 MMbbl/yr h,1 60.55 scf/bbli,ff 11,079.00 

Compressor Exhaust Vented

 Gas Engines 7,311 MMHPhr b,2 0.25 scf/HPhrb 34,530.94 

Liquids Unloading

 Liquids Unloading (with plunger lifts) 1,379 venting wells a,j,2 2,856 scfy/venting wellj,gg 75.85 

 Liquids Unloading (without plunger lifts) 8,078 venting wells a,j,2 77,899 scfy/venting wellj,gg 12,119.67 

Blowdowns

 Vessel Blowdown 40,343 vessels b,2 80 scfy/vesselb 61.93 

 Pipeline Blowdown 60,832 miles (gathering) c,2 316 scfy/mileb 369.92 
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

 Compressor Blowdown 5,642 compressors b,2 3,856 scfy/compressorb 419.06 

 Compressor Starts 5,642 compressors b,2 8,627 scfy/compressorb 937.49 

Upsets

 Pressure Relief Valves 92,590 PRV b,2 35 scfy/PRVb 61.95 

 Mishaps 15,208 miles c,2 684 scf/mileb 200.22 

West Coast

Gas Wells

 WC - Associated Gas Wellscc,dd 29,726 wells a,1 NA - 

 WC - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 1,999 wells a,1 42.49 scfd/wellb

597.13 

 WC - Gas Wells with Hydraulic Fracturing 95 wells a,1 42.49 scfd/wellb 28.38 

Field Separation Equipment

 Heaters 2,094 heaters b,2 67.29 scfd/heaterb 990.60 

 Separators 1,529 separators b,2 142 scfd/separatorb 1,528.80 

 Dehydrators 292 dehydrators b,2 106 scfd/dehydratorb 218.06 

 Meters/Piping 3,994 meters c,2 61.68 scfd/meterb 1,732.08 

Gathering Compressors

 Small Reciprocating Compressors 2,431 compressors b,2 312 scfd/compressorb 5,335.45 

 Large Reciprocating Compressors 8 compressors b,2 17,728 scfd/compressorb 997.03 

 Large Reciprocating Stations 1 stations b,2 9,615 scfd/stationb 67.59 

 Pipeline Leaks 16,712 miles c,2 61.97 scfd/mileb 7,280.76 

Drilling, Well Completion, and Well Workover

 Gas Well Completions without Hydraulic 

Fracturingee 4 completions/yr d,2 855 scf/completionb

0.06 

 Gas Well Workovers without Hydraulic Fracturing 87 workovers/yr a,1 2,861 scf/workoverb 4.79 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

2 completions/yro

See Table A-126p 1 workovers/yr 123.0 o

 Well Drilling 84 wells f,1 2,965 scf/wellg 4.78 

Normal Operations

 Pneumatic Device Vents 2,098 controllers b,2 402 scfd/deviceb 5,933.29 

 Chemical Injection Pumps 1,422 active pumps b,2 289 scfd/pumpb 2,890.21 

 Kimray Pumps 85,450 MMscf/yr b,2 1,157 scf/MMscfb 1,903.54 

 Dehydrator Vents 95,903 MMscf/yr b,2 321 scf/MMscfb 593.54 

Condensate Tank Vents

 Condensate Tanks without Control Devices 10 MMbbl/yr h,1 21.87 scf/bbli,ff 4,212.16 

 Condensate Tanks with Control Devices 10 MMbbl/yr h,1 4.37 scf/bbli,ff 842.43 

Compressor Exhaust Vented

 Gas Engines 369 MMHPhr b,2 0.28 scf/HPhrb 1,988.81 

Liquids Unloading

 Liquids Unloading (with plunger lifts) 159 wells a,j,2 317,292 scfy/venting wellj,gg 971.66 

 Liquids Unloading (without plunger lifts) 142 wells a,j,2 279,351 scfy/venting wellj,gg 764.00 

Blowdowns

 Vessel Blowdown 3,915 vessels b,2 90.94 scfy/vesselb 6.86 

 Pipeline Blowdown 16,712 miles (gathering) c,2 360 scfy/mileb 115.96 

 Compressor Blowdown 2,431 compressors b,2 4,400 scfy/compressorb 206.04 

 Compressor Starts 2,431 compressors b,2 9,844 scfy/compressorb 460.94 

Upsets

 Pressure Relief Valves 4,673 PRV b,2 40 scfy/PRVb 3.57 

 Mishaps 4,178 miles c,2 780 scf/mileb 62.77 

Gulf Coast

Gas Wells

 GC - Associated Gas Wellscc,dd 39,709 wells a,1 NA - 

 GC - Non-associated Gas Wells (less wells with 

hydraulic fracturing) 27,024 wells a,1 7.96 scfd/wellb

1,511.56 

 GC - Gas Wells with Hydraulic Fracturing 49,862 wells a,1 7.96 scfd/wellb 2,789.34 

Field Separation Equipment

 Heaters 17,222 heaters b,2 64.60 scfd/heaterb 7,821.18 

 Separators 50,591 separators b,2 136.57 scfd/separatorb 48,571.24 

 Dehydrators 10,719 dehydrators b,2 102.00 scfd/dehydratorb 7,686.19 

 Meters/Piping 90,288 meters c,2 59.21 scfd/meterb 37,584.36 

Gathering Compressors

 Small Reciprocating Compressors 6,228 compressors b,2 300 scfd/compressorb 13,120.51 

 Large Reciprocating Compressors 32 compressors b,2 17,019 scfd/compressorb 3,828.46 
A-183

 Large Reciprocating Stations 4 stations b,2 9,230 scfd/stationb 259.55 

 Pipeline Leaks 100,479 miles c,2 59.49 scfd/mileb 42,022.30 

Drilling, Well Completion, and Well Workover

 Gas Well Completions without Hydraulic 

Fracturingee 130 completions/yr d,2 820 scf/completionb

2.05 

 Gas Well Workovers without Hydraulic Fracturing 1,176 workovers/yr a,1 2,746 scf/workoverb 62.18 

Gas Well Completions and Workovers with 

Hydraulic Fracturing

2,357 completions/yro

See Table A-126p 279 workovers/yr 53,643.0 o

 Well Drilling 3,076 wells f,1 2,846 scf/wellg 168.61 

Normal Operations

 Pneumatic Device Vents 53,436 controllers b,2 386 scfd/deviceb 145,057.07 

 Chemical Injection Pumps 2,537 active pumps b,2 278 scfd/pumpb 4,951.09 

 Kimray Pumps 3,137,482 MMscf/yr b,2 1,110 scf/MMscfb 67,094.63 

 Dehydrator Vents 3,521,304 MMscf/yr b,2 308 scf/MMscfb 20,920.77 

Condensate Tank Vents

 Condensate Tanks without Control Devices 43 MMbbl/yr h,1 21.87 scf/bbli,ff 17,901.69 

 Condensate Tanks with Control Devices 43 MMbbl/yr h,1 4.37 scf/bbli,ff 3,580.34 

Compressor Exhaust Vented

 Gas Engines 13,549 MMHPhr b,2 0.27 scf/HPhrb 70,100.25 

Liquids Unloading

 Liquids Unloading (with plunger lifts) 1,784 venting wells a,j,2 61,758 scfy/venting wellj,gg 2,121.99 

 Liquids Unloading (without plunger lifts) 5,445 venting wells a,j,2 265,120 scfy/venting wellj,gg 27,803.30 

Blowdowns

 Vessel Blowdown 78,533 vessels b,2 87 scfy/vesselb 132.05 

 Pipeline Blowdown 100,479 miles (gathering) c,2 346 scfy/mileb 669.31 

 Compressor Blowdown 6,228 compressors b,2 4,224 scfy/compressorb 506.67 

 Compressor Starts 6,228 compressors b,2 9,450 scfy/compressorb 1,133.51 

Upsets

 Pressure Relief Valves 171,593 PRV b,2 38 scfy/PRVb 125.77 

 Mishaps 25,120 miles c,2 749 scf/mileb 362.28 

Produced Water from Coal Bed Methane Wells

 Black Warrior 5,517 wells l,1 2.33E-03 Gg/well l 12,779.20 

Offshore Platforms

 Shallow water Gas Platforms (GoM and Pacific) 1,973 

shallow water gas 

platforms m,3 19,178 scfd/platformn

266,066.46 

Deepwater Gas Platforms (GoM and Pacific) 41 

deepwater gas 

platforms m,3 79,452 scfd/platformn 22,950.41 

Regulatory Reductions (Gg) (99.2)

Voluntary Reductions (Gg) (1,635.7)

Total Reductions (Gg) (1,734.8)

Total Potential Emissions (Gg) 3,726.6 

Total Net Emissions (Gg) 1,991.8

a DI Desktop (2014)

b

.EPA/GRI (1996), Methane Emissions from the Natural Gas Industry

c ICF (1996), Estimation of Activity Factors for the Natural Gas Exploration and Production Industry in the U.S.

d API/ICF memo (1997)

e EPA NSPS Technical Support Document (2012)

f EIA Monthly Energy Review

g Radian (1992), Global Emissions of Methane Sources

h EIA US Crude Oil, Natural Gas, and Natural Gas Liquids Reserves Annual Report

I EP&P/API Tank Calc runs

j API/ANGA (2012), Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production – Summary and Analysis of API and ANGA Survey 

Responses

k Wyoming Oil and Gas Conservation Commission (2013)

l Alabama State Oil and Gas Board (2013)

m Bureau of Ocean Energy Management, Regulation and Enforcement (2011)

n MMS (2000), 2000 Gulfwide Offshore Activity Data System

o 2012 GHGRP - Subpart W data

p Emissions for hydraulic fracturing completions and workovers are split into 4 categories and the same emission factors (shown in Table A-2) are used for all 

NEMS regions. For more details, refer to EPA memo “Updating GHG Inventory Estimate for Hydraulically Fractured.” The factors for hydraulically fractured 

completions and workovers in Table A-2 represent actual emissions and can be used to calculate emissions directly

q Emissions for hydraulic fracturing completions and workovers are calculated together. 
A-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission reductions are 

subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot be used to directly estimate net 

emissions from these sources. See detailed explanation of methodology above.

bb Totals may not sum due to independent rounding.

cc Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems model. In the Natural Gas Systems model, the oil wells counts are 

used as a driver only.

dd NA = not applicable (i.e., this data is not applicable for the Natural Gas Systems model). 

ff Emission factors for condensate tanks represent actual emissions and can be used to calculate emissions directly.

gg Emission factors for liquids unloading represent actual emissions and can be used to calculate emissions directly. 

1 Activity data for 2012 available from source.

2 Ratios relating other factors for which activity data are available.

3 2011 activity data are used to determine some or all of the 2012 activity.

Table A-126: 2012 National Activity Data and Emission Factors, and Emissions (Mg), by category for Hydraulically Fractured 

Gas Well Completions and Workovers

2012 EPA Inventory Values

Activity Activity Data Emission Factor

Emissions 

(Mg)aa

 Hydraulic Fracturing Completions and Workovers 

that vent 4,688

completions and 

workovers/yeara 41

Mg/comp or 

workoverb 192,208

 Flared Hydraulic Fracturing Completions and 

Workovers 775

completions and 

workovers/yeara 5

Mg/comp or 

workoverb 3,875

 Hydraulic Fracturing Completions and Workovers 

with RECs 3,386

completions and 

workovers/yeara 3

Mg/comp or 

workoverb 10,158

 Hydraulic Fracturing Completions and Workovers 

with RECs that flare 1,815

completions and 

workovers/yeara 6

Mg/comp or 

workoverb 10,890

a 2012 GHGRP - Subpart W data. The GHGRP data represents a subset of national completions and workovers, due to the reporting threshold. Please see the 

section on “Activity Data” above for more information and the Planned Improvements section of the Inventory report. 

b Emissions for hydraulic fracturing completions and workovers are split into 4 categories and the same emission factors are used for all NEMS regions. For more 

details, refer to EPA memo “Updating GHG Inventory Estimate for Hydraulically Fractured.”

aa Totals may not sum due to independent rounding.

Table A-127: U.S. Activity Data for Hydraulic Fracturing Completions and Workovers split by 4 categories

Activity 1990 1995 2000 2005 2011a 2012a

 Hydraulic Fracturing Completions and 

Workovers that vent 5,345 4,852 7,434 10,129 4,678 4,688 

 Flared Hydraulic Fracturing Completions and 

Workovers 591 536 823 1,464 1,381 775 

 Hydraulic Fracturing Completions and 

Workovers with RECs - - - 2,302 3,882 3,386 

 Hydraulic Fracturing Completions and 

Workovers with RECs that flare - - - 767 1,292 1,815 

Total 5,936 5,388 8,257 14,662 11,233 10,664 

a 2011 and 2012 GHGRP - Subpart W data
A-185

Table A-128: 2012 Data and CH4 Emissions (Mg) for the Natural Gas Processing Stage

2012 EPA Inventory Values

Activity Activity Data Emission Factor (Potential)aa

Calculated 

Potential 

Emissions (Mg)

Normal Fugitives

Plants 606 plantsa,1 7,906 scfd/plantb 33,680.5 

Reciprocating Compressors 5,624 compressors c,2 11,196 scfd/compressorb 442,633.5 

Centrifugal Compressors (wet seals) 658 compressors d,2 51,370 scfd/compressord 237,724.1 

Centrifugal Compressors (dry seals) 248 compressors d,2 25,189 scfd/compressord 43,936.6 

Compressor Exhaust

Gas Engines 40,403 MMHPhr c,2 0.24 scf/HPhrb 186,760.5

Gas Turbines 47,907 MMHPhr c,2 0.01 scf/HPhrb 5,259.3 

AGR Vents 307 AGR unitsb,2 6,083 scfd/AGRb 13,134.2 

Kimray Pumps 1,463,675 MMscf/yr c,2 178 scf/MMscfb 5,010.8 

Dehydrator Vents 13,186,262 MMscf/yr c,2 122 scf/MMscfb 30,869.7 

Pneumatic Devices 606 gas plants a,1 164,721 scfy/plantb 1,922.6 

Compressor Exhaust 

Blowdowns/Venting 606 gas plants a,1 4,060 Mscfy/plantb 47,386.5

Regulatory Reductions (Gg) (16.3)

Voluntary Reductions (Gg) (140.4)

Total Reductions (Gg) (156.8)

Total Potential Emissions (Gg) 1,048.3 

Total Net Emissions (Gg) 891.5

a Oil and Gas Journal

b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry

c

ICF (2008), Natural Gas Model Activity Factor Basis Change

d

ICF (2010), Emissions from Centrifugal Compressors

aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission 

reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot be used 

to directly estimate net emissions from these sources. See detailed explanation of methodology above.

1 Activity data for 2012 available from source.

2 Ratios relating other factors for which activity data are available.
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-129: 2012 Data and CH4 Emissions (Mg) for the Natural Gas Transmission Stage

2012 EPA Inventory Values

Activity Activity Data Emission Factor (Potential)aa

Calculated 

Potential 

Emissions 

(Mg)

Fugitives

Pipeline Leaks 303,126 milesa,1 1.55 Scfd/ mileb 3,311.4 

Compressor Stations (Transmission) 

 Station 1,799 stationsc,2 8,778 Scfd/stationb 111,037.1 

 Reciprocating Compressor 7,235 compressors c,2 15,205 Scfd/ compressorb 773,294.3 

 Centrifugal Compressor (wet seals) 659 compressors d,2 50,222 Scfd/ compressord 232,825.7 

 Centrifugal Compressor (dry seals) 66 compressors d,2 32,208 Scfd/ compressord 14,971.9 

Compressor Stations (Storage) 

 Station 344 stations e,2 21,507 Scfd/ stationb 52,013.2 

 Reciprocating Compressor 1,012 compressors e,2 21,116 Scfd/ compressorb 150,224.7 

 Centrifugal Compressor (wet seals) 70 compressors d,2 45,441 Scfd/ compressord 22,346.7 

 Centrifugal Compressor (dry seals) 29 compressors d,2 31,989 Scfd/ compressord 6,531.7 

Wells (Storage) 16,042 wells b,2 115 Scfd/wellb 12,912.2 

M&R (Trans. Co. Interconnect) 2,698 stations c,2 3,984 scfd/stationb 75,573.6 

M& R (Farm Taps + Direct Sales) 80,009 stations c,2 31 scfd/stationb 17,548.7 

Vented and Combusted 

Dehydrator vents (Transmission) 1,146,991 MMscf/year b,2 93.72 scf/MMscfb 2,070.4 

Dehydrator vents (Storage) 1,782,492 MMscf/year b,2 117 scf/MMscfb 4,022.9 

Compressor Exhaust 

 Engines (Transmission) 50,908 MMHPhr b,2 0.24 scf/HPhrb 235,315.3 

 Turbines (Transmission) 12,147 MMHPhr b,2 0.01 scf/HPhrb 1,333.5 

 Engines (Storage) 4,387 MMHPhr b,2 0.24 scf/HPhrb 20,277.1 

 Turbines (Storage) 1,541 MMHPhr b,2 0.01 scf/HPhrb 169.2 

 Generators (Engines) 2,491 MMHPhr b,2 0.24 scf/HPhrb 11,515.2 

 Generators (Turbines) 29 MMHPhr b,2 0.01 scf/HPhrb 3.2 

Pneumatic Devices Transmission + 

Storage 

 Pneumatic Devices Transmission 70,827 devices f,2 162,197 Scfy/deviceb 221,256.9 

 Pneumatic Devices Storage 13,542 devices e,2 162,197 Scfy/deviceb 42,304.0 

Routine Maintenance/Upsets 

Pipeline venting 303,126 miles a,1 31.65 Mscfy/mileb 184,779.2 

Station Venting Transmission + Storage 

Station Venting Transmission 1,799 

compressor 

stations c,2 4,359 Mscfy/stationb 151,065.9 

Station Venting Storage 344 

compressor 

stations e,2 4,359 Mscfy/stationb 28,882.0 

LNG Storage - 

LNG Stations 70 stations f,g,3 21,507 scfd/stationb 10,622.8 

LNG Reciprocating Compressors 270 

compressors

f,g,3

 21,116 scfd/compb 40,146.5 

LNG Centrifugal Compressors 64 

compressors

f,g,3

 30,573 scfd/compb 13,766.0 

LNG Compressor Exhaust 

 LNG Engines 579 MMHPhr f,g,3 0.24 scf/HPhrb 2,677.7 

 LNG Turbines 113 MMHPhr f,g,3 0.01 scf/HPhrb 12.4 

LNG Station Venting 70 stations f,g,3 4,359 Mscfy/stationb 5,898.6 

LNG Import Terminals 

LNG Stations 8 stations f,g,3 21,507 scfd/stationb 1,164.2 

LNG Reciprocating Compressors 37 compressors f,g,3 21,116 scfd/compressorb 5,551.8 

LNG Centrifugal Compressors 7 compressors f,g,3 30,573 scfd/compressorb 1,418.5 

LNG Compressor Exhaust 

 LNG Engines 774 MMHPhr f,g,3 0.24 scf/HPhrb 3,576.6 

 LNG Turbines 178 MMHPhr f,g,3 0.01 scf/HPhrb 19.5 

LNG Station Venting 8 stations f,g,3 4,359 Mscfy/stationb 646.4 

Regulatory Reductions (Gg) (0.0) 

Voluntary Reductions (Gg) (390.1)
A-187

Total Reductions (Gg) (390.1)

Total Potential Emissions (Gg) 2,461.1

Total Net Emissions (Gg) 2,071.0

a Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2013)

b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry

c

ICF (2008), Natural Gas Model Activity Factor Basis Change

d

ICF (2010), Emissions from Centrifugal Compressors

e

ICF (1997), Additional Changes to Activity Factors for Portions of the Gas Industry

f

ICF (1996), Estimation of Activity Factors for the Natural Gas Exploration and Production Industry in the U.S.

g EIA (2004), US LNG Markets and Uses

1 Activity data for 2012 available from source.

2 Ratios relating other factors for which activity data are available.

3 2011 activity data are used to determine some or all of the 2012 activity (to be updated).

aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission 

reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables 

cannot be used to directly estimate net emissions from these sources. See detailed explanation of methodology above.
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-130: 2012 Data and CH4 Emissions (Mg) for the Natural Gas Distribution Stage

2012 EPA Inventory Values

Activity

Activity Data Emission Factor (Potential)aa

Calculated 

Potential 

Emissions (Mg)

Pipeline Leaks

 Mains—Cast Iron 32,418 miles a,1 239 Mscf/mile-yrb 149,037.3 

 Mains—Unprotected steel 63,727 miles a,1 110 Mscf/mile-yrb 135,245.1 

 Mains—Protected steel 487,225 miles a,1 3.07 Mscf/mile-yrb 28,779.7 

 Mains—Plastic 661,100 miles a,1 9.91 Mscf/mile-yrc 126,181.9 

 Services—Unprotected steel 3,916,353 services a,1 1.70 Mscf/serviceb 128,287.1 

 Services Protected steel 14,951,473 services a,1 0.18 Mscf/serviceb 50,824.7 

 Services—Plastic 45,147,410 services a,1 0.01 Mscf/serviceb 8,085.1 

 Services—Copper 1,009,255 services a,1 0.25 Mscf/serviceb 4,943.6 

Meter/Regulator (City Gates) 

 M&R >300 3,465 stations d,2 180 scfh/stationb 105,101.3 

 M&R 100-300 12,644 stations d,2 95.60 scfh/stationb 203,935.5 

 M&R <100 6,758 stations d,2 4.31 scfh/stationb 4,914.3 

 Reg >300 3,788 stations d,2 162 scfh/stationb 103,468.3 

 R-Vault >300 2,225 stations d,2 1.30 scfh/stationb 487.9 

 Reg 100-300 11,459 stations d,2 40.50 scfh/stationb 78,300.9 

 R-Vault 100-300 5,148 stations d,2 0.18 scfh/stationb 156.3 

 Reg 40-100 34,387 stations d,2 1.04 scfh/stationb 6,033.8 

 R-Vault 40-100 30,494 stations d,2 0.09 scfh/stationb 445.0 

 Reg <40 14,581 stations d,2 0.13 scfh/stationb 327.2 

Customer Meters 

 Residential 35,693,769 

outdoor 

meters b,2 143 scfy/meterb 98,493.2 

 Commercial/Industry 4,481,003 meters b,2 47.90 scfy/meterb 4,134.0 

 Routine Maintenance 

 Pressure Relief Valve Releases 1,244,470 mile main a,1 0.05 Mscf/mileb 1,198.4 

 Pipeline Blowdown 1,156,453 miles b,2 0.10 Mscfy/mileb 2,271.9 

Upsets 

 Mishaps (Dig-ins) 1,156,453 miles b,2 1.59 Mscfy/mileb 35,414.5 

Regulatory Reductions (Gg) (0.0)

Voluntary Reductions (Gg) (44.8)

Total Reductions (Gg) (44.8)

Total Potential Emissions (Gg) 1,276.1

Total Net Emissions (Gg) 1,231.3

a Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2012)

b EPA/GRI (1996), Methane Emissions from the Natural Gas Industry

c

ICF (2005), Plastic Pipe Emission Factors

d ICF (2008), Natural Gas Model Activity Factor Basis Change

aa Emission factors listed in this table are for potential emissions (unless otherwise indicated in a footnote). For many of these sources, emission 

reductions are subtracted from potential emissions to calculate net emissions. For this reason, emission factors presented in these tables cannot 

be used to directly estimate net emissions from these sources. See detailed explanation of methodology above.

1 Activity data for 2012 available from source.

2 Ratios relating other factors for which activity data are available.
A-189

Table A-131: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (General Sources)

U.S. Region

Year North East Midcontinent

Rocky 

Mountain South West West Coast Gulf Coast Lower 48 States

1990 84.0% 78.3% 67.4% 64.4% 75.3% 79.8% n/a

1991 83.8% 78.7% 69.3% 67.1% 78.1% 80.1% n/a

1992 83.5% 79.1% 71.2% 74.4% 80.8% 82.7% n/a

1993 82.9% 79.9% 73.4% 76.1% 83.6% 84.1% n/a

1994 82.0% 80.7% 75.5% 77.4% 86.4% 85.6% n/a

1995 81.5% 81.6% 77.6% 79.0% 89.1% 87.2% n/a

1996 81.2% 82.6% 80.5% 80.5% 91.9% 88.7% 84.2%

1997 80.3% 82.5% 80.4% 80.5% 91.9% 88.6% 84.1%

1998 81.0% 82.5% 80.5% 80.5% 91.9% 88.6% 84.2%

1999 80.5% 82.5% 80.4% 80.5% 91.9% 88.7% 84.2%

2000 80.8% 82.5% 80.2% 80.5% 91.9% 88.7% 84.0%

2001 80.3% 82.5% 79.5% 80.5% 91.9% 88.7% 83.8%

2002 80.4% 82.5% 79.3% 80.5% 91.9% 88.6% 83.5%

2003 76.4% 82.6% 79.1% 80.5% 91.9% 88.6% 83.2%

2004 80.4% 82.7% 79.0% 80.5% 91.9% 88.6% 83.4%

2005 80.1% 82.7% 79.0% 80.5% 91.9% 88.6% 83.4%

2006 79.5% 83.0% 79.0% 80.5% 91.9% 88.6% 83.5%

2007 85.8% 82.7% 77.5% 80.5% 91.9% 88.6% 83.9%

2008 86.0% 82.7% 77.7% 80.5% 91.9% 88.5% 83.9%

2009 85.1% 82.7% 77.5% 80.5% 91.9% 88.5% 83.6%

2010 84.3% 82.8% 77.4% 80.5% 91.9% 88.3% 83.4%

2011 85.2% 82.6% 77.5% 80.5% 91.9% 88.2% 83.3%

2012 85.2% 82.6% 77.5% 80.5% 91.9% 88.2% 83.3%

Table A-132: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells Without Hydraulic Fracturing)

U.S. Region

Year North East Midcontinent

Rocky 

Mountain South West West Coast Gulf Coast Lower 48 States

1990 84.0% 78.3% 67.4% 64.4% 75.3% 79.8% n/a

1991 83.8% 78.7% 69.3% 67.1% 78.1% 80.1% n/a

1992 83.5% 79.1% 71.2% 74.4% 80.8% 82.7% n/a

1993 82.9% 79.9% 73.4% 76.1% 83.6% 84.1% n/a

1994 82.0% 80.7% 75.5% 77.4% 86.4% 85.6% n/a

1995 81.5% 81.6% 77.6% 79.0% 89.1% 87.2% n/a

1996 81.2% 82.5% 79.6% 80.5% 91.9% 88.6% 84.0%

1997 80.5% 82.5% 79.5% 80.5% 91.9% 88.6% 83.9%

1998 81.2% 82.5% 79.6% 80.5% 91.9% 88.6% 84.0%

1999 80.7% 82.5% 79.5% 80.5% 91.9% 88.7% 83.9%

2000 81.0% 82.5% 79.2% 80.5% 91.9% 88.7% 83.8%

2001 80.4% 82.5% 78.3% 80.5% 91.9% 88.6% 83.5%

2002 80.5% 82.5% 78.1% 80.5% 91.9% 88.6% 83.2%

2003 76.5% 82.6% 77.9% 80.5% 91.9% 88.6% 82.9%

2004 80.5% 82.6% 77.8% 80.5% 91.9% 88.6% 83.1%

2005 80.3% 82.7% 77.7% 80.5% 91.9% 88.6% 83.1%

2006 79.6% 83.0% 77.7% 80.5% 91.9% 88.6% 83.1%

2007 85.6% 82.7% 75.8% 80.5% 91.9% 88.6% 83.5%

2008 85.6% 82.7% 76.0% 80.5% 91.9% 88.5% 83.5%

2009 84.7% 82.7% 75.8% 80.5% 91.9% 88.5% 83.2%

2010 83.8% 82.8% 75.7% 80.5% 91.9% 88.3% 82.9%

2011 85.0% 82.6% 75.8% 80.5% 91.9% 88.2% 82.8%

2012 85.0% 82.6% 75.8% 80.5% 91.9% 88.2% 82.8%

Table A-133: U.S. Production Sector CH4 Content in Natural Gas by NEMS Region (Gas Wells With Hydraulic Fracturing)

U.S. Region

Year North East Midcontinent

Rocky 

Mountain South West West Coast Gulf Coast Lower 48 States

1990 84.0% 78.3% 67.4% 64.4% 75.3% 79.8% n/a

1991 83.8% 78.7% 69.3% 67.1% 78.1% 80.1% n/a

1992 83.5% 79.1% 71.2% 74.4% 80.8% 82.7% n/a
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

1993 82.9% 79.9% 73.4% 76.1% 83.6% 84.1% n/a

1994 82.0% 80.7% 75.5% 77.4% 86.4% 85.6% n/a

1995 81.5% 81.6% 77.6% 79.0% 89.1% 87.2% n/a

1996 83.2% 92.6% 74.4% 80.5% 91.9% 88.7% 82.1%

1997 83.1% 92.6% 74.9% 80.5% 91.9% 88.6% 82.1%

1998 83.1% 92.6% 75.5% 80.5% 91.9% 88.6% 82.3%

1999 83.1% 92.6% 75.4% 80.5% 91.9% 88.7% 82.0%

2000 83.0% 92.6% 76.4% 80.5% 91.9% 88.7% 82.5%

2001 83.0% 92.6% 78.9% 80.5% 91.9% 88.7% 83.6%

2002 83.0% 92.6% 80.5% 80.5% 91.9% 88.6% 84.4%

2003 83.1% 92.6% 81.4% 80.5% 91.9% 88.6% 84.9%

2004 83.0% 92.6% 81.7% 80.5% 91.9% 88.6% 85.2%

2005 83.0% 92.6% 82.0% 80.5% 91.9% 88.6% 85.3%

2006 83.0% 92.6% 82.3% 80.5% 91.9% 88.6% 85.5%

2007 83.5% 92.6% 86.5% 80.5% 91.9% 88.6% 88.7%

2008 84.1% 92.6% 86.2% 80.5% 91.9% 88.5% 88.4%

2009 84.1% 92.6% 86.8% 80.5% 91.9% 88.5% 88.7%

2010 84.3% 92.6% 86.8% 80.5% 91.9% 88.3% 89.0%

2011 83.6% 92.6% 87.9% 80.5% 91.9% 88.2% 89.4%

2012 83.6% 92.6% 88.1% 80.5% 91.9% 88.2% 89.5%
A-191

Table A-134: Key Activity Data Drivers

Variable Units 1990 1995 2000 2005 2011 2012

Transmission Pipelines Length miles 291,925 296,947 298,957 300,468 304,954 303,126 

Wells

NE—Associated Gas Wellsa,1 # wells 25,835 23,534 24,522 29,536 37,115 38,770 

NE—Non-associated Gas Wells a,1 # wells 64,134 90,350 103,760 129,765 156,158 158,974 

MC—Associated Gas Wells a,1 # wells 37,568 32,508 28,605 27,496 27,926 27,470 

MC—Non-associated Gas Wells a,1 # wells 65,317 69,934 75,000 89,817 108,559 108,052 

RM—Associated Gas Wells a,1 # wells 18,565 19,852 19,332 21,032 29,243 32,598 

RM—Non-associated Gas Wells a,1 # wells 26,036 31,574 42,324 66,975 84,349 83,420 

SW—Associated Gas Wells a,1 # wells 233,236 175,589 156,865 148,294 154,144 155,119 

SW—Non-associated Gas Wells a,1 # wells 22,146 22,653 25,472 33,600 41,380 41,487 

WC—Associated Gas Wells a,1 # wells 17,130 14,035 15,775 18,765 29,318 29,726 

WC—Non-associated Gas Wells a,1 # wells 2,041 1,929 2,029 2,030 2,152 2,094 

GC—Associated Gas Wells a,1 # wells 101,151 69,484 50,089 41,391 38,319 39,709 

GC—Non-associated Gas Wells a,1 # wells 36,692 38,279 43,419 57,415 76,828 76,886 

Platformsaa

Gulf of Mexico and Pacific OCS Off-shore 

Platforms b,2 # platforms 3,941 3,978 4,016 3,909 3,432 3,432 

GoM and Pacific OCS Deep Water 

Platforms b,2 # platforms 17 23 38 59 70 70 

Gas Plantsc,1 # gas plants 761 675 585 566 606 606

Distribution Services # of services 47,883,083 54,644,033 56,761,042 61,832,574 64,731,838 65,024,491 

Steel—Unprotected d,1 # of services 7,633,526 6,151,653 5,675,520 5,507,356 4,140,616 3,916,353 

Steel—Protected d,1 # of services 19,781,581 21,002,455 17,855,560 16,529,118 15,267,357 14,951,473 

Plastic d,1 # of services 18,879,865 26,044,545 31,795,871 38,549,089 44,269,061 45,147,410 

Copper d,1 # of services 1,588,111 1,445,380 1,434,091 1,247,011 1,054,804 1,009,255 

Distribution Mains miles 944,157 1,001,706 1,048,485 1,162,560 1,231,045 1,244,470 

Cast Iron d,1 miles 58,292 50,625 44,750 39,645 33,586 32,418 

Steel—Unprotected d,1 miles 108,941 94,058 82,800 72,458 64,092 63,727 

Steel—Protected d,1 miles 465,538 503,288 471,510 490,156 488,265 487,225 

Plastic d,1 miles 311,386 353,735 449,425 560,301 645,102 661,100 

aa Number of platforms include both oil and gas platforms

a DI Desktop (2014)

b Bureau of Ocean Energy Management, Regulation and Enforcement (2011)

c Oil and Gas Journal

d Pipeline and Hazardous Materials Safety Administration (PHMSA), Office of Pipeline Safety (OPS) (2013)

1 Activity data for 2012 available from source.

2 2011 activity data are used to determine some or all of the 2012 activity (to be updated).
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-135: CH4 Reductions Derived from the Natural Gas STAR Program (Gg)

Process 1990 1995 2000 2005 2011 2012

Production (9.0) (86.4) (302.5) (579.0) (1,603.0) (1,635.7)

Pipeline Leaks (0.0) (0.0) - (2.4) - - 

Pneumatic Device Vents (5.4) (25.8) (118.4) (226.4) (806.7) (873.1)

Chemical Injection Pumps - - - (0.0) (2.1) (2.8)

Gas Engines (0.0) (13.8) (53.2) (97.9) (137.2) (139.9)

Compressor Starts - (0.0) (0.1) (0.2) (0.5) (0.5)

Other Production (3.5) (46.8) (130.9) (251.9) (656.5) (619.3)

Processing (1.5) (21.8) (42.8) (155.5) (140.4) (140.4)

Fugitives Reciprocating Compressors - - - - - - 

Gas Engines - - (0.1) (1.1) (6.1) (6.1)

AGR Vents - - - - - - 

Dehydrator Vents (1.3) - (0.2) (2.1) (9.3) (9.3)

Other Processing (0.2) (21.8) (42.6) (152.2) (125.0) (125.0)

Transmission and Storage - (107.7) (264.0) (506.8) (355.0) (390.1)

Fugitives Reciprocating Compressors - (0.6) - (0.2) (0.2) (0.7)

Engines - (12.5) (49.3) (83.2) (121.6) (123.9)

Pneumatic Device Vents (Transmission) - (5.4) (8.9) (10.5) (13.0) (14.1)

Pipeline Vents - (36.3) (33.3) (124.9) (58.9) (100.1)

Other Transmission - (52.8) (172.5) (288.1) (161.3) (151.2)

Distribution - (19.7) (29.9) (48.4) (58.1) (44.8)

Fugitives Cast Iron - (0.0) (0.1) (0.1) (0.1) (0.1)

Mishaps (Dig-ins) - - - (0.3) (4.7) (0.7)

Other Distribution - (19.7) (29.9) (48.0) (53.3) (44.0)

Total (10.5) (235.6) (639.3) (1,289.6) (2,156.5) (2,211.0)

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.

This table presents aggregate Gas STAR reduction data for each natural gas system stage, and also presents reductions for select technologies for which 

disaggregated Gas STAR data can be matched to an Inventory source category. In general, the Inventory uses aggregated Gas STAR reductions by natural gas 

system stage (i.e., production, processing, transmission and storage, and distribution). In some cases, emissions reductions reported to Gas STAR have been 

matched to potential emissions calculated in the Inventory, to provide a net emissions number for specific emissions sources. This table presents sources for 

which Gas STAR reductions can be matched to Inventory emissions sources. Net emissions values for these sources are presented in Table A-141. Some 

reported Gas STAR reduction activities are cross-cutting and cover multiple Inventory sources. It is not possible to attribute those reductions to specific Inventory 

source categories, and they are included in the “Other” category. 

Table A-136: CH4 Reductions Derived from Regulations (Gg)

Process 1990 1995 2000 2005 2011 2012

Production NA NA (45.5) (62.7) (99.0) (99.2)

Dehydrator vents (NESHAP) NA NA (23.8) (30.8) (38.7) (38.9)

Condensate tanks (NESHAP) NA NA (21.7) (31.9) (60.3) (60.3)

Processing (0.0) (0.0) (12.9) (12.1) (15.5) (16.3)

Dehydrator vents (NESHAP) (0.0) (0.0) (12.9) (12.1) (15.5) (16.3)

Transmission and Storage NA NA NA NA NA NA 

Distribution NA NA NA NA NA NA 

Total (0.0) (0.0) (58.4) (74.8) (114.6) (115.5)

NA Not applicable

Note: Totals may not sum due to independent rounding.

Table A-137: National CH4 Potential Emission Estimates from the Natural Gas Production Stage, and Reductions from the 

Natural Gas STAR Program and Regulations (Gg)

Activity 1990 1995 2000 2005 2011 2012

Normal Fugitives

Associated Gas Wells IE IE IE IE IE IE

Non-Associated Gas Wells (less wells 

with hydraulic fracturing) 10.8 12.7 13.7 15.8 18.3 18.3 

Gas Wells with Hydraulic Fracturing 8.5 12.1 15.9 26.0 35.5 35.4 

 Field Separation Equipment

 Heaters 12.9 15.9 19.4 26.9 33.5 33.3 
A

-193

Activity 1990 1995 2000 2005 2011 2012

 Separators 40.9 50.1 60.7 84.0 107.1 106.7 

 Dehydrators 14.4 17.2 20.0 26.3 32.9 32.8 

 Meters/ Piping 46.2 54.1 63.4 84.9 106.5 106.2 

 Gathering Compressors

 Small Reciprocating Compressors 33.2 39.1 45.2 58.1 71.3 70.9 

 Large Reciprocating Compressors 7.3 8.9 10.0 11.7 15.4 15.4 

 Large Reciprocating Stations 0.5 0.6 0.7 0.8 1.0 1.0 

 Pipeline Leaks 89.1 105.7 118.8 146.3 175.5 175.5 

Vented and Combusted

 Drilling, Well Completion, and Well 

Workover

Gas Well Completions without 

Hydraulic Fracturing 0.0 0.0 0.0 0.0 0.0 0.0 

Gas Well Workovers without 

Hydraulic Fracturing 0.3 0.4 0.4 0.5 0.5 0.5 

Hydraulic Fracturing Completions 

and Workovers that vent

 

219.1 

 

198.9 304.8 415.3 191.8 192.2 

Flared Hydraulic Fracturing

Completions and Workovers

 

3.0 

 

2.7 4.1 7.3 6.9 3.9 

Hydraulic Fracturing Completions 

and Workovers with RECs- - - 5.4 11.6 10.2 

Hydraulic Fracturing Completions 

and Workovers with RECs that 

flare- - - 3.6 7.8 10.9 

Well Drilling 0.7 0.5 1.0 1.6 1.0 1.0 

 Produced Water from Coal Bed 

Methane Wells 

 Powder River 0.0 1.5 31.4 50.0 47.2 47.2 

 Black Warrior 2.7 6.3 6.8 9.9 12.7 12.8 

Normal Operations

Pneumatic Device Vents 529.6 642.7 745.8 973.5 1,209.0 1,207.5 

Chemical Injection Pumps 30.0 35.8 42.1 55.5 67.8 67.3 

Kimray Pumps 165.9 206.0 238.0 307.6 387.1 388.4 

Dehydrator Vents 51.7 64.2 74.2 95.9 120.7 121.1 

Condensate Tank Vents

Condensate Tanks without Control 

Device 77.7 58.1 67.5 99.3 187.7 187.7 

Condensate Tanks with Control 

Device 15.5 11.6 13.5 19.9 37.5 37.5 

Compressor Exhaust Vented 

 Gas Engines 117.6 139.0 162.0 213.9 266.8 265.7 

Liquids Unloading

Liquids Unloading (with plunger lifts)- 16.5 37.8 72.2 118.8 118.9 

Liquids Unloading (without plunger lifts) 893.0 912.5 831.3 678.5 154.4 154.7 

Blowdowns

 Vessel Blowdown 0.3 0.4 0.4 0.6 0.7 0.7 

 Pipeline Blowdown 1.4 1.7 1.9 2.3 2.8 2.8 

 Compressor Blowdown 1.3 1.5 1.7 2.2 2.8 2.7 

 Compressor Starts 2.9 3.4 3.9 5.0 6.2 6.1 

Upsets

 Pressure Relief Valves 0.3 0.4 0.4 0.6 0.7 0.7 

 Mishaps 0.8 0.9 1.0 1.3 1.5 1.5 

Offshore

 Offshore Water Gas Platforms (Gulf of 

Mexico & Pacific) 290.5 307.3 323.7 321.8 266.1 266.1 

 Deepwater Gas Platforms (Gulf of 

Mexico & Pacific) 5.2 7.4 12.8 20.4 23.0 23.0 

Regulatory Reductions- - (45.5) (62.7) (99.0) (99.2)

Voluntary Reductions (9.0) (86.4) (302.5) (579.0) (1,603.0) (1,635.7)

Total Reductions (9.0) (86.4) (348.0) (641.6) (1,702.0) (1,734.8)

Total Potential Emissions 2,673.4 2,936.2 3,274.5 3,847.6 3,729.8 3,726.6 

Total Net Emissions 2,664.5 2,849.8 2,926.5 3,206.0 2,027.7 1,991.8 

Note 1: Totals may not sum due to independent rounding.
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

IE: Included Elsewhere. These emissions are included in the Petroleum Systems estimates. 

Table A-138: Potential CH4 Emission Estimates from the Natural Gas Processing Plants, and Reductions from the Natural 

Gas STAR Program and Regulations (Gg)

Activity 1990 1995 2000 2005 2011 2012

Normal Fugitives 

Plants 42.3 37.5 32.5 31.5 33.7 33.7

Reciprocating Compressors 324.9 338.4 349.5 327.9 420.9 442.6

Centrifugal Compressors (wet seals) 240.3 248.6 251.3 229.2 236.1 237.7

Centrifugal Compressors 

 (dry seals) - 0.8 3.5 6.5 36.8 43.9

Vented and Combusted -

Normal Operations

Compressor Exhaust

 Gas Engines 137.1 142.8 147.5 138.3 177.6 186.8

 Gas Turbines 3.9 4.0 4.2 3.9 5.0 5.3

AGR Vents 16.5 14.6 12.7 12.3 13.1 13.1

Kimray Pumps 3.7 3.8 4.0 3.7 4.8 5.0

Dehydrator Vents 22.7 23.6 24.4 22.9 29.4 30.9

Pneumatic Devices 2.4 2.1 1.9 1.8 1.9 1.9

Routine Maintenance

Blowdowns/Venting 59.5 52.8 45.7 44.3 47.4 47.4

Regulatory Reductions - - (12.9) (12.1) (15.5) (16.3)

Voluntary Reductions (1.5) (21.8) (42.8) (155.5) (140.4) (140.4)

Total Reductions (1.5) (21.8) (55.7) (167.6) (155.9) (156.8)

Total Potential Emissions 853.2 869.2 877.1 822.2 1,006.6 1,048.3

Total Net Emissions 851.8 847.3 821.3 654.6 850.7 891.5

Note 1: Totals may not sum due to independent rounding.

Table A-139: Potential CH4 Emission Estimates from the Natural Gas Transmission and Storage, and Reductions from the 

Natural Gas STAR Program and Regulations (Gg)

Activity 1990 1995 2000 2005 2011 2012

Fugitives

 Pipelines Leaks 3.2 3.2 3.3 3.3 3.3 3.3

Compressor Stations (Transmission)

 Station 106.9 108.8 109.5 110.1 111.7 111.0

 Recip Compressor 744.7 757.5 762.7 766.5 778.0 773.3

Centrifugal Compressor (wet seals) 246.7 249.7 243.0 234.1 234.4 232.8

Centrifugal Compressor (dry seals) - 0.8 6.2 12.7 15.0 15.0

Compressor Stations (Storage)

Station 54.6 60.4 62.2 60.1 58.7 52.0

Recip Compressor 157.8 174.3 179.6 173.5 169.4 150.2

Centrifugal Compressor (wet seals) 33.2 36.6 34.4 30.9 26.5 22.3

Centrifugal Compressor (dry seals) - 0.1 2.5 4.1 6.5 6.5

Wells (Storage) 13.6 15.0 15.4 14.9 14.6 12.9

M&R (Trans. Co. Interconnect) 72.8 74.0 74.5 74.9 76.0 75.6

M&R (Farm Taps + Direct Sales) 16.9 17.2 17.3 17.4 17.7 17.5

Vented and Combusted

Normal Operation

Dehydrator Vents (Transmission) 2.0 2.0 2.0 2.1 2.1 2.1

Dehydrator Vents (Storage) 4.2 4.7 4.8 4.6 4.5 4.0

Compressor Exhaust

Engines (Transmission) 176.9 204.9 215.3 203.1 225.0 235.3

Turbines (Transmission) 1.0 1.2 1.2 1.2 1.3 1.3

Engines (Storage) 21.3 23.5 24.2 23.4 22.9 20.3

Turbines (Storage) 0.2 0.2 0.2 0.2 0.2 0.2

Generators (Engines) 8.7 10.0 10.5 9.9 11.0 11.5

Generators (Turbines) 0.0 0.0 0.0 0.0 0.0 0.0

Pneumatic Devices Transmission + Storage

Pneumatic Devices Trans 213.1 216.7 218.2 219.3 222.6 221.3

Pneumatic Devices Storage 44.4 49.1 50.6 48.8 47.7 42.3

Routine Maintenance/Upsets
A-195

Activity 1990 1995 2000 2005 2011 2012

Pipeline Venting 178.0 181.0 182.2 183.2 185.9 184.8

Station venting Transmission + Storage

Station Venting Transmission 145.5 148.0 149.0 149.7 152.0 151.1

Station Venting Storage 30.3 33.5 34.5 33.4 32.6 28.9

LNG Storage

LNG Stations 9.2 9.8 10.3 10.6 10.6 10.6

LNG Reciprocating Compressors 34.5 36.7 38.8 40.1 40.1 40.1

LNG Centrifugal Compressors 11.8 12.5 13.3 13.8 13.8 13.8

LNG Compressor Exhaust

LNG Engines 2.6 2.6 2.7 2.7 2.7 2.7

LNG Turbines 0.0 0.0 0.0 0.0 0.0 0.0

LNG Station Venting 5.1 5.4 5.7 5.9 5.9 5.9

LNG Import Terminals

LNG Stations 0.2 0.2 0.2 0.4 1.2 1.2

LNG Reciprocating Compressors 1.0 1.0 1.0 2.0 5.6 5.6

LNG Centrifugal Compressors 0.3 0.3 0.3 0.5 1.4 1.4

LNG Compressor Exhaust

LNG Engines 1.7 0.5 4.4 12.2 6.9 3.6

LNG Turbines 0.0 0.0 0.0 0.1 0.0 0.0

LNG Station Venting 0.1 0.1 0.1 0.2 0.6 0.6

Regulatory Reductions - - - - - -

Voluntary Reductions - (107.7) (264.0) (506.8) (355.0) (390.1)

Total Reductions - (107.7) (264.0) (506.8) (355.0) (390.1)

Total Potential Emissions 2,342.6 2,441.6 2,480.4 2,470.0 2,508.2 2,461.1

Total Net Emissions 2,342.6 2,333.9 2,216.4 1,963.2 2,153.1 2,071.0

Note: Totals may not sum due to independent rounding.

Table A-140: Potential CH4 Emission Estimates from the Natural Gas Distribution Stage, and Reductions from the Natural 

Gas STAR Program, and Regulations (Gg)

Activity 1990 1995 2000 2005 2011 2012

Pipeline Leaks

 Mains—Cast Iron 268.0 232.7 205.7 182.3 154.4 149.0

 Mains—Unprotected steel 231.2 199.6 175.7 153.8 136.0 135.2

 Mains—Protected steel 27.5 29.7 27.9 29.0 28.8 28.8

 Mains—Plastic 59.4 67.5 85.8 106.9 123.1 126.2

Services—Unprotected steel 250.0 201.5 185.9 180.4 135.6 128.3

 Services Protected steel 67.2 71.4 60.7 56.2 51.9 50.8

 Services—Plastic 3.4 4.7 5.7 6.9 7.9 8.1

 Services—Copper 7.8 7.1 7.0 6.1 5.2 4.9

Meter/Regulator (City Gates)

 M&R >300 110.4 122.0 125.6 121.4 118.5 105.1

 M&R 100-300 214.2 236.6 243.8 235.5 230.0 203.9

 M&R <100 5.2 5.7 5.9 5.7 5.5 4.9

 Reg >300 108.7 120.1 123.7 119.5 116.7 103.5

 R-Vault >300 0.5 0.6 0.6 0.6 0.6 0.5

 Reg 100-300 82.3 90.9 93.6 90.4 88.3 78.3

 R-Vault 100-300 0.2 0.2 0.2 0.2 0.2 0.2

 Reg 40-100 6.3 7.0 7.2 7.0 6.8 6.0

 R-Vault 40-100 0.5 0.5 0.5 0.5 0.5 0.4

 Reg <40 0.3 0.4 0.4 0.4 0.4 0.3

Customer Meters

 Residential 103.5 114.3 117.7 113.7 111.1 98.5

 Commercial/Industry 4.0 4.8 4.7 3.9 4.1 4.1

Routine Maintenance

 Pressure Relief Valve Releases 0.9 1.0 1.0 1.1 1.2 1.2

 Pipeline Blowdown 2.4 2.6 2.7 2.6 2.6 2.3

Upsets

 Mishaps (Dig-ins) 37.2 41.1 42.3 40.9 39.9 35.4

Regulatory Reductions - - - - - -
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Activity 1990 1995 2000 2005 2011 2012

Voluntary Reductions - (19.7) (29.9) (48.4) (58.1) (44.8)

Total Reductions - (19.7) (29.9) (48.4) (58.1) (44.8)

Total Potential Emissions 1,591.1 1,561.9 1,524.3 1,464.9 1,369.4 1,276.1

Total Net Emissions 1,591.1 1,542.1 1,494.4 1,416.5 1,311.3 1,231.3

 Note: Totals may not sum due to independent rounding.

Table A-141: Net emissions for select sources (Gg)

Stage/Activity 1990 1995 2000 2005 2011 2012

Production 2,664.5 2,849.8 2,926.5 3,206.0 2,027.7 1,991.8 

Hydraulic Fracturing Completions and Workovers 222.1 201.6 308.9 434.1 218.1 217.1 

Liquids Unloading 893.0 929.0 869.1 750.7 273.2 273.6 

Dehydrator Vents 51.7 64.2 50.4 65.1 82.0 82.2 

Condensate Tanks 93.2 69.7 59.3 87.3 164.9 164.9 

Pipeline Leaks 89.1 105.7 118.8 143.8 175.5 175.5 

Pneumatic Device Vents 524.2 616.9 627.4 747.1 402.2 334.4 

Chemical Injection Pumps 30.0 35.8 42.1 55.5 65.7 64.6 

Gas Engines 117.6 125.3 108.8 116.0 129.6 125.8 

Compressor Starts 2.9 3.4 3.8 4.8 5.6 5.6 

Other Production 640.7 698.2 737.9 801.5 510.8 548.1 

Processing 851.8 847.3 821.3 654.6 850.7 891.5

Fugitives Reciprocating Compressors 324.9 338.4 349.5 327.9 420.9 442.6

Gas Engines 137.1 142.8 147.4 137.2 171.5 180.7

AGR Vents 16.5 14.6 12.7 12.3 13.1 13.1

Dehydrator Vents 21.3 23.6 11.3 8.6 4.5 5.2

Other Processing 351.9 327.9 300.5 168.6 240.8 249.9

Transmission and Storage 2,342.6 2,333.9 2,216.4 1,963.2 2,153.1 2071.0

Fugitives Reciprocating Compressors 744.7 756.9 762.7 766.3 777.7 772.6

Engines 176.9 192.4 166.0 119.9 103.4 111.4

Pneumatic Device Vents (Transmission) 213.1 211.4 209.3 208.9 209.6 207.2

Pipeline Vents 178.0 144.7 149.0 58.3 126.9 84.7

Other Transmission 1,030.0 1,028.6 929.4 809.8 935.4 895.2

Distribution 1,591.1 1,542.1 1,494.4 1,416.5 1,311.3 1231.3

Fugitives Cast Iron 268 233 206 182 154 149

Mishaps (Dig-ins) 37 41 42 41 35 34.7

Other Distribution 1,286 1,268 1,246 1,194 1,222 1,048

Total 7,450 7,573 7,459 7,240 6,343 6,186

Note: This table presents net emissions for each natural gas system stage, and also presents net emissions for select emissions sources for which disaggregated 

Gas STAR data and/or regulation reduction data can be matched to an Inventory source category, and sources for which emissions are calculated using net

emission factors. In general, the Inventory uses aggregated Gas STAR reductions by natural gas system stage (i.e., production, processing, transmission and 

storage, and distribution). In some cases, emissions reductions reported to Gas STAR have been matched to potential emissions calculated in the Inventory, to 

provide a net emissions number for specific emissions sources. This table presents sources for which Gas STAR reductions and/or regulatory reductions can be 

matched to Inventory emissions sources. Net emission values presented here were calculated by deducting the voluntary reductions (Table A-135) and the 

regulatory reductions (Table A-136) from the potential emissions values in Table A-137 throughTable A-140. Some reported Gas STAR reduction activities are 

cross-cutting and cover multiple Inventory sources. It is not possible to attribute those reductions to specific Inventory source categories, and they are included in 

the “Other” category. 

Table A-142: U.S. Production Sector CO2 Content in Natural Gas by NEMS Region and Formation Type for all years

U.S. Region

Formation Types North East Midcontinent 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%

Non-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%

Source: GRI-01/0136 GTI's Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. August, 2001

*In GTI, this refers to shale, coal bed methane, and tight geologic formations.

Table A-143: CO2 Emission Estimates from the Natural Gas Production Stage (Gg)

Activity 1990 1995 2000 2005 2011 2012

Normal Fugitives

 Gas Wells

 Non-Associated Gas Wells 1.0 1.1 1.1 1.3 1.5 1.5 
A

-197

Activity 1990 1995 2000 2005 2011 2012

 Gas Wells with Hydraulic Fracturing 0.4 0.5 0.7 0.9 1.0 1.0 

 Field Separation Equipment 

 Heaters 1.9 2.2 2.8 4.2 5.3 5.2 

 Separators 5.9 6.6 8.2 12.0 15.3 15.2 

 Dehydrators 1.4 1.6 2.0 2.8 3.6 3.5 

 Meters/ Piping 6.3 6.8 8.3 11.9 15.0 14.9 

 Gathering Compressors

 Small Reciprocating Compressors 3.0 3.3 4.1 6.0 7.5 7.4 

 Large Reciprocating Compressors 0.8 0.9 1.0 1.3 1.7 1.7 

 Large Reciprocating Stations 0.1 0.1 0.1 0.1 0.1 0.1 

 Pipeline Leaks 9.8 10.9 12.6 16.7 20.1 20.1 

Vented and Combusted

 Drilling, Well Completion, and Well 

Workover

Gas Well Completions without 

Hydraulic Fracturing

b 0.0 0.0 0.0 0.0 0.0 0.0 

Gas Well Workovers without Hydraulic 

Fracturing

b 0.0 0.0 0.0 0.0 0.0 0.0 

Gas Well Completions with Hydraulic 

Fracturing 106.8 96.5 211.3 296.6 242.5 208.9 

Gas Well Workovers with Hydraulic 

Fracturing 16.9 21.5 28.2 43.1 44.1 26.1 

Well Drilling 0.1 0.1 0.1 0.2 0.1 0.1 

 Produced Water from Coal Bed 

Methane Wells 

 Powder River

1 NE NE NE NE NE NE 

 Black Warrior

a NE NE NE NE NE NE 

 Normal Operations

 Pneumatic Device Vents 52.4 61.5 75.4 108.0 134.8 134.3 

 Chemical Injection Pumps 3.1 3.6 4.6 6.8 8.6 8.5 

 Kimray Pumps 16.2 19.7 23.6 32.7 40.6 40.7 

 Dehydrator Vents 5.1 6.1 7.4 10.2 12.7 12.7 

 Condensate Tank Vents 

Condensate Tanks without Control 

Device 10.3 8.8 9.3 10.3 15.7 15.7 

Condensate Tanks with Control Device 2.1 1.8 1.9 2.1 3.1 3.1 

 Compressor Exhaust Vented 

 Gas Engines

a NE NE NE NE NE NE 

Liquids Unloading

Liquids Unloading – Vent with plunger 

Lifts- 1.7 4.2 8.9 14.7 14.6 

Liquids Unloading – Vent without 

plunger Lifts 266.2 244.3 220.8 183.2 27.2 27.1 

 Blowdowns 

 Vessel Blowdowns

b 0.0 0.0 0.0 0.1 0.1 0.1 

 Pipeline Blowdowns 0.2 0.2 0.2 0.3 0.3 0.3 

 Compressor Blowdowns 0.1 0.1 0.2 0.2 0.3 0.3 

 Compressor Starts 0.3 0.3 0.4 0.5 0.6 0.6 

 Upsets 

 Pressure Relief Valves

b 0.0 0.0 0.0 0.1 0.1 0.1 

 Mishaps 0.1 0.1 0.1 0.1 0.2 0.2 

Flaring Emissions – Onshore

Production and Processing 9,092.7 17,167.8 5,525.0 7,193.0 12,534.7 12,738.8 

Offshore 

Shallow water Gas Platforms (Gulf of 

Mexico & Pacific) 1.5 1.6 1.6 1.6 1.4 1.4 

Deepwater Gas Platforms (Gulf of 

Mexico & Pacific)

b 0.0 0.0 0.1 0.1 0.1 0.1 

Flaring Emissions – Offshore 230.4 197.2 204.3 180.7 358.0 358.0 

Total 9,834.9 17,866.9 6,359.7 8,135.8 13,511.1 13,662.6 
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

a

 Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.

b

 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-144: CO2 Emission Estimates from the Natural Gas Processing Stage (Gg)

Activity 1990 1995 2000 2005 2011 2012

Normal Fugitives 

Plants – Before CO2 removal 2.6 2.3 2.0 1.9 2.0 2.0

Plants – After CO2 removal 0.6 0.5 0.4 0.4 0.5 0.5

Reciprocating Compressors – 

Before CO2 removal 19.7 20.5 21.2 19.8 25.5 26.8

Reciprocating Compressors – After 

CO2 removal 4.4 4.5 4.7 4.4 5.7 5.9

Centrifugal Compressors (wet 

seals) – Before CO2 removal 14.5 15.0 15.2 13.9 14.3 14.4

Centrifugal Compressors (wet 

seals) – After CO2 removal 3.2 3.3 3.4 3.1 3.2 3.2

Centrifugal Compressors (dry seals) 

– Before CO2 removal - 0.0 0.2 0.4 2.2 2.7

Centrifugal Compressors (dry seals) 

– After CO2 removal - 0.0 0.0 0.1 0.5 0.6

Vented and Combusted

 Normal Operations

 Compressor Exhaust

 Gas Enginesa NE NE NE NE NE NE

 Gas Turbinesa NE NE NE NE NE NE

 AGR Vents 27,708.2 24,576.9 23,288.2 21,694.3 21,403.6 21,403.6

 Kimray Pumps 0.4 0.4 0.4 0.4 0.5 0.5

 Dehydrator Vents 2.4 2.5 2.6 2.4 3.1 3.3

 Pneumatic Devices 0.3 0.3 0.2 0.2 0.2 0.2

Routine Maintenance

 Blowdowns/Venting 6.4 5.6 4.9 4.7 5.1 5.1

Total 27,762.6 24,632.0 23,343.5 21,746.1 21,466.3 21,468.8

a Energy use CO2 emissions not estimated to avoid double counting. NE = not estimated.

