A-1 

Annexes 

The following seven annexes provide additional information related to the material presented in the main body of 

this report as directed in the UNFCCC Guidelines on Reporting and Review (GE.03-60887). Annex 1 contains an analysis 

of the key categories of emissions discussed in this report and a review of the methodology used to identify those key 

categories. Annex 2 describes the methodologies used to estimate CO2 emissions from fossil fuel combustion, the carbon 

content of fossil fuels, and the amount of carbon stored in products from non-energy uses of fossil fuels. Annex 3 

discusses the methodologies used for a number of individual source categories in greater detail than was presented in the 

main body of the report and includes explicit activity data and emission factor tables. Annex 4 presents the IPCC 

reference approach for estimating CO2 emissions from fossil fuel combustion. Annex 5 addresses the criteria for the 

inclusion of an emission source category and discusses some of the sources that are excluded from U.S. estimates. Annex 

6 provides a range of additional information that is relevant to the contents of this report. Finally, Annex 7 provides data 

on the uncertainty of the emission estimates included in this report. 

ANNEX 1 Key Category Analysis ..................................................................................................................................................A-3

ANNEX 2 Methodology and Data for Estimating CO2 Emissions from Fossil Fuel Combustion..................................................A-21

2.1. Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion..............................................................A-21

2.2. Methodology for Estimating the Carbon Content of Fossil Fuels.................................................................................A-46

2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil Fuels..................................................A-72

ANNEX 3 Methodological Descriptions for Additional Source or Sink Categories.......................................................................A-97

3.1. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse Gases from Stationary Combustion .A-97

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...................................A-104

3.3. Methodology for Estimating CH4 Emissions from Coal Mining ..................................................................................A-124

3.4. Methodology for Estimating CH4 and CO2 Emissions from Natural Gas Systems .....................................................A-131

3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum Systems........................................................A-149

3.6. Methodology for Estimating CO2 and N2O Emissions from the Incineration of Waste...............................................A-154

3.7. Methodology for Estimating Emissions from International Bunker Fuels used by the U.S. Military ...........................A-159

3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone Depleting Substances...............A-164

3.9. Methodology for Estimating CH4 Emissions from Enteric Fermentation ....................................................................A-182

3.10. Methodology for Estimating CH4 and N2O Emissions from Manure Management.....................................................A-194

3.11. Methodology for Estimating N2O Emissions from Agricultural Soil Management ......................................................A-213

3.12. Methodology for Estimating Net Carbon Stock Changes in Forest Lands Remaining Forest Lands.........................A-233

3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral and Organic Soils on Cropland and Grassland

 260

3.14. Methodology for Estimating CH4 Emissions from Landfills ........................................................................................A-279

ANNEX 4 IPCC Reference Approach for Estimating CO2 Emissions from Fossil Fuel Combustion .........................................A-285

ANNEX 5 Assessment of the Sources and Sinks of Greenhouse Gas Emissions Excluded ....................................................A-295

ANNEX 6 Additional Information................................................................................................................................................A-301

6.1. Global Warming Potential Values ..............................................................................................................................A-301

6.2. Ozone Depleting Substance Emissions.....................................................................................................................A-307

6.3. Sulfur Dioxide Emissions ...........................................................................................................................................A-309

6.4. Complete List of Source Categories ..........................................................................................................................A-310

6.5. Constants, Units, and Conversions............................................................................................................................A-311

6.6. Abbreviations .............................................................................................................................................................A-313

6.7. Chemical Formulas ....................................................................................................................................................A-317

ANNEX 7 Uncertainty ................................................................................................................................................................A-321

7.1. Overview ....................................................................................................................................................................A-321

7.2. Methodology and Results...........................................................................................................................................A-321

7.3. Planned Improvements ..............................................................................................................................................A-327

7.4. Additional Information on Uncertainty Analyses by Source .......................................................................................A-328
A-2 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 
A-3 

ANNEX 1 Key Category Analysis 

The United States has identified national key categories based on the estimates presented in this report. The 

IPCC’s Good Practice Guidance (IPCC 2000) describes a key category as a “[category] that is prioritized within the 

national inventory system because its estimate has a significant influence on a country’s total inventory of direct 

greenhouse gases in terms of the absolute level of emissions, the trend in emissions, or both.”1 By definition, key 

categories are sources or sinks that have the greatest contribution to the absolute overall level of national emissions in any 

of the years covered by the time series. In addition, when an entire time series of emission estimates is prepared, a 

determination of key categories must also account for the influence of the trends of individual categories. Therefore, a 

trend assessment is conducted to identify source and sink categories for which significant uncertainty in the estimate 

would have considerable effects on overall emission trends. Finally, a qualitative evaluation of key categories should be 

performed, in order to capture any key categories that were not identified in either of the quantitative analyses, but can be 

considered key because of the unique country-specific estimation methods. 

The methodology for conducting a key category analysis, as defined by IPCC’s Good Practice Guidance (IPCC 

2000) and IPCC’s Good Practice Guidance for Land Use, Land-Use Change, and Forestry (IPCC 2003), includes: 

 Tier 1 approach (including both level and trend assessments); 

 Tier 2 approach (including both level and trend assessments, and incorporating uncertainty analysis); and 

 Qualitative approach. 

This Annex presents an analysis of key categories, both for sources only and also for sources and sinks (i.e., 

including LULUCF); discusses Tier 1, Tier 2, and qualitative approaches to identifying key categories; provides level and 

trend assessment equations; and provides a brief statistical evaluation of IPCC’s quantitative methodologies for defining 

key categories. 

Table A-1 presents the key categories for the United States (including and excluding LULUCF categories) using 

emissions and uncertainty data in this report, and ranked according to their sector and global warming potential-weighted 

emissions in 2007. The table also indicates the criteria used in identifying these categories (i.e., level, trend, Tier 1, Tier 2, 

and/or qualitative assessments). 
A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-1: Key Source Categories for the United States (1990-2007) 

Tier 1 Tier 2 

IPCC Source Categories Gas 

Level 

Without 

LULUCF 

Trend 

Without 

LULUCF 

Level With 

LULUCF 

Trend 

With 

LULUCF 

Level 

Without 

LULUCF 

Trend 

Without 

LULUCF 

Level Wit

LULUCF 

Energy CO2 Emissions from Stationary Combustion - Coal CO2 • • • • • • • Mobile Combustion: Road & Other CO2 • • • • • • • CO2 Emissions from Stationary Combustion - Gas CO2 • • • • • • • CO2 Emissions from Stationary Combustion - Oil CO2 • • • • • • • Mobile Combustion: Aviation CO2 • • • • • • CO2 Emissions from Non-Energy Use of Fuels CO2 • • • • Mobile Combustion: Marine CO2 • • CO2 Emissions from Natural Gas Systems CO2 • • • • • • • CO2 Emissions from Waste Incineration CO2 • • Fugitive Emissions from Natural Gas Systems CH4 • • • • • • • Fugitive Emissions from Coal Mining CH4 • • • • • • • Fugitive Emissions from Petroleum Systems CH4 • • • • • • • Non-CO2 Emissions from Stationary Combustion CH4 • Mobile Combustion: Road & Other N2O • • • • • Non-CO2 Emissions from Stationary Combustion N2O • • International Bunker Fuelsb Several Industrial Processes CO2 Emissions from Iron and Steel Production & Metallurgical 

Coke Production 

CO2 • • • • • • • 

CO2 Emissions from Cement Production CO2 • • CO2 Emissions from Ammonia Production and Urea 

Consumption 

CO2 • • 

N2O Emissions from Adipic Acid Production N2O • • Emissions from Substitutes for Ozone Depleting Substances Several • • • • • HFC-23 Emissions from HCFC-22 Production HFCs • • • • • SF6 Emissions from Electrical Transmission and Distribution SF6 • • • PFC Emissions from Aluminum Production PFCs • • Agriculture CH4 Emissions from Enteric Fermentation CH4 • • • • • • • CH4 Emissions from Manure Management CH4 • • • • CH4 Emissions from Rice Cultivation CH4 • Direct N2O Emissions from Agricultural Soil Management N2O • • • • • • • Indirect N2O Emissions from Applied Nitrogen N2O • • • • • • • Waste CH4 Emissions from Landfills CH4 • • • • • • • CH4 Emissions from Wastewater Treatment CH4 • Land Use, Land Use Change, and Forestry CO2 from Changes in Forest Carbon Stocks CO2 • • • CO2 Emissions from Urban Trees CO2 • • • 
Tier 1 Tier 2 

IPCC Source Categories Gas 

Level 

Without 

LULUCF 

Trend 

Without 

LULUCF 

Level With 

LULUCF 

Trend 

With 

LULUCF 

Level 

Without 

LULUCF 

Trend 

Without 

LULUCF 

Level Wit

LULUCF 

CO2 Emissions from Cropland Remaining Cropland CO2 • • CO2 Emissions from Landfilled Yard Trimmings and Food Scraps CO2 • • CO2 Emissions from Grassland Remaining Grassland CO2 • • • CH4 Emissions from Forest Fires CH4 • • • N2O Emissions from Forest Fires N2O Subtotal Without LULUCF Total Emissions Without LULUCF Percent of Total Without LULUCF Subtotal With LULUCF Total Emissions With LULUCF Percent of Total With LULUCF 

aQualitative criteria. bPercent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive. cEmissions from this source not included in totals.
A-6 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-2 provides a complete listing of source categories by IPCC sector, along with comments on the criteria 

used in identifying key categories, without LULUCF sources and sinks. Similarly, Table A-3 provides a complete listing 

of source and sink categories by IPCC sector, along with comments on the criteria used in identifying key categories, 

including LULUCF sources and sinks. The comments refer specifically to the year(s) over the course of the entire 

inventory time series (i.e., 1990 to 2007) in which each source category reached the threshold for being a key source based 

on either a Tier 1 or Tier 2 level assessment. 

In addition to conducting Tier 1 and 2 level and trend assessments, a qualitative assessment of the source 

categories, as described in the IPCC’s Good Practice Guidance (IPCC 2000), was conducted to capture any key categories 

that were not identified by either quantitative method. One additional key category, international bunker fuels, was 

identified using this qualitative assessment. International bunker fuels are fuels consumed for aviation or marine 

international transport activities, and emissions from these fuels are reported separately from totals in accordance with 

IPCC guidelines. If these emissions were included in the totals, bunker fuels would qualify as a key category according to 

the Tier 1 approach. The amount of uncertainty associated with estimation of emissions from international bunker fuels 

also supports the qualification of this source category as key, which would qualify it as a key category according to the 

Tier 2 approach. 

Table A-2: U.S Greenhouse Gas Inventory Source Categories without LULUCF

IPCC Source Categories 

Direct 

GHG 

2007

Emissions

(Tg CO2 Eq.)

Key 

Category? 

ID 

Criteria 

Level in which

year(s)?

Energy 

CO2 Emissions from Stationary Combustion - Coal CO2 2,086.5 • L1 T1 L2 T2 1990, 2007

Mobile Combustion: Road & Other CO2 1,649.1 • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Stationary Combustion - Gas CO2 1,181.1 • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Stationary Combustion - Oil CO2 580.4 • L1 T1 L2 T2 1990, 2007

Mobile Combustion: Aviation CO2 187.5 • L1 T1 L2 1990, 2007

CO2 Emissions from Non-Energy Use of Fuels CO2 133.9 • L1 L2 1990, 2007

Mobile Combustion: Marine CO2 50.8 • L1 1990, 2007

CO2 Emissions from Natural Gas Systems CO2 28.7 • L1 T1 L2 T2 19901; 19902,20072

CO2 Emissions from Waste Incineration CO2 20.8 • T1 

CO2 Emissions from Stationary Combustion - Geothermal 

Energy CO2 0.4 

CO2 Emissions from Petroleum Systems CO2 0.3 

Fugitive Emissions from Natural Gas Systems CH4 104.7 • L1 T1 L2 T2 1990, 2007

Fugitive Emissions from Coal Mining CH4 57.6 • L1 T1 L2 T2 1990, 2007

Fugitive Emissions from Petroleum Systems CH4 28.8 • L1 T1 L2 T2 19901; 19902,20072

Non-CO2 Emissions from Stationary Combustion CH4 6.6 • T2 

Fugitive Emissions from Abandoned Underground Coal Mines CH4 5.7 

Mobile Combustion: Road & Other CH4 2.0 

Mobile Combustion: Aviation CH4 0.1 

Mobile Combustion: Marine CH4 0.1 

Mobile Combustion: Road & Other N2O 27.9 • L1 T1 T2 1990

Non-CO2 Emissions from Stationary Combustion N2O 14.7 • L2 1990, 2007

Mobile Combustion: Aviation N2O 1.8 

N2O Emissions from Waste Incineration N2O 0.4 

Mobile Combustion: Marine N2O 0.4 

International Bunker Fuelsa Several 109.9 • Q 

Industrial Processes 

CO2 Emissions from Iron and Steel Production & Metallurgical 

Coke Production CO2 77.4 • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Cement Production CO2 44.5 • L1 1990, 2007

CO2 Emissions from Lime Production CO2 14.6 

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 13.8 • T1 

CO2 Emissions from Limestone and Dolomite Use CO2 6.2 

CO2 Emissions from Aluminum Production CO2 4.3 

CO2 Emissions from Soda Ash Production and Consumption CO2 4.1 

CO2 Emissions from Petrochemical Production CO2 2.6 

CO2 Emissions from Titanium Dioxide Production CO2 1.9 

CO2 Emissions from CO2 Consumption CO2 1.9 
A-7 

IPCC Source Categories 

Direct 

GHG 

2007

Emissions

(Tg CO2 Eq.)

Key 

Category? 

ID 

Criteria 

Level in which

year(s)?

CO2 Emissions from Ferroalloy Production CO2 1.6 

CO2 Emissions from Phosphoric Acid Production CO2 1.2 

CO2 Emissions from Zinc Production CO2 0.5 

CO2 Emissions from Lead Production CO2 0.3 

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.2 

CH4 Emissions from Petrochemical Production CH4 1.0 

CH4 Emissions from Iron and Steel Production & Metallurgical 

Coke Production CH4 0.7 

CH4 Emissions from Ferroalloy Production CH4 + 

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + 

N2O Emissions from Nitric Acid Production N2O 21.7 

N2O Emissions from Adipic Acid Production N2O 5.9 • T1 

N2O Emissions from N2O Product Uses N2O 4.4 

Emissions from Substitutes for Ozone Depleting Substances HiGWP 108.3 • L1 T1 T2 2007

HFC-23 Emissions from HCFC-22 Production HiGWP 17.0 • L1 T1 T2 1990

SF6 Emissions from Electrical Transmission and Distribution HiGWP 12.7 • T1 T2 

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture HiGWP 4.7 

PFC Emissions from Aluminum Production HiGWP 3.8 • T1 

SF6 Emissions from Magnesium Production and Processing HiGWP 3.0 

Agriculture 

CH4 Emissions from Enteric Fermentation CH4 139.0 • L1 T1 L2 T2 1990, 2007

CH4 Emissions from Manure Management CH4 44.0 • L1 T1 2007

CH4 Emissions from Rice Cultivation CH4 6.2 • T2 

CH4 Emissions from Field Burning of Agricultural Residues CH4 0.9 

Direct N2O Emissions from Agricultural Soil Management N2O 172.0 • L1 T1 L2 T2 1990, 2007

Indirect N2O Emissions from Applied Nitrogen N2O 35.9 • L1 T1 L2 T2 1990, 2007

N2O Emissions from Manure Management N2O 14.7 

N2O Emissions from Field Burning of Agricultural Residues N2O 0.5 

Waste 

CH4 Emissions from Landfills CH4 132.9 • L1 T1 L2 T2 1990, 2007

CH4 Emissions from Wastewater Treatment CH4 24.4 

CH4 Emissions from Composting CH4 1.7 

N2O Emissions from Wastewater Treatment N2O 4.9 

N2O Emissions from Composting N2O 1.8 aEmissions from these sources not included in totals. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Note: LULUCF sources and sinks are not included in this analysis. 

Table A-3: U.S Greenhouse Gas Inventory Source Categories with LULUCF

IPCC Source Categories Gas 

2007

Emissions

(Tg CO2 Eq.)

Key 

Category? ID Criteria 

Level in which

year(s)?

Energy 

CO2 Emissions from Stationary Combustion - Coal CO2 2,086.5 • L1 T1 L2 T2 1990, 2007

Mobile Combustion: Road & Other CO2 1,649.1 • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Stationary Combustion - Gas CO2 1,181.1 • L1 T1 L2 1990, 2007

CO2 Emissions from Stationary Combustion - Oil CO2 580.4 • L1 T1 L2 T2 1990, 2007

Mobile Combustion: Aviation CO2 187.5 • L1 T1 L2 1990, 2007

CO2 Emissions from Non-Energy Use of Fuels CO2 133.9 • L1 L2 1990, 2007

Mobile Combustion: Marine CO2 50.8 • L1 1990, 2007

CO2 Emissions from Natural Gas Systems CO2 28.7 • L1 T1 L2 T2 19901; 19902,20072

CO2 Emissions from Waste Incineration CO2 20.8 • T1 

CO2 Emissions from Stationary Combustion - Geothermal 

Energy CO2 0.4 

CO2 Emissions from Petroleum Systems CO2 0.3 

Fugitive Emissions from Natural Gas Systems CH4 104.7 • L1 T1 L2 T2 1990, 2007
A-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

IPCC Source Categories Gas 

2007

Emissions

(Tg CO2 Eq.)

Key 

Category? ID Criteria 

Level in which

year(s)?

Fugitive Emissions from Coal Mining CH4 57.6 • L1 T1 L2 T2 1990, 2007

Fugitive Emissions from Petroleum Systems CH4 28.8 • L1 T1 L2 T2 19901; 19902,20072

Non-CO2 Emissions from Stationary Combustion CH4 6.6 

Fugitive Emissions from Abandoned Underground Coal Mines CH4 5.7 

Mobile Combustion: Road & Other CH4 2.0 

Mobile Combustion: Aviation CH4 0.1 

Mobile Combustion: Marine CH4 0.1 

Mobile Combustion: Road & Other N2O 27.9 • L1 T1 T2 1990

Non-CO2 Emissions from Stationary Combustion N2O 14.7 • L2 1990, 2007

Mobile Combustion: Aviation N2O 1.8 

N2O Emissions from Waste Incineration N2O 0.4 

Mobile Combustion: Marine N2O 0.4 

International Bunker Fuelsa Several 109.9 • Q 

Industrial Processes 

CO2 Emissions from Iron and Steel Production & Metallurgical 

Coke Production CO2 77.4 • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Cement Production CO2 44.5 • L1 1990, 2007

CO2 Emissions from Lime Production CO2 14.6 

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 13.8 • T1 

CO2 Emissions from Limestone and Dolomite Use CO2 6.2 

CO2 Emissions from Aluminum Production CO2 4.3 

CO2 Emissions from Soda Ash Production and Consumption CO2 4.1 

CO2 Emissions from Petrochemical Production CO2 2.6 

CO2 Emissions from Titanium Dioxide Production CO2 1.9 

CO2 Emissions from CO2 Consumption CO2 1.9 

CO2 Emissions from Ferroalloy Production CO2 1.6 

CO2 Emissions from Phosphoric Acid Production CO2 1.2 

CO2 Emissions from Zinc Production CO2 0.5 

CO2 Emissions from Lead Production CO2 0.3 

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.2 

CH4 Emissions from Petrochemical Production CH4 1.0 

CH4 Emissions from Iron and Steel Production & Metallurgical 

Coke Production CH4 0.7 

CH4 Emissions from Ferroalloy Production CH4 + 

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + 

N2O Emissions from Nitric Acid Production N2O 21.7 

N2O Emissions from Adipic Acid Production N2O 5.9 • T1 

N2O Emissions from N2O Product Uses N2O 4.4 

Emissions from Substitutes for Ozone Depleting Substances HiGWP 108.3 • L1 T1 T2 2007

HFC-23 Emissions from HCFC-22 Production HiGWP 17.0 • L1 T1 1990

SF6 Emissions from Electrical Transmission and Distribution HiGWP 12.7 • T1 T2 

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture HiGWP 4.7 

PFC Emissions from Aluminum Production HiGWP 3.8 • T1 

SF6 Emissions from Magnesium Production and Processing HiGWP 3.0 

Agriculture 

CH4 Emissions from Enteric Fermentation CH4 139.0 • L1 T1 L2 T2 1990, 2007

CH4 Emissions from Manure Management CH4 44.0 • L1 T1 1990, 2007

CH4 Emissions from Rice Cultivation CH4 6.2 • T2 

CH4 Emissions from Field Burning of Agricultural Residues CH4 0.9 

Direct N2O Emissions from Agricultural Soil Management N2O 172.0 • L1 T1 L2 T2 1990, 2007

Indirect N2O Emissions from Applied Nitrogen N2O 35.9 • L1 T1 L2 T2 1990, 2007

N2O Emissions from Manure Management N2O 14.7 

N2O Emissions from Field Burning of Agricultural Residues N2O 0.5 

Waste 

CH4 Emissions from Landfills CH4 132.9 • L1 T1 L2 T2 1990, 2007
A-9 

IPCC Source Categories Gas 

2007

Emissions

(Tg CO2 Eq.)

Key 

Category? ID Criteria 

Level in which

year(s)?

CH4 Emissions from Wastewater Treatment CH4 24.4 • L2 2007

CH4 Emissions form Composting CH4 1.7 

N2O Emissions from Wastewater Treatment N2O 4.9 

N2O Emissions from Composting N2O 1.8 

Land Use, Land Use Change, and Forestry 

CO2 from Changes in Forest Carbon Stocks CO2 (910.1) • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Urban Trees CO2 (97.6) • L1 T1 L2 T2 1990, 2007

CO2 Emissions from Land Converted to Grassland CO2 (26.7)

CO2 Emissions from Cropland Remaining Cropland CO2 (11.6) • T1 L2 T2 1990

CO2 Emissions from Landfilled Yard Trimmings and Food 

Scraps CO2 (9.8) • T1 L2 T2 1990

CO2 Emissions from Grassland Remaining Grassland CO2 (4.7) • L1 T1 L2 T2 1990

CO2 Emissions from Land Converted to Cropland CO2 5.9 

CO2 Emissions from Wetlands Remaining Wetlands CO2 1.0 

CH4 Emissions from Forest Fires CH4 29.0 • L1 T1 L2 T2 2007

N2O Emissions from Forest Fires N2O 2.9 • T2 

N2O Emissions from Soils N2O 1.9 

N2O Emissions from Wetlands Remaining Wetlands N2O + aEmissions from these sources not included in totals. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Evaluation of Key Categories 

Level Assessment 

When using a Tier 1 approach for the level assessment, a predetermined cumulative emissions threshold is used 

to identify key categories. When source and sink categories are sorted in order of decreasing absolute emissions, those 

that fall at the top of the list and cumulatively account for 95 percent of emissions are considered key categories. The 95 

percent threshold in the IPCC Good Practice Guidance (IPCC 2000) was designed to establish a general level where the 

key category analysis covers approximately 75 to 92 percent of inventory uncertainty. 

Including the Tier 2 approach provides additional insight into why certain source categories are considered key, 

and how to prioritize inventory improvements. In the Tier 2 approach, the level assessment for each category from the Tier 

1 approach is multiplied by its percent relative uncertainty. If the uncertainty reported is asymmetrical, the larger 

uncertainty is used. When source and sink categories are sorted in decreasing order of this calculation, those that fall at the

top of the list and cumulatively account for 90 percent of emissions are considered key categories. The key categories 

identified by the Tier 2 level assessment may differ from those identified by the Tier 1 assessment. The final set of key 

categories includes all source and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in 

mind that the two assessments are not mutually exclusive. 

It is important to note that a key category analysis can be sensitive to the definitions of the source and sink 

categories. If a large source category is split into many subcategories, then the subcategories may have contributions to 

the total inventory that are too small for those source categories to be considered key. Similarly, a collection of small, 

non-key source categories adding up to less than 5 percent of total emissions could become key source categories if those 

source categories were aggregated into a single source category. The United States has attempted to define source and 

sink categories by the conventions which would allow comparison with other international key categories, while still 

maintaining the category definitions that constitute how the emissions estimates were calculated for this report. As such, 

some of the category names used in the key category analysis may differ from the names used in the main body of the 

report. Additionally, the United States accounts for some source categories, including fossil fuel feedstocks, international 

bunkers, and emissions from U.S. territories, that are derived from unique data sources using country-specific 

methodologies. 

Table A- 4 through Table A- 7 contain the 1990 and 2007 level assessments for both with and without LULUCF 

sources and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are 

shaded dark gray. Additional key categories identified by the Tier 2 assessment are shaded light gray. 
A-10 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Trend Assessment 

The Tier 1 approach for trend assessment is defined as the product of the source or sink category level 

assessment and the absolute difference between the source or sink category trend and the total trend. In turn, the source or 

sink category trend is defined as the change in emissions from the base year to the current year, as a percentage of current 

year emissions from that source or sink category. The total trend is the percentage change in total inventory emissions 

from the base year to the current year. 

Thus, the source or sink category trend assessment will be large if the source or sink category represents a large 

percentage of emissions and/or has a trend that is quite different from the overall inventory trend. To determine key 

categories, the trend assessments are sorted in decreasing order, so that the source or sink categories with the highest trend 

assessments appear first. The trend assessments are summed until the threshold of 95 percent is reached; all categories 

that fall within that cumulative 95 percent are considered key categories. 

For the Tier 2 approach, the trend assessment for each category from the Tier 1 approach is multiplied by its 

percent relative uncertainty. If the uncertainty reported is asymmetrical, the larger uncertainty is used. When source and 

sink categories are sorted in decreasing order of this calculation, those that fall at the top of the list and cumulatively 

account for 90 percent of emissions are considered key categories. The key categories identified by the Tier 2 trend 

assessment may differ from those identified by the Tier 1 assessment. The final set of key categories includes all source 

and sink categories identified as key by either the Tier 1 or the Tier 2 assessment, keeping in mind that the two 

assessments are not mutually exclusive. 

Table A- 8 and Table A- 9 contain the 1990 – 2007 trend assessment for both with and without LULUCF sources 

and sinks, and contain further detail on where each source falls within the analysis. Tier 1 key categories are shaded dark 

gray. Additional key categories identified by the Tier 2 assessment are shaded light gray 

Table A- 4: 1990 Key Source Category Tier 1 and Tier 2 Analysis—Level Assessment, without LULUCF 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

Tier 1 Level

Assessment

Cumulative

Total Uncertaintya

Tier 2 Level

Assessment

CO2 Emissions from Stationary Combustion - Coal CO2 1,695.9 0.28 0.28 9% 0.026

Mobile Combustion: Road & Other CO2 1,258.7 0.21 0.49 8% 0.016

CO2 Emissions from Stationary Combustion - Gas CO2 965.5 0.16 0.64 7% 0.010

CO2 Emissions from Stationary Combustion - Oil CO2 562.7 0.09 0.74 6% 0.006

Mobile Combustion: Aviation CO2 179.4 0.03 0.77 8% 0.002

Direct N2O Emissions from Agricultural Soil Management N2O 158.9 0.03 0.79 54% 0.014

CH4 Emissions from Landfills CH4 149.2 0.02 0.82 39% 0.010

CH4 Emissions from Enteric Fermentation CH4 133.2 0.02 0.84 18% 0.004

Fugitive Emissions from Natural Gas Systems CH4 129.6 0.02 0.86 43% 0.009

CO2 Emissions from Non-Energy Use of Fuels CO2 117.0 0.02 0.88 20% 0.004

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 109.8 0.02 0.90 22% 0.004

Fugitive Emissions from Coal Mining CH4 84.1 0.01 0.91 24% 0.003

Mobile Combustion: Marine CO2 46.5 0.01 0.92 8% 0.001

Mobile Combustion: Road & Other N2O 41.6 0.01 0.93 19% 0.001

Indirect N2O Emissions from Applied Nitrogen N2O 41.5 0.01 0.93 136% 0.009

HFC-23 Emissions from HCFC-22 Production HFCs 36.4 0.01 0.94 10% 0.001

Fugitive Emissions from Petroleum Systems CH4 33.9 0.01 0.94 144% 0.008

CO2 Emissions from Natural Gas Systems CO2 33.7 0.01 0.95 43% 0.002

CO2 Emissions from Cement Production CO2 33.3 0.01 0.95 13% 0.001

CH4 Emissions from Manure Management CH4 30.4 <0.01 0.96 20% 0.001

SF6 Emissions from Electrical Transmission and 

Distribution SF6 26.8 <0.01 0.96 22% 0.001

CH4 Emissions from Wastewater Treatment CH4 23.5 <0.01 0.97 49% 0.002

N2O Emissions from Nitric Acid Production N2O 20.0 <0.01 0.97 44% 0.001

PFC Emissions from Aluminum Production PFCs 18.5 <0.01 0.97 11% <0.001

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 16.8 <0.01 0.98 12% <0.001

N2O Emissions from Adipic Acid Production N2O 15.3 <0.01 0.98 20% <0.001

Non-CO2 Emissions from Stationary Combustion N2O 12.8 <0.01 0.98 187% 0.004

N2O Emissions from Manure Management N2O 12.1 <0.01 0.98 24% <0.001

CO2 Emissions from Lime Production CO2 11.5 <0.01 0.99 9% <0.001

CO2 Emissions from Waste Incineration CO2 10.9 <0.01 0.99 27% <0.001

Non-CO2 Emissions from Stationary Combustion CH4 7.4 <0.01 0.99 128% 0.002
A-11 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

Tier 1 Level

Assessment

Cumulative

Total Uncertaintya

Tier 2 Level

Assessment

CH4 Emissions from Rice Cultivation CH4 7.1 <0.01 0.99 164% 0.002

CO2 Emissions from Aluminum Production CO2 6.8 <0.01 0.99 4% <0.001

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 6.0 <0.01 0.99 23% <0.001

SF6 Emissions from Magnesium Production and 

Processing SF6 5.4 <0.01 0.99 13% <0.001

CO2 Emissions from Limestone and Dolomite Use CO2 5.1 <0.01 0.99 16% <0.001

Mobile Combustion: Road & Other CH4 4.5 <0.01 0.99 8% <0.001

N2O Emissions from N2O Product Uses N2O 4.4 <0.01 1.00 2% <0.001

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 <0.01 1.00 7% <0.001

N2O Emissions from Wastewater Treatment N2O 3.7 <0.01 1.00 94% 0.001

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 2.9 <0.01 1.00 9% <0.001

CO2 Emissions from Petrochemical Production CO2 2.2 <0.01 1.00 40% <0.001

CO2 Emissions from Ferroalloy Production CO2 2.2 <0.01 1.00 12% <0.001

Mobile Combustion: Aviation N2O 1.7 <0.01 1.00 19% <0.001

CO2 Emissions from Phosphoric Acid Production CO2 1.5 <0.01 1.00 18% <0.001

CO2 Emissions from Carbon Dioxide Consumption CO2 1.4 <0.01 1.00 22% <0.001

CO2 Emissions from Titanium Dioxide Production CO2 1.2 <0.01 1.00 13% <0.001

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 1.0 <0.01 1.00 8% <0.001

CO2 Emissions from Zinc Production CO2 0.9 <0.01 1.00 25% <0.001

CH4 Emissions from Petrochemical Production CH4 0.9 <0.01 1.00 31% <0.001

CH4 Emissions from Field Burning of Agricultural Residues CH4 0.7 <0.01 1.00 94% <0.001

N2O Emissions from Waste Incineration N2O 0.5 <0.01 1.00 191% <0.001

CO2 Emissions from Stationary Combustion - Geothermal 

Energy CO2 0.4 <0.01 1.00 5% <0.001

CO2 Emissions from Petroleum Systems CO2 0.4 <0.01 1.00 144% <0.001

Mobile Combustion: Marine N2O 0.4 <0.01 1.00 19% <0.001

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.4 <0.01 1.00 10% <0.001

N2O Emissions from Field Burning of Agricultural Residues N2O 0.4 <0.01 1.00 85% <0.001

N2O Emission from Composting N2O 0.4 <0.01 1.00 50% <0.001

Emissions from Substitutes for Ozone Depleting 

Substances Several 0.3 <0.01 1.00 9% <0.001

CH4 Emissions from Composting CH4 0.3 <0.01 1.00 50% <0.001

CO2 Emissions from Lead Production CO2 0.3 <0.01 1.00 17% <0.001

Mobile Combustion: Aviation CH4 0.2 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH4 0.1 <0.01 1.00 8% <0.001

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + <0.01 1.00 10% <0.001

CH4 Emissions from Ferroalloy Production CH4 + <0.01 1.00 12% <0.001 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive. 

Note: LULUCF sources and sinks are not included in this analysis. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Table A- 5: 1990 Key Source Category Tier 1 and Tier 2 Analysis—Level Assessment, with LULUCF 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

Tier 1 Level 

Assessment

Cumulative 

Total Uncertainty a

Tier 2 Level 

Assessment

CO2 Emissions from Stationary Combustion - Coal CO2 1,695.9 0.24 0.245 9% 0.023

Mobile Combustion: Road & Other CO2 1,258.7 0.18 0.426 8% 0.014

CO2 Emissions from Stationary Combustion - Gas CO2 965.5 0.14 0.566 7% 0.009

CO2 from Changes in Forest Carbon Stocks CO2 661.1 0.10 0.661 19% 0.018

CO2 Emissions from Stationary Combustion - Oil CO2 562.7 0.08 0.742 6% 0.005

Mobile Combustion: Aviation CO2 179.4 0.03 0.768 8% 0.002

Direct N2O Emissions from Agricultural Soil Management N2O 158.9 0.02 0.791 54% 0.012

CH4 Emissions from Landfills CH4 149.2 0.02 0.813 39% 0.008

CH4 Emissions from Enteric Fermentation CH4 133.2 0.02 0.832 18% 0.003

Fugitive Emissions from Natural Gas Systems CH4 129.6 0.02 0.850 43% 0.008
A-12 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

Tier 1 Level 

Assessment

Cumulative 

Total Uncertainty a

Tier 2 Level 

Assessment

CO2 Emissions from Non-Energy Use of Fuels CO2 117.0 0.02 0.867 20% 0.003

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 109.8 0.02 0.883 22% 0.004

Fugitive Emissions from Coal Mining CH4 84.1 0.01 0.895 24% 0.003

CO2 Emissions from Urban Trees CO2 60.6 0.01 0.904 21% 0.002

CO2 Emissions from Grassland Remaining Grassland CO2 46.7 0.01 0.911 54% 0.004

Mobile Combustion: Marine CO2 46.5 0.01 0.918 8% 0.001

Mobile Combustion: Road & Other N2O 41.6 0.01 0.924 19% 0.001

Indirect N2O Emissions from Applied Nitrogen N2O 41.5 0.01 0.930 136% 0.008

HFC-23 Emissions from HCFC-22 Production HFCs 36.4 0.01 0.935 10% 0.001

Fugitive Emissions from Petroleum Systems CH4 33.9 <0.01 0.940 144% 0.007

CO2 Emissions from Natural Gas Systems CO2 33.7 <0.01 0.945 43% 0.002

CO2 Emissions from Cement Production CO2 33.3 <0.01 0.949 13% 0.001

CH4 Emissions from Manure Management CH4 30.4 <0.01 0.954 20% 0.001

SF6 Emissions from Electrical Transmission and 

Distribution SF6 26.8 <0.01 0.958 22% 0.001

CH4 Emissions from Wastewater Treatment CH4 23.5 <0.01 0.961 49% 0.002

CO2 Emissions from Landfilled Yard Trimmings and Food 

Scraps CO2 23.5 <0.01 0.964 84% 0.003

CO2 Emissions from Cropland Remaining Cropland CO2 22.3 <0.01 0.968 56% 0.002

CO2 Emissions from Land Converted to Grassland CO2 22.3 <0.01 0.971 9% <0.001

N2O Emissions from Nitric Acid Production N2O 20.0 <0.01 0.974 44% 0.001

PFC Emissions from Aluminum Production PFCs 18.5 <0.01 0.976 11% <0.001

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 16.8 <0.01 0.979 12% <0.001

N2O Emissions from Adipic Acid Production N2O 15.3 <0.01 0.981 20% <0.001

Non-CO2 Emissions from Stationary Combustion N2O 12.8 <0.01 0.983 187% 0.003

N2O Emissions from Manure Management N2O 12.1 <0.01 0.985 24% <0.001

CO2 Emissions from Lime Production CO2 11.5 <0.01 0.986 9% <0.001

CO2 Emissions from Waste Incineration CO2 10.9 <0.01 0.988 27% <0.001

Non-CO2 Emissions from Stationary Combustion CH4 7.4 <0.01 0.989 128% 0.001

CH4 Emissions from Rice Cultivation CH4 7.1 <0.01 0.990 164% 0.002

CO2 Emissions from Aluminum Production CO2 6.8 <0.01 0.991 4% <0.001

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 6.0 <0.01 0.992 23% <0.001

SF6 Emissions from Magnesium Production and 

Processing SF6 5.4 <0.01 0.993 13% <0.001

CO2 Emissions from Limestone and Dolomite Use CO2 5.1 <0.01 0.993 16% <0.001

CH4 Emissions from Forest Fires CH4 4.6 <0.01 0.994 155% 0.001

Mobile Combustion: Road & Other CH4 4.5 <0.01 0.995 8% <0.001

N2O Emissions from N2O Product Uses N2O 4.4 <0.01 0.995 2% <0.001

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 <0.01 0.996 7% <0.001

N2O Emissions from Wastewater Treatment N2O 3.7 <0.01 0.996 94% <0.001

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 2.9 <0.01 0.997 9% <0.001

CO2 Emissions from Petrochemical Production CO2 2.2 <0.01 0.997 40% <0.001

CO2 Emissions from Ferroalloy Production CO2 2.2 <0.01 0.997 12% <0.001

CO2 Emissions from Land Converted to Cropland CO2 2.2 <0.01 0.998 40% <0.001

Mobile Combustion: Aviation N2O 1.7 <0.01 0.998 19% <0.001

CO2 Emissions from Phosphoric Acid Production CO2 1.5 <0.01 0.998 18% <0.001

CO2 Emissions from Carbon Dioxide Consumption CO2 1.4 <0.01 0.998 22% <0.001

CO2 Emissions from Titanium Dioxide Production CO2 1.2 <0.01 0.999 13% <0.001

N2O Emissions from Soils N2O 1.0 <0.01 0.999 211% <0.001

CO2 Emissions from Wetlands Remaining Wetlands CO2 1.0 <0.01 0.999 31% <0.001

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 1.0 <0.01 0.999 8% <0.001

CO2 Emissions from Zinc Production CO2 0.9 <0.01 0.999 25% <0.001

CH4 Emissions from Petrochemical Production CH4 0.9 <0.01 0.999 31% <0.001

CH4 Emissions from Field Burning of Agricultural Residues CH4 0.7 <0.01 0.999 94% <0.001
A-13 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

Tier 1 Level 

Assessment

Cumulative 

Total Uncertainty a

Tier 2 Level 

Assessment

N2O Emissions from Waste Incineration N2O 0.5 <0.01 0.999 191% <0.001

N2O Emissions from Forest Fires N2O 0.5 <0.01 1.000 152% <0.001

CO2 Emissions from Stationary Combustion - Geothermal 

Energy CO2 0.4 <0.01 1.000 5% <0.001

CO2 Emissions from Petroleum Systems CO2 0.4 <0.01 1.000 144% <0.001

Mobile Combustion: Marine N2O 0.4 <0.01 1.000 19% <0.001

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.4 <0.01 1.000 10% <0.001

N2O Emissions from Field Burning of Agricultural Residues N2O 0.4 <0.01 1.000 85% <0.001

N2O Emission from Composting N2O 0.4 <0.01 1.000 50% <0.001

Emissions from Substitutes for Ozone Depleting 

Substances Several 0.3 <0.01 1.000 9% <0.001

CH4 Emissions from Composting CH4 0.3 <0.01 1.000 50% <0.001

CO2 Emissions from Lead Production CO2 0.3 <0.01 1.000 17% <0.001

Mobile Combustion: Aviation CH4 0.2 <0.01 1.000 8% <0.001

Mobile Combustion: Marine CH4 0.1 <0.01 1.000 8% <0.001

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + <0.01 1.000 10% <0.001

CH4 Emissions from Ferroalloy Production CH4 + <0.01 1.000 12% <0.001

N2O Emissions from Wetlands Remaining Wetlands N2O + <0.01 1.000 73% <0.001 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Table A- 6: 2007 Key Source Category Tier 1 and Tier 2 Analysis—Level Assessment, without LULUCF 

IPCC Source Categories 

Direct 

GHG 

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Level

Assessment

Cumulative

Total Uncertaintya

Tier 2 Level

Assessment

CO2 Emissions from Stationary Combustion - Coal CO2 2,086.5 0.29 0.29 9% 0.028

Mobile Combustion: Road & Other CO2 1,649.1 0.23 0.53 8% 0.018

CO2 Emissions from Stationary Combustion - Gas CO2 1,181.1 0.17 0.69 7% 0.011

CO2 Emissions from Stationary Combustion - Oil CO2 580.4 0.08 0.77 6% 0.005

Mobile Combustion: Aviation CO2 187.5 0.03 0.80 8% 0.002

Direct N2O Emissions from Agricultural Soil Management N2O 172.0 0.02 0.82 54% 0.013

CH4 Emissions from Enteric Fermentation CH4 139.0 0.02 0.84 18% 0.004

CO2 Emissions from Non-Energy Use of Fuels CO2 133.9 0.02 0.86 20% 0.004

CH4 Emissions from Landfills CH4 132.9 0.02 0.88 39% 0.007

Emissions from Substitutes for Ozone Depleting 

Substances Several 108.3 0.02 0.90 9% 0.001

Fugitive Emissions from Natural Gas Systems CH4 104.7 0.01 0.91 43% 0.006

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 77.4 0.01 0.92 22% 0.002

Fugitive Emissions from Coal Mining CH4 57.6 0.01 0.93 24% 0.002

Mobile Combustion: Marine CO2 50.8 0.01 0.94 8% 0.001

CO2 Emissions from Cement Production CO2 44.5 0.01 0.94 13% 0.001

CH4 Emissions from Manure Management CH4 44.0 0.01 0.95 20% 0.001

Indirect N2O Emissions from Applied Nitrogen N2O 35.9 0.01 0.95 136% 0.007

Fugitive Emissions from Petroleum Systems CH4 28.8 <0.01 0.96 144% 0.006

CO2 Emissions from Natural Gas Systems CO2 28.7 <0.01 0.96 43% 0.002

Mobile Combustion: Road & Other N2O 27.9 <0.01 0.97 19% 0.001

CH4 Emissions from Wastewater Treatment CH4 24.4 <0.01 0.97 49% 0.002

N2O Emissions from Nitric Acid Production N2O 21.7 <0.01 0.97 44% 0.001

CO2 Emissions from Waste Incineration CO2 20.8 <0.01 0.98 27% 0.001

HFC-23 Emissions from HCFC-22 Production HFCs 17.0 <0.01 0.98 10% <0.001

N2O Emissions from Manure Management N2O 14.7 <0.01 0.98 24% <0.001

Non-CO2 Emissions from Stationary Combustion N2O 14.7 <0.01 0.98 187% 0.004

CO2 Emissions from Lime Production CO2 14.6 <0.01 0.98 9% <0.001

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 13.8 <0.01 0.99 12% <0.001

SF6 Emissions from Electrical Transmission and 

Distribution SF6 12.7 <0.01 0.99 22% <0.001
A-14 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

IPCC Source Categories 

Direct 

GHG 

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Level

Assessment

Cumulative

Total Uncertaintya

Tier 2 Level

Assessment

Non-CO2 Emissions from Stationary Combustion CH4 6.6 <0.01 0.99 128% 0.001

CO2 Emissions from Limestone and Dolomite Use CO2 6.2 <0.01 0.99 16% <0.001

CH4 Emissions from Rice Cultivation CH4 6.2 <0.01 0.99 164% 0.001

N2O Emissions from Adipic Acid Production N2O 5.9 <0.01 0.99 20% <0.001

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 5.7 <0.01 0.99 23% <0.001

N2O Emissions from Wastewater Treatment N2O 4.9 <0.01 0.99 94% 0.001

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 4.7 <0.01 0.99 9% <0.001

N2O Emissions from N2O Product Uses N2O 4.4 <0.01 0.99 2% <0.001

CO2 Emissions from Aluminum Production CO2 4.3 <0.01 1.00 4% <0.001

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 <0.01 1.00 7% <0.001

PFC Emissions from Aluminum Production PFCs 3.8 <0.01 1.00 11% <0.001

SF6 Emissions from Magnesium Production and 

Processing SF6 3.0 <0.01 1.00 13% <0.001

CO2 Emissions from Petrochemical Production CO2 2.6 <0.01 1.00 40% <0.001

Mobile Combustion: Road & Other CH4 2.0 <0.01 1.00 8% <0.001

CO2 Emissions from Titanium Dioxide Production CO2 1.9 <0.01 1.00 13% <0.001

CO2 Emissions from Carbon Dioxide Consumption CO2 1.9 <0.01 1.00 22% <0.001

N2O Emission from Composting N2O 1.8 <0.01 1.00 50% <0.001

Mobile Combustion: Aviation N2O 1.8 <0.01 1.00 19% <0.001

CH4 Emissions from Composting CH4 1.7 <0.01 1.00 50% <0.001

CO2 Emissions from Ferroalloy Production CO2 1.6 <0.01 1.00 12% <0.001

CO2 Emissions from Phosphoric Acid Production CO2 1.2 <0.01 1.00 18% <0.001

CH4 Emissions from Petrochemical Production CH4 1.0 <0.01 1.00 31% <0.001

CH4 Emissions from Field Burning of Agricultural 

Residues CH4 0.9 <0.01 1.00 94% <0.001

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 0.7 <0.01 1.00 8% <0.001

CO2 Emissions from Zinc Production CO2 0.5 <0.01 1.00 25% <0.001

N2O Emissions from Field Burning of Agricultural 

Residues N2O 0.5 <0.01 1.00 85% <0.001

Mobile Combustion: Marine N2O 0.4 <0.01 1.00 19% <0.001

N2O Emissions from Waste Incineration N2O 0.4 <0.01 1.00 191% <0.001

CO2 Emissions from Stationary Combustion - Geothermal 

Energy CO2 0.4 <0.01 1.00 5% <0.001

CO2 Emissions from Petroleum Systems CO2 0.3 <0.01 1.00 144% <0.001

CO2 Emissions from Lead Production CO2 0.3 <0.01 1.00 17% <0.001

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.2 <0.01 1.00 10% <0.001

Mobile Combustion: Aviation CH4 0.1 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH4 0.1 <0.01 1.00 8% <0.001

CH4 Emissions from Ferroalloy Production CH4 + <0.01 1.00 12% <0.001

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + <0.01 1.00 10% <0.001 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive. 

Note: LULUCF sources and sinks are not included in this analysis. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Table A- 7: 2007 Key Source Category Tier 1 and Tier 2 Analysis—Level Assessment with LULUCF 

IPCC Source Categories 

Direct 

GHG 

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Level

Assessment

Cumulative

Total Uncertaintya

Tier 2 Level

Assessment

CO2 Emissions from Stationary Combustion - Coal CO2 2,086.5 0.25 0.25 9% 0.024

Mobile Combustion: Road & Other CO2 1,649.1 0.20 0.46 8% 0.015

CO2 Emissions from Stationary Combustion - Gas CO2 1,181.1 0.14 0.60 7% 0.009

CO2 from Changes in Forest Carbon Stocks CO2 910.1 0.11 0.71 19% 0.021

CO2 Emissions from Stationary Combustion - Oil CO2 580.4 0.07 0.78 6% 0.004

Mobile Combustion: Aviation CO2 187.5 0.02 0.80 8% 0.002

Direct N2O Emissions from Agricultural Soil Management N2O 172.0 0.02 0.82 54% 0.011

CH4 Emissions from Enteric Fermentation CH4 139.0 0.02 0.84 18% 0.003
A-15 

CO2 Emissions from Non-Energy Use of Fuels CO2 133.9 0.02 0.86 20% 0.003

CH4 Emissions from Landfills CH4 132.9 0.02 0.87 39% 0.006

Emissions from Substitutes for Ozone Depleting 

Substances Several 108.3 0.01 0.89 9% 0.001

Fugitive Emissions from Natural Gas Systems CH4 104.7 0.01 0.90 43% 0.006

CO2 Emissions from Urban Trees CO2 97.6 0.01 0.91 21% 0.002

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 77.4 0.01 0.92 22% 0.002

Fugitive Emissions from Coal Mining CH4 57.6 0.01 0.93 24% 0.002

Mobile Combustion: Marine CO2 50.8 0.01 0.93 8% <0.001

CO2 Emissions from Cement Production CO2 44.5 0.01 0.94 13% 0.001

CH4 Emissions from Manure Management CH4 44.0 0.01 0.95 20% 0.001

Indirect N2O Emissions from Applied Nitrogen N2O 35.9 <0.01 0.95 136% 0.006

CH4 Emissions from Forest Fires CH4 29.0 <0.01 0.95 155% 0.005

Fugitive Emissions from Petroleum Systems CH4 28.8 <0.01 0.96 144% 0.005

CO2 Emissions from Natural Gas Systems CO2 28.7 <0.01 0.96 43% 0.002

Mobile Combustion: Road & Other N2O 27.9 <0.01 0.96 19% 0.001

CO2 Emissions from Land Converted to Grassland CO2 26.7 <0.01 0.97 9% <0.001

CH4 Emissions from Wastewater Treatment CH4 24.4 <0.01 0.97 49% 0.001

N2O Emissions from Nitric Acid Production N2O 21.7 <0.01 0.97 44% 0.001

CO2 Emissions from Waste Incineration CO2 20.8 <0.01 0.97 27% 0.001

HFC-23 Emissions from HCFC-22 Production HFCs 17.0 <0.01 0.98 10% <0.001

N2O Emissions from Manure Management N2O 14.7 <0.01 0.98 24% <0.001

Non-CO2 Emissions from Stationary Combustion N2O 14.7 <0.01 0.98 187% 0.003

CO2 Emissions from Lime Production CO2 14.6 <0.01 0.98 9% <0.001

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 13.8 <0.01 0.98 12% <0.001

SF6 Emissions from Electrical Transmission and 

Distribution SF6 12.7 <0.01 0.99 22% <0.001

CO2 Emissions from Cropland Remaining Cropland CO2 11.6 <0.01 0.99 56% 0.001

CO2 Emissions from Landfilled Yard Trimmings and Food

Scraps CO2 9.8 <0.01 0.99 84% 0.001

Non-CO2 Emissions from Stationary Combustion CH4 6.6 <0.01 0.99 128% 0.001

CO2 Emissions from Limestone and Dolomite Use CO2 6.2 <0.01 0.99 16% <0.001

CH4 Emissions from Rice Cultivation CH4 6.2 <0.01 0.99 164% 0.001

CO2 Emissions from Land Converted to Cropland CO2 5.9 <0.01 0.99 40% <0.001

N2O Emissions from Adipic Acid Production N2O 5.9 <0.01 0.99 20% <0.001

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 5.7 <0.01 0.99 23% <0.001

N2O Emissions from Wastewater Treatment N2O 4.9 <0.01 0.99 94% 0.001

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 4.7 <0.01 0.99 9% <0.001

CO2 Emissions from Grassland Remaining Grassland CO2 4.7 <0.01 0.99 54% <0.001

N2O Emissions from N2O Product Uses N2O 4.4 <0.01 0.99 2% <0.001

CO2 Emissions from Aluminum Production CO2 4.3 <0.01 1.00 4% <0.001

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 <0.01 1.00 7% <0.001

PFC Emissions from Aluminum Production PFCs 3.8 <0.01 1.00 11% <0.001

SF6 Emissions from Magnesium Production and 

Processing SF6 3.0 <0.01 1.00 13% <0.001

N2O Emissions from Forest Fires N2O 2.9 <0.01 1.00 152% 0.001

CO2 Emissions from Petrochemical Production CO2 2.6 <0.01 1.00 40% <0.001

Mobile Combustion: Road & Other CH4 2.0 <0.01 1.00 8% <0.001

N2O Emissions from Soils N2O 1.9 <0.01 1.00 211% <0.001

CO2 Emissions from Titanium Dioxide Production CO2 1.9 <0.01 1.00 13% <0.001

CO2 Emissions from Carbon Dioxide Consumption CO2 1.9 <0.01 1.00 22% <0.001

N2O Emission from Composting N2O 1.8 <0.01 1.00 50% <0.001

Mobile Combustion: Aviation N2O 1.8 <0.01 1.00 19% <0.001

CH4 Emissions from Composting CH4 1.7 <0.01 1.00 50% <0.001

CO2 Emissions from Ferroalloy Production CO2 1.6 <0.01 1.00 12% <0.001

CO2 Emissions from Phosphoric Acid Production CO2 1.2 <0.01 1.00 18% <0.001
A-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

CH4 Emissions from Petrochemical Production CH4 1.0 <0.01 1.00 31% <0.001

CO2 Emissions from Wetlands Remaining Wetlands CO2 1.0 <0.01 1.00 31% <0.001

CH4 Emissions from Field Burning of Agricultural 

Residues CH4 0.9 <0.01 1.00 94% <0.001

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 0.7 <0.01 1.00 8% <0.001

CO2 Emissions from Zinc Production CO2 0.5 <0.01 1.00 25% <0.001

N2O Emissions from Field Burning of Agricultural 

Residues N2O 0.5 <0.01 1.00 85% <0.001

Mobile Combustion: Marine N2O 0.4 <0.01 1.00 19% <0.001

N2O Emissions from Waste Incineration N2O 0.4 <0.01 1.00 191% <0.001

CO2 Emissions from Stationary Combustion - 

Geothermal Energy CO2 0.4 <0.01 1.00 5% <0.001

CO2 Emissions from Petroleum Systems CO2 0.3 <0.01 1.00 144% <0.001

CO2 Emissions from Lead Production CO2 0.3 <0.01 1.00 17% <0.001

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.2 <0.01 1.00 10% <0.001

Mobile Combustion: Aviation CH4 0.1 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH4 0.1 <0.01 1.00 8% <0.001

CH4 Emissions from Ferroalloy Production CH4 + <0.01 1.00 12% <0.001

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + <0.01 1.00 10% <0.001

N2O Emissions from Wetlands Remaining Wetlands N2O + <0.01 1.00 73% <0.001 a Percent relative uncertainty. If the corresponding uncertainty is asymmetrical, the uncertainty given here is the larger and always positive. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Table A- 8: 1990-2007 Key Source Category Tier 1 Analysis—Trend Assessment, without LULUCF 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Trend

Assessment

Tier 2 Trend

Assessment

Percent

Contribution to

Trend (%)

Cumulative

Contribution to

Trend (%)

Mobile Combustion: Road & Other CO2 1258.7 1649.1 0.02 0.002 18.8 19

Emissions from Substitutes for Ozone Depleting 

Substances Several 0.3 108.3 0.01 0.001 11.4 30

CO2 Emissions from Stationary Combustion - Coal CO2 1695.9 2086.5 0.01 0.001 11.1 41

CO2 Emissions from Stationary Combustion - Oil CO2 562.7 580.4 0.01 0.001 8.1 49

CO2 Emissions from Stationary Combustion - Gas CO2 965.5 1181.1 0.01 <0.001 5.6 55

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 109.8 77.4 0.01 0.001 5.3 60

Fugitive Emissions from Natural Gas Systems CH4 129.6 104.7 0.01 0.002 4.9 65

CH4 Emissions from Landfills CH4 149.2 132.9 <0.01 0.002 4.4 70

Fugitive Emissions from Coal Mining CH4 84.1 57.6 <0.01 0.001 4.3 74

HFC-23 Emissions from HCFC-22 Production HFCs 36.4 17.0 <0.01 <0.001 2.7 77

Mobile Combustion: Aviation CO2 179.4 187.5 <0.01 <0.001 2.3 79

Mobile Combustion: Road & Other N2O 41.6 27.9 <0.01 <0.001 2.2 81

SF6 Emissions from Electrical Transmission and 

Distribution SF6 26.8 12.7 <0.01 0.001 2.0 83

PFC Emissions from Aluminum Production PFCs 18.5 3.8 <0.01 <0.001 1.9 85

CH4 Emissions from Enteric Fermentation CH4 133.2 139.0 <0.01 <0.001 1.7 87

Direct N2O Emissions from Agricultural Soil 

Management N2O 158.9 172.0 <0.01 0.001 1.4 88

Indirect N2O Emissions from Applied Nitrogen N2O 41.5 35.9 <0.01 0.002 1.3 89

N2O Emissions from Adipic Acid Production N2O 15.3 5.9 <0.01 <0.001 1.3 91

Fugitive Emissions from Petroleum Systems CH4 33.9 28.8 <0.01 0.002 1.1 92

CO2 Emissions from Natural Gas Systems CO2 33.7 28.7 <0.01 0.001 1.1 93

CH4 Emissions from Manure Management CH4 30.4 44.0 <0.01 <0.001 0.9 94

CO2 Emissions from Waste Incineration CO2 10.9 20.8 <0.01 <0.001 0.8 95

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 16.8 13.8 <0.01 <0.001 0.6 95

CO2 Emissions from Cement Production CO2 33.3 44.5 <0.01 <0.001 0.6 96

CO2 Emissions from Aluminum Production CO2 6.8 4.3 <0.01 <0.001 0.4 96

Mobile Combustion: Marine CO2 46.5 50.8 <0.01 <0.001 0.4 97
A-17 

IPCC Source Categories 

Direct 

GHG 

1990 Estimate

(Tg CO2 Eq.)

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Trend

Assessment

Tier 2 Trend

Assessment

Percent

Contribution to

Trend (%)

Cumulative

Contribution to

Trend (%)

SF6 Emissions from Magnesium Production and 

Processing SF6 5.4 3.0 <0.01 <0.001 0.4 97

Mobile Combustion: Road & Other CH4 4.5 2.0 <0.01 <0.001 0.3 97

CH4 Emissions from Wastewater Treatment CH4 23.5 24.4 <0.01 <0.001 0.3 98

CO2 Emissions from Non-Energy Use of Fuels CO2 117.0 133.9 <0.01 <0.001 0.3 98

CH4 Emissions from Rice Cultivation CH4 7.1 6.2 <0.01 <0.001 0.2 98

Non-CO2 Emissions from Stationary Combustion CH4 7.4 6.6 <0.01 <0.001 0.2 98

N2O Emissions from Nitric Acid Production N2O 20.0 21.7 <0.01 <0.001 0.2 99

N2O Emission from Composting N2O 0.4 1.8 <0.01 <0.001 0.1 99

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 6.0 5.7 <0.01 <0.001 0.1 99

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 2.9 4.7 <0.01 <0.001 0.1 99

CH4 Emissions from Composting CH4 0.3 1.7 <0.01 <0.001 0.1 99

CO2 Emissions from Lime Production CO2 11.5 14.6 <0.01 <0.001 0.1 99

CO2 Emissions from Ferroalloy Production CO2 2.2 1.6 <0.01 <0.001 0.1 99

N2O Emissions from N2O Product Uses N2O 4.4 4.4 <0.01 <0.001 0.1 99

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 4.1 <0.01 <0.001 0.1 99

CO2 Emissions from Phosphoric Acid Production CO2 1.5 1.2 <0.01 <0.001 0.1 100

N2O Emissions from Manure Management N2O 12.1 14.7 <0.01 <0.001 0.1 100

CO2 Emissions from Zinc Production CO2 0.9 0.5 <0.01 <0.001 0.1 100

N2O Emissions from Wastewater Treatment N2O 3.7 4.9 <0.01 <0.001 0.1 100

CO2 Emissions from Titanium Dioxide Production CO2 1.2 1.9 <0.01 <0.001 0.1 100

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 1.0 0.7 <0.01 <0.001 <0.1 100

Non-CO2 Emissions from Stationary Combustion N2O 12.8 14.7 <0.01 <0.001 <0.1 100

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.4 0.2 <0.01 <0.001 <0.1 100

CO2 Emissions from Carbon Dioxide Consumption CO2 1.4 1.9 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation N2O 1.7 1.8 <0.01 <0.001 <0.1 100

CO2 Emissions from Limestone and Dolomite Use CO2 5.1 6.2 <0.01 <0.001 <0.1 100

N2O Emissions from Waste Incineration N2O 0.5 0.4 <0.01 <0.001 <0.1 100

CO2 Emissions from Petroleum Systems CO2 0.4 0.3 <0.01 <0.001 <0.1 100

CH4 Emissions from Field Burning of Agricultural 

Residues CH4 0.7 0.9 <0.01 <0.001 <0.1 100

CO2 Emissions from Stationary Combustion - 

Geothermal Energy CO2 0.4 0.4 <0.01 <0.001 <0.1 100

CO2 Emissions from Lead Production CO2 0.3 0.3 <0.01 <0.001 <0.1 100

N2O Emissions from Field Burning of Agricultural 

Residues N2O 0.4 0.5 <0.01 <0.001 <0.1 100

CO2 Emissions from Petrochemical Production CO2 2.2 2.6 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation CH4 0.2 0.1 <0.01 <0.001 <0.1 100

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + + <0.01 <0.001 <0.1 100

CH4 Emissions from Petrochemical Production CH4 0.9 1.0 <0.01 <0.001 <0.1 100

Mobile Combustion: Marine N2O 0.4 0.4 <0.01 <0.001 <0.1 100

CH4 Emissions from Ferroalloy Production CH4 + + <0.01 <0.001 <0.1 100

Mobile Combustion: Marine CH4 0.1 0.1 <0.01 <0.001 <0.1 100

Note: LULUCF sources and sinks are not included in this analysis. 

+ Does not exceed 0.05 Tg CO2 Eq. 

Table A- 9: 1990-2007 Key Source Category Tier 1 Analysis—Trend Assessment, with LULUCF 

IPCC Source Categories 

Direct 

GHG 

1990 

Estimate

(Tg CO2 Eq.)

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Trend

Assessment

Tier 2 Trend

Assessment

Percent

Contribution to

Trend (%)

Cumulative

Contribution to

Trend (%)

Mobile Combustion: Road & Other CO2 1258.7 1649.1 0.02 0.001 13.4 13

CO2 from Changes in Forest Carbon Stocks CO2 661.1 910.1 0.01 0.003 10.7 24
A-18 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

IPCC Source Categories 

Direct 

GHG 

1990 

Estimate

(Tg CO2 Eq.)

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Trend

Assessment

Tier 2 Trend

Assessment

Percent

Contribution to

Trend (%)

Cumulative

Contribution to

Trend (%)

Emissions from Substitutes for Ozone Depleting 

Substances Several 0.3 108.3 0.01 0.001 9.1 33

CO2 Emissions from Stationary Combustion - Oil CO2 562.7 580.4 0.01 0.001 7.3 40

CO2 Emissions from Stationary Combustion - Coal CO2 1695.9 2086.5 0.01 0.001 6.6 47

CO2 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CO2 109.8 77.4 0.01 0.001 4.4 51

CO2 Emissions from Grassland Remaining Grassland CO2 46.7 4.7 0.01 0.003 4.3 56

Fugitive Emissions from Natural Gas Systems CH4 129.6 104.7 0.01 0.002 4.1 60

CH4 Emissions from Landfills CH4 149.2 132.9 <0.01 0.002 3.7 64

Fugitive Emissions from Coal Mining CH4 84.1 57.6 <0.01 0.001 3.5 67

CO2 Emissions from Stationary Combustion - Gas CO2 965.5 1181.1 <0.01 <0.001 3.2 70

HFC-23 Emissions from HCFC-22 Production HFCs 36.4 17.0 <0.01 <0.001 2.2 72

CO2 Emissions from Urban Trees CO2 60.6 97.6 <0.01 0.001 2.2 75

Mobile Combustion: Aviation CO2 179.4 187.5 <0.01 <0.001 2.1 77

CH4 Emissions from Forest Fires CH4 4.6 29.0 <0.01 0.004 2.0 79

Mobile Combustion: Road & Other N2O 41.6 27.9 <0.01 <0.001 1.8 81

SF6 Emissions from Electrical Transmission and 

Distribution SF6 26.8 12.7 <0.01 <0.001 1.6 82

CH4 Emissions from Enteric Fermentation CH4 133.2 139.0 <0.01 <0.001 1.6 84

PFC Emissions from Aluminum Production PFCs 18.5 3.8 <0.01 <0.001 1.5 85

CO2 Emissions from Landfilled Yard Trimmings and Food

Scraps CO2 23.5 9.8 <0.01 0.002 1.5 87

Direct N2O Emissions from Agricultural Soil Management N2O 158.9 172.0 <0.01 0.001 1.4 88

CO2 Emissions from Cropland Remaining Cropland CO2 22.3 11.6 <0.01 0.001 1.2 89

Indirect N2O Emissions from Applied Nitrogen N2O 41.5 35.9 <0.01 0.002 1.1 91

N2O Emissions from Adipic Acid Production N2O 15.3 5.9 <0.01 <0.001 1.0 92

Fugitive Emissions from Petroleum Systems CH4 33.9 28.8 <0.01 0.002 1.0 93

CO2 Emissions from Natural Gas Systems CO2 33.7 28.7 <0.01 0.001 1.0 93

CH4 Emissions from Manure Management CH4 30.4 44.0 <0.01 <0.001 0.7 94

CO2 Emissions from Waste Incineration CO2 10.9 20.8 <0.01 <0.001 0.7 95

CO2 Emissions from Ammonia Production and Urea 

Consumption CO2 16.8 13.8 <0.01 <0.001 0.5 95

CO2 Emissions from Cement Production CO2 33.3 44.5 <0.01 <0.001 0.4 96

CO2 Emissions from Non-Energy Use of Fuels CO2 117.0 133.9 <0.01 <0.001 0.4 96

Mobile Combustion: Marine CO2 46.5 50.8 <0.01 <0.001 0.4 97

CO2 Emissions from Aluminum Production CO2 6.8 4.3 <0.01 <0.001 0.3 97

CH4 Emissions from Wastewater Treatment CH4 23.5 24.4 <0.01 <0.001 0.3 97

SF6 Emissions from Magnesium Production and 

Processing SF6 5.4 3.0 <0.01 <0.001 0.3 97

CO2 Emissions from Land Converted to Cropland CO2 2.2 5.9 <0.01 <0.001 0.3 98

Mobile Combustion: Road & Other CH4 4.5 2.0 <0.01 <0.001 0.3 98

N2O Emissions from Forest Fires N2O 0.5 2.9 <0.01 <0.001 0.2 98

CH4 Emissions from Rice Cultivation CH4 7.1 6.2 <0.01 <0.001 0.2 98

Non-CO2 Emissions from Stationary Combustion CH4 7.4 6.6 <0.01 <0.001 0.2 99

N2O Emissions from Nitric Acid Production N2O 20.0 21.7 <0.01 <0.001 0.2 99

Fugitive Emissions from Abandoned Underground Coal 

Mines CH4 6.0 5.7 <0.01 <0.001 0.1 99

N2O Emission from Composting N2O 0.4 1.8 <0.01 <0.001 0.1 99

CH4 Emissions from Composting CH4 0.3 1.7 <0.01 <0.001 0.1 99

PFC, HFC, and SF6 Emissions from Semiconductor 

Manufacture Several 2.9 4.7 <0.01 <0.001 0.1 99

CO2 Emissions from Ferroalloy Production CO2 2.2 1.6 <0.01 <0.001 0.1 99

CO2 Emissions from Lime Production CO2 11.5 14.6 <0.01 <0.001 0.1 99

N2O Emissions from N2O Product Uses N2O 4.4 4.4 <0.01 <0.001 0.1 99

CO2 Emissions from Soda Ash Production and 

Consumption CO2 4.1 4.1 <0.01 <0.001 0.1 99

N2O Emissions from Soils N2O 1.0 1.9 <0.01 <0.001 0.1 100

CO2 Emissions from Phosphoric Acid Production CO2 1.5 1.2 <0.01 <0.001 0.1 100

CO2 Emissions from Zinc Production CO2 0.9 0.5 <0.01 <0.001 0.1 100
A-19 

IPCC Source Categories 

Direct 

GHG 

1990 

Estimate

(Tg CO2 Eq.)

2007 Estimate

(Tg CO2 Eq.)

Tier 1 Trend

Assessment

Tier 2 Trend

Assessment

Percent

Contribution to

Trend (%)

Cumulative

Contribution to

Trend (%)

N2O Emissions from Wastewater Treatment N2O 3.7 4.9 <0.01 <0.001 <0.1 100

CO2 Emissions from Titanium Dioxide Production CO2 1.2 1.9 <0.01 <0.001 <0.1 100

Non-CO2 Emissions from Stationary Combustion N2O 12.8 14.7 <0.01 <0.001 <0.1 100

CH4 Emissions from Iron and Steel Production & 

Metallurgical Coke Production CH4 1.0 0.7 <0.01 <0.001 <0.1 100

N2O Emissions from Manure Management N2O 12.1 14.7 <0.01 <0.001 <0.1 100

CO2 Emissions from Land Converted to Grassland CO2 22.3 26.7 <0.01 <0.001 <0.1 100

CO2 Emissions from Silicon Carbide Production and 

Consumption CO2 0.4 0.2 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation N2O 1.7 1.8 <0.01 <0.001 <0.1 100

CO2 Emissions from Wetlands Remaining Wetlands CO2 1.0 1.0 <0.01 <0.001 <0.1 100

CO2 Emissions from Carbon Dioxide Consumption CO2 1.4 1.9 <0.01 <0.001 <0.1 100

N2O Emissions from Waste Incineration N2O 0.5 0.4 <0.01 <0.001 <0.1 100

CO2 Emissions from Petroleum Systems CO2 0.4 0.3 <0.01 <0.001 <0.1 100

CO2 Emissions from Limestone and Dolomite Use CO2 5.1 6.2 <0.01 <0.001 <0.1 100

CO2 Emissions from Stationary Combustion - 

Geothermal Energy CO2 0.4 0.4 <0.01 <0.001 <0.1 100

CH4 Emissions from Field Burning of Agricultural 

Residues CH4 0.7 0.9 <0.01 <0.001 <0.1 100

CO2 Emissions from Lead Production CO2 0.3 0.3 <0.01 <0.001 <0.1 100

N2O Emissions from Field Burning of Agricultural 

Residues N2O 0.4 0.5 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation CH4 0.2 0.1 <0.01 <0.001 <0.1 100

CH4 Emissions from Silicon Carbide Production and 

Consumption CH4 + + <0.01 <0.001 <0.1 100

Mobile Combustion: Marine N2O 0.4 0.4 <0.01 <0.001 <0.1 100

CH4 Emissions from Ferroalloy Production CH4 + + <0.01 <0.001 <0.1 100

CO2 Emissions from Petrochemical Production CO2 2.2 2.6 <0.01 <0.001 <0.1 100

CH4 Emissions from Petrochemical Production CH4 0.9 1.0 <0.01 <0.001 <0.1 100

Mobile Combustion: Marine CH4 0.1 0.1 <0.01 <0.001 <0.1 100

N2O Emissions from Wetlands Remaining Wetlands N2O + + <0.01 <0.001 <0.1 100

+ Does not exceed 0.05 Tg CO2 Eq. 

References 

Flugsrud, K., W. Irving, and K. Rypdal (1999) Methodological Choice in Inventory Preparation. Suggestions for 

Good Practice Guidance. Statistics Norway Department of Economic Statistics. 1999/19. 

IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, 

Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme. 

IPCC (2003) Good Practice Guidance for Land Use, Land-Use Change, and Forestry. National Greenhouse Gas 

Inventories Programme, The Intergovernmental Panel on Climate Change, J. Penman, et al. (eds.). Available online at 

<http://www.ipcc-nggip.iges.or.jp/public/gpglulucf/gpglulucf.htm>. August 13, 2004. 

A-21 

ANNEX 2 Methodology and Data for 

Estimating CO2 Emissions from Fossil 

Fuel Combustion 

2.1. Methodology for Estimating Emissions of CO2 from Fossil Fuel Combustion 

Carbon dioxide (CO2) emissions from fossil fuel combustion were estimated using a “bottom-up” methodology 

characterized by seven steps. These steps are described below. 

Step 1: Determine Total Fuel Consumption by Fuel Type and Sector 

The bottom-up methodology used by the United States for estimating CO2 emissions from fossil fuel combustion 

is conceptually similar to the approach recommended by the Intergovernmental Panel on Climate Change (IPCC) for 

countries that intend to develop detailed, sectoral-based emission estimates (IPCC/UNEP/OECD/IEA 1997). Total 

consumption data and adjustments to consumption are presented in Columns 2 through 13 of Table A-10. Adjusted 

consumption data are presented in Columns 2 through 8 of Table A-11 through Table A-28, with totals by fuel type in 

Column 8 and totals by end-use sector in the last rows. Fuel consumption data for the bottom-up approach were obtained 

directly from the Energy Information Administration (EIA) of the U.S. Department of Energy. These data were first 

gathered in physical units, and then converted to their energy equivalents (see the Constants, Units, and Conversions 

Annex). The EIA data were collected through a variety of consumption surveys at the point of delivery or use and 

qualified with survey data on fuel production, imports, exports, and stock changes. Individual data elements were supplied 

by a variety of sources within EIA. Most information was taken from published reports, although some data were drawn 

from unpublished energy studies and databases maintained by EIA. 

Energy consumption data were aggregated by sector (i.e., residential, commercial, industrial, transportation, 

electricity generation, and U.S. territories), primary fuel type (e.g., coal, natural gas, and petroleum), and secondary fuel 

type (e.g., motor gasoline, distillate fuel, etc.). The 2007 total adjusted energy consumption across all sectors, including 

territories, and energy types was 78,873.5 trillion British thermal units (TBtu), as indicated in the last entry of Column 8 in

Table A-11. This total excludes fuel used for non-energy purposes and fuel consumed as international bunkers, both of 

which were deducted in earlier steps. 

Electricity consumption information was allocated to each sector based on EIA’s distribution of electricity retail 

sales to ultimate customers (i.e., residential, commercial, industrial, and other). Because the “other” fuel use includes sales 

to both the commercial and transportation sectors, EIA’s limited transportation electricity use data were subtracted from 

“other” electricity use and also reported separately. This total was consequently combined with the commercial electricity 

data. Further information on these electricity end uses is described in EIA’s Annual Energy Review (EIA 2008a). 

There are also three basic differences between the consumption data presented in Table A-11 through Table A-28 

and those recommended in the IPCC emission inventory methodology. 

First, consumption data in the U.S. inventory are presented using higher heating values (HHV)1 rather than the 

lower heating values (LHV)2 reflected in the IPCC emission inventory methodology. This convention is followed because 

data obtained from EIA are based on HHV. Of note, however, is that EIA renewable energy statistics are often published 

using LHV. The difference between the two conventions relates to the treatment of the heat energy that is consumed in the 

process of evaporating the water contained in the fuel. The simplified convention used by the International Energy 

Agency for converting from HHV to LHV is to multiply the energy content by 0.95 for petroleum and coal and by 0.9 for 

natural gas. 

Second, while EIA's energy use data for the United States includes only the 50 U.S. states and the District of 

Columbia, the data reported to the Framework Convention on Climate Change are to include energy consumption within 

 

1 Also referred to as Gross Calorific Values (GCV). 

2 Also referred to as Net Calorific Values (NCV). 
A-22 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

territories. Therefore, consumption estimates for U.S. territories were added to domestic consumption of fossil fuels. 

Energy consumption data from U.S. territories are presented in Column 7 of Table A-11 through Table A-28. It is 

reported separately from domestic sectoral consumption, because it is collected separately by EIA with no sectoral 

disaggregation. 

Third, there were a number of modifications made in this report that may cause consumption information herein 

to differ from figures given in the cited literature. These are (1) the reallocation of select amounts of coking coal, 

petroleum coke, natural gas, residual fuel oil, and other oil (>401F) for processes accounted for in the Industrial Processes 

chapter, (2) corrections for synthetic natural gas production, (3) corrections for ethanol added to motor gasoline, and (4) 

corrections for biogas in natural gas, (5) subtraction of other fuels used for non-energy purposes, and (6) subtraction of 

international bunker fuels. These adjustments are described in the following steps. 

Step 2: Subtract uses accounted for in the Industrial Processes chapter. 

Portions of the fuel consumption data for seven fuel categories—coking coal, distillate fuel, industrial other coal, 

petroleum coke, natural gas, residual fuel oil, and other oil (>401 F)—were reallocated to the Industrial Processes chapter, 

as these portions were consumed as raw materials during non-energy related industrial processes. Emissions from these 

fuels used as raw materials are presented in the Industrial Processes chapter, and is removed from the energy and nonenergy consumption estimates within the Energy chapter. 

 Coking coal, also called “coal coke,” is used as a raw material (specifically as a reducing agent) in the blast 

furnace process to produce iron and steel, lead, and zinc and therefore is not used as a fuel for this process. 

 Similarly, petroleum coke is used in multiple processes as a raw material, and is thus not used as a fuel in 

those applications. The processes in which petroleum coke is used include (1) ferroalloy production, (2) 

aluminum production (for the production of C anodes and cathodes), (3) titanium dioxide production (in the 

chloride process), (4) ammonia production, and (5) silicon carbide. 

 Natural gas consumption is used for the production of ammonia, and blast furnace and coke oven gas used in 

iron and steel production. 

 Residual fuel oil and other oil (>401F) are both used in the production of C black. 

 Natural gas, distillate fuel, coal, and metallurgical coke are used to produce pig iron through the reduction of 

iron ore in the production of iron and steel. 

Step 3: Adjust for Biofuels and Conversion of Fossil Fuels 

First, a portion of industrial “other” coal that is accounted for in EIA coal combustion statistics is actually used to 

make “synthetic natural gas” via coal gasification at the Dakota Gasification Plant, a synthetic natural gas plant. The plant 

produces synthetic natural gas and byproduct CO2. The synthetic natural gas enters the natural gas distribution system. 

Since October 2000, a portion of the CO2 produced by the coal gasification plant has been exported to Canada by pipeline. 

The remainder of the CO2 byproduct from the plant is released to the atmosphere. The energy in this synthetic natural gas 

enters the natural gas distribution stream, and is accounted for in EIA natural gas combustion statistics. Because this 

energy of the synthetic natural gas is already accounted for as natural gas combustion, this amount of energy is deducted 

from the industrial coal consumption statistics to avoid double counting. The exported CO2 is not emitted to the 

atmosphere in the United States, and therefore the energy used to produce this amount of CO2 is subtracted from industrial 

other coal. 

Second, ethanol has been added to the motor gasoline stream for several years, but prior to 1993 this addition 

was not captured in EIA motor gasoline statistics. Starting in 1993, ethanol was included in gasoline statistics. However, 

because ethanol is a biofuel, which is assumed to result in no net CO2 emissions, the amount of ethanol added is subtracted 

from total gasoline consumption. Thus, motor gasoline consumption statistics given in this report may be slightly lower 

than in EIA sources. 

Third, EIA natural gas consumption statistics include “biomass gas,” which is upgraded landfill methane that is 

sold to pipelines. However, because this gas is biogenic, the biomass gas total is deducted from natural gas consumption. 

The subtraction is done only from natural gas in the industrial sector, as opposed to all end-sectors, because the biogas 

amount is small. Due to this adjustment—and the ammonia adjustment mentioned previously—industrial natural gas 

consumption in this report is slightly lower than in EIA sources. 
A-23 

Step 4: Subtract Consumption for Non-Energy Use 

U.S. aggregate energy statistics include consumption of fossil fuels for non-energy purposes. Depending on the 

end-use, non-energy uses of fossil fuels can result in long term storage of some or all of the C contained in the fuel. For 

example, asphalt made from petroleum can sequester up to 100 percent of the C contained in the petroleum feedstock for 

extended periods of time. Other non-energy fossil fuel products, such as lubricants or plastics also store C, but can lose or 

emit some of this C when they are used and/or burned as waste.3 As the emission pathways of C used for non-energy 

purposes are vastly different than fuel combustion, these emissions are estimated separately in the Carbon Emitted in 

Products from Non-Energy Uses of Fossil Fuels section in this chapter. Therefore, the amount of fuels used for nonenergy purposes, shown in Table A-29, was subtracted from total fuel consumption. 

Step 5: Subtract Consumption of International Bunker Fuels 

Emissions from international transport activities, or international bunker fuel consumption, are not included in 

national totals, as required by the IPCC (IPCC/UNEP/OECD/IEA 1997). There is currently disagreement internationally 

as to how these emissions should be allocated, and until this issue is resolved, countries are asked to report them 

separately. EIA energy statistics, however, include these bunker fuelsjet fuel for aircraft, and distillate fuel oil and 

residual fuel oil for marine shippingas part of fuel consumption by the transportation end-use sector. Therefore, the 

amount of consumption for international bunker fuels was estimated and subtracted from total fuel consumption (see Table 

A-30). Emissions from international bunker fuels have been estimated separately and not included in national totals.4 

Step 6: Determine the C Content of All Fuels 

The C content of combusted fossil fuels was estimated by multiplying adjusted energy consumption (Columns 2 

through 8 of Table A-11 through Table A-28) by fuel-specific C content coefficients (see Table A-31 and Table A-32) that 

reflect the amount of C per unit of energy in each fuel. The C content coefficients used in the U.S. inventory were derived 

by EIA from detailed fuel information and are similar to the C content coefficients contained in the IPCC's default 

methodology (IPCC/UNEP/OECD/IEA 1997), with modifications reflecting fuel qualities specific to the United States. 

Step 7: Estimate CO2 Emissions 

Actual CO2 emissions in the United States were summarized by major fuel (i.e., coal, petroleum, natural gas, 

geothermal) and consuming sector (i.e., residential, commercial, industrial, transportation, electricity generation, and U.S. 

territories). Emission estimates are expressed in teragrams of carbon dioxide equivalents (Tg CO2 Eq.). To convert from 

C content to CO2 emissions, the fraction of C that is oxidized was applied. This fraction was 100 percent based on 

guidance in IPCC (2006). 

To determine total emissions by final end-use sector, emissions from electricity generation were distributed to 

each end-use sector according to its share of aggregate electricity consumption (see Table A-33). This pro-rated approach 

to allocating emissions from electricity generation may overestimate or underestimate emissions for particular sectors due 

to differences in the average C content of fuel mixes burned to generate electricity. 

 

3 See the Waste Incineration section of the Energy chapter and the Waste Incineration Annex for a discussion of emissions 

from the combustion of plastics in the municipal solid waste stream. 

4 Refer to the International Bunker Fuels section of the Energy chapter for a description of the methodology for distinguishing 

between bunker and non-bunker fuel consumption. 
A-24 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-10: 2007 Energy Consumption Data and Adjusted Energy Consumption Data by Fuel Type (TBtu) 

1 2 3 4 5 6 7 8 9 10 11 12 1

 Total Consumption (TBtu)a Adjustments (TBtu)b

Unadjusted NEU Consumption

Fuel Type 

Res. Comm. Ind. Trans. Elec. Terr. Total

Bunker

Fuel Ind. Trans. Terr.

Cons

Total Coal 6.0 71.0 1,187.8 NE 20,835.0 44.5 22,144.4 45.3 

Residential Coal 6.0 6.0 Commercial Coal 71.0 71.0 Industrial Other Coal 1,154.9 1,154.9 12.4 Transportation Coal NE NEElectric Power Coal 20,835.0 20,835.0 U.S. Territory Coal (bit) 44.5 44.5 

Natural Gas 4,842.0 3,080.0 7,680.9 667.0 7,046.0 26.7 23,342.6 413.7 Total Petroleum 1,201.1 611.2 9,746.7 27,505.8 659.0 710.2 40,434.0 1,462.9 4,923.3 152.0 100.9 

Asphalt & Road Oil 1,197.0 1,197.0 1,197.0 Aviation Gasoline 32.0 32.0 Distillate Fuel Oil 698.1 369.1 1,204.5 6,544.5 92.0 123.7 9,032.0 111.3 11.7 Jet Fuel 3,358.0 NA 65.1 3,423.1 744.1 Kerosene 40.0 9.0 18.0 5.1 72.1 LPG 463.0 82.0 2,162.0 26.0 0.7 2,733.7 1,540.0 Lubricants 161.0 152.0 4.9 317.9 161.0 152.0 4.9Motor Gasoline 71.8 578.0 16,460.3 186.0 17,296.1 Residual Fuel 79.0 204.0 933.0 399.0 228.9 1,843.9 607.5 Other Petroleum 

AvGas Blend Components 1.8 1.8 Crude Oil MoGas Blend Components Misc. Products 133.5 96.0 229.4 133.5 96.0Naphtha (<401 deg. F) 562.5 562.5 562.5 Other Oil (>401 deg. F) 744.1 744.1 744.1 Pentanes Plus 179.4 179.4 137.1 Petroleum Coke 0.3 909.0 168.0 1,077.3 248.2 Still Gas 1,526.9 1,526.9 88.4 Special Naphtha 78.0 78.0 78.0 Unfinished Oils 65.2 65.2 Waxes 21.9 21.9 21.9 

Geothermal 50.6 50.6 TOTAL (All Fuels) 6,049.1 3,762.2 18,615.4 28,172.8 28,590.6 781.4 85,971.6 1,462.9 5,382.3 152.0 100.9 a Expressed as gross calorific values (i.e., higher heating values). b Adjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption (see Table 

NE (Not Estimated) 
Table A-11: 2007 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 6.0 71.0 1,142.5 NE 20,835.0 44.5 22,099.0 0.6 6.8 107.4 NE 

Residential Coal 6.0 6.0 0.6 Commercial Coal 71.0 71.0 6.8 Industrial Other Coal 1,142.5 1,142.5 107.4 Transportation Coal NE Electric Power Coal 20,835.0 20,835.0 U.S. Territory Coal (bit) 44.5 44.5 

Natural Gas 4,842.0 3,080.0 7,267.3 667.0 7,046.0 26.7 22,929.0 256.9 163.4 385.6 35.4 Total Petroleum 1,201.1 611.2 4,823.4 25,890.9 659.0 609.3 33,794.9 83.2 44.2 352.5 1,852.0 

Asphalt & Road Oil Aviation Gasoline 32.0 32.0 2.2 Distillate Fuel Oil 698.1 369.1 1,192.8 6,433.2 92.0 123.7 8,909.0 51.1 27.0 87.3 470.6 Jet Fuel 2,613.9 NA 65.1 2,679.0 185.3 Kerosene 40.0 9.0 18.0 5.1 72.1 2.9 0.7 1.3 LPG 463.0 82.0 622.0 26.0 0.7 1,193.6 29.2 5.2 39.2 1.6 Lubricants Motor Gasoline 71.8 578.0 16,460.3 186.0 17,296.1 5.1 41.0 1,166.7 Residual Fuel 79.0 204.0 325.5 399.0 228.9 1,236.3 6.2 16.1 25.6 Other Petroleum 

AvGas Blend Components 1.8 1.8 0.1 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 42.3 42.3 2.8 Petroleum Coke 0.3 660.8 168.0 829.1 0.0 67.5 Still Gas 1,438.4 1,438.4 92.4 Special Naphtha Unfinished Oils 65.2 65.2 4.9 Waxes 

Geothermal 50.6 50.6 TOTAL (All Fuels) 6,049.1 3,762.2 13,233.1 26,557.9 28,590.6 680.5 78,873.5 340.6 214.4 845.4 1,887.4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-26 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-12: 2006 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 5.7 65.1 1,213.8 NE 20,461.4 43.6 21,789.5 0.5 6.2 114.1 NE 

Residential Coal 5.7 5.7 0.5 Commercial Coal 65.1 65.1 6.2 Industrial Other Coal 1,213.8 1,213.8 114.1 Transportation Coal NE Electric Power Coal 20,461.4 20,461.4 U.S. Territory Coal (bit) 43.6 43.6 

Natural Gas 4,463.5 2,899.3 7,091.0 630.7 6,388.2 26.1 21,498.8 236.8 153.8 376.2 33.5 Total Petroleum 1,219.1 636.8 4,841.0 25,830.7 648.1 664.9 33,840.5 84.5 46.0 354.2 1,847.4 

Asphalt & Road Oil Aviation Gasoline 33.4 33.4 2.3 Distillate Fuel Oil 694.9 391.6 1,208.6 6,352.2 73.7 134.7 8,855.7 50.8 28.6 88.4 464.7 Jet Fuel 2,609.6 NA 69.4 2,679.0 185.0 Kerosene 66.4 15.2 29.6 5.5 116.6 4.8 1.1 2.1 LPG 457.8 80.8 619.4 25.8 0.7 1,184.5 28.9 5.1 39.1 1.6 Lubricants Motor Gasoline 73.7 582.2 16,503.3 203.6 17,362.9 5.2 41.3 1,169.7 Residual Fuel 75.3 195.3 306.3 360.5 250.9 1,188.3 5.9 15.4 24.1 Other Petroleum 

AvGas Blend Components 0.6 0.6 0.0 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 32.8 32.8 2.2 Petroleum Coke 0.3 672.8 213.9 886.9 0.0 68.7 Still Gas 1,429.4 1,429.4 91.8 Special Naphtha Unfinished Oils 70.3 70.3 5.2 Waxes 

Geothermal 49.7 49.7 TOTAL (All Fuels) 5,688.2 3,601.2 13,145.7 26,461.4 27,547.4 734.6 77,178.5 321.9 206.0 844.5 1,880.9 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-13: 2005 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 8.3 95.7 1,236.2 NE 20,736.9 40.1 22,117.2 0.8 9.1 116.2 NE 

Residential Coal 8.3 8.3 0.8 Commercial Coal 95.7 95.7 9.1 Industrial Other Coal 1,236.2 1,236.2 116.2 Transportation Coal NE Electric Power Coal 20,736.9 20,736.9 U.S. Territory Coal (bit) 40.1 40.1 

Natural Gas 4,939.0 3,073.7 7,196.3 626.3 6,030.1 24.3 21,889.7 262.0 163.1 381.8 33.2 Total Petroleum 1,373.7 682.7 4,500.7 25,853.2 1,234.5 648.7 34,293.5 95.2 49.6 330.0 1,848.2 

Asphalt & Road Oil Aviation Gasoline 35.4 35.4 2.4 Distillate Fuel Oil 773.1 405.0 1,129.7 6,192.2 114.6 131.5 8,746.1 56.6 29.6 82.6 453.0 Jet Fuel 2,678.7 NA 67.7 2,746.4 189.9 Kerosene 83.8 21.6 39.1 5.4 149.8 6.1 1.6 2.8 LPG 516.8 91.2 559.2 26.6 0.7 1,194.4 32.6 5.7 35.3 1.7 Lubricants Motor Gasoline 48.8 388.7 16,663.9 198.7 17,300.2 3.5 27.6 1,181.1 Residual Fuel 115.8 237.4 256.4 876.5 244.7 1,730.8 9.1 18.7 20.2 Other Petroleum 

AvGas Blend Components 8.3 8.3 0.6 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 45.9 45.9 3.1 Petroleum Coke 0.3 660.1 243.5 903.9 0.0 67.4 Still Gas 1,429.4 1,429.4 91.8 Special Naphtha Unfinished Oils 2.8 2.8 0.2 Waxes 

Geothermal 50.1 50.1 TOTAL (All Fuels) 6,321.0 3,852.1 12,933.1 26,479.5 28,051.6 713.1 78,350.5 358.0 221.8 828.0 1,881.5 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-28 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-14: 2004 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 11.4 102.9 1,261.8 NE 20,304.9 39.1 21,720.1 1.1 9.8 118.6 NE 

Residential Coal 11.4 11.4 1.1 Commercial Coal 102.9 102.9 9.8 Industrial Other Coal 1,261.8 1,261.8 118.6 Transportation Coal NE Electric Power Coal 20,304.9 20,304.9 U.S. Territory Coal (bit) 39.1 39.1 

Natural Gas 4,949.2 3,181.0 7,729.2 603.1 5,602.4 24.7 22,089.6 262.6 168.8 410.1 32.0 Total Petroleum 1,476.4 698.2 4,308.4 25,586.8 1,212.4 653.6 33,935.8 102.6 50.8 315.9 1,828.4 

Asphalt & Road Oil Aviation Gasoline 31.2 31.2 2.2 Distillate Fuel Oil 859.6 437.6 1,115.2 5,970.0 111.3 134.4 8,628.1 62.9 32.0 81.6 436.7 Jet Fuel 2,525.0 NA 68.8 2,593.7 179.0 Kerosene 84.8 20.5 28.2 6.0 139.5 6.1 1.5 2.0 LPG 532.1 93.9 603.3 18.0 0.8 1,248.2 33.5 5.9 38.0 1.1 Lubricants Motor Gasoline 23.4 199.5 16,856.2 198.6 17,277.7 1.7 14.1 1,194.7 Residual Fuel 122.5 204.7 186.4 879.0 245.0 1,637.6 9.7 16.1 14.7 Other Petroleum 

AvGas Blend Components 10.6 10.6 0.7 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 52.1 52.1 3.5 Petroleum Coke 0.3 687.7 222.1 910.0 0.0 70.2 Still Gas 1,482.6 1,482.6 95.2 Special Naphtha Unfinished Oils (75.6) (75.6) (5.6) Waxes 

Geothermal 50.5 50.5 TOTAL (All Fuels) 6,437.1 3,982.1 13,299.4 26,189.9 27,170.2 717.4 77,796.0 366.2 229.3 844.6 1,860.4 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-15: 2003 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 12.4 82.9 1,248.9 NE 20,184.5 41.1 21,569.8 1.2 7.9 117.4 NE 

Residential Coal 12.4 12.4 1.2 Commercial Coal 82.9 82.9 7.9 Industrial Other Coal 1,248.9 1,248.9 117.4 Transportation Coal NE Electric Power Coal 20,184.5 20,184.5 U.S. Territory Coal (bit) 41.1 41.1 

Natural Gas 5,206.8 3,276.9 8,064.6 632.6 5,256.2 26.9 22,464.0 276.3 173.9 427.9 33.6 Total Petroleum 1,503.2 714.3 4,073.7 24,884.8 1,205.0 621.8 33,002.8 104.2 51.8 299.0 1,777.2 

Asphalt & Road Oil Aviation Gasoline 30.2 30.2 2.1 Distillate Fuel Oil 868.9 462.4 1,077.6 5,661.8 161.0 120.5 8,352.3 63.6 33.8 78.8 414.2 Jet Fuel 2,480.8 NA 76.1 2,556.9 175.8 Kerosene 70.3 18.6 24.1 10.7 123.7 5.1 1.3 1.7 LPG 564.0 99.5 523.1 15.6 10.5 1,212.8 35.6 6.3 33.0 1.0 Lubricants Motor Gasoline 22.3 122.7 16,597.1 210.1 16,952.2 1.6 8.7 1,176.4 Residual Fuel 111.1 176.4 99.1 869.4 193.9 1,450.0 8.8 13.9 7.8 Other Petroleum 

AvGas Blend Components 7.5 7.5 0.5 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 51.7 51.7 3.5 Petroleum Coke 0.3 663.7 174.7 838.7 0.0 67.8 Still Gas 1,477.3 1,477.3 94.8 Special Naphtha Unfinished Oils (50.4) (50.4) (3.7) Waxes 

Geothermal 49.2 49.2 TOTAL (All Fuels) 6,722.3 4,074.1 13,387.2 25,517.4 26,694.9 689.8 77,085.8 381.7 233.6 844.3 1,810.8 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-30 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-16: 2002 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 12.0 87.8 1,243.5 NE 19,782.9 17.9 21,144.2 1.1 8.4 116.9 NE 

Residential Coal 12.0 12.0 1.1 Commercial Coal 87.8 87.8 8.4 Industrial Other Coal 1,243.5 1,243.5 116.9 Transportation Coal NE Electric Power Coal 19,782.9 19,782.9 U.S. Territory Coal (bit) 17.9 17.9 

Natural Gas 4,971.0 3,208.1 8,318.2 711.1 5,785.0 22.8 23,016.2 263.7 170.2 441.3 37.7 Total Petroleum 1,365.5 601.4 3,873.8 24,838.1 961.3 556.8 32,196.9 94.4 43.4 283.8 1,776.0 

Asphalt & Road Oil Aviation Gasoline 33.7 33.7 2.3 Distillate Fuel Oil 762.8 394.0 1,049.9 5,592.7 127.4 92.8 8,019.7 55.8 28.8 76.8 409.1 Jet Fuel 2,448.5 NA 61.8 2,510.2 173.5 Kerosene 59.9 15.9 13.8 8.2 97.8 4.3 1.2 1.0 LPG 542.8 95.8 579.5 13.5 11.2 1,242.7 34.2 6.0 36.6 0.8 Lubricants Motor Gasoline 15.6 106.9 16,521.9 189.4 16,833.7 1.1 7.6 1,172.2 Residual Fuel 79.8 146.1 227.9 658.7 193.6 1,306.1 6.3 11.5 18.0 Other Petroleum 

AvGas Blend Components 7.5 7.5 0.5 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 52.4 52.4 3.5 Petroleum Coke 0.2 650.0 175.2 825.4 0.0 66.4 Still Gas 1,403.3 1,403.3 90.1 Special Naphtha Unfinished Oils (135.7) (135.7) (10.1) Waxes 

Geothermal 49.4 49.4 TOTAL (All Fuels) 6,348.5 3,897.3 13,435.5 25,549.2 26,578.7 597.6 76,406.8 359.3 222.0 842.1 1,813.8 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-17: 2001 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 10.9 88.3 1,358.3 NE 19,613.6 10.8 21,081.9 1.0 8.4 127.7 NE 

Residential Coal 10.9 10.9 1.0 Commercial Coal 88.3 88.3 8.4 Industrial Other Coal 1,358.3 1,358.3 127.7 Transportation Coal NE Electric Power Coal 19,613.6 19,613.6 U.S. Territory Coal (bit) 10.8 10.8 

Natural Gas 4,871.9 3,089.3 7,882.3 655.5 5,488.3 22.9 22,010.1 258.5 163.9 418.2 34.8 Total Petroleum 1,472.4 675.8 3,993.2 24,379.7 1,276.6 632.2 32,429.9 102.2 48.8 293.2 1,742.0 

Asphalt & Road Oil Aviation Gasoline 34.9 34.9 2.4 Distillate Fuel Oil 842.1 471.3 1,181.6 5,417.4 170.5 109.4 8,192.3 61.6 34.5 86.4 396.3 Jet Fuel 2,605.2 NA 98.9 2,704.0 184.7 Kerosene 95.1 31.4 23.2 0.9 150.6 6.9 2.3 1.7 LPG 535.2 94.5 500.6 12.9 7.0 1,150.2 33.8 6.0 31.6 0.8 Lubricants Motor Gasoline 8.6 68.1 16,149.8 187.6 16,414.2 0.6 4.8 1,145.2 Residual Fuel 69.9 146.7 159.5 1,002.8 228.4 1,607.2 5.5 11.6 12.6 Other Petroleum 

AvGas Blend Components 6.1 6.1 0.4 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 61.6 61.6 4.1 Petroleum Coke 0.2 650.0 103.2 753.4 0.0 66.4 Still Gas 1,430.7 1,430.7 91.9 Special Naphtha Unfinished Oils (75.4) (75.4) (5.6) Waxes 

Geothermal 46.9 46.9 TOTAL (All Fuels) 6,355.3 3,853.4 13,233.8 25,035.2 26,425.3 665.9 75,568.8 361.8 221.2 839.1 1,776.8 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-32 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-18: 2000 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 10.6 85.8 1,348.7 NE 20,219.8 10.3 21,675.1 1.0 8.2 126.8 NE 

Residential Coal 10.6 10.6 1.0 Commercial Coal 85.8 85.8 8.2 Industrial Other Coal 1,348.7 1,348.7 126.8 Transportation Coal NE Electric Power Coal 20,219.8 20,219.8 U.S. Territory Coal (bit) 10.3 10.3 

Natural Gas 5,067.2 3,233.7 8,524.2 671.7 5,311.0 12.7 22,820.4 268.8 171.6 452.3 35.6 Total Petroleum 1,452.6 651.9 3,630.5 24,668.8 1,144.3 471.7 32,019.7 100.5 47.2 265.5 1,764.7 

Asphalt & Road Oil Aviation Gasoline 36.3 36.3 2.5 Distillate Fuel Oil 794.3 431.0 1,025.3 5,395.7 174.8 71.3 7,892.5 58.1 31.5 75.0 394.7 Jet Fuel 2,766.4 NA 74.1 2,840.5 196.1 Kerosene 94.6 29.7 15.6 2.4 142.2 6.8 2.1 1.1 LPG 563.7 99.5 605.3 11.2 8.0 1,287.7 35.6 6.3 38.2 0.7 Lubricants Motor Gasoline 16,015.7 185.1 16,200.8 1,135.7 Residual Fuel 91.6 184.1 443.5 870.8 130.9 1,720.8 7.2 14.5 34.9 Other Petroleum 

AvGas Blend Components 3.8 3.8 0.3 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 106.5 106.5 7.1 Petroleum Coke 0.2 655.4 98.6 754.2 0.0 66.9 Still Gas 1,435.6 1,435.6 92.2 Special Naphtha Unfinished Oils (401.2) (401.2) (29.8) Waxes 

Geothermal 48.1 48.1 TOTAL (All Fuels) 6,530.3 3,971.4 13,503.3 25,340.4 26,723.2 494.6 76,563.3 370.4 226.9 844.6 1,800.3 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-19: 1999 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 13.9 101.6 1,317.3 NE 19,279.1 10.2 20,722.0 1.3 9.7 123.8 NE 

Residential Coal 13.9 13.9 1.3 Commercial Coal 101.6 101.6 9.7 Industrial Other Coal 1,317.3 1,317.3 123.8 Transportation Coal NE Electric Power Coal 19,279.1 19,279.1 U.S. Territory Coal (bit) 10.2 10.2 

Natural Gas 4,822.0 3,110.6 8,407.9 675.0 4,916.5 - 21,932.0 255.8 165.0 446.1 35.8 Total Petroleum 1,377.4 582.5 3,516.6 23,990.0 1,211.4 461.0 31,138.9 95.4 42.1 259.0 1,713.3 

Asphalt & Road Oil Aviation Gasoline 39.2 39.2 2.7 Distillate Fuel Oil 732.5 388.0 1,022.8 5,169.8 140.1 79.4 7,532.6 53.6 28.4 74.8 378.2 Jet Fuel 2,677.9 NA 59.5 2,737.4 189.8 Kerosene 111.2 26.9 12.8 3.7 154.7 8.0 1.9 0.9 LPG 533.7 94.2 435.0 13.5 8.3 1,084.7 33.8 6.0 27.5 0.9 Lubricants Motor Gasoline 15,913.9 164.0 16,078.0 1,127.9 Residual Fuel 73.3 150.9 175.7 958.7 146.0 1,504.6 5.8 11.9 13.8 Other Petroleum 

AvGas Blend Components 6.4 6.4 0.4 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 103.5 103.5 6.9 Petroleum Coke 0.1 652.0 112.5 764.6 0.0 66.6 Still Gas 1,421.1 1,421.1 91.2 Special Naphtha Unfinished Oils (287.9) (287.9) (21.3) Waxes 

Geothermal 50.6 50.6 TOTAL (All Fuels) 6,213.3 3,794.7 13,241.8 24,665.0 25,457.5 471.2 73,843.5 352.5 216.8 828.9 1,749.1 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-34 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-20: 1998 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 12.4 100.6 1,356.3 NE 19,215.8 10.5 20,695.6 1.2 9.6 127.5 NE 

Residential Coal 12.4 12.4 1.2 Commercial Coal 100.6 100.6 9.6 Industrial Other Coal 1,356.3 1,356.3 127.5 Transportation Coal NE Electric Power Coal 19,215.8 19,215.8 U.S. Territory Coal (bit) 10.5 10.5 

Natural Gas 4,637.3 3,076.0 8,888.2 666.6 4,689.7 - 21,957.8 246.0 163.2 471.6 35.4 Total Petroleum 1,242.9 585.4 3,444.6 23,055.7 1,306.2 445.4 30,080.3 86.5 42.5 254.8 1,645.6 

Asphalt & Road Oil Aviation Gasoline 35.5 35.5 2.5 Distillate Fuel Oil 701.0 389.5 1,068.5 4,874.3 135.7 71.9 7,240.8 51.3 28.5 78.2 356.6 Jet Fuel 2,484.2 NA 59.9 2,544.1 176.1 Kerosene 108.3 31.2 22.1 6.3 167.8 7.8 2.3 1.6 LPG 433.6 76.5 303.9 16.6 5.9 836.6 27.4 4.8 19.2 1.1 Lubricants Motor Gasoline 3.0 15.1 15,566.1 160.3 15,744.6 0.2 1.1 1,103.3 Residual Fuel 85.2 173.3 78.9 1,047.0 141.1 1,525.5 6.7 13.7 6.2 Other Petroleum 

AvGas Blend Components 4.0 4.0 0.3 Crude Oil MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 89.7 89.7 6.0 Petroleum Coke 0.1 644.8 123.6 768.4 0.0 65.8 Still Gas 1,437.3 1,437.3 92.3 Special Naphtha Unfinished Oils (313.9) (313.9) (23.3) Waxes 

Geothermal 50.4 50.4 TOTAL (All Fuels) 5,892.6 3,762.1 13,689.1 23,722.2 25,262.1 456.0 72,784.1 333.7 215.3 853.8 1,681.0 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-21: 1997 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 15.9 128.7 1,435.6 NE 18,902.9 10.4 20,493.6 1.5 12.3 134.9 NE 

Residential Coal 15.9 15.9 1.5 Commercial Coal 128.7 128.7 12.3 Industrial Other Coal 1,435.6 1,435.6 134.9 Transportation Coal NE Electric Power Coal 18,902.9 18,902.9 U.S. Territory Coal (bit) 10.4 10.4 

Natural Gas 5,088.0 3,282.9 9,174.3 789.9 4,140.3 - 22,475.4 270.0 174.2 486.8 41.9 Total Petroleum 1,343.4 623.9 3,801.3 22,604.2 926.8 445.3 29,745.0 93.5 45.4 279.0 1,615.0 

Asphalt & Road Oil Aviation Gasoline 39.7 39.7 2.7 Distillate Fuel Oil 789.8 400.9 1,059.1 4,790.8 110.6 81.6 7,232.7 57.8 29.3 77.5 350.4 Jet Fuel 2,509.8 NA 62.1 2,571.9 177.9 Kerosene 92.9 24.6 18.8 4.0 140.3 6.7 1.8 1.4 LPG 460.7 81.3 463.7 13.4 6.5 1,025.7 29.0 5.1 29.2 0.8 Lubricants Motor Gasoline 5.8 28.5 15,113.9 160.0 15,308.2 0.4 2.0 1,072.3 Residual Fuel 111.2 235.6 136.5 714.6 131.1 1,329.0 8.8 18.6 10.8 Other Petroleum 

AvGas Blend Components 9.1 9.1 0.6 Crude Oil 4.6 4.6 0.3 MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 30.0 30.0 2.0 Petroleum Coke 0.1 609.7 101.6 711.4 0.0 62.3 Still Gas 1,445.1 1,445.1 92.8 Special Naphtha Unfinished Oils (102.9) (102.9) (7.6) Waxes 

Geothermal 50.2 50.2 TOTAL (All Fuels) 6,447.3 4,035.6 14,411.3 23,394.1 24,020.2 455.7 72,764.2 365.0 231.9 900.7 1,656.9 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-36 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-22: 1996 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 16.4 120.1 1,409.0 NE 18,427.6 10.3 19,983.4 1.6 11.4 132.3 NE 

Residential Coal 16.4 16.4 1.6 Commercial Coal 120.1 120.1 11.4 Industrial Other Coal 1,409.0 1,409.0 132.3 Transportation Coal NE Electric Power Coal 18,427.6 18,427.6 U.S. Territory Coal (bit) 10.3 10.3 

Natural Gas 5,350.2 3,224.8 9,090.7 740.5 3,875.7 - 22,281.8 283.9 171.1 482.3 39.3 Total Petroleum 1,436.3 699.3 3,832.0 22,294.9 817.4 434.6 29,514.4 100.2 51.1 281.8 1,594.4 

Asphalt & Road Oil Aviation Gasoline 37.4 37.4 2.6 Distillate Fuel Oil 874.2 455.9 1,091.8 4,500.4 109.4 76.5 7,108.1 63.9 33.4 79.9 329.2 Jet Fuel 2,460.3 NA 78.5 2,538.8 174.4 Kerosene 88.8 21.0 18.3 3.0 131.1 6.4 1.5 1.3 LPG 473.2 83.5 436.9 14.7 7.3 1,015.7 29.8 5.3 27.5 0.9 Lubricants Motor Gasoline 1.5 11.2 14,967.3 151.4 15,131.4 0.1 0.8 1,062.5 Residual Fuel 137.2 281.7 314.9 628.4 118.0 1,480.1 10.8 22.2 24.8 Other Petroleum 

AvGas Blend Components 7.0 7.0 0.5 Crude Oil 13.7 13.7 1.0 MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 38.5 38.5 2.6 Petroleum Coke 0.1 608.7 79.6 688.4 0.0 62.2 Still Gas 1,437.1 1,437.1 92.3 Special Naphtha Unfinished Oils (112.8) (112.8) (8.4) Waxes 

Geothermal 48.9 48.9 TOTAL (All Fuels) 6,802.9 4,044.1 14,331.7 23,035.4 23,169.5 445.0 71,828.4 385.6 233.6 896.5 1,633.7 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-23: 1995 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 17.4 116.3 1,485.6 NE 17,465.1 10.2 19,094.6 1.7 11.1 139.6 NE 

Residential Coal 17.4 17.4 1.7 Commercial Coal 116.3 116.3 11.1 Industrial Other Coal 1,485.6 1,485.6 139.6 Transportation Coal NE Electric Power Coal 17,465.1 17,465.1 U.S. Territory Coal (bit) 10.2 10.2 

Natural Gas 4,943.9 3,090.6 8,764.3 727.8 4,319.7 - 21,846.2 262.3 164.0 465.0 38.6 Total Petroleum 1,293.5 673.7 3,508.9 21,793.2 754.6 461.8 28,485.7 90.5 49.3 257.9 1,560.1 

Asphalt & Road Oil Aviation Gasoline 39.6 39.6 2.7 Distillate Fuel Oil 814.9 431.3 995.7 4,318.9 108.1 89.5 6,758.4 59.6 31.6 72.8 315.9 Jet Fuel 2,410.1 NA 75.7 2,485.9 170.9 Kerosene 74.3 22.1 15.4 3.6 115.4 5.4 1.6 1.1 LPG 404.2 71.3 432.5 16.7 5.6 930.5 25.5 4.5 27.3 1.1 Lubricants Motor Gasoline 7.3 81.2 14,620.5 146.7 14,855.7 0.5 5.8 1,038.9 Residual Fuel 141.5 284.7 387.3 566.0 140.7 1,520.1 11.1 22.4 30.5 Other Petroleum 

AvGas Blend Components 5.3 5.3 0.4 Crude Oil 14.5 14.5 1.1 MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 34.5 34.5 2.3 Petroleum Coke 0.1 588.7 80.6 669.4 0.0 60.1 Still Gas 1,377.3 1,377.3 88.4 Special Naphtha Unfinished Oils (320.9) (320.9) (23.8) Waxes 

Geothermal 45.6 45.6 TOTAL (All Fuels) 6,254.7 3,880.6 13,758.8 22,521.0 22,585.0 472.0 69,472.1 354.4 224.4 862.6 1,598.7 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-38 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-24: 1994 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 20.7 117.1 1,550.9 NE 17,259.1 10.0 18,957.8 2.0 11.1 145.8 NE 

Residential Coal 20.7 20.7 2.0 Commercial Coal 117.1 117.1 11.1 Industrial Other Coal 1,550.9 1,550.9 145.8 Transportation Coal NE Electric Power Coal 17,259.1 17,259.1 U.S. Territory Coal (bit) 10.0 10.0 

Natural Gas 4,941.2 2,952.1 8,300.7 709.7 3,992.8 - 20,896.6 262.2 156.6 440.4 37.7 Total Petroleum 1,325.5 712.9 3,652.2 21,355.9 1,058.8 506.3 28,611.5 92.9 52.4 268.2 1,532.1 

Asphalt & Road Oil Aviation Gasoline 38.1 38.1 2.6 Distillate Fuel Oil 865.1 451.5 985.5 4,164.4 120.1 118.8 6,705.5 63.3 33.0 72.1 304.6 Jet Fuel 2,360.9 NA 65.8 2,426.7 167.5 Kerosene 64.9 19.5 16.9 3.0 104.3 4.7 1.4 1.2 LPG 395.4 69.8 450.8 32.2 7.3 955.5 25.0 4.4 28.5 2.0 Lubricants Motor Gasoline 14,402.0 147.4 14,549.4 1,027.1 Residual Fuel 171.9 368.4 358.1 869.0 164.1 1,931.5 13.5 29.0 28.2 Other Petroleum 

AvGas Blend Components 6.1 6.1 0.4 Crude Oil 18.7 18.7 1.4 MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 80.8 80.8 5.4 Petroleum Coke 0.1 586.8 69.7 656.6 0.0 59.9 Still Gas 1,417.5 1,417.5 91.0 Special Naphtha Unfinished Oils (279.2) (279.2) (20.7) Waxes 

Geothermal 53.0 53.0 TOTAL (All Fuels) 6,287.4 3,782.1 13,503.8 22,065.6 22,363.7 516.3 68,518.8 357.1 220.2 854.4 1,569.8 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-25: 1993 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 25.6 116.5 1,543.1 NE 17,194.4 9.6 18,889.2 2.4 11.1 144.9 NE 

Residential Coal 25.6 25.6 2.4 Commercial Coal 116.5 116.5 11.1 Industrial Other Coal 1,543.1 1,543.1 144.9 Transportation Coal NE Electric Power Coal 17,194.4 17,194.4 U.S. Territory Coal (bit) 9.6 9.6 

Natural Gas 5,054.2 2,916.4 8,305.3 646.0 3,552.0 - 20,473.9 268.2 154.7 440.7 34.3 Total Petroleum 1,358.4 704.9 3,524.2 20,856.7 1,123.8 459.9 28,027.9 95.3 51.8 259.7 1,495.4 

Asphalt & Road Oil Aviation Gasoline 38.4 38.4 2.7 Distillate Fuel Oil 884.2 447.7 991.1 3,886.9 86.5 104.9 6,401.3 64.7 32.7 72.5 284.3 Jet Fuel 2,307.2 NA 62.1 2,369.3 163.9 Kerosene 75.6 14.0 13.1 3.8 106.5 5.5 1.0 0.9 LPG 398.6 70.3 443.3 19.0 4.9 936.2 25.2 4.4 28.0 1.2 Lubricants Motor Gasoline 14,237.7 128.3 14,365.9 1,014.3 Residual Fuel 172.7 391.5 367.5 958.6 155.9 2,046.3 13.6 30.8 29.0 Other Petroleum 

AvGas Blend Components 0.1 0.1 0.0 Crude Oil 21.2 21.2 1.6 MoGas Blend Components Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 56.4 56.4 3.8 Petroleum Coke 0.2 601.8 78.6 680.6 0.0 61.5 Still Gas 1,401.8 1,401.8 90.0 Special Naphtha Unfinished Oils (396.0) (396.0) (29.4) Waxes 

Geothermal 57.3 57.3 TOTAL (All Fuels) 6,438.1 3,737.8 13,372.7 21,502.7 21,927.5 469.5 67,448.3 365.9 217.7 845.3 1,529.7 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-40 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-26: 1992 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 25.6 116.8 1,530.8 NE 16,463.6 8.8 18,145.7 2.5 11.2 143.8 NE 

Residential Coal 25.6 25.6 2.5 Commercial Coal 116.8 116.8 11.2 Industrial Other Coal 1,530.8 1,530.8 143.8 Transportation Coal NE Electric Power Coal 16,463.6 16,463.6 U.S. Territory Coal (bit) 8.8 8.8 

Natural Gas 4,777.7 2,855.3 8,203.1 610.6 3,528.1 - 19,974.9 253.5 151.5 435.2 32.4 Total Petroleum 1,381.9 751.0 3,850.9 20,274.8 990.7 444.9 27,694.3 97.2 55.3 280.3 1,453.4 

Asphalt & Road Oil Aviation Gasoline 41.1 41.1 2.8 Distillate Fuel Oil 934.4 483.3 1,032.0 3,657.7 73.5 91.8 6,272.7 68.4 35.4 75.5 267.6 Jet Fuel 2,268.0 NA 61.3 2,329.3 161.2 Kerosene 65.0 11.1 9.8 3.3 89.2 4.7 0.8 0.7 LPG 382.5 67.5 469.1 18.3 11.9 949.3 24.1 4.3 29.6 1.2 Lubricants Motor Gasoline 13,889.5 122.1 14,011.7 989.0 Residual Fuel 189.1 328.4 400.1 872.2 154.6 1,944.3 14.9 25.9 31.5 Other Petroleum 

AvGas Blend Components 0.2 0.2 0.0 Crude Oil 27.4 27.4 2.0 MoGas Blend Components 75.7 75.7 5.4 Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 261.0 261.0 17.5 Petroleum Coke 0.1 566.6 45.0 611.7 0.0 57.9 Still Gas 1,435.7 1,435.7 92.2 Special Naphtha Unfinished Oils (354.8) (354.8) (26.3) Waxes 

Geothermal 55.1 55.1 TOTAL (All Fuels) 6,185.3 3,723.2 13,584.9 20,885.4 21,037.5 453.7 65,869.9 353.1 218.0 859.3 1,485.7 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-27: 1991 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 25.3 115.4 1,580.9 NE 16,247.9 7.7 17,977.4 2.4 11.0 148.4 NE 

Residential Coal 25.3 25.3 2.4 Commercial Coal 115.4 115.4 11.0 Industrial Other Coal 1,580.9 1,580.9 148.4 Transportation Coal NE Electric Power Coal 16,247.9 16,247.9 U.S. Territory Coal (bit) 7.7 7.7 

Natural Gas 4,642.8 2,779.7 7,833.6 621.8 3,391.1 - 19,269.1 246.3 147.5 415.6 33.0 Total Petroleum 1,386.6 837.8 3,523.4 19,689.0 1,198.3 425.4 27,060.5 97.5 61.7 256.6 1,409.5 

Asphalt & Road Oil Aviation Gasoline 41.7 41.7 2.9 Distillate Fuel Oil 924.8 514.3 1,043.6 3,466.5 83.6 71.4 6,104.1 67.6 37.6 76.3 253.6 Jet Fuel 2,329.3 NA 78.2 2,407.5 165.7 Kerosene 72.3 12.1 11.4 2.8 98.6 5.2 0.9 0.8 LPG 389.5 68.7 371.4 19.9 13.8 863.3 24.6 4.3 23.4 1.3 Lubricants Motor Gasoline 30.8 70.0 13,607.2 124.7 13,832.6 2.2 5.0 968.4 Residual Fuel 211.9 270.9 224.4 1,085.3 134.6 1,927.1 16.7 21.3 17.7 Other Petroleum 

AvGas Blend Components (0.1) (0.1) (0.0) Crude Oil 38.9 38.9 2.9 MoGas Blend Components (25.9) (25.9) (1.8) Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 249.2 249.2 16.7 Petroleum Coke 539.6 29.3 569.0 55.1 Still Gas 1,404.5 1,404.5 90.2 Special Naphtha Unfinished Oils (450.2) (450.2) (33.3) Waxes 

Geothermal 54.5 54.5 TOTAL (All Fuels) 6,054.7 3,733.0 12,938.0 20,310.9 20,891.7 433.2 64,361.4 346.2 220.2 820.6 1,442.5 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
A-42 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-28: 1990 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type 

1 2 3 4 5 6 7 8 9 10 11 12 

 Adjusted Consumption (TBtu)a Emissionsb (Tg CO2 Eq.) fro

Fuel Type Res. Comm. Ind. Trans. Elec. Terr. Total Res. Comm. Ind. Trans. Total Coal 31.0 123.8 1,593.5 NE 16,259.0 7.0 18,014.3 2.9 11.8 149.5 NE 

Residential Coal 31.0 31.0 2.9 Commercial Coal 123.8 123.8 11.8 Industrial Other Coal 1,593.5 1,593.5 149.5 Transportation Coal NE Electric Power Coal 16,259.0 16,259.0 U.S. Territory Coal (bit) 7.0 7.0 

Natural Gas 4,473.9 2,667.7 7,729.7 682.7 3,325.8 - 18,879.9 237.4 141.5 410.1 36.2 Total Petroleum 1,382.3 828.4 3,766.3 20,222.7 1,289.4 374.8 27,864.0 97.4 61.2 274.6 1,448.3 

Asphalt & Road Oil Aviation Gasoline 45.0 45.0 3.1 Distillate Fuel Oil 953.4 522.3 1,091.7 3,570.5 96.5 74.0 6,308.4 69.7 38.2 79.9 261.2 Jet Fuel 2,477.5 NA 61.0 2,538.5 176.2 Kerosene 63.9 11.8 12.3 2.6 90.6 4.6 0.9 0.9 LPG 365.0 64.4 406.4 21.6 14.4 871.9 23.0 4.1 25.7 1.4 Lubricants Motor Gasoline 13,807.8 101.0 13,908.8 982.7 Residual Fuel 229.8 364.1 300.3 1,162.6 121.8 2,178.7 18.1 28.7 23.7 Other Petroleum 

AvGas Blend Components 0.2 0.2 0.0 Crude Oil 50.9 50.9 3.8 MoGas Blend Components 53.7 53.7 3.8 Misc. Products Naphtha (<401 deg. F) Other Oil (>401 deg. F) Pentanes Plus 167.8 167.8 11.2 Petroleum Coke 536.2 30.4 566.6 54.8 Still Gas 1,451.9 1,451.9 93.2 Special Naphtha Unfinished Oils (369.0) (369.0) (27.3) Waxes 

Geothermal 52.7 52.7 TOTAL (All Fuels) 5,887.2 3,619.9 13,089.5 20,905.5 20,926.9 381.9 64,810.8 337.7 214.5 834.2 1,484.5 a Expressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunker fuel consumption b Consumption and/or emissions of select fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses. NE (Not Estimated) 
Table A-29: Unadjusted Non-Energy Fuel Consumption (TBtu) 

Sector/Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Industry 4,534.9 4,571.5 4,718.8 4,877.6 5,223.6 5,253.0 5,295.3 5,571.8 5,820.2 6,039.9 5,652.3 5,297.1 5,416.6 5,332.6 

Industrial Coking Coal 0.0 11.9 42.5 33.3 71.7 75.0 28.0 57.1 84.4 72.5 82.2 36.8 76.4 63.5 Industrial Other Coal 8.2 8.5 9.5 10.0 10.9 11.3 11.4 11.2 10.4 11.1 12.4 11.3 12.0 11.9 Natural Gas to Chemical Plants, Other Uses 299.9 301.1 259.4 292.4 354.0 357.2 360.3 386.8 426.6 437.5 439.8 412.3 379.5 383.0 Asphalt & Road Oil 1,170.2 1,076.5 1,102.2 1,149.0 1,172.9 1,178.2 1,175.9 1,223.6 1,262.6 1,324.4 1,275.7 1,256.9 1,239.9 1,219.5 LPG 1,201.4 1,377.9 1,390.7 1,351.0 1,545.7 1,586.9 1,652.0 1,670.4 1,744.4 1,820.7 1,665.4 1,553.4 1,620.3 1,545.1 Lubricants 186.3 166.7 170.0 173.1 180.9 177.8 172.5 182.3 190.8 192.8 189.9 174.0 171.9 159.0 Pentanes Plus 82.6 44.8 61.5 275.9 257.9 303.4 316.5 298.9 204.3 261.4 236.7 201.6 171.4 169.1 Naphtha (<401 deg. F) 347.8 298.9 377.1 350.6 398.3 373.0 479.3 536.4 584.0 502.1 613.5 493.7 582.6 613.0 Other Oil (>401 deg. F) 753.9 827.3 814.5 844.1 838.6 801.0 729.6 861.3 818.7 811.1 722.2 662.4 632.1 699.4 Still Gas 21.3 22.0 11.3 28.4 21.9 40.1 0.0 2.1 0.0 16.1 12.6 35.8 57.8 59.0 Petroleum Coke 178.0 153.0 231.1 123.5 136.4 132.6 148.2 117.7 213.7 283.8 140.7 207.6 192.1 161.0 Special Naphtha 107.1 88.0 104.6 104.6 81.1 70.8 74.5 72.3 107.3 145.4 97.4 78.5 102.4 80.5 Other (Wax/Misc.) Distillate Fuel Oil 7.0 7.1 7.0 6.9 6.8 8.0 9.2 10.4 11.7 11.7 11.7 11.7 11.7 11.7 Waxes 33.3 35.1 37.3 40.0 40.6 40.6 48.7 43.7 42.4 37.4 33.1 36.3 32.2 31.0 Miscellaneous Products 137.8 152.6 100.1 94.7 105.9 97.1 89.0 97.8 119.0 111.9 119.2 124.9 134.2 126.0 

Transportation 176.0 157.5 160.5 163.5 170.8 167.9 163.0 172.1 180.2 182.1 179.4 164.3 162.4 150.1 

Lubricants 176.0 157.5 160.5 163.5 170.8 167.9 163.0 172.1 180.2 182.1 179.4 164.3 162.4 150.1 

U.S. Territories 86.7 114.4 62.6 74.2 54.8 90.8 121.0 131.6 135.0 139.3 165.5 80.3 138.6 127.9 

Lubricants 0.7 0.6 1.5 3.3 1.9 2.0 0.8 2.5 1.3 1.4 16.4 0.0 1.5 9.3 Other Petroleum (Misc. Prod.) 86.0 113.8 61.2 71.0 53.0 88.8 120.2 129.1 133.8 138.0 149.1 80.3 137.2 118.6 

Total 4,797.6 4,843.3 4,941.9 5,115.3 5,449.3 5,511.8 5,579.3 5,875.5 6,135.4 6,361.3 5,997.1 5,541.7 5,717.6 5,610.7 Note: These values are unadjusted non-energy fuel use provided by EIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for in the Industrial ProceTable A-30: International Bunker Fuel Consumption (TBtu) Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Marine Residual Fuel Oil 715.7 801.5 669.8 533.9 524.5 523.2 536.4 575.2 594.8 489.7 444.1 426.0 448.9 471.8 Marine Distillate Fuel Oil & Other 158.0 149.3 145.9 146.6 121.2 125.7 114.1 125.5 158.8 113.6 85.9 72.4 82.6 103.9 

Aviation Jet Fuel 652.0 695.7 733.3 720.8 793.6 722.1 813.9 798.3 872.6 783.9 814.0 820.8 891.9 784.6 

Total 1,525.7 1,646.5 1,549.1 1,401.4 1,439.2 1,371.0 1,464.5 1,499.1 1,626.2 1,387.2 1,343.9 1,319.3 1,423.3 1,360.3 
A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-31: Key Assumptions for Estimating CO2 Emissions 

Fuel Type 

C Content Coefficient 

(Tg C/QBtu) 

Coal 

Residential Coal [a] 

Commercial Coal [a] 

Industrial Coking Coal 31.00 

Industrial Other Coal [a] 

Electric Power Coal [a] 

U.S. Territory Coal (bit) 25.14 

Natural Gas 14.47 

Petroleum 

Asphalt & Road Oil 20.62 

Aviation Gasoline 18.87 

Distillate Fuel Oil 19.95 

Jet Fuel [a] 

Kerosene 19.72 

LPG (energy use) [a] 

LPG (non-energy use) [a] 

Lubricants 20.24 

Motor Gasoline [a] 

Residual Fuel Oil 21.49 

Other Petroleum 

AvGas Blend Components 18.87 

Crude Oil [a] 

MoGas Blend Components [a] 

Misc. Products [a] 

Misc. Products (Territories) 20.00 

Naphtha (<401 deg. F) 18.14 

Other Oil (>401 deg. F) 19.95 

Pentanes Plus 18.24 

Petrochemical Feedstocks 19.37 

Petroleum Coke 27.85 

Still Gas 17.51 

Special Naphtha 19.86 

Unfinished Oils [a] 

Waxes 19.81 

Geothermal 2.05 

Sources: C coefficients from EIA (2008b). 

[a] These coefficients vary annually due to fluctuations in fuel quality (see Table A-32). 
Table A-32: Annually Variable C Content Coefficients by Year (Tg C/QBtu) Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Residential Coal 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 Commercial Coal 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 

Industrial Other Coal 25.58 25.60 25.62 25.61 25.63 25.63 25.61 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 

Electric Power Coal 25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 LPG (energy use) 16.99 16.98 16.99 16.97 17.01 17.00 16.99 16.99 16.99 16.99 16.98 16.99 16.99 16.99 16.99 16.99 

LPG (non-energy use) 17.21 17.21 17.21 17.22 17.22 17.20 17.20 17.18 17.23 17.25 17.20 17.21 17.20 17.21 17.20 17.19 

Motor Gasoline 16.83 16.84 16.84 16.80 16.88 16.87 16.86 16.88 16.88 16.84 16.81 16.83 16.82 16.84 16.81 16.81 

Jet Fuel 19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.33 19.33 19.34 19.34 19.35 19.33 19.33 19.33 MoGas Blend Components 19.40 19.40 19.39 19.37 19.35 19.34 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 

Misc. Products 19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.33 19.33 19.34 19.34 19.35 19.33 19.33 19.33 

Unfinished Oils 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.24 20.19 20.23 20.29 20.30 20.28 20.33 20.33 

Crude Oil 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.24 20.19 20.23 20.29 20.30 20.28 20.33 20.33 

Source: EIA (2008b) Table A-33: Electricity Consumption by End-Use Sector (Billion Kilowatt-Hours) 

End-Use Sector 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Residential 924 955 936 995 1,008 1,043 1,083 1,076 1,130 1,145 1,192 1,202 1,265 1,276 1,292 1,359 Commercial 838 855 850 885 913 953 980 1,027 1,078 1,104 1,159 1,191 1,205 1,199 1,230 1,275 Industrial 1,070 1,071 1,107 1,116 1,154 1,163 1,186 1,194 1,212 1,230 1,235 1,159 1,156 1,181 1,186 1,169 Transportation 5 5 5 5 5 5 5 5 5 5 5 6 6 7 7 8 Total 2,837 2,886 2,897 3,001 3,081 3,164 3,254 3,302 3,425 3,484 3,592 3,557 3,632 3,662 3,716 3,811 

Note: Does not include the U.S. territories. Source: EIA (2008a) 
A-46 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

2.2. Methodology for Estimating the Carbon Content of Fossil Fuels 

This sub-annex presents the background and methodology for estimating the carbon (C) content of fossil fuels 

combusted in the United States. The C content of a particular fossil fuel represents the maximum potential emissions to 

the atmosphere if all C in the fuel is oxidized during combustion. The C content coefficients used in this report were 

developed using methods first outlined in EIA’s Emissions of Greenhouse Gases in the United States: 1987-1992 (1994) 

and were developed primarily by EIA. This sub-annex describes an updated methodology for estimating the C content of 

coal, and presents a time-series analysis of changes in U.S. C content coefficients. A summary of C content coefficients 

used in this report appears in Table A- 34. 

