### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### Emissions from Fossil Fuel Combustion..................................................A-21

2.1. Methodology for Estimating Emissions of CO 2 

### 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 fr om Non-Energy Uses of Fossil Fuels................................................. .A-72

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

3.1. Methodology for Estimating Emissions of CH 4 

### , N 2 

### O, and Indirect Greenhouse Gases from Stationary Combustion .A-97

3.2. Methodology for Estimating Emissions of CH 4 

### , N 2 

### O, 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 CH 4 

### Emissions from Coal Mining ..................................................................................A-124

3.4. Methodology for Estimating CH 4 

### and CO 2 

### Emissions from Natural Gas Systems .....................................................A-131

3.5. Methodology for Estimating CH 4 

### and CO 2 

### Emissions from Petroleum Systems........................................................A-149

3.6. Methodology for Estimating CO 2 

### and N 2 

### O Emissions from the Incineration of Waste...............................................A-154

3.7. Methodology for Estimating Emissions from Internati onal 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 CH 4 

### Emissions from Enteric Fermentation ....................................................................A-182

3.10. Methodology for Estimating CH 4 

### and N 2 

### O Emissions from Manure Management.....................................................A-194

3.11. Methodology for Estimating N 2 

### O 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 CH 4 

### Emissions from Landfills ........................................................................................A-279

ANNEX 4 IPCC Reference Approach for Estimating CO 2 

### 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 Unce rtainty 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 influe nce of the trends of individual categories. Therefore, a

trend assessment is conducted to identify source and sink cat egories 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 assessm ents, 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 acc ording 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, Tie r 2,

and/or qualitative assessments).A-4 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A-1: Key Source Categoriesfor the United States (1990-2007)Tier 1 Tier 2IPCC Source Categories GasLevelWithoutLULUCFTrendWithoutLULUCFLevel WithLULUCFTrendWithLULUCFLevelWithoutLULUCFTrendWithoutLULUCFLevel WithLULUCFTrend WithLULUCF Quala2007 Emissions(Tg CO2 Eq.)EnergyCO2Emissions from Stationary Combustion - CoalCO2••••••••2,086.5Mobile Combustion: Road & Other CO2• • • • • • • •1,649.1CO2Emissions from Stationary Combustion - GasCO2•••••••1,181.1CO2Emissions from Stationary Combustion - Oil CO2• • • • • • • •580.4Mobile Combustion: AviationCO2••••••187.5CO2Emissions from Non-Energy Use of Fuels CO2• • • •133.9Mobile Combustion: MarineCO2••50.8CO2Emissions from Natural Gas Systems CO2• • • • • • • •28.7CO2Emissions from Waste IncinerationCO2••20.8Fugitive Emissions from Natural Gas Systems CH4• • • • • • • •104.7Fugitive Emissions from Coal MiningCH4••••••••57.6Fugitive Emissions from Petroleum Systems CH4• • • • • • • •28.8Non-CO2Emissions from Stationary CombustionCH4•6.6Mobile Combustion: Road & Other N2O• • • • • •27.9Non-CO2Emissions from Stationary CombustionN2O••14.7International Bunker FuelsbSeveral•109.9Industrial ProcessesCO2Emissions from Iron and Steel Production & MetallurgicalCoke ProductionCO2••••••••77.4CO2Emissions from Cement Production CO2• •44.5CO2Emissions from Ammonia Production and UreaConsumptionCO2••13.8N2O Emissions from AdipicAcid Production N2O• •5.9Emissions from Substitutes for Ozone Depleting SubstancesSeveral••••••108.3HFC-23 Emissions from HCFC-22 Production HFCs• • • • •17.0SF6Emissions from Electrical Transmission and DistributionSF6••••12.7PFC Emissions from Aluminum Production PFCs• •3.8AgricultureCH4Emissions from Enteric FermentationCH4••••••••139.0CH4Emissions from Manure Management CH4• • • •44.0CH4Emissions from Rice CultivationCH4••6.2Direct N2O Emissions from Agricultural Soil Management N2O• • • • • • • •172.0Indirect N2O Emissions from Applied NitrogenN2O••••••••35.9WasteCH4Emissions from LandfillsCH4••••••••132.9CH4Emissions from Wastewater Treatment CH4•24.4Land Use, Land Use Change, and ForestryCO2from Changes in Forest Carbon Stocks CO2• • • •-910.1CO2Emissions from Urban TreesCO2••••-97.6A-5Tier 1 Tier 2IPCC Source Categories GasLevelWithoutLULUCFTrendWithoutLULUCFLevel WithLULUCFTrendWithLULUCFLevelWithoutLULUCFTrendWithoutLULUCFLevel WithLULUCFTrend WithLULUCF Quala2007 Emissions(Tg CO2 Eq.)CO2Emissions from Cropland Remaining Cropland CO2• • •-11.6CO2Emissions from Landfilled Yard Trimmings and Food ScrapsCO2•••-9.8CO2Emissions from Grassland Remaining Grassland CO2• • • •-4.7CH4Emissions from Forest FiresCH4••••29.0N2O Emissions from Forest Fires N2O•2.9Subtotal Without LULUCF6,972.3Total Emissions Without LULUCF7,107.2Percent of Total Without LULUCF98%Subtotal With LULUCF5,991.9Total Emissions With LULUCF6,087.5Percent of Total With LULUCF98%aQualitative criteria.bPercent relative uncertainty. If the corresponding uncertainty isasymmetrical, the uncertaintygiven here is the larger and always positive.cEmissions from this sourcenot 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 categor ies by IPCC sector, along with comments on the criteria