Note: Totals may not sum due to independent rounding.

Table A-145: CO2 Emission Estimates from the Natural Gas Transmission and Storage Stage (Gg)

Activity 1990 1995 2000 2005 2011 2012

Fugitives

 Pipelines Leaks 0.1 0.1 0.1 0.1 0.1 0.1

Compressor Stations (Transmission)

 Station 3.1 3.1 3.2 3.2 3.2 3.2

 Reciprocating Compressor 21.5 21.9 22.0 22.1 22.4 22.3

Centrifugal Compressor (wet seals) 7.1 7.2 7.0 6.8 6.8 6.7

Centrifugal Compressor (dry seals) - 0.0 0.2 0.4 0.4 0.4

 Compressor Stations (Storage)

Station 1.6 1.7 1.8 1.7 1.7 1.5

Reciprocating Compressor 4.6 5.0 5.2 5.0 4.9 4.3

Centrifugal Compressor (wet seals) 1.0 1.1 1.0 0.9 0.8 0.6

Centrifugal Compressor (dry seals) - 0.0 0.1 0.1 0.2 0.2

 Wells (Storage) 0.4 0.4 0.4 0.4 0.4 0.4

 M&R (Trans. Co. Interconnect) 2.1 2.1 2.1 2.2 2.2 2.2

 M&R (Farm Taps + Direct Sales) 0.5 0.5 0.5 0.5 0.5 0.5

Vented and Combusted

 Normal Operation

 Dehydrator Vents (Transmission) 0.1 0.1 0.1 0.1 0.1 0.1

 Dehydrator Vents (Storage) 0.1 0.1 0.1 0.1 0.1 0.1

 Compressor Exhaust

 Engines (Transmission)1 NE NE NE NE NE NE

 Turbines (Transmission)1 NE NE NE NE NE NE

 Engines (Storage)1 NE NE NE NE NE NE

 Turbines (Storage)1 NE NE NE NE NE NE

 Generators (Engines)1 NE NE NE NE NE NE

 Generators (Turbines)1 NE NE NE NE NE NE
A-199

Activity 1990 1995 2000 2005 2011 2012

 Pneumatic Devices Transmission + Storage

 Pneumatic Devices Transmission 6.1 6.3 6.3 6.3 6.4 6.4

 Pneumatic Devices Storage 1.3 1.4 1.5 1.4 1.4 1.2

 Routine Maintenance/Upsets

 Pipeline Venting 5.1 5.2 5.3 5.3 5.4 5.3

 Station Venting Transmission + Storage

 Station Venting Transmission 4.2 4.3 4.3 4.3 4.4 4.4

 Station Venting Storage 0.9 1.0 1.0 1.0 0.9 0.8

LNG Storage

 LNG Stations 0.3 0.3 0.3 0.4 0.4 0.4

 LNG Reciprocating Compressors 1.2 1.2 1.3 1.3 1.3 1.3

 LNG Centrifugal Compressors 0.4 0.4 0.4 0.5 0.5 0.5

 LNG Compressor Exhaust

 LNG Engines1 NE NE NE NE NE NE

 LNG Turbines1 NE NE NE NE NE NE

 LNG Station Venting 0.2 0.2 0.2 0.2 0.2 0.2

LNG Import Terminals

 LNG Stations 0.0 0.0 0.0 0.0 0.0 0.0

 LNG Reciprocating Compressors 0.0 0.0 0.0 0.1 0.2 0.2

 LNG Centrifugal Compressors 0.0 0.0 0.0 0.0 0.0 0.0

 LNG Compressor Exhaust

 LNG Engines1 NE NE NE NE NE NE

 LNG Turbines1 NE NE NE NE NE NE

 LNG Station Venting2 0.0 0.0 0.0 0.0 0.0 0.0

Total 61.7 63.7 64.4 64.3 64.9 63.4

a 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-146: CO2 Emission Estimates from the Natural Gas Distribution Stage (Gg)

Activity 1990 1995 2000 2005 2011 2012

Pipeline Leaks

 Mains—Cast Iron 7.7 6.7 5.9 5.3 4.5 4.3

 Mains—Unprotected steel 6.7 5.8 5.1 4.4 3.9 3.9

 Mains—Protected steel 0.8 0.9 0.8 0.8 0.8 0.8

 Mains—Plastic 1.7 1.9 2.5 3.1 3.6 3.6

Total Pipeline Miles

 Services—Unprotected steel 7.2 5.8 5.4 5.2 3.9 3.7

 Services Protected steel 1.9 2.1 1.8 1.6 1.5 1.5

 Services—Plastic 0.1 0.1 0.2 0.2 0.2 0.2

 Services—Copper 0.2 0.2 0.2 0.2 0.1 0.1

Meter/Regulator (City Gates)

 M&R >300 3.2 3.5 3.6 3.5 3.4 3.0

 M&R 100-300 6.2 6.8 7.0 6.8 6.6 5.9

 M&R <100 0.1 0.2 0.2 0.2 0.2 0.1

 Reg >300 3.1 3.5 3.6 3.4 3.4 3.0

 R-Vault >300 0.0 0.0 0.0 0.0 0.0 0.0

 Reg 100-300 2.4 2.6 2.7 2.6 2.5 2.3

 R-Vault 100-300 0.0 0.0 0.0 0.0 0.0 0.0

 Reg 40-100 0.2 0.2 0.2 0.2 0.2 0.2

 R-Vault 40-100 0.0 0.0 0.0 0.0 0.0 0.0

 Reg <40 0.0 0.0 0.0 0.0 0.0 0.0

Customer Meters

 Residential 3.0 3.3 3.4 3.3 3.2 2.8

 Commercial/Industry 0.1 0.1 0.1 0.1 0.1 0.1

Routine Maintenance

 Pressure Relief Valve Releases 0.0 0.0 0.0 0.0 0.0 0.0

 Pipeline Blowdown 0.1 0.1 0.1 0.1 0.1 0.1

Upsets

 Mishaps (Dig-ins) 1.1 1.2 1.2 1.2 1.2 1.0
A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Activity 1990 1995 2000 2005 2011 2012

Total 45.9 45.1 44.0 42.3 39.5 36.8

Note: Totals may not sum due to independent rounding.

References

AGA (1991 through 1998) Gas Facts. American Gas Association. Washington, DC.

Alabama (2013) Alabama State Oil and Gas Board. Available online at <http://www.ogb.state.al.us>.

API/ANGA (2012) Characterizing Pivotal Sources of Methane Emissions from Natural Gas Production – Summary and 

Analysis of API and ANGA Survey Responses. Final Report. American Petroleum Institute and America’s Natural Gas 

Alliance. September 21.

BOEMRE (2004) Gulfwide Emission Inventory Study for the Regional Haze and Ozone Modeling Effort. OCS Study 

MMS 2004-072.

BOEMRE (2011a) Gulf of Mexico Region Offshore Information. Bureau of Ocean Energy Management, Regulation 

and Enforcement, U.S. Department of Interior. 

BOEMRE (2011b) Pacific OCS Region Offshore Information. Bureau of Ocean Energy Management, Regulation 

and Enforcement, U.S. Department of Interior. 

BOEMRE (2011c) GOM and Pacific OCS Platform Activity. Bureau of Ocean Energy Management, Regulation and 

Enforcement, U.S. Department of Interior. 

BOEMRE (2011d) Pacific OCS Region. Bureau of Ocean Energy Management, Regulation and Enforcement, U.S. 

Department of Interior. 

DrillingInfo (2014) DI Desktop® February 2014 Download. DrillingInfo, Inc.

EIA (2012a) Formation crosswalk. Energy Information Administration, U.S. Department of Energy, Washington, 

DC. Provided July 7.

EIA (2012b) Lease Condensate Production, 1989-2011, Natural Gas Navigator. Energy Information Administration, 

U.S. Department of Energy, Washington, DC. Available online at 

<http://www.eia.gov/dnav/ng/ng_prod_lc_s1_a.htm >. 

EIA (2013a) “Table 1—Summary of natural gas supply and disposition in the United States 2008-2013.” Natural 

Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, DC. Available 

online at <http://www.eia.doe.gov>.

EIA (2013b) “Table 2—Natural Gas Consumption in the United States 2008-2013.” Natural Gas Monthly, Energy 

Information Administration, U.S. Department of Energy, Washington, DC. Available online at 

<http://www.eia.doe.gov>.

EIA (2013c) “Table 7— Marketed production of natural gas in selected states and the Federal Gulf of Mexico, 2008-

2013.” Natural Gas Monthly, Energy Information Administration, U.S. Department of Energy, Washington, 

DC. Available online at <http://www.eia.doe.gov>.

EIA (2014a) U.S. Imports by Country. Energy Information Administration, U.S. Department of Energy, 

Washington, DC. Available online at <http://www.eia.doe.gov>.

EIA (2014b) Natural Gas Gross Withdrawals and Production. Energy Information Administration, U.S. Department of 

Energy, Washington, DC. Available online at <http://www.eia.doe.gov>.

EIA (2005) “Table 5—U.S. Crude Oil, Natural Gas, and Natural Gas Liquids Reserves, 1977-2003.” Energy 

Information Administration, Department of Energy, Washington, DC.

EIA (2004) US LNG Markets and Uses. Energy Information Administration, U.S. Department of Energy, 

Washington, DC. June 2004. Available online at <http://www.eia.doe.gov/pub/oil_gas/natural_gas/ 

feature_articles/2004/lng/lng2004.pdf>.
A-201

EIA (2001) “Documentation of the Oil and Gas Supply Module (OGSM).” Energy Information Administration, U.S. 

Department of Energy, Washington, DC. Available online at 

<http://tonto.eia.doe.gov/FTPROOT/modeldoc/m063(2001).pdf>.

EIA (1996) “Emissions of Greenhouse Gases in the United States” Carbon Dioxide Emissions. Energy Information 

Administration, U.S. Department of Energy, Washington, DC. 

EPA (2012) Greenhouse Gas Reporting Program- Subpart W – Petroleum and Natural Gas Systems. Environmental 

Protection Agency.

EPA (2013a) Oil and Natural Gas Sector: Standards of Performance for Crude Oil and Natural Gas Production, 

Transmission, and Distribution. Background Supplemental Technical Support Document for the Final New Source 

Performance Standards. Environmental Protection Agency. September 2013.

EPA (2013b) Oil and Natural Gas Sector: New Source Performance Standards and National Emission Standards for 

Hazardous Air Pollutants Reviews. Environmental Protection Agency, 40 CFR Parts 60 and 63, [EPA-HQ-OAR2010-0505; FRL-9665-1], RIN 2060-AP76. 

EPA (2013c) Natural GasSTAR Reductions 1990-2012. Natural GasSTAR Program. September 2013.

EPA (2013d) Updating GHG Inventory Estimate for Hydraulically Fractured Gas Well Completions and Workovers. 

Available online at: http://www.epa.gov/climatechange/Downloads/ghgemissions/memo-update-emissions-forhydraulically-workovers.pdf. 

EPA (2013e) Greenhouse Gas Reporting Program- Subpart W – Petroleum and Natural Gas Systems. Environmental 

Protection Agency. September 2013.

EPA (1999) Estimates of Methane Emissions from the U.S. Oil Industry (Draft Report). Prepared by ICF-Kaiser, 

Office of Air and Radiation, U.S. Environmental Protection Agency. October 1999.

EPA/GRI (1996) Methane Emissions from the Natural Gas Industry. Prepared by Harrison, M., T. Shires, J. 

Wessels, and R. Cowgill, eds., Radian International LLC for National Risk Management Research Laboratory, 

Air Pollution Prevention and Control Division, Research Triangle Park, NC. EPA-600/R-96-080a.

FERC (2011) North American LNG Terminals. Federal Energy Regulatory Commission, Washington, DC. 

GTI (2001) Gas Resource Database: Unconventional Natural Gas and Gas Composition Databases. Second Edition. 

GRI-01/0136.

IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas 

Inventories Programme, H.S. Eggleston, L. Buenida, K. Miwa, T Ngara, and K. Tanabe, eds.; Institute for 

Global Environmental Strategies (IGES). Hayama, Kanagawa, Japan.

OGJ (1997-2013) “Worldwide Gas Processing.” Oil & Gas Journal, PennWell Corporation, Tulsa, OK. Available 

online at <http:// http://www.ogj.com/>.

PHMSA (2013a) Transmission Annuals Data. Pipeline and Hazardous Materials Safety Administration, U.S. Department 

of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-stats >.

PHMSA (2013b) Gas Distribution Annual Data. Pipeline and Hazardous Materials Safety Administration, U.S. 

Department of Transportation, Washington, DC. Available online at < http://phmsa.dot.gov/pipeline/library/data-stats 

>.

Wyoming (2013) Wyoming Oil and Gas Conservation Commission. Available online at 

<http://wogcc.state.wy.us/coalbedchart.cfm>.
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.6. 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 over 98 percent of total oil industry CH4 emissions. 

Crude transportation and refining accounted for the remaining CH4 emissions of less than 0.4 and less than 1.3 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: Calculate Potential Methane and Carbon Dioxide

Activity Data

Activity levels change from year to year. Some data changes 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 data for 1995 found in EPA/GRI 1996 are extrapolated to other years using publiclyavailable data sources. For the remaining sources, the activity data are obtained directly from publicly available data.

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 (BOEM), 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 data from the base year to each year between 1990 and 2012. 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 are the same as for CH4 emission sources.

Potential methane factors and emission factors

The CH4 emission factors for the majority of the activities 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 were assumed to be 

representative of emissions from each source type over the period 1990 through 2012. 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-151. 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).
A-203

Step 2: Compile Reductions Data

The methane emissions calculated in Step 1 above generally represent expected emissions from an activity in the 

absence of emissions controls, and do not take into account any use of technologies or practices that reduce emissions. To 

take into account use of such technologies, data were collected on voluntary reductions. Voluntary reductions included in 

the Petroleum Sector calculations were those reported to Gas STAR for the following activities: Artificial lift: gas lift, 

Artificial lift: use compression, Artificial lift: use pumping unit, Consolidate crude oil prod and water storage tanks, Lower 

heater-treater temperature, Re-inject gas for enhanced oil recovery, Re-inject gas into crude, and Route casinghead gas to 

VRU or compressor. 

Industry partners report CH4 emission reductions by project to the Natural Gas STAR Program. The reductions 

from the implementation of specific technologies and practices are calculated by the reporting partners 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. The Inventory 

uses aggregated Gas STAR reductions for the petroleum sector. 

Step 3: Calculate Net 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. Gas Star reductions data is summed for each year and deducted from the potential CH4 calculated in Step 1 

to estimate net CH4 emissions for the Inventory. 

Table A-147, Table A-148, Table A-149, and Table A-152 provide 2012 activity data, emission factors, and 

emission estimates and Table A-150 and Table A-153 provide a summary of emission estimates for the years 1990, 1995, 

2000, and 2005 through 2012. Table A-151 provides the CO2 content in natural gas for equipment in different crude 

streams to estimate CO2 emission factors using CH4 emission factors.

The tables provide references for emission factors and activity data in footnotes (the lettered footnotes). The 

tables also provide information on which method was used for supplying activity data for 2012 (the numbered footnotes). 

Key to table notations on methods for supplying activity data for 2012 for all tables:

1. Ratios relating other factors for which activity data are available. For example, EPA (1996) found that the 

number of heater treaters (a source of CH4 emissions) is related to both number of producing wells and 

annual production. To estimate the activity data for heater treaters, reported statistics for wells and 

production were used, along with the ratios developed for EPA (1996).

2. Activity data for 2012 available from source.

3. Activity data was held constant from 1990 through 2012 based on EPA (1999).

4. 2009, 2010, or 2011 activity data are used to determine some or all of the 2012 activity data.

Table A-147: 2012 CH4 Emissions from Petroleum Production Field Operations 

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Data Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 71.623 1,377.373

Oil Tanks

7.39 scf of CH4/bbl crudea 1,878

MMbbl/yr (non stripper 

wells)b,c,d,1,4 13.890 267.121

Pneumatic Devices, High Bleed

330 scfd CH4/devicef 145,179 

No. of high-bleed 

devicesc,e,g,1 17.508 336.692

Pneumatic Devices, Low Bleed

52 scfd CH4/devicef 269,618 

No. of low-bleed 

devicesc,e,g,1 5.117 98.411

Chemical Injection Pumps 248 scfd CH4/pumph 28,702 No. of pumpsg,i,p1 2.599 49.973

Vessel Blowdowns 78 scfy CH4/vesselh 189,710 No. of vesselsc,g,i,1 0.015 0.285

Compressor Blowdowns 3,775 scf/yr of CH4/compressorh 2,582 No. of compressorsc,g,i,1 0.010 0.187

Compressor Starts 8,443 scf/yr of CH4/compressorh 2,582 No. of compressorsc,g,i,1 0.022 0.419

Stripper wells

2,345 scf/yr of CH4/stripper wellf 315,213 

No. of stripper wells 

ventedf,1,4 0.739 14.21
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Data Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Well Completion Venting 733 scf/completionh 15,753 Oil well completionsc,2 0.012 0.222

Well Workovers 96 scf CH4/workoveri 40,200 Oil well workoversg,i,1 0.004 0.074

Pipeline Pigging 2.40 scfd of CH4/pig stationj 0 No. of crude pig stationse,3 - -

Offshore Platforms, Shallow water Oil, 

fugitive, vented and combusted 54,795 scfd CH4/platformk 1,447

No. of shallow water oil 

platformsl,4 28.930 556.353

Offshore Platforms, Deepwater oil, 

fugitive, vented and combusted 260,274 scfd CH4/platformk 29 

No. of deep water oil 

platformsl,4 2.778 53.419

Fugitive Emissions 2.669 51.330

Oil Wellheads (heavy crude)

0.13 scfd/welle,m 15,565

No. of hvy. crude 

wellsd,g,i,1,4 0.001 0.014

Oil Wellheads (light crude) 16.6 scfd/welle,m 205,222 No. of lt. crude wellsd,g,i,1,4 1.246 23.956

Separators (heavy crude)

0.15 scfd CH4/separatore,m 11,142 

No. of hvy. crude 

seps.c,g,i,1 0.001 0.012

Separators (light crude) 14 scfd CH4/separatore,m 101,402 No. of lt. crude seps.c,g,i,1 0.513 9.860

Heater/Treaters (light crude) 19 scfd CH4/heatere,m 77,166 No. of heater treatersc,g,i,1 0.541 10.396

Headers (heavy crude) 0.08 scfd CH4/headere,m 13,982 No. of hvy. crude hdrs.g,i,1 0.000* 0.008

Headers (light crude) 11 scfd CH4/headere,m 43,344 No. of lt. crude hdrs.g,i,1 0.172 3.303

Floating Roof Tanks 338,306 scf CH4/floating roof tank/yrm,n 24 No. of floating roof tankse,3 0.008 0.159

Compressors 100 scfd CH4/compressore 2,582 No. of compressorsc,g,i,1 0.094 1.812

Large Compressors 16,360 scfd CH4/compressore 0 No. of large comprs.e,3 - -

Sales Areas 41 scf CH4/loadinge 1,974,334 Loadings/yearc,1 0.080 1.540

Pipelines NE scfd of CH4/mile of pipeline 14,077 Miles of gathering lineo,2 NE NE

Well Drilling NE scfd of CH4/oil well drilled 17,774 No. of oil wells drilledc,2 NE NE

Battery Pumps 0.24 scfd of CH4/pumpm 160,800 No. of battery pumpsg,e,1 0.014 0.271

Combustion Emissions 5.188 99.765

Gas Engines 0.24 scf CH4/HP-hrh 16,264 MMHP-hrc,g,i,1 3.903 75.063

Heaters 0.52 scf CH4/bbln 2,369.6 MMbbl/yrc,2 1.235 23.746

Well Drilling 2,453 scf CH4/well drilledm 17,774 Oil wells drilledc,2 0.044 0.838

Flares 20 scf CH4/Mcf flaredj 305,587 Mcf flared/yr b,c,d,1,4 0.006 0.118

Process Upset Emissions 0.155 2.980

Pressure Relief Valves 35 scf/yr/PR valve 197,931 No. of PR valves 0.007 0.132

Well Blowouts Onshore 2.5 MMscf/blowout 59.2 No. of blowouts/yr 0.148 2.848

Voluntary Reductions 2.34 45

Total Potential Emissions 79.64 1,531.447

Total Net Emissions 77.29 1,486

a TankCALC

b EPA / ICF International (1999)

c Energy Information Administration (EIA) Monthly Energy Review

d Interstate Oil & Gas Compact Commission (IOGCC) Marginal Wells Report

e Consensus of Industrial Review Panel

f Expert Judgment

g EIA Annual Energy Review

h Gas Research Institute (GRI) / EPA (1996)

i Radian (1999)

j Canadian Association of Petroleum Producers (CAPP) (1992)

k Adapted from the Minerals Management Service (MMS) Gulfwide Offshore Activities Data System (GOADS) by ICF (2005)

l Bureau of Ocean Energy Management (BOEM)

m American Petroleum Institute (API) (1996)

n EPA, AP 42 Compilation of Air Pollutant Emission Factors

o Oil and Gas Journal (OGJ) Petroleum Economics Issue

p Percentage of chemical injection pumps (CIPs) that are gas-driven was determined through based on an estimate provided in 1997 by Ron Rayman. From Dresser 

Texsteam, a major manufacturer of CIPs at the time. 

- Zero Emissions

* Emissions are not actually 0, but too small to show at this level of precision.
A-205

Table A-148: 2012 CH4 Emissions from Petroleum Transportation

Activity/Equipment

Emission

Factor Units

Activity

Factor Units

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 0.256 4.928

Tanks 0.021 scf CH4/yr/bbl of crude delivered to refineriesa 5,490 MMbbl crude feed/yrb,2 0.113 2.175

Truck Loading 

0.520 scf CH4/yr/bbl of crude transported by truckc 131.3

MMbbl crude trans. by 

truckd,2 0.068 1.313

Marine Loading 2.544 scf CH4/1000 gal crude marine loadingsc 17,518,599 1,000 gal/yr loadede,1,4 0.045 0.857

Rail Loading

0.520 scf CH4/yr/bbl of crude transported by railc 30.1

MMbbl Crude by 

rail/yrd,2 0.016 0.301

Pump Station Maintenance 36.80 scf CH4/station/yrf 513 No. of pump stationsg,1 0.000* 0.000*

Pipeline Pigging 39 scfd of CH4/pig stationh 1,027 No. of pig stationsg,1 0.015 0.281

Fugitive Emissions 0.050 0.959

Pump Stations

25 scf CH4/mile/yrf 51,349

No. of miles of crude 

p/lg,2 0.001 0.025

Pipelines NE scf CH4/bbl crude transported by pipelinef 7,471 MMbbl crude pipedg,2 NE NE

Floating Roof Tanks

58,965 scf CH4/floating roof tank/yri 824

No. of floating roof 

tanks3 0.049 0.934

Combustion Emissions NE NE

Pump Engine Drivers 0.24 scf CH4/hp-hrj NE No. of hp-hrs NE NE

Heaters 0.521 scf CH4/bbl burnedk NE No. of bbl Burned NE NE

Total 0.306 5.887

a API (1992)

b Energy Information Administration (EIA) Petroleum Supply Annual, Volume 1.

c EPA, AP 42 Compilation of Air Pollutant Emission Factors

d EIA Refinery Capacity Report

e EIA Monthly Energy Review

f Radian (1996)

g OGJ Petroleum Economics Issue

h CAPP (1992)

i API TANK

j GRI / EPA (1996)

k EPA / ICF International (1999)

* Emissions are not actually 0, but too small to show at this level of precision.

NE: Not estimated for lack of data

Table A-149: 2012 CH4 Emissions from Petroleum Refining

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 0.798 15.349

 Tanks

20.6 scf CH4/Mbbla 1,930

Mbbl/calendar day heavy crude 

feedb,c,1 0.015 0.279

 System Blowdowns

137 scf CH4/Mbblc 15,040

Mbbl/calendar day refinery 

feedb,2 0.751 14.443

 Asphalt Blowing 2,555 scf CH4/Mbblc 35 Mbbl/calendar day productionb,2 0.033 0.627

Fugitive Emissions 0.087 1.672

 Fuel Gas System 439 Mscf CH4/refinery/yra 144 Refineriesd,2 0.063 1.216

 Floating Roof Tanks 587 scf CH4/floating roof tank/yre 767 No. of floating roof tanks3 0.000* 0.009

 Wastewater Treating 1.88 scf CH4/Mbblc 15,040

Mbbl/calendar day refinery 

feedb,2 0.010 0.199

 Cooling Towers 2.36 scf CH4/Mbblc 15,040

Mbbl/calendar day refinery 

feedb,2 0.013 0.249

Combustion Emissions 0.096 1.842

 Atmospheric Distillation 3.61 scf CH4/Mbblc 15,362 Mbbl/calendar day refinery 0.020 0.389
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

feedd,2

 Vacuum Distillation 3.61 scf CH4/Mbblc 7,133 Mbbl/calendar day feedf,1 0.009 0.181

 Thermal Operations 6.01 scf CH4/Mbblc 2,295 Mbbl/calendar day feed f,1 0.005 0.097

 Catalytic Cracking 5.17 scf CH4/Mbblc 5,034 Mbbl/calendar day feed f,1 0.010 0.183

 Catalytic Reforming 7.22 scf CH4/Mbblc 3,112 Mbbl/calendar day feed f,1 0.008 0.158

 Catalytic Hydrocracking 7.22 scf CH4/Mbblc 1,548 Mbbl/calendar day feed f,1 0.004 0.078

 Hydrorefining 2.17 scf CH4/Mbblc 2,268 Mbbl/calendar day feed f,1 0.002 0.034

 Hydrotreating 6.50 scf CH4/Mbblc 10,401 Mbbl/calendar day feed f,1 0.025 0.474

 Alkylation/Polymerization 12.6 scf CH4/Mbblc 1,088 Mbbl/calendar day feed f,1 0.005 0.096

 Aromatics/Isomeration 1.80 scf CH4/Mbblc 1,011 Mbbl/calendar day feed f,1 0.001 0.013

 Lube Oil Processing 0.00 scf CH4/Mbblc 171 Mbbl/calendar day feed f,1 0.000 0.000

 Engines 0.006 scf CH4/hp-hrg 1,084 MMhp-hr/yrf,1 0.006 0.119

 Flares

0.189 scf CH4/Mbblc,e 15,040

Mbbl/calendar day refinery 

feedb,2 0.001 0.020

Total 0.981 18.864

a API (1992)

b EIA Petroleum Supply Annual, Volume 1.

c Radian (1996)

d EIA Petroleum Navigator

e EPA, AP 42 Compilation of Air Pollutant Emission Factors

f OGJ Refinery Worldwide Report

g GRI / EPA (1996)

* Emissions are not actually 0, but too small to show at this level of precision.

Table A-150: Summary of CH4 Emissions from Petroleum Systems (Gg)

Activity 1990 1995 2000 2007 2008 2009 2010 2011 2012

Production Field Operations 1,680 1,581 1,488 1,415 1,425 1,432 1,443 1,474 1,531

Pneumatic device venting 489 463 428 398 416 419 416 428 435

Tank venting 250 226 214 193 185 202 211 222 267

Combustion & process upsets 115 106 97 90 94 94 95 98 103

Misc. venting & fugitives 799 762 726 714 706 694 700 702 703

Wellhead fugitives 26 25 22 20 24 23 22 24 24

Crude Oil Transportation 7 6 5 5 5 5 5 5 6

Refining 18 18 19 19 19 18 19 19 19

Voluntary Reductions 0.2 1.4 17 64 77 67 60 45 45

Total Potential Emissions 1,704 1,605 1,513 1,439 1,449 1,455 1,467 1,498 1,556

Total Net Emissions 1,704 1,604 1,496 1,376 1,372 1,388 1,407 1,453 1,511

Note: Totals may not sum due to independent rounding.

Table A-151: 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-152: 2012 CO2 Emissions from Petroleum Production Field Operations and Petroleum Refining

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 7.363 389.364

Oil Tanks 3.53 scf of CO2/bbl crudea 1,878 MMbbl/yr (non stripper 

wells)b,c,d,1,4

6.627 350.477

Pneumatic Devices, High Bleed 6.704 scfd CO2/devicef 145,179 No. of high-bleed 

devicesc,e,g,1

0.355 18.787
A-207

Activity/Equipment

2012 EPA Inventory Values

Emission Factor Activity Factor Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Pneumatic Devices, Low Bleed 1.055 scfd CO2/devicef 269,618 No. of low-bleed 

devicesc,e,g,1

0.104 5.491

Chemical Injection Pumps 5.033 scfd CO2/pumph 28,702 No. of pumpsg,i,1 0.053 2.788

Vessel Blowdowns 1.583 scfy CO2/vesselh 189,710 No. of vesselsc,g,i,1 0.000* 0.016

Compressor Blowdowns 77 scf/yr of CO2/compressorh 2,582 No. of compressorsc,g,i,1 0.000* 0.010

Compressor Starts 171 scf/yr of CO2/compressorh 2,582 No. of compressorsc,g,i,1 0.000* 0.023

Stripper wells 48 scf/yr of CO2/stripper wellf 315,213 No. of stripper wells 

ventedf,1,4

0.015 0.793

Well Completion Venting 14.87 scf/completionh 15,753 Oil well completionsc,2 0.000* 0.012

Well Workovers 1.95 scf CO2/workoveri 40,200 Oil well workoversg,i,1 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/platformk 1,447 No. of shallow water oil 

platformsl,4

0.189 10.00

Offshore Platforms, Deepwater oil, 

fugitive, vented and combusted

1,701 scfd CO2/platformk 29 No. of deep water oil 

platformsl,4

0.018 0.960

Fugitive Emissions 0.055 2.929

Oil Wellheads (heavy crude) 0.003 scfd/welle,m 15,565 No. of hvy. crude wellsd,g,i,1,4 0.000* 0.001

Oil Wellheads (light crude) 0.337 scfd/welle,m 205,222 No. of lt. crude wellsd,g,i,1,4 0.025 1.337

Separators (heavy crude) 0.003 scfd CO2/separatore,m 11,142 No. of hvy. crude seps.c,g,i,1 0.000* 0.001

Separators (light crude) 0.281 scfd CO2/separatore,m 101,402 No. of lt. crude seps.c,g,i,1 0.010 0.550

Heater/Treaters (light crude) 0.319 scfd CO2/heatere,m 77,166 No. of heater treatersc,g,i,1 0.009 0.475

Headers (heavy crude) 0.002 scfd CO2/headere,m 13,982 No. of hvy. crude hdrs.g,i,1 0.000* 0.000*

Headers (light crude) 0.220 scfd CO2/headere,m 43,344 No. of lt. crude hdrs.g,i,1 0.003 0.184

Floating Roof Tanks 17,490 scf CO2/floating roof 

tank/yrm,n

24 No. of floating roof tankse,3

0.000* 

0.023

Compressors 2.029 scfd CO2/compressore 2,582 No. of compressorsc,g,i,1 0.002 0.101

Large Compressors 332 scfd CO2/compressore 0 No. of large comprs.e,3 0.000 0.000

Sales Areas 2.096 scf CO2/loadinge 1,974,334 Loadings/yearc,1 0.004 0.219

Pipelines NE scfd of CO2/mile of 

pipeline

14,077 Miles of gathering lineo,2 NE NE

Well Drilling NE scfd of CO2/oil well drilled 17,774 No. of oil wells drilledc,2 NE NE

Battery Pumps 0.012 scfd of CO2/pumpm 160,800 No. of battery pumpsg,e,1 0.001 0.039

Process Upset Emissions 0.003 0.178

Pressure Relief Valves 1.794 scf/yr/PR valveh 197,931 No. of PR valvesc,e,1 0.000* 0.019

Well Blowouts Onshore 0.051 MMscf/blowoute 59.0 No. of blowouts/yrc,e,1 0.003 0.159

Refining Emissions 0.265 14.00

 Asphalt Blowing† 20,736 scf CO2/Mbblm 35 Mbbl/calendar day 

productionp,2

0.265 13.998

Total 7.686 406.468

a TankCALC

b EPA / ICF International (1999)

c EIA Monthly Energy Review

d IOGCC Marginal Wells Report

e Consensus of Industrial Review Panel

f Expert Judgement

g EIA Annual Energy Review

h GRI / EPA (1996)

i Radian (1996)

j CAPP (1992)

k Adapted from the MMS GOADS by ICF (2005)

l BOEM

m API (1996)
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

n EPA, AP 42 Compilation of Air Pollutant Emission Factors

o OGJ Petroleum Economics Issue

p EIA Petroleum Supply Annual, Volume 1

* Emissions are not actually 0, but too small to show at this level of precision.

† 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-153: Summary of CO2 Emissions from Petroleum Systems (Gg)

Activity 1990 1995 2000 2007 2008 2009 2010 2011 2012

Production Field Operations 376 341 323 293 284 306 317 332 392

Pneumatic device venting 27 26 24 22 23 23 23 24 24

Tank venting 328 296 281 253 243 265 276 291 350

Misc. venting & fugitives 18 18 17 16 16 16 16 16 16

Wellhead fugitives 1 1 1 1 1 1 1 1 1

Refining 18 19 21 18 16 14 15 15 14

Asphalt Blowing 18 19 21 18 16 14 15 15 14

Total 394 360 344 311 300 320 332 347 406
A-209

3.7. 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 2014), the flows of plastics in the U.S. waste stream are reported for seven 

resin categories. For 2012, the quantity generated, recovered, and discarded for each resin is shown in Table A-154. The 

data set for 1990 through 2012 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-154: 2012 Plastics in the Municipal Solid Waste Stream by Resin (Gg)

Waste Pathway PET HDPE PVC

LDPE/

LLDPE PP PS Other Total

Generation 4,101 5,017 789 6,668 6,523 2,032 3,629 28,758

Recovery 798 517 0 354 36 18 816 2,540

Discard 3,302 4,500 789 6,314 6,486 2,014 2,812 26,218

 Landfill 3,011 4,103 720 5,757 5,915 1,836 2,564 23,907

 Combustion 291 397 70 557 572 178 248 2,311

Recovery* 19% 10% 0% 5% 1% 1% 23% 9%

Discard* 81% 90% 100% 95% 99% 99% 78% 91%

 Landfill* 73% 82% 91% 86% 91% 90% 71% 83%

 Combustion* 7% 8% 9% 8% 9% 9% 7% 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-155). 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-155: 2012 Plastics Incinerated (Gg), Carbon Content (%), Fraction Oxidized (%) and Carbon Incinerated (Gg)

Factor PET HDPE PVC

LDPE/

LLDPE PP PS Other Total

Quantity Combusted 291 397 70 557 572 178 248 2,311

Carbon Content of Resin 63% 86% 38% 86% 86% 92% 66% -

Fraction Oxidized 98% 98% 98% 98% 98% 98% 98% -

Carbon in Resin Combusted 178 333 26 468 480 161 160 1,806

Emissions (Tg CO2 Eq.) 0.7 1.2 0.1 1.7 1.8 0.6 0.6 6.6

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. “U.S. Scrap Tire Management Summary 2005-2009” (RMA 2011) reports that 2,085 thousand of the 

4,391 thousand tons of scrap tires generated in 2009 (approximately 47 percent of generation) were used for fuel purposes. 
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.60 Table A-156 shows consumption and C content of 

elastomers used for tires and other products in 2002, the most recent year for which data are available. 

 C black is 100 percent C (Aslett Rubber Inc. n.d.). 

Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber, C black portions of 

scrap tires, and then by a 98 percent oxidation factor, yielded CO2 emissions, as shown in Table A- 157. The disposal rate 

of rubber in tires (0.4 Tg C/yr) is smaller than the consumption rate for tires based on summing the elastomers listed in 

Table A-154 (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 2009 were taken from RMA 2006, RMA 2009, RMA 2011; where 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. 

In 2009, RMA changed the reporting of scrap tire data from millions of tires to thousands of short tons of scrap 

tire. As a result, the average weight and percent of the market of light duty and commercial scrap tires was used to convert 

the previous years from millions of tires to thousands of short tons (STMC 1990 through 1997; RMA 2002 through 2006, 

2012a). 

Table A-156: 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.

Table A-157: Scrap Tire Constituents and CO2 Emissions from Scrap Tire Incineration in 2012

Material

Weight of Material 

(Tg) Fraction Oxidized Carbon Content Emissions (Tg CO2 Eq.)

Synthetic Rubber 0.4 98% 90% 1.6

Carbon Black 0.5 98% 100% 1.9

Total 1.0 - - 3.5

- Not applicable

 

60 The carbon content of tires (1,174 Gg C) divided by the mass of rubber in tires (1,307 Gg) equals 90 percent.
A-211

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 data61 provided in the Municipal Solid Waste Generation, 

Recycling, and Disposal in the United States: Facts and Figures reports (EPA 1999 through 2003, 2005 through 2014) 

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 (2014) did not report rubber found in the product category “containers and packaging;” 

however, containers and packaging from miscellaneous material types were reported for 2009 through 2012. As a result, 

EPA assumes that rubber containers and packaging are reported under the “miscellaneous” category; and therefore, the 

quantity reported for 2009 through 2012 were 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 percent was assigned to each product type, as shown in Table A-158.

62

 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-158: Rubber and Leather in Municipal Solid Waste in 2012

Product Type

Incinerated 

(Gg)

Synthetic 

Rubber (%)

Carbon Content 

(%)

Fraction Oxidized 

(%)

Emissions 

(Tg CO2 Eq.)

Durables (not Tires) 280 70% 85% 98% 0.9

Non-Durables 81 - - - 0.3

Clothing and Footwear 62 70% 85% 98% 0.2

Other Non-Durables 19 70% 85% 98% 0.1

Containers and Packaging 2 70% 85% 98% 0.0

Total 363 - - - 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 2014) 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-159). 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 types (polyester, nylon, olefin, and acrylic) produced in 1999 (see Table A-160). 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)

 

61 Discards = Generation minus recycling.

62 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions.
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-159: 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 4,706 655 4,051 417

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 5,714 913 4,800 480

2006 5,893 933 4,959 479

2007 6,041 953 5,088 470

2008 6,305 968 5,337 470

2009 6,424 978 5,446 480

2010 6,508 998 5,510 486

2011 6,513 1,003 5,510 486

2012 7,114 1,117 5,997 529

Table A-160: 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 2014) 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 through 2012, so these values were

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-160).
A-213

Table A-161: 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

2010 26,544,672 NA

2011 26,544,672 NA

2012 26,544,672 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
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.8. Methodology for Estimating Emissions from International Bunker Fuels used by 

the U.S. Military

Bunker fuel emissions estimates for the Department of Defense (DoD) 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 2012, 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 DoD63 and NATO64 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-162 displays DoD’s consumption of fuels that remain at the completion of Step 1, summarized by fuel 

type. Table A-162 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.

 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 

 

63 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Directive 4140.25, DoD Management Policy for Energy Commodities and 

Related Services, 1999.

64 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipments Using Compression Ignition or Turbine 

Engines, 1987.
A-215

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 DLA Energy 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-163 and Table A-164 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-163 and Table A-164, below. CO2 emissions 

from aviation bunkers and distillate marine bunkers presented in Table A-167, and are based on emissions from fuels 

tallied in Table A-163 and Table A-164
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-162: Transportation Fuels from Domestic Fuel Deliveriesa (Million Gallons)

Vehicle Type/Fuel 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

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 2,092.0 2,081.0 2,067.8 1,814.5 1,663.9 1,405.0 1,449.7 

 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 2,091.9 2,080.9 2,067.7 1,814.3 1,663.7 1,404.8 1,449.5 

 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 1,709.3 1,618.5 1,616.2 1,358.2 1,100.1 882.8 865.2 

 JP5 1,025.4 691.2 615.8 552.8 515.6 505.5 472.1 503.2 442.2 409.1 433.7 421.6 325.5 376.1 362.2 361.2 399.3 372.3 362.5 

 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 57.0 86.3 89.2 94.8 164.3 149.7 221.8 

 Aviation Gasoline + + + + + + + + 0.1 0.1 + 0.1 0.1 0.2 0.1 0.2 0.2 0.2 0.3 

Marine 686.8 438.9 493.3 639.8 674.2 598.9 454.4 418.4 455.8 609.1 704.5 604.9 531.6 572.8 563.4 485.8 578.8 489.9 490.4 

 Middle Distillate (MGO) + + 38.5 47.5 51.1 49.2 48.3 33.0 41.2 88.1 71.2 54.0 45.8 45.7 55.2 56.8 48.4 37.3 52.9 

 Naval Distillate (F76) 686.8 438.9 449.0 583.4 608.4 542.9 398.0 369.1 395.1 460.9 583.5 525.9 453.6 516.0 483.4 399.0 513.7 440.0 428.4 

 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 32.2 11.1 24.9 30.0 16.7 12.5 9.1 

Other c 717.1 310.9 276.9 263.3 256.8 256.0 248.2 109.8 211.1 221.2 170.9 205.6 107.3 169.0 173.6 206.8 224.0 208.6 193.8 

 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 30.6 47.3 49.1 58.3 64.1 60.9 57.9 

 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 11.7 19.2 19.7 25.2 25.5 22.0 19.6 

 Jet Fuel d + + + 11.7 23.4 35.0 46.7 58.4 125.9 133.9 105.1 124.4 65.0 102.6 104.8 123.3 134.4 125.6 116.2 

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 2,730.9 2,822.8 2,804.9 2,507.1 2,466.7 2,103.5 2,133.9 

Note: Totals may not sum due to independent rounding.

a

 Includes fuel consumption in the United States and U.S. Territories.

b

 Intermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO is used by the Military Sealift Command.

c

 Prior to 2001, gasoline and diesel fuel totals were estimated using data provided by the military Services for 1990 and 1996. The 1991 through 1995 data points were interpolated from the Service inventory data. The 

1997 through 1999 gasoline and diesel fuel data were initially extrapolated from the 1996 inventory data. Growth factors used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel 

estimates from 1997 through 2000 were reduced according to the estimated consumption of jet fuel that is assumed to have replaced the diesel fuel consumption in land-based vehicles. Data sets for other diesel and 

gasoline consumed by the military in 2000 were estimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000. In 2001, other gasoline 

and diesel fuel totals were generated by DESC/DLA.

d

 The fraction of jet fuel consumed in land-based vehicles was estimated using Service data, DESC/DLA Energy data, and expert judgment.

+ Does not exceed 0.05 million gallons.
A-217

Table A-163: 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 2008 2009 2010 2011 2012

JP8 56.7 300.4 308.8 292.0 306.4 301.4 307.6 341.2 309.5 305.1 309.8 285.6 262.5 249.1 229.4 211.4 182.5 143.4 141.2

 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 59.2 55.4 60.8 47.1 50.4 

 Air Force + 262.2 269.0 245.1 252.6 245.9 254.2 267.4 222.9 228.7 230.6 214.7 197.8 186.5 170.3 156.0 121.7 96.2 90.8 

JP5 370.5 249.8 219.4 194.2 184.4 175.4 160.3 169.7 158.3 146.1 157.9 160.6 125.0 144.5 139.2 137.0 152.5 144.9 141.2

 Navy 365.3 246.3 216.1 191.2 181.4 170.6 155.6 163.7 153.0 141.3 153.8 156.9 122.8 141.8 136.5 133.5 149.7 143.0 139.5 

 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 2.6 3.5 2.8 1.8 1.7 

JP4 420.8 21.5 1.1 0.1 + + + + + + + + + + + + 0.1 + + 

 Navy + + + + + + + + + + + + + + + + + + + 

 Air Force 420.8 21.5 1.1 0.1 + + + + + + + + + + + + 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 16.8 18.1 31.4 31.1 38.6

 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 12.5 12.3 13.7 14.6 14.8 

 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 4.3 5.9 17.7 16.5 23.8 

JA1 + + + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.1 1.0 0.6 0.3 -+ -+ 

 Navy + + + + + + + + + + + + + 0.1 0.1 0.1 0.1 -+ -+ 

 Air Force + + + + + + + 0.1 0.6 0.2 0.5 0.5 0.4 1.0 0.8 0.5 0.1 -+ -+ 

JAB + + + + + + + + + + + + + + + + + + + 

 Navy + + + + + + + + + + + + + + + + + + + 

 Air Force + + + + + + + + + + + + + + + + + + + 

Navy Subtotal 430.5 290.2 262.5 244.0 241.8 232.4 216.9 245.5 249.4 233.1 254.4 239.4 196.6 216.3 208.3 201.3 224.4 204.3 204.5

Air Force Subtotal 431.3 290.7 277.0 251.7 259.9 255.2 263.5 278.1 232.7 239.9 243.7 222.9 203.1 194.0 178.1 165.9 142.4 114.5 116.3

Total 861.8 580.9 539.5 495.6 501.7 487.5 480.4 523.6 482.1 473.0 498.1 462.3 399.7 410.3 386.3 367.2 366.7 318.8 320.8

+ Does not exceed 0.05 million gallons.

Note: Totals may not sum due to independent rounding.

Table A-164: 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 2008 2009 2010 2011 2012

Navy—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 40.9 39.9 32.9 25.5 36.5

Navy—F76 522.4 333.8 331.9 441.7 474.2 466.0 298.6 282.6 305.6 347.8 434.4 413.1 355.9 404.1 376.9 311.4 402.2 346.6 337.9

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 19.0 23.1 12.9 9.5 6.1

Total 522.4 333.8 366.8 484.3 517.7 511.0 328.8 318.0 348.2 459.0 530.0 470.7 414.3 444.4 436.7 374.4 448.0 381.5 380.6

+ Does not exceed 0.05 million gallons.

Note: Totals may not sum due to independent rounding.
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-165: 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-166: 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 2010 2011 2012

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 19.70 19.70 19.70

Source: EPA (2010)

Table A-167: 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 2010 2011 2012

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 3.6 3.1 3.1

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 3.8 4.6 3.9 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 7.4 8.2 7.0 7.0

Note: Totals may not sum due to independent rounding.
A-219

3.9. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone

Depleting Substances 

Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a 

country-specific modeling approach. The Vintaging 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 60 independently modeled 

end-uses. The model requires information on the market growth for each of the end-uses, a history of the market transition 

from ODS to alternatives, and the characteristics of each end-use such as market size or charge sizes and loss rates. As 

ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to each of its substitutes.

The model, named for its method of tracking the emissions of annual “vintages” of new equipment that enter into 

service, is a “bottom-up” model. It models the consumption of chemicals based on estimates of the quantity of equipment 

or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain 

the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of 

the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a “business-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.
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Methodology

The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps:

1. Gather historical data. The Vintaging Model is populated with information on each end-use, taken 

from published sources and industry experts.

2. Simulate the implementation of new, non-ODS technologies. The Vintaging Model uses detailed 

characterizations of the existing uses of the ODS, as well as data on how the substitutes are replacing the ODS, to simulate 

the implementation of new technologies that enter the market in compliance with ODS phase-out policies. As part of this 

simulation, the ODS substitutes are introduced in each of the end-uses over time as seen historically and as needed to 

comply with the ODS phase-out.

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-221

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- 168, 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-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-168: Refrigeration and Air-Conditioning Market Transition Assumptions

Initial Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start Date Date of Full 

Penetration in 

New 

Equipment

Maximum 

Market 

Penetration

Centrifugal Chillers

CFC-11 HCFC-123 1993 1993 45% Unknown 0.5%

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 0.5%

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 0.5%

HCFC-22 1991 1994 16% HFC-134a 2000 2010 100% None

HCFC-123 1993 1994 31% Unknown

CFC-114 HFC-236fa 1993 1996 100% HFC-134a 1998 2009 100% None 0.2%

Cold Storage

CFC-12 HCFC-22 1990 1993 65% R-404A 1996 2010 75% None 2.5%

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 2.5%

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 2.5%

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 0.8%

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 0.8%

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 0.2%

R-410A 2007 2010 11% None
A-223

Initial Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start Date Date of Full 

Penetration in 

New 

Equipment

Maximum 

Market 

Penetration

Ice Makers

CFC-12 HFC-134a 1993 1995 100% None 2.5%

Industrial Process Refrigeration

CFC-11 HCFC-123 1992 1994 70% Unknown 2.5%

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 2.5%

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 HFC-134a 1995 2009 2% None 2.5%

R-404A 1995 2009 5% None

R-410A 1999 2009 2% None

R-507 1995 2009 2% None

HFC-134a 2009 2010 14% None

R-404A 2009 2010 45% None

R-410A 2009 2010 18% None

R-507 2009 2010 14% None

Mobile Air Conditioners (Passenger Cars)

CFC-12 HFC-134a 1992 1994 100% HFO-1234yf 2012 2015 1% None 0.5%

HFO-1234yf 2016 2021 99% None

Mobile Air Conditioners (Light Duty Trucks)

CFC-12 HFC-134a 1993 1994 100% HFO-1234yf 2012 2015 1% None 2%

HFO-1234yf 2016 2021 99% 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 2.6%

Mobile Air Conditioners (Transit Buses)

HCFC-22 HFC-134a 1995 2009 100% None 2.6%
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Initial Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start Date Date of Full 

Penetration in 

New 

Equipment

Maximum 

Market 

Penetration

Mobile Air Conditioners (Trains)

HCFC-22 HFC-134a

R-407C

2002

2002

2009

2009

50%

50%

None

None

2.6%

Packaged Terminal Air Conditioners and Heat Pumps

HCFC-22 R-410A 2006 2009 10% None 0.8%

R-410A 2009 2010 90% None

Positive Displacement Chillers

HCFC-22 HFC-134a 2000 2009 9% R-407C 2010 2020 60% None 0.5%

R-410A 2010 2020 40% None

R-407C 2000 2009 1% None

HFC-134a 2009 2010 81% R-407C 2010 2020 60% None

R-410A 2010 2020 40%

R-407C 2009 2010 9% None

CFC-12 HCFC-22 1993 1993 100% HFC-134a 2000 2009 9% R-407C 2010 2020 60% 0.2%

R-410A 2010 2020 40%

R-407C 2000 2009 1% None

HFC-134a 2009 2010 81% R-407C 2010 2020 60%

R-410A 2010 2020 40%

R-407C 2009 2010 9% None

Refrigerated Appliances

CFC-12 HFC-134a 1994 1995 100% None 0.5%

Residential Unitary Air Conditioners

HCFC-22 HCFC-22 2006 2006 70% R-410A 2007 2010 29% None 0.8%

R-410A 2010 2010 71% None

R-410A 2000 2005 5% R-410A 2006 2006 100% None

R-410A 2000 2006 5% None

R-410A 2006 2006 20% None

Retail Food (Large; Technology Transition)

DX65 DX 2000 2010 85% DX 2010 2010 66% None 0.8%

 

65 DX refers to direct expansion systems where the compressors are mounted together in a rack and share suction and discharge refrigeration lines that run throughout the store, feeding refrigerant to the 

display cases in the sales area.

2 DR refers to distributed refrigeration systems that consist of multiple smaller units that are located close to the display cases that they serve such as on the roof above the cases, behind a nearby wall, or 

on top of or next to the case in the sales area.

3 SLS refers to secondary loop systems wherein a secondary fluid such as glycol or carbon dioxide is cooled by the primary refrigerant in the machine room and then pumped throughout the store to 

remove heat from the display equipment.

4 The CFC-12 large retail food market for new systems transitioned to R-502 from 1998 to 1990, and subsequently transitioned to HCFC-22 from 1990 to 1993. These transitions are not shown in the 

table in order to provide the HFC transitions in greater detail.

5 HCFC-22 for new equipment after 2010 is assumed to be reclaimed material.
A-225

Initial Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start Date Date of Full 

Penetration in 

New 

Equipment

Maximum 

Market 

Penetration

DR2 2010 2010 30% None

SLS3 2010 2010 4% None

DR 2000 2010 13.5% None

SLS 2000 2010 1.5% None

Retail Food (Large; Refrigerant Transition)

CFC-12 R-404A 1995 2000 17.5% R-404A 2000 2009 17.9% None 0.8%

and R-507 2000 2009 1.7% None

R-5024 R-407A 2000 2009 0.4% None

R-507 1995 2000 7.5% R-404A 2000 2009 17.9% None

R-507 2000 2009 1.7% None

R-407A 2000 2009 0.4% None

HCFC-22 1995 2000 75% R-404A 2001 2010 17.9% None

R-507 2001 2010 1.7% None

R-407A 2001 2010 0.4% None

R-404A 2009 2010 68% R-404A 2010 2010 35.8%

R-507 2010 2010 3.6%

R-407A 2010 2010 0.7%

R-507 2009 2010 8.0% R-404A 2010 2010 35.8%

R-507 2010 2010 3.6%

R-407A 2010 2010 0.7%

R-407A 2009 2010 4.0% R-404A 2010 2010 35.8%

R-507 2010 2010 3.6%

R-407A 2010 2010 0.7%

Retail Food (Large Condensing Units)

HCFC-22 R-402A 1995 2005 5% R-404A 2006 2006 100% None 0.9%

R-404A 1995 2005 25% None

R-507 1995 2005 10% None

R-404A 2008 2010 45% None

R-507 2008 2010 15% None

Retail Food (Small Condensing Units)

HCFC-22 R-401A 1995 2005 6% HFC-134a 2006 2006 100% None 0.9%

 
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Initial Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start Date Date of Full 

Penetration in 

New 

Equipment

Maximum 

Market 

Penetration

R-402A 1995 2005 4% HFC-134a 2006 2006 100% None

HFC-134a 1993 2005 30%

R-404A 1995 2005 30%

R-404A 2008 2010 30%

Retail Food (Small)

CFC-12 HCFC-22 1990 1993 90% HFC-134a 1993 1995 90% CO2 2010 2010 5% 0.8%

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 2.5%

HCFC-22 1993 1995 2% HFC-134a 1995 1999 100% None

R-502 HFC-134a 1993 1995 55% None 2.5%

R-404A 1993 1995 45% None

Water-Source and Ground-Source Heat Pumps

HCFC-22 R-407C 2000 2006 5% None 0.8%

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-410A 2008 2009 10% None 5.0%

R-410A 2009 2010 90% None
A-227

Table A- 169 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-169. 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 –16 2.3 – 18.0

Positive Displacement Chillers 20 0.5 – 1.5

PTAC/PTHP 12 3.9

Retail Food 18 – 20 1.0 – 25

Refrigerated Appliances 14 0.6

Residential Unitary A/C 15 11.8

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- 170.
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-170. 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- 171.
A-229

Table A-171. Solvent Market Transition Assumptions

Initial Market 

Segment

Primary Substitute Secondary Substitute Growth 

Name of Substitute Start Rate

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

CH3CCl3 Non-ODP/GWP 1994 1995 100% None 2.0%

Electronics

CFC-113 Semi-Aqueous 1994 1995 52% None 2.0%

HCFC-225ca/cb 1994 1995 0.2% Unknown

HFC-43-10mee 1995 1996 0.7% None

HFE-7100 1994 1995 0.7% None

nPB 1992 1996 5% None

Methyl Siloxanes 1992 1996 0.8% None

No-Clean 1992 1996 40% None

CH3CCl3 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

CH3CCl3 Non-ODP/GWP 1992 1996 100% None 2.0%

CFC-113 Non-ODP/GWP 1992 1996 100% None 2.0%

CCl4 Non-ODP/GWP 1992 1996 100% None 2.0%

Precision

CH3CCl3 Non-ODP/GWP 1995 1996 99.3% None 2.0%

HFC-43-10mee 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.
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.

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- 172.

Table A-172. 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 2020 2020 25%

HFC-236fa 2020 2020 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.
A-231

j = Year of emission.

Step 2: Calculate lifetime emissions (closed-cell foams)

Lifetime emissions occur annually from closed-cell foams throughout the lifetime of the foam, as calculated as 

presented in the following equation.

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 + 26

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.
A-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year.

k = Lifetime. The average lifetime of foam product.

m = Counter. Runs from lifetime (k) to (k+26).

j = Year of emission.

Step 5: Calculate total emissions (open-cell and closed-cell foams)

To calculate total emissions from foams in any given year, emissions from all foam stages must be summed, as 

presented in the following equation.

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- 173. The emission profiles of these 13 foam types are shown in Table A- 174.
A-233

Table A-173. Foam Blowing Market Transition Assumptions

Initial 

Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start 

Date

Date of Full 

Penetration in 

New Equipment

Maximum 

Market 

Penetration

Commercial Refrigeration Foam

CFC-11 HCFC-141b 1989 1996 40% HFC-245fa 2002 2003 80% None 6.0%

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 2.0%

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 2.0%

Phenolic Foam

CFC-11 HCFC-141b 1989 1990 100% Non-ODP/GWP 1992 1992 100% None 2.0%

Polyolefin Foam

CFC-114 HFC-152a 1989 1993 10% Non-ODP/GWP 2005 2010 100% None 2.0%

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 6.0%

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 2003 100% 0.8%

HFC-245fa 2001 2003 50% Non-ODP/GWP 2015 2029 100%

HFC-245fa 2006 2009 10% Non-ODP/GWP 2015 2029 100%

Non-ODP/GWP 2002 2005 10% None

Non-ODP/GWP 2006 2009 3% None

Non-ODP/GWP 2009 2014 20% None
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Initial 

Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start 

Date

Date of Full 

Penetration in 

New Equipment

Maximum 

Market 

Penetration

PU Rigid: One Component Foam

CFC-12 HCFC-142b/22 

Blend 1989 1996 70% Non-ODP/GWP 2009 2010 80% None 4.0%

HFC-134a 2009 2010 10% None

HFC-152a 2009 2010 10% None

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 2.0%

Non-ODP/GWP 2001 2003 45% None

HCFC-22 2003 2003 10% Non-ODP/GWP 2009 2010 100%

PU Rigid: Sandwich Panels: Continuous and Discontinuous

CFC-11 HCFC-141b 1989 1996 82%

HCFC-22/Water 

Blend 2001 2003 20%

HFC-245fa/CO2

Blend 2009 2010 50% 6.0%

Non-ODP/GWP 2009 2010 50%

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 6.0%

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 2.5%

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
A-235

Initial 

Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start 

Date

Date of Full 

Penetration in 

New Equipment

Maximum 

Market 

Penetration

CFC-12 CO2 1989 1994 1% None 2.0%

Non-ODP/GWP 1989 1994 99% CO2 1995 1999 9% None

HFC-152a 1995 1999 10% None
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-174. 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- 175

.
A-237

Table A-175. Sterilization Market Transition Assumptions

Initial 

Market 

Segment

Primary Substitute Secondary Substitute Tertiary 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

Name of 

Substitute

Start 

Date

Date of Full 

Penetration in 

New Equipment

Maximum 

Market 

Penetration

Commercial Refrigeration Foam

12/88 EtO 1994 1995 95% None 2.0%

Non-ODP/GWP 1994 1995 1% None

HCFC/EtO Blends 1993 1994 4% Non-ODP/GWP 2010 2010 100% None
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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- 176 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 1990-2012.
A-239

Table A-176. Banks of ODS and ODS Substitutes, 1990-2012 (MT)

CFC HCFC HFC

1990 711,534 283,302 868

1995 807,182 497,582 53,696 

2000 674,734 923,281 197,661 

2001 645,931 992,260 227,436 

2002 620,996 1,045,199 257,279 

2003 596,076 1,081,664 293,046 

2004 570,703 1,118,168 330,397 

2005 547,210 1,159,858 368,806 

2006 523,580 1,197,264 412,438 

2007 502,928 1,226,849 456,497 

2008 487,552 1,246,198 497,005 

2009 480,177 1,241,550 544,053 

2010 466,203 1,206,140 612,013 

2011 452,162 1,161,244 679,111 

2012 437,702 1,115,819 753,596 

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-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.10. Methodology for Estimating CH4 Emissions from Enteric Fermentation

Methane emissions from enteric fermentation were estimated for seven livestock categories: cattle, horses, sheep, 

swine, goats, American bison, and the non-horse equines (mules and asses). Emissions from cattle represent the majority 

of U.S. emissions from enteric fermentation; consequently, a more detailed IPCC Tier 2 methodology was used to estimate 

emissions from cattle. The IPCC Tier 1 methodology was used to estimate emissions for the other types of livestock, 

including horses, goats, sheep, swine, American bison, and mules and asses.