 Though the methods for estimating C contents for coal, natural gas, and petroleum products differ in their 

details, they each follow the same basic approach. First, because C coefficients are presented in terms of mass per unit 

energy (i.e., teragrams C per quadrillion Btu or Tg C/QBtu), those fuels that are typically described in volumetric units 

(petroleum products and natural gas) are converted to units of mass using an estimated density. Second, C contents are 

derived from fuel sample data, using descriptive statistics to estimate the C share of the fuel by weight. The heat content 

of the fuel is then estimated based on the sample data, or where sample data are unavailable or unrepresentative, by default 

values that reflect the characteristics of the fuel as defined by market requirements. A discussion of each fuel appears 

below. 

The C content of coal is described first because approximately one-third of all U.S. C emissions from fossil fuel 

combustion are associated with coal consumption. The methods and sources for estimating the C content of natural gas 

are provided next. Approximately one-fifth of U.S. greenhouse gas emissions from fossil fuel combustion are attributable 

to natural gas consumption. Finally, this sub-annex examines C contents of petroleum products. U.S. energy consumption 

statistics account for more than 20 different petroleum products. 
Table A- 34: Carbon Content Coefficients Used in this Report (Tg Carbon/QBtu) 

Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 20Coal 

Residential Coala 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 2

Commercial Coala 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 2

Industrial Coking Coala 25.51 25.51 25.51 25.51 25.52 25.53 25.55 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56 2

Industrial Other Coala 25.58 25.60 25.62 25.61 25.63 25.63 25.61 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 2Utility Coala,b 25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 2

Natural Gas 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 1

Petroleum 

Asphalt and Road Oil 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 20.62 2Aviation Gasoline 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 1Distillate Fuel Oil 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 1Jet Fuela 19.40 19.40 19.39 19.37 19.35 19.34 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 1

Kerosene 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 1

LPG (energy use)a 17.21 17.21 17.21 17.22 17.22 17.20 17.20 17.18 17.23 17.25 17.20 17.21 17.20 17.21 17.20 1

LPG (non-energy use)a 16.83 16.84 16.84 16.80 16.88 16.87 16.86 16.88 16.88 16.84 16.81 16.83 16.82 16.84 16.81 1

Lubricants 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 2Motor Gasolinea 19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.33 19.33 19.34 19.34 19.35 19.33 19.33 1

Residual Fuel 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 21.49 2

Other Petroleum 

Av Gas Blend Comp. 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 18.87 1Mo Gas Blend Compa 19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.33 19.33 19.34 19.34 19.35 19.33 19.33 1

Crude Oila 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.24 20.19 20.23 20.29 20.30 20.28 20.33 2

Misc. Productsa 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.24 20.19 20.23 20.29 20.30 20.28 20.33 2

Misc. Products (Terr.) 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 2

Naphtha (<401 deg. F) 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 1

Other oil (>401 deg. F) 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 1

Pentanes Plus 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 18.24 1

Petrochemical Feed. 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 19.37 1Petroleum Coke 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 27.85 2

Still Gas 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 1

Special Naphtha 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 19.86 1

Unfinished Oilsa 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.24 20.19 20.23 20.29 20.30 20.28 20.33 2

Waxes 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 1

Other Wax and Misc. 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 1

Geothermal 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 

aC contents vary annually based on changes in fuel composition. 

bC content for utility coal used in the electric power calculations. All coefficients based on higher heating value. Higher heating value (gross heating value) is the total amount of heat released when a f

natural gas all include chemical compounds of carbon and hydrogen. When those fuels are burned, the carbon and hydrogen combine with oxygen in the air to produce CO2 and water. Some of the en

transforming the water into steam and is usually lost. The amount of heat spent in transforming the water into steam is counted as part of gross heat content. Lower heating value (net heating value), i

spent in transforming the water into steam. Using a simplified methodology based on International Energy Agency defaults, higher heating value can be converted to lower heating value for coal and p0.95 and for natural gas by multiplying by 0.90. Carbon content coefficients are presented in higher heating value because U.S. energy statistics are reported by higher heating value.
A-48 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Coal 

Approximately one-third of all U.S. CO2 emissions from fossil fuel combustion are associated with coal 

consumption. Although the IPCC guidelines provide C contents for coal according to rank, it was necessary to develop C 

content coefficients by consuming sector to match the format in which coal consumption is reported by EIA. Because the 

C content of coal varies by the state in which it was mined and by coal rank, and because the sources of coal for each 

consuming sector vary by year, the weighted average C content for coal combusted in each consuming sector also varies 

over time. A time series of C contents by coal rank and consuming sector appears in Table A-35.1 

Methodology 

The methodology for developing C contents for coal by consuming sector consists of four steps. 

Step 1. Determine carbon contents by rank and by state of origin 

C contents by rank are estimated on the basis of 6,588 coal samples collected by the U.S. Geological Survey 

between 1973 and 1989. These coal samples are classified according to rank and state of origin. For each rank in each 

state, the average heat content and C content of the coal samples are calculated. Dividing the C content (reported in 

pounds CO2) by the heat content (reported in million Btu or MMBtu) yields an average C content coefficient. This 

coefficient is then converted into units of Tg C/QBtu. 

Step 2. Allocate sectoral consumption by rank and state of origin 

U.S. energy statistics provide data on the origin of coal used in four areas: 1) the electric power industry, 2) 

industrial coking, 3) all other industrial uses, and 4) the residential and commercial end-use sectors. Because U.S. energy 

statistics do not provide the distribution of coal rank consumed by each consuming sector, it is assumed that each sector 

consumes a representative mixture of coal ranks from a particular state that matches the mixture of all coal produced in 

that state during the year. 

Step 3. Weight sectoral carbon contents to reflect the rank and state of origin of coal consumed 

Sectoral C contents are calculated by multiplying the share of coal purchased from each state by rank by the C 

content estimated in Step 1. The resulting partial C contents are then totaled across all states and ranks to generate a 

national sectoral C content. 

Csector = Srank1×Crank1 + Srank2×Crank2 +.... + Srank50×Crank50 

Where, 

Csector = The C content by consuming sector; 

Srank = The portion of consuming sector coal consumption attributed to a given rank in each state; 

Crank = The estimated C content of a given rank in each state. 

 

1 For a comparison to earlier estimated carbon contents please see Chronology and Explanation of Changes in Individual 

Carbon Content Coefficients of Fossil Fuels near the end of this annex. 
Table A-35: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank (Tg C/QBtu) (1990-2007) 

Consuming Sector 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 20

Electric Power 25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.76 25.76 25.76 25.76 25.76 25.76 25.Industrial Coking 25.51 25.51 25.51 25.51 25.52 25.53 25.55 25.56 25.56 25.56 25.56 25.56 25.56 25.Other Industrial 25.58 25.60 25.62 25.61 25.63 25.63 25.61 25.63 25.63 25.63 25.63 25.63 25.63 25.Residential/ Commercial 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26.

Coal Rank 

 Anthracite 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.

 Bituminous 25.43 25.45 25.44 25.45 25.46 25.47 25.47 25.48 25.47 25.48 25.49 25.49 25.49 25. Sub-bituminous 26.50 26.49 26.49 26.48 26.49 26.49 26.49 26.49 26.49 26.49 26.48 26.48 26.48 26.

 Lignite 26.19 26.21 26.22 26.21 26.52 26.51 26.47 26.50 26.53 26.55 26.57 26.57 26.57 26.

p Preliminary Sources: C content coefficients by consuming sector from EIA (2008a). C content coefficients by coal rank from USGS (1998) and SAIC (2007). 
A-50 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 4. Develop national-level carbon contents by rank for comparison to IPCC defaults 

Although not used to calculate emissions, national-level C contents by rank are more easily compared to C 

contents of other countries than are sectoral C contents. This step requires weighting the state-level C contents by rank 

developed under Step 1 by overall coal production by state and rank (consumption by rank is unavailable in U.S. energy 

statistics). Each state-level C content by rank is multiplied by the share of national production of that rank that each state

represents. The resulting partial C contents are then summed across all states to generate an overall C content for each 

rank. 

 Nrank = Prank1 × Crank1 + Prank2 × Crank2 +...+ Prankn× Crankn

Where, 

Nrank = The national C content by rank; 

Prank = The portion of U.S. coal production attributed to a given rank in each state; and 

Crank = The estimated C content of a given rank in each state. 

Data Sources 

The ultimate analysis of coal samples was based on the 6,588 coal samples from USGS (1998). Data contained 

in the CoalQual Database are derived primarily from samples taken between 1973 and 1989, and were largely reported in 

State Geological Surveys. 

Data on coal distribution by state and consumption by sector, as well as coal production by state and rank, was 

obtained from EIA (2002). 

Uncertainty 

C contents vary considerably by state. Bituminous coal production and sub-bituminous coal production 

represented 53.4 percent and 38.1 percent of total U.S. supply in 2000, respectively. C content coefficients for bituminous 

coal vary from a low of 90.94 kg CO2 per MMBtu in Kansas to a high of 105.23 kg CO2 per MMBtu in Montana. In 2000, 

however, just 200 tons of bituminous coal was produced in Kansas, and none was produced in Montana. In 2000, more 

than 60 percent of bituminous coal was produced in three states: West Virginia, Kentucky, and Pennsylvania, and this 

share has remained fairly constant since 1990. These three states show a variation in C content for bituminous coals of 

0.7 percent, based on more than 2,000 samples (see Table A-36). 

Similarly, the C content coefficients for sub-bituminous coal range from 91.31 kg CO2 per MMBtu in Utah to 

98.66 kg CO2 per MMBtu in Washington. Utah showed no sub-bituminous coal production in 2000, and Washington 

produced just 4,000 tons. Wyoming, however, has represented between 75 percent and 82 percent of total sub-bituminous 

coal production in the United States since 1990. Thus, the C content coefficient for Wyoming, based on 435 samples, 

dominates. 

The interquartile range of C content coefficients among samples of sub-bituminous coal in Wyoming was 1.5 

percent from the mean. Similarly, this range among samples of bituminous coal from West Virginia, Kentucky, and 

Pennsylvania was 1.0 percent or less for each state. The large number of samples and the low variability within the 

sample set of the states that represent the predominant source of supply for U.S. coal suggest that the uncertainty in this 

factor is very low, on the order of 1.0 percent. 

Table A-36: Variability in Carbon Content Coefficients by Rank Across States (Kilograms CO2 Per MMBtu) 

State 

Number of 

Samples Bituminous 

Subbituminous Anthracite Lignite 

Alabama 946 92.85 - - 99.11 

Alaska 90 98.34 98.11 - 98.66 

Arizona 11 - 97.52 - - 

Arkansas 70 96.52 - - 94.98 

Colorado 292 94.39 96.48 - 96.48 

Georgia 35 95.03 - - - 

Idaho 1 - 94.89 - - 

Illinois 16 93.35 - - - 

Indiana 125 92.67 - - - 

Iowa 89 91.94 - - - 

Kansas 28 90.94 - - - 

Kentucky 870 92.58 - - - 
A-51

Louisiana 1 - - - 96.03 

Maryland 46 94.35 - - - 

Massachusetts 3 - - 114.82 - 

Michigan 3 92.85 - - - 

Mississippi 8 - - - 98.20 

Missouri 91 91.85 - - - 

Montana 301 105.23 97.75 103.60 99.38 

Nevada 2 94.39 - - 99.84 

New Mexico 167 95.25 94.89 103.92 - 

North Dakota 186 - - - 99.56 

Ohio 646 91.85 - - - 

Oklahoma 46 92.67 - - - 

Pennsylvania 739 93.39 - 103.65 - 

Tennessee 58 92.80 - - - 

Texas 48 - - - 94.76 

Utah 152 96.07 91.31 - - 

Virginia 456 93.53 - 98.52 - 

Washington 14 95.39 98.66 102.51 106.55 

West Virginia 566 93.89 - - - 

Wyoming 476 94.66 97.20 - - 

- No Sample Data Available 

Sources: USGS (1998) and SAIC (2007). 

Natural Gas 

Natural gas is predominantly composed of methane, which is 75 percent C by weight and contains 14.2 Tg 

C/QBtu (Higher Heating Value), but it may also contain many other compounds that can lower or raise its overall C 

content. These other compounds may be divided into two classes: 1) natural gas liquids (NGLs), and 2) non-hydrocarbon 

gases. The most common NGLs are ethane (C2H6), propane (C3H8), butane (C4H10), and, to a lesser extent, pentane (C5H12) 

and hexane (C6H14). Because the NGLs have more C atoms than methane (which has only one), their presence increases 

the overall C content of natural gas. NGLs have a commercial value greater than that of methane, and therefore are 

usually separated from raw natural gas at gas processing plants and sold as separate products. Ethane is typically used as a 

petrochemical feedstock, propane and butane have diverse uses, and natural gasoline1

 contributes to the gasoline/naphtha 

"octane pool," used primarily to make motor gasoline. 

Raw natural gas can also contain varying amounts of non-hydrocarbon gases, such as CO2, nitrogen, helium and 

other noble gases, and hydrogen sulfide. The share of non-hydrocarbon gases is usually less than 5 percent of the total, 

but there are individual natural gas reservoirs where the share can be much larger. The treatment of non-hydrocarbon 

gases in raw gas varies. Hydrogen sulfide is always removed. Inert gases are removed if their presence is substantial 

enough to reduce the energy content of the gas below pipeline specifications. Otherwise, inert gases will usually be left in 

the natural gas. Because the raw gas that is usually flared contains NGLs and CO2, it will typically have a higher overall C 

content than gas that has been processed and moved to end-use customers via transmission and distribution pipelines. 

Methodology 

The methodology for estimating the C contents of natural gas can be described in five steps. 

Step 1. Define pipeline-quality natural gas 

In the United States, pipeline-quality natural gas is expected to have an energy content greater than 970 Btu per 

cubic foot, but less than 1,100 Btu per cubic foot. Hydrogen sulfide content must be negligible. Typical pipeline-quality 

natural gas is about 95 percent methane, 3 percent NGLs, and 2 percent non-hydrocarbon gases, of which approximately 1 

percent is CO2. 

However, there is a range of gas compositions that are consistent with pipeline specifications. The minimum C 

content coefficient for natural gas would match that for pure methane, which equates to an energy content of 1,005 Btu per 

standard cubic foot. Gas compositions with higher or lower Btu content tend to have higher C emission factors, because 

the "low" Btu gas has a higher content of inert gases (including CO2 offset with more NGLs), while "high" Btu gas tends 

to have more NGLs. 

 

1 A term used in the gas processing industry to refer to a mixture of liquid hydrocarbons (mostly pentanes and heavier 

hydrocarbons) extracted from natural gas. 
A-52 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 2. Define flared gas 

Every year, a certain amount of natural gas is flared in the United States. There are several reasons that gas is 

flared: 

 There may be no market for some batches of natural gas, the amount may be too small or too variable, or the 

quality might be too poor to justify treating the gas and transporting it to market (such is the case when gas 

contains large shares of CO2). All natural gas flared for these reasons is probably "rich" associated gas, with 

relatively high energy content, high NGL content, and a high C content. 

 Gas treatment plants may flare substantial volumes of natural gas because of "process upsets," because the gas is 

"off spec," or possibly as part of an emissions control system. Gas flared at processing plants may be of variable 

quality. 

Data on the energy content of flare gas, as reported by states to EIA, indicate an energy content of 1,130 Btu per 

standard cubic foot. Flare gas may have an even higher energy content than reported by EIA since rich associated gas can 

have energy contents as high as 1,300 to 1,400 Btu per cubic foot. 

Step 3. Determine a relationship between carbon content and heat content 

A relationship between C content and heat content may be used to develop a C content coefficient for natural gas 

consumed in the United States. In 1994, EIA examined the composition (and therefore C contents) of 6,743 samples of 

pipeline-quality natural gas from utilities and/or pipeline companies in 26 cities located in 19 states. To demonstrate that 

these samples were representative of actual natural gas "as consumed" in the United States, their heat content was 

compared to that of the national average. For the most recent year, the average heat content of natural gas consumed in the 

United States was 1,025 Btu per cubic foot, varying by less than 1 percent (1,025 to 1,031 Btu per cubic foot) over the past 

5 years. Meanwhile, the average heat content of the 6,743 samples was 1,027 Btu per cubic foot, and the median heat 

content was 1,031 Btu per cubic foot. Thus, the average heat content of the sample set falls well within the typical range 

of natural gas consumed in the United States, suggesting that these samples continue to be representative of natural gas “as 

consumed” in the United States. The average and median composition of these samples appears in Table A-37. 

Table A-37: Composition of Natural Gas (Percent) 

Compound Average Median 

Methane 93.07 95.00 

Ethane 3.21 2.79 

Propane 0.59 0.48 

Higher Hydrocarbons 0.32 0.30 

Non-hydrocarbons 2.81 1.43 

Higher Heating Value (Btu per cubic foot) 1,027 1,032 

Source: Gas Technology Institute (1992) 

C contents were then calculated for a series of sub samples stratified by heat content. C contents were developed 

for eight separate sub-samples based on heat content and are shown in Table A-38. 

Table A-38: Carbon Content of Pipeline-Quality Natural Gas by Energy Content (Tg C/QBtu) 

Sample Average Carbon Content 

GRI Full Sample 14.51 

Greater than 1,000 Btu 14.47 

1,025 to 1,035 Btu 14.45 

975 to 1,000 Btu 14.73 

1,000 to 1,025 Btu 14.43 

1,025 to 1,050 Btu 14.47 

1,050 to 1,075 Btu 14.58 

1,075 to 1,100 Btu 14.65 

Greater than 1,100 Btu 14.92 

Weighted National Average 14.47 

Source: EIA (1994). 

Step 4. Apply carbon content coefficients developed in Step 3 to pipeline natural gas 

Because there is some regional variation in the energy content of natural gas consumed, a weighted national 

average C content was calculated using the average C contents for each sub-sample of gas that conformed with an 

individual state’s typical cubic foot of natural gas. The result was a weighted national average of 14.47 Tg C/QBtu. This 
A-53

was identical to the average C content of all samples with more than 1,000 Btu per cubic foot and the average C content 

for all samples with a heat content between 1,025 and 1,050 Btu per cubic foot. Because those samples with a heat content 

below 1,000 Btu had an unusually high C content coefficient attributable to large portions of CO2 (not seen in the median 

sample), they were excluded so as not to bias the C content coefficient upwards by including them in the final sample used 

to select a C content. 

Step 5. Apply carbon content coefficients developed in Step 3 to flare gas 

Selecting a C content coefficient for flare gas was much more difficult than for pipeline natural gas because of 

the uncertainty of its composition and uncertainty of the combustion efficiency of the flare. Because EIA estimates the 

heat content of flare gas at 1,130 Btu per cubic foot, the average C content for samples with more than 1,100 Btu per cubic 

foot, 14.92 Tg C/QBtu, was adopted as the coefficient for flare gas. It should be noted that the sample data set did not 

include any samples with more than 1,130 Btu per cubic foot. 

Data Sources 

Natural gas samples were obtained from the Gas Technology Institute (1992). Average heat content data for 

natural gas consumed in the United States and on a state-by-state basis were taken from EIA (2008a) and EIA (2003), 

respectively.

Uncertainty 

The assignment of C content coefficients for natural gas, and particularly for flare gas, requires more subjective 

judgment than the methodology used for coal. This subjective judgment may introduce additional uncertainty. 

Figure A-1 shows the relationship between the calculated C contents for each natural gas sample and its energy 

content. This figure illustrates the relatively restricted range of variation in both the energy content (which varies by about 

6 percent from average) and the C emission coefficient of natural gas (which varies by about 5 percent). Thus, the 

knowledge that gas has been sold via pipeline to an end-use consumer allows its C emission coefficient to be predicted 

with an accuracy of 5.0 percent. 

Figure A- 1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute 

Database 

Natural gas suppliers may achieve the same energy contents with a wide variety of methane, higher hydrocarbon, 

and non-hydrocarbon gas combinations. Thus, the plot reveals large variations in C content for a single Btu value. In fact, 

the variation in C content for a single Btu value may be nearly as great as the variation for the whole sample. As a result, 

while energy content has some predictive value, the specific energy content does not substantially improve the accuracy of 

an estimated C content coefficient beyond the 5.0 percent offered with the knowledge that it is of pipeline-quality. 

The plot of C content also reveals other interesting anomalies. Samples with the lowest emissions coefficients 

tend to have energy contents of about 1,000 Btu per cubic foot. They are composed of almost pure methane. Samples 

with a greater proportion of NGLs (e.g., ethane, propane, and butane) tend to have energy contents greater than 1,000 Btu 

per cubic foot, along with higher emissions coefficients. Samples with a greater proportion of inert gases tend to have 

lower energy content, but they usually contain carbon dioxide as one of the inert gases and, consequently, also tend to 

have higher emission coefficients (see left side of Figure A- 1). 

For the full sample (N=6,743), the average C content of a cubic foot of gas was 14.51 Tg C/QBtu (see Table A38). However, this average was raised by both the samples with less than 1,000 Btu per cubic foot that contain large 

amounts of inert carbon dioxide and those samples with more than 1,050 Btu per cubic foot that contain an unusually large 

amount of NGLs. Because typical gas consumed in the United States does not contain such a large amount of carbon 
A-54 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

dioxide or natural gas liquids, a weighted national average of 14.47 Tg C/QBtu that represents fuels more typically 

consumed is used.2 

Petroleum 

There are four critical determinants of the C content coefficient for a petroleum-based fuel: 

 The density of the fuel (e.g., the weight in kilograms of one barrel of fuel); 

 The fraction by mass of the product that consists of hydrocarbons, and the fraction of non-hydrocarbon 

impurities; 

 The specific types of ‘families’ of hydrocarbons that make up the hydrocarbon portion of the fuel; and 

 The heat content of the fuel. 

Cfuel = (Dfuel× Sfuel) / Efuel

Where, 

Cfuel = The C content coefficient of the fuel; 

Dfuel = The density of the fuel; 

Sfuel = The share of the fuel that is C; and 

Efuel = The heat content of the fuel. 

Petroleum products vary between 5.6 degrees API gravity (dense products such as asphalt and road oil) and 247 

degrees (ethane).3 This is a range in density of 60 to 150 kilograms per barrel, or 50 percent. The variation in C content, 

however, is much smaller (5 to 7 percent): ethane is 80 percent C by weight, while petroleum coke is 90 to 92 percent C. 

The tightly bound range of C contents can be explained by basic petroleum chemistry. 

Petroleum Chemistry 

Crude oil and petroleum products are typically mixtures of several hundred distinct compounds, predominantly 

hydrocarbons. All hydrocarbons contain hydrogen and C in various proportions. When crude oil is distilled into 

petroleum products, it is sorted into fractions by the boiling temperature of these hundreds of organic compounds. Boiling 

temperature is strongly correlated with the number of C atoms in each molecule. Petroleum products consisting of 

relatively simple molecules and few C atoms have low boiling temperatures, while larger molecules with more C atoms 

have higher boiling temperatures. 

Products that boil off at higher temperatures are usually more dense, which implies greater C content as well. 

Petroleum products with higher C contents, in general, have lower energy content per unit mass and higher energy content 

per unit volume than products with lower C contents. Empirical research led to the establishment of a set of quantitative 

relationships between density, energy content per unit weight and volume, and C and hydrogen content. Figure A- 2 

compares C content coefficients calculated on the basis of the derived formula with actual C content coefficients for a 

range of crude oils, fuel oils, petroleum products, and pure hydrocarbons. The actual fuel samples were drawn from the 

sources described below in the discussions of individual petroleum products. 

Figure A- 2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon 

Density 

 

2 The national average was weighted by applying the carbon content associated with the average heat content of natural gas 

consumed in each state by the portion of national natural gas consumption represented by that state. 

3 API gravity is an arbitrary scale expressing the gravity or density of liquid petroleum products, as established by the 

American Petroleum Institute (API). The measuring scale is calibrated in terms of degrees API. The higher the API gravity, the lighter 

the compound. Light crude oils generally exceed 38 degrees API and heavy crude oils are all crude oils with an API gravity of 22 

degrees or below. Intermediate crude oils fall in the range of 22 degrees to 38 degrees API gravity. API gravity can be calculated with 

the following formula: API Gravity = (141.5/Specific Gravity) – 131.5. Specific gravity is the density of a material relative to that of 

water. At standard temperature and pressure, there are 62.36 pounds of water per cubic foot, or 8.337 pounds water per gallon. 
A-55

The derived empirical relationship between C content per unit heat and density is based on the types of 

hydrocarbons most frequently encountered. Actual petroleum fuels can vary from this relationship due to nonhydrocarbon impurities and variations in molecular structure among classes of hydrocarbons. In the absence of more exact 

information, this empirical relationship offers a good indication of C content. 

Non-hydrocarbon Impurities 

Most fuels contain a certain share of non-hydrocarbon material. This is also primarily true of crude oils and fuel 

oils. The most common impurity is sulfur, which typically accounts for between 0.5 and 4 percent of the mass of most 

crude oils, and can form an even higher percentage of heavy fuel oils. Some crude oils and fuel oils also contain 

appreciable quantities of oxygen and nitrogen, typically in the form of asphaltenes or various acids. The nitrogen and 

oxygen content of crude oils can range from near zero to a few percent by weight. Lighter petroleum products have much 

lower levels of impurities, because the refining process tends to concentrate all of the non-hydrocarbons in the residual oil 

fraction. Light products usually contain less than 0.5 percent non-hydrocarbons by mass. Thus, the C content of heavy 

fuel oils can often be several percent lower than that of lighter fuels, due entirely to the presence of non-hydrocarbons. 

Variations in Hydrocarbon Classes 

Hydrocarbons can be divided into five general categories, each with a distinctive relationship between density 

and C content and physical properties. Refiners tend to control the mix of hydrocarbon types in particular products in 

order to give petroleum products distinct properties. The main classes of hydrocarbons are described below. 

Paraffins. Paraffins are the most common constituent of crude oil, usually comprising 60 percent by mass. 

Paraffins are straight-chain hydrocarbons with the general formula CnH2n+2. Paraffins include ethane (C2H6), propane 

(C3H8), butane (C4H10), and octane (C8H18). As the chemical formula suggests, the C content of the paraffins increases 

with their C number: ethane is 80 percent C by weight, octane 84 percent. As the size of paraffin molecules increases, the 

C content approaches the limiting value of 85.7 percent asymptotically (see Figure A- 3). 

Cycloparaffins. Cycloparaffins are similar to paraffins, except that the C molecules form ring structures rather 

than straight chains, and consequently require two fewer hydrogen molecules than paraffins. Cycloparaffins always have 

the general formula CnH2n and are 85.7 percent C by mass, regardless of molecular size. 

Olefins. Olefins are a reactive and unstable form of paraffin: a straight chain with the two hydrogen atoms at 

each end of the chain missing. They are never found in crude oil but are created in moderate quantities by the refining 

process. Thus, gasoline, for example, may contain 2 percent olefins. They also have the general formula CnH2n, and hence 

are also always 85.7 percent C by weight. Propylene (C3H6), a common intermediate petrochemical product, is an olefin. 

Aromatics. Aromatics are very reactive hydrocarbons that are relatively uncommon in crude oil (10 percent or 

less). Light aromatics increase the octane level in gasoline, and consequently are deliberately created by steam reforming 

of naphtha. Aromatics also take the form of ring structures with some double bonds between C atoms. The most common 

aromatics are benzene (C6H6), toluene (C7H8), and xylene (C8H10). The general formula for aromatics is CnH2n-6. Benzene 

is 92 percent C by mass, while xylene is 90.6 percent C by mass. Unlike the other hydrocarbon families, the C content of 

aromatics declines asymptotically toward 85.7 percent with increasing C number and density (see Figure A- 3) 

Polynuclear Aromatics. Polynuclear aromatics are large molecules with a multiple ring structure and few 

hydrogen atoms, such as naphthalene (C10H2 and 94.4 percent C by mass) and anthracene (C14H4 and 97.7 percent C). 

They are relatively rare but do appear in heavier petroleum products. 

Figure A- 3 illustrates the share of C by weight for each class of hydrocarbon. Hydrocarbon molecules 

containing 2 to 4 C atoms are all natural gas liquids; hydrocarbons with 5 to 10 C atoms are predominantly found in 

naphtha and gasoline; and hydrocarbon compounds with 12 to 20 C atoms comprise "middle distillates," which are used to 

make diesel fuel, kerosene and jet fuel. Larger molecules are generally used as lubricants, waxes, and residual fuel oil. 

Figure A- 3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number 

If one knows nothing about the composition of a particular petroleum product, assuming that it is 85.7 percent C 

by mass is not an unreasonable first approximation. Since denser products have higher C numbers, this guess would be 
A-56 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

most likely to be correct for crude oils and fuel oils. The C content of lighter products is more affected by the shares of 

paraffins and aromatics in the blend. 

Energy Content of Petroleum Products 

The exact energy content (gross heat of combustion) of petroleum products is not generally known. EIA 

estimates energy consumption in Btu on the basis of a set of industry-standard conversion factors. These conversion 

factors are generally accurate to within 3 to 5 percent. 

Individual Petroleum Products 

The United States maintains data on the consumption of more than 20 separate petroleum products and product 

categories. The C contents, heat contents, and density for each product are provided below in Table A-39. A description 

of the methods and data sources for estimating the key parameters for each individual petroleum product appears below. 

Table A-39: Carbon Content Coefficients and Underlying Data for Petroleum Products 

Fuel 2007 Carbon Content 

(Tg C/QBtu) 

Gross Heat of Combustion 

(MMBtu/Barrel) 

Density 

(API Gravity) 

Percent Carbon 

Motor Gasoline 19.33 5.219 59.1 86.60 

LPG(total) 16.99 a a a 

LPG (energy use) 17.18 a a a 

LPG (non-energy use) 16.76 a a a 

Jet Fuel 19.33 5.670 42.0 86.30 

Distillate Fuel 19.95 5.825 35.5 86.34 

Residual Fuel 21.49 6.287 11.0 85.68 

Asphalt and Road Oil 20.62 6.636 5.6 83.47 

Lubricants 20.24 6.065 25.6 85.80 

Petrochemical Feedstocks 19.37 5.248 b 67.1 b 84.11 b

Aviation Gas 18.87 5.048 69.0 85.00 

Kerosene 19.72 5.670 41.4 86.01 

Petroleum Coke 27.85 6.024 - 92.28 

Special Naphtha 19.86 5.248 51.2 84.76 

Petroleum Waxes 19.81 5.537 43.3 85.29 

Still Gas 17.51 6.000 - - 

Crude Oil 20.33 5.800 30.5 85.49 

Unfinished Oils 20.33 5.825 30.5 85.49 

Miscellaneous Products 20.33 5.796 30.5 85.49 

Pentanes Plus 18.24 4.620 81.7 83.70 

Natural Gasoline 14.47 4.620 81.7 83.70 

a LPG is a blend of multiple paraffinic hydrocarbons: ethane, propane, isobutane, and normal butane, each with their own heat content, density and C content, see Table A42. 

b Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks with higher boiling points are assumed to 

have the same characteristics as distillate fuel. 

- No sample data available 

Sources: EIA (1994), EIA (2008a), and SAIC (2007). 

Motor Gasoline and Motor Gasoline Blending Components 

Motor gasoline is a complex mixture of relatively volatile hydrocarbons with or without small quantities of 

additives, blended to form a fuel suitable for use in spark-ignition engines.4 “Motor Gasoline” includes conventional 

gasoline; all types of oxygenated gasoline, including gasohol; and reformulated gasoline; but excludes aviation gasoline. 

Gasoline is the most widely used petroleum product in the United States, and its combustion accounts for nearly 

20 percent of all U.S. CO2 emissions. EIA collects consumption data (i.e., "petroleum products supplied" by wholesalers) 

for several types of gasoline: leaded regular, unleaded regular, and unleaded high octane. The American Society for 

Testing and Materials (ASTM) standards permit a broad range of densities for gasoline, ranging from 50 to 70 degrees 

 

4 Motor gasoline, as defined in ASTM Specification D 4814 or Federal Specification VV-G-1690C, is characterized as having 

a boiling range of 122 degrees to 158 degrees Fahrenheit at the 10-percent recovery point to 365 degrees to 374 degrees Fahrenheit at the 

90-percent recovery point. 
A-57

API gravity, or 111.52 to 112.65 kilograms per barrel, which implies a range of possible C and energy contents per barrel. 

Table A-40 reflects changes in the density of gasoline over time and across grades of gasoline through 2007. 

Table A-40: Motor Gasoline Density, 1990 – 2007 (Degrees API) 

Fuel Grade 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Winter Grade 

 Low Octane 62.0 61.8 61.4 61.0 60.1 59.8 60.6 61.5 61.8 61.6 61.6 61.7 61.6 61.8 62.4 62.4 62.4 62.4

 Mid Octane 60.8 60.4 60.2 59.9 59.4 59.1 59.9 60.7 61.2 61.3 61.2 61.2 61.2 61.2 61.2 61.2 61.2 61.2

 High Octane 59.0 59.3 59.0 58.7 58.5 58.0 58.5 59.3 60.0 60.3 59.7 59.1 59.0 59.9 60.7 60.7 60.7 60.7

Summer Grade 

 Low Octane 58.2 58.0 57.4 56.1 55.7 56.1 56.9 57.1 57.6 57.7 56.8 57.2 56.5 56.8 57.4 57.4 57.4 57.4

 Mid Octane 57.4 57.1 56.4 55.5 54.8 55.6 56.2 56.6 56.7 57.4 58.0 58.0 58.0 58.0 58.0 58.0 58.0 58.0

 High Octane 55.5 55.7 55.6 54.4 53.8 55.1 55.3 56.4 55.7 57.4 55.8 55.5 55.7 56.0 57.0 57.0 57.0 57.0

Source: National Institute of Petroleum and Energy Research (1990 through 2007). 

The density of motor gasoline increased across all grades through 1994, partly as a result of the leaded gasoline 

phase-out. In order to maintain the “anti-knock” quality and octane ratings of gasoline in the absence of lead, the portion 

of aromatic hydrocarbons used in gasoline increased. As discussed above, aromatic hydrocarbons have a lower ratio of 

hydrogen to C than other hydrocarbons typically found in gasoline, and therefore increase fuel density. 

The trend in gasoline density was reversed beginning in 1996 with the development of fuel additives that raised 

oxygen content. In 1995, a requirement for reformulated gasoline in non-attainment areas implemented under the Clean 

Air Act Amendments further changed the composition of gasoline consumed in the United States. In reformulated 

gasoline, methyl tertiary butyl ether (MTBE) and tertiary amyl methyl ether (TAME) are often added to standard gasoline 

to boost its oxygen content. The increased oxygen reduces the emissions of carbon monoxide and unburned hydrocarbons. 

These oxygen-rich blending components are also much lower in C than standard gasoline. The average gallon of 

reformulated gasoline consumed in 2001 contained 8 percent MTBE and 0.5 percent TAME. The characteristics of 

reformulated fuel additives appear in Table A-41. 

Table A-41: Characteristics of Major Reformulated Fuel Additives 

Additive Density (Degrees API) Carbon Share (Percent) Carbon Content (Tg C/QBtu) 

MTBE 59.1 68.2 16.92 

ETBE 59.1 70.5 17.07 

TAME 52.8 70.5 17.00 

Source: API (1988). 

Methodology

Step 1. Disaggregate U.S. gasoline consumption by grade and type 

U.S. gasoline consumption was divided by product grade and season for both standard gasoline and reformulated 

gasoline. 

Step 2. Develop carbon content coefficients for each grade and type 

C content coefficients for each grade and type are derived from three parameters: gasoline density, share of the 

gasoline mixture that is C; and the energy content of a gallon of gasoline. C content coefficients for reformulated fuels 

were calculated by applying the C content coefficient for the fuel additives listed in Table A-41 to the increased share of 

reformulated gasoline represented by these additives (standard gasoline contains small amounts of MTBE and TAME) and 

weighting the gasoline C content accordingly. 

Step 3. Weight overall gasoline carbon content coefficient for consumption of each grade and type 

The C content for each grade and type of fuel is multiplied by the share of overall consumption represented by 

the grade and fuel type. Individual coefficients are then summed and totaled to yield an overall C content coefficient. 

Data Sources

Data for the density of motor gasoline were obtained from the National Institute for Petroleum and Energy 

Research (1990 through 2007). Data on the characteristics of reformulated gasoline were taken from API (1988). C 

contents of motor gasoline were obtained from the following: DeLuchi (1993), Applied Systems Corporation (1976), 

Ward, C.C. (1978), and Rose and Cooper (1977). 

Standard heat contents for motor gasoline of 5.253 MMBtu per barrel conventional gasoline and 5.150 MMBtu 

per barrel reformulated gasoline were adopted from EIA (2008a). 
A-58 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Uncertainty

There are two primary contributors to the uncertainty of C content coefficients for motor gasoline. The first is 

the small number of motor gasoline samples and ultimate analyses from Deluchi et al. However, as demonstrated above 

in Figure A- 3, the amount of variation in C content of gasoline is restricted by the compounds in the fuel to 4 percent. 

The second primary contributor to uncertainty is the assumed heat content. The heat contents are industry 

standards established many years ago. The heat contents are standard conversion factors used by EIA to convert 

volumetric energy data to energy units. Because the heat contents of fuels change over time, without necessarily and 

directly altering their volume, the conversion of known volumetric data to energy units may introduce bias. Thus, a more 

precise approach to estimating emissions factors would be to calculate C content per unit of volume, rather than per unit of 

energy. Adopting this approach, however, makes it difficult to compare U.S. C content coefficients with those of other 

nations. 

The changes in density of motor gasoline over the last decade suggest that the heat content of the fuels is also 

changing. However, that change within any season grade has been less than 1 percent over the decade. Of greater concern 

is the use of a standardized heat content across grades that show a variation in density of 1.5 percent. 

Jet Fuel 

Jet fuel is a refined petroleum product used in jet aircraft engines. There are two classes of jet fuel used in the 

United States: “naphtha-based” jet fuels and “kerosene-based” jet fuels. In 1989, 13 percent of U.S. consumption was 

naphtha-based fuel, with the remainder kerosene-based jet fuel. In 1993, the U.S. Department of Defense began a 

conversion from naphtha-based JP-4 jet fuel to kerosene-based jet fuel, because of the possibility of increased demand for 

reformulated motor gasoline limiting refinery production of naphtha-based jet fuel. By 1996, naphtha-based jet fuel 

represented less than one-half of one percent of all jet fuel consumption. The C content coefficient for jet fuel used in this

report represents a consumption-weighted combination of the naphtha-based and kerosene-based coefficients. 

Methodology

Step 1. Estimate the carbon content for naphtha-based jet fuels 

Because naphtha-based jet fuels are used on a limited basis in the United States, sample data on its characteristics 

are limited. The density of naphtha-based jet fuel (49 degrees) was estimated as the central point of the acceptable API 

gravity range published by ASTM. The heat content of the fuel was assumed to be 5.355 MMBtu per barrel based on EIA 

industry standards. The C fraction was derived from an estimated hydrogen content of 14.1 percent (Martel and Angello 

1977), and an estimated content of sulfur and other non-hydrocarbons of 0.1 percent. 

Step 2. Estimate the carbon content for kerosene-based jet fuels 

The density and C share of kerosene-based jet fuels was based on the average composition of 39 fuel samples 

taken by Boeing Corporation (the leading U.S. commercial airline manufacturer) in 1989. The EIA’s standard heat 

content of 5.670 MMBtu per barrel was adopted for kerosene-based jet fuel. 

Step 3. Weight the overall jet fuel carbon content coefficient for consumption of each type of fuel 

The C content for each jet fuel type is multiplied by the share of overall consumption of that fuel type. Individual 

coefficients are then summed and totaled to yield an overall C content coefficient 

Data Sources

Data on the C content of naphtha-based jet fuel was taken from C.R. Martel and L.C. Angello (1977). Data on 

the density of naphtha-based jet fuel was taken from ASTM (1985). Standard heat contents for kerosene and naphthabased jet fuels were adopted from EIA (2008a). Data on the C content and density of kerosene-based jet fuel was taken 

from Hadallar and Momenthy (1990). 

Uncertainty

Variability in jet fuel is relatively small with the average C share of kerosene-based jet fuel varying by less than 

1 percent and the density varying by 1 percent. This is because the ratio of fuel mass to useful energy must be tightly 

bounded to maximize safety and range. There is more uncertainty associated with the density and C share of naphthabased jet fuel because sample data were unavailable and default values were used. This uncertainty has only a small 

impact on the overall uncertainty of the C content coefficient for jet fuels, however, because naphtha-based jet fuel 

represents a small and declining share of total jet fuel consumption in the United States. 
A-59

Distillate Fuel 

Distillate fuel is a general classification for diesel fuels and fuel oils. Products known as No. 1, No. 2, and No. 4 

diesel fuel are used in on-highway diesel engines, such as those in trucks and automobiles, as well as off-highway engines, 

such as those in railroad locomotives and agricultural machinery. No. 1, No. 2, and No. 4 fuel oils are also used for space 

heating and electric power generation. 

Methodology

For the purposes of this report, the C content of No. 2 fuel oil is assumed to typify the C content of distillate fuel 

generally. The C share in No. 2 fuel oil was estimated based on the average of 11 ultimate analyses. This C share was 

combined with EIA’s standard heat content of 5.825 MMBtu per barrel and the density of distillate assumed to be 35.5 

degrees API, in accord with its heat content. 

Data Sources

Data on C contents and density were derived from four samples from C. T. Hare and R.L. Bradow (1979). 

Samples were taken from the following sources: Funkenbush, et al. (1979), Mason (1981), and Black and High (1979). 

A standard heat content was adopted from EIA (2008a). 

 Uncertainty

The primary source of uncertainty for the estimated C content of distillate fuel is the selection of No.2 fuel oil as 

the typical distillate fuel. No.2 fuel oil is generally consumed for home heating. No.1 fuel oil is generally less dense and if 

it is consumed in large portions for mobile sources, the C content estimated for this report is likely to be too high. The 

five No.1 fuel oil samples obtained by EIA contained an average of 86.01 percent C compared to the 86.34 percent 

contained in samples of No.2 fuel oil. A C content coefficient based on No.1 fuel oil would equal 19.72 Tg C/QBtu rather 

than the 19.95 Tg C/QBtu for No. 2 fuel oil. There is also small uncertainty in the share of C based on the limited sample 

size of 1 percent. 

Residual Fuel 

Residual fuel is a general classification for the heavier oils, known as No. 5 and No. 6 fuel oils, that remain after 

the distillate fuel oils and lighter hydrocarbons are distilled away in refinery operations. Residual fuel conforms to ASTM 

Specifications D 396 and D 975 and Federal Specification VV-F-815C. No. 5, a residual fuel oil of medium viscosity, is 

also known as Navy Special and is defined in Military Specification MIL-F-859E, including Amendment 2 (NATO 

Symbol F-770). It is used in steam-powered vessels in government service and inshore power plants. No. 6 fuel oil 

includes Bunker C fuel oil and is used for the production of electric power, space heating, vessel bunkering, and various 

industrial purposes. 

In the United States, electric utilities purchase about a third of the residual oil consumed. A somewhat larger 

share is used for vessel bunkering, and the balance is used in the commercial and industrial sectors. The residual oil 

(defined as No.6 fuel oil) consumed by electric utilities has an energy content of 6.287 MMBtu per barrel and an average 

sulfur content of 1 percent (EIA 2001). This implies a density of about 17 degrees API. 

Methodology

For this report, residual fuel was defined as No.6 fuel oil. The National Institute of Petroleum and Energy 

Research, Fuel Oil Survey shows an average density for fuel oil of 11.3 API gravity and anecdotal evidence suggests that 

marine residual fuel is also very dense, with typical gravity of 10.5 to 11.5 degrees API (EIA 1993). Because the largest 

share of fuel oil consumption is for marine vessels, a density of 11 degrees API was adopted when developing the C 

content coefficient for this report. An average share of C in residual fuel of 85.67 percent by mass was used based on 

ultimate analyses of a dozen samples. 

Data Sources

Data on C content were derived from three samples of residual fuel from the Middle East and one sample from 

Texas. These data were found in Mosby, et al. (1976). Three samples of heavy fuel oils were taken from Longwell 

(1991); three samples from Ward (1978); two samples from Vorum (1974); and one sample from Rose and Cooper (1977). 

Density of residual fuel consumed for electric power generation was obtained from EIA (2001). Density of residual fuel 

consumed in marine vessels was obtained from EIA (1993). A standard heat content was adopted from EIA (2008a). 

Uncertainty
A-60 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

The largest source of uncertainty for estimating the C content of residual fuel centers on the estimates of density, 

which differ from power generation to marine vessel fuels. The difference between the density implied by the energy 

content of utility fuels and the density observed in the NIPER surveys is probably due to nonsulfur impurities, which 

reduce the energy content without greatly affecting the density of the product. Impurities of several percent are commonly 

observed in residual oil. The presence of these impurities also affects the share of the fuel that is C. Overall, the 

uncertainty associated with the C content of residual fuel is probably 1 percent. 

Liquefied Petroleum Gases (LPG) 

EIA identifies four categories of paraffinic hydrocarbons as LPG: ethane, propane, isobutane, and n-butane. 

Because each of these compounds is a pure paraffinic hydrocarbon, their C shares are easily derived by taking into account 

the atomic weight of C (12) and the atomic weight of hydrogen (1). Thus, for example, the C share of propane, C3H8, is 

81.8 percent. The densities and heat content of the compounds are also well known allowing C content coefficients to be 

calculated directly. Table A-42 summarizes the physical characteristic of LPG. 

Table A-42: Physical Characteristics of Liquefied Petroleum Gases 

Compound Chemical 

Formula 

Density (Barrels Per 

Metric Ton) 

Carbon Content 

(Percent) 

Energy Content 

(MMBtu/Barrel) 

Carbon Content 

Coefficient (Tg C/QBtu) 

Ethane C2H6 16.88 80.0 2.916 16.25 

Propane C3H8 12.44 81.8 3.824 17.20 

Isobutane C4H10 11.20 82.8 4.162 17.75 

n-butane C4H10 10.79 82.8 4.328 17.72 

 Source: Guthrie (1960). 

Methodology

Step 1. Assign carbon content coefficients to each pure paraffinic compound 

Based on their known physical characteristics, a C content coefficient is assigned to each compound contained in 

the U.S. energy statistics category, Liquefied Petroleum Gases. 

Step 2. Weight individual LPG coefficients for share of fuel use consumption 

A C content coefficient for LPG used as fuel is developed based on the consumption mix of the individual 

compound reported in U.S. energy statistics. 

Step 3. Weight individual LPG coefficients for share of non-fuel use consumption 

The mix of LPG consumed for non-fuel use differs significantly from the mix of LPG that is combusted. While 

the majority of LPG consumed for fuel use is propane, ethane is the largest component of LPG used for non-fuel 

applications. A C content coefficient for LPG used for non-fuel applications is developed based on the consumption mix 

of the individual compound reported in U.S. energy statistics. 

Step 4. Weight the carbon content coefficients for fuel use and non-fuel use by their respective shares of 

consumption 

The changing shares of LPG fuel use and non-fuel use consumption appear below in Table A-43. 

Data Sources

Data on C share, density, and heat content of LPG was obtained from Guthrie (1960). LPG consumption was 

based on data obtained from API (1990-2007) and EIA (2008b). Non-fuel use of LPG was obtained from API (1990 

through 2007). 

Uncertainty

Because LPG consists of pure paraffinic compounds whose density, heat content and C share are physical 

constants, there is limited uncertainty associated with the C content coefficient for this petroleum product. Any 

uncertainty is associated with the collection of consumption data and non-fuel data in U.S. energy statistics. This 

uncertainty is probably less than 3 percent. 
Table A-43: Consumption and Carbon Content Coefficients of Liquefied Petroleum Gases, 1990-2007  1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Energy Consumption (QBtu) Fuel Use 0.86 0.86 0.94 0.94 0.96 0.93 1.02 1.03 0.84 1.09 1.29 1.15 1.24 1.21 

 Ethane 0.03 0.02 0.03 0.02 0.02 0.03 0.03 0.05 0.00 0.00 0.08 0.05 0.05 0.05  Propane 0.75 0.79 0.84 0.86 0.86 0.86 0.95 0.92 0.80 0.97 1.08 1.01 1.10 1.07 

 Butane 0.08 0.05 0.07 0.06 0.07 0.05 0.04 0.05 0.04 0.11 0.14 0.10 0.09 0.09 

Non-Fuel Use 1.20 1.38 1.39 1.35 1.55 1.59 1.65 1.67 1.74 1.82 1.67 1.55 1.62 1.55 

 Ethane 0.55 0.62 0.62 0.65 0.65 0.68 0.74 0.71 0.73 0.82 0.80 0.73 0.77 0.70  Propane 0.53 0.59 0.61 0.55 0.65 0.67 0.65 0.71 0.77 0.77 0.66 0.59 0.65 0.63 

 Butane 0.13 0.17 0.16 0.15 0.25 0.24 0.26 0.25 0.24 0.22 0.21 0.23 0.21 0.22 

Carbon Content (Tg C/QBtu) 

Fuel Use 17.21 17.21 17.21 17.22 17.22 17.20 17.20 17.18 17.23 17.25 17.20 17.21 17.20 17.21 

Non-Fuel Use 16.83 16.84 16.84 16.80 16.88 16.87 16.86 16.88 16.88 16.84 16.81 16.83 16.82 16.84 

Sources: Fuel use of LPG based on data from EIA (2008b) and API (1990 through 2007). Non-fuel use of LPG from API (1990 through 2007). C contents from EIA (2008a).
A-62 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Aviation Gasoline 

Aviation gasoline is used in piston-powered airplane engines. It is a complex mixture of relatively volatile 

hydrocarbons with or without small quantities of additives, blended to form a fuel suitable for use in aviation reciprocating 

engines. Fuel specifications are provided in ASTM Specification D910 and Military Specification MIL-G-5572. Aviation 

gas is a relatively minor contributor to greenhouse gas emissions compared to other petroleum products, representing 

approximately 0.1 percent of all consumption. 

The ASTM standards for boiling and freezing points in aviation gasoline effectively limit the aromatics content 

to a maximum of 25 percent (ASTM D910). Because weight is critical in the operation of an airplane, aviation gas must 

have as many Btu per pound (implying a lower density) as possible, given other requirements of piston engines such as 

high anti-knock quality. 

Methodology

A C content coefficient for aviation gasoline was calculated on the basis of the EIA standard heat content of 

5.048 MMBtu per barrel. This implies a density of approximately 69 degrees API gravity or 5.884 pounds per gallon. To 

estimate the share of C in the fuel, it was assumed that aviation gasoline is 87.5 percent isooctane, 9.0 percent toluene, and 

3.5 percent xylene. The maximum allowable sulfur content in aviation gasoline is 0.05 percent, and the maximum 

allowable lead content is 0.1 percent. These amounts were judged negligible and excluded for the purposes of this 

analysis. This yielded a C share of 85 percent and a C content coefficient of 18.87 Tg C/QBtu. 

Data Sources

Data sources include ASTM (1985). A standard heat content for aviation gas was adopted from EIA (2008a). 

Uncertainty

The uncertainty associated with the C content coefficient for aviation gasoline is larger than that for other liquid 

petroleum products examined because no ultimate analyses of samples are available. Given the requirements for safe 

operation of piston-powered aircraft the composition of aviation gas is well bounded and the uncertainty of the C content 

coefficient is likely to be 5 percent. 

Still Gas 

Still gas, or refinery gas, is composed of light hydrocarbon gases that are released as petroleum is processed in a 

refinery. The composition of still gas is highly variable, depending primarily on the nature of the refining process and 

secondarily on the composition of the product being processed. Petroleum refineries produce still gas from many different 

processes. Still gas can be used as a fuel or feedstock within the refinery, sold as a petrochemical feedstock, or purified 

and sold as pipeline-quality natural gas. In general, still gas tends to include large amounts of free hydrogen and methane, 

as well as smaller amounts of heavier hydrocarbons. Because different refinery operations result in different gaseous 

byproducts, it is difficult to determine what represents typical still gas. 

Methodology

The EIA obtained data on four samples of still gas. Table A-44 below shows the composition of those samples. 

Table A-44: Composition, Energy Content, and Carbon Content Coefficient for Four Samples of Still Gas 

Sample Hydrogen 

(%) 

Methane 

(%) 

Ethane 

(%) 

Propane 

(%) 

Btu Per Cubic 

Foot 

Carbon Content 

(Tg C/QBtu) 

One 12.7 28.1 17.1 11.9 1,388 17.51 

Two 34.7 20.5 20.5 6.7 1,143 14.33 

Three 72.0 12.8 10.3 3.8 672 10.23 

Four 17.0 31.0 16.2 2.4 1,100 15.99 

Because gas streams with a large free hydrogen content are likely to be used as refinery or chemical feedstocks, 

EIA selected the C content coefficient from the sample with the lowest hydrogen content as the representative value for 

still gas. 

Data Sources

Data sources include one still gas sample from American Gas Association (1974) and three still gas samples from 

Guerra, et al. (1979). 
A-63

Uncertainty

Because the composition of still gas is highly heterogeneous, the C content coefficient for this product is highly 

uncertain, with an accuracy of 33 percent. The C content coefficient used for this report is probably at the high end of 

the plausible range. 

Asphalt 

Asphalt is used to pave roads. Because most of its C is retained in those roads, it is a small source of emissions. 

It is derived from a class of hydrocarbons called "asphaltenes," abundant in some crude oils but not in others. Asphaltenes 

have oxygen and nitrogen atoms bound into their molecular structure, so that they tend to have lower C contents than other 

hydrocarbons. 

Methodology

Ultimate analyses of twelve samples of asphalts showed an average C content of 83.5 percent. The EIA standard 

Btu content for asphalt of 6.636 MMBtu per barrel was assumed. The ASTM petroleum measurement tables show a 

density of 5.6 degrees API or 8.605 pounds per gallon for asphalt. Together, these variables generate C content coefficient 

of 20.62 Tg C/QBtu. 

Data Sources

A standard heat content for asphalt was adopted from EIA (2008a). The density of asphalt was determined by 

the ASTM (1985). 

Uncertainty

The share of C in asphalt ranges from 79 to 88 percent by weight. Also present in the mixture are hydrogen and 

sulfur, with shares by weight ranging from seven to 13 percent for hydrogen, and from trace levels to eight percent for 

sulfur. Because C share and total heat content in asphalts do vary systematically, the overall C content coefficient is likely

to be accurate to 5 percent. 

Lubricants 

Lubricants are substances used to reduce friction between bearing surfaces, or incorporated into processing 

materials used in the manufacture of other products, or used as carriers of other materials. Petroleum lubricants may be 

produced either from distillates or residues. Lubricants include all grades of lubricating oils, from spindle oil to cylinder 

oil to those used in greases. Lubricant consumption is dominated by motor oil for automobiles, but there is a large range 

of product compositions and end uses within this category. 

Methodology

The ASTM Petroleum Measurement tables give the density of lubricants at 25.6 degrees API. Ultimate analysis 

of a single sample of motor oil yielded a C content of 85.8 percent. A standard heat content of 6.065 MMBtu per barrel 

was adopted from EIA. These factors produce a C content coefficient of 20.24 Tg C/QBtu. 

Data Sources

A standard heat content was adopted from the EIA (2008a). The density of asphalt was determined by ASTM 

(1985). 

Uncertainty

Uncertainty in the estimated C content coefficient for lubricants is driven by the large range of product 

compositions and end uses in this category combined with an inability to establish the shares of the various products 

captured under this category in U.S. energy statistics. Because lubricants may be produced from either the distillate or 

residual fractions during refineries, the possible C content coefficients range from just under 20.0 Tg C/QBtu to about 21.5 

Tg C/QBtu or an uncertainty band from –1 percent to + 6 percent of the estimated value. 

Petrochemical Feedstocks 

U.S. energy statistics distinguish between two different kinds of petrochemical feedstocks: those with a boiling 

temperature below 400 degrees Fahrenheit, generally called “naphtha,” and those with a boiling temperature 400 degrees 

Fahrenheit and above. 
A-64 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Methodology

The method for estimating the C content of petrochemical feedstocks includes three steps. 

Step 1. Estimate the carbon content coefficient for naphtha 

Because reformed naphtha is used to make motor gasoline (hydrogen is released to raise aromatics content and 

octane rating), “straight-run” naphtha is assumed to be used as a petrochemical feedstock. Ultimate analyses of five 

samples of naphtha were examined and showed an average C share of 84.11 percent and an average density of 67.1 

degrees API gravity. The standard EIA heat content of 5.248 MMBtu per barrel is used to estimate a C content coefficient 

of 18.14 Tg C/QBtu. 

Step 2. Estimate the carbon content coefficient for petrochemical feedstocks with a boiling temperature 400 

degrees Fahrenheit and above 

The boiling temperature of this product places it into the “middle distillate” fraction in the refining process, and 

EIA estimates that these petrochemical feedstocks have the same heat content as distillate fuel. Thus, the C content 

coefficient of 19.95 Tg C/QBtu used for distillate fuel is also adopted for this portion of petrochemical feedstocks. 

Step 3. Weight the carbon content coefficients for the two classes of petrochemical feedstock by consumption 

The weighted average of the two C content coefficients for petroleum feedstocks equals 19.37 Tg C/QBtu. 

Data Sources

Data on the C content and density of naphtha was taken from Unzelman (1992). A standard heat content for 

petrochemical feedstock was adopted from EIA (2008a). 

Uncertainty

Petrochemical feedstocks are not so much distinguished on the basis of chemical composition as on the identity 

of the purchaser, who may be presumed to be a chemical company or a petrochemical unit co-located on the refinery 

grounds. This produces a considerable degree of uncertainty about the exact composition of petrochemical feedstocks. 

Since the C content coefficient for petrochemical feedstocks is a weighted average of the coefficients for naphtha and 

some class of middle distillates, the accurate coefficient is likely bounded by the two individual coefficients, suggesting an 

uncertainty of 6 percent. 

Kerosene 

A light petroleum distillate that is used in space heaters, cook stoves, and water heaters and is suitable for use as 

a light source when burned in wick-fed lamps, kerosene is drawn from the same petroleum fraction as jet fuel. Kerosene is 

generally comparable to No.1 fuel oil. 

Methodology

The average density of 41.4 degrees API and average C share of 86.01 percent found in five ultimate analyses of 

No. 1 fuel oil samples were applied to a standard heat content of 5.670 MMBtu per barrel to yield a C content coefficient 

of 19.72 Tg C/QBtu. 

Data Sources

A standard heat content was adopted from EIA (2008a). 

Uncertainty

Uncertainty in the estimated C content for kerosene is driven by the selection of No. 1 fuel oil as a proxy for 

kerosene. If kerosene is more like kerosene-based jet fuel, the true C content coefficient is likely to be some 2 percent 

lower. If kerosene is more aptly compared to No. 2 fuel oil, then the true C content coefficient is likely to be about 1 

percent higher. 

Petroleum Coke 

Petroleum coke is the solid residue by-product of the extensive processing of crude oil. It is a coal-like solid, 

usually with a C content greater than 90 percent, that is used as a boiler fuel and industrial raw material.

Methodology
A-65

Ultimate analyses of two samples of petroleum coke showed an average C share of 92.3 percent. The ASTM 

standard density of 9.543 pounds per gallon was adopted and the EIA standard energy content of 6.024 MMBtu per barrel 

assumed. Together, these factors produced an estimated C content coefficient of 27.85 Tg C/QBtu. 

Data Sources

C content was derived from two samples from Martin, S.W. (1960). The density of petroleum coke was taken 

from the ASTM (1985). A standard heat content for petroleum coke was adopted from EIA (2008a). 

Uncertainty

The uncertainty associated with the estimated C content coefficient of petroleum coke can be traced to two 

factors: the use of only two samples to establish C contents and a standard heat content which may be too low. Together, 

these uncertainties are likely to bias the C content coefficient upwards by as much as 6 percent. 

Special Naphtha 

Special naphtha is defined as a light petroleum product to be used for solvent applications, including commercial 

hexane and four classes of solvent: stoddard solvent, used in dry cleaning; high flash point solvent, used as an industrial 

paint because of its slow evaporative characteristics; odorless solvent, most often used for residential paints; and high 

solvency mineral spirits, used for architectural finishes. These products differ in both density and C percentage, requiring 

the development of multiple coefficients. 

Methodology

The method for estimating the C content coefficient of special naphtha includes three steps. 

Step 1. Estimate the carbon content coefficient for hexane 

Hexane is a pure paraffin containing 6 C atoms and 14 hydrogen atoms; thus, it is 83.7 percent C. Its density is 

76.6 degrees API or 5.649 pounds per gallon and its derived C content coefficient is 17.17 Tg C/QBtu. 

Step 2. Estimate the carbon contents of non-hexane special naphthas 

The hydrocarbon compounds in special naphthas are assumed to be either paraffinic or aromatic (see discussion 

above). The portion of aromatics in odorless solvents is estimated at less than 1 percent, Stoddard and high flash point 

solvents contain 15 percent aromatics and high solvency mineral spirits contain 30 percent aromatics (Boldt and Hall 

1977). These assumptions, when combined with the relevant densities, yield the C content factors contained in Table A45, below. 

Table A-45: Characteristics of Non-hexane Special Naphthas 

Special Naphtha 

Aromatic Content 

(Percent) 

Density 

(Degrees API) 

Carbon Content 

(Percent) 

Carbon Content (Tg 

C/QBtu) 

Odorless Solvent 1 55.0 84.51 19.41 

Stoddard Solvent 15 47.9 84.44 20.11 

High Flash Point 15 47.6 84.70 20.17 

Mineral Spirits 30 43.6 85.83 20.99 

Step 3. Develop weighted carbon content coefficient based on consumption of each special naphtha 

EIA reports only a single consumption figure for special naphtha. The C contents of the five special naphthas are 

weighted according to the following formula: approximately 10 percent of all special naphtha consumed is hexane; the 

remaining 90 percent is assumed to be distributed evenly among the four other solvents. The resulting emissions 

coefficient for special naphthas is 19.86 Tg C/QBtu. 

Data Sources

A standard heat content for special naphtha was adopted from EIA (2008a). Density and aromatic contents were 

adopted from Boldt and Hall (1977). 

Uncertainty

The principal uncertainty associated with the estimated C content coefficient for special naphtha is the allocation 

of overall consumption across individual solvents. The overall uncertainty is bounded on the low end by the C content of 

hexane and on the upper end by the C content of high solvency mineral spirits. This implies an uncertainty band of –15 

percent to +6 percent. 
A-66 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Petroleum Waxes 

The ASTM standards define petroleum wax as a product separated from petroleum that is solid or semi-solid at 

77 degrees Fahrenheit (25 degrees Celsius). The two classes of petroleum wax are paraffin waxes and microcrystalline 

waxes. They differ in the number of C atoms and the type of hydrocarbon compounds. Microcrystalline waxes have 

longer C chains and more variation in their chemical bonds than paraffin waxes. 

Methodology

The method for estimating the C content coefficient for petroleum waxes includes three steps. 

Step 1. Estimate the carbon content of paraffin waxes 

For the purposes of this analysis, paraffin waxes are assumed to be composed of 100 percent paraffinic 

compounds with a chain of 25 C atoms. The resulting C share for paraffinic wax is 85.23 percent and the density is 

estimated at 45 degrees API or 6.684 pounds per gallon. 

Step 2. Estimate the carbon content of microcrystalline waxes 

Microcrystalline waxes are assumed to consist of 50 percent paraffinic and 50 percent cycloparaffinic 

compounds with a chain of 40 C atoms, yielding a C share of 85.56 percent. The density of microcrystalline waxes is 

estimated at 36.7 degrees API, based on a sample of 10 microcrystalline waxes found in the Petroleum Products 

Handbook. 

Step 3. Develop a carbon content coefficient for petroleum waxes by weighting the density and carbon content of 

paraffinic and microcrystalline waxes 

A weighted average density and C content was calculated for petroleum waxes, assuming that wax consumption 

is 80 percent paraffin wax and 20 percent microcrystalline wax. The weighted average C content is 85.29 percent, and the 

weighted average density is 6.75 pounds per gallon. EIA’s standard heat content for waxes is 5.537 MMBtu per barrel. 

These inputs yield a C content coefficient for petroleum waxes of 19.81 Tg C/QBtu. 

Data Sources

Density of paraffin wax was taken from ASTM (1985). Density of microcrystalline waxes was derived from 10 

samples found in Guthrie (1960). A standard heat content for petroleum waxes was adopted from EIA (2008a). 

Uncertainty

Although there is considerable qualitative uncertainty associated with the allocation of petroleum waxes and 

microcrystalline waxes, the quantitative variation in the C contents for all waxes is limited to  1 percent because of the 

nearly uniform relationship between C and other elements in petroleum waxes broadly defined. 

Crude Oil, Unfinished Oils, and Miscellaneous 

U.S. energy statistics include several categories of petroleum products designed to ensure that reported refinery 

accounts “balance” and cover any “loopholes” in the taxonomy of petroleum products. These categories include crude oil, 

unfinished oils, and miscellaneous products. Crude oil is rarely consumed directly, miscellaneous products account for 

less than one percent of oil consumption, and unfinished oils are a balancing item that may show negative consumption. 

For C accounting purposes, it was assumed that all these products have the same C content as crude oil. 

Methodology

EIA reports on the average density and sulfur content of U.S. crude oil purchased by refineries. To develop a 

method of estimating C content based on this information, ultimate analyses of 182 crude oil samples were collected. 

Within the sample set, C content ranged from 82 to 88 percent C, but almost all samples fell between 84 percent and 86 

percent C. The density and sulfur content of the crude oil data were regressed on the C content, producing the following 

equation: 

Percent C = 76.99 + (10.19 × Specific Gravity) + (-0.76 × Sulfur Content) 

Absent the term representing sulfur content, the equation had an R-squared of only 0.35.1 When C content was 

adjusted to exclude sulfur, the R-squared value rose to 0.65. While sulfur is the most important nonhydrocarbon impurity, 

 

1 R-squared represents the percentage of variation in the dependent variable (in this case carbon content) explained by 

variation in the independent variables. 
A-67

nitrogen and oxygen can also be significant, but they do not seem to be correlated with either density or sulfur content. 

Restating these results, density accounts for about 35 percent of the variation in C content, impurities account for about 30 

percent of the variation, and the remaining 35 percent is accounted for by other factors, including (presumably) the degree 

to which aromatics and polynuclear aromatics are present in the crude oil. Applying this equation to the 2001 crude oil 

quality data (30.49 degrees API and 1.42 percent sulfur) produces an estimated C content of 85.81 percent. Applying the 

density and C content to the EIA standard energy content for crude oil of 5.800 MMBtu per barrel produced an emissions 

coefficient of 20.33 Tg C/QBtu. 

Data Sources

C content was derived from 150 crude oil samples from U.S. National Research Council (1927). A standard heat 

content for crude oil was adopted from EIA (2008a). 

Uncertainty

The uncertainty of the estimated C content for crude oil centers on the 35 percent of variation that cannot be 

explained by density and sulfur content. This variation is likely to alter the C content coefficient by 3 percent. Since 

unfinished oils and miscellaneous products are impossible to define, the uncertainty of applying a crude oil C content is 

likely to be bounded by the range of petroleum products described in this chapter at 10 percent. 

Chronology and Explanation of Changes in Individual Carbon Content Coefficients of Fossil Fuels 

Coal 

The estimates of C content coefficients for coal were updated and revised in 2007. The methodology employed 

for these estimates was unchanged from previous years; however, the underlying coal data sample set was updated. 

Previously a set of 5,426 coal samples from the EIA Coal Analysis File was used to develop C content estimates. The 

results from that sample set appear below in Table A-46. The EIA Coal Analysis File was originally developed by the 

U.S. Bureau of Mines and contained over 60,000 coal samples obtained through numerous coal seams throughout the 

United States. Many of the samples were collected starting in the 1940s and 1950s through the 1980s and analyzed in 

U.S. government laboratories. The updated sample set included 6,588 coal samples collected by the U.S. Geological 

Survey between 1973 and 1989. 

Petroleum Products 

Jet Fuel 

Between 1994 and 1995, the C content coefficient for kerosene-based jet fuel was revised downward from 19.71 

Tg C/QBtu to 19.33 Tg C/QBtu. This downward revision was the result of a shift in the sample set used from one 

collected between 1959 and 1972 and reported on by Martel and Angello in 1977 to one collected by Boeing in 1989 and 

published by Hadaller and Momenthy in 1990. The downward revision was a result of a decrease in density, as well as 

slightly lower C shares than in the earlier samples. However, the assumed heat content is unchanged because it is based 

on an EIA standard and probably yields a downward bias in the revised C content coefficient. 

Liquefied Petroleum Gases (LPG)

The C content coefficient of LPG is updated annually to reflect changes in the consumption mix of the 

underlying compounds: ethane; propane; isobutane; and normal butane. In 1994, EIA included pentanes plus— assumed 

to have the characteristics of hexane—in the mix of compounds broadly described as LPG. In 1995, EIA removed 

pentanes plus from this fuel category. Because pentanes plus is relatively rich in C per unit of energy, its removal from 

the consumption mix lowered the C content coefficient for LPG from 17.26 Tg C/QBtu to 16.99 Tg C/QBtu. In 1998, EIA 

began separating LPG consumption into two categories: energy use and non-fuel use and providing individual coefficients 

for each. Because LPG for fuel use typically contains higher proportions of propane than LPG for non-fuel use, the C 

content coefficient for fuel use is about 2 percent higher than the coefficient for non-fuel use. 

Motor Gasoline 

The C content coefficient for motor gasoline varies annually based on the density of and proportion of additives 

in a representative sample of motor gasoline examined each year. However, in 1997 EIA began incorporating the effects 
A-68 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

of the introduction of reformulated gasoline into its estimate of C content coefficients for motor gasoline. This change 

resulted in a downward step function in C content coefficients for gasoline of approximately 0.3 percent beginning in 

1995. 
Table A-46: Carbon Content Coefficients for Coal by Consuming Sector and Coal Rank, 1990-2007 [Tg C/QBtu)  1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 20

Consuming Sector

 Electric Power 25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.76 25.76 25.76 25.76 25.76 25.76 25 Industrial Coking 25.51 25.51 25.51 25.51 25.52 25.53 25.55 25.56 25.56 25.56 25.56 25.56 25.56 25 Other Industrial 25.58 25.60 25.62 25.61 25.63 25.63 25.61 25.63 25.63 25.63 25.63 25.63 25.63 25Residential/Commercial 25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.00 26.00 26.00 26.00 26

Coal Rank 

 Anthracite 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28.26 28

 Bituminous 25.43 25.45 25.44 25.45 25.46 25.47 25.47 25.48 25.47 25.48 25.49 25.49 25.49 25 Sub-bituminous 26.50 26.49 26.49 26.48 26.49 26.49 26.49 26.49 26.49 26.49 26.48 26.48 26.48 26

 Lignite 26.19 26.21 26.22 26.21 26.24 26.22 26.17 26.20 26.23 26.26 26.30 26.30 26.30 26

p (Preliminary) Sources: C content coefficients by consuming sector from EIA (2008a). C content coefficients by coal rank from USGS (1998) and SAIC (2007). 
Figure A-1: Carbon Content for Samples of Pipeline-Quality Natural Gas Included in the Gas Technology Institute 

Database

Source: EIA (1994) Energy Information Administration, Emissions of Greenhouse Gases in the United States 1987-1992, U.S. Department of 

Energy, Washington, DC, November, 1994, DOE/EIA 0573, Appendix A. 
Figure A-2: Estimated and Actual Relationships Between Petroleum Carbon Content Coefficients and Hydrocarbon 

Density

Source: Carbon content factors for paraffins are calculated based on the properties of hydrocarbons in V. Guthrie (ed.), Petroleum Products 

Handbook (New York: McGraw Hill, 1960) p. 33. Carbon content factors from other petroleum products are drawn from sources described 

below. Relationship between density and emission factors based on the relationship between density and energy content in U.S. Department of 

Commerce, National Bureau of Standards, Thermal Properties of Petroleum Products, Miscellaneous Publication, No. 97 (Washington, D.C., 

1929), pp.16-21, and relationship between energy content and fuel composition in S. Ringen, J. Lanum, and F.P. Miknis, “Calculating Heating 

Values from the Elemental Composition of Fossil Fuels,’ Fuel, Vol. 58 (January 1979), p.69.
Figure A-3: Carbon Content of Pure Hydrocarbons as a Function of Carbon Number 

Source: J.M. Hunt, Petroleum Geochemistry and Geology (San Francisco, CA, W.H. Freeman and Company, 1979), pp. 31-37. 
A-70 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

References 

American Gas Association (1974) Gas Engineer's Handbook, New York, NY, Industrial Press, pp. 3/71, 3.87. 

API (1990 through 2007) Sales of Natural Gas Liquids and Liquefied Refinery Gases, American Petroleum 

Institute. 

API (1988) Alcohols and Ethers: A Technical Assessment of Their Applications as Fuels and Fuel Components, 

American Petroleum Institute, API 4261. 

Applied Systems Corporation (1976) Compilation of Oil Shale Test Results, submitted to the Office of Naval 

Research, April 1976, p. 3-2. 

ASTM (1985) ASTM and Other Specifications for Petroleum Products and Lubricants, American Society for 

Testing and Materials. Philadelphia, PA. 

Black, F. and L. High (1979) “Methodology for Determining Particulate and Gaseous Diesel Emissions,” in, The 

Measurement and Control of Diesel Particulate Emissions, Society of Automotive Engineers, p. 128. 

Boldt, K. and B.R. Hall (1977) Significance of Tests for Petroleum Products, Philadelphia, PA, American 

Society for Testing and Materials, p. 30. 

DeLuchi (1993) Emissions of Greenhouse Gases from the Use of Transportation Fuels and Electricity, Volume 

2, ANL/ESD/TM-22, Vol. 2. Chicago, IL, Argonne National Laboratory. Appendix C, pp. C-1 to C-8. 

DOC (1929) Thermal Properties of Petroleum Products, U.S. Department of Commerce, National Bureau of 

Standards. Washington, DC. pp.16-21. 

EIA (1993) Btu Tax on Finished Petroleum Products, Energy Information Administration, Petroleum Supply 

Division (unpublished manuscript, April 1993). 

EIA (1994) Emissions of Greenhouse Gases in the United States 1987-1992, Energy Information Administration, 

U.S. Department of Energy. Washington, DC. November, 1994. DOE/EIA 0573. 

EIA (2001) Cost and Quality of Fuels for Electric Utility Plants 2000, Energy Information Administration. 

Washington, DC. August 2001. Available online at <http://www.eia.doe.gov/cneaf/electricity/cq/cq_sum.html>. 

EIA (2002) Coal Industry Annual, U.S. Department of Energy, Energy Information Administration. Washington, 

DC. 

EIA (2003) State Energy Data 2000: Consumption, Energy Information Administration, U.S. Department of 

Energy, Washington, DC. August 2003. Available online at <http://www.eia.doe.gov/emeu/states/_use_multistate.html>. 

EIA (2008a) Monthly Energy Review, September 2007 and Published Supplemental Tables on Petroleum Product 

detail. Energy Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2007/9). 

EIA (2008b) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, 

Washington, DC. Available online at 

<http://www.eia.doe.gov/oil_gas/petroleum/data_publications/petroleum_supply_annual/psa_volume1/ 

psa_volume1.html>. 

Funkenbush, E.F., D.G. Leddy, and J.H. Johnson (1979) “The Organization of the Soluble Organic Fraction of 

Diesel Particulate Matter,” in Society of Automotive Engineers, The Measurement and Control of Diesel Particulate 

Emissions, p. 128. 

Gas Technology Institute (1992) Database as documented in W.E. Liss, W.H. Thrasher, G.F. Steinmetz, P. 

Chowdiah, and A. Atari, Variability of Natural Gas Composition in Select Major Metropolitan Areas of the United States. 

GRI-92/0123. March 1992. 

Guerra, C.R., K. Kelton, and D.C. Nielsen (1979) “Natural Gas Supplementation with Refinery Gases and 

Hydrogen,” in Institute of Gas Technology, New Fuels and Advances in Combustion Technologies. Chicago, IL, June 

1979. 

Guthrie, V.B. (ed.) (1960) Characteristics of Compounds, Petroleum Products Handbook, p.3-3. New York, NY, 

McGraw-Hill. 

Hadaller, O.J. and A.M. Momenthy (1990) The Characteristics of Future Fuels, Part 1, “Conventional Heat 

Fuels”. Seattle, WA, Boeing Corp. September 1990. pp. 46-50. 
A-71

Hare, C.T. and R.L. Bradow (1979) “Characterization of Heavy-Duty Diesel Gaseous and Particulate Emissions, 

and the Effects of Fuel Composition,” in Society of Automotive Engineers, The Measurement and Control of Diesel 

Particulate Emissions, p. 128. 

Hunt, J.M. (1979) Petroleum Geochemistry and Geology. San Francisco, CA. W.H. Freeman and Company. pp. 

31-37. 

Longwell, J.P. (1991) “Interface Between Fuels and Combustion,” in Fossil Fuel Combustion: A Sourcebook, 

New York, NY, John Wiley & Sons. 

Martel, C.R., and L.C. Angello (1977) “Hydrogen Content as a Measure of the Combustion Performance of 

Hydrocarbon Fuels,” in Current Research in Petroleum Fuels, Volume I. New York, NY, MSS Information Company, p. 

116. 

Martin, S.W. (1960) “Petroleum Coke,” in Virgil Guthrie (ed.), Petroleum Processing Handbook, New York, 

NY, McGraw-Hill, pp. 14-15. 

Mason, R.L. (1981) “Developing Prediction Equations for Fuels and Lubricants,” SAE Paper 811218, p.34. 

October 1981. 

Mosby, F., G.B. Hoekstra, T.A. Kleinhenz, and J.M. Sokra (1976) “Pilot Plant Proves Resid Process,” in 

Chemistry of Petroleum Processing and Extraction, MSS Information Corporation, p.227. 

National Institute for Petroleum and Energy Research (1990 through 2007) Motor Gasolines, Summer and Motor 

Gasolines, Winter. 

Ringen, S., J. Lanum, and F.P. Miknis (1979) “Calculating Heating Values from the Elemental Composition of 

Fossil Fuels,” Fuel, Vol. 58, January 1979, p.69. 

Rose, J.W. and J.R. Cooper (1977) Technical Data on Fuel, The British National Committee, World Energy 

Conference, London, England. 

SAIC (2007) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and 

Radiation, Market Policies Branch. 

U.S. National Research Council (1927) International Critical Tables of Numerical Data, Physics, Chemistry, and 

Technology, New York, NY, McGraw-Hill. 

Unzelman, G.H. (1992) “A Sticky Point for Refiners: FCC Gasoline and the Complex Model,” Fuel 

Reformulation, July/August 1992, p. 29. 

USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey. 

Vorum, D.A. (1974) “Fuel and Synthesis Gases from Gaseous and Liquid Hydrocarbons,” in American Gas 

Association, Gas Engineer's Handbook, New York, NY, Industrial Press, p. 3/71. 

Ward, C.C (1978) “Petroleum and Other Liquid Fuels,” in Marks' Standard Handbook for Mechanical 

Engineers, New York, NY, McGraw-Hill, pp. 7-14 
A-72 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

2.3. Methodology for Estimating Carbon Emitted from Non-Energy Uses of Fossil 

Fuels 

C storage associated with the non-energy use of fossil fuels was calculated by multiplying each fuel’s potential 

emissions (i.e., each fuel’s total C content) by a fuel-specific storage factor, as listed in Table A-47. The remaining C—

i.e., that which is not stored—is emitted. This sub-annex explains the methods and data sources employed in developing 

the storage factors for petrochemical feedstocks (industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes 

plus, naphthas, other oils, still gas, special naphtha), asphalt and road oil, lubricants, waxes, and miscellaneous products. 

The storage factors11 for the remaining non-energy fuel uses are either based on values recommended for use by IPCC 

(2006), or when these were not available, assumptions based on the potential fate of C in the respective NEU products. 

Table A-47: Fuel Types and Percent of C Stored for Non-Energy Uses 

Sector/Fuel Type Storage Factor (%) 

Industry - 

Industrial Coking Coala 10 

Industrial Other Coal b 61 

Natural Gas to Chemical Plants b 61 

Asphalt & Road Oil 100 

LPG b 61 

Lubricants 9 

Pentanes Plus b 61 

Naphtha (<401 deg. F) b 61 

Other Oil (>401 deg. F) b 61 

Still Gas b 61 

Petroleum Cokec

 30 

Special Naphtha b 61 

Distillate Fuel Oil 50 

Waxes 58 

Miscellaneous Products 0 

Transportation - 

Lubricants 9 

U.S. Territories - 

Lubricants 9 

Other Petroleum (Misc. Prod.) 10 

- Not applicable 

a Includes processes for which specific coking coal consumption and emission factor data are not available. Consumption of coking coal for production of iron and steel is 

covered in the Industrial Processes chapter. 

b The storage factor listed is the value for 2007. As described in this annex, the factor varies over time. c Assumes petroleum coke consumption is for pigments. Consumption of petroleum coke for production of primary aluminum anodes, electric arc furnace anodes, titanium 

dioxide, ammonia, urea, and ferroalloys is covered in the Industrial Processes chapter. 

The following sections describe the non-energy uses in greater detail, outlining the methods employed and data 

used in estimating each storage factor. Several of the fuel types tracked by EIA are used in organic chemical synthesis and 

in other manufacturing processes, and are referred to collectively as “petrochemical feedstocks.” Because the methods and 

data used to analyze them overlap, they are handled as a group and are discussed first. Discussions of the storage factors 

for asphalt and road oil, lubricants, waxes, and miscellaneous products follow. 

 

11 Throughout this section, references to “storage factors” represent the proportion of carbon stored. 
A-73

Petrochemical Feedstocks

Petrochemical feedstocks—industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus, 

naphthas, other oils, still gas, special naphtha—are used in the manufacture of a wide variety of man-made chemicals and 

products. Plastics, rubber, synthetic fibers, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a few 

of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e., 

predominantly CH4) to heavier, more complex naphthas and other oils.12 

After adjustments for (1) use in industrial processes and (2) net exports, these eight fuel categories constituted 

approximately 206.9 Tg CO2 Eq., or 58% percent, of the 361.1 Tg CO2 Eq. of non-energy fuel consumption in 2007. For 

2007 the storage factor for the eight fuel categories was 61 percent. In other words, of the net consumption, 61 percent 

was destined for long-term storage in products—including products subsequently combusted for waste disposal—while 

the remaining 39 percent was emitted to the atmosphere directly as CO2 (e.g., through combustion of industrial 

byproducts) or indirectly as CO2 precursors (e.g., through evaporative product use). The indirect emissions include a 

variety of organic gases such as volatile organic compounds (VOCs) and carbon monoxide (CO), which eventually oxidize 

into CO2 in the atmosphere. The derivation of the storage factor is described in the following sections. 

Methodology and Data Sources 

The petrochemical feedstocks storage factor is equal to the ratio of C stored in the final products to total C 

content for the non-energy fossil fuel feedstocks used in industrial processes, after adjusting for net exports of feedstocks. 

One aggregate storage factor was calculated to represent all eight fuel feedstock types. The feedstocks were grouped 

because of the overlap of their derivative products. Due to the many reaction pathways involved in producing 

petrochemical products (or wastes), it becomes extraordinarily complex to link individual products (or wastes) to their 

parent fuel feedstocks. 

Import and export data for feedstocks were obtained from the Energy Information Administration (EIA) for the 

major categories of petrochemical feedstocks. EIA’s Petroleum Supply Annual (EIA 2007) publication tracks imports and 

exports of petrochemical feedstocks, including butanes, butylenes, ethane, ethylene, propane, propylene, LPG, and 

naphthas (i.e., most of the large volume primary chemicals produced by petroleum refineries). These imports and exports 

are already factored into the U.S. fuel consumption statistics. However, EIA does not track imports and exports of 

chemical intermediates and products produced by the chemical industry (e.g., xylenes, vinyl chloride), which are derived 

from the primary chemicals produced by the refineries. These products represent very large flows of C derived from fossil 

fuels (i.e., fossil C), so estimates of net flows not already considered in EIA’s dataset were developed for the entire time 

series from 1990 to 2007. 

The approach to estimate imports and exports involves three steps, listed here and then described in more detail 

below: 

Step 1. Identify commodities derived from petrochemical feedstocks, and calculate net import/export for each. 

Step 2. Estimate the C content for each commodity.

Step 3. Sum the net C imports/exports across all commodities.

Step 1 relies heavily on information provided by the National Petrochemical and Refiners Association (NPRA) 

and U.S. Bureau of the Census (BoC) trade statistics published by the U.S. International Trade Commission (USITC). 

NPRA provided a spreadsheet of the ten-digit BoC Harmonized Tariff Schedule (HTS) Commodity Codes used to compile 

import-export data for periodic reports issued to NPRA’s membership on trade issues. Additional feedstock commodities 

were identified by HTS code in the BoC data system and included in the net import/export analysis. 

One of the difficulties in analyzing trade data is that a large portion of the outputs from the refining industry are 

fuels and fuel components, and it was difficult to segregate these from the outputs used for non-energy uses. The NPRAsupplied codes identify fuels and fuel components, thus providing a sound basis for isolating net imports/exports of 

petrochemical feedstocks. Although MTBE and related ether imports are included in the published NPRA data, these 

commodities are not included in the total net imports/exports calculated here, because it is assumed that they are fuel 

additives and do not contribute to domestic petrochemical feedstocks. Net exports of MTBE and related ethers are also 

not included in the totals, as these commodities are considered to be refinery products that are already accounted for in the 

 

12 Naphthas are compounds distilled from petroleum containing 4 to 12 carbon atoms per molecule and having a boiling point 

less than 401° F. “Other oils” are distillates containing 12 to 25 carbon atoms per molecule and having a boiling point greater than 401° 

F. 
A-74 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

EIA data. Imports and exports of commodities for which production and consumption data are provided by EIA (e.g., 

butane, ethylene, and liquefied petroleum gases) are also not included in the totals, to avoid double-counting. 

Another difficulty is that one must be careful to assure that there is not double-counting of imports and exports in 

the data set. Other parts of the mass balance (described later) provide information on C flows, in some cases based on 

production data and in other cases based on consumption data. Production data relates only to production within the 

country; consumption data incorporates information on imports and exports as well as production. Because many 

commodities are emissive in their use, but not necessarily their production, consumption data is appropriately used in 

calculations for emissive fates. For purposes of developing an overall mass balance on U.S. non-energy uses of C, for 

those materials that are non-emissive (e.g., plastics), production data is most applicable. And for purposes of adjusting the 

mass balance to incorporate C flows associated with imports and exports, it was necessary to carefully review whether the 

mass balance already incorporated cross-boundary flows (through the use of consumption data) or not, and to adjust the 

import/export balance accordingly. 

The BoC trade statistics are publicly available13 and cover a complete time series from 1990 to 2007. These 

statistics include information on imports and exports of thousands of commodities. After collecting information on annual 

flows of the more than 100 commodities identified by NPRA, Step 2 involves calculating the C content for each 

commodity from its chemical formula. In cases where the imports and exports were expressed in units of volume, rather 

than mass, they were converted to mass based on the commodities’ densities. 

Step 3 involves summing the net C imports/exports across all commodities. The results of this step are shown in 

Table A-48. As shown in the table, the United States has been a net exporter of chemical intermediates and products 

throughout the 1990 to 2007 period. 

Table A-48: Net Exports of Petrochemical Feedstocks, 1990 – 2007 (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Net Exports 11.8 13.4 12.7 15.0 12.6 13.9 11.6 13.8 8.8 8.7 8.7 2.2 7.5 15.1 20.6 6.9 3.5 7.9

After adjusting for imports and exports, the C budget is adjusted for the quantity of C that is used in the 

Industrial Processes sector of the Inventory. Fossil fuels used for non-energy purposes in industrial processes—and for 

which C emissions and storage have been characterized through mass balance calculations and/or emission factors that 

directly link the non-energy use fossil fuel raw material and the industrial process product—are not included in the nonenergy use sector. These industrial processes (and their non-energy use fossil fuel raw materials) include iron and steel 

(coal coke), primary aluminum (petroleum coke), titanium oxide (petroleum coke), ferroalloys (petroleum coke), and 

ammonia and urea (petroleum coke and natural gas). 

For each year of the Inventory, the total C content of non-energy uses was calculated by starting with the EIA 

estimate of non-energy use, and reducing it by the adjustment factor for net exports (see Table A-48) to yield net domestic 

fuel consumption for non-energy. The balance was apportioned to either stored C or emissive C, based on a storage factor. 

The overall storage factor for the feedstocks was determined by developing a mass balance on the C in 

feedstocks, and characterizing products, uses, and environmental releases as resulting in either storage or emissions. The 

total C in the system was estimated by multiplying net domestic consumption for non-energy by the C content of each of 

the feedstocks (i.e., industrial other coal, natural gas for non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still 

gas, special naphtha). C content values for the fuel feedstocks are discussed in the Estimating Emissions from Fossil Fuel 

Combustion and Estimating the Carbon Content from Fossil Fuel Combustion Annexes. 

Next, C pools and releases in a variety of industrial releases, energy recovery processes, and products were 

characterized. The C fate categories are plastics, energy recovery, synthetic rubber, synthetic fibers, organic solvents, C 

black, detergents and personal cleansers, industrial non-methane volatile organic compound (NMVOC) emissions, 

hazardous waste incineration, industrial toxic chemical (i.e., TRI) releases, pesticides, food additives, antifreeze and 

deicers (glycols), and silicones.14 

The C in each product or waste produced was categorized as either stored or emitted. The aggregate storage 

factor is the C-weighted average of storage across fuel types. As discussed later in the section on uncertainty, the sum of 

stored C and emitted C (i.e., the outputs of the system) exceeded total C consumption (the inputs to the system) for some 

 

13 See the U.S International Trade Commission (USITC) Trade Dataweb at <http://dataweb.usitc.gov/>. 

14 For the most part, the releases covered by the U.S. Toxic Release Inventory (TRI) represent air emissions or water 

discharges associated with production facilities. Similarly, VOC emissions are generally associated with production facilities. These 

emissions could have been accounted for as part of the Waste chapter, but because they are not necessarily associated with waste 

management, they were included here. Toxic releases are not a “product” category, but they are referred to as such for ease of discussion. 
A-75

years in the time series.15 To address this mass imbalance, the storage factor was calculated as C storage divided by total 

C outputs (rather than C storage divided by C inputs). 

Note that the system boundaries for the storage factor do not encompass the entire life-cycle of fossil-based C 

consumed in the United States insofar as emissions of CO2 from waste combustion are accounted for separately in the 

Inventory and are discussed in the Waste Combustion section of the Energy chapter. 

The following sections provide details on the calculation steps, assumptions, and data sources employed in 

estimating and classifying the C in each product and waste shown in Table A-49. Summing the C stored and dividing it by 

total C outputs yields the overall storage factor, as shown in the following equation for 2007: 

Overall Storage Factor = C Stored / (C Stored + C Emitted) = 

148.5 Tg CO2 Eq. / (148.5 + 93.5) Tg CO2 Eq. = 61% 

Table A-49: C Stored and Emitted by Products from Feedstocks in 2007 (Tg CO2 Eq.) 

Product/Waste Type C Stored

(Tg CO2 Eq.)

C Emitted

(Tg CO2 Eq.)

Industrial Releases 0.4 5.3

 TRI Releases 0.4 1.0

 Industrial VOCs 2.1

 Non-combustion CO 0.7

 Hazardous Waste Incin. 1.5

Energy Recovery 71.9

Products 148.1 16.4

 Plastics 125.0

 Synthetic Rubber 13.0

 Antifreeze and deicers 1.1

 Abraded tire rubber 0.7

 Food additives 0.6

 Silicones 0.5

 Synthetic Fiber 9.4

 Pesticides 0.3 0.2

 Soaps, shampoos, detergents 5.7

 Solvent VOCs 8.0

Total 148.5 93.5

- Not applicable 

Note: Totals may not sum due to independent rounding. 

The three categories of C accounted for in the table are industrial releases, energy recovery, and products. Each 

is discussed below. 

 Industrial Releases 

Industrial releases include toxic chemicals reported through the Toxics Release Inventory, industrial emissions of 

volatile organic compounds (VOCs), CO emissions (other than those related to fuel combustion), and emissions from 

hazardous waste incineration. 

TRI Releases 

Fossil-derived C is found in many toxic substances released by industrial facilities. The Toxics Release 

Inventory (TRI), maintained by EPA, tracks these releases by chemical and environmental release medium (i.e., land, air, 

or water) on a biennial basis (EPA 2000). By examining the C contents and receiving media for the top 35 toxic chemicals 

released, which account for 90 percent of the total mass of chemicals, the quantity of C stored and emitted in the form of 

toxic releases can be estimated. 

 

15 Overall, there was fairly close agreement between inputs and outputs; for the entire 1990 through 2007 time series, inputs 

exceeded outputs by 0.4 percent. During the period 1990 through 1999, carbon inputs exceeded carbon outputs (i.e., the sum of carbon 

stored and carbon emitted), and for those years, the assumption was made that the “missing” carbon was lost through fates leading to 

emissions. 
A-76 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

The TRI specifies releases by chemical, so C contents were assigned to each chemical based on molecular 

formula. The TRI also classifies releases by disposal location as either off-site or on-site. The on-site releases are further 

subdivided into air emissions, surface water discharges, underground injection, and releases to land; the latter is further 

broken down to disposal in a RCRA Subtitle C (i.e., hazardous waste) landfill or to “Other On-Site Land Disposal.”16 The 

C released in each disposal location is provided in Table A-50. 

Each on-site classification was assigned a storage factor. A one hundred percent storage factor was applied to 

disposition of C to underground injection and to disposal to RCRA-permitted landfills, while the other disposition 

categories were assumed to result in an ultimate fate of emission as CO2 (i.e., a storage factor of zero was applied to these 

categories.) The release allocation is not reported for off-site releases; therefore, the approach was to develop a Cweighted average storage factor for the on-site C and apply it to the off-site releases. 

For the remaining 10 percent of the TRI releases, the weights of all chemicals were added and an average C 

content value, based upon the top 35 chemicals’ C contents, was applied. The storage and emission allocation for the 

remaining 10 percent of the TRI releases was carried out in the same fashion as for the 35 major chemicals. 

Data on TRI releases for the full 1990 through 2007 time series were not readily available. Since this category is 

small (less than 1 Tg C emitted and stored), the 1998 value was applied for the entire time series. 

Table A-50: 1998 TRI Releases by Disposal Location (Gg CO2 Eq.) 

Disposal Location Carbon Stored 

(Gg CO2 Eq.) 

Carbon Emitted 

(Gg CO2 Eq.) 

Air Emissions - 924.0 

Surface Water Discharges - 6.7 

Underground Injection 89.4 - 

RCRA Subtitle C Landfill Disposal 1.4 - 

Other On-Site Land Releases - 15.9 

Off-site Releases 6.4 36.0 

Total 97.2 982.6 

- Not applicable 

Note: Totals may not sum due to independent rounding. 

Volatile Organic Compound Emissions from Industrial Processes and Solvent Evaporation Emissions 

Data on annual non-methane volatile organic compound (NMVOC) emissions were obtained from the National 

Emissions Inventory (NEI) Air Pollutant Emissions Trends Data (EPA 2008). The 1990-2007 Trends data include 

information on NMVOC emissions by end-use category; some of these fall into the heading of “industrial releases” in 

Table A-50 above, and others are related to “product use”; for ease of discussion, both are covered here. The end-use 

categories that represent “Industrial NMVOC Emissions” include some chemical and allied products, certain petroleum 

related industries, and other industrial processes. NMVOC emissions from solvent utilization (product use) were 

considered to be a result of non-energy use of petrochemical feedstocks. These categories were used to distinguish nonenergy uses from energy uses; other categories where VOCs could be emitted due to combustion of fossil fuels were 

excluded to avoid double counting. 

Because solvent evaporation and industrial NMVOC emission data are provided in tons of total NMVOCs, 

assumptions were made concerning the average C content of the NMVOCs for each category of emissions. The 

assumptions for calculating the C fraction of industrial and solvent utilization emissions were made separately and differ 

significantly. For industrial NMVOC emissions, a C content of 85 percent was assumed. This value was chosen to reflect 

the C content of an average volatile organic compound based on the list of the most abundant NMVOCs provided in the 

Trends Report. The list contains only pure hydrocarbons, including saturated alkanes (C contents ranging from 80 to 85 

percent based upon C number), alkenes (C contents approximately 85 percent), and some aromatics (C contents 

approximately 90 percent, depending upon substitution). 

An EPA solvent evaporation emissions dataset (Tooly 2001) was used to estimate the C content of solvent 

emissions. The dataset identifies solvent emissions by compound or compound category for six different solvent end-use 

categories: degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial processes. 

The percent C of each compound identified in the dataset was calculated based on the molecular formula of the individual 

compound (e.g., the C content of methylene chloride is 14 percent; the C content of toluene is 91 percent). For solvent 

 

16 Only the top 9 chemicals had their land releases separated into RCRA Landfills and Other Land Disposal. For the 

remaining chemicals, it was assumed that the ratio of disposal in these two categories was equal to the carbon-weighted average of the 

land disposal fate of the top 9 chemicals (i.e., 8 percent attributed to RCRA Landfills and 92 percent in the “Other” category). 
A-77

emissions that are identified in the EPA dataset only by chemical category (e.g., butanediol derivatives) a single individual 

compound was selected to represent each category, and the C content of the category was estimated based on the C content 

of the representative compound. The overall C content of the solvent evaporation emissions for 1998, estimated to be 56 

percent, is assumed to be constant across the entire time series. 

The results of the industrial and solvent NMVOC emissions analysis are provided in Table A-51 for 1990 

through 2007. Solvent evaporation emissions in 2007 were 8.0 Tg CO2 Eq., and industrial NMVOC emissions in 2007 

were 2.1 Tg CO2 Eq. In 2007, NMVOC and solvent activity data were revised across the entire time series to reflect 

updated information from the NEI Air Pollutant Emissions Trends Data. 

Table A-51: Industrial and Solvent NMVOC Emissions 

 1990 1995 2000 2005 2006 2007 

Industrial NMVOCsa 

NMVOCs (‘000 Short Tons) 1,120 1,181 744 779 759 739 

Carbon Content (%) 85% 85% 85% 85% 85% 85% 

Carbon Emitted (Tg CO2 Eq.) 3.2 3.3 2.1 2.2 2.1 2.1 

Solvent Evaporationb 

Solvents (‘000 Short Tons) 5,750 6,183 4,832 4,278 4,263 4,249 

Carbon Content (%) 56% 56% 56% 56% 56% 56% 

Carbon Emitted (Tg CO2 Eq.) 10.8 11.6 9.0 8.0 8.0 8.0 

a Includes emissions from chemical and allied products, petroleum and related industries, and other industrial processes categories. b Includes solvent usage and solvent evaporation emissions from degreasing, graphic arts, dry cleaning, surface coating, other industrial processes, and non-industrial 

processes. 

Non-Combustion Carbon Monoxide Emissions 

Carbon monoxide (CO) emissions data were also obtained from the NEI Air Pollutant Emissions Trends Data 

(EPA 2008). There are three categories of CO emissions in the report that are classified as process-related emissions not 

related to fuel combustion. These include chemical and allied products manufacturing, metals processing, and other 

industrial processes. Some of these CO emissions are accounted for in the Industrial Processes section of this report, and 

are therefore not accounted for in this section. These include total C emissions from the primary aluminum, titanium 

dioxide, iron and steel, and ferroalloys production processes. The total C (CO and CO2) emissions from oil and gas 

production, petroleum refining, and asphalt manufacturing are also accounted for elsewhere in this Inventory. Sustainably 

harvested biogenic emissions (e.g., pulp and paper process emissions) are also excluded from calculation of CO emissions 

in this section. Those CO emissions that are not accounted for elsewhere are considered to be byproducts of non-fuel use 

of feedstocks and are included in the calculation of the petrochemical feedstocks storage factor. Table A-52 lists the CO 

emissions that remain after taking into account the exclusions listed above. 

Table A-52: Non-Combustion Carbon Monoxide Emissionsa

 

 1990 1995 2000 2005 2006 2007 

Thousand short tons CO 489 481 623 493 492 492 

Carbon Emitted (Tg CO2 Eq.) 0.7 0.7 0.9 0.7 0.7 0.7 

Includes emissions from chemical and allied products, petroleum and related industries, metals processing, and other industrial processes categories. 

Hazardous Waste Incineration 

Hazardous wastes are defined by the EPA under the Resource Conservation and Recovery Act (RCRA).17 

Industrial wastes, such as rejected products, spent reagents, reaction by-products, and sludges from wastewater or air 

pollution control, are federally regulated as hazardous wastes if they are found to be ignitable, corrosive, reactive, or toxic

according to standardized tests or studies conducted by the EPA. 

Hazardous wastes must be treated prior to disposal according to the federal regulations established under the 

authority of RCRA. Combustion is one of the most common techniques for hazardous waste treatment, particularly for 

those wastes that are primarily organic in composition or contain primarily organic contaminants. Generally speaking, 

combustion devices fall into two categories: incinerators that burn waste solely for the purpose of waste management, and 

boilers and industrial furnaces (BIFs) that burn waste in part to recover energy from the waste. More than half of the 

hazardous waste combusted in the United States is burned in BIFs; because these processes are included in the energy 

recovery calculations described below, they are not included as part of hazardous waste incineration. 

 

17 [42 U.S.C. §6924, SDWA §3004] 
A-78 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

EPA’s Office of Solid Waste requires biennial reporting of hazardous waste management activities, and these 

reports provide estimates of the amount of hazardous waste burned for incineration or energy recovery. EPA stores this 

information in its Biennial Reporting System (BRS) database (EPA 2000a, 2004a, 2006, 2007a). Combusted hazardous 

wastes are identified based on EPA-defined management system types M041 through M049 (incineration). Combusted 

quantities are grouped into four representative waste form categories based on the form codes reported in the BRS: 

aqueous liquids, organic liquids and sludges, organic solids, and inorganic solids. To relate hazardous waste quantities to 

C emissions, “fuel equivalent” factors were derived for hazardous waste by assuming that the hazardous wastes are simple 

mixtures of a common fuel, water, and noncombustible ash. For liquids and sludges, crude oil is used as the fuel 

equivalent and coal is used to represent solids. 

Fuel equivalent factors were multiplied by the tons of waste incinerated to obtain the tons of fuel equivalent. 

Multiplying the tons of fuel equivalent by the C content factors (discussed in the Estimating the Carbon Content from 

Fossil Fuel Comubstion Annex) yields tons of C emitted. Implied C content is calculated by dividing the tons of C 

emitted by the associated tons of waste incinerated. 

Waste quantity data for hazardous wastes were obtained from EPA’s BRS database for reporting years 1989, 

1991, 1993, 1995, 1997, 1999, 2001, 2003, and 2005 (EPA 2000a, 2004a, 2006, 2007a). Values for 2007 and 2006 were 

held constant at the 2005 level. Combusted waste quantities were obtained from Form GM (Generation and Management) 

for wastes burned on site and Form WR (Wastes Received) for waste received from off-site for combustion. For each of 

the waste types, assumptions were developed on average waste composition (see Table A-53). Regulations require 

incinerators to achieve at least 99.99 percent destruction of organics; this formed the basis for assuming the fraction of C 

oxidized. Emissions from hazardous waste incineration in 2005 were 1.5 Tg CO2 Eq. Table A-54 lists the CO2 emissions 

from hazardous waste incineration. 

Table A-53: Assumed Composition of Combusted Hazardous Waste by Weight (Percent) 

Waste Type Water 

(%) 

Noncombustibles (%) Fuel Equivalent (%) 

Aqueous Waste 90 5 5 

Organic Liquids and Sludges 40 20 40 

Organic Solids 20 40 40 

Inorganic Solids 20 70 10 

Table A-54: CO2 Emitted from Hazardous Waste Incineration (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

CO2 Emissions 1.1 1.1 1.1 1.2 1.5 1.7 1.7 1.8 1.6 1.4 1.4 1.4 1.4 1.5 1.5 1.5 1.5 1.5

Energy Recovery 

The amount of feedstocks combusted for energy recovery was estimated from data included in EIA’s 

Manufacturers Energy Consumption Survey (MECS) for 1991, 1994, 1998, and 2002 (EIA 1994, 1997, 2001b, 2004). 

Some fraction of the fossil C exiting refineries and designated for use for feedstock purposes actually ends up being 

combusted for energy recovery (despite the designation of feedstocks as a “non-energy” use) because the chemical 

reactions in which fuel feedstocks are used are not 100 percent efficient. These chemical reactions may generate 

unreacted raw material feedstocks or generate byproducts that have a high energy content. The chemical industry and 

many downstream industries are energy-intensive and often have boilers or other energy recovery units on-site, and thus 

these unreacted feedstocks or byproducts are often combusted for energy recovery. Also, as noted above in the section on 

hazardous waste incineration, regulations provide a strong incentive—and in some cases require—burning of organic 

wastes generated from chemical production processes. 

Information available from the MECS include data on the consumption for energy recovery of “other” fuels in 

the petroleum and coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors. These 

“other” fuels include refinery still gas; waste gas; waste oils, tars, and related materials; petroleum coke, coke oven and 

blast furnace gases; and other uncharacterized fuels. Fuel use of petroleum coke is included separately in the fuel use data 

provided annually by EIA, and energy recovery of coke oven gas and blast furnace gas (i.e., byproducts of the iron and 

steel production process) is addressed in the Iron and Steel production section in the Industrial Processes chapter. 

Consumption of refinery still gas in the refinery sector is also included separately in the fuel use data from EIA. 

Consumption of net steam, assumed to be generated from fossil fuel combustion, is also included separately in the fuel use 

data from EIA. Therefore, these categories of “other” fuels are addressed elsewhere in the Inventory and not considered as 

part of the petrochemical feedstocks energy recovery analysis. The remaining categories of fuels, including waste gas; 

waste oils, tars, and related materials; and other uncharacterized fuels are assumed to be petrochemical feedstocks burned 

for energy recovery (see Table A-55). The conversion factors listed in the Estimating Emissions from Fossil Fuel 

Combustion Annex were used to convert the Btu values for each fuel feedstock to Tg CO2. Petrochemical feedstocks 
A-79

combusted for energy recovery corresponded to 42.7 Tg CO2 Eq. in 1991, 35.8 Tg CO2 Eq. in 1994, 58.7 Tg CO2 Eq. in 

1998, and 71.9 Tg CO2 in 2002. Values for petrochemical feedstocks burned for energy recovery for years between 1991 

and 1994, between 1994 and 1998, and between 1998 and 2002 have been estimated by interpolation. The value for 1990 

is assumed to be the same as the value for 1991, and values for years subsequent to 2002 are assumed to be the same as the 

value for 2002 (Table A-56). 

Table A-55: Summary of 2002 MECS Data for Other Fuels Used in Manufacturing/Energy Recovery (Trillion Btu) 

Subsector and Industry NAICS CODE Waste Gasa Waste Oils/Tarsb

Refinery Still

Gasc Net Steamd Other Fuelse

Printing and Related Support 323 0 0 0 0 1

Petroleum and Coal Products 324 0 2 1396 89 67

Chemicals 325 483 10 0 261 394

Plastics and Rubber Products 326 0 0 0 4 1

Nonmetallic Mineral Products 327 0 0 0 0 43

Primary Metals 331 1 1 0 31 4

Fabricated Metal Products 332 0 0 0 0 2

Machinery 333 0 0 0 2 2

Computer and Electronic Products 334 0 0 0 1 1

Electrical Equip., Appliances, Components 335 0 0 0 1 0

Transportation Equipment 336 1 0 0 7 18

Furniture and Related Products 337 0 8 0 1 2

Miscellaneous 339 0 0 0 1 1

Total (Trillion Btu) 485 21 1396 397 536

Average C Content (Tg/QBtu) 18.14 20.62 17.51 0 19.37

Fraction Oxidized 1 1 1 0 1

Total C (Tg) 8.80 0.43 24.44 10.38

Total C (Tg) (ex. still gas from refining) 8.80 0.43 0.00 10.38 a C content: Waste Gas is assumed to be same as naphtha <401 deg. F b C content: Waste Oils/Tars is assumed to be same as asphalt/road oil c Refinery "still gas" fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate d Net steam fuel consumption is reported elsewhere in the Inventory and is excluded from the total C content estimate e C content: "Other" is assumed to be the same as petrochemical feedstocks 

Table A-56: Carbon Emitted from Fuels Burned for Energy Recovery (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 

C Emissions 42.7 42.7 40.4 38.1 35.8 41.5 47.2 53.0 58.7 62.0 65.3 68.6 71.9 71.9 71.9 71.9 71.9 71.9 

Products 

More C is found in products than in industrial releases or energy recovery. The principal types of products are 

plastics; synthetic rubber; synthetic fiber; C black; pesticides; soaps, detergents, and cleansers; food additives; antifreeze 

and deicers (glycols); silicones; and solvents. Solvent evaporation was discussed previously along with industrial releases 

of NMVOCs; the other product types are discussed below. 

Plastics 

Data on annual production of plastics were taken from the American Plastics Council (APC), as published in 

Chemical & Engineering News and on the APC and Society of Plastics Industry (SPI) websites, and through direct 

communication with the APC (APC 2000, 2001, 2003, through 2007; SPI 2000; Eldredge-Roebuck 2000). Production 

was organized by resin type (see Table A-57) and by year. Several of the resin categories included production from 

Canada and/or Mexico, in addition to the U.S. values for part of the time series. The production data for the affected 

resins and years were corrected using an economic adjustment factor, based on the percent of North American production 

value in this industry sector accounted for by the United States. A C content was then assigned for each resin. These C 

contents were based on molecular formulas and are listed in Table A-58 and Table A-59. In cases where the resin type is 

generic, referring to a group of chemicals and not a single polymer (e.g., phenolic resins, other styrenic resins), a 

representative compound was chosen. For engineering resins and other resins, a weighted C content of 68 percent was 

assumed (i.e., it was assumed that these resins had the same content as those for which a representative compound could 

be assigned). 

There were no emissive uses of plastics identified, so 100 percent of the C was considered stored in products. 

However, an estimate of emissions related to the combustion of these plastics in the municipal solid waste stream can be 

found in the Incineration of Waste section of the Energy chapter. 
A-80 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-57: 2007 Plastic Resin Production (Tg dry weight) and C Stored (Tg CO2 Eq.) 

Resin Type 

2007 Productiona 

(Tg dry weight) 

Carbon Stored 

(Tg CO2 Eq.) 

Epoxy 0.29 0.8 

Urea 0.68 0.9 

Melamine 0.68 0.7 

Phenolic 1.90 5.3 

Low-Density Polyethylene (LDPE) 3.11 9.8 

Linear Low-Density Polyethylene (LLDPE) 5.33 16.7 

High Density Polyethylene (HDPE) 7.15 22.5 

Polypropylene (PP) 7.63 24.0 

Acrylonitrile-butadiene-styrene (ABS) 0.48 1.5 

Other Styrenics c 0.65 2.2 

Polystyrene (PS) 2.36 8.0 

Nylon 0.49 1.2 

Polyvinyl chloride (PVC) b 5.73 8.1 

Thermoplastic Polyester 3.30 7.6 

Engineering Resins - - 

All Other (including Polyester (unsaturated)) 6.35 15.9 

Total 46.12 125.0 a Originally included production from Canada for Urea, Melamine, LDPE, LLDPE, HDPE, PP, ABS, SAN, Phenolic, Other Styrenics, PS, Nylon, PVC, Thermoplastic 

Polyester, and Engineering Resins, and production from Mexico for ABS, SAN, Other Styrenics, Nylon, and Thermoplastic Polyester. Values have been adjusted to account 

just for U.S. production. 

b Includes copolymers 

c Includes Styrene-acrylonitrile (SAN) 

Note: Totals may not sum due to independent rounding. 

Table A-58: Assigned C Contents of Plastic Resins (% by weight) 

Resin Type C Content Source of C Content Assumption 

Epoxy 76% Typical epoxy resin made from epichlorhydrin and bisphenol A 

Polyester (Unsaturated) 63% Poly (ethylene terephthalate) (PET) 

Urea 34% 50% carbamal, 50% N-(hydroxymethyl) urea * 

Melamine 29% Trimethylol melamine * 

Phenolic 77% Phenol 

Low-Density Polyethylene (LDPE) 86% Polyethylene 

Linear Low-Density Polyethylene (LLDPE) 86% Polyethylene 

High Density Polyethylene (HDPE) 86% Polyethylene 

Polypropylene (PP) 86% Polypropylene 

Acrylonitrile-Butadiene-Styrene (ABS) 85% 50% styrene, 25% acrylonitrile, 25% butadiene 

Styrene-Acrylonitrile (SAN) 80% 50% styrene, 50% acrylonitrile 

Other Styrenics 92% Polystyrene 

Polystyrene (PS) 92% Polystyrene 

Nylon 65% Average of nylon resins (see Table A-59) 

Polyvinyl Chloride (PVC) 38% Polyvinyl chloride 

Thermoplastic Polyester 63% Polyethylene terephthalate 

Engineering Resins 68% Weighted average of other resin production 

All Other 68% Weighted average of other resin production 

*Does not include alcoholic hydrogens. 

Table A-59: Major Nylon Resins and their C Contents (% by weight) 

Resin C Content 

Nylon 6 64% 

Nylon 6,6 64% 

Nylon 4 52% 

Nylon 6,10 68% 

Nylon 6,11 69% 

Nylon 6,12 70% 

Nylon 11 72% 

Synthetic Rubber 

Data on synthetic rubber in tires were derived from data on the scrap tire market and the composition of scrap 

tires from the Rubber Manufacturers’ Association’s (RMA) Scrap Tire Management Council (STMC). The market 

information is presented in the report Scrap Tire Markets in the United States 2005 Edition (RMA 2006), while the tire 

composition information is from the “Scrap Tires, Facts and Figures” section of the organization’s website (STMC 2003). 
A-81

Data on synthetic rubber in other products (durable goods, nondurable goods, and containers and packaging) were 

obtained from EPA’s Municipal Solid Waste in the United States reports (1996 through 2003, 2005, 2007b, and 2008) and 

detailed unpublished backup data for some years not shown in the Characterization of Municipal Solid Waste in the 

United States reports (Schneider 2007). The abraded rubber from scrap passenger tires was assumed to be 5 lbs per scrap 

tire, while the abraded rubber from scrap truck tires was assumed to be 20 lbs per scrap tire. Data on abraded rubber 

weight were obtained by calculating the average weight difference between new and scrap tires (STMC 2003). 

A C content for synthetic rubber (90 percent for tire synthetic rubber and 85 percent for non-tire synthetic 

rubber) was assigned based on the weighted average of C contents (based on molecular formula) by elastomer type 

consumed in 1998, 2001, and 2002 (see Table A-60). The 1998 consumption data were obtained from the International 

Institute of Synthetic Rubber Producers (IISRP) press release “Synthetic Rubber Use Growth to Continue Through 2004, 

Says IISRP and RMA” (IISRP 2000). The 2001 and 2002 consumption data were obtained from the IISRP press release, 

“IISRP Forecasts Moderate Growth in North America to 2007” (IISRP 2003). 

The rubber in tires that is abraded during use (the difference between new tire and scrap tire rubber weight) was 

considered to be 100 percent emitted. Other than abraded rubber, there were no emissive uses of scrap tire and non-tire 

rubber identified, so 100 percent of the non-abraded amount was assumed stored. Emissions related to the combustion of 

rubber in scrap tires and consumer goods can be found in the Incineration of Waste section of the Energy chapter. 

Table A-60: 2002 Rubber Consumption (Gg) and C Content (%) 

Elastomer Type 2002 Consumption (Gg)* C Content 

SBR Solid 768 91% 

Polybutadiene 583 89% 

Ethylene Propylene 301 86% 

Polychloroprene 54 59% 

NBR Solid 84 77% 

Polyisoprene 58 88% 

Others 367 88% 

Weighted Average - 90% 

Total 2,215 - 

* Includes consumption in Canada. 

- Not applicable 

Note: Totals may not sum due to independent rounding. 

Synthetic Fibers 

Annual synthetic fiber production data were obtained from the Fiber Economics Bureau, as published in 

Chemical & Engineering News (APC 2001, 2003, 2005 through 2007). These data are organized by year and fiber type. 

For each fiber, a C content was assigned based on molecular formula (see Table A-61). For polyester, the C content for 

poly (ethylene terephthalate) (PET) was used as a representative compound. For nylon, the average C content of nylon 6 

and nylon 6,6 was used, since these are the most widely produced nylon fibers. Cellulosic fibers, such as acetate and 

rayon, have been omitted from the synthetic fibers’ C accounting because much of their C is of biogenic origin. These 

fibers account for only 4 percent of overall fiber production by weight. 

There were no emissive uses of fibers identified, so 100 percent of the C was considered stored. Data for 2007 

are not yet available as of this writing, so 2007 fiber production was assumed equal to 2006 production (the most recent 

year for which data are available). Note that emissions related to the combustion of textiles in municipal solid waste are 

accounted for under the Incineration of Waste section of the Energy chapter. 

Table A-61: 2006 Fiber Production (Tg), C Content (%), and C Stored (Tg CO2 Eq.) 

Fiber Type 

Production 

(Tg) C Content 

C Stored 

(Tg CO2 Eq.) 

Polyester 1.3 63% 2.87 

Nylon 1.0 64% 2.39 

Olefin 1.3 86% 4.06 

Acrylic + 68% 0.06 

Total 3.6 - 9.38 

+ Less than 0.05 Tg 

- Not applicable 

Note: Totals may not sum due to independent rounding 
A-82 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Pesticides 

Pesticide consumption data were obtained from the 1994/1995, 1996/1997, 1998/1999, and 2000/2001 Pesticides 

Industry Sales and Usage Market Estimates (EPA 1998, 1999, 2002, 2004b) reports. The most recent data available were 

for 2001, so it was assumed that the 2002 through 2007 consumption was equal to that of 2001. Active ingredient 

compound names and consumption weights were available for the top 25 agriculturally-used pesticides and top 10 

pesticides used in the home and garden and the industry/commercial/government categories. The report provides a range 

of consumption for each active ingredient; the midpoint was used to represent actual consumption. Each of these 

compounds was assigned a C content value based on molecular formula. If the compound contained aromatic rings 

substituted with chlorine or other halogens, then the compound was considered persistent and the C in the compound was 

assumed to be stored. All other pesticides were assumed to release their C to the atmosphere. Over one-third of 2002 total 

pesticide active ingredient consumption was not specified by chemical type in the Sales and Usage report (EPA 2004b). 

This unspecified portion of the active ingredient consumption was treated as a single chemical and assigned a C content 

and a storage factor based on the weighted average of the known chemicals’ values. 

Table A-62: Active Ingredient Consumption in Pesticides (Million lbs.) and C Emitted and Stored (Tg CO2 Eq.) in 2001 

Pesticide Use* Active Ingredient 

(Million lbs.) 

C Emitted 

(Tg CO2 Eq.) 

C Stored 

(Tg CO2 Eq.) 

Agricultural Uses a 458.5 0.1 0.2 

Non-Agricultural Uses b 84.5 + + 

 Home & Garden 38.5 + + 

 Industry/Gov't/Commercial 46.0 + + 

Other 345.0 0.1 0.1 

Total 888.0 0.2 0.3 

+ Less than 0.05 Tg CO2 Eq. *

2001 estimates (EPA 2004b).

Note: Totals may not sum due to independent rounding. 

Soaps, Shampoos, and Detergents 

Cleansers—soaps, shampoos, and detergentsare among the major consumer products that may contain fossil 

C. All of the C in cleansers was assumed to be fossil-derived, and, as cleansers eventually biodegrade, all of the C was 

assumed to be emitted. The first step in estimating C flows was to characterize the “ingredients” in a sample of cleansers. 

For this analysis, cleansers were limited to the following personal household cleaning products: bar soap, shampoo, 

laundry detergent (liquid and granular), dishwasher detergent, and dishwashing liquid. Data on the annual consumption of 

household personal cleansers were obtained from the U.S. Census Bureau 1992, 1997, and 2002 Economic Census. 

Consumption values for 1990 and 1991 were assumed to be the same as the 1992 value; consumption was interpolated 

between 1992 and 1997 and between 1997 and 2002; consumption for 2003 through 2007 was assumed to equal the 2002 

value. Cleanser consumption values were adjusted by import and export data to develop US consumption estimates. 

Chemical formulae were used to determine C contents (as percentages) of the ingredients in the cleansers. Each 

product’s overall C content was then derived from the composition and contents of its ingredients. From these values the 

mean C content for cleansers was calculated to be 21.9 percent. 

The Census Bureau presents consumption data in terms of quantity (in units of million gallons or million pounds) 

and/or terms of value (thousands of dollars) for eight specific categories, such as “household liquid laundry detergents, 

heavy duty” and “household dry alkaline automatic dishwashing detergents.” Additionally, the report provides dollar 

values for the total consumption of “soaps, detergents, etc.—dry” and “soaps, detergents, etc.—liquid.” The categories for 

which both quantity and value data are available is a subset of total production. Those categories that presented both 

quantity and value data were used to derive pounds per dollar and gallons per dollar conversion rates, and they were 

extrapolated (based on the Census Bureau estimate of total value) to estimate the total quantity of dry and liquid18 cleanser 

categories, respectively. 

Next, the total tonnage of cleansers was calculated (wet and dry combined). Multiplying the mean C content 

(21.9 percent) by this value yielded an estimate of 4.5 Tg CO2 Eq. in cleansers for 1997. For 1992 and 2002 the estimates 

are 3.6 Tg CO2 Eq. and 5.1 Tg CO2 Eq. Estimates for other years are based on these values as described above, and are 

shown in Table A-63. 

 

18 A density of 1.05 g/mL—slightly denser than water—was assumed for liquid cleansers. 
A-83

Table A-63: C Emitted from Utilization of Soaps, Shampoos, and Detergents (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

C Emissions 3.6 3.6 3.6 3.8 4.0 4.2 4.4 4.6 4.7 4.5 4.5 4.8 5.1 5.0 4.9 4.9 5.7 5.7

Antifreeze and Deicers 

Glycol compounds, including ethylene glycol, propylene glycol, diethylene glycol, and triethylene glycol, are 

used as antifreeze in motor vehicles, deicing fluids for commercial aircraft, and other similar uses. These glycol 

compounds are assumed to ultimately enter wastewater treatment plants where they are degraded by the wastewater 

treatment process to CO2 or to otherwise biodegrade to CO2. Glycols are water soluble and degrade rapidly in the 

environment (Howard 1993). 

Annual production data for each glycol compound used as antifreeze and deicers were obtained from the Guide 

to the Business of Chemistry (ACC 2008). Import and export data were used to adjust annual production data to annual 

consumption data. The percentage of the annual consumption of each glycol compound used for antifreeze and deicing 

applications was estimated from Chemical Profiles data published on The Innovation Group website and from similar data 

published in the Chemical Market Reporter, which became ICIS Chemical Business in 2005. 

The consumption of glycol compounds in antifreeze and deicing applications is assumed to be 100 percent 

emitted as CO2. Emissions of CO2 from utilization of antifreeze and deicers are summarized in Table A-64. 

Table A-64: C Emitted from Utilization of Antifreeze and Deicers (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

C Emissions 1.1 1.1 1.1 1.1 1.2 1.2 1.2 1.3 1.3 1.3 1.3 1.1 1.1 1.1 1.2 1.0 1.1 1.1

Food Additives 

Petrochemical feedstocks are used to manufacture synthetic food additives, including preservatives, flavoring 

agents, and processing agents. These compounds include glycerin, propylene glycol, benzoic acid, and other compounds. 

These compounds are incorporated into food products, and are assumed to ultimately enter wastewater treatment plants 

where they are degraded by the wastewater treatment processes to CO2 or to otherwise biodegrade to CO2. Certain food 

additives, e.g., glycerin, are manufactured both from petrochemical feedstocks and from biogenic feedstocks. Food 

additives that are derived from biogenic feedstocks are not considered in this analysis. 

Annual production data for food additive compounds were obtained from the Guide to the Business of Chemistry 

(ACC 2008). Import and export data were used to adjust annual production data to annual consumption data. The 

percentage of the annual consumption of food additive compounds was estimated from Chemical Profiles data published 

on The Innovation Group website.19 The consumption of synthetic food additives is assumed to be 100 percent emitted as 

CO2. Emissions of CO2 from utilization of synthetic food additives are summarized in Table A-65. 

Table A-65: C Emitted from Utilization of Food Additives (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

C Emissions 0.6 0.6 0.6 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.9 0.8 0.9 0.8 0.8 0.8 0.8 0.6

Silicones 

Silicone compounds (e.g., polymethyl siloxane) are used as sealants and in manufactured products. Silicone 

compounds are manufactured from petrochemical feedstocks including methyl chloride. It is assumed that petrochemical 

feedstocks used to manufacture silicones are incorporated into the silicone products and not emitted as CO2 in the 

manufacturing process. It is also assumed that the C contained in the silicone products is stored, and not emitted as CO2. 

Annual production data for each silicone manufacturing compound were obtained from the Guide to the Business 

of Chemistry (ACC 2008). Import and export data were used to adjust annual production data to annual consumption data. 

The percentage of the annual consumption of each silicone manufacturing compound was estimated from Chemical 

Profiles data published on The Innovation Group website. The consumption of silicone manufacturing compounds is 

assumed to be 100 percent stored, and not emitted as CO2. Storage of silicone manufacturing compounds is summarized 

in Table A-66.

 

19 http://www.the-innovation-group.com/ChemProfiles 
A-84 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-66: C Stored in Silicone Products (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

C Storage 0.3 0.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5 0.5 0.4 0.5 0.5

Uncertainty 

A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty 

surrounding the estimates of the feedstocks C storage factor and the quantity of C emitted from feedstocks in 2007. The 

Tier 2 analysis was performed to allow the specification of probability density functions for key variables, within a 

computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of 

uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates 

were determined using assumptions based on source category knowledge. Uncertainty estimates for production data (the 

majority of the variables) were assumed to exhibit a normal distribution with a relative error of ±20 percent in the 

underlying EIA estimates, plus an additional ±15 percent to account for uncertainty in the assignment of imports and 

exports. An additional 10 percent (for a total of ±45 percent) was applied to the production of other oils (>401 deg. F) to 

reflect the additional uncertainty in the assignment of part of the production quantity to industrial processes. A relatively 

narrow uniform distribution ±1 percent to ±10 percent, depending on the fuel type) was applied to each C coefficient. 

The Monte Carlo analysis produced a storage factor distribution that approximates a normal curve around a mean 

of 61 percent, with a standard deviation of 1 percent, and 95 percent confidence limits of 59 percent and 63 percent. This 

compares to the calculated estimate, used in the Inventory, of 61 percent. The analysis produced a C emission distribution 

approximating a normal curve with a mean of 79.8 Tg CO2 Eq., standard deviation of 8.0 Tg CO2 Eq., and 95 percent 

confidence limits of 64.4 and 95.9 Tg CO2 Eq. This compares with a calculated estimate of 79.9 Tg CO2 Eq. 

The apparently tight confidence limits for the storage factor and C storage probably understate uncertainty, as a 

result of the way this initial analysis was structured. As discussed above, the storage factor for feedstocks is based on an 

analysis of six fates that result in long-term storage (e.g., plastics production), and eleven that result in emissions (e.g., 

volatile organic compound emissions). Rather than modeling the total uncertainty around all 17 of these fate processes, 

the current analysis addresses only the storage fates, and assumes that all C that is not stored is emitted. As the production

statistics that drive the storage factors are relatively well-characterized, this approach yields a result that is probably biased 

toward understating uncertainty. 

As far as specific sources of uncertainty, there are several cross-cutting factors that pervade the characterization 

of C flows for feedstocks. The aggregate storage factor for petrochemical feedstocks (industrial other coal, natural gas for 

non-fertilizer uses, LPG, pentanes plus, naphthas, other oils, still gas, special naphtha) is based on assuming that the 

ultimate fates of all of these fuel types —in terms of storage and emissions—are similar. In addition, there are 

uncertainties associated with the simplifying assumptions made for each end use category C estimate. Generally, the 

estimate for a product is subject to one or both of the following uncertainties: 

 The value used for estimating the C content has been assumed or assigned based upon a representative 

compound. 

 The split between C storage and emission has been assumed based on an examination of the environmental fate 

of the products in each end use category. 

 Environmental fates leading to emissions are assumed to operate rapidly, i.e., emissions are assumed to occur 

within one year of when the fossil C enters the non-energy mass balance. Some of the pathways that lead to 

emissions as CO2 may actually take place on a time-scale of several years or decades. By attributing the 

emissions to the year in which the C enters the mass balance (i.e., the year in which it leaves refineries as a nonenergy fuel use and thus starts being tracked by EIA), this approach has the effect of “front-end loading” the 

emission profile. 