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

of source and sink categories by IPCC sector, along with comm ents 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 fu els 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 Invent ory Source Categories without LULUCF

IPCC Source Categories Direct

GHG 2007

Emissions

(Tg CO 2 Eq.) Key

Category? ID

Criteria Level in which

year(s)?



### Energy

CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 2,086.5 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Mobile Combustion: Road & Other CO 2 

### 1,649.1 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 1,181.1 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 580.4 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Mobile Combustion: Aviation CO 2 

### 187.5 • 

### L 1 

### T 1 

### L 2 

### 1990, 2007

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 133.9 • 

### L 1 

### L 2 

### 1990, 2007

Mobile Combustion: Marine CO 2 

### 50.8 • 

### L 1 

### 1990, 2007

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 28.7 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990 1 

### ; 1990 2 

### ,2007 2



### CO 2 

### Emissions from Waste Incineration CO 2 

### 20.8 • 

### T 1



### CO 2 

### Emissions from Stationary Combustion - Geothermal

Energy CO 2 

### 0.4

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.3

Fugitive Emissions from Natural Gas Systems CH 4 

### 104.7 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Fugitive Emissions from Coal Mining CH 4 

### 57.6 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Fugitive Emissions from Petroleum Systems CH 4 

### 28.8 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990 1 

### ; 1990 2 

### ,2007 2



### Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 6.6 • 

### T 2 

### Fugitive Emissions from Abandoned Underground Coal Mines CH 4 

### 5.7

Mobile Combustion: Road & Other CH 4 

### 2.0

Mobile Combustion: Aviation CH 4 

### 0.1

Mobile Combustion: Marine CH 4 

### 0.1

Mobile Combustion: Road & Other N 2 

### O 27.9 • 

### L 1 

### T 1 

### T 2 

### 1990

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 14.7 • 

### L 2 

### 1990, 2007

Mobile Combustion: Aviation N 2 

### O 1.8

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.4

Mobile Combustion: Marine N 2 

### O 0.4

International Bunker Fuels a 

### Several 109.9 • 

### Q

Industrial Processes

CO 2 

### Emissions from Iron and Steel Production & Metallurgical

Coke Production CO 2 

### 77.4 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Cement Production CO 2 

### 44.5 • 

### L 1 

### 1990, 2007

CO 2 

### Emissions from Lime Production CO 2 

### 14.6

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 13.8 • 

### T 1



### CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 6.2

CO 2 

### Emissions from Aluminum Production CO 2 

### 4.3

CO 2 

### Emissions from Soda Ash Production and Consumption CO 2 

### 4.1

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.6

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.9

CO 2 

### Emissions from CO 2 

### Consumption CO 2 

### 1.9

### A-7 IPCC Source Categories Direct

GHG 2007

Emissions

(Tg CO 2 Eq.) Key

Category? ID

Criteria Level in which

year(s)?



### CO 2 

### Emissions from Ferroalloy Production CO 2 

### 1.6

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.2

CO 2 

### Emissions from Zinc Production CO 2 

### 0.5

CO 2 

### Emissions from Lead Production CO 2 

### 0.3

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.2

CH 4 

### Emissions from Petrochemical Production CH 4 

### 1.0

CH 4 

### Emissions from Iron and Steel Production & Metallurgical

Coke Production CH 4 

### 0.7

CH 4 

### Emissions from Ferroalloy Production CH 4 

### +

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### +

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 21.7

N 2 

### O Emissions from Adipic Acid Production N 2 

### O 5.9 • 

### T 1



### N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4

Emissions from Substitutes for Ozone Depleting Substances HiGWP 108.3 • 

### L 1 

### T 1 

### T 2 

### 2007

HFC-23 Emissions from HCFC-22 Production HiGWP 17.0 • 

### L 1 

### T 1 

### T 2 

### 1990

SF 6 

### Emissions from Electrical Transmission and Distribution HiGWP 12.7 • 

### T 1 

### T 2 

### PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture HiGWP 4.7

PFC Emissions from Aluminum Production HiGWP 3.8 • 

### T 1



### SF 6 

### Emissions from Magnesium Production and Processing HiGWP 3.0

Agriculture

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 139.0 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CH 4 

### Emissions from Manure Management CH 4 

### 44.0 • 

### L 1 

### T 1 

### 2007

CH 4 

### Emissions from Rice Cultivation CH 4 

### 6.2 • 

### T 2



### CH 4 

### Emissions from Field Burning of Agricultural Residues CH 4 

### 0.9

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 172.0 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 35.9 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

N 2 

### O Emissions from Manure Management N 2 

### O 14.7

N 2 

### O Emissions from Field Burning of Agricultural Residues N 2 

### O 0.5

Waste

CH 4 

### Emissions from Landfills CH 4 

### 132.9 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 24.4

CH 4 

### Emissions from Composting CH 4 

### 1.7

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 4.9

N 2 

### O Emissions from Composting N 2 

### O 1.8

a Emissions from these source s not included in totals.