Estimate Methane Emissions from Cattle

This section describes the process used to estimate CH4 emissions from enteric fermentation from cattle using the 

Cattle Enteric Fermentation Model (CEFM). The CEFM was developed based on recommendations provided in 

IPCC/UNEP/OECD/IEA (1997), IPCC (2000) and IPCC (2006), and uses information on population, energy 

requirements, digestible energy, and CH4 conversion rates to estimate CH4 emissions.66 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 state-level cattle population estimates are based on data obtained from the U.S. Department of Agriculture’s 

(USDA) National Agricultural Statistics Service Quick Stats database (USDA 2012). A summary of the annual average 

populations upon which all livestock-related emissions are based is provided in Table A-177. Cattle populations used in 

the Enteric Fermentation source category were estimated using the cattle transition matrix in the CEFM, which uses 

January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from birth to slaughter, 

and results in an estimate of the number of animals in a particular cattle grouping while taking into account the monthly 

rate of weight gain, the average weight of the animals, and the death and calving rates. The use of supplemental USDA 

data and the cattle transition matrix in the CEFM results in cattle population estimates for this sector differing slightly 

from the January 1 or July 1 USDA point estimates and the cattle population data obtained from the Food and Agriculture 

Organization of the United Nations (FAO). 

Table A-177: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2012 (1,000 head)

Livestock Type 1990 1995 2000 2005 2009 2010 2011 2012

Dairy

Dairy Calves (4-6 months) 5,369 5,091 4,951 4,628 4,791 4,666 4,706 4,772

Dairy Cows 10,015 9,482 9,183 9,004 9,333 9,086 9,150 9,230

Dairy Replacements 7-11 months 1,214 1,216 1,196 1,257 1,327 1,347 1,362 1,350

Dairy Replacements 12-23 months 2,915 2,892 2,812 2,905 3,101 3,179 3,210 3,236

Beef

Beef Calves (4-6 months) 16,909 18,177 17,431 16,918 16,051 16,043 15,795 15,186

Bulls 2,160 2,385 2,293 2,214 2,184 2,190 2,155 2,096

Beef Cows 32,455 35,190 33,575 32,674 31,712 31,371 30,850 30,158

Beef Replacements 7-11 months 1,269 1,493 1,313 1,363 1,290 1,239 1,230 1,250

Beef Replacements 12-23 months 2,967 3,637 3,097 3,171 3,098 3,055 2,890 2,957

Steer Stockers 10,321 11,716 8,724 8,185 8,515 8,223 7,628 7,209

Heifer Stockers 5,946 6,699 5,371 5,015 5,059 5,054 4,759 4,470

Feedlot Cattle 9,549 11,064 13,006 12,652 12,953 13,191 13,546 13,209

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.67 Since cattle often do not remain in 

a single population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and emission 

profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation 

model to obtain more accurate emission estimates than would be available from annual point estimates of population (such 

as available from USDA statistics) and weight for each cattle type. 

 

66 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006).

67 Mature animal populations are not assumed to have significant monthly fluctuations, and therefore the populations utilized are the January 

estimates downloaded from USDA (2013).
A-241

The transition matrix tracks both dairy and beef populations, and divides the populations into males and females, 

and subdivides the population further into specific cattle groupings for calves, replacements, 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., calves are usually weaned between 4 and 6 months of age), each is modeled on a per-month basis. The 

type of cattle also influences CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition 

matrix for each of three separate lifecycle phases, 1) calves, 2) replacements and stockers, and 3) feedlot animals. In 

addition, the number of mature cows and bulls are tabulated for both dairy and beef stock. The transition matrix estimates 

total monthly populations for all cattle subtypes. These populations are then reallocated to the state level based on the 

percent of the cattle type reported in each state in the January 1 USDA data. Each lifecycle is discussed separately below, 

and the categories tracked are listed in Table A-178. 

Table A-178: Cattle Population Categories Used for Estimating CH4 Emissions

Dairy Cattle Beef Cattle

Calves (4-6 months) Calves (4-6 months)

Heifer Replacements Heifer Replacements 

Cows Heifer and Steer Stockers 

Animals in Feedlots (Heifers & Steer)

Cows

Bullsa

a Bulls (beef and dairy) are accounted for in a single category.

The key variables tracked for each of these cattle population categories are as follows:

Calves. Although only the emissions for calves ages 4 to 6 months are calculated in the inventory, it is necessary 

to estimate populations from birth as estimates of populations for older cattle are reliant on understanding the available 

supply of calves from birth. The number of animals born on a monthly basis was used to initiate monthly cohorts and to 

determine population age structure. The number of calves born each month was obtained by multiplying annual births by 

the percentage of births per month. Annual birth information for each year was taken from USDA (2013). 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-179, 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 (approximately 61 

pounds per month). The total calf population is modified through time to account for veal calf slaughter at 4 months and a 

calf death loss of 0.35 percent annually (distributed across age cohorts up to six months of age). An example of a transition

matrix for calves is shown in Table A-180. 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-179: 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-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-180: Example of Monthly Average Populations from Calf Transition Matrix (1,000 head)

Note: The cohort starting at age 0 months on January 1 is tracked in order to illustrate how a single cohort moves through the transition matrix. Each month, the 

cohort reflects the decreases in population due to the estimated 0.35% annual death loss, and between months 4 and 5, a more significant loss is seen than in 

other months due to estimated veal slaughter.

Replacements and Stockers. At 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. Based on the USDA estimates for 

“replacement beef heifers” and “replacement dairy heifers,” the transition matrix for the replacements is back-calculated 

from the known animal totals from USDA, and the number of calves needed to fill that requirement for each month is 

subtracted from the known supply of female calves. All female calves remaining after those needed for beef and dairy 

replacements are removed become “stockers” that can be placed in feedlots (along with all male calves). During the 

stocker phase animals are subtracted out of the transition matrix for placement into feedlots based on feedlot placement 

statistics from USDA (2013). 

The data and calculations that occur for the stocker category include matrices that estimate the population of 

backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of 

year populations in January and model the progression of each cohort. The age structure of the January population is based 

on estimated births by month from the previous two years, although in order to balance the population properly, an 

adjustment is added that slightly reduces population percentages in the older populations. The populations are modified 

through addition of graduating calves (added in month 7, bottom row of Table A-181) and subtraction through death loss 

and animals placed in feedlots. Eventually, an entire cohort population of stockers may reach zero, indicating that the 

complete cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown in Table A181. 

Table A-181: Example of Monthly Average Populations from Stocker Transition Matrix (1,000 head)

Age

(month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

23 177 187 106 36 15 9 7 7 5 2 0 0

22 306 152 50 19 11 9 9 8 5 2 3 188

21 248 71 26 14 11 11 11 8 5 54 195 326

20 117 37 19 14 14 13 10 8 110 305 339 264

19 60 28 19 17 16 13 10 177 424 562 276 125

18 45 27 23 20 15 12 222 569 804 465 130 64

17 45 34 27 19 15 272 666 1,096 671 216 78 48

16 55 39 26 19 334 774 1,294 920 309 304 48 48

15 64 38 26 413 907 1,513 1,088 422 550 48 48 59

14 61 37 568 1,078 1,782 1,275 499 847 48 48 59 68

13 61 813 1,396 2,125 1,506 585 1,118 48 48 59 68 65

12 946 1,543 2,412 1,707 707 1,540 226 48 59 68 65 65

11 1,750 2,665 1,938 802 1,930 297 148 79 88 84 86 1,014

10 3,026 2,158 903 2,306 419 210 188 236 205 207 1,153 2,368

9 2,456 1,023 2,601 521 387 384 421 417 385 1,308 2,856 5,180

8 1,157 3,091 704 534 647 787 710 737 1,509 3,326 6,107 4,910

7 3,520 853 846 1,018 1,232 1,179 1,168 1,920 3,764 6,676 5,368 2,401

Note: The cohort starting at age 7 months on January 1 is tracked in order to illustrate how a single cohort moves through the transition matrix. Each month, the 

cohort reflects the decreases in population due to the estimated 0.35% annual death loss and loss due to placement in feedlots (the latter resulting in the majority 

of the loss from the matrix).

In order to ensure a balanced population of both stockers and placements, additional data tables are utilized in the 

stocker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the total 

number of animals within the population that are available to be placed in feedlots and the actual feedlot placement 

statistics provided by USDA (2013). In cases where there are discrepancies between the USDA estimated placements by 

weight class and the calculated animals available by weight, the model pulls available stockers from one higher weight 

category if available. If there are still not enough animals to fulfill requirements the model pulls animals from one lower 

Age (month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

6 1,187 1,178 1,381 1,621 1,561 1,547 2,391 4,476 7,771 6,291 2,934 1,459

5 1,179 1,382 1,622 1,561 1,547 2,392 4,478 7,774 6,294 2,935 1,460 1,089

4 1,450 1,683 1,620 1,603 2,451 4,535 7,843 6,368 3,000 1,533 1,159 1,150

3 1,684 1,622 1,604 2,452 4,536 7,846 6,371 3,001 1,533 1,160 1,151 1,411

2 1,623 1,605 2,453 4,538 7,849 6,373 3,002 1,534 1,160 1,152 1,412 1,637

1 1,607 2,454 4,540 7,852 6,375 3,003 1,534 1,161 1,152 1,413 1,638 1,577

0 2,457 4,543 7,856 6,378 3,004 1,535 1,161 1,152 1,413 1,639 1,579 1,561
A-243

weight category. In the current time series, this method was able to ensure that total placement data matched USDA 

estimates, and no shortfalls have occurred. 

In addition, average weights were tracked for each monthly age group using starting weight and monthly weight 

gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target 

weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be 

88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 pounds. Other reported target 

weights were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight 

was taken from measurements provided by a major British Bos taurus breed (Enns 2008) and increased during the time 

series through 2007.68 Bull mature weight was calculated as 1.5 times the beef cow mature weight (Doren et al. 1989). 

Beef replacement weight was calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at 

24 months. As dairy weights are not a trait that is typically tracked, mature weight for dairy cows was estimated at 1,500 

pounds for all years, based on a personal communication with Kris Johnson (2010) and an estimate from Holstein 

Association USA (2010).69 Dairy replacement weight at 15 months was assumed to be 875 pounds and 1,300 pounds at 

24 months. Live slaughter weights were estimated from dressed slaughter weight (USDA 2013) divided by 0.63. This 

ratio represents the dressed weight (i.e., weight of the carcass after removal of the internal organs), to the live weight (i.e., 

weight taken immediately before slaughter). The annual typical animal mass for each livestock type are presented in 

Table A-182.

Weight gain for stocker animals was based on monthly gain estimates from Johnson (1999) for 1989, and from 

average daily estimates from Lippke et al. (2000), Pinchack et al. (2004), Platter et al. (2003), and Skogerboe et al. (2000) 

for 2000. Interim years were calculated linearly, as shown in Table A-183, and weight gain was held constant starting in 

2000. Table A-183 provides weight gains that vary by year in the CEFM.

 

68 Mature beef weight is held constant after 2007 but future inventory submissions will incorporate known trends through 2007 

and extrapolate to future years, as noted in the Planned Improvements section.

69 Mature dairy weight is based solely on Holstein weight, so could be higher than the national average. Future inventory 

submissions will consider other dairy breeds, as noted in the Planned Improvements section.
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-182: Typical Animal Mass (lbs)

Year/Cattle 

Type

Calves Dairy 

Cowsa

Dairy 

Replacementsb

Beef

Cowsa

Bullsa Beef

Replacementsb

Steer

Stockersb

Heifer

Stockersb

Steer

Feedlotb

Heifer

Feedlotb

1990 269 1,500 900 1,221 1,832 820 692 652 923 846

1991 270 1,500 898 1,225 1,838 822 695 656 934 856

1992 269 1,500 897 1,263 1,895 841 714 673 984 878

1993 270 1,500 899 1,280 1,920 852 721 683 983 878

1994 270 1,500 898 1,280 1,920 854 721 689 1000 886

1995 270 1,500 898 1,282 1,923 858 735 701 1019 896

1996 269 1,500 898 1,285 1,928 859 739 707 1018 893

1997 270 1,500 900 1,286 1,929 861 737 708 1010 888

1998 270 1,500 897 1,296 1,944 866 736 710 1046 905

1999 270 1,500 899 1,292 1,938 862 731 709 1050 908

2000 270 1,500 897 1,272 1,908 849 720 702 1063 915

2001 270 1,500 898 1,272 1,908 850 726 707 1081 918

2002 270 1,500 897 1,276 1,914 852 726 708 1088 933

2003 270 1,500 900 1,308 1,962 872 719 702 1087 929

2004 270 1,500 897 1,323 1,985 878 719 702 1089 921

2005 270 1,500 895 1,327 1,991 880 718 706 1087 934

2006 270 1,500 898 1,341 2,012 890 725 713 1113 943

2007 270 1,500 897 1,348 2,022 895 721 707 1133 954

2008 270 1,500 898 1,348 2,022 895 721 705 1142 961

2009 270 1,500 897 1,348 2,022 894 731 715 1145 966

2010 270 1,500 898 1,348 2,022 897 727 714 1134 964

2011 270 1,500 897 1,348 2,022 892 723 714 1137 955

2012 270 1,500 899 1,348 2,022 893 715 708 1153 969

a Input into the model. 

b Annual average calculated in model based on age distribution.

Table A-183: Weight Gains that Vary by Year (lbs)

Year/Cattle Type Steer Stockers to 12 

months(lbs/day)

Steer Stockers to 24 

months (lbs/day)

Heifer Stockers to 12 

months(lbs/day)

Heifer Stockers to 24 

months(lbs/day)

1990 1.53 1.23 1.23 1.08

1991 1.56 1.29 1.29 1.15

1992 1.59 1.35 1.35 1.23

1993 1.62 1.41 1.41 1.30

1994 1.65 1.47 1.47 1.38

1995 1.68 1.53 1.53 1.45

1996 1.71 1.59 1.59 1.53

1997 1.74 1.65 1.65 1.60

1998 1.77 1.71 1.71 1.68

1999 1.80 1.77 1.77 1.75

2000 onwards 1.83 1.83 1.83 1.83

Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al. (2003), Skogerboe et al. (2000).

Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the 

stocker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number 

of animals from the stocker cohorts into the feedlot populations to match the reported placement data. After animals are 

placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become acclimated to 

the new feed type (e.g., more grain than forage, along with new dietary supplements), during this time weight gain is 

estimated to be 2.7 to 3 pounds per day (Johnson 1999). Animals are then switched to a finishing diet (concentrated, high 

energy) for a period of time before they are slaughtered. Weight gain during finishing diets is estimated to be 2.9 to 3.3 

pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the start weight (i.e., 

placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target weight (as 

determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are tracked for 

inclusion in the following year’s emission and population counts. For 1990 to 1995, only the total placement data were 

available, therefore placements for each weight category (categories displayed in Table A-184) 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-184 provides a summary of the reported feedlot placement statistics for 2012.
A-245

Table A-184: Feedlot Placements in the United States for 2012 (Number of animals placed in 1,000 Head)

Weight 

Placed When: Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

< 600 lbs 445 400 390 355 520 460 500 482 515 680 655 495

600 – 700 lbs 430 335 300 250 365 320 325 385 355 505 450 415

700 – 800 lbs 525 469 500 380 530 390 470 475 444 435 385 379

> 800 lbs 447 510 602 536 669 494 627 665 690 560 453 375

Total 1,847 1,714 1,792 1,521 2,084 1,664 1,922 2,007 2,004 2,180 1,943 1,664

Source: USDA (2013).

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 pounds per animal per month. The 

monthly estimates for daily milk production by beef cows are shown in Table A-9. Annual estimates for dairy cows were 

taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 2012 are shown in Table A-186. 

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-185: Estimates of Monthly Milk Production by Beef Cows (Pounds per Head)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Beef Cow Milk Production (lbs/ head) 3.3 5.1 8.7 12.0 13.6 13.3 11.7 9.3 6.9 4.4 3.0 2.8
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-186: Dairy Lactation Rates by State (lbs/ year/cow)

State/Year 1990 1995 2000 2005 2009 2010 2011 2012

Alabama 12,214 14,176 13,920 14,000 14,909 14,455 13,182 13,200

Alaska 13,300 17,000 14,500 12,273 10,000 11,833 13,800 14,250

Arizona 17,500 19,735 21,820 22,679 23,028 23,441 23,468 23,979

Arkansas 11,841 12,150 12,436 13,545 12,692 12,750 11,833 13,300

California 18,456 19,573 21,130 21,404 22,000 23,025 23,438 23,457

Colorado 17,182 18,687 21,618 22,577 23,081 23,664 23,430 23,978

Connecticut 15,606 16,438 17,778 19,200 18,579 19,158 19,000 19,889

Delaware 13,667 14,500 14,747 16,622 17,000 16,981 18,300 19,143

Florida 14,033 14,698 15,688 16,591 18,070 18,658 19,067 19,008

Georgia 12,973 15,550 16,284 17,259 18,182 17,671 18,354 19,125

Hawaii 13,604 13,654 14,358 12,889 14,200 13,316 14,421 14,200

Idaho 16,475 18,147 20,816 22,332 22,091 22,658 22,934 23,376

Illinois 14,707 15,887 17,450 18,827 18,873 19,170 19,357 19,510

Indiana 14,590 15,375 16,568 20,295 20,137 20,094 20,657 21,366

Iowa 15,118 16,124 18,298 20,641 20,367 20,724 21,309 21,730

Kansas 12,576 14,390 16,923 20,505 21,085 20,975 21,016 21,675

Kentucky 10,947 12,469 12,841 12,896 14,190 14,769 14,342 15,135

Louisiana 11,605 11,908 12,034 12,400 11,870 11,750 12,889 13,176

Maine 14,619 16,025 17,128 18,030 18,061 18,344 18,688 18,576

Maryland 13,461 14,725 16,083 16,099 18,255 18,537 18,654 19,196

Massachusetts 14,871 16,000 17,091 17,059 17,571 17,286 16,923 18,250

Michigan 15,394 17,071 19,017 21,635 22,445 23,277 23,164 23,704

Minnesota 14,127 15,894 17,777 18,091 19,230 19,366 18,996 19,508

Mississippi 12,081 12,909 15,028 15,280 13,889 13,118 14,571 14,357

Missouri 13,632 14,158 14,662 16,026 14,654 14,596 14,611 14,936

Montana 13,542 15,000 17,789 19,579 19,933 20,643 20,571 21,357

Nebraska 13,866 14,797 16,513 17,950 19,672 19,797 20,579 21,179

Nevada 16,400 18,128 19,000 21,680 21,821 23,500 23,138 22,966

New Hampshire 15,100 16,300 17,333 18,875 19,533 19,600 20,429 19,643

New Jersey 13,538 13,913 15,250 16,000 17,889 17,500 16,875 18,571

New Mexico 18,815 18,969 20,944 21,192 24,320 24,551 24,854 24,694

New York 14,658 16,501 17,378 18,639 20,071 20,807 21,046 21,633

North Carolina 15,220 16,314 16,746 18,741 19,644 19,636 20,089 20,435

North Dakota 12,624 13,094 14,292 14,182 16,739 18,286 18,158 19,278

Ohio 13,767 15,917 17,027 17,567 18,744 19,446 19,194 19,833

Oklahoma 12,327 13,611 14,440 16,480 16,983 17,125 17,415 17,688

Oregon 16,273 17,289 18,222 18,876 19,719 20,331 20,488 20,431

Pennsylvania 14,726 16,492 18,081 18,722 19,360 19,847 19,495 19,576

Rhode Island 14,250 14,773 15,667 17,000 17,818 17,727 17,909 18,300

South Carolina 12,771 14,481 16,087 16,000 19,000 17,875 17,438 17,313

South Dakota 12,257 13,398 15,516 17,741 20,128 20,478 20,582 21,391

Tennessee 11,825 13,740 14,789 15,743 16,232 16,346 16,200 16,100

Texas 14,350 15,244 16,503 19,646 20,898 21,375 22,232 22,009

Utah 15,838 16,739 17,573 18,875 21,036 21,400 21,068 21,678

Vermont 14,528 16,210 17,199 18,469 18,289 18,537 18,940 19,316

Virginia 14,213 15,116 15,833 16,990 18,083 18,095 17,906 17,990

Washington 18,532 20,091 22,644 23,270 23,171 23,510 23,727 23,794

West Virginia 11,250 12,667 15,588 14,923 14,727 15,700 15,600 15,800

Wisconsin 13,973 15,397 17,306 18,500 20,079 20,630 20,599 21,436

Wyoming 12,337 13,197 13,571 14,878 19,036 20,067 20,517 20,817

Source: USDA (2013).

Step 2: Characterize U.S. Cattle Population Diets

To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and 

CH4 conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population categories, 

data were collected on diets considered representative of different regions. For both grazing animals and animals being 

fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists, 

the United States Department of Agriculture, expert opinion, and other literature sources. The designated regions for this 

analysis for dairy cattle for all years and foraging beef cattle from 1990 through 2006 are shown in Table A-187. For 

foraging beef cattle from 2007 onwards, the regional designations were revised based on data available from the NAHMS 

2007-2008 survey on cow-calf system management practices (USDA:APHIS:VS 2010) and are shown in and Table A-
A-247

188. The data for each of the diets (e.g., proportions of different feed constituents, such as hay or grains) were used to 

determine feed chemical composition for use in estimating DE and Ym for each animal type. 

Table A-187: Regions used for Characterizing the Diets of Dairy Cattle (all years) and Foraging Cattle from 1990-2006

West California Northern Great 

Plains

Midwestern Northeast Southcentral Southeast

Alaska

Arizona

Hawaii

Idaho

Nevada

New Mexico

Oregon

Utah

Washington

California Colorado

Kansas

Montana

Nebraska

North Dakota

South Dakota

Wyoming

Illinois

Indiana

Iowa

Michigan

Minnesota

Missouri

Ohio

Wisconsin

Connecticut

Delaware

Maine

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

West Virginia

Arkansas

Louisiana

Oklahoma

Texas

Alabama

Florida

Georgia

Kentucky

Mississippi

North Carolina

South Carolina

Tennessee

Virginia

Source: USDA (1996).

Table A-188: Regions used for Characterizing the Diets of Foraging Cattle from 2007-2012

West Central Northeast Southeast

Alaska

Arizona

California

Colorado

Hawaii

Idaho

Montana

Nevada

New Mexico

Oregon

Utah

Washington

Wyoming

Illinois

Indiana

Iowa

Kansas

Michigan 

Minnesota

Missouri

Nebraska

North Dakota

Ohio

South Dakota

Wisconsin

Connecticut

Delaware

Maine 

Maryland

Massachusetts

New Hampshire

New Jersey

New York

Pennsylvania

Rhode Island

Vermont

West Virginia 

Alabama

Arkansas

Florida

Georgia

Kentucky

Louisiana

Mississippi

North Carolina

Oklahoma

South Carolina

Tennessee

Texas

Virginia

Source: Based on data from USDA:APHIS:VS (2010).

Note: States in bold represent a change in region from the 1990-2006 assessment.

DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle 

and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different 

regions and animal types in the United States, DE and Ym values unique to the United States were developed for dairy and 

beef cattle. Digestible energy and Ym values were estimated across the time series for each cattle population category 

based on physiological modeling, published values, and/or expert opinion. 

For dairy cows, ruminant digestion models were used to estimate Ym. The three major categories of input 

required by the models are animal description (e.g., cattle type, mature weight), animal performance (e.g., initial and final

weight, age at start of period), and feed characteristics (e.g., chemical composition, habitat, grain or forage). Data used to 

simulate ruminant digestion is provided for a particular animal that is then used to represent a group of animals with 

similar characteristics. The Ym values were estimated for 1990 using the Donovan and Baldwin model (1999), which 

represents physiological processes in the ruminant animals, as well as diet characteristics from USDA (1996). The 

Donovan and Baldwin model is able to account for differing diets (i.e., grain-based or forage-based), so that Ym values for 

the variable feeding characteristics within the U.S. cattle population can be estimated. Subsequently, a literature review of 

dairy diets was conducted and nearly 250 diets were analyzed from 1990 through 2009 across 23 states—the review 

indicated highly variable diets, both temporally and spatially. Kebreab et al. (2008) conducted an evaluation of models 

and found that the COWPOLL model was the best model for estimating Ym for dairy. The statistical analysis of the 

COWPOLL model showed a trend in predicting Ym, and inventory team experts determined that the most comprehensive 

approach was to use the 1990 baseline from Donovan and Baldwin and then scale Ym values for each of the diets beyond 
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

1990 with the COWPOLL model. A function based on the national trend observed from the analysis of the dairy diets was 

used to calculate 1991 and beyond regional values based on the regional 1990 Ym values from Donovan and Baldwin. The 

resulting scaling factor (incorporating both Donovan and Baldwin (1999) and COWPOLL) 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 annual trends observed in the 

data collection effort. The regional variability observed in the literature search was not statistically significant, and 

therefore DE was not varied by region, but did vary over time, and was grouped by the following years 1990-1993, 1994-

1998, 1999-200270, 2004-2006, 2007, and 2008 onwards. 

Considerably less data was available for dairy heifers and dairy calves. Therefore, for dairy heifers assumptions 

were based on the relationship of the collected data in the literature on dairy heifers to the data on dairy cow diets. From 

this relationship, DE was estimated as the mature cow DE minus three percent, and Ym was estimated as that of the mature 

dairy cow plus 0.1 percent. 

To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted DE

values for a combination of forage and supplemental diets. The forage portion makes up an estimated 85 to 95 percent of 

grazing beef cattle diets, and there is considerable variation of both forage type and quality across the US. Currently there

is no comprehensive survey of this data, so for this analysis two regional DE values were developed to account for the 

generally lower forage quality in the “West” region of the United States versus all other regions in Table A-11 (California, 

Northern Great Plains, Midwestern, Northeast, Southcentral, Southeast) and Table A-12 (Central, Northeast, and 

Southeast). For all non-western grazing cattle, the forage DE was an average of the estimated seasonal values for grass 

pasture diets for a calculated DE of 64.2 percent. For foraging cattle in the west, the forage DE was calculated as the 

seasonal average for grass pasture, meadow and range diets, for a calculated DE of 61.3 percent. The assumed specific 

components of each of the broad forage types, along with their corresponding DE value and the calculated regional DE 

values can be found in Table A-189. In addition, it was assumed that each region fed a supplemental diet, and two sets of 

supplemental diets were developed, one for 1990 through 2006 (Donovan 1999) and one for 2007 onwards (Preston 2010, 

Archibeque 2011, USDA:APHIS:VS 2010) as shown in 

Table A-190 and Table A-191 along with the percent of each total diet that is assumed to be made up of the supplemental 

portion. By weighting the calculated DE values from the forage and supplemental diets, the DE values for the composite 

diet were calculated.71 These values are used for steer and heifer stockers and beef replacements. Finally, for mature beef 

cows and bulls, the DE value was adjusted downward by two percent to reflect the lower digestibility diets of mature cattle 

based on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). The Ym

values and the resulting final weighted DE values by region for 2007 onwards are shown in Table A-192.

For feedlot animals, DE and Ym are adjusted over time as diet compositions in actual feedlots are adjusted based 

on new and improved nutritional information and availability of feed types. Feedlot diets are assumed to not differ 

significantly by state, and therefore only a single set of national diet values is utilized for each year. The DE and Ym values 

for 1990 were estimated by Dr. Don Johnson (1999). In the CEFM, the DE values for 1991 through 1999 were linearly 

extrapolated based on values for 1990 and 2000. DE and Ym values from 2000 through the current year were estimated 

using the MOLLY model as described in Kebreab et al. (2008), based on a series of average diet feed compositions from 

Galyean and Gleghorn (2001) for 2000 through 2006 and Vasconcelos and Galyean (2007) for 2007 onwards. In addition, 

feedlot animals are assumed to spend the first 25 days in the feedlot on a “step-up” diet to become accustomed to the 

higher quality feedlot diets. The step-up DE and Ym are calculated as the average of all state forage and feedlot diet DE 

and Ym values. 

For calves aged 4 through 6 months, a gradual weaning from milk is simulated, with calf diets at 4 months 

assumed to be 25 percent forage, increasing to 50 percent forage at age 5 months, and 75 percent forage at age 6 months. 

The portion of the diet allocated to milk results in zero emissions, as recommended by the IPCC (2006). For calves, the 

DE for the remainder of the diet is assumed to be similar to that of slightly older replacement heifers (both beef and dairy 

are calculated separately). The Ym for beef calves is also assumed to be similar to that of beef replacement heifers (6.5 

percent), as literature does not provide an alternative Ym for use in beef calves. For dairy calves, the Ym is assumed to be 

7.8 percent at 4 months, 8.03 percent at 5 months, and 8.27 percent at 6 months per estimates provided by Soliva (2006)

 

70 Due to inconsistencies in the 2003 literature values, the 2002 values were extended to include 2003 as well.

71 For example, the West has a forage DE of 61.3 which makes up 90 percent of the diet and a supplemented diet DE of 67.4 percent was 

used for 10 percent of the diet, for a total weighted DE of 61.9 percent, as shown in Table A-192. 
A-249

for Ym at 4 and 7 months of age. Estimates for 5 and 6 months are the result of linear interpolation between the values 

provided for 4 and 7 months.

Table A-193 shows the regional DE and Ym for U.S. cattle in each region for 2012. 

Table A-189: Feed Components and Digestible Energy Values Incorporated into Forage Diet Composition Estimates

Forage Type

DE, (% of GE)Grass pasture -SpringGrass pasture -SummerGrass pasture -FallRange JuneRange JulyRange AugustRange SeptemberRange WinterMeadow -SpringMeadow - Fall

Bahiagrass Paspalum notatum, fresh 61.38 x

Bermudagrass Cynodon dactylon, fresh 66.29 x

Bremudagrass, Coastal Cynodon dactylon, fresh 65.53 x

Bluegrass, Canada Poa compressa, fresh, early 

vegetative 73.99 x

Bluegrass, Kentucky Poa pratensis, fresh, early 

vegetative 75.62 x

Bluegrass, Kentucky Poa pratensis, fresh, mature 59.00 x x

Bluestem Andropagon spp, fresh, early vegetative 73.17 x

Bluestem Andropagon spp, fresh, mature 56.82 x x x x x

Brome Bromus spp, fresh, early vegetative 78.57 x

Brome, Smooth Bromus inermis, fresh, early 

vegetative 75.71 x

Brome, Smooth Bromus inermis, fresh, mature 57.58 x x x

Buffalograss, Buchloe dactyloides, fresh 64.02 x x

Clover, Alsike Trifolium hybridum, fresh, early 

vegetative 70.62 x

Clover, Ladino Trifolium repens, fresh, early 

vegetative 73.22 x

Clover, Red Trifolium pratense, fresh, early bloom 71.27 x

Clover, Red Trifolium pratense, fresh, full bloom 67.44 x x

Corn, Dent Yellow Zea mays indentata, aerial part 

without ears, without husks, sun-cured, 

(stover)(straw)

55.28 x

Dropseed, Sand Sporobolus cryptandrus, fresh, 

stem cured 64.69 x x x x

Fescue Festuca spp, hay, sun-cured, early 

vegetative 67.39 x

Fescue Festuca spp, hay, sun-cured, early bloom 53.57 x

Grama Bouteloua spp, fresh, early vegetative 67.02 x

Grama Bouteloua spp, fresh, mature 63.38 x x x

Millet, Foxtail Setaria italica, fresh 68.20 x x

Napiergrass Pennisetum purpureum, fresh, late 

bloom 57.24 x x

Needleandthread Stipa comata, fresh, stem cured 60.36 x x x

Orchardgrass Dactylis glomerata, fresh, early 

vegetative 75.54 x

Orchardgrass Dactylis glomerata, fresh, midbloom 60.13 x

Pearlmillet Pennisetum glaucum, fresh 68.04 x

Prairie plants, Midwest, hay, sun-cured 55.53 x x

Rape Brassica napus, fresh, early bloom 80.88 x

Rye Secale cereale, fresh 71.83 x

Ryegrass, Perennial Lolium perenne, fresh 73.68 x

Saltgrass Distichlis spp, fresh, post ripe 58.06 x x

Sorghum, Sudangrass Sorghum bicolor 

sudanense, fresh, early vegetative 73.27 x

Squirreltail Stanion spp, fresh, stem-cured 62.00 x x
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Forage Type

DE, (% of GE)Grass pasture -SpringGrass pasture -SummerGrass pasture -FallRange JuneRange JulyRange AugustRange SeptemberRange WinterMeadow -SpringMeadow - Fall

Summercypress, Gray Kochia vestita, fresh, stemcured 65.11 x x x

Timothy Phleum pratense, fresh, late vegetative 73.12 x

Timothy Phleum pratense, fresh, midbloom 66.87 x

Trefoil, Birdsfoot Lotus corniculatus, fresh 69.07 x

Vetch Vicia spp, hay, sun-cured 59.44 x

Wheat Triticum aestivum, straw 45.77 x

Wheatgrass, Crested Agropyron desertorum, 

fresh, early vegetative 79.78 x

Wheatgrass, Crested Agropyron desertorum, 

fresh, full bloom 65.89 x x

Wheatgrass, Crested Agropyron desertorum, 

fresh, post ripe 52.99 x x x

Winterfat, Common Eurotia lanata, fresh, stemcured 40.89 x

Weighted Average DE 72.99 62.45 57.26 67.11 62.70 60.62 58.59 52.07 64.03 55.11

Forage Diet for West 61.3 10% 10% 10% 10% 10% 10% 10% 10% 10% 10%

Forage Diet for All Other Regions 64.2 33.3% 33.3% 33.3% - - - - - - -

Sources: Preston (2010) and Archibeque (2011).

Note that forages marked with an x indicate that the DE from that a specific forage type is included in the general forage type for that column (e.g., grass 

pasture, range, meadow or meadow by month or season).

Table A-190: DE Values with Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 1990-2006

Source of representative regional diets: Donovan (1999). 

* Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. 

Feed

Source of DE

(NRC 1984)

Unweighted

DE (% of GE) California * West

Northern

Great Plains Southcentral Northeast Midwest Southeast

Alfalfa Hay Table 8, feed #006 61.79 65% 30% 30% 29% 12% 30%

Barley 85.08 10% 15%

Bermuda Table 8, feed #030 66.29 35%

Bermuda Hay Table 8, feed #031 50.79 40%

Corn Table 8, feed #089 88.85 10% 10% 25% 11% 13% 13%

Corn Silage Table 8, feed #095 72.88 25% 20% 20%

Cotton Seed 

Meal 7%

Grass Hay Table 8, feed #126, 

170, 274 58.37 40% 30%

Orchard Table 8, feed #147 60.13 40%

Soybean Meal 

Supplement 77.15 5% 5% 5%

Sorghum Table 8, feed #211 84.23 20%

Soybean Hulls 66.86 7%

Timothy Hay Table 8, feed #244 60.51 50%

Whole Cotton 

Seed 75.75 5% 5%

Wheat Middlings Table 8, feed #257 68.09 15% 13%

Wheat Table 8, feed #259 87.95 10%

Weighted Supplement DE (%) 70.1 67.4 73.0 62.0 67.6 66.9 68.0 

Percent of Diet that is Supplement 5% 10% 15% 10% 15% 10% 5%
A-251

Table A-191: DE Values and Representative Regional Diets for the Supplemental Diet of Grazing Beef Cattle for 2007-2012

Feed Source of DE

(NRC1984)

Unweighted

DE (% of GE) Westa Centrala Northeasta Southeasta

Alfalfa Hay Table 8, feed #006 61.79 65% 30% 12%

Bermuda Table 8, feed #030 66.29 20%

Bermuda Hay Table 8, feed #031 50.79 20%

Corn Table 8, feed #089 88.85 10% 15% 13% 10%

Corn Silage Table 8, feed #095 72.88 35% 20%

Grass Hay Table 8, feed #126, 170, 274 58.37 10%

Orchard Table 8, feed #147 60.13 30%

Protein supplement (West) Table 8, feed #082, 134, 225 b 81.01 10%

Protein Supplement (Central 

and Northeast) Table 8, feed #082, 134, 225 b80.76 10% 10%

Protein Supplement 

(Southeast) Table 8, feed #082, 134, 101 b77.89 10%

Sorghum Table 8, feed #211 84.23 5% 10%

Timothy Hay Table 8, feed #244 60.51 45%

Wheat Middlings Table 8, feed #257 68.09 5%

Wheat Table 8, feed #259 87.95 5%

Weighted Supplement DE 67.4 73.1 68.9 66.6

Percent of Diet that is Supplement 10% 15% 5% 15%

Sources of representative regional diets: Donovan (1999), Preston (2010), Archibeque (2011), and USDA:APHIS:VS (2010).

a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. 

b Not in equal proportions.

Table A-192: Foraging Animal DE (% of GE) and Ym Values for Each Region and Animal Type for 2007-2012

Animal Type Data West c Central Northeast Southeast

Beef Repl. Heifers DEa 61.9 65.6 64.5 64.6

Ym

b 6.5% 6.5% 6.5% 6.5%

Beef Calves (4-6 mo) DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Steer Stockers DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Heifer Stockers DE 61.9 65.6 64.5 64.6

Ym 6.5% 6.5% 6.5% 6.5%

Beef Cows DE 59.9 63.6 62.5 62.6

Ym 6.5% 6.5% 6.5% 6.5%

Bulls DE 59.9 63.6 62.5 62.6

Ym 6.5% 6.5% 6.5% 6.5%

a DE is the digestible energy in units of percent of GE (MJ/Day).

b Ym is the methane conversion rate, 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. To see the regional 

designation per state, please see Table A-188. 

Table A-193: Regional DE (% of GE) and Ym Rates for Dairy and Feedlot Cattle by Animal Type for 2012

Animal Type Data Californiac West

Northern 

Great Plains Southcentral Northeast Midwest Southeast

Dairy Repl. Heifers DEa 63.7 63.7 63.7 63.7 63.7 63.7 63.7

Ymb 6.0% 6.0% 5.7% 6.5% 6.4% 5.7% 7.0%

Dairy Calves (4-6 mo) DE 63.7 63.7 63.7 63.7 63.7 63.7 63.7

Ym 7.8% (4 mo), 8.03% (5 mo), 8.27% (6 mo)-all regions

Dairy Cows DE 66.7 66.7 66.7 66.7 66.7 66.7 66.7

Ym 5.9% 5.9% 5.6% 6.4% 6.3% 5.6% 6.9%

Steer Feedlot DE 82.5 82.5 82.5 82.5 82.5 82.5 82.5

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

Heifer Feedlot DE 82.5 82.5 82.5 82.5 82.5 82.5 82.5

Ym 3.9% 3.9% 3.9% 3.9% 3.9% 3.9% 3.9%

a DE is the digestible energy in units of percent of GE (MJ/Day).

b Ym is the methane conversion rate, the fraction of GE in feed converted to methane.
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

c Note that emissions are currently calculated on a state-by-state basis, but diets are applied in Table A-187 by the regions shown in the table above. To see the 

regional designation for foraging cattle per state, please see Table A-187.

Step 3: Estimate CH4 Emissions from Cattle

Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the Tier 2 IPCC 

(2006) equations, b) determine an emission factor using the GE values, Ym and a conversion factor, and c) sum the daily 

emissions for each animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. 

The necessary data values for each state and animal type include:

 Body Weight (kg) 

 Weight Gain (kg/day) 

 Net Energy for Activity (Ca, MJ/day)72

 Standard Reference Weight (kg)73

 Milk Production (kg/day) 

 Milk Fat (percent of fat in milk = 4) 

 Pregnancy (percent of population that is pregnant)

 DE (percent of GE intake digestible)

 Ym (the fraction of GE converted to CH4)

 Population

Step 3a: Determine Gross Energy, GE

As shown in the following equation, GE is derived based on the net energy estimates and the feed characteristics. 

Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require the NEl factor). 

All net energy equations are provided in IPCC (2006).





























































    



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 daily emission factor (DayEmit) was determined using the GE value and the methane conversion factor (Ym) 

for each category. This relationship is shown in the following equation:

55.65

GE× Ym DayEmit 

 

72 Zero for feedlot conditions, 0.17 for high quality confined pasture conditions, and 0.36 for extensive open range or hilly terrain 

grazing conditions. Ca factor for dairy cows is weighted to account for the fraction of the population in the region that grazes during the 

year (IPCC 2006). 

73

 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-253

where,

DayEmit = Emission factor (kg CH4/head/day)

GE = Gross energy intake (MJ/head/day)

Ym = CH4 conversion rate, which is the fraction of GE in feed converted to CH4 (%) 

55.65 = A factor for the energy content of methane (MJ/kg CH4) 

The daily emission factors were estimated for each animal type and state, calculated annual national emission 

factors are shown by animal type in Table A-194.

Table A-194: Calculated Annual National Emission Factors for Cattle by Animal Type (kg CH4/head/year)

Cattle Type 1990 1995 2000 2005 2009 2010 2011 2012

Dairy 

Calves 12 12 12 12 12 12 12 12

Cows 124 125 132 133 140 142 142 143

Replacements 7-11 months 48 46 46 45 46 46 46 46

Replacements 12-23 months 73 69 70 67 70 69 69 69

Beef

Calves 11 11 11 11 11 11 11 11

Bulls 91 94 94 97 98 98 98 98

Cows 89 92 91 94 95 95 95 95

Replacements 7-11 months 54 57 56 59 60 60 60 60

Replacements 12-23 months 63 66 66 68 70 70 70 70

Steer Stockers 55 57 58 58 58 58 58 58

Heifer Stockers 52 56 60 60 60 60 60 60

Feedlot Cattle 39 39 41 41 46 46 45 46

Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365 (the number of days in a year). 

For quality assurance purposes, U.S. emission factors for each animal type were compared to estimates provided 

by the other Annex I member countries of the United Nations Framework Convention on Climate Change (UNFCCC) (the 

most recently available summarized results for Annex I countries are through 2011 only). Results, presented in Table A195 indicate that U.S. emission factors are comparable to those of other Annex I countries. Results are presented in Table 

A-195 (along with Tier I emission factors provided by IPCC (2006). Throughout the time series, beef cattle in the United 

States generally emit more enteric CH4 than other Annex I member countries, while dairy cattle in the United States 

generally emit comparable enteric CH4. 

Table A-195: Annex I Countries’ Implied Emission Factors for Cattle by Year (kg CH4/head/year)

Dairy Cattle Beef Cattle

Year 

United States Implied 

Emission Factor

Mean of Implied Emission Factors for 

Annex I countries (excluding U.S.)

United States Implied 

Emission Factor

Mean of Implied Emission Factors for 

Annex I countries (excluding U.S.)

1990 107 96 71 53

1991 107 97 71 53

1992 107 96 72 54

1993 106 97 73 54

1994 106 98 73 54

1995 106 98 73 54

1996 105 99 73 54

1997 106 100 73 54

1998 107 101 73 55

1999 110 102 73 55

2000 111 103 73 55

2001 110 104 73 55

2002 111 105 73 55

2003 111 106 74 55

2004 109 107 74 55
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

2005 110 109 74 55

2006 110 110 74 55

2007 114 111 75 55

2008 115 112 76 55

2009 115 112 76 56

2010 115 113 75 55

2011 116 113 75 55

2012 117 N/A 75 N/A

Tier I EFs For North America, from IPCC (2006) 121 53

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,

Emissionsstate = Emissions for state during the month (kg CH4)

DayEmitstate = Emission factor for the subcategory and state (kg CH4/head/day)

Days/Month = Number of days in the month

SubPopstate = Number of animals in the subcategory and state during the month

This process was repeated for each month, and the monthly totals for each state subcategory were summed to 

achieve an emission estimate for a state for the entire year and state estimates were summed to obtain the national total. 

The estimates for each of the 10 subcategories of cattle are listed in Table A-196. The emissions for each subcategory 

were then aggregated to estimate total emissions from beef cattle and dairy cattle for the entire year. 

Table A-196: CH4 Emissions from Cattle (Gg)

Cattle Type 1990 1995 2000 2005 2009 2010 2011 2012

Dairy 1,574 1,498 1,519 1,503 1,639 1,626 1,643 1,668

Calves (4-6 months) 62 59 59 54 58 57 57 58

Cows 1,242 1,185 1,209 1,197 1,304 1,287 1,301 1,324

Replacements 7-11 months 58 56 55 56 61 62 63 62

Replacements 12-23 

months

212 202 196 196 216 221 222 224

Beef 4,763 5,438 5,098 5,037 5,062 5,019 4,911 4,789

Calves (4-6 months) 182 193 186 179 169 169 166 160

Bulls 196 225 215 214 216 215 211 205

Cows 2,884 3,222 3,058 3,056 3,002 2,970 2,921 2,855

Replacements 7-11 months 69 85 74 80 78 75 74 75

Replacements 12-23 

months 188 

241 204 217 216 213 202 207

Steer Stockers 563 662 509 473 491 475 439 415

Heifer Stockers 306 375 323 299 300 301 283 267

Feedlot Cattle 375 435 530 518 592 602 615 604

Total 6,338 6,936 6,617 6,540 6,701 6,645 6,555 6,458

Notes: Totals may not sum due to independent rounding. 

Emission Estimates from Other Livestock

“Other livestock” include horses, sheep, swine, goats, American bison, and mules and asses. All livestock 

population data, except for American bison for years prior to 2002, were taken from the U.S. Department of Agriculture 

(USDA) National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2013) or earlier census 

data (USDA 1992, 1997). The Manure Management Annex discusses the methods for obtaining annual average 

populations and disaggregating into state data where needed and provides the resulting population data for the other 

livestock that were used for estimating all livestock-related emissions (See Table A-199). For each animal category, the 

USDA publishes monthly, annual, or multi-year livestock population and production estimates. All data were downloaded 

from the USDA-NASS agricultural database (USDA 2013) or taken from older census reports (USDA 1992, 1997). 

American bison estimates prior to 2002 were estimated using data from the National Bison Association (1999).

Methane emissions from sheep, goats, swine, horses, mules and asses were estimated by multiplying national 

population estimates by the default IPCC emission factor (IPCC 2006). For American bison the emission factor for 
A-255

buffalo (IPCC 2006) was used and adjusted based on the ratio of live weights of 300 kg for buffalo (IPCC 2006) and 1,130 

pounds (513 kg) for American Bison (National Bison Association 2011) to the 0.75 power. This methodology for 

determining emission factors is recommended by IPCC (2006) for animals with similar digestive systems. Table A-197

shows the emission factors used for these other livestock. 

National enteric fermentation emissions from all livestock types are shown in Table A-198 and Table A-199. Enteric 

fermentation emissions from most livestock types, broken down by state, for 2012 are shown in Table A-200 and Table A201. Livestock Populations are shown in Table A- 202.

Table A-197: Emission Factors for Other Livestock (kg CH4/head/year)

Livestock Type Emission Factor

Sheep 8

Goats 5

Horses 18

Swine 1.5

Mules and Asses 10.0

American Bison 82.2

Source: IPCC (2006), except American Bison, as described in text.

Table A-198: CH4 Emissions from Enteric Fermentation (Tg CO2 Eq.)

Livestock Type 1990 1995 2000 2006 2007 2008 2009 2010 2011 2012

Beef Cattle 100.0 114.2 107.1 107.5 108.4 107.5 106.3 105.4 103.1 100.6

Dairy Cattle 33.1 31.5 31.9 32.2 33.6 34.1 34.4 34.1 34.5 35.0

Horses 0.8 1.0 1.3 1.5 1.5 1.6 1.6 1.6 1.6 1.7

Sheep 1.9 1.5 1.2 1.0 1.0 1.0 1.0 0.9 0.9 0.9

Swine 1.7 1.9 1.9 1.9 2.1 2.1 2.1 2.0 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

American Bison 0.1 0.2 0.3 0.4 0.3 0.3 0.3 0.3 0.3 0.3

Mules and Asses + + + 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Total 137.9 1,150.5 1,143.9 144.9 147.4 147.0 146.1 144.9 143.0 141.0

Notes: Totals may not sum due to independent rounding. 

+ indicates emissions are less than 0.05.

Table A-199: CH4 Emissions from Enteric Fermentation (Gg)

Livestock Type 1990 1995 2000 2006 2007 2008 2009 2010 2011 2012

Beef Cattle 4,763 5,438 5,098 5,117 5,163 5,119 5,062 5,019 4,911 4,789

Dairy Cattle 1,574 1,498 1,519 1,534 1,601 1,622 1,639 1,626 1,643 1,668

Horses 40 47 61 71 73 74 75 77 78 79

Sheep 91 72 56 50 49 48 46 45 44 43

Swine 81 88 88 93 98 101 99 97 98 100

Goats 13 12 12 15 16 16 16 16 16 16

American Bison 4 9 16 17 16 16 15 15 14 14

Mules and Asses 1 1 1 2 3 3 4 4 4 5

Total 6,566 7,165 6,852 6,899 7,019 6,999 6,956 6,898 6,809 6,714

Note: Totals may not sum due to independent rounding.
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-200: CH4 Emissions from Enteric Fermentation from Cattle (Gg), by State, for 2012

StateDairy CalvesDairy CowsDairy Repl. 

Heif. 0-12

Dairy Repl. 

Heif. 12-24

BullsBeef CalvesBeef CowsBeef Repl. Heif. 

0-12

Beef Repl. Heif. 

12-24

Steer StockersHeifer StockersFeedlotTotal

Alabama 63 1,312 93 337 4,379 3,426 61,173 1,348 3,695 1,440 1,101 492 78,860 

Alaska 3 57 3 10 301 31 553 24 67 11 3 3 1,066 

Arizona 1,203 28,933 937 3,390 2,078 1,024 18,084 536 1,461 8,455 456 11,191 77,748 

Arkansas 70 1,306 72 260 5,839 4,791 85,548 1,632 4,473 2,880 1,693 - 108,564 

California 11,270 265,624 11,240 40,681 7,272 3,528 62,291 1,684 4,591 17,192 6,689 20,456 452,518 

Colorado 829 18,872 950 3,440 5,194 4,318 76,256 2,373 6,470 26,210 15,507 46,929 207,348 

Conn. 117 2,668 134 487 59 24 425 36 98 63 14 15 4,139 

Delaware 32 706 47 169 39 19 330 10 27 63 14 15 1,470 

Florida 760 18,932 544 1,967 5,839 4,955 88,465 1,632 4,473 524 847 261 129,199 

Georgia 494 12,347 481 1,742 2,627 2,699 48,185 1,008 2,762 890 903 345 74,485 

Hawaii 12 216 13 48 519 444 7,847 153 417 169 182 63 10,085 

Idaho 3,679 87,096 4,014 14,529 3,636 2,668 47,120 1,378 3,756 8,455 5,777 9,626 191,735 

Illinois 627 12,615 596 2,156 2,378 1,700 30,454 775 2,126 5,362 3,219 6,418 68,425 

Indiana 1,108 23,508 887 3,210 1,998 1,001 17,941 429 1,177 2,528 1,623 4,292 59,703 

Iowa 1,298 27,816 2,028 7,338 5,708 4,596 82,345 1,936 5,315 33,196 17,059 52,143 240,779 

Kansas 779 16,664 1,077 3,898 8,562 7,430 133,132 3,458 9,492 48,773 42,648 95,061 370,975 

Kentucky 475 10,502 699 2,529 6,812 5,244 93,642 1,916 5,251 5,237 4,093 401 136,801 

Louisiana 114 2,127 86 312 2,725 2,382 42,539 1,150 3,151 602 677 246 56,110 

Maine 203 4,440 227 820 146 53 944 43 117 144 85 44 7,265 

Maryland 329 7,349 396 1,435 390 227 4,058 171 468 420 227 401 15,872 

Mass. 76 1,649 85 307 98 37 661 43 117 53 28 16 3,168 

Michigan 2,349 53,067 2,002 7,246 1,617 560 10,029 373 1,025 4,418 1,238 6,017 89,942 

Minnesota 2,944 59,246 3,612 13,071 3,330 1,874 33,582 1,245 3,417 11,874 5,090 12,434 151,719 

Miss. 89 1,910 109 393 3,795 2,562 45,738 1,164 3,190 1,597 1,016 507 62,071 

Missouri 589 10,266 507 1,835 9,513 9,536 170,854 3,734 10,251 9,193 6,604 2,206 235,087 

Montana 89 1,880 101 367 9,350 8,284 146,282 6,354 17,322 5,918 6,446 1,284 203,677 

Nebraska 355 7,484 190 688 9,513 9,674 173,338 5,325 14,617 58,221 39,071 106,292 424,769 

Nevada 184 4,300 134 484 1,454 1,343 23,711 628 1,711 1,184 1,125 281 36,538 

N. Hamp. 89 2,005 92 333 49 21 377 21 59 39 14 10 3,110 

N. Jersey 47 1,040 57 205 98 42 755 28 78 53 42 18 2,464 

N. Mexico 2,121 51,957 1,606 5,812 3,636 2,475 43,704 1,148 3,130 3,100 2,737 1,046 122,471 

New York 3,862 92,408 4,530 16,396 1,464 529 9,437 541 1,483 1,051 1,189 1,003 133,892 

N. Car. 285 7,393 326 1,180 3,309 1,898 33,880 923 2,528 1,021 734 340 53,817 

N. Dakota 114 2,278 127 459 5,423 4,426 79,309 2,531 6,948 6,512 5,503 2,407 116,035 

Ohio 1,710 34,729 1,521 5,504 2,378 1,541 27,602 761 2,088 4,852 1,926 7,220 91,830 

Oklahoma 329 7,136 287 1,039 11,677 9,372 167,331 4,542 12,447 24,091 13,266 15,242 266,760 

Oregon 779 17,013 1,004 3,632 4,155 3,112 54,956 1,684 4,591 4,368 2,889 2,808 100,991 

Penn 3,419 77,175 4,459 16,140 2,440 846 15,100 641 1,756 3,809 1,557 3,008 130,349 

R.Island 7 151 7 26 10 7 123 4 12 8 3 2 359 

S. Car. 101 2,400 93 337 1,557 970 17,317 511 1,400 288 423 136 25,534 

S. Dakota 570 12,098 444 1,605 8,562 8,267 148,129 4,426 12,149 17,620 14,858 12,835 241,563 

Tenn. 317 7,222 466 1,686 6,812 5,007 89,406 2,129 5,835 4,190 2,822 361 126,254 

Texas 2,754 67,646 3,015 10,911 32,113 24,061 429,622 7,806 21,393 62,061 42,901 118,325 822,611 

Utah 570 12,890 709 2,567 2,078 1,878 33,155 995 2,713 2,536 1,429 1,043 62,562 

Vermont 842 18,863 764 2,767 293 53 944 57 156 105 142 47 25,033 

Virginia 608 14,698 621 2,248 3,892 3,500 62,490 1,348 3,695 4,190 1,552 923 99,766 

Wash. 1,665 39,858 1,606 5,812 1,974 1,235 21,802 827 2,254 5,073 3,375 9,426 94,905 

W. Virg. 63 1,272 71 256 1,366 1,031 18,403 513 1,404 1,130 736 201 26,446 

Wisconsin 8,009 170,261 8,744 31,646 2,854 1,361 24,381 968 2,658 9,193 1,101 9,626 270,802 

Wyoming 38 794 63 229 4,155 4,062 71,735 2,526 6,887 5,073 4,257 3,008 102,828 
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.11.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 2012 for all livestock types, except American bison, goats, 

horses, mules and asses 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 Quick Stats

Database (USDA 2013a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998, 

1999, 2004a, 2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013b and 2013c). Goat 

population data for 1992, 1997, 2002, and 2007 were obtained from the Census of Agriculture (USDA 2009a), as were 

horse, mule and ass population data for 1987, 1992, 1997, 2002, and 2007, and American bison population for 2002 and 

2007. American bison population data for 1990-1999 were obtained from the National Bison Association (1999). 

Additional data sources used and adjustments to these data sets are described below. 

Cattle: For all cattle groups (cows, heifers, steers, bulls, and calves), the USDA data provide cattle inventories 

from January (for each state) and July (as a U.S. total only) of each year. Cattle inventories change over the course of the 

year, sometimes significantly, as new calves are born and as cattle are moved into feedlots and subsequently slaughtered; 

therefore, to develop the best estimate for the annual animal population, the populations and the individual characteristics, 

such as weight and weight gain, pregnancy, and lactation of each animal type were tracked in the Cattle Enteric 

Fermentation Model (CEFM—see section 6-1 Enteric Fermentation). For animals that have relatively static populations 

throughout the year, such as mature cows and bulls, the January 1 values were used. For animals that have fluctuating 

populations throughout the year, such as calves and growing heifers and steer, the populations are modeled based on a 

transition matrix that uses annual population data from USDA along with USDA data on animal births, placement into 

feedlots, and slaughter statistics. 

Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 50 pounds (under 

23 kg), market 50 to 119 pounds (23 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and over 

(greater than 82 kg). The average of the quarterly data was used in the emission calculations. For states where only 

December inventory is reported, the December data were used directly. 

Sheep: The USDA provides total state-level data annually for lambs and sheep. Population distribution data for 

lamb and sheep on feed are not available after 1993 (USDA 1994). The number of lamb and sheep on feed for 1994 

through 2012 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 2012. Data for 1993 

through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated based on the 1992, 1997, and 2002 Census 

data.

Horses: Annual horse population data by state were available for 1987, 1992, 1997, 2002, and 2007 (USDA 

2009a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003 through 2006, and 2008 through 2012 

were extrapolated based on the 1987, 1992, 1997, 2002, and 2007 Census data.

Mules and Asses: Annual mule and ass (burro and donkey) population data by state were available for 1987, 

1992, 1997, 2002, and 2007 (USDA 2009a). Data for 1990 through 1991, 1993 through 1996, 1998 through 2001, 2003 

through 2006, and 2008 through 2012 were extrapolated based on the 1987, 1992, 1997, 2002, and 2007 Census data.

American Bison: Annual American bison population data by state were available for 2002, and 2007 (USDA 

2009a). Data for 1990 through 1999 were obtained from the Bison Association (1999). Data for 2000, 2001, 2003 

through 2006, and 2008 through 2012 were extrapolated based on the Bison Association and 2002 and 2007 Census data.
A-259

Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one 

year old), other chickens, and production (slaughter) data for broilers and turkeys (USDA 1995a, 1995b, 1998, 1999, 

2004a, 2004b, 2009b, 2009c, 2009d, 2009e, 2010a, 2010b, 2011a, 2011b, 2012a, 2012b, 2013b, and 2013c). 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 nondisclosed states were estimated by equally distributing the population attributed to “other” states to each of the nondisclosed states.

Because only production data are available for boilers and turkeys, population data are calculated by dividing the 

number of animals produced by the number of production cycles per year, or the turnover rate. Based on personal 

communications with John Lange, an agricultural statistician with USDA NASS, the broiler turnover rate ranges from 3.4 

to 5.5 over the course of the inventory. For turkeys, the turnover rate ranges from 2.4 to 3.0. A summary of the livestock 

population characterization data used to calculate CH4 and N2O emissions is presented in Table A- 202.