Another cross-cutting source of uncertainty is that for several sources the amount of C stored or emitted was 

calculated based on data for only a single year. This specific year may not be representative of storage for the entire 

Inventory period. Sources of uncertainty associated with specific elements of the analysis are discussed below. 

Import and export data for petrochemical feedstocks were obtained from EIA, the National Petroleum Refiners 

Association, and the U.S. BoC for the major categories of petrochemical feedstocks (EIA 2001a, NPRA 2001, and U.S. 

BoC 2006). The complexity of the organic chemical industry, with multiple feedstocks, intermediates, and subtle 

differences in nomenclature, makes it difficult to ensure that the adjustments to the EIA data for imports and exports is 

accurate and the approach used here may underestimate or overestimate net exports of C. 
A-85

Oxidation factors have been applied to non-energy uses of petrochemical feedstocks in the same manner as for 

energy uses. However, for those fuels where IPCC storage factors are used, this “oxidation factor” may be inherent in the 

storage factor applied when calculating emissions from non-energy consumption, which would result in a double-counting 

of the unoxidized C. Oxidation factors are small corrections, on the order of 1 percent, and therefore application of 

oxidation factors to non-energy uses may result in a slight underestimation of C emissions from non-energy uses. 

The major uncertainty in using the TRI data are the possibility of double counting of emissions that are already 

accounted for in the NMVOC data (see above) and in the storage and emission assumptions used. The approach for 

predicting environmental fate simplifies some complex processes, and the balance between storage and emissions is very 

sensitive to the assumptions on fate. Extrapolating from known to unknown characteristics also introduces uncertainty. 

The two extrapolations with the greatest uncertainty are: 1) that the release media and fate of the off-site releases were 

assumed to be the same as for on-site releases, and 2) that the C content of the least frequent 10 percent of TRI releases 

was assumed to be the same as for the chemicals comprising 90 percent of the releases. However, the contribution of these 

chemicals to the overall estimate is small. The off-site releases only account for 3 percent of the total releases, by weight,

and, by definition, the less frequent compounds only account for 10 percent of the total releases. 

The principal sources of uncertainty in estimating CO2 emissions from solvent evaporation and industrial 

NMVOC emissions are in the estimates of (a) total emissions and (b) their C content. Solvent evaporation and industrial 

NMVOC emissions reported by EPA are based on a number of data sources and emission factors, and may underestimate 

or overestimate emissions. The C content for solvent evaporation emissions is calculated directly from the specific solvent 

compounds identified by EPA as being emitted, and is thought to have relatively low uncertainty. The C content for 

industrial emissions has more uncertainty, however, as it is calculated from the average C content of an average volatile 

organic compound based on the list of the most abundant measured NMVOCs provided in EPA (2002a). 

Uncertainty in the hazardous waste combustion analysis is introduced by the assumptions about the composition 

of combusted hazardous wastes, including the characterization that hazardous wastes are similar to mixtures of water, 

noncombustibles, and fuel equivalent materials. Another limitation is the assumption that all of the C that enters 

hazardous waste combustion is emitted—some small fraction is likely to be sequestered in combustion ash—but given that 

the destruction and removal efficiency for hazardous organics is required to meet or exceed 99.99 percent, this is a very 

minor source of uncertainty. C emission estimates from hazardous waste should be considered central value estimates that 

are likely to be accurate to within 50 percent. 

The amount of feedstocks combusted for energy recovery was estimated from data included in the Manufacturers 

Energy Consumption Surveys (MECS) for 1991, 1994, 1998, and 2002 (EIA 1994, 1997, 2001b, 2004). MECS is a 

comprehensive survey that is conducted every four years and intended to represent U.S. industry as a whole, but because 

EIA does not receive data from all manufacturers (i.e., it is a sample rather than a census), EIA must extrapolate from the 

sample. Also, the “other” fuels are identified in the MECS data in broad categories, including refinery still gas; waste gas; 

waste oils, tars, and related materials; petroleum coke, coke oven and blast furnace gases; and other uncharacterized fuels. 

Moreover, the industries using these “other” fuels are also identified only in broad categories, including the petroleum and 

coal products, chemicals, primary metals, nonmetallic minerals, and other manufacturing sectors. The “other” fuel 

consumption data are reported in BTUs (energy units) and there is uncertainty concerning the selection of a specific 

conversion factor for each broad “other” fuel category to convert energy units to mass units. Taken as a whole, the 

estimate of energy recovery emissions probably introduces more uncertainty than any other element of the non-energy 

analysis. 

Uncertainty in the C storage estimate for plastics arises primarily from three factors. First, the raw data on 

production for several resins include Canadian and/or Mexican production and may overestimate the amount of plastic 

produced from U.S. fuel feedstocks; this analysis includes adjustments to “back out” the Canadian and Mexican values, 

but these adjustments are approximate. Second, the assumed C content values are estimates for representative compounds, 

and thus do not account for the many formulations of resins available. This uncertainty is greater for resin categories that 

are generic (e.g., phenolics, other styrenics, nylon) than for resins with more specific formulations (e.g., polypropylene, 

polyethylene). Lastly, the assumption that all of the C contained in plastics is stored ignores certain end uses (e.g., 

adhesives and coatings) where the resin may be released to the atmosphere; however, these end uses are likely to be small 

relative to use in plastics.

The quantity of C stored in synthetic rubber only accounts for the C stored in scrap tire synthetic rubber. The 

value does not take into account the rubber stored in other durable goods, clothing, footwear, and other non-durable goods, 

or containers and packaging. This adds uncertainty to the total mass balance of C stored. There are also uncertainties as to 

the assignment of C content values; however, they are much smaller than in the case of plastics. There are probably fewer 

variations in rubber formulations than in plastics, and the range of potential C content values is much narrower. Lastly, 

assuming that all of the C contained in rubber is stored ignores the possibility of volatilization or degradation during 
A-86 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

product lifetimes. However, the proportion of the total C that is released to the atmosphere during use is probably 

negligible. 

A small degree of uncertainty arises from the assignment of C content values; however, the magnitude of this 

uncertainty is less than that for plastics or rubber. Although there is considerable variation in final textile products, the 

stock fiber formulations are standardized and proscribed explicitly by the Federal Trade Commission. 

For pesticides, the largest source of uncertainty involves the assumption that an active ingredient’s C is either 0 

percent stored or 100 percent stored. This split is a generalization of chemical behavior, based upon active-ingredient 

molecular structure, and not on compound-specific environmental data. The mechanism by which a compound is bound 

or released from soils is very complicated and can be affected by many variables, including the type of crop, temperature, 

application method, and harvesting practice. Another smaller source of uncertainty arises from the C content values 

applied to the unaccounted for portion of active ingredient. C contents vary widely among pesticides, from 7 to 72 

percent, and the remaining pesticides may have a chemical make-up that is very different from the 32 pesticides that have 

been examined. Additionally, pesticide consumption data were only available for 1987, 1993, 1995, 1997, 1999, and 

2001; the majority of the time series data were interpolated or held constant at the latest (2001) value. Another source of 

uncertainty is that only the “active” ingredients of pesticides are considered in the calculations; the “inactive” ingredients 

may also be derived from petrochemical feedstocks. 

It is important to note that development of this uncertainty analysis is a multi-year process. The current 

feedstocks analysis examines NEU fuels that end in storage fates Thus only C stored in pesticides, plastics, synthetic 

fibers, synthetic rubbers, silicones, and TRI releases to underground injection and Subtitle C landfills is accounted for in 

the uncertainty estimate above. In the future this analysis will be expanded to include the uncertainty surrounding emitted 

fates in addition to the storage fates. Estimates of variable uncertainty will also be refined where possible to include fewer

assumptions. With these major changes in future Inventories, the uncertainty estimate is expected to change, and likely 

increase. An increase in the uncertainty estimate in the coming years will not indicate that the Inventory calculations have 

become less certain, but rather that the methods for estimating uncertainty have become more comprehensive; thus, 

potential future changes in the results of this analysis will reflect a change in the uncertainty analysis, not a change in the

Inventory quality. 

Asphalt and Road Oil 

Asphalt is one of the principal non-energy uses of fossil fuels. The term “asphalt” generally refers to a mixture 

of asphalt cement and a rock material aggregate, a volatile petroleum distillate, or water. For the purposes of this analysis,

“asphalt” is used interchangeably with asphalt cement, a residue of crude oil. According to EPA (2000e), approximately 

100 Tg CO2 Eq. has been used in the production of asphalt cement annually. Though minor amounts of C are emitted 

during production, asphalt has an overall C storage factor of almost 100 percent, as discussed below. 

Paving is the primary application of asphalt cement, comprising 86 percent of production. The three types of 

asphalt paving produced in the United States are hot mix asphalt (HMA), cut-backs, and emulsified asphalt. HMA, which 

makes up 90 percent of total asphalt paving (EPA 2000c), contains asphalt cement mixed with an aggregate of rock 

materials. Cut-back asphalt is composed of asphalt cement thinned with a volatile petroleum distillate (e.g., naphtha). 

Emulsified asphalt contains only asphalt cement and water. Roofing products are the other significant end use of asphalt 

cement, accounting for approximately 14 percent of U.S. production (Kelly 2000). No data were available on the fate of C 

in asphalt roofing; it was assumed that it has the same fate as C in asphalt paving applications. 

Methodology and Data Sources 

A C storage factor was calculated for each type of asphalt paving. The fraction of C emitted by each asphalt type 

was multiplied by consumption data for asphalt paving (EPA 2000c, EIIP 1998) to estimate a weighted average C storage 

factor for asphalt as a whole. 

The fraction of C emitted by HMA was determined by first calculating the organic emissions (volatile organic 

compounds [VOCs], carbon monoxide [CO], polycyclic aromatic hydrocarbons [PAHs], hazardous air pollutants [HAPs], 

and phenol) from HMA paving, using emission factors reported in EPA (2000e) and total HMA production.20 The next 

step was to estimate the C content of the organic emissions. This calculation was based on the C content of CO and 

phenol, and an assumption of 85 percent C content for PAHs and HAPs. The C content of asphalt paving is a function of 

(1) the proportion of asphalt cement in asphalt paving, assumed to be 5 percent asphalt cement content based on personal 

 

20 The emission factors are expressed as a function of asphalt paving tonnage (i.e., including the rock aggregate as well as the 

asphalt cement). 
A-87

communication with an expert from the National Asphalt Paving Association (Connolly 2000), and (2) the proportion of C 

in asphalt cement. For the latter factor, all paving types were characterized as having a mass fraction of 85 percent C in 

asphalt cement, based on the assumption that asphalt is primarily composed of saturated paraffinic hydrocarbons. By 

combining these estimates, the result is that over 99.99 percent of the C in asphalt cement was retained (i.e., stored), and 

less than 0.01 percent was emitted. 

Cut-back asphalt is produced in three forms (i.e., rapid, medium and slow cure). All three forms emit C only 

from the volatile petroleum distillate used to thin the asphalt cement (EPA 1995). Because the petroleum distillates are 

not included in the EIA fuel use statistics for asphalt, the storage factor for cut-back is assumed to be 100 percent. 

It was also assumed that there was no loss of C from emulsified asphalt (i.e., the storage factor is 100 percent) 

based on personal communication with an expert from Akzo Nobel Coatings, Inc. (James 2000). 

Data on asphalt and road oil consumption and C content factors were supplied by EIA. Hot mix asphalt 

production and emissions factors were obtained from “Hot Mix Asphalt Plants Emissions Assessment Report” from EPA’s 

AP-42 (EPA 2004c) publication. The asphalt cement content of HMA was provided by Una Connolly of National Asphalt 

Paving Association (Connolly 2000). The consumption data for cut-back and emulsified asphalts were taken from a 

Moulthrop, et al. study used as guidance for estimating air pollutant emissions from paving processes (EIIP 1998). 

“Asphalt Paving Operation” AP-42 (EPA 2004c) provided the emissions source information used in the calculation of the 

C storage factor for cut-back asphalt. The storage factor for emulsified asphalt was provided by Alan James of Akzo 

Nobel Coatings, Inc. (James 2000). 

Uncertainty

A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty 

surrounding the estimates of the asphalt C storage factor and the quantity of C stored in asphalt in 2007. The Tier 2 

analysis was performed to allow the specification of probability density functions for key variables, within a 

computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of 

uncertainty were not available directly from the information sources for the activity variables; thus, uncertainty estimates 

were determined using assumptions based on source category knowledge. Uncertainty estimates for asphalt production 

were assumed to be ±20 percent, while the asphalt property variables were assumed to have narrower distributions. A 

narrow uniform distribution, with maximum 5 percent uncertainty around the mean, was applied to the C content 

coefficient. 

The Monte Carlo analysis, given a 95 percent confidence interval, produced a storage factor distribution that 

approximates a normal curve skewed to the right, around a mean of 99.5 percent, with a standard deviation less than 0.05 

percent and boundaries between 99.1 and 99.8 percent. This compares to the storage factor value used in the Inventory of 

100 percent. The analysis produced an emission distribution, skewed to the left, with an uncertainty range slightly below 

100 percent. The emission uncertainty range is not applicable since the Inventory calculation estimates that zero C is 

emitted from asphalts and road oil. 

The principal source of uncertainty is that the available data are from short-term studies of emissions associated 

with the production and application of asphalt. As a practical matter, the cement in asphalt deteriorates over time, 

contributing to the need for periodic re-paving. Whether this deterioration is due to physical erosion of the cement and 

continued storage of C in a refractory form or physicochemical degradation and eventual release of CO2 is uncertain. 

Long-term studies may reveal higher lifetime emissions rates associated with degradation. 

Many of the values used in the analysis are also uncertain and are based on estimates and professional judgment. 

For example, the asphalt cement input for hot mix asphalt was based on expert advice indicating that the range is 

variable—from about 3 to 5 percent—with actual content based on climate and geographical factors (Connolly 2000). 

Over this range, the effect on the calculated C storage factor is minimal (on the order of 0.1 percent). Similarly, changes 

in the assumed C content of asphalt cement would have only a minor effect. 

The consumption figures for cut-back and emulsified asphalts are based on information reported for 1994. More 

recent trends indicate a decrease in cut-back use due to high VOC emission levels and a related increase in emulsified 

asphalt use as a substitute. However, because the C storage factor of each is 100 percent, use of more recent data would 

not affect the overall result. 

Future improvements to this uncertainty analysis, and to the overall estimation of a storage factor for asphalt, 

include characterizing the long-term fate of asphalt. 
A-88 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Lubricants 

Lubricants are used in industrial and transportation applications. They can be subdivided into oils and greases, 

which differ in terms of physical characteristics (e.g., viscosity), commercial applications, and environmental fate. 

According to EIA (2008), the C content from U.S. production of lubricants in 2007 was approximately 20.2 Tg C. Based 

on apportioning oils and greases to various environmental fates, and characterizing those fates as resulting in either longterm storage or emissions, the overall C storage factor was estimated to be 9 percent; thus, emissions in 2007 were about 

5.8 Tg C, or 21.4 Tg CO2 Eq. 

Methodology and Data Sources 

For each lubricant category, a storage factor was derived by identifying disposal fates and applying assumptions 

as to the disposition of the C for each practice. An overall lubricant C storage factor was calculated by taking a 

production-weighted average of the oil and grease storage factors. 

Oils 

Regulation of used oil in the United States has changed dramatically over the past 20 years.21 The effect of these 

regulations and policies has been to restrict landfilling and dumping, and to encourage collection of used oil. The 

economics of the petroleum industry have generally not favored re-refining—instead, most of the used oil that has been 

collected has been combusted. 

Table A-67 provides an estimated allocation of the fates of lubricant oils (Rinehart 2000), along with an estimate 

of the proportion of C stored in each fate. The ultimate fate of the majority of oils (about 84 percent) is combustion, either

during initial use or after collection as used oil. Combustion results in 99 percent oxidation to CO2 (EIIP 1999), with 

correspondingly little long-term storage of C in the form of ash. Dumping onto the ground or into storm sewers, primarily 

by “do-it-yourselfers” who change their own oil, is another fate that results in conversion to CO2 given that the releases are 

generally small and most of the oil is biodegraded (based on the observation that land farming—application to soil—is one 

of the most frequently used methods for degrading refinery wastes). In the landfill environment, which tends to be 

anaerobic within municipal landfills, it is assumed that 90 percent of the oil persists in an underrated form, based on 

analogy with the persistence of petroleum in native petroleum-bearing strata, which are both anaerobic. Re-refining adds a 

recycling loop to the fate of oil. Re-refined oil was assumed to have a storage factor equal to the weighted average for the 

other fates (i.e., after re-refining, the oil would have the same probability of combustion, landfilling, or dumping as virgin 

oil), that is, it was assumed that about 97 percent of the C in re-refined oil is ultimately oxidized. Because of the 

dominance of fates that result in eventual release as CO2, only about 3 percent of the C in oil lubricants goes into longterm storage. 

Table A-67: Commercial and Environmental Fate of Oil Lubricants (Percent) 

Fate of Oil Portion of Total Oil C Stored 

Combusted During Use 20 1 

Not Combusted During Use 80 - 

Combusted as Used Oil * 64 1 

Dumped on the ground or in storm sewers 6 0 

Landfilled 2 90 

Re-refined into lube oil base stock and other products 8 3 

Weighted Average - 2.9 

*(e.g., in boilers or space heaters) 

- Not applicable 

Greases 

Table A-68 provides analogous estimates for lubricant greases. Unlike oils, grease is generally not combusted 

during use, and combustion for energy recovery and re-refining is thought to be negligible. Although little is known about 

the fate of waste grease, it was assumed that 90 percent of the non-combusted portion is landfilled, and the remainder is 

dumped onto the ground or storm sewers. Because much of the waste grease will be in containers that render it relatively 

inaccessible to biodegradation, and because greases contain longer chain paraffins, which are more persistent than oils, it 

was assumed that 90 percent and 50 percent of the C in landfilled and dumped grease, respectively, would be stored. The 

overall storage factor is 82 percent for grease. 

 

21 For example, the U.S. EPA “RCRA (Resource Conservation and Recovery Act) On-line” web site 

(<http://www.epa.gov/rcraonline/>) has over 50 entries on used oil regulation and policy for 1994 through 2000. 
A-89

Table A-68: Commercial and Environmental Fate of Grease Lubricants (Percent) 

Fate of Grease Portion of Total Grease C Stored 

Combusted During Use 5 1 

Not Combusted During Use 95 - 

Landfilled 85.5 90 

Dumped on the ground or in storm sewers 9.5 50 

Weighted Average - 81.8

- Not applicable 

Having derived separate storage factors for oil and grease, the last step was to estimate the weighted average for 

lubricants as a whole. No data were found apportioning the mass of lubricants into these two categories, but the U.S. 

Census Bureau (1999) does maintain records of the value of production of lubricating oils and lubricating greases. 

Assuming that the mass of lubricants can be allocated according to the proportion of value of production (92 percent oil, 8 

percent grease), applying these weights to the storage factors for oils and greases (3 percent and 82 percent) yields an 

overall storage factor of 9 percent. 

Uncertainty 

A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty 

surrounding the estimates of the lubricants weighted average C storage factor and the quantity of C emitted from 

lubricants in 2007. The Tier 2 analysis was performed to allow the specification of probability density functions for key 

variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or 

expert judgments of uncertainty were not available directly from the information sources for the activity variables; thus, 

uncertainty estimates were determined using assumptions based on source category knowledge. Uncertainty estimates for 

oil and grease variables were assumed to have a moderate variance, in triangular or uniform distribution. Uncertainty 

estimates for lubricants production were assumed to be rather high (±20 percent). A narrow uniform distribution, with 

maximum 6 percent uncertainty around the mean, was applied to the lubricant C content coefficient. 

The Monte Carlo analysis, given a 95 percent confidence interval, produced a storage factor distribution that 

approximates a normal curve, around a mean of 10 percent (with individual storage factors for oil and grease at 3 and 82 

percent), with a standard deviation of 4 percent and 95 percent confidence limits of 4 and 17 percent. This compares to the 

calculated estimate, used in the Inventory, of 9 percent. The analysis produced an emission distribution approximating a 

normal curve with a mean of 21.1 Tg CO2, standard deviation of 1.8 and 95 percent confidence limits of 17.7 and 24.9 Tg 

CO2. This compares with a calculated estimate of 21.4 Tg CO2. 

The principal sources of uncertainty for the disposition of lubricants are the estimates of the commercial use, 

post-use, and environmental fate of lubricants, which, as noted above, are largely based on assumptions and judgment. 

There is no comprehensive system to track used oil and greases, which makes it difficult to develop a verifiable estimate 

of the commercial fates of oil and grease. The environmental fate estimates for percent of C stored are less uncertain, but 

also introduce uncertainty in the estimate. 

The assumption that the mass of oil and grease can be divided according to their value also introduces 

uncertainty. Given the large difference between the storage factors for oil and grease, changes in their share of total 

lubricant production have a large effect on the weighted storage factor. 

Future improvements to the analysis of uncertainty surrounding the lubricants C storage factor and C stored 

include further refinement of the uncertainty estimates for the individual activity variables.

Waxes 

Waxes are organic substances that are solid at ambient temperature, but whose viscosity decreases as temperature 

increases. Most commercial waxes are produced from petroleum refining, though “mineral” waxes derived from animals, 

plants, and lignite [coal] are also used. An analysis of wax end uses in the United States, and the fate of C in these uses, 

suggests that about 42 percent of C in waxes is emitted, and 58 percent is stored. 

Methodology and Data Sources 

The National Petroleum Refiners Association (NPRA) considers the exact amount of wax consumed each year 

by end use to be proprietary (Maguire 2004). In general, about thirty percent of the wax consumed each year is used in 

packaging materials, though this percentage has declined in recent years. The next highest wax end use, and fastest 

growing end use, is candles, followed by construction materials and firelogs. Table A-69 categorizes some of the wax end 
A-90 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

uses, which the NPRA generally classifies into cosmetics, plastics, tires and rubber, hot melt (adhesives), chemically 

modified wax substances, and other miscellaneous wax uses (NPRA 2002) 

Table A-69: Emissive and Non-emissive (Storage) Fates of Waxes: Uses by Fate and Percent of Total Mass 

Use Emissive Non-emissive 

Packaging 6% 24% 

Non-packaging 36% 34% 

Candles 18% 2% 

Construction Materials 4% 14% 

Firelogs 7% 0% 

Cosmetics 1% 2% 

Plastics 1% 2% 

Tires/Rubber 1% 1% 

Hot Melts 1% 1% 

Chemically Modified 0% 1% 

Other 2% 9% 

Total 42% 58% 

A C storage factor for each wax end use was estimated and then summed across all end uses to provide an overall 

C storage factor for wax. Because no specific data on C contents of wax used in each end use were available, all wax 

products are assumed to have the same C content. Table A-70 categorizes wax end uses identified by the NPRA, and lists 

each end use’s estimated C storage factor. 

Table A-70: Wax End-Uses by Fate, Percent of Total Mass, Percent C Stored, and Percent of Total C Mass Stored 

Use 

Percent of Total 

Wax Mass 

Percent of C 

Stored 

Percent of Total C 

Mass Stored 

Candles 20% 10% 2% 

Firelogs 7% 1% + 

Hotmelts 3% 50% 1% 

Packaging 30% 79% 24% 

Construction Materials 18% 79% 14% 

Cosmetics 3% 79% 2% 

Plastics 3% 79% 2% 

Tires/Rubber 3% 47% 1% 

Chemically Modified 1% 79% 1% 

Other 12% 79% 9% 

Total 100% NA 58% 

+ Does not exceed 0.5 percent 

Source, mass percentages: NPRA 2002. Estimates of percent stored are based on professional judgment, ICF Consulting. 

Note: Totals may not sum due to independent rounding. 

Emissive wax end uses include candles, firelogs (synthetic fireplace logs), hotmelts (adhesives), matches, and 

explosives. At about 20 percent, candles consume the greatest portion of wax among emissive end uses. As candles 

combust during use, they release emissions to the atmosphere. For the purposes of the Inventory, it is assumed that 90 

percent of C contained in candles is emitted as CO2. In firelogs, petroleum wax is used as a binder and as a fuel, and is 

combusted during product use, likely resulting in the emission of nearly all C contained in the product. Similarly, C 

contained in hotmelts is assumed to be emitted as CO2 as heat is applied to these products during use. It is estimated that 

50 percent of the C contained in hot melts is stored. Together, candles, firelogs, and hotmelts constitute approximately 30 

percent of annual wax production (NPRA 2002). 

All of the wax utilized in the production of packaging, cosmetics, plastics, tires and rubber, and other products is 

assumed to remain in the product (i.e., it is assumed that there are no emissions of CO2 from wax during the production of 

the product). Wax is used in many different packaging materials including wrappers, cartons, papers, paperboard, and 

corrugated products (NPRA 2002). Davie (1993) and Davie et al. (1995) suggest that wax coatings in packaging products 

degrade rapidly in an aerobic environment, producing CO2; however, because packaging products ultimately enter landfills 

typically having an anaerobic environment, most of the C from this end use is assumed to be stored in the landfill. 

In construction materials, petroleum wax is used as a water repellent on wood-based composite boards, such as 

particle board (IGI 2002). Wax used for this end-use should follow the life-cycle of the harvested wood used in product, 

which is classified into one of 21 categories, evaluated by life-cycle, and ultimately assumed to either be disposed of in 

landfills or be combusted (EPA 2003). 
A-91

The fate of wax used for packaging, in construction materials, and most remaining end uses is ultimately to enter 

the municipal solid waste (MSW) stream, where they are either combusted or sent to landfill for disposal. Most of the C 

contained in these wax products will be stored. It is assumed that approximately 21 percent of the C contained in these 

products will be emitted through combustion or at landfill. With the exception of tires and rubber, these end uses are 

assigned a C storage factor of 79 percent. 

Waxes used in tires and rubber follow the life cycle of the tire and rubber products. Used tires are ultimately 

recycled, landfilled, or combusted. The life-cycle of tires is addressed elsewhere in this annex as part of the discussion of 

rubber products derived from petrochemical feedstocks. For the purposes of the estimation of the C storage factor for 

waxes, wax contained in tires and rubber products is assigned a C storage factor of 47 percent. 

Uncertainty 

A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty 

surrounding the estimates of the wax C storage factor and the quantity of C emitted from wax in 2007. A Tier 2 analysis 

was performed to allow the specification of probability density functions for key variables, within a computational 

structure that mirrors the calculation of the Inventory estimate. Statistical analyses or expert judgments of uncertainty 

were not available directly from the information sources for the activity variables; thus, uncertainty estimates were 

determined using assumptions based on source category knowledge. Uncertainty estimates for wax variables were 

assumed to have a moderate variance, in normal, uniform, or triangular distribution; uniform distributions were applied to 

total consumption of waxes and the C content coefficients. 

The Monte Carlo analysis produced a storage factor distribution that approximates a normal curve around a mean 

of 58 percent, with a standard deviation of 7 percent and 95 percent confidence limits of 44 percent and 70 percent. This 

compares to the calculated estimate, used in the Inventory, of 58 percent. The analysis produced an emission distribution 

approximating a normal curve with a mean of 0.8 Tg CO2, standard deviation of 0.2 Tg CO2, and 95 percent confidence 

limits of 0.5 and 1.1 Tg CO2. This compares with a calculated estimate of 0.7 Tg CO2. This value is within the range of 

95 percent confidence limits established by this quantitative uncertainty analysis. Uncertainty associated with the wax 

storage factor is considerable due to several assumptions pertaining to wax imports/exports, consumption, and fates. 

Miscellaneous Products 

Miscellaneous products are defined by the U.S. Energy Information Administration as: “all finished [petroleum] 

products not classified elsewhere, e.g., petrolatum; lube refining byproducts (e.g., aromatic extracts and tars); absorption 

oils; ram-jet fuel; petroleum rocket fuel; synthetic natural gas feedstocks; and specialty oils." 

Methodology and Data Sources 

Data are not available concerning the distribution of each of the above-listed subcategories within the 

"miscellaneous products" category. However, based on the anticipated disposition of the products in each subcategory, it is 

assumed that all of the C content of miscellaneous products is emitted rather than stored. Petrolatum and specialty oils 

(which include greases) are likely to end up in solid waste or wastewater streams rather than in durable products, and 

would be emitted through waste treatment. Absorption oil is used in natural gas processing and is not a feedstock for 

manufacture of durable products Jet fuel and rocket fuel are assumed to be combusted in use, and synthetic natural gas 

feedstocks are assumed to be converted to synthetic natural gas that is also combusted in use. Lube refining byproducts 

could potentially be used as feedstocks for manufacture of durable goods, but such byproducts are more likely to be used 

in emissive uses. Lube refining byproducts and absorption oils are liquids and are precluded from disposal in landfills. 

Because no sequestering end uses of any of the miscellaneous products subcategories have been identified, a zero percent 

storage factor is assigned to miscellaneous products. According to EIA (2008), the C content of miscellaneous petroleum 

products in 2007 was approximately 20.3 Tg C. One hundred percent of the C content is assumed to be emitted to the 

atmosphere, where it is oxidized to CO2. 

Uncertainty 

A separate uncertainty analysis was not conducted for miscellaneous products, though this category was included 

in the uncertainty analysis of other non-energy uses discussed in the following section. 
A-92 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Other Non-Energy Uses 

The remaining fuel types use storage factors that are not based on U.S.-specific analysis. For industrial coking 

coal and distillate fuel oil, storage factors were taken from IPCC (2006), which in turn draws from Marland and Rotty 

(1984). These factors are 0.1 and 0.5, respectively. 

IPCC does not provide guidance on storage factors for the remaining fuel types (petroleum coke, miscellaneous 

products, and other petroleum), ,and assumptions were made based on the potential fate of C in the respective NEUs. 

Specifically, the storage factor for petroleum coke is 0.3, based on information from Huurman (2006) indicating that 

petroleum coke is used in the Netherlands for production of pigments, with 30% being stored long-term. EIA defines 

“miscellaneous products” as “all finished products not classified elsewhere (e.g., petrolatum, lube refining byproducts 

(aromatic extracts and tars), absorption oils, ram-jet fuel, petroleum rocket fuels, synthetic natural gas feedstocks, and 

specialty oils).” All of these uses are emissive, and therefore the storage factor for miscellaneous products is set at zero 

(EIA 2007). The “other petroleum” category is reported by U.S. territories and accounts mostly for the same products as 

miscellaneous products, but probably also includes some asphalt, known to be non-emissive. The exact amount of asphalt 

or any of the other miscellaneous products is confidential business information, but based on judgment the storage factor 

for this category was estimated at 0.1. 

For all these fuel types, the overall methodology simply involves multiplying C content by a storage factor, 

yielding an estimate of the mass of C stored. To provide a complete analysis of uncertainty for the entire NEU 

subcategory, the uncertainty around the estimate of “other” NEUs was characterized, as discussed below. 

Uncertainty 

A Tier 2 Monte Carlo analysis was performed using @RISK software to determine the level of uncertainty 

surrounding the weighted average of the remaining fuels’ C storage factors and the total quantity of C emitted from these 

other fuels in 2007. A Tier 2 analysis was performed to allow the specification of probability density functions for key 

variables, within a computational structure that mirrors the calculation of the Inventory estimate. Statistical analyses or 

expert judgments of uncertainty were not available directly from the information sources for some of the activity variables; 

thus, uncertainty estimates were determined using assumptions based on source category knowledge. A uniform 

distribution was applied to coking coal consumption, while the remaining consumption inputs were assumed to be 

normally distributed. The C content coefficients were assumed to have a uniform distribution; the greatest uncertainty 

range, 10 percent, was applied to coking coal and miscellaneous products. C coefficients for distillate fuel oil ranged from 

19.52 to 20.15 Tg C/QBtu. The fuel-specific storage factors were assigned wide triangular distributions indicating greater 

uncertainty. 

The Monte Carlo analysis produced a storage factor distribution that approximates a normal curve around a mean 

of 39 percent, with a standard deviation of 12 percent and 95 percent confidence limits of 17 percent and 64 percent. This 

compares to the calculated, weighted average (across the various fuels) storage factor of 17 percent. The analysis 

produced an emission distribution approximating a normal curve with a mean of 23.5 Tg CO2 and a standard deviation of 

5.0 Tg CO2, and 95 percent confidence limits of13.7 Tg CO2 and 33.0 Tg CO2. This compares with the Inventory estimate 

of 31.9 Tg CO2, which falls closer to the upper boundary of the confidence limit. The uncertainty analysis results are 

driven primarily by the very broad uncertainty inputs for the storage factors. 

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A-97

ANNEX 3 Methodological Descriptions for 

Additional Source or Sink Categories 

3.1. Methodology for Estimating Emissions of CH4, N2O, and Indirect Greenhouse 

Gases from Stationary Combustion 

Estimates of CH4 and N2O Emissions 

Methane (CH4) and nitrous oxide (N2O) emissions from stationary combustion were estimated using IPCC 

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

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

below. Data are presented in Table A-71 through Table A-75. 

Step 1: Determine Energy Consumption by Sector and Fuel Type 

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

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

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

December 2008 and Published Supplemental Tables on Petroleum Product detail (EIA 2008a). Wood consumption data 

for the United States was obtained from EIA’s Annual Energy Review (EIA 2008b). Because the United States does not 

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

EIA from Grillot (2008).22 Fuel consumption for the industrial sector was adjusted to subtract out construction and 

agricultural use, which is reported under mobile sources.23 Construction and agricultural fuel use was obtained from EPA 

(2006). 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-71 provides annual energy consumption data for the years 1990 through 2007. 

Step 2: Determine the Amount of CH4 and N2O Emitted 

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

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

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

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

estimated based on the emission factor for the primary sector in which each fuel was combusted. Table A-72 provides 

emission factors used for each sector and fuel type. 

Estimates of NOx, CO, and NMVOC Emissions 

Emissions estimates for NOx, CO, and NMVOCs were obtained from preliminary data (EPA 2008) and 

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

(NEI) Air Pollutant Emission Trends web site. 

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

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

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

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

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

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

 

22 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. 

23 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-98 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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. Table A-73 through Table A-75 

present indirect greenhouse gas emission estimates for 1990 through 2007. 

The basic calculation procedure for most source categories presented in EPA (2003) and EPA (2008) is 

represented by the following equation: 

Ep,s = As × EFp,s × (1 - Cp,s/100) 

Where, 

 E = Emissions 

 p = Pollutant 

 s = Source category 

 A = Activity level 

 EF = Emission factor 

 C = Percent control efficiency 

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

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

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

combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/IEA 1997). 
Table A-71: Fuel Consumption by Stationary Combustion for Calculating CH4 and N2O Emissions (TBtu) Fuel/End-Use Sector 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Coal 18,014 17,977 18,146 18,889 18,958 19,095 19,983 20,494 20,696 20,722 21,675 21,082 21,144 21,570 

Residential 31 25 26 26 21 17 16 16 12 14 11 11 12 12 Commercial 124 115 117 117 117 116 120 129 101 102 86 88 88 83 Industrial 1,593 1,581 1,531 1,543 1,551 1,486 1,409 1,436 1,356 1,317 1,349 1,358 1,244 1,249 

Electric Power 16,259 16,248 16,464 17,194 17,259 17,465 18,428 18,903 19,216 19,279 20,220 19,614 19,783 20,185 

U.S. Territories 7 8 9 10 10 10 10 10 11 10 10 11 18 41 

Petroleum 6,649 6,355 6,369 6,087 6,127 5,529 6,025 5,905 5,774 5,899 6,074 6,684 5,954 6,675 

Residential 1,382 1,387 1,382 1,358 1,325 1,293 1,436 1,343 1,243 1,377 1,453 1,472 1,366 1,503 

Commercial 828 838 751 705 713 674 699 624 585 583 652 676 601 714 Industrial 2,774 2,507 2,800 2,440 2,524 2,346 2,638 2,565 2,194 2,267 2,353 2,627 2,469 2,631 Electric Power 1,289 1,198 991 1,124 1,059 755 817 927 1,306 1,211 1,144 1,277 961 1,205 

U.S. Territories 375 425 445 460 506 462 435 445 445 461 472 632 557 622 

Natural Gas 18,197 18,647 19,364 19,828 20,187 21,118 21,541 21,685 21,291 21,257 22,149 21,355 22,305 21,831 

Residential 4,474 4,643 4,778 5,054 4,941 4,944 5,350 5,088 4,637 4,822 5,067 4,872 4,971 5,207 

Commercial 2,668 2,780 2,855 2,916 2,952 3,091 3,225 3,283 3,076 3,111 3,234 3,089 3,208 3,277 Industrial 7,730 7,834 8,203 8,305 8,301 8,764 9,091 9,174 8,888 8,408 8,524 7,882 8,318 8,065 Electric Power 3,326 3,391 3,528 3,552 3,993 4,320 3,876 4,140 4,690 4,917 5,311 5,488 5,785 5,256 

U.S. Territories 0 0 0 0 0 0 0 0 0 0 13 23 23 27 

Wood 2,216 2,214 2,313 2,260 2,324 2,370 2,437 2,371 2,184 2,214 2,262 2,006 1,995 2,002 

Residential 580 610 640 550 520 520 540 430 380 390 420 370 380 400 Commercial 66 68 72 76 72 72 76 73 64 67 71 67 69 71 Industrial 1,442 1,410 1,461 1,484 1,580 1,652 1,683 1,731 1,603 1,620 1,636 1,443 1,396 1,363 

Electric Power 129 126 140 150 152 125 138 137 137 138 134 126 150 167 U.S. Territories 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-100 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-72: CH4 and N2O Emission Factors by Fuel Type and Sector (g/GJ)24

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) 

 

24 GJ (Gigajoule) = 109

 joules. One joule = 9.486×10-4 Btu 
Table A-73: NOx Emissions from Stationary Combustion (Gg) Sector/Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Electric Power 6,045 5,914 5,900 6,034 5,956 5,792 5,581 5,683 5,637 5,183 4,829 4,453 4,265 4,007 

Coal 5,119 5,043 5,061 5,211 5,113 5,061 5,079 5,118 4,932 4,437 4,130 3,802 3,634 3,414 Fuel Oil 200 192 154 163 148 87 107 131 202 179 147 149 142 133 Natural gas 513 526 526 500 536 510 248 277 329 393 376 325 310 292 Wood NA NA NA NA NA NA 5 6 24 33 36 37 36 34 Other Fuelsa NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

Internal Combustion 213 152 159 160 159 134 142 150 149 141 140 140 143 134 

Industrial 2,559 2,498 2,575 2,645 2,646 2,650 2,666 2,614 2,570 2,283 2,278 2,296 1,699 1,699 

Coal 530 517 521 534 546 541 490 487 475 475 484 518 384 384 Fuel Oil 240 215 222 222 219 224 203 196 190 190 166 153 114 114 Natural gas 877 929 969 993 1,000 999 900 880 869 706 710 711 526 526 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

Other Fuelsa 119 117 115 113 113 111 109 103 104 100 109 116 86 86 

Internal Combustion 792 720 749 783 767 774 965 948 932 813 809 798 591 591 

Commercial 336 333 348 360 365 365 360 369 347 255 257 261 247 246 

Coal 36 33 35 37 36 35 30 32 34 23 21 21 19 19 Fuel Oil 88 80 84 84 86 94 86 88 73 54 52 52 50 49 Natural gas 181 191 204 211 215 210 224 229 220 156 161 165 157 156 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

Other Fuelsa 31 29 25 28 28 27 20 21 21 22 22 22 21 21 

Residential 749 829 879 827 817 813 726 699 651 441 439 446 423 421 

Coalb 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 Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA NA Wood 42 45 48 40 40 44 27 27 27 25 21 22 21 21 

Other Fuelsa 707 784 831 786 778 769 699 671 624 416 417 424 402 401 

Total 9,689 9,574 9,703 9,865 9,783 9,619 9,333 9,364 9,206 8,162 7,802 7,456 6,634 6,375 NA (Not Applicable) a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2008). 

Note: Totals may not sum due to independent rounding. 
A-102 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-74: CO Emissions from Stationary Combustion (Gg) 

Sector/Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Electric Power 329 317 318 329 336 337 369 385 410 450 439 439 595 594 

Coal 213 212 214 224 224 227 228 233 220 187 221 220 298 298 

Fuel Oil 18 17 14 15 13 9 11 13 17 36 27 28 38 37 

Natural gas 46 46 47 45 48 49 72 76 88 150 96 92 125 125 

Wood NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Other Fuelsa NA NA NA NA N

A NA 7 8 30 24 31 32 44 44 

Internal Combustion 52 41 43 46 50 52 52 54 55 52 63 67 91 91 

Industrial 797 835 866 946 944 958 1,078 1,054 1,044 1,100 1,106 1,137 1,149 1,149 

Coal 95 92 92 92 91 88 100 99 96 114 118 125 127 127 

Fuel Oil 67 54 58 60 60 64 49 47 46 54 48 45 46 46 

Natural gas 205 257 272 292 306 313 307 307 305 350 355 366 370 370 

Wood NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Other Fuelsa 253 242 239 259 260 270 316 302 303 286 300 321 325 325 

Internal Combustion 177 189 205 244 228 222 305 299 294 296 285 279 282 282 

Commercial 205 196 204 207 212 211 122 126 122 151 151 154 177 173 

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

Fuel Oil 16 16 16 16 16 17 17 18 15 17 17 17 20 19 Natural gas 40 40 46 48 49 49 58 59 57 81 83 84 97 95 Wood NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Other Fuelsa 136 128 128 129 134 132 34 36 36 36 36 38 44 43 

Residential 3,668 3,965 4,195 3,586 3,515 3,877 2,364 2,361 2,352 3,323 2,644 2,648 3,044 2,986 

Coalb NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Fuel Oilb NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Natural Gasb NA NA NA NA N

A NA NA N

A NA N

A NA NA N

A NA 

Wood 3,430 3,710 3,930 3,337 3,272 3,629 2,133 2,133 2,133 3,094 2,416 2,424 2,787 2,734 

Other Fuelsa 238 255 265 249 243 248 231 229 220 229 228 224 257 252 

Total 5,000 5,313 5,583 5,069 5,007 5,383 3,934 3,926 3,928 5,024 4,340 4,377 4,965 4,902 

NA (Not Applicable) a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2008). 

Note: Totals may not sum due to independent rounding. 
Table A-75: NMVOC Emissions from Stationary Combustion (Gg) 

Sector/Fuel Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Electric Power 43 40 40 41 41 40 44 47 51 49 56 55 44 44 

Coal 24 24 25 26 26 26 25 26 26 25 27 26 21 21 Fuel Oil 5 5 4 4 4 2 3 4 5 4 4 4 4 4 Natural gas 2 2 2 2 2 2 7 7 9 9 12 12 10 10 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

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

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

Industrial 165 178 170 169 178 187 163 160 159 156 157 160 138 138 

Coal 7 5 7 5 7 5 6 6 6 9 9 10 9 9 Fuel Oil 11 10 11 11 11 11 8 7 7 10 9 9 7 7 Natural gas 52 54 48 46 57 66 54 54 54 52 53 54 47 47 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

Other Fuelsa 46 47 45 46 45 45 33 31 31 26 27 29 25 25 

Internal Combustion 49 61 60 60 58 60 63 62 61 60 58 57 49 49 

Commercial 18 18 20 22 21 21 22 22 21 25 28 29 61 60 

Coal 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Fuel Oil 3 2 3 3 3 3 3 3 3 3 4 4 6 6 Natural gas 7 8 9 10 10 10 13 13 12 11 14 14 23 22 Wood NA NA NA NA NA NA NA NA NA NA NA NA NA NA 

Other Fuelsa 8 7 7 8 8 8 5 5 5 10 9 10 31 30 

Residential 686 739 782 670 657 725 788 788 786 815 837 837 1,341 1,298 

Coalb 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 Natural Gasb NA NA NA NA NA NA NA NA NA NA NA NA NA NA Wood 651 704 746 633 621 688 756 757 756 794 809 809 1,298 1,256 

Other Fuelsa 35 35 36 36 36 37 33 32 30 21 27 27 43 42 

Total 912 975 1,012 901 897 973 1,018 1,017 1,016 1,045 1,077 1,081 1,585 1,540 NA (Not Applicable) a “Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008). b Residential coal, fuel oil, and natural gas emissions are included in the “Other Fuels” category (EPA 2003, 2008). 

Note: Totals may not sum due to independent rounding. 
A-104 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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 all other 

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

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

Administration (EIA 2007b and EIA 2007 through 2008) and apportioned to individual modes (considered a “top down” 

approach). 

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

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

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

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

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

VM-1 (FHWA 1996 through 2008), 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 2008) 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 with the fuel consumption estimates in Highway Statistics’ Table MF-21 (FHWA 1996 through 2008). 

This resulted in a final estimate of motor gasoline and diesel fuel use by vehicle type, consistent with the FHWA total for 

on-road motor gasoline and diesel fuel use. 

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

2008) for Class I railroads, the American Public Transportation Association (APTA 2007 through 2008 and APTA 2006) 

and Gaffney (2007) for commuter rail, the Upper Great Plains Transportation Institute (Benson 2002 through 2004) and 

Whorton (2006 through 2008) for Class II and III railroads, and DOE’s Transportation Energy Data Book (DOE 1993 

through 2008) for passenger rail. Estimates of diesel from ships and boats were taken from EIA’s Fuel Oil and Kerosene 

Sales (1991 through 2008). 

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 2006), and these estimates along 

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

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

sectors were adjusted downward proportionately. 

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

to specific transportation sources. For jet fuel, estimates come from: DOT (1991 through 2008) and FAA (2006) for 

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

Table A- 76 displays estimated fuel consumption by fuel and vehicle type. Table A- 77 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 subtracted out to calculate CO2 emissions. 
A-105

Table A- 76. Fuel Consumption by Fuel and Vehicle Type (million gallons unless otherwise specified) 

Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 

Motor Gasolinea 110,399.4 117,748.8 129,109.4 134,128.7 132,836.1 132.464.6 

 Passenger Cars 69,761.8 67,667.1 72,747.5 74,295.9 71,580.2 70,502.6 

 Light-Duty Trucks 34,698.1 44,185.8 50,695.6 54,053.2 55,408.5 56,079.9 

 Motorcycles 194.0 199.5 209.5 182.8 212.0 230.8 

 Buses 38.9 41.6 43.5 41.5 42.4 41.9 

 Medium- and Heavy- Duty 

Trucks 4,349.8 4,054.7 4,089.2 3,945.6 4,004.6 4,041.0 

 Recreational Boatsb 1,356.9 1,600.1 1,324.0 1,609.7 1,588.4 1,568.3 

Distillate Fuel Oil (Diesel 

Fuel) 25,744.2 31,140.8 38,904.8 44,647.7 45,801.6 46,385.0 

 Passenger Cars 770.5 762.0 354.8 413.7 402.5 399.3 

 Light-Duty Trucks 1,117.5 1,446.3 1,953.8 2,516.8 2,605.2 2,655.7 

 Buses 780.2 847.8 993.1 1,042.4 1,073.3 1,069.7 

 Medium- and Heavy-Duty 

Trucks 18,557.4 23,149.7 30,074.9 35,138.0 36,013.2 36,601.6 

 Recreational Boats 190.4 228.1 265.6 305.4 313.4 321.4 

 Ships and Other Boats 867.4 843.4 1,156.8 785.3 729.5 799.8 

 Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,537.5 

Jet Fuelc 18,381.1 17,922.9 18,866.7 17,983.8 17,483.8 17,122.0 

 Commercial Air craft 14,128.0 14,856.5 15,976.6 14,984.1 14,546.7 14,198.2 

 General Aviation Aircraft 663.9 562.0 894.9 1,313.4 1,418.9 1,459.2 

 Military Aircraft 3,589.1 2,504.4 1,995.2 1,686.2 1,518.2 1,464.6 

Aviation Gasolineb 374.2 329.3 301.9 294.3 278.3 266.2 

 General Aviation Aircraft 374.2 329.3 301.9 294.3 278.3 266.2 

Residual Fuel Oilc, d 2,006.2 2,587.4 2,962.6 1,713.1 2,046.3 2,174.2 

 Ships and Other Boats 2,006.2 2,587.4 2,962.6 1,713.1 2,046.3 2,174.2 

Natural Gas c (million cubic 

feet) 0.7 0.7 0.7 0.6 0.6 0.6 

 Passenger Cars - + - - - - 

 Light-Duty Trucks - + - - - - 

 Buses - + + + + + 

 Pipelines 0.7 0.7 0.6 0.6 0.6 0.6 

LPGc 250.6 194.1 130.1 308.7 300.7 304.0 

 Buses - 1.5 1.4 1.0 1.0 1.0 

 Light-Duty Trucks 100.2 92.9 82.8 233.2 215.6 218.0 

 Medium- and Heavy-Duty 

Trucks 150.3 99.7 45.9 74.6 84.1 85.0 

Electricityc 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 7,737.7 

 Rail 4,751.2 4,974.5 5,381.9 7,506.4 7,357.6 7,737.7 a Figures do not include ethanol blended in motor gasoline. 

b Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates. c Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up activity data used for apportionment to modes. d Fluctuations in reported fuel consumption may reflect data collection problems. 

+ Less than 0.05 million gallons or 0.05 million cubic feet 

- Unreported or zero 
A-106 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 77: Energy Consumption by Fuel and Vehicle Type (Tbtu) 

Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007 

Motor Gasolinea 13,808 14,620 16,016 16,664 16,503 16,460 

 Passenger Cars 8,725 8,402 9,024 9,230 8,893 8,761 

 Light-Duty Trucks 4,340 5,486 6,289 6,715 6,884 6,969 

 Motorcycles 24 25 26 23 26 29 

 Buses 5 5 5 5 5 5 

 Medium- and Heavy-Duty 

Trucks 544 503 507 490 498 502 

 Recreational Boatsb 170 199 164 200 197 195 

Distillate Fuel Oil (Diesel 

Fuel) 3,570 4,319 5,396 6,192 6,352 6,433 

 Passenger Cars 107 106 49 57 56 55 

 Light-Duty Trucks 155 201 271 349 361 368 

 Buses 108 118 138 145 149 148 

 Medium- and Heavy-Duty 

Trucks 2,574 3,211 4,171 4,873 4,995 5,076 

 Recreational Boats 26 32 37 42 43 45 

 Ships and Other Boats 120 117 160 109 101 111 

 Rail 480 536 569 617 647 629 

Jet Fuelc 2,481 2,420 2,547 2,428 2,360 2,311 

 Commercial Aircraft 1,907 2,006 2,157 2,023 1,964 1,917 

 General Aviation Aircraft 90 76 121 177 192 197 

 Military Aircraft 485 338 269 228 205 198 

Aviation Gasolinec 45 40 36 35 33 32 

 General Aviation Aircraft 45 40 36 35 33 32 

Residual Fuel Oilc, d 300 387 443 256 306 325 

 Ships and Other Boats 300 387 443 256 306 325 

Natural Gasc 683 728 672 626 631 667 

 Passenger Cars - 2 - - - - 

 Light-Duty Trucks - + - - - - 

 Buses - 1 8 16 16 16 

 Pipelines 683 725 663 610 615 651 

LPGc 22 17 11 27 26 26 

 Buses - + + + + + 

 Light-Duty Trucks 9 8 7 20 19 19 

 Medium- and Heavy-Duty 

Trucks 13 9 4 6 7 7 

Electricityc 16 17 18 26 25 26 

 Rail 16 17 18 26 25 26 

Total 20,926 22,547 25,139 26,254 26,237 26,282 a Figures do not include ethanol blended in motor gasoline. 

b Fluctuations in recreational boat gasoline estimates reflect the use of this category to reconcile bottom-up values with EIA total gasoline estimates. c Estimated based on EIA transportation sector energy estimates, with bottom-up data used for apportionment to modes d Fluctuations in reported fuel consumption may reflect data collection problems. 

+ Less than 0.5 million Tbtu 

- Unreported or zero 

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. 
 

A-107

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,25 buses, and 

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

through 2008). 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 2008) 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 2008). These fuel shares are drawn 

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

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

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

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

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

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

category.26 The resulting national VMT estimates for gasoline and diesel on-road vehicles are presented in Table A- 78 

and Table A- 79, 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 2007 in Table A- 82. Distributions for 1990-2007 are 

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

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

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

EPA’s MOVES model for years 1999 forward (EPA 2007c).27 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- 86 through Table A- 89. The categories “EPA Tier 0” and “EPA Tier 1” were used instead of the early three-way 

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

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

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

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

correspond to the introduction of EPA Tier 0 and EPA Tier 1 regulations (EPA 1998).28 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. 

 

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

include those with a GVWR of over 14,000 lbs. 

26 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. 

27 Age distributions were held constant for the period 1990-1998, and reflect a 25-year vehicle age span. EPA (2007c) provides a 

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

28 For further description, see “Definitions of Emission Control Technologies and Standards” section of this annex. 
A-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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

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

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

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

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

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

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

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

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

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

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

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

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

technology type by the emission factors developed in Step 3. 

Methodology for Alternative Fuel Vehicles (AFVs) 

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

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

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

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

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

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

or some combination.29 The data obtained include vehicle fuel use and total number of vehicles in use from 1992 through 

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

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

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

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

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

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

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

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

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

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

(passenger car, light-duty truck, heavy-duty vehicles) are shown in Table A- 80, while more detailed estimates of VMT by 

control technology are shown in Table A- 81. 

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- 91. 

 

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

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

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

likely to run on a combination of diesel fuel and either natural gas, LPG, ethanol, or methanol. 
 

A-109

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

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

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

consumption, and this total was reduced by the amount of fuel used for international bunkers.30 Gasoline consumption by 

recreational boats was obtained from EPA’s NONROAD model (EPA 2006b). Annual diesel consumption for Class I rail 

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

obtained from APTA (2007 through 2008 and 2006) and Gaffney (2007), and consumption by Class II and III rail was 

provided by Benson (2002 through 2004) and Whorton (2006 through 2008). Diesel consumption by commuter and 

intercity rail was obtained from DOE (1993 through 2008). Data on the consumption of jet fuel and aviation gasoline in 

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

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

consumption was obtained from EIA (2007 through 2008) (note: pipelines are a transportation source but are stationary, 

not mobile, sources). Data on fuel consumption by all non-transportation mobile sources31 were obtained from EPA’s 

NONROAD model (EPA 2006b) and from FHWA (1996 through 2008 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- 92). 

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 2008), 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- 93 through Table A- 95 provide complete emission estimates for 1990 through 2007. 

Table A- 78: 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 23.8 10.1 

 

30 See International Bunker Fuels section of the Energy Chapter. 

31 “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-110 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

2005 1,698.5 997.2 23.9 10.5 

2006 1,680.8 1,037.2 24.0 12.0 

2007 1,661.3 1,064.9 24.5 13.6 

Source: Derived from FHWA (1996 through 2008). 

Table A- 79: Vehicle Miles Traveled for Diesel On-Road Vehicles (109 Miles) 

Year Passenger 

Cars 

Light-Duty 

Trucks 

Heavy-Duty 

Vehicles 

1990 16.9 19.7 125.5 

1991 16.3 21.6 129.3 

1992 16.5 23.4 133.5 

1993 17.9 24.7 140.3 

1994 18.3 25.3 150.5 

1995 17.3 26.9 158.7 

1996 14.7 27.8 164.3 

1997 13.5 29.0 173.4 

1998 12.4 30.5 178.4 

1999 9.4 32.6 185.3 

2000 8.0 35.2 188.0 

2001 8.1 37.0 191.1 

2002 8.3 38.9 196.4 

2003 8.3 39.6 199.1 

2004 8.4 41.3 201.6 

2005 8.4 41.9 202.8 

2006 8.3 43.5 201.4 

2007 8.1 44.6 204.5 

Source: Derived from FHWA (1996 through 2008). 

Table A- 80: Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (109 Miles) 

Year Passenger 

Cars 

Light-Duty 

Trucks 

Heavy-Duty 

Vehicles 

1990 0.2 0.0 0.1 

1991 0.2 0.0 0.1 

1992 0.3 0.0 0.1 

1993 0.3 0.0 0.1 

1994 0.3 0.0 0.1 

1995 0.4 0.0 0.1 

1996 0.5 0.0 0.1 

1997 0.6 0.0 0.1 

1998 0.6 0.0 0.1 

1999 0.6 0.0 0.1 

2000 0.8 0.0 0.1 

2001 0.9 0.0 0.1 

2002 1.0 0.0 0.1 

2003 1.2 0.0 0.1 

2004 1.3 0.0 0.1 

2005 1.5 0.0 0.1 

2006 1.5 0.0 0.1 

2007 1.6 0.0 0.1 

Source: Derived from Browning (2003). 

Table A- 81: Detailed Vehicle Miles Traveled for Alternative Fuel On-Road Vehicles (106 Miles) 

Vehicle Type 1990 1995 2000 2005 2006 2007 

Passenger Cars 206.3 400.6 788.1 1,515.4 1,517.1 1,606.4 

 Methanol-Flex Fuel ICE 0.0 40.9 13.2 0.0 0.0 0.0 

 Ethanol-Flex Fuel ICE 0.0 2.2 120.4 399.1 432.8 500.2 

 CNG ICE 10.6 28.0 68.9 147.0 131.6 130.2 

 CNG Bi-fuel 28.2 75.1 202.9 253.3 244.5 232.5 

 LPG ICE 20.6 40.3 41.9 50.0 37.3 31.0 

 LPG Bi-fuel 146.9 201.7 197.6 250.2 198.8 148.8 

 Biodiesel (BD20) 0.0 0.0 8.2 125.1 186.4 248.0 

 NEVs 0.0 5.2 62.4 135.5 130.5 130.8 

 Electric Vehicle 0.0 7.2 72.6 155.2 155.2 184.9 
 

A-111

Light-Duty Trucks 660.7 606.8 1,161.6 1,968.6 1,841.2 1,780.8 

 Ethanol-Flex Fuel ICE 0.0 1.3 122.6 415.0 516.3 610.4 

 CNG ICE 10.9 29.6 145.9 161.1 158.3 159.0 

 CNG Bi-fuel 24.2 71.0 280.1 432.8 429.2 333.8 

 LPG ICE 56.9 48.5 58.4 89.9 56.0 39.2 

 LPG Bi-fuel 568.7 449.4 511.9 684.1 439.6 335.0 

 Biodiesel (BD20) 0.0 0.0 7.8 109.2 164.8 224.0 

 Electric Vehicle 0.0 7.1 35.0 76.4 76.9 78.4 

Medium-Duty Trucks 508.0 458.4 629.7 1,107.0 992.9 1,063.2 

 CNG Bi-fuel 2.3 20.1 117.0 290.8 322.4 341.0 

 LPG ICE 24.3 20.0 29.7 52.8 42.3 40.2 

 LPG Bi-fuel 481.4 418.3 475.9 687.3 483.2 483.6 

 Biodiesel (BD20) 0.0 0.0 7.1 76.1 145.0 198.4 

Heavy-Duty Trucks 523.9 627.0 716.2 1,490.8 2,519.5 3,403.7 

 Neat Methanol ICE 3.0 7.8 0.0 0.0 0.0 0.0 

 Neat Ethanol ICE 0.0 2.0 0.1 0.0 0.0 0.0 

 CNG ICE 12.7 32.2 83.7 186.4 225.8 411.3 

 LPG ICE 36.3 46.3 48.3 82.8 65.8 70.0 

 LPG Bi-fuel 471.9 531.9 529.7 638.4 524.3 546.0 

 LNG 0.0 6.9 22.2 117.3 125.0 136.4 

 Biodiesel (BD20) 0.0 0.0 32.2 465.9 1,578.6 2,240.0 

Buses 41.4 80.5 112.6 275.9 375.1 433.9 

 Neat Methanol ICE 1.9 3.7 0.0 0.0 0.0 0.0 

 Neat Ethanol ICE 0.1 2.2 0.0 0.0 0.0 0.0 

 CNG ICE 11.2 37.5 53.4 122.9 128.1 170.0 

 LPG ICE 28.2 30.9 35.6 40.7 38.3 37.4 

 LNG 0.0 4.3 13.3 27.8 28.9 29.4 

 Biodiesel (BD20) 0.0 0.0 5.2 78.1 173.4 190.8 

 Electric 0.0 2.0 5.1 6.5 6.4 6.4 

Total VMT 1,940.3 2,173.4 3,408.3 6,357.7 7,245.9 8,288.0 

Source: Derived from Browning (2003). 

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

run on a slightly different range of fuel types. 

Table A- 82: Age Distribution by Vehicle/Fuel Type for On-Road Vehicles,a 2007 

Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MC 

0 6.19% 7.53% 6.30% 6.19% 7.53% 6.30% 8.64% 

1 5.84% 8.11% 6.28% 5.84% 8.11% 6.28% 8.08% 

2 5.72% 8.19% 5.73% 5.72% 8.19% 5.73% 7.70% 

3 5.75% 7.62% 4.76% 5.75% 7.62% 4.76% 7.45% 

4 6.08% 7.32% 4.49% 6.08% 7.32% 4.49% 7.12% 

5 6.25% 7.17% 4.98% 6.25% 7.17% 4.98% 7.04% 

6 6.48% 6.75% 6.15% 6.48% 6.75% 6.15% 7.10% 

7 6.25% 6.30% 6.61% 6.25% 6.30% 6.61% 6.64% 

8 5.72% 5.47% 5.15% 5.72% 5.47% 5.15% 6.22% 

9 5.56% 4.73% 4.45% 5.56% 4.73% 4.45% 4.72% 

10 5.50% 4.40% 4.14% 5.50% 4.40% 4.14% 3.99% 

11 5.40% 3.84% 4.75% 5.40% 3.84% 4.75% 3.41% 

12 5.28% 3.58% 4.15% 5.28% 3.58% 4.15% 3.00% 

13 4.58% 2.94% 3.22% 4.58% 2.94% 3.22% 2.57% 

14 3.72% 2.35% 2.36% 3.72% 2.35% 2.36% 2.00% 

15 3.06% 1.91% 2.37% 3.06% 1.91% 2.37% 1.62% 

16 2.85% 1.90% 2.89% 2.85% 1.90% 2.89% 1.47% 

17 2.40% 1.72% 3.27% 2.40% 1.72% 3.27% 1.32% 

18 2.00% 1.60% 2.99% 2.00% 1.60% 2.99% 1.42% 

19 1.45% 1.37% 2.54% 1.45% 1.37% 2.54% 1.58% 

20 1.16% 1.21% 2.67% 1.16% 1.21% 2.67% 1.37% 

21 0.84% 1.02% 2.14% 0.84% 1.02% 2.14% 1.17% 

22 0.59% 0.77% 1.48% 0.59% 0.77% 1.48% 0.98% 
A-112 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

23 0.39% 0.53% 1.47% 0.39% 0.53% 1.47% 0.82% 

24 0.24% 0.39% 0.84% 0.24% 0.39% 0.84% 0.68% 

25 0.20% 0.30% 0.77% 0.20% 0.30% 0.77% 0.55% 

26 0.15% 0.29% 0.76% 0.15% 0.29% 0.76% 0.44% 

27 0.12% 0.23% 0.63% 0.12% 0.23% 0.63% 0.34% 

28 0.09% 0.20% 0.72% 0.09% 0.20% 0.72% 0.25% 

29 0.07% 0.14% 0.41% 0.07% 0.14% 0.41% 0.17% 

30 0.06% 0.15% 0.53% 0.06% 0.15% 0.53% 0.15% 

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

Source: EPA (2007c). 

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). 

Table A- 83: Annual Average Vehicle Mileage Accumulation per Vehicle a (miles) 

Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MCb

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

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

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

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

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

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

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

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

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

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

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

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

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

13 7,723 7,925 8,662 7,723 8,420 8,962 1,368

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Source: EPA (2000). 

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

LDDV (light-duty diesel vehicles), LDDT (light-duty diesel trucks), HDDV (heavy-duty diesel vehicles), and MC (motorcycles). b Because of a lack of data, all motorcycles over 12 years old are considered to have the same emissions and travel characteristics, and therefore are presented in 

aggregate. 

Table A- 84: VMT Distribution by Vehicle Age and Vehicle/Fuel Type, a 2007 

Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MC 

0 9.09% 11.62% 10.84% 9.09% 12.87% 12.68% 14.30% 

1 8.16% 11.77% 10.12% 8.16% 12.69% 11.55% 12.50% 

2 7.60% 11.15% 8.34% 7.60% 11.72% 9.64% 11.08% 

3 7.26% 9.72% 6.73% 7.26% 9.98% 7.31% 9.92% 

4 7.29% 8.75% 5.94% 7.29% 8.78% 6.30% 8.72% 

5 7.14% 8.01% 6.18% 7.14% 7.88% 6.39% 7.87% 

6 7.02% 7.04% 7.14% 7.02% 6.79% 7.21% 7.17% 

7 6.44% 6.12% 7.19% 6.44% 5.81% 7.08% 5.99% 

8 5.61% 4.95% 5.25% 5.61% 4.62% 5.05% 4.94% 

9 5.18% 3.98% 4.26% 5.18% 3.66% 3.99% 3.24% 

10 4.87% 3.43% 3.71% 4.87% 3.12% 3.39% 2.32% 
 

A-113

11 4.55% 2.77% 3.99% 4.55% 2.50% 3.56% 1.61% 

12 4.23% 2.39% 3.27% 4.23% 2.13% 2.84% 1.42% 

13 3.49% 1.81% 2.38% 3.49% 1.60% 2.02% 1.21% 

14 2.69% 1.33% 1.61% 2.69% 1.17% 1.35% 0.95% 

15 2.10% 0.99% 1.53% 2.10% 0.88% 1.24% 0.77% 

16 1.86% 0.90% 1.76% 1.86% 0.80% 1.39% 0.70% 

17 1.49% 0.75% 1.86% 1.49% 0.66% 1.43% 0.62% 

18 1.18% 0.63% 1.59% 1.18% 0.56% 1.20% 0.67% 

19 0.81% 0.49% 1.27% 0.81% 0.45% 0.93% 0.75% 

20 0.62% 0.39% 1.25% 0.62% 0.36% 0.90% 0.65% 

21 0.43% 0.30% 0.94% 0.43% 0.28% 0.66% 0.55% 

22 0.28% 0.21% 0.61% 0.28% 0.19% 0.42% 0.46% 

23 0.18% 0.13% 0.57% 0.18% 0.12% 0.38% 0.39% 

24 0.11% 0.09% 0.30% 0.11% 0.08% 0.20% 0.32% 

25 0.09% 0.06% 0.28% 0.09% 0.06% 0.18% 0.26% 

26 0.07% 0.06% 0.28% 0.07% 0.06% 0.18% 0.21% 

27 0.05% 0.05% 0.23% 0.05% 0.05% 0.15% 0.16% 

28 0.04% 0.04% 0.26% 0.04% 0.04% 0.17% 0.12% 

29 0.03% 0.03% 0.15% 0.03% 0.03% 0.10% 0.08% 

30 0.03% 0.03% 0.19% 0.03% 0.03% 0.13% 0.07% 

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

Note: Estimated by weighting data in Table A- 82 by data in Table A- 83. 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). 

Table A- 85: Fuel Consumption for Off-Road Sources by Fuel Type (million gallons) 

Vehicle Type/Year 1990 1995 2000 2005 2006 2007 

Aircrafta 18,726.3 18,182.1 20,793.7 20,136.6 19,608.6 19,628.7 

 Gasoline 374.2 329.3 301.9 294.3 278.3 266.2 

 Jet Fuel 18,352.0 17,852.8 20,491.8 19,842.3 19,330.3 19,362.5 

Ships and Other Boats 4,665.0 5,443.6 6,217.7 4,893.7 5,169.0 5,365.4 

 Diesel 1,175.2 1,184.8 1,525.5 1,470.2 1,422.4 1,500.8 

 Gasoline 1,429.0 1,612.7 1,664.7 1,641.9 1,631.8 1,622.0 

 Residual 2,060.7 2,646.1 3,027.6 1,781.5 2,114.7 2,242.6 

Construction/Mining 

Equipmentb 4,160.5 4,834.8 5,439.3 6,520.7 6,656.6 6,683.6 

 Diesel 3,674.5 4,387.1 5,095.1 5,823.6 5,968.4 6,113.1 

 Gasoline 485.9 447.8 344.2 697.1 688.3 570.5 

Agricultural Equipmentc 3,133.7 3,698.3 3,874.5 4,715.0 4,947.8 4,861.5 

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

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

Rail 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,537.5 

 Diesel 3,460.6 3,863.5 4,105.9 4,446.1 4,664.5 4,537.5 

Otherd 5,915.7 6,523.8 6,825.7 8,282.3 8,397.4 8,246.9 

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

 Gasoline 4,492.4 4,803.7 4,809.8 5,942.0 5,992.0 5,776.3 

Total 40,061.7 42,546.1 47,256.9 48,994.2 49,443.9 49,323.7 

Sources: AAR (2008), APTA (2007 through 2008 and 2006), BEA (1991 through 2005), Benson (2002 through 2004), DHS (2008), DOC (1991 

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

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

a For aircraft, this is aviation gasoline. For all other categories, this is motor gasoline. b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

 

Table A- 86: 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% - - -
A-114 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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%

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-87: 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%

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

a Detailed descriptions of emissions control technologies are provided in the following section of this annex. b The proportion of LEVs as a whole has decreased since 2001, as carmakers have been able to achieve greater emission reductions with certain types of LEVs, such as 

ULEVs. Because ULEVs emit about half the emissions of LEVs, a carmaker can reduce the total number of LEVs they need to build to meet a specified emission average 

for all of their vehicles in a given model year.

- Not applicable. 

Table A- 88: 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% -
 

A-115

2000 - - - - 54% 46% -

2001 - - - - 64% 36% -

2002 - - - - 69% 31% -

2003 - - - - 65% 30% 5%

2004 - - - - 5% 37% 59%

2005 - - - - - 23% 77%

2006 - - - - - 22% 78%

2007 - - - - - 21% 79%

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- 89: 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 1996-2007 

Diesel Medium- and Heavy-Duty Trucks and Buses 

Uncontrolled 1960-1982 

Moderate control 1983-1995 

Advanced control 1996-2007 

Aftertreatment 2007 

Motorcycles 

Uncontrolled 1960-1995 

Non-catalyst controls 1996-2007 

Source: EPA (1998) and Browning (2005) 

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

Table A- 90: Emission Factors for CH4 and N2O for On-Road Vehicles 

Vehicle Type/Control Technology N2O CH4

(g/mi) (g/mi) 

Gasoline Passenger Cars 

 EPA Tier 2 0.0036 0.0173 

 Low Emission Vehicles 0.0150 0.0105 

 EPA Tier 1a 0.0429 0.0271 

 EPA Tier 0 a 0.0647 0.0704 

 Oxidation Catalyst 0.0504 0.1355 

 Non-Catalyst Control 0.0197 0.1696 

 Uncontrolled 0.0197 0.1780 

Gasoline Light-Duty Trucks 

 EPA Tier 2 0.0066 0.0163 

 Low Emission Vehicles 0.0157 0.0148 

 EPA Tier 1a 0.0871 0.0452 

 EPA Tier 0a 0.1056 0.0776 

 Oxidation Catalyst 0.0639 0.1516 

 Non-Catalyst Control 0.0218 0.1908 

 Uncontrolled 0.0220 0.2024 

Gasoline Heavy-Duty Vehicles 

 EPA Tier 2 0.0134 0.0333 

 Low Emission Vehicles 0.0320 0.0303 

 EPA Tier 1a 0.1750 0.0655 

 EPA Tier 0a 0.2135 0.2630 

 Oxidation Catalyst 0.1317 0.2356 

 Non-Catalyst Control 0.0473 0.4181 

 Uncontrolled 0.0497 0.4604 

Diesel Passenger Cars 

 Advanced 0.0010 0.0005 

 Moderate 0.0010 0.0005 

 Uncontrolled 0.0012 0.0006 

Diesel Light-Duty Trucks 

 Advanced 0.0015 0.0010 

 Moderate 0.0014 0.0009 
A-116 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 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- 91: Emission Factors for CH4 and N2O for Alternative Fuel Vehicles

 N2O (g/mi) CH4 (g/mi) 

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

Vehicle Type/Fuel Type N2O CH4

Ships and Boats 

Residual 0.08 0.23 

Distillate 0.08 0.23 

Gasoline 0.08 0.23 

Rail 

Diesel 0.08 0.25 

Agricultural Equipmenta 

Gasoline 0.08 0.45 

Diesel 0.08 0.45 

Construction/Mining 

Equipmentc 

Gasoline 0.08 0.18 

Diesel 0.08 0.18 

Other Non-Road 

All “Other” Categoriesc 0.08 0.18 

Aircraft 

Jet Fuel 0.10 0.087 

Aviation Gasoline 0.04 2.64 

Source: IPCC/UNEP/OECD/IEA (1997). 

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. 
 

A-117

Table A- 93: NOx Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 

Gasoline On-Road 5,746 4,560 3,812 3,062 2,851 2,658 

Passenger Cars 3,847 2,752 2,084 1,670 1,556 1,450 

Light-Duty Trucks 1,364 1,325 1,303 1,059 986 919 

Medium- and Heavy-Duty 

Trucks and Buses 515 469 411 321 299 279 

Motorcycles 20 14 13 11 10 10 

Diesel On-Road 2,956 3,493 3,803 2,751 2,562 2,388 

Passenger Cars 39 19 7 5 5 4 

Light-Duty Trucks 20 12 6 5 4 4 

Medium- and Heavy-Duty 

Trucks and Buses 2,897 3,462 3,791 2,741 2,553 2,380 

Alternative Fuel On-Roada IE IE IE IE IE IE 

Non-Road 2,218 2,569 2,695 2,944 2,858 2,785 

Ships and Boats 402 488 506 582 565 550 

Rail 338 433 451 519 504 491 

Aircraftb 25 31 40 42 41 40 

Agricultural Equipmentc 437 478 484 509 494 481 

Construction/Mining 

Equipmentd 641 697 697 731 709 691 

Othere 376 442 518 562 546 532 

Total 10,920 10,622 10,310 8,757 8,271 7,831 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. e “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Table A- 94: CO Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 

Gasoline On-Road 98,328 74,673 60,657 43,087 39,963 37,179 

Passenger Cars 60,757 42,065 32,867 24,006 22,265 20,714 

Light-Duty Trucks 29,237 27,048 24,532 17,149 15,906 14,798 

Medium- and Heavy-Duty 

Trucks and Buses 8,093 5,404 3,104 1,832 1,699 1,581 

Motorcycles 240 155 154 100 93 86 

Diesel On-Road 1,696 1,424 1,088 660 612 570 

Passenger Cars 35 18 7 4 4 4 

Light-Duty Trucks 22 16 6 4 4 3 

Medium- and Heavy-Duty 

Trucks and Buses 1,639 1,391 1,075 652 605 562 

Alternative Fuel On-Roada IE IE IE IE IE IE 

Non-Road 19,337 21,533 21,814 18,772 17,746 16,929 

Ships and Boats 1,559 1,781 1,825 1,544 1,460 1,392 

Rail 85 93 90 76 72 68 

Aircraftb 217 224 245 197 186 178 

Agricultural Equipmentc 581 628 626 524 495 472 

Construction/Mining 

Equipmentd 1,090 1,132 1,047 878 830 792 

Othere 15,805 17,676 17,981 15,553 14,704 14,027 

Total 119,360 97,630 83,559 62,519 58,322 54,678 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. e “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Table A- 95: NMVOCs Emissions from Mobile Combustion (Gg) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 

Gasoline On-Road 8,110 5,819 4,615 3,529 3,307 3,117 
A-118 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Passenger Cars 5,120 3,394 2,610 1,971 1,847 1,741 

Light-Duty Trucks 2,374 2,019 1,750 1,370 1,284 1,210 

Medium- and Heavy-Duty 

Trucks and Buses 575 382 232 170 159 150 

Motorcycles 42 24 23 18 17 16 

Diesel On-Road 406 304 216 171 160 151 

Passenger Cars 16 8 3 3 2 2 

Light-Duty Trucks 14 9 4 3 3 3 

Medium- and Heavy-Duty 

Trucks and Buses 377 286 209 165 155 146 

Alternative Fuel On-Roada IE IE IE IE IE IE 

Non-Road 2,415 2,622 2,399 2,593 2,487 2,404 

Ships and Boats 608 739 744 796 763 728 

Rail 33 36 35 39 37 36 

Aircraftb 28 28 24 21 20 19 

Agricultural Equipmentc 85 86 76 79 76 73 

Construction/Mining 

Equipmentd 149 152 130 136 131 126 

Othere 1,512 1,580 1,390 1,523 1,460 1,412 

Total 10,932 8,745 7,229 6,292 5,954 5,672 a NOx emissions from alternative fuel on-road vehicles are included under gasoline and diesel on-road. b Aircraft estimates include only emissions related to LTO cycles, and therefore do not include cruise altitude emissions. c Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. d Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. e “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

Note: Totals may not sum due to independent rounding. 

IE = Included Elsewhere 

Definitions of Emission Control Technologies and Standards 

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

of control technology for each vehicle type. Table A- 86 through Table A- 89 show the years in which these technologies 

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

Uncontrolled 

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

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

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

Gasoline Emission Controls 

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

Non-catalyst 

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

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

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

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

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

Oxidation Catalyst 

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

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

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

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

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

function properly with leaded gasoline. 
 

A-119

EPA Tier 0 

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

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

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

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

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

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

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

g/mile by 1981 and NOx to 1.0 g/mile by 1981. 

EPA Tier 1 

This emission standard created through the 1990 amendments to the Clean Air Act limited passenger car NOx

emissions to 0.4 g/mi, and HC emissions to 0.25 g/mi. These bounds respectively amounted to a 60 and 40 percent 

reduction from the EPA Tier 0 standard set in 1981. For light-duty trucks, this standard set emissions at 0.4 to 1.1 g/mi for 

NOx, and 0.25 to 0.39 g/mi for HCs, depending on the weight of the truck. Emission reductions were met through the use 

of more advanced emission control systems, and applied to light-duty gasoline vehicles beginning in 1994. These 

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

ignition timing, EGR, and air injection. 

EPA Tier 2 

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

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

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

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

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

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

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

injection. 

Low Emission Vehicles (LEV) 

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

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

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

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

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

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

and advanced technology vehicle assessments.

Diesel Emission Controls 

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

Moderate control 

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

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

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

1994. 
A-120 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Advanced control 

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

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

from 1996 through 2006. 

Aftertreatment

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

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

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

Supplemental Information on GHG Emissions from Transportation and Other Mobile Sources 

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

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

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

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

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

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

transportation estimates presented throughout the repot in one place. 

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

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

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

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

Estimation of CO2 from Non-Transportation Mobile Sources 

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

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

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

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

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

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

Table A- 96 provides estimates of CO2 from these other mobile sources, developed from EPA’s NONROAD model and 

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

utilized in developing the N2O and CH4 estimates. 

Table A- 96: CO2 Emissions from Non-Transportation Mobile Sources (Tg CO2 Eq.) 

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007 

Agricultural Equipmenta 30.8 36.3 38.4 46.4 48.6 47.9 

Construction/Mining 

Equipmentb 41.6 48.5 54.7 65.2 66.6 67.0 

Other Sourcesc 54.3 59.7 62.6 75.9 77.0 75.8 

Total 126.7 144.4 155.7 187.5 192.2 190.7 a Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. b Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. c “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

Estimation of HFC Emissions from Transportation Sources 

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

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

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

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

Comfort Cooling represents the emissions from air conditioning units in passenger trains and buses, and 

Refrigerated Transport represents the emissions from units used to cool freight during transportation. 

Table A- 97 below presents these HFC emissions. Table A- 98 presents all transportation and mobile 

source greenhouse gas emissions, including HFC emissions.
 

A-121

Table A- 97: HFC Emissions from Transportation Sources 

Vehicle Type 1990 1995 2000 2005 2006 2007 

Mobile AC + 16.2 42.9 56.2 55.6 52.9 

Passenger Cars + 10.1 24.3 28.5 27.2 24.9 

Light-Duty Trucks + 6.1 18.6 27.7 28.3 27.9 

Comfort Cooling for Trains 

and Buses + + 0.1 0.2 0.3 0.3 

School and Tour Buses + + 0.1 0.2 0.3 0.3 

Transit Buses + + + + + + 

Rail + + + + + + 

Refrigerated Transport + 2.3 9.6 13.2 13.6 13.8 

Medium- and Heavy-Duty 

Trucks + 0.3 1.6 2.1 2.2 2.2 

Rail + 1.4 4.6 6.4 6.5 6.6 

Ships and Other Boats + 0.6 3.4 4.7 4.9 4.9 

Total + 18.6 52.6 69.7 69.5 67.0 

Note: Totals may not sum due to independent rounding. 

+ Less than 0.05 Tg CO2 Eq. 

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

Mode/Vehicle Type/Fuel Type 

Table A- 98 presents estimates of greenhouse gas emissions from all transportation and other mobile sources in 

CO2 equivalents. In total, transportation and mobile sources emitted 2,192.6 Tg CO2 Eq. in 2007, an increase of 31 

percent from 1990. 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- 96 above. 

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

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

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

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

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

fuel type (jet fuel and aviation gasoline) by the portion of fuel used by each aircraft type (from FAA 2008 and 2006). 

Similarly, N2O and CH4 estimates were developed for medium- and heavy-duty trucks and buses by multiplying the 

emission estimates for heavy-duty vehicles for each fuel type (gasoline, diesel) from the Mobile Combustion sectionin the 

Energy chapter, by the portion of fuel used by each vehicle type (from DOE 1993 through 2008). 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 2,000.1 Tg 

CO2 Eq. in 2007. 

On-road vehicles were responsible for about 75 percent of transportation GHG emissions in 2007. 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 2007, 

greenhouse gas emissions from passenger cars increased 1 percent, while emissions from light-duty trucks increased 67 

percent, largely due to the increased use of sport-utility vehicles and other light-duty trucks. Meanwhile, greenhouse gas 

emissions from medium- and heavy-duty trucks increased 80 percent, reflecting the increased volume of total freight 

movement and an increasing share transported by trucks. 

Greenhouse gas emissions from aircraft increased 5 percent between 1990 and 2007, despite a substantial rise in 

passenger miles traveled. The overall increase occurred despite a large decline in emissions from military aircraft (54 

percent). Greenhouse gas emissions from commercial aircraft rose 23 percent between 1990 and 2000, but then declined in 

2001 and 2002, due largely to a decrease in air travel following the September 11, 2001 terrorist attacks. Commercial 

aircraft emissions rose 13 percent between 1990 and 2007. 
A-122 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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

industrial/commercial equipment, emitted approximately 192.5 Tg CO2 Eq. in 2007. Together, these sources emitted more 

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

increased rapidly, growing approximately 51 percent between 1990 and 2007. 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- 99 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 95 percent in 2007). Emissions of CO2 from transportation and mobile sources increased by 

467.1 Tg CO2 Eq. between 1990 and 2007. In contrast, the combined emissions of CH4 and N2O decreased by 16.1 Tg 

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

emissions.32 Meanwhile, HFC emissions from mobile air conditioners and refrigerated transport increased from virtually 

no emissions in 1990 to 67.0 Tg CO2 Eq. in 2007 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- 100 and Table A- 101 present greenhouse gas estimates from transportation, broken down into the 

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

aviation and commercial aircraft), recreational boats, and mobile air conditioners, and are illustrated in Table A- 100. 

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

pipelines, and commercial aircraft and are illustrated in Table A- 101. Commercial aircraft do carry some freight, in 

addition to passengers, and 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- 98. In addition, estimates of 

fuel consumption from DOE (1993 through 2008) were used to allocate rail emissions between passenger and freight 

categories. 

In 2007, passenger transportation modes emitted 1,415.1 Tg CO2 Eq., while freight transportation modes emitted 

558.7 Tg CO2 Eq. Since 1990, the rate of growth of greenhouse gas emissions from freight sources has been more than 

twice times as fast, due largely to the rapid increase in emissions associated with medium- and heavy-duty trucks. 

Table A- 98: Total U.S. Greenhouse Gas Emissions from Transportation and Mobile Sources (Tg CO2 Eq.) 

Mode / Vehicle Type / 

Fuel Type 1990 1995 2000 2005 2006 2007

Percent

Change

1990-2007

Transportation Totala 1,546.7 1,688.3 1,923.2 2,003.6 1,999.0 2,000.1 29%

On-Road Vehicles 1,231.9 1,362.4 1,560.1 1,659.4 1,654.2 1,651.6 34%

Passenger Cars 656.9 644.1 694.6 705.8 678.3 664.6 1%

 Gasoline 649.0 626.1 666.7 673.1 647.0 635.5 -2%

 Diesel 7.8 7.7 3.6 4.2 4.1 4.1 -48%

 AFVs + 0.1 + + + + 444%

 HFCs from Mobile AC + 10.1 24.3 28.5 27.2 24.9 NA

Light-Duty Trucks 336.2 434.7 508.3 544.8 557.1 561.7 67%

 Gasoline 324.3 413.4 469.4 490.3 501.1 505.6 56%

 Diesel 11.3 14.7 19.8 25.6 26.5 27.0 138%

 AFVs 0.6 0.5 0.5 1.3 1.2 1.2 116%

 HFCs from Mobile AC + 6.1 18.6 27.7 28.3 27.9 NA

Medium- and HeavyDuty Trucks 228.8 272.7 344.2 395.1 404.5 410.8 80%

Gasoline 39.5 36.7 36.9 35.6 36.0 36.3 -8%

 

32 The decline in CFC emissions is not captured in the official transportation estimates. 
 

A-123

Diesel 188.5 235.1 305.4 356.8 365.7 371.7 97%

AFVs 0.9 0.6 0.3 0.5 0.6 0.6 -30%

HFCs from Refrigerated 

Transport + 0.3 1.6 2.1 2.2 2.2 NA

Buses 8.3 9.1 11.1 12.1 12.4 12.4 50%

Gasoline 0.4 0.4 0.4 0.4 0.4 0.4 7%

Diesel 7.9 8.6 10.1 10.6 10.9 10.9 37%

AFVs + 0.1 0.5 0.9 0.9 0.9 30,877%

HFCs from Comfort 

Cooling + + 0.1 0.2 0.3 0.3 N/A

Motorcycles 1.8 1.8 1.9 1.6 1.9 2.1 18%

Gasoline 1.8 1.8 1.9 1.6 1.9 2.1 18%

Aircraft 181.2 175.4 200.7 194.3 189.2 189.4 5%

Commercial Aircraft 136.9 143.1 167.8 159.8 155.5 155.2 13%

Jet Fuel 136.9 143.1 167.8 159.8 155.5 155.2 13%

General Aviation 

Aircraft 9.6 8.2 12.0 16.5 17.5 18.2 90%

 Jet Fuel 6.4 5.4 9.4 14.0 15.2 16.0 148%

 Aviation Gasoline 3.2 2.8 2.6 2.5 2.4 2.3 -29%

Military Aircraft 34.8 24.1 21.0 18.0 16.2 16.0 -54%

Jet Fuel 34.8 24.1 21.0 18.0 16.2 16.0 -54%

Ships and Boatsb 46.9 56.6 65.1 50.7 54.1 56.3 20%

Gasoline 12.2 14.3 11.8 14.3 14.1 13.9 14%

Distillate Fuel 10.8 11.0 14.6 11.2 10.7 11.5 6%

Residual Fuel 23.9 30.8 35.3 20.4 24.4 25.9 8%

HFCs from Refrigerated 

Transport + 0.6 3.4 4.7 4.9 4.9 NA

Rail 38.6 44.1 50.1 56.7 58.9 58.0 50%

Distillate Fuel 35.4 39.6 42.0 45.5 47.8 46.5 31%

Electricity 3.0 3.1 3.4 4.7 4.5 4.8 57%

HFCs from Comfort 

Cooling + + + + + + NA

HFCs from Refrigerated 

Transport + 1.4 4.6 6.4 6.5 6.6 NA

Other Emissions from Rail 

Electricity Use 0.1 0.1 0.1 0.1 0.1 0.1 6%

Pipelinesc 36.2 38.5 35.2 32.4 32.6 34.6 -5%

Natural Gas 36.2 38.5 35.2 32.4 32.6 34.6 -5%

Other Transportation 11.9 11.3 12.1 10.2 9.9 10.2 -14%

Lubricants 11.9 11.3 12.1 10.2 9.9 10.2 -14%

Non-Transportation 

Mobile Total 127.9 145.8 157.2 189.3 194.0 192.5 51%

Agricultural Equipmentd 31.1 36.7 38.8 46.9 49.1 48.4 56%

Gasoline 7.3 8.3 5.8 9.6 10.9 9.4 29%

Diesel 23.8 28.4 33.0 37.3 38.1 39.0 64%

Construction/ Mining 

Equipmente 42.0 48.9 55.2 65.8 67.2 67.6 61%

Gasoline 4.4 4.0 3.1 6.2 6.1 5.1 16%

Diesel 37.6 44.9 52.2 59.6 61.1 62.6 66%

Other Equipmentf 54.8 60.2 63.2 76.6 77.7 76.5 40%

Gasoline 40.2 42.6 42.5 52.6 53.1 51.2 27%

Diesel 14.6 17.6 20.6 24.0 24.6 25.3 74%

Transportation and NonTransportation Mobile 

Total 1,674.9 1,834.1 2,080.4 2,192.9 2,193.0 2,192.6 31% a Not including emissions from international bunker fuels. 

b Fluctuations in emission estimates reflect data collection problems. 

c Includes only CO2 from natural gas used to power pipelines; does not include emissions from electricity use or non-CO2 gases. d Includes equipment, such as tractors and combines, as well as fuel consumption from trucks that are used off-road in agriculture. e Includes equipment, such as cranes, dumpers, and excavators, as well as fuel consumption from trucks that are used off-road in construction. f “Other" includes snowmobiles and other recreational equipment, logging equipment, lawn and garden equipment, railroad equipment, airport equipment, commercial 

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

+ Less than 0.05 Tg CO2 Eq. 

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-124 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 99: Transportation and Mobile Source Emissions by Gas (Tg CO2 Eq.) 

Gas 1990 1995 2000 2005 2006 2007 

Percent 

Change 

1990-2007 

CO2 1,626.1 1,757.5 1,971.6 2,083.9 2,087.4 2,093.1 29% 

N2O 43.7 53.7 52.8 36.7 33.5 30.1 -31% 

CH4 4.7 4.3 3.4 2.5 2.4 2.3 -52% 

HFC + 18.6 52.6 69.7 69.5 67.0 NA 

Total 1,674.5 1,834.0 2,080.3 2,192.8 2,192.9 2,192.5 31% 

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

Figure A-4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2007 (Tg CO2 Eq.) 

Table A- 100: Greenhouse Gas Emissions from Passenger Transportation (Tg CO2 Eq.) 

Vehicle Type 1990 1995 2000 2005 2006 2007 

Percent 

Change 

1990-2007 

On-Road Vehicles 1,003.1 1,089.7 1,215.8 1,264.4 1,249.8 1,240.8 24% 

Passenger Cars 656.9 644.1 694.6 705.8 678.3 664.6 1% 

Light-duty Trucks 336.2 434.7 508.3 544.8 557.1 561.7 67% 

Buses 8.3 9.1 11.1 12.1 12.4 12.4 50% 

Motorcycles 1.8 1.8 1.9 1.6 1.9 2.1 18% 

Aircraft 122.8 126.5 150.7 150.8 148.5 150.8 23% 

General Aviation 9.6 8.2 12.0 16.5 17.5 18.2 90% 

Commercial Aircraft 113.2 118.3 138.7 134.3 131.0 132.6 17% 

Recreational Boats 14.1 16.6 14.5 17.4 17.3 17.2 22% 

Passenger Rail 4.3 4.4 5.1 6.2 6.0 6.2 44% 

Total 1,144.3 1,237.2 1,386.1 1,438.8 1,421.6 1,415.1 24% 

Note: Data from DOE (1993 through 2008) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. 

Table A- 101: Greenhouse Gas Emissions from Domestic Freight Transportation (Tg CO2 Eq.) 

By Mode 1990 1995 2000 2005 2006 2007 

Percent 

Change 

1990-2007 

Trucking 228.8 272.7 344.2 395.1 404.5 410.8 80% 

Freight Rail 34.1 39.6 44.9 50.4 52.8 51.6 51% 

Ships and Other Boats 32.8 40.1 50.6 33.2 36.8 39.1 19% 

Pipelines 36.2 38.5 35.2 32.4 32.4 34.6 -5% 

Commercial Aircraft 23.7 24.8 29.0 25.5 24.5 22.6 -5% 

Total 355.7 415.6 504.0 536.6 551.2 558.7 57% 

Note: Data from DOE (1993 through 2008) were used to disaggregate emissions from rail and buses. Emissions from HFCs have been included in these estimates. 

3.3. Methodology for Estimating CH4 Emissions from Coal Mining 

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

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

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

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

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

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

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

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

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

degasification systems, minus CH4 recovered and used. 
 

A-125

Step 1.1: Estimate CH4 Liberated from Ventilation Systems 

All coal mines with detectable CH4 emissions33 use ventilation systems to ensure that CH4 levels remain within 

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

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

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

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

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

For the years 1990 through 1996 and 1998 through 2006, 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- 102. Well over 90 percent of all ventilation 

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

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

100 percent of CH4 liberated from underground mines. Using the complete database from 1997, the proportion of total 

emissions accounted for by mines emitting less than 0.1 MMCFD or 0.5 MMCFD was estimated (see Table A- 102). 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. The full 2007 MSHA dataset will be used in future years to account for the less 

than 5 percent of ventilations 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 2007, 

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. 

Table A- 102: 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 

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) 

* Factor derived from a complete set of individual mine data collected for 1997. 

 

33 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-126 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 1.2: Estimate CH4 Liberated from Degasification Systems 

Coal mines use several different types of degasification systems to remove CH4, including vertical wells and 

horizontal boreholes to recover CH4 prior to mining of the coal seam. Gob wells and cross-measure boreholes recover 

CH4 from the overburden (i.e., GOB area) after mining of the seam (primarily in longwall mines). 

MSHA collects information about the presence and type of degasification systems in some mines, but does not 

collect quantitative data on the amount of CH4 liberated. Thus, the methodology estimated degasification emissions on a 

mine-by-mine basis based on other sources of available data. Many of the coal mines employing degasification systems 

have provided EPA with information regarding CH4 liberated from their degasification systems. For these mines, this 

reported information was used as the estimate. In other cases in which mines sell CH4 recovered from degasification 

systems to a pipeline, gas sales were used to estimate CH4 liberated from degasification systems (see Step 1.3). Finally, 

for those mines that do not sell CH4 to a pipeline and have not provided information to EPA, CH4 liberated from 

degasification systems was estimated based on the type of system employed. For example, for coal mines employing gob 

wells and horizontal boreholes, the methodology assumes that degasification emissions account for 40 percent of total CH4

liberated from the mine. 

Step 1.3: Estimate CH4 Recovered from Degasification Systems and Utilized (Emissions Avoided) 

In 2007, fifteen active coal mines had CH4 recovery and use projects. Thirteen mines sold the recovered CH4 to 

a pipeline, one used the CH4 on site to heat mine ventilation air, and one of the coal mines used CHB

4B

 to generate electricity. 

One of the mines that sold gas to a pipeline also used CHB

4 to fuel a thermal coal dryer. In order to calculate emissions 

avoided from pipeline sales, information was needed regarding the amount of gas recovered and the number of years in 

advance of mining that wells were drilled. Several state agencies provided gas sales data, which were used to estimate 

emissions avoided for these projects. Additionally, coal mine operators provided information on gas sales and/or the 

number of years in advance of mining. Emissions avoided were attributed to the year in which the coal seam was mined. 

For example, if a coal mine recovered and sold CH4 using a vertical well drilled five years in advance of mining, the 

emissions avoided associated with those gas sales (cumulative production) were attributed to the well at the time it was 

mined through (e.g., five years of gas production). Where individual well data is not available, estimated percentages of 

the operator’s annual gas sales within the field around the coal mine are attributed to emissions avoidance. For some 

mines, individual well data were used to assign gas sales to the appropriate emissions avoided year. In most cases, coal 

mine operators provided this information, which was then used to estimate emissions avoided for a particular year. 

Additionally, several state agencies provided production data for individual wells. 

Step 2: Estimate CH4 Emitted from Surface Mines and Post-Mining Activities 

Mine-specific data were not available for estimating CH4 emissions from surface coal mines or for post-mining 

activities. For surface mines and post-mining activities, basin-specific coal production was multiplied by a basin-specific 

emission factor to determine CH4 emissions. 

Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data 

The first step in estimating CH4 emissions from surface mining and post-mining activities was to define the 

geographic resolution of the analysis and to collect coal production data at that level of resolution. The analysis was 

conducted by coal basin as defined in Table A- 103, which presents coal basin definitions by basin and by state. 

The Energy Information Administration’s (EIA) Annual Coal Report includes state- and county-specific 

underground and surface coal production by year. To calculate production by basin, the state level data were grouped into 

coal basins using the basin definitions listed in Table A- 103. For two statesWest Virginia and Kentuckycounty-level 

production data was used for the basin assignments because coal production occurred from geologically distinct coal 

basins within these states. Table A- 104 presents the coal production data aggregated by basin. 

Step 2.2: Estimate Emissions Factors for Each Emissions Type 

Emission factors for surface mined coal were developed from the in situ CH4 content of the surface coal in each 

basin. Based on an analysis presented in EPA (1993), surface mining emission factors were estimated to be from 1 to 3 

times the average in situ CH4 content in the basin. For this analysis, the surface mining emission factor was determined to 

be twice the in situ CH4 content in the basin. Furthermore, the post-mining emission factors used were estimated to be 25 

to 40 percent of the average in situ CH4 content in the basin. For this analysis, the post-mining emission factor was 
 

A-127

determined to be 32.5 percent of the in situ CH4 content in the basin. Table A- 105 presents the average in situ content for 

each basin, along with the resulting emission factor estimates. 

Step 2.3: Estimate CH4 Emitted 

The total amount of CH4 emitted was calculated by multiplying the coal production in each basin by the 

appropriate emission factors. 

Total annual CH4 emissions are equal to the sum of underground mine emissions plus surface mine emissions 

plus post-mining emissions. Table A- 106 and Table A- 107 present estimates of CH4 liberated, used, and emitted for 

1990 through 2007. Table A- 108 provides emissions by state. 

Table A- 103: Coal Basin Definitions by Basin and by State 

Basin States 

Northern Appalachian Basin Maryland, Ohio, Pennsylvania, West Virginia North 

Central Appalachian Basin Kentucky East, Tennessee, Virginia, West Virginia South 

Warrior Basin Alabama, Mississippi 

Illinois Basin Illinois, Indiana, Kentucky West 

South West and Rockies Basin Arizona, California, Colorado, New Mexico, Utah 

North Great Plains Basin Montana, North Dakota, Wyoming 

West Interior Basin Arkansas, Iowa, Kansas, Louisiana, Missouri, Oklahoma, Texas 

Northwest Basin Alaska, Washington 

State Basin 

Alabama Warrior Basin 

Alaska Northwest Basin 

Arizona South West and Rockies Basin 

Arkansas West Interior Basin 

California South West and Rockies Basin 

Colorado South West and Rockies Basin 

Illinois Illinois Basin 

Indiana Illinois Basin 

Iowa West Interior Basin 

Kansas West Interior Basin 

Kentucky East Central Appalachian Basin 

Kentucky West Illinois Basin 

Louisiana West Interior Basin 

Maryland Northern Appalachian Basin 

Mississippi Warrior Basin 

Missouri West Interior Basin 

Montana North Great Plains Basin 

New Mexico South West and Rockies Basin 

North Dakota North Great Plains Basin 

Ohio Northern Appalachian Basin 

Oklahoma West Interior Basin 

Pennsylvania. Northern Appalachian Basin 

Tennessee Central Appalachian Basin 

Texas West Interior Basin 

Utah South West and Rockies Basin 

Virginia Central Appalachian Basin 

Washington Northwest Basin 

West Virginia South Central Appalachian Basin 

West Virginia North Northern Appalachian Basin 

Wyoming North Great Plains Basin 
A-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 104: Annual Coal Production (Thousand Short Tons) 

Basin 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 200Underground Coal Production 423,556 406,344 406,335 351,056 399,102 396,249 409,850 420,657 417,729 391,791 372,766 380,627 357,384 352,78N. Appalachia 103,865 103,450 105,220 77,032 100,122 98,103 106,729 112,135 116,718 107,575 105,374 107,025 98,643 98,36

Cent. Appalachia 198,412 181,873 177,777 164,845 170,893 166,495 171,845 177,720 171,279 157,058 150,584 152,457 137,224 130,72Warrior 17,531 17,062 15,944 15,557 14,471 17,605 18,217 18,505 17,316 14,799 15,895 15,172 14,916 15,37

Illinois 69,167 69,947 73,154 55,967 69,050 69,009 67,046 64,728 64,463 63,529 53,720 54,364 54,016 51,78S. West/Rockies 32,754 31,568 31,670 35,409 41,681 42,994 43,088 44,503 45,983 46,957 45,742 51,193 52,121 56,1N. Great Plains 1,722 2,418 2,511 2,146 2,738 2,018 2,788 2,854 1,723 1,673 1,210 0 0 3West Interior 105 26 59 100 147 25 137 212 247 200 241 416 464 39

Northwest 0 0 0 0 0 0 0 0 0 0 0 0 0 

Surface Coal Production 602,753 587,143 588,944 594,372 634,401 636,726 654,007 669,271 699,608 708,639 700,608 745,306 735,912 717,689 

N. Appalachia 60,761 51,124 50,512 48,641 44,960 39,372 39,788 40,179 41,043 33,928 34,908 35,334 30,008 27,37Cent. Appalachia 94,343 91,785 95,163 94,433 106,129 106,250 108,869 113,275 108,345 107,507 110,479 116,983 111,340 99,4

Warrior 11,413 10,104 9,775 9,211 8,795 7,036 6,420 5,963 5,697 4,723 4,252 4,796 6,320 8,43

Illinois 72,000 63,483 58,814 50,535 51,868 40,376 44,754 46,862 47,715 40,474 33,631 40,894 39,380 36,67S. West/Rockies 43,863 42,985 46,052 48,765 49,119 46,643 43,814 48,374 49,635 50,349 49,587 52,180 50,006 41,23N. Great Plains 249,356 259,194 258,281 275,873 308,279 331,367 343,404 349,612 385,438 407,683 407,670 438,367 441,346 444,00

West Interior 64,310 61,889 63,562 60,574 58,791 59,116 60,912 59,061 57,951 58,309 54,170 50,613 50,459 53,4

Northwest 6,707 6,579 6,785 6,340 6,460 6,566 6,046 5,945 5,982 5,666 5,911 6,138 6,973 7,3

Total Coal Production 1,026,309 993,487 995,279 945,428 1,033,503 1,032,975 1,063,857 1,089,928 1,118,132 1,093,975 1,073,374 1,127,689 1,093,296 1,070,65

N. Appalachia 164,626 154,574 155,732 125,673 145,082 137,475 146,517 152,314 157,761 141,145 140,282 142,360 128,731 125,73Cent. Appalachia 292,755 273,658 272,940 259,278 277,022 272,745 280,714 290,995 279,624 262,660 261,063 269,440 248,564 230,14

Warrior 28,944 27,166 25,719 24,768 23,266 24,641 24,637 24,468 23,013 19,499 20,147 19,967 21,236 23,81

Illinois 141,167 133,430 131,968 106,502 120,918 109,385 111,800 111,590 110,176 103,966 87,351 95,258 93,396 88,45S. West/Rockies 76,617 74,553 77,722 84,174 90,800 89,637 86,902 92,877 95,618 96,207 95,239 103,373 102,127 97,34N. Great Plains 251,078 261,612 260,792 278,019 311,017 333,385 346,192 352,466 387,161 406,324 408,880 438,367 441,346 444,03

West Interior 64,415 61,915 63,621 60,674 58,938 59,141 61,049 59,273 58,198 58,509 54,411 51,028 50,923 53,80

Northwest 6,707 6,579 6,785 6,340 6,460 6,566 6,046 5,945 5,982 5,665 5,911 6,138 6,973 7,31

Source for 1990-2007 data: EIA (1990 through 07), Annual Coal Report. U.S. Department of Energy, Washington, DC, Table 1. Note: Totals may not sum due to independent rounding. 
Table A- 105: Coal Surface and Post-Mining CH4 Emission Factors (ft3 per Short Ton) Basin 

Surface Average

in situ Content

Underground Average

In situ Content

Surface Mine

Factors

Post-Mining

Surface Factors

Post Mining

Underground

Northern Appalachia 59.5 138.4 119.0 19.3 45.0Central Appalachia (WV) 24.9 136.8 49.8 8.1 44.5Central Appalachia (VA) 24.9 399.1 49.8 8.1 129.7Central Appalachia (E KY) 24.9 61.4 49.8 8.1 20.0Warrior 30.7 266.7 61.4 10.0 86.7Illinois 34.3 64.3 68.6 11.1 20.9Rockies (Piceance Basin) 33.1 196.4 66.2 10.8 63.8Rockies (Uinta Basin) 16.0 99.4 32.0 5.2 32.3Rockies (San Juan Basin) 7.3 104.8 14.6 2.4 34.1Rockies (Green River Basin) 33.1 247.2 66.2 10.8 80.3Rockies (Raton Basin) 33.1 127.9 66.2 10.8 41.6N. Great Plains (WY, MT) 20.0 15.8 40.0 6.5 5.1N. Great Plains (ND) 5.6 15.8 11.2 1.8 5.1West Interior (Forest City, Cherokee Basins) 34.3 64.3 68.6 11.1 20.9West Interior (Arkoma Basin) 74.5 331.2 149.0 24.2 107.6West Interior (Gulf Coast Basin) 11.0 127.9 22.0 3.6 41.6Northwest (AK) 16.0 160.0 32.0 1.8 52.0Northwest (WA) 16.0 47.3 32.0 5.2 15.4Sources: 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 Coalb

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- 106: Underground Coal Mining CH4 Emissions (Billion Cubic Feet) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 Ventilation Output 112 NA NA 95 96 100 90 96 94 92 87 84 79 Adjustment Factor for Mine Data* 97.8% NA NA 97.8% 97.8% 91.4% 91.4% 100% 97.8% 97.8% 97.8% 97.8% 97.8% 

Adjusted Ventilation Output 114 NA NA 97 98 109 99 96 96 94 89 86 80 

Degasification System Liberated 54 NA NA 45 46 47 49 42 49 40 45 49 50 Total Underground Liberated 168 164 162 142 144 153 148 139 146 134 134 135 131 

Recovered & Used (14) (14) (16) (23) (27) (31) (35) (28) (35) (31) (37) (41) (43) 

Total 154 150 145 120 117 116 113 115 111 103 97 94 88 

* Refer to Table A- 102. Note: Totals may not sum due to independent rounding. Table A- 107: Total Coal Mining CH4 Emissions (Billion Cubic Feet) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Underground Mining 154 150 145 120 117 116 113 115 111 103 97 94 88 89 Surface Mining 30 28 29 28 29 28 29 31 30 31 30 33 32 31 Post-Mining (Underground) 19 18 18 16 17 17 18 18 18 17 17 17 16 16 Post-Mining (Surface) 5 5 5 4 5 5 5 5 5 5 5 5 5 5 Total 208 200 196 167 168 166 155 169 166 156 149 149 141 141 Note: Totals may not sum due to independent rounding. 
A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 108: Total Coal Mining CH4 Emissions by State (Million Cubic Feet) 

State 1990 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Alabama 32,272 29,618 33,735 29,556 30,942 27,111 25,702 23,342 21,896 18,686 19,288 Alaska 63 58 63 55 54 50 58 61 56 43 40 

Arizona 192 222 203 177 199 192 200 223 228 217 205 

Arkansas 7 8 5 4 3 4 4 2 2 2 1 California 1 - - - - 0 0 0 0 0 0 

Colorado 10,325 9,192 7,582 5,972 9,189 9,181 9,390 10,808 11,117 12,082 13,216 

Illinois 10,502 10,585 11,563 10,876 8,534 7,847 7,810 8,542 7,270 5,972 4,744 

Indiana 2,795 2,495 2,025 2,192 2,742 2,878 2,650 2,231 3,373 3,496 3,821 Iowa 30 4 - - - 0 0 0 0 0 0 

Kansas 57 23 23 19 29 27 33 16 14 16 12 

Kentucky 10,956 11,259 10,269 8,987 10,451 10,005 9,561 9,105 9,363 8,464 8,028 

Louisiana 81 89 95 82 91 82 76 94 95 97 103 

Maryland 519 237 237 259 267 251 225 331 340 401 391 

Mississippi - - - - - 0 1 57 43 165 264 Missouri 211 67 44 57 32 30 31 35 29 20 43 

Montana 1,749 1,936 1,834 1,756 1,906 1,992 1,911 1,783 1,820 1,738 1,719 

New Mexico 451 679 586 408 459 489 497 464 630 1,280 1,864 North Dakota 380 420 392 389 385 389 405 407 397 401 401 

Ohio 5,065 4,583 4,189 4,068 4,349 4,350 3,914 3,519 3,619 2,831 2,649 

Oklahoma 285 359 323 286 385 395 469 454 620 660 620 

Pennsylvania 22,735 24,024 25,611 26,440 30,026 29,491 24,867 24,830 22,252 19,668 20,281 Tennessee 296 101 112 143 148 116 119 99 142 142 124 Texas 1,426 1,339 1,347 1,411 1,364 1,345 1,357 1,240 1,152 1,157 1,215 

Utah 3,587 2,626 2,570 2,810 3,566 3,859 3,633 2,816 2,080 2,709 3,408 Virginia 46,137 26,742 20,059 19,771 16,851 13,978 13,321 12,065 11,506 11,227 11,906 

Washington 186 182 181 170 167 173 153 159 172 217 232 

West Virginia 49,039 30,664 30,552 36,384 33,554 36,962 33,660 30,827 33,833 31,482 28,599 

Wyoming 8,496 10,912 12,185 12,838 12,994 14,549 15,607 15,725 17,147 17,352 17,497 Total 207,844 168,422 165,785 165,109 168,687 165,747 155,654 149,235 149,196 140,526 140,669 14

+ Does not exceed 0.5 Million Cubic Feet Note: The emission estimates provided above are inclusive of emissions from underground mines, surface mines and post-mining activities. The following states have neither underground nor surface 

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 CaCarolina, South Dakota, Vermont, and Wisconsin.
 

A-131

3.4. Methodology for Estimating CH4 and CO2 Emissions from Natural Gas Systems 

The following steps were used to estimate CH4 and non-energy CO2 emissions from natural gas systems. 

Step 1: Calculate Emission Estimates for Base Year 1992 Using EPA Adjusted GRI/EPA Study 

The first step in estimating CH4 and non-energy related (i.e., fugitive, vented and flared) CO2 emissions from 

natural gas systems was to develop a detailed base year estimate of emissions. The study by EPA/GRI (1996) divides the 

industry into four stages to construct a detailed emission inventory for the year 1992. These stages include: field 

production, processing, transmission and storage (i.e., both underground and liquefied gas storage), and distribution. This 

study produced emission factors and activity data for over 80 different emission sources within the natural gas system. 

Emissions for 1992 were estimated by multiplying activity levels by emission factors for each system component and then 

summing by stage. Since publication, the EPA has updated activity data for some of the components in the system based 

on publicly available data. For other sources where annual activity data are not available, a set of industry activity factor 

drivers was developed that can be used to update activity data. Table A- 109 through Table A- 112 display the 1992 

GRI/EPA activity levels and CH4 emission factors for each stage, and the current EPA adjusted activity levels and 

emission factors. These data are shown to illustrate the kind of data used to calculate CH4 and non-energy CO2 emissions 

from all stages. For most sources, the CH4 emission factors were adjusted for CO2 content when estimating fugitive and 

vented non-energy CO2 emissions. In the case of non-energy CO2 emissions from flared sources, acid gas removal units 

and condensate tanks, specific industry data related to those sources was used to derive their respective emission factors. 

Step 2: Collect Aggregate Statistics on Main Driver Variables 

As detailed data on each of the over 80 sources were not available for the period 1990 through 2007, activity 

levels were estimated using aggregate statistics on key drivers, including: number of producing wells (EIA 2008a-b, New 

Mexico 2008a-b, Texas 2008a-b), number of gas plants (AGA 1991-1998; OGJ 1997-2008), number of shallow and deep 

offshore platforms (MMS 2008a-d), miles of transmission pipeline (OPS 2008a), miles of distribution pipeline (OPS 

2008b), miles of distribution services (OPS 2008b), and energy consumption (EIA 2008c). Table A- 113 provides the 

activity levels of some of the key drivers in the natural gas analysis. 

Step 3: Estimate CH4 Emissions for Each Year and Stage 

Emissions from each stage of the natural gas industry were estimated by multiplying the activity factors by the 

appropriate emission factors, summing all sources for each stage and then accounting for CH4 reductions reported to the 

Natural Gas STAR Program and CH4 reductions resulting from National Emission Standards for Hazardous Air Pollutants 

(NESHAP) regulations. 

Industry partners report CH4 emission reductions by project to the Natural Gas STAR Program. The reductions 

are estimates using actual measurement data or equipment-specific emission factors. Before incorporating the reductions 

into the Inventory, quality assurance and quality control checks are undertaken to identify errors, inconsistencies, or 

irregular data. The checks include matching Natural Gas STAR reported reductions to specific inventory sources to make 

sure that a reported reduction for one source is not greater than the emission estimate for that source. Total emissions were 

estimated by adding the emission estimates from each stage. The base year of the inventory is 1992; therefore any 

reductions reported for 1992 or earlier are considered to be already included in the base-year emission factors and are not 

subtracted from the inventory estimate. If the reported reduction occurred between 1990 and 1992, then the reduction is 

added back into the estimate for the appropriate year(s). The reductions are also adjusted to remove the sunsetting time 

period which is relevant to Natural Gas STAR accounting but not the Inventory. For example, replacing a gas-assisted 

pump with an electric pump permanently reduces the vented methane emissions from that source, even after the Natural 

Gas STAR sunsetting period. CH4 emission reductions from the Natural Gas STAR Program beyond the efforts reflected 

in the 1992 base year are summarized in Table A- 114. 

The 1990 Clean Air Act (CAA) sets the limits on the amount of hazardous air pollutants (HAPs) that can be 

emitted in the United States. The NESHAP regulations set the standards to limit emissions of HAPs. The emission sources 

are required to use the Maximum Achievable Control Technology, giving the operators flexibility to choose the type of 

control measure(s) to implement. In regards to the oil and natural gas industry, the NESHAP regulation addresses HAPs 

from the oil and natural gas production sectors and the natural gas transmission and storage sectors of the industry. 

Though the regulation deals specifically with HAPs reductions, methane emissions are also reduced. 
A-132 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 The NESHAP regulation requires that glycol dehydration unit vents and storage tanks that have HAP emissions 

and exceed a gas throughput and liquids throughput value, respectively, be connected to a closed loop emission control 

system that reduces emissions by 95 percent. Also, gas processing plants exceeding the threshold natural gas throughput 

limit are required to routinely implement Leak Detection and Survey (LDAR) programs. The emissions reductions 

achieved as a result of NESHAP regulations were estimated using data provided in the Federal Register Background 

Information Document (BID) for this regulation. The BID provides the levels of control measures in place before the 

enactment of regulation. The emissions reductions were estimated by analyzing the portion of the industry without control 

measures already in place that would be impacted by the regulation. The reductions are representative of the control 

measures in both the oil and natural gas industry. CH4 emission reductions from the NESHAP regulation are summarized 

in Table A- 115. 

Step 4: Estimate CO2 Emissions for Each Year and Stage 

The same procedure for estimating CH4 emissions holds true for estimating non-energy related CO2 emissions, 

except the emission estimates are not corrected for reductions due to the Natural Gas STAR program or the NESHAP 

regulations. 

Produced natural gas is composed of primarily CH4, but as shown in Table A- 120, the natural gas contains, in 

some cases, as much as 8 percent CO2. The same vented and fugitive emissions of natural gas that led to CH4 emissions 

also contain a certain volume of CO2. Accordingly, the CO2 emissions for each sector can be estimated using the same 

methane activity and emissions factors for these vented and fugitive sources. The primary difference is that EPA/GRI 

emission factors are adjusted for CO2 content in each sector. 

 Using the default CH4 content of produced natural gas from EPA/GRI (1996) of 78.8 percent, the corresponding 

amount of CO2 emissions from the production sector can be estimated. Each sector of the natural gas system has varying 

CO2 contents, similar to the way the CH4 content varies among the sectors. Table A- 120 shows the CO2 content for the 

different well types in the production sector of the natural gas system. In the estimation of CO2 emissions from the 

production sector, the production sector CH4 emission factors were used as a basis to estimate CO2 emissions; however, 

they were converted to CO2 emission factors by multiplication by a conversion factor. This conversion factor is the ratio 

of CO2 content in the gas stream (in this case the production concentrations provided in Table A- 120) to the 

corresponding CH4 content of the same gas stream. The three exceptions to this methodology are CO2 emissions from 

flares, acid gas removal units, and condensate tanks. In the case of flare emissions, a direct CO2 emission factor from EIA 

(1996) was used. This emission factor was applied to the portion of offshore gas that is not vented and all of the gas 

reported as vented and flared onshore by EIA. The amount of CO2 emissions from an acid gas unit in a processing plant is 

equal to the difference in CO2 concentrations between produced natural gas and pipeline quality gas applied to the 

throughput of the plant. This methodology was applied to the national gas throughput using national average CO2

concentrations in produced gas (3.45 percent) and transmission quality gas (1 percent). For condensate tanks, a series of 

API Tankcalc (EPA 1999) simulations provide the total CO2 vented per barrel of condensate throughput from fixed roof 

tank flash gas for condensate gravities of API 44 degree and higher. The ratios of emissions to throughput were used to 

estimate the CO2 emission factor for condensate passing through fixed roof tanks. The detailed source emission estimates 

for CH4 and CO2 from the production sector are presented in Table A- 116 and Table A- 121, respectively. 

In the processing sector, the CO2 content of the natural gas remains the same as the CO2 content in the 

production sector for the equipment upstream of the acid gas removal unit because produced natural gas is usually only 

minimally treated after being produced and then transported to natural gas processing plants via gathering pipelines. The 

CO2 content in gas for the remaining equipment that is downstream of the acid gas removal is the same as in pipeline 

quality gas. The EPA/GRI study estimates the average CH4 content of natural gas in the processing sector to be 87 percent 

CH4. Consequently, the processing sector CH4 emission factors were proportioned to reflect the CO2 content of either 

produced natural gas or pipeline quality gas using the same methodology as the production sector. The detailed source 

emission estimates for CH4 and CO2 from the processing sector are presented in Table A- 117 and Table A- 122, 

respectively. 

For the transmission sector, CO2 content in natural gas transmission pipelines was estimated for the top twenty 

transmission pipeline companies in the United States (separate analyses identified the top twenty companies based on gas 

throughput and total pipeline miles). The weighted average CO2 content in the transmission pipeline quality gas in both 

cases—total gas throughput and total miles of pipeline—was estimated to be about 1 percent. To estimate the CO2

emissions for the transmission sector the CH4 emission factors were proportioned from the 93.4 percent CH4 reported in 

EPA/GRI (1996) to reflect the 1 percent CO2 content found in transmission quality natural gas. The detailed source 

emissions estimates for CH4 and CO2 for the transmission sector are presented in Table A- 118 and Table A- 123, 

respectively. 
 

A-133

The natural gas in the distribution sector of the system has the same characteristics as the natural gas in the 

transmission sector. The CH4 content (93.4 percent) and CO2 content (1 percent) are identical due to the absence of any 

further treatment between sector boundaries. Thus, the CH4 emissions factors were converted to CO2 emission factors 

using the same methodology as discussed for the transmission sector. The detailed source emission estimates for CH4 and 

CO2 for the distribution sector are presented in Table A- 119 and Table A- 124, respectively. 

 Because Partners report only CH4 emission reductions to the Natural Gas STAR Program, there was no need to 

adjust for the Natural Gas STAR program in the CO2 emissions estimates for any of the sectors in the natural gas system. 

The impact of NESHAP regulations on CO2 emission reductions are not currently addressed in the CO2 emission 

estimates. 
A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 109: 1992 Data and CH4 Emissions (Mg) for the Natural Gas Production Stage 

GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data North East Gas Wells NE - Associated Gas Wells2 67,836 wells NA3 67,489 wells NE - Non-associated Gas Wells (less Unconventional) 129,407 wells 7.5 scfd/well 9,752 129,157 wells NE - Unconventional Gas Wells NA NA 0 wells Field Separation Equipment Heaters 260 heaters 15.1 scfd/heater 39 258 heaters Separators 91,670 separators 1.0 scfd/sep 873 91,701 separators Dehydrators 1,047 dehydrators 23.1 scfd/dehy 241 686 dehydrators Meters/Piping 76,262meters 9.6 scfd/meter 7,284 7,866meters Gathering Compressors Small Reciprocating Comp. 129 compressors 283.8 scfd/comp 366 129 compressors Large Reciprocating Comp. 0 compressors/station 16,116.9 scfd/comp 0 16 compressors Large Reciprocating Stations NA 8,741.2 scfd/station 2 stations Pipeline Leaks 159,406miles (gathering) 56.3 scfd/mile 89,809 66,029miles Drilling and Well Completion Completion Flaring 396 Completions 777.0 scf/comp 3,074 187 completions/ year Well Drilling NA NA 5,227 wells Normal Operations Pneumatic Device Vents 66,903 device 365.7 scfd/device 244,660 62,770 controllers Chemical Injection Pumps 4,659 active pumps 262.9 scfd/pump 12,246 646 active pumps Kimray Pumps 5,176,010MMscf/yr 1,051.5 scf/MMscf 54,425,537 200,673MMscf/yr Dehydrator Vents 5,813,634MMscf/yr 292.1 scf/MMscf 16,983,331 225,222MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA scf/bbl 1MMbbl/yr Condensate Tanks with Control Devices NA NA scf/bbl 1MMbbl/yr Compressor Exhaust Vented Gas Engines 12,874MMHPhr 0.3 scf/HPhr 33 0.00MMHPhr Well Workovers Gas Wells 4,374 workovers/yr 2,601.2 scfy/w.o. 219 5,618 workovers/yr Well Clean Ups (LP Gas Wells) 53,513 LP Gas Wells 52,543.0 scfy/LP well 54,154 53,471 LP Gas Wells Blowdowns Vessel BD 120,022 vessels 82.7 scfy/vessel 191 92,645 vessels Pipeline BD 159,406miles (gathering) 327.5 scfy/mile 1,006 66,029miles (gathering) Compressor BD 8,023 compressors 4,000.3 scfy/comp 618 129 compressors Compressor Starts 8,023 compressors 8,949.4 scfy/comp 1,383 129 compressors Upsets Pressure Relief Valves 248,223 PRV 36.0 PRV 172 247,749 PRV Mishaps 159,406miles (gathering) 709.1 scf/mile 2,177 16,507miles Mid-Central Gas Wells 
GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data MC - Associated Gas Wells2 71,002 wells NA 70,640 wells MC - Non-associated Gas Wells 59,143 wells 7.14 scfd/well 4,221 59,029 wells MC - Unconventional Gas Wells 329 wells 7.14 scfd/well 23 329 wells Field Separation Equipment Heaters 30,124 heaters 14.27 scfd/heater 4,298 24,099 heaters Separators 20,469 separators 0.90 scfd/sep 185 25,821 separators Dehydrators 14,836 dehydrators 91.48 scfd/dehy 13,572 9,987 dehydrators Meters/Piping 121,500meters 9.05 scfd/meter 10,990 82,475meters Gathering Compressors Small Reciprocating Comp. 6,824 compressors 268.77 scfd/comp 18,340 6,707 compressors Large Reciprocating Comp. 39 compressors 15,263.01 scfd/comp 5,911 16 compressors Large Reciprocating Stations 5 stations 8,278.04 scfd/station 401 2 stations Pipeline Leaks 72,854miles (gathering) 53.35 scfd/mile 38,871 51,169miles Drilling and Well Completion Completion Flaring 181 Completions 735.80 scf/comp 1,331 86 completions/ year Well Drilling NA NA 2,402 wells Normal Operations Pneumatic Device Vents 67,285 device 346.32 scfd/device 233,019 92,064 controllers Chemical Injection Pumps 2,129 active pumps 248.95 scfd/pump 5,300 8,429 active pumps Kimray Pumps 2,365,609MMscf/yr 995.78 scf/MMscf 23,556,366 2,923,053MMscf/yr Dehydrator Vents 2,657,024MMscf/yr 276.65 scf/MMscf 7,350,696 3,280,643MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA 5MMbbl/yr Condensate Tanks with Control Devices NA NA 5MMbbl/yr Compressor Exhaust Vented Gas Engines 5,884MMHPhr 0.24 scf/HPhr 14 11,099MMHPhr Well Workovers Gas Wells 1,999 workovers/yr 2,463.36 scfy/w.o. 95 2,582 workovers/yr Well Clean Ups (LP Gas Wells) 24,457 LP Gas Wells 49,759.11 scfy/LP well 23,439 24,574 LP Gas Wells Blowdowns Vessel BD 54,854 vessels 78.30 scfy/vessel 83 59,907 vessels Pipeline BD 72,854miles (gathering) 310.18 scfy/mile 435 51,169miles (gathering) Compressor BD 3,667 compressors 3,788.40 scfy/comp 268 6,707 compressors Compressor Starts 3,667 compressors 8,475.21 scfy/comp 599 6,707 compressors Upsets Pressure Relief Valves 113,446 PRV 34.13 PRV 75 113,860 PRV Mishaps 72,854miles (gathering) 671.55 scf/mile 942 12,792miles Rocky Mountain Gas Wells RM - Associated Gas Wells2 14,215 wells NA 14,142 wells RM - Non-associated Gas Wells 26,274 wells 32.95 scfd/well 8,658 26,224 wells RM - Unconventional Gas Wells 100 wells 6.43 scfd/well 6 100 wells Field Separation Equipment Heaters 13,383 heaters 52.18 scfd/heater 6,984 12,004 heaters 
A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data Separators 9,093 separators 110.32 scfd/sep 10,032 13,135 separators Dehydrators 6,591 dehydrators 82.40 scfd/dehy 5,431 4,429 dehydrators Meters/Piping 53,976meters 47.83 scfd/meter 25,819 30,941meters Gathering Compressors Small Reciprocating Comp. 3,031 compressors 242.09 scfd/comp 7,339 2,922 compressors Large Reciprocating Comp. 17 compressors 13,748.02 scfd/comp 2,365 16 compressors Large Reciprocating Stations 2 stations 7,456.37 scfd/station 160 2 stations Pipeline Leaks 32,365miles (gathering) 48.06 scfd/mile 15,554 43,849miles Drilling and Well Completion Completion Flaring1 80 Completions 662.76 scf/comp 532 38 completions/ year Well Drilling NA NA 1,065 wells Normal Operations Pneumatic Device Vents 20,933 device 311.94 scfd/device 65,298 38,538 controllers Chemical Injection Pumps 946 active pumps 224.24 scfd/pump 2,121 4,686 active pumps Kimray Pumps 1,050,921MMscf/yr 896.94 scf/MMscf 9,426,175 1,296,285MMscf/yr Dehydrator Vents 1,180,383MMscf/yr 249.19 scf/MMscf 2,941,411 1,454,865MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA 10MMbbl/yr Condensate Tanks with Control Devices NA NA 10MMbbl/yr Compressor Exhaust Vented Gas Engines 2,614MMHPhr 0.22 scf/HPhr 6 4,922MMHPhr Well Workovers Gas Wells 888 workovers/yr 2,218.85 scfy/w.o. 38 1,145 workovers/yr Well Clean Ups (LP Gas Wells) 10,865 LP Gas Wells 44,820.07 scfy/LP well 9,379 10,898 LP Gas Wells Blowdowns Vessel BD 24,369 vessels 70.53 scfy/vessel 33 29,568 vessels Pipeline BD 32,365miles (gathering) 279.39 scfy/mile 174 43,849miles (gathering) Compressor BD 1,629 compressors 3,412.36 scfy/comp 107 2,922 compressors Compressor Starts 1,629 compressors 7,633.97 scfy/comp 240 2,922 compressors Upsets Pressure Relief Valves 50,399 PRV 30.74 PRV 30 50,494 PRV Mishaps 32,365miles (gathering) 604.89 scf/mile 377 10,962miles Coal Bed Methane  Powder River NA NA 254,507,484 gal produced water South West Gas Wells SW - Associated Gas Wells2 68,479 wells NA 68,130 wells SW - Non-associated Gas Wells 22,554 wells 34.40 scfd/well 7,758 22,510 wells SW - Unconventional Gas Wells 99 wells NA 99 wells Field Separation Equipment Heaters 11,488 heaters 54.47 scfd/heater 6,258 6,127 heaters Separators 7,806 separators 115.17 scfd/sep 8,990 12,706 separators Dehydrators 5,658 dehydrators 86.01 scfd/dehy 4,866 3,804 dehydrators 
GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data Meters/Piping 46,333meters 49.93 scfd/meter 23,136 29,576meters Gathering Compressors Small Reciprocating Comp. 2,602 compressors 252.72 scfd/comp 6,576 3,075 compressors Large Reciprocating Comp. 15 compressors 14,351.40 scfd/comp 2,119 8 compressors Large Reciprocating Stations 2 stations 7,783.62 scfd/station 144 1 stations Pipeline Leaks 27,782miles (gathering) 50.17 scfd/mile 13,938 39,689miles Drilling and Well Completion Completion Flaring1 69 Completions 691.85 scf/comp 477 33 completions/ year Well Drilling NA NA 915 wells Normal Operations Pneumatic Device Vents 47,064 device 325.63 scfd/device 153,256 30,025 controllers Chemical Injection Pumps 812 active pumps 234.08 scfd/pump 1,901 1,379 active pumps Kimray Pumps 902,104MMscf/yr 936.31 scf/MMscf 8,446,489 1,113,378MMscf/yr Dehydrator Vents 1,013,233MMscf/yr 260.13 scf/MMscf 2,635,702 1,249,582MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA 3MMbbl/yr Condensate Tanks with Control Devices NA NA 3MMbbl/yr Compressor Exhaust Vented Gas Engines 2,244MMHPhr 0.23 scf/HPhr 5 4,228MMHPhr Well Workovers Gas Wells 762 workovers/yr 2,316.23 scfy/w.o. 34 984 workovers/yr Well Clean Ups (LP Gas Wells) 9,327 LP Gas Wells 46,787.16 scfy/LP well 8,404 9,360 LP Gas Wells Blowdowns Vessel BD 20,918 vessels 73.62 scfy/vessel 30 22,637 vessels Pipeline BD 27,782miles (gathering) 291.65 scfy/mile 156 39,689miles (gathering) Compressor BD 1,398 compressors 3,562.13 scfy/comp 96 3,075 compressors Compressor Starts 1,398 compressors 7,969.01 scfy/comp 215 3,075 compressors Upsets Pressure Relief Valves 43,262 PRV 32.09 PRV 27 43,369 PRV Mishaps 27,782miles (gathering) 631.44 scf/mile 338 9,922miles West Coast Gas Wells WC - Associated Gas Wells2 19,921 wells NA 19,819 wells WC - Non-associated Gas Wells 1,256 wells 37.38 scfd/well 470 1,254 wells WC - Unconventional Gas Wells 0 wells 0 scfd/well 0 0 wells Field Separation Equipment Heaters 640 heaters 59.20 scfd/heater 379 1,254 heaters Separators 435 separators 125.15 scfd/sep 544 915 separators Dehydrators 315 dehydrators 93.47 scfd/dehy 295 211 dehydrators Meters/Piping 2,581meters 54.26 scfd/meter 1,401 2,500meters Gathering Compressors Small Reciprocating Comp. 145 compressors 274.63 scfd/comp 398 1,456 compressors Large Reciprocating Comp. 1 compressors 15,595.89 scfd/comp 128 8 compressors Large Reciprocating Stations 0 stations 8,458.58 scfd/station 1 stations 
A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data Pipeline Leaks 1,548miles (gathering) 54.52 scfd/mile 844 15,771miles Drilling and Well Completion 0Completion Flaring1 4 Completions 751.84 scf/comp 29 2 completions/ year Well Drilling NA NA 51 wells Normal Operations Pneumatic Device Vents 10,949 device 353.87 scfd/device 38,744 1,257 controllers Chemical Injection Pumps 45 active pumps 254.38 scfd/pump 115 851 active pumps Kimray Pumps 50,254MMscf/yr 1,017.50 scf/MMscf 511,341 61,753MMscf/yr Dehydrator Vents 56,445MMscf/yr 282.69 scf/MMscf 159,562 69,307MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA scf/bbl 0MMbbl/yr Condensate Tanks with Control Devices NA NA scf/bbl 0MMbbl/yr Compressor Exhaust Vented Gas Engines 125MMHPhr 0.25 scf/HPhr 0 234MMHPhr Well Workovers Gas Wells 42 workovers/yr 2,517.09 scfy/w.o. 2 55 workovers/yr Well Clean Ups (LP Gas Wells) 520 LP Gas Wells 50,844.36 scfy/LP well 509 519 LP Gas Wells Blowdowns Vessel BD 1,165 vessels 80.01 scfy/vessel 2 2,380 vessels Pipeline BD 1,548miles (gathering) 316.94 scfy/mile 9 15,771miles (gathering) Compressor BD 78 compressors 3,871.02 scfy/comp 6 1,456 compressors Compressor Starts 78 compressors 8,660.05 scfy/comp 13 1,456 compressors Upsets Pressure Relief Valves 2,410 PRV 34.87 PRV 2 2,405 PRV Mishaps 1,548miles (gathering) 686.20 scf/mile 20 3,943miles Gulf Coast Gas Wells GC - Associated Gas Wells2 35,558 wells NA 35,376 wells GC - Non-associated Gas Wells 37,379 wells 38.25 scfd/well 14,298 37,307 wells GC - Unconventional Gas Wells NA wells NA 0 wells Field Separation Equipment Heaters 19,039 heaters 60.58 scfd/heater 11,534 8,357 heaters Separators 12,937 separators 128.07 scfd/sep 16,568 24,548 separators Dehydrators 9,377 dehydrators 95.65 scfd/dehy 8,969 6,277 dehydrators Meters/Piping 76,790meters 55.53 scfd/meter 42,641 44,674meters Gathering Compressors Small Reciprocating Comp. 4,313 compressors 281.04 scfd/comp 12,120 3,022 compressors Large Reciprocating Comp. 24 compressors 15,959.44 scfd/comp 3,906 16 compressors Large Reciprocating Stations 3 stations 8,655.76 scfd/station 265 2 stations Pipeline Leaks 46,045miles (gathering) 55.79 scfd/mile 25,688 56,151miles Drilling and Well Completion Completion Flaring 114 Completions 769.37 scf/comp 879 54 completions/ year Well Drilling NA NA 1,510 wells Normal Operations 
GRI/EPA Values EPA Adjust

Activity Activity Data Emission Factor Emissions Activity Data Pneumatic Device Vents 37,709 device 362.12 scfd/device 136,550 25,929 controllers Chemical Injection Pumps 1,346 active pumps 260.31 scfd/pump 3,503 1,231 active pumps Kimray Pumps 1,495,101MMscf/yr 1,041.22 scf/MMscf 15,567,310 1,837,175MMscf/yr Dehydrator Vents 1,679,280MMscf/yr 289.27 scf/MMscf 4,857,734 2,061,925MMscf/yr Condensate Tank Vents Condensate Tanks without Control Devices NA NA 34MMbbl/yr Condensate Tanks with Control Devices NA NA 34MMbbl/yr Compressor Exhaust Vented Gas Engines 3,719MMHPhr 0.25 scf/HPhr 9 6,976MMHPhr Well Workovers Gas Wells 1,263 workovers/yr 2,575.76 scfy/w.o. 63 1,623 workovers/yr Well Clean Ups (LP Gas Wells) 14,704 LP Gas Wells 52,029.57 scfy/LP well 14,735 15,445 LP Gas Wells Blowdowns Vessel BD 34,668 vessels 81.87 scfy/vessel 55 39,182 vessels Pipeline BD 46,045miles (gathering) 324.33 scfy/mile 288 56,151miles (gathering) Compressor BD 2,317 compressors 3,961.26 scfy/comp 177 3,022 compressors Compressor Starts 2,317 compressors 8,861.93 scfy/comp 396 3,022 compressors Upsets Pressure Relief Valves 71,700 PRV 35.69 PRV 49 71,563 PRV Mishaps 46,045miles (gathering) 702.19 scf/mile 623 14,038miles Coal Bed Methane  Black Warrior NA NA 2,230 wells Offshore Platforms Shallow water Gas Platforms (GoM and Pacific) NA NA 2,210

Shallow water gas platforms 

Deepwater Gas Platforms (GoM and Pacific) NA NA 11

Deepwater gas platforms 

1 Emissions are not actually 0, but too small to show at this level of precision. 2 Emissions from oil wells that produce associated gas are estimated in the Petroleum Systems model. Here the oil wells count is used as a driver only. 3 NA = not available; i.e. this data is not available from the specified source. Note: The mapping of regions differs between GRI and NEMS. The GRI maps the states to the following regions: Gulf Coast Onshore, Gulf Coast Offshore, Central Plains (onshore), Atlantic and Grea

Mountain (onshore) and Pacific Offshore., while the NEMS has the following regions: Northeast, Mid-Central, Rocky Mountain, South West, West Coast and Gulf Coast. Therefore, it is not possible to 

activity factors between the two modeling systems. Hence, the GRI national AF estimates were allocated to the NEMS regions using the NEMS regional gas well counts to national well count ratios. 