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- 203 presents a summary of the waste characteristics used in the emissions estimates. Published sources 

were reviewed for U.S.-specific livestock waste characterization data that would be consistent with the animal population 

data discussed in Step 1. The USDA’s Agricultural Waste Management Field Handbook (AWMFH; USDA 1996, 2008) 

is one of the primary sources of waste characteristics. 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- 204 (ERG 

2010b and 2010c). The 1996 AWMFH data were based on measured values from U.S. farms; the 2008 AWMFH data 

were developed using the calculation method created by the American Society of Agricultural and Biological Engineers, 

which is based on U.S. animal dietary intake and performance measures. Since the values from each of the two AWMFHs 

result from different estimation methods and reflect changes in animal genetics and nutrition over time, both data sources 

were used to create a time series across the inventory as neither value would be appropriate to use across the entire span of 

inventory years. Although the AWMFH values are lower than the IPCC values, these values are more appropriate for U.S.

systems because they have been calculated using U.S.-specific data. Animal-specific notes about VS and Nex are 

presented below:

 Swine: The VS and Nex data for breeding swine are from a combination of the types of animals that make 

up this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of 

breeding swine is typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 

percent boars (Safley 2000). 

 Poultry: 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. 

 Goats, Sheep, Horses, Mules and Asses: In cases where data were not available in the USDA documents,

data from the American Society of Agricultural Engineers, Standard D384.1 (ASAE 1998) or the 2006 IPCC 

Guidelines were used as a supplement.

The method for calculating VS excretion and Nex from American bison, beef and dairy cows, bulls, heifers, and 

steers is based on the relationship between animal performance characteristics such as diet, lactation, and weight gain and 

energy utilization. The method used is outlined by the IPCC 2006 Guidelines Tier II methodology, and is modeled using 

the CEFM described in the enteric fermentation portion of the inventory (documented in Moffroid and Pape 2013) in order 
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

to take advantage of the detailed diet and animal performance data assembled as part of the Tier II analysis for cattle. For 

American bison, VS and Nex were assumed to be the same as beef NOF bulls.

The VS content of manure is the fraction of the diet consumed by cattle that is not digested and thus excreted as 

fecal material; fecal material combined with urinary excretions constitutes manure. The CEFM uses the input of digestible 

energy (DE) and the energy requirements of cattle to estimate gross energy (GE) intake and enteric CH4 emissions. GE 

and DE are used to calculate the indigestible energy per animal as gross energy minus digestible energy plus the amount of 

gross energy for urinary energy excretion per animal (2 or 4 percent). This value is then converted to VS production per 

animal using the typical conversion of dietary gross energy to dry organic matter of 18.45 MJ/kg, after subtracting out the 

ash content of manure. The current equation recommended by the 2006 IPCC Guidelines is:

   

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.

Total nitrogen ingestion in cattle is determined by dietary protein intake. When feed intake of protein exceeds 

the nutrient requirements of the animal, the excess nitrogen is excreted, primarily through the urine. To calculate the 

nitrogen excreted by each animal type, the CEFM utilizes the energy balance calculations recommended by the 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. 

  Nexcreted  Nconsumed  Ngrowth  Nmilk

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 N 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 N 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 
A-261

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, as input into the CEFM transition matrix, kg per day

268 = Constant from 2006 IPCC Guidelines

7.03 = Constant from 2006 IPCC Guidelines

NEg = Net energy required for growth, as calculated in the CEFM, 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, and is calculated using milk 

production and percent protein, along with conversion factors. Milk N retained as product is calculated using the 

following equation:

6.38

100

%

* 













pr milk

N milk

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

(Fat assumed to be 4%)

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, animal type (heifers and 

steer on feed, heifers and steer not on feed, bulls and American bison), and year. Table A- 205 presents the state-specific 

VS and Nex production rates used for cattle in 2012. 

Step 3: Waste Management System Usage Data

Table A- 206 summarizes 2012 manure distribution data among waste management systems (WMS) at beef 

feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining 

animal types (beef cattle not on feed, American bison, goats, horses, mules and asses and sheep) is managed on pasture, 

range, or paddocks, on drylot, or with solids storage systems. 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 A207. 

Beef Cattle, Dairy Heifers and American Bison: 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 personal communication with the national USDA office to estimate the 
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

percent of beef steers and heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots. 

Therefore, for these animal groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition, 

there is a small amount of manure contained in runoff, which may or may not be collected in runoff ponds. Using the 

expert opinions and EPA and USDA data, the runoff from feedlots was calculated by region in Calculations: Percent 

Distribution of Manure for Waste Management Systems (ERG 2000a) 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. American bison WMS data were assumed to be the same as beef cattle not on 

feed.

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, 2002, and 2007 

Census of Agriculture (USDA 2009a). It was assumed that the Census data provided for 1992 were the same as that for 

1990 and 1991, and data provided for 2007 were the same as that for 2008 through 2012. Data for 1993 through 1996, 

1998 through 2001, and 2003 through 2006 were extrapolated using the 1992, 1997, 2002, and 2007 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 the expert opinion of state contacts, manure from dairy cows at medium (200 

through 700 head) and large (greater than 700 head) operations are managed using either flush systems or scrape/slurry 

systems. In addition, they may have a solids separator in place prior to their storage component. Estimates of the percent 

of farms that use each type of system (by geographic region) were developed by EPA's Office of Water, and were used to 

estimate the percent of waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage 

(separated solids) (EPA 2002b).

Manure management system data for small (fewer than 200 head) dairies were obtained from USDA’s Animal 

and Plant Health Inspection Service (APHIS)’s National Animal Health Monitoring System (Ott 2000). These data are 

based on a statistical sample of farms in the 20 U.S. states with the most dairy cows. Small operations are more likely to 

use liquid/slurry and solid storage management systems than anaerobic lagoon systems. The reported manure 

management systems were deep pit, liquid/slurry (includes slurry tank, slurry earth-basin, and aerated lagoon), anaerobic 

lagoon, and solid storage (includes manure pack, outside storage, and inside storage).

Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from 

personal communications with personnel from several organizations. These organizations include state NRCS offices, 

state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000,

Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure 

management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, 2002, and 

2007 (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 2012, which were obtained from the 

USDA NASS (USDA 2013a).

Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies 

reported using more than one type of manure management system. Due to limitations in how USDA APHIS collects the 

manure management data, the total percent of systems for a region and farm size is greater than 100 percent. However, 

manure is typically partitioned to use only one manure management system, rather than transferred between several 

different systems. Emissions estimates are only calculated for the final manure management system used for each portion 

of manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total 

of 100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep 

pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent 

(i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided 

by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a).

Finally, the percentage of manure managed with anaerobic digestion (AD) systems with methane capture and 

combustion was added to the WMS distributions. AD system data were obtained from EPA’s AgSTAR Program’s project 

database (EPA 2012). This database includes basic information for AD systems in the U.S., based on publically available 

data and data submitted by farm operators, project developers, financiers, and others involved in the development of farm 

AD projects. 

Swine: The distribution of manure managed in each WMS was estimated using data from a USDA APHIS report 

and EPA’s Office of Water site visits (Bush 1998, ERG 2000a). The USDA APHIS data are based on a statistical sample 

of farms in the 16 U.S. states with the most hogs. For operations with less than 200 head, manure management system 
A-263

data were obtained from USDA APHIS (Bush 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, 2002, and 2007 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 2007 were the same as that for 2008 through 2012. Data for 

1993 through 1996, 1998 through 2001, and 2003 through 2006 were extrapolated using the 1992, 1997, 2002, and 2007 

data. The manure management systems reported in the census were deep pit, liquid/slurry (includes above- and belowground 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. As with dairy, AD WMS were 

added to the WMS distribution based on data from EPA’s AgSTAR database (EPA 2012).

Sheep: WMS data for sheep were obtained from USDA NASS sheep report for years 1990 through 1993 (USDA 

1994). Data for 2001 are obtained from USDA APHIS’s national sheep report (USDA, APHIS 2003). The USDA APHIS 

data are based on a statistical sampled of farms in the 22 U.S. states with the most sheep. The data for years 1994-2000 

are calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for years 2002 and 

beyond are assumed to be the same as 2001. Based on expert opinion, it was assumed that all sheep manure not deposited 

in feedlots was deposited on pasture, range, or paddock lands (Anderson 2000). 

Goats, Horses, and Mules and Asses: WMS data for 1990 to 2012 were obtained from Appendix H of Global 

Methane Emissions from Livestock and Poultry Manure (EPA 1992). This report presents state WMS usage in 

percentages for the major animal types in the U.S., based on information obtained from extension service personnel in 

each state. It was assumed that all manure not deposited in pasture, range, or paddock lands was managed in dry systems. 

For mules and asses, the WMS was assumed to be the same as horses.

Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: WMS data for 

1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). These data were 

also used to represent 1990 and 1991. The percentage of layer operations using a shallow pit flush house with anaerobic 

lagoon or high-rise house without bedding was obtained for 1999 from a United Egg Producers voluntary survey (UEP 

1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, IN, MN, MO, NC, NE, OH, PA, TX, 

and WA) with USDA data (USDA, APHIS 2000). It was assumed that the change in system usage between 1990 and 

1999 is proportionally distributed among those years of the inventory. It was also assumed that system usage in 2000 

through 2012 was equal to that estimated for 1999. Data collected for EPA's Office of Water, including information 

collected during site visits (EPA 2002b), were used to estimate the distribution of waste by management system and 

animal type. As with dairy and swine, using information about AD WMS from EPA’s AgSTAR database (EPA 2012), 

AD was added to the WMS distribution for poultry operations.

Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from the Office 

of Air and Radiation’s Global Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that 

one percent of poultry waste is deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is 

assumed to be deposited in operations with bedding management. As with dairy, swine, and other poultry, AD systems 

were added to the WMS distributions based on information from EPA’s AgSTAR database (EPA 2012).

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-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Methane Conversion Factors (MCFs)

Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in 

Table A- 208. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems. 

For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual 

average temperature data. The average county and state temperature data were obtained from the National Climate Data 

Center (NOAA 2012) and each state and year in the inventory was assigned a climate classification of cool, temperate or 

warm. Although there are some specific locations in the U.S. that may be included in the warm climate category, no 

aggregated state-level annual average temperatures are included in this category. In addition, some counties in a particular 

state may be included in the cool climate category, although the aggregated state-level annual average temperature may be 

included in the temperate category. Although considering the temperatures at a state level instead of a county level may 

be causing some specific locations to be classified into an inappropriate climate category, using the state level annual 

average temperature provides an estimate that is appropriate for calculating the national average. 

For anaerobic lagoons and other liquid systems a climate-based approach based on the van’t Hoff-Arrhenius 

equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for 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,

f = van’t Hoff-Arrhenius f factor, the proportion of VS that are biologically available for conversion to 

CH4 based on the temperature of the system

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)

For those animal populations using liquid manure management systems or manure runoff ponds (i.e., dairy cow, 

dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on the counties where the 

specific animal population resides (i.e., the temperatures were weighted based on the percent of animals located in each 

county). County population data were calculated from state-level population data from NASS and county-state 

distribution data from the 1992, 1997, 2002, and 2007 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; county population data for 2003 through 2006 were extrapolated based on 2002 and 2007 data; and county 

population data for 2008 to 2012 were assumed to be the same as 2007.

Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory:

 The weighted-average temperature for a state is calculated using the county population estimates and average 

monthly temperature in each county. Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius 

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 animal type, number of animals present, 

and the volatile solids excretion rate of the animals. 

 For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This 

factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from 

anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents management and design 

factors which cause a system to operate at a less than optimal level.

 For all systems other than anaerobic lagoons, the amount of VS available for conversion to CH4 each month is 

assumed to be equal to the amount of VS produced during the month (from Step 3). For anaerobic lagoons, the 

amount of VS available also includes VS that may remain in the system from previous months.











 



1 2

2 1

( )

exp RT T

E T T

f
A-265

 The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the f

factor.

 For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount 

available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once 

per year, occurring in the month of October.

 The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by 

the maximum CH4 potential of the waste (Bo).

The annual MCF is then calculated as:

annual o

annual 4

annual VS produced 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- 209 by state, WMS, and animal group for 2012. 

Nitrous Oxide Emission Factors

Direct N2O EFs 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- 210. 

Indirect N2O EFs account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and NOX (Fracgas) 

and runoff/leaching (Fracrunoff/leach). IPCC default indirect N2O EFs 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. 

Country-specific estimates of N losses were developed for Fracgas and Fracrunoff/leach for the United States. The 

vast majority of volatilization losses are NH3. Although there are also some small losses of NOX, no quantified estimates 

were available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. 

Therefore, Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA’s National Emission 

Inventory - Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracrunoff/leach, data from 

EPA’s Office of Water were used that estimate the amount of runoff from beef, dairy, and heifer operations in five 

geographic regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, 

WMS, and region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses and there are 

a lack of data to quantify these losses. Therefore, leaching losses were assumed to be zero and Fracrunoff/leach was set equal 

to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A- 211.

Step 5: CH4 Emission Calculations

To calculate CH4 emissions for animals other than cattle, first the amount of VS excreted in manure that is 

managed in each WMS was estimated:
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012 VS WMS 365.25

1000

TAM VS excreted State, Animal, WMS  PopulationState, Animal    

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)

TAM = Typical animal mass (kg)

VS = Volatile solids production rate (kg VS/1000 kg animal mass/day)

WMS = Distribution of manure by WMS for each animal type in a state (percent)

365.25 = Days per year

Using the CEFM VS data for cattle, the amount of VS excreted in manure that is managed in each WMS was 

estimated using the following equation:

VS excreted State,Animal, WMS  PopulationState,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 excreted into a WMS was used to calculate CH4 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)

A calculation was developed to estimate the amount of CH4 emitted from AD systems utilizing CH4 capture and 

combustion technology. First, AD systems were assumed to produce 90 percent of the maximum CH4 producing capacity. 

This value is applied for all climate regions and AD system types. However, 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  PopulationADADSystem   

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 CH4 at 25o C (kg CH4/m3 CH4)

365.25 = Days/year

0.90 = CH4 production factor for AD systems
A-267

The total amount of CH4 produced by AD is calculated only as a means to estimate the emissions from AD; i.e., 

only the estimated amount of CH4 actually entering the atmosphere from AD is reported in the inventory. The emissions 

to the atmosphere from AD are a result of leakage and incomplete combustion and are calculated using the collection 

efficiency (CE) and destruction efficiency (DE) of the AD system. The three primary types of AD systems in the U.S. are 

covered lagoons, complete mix and plug flow systems. The CE of covered lagoon systems was assumed to be 75 percent, 

and the CE of complete mix and plug flow AD systems was assumed to be 99 percent (EPA 2008). The CH4 DE from 

flaring or burning in an engine was assumed to be 98 percent; therefore, the amount of CH4 that would not be flared or 

combusted was assumed to be 2 percent (EPA 2008). The amount of CH4 produced by systems with AD was calculated 

with the following equation:

  

  

 















 

  



State, Animal,ADSystems 4 ADsystem ADsystem

4 ADsystem ADsystem

4

CH Production AD 1-CE

CH Production AD CE 1 DE

CH EmissionsAD

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

Step 6: N2O Emission Calculations

In addition to CH4 emissions, total N2O emissions were also estimated 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 N excreted in manure and managed in each WMS. For calves and 

animals other than cattle the following equation was used: 

Nex 365.25

1000

TAM N excreted State, Animal, WMS  PopulationState, 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 other than calves, the amount of N excreted was calculated using the 

following equation:

N excreted State, Animal, WMS  PopulationState, Animal  WMS Nex

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)
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

WMS = Distribution of manure by waste management system for each animal type 

in a state (percent)

Nex = Total Kjeldahl N 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

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- 212

and Table A- 213 respectively. Emission estimates for 2012 are presented by animal type and state in Table A- 214 and 

Table A- 215 respectively.
A-269

Table A- 202: Livestock Population (1,000 Head) 

Animal Type 1990 1995 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Dairy Cattle 14,144 13,590 13,217 13,165 13,215 13,021 13,165 13,398 13,487 13,658 13,761 13,612 13,722 13,816

 Dairy Cows 10,015 9,482 9,172 9,106 9,142 8,988 9,004 9,104 9,145 9,257 9,333 9,086 9,150 9,230

 Dairy Heifer 4,129 4,108 4,045 4,060 4,073 4,033 4,162 4,294 4,343 4,401 4,429 4,526 4,572 4,586

Swinea 53,941 58,899 58,913 60,028 59,827 60,735 61,073 61,887 65,417 67,408 65,990 64,768 65,589 66,516

 Market <50 lb. 18,359 19,656 19,659 19,863 19,929 20,222 20,228 20,514 21,812 19,964 19,444 19,124 19,385 19,580

 Market 50-119 lb. 11,734 12,836 12,900 13,284 13,138 13,400 13,519 13,727 14,557 17,219 16,995 16,699 16,966 17,257

 Market 120-179 lb. 9,440 10,545 10,708 11,013 11,050 11,227 11,336 11,443 12,185 12,931 12,567 12,313 12,438 12,660

 Market >180 lb. 7,510 8,937 9,465 9,738 9,701 9,922 9,997 10,113 10,673 11,193 11,079 10,854 11,009 11,181

 Breeding 6,899 6,926 6,181 6,129 6,011 5,963 5,993 6,090 6,190 6,102 5,905 5,778 5,791 5,837

Beef Cattleb 87,228 95,683 89,118 89,102 88,232 86,441 86,954 88,070 87,639 86,450 85,812 85,183 83,705 81,443

 Feedlot Steers 6,357 7,233 7,932 8,116 8,416 8,018 8,116 8,724 8,674 8,481 8,446 8,563 8,743 8,515

 Feedlot Heifers 3,192 3,831 4,569 4,557 4,676 4,521 4,536 4,801 4,730 4,589 4,508 4,628 4,803 4,695

 NOF Bulls 2,160 2,385 2,274 2,244 2,248 2,201 2,214 2,258 2,214 2,207 2,184 2,190 2,155 2,096

 NOF Calves 22,561 23,499 22,389 22,325 21,997 21,781 21,678 21,621 21,483 21,155 21,001 20,861 20,648 20,094

 NOF Heifers 10,182 11,829 9,832 9,843 9,564 9,321 9,550 9,716 9,592 9,350 9,448 9,348 8,878 8,677

 NOF Steers 10,321 11,716 8,724 8,883 8,347 8,067 8,185 8,248 8,302 8,233 8,515 8,223 7,628 7,209

 NOF Cows 32,455 35,190 33,398 33,134 32,983 32,531 32,674 32,703 32,644 32,435 31,712 31,371 30,850 30,158

Sheep 11,358 8,989 6,908 6,623 6,321 6,065 6,135 6,200 6,120 5,950 5,747 5,620 5,480 5,365

 Sheep On Feed 1,180 1,771 3,256 3,143 3,049 2,923 2,971 3,026 3,000 2,911 2,806 2,778 2,692 2,661

 Sheep NOF 10,178 7,218 3,652 3,480 3,272 3,142 3,164 3,174 3,120 3,039 2,941 2,842 2,788 2,704

Goats 2,516 2,357 2,475 2,530 2,652 2,774 2,897 3,019 3,141 3,141 3,141 3,141 3,141 3,141

Poultryc 1,537,074 1,826,977 2,060,398 2,097,691 2,085,268 2,130,877 2,150,410 2,154,236 2,166,936 2,175,990 2,088,828 2,104,335 2,095,951 2,074,269

 Hens >1 yr. 273,467 299,071 340,317 340,209 340,979 343,922 348,203 349,888 346,613 339,859 341,005 341,884 338,944 345,730

 Pullets 73,167 81,369 95,656 95,289 100,346 101,429 96,809 96,596 103,816 99,458 102,301 105,738 102,233 103,058

 Chickens 6,545 7,637 8,126 8,353 8,439 8,248 8,289 7,938 8,164 7,589 8,487 7,390 6,922 6,817

 Broilers 1,066,209 1,331,940 1,525,413 1,562,015 1,544,155 1,589,209 1,613,091 1,612,327 1,619,400 1,638,055 1,554,582 1,567,927 1,565,018 1,534,164

 Turkeys 117,685 106,960 90,887 91,826 91,349 88,069 84,018 87,487 88,943 91,029 82,453 81,396 82,833 84,500

Horses 2,212 2,632 3,519 3,644 3,721 3,798 3,875 3,952 4,029 4,106 4,183 4,260 4,336 4,413

Mules and Asses 63 101 109 105 141 177 212 248 284 319 355 391 427 462

American Bison 47 104 213 232 225 218 212 205 198 192 185 179 173 167

Note: Totals may not sum due to independent rounding. 

a Prior to 2008, the Market <50 lbs category was <60 lbs and the Market 50-119 lbs category was Market 60-119 lbs; USDA updated the categories to be more consistent with international animal categories.

b NOF = Not on Feed

c Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months.
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 203: Waste Characteristics Data

Animal Group

Typical Animal Mass, TAM Total Kjeldahl Nitrogen Excreted, Nexa Maximum Methane Generation Potential, B0 Volatile Solids Excreted, VSa

Value 

(kg) Source Value Source

Value

(m3 CH4/kg VS added) Source Value Source

Dairy Cows 680 CEFM Table A- 94 CEFM 0.24 Morris 1976 Table A- 94 CEFM

Dairy Heifers 406-408 CEFM Table A- 94 CEFM 0.17 Bryant et al. 1976 Table A- 94 CEFM

Feedlot Steers 419-457 CEFM Table A- 94 CEFM 0.33 Hashimoto 1981 Table A- 94 CEFM

Feedlot Heifers 384-430 CEFM Table A- 94 CEFM 0.33 Hashimoto 1981 Table A- 94 CEFM

NOF Bulls 831-917 CEFM Table A- 94 CEFM 0.17 Hashimoto 1981 Table A- 94 CEFM

NOF Calves 118 ERG 2003b Table A- 205 USDA 1996, 2008 0.17 Hashimoto 1981 Table A- 205 USDA 1996, 2008

NOF Heifers 296-407 CEFM Table A- 199 CEFM 0.17 Hashimoto 1981 Table A- 199 CEFM

NOF Steers 314-335 CEFM Table A- 94 CEFM 0.17 Hashimoto 1981 Table A- 94 CEFM

NOF Cows 554-611 CEFM Table A- 94 CEFM 0.17 Hashimoto 1981 Table A- 94 CEFM

American Bison 578.5 Meagher 1986 Table A- 199 CEFM 0.17 Hashimoto 1981 Table A- 199 CEFM

Market Swine <50 lbs. 13 ERG 2010a Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Market Swine <60 lbs. 16 Safley 2000 Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Market Swine 50-119 lbs. 39 ERG 2010a Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Market Swine 60-119 lbs. 41 Safley 2000 Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Market Swine 120-179 lbs. 68 Safley 2000 Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Market Swine >180 lbs. 91 Safley 2000 Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Breeding Swine 198 Safley 2000 Table A- 205 USDA 1996, 2008 0.48 Hashimoto 1984 Table A- 205 USDA 1996, 2008

Feedlot Sheep 25 EPA 1992 Table A- 205 ASAE 1998, USDA 2008 0.36 EPA 1992 Table A- 205 ASAE 1998, USDA 2008

NOF Sheep 80 EPA 1992 Table A- 205 ASAE 1998, USDA 2008 0.19 EPA 1992 Table A- 205 ASAE 1998, USDA 2008

Goats 64 ASAE 1998 Table A- 205 ASAE 1998 0.17 EPA 1992 Table A- 205 ASAE 1998

Horses 450 ASAE 1998 Table A- 205 ASAE 1998, USDA 2008 0.33 EPA 1992 Table A- 205 ASAE 1998, USDA 2008

Mules and Asses 130 IPCC 2006 Table A- 205 IPCC 2006 0.33 EPA 1992 Table A- 205 IPCC 2006

Hens >/= 1 yr 1.8 ASAE 1998 Table A- 205 USDA 1996, 2008 0.39 Hill 1982 Table A- 205 USDA 1996, 2008

Pullets 1.8 ASAE 1998 Table A- 205 USDA 1996, 2008 0.39 Hill 1982 Table A- 205 USDA 1996, 2008

Other Chickens 1.8 ASAE 1998 Table A- 205 USDA 1996, 2008 0.39 Hill 1982 Table A- 205 USDA 1996, 2008

Broilers 0.9 ASAE 1998 Table A- 205 USDA 1996, 2008 0.36 Hill 1984 Table A- 205 USDA 1996, 2008

Turkeys 6.8 ASAE 1998 Table A- 205 USDA 1996, 2008 0.36 Hill 1984 Table A- 205 USDA 1996, 2008

a Nex and VS values vary by year; Table A- 203 shows state-level values for 2012 only. 
A-271

Table A- 204: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Nex) Production Rates by year for Swine, Poultry, Sheep, Goats, Horses, Mules and Asses, and 

Cattle Calves (kg/day/1000 kg animal mass)

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

VS

Swine, Market 

 <50 lbs. 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8 8.8

Swine, Market

 50-119 lbs. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4

Swine, Market 

 120-179 lbs. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4

Swine, Market

 >180 lbs. 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4 5.4

Swine, Breeding 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7 2.7

NOF Cattle Calves 6.4 6.4 6.4 6.4 6.4 6.4 6.4 6.5 6.6 6.7 6.8 6.9 7.1 7.2 7.3 7.4 7.5 7.6 7.7 7.7 7.7 7.7 7.7

Sheep 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.2 9.1 9.0 8.9 8.8 8.8 8.7 8.6 8.5 8.4 8.3 8.3 8.3 8.3 8.3

Goats 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5 9.5

Hens >1yr. 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.2 10.2 10.2 10.2 10.2 10.2 10.2

Pullets 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.1 10.2 10.2 10.2 10.2 10.2 10.2 10.2

Chickens 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.8 10.9 10.9 10.9 10.9 10.9 10.9 11.0 11.0 11.0 11.0 11.0 11.0 11.0 11.0

Broilers 15.0 15.0 15.0 15.0 15.0 15.0 15.0 15.2 15.3 15.5 15.7 15.8 16.0 16.2 16.3 16.5 16.7 16.8 17.0 17.0 17.0 17.0 17.0

Turkeys 9.7 9.7 9.7 9.7 9.7 9.7 9.7 9.6 9.5 9.4 9.3 9.2 9.1 9.0 8.9 8.8 8.7 8.6 8.5 8.5 8.5 8.5 8.5

Horses 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 10.0 9.6 9.2 8.8 8.4 8.1 7.7 7.3 6.9 6.5 6.1 6.1 6.1 6.1 6.1

Mules and Asses 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2 7.2

Nex

Swine, Market 

 <50 lbs. 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 0.50 0.51 0.52 0.53 0.54 0.54 0.54 0.54 0.54

Swine, Market

 50-119 lbs. 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 0.50 0.51 0.52 0.53 0.54 0.54 0.54 0.54 0.54

Swine, Market 

 120-179 lbs. 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 0.50 0.51 0.52 0.53 0.54 0.54 0.54 0.54 0.54

Swine, Market

 >180 lbs. 0.24 0.24 0.24 0.24 0.24 0.24 0.24 0.23 0.23 0.23 0.22 0.22 0.22 0.22 0.21 0.21 0.21 0.21 0.20 0.20 0.20 0.20 0.20

Swine, Breeding 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.31 0.33 0.34 0.35 0.36 0.38 0.39 0.40 0.41 0.43 0.44 0.45 0.45 0.45 0.45 0.45

NOF Cattle Calves 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.42 0.43 0.43 0.43 0.44 0.44 0.44 0.44 0.45 0.45 0.45 0.45 0.45 0.45

Sheep 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45 0.45

Goats 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.71 0.72 0.73 0.73 0.74 0.75 0.76 0.77 0.77 0.78 0.79 0.79 0.79 0.79 0.79

Hens >1yr. 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.70 0.71 0.72 0.73 0.73 0.74 0.75 0.76 0.77 0.77 0.78 0.79 0.79 0.79 0.79 0.79

Pullets 0.83 0.83 0.83 0.83 0.83 0.83 0.83 0.85 0.88 0.90 0.92 0.94 0.97 0.99 1.01 1.03 1.06 1.08 1.10 1.10 1.10 1.10 1.10

Chickens 1.10 1.10 1.10 1.10 1.10 1.10 1.10 1.09 1.08 1.07 1.05 1.04 1.03 1.02 1.01 1.00 0.98 0.97 0.96 0.96 0.96 0.96 0.96

Broilers 0.74 0.74 0.74 0.74 0.74 0.74 0.74 0.73 0.72 0.71 0.70 0.69 0.68 0.67 0.66 0.65 0.64 0.63 0.63 0.63 0.63 0.63 0.63
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Turkeys 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.28 0.28 0.27 0.27 0.26 0.26 0.25 0.25 0.25 0.25 0.25 0.25

Horses 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.30

Mules and Asses 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 0.50 0.51 0.52 0.53 0.54 0.54 0.54 0.54 0.54
A-273

Table A- 205: Estimated Volatile Solids (VS) and Total Kjeldahl Nitrogen Excreted (Nex) Production Rates by State for Cattle (other than Calves) and American Bisonafor 2012 

(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

Beef 

NOF Bull

American 

Bison

Dairy 

Cow

Dairy

Heifers

Beef 

NOF Cow

Beef 

NOF 

Heifers

Beef 

NOF 

Steer

Beef OF 

Heifers

Beef OF 

Steer

Beef 

NOF Bull

American 

Bison

Alabama 2,140 1,255 1,664 2,522 968 675 656 1,721 1,721 130 69 73 50 41 54 55 83 83

Alaska 2,164 1,255 1,891 3,966 1,112 675 658 1,956 1,956 130 69 59 42 33 55 56 69 69

Arizona 2,891 1,255 1,891 2,861 1,112 675 656 1,956 1,956 161 69 59 41 33 54 55 69 69

Arkansas 2,097 1,255 1,664 2,357 968 670 669 1,721 1,721 127 69 73 50 41 58 62 83 83

California 2,833 1,255 1,891 1,790 1,112 675 656 1,956 1,956 158 69 59 39 33 54 55 69 69

Colorado 2,890 1,255 1,891 1,574 1,112 675 656 1,956 1,956 161 69 59 38 33 54 55 69 69

Connecticut 2,585 1,255 1,674 2,994 974 675 656 1,731 1,731 148 69 74 52 42 54 55 84 84

Delaware 2,529 1,255 1,674 2,156 974 675 656 1,731 1,731 145 69 74 50 42 54 55 84 84

Florida 2,574 1,255 1,664 2,819 968 676 655 1,721 1,721 149 69 73 51 41 54 55 83 83

Georgia 2,582 1,255 1,664 2,458 968 675 656 1,721 1,721 149 69 73 50 41 54 55 83 83

Hawaii 2,160 1,255 1,891 2,596 1,112 675 656 1,956 1,956 129 69 59 41 33 54 55 69 69

Idaho 2,845 1,255 1,891 1,763 1,112 675 656 1,956 1,956 159 69 59 39 33 54 55 69 69

Illinois 2,557 1,255 1,589 1,464 921 675 656 1,643 1,643 146 69 75 49 43 54 55 85 85

Indiana 2,695 1,255 1,589 1,503 921 675 656 1,643 1,643 152 69 75 49 43 54 56 85 85

Iowa 2,723 1,255 1,589 1,223 921 675 656 1,643 1,643 154 69 75 48 43 54 55 85 85

Kansas 2,718 1,255 1,589 1,152 921 675 656 1,643 1,643 153 69 75 47 43 54 55 85 85

Kentucky 2,284 1,255 1,664 1,883 968 675 656 1,721 1,721 137 69 73 49 41 54 55 83 83

Louisiana 2,088 1,255 1,664 2,739 968 675 656 1,721 1,721 126 69 73 51 41 54 55 83 83

Maine 2,487 1,255 1,674 1,938 974 676 655 1,731 1,731 143 69 74 50 42 54 55 84 84

Maryland 2,533 1,255 1,674 2,203 974 675 656 1,731 1,731 145 69 74 51 42 54 55 84 84

Massachusetts 2,462 1,255 1,674 2,678 974 675 656 1,731 1,731 142 69 74 51 42 54 55 84 84

Michigan 2,870 1,255 1,589 1,563 921 675 656 1,643 1,643 160 69 75 49 43 54 55 85 85

Minnesota 2,556 1,255 1,589 1,471 921 675 656 1,643 1,643 146 69 75 49 43 54 55 85 85

Mississippi 2,226 1,255 1,664 2,476 968 676 655 1,721 1,721 134 69 73 50 41 54 55 83 83

Missouri 2,215 1,255 1,589 1,903 921 675 656 1,643 1,643 132 69 75 51 43 54 55 85 85

Montana 2,695 1,255 1,664 2,372 968 675 656 1,721 1,721 152 69 73 50 41 54 55 83 83

Nebraska 2,681 1,255 1,891 1,535 1,112 675 656 1,956 1,956 152 69 59 38 33 54 55 69 69

Nevada 2,815 1,255 1,589 1,893 921 675 656 1,643 1,643 157 69 75 51 43 54 55 85 85

New Hampshire 2,567 1,255 1,891 3,055 1,112 676 655 1,956 1,956 147 69 59 41 33 54 55 69 69

New Jersey 2,487 1,255 1,674 2,123 974 675 656 1,731 1,731 143 69 74 50 42 54 55 84 84

New Mexico 2,944 1,255 1,674 1,829 974 675 656 1,731 1,731 163 69 74 49 42 54 55 84 84

New York 2,715 1,255 1,891 2,143 1,112 675 656 1,956 1,956 153 69 59 40 33 54 55 69 69

North Carolina 2,680 1,255 1,674 2,555 974 675 656 1,731 1,731 154 69 74 51 42 54 55 84 84

North Dakota 2,539 1,255 1,664 1,873 968 675 656 1,721 1,721 146 69 73 49 41 54 55 83 83
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Volatile Solids Nitrogen Excreted

State Dairy 

Cow

Dairy 

Heifers

Beef 

NOF Cow

Beef 

NOF 

Heifers

Beef 

NOF 

Steer

Beef OF 

Heifers

Beef OF 

Steer

Beef 

NOF Bull

American 

Bison

Dairy 

Cow

Dairy

Heifers

Beef 

NOF Cow

Beef 

NOF 

Heifers

Beef 

NOF 

Steer

Beef OF 

Heifers

Beef OF 

Steer

Beef 

NOF Bull

American 

Bison

Ohio 2,581 1,255 1,589 1,698 921 675 656 1,643 1,643 147 69 75 50 43 54 55 85 85

Oklahoma 2,425 1,255 1,589 1,624 921 675 656 1,643 1,643 141 69 75 50 43 54 55 85 85

Oregon 2,625 1,255 1,664 2,019 968 675 656 1,721 1,721 149 69 73 49 41 54 55 83 83

Pennsylvania 2,562 1,255 1,891 2,076 1,112 675 656 1,956 1,956 147 69 59 40 33 54 55 69 69

Rhode Island 2,466 1,255 1,674 2,678 974 675 656 1,731 1,731 143 69 74 51 42 54 56 84 84

South Carolina 2,447 1,255 1,674 2,527 974 675 656 1,731 1,731 143 69 74 51 42 54 55 84 84

South Dakota 2,697 1,255 1,664 1,637 968 675 656 1,721 1,721 152 69 73 48 41 54 56 83 83

Tennessee 2,356 1,255 1,589 2,083 921 675 656 1,643 1,643 140 69 75 51 43 54 55 85 85

Texas 2,748 1,255 1,664 1,429 968 675 656 1,721 1,721 155 69 73 47 41 54 55 83 83

Utah 2,719 1,255 1,664 2,135 968 675 656 1,721 1,721 153 69 73 49 41 54 55 83 83

Vermont 2,542 1,255 1,891 2,062 1,112 676 656 1,956 1,956 146 69 59 40 33 54 55 69 69

Virginia 2,498 1,255 1,674 2,299 974 676 656 1,731 1,731 146 69 74 51 42 54 55 84 84

Washington 2,877 1,255 1,664 1,546 968 675 656 1,721 1,721 160 69 73 48 41 54 55 83 83

West Virginia 2,279 1,255 1,891 2,452 1,112 675 656 1,956 1,956 134 69 59 40 33 54 55 69 69

Wisconsin 2,701 1,255 1,589 2,183 921 675 656 1,643 1,643 153 69 75 51 43 54 55 85 85

Wyoming 2,654 1,255 1,891 2,331 1,112 675 656 1,956 1,956 151 69 59 40 33 54 55 69 69

Source: CEFM

a Beef NOF Bull values were used for American bison Nex and VS.

Table A- 206: 2012 Manure Distribution Among Waste Management Systems by Operation (Percent)

Beef Feedlots

Beef Not 

on Feed 

Operations Dairy Cow Farmsa Dairy Heifer Facilities Swine Operationsa Layer Operations

Broiler and Turkey 

Operations

State Dry 

Lotb

Liquid/ 

Slurryb

Pasture, 

Range, 

Paddock

Pasture, 

Range, 

Paddock

Daily 

Spread

Solid

Storage

Liquid/ 

Slurry

Anaerobic

Lagoon

Deep 

Pit

Daily 

Spreadb

Dry 

Lotb

Liquid/ 

Slurryb

Pasture, 

Range, 

Paddockb

Pasture, 

Range, 

Paddock

Solid

Storage

Liquid/ 

Slurry

Anaerobic

Lagoon

Deep 

Pit

Anaerobic

Lagoon

Poultry 

without

Litter

Pasture, 

Range, 

Paddock

Poultry 

with

Litter

Alabama 100 1 100 51 16 7 10 16 0 17 38 0 45 5 4 7 54 31 42 58 1 99

Alaska 100 1 100 5 9 34 19 24 9 6 90 1 4 64 2 10 7 17 25 75 1 99

Arizona 100 0 100 0 10 9 19 61 0 10 90 0 0 6 3 6 55 29 60 40 1 99

Arkansas 100 1 100 60 14 10 7 9 0 15 28 0 57 4 4 13 45 35 0 100 1 99

California 100 1 100 1 11 9 20 59 0 11 88 1 1 10 3 7 50 29 12 88 1 99

Colorado 100 0 100 1 1 11 23 64 0 1 98 0 1 1 6 26 17 50 60 40 1 99

Connecticut 100 1 100 6 43 16 20 13 2 43 51 0 6 78 1 6 5 11 5 95 1 99

Delaware 100 1 100 6 44 19 19 10 2 44 50 0 6 8 5 25 17 46 5 95 1 99

Florida 100 1 100 13 22 7 15 43 0 22 61 1 17 72 1 8 6 13 42 58 1 99

Georgia 100 1 100 37 18 9 12 23 0 18 42 0 40 4 4 8 53 31 42 58 1 99

Hawaii 100 1 100 10 0 9 23 57 0 0 99 1 1 31 3 19 14 32 25 75 1 99

Idaho 100 0 100 0 0 11 23 65 0 1 99 0 0 12 5 23 15 44 60 40 1 99

Illinois 100 1 100 4 6 39 31 16 5 8 87 0 5 1 5 29 14 52 2 98 1 99

Indiana 100 1 100 5 8 29 31 24 3 13 79 0 8 1 5 28 14 52 0 100 1 99

Iowa 100 1 100 4 8 34 30 20 4 10 83 0 6 1 4 9 54 33 0 100 1 99

Kansas 100 1 100 2 3 21 37 36 2 5 92 0 3 2 5 28 13 52 2 98 1 99

Kentucky 100 1 100 60 14 14 7 3 2 14 24 0 61 5 4 10 48 33 5 95 1 99
A-275

Beef Feedlots

Beef Not 

on Feed 

Operations Dairy Cow Farmsa Dairy Heifer Facilities Swine Operationsa Layer Operations

Broiler and Turkey 

Operations

State Dry 

Lotb

Liquid/ 

Slurryb

Pasture, 

Range, 

Paddock

Pasture, 

Range, 

Paddock

Daily 

Spread

Solid

Storage

Liquid/ 

Slurry

Anaerobic

Lagoon

Deep 

Pit

Daily 

Spreadb

Dry 

Lotb

Liquid/ 

Slurryb

Pasture, 

Range, 

Paddockb

Pasture, 

Range, 

Paddock

Solid

Storage

Liquid/ 

Slurry

Anaerobic

Lagoon

Deep 

Pit

Anaerobic

Lagoon

Poultry 

without

Litter

Pasture, 

Range, 

Paddock

Poultry 

with

Litter

Louisiana 100 1 100 59 15 10 7 9 1 14 26 0 60 88 1 3 3 6 60 40 1 99

Maine 100 1 100 7 45 20 17 10 2 45 48 0 7 65 2 10 7 16 5 95 1 99

Maryland 100 1 100 7 44 22 16 8 3 44 49 0 7 7 5 25 17 47 5 95 1 99

Massachusetts 100 1 100 7 44 22 16 8 3 45 47 0 7 56 2 12 9 20 5 95 1 99

Michigan 100 1 100 2 4 24 38 29 3 6 91 0 3 4 5 26 17 48 2 98 1 99

Minnesota 100 1 100 5 8 39 28 17 4 10 84 0 6 1 5 26 18 50 0 100 1 99

Mississippi 100 1 100 54 15 10 8 12 0 15 28 0 57 2 4 6 58 31 60 40 1 99

Missouri 100 1 100 7 12 42 22 11 5 14 77 0 8 2 5 28 13 52 0 100 1 99

Montana 100 0 100 2 4 19 28 42 4 4 93 0 3 3 5 25 17 49 60 40 1 99

Nebraska 100 1 100 2 4 26 35 29 3 6 90 0 4 1 5 28 14 51 2 98 1 99

Nevada 100 0 100 0 0 10 24 65 0 0 99 0 0 34 3 18 14 31 0 100 1 99

New 

Hampshire 100 1 100 7 44 19 18 10 2 44 49 0 7 64 2 10 8 17 5 95 1 99

New Jersey 100 1 100 7 45 25 13 6 3 45 47 0 8 36 3 18 14 30 5 95 1 99

New Mexico 100 0 100 0 10 9 19 61 0 10 90 0 0 100 0 0 0 0 60 40 1 99

New York 100 1 100 6 44 17 18 13 2 45 48 0 7 13 5 23 15 44 5 95 1 99

North Carolina 100 1 100 46 17 11 15 10 2 15 31 0 54 0 4 7 57 32 42 58 1 99

North Dakota 100 1 100 7 11 38 26 15 4 11 83 0 6 5 5 25 17 48 2 98 1 99

Ohio 100 1 100 6 11 38 26 15 4 14 78 0 8 3 5 28 14 51 0 100 1 99

Oklahoma 100 0 100 0 7 21 22 45 4 6 94 0 0 1 4 6 58 31 60 40 1 99

Oregon 100 1 100 16 0 11 22 50 1 0 80 1 20 48 2 14 11 24 25 75 1 99

Pennsylvania 100 1 100 8 46 24 12 6 2 47 44 0 9 4 5 26 18 48 0 100 1 99

Rhode Island 100 1 100 9 47 25 13 5 2 47 44 0 9 72 1 8 6 13 5 95 1 99

South Carolina 100 1 100 47 17 8 11 18 0 15 31 0 54 3 4 7 55 31 60 40 1 99

South Dakota 100 1 100 3 4 24 36 31 2 8 87 0 5 1 5 26 17 50 2 98 1 99

Tennessee 100 1 100 58 15 12 9 4 2 15 26 0 59 13 3 11 41 32 5 95 1 99

Texas 100 0 100 0 9 11 22 58 1 8 92 0 0 3 4 6 57 30 12 88 1 99

Utah 100 0 100 1 1 15 26 56 2 1 98 0 1 1 6 26 17 51 60 40 1 99

Vermont 100 1 100 6 44 17 19 13 2 44 49 0 7 63 2 10 8 18 5 95 1 99

Virginia 100 1 100 56 15 11 10 5 2 15 28 0 57 4 4 7 54 31 5 95 1 99

Washington 100 1 100 11 0 11 22 56 1 0 83 1 17 43 3 15 11 28 12 88 1 99

West Virginia 100 1 100 8 46 23 14 7 2 45 48 0 7 59 2 11 7 20 5 95 1 99

Wisconsin 100 1 100 5 9 38 28 17 4 12 82 0 7 13 4 23 17 42 2 98 1 99

Wyoming 100 0 100 4 6 19 23 43 4 12 81 0 7 4 5 25 16 49 60 40 1 99
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

a

In the methane inventory for manure management, the percent of dairy cows and swine with AD systems is estimated using data from EPA’s AgSTAR Program. 

b Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generates emissions is greater than 100 percent.
A-277

Table A- 207: Manure Management System Descriptions

Manure Management System Descriptiona

Pasture, Range, Paddock The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. Methane 

emissions are accounted for under Manure Management, but the N2O emissions from manure deposited on 

PRP are included under the Agricultural Soil Management category.

Daily Spread Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of 

excretion. Methane emissions are accounted for under Manure Management, but the N2O emissions during 

storage and treatment are assumed to be zero. N2O emissions from land application are covered under the 

Agricultural Soil Management category. 

Solid Storage The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to 

be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation. 

Dry Lot A paved or unpaved open confinement area without any significant vegetative cover where accumulating 

manure may be removed periodically. Dry lots are most typically found in dry climates but also are used in 

humid climates. 

Liquid/ Slurry Manure is stored as excreted or with some minimal addition of water to facilitate handling and is stored in either 

tanks or earthen ponds, usually for periods less than one year. 

Anaerobic Lagoon Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon 

supernatant is usually used to remove manure from the associated confinement facilities to the lagoon. 

Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the 

climate region, the VS loading rate, and other operational factors. Anaerobic lagoons accumulate sludge over 

time, diminishing treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and the sludge is 

typically applied to agricultural lands. The water from the lagoon may be recycled as flush water or used to 

irrigate and fertilize fields. Lagoons are sometimes used in combination with a solids separator, typically for 

dairy waste. Solids separators help control the buildup of nondegradable material such as straw or other 

bedding materials. 

Anaerobic Digester Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel

(complete mix or plug flow digester) or covered lagoon. Digesters are designed and operated for waste 

stabilization by the microbial reduction of complex organic compounds to 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.

a Manure management system descriptions are based on the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry 

and Other Land Use, Chapter 10: Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development Document for the Final 

Revisions to the National Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821-

R-03-001, December 2002).

Table A- 208: Methane Conversion Factors (percent) for Dry Systems

Waste Management System Cool Climate MCF Temperate Climate MCF Warm Climate MCF

Aerobic Treatment 0 0 0

Anaerobic Digester 0 0 0

Cattle Deep Litter (<1 month) 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
A-278 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Waste Management System Cool Climate MCF Temperate Climate MCF Warm Climate MCF

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

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

Source: IPCC 2006

Table A- 209: Methane Conversion Factors by State for Liquid Systems for 2012 (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 0.77 0.41 0.76 0.40 0.42 0.76

Alaska 0.47 0.15 0.47 0.15 0.15 0.47

Arizona 0.79 0.58 0.78 0.49 0.54 0.75

Arkansas 0.76 0.39 0.77 0.43 0.40 0.76

California 0.75 0.33 0.73 0.31 0.41 0.75

Colorado 0.65 0.22 0.68 0.24 0.24 0.65

Connecticut 0.70 0.26 0.71 0.26 0.26 0.70

Delaware 0.76 0.35 0.76 0.35 0.35 0.76

Florida 0.80 0.55 0.80 0.54 0.54 0.80

Georgia 0.78 0.44 0.77 0.42 0.42 0.76

Hawaii 0.76 0.57 0.76 0.57 0.57 0.76

Idaho 0.67 0.23 0.65 0.21 0.21 0.67

Illinois 0.73 0.31 0.72 0.30 0.29 0.73

Indiana 0.71 0.28 0.72 0.29 0.29 0.72

Iowa 0.70 0.26 0.70 0.26 0.26 0.70

Kansas 0.75 0.36 0.75 0.35 0.35 0.75

Kentucky 0.74 0.33 0.74 0.33 0.32 0.74

Louisiana 0.78 0.48 0.78 0.50 0.50 0.78

Maine 0.65 0.21 0.65 0.21 0.21 0.65

Maryland 0.74 0.32 0.74 0.32 0.33 0.75

Massachusetts 0.68 0.24 0.69 0.25 0.25 0.69

Michigan 0.68 0.24 0.69 0.25 0.24 0.68

Minnesota 0.68 0.24 0.69 0.25 0.25 0.67

Mississippi 0.77 0.44 0.76 0.42 0.44 0.77

Missouri 0.74 0.34 0.74 0.33 0.33 0.74

Montana 0.61 0.19 0.63 0.20 0.21 0.63

Nebraska 0.71 0.27 0.71 0.28 0.27 0.71

Nevada 0.69 0.24 0.71 0.26 0.23 0.70

New Hampshire 0.66 0.23 0.67 0.23 0.22 0.67

New Jersey 0.73 0.31 0.74 0.31 0.30 0.74

New Mexico 0.75 0.35 0.73 0.30 0.32 0.71

New York 0.68 0.24 0.69 0.24 0.24 0.69

North Carolina 0.75 0.34 0.77 0.40 0.32 0.75

North Dakota 0.66 0.22 0.66 0.22 0.22 0.65

Ohio 0.71 0.28 0.72 0.28 0.28 0.71

Oklahoma 0.77 0.47 0.77 0.43 0.44 0.77

Oregon 0.63 0.20 0.64 0.20 0.22 0.64

Pennsylvania 0.71 0.28 0.72 0.29 0.28 0.72

Rhode Island 0.71 0.27 0.71 0.27 0.27 0.71

South Carolina 0.77 0.42 0.78 0.43 0.41 0.77

South Dakota 0.69 0.25 0.70 0.25 0.25 0.69

Tennessee 0.74 0.33 0.75 0.36 0.34 0.74

Texas 0.77 0.48 0.78 0.50 0.44 0.78

Utah 0.67 0.23 0.69 0.24 0.24 0.67

Vermont 0.65 0.22 0.65 0.22 0.22 0.66

Virginia 0.73 0.30 0.75 0.34 0.31 0.74

Washington 0.63 0.20 0.64 0.21 0.22 0.64

West Virginia 0.71 0.28 0.71 0.28 0.28 0.71
A-279

State

Dairy Swine Beef Poultry

Anaerobic 

Lagoon

Liquid/Slurry and 

Deep Pit

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit Liquid/Slurry

Anaerobic 

Lagoon

Wisconsin 0.67 0.23 0.68 0.24 0.24 0.68

Wyoming 0.63 0.20 0.64 0.21 0.22 0.64

Note: MCFs developed using Tier 2 methods described in IPCC 2006, Section 10.4.2.
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 210: Direct Nitrous Oxide Emission Factors for 2012 (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

Source: IPCC 2006

Table A- 211: Indirect Nitrous Oxide Loss Factors (percent)

Animal Type

Waste Management 

System

Volatilization 

Nitrogen Loss

Runoff/Leaching Nitrogen Lossa

Central Pacific Mid-Atlantic Midwest South

Beef Cattle Dry Lot 23 1.1 3.9 3.6 1.9 4.3

Beef Cattle Liquid/Slurry 26 0 0 0 0 0

Beef Cattle Pasture 0 0 0 0 0 0

Dairy Cattle Anaerobic Lagoon 43 0.2 0.8 0.7 0.4 0.9

Dairy Cattle Daily Spread 10 0 0 0 0 0

Dairy Cattle Deep Pit 24 0 0 0 0 0

Dairy Cattle Dry Lot 15 0.6 2 1.8 0.9 2.2

Dairy Cattle Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9

Dairy Cattle Pasture 0 0 0 0 0 0

Dairy Cattle Solid Storage 27 0.2 0 0 0 0

American Bison Pasture 0 0 0 0 0 0

Goats Dry Lot 23 1.1 3.9 3.6 1.9 4.3

Goats Pasture 0 0 0 0 0 0

Horses Dry Lot 23 0 0 0 0 0

Horses Pasture 0 0 0 0 0 0

Mules and Asses Dry Lot 23 0 0 0 0 0

Mules and Asses Pasture 0 0 0 0 0 0

Poultry Anaerobic Lagoon 54 0.2 0.8 0.7 0.4 0.9

Poultry Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9

Poultry Pasture 0 0 0 0 0 0

Poultry Poultry with bedding 26 0 0 0 0 0

Poultry Poultry without bedding 34 0 0 0 0 0

Poultry Solid Storage 8 0 0 0 0 0

Sheep Dry Lot 23 1.1 3.9 3.6 1.9 4.3

Sheep Pasture 0 0 0 0 0 0

Swine Anaerobic Lagoon 58 0.2 0.8 0.7 0.4 0.9

Swine Deep Pit 34 0 0 0 0 0

Swine Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9

Swine Pasture 0 0 0 0 0 0

Swine Solid Storage 45 0 0 0 0 0

Source: EPA 2002b, 2005.

a Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff.
A-281

Table A- 212: Methane Emissions from Livestock Manure Management (Gg)a

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Dairy Cattle 599 615 598 623 663 693 702 734 781 854 900 960 997 1047 1000 1069 1101 1224 1238 1233 1239 1262 1291

Dairy Cows 592 608 591 616 656 686 695 727 774 846 893 952 990 1039 993 1062 1094 1216 1230 1225 1231 1254 1283

Dairy Heifer 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 9

Swine 624 676 639 680 741 764 730 783 892 849 834 854 879 860 857 914 901 982 938 896 948 941 957

Market Swine 484 524 500 534 585 608 582 626 720 692 680 696 720 706 706 753 741 814 780 748 792 787 801

Market <50 lbs. 102 110 104 109 119 121 116 125 141 133 131 134 137 135 135 142 141 155 109 103 110 110 112

Market 50-119 lbs. 101 111 105 110 120 124 117 127 144 138 136 138 144 140 141 150 148 163 174 167 177 176 180

Market 120-179 lbs. 136 147 140 151 164 170 163 175 201 193 189 192 199 196 196 210 206 227 229 218 231 228 233

Market >180 lbs. 145 156 152 165 182 194 185 198 235 229 225 232 240 234 234 251 246 269 268 259 273 272 276

Breeding Swine 140 152 139 146 156 155 148 157 172 157 155 158 158 154 151 161 160 168 157 148 156 154 156

Beef Cattle 128 128 131 131 137 141 139 136 139 139 133 136 133 133 131 135 138 136 132 131 134 132 128

Feedlot Steers 14 14 14 13 14 14 14 13 13 14 15 15 15 16 15 15 16 16 16 16 16 17 16

Feedlot Heifers 7 7 7 7 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9

NOF Bulls 5 5 5 5 5 5 5 5 5 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 9 9 10 10 9 9 9 9 9

NOF Heifers 12 12 13 14 14 15 15 14 15 14 13 13 13 13 12 13 13 13 13 13 13 12 12

NOF Steers 12 12 13 14 13 14 14 13 13 12 11 11 11 10 10 10 10 11 10 11 11 10 9

NOF Cows 69 69 70 71 74 76 76 74 76 76 71 73 71 71 71 73 74 73 70 69 71 70 68

Sheep 7 7 7 6 6 5 5 5 5 4 4 4 4 4 3 3 3 3 3 3 3 3 3

Goats 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Poultry 131 132 127 131 131 128 126 128 130 126 127 131 129 130 129 129 131 134 129 128 129 127 127

Hens >1 yr. 73 72 70 73 72 69 68 67 70 66 67 70 67 68 66 66 66 67 64 64 64 64 64

Total Pullets 25 26 23 23 23 22 21 23 23 21 22 22 22 22 23 22 23 25 23 23 24 23 24

Chickens 4 4 4 4 4 4 3 3 4 4 3 3 4 4 3 3 3 3 3 4 3 3 3

Broilers 19 20 20 21 22 23 24 25 26 27 28 28 29 29 30 31 32 32 33 31 31 31 31

Turkeys 10 10 10 10 9 9 9 9 8 7 7 7 7 7 7 7 7 7 7 6 6 6 6

Horses 9 9 9 9 10 11 11 12 13 13 13 13 13 13 12 12 12 11 10 11 11 11 12

Mules and Asses + + + + + + + + + + + + + + + + + + + + + + +

American Bison + + + + + + + + + + + + + + + + + + + + + + +

a Accounts for CH4 reductions due to capture and destruction of CH4 at facilities using anaerobic digesters.

+ Emission estimate is less than 0.5 Gg.
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 213: 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 2004 2005 2006 2007 2008 2009 2010 2011 2012

Dairy Cattle 17.1 17.0 17.0 17.3 17.4 17.7 17.7 17.9 18.0 17.6 17.9 18.2 18.5 18.7 17.8 18.3 18.9 18.9 18.6 18.8 18.9 19.1 19.4

 Dairy Cows 10.0 10.1 10.0 10.0 10.1 10.3 10.3 10.4 10.5 10.2 10.5 10.5 10.6 10.8 10.3 10.5 10.8 10.8 10.6 10.8 10.6 10.8 11.0

 Dairy Heifer 7.0 6.9 7.1 7.2 7.3 7.4 7.4 7.4 7.5 7.4 7.5 7.7 7.8 7.9 7.5 7.8 8.1 8.1 8.0 8.0 8.2 8.3 8.4

Swine 4.0 4.2 4.3 4.4 4.6 4.5 4.4 4.7 5.1 5.0 5.0 5.1 5.3 5.4 5.6 5.7 5.9 6.3 6.5 6.3 6.2 6.3 6.4

 Market Swine 3.0 3.1 3.3 3.3 3.5 3.5 3.3 3.6 4.0 4.1 4.1 4.2 4.4 4.5 4.7 4.9 5.0 5.5 5.6 5.5 5.4 5.5 5.6

 Market <50 lbs. 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.7 0.7 0.7 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.1 0.8 0.8 0.8 0.8 0.8

 Market 50-119 lbs. 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.9 0.9 0.9 1.0 1.0 1.1 1.3 1.2 1.2 1.2 1.3

 Market 120-179 lbs. 0.9 0.9 0.9 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.2 1.2 1.3 1.3 1.4 1.4 1.5 1.6 1.6 1.6 1.6 1.6

 Market >180 lbs. 0.9 1.0 1.0 1.0 1.1 1.1 1.1 1.2 1.3 1.3 1.3 1.4 1.5 1.5 1.6 1.6 1.6 1.8 1.9 1.9 1.8 1.9 1.9

 Breeding Swine 1.0 1.1 1.1 1.1 1.1 1.1 1.0 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8

Beef Cattle 19.8 20.3 20.1 19.1 20.9 21.8 21.4 21.5 21.6 24.0 25.0 24.1 24.8 25.0 23.6 24.0 25.7 25.6 25.2 25.1 25.3 25.8 25.6

 Feedlot Steers 13.4 13.6 13.5 12.8 13.9 14.4 14.0 13.9 14.1 15.5 16.1 15.4 16.0 16.3 15.3 15.5 16.7 16.7 16.5 16.5 16.6 16.8 16.6

 Feedlot Heifers 6.4 6.6 6.6 6.3 7.0 7.4 7.4 7.6 7.6 8.5 8.9 8.6 8.7 8.8 8.4 8.5 9.0 8.9 8.7 8.6 8.7 9.0 9.0

Sheep 0.4 0.4 0.4 0.4 0.6 0.7 0.8 0.9 0.9 1.0 1.1 1.2 1.2 1.2 1.1 1.2 1.2 1.2 1.2 1.1 1.1 1.1 1.1

Goats 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1

Poultry 4.7 4.8 4.9 5.0 5.1 5.1 5.3 5.3 5.3 5.3 5.3 5.3 5.4 5.3 5.4 5.4 5.4 5.4 5.4 5.2 5.2 5.2 5.1

 Hens >1 yr. 1.0 1.0 1.0 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.2 1.3 1.3 1.3 1.3 1.3 1.3 1.3 1.3

 Total Pullets 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 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4

 Chickens + + + + + + + + + + + + + + + + + + + + + + +

 Broilers 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.8 2.9 2.9 2.9 2.9 3.0 2.9 2.9 3.0 2.9 2.9 2.9 2.7 2.8 2.8 2.7

 Turkeys 1.2 1.2 1.2 1.1 1.1 1.1 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.8 0.8 0.8 0.8 0.8 0.7 0.7 0.7 0.7

Horses 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 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

Mules and Asses + + + + + + + + + + + + + + + + + + + + + + +

American Bison NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA

+ Emission estimate is less than 0.5 Gg.