Table A- 110: 1992 Data and CH4 Emissions (Mg) for the Natural Gas Processing Stage 

GRI/EPA Values EPA Adjuste

Activity Activity Data Emission Factor Emissions Activity Data Plants 726 plants 7,906 scfd/plant 40,349 732 plants Recip. Compressors 4,092 compressors 11,196 scfd/comp 322,068 4,126 compressors 1Centrifugal Compressors 726 compressors 21,230 scfd/comp 108,351 665 compressors 2Compressor Exhaust Gas Engines 27,760 MMHPhr 0.24 scf/HPhr 128,317 29,642 MMHPhr Gas Turbines 32,910 MMHPhr 0.01 scf/HPhr 3,612 35,147 MMHPhr AGR Vents 371 AGR units 6,083 scfd/AGR 15,865 371 AGR units 
A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Kimray Pumps 957,900 MMscf/yr 178 scf/MMscf 3,279 1,073,845 MMscf/yr Dehydrator Vents 8,630,000 MMscf/yr 122 scf/MMscf 20,203 9,674,280 MMscf/yr Pneumatic Devices 726 gas plants 164,721 scfy/plant 2,303 732 gas plants 16Blowdowns/Venting 726 gas plants 4,060 Mscfy/plant 56,770 732 gas plants 

Table A- 111: 1992 Data and CH4 Emissions (Mg) for the Natural Gas Transmission Stage 

GRI/EPA Values EPA Adjusted Values 

Activity Activity Data Emission Factor Emissions Activity Data EmisPipeline Leaks 284,500 miles 1.55 scfd/ mile 3,107 291,468 miles Compressor Stations (Transmission) Station 1,700 Stations 8,778 scfd/station 104,931 1,730 Stations Recip Compressor 6,799 Compressors 15,205 scfd/ comp 726,742 6,956 Compressors 1Centrifugal Compressor 681 Compressors 30,305 scfd/ comp 145,081 698 Compressors 3Compressor Stations (Storage) Station 386 Stations 21,507 scfd/station 58,360 386 Stations 2Recip Compressor 1,135 Compressors 21,116 scfd/comp 168,483 1,135 Compressors 2Centrifugal Compressor 111 Compressors 30,573 scfd/comp 23,856 111 Compressors 3Wells (Storage) 17,999 Stations 114 scfd/well 14,487 17,999 Stations M&R (Trans. Co. Interconnect) 2,532 Stations 3,984 scfd/station 70,915 2,595 Stations M& R (Farm Taps + Direct Sales) 72,630 Stations 31.20 scfd/station 15,930 76,932 Stations Dehydrator vents (Transmission) 1,083,926 MMscf/Year 93.72 scf/MMscf 1,956 1,102,878 MMscf/Year Dehydrator vents (Storage) 2,000,001 MMscf/Year 117.18 scf/MMscf 4,513 2,000,001 MMscf/Year Compressor Exhaust Engines (Transmission) 40,380 MMHPhr 0.24 scf/HPhr 186,652 40,380 MMHPhr Turbines (Transmission) 9,635 MMHPhr 0.01 scf/HPhr 1,057 9,635 MMHPhr Engines (Storage) 4,922 MMHPhr 0.24 scf/HPhr 22,751 4,922 MMHPhr Turbines (Storage) 1,729 MMHPhr 0.01 scf/HPhr 189 1,729 MMHPhr Generators (Engines) 1,976 MMHPhr 0.24 scf/HPhr 9,133 1,976 MMHPhr Generators (Turbines) 23 MMHPhr 0.01 scf/HPhr 3 23 MMHPhr Pneumatic Devices Trans + Stor Pneumatic Devices Trans 68,103 Devices 162,197 scfy/device 212,747 68,103 Devices 16Pneumatic Devices Storage 15,460 Devices 162,197 scfy/device 48,295 15,194 Devices 16Routine Maintenance/Upsets Pipeline venting 284,500 Miles 32 Mscfy/mile 173,425 291,468 Miles Station Venting Trans + Storage Station Venting Transmission 1,700 

Compressor Stations 4,359 Mscfy/station 148,206 1,730 

Compressor Stations 

Station Venting Storage 386 

Compressor Stations 4,359 Mscfy/station 33,651 386 

Compressor Stations 

LNG Storage LNG Stations NA NA 63 Stations 2LNG Reciprocating Compressors NA NA 238 Compressors 2LNG Centrifugal Compressors NA NA 56 Compressors 3LNG Compressor Exhaust LNG Engines NA NA 699 MMHPhr 
LNG Turbines NA NA 110 MMHPhr LNG Station Venting NA NA 63 Stations LNG Import Terminals LNG Stations NA NA 1 Stations 2LNG Reciprocating Compressors NA NA 7 Compressors 2LNG Centrifugal Compressors NA NA 1 Compressors 3LNG Compressor Exhaust LNG Engines NA NA 208 MMHPhr LNG Turbines NA NA 44 MMHPhr LNG Station Venting NA NA 1 Stations 

Table A- 112: 1992 Data and CH4 Emissions (Mg) for the Natural Gas Distribution Stage 

GRI/EPA Values EPA Adjusted 

Activity Activity Data Emission Factor Emissions Activity Data EmissPipeline Leaks Mains—Cast Iron 55,288 miles 238.7 Mscf/mile-yr 254,178.95 52,917 miles 23Mains—Unprotected steel 174,657 equiv leaks 51.8 Mscf/leak-yr 174,249.70 99,619 miles 11Mains—Protected steel 68,308 equiv leaks 20.3 Mscf/leak-yr 26,706.93 469,106 miles Mains—Plastic 49,226 equiv leaks 99.8 Mscf/leak-yr 94,619.66 267,283 miles Services—Unprotected steel 458,476 equiv leaks 20.2 Mscf/leak-yr 178,371.00 7,138,563 services Services Protected steel 390,628 equiv leaks 9.2 Mscf/leak-yr 69,216.16 19,742,086 services Services—Plastic 68,903 equiv leaks 2.39 Mscf/leak-yr 3,171.70 20,692,674 services Services—Copper 7,720 equiv leaks 7.68 Mscf/leak-yr 1,141.92 1,568,685 services Meter/Regulator (City Gates) M&R >300 3,460 stations 179.8 scfh/station 104,960.57 3,887 stations 17M&R 100-300 13,335 stations 95.6 scfh/station 215,085.58 14,187 stations 9M&R <100 7,127 stations 4.31 scfh/station 5,182.56 7,583 stations Reg >300 3,995 stations 161.9 scfh/station 109,124.94 4,250 stations 16R-Vault >300 2,346 stations 1.3 scfh/station 514.55 2,496 stations Reg 100-300 12,273 stations 40.5 scfh/station 83,862.18 12,857 stations 4R-Vault 100-300 5,514 stations 0.18 scfh/station 167.46 5,777 stations Reg 40-100 36,328 stations 1.04 scfh/station 6,374.34 38,583 stations R-Vault 40-100 32,215 stations 0.09 scfh/station 470.15 34,216 stations Reg <40 15,377 stations 0.13 scfh/station 345.05 16,360 stations Customer Meters Residential 40,049,306 

outdoor meters 138.5 scfy/meter 106,831.92 40,049,306 outdr meters 14

Commercial/Industry 4,608,000 meters 47.9 scfy/meter 4,251.13 4,607,983 meters 4Routine Maintenance Pressure Relief Valve Releases 836,760 mile main 0.05 Mscf/mile 805.8 888,925 mile main Pipeline Blowdown 1,297,569 miles 0.1 Mscfy/mile 2,549.10 1,297,569 miles Upsets Mishaps (Dig-ins) 1,297,569 miles 1.59 mscfy/mile 39,735.97 1,297,569 miles 
A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 113: Key Activity Data Drivers Variable Units 1990 1995 2001 2002 2003 2004 2Transmission Pipelines Length miles 291,990 296,947 290,456 303,541 301,816 303,084 300,Wells 

NE—Associated Gas Wells* # wells 68,261 66,102 54,727 52,928 47,803 47,412 46,NE—Non-associated Gas Wells* # wells 124,241 129,789 149,436 154,590 156,320 155,257 158,MC—Associated Gas Wells* # wells 64,379 72,483 67,278 65,786 65,864 65,902 65,MC—Non-associated Gas Wells* # wells 53,940 65,585 63,595 67,861 70,377 72,809 71,RM—Associated Gas Wells* # wells 13,749 13,745 12,148 12,446 12,495 12,723 13,RM—Non-associated Gas Wells* # wells 24,339 32,668 70,450 72,438 71,239 70,770 72,SW—Associated Gas Wells* # wells 69,339 59,954 57,188 60,623 60,315 59,381 54,SW—Non-associated Gas Wells* # wells 24,217 27,392 33,936 35,025 36,648 37,219 34,WC—Associated Gas Wells* # wells 20,672 19,109 20,808 22,503 22,263 22,584 21,WC—Non-associated Gas Wells* # wells 1,292 1,114 1,434 1,415 1,459 1,388 1,GC—Associated Gas Wells* # wells 36,279 34,792 32,549 29,880 28,078 27,675 28,GC—Non-associated Gas Wells* # wells 41,753 41,978 51,182 53,198 54,245 54,544 57,

Platforms

Gulf of Mexico and Pacific OCS Off-shore 

Platforms # platforms 3,942 3,981 4,074 4,058 4,010 3,938 3,

GoM and Pacific OCS Deep Water Platforms # platforms 17 23 40 44 46 50 

Gas Plants # gas plants 761 675 570 590 574 572 Distribution Services # of services 47,883,083 54,644,033 57,461,795 58,876,416 58,537,395 61,089,889 58,556,

Steel—Unprotected # of services 7,633,526 6,151,653 5,449,653 5,186,134 4,840,347 4,791,652 5,308,Steel—Protected # of services 19,781,581 21,002,455 17,911,402 17,778,463 17,258,710 18,147,587 15,883,Plastic # of services 18,879,865 26,044,545 32,706,753 34,547,274 35,071,961 36,811,107 36,152,Copper # of services 1,588,111 1,445,380 1,393,987 1,364,545 1,366,377 1,339,543 1,212,

Distribution Mains miles 944,157 1,001,706 1,099,137 1,133,625 1,104,683 1,151,995 1,093,

Cast Iron miles 58,292 50,625 44,283 42,025 41,091 40,600 37,Steel—Unprotected miles 108,941 94,058 81,291 78,119 74,042 75,859 69,Steel—Protected miles 465,538 503,288 475,329 480,982 483,782 495,861 461,Plastic miles 311,386 353,735 498,234 532,499 505,768 539,675 525,

* NEMS (National Energy Modeling System) projects the production, imports, conversion, consumption, and prices of energy, subject to assumptions on macroeconomic and financial factors, world encosts, behavioral and technological choice criteria, cost and performance characteristics of energy technologies, and demographics
A-143

Table A- 114: CH4 reductions derived from the Natural Gas STAR program (Gg) 

Process 1992 1995 2001 2002 2003 2004 2005 2006 2007 

Production 0 75 383 420 565 917 1,308 1,336 1,667 

Processing 0 5 29 34 65 60 123 135 133 

Transmission and Storage 0 121 336 344 330 416 512 505 450 

Distribution 0 19 33 161 110 95 34 108 28 

Note: These reductions will not match the Natural Gas STAR program reductions. These numbers are adjusted for reductions prior to the 1992 base year, and do not 

include a sunsetting period. 

Table A- 115: CH4 reductions derived from NESHAP regulations (Gg) 

Process 1999 2000 2001 2002 2003 2004 2005 2006 2007 

Production 24.0 25.4 27.6 28.6 28.6 28.4 28.9 30.5 31.7 

Processing 12.6 12.9 13.1 12.7 12.8 12.5 12.1 12.4 13.0 

Transmission and Storage 0 0 0 0 0 0 0 0 0 

Distribution 0 0 0 0 0 0 0 0 0 

Note: NESHAP regulations went into effect in 1999. 

Table A- 116: CH4 Emission Estimates from the Natural Gas Production Stage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Normal Fugitives 

 Gas Wells 

Non-Associated Gas Wells (less 

conventional) 30.82 31.68 37.41 50.22 51.09 50.24 49.97 49.21 50.71 51.79

 Unconventional Gas Wells 0.01 0.02 0.04 0.47 0.67 0.85 0.98 1.20 1.32 1.51

 Field Separation Equipment 

 Heaters 12.57 13.28 16.48 25.61 26.44 26.60 26.67 26.94 28.00 28.42

 Separators 43.83 44.16 54.79 81.55 84.15 85.03 85.25 86.52 90.23 94.17

 Dehydrators 14.75 15.76 17.89 22.42 23.28 23.62 23.73 23.96 24.90 26.19

 Meter/ Piping 43.61 44.95 54.89 79.34 81.96 82.73 83.08 83.81 87.15 90.32

 Gathering Compressors 

 Small Reciprocating Comp. 30.22 32.15 38.27 50.61 52.53 53.52 53.99 53.89 53.03 58.48

 Large Reciprocating Comp. 7.50 8.56 9.80 12.62 12.62 12.48 12.61 12.60 12.58 12.58

 Large Reciprocating Stations 0.51 0.58 0.66 0.86 0.86 0.85 0.86 0.85 0.85 0.85

 Pipeline Leaks 98.03 102.22 114.49 141.52 145.52 145.56 147.60 148.53 152.90 158.31

Vented and Combusted 

 Drilling and Well Completion 

 Completion Flaring 0.0055 0.0058 0.0063 0.0079 0.0082 0.0081 0.0083 0.0084 0.0087 0.0091

 Well Drilling 0.74 0.56 0.57 1.25 0.98 1.11 1.24 1.52 1.80 1.89

 Coal Bed Methane 

 Powder River 0.04 0.47 1.48 40.58 47.17 46.85 44.74 46.37 56.55 53.62

 Black Warrior 2.72 4.84 6.25 7.32 7.81 8.54 9.22 9.88 10.45 11.08

 Normal Operations 

 Pneumatic Device Vents 569.76 607.88 697.61 895.16 932.14 937.52 955.82 955.56 990.34 1031.68

 Chemical Injection Pumps 26.89 29.38 35.04 48.86 49.42 51.28 51.80 51.93 53.85 54.91

 Kimray Pumps 131.82 140.65 159.45 199.72 207.39 210.19 211.38 213.42 221.79 233.18

 Dehydrator Vents 41.10 43.86 49.72 62.28 64.67 65.54 65.91 66.55 69.16 72.71

 Condensate Tank Vents 

 Condensate Tanks without 

 Control Device 23.59 21.69 20.22 23.80 24.43 23.59 22.54 23.59 25.90 25.90

 Condensate Tanks with Control 

 Device 4.72 4.34 4.04 4.76 4.89 4.72 4.51 4.72 5.18 5.18

 Compressor Exhaust Vented 

 Gas Engines 119.06 125.48 151.43 200.20 208.50 212.13 214.62 215.27 223.88 235.24

 Well Workovers 

 Gas Wells 0.56 0.58 0.64 0.79 0.82 0.82 0.84 0.84 0.87 0.92

Well Clean Ups (LP Gas Wells) 106.80 111.49 122.33 151.71 157.67 156.89 160.77 162.03 167.93 176.38

 Blowdowns 

 Vessel BD 0.36 0.38 0.42 0.53 0.55 0.55 0.56 0.56 0.58 0.61

 Pipeline BD 1.56 1.63 1.82 2.25 2.32 2.32 2.35 2.37 2.44 2.52

 Compressor BD 1.17 1.24 1.48 1.95 2.03 2.07 2.08 2.08 2.16 2.26

 Compressor Starts 2.61 2.78 3.31 4.37 4.54 4.62 4.66 4.66 4.84 5.05
A-144 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 Upsets 

 Pressure Relief Valves 0.34 0.35 0.39 0.48 0.50 0.50 0.51 0.51 0.53 0.56

 Mishaps 0.85 0.88 0.99 1.22 1.25 1.25 1.27 1.28 1.32 1.36

Offshore 

 Offshore water Gas Platforms (GoM & 

Pacific) 290.56 297.92 307.56 331.26 332.57 331.34 324.99 322.32 322.18 306.06

 Deepwater Gas Platforms (GoM & 

Pacific) 5.21 5.94 7.40 13.61 15.10 15.93 17.31 20.43 23.20 21.10

Table A- 117: CH4 Emission Estimates from the Natural Gas Processing Plants (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007 

Normal Fugitives 

 Plants 42.30 40.68 37.52 31.68 32.79 31.90 31.79 31.46 31.74 31.90 

 Reciprocating Compressors 324.74 324.74 337.77 356.69 344.02 346.72 337.58 327.24 335.95 350.90 

 Centrifugal Compressors 99.25 99.25 103.23 109.01 105.14 105.96 103.17 100.01 102.67 107.24 

Vented and Combusted 

 Normal Operations 

 Compressor Exhaust 

 Gas Engines 137.02 137.02 142.52 150.50 145.15 146.29 142.44 138.07 141.75 148.05 

 Gas Turbines 3.86 3.86 4.01 4.24 4.09 4.12 4.01 3.89 3.99 4.17 

 AGR Vents 16.49 15.87 14.63 12.35 12.79 12.44 12.40 12.27 12.38 12.44 

 Kimray Pumps 3.68 3.68 3.82 4.04 3.89 3.93 3.82 3.70 3.80 3.97 

 Dehydrator Vents 22.65 22.65 23.56 24.88 23.99 24.18 23.54 22.82 23.43 24.47 

 Pneumatic Devices 2.41 2.32 2.14 1.81 1.87 1.82 1.81 1.80 1.81 1.82 

Routine Maintenance 

 Blowdowns/Venting 59.51 57.24 52.78 44.57 46.14 44.88 44.73 44.26 44.65 44.88 

Table A- 118: CH4 Emission Estimates from the Natural Gas Transmission and Storage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Fugitives 

 Pipelines Leaks 3.19 3.18 3.24 3.17 3.32 3.30 3.31 3.28 3.28 3.28

Compressor Stations 

(Transmission) 

 Station 106.96 106.77 108.77 106.40 111.19 110.56 111.02 110.14 110.02 109.86

 Recip Compressor 744.89 743.55 757.53 740.97 774.35 769.95 773.19 767.07 766.21 765.12

 Centrifugal 

 Compressor 148.90 148.63 151.43 148.12 154.79 153.91 154.56 153.34 153.16 152.94

Compressor Stations (Storage) 

 Station 54.64 58.36 60.35 59.37 60.84 63.20 60.59 59.80 54.35 58.78

 Recip Compressor 157.80 168.48 174.27 171.45 175.76 182.59 175.01 172.79 157.05 169.82

 Centrifugal 

 Compressor 22.35 23.86 24.72 24.29 24.93 26.01 24.93 24.50 22.35 24.07

 Wells (Storage) 13.56 14.49 14.98 14.74 15.10 15.69 15.04 14.85 13.49 14.59

 M&R (Trans. Co. 

 Interconnect) 72.80 72.67 74.03 72.41 75.68 75.25 75.56 74.97 74.88 74.77

 M&R (Farm Taps + 

 Direct Sales) 16.90 16.87 17.19 16.82 17.57 17.47 17.55 17.41 17.39 17.36

Vented and Combusted 

Normal Operation 

 Dehydrator Vents 

(Transmission) 1.99 1.99 2.03 1.98 2.07 2.06 2.07 2.05 2.05 2.05

Dehydrator Vents (Storage) 4.23 4.51 4.67 4.59 4.71 4.89 4.69 4.63 4.20 4.55

Compressor Exhaust 

Engines (Transmission) 178.73 186.65 206.10 212.39 219.72 212.75 213.82 210.20 206.79 220.20

Turbines (Transmission) 1.01 1.06 1.17 1.20 1.25 1.21 1.21 1.19 1.17 1.25

Engines (Storage) 21.30 22.75 23.53 23.14 23.72 24.64 23.62 23.31 21.19 22.92

Turbines (Storage) 0.18 0.19 0.20 0.19 0.20 0.21 0.20 0.19 0.18 0.19

Generators (Engines) 8.75 9.13 10.09 10.39 10.75 10.41 10.46 10.29 10.12 10.78

Generators (Turbines) 0.0024 0.0026 0.0028 0.0029 0.0030 0.0029 0.0029 0.0029 0.0028 0.0030

Pneumatic Devices Trans+Stor 

Pneumatic Devices Trans 213.13 212.75 216.75 212.01 221.56 220.30 221.23 219.48 219.23 218.92

Pneumatic Devices Storage 44.44 47.46 49.09 48.28 49.48 51.40 49.28 48.64 44.21 47.81

Routine Maintenance/Upsets 
A-145

Pipeline Venting 177.99 177.67 181.01 177.06 185.03 183.98 184.75 183.29 183.09 182.82

Station venting Trans+Storage 

Station Venting Transmission 145.52 145.26 147.99 144.75 151.27 150.41 151.04 149.85 149.68 149.47

Station Venting Storage 30.34 32.41 33.51 32.97 33.78 35.09 33.64 33.21 30.18 32.64

LNG Storage 

LNG Stations 9.24 9.45 9.77 10.41 10.52 10.62 10.62 10.62 10.62 10.62

LNG Reciprocating Compressors 34.50 35.37 36.67 39.28 39.71 40.15 40.15 40.15 40.15 40.15

LNG Centrifugal Compressors 11.78 12.09 12.55 13.46 13.61 13.77 13.77 13.77 13.77 13.77

LNG Compressor Exhaust 

LNG Engines 3.21 3.23 3.26 3.30 3.31 3.32 3.32 3.32 3.32 3.32

LNG Turbines 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

LNG Station Venting 5.13 5.25 5.43 5.78 5.84 5.90 5.90 5.90 5.90 5.90

LNG Import Terminals 

LNG Stations 0.21 0.21 0.21 0.32 0.32 0.42 0.42 0.42 0.42 0.42

LNG Reciprocating Compressors 1.01 1.01 1.01 1.51 1.51 2.02 2.02 2.02 2.02 2.02

LNG Centrifugal Compressors 0.26 0.26 0.26 0.39 0.39 0.52 0.52 0.52 0.52 0.52

LNG Compressor Exhaust 

LNG Engines 1.74 0.96 0.49 4.70 4.52 9.83 12.57 12.18 11.28 14.81

LNG Turbines1 0.01 0.00 0.00 0.03 0.03 0.06 0.07 0.07 0.07 0.09

LNG Station Venting 0.12 0.12 0.12 0.18 0.18 0.24 0.24 0.24 0.24 0.24 1 Emissions are not actually 0, but too small to show at this level of precision. 

Table A- 119: CH4 Emission Estimates from the Natural Gas Distribution Stage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Pipeline Leaks 

 Mains—Cast Iron 267.99 243.28 232.74 203.58 193.20 188.91 186.65 171.81 170.00 173.18

 Mains—Unprotected steel 231.20 211.42 199.62 172.52 165.79 157.14 160.99 147.05 152.25 147.55

 Mains—Protected steel 27.50 27.71 29.73 28.08 28.41 28.58 29.29 27.26 28.46 28.93

 Mains—Plastic 59.43 51.02 67.52 95.10 101.64 96.53 103.01 100.36 118.13 114.82

 Services—Unprotected steel 250.05 233.84 201.51 178.51 169.88 158.55 156.96 173.89 184.83 178.49

 Services Protected steel 67.24 67.11 71.39 60.89 60.43 58.67 61.69 53.99 53.48 53.56

 Services—Plastic 3.38 3.71 4.66 5.86 6.19 6.28 6.59 6.47 7.10 7.36

 Services—Copper 7.78 7.68 7.08 6.83 6.68 6.69 6.56 5.94 6.12 6.01

Meter/Regulator (City Gates) 

 M&R >300 110.41 117.93 121.96 119.96 122.93 127.71 122.43 120.84 109.83 118.78

 M&R 100-300 214.25 228.82 236.64 232.77 238.53 247.80 237.55 234.48 213.11 230.47

 M&R <100 5.16 5.51 5.70 5.61 5.75 5.97 5.72 5.65 5.14 5.55

 Reg >300 108.70 116.09 120.06 118.10 121.02 125.72 120.52 118.96 108.12 116.93

 R-Vault >300 0.51 0.55 0.57 0.56 0.57 0.59 0.57 0.56 0.51 0.55

 Reg 100-300 82.26 87.86 90.86 89.37 91.58 95.14 91.21 90.03 81.82 88.49

 R-Vault 100-300 0.16 0.18 0.18 0.18 0.18 0.19 0.18 0.18 0.16 0.18

 Reg 40-100 6.34 6.77 7.00 6.89 7.06 7.33 7.03 6.94 6.31 6.82

 R-Vault 40-100 0.47 0.50 0.52 0.51 0.52 0.54 0.52 0.51 0.47 0.50

 Reg <40 0.34 0.37 0.38 0.37 0.38 0.40 0.38 0.38 0.34 0.37

Customer Meters 

 Residential 103.47 110.51 114.29 112.42 115.20 119.68 114.73 113.24 102.92 111.31

 Commercial/Industry 3.97 4.25 4.78 4.27 4.38 4.25 4.27 3.95 3.84 3.97

Routine Maintenance 

 Pressure Relief Valve Releases 0.91 0.86 0.96 1.06 1.09 1.06 1.11 1.05 1.16 1.15

 Pipeline Blowdown 2.39 2.55 2.64 2.59 2.66 2.76 2.65 2.61 2.37 2.57

Upsets 

 Mishaps (Dig-ins) 37.20 39.74 41.09 40.42 41.42 43.03 41.25 40.72 37.01 40.02

Table A- 120: U.S. Production Sector CO2 Content in Natural Gas by NEMS Region and Natural Gas Well type 

 U.S. Region 

Well Types North East Mid-Central Gulf Coast South West Rocky Mountain West Coast Lower-48 States 

Conventional 0.92% 0.79% 2.17% 3.81% 7.95% 0.16% 3.41% 

Un-conventional 7.42% 0.31% 0.23% NA 0.64% NA 4.83% 

All types 3.04% 0.79% 2.17% 3.81% 7.58% 0.16% 3.45% 
A-146 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 121: CO2 Emission Estimates from the Natural Gas Production Stage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Normal Fugitives 

 Gas Wells 

 Associated Gas Wells 3.56 3.57 4.25 6.69 6.67 6.43 6.26 6.02 6.16 5.82

 Unconventional Gas Wells2 0.00 0.00 0.00 0.01 0.01 0.02 0.02 0.03 0.03 0.03

 Field Separation Equipment 

 Heaters 1.86 1.91 2.32 4.29 4.42 4.39 4.38 4.44 4.61 4.47 

 Separators 6.04 5.97 7.17 12.17 12.55 12.56 12.56 12.70 13.20 13.13

 Dehydrators 1.50 1.54 1.73 2.73 2.81 2.81 2.79 2.83 2.94 2.92

 Meter/ Piping 5.85 5.85 6.95 11.76 12.12 12.12 12.11 12.23 12.69 12.53

 Gathering Compressors 

 Small Reciprocating Comp. 2.95 2.98 3.57 5.94 6.13 6.15 6.16 6.18 6.41 6.36

 Large Reciprocating Comp. 0.69 0.91 1.02 1.40 1.40 1.40 1.40 1.40 1.40 1.40

 Large Reciprocating Stations 0.05 0.06 0.07 0.09 0.09 0.09 0.09 0.09 0.09 0.09

 Pipeline Leaks 10.75 10.85 11.99 17.02 17.47 17.50 17.49 17.66 18.21 18.21

Vented and Combusted 

 Drilling and Well Completion 

 Completion Flaring2 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Well Drilling 0.07 0.05 0.06 0.14 0.11 0.13 0.14 0.17 0.20 0.20 

 Coal Bed Methane 

 Powder River1 - - - - - - - - - -

 Black Warrior1 - - - - - - - - - -

 Normal Operations 

 Pneumatic Device Vents 58.49 60.24 69.38 110.26 113.87 114.04 114.00 115.02 119.29 118.44

 Chemical Injection Pumps 2.95 3.11 3.75 6.93 7.00 7.09 7.08 7.19 7.45 7.17

 Kimray Pumps 13.41 13.71 15.43 24.22 24.94 24.93 24.78 25.18 26.12 26.00

 Dehydrator Vents 4.18 4.28 4.81 7.55 7.78 7.77 7.73 7.85 8.15 8.11

 Condensate Tank Vents 

 Condensate Tanks without 

 Control Device 10.25 9.43 8.79 10.34 10.62 10.25 9.79 10.25 11.26 11.26

 Condensate Tanks with Control 

 Device 2.05 1.89 1.76 2.07 2.12 2.05 1.96 2.05 2.25 2.25

 Compressor Exhaust Vented 

 Gas Engines1 - - - - - - - - - -

 Well Workovers 

 Gas Wells 0.06 0.06 0.06 0.09 0.09 0.09 0.09 0.09 0.10 0.10

 Well Clean Ups (LP Gas Wells) 10.64 10.89 12.02 17.28 17.86 17.95 17.92 18.15 18.84 19.10

 Blowdowns 

 Vessel BD 0.04 0.04 0.04 0.06 0.06 0.06 0.06 0.07 0.07 0.07

 Pipeline BD 0.17 0.17 0.19 0.27 0.28 0.28 0.28 0.28 0.29 0.29

 Compressor BD 0.11 0.12 0.14 0.23 0.24 0.24 0.24 0.24 0.25 0.25

 Compressor Starts 0.25 0.26 0.31 0.51 0.53 0.53 0.53 0.53 0.55 0.55

 Upsets 

 Pressure Relief Valves 0.03 0.03 0.04 0.05 0.06 0.06 0.06 0.06 0.06 0.06

 Mishaps 0.09 0.09 0.10 0.15 0.15 0.15 0.15 0.15 0.16 0.16

 Flaring Emissions - Onshore 5,509 5,062 8,729 5,858 6,001 5,936 5,828 7,200 7,826 6,969

Offshore 

Offshore water Gas Platforms (GoM 

& Pacific) 1.47 1.51 1.56 1.68 1.69 1.68 1.65 1.64 1.64 1.55

Deepwater Gas Platforms (GoM & 

Pacific) 0.03 0.03 0.04 0.07 0.08 0.08 0.09 0.10 0.12 0.11

 Flaring Emissions - Offshore 230.37 163.13 197.22 204.93 211.02 155.39 164.74 172.54 146.48 159.22 1 Energy use CO2 emissions not estimated to avoid double counting. 2

 Emissions are not actually 0, but too small to show at this level of precision. 

 

Table A- 122: CO2 Emission Estimates from the Natural Gas Processing Plants (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Normal Fugitives 

 Plants – Before CO2 removal 2.56 2.46 2.27 1.92 1.98 1.93 1.92 1.90 1.92 1.93

 Plants – After CO2 removal 0.57 0.55 0.50 0.43 0.44 0.43 0.43 0.42 0.43 0.43

 Reciprocating Compressors – 

Before CO2 removal 19.66 19.66 20.45 21.59 20.82 20.99 20.43 19.81 20.34 21.24
A-147

 Reciprocating Compressors – 

After CO2 removal 4.36 4.36 4.54 4.79 4.62 4.66 4.54 4.40 4.52 4.72

 Centrifugal Compressors – 

Before CO2 removal 6.01 6.01 6.25 6.60 6.36 6.41 6.25 6.05 6.21 6.49

 Centrifugal Compressors – 

After CO2 removal 1.33 1.33 1.39 1.47 1.41 1.42 1.39 1.34 1.38 1.44

Vented and Combusted 

 Normal Operations 

 Compressor Exhaust 

 Gas Engines1 - - - - - - - - - -

 Gas Turbines1 - - - - - - - - - -

 AGR Vents 27,708 26,652 24,577 22,342 23,022 21,958 21,737 21,694 21,161 21,144

 Kimray Pumps 0.39 0.39 0.41 0.43 0.42 0.42 0.41 0.40 0.41 0.42

 Dehydrator Vents 2.42 2.42 2.52 2.66 2.57 2.59 2.52 2.44 2.51 2.62

 Pneumatic Devices 0.29 0.27 0.25 0.21 0.22 0.22 0.21 0.21 0.21 0.22

Routine Maintenance 

 Blowdowns/Venting 6.36 6.12 5.64 4.77 4.93 4.80 4.78 4.73 4.78 4.80 1 Energy use CO2 emissions not estimated to avoid double counting. 

Table A- 123: CO2 Emission Estimates from the Natural Gas Transmission and Storage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007 

Fugitives 

 Pipelines Leaks 0.09 0.09 0.09 0.09 0.10 0.10 0.10 0.09 0.09 0.09 

 Compressor Stations (Transmission) 

 Station 3.09 3.08 3.14 3.07 3.21 3.19 3.20 3.18 3.17 3.17

 Recip Compressor 21.49 21.45 21.85 21.37 22.34 22.21 22.30 22.13 22.10 22.07

 Centrifugal Compressor 4.30 4.29 4.37 4.27 4.47 4.44 4.46 4.42 4.42 4.41

 Compressor Stations (Storage) 

 Station 1.58 1.68 1.74 1.71 1.75 1.82 1.75 1.73 1.57 1.70

 Recip Compressor 4.55 4.86 5.03 4.95 5.07 5.27 5.05 4.98 4.53 4.90

 Centrifugal Compressor 0.64 0.69 0.71 0.70 0.72 0.75 0.72 0.71 0.64 0.69

 Wells (Storage) 0.39 0.42 0.43 0.43 0.44 0.45 0.43 0.43 0.39 0.42

 M&R (Trans. Co. Interconnect) 2.10 2.10 2.14 2.09 2.18 2.17 2.18 2.16 2.16 2.16

 M&R (Farm Taps + Direct Sales) 0.49 0.49 0.50 0.49 0.51 0.50 0.51 0.50 0.50 0.50

Vented and Combusted 

 Normal Operation 

 Dehydrator Vents (Transmission) 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 0.06 

 Dehydrator Vents (Storage) 0.12 0.13 0.13 0.13 0.14 0.14 0.14 0.13 0.12 0.13

 Compressor Exhaust 

 Engines (Transmission)1 - - - - - - - - - -

 Turbines (Transmission)1 - - - - - - - - - -

 Engines (Storage)1 - - - - - - - - - -

 Turbines (Storage)1 - - - - - - - - - -

 Generators (Engines)1 - - - - - - - - - -

 Generators (Turbines)1 - - - - - - - - - -

 Pneumatic Devices Trans+Stor 

 Pneumatic Devices Trans 6.15 6.14 6.25 6.12 6.39 6.35 6.38 6.33 6.32 6.31

 Pneumatic Devices Storage 1.28 1.37 1.42 1.39 1.43 1.48 1.42 1.40 1.28 1.38

 Routine Maintenance/Upsets 

 Pipeline Venting 5.13 5.13 5.22 5.11 5.34 5.31 5.33 5.29 5.28 5.27

 Station venting Trans+Storage 

 Station Venting Transmission 4.20 4.19 4.27 4.18 4.36 4.34 4.36 4.32 4.32 4.31

 Station Venting Storage 0.88 0.93 0.97 0.95 0.97 1.01 0.97 0.96 0.87 0.94

LNG Storage 

 LNG Stations 0.31 0.32 0.33 0.35 0.35 0.36 0.36 0.36 0.36 0.36

 LNG Reciprocating Compressors 1.16 1.18 1.23 1.32 1.33 1.34 1.34 1.34 1.34 1.34

 LNG Centrifugal Compressors 0.39 0.41 0.42 0.45 0.46 0.46 0.46 0.46 0.46 0.46

 LNG Compressor Exhaust 

 LNG Engines1 - - - - - - - - - -

 LNG Turbines1 - - - - - - - - - -

 LNG Station Venting 0.17 0.18 0.18 0.19 0.20 0.20 0.20 0.20 0.20 0.20

LNG Import Terminals 
A-148 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 LNG Stations 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

 LNG Reciprocating Compressors 0.03 0.03 0.03 0.05 0.05 0.07 0.07 0.07 0.07 0.07

 LNG Centrifugal Compressors 0.01 0.01 0.01 0.01 0.01 0.02 0.02 0.02 0.02 0.02

 LNG Compressor Exhaust 

 LNG Engines1 - - - - - - - - - -

 LNG Turbines1 - - - - - - - - - -

 LNG Station Venting2 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 1 Energy use CO2 emissions not estimated to avoid double counting. 2

 Emissions are not actually 0, but too small to show at this level of precision. 

Table A- 124: CO2 Emission Estimates from the Natural Gas Distribution Stage (Gg) 

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Pipeline Leaks 

 Mains—Cast Iron 7.73 7.02 6.71 5.87 5.57 5.45 5.38 4.96 4.90 5.00

 Mains—Unprotected steel 6.67 6.10 5.76 4.98 4.78 4.53 4.64 4.24 4.39 4.26

 Mains—Protected steel 0.79 0.80 0.86 0.81 0.82 0.82 0.84 0.79 0.82 0.83

 Mains—Plastic 1.71 1.47 1.95 2.74 2.93 2.78 2.97 2.89 3.41 3.31

Total Pipeline Miles 

 Services—Unprotected steel 7.21 6.75 5.81 5.15 4.90 4.57 4.53 5.02 5.33 5.15

 Services Protected steel 1.94 1.94 2.06 1.76 1.74 1.69 1.78 1.56 1.54 1.54

 Services—Plastic 0.10 0.11 0.13 0.17 0.18 0.18 0.19 0.19 0.20 0.21

 Services—Copper 0.22 0.22 0.20 0.20 0.19 0.19 0.19 0.17 0.18 0.17

Meter/Regulator (City Gates) 

 M&R >300 3.18 3.40 3.52 3.46 3.55 3.68 3.53 3.49 3.17 3.43

 M&R 100-300 6.18 6.60 6.83 6.71 6.88 7.15 6.85 6.76 6.15 6.65

 M&R <100 0.15 0.16 0.16 0.16 0.17 0.17 0.17 0.16 0.15 0.16

 Reg >300 3.14 3.35 3.46 3.41 3.49 3.63 3.48 3.43 3.12 3.37

 R-Vault >300 0.01 0.02 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.02 

 Reg 100-300 2.37 2.53 2.62 2.58 2.64 2.74 2.63 2.60 2.36 2.55

 R-Vault 100-3001 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.00 0.01 

 Reg 40-100 0.18 0.20 0.20 0.20 0.20 0.21 0.20 0.20 0.18 0.20

 R-Vault 40-100 0.01 0.01 0.01 0.01 0.02 0.02 0.01 0.01 0.01 0.01

 Reg <40 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01 

Customer Meters 

 Residential 2.98 3.19 3.30 3.24 3.32 3.45 3.31 3.27 2.97 3.21

 Commercial/Industry 0.11 0.12 0.14 0.12 0.13 0.12 0.12 0.11 0.11 0.11

Routine Maintenance 

 Pressure Relief Valve Releases 0.03 0.02 0.03 0.03 0.03 0.03 0.03 0.03 0.03 0.03 

 Pipeline Blowdown 0.07 0.07 0.08 0.07 0.08 0.08 0.08 0.08 0.07 0.07

Upsets 

 Mishaps (Dig-ins) 1.07 1.15 1.19 1.17 1.19 1.24 1.19 1.17 1.07 1.15 1 Emissions are not actually 0, but too small to show at this level of precision. 
A-149

3.5. Methodology for Estimating CH4 and CO2 Emissions from Petroleum Systems 

The methodology for estimating CH4 and non-combustion CO2 emissions from petroleum systems is based on 

the 1999 EPA draft report, Estimates of Methane Emissions from the U.S. Oil Industry (EPA 1999) and the study, Methane 

Emissions from the U.S. Petroleum Industry (EPA/GRI 1996). Sixty-four activities that emit CH4 and twenty-nine 

activities that emit non-combustion CO2 from petroleum systems were examined from these reports. Most of the activities 

analyzed involve crude oil production field operations, which accounted for about 97.7 percent of total oil industry CH4 

emissions. Crude transportation and refining accounted for the remaining CH4 emissions of less than one half and about 

two percent, respectively. Non-combustion CO2 emissions were analyzed for the production operations only, since the 

emissions from transportation and refining operations are negligible. The following steps were taken to estimate CH4 and 

CO2 emissions from petroleum systems. 

Step 1: Determine Emission Factors for all Activities 

The CH4 emission factors for the majority of the activities for 1995 are taken from the 1999 EPA draft report, 

which contained the most recent and comprehensive determination of CH4 emission factors for the 64 CH4-emitting 

activities in the oil industry at that time. Emission factors for pneumatic devices in the production sector were recalculated

in 2002 using emissions data in the EPA/GRI 1996 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 average for API gravity less than 

44 API degrees. Offshore emissions from shallow water and deep water oil platforms are taken from analysis of the Gulfwide Offshore Activity Data System (GOADS) report (EPA 2005, MMS 2004). The emission factors determined for 1995 

were assumed to be representative of emissions from each source type over the period 1990 through 2007. 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, 

three distinct stages/streams with varying CO2 contents exist. The three 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), and whole crude oil itself when it leaks downstream. 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. Only the emissions factor for storage tanks is estimated using API E&P Tank 

Calc simulation runs of tank emissions for crude oil of different gravity less than 44 API degrees. 

Step 2: Determine Activity Levels for Each Year 

Activity levels change from year to year. Some factors change in proportion to crude oil rates: production, 

transportation, refinery runs. Some change in proportion to the number of facilities: oil wells, petroleum refineries. Some 

factors change proportional to both the rate and number of facilities. 

For most sources, activity levels found in the EPA/GRI 1996 for the 1995 base year are extrapolated to other 

years using publicly-available data sources. For the remaining sources, the activity levels are obtained directly from 

publicly available data and are not extrapolated from the 1995 base year. 

For both sets of data, a determination was made on a case-by-case basis as to which measure of petroleum 

industry activity best reflects the change in annual activity. Publicly-reported data from the Minerals Management Service 

(MMS), Energy Information Administration (EIA), American Petroleum Institute (API), and the Oil & Gas Journal 

(O&GJ) were used to extrapolate the activity levels from the base year to each year between 1990 and 2007. Data used 

include total domestic crude oil production, number of domestic crude oil wells, total imports and exports of crude oil, and 

total petroleum refinery crude runs. The activity data for the transportation sector are 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, such as average stripper well production, oil 

industry data based on expert judgment was used. In the case of non-combustion CO2 emission sources, the activity 

factors remain the same as for CH4 emission sources. 
A-150 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 3: Estimate Methane and Carbon Dioxide Emissions for Each Activity for Each Year 

Annual CH4 emissions from each of the 64 petroleum system activities and CO2 emissions from the 29 petroleum system 

activities were estimated by multiplying the activity data for each year by the corresponding emission factor. These 

annual emissions for each activity were then summed to estimate the total annual CH4 and CO2 emissions, respectively. 

Table A- 125, Table A- 126, Table A- 127, and Table A- 129 provide 2007 activity factors, emission factors, and 

emission estimates and Table A- 128 and Table A- 130 provide a summary of emission estimates for the years 1990 

through 2007. 

Table A- 125: 2007 CH4 Emissions from Petroleum Production Field Operations 

Activity/Equipment 

Emission

Factor Units 

Activity

Factor

 

Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 63.652 1,224

Oil Tanks 5.28 scf of CH4/bbl crude 1,326 MMbbl/yr (non stripper 

wells) 

7.001 134.6

Pneumatic Devices, High Bleed 330 scfd CH4/device 132,803 No. of high-bleed devices 16.015 308.0

Pneumatic Devices, Low Bleed 52 scfd CH4/device 246,633 No. of low-bleed devices 4.681 90.02

Chemical Injection Pumps 248 scfd CH4/pump 26,774 No. of pumps 2.424 46.62

Vessel Blowdowns 78 scfy CH4/vessel 173,315 No. of vessels 0.014 0.260

Compressor Blowdowns 3,775 scf/yr of CH4/compressor 2,348 No. of compressors 0.009 0.170

Compressor Starts 8,443 scf/yr. of CH4/compressor 2,348 No. of compressors 0.020 0.381

Stripper wells 2,345 scf/yr of CH4/stripper well 337,804 No. of stripper wells vented 0.792 15.23

Well Completion Venting 733 scf/completion 14,477 Oil well completions 0.011 0.204

Well Workovers 96 scf CH4/workover 37,500 Oil well workovers 0.004 0.069

Pipeline Pigging 2.40 scfd of CH4/pig station NE No. of crude pig stations NE NE

Offshore Platforms, Shallow water Oil, 

fugitive, vented and combusted 

54,795 scfd CH4/platform 1,514 No. of shallow water oil 

platforms 

30.288 582.5

Offshore Platforms, Deepwater oil, 

fugitive, vented and combusted 

260,274 scfd CH4/platform 25 No. of deep water oil 

platforms 

2.394 46.04

Fugitive Emissions 2.208 42.46

Oil Wellheads (heavy crude) 0.13 scfd/well 11,435 No. of hvy. crude wells 0.001 0.010

Oil Wellheads (light crude) 16.6 scfd/well 150,761 No. of lt. crude wells 0.915 17.60

Separators (heavy crude) 0.15 scfd CH4/separator 10,203 No. of hvy. crude seps. 0.001 0.011

Separators (light crude) 14 scfd CH4/separator 92,858 No. of lt. crude seps. 0.470 9.029

Heater/Treaters (light crude) 19 scfd CH4/heater 70,254 No. of heater treaters 0.492 9.465

Headers (heavy crude)1 0.08 scfd CH4/header 13,043 No. of hvy. crude hdrs. 0.000 0.007

Headers (light crude) 11 scfd CH4/header 40,433 No. of lt. crude hdrs. 0.160 3.081

Floating Roof Tanks 338,306 scf CH4/floating roof 

tank/yr. 

24 No. of floating roof tanks 0.008 0.159

Compressors 100 scfd CH4/compressor 2,348 No. of compressors 0.086 1.648

Large Compressors 16,360 scfd CH4/compressor 0 No. of large comprs. 0.000 0.000

Sales Areas 41 scf CH4/loading 1,540,054 Loadings/year 0.062 1.201

Pipelines NE scfd of CH4/mile of 

pipeline 

14,911 Miles of gathering line NE NE

Well Drilling NE scfd of CH4/oil well drilled 16,362 No. of oil wells drilled NE NE

Battery Pumps 0.24 scfd of CH4/pump 150,000 No. of battery pumps 0.013 0.253

Combustion Emissions 3.595 69.13

Gas Engines 0.24 scf CH4/HP-hr 14,795 MMHP-hr 3.551 68.29

Heaters 0.52 scf CH4/bbl 1,848 MMbbl/yr 0.001 0.019

Well Drilling 2,453 scf CH4/well drilled 16,362 Oil wells drilled 0.040 0.772

Flares 20 scf CH4/Mcf flared 154,017 Mcf flared/yr 0.003 0.059

Process Upset Emissions 0.142 2.725

Pressure Relief Valves 35 scf/yr/PR valve 154,394 No. of PR valves 0.005 0.103

Well Blowouts Onshore 2.5 MMscf/blowout 54.5 No. of blowouts/yr 0.136 2.622

Total 69.60 1,338 1 Emissions are not actually 0, but too small to show at this level of precision. 

 NE: Not estimated for lack of activity factor data 

Table A- 126: 2007 CH4 Emissions from Petroleum Transportation 

Activity/Equipment 

Emission 

Factor Units 

Activity

Factor Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)
A-151

Activity/Equipment 

Emission 

Factor Units 

Activity

Factor Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 0.215 4.127

Tanks 0.021 scf CH4/yr/bbl of crude delivered to refineries 5,532 MMbbl crude feed/yr 0.114 2.192

Truck Loading 0.520 scf CH4/yr/bbl of crude transported by truck 65.8 MMbbl trans. by truck 0.034 0.658

Marine Loading 2.544 scf CH4/1000 gal. crude marine loadings 20,070,765 1,000 gal./yr loaded 0.051 0.982

Rail Loading 0.520 scf CH4/yr/bbl of crude transported by rail 4.0 MMbbl. Crude by rail/yr 0.002 0.040

Pump Station Maintenance1 36.80 scf CH4/station/yr 467 No. of pump stations 0.000 0.000

Pipeline Pigging 39 scfd of CH4/pig station 933 No. of pig stations 0.013 0.255

Fugitive Emissions 0.050 0.957

Pump Stations 25 scf CH4/mile/yr. 46,658 No. of miles of crude p/l 0.001 0.022

Pipelines NE scf CH4/bbl crude transported by pipeline 7,038 MMbbl crude piped NE NE

Floating Roof Tanks 58,965 scf CH4/floating roof tank/yr. 824 No. of floating roof tanks 0.049 0.934

Combustion Emissions NE NE

Pump Engine Drivers 0.24 scf CH4/hp-hr NE No. of hp-hrs NE NE

Heaters 0.521 scf CH4/bbl.burned NE No. of bbl. Burned NE NE

Total 0.264 5.084 1 Emissions are not actually 0, but too small to show at this level of precision. 

 NE: Not estimated for lack of data 

Table A- 127: 2007 CH4 Emissions from Petroleum Refining 

Activity/Equipment 

Emission

Factor Units 

Activity

Factor Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 1.196 23.00

 Tanks 20.6 scf CH4/Mbbl 1,945 Mbbl/cd heavy crude feed 0.015 0.281

 System Blowdowns 137 scf CH4/Mbbl 15,156 Mbbl/cd refinery feed 0.757 14.55

 Asphalt Blowing 2,555 scf CH4/Mbbl 456 Mbbl/cd production 0.425 8.169

Fugitive Emissions 0.089 1.718

 Fuel Gas System 439 Mscf CH4/refinery/yr 149 Refineries 0.065 1.258

 Floating Roof Tanks1 587 scf CH4/floating roof tank/yr. 767 No. of floating roof tanks 0.000 0.009

 Wastewater Treating 1.88 scf CH4/Mbbl 15,156 Mbbl/cd refinery feed 0.010 0.200

 Cooling Towers 2.36 scf CH4/Mbbl 15,156 Mbbl/cd refinery feed 0.013 0.251

Combustion Emissions 0.095 1.819

 Atmospheric Distillation 3.61 scf CH4/Mbbl 15,450 Mbbl/cd refinery feed 0.020 0.391

 Vacuum Distillation 3.61 scf CH4/Mbbl 6,933 Mbbl/cd feed 0.009 0.176

 Thermal Operations 6.01 scf CH4/Mbbl 2,179 Mbbl/cd feed 0.005 0.092

 Catalytic Cracking 5.17 scf CH4/Mbbl 5,067 Mbbl/cd feed 0.010 0.184

 Catalytic Reforming 7.22 scf CH4/Mbbl 3,183 Mbbl/cd feed 0.008 0.161

 Catalytic Hydrocracking 7.22 scf CH4/Mbbl 1,431 Mbbl/cd feed 0.004 0.073

 Hydrorefining 2.17 scf CH4/Mbbl 2,172 Mbbl/cd feed 0.002 0.033

 Hydrotreating 6.50 scf CH4/Mbbl 9,960 Mbbl/cd feed 0.024 0.454

 Alkylation/Polymerization 12.6 scf CH4/Mbbl 1,082 Mbbl/cd feed 0.005 0.096

 Aromatics/Isomeration 1.80 scf CH4/Mbbl 992 Mbbl/cd feed 0.001 0.013

 Lube Oil Processing 0.00 scf CH4/Mbbl 176 Mbbl/cd feed 0.000 0.000

 Engines 0.006 scf CH4/hp-hr 1,158 MMhp-hr/yr 0.007 0.127

 Flares 0.189 scf CH4/Mbbl 15,156 Mbbl/cd refinery feed 0.001 0.020

Total 1.380 26.54 1 Emissions are not actually 0, but too small to show at this level of precision. 

Table A- 128: Summary of CH4 Emissions from Petroleum Systems (Gg) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Production Field 

Operations 1,581 1,593 1,552 1,515 1,505 1,493 1,490 1,481 1,469 1,427 1,408 1,404 1,390 1,357 1,335 1,314 1,314 1,338

Pneumatic device 

venting 489 498 482 472 469 463 462 461 452 437 428 425 424 412 408 397 396 398

Tank venting 179 179 173 167 161 161 161 165 163 153 154 154 151 150 142 135 135 135

Combustion & 

process upsets 88 90 86 84 83 82 82 82 80 76 76 75 75 73 72 71 71 72

Misc. venting & 

fugitives 799 799 787 768 768 762 760 747 749 736 727 727 717 700 691 691 694 716

Wellhead fugitives 26 26 24 24 24 25 25 26 26 24 22 22 23 22 21 19 17 18
A-152 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Crude Oil 

Transportation 7 6 6 6 6 6 6 6 6 6 5 5 5 5 5 5 5 5

Refining 25 24 24 25 25 25 26 27 27 27 28 27 27 27 28 28 28 27

Total 1,613 1,624 1,583 1,546 1,536 1,524 1,522 1,514 1,502 1,459 1,441 1,436 1,422 1,389 1,368 1,346 1,346 1,370

Table A- 129: 2007 CO2 Emissions from Petroleum Production Field Operations 

Activity/Equipment 

Emission

Factor Units 

Activity

Factor

 

Units 

Emissions

(Bcf/yr)

Emissions

(Gg/yr)

Vented Emissions 5.378 103.4

Oil Tanks 3.53 scf of CO2/bbl crude 1,326 MMbbl/yr (non stripper 

wells) 

4.678 89.96

Pneumatic Devices, High Bleed 6.704 scfd CO2/device 132,803 No. of high-bleed devices 0.325 6.249

Pneumatic Devices, Low Bleed 1.055 scfd CO2/device 246,633 No. of low-bleed devices 0.095 1.827

Chemical Injection Pumps 5.033 scfd CO2/pump 26,774 No. of pumps 0.049 0.946

Vessel Blowdowns1 1.583 scfy CO2/vessel 173,315 No. of vessels 0.000 0.005

Compressor Blowdowns1 77 scf/yr of CO2/compressor 2,348 No. of compressors 0.000 0.003

Compressor Starts1 171 scf/yr. of CO2/compressor 2,348 No. of compressors 0.000 0.008

Stripper wells 48 scf/yr of CO2/stripper well 337,804 No. of stripper wells vented 0.016 0.309

Well Completion Venting1 14.87 scf/completion 14,477 Oil well completions 0.000 0.004

Well Workovers1 1.95 scf CO2/workover 37,500 Oil well workovers 0.000 0.001

Pipeline Pigging NE scfd of CO2/pig station NE No. of crude pig stations NE NE

Offshore Platforms, Shallow water Oil, 

fugitive, vented and combusted 

 358 scfd CO2/platform 1,514 No. of shallow water oil 

platforms 

0.198 3.807

Offshore Platforms, Deepwater oil, 

fugitive, vented and combusted 

 1,701 scfd CO2/platform 25 No. of deep water oil 

platforms 

0.016 0.301

Fugitive Emissions 0.046 0.877

Oil Wellheads (heavy crude)1 0.003 scfd/well 11,435 No. of hvy. crude wells 0.000 0.000

Oil Wellheads (light crude) 0.337 scfd/well 150,761 No. of lt. crude wells 0.019 0.357

Separators (heavy crude)1 0.003 scfd CO2/separator 10,203 No. of hvy. crude seps. 0.000 0.000

Separators (light crude) 0.281 scfd CO2/separator 92,858 No. of lt. crude seps. 0.010 0.183

Heater/Treaters (light crude) 0.319 scfd CO2/heater 70,254 No. of heater treaters 0.008 0.157

Headers (heavy crude)1 0.002 scfd CO2/header 13,043 No. of hvy. crude hdrs. 0.000 0.000

Headers (light crude) 0.220 scfd CO2/header 40,433 No. of lt. crude hdrs. 0.003 0.063

Floating Roof Tanks1 17,490 scf CO2/floating roof 

tank/yr. 

24 No. of floating roof tanks 0.000 0.008

Compressors 2.029 scfd CO2/compressor 2,348 No. of compressors 0.002 0.033

Large Compressors 332 scfd CO2/compressor 0 No. of large comprs. 0.000 0.000

Sales Areas 2.096 scf CO2/loading 1,540,054 Loadings/year 0.003 0.062

Pipelines NE scfd of CO2/mile of 

pipeline 

14,911 Miles of gathering line NE NE

Well Drilling NE scfd of CO2/oil well drilled 16,362 No. of oil wells drilled NE NE

Battery Pumps 0.012 scfd of CO2/pump 150,000 No. of battery pumps 0.001 0.013

Process Upset Emissions 0.003 0.059

Pressure Relief Valves1 1.794 scf/yr/PR valve 154,394 No. of PR valves 0.000 0.005

Well Blowouts Onshore 0.051 MMscf/blowout 55 No. of blowouts/yr 0.003 0.053

Total 5.426 104.4 1 Emissions are not actually 0, but 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- 130: Summary of CO2 Emissions from Petroleum Systems (Gg) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Production Field 

Operations 

Pneumatic device venting 27 28 27 26 26 26 26 26 25 24 24 24 24 23 23 22 22 22

Tank venting 328 330 319 307 296 296 296 303 299 282 283 283 278 276 262 248 249 247

Misc. venting & fugitives 18 18 18 18 18 18 17 17 17 17 17 17 16 16 16 16 16 16

Wellhead fugitives 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

Total 376 378 365 352 341 341 341 348 343 325 325 325 320 316 302 287 288 287
A-153
A-154 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

3.6. Methodology for Estimating CO2 and N2O 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. 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 1996 through 1999, 2000b, 2002, 2003, 2005a, 2006b, 2007, 2008), the flows of plastics in the U.S. waste 

stream are reported for seven resin categories. For 2007, the most recent year for which these data are reported, the 

quantity generated, recovered, and discarded for each resin is shown in Table A- 131. The data set for 1990 through 2007 

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. 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- 131: 2007 Plastics in the Municipal Solid Waste Stream by Resin (Gg) 

Waste Pathway PET HDPE PVC 

LDPE/ 

LLDPE PP PS Other Total 

Generation 3,411 5,126 1,506 5,806 4,146 2,377 5,507 27,878 

Recovery 617 517 0 281 9 18 454 1,896 

Discard 2,794 4,609 1,506 5,525 4,137 2,359 5,053 25,982 

 Landfill 2,278 3,757 1,228 4,503 3,372 1,923 4,119 21,179 

 Incineration 528 871 285 1,044 782 446 955 4,910 

Recovery* 18% 10% 0% 5% 0% 1% 8% 7% 

Discard* 82% 90% 100% 95% 100% 99% 92% 93% 

 Landfill* 67% 73% 82% 78% 81% 81% 75% 76% 

 Incineration* 15% 17% 19% 18% 19% 19% 17% 18% 

*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- 132). 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- 132: 2007 Plastics Incinerated (Gg), Carbon Content (%), Fraction Oxidized (%) and Carbon Incinerated (Gg) 

Factor PET HDPE PVC 

LDPE/ 

LLDPE PP PS Other Total 

Quantity Incinerated 528 871 285 1044 782 446 955 4,910 

Carbon Content of Resin 63% 86% 38% 86% 86% 92% 66% - 

Fraction Oxidized 98% 98% 98% 98% 98% 98% 98% - 

Carbon in Resin Incinerated 323 732 107 877 657 403 616 3,715 

Emissions (Tg CO2 Eq.) 1.2 2.7 0.4 3.2 2.4 1.5 2.3 13.6 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 Markets in the United States 2005 (RMA 2006) reports that 155.1 million of the 299.2 

million scrap tires generated in 2005 (approximately 52 percent of generation) were used for fuel purposes. Using RMA’s 
A-155

Scrap Tire Management Council (STMC) 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.34 Table A- 133 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 (Miller 1999). 

Multiplying the mass of scrap tires incinerated by the total C content of the synthetic rubber and C black portions 

of scrap tires and by a 98 percent oxidation factor yielded CO2 emissions, as shown in Table A- 134. The disposal rate of 

rubber in tires (0.3 Tg C/yr) is smaller than the consumption rate for tires based on summing the elastomers listed in Table 

A- 133 (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 

2001 were taken from RMA 2004; when data were not reported, they were linearly interpolated between bracketing years’ 

data or, for the ends of time series, set equal to the closest year with reported data. 

Table A- 133: 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- 134: Scrap Tire Constituents and CO2 Emissions from Scrap Tire Incineration in 2007 

Material 

Weight of Material 

(Tg) 

Fraction Oxidized 

 Carbon Content 

Emissions 

(Tg CO2 Eq.) 

Synthetic Rubber 0.4 98% 90% 1.2 

Carbon Black 0.4 98% 100% 1.6 

Total 0.8 - - 2.8 

- Not applicable 

 

34 The carbon content of tires (1,158 Gg C) divided by the mass of rubber in tires (1,285 Gg) equals 90 percent. 
A-156 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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 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 1996 through 1999, 2000b, 2002, 2003, 2005a, 2006b, 2007, 2008) 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. Since there was negligible recovery for these product types, all the waste generated can be considered 

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 was assigned to each product type, as shown in Table A-135.35 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-135: Rubber and Leather in Municipal Solid Waste in 2007 

Product Type 

Incinerated 

(Gg) 

 Synthetic Rubber 

(%) 

Carbon Content 

(%) 

Fraction Oxidized 

(%) 

Emissions 

(Tg CO2 Eq.) 

Durables (not Tires) 569.2 100% 85% 98% 1.8

Non-Durables 65.6 - - - 0.2

Clothing and Footwear 29.6 25% 85% 98% 0.1

Other Non-Durables 36.0 75% 85% 98% 0.1

Containers and Packaging 5.1 100% 85% 98% 0.0

Total 639.9 - - - 2.0

+ 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 2000b, 

2002, 2003, 2005a, 2006b, 2007, 2008) for textiles. Production data for the synthetic fibers was based on data from the 

American Chemical Society (FEB 2006). The amount of synthetic fiber in MSW was estimated by subtracting (a) the 

amount recovered from (b) the waste generated (see Table A-136). 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. 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 1998 (see Table A-137). 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) 

 

35 As a sustainably harvested biogenic material, the incineration of leather is assumed to have no net CO2 emissions. 
A-157

Table A-136: Synthetic Textiles in MSW (Gg) 

Year Generation Recovery Discards Incineration 

1990 2,884 328 2,557 474 

1991 3,008 347 2,661 530 

1992 3,286 387 2,899 556 

1993 3,386 397 2,988 578 

1994 3,604 432 3,172 619 

1995 3,674 447 3,227 723 

1996 3,832 472 3,361 789 

1997 4,090 526 3,564 810 

1998 4,269 556 3,713 781 

1999 4,498 611 3,887 789 

2000 4,686 655 4,031 800 

2001 4,870 715 4,155 824 

2002 5,123 750 4,373 864 

2003 5,257 755 4,503 883 

2004 5,451 884 4,567 838 

2005 5,649 918 4,731 875 

2006 5,893 933 4,959 921 

2007 5,917 943 4,974 920 

Table A-137: Synthetic Fiber Production in 1998 

Fiber Production (Tg) Carbon Content 

Polyester 1.8 63% 

Nylon 1.2 64% 

Olefin 1.3 86% 

Acrylic 0.2 68% 

Total 4.5 70% 

N2O 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 (2000a, 2002, 2003, 2005a, 

2006b, 2007, 2008) 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 (Arsova et al. 2008). For waste incineration in the United States, an emission factor 

of 44 g N2O/metric ton MSW (the average of the values provided for hearth/grate combustors as listed in the IPCC Good 

Practice Guidance, 2000) and an estimated emissions control removal efficiency of zero percent were used. No 

information was available on the mass of waste incinerated in 2005-2007, so the value was assumed to remain constant at 

the 2004 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-138). 
A-158 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-138: U.S. Municipal Solid Waste Incinerated, as Reported by EPA and BioCycle (Metric Tons) 

Year EPA BioCycle 

1990 28,939,680 30,632,057 

1991 30,209,760 25,462,836 

1992 29,656,368 29,086,574 

1993 29,865,024 27,838,884 

1994 29,474,928 29,291,583 

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 25,951,892a

2002 30,255,120 25,802,917 

2003 30,527,280 25,932,940b

2004 28,585,872 26,037,823 

2005 28,685,664 25,974,142c

2006 28,985,040 25,853,401 

2007 29,003,184 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. 
A-159

3.7. Methodology for Estimating Emissions from International Bunker Fuels used by 

the U.S. Military 

Bunker fuel emissions estimates for the Department of Defense (DoD) are developed using data generated by the 

Defense Energy Support Center (DESC) for aviation and naval fuels. The DESC of the Defense Logistics Agency (DLA) 

prepared a special report based on data in the Fuels Automated System (FAS), a database that recently replaced the 

Defense Fuels Automated Management System (DFAMS). Data for intermediate fuel oil, however, currently remains in 

the original DFAMS database. DFAMS/FAS contains data for 1995 through 2007, 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 DoD36 and NATO37 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-139 displays DoD’s consumption of fuels that remain at the completion of Step 1, summarized by fuel 

type. Table A-139 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. 

 

36 DoD Directive 4140.43, Fuel Standardization, 1998; DoD Directive 4140.25, DoD Management Policy for Energy 

Commodities and Related Services, 1999. 

37 NATO Standard Agreement NATO STANAG 4362, Fuels for Future Ground Equipments Using Compression Ignition or 

Turbine Engines, 1987. 
A-160 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 Only fuel delivered to a ship, aircraft, or installation in the United States was considered a potential 

international bunker fuel. Fuel consumed in international aviation or marine transport was included in the 

bunker fuel estimate of the country where the ship or aircraft was fueled. Fuel consumed entirely within a 

country’s borders was not considered a bunker fuel. 

 Based on discussions with the Army staff, only an extremely small percentage of Army aviation emissions, 

and none of its watercraft emissions, qualified as bunker fuel emissions. The magnitude of these emissions 

was judged to be insignificant when compared to Air Force and Navy emissions. Based on this, Army 

bunker fuel emissions were assumed to be zero. 

 Marine Corps aircraft operating while embarked consumed fuel reported as delivered to the Navy. Bunker 

fuel emissions from embarked Marine Corps aircraft were reported in the Navy bunker fuel estimates. 

Bunker fuel emissions from other Marine Corps operations and training were assumed to be zero. 

 Bunker fuel emissions from other DoD and non-DoD activities (i.e., other federal agencies) that purchased 

fuel from DESC were assumed to be zero. 

Step 5: Determine Bunker Fuel Percentages 

Next it was necessary to determine what percent of the marine and aviation fuels were used as international 

bunker fuels. Military aviation bunkers include international operations (i.e., sorties that originate in the United States and 

end in a foreign country), operations conducted from naval vessels at sea, and operations conducted from U.S. installations 

principally over international water in direct support of military operations at sea (e.g., anti-submarine warfare flights). 

For the Air Force, a bunker fuel weighted average was calculated based on flying hours by major command. International 

flights were weighted by an adjustment factor to reflect the fact that they typically last longer than domestic flights. In 

addition, a fuel use correction factor was used to account for the fact that transport aircraft burn more fuel per hour of 

flight than most tactical aircraft. The Air Force bunker fuel percentage was determined to be 13.2 percent. This 

percentage was multiplied by total annual Air Force aviation fuel delivered for U.S. activities, producing an estimate for 

international bunker fuel consumed by the Air Force. The Naval Aviation bunker fuel percentage of total fuel was 

calculated using flying hour data from Chief of Naval Operations Flying Hour Projection System Budget for fiscal year 

1998, and estimates of bunker fuel percent of flights provided by the fleet. The Naval Aviation bunker fuel percentage, 

determined to be 40.4 percent, was multiplied by total annual Navy aviation fuel delivered for U.S. activities, yielding 

total Navy aviation bunker fuel consumed. 

For marine bunkers, fuels consumed while ships were underway were assumed to be bunker fuels. In 2000, the 

Navy reported that 79 percent of vessel operations were underway, while the remaining 21 percent of operations occurred 

in port (i.e., pierside). Therefore, the Navy maritime bunker fuel percentage was determined to be 79 percent. The 

percentage of time underway may vary from year-to-year. For example, for years prior to 2000, the bunker fuel 

percentage was 87 percent. Table A-140 and Table A-141 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-140 and Table A-141, below. CO2 emissions 

from aviation bunkers and distillate marine bunkers presented in Table A-144, and are based on emissions from fuels 

tallied in Table A-140 and Table A-141. 
Table A-139: Transportation Fuels from Domestic Fuel Deliveriesa (Million Gallons) Vehicle Type/Fuel 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 200Aviation 4,598.4 4,562.8 3,734.5 3,610.8 3,246.2 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. Total Jet Fuels 4,598.4 4,562.8 3,734.5 3,610.8 3,246.2 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.

 JP8 285.7 283.5 234.5 989.4 1,598.1 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. JP5 1,025.4 1,017.4 832.7 805.1 723.8 691.2 615.8 552.8 515.6 505.5 472.1 503.2 442.2 409.

 Other Jet Fuels 3,287.3 3,261.9 2,667.3 1,816.3 924.3 225.9 72.9 60.9 103.3 63.3 69.6 71.2 76.1 111.

 Aviation Gasoline + + + + + + + + + + + + 0.1 0.

Marine 686.8 632.6 646.2 589.4 478.6 438.9 493.3 639.8 674.2 598.9 454.4 418.4 455.8 609.

 Middle Distillate (MGO) 0.0 0.0 0.0 0.0 0.0 0.0 38.5 47.5 51.1 49.2 48.3 33.0 41.2 88.

 Naval Distillate (F76) 686.8 632.6 646.2 589.4 478.6 438.9 449.0 583.4 608.4 542.9 398.0 369.1 395.1 460.

 Intermediate Fuel Oil (IFO)b 0.0 0.0 0.0 0.0 0.0 0.0 5.9 9.0 14.7 6.7 8.1 16.3 19.5 60.

Other c 717.1 590.4 491.7 415.1 356.1 310.9 276.9 263.3 256.8 256.0 248.2 109.8 211.1 221.

 Diesel 93.0 97.9 103.0 108.3 113.9 119.9 126.1 132.6 139.5 146.8 126.6 26.6 57.7 60.

 Gasoline 624.1 492.5 388.7 306.8 242.1 191.1 150.8 119.0 93.9 74.1 74.8 24.7 27.5 26. Jet Fuel d 0.0 0.0 0.0 0.0 0.0 0.0 0.0 11.7 23.4 35.0 46.7 58.4 125.9 133.

Total (Including Bunkers) 6,002.4 5,785.9 4,872.3 4,615.3 4,080.9 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.

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 in

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 diese2000 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 conestimated 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 wer

d The fraction of jet fuel consumed in land-based vehicles was estimated using Service data, DESC data, and expert judgment. 

+ Does not exceed 0.05 million gallons. 
A-162 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-140: Total U.S. Military Aviation Bunker Fuel (Million Gallons) 

Fuel Type/Service 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 JP8 56.7 56.3 46.4 145.3 224.0 300.4 308.8 292.0 306.4 301.4 307.6 341.2 309.5 305.1  Navy 56.7 56.3 46.1 44.6 40.1 38.3 39.8 46.9 53.8 55.5 53.4 73.8 86.6 76.3  Air Force + + 0.3 100.8 183.9 262.2 269.0 245.1 252.6 245.9 254.2 267.4 222.9 228.7 JP5 370.5 367.7 300.9 291.0 261.6 249.8 219.4 194.2 184.4 175.4 160.3 169.7 158.3 146.1  Navy 365.3 362.5 296.7 286.8 257.9 246.3 216.1 191.2 181.4 170.6 155.6 163.7 153.0 141.3  Air Force 5.3 5.2 4.3 4.1 3.7 3.5 3.3 3.0 3.0 4.8 4.7 6.1 5.3 4.9 JP4 420.8 417.5 341.4 229.6 113.1 21.5 1.1 0.1 + + + + + +  Navy + + + + + + 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0  Air Force 420.8 417.5 341.4 229.6 113.1 21.5 1.1 0.1 + + + + + + JAA 13.7 13.6 11.1 10.8 9.7 9.2 10.3 9.4 10.8 10.8 12.5 12.6 13.7 21.7  Navy 8.5 8.4 6.9 6.6 6.0 5.7 6.6 5.9 6.6 6.3 7.9 8.0 9.8 15.5  Air Force 5.3 5.2 4.3 4.1 3.7 3.5 3.7 3.5 4.2 4.5 4.5 4.6 3.8 6.2 JA1 + + + + + + + + + + + 0.1 0.6 0.2  Navy + + + + + + + + + 0.0 + + + +  Air Force + + + + + + + + + 0.0 + 0.1 0.6 0.2 JAB 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  Navy 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  Air Force 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 Navy Subtotal 430.5 427.2 349.6 338.1 303.9 290.2 262.5 244.0 241.8 232.4 216.9 245.5 249.4 233.1 Air Force Subtotal 431.3 427.9 350.2 338.6 304.4 290.7 277.0 251.7 259.9 255.2 263.5 278.1 232.7 239.9 Total 861.8 855.1 699.9 676.7 608.4 580.9 539.5 495.6 501.7 487.5 480.4 523.6 482.1 473.0 + Does not exceed 0.05 million gallons. Note: Totals may not sum due to independent rounding. Table A-141: Total U.S. DoD Maritime Bunker Fuel (Million Gallons) Marine Distillates 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Navy—MGO 0.0 0.0 0.0 0.0 0.0 0.0 30.3 35.6 31.9 39.7 23.8 22.5 27.1 63.7 Navy—F76 522.4 481.2 491.5 448.3 364.0 333.8 331.9 441.7 474.2 466.0 298.6 282.6 305.6 347.8 Navy—IFO 0.0 0.0 0.0 0.0 0.0 0.0 4.6 7.1 11.6 5.3 6.4 12.9 15.4 47.5 Total 522.4 481.2 491.5 448.3 364.0 333.8 366.8 484.3 517.7 511.0 328.8 318.0 348.2 459.0 + Does not exceed 0.005 million gallons. Note: Totals may not sum due to independent rounding. 
A-163

Table A-142: 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) 19.95 1.00

Marine (Residual) 21.49 1.00

Source: EIA (2008) and IPCC (2006) 

Table A-143: Annual Variable Carbon Content Coefficient for Jet Fuel (Tg Carbon/QBtu) 

Fuel 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Jet Fuel 19.40 19.40 19.39 19.37 19.35 19.34 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33

Source: EIA (2008) 

Table A-144: Total U.S. DoD CO2 Emissions from Bunker Fuels (Tg CO2 Eq.) 

Mode 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Aviation 8.1 8.0 6.5 6.4 5.8 5.5 5.1 4.7 4.8 4.6 4.6 5.0 4.6 4.5 4.7 4.4 3.8 3.9

Marine 5.3 4.9 5.0 4.5 3.7 3.4 3.7 4.9 5.3 5.2 3.3 3.2 3.5 4.7 5.4 4.8 4.2 4.5

Total 13.4 12.9 11.5 10.9 9.5 8.9 8.9 9.6 10.0 9.8 7.9 8.2 8.1 9.2 10.1 9.2 8.0 8.4

Note: Totals may not sum due to independent rounding. 
A-164 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

3.8. Methodology for Estimating HFC and PFC Emissions from Substitution of Ozone 

Depleting Substances 

Emissions of HFCs and PFCs from the substitution of ozone depleting substances (ODS) are developed using a 

country-specific modeling approach. The Vintaging Model was developed as a tool for estimating the annual chemical 

emissions from industrial sectors that have historically used ODS in their products. Under the terms of the Montreal 

Protocol and the United States’ Clean Air Act Amendments of 1990, the domestic U.S. production 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 61 independently modeled 

end-uses. The model requires information on the market growth for each of the end-uses, as well as a history of the 

market transition from ODS to alternatives, as well as the characteristics of each end-use such as market size or charge 

sizes and loss rates. As ODS are phased out, a percentage of the market share originally filled by the ODS is allocated to 

each of its substitutes. 

The model, named for its method of tracking the emissions of annual “vintages” of new equipment that enter into 

service, is a “bottom-up” model. It models the consumption of chemicals based on estimates of the quantity of equipment 

or products sold, serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain 

the equipment. The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of 

the equipment and ODS and ODS substitute in each of the end-uses. The simulation is considered to be a “business-asusual” baseline case, and does not incorporate measures to reduce or eliminate the emissions of these gases other than 

those regulated by U.S. law or otherwise common in the industry. Emissions are estimated by applying annual leak rates, 

service emission rates, and disposal emission rates to each population of equipment. By aggregating the emission and 

consumption output from the different end-uses, the model produces estimates of total annual use and emissions of each 

chemical. 

The Vintaging Model synthesizes data from a variety of sources, including data from the ODS Tracking System 

maintained by the Stratospheric Protection Division and information from submissions to EPA under the Significant New 

Alternatives Policy (SNAP) program. Published sources include documents prepared by the United Nations Environment 

Programme (UNEP) Technical Options Committees, reports from the Alternative Fluorocarbons Environmental 

Acceptability Study (AFEAS), and conference proceedings from the International Conferences on Ozone Protection 

Technologies and Earth Technologies Forums. EPA also coordinates extensively with numerous trade associations and 

individual companies. For example, the Alliance for Responsible Atmospheric Policy, the Air-Conditioning, Heating and 

Refrigeration Institute, the Association of Home Appliance Manufacturers, the American Automobile Manufacturers 

Association, and many of their member companies, have provided valuable information over the years. In some instances 

the unpublished information that the EPA uses in the model is classified as Confidential Business Information (CBI). The 

annual emissions inventories of chemicals are aggregated in such a way that CBI cannot be inferred. Full public 

disclosure of the inputs to the Vintaging Model would jeopardize the security of the CBI that has been entrusted to the 

EPA. 

The following sections discuss the emission equations used in the Vintaging Model for each broad end-use 

category. These equations are applied separately for each chemical used within each of the different end-uses. In the 

majority of these end-uses, more than one ODS substitute chemical is used. 

In general, the modeled emissions are a function of the amount of chemical consumed in each end-use market. 

Estimates of the consumption of ODS alternatives can be inferred by extrapolating forward in time from the amount of 

regulated ODS used in the early 1990s. Using data gleaned from a variety of sources, assessments are made regarding 

which alternatives will likely be used, and what fraction of the ODS market in each end-use will be captured by a given 

alternative. By combining this with estimates of the total end-use market growth, a consumption value can be estimated 

for each chemical used within each end-use. 

Methodology 

The Vintaging Model estimates the use and emissions of ODS alternatives by taking the following steps: 
A-165

1. Gather historical emissions 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 ODSs, as well as data on how the substitutes are replacing the ODSs, 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 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-166 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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 normal leakage and servicing 

(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- 145, 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. 
Table A- 145: Refrigeration and Air-Conditioning Market Transition Assumptions 

Initial Market Primary Substitute Secondary Substitute Tertiary 

Segment 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 

Centrifugal Chillers 

CFC-11 HCFC-123 1993 1993 45% Unknown  HCFC-22 1991 1993 16% HCFC-22 1996 1996 100% HFC-134a 2000  HFC-134a 1992 1993 39% None CFC-12 HFC-134a 1992 1994 53% None  HCFC-22 1991 1994 16% HCFC-22 1996 1996 100% HFC-134a 2000  HCFC-123 1993 1994 31% Unknown R-500 HFC-134a 1992 1994 53% None  HCFC-22 1991 1994 16% HCFC-22 1996 1996 100% HFC-134a 2000  HCFC-123 1993 1994 31% Unknown CFC-114 HFC-236fa 1993 1994 100% HFC-236fa 1996 1996 100% HFC-134a 1998 

Cold Storage 

CFC-12 HCFC-22 1990 1993 65% R-404A 1996 2010 75%  R-507 1996 2010 25%  R-404A 1994 1996 26% None  R-507 1994 1996 9% None HCFC-22 HCFC-22 1992 1993 100% R-404A 1996 2009 8%  R-507 1996 2009 3%  R-404A 2009 2010 68%  R-507 2009 2010 23% R-502 HCFC-22 1990 1993 40% R-404A 1996 2010 38%  R-507 1996 2010 12%  Non-ODP/GWP 1996 2010 50%  R-404A 1993 1996 45% None  R-507 1994 1996 15% None 

Commercial Unitary Air Conditioners (Large) 

HCFC-22 HCFC-22 1992 1993 100% R-410A 2001 2005 5% None  R-407C 2006 2009 1% None  R-410A 2006 2009 9% None  R-407C 2009 2010 5% None  R-410A 2009 2010 81% None 

Commercial Unitary Air Conditioners (Small) 

HCFC-22 HCFC-22 1992 1993 100% R-410A 1996 2000 3% None  R-410A 2001 2005 18% None  R-410A 2006 2009 8% None 
A-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Initial Market Primary Substitute Secondary Substitute Tertiary 

Segment 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 

 R-410A 2009 2010 71% None 

Dehumidifiers 

HCFC-22 HFC-134a 1997 1997 89% None  R-410A 2007 2010 11% None 

Ice Makers 

CFC-12 HFC-134a 1993 1995 100% None 

Industrial Process Refrigeration 

CFC-11 HCFC-123 1992 1994 70% Unknown  HFC-134a 1992 1994 15% None  HCFC-22 1991 1994 15% HFC-134a 1995 2010 100% CFC-12 HCFC-22 1991 1994 10% HFC-134a 1995 2010 15%  R-404A 1995 2010 50%  R-410A 1999 2010 20%  R-507 1995 2010 15%  HCFC-123 1992 1994 35% Unknown  HFC-134a 1992 1994 50% None  R-401A 1995 1996 5% HFC-134a 1997 2000 100% None HCFC-22 HCFC-22 1992 1993 100% HFC-134a 1995 2009 2%  R-404A 1995 2009 5%  R-410A 1999 2009 2%  R-507 1995 2009 2%  HFC-134a 2009 2010 14%  R-404A 2009 2010 45%  R-410A 2009 2010 18%  R-507 2009 2010 14% 

Mobile Air Conditioners (Passenger Cars) 

CFC-12 HFC-134a 1992 1994 100% 

Mobile Air Conditioners (Light Duty Trucks) 

CFC-12 HFC-134a 1993 1994 100% 

Mobile Air Conditioners (School and Tour Buses) 

CFC-12 HCFC-22 

HFC-134a 

1994 1994 

1995 1997 

0.5% 

99.5% 

HFC-134a None 

2006 2007 100% None 

Mobile Air Conditioners (Transit Buses and Trains)

HCFC-22 HFC-134a 1995 2009 100% None HCFC-22 

HFC-134a R-407C 

2002 2002 

2009 2009 

50% 50% 

None None 

Positive Displacement Chillers 
Initial Market Primary Substitute Secondary Substitute Tertiary 

Segment 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 

HCFC-22 HCFC-22 1996 1996 100% HFC-134a 2000 2009 9% R-407C 2010  R-410A 2010  R-407C 2000 2009 1% None  HFC-134a 2009 2010 81% R-407C 2010  R-410A 2010  R-407C 2009 2010 9% None CFC-12 HCFC-22 1993 1993 100% HFC-134a 2000 2009 9% R-407C 2010  R-410A 2010  R-407C 2000 2009 1% None  HFC-134a 2009 2010 81% R-407C 2010  R-410A 2010  R-407C 2009 2010 9% None 

Refrigerated Appliances 

CFC-12 HFC-134a 1994 1995 100% HFC-134a 2005 2005 100% None 

Residential Unitary Air Conditioners 

HCFC-22* HCFC-22 1992 1993 100% HCFC-22 2006 2006 70% R-410A 2007  R-407C 2010  R-410A 2010  R-410A 2000 2005 5% R-410A 2006  R-410A 2000 2006 5% None  R-410A 2006 2006 20% None 

Retail Food (Large) 

CFC-12 R-502 1988 1990 100% HCFC-22 1991 1993 100% R-404A 1995  R-507 1995  R-404A 2000  R-507 2000  R-404A 2005  R-507 2005 R-502 HCFC-22 1990 1993 100% R-404A 1995 2000 17.5% None  R-507 1995 2000 7.5% None  R-404A 2000 2005 31.5% None  R-507 2000 2005 13.5% None  R-404A 2005 2010 18% None  R-507 2005 2010 12% None 

Retail Food (Medium) 

HCFC-22 R-404A 1995 2000 17.5% None  R-507 1995 2000 7.5% None  R-404A 2000 2005 31.5% None 
A-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Initial Market Primary Substitute Secondary Substitute Tertiary 

Segment 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 

 R-507 2000 2005 13.5% None  R-404A 2005 2010 18% None  R-507 2005 2010 12% None 

Retail Food (Small) 

CFC-12 HCFC-22 1990 1993 90% HFC-134a 1993 1995 90% CO2 2010  R-404A 2000 2009 7.5% None  R-507 2000 2009 2.5% None  R-404A 1993 1996 7.5% None  R-507 1993 1996 2.5% None 

Transport Refrigeration 

CFC-12 HFC-134a 1993 1995 98% None  HCFC-22 1993 1995 2% HFC-134a 1995 1999 100% R-502 HFC-134a 1993 1995 55% None  R-404A 1993 1995 45% None 

Water-Source and Ground-Source Heat Pumps 

HCFC-22 HCFC-22 1992 1993 100% R-407C 2000 2006 5% None  R-410A 2000 2006 5% None  HFC-134a 2000 2009 2% None  R-407C 2006 2009 2.5% None  R-410A 2006 2009 4.5% None  HFC-134a 2009 2010 18% None  R-407C 2009 2010 22.5% None  R-410A 2009 2010 40.5% None 

Packaged Terminal Air Conditioners and Heat Pumps 

HCFC-22 HCFC-22 1992 1993 100% R-410A 2006 2009 10% None  R-410A 2009 2010 90% None 

Window Units 

HCFC-22 R-407C 2003 2009 3% None  R-407C 2009 2010 35% None  R-410A 2003 2009 7% None  R-410A 2009 2010 55% None 

*HCFC-22 residential unitary air conditioners have a quaternary substitute.R-407C transitions to R-410A starting in 2011 and fully penetrating 100 percent of the market in 2015
A-171

Table A- 146 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- 146. Refrigeration and Air-conditioning Lifetime Assumptions 

End Use Lifetime HFC Emission Rates 

 (Years) (%) 

Centrifugal Chillers 20 – 27 2.0 – 10.9 

Cold Storage 20 - 25 15.0 

Commercial Unitary A/C 15 7.9 – 8.6 

Dehumidifiers 11 0.5 

Ice Makers 20 3.0 

Industrial Process Refrigeration 25 3.6 – 12.3 

Mobile Air Conditioners 5-12 2.3 – 18.0 

Positive Displacement Chillers 20 0.5 – 1.5 

PTAC/PTHP 12 3.9 

Retail Food 15 – 20 7.8 – 29.9 

Refrigerated Appliances 14 0.6 

Residential Unitary A/C 15 7.2 – 9.3 

Transport Refrigeration 12 20.6 – 27.9 

Water & Ground Source Heat Pumps 20 3.9 

Window Units 12 0.6 

Aerosols 

ODSs, HFCs and many other chemicals are used as propellant aerosols. Pressurized within a container, a nozzle 

releases the chemical, which allows the product within the can to also be released. Two types of aerosol products are 

modeled: metered dose inhalers (MDI) and consumer aerosols. In the United States, the use of CFCs in consumer aerosols 

was banned in 1977, 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. 

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- 147. 

Table A- 147. Aerosol Product Transition Assumptions 

Initial Primary Substitute Secondary Substitute 

Market 

Segment 

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 

Growth 

Rate 

MDIs 

CFC-11 HFC-134a 1997 2008 9% None 1.5% 

 HFC-227ea 1997 2008 1% None 

 HFC-134a 2009 2009 27% None 

 HFC-227ea 2009 2009 3% None 
A-172 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Initial Primary Substitute Secondary Substitute 

Market 

Segment 

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 

Growth 

Rate 

 HFC-134a 2009 2015 54% None 

 HFC-227ea 2009 2015 6% None 

CFC-12 HFC-134a 1997 2008 9% None 1.5% 

 HFC-227ea 1997 2008 1% None 

 HFC-134a 2009 2009 27% None 

 HFC-227ea 2009 2009 3% None 

 HFC-134a 2009 2015 54% None 

 HFC-227ea 2009 2015 6% None 

CFC-114 HFC-134a 1997 2008 9% None 1.5% 

 HFC-227ea 1997 2008 1% None 

 HFC-134a 2009 2009 27% None 

 HFC-227ea 2009 2009 3% None 

 HFC-134a 2009 2015 54% None 

 HFC-227ea 2009 2015 6% None 

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% 

*Consumer Aerosols transitioned away from ODS prior to the beginning of the Vintaging Model, which begins in 1985. 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. 

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- 148. 

Table A- 148. Solvent Market Transition Assumptions 

Initial Primary Substitute Secondary Substitute 

Market 

Segment 

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 

Growth 

Rate 

Adhesives 

CH3CCI3 Non-ODP/GWP 1994 1995 100% None 2.0% 

Electronics 

CFC-113 Semi-Aqueous 1994 1995 52% None 2.0% 

 HCFC-225ca/cb 1994 1995 0.2% Unknown 
A-173

Initial Primary Substitute Secondary Substitute 

Market 

Segment 

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 

Growth 

Rate 

 HFC-4310mee 1995 1996 1% None 

 HFE-7100 1994 1995 1% None 

 

nPB,Methyl 

Siloxanes 1992 1996 6% None 

 No-Clean 1992 1996 40% None 

CH3CCI3 Non-ODP/GWP 1996 1997 99.8% None 2.0% 

 PFC/PFPE 1996 1997 0.2% Non-ODP/GWP 2000 2003 90% 

 Non-ODP/GWP 2005 2009 10% 

Metals 

CH3CCI3 Non-ODP/GWP 1992 1996 100% None 2.0% 

CFC-113 Non-ODP/GWP 1992 1996 100% None 2.0% 

CCI4 Non-ODP/GWP 1992 1996 100% None 2.0% 

Precision 

CH3CCI3 Non-ODP/GWP 1995 1996 99% None 2.0% 

 HFC-4310mee 1995 1996 1% None 

 PFC/PFPE 1995 1996 0.2% 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, 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. The equation is applied for a single year, accounting for all fire protection equipment in 

operation in that year. Each fire protection agent is modeled separately. In the Vintaging Model, streaming applications 

have a 12-year lifetime and flooding applications have a 20-year lifetime. 

 Ej = r × Σ Qcj-i+1 for i=1→k

Where: 

E = Emissions. Total emissions of a specific chemical in year j for streaming fire extinguishing equipment, 

by weight. 

r = Percent Released. The percentage of the total chemical in operation that is released to the atmosphere. 

Qc = Quantity of Chemical. Total amount of a specific chemical used in new fire extinguishing equipment in 

a given year, j-i+1, by weight. 

i = Counter, runs from 1 to lifetime (k).

j = Year of emission. 

k = Lifetime. The average lifetime of the equipment. 

Assumptions 

Transition assumptions and growth rates for these two fire extinguishing types are presented in Table A- 149. 

Table A- 149. Fire Extinguishing Market Transition Assumptions 

Initial Market Primary Substitute Secondary Substitute 
A-174 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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% 

NonODP/GWP 2015 2015 25% 

 HFC-236fa 2015 2015 75% 

 Non-ODP/GWP 1993 1994 58% None 

 Non-ODP/GWP 1995 2024 20% None 

 Non-ODP/GWP 1999 2018 10% None 

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 or PFC 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) 

Emissions from foams occur at many different stages, including manufacturing, lifetime, disposal and postdisposal. Manufacturing emissions occur in the year of foam manufacture, and are calculated as presented in the following 

equation. 

 Emj = lm × Qcj

Where: 

Emj = Emissions from manufacturing. Total emissions of a specific chemical in year j due to manufacturing 

losses, by weight. 

lm = Loss Rate. Percent of original blowing agent emitted during foam manufacture. For open-cell foams, 

lm is 100%. 

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year. 

j = Year of emission. 

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. 
A-175

lu = Leak Rate. Percent of original blowing agent emitted 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, and are assumed to occur while the 

disposed foam is in a landfill. Currently, the only foam type assumed to have post-disposal emissions is polyurethane 

appliance foam, which is expected to continue to emit for 32 years post-disposal, calculated as presented in the following 

equation. 

Epj = lp × Σ Qcj-m for m=k→k + 32 

Where: 

Epj = Emissions from post disposal. Total post-disposal emissions of a specific chemical in year j, by weight. 

lp = Leak Rate. Percent of original blowing agent emitted post disposal. 

Qc = Quantity of Chemical. Total amount of a specific chemical used to manufacture closed-cell foams in a 

given year. 

k = Lifetime. The average lifetime of foam product. 

m = Counter. Runs from lifetime (k) to (k+32). 

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. 
A-176 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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- 150. The emission profiles of the foam types estimating in the Vintaging Model are shown in 

Table A- 151. 
Table A- 150. Foam Blowing Market Transition Assumptions 

Initial Market Primary Substitute Secondary Substitute Ter

Segment 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 

Sta

Dat

Commercial Refrigeration Foam 

CFC-11 HCFC-141b 1989 1996 40% HFC-245fa 2002 2003 80% None  Non-ODP/GWP 2002 2003 20% None  HCFC-142b 1989 1996 8% Non-ODP/GWP 2009 2010 80% None  HFC-245fa 2009 2010 20% None  HCFC-22 1989 1996 52% Non-ODP/GWP 2009 2010 80% None  HFC-245fa 2009 2010 20% None 

Flexible PU Foam: Integral Skin Foam 

CFC-11 HCFC-141b 1989 1990 100% HFC-134a 1993 1996 25% None  HFC-134a 1994 1996 25% None  CO2 1993 1996 25% None  CO2 1994 1996 25% None 

Flexible PU Foam: Slabstock Foam, Moulded Foam 

CFC-11 Non-ODP/GWP 1992 1992 100% None 

Phenolic Foam 

CFC-11 HCFC-141b 1989 1990 100% Non-ODP/GWP 1992 1992 100% None 

Polyolefin Foam 

CFC-114 HFC-152a 1989 1993 10% Non-ODP/GWP 2005 2010 100% None  HCFC-142b 1989 1993 90% Non-ODP/GWP 1994 1996 100% None 

PU and PIR Rigid: Boardstock 

CFC-11 HCFC-141b 1993 1996 100% Non-ODP/GWP 2000 2003 95% None 

HC/HFC-245fa Blend 2000 2003 5% None 

PU Rigid: Domestic Refrigerator and Freezer Insulation 

CFC-11 HCFC-141b 1993 1996 89% HFC-134a 1996 2003 10% None  HFC-245fa 2002 2003 85% None  Non-ODP/GWP 2002 2003 5% None 

HCFC-142b/22 Blend 1993 1996 1% HFC-245fa 2009 2010 50% None 

 HFC-134a 2009 2010 50% None  HCFC-22 1993 1996 10% HFC-134a 2009 2010 100% None 

PU Rigid: One Component Foam 

CFC-12 

HCFC-142b/22 Blend 1989 1996 70% Non-ODP/GWP 2009 2010 80% None 

 HFC-134a 2009 2010 10% None  HFC-152a 2009 2010 10% None  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 
A-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Initial Market Primary Substitute Secondary Substitute Ter

Segment 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 

Sta

Dat

CFC-11 HCFC-141b 1989 1996 100% CO2 1999 2003 45% None  Non-ODP/GWP 2001 2003 45% None  HCFC-22 2003 2003 10% Non-ODS/GWP 2009 

PU Rigid: Sandwich Panels: Continuous and Discontinuous 

CFC-11 HCFC-141b 1989 1996 82% 

HCFC-22/Water Blend 2001 2003 20% HFC Blend 2009 

 Non-ODP/GWP 2009 

HFC-245fa/CO2

Blend 2002 2004 20% 

 Non-ODP/GWP 2001 2004 40% None  HFC-134a 2002 2004 20% None  HCFC-22 1989 1996 18% 

HFC-245fa/CO2

Blend 2009 2010 40% None 

 Non-ODP/GWP 2009 2010 20% None  CO2 2009 2010 20% None  HFC-134a 2009 2010 20% None 

PU Rigid: Spray Foam 

CFC-11 HCFC-141b 1989 1996 100% HFC-245fa 2002 2003 30% None 

HFC-245fa/CO2

Blend 2002 2003 60% None 

 Non-ODP/GWP 2001 2003 10% None 

XPS: Boardstock Foam 

CFC-12 

HCFC-142b/22 Blend 1989 1994 10% HFC-134a 2009 2010 70% None 

 HFC-152a 2009 2010 10% None  CO2 2009 2010 10% None  Non-ODP/GWP 2009 2010 10% None  HCFC-142b 1989 1994 90% HFC-134a 2009 2010 70% None  HFC-152a 2009 2010 10% None  CO2 2009 2010 10% None  Non-ODP/GWP 2009 2010 10% None 

XPS: Sheet Foam 

CFC-12 CO2 1989 1994 1% None None  Non-ODP/GWP 1989 1994 99% CO2 1995 1999 9% None  HFC-152a 1995 1999 10% None 
A-179

Table A- 151. 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 27.3 34.8 

PU and PIR Rigid: Boardstock 6 1 50 44 100 

PU Sandwich Panels: Continuous and Discontinuous 5.5 0.5 50 69.5 100 

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 32 years. 

Sterilization 

Sterilization is used to control microorganisms and pathogens during the growing, collecting, storing and 

distribution of flowers as well as various foods including grains, vegetables and fruits. Currently, the Vintaging Model 

assumes that the sterilization sector has not transitioned to any HFC or PFC as an ODS substitute, however, the modeling 

methodology is provided below for completeness. 

 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. 

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 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: 
A-180 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 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- 152 provides the bank for ODS and ODS substitutes by chemical grouping in metric tons (MT) for 

1990-2007. 
Table A- 152. Banks of ODs and ODS Substitutes, 1990-2007 (MT) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 CFC 651,935 713,209 751,697 784,086 795,636 793,517 773,996 750,718 728,247 698,645 665,619 634,720 605,686 577,491 HCFC 254,090 289,536 328,329 380,761 444,600 518,886 606,446 697,038 791,416 889,736 972,930 1,044,176 1,095,551 1,126,924 1HFC 997 4,058 13,466 30,335 49,855 75,182 100,630 126,231 152,926 180,028 206,862 240,352 284,089 328,371 

References IPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The 

Climate Change, H.S. Eggleston, L. Buendia, K. Miwa, T Ngara, and K. Tanabe (eds.). Hayama, Kanagawa, Japan. 
A-182 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

3.9. Methodology for Estimating CH4 Emissions from Enteric Fermentation 

Methane emissions from enteric fermentation were estimated for five livestock categories: cattle, horses, sheep, 

swine, and goats. Emissions from cattle represent the majority of U.S. emissions; consequently, the more detailed IPCC 

Tier 2 methodology was used to estimate emissions from all cattle (except for bulls). The IPCC Tier 1 methodology was 

used to estimate emissions from bulls and the other types of livestock. 

Estimate Methane Emissions from Cattle 

This section describes the process used to estimate methane emissions from cattle enteric fermentation using the 

Cattle Enteric Fermentation Model (CEFM).38 The CEFM, based on recommendations provided in 

IPCC/UNEP/OECD/IEA (1997), IPCC (2000) and IPCC (2006), uses information on population, energy requirements, 

digestible energy, and methane conversion rates to estimate methane emissions.39 The emission methodology consists of 

the following three steps: (1) characterize the cattle population to account for animal population categories with different 

emission profiles; (2) characterize cattle diets to generate information needed to estimate emission factors; and (3) estimate 

emissions using these data and the IPCC Tier 2 equations. 

Step 1: Characterize U.S. Cattle Population 

The national cattle population estimates in the inventory submission are based on data obtained from the U.S. 

Department of Agriculture’s (USDA) National Agricultural Statistics Service Quick Stats database (USDA 2008). A 

summary of the annual average populations upon which all livestock-related emissions are based is provided in Table A153. Cattle populations used in the Enteric Fermentation sector were estimated using the cattle transition matrix in the 

CEFM, which uses January 1 USDA population estimates and weight data to simulate the population of U.S. cattle from 

birth to slaughter, and results in an estimate of the number of animals in a particular cattle grouping while taking into 

account the monthly rate of weight gain, the average weight of the animals, and the death and calving rates. The use of 

supplemental USDA data and the cattle transition matrix in the CEFM results in the cattle population estimates for Enteric 

Fermentation differing slightly from the January 1 USDA point estimates used in the Manure Management Estimates in 

the Agriculture Chapter and the cattle population data obtained from the Food and Agriculture Organization of the United 

Nations (FAO). 

Table A-153: Cattle Population Estimates from the CEFM Transition Matrix for 1990-2007 

Livestock Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007

Calves 0-6 months 22,561 23,499 22,569 22,389 22,325 22,156 21,929 21,936 21,912 21,827

Dairy 

Dairy Cows 10,015 9,482 9,183 9,172 9,106 9,142 8,990 9,005 9,063 9,132

Dairy Replacements 7-11 months 1,214 1,216 1,196 1,199 1,211 1,196 1,210 1,251 1,273 1,308

Dairy Replacements 12-23 months 2,915 2,892 2,812 2,846 2,848 2,877 2,829 2,902 3,003 3,039

Beef 

Bulls 2,180 2,392 2,197 2,187 2,172 2,174 2,128 2,160 2,181 2,158

Beef Cows 32,455 35,190 33,575 33,398 33,134 32,983 32,861 32,915 32,994 32,891

Beef Replacements 7-11 months 1,269 1,493 1,313 1,316 1,322 1,312 1,326 1,375 1,392 1,357

Beef Replacements 12-23 months 2,967 3,637 3,097 3,140 3,129 3,160 3,103 3,199 3,321 3,300

Steer Stockers 10,337 11,732 8,728 8,730 8,886 8,415 8,259 8,455 8,623 8,848

Heifer Stockers 5,930 6,682 5,366 5,370 5,388 5,139 5,037 5,219 5,293 5,280

Feedlot Cattle 9,548 11,017 12,962 12,452 12,615 12,895 12,120 12,248 13,004 12,988 

Note: Bull populations are not run through the transition matrix, and therefore the populations listed here represent an average of the January and July 

populations downloaded from USDA (2008). 

The population transition matrix in the CEFM simulates the U.S. cattle population over time and provides an 

estimate of the population age and weight structure by cattle type on a monthly basis. Since cattle often do not remain in a 

single population type for an entire year (e.g., calves become stockers, stockers become feedlot animals), and emission 

 

38 Emissions from bulls are estimated in the CEFM using a Tier 1 approach based on published population statistics and 

national average emission factors because the variation in diets and within year population is assumed to be minimal. The IPCC 

recommends the use of a methane conversion factor of zero for calves, because they consume mainly milk, therefore this results in no 

methane emissions from calves through 6 months. 

39 Additional information on the Cattle Enteric Fermentation Model can be found in ICF (2006). 
A-183

profiles vary both between and within each cattle type, these monthly age groups are tracked in the enteric fermentation 

model to obtain more accurate emission estimates than would be available from annual point estimates of population (such 

as available from USDA statistics) and weight for each cattle type. 

The transition matrix tracks both dairy and beef populations, and divides the populations into males and females, 

and subdivides the population further into specific cattle groupings for replacement, stocker, feedlot, and mature animals. 

The matrix is based primarily on two types of data: population statistics and weight statistics (including target weights, 

slaughter weights, and weight gain). Using the weight data, the transition matrix simulates the growth of animals over 

time by month. The matrix also relies on supplementary data, such as feedlot placement statistics, slaughter statistics, 

death rates, and calving rates. 

The basic method for tracking population of animals per category is based on the number of births (or graduates) 

into the monthly age group minus those animals that die or are slaughtered and those that graduate to the next category 

(such as stockers to feedlot placements). 

Each stage in the cattle lifecycle was modeled to simulate the cattle population from birth to slaughter. This 

level of detail accounts for the variability in CH4 emissions associated with each life stage. Given that a stage can last less 

than one year (e.g., beef calves are weaned at 7 months), each is modeled on a per-month basis. The type of cattle also 

impacts CH4 emissions (e.g., beef versus dairy). Consequently, there is an independent transition matrix for each of three 

separate lifecycle phases, 1) calves, 2) replacements and stockers, and 3) feedlot animals. In addition, the number of 

mature cows is tracked for both dairy and beef stock. Each lifecycle is discussed separately below, and the categories 

tracked are listed in Table A-154. 

Table A-154: Cattle Population Categories Used for Estimating CH4 Emissions 

Dairy Cattle Beef Cattle 

Calves Calves 

Heifer Replacements Heifer Replacements 

Cows Heifer and Steer Stockers 

 Animals in Feedlots (Heifers & Steers) 

 Cows 

 Bulls* 

* Bulls (beef and dairy) are accounted for in a single category. 

The key variables tracked for each of these cattle population categories (except bulls) are as follows: 

Calves. The number of animals born on a monthly basis was used to initiate monthly cohorts and to determine 

population age structure. The number of calves born each month was obtained by multiplying annual births by the 

percentage of births by month. Annual birth information for each year was taken from USDA (2008). 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-155, 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 (varies depending on birth month). 

Total calf population is modified through time to account for veal calf slaughter at 4 months and a calf death loss of 0.35 

percent annually (distributed across age cohorts up to six months of age). An example of a transition matrix for calves is 

shown in Table A-156. 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-155: 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-184 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-156: Example of Monthly Average Populations from Calf Transition Matrix 

Replacements and Stockers. At seven months of age, calves ‘graduate’ and are separated into the applicable 

cattle types. First the number of replacements required for beef and dairy cattle are calculated based on estimated death 

losses and population changes between beginning and end of year population estimates. All steer, and remaining heifers 

(after subtracting required replacements), are considered ‘stockers,’ that is backgrounding animals that are eligible for 

placement into feedlots as they reach the appropriate weight class. During the stocker phase animals are subtracted out of 

the transition matrix for placement into feedlots based on feedlot placement statistics from USDA (2008). 

The data and calculations that occur for the stocker category include matrices that estimate the population of 

backgrounding heifers and steer, as well as a matrix for total combined stockers. The matrices start with the beginning of 

year populations in January and model the progression of each cohort. The age structure of the January population is based 

on estimated births by month from the previous two years, although in order to balance the population properly, an 

adjustment is added that slightly reduces population percentages in the older populations. The populations are modified 

through addition of graduating calves (month 7, bottom row of Table A-157) and subtraction through death loss and 

animals placed in feedlots. Eventually, an entire cohort population of stockers will be zero, indicating that the complete 

cohort has been transitioned into feedlots. An example of the transition matrix for stockers is shown in Table A-157. 

Table A-157: Example of Monthly Average Populations from Stocker Transition Matrix 

Age 

(month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

23 202 214 126 47 20 12 10 11 9 7 5 51 

22 353 174 59 23 15 12 13 13 9 7 103 311 

21 286 81 29 17 15 15 15 12 9 153 421 559 

20 133 40 22 17 18 18 14 12 197 526 765 457 

19 66 30 22 21 21 17 14 273 620 965 629 212 

18 49 30 27 25 20 17 321 779 1,141 795 291 305 

17 49 37 31 24 20 379 882 1,442 942 368 546 49 

16 60 43 30 24 453 1,004 1,636 1,193 435 779 49 49 

15 71 41 30 531 1,160 1,867 1,355 551 985 49 49 61 

14 68 41 669 1,324 2,162 1,547 625 1,337 49 49 61 71 

13 67 918 1,607 2,471 1,796 716 1,669 49 49 61 71 68 

12 1,076 1,774 2,813 2,017 857 2,242 282 49 61 71 68 67 

11 2,027 3,105 2,297 962 2,765 365 179 82 91 76 72 1,094 

10 3,532 2,542 1,082 3,229 507 253 209 261 213 128 1,151 2,683 

9 2,860 1,204 3,588 607 451 425 504 464 429 1,353 2,916 6,195 

8 1,328 4,083 770 617 664 915 782 831 1,682 3,596 6,832 5,605 

7 4,450 878 871 1,060 1,295 1,232 1,230 2,114 4,188 7,484 6,012 2,665 

In order to ensure a balanced population of both stockers and placements, additional data tables are utilized in the 

stocker matrix calculations. The tables summarize the placement data by weight class and month, and is based on the total 

number of animals within the population that are available to be placed in feedlots and the actual feedlot placement 

statistics provided by USDA (2008). In cases where there are discrepancies between these values, the model performs one 

of two actions. If there are not enough stockers in the 700-800 lb animal category, the model pulls available stockers from 

the highest weight category, animals greater than 800 lbs. If there are still not enough animals to fulfill requirements the 

model reports this as a shortfall in the stocker population. Note that in the current time series, placement shortfalls have 

only occurred in instances where there are not enough stockers to fulfill placements at particular weight classes, even 

though total populations have been sufficient. This results in a slight overestimation of emissions, as these animals remain 

stockers, which have a higher emission factor than feedlot animals. 

Age 

(month) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 

6 1,207 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 

5 1,199 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 

4 1,494 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 

3 1,751 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 

2 1,684 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 

1 1,666 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 

0 2,665 5,005 8,720 7,062 3,279 1,631 1,212 1,202 1,495 1,748 1,680 1,661 
A-185

In addition, average weights were tracked for each monthly age group using starting weight and monthly weight 

gain estimates. Weight gain (i.e., pounds per month) was estimated based on weight gain needed to reach a set target 

weight, divided by the number of months remaining before target weight was achieved. Birth weight was assumed to be 

88 pounds for both beef and dairy animals. Weaning weights were estimated at 515 lbs. Other reported target weights 

were available for 12, 15, 24, and 36 month-old animals, depending on the animal type. Beef cow mature weight was 

taken from measurements provided by a major British Bos taurus breed (Enns 2008). Beef replacement weight was 

calculated as 70 percent of mature weight at 15 months and 85 percent of mature weight at 24 months. Mature weight for 

dairy cows was estimated at 1,550 for all years, based on Patton et al. (2008). Dairy replacement at 15 months was 

assumed to be 875 lbs and replacement at 24 months is 1300 lbs. Live slaughter weights were derived from dressed 

slaughter weight data for each year (USDA 2008). Weight gain for stocker animals was based on monthly gain estimates 

from Johnson (1999) for 1989, and from average daily estimates from Lippke et al. 2000, Pinchack et al., 2004, Platter et 

al., 2003, and Skogerboe et al., 2000 for 2000 through 2007. Interim years were calculated linearly, as shown in Table A158. Live slaughter weight was estimated as dressed weight divided by 0.63 USDA (1999c). This ratio represents the 

dressed weight (i.e. weight of the carcass after removal of the internal organs), to the live weight (i.e. weight taken 

immediately before slaughter). Table A-158 provides weights and weight gains that vary by year in the CEFM. 

Table A-158: Mature Weights and Weight Gains that vary by Year (lbs) 

Year/Cattle 

Type 

Mature Beef Cows 

 (lbs) 

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 1,221 1.53 1.23 1.23 1.08 

1991 1,225 1.56 1.29 1.29 1.15 

1992 1,263 1.59 1.35 1.35 1.23 

1993 1,280 1.62 1.41 1.41 1.30 

1994 1,280 1.65 1.47 1.47 1.38 

1995 1,282 1.68 1.53 1.53 1.45 

1996 1,285 1.71 1.59 1.59 1.53 

1997 1,286 1.74 1.65 1.65 1.60 

1998 1,296 1.77 1.71 1.71 1.68 

1999 1,292 1.80 1.77 1.77 1.75 

2000 1,272 1.83 1.83 1.83 1.83 

2001 1,272 1.83 1.83 1.83 1.83 

2002 1,276 1.83 1.83 1.83 1.83 

2003 1,308 1.83 1.83 1.83 1.83 

2004 1,323 1.83 1.83 1.83 1.83 

2005 1,327 1.83 1.83 1.83 1.83 

2006 1,341 1.83 1.83 1.83 1.83 

2007 1,348 1.83 1.83 1.83 1.83 

Sources: Enns (2008), Johnson (1999), Lippke et al. (2000), NRC (1999), Pinchack et al. (2004), Platter et al. (2003), Skogerboe et al. (2000) 

Feedlot Animals. Feedlot placement statistics from USDA provide data on the placement of animals from the 

stocker population into feedlots on a monthly basis by weight class. The model uses these data to shift a sufficient number 

of animals from the stocker cohorts into the feedlot populations to match the reported placement data. After animals are 

placed in feedlots they progress through two steps. First, animals spend 25 days on a step-up diet to become acclimated to 

the new feed type, during this time weight gain is estimated to be 2.8 to 3 pounds per day (Johnson 1999). Animals are 

then switched to a finishing diet for a period of time before they are slaughtered. Weight gain during finishing diets is 

estimated to be 3 to 3.3 pounds per day (Johnson 1999). The length of time an animal spends in a feedlot depends on the 

start weight (i.e., placement weight), the rate of weight gain during the start-up and finishing phase of diet, and the target 

weight (as determined by weights at slaughter). Additionally, animals remaining in feedlots at the end of the year are 

tracked for inclusion in the following year’s emission and population counts. For 1990 to 1995, only the total placement 

data were available, therefore placements for each weight category (displayed in Table A-159) 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-159 provides a summary of the reported feedlot placement statistics for 2007. 

Table A-159: Feedlot Placements in the United States for 2007 (Number of animals placed in Thousand Head)

Weight When Placed Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

< 600 lbs 320 325 345 375 420 405 360 490 865 685 480 610 

600 – 700 lbs 390 334 375 263 374 325 312 440 745 660 505 505 

700 – 800 lbs 545 505 640 430 645 457 445 549 555 420 420 565 

> 800 lbs 435 495 600 500 720 470 505 640 560 360 296 735 

Total 1,690 1,659 1,960 1,568 2,159 1,657 1,622 2,119 2,725 2,125 1,701 2,415

Source: USDA (2008). 

Note: Totals may not sum due to independent rounding. 
A-186 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Mature Animals. Energy requirements and hence, composition of diets, level of intake, and emissions for 

particular animals, are greatly influenced by whether the animal is pregnant or lactating. Information is therefore needed 

on the percentage of all mature animals that are pregnant each month, as well as milk production, to estimate CH4

emissions. A weighted average percent of pregnant cows each month was estimated using information on births by month 

and average pregnancy term. For beef cattle, a weighted average total milk production per animal per month was 

estimated using information on typical lactation cycles and amounts (NRC 1999), and data on births by month. This 

process results in a range of weighted monthly lactation estimates expressed as lbs/animal/month. The monthly estimates 

from January to December are 3.3, 5.1, 8.7, 12.0, 13.6, 13.3, 11.7, 9.3, 6.9, 4.4, 3.0, and 2.8 lbs milk/animal/day. Annual 

estimates for dairy cattle were taken from USDA milk production statistics. Dairy lactation estimates for 1990 through 

2007 are shown in Table A-160. Beef and dairy cow 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. 
A-187

Table A-160: Dairy Lactation by State (lbs/ year/cow)* 

State/Year 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007 

Alabama 12,214 14,176 13,920 14,286 13,850 14,000 14,412 14,000 14,500 15,154 

Alaska 13,300 17,000 14,500 13,055 13,600 12,846 12,167 12,273 12,250 14,667 

Arizona 17,500 19,735 21,820 22,036 23,333 22,916 22,788 22,679 22,855 23,260 

Arkansas 11,841 12,150 12,436 12,343 12,281 12,207 13,458 13,545 12,900 12,941 

California 18,456 19,573 21,130 20,904 21,277 20,993 21,139 21,404 21,815 22,440 

Colorado 17,182 18,687 21,618 21,413 21,590 21,530 21,412 22,577 23,155 22,915 

Connecticut 15,606 16,438 17,778 18,240 18,625 18,773 19,600 19,200 19,316 19,211 

Delaware 13,667 14,500 14,747 16,667 16,667 15,904 17,230 17,662 16,286 16,618 

Florida 14,033 14,698 15,688 15,758 15,387 15,218 16,326 16,591 16,447 16,622 

Georgia 12,973 15,550 16,284 16,663 17,294 16,988 16,857 17,259 18,234 18,169 

Hawaii 13,604 13,654 14,358 14,107 14,667 14,154 13,197 12,889 13,256 12,241 

Idaho 16,475 18,147 20,816 21,194 21,018 21,718 21,446 22,332 22,346 22,513 

Illinois 14,707 15,887 17,450 17,414 17,835 18,441 18,486 18,827 19,252 18,612 

Indiana 14,590 15,375 16,568 16,778 17,603 19,725 20,046 20,295 19,861 20,307 

Iowa 15,118 16,124 18,298 18,024 18,201 18,955 19,953 20,641 20,127 20,085 

Kansas 12,576 14,390 16,923 17,312 18,972 19,189 19,611 20,505 20,938 19,734 

Kentucky 10,947 12,469 12,841 12,969 13,230 12,629 12,927 12,896 13,296 13,889 

Louisiana 11,605 11,908 12,034 11,704 12,063 12,070 12,605 12,400 12,375 12,034 

Maine 14,619 16,025 17,128 17,211 17,730 17,829 18,000 18,030 17,938 17,788 

Maryland 13,461 14,725 16,083 15,780 16,062 15,577 15,703 15,986 17,281 18,017 

Massachusetts 14,871 16,000 17,091 17,000 17,190 17,474 17,412 17,059 17,375 17,000 

Michigan 15,394 17,071 19,017 19,373 20,332 21,109 20,891 21,635 22,234 22,681 

Minnesota 14,127 15,894 17,777 17,278 17,368 17,459 17,499 18,091 18,600 18,817 

Mississippi 12,081 12,909 15,028 14,200 14,059 13,645 14,037 15,280 14,957 15,429 

Missouri 13,632 14,158 14,662 13,441 14,204 14,620 15,139 16,026 16,000 14,982 

Montana 13,542 15,000 17,789 18,211 18,944 19,167 19,333 19,579 18,632 18,500 

Nebraska 13,866 14,797 16,513 16,194 17,418 17,641 17,197 17,950 18,328 18,220 

Nevada 16,400 18,128 19,000 19,412 20,040 19,400 20,360 21,680 20,148 20,370 

New Hampshire 15,100 16,300 17,333 17,889 18,222 19,063 18,938 18,875 19,533 20,714 

New Jersey 13,538 13,913 15,250 16,643 18,154 16,615 16,667 16,000 16,182 16,800 

New Mexico 18,815 18,969 20,944 20,750 20,983 21,028 20,666 21,192 21,493 21,363 

New York 14,658 16,501 17,378 17,530 18,101 17,812 17,786 18,639 18,879 19,303 

North Carolina 15,220 16,314 16,746 17,224 17,766 17,115 17,649 18,741 18,510 19,188 

North Dakota 12,624 13,094 14,292 14,000 14,825 14,857 15,471 14,182 14,688 15,310 

Ohio 13,767 15,917 17,027 16,519 17,080 17,269 17,338 17,567 17,737 18,043 

Oklahoma 12,327 13,611 14,440 15,407 15,560 16,000 16,192 16,480 16,630 16,580 

Oregon 16,273 17,289 18,222 18,074 18,360 18,294 18,917 18,876 19,000 19,417 

Pennsylvania 14,726 16,492 18,081 18,112 18,419 17,979 17,904 18,722 19,390 19,422 

Rhode Island 14,250 14,773 15,667 16,571 16,357 17,000 16,333 17,000 17,273 16,455 

South Carolina 12,771 14,481 16,087 17,476 18,200 16,737 16,882 16,000 16,294 17,889 

South Dakota 12,257 13,398 15,516 15,393 14,988 16,220 16,838 17,741 18,580 19,306 

Tennessee 11,825 13,740 14,789 14,511 14,943 15,253 15,400 15,743 15,657 15,857 

Texas 14,350 15,244 16,503 15,666 16,719 17,649 18,837 20,131 21,328 21,143 

Utah 15,838 16,739 17,573 17,211 17,914 17,824 18,284 18,875 20,314 20,376 

Vermont 14,528 16,210 17,199 17,444 17,552 17,698 17,890 18,469 18,383 18,079 

Virginia 14,213 15,116 15,833 15,975 15,891 15,319 16,486 16,990 17,363 17,530 

Washington 18,532 20,091 22,644 22,324 22,753 22,780 22,852 23,270 23,055 23,239 

West Virginia 11,250 12,667 15,588 15,563 15,188 14,400 14,923 14,923 15,385 15,308 

Wisconsin 13,973 15,397 17,306 17,182 17,367 17,728 17,796 18,500 18,824 19,310 

Wyoming 12,337 13,197 13,571 14,000 14,409 14,211 14,744 14,878 17,612 18,831 

Source: USDA (2008). 

* Beef lactation data were developed using the methodology described in Step 1. 

Step 2: Characterize U.S. Cattle Population Diets 

To support development of digestible energy (DE, the percent of gross energy intake digested by the animal) and 

CH4 conversion rate (Ym, the fraction of gross energy converted to CH4) values for each of the cattle population categories, 

data were collected on diets considered representative of different regions. For both grazing animals and animals being 

fed mixed rations, representative regional diets were estimated using information collected from state livestock specialists 

and from USDA (1996). The data for each of the diets (e.g., proportions of different feed constituents, such as hay or 

grains) were used to determine feed chemical composition for use in estimating digestible energy and Ym for each animal 

type. Additional detail on the regional diet characterization is provided in EPA (2000). 
A-188 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

DE and Ym vary by diet and animal type. The IPCC recommends Ym values of 3.0+1.0 percent for feedlot cattle 

and 6.5+1.0 percent for all other cattle (IPCC 2006). Given the availability of detailed diet information for different 

regions and animal types in the United States, digestible energy and Ym values unique to the United States40 were 

developed. Digestible energy and Ym values were estimated for each cattle population category, for each year in the time 

series based on physiological modeling, published values, and/or expert opinion. 

DE and Ym values for dairy cows were estimated using a model (Donovan and Baldwin 1999) that represents 

physiological processes in the ruminant animals. The three major categories of input required by the model 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 (USDA 1996)). 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 Donovan and Baldwin model accounts for differing diets (i.e., grain-based or forage-based, so that Ym values for the 

variable feeding characteristics within the U.S. cattle population can be estimated. 

To calculate the DE values for grazing beef cattle, diet composition assumptions were used to estimate weighted 

DE values for a combination of forage and supplemental diets. Where DE values were not available for specific feed 

types, total digestible nutrients (TDN) as a percent of dry matter (DM) intake was used as a proxy for DE, as listed in NRC 

(2000). Forage diets make up the majority of beef cattle diets, and two separate regional DE values were used to account 

for the generally lower forage quality in the western United States. For all non-western beef cattle, the forage DE was an 

average of the seasonal values for Grass Pasture diets listed in Appendix Table 1 of the NRC (2000). This resulted in a 

DE of 64.7 percent for the forage portion of the diet for all beef cattle in regions other than the west. For beef cattle in the 

west, the forage DE was calculated as the seasonal average for meadow and range diets listed in Appendix Table 1 of the 

NRC (2000). This resulted in a DE for the forage portion of the western region diet of 58.5 percent. In addition, it was 

assumed that each region fed a supplemental diet, as shown in Table A-161. By weighting the calculated DE values from 

the forage and supplemental diets, the DE values for the composite diet were calculated.41 The percent of each diet that is 

assumed to be supplemental and the forage DE values used for each region, and the resulting weighted DE values are 

shown in Table A-162. These values are used for steer and heifer stockers and beef replacements. Finally, for beef cows, 

the DE value was adjusted downward by two percent to reflect the lower digestibility diets of the mature beef cow based 

on Johnson (2002). Ym values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002). 