Note: American bison are maintained entirely on unmanaged WMS; there are no American bison N2O emissions from managed systems.
A-283

Table A- 214: Methane Emissions by State from Livestock Manure Management for 2012(Gg)a

State

Beef on 

Feedlots

Beef Not 

on Feedb Dairy Cow

Dairy 

Heifer

Swine—

Market

Swine—

Breeding Layer Broiler Turkey Sheep Goats Horses

Mules and 

Asses

American 

Bison

Alabama 0.0336 2.4197 0.5926 0.0161 2.0727 0.5977 8.7998 3.6453 0.0267 0.0085 0.0302 0.3572 0.0203 0.0006

Alaska 0.0001 0.0212 0.0281 0.0003 0.0020 0.0010 0.1918 + 0.0266 0.0057 0.0001 0.0057 0.0001 0.0038

Arizona 0.6397 1.0594 52.3809 0.1643 2.7052 0.7384 0.6810 + 0.0267 0.0987 0.0158 0.2977 0.0039 0.0011

Arkansas 0.0026 3.3507 0.3986 0.0133 0.6992 2.1303 0.5260 3.5490 0.7252 0.0085 0.0190 0.2814 0.0139 0.0016

California 1.3823 4.0028 407.5697 2.1559 1.5979 0.1513 4.3435 0.1939 0.3876 0.4017 0.0490 0.7540 0.0133 0.0101

Colorado 1.7186 2.8226 28.3952 0.1119 4.8006 2.7184 3.5109 + 0.0266 0.2161 0.0122 0.2869 0.0060 0.0412

Connecticut 0.0006 0.0192 1.0254 0.0161 0.0040 0.0027 0.2534 + 0.0266 0.0035 0.0011 0.0296 0.0005 0.0001

Delaware 0.0007 0.0112 0.3107 0.0059 0.0280 0.0191 0.0773 0.7675 0.0266 0.0057 0.0009 0.0098 0.0001 +

Florida 0.0187 3.3388 21.0759 0.1008 0.0581 0.0376 6.2130 0.2161 0.0267 0.0085 0.0216 0.4643 0.0122 0.0047

Georgia 0.0246 1.8624 7.4419 0.0841 2.2272 0.8186 14.7767 4.9460 0.0267 0.0085 0.0315 0.2609 0.0171 0.0017

Hawaii 0.0048 0.3194 0.3757 0.0029 0.1056 0.0639 0.2996 + 0.0267 0.0085 0.0034 0.0280 0.0005 0.0006

Idaho 0.3558 1.6026 121.2554 0.4488 0.1854 0.0941 0.6031 + 0.0266 0.1128 0.0044 0.1420 0.0047 +

Illinois 0.2545 0.9485 8.8046 0.0759 43.2643 10.7696 0.2349 0.1932 0.0266 0.0268 0.0084 0.2175 0.0053 0.0028

Indiana 0.1772 0.6055 14.2386 0.1118 35.9163 5.9036 0.8871 0.1932 0.4113 0.0258 0.0118 0.1400 0.0050 0.0033

Iowa 2.0677 3.0499 20.7412 0.2534 298.3233 31.2223 1.6782 0.1932 0.0266 0.0916 0.0140 0.1465 0.0047 0.0056

Kansas 3.9061 5.0245 21.7396 0.1402 21.5870 4.0160 0.0441 + 0.0266 0.0329 0.0124 0.2461 0.0056 0.0210

Kentucky 0.0180 2.5696 1.5901 0.0797 4.7269 1.1379 0.5523 1.1204 0.0266 0.0188 0.0245 0.4414 0.0146 0.0003

Louisiana 0.0175 1.6358 0.7189 0.0167 0.0127 0.0099 2.1974 0.1939 0.0267 0.0085 0.0081 0.2407 0.0095 0.0005

Maine 0.0018 0.0371 1.3859 0.0263 0.0096 0.0069 0.2850 + 0.0266 0.0035 0.0015 0.0254 0.0005 0.0008

Maryland 0.0181 0.1329 2.6381 0.0493 0.1784 0.0565 0.2867 1.1005 0.0266 0.0057 0.0042 0.0779 0.0014 0.0024

Massachusetts 0.0007 0.0245 0.4907 0.0100 0.0428 0.0093 0.0120 + 0.0266 0.0035 0.0021 0.0562 0.0013 0.0001

Michigan 0.2390 0.4683 46.7963 0.2481 8.8211 2.0014 0.7397 0.1932 0.0266 0.0371 0.0070 0.2132 0.0054 0.0059

Minnesota 0.4858 1.3667 32.0661 0.4477 66.0579 10.9262 0.3392 0.1647 1.1466 0.0705 0.0092 0.1917 0.0046 0.0004

Mississippi 0.0338 1.8562 0.6039 0.0190 5.8406 1.9556 7.6372 2.7289 0.0267 0.0085 0.0115 0.2098 0.0134 +

Missouri 0.0878 4.5551 6.1014 0.0656 23.2430 7.8459 0.2893 0.9832 0.4362 0.0390 0.0241 0.3438 0.0145 0.0038

Montana 0.0465 3.5868 1.7686 0.0119 1.1836 0.3580 0.3657 + 0.0266 0.1057 0.0031 0.2543 0.0042 0.0186

Nebraska 4.1448 7.0122 7.5325 0.0239 26.3869 7.7471 0.5646 0.1932 0.0266 0.0362 0.0086 0.1584 0.0031 0.0468

Nevada 0.0107 0.5581 6.6532 0.0150 0.0172 0.0042 0.0223 + 0.0266 0.0329 0.0030 0.0453 0.0005 0.0004

New Hampshire 0.0004 0.0160 0.6541 0.0108 0.0111 0.0052 0.0705 + 0.0266 0.0035 0.0010 0.0260 0.0009 0.0001

New Jersey 0.0008 0.0235 0.2943 0.0070 0.0600 0.0160 0.0758 + 0.0266 0.0057 0.0027 0.0731 0.0014 0.0003

New Mexico 0.0406 1.1678 82.3978 0.1855 0.0003 0.0003 0.6441 + 0.0266 0.0470 0.0089 0.1326 0.0022 0.0074

New York 0.0421 0.4686 31.8012 0.5337 0.5247 0.1025 0.5093 0.1932 0.0266 0.0291 0.0100 0.2075 0.0032 0.0035

North Carolina 0.0149 0.9605 2.7022 0.0522 141.3090 32.2132 11.0142 2.9047 0.9003 0.0183 0.0369 0.3041 0.0167 0.0013

North Dakota 0.0922 2.1982 1.2472 0.0156 0.7981 0.6028 0.0413 + 0.0266 0.0343 0.0011 0.1009 0.0011 0.0018

Ohio 0.2825 0.8934 20.5114 0.1915 18.4559 3.3987 0.9321 0.2288 0.1371 0.0592 0.0174 0.2279 0.0072 +

Oklahoma 0.8635 7.2239 9.6670 0.0459 35.0261 15.7762 3.4508 0.7693 0.0267 0.0493 0.0470 0.5946 0.0252 0.0202

Oregon 0.1238 1.4227 17.4305 0.1266 0.0458 0.0290 0.8411 0.1932 0.0266 0.0940 0.0095 0.1893 0.0050 0.0010
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

State

Beef on 

Feedlots

Beef Not 

on Feedb Dairy Cow

Dairy 

Heifer

Swine—

Market

Swine—

Breeding Layer Broiler Turkey Sheep Goats Horses

Mules and 

Asses

American 

Bison

Pennsylvania 0.1306 0.7368 17.4491 0.5388 10.7082 2.0211 0.7737 0.5593 0.1745 0.0418 0.0148 0.2612 0.0119 0.0042

Rhode Island 0.0001 0.0036 0.0358 0.0009 0.0039 0.0046 0.0741 + 0.0266 0.0035 0.0002 0.0110 0.0001 +

South Carolina 0.0097 0.7102 1.2038 0.0161 4.8058 0.3713 4.9170 0.8093 0.3001 0.0085 0.0163 0.1506 0.0089 0.0004

South Dakota 0.5241 4.2889 11.6592 0.0552 10.5354 3.4490 0.1377 + 0.1147 0.1339 0.0027 0.1553 0.0021 0.0721

Tennessee 0.0167 2.3424 1.4279 0.0533 2.2084 0.4569 0.2311 0.6067 0.0266 0.0164 0.0327 0.2965 0.0227 0.0017

Texas 6.6601 19.1277 102.5671 0.4834 14.0303 3.0580 4.5769 2.1884 0.0267 0.4722 0.4273 1.6602 0.1112 0.0194

Utah 0.0383 0.8478 16.9664 0.0793 5.6764 1.4043 3.2231 + 0.1022 0.1433 0.0043 0.1275 0.0025 0.0045

Vermont 0.0019 0.0628 6.0471 0.0889 0.0131 0.0045 0.0183 + 0.0266 0.0035 0.0016 0.0336 0.0011 0.0003

Virginia 0.0399 1.7150 2.9815 0.0696 3.9370 0.1651 0.3398 0.8706 0.4237 0.0395 0.0158 0.2177 0.0082 0.0021

Washington 0.4017 0.7238 45.8262 0.2024 0.1247 0.0626 1.2451 0.1932 0.0266 0.0244 0.0082 0.2268 0.0046 0.0029

West Virginia 0.0087 0.5604 0.3619 0.0086 0.0235 0.0185 0.1649 0.3403 0.0823 0.0155 0.0070 0.0955 0.0032 0.0005

Wisconsin 0.3756 1.1479 93.7919 1.0791 2.2459 0.8086 0.3269 0.1872 0.0266 0.0395 0.0140 0.3025 0.0070 0.0073

Wyoming 0.1082 2.0454 0.8400 0.0074 0.2272 0.3644 0.0087 + 0.0266 0.1739 0.0021 0.2147 0.0028 0.0277

+ 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.

b Beef Not on Feed includes calves.
A-285

Table A- 215: Nitrous Oxide Emissions by State from Livestock Manure Management for 2012(Gg)

State

Beef 

FeedlotHeifer

Beef 

FeedlotSteers Dairy CowDairy 

Heifer

SwineMarketSwineBreeding Layer Broiler Turkey Sheep Goats HorsesMules and 

Asses

Alabama 0.0078 0.0144 0.0041 0.0053 0.0104 0.0022 0.0628 0.3225 0.0031 0.0046 0.0024 0.0123 0.0007

Alaska + 0.0001 0.0006 0.0004 + + 0.0033 + 0.0031 0.0015 + 0.0003 +

Arizona 0.1737 0.3229 0.2326 0.1465 0.0125 0.0026 0.0035 + 0.0031 0.0154 0.0012 0.0102 0.0001

Arkansas 0.0007 0.0012 0.0036 0.0033 0.0038 0.0085 0.0743 0.3140 0.0840 0.0040 0.0015 0.0097 0.0005

California 0.3213 0.5971 2.1362 1.7345 0.0089 0.0006 0.0906 0.0172 0.0449 0.0710 0.0039 0.0259 0.0005

Colorado 0.7310 1.3583 0.1780 0.1711 0.0507 0.0212 0.0210 + 0.0031 0.0507 0.0014 0.0148 0.0003

Connecticut 0.0002 0.0004 0.0148 0.0114 + + 0.0108 + 0.0031 0.0029 0.0001 0.0015 +

Delaware 0.0002 0.0004 0.0039 0.0039 0.0002 0.0001 0.0031 0.0681 0.0031 0.0046 0.0001 0.0005 +

Florida 0.0040 0.0074 0.1001 0.0501 0.0003 0.0001 0.0414 0.0191 0.0031 0.0046 0.0017 0.0160 0.0004

Georgia 0.0057 0.0106 0.0486 0.0305 0.0111 0.0030 0.1042 0.4376 0.0031 0.0046 0.0025 0.0090 0.0006

Hawaii 0.0010 0.0019 0.0019 0.0023 0.0006 0.0002 0.0033 + 0.0031 0.0015 0.0003 0.0010 +

Idaho 0.1534 0.2845 0.7848 0.6894 0.0021 0.0008 0.0035 + 0.0031 0.0265 0.0005 0.0073 0.0003

Illinois 0.1017 0.1887 0.1418 0.0952 0.3919 0.0713 0.0168 0.0172 0.0031 0.0187 0.0010 0.0112 0.0003

Indiana 0.0711 0.1315 0.2426 0.1281 0.3354 0.0403 0.1232 0.0172 0.0478 0.0180 0.0014 0.0072 0.0003

Iowa 0.8372 1.5524 0.2943 0.3101 1.8140 0.1390 0.2330 0.0172 0.0031 0.0640 0.0017 0.0075 0.0003

Kansas 1.5100 2.8022 0.1780 0.1816 0.1754 0.0242 0.0031 + 0.0031 0.0230 0.0015 0.0127 0.0003

Kentucky 0.0064 0.0119 0.0297 0.0257 0.0268 0.0047 0.0229 0.0995 0.0031 0.0152 0.0029 0.0228 0.0008

Louisiana 0.0038 0.0071 0.0062 0.0036 0.0001 + 0.0112 0.0172 0.0031 0.0040 0.0006 0.0083 0.0003

Maine 0.0007 0.0013 0.0240 0.0180 0.0001 0.0001 0.0129 + 0.0031 0.0029 0.0002 0.0013 +

Maryland 0.0064 0.0119 0.0403 0.0321 0.0015 0.0003 0.0117 0.0977 0.0031 0.0046 0.0005 0.0040 0.0001

Massachusetts 0.0002 0.0005 0.0092 0.0066 0.0004 0.0001 0.0005 + 0.0031 0.0029 0.0002 0.0029 0.0001

Michigan 0.0981 0.1817 0.5678 0.3330 0.0905 0.0153 0.0547 0.0172 0.0031 0.0259 0.0008 0.0110 0.0003

Minnesota 0.1984 0.3680 0.6451 0.5568 0.6568 0.0801 0.0471 0.0146 0.1333 0.0492 0.0011 0.0099 0.0002

Mississippi 0.0077 0.0143 0.0057 0.0046 0.0291 0.0070 0.0397 0.2414 0.0031 0.0046 0.0009 0.0072 0.0005

Missouri 0.0342 0.0637 0.1082 0.0718 0.2076 0.0510 0.0403 0.0873 0.0507 0.0272 0.0029 0.0177 0.0008

Montana 0.0200 0.0371 0.0189 0.0173 0.0139 0.0031 0.0022 + 0.0031 0.0248 0.0004 0.0131 0.0002

Nebraska 1.6696 3.1011 0.0815 0.0315 0.2539 0.0548 0.0409 0.0172 0.0031 0.0253 0.0010 0.0082 0.0002

Nevada 0.0046 0.0085 0.0391 0.0231 0.0002 + 0.0031 + 0.0031 0.0077 0.0004 0.0023 +

New Hampshire 0.0002 0.0003 0.0109 0.0075 0.0001 + 0.0031 + 0.0031 0.0029 0.0001 0.0013 +

New Jersey 0.0003 0.0005 0.0056 0.0044 0.0005 0.0001 0.0031 + 0.0031 0.0046 0.0003 0.0038 0.0001

New Mexico 0.0168 0.0312 0.4155 0.2511 + + 0.0035 + 0.0031 0.0110 0.0011 0.0068 0.0001

New York 0.0160 0.0297 0.4926 0.3598 0.0053 0.0008 0.0220 0.0172 0.0031 0.0236 0.0012 0.0107 0.0002

North Carolina 0.0053 0.0099 0.0267 0.0150 0.7058 0.1187 0.0790 0.2570 0.1043 0.0099 0.0029 0.0104 0.0006

North Dakota 0.0381 0.0708 0.0233 0.0192 0.0086 0.0048 0.0031 + 0.0031 0.0239 0.0001 0.0052 0.0001

Ohio 0.1130 0.2099 0.3585 0.2187 0.1735 0.0234 0.1293 0.0203 0.0159 0.0477 0.0021 0.0117 0.0004

Oklahoma 0.2406 0.4466 0.0627 0.0434 0.1695 0.0557 0.0178 0.0681 0.0031 0.0229 0.0037 0.0204 0.0009

Oregon 0.0456 0.0845 0.1379 0.1400 0.0005 0.0002 0.0109 0.0172 0.0031 0.0249 0.0011 0.0098 0.0003

Pennsylvania 0.0480 0.0891 0.3982 0.3258 0.0975 0.0138 0.1075 0.0496 0.0203 0.0339 0.0018 0.0135 0.0006

Rhode Island + 0.0001 0.0008 0.0005 + + 0.0031 + 0.0031 0.0029 + 0.0006 +

South Carolina 0.0023 0.0042 0.0080 0.0043 0.0234 0.0013 0.0252 0.0716 0.0348 0.0046 0.0013 0.0052 0.0003
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

+ Emission estimate is less than 0.00005 Gg.

South Dakota 0.2145 0.3967 0.1300 0.0711 0.1031 0.0248 0.0101 + 0.0133 0.0935 0.0003 0.0080 0.0001

Tennessee 0.0059 0.0109 0.0209 0.0185 0.0126 0.0019 0.0097 0.0539 0.0031 0.0133 0.0039 0.0153 0.0012

Texas 1.8540 3.4433 0.5287 0.4487 0.0751 0.0120 0.0919 0.1936 0.0031 0.0739 0.0337 0.0570 0.0039

Utah 0.0163 0.0303 0.1216 0.1203 0.0584 0.0113 0.0188 + 0.0119 0.0336 0.0005 0.0066 0.0001

Vermont 0.0007 0.0014 0.1029 0.0617 0.0001 + 0.0008 + 0.0031 0.0029 0.0002 0.0017 0.0001

Virginia 0.0143 0.0266 0.0432 0.0260 0.0212 0.0007 0.0142 0.0773 0.0493 0.0320 0.0019 0.0112 0.0004

Washington 0.1478 0.2748 0.3294 0.2340 0.0014 0.0005 0.0298 0.0172 0.0031 0.0065 0.0010 0.0117 0.0002

West Virginia 0.0032 0.0059 0.0069 0.0056 0.0002 0.0001 0.0071 0.0302 0.0096 0.0126 0.0008 0.0049 0.0002

Wisconsin 0.1539 0.2855 1.7964 1.3101 0.0222 0.0059 0.0242 0.0166 0.0031 0.0276 0.0017 0.0156 0.0004

Wyoming 0.0462 0.0860 0.0075 0.0094 0.0041 0.0048 0.0001 + 0.0031 0.0408 0.0002 0.0111 0.0001
A-287

3.12.Methodology for Estimating N2O Emissions and Soil Organic C Stock Changes 

from Agricultural Soil Management (Cropland and Grassland)

Nitrous oxide (N2O) 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. Management also influences soil 

organic C stocks in agricultural soils by modifying the natural processes of photosynthesis (i.e., crop and forage 

production) and microbial decomposition. This sub-annex describes the methodologies used to calculate N2O 

emissions from agricultural soil management74 and annual carbon (C) stock changes from mineral and organic soils 

classified as Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and 

Land Converted to Grassland.

75

 This annex provides the underlying methodologies for both N2O emissions from 

agricultural soil management and soil organic C stock change from mineral and organic soils. There is considerable 

overlap in the methods and data sets used for these source categories, and the majority of emission are estimated 

with the same inventory analysis using the DAYCENT biogeochemical simulation model. 

A combination of Tier 1, 2 and 3 approaches is used to estimate direct and indirect N2O emissions, in 

addition to C stock changes in agricultural soils. More specifically, the methodologies used to estimate soil N2O 

emissions include: 

1) Tier 3 method to estimate direct emissions from mineral soils for alfalfa hay, barley, corn, cotton, dry 

beans, grass hay, grass-clover hay, oats, onions, peanuts, potatoes, rice, sorghum, soybeans, sugar beets, 

sunflowers, tomatoes, and wheat, as well as non-federal grasslands and land use change between cropland

(with the crops listed above) and grassland, using the DAYCENT biogeochemical simulation model; 

2) Tier 1 method to estimate direct N2O emissions from mineral soils for some vegetables, tobacco, 

perennial/horticultural crops (i.e., all crops that are not listed in Item 1 above), and crops that are rotated 

with these crops, in addition to federal grasslands (i.e., not simulated by DAYCENT); 

3) Tier 1 method to estimate direct N2O emissions due to drainage of organic soils in croplands and 

grasslands;

4) A combination of the Tier 3 and 1 methods to estimate indirect N2O emissions associated with management 

of Tier 3 crops (i.e., simulated by DAYCENT; see list above), non-federal grasslands and land use change 

between cropland and grassland; and 

5) Tier 1 method to estimate indirect emissions from some vegetables, tobacco, perennial/horticultural crops, 

and crops that are rotated with these crops, in addition to federal grasslands and all other land uses. 

The methodologies used to estimate soil organic C stock changes include: 

1) Tier 3 method to estimate soil organic C stock changes in mineral soils for crops (See crop list in Item 1 for 

N2O emissions), non-federal grasslands and land use change between grasslands and croplands, using the 

DAYCENT biogeochemical simulation model;

2) Tier 2 methods with country-specific stock change factors for estimating mineral soil organic C stock 

changes for crop rotations and land use changes to cropland and grassland (other than the conversions 

between cropland and grassland) that were not simulated with DAYCENT;

3) Tier 2 methods with country-specific emission factors for estimating losses of C from organic soils that are 

partly or completely drained for agricultural production; and 

4) 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 2007.

 

74 Direct Soil N2O methods from forestlands and settlements are described elsewhere in Forestland Remaining Forestland and 

Settlements Remaining Settlements.

75 Soil C stock change methods for forestland are described elsewhere in the Forestland Remaining Forestland section.
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

As described above, the Inventory uses a Tier 3 approach to estimate direct soil N2O emissions and 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 sub-county scales, rather than a broad climate region classification, 

enabling quantification of inter-annual variability in N2O emissions and C stock changes at finer spatial 

scales;

 The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C 

and N dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology;

 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 N2O emissions and C stock changes are estimated on a more continuous, daily basis as a function of 

the interaction of climate, soil, and land management, compared with the linear rate changes that are 

estimated with the Tier 1 and 2 methods.

The DAYCENT process-based simulation model (daily time-step version of the Century model) has been 

selected for the Tier 3 approach based on several criteria:

 The model has been developed in the United States and extensively tested and verified for U.S. conditions 

(e.g., Parton et al. 1987, 1993). In addition, the model has been widely used by researchers and agencies in 

many other parts of the world for simulating soil C dynamics at local, regional and national scales (e.g., 

Brazil, Canada, India, Jordan, Kenya, Mexico), and soil N2O emissions (e.g., Canada, China, Ireland, New 

Zealand) (Abdalla et al. 2010, Li et al., Smith et al. 2008, Stehfest and Muller 2004) . 

 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 is designed to simulate management practices that influence soil C dynamics and direct N2O 

emissions, with the exception of cultivated organic soils; cobbly, gravelly, or shaley soils; and a few crops 

that have not been parameterized for DAYCENT simulations (e.g., some vegetables, tobacco, 

perennial/horticultural crops, and crops that are rotated with these crops). For these latter cases, an IPCC 

Tier 2 method has been used for soil C stock changes and IPCC Tier 1 method for N2O emissions. The 

model can also be used estimate the amount of N leaching and runoff, as well as volatilization of N, which 

is subject to indirect N2O emissions. 

 Much of the data needed for the model is available from existing national databases. The exceptions are 

CRP enrollment after 2007, management of federal grasslands, and sewage sludge amendments to soils, 

which are not known at a sufficient resolution to use the Tier 3 model. Soil N2O emissions and C stock 

changes associated with these practices are addressed with a Tier 1 and 2 method, respectively.

Overall, the Tier 3 approach is used to estimate approximately 82 to 88 percent of direct soil N2O emissions 

and 85 to 87 percent of the land area associated with estimation of soil organic C stock changes under agricultural 

management in the United States.

Tier 3 Method Description and Model Evaluation

The DAYCENT biogeochemical model (Parton et al. 1998; Del Grosso et al. 2001, 2011) simulates 

biogeochemical C and N fluxes between the atmosphere, vegetation, and soil; and provides a more complete 

estimation of soil C stock changes and N2O emissions than IPCC Tier 1/2 methods by more thoroughly accounting 

for the influence of environmental conditions including soil characteristics and weather patterns, specific crop and 

forage qualities that influence the C and N cycle, and management practices. The DAYCENT model utilizes the 

soil C modeling framework developed in the Century model (Parton et al. 1987, 1988, 1994; Metherell et al. 1993), 

but has been refined to simulate dynamics at a daily time-step. Carbon and N dynamics are linked in plant-soil 

systems through biogeochemical processes of microbial decomposition and plant production (McGill and Cole 

1981). Coupling the two source categories (i.e., agricultural soil C and N2O) in a single inventory analysis ensures 

that there is a consistent treatment of the processes and interactions between C and N cycling in soils. For example, 

plant growth is controlled by nutrient availability, water, and temperature stress. Plant growth along with the 

residue management determines the C inputs to soils, which influence the C stock changes, and removal of mineral 

N from the soil with plant growth influences the amount of N that can 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 
A-289

increasing decomposition can lead to a reduction in soil organic C stocks due to microbial respiration, and greater 

N2O emissions by enhancing mineral N availability in soils.

Key processes simulated by DAYCENT include plant production, organic matter formation and 

decomposition, soil water and temperature regimes by layer, in addition to nitrification and denitrification processes 

(Figure A-7). The plant-growth submodel simulates C assimilation through photosynthesis; N uptake; dry matter 

production; partitioning of C within the crop or forage; senescence; and mortality. The primary function of the 

growth submodel is to estimate the amount, type, and timing of organic matter inputs to soil, and to represent the 

influence of the plant on soil water, temperature, and N balance. Yield and removal of harvested biomass are also 

simulated. Separate submodels are designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and 

woody vegetation (i.e., trees and scrub). Maximum daily net primary production (NPP) is estimated using the 

NASA-CASA production algorithm (Potter et al.1993, 2007) and MODIS Enhanced Vegetation Index (EVI) 

products, MOD13Q1 and MYD13Q1, or an approximation of EVI data derived from the MODIS products (Gurung 

et al. 2009). The NASA-CASA production algorithm is only used in the central United States for the following 

major crops: corn, soybeans, sorghum, cotton and wheat.76 Other regions and crops are simulated with a single 

value for the maximum daily net primary production (NPP), instead of the more dynamic NASA-CASA algorithm. 

The maximum daily NPP rate is modified by air temperature and available water to capture temperature and 

moisture stress. If the NASA-CASA algorithm is not used in the simulation, then production is further subject to 

nutrient limitations (i.e., nitrogen). Model evaluation has shown that the NASA-CASA algorithm improves the 

precision of NPP estimates using the EVI products to inform the production model. The r2is 83 percent for the 

NASA-CASA algorithm and 64 percent for the single parameter value approach).

 

Figure A-7: DAYCENT Model Flow Diagram 

 

76 It is a planned improvement to estimate NPP for additional crops and grass forage with the NASA-CASA method

in the future.
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-8: Modeled versus measured net primary production (g C m-2) 

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.

Dynamics of soil organic C and N (Figure A-7) are simulated for the surface and belowground litter pools 

and soil organic matter in 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 (metabolic and structural) and three soil 

organic matter (SOM) pools (active, slow, and passive). The metabolic litter pool represents the easily 

decomposable constituents of plant residues, while the structural litter pool is composed of more recalcitrant, lignocellulose plant materials. The three SOM pools represent a gradient in decomposability, from active SOM 

(representing microbial biomass and associated metabolites) having a rapid turnover (months to years), to passive 

SOM (representing highly processed, humified, condensed decomposition products), which is highly recalcitrant, 

with mean residence times on the order of several hundred years. The slow pool represents decomposition products 

of intermediate stability, having a mean residence time on the order of decades and is the fraction that tends to 

change the most 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 decomposition, leading to enhanced SOM stabilization in finely textured soils. Soil temperature and 

moisture, tillage disturbance, aeration, and other factors influence decomposition and loss of C from the soil organic 

matter pools.

Soil mineral N dynamics are modeled based on N inputs from fertilizer inputs (synthetic and organic), 

residue N inputs, soil organic matter mineralization, symbiotic and asymbiotic N fixation. Mineral N is available for 

plant and microbial uptake, and is largely controlled by the specified stoichiometric limits for these organisms (i.e., 

C:N ratios). Mineral and organic N losses are simulated with leaching and runoff, and nitrogen can be volatilized 

and lost from the soil during a variety of processes including nitrification and denitrification. N2O emissions from 
A-291

denitrification are a function of soil NO3

-

concentration, water filled pore space (WFPS), heterotrophic (i.e., 

microbial) respiration, and texture. Nitrification is controlled by soil ammonium (NH4

+

) concentration, water filled 

pore space, temperature, and pH (See Box 2 for more information).

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 day 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 with the model algorithms. Userspecified 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, 1998), Del Grosso et al. (2001, 2011) 

and Metherell et al. (1993), and archived copies of the model source code are available.

[BEGIN TEXT BOX]

Box 2. 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, where nitrification mostly occurs, while denitrification is calculated for the entire soil profile 

accounting for denitrification near the surface and subsurface as nitrate leaches through the profile. The equations 

and key parameters controlling N2O emissions from nitrification and denitrification are described below. 

Nitrification is controlled by soil ammonium (NH4

+

) concentration, 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-9a)

F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A-9b)

F(pH) = the effect of soil pH on nitrification (Figure A-9c)

The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O.

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. These rates vary for particular levels 

of electron acceptor and C substrate, and account for limitations of oxygen availability to estimate daily 

denitrification rates according to the following equation: 

Den = min[F(CO2), F(NO3)] × F(WFPS)

where,

Den = the soil denitrification rate (g N/g soil/day)
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

F(CO2) = a function relating N gas flux to soil respiration (Figure A-10a)

F(NO3) = a function relating N gas flux to nitrate levels (Figure A-10b)

F(WFPS) = a dimensionless multiplier (Figure A-10c). 

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. In technical terms, the inflection point is the domain 

where either F(WFPS) is not differentiable or its derivative is 0. In this case, the inflection point can be 

interpreted as the WFPS value at which denitrification reaches half of its maximum rate. 

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-10c). 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-293

Figure A-9: Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates 
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-10: Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on 

Denitrification Rates 

Comparison of model results and plot level data show that DAYCENT reliably simulates soil organic

matter levels (Ogle et al. 2007). The model was tested and shown to capture the general trends in C storage across 

approximately 870 field plots from 47 experimental sites (Figure A-11). Some biases and imprecision occur in 

predictions of soil organic C, which is reflected in the uncertainty associated with DAYCENT model results. 

Regardless, the Tier 3 approach has considerably less uncertainty than Tier 1 and 2 methods (Del Grosso et al., 201; 

Figure A-11). 

Similarly, DAYCENT model results have been compared to trace gas N2O fluxes for a number of native 

and managed systems (Del Grosso et al. 2001, 2005, 2010) (Figure A-12). In general, the model simulates accurate 
A-295

emissions, but some bias and imprecision does occur in predictions, which is reflected in the uncertainty associated 

with DAYCENT model results. Comparisons with measured data showed that DAYCENT estimated N2O emissions 

more accurately and precisely than the IPCC Tier 1 methodology (See Figure 6-7: Comparison of Measured 

Emissions at Field Sites and Modeled Emissions Using the DAYCENT Simulation Model and IPCC Tier 1 

Approach in the main chapter text). 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). Volatilization of N gases that contribute to indirect soil N2O emissions 

is the only component that has not been thoroughly tested, which is due to a lack of measurement data. Thus, the 

Tier 3 approach has reduced uncertainties in the agricultural soil C stock changes and N2O emissions compared to 

using lower Tier methods. 
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-11: Comparisons of Results from DAYCENT Model and Measurements of Soil Organic C Stocks
A-297

Figure A-12: Comparison of Estimated Soil Organic C Stock Changes and Uncertainties using Tier 1 (IPCC 2006), Tier 2 

(Ogle et al. 2003, 2006) and Tier 3 Methods 

Inventory Compilation Steps

There are five steps in estimating soil organic C stock changes for Cropland Remaining Cropland, Land 

Converted to Cropland, Grassland Remaining Grassland and Land Converted to Grassland; 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, soil organic C stock changes, direct and indirect N2O emissions are estimated using 

DAYCENT and/or the Tier 1 and 2 methods. In the fifth step, total emissions are computed by summing all 

components separately for soil organic C stock changes and N2O emissions. The remainder of this annex describes 

the methods underlying each step.

Step 1: Derive Activity Data

The following describes how the activity data are derived to estimate soil organic C stock changes, in 

addition to direct and indirect N2O emissions. The activity data requirements include: (1) land base and history data, 

(2) crop-specific mineral N fertilizer rates,77 (3) crop-specific manure amendment N rates and timing, (4) other N 

inputs, (5) tillage practices, (6) irrigation data, (7) Enhanced Vegetation Index (EVI), (8) daily weather data, and (9) 

edaphic characteristics. 

Step 1a: Activity Data for the Agricultural Land Base and Histories

The U.S. Department of Agriculture’s National Resources Inventory (NRI) (USDA-NRCS 2009) provides 

the basis for identifying the U.S. agricultural land base on non-federal lands, and classifying parcels into Cropland 

Remaining Cropland, Land Converted to Cropland, Grassland Remaining Grassland, and Land Converted to 

Grassland. Note that the Inventory does not include estimates of C stock changes and N2O emissions for federal 

grasslands (with the exception of soil N2O from PRP manure N, i.e., manure deposited directly onto pasture, range 

or paddock by grazing livestock) and a minor amount of croplands on federal lands, even though these areas are part 

 

77 No data are currently available at the national scale to distinguish the type of fertilizer applied or timing of applications rates. 

It is a planned improvement to address variation in these practices in future inventories.
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

of the managed land base for the United States. Methods are under development for estimating greenhouse gas 

emissions from soils on federal croplands and grasslands, and will be included in future inventories. 

Figure A-13: Comparisons of Results from DAYCENT Model and Measurements of Soil Nitrous Oxide Emissions

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 uses a sampling approach, 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 
A-299

(Nusser et al. 1998).78 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 2007, with the exception 

of 1983, 1988, and 1993. These years are gap-filled using an automated set of rules so that cropping sequences are 

filled with the most likely crop type given the historical cropping pattern at each NRI point location. Grassland data 

are reported on 5-year increments prior to 1998, but it is assumed that the land use is also grassland between the 

years of data collection (see Easter et al. 2008 for more information).

NRI points are included in the land base for the agricultural soil C and N2O emissions inventories if they 

are identified as cropland or grassland79 between 1990 and 2007 (Table A-216).80 The NRI data are reconciled with 

the Forest Inventory and Analysis Dataset, and in this process, the time series for Grassland Remaining Grassland

and Land Converted to Grassland is modified to account for differences in forest land area between the two national 

surveys (See Section 7.1 for more information on the U.S. land representation). Overall, 529,687 NRI survey points 

are included in the inventory. 

For each year, land parcels are subdivided into Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a 

specific year are classified as Cropland Remaining Cropland if the parcel is cropland for at least 20 years. Similarly 

land parcels under grassland management in a specific year of the inventory are classified as Grassland Remaining 

Grassland if they are designated as grassland for at least 20 years.81 Otherwise, land parcels are classified as Land 

Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. 

Lands are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to 

Grassland) for 20 years as recommended by the IPCC guidelines (IPCC 2006). Lands converted into Cropland and 

Grassland are further subdivided into the specific land use conversions (e.g., Forest Land Converted to Cropland).

Table A-216: Total Land Areas for the Agricultural Soil C and N2O Inventory, Subdivided by Land Use Categories (Million 

Hectares)

Land Areas (million ha)

Category 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000

Mineral Soils 349.74 349.63 349.49 344.46 344.20 344.25 344.34 344.45 338.83 339.00 339.11

Croplands 

Cropland Remaining Cropland 152.87 152.66 152.41 148.11 146.95 146.62 146.26 145.87 141.61 141.65 141.67

Grassland Converted to Cropland 12.82 13.12 13.38 15.19 16.75 16.97 17.31 17.46 18.08 17.64 17.25

Forest Converted to Cropland 0.62 0.62 0.62 1.24 1.24 1.24 1.24 1.24 0.41 0.41 0.41

Other Lands Converted to Cropland 0.11 0.11 0.11 0.25 0.25 0.25 0.25 0.25 0.13 0.13 0.13

Settlements Converted to Croplands 0.24 0.24 0.24 0.66 0.66 0.66 0.66 0.66 0.33 0.33 0.33

Wetlands Converted to Croplands 0.08 0.08 0.08 0.22 0.22 0.22 0.22 0.22 0.10 0.10 0.10

Grasslands

Grasslands Remaining Grasslands 174.02 173.75 173.48 167.45 166.20 166.12 166.05 166.01 163.07 163.18 163.19

Croplands Converted to Grasslands 7.29 7.35 7.47 8.84 9.42 9.67 9.86 10.23 12.32 12.78 13.25

Forest Converted to Grasslands 1.15 1.15 1.15 1.72 1.72 1.72 1.72 1.72 1.79 1.79 1.79

Other Lands Converted to Grasslands 0.25 0.25 0.25 0.40 0.40 0.40 0.40 0.40 0.55 0.55 0.55

Settlements Converted to Grasslands 0.08 0.08 0.08 0.14 0.14 0.14 0.14 0.14 0.18 0.18 0.18

Wetlands Converted to Grasslands 0.22 0.22 0.22 0.25 0.25 0.25 0.25 0.25 0.25 0.25 0.25

Organic Soils 1.20 1.19 1.18 1.16 1.17 1.16 1.15 1.14 1.13 1.12 1.11

Croplands 

 

78 In the current Inventory, NRI data only provide land-use and management statistics through 2007, but additional data will be 

incorporated in the future to extend the time series of land use and management data. 

79 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).

80 Land use for 2008 to 2012 is assumed to be the same as 2007, but will be updated after new NRI data are released.

81

 NRI points are classified according to land-use history records starting in 1982 when the NRI survey began, and consequently the 

classifications are based on less than 20 years from 1990 to 2001.
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Cropland Remaining Cropland 0.59 0.59 0.58 0.56 0.55 0.55 0.54 0.54 0.52 0.52 0.52

Grassland Converted to Cropland 0.06 0.07 0.06 0.07 0.08 0.08 0.08 0.08 0.09 0.09 0.09

Forest Converted to Cropland 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.02

Other Lands Converted to Cropland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Settlements Converted to Croplands 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Wetlands Converted to Croplands 0.02 0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01

Grasslands

Grasslands Remaining Grasslands 0.44 0.43 0.43 0.42 0.41 0.40 0.39 0.38 0.37 0.36 0.36

Croplands Converted to Grasslands 0.05 0.05 0.05 0.05 0.06 0.07 0.06 0.07 0.08 0.08 0.08

Forest Converted to Grasslands 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Other Lands Converted to Grasslands 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Settlements Converted to Grasslands 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wetlands Converted to Grasslands 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02 0.02

Total 350.94 350.82 350.67 345.62 345.37 345.41 345.49 345.58 339.96 340.12 340.23

Category 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Mineral Soils 339.09 339.12 339.23 338.85 338.35 337.90 337.53 337.30 337.06 336.83 336.60 336.37

Croplands 

Cropland Remaining Cropland 141.68 142.22 144.37 143.42 143.68 143.96 144.57 144.57 144.57 144.57 144.57 144.57

Grassland Converted to Cropland 16.96 16.34 14.21 14.08 13.60 13.12 12.27 12.27 12.27 12.27 12.27 12.27

Forest Converted to Cropland 0.41 0.41 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

Other Lands Converted to Cropland 0.13 0.13 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06

Settlements Converted to Croplands 0.33 0.33 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18 0.18

Wetlands Converted to Croplands 0.10 0.10 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04 0.04

Grasslands

Grasslands Remaining Grasslands 163.03 163.56 165.91 165.79 165.53 165.47 165.86 165.69 165.51 165.33 165.15 164.97

Croplands Converted to Grasslands 13.67 13.25 12.57 13.38 13.38 13.18 12.66 12.60 12.55 12.50 12.44 12.39

Forest Converted to Grasslands 1.79 1.79 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07 1.07

Other Lands Converted to Grasslands 0.55 0.55 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39 0.39

Settlements Converted to Grasslands 0.18 0.18 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13 0.13

Wetlands Converted to Grasslands 0.25 0.25 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15

Organic Soils 1.10 1.09 1.06 1.05 1.05 1.04 1.04 1.03 1.03 1.03 1.03 1.03

Croplands 

Cropland Remaining Cropland 0.52 0.52 0.54 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53 0.53

Grassland Converted to Cropland 0.11 0.10 0.10 0.09 0.09 0.09 0.08 0.08 0.08 0.08 0.08 0.08

Forest Converted to Cropland 0.01 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Other Lands Converted to Cropland 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Settlements Converted to Croplands 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Wetlands Converted to Croplands 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Grasslands

Grasslands Remaining Grasslands 0.32 0.32 0.31 0.31 0.31 0.30 0.30 0.30 0.30 0.30 0.30 0.30

Croplands Converted to Grasslands 0.09 0.10 0.09 0.10 0.10 0.09 0.08 0.08 0.08 0.08 0.08 0.08

Forest Converted to Grasslands 0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Other Lands Converted to Grasslands 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Settlements Converted to Grasslands 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Wetlands Converted to Grasslands 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Total 340.19 340.20 340.29 339.90 339.40 338.94 338.56 338.32 338.09 337.86 337.63 337.39

Note: The area estimates are not consistent with the land representation chapter because the current Inventory does not cover all 

of the managed land, including grassland and cropland in Alaska, as well as grasslands and croplands on federal lands in the 

conterminous United States.

The Tier 3 method using the DAYCENT model is applied to estimate soil C stock changes and N2O 

emissions for most of the NRI points that occur on mineral soils. Parcels of land that are not simulated with 

DAYCENT are allocated to the Tier 2 approach for estimating soil organic C stock change, and a Tier 1 method 

(IPCC 2006) to estimate soil N2O emissions (Table A- 214) (Note: Tier 1 method for soil N2O does not require land 

area data with the exception of emissions from drainage and cultivation of organic soils so in practice it is only the 

amount of N input to mineral soils that is addressed by the Tier 1 method and not the actual land area). The land 

base that is not simulated with DAYCENT includes (1) land parcels occurring on organic soils; (2) land parcels that 

include non-agricultural uses such as forest and federal lands in one or more years of the inventory; (3) land parcels 

on mineral soils that are very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent of 

soil volume comprised of gravel, cobbles, or shale); or (4) land parcels that are used to produce vegetables, 

perennial/horticultural crops, tobacco or rice, which are either grown continuously or in rotation with other crops. 

DAYCENT has not been fully tested or developed to simulate biogeochemical processes in soils used to produce 

some annual (e.g., tobacco), horticultural (e.g., flowers), or perennial (e.g., vineyards, orchards) crops and 
A-301

agricultural use of organic soils. In addition, DAYCENT has not been adequately tested for soils with a high gravel, 

cobble, or shale content. 

Table A-217: Total Land Area Estimated with Tier 2 and 3 Inventory Approaches (Million Hectares)

Land Areas (million ha)

Mineral Organic

Year Tier 1/2 Tier 3 Total Tier 1/2 Total

1990 31.78 317.96 349.74 1.20 350.94

1991 31.78 317.85 349.63 1.19 350.82

1992 31.78 317.71 349.49 1.18 350.67

1993 26.90 317.56 344.46 1.16 345.62

1994 26.90 317.30 344.20 1.17 345.37

1995 26.90 317.35 344.25 1.16 345.41

1996 26.90 317.44 344.34 1.15 345.49

1997 26.90 317.55 344.45 1.14 345.58

1998 21.50 317.33 338.83 1.13 339.96

1999 21.50 317.50 339.00 1.12 340.12

2000 21.50 317.61 339.11 1.11 340.23

2001 21.50 317.58 339.09 1.10 340.19

2002 21.50 317.61 339.12 1.09 340.20

2003 21.63 317.59 339.23 1.06 340.29

2004 21.63 317.21 338.85 1.05 339.90

2005 21.63 316.71 338.35 1.05 339.40

2006 21.63 316.27 337.90 1.04 338.94

2007 21.63 315.89 337.53 1.04 338.56

2008 21.63 315.66 337.30 1.03 338.32

2009 21.63 315.43 337.06 1.03 338.09

2010 21.63 315.20 336.83 1.03 337.86

2011 21.63 314.96 336.60 1.03 337.63

2012 21.63 314.73 336.37 1.03 337.39

NRI points on mineral soils are 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-218). NRI points are 

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, 

probability distribution functions (PDFs) for the NRI land-use data are 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 are taken into account resulting from the likelihood that current use is correlated 

with past use. These dependencies occur because as some land use/management categories increase in area, the area 

of other land use/management categories will decline. The covariance matrix addresses these relationships.
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-218: Total Land Areas by Land-Use and Management System for the Tier 2 Mineral Soil Organic C Approach 

(Million Hectares)

Land Areas (million ha)

Land-Use/Management System

1990-1992 

(Tier 2)

1993-1997 

(Tier 2)

1998-2002 

(Tier 2)

2003-2007

(Tier 2)

Cropland Systems 17.20 15.16 15.04 13.50

Aquaculture 0.00 0.00 0.01 0.01

Conservation Reserve Program 0.86 0.80 0.40 0.45

Continuous Hay 1.20 1.16 1.32 1.36

Continuous Hay with Legumes or Irrigation 0.29 0.27 0.31 0.29

Continuous Perennial or Horticultural Crops 0.71 0.59 0.51 0.41

Continuous Rice 0.00 0.00 0.00 0.00

Continuous Row Crops 2.96 2.31 2.55 2.50

Continuous Row Crops and Small Grains 2.01 1.57 1.37 1.29

Continuous Small Grains 0.66 0.57 0.53 0.44

Irrigated Crops 5.61 5.41 5.76 5.04

Low Residue Annual Crops (e.g., Tobacco or Cotton) 0.79 0.90 0.72 0.57

Miscellaneous Crop Rotations 0.00 0.01 0.00 0.00

Rice in Rotation with other crops 0.01 0.00 0.01 0.03

Row Crops and Small Grains in with Hay and/or Pasture 0.47 0.35 0.41 0.22

Row Crops and Small Grains with Fallow 0.05 0.04 0.04 0.04

Row Crops in Rotation with Hay and/or Pasture 0.28 0.30 0.35 0.20

Row Crops with Fallow 0.03 0.01 0.03 0.00

Small Grains in Rotation with Hay and/or Pasture 0.19 0.11 0.10 0.06

Small Grains with Fallow 0.47 0.29 0.18 0.21

Vegetable Crops 0.61 0.47 0.44 0.38

Grassland Systems 10.63 7.51 8.53 8.72

Rangeland 3.71 2.88 3.27 3.43

Continuous Pasture 6.84 4.56 5.17 5.16

Continuous Pasture with Legumes or Irrigation 0.08 0.07 0.10 0.13

CRP 0.00 0.00 0.00 0.00

Total 27.83 22.67 23.57 22.22

For the Tier 3 inventory estimates, the actual cropping and grassland histories are simulated with the DAYCENT 

model so it is not necessary to classify NRI points into management systems. Uncertainty in the areas associated with 

each management system is determined from the estimated sampling variance from the NRI survey (Nusser and Goebel 

1997). See Step 2b for additional discussion. 

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 or soil N2O emissions. Drained soils are subdivided into those used for cultivated 

cropland, which are assumed to have high drainage and relatively large losses of C, and those used for managed pasture, 

which are assumed to have less drainage with smaller losses of C. N2O emissions are assumed to be similar for both 

drained croplands and grasslands. Overall, the area of organic soils drained for cropland and grassland has remained 

relatively stable since 1990 (see Table A-219). 

Table A-219: Total Land Areas for Drained Organic Soils By Land Management Category and Climate Region (Million 

Hectares) 

Land Areas (million ha)

IPCC Land-Use Category for 

Organic Soils 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003

Cold Temperate

Cultivated Cropland 

(high drainage) 0.37 0.36 0.36 0.36 0.37 0.37 0.36 0.37 0.36 0.36 0.35 0.35 0.34 0.34

Managed Pasture 

(low drainage) 0.31 0.30 0.30 0.30 0.30 0.29 0.29 0.28 0.29 0.29 0.28 0.27 0.28 0.27

Undrained 0.05 0.05 0.05 0.04 0.03 0.03 0.04 0.03 0.03 0.03 0.04 0.03 0.03 0.02

Total 0.72 0.72 0.71 0.70 0.70 0.69 0.69 0.68 0.68 0.67 0.67 0.65 0.64 0.63

Warm Temperate

Cultivated Cropland 

(high drainage) 0.09 0.09 0.09 0.08 0.09 0.09 0.09 0.08 0.09 0.09 0.09 0.08 0.09 0.09

Managed Pasture 

(low drainage) 0.07 0.07 0.07 0.07 0.07 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Undrained 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.00 0.01 0.00 0.00 0.00

Total 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.16 0.16 0.16

Tropical
A-303

Cultivated Cropland 

(high drainage) 0.17 0.17 0.17 0.17 0.17 0.17 0.17 0.16 0.17 0.17 0.17 0.19 0.19 0.19

Managed Pasture 

(low drainage) 0.13 0.12 0.12 0.12 0.12 0.12 0.12 0.11 0.11 0.11 0.11 0.08 0.08 0.07

Undrained 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.00 0.00 0.00 0.00 0.01 0.00

Total 0.30 0.30 0.30 0.30 0.30 0.29 0.29 0.28 0.28 0.28 0.28 0.28 0.28 0.26

Land Areas (million ha)

IPCC Land-Use Category for 

Organic Soils 2004 2005 2006 2007 2008 2009 2010 2011 2012

Cold Temperate

Cultivated Cropland 

(high drainage) 0.33 0.33 0.32 0.32 0.32 0.32 0.32 0.32 0.32

Managed Pasture 

(low drainage) 0.27 0.28 0.27 0.27 0.27 0.27 0.27 0.27 0.27

Undrained 0.02 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03

Total 0.63 0.63 0.62 0.62 0.62 0.62 0.62 0.62 0.62

Warm Temperate

Cultivated Cropland 

(high drainage) 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09 0.09

Managed Pasture 

(low drainage) 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Undrained 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Total 0.17 0.17 0.16 0.16 0.16 0.16 0.16 0.16 0.16

Tropical

Cultivated Cropland 

(high drainage) 0.19 0.18 0.19 0.18 0.18 0.18 0.18 0.18 0.18

Managed Pasture 

(low drainage) 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07 0.07

Undrained 0.00 0.01 0.00 0.01 0.01 0.01 0.01 0.01 0.01

Total 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26 0.26

Step 1b: Obtain Management Activity Data for the Tier 3 Method to estimate Soil C Stock Changes and 

N2O Emissions from Mineral Soils

Synthetic N Fertilizer Application: Data on N fertilizer rates are based primarily on the USDA–Economic 

Research Service Cropping Practices Survey (USDA-ERS 1997, 2011). In these surveys, data on inorganic N 

fertilization rates are collected for crops simulated by DAYCENT (barley, corn, cotton, dry beans, oats, onions, 

peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat) in the high production 

states and for a subset of low production states. These data are used to build a time series of fertilizer application 

rates for specific crops and states for the 1990 through 1999 time period and 2000 through 2012 time period. If only 

a single survey is available for a crop, as is the case with sorghum, the rates for the one survey are used for both time 

periods. 

Mean fertilizer rates and standard deviations for irrigated and rainfed crops are produced for each state. If a

state is not surveyed for a particular crop or if there are not enough data to produce a state-level estimate, then data 

are aggregated to USDA 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 are not available, crop data are 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 are used 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). The survey summaries also present estimates for fraction of crop acres receiving fertilizer, 

and these fractions are used to determine if a crop is receiving fertilizer. Alfalfa hay and grass-clover hay are 

assumed to not be fertilized, but grass hay is fertilized according to rates from published farm enterprise budgets 

(NRIAI 2003). Total fertilizer application data are found in Table A- 220.

Simulations are conducted for the period prior to 1990 in order to initialize the DAYCENT model (see Step 

2a), and crop-specific regional fertilizer rates prior to 1990 are based largely on extrapolation/interpolation of 

fertilizer rates from the years with available data. For crops in some states, little or no data are available, and, 

therefore, a geographic regional mean is used to simulate N fertilization rates (e.g., no data are available for the State 
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

of Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the southeastern 

United States are used to simulate fertilization to corn fields in this state). 

Managed Livestock Manure82Amendments: County-level manure addition estimates have been derived 

from manure N addition rates developed by the USDA Natural Resources Conservation Service (NRCS) (Edmonds 

et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, USDA-NRCS 

has coupled estimates of manure N produced with estimates of manure N recoverability by animal waste 

management system to produce county-level rates of manure N application to cropland and pasture. Edmonds et al. 

(2003) defined a hierarchy that included 24 crops, permanent pasture, and cropland used as 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 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. The estimates were based on 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. In order to address 

uncertainty in these data, uniform probability distributions are 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 is 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 is 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 is divided by the manure N available in 1997. If the ratio is greater than 1, there is more

manure N available in that county relative to the amount in 1997, and so it is assumed a larger area is amended with 

manure. In contrast, ratios less than one imply less area is amended with manure because there is a lower amount 

available in the year compared to 1997. The amendment area in each county for 1997 is 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 is calculated by determining the populations of livestock that are on feedlots or otherwise 

housed, requiring collection and management of the manure, and the methods are described in the Manure 

Management section (Section 6.2) and annex (Annex 3.11). The total managed manure N applied to soils is found in 

Table A- 221. 

To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003), 

carbon-nitrogen (C:N) ratios for livestock-specific manure types are 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 are 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 is 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 are 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). Mineral N 

fertilization rates are reduced for crops receiving manure N based on a fraction of the amount of manure N applied, 

depending on the crop and whether it is irrigated or rainfed. The reduction factors are randomly selected from PDFs 

 

82 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 drylots, 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-305

with normal densities in order to address uncertainties in the dependence between manure amendments and mineral 

fertilizer application.

PRP Manure N: Another key source of N for grasslands is Pasture/Range/Paddock (PRP) manure N 

deposition (i.e., manure deposited by grazing livestock). The total amount of PRP manure N was estimated using 

methods described in the Manure Management section (Section 6.2) and annex (Annex 3.11). Nitrogen from PRP 

animal waste deposited on non-federal grasslands in a county was generated by multiplying the total PRP N (based 

on animal type and population data in a county) by the fraction of non-federal grassland area in the county. PRP 

manure N input rates for the Tier 3 DAYCENT simulations were estimated by dividing the total PRP manure N 

amount by the land area associated with non-federal grasslands in the county from the NRI survey data. The total 

PRP manure N added to soils is found in Table A- 221.

Residue N Inputs: Crop residue N, fixation by legumes, and N residue inputs from senesced grass litter 

are included as sources of N to the soil, and are estimated in the DAYCENT simulations as a function of vegetation 

type, weather, and soil properties. 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. The simulated total N inputs of above- and below-ground residue N and fixed N that is not 

harvested and not burned (the DAYCENT simulations assumed that 3 percent of non-harvested above ground 

residues for crops are burned83) are provided in Table A-222. 

Other N Inputs: Other N inputs are estimated within the DAYCENT simulation, and thus input data are 

not required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from 

the atmosphere. Mineralization of soil organic matter will also include the effect of land use change on this process 

as recommended by the IPCC (2006). The influence of additional inputs of N are estimated in the simulations so that 

there is full 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-222.

Tillage Practices: Tillage practices are estimated for each cropping system based on data from the 

Conservation Technology Information Center84 (CTIC 2004). 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 are 

provided by experts at CTIC to account for intermittent tillage activity and its impact on soil C (Towery 2001). 

Tillage practices are grouped into 3 categories: full, reduced, and no-tillage. Full tillage is defined as 

multiple tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low 

surface residue coverage. This definition corresponds to the intensive tillage and “reduced” tillage systems as 

defined by CTIC (2004). No-till is defined as not disturbing the soil except through the use of fertilizer and seed 

drills and where no-till is applied to all crops in the rotation. Reduced tillage made up the remainder of the 

cultivated area, including mulch tillage and ridge tillage as defined by CTIC and intermittent no-till. The specific 

tillage implements and applications used for different crops, rotations, and regions to represent the three tillage 

classes are derived from the 1995 Cropping Practices Survey by the Economic Research Service (ERS 1997).

Tillage data are further processed to construct probability distribution functions (PDFs). Transitions 

between tillage systems are 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 are modeled and 

applied to NRI points occurring within a county are full tillage to reduced and no-till, and reduced tillage to no-till. 

 

83 Another improvement is to reconcile the amount of crop residues burned with the Field Burning of Agricultural 

Residues source category (Section 6.5).

84 National scale tillage data are no longer collected by CTIC, and a new data source will be needed, which is a 

planned improvement.
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

The remaining amount of cropland is assumed to have no change in tillage (e.g., full tillage remained in full tillage). 

Transition matrices are constructed from CTIC data to represent tillage changes for three time periods, 1980-1989, 

1990-1999, 2000-2007. 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 are available) are used for the first time period, data from 1997 are used for the 

second time period, and data from 2004 are used for the last time period. Percentage areas of cropland in each 

county are 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. It is assumed that there are no transitions for NT→FT or 

NT→NT after accounting for NT systems that have intermittent tillage. Uniform probability distributions are 

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 is 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 is greater than or equal to 0 and less than 80, a transition from full to reduced till if the random draw is 

equal to or greater than 80 and less than 95, or a transition from full to no-till if the draw is greater than or equal to 

95. See step 2b for additional discussion of the uncertainty analysis.

Irrigation: NRI (USDA-NRCS 2009) differentiates between irrigated and non-irrigated land, but does not 

provide more detailed information on the type and intensity of irrigation. Hence, irrigation is modeled by assuming 

that applied water to field capacity with intervals between irrigation events where the soils drain to about 60 percent

of field capacity.

Daily Weather Data: Daily maximum/minimum temperature and precipitation data are based on gridded 

weather data from the North America Regional Reanalysis Product (NARR) (Mesinger et al. 2006). It is necessary 

to use computer-generated weather data because weather station data do not exist near all NRI points, and moreover 

weather station data are for a point in space. The NARR product uses this information with interpolation algorithms 

to derive weather patterns for areas between these stations. NARR weather data are available for the U.S. from 

1980 through 2007 at a 32 km resolution. Each NRI point is assigned the NARR weather data for the grid cell 

containing the point. 

Enhanced Vegetation Index: The Enhanced Vegetation Index (EVI) from the MODIS vegetation products, 

(MOD13Q1 and MYD13Q1) is an input to DAYCENT for estimating net primary production using the NASACASA production algorithm (Potter et al. 1993, 2007). MODIS imagery is collected on a nominal 8 day-time 

frequency when combining the two products. A best approximation of the daily time series of EVI data is derived 

using a smoothing process based on the Savitzky-Golay Filter (Savitzky and Golay 1964) after pre-screening for 

outliers and for cloud-free, high quality data as identified in the MODIS data product quality layer. The NASACASA production algorithm is only used for the following crops: corn, soybeans, sorghum, cotton, wheat and other 

close-grown crops such as barley and oats.85

The MODIS EVI products have a 250 m spatial resolution, and some pixels in images have mixed land 

uses and crop types at this resolution, which is problematic for estimating NPP associated with a specific crop at a 

NRI point. Therefore, a threshold of 90 percent purity in an individual pixel is the cutoff for estimating NPP using 

the EVI data derived from the imagery (i.e., pixels with less than 90 percent purity for a crop are assumed to 

generate bias in the resulting NPP estimates). The USDA-NASS Crop Data Layer (CDL) (Johnson and Mueller 

2010) is used to determine the purity levels of the EVI data. CDL data have a 30 to 58 m spatial resolution, 

depending on the year. The level of purity for individual pixels in the MODIS EVI products is determined by 

aggregating the crop cover data in CDL to the 250m resolution of the EVI data. In this step, the percent cover of 

individual crops is determined for the 250m EVI pixels. Pixels that did not meet a 90 percent purity level for any 

crop are eliminated from the dataset. CDL did not provide full coverage of crop maps for the conterminous United 

States until 2009 so it is not possible to evaluate purity for the entire cropland area prior to 2009.

The nearest pixel with at least 90 percent purity for a crop is assigned to the NRI point based on a 50 km 

buffer surrounding the survey location. EVI data are not assigned to a point if there are no pixels with at least 90 

percent purity within the 50 km buffer. Furthermore, MODIS products do not provide any data on EVI prior to 

2000, which preceded the launch of the MODIS sensor on the Aqua and Terra Satellites. It is good practice to apply 

a method consistently across a time series (IPCC 2006), and so a statistical model is used to estimate EVI for the 

 

85 Additional crops and grassland will be used with the NASA-CASA method in the future, as a planned improvement.
A-307

inventory time series prior to 2000 and also to fill gaps if no pixel has at least 90 percent purity within the 50 km 

buffer due to purity limitations, lack of CDL data to evaluate purity, or low quality data (Gurung et al. 2009). 

Soil Properties: Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) 

are the main soil variables used as input to the DAYCENT model. Texture is one of the main controls on soil C 

turnover and stabilization in the DAYCENT model, which uses particle size fractions of sand (50-2,000 μm), silt (2-

50 μm), and clay (< 2 μm) as inputs. 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.86 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 function that varies from 0.3 under completely anaerobic conditions to 1.0 under fully aerobic 

conditions (default parameters in DAYCENT).87 Other soil characteristics needed in the simulation, such as field 

capacity and wilting-point water contents, are estimated from soil texture data using a standardized hydraulic 

properties calculator (Saxton et al. 1986). Soil input data are derived from Soil Survey Geographic Database 

(SSURGO) (Soil Survey Staff 2011). The data are based on field measurements collected as part of soil survey and 

mapping. Each NRI point is assigned the dominant soil component in the polygon containing the point from the 

SSURGO data product.

Step 1c: Obtain Additional Management Activity Data for the Tier 1 Method to estimate Soil N2O 

Emissions from Mineral Soils

Synthetic N Fertilizer: A process-of-elimination approach is used to estimate synthetic N fertilizer 

additions to Tier 1 crops estimates. 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 through 2012, countylevel fertilizer used on-farms is adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 

through 2012). Fertilizer application data are available for crops and grasslands simulated by DAYCENT (discussed 

in Step 1a section for Tier 3 crops and non-federal grasslands). Thus, the amount of N applied to Tier 1 crops (i.e., 

not simulated by DAYCENT) is assumed to be the remainder of the fertilizer used on farms after subtracting the 

amount applied to Tier 3 crops and non-federal grasslands (i.e., simulated by DAYCENT). The differences are 

aggregated to the state level, and PDFs are derived based on uncertainties in the amount of N applied to Tier 3 crops 

and non-federal grasslands. Total fertilizer application to Tier 1 crops is found in Table A- 223.

Managed Livestock Manure and Other Organic Amendments: Manure N that is not applied to crops and 

grassland simulated by DAYCENT is assumed to be applied to other crops that are included in the Tier 1 method. 

Estimates of total national annual N additions from other commercial organic fertilizers are derived from organic 

fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 through 2011). Commercial organic fertilizers include 

dried blood, tankage, compost, and other; dried manure and sewage sludge that are used as commercial fertilizer are 

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 content of 0.5 percent (AAPFCO 2000a). The fertilizer consumption data are 

recorded in “fertilizer year” totals, (i.e., July to June), but are converted to calendar year totals. This is done by 

assuming that approximately 35 percent of fertilizer usage occurred from July to December and 65 percent from 

January to June (TVA 1992b). Values for July to December are not available for calendar year 2012 so a “least 

squares line” statistical extrapolation using the previous 5 years of data is used to arrive at an approximate value. 

PDFs are derived for the organic fertilizer applications assuming a default ±50 percent uncertainty. Annual 

consumption of other organic fertilizers is presented in Table A- 224. The fate of manure N is summarized in Table 

A- 221.

 

86 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable. 

87 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 DAYCENT model.
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

PRP Manure N: Soil N2O emissions from PRP manure N deposited on federal grasslands is estimated with 

a Tier 1 method. PRP manure N data are derived using methods described in the Manure Management section 

(Section 6.2) and Annex 3.11. PRP N deposited on federal grasslands is calculated using a process of elimination 

approach. The amount of PRP N generated by DAYCENT model simulations of non-federal grasslands was 

subtracted from total PRP N and this difference was assumed to be applied to federal grasslands. The total PRP 

manure N added to soils is found in Table A- 221.

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 are derived from national 

data on sewage sludge generation, disposition, and N content. Total sewage sludge generation data for 1990-2011, 

in dry mass units, are obtained from AAPFCO (1990-2011). Values for 2012 were not available so a “least squares 

line” statistical extrapolation using the previous 5 years of data was used to arrive at an approximate value. The 

total sludge generation estimates are then converted to units of N by applying an average N content of 69 percent 

(AAPFCO 2000a), and disaggregated into use and disposal practices using historical data in EPA (1993) and 

NEBRA (2007). The use and disposal practices are agricultural land application, other land application, surface 

disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The resulting estimates of 

sewage sludge N applied to agricultural land are used to estimate N2O emissions from agricultural soil management; 

the estimates of sewage sludge N applied to other land and surface-disposed are 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- 225).

Residue N Inputs: Soil N2O emissions for residue N inputs from crops that are not simulated by 

DAYCENT are estimated with a Tier 1 method. Annual crop yield (metric tons per hectare) and area harvested 

(hectare) statistics for N-fixing crops, including bean and pulse crops, are taken from U.S. Department of 

Agriculture crop production reports (USDA 1994, 1998, 2003, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012). 

Crop yield per hectare and area planted are multiplied to determine total crop yield for each crop, which is then 

converted to tons of dry matter product using the residue dry matter fractions shown in Table A- 226. Dry matter 

yield is then converted to tons of above- and below-ground biomass N. Above-ground biomass is calculated by 

using linear equations to estimate above-ground biomass given dry matter crop yields, and below-ground biomass is 

calculated by multiplying above-ground biomass by the below-to-above-ground biomass ratio. N inputs are 

estimated by multiplying above- and below-ground biomass by respective N concentrations. All ratios and 

equations used to calculate residue N inputs are from IPCC (2006) and Williams (2006). PDFs are derived 

assuming a ±50 percent uncertainty in the yield estimates (NASS does not provide uncertainty), along with 

uncertainties provided by the IPCC (2006) for dry matter fractions, above-ground residue, ratio of below-ground to 

above-ground biomass, and residue N fractions. The resulting annual biomass N inputs are presented in Table A227.

Step 1d: Obtain Additional Management Activity Data for the Tier 2 Method to estimate Soil C Stock 

Changes in Mineral Soils 

Tillage Practices: For the Tier 2 method that is used to estimate soil organic C stock changes, PDFs are 

constructed for the CTIC tillage data (CTIC 2004) as bivariate normal on a log-ratio scale to reflect negative 

dependence among tillage classes. This structure ensured that simulated tillage percentages are non-negative and 

summed to 100 percent. CTIC data do not differentiate between continuous and intermittent use of no-tillage, which 

is important for estimating SOC storage. Thus, regionally based estimates for continuous no-tillage (defined as 5 or 

more years of continuous use) are modified based on consultation with CTIC experts, as discussed in Step 1a 

(downward adjustment of total no-tillage area based on the amount of no-tillage that is rotated with more intensive 

tillage practices, Towery 2001).

Managed Livestock Manure Amendments: 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 DAYCENT model discussion in 

Step1b, the amount of land receiving manure is 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 

are used to re-classify a portion of crop and grassland into a new management category. Specifically, a portion of 

medium input cropping systems is re-classified as high input, and a portion of the high input systems is re-classified 

as high input with amendment. In grassland systems, the estimated proportions for land amended with manure are 
A-309

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 2006). Uncertainties in the amount of land amended with manure are based on the sample variance at the 

climate region scale, assuming normal density PDFs (i.e., variance of the climate region estimates, which are 

derived from county-scale proportions).

Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or 

private wastewater treatment facilities and is typically used as a soil amendment or is sent for waste disposal to

landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to 

grasslands. See section on sewage sludge in Step 1c for more information about the methods used to derive sewage 

sludge N estimates, and the total amount of sewage sludge N is given in Table A- 225. Sewage sludge N is assumed 

to be applied at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up 

by a crop and removed at harvest, representing the recommended application rate for manure amendments. 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 is divided by the assimilative capacity to estimate the 

total land area over which sewage sludge had been applied. The resulting estimates are used for the estimation of 

soil C stock change.

CRP Enrollment after 2007: The change in enrollment for the Conservation Reserve Program after 2007 

is based on the amount of land under active contracts from 2008 through 2012 relative to 2007 (USDA-FSA 2012).

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 is estimated from contract agreements (Euliss and Gleason 2002). While the 

contracts provide reasonable estimates of the amount of land restored in the region, they do not provide the 

information necessary to estimate uncertainty. Consequently, a ±50 percent range is used to construct the PDFs for 

the uncertainty analysis.

Table A- 220: Synthetic Fertilizer N Added to Tier 3 Crops (Gg N) 

1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Fertilizer N 8,994 8,760 8,906 8,540 8,908 8,748 9,052 8,719 8,594 9,594 9,609 9,724 9,842 9,620 9,823

Table A- 221: Fate of Livestock Manure Nitrogen (Gg N)

Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Managed Manure N 

Applied to Tier 3 

Crops and Nonfederal Grasslandsa

,

b 992 908 1,098 1,055 1,060 1,051 1,038 1,037 1,004 1,107 1,107 1,107 1,107 1,105 1,104

Managed Manure N 

Applied to Tier 1 

CropsC 1,665 1,804 1,750 1,772 1,795 1,822 1,750 1,792 1,889 1,829 1,803 1,790 1,791 1,815 1,831

Managed Manure N 

Applied to 

Grasslands 62 61 60 61 62 60 59 59 59 57 57 57 57 57 57

Pasture, Range, & 

Paddock Manure N 4,293 4,695 4,331 4,319 4,322 4,313 4,256 4,308 4,349 4,253 4,210 4,169 4,123 4,033 4,082

Total 7,012 7,468 7,239 7,208 7,239 7,246 7,104 7,196 7,300 7,247 7,177 7,124 7,078 7,010 7,075

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-222: Crop Residue N and Other N Inputs to Tier 3 Crops as Simulated by DAYCENT (Gg N)

Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Residue Na 578 623 619 631 589 636 664 657 629 628 628 628 628 628 628

Mineralization & 

Asymbiotic 

Fixation 11,001 11,834 10,455 11,653 11,300 11,303 12,061 11,343 11,642 11,116 11,116 11,116 11,116 11,116 11,116

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 223: Synthetic Fertilizer N Added to Tier 1 Crops (Gg N)

Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Fertilizer N 1,969 2,181 2,532 2,311 2,261 2,594 2,554 2,450 2,630 2,298 1,914 1,656 1,866 1,974 1,640

Table A- 224: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N)

Activity 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Other Commercial Organic 

Fertilizer Na 4 10 9 7 8 8 9 10 12 15 12 10 10 10 9

a

Includes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double 

counting.

Table A- 225: Sewage Sludge Nitrogen by Disposal Practice (Gg N)

Disposal Practice 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Applied to Agricultural Soils 52 69 84 86 89 91 94 98 101 104 106 109 112 115 118 

Other Land Application 25 28 30 30 30 30 30 31 31 32 32 32 32 32 32 

Surface Disposal 20 16 10 9 8 6 5 5 4 4 3 3 3 2 2 

Total 98 113 124 125 127 128 130 134 136 139 141 144 147 149 152 

Note: Totals may not sum due to independent rounding.

Table A- 226: Key Assumptions for Production of Tier 1 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

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

Millet 0.90 1.43 0.14 0.22 0.007 0.009

Table A- 227: Nitrogen in Crop Residues Retained on Soils Producing Tier 1 Crops (Gg N)

Crop 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Dry Edible Peas 9 11 10 10 10 11 14 15 8 8 14 17 15 11 14

Austrian Winter Peas 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8

Lentils 9 9 10 10 9 9 10 11 10 10 9 11 12 10 11

Wrinkled Seed Peas 9 9 8 8 8 8 9 8 8 8 8 9 8 8 8

Millet 7 7 3 7 2 5 6 5 4 6 6 4 5 4 2

Total 41 43 40 43 39 41 47 48 39 41 46 48 49 42 43

Step 1e: Additional Activity Data for Indirect N2O Emissions from Managed Soils of all Land-Use Types

A portion of the N that is applied as synthetic fertilizer, livestock manure, sewage sludge, and other organic 

amendments volatilizes as NH3 and NOx. 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 is assumed 

to be an insignificant source of indirect N2O in cropland and grassland systems where the amount of precipitation 

plus irrigation does not exceed 80 percent of the potential evapotranspiration. These areas are typically semi-arid to 

arid, 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- 220 through Table A- 224, Table A- 227, and Table A225. 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 section of this report.
A-311

Using the DAYCENT model, volatilization and leaching/surface run-off of N from soils is computed 

internally for Tier 3 crops 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 model 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 is also estimated by DAYCENT. Estimated leaching losses 

of N from DAYCENT are 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 are summed for each day in the 

annual cycle to provide an estimate of the amount of N subject to indirect N2O emissions. In addition, the daily 

losses of N through leaching and runoff in overland flow are summed for the annual cycle. The implied emission 

factor for N volatilization ranges from 7 to 9 percent for cropland (Table A-15, Tier 1 default value is 10 percent). 

The implied emission factor for NO3

-

from leaching/runoff ranges from 25 to 31 percent for cropland (Table A-15, 

Tier 1 default value is 30 percent). The implied emission factor for N volatilization ranges from 21 to 57 percent for 

grassland (Table A-16, Tier 1 default value is 20 percent). The implied emission factor for NO3

-

from 

leaching/runoff ranges from 14 to 22 percent for grassland (Table A-16, Tier 1 default value is 30 percent). 

Uncertainty in the estimates is 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 is used to estimate N losses from mineral soils due to volatilization and leaching/runoff 

for crops, 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 are multiplied by the fraction subject to gaseous losses using the respective default values of 

0.1 kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the 

volatilized N ranges from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOxN/kg N for organic amendments (IPCC 2006). Leaching/runoff losses of N are estimated by summing the N 

additions from synthetic and other organic fertilizers, manure, 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 is 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 are derived for each of the N inputs in the same manner as direct 

N2O emissions, discussed in Steps 1a and 1c. 

Volatilized N is summed for losses from Tier 3 crop types, Tier 1 crop types, federal and non-federal 

grasslands, settlements, and forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching 

and surface runoff are summed to obtain the total losses for this pathway.

Step 2: Estimate Soil Organic C Stock Changes and Direct N2O Emissions from Mineral Soils

In this step, soil organic C stock changes and N2O emissions are estimated for Tier 3 crops, Tier 1 crops, 

federal and non-federal grasslands. Three methods are used to estimate soil organic C stock changes and direct N2O 

emissions from mineral soils. The DAYCENT process-based model is used for Tier 3 croplands and non-federal 

grasslands. A Tier 2 method is used to estimate soil organic C stock changes for crop histories that included crops 

that were not simulated by DAYCENT and land use change other than conversions between cropland and grassland. 

A Tier 1 methodology is used to estimate N2O emissions from Tier 1 crops, which are grown on a considerably 

smaller portion of land than the Tier 3 crops, as well as PRP manure N deposition on federal grasslands. Soil organic 

C stock changes and N2O emissions are not estimated for federal grasslands (other than the effect of PRP manure 

N), but are under evaluation as a planned improvement and may be estimated in future inventories.

Step 2a: Estimate Soil Organic C Stock Changes and N2O Emissions for Crops and Non-Federal Grassland with the 

Tier 3 DAYCENT Model 

Tier 3 crops include alfalfa hay, barley, corn, cotton, dry beans, grass hay, grass-clover hay, oats, onions, 

peanuts, potatoes, rice, sorghum, soybeans, sugar beets, sunflowers, tomatoes, and wheat, which represent 

approximately 85-87 percent of total cropland in the United States. The DAYCENT simulations also included all 

non-federal grasslands in the United States.
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

The methodology description is divided into two sub-steps. First, the model is used to establish the initial 

conditions and C stocks for 1979, which is the last year before the NRI survey is initiated. In the second sub-step, 

DAYCENT is used to estimate changes in soil organic C stocks and direct N2O emissions based on the land-use and 

management histories recorded in the NRI from 1990 through 2007 (USDA-NRCS 2009). 

Simulate Initial Conditions (Pre-NRI Conditions): DAYCENT model initialization involves two steps, 

with the goal of estimating the most accurate stock for the pre-NRI history, and the distribution of organic C among 

the pools represented in the model (e.g., Structural, Metabolic, Active, Slow, and Passive). Each pool has a different 

turnover rate (representing the heterogeneous nature of soil organic matter), and the amount of C in each pool at any 

point in time influences the forward trajectory of the total soil organic C storage. There is currently no national set 

of soil C measurements that can be used for establishing initial conditions in the model. Sensitivity analysis of the 

soil organic C algorithms showed that the rate of change of soil organic matter is relatively insensitive to the amount

of total soil organic C but is highly sensitive to the relative distribution of C among different pools (Parton et al. 

1987). By simulating the historical land use prior to the inventory period, initial pool distributions are estimated in 

an unbiased way.

The first step involves running the model to a steady-state condition (e.g., equilibrium) under native 

vegetation, historical climate data based on the NARR product (1980-2007), 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 simulates 5,000 years in the pre-settlement era in order to achieve a steady-state condition. 

The second step is to simulate the period of time from European settlement and expansion of agriculture to 

the beginning of the NRI survey, representing the influence of historic land-use change and management, 

particularly the conversion of native vegetation to agricultural uses. This encompasses a varying time period from 

land conversion (depending on historical settlement patterns) to 1979. The information on historical cropping 

practices used for DAYCENT simulations has been gathered from a variety of sources, ranging from the historical 

accounts of farming practices reported in the literature (e.g., Miner 1998) to national level databases (e.g., NASS 

2004). A detailed description of the data sources and assumptions used in constructing the base history scenarios of 

agricultural practices can be found in Williams and Paustian (2005).

NRI History Simulations: After model initialization, DAYCENT is used to simulate the NRI land use and 

management histories from 1979 through 2007.88 The simulations address the influence of soil management on 

direct N2O emissions, soil organic C stock changes and losses of N from the profile through leaching/runoff and 

volatilization. The NRI histories identify the land use and land use change histories for the NRI survey locations, as 

well as cropping patterns and irrigation history (see Step 1a for description of the NRI data). The input data for the 

model simulations also include the NARR weather dataset and SSURGO soils data, synthetic N fertilizer rates, 

managed manure amendments to cropland and grassland, manure deposition on grasslands (i.e., PRP), tillage 

histories and EVI data (See Step 1b for description of the inputs). The total number of DAYCENT simulations is 

over 18 million with a 100 repeated simulations (i.e., iterations) for each NRI point location in a Monte Carlo 

Analysis. The simulation system incorporates a dedicated MySQL database server and a 30-node parallel processing 

computer cluster. Input/output operations are managed by a set of run executive programs written in PERL. 

The simulations for the NRI history are integrated with the uncertainty analysis. Evaluating uncertainty is 

an integral part of the analysis, and includes three components: (1) uncertainty in the main activity data inputs 

affecting soil C and N2O emissions (input uncertainty); (2) uncertainty in the model formulation and 

parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling 

uncertainty) (Ogle et al. 2010, Del Grosso et al. 2010). For component 1, input uncertainty is evaluated for 

fertilization management, manure applications, and tillage, which are primary management activity data that are 

supplemental to the NRI observations and have significant influence on soil organic C dynamics and N2O emissions. 

As described in Step 1b, PDFs are derived from surveys at the county scale for the inputs in most cases. In addition, 

uncertainty is included for predictions of EVI data that are needed to fill-data gaps and extend the time series (see 

Enhanced Vegetation Index in Step 1b). To represent uncertainty in all of these inputs, a Monte-Carlo Analysis is 

used with 100 iterations for each NRI point; random draws are made from PDFs for fertilizer, manure application, 

tillage, and EVI predictions. As described above, an adjustment factor is also selected from PDFs with normal 

densities to represent the dependence between manure amendments and N fertilizer application rates. 

 

88 The estimated soil C stock change in 2007 is currently assumed to represent the changes between 2008 and 2012. New 

estimates will be available in the future to extend the time series of land use and management data.
A-313

The second component deals with uncertainty inherent in model formulation and parameterization. This 

component is the largest source of uncertainty in the Tier 3 model-based inventory analysis, accounting for more 

than 80 percent of the overall uncertainty in the final estimates (Ogle et al. 2010, Del Grosso et al. 2010). An 

empirically-based procedure is applied to develop a structural uncertainty estimator from the relationship between 

modeled results and field measurements from agricultural experiments (Ogle et al. 2007). For soil organic C, the 

DAYCENT model is evaluated with measurements from 84 long-term field experiments that have over 900 

treatments, representing a variety of management conditions (e.g., variation in crop rotation, tillage, fertilization 

rates, and manure amendments). There are 24 experimental sites available to evaluate structural uncertainty in the 

N2O emission predictions from DAYCENT (Del Grosso et al. 2010). The inputs to the model are essentially known 

in the simulations for the long-term experiments, and, therefore, the analysis is designed to evaluate uncertainties 

associated with the model structure (i.e., model algorithms and parameterization). USDA is developing a national 

soil monitoring network to evaluate the Inventory in the future (Spencer et al. 2011).

The relationship between modeled soil organic C stocks and field measurements are statistically analyzed 

using linear-mixed effect modeling techniques. Additional fixed effects are 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 are 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 cropland model includes variables for modeled soil organic C 

inclusion of hay/pasture in cropping rotations, use of no-till, set-aside lands, organic matter amendments, and 

inclusion of bare fallow in the rotation, which are significant at an alpha level of 0.05. The final grassland model 

only included the model soil organic C. These fixed effects are used to make an adjustment to modeled values due to 

biases that are creating significant mismatches between the modeled and measured stocks. For soil N2O, simulated 

DAYCENT emissions are a highly significant predictor of the measurements, with a p-value of <0.01. Several other 

variables are considered in the statistical model to evaluate if DAYCENT exhibits bias under certain conditions 

related to climate, soil types, and management practices. The type of crop is significant at an alpha level of 0.05, 

demonstrating that DAYCENT tends to over-estimate emissions for small grains systems, but is accurate in 

predicting the N2O emissions for other crops and grassland. Random effects are included in the model to capture the 

dependence in time series and data collected from the same site, which are needed to estimate appropriate standard 

deviations for parameter coefficients. 

A Monte Carlo approach is used to apply the uncertainty estimator (Ogle et al. 2010). Parameter values for 

the statistical equation (i.e., fixed effects) are selected from their joint probability distribution, as well as random 

error associated with fine-scale estimates at NRI points, and the residual or unexplained error associated with the 

linear mixed-effect model. The estimate and associated management information is then used as input into the 

equation, and adjusted values are computed for each C stock and N2O emissions estimate. The variance of the 

adjusted estimates is computed from the 100 simulated values from the Monte Carlo analysis. 

The third element is the uncertainty associated with scaling the DAYCENT results for each NRI point to 

the entire land base, using the expansion factors provided with the NRI survey dataset. The expansion factors 

represent the number of hectares associated with the land-use and management history for a particular point. This 

uncertainty is determined by computing the variances from a set of replicated weights for the expansion factor. 

For the land base that is simulated with the DAYCENT model, soil organic C stock changes are provided in 

Table A-228, and soil N2O emissions are provided in Table A-229.

Table A-228: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with 

the Tier 3 DAYCENT 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 (69.39) (111.58) to (27.19) 19.99 9.49 to 30.49 (13.36) (48.70) to 21.97 (4.57) (9.48) to 0.33

1991 (74.40) (113.19) to (35.60) 17.32 5.98 to 28.65 (2.56) (41.30) to 36.18 (5.31) (10.02) to (0.60)

1992 (66.23) (104.48) to (27.97) 19.15 8.63 to 29.66 (11.34) (46.88) to 24.19 (4.86) (10.16) to 0.43

1993 (45.70) (82.77) to (8.64) 20.15 7.94 to 32.36 (0.65) (35.56) to 34.26 (3.85) (9.06) to 1.36

1994 (53.00) (86.46) to (19.53) 12.25 (1.92) to 26.42 (18.10) (56.56) to 20.35 (5.35) (10.68) to (0.02)

1995 (41.87) (78.60) to (5.15) 20.28 7.18 to 33.38 1.68 (36.44) to 39.81 (5.53) (10.54) to (0.52)
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

1996 (51.50) (90.24) to (12.77) 16.20 3.96 to 28.44 (21.59) (61.91) to 18.73 (6.34) (12.09) to (0.58)

1997 (46.79) (85.92) to (7.66) 16.58 3.03 to 30.13 (8.46) (42.26) to 25.35 (6.03) (11.91) to (0.14)

1998 (38.32) (78.24) to 1.59 11.87 (1.95) to 25.70 (9.66) (45.71) to 26.39 (6.20) (14.47) to 2.08

1999 (41.27) (76.37) to (6.16) 12.08 (1.74) to 25.89 0.53 (33.43) to 34.49 (6.62) (14.01) to 0.77

2000 (57.32) (97.85) to (16.78) 11.94 0.30 to 23.57 (33.46) (72.15) to 5.22 (8.67) (17.0) to (0.35)

2001 (45.18) (83.56) to (6.81) 13.38 (1.54) to 28.30 (11.47) (46.22) to 23.28 (7.32) (14.44) to (0.20)

2002 (37.86) (75.34) to (0.38) 12.46 0.97 to 23.95 (14.69) (54.98) to 25.60 (6.98) (15.62) to 1.65

2003 (35.71) (72.65) to 1.23 12.91 0.60 to 25.21 (10.48) (48.22) to 27.26 (6.96) (15.04) to 1.12

2004 (38.94) (77.67) to (0.21) 10.59 (0.49) to 21.66 (0.20) (38.01) to 37.61 (8.18) (16.58) to 0 .21

2005 (48.71) (87.49) to (9.93) 14.25 2.01 to 26.50 3.56 (35.96) to 43.08 (7.02) (15.82) to 1.78

2006 (50.85) (90.12) to (11.58) 10.97 (1.38) to 23.33 (19.74) (53.30) to 13.82 (7.04) (15.70) to 1.62

2007 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.83 (30.74) to 40.40 (7.32) (16.67) to 2.03

2008 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.85 (30.71) to 40.41 (7.28) (16.59) to 2.03

2009 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.88 (30.68) to 40.43 (7.24) (16.50) to 2.03

2010 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.90 (30.65) to 40.45 (7.20) (16.42) to 2.02

2011 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.92 (30.62) to 40.46 (7.15) (16.33) to 2.02

2012 (50.58) (93.40) to (7.75) 10.51 (.41) to 21.42 4.95 (30.59) to 40.48 (7.11) (16.25) to 2.02

Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part 

of the time series.

Table A-229: Annual N2O Emissions (95% Confidence Interval) for the Land Base Simulated with the Tier 3 DAYCENT ModelBased Approach (Tg CO2 Eq.)

Year

Tier 3 Crops Non-Federal Grasslands

Estimate 95% CI Estimate 95% CI

1990 132.1 123.77 to 144.01 74.3 67.37 to 84.96

1991 144.6 135.44 to 157.83 89.7 82.14 to 101.2

1992 146.3 136.76 to 160.06 73.1 68.03 to 80.41

1993 154.6 144.86 to 168.56 79.3 74.12 to 86.92

1994 138.1 129.86 to 150.44 68.0 63.75 to 74.02

1995 141.0 132.4 to 153.96 77.4 72.38 to 84.61

1996 151.4 142.2 to 165.16 84.8 79.24 to 92.74

1997 147.0 137.75 to 160.98 86.3 80.52 to 94.61

1998 125.4 117.28 to 138.09 67.1 62.5 to 73.54

1999 129.3 121.27 to 141.39 66.0 61.86 to 71.77

2000 121.1 113.85 to 131.88 61.5 57.33 to 67.25

2001 133.6 125.47 to 145.5 69.8 64.82 to 76.9

2002 131.5 123.57 to 143.07 64.2 59.92 to 70.37

2003 130.0 122.3 to 141.21 63.9 59.84 to 69.71

2004 138.4 130.06 to 150.46 77.9 72.46 to 85.71

2005 137.4 128.97 to 149.53 74.3 69.61 to 80.97

2006 136.8 128.43 to 148.91 71.3 66.59 to 78.04

2007 144.1 135.14 to 156.8 86.8 80.21 to 96.4

2008 143.7 134.78 to 156.45 86.6 80.01 to 96.18

2009 143.7 134.75 to 156.42 86.3 79.76 to 95.91

2010 139.6 130.73 to 152.46 86.1 79.5 to 95.64

2011 137.9 129.08 to 150.83 85.8 79.25 to 95.36

2012 138.2 129.33 to 151.09 85.6 78.99 to 95.09

In DAYCENT, the model cannot distinguish among the original sources of N after the mineral N enters the 

soil pools in order 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 NRI 

point, the N inputs in a simulation are determined for anthropogenic practices discussed in IPCC (2006), including 

synthetic mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). 

The percentage of N input for anthropogenic practices is divided by the total N input, and this proportion is used to 

determine the amount of N2O emissions assigned to each of the practices.89 For example, if 70 percent of the 

 

89 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 
A-315

mineral N made available in the soil is due to mineral fertilization, then 70 percent of the N2O emissions are 

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 N fixation 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 cropland soils compared to application of synthetic 

fertilizers and manure amendments, along with symbiotic fixation. Mineralization of soil organic matter accounts 

for the majority of available N in grassland soils. Accounting for the influence of “other N inputs” is necessary in 

order to meet the recommendation for 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 results are provided in Table A- 230 and Table A- 231 associated with

subdividing the N2O emissions based on N inputs.

 

mineral N so this approximation is the only approach that can be used currently 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). Further research and model development may allow for other approaches in the future. 
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 230: Direct N2O Emissions from Cropland Soils (Tg CO2 Eq.)

N Source 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Total Mineral Soils 150.4 161.0 142.6 154.2 151.9 152.2 160.0 158.7 159.5 164.8 162.5 161.1 158.1 157.0 155.7 

Tier 3 Crops 132.1 141.0 121.1 133.6 131.5 130.0 138.4 137.4 136.8 144.1 143.7 143.7 139.6 137.9 138.2 

Synthetic Fertilizer 55.9 55.8 50.6 52.1 53.3 51.5 54.8 53.8 51.7 60.4 60.1 61.0 59.5 57.8 59.3 

Managed Manure 5.8 5.8 6.1 6.4 6.4 6.2 6.5 6.5 6.5 7.0 7.0 6.9 6.6 6.6 6.6 

Residue Na 3.3 3.9 3.4 3.8 3.4 3.7 3.9 4.2 3.9 4.0 4.0 4.0 3.9 3.9 3.8 

Mineralization and Asymbiotic 

Fixation 67.0 75.4 61.0 71.4 68.3 68.7 73.2 72.9 74.7 72.6 72.5 71.8 69.5 69.6 68.5 

Tier 1 Crops 18.3 20.1 21.5 20.5 20.4 22.1 21.6 21.3 22.6 20.8 18.8 17.5 18.5 19.1 17.5 

Synthetic Fertilizer 9.6 10.6 12.3 11.3 11.0 12.6 12.4 11.9 12.8 11.2 9.3 8.1 9.1 9.6 8.0 

Managed Manure and Other 

Organic Commercial Fertilizer 8.1 8.8 8.6 8.7 8.8 8.9 8.6 8.8 9.3 9.0 8.8 8.8 8.8 8.9 9.0 

Residue N 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 0.6 

Organic Soils 4.7 4.5 4.4 4.3 4.3 4.2 4.2 4.1 4.1 4.0 4.0 4.0 4.0 4.0 4.0 

Total* 155.1 165.6 146.9 158.5 156.1 156.3 164.2 162.8 163.5 168.9 166.5 165.2 162.1 161.0 159.8 

+ 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- 231: Direct N2O Emissions from Grasslands (Tg CO2 Eq.)

N Source 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Tier 3 74.3 82.0 63.3 71.9 66.1 66.6 80.0 77.8 74.1 90.4 90.1 89.9 89.6 89.4 89.2 

Synthetic Fertilizer 0.5 0.7 0.6 0.7 0.8 0.7 1.0 1.0 1.2 1.0 1.0 1.0 1.0 1.0 0.9 

PRP Manure 13.4 14.2 12.3 14.1 13.5 12.3 13.2 13.2 13.1 14.3 14.3 14.2 14.2 14.1 14.1 

Managed Manure + + + + + + + + + + + + + + + 

Residue Na 2.0 2.5 1.6 2.0 1.9 1.9 2.2 2.4 2.2 2.6 2.6 2.6 2.6 2.5 2.5 

Mineralization and 

Asymbiotic Fixation 58.2 64.4 48.6 54.8 49.6 51.5 63.4 60.8 57.2 72.2 72.0 71.9 71.7 71.5 71.3 

Tier 1 11.3 15.5 12.4 11.9 11.4 12.3 12.3 12.7 13.5 13.2 12.9 12.6 12.2 11.5 11.9 

PRP Manure 11.0 15.2 12.0 11.5 11.0 11.9 11.8 12.3 13.0 12.7 12.4 12.1 11.7 10.9 11.4 

Sewage Sludge 0.3 0.3 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.6 0.6 

Total 85.6 97.6 75.7 83.8 77.5 78.9 92.3 90.5 87.6 103.6 103.0 102.5 101.9 100.9 101.1 

+ Less than 0.05 Tg CO2 Eq.

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-317

Step 2b: Soil N2O Emissions from Agricultural Lands on Mineral Soils Approximated with the Tier 1 

Approach

To estimate direct N2O emissions from N additions to Tier 1 crops, the amount of N in applied synthetic 

fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) is added to 

N inputs from crop residues, and the resulting annual totals are multiplied by the IPCC default emission factor of 

0.01 kg N2O-N/kg N (IPCC 2006). The uncertainty is determined based on simple error propagation methods (IPCC 

2006). The uncertainty in the default emission factor ranges from 0.3–3.0 kg N2O-N/kg N (IPCC 2006). For 

flooded rice soils, the IPCC default emission factor is 0.003 kg N2O-N/kg N and the uncertainty range is 0.000–

0.006 kg N2O-N/kg N (IPCC 2006). Uncertainty in activity data is ± 20 percent for fertilizer additions (Mosier 

2004).90 Uncertainties in the emission factor and fertilizer additions are combined with uncertainty in the equations 

used to calculate residue N additions from above- and below-ground biomass dry matter and N concentration to 

derive overall uncertainty. 

The Tier 1 method is also used to estimate emissions from manure N deposited by livestock on federal 

lands (i.e., PRP manure N), and from sewage sludge application to grasslands. These two sources of N inputs to 

soils are multiplied by the IPCC (2006) default emission factors (0.01 kg N2O-N/kg N for sludge and horse, sheep, 

and goat manure, and 0.02 kg N2O-N/kg N for cattle, swine, and poultry manure) to estimate N2O emissions (Table 

A- 231). The uncertainty is 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 2c: Soil Organic C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 

Approach

Mineral soil organic C stock values are derived for crop rotations that were not simulated by DAYCENT 

and land converted from non-agricultural land uses to cropland or grassland in 1982, 1992, 1997, 2002 and 2007, 

based on the land-use and management activity data in conjunction with appropriate reference C stocks, land-use 

change, management, input, and wetland restoration factors. Each input to the inventory calculations for the Tier 2 

approach has some level of uncertainty that is quantified in PDFs, including the land-use and management activity 

data, reference C stocks, and management factors. A Monte Carlo Analysis is used to quantify uncertainty in soil 

organic C stock changes for the inventory period based on uncertainty in the inputs. Input values are 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.

Derive Mineral Soil Organic C Stock Change Factors: Stock change factors representative of U.S. 

conditions are estimated from published studies (Ogle et al. 2003, Ogle et al. 2006). The numerical factors quantify 

the impact of changing land use and management on SOC storage in mineral soils, including tillage practices, 

cropping rotation or intensification, and land conversions between cultivated and native conditions (including setasides in the Conservation Reserve Program). Studies from the United States and Canada are used in this analysis 

under the assumption that they would best represent management impacts for the Inventory. 

The IPCC inventory methodology for agricultural soils divides climate into eight distinct zones based upon 

average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006) (Table A232). Six of these climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001). 

Table A-232: 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

 

90 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-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Warm Temperate, Moist 10 – 20 ≥ Potential Evapotranspiration NA

Sub-Tropical, Drya > 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) are used to classify climate 

zones. Mean annual precipitation and annual temperature data are averaged (weighted by area) for each of the 4×4 

km grid cells occurring within a MLRA region. These averages are used to assign a climate zone to each MLRA 

according to the IPCC climate classification (Figure A-14). 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-14: Major Land Resource Areas by IPCC Climate Zone

Soils are classified into one of seven classes based upon texture, morphology, and ability to store organic 

matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, 

representing estimates from conventionally managed cropland, are computed for each of the mineral soil types 

across the various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization 

Database (NRCS 1997) (Table A-233). These stocks are used in conjunction with management factors to estimate

the change in SOC stocks that result from management and land-use activity. PDFs, which represent the variability 

in the stock estimates, are constructed as normal densities based on the mean and variance from the pedon data. 

Pedon locations are clumped in various parts of the country, which reduces the statistical independence of individual 

pedon estimates. To account for this lack of independence, samples from each climate by soil zone are tested for 

spatial autocorrelation using the Moran’s I test, and variance terms are inflated by 10 percent for all zones with 

significant p-values.
A-319

Table A-233: 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

Sub-Tropical, 

Dry

Sub-Tropical, 

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)

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 are 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).

To estimate the land use, management and input factors, 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 are 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 are not aggregated for the C stock 

measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 10-30 cm) is included as a separate point in the 

dataset. Similarly, time series data are not aggregated in these datasets. Linear regression models assume that the 

underlying data are independent observations, but this is not the case with data from the same experimental site, or 

plot in a time series. These data are more related to each other than data from other sites (i.e., not independent). 

Consequently, random effects are 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 are estimated for the effect 

of management practices at 20 years for the top 30 cm of the soil (Table A-234). Variance is 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 

influences C stock changes in 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.91 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 are insufficient field 

studies to derive factor values for these systems from the published literature, and, thus, estimates from IPCC (2006) 

are 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 is provided for these factors in IPCC (2006), which is used to 

construct PDFs.

Table A-234: 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

 

91 Improved grasslands are identified in the 2007 National Resources Inventory as grasslands that are irrigated or seeded with 

legumes, in addition to those reclassified as improved with manure amendments.
A-320 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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 Factorb 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 amendmentb 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) are converted to represent changes in SOC storage from a cultivated condition rather than a native condition.

b U.S.-specific factors are not estimated for land improvements, rice production, or high input with amendment because of few studies addressing the impact of 

legume mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland rice production in the US. Factors 

provided in IPCC (2006) are used as the best estimates of these impacts. 

Wetland restoration management also influences 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 is estimated 

assessing the difference in SOC storage between restored and unrestored wetlands enrolled in the Conservation 

Reserve Program (Euliss and Gleason 2002), which represents an initial increase of C in the restored soils over the 

first 10 years (Table A-235). A PDF with a normal density is constructed from these data based on results from a 

linear regression model. Following the initial increase of C, natural erosion and deposition leads to additional 

accretion of C in these wetlands. The mass accumulation rate of organic C is estimated using annual sedimentation 

rates (cm/yr) in combination with percent organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002). 

Procedures for calculation of mass accumulation rate are described in Dean and Gorham (1998); the resulting rate 

and variance are used to construct a PDF with a normal density (Table A-235).

Table A-235: Factor Estimate for the Initial Increase and Subsequent Annual Mass Accumulation Rate (Mg C/ha-yr) in Soil 

Organic C Following Wetland Restoration of Conservation Reserve Program

Variable Value

Factor (Initial Increase—First 10 Years) 1.22±0.18

Mass Accumulation (After Initial 10 Years) 0.79±0.05

Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002). 

Estimate Annual Changes in Mineral Soil Organic C Stocks: In accordance with IPCC methodology, 

annual changes in mineral soil C are calculated by subtracting the beginning stock from the ending stock and then 

dividing by 20.92 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 1997 is the difference between the 1997 

and 1992 C stocks. The annual average change between 1998 and 2002 is the difference between the 1998 and 2002 

C stocks. The annual average change between 2003 and 2012 is the difference between the 2003 and 2007. Using 

the Monte Carlo approach, SOC stock changes for mineral soils are estimated 50,000 times between 1982 and 1992, 

1993 and 1997, 1998 and 2002, and 2003 and 2007. From the final distribution of 50,000 values, a 95 percent 

confidence interval is generated based on the simulated values at the 2.5 and 97.5 percentiles in the distribution 

(Ogle et al. 2003). Soil organic C stock changes are provided in Table A-236.

 

92 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period. 
A-321

Table A-236: 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 and 

Reference Carbon Stocks (Tg CO2 Eq./yr)

Croplands: Cropland Remaining Cropland Grassland Converted to 

Cropland

Forest Converted to 

Cropland

Other Land Converted to 

Cropland

Settlements Converted to 

Cropland

Wetlands Converted to 

Cropland

Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI

Mineral Soils

1990-1992 -6.49 (9.24) to (4.07) 2.34 1.29 to 3.48 1.47 0.81 to 2.18 0.26 0.14 to 0.38 0.56 .31 to .83 0.18 .10 to .27

1993-1997 -7.64 (10.70) to (4.79) 2.02 1.06 to 3.06 1.39 0.73 to 2.12 0.28 0.15 to 0.43 0.74 .39 to 1.13 0.25 .13 to .38

1998-2002 -6.93 (9.67) to (4.44) 1.78 0.90 to 2.73 0.82 0.41 to 1.25 0.27 0.13 to 0.41 0.65 .33 to 1.0 0.21 .10 to .32

2003-2012 -2.82 (5.08) to (0.91) 0.78 0.40 to 1.20 0.26 0.13 to 0.40 0.11 0.06 to 0.17 0.32 .16 to .50 0.08 .04 to .12

Organic Soils

1990 23.98 15.43 to 34.78 2.51 1.36 to 4.05 0.83 0.34 to 1.51 - - 0.14 0.06 to 0.26 0.67 0.37 to 1.09

1991 23.72 15.21 to 34.31 2.59 1.40 to 4.12 0.83 0.35 to 1.51 - - 0.09 0.03 to 0.16 0.67 0.36 to 1.09

1992 23.74 15.27 to 34.52 2.43 1.33 to 3.90 0.77 0.29 to 1.43 - - 0.09 0.03 to 0.16 0.62 0.34 to .98

1993 23.01 14.68 to 33.58 2.75 1.56 to 4.32 0.81 0.32 to 1.50 - - 0.19 0.09 to 0.33 0.68 0.39 to 1.06

1994 22.40 14.36 to 32.77 3.10 1.78 to 4.83 0.85 0.35 to 1.55 - - 0.35 0.18 to 0.59 0.79 0.47 to 1.18

1995 22.19 14.12 to 32.46 3.11 1.77 to 4.90 0.81 0.32 to 1.52 - - 0.35 0.18 to 0.58 0.80 0.49 to 1.20

1996 21.83 13.88 to 31.93 3.25 1.87 to 5.09 0.93 0.40 to 1.67 - - 0.36 0.19 to 0.59 0.81 0.49 to 1.22

1997 21.69 13.75 to 31.76 3.33 1.92 to 5.20 0.93 0.40 to 1.67 - - 0.36 0.19 to 0.59 0.81 0.49 to 1.21

1998 21.72 13.63 to 32.09 3.44 1.85 to 5.55 0.83 0.30 to 1.58 - - 0.36 0.08 to 0.73 0.86 0.25 to 1.68

1999 21.64 13.63 to 31.77 3.34 1.77 to 5.44 0.76 0.31 to 1.39 - - 0.36 0.08 to 0.72 0.67 0.28 to 1.26

2000 21.52 13.51 to 31.60 3.26 1.77 to 5.27 0.70 0.27 to 1.31 - - 0.26 0.04 to 0.55 0.62 0.24 to 1.20

2001 21.96 13.84 to 32.17 4.68 1.91 to 9.31 0.42 0.14 to 0.80 - - 0.29 0.06 to 0.59 0.62 0.24 to 1.19

2002 21.92 13.85 to 32.08 4.34 1.73 to 8.88 0.29 0.04 to 0.63 - - 0.27 0.05 to 0.57 0.48 0.17 to 1.01

2003 22.92 14.50 to 33.46 4.04 1.70 to 7.89 0.26 0.02 to 0.60 - - 0.27 0.04 to 0.56 0.30 0.15 to 0.50

2004 22.61 14.24 to 33.46 4.36 1.03 to 11.27 0.29 0.0 to 0.95 - - 0.21 0.12 to 0.34 0.30 0.14 to 0.50

2005 22.39 14.06 to 33.01 4.29 0.95 to 11.22 0.27 0.0 to 0.91 - - 0.21 0.12 to 0.34 0.30 0.14 to 0.50

2006 22.29 13.98 to 32.83 4.17 0.86 to 11.10 0.22 0.0 to 0.81 - - 0.20 0.11 to 0.32 0.30 0.14 to 0.50

2007 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

2008 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

2009 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

2010 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

2011 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

2012 22.14 14.05 to 32.46 4.02 0.69 to 10.93 0.23 0.0 to 0.81 - - 0.18 0.10 to 0.29 0.36 0.17 to 0.61

Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part of the time series.

Grasslands: Grassland Remaining 

Grassland

Cropland Converted to 

Grassland

Forest Converted to 

Grassland

Other Land Converted to 

Grassland

Settlements Converted to 

Grassland

Wetlands Converted to 

Grassland

Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI

Mineral Soils

1990-1992 -0.19 (0.38) to (0.03) -1.73 (2.41) to (1.06) -1.07 (1.51) to (0.67) -0.19 (0.27) to (0.12) -0.41 (0.58) to (0.26) -0.13 (0.19) to (0.08)

1993-1997 -0.08 (0.18) to 0.0 -1.56 (2.18) to (0.94) -1.06 (1.51) to (0.65) -0.21 (0.31) to (0.13) -0.56 (0.80) to (0.35) -0.19 (0.27) to (0.12)

1998-2002 -0.01 (0.08) to 0.06 -1.74 (2.47) to (1.03) -0.79 (1.14) to (0.47) -0.26 (0.37) to (0.15) -0.63 (0.91) to (0.38) -0.20 (0.29) to (0.12)
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

2003-2012 0.10 0.01 to 0.21 -1.28 (1.86) to (0.71) -0.42 (0.62) to (0.24) -0.18 (0.27) to (0.10) -0.52 (0.77) to (0.29) -0.13 (0.19) to (0.07)

Organic Soils

1990 4.60 2.54 to 7.33 0.54 0.24 to 0.98 0.11 0.03 to 0.23 - - 0.01 0.0 to 0.05 0.10 0.02 to 0.22

1991 4.50 2.47 to 7.19 0.50 0.22 to 0.92 0.11 0.03 to 0.24 0.02 0.0 to 0.05 0.01 0.0 to 0.05 0.10 0.02 to 0.22

1992 4.47 2.45 to 7.14 0.55 0.24 to 0.99 0.11 0.03 to 0.23 - - 0.01 0.0 to 0.05 0.10 0.01 to 0.24

1993 4.40 2.42 to 7.03 0.56 0.25 to 1.02 0.10 0.03 to 0.22 - - 0.02 0.0 to 0.06 0.11 0.03 to 0.24

1994 4.29 2.37 to 6.87 0.69 0.31 to 1.23 0.11 0.02 to 0.23 - - 0.02 0.0 to 0.06 0.16 0.07 to 0.31

1995 4.14 2.28 to 6.61 0.73 0.34 to 1.30 0.11 0.02 to 0.23 - - 0.02 0.0 to 0.06 0.17 0.07 to 0.32

1996 4.04 2.22 to 6.47 0.71 0.33 to 1.25 0.10 0.02 to 0.23 - - 0.02 0.0 to 0.06 0.17 0.07 to 0.32

1997 3.91 2.14 to 6.28 0.74 0.35 to 1.30 0.11 0.02 to 0.23 0.01 0.0 to 0.04 0.01 0.0 to 0.03 0.17 0.07 to 0.32

1998 3.80 1.96 to 6.32 0.89 0.40 to 1.63 0.10 0.0 to 0.27 - - 0.02 0.0 to 0.06 0.18 0.05 to 0.38

1999 3.73 1.92 to 6.25 0.89 0.40 to 1.62 0.10 0.0 to 0.27 - - 0.02 0.0 to 0.06 0.17 0.05 to 0.38

2000 3.69 1.91 to 6.14 0.88 0.40 to 1.62 0.09 0.0 to 0.23 - - 0.03 0.01 to 0.07 0.17 0.05 to 0.38

2001 3.28 1.76 to 5.33 0.94 0.42 to 1.70 0.08 0.0 to 0.22 - - 0.02 0.0 to 0.06 0.16 0.04 to 0.35

2002 3.24 1.72 to 5.24 1.05 0.45 to 1.96 0.05 0.0 to 0.16 - - 0.02 0.0 to 0.06 0.16 0.04 to 0.34

2003 3.08 1.66 to 4.97 0.92 0.39 to 1.73 0.05 0.0 to 0.16 0.01 .0 to 0.05 0.02 0.0 to 0.06 0.09 0.05 to 0.16

2004 3.05 1.66 to 4.90 1.03 0.39 to 2.03 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.19

2005 3.06 1.67 to 4.91 1.05 0.40 to 2.05 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.12 0.06 to 0.22

2006 2.97 1.59 to 4.81 0.97 0.36 to 1.93 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.21

2007 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

2008 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

2009 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

2010 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

2011 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

2012 3.03 1.62 to 4.93 0.90 0.33 to 1.78 0.07 0.0 to 0.24 - - 0.03 0.0 to 0.07 0.11 0.05 to 0.20

Note: Estimates after 2007 are based on NRI data from 2007 and therefore do not fully reflect changes occurring in the latter part of the time series.
A-323

Step 2d: Estimate Additional Changes in Soil Organic C Stocks Due to CRP Enrollment after 2007 and Sewage Sludge 

Amendments

There are two additional land use and management activities in U.S. agricultural lands that are not 

estimated in Steps 2a and 2b. The first activity involves 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 is assumed that sewage sludge for agricultural land application is 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 is 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-238), by calculating the effect of converting 

nominal, medium-input grassland to high input improved grassland. The assumptions are that reference C stock are 

50 metric tonnes C/ha, which represents a mid-range value of reference C stocks for the cropland soils in the United 

States,

93 that the land use factor for grassland of 1.4 and 1.11 for high input improved grassland are representative of 

typical conditions, and that the change in stocks are occurring over a 20 year (default value) time period (i.e., [50 × 

1.4 × 1.11 – 50 × 1.4] / 20 = 0.38). A nominal ±50 percent uncertainty is 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 

influence of sewage sludge on soil organic C stocks are provided in Table A-238. 

The second activity is the change in enrollment for the Conservation Reserve Program after 2007 for 

mineral soils. Relative to the enrollment in 2007, the total area in the Conservation Reserve Program has decreased 

from 2008 to 2012 (USDA-FSA 2012). An average annual change in SOC of 0.5 metric tonnes C/ha-yr is 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-234) by estimating the impact of setting aside a medium input cropping system in the 

Conservation Reserve Program. The assumptions are that reference C stock are 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 is 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 year (default 

value) time period equal to 0.5 (i.e., [50 × 1.2 – 50] / 20 = 0.5). A nominal ±50 percent uncertainty is attached to 

these estimates due to limited information about the enrollment trends at subregional scales, which creates 

uncertainty in the rate of soil C stock change (stock change factors for set-aside lands vary by climate region). 

Estimates and uncertainties are provided in Table A-242. 

Step 3: Estimate Soil Organic C Stock Changes and Direct N2O Emissions from Organic Soils

In this step, soil organic C losses and N2O emissions are estimated for organic soils that are drained for 

agricultural production.

Step 3a: Direct N2O Emissions Due to Drainage of Organic Soils in Cropland and Grassland

To estimate annual N2O emissions from drainage of organic soils in cropland and grassland, the area of 

drained organic soils in croplands and grasslands for temperate regions is multiplied by the IPCC (2006) default 

emission factor for temperate soils and the corresponding area in sub-tropical regions is multiplied by the average 

(12 kg 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 is 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 3b: Soil Organic C Stock Changes Due to Drainage of Organic Soils in Cropland and Grassland

Change in soil organic C stocks due to drainage of cropland and grassland soils are estimated annually from 

1990 through 2007, based on the land-use and management activity data in conjunction with appropriate loss rate 

emission factors. Each input to the inventory for the Tier 2 approach has some level of uncertainty that is quantified 

 

93 Reference C stocks are based on cropland soils for the Tier 2 method applied in this Inventory.
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

in PDFs. A Monte Carlo Analysis is used to quantify uncertainty in soil organic C stock changes for the inventory 

period based on uncertainty in the inputs. Input values are 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.

Derive Organic Soil Emission Factors: Organic Soil emission factors representative of U.S. conditions 

have been estimated from published studies (Ogle et al. 2003), based on subsidence studies in the United States and 

Canada (Table A-238). PDFs are constructed as normal densities based on the mean C loss rates and associated 

variances.

Estimate Annual C Emissions from Organic Soils: Losses of C are estimated by applying the Monte Carlo 

approach to the annual data from 1990 through 2007 from the NRI. The results for 2007 are applied to the years 

2007 through 2012. Losses of soil organic C from drainage of cropland and grassland soils are provided in Table A239.

Step 4: Estimate Indirect N2O Emissions for All Land-Use Types 

In this step, N2O emissions are estimated for the two indirect emission pathways (N2O emissions due to 

volatilization, and N2O emissions due to leaching and runoff of N), which are summed to yield total indirect N2O 

emissions from croplands, grasslands, forest lands, and settlements. 

Step 4a: Indirect Soil N2O Emissions Due to Volatilization

Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and 

PRP manure, are calculated according to the amount of mineral N that is transported in gaseous forms from the soil 

profile and later emitted as soil N2O following atmospheric deposition. See Step 1e 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 is multiplied by the IPCC default emission factor of 0.01 kg N2O-N/kg N (IPCC 2006) to 

estimate total N2O emissions from volatilization. The uncertainty is estimated using simple error propagation 

methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the default 

emission factor ranging from 0.002–0.05 kg N2O-N/kg N (IPCC 2006). The estimates and uncertainties are 

provided in Table A- 239.

Step 4b: Indirect Soil N2O Emissions Due to Leaching and Runoff

The amount of mineral N from synthetic fertilizers, commercial organic fertilizers, PRP manure, crop 

residue, N mineralization, asymbiotic fixation that is transported from the soil profile in aqueous form is used to 

calculate indirect emissions from leaching of mineral N from soils and losses in runoff of water associated with 

overland flow. See Step 1e for additional information about the methods used to compute N losses from soils due to 

leaching and runoff in overland water flows.

The total amount of N transported from soil profiles through leaching and surface runoff is multiplied by 

the IPCC default emission factor of 0.0075 kg N2O-N/kg N (IPCC 2006) to estimate emissions for this source. The 

emission estimates are provided in Table A-241. The uncertainty is estimated based on simple error propagation 

methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg N2ON/kg N (IPCC 2006). 

Step 5: Estimate Total Soil Organic C Stock Changes and N2O Emissions for U.S. Soils

Step 5a: Estimate Total Soil N2O Emissions

Total emissions are estimated by adding total direct emissions (from Tier 3 crop types and Tier 1 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. Uncertainties in the final estimate are combined using simple 

error propagation methods (IPCC 2006), and expressed as a 95 percent confidence interval. Estimates and 

uncertainties are provided in Table A- 237.

Direct and indirect simulated emissions of soil N2O vary regionally in both croplands and grasslands as a function of 

N input amount and timing of fertilization, tillage intensity, crop rotation sequence, weather, and soil type. Note that 

there are other management practices, such as fertilizer formulation (Halvorson et al. 2013), that influence emissions 

but are not represented in the model simulations. The highest total N2O emissions from Tier 3 crops occur in Iowa, 
A-325

Illinois, Missouri, Minnesota, the Dakotas, and Kansas (Table A- 243). On a per area unit basis, direct N2O 

emissions are high in the northeast and many of the Mississippi River Basin states where there are high N inputs to 

hay, corn and soybean crops, and in some western states where irrigated crops are grown that require high N inputs 

(Figure A-15). Note that although the total crop area in the northeast is relatively low, emissions are high on a per 

unit area basis because a large portion of the cropped area in these states is used for hay production that receives 

large N inputs from both fertilizer and symbiotic fixation. Indirect emissions tend to be high on a per unit of area 

basis in some northeastern states and Florida because relatively high rainfall and coarse textured soils facilitates N 

losses from leaching and runoff and in some Great Plains states where irrigation can contribute to leaching and

runoff (Figure A-16).

Direct and indirect emissions from non-federal grasslands are typically lower than those from croplands 

(Table A- 243, Figure A-17, and Figure A-18) because N inputs tend to be lower, particularly from synthetic 

fertilizer. Texas, Oklahoma, Kansas, Nebraska, Missouri, Colorado, South Dakota and Montana are the highest 

emitters for this category because large land areas are suded for pastures and rangeland. On a per unit of area basis, 

emissions are higher in the Northeastern United States and some of the Great Lakes and Midwestern states because 

these grasslands 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, but N volatilization can be substantial.

Figure A-15: Tier 3 Crops,2012 Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, (kg N./ha/year) 
A-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-16: Tier 3 Crops, 2012 Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the DAYCENT Model, 

(kg N/ha/year)
A-327

Figure A-17: Non-federal Grasslands, 2012 Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, (kg 

N/ha/year) 
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A-18: Non-federal Grasslands, 2012 Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the 

DAYCENT Model, (kg N/ha/year)

Step 5b: Estimate Total Soil Organic Stock Change

The sum of total CO2 emissions and removals from the Tier 3 DAYCENT Model Approach, Tier 2 IPCC 

Methods and additional land-use and management considerations are provided in Table A-242. The total change in soil 

organic C stocks (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-19 and Figure A-20). The states with highest total amounts of C 

sequestration are Illinois, Iowa, Kansas, Minnesota, Missouri, Ohio and Tennessee (Table A- 244). On a per hectare 

basis, the highest rates of C accumulation occur in states found in the Southeast, Northeast and Midwest. For 

organic soils, emission rates are highest in the regions that contain the majority of drained organic soils, including 

California, Florida, Indiana, Michigan, Minnesota, North Carolina and Wisconsin. On a per unit of area basis, the 

emission rate patterns are very similar to the total emissions in each state, with the highest rates in coastal states of 

the Southeast, states surrounding the Great Lakes, and California.
A-329

Figure A-19: Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2012
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Figure A- 20: Net C Stock Change, per Hectare, for Organic Soils Under Agricultural Management, 2012
A-331

Table A- 237: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of Sewage Sludge to Mineral Soils

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001

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

Assimilative Capacity (Mg N/ha)b 0.12 0.12 0.12 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

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

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

2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Sewage Sludge N Applied to Agricultural 

Land (Mg N)a 88,736 91,358 93,991 98,081 100,887 103,682 106,468 109,245 112,015 114,778 117,536

Assimilative Capacity (Mg N/ha)b 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 727,341 748,836 770,418 803,942 826,940 849,851 872,686 895,452 918,156 940,805 963,407

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

Contribution to Soil C (TgCO2/yr)e,f -1.01 -1.04 -1.07 -1.12 -1.15 -1.18 -1.22 -1.25 -1.28 -1.31 -1.34

 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 

 1990 – 1992 and the 1997 rate was applied to 1993-2012.

 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.
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-238: 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 1 11.2±2.5 0.25 2.8±0.5a

a There are not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values for cropland, which is an assumption that is used for the IPCC default 

organic soil C losses on grassland.

Table A- 239: Indirect N2O Emissions from Volatilization (Tg CO2 Eq.)

Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Croplands 15.1 16.1 15.8 15.9 16.4 15.8 15.5 15.5 15.5 16.0 15.8 15.9 16.5 15.9 15.5 15.3 15.3 15.5 15.4 

Settlements 0.1 0.2 0.2 0.2 0.1 0.1 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 

Forest Land + + + + + + + + + + + + + + + + + + + 

Grasslands 4.9 5.6 5.4 5.4 5.5 5.1 4.9 5.0 4.9 5.2 5.7 5.5 5.3 5.5 5.5 5.5 5.4 5.3 5.4 

Total 20.2 21.9 21.4 21.5 22.1 21.0 20.6 20.8 20.6 21.4 21.7 21.6 22.0 21.7 21.3 21.0 20.9 21.0 21.0 

+ Less than 0.05 Tg CO2 Eq.