For feedlot animals, DE and Ym values for 1996 through 2007 were taken from Johnson (1999). Values for 1990 

through 1995 were linearly extrapolated from the 1996 value based on Johnson (1999). 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. In 

response to peer reviewer comments (Johnson 2000), values for dairy replacement heifers are based on EPA (1993). 

Table A-163 shows the regional DE and Ym for U.S. cattle in each region for 2007. 

 

40 In some cases, the Ym values used for this analysis extend beyond the range provided by the IPCC. However, EPA 

believes that these values are representative for the U.S. due to research conducted to characterize the diets of U.S. cattle and assess the 

Ym values associated with different animal performance and feed characteristics in the United States. 

41 For example, in California the forage DE of 64.7 was used for 95 percent of the grazing cattle diet and a supplemented diet 

DE of 69.3 percent was used for five percent of the diet, for a total weighted DE of 64.9 percent, as shown in Table A-162. 
A-189

Table A-161: DE Values and Representative Regional Diets (Percent of Diet for each Region) for the Supplemental Diet of 

Grazing Beef Cattle 

Source of representative regional diets: Donovan (1999). a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, 

OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, ME, and WV; the Southcentral region includes AR, LA, OK, 

and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern 

region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL. 

Table A-162: Percent of Each Diet that is Supplemental and the Resulting DE Values for Each Region 

Regiona

Percent Supplement Percent Forage Forage DE Used Calculated Weighted 

Average DE 

West 10 90 59 59 

Northeast 15 85 65 65 

Southcentral 10 90 65 64 

Midwest 15 85 65 65 

Northern Great Plains 15 85 65 66 

Southeast 5 95 65 64 

California 5 95 65 65 

Sources: Percent of total diet that is supplemental diet, Donovan (1999); Forage DE, NRC (2000). a Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, 

OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, ME, and WV; the Southcentral region includes AR, LA, OK, 

and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern 

region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL. 

Feed 

Source of TDN 

(NRC 2000) 

Unweighted 

TDN or DE California a West 

Northern 

Great 

Plains Southcentral Northeast Midwest Southeast 

Alfalfa Hay Table 11-1, feed #4 59.6% 65% 30% 30% 29% 12% 30%

Barley Table 11-1, feed #12 86.3% 10% 15%

Bermuda Table 11-1, feed #17 48.5% 35%

Bermuda Hay Table 11-1, feed #17 48.5% 40%

Corn Table 11-1, feed #38 88.1% 10% 10% 25% 11% 13% 13%

Corn Silage Table 11-1, feed #39 71.2% 25% 20% 20%

Cotton Seed Meal Table 11-1, feed #42 74.4% 7%

Grass Hay Table 1a, feed #129, 

147, 148 53.7% 40% 30%

Orchard Table 11-1, feed #61 53.5% 40%

Soybean Meal 

Supplement Table 11-1, feed #70 83.1% 5% 5% 5%

Sorghum Table 11-1, feed #67 81.3% 20%

Soybean Hulls Table 11-1, feed #69 76.4% 7%

Timothy Hay Table 11-1, feed #77 55.5% 50%

Whole Cotton Seed Table 11-1, feed #41 89.2% 5% 5%

Wheat Middlings Table 1a, feed #433 83.0% 15% 13%

Wheat Table 11-1, feed #83 87.2% 10%

Weighted Total 69% 65% 74% 62% 65% 65% 59%
A-190 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-163: Regional Digestible Energy (DE) and CH4 Conversion Rates (Ym) for Cattle in 2007 

Animal Type Data California c West 

Northern 

Great Plains Southcentral Northeast Midwest Southeast 

Beef Repl. Heif. DEa 65 59 66 64 65 65 64 

 Ymb 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Dairy Repl. Heif. DE 66 66 66 64 68 66 66 

 Ym 5.9% 5.9% 5.6% 6.4% 6.3% 5.6% 6.9% 

Steer Stockers DE 65 59 66 64 65 65 64 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Heifer Stockers DE 65 59 66 64 65 65 64 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Steer Feedlot DE 85 85 85 85 85 85 85 

 Ym 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 

Heifer Feedlot DE 85 85 85 85 85 85 85 

 Ym 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 3.0% 

Beef Cows DE 63 57 64 62 63 63 62 

 Ym 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 6.5% 

Dairy Cows DE 69 66 69 68 69 69 68 

 Ym 4.8% 5.8% 5.8% 5.7% 5.8% 5.8% 5.6% 

Steer Step-Up DE 73 73 73 73 73 73 73 

 Ym 4.8% 4.8% 4.8% 4.8% 4.8% 4.8% 4.8% 

Heifer Step-Up DE 73 73 73 73 73 73 73 

 Ym 4.8% 4.8% 4.8% 4.8% 4.8% 4.8% 4.8% a Digestible Energy in units of percent of GE (MJ/Day). b Methane Conversion Rate is the fraction of GE in feed converted to methane. c Note that emissions are currently calculated on a state-by-state basis, but diets are applied by the regions shown in the table above. The Western region includes AK, WA, 

OR, ID, NV, UT, AZ, HI, and NM; the Northeastern region includes PA, NY, MD, DE, NJ, CT, RI, MA, VT, NH, ME, and WV; the Southcentral region includes AR, LA, OK, 

and TX; the Midwestern region includes MO, IL, IN, OH, MN, WI, MI, and IA; the Northern Great Plains include MT, WY, ND, SD, NE, KS, and CO; and the Southeastern 

region includes VA, NC, KY, TN, MS, AL, GA, SC, and FL. 

Step 3: Estimate CH4 Emissions from Cattle 

Emissions by state were estimated in three steps: a) determine gross energy (GE) intake using the IPCC (2006) 

equations, b) determine an emission factor using the GE values and other factors, and c) sum the daily emissions for each 

animal type. Finally, the state emissions were aggregated to obtain the national emissions estimate. The necessary data 

values for each state and animal type include: 

 Body Weight (kg) 

 Weight Gain (kg/day) 

 Net Energy for Activity (Ca, MJ/day)42

 Standard Reference Weight (kg)43

 Milk Production (kg/day) 

 Milk Fat (percent of fat in milk = 4) 

 Pregnancy (percent of population that is pregnant) 

 DE (percent of gross energy intake digestible) 

 Ym (the fraction of gross energy converted to CH4) 

 Population 

Step 3a: Determine Gross Energy, GE 

As shown in the following equation, gross energy (GE) is derived based on the net energy estimates and the feed 

characteristics. Only variables relevant to each animal category are used (e.g., estimates for feedlot animals do not require 

the NEl factor). All net energy equations are provided in IPCC (2006). 

 

42 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. 

43 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-191



























































    



100

DE%

REG

NE

REM

NE NE NE NE NE

GE

m a l work p g

Where, 

GE = Gross energy (MJ/day) 

NEm = Net energy required by the animal for maintenance (MJ/day) 

NEa = Net energy for animal activity (MJ/day) 

NEl = Net energy for lactation (MJ/day) 

NEwork = Net energy for work (MJ/day) 

NEp = Net energy required for pregnancy (MJ/day) 

REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed 

NEg = Net energy needed for growth (MJ/day) 

REG = Ratio of net energy available for growth in a diet to digestible energy consumed 

DE = Digestible energy expressed as a percent of gross energy (percent) 

Step 3b: Determine Emission Factor 

The emission factor (DayEmit) was determined using the gross energy value and the methane conversion factor 

(Ym) for each category. This relationship is shown in the following equation: 

55.65

GE × Ym DayEmit 

Where, 

DayEmit = Emission factor (kg CH4/head/day) 

GE = Gross energy intake (MJ/head/day) 

Ym = CH4 conversion rate, which is the fraction of gross energy in feed converted to CH4 (%) 

55.65 = A factor for the energy content of methane (MJ/kg CH4) 

The daily emission factors were estimated for each animal type and state, calculated national emission factors are 

shown by animal type in Table A-164. 

Table A-164: Calculated National Emission Factors for Cattle by Animal Type (kg CH4/head/year) 

Cattle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007

Dairy 

Cows 130 130 130 129 131 132 132 135 136 137

Replacements 7-11 months 46 46 45 45 45 45 45 45 45 45

Replacements 12-23 months 69 69 69 69 69 69 68 69 69 69

Beef 

Bulls 53 53 53 53 53 53 53 53 53 53

Cows 89 92 91 91 91 93 93 94 94 94

Replacements 7-11 months 54 57 57 57 57 58 59 59 60 60

Replacements 12-23 months 63 66 66 66 66 68 68 68 69 69

Steer Stockers 55 56 58 58 58 58 57 58 57 57

Heifer Stockers 51 56 60 60 60 60 59 59 59 59

Feedlot Cattle 39 34 33 33 33 33 32 33 32 32

Note: To convert to a daily emission factor, the yearly emission factor can be divided by 365.25 (the average number of days in a year). 

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: 
A-192 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Emissionsstate = DayEmitstate × Days/Month × SubPopstate 

Where, 

DayEmitState = The emission factor for the subcategory and state (kg CH4/head/day) 

Days/Month = The number of days in the month 

SubPopState = The number of animals in the subcategory and state during the month 

This process was repeated for each month, and the totals for each state subcategory were summed to achieve an 

emission estimate for a state for the entire year and state estimates were summed to obtain the national total. The 

estimates for each of the 10 subcategories of cattle are listed in Table A-165. The emissions for each subcategory were 

then summed to estimate total emissions from beef cattle and dairy cattle for the entire year. 

Table A-165: CH4 Emissions from Cattle (Gg) 

Cattle Type 1990 1995 2000 2001 2002 2003 2004 2005 2006 2007

Dairy 1,563 1,490 1,440 1,436 1,444 1,455 1,438 1,468 1,497 1,521

Cows 1,306 1,235 1,192 1,187 1,193 1,203 1,190 1,212 1,233 1,253 

Replacements 7-11 months 55 56 54 55 55 54 55 57 58 59 

Replacements 12-23 months 201 200 193 195 195 198 194 199 206 208 

Beef 4,504 5,082 4,707 4,671 4,674 4,676 4,634 4,687 4,762 4,772

Bulls 116 127 116 116 115 115 113 114 116 114 

Cows 2,887 3,223 3,059 3,041 3,022 3,056 3,068 3,078 3,106 3,106 

Replacements 7-11 months 69 85 74 74 75 76 78 81 83 82 

Replacements 12-23 months 188 241 204 207 207 214 212 219 230 229 

Steer Stockers 564 662 507 506 516 488 474 487 495 508 

Heifer Stockers 305 373 322 320 322 306 297 309 312 311 

Feedlot Cattle 375 372 425 408 417 421 392 398 421 422 

Total 6,067 6,573 6,147 6,108 6,118 6,131 6,072 6,155 6,259 6,293

Notes: Totals may not sum due to independent rounding. Because calves younger than 7 months consume mainly milk the IPCC recommends the use of methane 

conversion factor of zero, resulting in no methane emissions from this subcategory of cattle. 

Emission Estimates from Other Livestock 

All livestock population data, except for horses, were taken from the U.S. Department of Agriculture (USDA) 

National Agricultural Statistics Service (NASS) agricultural statistics database (USDA 2008). Table A- 169 of the 

Manure Management Annex shows the population data for all livestock species that were used for estimating all livestockrelated emissions. For each animal category, the USDA publishes monthly, annual, and multi-year livestock population 

and production estimates. All data were downloaded from the USDA-NASS agricultural database (USDA 2008). The 

Food and Agriculture Organization (FAO) publishes horse population data. These data were accessed from the 

FAOSTAT database (FAO 2008). Methane emissions from sheep, goats, swine, and horses were estimated by multiplying 

published national population estimates by the IPCC emission factor for each year. Table A-166 shows the emission 

factors used for these other livestock. 

Enteric fermentation emissions from all livestock types are shown in Table A-167 and Table A-168. 

Table A-166: Emission Factors for Other Livestock (kg CH4/head/year) 

Livestock Type Emission Factor 

Sheep 8 

Goats 5 

Horses 18 

Swine 1.5 

Source: IPCC (2006) 

Table A-167: CH4 Emissions from Enteric Fermentation (Tg CO2 Eq.) 

Livestock Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Beef Cattle 94.6 94.8 99.1 100.9 103.5 106.7 106.1 103.4 102.0 101.3 98.8 98.1 98.2 98.2 97.3 98.4 100.0 100.2

Dairy Cattle 32.8 32.9 32.7 31.3 31.2 31.3 29.7 29.7 29.6 29.8 30.2 30.2 30.3 30.6 30.2 30.8 31.4 31.9

Horses 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.0 2.0 2.0 2.0 2.1 2.3 2.6 3.0 3.5 3.5 3.5

Sheep 1.9 1.9 1.8 1.7 1.7 1.5 1.4 1.3 1.3 1.2 1.2 1.2 1.1 1.1 1.0 1.0 1.0 1.0

Swine 1.7 1.8 1.8 1.8 1.9 1.9 1.8 1.8 2.0 1.9 1.9 1.9 1.9 1.9 1.9 1.9 1.9 2.0

Goats 0.3 0.3 0.3 0.3 0.3 0.2 0.2 0.2 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Total 133.2 133.5 137.7 137.9 140.4 143.6 141.2 138.5 137.1 136.5 134.4 133.6 134.0 134.6 133.7 136.0 138.2 139.0

Note: Totals may not sum due to independent rounding. 
A-193

Table A-168: CH4 Emissions from Enteric Fermentation (Gg) 

Livestock

Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Beef 

Cattle 4,504 4,514 4,720 4,803 4,930 5,082 5,051 4,924 4,859 4,824 4,707 4,671 4,674 4,676 4,634 4,687 4,762 4,772

Dairy 

Cattle 1,563 1,565 1,558 1,489 1,485 1,490 1,416 1,414 1,408 1,421 1,440 1,436 1,444 1,455 1,438 1,468 1,497 1,521

Horses 91 92 92 92 92 92 93 93 94 93 94 99 108 126 144 166 166 166

Sheep 91 89 86 82 79 72 68 64 63 58 56 55 53 51 49 49 50 49

Swine 81 85 88 87 90 88 84 88 93 90 88 88 90 90 91 92 93 98

Goats 13 13 13 12 12 12 12 11 12 12 12 12 13 13 13 13 13 13

Total 6,342 6,357 6,557 6,566 6,688 6,837 6,723 6,595 6,529 6,499 6,398 6,363 6,382 6,410 6,369 6,474 6,580 6,618

Note: Totals may not sum due to independent rounding. 
A-194 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

3.10. Methodology for Estimating CH4 and N2O Emissions from Manure Management 

The following steps were used to estimate methane (CH4) and nitrous oxide (N2O) emissions from the 

management of livestock manure. Nitrous oxide emissions associated with pasture, range, or paddock systems and daily 

spread systems are included in the emission estimates for Agricultural Soil Management (see the Agricultural Soil 

Management Annex).

Step 1: Livestock Population Characterization Data 

Annual animal population data for 1990 through 2007 for all livestock types, except horses and goats were 

obtained from the USDA National Agricultural Statistics Service (NASS). The population data used in the emissions 

calculations for cattle, swine, and sheep were downloaded from the USDA NASS Population Estimates Database (USDA 

2008a). Poultry population data were obtained from USDA NASS reports (USDA 1995a, 1995b, 1998a, 1999, 2004a, 

2004b, 2008b, and 2008c). Horse population data were obtained from the Food and Agriculture Organization (FAO) 

FAOSTAT database (FAO 2008). Goat population data for 1992, 1997, and 2002 were obtained from the Census of 

Agriculture (USDA 2005). Additional data sources used and adjustments to these data sets are described below. 

Beef Cattle: Additional information regarding the percent of beef steers and heifers in feedlots was obtained 

from contacts with the national USDA office (Milton 2000). Data for beef feedlots were also obtained from EPA’s Office 

of Water (ERG 2000a, EPA 2002a, 2002b). 

For all beef 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 fattened cattle are slaughtered; therefore, to develop the best 

estimate for the annual animal population, the average inventory of cattle by state was calculated. In order to estimate 

average annual populations by state, a “scaling factor” was developed that adjusts the January state-level data to reflect 

July inventory changes. This factor, as shown in the equation below, equals the average of the U.S. January and July data 

divided by the January data. The scaling factor is derived for each cattle group and is then applied to the January statelevel data to arrive at the state-level annual population estimates. 

USCattle

USCattle USCattle

January 

2

January July 

CattleScalingFactor 















  

Swine: The USDA provides quarterly data for each swine subcategory: breeding, market under 60 pounds (less 

than 27 kg), market 60 to 119 pounds (27 to 54 kg), market 120 to 179 pounds (54 to 81 kg), and market 180 pounds and 

over (greater than 82 kg). The average of the quarterly data was used in the emissions calculations. For states where only 

December inventory is reported, the December data were used directly. 

Sheep: Population data for lamb and sheep on feed are not available after 1993 (USDA 1994). The number of 

lamb and sheep on feed for 1994 through 2006 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 only available for 1992, 1997, and 2002 (USDA 2005). The 

data for 1992 were used for 1990 through 1992 and the data for 2002 were used for 2002 through 2006. Data for 1993 

through 1996 were estimated based on the 1992 and 1997 Census data. Data for 1998 through 2001 were extrapolated 

using the 1997 and 2002 Census data. 

Poultry: The USDA provides population data for hens (one year old or older), pullets (hens younger than one 

year old), broilers, other chickens, and turkeys (USDA 1995a, 1995b, 1998a, 1999, 2004a, 2004b, 2008b, and 2008c). The 

annual population data for boilers and turkeys were adjusted for the turnover (i.e., slaughter) rate (Lange 2000). All 

poultry population data were adjusted to account for states that report non-disclosed populations to USDA NASS. The 

combined populations of the states reporting non-disclosed populations are reported as “other” states. State populations 

for the non-disclosed states were estimated by equally distributing the population attributed to “other” states to each of the 

non-disclosed states. 
A-195

Horses: The FAO publishes annual total U.S. horse population, which were accessed from the FAOSTAT 

database (FAO 2008). State horse population data were estimated using state population distributions from the 1992, 

1997, and 2002 Census of Agriculture and the FAO national population data. A summary of the livestock population 

characterization data used to calculate CH4 and N2O emissions is presented in Table A- 169.

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-170 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 (USDA 1996a) is one of the 

primary sources of waste characteristics. In some cases, data from the American Society of Agricultural Engineers, 

Standard D384.1 (ASAE 1999) were used to supplement the USDA data. The dairy cow populations are assumed to be 

comprised of both lactating and dry cows: 17 percent of a dairy herd is assumed to be dry and 83 percent is assumed to be 

lactating. Nex rates were collected from the sources indicated in Table A-170 and are based on measurement data from 

excreted manure. The VS and Nex data for breeding swine are from a combination of the types of animals that make up 

this animal group, namely gestating and farrowing swine and boars. It is assumed that a group of breeding swine is 

typically broken out as 80 percent gestating sows, 15 percent farrowing swine, and 5 percent boars (Safley 2000). Due to 

the change in USDA reporting of hens and pullets, new nitrogen and VS excretion rates were calculated for the combined 

population of hens and pullets. A weighted average rate was calculated based on hen and pullet population data from 1990 

to 2004, and the 2004 inventory VS and Nex values of 10.8 (hens) and 9.7 (pullets) kg VS/day per 1,000 kg mass, and 

0.83 (hens) and 0.62 (pullets) kg Total Kjeldahl Nitrogen/day per 1,000 kg mass. The new hen and pullet VS and Nex 

values were applied to hens and pullets for all years of the inventory. 

The method for calculating VS production from beef and dairy cows, heifers, and steers is based on the 

relationship between animal diet and energy utilization, which is modeled in the enteric fermentation portion of the 

inventory. Volatile solids content of manure is the fraction of the diet consumed by cattle that is not digested and thus 

excreted as fecal material; fecal material combined with urinary excretions constitutes manure. The enteric fermentation 

model requires the estimation of gross energy intake and its fractional digestibility to estimate enteric CH4 emissions (see 

the Enteric Fermentation Annex for details on the enteric energy model). These two inputs are used to calculate the 

indigestible energy per animal unit as gross energy minus digestible energy plus an additional two percent of gross energy 

for urinary energy excretion per animal unit. This value is then converted to VS production per animal unit using the 

typical conversion of dietary gross energy to dry organic matter of 20.1 MJ/kg (Garrett and Johnson 1983). The equation 

used for calculating VS is as follows: 

 

20.1MJ/kg

GE DE 0.02 GE VS production (kg)    

Where, 

 GE = Gross energy intake (MJ) 

 DE = Digestible energy (MJ) 

This equation is used to calculate VS rates for each state, cattle type, and year. (Pederson et al., 2007). Table A171 presents the state-specific VS production rates used for 2007. 
A-196 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 3: Waste Management System Usage Data 

Table A- 172 summarizes 2007 manure distribution data among waste management systems (WMS) at beef 

feedlots, dairies, dairy heifer facilities, and swine, layer, broiler, and turkey operations. Manure from the remaining 

animal types (beef cattle not on feed, sheep, horses, and goats) is managed on pasture, range, or paddocks, on drylot, or 

with solids storage systems. Additional information on the development of the manure distribution estimates for each 

animal type is presented below. Definitions of each WMS type are presented in Table A- 173. 

Beef Cattle and Dairy Heifers: The beef feedlot and dairy heifer WMS data were developed using information 

from EPA's Office of Water's engineering cost analyses conducted to support the development of effluent limitations 

guidelines for Concentrated Animal Feeding Operations (EPA 2002b). Based on EPA site visits and state contacts 

supporting this work and additional contacts with the national USDA office to estimate the percent of beef steers and 

heifers in feedlots (Milton 2000), feedlot manure is almost exclusively managed in drylots. Therefore, for these animal 

groups, the percent of manure deposited in drylots is assumed to be 100 percent. In addition, there is a small amount of 

manure contained in runoff, which may or may not be collected in runoff ponds. The runoff from feedlots was calculated 

by region in Calculations: Percent Distribution of Manure for Waste Management Systems (ERG 2000b) and was used to 

estimate the percentage of manure managed in runoff ponds in addition to drylots; this percentage ranges from 0.4 to 1.3 

percent. The percentage of manure generating emissions from beef feedlots is therefore greater than 100 percent. The 

remaining population categories of beef cattle outside of feedlots are managed through pasture, range, or paddock systems, 

which are utilized for the majority of the population of beef cattle in the country. 

Dairy Cows: The WMS data for dairy cows were developed using data from the Census of Agriculture, EPA’s 

Office of Water, USDA, and expert sources. Farm-size distribution data are reported in the 1992, 1997, and 2002 Census 

of Agriculture (USDA 2005). It was assumed that the data provided for 1992 were the same as that for 1990 and 1991, 

and data provided for 2002 were the same as that for 2003 through 2007. Data for 1993 through 1996 and 1998 through 

2001 were extrapolated using the 1992, 1997, and 2002 data. The percent of waste by system was estimated using the 

USDA data broken out by geographic region and farm size. 

Based on EPA site visits and state contacts, manure from dairy cows at medium (200 through 700 head) and 

large (greater than 700 head) operations are managed using either flush systems or scrape/slurry systems. In addition, they 

may have a solids separator in place prior to their storage component. Estimates of the percent of farms that use each type 

of system (by geographic region) were developed by EPA's Office of Water, and were used to estimate the percent of 

waste managed in lagoons (flush systems), liquid/slurry systems (scrape systems), and solid storage (separated solids) 

(EPA 2002b). Manure management system data for small (fewer than 200 head) dairies were obtained from USDA 

(2000a). These operations are more likely to use liquid/slurry and solid storage management systems than anaerobic 

lagoon systems. The reported manure management systems were deep pit, liquid/slurry (includes slurry tank, slurry earthbasin, and aerated lagoon), anaerobic lagoon, and solid storage (includes manure pack, outside storage, and inside storage). 

Data regarding the use of daily spread and pasture, range, or paddock systems for dairy cattle were obtained from 

personal communications with personnel from several organizations. These organizations include state NRCS offices, 

state extension services, state universities, USDA NASS, and other experts (Deal 2000, Johnson 2000, Miller 2000, 

Stettler 2000, Sweeten 2000, and Wright 2000). Contacts at Cornell University provided survey data on dairy manure 

management practices in New York (Poe et al. 1999). Census of Agriculture population data for 1992, 1997, and 2002 

(USDA 2005) 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 2007, which were obtained from the USDA 

NASS (USDA 2008a). 

Of the dairies using systems other than daily spread and pasture, range, or paddock systems, some dairies 

reported using more than one type of manure management system. Due to limitations in how USDA reports the manure 

management data, the total percent of systems for a region and farm size is greater than 100 percent. However, manure is 

typically partitioned to use only one manure management system, rather than transferred between several different 

systems. Emissions estimates are only calculated for the final manure management system used for each portion of 

manure. To avoid double counting emissions, the reported percentages of systems in use were adjusted to equal a total of 

100 percent using the same distribution of systems. For example, if USDA reported that 65 percent of dairies use deep 

pits to manage manure and 55 percent of dairies use anaerobic lagoons to manage manure, it was assumed that 54 percent 

(i.e., 65 percent divided by 120 percent) of the manure is managed with deep pits and 46 percent (i.e., 55 percent divided 

by 120 percent) of the manure is managed with anaerobic lagoons (ERG 2000a). 

Swine: The distribution of manure managed in each WMS was estimated using data from a USDA report and 

EPA’s Office of Water site visits (USDA 1998, ERG 2000a). For operations with less than 200 head, manure 

management system data were obtained from USDA (USDA 1998). It was assumed that those operations use pasture, 
A-197

range, or paddock systems. For swine operations with greater than 200 head, the percent of waste managed in each system 

was estimated using the EPA and USDA data broken out by geographic region and farm size. Farm-size distribution data 

reported in the 1992, 1997, and 2002 Census of Agriculture (USDA 2005) were used to determine the percentage of all 

swine utilizing the various manure management systems. It was assumed that the swine farm size data provided for 1992 

were the same as that for 1990 and 1991, and data provided for 2002 were the same as that for 2003 through 2007. Data 

for 1993 through 1996 and 1998 through 2001 were extrapolated using the 1992, 1997, and 2002 data. The reported 

manure management systems were deep pit, liquid/slurry (includes above- and below-ground slurry), anaerobic lagoon, 

and solid storage (includes solids separated from liquids). 

Some swine operations reported using more than one management system; therefore, the total percent of systems 

reported by USDA for a region and farm size was greater than 100 percent. Typically, this means that a portion of the 

manure at a swine operation is handled in one system (e.g., liquid system), and a separate portion of the manure is handled 

in another system (e.g., dry system). However, it is unlikely that the same manure is moved from one system to another, 

which could result in increased emissions, so reported systems data were normalized to 100 percent for incorporation into 

the WMS distribution, using the same method as described above for dairy operations. 

Sheep: Waste management system data for sheep were obtained from USDA NASS sheep report for years 1990 

through 1993 (USDA 1994). Data for 2001 are obtained from USDA APHIS sheep report (USDA 2003). The data for 

years 1994-2000 are calculated assuming a linear progression from 1993 to 2001. Due to lack of additional data, data for 

years 2002 and beyond are assumed to be the same as 2001. It was assumed that all sheep manure not deposited in feedlots 

was deposited on pasture, range, or paddock lands (Anderson 2000). 

Goats and Horses: Waste management system data for 1990 to 2007 were obtained from Appendix H of Global 

Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that all manure not deposited in 

pasture, range, or paddock lands was managed in dry systems. 

Poultry—Hens (one year old or older), Pullets (hens less than one year old), and Other Chickens: Waste 

management system data for 1992 were obtained from Global Methane Emissions from Livestock and Poultry Manure

(EPA 1992). These data were also used to represent 1990 and 1991. The percentage of layer operations using a shallow 

pit flush house with anaerobic lagoon or high-rise house without bedding was obtained for 1999 from a United Egg 

Producers voluntary survey (UEP 1999). These data were augmented for key poultry states (AL, AR, CA, FL, GA, IA, 

IN, MN, MO, NC, NE, OH, PA, TX, and WA) with USDA data (USDA 2000b). It was assumed that the change in 

system usage between 1990 and 1999 is proportionally distributed among those years of the inventory. It was assumed 

that system usage in 2000 through 2007 was equal to that estimated for 1999. Data collected for EPA's Office of Water, 

including information collected during site visits (EPA 2002b), were used to estimate the distribution of waste by 

management system and animal type. 

Poultry—Broilers and Turkeys: The percentage of turkeys and broilers on pasture was obtained from Global 

Methane Emissions from Livestock and Poultry Manure (EPA 1992). It was assumed that one percent of poultry waste is 

deposited in pastures, ranges, and paddocks (EPA 1992). The remainder of waste is assumed to be deposited in operations 

with bedding management.

Step 4: Emission Factor Calculations 

Methane conversion factors (MCFs) and N2O emission factors (EFs) used in the emission calculations were 

determined using the methodologies presented below. 

Methane Conversion Factors (MCFs) 

Climate-based IPCC default MCFs (IPCC 2006) were used for all dry systems; these factors are presented in 

Table A- 174. A U.S.-specific methodology was used to develop MCFs for all lagoon and liquid systems. 

For animal waste managed in dry systems, the appropriate IPCC default MCF was applied based on annual 

average temperature data. Each state and year in the inventory was assigned a climate classification of cool, temperate or 

warm. 

For anaerobic lagoons and other liquid systems a climate-based approach based on the van’t Hoff-Arrhenius 

equation was developed to estimate MCFs that reflects the seasonal changes in temperatures, and also accounts for longterm retention time. This approach is consistent with the recently revised guidelines from IPCC (IPCC 2006). The van’t 

Hoff-Arrhenius equation, with a base temperature of 30°C, is shown in the following equation (Safley and Westerman 

1990): 
A-198 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Where, 

T1 = 303.15K 

T2 = Ambient temperature (K) for climate zone (in this case, a weighted value for each state) 

E = Activation energy constant (15,175 cal/mol) 

R = Ideal gas constant (1.987 cal/K mol) 

The factor f represents the proportion of VS that are biologically available for conversion to CH4 based on the 

temperature of the system. For those animal populations using liquid manure management systems or manure runoff 

ponds (i.e., dairy cow, dairy heifer, layers, beef in feedlots, and swine) monthly average state temperatures were based on 

the counties where the specific animal population resides (i.e., the temperatures were weighted based on the percent of 

animals located in each county). The average county and state temperature data were obtained from the National Climate 

Data Center (NOAA 2007). County population data were calculated from state-level population data from NASS and 

county-state distribution data from the 1992, 1997, and 2002 Census data (USDA 2005). County population distribution 

data for 1990 and 1991 were assumed to be the same as 1992; county population distribution data for 1993 through 1996 

were extrapolated based on 1992 and 1997 data; county population data for 1998 through 2001 were extrapolated based on 

1997 and 2002 data; and county population data for 2003 to 2007 were assumed to be the same as 2002. 

Annual MCFs for liquid systems are calculated as follows for each animal type, state, and year of the inventory: 

 The weighted-average temperature for a state is calculated using the population estimates and average monthly 

temperature in each county. 

 Monthly temperatures are used to calculate a monthly van't Hoff-Arrhenius “f” factor, using the equation 

presented above. A minimum temperature of 5°C is used for uncovered anaerobic lagoons and 7.5°C is used for 

liquid/slurry and deep pit systems. 

 Monthly production of VS added to the system is estimated based on the number of animals present. 

 For lagoon systems, the calculation of methane includes a management and design practices (MDP) factor. This 

factor, equal to 0.8, was developed based on model comparisons to empirical CH4 measurement data from 

anaerobic lagoon systems in the United States (ERG 2001). The MDP factor represents a variety of factors that 

may affect methane production in lagoon systems. 

 The amount of VS available for conversion to CH4 is assumed to be equal to the amount of VS produced during 

the month (from Step 3). For anaerobic lagoons, the amount of VS available also includes VS that may remain 

in the system from previous months. 

 The amount of VS consumed during the month is equal to the amount available for conversion multiplied by the 

“f” factor. 

 For anaerobic lagoons, the amount of VS carried over from one month to the next is equal to the amount 

available for conversion minus the amount consumed. Lagoons are also modeled to have a solids clean-out once 

per year, occurring after the month of September. 

 The estimated amount of CH4 generated during the month is equal to the monthly VS consumed multiplied by 

the maximum CH4 potential of the waste (Bo). 

The annual MCF is then calculated as: 

annual 0

annual 4

annual VSproduced B

CH generated MCF  

Where, 

MCF annual = Methane conversion factor

VS produced annual = Volatile solids excreted annually 

Bo = Maximum CH4 producing potential of the waste 





 



  

1 2

2 1 ( ) exp RTT

E T T f
A-199

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- 175 by state, WMS, and animal group for 2007. 

Nitrous Oxide Emission Factors 

Direct N2O emission factors for manure management systems (kg N2O-N/kg excreted N) were set equal to the 

most recent default IPCC factors (IPCC 2006), presented in Table A- 176. 

Indirect N2O emission factors account for two fractions of nitrogen losses: volatilization of ammonia (NH3) and 

NOX (Fracgas) and runoff/leaching (Fracrunoff/leach). IPCC default indirect N2O emission factors were used to estimate 

indirect N2O emissions. These factors are 0.010 kg N2O-N/kg N for volatilization and 0.0075 kg N2O/kg N for 

runoff/leaching. 

EPA developed country-specific estimates of nitrogen losses for Fracgas and Fracrunoff/leach for the U.S. The vast 

majority of volatilization losses are NH3. Although there are also some small losses of NOX, no quantified estimates were 

available for use and those losses are believed to be small (about 1 percent) in comparison to the NH3 losses. Therefore, 

Fracgas values were based on WMS-specific volatilization values estimated from U.S. EPA’s National Emission Inventory 

- Ammonia Emissions from Animal Agriculture Operations (EPA 2005). To estimate Fracrunoff/leach, EPA used data from 

EPA’s Office of Water that estimate the amount of runoff from beef, dairy, and heifer operations in five geographic 

regions of the country (EPA 2002b). These estimates were used to develop U.S. runoff factors by animal type, WMS, and 

region. Nitrogen losses from leaching are believed to be small in comparison to the runoff losses; therefore, Fracrunoff/leach 

was set equal to the runoff loss factor. Nitrogen losses from volatilization and runoff/leaching are presented in Table A177. 

Step 5: CH4 and N2O Emission Calculations 

To calculate methane emissions, EPA first estimated the amount of volatile solids excreted in manure that is 

managed in each WMS: 

VS WMS 365.25

1000

TAM VS excreted State, Animal, WMS  Population State, Animal    

Where, 

VS excreted State, Animal, WMS = The 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 

Next, the estimated amount of VS was used to calculate methane emissions using the following equation: 

      

State, Animal, WMS

CH 4 VS excreted State, Animal, WMS B0 MCF 0.662

Where, 

CH4 = CH4 emissions (kg CH4/yr) 

VS excreted WMS, State = The 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-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

A calculation was developed to estimate the amount of CH4 emitted from anaerobic digestion (AD) systems. 

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 CH 4 Production ADAD System  Population ADAD System    0   

Where, 

CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr) 

Population AD state = Number of animals on a particular AD system 

VS = Volatile solids production rate (kg VS/1000 kg animal mass-day) 

TAM = Typical Animal Mass (kg/head) 

Bo = Maximum CH4 producing capacity (CH4 m3

/kg VS) 

0.662 = Density of methane at 25o

 C (kg CH4/m3 CH4) 

365.25 = Days/year 

0.90 = CH4 production factor for AD systems 

 

 Next, the collection efficiency (CE) and destruction efficiency (DE) of the AD system were considered. 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 anaerobic digestion was calculated with the following equation: 

  

     















 

   

State, Animal, AD Systems 4 AD system AD system

4 AD system AD system

4 CH Production AD 1-CE

CH Production AD CE 1 DE

CH Emissions AD

Where: 

CH4 Emissions AD = CH4 emissions from AD systems, (kg/yr) 

CH4 Production ADAD system= CH4 production from a particular AD system, (kg/yr) 

CEAD system = Collection efficiency of the AD system, varies by AD system type 

DE = Destruction efficiency of the AD system, 0.98 for all systems 

In addition to 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 nitrogen excreted in manure and managed in each WMS using the 

following equation: 

Nex 365.25

1000

TAM N excreted State, Animal, WMS  Population State, Animal  WMS  

Where, 

N excreted State, Animal, WMS = The 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 
A-201

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 = The 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 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 = The 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- 178 and Table 

A- 179, respectively. Emission estimates for 2007 are presented by animal type and state in Table A- 180 and Table A181, respectively. 
A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 169: Livestock Population (1,000 Head) 

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 200Dairy Cattle 14,143 13,980 13,830 13,767 13,566 13,502 13,305 13,138 12,992 13,023 13,066 12,964 13,005 12,97 Dairy Cows 10,007 9,883 9,714 9,679 9,504 9,491 9,410 9,309 9,200 9,139 9,216 9,136 9,128 9,12 Dairy Heifer 4,135 4,097 4,116 4,088 4,062 4,011 3,895 3,829 3,793 3,884 3,850 3,828 3,877 3,85Swine 53,941 56,478 58,532 58,016 59,951 58,899 56,220 58,728 61,989 60,238 58,864 58,913 60,028 59,81 Market <60 lb. 18,359 19,212 19,851 19,434 20,157 19,656 18,851 19,886 20,692 19,928 19,574 19,659 19,863 19,92 Market 60-119 lb. 11,734 12,374 12,839 12,656 13,017 12,836 12,157 12,754 13,551 13,255 12,926 12,900 13,284 13,13 Market 120-179 lb. 9,440 9,840 10,253 10,334 10,671 10,545 10,110 10,480 11,234 11,041 10,748 10,708 11,013 11,04 Market >180 lb. 7,510 7,822 8,333 8,435 8,824 8,937 8,463 8,768 9,671 9,641 9,385 9,465 9,738 9,70 Breeding 6,899 7,231 7,255 7,157 7,282 6,926 6,639 6,840 6,841 6,373 6,231 6,181 6,129 6,00Beef Cattle 86,087 87,267 88,548 90,321 92,571 94,391 94,269 92,290 90,730 90,034 89,220 88,621 87,927 87,04 Feedlot Steers 7,338 7,920 7,581 7,984 7,797 7,763 7,380 7,644 7,845 7,805 8,338 8,622 8,423 7,94 Feedlot Heifers 3,621 4,035 3,626 3,971 3,965 4,047 3,999 4,396 4,459 4,587 4,899 5,066 4,852 4,57 NOF Bulls 2,180 2,198 2,220 2,239 2,306 2,392 2,392 2,325 2,235 2,241 2,197 2,187 2,172 2,17 NOF Calves 23,909 23,853 24,118 24,209 24,586 25,170 25,042 24,363 24,001 23,895 23,508 22,958 22,577 22,27 NOF Heifers 8,872 8,938 9,520 9,850 10,469 10,680 10,869 10,481 9,998 9,716 9,326 9,194 9,212 9,33 NOF Steers 7,490 7,364 8,031 7,935 8,346 8,693 9,077 8,452 8,050 7,840 7,190 6,946 7,249 7,45 NOF Cows 32,677 32,960 33,453 34,132 35,101 35,645 35,509 34,629 34,143 33,950 33,763 33,649 33,442 33,29Sheep 11,358 11,174 10,797 10,201 9,836 8,989 8,465 8,024 7,825 7,247 7,036 6,908 6,623 6,32 Sheep On Feed 1,180 1,115 1,187 1,195 1,537 1,771 2,042 2,294 2,536 2,689 2,963 3,256 3,143 3,04 Sheep NOF 10,178 10,058 9,610 9,006 8,298 7,218 6,423 5,730 5,289 4,558 4,073 3,652 3,480 3,27Goats 2,516 2,516 2,516 2,463 2,410 2,357 2,304 2,252 2,307 2,363 2,419 2,475 2,530 2,53Poultry 1,537,074 1,594,944 1,649,998 1,707,422 1,769,135 1,826,977 1,882,078 1,926,790 1,965,312 2,008,632 2,033,123 2,060,398 2,097,691 2,085,26 Hens >1 yr. 273,467 280,121 284,500 290,626 298,527 299,071 303,922 312,137 321,828 330,180 333,593 340,317 340,209 340,97 Pullets 73,167 76,616 79,870 81,774 79,853 81,369 81,572 90,344 95,845 97,562 95,159 95,656 95,289 100,34 Chickens 6,545 6,857 7,113 7,240 7,369 7,637 7,243 7,549 7,682 9,661 8,088 8,126 8,353 8,43 Broilers 1,066,209 1,115,845 1,164,089 1,217,147 1,275,916 1,331,940 1,381,229 1,411,673 1,442,593 1,481,165 1,506,127 1,525,413 1,562,015 1,544,15 Turkeys 117,685 115,504 114,426 110,635 107,469 106,960 108,112 105,088 97,365 90,064 90,155 90,887 91,826 91,34

Horses 5,069 5,100 5,121 5,130 5,110 5,130 5,150 5,170 5,237 5,170 5,240 5,500 6,000 7,00

Note: Totals may not sum due to independent rounding. 1Pullets includes laying pullets, pullets younger than 3 months, and pullets older than 3 months. 2NOF = Not on Feed 
Table A-170: Waste Characteristics Data Animal Group 

Average

TAM (kg) Source

Total Kjeldahl

Nitrogen, Nex

(kg/day per

1,000 kg

mass) Source

Maximum Methane

Generation

Potential, Bo (m3

CH4/kg VS added) Source

Volatile Solids,

VS (kg/day per

1,000 kg mass) 

Dairy Cows 604 Safley 2000 0.44 USDA 1996a 0.24 Morris 1976 Table A- 171 Dairy Heifers 476 Safley 2000 0.31 USDA 1996a 0.17 Bryant et. al. 1976 Table A- 171 Feedlot Steers 420 USDA 1996a 0.30 USDA 1996a 0.33 Hashimoto 1981 Table A- 171 Feedlot Heifers 420 USDA 1996a 0.30 USDA 1996a 0.33 Hashimoto 1981 Table A- 171 NOF Bulls 750 Shuyler 2000 0.31 USDA 1996a 0.17 Hashimoto 1981 6.04 NOF Calves 118 USDA 1996a 0.30 USDA 1996a 0.17 Hashimoto 1981 6.41 NOF Heifers 420 USDA 1996a 0.31 USDA 1996a 0.17 Hashimoto 1981 Table A- 171 NOF Steers 318 Safley 2000 0.31 USDA 1996a 0.17 Hashimoto 1981 Table A- 171 NOF Cows 533 Safley 2000 0.33 USDA 1996a 0.17 Hashimoto 1981 Table A- 171 Market Swine <60 lbs. 16 Safley 2000 0.60 USDA 1996a 0.48 Hashimoto 1984 8.80 Market Swine 60-119 lbs. 41 Safley 2000 0.42 USDA 1996a 0.48 Hashimoto 1984 5.40 Market Swine 120-179 lbs. 68 Safley 2000 0.42 USDA 1996a 0.48 Hashimoto 1984 5.40 Market Swine >180 lbs. 91 Safley 2000 0.42 USDA 1996a 0.48 Hashimoto 1984 5.40 Breeding Swine 198 Safley 2000 0.24 USDA 1996a 0.48 Hashimoto 1984 2.60 Feedlot Sheep 25 EPA 1992 0.42 ASAE 1999 0.36 EPA 1992 9.20 NOF Sheep 80 EPA 1992 0.42 ASAE 1999 0.19 EPA 1992 9.20 Goats 64 ASAE 1999 0.45 ASAE 1999 0.17 EPA 1992 9.50 Horses 450 ASAE 1999 0.30 ASAE 1999 0.33 EPA 1992 10.0 Hens >/= 1 yr 1.8 ASAE 1999 0.83 USDA 1996a 0.39 Hill 1982 10.8 Pullets 1.8 ASAE 1999 0.62 USDA 1996a 0.39 Hill 1982 9.7 Other Chickens 1.8 ASAE 1999 0.83 USDA 1996a 0.39 Hill 1982 10.8 Broilers 0.9 ASAE 1999 1.10 USDA 1996a 0.36 Hill 1984 15.0 Turkeys 6.8 ASAE 1999 0.74 USDA 1996a 0.36 Hill 1984 9.7 
A-204 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 171: Estimated Volatile Solids Production Rate by State for 2007 (kg/day/1000 kg animal mass) 

State Dairy Cow Dairy Heifers Beef NOF Cow

Beef NOF 

Heifers

Beef NOF 

Steer Beef OF Heifers Beef OF Steer

Alabama 8.02 7.42 7.02 7.82 8.08 3.55 3.21

Alaska 8.18 7.42 9.02 10.08 10.62 3.53 3.20

Arizona 10.55 7.42 9.02 10.41 10.62 3.48 3.15

Arkansas 7.11 8.22 7.00 7.87 8.05 3.88 3.48

California 8.98 7.42 6.85 7.92 7.86 3.48 3.15

Colorado 9.11 7.42 6.46 7.65 7.39 3.47 3.15

Connecticut 8.22 6.70 6.90 7.66 7.93 3.38 3.07

Delaware 7.60 6.70 6.90 7.89 7.93 3.35 3.05

Florida 8.40 7.42 7.02 7.77 8.08 3.36 3.06

Georgia 8.80 7.42 7.02 7.89 8.08 3.40 3.09

Hawaii 7.52 7.42 9.02 10.30 10.62 3.44 3.12

Idaho 10.34 7.42 9.02 10.80 10.62 3.51 3.18

Illinois 8.08 7.42 6.91 8.11 7.94 3.45 3.13

Indiana 8.49 7.42 6.91 8.01 7.94 3.51 3.18

Iowa 8.43 7.42 6.91 8.20 7.94 3.51 3.18

Kansas 8.35 7.42 6.46 7.68 7.39 3.46 3.14

Kentucky 7.70 7.42 7.02 7.97 8.08 3.33 3.03

Louisiana 6.88 8.22 7.00 7.75 8.05 3.47 3.14

Maine 7.88 6.70 6.90 7.66 7.93 3.69 3.32

Maryland 7.94 6.70 6.90 7.85 7.93 3.60 3.25

Massachusetts 7.69 6.70 6.90 7.78 7.93 3.38 3.07

Michigan 9.05 7.42 6.91 7.95 7.94 3.53 3.20

Minnesota 8.13 7.42 6.91 8.05 7.94 3.49 3.16

Mississippi 8.09 7.42 7.02 7.85 8.08 3.42 3.10

Missouri 7.21 7.42 6.91 7.88 7.94 3.44 3.12

Montana 8.05 7.42 6.46 7.21 7.39 3.48 3.15

Nebraska 7.98 7.42 6.46 7.64 7.39 3.46 3.14

Nevada 9.75 7.42 9.02 10.50 10.62 3.48 3.15

New Hampshire 8.58 6.70 6.90 7.78 7.93 3.48 3.16

New Jersey 7.64 6.70 6.90 7.92 7.93 3.64 3.28

New Mexico 10.03 7.42 9.02 10.64 10.62 3.51 3.18

New York 8.24 6.70 6.90 7.99 7.93 3.42 3.10

North Carolina 9.07 7.42 7.02 7.85 8.08 3.48 3.15

North Dakota 7.29 7.42 6.46 7.40 7.39 3.48 3.15

Ohio 7.94 7.42 6.91 7.94 7.94 3.46 3.14

Oklahoma 8.04 8.22 7.00 8.09 8.05 3.51 3.18

Oregon 9.49 7.42 9.02 10.61 10.62 3.31 3.02

Pennsylvania 8.27 6.70 6.90 8.03 7.93 3.48 3.15

Rhode Island 7.56 6.70 6.90 7.66 7.93 3.61 3.26

South Carolina 8.73 7.42 7.02 7.85 8.08 3.40 3.09

South Dakota 8.24 7.42 6.46 7.50 7.39 3.45 3.13

Tennessee 8.21 7.42 7.02 7.92 8.08 3.56 3.22

Texas 9.19 8.22 7.00 8.20 8.05 3.49 3.16

Utah 9.75 7.42 9.02 10.58 10.62 3.48 3.15

Vermont 7.95 6.70 6.90 7.92 7.93 3.48 3.16

Virginia 8.64 7.42 7.02 7.95 8.08 3.44 3.12

Washington 10.54 7.42 9.02 10.87 10.62 3.38 3.07

West Virginia 7.29 6.70 6.90 7.82 7.93 3.42 3.10

Wisconsin 8.25 7.42 6.91 7.88 7.94 3.45 3.13

Wyoming 8.13 7.42 6.46 7.34 7.39 3.41 3.10

Source: Lieberman and Pape 2005.
Table A- 172: 2007 Manure Distribution Among Waste Management Systems by Operation (Percent) 

 Beef Feedlots Dairies1

Dairy Heifer 

Facilities Swine Operations1 

State Dry Lot2

Liquid/ 

Slurry2 Pasture

Daily

Spread

Solid

Storage

Liquid/

Slurry

Anaerobic

Lagoon Deep Pit Dry Lot2

Liquid/

Slurry2 Pasture

Solid

Storage

Liquid/

Slurry

Anaerobic

Lagoon Deep Pit

Alabama 100 1.3 45 17 11 10 17 0 38 0.4 5 4 8 51 32 Alaska 100 1.3 4 6 28 24 30 8 90 0.9 53 2 13 10 22 Arizona 100 0.4 0 10 9 20 61 0 90 0.3 14 3 5 50 27 Arkansas 100 1.3 57 15 11 7 10 1 28 0.3 4 4 12 46 35 California 100 1.3 1 11 9 21 58 0 88 0.9 13 3 8 47 29 Colorado 100 0.4 1 1 12 24 62 1 98 0.3 2 5 26 17 50 Connecticut 100 1.0 6 43 17 20 12 2 51 0.4 57 2 12 9 20 Delaware 100 1.0 6 44 19 19 10 2 50 0.4 12 4 24 18 42 Florida 100 1.3 17 22 8 15 39 0 61 0.6 71 1 8 6 14 Georgia 100 1.3 40 18 10 11 21 0 42 0.4 8 4 10 47 32 Hawaii 100 1.3 1 0 11 21 67 0 99 1.0 23 3 18 21 34 Idaho 100 0.4 0 1 12 23 63 1 99 0.3 46 3 15 10 26 Illinois 100 0.6 5 8 43 26 13 5 87 0.4 2 5 28 15 50 Indiana 100 0.6 8 13 35 24 16 3 79 0.4 3 5 28 15 50 Iowa 100 0.6 6 10 41 25 14 4 83 0.4 1 4 12 48 35 Kansas 100 0.6 3 5 28 33 29 3 92 0.5 2 5 28 13 51 Kentucky 100 1.0 61 14 14 6 2 2 24 0.2 5 4 12 45 34 Louisiana 100 1.3 60 14 10 6 9 1 26 0.3 54 2 13 10 22 Maine 100 1.0 7 45 20 17 9 2 48 0.4 73 1 7 6 13 Maryland 100 1.0 7 44 23 15 8 3 49 0.4 21 4 21 16 38 Massachusetts 100 1.0 7 45 24 15 6 3 47 0.4 31 3 19 14 32 Michigan 100 0.6 3 6 32 33 22 4 91 0.5 5 5 25 17 48 Minnesota 100 0.6 6 10 44 24 12 5 84 0.4 2 5 26 18 49 Mississippi 100 1.3 57 15 10 7 10 0 28 0.3 2 4 6 57 31 Missouri 100 0.6 8 14 48 18 6 5 77 0.4 3 5 28 14 51 Montana 100 0.4 3 4 25 26 36 6 93 0.3 6 5 25 17 47 Nebraska 100 0.6 4 6 35 30 21 4 90 0.4 3 5 28 15 50 Nevada 100 0.4 0 0 11 24 64 0 99 0.3 35 2 4 39 20 New Hampshire 100 1.0 7 44 21 16 9 3 49 0.4 57 2 12 9 20 New Jersey 100 1.0 8 45 24 14 6 3 47 0.4 31 3 19 14 33 New Mexico 100 0.4 0 10 9 19 61 0 90 0.3 92 0 2 2 4 New York 100 1.0 7 45 20 16 10 2 48 0.4 20 4 21 15 40 North Carolina 100 1.0 54 15 12 11 6 1 31 0.2 0 4 6 58 31 North Dakota 100 0.6 6 11 45 22 12 4 83 0.4 9 5 24 17 45 Ohio 100 0.6 8 14 41 23 11 4 78 0.4 7 4 27 15 47 Oklahoma 100 0.4 0 6 25 23 40 6 94 0.3 1 4 6 58 31 Oregon 100 1.3 20 0 13 21 44 2 80 0.8 58 2 12 9 20 Pennsylvania 100 1.0 9 47 25 12 5 2 44 0.4 5 5 25 18 47 Rhode Island 100 1.0 9 47 26 12 4 3 44 0.4 56 2 12 9 21 South Carolina 100 1.3 54 15 8 9 13 0 31 0.3 6 4 9 50 32 South Dakota 100 0.6 5 8 38 28 18 4 87 0.4 3 5 26 18 48 
A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 Beef Feedlots Dairies1

Dairy Heifer 

Facilities Swine Operations1 

State Dry Lot2

Liquid/ 

Slurry2 Pasture

Daily

Spread

Solid

Storage

Liquid/

Slurry

Anaerobic

Lagoon Deep Pit Dry Lot2

Liquid/

Slurry2 Pasture

Solid

Storage

Liquid/

Slurry

Anaerobic

Lagoon Deep Pit

Tennessee 100 1.0 59 15 12 9 4 2 26 0.2 10 4 12 41 33 Texas 100 0.4 0 8 13 24 53 2 92 0.3 7 3 6 54 29 Utah 100 0.4 1 1 17 26 51 3 98 0.3 1 6 26 17 51 Vermont 100 1.0 7 44 19 17 10 2 49 0.4 86 1 4 3 7 Virginia 100 1.0 57 15 12 9 4 2 28 0.2 3 4 7 55 31 Washington 100 1.3 17 0 11 22 49 1 83 0.8 37 3 17 12 31 West Virginia 100 1.0 7 45 23 16 7 3 48 0.4 58 2 11 8 21 Wisconsin 100 0.6 7 12 42 24 12 4 82 0.4 13 4 24 17 42 Wyoming 100 0.4 7 12 22 23 30 6 81 0.2 3 5 26 17 49 

1 In the methane inventory for manure management, the percent of dairy cows and swine with anaerobic digestion systems is estimated using data from EPA’s AgSTAR Program. 2 Because manure from beef feedlots and dairy heifers may be managed for long periods of time in multiple systems (i.e., both drylot and runoff collection pond), the percent of manure that generates 
A-207

Table A- 173: Manure Management System Descriptions 

Manure Management System Description1

Pasture The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. N2O emissions 

from deposited manure are covered under the N2O from Agricultural Soils category. 

Daily Spread Manure is routinely removed from a confinement facility and is applied to cropland or pasture within 24 hours of 

excretion. N2O emissions during storage and treatment are assumed to be zero. N2O emissions from land 

application are covered under the Agricultural Soils category. 

Solid Storage The storage of manure, typically for a period of several months, in unconfined piles or stacks. Manure is able to 

be stacked due to the presence of a sufficient amount of bedding material or loss of moisture by evaporation. 

Dry Lot A paved or unpaved open confinement area without any significant vegetative cover where accumulating 

manure may be removed periodically. Dry lots are most typically found in dry climates but also are used in 

humid climates. 

Liquid/ Slurry Manure is stored as excreted or with some minimal addition of water to facilitate handling and is stored in either 

tanks or earthen ponds, usually for periods less than one year. 

Anaerobic Lagoon Uncovered anaerobic lagoons are designed and operated to combine waste stabilization and storage. Lagoon 

supernatant is usually used to remove manure from the associated confinement facilities to the lagoon. 

Anaerobic lagoons are designed with varying lengths of storage (up to a year or greater), depending on the 

climate region, the volatile solids loading rate, and other operational factors. Anaerobic lagoons accumulate 

sludge over time, diminishing treatment capacity. Lagoons must be cleaned out once every 5 to 15 years, and 

the sludge is typically applied to agricultural lands. The water from the lagoon may be recycled as flush water or 

used to irrigate and fertilize fields. Lagoons are sometimes used in combination with a solids separator, typically 

for dairy waste. Solids separators help control the buildup of nondegradable material such as straw or other 

bedding materials. 

Anaerobic Digester Animal excreta with or without straw are collected and anaerobically digested in a large containment vessel or 

covered lagoon. Digesters are designed and operated for waste stabilization by the microbial reduction of 

complex organic compounds to CO2 and CH4, which is captured and flared or used as a fuel. 

Deep Pit Collection and storage of manure usually with little or no added water typically below a slatted floor in an 

enclosed animal confinement facility. Typical storage periods range from 5 to 12 months, after which manure is 

removed from the pit and transferred to a treatment system or applied to land. 

Poultry with Litter Enclosed poultry houses use bedding derived from wood shavings, rice hulls, chopped straw, peanut hulls, or 

other products, depending on availability. The bedding absorbs moisture and dilutes the 

manure produced by the birds. Litter is typically cleaned out completely once a year. These manure systems 

are typically used for all poultry breeder flocks and for the production of meat type chickens (broilers) and other 

fowl. 

Poultry without Litter In high-rise cages or scrape-out/belt systems, manure is excreted onto the floor below with no bedding to 

absorb moisture. The ventilation system dries the manure as it is stored. When designed and operated 

properly, this high-rise system is a form of passive windrow composting. 

1 Manure management system descriptions are from the 2006 IPCC Guidelines for National Greenhouse Gas Inventories (Volume 4: Agriculture, Forestry and Other Land 

Use, Chapter 10: Emissions from Livestock and Manure Management, Tables 10.18 and 10.21) and the Development Document for the Final Revisions to the National 

Pollutant Discharge Elimination System Regulation and the Effluent Guidelines for Concentrated Animal Feeding Operations (EPA-821-R-03-001, December 2002). 

Table A- 174: Methane Conversion Factors (percent) for Dry Systems 

Waste Management System Cool Climate MCF Temperate Climate MCF Warm Climate MCF 

Aerobic Treatment 0 0 0

Cattle Deep Litter (<1 month) 0.03 0.03 0.3 

Cattle Deep Litter (>1 month) 0.21 0.44 0.76

Composting - In Vessel 0.005 0.005 0.005 

Composting - Static Pile 0.005 0.005 0.005 

Composting-Extensive/ Passive 0.005 0.01 0.015 

Composting-Intensive 0.005 0.01 0.015 

Daily Spread 0.001 0.005 0.01 

Dry Lot 0.01 0.015 0.05 

Fuel 0.1 0.1 0.1

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 
A-208 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 175: Methane Conversion Factors by State for Liquid Systems for 2007 (percent) 

State Dairy Swine Beef Poultry 

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit 

Anaerobic 

Lagoon

Liquid/Slurry 

and Deep Pit

Liquid/Slurry Anaerobic 

Lagoon 

Alabama 0.76 0.42 0.78 0.42 0.43 0.78 

Alaska 0.50 0.15 0.50 0.14 0.15 0.50 

Arizona 0.80 0.62 0.76 0.39 0.50 0.77 

Arkansas 0.76 0.38 0.77 0.39 0.38 0.76 

California 0.74 0.35 0.73 0.32 0.41 0.74 

Colorado 0.66 0.22 0.70 0.24 0.24 0.66 

Connecticut 0.69 0.25 0.69 0.24 0.26 0.69 

Delaware 0.74 0.32 0.74 0.32 0.33 0.74 

Florida 0.79 0.54 0.79 0.53 0.54 0.79 

Georgia 0.77 0.41 0.77 0.41 0.40 0.77 

Hawaii 0.77 0.59 0.77 0.59 0.59 0.77 

Idaho 0.69 0.25 0.68 0.23 0.23 0.67 

Illinois 0.74 0.32 0.74 0.30 0.30 0.74 

Indiana 0.73 0.29 0.73 0.29 0.30 0.73 

Iowa 0.71 0.27 0.71 0.26 0.28 0.71 

Kansas 0.75 0.33 0.75 0.32 0.33 0.75 

Kentucky 0.76 0.35 0.76 0.35 0.35 0.76 

Louisiana 0.79 0.47 0.79 0.48 0.48 0.79 

Maine 0.62 0.20 0.62 0.19 0.20 0.63 

Maryland 0.73 0.31 0.74 0.31 0.31 0.74 

Massachusetts 0.67 0.24 0.68 0.23 0.24 0.67 

Michigan 0.69 0.25 0.70 0.25 0.25 0.70 

Minnesota 0.68 0.25 0.69 0.24 0.25 0.68 

Mississippi 0.78 0.44 0.78 0.43 0.43 0.78 

Missouri 0.75 0.34 0.75 0.33 0.34 0.76 

Montana 0.63 0.21 0.65 0.20 0.23 0.65 

Nebraska 0.72 0.29 0.72 0.28 0.28 0.72 

Nevada 0.71 0.27 0.72 0.28 0.28 0.71 

New Hampshire 0.64 0.21 0.66 0.21 0.21 0.65 

New Jersey 0.72 0.29 0.72 0.28 0.29 0.72 

New Mexico 0.73 0.30 0.72 0.28 0.30 0.70 

New York 0.66 0.23 0.67 0.22 0.23 0.68 

North Carolina 0.75 0.34 0.77 0.39 0.35 0.76 

North Dakota 0.66 0.23 0.66 0.21 0.23 0.65 

Ohio 0.71 0.28 0.72 0.27 0.28 0.72 

Oklahoma 0.77 0.39 0.76 0.35 0.36 0.77 

Oregon 0.63 0.21 0.63 0.20 0.24 0.63 

Pennsylvania 0.70 0.26 0.71 0.27 0.27 0.72 

Rhode Island 0.70 0.26 0.70 0.25 0.26 0.70 

South Carolina 0.78 0.41 0.78 0.42 0.41 0.78 

South Dakota 0.70 0.26 0.70 0.25 0.26 0.70 

Tennessee 0.76 0.35 0.77 0.39 0.37 0.77 

Texas 0.78 0.43 0.77 0.41 0.36 0.78 

Utah 0.69 0.25 0.68 0.23 0.25 0.68 

Vermont 0.63 0.21 0.63 0.20 0.21 0.63 

Virginia 0.73 0.30 0.74 0.32 0.31 0.74 

Washington 0.63 0.21 0.65 0.21 0.23 0.64 

West Virginia 0.72 0.28 0.72 0.27 0.28 0.71 

Wisconsin 0.68 0.24 0.69 0.24 0.25 0.69 

Wyoming 0.63 0.20 0.67 0.22 0.24 0.66 

Table A- 176: Direct Nitrous Oxide Emission Factors for 2007 (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
A-209

Cattle Deep Bed (no mix) 0.01

Composting_in vessel 0.006

Composting_intensive 0.1

Composting_passive 0.01

Composting_static 0.006

Daily Spread 0

Deep Pit 0.002

Dry Lot 0.02

Fuel 0

Liquid/Slurry 0.005

Pasture 0

Poultry with bedding 0.001

Poultry without bedding 0.001

Solid Storage 0.005

Table A- 177: Indirect Nitrous Oxide Loss Factors (percent) 

Runoff/Leaching Nitrogen Loss Animal Type Waste Management 1

System 

Volatilization 

Nitrogen Loss 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

Goats Dry Lot 23 1.1 3.9 3.6 1.9 4.3

Goats Pasture 0 0 0 0 0 0

Horses Dry Lot 23 0 0 0 0 0

Horses Pasture 0 0 0 0 0 0

Poultry Anaerobic Lagoon 54 0.2 0.8 0.7 0.4 0.9

Poultry Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9

Poultry Pasture 0 0 0 0 0 0

Poultry Poultry with bedding 26 0 0 0 0 0 

Poultry Poultry without bedding 34 0 0 0 0 0 

Poultry Solid Storage 8 0 0 0 0 0 

Sheep Dry Lot 23 1.1 3.9 3.6 1.9 4.3 

Sheep Pasture 0 0 0 0 0 0 

Swine Anaerobic Lagoon 58 0.2 0.8 0.7 0.4 0.9 

Swine Deep Pit 34 0 0 0 0 0 

Swine Liquid/Slurry 26 0.2 0.8 0.7 0.4 0.9 

Swine Pasture 0 0 0 0 0 0 

Swine Solid Storage 45 0 0 0 0 0 1 Data for nitrogen losses due to leaching were not available, so the values represent only nitrogen losses due to runoff.
A-210 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 178: Methane Emissions from Livestock Manure Management (Gg) a

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 200Dairy Cattle 538 548 536 530 571 597 572 598 637 663 701 742 775 798 77 Dairy Cows 530 540 529 522 563 590 565 591 629 655 694 734 768 791 77 Dairy Heifer 8 8 8 8 8 8 7 7 7 7 7 7 7 7 Swine 624 676 639 680 741 764 730 783 891 847 832 851 875 840 83 Market Swine 484 524 500 534 585 608 582 626 720 692 680 696 720 693 69 Market <60 lbs. 102 110 104 109 119 121 116 125 141 133 131 134 137 133 13 Market 60-119 lbs. 101 111 105 110 120 124 117 127 144 138 136 138 144 138 13 Market 120-179 lbs. 136 147 140 151 164 170 163 175 201 193 189 192 199 193 19 Market >180 lbs. 145 156 152 165 182 194 185 198 235 229 225 232 240 230 23 Breeding Swine 140 152 139 146 156 155 148 157 170 155 152 154 154 147 14Beef Cattle 124 124 121 122 125 125 124 121 124 123 118 121 117 115 11 Feedlot Steers 17 16 14 15 13 12 11 11 12 11 12 13 12 11 1 Feedlot Heifers 9 9 7 8 7 7 6 7 7 7 8 8 8 7  NOF Bulls 5 5 5 5 5 5 5 5 5 5 5 5 5 5  NOF Calves 9 9 9 9 9 9 9 9 9 9 9 9 8 8  NOF Heifers 14 14 15 15 16 16 17 16 16 16 15 15 15 15 1 NOF Steers 9 9 9 9 10 10 10 10 10 9 8 8 8 9  NOF Cows 62 63 62 62 65 66 65 63 65 65 62 63 61 60 6Sheep 7 7 7 6 6 5 5 5 5 4 4 4 4 4 Goats 1 1 1 1 1 1 1 1 1 1 1 1 1 1 Poultry 131 132 127 131 131 128 126 127 130 125 126 130 128 128 12 Hens >1 yr. 73 72 70 73 72 69 68 67 70 66 66 70 67 68 6 Total Pullets 25 26 23 23 23 22 21 23 23 21 22 22 22 22 2 Chickens 4 4 4 4 4 4 3 3 4 3 3 3 4 4  Broilers 19 20 20 21 22 23 24 25 25 26 27 27 28 27 2 Turkeys 10 10 10 10 9 9 9 9 8 8 8 8 8 8 Horses 22 22 21 21 21 21 21 21 22 22 22 23 25 29 3

a Methane emissions estimates presented in this table account for anaerobic digestion reductions. 
Table A- 179: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (Gg) 

Animal Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 200Dairy Cattle 11.2 11.10 11.06 11.16 11.2 11.3 11.2 11.2 11.3 11.5 11.7 11.7 11.9 11.9 11. Dairy Cows 6.4 6.36 6.25 6.31 6.3 6.3 6.4 6.4 6.4 6.4 6.5 6.5 6.6 6.6 6. Dairy Heifer 4.8 4.74 4.80 4.85 4.9 5.0 4.9 4.8 4.9 5.1 5.1 5.2 5.4 5.3 5.Swine 4.0 4.18 4.33 4.37 4.6 4.5 4.4 4.6 4.9 4.8 4.7 4.7 4.8 4.7 4. Market Swine 3.0 3.11 3.26 3.29 3.5 3.5 3.3 3.5 3.8 3.7 3.7 3.7 3.8 3.8 3. Market <60 lbs. 0.6 0.59 0.61 0.60 0.6 0.6 0.6 0.6 0.7 0.6 0.6 0.6 0.7 0.7 0. Market 60-119 lbs. 0.6 0.67 0.70 0.70 0.7 0.7 0.7 0.7 0.8 0.8 0.8 0.8 0.8 0.8 0. Market 120-179 lbs. 0.9 0.90 0.94 0.95 1.0 1.0 1.0 1.0 1.1 1.1 1.0 1.0 1.1 1.1 1. Market >180 lbs. 0.9 0.95 1.02 1.04 1.1 1.1 1.1 1.1 1.3 1.3 1.2 1.2 1.3 1.3 1. Breeding Swine 1.0 1.07 1.08 1.07 1.1 1.1 1.0 1.1 1.1 1.0 1.0 1.0 1.0 1.0 1.Beef Cattle 17.8 19.43 18.22 19.43 19.1 19.2 18.5 19.6 20.0 20.1 21.5 22.2 21.6 20.3 20. Feedlot Steers 11.9 12.87 12.32 12.98 12.7 12.6 12.0 12.4 12.7 12.7 13.6 14.0 13.7 12.9 13. Feedlot Heifers 5.9 6.56 5.89 6.45 6.4 6.6 6.5 7.1 7.2 7.5 8.0 8.2 7.9 7.4 7.Sheep 0.4 0.41 0.44 0.44 0.6 0.7 0.8 0.9 0.9 1.0 1.1 1.2 1.2 1.1 1.Goats 0.1 0.07 0.07 0.07 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.Poultry 4.7 4.79 4.88 4.96 5.1 5.1 5.3 5.3 5.3 5.4 5.4 5.5 5.6 5.5 5. Hens >1 yr. 1.0 1.02 1.02 1.03 1.0 1.0 1.0 1.0 1.1 1.1 1.1 1.1 1.1 1.1 1. Total Pullets 0.3 0.29 0.29 0.29 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0. Chickens 0.0 0.03 0.03 0.03 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0. Broilers 2.2 2.26 2.36 2.46 2.6 2.7 2.8 2.9 2.9 3.0 3.1 3.1 3.2 3.1 3. Turkeys 1.2 1.19 1.18 1.14 1.1 1.1 1.1 1.1 1.0 0.9 0.9 0.9 0.9 0.9 0.Horses 0.7 0.70 0.71 0.71 0.7 0.7 0.7 0.7 0.7 0.7 0.7 0.8 0.8 1.0 1.
A-212 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 180: Methane Emissions by State from Livestock Manure Management for 2007(Gg) a

State 

Beef on 

Feedlots 

Beef Not 

on Feed 

Dairy 

Cow 

Dairy 

Heifer

Swine—

Market

Swine—

Breeding Layer Broiler Turkey Sheep Goats Horses

Alabama 0.0102 2.3514 0.6839 0.0133 2.6619 0.6055 8.8832 3.2526 + 0.0085 0.0190 0.8905

Alaska 0.0001 0.0231 0.0355 0.0005 0.0019 0.0019 0.1898 + + 0.0056 0.0001 0.0174

Arizona 0.5616 0.7887 39.8714 0.1166 2.5857 0.5101 0.6515 + + 0.0859 0.0133 0.6372

Arkansas 0.0067 3.1189 0.5443 0.0201 2.4680 2.7183 0.6078 3.7571 0.8899 0.0085 0.0122 0.9828

California 1.1222 3.2300 281.3490 2.0159 1.9453 0.5347 4.0780 0.2719 0.4593 0.4766 0.0387 1.7941

Colorado 1.2553 1.6712 17.1198 0.0885 6.0828 2.6852 3.3648 + 0.1081 0.2083 0.0046 0.9707

Connecticut 0.0002 0.0226 0.6924 0.0150 0.0135 0.0068 0.3054 0.2710 + 0.0038 0.0006 0.0861

Delaware 0.0003 0.0135 0.2796 0.0041 0.0871 0.0513 0.0715 0.7851 + 0.0056 0.0004 0.0313

Florida 0.0062 2.9555 14.9124 0.0847 0.0661 0.0522 7.8750 0.2349 + 0.0085 0.0150 1.3585

Georgia 0.0094 1.9132 4.8111 0.0608 3.6186 1.2705 15.8878 4.4830 + 0.0085 0.0261 1.0063

Hawaii 0.0017 0.3532 0.6457 0.0029 0.1430 0.1019 0.1384 + + 0.0085 0.0020 0.0623

Idaho 0.2973 1.6578 89.8289 0.3864 0.1094 0.0500 0.6063 + + 0.1354 0.0029 0.7544

Illinois 0.2550 1.1175 5.5310 0.0832 40.0750 9.2770 0.3037 + 0.0801 0.0365 0.0043 0.5407

Indiana 0.1322 0.5896 8.7882 0.1125 32.4593 6.3475 0.8248 0.2710 0.4378 0.0276 0.0069 0.8919

Iowa 1.0369 3.2133 11.2956 0.1883 258.1416 30.2537 1.6695 0.2710 0.2375 0.1224 0.0047 0.6991

Kansas 3.1955 3.9774 10.9657 0.0968 21.1989 3.6085 0.0415 + 0.1081 0.0557 0.0062 0.6112

Kentucky 0.0262 2.8204 1.3561 0.0707 5.3424 1.0521 0.5916 0.9742 + 0.0193 0.0171 1.3548

Louisiana 0.0048 1.5553 0.8402 0.0272 0.0744 0.0224 1.9434 0.2719 + 0.0085 0.0055 0.6498

Maine 0.0003 0.0361 0.9091 0.0232 0.0087 0.0044 0.4217 + + 0.0038 0.0008 0.1155

Maryland 0.0137 0.1303 1.9743 0.0467 0.2472 0.0898 0.2796 0.9417 0.0215 0.0120 0.0024 0.2352

Massachusetts 0.0002 0.0211 0.3705 0.0104 0.0701 0.0198 0.0099 + 0.0017 0.0038 0.0015 0.1405

Michigan 0.2009 0.3828 24.6370 0.2098 8.8588 1.8690 0.5393 0.2710 0.1373 0.0422 0.0053 0.9513

Minnesota 0.3281 1.4009 20.3354 0.4197 61.4660 11.1449 0.3524 0.1489 1.3734 0.0781 0.0049 0.8409

Mississippi 0.0095 1.7479 0.8063 0.0268 5.9739 1.2732 8.9086 2.6408 + 0.0085 0.0100 0.9085

Missouri 0.0906 5.0055 3.9709 0.1134 27.9268 8.1669 0.2448 0.2710 0.5722 0.0406 0.0122 1.2814

Montana 0.0611 3.0076 1.5137 0.0147 1.3335 0.2755 0.3294 + + 0.1510 0.0022 0.8557

Nebraska 3.1774 4.6686 4.3533 0.0316 27.8401 6.9537 0.6122 0.0153 0.1081 0.0495 0.0029 0.5342

Nevada 0.0090 0.7028 4.8431 0.0195 0.0309 0.0077 0.0209 + + 0.0391 0.0016 0.1462

New Hampshire 0.0001 0.0130 0.4632 0.0095 0.0118 0.0040 0.0215 + + 0.0038 0.0009 0.0718

New Jersey 0.0003 0.0245 0.2718 0.0077 0.0760 0.0151 0.1387 + 0.0011 0.0056 0.0021 0.2438

New Mexico 0.1553 1.4387 64.7004 0.1811 0.0023 0.0005 0.5951 + + 0.0677 0.0048 0.4232

New York 0.0250 0.4085 19.4959 0.4950 0.6199 0.1534 0.4372 0.2710 0.0183 0.0385 0.0083 0.6823

North Carolina 0.0076 1.3606 1.5822 0.0638 161.2220 36.5538 11.6261 2.5037 1.1196 0.0164 0.0252 0.8728

North Dakota 0.0694 2.1568 1.1714 0.0277 1.0468 0.5938 0.0387 + 0.0544 0.0521 0.0006 0.3935

Ohio 0.2162 0.7535 12.1791 0.1731 14.8551 3.1235 0.8509 0.1591 0.1459 0.0734 0.0113 1.2180

Oklahoma 0.4141 5.6465 8.4849 0.0372 35.6155 12.4006 3.8077 0.7781 0.1085 0.0625 0.0310 2.0415

Oregon 0.1138 1.7314 11.6346 0.1084 0.0870 0.0300 0.9251 0.2710 + 0.1120 0.0077 0.8378

Pennsylvania 0.0952 0.6316 11.5482 0.4082 10.6228 1.8833 0.6684 0.4830 0.3147 0.0562 0.0100 1.0279

Rhode Island + 0.0039 0.0215 0.0008 0.0081 0.0054 0.0684 + + 0.0038 0.0001 0.0177

South Carolina 0.0027 0.6833 0.7998 0.0184 5.3317 0.9670 5.2096 0.7531 0.3014 0.0085 0.0154 0.5537

South Dakota 0.4903 3.8395 4.9879 0.0624 11.8340 2.8178 0.1707 + 0.1288 0.1979 0.0018 0.6306

Tennessee 0.0137 3.9494 1.5941 0.0986 2.1156 0.5227 0.1967 0.6634 + 0.0195 0.0430 2.0212

Texas 4.8334 19.1224 58.0716 0.3745 15.3030 3.0367 4.6967 1.9752 0.1085 0.8203 0.4477 5.0627

Utah 0.0335 1.0661 12.5926 0.0668 6.2387 1.7701 2.9859 + 0.1081 0.1536 0.0023 0.5563

Vermont 0.0006 0.0526 3.9806 0.0846 0.0032 0.0017 0.0181 + 0.0015 0.0038 0.0010 0.1019

Virginia 0.0392 1.6849 2.2398 0.0685 5.8481 0.9510 0.3997 0.7992 0.6295 0.0375 0.0103 0.7374

Washington 0.2401 0.9021 30.9029 0.2055 0.1590 0.0433 1.0385 0.2710 + 0.0266 0.0058 0.6885

West Virginia 0.0128 0.4871 0.3558 0.0061 0.0402 0.0181 0.1723 0.2840 0.1001 0.0177 0.0044 0.2887

Wisconsin 0.2692 1.0455 53.8634 1.0149 3.1120 0.8429 0.2786 0.1504 0.1081 0.0479 0.0088 0.9246

Wyoming 0.0988 1.6624 0.4995 0.0073 0.3702 0.3506 0.0096 + + 0.2396 0.0013 0.5703

+ Emission estimate is less than 0.00005 Gg. 

a Methane emissions estimates presented in this table account for anaerobic digestion reductions. 
A-213

Table A- 181: Total (Direct and Indirect) Nitrous Oxide Emissions by State from Livestock Manure Management for 2007

(Gg) 

State 

Beef 

FeedlotHeifer 

Beef 

FeedlotSteers Dairy 

Cow 

Dairy 

Heifer

Swine—

Market

Swine—

Breeding Layer Broiler Turkey Sheep Goats Horses

Alabama 0.0025 0.0048 0.0046 0.0033 0.0100 0.0027 0.0543 0.3737 + 0.0039 0.0015 0.0229

Alaska + 0.0001 0.0005 0.0005 + + 0.0027 + + 0.0013 + 0.0007

Arizona 0.0233 0.4807 0.1297 0.0784 0.0096 0.0023 0.0029 + + 0.0113 0.0010 0.0164

Arkansas 0.0015 0.0031 0.0053 0.0034 0.0097 0.0132 0.0754 0.4317 0.1064 0.0033 0.0010 0.0252

California 0.1669 0.6734 1.3232 1.2223 0.0088 0.0028 0.0755 0.0312 0.0549 0.0709 0.0031 0.0460

Colorado 0.6285 1.0767 0.0953 0.1025 0.0454 0.0246 0.0176 + 0.0130 0.0412 0.0005 0.0374

Connecticut + 0.0002 0.0101 0.0090 0.0001 0.0001 0.0117 0.0312 + 0.0026 0.0001 0.0033

Delaware 0.0001 0.0003 0.0037 0.0023 0.0005 0.0004 0.0026 0.0905 + 0.0038 + 0.0012

Florida 0.0025 0.0017 0.0678 0.0322 0.0003 0.0002 0.0473 0.0270 + 0.0039 0.0012 0.0349

Georgia 0.0029 0.0042 0.0289 0.0170 0.0141 0.0059 0.0986 0.5151 + 0.0039 0.0021 0.0258

Hawaii 0.0004 0.0007 0.0031 0.0017 0.0005 0.0005 0.0013 + + 0.0013 0.0002 0.0016

Idaho 0.1554 0.2445 0.4175 0.4477 0.0008 0.0005 0.0031 + + 0.0268 0.0003 0.0290

Illinois 0.1151 0.2104 0.0957 0.0789 0.2662 0.0729 0.0190 + 0.0096 0.0214 0.0005 0.0208

Indiana 0.0550 0.1115 0.1364 0.0974 0.2240 0.0520 0.1014 0.0312 0.0525 0.0162 0.0008 0.0343

Iowa 0.4784 0.8419 0.1860 0.1743 1.2511 0.1733 0.2053 0.0312 0.0285 0.0720 0.0006 0.0269

Kansas 1.7435 2.2235 0.1006 0.0961 0.1363 0.0275 0.0026 + 0.0130 0.0328 0.0007 0.0235

Kentucky 0.0105 0.0199 0.0249 0.0171 0.0230 0.0054 0.0212 0.1123 + 0.0132 0.0020 0.0522

Louisiana 0.0015 0.0018 0.0077 0.0041 0.0003 0.0001 0.0086 0.0312 + 0.0033 0.0004 0.0167

Maine 0.0001 0.0003 0.0162 0.0135 0.0001 + 0.0172 + + 0.0026 0.0001 0.0044

Maryland 0.0048 0.0105 0.0312 0.0257 0.0015 0.0007 0.0102 0.1085 0.0026 0.0082 0.0003 0.0091

Massachusetts 0.0001 0.0002 0.0079 0.0058 0.0005 0.0002 0.0004 + 0.0002 0.0026 0.0002 0.0054

Michigan 0.0421 0.2229 0.3044 0.2132 0.0671 0.0168 0.0349 0.0312 0.0165 0.0248 0.0006 0.0366

Minnesota 0.1163 0.3153 0.4103 0.3953 0.4620 0.1001 0.0434 0.0172 0.1647 0.0459 0.0006 0.0324

Mississippi 0.0030 0.0040 0.0061 0.0049 0.0216 0.0055 0.0398 0.3034 + 0.0039 0.0008 0.0233

Missouri 0.0412 0.0724 0.0967 0.0942 0.1731 0.0612 0.0301 0.0312 0.0686 0.0239 0.0014 0.0493

Montana 0.0349 0.0481 0.0157 0.0162 0.0116 0.0028 0.0017 + + 0.0298 0.0003 0.0329

Nebraska 1.5034 2.5847 0.0557 0.0315 0.1969 0.0587 0.0389 0.0018 0.0130 0.0291 0.0003 0.0206

Nevada 0.0041 0.0079 0.0224 0.0225 0.0001 + 0.0026 + + 0.0077 0.0002 0.0056

New Hampshire + 0.0001 0.0075 0.0056 0.0001 + 0.0009 + + 0.0026 0.0001 0.0028

New Jersey 0.0002 0.0002 0.0053 0.0041 0.0005 0.0001 0.0052 + 0.0001 0.0038 0.0002 0.0094

New Mexico 0.0648 0.1404 0.2666 0.1881 + + 0.0029 + + 0.0134 0.0006 0.0163

New York 0.0178 0.0127 0.3166 0.2822 0.0051 0.0015 0.0169 0.0312 0.0022 0.0263 0.0010 0.0263

North Carolina 0.0026 0.0035 0.0153 0.0139 0.6023 0.1631 0.0730 0.2876 0.1338 0.0075 0.0020 0.0224

North Dakota 0.0409 0.0499 0.0275 0.0259 0.0085 0.0058 0.0026 + 0.0065 0.0306 0.0001 0.0152

Ohio 0.0783 0.2018 0.2309 0.1502 0.1042 0.0261 0.1046 0.0183 0.0175 0.0499 0.0013 0.0469

Oklahoma 0.1651 0.3706 0.0601 0.0244 0.1361 0.0563 0.0173 0.0894 0.0130 0.0244 0.0025 0.0524

Oregon 0.0530 0.0845 0.0808 0.0909 0.0007 0.0003 0.0107 0.0312 + 0.0250 0.0009 0.0323

Pennsylvania 0.0323 0.0820 0.2653 0.2093 0.0754 0.0160 0.0822 0.0557 0.0377 0.0384 0.0012 0.0396

Rhode Island + + 0.0005 0.0004 0.0001 + 0.0026 + + 0.0026 + 0.0007

South Carolina 0.0010 0.0010 0.0050 0.0038 0.0205 0.0044 0.0234 0.0865 0.0360 0.0039 0.0012 0.0142

South Dakota 0.2721 0.3638 0.0739 0.0609 0.0868 0.0246 0.0110 + 0.0154 0.1164 0.0002 0.0243

Tennessee 0.0042 0.0065 0.0188 0.0184 0.0088 0.0026 0.0070 0.0762 + 0.0089 0.0034 0.0519

Texas 1.5038 2.8423 0.2759 0.2401 0.0661 0.0155 0.0835 0.2269 0.0130 0.1080 0.0353 0.1299

Utah 0.0161 0.0291 0.0745 0.0765 0.0499 0.0166 0.0152 + 0.0130 0.0304 0.0003 0.0214

Vermont 0.0003 0.0003 0.0717 0.0497 + + 0.0007 + 0.0002 0.0026 0.0001 0.0039

Virginia 0.0157 0.0301 0.0295 0.0195 0.0241 0.0046 0.0147 0.0921 0.0755 0.0256 0.0012 0.0284

Washington 0.1198 0.1659 0.1699 0.1797 0.0013 0.0004 0.0219 0.0312 + 0.0059 0.0007 0.0265

West Virginia 0.0056 0.0096 0.0067 0.0034 0.0003 0.0001 0.0065 0.0327 0.0120 0.0121 0.0005 0.0111

Wisconsin 0.0505 0.3128 1.0894 0.9319 0.0231 0.0075 0.0181 0.0173 0.0130 0.0282 0.0010 0.0356

Wyoming 0.0578 0.0781 0.0053 0.0070 0.0042 0.0047 0.0001 + + 0.0473 0.0002 0.0220

+ Emission estimate is less than 0.00005 Gg. 

3.11. Methodology for Estimating N2O Emissions from Agricultural Soil Management 

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. 
A-214 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Emissions can occur directly in the soil where the N is made available or can be transported to another location following 

volatilization, leaching, or runoff, and then converted into N2O. 

A combination of Tier 1 and Tier 3 approaches was used to estimate direct and indirect N2O emissions from 

agricultural soils. The process-based biogeochemical model DAYCENT (a Tier 3 approach) was used to estimate N2O 

emissions resulting from mineral soil croplands that were used to produce major crops, while the IPCC (2006) Tier 1 

methodology was applied to estimate N2O emissions for non-major crop types on mineral soils. The Tier 1 method was 

also used to estimate direct N2O emissions due to drainage and cultivation of organic cropland soils. Direct N2O 

emissions from grasslands were estimated by using a combination of DAYCENT and IPCC (2006) Tier 1 methods. A 

combination of DAYCENT and Tier 1 methods was also used to estimate indirect emissions from all managed lands (i.e., 

croplands, grasslands, forest lands, and settlements). Specifically, the amount of N volatilized from soils, as well as 

leaching or transport of nitrate (NO3

-

) off-site in surface runoff waters was computed by DAYCENT for the direct 

emission analyses, while IPCC default factors were used to estimate N transport for the analyses using the Tier 1 

methodology. The indirect N2O emissions resulting from off-site transport of N were then computed using the IPCC 

(2006) Tier 1 default emission factor. Overall, the Tier 3 approach is used to estimate approximately 85 percent of direct 

soil emissions and 70 percent of total soil N2O emissions associated with agricultural soil management in the United 

States. 

DAYCENT (Del Grosso et al. 2001, Parton et al. 1998) simulates biogeochemical N fluxes between the 

atmosphere, vegetation, and soil, allowing for a more complete estimation of N2O emissions than IPCC Tier 1 methods by 

accounting for the influence of environmental conditions including soil characteristics and weather patterns, specific crop 

and forage qualities that influence the N cycle, and management practices at a daily time step. For example, plant growth 

is controlled by nutrient availability, water, and temperature stress; moreover, growth removes mineral N from the soil 

before it can potentially be converted into N2O. Nutrient supply is a function of external nutrient additions as well as litter 

and soil organic matter (SOM) decomposition rates, and increasing decomposition can lead to greater N2O emissions by 

enhancing mineral N availability in soils. In this model-based assessment framework, daily maximum/minimum 

temperature and precipitation, timing and description of management events (e.g., fertilization, tillage, harvest), and soil 

texture data are model inputs to DAYCENT, which form the basis to simulate key processes and generate robust estimates 

of N2O emissions from soils. Key processes simulated within sub-models of DAYCENT include plant production, organic 

matter formation and decomposition, soil water and soil temperature regimes by layer, and nitrification and denitrification 

processes (Figure A- 7). Comparison of model results and plot level data show that DAYCENT reliably simulates crop 

yields, soil organic matter levels, and trace gas fluxes for a number of native and managed systems (Del Grosso et al. 

2001, 2005). Comparisons with measured data showed that DAYCENT estimated emissions more accurately and 

precisely than the IPCC Tier 1 methodology (Figure A- 8). The linear regression of simulated vs. measured emissions for 

DAYCENT had higher r2

 and a fitted line closer to a perfect 1:1 relationship between measured and modeled N2O 

emissions (Del Grosso et al. 2005, 2008). This is not surprising, since DAYCENT includes site-specific factors (climate, 

soil properties, and previous management) that influence N2O emissions. Furthermore, DAYCENT also simulated NO3

-

leaching (root mean square error = 20 percent) more accurately than IPCC Tier 1 methodology (root mean square error = 

69 percent) (Del Grosso et al. 2005). Thus, the Tier 3 approach has reduced uncertainties in the agricultural soil 

management section relative to earlier Inventory years where the IPCC Tier 1 method was used. The latest operational 

version of DAYCENT has several improvements, including (1) elimination of the influence of labile (i.e., easily 

decomposable by microbes) C availability on surface litter denitrification rates, (2) incorporation of precipitation events on 

surface litter denitrification, and (3) having the wettest soil layer within the rooting zone control plant transpiration. 

[Begin Text Box] 

Box 1. DAYCENT Model Simulation of Nitrification and Denitrification 

The DAYCENT model simulates the two biogeochemical processes, nitrification and denitrification, that result 

in N2O emissions from soils (Del Grosso et al. 2000, Parton et al. 2001). Nitrification is calculated for the top 15 cm of 

soil, while denitrification is calculated for the entire soil profile. The equations and key parameters controlling N2O 

emissions from nitrification and denitrification are described below. 

Nitrification is controlled by soil ammonium (NH4

+

) concentration, water filled pore space (WFPS), temperature 

(t), and pH according to the following equation: 

Nit = NH4 × Kmax × F(t) × F(WFPS) × F(pH) 

where, 

Nit = the soil nitrification rate (g N/m2

/day) 
A-215

NH4 = the model-derived soil ammonium concentration (g N/m2) 

Kmax = the maximum fraction of NH4

+

 nitrified (Kmax = 0.10/day) 

F(t) = the effect of soil temperature on nitrification (Figure A- 5a) 

F(WFPS) = the effect of soil water content and soil texture on nitrification (Figure A- 5b) 

F(pH) = the effect of soil pH on nitrification (Figure A- 5c) 

The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N2O. 

N2O emissions from denitrification are a function of soil NO3

-

 concentration, WFPS, heterotrophic (i.e., 

microbial) respiration, and texture. Denitrification is calculated for each soil layer in the profile, and N2O emissions from 

each layer are summed to obtain total soil emissions. The model assumes that denitrification rates are controlled by the 

availability of soil NO3

-

 (electron acceptor), labile C compounds (electron donor) and oxygen (competing electron 

acceptor). Heterotrophic soil respiration is used as a proxy for labile C availability, while oxygen availability is a function 

of soil physical properties that influence gas diffusivity, soil WFPS, and oxygen demand. The model selects the minimum 

of the NO3

-

 and CO2 functions to establish a maximum potential denitrification rate for particular levels of electron 

acceptor and C substrate and accounts for limitations of oxygen availability to estimate daily denitrification rates 

according to the following equation: 

Den = min[F(CO2), F(NO3)] × F(WFPS) 

where, 

Den = the soil denitrification rate (g N/g soil/day) 

F(CO2) = a function relating N gas flux to soil respiration (Figure A- 6a) 

F(NO3) = a function relating N gas flux to nitrate levels (Figure A- 5b) 

F(WFPS) = a dimensionless multiplier (Figure A- 6c). 

The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (DFC): 

x inflection = 0.90 - M(CO2) 

where, 

M = a multiplier that is a function of DFC. 

Respiration has a much stronger effect on the water curve in clay soils with low DFC than in loam or sandy soils 

with high DFC (Figure A- 6c). The model assumes that microsites in fine-textured soils can become anaerobic at relatively 

low water contents when oxygen demand is high. 

After calculating total N gas flux, the ratio of N2/N2O is estimated so that total N gas emissions can be 

partitioned between N2O and N2: 

RN2/N2O = Fr(NO3/CO2) × Fr(WFPS). 

where, 

RN2/N2O = the ratio of N2/N2O 

Fr(NO3/CO2) = a function estimating the impact of the availability of electron donor relative to substrate 

Fr(WFPS) = a multiplier to account for the effect of soil water on N2:N2O. 

For Fr(NO3/CO2), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be 

in the form of N2O. For Fr(WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N2. 

[End Box] 

Figure A- 5: Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates 

Figure A- 6: Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on 

Denitrification Rates 
A-216 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

There are five steps in estimating direct N2O emissions from cropland and grassland soils, and indirect N2O 

emissions from volatilization, leaching, and runoff from all managed lands (i.e., croplands, grasslands, forest lands, and 

settlements). First, the activity data are derived from a combination of land-use, livestock, crop, and grassland 

management records, as well as expert knowledge. In the second, third, and fourth steps, direct and indirect N2O 

emissions are estimated using DAYCENT and/or the Tier 1 method. In the fifth step, total emissions are computed by 

summing all components. The remainder of this annex describes the methods underlying each step. 

Step 1: Derive Activity Data 

The activity data requirements vary for major crops, non-major crops, grasslands, organic cropland soils, 

settlements and forest lands. Activity data were derived for direct and indirect N2O emission calculations as described 

below. 

Step 1a: Activity Data for Direct Emissions from Crop Production on Mineral Soils 

Nitrous oxide emissions from mineral cropland soils include emissions from both major and non-major cropping 

systems and were estimated using a Tier 3 and a Tier 1 approach, respectively. 

Major Crop Types: Tier 3 DAYCENT Simulations 

The activity data requirements for estimating N2O emissions from major crop types (corn, soybeans, wheat, 

alfalfa hay, other hay, sorghum, and cotton) include the following: (1) crop-specific mineral N fertilizer rates and timing, 

(2) crop-specific manure amendment N rates and timing, (3) other N inputs, (4) crop-specific land management 

information, (5) native vegetation, (6) daily weather data for every county, (7) sub-county-level soil texture data, and (8) 

county-level crop areas. The United States was divided into 63 agricultural regions based on common cropping practices 

as defined by McCarl et al. (1993), and data were assembled and provided as inputs to the DAYCENT biogeochemical 

ecosystem model. 

Unlike the Tier 1 approach, N inputs from crop residues are not considered activity data in the DAYCENT 

analysis because N availability from this source is simulated by the model based on N uptake during crop growth 

according to environmental and management conditions, such as temperature, precipitation, and edaphic (i.e., soil) 

characteristics, in combination with the harvest practices. That is, while the model accounts for the contribution of N from 

crop residues to the soil profile and subsequent N2O emissions, this source of mineral soil N is not activity data in the 

sense that it is not a model input. Similarly, N from mineralization of soil organic matter and asymbiotic N fixation are 

also simulated by the model. 

Synthetic N Fertilizer Application: Data on N fertilizer rates were obtained primarily from the U.S. Department 

of Agriculture–Economic Research Service 1995 Cropping Practices Survey (ERS 1997). In this survey, data on 

inorganic N fertilization rates were collected for major crops (corn, cotton, soybeans, and wheat) in the high production 

states during 1995. It is assumed that the fertilization rates have not changed much during the Inventory reporting period, 

which is confirmed by the sales data showing relatively minor change in the amount of fertilizer sold for on-farm use 

during this time period (Ruddy et al. 2006). The trend and therefore the rates and uncertainties reflected in the 1995 

survey data are considered representative for 1990 through 2007 (trends will be re-evaluated when new fertilization data 

are released by U.S. Department of Agriculture). Note that all wheat data were combined into one category and assumed to 

represent small grains in aggregate. Estimates for sorghum fertilizer rates were derived from corn fertilizer rates using a 

ratio of national average corn fertilizer rates to national average sorghum fertilizer rates derived from additional 

publications (NASS 2004, 1999, 1992; ERS 1988; Grant and Krenz 1985; USDA 1966, 1957, 1954). Alfalfa hay is 

assumed to not be fertilized, but grass hay is fertilized according to rates from published farm enterprise budgets (NRIAI 

2003). 

The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records 

deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for 

each region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state with 

a minimum of 15 data points for irrigated and rainfed, respectively. If a state was not surveyed for a particular crop or if 

fewer than 15 data points existed for one of the categories, then data were aggregated to U.S. Department of Agriculture 

Farm Production Regions in order to estimate a mean and standard deviation for fertilization rates (Farm Production 

Regions are groups of states in the United States with similar agricultural commodities). If Farm Production Region data 
A-217

were not available, crop data were aggregated to the entire United States (all major states surveyed) to estimate a mean and 

standard deviation for a particular crop in a state lacking sufficient data. Standard deviations for fertilizer rates were used 

to construct probability distribution functions (PDFs) with log-normal densities in order to address uncertainties in 

application rates (see Step 2a for discussion of uncertainty methods). Total fertilizer application data are found in Table 

A- 182. 

Simulations were conducted prior to 1990 in order to initialize the DAYCENT model (see Step 2a), and 

estimates for crop-specific regional fertilizer rates prior to 1990 were based largely on extrapolation/interpolation of 

fertilizer rates from the years with available data. For crops in some agricultural regions, little or no data were available,

and, therefore, a geographic regional mean was used to simulate N fertilization rates (e.g., no data were available from 

Alabama during the 1970s and 1980s for corn fertilization rates; therefore, mean values from the southeastern United 

States were used to simulate fertilization to corn fields in this state). 

Managed Livestock Manure44 N Amendment Rates and Timing: County-level manure addition estimates have 

been derived from manure N addition rates developed by the Natural Resources Conservation Service (NRCS) (Edmonds 

et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of Agriculture, NRCS has coupled 

estimates of manure N produced with estimates of manure N recoverability by animal waste management system to 

produce county-level estimates of manure N applied to cropland and pasture. Edmonds et al. (2003) defined a hierarchy 

that included 24 crops, cropland used as pasture, and permanent pasture. They estimated the area amended with manure 

and application rates in 1997 for both manure-producing farms and manure-receiving farms within a county and for two 

scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation 

(Edmonds et al. 2003). The goal of nutrient management plans is to apply manure nutrients at a rate meeting plant 

demand, thus limiting leaching losses of nutrients to groundwater and waterways. For DAYCENT simulations, the 

baseline scenario estimates have been used as the basis for manure amendment applications under the assumption that 

Comprehensive Nutrient Management Plans have not been fully implemented. This is a conservative assumption because 

it allows for higher leaching rates due to some over-application of manure to soils. The rates for manure-producing farms 

and manure-receiving farms have been area-weighted and combined to produce a single county-level estimate for the 

amount of land amended with manure and the manure N application rate for each crop in each county. Several of the 

crops in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all 

small grain crops have been combined into one category. In order to address uncertainty in these data, uniform probability 

distributions were constructed based on the proportion of land receiving manure versus the amount not receiving manure 

for each crop type and pasture. For example, if 20 percent of land producing corn in a county was amended with manure, 

randomly drawing a value equal to or greater than 0 and less than 20 would lead to 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 provide manure application rate data for 1997, but the amount of managed manure 

available for soil application changes annually, so the area amended with manure was adjusted relative to 1997 to account 

for all the manure available for application in other years. Specifically, the manure N available for application in other 

years was divided by the manure N available in 1997. If the ratio was greater than 1, there was more manure N available 

in that county relative to the amount in 1997, and so it was assumed a larger area was amended with manure. In contrast, 

ratios less than one implied less area was amended with manure because there was a lower amount available in the year 

compared to 1997. The amendment area in each county for 1997 was multiplied by the ratio to reflect the impact of 

manure N availability on the area amended. The amount of managed manure N available for application to soils was 

calculated by determining the populations of animals that were on feedlots or otherwise housed in order to collect and 

manage the manure, as described in the Manure Management section (Section 6.2) and annex (Annex 3.10). 

To estimate C inputs associated with manure N application rates derived from Edmonds et al. (2003), carbonnitrogen (C:N) ratios for livestock-specific manure types were adapted from the Agricultural Waste Management Field 

Handbook (USDA 1996), On-Farm Composting Handbook (NRAES 1992), and recoverability factors provided by 

Edmonds et al (2003). The C:N ratios were applied to county-level estimates of manure N excreted by animal type and 

management system to produce a weighted county average C:N ratio for manure amendments. The average C:N ratio was 

used to determine the associated C input for crop amendments derived from Edmonds et al. (2003). 

 

44 For purposes of the Inventory, total livestock manure is divided into two general categories: (1) managed manure, and (2) 

unmanaged manure. Managed manure includes manure that is stored in manure management systems such as pits and lagoons, as well

as manure applied to soils through daily spread manure operations. Unmanaged manure encompasses all manure deposited on soils by 

animals on PRP. 
A-218 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

To account for the common practice of reducing inorganic N fertilizer inputs when manure is added to a cropland 

soil, crop-specific reduction factors were derived from mineral fertilization data for land amended with manure versus land 

not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). In addition, mineral N fertilization 

rates were reduced for crops receiving manure N based on a fraction of the amount of manure N applied, depending on the 

crop and whether it was irrigated or rainfed. The reduction factors were randomly selected from PDFs with normal 

densities in order to address uncertainties in the dependence between manure amendments and mineral fertilizer 

application. 

Manure N that was not applied to major crops and grassland was assumed to be applied to non-major crop types. 

The fate of manure N is summarized in Table A- 183. 

Residue N Inputs: Residue N input is estimated as part of the DAYCENT simulation and is not an input to the 

model. The simulated total N inputs of above- and below-ground residue N and fixed N that was not harvested are 

provided in Table A- 184. 

Other N Inputs: Other N inputs are estimated within the DAYCENT simulation, and thus input data are not 

required, including mineralization from decomposition of soil organic matter and asymbiotic fixation of N from the 

atmosphere. 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- 184. 

Crop Rotation and Land Management Information: Data were obtained on specific timing and type of 

cultivation, timing of planting/harvest, and crop rotation schedules for the 63 agricultural regions (Hurd 1930, 1929, Latta 

1938, Iowa State College Staff Members 1946, Bogue 1963, Hurt 1994, USDA 2000a, 2000b, CTIC 1998, Piper et al. 

1924, Hardies and Hume 1927, Holmes 1902, 1929, Spillman 1902, 1905, 1907, 1908, Chilcott 1910, Smith 1911, Kezer 

ca 1917, Hargreaves 1993, ERS 2002, Warren 1911, Langston et al. 1922, Russell et al. 1922, Elliott and Tapp 1928, 

Elliott 1933, Ellsworth 1929, Garey 1929, Holmes 1929, Hodges et al. 1930, Bonnen and Elliott 1931, Brenner et al. 2002, 

2001, Smith et al. 2002). As with N fertilizer and manure additions, data were not complete, so regional averages were 

used to fill spatial gaps in the data sets and interpolation/extrapolation was used to fill temporal gaps. The amount of 

agricultural residue burning was based on state inventory data (ILENR 1993, Oregon Department of Energy 1995, Noller 1996, 

Wisconsin Department of Natural Resources 1993, and Cibrowski 1996). 

Native Vegetation by County: Pre-agricultural land cover for each county was designated according to the 

potential native vegetation used in the Vegetation-Ecosystem Modeling and Analysis Project (VEMAP 1995) analysis, 

which was based on the Kuchler (1964) Potential Vegetation Map for the conterminous United States. 

Daily Weather Data by County: Daily maximum/minimum temperature and precipitation data were obtained 

from the DAYMET model, which generates daily surface precipitation, temperature, and other meteorological data at 1 

km2 resolution driven by weather station observations and an elevation model (Thornton et al. 2000 and 1997, Thornton 

and Running 1999, DAYMET no date). It is necessary to use computer-generated weather data because weather station 

data do not exist in each county, and moreover weather station data are for a point in space, while the DAYMET modeling 

process uses this information with interpolation algorithms to derive weather patterns for areas between these stations. 

DAYMET weather data are available for the United States at 1 km2

 resolution for 1980 through 2003. For each county, 

DAYMET weather data were selected from the 1 km2

 cell that occurred in agricultural lands according the National Land 

Cover Dataset (Vogelman et al. 2001). The grid cells formed the basis for county-scale PDFs based on the frequency of 

cells with same weather patterns. Separate PDFs were developed for cropland, pasture/hay land, and rangeland. A 

weather record was then randomly selected from the PDFs in each iteration of the Monte Carlo analysis to represent 

variation in precipitation and temperature at the county scale. Weather data were randomly selected from the previous 23 

years to represent 2004 through 2007, accounting for uncertainty in the weather during the years that have no data. The 

time series will be updated when new weather data are available. 

Soil Properties by County: Soil texture data required by DAYCENT were obtained from STATSGO (Soil Survey 

Staff 2005). Observed data for soil hydraulic properties needed for model inputs were not available, so they were 

calculated from STATSGO (Soil Survey Staff 2005) texture class and Saxton et al.’s (1986) hydraulic properties 

calculator. Similar to the weather data, soil types within the STATSGO map that occurred in agricultural lands according 

to the National Land Cover Dataset (Vogelman et al. 2001) were used to form a county-scale PDF. Specifically, the PDFs 

were an area-weighted proportion for the extent of overlap between STATSGO map units and agricultural land. Separate 

PDFs were developed for cropland, pasture/hay land, and rangeland. Individual soil types were randomly selected from 

the PDFs during each iteration of the Monte Carlo analysis to represent variation in soil texture and depth at the county 

scale. 
A-219

 Crop Areas by County: County-level total crop area data were downloaded from the NASS web site for the 

years 1990 through 2007 (NASS 2007), and these data formed the basis to scale emissions from individual crop types to 

an entire county. 

Non-Major Crop Types: Tier 1 Method 

The activity data required for calculating emissions from non-major crop types include: (1) the amount of 

mineral N in synthetic fertilizers that are applied annually, (2) managed manure N, (3) the amount of N in other 

commercial organic fertilizers and (4) the amount of N in the above- and below-ground residue retained on and in soils of 

all non-major crops. 

Application of Synthetic Commercial Fertilizers: A process-of-elimination approach was used to estimate 

synthetic N fertilizer additions to non-major crop types. The total amount of fertilizer used on farms has been estimated 

by the USGS from 1990-2001 on a county scale from fertilizer sales data (Ruddy et al. 2006). For 2002-2007, countylevel fertilizer used on farms was adjusted based on annual fluctuations in total U.S. fertilizer sales (AAPFCO 1995 

through 2008). In addition, fertilizer application data are available for major crops and grasslands (discussed in Step 1 

sections for Major Crops and Grasslands). Thus, the amount of N applied to non-major crops was assumed to be the 

remainder of the fertilizer used on farms after subtracting the amount applied to major crops and grasslands. The 

differences were aggregated to the state level and PDFs were derived based on uncertainties in the amount of N applied to 

major crops and grasslands. Total fertilizer application is found in Table A- 185. 

Manure and Other Commercial Organic Fertilizers:45 Manure N applied to non-major crops was estimated 

using the activity data described for major crops (Table A- 183). Estimates of total national annual N additions from other 

commercial organic fertilizers were derived from organic fertilizer statistics (TVA 1991 through 1994; AAPFCO 1995 

through 2008). Commercial organic fertilizers include dried blood, tankage, compost, and other; dried manure and sewage 

sludge that are used as commercial fertilizer were subtracted from totals to avoid double counting. The dried manure N is 

counted with the non-commercial manure applications, and sewage sludge is assumed to be applied only to grasslands. 

The organic fertilizer data, which are recorded in mass units of fertilizer, had to be converted to mass units of N by 

multiplying the consumption values by the average organic fertilizer N contents provided in the annual fertilizer 

publications. These N contents are weighted average values, and vary from year to year (ranging from 2.3 percent to 3.9 

percent over the period 1990 through 2008). The fertilizer consumption data are recorded in “fertilizer year” totals, (i.e., 

July to June), but were converted to calendar year totals. This was done by assuming that approximately 35 percent of 

fertilizer usage occurred from July to December and 65 percent from January to June (TVA 1992b). July to December 

values were not available for calendar year 2006, so a “least squares line” statistical extrapolation using the previous 14 

years of data was used to arrive at an approximate value. PDFs were derived for the organic fertilizer applications 

assuming a default ±50 percent uncertainty. Annual consumption of other organic fertilizers is presented in Table A- 186. 

Retention of Crop Residue: Annual crop yield (metric tons per hectare) and area harvested (hectare) statistics for 

non-major N-fixing crops, including bean and pulse crops, were taken from U.S. Department of Agriculture crop 

production reports (USDA 1994, 1998, 2003, 2005, 2006, 2008a,b). Crop yield per hectare and area planted were 

multiplied to determine total crop yield for each crop, which was then converted to tons of dry matter product using the 

residue dry matter fractions shown in Table A- 187. Dry matter yield was then converted to tons of above- and belowground biomass N. Above-ground biomass was calculated by using linear equations to estimate above-ground biomass 

given dry matter crop yields, and below-ground biomass was calculated by multiplying above-ground biomass by the 

below-to-above-ground biomass ratio. N inputs were estimated by multiplying above- and below-ground biomass by 

respective N concentrations. All ratios and equations used to calculate residue N inputs (shown in Table A- 188) are from 

IPCC (2006) and Williams (2006). PDFs were derived assuming a ±50 percent uncertainty in the yield estimates (NASS 

does not provide uncertainty), along with uncertainties provided by the IPCC (2006) for dry matter fractions, aboveground residue, ratio of below-ground to above-ground biomass, and residue N fractions. The resulting annual biomass N 

inputs are presented in Table A- 188. 

 

45 Other commercial organic fertilizers include, dried blood, dried manure, tankage, compost, sewage, and other minor 

organic fertilizer types, but manure and sewage sludge have been excluded in order to avoid double-counting and ensure consistency 

across the Inventory as these inputs are calculated using alternative data sources and methods. 
A-220 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 1b: Activity Data for Direct Emissions from Drainage and Cultivation of Organic Cropland Soils 

Tier 1 Method 

Estimates and associated uncertainty for the area of drained and cultivated organic cropland soils in 1992 and 

1997 were obtained from the U.S. Department of Agriculture 1997 National Resources Inventory (USDA 2000a, as 

extracted by Eve 2001, and revised by Ogle 2002).46 These areas were grouped by broad climatic region47 using 

temperature and precipitation estimates from Daly et al. (1994, 1998) and then further aggregated to derive total land in 

temperate and sub-tropical regions. Areas for 1992 were assumed to represent 1990 through 1992 and areas for 1997 were 

assumed to represent 1993 through 2007 (Table A- 189). 

Step 1c: Activity Data for Direct Emissions from Grassland Management 

N2O emissions from non-federal grasslands were estimated using DAYCENT. DAYCENT simulations 

addressed the influence of legume seeding, managed manure N amendments, unmanaged manure N excreted by livestock 

and deposited directly onto pasture, range, and paddock (PRP) soils, and synthetic fertilizer applications. N2O emissions 

from PRP manure N deposition on federal grasslands and sewage sludge amendments to agricultural soils were addressed 

using the Tier 1 method. 

Tier 3 DAYCENT Simulations 

Activity data for DAYCENT simulations of grasslands (i.e., climate, soils, and N inputs) were based on the same 

sources as those used for major crop types described in Step 1a. In addition to the data sources used for major crops, 

county-level area data on non-federal pasture and rangeland (i.e., mostly privately-owned) were needed for U.S. 

grasslands. This information was based on data compiled from the U.S. Department of Agriculture National Resources 

Inventory (USDA 2000a). Another key source of N for grasslands is PRP manure N deposition. Activity data for PRP 

manure N excretion from dairy cattle, beef cattle, swine, sheep, goats, poultry, and horses were derived from multiple 

sources as described in the Manure Management section (Section 6.2) and annex (Annex 3.10). The amount of PRP 

manure N deposited on non-federal grasslands (non-federal grasslands are mostly under private ownership) was based on 

the relative proportion of federal and non-federal grasslands in each county. For example, if 75 percent of the grasslands 

in a county were non-federal then 75 percent of PRP manure N was assumed to be deposited on non-federal grasslands. 

The relative proportions of non-federal and federal grasslands areas were based on the U.S. Department of Agriculture’s 

National Resources Inventory (USDA 2000a) and the National Land Cover Dataset (Vogelman et al. 2001), respectively. 

Nitrogen fixation by legumes, and N residue inputs from senesced grass litter were included as sources of N to 

the soil, which are estimated in the DAYCENT simulations as a function of vegetation type, weather, and soil properties. 

Similar to the methodology for major crops, “other N inputs” were simulated within the DAYCENT model framework in 

order to estimate soil N2O emissions from managed lands (IPCC 2006), including mineralization from decomposition of 

soil organic matter and litter, as well as asymbiotic N fixation from the atmosphere. Decomposition rates are a function of 

litter quality and quantity, soil texture, water content and temperature, and other factors. Total annual amounts of PRP 

manure N, mineral N fertilizer application, manure N amendments, forage legume N and “other N inputs” can be found in 

Table A- 190. 

Tier 1 Method: Additional Direct Soil N2O Emissions 

The Tier 1 method was used to estimate emissions from PRP manure that were not simulated with DAYCENT, 

in addition to emissions due to sewage sludge amendments to agricultural soils. 

PRP Manure: PRP manure N data were derived using methods described in the Manure Management section 

(Section 6.2) and annex (Annex 3.10). The amount of PRP manure N deposited on federal grasslands was based on the 

relative proportion of federal to non-federal grassland area in each county. The area data were based on the U.S. 

Department of Agriculture’s National Resources Inventory (USDA 2000a) and the National Land Cover Dataset 

 

46 These areas do not include Alaska, but Alaska’s cropland area accounts for less than 0.1 percent of total U.S. cropland area, 

so this omission is not significant. 

47 These climatic regions were: (1) cold temperate, dry, (2) cold temperate, moist, (3) sub-tropical, dry, (4) sub-tropical, moist, 

(5) warm temperate, dry, and (6) warm temperate, moist. 
A-221

(Vogelman et al. 2001), respectively. Soil N2O emissions from the proportion of PRP manure N deposited on federal 

grasslands were estimated with the Tier 1 method. 

Sewage Sludge Amendments: Sewage sludge is generated from the treatment of raw sewage in public or private 

wastewater treatment works and is typically used as a soil amendment or is sent to waste disposal facilities such as 

landfills. In this Inventory, all sewage sludge that is amended to agricultural soils is assumed to be applied to grasslands. 

Estimates of the amounts of sewage sludge N applied to agricultural lands were derived from national data on sewage 

sludge generation, disposition, and N content. Total sewage sludge generation data for 1988, 1996, and 1998, in dry mass 

units, were obtained from EPA (1999) and estimates for 2004 were obtained from an independent national biosolids 

survey (NEBRA 2007). These values were linearly interpolated to estimate values for the intervening years. Sewage 

sludge generation data are not available after 2004 (Bastian 2007), so the 1990 through 2004 data were linearly 

extrapolated for the most recent years. The total sludge generation estimates were then converted to units of N by 

applying an average N content of 3.9 percent (McFarland 2001), and disaggregated into use and disposal practices using 

historical data in EPA (1993) and NEBRA (2007). The use and disposal practices were agricultural land application, other 

land application, surface disposal, incineration, landfilling, ocean dumping (ended in 1992), and other disposal. The 

resulting estimates of sewage sludge N applied to agricultural land were used here; the estimates of sewage sludge N 

applied to other land and surface-disposed were used in estimating N2O fluxes from soils in Settlements Remaining 

Settlements (see section 7.5 of the Land Use, Land-Use Change, and Forestry chapter). Sewage sludge disposal data are 

provided in Table A- 191. 

Step 1d: Activity Data for Indirect N2O Emissions from Managed Soils of all Land-Use Types and Managed Manure 

Systems 

Volatilization of N that was applied or deposited as synthetic fertilizer, livestock manure, sewage sludge, and 

other organic amendments leads to emissions of NH3 and NOx to the atmosphere. In turn, this N is returned to soils 

through atmospheric deposition, thereby increasing mineral N availability and enhancing N2O production. Additional N is 

lost from soils through leaching as water percolates through a soil profile and through runoff with overland water flow. N 

losses from leaching and runoff enter groundwater and waterways, from which a portion is emitted as N2O. However, N 

leaching was assumed to be an insignificant source of indirect N2O in cropland and grassland systems where the amount of 

precipitation plus irrigation did not exceed the potential evapotranspiration, as recommended by IPCC (2006). These 

areas are typically semi-arid to arid, and nitrate leaching to groundwater is a relatively uncommon event. Note that the 

sources of N that is volatilized and N that is leached/runoff are not necessarily the same (e.g., N from crop residues is not 

volatilized, but is subject to leaching/runoff). 

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- 182 through Table A- 186, Table A- 188, and Table A- 191. The activity data for 

computing direct and indirect N2O emissions from settlements and forest lands are described in the Land Use, Land-Use 

Change, and Forestry chapter. 

Using the DAYCENT model, volatilization and leaching/surface run-off of N from soils was computed internally 

for major crop types and non-federal grasslands. DAYCENT simulates the processes leading to these losses of N based on 

environmental conditions (i.e., weather patterns and soil characteristics), management impacts (e.g., plowing, irrigation, 

harvest), and soil N availability. Note that the DAYCENT method accounts for losses of N from all anthropogenic 

activity, not just the inputs of N from mineral fertilization and organic amendments, which are addressed in the Tier 1 

methodology. Similarly, the N available for producing indirect emissions resulting from grassland management as well as 

deposited PRP manure was also estimated by DAYCENT. Estimated leaching losses of N from DAYCENT were not used 

in the indirect N2O calculation if the amount of precipitation plus irrigation did not exceed the potential 

evapotranspiration, as recommended by IPCC (2006). Volatilized losses of N were 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 were summed for the annual cycle. Uncertainty in the estimates was derived 

from uncertainties in the activity data for the N inputs (i.e., fertilizer and organic amendments; see Step 1a for further 

information) 

The Tier 1 method was used to estimate N losses from mineral soils due to volatilization and leaching/runoff for 

non-major crop types, forestland, settlements, sewage sludge applications, and PRP manure on federal grasslands not 

accounted for by DAYCENT simulations. To estimate volatilized losses, synthetic fertilizers, manure, sewage sludge, and 

other organic N inputs were multiplied by the fraction subject to gaseous losses using the respective default values of 0.1 

kg N/kg N added as mineral fertilizers and 0.2 kg N/kg N added as manure (IPCC 2006). Uncertainty in the volatilized N 

ranged from 0.03-0.3 kg NH3-N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH3-N+NOx-N/kg N for organic 
A-222 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

amendments (IPCC 2006). Leaching/runoff losses of N were estimated by summing the N additions from synthetic and 

other organic fertilizers, manure, sewage sludge, and above- and below-ground crop residues, and then multiplying by the 

default fraction subject to leaching/runoff losses of 0.3 kg N/kg N applied, with an uncertainty from 0.1–0.8 kg NO3-N/kg 

N (IPCC 2006). However, N leaching was assumed to be an insignificant source of indirect N2O emissions if the amount 

of precipitation plus irrigation did not exceed the potential evapotranspiration (IPCC 2006). PDFs were derived for each of 

the N inputs in the same manner as direct N2O emissions, discussed in Steps 1a and 1c. 

Volatilized N was summed for losses from major crop types, minor crop types, grasslands, settlements, and 

forest lands. Similarly, the annual amounts of N lost from soil profiles through leaching and surface runoff were summed 

to obtain the total losses for this pathway. 

Step 2: Estimate Direct N2O Emissions from Cropland Soils 

In this step, N2O emissions were calculated for major crop types and non-major crop types on mineral soils, in 

addition to emissions associated with drainage and cultivation of organic soils. 

Step 2a: Direct N2O Emissions from Cropland Mineral Soils 

Two methods were used to estimate direct N2O emissions from N additions and crop production on mineral soils. 

The DAYCENT ecosystem model was used to estimate emissions from major crop types, while the Tier 1 methodology 

was used to estimate emissions from crops considered non-major types, which are grown on a considerably smaller 

portion of land than the major types. 

Major Crops: Tier 3 DAYCENT Simulations 

Simulations were performed over three major time periods for each county in the United States using the 

DAYCENT model. The first time period was used for simulation of native vegetation up to date of cultivation in the 

county (1 A.D. to plow out). Plow out was assumed to occur between 1600 and 1850, depending on the state in which the 

county lies. Simulation of at least 1600 years of native vegetation was needed to initialize soil organic matter (SOM) 

pools in the model. The second time period of the simulation started at plowout and represents historical agricultural 

practices up to the modern period (plow out to 1970). Simulation of the historical cropping period was needed to establish 

modern day SOM levels, which is important because N2O emissions are sensitive to the amount of SOM. Lastly, 

simulations were performed for the modern agricultural period (1971 through 2007). 

Corn, soybeans, wheat, alfalfa hay, other hay, sorghum, and cotton are defined as major crops and were 

simulated in every county where they were grown. These crops represent approximately 90 percent of total principal 

cropland in the United States as defined by the U.S. Department of Agriculture (USDA 2003). Overall, the DAYCENT 

simulations included approximately 86 percent of total cropped area. For rotations that include a cycle that repeats every 

two or more years (e.g., corn/soybeans, wheat/corn/fallow), different simulations were performed where each phase of the 

rotation was simulated every year. For example, 3 rotations were simulated in regions where wheat/corn/fallow cropping 

is a dominant rotation—one with wheat grown the first year, a second with corn the first year and a third with fallow the 

first year. This ensured that each crop was represented during each year in one of the three simulations. In cases where 

the same crop was grown in the same year in two or more distinct rotations for a region, N2O emissions were averaged 

across the different rotations to obtain a value for that crop. Emissions from cultivated fallow land were also included. 

Fallow area was assumed to be equal to winter wheat area in regions where winter wheat/fallow rotations are the dominant 

land management for winter wheat. 

The simulations reported here assumed conventional tillage cultivation, gradual improvement of cultivars, and 

gradual increases in fertilizer application until 1989. Note that there is a planned improvement to incorporate use of 

conservation tillage in the United States into this analysis. The productivity of cultivars (cultivated varieties) has steadily 

improved over the last century and therefore it is unrealistic to assume that modern varieties of crops, such as corn, are 

identical to the popular varieties grown in 1900 in terms of yield potential, N demand, etc. Realistic simulations of 

historical land management and vegetation type are important because they influence present day soil C and N levels, 

which influence present-day N cycling and associated N2O emissions. 

Uncertainty estimation was an integral part of this analysis; uncertainty in the input data for the county-scale 

simulations and structural uncertainty associated with the DAYCENT model predictions were both addressed. In the first 

step, a Monte Carlo Analysis was used to propagate input data uncertainty through the modeling process. Thus, input data 

were randomly selected from PDFs for weather records, soil type, mineral N fertilization rate, and organic amendments. 

See Step 1a for additional discussion about the PDFs. After selecting a set of inputs for a county, DAYCENT was used to 
A-223

simulate each crop and then the process was repeated until 100 iterations were completed. Due to the computationally 

intensive requirements for DAYCENT, it was not possible to simulate every county with the Monte Carlo Analysis. Two 

counties were selected from each of the 63 agricultural regions, and additional counties were added based on the variance 

in N2O emissions across regions from the past year’s Inventory, using a Neyman allocation (Cochran 1977). A Neyman 

allocation is based on the variance in N2O emissions across the 63 regions; regions with larger variances were allocated a 

larger number of counties for the Monte Carlo Analysis. A total of 300 counties were included in the Monte Carlo 

Analysis, which is approximately 10 percent of all counties in the analysis. In addition, all counties were simulated once 

based on the dominant conditions from the PDFs (i.e., most common soil type, weather condition, manure amendment, 

and mineral fertilizer rate). 

In the second step of the uncertainty analysis, a structural uncertainty estimator was developed to account for 

uncertainty inherent in model formulation and parameterization using an empirically-based procedure described by Ogle et 

al. (2007). The procedure is based on developing a statistical relationship between modeled results and field 

measurements. Specifically, DAYCENT was used to simulate 11 agricultural experiments with 108 treatments, and the 

results were analyzed using a linear-mixed effect model in which measurements were statistically modeled as a function of 

simulated emissions. DAYCENT results were a highly significant predictor of the measurements, with a p-value of <0.01. 

Several other variables were tested in the statistical model to evaluate if DAYCENT exhibited bias under certain 

conditions related to climate, soil types, and management practices. The type of crop or grassland was significant at an 

alpha level of 0.05, demonstrating that DAYCENT tended to over-estimate emissions for small grains systems and 

grassland, but was accurate in predicting the N2O emissions for other crops. Random effects were included in the model 

to capture the dependence in time series and data collected from the same site, which were needed to estimate appropriate 

standard deviations for parameter coefficients. 

The structural uncertainty estimator accounted for bias and prediction error in the DAYCENT model results, as 

well as random error associated with fine-scale emission predictions in counties over a time series from 1990 to 2007. To 

apply the uncertainty estimator, DAYCENT N2O emission estimates were used as an input to the linear mixed effect 

model after randomly selecting statistical parameter coefficients from their joint probability distribution, in addition to 

random draws from PDFs representing the uncertainty due to site, site by year random effects and the residual error from 

the linear-mixed effect model (Del Grosso et al., in prep.). 

In DAYCENT, once N enters the plant/soil system, the model cannot distinguish among the original sources of 

the N to determine which management activity led to specific N2O emissions. This means, for example, that N2O 

emissions from applied synthetic fertilizer cannot be separated from emissions due to other N inputs, such as crop 

residues. It is desirable, however, to report emissions associated with specific N inputs. Thus, for each crop in a county, 

the N inputs in a simulation were determined for anthropogenic practices discussed in IPCC (2006), including synthetic 

mineral N fertilization, organic amendments, and crop residue N added to soils (including N-fixing crops). The percentage 

of N input for anthropogenic practices was divided by the total N input, and this proportion was used to determine the 

amount of N2O emissions assigned to each of the practices.48 For example, if 70 percent of the mineral N made available 

in the soil was due to mineral fertilization, then 70 percent of the N2O emissions were assigned to this practice. The 

remainder of soil N2O emissions is reported under “other N inputs,” which includes mineralization due to decomposition 

of soil organic matter and litter, as well as asymbiotic fixation of mineral N in soils from the atmosphere. Asymbiotic N 

fixation by soil bacteria is a minor source of N, typically not exceeding 10 percent of total N inputs to agroecosystems. 

Mineralization of soil organic matter is a more significant source of N, but is still typically less than half of the amount of

N made available in the soil compared to fertilization, manure amendments, and symbiotic fixation. Accounting for the 

influence of “other N inputs” was necessary in order to meet the recommendation of reporting all emissions from managed 

lands (IPCC 2006). 

The final N2O emission estimate was determined by summing the estimates from the single simulation conducted 

in each county for the dominant condition to the 63 regions. Estimates were then adjusted to account for the difference 

between the emissions estimated in the Monte Carlo analysis and the dominant condition simulation on a region-by-region 

basis (i.e., if the Monte Carlo mean was slightly higher than the dominant condition among the counties included in the 

Monte Carlo analysis, the total emission estimate for the region would be raised by the difference) (Del Grosso et al., in 

prep). In turn, regional values were summed to produce the national total. The uncertainty was based on the variance in 

simulated N2O emissions for the iterations in the Monte Carlo Analysis and the variance associated with difference 

 

48 This method is a simplification of reality to allow partitioning of N2O emissions, as it assumes that all N inputs have an 

identical chance of being converted to N2O. This is unlikely to be the case, but DAYCENT does not track N2O emissions by source of 

mineral N so this approximation is the only approach that can be used for approximating the portion of N2O emissions by source of N 

input. Moreover, this approach is similar to the IPCC Tier 1 method (IPCC 2006), which uses the same direct emissions factor for most 

N sources (e.g., PRP). 
A-224 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

between the means from the Monte Carlo Analysis and the simulated N2O emissions for the dominant condition, expressed 

as a 95 percent confidence interval. 

Non-Major Crops: Tier 1 Method 

To estimate direct N2O emissions from N additions to non-major crops, the amount of N in applied synthetic 

fertilizer, manure and other commercial organic fertilizers (i.e., dried blood, tankage, compost, and other) was added to N 

inputs from crop residues, and the resulting annual totals were multiplied by the IPCC default emission factor of 0.01 kg 

N2O-N/kg N (IPCC 2006). The uncertainty was determined based on simple error propagation methods (IPCC 2006). 

The uncertainty in the default emission factor ranged from 0.3–3.0 kg N2O-N/kg N (IPCC 2006). Uncertainty in activity 

data is ± 20 percent for fertilizer additions (Mosier 2004).49 Uncertainties in the emission factor and fertilizer additions 

were combined with uncertainty in the equations used to calculate residue N additions from above- and below-ground 

biomass dry matter and N concentration to derive overall uncertainty. 

Step 2b: Direct N2O Emissions Due to Drainage and Cultivation of Organic Cropland Soils 

To estimate annual N2O emissions from drainage and cultivation of organic soils, the area of cultivated organic 

soils in temperate regions was multiplied by the IPCC (2006) default emission factor for temperate soils and the 

corresponding area in sub-tropical regions was multiplied by the average (12 kg N2O-N/ha cultivated) of IPCC (2006) 

default emission factors for temperate (8 kg N2O-N/ha cultivated) and tropical (16 kg N2O-N/ha cultivated) organic soils. 

The uncertainty was determined based on simple error propagation methods (IPCC 2006), including uncertainty in the 

default emission factor ranging from 2–24 kg N2O-N/ha (IPCC 2006). 

Step 2c: Estimate Total Direct N2O Emissions from Cropland Soils 

In this step, total direct N2O emissions from cropland soils are calculated by summing direct emissions on 

mineral soils with emissions resulting from the drainage and cultivation of organic cropland soils (Table A- 192). 

Uncertainties were combined using the simple error propagation method (IPCC 2006). 