Table A- 240: Indirect N2O Emissions from Leaching and Runoff (Tg CO2 Eq.)

Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Croplands 16.4 21.3 21.0 16.6 22.1 24.4 14.5 17.9 16.6 14.7 18.8 16.8 14.5 23.6 22.7 22.3 19.8 19.8 19.5 

Settlements 0.2 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 

Forest Land + + 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Grasslands 4.5 6.2 6.3 5.8 5.6 5.3 5.0 4.8 4.6 4.9 4.9 5.1 5.4 5.2 5.1 5.0 4.8 4.5 4.8 

Total 21.2 27.9 27.7 22.8 28.0 30.1 19.9 23.2 21.7 20.1 24.2 22.4 20.4 29.3 28.2 27.8 25.2 24.8 24.8 

+ Less than 0.05 Tg CO2 Eq.
A-333

Table A-241: Total N2O Emissions from Agricultural Soil Management (Tg CO2 Eq.)

Activity 1990 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Total Direct 240.7 263.2 282.0 278.1 238.8 239.6 222.7 242.3 233.6 235.2 256.5 253.3 251.1 272.4 269.5 267.6 264.0 261.9 260.9 

Direct Emissions from Mineral 

Cropland Soils 150.4 161.0 173.3 169.0 149.2 153.2 142.6 154.2 151.9 152.2 160.0 158.7 159.5 164.8 162.5 161.1 158.1 157.0 155.7 

Synthetic Fertilizer 65.5 66.4 74.4 72.2 62.3 66.0 62.9 63.3 64.3 64.1 67.2 65.8 64.5 71.6 69.5 69.0 68.6 67.4 67.3 

Organic Amendmenta 14.0 14.7 15.2 15.5 14.3 14.9 14.7 15.1 15.2 15.1 15.0 15.3 15.8 16.0 15.8 15.7 15.4 15.5 15.5 

Residue Nb 3.9 4.5 4.7 4.6 4.1 4.4 4.0 4.4 4.0 4.3 4.5 4.8 4.4 4.6 4.6 4.6 4.5 4.5 4.4 

Mineralization and Asymbiotic Fixation 67.0 75.4 78.9 76.7 68.6 67.9 61.0 71.4 68.3 68.7 73.2 72.9 74.7 72.6 72.5 71.8 69.5 69.6 68.5 

Direct Emissions from Drained 

Organic Cropland Soils 4.7 4.5 4.5 4.4 4.5 4.4 4.4 4.3 4.3 4.2 4.2 4.1 4.1 4.0 4.0 4.0 4.0 4.0 4.0 

Direct Emissions from Grasslands 85.6 97.6 104.3 104.7 85.1 82.0 75.7 83.8 77.5 78.9 92.3 90.5 87.6 103.6 103.0 102.5 101.9 100.9 101.1 

Synthetic Mineral Fertilizer 0.5 0.7 0.8 0.7 0.5 0.7 0.6 0.7 0.8 0.7 1.0 1.0 1.2 1.0 1.0 1.0 1.0 1.0 0.9 

PRP Manure 24.5 29.4 31.5 29.8 26.1 26.3 24.3 25.6 24.5 24.1 25.0 25.5 26.2 27.0 26.6 26.3 25.8 25.0 25.4 

Managed Manure + + + + + + + + + + + + + + + + + + + 

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 0.5 0.5 0.5 0.5 0.5 0.6 0.6 

Residueb 2.0 2.5 2.7 2.6 2.0 2.0 1.6 2.0 1.9 1.9 2.2 2.4 2.2 2.6 2.6 2.6 2.6 2.5 2.5 

Mineralization and Asymbiotic Fixation 58.2 64.4 68.6 70.9 55.8 52.3 48.6 54.8 49.6 51.5 63.4 60.8 57.2 72.2 72.0 71.9 71.7 71.5 71.3 

Total Indirect 41.4 49.8 49.1 44.3 50.1 51.1 40.5 43.9 42.3 41.5 45.9 44.0 42.4 51.0 49.5 48.8 46.1 45.8 45.7 

Volatilization 20.2 21.9 21.4 21.5 22.1 21.0 20.6 20.8 20.6 21.4 21.7 21.6 22.0 21.7 21.3 21.0 20.9 21.0 21.0 

Leaching/Runoff 21.2 27.9 27.7 22.8 28.0 30.1 19.9 23.2 21.7 20.1 24.2 22.4 20.4 29.3 28.2 27.8 25.2 24.8 24.8 

Total Emissions 282.1 312.9 331.2 322.5 288.8 290.7 263.2 286.2 275.9 276.7 302.4 297.3 293.6 323.4 319.0 316.4 310.1 307.8 306.6 

+ Less than 0.05 Tg CO2 Eq.

a Organic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e., dried blood, tankage, compost, and other).

b Residue N inputs include unharvested fixed N from legumes as well as crop residue N.
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-242: Annual Soil C Stock Change in Cropland Remaining Cropland (CRC), Land Converted to Cropland (LCC), Grassland Remaining Grassland (GRG), and Land 

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 2007 2008 2009 2010 2011 2012

Net emissions based on Tier 3 Century-based analysis (Step 2)

 CRC (69.4) (74.4) (66.2) (45.7) (53.0) (41.9) (51.5) (46.8) (38.3) (41.3) (57.3) (45.2) (37.9) (35.7) (38.9) (48.7) (50.9) (50.6) (50.6) (50.6) (50.6) (50.6) (50.6)

 GCC 20.0 17.3 19.1 20.2 12.3 20.3 16.2 16.6 11.9 12.1 11.9 13.4 12.5 12.9 10.6 14.3 11.0 10.5 10.5 10.5 10.5 10.5 10.5 

 GRG (13.4) (2.6) (11.3) (0.7) (18.1) 1.7 (21.6) (8.5) (9.7) 0.5 (33.5) (11.5) (14.7) (10.5) (0.2) 3.6 (19.7) 4.8 4.9 4.9 4.9 4.9 4.9 

 CCG (4.6) (5.3) (4.9) (3.8) (5.4) (5.5) (6.3) (6.0) (6.2) (6.6) (8.7) (7.3) (7.0) (7.0) (8.2) (7.0) (7.0) (7.3) (7.3) (7.2) (7.2) (7.2) (7.1)

Net emissions based on the IPCC Tier 2 analysis (Step 3)

Mineral Soils

CRC (6.5) (6.5) (6.5) (7.6) (7.6) (7.6) (7.6) (7.6) (6.9) (6.9) (6.9) (6.9) (6.9) (2.8) (2.8) (2.8) (2.8) (2.8) (2.8) (2.8) (2.8) (2.8) (2.8)

GCC 2.3 2.3 2.3 2.0 2.0 2.0 2.0 2.0 1.8 1.8 1.8 1.8 1.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 0.8 

FCC 1.5 1.5 1.5 1.4 1.4 1.4 1.4 1.4 0.8 0.8 0.8 0.8 0.8 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 

OCC 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 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

SCC 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 

WCC 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 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

GRG (0.2) (0.2) (0.2) (0.1) (0.1) (0.1) (0.1) (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

CCG (1.7) (1.7) (1.7) (1.6) (1.6) (1.6) (1.6) (1.6) (1.7) (1.7) (1.7) (1.7) (1.7) (1.3) (1.3) (1.3) (1.3) (1.3) (1.3) (1.3) (1.3) (1.3) (1.3)

FCG (1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (1.1) (0.8) (0.8) (0.8) (0.8) (0.8) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4) (0.4)

OCG (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3) (0.3) (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)

SCG (0.4) (0.4) (0.4) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5)

WCG (0.1) (0.1) (0.1) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1) (0.1)

Organic Soils

CRC 24.0 23.7 23.7 23.0 22.4 22.2 21.8 21.7 21.7 21.6 21.5 22.0 21.9 22.9 22.6 22.4 22.3 22.1 22.1 22.1 22.1 22.1 22.1 

GCC 2.5 2.6 2.4 2.7 3.1 3.1 3.3 3.3 3.4 3.3 3.3 4.7 4.3 4.0 4.4 4.3 4.2 4.0 4.0 4.0 4.0 4.0 4.0 

FCC (0.2) (0.2) 0.8 0.8 0.9 0.8 0.9 0.9 0.8 0.8 0.7 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 

OCC 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

SCC (0.0) (0.0) 0.1 0.2 0.4 0.4 0.4 0.4 0.4 0.4 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 

WCC (0.2) (0.2) 0.6 0.7 0.8 0.8 0.8 0.8 0.9 0.7 0.6 0.6 0.5 0.3 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 

GRG 4.6 4.5 4.5 4.4 4.3 4.1 4.0 3.9 3.8 3.7 3.7 3.3 3.2 3.1 3.1 3.1 3.0 3.0 3.0 3.0 3.0 3.0 3.0 

CCG 0.5 0.5 0.5 0.6 0.7 0.7 0.7 0.7 0.9 0.9 0.9 0.9 1.1 0.9 1.0 1.0 1.0 0.9 0.9 0.9 0.9 0.9 0.9 

FCG 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

OCG 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

SCG 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 

WCG 0.1 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

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) (1.2) (1.2) (1.2) (1.3) (1.3) (1.3)

Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4)

CRC - - - - - - - - - - - - - - - - - - 1.4 2.0 3.6 3.7 4.8

Total Stock Changes by Land Use/Land-Use Change Category (Step 5)

CRC (51.9) (57.2) (49.0) (30.3) (38.2) (27.3) (37.3) (32.7) (23.5) (26.6) (42.7) (30.2) (22.9) (15.6) (19.2) (29.1) (31.4) (31.3) (29.8) (29.2) (27.6) (27.5) (26.5)

GCC 24.8 22.2 23.9 24.9 17.4 25.4 21.5 21.9 17.1 17.2 17.0 19.8 18.6 17.7 15.7 19.3 15.9 15.3 15.3 15.3 15.3 15.3 15.3 

FCC 1.2 1.2 2.2 2.2 2.2 2.2 2.3 2.3 1.7 1.6 1.5 1.2 1.1 0.5 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

OCC 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 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

SCC 0.5 0.5 0.6 0.9 1.1 1.1 1.1 1.1 1.0 1.0 0.9 0.9 0.9 0.6 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 0.5 

WCC 0.0 (0.0) 0.8 0.9 1.0 1.0 1.1 1.1 1.1 0.9 0.8 0.8 0.7 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.4 

GRG (9.6) 1.1 (7.7) 3.0 (14.6) 5.0 (18.4) (5.5) (6.8) 3.3 (30.7) (9.2) (12.5) (8.3) 1.9 5.6 (17.8) 6.8 6.8 6.8 6.7 6.7 6.7 

CCG (5.8) (6.5) (6.0) (4.8) (6.2) (6.4) (7.2) (6.8) (7.0) (7.5) (9.5) (8.1) (7.7) (7.3) (8.4) (7.3) (7.3) (7.7) (7.7) (7.6) (7.6) (7.5) (7.5)
A-335

FCG (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (1.0) (0.7) (0.7) (0.7) (0.7) (0.7) (0.4) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3) (0.3)

OCG (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.3) (0.3) (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)

SCG (0.4) (0.4) (0.4) (0.5) (0.5) (0.5) (0.5) (0.6) (0.6) (0.6) (0.6) (0.6) (0.6) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5) (0.5)

WCG (0.0) (0.0) (0.0) (0.1) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0) (0.0)

Total * (41.9) (39.9) (36.5) (4.7) (38.8) (0.4) (38.4) (20.1) (17.8) (11.3) (64.1) (26.0) (23.1) (13.0) (9.4) (11.0) (40.2) (16.4) (14.9) (14.3) (12.7) (12.5) (11.4)

Note: Totals may not sum due to independent rounding.
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 243: Total 2012 N2O Emissions (Direct and Indirect) from Agricultural Soil Management by State (Tg CO2 Eq.)

a Emissions from non-manure organic N inputs for minor crops were not estimated (n.e.) at the state level.

b Emissions from sewage sludge applied to grasslands and were not estimated (n.e.) at the state level

c Emissions from sewage sludge applied to settlements were not estimated (n.e.) at the state level.

d Forestland emissions were not estimated (n.e.) at the state level.

e N2O emissions are not reported for Hawaii except from cropland organic soils.

State Croplandsa Grasslandsb Settlementsc Forest Landsd Total

Lower 

Bound

Upper 

Bound

AL 1.19 0.88 0.03 NE 2.14 1.39 3.77

AR 4.20 1.40 0.02 NE 5.70 4.24 8.44

AZ 0.98 1.30 0.01 NE 2.35 1.34 4.34

CA 7.51 2.17 0.18 NE 10.35 3.63 21.08

CO 4.08 6.10 0.02 NE 10.24 7.16 15.15

CT 0.21 0.04 0.02 NE 0.27 0.21 0.42

DE 0.24 0.01 0.01 NE 0.27 0.19 1.12

FL 4.91 1.59 0.21 NE 6.97 2.85 14.02

GA 1.58 0.60 0.01 NE 2.25 1.25 4.41

HIe 0.03 n.e. n.e. NE 0.03 0.01 0.11

IA 16.46 2.23 0.07 NE 18.76 14.46 25.47

ID 4.00 2.96 0.02 NE 7.05 5.33 10.13

IL 14.94 1.44 0.11 NE 16.51 12.92 22.07

IN 7.65 2.71 0.07 NE 10.45 7.16 15.52

KS 12.26 4.63 0.06 NE 16.98 13.22 22.74

KY 2.40 2.20 0.03 NE 4.66 3.23 7.13

LA 1.32 0.89 0.04 NE 2.25 1.54 3.82

MA 0.38 0.06 0.04 NE 0.49 0.37 1.36

MD 0.59 0.19 0.05 NE 0.85 0.57 1.92

ME 0.68 0.14 0.01 NE 0.84 0.60 1.79

MI 5.08 1.31 0.08 NE 6.56 4.90 9.60

MN 9.96 1.48 0.03 NE 11.53 9.18 15.90

MO 7.67 6.07 0.07 NE 13.82 10.85 18.75

MS 2.80 0.74 0.03 NE 3.59 2.73 5.50

MT 5.64 10.59 0.01 NE 16.24 11.97 22.52

NC 2.13 0.56 0.05 NE 2.85 1.23 5.88

ND 7.57 1.83 0.03 NE 9.43 7.39 12.44

NE 8.62 5.45 0.06 NE 14.16 7.10 24.40

NH 0.22 0.04 0.01 NE 0.27 0.19 1.17

NJ 0.38 0.07 0.07 NE 0.54 0.28 1.52

NM 1.82 3.10 0.01 NE 5.00 3.09 8.33

NV 0.33 1.12 0.01 NE 1.47 0.97 2.73

NY 6.01 1.82 0.07 NE 7.94 6.37 10.79

OH 6.40 1.18 0.10 NE 7.89 5.02 12.59

OK 3.19 5.79 0.03 NE 9.14 6.34 13.53

OR 2.10 3.90 0.01 NE 6.04 4.73 8.25

PA 4.96 0.93 0.06 NE 6.03 4.54 9.42

RI 0.02 0.01 0.01 NE 0.04 0.02 0.84

SC 0.68 0.29 0.03 NE 1.02 0.65 2.18

SD 6.66 4.70 0.02 NE 11.38 8.45 15.26

TN 1.60 1.33 0.05 NE 3.00 1.98 4.97

TX 9.06 14.01 0.09 NE 23.26 16.07 33.96

UT 0.99 1.73 0.01 NE 2.75 1.97 4.40

VA 0.88 1.47 0.05 NE 2.43 1.35 4.56

VT 1.22 0.28 0.00 NE 1.50 1.19 22.27

WA 3.72 2.57 0.03 NE 6.39 4.83 9.05

WI 4.40 1.58 0.04 NE 6.12 4.41 10.37

WV 0.56 0.47 0.00 NE 1.06 0.57 2.39

WY 1.13 4.51 0.01 NE 5.68 1.86 1.90
A-337

Table A- 244: Soil C Stock Change for Mineral and Organic Soils during 2012 within individual states (Tg CO2 Eq.)

Note: Parentheses indicate net C accumulation. Estimates do not include soil C stock change associated with CRP enrollment after 2007 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 2007 are not included, and differences arise due to rounding of 

values in this table.

State Mineral Soil Organic Soil Total

AL (1.31) - (1.31)

AR (0.17) - (0.17)

AZ 0.20 - 0.20 

CA (0.94) 1.55 0.61 

CO 0.16 0.00 0.17 

CT (0.05) 0.00 (0.04)

DE (0.12) 0.01 (0.11)

FL 0.28 10.11 10.39 

GA (0.67) - (0.67)

HI (0.02) 0.25 0.24 

IA (8.59) 0.54 (8.05)

ID (1.07) 0.08 (0.98)

IL (5.00) 0.62 (4.38)

IN (1.71) 2.37 0.67 

KS (2.99) - (2.99)

KY (1.82) - (1.82)

LA (1.45) 0.33 (1.11)

MA (0.07) 0.12 0.05 

MD (0.37) 0.02 (0.34)

ME (0.22) 0.00 (0.21)

MI (0.75) 3.05 2.29 

MN (2.07) 5.85 3.78 

MO (2.47) - (2.47)

MS (1.03) 0.00 (1.03)

MT 1.72 0.16 1.87 

NC (1.21) 1.90 0.68 

ND (1.63) - (1.63)

NE (0.52) 0.00 (0.52)

NH (0.03) 0.05 0.02 

NJ (0.15) 0.06 (0.10)

NM 1.13 - 1.13 

NV (0.12) 0.00 (0.12)

NY 0.19 0.41 0.61 

OH (2.11) 0.47 (1.64)

OK (0.26) - (0.26)

OR (1.71) 0.34 (1.38)

PA (0.84) 0.02 (0.82)

RI (0.01) 0.02 0.01 

SC (0.64) 0.02 (0.62)

SD (0.68) - (0.68)

TN (2.14) - (2.14)

TX 0.07 - 0.07 

UT 0.48 0.08 0.56 

VA (1.23) 0.00 (1.23)

VT (0.02) 0.06 0.03 

WA (1.10) 0.31 (0.79)

WI 0.48 2.28 2.76 

WV (0.59) - (0.59)

WY 0.93 - 0.93 
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

3.13.Methodology for Estimating Net Carbon Stock Changes in Forest Lands 

Remaining Forest Lands

This sub-annex expands on the methodology used to estimate 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. Carbon stocks are estimated based 

on data from each inventory, at the level of permanent inventory plots. Carbon 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 from forest inventory surveys. Forest inventory data were 

obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Frayer and Furnival 1999, USDA 

Forest Service 2013a, USDA Forest Service 2013b). Forest Inventory and Analysis 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 downed dead wood, litter, 

and soil C. However, the technical advances needed to estimate C stocks from these data are still under development (e.g., 

forest floor, Woodall et al. 2012). The field protocols are thoroughly documented and available for download from the 

USDA Forest Service (2013c). Bechtold and Patterson (2005) provide the estimation procedures for standard forest 

inventory results. The data are freely available for download at USDA Forest Service (2011b) as the Forest Inventory and 

Analysis Database (FIADB) Version 5.1.6 (USDA Forest Service 2013b, USDA Forest Service 2013c); 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 million hectares of forest land, and these 

inventoried lands, comprising an estimated 12 percent of the total forest land in Alaska, contribute to the forest C 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.94 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 help 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).

 

94 More information on agroforestry practices can be found online at <http://www.unl.edu/nac>.
A-339

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 essentially based on methodology and 

algorithms coded into the computer tool described in Smith et al. (2010). A recent change in methods for the inventory 

involves a modification of the downed dead wood estimates to incorporate population estimates of down woody material 

measured on a subset of the inventory plots (Domke et al. 2013, Woodall and Monleon 2008, Woodall et al. 2013). The C 

calculation tool 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. 

Carbon 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).95 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-245: 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 5.1.6, 1982 1982

FIADB 5.1.6, 1990 1990

FIADB 5.1.6, 2000 1999

FIADB 5.1.6, 2005 2003

FIADB 5.1.6, 2012 2009

Alaska, Coastal east non-reserved FIADB 5.1.6, 2003 1997

FIADB 5.1.6, 2011 2008

Alaska, Coastal reserved FIADB 5.1.6, 2011 2006

Alaska, Coastal west non-reserved FIADB 5.1.6, 2003 2001

FIADB 5.1.6, 2011 2008

Arizona, NFS non-woodlands 1987 RPA 1985

FIADB 5.1.6, 1999 1996

FIADB 5.1.6, 2010 2006

Arizona, NFS woodlands 1987 RPA 1984

 

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-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

FIADB 5.1.6, 1999 1996

FIADB 5.1.6, 2010 2006

Arizona, non-NFS non-woodlands FIADB 5.1.6, 1985 1986

FIADB 5.1.6, 1999 1996

FIADB 5.1.6, 2010 2007

Arizona, non-NFS woodlands FIADB 5.1.6, 1999 1990

FIADB 5.1.6, 2010 2006

Arkansas FIADB 5.1.6, 1988 1988

FIADB 5.1.6, 1995 1996

FIADB 5.1.6, 2005 2003

FIADB 5.1.6, 2010 2008

California, NFS IDB, 1990s 1997

FIADB 5.1.6, 2010 2006

California, non-NFS IDB, 1990s 1993

FIADB 5.1.6, 2010 2006

Colorado, NFS non-woodlands 1997 RPA 1981

FIADB 5.1.6, 2011 2007

Colorado, NFS woodlands FIADB 5.1.6, 2011 2007

Colorado, non-NFS non-woodlands Westwide, 1983 1980

FIADB 5.1.6, 2011 2007

Colorado, non-NFS woodlands Westwide, 1983 1983

FIADB 5.1.6, 2011 2007

Connecticut FIADB 5.1.6, 1985 1985

FIADB 5.1.6, 1998 1998

FIADB 5.1.6, 2007 2006

FIADB 5.1.6, 2011 2010

Delaware FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1999 1999

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2011 2010

Florida FIADB 5.1.6, 1987 1987

FIADB 5.1.6, 1995 1995

FIADB 5.1.6, 2007 2005

FIADB 5.1.6, 2011 2009

Georgia FIADB 5.1.6, 1989 1989

FIADB 5.1.6, 1997 1997

FIADB 5.1.6, 2004 2002

FIADB 5.1.6, 2009 2007

FIADB 5.1.6, 2012 2010

Idaho, Caribou-Targhee NF Westwide, 1991 1992

FIADB 5.1.6, 2011 2008

Idaho, Kootenai NF 1987 RPA 1988

FIADB 5.1.6, 1991 1995

FIADB 5.1.6, 2011 2008

Idaho, Payette NF 1987 RPA 1982

FIADB 5.1.6, 2011 2008

Idaho, Salmon-Challis NF 1987 RPA 1978

FIADB 5.1.6, 2011 2008
A

-341

Idaho, Sawtooth NF Westwide, 1991 1983

FIADB 5.1.6, 1991 1996

FIADB 5.1.6, 2011 2008

Idaho, non-NFS non-woodlands FIADB 5.1.6, 1991 1990

FIADB 5.1.6, 2011 2008

Idaho, non-NFS woodlands FIADB 5.1.6, 1991 1982

FIADB 5.1.6, 2011 2008

Idaho, other NFS Westwide, 1991 1988

FIADB 5.1.6, 1991 2000

FIADB 5.1.6, 2011 2008

Illinois FIADB 5.1.6, 1985 1985

FIADB 5.1.6, 1998 1998

FIADB 5.1.6, 2005 2004

FIADB 5.1.6, 2010 2008

Indiana FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1998 1998

FIADB 5.1.6, 2003 2001

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2012 2010

Iowa FIADB 5.1.6, 1990 1990

FIADB 5.1.6, 2003 2002

FIADB 5.1.6, 2008 2006

FIADB 5.1.6, 2012 2010

Kansas FIADB 5.1.6, 1981 1981

FIADB 5.1.6, 1994 1994

FIADB 5.1.6, 2005 2003

FIADB 5.1.6, 2010 2009

Kentucky FIADB 5.1.6, 1988 1987

FIADB 5.1.6, 2004 2002

FIADB 5.1.6, 2009 2008

Louisiana FIADB 5.1.6, 1984 1984

FIADB 5.1.6, 1991 1991

FIADB 5.1.6, 2005 2004

Maine Eastwide, 1982 1983

FIADB 5.1.6, 1995 1995

FIADB 5.1.6, 2003 2002

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2012 2010

Maryland FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1999 2000

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2011 2010

Massachusetts FIADB 5.1.6, 1985 1985

FIADB 5.1.6, 1998 1998

FIADB 5.1.6, 2007 2006

FIADB 5.1.6, 2011 2010

Michigan FIADB 5.1.6, 1980 1980
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

FIADB 5.1.6, 1993 1993

FIADB 5.1.6, 2004 2003

FIADB 5.1.6, 2009 2007

FIADB 5.1.6, 2012 2010

Minnesota FIADB 5.1.6, 1990 1989

FIADB 5.1.6, 2003 2001

FIADB 5.1.6, 2008 2006

FIADB 5.1.6, 2012 2010

Mississippi FIADB 5.1.6, 1987 1987

FIADB 5.1.6, 1994 1994

FIADB 5.1.6, 2006 2007

FIADB 5.1.6, 2012 2009

Missouri FIADB 5.1.6, 1989 1988

FIADB 5.1.6, 2003 2002

FIADB 5.1.6, 2008 2006

FIADB 5.1.6, 2012 2010

Montana, NFS 1987 RPA 1988

FIADB 5.1.6, 1989 1996

FIADB 5.1.6, 2011 2008

Montana, non-NFS non-reserved FIADB 5.1.6, 1989 1989

FIADB 5.1.6, 2011 2008

Montana, non-NFS reserved 1997 RPA 1990

FIADB 5.1.6, 2011 2008

Nebraska FIADB 5.1.6, 1983 1983

FIADB 5.1.6, 1994 1995

FIADB 5.1.6, 2005 2004

FIADB 5.1.6, 2010 2008

Nevada, NFS non-woodlands 1987 RPA 1974

FIADB 5.1.6, 1989 1997

FIADB 5.1.6, 2005 2005

Nevada, NFS woodlands 1987 RPA 1978

FIADB 5.1.6, 1989 1997

FIADB 5.1.6, 2005 2005

Nevada, non-NFS non-woodlands 1997 RPA 1985

FIADB 5.1.6, 2005 2005

Nevada, non-NFS woodlands FIADB 5.1.6, 1989 1980

FIADB 5.1.6, 2005 2005

New Hampshire FIADB 5.1.6, 1983 1983

FIADB 5.1.6, 1997 1997

FIADB 5.1.6, 2007 2005

FIADB 5.1.6, 2012 2011

New Jersey FIADB 5.1.6, 1987 1987

FIADB 5.1.6, 1999 1999

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2011 2010

New Mexico, NFS non-woodlands 1987 RPA 1986

FIADB 5.1.6, 1999 1997

FIADB 5.1.6, 2012 2011
A

-343

New Mexico, NFS woodlands 1987 RPA 1986

FIADB 5.1.6, 1999 1997

FIADB 5.1.6, 2012 2011

New Mexico, non-NFS non-timberlands FIADB 5.1.6, 2012 2011

New Mexico, non-NFS timberlands FIADB 5.1.6, 1987 1987

FIADB 5.1.6, 1999 1999

FIADB 5.1.6, 2012 2011

New York, non-reserved Eastwide, 1980 1981

FIADB 5.1.6, 1993 1993

FIADB 5.1.6, 2007 2005

FIADB 5.1.6, 2011 2010

New York, reserved 1987 RPA 1988

FIADB 5.1.6, 2007 2005

FIADB 5.1.6, 2011 2010

North Carolina FIADB 5.1.6, 1984 1984

FIADB 5.1.6, 1990 1990

FIADB 5.1.6, 2002 2001

FIADB 5.1.6, 2007 2006

FIADB 5.1.6, 2012 2009

North Dakota FIADB 5.1.6, 1980 1979

FIADB 5.1.6, 1995 1995

FIADB 5.1.6, 2005 2003

FIADB 5.1.6, 2010 2009

Ohio FIADB 5.1.6, 1991 1991

FIADB 5.1.6, 2006 2005

FIADB 5.1.6, 2011 2010

Oklahoma, Central & West FIADB 5.1.6, 2011 2011

Oklahoma, East FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1993 1993

FIADB 5.1.6, 2008 2008

Oregon, NFS East IDB, 1990s 1995

FIADB 5.1.6, 2010 2006

Oregon, NFS West IDB, 1990s 1996

FIADB 5.1.6, 2010 2006

Oregon, non-NFS East Westwide, 1992 1991

IDB, 1990s 1999

FIADB 5.1.6, 2010 2006

Oregon, non-NFS West Westwide, 1992 1989

IDB, 1990s 1997

FIADB 5.1.6, 2010 2006

Pennsylvania FIADB 5.1.6, 1989 1990

FIADB 5.1.6, 2004 2003

FIADB 5.1.6, 2009 2008

Rhode Island FIADB 5.1.6, 1985 1985

FIADB 5.1.6, 1998 1999

FIADB 5.1.6, 2007 2006

FIADB 5.1.6, 2011 2010
A-344 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

South Carolina FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1993 1993

FIADB 5.1.6, 2001 2001

FIADB 5.1.6, 2006 2005

FIADB 5.1.6, 2011 2009

South Dakota, NFS 1997 RPA 1986

FIADB 5.1.6, 1995 1999

FIADB 5.1.6, 2005 2004

FIADB 5.1.6, 2010 2009

South Dakota, non-NFS 1987 RPA 1986

FIADB 5.1.6, 1995 1995

FIADB 5.1.6, 2005 2004

FIADB 5.1.6, 2010 2008

Tennessee FIADB 5.1.6, 1989 1989

FIADB 5.1.6, 1999 1998

FIADB 5.1.6, 2004 2003

FIADB 5.1.6, 2009 2008

Texas, Central & West FIADB 5.1.6, 2010 2008

Texas, East FIADB 5.1.6, 1986 1986

FIADB 5.1.6, 1992 1992

FIADB 5.1.6, 2003 2003

FIADB 5.1.6, 2008 2006

FIADB 5.1.6, 2012 2010

Utah, non-woodlands FIADB 5.1.6, 1993 1993

FIADB 5.1.6, 2009 2005

Utah, woodlands FIADB 5.1.6, 1993 1994

FIADB 5.1.6, 2009 2005

Vermont FIADB 5.1.6, 1983 1983

FIADB 5.1.6, 1997 1997

FIADB 5.1.6, 2007 2006

FIADB 5.1.6, 2011 2010

Virginia FIADB 5.1.6, 1985 1985

FIADB 5.1.6, 1992 1991

FIADB 5.1.6, 2001 2000

FIADB 5.1.6, 2007 2005

FIADB 5.1.6, 2011 2010

Washington, NFS East IDB, 1990s 1996

FIADB 5.1.6, 2011 2007

Washington, NFS West IDB, 1990s 1996

FIADB 5.1.6, 2011 2007

Washington, non-NFS East IDB, 1990s 1992

FIADB 5.1.6, 2011 2007

Washington, non-NFS West IDB, 1990s 1990

FIADB 5.1.6, 2011 2007

West Virginia FIADB 5.1.6, 1989 1988

FIADB 5.1.6, 2000 2001

FIADB 5.1.6, 2008 2007

FIADB 5.1.6, 2011 2010
A-345

Wisconsin FIADB 5.1.6, 1983 1982

FIADB 5.1.6, 1996 1995

FIADB 5.1.6, 2004 2002

FIADB 5.1.6, 2009 2007

FIADB 5.1.6, 2012 2010

Wyoming, NFS 1997 RPA 1982

FIADB 5.1.6, 2000 2000

Wyoming, non-NFS non-reserved non-woodlands FIADB 5.1.6, 1984 1984

FIADB 5.1.6, 2000 2002

Wyoming, non-NFS non-reserved woodlands FIADB 5.1.6, 1984 1984

FIADB 5.1.6, 2000 2002

Wyoming, non-NFS reserved 1997 RPA 1985

FIADB 5.1.6, 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 2013c), non-woodlands (used 

for clarity to emphasize that woodlands are classified separately), reserved (forest land withdrawn from timber utilization through statute, administrative 

regulation, or designation, Smith et al. (2009)), and non-reserved (forest land that is not reserved, used for clarity). Some National Forests are listed 

individually by name, e.g., Payette NF. Oregon and Washington were divided into eastern and western forests (east or west of the crest of the Cascade 

Mountains). Oklahoma and Texas are divided into East versus Central & West according to forest inventory survey units (USDA Forest Service 2013d). 

Alaska is represented by a portion of forest land, in the southcentral and southeast part of the state.

b FIADB 5.1.6 is the current, publicly available, format of FIA inventory data, and these files were downloaded from the Internet 11 July 2013 (USDA 

Forest Service 2013b). 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 national plot design and annualized sampling (USDA Forest Service 2013a) 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 July 2013. 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 one or two cycles 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. The specific surveys used in this report are listed in Table A-245, and this list can be compared with the 

full set of summaries available for download (USDA Forest Service 2013b).

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. Carbon stock and flux estimates for each pool are 

summed over all forest land in all states as identified in the FIADB to form estimates for the United States. Summed net 

annual stock change and stocks are presented in Table A-245 and Table A-246 , respectively. An estimate of forest area 

based on the interpolation and extrapolation procedure described above is also provided in Table A-247. Estimated net 
A-346 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

stock change of non-soil forest ecosystem carbon for each of the states is shown in Table A-248, which also includes 

estimated forest area and total non-soil forest C stock. The state-level forest areas and C stocks are from the most recent 

inventory available (USDA Forest Service 2011a), and the estimate for net stock change is the 10-year mean of the 2003 

through 2012 estimates from the C calculator (Smith et al. 2010).
A-347

Table A-246: Estimated Net Annual Changes in Carbon Stocks (Tg C yr-1) in Forest and Harvested Wood Pools, 1990–2012

Year Total Net Flux Forest Total

Live, 

aboveground

Live, 

belowground Dead Wood Litter

Soil Organic 

Carbon

Harvested 

Wood Total

Products in 

Use SWDS

1990 (192.2) (156.2) (96.7) (18.9) (13.8) (6.5) (20.3) (35.9) (17.7) (18.3)

1991 (189.8) (156.0) (96.6) (18.9) (14.0) (6.5) (20.1) (33.8) (14.9) (18.8)

1992 (188.3) (154.5) (97.5) (19.1) (14.1) (6.5) (17.3) (33.8) (16.3) (17.4)

1993 (188.9) (155.9) (103.4) (20.3) (14.9) (5.6) (11.7) (32.9) (15.0) (17.9)

1994 (195.6) (162.2) (106.5) (20.9) (14.9) (5.1) (14.8) (33.4) (15.9) (17.5)

1995 (197.0) (164.7) (109.7) (21.6) (14.2) (3.4) (15.8) (32.3) (15.1) (17.2)

1996 (193.3) (162.7) (109.1) (21.5) (19.9) (2.5) (9.6) (30.6) (14.1) (16.5)

1997 (184.3) (152.3) (110.1) (21.7) (18.6) (2.3) 0.4 (32.0) (14.7) (17.3)

1998 (169.5) (138.3) (105.2) (20.7) (18.6) 1.7 4.6 (31.1) (13.4) (17.7)

1999 (152.7) (120.2) (101.2) (20.0) (18.2) 5.0 14.2 (32.5) (14.1) (18.4)

2000 (147.1) (116.3) (100.3) (19.8) (18.6) 6.8 15.5 (30.8) (12.8) (18.0)

2001 (175.2) (149.7) (109.4) (21.6) (19.0) 1.5 (1.2) (25.5) (8.7) (16.8)

2002 (210.8) (184.0) (112.0) (22.0) (20.2) (5.1) (24.6) (26.8) (9.6) (17.2)

2003 (240.3) (214.7) (118.5) (23.3) (19.3) (9.4) (44.3) (25.6) (9.4) (16.2)

2004 (250.4) (222.0) (120.3) (23.7) (18.7) (11.1) (48.2) (28.3) (12.0) (16.3)

2005 (252.9) (224.8) (120.6) (23.7) (17.6) (12.6) (50.3) (28.0) (11.7) (16.3)

2006 (249.0) (219.6) (119.1) (23.6) (19.2) (13.4) (44.4) (29.4) (12.1) (17.3)

2007 (244.8) (217.0) (118.2) (23.5) (19.9) (14.0) (41.4) (27.8) (10.4) (17.4)

2008 (237.6) (216.9) (118.6) (23.6) (20.0) (14.0) (40.7) (20.7) (3.6) (17.0)

2009 (231.6) (216.9) (118.6) (23.6) (20.0) (14.0) (40.7) (14.8) 1.8 (16.6)

2010 (233.4) (217.2) (118.6) (23.6) (20.3) (14.0) (40.7) (16.2) 0.3 (16.5)

2011 (236.5) (218.2) (118.6) (23.6) (21.3) (14.0) (40.7) (18.3) (1.6) (16.7)

2012 (236.3) (218.2) (118.6) (23.6) (21.3) (14.0) (40.7) (18.1) (1.3) (16.8)
A-348 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-247: Estimated Carbon Stocks (Tg C) in Forest and Harvested Wood Pools, 1990–2013

Total Carbon 

Stock

Forest Harvested Wood

Forest Area 

Year Total (1000 ha)

Live, 

aboveground

Live, 

belowground Dead Wood Litter

Soil Organic 

Carbon Total

Products in 

Use SWDS

1990 40,826 38,967 12,318 2,437 2,147 4,897 17,168 1,859 1,231 628 275,399

1991 41,018 39,123 12,415 2,456 2,161 4,903 17,188 1,895 1,249 646 276,027

1992 41,208 39,279 12,511 2,475 2,175 4,910 17,208 1,929 1,264 665 276,663

1993 41,396 39,434 12,609 2,494 2,189 4,916 17,225 1,963 1,280 683 277,275

1994 41,585 39,590 12,712 2,515 2,204 4,922 17,237 1,996 1,295 701 277,845

1995 41,781 39,752 12,819 2,535 2,219 4,927 17,252 2,029 1,311 718 278,409

1996 41,978 39,917 12,928 2,557 2,233 4,931 17,268 2,061 1,326 735 278,962

1997 42,171 40,079 13,038 2,579 2,253 4,933 17,277 2,092 1,340 752 279,401

1998 42,356 40,232 13,148 2,600 2,272 4,935 17,277 2,124 1,355 769 279,769

1999 42,525 40,370 13,253 2,621 2,290 4,934 17,272 2,155 1,368 787 280,134

2000 42,678 40,490 13,354 2,641 2,308 4,929 17,258 2,188 1,382 805 280,400

2001 42,825 40,606 13,454 2,661 2,327 4,922 17,243 2,218 1,395 823 280,613

2002 43,000 40,756 13,564 2,682 2,346 4,920 17,244 2,244 1,404 840 280,895

2003 43,211 40,940 13,676 2,704 2,366 4,925 17,268 2,271 1,414 857 281,323

2004 43,451 41,155 13,794 2,728 2,385 4,935 17,313 2,296 1,423 873 281,929

2005 43,701 41,377 13,915 2,751 2,404 4,946 17,361 2,325 1,435 890 282,583

2006 43,954 41,602 14,035 2,775 2,422 4,958 17,411 2,353 1,447 906 283,263

2007 44,203 41,821 14,154 2,799 2,441 4,972 17,456 2,382 1,459 923 283,829

2008 44,448 42,038 14,272 2,822 2,461 4,986 17,497 2,410 1,469 940 284,345

2009 44,686 42,255 14,391 2,846 2,481 5,000 17,538 2,430 1,473 958 284,858

2010 44,917 42,472 14,510 2,869 2,501 5,014 17,578 2,445 1,471 974 285,371

2011 45,151 42,689 14,628 2,893 2,521 5,028 17,619 2,461 1,471 991 285,884

2012 45,387 42,907 14,747 2,916 2,542 5,042 17,660 2,480 1,472 1,007 286,397

2013 45,623 43,126 14,866 2,940 2,564 5,056 17,700 2,498 1,474 1,024 286,910
A-349

Table A-248: 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 (Tg C)

Mean net annual 

nonsoil stock change 

2003–2012 (Tg C/yr)

Alabama 2009 9,268 684 (7.0)

Alaska 2007 6,161 993 (5.0)

Arizona 2007 7,545 397 0.9 

Arkansas 2010 7,666 584 (4.1)

California 2007 13,200 1,798 (6.5)

Colorado 2007 9,242 746 (1.1)

Connecticut 2010 693 83 (0.8)

Delaware 2010 137 16 (0.1)

Florida 2009 7,066 449 (4.3)

Georgia 2010 10,017 737 (6.2)

Idaho 2008 8,680 932 (0.2)

Illinois 2010 1,977 167 (3.2)

Indiana 2010 1,965 185 (2.5)

Iowa 2010 1,208 89 (1.6)

Kansas 2010 1,013 62 (1.4)

Kentucky 2010 5,047 444 (4.3)

Louisiana 2008 5,954 427 (2.7)

Maine 2010 7,138 621 (3.0)

Maryland 2010 996 118 (1.0)

Massachusetts 2010 1,224 146 (1.3)

Michigan 2010 8,214 698 (9.8)

Minnesota 2010 7,055 446 (4.8)

Mississippi 2009 7,886 574 (8.6)

Missouri 2010 6,266 464 (5.9)

Montana 2008 10,349 986 (7.3)

Nebraska 2009 638 38 (1.1)

Nevada 2005 4,520 193 (1.2)

New Hampshire 2011 1,956 219 (1.4)

New Jersey 2010 795 77 (0.7)

New Mexico 2011 10,052 499 (0.0)

New York 2010 7,675 850 (7.6)

North Carolina 2009 7,536 672 (7.4)

North Dakota 2010 305 16 (0.1)

Ohio 2010 3,273 329 (4.1)

Oklahoma 2010 5,118 237 (1.4)

Oregon 2007 12,061 1,781 (5.8)

Pennsylvania 2010 6,791 742 (7.0)

Rhode Island 2010 145 16 (0.3)

South Carolina 2010 5,285 423 (5.8)

South Dakota 2011 770 42 (0.7)

Tennessee 2010 5,642 572 (4.4)

Texas 2008 25,234 894 (0.9)

Utah 2007 7,339 423 (4.1)

Vermont 2010 1,858 221 (1.5)

Virginia 2010 6,437 616 (5.0)

Washington 2007 9,079 1,598 (8.5)

West Virginia 2010 4,919 547 (7.3)

Wisconsin 2010 6,909 508 (6.0)

Wyoming 2001 4,633 413 (1.1)
A-350 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A-249 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 11 July 2013 (USDA Forest Service 2013b). Carbon 

density values reflect the most recent survey for each state as available in the FIADB, not potential maximum C storage. 

Carbon 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-249: 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 2009

Region

(States)

Forest Types

Above-ground 

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 79.0 16.3 6.9 13.8 78.1 1,661

Spruce/Fir 39.6 8.3 7.5 30.8 98.0 3,077

Oak/Pine 70.9 14.0 5.6 27.9 66.9 1,209

Oak/Hickory 78.1 14.8 6.3 8.2 53.1 13,027

Elm/Ash/Cottonwood 55.5 10.5 5.4 7.1 111.7 1,479

Maple/Beech/Birch 71.1 13.6 6.8 27.3 69.6 13,763

Aspen/Birch 42.4 8.3 5.9 8.7 87.4 1,551

Minor Types and Nonstocked 45.8 9.0 5.8 11.2 73.9 1,833

All 68.3 13.1 6.5 18.1 69.1 37,601

Northern Lake States

(MI,MN,WI)

White/Red/Jack Pine 46.3 9.6 5.6 12.4 120.8 1,915

Spruce/Fir 29.4 6.2 5.6 33.3 261.8 3,197

Oak/Hickory 54.4 10.3 7.0 8.1 97.1 4,002

Elm/Ash/Cottonwood 42.4 8.1 5.5 7.6 179.9 2,287

Maple/Beech/Birch 59.0 11.3 6.7 27.7 134.3 4,495

Aspen/Birch 31.8 6.1 6.1 8.3 146.1 5,096

Minor Types and Nonstocked 29.3 5.8 6.3 18.0 120.0 1,186

All 43.2 8.4 6.2 16.6 151.4 22,178

Northern Prairie States

(IA,IL,IN,KS,MO,ND,NE,SD)

Ponderosa Pine 32.1 6.7 4.5 14.4 48.5 550

Oak/Pine 40.0 7.7 4.4 26.1 40.7 572

Oak/Hickory 53.2 10.0 6.0 7.9 49.6 9,545

Elm/Ash/Cottonwood 56.4 10.5 6.4 6.9 83.1 2,041

Minor Types and Nonstocked 31.2 6.1 5.2 17.8 60.3 1,434

All 50.1 9.5 5.8 9.8 55.1 14,142

South Central

(AL,AR,KY,LA,MS,OK,TN,TX)

Loblolly/Shortleaf Pine 48.7 10.0 7.4 9.6 41.9 13,816

Pinyon/Juniper 12.1 2.3 4.0 12.2 37.7 4,030

Oak/Pine 44.8 8.8 6.6 9.3 41.7 5,121

Oak/Hickory 48.1 9.0 7.4 6.4 38.6 25,087

Oak/Gum/Cypress 63.7 12.1 7.5 6.5 52.8 5,278

Elm/Ash/Cottonwood 38.6 7.2 6.1 5.9 49.9 4,047

Woodland Hardwoods 9.6 1.5 1.9 5.0 65.0 9,454

Minor Types and Nonstocked 27.9 5.4 6.6 6.8 54.3 4,983

All 40.1 7.7 6.3 7.4 45.7 71,815

Southeast

(FL,GA,NC,SC,VA)

Longleaf/Slash Pine 41.0 8.5 4.9 10.0 110.0 4,174

Loblolly/Shortleaf Pine 53.1 11.0 4.3 9.8 72.9 9,309

Oak/Pine 51.2 10.0 4.0 9.4 61.4 4,066

Oak/Hickory 65.2 12.3 5.3 6.5 45.3 11,817

Oak/Gum/Cypress 64.2 12.5 5.3 6.5 158.0 4,622
A

-351

Elm/Ash/Cottonwood 49.1 9.3 3.9 5.6 95.7 867

Minor Types and Nonstocked 31.4 6.0 7.0 5.7 110.9 1,485

All 55.9 10.9 4.9 8.0 79.8 36,341

Coastal Alaska

(approximately 12 percent of forest 

land in Alaska)

Spruce/Fir 14.7 2.9 7.8 33.5 62.1 395

Fir/Spruce/Mountain Hemlock 64.4 13.5 17.2 43.0 62.1 2,275

Hemlock/Sitka Spruce 116.5 24.5 27.4 50.4 116.3 2,808

Aspen/Birch 28.7 5.4 8.3 10.6 42.5 294

Minor Types and Nonstocked 29.8 5.8 7.3 22.9 74.6 389

All 81.1 17.0 20.2 42.9 86.7 6,161

Pacific Northwest, Westside

(Western OR and WA)

Douglas-fir 140.0 29.3 31.4 32.2 94.8 5,954

Fir/Spruce/Mountain Hemlock 130.3 27.5 35.7 38.5 62.1 1,210

Hemlock/Sitka Spruce 169.2 35.6 41.5 37.8 116.3 1,545

Alder/Maple 78.9 15.4 16.0 7.7 115.2 1,180

Minor Types and Nonstocked 56.0 11.0 17.5 12.9 86.0 1,234

All 127.2 26.5 30.1 28.9 95.4 11,124

Pacific Northwest, Eastside

(Eastern OR and WA)

Douglas-fir 62.9 13.1 18.3 36.3 94.8 2,020

Ponderosa Pine 41.1 8.5 9.7 22.5 50.7 2,809

Fir/Spruce/Mountain Hemlock 74.1 15.6 27.1 37.9 62.1 1,797

Lodgepole Pine 35.7 7.5 13.0 21.1 52.0 1,019

Western Larch 72.4 15.1 20.8 36.1 45.1 211

Other Western Softwoods 12.0 2.2 3.5 36.3 78.8 1,143

Minor Types and Nonstocked 27.3 5.3 17.0 24.4 80.7 1,016

All 46.8 9.7 15.2 30.0 67.9 10,017

Pacific Southwest

(CA)

Pinyon/Juniper 13.7 2.5 2.4 21.1 26.3 739

Douglas-fir 144.6 30.0 23.8 35.5 40.1 455

Ponderosa Pine 53.5 11.1 9.6 22.4 41.3 912

Fir/Spruce/Mountain Hemlock 111.3 23.5 29.6 38.4 51.9 817

Redwood 243.2 50.8 35.9 60.4 53.8 287

Other Western Softwoods 23.0 4.4 5.5 37.8 49.8 822

California Mixed Conifer 105.8 22.1 21.3 37.9 49.8 3,157

Western Oak 49.3 9.3 5.3 30.0 27.6 3,827

Tanoak/Laurel 126.0 24.7 12.4 28.3 27.6 805

Minor Types and Nonstocked 48.5 9.7 15.5 26.0 37.0 1,378

All 75.4 15.3 13.6 32.2 38.7 13,200

Rocky Mountain, North

Rocky Mountains, North

(ID,MT)

Douglas-fir 11.2 2.2 1.8 21.1 41.7 638

Ponderosa Pine 50.9 10.7 13.1 37.0 38.8 5,473

Fir/Spruce/Mountain Hemlock 32.2 6.6 8.0 22.9 34.3 1,733

Lodgepole Pine 55.5 11.7 21.8 37.4 44.1 4,708

Western Larch 44.8 9.5 16.4 22.9 37.2 2,715

Other Western Softwoods 27.7 5.7 13.1 38.8 31.4 648

Aspen/Birch 22.2 4.1 14.7 26.8 56.6 504

Minor Types and Nonstocked 33.2 6.7 21.0 25.2 41.7 2,609

All 44.1 9.2 16.0 31.4 40.2 19,029

Rocky Mountain, South

(AZ,CO,NM,NV,UT,WY)

Pinyon/Juniper 15.1 3.0 1.8 21.1 19.7 20,800

Douglas-fir 49.7 10.5 12.8 38.3 30.9 1,694

Ponderosa Pine 37.0 7.7 7.5 23.8 24.1 3,264

Fir/Spruce/Mountain Hemlock 55.9 11.8 19.9 38.9 31.5 4,210

Lodgepole Pine 46.8 9.9 18.7 23.9 27.0 1,974

Aspen/Birch 39.9 7.6 10.6 28.5 58.8 2,569
A-352 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Woodland Hardwoods 13.9 2.5 4.9 28.0 25.9 5,728

Minor Types and Nonstocked 13.1 2.4 9.6 22.8 25.5 3,092

All 24.7 5.0 6.7 25.3 25.5 43,330

United States (forest land included in 

Inventory) 50.7 10.0 8.7 17.6 61.7 284,938

Note: The forest area values in this table do not equal the forest area values reported in Table A-247, because the forest area values in this table 

are estimated using the most recent dataset per state, with an average year of 2009. The time series of forest area values reported in Table A-247, 

in contrast, is constructed following the CCT methods used to construct the C stock series. The forest area values reported in Table A-247 and 

Table A-249 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 FIADB 5.1.6 data (USDA Forest Service 2013b), 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 2013a), and compilations of analytical estimates based on these databases are found in Waddell et 

al. (1989) and Smith et al. (2001). Some differences between the RPA database and the FIADB are that the FIADB 

includes individual-tree data and includes additional land use categories such as “other wooded lands”.

 Having only plot-level information (such as volume per hectare) limits the conversion to biomass. This does 

not constitute a substantial difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et 

al. 2004). In the past, FIA made their data available in tree-level Eastwide (Hansen et al. 1992) or Westwide (Woudenberg 

and Farrenkopf 1995) formats, which included inventories for Eastern and Western states, respectively. The current 

Inventory estimates rely, in part, on older tree-level data that are not available on the current FIADB site. The Integrated 

Database (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. (2011) as the most appropriate non-FIADB 

sources for these three states.

A 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 individual-tree data or even stand-level characteristics such as 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 (for further discussion see Domke et al. In 

Review). This approach to filling gaps in older data also extends to timberlands where individual-tree data was not 

available (e.g., standing dead trees).

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. 

Most of the conversion factors and models used for inventory-based forest C estimates (Smith et al. 2010, Heath et al. 

2011) were initially developed as an offshoot of the forest C simulation model FORCARB (Heath et al. 2010) and are 

incorporated into a number of applications (Birdsey and Heath 1995, Birdsey and Heath 2001, Heath et al. 2003, Smith et 

al. 2004, Hoover and Rebain 2008). The conversion factors and model coefficients are usually categorized by region and 

forest type. Classifications for both of these categories 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. Carbon 

density for live trees, standing dead trees, understory vegetation, downed 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 downed 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

Live tree C pools include aboveground and belowground (coarse root) biomass of live trees with diameter at 

diameter breast height (d.b.h.) of at least 2.54 cm at 1.37 m above the forest floor. Separate estimates are made for above-
A-353

and below-ground biomass components. If inventory plots include data on individual trees, tree C is based on Woodall et 

al. (2011), which is also known as the component ratio method (CRM), and is a function of volume, species, diameter, 

and, in some regions, tree height and site quality. The estimated sound volume provided in the tree table of the FIADB is 

the principal input to the CRM biomass calculation for each tree. The estimated volumes of wood and bark are converted 

to biomass based on the density of each. Additional components of the trees such as tops, branches, and coarse roots, are 

estimated according to adjusted component estimates from Jenkins et al. (2003). Live trees with d.b.h of less than 12.7 cm 

do not have estimates of sound volume in the FIADB, and CRM biomass estimates follow a separate process (see Woodall 

et al. 2011 for details). An additional component of foliage, which was not explicitly included in Woodall et al. (2011), 

was added to each tree following the same CRM method. Carbon is estimated by multiplying the estimated oven-dry 

biomass by a C constant of 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 1997). Further 

discussion and example calculations are provided in Woodall et al. 2011 and Domke et al. 2012.

Some of the older forest inventory data in use for these estimates do not provide measurements of individual 

trees. Examples of these data include plots with incomplete or missing tree data (e.g., some of the non-timberland plots in 

older surveys) or the RPA plot-level summaries. The C estimates for these plots are based on average densities (metric 

tons C per hectare) obtained from plots of more recent surveys with similar stand characteristics and location. This applies 

to less than 5 percent of the forest land inventory-plot-to-C conversions within the 193 state-level surveys utilized here. 

Understory vegetation

Understory vegetation is a minor component of total forest ecosystem biomass. Understory vegetation is defined 

as all biomass of undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this 

inventory, it is assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is 

near the lower range of temperate forest values provided in IPCC (2006) and was selected based on two general 

assumptions: ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a 

greater proportion of all root mass will be less than 2 mm diameter.

Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent plots. 

These were fit to the model:

Ratio = e(A − B × ln(live tree C density))

In this model, the ratio is the ratio of understory C density (Mg C/ha) to live tree C density (above- and belowground) according to Jenkins et al. (2003) and expressed in Mg C/ha. An additional coefficient is provided as a maximum 

ratio; that is, any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum 

ratio. A full set of coefficients are in Table A-250. 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 

model. 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 C density (Mg C/ha) for these types only.

Table A-250: 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
A-354 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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

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-355

aPrediction of ratio of understory C to live tree C is based on the model: 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).

cMaximum ratio: any estimate predicted from the model that is greater than the maximum ratio is set equal to the maximum ratio.

Dead Wood

The standing dead tree estimates are primarily based on plot-level measurements (Domke et al. 2011, Woodall et 

al. 2011). This C pool includes aboveground and belowground (coarse root) mass and includes trees of at least 12.7 cm 

d.b.h. Calculations follow the basic CRM method applied to live trees (Woodall et al. 2011) with additional modifications 

to account for decay and structural loss. In addition to the lack of foliage, two characteristics of standing dead trees that 

can significantly affect C mass are decay, which affects density and thus specific C content (Domke et al. 2011, Harmon et 

al. 2011), and structural loss such as branches and bark (Domke et al. 2011). Dry weight to C mass conversion is by 

multiplying by 0.5.

Some of the older forest inventory data in use for these estimates do not provide measurements of individual 

standing dead trees. In addition to the RPA data, which are plot-level summaries, some of the older surveys that otherwise 

include individual-tree data may not completely sample dead trees on non-timberlands and in some cases timberlands. 

The C estimates for these plots are based on average densities (metric tons C per hectare) obtained from plots of more 

recent surveys with similar stand characteristics and location. This applies to 23 percent of the forest land inventory-plotto-C conversions within the 193 state-level surveys utilized here.

Downed dead wood, inclusive of logging residue, are sampled on a subset of FIA plots. Despite a reduced 

sample intensity, a single down woody material population estimate (Woodall et al. 2010, Domke et al. 2013, Woodall et 

al. 2013) per state is now incorporated into these empirical downed dead wood estimates. Downed dead wood is defined 

as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, that are not attached to live or standing dead

trees. It also includes stumps and roots of harvested trees. Ratio estimates of downed dead wood to live tree biomass 

were developed using FORCARB2 simulations and applied at the plot level (Smith et al. 2004). Estimates for downed 

dead wood correspond to the region and forest type classifications described in Smith et al. (2003). A full set of ratios is 

provided in Table A-251. An additional component of downed dead wood is a regional average estimate of logging 

residue based on Smith et al. (2006) applied at the plot level. These are based on a regional average C density at age zero 

and first order decay; initial densities and decay coefficients are provided in Table A-252. These amounts are added to 

explicitly account for downed dead wood following harvest. The sum of these two components are then adjusted by the 

ratio of population totals; that is, the ratio of plot-based to modeled estimates (Domke et al. 2013). An example of this 3-

part calculation for downed dead wood in a 25-year-old naturally regenerated loblolly pine forest with 82.99 Mg C/ha in 

live trees (Jenkins et al. 2003) in Louisiana is as follows:

First, an initial estimate from live tree C density and Table A-251 (SC, Natural Pine)

C density = 82.99 × 0.068 = 5.67 (Mg C/ha)

Second, an average logging residue from age and Table A-252 (SC, softwood) C density = 5.5 × e(−25/17.9) = 1.37 

(Mg C/ha)

Third, adjust the sum by the downed dead wood ratio plot-to-model for Louisiana, which was 27.6/31.1 = 0.886

C density = (5.67 + 1.37) × 0.886 = 6.24 (Mg C/ha)

Table A-251: 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 (Mg C/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
A-356 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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

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-252: 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.
A-357

Litter carbon

Carbon 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-245. 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 models of Smith and Heath (2002) and 

applied at the plot level. The models 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.

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 C. The 2006 IPCC Guidelines provide methods that allow Parties 

to report HWP Contribution using one of several different accounting approaches: production, stock change, and 

atmospheric flow, as well as a default method. The various approaches are described below. The approaches differ in 

how HWP Contribution is allocated based on production or consumption as well as what processes (atmospheric fluxes or 

stock changes) are emphasized.

 Production approach: Accounts for the net changes in C stocks in forests and in the wood products pool, 

but attributes both to the producing country.

 Stock change approach: Accounts for changes in the product pool within the boundaries of the consuming 

country.

 Atmospheric flow approach: Accounts for net emissions or removals of C to and from the atmosphere 

within national boundaries. Carbon removal due to forest growth is accounted for in the producing country 

while C emissions to the atmosphere from oxidation of wood products are accounted for in the consuming 

country.

 Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an assumption 

be justified if this is how a Party is choosing to report.