Step 3: Estimate Direct N2O Emissions from Grasslands 

DAYCENT was used to estimate direct N2O emissions from soils in non-federal grasslands (pastures and 

rangeland), and the Tier 1 method was used for federal grasslands. Managed pastures were simulated with DAYCENT by 

assuming that the vegetation mix includes forage legumes and grasses, and that grazing intensity was moderate to heavy. 

Rangelands were simulated without forage legumes and grazing intensity was assumed to be light to moderate. The 

methodology used to conduct the DAYCENT simulations of grasslands was similar to major crop types described above in 

Step 2a, including the analysis addressing uncertainty in the model inputs and model structure. Carbon and nitrogen 

additions to grasslands from grazing animals were obtained from county level animal excretion data and area data for 

federal and non-federal grasslands, as described in Step1c. 

A Tier 1 method was used to estimate emissions from N excreted by livestock on federal lands (i.e., PRP manure 

N). A Tier 1 method was also used to estimate emissions from sewage sludge application to grasslands. These two 

sources of N inputs to soils were multiplied by the IPCC (2006) default emission factors (0.01 kg N2O-N/kg N from 

sludge and 0.02 kg N2O-N/kg N from manure) to estimate N2O emissions. This emission estimate was summed with the 

DAYCENT simulated emissions to provide the national total for direct N2O losses from grasslands (Table A- 193). The 

uncertainty was determined based on the Tier 1 error propagation methods provided by the IPCC (2006) with uncertainty 

in the default emission factor ranging from 0.007 to 0.06 kg N2O-N/kg N (IPCC 2006). 

Step 4: Estimate Indirect N2O Emissions for All Land-Use Types 

In this step, N2O emissions were calculated for the two indirect emission pathways (N2O emissions due to 

volatilization, and N2O emissions due to leaching and runoff of N), which were then summed to yield total indirect N2O 

emissions from croplands, grasslands, forest lands, and settlements. 

 

49 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-225

Step 4a: Indirect Emissions Due to Volatilization 

Indirect emissions from volatilization of N inputs from synthetic and commercial organic fertilizers, and PRP 

manure, were calculated according to the amount of mineral N that was transported in gaseous forms from the soil profile 

and later emitted as soil N2O following atmospheric deposition. See Step 1d for additional information about the methods 

used to compute N losses due to volatilization. The estimated N volatilized for all land-use and livestock activities was 

multiplied by the IPCC default emission factor of 0.01 kg N2O-N/kg N (IPCC 2006) to compute total N2O emissions from 

volatilization. The resulting estimates are provided in Table A- 194. The uncertainty was determined using simple error 

propagation methods (IPCC 2006), by combining uncertainties in the amount of N volatilized, with uncertainty in the 

default emission factor ranging from 0.002–0.05 kg N2O-N/kg N (IPCC 2006). 

Step 4b: Indirect Emissions Due to Leaching and Runoff 

The amount of mineral N (i.e., synthetic fertilizers, commercial organic fertilizers, PRP manure, crop residue, N 

mineralization, asymbiotic fixation) that was transported from the soil profile in aqueous form was used to calculate 

indirect emissions from (1) leaching of mineral N from soils and (2) losses in runoff of water associated with overland 

flow. See Step 1d for additional information about the methods used to compute N losses from soils due to leaching and 

runoff in overland water flows. 

The total amount of N transported from soil profiles through leaching and surface runoff was multiplied by the 

IPCC default emission factor of 0.0075 kg N2O-N/kg N (IPCC 2006) to provide the emission estimate for this source. The 

resulting emission estimates are provided in Table A- 195. The uncertainty was determined based on simple error 

propagation methods (IPCC 2006), including uncertainty in the default emission factor ranging from 0.0005 to 0.025 kg 

N2O-N/kg N (IPCC 2006). 

Step 5: Estimate Total N2O Emissions for U.S. Soils 

Total emissions were estimated by adding total direct emissions (from major crop types and non-major crop 

types on mineral cropland soils, drainage and cultivation of organic soils, and grassland management) to indirect 

emissions for all land use and management activities. U.S. national estimates for this source category are provided in 

Table A- 195. Uncertainties in the final estimate were combined using simple error propagation methods (IPCC 2006), 

and expressed as a 95 percent confidence interval. 

Direct and indirect emissions of soil N2O vary regionally in both croplands and grasslands as a function of N 

inputs, weather, and soil type. About half of the total N2O emissions from major crops occur in Iowa, Illinois, Nebraska, 

Minnesota, Kansas and Texas, where N inputs associated with corn rotations are high or where large land areas are 

cropped (Table A- 196). On a per area unit basis, direct N2O is high in Nevada, Utah, West Virginia and some New 

England states (Figure A- 9). Only a small portion of the land in these regions is used for crop production, but 

management and conditions lead to higher emissions on a per unit area basis than other regions. For example, emissions 

are high in Nevada and Utah due to dominance of intensive irrigated management systems. For West Virginia and some 

New England states, emissions are high on a per unit area because subsurface soil layers remain frozen when surface soil 

layers thaw in spring. This creates saturated conditions near the surface that facilitate denitrification and N2O emissions. 

Indirect emissions tend to be high on an area basis in the central and eastern United States because relatively high rainfall 

facilitates N losses from leaching and runoff and in some western states where irrigation can contribute to leaching and 

runoff (Figure A- 10). 

Direct and indirect emissions from grasslands are typically lower than those from croplands (Table A- 196, 

Figure A- 11, and Figure A- 12) because N inputs tend to be lower, particularly from synthetic fertilizer. The highest total 

emissions occurred in Texas, Missouri, and Oklahoma. On a per area unit basis, emissions are lower in the western United 

States because grasslands in the East 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 little leaching and runoff of N occurs in these grasslands. 

Figure A- 7: DAYCENT Model Flow Diagram 

Figure A- 8: Comparisons of Results from DAYCENT Model and IPCC Tier 1 Method with Measurements of Soil N2O Emissions 
A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Figure A- 9: Major Crops, Average Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, 1990-2007 (Metric 

Tons CO2 Eq./ha/year) 

Figure A- 10: Major Crops, Average Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the DAYCENT Model, 

1990-2007 (kg N/ha/year) 

Figure A- 11: Grasslands, Average Annual Direct N2O Emissions, Estimated Using the DAYCENT Model, 1990-2007 (Metric 

Tons CO2 Eq./ha/year) 

Figure A- 12: Grasslands, Average Annual N Losses Leading to Indirect N2O Emissions, Estimated Using the DAYCENT Model, 

1990-2007 (kg N/ha/year) 
Table A- 182: Synthetic Fertilizer N Added to Major Crops (Gg N)  1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004  Fertilizer N 7,482 7,444 7,737 7,399 7,680 7,346 7,939 7,736 7,660 7,445 7,585 7,476 7,561 7,386 7,35

Table A- 183: Fate of Livestock Manure Nitrogen (Gg N) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Managed Manure N Applied to Major Crops and Grasslandsa,b 972 1,041 1,053 1,056 1,017 938 1,000 1,031 1,084 1,070 1,077 1,089 1,147 1,094 Managed Manure N Applied to NonMajor Cropsb 790 797 773 794 849 915 852 892 856 883 906 938 947 920 Managed Manure N Applied to Grasslands 448 437 431 417 413 408 401 389 417 408 408 409 409 414 Pasture, Range, & Paddock Manure N 3,856 3,875 3,959 3,992 4,068 4,094 4,072 3,929 3,859 3,823 3,760 3,745 3,762 3,802 Total 6,066 6,150 6,216 6,260 6,348 6,354 6,325 6,241 6,216 6,185 6,151 6,181 6,266 6,230 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- 184: Crop Residue N and Other N Inputs to Major Crops as Simulated by DAYCENT (Gg N) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 20

Residue Na 1,358 1,313 1,373 1,324 1,343 1,476 1,447 1,551 1,413 1,649 1,524 1,456 1,462 1,574 1,4Mineralization & Asymbiotic Fixation 12,362 12,337 12,120 12,361 12,513 11,972 12,539 12,760 14,116 12,866 13,497 13,499 13,187 13,194 13,3

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N.Table A- 185: Synthetic Fertilizer N Added to Non-Major Crops (Gg N) 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2

 Fertilizer N 2,446 2,659 2,397 3,057 2,865 3,423 2,824 3,133 3,175 3,379 2,995 2,999 2,933 3,340 3Table A- 186: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N) 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 

 Other Commercial Organic Fertilizer Na 4 8 6 5 8 10 13 14 12 11 9 7 8 8 a Includes dried blood, tankage, compost, other. Excludes dried manure and sewage sludge used as commercial fertilizer to avoid double counting. Table A- 187: Key Assumptions for Production of Non-Major Crops and Retention of Crop Residues 

Above-ground Residue Residue N Fraction 

Crop 

Dry Matter 

Fraction of 

Harvested 

Product Slope Intercept 

Ratio of 

Below-ground 

Residue to Aboveground Biomass Above-ground Below-ground 

Peanuts for Nuts 0.94 1.07 1.54 0.20 0.016 0.014 

Dry Edible Beans 0.90 0.36 0.68 0.19 0.010 0.010 Dry Edible Peas 0.91 1.13 0.85 0.19 0.008 0.008 Austrian Winter Peas 0.91 1.13 0.85 0.19 0.008 0.008 
A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Lentils 0.91 1.13 0.85 0.19 0.008 0.008 Wrinkled Seed Peas 0.91 1.13 0.85 0.19 0.008 0.008 

Barley 0.89 0.98 0.59 0.22 0.007 0.014 Oats 0.89 0.91 0.89 0.25 0.007 0.008 Rye 0.88 1.09 0.88 0.22 0.005 0.011 Millet 0.90 1.43 0.14 0.22 0.007 0.009 Rice 0.89 0.95 2.46 0.16 0.007 0.009 

Table A- 188: Nitrogen in Crop Residues Retained on Soils Producing Non-Major Crops (Gg N) 

Crop 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2Peanuts for Nuts 64 77 71 62 70 63 65 64 68 66 61 71 61 69 Dry Edible Beans 16 17 14 14 15 16 15 16 16 16 15 13 16 14 

Dry Edible Peas 9 10 9 10 9 11 9 11 11 10 10 10 10 11 

Austrian Winter Peas 8 8 8 8 8 8 8 8 8 8 8 8 8 8 

Lentils 9 9 9 9 9 9 9 9 9 9 10 10 9 9 

Wrinkled Seed Peas 9 9 8 9 8 9 8 8 8 8 8 8 8 8 

Barley 112 123 120 106 100 96 105 96 94 74 86 68 63 76 

Oats 55 40 47 35 38 29 28 30 30 27 28 24 23 27 Rye 9 9 9 9 9 9 9 8 9 9 9 8 8 9 

Millet 7 7 7 7 7 7 7 7 7 7 3 7 2 5 Rice 80 82 89 80 96 87 86 91 91 99 94 103 101 97 

Total 378 389 392 348 371 343 349 348 352 336 331 330 311 333 

Table A- 189: Drained and Cultivated Organic Soil Area (Thousand Hectares) 

Year Temperate Area Sub-Tropical Area 

1990 444 194 1991 444 194 1992 444 194 1993 450 196 1994 450 196 1995 450 196 1996 450 196 1997 450 196 1998 450 196 1999 450 196 2000 450 196 2001 450 196 2002 450 196 2003 450 196 2004 450 196 2005 450 196 2006 450 196 2007 450 196 
Table A- 190: Synthetic Fertilizer N, PRP Manure N, Organic Manure N Amendment, Forage Legume N, and Other N Inputs Simulated with the DAY

Fertilizer Type 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2Fertilizer N 7,966 7,924 8,214 7,877 8,155 7,822 8,415 8,212 8,133 7,908 8,041 7,925 8,000 7,819 7,PRP Manure N 3,479 3,494 3,568 3,601 3,664 3,691 3,666 3,528 3,460 3,431 3,379 3,371 3,391 3,434 3,4Managed Manure 1,420 1,478 1,484 1,474 1,431 1,346 1,401 1,420 1,501 1,479 1,485 1,498 1,557 1,508 1,Residue Na 6,681 6,592 6,820 6,790 6,409 6,935 6,563 6,806 6,458 7,127 6,423 6,441 6,171 6,584 6,4Mineralization & 

Asymbiotic Fixation 24,832 24,901 24,684 24,586 24,479 24,236 24,619 25,114 26,594 24,519 25,159 25,319 24,736 24,892 25,

a Residue N inputs include unharvested fixed N from legumes as well as crop residue N. 

Table A- 191: Sewage Sludge Nitrogen by Disposal Practice (Gg N) 

Disposal Practice 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Applied to Agricultural Soils 52 56 59 63 66 69 72 75 78 81 84 86 89 91 Other Land Application 25 26 27 27 28 28 29 29 29 30 30 30 30 30 Surface Disposal 20 20 19 18 17 16 15 14 13 12 10 9 8 6 Total 98 101 105 109 111 113 116 118 121 122 124 125 127 128 Note: Totals may not sum due to independent rounding. 

Table A- 192: Direct N2O Emissions from Cropland Soils (Tg CO2 Eq.) 

Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Mineral Soils 103.5 113.0 104.4 112.8 115.4 111.3 119.4 117.1 120.6 115.3 116.5 124.1 116.2 114.1 Major Crops 85.8 94.3 87.0 92.3 95.5 88.4 99.7 95.7 99.2 92.8 95.8 103.2 95.7 91.7 

Synthetic Fertilizer 29.1 31.9 30.4 30.9 32.5 29.9 34.5 32.1 31.4 30.0 30.8 32.6 31.1 29.2 Managed Manure 3.8 4.4 4.2 4.5 4.4 3.8 4.4 4.3 4.4 4.3 4.3 4.8 4.6 4.3 Residue Na 5.2 5.5 5.3 5.4 5.7 6.0 6.3 6.4 5.7 6.7 6.1 6.3 6.0 6.2 

Mineralization and Asymbiotic 

Fixation 47.8 52.4 47.1 51.5 52.9 48.7 54.4 52.9 57.7 51.8 54.6 59.5 54.0 52.0 

Non-Major Crops 17.6 18.8 17.4 20.5 19.9 22.8 19.7 21.4 21.4 22.5 20.7 20.8 20.5 22.4 

Synthetic Fertilizer 11.9 13.0 11.7 14.9 14.0 16.7 13.8 15.3 15.5 16.5 14.6 14.6 14.3 16.3 

Managed Manure and Other 

Organic Commercial Fertilizer 3.9 3.9 3.8 3.9 4.2 4.5 4.2 4.4 4.2 4.4 4.5 4.6 4.6 4.5 

Residue N 1.8 1.9 1.9 1.7 1.8 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.5 1.6 

Organic Soils 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 

Total* 106.3 115.9 107.2 115.7 118.3 114.2 122.3 120.0 123.5 118.2 119.4 127.0 119.1 117.0 + 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- 193: Direct N2O Emissions from Grasslands (Tg CO2 Eq.) 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2
A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

DAYCENT 48.6 50.9 46.0 48.9 49.4 47.4 52.5 48.1 49.9 44.9 45.7 49.6 50.0 45.1 4

Synthetic Fertilizer 1.0 1.0 1.0 1.0 1.1 1.0 1.1 1.0 1.0 0.9 0.9 1.1 1.1 0.9 

PRP Manure 6.7 7.0 6.5 7.0 7.3 7.0 7.7 6.8 6.9 6.3 6.5 6.9 7.2 6.5 

Managed Manure 1.0 1.0 0.9 1.0 1.0 0.9 1.0 0.9 0.9 0.9 0.9 1.0 1.0 0.9 Residue Na 12.0 12.4 11.5 12.4 12.0 11.9 12.8 11.9 11.8 11.9 11.1 12.2 12.0 11.1 

Mineralization and Asymbiotic Fixation 27.9 29.4 26.1 27.5 28.0 26.6 30.0 27.5 29.2 24.8 26.3 28.5 28.7 25.7 2

Tier 1 3.9 4.0 4.1 4.1 4.3 4.3 4.3 4.3 4.3 4.2 4.1 4.1 4.0 4.0 

PRP Manure 3.7 3.7 3.8 3.8 3.9 3.9 4.0 3.9 3.9 3.8 3.7 3.6 3.6 3.6 

Sewage Sludge 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 

Total 52.5 54.8 50.1 53.0 53.6 51.6 56.8 52.4 54.2 49.1 49.9 53.6 54.1 49.1 5

+ 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- 194: Indirect N2O Emissions (Tg CO2 Eq.) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Volatilization and Atm. Deposition 13.5 13.7 13.5 13.6 14.0 14.7 14.0 14.6 14.5 14.5 14.2 14.4 14.3 14.5 

Croplands 7.8 7.9 7.7 8.0 8.5 8.9 8.4 8.8 8.8 8.9 9.0 8.9 9.0 9.0 

Settlements 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 0.2 Forest Land + + + + + + + + + + + + + + Grasslands 5.6 5.6 5.6 5.5 5.4 5.6 5.4 5.6 5.5 5.4 5.0 5.2 5.1 5.2 

Surface Leaching & Run-off 28.0 21.0 24.5 29.0 17.3 21.9 26.4 19.3 26.3 14.2 21.1 25.4 20.1 22.1 

Croplands 21.3 15.1 17.7 22.1 11.7 15.9 20.4 13.8 20.1 10.5 16.6 19.1 14.2 16.6 

Settlements 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.4 0.4 0.4 

Forest Land + + + + + + + 0.1 0.1 0.1 0.1 0.1 0.1 0.1 

Grasslands 6.4 5.7 6.5 6.6 5.2 5.6 5.7 5.2 5.9 3.3 4.0 5.8 5.4 5.0 

Total 41.5 34.7 38.0 42.6 31.3 36.5 40.4 33.9 40.8 28.6 35.3 39.8 34.5 36.6 

+ Less than 0.05 Tg CO2 Eq. Table A- 195: Total N2O Emissions from Agricultural Soil Management (Tg CO2 Eq.) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 Total Direct 158.9 170.7 157.4 168.7 171.9 165.8 179.1 172.4 177.7 167.2 169.2 180.6 173.1 166.2 Direct Emissions from Mineral 

Cropland Soils 103.5 113.0 104.4 112.8 115.4 111.3 119.4 117.1 120.6 115.3 116.5 124.1 116.2 114.1 

Synthetic Fertilizer 41.0 44.8 42.1 45.8 46.4 46.6 48.3 47.4 46.9 46.4 45.4 47.2 45.4 45.5 

Organic Amendmenta 7.6 8.3 8.0 8.4 8.6 8.3 8.6 8.7 8.6 8.7 8.8 9.4 9.3 8.8 

Residue Nb 7.0 7.4 7.2 7.1 7.5 7.7 8.0 8.1 7.4 8.3 7.7 7.9 7.5 7.8 

Mineralization and Asymbiotic Fixation 47.8 52.4 47.1 51.5 52.9 48.7 54.4 52.9 57.7 51.8 54.6 59.5 54.0 52.0 

Direct Emissions from Drained 

Organic Cropland Soils 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 2.9 

Direct Emissions from Grasslands* 52.5 54.8 50.1 53.0 53.6 51.6 56.8 52.4 54.2 49.1 49.9 53.6 54.1 49.1 

Synthetic Mineral Fertilizer 1.0 1.0 1.0 1.0 1.1 1.0 1.1 1.0 1.0 0.9 0.9 1.1 1.1 0.9 

PRP Manure* 10.3 10.7 10.3 10.8 11.2 10.9 11.7 10.7 10.8 10.1 10.2 10.6 10.8 10.1 

Managed Manure 1.0 1.0 0.9 1.0 1.0 0.9 1.0 0.9 0.9 0.9 0.9 1.0 1.0 0.9 

Sewage Sludge 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 Residueb 12.0 12.4 11.5 12.4 12.0 11.9 12.8 11.9 11.8 11.9 11.1 12.2 12.0 11.1 
Mineralization and Asymbiotic Fixation 27.9 29.4 26.1 27.5 28.0 26.6 30.0 27.5 29.2 24.8 26.3 28.5 28.7 25.7 

Total Indirect 41.5 34.7 38.0 42.6 31.3 36.5 40.4 33.9 40.8 28.6 35.3 39.8 34.5 36.6 

 Volatilization 13.5 13.7 13.5 13.6 14.0 14.7 14.0 14.6 14.5 14.5 14.2 14.4 14.3 14.5 

 Leaching/Runoff 28.0 21.0 24.5 29.0 17.3 21.9 26.4 19.3 26.3 14.2 21.1 25.4 20.1 22.1 

Total Emissions 200.3 205.5 195.4 211.4 203.2 202.3 219.5 206.3 218.5 195.9 204.5 220.4 207.6 202.8 

+ 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-232 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 196: Total 2007 N2O Emissions (Direct and Indirect) from Agricultural Soil Management by State (Tg CO2 Eq.) 

State Croplands1 Grasslands2 Settlements3 Forest Lands4 Total 

Lower 

Bound 

Upper 

Bound 

AL 1.02 0.90 0.03 n.e. 1.95 0.89 4.03 

AR 3.73 1.18 0.02 n.e. 4.94 2.46 8.32 

AZ 0.76 0.89 0.02 n.e. 1.68 0.70 3.84 

CA 5.89 1.89 0.19 n.e. 7.97 4.03 15.53 

CO 1.76 2.47 0.02 n.e. 4.26 1.94 2.12 

CT 0.10 0.03 0.02 n.e. 0.15 0.00 0.00 

DE 0.23 0.02 0.01 n.e. 0.26 -0.02 2.42 

FL 2.34 0.94 0.22 n.e. 3.50 2.18 6.70 

GA 1.58 0.48 0.01 n.e. 2.07 1.19 4.96 

HI5 0.03 n.e. n.e. n.e. 0.03 0.01 0.11 

IA 14.50 0.83 0.07 n.e. 15.40 2.70 27.29 

ID 1.57 1.74 0.02 n.e. 3.33 1.73 5.45 

IL 11.79 0.45 0.12 n.e. 12.36 3.00 21.64 

IN 6.77 0.36 0.07 n.e. 7.20 1.99 11.99 

KS 6.42 1.91 0.07 n.e. 8.40 2.97 13.10 

KY 2.29 1.59 0.03 n.e. 3.91 1.13 7.52 

LA 1.84 0.56 0.04 n.e. 2.44 1.24 4.43 

MA 0.12 0.03 0.04 n.e. 0.19 -0.07 2.28 

MD 0.59 0.22 0.05 n.e. 0.86 0.14 4.24 

ME 0.19 0.03 0.01 n.e. 0.24 -0.03 2.35 

MI 4.02 0.54 0.08 n.e. 4.64 2.16 7.52 

MN 9.29 0.89 0.03 n.e. 10.21 2.80 16.97 

MO 6.06 2.21 0.07 n.e. 8.33 2.81 12.95 

MS 2.35 0.60 0.03 n.e. 2.99 1.38 4.98 

MT 1.56 3.12 0.01 n.e. 4.69 1.86 8.05 

NC 2.38 0.82 0.05 n.e. 3.25 1.36 6.82 

ND 5.44 1.22 0.03 n.e. 6.70 2.71 10.61 

NE 8.54 2.49 0.07 n.e. 11.10 3.19 17.98 

NH 0.04 0.06 0.01 n.e. 0.11 -0.16 2.23 

NJ 0.19 0.02 0.07 n.e. 0.29 0.02 2.37 

NM 0.47 2.63 0.01 n.e. 3.11 0.62 5.60 

NV 0.31 0.58 0.01 n.e. 0.89 0.54 2.74 

NY 2.76 0.55 0.07 n.e. 3.38 1.48 5.43 

OH 7.11 0.60 0.11 n.e. 7.82 2.62 13.25 

OK 2.21 3.11 0.03 n.e. 5.34 2.06 8.63 

OR 1.03 1.63 0.01 n.e. 2.68 1.46 4.94 

PA 2.32 0.41 0.06 n.e. 2.79 0.80 6.48 

RI 0.01 0.00 0.01 n.e. 0.02 -0.25 2.11 

SC 0.63 0.18 0.03 n.e. 0.85 0.44 2.91 

SD 4.52 2.42 0.02 n.e. 6.96 3.10 10.87 

TN 1.84 1.06 0.05 n.e. 2.95 1.52 5.91 

TX 8.59 12.15 0.09 n.e. 20.83 7.44 34.19 

UT 0.50 1.07 0.01 n.e. 1.58 0.87 3.47 

VA 1.06 0.71 0.06 n.e. 1.83 0.95 3.61 

VT 0.35 0.11 0.00 n.e. 0.46 0.20 2.39 

WA 2.23 0.85 0.03 n.e. 3.11 1.56 5.51 

WI 6.17 0.68 0.04 n.e. 6.89 2.24 11.13 

WV 0.31 0.20 0.01 n.e. 0.52 0.25 2.34 

WY 0.35 2.21 0.01 n.e. 2.57 0.87 4.61 1 Emissions from non-manure organic N inputs for minor crops were not estimated (n.e.) at the state level. 2 Emissions from sewage sludge applied to grasslands and were not estimated (n.e.) at the state level 3 Emissions from sewage sludge applied to settlements were not estimated (n.e.) at the state level. 4 Forestland emissions were not estimated (n.e.) at the state level. 5 N2O emissions are not reported for Hawaii except from cropland organic soils. 
A-233

3.12. Methodology for Estimating Net Carbon Stock Changes in Forest Lands 

Remaining Forest Lands 

This sub-annex expands on the methodology used to calculate net changes in carbon (C) stocks in forest 

ecosystems and in harvested wood products. Some of the details of C conversion factors and procedures for 

calculating net CO2 flux for forests are provided below; full details of selected topics may be found in the cited 

references.

Carbon Stocks and Net Changes in Forest Ecosystem Carbon Stocks 

At least two forest inventories exist for the managed forests of Alaska and for most forest land in the 48 

states of the conterminous United States. C stocks are estimated based on data from each inventory, at the level of 

permanent inventory plots. C per hectare (for a sample location) is multiplied by the total number of hectares that 

the plot represents, and then totals are summed for an area of interest, such as the state of Arizona. Net annual C 

stock changes are calculated by taking the difference between the inventories and dividing by the number of years 

between the inventories for a selected state or sub-state area. 

Forest inventory data 

The estimates of forest C stocks are based on data derived from forest inventory surveys. Forest inventory 

data were obtained from the USDA Forest Service, Forest Inventory and Analysis (FIA) program (Frayer and 

Furnival 1999). FIA data include remote sensing information to determine forest areas, and collection of 

measurements in the field at sample locations called plots. Tree measurements include diameter and species. On a 

subset of plots, additional measurements or samples are taken of down dead wood, litter, and soil C; however, these 

are not yet available nationwide for C estimation. The field protocols are thoroughly documented and available for 

download from the USDA Forest Service (2008c). The inventory was designed for timber volume estimation rather 

than C stock estimation, so most C pools are not measured or sampled directly. Bechtold and Patterson (2005) 

provide the estimation procedures for standard forest inventory results. The data are freely available for download 

at USDA Forest Service (2008b) as the Forest Inventory and Analysis Database (FIADB) Version 3.0; these data are 

the primary sources of forest inventory used to estimate forest C stocks. 

Forest surveys are currently being conducted in the U.S. territories and are planned for 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). Data are publicly posted for 3.8 million 

hectares of forest land. This inventory covers most of the managed forestlands of Alaska. Data are available for 

areas that are administratively designated as reserved, or areas where no harvesting is to occur; however, for these 

reserved areas, the first available inventory covered a much smaller land base compared to the second inventory, so 

that data was not used. The assumption applied here is that the reserved areas in this part of Alaska account for a net 

C change of zero. 

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.50 These practices are used 

to address many issues facing agricultural lands, such as economic diversification, habitat fragmentation, and water 

quality. While providing these services and regardless of intent, these tree-based plantings will also reduce 

 

50 More information on agroforestry practices can be found online at <http://www.unl.edu/nac>. 
A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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 (Brandle et al. 1992). These practices are not worth reporting individually, but 

can potentially be quite large when taken into account within a whole-farm or within an aggregating larger entity 

(i.e., state-level) (Brandle et al. 1992, Schoeneberger 2006). The sequestration potential through agroforestry 

practices in the United States has been estimated to be approximately 90.3 Mt C/year by 2025 (Nair and Nair 2003). 

Summing state-level C stocks to calculate United States net C flux in forest ecosystems 

The overall approach for determining forest C stocks and stock change is based on methodology and 

algorithms coded into the computer tool described in a revised version of Smith et al. (2007) and Smith et al. (in 

preparation) and 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 2 or more surveys available for download for 

each state. C stocks are calculated separately for each state based on available inventories conducted since 1990 and 

for the inventory closest to, but prior to, 1990 if such data are available and consistent with these methods. This 

approach ensures that the period 1990 (the base year) to present can be adequately represented. Surveys conducted 

prior to and in the early to mid 1990s focused on land capable of supporting timber production (timberland).51 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 slowergrowing (and therefore slowly changing) forests. Older surveys for some states, particularly in the West, also have 

National Forest System (NFS) lands or reserved lands 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-197 provides a list of the 

specific surveys used here and Smith et al. (2007, in preparation) provides further details. 

Table A-197: Source of 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 Southern, 1982 1982 

 FIADB 3.0, 1990 1990 

 FIADB 3.0, 2000 1999 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

Alaska, non-reserved Southcentral FIADB 2.1, 1998 2001 

 FIADB 3.0, 2007 2006 

Alaska, non-reserved Southeast FIADB 2.1, 1998 1997 

 FIADB 3.0, 2007 2006 

Arizona, NFS non-woodlands 1987 RPA 1985 

 FIADB 3.0, 1999 1996 

 FIADB 3.0, 2007 2005 

Arizona, NFS woodlands 1987 RPA 1984 

 FIADB 3.0, 1999 1996 

 FIADB 3.0, 2007 2004 

Arizona, non-NFS non-woodlands FIADB 3.0, 1985 1986 

 FIADB 3.0, 1999 1995 

 

51 Forest land in the United States includes land that is at least 10 percent stocked with trees of any size. Timberland is 

the most productive type of forest land, which is on unreserved land and is producing or capable of producing crops of industrial 

wood. Productivity is at a minimum rate of 20 cubic feet of industrial wood per acre per year. The remaining portion of forest

land is classified as either reserved forest land, which is forest land withdrawn from timber use by statute or regulation, or other 

forest land, which includes less productive forests on which timber is growing at a rate less than 20 cubic feet per acre per year. 

Forest land includes woodlands, which describes forest types consisting primarily of species that have their diameter measured at 

root collar, and for which there is no site index equations, nor stocking guides (Woudenberg 2006). 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. 2008). 
A-235

 FIADB 3.0, 2007 2006 

Arizona, non-NFS woodlands FIADB 3.0, 1985 1987 

 FIADB 3.0, 1999 1989 

 FIADB 3.0, 2007 2005 

Arkansas Eastwide, 1988 1988 

 FIADB 3.0, 1995 1996 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

California, NFS 1997 RPA 1991 

 FIADB 3.0, 2007 2005 

California, non-NFS 1987 RPA 1982 

 Westwide, 1994 1994 

 FIADB 3.0, 2007 2005 

Colorado, NFS non-woodlands 1997 RPA 1981 

 FIADB 3.0, 2007 2005 

Colorado, NFS woodlands 1997 RPA 1975 

 FIADB 3.0, 1984 1997 

 FIADB 3.0, 2007 2005 

Colorado, non-NFS non-woodlands Westwide, 1983 1980 

 FIADB 3.0, 2007 2005 

Colorado, non-NFS woodlands Westwide, 1983 1983 

 FIADB 3.0, 2007 2005 

Connecticut FIADB 3.0, 1985 1985 

 FIADB 3.0, 1998 1998 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2005 

Delaware FIADB 3.0, 1986 1986 

 FIADB 3.0, 1999 1999 

 FIADB 3.0, 2004 2005 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2006 

Florida FIADB 3.0, 1987 1987 

 FIADB 3.0, 1995 1995 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2004 

Georgia FIADB 3.0, 1989 1989 

 FIADB 3.0, 1997 1997 

 FIADB 3.0, 2004 2001 

 FIADB 3.0, 2005 2002 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

Idaho, Caribou-Targhee NF Westwide, 1991 1992 

 FIADB 3.0, 2007 2006 

Idaho, Kootenai NF 1987 RPA 1988 

 FIADB 3.0, 1991 1995 

 FIADB 3.0, 2007 2007 

Idaho, Payette NF 1987 RPA 1982 

 FIADB 3.0, 2007 2006 

Idaho, Salmon-Challis NF 1987 RPA 1978 

 FIADB 3.0, 2007 2006 

Idaho, Sawtooth NF Westwide, 1991 1983 

 FIADB 3.0, 1991 1996 

 FIADB 3.0, 2007 2006 

Idaho, non-NFS non-woodlands FIADB 3.0, 1991 1990 

 FIADB 3.0, 2007 2006 

Idaho, non-NFS woodlands FIADB 3.0, 1991 1982 

 FIADB 3.0, 2007 2006 

Idaho, other NFS Westwide, 1991 1988 

 FIADB 3.0, 1991 2000 

 FIADB 3.0, 2007 2006 
A-236 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Illinois FIADB 3.0, 1985 1985 

 FIADB 3.0, 1998 1998 

 FIADB 3.0, 2003 2003 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Indiana FIADB 3.0, 1986 1986 

 FIADB 3.0, 1998 1998 

 FIADB 3.0, 2003 2001 

 FIADB 3.0, 2004 2002 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2006 

Iowa FIADB 3.0, 1990 1990 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2004 

Kansas FIADB 3.0, 1981 1981 

 FIADB 3.0, 1994 1994 

 FIADB 3.0, 2003 2003 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

Kentucky FIADB 3.0, 1988 1987 

 FIADB 3.0, 2004 2002 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

Louisiana Southern, 1984 1984 

 FIADB 3.0, 1991 1991 

 FIADB 3.0, 2005 2004 

Maine Eastwide, 1982 1983 

 FIADB 3.0, 1995 1995 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Maryland FIADB 3.0, 1986 1986 

 FIADB 3.0, 1999 2000 

 FIADB 3.0, 2004 2005 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2006 

Massachusetts FIADB 3.0, 1985 1985 

 FIADB 3.0, 1998 1998 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2005 

Michigan FIADB 3.0, 1980 1980 

 FIADB 3.0, 1993 1993 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

Minnesota FIADB 3.0, 1977 1977 

 FIADB 3.0, 1990 1989 

 FIADB 3.0, 2003 2001 

 FIADB 3.0, 2004 2002 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

Mississippi Eastwide, 1987 1987 

 FIADB 3.0, 1994 1994 

 FIADB 3.0, 2006 2007 

Missouri FIADB 3.0, 1989 1988 
A-237

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2004 

Montana, NFS 1987 RPA 1988 

 FIADB 3.0, 1989 1996 

 FIADB 3.0, 2007 2006 

Montana, non-NFS non-reserved FIADB 3.0, 1989 1989 

 FIADB 3.0, 2007 2006 

Montana, non-NFS reserved 1997 RPA 1990 

 FIADB 3.0, 2007 2006 

Nebraska FIADB 3.0, 1983 1983 

 FIADB 3.0, 1994 1995 

 FIADB 3.0, 2003 2003 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2004 

Nevada, NFS non-woodlands 1987 RPA 1974 

 FIADB 3.0, 1989 1997 

 FIADB 3.0, 2005 2005 

Nevada, NFS woodlands 1987 RPA 1978 

 FIADB 3.0, 1989 1997 

 FIADB 3.0, 2005 2005 

Nevada, non-NFS non-woodlands 1997 RPA 1985 

 FIADB 3.0, 2005 2005 

Nevada, non-NFS woodlands FIADB 3.0, 1989 1980 

 FIADB 3.0, 2005 2005 

New Hampshire FIADB 3.0, 1983 1983 

 FIADB 3.0, 1997 1997 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

New Jersey FIADB 3.0, 1987 1987 

 FIADB 3.0, 1999 1999 

 FIADB 3.0, 2004 2005 

 FIADB 3.0, 2005 2006 

 FIADB 3.0, 2006 2006 

New Mexico, NFS non-woodlands 1987 RPA 1986 

 FIADB 3.0, 1999 1997 

New Mexico, NFS woodlands 1987 RPA 1986 

 FIADB 3.0, 1999 1997 

New Mexico, non-NFS non-woodlands FIADB 3.0, 1987 1987 

 FIADB 3.0, 1999 1999 

New Mexico, non-NFS woodlands FIADB 3.0, 1999 1989 

New York, non-reserved Eastwide, 1980 1981 

 FIADB 3.0, 1993 1993 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

New York, reserved 1987 RPA 1988 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

North Carolina FIADB 3.0, 1984 1984 

 FIADB 3.0, 1990 1990 

 FIADB 3.0, 2002 2001 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2005 

North Dakota FIADB 3.0, 1980 1979 

 FIADB 3.0, 1995 1995 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2005 
A-238 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Ohio FIADB 3.0, 1991 1991 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Oklahoma, Central & West FIADB 3.0, 1989 1989 

Oklahoma, East Southern, 1986 1986 

 FIADB 3.0, 1993 1993 

Oregon, NFS East 1987 RPA 1988 

 Westwide, 1992 1995 

 FIADB 3.0, 2007 2005 

Oregon, NFS West 1987 RPA 1987 

 Westwide, 1992 1995 

 FIADB 3.0, 2007 2005 

Oregon, non-NFS East FIADB 2.1, 1992 1990 

 FIADB 2.1, 1999 1999 

 FIADB 3.0, 2007 2004 

Oregon, non-NFS West Westwide, 1992 1989 

 FIADB 3.0, 2007 2005 

Pennsylvania FIADB 3.0, 1989 1990 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Rhode Island FIADB 3.0, 1985 1985 

 FIADB 3.0, 1998 1999 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2005 

South Carolina FIADB 3.0, 1986 1986 

 FIADB 3.0, 1993 1993 

 FIADB 3.0, 2001 2001 

 FIADB 3.0, 2006 2005 

South Dakota, NFS 1997 RPA 1986 

 FIADB 3.0, 1995 1999 

 FIADB 3.0, 2003 2003 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2004 

South Dakota, non-NFS 1987 RPA 1986 

 FIADB 3.0, 1995 1995 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Tennessee FIADB 3.0, 1989 1989 

 FIADB 3.0, 1999 1998 

 FIADB 3.0, 2004 2003 

 FIADB 3.0, 2005 2004 

 FIADB 3.0, 2006 2005 

Texas, East Southern, 1986 1986 

 FIADB 3.0, 1992 1992 

 FIADB 3.0, 2003 2003 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

Utah, non-woodlands FIADB 3.0, 1993 1993 

 FIADB 3.0, 2007 2004 

Utah, woodlands FIADB 3.0, 1993 1994 

 FIADB 3.0, 2007 2004 

Vermont FIADB 3.0, 1983 1983 

 FIADB 3.0, 1997 1997 

 FIADB 3.0, 2005 2005 

 FIADB 3.0, 2006 2006 

Virginia FIADB 3.0, 1984 1985 
A-239

 FIADB 3.0, 1992 1991 

 FIADB 3.0, 2001 2000 

 FIADB 3.0, 2005 2002 

 FIADB 3.0, 2006 2004 

 FIADB 3.0, 2007 2005 

Washington, NFS East 1987 RPA 1988 

 Westwide, 1991 1995 

 FIADB 3.0, 2007 2005 

Washington, NFS West 1987 RPA 1988 

 Westwide, 1991 1995 

 FIADB 3.0, 2007 2005 

Washington, non-NFS East FIADB 2.1, 1991 1992 

 FIADB 3.0, 2007 2005 

Washington, non-NFS West 1987 RPA 1979 

 FIADB 2.1, 1991 1991 

 FIADB 3.0, 2007 2005 

West Virginia FIADB 3.0, 1989 1988 

 FIADB 3.0, 2000 2001 

 FIADB 3.0, 2004 2005 

 FIADB 3.0, 2005 2006 

 FIADB 3.0, 2006 2006 

Wisconsin FIADB 3.0, 1983 1982 

 FIADB 3.0, 1996 1995 

 FIADB 3.0, 2003 2002 

 FIADB 3.0, 2004 2002 

 FIADB 3.0, 2005 2003 

 FIADB 3.0, 2006 2004 

Wyoming, NFS 1997 RPA 1982 

 2002 RPA 1997 

 FIADB 3.0, 2000 2000 

Wyoming, non-NFS non-reserved nonwoodlands FIADB 3.0, 1984 1984 

 FIADB 3.0, 2000 2002 

Wyoming, non-NFS non-reserved woodlands FIADB 3.0, 1984 1984 

 FIADB 3.0, 2000 2002 

Wyoming, non-NFS reserved 1997 RPA 1985 

 FIADB 3.0, 2000 2000 

a Substate areas (Smith et al. 2007) 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 2008c]), 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. (2008)), 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). In Oklahoma, east 

refers to the eastern 18 counties. Texas and Alaska represent a portion of each state: eastern Texas and southcentral and southeast Alaska only. b FIADB 3.0 is the current, publicly available, format of FIA inventory data, and these files were downloaded from the Internet September 18, 2008 (USDA Forest 

Service 2008b). FIADB 2.1 formatted data were used for a few surveys that were not yet available in the 3.0 format; in these cases data from the previous 

inventory (EPA 2008) were reprocessed with the 3.0 data. Eastwide (Hansen et al. 1992) and Westwide (Woudenberg and Farrenkopf 1995) inventory data are 

formats that predate the FIADB data. Southern inventories are from older surveys compiled for the Southern region (CD of Southern data from Linda Heatherly, 

FIA Southern Region, July 7, 2004). RPA data are periodic national summaries. The year is the nominal, or reporting, year associated with each dataset. c Average year is based on average measurement year of forest land survey plots and rounded to the nearest integer year. 

A new national plot design and annual sampling (USDA Forest Service 2008a) was introduced by FIA 

about ten years ago. Most states have only recently been brought into this system, though. Annual sampling means 

that a portion of plots throughout each 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. Sampling is designed such that partial inventory cycles 

provide usable, unbiased samples of forest inventory, but with higher standard errors than the full cycle. Once all 

plots have been measured, the sequence continues with re-measurement of the first year’s plots. Thus, many states 

have relatively recent partial inventories; that is, they are part-way through an annual inventory cycle. Twenty one 

states have remeasurement data available for at least a portion of the state. All annual surveys initiated since 1998 

have followed the new national plot design for all forest land – this includes sampling reserved and lower 

productivity lands. 
A-240 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

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. Stocks and 

fluxes since the most recent survey are based on extrapolation. Extrapolation beyond the most recent survey is 

based on an average flux obtained from the last three whole-year interpolated values (Smith et al. In preparation). C 

stock and flux estimates for each pool are summed over all forest land in all states as identified in Table A-197 to 

form estimates for the United States. Summed net annual change and stock are presented in Table A- 198 and Table 

A- 199, respectively. Table A- 199 also provides an estimate of forest area based on the interpolation and 

extrapolation procedure described above. 
Table A- 198.: Net Annual Changes in Carbon Stocks (Tg C yr-1) in Forest and Harvested Wood Pools, 1990-2007 

Carbon Pool 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007Forest (144.3) (155.9) (154.0) (143.1) (153.8) (155.0) (148.2) (154.5) (136.5) (118.8) (109.0) (133.6) (205.7) (280.5) (284.9) (237.7) (215.9) (220.8) Live, aboveground (87.7) (94.7) (95.9) (92.8) (96.3) (106.6) (102.9) (109.2) (103.0) (98.9) (96.0) (103.0) (119.7) (144.3) (145.0) (128.0) (120.7) (123.4) Live, belowground (16.8) (18.3) (18.6) (18.1) (19.0) (21.3) (20.6) (22.0) (20.8) (20.1) (19.5) (20.9) (24.1) (28.8) (28.8) (25.5) (24.2) (24.7) Dead Wood (4.2) (4.8) (4.7) (4.9) (8.2) (7.4) (7.5) (6.4) (4.1) (4.0) (5.0) (5.8) (8.5) (11.6) (12.1) (10.7) (9.7) (10.0) Litter (18.5) (19.4) (18.9) (17.7) (17.7) (10.1) (8.8) (8.6) (5.6) (5.7) (4.0) (9.6) (18.7) (25.1) (25.4) (21.7) (18.7) (19.3) Soil Organic Carbon (17.1) (18.6) (15.8) (9.6) (12.5) (9.4) (8.4) (8.4) (2.9) 10.0 15.5 5.8 (34.7) (70.6) (73.7) (51.9) (42.5) (43.3)Harvested Wood (35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1) (32.5) (30.8) (25.5) (26.7) (25.9) (28.7) (28.3) (29.6) (27.4) Products in Use (17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.5) (9.7) (12.4) (12.0) (12.3) (10.1) SWDS (18.3) (18.8) (17.4) (18.0) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16.3) (16.3) (17.3) (17.3)Total Net Flux (180.3) (189.6) (187.7) (176.1) (187.2) (187.2) (178.8) (186.6) (167.6) (151.2) (139.8) (159.1) (232.4) (306.4) (313.7) (266.1) (245.5) (248.2)

Table A- 199: Carbon Stocks (Tg C) in Forest and Harvested Wood Pools, 1990-2008 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Forest Area (1000 ha) 245,697 246,440 247,194 247,922 248,594 249,240 249,798 250,312 250,858 251,345 251,732 252,096 252,520 253,183 254,194 255,358 Carbon Pool Forest 40,011 40,155 40,311 40,465 40,608 40,762 40,917 41,065 41,219 41,356 41,475 41,584 41,717 41,923 42,203 42,488  Live, aboveground 14,378 14,465 14,560 14,656 14,749 14,845 14,951 15,054 15,164 15,267 15,365 15,462 15,564 15,684 15,829 15,974  Live, belowground 2,860 2,876 2,895 2,913 2,931 2,950 2,972 2,992 3,014 3,035 3,055 3,075 3,096 3,120 3,149 3,177  Dead Wood 2,541 2,545 2,550 2,554 2,559 2,567 2,575 2,582 2,589 2,593 2,597 2,602 2,608 2,616 2,628 2,640  Litter 4,558 4,577 4,596 4,615 4,633 4,651 4,661 4,670 4,678 4,684 4,690 4,694 4,703 4,722 4,747 4,772  Soil Organic Carbon 15,675 15,692 15,710 15,726 15,736 15,748 15,758 15,766 15,774 15,777 15,767 15,752 15,746 15,781 15,851 15,925 Harvested Wood 1,783 1,821 1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124 2,155 2,188 2,218 2,244 2,271  Products in Use 1,193 1,212 1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413  SWDS 590 609 628 646 665 683 701 718 735 752 769 787 805 823 840 857 Total Carbon Stock 41,794 41,976 42,170 42,360 42,537 42,724 42,912 43,094 43,281 43,448 43,599 43,739 43,905 44,141 44,447 44,759 
A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 200 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 (Table A-197). C density values reflect the 

most recent survey for each state as available in the FIADB, not potential maximum C storage. C densities are 

affected by the distribution of stand sizes within a forest type, which can range from regenerating to mature stands. 

A large proportion of young stands in a particular forest type are likely to reduce the regional average for C density. 

Table A- 200: 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 (see Table A-197), 

corresponding to an average year of 2004 

Region 

(States) 

Forest Types 

Aboveground 

Biomass 

Belowground 

Biomass 

Dead 

Wood Litter 

Soil 

Organic 

Carbon 

Forest 

Area 

 Carbon Density (Mg/ha) (1,000 ha) 

Northeast 

(CT,DE,MA,MD,ME,NH,NJ,NY,OH,PA,RI,VT,WV) 

White/Red/Jack Pine 91.8 19.0 11.2 13.8 78.1 2,007 

Spruce/Fir 51.1 10.8 11.7 30.6 98.0 2,910 

Oak/Pine 75.7 15.0 9.1 27.3 66.9 1,252 

Oak/Hickory 81.1 15.4 10.4 8.1 53.1 11,105 

Elm/Ash/Cottonwood 54.0 10.2 8.3 23.7 111.7 1,138 

Maple/Beech/Birch 75.8 14.6 12.5 26.5 69.6 16,136 

Aspen/Birch 47.0 9.3 7.8 8.5 87.4 1,651 

Minor Types and Nonstocked 48.7 9.8 6.9 14.7 82.7 1,219 

All 73.5 14.2 11.1 19.4 69.7 37,417 

Northern Lake States 

(MI,MN,WI) 

White/Red/Jack Pine 54.8 11.4 8.1 12.3 120.8 1,832 

Spruce/Fir 40.4 8.5 8.2 32.8 261.8 3,126 

Oak/Hickory 71.8 13.6 10.5 7.9 97.1 3,033 

Elm/Ash/Cottonwood 50.7 9.7 8.6 25.6 179.9 1,746 

Maple/Beech/Birch 70.6 13.5 10.8 26.6 134.3 5,275 

Aspen/Birch 41.1 8.0 8.2 8.3 146.1 5,313 

Minor Types and Nonstocked 37.7 7.5 6.2 11.0 125.8 938 

All 54.5 10.7 9.1 18.3 152.9 21,263 

Northern Prairie States 

(IA,IL,IN,KS,MO,ND,NE,SD) 

Ponderosa Pine 41.1 8.7 6.7 14.2 48.5 557 

Oak/Pine 51.7 10.0 7.1 25.3 39.9 587 

Oak/Hickory 69.8 13.2 9.4 7.7 48.9 8,465 

Elm/Ash/Cottonwood 72.7 13.6 11.2 23.7 83.2 1,865 

Maple/Beech/Birch 65.1 12.3 8.9 25.1 70.7 1,182 

Minor Types and Nonstocked 36.1 7.2 5.8 12.6 57.5 917 

All 65.5 12.4 9.1 12.8 55.7 13,574 

South Central 

(AL,AR,KY,LA,MS,OK,TN,TX) 

Longleaf/Slash Pine 40.6 8.3 4.2 10.9 55.5 1,215 

Loblolly/Shortleaf Pine 45.7 9.4 5.1 9.5 41.9 13,152 

Oak/Pine 48.1 9.3 6.0 9.2 41.7 5,065 

Oak/Hickory 63.2 11.9 7.2 6.3 38.6 19,394 

Oak/Gum/Cypress 77.6 14.8 9.2 6.5 52.8 4,717 

Elm/Ash/Cottonwood 55.0 10.4 7.5 5.8 49.9 2,572 

Minor Types and Nonstocked 40.6 7.8 5.6 6.9 49.6 1,839 

All 56.4 10.9 6.6 7.6 42.7 47,954 

Southeast 

(FL,GA,NC,SC,VA) 

Longleaf/Slash Pine 34.4 7.0 3.7 9.7 110.0 4,053 

Loblolly/Shortleaf Pine 48.3 9.9 6.0 9.5 72.9 9,095 

Oak/Pine 51.7 10.1 5.7 9.2 61.4 4,051 

Oak/Hickory 72.2 13.6 8.2 6.4 45.3 12,359 

Oak/Gum/Cypress 74.3 14.3 9.2 6.4 158.0 4,369 
A-243

Elm/Ash/Cottonwood 58.2 11.0 8.9 5.6 95.7 708 

Minor Types and Nonstocked 44.9 8.6 5.8 5.8 101.4 1,314 

All 58.5 11.4 6.9 7.8 78.1 35,948 

Coastal Alaska 

(approximately 7 percent of forest 

land) 

Fir/Spruce/Mt. Hemlock 88.7 18.7 26.0 44.1 62.1 1,233 

Lodgepole Pine 30.4 6.3 5.7 41.5 52.0 191 

Hemlock/Sitka Spruce 136.2 28.7 36.5 49.6 116.3 1,947 

Aspen/Birch 34.2 6.5 9.5 11.0 42.5 142 

Minor Types and Nonstocked 37.1 7.2 8.8 18.1 77.3 329 

All 103.4 21.7 28.2 43.3 89.7 3,843 

Pacific Northwest, Westside 

(Western OR and WA) 

Douglas-fir 144.8 30.4 31.5 32.0 94.8 5,802 

Fir/Spruce/Mt. Hemlock 152.0 32.1 39.1 38.4 62.1 1,227 

Hemlock/Sitka Spruce 175.7 37.0 45.3 37.7 116.3 1,573 

Alder/Maple 84.4 16.5 21.5 7.5 115.2 1,222 

Minor Types and Nonstocked 66.1 13.1 11.7 13.3 85.7 1,208 

All 134.7 28.1 31.0 28.7 95.5 11,032 

Pacific Northwest, Eastside 

(Eastern OR and WA) 

Pinyon/Juniper 12.4 2.4 2.3 21.1 46.9 1,256 

Douglas-fir 76.5 16.0 18.1 36.1 94.8 2,071 

Ponderosa Pine 48.8 10.2 9.8 22.5 50.7 2,827 

Fir/Spruce/Mt. Hemlock 92.3 19.5 26.3 37.9 62.1 1,816 

Lodgepole Pine 42.2 8.9 9.9 21.1 52.0 1,023 

Western Larch 65.5 13.7 17.9 35.9 45.1 209 

Minor Types and Nonstocked 37.9 7.6 11.7 25.3 81.9 1,045 

All 56.2 11.7 13.9 28.2 64.4 10,247 

Pacific Southwest 

(CA) 

Pinyon/Juniper 22.1 4.3 1.7 21.1 26.3 1,397 

Douglas-fir 162.1 33.7 33.8 35.5 40.1 447 

Ponderosa Pine 60.9 12.6 11.4 35.7 41.3 955 

Fir/Spruce/Mt. Hemlock 154.4 32.6 42.8 38.1 51.9 850 

Lodgepole Pine 101.6 21.5 20.7 39.8 35.2 415 

Redwood 202.6 42.3 42.3 60.4 53.8 286 

California Mixed Conifer 128.5 26.9 31.2 37.9 49.8 3,227 

Western Oak 65.2 12.4 7.4 29.5 27.6 3,819 

Tanoak/Laurel 133.7 26.3 19.7 27.7 27.6 835 

Minor Types and Nonstocked 47.6 9.4 9.3 28.4 40.1 1,144 

All 91.4 18.6 18.1 32.6 37.6 13,376 

Rocky Mountain, North 

(ID,MT) 

Douglas-fir 73.2 15.5 13.8 37.0 38.8 5,567 

Ponderosa Pine 41.0 8.5 7.5 22.9 34.3 1,927 

Fir/Spruce/Mt. Hemlock 67.6 14.3 21.2 37.4 44.1 4,506 

Lodgepole Pine 52.0 11.1 9.9 23.1 37.2 2,727 

Western Larch 61.1 12.9 14.4 36.4 34.2 483 

Minor Types and Nonstocked 31.2 6.3 10.0 25.6 43.2 3,841 

All 56.8 11.9 13.6 31.4 40.1 19,051 

Rocky Mountain, South 

(AZ,CO,NM,NV,UT,WY) 

Pinyon/Juniper 23.0 4.7 0.9 21.1 19.7 18,633 

Douglas-fir 74.2 15.8 16.7 38.0 30.9 1,813 

Ponderosa Pine 47.9 10.1 8.1 23.6 24.1 3,615 

Fir/Spruce/Mt. Hemlock 80.1 17.0 22.8 38.8 31.5 4,174 

Lodgepole Pine 53.3 11.3 12.9 24.1 27.0 2,060 

Aspen/Birch 58.3 11.3 12.1 28.5 58.8 2,539 

Western Oak 19.9 3.8 2.3 27.5 38.0 3,105 

Minor Types and Nonstocked 18.4 3.5 4.1 24.6 25.6 4,070 
A-244 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

All 36.6 7.5 6.3 25.4 26.7 40,008 

United States (forest land included 

in Inventory) 62.4 12.4 10.4 18.8 62.5 253,713 

Note: The forest area values in this table do not equal the forest area values reported in Table A- 199, because the forest area values in this table are estimated 

using the most recent dataset per state (see Table A-1), with an average year of 2004. The time series of forest area values reported in Table A- 199, in 

contrast, is constructed following the CCT methods used to construct the carbon stock series. The forest area values reported in Table A- 199 and Table A- 200 

would only be identical if all states were measured simultaneously or they all had identical rates of change. 

The Inventory is derived primarily from the current FIADB 3.0 data (USDA Forest Service 2008b), 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. The basic difference between the RPA 

database and the FIADB is that the FIADB includes some informative additional details such as individual-tree data. 

Having only plot-level information (such as volume per hectare) limits the conversion to biomass. This does not 

constitute a substantial difference for the overall state-wide estimates, but it does affect plot-level precision (Smith et 

al. 2004a). 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 and older tree-level inventories from the Southern FIA unit (Heatherly 2006). More complete 

information about these data is available on the Internet (USDA Forest Service 2008a), and the results of these 

databases are found in Waddell et al. (1989), Smith et al. (2001), and Smith et al. (2004b). 

An historical focus of the FIA program was to provide information on timber resources of the United 

States. For this reason, prior to 1998, some forest land, which were less productive or reserved (i.e., land where 

harvesting was prohibited by law), were less intensively surveyed. This generally meant that on these less 

productive lands, forest type and area were identified but data were not collected on individual tree measurements. 

The practical effect that this evolution in inventories has had on estimating forest C stocks from 1990 through the 

present is that some older surveys of lands do not have the stand-level values for merchantable volume of wood or 

stand age. Any data gaps identified in the surveys taken before 1998 were filled by assigning average C densities 

calculated from the more complete, later inventories from the respective states. The overall effect of this necessary 

approach to generate estimates for C stock is that no net change in C density occurs on those lands with gaps in past 

surveys.

Estimating C stocks from forest inventory data 

For each inventory summary in each state, data are converted to C units or augmented by other ecological 

data. This collection of conversion factors and models is referred to as FORCARB2 (Birdsey and Heath 1995, 

Birdsey and Heath 2001, Heath et al. 2003, Smith et al. 2004a, Smith et al. 2007). The conversion factors and 

model coefficients are usually categorized by region, and forest type. Classifications for both region and forest type 

are subject to change depending on the particular coefficient set. Thus, region and type are specifically defined for 

each set of estimates. Factors are applied to the survey data at the scale of FIA inventory plots. The results are 

estimates of C density (Mg per hectare) for the various forest pools. C density for live trees, standing dead trees, 

understory vegetation, down dead wood, litter, and soil organic matter are estimated. All non-soil pools except litter 

can be separated into aboveground and belowground components. The live tree and understory C pools are pooled 

as biomass in this inventory. Similarly, standing dead trees and down dead wood are pooled as dead wood in this 

inventory. C stocks and fluxes for Forest Land Remaining Forest Land are reported in pools following IPCC 

(2003). 

Live tree C pools 

The tree C pools include aboveground and belowground (coarse root) C mass of live trees. Separate 

estimates are made for full-tree and aboveground-only biomass to estimate the belowground component. Most tree 

C estimates are based on Jenkins et al. (2003) and are functions of species groups and diameter. For example, the 

equation for estimating aboveground biomass for a live tree of a species in the aspen/alder/cottonwood/willow group 

is: 

Biomass (kg dry weight) = e(-2.2094 + 2.3867 × ln(diameter))
A-245

Diameter is cm at diameter breast height (d.b.h.), which is measured at 1.37 m above the forest floor. C is 

calculated by multiplying biomass by 0.5 because biomass is 50 percent of dry weight (IPCC/UNEP/OECD/IEA 

1997). A full set of coefficients can be found in Jenkins et al. (2003; Table 4). Belowground root biomass is 

estimated as a ratio of roots to total aboveground biomass. The equation for ratio of root biomass of a live tree in 

the aspen/alder/cottonwood/willow group is: 

Ratio = e(-1.6911 + 0.8160/diameter)

Belowground biomass is calculated by multiplying the ratio by total aboveground biomass. A full set of 

coefficients can be found in Jenkins et al. (2003; Table 6). The C per tree is summed for each plot, and multiplied 

by the appropriate expansion factors to obtain a C stock estimate for the plot. 

Some inventory data do not provide measurements of individual trees; tree C in these plots is estimated 

from plot-level growing stock volume of live trees and equations given in Table A- 201 and Table A- 202. These 

equations are updates of those in Smith et al. (2003), modified to reduce error and correspond to common forest 

types defined by inventories. Separate estimates are made for whole-tree and aboveground-only biomass based on 

forest type group and region. The belowground portion is determined as the difference between the two estimates. 

C density is estimated based on the growing stock volume of the plot, where growing stock includes live trees of 

commercial species meeting specified standards. Only trees 5.0 inches d.b.h. and larger are included in growing 

stock volume (Smith et al. 2008). The full sets of coefficients are in Table A- 201 and Table A- 202. For example, 

the total C in tree biomass per hectare of aspen-birch in the North averages 8.1 Mg C/ha if growing-stock volume is 

zero. If growing-stock volume is greater than zero, the estimate is in two parts. Average C density of non-growingstock trees (sapling and cull trees) is 14.3 Mg C/ha, and the equation for C in growing-stock trees is: 

Growing-stock trees (Mg C/ha) = e(-0.337 + ln(volume) × 0.933)

Units for volume are m3

/ha. 

Table A- 201: Coefficients for estimating carbon density of live trees (above- and below-ground, MgC/ha) by region 

and type for plot-level data such as RPA dataa

Regionb Forest type groupc

Carbon density, 

if Growing Stock 

Volume (GSV)=0 

C density for nonGrowing Stock 

(GS), if GSV > 0 

Coefficient A Coefficient B 

Aspen/Birch 8.138 14.335 -0.337 0.933 

Elm/Ash/Cottonwood 16.187 18.707 -0.206 0.920 

Maple/Beech/Birch 6.938 17.054 -0.170 0.925 

Oak/Hickory 13.083 15.914 -0.079 0.932 

Hardwood minor types 10.376 14.127 0.002 0.890 

Oak/Pine 4.079 15.473 -0.146 0.908 

Ponderosa Pine & Exotic 

Softwood 2.595 6.895 -0.074 0.886 

Loblolly/Shortleaf Pine 6.277 9.766 -0.415 0.943 

Spruce/Fir 6.424 16.903 -0.487 0.947 

White/Red/Jack Pine 3.908 12.117 -0.349 0.924 

Softwood minor types 6.277 17.234 -0.380 0.970 

North 

Non-stocked 1.054 1.238 -0.174 0.866 

Alder/Maple 8.425 4.444 0.056 0.828 

Other Western Hardwoods 8.425 10.483 0.041 0.864 

Tanoak/Laurel 8.425 10.203 -0.167 0.917 

Western Oak 8.425 7.400 0.344 0.850 

Hardwood minor types 8.425 4.802 0.333 0.770 

California Mixed Conifer 10.102 4.727 0.137 0.843 

Douglas-fir 2.752 4.961 0.180 0.834 

Fir/Spruce/Mt. Hemlock 10.102 6.462 0.171 0.834 

Hemlock/Sitka Spruce 10.102 8.034 0.085 0.830 

Lodgepole Pine 10.102 5.733 -0.129 0.857 

Pinyon/Juniper 22.552 5.065 -0.070 0.842 

Ponderosa Pine 10.102 2.262 0.145 0.813 

Western Larch 10.102 5.254 -0.264 0.853 

Softwood minor types 10.102 6.771 0.466 0.783 

RPA Western Hardwoods 8.425 7.460 0.302 0.831 

Pacific Coast 

Non-stocked 0.880 0.300 0.049 0.806 
A-246 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Aspen/Birch 4.594 9.516 0.324 0.792 

Harwood minor types 4.866 11.844 0.266 0.814 

Douglas-fir 1.987 5.363 0.331 0.825 

Fir/Spruce/Mt. Hemlock 1.987 6.693 0.065 0.825 

Lodgepole Pine 1.080 8.051 0.003 0.804 

Other Western Softwoods 1.987 12.217 0.361 0.796 

Ponderosa Pine 1.987 5.574 0.382 0.771 

Softwood minor types 1.987 5.496 -0.152 0.836 

RPA Western Hardwood 13.714 11.678 0.246 0.807 

Pinyon/Juniper 22.927 23.301 0.254 0.794 

West. Oak/Other West. 

Hardwoods 14.441 18.544 0.215 0.796 

Rocky 

Mountain 

Non-stocked 1.111 0.568 0.257 0.732 

Elm/Ash/Cottonwood 12.841 21.633 -0.144 0.896 

Oak/Gum/Cypress 7.176 23.919 -0.216 0.907 

Oak/Hickory 14.594 20.007 -0.031 0.886 

Hardwood minor types 47.316 40.194 -0.442 0.960 

South 

Oak/Pine 4.106 17.933 -0.086 0.858 

Loblolly/Shortleaf Pine 3.892 12.466 0.206 0.773 

Longleaf/Slash Pine 4.441 8.694 0.110 0.772 

Softwood minor types 7.161 20.189 -0.085 0.868 

Non-stocked 0.467 0.943 0.019 0.734 a Prediction of C in growing-stock trees is based on exp(A + B*ln(growing stock volume)). b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast (CA, OR, WA); Rocky 

Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, VA). 

c Forest type groups are identified in appendix D of the FISDB Users Guide (USDA Forest Service, 2008d). 

Table A- 202: Coefficients for estimating carbon density of live trees (aboveground only, MgC/ha) by region and 

type for plot-level data such as RPA dataa

 

Regionb Forest type groupc

Carbon density, 

if Growing Stock 

Volume (GSV)=0 

C density for nonGS, if GSV > 0 Coefficient 

A 

Coefficient 

B 

Aspen/Birch 6.697 11.880 -0.521 0.934 

Elm/Ash/Cottonwood 13.585 15.653 -0.387 0.922 

Maple/Beech/Birch 5.762 14.219 -0.352 0.926 

Oak/Hickory 10.960 13.306 -0.260 0.933 

Hardwood minor types 8.647 11.796 -0.166 0.888 

Oak/Pine 3.368 12.881 -0.335 0.909 

Ponderosa Pine & Exotic Softwood 2.116 5.671 -0.269 0.886 

Loblolly/Shortleaf Pine 5.098 8.070 -0.620 0.946 

Spruce/Fir 5.206 13.833 -0.684 0.948 

White/Red/Jack Pine 3.174 10.010 -0.548 0.926 

Softwood minor types 5.098 14.246 -0.570 0.971 

North 

Non-stocked 0.880 1.032 -0.357 0.866 

Alder/Maple 7.006 3.676 -0.138 0.830 

Other Western Hardwoods 7.006 8.709 -0.154 0.867 

Tanoak/Laurel 7.006 8.469 -0.355 0.918 

Western Oak 7.006 6.163 0.167 0.850 

Hardwood minor types 7.006 3.974 0.136 0.773 

California Mixed Conifer 8.309 3.883 -0.061 0.844 

Douglas-fir 2.235 4.072 -0.017 0.835 

Fir/Spruce/Mt. Hemlock 8.309 5.285 -0.027 0.835 

Hemlock/Sitka Spruce 8.309 6.586 -0.113 0.831 

Lodgepole Pine 8.309 4.674 -0.327 0.858 

Pinyon/Juniper 18.583 4.170 -0.263 0.842 

Ponderosa Pine 8.309 1.849 -0.053 0.814 

Western Larch 8.309 4.282 -0.461 0.853 

Softwood minor types 8.309 5.563 0.267 0.784 

RPA Western Hardwoods 7.006 6.202 0.119 0.831 

Pacific 

Coast 

Non-stocked 0.724 0.247 -0.146 0.808 

Rocky Aspen/Birch 3.798 7.914 0.139 0.793 

Mountain Harwood minor types 4.027 9.936 0.084 0.815 
A-247

Douglas-fir 1.616 4.388 0.134 0.826 

Fir/Spruce/Mt. Hemlock 1.616 5.466 -0.133 0.826 

Lodgepole Pine 0.871 6.571 -0.195 0.805 

Other Western Softwoods 1.616 10.031 0.165 0.797 

Ponderosa Pine 1.616 4.569 0.185 0.772 

Softwood minor types 1.616 4.473 -0.350 0.837 

RPA Western Hardwood 11.341 9.704 0.054 0.809 

Pinyon/Juniper 18.867 19.173 0.059 0.794 

West. Oak/Other West. Hardwoods 11.942 15.353 0.021 0.796 

Non-stocked 0.916 0.466 0.061 0.733 

Elm/Ash/Cottonwood 10.749 18.129 -0.323 0.897 

Oak/Gum/Cypress 5.987 20.004 -0.400 0.909 

Oak/Hickory 12.223 16.731 -0.215 0.888 

Hardwood minor types 39.737 33.739 -0.631 0.964 

Oak/Pine 3.394 14.923 -0.277 0.859 

Loblolly/Shortleaf Pine 3.172 10.288 0.012 0.773 

Longleaf/Slash Pine 3.634 7.176 -0.088 0.773 

Softwood minor types 5.893 16.751 -0.280 0.869 

South 

Non-stocked 0.388 0.788 -0.171 0.735 a Prediction of aboveground C in growing-stock trees is based on exp(A + B*ln(growing stock volume). b Regions are North (CT, DE, IA, IL, IN, KS, MA, MD, ME, MI, MN, MO, ND, NE, NH, NJ, NY, OH, PA, RI, SD, VT, WI, WV); Pacific Coast (CA, OR, WA); Rocky 

Mountain (AZ, CO, ID, MT, NM, NV, UT, WY); and South (AL, AR, FL, GA, KY, LA, MS, NC, OK, SC, TN, TX, VA). 

c Forest type groups are identified in appendix D of the FISDB Users Guide (USDA Forest Service, 2008d).

Understory vegetation 

Understory vegetation is a minor component of biomass. Understory vegetation is defined as all biomass 

of undergrowth plants in a forest, including woody shrubs and trees less than one-inch d.b.h. In this inventory, it is 

assumed that 10 percent of understory C mass is belowground. This general root-to-shoot ratio (0.11) is near the 

lower range of temperate forest values provided in IPCC (2006) and was selected based on two general assumptions: 

ratios are likely to be lower for light-limited understory vegetation as compared with larger trees, and a greater 

proportion of all root mass will be less than 2 mm diameter. 

Estimates of C density are based on information in Birdsey (1996), which was applied to FIA permanent 

plots. These were fit to the equation: 

Ratio = e(A - B × ln(live tree C density)

In this equation, “ratio” is the ratio of understory C density (Mg C/ha) to live tree C density (above- and 

below-ground) in Mg C/ha. An additional coefficient is provided as a maximum ratio; that is, any estimate 

predicted from the equation that is greater than the maximum ratio is set equal to the maximum ratio. A full set of 

coefficients is in Table A-203. Regions and forest types are the same classifications described in Smith et al. 

(2003). As an example, the basic calculation for understory C in aspen-birch forests in the Northeast is: 

Understory (Mg C/ha) = (live tree C density) × e(0.855 – 1.03 × ln(tree C density)

This calculation is followed by three possible modifications. First, the maximum value for the ratio is set 

to 2.02 (see value in column “maximum ratio”); this also applies to stands with zero tree C, which is undefined in 

the above equation. Second, the minimum ratio is set to 0.005 (Birdsey 1996). Third, nonstocked and 

pinyon/juniper stands are set to constant ratios defined by coefficient A. 

Table A-203: 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

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 

NE 

Spruce-Fir 0.825 1.121 2.140 
A-248 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

White-Red-Jack Pine 1.000 1.116 2.098 

Nonstocked 2.020 2.020 2.060 

Aspen-Birch 0.777 1.018 2.023 

Lowland Hardwood 0.650 0.997 2.037 

Maple-Beech-Birch 0.863 1.120 2.129 

Oak-Hickory 0.965 1.091 2.072 

Pine 0.740 1.014 2.046 

Spruce-Fir 1.656 1.318 2.136 

NLS 

Nonstocked 1.928 1.928 2.117 

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 

NPS 

Nonstocked 2.052 2.052 2.072 

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 

PSW 

Nonstocked 4.431 4.431 4.626 

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 

PWE 

Nonstocked 4.315 4.315 4.626 

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 

PWW 

Nonstocked 4.401 4.401 4.589 

Douglas-fir 2.342 1.360 4.731 

Fir-Spruce 2.129 1.315 4.749 

Hardwoods 1.860 1.110 4.745 

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 

RMN 

Nonstocked 4.430 4.430 4.773 

Douglas-fir 5.145 2.232 4.829 

Fir-Spruce 2.861 1.568 4.822 

Hardwoods 1.858 1.110 4.745 

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 

RMS 

Nonstocked 4.243 4.243 4.797 

Bottomland Hardwood 0.917 1.109 1.842 

Misc. Conifer 1.601 1.129 4.191 

Natural Pine 2.166 1.260 4.161 

Oak-Pine 1.903 1.190 4.173 

Planted Pine 1.489 1.037 4.124 

Upland Hardwood 2.089 1.235 4.170 

SC 

Nonstocked 4.044 4.044 4.170 

Bottomland Hardwood 0.834 1.089 1.842 

Misc. Conifer 1.601 1.129 4.191 

SE 

Natural Pine 1.752 1.155 4.178 
A-249

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 aPrediction of ratio of understory C to live tree C is based on the equation: Ratio=exp(A-B*ln(tree_carbon_tph)), where “ratio” is the ratio of understory C density 

to live tree (above-and below- ground) C density, and “tree_carbon_density” is live tree (above-and below- ground) C density in Mg C/ha. b Regions and types as defined in Smith et al. (2003)

cMaximum ratio: any estimate predicted from the equation that is greater than the maximum ratio is set equal to the maximum ratio. 

Dead Wood 

The standing dead tree C pools include aboveground and belowground (coarse root) mass. Estimates for 

standing dead tree C are not based on FIA standing dead tree data because of yet unresolved problems with 

consistency among the state inventories. Instead, the estimates are based on a ratio of growing stock volume of live 

trees by region and forest type groups, applied at the FIA plot-level. The standing dead tree equations estimate 

mass; they are converted to C mass by multiplying by 0.5. An example calculation for standing dead tree C in 

aspen-birch forests in the Northeast is: 

Dry weight (Mg/ha) = 1.0 × (growing stock volume)0.499

It is multiplied by 0.5 to obtain Mg C/ha. All coefficients are provided in Table A- 204. Note that 

nonstocked stands are assigned a constant C density (the value of Coefficient A). 

Down dead wood is defined as pieces of dead wood greater than 7.5 cm diameter, at transect intersection, 

that are not attached to live or standing dead trees. Down dead wood includes stumps and roots of harvested trees. 

Ratio estimates of down dead wood to live tree biomass were developed using FORCARB2 simulations and applied 

at the plot level (Smith et al. 2004a). Estimates for down dead wood correspond to the region and forest type 

classifications described in Smith et al. (2003). A full set of ratios is provided in Table A-205. An example 

calculation for down dead wood in aspen-birch forests in the Northeast is: 

C density (Mg C/ha) = (live tree C density, above- and below-ground) × (0.078) = 7.8% of live tree C 

Conversion to C mass is not necessary because the live tree value is already in terms of C. 

Table A- 204: Coefficients for estimating standing dead tree carbon (MgC/ha) by region and forest type groupa

Regionb Forest type groupc Coefficient A Coefficient B 

Douglas-fir 3.935 0.312

Fir/Spruce/Mt. Hemlock 4.550 0.358

Hemlock/Sitka Spruce 1.000 0.569

Lodgepole Pine 1.177 0.501

Ponderosa Pine 1.000 0.455

MTN 

Nonstocked 12.855 -- 

Douglas-fir 2.200 0.460

Fir/Spruce/Mt. Hemlock 6.923 0.293

Lodgepole Pine 1.177 0.501

Ponderosa Pine 1.944 0.292

MTS 

Nonstocked 4.232 -- 

Aspen/Birch 1.962 0.400

Elm/Ash/Cottonwood 3.755 0.253

Maple/Beech/Birch 3.442 0.219

Planted Pine 1.000 0.298

Oak/Hickory 2.949 0.236

Oak/Pine 1.364 0.394

Spruce/Fir 1.320 0.472

White/Red/Jack Pine 2.844 0.266

NC 

Nonstocked 2.634 -- 

Aspen/Birch 1.000 0.499

Elm/Ash/Cottonwood 4.992 0.134

Maple/Beech/Birch 3.041 0.306

Oak/Hickory 3.332 0.191

NE 

Oak/Pine 1.725 0.311
A-250 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Spruce/Fir 5.893 0.190

White/Red/Jack Pine 2.841 0.254

Nonstocked 2.876 -- 

California Mixed Conifer 1.000 0.608

Douglas-fir 1.237 0.559

Douglas-fir Planted 10.145 0.112

Fir/Spruce/Mt. Hemlock 4.235 0.415

Hemlock/Sitka Spruce 1.546 0.562

Redwood 5.385 0.287

PC 

Nonstocked 7.377 -- 

Elm/Ash/Cottonwood 2.393 0.284

Loblolly/Shortleaf Pine 1.203 0.271

Loblolly/Shortleaf Planted 1.000 0.138

Oak/Gum/Cypress 4.234 0.121

Oak/Hickory 2.396 0.186

Oak/Pine 1.133 0.337

SC 

Nonstocked 0.286 -- 

Elm/Ash/Cottonwood 1.358 0.476 

Loblolly/Shortleaf Pine 1.000 0.324 

Loblolly/Shortleaf Planted 1.000 0.265 

Oak/Gum/Cypress 1.770 0.329 

Oak/Hickory 2.256 0.257 

Oak/Pine 1.000 0.351 

SE 

Nonstocked 0.349 -- 

Longleaf/Slash Pine 1.000 0.184 South Longleaf/Slash Planted 1.000 0.106 

Alder/Maple 2.190 0.466 

Aspen/Birch 3.062 0.376 

Pinyon/Juniper 3.163 0.100 

Tanoak/Laurel 1.000 0.593 

Western Hardwood/Woodlands 5.595 0.181 

Western Larch 2.049 0.449 

West 

Western Oak 1.996 0.348 aStanding dead tree C is based on the equation: mass (Mg/ha) = A* (live-tree growing stock volume)^B. Note that nonstocked stands are assigned a constant C 

density (the value listed under coefficient A). Note that the standing dead tree equations are for biomass. To convert to C mass, multiply by 0.5. bRegions are PC (CA,OR-West,WA-West), MTN (OR-East,WA-East,ID,MT), MTS (AZ,CO,NM,NV,UT,WY), West (regions PC, MTN, and MTS), NC (IA, IL, IN, 

KS, MI, MN, MO, ND, NE, SD, WI), NE (CT, DE, MA, MD, ME, NH, NJ, NY, OH, PA, RI, VT, WV), SC (AL, AR, KY, LA, MS, OK, TN, TX), SE (FL, GA, NC, SC, 

VA), and South (regions SC and SE). 

cForest types are described in appendix D of the FISDB users guide (USDA Forest Service, 2008d). Minor forest types within a region that are not explicitly 

defined/listed in the table of coefficients are assigned to a similar hardwood or softwood forest type. 

Table A-205: Ratio for estimating down dead wood by region and forest type. The ratio is multiplied by the live tree 

carbon density on a plot to produce down dead wood carbon density (MgC/ha) 

Regiona Forest typea Ratio Region 

(cont’d) Forest type (cont’d) Ratio 

(cont’d) 

Aspen-Birch 0.078 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 

PWW 

Nonstocked 0.020 

NE 

Nonstocked 0.019 Douglas-fir 0.062 

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 

NLS 

Nonstocked 0.027 

RMN 

Nonstocked 0.018 

NPS Conifer 0.073 RMS Douglas-fir 0.077 
A-251

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 

Douglas-fir 0.091 Ponderosa Pine 0.082 

Fir-Spruce 0.109 Nonstocked 0.020 

Hardwoods 0.042 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 

PSW 

Nonstocked 0.022 Planted Pine 0.077 

Douglas-fir 0.103 Upland Hardwood 0.067 

Fir-Spruce 0.106 

SC 

Nonstocked 0.013 

Hardwoods 0.027 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 

PWE 

Nonstocked 0.024 Planted Pine 0.075 

 Upland Hardwood 0.059 

 

SE 

Nonstocked 0.012 a Regions and types as defined in Smith et al. (2003). 

Litter carbon 

C of the litter layer is sampled on a subset of the FIA plots. However, the data are not yet available. 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 are based on equations of Smith and Heath 

(2002) and applied at the plot level. The equations describe processes for decay or loss of forest floor following 

harvest and the net accumulation of new forest floor material following stand growth. For example, total forest floor 

C at a given number of years after a clearcut harvest for aspen-birch forests in the North is: 

Total forest floor C (Mg C/ha) = (18.4×years)/(53.7+years) + 10.2× e(-years/9.2)

See Table 4 of Smith and Heath (2002) for the complete set of coefficients. Note that these are direct 

estimates of C density; the 0.5 conversion does not apply to litter.

Soil organic carbon 

Soil organic carbon (SOC) is currently sampled to a 20 cm depth on subsets of FIA plots, however, these 

data are not available for the entire United States. Thus, estimates of SOC are based on the national STATSGO 

spatial database (USDA 1991), and the general approach described by Amichev and Galbraith (2004). In their 

procedure, SOC was calculated for the conterminous United States using the STATSGO database, and data gaps 

were filled by representative values from similar soils. Links to region and forest type groups were developed with 

the assistance of the USDA Forest Service FIA Geospatial Service Center by overlaying FIA forest inventory plots 

on the soil C map. The average SOC densities are provided in Table A- 200. 

Carbon in Harvested Wood Products 

Estimates of the harvested wood product (HWP) contribution to forest C sinks and emissions (hereafter 

called “HWP Contribution”) are based on methods described in Skog (2008) using the WOODCARB II model. 

These methods are based on IPCC (2006) guidance for estimating HWP carbon. The 2006 IPCC Guidelines provide 

methods that allow Parties to report HWP Contribution using one of several different accounting approaches: 

production, stock change and atmospheric flow, as well as a default method. The various approaches are described 

below. The approaches differ in how HWP Contribution is allocated based on production or consumption as well as 

what processes (atmospheric fluxes or stock changes) are focused on. 

 Production approach: Accounts for the net changes in carbon stocks in forests and in the wood 

products pool, but attributes both to the producing country. 
A-252 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 Stock change approach: Accounts for changes in the product pool within the boundaries of the 

consuming country. 

 Atmospheric Flow: Accounts for net emissions or removals of carbon to and from the atmosphere 

within national boundaries. C removal due to forest growth is accounted for in the producing country 

while C emissions to the atmosphere from oxidation of wood products are accounted for in the 

consuming country. 

 Default approach: Assumes no change in C stocks in HWP. IPCC (2006) requests that such an 

assumption be justified if this is how a Party is choosing to report. 

The United States uses the production accounting approach (as in previous years) to report HWP Contribution. 

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-207). 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 5 HWP variables that can be used to calculate HWP contribution for the stock change and 

atmospheric flow approaches for imports and exports are provided in Table A-206. The HWP variables estimated 

are: 

1A) annual change of C in wood and paper products in use in the United States, 

1B) annual change of C in wood and paper products in SWDS in the United States, 

2A) annual change of C in wood and paper product in use in the United States and other countries where 

the wood came from trees harvested in the United States, 

2B) annual change of C in wood and paper products in SWDS in the United States and other countries 

where the wood came from trees harvested in the United States, 

3) C in imports of wood, pulp, and paper to the United States, 

4) C in exports of wood, pulp and paper from the United States, and 

5) C in annual harvest of wood from forests in the United States. 
Table A-206: Harvested wood products from wood harvested in United States – Annual additions of carbon to stocks and total stocks 

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 20

Net carbon additions per year (Tg C per year) Total Harvested wood carbon (35.9) (33.8) (33.8) (32.9) (33.4) (32.3) (30.6) (32.0) (31.1) (32.5) (30.8) (25.5) (26.7) (25.9) (28 Products in use (17.7) (14.9) (16.3) (15.0) (15.9) (15.1) (14.1) (14.7) (13.4) (14.1) (12.8) (8.7) (9.5) (9.7) (12 Solid wood products (14.4) (11.9) (12.6) (12.2) (12.1) (11.2) (11.5) (11.8) (11.4) (12.1) (11.9) (10.1) (10.7) (10.1) (11 Paper products (3.3) (3.1) (3.7) (2.8) (3.8) (3.8) (2.6) (3.0) (2.0) (2.0) (1.0) 1.4 1.2 0.4 (0

 Products in SWDS (18.3) (18.8) (17.4) (18.0) (17.5) (17.2) (16.5) (17.3) (17.7) (18.4) (18.0) (16.8) (17.2) (16.2) (16 Solid wood products (9.9) (11.1) (9.5) (9.7) (9.8) (10.7) (10.6) (10.3) (10.2) (10.6) (10.7) (10.7) (11.1) (11.1) (11

 Paper products (8.3) (7.7) (7.9) (8.3) (7.7) (6.5) (6.0) (6.9) (7.5) (7.8) (7.3) (6.0) (6.1) (5.1) (5Total Carbon stocks (Tg C) Total Harvested wood carbon 1,783 1,821 1,859 1,895 1,929 1,963 1,996 2,029 2,061 2,092 2,124 2,155 2,188 2,218 2,24 Products in use 1,193 1,212 1,231 1,249 1,264 1,280 1,295 1,311 1,326 1,340 1,355 1,368 1,382 1,395 1,40

 Products in SWDS 590 609 628 646 665 683 701 718 735 752 769 787 805 823 84

Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere). Table A-207: 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 (116.4) (131.5) (123.8) 1992 (120.0) (131.6) (123.8) 1993 (126.8) (127.8) (120.7) 1994 (130.0) (129.9) (122.5) 1995 (126.0) (128.0) (118.4) 1996 (122.4) (122.5) (112.2) 1997 (131.5) (127.4) (117.4) 1998 (139.8) (122.7) (114.1) 1999 (149.4) (127.4) (119.1) 2000 (143.2) (120.3) (112.9) 2001 (128.3) (100.3) (93.5) 2002 (135.6) (103.1) (98.0) 2003 (134.6) (99.2) (94.8) 2004 (163.0) (109.1) (105.4) 2005 (161.4) (109.0) (103.9) 2006 (139.4) (114.2) (108.6) 2007 (115.4) (107.8) (100.4) Note: Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere). 
A-254 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-208: Harvested Wood Products Sectoral Background Data for LULUCF - United States (production approach) 

1A 1B 2A 2B 3 4 5 6 

Inventory 

year 

Annual Change 

in stock of HWP 

in use from 

consumption 

Annual Change 

in stock of HWP 

in SWDS from 

consumption 

Annual 

Change in 

stock of 

HWP in use 

produced 

from 

domestic 

harvest 

Annual 

Change in 

stock of HWP 

in SWDS 

produced from 

domestic 

harvest 

Annual 

Imports of 

wood, and 

paper 

products + 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips 

Annual 

Exports of wood, and 

paper 

products + 

wood fuel, 

pulp, 

recovered 

paper, 

roundwood/ 

chips 

Annual 

Domestic 

Harvest 

Annual release 

of carbon to 

the atmosphere 

from HWP 

consumption 

(from fuelwood 

& products in 

use and 

products in 

SWDS) 

A

rele

carbon 

atmos

from 

(inc

fire

where 

cam

dom

harvest 

produ

us

produ

S

∆CHWP IU DC ∆CHWP SWDS DC ∆C HWP IU DH ∆CHWP SWDS DH PIM PEX H ↑CHWP DC ↑C

Gg 

1990 17,000 18,300 17,700 18,300 12,700 15,100 142,300 104,500 101991 13,100 18,600 14,900 18,800 11,600 15,700 144,400 108,600 11992 15,700 17,000 16,300 17,400 12,900 16,000 139,400 103,500 101993 17,000 17,600 15,000 18,000 14,500 14,800 134,600 99,700 101994 18,200 17,200 15,900 17,500 15,700 15,700 134,800 99,300 101995 17,300 17,100 15,100 17,200 16,700 17,300 137,000 102,100 101996 17,000 16,400 14,100 16,500 16,700 16,700 134,500 101,100 101997 18,800 17,100 14,700 17,300 18,000 16,900 135,400 100,700 101998 20,300 17,800 13,400 17,700 19,700 15,100 135,000 101,600 101999 22,000 18,700 14,100 18,400 21,300 15,200 134,900 100,200 102000 20,500 18,600 12,800 18,000 22,400 16,200 134,500 101,600 102001 17,300 17,700 8,700 16,800 23,000 15,300 128,600 101,300 102002 18,600 18,400 9,500 17,200 24,600 15,700 127,600 99,400 102003 19,200 17,500 9,700 16,200 26,000 16,300 124,900 97,900 2004 26,400 18,100 12,400 16,300 31,700 16,900 130,500 100,700 102005 25,800 18,300 12,000 16,300 31,700 17,400 131,700 102,000 10

2006 19,200 18,800 12,300 17,300 25,700 18,800 127,400 96,300 9

2007 13,000 18,500 10,100 17,300 22,000 19,900 123,100 93,700 9

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 
A-255

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 & forthcoming). 

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 validation 

criteria, are shown in Table A-209. The first validation criteria 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 criteria 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. 

Decay parameters for products in SWDS are shown in Table A-210. 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- 198 and Table A- 199. 

Table A-209: Half-life of solidwood and paper products in end uses 

Parameter Value Units 

Half life of wood in single family housing 1920 and before 78.0 Years 

Half life of wood in single family housing 1920 – 1939 78.0 Years 

Half life of wood in single family housing 1940 – 1959 80.0 Years 

Half life of wood in single family housing 1960 – 1979 81.9 Years 

Half life of wood in single family housing 1980 + 83.9 Years 

Ratio of multifamily half live to single family half life 0.61 

Ratio of repair and alterations half life to single family half life 0.30 

Half life for other solidwood product in end uses 38.0 Years 

Half life of paper in end uses 2.54 Years 

Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72. 

Table A-210: Parameters determining decay of wood and paper in SWDS 

Parameter Value Units 

Percentage of wood and paper in dumps that is subject to decay 100% 

Percentage of wood in landfills that is subject to decay 23% 

Percentage of paper in landfills that is subject to decay 56% 

Half life of wood in landfills / dumps (portion subject to decay) 29 Years 

Half life of paper in landfills/ dumps (portion subject to decay) 14.5 Years 

Source: Skog, K.E. (2008) “Sequestration of carbon in harvested wood products for the United States.” Forest Products Journal 58:56-72 

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 (2000b), Smith and Heath (2000), and 

Skog et al. (2004). Uncertainties surrounding input data or model processes are quantified as probability 
A-256 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

distribution functions (PDFs), so that a series of sample values can be randomly selected from the distributions. 

Model simulations are repeated a large number of times to numerically simulate the effect of the random PDF 

selections on estimated total C flux. The separate results from the ecosystem and HWP simulations are pooled for 

total uncertainty (see uncertainty table in LULUCF chapter). 

Uncertainty surrounding current net C flux in forest ecosystems is based on the value for 2007 as obtained 

from the Monte Carlo simulation. C stocks are based on forest condition level (plot-level) calculations, and, 

therefore, uncertainty analysis starts probabilistic sampling at the plot level. Uncertainty surrounding C density 

(Mg/ha) is defined for each of six C pools for each inventory plot. Live trees are generally assigned normal PDFs, 

which are defined according to variability information in Jenkins et al. (2003) and the species and number of trees 

measured on each FIA plot. Plot-level live tree C estimates from RPA data are based on volume; these PDFs also 

include an additional level of uncertainty based on their respective regression equations. Similarly, the normallydistributed PDFs for standing dead trees are based on both volume regression and the individual-tree uncertainties 

related to the Jenkins et al. (2003) based estimates. Definitions of these normal distributions, which centered on 

expected values, depend on region, type, and specific plot information. Where data gaps—tree or volume—are 

identified for older inventory data, corresponding averages from later data are applied for live and standing dead tree 

C densities. Uniform PDFs with a range of ±90 percent of the average are used for these plots. 

Distributions for the remaining C pools are triangular or uniform, which partly reflects the lower level of 

information available about these estimates. Down dead wood, understory, and litter are assigned triangular 

distributions with the mean at the expected value for each plot and the minimum and mode at 10 percent of the 

expected value. The use of these PDFs skewed to the right reflects the assumption that a small proportion of plots 

will have relatively high C densities. Joint sampling of PDFs is specified for two pairs of samples: understory PDF 

sampling is slightly negatively correlated with live tree PDF sampling, and down dead wood sampling is slightly 

positively correlated with live tree sampling. This also reflects the structure of the estimates, which are dependent 

on live tree C. Soil organic C is defined as a uniform PDF at ±50 percent of the mean. Sub-state or state total 

carbon stocks associated with each survey are the cumulative sum of random samples from the plot-level PDFs, 

which are then appropriately expanded to population estimates. These expected values for each carbon pool include 

uncertainty associated with sampling, which is also incorporated in the Monte Carlo simulation. Sampling errors are 

determined according to methods described for the FIADB (USDA Forest Service 2008d), are normally distributed, 

and are assigned a slight positive correlation between successive surveys for Monte Carlo sampling. More recent 

annual inventories are assigned higher sampling correlation between successive surveys based on the proportion of 

plot data jointly included in each. Errors for older inventory data are not available, and these surveys are assigned 

values consistent with those obtained from the FIADB. 

Uncertainty about net C flux in HWP is based on Skog et al. (2004) and Skog (2008). Latin hypercube 

sampling is the basis for the HWP Monte Carlo simulation. Estimates of the HWP variables and HWP Contribution 

under the production approach are subject to many sources of uncertainty. An estimate of uncertainty is provided 

that evaluated the effect of uncertainty in 13 sources, including production and trade data and parameters used to 

make the estimate. Uncertain data and parameters include data on production and trade and factors to convert them 

to C, the Census-based estimate of C in housing in 2001, the EPA estimate of wood and paper discarded to SWDS 

for 1990 to 2000, the limits on decay of wood and paper in SWDS, the decay rate (half-life) of wood and paper in 

SWDS, the proportion of products produced in the United States made with wood harvested in the United States, 

and the rate of storage of wood and paper C in other countries that came from United States harvest, compared to 

storage in the United States. 

A total of ten thousand samples are drawn from the PDF input to separately determine uncertainties about 

forest ecosystem and HWP flux before they are combined for a quantitative estimate of total forest carbon 

uncertainty (see uncertainty table in LULUCF chapter). The only significant change in forest ecosystem simulation 

as compared with the previous Inventory is the carbon-pool-specific calculation of sampling error, which is now 

possible with the FIADB 3.0 (USDA Forest Service 2008d) and replaces the approximation of sampling errors used 

in previous years (EPA 2008). Again this year, true Monte Carlo sampling is used for the forest ecosystem 

estimates (in contrast to Latin hypercube sampling, which was used in some previous estimates), and a part of the 

QA/QC process includes verifying that the PDFs are adequately sampled. 
A-257

Emissions from fires 

CO2

As stated in other sections, the forest inventory approach implicitly accounts for emissions due to 

disturbances. Net C stock change is estimated by subtracting consecutive C stock estimates. A disturbance removes 

C from the forest. The inventory data, on which net C stock estimates are based, already reflects the C loss because 

only C remaining in the forest is estimated. Estimating the CO2 emissions from a disturbance such as fire and 

adding those emissions to the net CO2 change in forests would result in double-counting the loss from fire because 

the inventory data already reflect the loss. There is interest, however, in the size of the CO2 emissions from 

disturbances such as fire. The IPCC (2003) methodology and IPCC (2006) default combustion factor for wildfire 

were employed to estimate emissions from forest fires. 

The same methodology was used to estimate emissions from both wildfires and prescribed fires occurring 

in the lower 48 states. Wildfire area statistics are available, but they include non-forest land, such as shrublands and 

grasslands. It was thus necessary to develop a rudimentary estimate of the percent of area burned in forest by 

multiplying the reported area burned by a ratio of total forest land area to the total area considered to be under 

protection from fire. Data on total area of forest land were obtained from FIA (USDA Forest Service 2008b). Data 

on “total area considered to be under protection from fire” were available at the state level and obtained for the year 

1990 from 1984-1990 Wildfire Statistics prepared by the U.S. Forest Service (USDA Forest Service 1992). Data for 

years 1998, 2002, 2004, and 2006 were obtained from the National Association of State Foresters (NASF 1998, 

2002, 2004, 2008). For states where data were available for all four years, the 1990 value was assumed for years 

1990 to 1994, values for 1998 were assumed for years 1995 to 1998, values for 2002 were assumed for years 1999 

to 2002, values for 2004 were assumed for years 2003 and 2004, and values for 2006 were assumed for years 2005 

to 2007. 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 

2007. For years where data were available for all years except 2006, 2004 data were assumed for years 2003 to 

2007. Since no 1998 value was available for Alaska, the 1990 value was assumed for years 1990 to 1997, the 2002 

value was assumed for years 1998 to 2002, and the 2004 value was assumed for years 2003 and 2005 to 2007. Data 

for 1990, 1998, and 2006 were available for New Mexico, so the 1990 value was assumed for years 1990-1995, 

while the 1998 value was assumed for year 1996 through 2001, and 2006 data were assumed for all remaining years. 

Data for 1990, 1998, and 2002 were available for Illinois, so the 1990 value was assumed for years 1990-1995, 

while the 1998 value was assumed for years 1995 through 2001, and the 2002 value was assumed for all remaining 

years. Total forestland area for the lower 48 states was divided by total area considered to be under protection from 

wildfire for the lower 48 states across the 1990 to 2007 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 2007 from the National Interagency Fire Center (NIFC 2008). 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). Data are acres of land which experienced fire activity on forest service 

land. Based on personal communication with USFS, forest areas under the protection of USFS serve as a proxy for 

coastal areas, which is where the majority of fires in Alaska occur (Heath 2008). According to expert judgment, the 

coastal area of Alaska included in this Inventory is mostly temperate rainforest and, therefore, there is little call for 

prescribed burns (Smith 2008a). It was, thus, assumed that reported area burned for prescribed fires covers only 

prescribed fires in the lower 48 states. 

The average C density in the lower 48 states for aboveground biomass C, dead wood C, and litter layer is 

91 Mg/ha, according to data from FIA. A default value of 0.45 from IPCC (2006) was assumed for the amount of 

biomass burned by wildfire (combustion factor value). Thus, approximately 41.0 Mg C/ha is estimated to be 

emitted by wildfire. For Alaska, an average C density of 179 Mg/ha was used based on data from FIA; this 

translates into 80.6 Mg C/ha emitted. A value of 30 Mg/ha was used as average C density for prescribed fires based 

on data from the U.S. Forest Service (Smith 2008a). Thus, prescribed fires are estimated to emit 13.5 Mg C/ha. 

Estimates for Mg C/ha were multiplied by estimates of forest area burned by year; the resulting estimates are 

displayed in Table A-211. C estimates were multiplied by 92.8 percent to account for the proportion of carbon 
A-258 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

emitted as CO2 and by 3.67 to yield CO2 units. Total CO2 emissions for wildfires and prescribed fires in the lower 

48 states and wildfires in Alaska in 2007 were estimated to be 224.2 Tg/yr. 

Table A-211. 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 579,589 277,223 11 39 355,432 170,006 2 8 8 3 0.0003 0.0009 

1991 486,807 233,561 10 33 355,432 170,529 2 8 557 225 0.0182 0.0618 

1992 785,892 378,229 15 53 355,432 171,060 2 8 47 19 0.0015 0.0052 

1993 438,865 211,846 9 30 355,432 171,572 2 8 110 45 0.0036 0.0122 

1994 1,540,987 745,911 31 104 355,432 172,046 2 8 23 9 0.0007 0.0025 

1995 727,051 384,916 16 54 355,432 188,173 3 9 7 3 0.0002 0.0008 

1996 2,212,309 1,207,118 49 168 355,432 193,937 3 9 103 42 0.0034 0.0115 

1997 335,914 183,674 8 26 355,432 194,346 3 9 33 13 0.0011 0.0036 

1998 489,246 268,111 11 37 355,432 194,780 3 9 2 1 0.0001 0.0002 

1999 1,869,918 1,032,546 42 144 806,780 445,494 6 20 7 3 0.0002 0.0007 

2000 2,685,981 1,485,517 61 207 77,789 43,022 1 2 1 1 0.0000 0.0001 

2001 1,356,830 751,533 31 105 667,428 369,681 5 17 2,078 841 0.0677 0.2305 

2002 2,023,976 1,119,090 46 156 1,086,503 600,746 8 28 28 11 0.0009 0.0031 

2003 1,358,986 660,419 27 92 1,147,695 557,739 8 26 17 7 0.0006 0.0019 

2004 637,258 315,835 13 44 996,453 493,857 7 23 23 9 0.0008 0.0026 

2005 1,629,067 867,629 36 121 934,965 497,955 7 23 353 143 0.0115 0.0392 

2006 3,888,011 2,077,888 85 290 1,100,966 588,395 8 27 8 3 0.0003 0.0009 

2007 3,512,122 1,882,768 77 262 1,274,383 683,168 9 31 2 1 0.0001 0.0002 1 Note that these emissions have already been accounted for in the estimates of net annual changes in carbon stocks, which accounts for the amount 

sequestered minus any emissions, including the assumption that combusted wood may continue to decay through time. 2 National Interagency Fire Center (2008). 3 Ratios calculated using forest land area estimates from FIA (USDA Forest Service 2008b) and wildland area under protection estimates from USDA Forest 

Service (1992) and the National Association of State Foresters (2007). 4 Alaska Department of Natural Resources (2008). 

Non-CO2 

Emissions of non-CO2 gases from forest fires were estimated using the default IPCC (2003) methodology 

and default IPCC (2006) combustion factor for wildfires. Emissions estimates for CH4 are calculated by multiplying 

the total estimated C emitted from forest burned by gas-specific emissions ratios and conversion factors. N2O 

emissions are calculated in the same manner, but are also multiplied by an N-C ratio of 0.01 as recommended by 

IPCC (2003). The equations used are: 

CH4 Emissions = (C released) × (emission ratio) × 16/12 

N2O Emissions = (C released) × (N/C ratio) × (emission ratio) × 44/28 

The resulting estimates are presented in Table A-212. 

Table A-212. 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 13.648 4.586 0.465 

1991 11.885 3.993 0.405 

1992 17.799 5.981 0.607 

1993 10.995 3.694 0.375 

1994 32.868 11.044 1.121 
A-259

1995 18.303 6.150 0.624 

1996 52.053 17.490 1.775 

1997 10.146 3.409 0.346 

1998 13.609 4.573 0.464 

1999 48.297 16.228 1.647 

2000 61.413 20.635 2.094 

2001 35.834 12.040 1.222 

2002 53.938 18.123 1.839 

2003 34.574 11.617 1.179 

2004 19.601 6.586 0.668 

2005 42.263 14.200 1.441 

2006 93.033 31.259 3.172 

2007 86.322 29.004 2.944 1 Calculated based on C emission estimates in Table A-211 and default factors in IPCC (2003, 2006)
A-260 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

3.13. Methodology for Estimating Net Changes in Carbon Stocks in Mineral and 

Organic Soils on Cropland and Grassland 

This sub-annex describes the methodologies used to calculate annual carbon (C) stock changes from mineral and 

organic soils under agricultural management, including Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland. Three types of methodologies were applied: (1) a 

Tier 3 approach, employing the Century simulation model, (2) Tier 2 methods with country-specific stock change and 

emission factors; and (3) Tier 2 methods for estimating additional changes in mineral soil C stocks due to sewage sludge 

additions to soils and enrollment changes in the Conservation Reserve Program (CRP) after 2003. 

The Inventory uses a Tier 3 approach to estimate soil C stock changes for the majority of agricultural lands. This 

approach has several advantages over the IPCC Tier 1 or 2 approaches: 

 It utilizes actual weather data at county scales, rather than a broad climate region classification, enabling 

quantification of inter-annual variability in C fluxes at finer spatial scales; 

 The model uses a more detailed characterization of spatially-mapped soil properties that influence soil C 

dynamics, as opposed to the broad soil taxonomic classifications of the IPCC methodology; 

 The simulation approach provides a more detailed representation of management influences and their 

interactions than are represented by a discrete factor-based approach in the Tier 1 and 2 methods; and 

 Soil C changes are estimated on a more continuous basis (monthly) as a function of the interaction of 

climate, soil, and land management, compared with the linear change between the start and end of the 

inventory that is used with the Tier 1 and 2 methods. 

The Century model was chosen as an appropriate tool for a Tier 3 approach based on several criteria: 

 The model was developed in the United States and has been extensively tested and verified for U.S. 

conditions. In addition, the model has been widely used by researchers and agencies in many other parts of 

the world for simulating soil C dynamics at local, regional and national scales (e.g., Brazil, Canada, India, 

Jordan, Kenya, Mexico). 

 The model is capable of simulating cropland, grassland, forest, and savanna ecosystems, and land-use 

transitions between these different land uses. It is, thus, well suited to model land-use change effects. 

 The model was designed to simulate all major types of management practices that influence soil C 

dynamics, with the exception of cultivated organic soils and a few crops that have not been parameterized 

for Century simulations (e.g., rice, perennial/horticultural crops, and tobacco). For these latter cases, an 

IPCC Tier 2 method has been used. 

 Much of the data needed for the model was obtainable from existing national databases. The exceptions are 

CRP enrollment after 2003 and sewage sludge amendments to soils, which are not known at a sufficient 

resolution to use the Tier 3 model. Soil C stock changes associated with these practices are addressed with a 

Tier 2 method. 

Century Model Description 

The Century model simulates C (and also N, P, and S) dynamics, soil temperature, and water dynamics for 

cropland, grassland, forest, and savanna (mixed forest-grassland) systems. For this analysis, only C and N dynamics have 

been included for several reasons: to simplify the analysis and reduce data requirements, and because P and S interactions 

are less important as determinants of land-use- and management-induced changes in soil C stocks for U.S. agricultural 

systems. 

The model has four main components: (1) soil organic matter and nutrient dynamics; (2) plant growth processes; 

(3) water and temperature dynamics; and (4) management practices. The model was designed to work with readily 

available input data: monthly weather data (e.g., temperature and precipitation); soil physical properties (e.g., soil texture,

drainage condition, rooting depth); and information about land use/land cover (e.g., vegetation attributes) and management 

activities (see below). The model operates on a monthly time step (with weekly time steps used for soil water dynamics). 

Dynamics of organic C and N (Figure A-13) are simulated for the surface and subsurface litter pools and the top 

20 cm of the soil profile; mineral N dynamics are simulated through the whole soil profile. Organic C and N stocks are 

represented by two plant litter pools (termed metabolic and structural) and three soil organic matter (SOM) pools (termed 

active, slow, and passive). The metabolic litter pool represents the easily decomposable constituents of plant residues, 

while the structural litter pool is composed of more recalcitrant, ligno-cellulose plant materials. The three SOM pools 

represent a gradient in decomposability, from active SOM (representing microbial biomass and associated metabolites) 
A-261

having a rapid turnover (months to years), to passive SOM (representing highly processed, humified, condensed 

decomposition products), which is highly recalcitrant, with mean residence times on the order of several hundred years. 

The slow pool represents decomposition products of intermediate stability, having a mean residence time on the order of 

decades and is the fraction that tends to change the most in terms of C content in response to changes in land use and 

management. Soil texture influences turnover rates of the slow and passive pools The clay and silt-sized mineral fraction 

of the soil provides physical protection from microbial attack, leading to slower decomposition and greater SOM 

stabilization in finely textured soils. Soil temperature and moisture, tillage disturbance, aeration, and other factors 

influence the decomposition and loss of C from the soil organic matter pools. 

Figure A-13: Flow diagram of Carbon submodel (A) and Nitrogen submodel (B) 

The plant-growth submodel simulates C assimilation through photosynthesis, N uptake, dry matter production, 

partitioning of C within the crop or forage, senescence, and mortality. The primary function of the growth submodel is to 

estimate the amount, type, and timing of organic matter inputs to soil and to represent the influence of the plant on soil 

water, temperature, and N balance. Yield and removal of harvested biomass are also simulated. Separate submodels are 

designed to simulate herbaceous plants (i.e., agricultural crops and grasses) and woody vegetation (i.e., trees and scrubs). 

Only the herbaceous plant submodel is currently used. Maximum monthly net primary production (NPP) rate (a parameter 

of crop and forage species/variety, specified in the model input files) is modified by air temperature and available water to 

estimate a potential monthly NPP, which is then further subject to nutrient limitations in order to estimate actual NPP and 

biomass allocation. 

The soil water balance submodel calculates water balance components and changes in soil water availability, 

which influences both plant growth and decomposition/nutrient cycling processes. The moisture content of soils are 

simulated through a multi-layer profile based on precipitation, snow accumulation and melting, interception, soil and 

canopy evaporation, transpiration, soil water movement, runoff, and drainage. 

The final main component of the model is the management submodel, which includes options for specifying crop 

type, crop sequence (e.g., rotation), tillage, fertilization, organic matter addition (e.g., manure amendments), harvest (with 

variable residue removal), drainage, irrigation, burning, and grazing intensity. An input “schedule” file is used to simulate 

the timing of management activities and temporal trends; schedules can be organized into discrete time blocks to define a 

repeated sequence of events (e.g., a crop rotation or a frequency of disturbance such as a burning cycle for perennial 

grassland). Management options can be specified for any month of a year within a scheduling block, where management 

codes point to operation-specific parameter files (referred to as *.100 files), which contain the information used to 

simulate management effects within the model process algorithms. User-specified management activities can be defined 

by adding to or editing the contents of the *.100 files. Additional details of the model formulation are given in Parton et 

al. (1987, 1988, 1994) and Metherell et al. (1993), and archived copies of the model source code are available. 

The model has been tested for application in U.S. agricultural lands and has been shown to capture the general 

trends in C storage across approximately 870 field plots from 47 experimental sites (Figure A-14). Some biases and 

imprecision were found in predictions of soil organic C, which is reflected in the uncertainty associated with Century 

model results as described in Step 2b of this sub-annex. Additional discussion is provided in Ogle et al. (2007). 

Figure A-14: Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century 

Model 

IPCC Tier 2 Method Description 

The IPCC Tier 2 method has been developed to estimate C stock changes and CO2 fluxes between soils and the 

atmosphere based on land-use and management activity (IPCC 2003, 2006; Ogle et al. 2003). For mineral soils (i.e., all 

soil orders from the USDA taxonomic classification except Histosols), the Tier 2 method uses reference C values to 

establish baseline C stocks that are modified based on agricultural activities using land-use change, tillage, and input 

factors. The standard IPCC approach was modified to use agricultural SOC stocks as the reference condition, rather than 

uncultivated soils under native vegetation. This modification was needed because soil measurements under agricultural 
A-262 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

management are much more common and easily identified in the National Soil Survey Characterization Database (NRCS 

1997); thus, these measurements formed the basis to estimate reference C stocks. Measurements of soils under native 

vegetation are uncommon in the major agricultural regions of the United States because most of the area has been 

converted into cropland. In addition, country-specific factors were derived for land-use change, tillage and input factors. 

Organic soils used for agricultural production are treated in a separate calculation. These soils are made up of 

deep (greater than 30 cm) layers of organic material that can decompose at a steady rate over several decades following 

drainage for crop production or grazing (IPCC 2006). The IPCC approach uses an emission factor to estimate annual 

losses of CO2 from cultivated organic soils, rather than an explicit stock change approach. 

Methodological Steps for Derivation of Soil Organic C Stock Change Estimates 

The inventory of soil C stock changes in U.S. agricultural land combines Tier 2 and 3 approaches. A simulationbased Tier 3 approach was used to estimate soil C changes for most agricultural land (approximately 90 percent of total 

cropland and grassland) comprising the dominant cropping and grazing systems in the United States, for which the model 

has been well-tested. Estimates for the remaining area, comprised of less common crop systems (e.g., horticultural, 

vegetable, tobacco, rice), land converted between non-agricultural and agricultural uses, and all agricultural land occurring 

on drained organic soils, were developed using the Tier 2 approach. Tier 2 methods were used to estimate additional 

changes in mineral soil C stocks due to sewage sludge additions to soils, and enrollment changes in the Conservation 

Reserve Program after 2003. Most of the activity data sources were common to the Tier 2 and Tier 3 approaches, and, 

hence, they are described in an integrated fashion below. Additional activity data required for the methods are described 

in adjoining sections, followed by the computation steps. 

Step 1: Derive Activity Data 

Activity data were compiled for the Tier 3 Century biogeochemical model and Tier 2 IPCC methods, including 

climate data, soil characteristics, and land-use/management activity data. The first step was to obtain landuse/management activity data, and determine the land base for areas under agricultural management. The areas modeled 

with Century and those estimated with the Tier 2 IPCC method were also subdivided. Finally, additional data were 

collected specific for each method on other key management activities (e.g., tillage management, fertilizer and manure 

addition rates) and environmental conditions (e.g., climate and soil characteristics). 

Step 1a: Determine the Land Base and Classify Management Systems 

Land Base—The National Resources Inventory (NRI) provided the basis for identifying the U.S. agricultural 

land base, and classifying parcels into Cropland Remaining Cropland, Land Converted to Cropland, Grassland Remaining 

Grassland, and Land Converted to Grassland (USDA-NRCS 2000). 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). An 

extensive amount of soils, land-use, and land management data have been collected through the survey, which occurs 

every five years (Nusser et al. 1998).52 Primary sources for data include aerial photography and remote sensing imagery 

as well as field visits and county office records. The annual NRI data product provides crop data for most years between 

1979 and 2003, with the exception of 1983, 1988, and 1993. These years were gap-filled using an automated set of rules 

so that cropping sequences were filled with the most likely crop type given the historical cropping pattern at each NRI 

point location. Grassland data were reported on 5-year increments prior to 1998, but it was assumed that the land use was 

also grassland between the years of data collection (see Easter et al. 2008 for more information). 

NRI points were included in the land base for the agricultural soil C inventory if they were identified as cropland 

or grassland53 between 1990 and 2003 (Table A-213). The most recent national-level data available for NRI were for 

2003; and so the designation for 2003 was extended to 2007 in order to provide C stock changes over the entire time 

series. Overall, more than 260,000 NRI points were included in the inventory calculations, and the total agricultural land 

 

52 In the current Inventory, NRI data only provide land-use and management statistics through 2003, but additional data will 

be incorporated in the future to extend the time series of land use and management data. 

53 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). 
A-263

base varied from 375 to 384 million hectares from 1990 through 2003. Each NRI point represents a specific land parcel 

based upon the weighted expansion factors. 

For each year, land parcels were subdivided into Cropland Remaining Cropland, Land Converted to Cropland, 

Grassland Remaining Grassland, and Land Converted to Grassland. Land parcels under cropping management in a 

specific year were classified as Cropland Remaining Cropland if they had been cropland for at least 20 years. Similarly 

land parcels under grassland management in a specific year of the inventory were classified as Grassland Remaining 

Grassland if they had been designated as grassland for at least 20 years.54 Otherwise, land parcels were classified as Land 

Converted to Cropland or Land Converted to Grassland based on the most recent use in the inventory time period. Lands 

are retained in the land-use change categories (i.e., Land Converted to Cropland and Land Converted to Grassland) for 20 

years as recommended by the IPCC guidelines (IPCC 2006). 

Table A-213: Total Land Areas for the Agricultural Soil C Inventory, Subdivided by Land Use Categories (Million Hectares) 

Land Areas (106 ha) 

Category 1990 1991 1992 1993 1994 1995 1996 

Mineral Soils 

Cropland Remaining Cropland 154.04 153.41 152.74 150.45 148.79 147.98 147.19 

Land Converted to Cropland 15.20 15.49 15.70 16.79 18.93 19.15 19.54 

Grassland Remaining Grassland 198.79 197.71 196.71 194.78 192.66 191.80 190.87 

Land Converted to Grassland 10.01 10.27 10.71 12.25 13.31 13.64 13.79 

Non-Agricultural Usesa 2.46 2.46 2.46 8.08 8.08 8.08 8.08 

Organic Soils 

Cropland Remaining Cropland 0.70 0.70 0.70 0.72 0.72 0.72 0.72 

Land Converted to Cropland 0.07 0.07 0.07 0.03 0.03 0.03 0.03 

Grassland Remaining Grassland 0.50 0.50 0.50 0.48 0.48 0.48 0.48 

Land Converted to Grassland 0.04 0.04 0.04 0.07 0.07 0.07 0.07 

Non-Agricultural Usesa 0.04 0.04 0.04 0.05 0.05 0.05 0.05 

Total 381.85 380.68 379.67 383.68 383.10 381.98 380.80 

Land Areas (106 ha) 

Category 1997 1998 1999 2000 2001 2002 2003-07 

Mineral Soils 

Cropland Remaining Cropland 146.35 142.87 142.92 142.89 142.70 143.28 144.48 

Land Converted to Cropland 19.66 21.36 20.77 20.28 19.98 19.04 17.55 

Grassland Remaining Grassland 190.01 187.82 187.35 186.75 186.18 186.49 186.60 

Land Converted to Grassland 14.21 17.56 17.57 17.80 18.22 17.68 17.03 

Non-Agricultural Usesa 8.08 8.08 8.08 8.08 8.08 8.08 8.08 

Organic Soils 

Cropland Remaining Cropland 0.72 0.72 0.72 0.72 0.72 0.72 0.72 

Land Converted to Cropland 0.03 0.03 0.03 0.03 0.03 0.03 0.03 

Grassland Remaining Grassland 0.48 0.48 0.48 0.48 0.48 0.48 0.48 

Land Converted to Grassland 0.07 0.07 0.07 0.07 0.07 0.07 0.07 

Non-Agricultural Usesa 0.05 0.05 0.05 0.05 0.05 0.05 0.05 

Total 379.64 379.02 378.03 377.13 376.50 375.91 375.08 a These non-agricultural uses were converted to or from cropland or grassland between 1990 and 2003. 

Subdivide Land Base for Tier 2 and 3 Inventory Approaches – The Tier 3 method based on application of the 

Century model was used to model NRI points on most mineral soils (Table A-213). Parcels of land that were not simulated 

with Century were allocated to the Tier 2 approach, including (1) land parcels occurring on organic soils; (2) land parcels 

that included non-agricultural uses such as forest and federal lands in one or more years of the inventory;55 (3) land 

parcels on mineral soils that were very gravelly, cobbly, or shaley (i.e., classified as soils that have greater than 35 percent 

of soil volume comprised of gravel, cobbles or shale); or (4) land parcels that were used to produce vegetables, 

perennial/horticultural crops, tobacco or rice, which was either grown continuously or in rotation with other crops. 

Century has not been fully tested for non-major crops, horticultural or perennial crops, rice and agricultural use of organic 

soils. In addition, Century has not been adequately tested for soils with a high gravel, cobble or shale content, or fully 

tested for the transitions between agricultural and non-agricultural uses. 

 

54 NRI points were classified according to land-use history records starting in 1982 when the NRI survey began, and 

consequently the classifcations were based on less than 20 years from 1990 to 2001. 

55 Federal land is treated as forest or nominal grassland for purposes of these calculations, although the specific use is not 

identified in the NRI survey (USDA-NRCS 2000). 
A-264 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-214: Total Land Area Estimated with Tier 2a and 3 Inventory Approaches (Million Hectares) 

Land Areas (106 ha) 

Year Tier 2* Tier 3 Total 

1990 43.14 338.71 381.85 

1991 42.15 338.53 380.68 

1992 41.26 338.41 379.67 

1993 45.50 337.18 383.68 

1994 45.14 337.97 383.10 

1995 43.98 338.01 381.98 

1996 42.81 338.00 380.80 

1997 41.53 338.11 379.64 

1998 41.21 337.82 379.02 

1999 40.19 337.84 378.03 

2000 39.17 337.96 377.13 

2001 38.50 338.00 376.50 

2002 37.83 338.08 375.91 

2003-07 37.01 338.07 375.08 

a Land use data for 1998-2003 are based on the Revised 1997 NRI data product for the Tier 2 method. Consequently, area data estimates in this table are not used for the 

Tier 2 portion of the Inventory. 

Management System Classification—NRI points on mineral soils were classified into specific crop rotations, 

continuous pasture/rangeland, and other non-agricultural uses for the Tier 2 inventory analysis based on the survey data 

(Table A-215). NRI points were assigned to IPCC input categories (low, medium, high, and high with organic 

amendments) according to the classification provided in IPCC (2006). In addition, NRI differentiates between improved 

and unimproved grassland, where improvements include irrigation and interseeding of legumes. In order to estimate 

uncertainties, PDFs for the NRI land-use data were constructed as multivariate normal based on the total area estimates for 

each land-use/management category and associated covariance matrix. Through this approach, dependencies in land use 

were taken into account resulting from the likelihood that current use is correlated with past use. 

For the Tier 3 inventory estimates, the actual cropping and grassland histories were simulated with the Century 

model so it was not necessary to classify NRI points into management systems. Uncertainty in the areas associated with 

each management system was determined from the estimated sampling variance from the NRI survey (Nusser and Goebel 

1997). See Step 2b for additional discussion. 

Table A-215: Total Land Areas by Land-Use and Management System for the Tier 2 Approach (Million Hectares) 

 Land Areas (106 ha) 

Land-Use/Management System 1990-94 (Tier 2) 1995-2000 (Tier 2)

Cropland Systems 31.53 29.25 

Irrigated Crops 7.27 6.91 

Continuous Row Crops 4.12 3.63 

Continuous Small Grains 1.25 1.04 

Continuous Row Crops and Small Grains 2.30 1.95 

Row Crops in Rotation with Hay and/or Pasture 0.30 0.23 

Small Grains in Rotation with Hay and/or Pasture 0.06 0.06 

Row Crops and Small Grains in Rotation with Hay and/or Pasture 0.03 0.04 

Vegetable Crops 2.90 3.16 

Low Residue Annual Crops (e.g., Tobacco or Cotton) 0.87 1.03 

Small Grains with Fallow 2.01 1.31 

Row Crops and Small Grains with Fallow 1.72 1.80 

Row Crops with Fallow 0.52 0.34 

Miscellaneous Crop Rotations 0.54 0.43 

Continuous Rice 0.34 0.31 

Rice in Rotation with other crops 1.78 1.91 

Continuous Perennial or Horticultural Crops 2.57 2.50 

Continuous Hay 0.59 0.50 

Continuous Hay with Legumes or Irrigation 1.31 1.12 

CRP 1.03 0.96 

Aquaculture 0.01 0.01 

Grassland Systems 12.02 8.68 

Rangeland 5.98 5.16 

Continuous Pasture 3.76 2.49 

Continuous Pasture with Legumes or Irrigation (i.e., improved) 2.25 1.03 

CRP 0.02 0.00 

Non-Agricultural Systems 2.46 8.08 

Forest 1.53 3.95 
A-265

Federal 0.01 0.05 

Water 0.11 0.25 

Settlements 0.04 2.46 

Miscellaneous 0.77 1.36 

Total 46.01 46.01 

Organic soils are also categorized into land-use systems based on drainage (IPCC 2006). Undrained soils are 

treated as having no loss of organic C. Drained soils are subdivided into those used for cultivated cropland, which are 

assumed to have high drainage and greater losses of C, and those used for managed pasture, which are assumed to have 

less drainage and smaller losses of C. Overall, the area of organic soils drained for cropland and grassland has remained 

relatively stable since 1992 (see Table A-216). 

Table A-216: Total Land Areas for Drained Organic Soils By Land Management Category and Climate Region (Million 

Hectares) 

 Land Areas (106 ha) 

 Cold Temperate Warm Temperate Tropical 

IPCC Land-Use Category for Organic Soils 1992 1997 1992 1997 1992 1997 

Undrained 0.07 0.06 0.0020 0.0017 0.12 0.09 

Managed Pasture (Low Drainage) 0.42 0.42 0.0136 0.0119 0.07 0.08 

Cultivated Cropland (High Drainage) 0.33 0.34 0.0971 0.0974 0.19 0.20 

Other Land Usesa 0.02 0.01 0.0002 0.0017 0.00 0.02 

Total 0.84 0.84 0.11 0.11 0.39 0.39 aUrban, water, and miscellaneous non-cropland, which are part of the agricultural land base because these areas were converted from or into agricultural land uses during 

the 1990s. 

Step 1b: Obtain Additional Management Activity Data for the Tier 3 Century Model 

Tillage Practices—Tillage practices were estimated for each cropping system based on data compiled by the 

Conservation Technology Information Center (CTIC 1998). CTIC compiles data on cropland area under five tillage 

classes by major crop species and year for each county. Because the surveys involve county-level aggregate area, they do 

not fully characterize tillage practices as they are applied within a management sequence (e.g., crop rotation). This is 

particularly true for area estimates of cropland under no-till, which include a relatively high proportion of “intermittent” 

no-till, where no-till in one year may be followed by tillage in a subsequent year. For example, a common practice in 

maize-soybean rotations is to use tillage in the maize crop while no-till is used for soybean, such that no-till practices are 

not continuous in time. Estimates of the area under continuous no-till were provided by experts at CTIC to account for 

intermittent tillage activity and its impact on soil C (Towery 2001). 

Tillage practices were grouped into 3 categories: full, reduced, and no-tillage. Full tillage was defined as multiple 

tillage operations every year, including significant soil inversion (e.g., plowing, deep disking) and low surface residue 

coverage. This definition corresponds to the intensive tillage and “reduced” tillage systems as defined by CTIC (1998). 

No-till was defined as not disturbing the soil except through the use of fertilizer and seed drills and where no-till is applied 

to all crops in the rotation. Reduced tillage made up the remainder of the cultivated area, including mulch tillage and ridge 

tillage as defined by CTIC and intermittent no-till. The specific tillage implements and applications used for different 

crops, rotations, and regions to represent the three tillage classes were derived from the 1995 Cropping Practices Survey 

by the Economic Research Service (ERS 1997). 

Tillage data were further processed to construct probability distribution functions (PDFs) using CTIC tillage 

data. Transitions between tillage systems were based on observed county-level changes in the frequency distribution of 

the area under full, reduced, and no-till from the 1980s through 2004. Generally, the fraction of full tillage decreased 

during this time span, with concomitant increases in reduced till and no-till management. Transitions that were modeled 

and applied to NRI points occurring within a county were full tillage to reduced and no-till, and reduced tillage to no-till. 

The remaining amount of cropland was assumed to have no change in tillage (e.g., full tillage remained in full tillage). 

Transition matrices were constructed from CTIC data to represent tillage changes for three time periods, 1980-1989, 1990-

1999, 2000-2007. 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 were available) were used for the first time period, data from 1997 were used for the second time 

period, and data from 2004 were used for the last time period. Percentage areas of cropland in each county were 

calculated for each possible transition (e.g., FT→FT, FT→RT, FT→NT, RT→RT, RT→NT) to obtain a probability for 

each tillage transition at an NRI point. Since continuous NT constituted < 1 percent of total cropland prior to 1990, there 

were no transitions for NT→FT or NT→NT. Uniform probability distributions were established for each tillage scenario 

in the county. For example, a particular crop rotation had 80 percent chance of remaining in full tillage over the two 

decades, a 15 percent chance of a transition from full to reduced tillage and a 5 percent chance of a transition from full to 
A-266 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

no-till. The uniform distribution was subdivided into three segments with random draws in the Monte Carlo simulation 

(discussed in Step 2b) leading to full tillage over the entire time period if the value was greater than or equal to 0 and less

than 80, a transition from full to reduced till if the random draw was equal to or greater than 80 and less than 95, or a 

transition from full to no-till if the draw was greater than or equal to 95. See step 2b for additional discussion of the 

uncertainty analysis. 

Mineral Fertilizer Application—Data on nitrogen fertilizer rates were obtained primarily from USDA’s 

Economic Research Service’s 1995 Cropping Practices Survey (ERS 1997). In this survey, data on inorganic nitrogen 

fertilization rates were collected for major crops (corn, cotton, soybeans, potatoes, winter wheat, durum wheat, and other 

spring wheat) in the key crop producing states. Note that all wheat data were combined into one category and assumed to 

represent small grains in general. Estimates for sorghum fertilizer rates were derived from corn rates using a ratio of 

national average corn fertilizer rates to national average sorghum fertilizer rates derived from additional publications 

(NASS 2004, 1999, 1992; ERS 1988; Grant and Krenz 1985; USDA 1954, 1957, 1966). 

The ERS survey parameter “TOT N” (total amount of N applied per acre), with a small number of records 

deleted as outliers, was used in determining the fraction of crop acres receiving fertilizer and the average fertilizer rates for 

a region. Mean fertilizer rates and standard deviations for irrigated and rainfed crops were produced for each state at the 

finest resolution available. State-level data were produced for surveyed states if a minimum of 15 data points existed for 

each of the two categories (irrigated and rainfed). If a state was not surveyed for a particular crop or if fewer than 15 data

points existed for one of the categories, then data at the Farm Production Region level were substituted. If Farm 

Production Region data were not available, then U.S.-level estimates (all major states surveyed) were used in the 

simulation for that particular crop in the state lacking sufficient data. Note that standard deviations for fertilizer rates on 

log scale were used to construct PDFs on a log-normal scale, in order to address uncertainties in application rates (see Step 

2b for discussion of uncertainty methods). 

Manure Application—County-level manure N addition estimates were obtained from the Natural Resources 

Conservation Service (Edmonds et al. 2003). Working with the farm-level crop and animal data from the 1997 Census of 

Agriculture, NRCS has coupled estimates of manure nitrogen produced with estimates of manure nitrogen recoverability 

by animal waste management system to produce county-level estimates of manure nitrogen applied to cropland and 

pasture. Edmonds et al. (2003) defined a hierarchy of land use systems to which manure is applied, that included 24 crops, 

cropland used as pasture, and permanent pasture. They estimated the area amended with manure and manure nitrogen 

application rates in 1997 for both manure-producing farms and manure-receiving farms within a county, for two 

scenarios—before implementation of Comprehensive Nutrient Management Plans (baseline) and after implementation. 

The application rates for the baseline scenario were used in the inventory under the assumption that Comprehensive 

Nutrient Management Plans have not been fully implemented.

In order to derive estimates of manure application rates over time, the availability of managed manure N for 

application to soils (which are available annually) was used to adjust the amount of area amended with manure on a county 

scale (Note: Edmonds et al. (2003) only provide information on application rates for 1997). Specifically, the estimated 

available managed manure N in another year was divided by the managed manure N available in 1997. The amendment 

area in a county for 1997 was then multiplied by the ratio to reflect the probability of manure amendments based on the 

variation in available manure N across time. If more managed manure N was available in a given year for a county 

relative to the amount available in 1997 (ratio > 1), it was assumed that there was a higher probability of a manure 

amendment. In contrast, if less managed manure N was available (ratio < 1), the probability of an amendment declined in 

comparison to 1997. A detailed description of the derivation of the managed manure N availability data is provided in the 

Manure Management section (Section 6.2) and Annex (Annex 3.10). Managed manure N availability in the 1980s was 

based on USDA estimates (Kellogg et al. 2000) after adjusting for relative differences in manure N production between 

the USDA dataset and estimates derived from the method described in Annex 3.10. Unmanaged manure classified as 

pasture/range/paddock manure was assumed to have negligible impacts on soil C stocks because of the tradeoff between 

reduced litterfall C versus C ingested by livestock and deposited on soils in manure. 