The United States uses the production accounting approach (as in previous years) to report HWP Contribution 

(Table A-256). 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-257). 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-256. 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,
A-358 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

(2A) annual change of C in wood and paper products in use in the United States and other countries where the 

wood came from trees harvested in the United States,

(2B) annual change of C in wood and paper products in SWDS in the United States and other countries where the 

wood came from trees harvested in the United States,

(3) Carbon in imports of wood, pulp, and paper to the United States,

(4) Carbon in exports of wood, pulp and paper from the United States, and

(5) Carbon in annual harvest of wood from forests in the United States.
A-359

Table A-253: Harvested Wood Products from Wood Harvested in United States—Annual Additions of Carbon to Stocks and Total Stocks Under the Production Approach 

(Parentheses Indicate Net C Sequestration (i.e., a Net Removal of C from the Atmosphere)

Year

Net carbon additions per year (Tg C per year) Total Carbon stocks (Tg C)

Total

Products in use Products in SWDS

Total

Solid wood 

products

Paper 

products Total

Solid wood 

products

Paper 

products Total

Products in 

use

Products in 

SWDS

1990 (35.9) (17.7) (14.4) (3.3) (18.3) (9.9) (8.3) 1,859 1,231 628

1991 (33.8) (14.9) (11.9) (3.1) (18.8) (11.1) (7.7) 1,895 1,249 646

1992 (33.8) (16.3) (12.6) (3.7) (17.4) (9.5) (7.9) 1,929 1,264 665

1993 (32.9) (15.0) (12.2) (2.8) (17.9) (9.7) (8.3) 1,963 1,280 683

1994 (33.4) (15.9) (12.1) (3.8) (17.5) (9.8) (7.7) 1,996 1,295 701

1995 (32.3) (15.1) (11.2) (3.8) (17.2) (10.7) (6.5) 2,029 1,311 718

1996 (30.6) (14.1) (11.5) (2.6) (16.5) (10.6) (6.0) 2,061 1,326 735

1997 (32.0) (14.7) (11.8) (3.0) (17.3) (10.3) (6.9) 2,092 1,340 752

1998 (31.1) (13.4) (11.4) (2.0) (17.7) (10.2) (7.5) 2,124 1,355 769

1999 (32.5) (14.1) (12.1) (2.0) (18.4) (10.6) (7.8) 2,155 1,368 787

2000 (30.8) (12.8) (11.9) (1.0) (18.0) (10.7) (7.3) 2,188 1,382 805

2001 (25.5) (8.7) (10.1) 1.4 (16.8) (10.7) (6.0) 2,218 1,395 823

2002 (26.8) (9.6) (10.7) 1.1 (17.2) (11.1) (6.1) 2,244 1,404 840

2003 (25.6) (9.4) (9.9) 0.5 (16.2) (11.0) (5.1) 2,271 1,414 857

2004 (28.3) (12.0) (11.3) (0.8) (16.3) (11.3) (5.0) 2,296 1,423 873

2005 (28.0) (11.7) (11.3) (0.4) (16.3) (11.5) (4.8) 2,325 1,435 890

2006 (29.4) (12.1) (10.5) (1.7) (17.3) (11.6) (5.7) 2,353 1,447 906

2007 (27.8) (10.4) (8.5) (1.9) (17.4) (11.6) (5.7) 2,382 1,459 923

2008 (20.7) (3.6) (2.9) (0.8) (17.0) (11.4) (5.7) 2,410 1,469 940

2009 (14.8) 1.8 0.5 1.3 (16.6) (11.2) (5.4) 2,430 1,473 958

2010 (16.2) 0.3 0.2 0.2 (16.5) (11.3) (5.2) 2,445 1,471 974

2011 (18.3) (1.6) (1.0) (0.6) (16.7) (11.4) (5.3) 2,461 1,471 991

2012 (18.1) (1.3) (1.4) 0.1 (16.8) (11.4) (5.4) 2,480 1,472 1,007

2013 2,498 1,474 1,024

Table A-254: 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)
A-360 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

1995 (126.0) (128.0) (118.4)

1996 (122.3) (122.5) (112.2)

1997 (131.4) (127.4) (117.3)

1998 (137.2) (122.8) (114.2)

1999 (147.1) (127.4) (119.2)

2000 (141.2) (120.4) (113.0)

2001 (125.0) (100.4) (93.5)

2002 (130.7) (103.3) (98.2)

2003 (125.8) (98.7) (93.8)

2004 (143.2) (108.5) (103.8)

2005 (142.1) (107.3) (102.8)

2006 (138.1) (113.9) (107.8)

2007 (115.1) (111.5) (101.8)

2008 (73.1) (88.4) (75.8)

2009 (42.3) (69.8) (54.1)

2010 (50.5) (79.4) (59.3)

2011 (52.9) (90.9) (67.1)

2012 (57.7) (90.8) (66.5)

Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).

Table A-255: Harvested Wood Products Sectoral Background Data for LULUCF—United States (production approach)

1A 1B 2A 2B 3 4 5 6 7 8

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 plus 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips

Annual 

Exports of 

wood, and 

paper 

products plus 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips

Annual 

Domestic 

Harvest

Annual release 

of carbon to the 

atmosphere 

from HWP 

consumption 

(from fuelwood 

and products in 

use and 

products in 

SWDS) 

Annual release 

of carbon to the 

atmosphere from 

HWP (including 

firewood) where 

wood came from 

domestic harvest 

(from products in 

use and products 

in SWDS )

HWP 

Contribution to 

AFOLU CO2

emissions/ 

removals

∆CHWP IU DC ∆CHWP SWDS DC ∆C HWP IU DH ∆CHWP SWDS DH PIM PEX H ↑CHWP DC ↑CHWP DH

Gg C/yr Gg CO2/yr

1990 17,044 18,308 17,659 18,278 12,680 15,078 142,297 104,547 106,359 (131,772)

1991 13,129 18,602 14,940 18,812 11,552 15,667 144,435 108,588 110,682 (123,758)

1992 15,718 17,006 16,334 17,427 12,856 16,032 139,389 103,489 105,627 (123,791)

1993 16,957 17,627 14,971 17,949 14,512 14,788 134,554 99,694 101,633 (120,708)

1994 18,221 17,221 15,930 17,479 15,685 15,665 134,750 99,328 101,342 (122,498)

1995 17,307 17,051 15,065 17,229 16,712 17,266 137,027 102,115 104,733 (118,411)

1996 17,018 16,348 14,092 16,513 16,691 16,733 134,477 101,069 103,872 (112,219)

1997 18,756 17,090 14,740 17,263 17,983 16,877 135,439 100,699 103,436 (117,344)

1998 19,654 17,769 13,404 17,738 18,994 15,057 134,206 100,720 103,064 (114,188)

1999 21,444 18,662 14,146 18,359 20,599 15,245 134,193 99,440 101,689 (119,182)

2000 20,000 18,508 12,840 17,970 21,858 16,185 133,694 100,859 102,884 (112,969)
A-361

2001 16,491 17,610 8,713 16,781 22,051 15,336 127,896 100,510 102,402 (93,479)

2002 17,414 18,235 9,566 17,213 23,210 15,744 126,866 98,683 100,087 (98,188)

2003 16,986 17,326 9,415 16,171 23,707 16,303 126,477 99,569 100,891 (93,815)

2004 21,409 17,643 12,045 16,268 26,428 16,953 131,738 102,160 103,425 (103,814)

2005 20,992 17,765 11,746 16,290 26,793 17,313 132,482 103,205 104,446 (102,798)

2006 19,085 18,587 12,135 17,269 25,443 18,836 129,529 98,464 100,125 (107,815)

2007 13,085 18,308 10,405 17,364 21,648 20,656 123,640 93,239 95,871 (101,820)

2008 2,414 17,510 3,627 17,043 16,980 21,157 106,096 81,996 85,426 (75,789)

2009 (5,104) 16,641 (1,836) 16,600 13,114 20,616 96,032 76,994 81,269 (54,133)

2010 (2,564) 16,324 (334) 16,501 14,161 22,052 97,555 75,904 81,388 (59,279)

2011 (1,761) 16,184 1,595 16,700 13,922 24,302 100,848 76,045 82,553 (67,082)

2012 (363) 16,113 1,345 16,787 13,950 22,961 103,019 78,258 84,887 (66,485)

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 net removal of C from the atmosphere).
A-362 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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-256. 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-257. 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-246 and Table A-247. The decline in net additions to HWP C stocks continued through 2009 from the recent high point 

in 2006. This is due to sharp declines in U.S. production of solidwood and paper products in 2009 primarily due to the 

decline in housing construction. The low level of gross additions to solidwood and paper products in use in 2009 was 

exceeded by discards from uses. The result is a net reduction in the amount of HWP C that is held in products in use 

during 2009. For 2009 additions to landfills still exceeded emissions from landfills and the net additions to landfills have

remained relatively stable. Overall, there were net C additions to HWP in use and in landfills combined.

Table A-256: 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 C in harvested wood products for the United States.” Forest Products Journal 58:56–72.

Table A-257: Parameters Determining Decay of Wood and Paper in SWDS

Parameter Value Units

Percentage of wood and paper in dumps that is subject to decay 100 Percent

Percentage of wood in landfills that is subject to decay 23 Percent

Percentage of paper in landfills that is subject to decay 56 Percent

Half life of wood in landfills / dumps (portion subject to decay) 29 Years

Half life of paper in landfills/ dumps (portion subject to decay) 14.5 Years

Source: Skog, K.E. (2008) “Sequestration of C in harvested wood products for the United States.” Forest Products Journal 58:56–72
A-363

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 effects 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 estimate for 2010 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 and standing dead tree C pools are generally assigned normal PDFs 

that represent total uncertainty of all trees measured on the plot, which varies according to species, number of trees, and 

per area representation. Error estimates for volume and the CRM for estimating biomass are not available, so an assumed 

10 percent error on biomass from volume is applied to the volume portion of the estimate; error information in Jenkins et 

al. (2003) is applied to uncertainty about the additional components (e.g., top, leaves, and roots). Uniform PDFs with a 

range of ±90 percent of the average are used for those plots where C densities from similarly classified forest stands were 

applied.

Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of 

information available about these estimates. The PDFs defined for these four pools were sampled as marginal 

distributions. Downed 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. Soil 

organic C is defined as a uniform PDF at ±50 percent of the mean. Sub-state or state total C 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 C 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 C 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.

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 
A-364 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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. Using the methodology provided in IPCC (2003), C emissions from forest fires were calculated as:

C Emissions = Forest area burned (ha) × Carbon density (Mg per ha of dry matter)

× Combustion efficiency (45%) × Mg to Tg conversion factor (10-6)

where a default value of 0.45 from IPCC (2006) was assumed for the amount of biomass burned by wildfires as well as 

prescribed fires (combustion factor value). 

This 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 2013b). 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 USDA Forest Service (USDA Forest Service 1992). Data for years 1998, 2002, 2004, 2006, and 2008 were obtained 

from the National Association of State Foresters (NASF 2011, 2008, 2007a, 2007b, 2007c). For states where data were 

available for all five 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, values for 2006 were assumed for years 2005 and 2006, and values for 2008 were assumed for years 2008 to 2012. 

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 both the 1998 and 2006 

values are missing from the NASF data for Alaska, the 1990 value was assumed for years 1990 to 1997, the 2002 value 

was assumed for years 1998 to 2002, the 2004 value was assumed for years 2003 to 2006, and the 2008 value was 

assumed for 2007 to 2012. Similarly, since the NASF data for New Mexico lacks values for 2002 and 2004, the 1990 

value was assumed for years 1990–1995, while the 1998 value was assumed for year 1996 through 2001, the 2006 data 

were assumed for 2002 to 2006, and the 2008 value was assumed for all remaining years. Illinois has not reported data on 

wildland since 2002, 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 2012 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 2012 from the National Interagency Fire Center (NIFC 2013). 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 2013). 

Data are acres of land which experienced fire activity on forest service land. The majority of wildfires in Alaska that 

occur on lands protected by the USDA Forest Service occur in the coastal areas (Southeast and South Central); as this is 

where the National Forest System land is located. 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 2008). 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 varied 

between 68.8 and 76.5 Mg/ha, according to annual (1990–2012) data from FIA. In order to estimate these annual C 

densities in the lower 48 states, the C contained in the aboveground, deadwood, and litter C pools was first summed for 

each state and year. The methodology assumes that wildfires burn only those pools, and leaves the belowground C and 

soil C un-burnt. The methodology estimates the C density value by taking a weighted average of these summed C pools in 

each state and year. The states’ C values are weighted according to area of forestland present in each state and year 

compared with the total. A default value of 0.45 from IPCC (2006) was assumed for the amount of biomass burned by 

wildfire (combustion factor value). According to the estimates, wildfires in the lower 48 states emit between 6.5 and 76.1 

Tg C. For Alaska, the average C density reported by the USDA Forest Service varies between 140.2 and 145.0 Mg/ha, 

based on data from FIA. In the case of wildfires in Alaska, Alaska’s C pool values are used instead of a weighted average 

for states. These values translate into 0 to 0.1 Tg C emitted. Based on data from the USDA Forest Service, the average C 

density for prescribed fires varied between 24.8 and 25.7 Mg C/ha. For prescribed fires, the methodology assumes that 

only the litter and deadwood C pools burn. The weighted average C densities estimated for prescribed fires therefore only 

include the sum of these two pools, and excludes aboveground biomass. It is assumed that prescribed fires only occur in 
A-365

the lower 48 states (Smith 2008). The default value of 0.45 from IPCC (2006) for wildfires was also assumed for the 

amount of biomass burned by prescribed fires (combustion factor value). As a result, prescribed fires are estimated to emit 

between 0.5 and 7.5 Tg C.

Carbon density estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting 

estimates are displayed in Table A-258. C estimates were multiplied by 92.8 percent to account for the proportion of C 

emitted as CO2 and by 3.67 (i.e., 44/12) to yield CO2 units. Total CO2 emissions for wildfires and prescribed fires in the 

lower 48 states and wildfires in Alaska in 2012 were estimated to be 242.7 Tg/yr.

Table A-258: 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 579,589 308,982 10 33 355,432 189,483 2 7 8 3 0.000 0.001 

1991 486,807 260,103 8 28 355,432 189,909 2 7 557 225 0.014 0.048 

1992 785,892 420,861 13 45 355,432 190,341 2 7 47 19 0.001 0.004 

1993 438,865 235,533 7 25 355,432 190,755 2 7 110 45 0.003 0.010 

1994 1,540,987 828,698 26 89 355,432 191,141 2 7 23 9 0.001 0.002 

1995 727,051 427,354 14 46 355,432 208,920 2 8 7 3 0.000 0.001 

1996 2,212,309 1,339,754 43 145 355,432 215,246 2 8 103 42 0.003 0.009 

1997 335,914 203,738 6 22 355,432 215,576 2 8 33 13 0.001 0.003 

1998 489,246 297,112 10 32 355,432 215,848 2 8 2 1 0.000 0.000 

1999 1,869,918 1,143,390 37 125 806,780 493,318 6 19 7 3 0.000 0.001 

2000 2,685,981 1,643,847 53 181 77,789 47,607 1 2 1 1 0.000 0.000 

2001 1,356,830 830,972 27 92 667,428 408,757 5 16 2,078 841 0.054 0.184 

2002 2,023,976 1,236,415 40 138 1,086,503 663,728 7 25 28 11 0.001 0.002 

2003 1,358,986 728,786 24 81 1,147,695 615,476 7 24 17 7 0.000 0.002 

2004 637,258 347,862 11 39 996,453 543,936 6 21 23 9 0.001 0.002 

2005 1,629,067 953,384 32 108 934,965 547,172 6 21 353 143 0.009 0.031 

2006 3,888,011 2,280,808 76 259 1,100,966 645,855 7 25 8 3 0.000 0.001 

2007 3,512,122 1,797,232 60 205 1,274,383 652,131 7 25 2 1 0.000 0.000 

2008 2,099,842 1,076,449 36 123 783,068 401,427 5 16 1 0 0.000 0.000 

2009 1,201,996 617,272 21 71 1,024,306 526,021 6 20 22 9 0.001 0.002 

2010 929,687 478,273 16 55 980,903 504,620 6 20 12 5 0.000 0.001 

2011 3,406,788 1,755,690 60 205 855,025 440,638 5 17 5 2 0.000 0.000 

2012 3,658,098 1,930,631 66 226 797,974 421,146 5 17 2 1 0.000 0.000 

1 Note that these emissions have already been accounted for in the estimates of net annual changes in C stocks, which accounts for the amount sequestered 

minus any emissions, including the assumption that combusted wood may continue to decay through time.

2 National Interagency Fire Center (2013).

3 Ratios calculated using forest land area estimates from FIA (USDA Forest Service 2012b) and wildland area under protection estimates from USDA Forest 

Service (1992) and the National Association of State Foresters (20011).

4 1990–2007 Alaskan forest fires data are from the Alaska Department of Natural Resources (2008). 2008–2011 data are from Alaska Interagency Coordination 

Center (2013).

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. The default IPCC (2003) methodology 

and default IPCC (2006) combustion factor for wildfires were used to calculate the C emissions from forest fires as 

discussed above. Carbon dioxide emissions were estimated by multiplying total C emitted by the C to CO2 conversion 

factor of 44/12 and by 92.8 percent, which is the estimated proportion of C emitted as CO2 (Smith 2008). Emissions 

estimates for CH4 and N2O are calculated by multiplying the total estimated CO2 emitted from forest burned by gasspecific emissions ratios from IPCC (2006). The models used are:

CH4 Emissions = (C released) × 92.8% × (44/12) × (CH4 to CO2 emission ratio)

N2O Emissions = (C released) × 92.8% × (44/12) × (N2O to CO2 emission ratio)

Where the CH4 and N2O to CO2 emission ratios were derived from IPCC (2006), in Table A-256 below.
A-366 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 259: Emission Factors for Extra Tropical Forest Burning and Emissions Ratios of CH4 and N2O to CO2

Emission Factor (g per kg dry 

matter burned)1

Emissions Ratios

CH4 4.70 CH4 to CO2 0.003

N2O 0.26 N2O to CO2 0.0002

CO2 1,569 CO2 to CO2 1.000

1 IPCC 2006

The resulting estimates are presented in Table A- 260.

Table A- 260: 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 11.681 0.119 0.007

1991 10.218 0.104 0.006

1992 15.257 0.156 0.009

1993 9.511 0.097 0.005

1994 28.204 0.287 0.016

1995 15.839 0.161 0.009

1996 44.928 0.458 0.025

1997 8.916 0.091 0.005

1998 11.950 0.122 0.007

1999 42.381 0.432 0.024

2000 53.743 0.548 0.030

2001 31.681 0.323 0.018

2002 47.904 0.488 0.027

2003 30.901 0.315 0.017

2004 17.644 0.180 0.010

2005 37.851 0.386 0.021

2006 83.387 0.850 0.047

2007 67.663 0.690 0.038

2008 40.856 0.416 0.023

2009 26.935 0.275 0.015

2010 22.082 0.225 0.012

2011 65.181 0.664 0.037

2012 71.333 0.727 0.040

1 Calculated based on C emission estimates in Table A-258 and default factors in IPCC (2003, 2006)
A-367

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.

96 The amount and rate of CH4 generation depends 

upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment. 

Not all CH4 generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared 

or utilized for energy, thus oxidizing the CH4 to 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.97 

To 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 (i.e., the first order decay waste model) based on the quantity of waste placed in landfills nationwide each year 

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 waste landfills were developed. A database of measured CH4 generation rates at MSW 

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 waste landfills, CH4 emissions from industrial waste 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 at MSW landfills98 and the amount 

oxidized in the soil at MSW and industrial landfills. The steps taken to estimate CH4 emissions from U.S. landfills for the 

years 1990 through the current inventory year are discussed in greater detail below. 

Figure A- 21 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 2012, 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 (SOG) in America reports (BioCycle 2010), adjusted 

to include U.S. territories.99 The SOG survey is the only continually updated nationwide survey of waste disposed in 

landfills in the United States. Table A-261 shows estimates of waste quantities contributing to CH4 emissions. The table 

shows SOG estimates of total waste generated and total waste landfilled (adjusted for U.S. territories) for various years 

over the 1990 to 2012 timeframe. 

State-specific landfill waste generation data and a national average disposal factor for 1989 through 2008 were 

obtained from the SOG survey for every two years (i.e., 2002, 2004, 2006, and 2008 as published in BioCycle 2006, 2008, 

and 2010). A linear interpolation was used for the amount of waste generated in 2001, 2003, 2005, 2007, 2009, 2010, 

2011, and 2012 because no BioCycle SOG surveys were published for those years. The most recent SOG survey was 

published in December 2010 representing 2008 data. Upon publication of the next SOG survey, the waste landfilled for 

2009 through 2012 will be updated. Estimates of the quantity of waste landfilled from 1989 to the current inventory year 

are determined by applying a waste disposal factor to the total amount of waste generated (i.e., the SOG data). A waste 

disposal factor is determined for each year a SOG survey is published and is the ratio of the total amount of waste 

landfilled to the total amount of waste generated. The waste disposal factor is interpolated for the years in-between the 

 

96 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. 

97 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. 

98 Landfill gas recovery is only estimated for MSW landfills due to a lack of national data on industrial waste landfills. Approximately 1 

percent of the industrial waste landfills reporting under the GHGRP have active landfill gas collection systems.

99 Since the SOG survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using population data for the 

U.S territories (U.S. Census Bureau 2013) and the per capita rate for waste landfilled from BioCycle (2010).
A-368 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

SOG surveys. Methodological changes have occurred over the time that the SOG survey has been published, and this has 

affected the fluctuating trends observed in the data (RTI 2013). 

Figure A- 21: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 

a

 BioCycle 2010 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 2010. 

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 
A-369

Table A-261: Solid Waste in MSW Landfills Contributing to CH4 Emissions (Tg unless otherwise noted)

Description 1990 1995 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Total Waste Generateda 271 302 416 455 459 462 459 455 430 404 372 411 414 417

Percent of Wastes Landfilleda 77% 63% 63% 66% 65% 64% 64% 65% 67% 69% 69% 69% 69% 69%

Total Wastes Landfilleda 205 187 259 294 293 291 290 289 283 275 278 280 282 284

Waste in Place (30 years)b 4,671 5,054 5,562 5,562 5,709 5,852 5,991 6,126 6,257 6,378 6,488 6,598 6,707 6,814

Waste Contributing to 

Emissionsc 6,808 7,772 9,340 9,340 9,632 9,924 10,214 10,503 10,786 11,061 11,339 11,619 11,901 12,185

a Source: BioCycle (2006, 2008, 2010), adjusted for missing data using U.S. Census Bureau (2009, 2013) 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, 2003, 2005, 2007, 2009, 2010, 2011, and 2012

are based on an interpolation between survey years and the increase in population because there were no surveys in these years.

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) for years 1940 to years 1988 (not presented in table) and BioCycle (2006, 2008, 2010) for years 1989 to 2012.

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 are 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 
A-370 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.

The FOD model was applied to the gas recovery data for the 52 landfills to calculate the decay 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- 262 and recommended in EPA’s 

compilation of emission factors (EPA 2008).

Table A- 262: 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-1for 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-263.

Table A-263: 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 waste landfills receive waste from factories, processing plants, and other manufacturing activities. In 

national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven 

percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). 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 were used for years 1990 to 2001 and production data published by the U.S. Department of Agriculture were 

used for years 2002 through 2011. An extrapolation based on U.S. real gross domestic product was used for years 1940 
A-371

through 1964. For the food processing industry, production levels were obtained or developed from the U.S. Department 

of Agriculture for the years 1990 through 2011 (ERG 2013). 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 waste 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 (RTI 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 at 0.20 (Lo of 99 m3/Mg); the DOC value for 

industrial food waste is estimated as 0.26 (Lo of 128 m3/Mg) (RTI 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 1990 to 2010 inventory year with the intent of updating values for Lo

and k in the pulp and paper industry. Where pulp and paper mill wastewater treatment residuals or sludge are the primary 

constituents of pulp and paper waste landfilled, values for k range from 0.01/yr to 0.1/yr, while values for Lo range from 

50 m3/Mg to 200 m3/Mg. 100 Values for these factors are highly variable and are dependent on the soil moisture content, 

which is generally related to rainfall amounts. At this time, sufficient data were not obtained to warrant a change for the 

current inventory year. EPA is considering an update to the Lo and k values for the pulp and paper sector and will work 

with stakeholders to gather data and other feedback on potential changes to these values. 

As with MSW landfills, a similar trend in disposal practices from open dumps to managed landfills was expected 

for industrial waste 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 that was land 

disposed transitioned from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed 

landfills in 1980 and on. For wastes disposed of in 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 waste 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 (86 to 99+ percent) 

recommended for flares in EPA’s AP-42 Compilation of Air Pollutant Emission Factors, Draft Chapter 2.4, Table 2.4-3 

(EPA 2008). A typical value of 97.7 percent was presented for the non-methane components (i.e., volatile organic 

compounds and non-methane organic compounds) in test results (EPA 2008). An arithmetic average of 98.3 percent and a 

median value of 99 percent are derived from the test results presented in EPA 2008. Thus, a value of 99 percent for the 

destruction efficiency of flares has been used in Inventory methodology. Other data sources supporting a 99 percent 

destruction efficiency include those used to establish new source performance standards (NSPS) for landfills and in 

recommendations for closed flares used in the 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 developed by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases 

(EIA 2007) and (2) a database compiled by LMOP (EPA 2012). 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 

 

100 Sources reviewed included Heath et al. 2010; Miner 2008; Skog 2008; Upton et al. 2008; Barlaz 2006; Sonne 2006; NCASI 

2005; and Skog 2000.
A-372 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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 

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 Inventory time series. 

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 
A-373

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 

A literature review was conducted in 2011 (RTI 2011) to provide recommendations for the most appropriate 

oxidation rate assumptions. It was found that oxidation values are highly variable and range from zero to over 100 percent 

(i.e., the landfill is considered to be an atmospheric sink by virtue of the landfill gas extraction system pulling atmospheric 

methane down through the cover). There is considerable uncertainty and variability surrounding estimates of the rate of 

oxidation because oxidation is difficult to measure and varies considerably with the presence of a gas collection system, 

thickness and type of the cover material, size and area of the landfill, climate, and the presence of cracks and/or fissures in 

the cover material through which methane can escape. IPCC (2006) notes that test results from field and laboratory 

studies may lead to over-estimations of oxidation in landfill cover soils because they largely determine oxidation using 

uniform and homogeneous soil layers. In addition, a number of studies note that gas escapes more readily through the side 

slopes of a landfill as compared to moving through the cover thus complicating the correlation between oxidation and 

cover type or gas recovery. 

Sites with landfill gas collection systems are generally designed and managed better to improve gas recovery. 

More recent research (2006 to 2012) on landfill cover methane oxidation has relied on stable isotope techniques that may 

provide a more reliable measure of oxidation. Results from this recent research consistently point to higher cover soil 

methane oxidation rates than the IPCC (2006) default of 10 percent. A continued effort will be made to review the peerreviewed literature to better understand how climate, cover type, and gas recovery influence the rate of oxidation at active 

and closed landfills. At this time, the IPCC recommended oxidation factor of 10 percent will continue to be used for all 

landfills. 

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- 264.
A-374 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Table A- 264: CH4 Emissions from Landfills (Gg)

Activity 1990 1995 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

MSW CH4 Generation 8,219 9,132 10,068 10,367 10,754 11,120 11,466 11,793 12,103 12,280 12,623 12,863 13,099 13,331

Industrial CH4 Generation 553 617 704 711 719 724 732 736 741 748 753 756 758 758

Potential Emissions 8,772 9,749 10,772 11,078 11,473 11,844 12,198 12,530 12,844 13,127 13,377 13,619 13,857 14,089

Landfill Gas-to-Energy (634) (1,070) (2,565) (2,554) (2,520) (2,630) (2,660) (2,766) (2,980) (3,189) (3,532) (3,927) (4,190) (4,608)

Flare (321) (1,298) (2,505) (2,772) (2,920) (3,399) (3,606) (3,880) (3,961) (3,880) (3,743) (3,876) (3,986) (4,040)

Emissions Avoided (954) (2,368) (5,070) (5,326) (5,440) (6,029) (6,266) (6,646) (6,942) (7,079) (7,274) (7,803) (8,177) (8,648)

Oxidation at MSW Landfills (726) (676) (500) (504) (531) (5109) (520) (515) (516) (530) (535) (506) (492) (468)

Oxidation at Industrial Landfills (55) (62) (70) (71) (72) (72) (73) (74) (74) (75) (75) (76) (76) (76)

Net Emissions 7,036 6,643 5,132 5,177 5,430 5,233 5,339 5,295 5,312 5,444 5,492 5,234 5,112 4,897

Note: Totals may not sum exactly to the last significant figure due to rounding.

Note: MSW generation in Table A-248 represents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation 

Note: Parentheses denote negative values.
A-375

References

Abdalla, M., Jones, J. Yeluripati, P. Smith, J. Burke and D M. Williams. 2010. Testing DayCent and DNDC model 

simulations of N2O fluxes and assessing the impacts of climate change on the gas flux and biomass production from a 

humid pasture. Atmos. Environ. 44: 2961–2970.

AAPFCO (1995 through 2000b, 2002 through 2009) Commercial Fertilizers. Association of American Plant Food Control 

Officials. University of Kentucky, Lexington, KY.

AAPFCO (2000a) 1999-2000 Commercial Fertilizers Data, ASCII files. Available from David Terry, Secretary, 

Association of American Plant Food Control Officials.

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) (2013). “2012 Alaska Fire Season: Wildland Fire Summary and Statistics 

Annual Report.” Available online at < http://fire.ak.blm.gov/content/aicc/stats/firestats.pdf > November 2013.

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.

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.

Barlaz, M.A. (2006) “Forest Products Decomposition in Municipal Solid Waste Landfills.” Waste Management, 26(4): 

321-333.

Bastian, R. (2007) Personal Communication. Robert Bastian, Office of Water, U.S. Environmental Protection Agency, 

Washington, DC and Victoria Thompson, ICF International. July 20, 2007.

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.

BioCycle (2006) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N. 

Themelis. BioCycle. April 2006. Available online at <http://www.seas.columbia.edu/earth/wtert/sofos/biocycle.pdf>

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>

BioCycle (2010) "The State of Garbage in America" By L. Arsova, R. Van Haaren, N. Goldstein, S. Kaufman, and N. 

Themelis. BioCycle. December 2010. Available online at < http://www.jgpress.com/archives/_free/002191.html>

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.

Chartwell (2004) Municipal Solid Waste Directory. The Envirobiz Group.
A-376 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Cibrowski, P. (1996) Personal Communication. Peter Cibrowski, Minnesota Pollution Control Agency and Heike 

Mainhardt, ICF Incorporated. July 29, 1996.

CTIC (2004) 2004 Crop Residue Management Survey. Conservation Technology Information Center. Available online at 

<http://www.ctic.purdue.edu/CRM/>.

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.

Daly, C., G.H. Taylor, W.P. Gibson, T. Parzybok, G.L. Johnson, and P.A. Pasteris (1998) “Development of high-quality 

spatial datasets for the United States.” Proc., 1st International Conference on Geospatial Information in Agriculture and 

Forestry, Lake Buena Vista, FL, I-512-I-519. June 1-3, 1998. 

Daly, C., R.P. Neilson, and D.L. Phillips (1994) “A statistical-topographic model for mapping climatological precipitation 

over mountainous terrain.” Journal of Applied Meteorology, 33:140-158.

Dean, W. E., and E. Gorham (1998) Magnitude and significance of carbon burial in lakes, reservoirs, and peatlands. 

Geology 26:535-538.

Delgado, J.A., S.J. Del Grosso, and S.M. Ogle (2009) “15N isotopic crop residue cycling studies and modeling suggest 

that IPCC methodologies to assess residue contributions to N2O-N emissions should be reevaluated.” Nutrient Cycling 

in Agroecosystems, DOI 10.1007/s10705-009-9300-9.

Del Grosso, S.J., S.M. Ogle, W.J. Parton. 2011. Soil Organic Matter Cycling and Greenhouse Gas Accounting 

Methodologies, Chapter 1, pp 3-13 DOI: 10.1021/bk-2011-1072.ch001. In: L. Guo, A. Gunasekara, L. McConnell 

(Eds.) Understanding Greenhouse Gas Emissions from Agricultural Management, American Chemical Society, 

Washington, D.C.

Del Grosso, S.J., W.J. Parton, C.A. Keough, and M. Reyes-Fox. (2011) Special features of the DayCent modeling package 

and additional procedures for parameterization, calibration, validation, and applications, in Methods of Introducing 

System Models into Agricultural Research, L.R. Ahuja and Liwang Ma, editors, p. 155-176, American Society of 

Agronomy, Crop Science Society of America, Soil Science Society of America, Madison, WI. USA.

Del Grosso, S.J., S.M. Ogle, W.J. Parton, and F.J. Breidt (2010) “Estimating Uncertainty in N2O Emissions from US 

Cropland Soils.” Global Biogeochemical Cycles, 24, GB1009, doi:10.1029/2009GB003544.

Del Grosso, S.J., T. Wirth, S.M. Ogle, W.J. Parton (2008) Estimating agricultural nitrous oxide emissions. EOS 89, 529-

530.

Del Grosso, S.J., A.R. Mosier, W.J. Parton, and D.S. Ojima (2005) “DAYCENT Model Analysis of Past and 

Contemporary Soil N2O and Net Greenhouse Gas Flux for Major Crops in the USA.” Soil Tillage and Research, 83: 9-

24. doi: 10.1016/j.still.2005.02.007.

Del Grosso, S.J., W.J. Parton, A.R. Mosier, M.D. Hartman, J. Brenner, D.S. Ojima, and D.S. Schimel (2001) “Simulated 

Interaction of Carbon Dynamics and Nitrogen Trace Gas Fluxes Using the DAYCENT Model.” In Schaffer, M., L. Ma, 

S. Hansen, (eds.); Modeling Carbon and Nitrogen Dynamics for Soil Management. CRC Press. Boca Raton, Florida. 

303-332.

Del Grosso, S.J., W.J. Parton, A.R. Mosier, D.S. Ojima, A.E. Kulmala and S. Phongpan (2000) General model for N2O 

and N2 gas emissions from soils due to denitrification. Global Biogeochem. Cycles, 14:1045-1060.

DeZan, D.(2000) Personal Communication between Diane DeZan, Fiber Economics Bureau and Joe Casola, ICF 

Consulting. 4 August 2000.

Domke, G.M., J.E. Smith, and C.W. Woodall. (2011) Accounting for density reduction and structural loss in standing 

dead trees: Implications for forest biomass and carbon stock estimates in the United States. Carbon Balance and 

Management. 6:14.

Domke, G.M., Woodall, C.W., Smith, J.E., Westfall, J.A., McRoberts, R.E. (2012) Consequences of alternative tree-level 

biomass estimation procedures on U.S. forest carbon stock estimates. Forest Ecology and Management. 270: 108-116

Domke, G.M., Woodall, C.W., Walters, B.F., McRoberts, R.E., Hatfield, M.A. In Review. Strategies to compensate for 

the effects of nonresponse on forest carbon baseline estimates from the national forest inventory of the United States. 

Forest Ecology and Management.Domke, G.M., Woodall, C.W., Walters, B.F., Smith, J.E. (2013) From models to 

measurements: comparing down dead wood carbon stock estimates in the U.S. forest inventory. PLoS ONE 8(3): 

e59949.
A-377

Easter, M. , S. Williams, and S. Ogle. (2008) Gap-filling NRI data for the Soil C Inventory. Natural Resource Ecology 

Laboratory, Colorado State University, Fort Collins, CO. Report provided to the US Environmental Protection Agency, 

Tom Wirth.

Edmonds, L., N. Gollehon, R.L. Kellogg, B. Kintzer, L. Knight, C. Lander, J. Lemunyon, D. Meyer, D.C. Moffitt, and J. 

Schaeffer (2003) “Costs Associated with Development and Implementation of Comprehensive Nutrient Management 

Plans.” Part 1. Nutrient Management, Land Treatment, Manure and Wastewater Handling and Storage, and 

Recordkeeping. Natural Resource Conservation Service, U.S. Department of Agriculture.

Energy Recovery Council (2009) “2007 Directory of Waste-to-Energy Plants in the United States,” accessed September 

29, 2009.

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/>.

EPA (2005 through 2013) 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/osw/nonhaz/municipal/msw99.htm >.

EPA (2012) Landfill Gas-to-Energy Project Database. Landfill Methane and Outreach Program. July 2012.

EPA (2008). Compilation of Air Pollution Emission Factors, Publication AP-42, Draft Section 2.4 Municipal Solid Waste 

Landfills. October 2008.

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 (1999) Biosolids Generation, Use and Disposal in the United States. Office of Solid Waste, U.S. Environmental 

Protection Agency. Available online at <http://biosolids.policy.net/relatives/18941.PDF>.

EPA (1998) Compilation of Air Pollution Emission Factors, Publication AP-42, Section 2.4 Municipal Solid Waste 

Landfills. November 1998.

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.

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 (1993) Federal Register. Part II. Standards for the Use and Disposal of Sewage Sludge; Final Rules. U.S. 

Environmental Protection Agency, 40 CFR Parts 257, 403, and 503.

EPA (1988) National Survey of Solid Waste (Municipal) Landfill Facilities, U.S. Environmental Protection Agency. 

Washington, DC. EPA/530-SW-88-011. September 1988.

ERG (2013). Production Data Supplied by ERG for 1990-2011 for Pulp and Paper, Fruits and Vegetables, and Meat. 

October.

Euliss, N., and R. Gleason (2002) Personal communication regarding wetland restoration factor estimates and restoration 

activity data. Ned Euliss and Robert Gleason of the U.S. Geological Survey, Jamestown, ND, to Stephen Ogle of the 

National Resource Ecology Laboratory, Fort Collins, CO. August 2002.

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>.

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.

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.

Gurung, R.B., F.J. Breidt, A. Dutin, and S.M. Ogle (2009) Predicting Enhanced Vegetation Index (EVI) for ecosystem 

modeling applications. Remote Sensing of Environment 113:2186-2193.

Hair. D. and A.H. Ulrich (1963) The Demand and price situation for forest products – 1963. U.S. Department of 
A-378 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.

Halvorson, A.D., C.S. Snyder, A.D. Blaylock, and S.J. Del Grosso. 2013. Enhanced Efficiency Nitrogen Fertilizers: 

Potential Role in Nitrous Oxide Emission Mitigation. Agronomy Journal, doi:10.2134/agronj2013.0081

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.

Harmon, M.E., C.W. Woodall, B. Fasth, J. Sexton, M. Yatkov. (2011) Differences between standing and downed dead 

tree wood density reduction factors: A comparison across decay classes and tree species. Res. Paper. NRS-15. 

Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 40 p

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 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.J. Nowak, and C.W. Woodall. (2011) Managed forest carbon estimates for the U.S. 

Greenhouse Gas Inventory, 1990-2008. Journal of Forestry 109(3):167-173.

Heath, L. S., J. E. Smith, C. W. Woodall, D. L. Azuma, and K. L. Waddell (2011). Carbon stocks on forestlands of the 

United States, with emphasis on USDA Forest Service ownership. Ecosphere 2(1), article 6, 21 p.

Heath, L.S., V. Maltby, R. Miner, K.E. Skog, J.E. Smith, J. Unwin, B. Upton (2010) “Greenhouse Gas and Carbon Profile 

of the U.S. Forest Products Industry Value Chain.” Environ. Sci. Technol., 44: 3999-4005. 

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. FPLRP-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 CoOperation 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.

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.
A-379

Johnson, D.M., and R. Mueller. 2010. The 2009 Cropland Data Layer. Photogrammetric engineering and remote sensing 

76:1201-1205.

Kellogg R.L., C.H. Lander, D.C. Moffitt, and N. Gollehon (2000) Manure Nutrients Relative to Capacity of Cropland and 

Pastureland to Assimilate Nutrients: Spatial and Temporal Trends for the United States. U.S. Department of Agriculture 

Publication Number nps00-0579.

Kraft, D.L. and H.C. Orender (1993) “Considerations for Using Sludge as a Fuel.” Tappi Journal, 76(3): 175-183. 

Li, Y., D. Chen, Y. Zhang, R. Edis and H. Ding. 2005. Comparison of three modeling approaches for simulating 

denitrification and nitrous oxide emissions from loam-textured arable soils. Global Biogeochemical Cycles, 19, 

GB3002.

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.

McFarland, M.J. (2001) Biosolids Engineering, New York: McGraw-Hill, p. 2.12. 

McGill, W.B., and C.V. Cole (1981) Comparative aspects of cycling of organic C, N, S and P through soil organic matter. 

Geoderma 26:267-286.

Mesinger, F., G. DiMego, E. Kalnay, K. Mitchell, P. C. Shafran, W. Ebisuzaki, D. Jovic, J. Woollen, E. Rogers, E. H. 

Berbery, M. B. Ek, Y. Fan, R. Grumbine, W. Higgins, H. Li, Y. Lin, G. Manikin, D. Parrish, and W. Shi (2006) North 

American regional reanalysis. Bulletin of the American Meteorological Society 87:343-360.

Metherell, A.K., L.A. Harding, C.V. Cole, and W.J. Parton (1993) “CENTURY Soil Organic Matter Model Environment.” 

Agroecosystem version 4.0. Technical documentation, GPSR Tech. Report No. 4, USDA/ARS, Ft. Collins, CO.

Miner, R. (2008). “Calculations documenting the greenhouse gas emissions from the pulp and paper industry.” 

Memorandum from Reid Minor, National Council for Air and Stream Improvement, Inc. (NCASI) to Becky 

Nicholson, RTI International, May 21, 2008.

Miner, C. (1998) Harvesting the High Plains: John Kriss and the business of wheat farming, 1920-1950. University Press 

of Kansas, Lawrence, KS.

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.

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.

NASS (2004) Agricultural Chemical Usage: 2003 Field Crops Summary. Report AgCh1(04)a, National Agricultural 

Statistics Service, U.S. Department of Agriculture. Available online at 

<http://usda.mannlib.cornell.edu/reports/nassr/other/pcu-bb/agcs0504.pdf>.

NASS (1999) Agricultural Chemical Usage: 1998 Field Crops Summary. Report AgCh1(99). National Agricultural 

Statistics Service, U.S. Department of Agriculture. Available online at 

<http://usda.mannlib.cornell.edu/reports/nassr/other/pcu-bb/agch0599.pdf>.

NASS (1992) Agricultural Chemical Usage: 1991 Field Crops Summary. Report AgCh1(92). National Agricultural 

Statistics Service, U.S. Department of Agriculture. Available online at 

<http://usda.mannlib.cornell.edu/reports/nassr/other/pcu-bb/agch0392.txt>.

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).

National Council for Air and Stream Improvement, Inc. (NCASI) (2005) “Calculation Tools for Estimating Greenhouse 

Gas Emissions from Pulp and Paper Mills, Version 1.1.” July 8, 2005. 

National Interagency Fire Center (NIFC) (2013). “Fire Information—Wildland Fire Statistics. Prescribed Fires (1998-

2012) and Wildland Fires and Acres (1960-2012).” Available online at 

<http://www.nifc.gov/fireInfo/fireInfo_statistics.html >. November 2013.

National Association of State Foresters (2011). State Forestry Statistics Spreadsheet. Available online at 

<http://www.stateforesters.org/publication-type/stats> November 2011.
A-380 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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.

NEBRA (2007) A National Biosolids Regulation, Quality, End Use & Disposal Survey. North East Biosolids and 

Residuals Association, July 21, 2007

NRAES (1992) On-Farm Composting Handbook (NRAES-54). Natural Resource, Agriculture, and Engineering Service. 

Available online at <http://compost.css.cornell.edu/OnFarmHandbook/onfarm_TOC.html>.

NRCS (1981) Land Resource Regions and Major Land Resource Areas of the United States, USDA Agriculture Handbook 

296, United States Department of Agriculture, Natural Resources Conservation Service, National Soil Survey Cente., 

Lincoln, NE, pp. 156.

NRCS (1997) “National Soil Survey Laboratory Characterization Data,” Digital Data, Natural Resources Conservation 

Service, U.S. Department of Agriculture. Lincoln, NE.

NRIAI (2003) Regional Budget and Cost Information. U.S. Department of Agriculture, Natural Resources Conservation 

Service, Natural Resources Inventory and Analysis Institute. Available online at 

http://www.economics.nrcs.usda.gov/care/budgets/index.html

Nusser, S.M., J.J. Goebel (1997) The national resources inventory: a long term monitoring programme. Environmental 

and Ecological Statistics, 4, 181-204.

Nusser, S.M., F.J. Breidt, and. W.A. Fuller (1998) “Design and Estimation for Investigating the Dynamics of Natural 

Resources, Ecological Applications, 8:234-245.

Ogle, S.M., F.J. Breidt, M. Easter, S. Williams, K. Killian, and K. Paustian (2010) “Scale and uncertainty in modeled soil 

organic carbon stock changes for US croplands using a process-based model.” Global Change Biology 16:810-822.

Ogle, S.M., F.J. Breidt, M. Easter, S. Williams and K. Paustian. (2007) “Empirically-Based Uncertainty Associated with 

Modeling Carbon Sequestration Rates in Soils.” Ecological Modeling 205:453-463.

Ogle, S.M., F.J. Breidt, and K. Paustian. (2006) “Bias and variance in model results due to spatial scaling of measurements 

for parameterization in regional assessments.” Global Change Biology 12:516-523.

Ogle, S.M., M.D. Eve, F.J. Breidt, and K. Paustian (2003) “Uncertainty in estimating land use and management impacts 

on soil organic carbon storage for U.S. agroecosystems between 1982 and 1997.” Global Change Biology 9:1521-1542.

Parton, W.J., D.S. Schimel, C.V. Cole, D.S. Ojima (1987) “Analysis of factors controlling soil organic matter levels in 

Great Plains grasslands.” Soil Science Society of America Journal 51:1173-1179.

Parton, W. J., J. M. O. Scurlock, D. S. Ojima, T. G. Gilmanov, R. J. Scholes, D. S. Schimel, T. Kirchner, J.-C. Menaut, T. 

Seastedt, E. G. Moya, A. Kamnalrut, and J. I. Kinyamario. 1993. Observations and modeling of biomass and soil 

organic matter dynamics for grassland biomes worldwide. Global Biogeochemical Cycles 7:785-809.

Parton, W.J., D.S. Ojima, C.V. Cole, and D.S. Schimel (1994) “A General Model for Soil Organic Matter Dynamics: 

Sensitivity to litter chemistry, texture and management,” in Quantitative Modeling of Soil Forming Processes. Special 

Publication 39, Soil Science Society of America, Madison, WI, 147-167.

Parton, W.J., M.D. Hartman, D.S. Ojima, and D.S. Schimel (1998) “DAYCENT: Its Land Surface Submodel: Description 

and Testing”. Glob. Planet. Chang. 19: 35-48.

Parton, W.J., E.A. Holland, S.J. Del Grosso, M.D. Hartman, R.E. Martin, A.R. Mosier, D.S. Ojima, and D.S. Schimel 

(2001) Generalized model for NOx and N2O emissions from soils. Journal of Geophysical Research. 106 (D15):17403-

17420.

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. 
A-381

Minnesota, St. Paul, MN, 12 p. Available online at <http://cinram.umn.edu/afta2005/> (verified 23 Sept 2006).

Potter, C. S., J.T. Randerson, C.B. Fields, P.A. Matson, P.M. Vitousek, H.A. Mooney, and S.A. Klooster. (1993) 

“Terrestrial ecosystem production: a process model based on global satellite and surface data.” Global Biogeochemical 

Cycles 7:811-841.

Potter, C., S. Klooster, A. Huete, and V. Genovese (2007) Terrestrial carbon sinks for the United States predicted from 

MODIS satellite data and ecosystem modeling. Earth Interactions 11, Article No. 13, DOI 10.1175/EI228.1.

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.

Quam, V.C., J. Gardner, J.R. Brandle, and T.K. Boes (1992) Windbreaks in Sustainable Agricultural Systems. EC-91-

1772. University of Nebraska Extension. Lincoln, NE.

RMA (2012a) “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 18 January 2012.

RMA (2012b) "Rubber FAQs." Rubber Manufacturers Association. Available online at: 

<http://www.rma.org/about_rma/rubber_faqs/>. Accessed 18 January 2012.

RMA (2011) “U.S. Scrap Tire Management Summary 2005-2009.” Rubber Manufacturers Association. October 

2011. Available online at: <http://www.rma.org/scrap_tires/scrap_tire_markets/2009_summary.pdf>.

RMA (2009) Scrap Tire Markets in the United States: 9

th Biennial Report. Rubber Manufacturers Association. 

Washington, DC. May 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>.

RTI (2013) Review of State of Garbage Data Used in the U.S. Non-CO2 Greenhouse Gas Inventory for Landfills. 

Memorandum prepared by K. Weitz and K. Bronstein (RTI) for R. Schmeltz (EPA), November 25, 2013.

RTI (2011) Updated Research on Methane Oxidation in Landfills. Memorandum prepared by K. Weitz (RTI) for R. 

Schmeltz (EPA), January 14, 2011.

RTI (2009) GHG Inventory Improvement – Construction & Demolition Waste DOC and Lo Value. Memorandum prepared 

by J. Coburn and K. Bronstein (RTI) for R. Schmeltz, April 15, 2010.

RTI (2008) Analysis of DOC Values for Industrial Solid Waste for the Pulp and Paper Industry and the Food Industry. 

Memorandum prepared by J. Coburn for M. Weitz (EPA), October 13, 2008.

RTI (2006) Methane Emissions for Industrial Landfills. Memorandum prepared by K. Weitz and M. Bahner for M. Weitz 

(EPA), September 5, 2006.

RTI (2004) Documentation for Changes to the Methodology for the Inventory of Methane Emissions from Landfills. 

Memorandum prepared by M. Branscome and J. Coburn (RTI) E. Scheehle (EPA), August 26, 2004.

Ruddy B.C., D.L. Lorenz, and D.K. Mueller (2006) County-level estimates of nutrient inputs to the land surface of the 

conterminous United States, 1982-2001. Scientific Investigations Report 2006-5012. US Department of the Interior.

Savitzky, A., and M. J. E. Golay. 1964. Smoothing and Differentiation of Data by Simplified Least Squares Procedures. 

Analytical Chemistry 36:1627-1639.

Saxton, K.E., W.J. Rawls, J.S. Romberger, and R.I. Papendick (1986) “Estimating Generalized Soil-Water Characteristics 

From Texture.” Soil Sci. Soc. Am. J. 50:1031-1036.

Schneider, S. (2007) E-mail communication Shelly Schneider, Franklin Associates to Sarah Shapiro, ICF International., A

Division of ERG. January 10, 2007.

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(6): 56-72. 
A-382 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

Skog, K. and G.A. Nicholson (2000) “Carbon Sequestration in Wood and Paper Products.” USDA Forest Service Gen. 

Tech. Rep. RMRS-GTR-59. 

Smith, P., J. Brenner, K. Paustian, G. Bluhm, J. Cipra, M. Easter, E.T. Elliott, K. Killian, D. Lamm, J. Schuler, and S. 

Williams (2002) Quantifying the Change in Greenhouse Gas Emissions Due to Natural Resource Conservation Practice 

Application in Indiana. Final Report to the Indiana Conservation Partnership, Colorado State University Natural 

Resource Ecology Laboratory and U.S. Department of Agriculture Natural Resources Conservation Service, Fort 

Collins, CO.

Smith, J. (2013a) Estimates of Forest Carbon Stocks and Flux: 1990-2013. E-mail correspondence between ICF and Jim 

Smith, USDA Forest Service. September 18, 2013.

Smith, J. (2013b) Estimates of Forest Carbon Stocks by State: 1990-2013. E-mail correspondence between ICF and Jim 

Smith, USDA Forest Service. November 7, 2013.

Smith, J. (2008) 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 NE722. 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 (2010). U.S. Forest Carbon Calculation Tool User’s Guide: Forestland Carbon 

Stocks and Net Annual Stock Change. General Technical Report NRS-13 revised, U.S. Department of Agriculture 

Forest Service, Northern Research Station, 34p.

Smith, 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.

Soil Survey Staff, Natural Resources Conservation Service, United States Department of Agriculture. (2005) State Soil 

Geographic (STATSGO) Database for State. Available online at 

<http://www.ncgc.nrcs.usda.gov/products/datasets/statsgo/index.html>.

Solid Waste Association of North America (SWANA) (1998) Comparison of Models for Predicting Landfill Methane 

Recovery. Publication No. GR-LG 0075. March 1998.

Sonne, E. (2006) “Greenhouse Gas Emissions from Forestry Operations: A Life Cycle Assessment.” J. Environ. Qual. 

35:1439-1450. 

Spencer, S., S.M. Ogle, F.J. Breidt, J. Goebel, and K. Paustian. 2011. Designing a national soil carbon monitoring 

network to support climate change policy: a case example for US agricultural lands. Greenhouse Gas Management & 

Measurement 1:167-178.

Steer, Henry B. (1948) Lumber production in the United States. Misc. Pub. 669, U.S. Department of Agriculture Forest 

Service. Washington, DC.
A-383

Stehfest, E., and C. Müller (2004), Simulation of N2O emissions from a urine-affected pasture in New Zealand with the 

ecosystem model DayCent, J. Geophys. Res., 109, D03109, doi:10.1029/2003JD004261.

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>.

Towery, D. (2001) Personal Communication. Dan Towery regarding adjustments to the CTIC (1998) tillage data to reflect 

long-term trends, Conservation Technology Information Center, West Lafayette, IN, and Marlen Eve, National 

Resource Ecology Laboratory, Fort Collins, CO. February 2001.

TVA (1991 through 1992a, 1993 through 1994) Commercial Fertilizers. Tennessee Valley Authority, Muscle Shoals, AL.

TVA (1992b) Fertilizer Summary Data 1992. Tennessee Valley Authority, Muscle Shoals, AL. 

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.

Upton, B., R. Miner, M. Spinney, L.S. Heath (2008) “The Greenhouse Gas and Energy Impacts of Using Wood Instead of 

Alternatives in Residential Construction in the United States.” Biomass and Bioenergy, 32: 1-10.

U.S. Census Bureau (2013) Population Division. Table 1: Annual Estimates of the Resident Population for the United 

States, Regions, States, and Puerto Rico: April 1, 2010 to July 1, 2012 (NST-EST2012-01). September 2013. 

Available online at < http://www.census.gov/popest/data/national/totals/2012/index.html >

U.S. Census Bureau (2009). Population Division. Table 1. Annual Estimates of the Resident Population for the United 

States, Regions, States, and Puerto Rico: April 1, 2000 to July 1, 2009 (NST-EST2009-01). December 2009. 

Available online at < http://www.census.gov/popest/data/historical/2000s/vintage_2009/index.html>.

USDA (2010a) Crop Production 2009 Summary, National Agricultural Statistics Service, Agricultural Statistics Board, 

U.S. Department of Agriculture, Washington, DC. Available online at <http://usda.mannlib.cornell.edu>, 

USDA (2010b) Quick Stats: U.S. & All States Data - Crops. National Agricultural Statistics Service, U.S. Department of 

Agriculture. Washington, DC. U.S. Department of Agriculture, National Agricultural Statistics Service. Washington, 

D.C., Available online at <http://quickstats.nass.usda.gov/>

USDA (2003, 2005 through 2006, 2008 through 2009) Crop Production Summary, National Agricultural Statistics 

Service, Agricultural Statistics Board, U.S. Department of Agriculture, Washington, DC. Available online at 

<http://usda.mannlib.cornell.edu>, 

USDA (1998) Field Crops Final Estimates 1992-1997. Statistical Bulletin Number 947a. National Agricultural Statistics 

Service, U.S. Department of Agriculture. Washington, DC. Available online at <http://usda.mannlib.cornell.edu>. 

Accessed July 2001. 

USDA (1996) Agricultural Waste Management Field Handbook, National Engineering Handbook (NEH), Part 651. 

Natural Resources Conservation Service, U.S. Department of Agriculture. July 1996.

USDA (1994) Field Crops: Final Estimates, 1987-1992. Statistical Bulletin Number 896, National Agriculture Statistics 

Service, U.S. Department of Agriculture. Washington, DC. Available online at <http://usda.mannlib.cornell.edu/datasets/crops/94896/sb896.txt>.

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 (2013a) Forest Inventory and Analysis National Program: Program Features. U.S. Department of 

Agriculture Forest Service. Washington, DC. Available online at < http://fia.fs.fed.us/program-features/>. Accessed 10 

September 2013.

USDA Forest Service. (2013b) Forest Inventory and Analysis National Program: FIA Data Mart. U.S. Department of 

Agriculture Forest Service. Washington, DC. Available online at <http://apps.fs.fed.us/fiadb-downloads/datamart.html>. 

Accessed 10 September 2013.

USDA Forest Service. (2013c) Forest Inventory and Analysis National Program, FIA library: Field Guides, Methods and 
A-384 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2012

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 10 September 2013.

USDA Forest Service (2013d) 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 10 September 2013.

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.

USDA-ERS (1997) Cropping Practices Survey Data—1995. Economic Research Service, United States Department of 

Agriculture. Available online at <http://www.ers.usda.gov/data/archive/93018/>.

USDA-ERS (2011) Agricultural Resource Management Survey (ARMS) Farm Financial and Crop Production Practices: 

Tailored Reports. Online at: http://ers.usda.gov/Data/ARMS/CropOverview.htm.

USDA-FSA (2012) Conservation Reserve Program Monthly Summary – September 2012. U.S. Department of 

Agriculture, Farm Service Agency, Washington, DC, Available online at 

http://www.fsa.usda.gov/Internet/FSA_File/crpstat0912.pdf.

USDA-NRCS (2009) Summary Report: 2007 National Resources Inventory, Natural Resources Conservation Service, 

Washington, DC, and Center for Survey Statistics and Methodology, Iowa State University, Ames, Iowa, 

http://www.nrcs.usda.gov/technical/NRI/2007/2007_NRI_Summary.pdf.

van Haaren, Rob, Thermelis, N., and Goldstein, N. (2010) "The State of Garbage in America." BioCycle, October 2010. 

Volume 51, Number 10, pg. 16-23.

Vogelman, J.E., S.M. Howard, L. Yang, C. R. Larson, B. K. Wylie, and J. N. Van Driel (2001) “Completion of the 1990’s 

National Land Cover Data Set for the conterminous United States.” Photogrammetric Engineering and Remote Sensing, 

67:650-662.

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.

Williams, S.A. (2006) Data compiled for the Consortium for Agricultural Soils Mitigation of Greenhouse Gases 

(CASMGS) from an unpublished manuscript. Natural Resource Ecology Laboratory, Colorado State University.

Williams, S. and K. Paustian (2005) Developing Regional Cropping Histories for Century Model U.S.-level Simulations. 

Colorado State University, Natural Resources Ecology Laboratory, Fort Collins, CO.

Woodall, C.W., and V.J. Monleon (2008) Sampling protocol, estimation, and analysis procedures for the down woody 

materials indicator of the FIA program. Gen. Tech. Rep. NRS-22. Newtown Square, PA: U.S. Department of 

Agriculture, Forest Service, Northern Research StationF. 68 p.

Woodall, C.W., Walters, B.F., Oswalt, S.N., Domke, G.M., Toney, C., Gray, A.N. 2013. Biomass and carbon attributes 

of downed woody materials in forests of the United States. Forest Ecology and Management 305: 48-59.

Woodall, C.W., B.L. Conkling, M.C. Amacher, J.W. Coulston, S. Jovan, C.H. Perry, B. Schulz, G.C. Smith, S. Will Wolf. 

(2010).. The Forest Inventory and Analysis Database Version 4.0: Database Description and Users Manual for Phase 3. 

Gen. Tech. Rep. NRS-61. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research 

Station. 180 p.

Woodall, C.W., L.S. Heath, G.M. Domke, and M.C. Nichols. (2011) Methods and equations for estimating aboveground 

volume, biomass, and carbon for trees in the U.S. forest inventory, 2010. Gen. Tech. Rep. NRS-88. Newtown Square, 

PA: U.S. Department of Agriculture, Forest Service, Northern Research Station. 30 p.

Woodall, C.W., Perry, C.H., Westfall, J.A. 2012. An empirical assessment of forest floor carbon stock components 

across the United States. Forest Ecology and Management 269: 1-9.

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.