For Century simulations, the amended areas were averaged for three time periods (1980-1989, 1990-1999, and 

2000-2007) similar to the tillage transitions. Rates for manure-producing farms and manure-receiving farms have been 

area-weighted and combined to produce a manure nitrogen application rate for each crop in a county. Several of the crops 

in Edmonds et al. (2003) have been area-weighted and combined into broader crop categories. For example, all small 

grain crops have been combined into one category. In order to address uncertainty, uniform probability distributions were 

constructed based on the proportion of land receiving manure versus the amount not receiving manure for each crop type 

and pasture. For example, if the 20 percent of land producing corn in a county was amended with manure, randomly 

drawing a value equal to or greater than 0 and less than 20 would lead to simulation with a manure amendment, while 

drawing a value greater than or equal to 20 and less than 100 would lead to no amendment in the simulation (see Step 2b 

for further discussion of uncertainty methods). 
A-267

To estimate the C inputs associated with the manure N application rates (from Edmonds et al. 2003), C:N ratios 

for various manure types (based on animal species and manure management system) were estimated from data in the 

Agricultural Waste Management Field Handbook (USDA 1996) and the On-Farm Composting Handbook (NRAES 1992). 

Weighted county-average C:N ratios for total manure applied were then calculated based on the C:N ratio and the manure 

N production rate for each manure type reported in the county. Manure C addition rates were then calculated by 

multiplying the county-average manure C:N ratio by the manure N application rates. 

To account for the common practice of reducing inorganic nitrogen fertilizer inputs when manure is added to a 

cropland soil, a set of crop-specific reduction factors were derived from mineral fertilization data for land amended with 

manure versus land not amended with manure in the ERS 1995 Cropping Practices Survey (ERS 1997). In the 

simulations, mineral N fertilization rates were reduced for crops receiving manure nitrogen based on a fraction of the 

amount of manure nitrogen applied, depending on the crop and whether it was irrigated or a rainfed system. The reduction 

factors were selected from PDFs with normal densities in order to address uncertainties in this dependence between 

manure amendments and mineral fertilizer application. 

Irrigation—NRI differentiates between irrigated and non-irrigated land but does not provide more detailed 

information on the type and intensity of irrigation. Hence, irrigation was modeled by assuming that applied water was 

sufficient to meet full crop demand (i.e., irrigation plus precipitation equaled potential evapotranspiration during the 

growing season). 

Step 1c—Obtain Additional Management Activity Data for Tier 2 IPCC Method

Tillage Practices—PDFs were also constructed for the CTIC tillage data, as bivariate normal on a log-ratio scale 

to reflect negative dependence among tillage classes. This structure ensured that simulated tillage percentages were nonnegative and summed to 100 percent. CTIC data do not differentiate between continuous and intermittent use of notillage, which is important for estimating SOC storage. Thus, regionally based estimates for continuous no-tillage (defined 

as 5 or more years of continuous use) were modified based on consultation with CTIC experts, as discussed in Step 1a 

(downward adjustment of total no-tillage acres reported, Towery 2001). 

Manure Amendments—Manure management is also a key practice in agricultural lands, with organic 

amendments leading to significant increases in SOC storage. USDA provides information on the amount of land amended 

with manure for 1997 based on manure production data and field-scale surveys detailing application rates that had been 

collected in the Census of Agriculture (Edmonds et al. 2003). Similar to the Century model discussion in Step1b, the 

amount of land receiving manure was based on the estimates provided by Edmonds et al. (2003), as a proportion of crop 

and grassland amended with manure within individual climate regions. The resulting proportions were used to re-classify 

a portion of crop and grassland into a new management category. Specifically, a portion of medium input cropping 

systems was re-classified as high input, and a portion of the high input systems was re-classified as high input with 

amendment. In grassland systems, the estimated proportions for land amended with manure were used to re-classify a 

portion of nominally-managed grassland as improved, and a portion of improved grassland as improved with high input. 

These classification approaches are consistent with the IPCC inventory methodology (IPCC 2003, 2006). Uncertainties in 

the amount of land amended with manure were based on the sample variance at the climate region scale, assuming normal 

density PDFs (i.e., variance of the climate region estimates, which were derived from county-scale proportions). 

Wetland Reserve—Wetlands enrolled in the Conservation Reserve Program have been restored in the Northern 

Prairie Pothole Region through the Partners for Wildlife Program funded by the U.S. Fish and Wildlife Service. The area 

of restored wetlands was estimated from contract agreements (Euliss and Gleason 2002). While the contracts provide 

reasonable estimates of the amount of land restored in the region, they do not provide the information necessary to 

estimate uncertainty. Consequently, a ±50 percent range was used to construct the PDFs for the uncertainty analysis. 

Step 1d—Obtain Management Activity Data to Compute Additional Changes in Soil Organic C Stocks in Mineral 

Soils Due to Sewage Sludge Applications and CRP Enrollment after 1997 

Two additional influences on soil organic C stocks in mineral soils were estimated using a Tier 2 method, 

including: sewage sludge additions to agricultural soils and changes in enrollment for the Conservation Reserve Program 

after 1997. 

Total sewage sludge generation data for 1988, 1996, 1998, in dry mass units, were obtained from an EPA report 

(EPA 1999) and estimates for 2004 were obtained from an independent national biosolids survey (NEBRA 2007). These 

values were linearly interpolated to estimate values for the intervening years. Sewage sludge generation data are not 

available for 2005 onwards (Bastian 2007), so the 1990 through 2004 data were linearly extrapolated for the most recent 

years. The total sludge generation estimates were then converted to units of N by applying an average N content of 3.9 
A-268 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

percent (McFarland 2001), and disaggregated into use and disposal practices using historical data in EPA (1993) and 

NEBRA (2007). The use and disposal practices were agricultural land application, other land application, surface disposal, 

incineration, landfilling, ocean dumping (ended in 1992), and other disposal. Sewage sludge N was assumed to be applied 

at the assimilative capacity provided in Kellogg et al. (2000), which is the amount of nutrients taken up by a crop and 

removed at harvest, representing the recommended application rate for manure amendments. This capacity varies from 

year to year, because it is based on specific crop yields during the respective year (Kellogg et al. 2000). Total sewage 

sludge N available for application was divided by the assimilative capacity to estimate the total land area over which 

sewage sludge had been applied. The resulting estimates were used for the estimation of soil C stock change. 

The change in enrollment for the Conservation Reserve Program after 2003 was based on the amount of land 

under active contracts from 2004 through 2007 relative to 2003 (USDA-FSA 2008). 

Step 1e: Obtain Climate and Soils Data 

Tier 3 Century Model—Monthly weather data (temperature and precipitation) from the PRISM database 

(Parameter-elevation Regressions on Independent Slopes Model) (Daly et al. 1994) were used as an input to the Century 

model simulations for the period 1895 through 2007. PRISM is based on observed weather data from the National 

Weather Service network database and statistical models for interpolation and orographic corrections. The primary 

database consists of approximately 4×4 km grid cells. These data were averaged (weighted by area) for each county in the 

United States, so that counties are the finest spatial scale represented in the Century simulations. 

Soil texture and natural drainage capacity (i.e., hydric vs. non-hydric soil characterization) were the main soil 

variables used as input to the Century model. Other soil characteristics needed in the simulation, such as field capacity 

and wilting-point water contents, were estimated from soil texture data using pedo-transfer functions available in the 

model. Soil input data are derived from the NRI database, which contain descriptions for the soil type at each NRI point 

(used to specify land-use and management time series-see below). The data are based on field measurements collected as 

part of soil survey and mapping. Soils are classified according to “soil-series,” which is the most detailed taxonomic level 

used for soil mapping in the United States. Surface soil texture and hydric condition were obtained from the soil attribute 

table in the NRI database. Texture is one of the main controls on soil C turnover and stabilization in the Century model, 

which uses particle size fractions of sand (50-2,000 μm), silt (2-50 μm), and clay (< 2 μm) as inputs. NRI points were 

assigned to one of twelve texture classes for the simulations. Hydric condition specifies whether soils are poorly-drained, 

and hence prone to water-logging, or moderately to well-drained (non-hydric), in their native (pre-cultivation) condition.56 

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 Century).57 

IPCC Tier 2 Method—The IPCC inventory methodology for agricultural soils divides climate into eight distinct 

zones based upon average annual temperature, average annual precipitation, and the length of the dry season (IPCC 2006) 

(Table A-217). Six of these climate zones occur in the conterminous United States and Hawaii (Eve et al. 2001). 

Table A-217: Characteristics of the IPCC Climate Zones that Occur in the United States 

Climate Zone 

Annual Average

Temperature ( ̊C)Average Annual Precipitation (mm)

Length of Dry Season

(months)

Cold Temperate, Dry < 10 < Potential Evapotranspiration NA

Cold Temperate, Moist < 10 ≥ Potential Evapotranspiration NA

Warm Temperate, Dry 10 – 20 < 600 NA

Warm Temperate, Moist 10 – 20 ≥ Potential Evapotranspiration NA

Sub-Tropical, Dry* > 20 < 1,000 Usually long

Sub-Tropical, Moist (w/short dry season)a > 20 1,000 – 2,000 < 5 a The climate characteristics listed in the table for these zones are those that correspond to the tropical dry and tropical moist zones of the IPCC. They have been renamed 

“sub-tropical” here. 

Mean climate (1961-1990) variables from the PRISM data set (Daly et al. 1994) were used to classify climate 

zones. Mean annual precipitation and annual temperature data were averaged (weighted by area) for each of the 4×4 km 

 

56 Artificial drainage (e.g., ditch- or tile-drainage) is simulated as a management variable. 

57 Hydric soils are primarily subject to anaerobic conditions outside the plant growing season (i.e., in the absence of active 

plant water uptake). Soils that are water-logged during much of the year are typically classified as organic soils (e.g., peat), which are 

not simulated with the Century model. 
A-269

grid cells occurring within a MLRA region. These averages were used to assign a climate zone to each MLRA according 

to the IPCC climate classification (Figure A-15). MLRAs represent geographic units with relatively similar soils, climate, 

water resources, and land uses; and there are approximately 180 MLRAs in the United States (NRCS 1981). 

Figure A-15: Major Land Resource Areas by IPCC Climate Zone 

Soils were classified into one of seven classes based upon texture, morphology, and ability to store organic 

matter (IPCC 2006). Six of the categories are mineral types and one is organic (i.e., Histosol). Reference C stocks, 

representing estimates from conventionally managed cropland, were computed for each of the mineral soil types across the 

various climate zones, based on pedon (i.e., soil) data from the National Soil Survey Characterization Database (NRCS 

1997) (Table A-218). These stocks are used in conjunction with management factors to compute the change in SOC 

stocks that result from management and land-use activity. PDFs, which represent the variability in the stock estimates, 

were constructed as normal densities based on the mean and variance from the pedon data. Pedon locations were clumped 

in various parts of the country, which reduces the statistical independence of individual pedon estimates. To account for 

this lack of independence, samples from each climate by soil zone were tested for spatial autocorrelation using the 

Moran’s I test, and variance terms were inflated by 10 percent for all zones with significant p-values. 

Table A-218: U.S. Soil Groupings Based on the IPCC Categories and Dominant Taxonomic Soil, and Reference Carbon 

Stocks (Metric Tons C/ha) 

Reference Carbon Stock in Climate Regions 

IPCC Inventory Soil 

Categories USDA Taxonomic Soil Orders 

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 were estimated from pedon data available in the National Soil Survey Characterization database (NRCS 1997); 

sample size provided in parentheses (i.e., ‘n’ values refer to sample size). 

Step 2: Estimate Organic C Stock Changes for Agricultural Lands on Mineral Soils Simulated with the Tier 3 Century 

Model 

This methodology description is divided into two sub-steps. First, the model was used to establish the initial 

conditions and C stocks for 1979, which was the last year before the NRI survey was initiated. In the second sub-step, 

Century was used to estimate changes in soil organic C stocks based on the land-use and management histories recorded in 

the NRI (USDA-NRCS 2000), including the reporting period starting in 1990. 

Step 2a: Simulate Initial Conditions (Pre-NRI Conditions) 

Century model initialization involves two steps, with the goal of estimating the most accurate stock for the preNRI history, and the distribution of organic C among the pools represented in the model (e.g., Structural, Metabolic, 

Active, Slow, Passive). Each pool has a different turnover rate (representing the heterogeneous nature of soil organic 

matter), and the amount of C in each pool at any point in time influences the forward trajectory of the total soil organic C 

storage. There is currently no national set of soil C measurements that can be used for establishing initial conditions in the

model. Sensitivity analysis of the Century model showed that the rate of change of soil organic matter is relatively 

insensitive to the amount of total soil organic C but is highly sensitive to the relative distribution of C among different 
A-270 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

pools (Parton et al. 1987). By simulating the historical land use prior to the inventory period, initial pool distributions are 

estimated in an unbiased way. 

The first step involves running the model to a steady-state condition (e.g., equilibrium) under native vegetation, 

with long-term mean climate based on 30-yr averages of the PRISM data (1960-1990), and the soil physical attributes for 

the NRI points. Native vegetation is represented at the MLRA level for pre-settlement time periods in the United States. 

The model was run for 7,000-year to represent a pre-settlement era and achieve a steady-state condition. 

The second step is to run the model for the period of time from settlement to the beginning of the NRI survey, 

representing the influence of historic land-use change and management, particularly the conversion of native vegetation to 

agricultural uses. This encompasses a varying time period from land conversion (depending on historical settlement 

patterns) to 1979. The information on historical cropping practices used for Century simulations was gathered from a 

variety of sources, ranging from the historical accounts of farming practices reported in the literature (e.g., Miner 1998) to 

national level databases (e.g., NASS 2004). A detailed description of the data sources and assumptions used in 

constructing the base history scenarios of agricultural practices can be found in Williams and Paustian (2005). 

Step 2b—Estimate Soil Organic C Stock Changes and Uncertainties 

After estimating model initialization, the model is used to simulate the NRI land use and management histories 

from 1979 through 2003.58 The simulation system incorporates a dedicated MySQL database server and a 24-node 

parallel processing computer cluster. Input/output operations are managed by a set of run executive programs written in 

PERL. The assessment framework for this analysis is illustrated in Figure A-16. 

Figure A-16: Uncertainty in Data Inputs 

Evaluating uncertainty was an integral part of the analysis, and included three components: (1) uncertainty in the 

main activity data inputs affecting soil C balance (input uncertainty); (2) uncertainty in the model formulation and 

parameterization (structural uncertainty); and (3) uncertainty in the land-use and management system areas (scaling 

uncertainty). For component 1, input uncertainty was evaluated for fertilization management, manure applications, and 

tillage, which are the primary management activity data that were supplemental to the NRI observations and have 

significant influence on soil C dynamics. As described in Step 1b, PDFs were derived from surveys at the county scale in 

most cases. To represent uncertainty in these inputs, a Monte-Carlo Analysis was used with 100 iterations for each NRI 

cluster-point in which random draws were made from PDFs for fertilizer, manure application, and tillage. As described 

above, an adjustment factor was also selected from PDFs with normal densities to represent the dependence between 

manure amendments and N fertilizer application rates. The total number of Century simulations was over 12 million for 

the Monte Carlo Analysis with 100 iterations. 

The second component dealt with uncertainty inherent in model formulation and parameterization. An 

empirically-based procedure was employed to develop a structural uncertainty estimator from the relationship between 

modeled results and field measurements from agricultural experiments (Ogle et al. 2007). The Century model was 

initialized for 45 long-term field experiments with over 800 treatments in which soil C was measured under a variety of 

management conditions (e.g., variation in crop rotation, tillage, fertilization rates, manure amendments). These studies 

were obtained from an extensive search of published studies. All studies located in North America that met minimum 

criteria of having sufficient site-level information and experimental designs were used, including C stock estimates, 

texture data, experimental designs with control plots, and land-use and management records for the experimental time 

period and pre-experiment condition. The inputs to the model were essentially known in the simulations for the long-term 

experiments, and, therefore, the analysis was designed to evaluate uncertainties associated with the model structure (i.e., 

model algorithms and parameterization). 

The relationship between modeled soil C stocks and field measurements was statistically analyzed using linearmixed effect modeling techniques. Additional fixed effects were included in the mixed effect model, which explained 

significant variation in the relationship between modeled and measured stocks (if they met an alpha level of 0.05 for 

significance). Several variables were tested including: land-use class; type of tillage; cropping system; geographic 

location; climate; soil texture; time since the management change; original land cover (i.e., forest or grassland); grain 

harvest as predicted by the model compared to the experimental values; and variation in fertilizer and residue 

 

58 The estimated soil C stock change in 2003 is currently assumed to represent the changes between 2004 and 2007. New 

estimates will be available in the future to extend the time series of land use and management data. 
A-271

management. The final model included variables for organic matter amendments, fertilizer rates, inclusion of hay/pasture 

in cropping rotations, use of no-till, and inclusion of bare fallow in the rotation, which were significant at an alpha level of 

0.05. These fixed effects were used to make an adjustment to modeled values due to biases that were creating significant 

mismatches between the modeled and measured stock values. Random effects captured the statistical dependence (i.e., the 

data are not fully independent) in time series and data collected from the same long-term experimental site. Accounting 

for this statistical dependency is needed to estimate appropriate standard deviations for parameter coefficients. 

A Monte Carlo approach was used to apply the uncertainty estimator. Parameter values for the statistical 

equation (i.e., fixed effects) were selected from their joint probability distribution, as well as random error associated with

fine-scale estimates at NRI points, and the residual or unexplained error associated with the linear mixed-effect model. 

The stock estimate and associated management information was then used as input into the equation, and adjusted stock 

values were computed for each C stock estimate produced in the evaluation of input uncertainty for Cropland Remaining 

Cropland (Component 1 of the uncertainty analysis). Note that the uncertainty estimator needs further development for 

application to Grassland Remaining Grassland and the land-use change categories. This development is a planned 

improvement for the soil C inventory. The variance of the adjusted C stock estimates were computed from the 100 

simulated values from the Monte Carlo analysis. 

The third element was the uncertainty associated with scaling the Century results for each NRI cluster to the 

entire land base, using the expansion factors provided with the NRI database. The expansion factors represent the number 

of hectares associated with the land-use and management history of a particular cluster. This uncertainty was determined 

by computing the variances of the expanded estimates, accounting for the two-stage sampling design of the NRI. 

For the land base that was simulated with the Century model (Table A-2132), soil organic C stocks ranged from 

losses of 7.8 Tg CO2 Eq. to gains of 64.0 Tg CO2 Eq. annually, depending on the land-use/land-use change category and 

inventory time period. Estimates and uncertainties are provided in Table A-219. 

Table A-219: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Simulated with 

the Tier 3 Century Model-Based Approach (Tg CO2 Eq.) 

Cropland Remaining Cropland Land Converted to Cropland Grassland Remaining 

Grassland Land Converted to Grassland 

Year Estimate* 95% CI * Estimate 95% CI Estimate 95% CI * Estimate 95% CI 

1990 (55.19) (111.72) to 1.33 (4.43) (5.06) to (3.80) (49.68) (50.07) to (49.28) (18.19) (18.74) to (17.65) 

1991 (57.50) (90.49) to (24.51) (4.30) (4.93) to (3.68) (32.24) (32.61) to (31.88) (16.29) (16.78) to (15.79) 

1992 (68.05) (99.81) to (36.28) (4.75) (5.38) to (4.13) (13.69) (15.06) to (12.32) (14.92) (15.39) to (14.45) 

1993 (64.25) (95.93) to (32.56) (8.66) (9.34) to (7.98) 3.61 3.26 to 3.96 (16.30) (16.80) to (15.80) 

1994 (62.38) (94.48) to (30.29) (12.61) (13.42) to (11.80) (75.68) (75.98) to (75.37) (19.32) (19.87) to (18.78) 

1995 (47.58) (83.91) to (11.25) (3.84) (4.51) to (3.17) (39.15) (39.41) to 

(38.89) 

(18.40) (18.91) to (17.90) 

1996 (54.99) (83.65) to (26.32) (4.55) (5.24) to (3.85) 21.24 21.00 to 21.48 (19.97) (20.49) to (19.44) 

1997 (54.08) (81.53) to (26.63) (4.21) (4.94) to (3.48) (22.71) (22.96) to 

(22.45) 

(20.98) (21.51) to (20.44) 

1998 (44.34) (77.06) to (11.61) (10.77) (11.60) to (9.94) 11.05 10.84 to 11.26 (22.32) (22.90) to (21.74) 

1999 (29.72) (58.21) to (1.23) (3.30) (4.02) to (2.59) (24.23) (24.41) to (24.05) (22.23) (22.74) to (21.72) 

2000 (54.83) (85.86) to (23.80) (4.41) (5.15) to (3.67) (53.97) (54.15) to (53.80) (27.86) (28.49) to (27.23) 

2001 (37.57) (68.05) to (7.08) (2.53) (3.24) to (1.83) (30.05) (30.22) to (29.88) (26.14) (26.70) to (25.58) 

2002 (36.14) (67.43) to (4.85) (1.97) (2.68) to (1.25) (45.67) (45.82) to (45.52) (24.15) (24.71) to (23.60) 

2003-2007 (42.32) (69.67) to (14.98) (0.84) (1.54) to (0.13) (6.99) (7.16) to (6.82) (22.60) (23.13) to (22.07) 

Note: Does not include the change in storage resulting from the annual application of sewage sludge, or the additional Conservation Reserve Program enrollment. 

Step 3: Estimate C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach, in 

Addition to CO2 Emissions from Agricultural Lands on Drained Organic Soils

Mineral and organic soil calculations were made for each climate by soil zone across the United States. Mineral 

stock values were derived for non-major crop rotations and land converted from non-agricultural uses to cropland in 1982, 

1992, and 1997 based on the land-use and management activity data in conjunction with appropriate reference C stocks, 

land-use change, tillage, input, and wetland restoration factors. C losses from organic soils were computed based on 1992 

and 1997 land use and management in conjunction with the appropriate C loss rate. Each input to the inventory 

calculations for the Tier 2 approach had some level of uncertainty that was quantified in PDFs, including the land-use and 

management activity data, reference C stocks, and management factors. A Monte Carlo Analysis was used to quantify 

uncertainty in SOC change for the inventory period based on uncertainty in the inputs. Input values were randomly 

selected from PDFs in an iterative process to estimate SOC change for 50,000 times and produce a 95 percent confidence 

interval for the inventory results. 
A-272 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Step 3a: Derive Mineral Soil Stock Change and Organic Soil Emission Factors 

Stock change factors representative of U.S. conditions were estimated from published studies (Ogle et al. 2003, 

Ogle et al. 2006). The numerical factors quantify the impact of changing land use and management on SOC storage in 

mineral soils, including tillage practices, cropping rotation or intensification, and land conversions between cultivated and 

native conditions (including set-asides in the Conservation Reserve Program), as well as the net loss of SOC from organic 

soils attributed to agricultural production on drained soils. Studies from the United States and Canada were used in this 

analysis under the assumption that they would best represent management impacts for this inventory. 

For mineral soils, studies had to report SOC stocks (or information to compute stocks), depth of sampling, and 

the number of years since a management change to be included in the analysis. The data were analyzed using linear 

mixed-effect modeling, accounting for both fixed and random effects. Fixed effects included depth, number of years since 

a management change, climate, and the type of management change (e.g., reduced tillage vs. no-till). For depth 

increments, the data were not aggregated for the C stock measurements; each depth increment (e.g., 0-5 cm, 5-10 cm, and 

10-30 cm) was included as a separate point in the dataset. Similarly, time series data were not aggregated in these 

datasets. Consequently, random effects were needed to account for the dependence in time series data and the dependence 

among data points representing different depth increments from the same study. Factors were estimated for the effect of 

management practices at 20 years for the top 30 cm of the soil (Table A-220). Variance was calculated for each of the 

U.S. factor values, and used to construct PDFs with a normal density. In the IPCC method, specific factor values are 

given for improved grassland, high input cropland with organic amendments, and for wetland rice, each of which 

influences the C balance of soils. Specifically, higher stocks are associated with increased productivity and C inputs 

(relative to native grassland) on improved grassland with both medium and high input.59 Organic amendments in annual 

cropping systems also increase SOC stocks due to greater C inputs, while high SOC stocks in rice cultivation are 

associated with reduced decomposition due to periodic flooding. There were insufficient field studies to re-estimate factor 

values for these systems, and, thus, estimates from IPCC (2003) were used under the assumption that they would best 

approximate the impacts, given the lack of sufficient data to derive U.S.-specific factors. A measure of uncertainty was 

provided for these factors in IPCC (2003), which was used to construct PDFs. 

Table A-220: Stock Change Factors for the United States and the IPCC Default Values Associated with Management 

Impacts on Mineral Soils 

 U.S. Factor 

IPCC 

default 

Warm Moist 

Climate 

Warm Dry 

Climate 

Cool Moist 

Climate 

Cool Dry 

Climate 

Land-Use Change Factors 

 Cultivateda 1 1 1 1 1 

 General Uncult.a,b (n=251) 1.4 1.42±0.06 1.37±0.05 1.24±0.06 1.20±0.06 

 Set-Asidea (n=142) 1.25 1.31±0.06 1.26±0.04 1.14±0.06 1.10±0.05 

Improved Grassland Factorsc 

 Medium Input 1.1 1.14±0.06 1.14±0.06 1.14±0.06 1.14±0.06 

 High Input Na 1.11±0.04 1.11±0.04 1.11±0.04 1.11±0.04 

Wetland Rice Production Factorc 1.1 1.1 1.1 1.1 1.1 

Tillage Factors 

 Conv. Till 1 1 1 1 1 

 Red. Till (n=93) 1.05 1.08±0.03 1.01±0.03 1.08±0.03 1.01±0.03 

 No-till (n=212) 1.1 1.13±0.02 1.05±0.03 1.13±0.02 1.05±0.03 

Cropland Input Factors 

 Low (n=85) 0.9 0.94±0.01 0.94±0.01 0.94±0.01 0.94±0.01 

 Medium 1 1 1 1 1 

 High (n=22) 1.1 1.07±0.02 1.07±0.02 1.07±0.02 1.07±0.02 

 High with amendmentc 1.2 1.38±0.06 1.34±0.08 1.38±0.06 1.34±0.08 

Note: The “n” values refer to sample size. 

a Factors in the IPCC documentation (IPCC 2006) were converted to represent changes in SOC storage from a cultivated condition rather than a native condition. b Default factor was higher for aquic soils at 1.7. The U.S. analysis showed no significant differences between aquic and non-aquic soils, so a single U.S. factor was 

estimated for all soil types. 

c U.S.-specific factors were not estimated for land improvements, rice production, or high input with amendment because of few studies addressing the impact of legume 

mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland rice production in the US. Factors provided in IPCC (2003) 

were used as the best estimates of these impacts. 

Wetland restoration management also influences SOC storage in mineral soils, because restoration leads to 

higher water tables and inundation of the soil for at least part of the year. A stock change factor was estimated assessing 

 

59 Improved grasslands are identified in the 1997 National Resources Inventory as grasslands that were irrigated or seeded 

with legumes, in addition to those reclassified as improved with manure amendments. 
A-273

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 A221). A PDF with a normal density was constructed from these data based on results from a linear regression model. 

Following the initial increase of C, natural erosion and deposition leads to additional accretion of C in these wetlands. The 

mass accumulation rate of organic C was estimated using annual sedimentation rates (cm/yr) in combination with percent 

organic C, and soil bulk density (g/cm3) (Euliss and Gleason 2002). Procedures for calculation of mass accumulation rate 

are described in Dean and Gorham (1998); the resulting rate and variance were used to construct a PDF with a normal 

density (Table A-221). 

Table A-221: Factor Estimate for the Initial and Subsequent Increase in Organic Soil C Following Wetland Restoration of 

Conservation Reserve Program 

Variable Value 

Factor (Initial Increase—First 10 Years) 1.22±0.18

Mass Accumulation (After Initial 10 Years) 0.79±0.05 Mg C/ha-yr

Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 2002). 

In addition, C loss rates were estimated for cultivated organic soils based on subsidence studies in the United 

States and Canada (Table A-222). PDFs were constructed as normal densities based on the mean C loss rates and 

associated variances. 

Table A-222: Carbon Loss Rates for Organic Soils Under Agricultural Management in the United States, and IPCC Default 

Rates (Metric Ton C/ha-yr) 

 Cropland Grassland 

Region IPCC U.S. Revised IPCC U.S. Revised 

Cold Temperate, Dry & Cold Temperate, Moist 1 11.2±2.5 0.25 2.8±0.5a

Warm Temperate, Dry & Warm Temperate, Moist 10 14.0±2.5 2.5 3.5±0.8a

Sub-Tropical, Dry & Sub-Tropical, Moist 20 14.0±3.3 5 3.5±0.8a

a There were not enough data available to estimate a U.S. value for C losses from grassland. Consequently, estimates are 25 percent of the values for cropland, which was 

an assumption used for the IPCC default organic soil C losses on grassland. 

Step 3b: Estimate Annual Changes in Mineral Soil Organic C Stocks and CO2 Emissions from Organic Soils 

In accordance with IPCC methodology, annual changes in mineral soil C were calculated by subtracting the 

beginning stock from the ending stock and then dividing by 20.60 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 

2006 is the difference between the 1997 and 1992 C stocks. Using the Monte Carlo approach, SOC stock change for 

mineral soils was estimated 50,000 times between 1982 and 1992, and between 1992 and 1997. From the final 

distribution of 50,000 values, a 95 percent confidence interval was generated based on the simulated values at the 2.5 and 

97.5 percentiles in the distribution (Ogle et al. 2003). 

For organic soils, annual losses of CO2 were estimated for 1992 and 1997 by applying the Monte Carlo approach 

to 1992 and 1997 land-use data in the United States. The results for 1992 were applied to the years 1990 through 1992, 

and the results for 1997 were applied to the years 1993 through 2007. 

 Mineral soils for the land base estimated with the Tier 2 approach accumulated about 1.7 to 3.0 Tg CO2 Eq. 

annually in Cropland Remaining Cropland, while mineral soils in Land Converted to Cropland lost C at a rate of about 

4.1 to 4.2 Tg CO2 Eq. annually. Minerals soils in Grassland Remaining Grassland had small gains of about 0.2 to 0.3 Tg 

CO2 Eq. annually and sequestered from 4.5 to 5.0 Tg CO2 Eq. annually in Land Converted to Grassland. Organic soils lost 

about 27.4 to 27.7 Tg CO2 Eq. annually in Cropland Remaining Cropland and 2.4 to 2.6 Tg CO2 Eq. annually in Land 

Converted to Cropland, as well as an additional 3.7 to 3.9 Tg CO2 Eq. annually in Grassland Remaining Grassland (Table 

A-223) and 0.5 to 0.9 Tg CO2 Eq. annually in Land Converted to Grassland. Estimates and uncertainties are provided in 

Table A-223. 

Table A-223: Annual Change in Soil Organic Carbon Stocks (95% Confidence Interval) for the Land Base Estimated with 

the Tier 2 Analysis using U.S. Factor Values, Reference Carbon Stocks, and Carbon Loss Rates (Tg CO2 Eq./yr) 

Cropland Remaining 

Cropland Land Converted to Cropland Grassland Remaining Grassland 

Land Converted to 

Grassland 

Year Estimate 95% CI Estimate 95% CI Estimate 95% CI Estimate 95% CI 

Mineral Soils 

 

60 The difference in C stocks is divided by 20 because the stock change factors represent change over a 20-year time period. 
A-274 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

 1990-1992 (1.65) (2.6) to 5.8 4.16 2.5 to 5.98 (0.31) (0.6) to (0.1) (4.54) (6.5) to (2.74) 

 1993-2007 (3.02) (6.9) to 0.8 4.14 2.3 to 5.8 (0.15) (0.3) to 0.04 (4.99) (6.96) to (2.84) 

Organic Soils 

 1990-1992 27.43 18.3 to 39.4 2.42 1.4 to 3.8 3.85 1.97 to 6.4 0.47 0.22 to 0.8 

 1993-2007 27.67 15.8 to 36.9 2.64 1.2 to 3.7 3.69 1.2 to 5.5 0.88 0.2 to 1.8 

Step 4: Compute Additional Changes in Soil Organic C Stocks Due to Organic Amendments and CRP Enrollment 

after 2003 

There are two additional land-use and management activities in U.S. agricultural lands that were not estimated in 

Steps 2 and 3. The first activity involved the application of sewage sludge to agricultural lands. Minimal data exist on 

where and how much sewage sludge is applied to U.S. agricultural soils, but national estimates of mineral soil land area 

receiving sewage sludge can be approximated based on sewage sludge N production data, and the assumption that 

amendments are applied at a rate equivalent to the assimilative capacity from Kellogg et al. (2000). It was assumed that 

sewage sludge for agricultural land application was applied to grassland because of the high heavy metal content and other 

pollutants found in human waste, which limits its application to crops. The impact of organic amendments on SOC was 

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-224), by calculating the effect of converting nominal, medium-input grassland to high input improved grassland 

(assuming a reference C stock of 50 metric tonnes C/ha, which represents a mid-range value for the dominant cropland 

soils in the United States, the land use factor for grassland (1.4) and the country-specific factor of 1.11 for high input 

improved grassland, with the change in stocks occurring over a 20 year (default value) time period; i.e., [50 × 1.4 × 1.11 – 

50 × 1.4] / 20 = 0.38). From 1990 through 2007, sewage sludge applications in agricultural lands increased SOC storage 

from 0.6 to 1.2 Tg CO2 Eq./year (Table A-224). A nominal ±50 percent uncertainty was attached to these estimates due to 

limited information on application and the rate of change in soil C stock change with sewage sludge amendments. 

The second activity was the change in enrollment for the Conservation Reserve Program after 2003 for mineral 

soils. Relative to the enrollment in 2003, the total area in the Conservation Reserve Program increased from 2004 to 2007, 

leading to an additional enrollment of 1.4 million ha over the five-year period (USDA-FSA 2007). An average annual 

change in SOC of 0.5 metric tonnes C/ha-yr was used to estimate the effect of the enrollment changes. This rate is based 

on the IPCC default method and country-specific factors (see Table A-220) by calculating the impact of setting aside a 

medium input cropping system in the Conservation Reserve Program (assuming a reference C stock of 50 metric tonnes 

C/ha, which represents a mid-range value for the dominant cropland soils in the United States and the average countryspecific factor of 1.2 for setting-aside cropland from production, with the change in stocks occurring over a 20 yr (default 

value) time period; i.e., [50 × 1.2 – 50] / 20 = 0.5). The change in enrollment generated an additional accumulation of 0.4 

to 2.0 Tg CO2 Eq. annually between 2004 and 2007, respectively (Table A-225). A nominal ±50 percent uncertainty was 

also attached to these estimates due to limited information about the enrollment trends at subregional scales, which creates 

uncertainty in the rate of the soil C stock change (stock change factors for set-aside lands vary by climate region). 

Step 5: Compute Net CO2 Emissions and Removals from Agricultural Lands 

The sum of total CO2 emissions and removals from the Tier 3 Century Model Approach (Step 2), Tier 2 IPCC 

Methods (Step 3) and additional land-use and management considerations (Step 4) are presented in Table A-225. Overall, 

there was a net accumulation of 138.1 Tg CO2 Eq. in 1990 for agricultural soils, and this rate had decreased by the end of 

the reporting period in 2007 to 60.2 Tg CO2 Eq. 

The total stock change (as seen in the Land Use, Land-Use Change, and Forestry chapter) as well as per hectare 

rate of change varies among the states (Figure A-17 and Figure A-18). On a per hectare basis, the highest rates of C 

accumulation occurred in the Northeast, Midwest, northern Great Plains and Northwest. The states with highest total 

amounts of C sequestration were Iowa, Illinois, Missouri, Montana, Oklahoma, North Dakota, and South Dakota (Table 

A-226). For organic soils, emission rates were highest in the regions that contain the majority of the drained organic soils, 

including California, Florida, Michigan, Minnesota, and New York. On a per hectare basis, the emission rate patterns were 

very similar to the total emissions in each state, with the highest rates in those regions with warmer climates and a larger 

proportion of the drained organic soil managed for crop production. 

Figure A-17: Net C Stock Change, per Hectare, for Mineral Soils Under Agricultural Management, 2007 
A-275

Figure A-18: Net C Stock Change, per Hectare, for Organic Soils Under Agricultural Management, 2007 
A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-224: Assumptions and Calculations to Estimate the Contribution to Soil Organic Carbon Stocks from Application of Sewage Sludge to 

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2

Sewage Sludge N 

Applied to Agricultural Land (Mg N)a 52,198 55,658 59,250 62,977 65,966 69,001 72,081 75,195 78,353 80,932 83,523 86,124 88,736 91,358 93

Assimilative Capacity 

(Mg N/ha)b 0.120 0.120 0.120 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0.122 0

Area covered by 

Available Sewage Sludge N (ha)c 434,985 463,816 493,746 516,202 540,707 565,583 590,828 616,357 642,240 663,381 684,612 705,932 727,341 748,836 770

Average Annual Rate 

of C storage (Mg C/ha-yr)d 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 0.38 

Contribution to Soil 

C (TgCO2/yr)e,f (0.61) (0.65) (0.69) (0.72) (0.75) (0.79) (0.82) (0.86) (0.89) (0.92) (0.95) (0.98) (1.01) (1.04) (

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 d Annual rate of C storage based on national average increase in C storage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr) e Contribution to Soil C is estimated as the product of the area covered by the available sewage sludge N and the average annual C storage attributed to an organic matter amendment. 

f Some small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands.
Table A-225: Annual Soil C Stock Change in Cropland Remaining Cropland (CRC), Land Converted to Cropland (LCC), Grassland Remaining 

Converted to Grassland (LCG), in U.S. Agricultural Soils (Tg CO2 Eq.)

 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2Net emissions based on Tier 3 Century-based analysis (Step 2)  CRC * (55.2) (57.5) (68.0) (64.2) (62.4) (47.6) (55.0) (54.1) (44.3) (29.7) (54.8) (37.6) (36.1) (42.3) (42.3) (42.3) (4

 LCC (4.4) (4.3) (4.8) (8.7) (12.6) (3.8) (4.5) (4.2) (10.8) (3.3) (4.4) (2.5) (2.0) (0.8) (0.8) (0.8) (

 GRG (49.7) (32.2) (13.7) 3.6 (75.7) (39.2) 21.2 (22.7) 11.1 (24.2) (54.0) (30.0) (45.7) (7.0) (7.0) (7.0) ( LCG (18.2) (16.3) (14.9) (16.3) (19.3) (18.4) (20.0) (21.0) (22.3) (22.2) (27.9) (26.1) (24.2) (22.6) (22.6) (22.6) (2

Net emissions based on the IPCC Tier 2 analysis (Step 3) 

Mineral Soils 

CRC (1.6) (1.6) (1.6) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (3.0) (

LCC 4.2 4.2 4.2 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 4.1 

GRG (0.3) (0.3) (0.3) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (0.2) (

LCG (4.5) (4.5) (4.5) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (5.0) (

Organic Soils 

CRC 27.4 27.4 27.4 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 LCC 2.4 2.4 2.4 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 GRG 3.9 3.9 3.9 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 3.7 LCG 0.5 0.5 0.5 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 0.9 

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) (

Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4) 

CRC - - - - - - - - - - - - - - (0.4) (0.6) (

Total Stock Changes by Land Use/Land-Use Change Category (Step 5)

CRC * (29.4) (31.7) (42.3) (39.6) (37.7) (22.9) (30.3) (29.4) (19.7) (5.1) (30.2) (12.9) (11.5) (17.7) (18.1) (18.3) (1

LCC 2.2 2.3 1.8 (1.9) (5.8) 2.9 2.2 2.6 (4.0) 3.5 2.4 4.2 4.8 5.9 5.9 5.9 

GRG (46.7) (29.3) (10.8) 6.4 (72.9) (36.4) 24.0 (20.0) 13.7 (21.6) (51.4) (27.5) (43.1) (4.5) (4.5) (4.6) (

LCG (22.3) (20.4) (19.0) (20.4) (23.4) (22.5) (24.1) (25.1) (26.4) (26.3) (32.0) (30.2) (28.3) (26.7) (26.7) (26.7) (2

Total * (96.3) (79.1) (70.3) (55.5) (139.9) (78.9) (28.2) (72.0) (36.4) (49.5) (111.2) (66.4) (78.1) (42.9) (43.4) (43.6) (4
A-278 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-226: Soil C Stock Change for Mineral and Organic Soils during 2007 within individual states (Tg CO2 Eq.)) 

State Mineral Soil Organic Soil Total 

AL (0.43) - (0.43) 

AR (1.04) - (1.04) 

AZ (0.80) - (0. 80) 

CA (0.16) 2.29 2.14 

CO (0.53) 0.00 (0.53) 

CT (0.04) - (0.04) 

DE 0.02 - (0.02) 

FL 0.25 10.84 11.08 

GA (0.21) - (0.21) 

HI (0.02) 0.25 0.24 

IA (4.82) 0.75 (4.07) 

ID (1.53) 0.11 (1.42) 

IL (5.31) 0.54 (4.76) 

IN (1.78) 2.93 1.15 

KS (2.35) - (2.35) 

KY (1.82) - (1.82) 

LA (0.93) 0.07 (0.86) 

MA (0.02) 0.03 0.02 

MD (0.19) 0.03 (0.16) 

ME (0.17) - (0.17) 

MI (2.28) 2.72 0.44 

MN (2.67) 7.30 4.62 

MO (10.05) - (10.05) 

MS (1.22) 0.00 (1.22) 

MT (6.10) 0.11 (5.99) 

NC (0.35) 2.25 1.90 

ND (6.03) - (6.03) 

NE (2.03) - (2.03) 

NH (0.03) 0.01 (0.03) 

NJ (0.09) 0.01 (0.08) 

NM (1.17) - (1.17) 

NV (0.23) 0.00 (0.23) 

NY (1.97) 0.61 (1.36) 

OH (2.83) 0.42 (2.41) 

OK (6.75) - (6.75) 

OR (2.11) 0.12 (1.99) 

PA (1.79) 0.01 (1.78) 

RI (0.01) 0.00 (0.00) 

SC 0.05 0.04 0.09 

SD (5.89) - (5.89) 

TN (1.91) - (1.91) 

TX 8.46 - 8.46 

UT (0.02) - (0.02) 

VA (0.50) 0.02 (0.48) 

VT (0.27) 0.00 (0.26) 

WA (2.19) 0.26 (1.93) 

WI (2.81) 2.88 0.07 

WV (0.41) - (0.41) 

WY 0.17 0.01 0.18 

Note: Parentheses indicate net C accumulation. Estimates do not include soil C stock change associated with CRP enrollment after 2003 or sewage sludge application to 

soils, which were only estimated at the national scale. 
 

A-279

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.

61 The amount and rate of CH4 generation depends 

upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment. 

Not all CH4 generated within a landfill is emitted to the atmosphere. The CH4 can be extracted and either flared 

or utilized for energy, thus oxidizing to CO2 during combustion. Of the remaining CH4, a portion oxidizes to CO2 as it 

travels through the top layer of the landfill cover. In general, landfill-related CO2 emissions are of biogenic origin and 

primarily result from the decomposition, either aerobic or anaerobic, of organic matter such as food or yard wastes.62

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). However, this

information is not available for all landfills in the United States. Consequently, to estimate CH4 generation, a 

methodology was developed based on the quantity of waste placed in landfills nationwide each year, the first order decay 

model, and model parameters from the analysis of measured CH4 generation rates for U.S. landfills with gas recovery 

systems. 

From various studies and surveys of the generation and disposal of solid waste, estimates of the amount of waste 

placed in MSW and industrial landfills were developed. A database of measured CH4 generation rates at landfills with gas 

recovery systems was compiled and analyzed. The results of this analysis and other studies were used to develop an 

estimate of the CH4 generation potential for use in the first order decay model. In addition, the analysis and other studies 

provided estimates of the CH4 generation rate constant as a function of precipitation. The first order decay model was 

applied to annual waste disposal estimates for each year and for three ranges of precipitation to estimate CH4 generation 

rates nationwide for the years of interest. Based on the organic content of industrial wastes and the estimates of the 

fraction of these wastes sent to industrial landfills, CH4 emissions from industrial landfills were also estimated using the 

first order decay model. Total CH4 emissions were estimated by adding the CH4 from MSW and industrial landfills and 

subtracting the amounts recovered for energy or flared and the amount oxidized in the soil. The steps taken to estimate 

CH4 emissions from U.S. landfills for the years 1990 through 2006 are discussed in greater detail below. 

Figure A-19 presents the CH4 emissions process—from waste generation to emissions—in graphical format. 

Step 1: Estimate Annual Quantities of Solid Waste Placed in Landfills 

For 1989 to 2007, estimates of the annual quantity of waste placed in MSW landfills were developed from a 

survey of State agencies as reported in BioCycle’s State of Garbage in America (BioCycle 2008), adjusted to include U.S. 

territories.63 

Table A-227 shows estimates of waste quantities contributing to CH4 emissions. The table shows BioCycle 

estimates of total waste landfilled each year from 1990 through 2000, 2002, 2004, and 2006 adjusted for U.S. territories. 

A linear interpolation was used for 2001, 2003, 2005, and 2007 because there were no BioCycle surveys for those years. 

Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 

Table A-227: Solid Waste in MSW Landfills Contributing to CH4 Emissions (Tg unless otherwise noted) 

Description 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Total Waste 

Generateda 271 259 269 283 298 302 302 314 346 353 377 416 455 462 470 459 448 452

 

61 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. 

62 See Box 3-3 in the Energy chapter for additional background on how biogenic emissions of landfill CO2 are addressed in 

the U.S. Inventory. 

63 Since the BioCycle survey does not include U.S. territories, waste landfilled in U.S. territories was estimated using 

population data for the U.S territories (U.S. Census Bureau 2007) and the per capita rate for waste landfilled from BioCycle (2008). 
A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Percent of Wastes 

Landfilleda 77% 76% 72% 71% 67% 63% 62% 61% 61% 60% 61% 63% 66% 65% 64% 64% 65% 65%

Total Wastes 

Landfilled 209 197 194 201 200 190 187 192 211 212 230 263 298 300 301 285 289 291

Waste in Place 

(30 years)b 4,674 4,768 4,848 4,922 5,001 5,075 5,137 5,194 5,252 5,327 5,400 5,488 5,608 5,759 5,909 6,058 6,198 6,328

Waste Contributing 

to Emissionsc

6,815 7,012 7,206 7,407 7,606 7,796 7,984 8,175 8,386 8,598 8,828 9,092 9,390 9,690 9,991 10,286 10,574 10,866 a Source: BioCycle (2008), adjusted for missing U.S. territories using U.S. Census Bureau (2007) population data and per capita generation rate from BioCycle. The data, 

originally reported in short tons, are converted to metric tons. Estimates shown for 2001 and 2003 are based on an interpolation because there were no surveys in 2001 

and 2003; estimates shown for 2005 and 2007 based on the increase in population. 

b This estimate represents the waste that has been in place for 30 years or less, which contributes about 90 percent of the CH4 generation. 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. 

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, and 190 million metric tons 

in the 1990s. 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 (m3

/Mg), 

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. 

Values for the CH4 generation potential (Lo) were evaluated from landfill gas recovery data for 52 landfills, 

which resulted in a best fit value for Lo of 99 m3

/Mg of waste (RTI 2004) and from other studies. 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) based on 

data from 21 landfills. Based on the results from these studies, a value of 100 m3

/Mg appears to be a reasonable best 

estimate to use in the FOD model for the national inventory. 
 

A-281

The FOD model was applied to the gas recovery data for the 52 landfills to calculate the rate constant (k) directly 

for L0 = 100 m3/Mg. The rate constant was found to increase with annual average precipitation; consequently, average 

values of k were developed for three ranges of precipitation, shown in Table A- 228. 

Table A- 228. 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) recommends a value of 0.02 yr-1 for arid areas (less than 20 inches/year of precipitation) 

and 0.04 yr-1 for non-arid areas. The SWANA study of 18 landfills reported a range in values of k from 0.03 to 0.06 yr-1

based on CH4 recovery data collected generally in the time frame of 1986 to 1995. 

Using data collected primarily for the year 2000, the distribution of waste in place vs. 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 vs. 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-229. 

Table A-229. 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 current 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 Minz et al. (2003), a timeline was developed for 

the transition from the use of open dumps for solid waste disposed to the use of managed landfills. Based on this timeline, 

it was estimated that 6 percent of the waste that was land disposed in 1940 was disposed of in managed landfills and 94 

percent was managed in open dumps. Between 1940 and 1980, the fraction of waste land disposed transitioned towards 

managed landfills until 100 percent of the waste was disposed of in managed landfills in 1980. For wastes disposed of in 

dumps, a methane correction factor (MCF) of 0.6 was used based on the recommended IPCC default value for 

uncharacterized land disposal (IPCC 2006); this MCF is equivalent to assuming 50 percent of the open dumps are deep 

and 50 percent are shallow. The recommended IPCC default value for the MCF for managed landfills of 1 was used for 

the managed landfills (IPCC 2006). 

Step 3: Estimate CH4 Generation at Industrial Landfills 

Industrial landfills receive waste from factories, processing plants, and other manufacturing activities. In 

national inventories prior to the 1990 through 2005 inventory, CH4 generation at industrial landfills was estimated as seven 

percent of the total CH4 generation from MSW landfills, based on a study conducted by EPA (1993). For the 1990 

through 2005 and current inventories, the methodology was updated and improved by using activity factors (industrial 

production levels) to estimate the amount of industrial waste landfilled each year and by applying the FOD model to 

estimate CH4 generation. A nationwide survey of industrial waste landfills found that over 99 percent of the organic waste 

placed in industrial landfills originated from two industries: food processing (meat, vegetables, fruits) and pulp and paper 

(EPA 1993). Data for annual nationwide production for the food processing and pulp and paper industries were taken 

from industry and government sources for recent years; estimates were developed for production for the earlier years for 

which data were not available. For the pulp and paper industry, production data published by the Lockwood-Post’s 

Directory (ERG 2008) and U.S. Department of Agriculture (2003) were the primary sources for years 1965 through 2007. 

An extrapolation based on U.S. real gross domestic product was used for years 1940 through 1964. For the food 

processing industry, production levels were obtained or developed from the U.S. Department of Agriculture (2008) for the 

years 1990 through 2007 (ERG 2008). An extrapolation based on U.S. population was used for the years 1940 through 

1989. 
A-282 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

In addition to production data for the pulp and paper and food processing industries, the following inputs were 

needed to use the FOD model for estimating CH4 generation from industrial landfills: 1) quantity of waste that is disposed 

in industrial landfills (as a function of production), 2) CH4 generation potential (L0) or DOC, and 3) FOD decay constant 

(k). Research into waste generation and disposal in landfills for the pulp and paper industry indicated that the quantity of 

waste landfilled was about 0.050 Mg/Mg of product compared to 0.046 Mg/Mg product for the food processing industry 

(Weitz and Bahner 2006). These factors were applied to estimates of annual production to estimate annual waste disposal 

in landfills. Estimates for DOC were derived from available data (Kraft and Orender, 1993; NCASI 2008; Flores et al. 

1999). The DOC value for industrial pulp and paper waste is estimated as 0.20 (Lo of 99 m3/Mg); the DOC value for 

industrial food waste is estimated as 0.26 (Lo of 128 m3

/Mg) (Coburn 2008). Estimates for k were taken from the default 

values in the 2006 IPCC Guidelines; the value of k given for food waste with disposal in a wet temperate climate is 0.19 

yr-1, and the value given for paper waste is 0.06 yr.-1

As with MSW landfills, a similar trend in disposal practices from open dumps to managed landfills was expected 

for industrial landfills; therefore, the same time line that was developed for MSW landfills was applied to the industrial 

landfills to estimate the average MCF. That is, between 1940 and 1980, the fraction of waste land disposed transitioned 

from 6 percent managed landfills in 1940 and 94 percent open dumps to 100 percent managed landfills in 1980 and on. 

For wastes disposed of in dumps, an MCF of 0.6 was used and for wastes disposed of in managed landfills, an MCF of 1 

was used, based on the recommended IPCC default values (IPCC 2006). 

The parameters discussed above were used in the integrated form of the FOD model to estimate CH4 generation 

from industrial landfills. 

Step 4: Estimate CH4 Emissions Avoided 

The estimate of CH4 emissions avoided (e.g., combusted) was based on landfill-specific data on landfill gas-toenergy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CH4 recovered to estimate CH4

emissions avoided. The value for efficiency was selected based on the range of efficiencies (98 to 100 percent) 

recommended for flares in EPA’s “AP-42 Compilation of Air Pollutant Emission Factors, Chapter 2.4," efficiencies used 

to establish new source performance standards (NSPS) for landfills, and in recommendations for closed flares used in the 

Landfill Methane Outreach Program (LMOP). 

Step 4a: Estimate CH4 Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects 

The quantity of CH4 avoided due to LFGTE systems was estimated based on information from two sources: (1) 

a database maintained by the Energy Information Administration (EIA) for the voluntary reporting of greenhouse gases 

(EIA 2007) and (2) a database compiled by LMOP (EPA 2008). The EIA database included location information for 

landfills with LFGTE projects, estimates of CH4 reductions, descriptions of the projects, and information on the 

methodology used to determine the CH4 reductions. Generally the CH4 reductions for each reporting year were based on 

the measured amount of landfill gas collected and the percent CH4 in the gas. For the LMOP database, data on landfill gas 

flow and energy generation (i.e., MW capacity) were used to estimate the total direct CH4 emissions avoided due to the 

LFGTE project. Detailed information on the landfill name, owner or operator, city, and state were available for both the 

EIA and LMOP databases; consequently, it was straightforward to identify landfills that were in both databases. The EIA 

database was given priority because reductions were reported for each year and were based on direct measurements. 

Landfills in the LMOP database that were also in the EIA database were dropped to avoid double counting. 

Step 4b: Estimate CH4 Emissions Avoided Through Flaring 

The quantity of CH4 flared was based on data from the EIA database and on information provided by flaring 

equipment vendors. To avoid double-counting, flares associated with landfills in the EIA and LMOP databases were 

excluded from the flare vendor database. As with the LFGTE projects, reductions from flaring landfill gas in the EIA 

database were based on measuring the volume of gas collected and the percent of CH4 in the gas. The information 

provided by the flare vendors included information on the number of flares, flare design flow rates or flare dimensions, 

year of installation, and generally the city and state location of the landfill. When a range of design flare flow rates was 

provided by the flare vendor, the median landfill gas flow rate was used to estimate CH4 recovered from each remaining 

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 
 

A-283

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 most LFGTE 

projects have flares. However, given that the flare vendor data only covers approximately 50 to 75 percent of the flare 

population, an associated flare was not identified for all LFGTE projects. These LFGTE projects likely have flares; 

however, flares were unable to be identified due to one of two reasons: 1) inadequate identifier information in the flare 

vendor data; or 2) the 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 on a state-by-state basis. This step likely underestimates CH4 avoided due to flaring. This 

approach was applied to be conservative in the estimates of CH4 emissions avoided. 

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, ten percent was oxidized in 

the soil (Jensen and Pipatti 2002; Mancinelli and McKay 1985; Czepiel et al 1996). The factor of 10 percent is consistent 

with the value recommended in the 2006 IPCC revised guidelines for managed and covered landfills. This oxidation 

factor was applied to the estimates of CH4 generation minus recovery for both MSW and industrial 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-230. 
A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-230: CH4 Emissions from Landfills (Gg) Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 MSW Generation 8,219 8,443 8,624 8,788 8,966 9,132 9,263 9,381 9,507 9,683 9,854 10,068 10,367 1Industrial Generation 554 563 574 585 598 615 630 645 660 673 687 701 707 

Potential Emissions 8,772 9,006 9,197 9,373 9,564 9,747 9,893 10,027 10,167 10,356 10,541 10,769 11,074 1

Emissions Avoided (878) (1,035) (1,167) (1,349) (1,596) (2,112) (2,437) (2,944) (3,438) (3,707) (4,069) (4,448) (4,627) (4Landfill Gas-to-Energy (635) (659) (725) (805) (850) (1,064) (1,270) (1,604) (1,921) (2,182) (2,348) (2,550) (2,602) (2

Flare (242) (376) (441) (545) (746) (1,048) (1,167) (1,341) (1,517) (1,526) (1,722) (1,898) (2,025) (2Oxidation at MSW Landfills (734) (741) (746) (744) (737) (702) (683) (644) (607) (598) (578) (562) (574) Oxidation at Industrial Landfills (55) (56) (57) (59) (60) (61) (63) (65) (66) (67) (69) (70) (71) 

Net Emissions 7,105 7,173 7,228 7,221 7,172 6,871 6,711 6,374 6,057 5,984 5,825 5,689 5,803 Note: Totals may not sum exactly to the last significant figure due to rounding. Note: MSW generation in Table A-230 represents emissions before oxidation. In other tables throughout the text, MSW generation estimates account for oxidation. Note: Parentheses denote negative values. 
Figure A- 4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2007 (Tg CO2 Eq.) 

0

500

1,000

1,500

2,000

2,500

199019911992199319941995199619971998199920002001200220032004200520062007

Tg CO2 Eq.

Passenger Cars/Mototcycles

Light-Duty Trucks

Medium- and Heavy-Duty Trucks Aircraft

Boats/Ships, Rail, and Pipelines

Mobile AC, Refrig. Transport, Lubricants

Non-Transportation Mobile Sources
Section Name X-7

Figure A-5

Effect of Soil Temperature, Water-Filled Pore Space, and pH on Nitrification Rates

0

0.2

0.4

0.6

0.8

1

1.2

0 10 20 30 40 50 60

F(temp)

Soil Temperature

0

0.2

0.4

0.6

0.8

1

1.2

F(WFPS)

0 0.2 0.4 0.6 0.8 1

WFPS

0

0.2

0.4

0.6

0.8

1

1.2

F(pH)

3 4 5 6 7 8 9 10

pH

fine

coarse
X-8 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Figure A-6

Effect of Soil Nitrite Concentration, Heterotrophic Respiration Rates, and Water-Filled Pore Space on Denitrification Rates

0

5

10

15

20

25

30

35

0 100 200 300 400

N +N 2 O μg N/g soil/day 2

NO3 μg N/g soil

0

5

10

15

20

25

30

35

40

N +N 2 O μg N/g soil/day 2

0 20 40 60 80 100

CO2 μg C/g soil/day

0

0.2

0.4

0.6

0.8

1

0 20 40 60 80 100

Relative Denitrification

WFPS %

 clay-low resp

clay-high resp

loam-low resp

loam-high resp
Figure A-7: DAYCENT model flow diagram 

DEAD PLANT

MATERIAL

METABOLIC

STRUCTURAL

PLANT 

COMPONENTS

LEAVES

 FINE ROOTS

BRANCHES

LARGE WOOD

LARGE

ROOTS

NO3

-

 NH4

+

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

0-15 cm

SOM

ACTIVE

0.5-1 yr

SLOW

10-50 yr

PASSIVE

1000-5000 yr

H2Osoil

Tsoil

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

N GAS

0-1 cm

1-4 cm

4-15 cm

15-30 cm

etc.

C:N

CO2

S,Rh

PPT,V,L

C:N

V

S

C:N

CO2

CO2

CH4

Uptake

Decomp

Rh S

Rh

S

S

NPP

DAYCENT MODEL

S=soil type

V=veg type

L=land use
y = 1.0765x - 0.6936

R2 = 0.9587

y = 0.1688x + 3.6132

R2 = 0.3931

0

5

10

15

20

25

30

35

40

0 10 20 30 40

N2O gN ha-1 day-1

N O gN ha 2 -1 day-1

DAYCENT

IPCC

1-1 line

Linear (DAYCENT)

Linear (IPCC)

Linear (1-1 line)

Figure A-8: Comparisons of Results from DAYCENT Model and IPCC Tier 1 

Method with Measurements of Soil N2O Emissions
Major Crops, Average Annual Direct N2

O Emissions, Estimated Using the DAYCENT Model, 

1990–2007 (Metric Tons CO2 Eq./ha/year)

Figure A-9

< 0.5

Metric Tons CO2

 Eq./

ha/year

0.5 – 1

1 – 1.5

1.5 – 2

2 – 3

3 – 5

> 5
Average Annual N Losses Leading to Indirect N2

O Emissions, Estimated Using the DAYCENT Model, 

1990–2007 (kg N/ha/year)

Figure A-10

< 10

kg N/ha/year

10 – 20

20 – 40

40 – 60

60 – 100

100 – 200

> 200
Grasslands, Average Annual Direct N2

O Emissions, Estimated Using the DAYCENT Model, 

1990–2007 (Metric Tons CO2 Eq./ha/year)

Figure A-11

< 0.1

Metric Tons CO2

 Eq./

ha/year

0.1 – 0.25

0.25 – 0.5

0.5 – 1

1 – 1.25

1.25 – 1.5

> 1.5
Grasslands, Average Annual N Losses Leading to Indirect N2

O Emissions, Estimated Using the DAYCENT Model, 

1990–2007 (kg N/ha/year)

Figure A-12

< 5

kg N/ha/year

5 – 10

10 – 15

15 – 20

20 – 30

30 – 50

> 50
X-12 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Flow diagram of Carbon submodel (A) and Nitrogen submodel (B)

Figure A-13 'MPXEJBHSBNPG$TVCNPEFM	"
BOE/TVCNPEFM	#


'JHVSF

A

B
Section Name X-13

Comparison of Measured Soil Organic C from Experimental Sites to Modeled Soil Organic C Using the Century Model

Figure A-14

30

50

70

90

30 50 70 90

Sqrt Modeled Soil Organic Carbon (g C m-2)

Sqrt Modeled Soil Organic Carbon (g C m-2)
X-14 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990–2006

Major Land Resource Areas by IPCC Climate Zone

Figure A-15
Section Name X-15

Uncertainty in Data Inputs

Figure A-16

6ODFSUBJOUZJO%BUB*OQVUT

'JHVSF

6ODFSUBJOUZJOEBUBJOQVUT	JFGFSUJMJ[FS
NBOVSFBOEUJMMBHFQSBDUJDFT
BSFFTUJNBUFEVTJOHB.POUF$BSMPQSPDFEVSFXJUISBOEPNESBXTGSPNJOQVU

EBUBQSPCBCJMJUZEJTUSJCVUJPOT
GPSFBDI/3*QPJOUTJNVMBUFE.PEFMVODFSUBJOUZJTFTUJNBUFEUISPVHIBOFNQJSJDBMCBTFEBQQSPBDI6ODFSUBJOUZJOUIF

MBOESFQSFTFOUBUJPOPG/3*JTFTUJNBUFEGSPNUIFTUBUJTUJDTDPNQJMFEGSPNUIF/3*TVSWFZTUPEFUFSNJOFUIFMBOEBSFBFYQBOTJPOGBDUPST
XIJDIBSFVTFE

UPVQTDBMFEBUBUPUIFOBUJPOBMMFWFM
Mg CO2 Eq./ha/year

> 0

-0.05 to 0

-0.1 to -0.05

-0.25 to -0.1

-0.5 to -0.25

< -0.5

Note: Values greater than zero represent emissions, 

and values less than zero represent sequestration.

Map accounts for fluxes associated with the Tier 2

and 3 inventory computations. See methodology

for additional details. 

Figure A-17

Net C Stock Change, per Hectare, for Mineral Soils Under 

Agricultural Management, 2007
Mg CO2 Eq./ha/year

> 1

0.5 to 1

0.25 to 0.5

0.1 to 0.25

0 to 0.1

No organic soils

Note: Values greater than zero represent emissions.

Figure A-18

Net C Stock Change, per Hectare, for Organic Soils 

Under Agricultural Management, 2007
Figure A-19: Methane Emissions Resulting from Landfilling Municipal and Industrial Waste 

a BioCycle 2006 for MSW and activity factors for industrial waste. b 1960 through 1988 based on EPA 1988 and EPA 1993; 1989 through 2006 based on BioCycle 2006. c 2006 IPCC Guidelines – First Order Decay Model. d EIA 2007 and flare vendor database. e EIA 2007 and EPA (LMOP) 2007. f 2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996

MSW and 

Industrial 

Waste 

Generateda

Total 

Methane 

Generated 

at 

Landfillsc

MSW and 

Industrial 

Waste 

Landfilledb

To

Met

Em

Nonrecovered 

Methane 

Generated 

at Landfills

Composted

Recycled

Incinerated

Flaredd Oxidized

Recovered 

for 

Energye

10%f
 

A-285

ANNEX 4 IPCC Reference Approach for 

Estimating CO2 Emissions from Fossil 

Fuel Combustion 

It is possible to estimate carbon dioxide (CO2) emissions from fossil fuel consumption using alternative 

methodologies and different data sources than those described in the Estimating Emissions from Fossil Fuel Combustion 

Annex. For example, the UNFCCC reporting guidelines request that countries, in addition to their “bottom-up” sectoral 

methodology, complete a "top-down" Reference Approach for estimating CO2 emissions from fossil fuel combustion. 

Section 1.3 of the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reporting Instructions states, 

“If a detailed, Sectoral Approach for energy has been used for the estimation of CO2 from fuel combustion you are still 

asked to complete...the Reference Approach...for verification purposes” (IPCC/UNEP/OECD/IEA 1997). This reference 

method estimates fossil fuel consumption by adjusting national aggregate fuel production data for imports, exports, and 

stock changes rather than relying on end-user consumption surveys. The basic principle is that once C-based fuels are 

brought into a national economy, they are either saved in some way (e.g., stored in products, kept in fuel stocks, or left 

unoxidized in ash) or combusted, and therefore the C in them is oxidized and released into the atmosphere. Accounting 

for actual consumption of fuels at the sectoral or sub-national level is not required. The following discussion provides the 

detailed calculations for estimating CO2 emissions from fossil fuel combustion from the United States using the IPCCrecommended Reference Approach. 

Step 1: Collect and Assemble Data in Proper Format 

To ensure the comparability of national inventories, the IPCC has recommended that countries report energy data 

using the International Energy Agency (IEA) reporting convention. National energy statistics were collected in physical 

units from several EIA documents in order to obtain the necessary data on production, imports, exports, and stock 

changes. 

It was necessary to make a number of modifications to these data to generate more accurate apparent 

consumption estimates of these fuels. The first modification adjusts for consumption of fossil fuel feedstocks accounted 

for in the Industrial Processes chapter, which include the following: unspecified coal for coal coke used in iron and steel 

production; natural gas, distillate fuel, and coal used in iron and steel production; natural gas used for ammonia 

production; petroleum coke used in the production of aluminum, ferroalloys, titanium dioxide, ammonia, and silicon 

carbide; and other oil and residual fuel oil used in the manufacture of C black. The second modification adjusts for the 

fact that EIA energy statistics include synthetic natural gas in both coal and natural gas data. The third modification 

adjusts for the inclusion of ethanol in motor gasoline statistics. Ethanol is a biofuel, and it is assumed that no net CO2

emissions occur due to its combustion. The fourth modification adjusts for consumption of bunker fuels, which refer to 

quantities of fuels used for international transportation estimated separately from U.S. totals. The fifth modification 

consists of the addition of U.S. territories data that are typically excluded from the national aggregate energy statistics. 

The territories include Puerto Rico, U.S. Virgin Islands, Guam, American Samoa, Wake Island, and U.S. Pacific Islands. 

These data, as well as the production, import, export, and stock change statistics, are presented in Table A-231. 

The C content of fuel varies with the fuel's heat content. Therefore, for an accurate estimation of CO2 emissions, 

fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics are 

typically provided in physical units (as in Table A-231 for 2007), they were converted to units of energy before CO2

emissions were calculated. Fuel statistics were converted to their energy equivalents by using conversion factors provided 

by EIA. These factors and their data sources are displayed in Table A-232. The resulting fuel type-specific energy data 

for 2007 are provided in Table A-233. 

Step 2: Estimate Apparent Fuel Consumption 

The next step of the IPCC Reference Approach is to estimate "apparent consumption" of fuels within the country. 

This requires a balance of primary fuels produced, plus imports, minus exports, and adjusting for stock changes. In this 

way, C enters an economy through energy production and imports (and decreases in fuel stocks) and is transferred out of 

the country through exports (and increases in fuel stocks). Thus, apparent consumption of primary fuels (including crude 
A-286 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2006 

oil, natural gas liquids, anthracite, bituminous, subbituminous and lignite coal, and natural gas) can be calculated as 

follows: 

Apparent Consumption = Production + Imports - Exports - Stock Change 

Flows of secondary fuels (e.g., gasoline, residual fuel, coke) should be added to primary apparent consumption. 

The production of secondary fuels, however, should be ignored in the calculations of apparent consumption since the C 

contained in these fuels is already accounted for in the supply of primary fuels from which they were derived (e.g., the 

estimate for apparent consumption of crude oil already contains the C from which gasoline would be refined). Flows of 

secondary fuels should therefore be calculated as follows: 

Secondary Consumption = Imports - Exports - Stock Change 

Note that this calculation can result in negative numbers for apparent consumption of secondary fuels. This 

result is perfectly acceptable since it merely indicates a net export or stock increase in the country of that fuel when 

domestic production is not considered. 

Next, the apparent consumption and secondary consumption need to be adjusted for feedstock uses of fuels 

accounted for in the Industrial Processes chapter, international bunker fuels, and U.S. territory fuel consumption. Bunker 

fuels and feedstocks accounted for in the Industrial Processes chapter are subtracted from these estimates, while fuel 

consumption in U.S. territories is added. 

The IPCC Reference Approach calls for estimating apparent fuel consumption before converting to a common 

energy unit. However, certain primary fuels in the United States (e.g., natural gas and steam coal) have separate 

conversion factors for production, imports, exports, and stock changes. In these cases, it is not appropriate to multiply 

apparent consumption by a single conversion factor since each of its components has different heat contents. Therefore, 

United States fuel statistics were converted to their heat equivalents before estimating apparent consumption. Results are 

provided in Table A-232. 

Step 3: Estimate Carbon Emissions 

Once apparent consumption is estimated, the remaining calculations are similar to those for the “bottom-up” 

Sectoral Approach (see Estimating Emissions from Fossil Fuel Combustion Annex). Potential CO2 emissions were 

estimated using fuel-specific C coefficients (see Table A-233).1 The C in products from non-energy uses of fossil fuels 

(e.g., plastics or asphalt) was then estimated and subtracted (see Table A-235). This step differs from the Sectoral 

Approach in that emissions from both fuel combustion and non-energy uses are accounted for in this approach. Finally, to 

obtain actual CO2 emissions, net emissions were adjusted for any C that remained unoxidized as a result of incomplete 

combustion (e.g., C contained in ash or soot).2 The fraction oxidized was assumed to be 100 percent for petroleum, coal, 

and natural gas based on guidance in IPCC (2006) (see Estimating Emissions from Fossil Fuel Combustion Annex). 

Step 4: Convert to CO2 Emissions 

Because the IPCC reporting guidelines recommend that countries report greenhouse gas emissions on a full 

molecular weight basis, the final step in estimating CO2 emissions from fossil fuel consumption was converting from units 

of C to units of CO2. Actual C emissions were multiplied by the molecular-to-atomic weight ratio of CO2 to C (44/12) to 

obtain total CO2 emitted from fossil fuel combustion in teragrams (Tg). The results are contained in Table A-234. 

Comparison Between Sectoral and Reference Approaches 

These two alternative approaches can both produce reliable estimates that are comparable within a few percent. 

Note that the reference approach includes emissions from non-energy uses. Therefore, these totals should be compared to 

the aggregation of fuel use and emission totals from Emissions of CO2 from Fossil Fuel Combustion and Carbon Emitted 

from Non-Energy Uses of Fossil Fuels Annexes. These two sections together are henceforth referred to as the Sectoral 

Approach. Other than this distinction, the major difference between methodologies employed by each approach lies in the 

energy data used to derive C emissions (i.e., the actual surveyed consumption for the Sectoral Approach versus apparent 

 

1 Carbon coefficients from EIA were used wherever possible. Because EIA did not provide coefficients for coal, the IPCCrecommended emission factors were used in the top-down calculations for these fuels. See notes in Table A-234 for more specific 

source information. 
 

A-287

consumption derived for the Reference Approach). In theory, both approaches should yield identical results. In practice, 

however, slight discrepancies occur. For the United States, these differences are discussed below. 

Differences in Total Amount of Energy Consumed 

Table A-237 summarizes the differences between the Reference and Sectoral approaches in estimating total 

energy consumption in the United States. Although theoretically the two methods should arrive at the same estimate for 

U.S. energy consumption, the Reference Approach provides an energy total that is 0.5 percent lower than the Sectoral 

Approach for 2007. The greatest differences lie in lower estimates for both coal and petroleum consumption for the 

Reference Approach (2.1 and 0.2 percent respectively). 

There are several potential sources for the discrepancies in consumption estimates: 

● Product Definitions. The fuel categories in the Reference Approach are different from those used in the 

Sectoral Approach, particularly for petroleum. For example, the Reference Approach estimates apparent 

consumption for crude oil. Crude oil is not typically consumed directly, but refined into other products. As 

a result, the United States does not focus on estimating the energy content of the various grades of crude oil, 

but rather estimating the energy content of the various products resulting from crude oil refining. The 

United States does not believe that estimating apparent consumption for crude oil, and the resulting energy 

content of the crude oil, is the most reliable method for the United States to estimate its energy consumption. 

Other differences in product definitions include using sector-specific coal statistics in the Sectoral Approach 

(i.e., residential, commercial, industrial coking, industrial other, and transportation coal), while the 

Reference Approach characterizes coal by rank (i.e. anthracite, bituminous, etc.). Also, the liquefied 

petroleum gas (LPG) statistics used in the bottom-up calculations are actually a composite category 

composed of natural gas liquids (NGL) and LPG. 

● Heat Equivalents. It can be difficult to obtain heat equivalents for certain fuel types, particularly for 

categories such as "crude oil" where the key statistics are derived from thousands of producers in the United 

States and abroad. 

● Possible inconsistencies in U.S. Energy Data. The United States has not focused its energy data collection 

efforts on obtaining the type of aggregated information used in the Reference Approach. Rather, the United 

States believes that its emphasis on collection of detailed energy consumption data is a more accurate 

methodology for the United States to obtain reliable energy data. Therefore, top-down statistics used in the 

Reference Approach may not be as accurately collected as bottom-up statistics applied to the Sectoral 

Approach. 

● Balancing Item. The Reference Approach uses apparent consumption estimates while the Sectoral 

Approach uses reported consumption estimates. While these numbers should be equal, there always seems 

to be a slight difference that is often accounted for in energy statistics as a “balancing item.” 

Differences in Estimated CO2 Emissions 

Given these differences in energy consumption data, the next step for each methodology involved estimating 

emissions of CO2. Table A-238 summarizes the differences between the two methods in estimated C emissions. 

As mentioned above, for 2007, the Reference Approach resulted in a 0.5 percent lower estimate of energy 

consumption in the United States than the Sectoral Approach. The resulting emissions estimate for the Reference 

Approach was 0.8 percent higher. Estimates of petroleum and natural gas emissions from the Reference Approach are 

higher (2.6 percent and 0.8 percent respectively), and coal emission estimates are lower (1.3 percent) than the Sectoral 

Approach. Potential reasons for these differences may include: 

● Product Definitions. Coal data is aggregated differently in each methodology, as noted above. The format 

used for the Sectoral Approach likely results in more accurate estimates than in the Reference Approach. 

Also, the Reference Approach relies on a "crude oil" category for determining petroleum-related emissions. 

Given the many sources of crude oil in the United States, it is not an easy matter to track potential 

differences in C content between many different sources of crude; particularly since information on the C 

content of crude oil is not regularly collected. 

● Carbon Coefficients. The Reference Approach relies on several default C coefficients by rank provided by 

IPCC (IPCC/UNEP/OECD/IEA 1997), while the Sectoral Approach uses annually updated category-specific 

coefficients by sector that are likely to be more accurate. Also, as noted above, the C coefficient for crude 

oil is more uncertain than that for specific secondary petroleum products, given the many sources and grades 

of crude oil consumed in the United States. 
A-288 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2006 

Although the two approaches produce similar results, the United States believes that the “bottom-up” Sectoral 

Approach provides a more accurate assessment of CO2 emissions at the fuel level. This improvement in accuracy is 

largely a result of the data collection techniques used in the United States, where there has been more emphasis on 

obtaining the detailed products-based information used in the Sectoral Approach than obtaining the aggregated energy 

flow data used in the Reference Approach. The United States believes that it is valuable to understand both methods. 

References 

EIA (2008a) Supplemental Tables on Petroleum Product detail. Monthly Energy Review, December 2008, Energy 

Information Administration, U.S. Department of Energy, Washington, DC. DOE/EIA-0035(2008/12). 

EIA (2008b) Annual Energy Review 2007. Energy Information Administration, U.S. Department of Energy, Washington, 

DC. DOE/EIA-0384(2006). June 2008. 

EIA (1995-2008) Petroleum Supply Annual, Energy Information Administration, U.S. Department of Energy, Washington, 

DC, Volume I. DOE/EIA-0340. 

IPCC (2006), 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Prepared by the National Greenhouse Gas 

Inventories Programme, Eggleston H.S., Buendia L., Miwa K., Ngara T., and Tanabe K. (eds.). Published: IGES, Japan. 

IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris: 

Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic CoOperation and Development, International Energy Agency. 

SAIC (2004) Analysis prepared by Science Applications International Corporation for EPA, Office of Air and Radiation, 

Market Policies Branch. 

USGS (1998) CoalQual Database Version 2.0, U.S. Geological Survey. 
Table A-231: 2007 U.S. Energy Statistics (Physical Units) 

Fuel Category (Units) Fuel Type Production Imports Exports 

Stock Change Adjustment 

Solid Fuels (Thousand Short Tons) Anthracite Coal 1,557 a a a

Bituminous Coal 534,867 a a a

Sub-bituminous Coal 530,622 a a a 440 

Lignite 78,520 a a a 3,784 

Coke 2,460 1,444 (52)

Unspecified Coal 36,347 59,163 2,497 22,780 

Gas Fuels (Million Cubic Feet) Natural Gas 19,085,266 4,601,511 808,883 (176,977) 310,410 

Liquid Fuels (Thousand Barrels) Crude Oil 1,848,450 3,661,404 10,006 (17,835) Nat Gas Liquids and LRGs 650,794 100,863 25,584 (19,239) 

 Other Liquids 0 546,948 32,049 9,611 

 Motor Gasoline 229,736 150,617 46,369 (4,671) 110,532 

 Aviation Gasoline 117 0 (167)

 Kerosene 1,276 3,148 (569) 

 Jet Fuel 79,034 15,010 329  Distillate Fuel 110,997 97,726 (9,707) 12 

 Residual Fuel 135,676 120,395 (3,059) 7,000 

 Naphtha for petrochemical feedstocks 34,346 0 205  Petroleum Coke 11,744 133,582 (128) 13,743  Other Oil for petrochemical feedstocks 54,817 0 (295) 35,930  Special Naphthas 6,083 6,487 (38) Lubricants 6,195 21,570 (1,809) 

Waxes 1,242 1,829 (36)

 Asphalt/Road Oil 14,479 6,757 (6,394) Still Gas 0 0 0  Misc. Products 119 2,365 (359) 

[a] Included in Unspecified Coal Data Sources: Solid and Gas Fuels: EIA (2008a); Liquid Fuels: EIA (1995-2008). 
A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-232: Conversion Factors to Energy Units (Heat Equivalents) Fuel Category (Units) Fuel Type Production Imports Exports 

Stock Change Adjustment 

Solid Fuels (Million Btu/Short Ton) Anthracite Coal 22.57

Bituminous Coal 23.89

Sub-bituminous Coal 17.14 28.16

Lignite 12.87 12.87

Coke 25.00 25.47 25.00

Unspecified 25.00 25.97 20.86 25.58 

Natural Gas (BTU/Cubic Foot) 1,028 1,025 1,009 1,028 1,026 Liquid Fuels (Million Btu/Barrel) Crude Oil 5.80 5.98 5.80 5.80 

Nat Gas Liquids and LRGs 3.70 3.70 3.70 3.70 

Other Liquids 5.83 5.83 5.83 5.83 

Motor Gasoline 5.22 5.22 5.22 5.22 5.22 

Aviation Gasoline 5.05 5.05 5.05 

Kerosene 5.67 5.67 5.67 

Jet Fuel 5.67 5.67 5.67 

Distillate Fuel 5.83 5.83 5.83 5.83 

Residual Oil 6.29 6.29 6.29 6.29 

Naphtha for petrochemical feedstocks 5.25 5.25 5.25 

Petroleum Coke 6.02 6.02 6.02 6.02 

Other Oil for petrochemical feedstocks 5.83 5.83 5.83 5.83 

Special Naphthas 5.25 5.25 5.25 

Lubricants 6.07 6.07 6.07 

Waxes 5.54 5.54 5.54 

Asphalt/Road Oil 6.64 6.64 6.64 

Still Gas 6.00 6.00 6.00 

Misc. Products 5.80 5.80 5.80 

Data Sources: Coal and lignite production: EIA (2008b); Unspecified Solid Fuels: EIA (2008a); Coke, Natural Gas and Petroleum Products: EIA (2008a). 
Table A-233: 2007 Apparent Consumption of Fossil Fuels (TBtu) Fuel Category Fuel Type Production Imports Exports 

Stock Change

Adjustment Bunkers 

Solid Fuels Anthracite Coal 35.2 

Bituminous Coal 12,778.0 Sub-bituminous Coal 9,094.9 12.4 Lignite 1,010.2 48.7 Coke 0.0 61.5 36.8 (1.3) Unspecified 0.0 908.7 1,536.6 52.1 582.7 

Gas Fuels Natural Gas 19,619.7 4,716.5 816.2 (181.9) 228.7 Liquid Fuels Crude Oil 10,721.0 21,898.9 58.0 (103.4) 0.0 

Nat Gas Liquids and LRGs 2,408.6 373.3 94.7 (71.2) 0.0 Other Liquids 0.0 3,186.0 186.7 56.0 0.0 Motor Gasoline 1,199.0 786.1 242.0 (24.4) 576.9 0.0 Aviation Gasoline 0.0 0.6 0.0 (0.8) 0.0 Kerosene 0.0 7.2 17.8 (3.2) 0.0 Jet Fuel 0.0 448.1 85.1 1.9 704.5 Distillate Fuel 0.0 646.6 569.3 (56.5) 107.2 Residual Oil 0.0 853.0 756.9 (19.2) 44.0 607.5 Naphtha for petrochemical feedstocks 0.0 180.2 0.0 1.1 0.0 Petroleum Coke 0.0 70.7 804.7 (0.8) 82.8 0.0 Other Oil for petrochemical feedstocks 0.0 319.3 0.0 (1.7) 209.3 0.0 Special Naphthas 0.0 31.9 34.0 (0.2) 0.0 Lubricants 0.0 37.6 130.8 (11.0) 0.0 Waxes 0.0 6.9 10.1 (0.2) 0.0 Asphalt/Road Oil 0.0 96.1 44.8 (42.4) 0.0 Still Gas 0.0 0.0 0.0 0.0 0.0 Misc. Products 0.0 0.7 13.7 (2.1) 0.0 

Total 56,866.5 34,629.9 5,438.3 (409.4) 1,785.4 1,419.2 Note: Totals may not sum due to independent rounding. 
A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-234: 2007 Potential CO2 Emissions Fuel Category Fuel Type 

Apparent Consumption (QBtu) Carbon Coefficients 

(Tg Carbon/QBtu) 

Potential Emissions 

(Tg CO2 Eq.) 

Solid Fuels Anthracite Coal 0.04 28.26 3.6

Bituminous Coal 12.78 25.49 1,194.3Sub-bituminous Coal 9.08 26.48 881.8Lignite 0.96 26.30 92.7Coke 0.03 31.00 3.0Unspecified (1.21) 25.34 (112.6

Gas Fuels Natural Gas 23.50 14.47 1,246.8Liquid Fuels Crude Oil 32.67 20.33 2,434.8

Nat Gas Liquids and LRGs 2.76 16.99 171.8Other Liquids 2.94 20.33 219.4Motor Gasoline 1.38 19.33 97.6Aviation Gasoline 0.00 18.87 0.1Kerosene (0.00) 19.72 (0.2Jet Fuel (0.28) 19.33 (19.7Distillate Fuel 0.15 19.95 11.0Residual Oil (0.31) 21.49 (24.2Naphtha for petrochemical feedstocks 0.18 18.14 11.9Petroleum Coke (0.82) 27.85 (83.3Other Oil for petrochemical feedstocks 0.11 19.95 8.2Special Naphthas (0.00) 19.86 (0.1Lubricants (0.08) 20.24 (5.7Waxes (0.00) 19.81 (0.2Asphalt/Road Oil 0.09 20.62 7.1Still Gas 0.00 17.51 0.0Misc. Products 0.08 20.33 6.3

Total 6,144.2Data Sources: C content coefficients by coal rank from USGS (1998) and SAIC (2004); Unspecified Solid Fuels, Natural Gas and Liquid Fuels: EIA (2008a).Note: Totals may not sum due to independent rounding. 
 

_____________________________________________________________________________________________ 

293 

Table A-235: 2007 Non-Energy Carbon Stored in Products 

Fuel Type 

Consumption

for Non-Energy

Use (TBtu)

Carbon

Coefficients

(Tg Carbon/QBtu)

Carbon

Content

(Tg Carbon)

Fraction

Sequestered Carbon Stored

(Tg CO2 Eq.)

Coal 33.0 31.00 1.0 0.10 0.37

Natural Gas 396.0 14.47 5.7 0.61 12.89

Asphalt & Road Oil 1,197.0 20.62 24.7 1.00 90.50

LPG 1,483.2 16.76 24.9 0.61 55.93

Lubricants 317.9 20.24 6.4 0.09 2.18

Pentanes Plus 132.4 18.24 2.4 0.61 5.43

Petrochemical Feedstocks A a a a 49.35

Petroleum Coke 165.4 27.85 4.6 0.30 5.07

Special Naphtha 75.6 19.86 1.5 0.61 3.38

Waxes/Misc. a a a a 1.35

Misc. U.S. Territories 

Petroleum a a a a 0.70

Total 227.2

[a] Values for Misc. U.S. Territories Petroleum, Petrochemical Feedstocks and Waxes/Misc. are not shown because these categories are aggregates of numerous smaller 

components. 

Note: Totals may not sum due to independent rounding. 

Table A-236: 2007 Reference Approach CO2 Emissions from Fossil Fuel Consumption (Tg CO2 Eq. unless otherwise noted) 

Fuel Category 

Potential

Emissions

Carbon

Sequestered

Net

Emissions

Fraction

Oxidized

Total

Emissions

Coal 2,062.8 0.4 2,062.4 100.0% 2,062.6

Petroleum 2,834.6 213.9 2,620.7 100.0% 2,620.7

Natural Gas 1,246.8 12.9 1,233.9 100.0% 1,233.9

Total 6,144.2 227.2 5,917.1 - 5,917.1

Note: Totals may not sum due to independent rounding. 
A-294 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-237: Fuel Consumption in the United States by Estimating Approach (TBtu)*

Approach 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 20Sectoral 69,555.8 69,150.2 70,756.8 72,506.3 73,915.1 74,938.3 77,358.8 78,589.4 78,869.2 80,154.2 82,512.4 81,063.6 82,075.0 82,647

Coal 18,022.5 17,997.8 18,197.7 18,932.5 19,040.4 19,180.9 20,022.8 20,561.8 20,790.4 20,805.6 21,769.7 21,130.0 21,232.7 21,645

Natural Gas 19,179.8 19,570.1 20,234.3 20,766.3 21,250.6 22,203.4 22,642.1 22,862.1 22,384.4 22,369.5 23,260.2 22,422.4 23,395.8 22,847Petroleum 32,353.5 31,582.3 32,324.8 32,807.5 33,624.2 33,553.9 34,694.0 35,165.5 35,694.3 36,979.1 37,482.5 37,511.3 37,446.6 38,155

Reference (Apparent) 68,953.1 68,123.8 69,651.2 71,461.6 73,046.3 74,070.9 76,435.2 77,952.9 77,970.7 79,263.5 81,707.2 80,709.7 81,414.2 81,901

Coal 17,602.2 17,400.1 17,724.7 18,259.7 18,723.7 18,609.4 19,517.3 20,159.6 20,031.6 20,080.2 21,038.6 20,780.8 20,880.7 21,179

Natural Gas 19,746.7 19,765.4 20,425.2 20,980.8 21,459.9 22,411.9 22,816.2 22,951.1 22,531.1 22,635.1 23,598.7 22,621.8 23,360.9 22,758Petroleum 31,604.1 30,958.3 31,501.3 32,221.0 32,862.7 33,049.6 34,101.7 34,842.2 35,408.0 36,548.2 37,069.9 37,307.1 37,172.5 37,962

Difference -0.9% -1.5% -1.6% -1.4% -1.2% -1.2% -1.2% -0.8% -1.1% -1.1% -1.0% -0.4% -0.8% -0.9

Coal -2.3% -3.3% -2.6% -3.6% -1.7% -3.0% -2.5% -2.0% -3.6% -3.5% -3.4% -1.7% -1.7% -2.2

Natural Gas 3.0% 1.0% 0.9% 1.0% 1.0% 0.9% 0.8% 0.4% 0.7% 1.2% 1.5% 0.9% -0.1% -0.4

Petroleum -2.3% -2.0% -2.5% -1.8% -2.3% -1.5% -1.7% -0.9% -0.8% -1.2% -1.1% -0.5% -0.7% -0.5

* Includes U.S. territories. Does not include international bunker fuels. + Does not exceed 0.05%. Note: Totals may not sum due to independent rounding. Table A-238: CO2 Emissions from Fossil Fuel Combustion by Estimating Approach (Tg CO2 Eq.) 

Approach 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 20Sectoral 4,825.5 4,790.8 4,890.6 5,016.2 5,101.6 5,151.0 5,326.8 5,414.7 5,450.1 5,529.1 5,705.6 5,629.0 5,676.5 5,73

Coal 1,696.2 1,694.8 1,714.1 1,783.8 1,795.2 1,810.0 1,889.0 1,941.2 1,963.0 1,964.3 2,055.3 1,994.6 2,004.6 2,04

Natural Gas 1,007.7 1,028.6 1,065.1 1,092.4 1,116.4 1,166.7 1,189.7 1,200.8 1,174.6 1,173.2 1,219.3 1,176.4 1,228.7 1,19Petroleum 2,121.6 2,067.4 2,111.5 2,140.0 2,190.0 2,174.3 2,248.2 2,272.7 2,312.5 2,391.7 2,431.0 2,458.0 2,443.2 2,49

Reference (Apparent) 4,802.1 4,743.2 4,848.2 4,980.1 5,083.1 5,136.4 5,313.5 5,429.5 5,436.1 5,511.2 5,696.8 5,660.6 5,700.3 5,75

Coal 1,658.1 1,641.2 1,671.3 1,724.9 1,769.5 1,760.5 1,846.0 1,907.1 1,898.8 1,907.7 1,996.8 1,974.1 1,983.5 2,01

Natural Gas 1,039.1 1,040.4 1,076.3 1,105.1 1,128.8 1,179.1 1,200.1 1,206.9 1,183.3 1,188.2 1,238.3 1,187.3 1,227.7 1,19Petroleum 2,105.0 2,061.7 2,100.6 2,150.1 2,184.8 2,196.7 2,267.4 2,315.4 2,354.0 2,415.3 2,461.7 2,499.2 2,489.1 2,55

Difference -0.5% -1.0% -0.9% -0.7% -0.4% -0.3% -0.3% 0.3% -0.3% -0.3% -0.2% 0.6% 0.4% 0.4

Coal -2.2% -3.2% -2.5% -3.3% -1.4% -2.7% -2.3% -1.8% -3.3% -2.9% -2.8% -1.0% -1.1% -1.

Natural Gas 3.1% 1.1% 1.1% 1.2% 1.1% 1.1% 0.9% 0.5% 0.7% 1.3% 1.6% 0.9% -0.1% -0.

Petroleum -0.8% -0.3% -0.5% 0.5% -0.2% 1.0% 0.9% 1.9% 1.8% 1.0% 1.3% 1.7% 1.9% 2.

* Includes U.S. territories. Does not include international bunker fuels. + Does not exceed 0.05%. Note: Totals may not sum due to independent rounding. 
 

A-295

ANNEX 5 Assessment of the Sources and 

Sinks of Greenhouse Gas Emissions 

Excluded 

Although this report is intended to be a comprehensive assessment of anthropogenic66 sources and sinks of 

greenhouse gas emissions for the United States, certain sources have been identified yet excluded from the estimates 

presented for various reasons. Before discussing these sources, however, it is important to note that processes or activities 

that are not anthropogenic in origin or do not result in a net source or sink of greenhouse gas emissions are intentionally 

excluded from a national inventory of anthropogenic greenhouse gas emissions. In general, processes or activities that are 

not anthropogenic are considered natural (i.e., not directly influenced by human activity) in origin and, as an example, 

would include the following: 

 Volcanic eruptions 

 Carbon dioxide (CO2) exchange (i.e., uptake or release) by oceans 

 Natural forest fires67

 Methane (CH4) emissions from wetlands not affected by human induced land-use changes 

Some processes or activities may be anthropogenic in origin but do not result in net emissions of greenhouse 

gases, such as the respiration of CO2 by people or domesticated animals.68 Given a source category that is both 

anthropogenic and results in net greenhouse gas emissions, reasons for excluding a source related to an anthropogenic 

activity include one or more of the following: 

 There is insufficient scientific understanding to develop a reliable method for estimating emissions at a 

national level. 

 Although an estimating method has been developed, data were not adequately available to calculate 

emissions. 

 Emissions were implicitly accounted for within another source category (e.g., CO2 from Fossil Fuel 

Combustion). 

It is also important to note that the United States believes the exclusion of the sources discussed below introduces 

only a minor bias in its overall estimate of U.S. greenhouse gas emissions. 

CO2 from Burning in Coal Deposits and Waste Piles 

Coal is periodically burned in deposits and waste piles. It has been estimated that the burning of coal in deposits 

and waste piles would represent less than 1.3 percent of total U.S. coal consumption, averaged over ten-years. Because 

there is currently no known source of data on the quantity of coal burned in waste piles and there is uncertainty as to the 

fraction of coal oxidized during such burnings, these CO2 emissions are not currently estimated. Further research would 

be required to develop accurate emission factors and activity data for these emissions to be estimated (see Revised 1996 

IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, p. 1.112 – 1.113). 

 

66 The term “anthropogenic,” in this context, refers to greenhouse gas emissions and removals that are a direct result of human 

activities or are the result of natural processes that have been affected by human activities (IPCC/UNEP/OECD/IEA 1997). 

67 In some cases forest fires that are started either intentionally or unintentionally are viewed as mimicking natural burning 

processes that have been suppressed by other human forest management activities. The United States does not consider forest fires 

within its national boundaries to be a net source of greenhouse emissions. 

68 Respiration of CO2 by biological organisms is simply part of the broader global carbon cycle that also includes uptake of 

CO2 by photosynthetic organisms. 
A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

CO2 from Enhanced Oil Recovery (EOR) 

The 2006 IPCC Guidelines include, for the first time, methodological guidance to estimate emissions from the 

capture, transport, injection, and geological storage of CO2. The methodology is based on the principle that the C capture 

and storage system should be handled in a complete and consistent manner across the entire Energy Sector. The approach 

accounts for CO2 captured at natural and industrial sites as well as emissions from capture, transport, and use. For storage 

specifically, a Tier 3 methodology is outlined for estimating and reporting emissions based on site-specific evaluations. If 

site-specific monitoring and reporting data are not available, and the C capture and storage system cannot, therefore, be 

considered in a complete and consistent manner, the assumption is that the captured CO2 is emitted. The assumption that, 

in the absence of site specific data, all CO2 injected in storage sites is emitted is opposite from the current methodology 

implemented by the United States. The new methodology will not affect emission estimates for CO2 consumption for nonEOR applications. The United States initiated data collection efforts to incorporate this new methodology for the current 

Inventory. However, time was not sufficient to fully implement this guidance and therefore estimates are not yet included 

in national totals. Preliminary estimates indicate that the amount of CO2 emitted from EOR operations and CO2 transport 

pipelines in 2005 was 35.16 Tg CO2 (35,156 Gg CO2). Site-specific monitoring and reporting data for CO2 injection sites 

(i.e., EOR operations) were not readily available. Therefore, these estimates assume that all of the CO2 used in EOR 

operations is emitted. 

CO2 from Natural Gas Processing 

CO2 is produced as a byproduct of natural gas production and processing. Natural gas produced from natural gas 

wells (referred to as non-associated natural gas) and natural gas produced from crude oil wells (referred to as associateddissolved natural gas) may contain naturally occurring CO2 that must be removed from the natural gas in order for it to 

meet pipeline specifications for CO2 content. A fraction of the CO2 remains in the natural gas delivered to end-users by 

pipeline, and is emitted when the natural gas is combusted. However, the majority of the CO2 is separated from natural 

gas at natural gas processing plants. CO2 removed at gas processing plants is generally vented to the atmosphere, but 

several gas processing plants in Wyoming and Texas and one gas processing plant in Michigan compress the CO2

separated from natural gas and transport this CO2 by pipeline for use in enhanced oil recovery. CO2 used for enhanced oil 

recovery is injected into oil reservoirs to improve the recovery of oil remaining in the reservoir through a number of 

processes, including reduction of crude oil viscosity and oil density, acid effects on carbonate reservoirs, and miscible and 

immiscible displacement. Preliminary estimates indicate that in 2005 approximately 5.99 Tg CO2 (5,992 Gg CO2) 

produced from natural gas processing plants (acid gas removal plants) was captured and used in enhanced oil recovery 

operations. As discussed under CO2 from Enhanced Oil Recovery (above) all of this CO2 used in EOR operations is 

assumed to be emitted.

CO2 from “Unaccounted for” Natural Gas 

There is a discrepancy between the amount of natural gas sold by producers and that reported as purchased by 

consumers. This discrepancy, known as “unaccounted for” or unmetered natural gas, was assumed to be the sum of 

leakage, measurement errors, data collection problems, undetected non-reporting, undetected over reporting, and 

undetected under reporting. Historically, the amount of gas sold by producers has always exceeded that reportedly 

purchased by consumers; therefore, some portion of unaccounted for natural gas was assumed to be a source of CO2

emissions. In other words, it was assumed that consumers were underreporting their usage of natural gas. In DOE/EIA’s 

energy statistics for 1996, however, reported consumption of natural gas exceeded the amount sold by producers. 

Therefore, the historical explanation given for this discrepancy has lost credibility and unaccounted for natural gas is no 

longer used to calculate CO2 emissions. 

CO2 from Shale Oil Production 

Oil shale is shale saturated with kerogen.69 It can be thought of as the geological predecessor to crude oil. CO2 

is released as a by-product of the process of producing petroleum products from shale oil. As of now, it is not costeffective to mine and process shale oil into usable petroleum products. The only identified large-scale oil shale processing 

facility in the United States was operated by Unocal during the years 1985 to 1990. There have been no known emissions 

from shale oil processing in the United States since 1990 when the Unocal facility closed. 

 

69 Kerogen is fossilized insoluble organic material found in sedimentary rocks, usually shales, which can be converted to 

petroleum products by distillation. 
 

A-297

CH4 from the Production of Carbides other than Silicon Carbide 

Methane (CH4) may be emitted from the production of carbides because the petroleum coke used in the process 

contains volatile organic compounds, which form CH4 during thermal decomposition. Methane emissions from the 

production of silicon carbide were estimated and accounted for, but emissions from the production of calcium carbide and 

other carbides were not. Further research is needed to estimate CH4 emissions from the production of calcium carbide and 

other carbides other than silicon carbide. (See Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: 

Reference Manual, pp. 2.20 – 2.21) 

CO2 from Calcium Carbide and Silicon Carbide Production 

CO2 is formed by the oxidation of petroleum coke in the production of calcium carbide. These CO2 emissions 

are implicitly accounted for in the storage factor calculation for the non-energy use of petroleum coke in the Energy 

chapter. There is currently not sufficient data on coke consumption to estimate emissions from this source. (See Revised 

1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, pp. 2.20 – 2.21) 

CO2 from Graphite Consumption in Ferroalloy and Steel Production 

Emissions from "graphite," "wood" or "biomass" in calculating CO2 emissions from ferroalloy production, iron 

and steel production or other "Industrial Processes" included in Chapter 4 of the inventory are not explicitly calculated. It 

is assumed that 100 percent of the C used in ferroalloy production is derived from petroleum coke and that all of the C 

used in iron and steel production is derived from coal coke or petroleum coke. It is also assumed that all of the C used in 

lead and zinc production is derived from coal coke. It is possible that some non-coke C is used in the production of 

ferroalloys, lead, zinc, and iron and steel, but no data are available to conduct inventory calculations for sources of C other

than petroleum coke and coal coke used in these processes. 

Non-fuel uses of coal coke and petroleum coke are accounted for in the Industrial Process chapter, either directly 

for iron and steel, aluminum, ferroalloy, lead, zinc, and titanium dioxide production, or indirectly by applying a storage 

factor to "uncharacterized" non-fuel uses of petroleum coke and coal coke. Non-fuel uses of wood and biomass are not 

accounted for in the Energy or Industrial Process chapters, as all uses of wood and biomass are accounted for in the Land 

Use, Land-Use Change, and Forestry chapter. It is assumed for the purposes of the CO2 emission calculation that no wood 

or other biogenic C is used in any of these industrial processes. Some biogenic C may be used in these industrial 

processes but sufficient data to estimate emissions are not available. 

Consumption of either natural or synthetic graphite is not explicitly accounted for in the Industrial Process 

chapter. It is assumed that all of the C used in manufacturing C anodes for production of aluminum, ferroalloys, and 

electric arc furnace (EAF) steel are derived directly from petroleum coke and coal tar pitch (a coal coke byproduct), not 

from natural graphite or synthetic graphite sources. Some amount of C used in these industrial processes may be derived 

from natural or synthetic graphite sources, but sufficient data to estimate emissions are not currently available. 

N2O from Caprolactam Production 

Caprolactam is a widely used chemical intermediate, primarily to produce nylon-6. All processes for producing 

caprolactam involve the catalytic oxidation of ammonia, with N2O being produced as a by-product. Caprolactam 

production could be a significant source of N2Oit has been identified as such in the Netherlands. More research is 

required to determine this source’s significance because there is currently insufficient information available on 

caprolactam production to estimate emissions in the United States. (See Revised 1996 IPCC Guidelines for National 

Greenhouse Gas Inventories: Reference Manual, pp. 2.22 – 2.23) 

N2O from Cracking of Certain Oil Fractions 

In order to improve the gasoline yield in crude oil refining, certain oil fractions are processed in a catcracker. 

Because crude oil contains some nitrogen, N2O emissions may result from this cracking process. There is currently 

insufficient data to develop a methodology for estimating these emissions. (See Revised 1996 IPCC Guidelines for 

National Greenhouse Gas Inventories: Reference Manual, p. 2.23) 
A-298 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

CH4 from Petroleum Coke Production 

Coke production may result in CH4 emissions. Detailed coke production statistics were not available for the 

purposes of estimating CH4 emissions from this minor source. (See Petrochemical Production in the Industrial Processes 

chapter and the Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, p. 2.23) 

CO2 from Metal Production 

Coke is used as a reducing agent in the production of some metals from their ores, including magnesium, 

chromium, , nickel, silicon, and tin. CO2 may be emitted during the metal’s production from the oxidization of this coke 

and, in some cases, from the carbonate ores themselves (e.g., some magnesium ores contain carbonate). The CO2

emissions from the carbonate ores are not presently accounted for, but their quantities are thought to be minor. (See 

Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, p. 2.37 – 2.38) 

N2O from Acrylonitrile Production 

Nitrous oxide may be emitted during acrylonitrile production. No methodology was available for estimating 

these emissions, and therefore further research is needed if these emissions are to be included. (See Revised 1996 IPCC 

Guidelines for National Greenhouse Gas Inventories: Reference Manual, p. 2.22) 

SF6 from Aluminum Fluxing and Degassing 

Occasionally, sulfur hexafluoride (SF6) is used by the aluminum industry as a fluxing and degassing agent in 

experimental and specialized casting operations. In these cases it is normally mixed with argon, nitrogen, and/or chlorine 

and blown through molten aluminum; however, this practice is not used by primary aluminum production firms in the 

United States and is not believed to be extensively used by secondary casting firms. Where it does occur, the 

concentration of SF6 in the mixture is small and a portion of the SF6 is decomposed in the process (Waite and Bernard 

1990, Corns 1990). It has been estimated that 230 Mg of SF6 were used by the aluminum industry in the United States and 

Canada (Maiss and Brenninkmeijer 1998); however, this estimate is highly uncertain. 

SF6 from Production/Leakage/Breakage of Soundproofed Double-glazed Windows 

Sulfur hexafluoride (SF6) may be emitted from the production, breakage, or leakage of soundproof double-glazed 

windows. No methodology was available for estimating these emissions, and therefore further research is needed if these 

emissions are to be included. 

SF6 from Production/Leakage/Dismantling of Radar, Tracer and Night Vision Equipment 

Sulfur hexafluoride (SF6) may be emitted from the production, leakage, and dismantling of radar, tracer, and 

night vision equipment. Emissions from this source are believed to be minor, and no data were available for estimating 

the emissions. 

SF6 from Applications in Sports Shoes, Tires, and Tennis Balls 

Sulfur hexafluoride (SF6) may be emitted from application involving the production of sport shoes, tires, and 

tennis balls. These emissions are believed to be minor, and no data were available for estimating emissions. 

SF6 from Applications to Trace Leakage of Pressure Vessels and Used as a Tracer Gas in Open Air 

Sulfur hexafluoride (SF6) may be emitted from application involving tracer gasses to detect leakage from 

pressure vessels and as a tracer gas in the open air. Although emissions from this source are believed to be minor, 

emissions estimation data and methodologies were not available. 

Miscellaneous SF6 Uses 

Sulfur hexafluoride may be used in foam insulation, for dry etching, in laser systems, for indoor air quality 

testing, for laboratory hood testing, for chromatography, in tandem accelerators, in loudspeakers, in shock absorbers, and 

for certain biomedical applications. Data need to be gathered and methodologies developed if these emissions are to be 
 

A-299

estimated. A preliminary global assessment of aggregate emissions from these applications can be found in Maiss, M. 

Brenninkmeijer, and C.A.M. Brenninkmeijer (1998). 

N2O from Domestic House Animal Waste Deposited on Soils 

A substantial amount of liquid and solid waste is produced by domestic animals that are kept as pets. A 

preliminary methodology was developed to estimate nitrous oxide (N2O) emissions from the deposition of domestic house 

animal (i.e., dogs and cats) waste on lawns, fields and parks. Estimates calculated with this methodology suggest that, in 

1990, approximately 330 Gg of nitrogen originating as domestic house animal waste were deposited on soils resulting in 

approximately 2.9 Tg CO2 Eq. of N2O emissions from soils. To estimate the amount of nitrogen deposited by domestic 

house animals, only those excretions that remained on land surfaces—as opposed to wastes that were collected by owners 

and are managed as municipal solid waste—were included. 

Annual dog and cat population numbers were obtained from the Pet Food Institute.70 Annual nitrogen excretion 

rates were estimated from protein intake. The recommended protein intake for an average size adult of each animal type71

was multiplied by the average amount of nitrogen per unit of protein (0.16 kg N/kg protein, from the Revised 1996 IPCC 

Guidelines) to estimate nitrogen consumption. It was then assumed that 95 percent of this nitrogen was excreted, either in 

solid or liquid form (i.e., it was assumed that 5 percent was retained for fur and milk production). Of the total nitrogen 

excretion, 90 percent was assumed to occur through liquid waste, with the balance from solid waste.72 Both cat and dog 

populations were divided into urban and rural fractions, using the metropolitan and non-metropolitan human population 

categories, respectively, of the U.S. Census Bureau.73 Both liquid and solid wastes from the urban cat population, and 

solid waste from the urban dog population were assumed to be collected (i.e., not deposited on soils). Nitrous oxide 

emission estimates from domestic house animal excretion were calculated in the same manner as performed for estimating 

emissions from livestock excretion. Producing these estimates involved making a number of simplifying assumptions 

regarding average animal size and protein consumption, as well as the proportions of animal populations residing in urban 

and rural areas and the proportions of wastes that are deposited on land. Further methodological development and data 

collection is required in order to reduce the uncertainty involved in the domestic house animal excretion estimates. 

CO2 from Non-Hazardous Industrial Waste Combustion 

Waste combustion is incorporated in two sections of the energy chapter of the inventory: in the section on CO2

emissions from waste combustion, and in the calculation of emissions and storage from non-energy uses of fossil fuels. 

The former section addresses fossil-derived materials (such as plastics) that are discarded as part of the municipal 

wastestream and combusted (generally for energy recovery). The latter addresses two types of combustion: hazardous 

waste incineration of organic materials (assumed to be fossil-derived), in which regulated wastes are burned without 

energy recovery, and burning of fossil-derived materials for energy recovery. There is one potentially significant category 

of waste combustion that is not included in our calculus: industrial non-hazardous waste, burned for disposal (rather than 

energy recovery). Data are not readily available for this source; further research is needed to estimate the magnitude of 

CO2 emissions. 

CH4 from Land-Use Changes Including Wetlands Creation or Destruction 

Wetlands are a known source of methane (CH4) emissions. When wetlands are destroyed, CH4 emissions may be 

reduced. Conversely, when wetlands are created (e.g., during the construction of hydroelectric plants), CH4 emissions 

may increase. Grasslands and forestlands may also be weak sinks for CH4 due to the presence of methanotrophic bacteria 

that use CH4 as an energy source (i.e., they oxidize CH4 to CO2). Currently, an adequate scientific basis for estimating 

these emissions and sinks does not exist, and therefore further research and methodological development is required. 

 

70 Pet Food Institute (1999) Pet Incidence Trend Report. Pet Food Institute, Washington DC. 

71 Bright, S. (1999) Personal communication between Marco Alcaraz of ICF Consulting and Susan Bright of the Dupont 

Animal Clinic, Washington, DC, August 1999. 

72 Swenson, M.J. and W.G. Reece, eds. (1993) Duke’s Physiology of Domestic Animals. Cornell University Press. 11th 

Edition. 

73 U.S. Census Bureau (1999) <http://www.census.gov/population/estimates/metro-city/ma96-08.txt> 
A-300 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

N2O from Wastewater Treatment and Biological Processes 

As a result of nitrification and denitrification processes, nitrous oxide (N2O) may be produced and emitted from 

large-scale composting, small scale composting (e.g. households), post-composting of anaerobic digested wastes, and both 

domestic and industrial wastewater treatment plants. Nitrogen-containing compounds are found in composted wastes and 

wastewater due to the presence of both human excrement and other nitrogen-containing constituents (e.g., effluent from 

garbage disposals, bath and laundry water, and industrial wastes). The portion of emitted N2O that originates from these 

sources is currently estimated under the Wastewater Treatment source category—based upon average dietary protein 

intake and assumptions on co-disposal of N that was not consumed. The portion of emitted N2O that originates from other 

nitrogen-containing constituents is not currently estimated. Further research and methodological development is needed if 

these emissions are to be accurately estimated. 

CH4 from Large and Small Scale Composting 

Methane (CH4) may be released through large and small scale (e.g. household) composting. Detailed composting 

data is necessary in order to estimate emissions but were not available. 

CH4 from Treatment of Dredging Sludge, Remediation of Groundwater, Intermediate Storage of Slaughter Waste, 

Production of Process Water from Groundwater, and Post Composting of Anaerobic Digested Wastes 

Methane (CH4) may be released through the treatment of dredging sludge, remediation of groundwater, 

intermediate storage of slaughter waste, production of process water from groundwater, and post composting of anaerobic 

digested wastes. No methodology was available for estimating these emissions, and therefore further research is needed if 

these emissions are to be included. 

References 

Census (2002) Industrial Gases: 2001. U.S. Census Bureau, Department of Commerce, Washington, DC. 

MQ325C(01)-5. 

EIA (2002) Emissions of Greenhouse Gases in the United States 2001. Energy Information Administration, 

Office of Integrated Analysis and Forecasting. DOE-EIA-0573(2001). 

EPA (2000) Toxics Release Inventory, 1998. U.S. Environmental Protection Agency, Office of Environmental 

Information, Office of Information Analysis and Access, Washington, DC. Available online at 

<http://www.epa.gov/triexplorer/chemical.htm>. 

Freedonia Group, Inc. (2000) Report 1091: Industrial Gases To 2003, Record 4, Carbon Dioxide Shipments and 

Production, 1989-2009. Cleveland, OH. 
 

A-301

ANNEX 6 Additional Information 

6.1. Global Warming Potential Values 

Global Warming Potentials (GWPs) are intended as a quantified measure of the globally averaged relative 

radiative forcing impacts of a particular greenhouse gas. It is defined as the cumulative radiative forcingboth direct and 

indirect effectsintegrated over a period of time from the emission of a unit mass of gas relative to some reference gas 

(IPCC 1996). Carbon dioxide (CO2) was chosen as this reference gas. Direct effects occur when the gas itself is a 

greenhouse gas. Indirect radiative forcing occurs when chemical transformations involving the original gas produce a gas 

or gases that are greenhouse gases, or when a gas influences other radiatively important processes such as the atmospheric 

lifetimes of other gases. The relationship between gigagrams (Gg) of a gas and Tg CO2 Eq. can be expressed as follows: 

   













   

1,000 Gg

Tg Tg CO2 Eq Gg of gas GWP

Where,

Tg CO2 Eq. = Teragrams of Carbon Dioxide Equivalents 

Gg = Gigagrams (equivalent to a thousand metric tons) 

GWP = Global Warming Potential 

Tg = Teragrams 

GWP values allow policy makers to compare the impacts of emissions and reductions of different gases. 

According to the IPCC, GWPs typically have an uncertainty of roughly 35 percent, though some GWPs have larger 

uncertainty than others, especially those in which lifetimes have not yet been ascertained. In the following decision, the 

parties to the UNFCCC have agreed to use consistent GWPs from the IPCC Second Assessment Report (SAR), based 

upon a 100 year time horizon, although other time horizon values are available (see Table A-239). 

In addition to communicating emissions in units of mass, Parties may choose also to use global 

warming potentials (GWPs) to reflect their inventories and projections in carbon dioxide-equivalent terms, using 

information provided by the Intergovernmental Panel on Climate Change (IPCC) in its Second Assessment 

Report. Any use of GWPs should be based on the effects of the greenhouse gases over a 100-year time horizon. 

In addition, Parties may also use other time horizons.74

Greenhouse gases with relatively long atmospheric lifetimes (e.g., CO2, CH4, N2O, HFCs, PFCs, and SF6) tend to 

be evenly distributed throughout the atmosphere, and consequently global average concentrations can be determined. The 

short-lived gases such as water vapor, carbon monoxide, tropospheric ozone, other indirect greenhouse gases (e.g., NOx, 

and NMVOCs), and tropospheric aerosols (e.g., SO2 products and black carbon), however, vary spatially, and 

consequently it is difficult to quantify their global radiative forcing impacts. GWP values are generally not attributed to 

these gases that are short-lived and spatially inhomogeneous in the atmosphere. 

Table A-239: Global Warming Potentials (GWP) and Atmospheric Lifetimes (Years) of Gases Used in this Report 

Gas Atmospheric Lifetime 100-year GWPa 20-year GWP 500-year GWP 

Carbon dioxide (CO2) 50-200 1 1 1 

Methane (CH4)b 123 21 56 6.5 

Nitrous oxide (N2O) 120 310 280 170 

HFC-23 264 11,700 9,100 9,800 

HFC-125 32.6 2,800 4,600 920 

HFC-134a 14.6 1,300 3,400 420 

HFC-143a 48.3 3,800 5,000 1,400 

HFC-152a 1.5 140 460 42 

 

74 Framework Convention on Climate Change; FCCC/CP/1996/15/Add.1; 29 October 1996; Report of the Conference of the 

Parties at its second session; held at Geneva from 8 to 19 July 1996; Addendum; Part Two: Action taken by the Conference of the Parties 

at its second session; Decision 9/CP.2; Communications from Parties included in Annex I to the Convention: guidelines, schedule and 

process for consideration; Annex: Revised Guidelines for the Preparation of National Communications by Parties Included in Annex I to 

the Convention; p. 18. FCCC (1996) 
A-302 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

HFC-227ea 36.5 2,900 4,300 950 

HFC-236fa 209 6,300 5,100 4,700 

HFC-4310mee 17.1 1,300 3,000 400 

CF4 50,000 6,500 4,400 10,000 

C2F6 10,000 9,200 6,200 14,000 

C4F10 2,600 7,000 4,800 10,100 

C6F14 3,200 7,400 5,000 10,700 

SF6 3,200 23,900 16,300 34,900 

Source: IPCC (1996) 

a GWPs used in this report are calculated over 100 year time horizon. b The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. The indirect effect due 

to the production of CO2 is not included. 

Table A-240 presents direct and net (i.e., direct and indirect) GWPs for ozone-depleting substances (ODSs). 

Ozone-depleting substances directly absorb infrared radiation and contribute to positive radiative forcing; however, their 

effect as ozone-depleters also leads to a negative radiative forcing because ozone itself is a potent greenhouse gas. There 

is considerable uncertainty regarding this indirect effect; therefore, a range of net GWPs is provided for ozone depleting 

substances. 

Table A-240: Net 100-year Global Warming Potentials for Select Ozone Depleting Substances* 

Gas Direct Netmin Netmax

CFC-11 4,600 (600) 3,600 

CFC-12 10,600 7,300 9,900 

CFC-113 6,000 2,200 5,200 

HCFC-22 1,700 1,400 1,700 

HCFC-123 120 20 100 

HCFC-124 620 480 590 

HCFC-141b 700 (5) 570 

HCFC-142b 2,400 1,900 2,300 

CHCl3 140 (560) 0 

CCl4 1,800 (3,900) 660 

CH3Br 5 (2,600) (500) 

Halon-1211 1,300 (24,000) (3,600) 

Halon-1301 6,900 (76,000) (9,300) 

Source: IPCC (2001) 

* Because these compounds have been shown to deplete stratospheric ozone, they are typically referred to as ozone depleting substances (ODSs). However, they are also 

potent greenhouse gases. Recognizing the harmful effects of these compounds on the ozone layer, in 1987 many governments signed the Montreal Protocol on Substances that 

Deplete the Ozone Layer to limit the production and importation of a number of CFCs and other halogenated compounds. The United States furthered its commitment to phaseout ODSs by signing and ratifying the Copenhagen Amendments to the Montreal Protocol in 1992. Under these amendments, the United States committed to ending the 

production and importation of halons by 1994, and CFCs by 1996. The IPCC Guidelines and the UNFCCC do not include reporting instructions for estimating emissions of ODSs 

because their use is being phased-out under the Montreal Protocol. The effects of these compounds on radiative forcing are not addressed in this report. 

The IPCC has published its Fourth Assessment Report (AR4), providing the most current and comprehensive 

scientific assessment of climate change (IPCC 2007). Within this report, the GWPs of several gases were revised relative 

to the SAR and the IPCC’s Third Assessment Report (TAR) (IPCC 2001). Thus the GWPs used in this report have been 

updated twice by the IPCC; although the SAR GWPs are used throughout this report, it is interesting to review the changes 

to the GWPs and the impact such improved understanding has on the total GWP-weighted emissions of the United States. 

All GWPs use CO2 as a reference gas; a change in the radiative efficiency of CO2 thus impacts the GWP of all other 

greenhouse gases. Since the SAR and TAR, the IPCC has applied an improved calculation of CO2 radiative forcing and an 

improved CO2 response function. The GWPs are drawn from IPCC/TEAP (2005) and the TAR, with updates for those 

cases where new laboratory or radiative transfer results have been published. Additionally, the atmospheric lifetimes of 

some gases have been recalculated. Because the revised radiative forcing of CO2 is about 8 percent lower than that in the 

TAR, the GWPs of the other gases relative to CO2 tend to be larger, taking into account revisions in lifetimes. However, 

there were some instances in which other variables, such as the radiative efficiency or the chemical lifetime, were altered 

that resulted in further increases or decreases in particular GWP values. In addition, the values for radiative forcing and 

lifetimes have been calculated for a variety of halocarbons, which were not presented in the SAR. Updates in some wellmixed HFC compounds (including HFC-23, HFC-32, HFC-134a, and HFC-227ea) for AR4 result from investigation into 

radiative efficiencies in these compounds, with some GWPs changing by up to 40 percent; with this change, the 

uncertainties associated with these well-mixed HFCs are thought to be approximately 12 percent.

Table A- 241 compares the lifetimes and GWPs for the SAR, TAR, and AR4. 

Table A- 241: Comparison of GWPs and lifetimes used in the SAR and AR4 

 Lifetime (years) GWP (100 year) Difference (relative to SAR) 

Gas SAR TAR AR4 SAR TAR AR4 TAR TAR (%) AR4 AR4 (%) 
 

A-303

Carbon dioxide (CO2) 50-200 5-200a 5-200a 1 1 1 NC NC NC NC

Methane (CH4)b 123 8.4/12c 8.7/12c 21 23 25 2 10% 4 19%

Nitrous oxide (N2O) 120 120/114 c 120/114 c 310 296 298 (14) (5%) (12) (4%)

Hydrofluorocarbons 

HFC-23 264 260 270 11,700 12,000 14,800 300 3% 3,100 26%

HFC-32 5.6 5.0 4.9 650 550 675 (100) (15%) 25 4%

HFC-125 32.6 29 29 2,800 3,400 3,500 600 21% 700 25%

HFC-134a 14.6 13.8 14 1,300 1,300 1,430 NC NC 130 10%

HFC-143a 48.3 52 52 3,800 4,300 4,470 500 13% 670 18%

HFC-152a 1.5 1.4 1.4 140 120 124 (20) (14%) (16) (11%)

HFC-227ea 36.5 33.0 34.2 2,900 3,500 3,220 600 21% 320 11%

HFC-236fa 209 220 240 6,300 9,400 9,810 3,100 49% 3,510 56%

HFC-245fa NA 7.2 7.6 NA 950 1,030 NA NA NA NA 

HFC-365mfc NA 9.9 6.6 NA 890 794 NA NA NA NA 

HFC-43-10mee 17.1 15 15.9 1,300 1,500 1,640 200 15% 340 26%

Fully Fluorinated Species 

SF6 3,200 3,200 3200 23,900 22,200 22,800 (1,900) (7%) (1,100) (5%)

CF4 50,000 50,000 50,000 6,500 5,700 7,390 (800) (12%) 890 14%

C2F6 10,000 10,000 10,000 9,200 11,900 12,200 2,700 29% 3,000 33%

C3F8 2,600 2,600 2,600 7,000 8,600 8,830 1,600 23% 1,830 26%

C4F10 2,600 2,600 2,600 7,000 8,600 8,860 1,600 23% 1,860 27%

c-C4F8 3,200 3,200 3,200 8,700 10,000 10,300 1,300 15% 1,600 18%

C5F12 4,100 4,100 4,100 7,500 8,900 9,160 1,400 19% 1,660 22%

C6F14 3,200 3,200 3,200 7,400 9,000 9,300 1,600 22% 1,900 26%

Othersd

NF3 NA 740 740 NA 10,800 17,200 NA NA NA NA a No single lifetime can be determined for CO2. (See IPCC 2001) b The methane GWP includes the direct effects and those indirect effects due to the production of tropospheric ozone and stratospheric water vapor. The indirect effect due 

to the production of CO2 is not included. c Methane and nitrous oxide have chemical feedback systems that can alter the length of the atmospheric response, in these cases, global mean atmospheric lifetime (LT) 

is given first, followed by perturbation time (PT). d Gases whose lifetime has been determined only via indirect means or for whom there is uncertainty over the loss process. 

Source: IPCC (2001) 

NC (No Change) 

NA (Not Applicable) 

The choice of GWPs between the SAR, TAR, and AR4 has an impact on both the overall emissions estimated by 

the inventory, as well as the trend in emissions over time. To summarize, Table A-242 shows the overall trend in U.S. 

greenhouse gas emissions, by gas, from 1990 through 2007 using the three sets of GWPs. The table also presents the 

impact of TAR and AR4 GWPs on the total emissions for 1990 and for 2007. 

Table A-242: Effects on U.S. Greenhouse Gas Emissions Using TAR, SAR, and AR4 GWPs (Tg CO2 Eq.) 

Gas Trend from 1990 to 2007 Revisions to Annual Estimates (relative to SAR) 

 TAR AR4 TAR AR4 

 SAR TAR AR4 1990 2007 

CO2 1,026.7 1,026.7 1,026.7 NC NC NC NC 

CH4 (31.2) (34.2) (37.2) 58.7 117.4 55.7 111.5 

N2O (3.1) (3.0) (3.0) (14.2) (12.2) (14.1) (12.1) 

HFCs, PFCs, and SF6* 59.0 61.1 53.1 (2.2) 11.9 (0.1) 5.9 

Total 1,051.3 1,050.6 1,039.6 42.3 117.1 41.6 105.3 

Percent Change 17% 17% 17% 0.7% 1.9% 0.6% 1.5% 

NC (No Change) 

*Includes NF3 

Note: Totals may not sum due to independent rounding. Excludes sinks. Parentheses indicate negative values. 

When the GWPs from the AR4 are applied to the emission estimates presented in this report, total emissions for 

the year 2007 are 7,225.4 Tg CO2 Eq., as compared to 7,150.1 Tg CO2 Eq. when the GWPs from the SAR are used (a 1.5 

percent difference). Table A-243 provides a detailed summary of U.S. greenhouse gas emissions and sinks for 1990 

through 2007, using the GWPs from the AR4. The adjusted greenhouse gas emissions are shown for each gas in units of 

Tg CO2 Eq. in Table A-244. The correlating percent change in emissions of each gas is shown in Table A-245. The 

percent change in emissions is equal to the percent change in the GWP; however, in cases where multiple gases are 

emitted in varying amounts the percent change is variable over the years, such as with substitutes for ozone depleting 

substances. Table A-246 summarizes the emissions and resulting change in emissions using GWPs from the SAR or the 

AR4 for 1990 and 2007. 
A-304 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A-243: Recent Trends in U.S. Greenhouse Gas Emissions and Sinks using the AR4 GWPs (Tg CO2 Eq.) 

Gas/Source 1990 1995 2000 2005 2006 2007

CO2 5,076.7 5,407.9 5,955.2 6,090.8 6,014.9 6,103.4

Fossil Fuel Combustion 4,708.9 5,013.9 5,561.5 5,723.5 5,635.4 5,735.8 

Electricity Generation 1,809.7 1,938.9 2,283.2 2,381.0 2,327.3 2,397.2 

Transportation 1,484.5 1,598.7 1,800.3 1,881.5 1,880.9 1,887.4 

Industrial 834.2 862.6 844.6 828.0 844.5 845.4 

Residential 337.7 354.4 370.4 358.0 321.9 340.6 

Commercial 214.5 224.4 226.9 221.8 206.0 214.4 

US Territories 28.3 35.0 36.2 53.2 54.8 50.8 

Non-Energy Use of Fuels 117.0 137.5 144.5 138.1 145.1 133.9 

Iron and Steel Production & 

Metallurgical Coke Production 109.8 103.1 95.1 73.2 76.1 77.4 

Cement Production 33.3 36.8 41.2 45.9 46.6 44.5 

Natural Gas Systems 33.7 33.8 29.4 29.5 29.5 28.7 

Waste Incineration 10.9 15.7 17.5 19.5 19.8 20.8 

Ammonia Production and Urea 

Consumption 16.8 17.8 16.4 12.8 12.3 13.8 

Lime Production 11.5 13.3 14.1 14.4 15.1 14.6 

Cropland Remaining Cropland 7.1 7.0 7.5 7.9 7.9 8.0 

Limestone and Dolomite Use 5.1 6.7 5.1 6.8 8.0 6.2 

Aluminum Production 6.8 5.7 6.1 4.1 3.8 4.3 

Soda Ash Production and 

Consumption 4.1 4.3 4.2 4.2 4.2 4.1 

Petrochemical Production 2.2 2.8 3.0 2.8 2.6 2.6 

Titanium Dioxide Production 1.2 1.5 1.8 1.8 1.9 1.9 

Carbon Dioxide Consumption 1.4 1.4 1.4 1.3 1.7 1.9 

Ferroalloy Production 2.2 2.0 1.9 1.4 1.5 1.6 

Phosphoric Acid Production 1.5 1.5 1.4 1.4 1.2 1.2 

Wetlands Remaining Wetlands 1.0 1.0 1.2 1.1 0.9 1.0 

Zinc Production 0.9 1.0 1.1 0.5 0.5 0.5 

Petroleum Systems 0.4 0.3 0.3 0.3 0.3 0.3 

Lead Production 0.3 0.3 0.3 0.3 0.3 0.3 

Silicon Carbide Production and 

Consumption 0.4 0.3 0.2 0.2 0.2 0.2 

Land Use, Land-Use Change, and 

Forestry (Sink)a (841.4) (851.0) (717.5) (1122.7) (1050.5) (1062.6)

Wood Biomass and Ethanol 

Consumptionb 219.3 236.8 227.3 231.5 240.4 247.8

International Bunker Fuelsb 114.3 101.6 99.0 111.5 110.5 108.8

CH4 734.0 733.1 703.7 668.7 692.8 696.8

Enteric Fermentation 158.6 170.9 160.0 161.9 164.5 165.4 

Landfills 177.6 171.8 145.6 152.2 155.3 158.2 

Natural Gas Systems 154.3 157.8 155.8 126.5 124.8 124.6 

Coal Mining 100.1 79.8 72.0 68.0 69.5 68.6 

Manure Management 36.2 41.0 45.1 49.8 49.8 52.3 

Forest Land Remaining Forest Land 5.5 7.3 24.6 16.9 37.2 34.5 

Petroleum Systems 40.3 38.1 36.0 33.7 33.7 34.3 

Wastewater Treatment 28.0 29.6 30.0 29.0 29.1 29.0 

Stationary Combustion 8.8 8.5 7.9 8.0 7.5 7.9 

Rice Cultivation 8.5 9.1 8.9 8.2 7.0 7.3 

Abandoned Underground Coal 

Mines 

7.2 

 9.8 8.8 6.6 6.6 6.8 

Mobile Combustion 5.6 5.2 4.1 3.0 2.9 2.7 

Composting 0.4 0.9 1.5 1.9 1.9 2.0 

Petrochemical Production 1.0 1.3 1.5 1.3 1.2 1.2 

Field Burning of Agricultural 

Residues 

0.8 

0.8 0.9 1.0 1.0 1.1 

Iron and Steel Production & 

Metallurgical Coke Production 

1.1 

 1.2 1.1 0.9 0.9 0.8 

Ferroalloy Production + + + + + + 

Silicon Carbide Production and 

Consumption + + + + + + 

International Bunker Fuelsb 0.2 0.2 0.1 0.2 0.2 0.2
 

A-305

N2O 302.8 321.2 316.4 303.7 300.0 299.8

Agricultural Soil Management 192.6 194.5 196.5 202.5 200.4 199.9 

Mobile Combustion 42.0 51.6 50.7 35.3 32.3 28.9 

Nitric Acid Production 19.2 21.4 21.1 17.9 17.5 20.9 

Manure Management 11.6 12.4 13.4 13.7 14.0 14.1 

Stationary Combustion 12.3 12.8 14.0 14.2 14.0 14.1 

Adipic Acid Production 14.7 16.7 6.0 5.7 5.7 5.7 

Wastewater Treatment 3.5 3.9 4.3 4.6 4.6 4.7 

N2O from Product Uses 4.2 4.4 4.7 4.2 4.2 4.2 

Forest Land Remaining Forest Land 0.5 0.7 2.3 1.7 3.3 3.1 

Composting 0.3 0.8 1.3 1.7 1.7 1.8 

Settlements Remaining Settlements 1.0 1.2 1.2 1.5 1.5 1.5 

Field Burning of Agricultural 

Residues 0.4 0.4 0.4 0.5 0.5 0.5 

Waste Incineration 0.5 0.4 0.4 0.4 0.4 0.4 

Wetlands Remaining Wetlands + + + + + + 

International Bunker Fuelsb 1.0 0.9 0.9 1.0 1.0 0.9

HFCs, PFCs, and SF6 102.3 116.0 142.3 145.3 147.1 155.4

HFCs 46.6 70.6 107.8 120.3 122.8 130.1

Substitution of Ozone Depleting 

Substances 0.3 28.5 71.2 100.0 105.0 108.3 

HCFC-22 Production 46.1 41.7 36.2 20.0 17.5 21.5 

Semiconductor Manufacture 0.2 0.4 0.3 0.3 0.3 0.3 

PFCs 24.4 18.6 16.2 7.9 8.0 9.6

Semiconductor Manufacture 2.9 4.8 6.3 4.5 5.1 5.1 

Aluminum Production 21.6 13.8 9.9 3.4 2.9 4.5 

SF6 31.3 26.8 18.3 17.1 16.2 15.7

Electrical Transmission and 

Distribution 25.6 20.6 14.4 13.4 12.6 12.1 

Magnesium Production and 

Processing 5.2 5.4 2.9 2.8 2.7 2.8 

Semiconductor Manufacture 0.5 0.9 1.0 0.9 0.9 0.8 

Total 6,215.8 6,578.2 7,117.6 7,208.6 7,154.8 7,255.4

+ Does not exceed 0.05 Tg CO2 Eq. a Sinks are only included in net emissions total, and are based partially on projected activity data. Parentheses indicate negative values (or sequestration). b Emissions from International Bunker Fuels and Biomass Combustion are not included in totals.

Note: Totals may not sum due to independent rounding. 

Table A-244: Change in U.S. Greenhouse Gas Emissions and Sinks Using TAR vs. AR4 GWPs (Tg CO2 Eq.) 

Gas 1990 1995 2000 2005 2006 2007

CO2 NC NC NC NC NC NC 

CH4 117.4 117.3 112.6 107.0 110.9 111.5 

N2O (12.2) (12.9) (12.7) (12.2) (12.1) (12.1) 

HFCs 9.7 8.8 7.7 4.3 3.7 4.6 

PFCs* 3.7 3.0 2.8 1.8 2.0 2.1 

SF6 (1.5) (1.3) (0.9) (0.8) (0.8) (0.8) 

Total 117.1 114.9 109.4 99.9 103.7 105.3

NC (No change) 

*Includes NF3 

Note: Totals may not sum due to independent rounding. Parentheses indicate negative values. 

Table A-245: Change in U.S. Greenhouse Gas Emissions Using TAR vs. AR4 GWPs (Percent) 

Gas/Source 1990 1995 2000 2005 2006 2007

CO2 NC NC NC NC NC NC 

CH4 19.0% 19.0% 19.0% 19.0% 19.0% 19.0% 

N2O (3.9%) (3.9%) (3.9%) (3.9%) (3.9%) (3.9%) 

SF6 (4.6%) (4.6%) (4.6%) (4.6%) (4.6%) (4.6%) 

HFCs 26.3% 10.0% 7.2% 3.7% 3.5% 4.0% 

Substitution of Ozone Depleting Substances 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 

HCFC-22 Productionb 26.5% 26.5% 26.5% 26.5% 26.5% 26.5% 

Semiconductor Manufacturec 26.5% 26.5% 26.5% 26.5% 26.5% 26.5% 

PFCs 17.7% 19.1% 20.4% 28.4% 33.3% 28.1% 

Semiconductor Manufacturec 28.4% 27.8% 29.1% 39.5% 45.1% 40.0% 

Aluminum Productiona 16.4% 16.4% 15.5% 16.3% 16.4% 16.9% 

Total 1.9% 1.8% 1.6% 1.4% 1.5% 1.5%

NC (No change) 
A-306 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

a PFC emissions from CF4 and C2F6

b HFC-23 emitted 

c Emissions from HFC-23, CF4, C2F6, C3F8, SF6, and the addition of NF3

Note: Excludes Sinks. Parentheses indicate negative values. 

Overall, these revisions to GWP values do not have a significant effect on U.S. emission trends, as shown in 

Table A-244 and Table A-245. Table A-246 below shows a comparison of total emissions estimates by sector using both 

the IPCC SAR and AR4 GWP values. For most sectors, the change in emissions was minimal. The effect on emissions 

from waste was by far the greatest (18 percent in 2007), due the predominance of CH4 emissions in this sector. Emissions 

from all other sectors were comprised of mainly CO2 or a mix of gases, which moderated the effect of the changes. 

Table A-246: Comparison of Emissions by Sector using IPCC SAR and AR4 GWP Values (Tg CO2 Eq.) 

Sector 1990 1995 2000 2005 2006 2007

Energy 

SAR GWP (Used in Inventory) 5,193.6 5,520.1 6,059.9 6,169.2 6,084.4 6,170.3 

AR4 GWP, Updated 5,242.0 5,565.3 6,102.8 6,206.5 6,121.7 6,207.8 

Difference (%) 0.9% 0.8% 0.7% 0.6% 0.6% 0.6% 

Industrial Processes 

SAR GWP (Used in Inventory) 325.2 345.8 356.3 337.6 343.9 353.8 

AR4 GWP, Updated 336.1 355.1 365.2 342.2 348.3 359.0 

Difference (%) 3.3% 2.7% 2.5% 1.4% 1.3% 1.5% 

Solvent and Other Product Use 

SAR GWP (Used in Inventory) 4.4 4.6 4.9 4.4 4.4 4.4 

AR4 GWP, Updated 4.2 4.4 4.7 4.2 4.2 4.2 

Difference (%) -3.9% -3.9% -3.9% -3.9% -3.9% -3.9% 

Agriculture 

SAR GWP (Used in Inventory) 384.2 402.0 399.4 410.8 410.3 413.1 

AR4 GWP, Updated 408.6 429.1 425.4 437.4 437.2 440.6 

Difference (%) 6.4% 6.8% 6.5% 6.5% 6.6% 6.7% 

Land Use, Land-Use Change, and 

Forestry 

SAR GWP (Used in Inventory) (827.2) (834.7) (684.5) (1,096.3) (1,005.5) (1,019.7) 

AR4 GWP, Updated (826.4) (834.7) (681.9) (1,094.8) (1,000.6) (1,015.4) 

Difference (%) -0.1% 0.0% -0.4% -0.1% -0.5% -0.4% 

Waste 

SAR GWP (Used in Inventory) 177.1 174.7 154.6 160.2 163.0 165.6 

AR4 GWP, Updated 209.9 206.9 182.7 189.3 192.6 195.6 

Difference (%) 18.5% 18.4% 18.2% 18.1% 18.1% 18.1% 

Net Emissions (Sources and Sinks) 

SAR GWP (Used in Inventory) 5,257.3 5,612.3 6,290.7 5,985.9 6,000.6 6,087.5

AR4 GWP 5,374.4 5,726.2 6,398.9 6,084.7 6,103.4 6,191.8

Difference (%) 2.2% 2.0% 1.7% 1.7% 1.7% 1.7%

NC (No change) 

Note: Totals may not sum due to independent rounding. Parentheses indicate negative values. 
 

A-307

6.2. Ozone Depleting Substance Emissions 

Ozone is present in both the stratosphere,75 where it shields the earth from harmful levels of ultraviolet radiation, 

and at lower concentrations in the troposphere,76 where it is the main component of anthropogenic photochemical “smog.” 

Chlorofluorocarbons (CFCs), halons, carbon tetrachloride, methyl chloroform, and hydrochlorofluorocarbons (HCFCs), 

along with certain other chlorine and bromine containing compounds, have been found to deplete the ozone levels in the 

stratosphere. These compounds are commonly referred to as ozone depleting substances (ODSs). If left unchecked, 

stratospheric ozone depletion could result in a dangerous increase of ultraviolet radiation reaching the earth’s surface. In 

1987, nations around the world signed the Montreal Protocol on Substances that Deplete the Ozone Layer. This landmark 

agreement created an international framework for limiting, and ultimately eliminating, the production of most ozone 

depleting substances. ODSs have historically been used in a variety of industrial applications, including refrigeration and 

air conditioning, foam blowing, fire extinguishing, as an aerosol propellant, sterilization, and solvent cleaning. 

In the United States, the Clean Air Act Amendments of 1990 provide the legal instrument for implementation of 

the Montreal Protocol controls. The Clean Air Act classifies ozone depleting substances as either Class I or Class II, 

depending upon the ozone depletion potential (ODP) of the compound.77 The production of CFCs, halons, carbon 

tetrachloride, and methyl chloroform─all Class I substances─has already ended in the United States. However, large 

amounts of these chemicals remain in existing equipment,78 and stockpiles of the ODSs are used for maintaining the 

equipment. In addition, U.S. regulations require the recovery of ODSs in order to minimize “venting” to the atmosphere. 

As a result, emissions of Class I compounds will continue, albeit in ever decreasing amounts, for many more years. Class 

II designated substances, all of which are hydrochlorofluorocarbons (HCFCs), are being phased out at later dates because 

they have lower ozone depletion potentials. These compounds serve as interim replacements for Class I compounds in 

many industrial applications. The use and emissions of HCFCs in the United States is anticipated to increase over the next 

several years as equipment that use Class I substances are retired from use. Under current controls, however, the 

production for domestic use of all HCFCs in the United States will end by the year 2030. 

In addition to contributing to ozone depletion, CFCs, halons, carbon tetrachloride, methyl chloroform, and 

HCFCs are also potent greenhouse gases. However, the depletion of the ozone layer has a cooling effect on the climate 

that counteracts the direct warming from tropospheric emissions of ODSs. Stratospheric ozone influences the earth’s 

radiative balance by absorption and emission of longwave radiation from the troposphere as well as absorption of 

shortwave radiation from the sun, overall, stratospheric ozone has a warming effect. 

The IPCC has prepared both direct GWPs and net (combined direct warming and indirect cooling) GWP ranges 

for some of the most common ozone depleting substances (IPCC 1996). See Global Warming Potential Values Annex for 

a listing of the net GWP values for ODS. 

Although the IPCC emission inventory guidelines do not require the reporting of emissions of ozone depleting 

substances, the United States believes that no inventory is complete without the inclusion of these compounds. Emission 

estimates for several ozone depleting substances are provided in Table A- 247. 

 

75 The stratosphere is the layer from the top of the troposphere up to about 50 kilometers. Approximately 90 percent of 

atmospheric ozone is within the stratosphere. The greatest concentration of ozone occurs in the middle of the stratosphere, in a region 

commonly called the ozone layer. 

76 The troposphere is the layer from the ground up to about 11 kilometers near the poles and 16 kilometers in equatorial 

regions (i.e., the lowest layer of the atmosphere, where humans live). It contains roughly 80 percent of the mass of all gases in the 

atmosphere and is the site for weather processes including most of the water vapor and clouds. 

77 Substances with an ozone depletion potential of 0.2 or greater are designated as Class I. All other substances that may 

deplete stratospheric ozone but which have an ODP of less than 0.2 are Class II. 

78 Older refrigeration and air-conditioning equipment, fire extinguishing systems, meter-dose inhalers, and foam products 

blown with CFCs/HCFCs may still contain ODS. 
A-308 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Table A- 247: Emissions of Ozone Depleting Substances (Gg) 

Compound 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Class I 

CFC-11 27.4 28.1 12.4 11.8 11.0 10.1 9.0 8.9 8.7 11.2 12.7 12.7 12.7 12.8 12.7 12.6 12.5 11.7 

CFC-12 106.8 108.9 110.4 108.0 86.4 65.1 59.0 54.2 46.7 39.6 33.4 27.3 22.4 17.8 13.7 10.1 7.8 7.5 

CFC-113 59.4 60.5 56.3 51.9 34.9 11.5 + + + + + + + + + + + + 

CFC-114 5.0 3.5 2.0 0.5 0.4 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 

CFC-115 5.5 5.8 5.9 5.7 5.6 5.3 5.1 4.9 4.6 4.4 4.1 3.6 3.2 2.7 2.0 1.3 0.6 0.2 

Carbon Tetrachloride 4.3 4.4 3.6 2.7 1.9 0.9 + + + + + + + + + + + + 

Methyl Chloroform 222.5 227.0 209.1 190.4 147.7 72.1 8.7 + + + + + + + + + + + 

Halon-1211 1.6 1.7 1.7 1.7 1.7 1.6 1.6 1.6 1.5 1.3 1.1 0.9 0.8 0.7 0.7 0.7 0.7 0.6 

Halon-1301 1.6 1.7 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.5 1.5 1.5 1.5 1.5 1.4 1.3 1.1 

Class II 

HCFC-22 37.2 40.3 42.7 45.3 49.1 52.8 56.1 59.6 63.1 66.7 74.3 77.7 80.0 81.4 82.8 83.6 84.7 85.4 

HCFC-123 + + 0.1 0.1 0.2 0.3 0.3 0.4 0.5 0.5 0.6 0.6 0.7 0.7 0.8 0.8 0.8 0.8 

HCFC-124 + + + 0.6 1.1 1.2 1.2 1.3 1.4 1.4 1.5 1.5 1.5 1.5 1.5 1.5 1.5 1.6 

HCFC-141b 1.0 1.2 1.1 2.0 2.9 3.9 5.1 5.7 6.3 6.9 7.0 6.8 5.5 3.8 3.9 4.0 4.1 4.8 

HCFC-142b 2.1 3.3 4.5 5.7 4.9 3.6 2.2 2.3 2.4 2.6 2.7 2.8 2.9 3.0 3.2 3.3 3.4 3.6 

HCFC-225ca/cb + + + + + 0.1 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.3 0.3 

+ Does not exceed 0.05 Gg. 

Methodology and Data Sources 

Emissions of ozone depleting substances were estimated using the EPA’s Vintaging Model. The model, named 

for its method of tracking the emissions of annual “vintages” of new equipment that enter into service, is a “bottom-up” 

model. It models the consumption of chemicals based on estimates of the quantity of equipment or products sold, 

serviced, and retired each year, and the amount of the chemical required to manufacture and/or maintain the equipment. 

The Vintaging Model makes use of this market information to build an inventory of the in-use stocks of the equipment in 

each of the end-uses. Emissions are estimated by applying annual leak rates, service emission rates, and disposal emission 

rates to each population of equipment. By aggregating the emission and consumption output from the different end-uses, 

the model produces estimates of total annual use and emissions of each chemical. Please see HFC and PFC Emissions 

from Substitution of Ozone Depleting Substances Annex of this Inventory for a more detailed discussion of the Vintaging 

Model. 

Uncertainties 

Uncertainties exist with regard to the levels of chemical production, equipment sales, equipment characteristics, 

and end-use emissions profiles that are used by these models. Please see the ODS Substitutes section of this report for a 

more detailed description of the uncertainties that exist in the Vintaging Model. 
 

A-309

6.3. Sulfur Dioxide Emissions 

Sulfur dioxide (SO2), emitted into the atmosphere through natural and anthropogenic processes, affects the 

Earth's radiative budget through photochemical transformation into sulfate aerosols that can (1) scatter sunlight back to 

space, thereby reducing the radiation reaching the Earth's surface; (2) affect cloud formation; and (3) affect atmospheric 

chemical composition (e.g., stratospheric ozone, by providing surfaces for heterogeneous chemical reactions). The overall 

effect of SO2-derived aerosols on radiative forcing is believed to be negative (IPCC 1996). However, because SO2 is 

short-lived and unevenly distributed through the atmosphere, its radiative forcing impacts are highly uncertain. Sulfur 

dioxide emissions have been provided below in Table A-248. Data on 2007 emissions have not yet been released, so 2007 

emissions were set equal to 2006 emissions until more updated data are provided. 

The major source of SO2 emissions in the United States is the burning of sulfur containing fuels, mainly coal. 

Metal smelting and other industrial processes also release significant quantities of SO2. The largest contributor to U.S. 

emissions of SO2 is electricity generation, accounting for 71 percent of total SO2 emissions in 2006 (see Table A-249); 

coal combustion accounted for approximately 92 percent of that total. The second largest source was industrial fuel 

combustion, which produced 6 percent of 2006 SO2 emissions. Overall, SO2 emissions in the United States decreased by 

41 percent from 1990 to 2006. The majority of this decline came from reductions from electricity generation, primarily 

due to increased consumption of low sulfur coal from surface mines in western states. 

Sulfur dioxide is important for reasons other than its effect on radiative forcing. It is a major contributor to the 

formation of urban smog and acid rain. As a contributor to urban smog, high concentrations of SO2 can cause significant 

increases in acute and chronic respiratory diseases. In addition, once SO2 is emitted, it is chemically transformed in the 

atmosphere and returns to earth as the primary contributor to acid deposition, or acid rain. Acid rain has been found to 

accelerate the decay of building materials and paints, and to cause the acidification of lakes and streams and damage trees. 

As a result of these harmful effects, the United States has regulated the emissions of SO2 under the Clean Air Act. The 

EPA has also developed a strategy to control these emissions via four programs: (1) the National Ambient Air Quality 

Standards program,79 (2) New Source Performance Standards,80 (3) the New Source Review/Prevention of Significant 

Deterioration Program,81 and (4) the sulfur dioxide allowance program.82

References 

EPA (2005) Air Emissions Trends—Continued Progress Through 2004. U.S. Environmental Protection Agency, 

Washington DC. August 18, 2005 <http://www.epa.gov/airtrends/2005/econ-emissions.html>. 

EPA (2003) E-mail correspondence containing preliminary ambient air pollutant data between EPA OAP and 

EPA OAQPS. December 22, 2003. 

Table A-248: SO2 Emissions (Gg) 

Sector/Source 1990 1995 2000 2005 2006 2007

Energy 19,628 15,772 13,797 12,496 11,413 10,885

Stationary Combustion 18,407 14,724 12,849 11,641 10,650 10,211

Mobile Combustion 793 672 632 600 520 442

Oil and Gas Activities 390 335 287 233 221 210

Waste Combustion 38 42 29 22 22 22

Industrial Processes 1,307 1,117 1,031 852 845 839

Chemical Manufacturing 269 259 307 235 234 234

Metals Processing 659 481 284 193 193 193

Storage and Transport 6 2 5 5 5 5

Other Industrial Processes 362 366 372 297 295 293

Miscellaneous* 11 9 64 122 118 114

Solvent Use + 1 1 + + +

Degreasing + + 0 0 0 0

Graphic Arts + + 0 0 0 0

Dry Cleaning NA + 0 0 0 0

Surface Coating + 1 0 0 0 0

 

79 [42 U.S.C § 7409, CAA § 109] 

80 [42 U.S.C § 7411, CAA § 111] 

81 [42 U.S.C § 7473, CAA § 163] 

82 [42 U.S.C § 7651, CAA § 401] 
A-310 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Other Industrial + + 1 0 0 0

Non-industrial NA NA NA NA NA NA

Agriculture NA NA NA NA NA NA

Agricultural Burning NA NA NA NA NA NA

Waste + 1 1 1 1 1

Landfills + + 1 1 1 1

Wastewater Treatment + + + + + + 

Miscellaneous Waste + + + + + + 

Total 20,935 16,891 14,830 13,348 12,259 11,725

Source: Data taken from EPA (2005) and disaggregated based on EPA (2003). 

* Miscellaneous includes other combustion and fugitive dust categories. 

+ Does not exceed 0.5 Gg 

NA (Not Available) 

Note: Totals may not sum due to independent rounding. 

Table A-249: SO2 Emissions from Electricity Generation (Gg) 

Fuel Type 1990 1995 2000 2005 2006 2007

Coal 13,808 10,526 9,620 8,734 7,861 7,486 

Petroleum 580 375 428 461 415 395 

Natural Gas 1 8 157 175 157 150 

Misc. Internal Combustion 45 50 54 57 52 49 

Other NA NA 78 71 64 61 

Total 14,433 10,959 10,338 9,498 8,549 8,141

Source: Data taken from EPA (2005) and disaggregated based on EPA (2003). 

Note: Totals may not sum due to independent rounding.

1 6.4. Complete List of Source Categories 

Chapter/Source Gas(es) 

Energy 

Fossil Fuel Combustion CO2 

Non-Energy Use of Fossil Fuels CO2

Stationary Combustion (excluding CO2) CH4, N2O, CO, NOx, NMVOC 

Mobile Combustion (excluding CO2) CH4, N2O, CO, NOx, NMVOC 

Coal Mining CH4

Abandoned Underground Coal Mines CH4

Natural Gas Systems CH4

Petroleum Systems CH4

Waste Incineration CO2, N2O 

Industrial Processes 

Titanium Dioxide Production CO2

Aluminum Production CO2, CF4, C2F6

Iron and Steel Production CO2, CH4

Ferroalloy Production CO2, CH4

Ammonia Production and Urea Consumption CO2

Cement Production CO2

Lime Production CO2

Limestone and Dolomite Use CO2

Soda Ash Production and Consumption CO2

Carbon Dioxide Consumption CO2

Phosphoric Acid Production CO2

Petrochemical Production CH4, CO2

Silicon Carbide Production and Consumption CH4, CO2

Lead Production CO2

Zinc Production CO2

Adipic Acid Production N2O 

Nitric Acid Production N2O 

Substitution of Ozone Depleting Substances HFCs, PFCsa 

HCFC-22 Production HFC-23 

Semiconductor Manufacture HFCs, PFCs, SF6b

Electrical Transmission and Distributing SF6

Magnesium Production and Processing SF6

Solvent and Other Product Use CO, NOx, NMVOC 

N2O Product Usage N2O 

Agriculture 

Enteric Fermentation CH4
 

A-311

Manure Management CH4, N2O 

Rice Cultivation CH4

Field Burning of Agricultural Residues CH4, N2O 

Agricultural Soil Management N2O, CO, NOx

Land Use, Land-Use Change, and Forestry 

CO2 Flux CO2 (sink) 

Cropland Remaining Cropland CO2

Settlements Remaining Settlements N2O 

Forestland Remaining Forestland CH4, N2O 

Wetlands Remaining Wetlands CO2, N2O 

Waste 

Landfills CH4

Wastewater Treatment CH4, N2O 

Composting CH4, N2O a 1 Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF4, HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and PFC/PFPEs. b 2 Includes such gases as HFC-23, CF4, C2F6, SF6.

6.5. Constants, Units, and Conversions 

Metric Prefixes 

Although most activity data for the United States is gathered in customary U.S. units, these units are 

converted into metric units per international reporting guidelines. Table A- 250 provides a guide for determining 

the magnitude of metric units. 

Table A- 250: Guide to Metric Unit Prefixes 

Prefix/Symbol Factor 

atto (a) 10-18

femto (f) 10-15

pico (p) 10-12

nano (n) 10-9

micro (μ ) 10-6

milli (m) 10-3

centi (c) 10-2

deci (d) 10-1

deca (da) 10 

hecto (h) 102

kilo (k) 103

mega (M) 106

giga (G) 109

tera (T) 1012

peta (P) 1015

exa (E) 1018

Unit Conversions 

1 kilogram = 2.205 pounds 

1 pound = 0.454 kilograms 

1 short ton = 2,000 pounds = 0.9072 metric tons 

1 metric ton = 1,000 kilograms = 1.1023 short tons 

1 cubic meter = 35.315 cubic feet 

1 cubic foot = 0.02832 cubic meters 

1 U.S. gallon = 3.785412 liters 

1 barrel (bbl) = 0.159 cubic meters 

1 barrel (bbl) = 42 U.S. gallons 

1 liter = 0.001 cubic meters 

1 foot = 0.3048 meters 

1 meter = 3.28 feet 

1 mile = 1.609 kilometers 
A-312 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

1 kilometer = 0.622 miles 

1 acre = 43,560 square feet = 0.4047 hectares = 4,047 square meters 

1 square mile = 2.589988 square kilometers 

To convert degrees Fahrenheit to degrees Celsius, subtract 32 and multiply by 5/9 

To convert degrees Celsius to Kelvin, add 273.15 to the number of Celsius degrees 

Density Conversions83

Methane 1 cubic meter = 0.67606 kilograms 

Carbon dioxide 1 cubic meter = 1.85387 kilograms 

Natural gas liquids 1 metric ton = 11.6 barrels = 1,844.2 liters 

Unfinished oils 1 metric ton = 7.46 barrels = 1,186.04 liters 

Alcohol 1 metric ton = 7.94 barrels = 1,262.36 liters 

Liquefied petroleum gas 1 metric ton = 11.6 barrels = 1,844.2 liters 

Aviation gasoline 1 metric ton = 8.9 barrels = 1,415.0 liters 

Naphtha jet fuel 1 metric ton = 8.27 barrels = 1,314.82 liters 

Kerosene jet fuel 1 metric ton = 7.93 barrels = 1,260.72 liters 

Motor gasoline 1 metric ton = 8.53 barrels = 1,356.16 liters 

Kerosene 1 metric ton = 7.73 barrels = 1,228.97 liters 

Naphtha 1 metric ton = 8.22 barrels = 1,306.87 liters 

Distillate 1 metric ton = 7.46 barrels = 1,186.04 liters 

Residual oil 1 metric ton = 6.66 barrels = 1,058.85 liters 

Lubricants 1 metric ton = 7.06 barrels = 1,122.45 liters 

Bitumen 1 metric ton = 6.06 barrels = 963.46 liters 

Waxes 1 metric ton = 7.87 barrels = 1,251.23 liters 

Petroleum coke 1 metric ton = 5.51 barrels = 876.02 liters 

Petrochemical feedstocks 1 metric ton = 7.46 barrels = 1,186.04 liters 

Special naphtha 1 metric ton = 8.53 barrels = 1,356.16 liters 

Miscellaneous products 1 metric ton = 8.00 barrels = 1,271.90 liters 

Energy Conversions 

Converting Various Energy Units to Joules 

The common energy unit used in international reports of greenhouse gas emissions is the joule. A joule 

is the energy required to push with a force of one Newton for one meter. A terajoule (TJ) is one trillion (1012) 

joules. A British thermal unit (Btu, the customary U.S. energy unit) is the quantity of heat required to raise the 

temperature of one pound of water one degree Fahrenheit at or near 39.2 Fahrenheit. 

2.3881011 calories 

23.88 metric tons of crude oil equivalent 

947.8 million Btus 1 TJ = 

277,800 kilowatt-hours 

Converting Various Physical Units to Energy Units 

Data on the production and consumption of fuels are first gathered in physical units. These units must 

be converted to their energy equivalents. The conversion factors in Table A-251 can be used as default factors, if 

local data are not available. See Appendix A of EIA’s Annual Energy Review 2006 (EIA 2007a) for more 

detailed information on the energy content of various fuels. 

 

83 Reference: EIA (2007a) 
 

A-313

Table A-251: Conversion Factors to Energy Units (Heat Equivalents) 

Fuel Type (Units) Factor

Solid Fuels (Million Btu/Short ton) 

Anthracite coal 22.573

Bituminous coal 23.89

Sub-bituminous coal 17.14

Lignite 12.866

Coke 24.8

Natural Gas (Btu/Cubic foot) 1,027

Liquid Fuels (Million Btu/Barrel) 

Crude oil 5.800

Natural gas liquids and LRGs 3.777

Other liquids 5.825

Motor gasoline 5.218

Aviation gasoline 5.048

Kerosene 5.670

Jet fuel, kerosene-type 5.670

Distillate fuel 5.825

Residual oil 6.287

Naphtha for petrochemicals 5.248

Petroleum coke 6.024

Other oil for petrochemicals 5.825

Special naphthas 5.248

Lubricants 6.065

Waxes 5.537

Asphalt 6.636

Still gas 6.000

Misc. products 5.796

Note: For petroleum and natural gas, Annual Energy Review 2006 (EIA 2007b). For coal ranks, State Energy Data Report 1992 (EIA 1993). All values are 

given in higher heating values (gross calorific values). 

References 

EIA (2007a) Emissions of Greenhouse Gases in the United States 2006, Draft Report. Office of 

Integrated Analysis and Forecasting, Energy Information Administration, U.S. Department of Energy, 

Washington, DC. DOE-EIA-0573(2006). 

EIA (2007b) Annual Energy Review 2006. Energy Information Administration, U.S. Department of 

Energy, Washington, DC. DOE/EIA-0384(2006). June 2007. 

EIA (1998) Emissions of Greenhouse Gases in the United States, DOE/EIA-0573(97), Energy 

Information Administration, U.S. Department of Energy. Washington, DC. October. 

EIA (1993) State Energy Data Report 1992, DOE/EIA-0214(93), Energy Information Administration, 

U.S. Department of Energy. Washington, DC. December. 

6.6. Abbreviations 

AAPFCO American Association of Plant Food Control Officials 

ABS Acrylonitrile Butadiene Styrene 

AFEAS Alternative Fluorocarbon Environmental Acceptability Study 

AFV Alternative Fuel Vehicle 

AGA American Gas Association 

AHEF Atmospheric and Health Effect Framework 

APC American Plastics Council 

API American Petroleum Institute 

ASAE American Society of Agricultural Engineers 

ASTM American Society for Testing and Materials 

BEA Bureau of Economic Analysis, U.S. Department of Commerce 

BoC Bureau of Census 

BOD5 Biochemical oxygen demand over a 5-day period 

BRS Biennial Reporting System 

BTS Bureau of Transportation Statistics, U.S. Department of Transportation 

Btu British thermal unit 

C&EN Chemical and Engineering News 
A-314 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

CAAA Clean Air Act Amendments of 1990 

CAPP Canadian Association of Petroleum Producers 

CBI Confidential Business Information 

CFC Chlorofluorocarbon 

CFR Code of Federal Regulations 

CMA Chemical Manufacturer’s Association 

CMOP Coalbed Methane Outreach Program 

CNG Compressed Natural Gas 

CRF Common Reporting Format 

CRM Crop Residue Management 

CRP Conservation Reserve Program 

CTIC Conservation Technology Information Center 

CVD Chemical vapor deposition 

DE Digestible Energy 

DESC Defense Energy Support Center-DoD’s defense logistics agency 

DFAMS Defense Fuels Automated Management System 

DIC Dissolved inorganic carbon 

DM Dry Matter 

DOC U.S. Department of Commerce 

DoD U.S. Department of Defense 

DOE U.S. Department of Energy 

DOI U.S. Department of the Interior 

DOT U.S. Department of Transportation 

EAF Electric Arc Furnace 

EF Emission Factor 

EGR Exhaust Gas Recirculation 

EIA Energy Information Administration, U.S. Department of Energy 

EIIP Emissions Inventory Improvement Program 

EOR Enhanced oil recovery 

EPA U.S. Environmental Protection Agency 

FAA Federal Aviation Administration 

FAO Food and Agricultural Organization 

FCCC Framework Convention on Climate Change 

FEB Fiber Economics Bureau 

FHWA Federal Highway Administration 

FIA Forest Inventory and Analysis 

GAA Governmental Advisory Associates 

GCV Gross calorific value 

GDP Gross domestic product 

Gg Gigagram 

GHG Greenhouse gas 

GRI Gas Research Institute 

GSAM Gas Systems Analysis Model 

GWP Global warming potential 

HBFC Hydrobromofluorocarbon 

HC Hydrocarbon 

HCFC Hydrochlorofluorocarbon 

HDDV Heavy duty diesel vehicle 

HDGV Heavy duty gas vehicle 

HDPE High density polyethylene 

HFC Hydrofluorocarbon 

HFE Hydrofluoroethers 

HHV Higher Heating Value 

HMA Hot Mix Asphalt 

HTS Harmonized Tariff Schedule 

ICAO International Civil Aviation Organization 

IEA International Energy Association 

IFO Intermediate Fuel Oil 

IISRP International Institute of Synthetic Rubber Products 

ILENR Illinois Department of Energy and Natural Resources 

IMO International Maritime Organization 

IPAA Independent Petroleum Association of America 

IPCC Intergovernmental Panel on Climate Change 

LDDT Light duty diesel truck 

LDDV Light duty diesel vehicle 

LDGT Light duty gas truck 
 

A-315

LDGV Light duty gas vehicle 

LDPE Low density polyethylene 

LEV Low emission vehicles 

LFG Landfill gas 

LFGTE Landfill gas-to-energy 

LHV Lower Heating Value 

LLDPE Linear low density polyethylene 

LMOP EPA’s Landfill Methane Outreach Program 

LNG Liquefied Natural Gas 

LPG Liquefied petroleum gas(es) 

LTO Landing and take-off 

LULUCF Land use, land-use change, and forestry 

MC Motorcycle 

MCF Methane conversion factor 

MGO Marine Gas Oil 

MLRA Major Land Resource Area 

MMCFD Million Cubic Feet Per Day 

MMS Minerals Management Service 

MMTCE Million metric tons carbon equivalent 

MSHA Mine Safety and Health Administration 

MSW Municipal solid waste 

MTBE Methyl Tertiary Butyl Ether 

NAHMS National Animal Health Monitoring System 

NAPAP National Acid Precipitation and Assessment Program 

NASS USDA’s National Agriculture Statistics Service 

NCV Net calorific value 

NEU Non-Energy Use 

NEV Neighborhood Electric Vehicle 

NGL Natural Gas Liquids 

NIAR Norwegian Institute for Air Research 

NIR National Inventory Report 

NMVOC Non-methane volatile organic compound 

NOx Nitrogen Oxides 

NPRA National Petroleum and Refiners Association 

NRC National Research Council 

NRCS Natural Resources Conservation Service 

NRI National Resources Inventory 

NSCR Non-selective catalytic reduction 

NVFEL National Vehicle Fuel Emissions Laboratory 

NWS National Weather Service 

OAP EPA Office of Atmospheric Programs 

OAQPS EPA Office of Air Quality Planning and Standards 

ODP Ozone Depleting Potential 

ODS Ozone depleting substances 

OECD Organization of Economic Co-operation and Development 

OMS EPA Office of Mobile Sources 

ORNL Oak Ridge National Laboratory 

OSHA Occupational Safety and Health Administration 

OTA Office of Technology Assessment 

OTAQ EPA Office of Transportation and Air-Quality 

PAH Polycyclic Aromatic Hydrocarbons 

PDF Probability Density Function 

PET Polyethylene Terephthalate 

PFC Perfluorocarbon 

PFPE Perfluoropolyether 

POTW Publicly Owned Treatment Works 

Ppbv Parts per billion (10

9) by volume 

PPC Precipitated calcium carbonate 

Ppmv Parts per million(10

6) by volume 

Pptv Parts per trillion (1012) by volume 

PS Polystyrene 

PSU Primary Sample Unit 

PVC Polyvinyl chloride 

QA/QC Quality Assurance and Quality Control 

QBtu Quadrillion Btu 

RCRA Resource Conservation and Recovery Act 
A-316 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

SAE Society of Automotive Engineers 

SAN Styrene Acrylonitrile 

SAR IPCC Second Assessment Report 

SBSTA Subsidiary Body for Scientific and Technical Advice 

SCR Selective catalytic reduction 

SNAP Significant New Alternative Policy Program 

SNG Synthetic natural gas 

SOC Soil Organic Carbon 

STMC Scrap Tire Management Council 

SULEV Super Ultra Low Emissions Vehicle 

SWANA Solid Waste Association of North America 

TAME Tertiary Amyl Methyl Ether 

TAR IPCC Third Assessment Report 

TBtu Trillion Btu 

TDN Total Digestible Nutrients 

Tg CO2 Eq. Teragrams carbon dioxide equivalent 

TJ Terajoule 

TLEV Traditional Low Emissions Vehicle 

TRI Toxic Release Inventory 

TSDF Hazardous waste treatment, storage, and disposal facility 

TVA Tennessee Valley Authority 

U.S. United States 

UEP United Egg Producers 

ULEV Ultra Low Emission Vehicle 

UNEP United Nations Environmental Programme 

UNFCCC United Nations Framework Convention on Climate Change 

USAF United States Air Force 

USDA United States Department of Agriculture 

USFS United States Forest Service 

USGS United States Geological Survey 

VAIP EPA’s Voluntary Aluminum Industrial Partnership 

VKT Vehicle kilometers traveled 

VMT Vehicle miles traveled 

VOCs Volatile Organic Compounds 

VS Volatile Solids 

WIP Waste In Place 

WMO World Meteorological Organization 

ZEVs Zero Emissions Vehicles 
 

A-317

6.7. Chemical Formulas 

Table A-252: Guide to Chemical Formulas 

Symbol Name 

Al Aluminum 

Al

2

O

3 Aluminum Oxide 

Br Bromine 

C Carbon 

CH

4 Methane 

C

2

H

6 Ethane 

C

3

H

8 Propane 

CF

4 Perfluoromethane 

C

2

F

6 Perfluoroethane, hexafluoroethane 

c-C

3

F

6 Perfluorocyclopropane 

C

3

F

8 Perfluoropropane 

c-C

4

F

8 Perfluorocyclobutane 

C

4

F10 Perfluorobutane 

C

5

F12 Perfluoropentane 

C

6

F14 Perfluorohexane 

CF

3I Trifluoroiodomethane 

CFCl

3 Trichlorofluoromethane (CFC-11) 

CF

2Cl2 Dichlorodifluoromethane (CFC-12) 

CF

3Cl Chlorotrifluoromethane (CFC-13) 

C

2

F

3Cl3 Trichlorotrifluoroethane (CFC-113)* 

CCl

3CF3 CFC-113a* 

C

2

F

4Cl2 Dichlorotetrafluoroethane (CFC-114) 

C

2

F

5Cl Chloropentafluoroethane (CFC-115) 

CHCl

2F HCFC-21 

CHF

2Cl Chlorodifluoromethane (HCFC-22) 

C

2

F

3HCl2 HCFC-123 

C

2

F

4HCl HCFC-124 

C

2FH3Cl2 HCFC-141b 

C

2

H

3

F

2Cl HCFC-142b 

CF

3CF2CHCl2 HCFC-225ca 

CClF

2CF2CHClF HCFC-225cb 

CCl

4 Carbon tetrachloride 

CHClCCl

2 Trichloroethylene 

CCl

2CCl2 Perchloroethylene, tetrachloroethene 

CH

3Cl Methylchloride 

CH

3CCl3 Methylchloroform 

CH

2Cl2 Methylenechloride 

CHCl3 Chloroform, trichloromethane 

CHF

3 HFC-23 

CH

2

F

2 HFC-32 

CH

3F HFC-41 

C

2HF5 HFC-125 

C

2

H

2

F

4 HFC-134 

CH

2FCF3 HFC-134a 

C

2

H

3

F

3 HFC-143* 

C

2

H

3

F

3 HFC-143a* 

CH

2FCH2F HFC-152*

C

2

H

4

F

2 HFC-152a* 

CH

3CH2F HFC-161 

C

3HF7 HFC-227ea 

CF

3CF2CH2F HFC-236cb 

CF

3CHFCHF2 HFC-236ea 

C

3

H

2

F

6 HFC-236fa 

C

3

H

3

F

5 HFC-245ca 

CHF

2CH2CF3 HFC-245fa 

CF

3CH2CF2CH3 HFC-365mfc 

C

5

H

2

F10 HFC-43-10mee 

CF

3OCHF2 HFE-125 

CF

2HOCF2H HFE-134 

CH

3OCF3 HFE-143a 

CF

3CHFOCF3 HFE-227ea 
A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

CF3CHClOCHF2 HCFE-235da2 

CF3CHFOCHF2 HFE-236ea2 

CF3CH2OCF3 HFE-236fa 

CF3CF2OCH3 HFE-245cb2 

CHF2CH2OCF3 HFE-245fa1 

CF3CH2OCHF2 HFE-245fa2 

CHF2CF2OCH3 HFE-254cb2 

CF3CH2OCH3 HFE-263fb2 

CF3CF2OCF2CHF2 HFE-329mcc2 

CF3CF2OCH2CF3 HFE-338mcf2 

CF3CF2CF2OCH3 HFE-347mcc3 

CF3CF2OCH2CHF2 HFE-347mcf2 

CF3CHFCF2OCH3 HFE-356mec3 

CHF2CF2CF2OCH3 HFE-356pcc3 

CHF2CF2OCH2CHF2 HFE-356pcf2 

CHF2CF2CH2OCHF2 HFE-356pcf3 

CF3CF2CH2OCH3 HFE-365mcf3 

CHF2CF2OCH2CH3 HFE-374pcf2 

C4F9OCH3 HFE-7100 

C4F9OC2H5 HFE-7200 

CHF2OCF2OC2F4OCHF2 H-Galden 1040x 

CHF2OCF2OCHF2 HG-10 

CHF2OCF2CF2OCHF2 HG-01 

CH3OCH3 Dimethyl ether 

CH2Br2 Dibromomethane 

CH2BrCl Dibromochloromethane 

CHBr3 Tribromomethane 

CHBrF2 Bromodifluoromethane 

CH3Br Methylbromide 

CF2BrCl Bromodichloromethane (Halon 1211) 

CF3Br(CBrF3) Bromotrifluoromethane (Halon 1301) 

CF3I FIC-13I1 

CO Carbon monoxide 

CO2 Carbon dioxide 

CaCO3 Calcium carbonate, Limestone 

CaMg(CO3)2 Dolomite 

CaO Calcium oxide, Lime 

Cl atomic Chlorine 

F Fluorine 

Fe Iron 

Fe2O3 Ferric oxide 

FeSi Ferrosilicon 

H, H2 atomic Hydrogen, molecular Hydrogen 

H2O Water 

H2O2 Hydrogen peroxide 

OH Hydroxyl 

N, N2 atomic Nitrogen, molecular Nitrogen 

NH3 Ammonia 

NH4+ Ammonium ion 

HNO3 Nitric acid 

NF3 Nitrogen trifluoride 

N2O Nitrous oxide 

NO Nitric oxide 

NO2 Nitrogen dioxide 

NO3 Nitrate radical 

Na Sodium 

Na2CO3 Sodium carbonate, soda ash 

Na3AlF6 Synthetic cryolite 

O, O2 atomic Oxygen, molecular Oxygen 

O3 Ozone 

S atomic Sulfur 

H2SO4 Sulfuric acid 

SF6 Sulfur hexafluoride 

SF5CF3 Trifluoromethylsulphur pentafluoride 

SO2 Sulfur dioxide 

Si Silicon 
 

A-319

SiC Silicon carbide 

SiO2 Quartz 

* Distinct isomers. 

 

A-321

ANNEX 7 Uncertainty 

The annual U.S. Inventory presents the best effort to produce estimates for greenhouse gas source and sink 

categories in the United States. These estimates were generated according to the UNFCCC reporting guidelines, 

following the recommendations set forth in the Revised 1996 IPCC Guidelines for National Greenhouse Gas 

Inventories (IPCC/UNEP/OECD/IEA 1997), the IPCC Good Practice Guidance (IPCC 2000), the Good Practice 

Guidance for Land Use, Land-Use Change and Forestry (IPCC 2003), and the 2006 Guidelines for National 

Greenhouse Gas Inventories (IPCC 2006). This Annex provides an overview of the uncertainty analysis conducted 

to support the U.S. Inventory, describes the sources of uncertainty characterized throughout the Inventory associated 

with various source categories (including emissions and sinks), and describes the methods through which 

uncertainty information was collected, quantified, and presented. 

7.1. Overview 

The current inventory estimates for some source categories, such as for CO2 Emissions from Fossil Fuel 

Combustion, have relatively low level of uncertainty associated with them. However, for some other source 

categories, the inventory emission estimates are considered less certain. The two major types of uncertainty 

associated with these inventory estimates are (1) model uncertainty, which arises when the emission and/or removal 

estimation models used in developing the inventory estimates do not fully and accurately characterize the respective 

emission and/or removal processes (due to a lack of technical details or other resources), resulting in the use of 

incorrect or incomplete estimation methodologies and (2) parameter uncertainty, which arises due to a lack of 

precise input data such as emission factors and activity data. 

The model uncertainty can be analyzed by comparing model results with those of other models developed 

to characterize the same emission (or removal) process. However, it would be very difficult—if not impossible—to 

quantify the model uncertainty associated with the inventory estimates (primarily because, in most cases, only a 

single model has been developed to estimate emissions from any one source). Therefore, model uncertainty was not 

quantified in this report. Nonetheless, it has been discussed qualitatively, where appropriate, along with the 

individual source category description and inventory estimation methodology. 

Parameter uncertainty is, therefore, the principal type and source of uncertainty associated with the national 

inventory estimates and is the main focus of the quantitative uncertainty analyses in this report. Parameter 

uncertainty has been quantified for all of the emission sources and sinks in the U.S. Inventory, with the exception of 

two source categories, whose emissions are not included in the Inventory totals. 

The primary purpose of the uncertainty analysis conducted in support of the U.S. Inventory is (i) to 

determine the quantitative uncertainty associated with the emission (and removal) estimates presented in the main 

body of this report [based on the uncertainty associated with the input parameters used in the emission (and 

removal) estimation methodologies] and (ii) to evaluate the relative importance of the input parameters in 

contributing to uncertainty in the associated source category inventory estimate and in the overall inventory 

estimate. Thus, the U.S. Inventory uncertainty analysis provides a strong foundation for developing future 

improvements and revisions to the Inventory estimation process. For each source category, the analysis highlights 

opportunities for changes to data measurement, data collection, and calculation methodologies. These are presented 

in the “Planned Improvements” sections of each source category’s discussion in the main body of the report. 

7.2. Methodology and Results 

The United States has developed a QA/QC and uncertainty management plan in accordance with the IPCC

Good Practice Guidance. Like the quality assurance/quality control plan, the uncertainty management plan is part 

of a continually evolving process. The uncertainty management plan provides for a quantitative assessment of the 

inventory analysis itself, thereby contributing to continuing efforts to understand both what causes uncertainty and 

how to improve inventory quality (EPA 2002). Although the plan provides both general and specific guidelines for 

implementing quantitative uncertainty analysis, its components are intended to evolve over time, consistent with the 

inventory estimation process. The U.S. plan includes procedures and guidelines, and forms and templates, for 

developing quantitative assessments of uncertainty in the national Inventory estimates. 
A-322 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

The IPCC Good Practice Guidance recommends two approaches—Tier 1 and Tier 2—for developing 

quantitative estimates of uncertainty in the inventory estimate of individual source categories and the overall 

inventory. Of these, the Tier 2 approach is both more flexible and reliable than Tier 1; both methods are described in 

the next section. The United States is currently in the process of implementing a multi-year strategy to develop 

quantitative estimates of uncertainty for all source categories using the Tier 2 approach. This year, a Tier 2 

approach was implemented for all source categories with the exception of Composting and parts of Agricultural Soil 

Management source categories. 

The current Inventory reflects significant improvements over the previous publication in the extent to 

which the Tier 2 approach to uncertainty analysis was adopted. Each of the new Tier 2 analyses reflect additional 

detail and characterization of input parameters using statistical data collection, expert elicitation methods and more 

informed judgment. Emissions and sinks from International Bunker Fuels, Biomass Combustion, and Indirect 

Greenhouse Gas Emissions are not included in total emissions estimated for the U.S. Inventory; therefore, no 

quantitative uncertainty estimates have been developed for these source categories. 

Tier 1 and Tier 2 Approach 

The Tier 1 method for estimating uncertainty is based on the error propagation equation. This equation 

combines the uncertainty associated with the activity data and the uncertainty associated with the emission (or the 

other) factors. The Tier 1 approach is applicable where emissions (or removals) are usually estimated as the product 

of an activity value and an emission factor or as the sum of individual sub-source category values. Inherent in 

employing the Tier 1 method are the assumptions that, for each source category, (i) both the activity data and the 

emission factor values are approximately normally distributed, (ii) the coefficient of variation associated with each 

input variable is less than 30 percent, and (iii) the input variables (i.e., values to be combined) are not correlated. 

The Tier 2 method is preferred (i) if the uncertainty associated with the input variables are significantly 

large, (ii) if the distributions underlying the input variables are not normal, (iii) if the estimates of uncertainty 

associated with the input variables are significantly correlated, and/or (iv) if a sophisticated estimation methodology 

and/or several input variables are used to characterize the emission (or removal) process correctly. In practice, the 

Tier 2 is the preferred method of uncertainty analysis for all source categories where sufficient and reliable data are 

available to characterize the uncertainty of the input variables. 

The Tier 2 method employs the Monte Carlo Stochastic Simulation technique (also referred to as the Monte 

Carlo method). Under this method, estimates of emissions (or removals) for a particular source category are 

generated many times (equal to the number of iterations specified) using an uncertainty model--which is an emission 

(or removal) estimation equation that simulates or is the same as the inventory estimation model for a particular 

source category. These estimates are generated using the respective, randomly-selected values for the constituent 

input variables using a simulation-software such as @RISK or Crystal Ball. 

Characterization of Uncertainty in Input Variables 

Both Tier 1 and Tier 2 uncertainty analyses require that all the input variables are well-characterized in 

terms of their Probability Distribution Functions (PDFs). In the absence of particularly convincing data 

measurements, sufficient data samples, or expert judgments that determined otherwise, the PDFs incorporated in the 

current source category uncertainty analyses were limited to uniform, PERT- beta, triangular, lognormal, or normal. 

The choice among these four PDFs depended largely on the observed or measured data and expert judgment. 

Source Category Inventory Uncertainty Estimates 

Discussion surrounding the input parameters and sources of uncertainty for each source category appears in 

the body of this report. Table A-253 summarizes results based on assessments of source category-level uncertainty. 

The table presents base year (1990 or 1995) and current year (2007) emissions for each source category. The 

combined uncertainty (at the 95 percent confidence interval) for each source category is expressed as the percentage 

deviation above and below the total 2007 emissions estimated for that source category. Source category trend 

uncertainty is described subsequently in this Appendix. 
 

A-323

Table A-253: Summary Results of Source Category Uncertainty Analyses 

 

Source Category 

Base Year 

Emissions* 

2007 

Emissions 2007 Uncertainty 

 Tg CO2 Eq. Tg CO2 Eq. Low High 

CO2 5,076.7 6,103.4 -2% 5% 

Fossil Fuel Combustion 4,708.9 5,735.8 -2% 5% 

Non-Energy Use of Fuels 117.0 133.9 -20% 8% 

Natural Gas Systems 33.7 28.7 -24% 43% 

Cement Production 33.3 44.5 -13% 13% 

Lime Production 11.5 14.6 -8% 9% 

Limestone and Dolomite Use 5.1 6.2 -12% 16% 

Soda Ash Production and Consumption 4.1 4.1 -7% 7% 

Carbon Dioxide Consumption 1.4 1.9 -18% 22% 

Waste Incineration 10.9 20.8 -27% 20% 

Titanium Dioxide Production 1.2 1.9 -12% 13% 

Aluminum Production 6.8 4.3 -4% 4% 

Iron and Steel Production & Metallurgical Coke Productiond 109.8 73.6 -22% 20% 

Ferroalloy Production 2.2 1.6 -12% 12% 

Ammonia Production and Urea Consumption 16.8 13.8 -12% 11% 

Phosphoric Acid Production 1.5 1.2 -18% 18% 

Petrochemical Production 2.2 2.6 -34% 40% 

Silicon Carbide Production and Consumption 0.4 0.2 -10% 10% 

Lead Production 0.3 0.3 -16% 17% 

Zinc Production 0.9 0.5 -21% 25% 

Cropland Remaining Cropland 7.1 8.0 -56% 51% 

Wetlands Remaining Wetlands 1.0 1.0 -31% 29% 

Petroleum Systems 0.4 0.3 -28% 144% 

Land Use, Land-Use Change, and Forestry (Sink)a (841.4) (1,062.6) 18% -15% 

International Bunker Fuelsb 114.3 108.8 NE NE 

Wood Biomass and Ethanol Consumptionb 219.3 247.8 NE NE 

CH4 616.6 585.3 -10% 18% 

Stationary Combustion 7.4 6.6 -34% 128% 

Mobile Combustionc 4.2 1.7 -16% 18% 

Coal Mining 84.1 57.6 -16% 24% 

Abandoned Underground Coal Mines 6.0 5.7 -19% 23% 

Natural Gas Systems 129.6 104.7 -24% 43% 

Petroleum Systems 33.9 28.8 -28% 144% 

Petrochemical Production 0.9 1.0 -31% 31% 

Silicon Carbide Production and Consumption + + -9% 10% 

Iron and Steel Production & Metallurgical Coke Productiond 1.0 0.7 -8% 8% 

Ferroalloy Production + + -12% 12% 

Enteric Fermentation 133.2 139.0 -11% 18% 

Manure Management 30.4 44.0 -18% 20% 

Rice Cultivation 7.1 6.2 -66% 164% 

Field Burning of Agricultural Residues 0.7 0.9 -73% 94% 

Forest Land Remaining Forest Land 4.6 29.0 -73% 155% 

Landfills 149.2 132.9 33% -39% 

Wastewater Treatment 23.5 24.4 -38% 49% 

Composting 0.3 1.7 -50% 50% 

International Bunker Fuelsb 0.2 0.1 NE NE 

N2O 315.0 311.9 -11% 41% 

Stationary Combustion 12.8 14.7 -24% 187% 

Mobile Combustionc 40.4 26.0 -19% 19% 

Adipic Acid Production 15.3 5.9 -18% 20% 

Nitric Acid Production 20.0 21.7 -42% 44% 

Manure Management 12.1 14.7 -16% 24% 

Agricultural Soil Management 200.3 207.9 -22% 53% 

Field Burning of Agricultural Residues 0.4 0.5 -73% 85% 

Wastewater Treatment 3.7 4.9 -75% 94% 
A-324 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

N2O from Product Uses 4.4 4.4 -2% 2% 

Waste Incineration 0.5 0.4 -71% 191% 

Settlements Remaining Settlements 1.0 1.6 -49% 163% 

Forest Land Remaining Forest Land 0.5 3.3 -66% 146% 

Composting 0.4 1.8 -50% 50% 

Wetlands Remaining Wetlands + + -73% 37% 

International Bunker Fuelsb 1.1 1.0 NE NE 

HFCs, PFCs, and SF6 90.5 149.5 -5% 7% 

Substitution of Ozone Depleting Substancese 28.5 105.9 -8% 9% 

HCFC-22 Production 36.4 17.0 -7% 10% 

Semiconductor Manufacture 2.9 5.2 -9% 9% 

Aluminum Production 18.5 3.8 -11% 11% 

Electrical Transmission and Distribution 26.8 12.7 -21% 22% 

Magnesium Production and Processing 5.4 3.0 -12% 13% 

Total 6,098.7 7,150.1 -1% 5% 

Net Emission (Sources and Sinks) 5,257.3 6,087.5 -3% 7% 

Notes: 

Totals may not sum due to independent rounding. 

*Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen to use 1995. 

+ Does not exceed 0.05 Tg CO2 Eq. a Sinks are only included in net emissions total. b Emissions from International Bunker Fuels and Biomass Combustion are not included in totals. c Uncertainty analysis for Mobile Combustion was not conducted for non-road sources. Therefore, the emission estimate presented here only includes on-road 

sources and do not match the emission estimate presented in the Energy chapter of this Inventory. d Uncertainty analysis for Iron and Steel and Metallurgical Coke Production was not conducted for Metallurgical Coke Production. Therefore, the emission 

estimate presented here only includes iron and steel production and do not match the emission estimate presented in the Industrial Processes chapter of this 

Inventory. 

e Uncertainty analysis for Substitution of Ozone Depleting Substances was conducted for aerosols, foams, solvents, fire extinguishing agents, and refrigerants, 

but not for other remaining categories. Therefore, the emission estimate presented here does not include “other” ODS substitues and does not match the 

emission estimate presented in the Industrial Processes chapter of this Inventory. 

Overall (Aggregate) Inventory Uncertainty Estimate 

 The overall uncertainty estimate for the U.S. greenhouse gas emissions inventory was developed using the 

IPCC Tier 2 uncertainty estimation methodology. The uncertainty models of all the emission source categories could 

not be directly integrated to develop the overall uncertainty estimates due to software constraints in integrating 

multiple, large uncertainty models. Therefore, an alternative approach was adopted to develop the overall 

uncertainty estimates. The Monte Carlo simulation output data for each emission source category uncertainty 

analysis were combined and the probability distribution was fitted to the combined simulation output data, where 

such simulated output data were available. If such detailed output data were not available for particular emissions 

sources, individual probability distributions were assigned to those source category emission estimates based on the 

most detailed data available from the quantitative uncertainty analysis performed. 

For the Composting and for parts of Agricultural Soil Management source categories, Tier 1 uncertainty 

results were used in the overall uncertainty analysis estimation. However, for all other emission sources (excluding 

international bunker fuels, CO2 from biomass combustion), Tier 2 uncertainty results were used in the overall 

uncertainty estimation. 

The results from the overall uncertainty model results indicate that the 2007 U.S. greenhouse gas emissions 

are estimated to be within the range of approximately 7,000 to 7,500 Tg CO2 Eq., reflecting a relative 95 percent 

confidence interval uncertainty range of -1 percent to 5 percent with respect to the total U.S. greenhouse gas 

emission estimate of approximately 7,150 Tg CO2 Eq. The uncertainty interval associated with total CO2 emissions, 

which constitute about 85 percent of the total U.S. greenhouse gas emissions in 2007, ranges from -2 percent to 5 

percent of total CO2 emissions estimated. The results indicate that the uncertainty associated with the inventory 

estimate of the total CH4 emissions ranges from -10 percent to 18 percent, uncertainty associated with the total 

inventory N2O emission estimate ranges from -11 percent to 41 percent, and uncertainty associated with high GWP 

gas emissions ranges from -5 percent to 7 percent. 

A summary of the overall quantitative uncertainty estimates are shown below. 
 

A-325

Table A-254. Quantitative Uncertainty Assessment of Overall National Inventory Emissions (Tg CO2 Eq. and Percent) 

2007 Emission 

Estimate 

Uncertainty Range Relative to Emission 

Estimatea Meanb

Standard 

Deviation 

Gas (Tg CO2 Eq.) (Tg CO2 Eq.) (%) (Tg CO2 Eq.) 

 

Lower 

Boundc

Upper 

Boundc

Lower 

Bound 

Upper 

Boundc 

CO2 6,103.4 5974.9 6390.0 -2% 5% 6181.5 106.8 

CH4 585.3 527.0 689.0 -10% 18% 599.3 41.3 

N2O 311.9 278.7 440.6 -11% 41% 352.4 42.8 

PFC, HFC & SF6

d 149.5 141.6 160.3 -5% 7% 148.1 4.7 

Total 7,150.1 7047.8 7525.1 -1% 5% 7281.3 121.9 

Net Emissions (Sources and Sinks) 6,087.5 5,917.7 6,503.9 -3% 7% 6,205.6 150.1 

Notes: 

a The emission estimates correspond to a 95 percent confidence interval. 

b Mean value indicates the arithmetic average of the simulated emission estimates; 

Standard deviation indicates the extent of deviation of the simulated values from the mean. c The low and high estimates for total emissions were separately calculated through simulations and, hence, the low and high emission estimates for the subsource categories do not sum to total emissions. 

d The overall uncertainty estimate did not take into account the uncertainty in the GWP values for CH4, N2O and high GWP gases used in the inventory emission 

calculations for 2007. 

Trend Uncertainty 

In addition to estimates of uncertainty associated with the current year’s emission estimates, this Annex 

also presents estimates of trend uncertainty. The IPCC Good Practice Guidance defines trend as the difference in 

emissions between the base year (i.e., 1990) and the current year (i.e., 2007) inventory estimates. However, for 

purposes of understanding the concept of trend uncertainty, the emission trend is defined in this report as the 

percentage change in the emissions (or removal) estimated for the current year, relative to the emission (or removal) 

estimated for the base year. The uncertainty associated with this emission trend is referred to as trend uncertainty. 

Under the Tier 1 approach, the trend uncertainty for a source category is estimated using the sensitivity of 

the calculated difference between base year and 2007 emissions to an incremental (i.e., 1 percent) increase in one or 

both of these values for that source category. The two sensitivities are expressed as percentages: Type A sensitivity 

highlights the effect on the difference between the base and the current year emissions caused by a 1 percent change 

in both, while Type B sensitivity highlights the effect caused by a change to only the current year’s emissions. Both 

sensitivities are simplifications introduced in order to analyze correlation between base and current year estimates. 

Once calculated, the two sensitivities are combined using the error propagation equation to estimate overall trend 

uncertainty. 

Under the Tier 2 approach, the trend uncertainty is estimated using Monte Carlo Stochastic Simulation 

technique. The trend uncertainty analysis takes into account the fact that base and the current year estimates often 

share input variables. For purposes of the current Inventory, a simple approach has been adopted, under which the 

base year source category emissions (or removals) are assumed to exhibit the same uncertainty characteristics as the 

current year emissions (or removals). Source category-specific PDFs for base year estimates were developed using 

2006 uncertainty output data. These were adjusted to account for differences in magnitude between the two years’ 

inventory estimates. Then, for each source category, a trend uncertainty estimate was developed using the Monte 

Carlo method. The overall inventory trend uncertainty estimate was developed by combining all source categoryspecific trend uncertainty estimates. These preliminary trend uncertainty estimates present the range of likely 

change from base year to 2007, and are shown in Table A- 255. 

Table A- 255. Quantitative Assessment of Trend Uncertainty (Tg CO2 Eq. and Percent) 

Gas/Source 

Base 

Year* 2007 

Emissions

Trend Trend Rangea

(Tg CO2 Eq.) (%) (%) 

 

Lower 

Bound 

Upper 

Bound 

CO2 5,076.7 6,103.4 20% 15% 26% 

Fossil Fuel Combustion 4,708.9 5,735.8 22% 16% 28% 
A-326 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Non-Energy Use of Fuels 117.0 133.9 14% -8% 42% 

Natural Gas Systems 33.7 28.7 -15% -46% 35% 

Cement Production 33.3 44.5 34% 11% 62% 

Lime Production 11.5 14.6 27% 13% 42% 

Limestone and Dolomite Use 5.1 6.2 21% -1% 47% 

Soda Ash Production and Consumption 4.1 4.1 0% -10% 10% 

Carbon Dioxide Consumption 1.4 1.9 32% -1% 75% 

Waste Incineration 10.9 20.8 90% 34% 172% 

Titanium Dioxide Production 1.2 1.9 57% 30% 89% 

Aluminum Production 6.8 4.3 -38% -41% -34% 

Iron and Steel Production & Metallurgical Coke 

Productiond 109.8 73.6 -30% -48% -6% 

Ferroalloy Production 2.2 1.6 -28% -39% -14% 

Ammonia Production and Urea Consumption 16.8 13.8 -18% -31% -4% 

Phosphoric Acid Production 1.5 1.2 -24% -42% -1% 

Petrochemical Production 2.2 2.6 19% -31% 107% 

Silicon Carbide Production and Consumption 0.4 0.2 -48% -55% -40% 

Lead Production 0.3 0.3 -7% -26% 19% 

Zinc Production 0.9 0.5 -44% -60% -22% 

Cropland Remaining Cropland 7.1 8.0 13% -53% 172% 

Wetlands Remaining Wetlands 1.0 1.0 -2% -38% 52% 

Petroleum Systems 0.4 0.3 -24% -70% 91% 

Land Use, Land-Use Change, and Forestry (Sink)a (841.4) (1,062.6) 26% 0% 59% 

International Bunker Fuelsb 114.3 108.8 -5% 

Wood Biomass and Ethanol Consumptionb 219.3 247.8 13% 

CH4 616.6 585.3 -5% -21% 15% 

Stationary Combustion 7.4 6.6 -10% -66% 135% 

Mobile Combustionc

 4.2 1.7 -61% -65% -56% 

Coal Mining 84.1 57.6 -31% -48% -10% 

Abandoned Underground Coal Mines 6.0 5.7 -5% -30% 28% 

Natural Gas Systems 129.6 104.7 -19% -50% 30% 

Petroleum Systems 33.9 28.8 -15% -65% 100% 

Petrochemical Production 0.9 1.0 18% -26% 88% 

Silicon Carbide Production and Consumption + + -67% -71% -62% 

Iron and Steel Production & Metallurgical Coke 

Productiond 1.0 0.7 -27% -35% -18% 

Ferroalloy Production + + -34% -44% -22% 

Enteric Fermentation 133.2 139.0 4% -15% 28% 

Manure Management 30.4 44.0 45% 10% 89% 

Rice Cultivation 7.1 6.2 -14% -80% 264% 

Field Burning of Agricultural Residues 0.7 0.9 29% -69% 459% 

Forest Land Remaining Forest Land 4.6 29.0 533% 18% 3321% 

Landfills 149.2 132.9 -11% -49% 58% 

Wastewater Treatment 23.5 24.4 4% -43% 92% 

Composting 0.3 1.7 417% 99% 1931% 

International Bunker Fuelsb 0.2 0.1 -13% 

N2O 315.0 311.9 -1% -16% 24% 

Stationary Combustion 12.8 14.7 15% -59% 227% 

Mobile Combustionc

 40.4 26.0 -36% -51% -16% 

Adipic Acid Production 15.3 5.9 -61% -71% -50% 

Nitric Acid Production 20.0 21.7 9% -42% 106% 

Manure Management 12.1 14.7 22% -8% 59% 

Agricultural Soil Management 200.3 207.9 4% -36% 68% 

Field Burning of Agricultural Residues 0.4 0.5 29% -67% 400% 

Wastewater Treatment 3.7 4.9 32% -71% 491% 

N2O from Product Uses 4.4 4.4 0% -3% 2% 

Waste Incineration 0.5 0.4 -16% -83% 360% 

Settlements Remaining Settlements 1.0 1.6 61% -49% 412% 

Forest Land Remaining Forest Land 0.5 3.3 536% 59% 2463% 

Composting 0.4 1.8 -2% -69% 226% 

Wetlands Remaining Wetlands + + 417% 107% 1930% 

International Bunker Fuelsb 1.1 1.0 -6% 
 

A-327

HFCs, PFCs, and SF6

* 90.5 149.5 65% 14% 36% 

Substitution of Ozone Depleting Substancese 28.5 105.9 271% 237% 327% 

HCFC-22 Production 36.4 17.0 -53% -58% -48% 

Semiconductor Manufacture 2.9 5.2 63% 55% 101% 

Aluminum Production 18.5 3.8 -79% -82% -76% 

Electrical Transmission and Distribution 26.8 12.7 -53% -65% -36% 

Magnesium Production and Processing 5.4 3.0 -45% -54% -35% 

Total 6,098.7 7,150.1 17% 11% 22% 

Net Emission (Sources and Sinks) 5,257.3 6,087.5 16% 8% 22% 

Notes: 

Totals may not sum due to independent rounding. 

*Base Year is 1990 for all sources except Substitution of Ozone Depleting Substances, for which the United States has chosen to use 1995. As a result, the 

trend uncertainty for HFCs, PFCs, and SF6 is reported relative to the base year total of 118 Tg CO2 Eq., and not the 1990 total of 90.5 Tg CO2 Eq. For this 

reason, the trend uncertainty falls outside of the range of 14% and 36%. 

+ Does not exceed 0.05 Tg CO2 Eq. a Trend Range represents the 95% confidence interval for the change in emissions from Base Year to 2007. b Sinks are only included in net emissions total. c Emissions from International Bunker Fuels and Biomass Combustion are not included in totals. d Uncertainty analysis for Mobile Combustion was not conducted for non-road sources. Therefore, the emission estimate prestend here only includes on-road 

sources and do not match the emission estimate presented in the Energy chapter of this Inventory. e Uncertainty analysis for Iron and Steel and Metallurgical Coke Production was not conducted for Metallurgical Coke Production. Therefore, the emission 

estimate presented here only includes iron and steel production and do not match the emission estimate presented in the Industrial Processes chapter of this 

Inventory. 

f

 Uncertainty analysis for Substitution of Ozone Depleting Substances was conducted for aerosols, foams, solvents, fire extinguishing agents, and refrigerants, 

but not for other remaining categories. Therefore, the emission estimate presented here does not include “other” ODS substitues and does not match the 

emission estimate presented in the Industrial Processes chapter of this Inventory. 

7.3. Planned Improvements 

Identifying the sources of uncertainties in the emission and sink estimates of the Inventory and quantifying 

the magnitude of the associated uncertainty is the crucial first step towards improving those estimates. Quantitative 

assessment of the parameter uncertainty may also provide information about the relative importance of input 

parameters (such as activity data and emission factors), based on their relative contribution to the uncertainty within 

the source category estimates. Such information can be used to prioritize resources with a goal of reducing 

uncertainties over time within or among inventory source categories and their input parameters. In the current 

Inventory, potential sources of model uncertainty have been identified for some emission sources, and preliminary 

uncertainty estimates based on their parameters’ uncertainty have been developed for all the emission source 

categories, with the exception of international bunker fuels and wood biomass and ethanol combustion source 

categories, which are not included in the inventory totals. 

Specific areas that require further research include: 

 Incorporating excluded emission sources. Quantitative estimates for some of the sources and sinks of 

greenhouse gas emissions, such as from some land-use activities, industrial processes, and parts of mobile 

sources, could not be developed at this time either because data are incomplete or because methodologies 

do not exist for estimating emissions from these source categories. See Annex 5 of this report for a 

discussion of the sources of greenhouse gas emissions and sinks excluded from this report. In the future, 

efforts will focus on estimating emissions from excluded emission sources and developing uncertainty 

estimates for all source categories for which emissions are estimated. 

 Improving the accuracy of emission factors. Further research is needed in some cases to improve the 

accuracy of emission factors used to calculate emissions from a variety of sources. For example, the 

accuracy of current emission factors applied to CH4 and N2O emissions from stationary and mobile 

combustion are highly uncertain. 

 Collecting detailed activity data. Although methodologies exist for estimating emissions for some sources, 

problems arise in obtaining activity data at a level of detail in which aggregate emission factors can be 

applied. For example, the ability to estimate emissions of SF6 from electrical transmission and distribution 

is limited due to a lack of activity data regarding national SF6 consumption or average equipment leak 

rates. 
A-328 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

In improving the quality of uncertainty estimates the following include areas that deserve further attention: 

 Refine Source Category and Overall Uncertainty Estimates. For many individual source categories, further 

research is needed to more accurately characterize PDFs that surround emissions modeling input variables. 

This might involve using measured or published statistics or adopting rigorous elicitation protocol to elicit 

expert judgments, if published or measured data are not available.

 Include GWP uncertainty in the estimation of Overall level and trend uncertainty. The current year’s 

Inventory does not include the uncertainty associated with the GWP values in the estimation of the overall 

uncertainty for the Inventory. Including this source would contribute to a better characterization of overall 

uncertainty and help assess the level of attention that this source of uncertainty warrants in the future. 

 Improve characterization of trend uncertainty associated with base year Inventory estimates. The 

characterization of base year uncertainty estimates could be improved, by developing explicit uncertainty 

models for the base year. This would then improve the analysis of trend uncertainty. However, not all of 

the simplifying assumptions described in the “Trend Uncertainty” section above may be eliminated through 

this process due to a lack of availability of more appropriate data. 

7.4. Additional Information on Uncertainty Analyses by Source 

The quantitative uncertainty estimates associated with each emission and sink source category are reported 

in each chapter of this Inventory following the discussions of inventory estimates and their estimation methodology. 

This section provides additional descriptions of the uncertainty analyses performed for some of the sources, 

including the models and methods used to calculate the emission estimates and the potential sources of uncertainty 

surrounding them. These sources are organized below in the same order as the sources in each chapter of the main 

section of this Inventory. To avoid repetition, the following uncertainty analysis discussions of individual source 

categories do not include descriptions of these source categories. Hence, to better understand the details provided 

below, refer to the respective chapters and sections in the main section of this Inventory, as needed. All uncertainty 

estimates are reported relative to the 2007 Inventory estimates for the 95 percent confidence interval, unless 

otherwise specified. 

Energy 

The uncertainty analysis descriptions in this section correspond to some source categories included in the 

Energy Chapter of the Inventory. 

Mobile Combustion (excluding CO2) 

Mobile combustion emissions of CH4 and N2O per vehicle mile traveled vary significantly due to fuel type 

and composition, technology type, operating speeds and conditions, type of emission control equipment, equipment 

age, and operating and maintenance practices. 

The primary activity data, VMT, are collected and analyzed each year by government agencies. To 

determine the uncertainty associated with the activity data used in the calculations of CH4 and N2O emissions, the 

agencies and the experts that supply the data were contacted. Because few of these sources were able to provide 

quantitative estimates of uncertainty, expert judgment was used to assess the quantitative uncertainty associated with 

the activity data. 

The emission factors for on-road vehicles used in the Inventory were obtained from ICF (2006b) and ICF 

(2004). These factors were based on laboratory testing of vehicles. While the controlled testing environment 

simulates real driving conditions, emission results from such testing can only approximate real world conditions and 

emissions. For some vehicle and control technology types, because the testing did not yield statistically significant 

results within the 95 percent confidence interval, expert judgments were adopted in developing the emission factors. 

In those cases, the emission factors were developed based on comparisons of fuel consumption between similar 

vehicle and control technology categories. 

The estimates of VMT for on-road vehicles by vehicle type in the United States were provided by FHWA 

(1996 through 2008), and were generated though the cooperation of FHWA and state and local governments. These 

estimates are subject to several possible sources of error, such as unregistered vehicles, and measurement and 

estimation errors. These VMT were apportioned by fuel type, based on data from DOE (1993 through 2008), and 
 

A-329

then allocated to individual model years using temporal profiles of both the vehicle fleet by age and vehicle usage by 

model year in the United States provided by EPA (2007c) and EPA (2000). While the uncertainty associated with 

the total national VMT is believed to be low, the uncertainty within individual source categories was considered to 

be higher due to the uncertainty associated with apportioning total VMT into individual vehicle categories, by fuel 

type, technology type, and by equipment age. The uncertainty in the individual estimates was assumed to be 

inversely related to the magnitude of estimated VMT (i.e., it was assumed that smaller sources had greater 

percentage uncertainty and vice-versa). Another source of uncertainty in the estimates occurs due to differences in 

FHWA and EPA data sources. For example, FHWA data are used for defining vehicle types and for developing the 

estimates of VMT by vehicle type, the estimates of VMT by fuel types are calculated using EPA’s definition of 

vehicle categories (which differ from those of the FHWA). 

A total of 105 highway data input variables were simulated through Monte Carlo Simulation technique 

using @RISK software. Variables included VMT and emission factors for individual conventional and alternative 

fuel vehicle categories and technologies. In developing the uncertainty estimation model, a normal distribution was 

assumed for all but two activity-related input variables (e.g., VMT); in the case of the two input variables, buses and 

percent of diesel combination trucks, triangular distributions were assumed. The dependencies and other 

correlations among the activity data were incorporated into the model to ensure consistency in the model 

specification and simulation. Emission factors were assigned uniform distributions, with upper and lower bounds 

assigned to input variables based on 95 percent confidence intervals of laboratory test data. In cases where data did 

not yield statistically significant results within the 95 percent confidence interval, estimates of upper and lower 

bounds were determined using expert judgments. For biodiesel vehicles, because no test data were available, 

consistent with the assumptions underlying the ANL GREET model, their N2O and CH4 emissions were assumed to 

be same as those for diesel vehicles of similar types. For other alternative fuel vehicles (AFVs), uncertainty 

estimates were developed based on conventional fuel vehicle emission factors and applicable multipliers, as 

described in the ICF’s AFV emission factors memorandum to EPA (ICF 2006a). The results of the quantitative 

uncertainty analysis are reported as quantitative uncertainty estimates following the mobile source category 

emissions description in the Energy Chapter of this Inventory. 

Emissions from non-road vehicles account for 27 percent of CH4 emissions from mobile sources and 14 

percent of N2O emissions from mobile sources in 2007. A quantitative analysis of uncertainty in the inventory 

estimates of emissions from non-highway vehicles has not been performed. However, sources of uncertainty for 

non-highway vehicles are being investigated by examining the underlying uncertainty of emission factors and fuel 

consumption data, and in the future, EPA will consider conducting a quantitative analysis of uncertainty for these 

sources. 

Estimates of fuel consumption for non-road vehicles (i.e., equipment used for agriculture, construction, 

lawn and garden, railroad, airport ground support, etc., as well as recreational vehicles) were generated by the EPA’s 

NONROAD model (EPA 2006a). This model estimates fuel consumption based on estimated equipment/vehicle use 

(in hours) and average fuel consumed per hour of use. Since the fuel estimates are not based upon documented fuel 

sales or consumption, a fair degree of uncertainty accompanies these estimates.

Estimates of distillate fuel sales for ships and boats were obtained from EIA’s Fuel Oil and Kerosene Sales

(EIA 1991 through 2008). These estimates have a moderate level of uncertainty since EIA’s estimates are based on 

survey data and reflect sales to economic sectors, which may include use by both mobile and non-mobile sources 

within a sector. Domestic consumption of residual fuel by ships and boats is obtained from EIA (2008b). These 

estimates fluctuate widely from year to year, and are believed to be highly uncertain. In addition, estimates of 

distillate and residual fuel sales for ships and boats are adjusted for bunker fuel consumption, which introduces an 

additional (and much higher) level of uncertainty. 

Jet fuel and aviation gasoline consumption data are obtained from DOT (1991 through 2008), EIA (2007a), 

FAA (2008), and FAA (2006). Additionally, all jet fuel consumption in the transportation sector is assumed to be 

consumed by aircraft. Some jet fuel may also be used for other purposes such as blending with diesel fuel or heating 

oil. 

In calculating CH4 emissions from aircraft, an average emission factor is applied to total jet fuel 

consumption. This average emission factor takes into account the fact that CH4 emissions occur only during the 

landing and take-off (LTO) cycles, with no CH4 being emitted during the cruise cycle. However, a better approach 

would be to apply emission factors based on the number of LTO cycles. 
A-330 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Incineration of Waste 

The upper and lower bounds of uncertainty in the CO2 emissions estimate for Incineration of Waste are 20 

percent and -27 percent respectively, and in the N2O emission estimates are 191 percent and -71 percent 

respectively, relative to the respective 2007-Inventory estimates, at the 95% confidence interval. The uncertainties in 

the waste combustion emission estimates arise from both the assumptions applied to the data and from the quality of 

the data. Key factors include MSW combustion rate, fraction oxidized, missing data on MSW composition, average 

carbon content of MSW components, assumptions on the synthetic/biogenic carbon ratio, and combustion conditions 

affecting N2O emissions. For more information on emission estimates from MSW combustion, please refer to the 

Incineration of Waste section of the Energy chapter. The highest levels of uncertainty surround the variables, whose 

estimates were developed based on assumptions (e.g., percent of clothing and footwear composed of synthetic 

rubber); the lowest levels of uncertainty surround variables that were determined by quantitative measurements (e.g., 

combustion efficiency, carbon content of carbon black). Important sources of uncertainty are as follows: 

 MSW Combustion Rate. A source of uncertainty affecting both fossil CO2 and N2O emissions is the 

estimate of the MSW combustion rate. The EPA (2000a, 2003, 2005a, 2006 through 2008; Schneider 

2007) estimates of materials generated, discarded, and combusted carry considerable uncertainty associated 

with the material flows methodology used to generate them. Similarly, the BioCycle (Glenn 1999, 

Goldstein and Matdes 2000, Goldstein and Matdes 2001, Kaufman et al. 2004a, Kaufman et al. 2004b, 

Simmons et al. 2006, Arsova et al. 2008) estimate of total waste combustionused for the N2O emissions 

estimateis based on a survey of state officials, who use differing definitions of solid waste and who draw 

from a variety of sources of varying reliability and accuracy. The survey methodology changed 

significantly in 2003 and thus the results reported for 2002 are not directly comparable to the earlier results 

(Kaufman et al. 2004a, 2004b), introducing further uncertainty. 

 Fraction Oxidized. Another source of uncertainty for the CO2 emissions estimate is fraction oxidized. 

Municipal waste combustors vary considerably in their efficiency as a function of waste type, moisture 

content, combustion conditions, and other factors. A value of 98 percent was assumed for this analysis. 

 Missing Data on Municipal Solid Waste Composition. Disposal rates have been interpolated when there is 

an incomplete interval within a time series. Where data are not available for years at the end of a time 

series, they are set equal to the most recent years for which estimates are available. 

 Average Carbon Contents. Average carbon contents were applied to the mass of “Other” plastics 

combusted, synthetic rubber in tires and municipal solid waste, and synthetic fibers. These average values 

were estimated from the average carbon content of the known products recently produced. The actual 

carbon content of the combusted waste may differ from this estimate depending on differences in the 

chemical formulation between the known and unspecified materials, and differences between the 

composition of the material disposed and that produced. For rubber, this uncertainty is probably small 

since the major elastomers’ carbon contents range from 77 to 91 percent; for plastics, it may be more 

significant, as their carbon contents range from 29 to 92 percent. However, overall, this is a small source of 

uncertainty. 

 Synthetic/Biogenic Assumptions. A portion of the fiber and rubber in municipal solid waste is biogenic in 

origin. Assumptions have been made concerning the allocation between synthetic and biogenic materials 

based primarily on expert judgment. 

 Combustion Conditions Affecting N2O Emissions. Because insufficient data exist to provide detailed 

estimates of N2O emissions for individual combustion facilities, the estimates presented exhibit high 

uncertainty. The emission factor for N2O from municipal solid waste combustion facilities used in the 

analysis is an average of default values used to estimate N2O emissions from facilities worldwide (Johnke 

1999, UK: Environment Agency 1999, Yasuda 1993). These factors span an order of magnitude, reflecting 

considerable variability in the processes from site to site. Due to a lack of information on the control of 

N2O emissions from MSW combustion facilities in the United States, the estimate of zero percent for N2O 

emissions control removal efficiency also exhibits uncertainty. 
 

A-331

Industrial Processes 

The uncertainty analysis descriptions in this section correspond to some source categories included in the 

Industrial Processes Chapter of the Inventory. 

Ammonia Manufacture and Urea Consumption 

The uncertainty upper and lower bounds of the emission estimate for Ammonia Manufacture and Urea 

Consumption were 11 percent and -12 percent, respectively, at the 95 percent confidence interval. The European 

Fertilizer Manufacturer’s Association (EFMA) reported an emission factor range of 1.15 to 1.30 ton CO2/ton NH3, 

with 1.2 ton CO2/ton NH3 reported as a typical value. The actual emission factor depends upon the amount of air 

used in the ammonia production process, with 1.15 ton CO2/ton NH3 being the approximate stoichiometric minimum 

that is achievable for the conventional reforming process. By using natural gas consumption data for each ammonia 

plant, more accurate estimates of CO2 emissions from ammonia production could be calculated. However, these 

consumption data are often considered confidential. Also, natural gas is consumed at ammonia plants both as a 

feedstock to the reforming process and for generating process heat and steam. Natural gas consumption data, if 

available, would need to be divided into feedstock use (non-energy) and process heat and steam (fuel) use, as CO2

emissions from fuel use and non-energy use are calculated separately.84 

Natural gas feedstock consumption data for the U.S. ammonia industry as a whole are available from the 

Energy Information Administration (EIA) Manufacturers Energy Consumption Survey (MECS) for the years 1985, 

1988, 1991, 1994 and 1998 (EIA 1994, 1998). These feedstock consumption data collectively correspond to an 

effective average emission factor of 1.0 ton CO2/ton NH3, which appears to be below the stoichiometric minimum 

that is achievable for the conventional steam reforming process. The EIA data for natural gas consumption for the 

years 1994 and 1998 correspond more closely to the CO2 emissions calculated using the EFMA emission factor than 

do data for previous years. The 1994 and 1998 data alone yield an effective emission factor of 1.1 ton CO2/ton NH3, 

corresponding to CO2 emissions estimates that are approximately 1.5 Tg CO2 Eq. below the estimates calculated 

using the EFMA emission factor of 1.2 ton CO2/ton NH3. Natural gas feedstock consumption data are not available 

from EIA for other years, and data for 1991 and previous years may underestimate feedstock natural gas 

consumption, and therefore the EFMA emission factor was used to estimate CO2 emissions from ammonia 

production, rather than EIA data. 

Research indicates that there is only one U.S. plant that manufactures ammonia from petroleum coke. CO2

emissions from this plant are explicitly accounted for in the Inventory estimates. No data for ammonia plants using 

naphtha or other feedstocks other than natural gas have been identified. Therefore, all other CO2 emissions from 

ammonia plants are calculated using the emission factor for natural gas feedstock. However, actual emissions may 

differ because processes other than catalytic steam reformation and feedstocks other than natural gas may have been 

used for ammonia production. Urea is also used for other purposes than as a nitrogenous fertilizer. Currently, urea 

used as a nitrogenous fertilizer is accounted for in the LULUCF chapter. Research has identified one ammonia 

production plant that is recovering byproduct CO2 for use in EOR. Such CO2 is currently assumed to remain 

sequestered (see the section of this chapter on CO2 Consumption); however, time series data for the amount of CO2

recovered from this plant are not available and therefore all of the CO2 produced by this plant is assumed to be 

emitted to the atmosphere and allocated to Ammonia Manufacture. 

 

84 It appears that the IPCC emission factor for ammonia production of 1.5 ton CO2 per ton ammonia may include both 

CO2 emissions from the natural gas feedstock to the process and some CO2 emissions from the natural gas used to generate 

process heat and steam for the process. Table 2-5, Ammonia Production Emission Factors, in Volume 3 of the Revised 1996 

IPCC Guidelines for National Greenhouse Gas Inventories Reference Manual (IPCC 1997) includes two emission factors, one 

reported for Norway and one reported for Canada. The footnotes to the table indicate that the factor for Norway does not include 

natural gas used as fuel but that it is unclear whether the factor for Canada includes natural gas used as fuel. However, the factors 

for Norway and Canada are nearly identical (1.5 and 1.6 tons CO2 per ton ammonia, respectively) and it is likely that if one value 

does not include fuel use, the other value also does not. For the conventional steam reforming process, however, the EFMA 

reports an emission factor range for feedstock CO2 of 1.15 to 1.30 ton per ton (with a typical value of 1.2 ton per ton) and an 

emission factor for fuel CO2 of 0.5 tons per ton. This corresponds to a total CO2 emission factor for the ammonia production 

process, including both feedstock CO2 and process heat CO2, of 1.7 ton per ton, which is closer to the emission factors reported in 

the IPCC 1996 Reference Guidelines than to the feedstock-only CO2 emission factor of 1.2 ton CO2 per ton ammonia reported by 

the EFMA. Because it appears that the emission factors cited in the IPCC Guidelines may actually include natural gas used as 

fuel, we use the 1.2 tons/ton emission factor developed by the EFMA. 
A-332 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Phosphoric Acid Production 

The uncertainty upper and lower bounds of the emissions estimate for Phosphoric Acid Production were 18 

percent and -18 percent, respectively, at the 95 percent confidence interval. Factors such as the composition of 

phosphate rock affect CO2 emissions from phosphoric acid production. For more information on how emissions 

estimates were calculated, please refer to the Phosphoric Acid Production section of the Industrial Processes chapter. 

Only one set of data from the Florida Institute of Phosphate Research (FIPR) was available for the composition of 

phosphate rock mined domestically and imported, and data for uncalcined phosphate rock mined in North Carolina 

and Idaho were unavailable. Inorganic carbon content (as CO2) of phosphate rock could vary ±1 percent, resulting 

in a variation in CO2 emissions of ±20 percent. 

Organic C is not included in the calculation of CO2 emissions from phosphoric acid production. However, 

if, for example, 50 percent of the organic carbon content of the phosphate rock were to be emitted as CO2 in the 

phosphoric acid production process, the CO2 emission estimate would increase by on the order of 50 percent. If it is 

assumed that 100 percent of the reported domestic production of phosphate rock for Idaho and Utah was first 

calcined, and it is assumed that 50 percent of the organic carbon content of the total production for Idaho and Utah 

was converted to CO2 in the calcination process, the CO2 emission estimate would increase on the order of 10 

percent. If it were assumed that there are zero emissions from other uses of phosphate rock, CO2 emissions would 

fall 10 percent. 

Iron and Steel Production 

The uncertainty upper and lower bounds of the CO2 emission estimate for Iron and Steel Production were 

20 percent and -22 percent, respectively, at the 95 percent confidence interval. Factors such as the composition of C 

anodes and the C content of pig iron and crude steel affect CO2 emissions from Iron and Steel Production. For more 

information on emission estimates, please refer to the Iron and Steel Production section of the Industrial Processes 

chapter. Simplifying assumptions were made concerning the composition of C anodes, (80 percent petroleum coke 

and 20 percent coal tar). For example, within the aluminum industry, the coal tar pitch content of anodes can vary 

from 15 percent in prebaked anodes to 24 to 28 percent in Soderberg anode pastes (DOE 1997). An average value 

was assumed and applied to all carbon anodes utilized during aluminum and steel production. It was also assumed 

that the C contents of all pig iron and crude steel have carbon contents of 4 percent and 0.5 percent, respectively. 

The carbon content of pig iron can vary between 3 and 5 percent, while crude steel can have a carbon content of up 

to 2 percent, although it is typically less than 1 percent (IPCC 2000). 

Aluminum Production 

The uncertainty upper and lower bounds of the PFCs emissions estimate for Aluminum Production were 11 

percent and -11 percent, respectively, at the 95 percent confidence interval. The uncertainties associated with three 

variables were estimated for each smelter: (1) the quantity of aluminum produced, (2) the anode effect minutes per 

cell day (which may be reported directly or calculated as the product of anode effect frequency and anode effect 

duration), and (3) the smelter- or technology-specific slope coefficient (or weight fraction). For more information on 

the effect of these variables on PFC emissions, please refer the Aluminum Production section of the Industrial 

Processes chapter. All three types of data are assumed to be characterized by a normal distribution. The uncertainty 

in aluminum production estimates was assumed to be 1 percent for reported data (IPCC 2006). For reported anode 

effect frequency and duration data, the uncertainties were assumed to be 2 percent and 5 percent, respectively 

(Kantamaneni et al. 2001). For calculated smelter-specific CF4 and C2F6 slope coefficients the uncertainties were 

assumed to be 15 percent (IPCC 2006). For smelters applying technology-specific slope coefficients or weight 

fractions, the uncertainty in the coefficients was based on the standard deviation of the individual measurements 

used to determine the average value given by the IPCC guidance for technology-specific (Tier 2) slope coefficients. 

Consequently, the uncertainties values assigned to the technology-specific slope coefficients for CF4 for CWPB and 

SWPB were 0.036 and 0.038, respectively, and for C2F6 for CWPB and SWPB were 0.060 and 0.056, respectively. 

(The uncertainty in CF4 emissions for CWPB is reported as 6 percent in IPCC (2001), but was increased to 50 

percent in this analysis to better account for measurement uncertainty. The uncertainty in PFC emissions for CWPB 

facilities (the best behaved of the technology types) is about 50 percent for any given facility using the Tier 2 

calculation.) In general, where precise quantitative information was not available on the uncertainty of a parameter, 

an upper-bound value was used. 
 

A-333

Magnesium Production 

The uncertainty information below pertains to the emission estimates presented in the Magnesium 

Production section of the Industrial Processes chapter. Please refer to that section for more information about this 

source. The uncertainty upper and lower bounds of the emissions estimate for Magnesium Production were 13 

percent and -12 percent, respectively, at the 95 percent confidence interval. An uncertainty of 5 percent was 

assigned to the data reported by each participant in the Partnership. If Partners did not report emissions data during 

the current reporting year, SF6 emissions data were estimated using available emission factor and production 

information reported in prior years; the extrapolation was based on the average trend for Partners reporting in the 

current reporting year and the year prior. The uncertainty associated with the SF6 usage estimate generated from the 

extrapolated emission factor and production information was determined using a sum of squares method. A 5% 

uncertainty for the year the Partner last reported was assumed and a 30% uncertainty for each subsequent year was 

assumed. For those industry processes that are not represented in Partnership, such as permanent mold and wrought 

casting, SF6 emissions were estimated using production and consumption statistics reported by USGS and estimated 

process-specific emission factors (see Table 4-63). The uncertainties associated with the emission factors and 

USGS-reported statistics were assumed to be 75 percent and 25 percent, respectively. Emissions associated with 

sand casting activities not entirely captured by the Partnership utilized a Partner-reported emission factor with an 

uncertainty of 75 percent. In general, where precise quantitative information was not available on the uncertainty of 

a parameter, a conservative (upper-bound) value was used. 

Electric Transmission and Distribution 

The uncertainty upper and lower bounds of the emissions estimate for Electric Transmission and 

Distribution at the 95 percent confidence interval were 22 percent and -21 percent, respectively. Uncertainty 

associated with emissions of SF6 from electric transmission and distribution stem from the following three 

quantities: (1) emissions from partners, (2) emissions from non-partners, and (3) emissions from manufacturers of 

electrical equipment. The uncertainty of partner emissions is related to whether the partner emissions are reported 

or estimated. For reported partner emissions, individual partner submitted SF6 data was assumed to have an 

uncertainty of 10 percent. Based on a Monte Carlo analysis, the cumulative uncertainty of the total partner reported 

data was estimated to be 3.6 percent. For partner estimated emissions, the uncertainty associated with emissions 

extrapolated or interpolated from reported emissions data was assumed to be 20 percent. There are two sources of 

uncertainty which contribute to the non-partner emissions uncertainty, The first is the uncertainty in the coefficients 

of the regression equations used to estimate emissions from non-partners, and the second is the uncertainty in the 

total transmission miles for non-partners—the independent variable in the regression equation. The uncertainty in 

the coefficients (as defined by the regression standard error estimate) is estimated to be ±21 percent for small 

utilities and ±64 percent for large utilities, while the uncertainty in the transmission miles is assumed to be 10 

percent. For equipment manufacturers, the quantity of SF6 charged into equipment by equipment manufacturers is 

estimated using partner reported new nameplate capacity data and the estimate for the total industry nameplate 

capacity. The quantity of SF6 charged into equipment in 2007 is estimated to have an uncertainty of 70.2 percent, 

and is derived from the uncertainty in partner reported new nameplate capacity (estimated as 4.2 percent using error 

propagation) and the uncertainty in the estimate for U.S. total nameplate capacity (assumed to be 70 percent). 

A Monte Carlo analysis was applied to estimate the overall uncertainty of the 2007 emission estimate for 

SF6 from electrical transmission and distribution. For each defined parameter (i.e., regression coefficient, 

transmission mileage, partner-reported and partner-estimated SF6 emissions data for electric power systems; and 

SF6 emission rate and statistics for manufacturers), random variables were selected from probability density 

functions, all assumed to have normal distributions about the mean. 

Agriculture 

The uncertainty analysis descriptions in this section correspond to some source categories included in the 

Agriculture Chapter of the Inventory. 

Agriculture Manure Management 

The uncertainty information below pertains to the emission estimates presented in the Agriculture Manure 

Management section of the Agriculture chapter. Please refer to that section for information about various manure 

management systems and their affect on emissions from this source. The uncertainty upper and lower bounds of the 

CH4 emissions estimate for Manure Management were 20 percent and -18 percent, respectively, at the 95 percent 
A-334 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

confidence interval. The primary factors that contribute to the uncertainty in emission estimates are a lack of 

information on the usage of various manure management systems in each regional location and the exact CH4

generating characteristics of each type of manure management system. Because of significant shifts in the swine 

and dairy sectors toward larger farms, it is believed that increasing amounts of manure are being managed in liquid 

manure management systems. The existing estimates reflect these shifts in the weighted MCFs based on the 1992, 

1997, and 2002 farm-size data. However, the assumption of a direct relationship between farm size and liquid 

system usage may not apply in all cases and may vary based on geographic location. In addition, the CH4

generating characteristics of each manure management system type are based on relatively few laboratory and field 

measurements, and may not match the diversity of conditions under which manure is managed nationally. 

Previously, IPCC published a default range of MCFs for anaerobic lagoon systems of 0 to 100 percent, 

reflecting the wide range in performance that may be achieved with these systems (IPCC 2000). There exist 

relatively few data points on which to determine country-specific MCFs for these systems. In the United States, 

many livestock waste treatment systems classified as anaerobic lagoons are actually holding ponds that are 

substantially organically overloaded and therefore not producing CH4 at the same rate as a properly designed lagoon. 

In addition, these systems may not be well operated, contributing to higher loading rates when sludge is allowed to 

enter the treatment portion of the lagoon or the lagoon volume is pumped too low to allow treatment to occur. 

Rather than setting the MCF for all anaerobic lagoon systems in the United States based on data available from 

optimized lagoon systems, a MCF methodology utilizing the van’t Hoff-Arrhenius equation was developed to more 

closely match observed system performance and account for the affect of temperature on system performance. 

The MCF methodology used in the inventory includes a factor to account for management and design 

practices that result in the loss of VS from the management system. This factor is currently estimated based on data 

from anaerobic lagoons in temperate climates, and from only three systems. However, this methodology is intended 

to account for systems across a range of management practices. 

Uncertainty also exists with the maximum CH4 producing potential of VS excreted by different animal 

groups (i.e., Bo). The Bo values used in the CH4 calculations are published values for U.S. animal waste. However, 

there are several studies that provide a range of Bo values for certain animals, including dairy and swine. The Bo

values chosen for dairy assign separate values for dairy cows and dairy heifers to better represent the feeding 

regimens of these animal groups. For example, dairy heifers do not receive an abundance of high energy feed and 

consequently, dairy heifer manure will not produce as much CH4 as manure from a milking cow. However, the data 

available for Bo values are sparse, and do not necessarily reflect the rapid changes that have occurred in this industry 

with respect to feed regimens. 

Rice Cultivation 

The uncertainty upper and lower bounds of the emissions estimate for Rice Cultivation were 164 percent 

and -66 percent, respectively, at the 95 percent confidence interval. Factors such as primary rice-cropped area, 

rationing, and flooding affect greenhouse gas emissions from this source. For more information on emissions 

estimates for Rice Cultivation, please refer to that section in the Agriculture Chapter. Uncertainty associated with 

primary rice-cropped area for each state was assumed to range from 1 percent to 5 percent of the mean area based on 

expert judgment. A normal distribution of uncertainty, truncated to avoid negative values, was assumed about the 

mean for areas. 

Ratooned area data are an additional source of uncertainty. Although ratooning accounts for only 5 to 10 

percent of the total rice-cropped area, it is responsible for about 15 to 30 percent of total emissions. For states that 

have never reported any ratooning, it is assumed with complete certainty that no ratooning occurred in 2007. For 

states that regularly report ratooning, uncertainty is estimated to be between 3 percent and 5 percent (based on expert 

judgment) and is assumed to have a normal distribution, truncated to avoid negative values. For Arkansas, which 

reported ratooning in 1998 and 1999 only, a triangular distribution was assumed, with a lower boundary of 0 percent 

ratooning and an upper boundary of 0.034 percent ratooning based on the maximum ratooned area reported in 1998 

and 1999. 

The practice of flooding outside of the normal rice season is also an uncertainty. According to agricultural 

extension agents, all of the rice-growing states practice this on some part of their rice acreage. Estimates of these 

areas range from 5 to 68 percent of the rice acreage. Fields are flooded for a variety of reasons: to provide habitat 

for waterfowl, to provide ponds for crawfish production, and to aid in rice straw decomposition. To date, however, 
 

A-335

CH4 flux measurements have not been undertaken over a sufficient geographic range or under a broad enough range 

of representative conditions to account for this source in the emission estimates or its associated uncertainty. 

Agricultural Soil Management 

The uncertainty information below pertains to the emission estimates presented in the Agricultural Soil 

Management section of the Agriculture chapter. Please refer to that section for information about this source. For 

direct emissions calculated using DAYCENT, uncertainty in the results was attributed to model inputs (i.e., activity 

data, weather and soil conditions) and the structure of the model (i.e., underlying model equations and 

parameterization). A Monte Carlo analysis was implemented to address these uncertainties and propagate errors 

through the modeling process (Del Grosso et al., in prep). The analysis was conducted using probability distribution 

functions (PDFs) for weather, soil characteristics, and N inputs to simulate direct N2O emissions for each crop- or 

grassland type in a county. A joint PDF was used to address the structural uncertainty for direct N2O emissions 

from crops, which was derived using an empirically-based method (Ogle et al. 2007). This same Monte Carlo 

analysis was used to derive uncertainty for the volatilization, runoff, and leaching of N that had been estimated with 

DAYCENT. County-scale PDFs for weather were based on the variation in temperature and precipitation as 

represented in DAYMET weather data grid cells (1x1 km) occurring in croplands and grasslands in a county. The 

National Land Cover Dataset (Vogelman et al. 2001) provided the data on distribution of croplands and grasslands. 

Similarly, county-scale PDFs for soil characteristics were based on STATSGO Soil Map Units (Soil Survey Staff 

2005), that occurred in croplands and grasslands. PDFs for fertilizer were derived from survey data for major U.S. 

crops, both irrigated and rainfed (ERS 1997; NASS 2004, 1999, 1992; Grant and Krenz 1985). State-level PDFs 

were developed for each crop if a minimum of 15 data points existed for each of the two categories (irrigated and 

rainfed). Where data were insufficient at the state-level, PDFs were developed for multi-state Farm Production 

Regions. Uncertainty in manure application for specific crops was incorporated into the analysis based on total 

manure available for application in each county, a weighted average application rate, and the crop-specific land area 

amended with manure for 1997 (compiled from USDA data on animal numbers, manure production, storage 

practices, application rates and associated land areas receiving manure amendments; see Edmonds et al. 2003). 

Together with the total area for each crop within a county, the result yielded a probability that a given crop in a 

specific county would either receive manure or not in the Monte Carlo analysis. A ratio of manure N available for 

application in each year of the inventory relative to 1997 was used to adjust the amount of area amended with 

manure, under the assumption that changing the amount of manure N available for application would lead to a 

proportional change in amended area (see the section on Major Crop Types on Mineral Soils for data sources on 

manure N availability). If soils were amended with manure, a reduction factor was applied to the N fertilization rate 

accounting for the interaction between fertilization and manure N amendments (i.e., producers reduce mineral 

fertilization rates if applying manure). Reduction factors were randomly selected from probability distribution 

factors based on relationships between manure N application and fertilizer rates from USDA cropping survey data 

(ERS 1997). 

An empirically-based uncertainty estimator was developed using a method described by Ogle et al. (2007) 

to assess uncertainty in model structure associated with the algorithms and parameterization. The estimator was 

based on a linear mixed-effect modeling analysis comparing N2O emission estimates from eight agricultural 

experiments with 50 treatments. Although the dataset was relatively small, modeled emissions were significantly 

related to measurements with a p-value of less than 0.01. Random effects were included to capture the dependence 

in time series and data collected from the same experimental site, which were needed to estimate appropriate 

standard deviations for parameter coefficients. The structural uncertainty estimator accounted for bias and 

prediction error in the DAYCENT model results, as well as random error associated with fine-scale emission 

predictions in counties over a time series from 1990 to 2006. Note that the current application only addresses 

structural uncertainty in cropland estimates; further development will be needed to address this uncertainty in model 

estimates for grasslands, which is a planned improvement as more soil N2O measurement data become available for 

grassland sites. In general, DAYCENT tended to underestimate emissions if the rates were above 6 g N2O/ha/day 

(Del Grosso et al., in prep). Model structural uncertainty was not assessed for N volatilization and leaching/runoff, 

because sufficient data from field experiments were not available. 

A simple error propagation method (IPCC 2006) was used to estimate uncertainties for direct emissions 

estimated with Tier 1 methods, including management of non-major crops (mineral fertilization, crop residues, 

organic fertilizers) and N inputs that were not addressed in the DAYCENT simulations (i.e., sewage sludge N, PRP 

manure N excreted on federal grasslands). Similarly, indirect emissions from N inputs that were not simulated with 
A-336 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

DAYCENT were calculated according to the IPCC methodology using the simple error propagation method (IPCC 

2006). PDFs for the proportion of N subject to volatilization, leaching and runoff, as well as indirect N2O emission 

factors were based on IPCC (2006), and PDFs for the activity data were based on the uncertainties associated 

underlying survey information and calculations.85 For lands simulated by DAYCENT, uncertainty in indirect 

emissions was derived using the simple error propagation approach, combining uncertainty from the DAYCENT 

outputs for N volatilization and leaching/runoff with uncertainty in the indirect N2O emission factors (IPCC 2006). 

Field Burning of Agricultural Residues 

The uncertainty upper and lower bounds of the CH4 emissions estimate for Field Burning of Agricultural 

Residues were 94 percent and -73 percent, respectively, and of the N2O emissions estimate were 85 percent and -73 

percent respectively, at the 95 percent confidence interval. Variables such as crop production, residue/crop product 

ratios, and burning and combustion efficiencies affect greenhouse gas emission estimates for Field Burning of 

Agricultural Residues. For more information on emission estimates, please refer to the Field Burning of 

Agricultural Residues section of the Agriculture Chapter. The uncertainty in production for all crops considered here 

is estimated to be 5 percent, based on expert judgment. Residue/crop product ratios can vary among cultivars. 

Generic residue/crop product ratios, rather than ratios specific to the United States, have been used for all crops 

except sugarcane. An uncertainty of 10 percent was applied to the residue/crop product ratios for all crops. Based 

on the range given for measurements of soybean dry matter fraction (Strehler and Stützle 1987), residue dry matter 

contents were assigned an uncertainty of 3.1 percent for all crop types. Burning and combustion efficiencies were 

assigned an uncertainty of 5 percent based on expert judgment. 

The N2O emission ratio was estimated to have an uncertainty of 28.6 percent based on the range reported in 

IPCC/UNEP/OECD/IEA (1997). The uncertainty estimated for the CH4 emission ratio was 40 percent based on the 

range of ratios reported in IPCC/UNEP/OECD/IEA (1997). 

Land Use, Land-Use Change, and Forestry 

Forest Land Remaining Forest Land 

Changes in Forest Carbon Stocks 

Forest area data from the USDA Forest Service and C density data affect total net flux of forest C 

estimates. For more information on net forest C flux, please refer to the Changes in Forest Carbon Stocks section of 

the Land Use, Land-Use Change, and Forestry (LULUCF) chapter. The USDA Forest Service inventories are 

designed to be accurate within 3 percent at the 67 percent confidence level (one standard error) per 405,000 ha (1 

million acres) of timberland (USDA Forest Service 2006c). For larger areas, the uncertainty in area is 

concomitantly smaller, and precision at plot levels is larger. An analysis of uncertainty in growing stock volume 

data for timber producing land in the Southeast by Phillips et al. (2000) found that nearly all of the uncertainty in 

their analysis was due to sampling rather than the regression equations used to estimate volume from tree height and 

diameter. The quantitative uncertainty analysis summarized here primarily focuses on uncertainties associated with 

the estimates of specific C stocks at the plot level and does not address error in tree diameters or volumes. 

Estimates for stand-level C pools are derived from extrapolations of site-specific studies to all forest land, 

because survey data on these pools are not generally available. Such extrapolation introduces uncertainty because 

available studies may not adequately represent regional or national averages. Uncertainty may also arise due to: (1) 

modeling errors (e.g., relying on coefficients or relationships that are not well known); and (2) errors in converting 

estimates from one reporting unit to another (Birdsey and Heath 1995). An important source of uncertainty is that 

there is little consensus from available data sets on the effect of land-use change and forest management activities 

(such as harvest) on soil C stocks. For example, while Johnson and Curtis (2001) found little or no net change in 

soil C following harvest, on average, across a number of studies, many of the individual studies did exhibit 

differences. Heath and Smith (2000) noted that the experimental design in a number of soil studies limited their 

usefulness for determining effects of harvesting on soil C. Because soil C stocks are large, estimates need to be very 

precise, since even small relative changes in soil C sum to large differences when integrated over large areas. The 

 

85 With the exception of organic fertilizers and crop yields, which were assumed to have a default ±50 percent 

uncertainty. 
 

A-337

soil C stock and stock change estimates presented here are based on the assumption that soil C density for each 

broad forest type group stays constant over time. The state of information and modeling are improving in this 

regard (Woodbury et al. 2006, 2007); the effects of land use and of changes in land use and forest management will 

be better accounted for in future estimates of soil C. 

Uncertainty in estimates about the HWP Contribution is based on Monte Carlo simulation of the production 

approach. The uncertainty analysis is based on Skog et al. (2004), with later revisions made in conjunction with 

overall revisions in the HWP model (Skog in preparation). The uncertainty analysis for HWP includes an evaluation 

of the effect of uncertainty in 13 sources including production and trade data, factors to convert products to 

quantities of C, rates at which wood and paper are discarded, and rates and limits for decay of wood and paper in 

SWDS. 

Direct N2O fluxes from Forest Soils 

The uncertainty upper and lower bounds of the emissions estimate for Direct N2O Fluxes from Forest Soils 

were 211 percent and -59 percent, respectively, at the 95 percent confidence interval. Variables such as the emission 

factor for synthetic fertilizer applied to soil, and the area of forest land receiving fertilizer affect direct N2O fluxes 

from Forest Soils. For more information, please refer to that section of the LULUCF chapter. The uncertainty range 

of the IPCC default emission factor for synthetic fertilizer applied to soil, according to IPCC (2006), ranges from 0.3 

to 3 percent. Because IPCC does not provide further information on whether this range represents the 95 percent 

confidence interval or the absolute minimum and maximum values, a triangular distribution was used to represent 

the uncertainty of the emission factor. The uncertainty in the area of forest land receiving fertilizer was 

conservatively estimated at ±20 percent and in fertilization rates at ±50 percent (Binkley 2004). 

Cropland Remaining Cropland 

The uncertainty information below pertains to the emission estimates presented in the Cropland Remaining 

Cropland section of the LULUCF chapter. Please refer to that section for information about this source. The 

uncertainty upper and lower bounds of the emissions estimate for Cropland Remaining Cropland were 148 percent 

and -152 percent, respectively, at the 95 percent confidence interval. Probability Distribution Functions (PDFs) for 

fertilizer were based on survey data for major U.S. crops, both irrigated and rainfed (ERS 1997; NASS 2004, 1999, 

1992; Grant and Krenz 1985). State-level PDFs were developed for each crop if a minimum of 15 data points 

existed for each of the two categories (irrigated and rainfed). Where data were insufficient at the state-level, PDFs 

were developed for multi-state Farm Production Regions. Uncertainty in manure applications for specific crops was 

incorporated in the analysis based on total manure available for use in each county, a weighted average application 

rate, and the crop-specific land area amended with manure (compiled from USDA data on animal numbers, manure 

production, storage practices, application rates and associated land areas receiving manure amendments; see 

Edmonds et al. 2003). Together with the total area for each crop within a county, this yielded a probability that a 

given crop at a specific NRI point would either receive manure or not. A ratio of managed manure N production in 

each year of the inventory relative to 1997 was used to adjust the probability of an area receiving an amendment, 

under the assumption that greater or less managed manure N production would lead to a proportional change in 

amended area (see Tier 3 Methods Section for data sources on manure N production). Manure amendment areas 

were averaged across decades to produce the PDF for the Monte Carlo Analysis (i.e., 1980-1989, 1990-2000). If 

soils were amended with manure, a reduction factor was applied to the N fertilization rate accounting for the 

interaction between fertilization and manure N amendments (i.e., producers often reduce mineral fertilization rates if 

applying manure). Reduction factors were randomly selected from probability distribution factors based on 

relationships between manure N application and fertilizer rates (ERS 1997). For tillage uncertainty, transition 

matrices were constructed from CTIC data to represent tillage changes for two time periods, combining the first two 

and the second two management blocks (i.e., 1980-1989, 1990-2000). A Monte Carlo analysis was conducted with 

100 iterations in which inputs values were randomly drawn from the PDFs to simulate the soil C stocks for each 

NRI cluster of points (i.e., inventory points in the same county were grouped into clusters if they had the same landuse/management history and soil type) using the Century model. 

An empirically-based uncertainty estimator was developed to assess uncertainty in model structure 

associated with the algorithms and parameterization. The estimator was based on a linear mixed effect modeling 

analysis comparing modeled soil C stocks with field measurements from 45 long-term agricultural experiments with 

over 800 treatments, representing a variety of tillage, cropping, and fertilizer management practices (Ogle et al. 

2006b). The final model included variables for organic matter amendments, N fertilizer rates, inclusion of 
A-338 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

hay/pasture in cropping rotations, use of no-till, setting-aside cropland from production and inclusion of bare fallow 

in the rotation. Each of these variables were found to be significant at a 95 percent probability level, and accounted 

for statistically significant biases in the modeled estimates from Century. For example, Century tended to underestimate the influence of organic amendments on soil C storage, so a variable was added to adjust the estimate from 

Century. Random effects captured the dependence in time series and data collected from the same long-term 

experimental site, which were needed to estimate appropriate standard deviations for parameter coefficients. For 

each C stock estimate from the Monte Carlo analysis, the structural uncertainty estimator was applied to adjust the 

value accounting for bias and prediction error in the modeled values. The structural uncertainty estimator was 

applied by randomly drawing parameter coefficients from their joint probability distribution, in addition to random 

draws from PDFs representing the uncertainty due to site and site by year random effects. Finally, uncertainty in the 

land-use and management statistics from the NRI were incorporated into the analysis based on the sampling 

variance for the clusters of NRI points. 

The NRI has a two-stage sampling design that allowed PDFs to be constructed assuming a multivariate 

normal distribution accounting for dependencies in activity data. PDFs for the tillage activity data, as provided by 

the CTIC, were constructed on a bivariate normal distribution with a log-ratio scale, accounting for the negative 

dependence among the proportions of land under conventional and conservation tillage practices. PDFs for the 

agricultural areas receiving manure were derived assuming a normal distribution from county-scale area amendment 

estimates derived from the USDA Census of Agriculture (Edmonds et al. 2003). Lastly, enrollment in wetland 

restoration programs was estimated from contract agreements, but due to a lack of information on the margin of 

error, PDFs were constructed assuming a nominal ±50 percent uncertainty range. 

Uncertainties in Mineral Soil Carbon Stock Changes 

Tier 3 Approach 

The uncertainty information below pertains to the emission estimates presented in the Mineral Soil Carbon 

Stock Changes section of the LULUCF chapter. Please refer to that section for information about this source. The 

uncertainty analysis for the Tier 3 Century inventory had three components: 1) a Monte Carlo approach to address 

uncertainties in model inputs, 2) an empirically-based approach for quantifying uncertainty inherent in the structure 

of the Century model, and 3) scaling uncertainty associated with the NRI survey (i.e., scaling from the individual 

NRI points to the entire U.S. agricultural land base using the expansion factors). 

For the model input uncertainty, probability distribution functions (PDFs) were developed for fertilizer 

rates, manure application and tillage practices. An empirically-based uncertainty estimator was developed to assess 

uncertainty in model structure associated with the algorithms and parameterization. The estimator was based on a 

linear mixed effect modeling analysis comparing modeled soil C stocks with field measurements from 45 long-term 

agricultural experiments with over 800 treatments, representing a variety of tillage, cropping, and fertilizer 

management practices (Ogle et al. 2007). The final model included variables for organic matter amendments, N 

fertilizer rates, inclusion of hay/pasture in cropping rotations, use of no-till, setting-aside cropland from production, 

and inclusion of bare fallow in the rotation. Each of these variables were found to be significant at a 0.05 alpha 

level, and accounted for statistically significant biases in modeled estimates from the Century model. Uncertainty in 

land-use and management statistics from the NRI were incorporated into the analysis based on the sampling 

variance for the clusters of NRI points. 

Tier 2 Approach 

For the Tier 2 IPCC method, a Monte Carlo approach was used (Ogle et al. 2003). PDFs for stock change 

factors were derived from a synthesis of 91 published studies, which addressed the impact of management on SOC 

storage. Uncertainties in land-use and management activity data were also derived from a statistical analysis. 

Additional Mineral C Stock Change Calculations 

A ±50 percent uncertainty was assumed for additional adjustments to the mineral soil C stocks between 

1990 and 2006, accounting for additional C stock changes associated gains or losses in C sequestration after 1997 

due to changes in Conservation Reserve Program enrollment. 

Uncertainties in Organic Soil C Stock Changes 
 

A-339

Uncertainty in C emissions from organic soils was estimated in the same manner described for mineral soil 

using the Tier 2 method and Monte Carlo analysis. PDFs for emission factors were derived from a synthesis of 10 

studies, and combined with uncertainties in the NRI land use and management data for organic soils in the Monte 

Carlo analysis. Please refer to the Organic Soil C Stock Changes section of the LULUCF chapter for more 

information on C emissions from organic soils. 

Uncertainties in CO2 Emissions from Liming 

The uncertainty information below pertains to the emission estimates presented in the Mineral Soil Carbon 

Stock Changes section of the LULUCF chapter. Please refer to that section for information about liming activity 

data and the emission factors used for this source. A Monte Carlo (Tier 2) uncertainty analysis was applied to 

estimate the uncertainty of CO2 emissions from liming. Uncertainties in the estimates of emissions from liming 

result from both the emission factors and the activity data. The emission factors used for limestone and dolomite 

take into account the fate of C following application to soils, including: dissolution of liming constituents; leaching 

of bicarbonates into the soil and transport to the ocean; and emissions to the atmosphere (West and McBride 2005). 

The C accounting behind these emission factors entails assumptions about several uncertain factors. First, it is 

uncertain what fraction of agricultural lime is dissolved by nitric acid (HNO3)—a process that releases CO2—and 

what portion reacts with carbonic acid (H2CO3), resulting in the uptake of CO2. The fractions can vary depending 

on soil pH and N fertilizer use. The second major source of uncertainty is the fraction of bicarbonate (HCO3

-

) that 

leaches through the soil profile and is transported into groundwater, which can eventually be transferred into rivers 

and into the ocean. This fraction can vary depending on the soil pH and whether calcium (Ca2+) and magnesium 

(Mg2+) liming constituents that might otherwise accompany HCO3

-

, are taken up by crops, remain in the upper soil 

profile, or are transported through or out of the soil profile. Finally, the emission factors do not account for the time 

that is needed for leaching and transport processes to occur. 

There are several sources of uncertainty in the limestone and dolomite activity data. When reporting data 

to the USGS (or U.S. Bureau of Mines), some producers do not distinguish between limestone and dolomite. In 

these cases, data are reported as limestone, so this reporting could lead to an overestimation of limestone and an 

underestimation of dolomite. In addition, the total quantity of crushed stone listed each year in the Minerals 

Yearbook excludes American Samoa, Guam, Puerto Rico, and the U.S. Virgin Islands. 

Land Converted to Cropland 

Tier 2 Approach 

The uncertainty upper and lower bounds of the emissions estimate for Land Converted to Cropland were 36 

percent and -40 percent, respectively, at the 95 percent confidence interval. The uncertainty analysis for Land 

Converted to Cropland using the Tier 2 approach was based on the same method described for Cropland Remaining 

Cropland. 

Uncertainties in Mineral and Organic Soil C Stock Changes 

The quantitative estimates of uncertainty presented above are missing several components. This section 

qualitatively describes these contributors to overall uncertainty. The agricultural soil C inventory has undergone 

several improvements during the past few years, such as the development of the Tier 3 inventory method to estimate 

mineral soil C stock changes for the majority of U.S. cropland. However, some limitations remain in the analysis. 

First, the current agricultural soil C inventory includes some points designated as non-agricultural land-uses in the 

NRI if the points were categorized as cropland in either 1992 or 1997, but were urban, water, or miscellaneous noncropland (e.g., roads and barren areas) in another year. The impact on soil organic C storage that results from 

converting non-agricultural uses to cropland is not well-understood, and therefore, those points were not included in 

the calculations for mineral soils (emissions from organic soils, however, were computed for those points in the 

years that they were designated as an agricultural use). Similarly, the effect of aquaculture (e.g., rice cultivation 

followed by crayfish production in flooded fields) on soil C stocks has not been estimated due to a lack of 

experimental data. Second, the current estimates may underestimate losses of C from organic soils because the 1997

National Resources Inventory was not designed as a soil survey and organic soils frequently occur as relatively 

small inclusions within major soil types. Lastly, the IPCC Tier 2 methodology does not take into account changes in 

SOC stocks due to pre-1982 land use and land-use change.
A-340 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

Grassland Remaining Grassland 

Tier 2 Approach 

The uncertainty upper and lower bounds of the emissions estimate for Grassland Remaining Grassland 

were 41 percent and -54 percent, respectively, at the 95 percent confidence interval. The uncertainty analysis for 

Grassland Remaining Grassland using the Tier 2 approach was based on the same method described for Cropland 

Remaining Cropland. The uncertainty in the inventory estimate of a 0.2 Tg CO2 Eq. removal was 89 percent below 

the mean and 127 percent above the mean. 

Additional Uncertainties in Mineral and Organic Soil C Stock Changes 

The quantitative estimates of uncertainty presented above are missing several components. This section 

qualitatively describes these contributors to overall uncertainty. Minimal data exist on where and how much sewage 

sludge has been applied to U.S. agricultural land and the accounting of this activity appears to be much more 

difficult than the related-activity of using manure to amend agricultural soils. Consequently, there is considerable 

uncertainty in the application of sewage sludge, which is assumed to be applied to Grassland Remaining Grassland. 

However, some sludge may be applied to other agricultural land, but there is not sufficient information to further 

subdivide application among the agricultural land use/land-use change categories. Another limitation is that the 

current estimates may underestimate losses of C from organic soils because the 1997 National Resources Inventory

was not designed as a soil survey and organic soils frequently occur as relatively small inclusions within major soil 

types. Lastly, the IPCC Tier 2 methodology does not take into account changes in SOC stocks due to pre-1982 land 

use and land-use change. 

Land Converted to Grassland 

Tier 2 Approach 

The uncertainty upper and lower bounds of the emissions estimate for Land Converted to Grassland were 9 

percent and -8 percent, respectively, at the 95 percent confidence interval, The uncertainty analysis for Land 

Converted to Grassland using the Tier 2 approach was based on the same method described for Cropland 

Remaining Cropland. See the Tier 2 section under minerals soils in the Cropland Remaining Cropland section for 

additional discussion. 

Additional Uncertainties in Mineral and Organic Soil Carbon Stock Changes 

The quantitative estimates of uncertainty presented above are missing several components. This section 

qualitatively describes these contributors to overall uncertainty. The agricultural soil C inventory has undergone 

several improvements during the past few years, such as the development of the Tier 3 inventory method to estimate 

mineral soil C stock changes for the majority of U.S. grassland. However, some limitations remain in the analysis. 

First, the current agricultural soil C inventory includes some points designated as non-agricultural land-uses in the 

NRI if the points were categorized as agricultural land use in either 1992 or 1997, but were urban, water, or 

miscellaneous non-cropland (e.g., roads and barren areas) in another year. The impact on SOC storage that results 

from converting non-agricultural uses to grassland is not well-understood, and therefore, those points were not 

included in the calculations for mineral soils (emissions from organic soils, however, were computed for those 

points in the years that they were designated as grassland). Second, the current estimates may underestimate losses 

of C from organic soils because the 1997 National Resources Inventory was not designed as a soil survey and 

organic soils frequently occur as relatively small inclusions within major soil types. Lastly, this IPCC Tier 2 

methodology does not take into account changes in SOC stocks due to pre-1982 land use and land-use change. 

Settlements Remaining Settlements 

N2O Fluxes from Settlement Soil 
 

A-341

The uncertainty information below pertains to the emission estimates presented in the N2O Fluxes from 

Settlement Soils section of the LULUCF chapter. Please refer to that section for information about synthetic 

fertilizer N, the amounts of sewage sludge applied to non-agricultural lands, and other variables that affect this 

source. The uncertainty upper and lower bounds of the emissions estimate for N2O fluxes from Settlement Soil were 

163 percent and -49 percent, respectively, at the 95 percent confidence interval. The uncertainty range for the 

IPCC’s default emission factor for mineral and organic N additions applied to soil ranges from 0.3 to 3 percent 

(IPCC 2006). Because the IPCC does not provide further information on whether this range represents the 95 

percent confidence interval or the absolute minimum and maximum values, a triangular distribution was used to 

represent the uncertainty of the emission factor. 

The uncertainty in the total amount of synthetic fertilizer N applied in the United States was estimated to be 

±3 percent (Terry 2005). The uncertainty in the amount of synthetic fertilizer N applied to settlement soils was 

conservatively estimated to be ±50 percent, since no uncertainty was provided in Ruddy et al. (2006). The 

uncertainty in the amounts of sewage sludge applied to non-agricultural lands and used in surface disposal was 

based on the uncertainty of the following data points: (1) N content of sewage sludge; (2) total sludge applied in 

2000; (3) wastewater existing flow in 1996 and 2000; and (4) the sewage sludge disposal practice distributions to 

non-agricultural land application and surface disposal. 

(1) The value assumed for N content of sewage sludge could range from around 0.1 percent to around 17 

percent (McFarland 2001). Because information was not available on the distribution, a triangular 

distribution was assumed based on IPCC guidelines. 

(2) The uncertainty in the total amount of sludge applied in 2000 was based on a comparison with similar data 

available from other publications, which were all within 3 percent of the value used in the Inventory 

calculations (BioCycle 2000, NRC 2002, WEF 1997, Bastian 1997). The distribution was estimated to be 

normal based on expert opinion (Boucher 2006). 

(3) The uncertainty in the wastewater existing flow values for 1996 and 2000 was estimated at 0.0625 percent 

with a lognormal distribution (Plastino 2006). 

(4) The uncertainty in the sewage sludge disposal practice distributions was based on a comparison with 

similar data available from other publications, which were at most 12 percent different than the distribution 

for non-agricultural land application used in the Inventory calculations and at most 69 percent different 

than the distribution for surface disposal used in the Inventory calculations (Biocycle 2000, NRC 2002). 

Other 

The uncertainty analysis descriptions in this section correspond to Changes in Yard Trimming and Food 

Scrap Carbon Stocks in Landfills source category included in the Other Chapter of the Inventory. 

Changes in Yard Trimming and Food Scrap Carbon Stocks in Landfills

The uncertainty ranges were assigned based on expert judgment and are assumed to be normally distributed 

around the inventory estimate, except for the values for decomposition rate, proportion of C stored, and moisture 

content for branches. The uncertainty ranges associated with these values are highlighted separately in this section. 

The uncertainty range selected for input variables for the proportions of both grass and leaves in yard 

trimmings was 20 to 60 percent. The initial C content for grass, leaves, and food scraps (all expressed as 

percentages in the calculations for the inventory) were plus or minus 10 percent. For the moisture content of 

branches (where the inventory estimate is 10 percent), the uncertainty range was assumed to be 5 to 30 percent, 

within a lognormal distribution. 

The uncertainty ranges associated with the disposal of grass, leaves, branches, and food scraps were bound 

at 50 percent to 150 percent of the inventory estimates. The half-life of grass was assumed to range from 1 to 15 

years, the half-life of food scraps was assumed to range from 1 to 20 years, the half-life of leaves was assumed to 

range from 2 to 30 years, and the half life of branches was assumed to range from 5 to 50 years. Finally, the 

proportion of C stored in grass, leaves, branches, and food scraps was assumed to vary plus or minus 20 percent 

from the best estimate, with a uniform distribution. The proportion of C stored in food scraps was truncated at a 

lower bound of 2 percent. 
A-342 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

References 

EPA (2002) Quality Assurance / Quality Control and Uncertainty Management Plan for the U.S. Greenhouse Gas 

Inventory: Background on the U.S. Greenhouse Gas Inventory Process, U.S. Environmental Protection Agency, 

Office of Atmospheric Programs, Greenhouse Gas Inventory Program, Washington, D.C., EPA 430-R-02-007A, 

June 2002. 

IPCC/UNEP/OECD/IEA (1997) Revised 1996 IPCC Guidelines for National Greenhouse Gas Inventories, Paris: 

Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic 

Co-Operation and Development, International Energy Agency. 

IPCC (2000) Good Practice Guidance and Uncertainty Management in National Greenhouse Gas Inventories, 

Intergovernmental Panel on Climate Change, National Greenhouse Gas Inventories Programme, Montreal, IPCCXVI/Doc. 10 (1.IV.2000), May 2000. 

IPCC/UNEP/OECD/IEA (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories, Paris: 

Intergovernmental Panel on Climate Change, United Nations Environment Programme, Organization for Economic 

Co-Operation and Development, International Energy Agency.