+ Does not exceed 0.05 Tg CO 2 Eq.

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



### Table A-3: U.S Greenhouse Gas Invent ory Source Categories with LULUCF

IPCC Source Categories Gas 2007

Emissions

(Tg CO 2 Eq.) Key

Category? ID Criteria Level in which

year(s)?



### Energy

CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 2,086.5 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Mobile Combustion: Road & Other CO 2 

### 1,649.1 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 1,181.1 • 

### L 1 

### T 1 

### L 2 

### 1990, 2007

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 580.4 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Mobile Combustion: Aviation CO 2 

### 187.5 • 

### L 1 

### T 1 

### L 2 

### 1990, 2007

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 133.9 • 

### L 1 

### L 2 

### 1990, 2007

Mobile Combustion: Marine CO 2 

### 50.8 • 

### L 1 

### 1990, 2007

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 28.7 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990 1 

### ; 1990 2 

### ,2007 2



### CO 2 

### Emissions from Waste Incineration CO 2 

### 20.8 • 

### T 1



### CO 2 

### Emissions from Stationary Combustion - Geothermal

Energy CO 2 

### 0.4

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.3

Fugitive Emissions from Natural Gas Systems CH 4 

### 104.7 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

### A-8 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 IPCC Source Categories Gas 2007

Emissions

(Tg CO 2 Eq.) Key

Category? ID Criteria Level in which

year(s)?



### Fugitive Emissions from Coal Mining CH 4 

### 57.6 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Fugitive Emissions from Petroleum Systems CH 4 

### 28.8 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990 1 

### ; 1990 2 

### ,2007 2



### Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 6.6

Fugitive Emissions from Abandoned Underground Coal Mines CH 4 

### 5.7

Mobile Combustion: Road & Other CH 4 

### 2.0

Mobile Combustion: Aviation CH 4 

### 0.1

Mobile Combustion: Marine CH 4 

### 0.1

Mobile Combustion: Road & Other N 2 

### O 27.9 • 

### L 1 

### T 1 

### T 2 

### 1990

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 14.7 • 

### L 2 

### 1990, 2007

Mobile Combustion: Aviation N 2 

### O 1.8

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.4

Mobile Combustion: Marine N 2 

### O 0.4

International Bunker Fuels a 

### Several 109.9 • 

### Q

Industrial Processes

CO 2 

### Emissions from Iron and Steel Production & Metallurgical

Coke Production CO 2 

### 77.4 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Cement Production CO 2 

### 44.5 • 

### L 1 

### 1990, 2007

CO 2 

### Emissions from Lime Production CO 2 

### 14.6

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 13.8 • 

### T 1



### CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 6.2

CO 2 

### Emissions from Aluminum Production CO 2 

### 4.3

CO 2 

### Emissions from Soda Ash Production and Consumption CO 2 

### 4.1

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.6

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.9

CO 2 

### Emissions from CO 2 

### Consumption CO 2 

### 1.9

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 1.6

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.2

CO 2 

### Emissions from Zinc Production CO 2 

### 0.5

CO 2 

### Emissions from Lead Production CO 2 

### 0.3

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.2

CH 4 

### Emissions from Petrochemical Production CH 4 

### 1.0

CH 4 

### Emissions from Iron and Steel Production & Metallurgical

Coke Production CH 4 

### 0.7

CH 4 

### Emissions from Ferroalloy Production CH 4 

### +

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### +

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 21.7

N 2 

### O Emissions from Adipic Acid Production N 2 

### O 5.9 • 

### T 1



### N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4

Emissions from Substitutes for Ozone Depleting Substances HiGWP 108.3 • 

### L 1 

### T 1 

### T 2 

### 2007

HFC-23 Emissions from HCFC-22 Production HiGWP 17.0 • 

### L 1 

### T 1 

### 1990

SF 6 

### Emissions from Electrical Transmission and Distribution HiGWP 12.7 • 

### T 1 

### T 2 

### PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture HiGWP 4.7

PFC Emissions from Aluminum Production HiGWP 3.8 • 

### T 1



### SF 6 

### Emissions from Magnesium Production and Processing HiGWP 3.0

Agriculture

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 139.0 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CH 4 

### Emissions from Manure Management CH 4 

### 44.0 • 

### L 1 

### T 1 

### 1990, 2007

CH 4 

### Emissions from Rice Cultivation CH 4 

### 6.2 • 

### T 2



### CH 4 

### Emissions from Field Burning of Agricultural Residues CH 4 

### 0.9

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 172.0 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 35.9 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

N 2 

### O Emissions from Manure Management N 2 

### O 14.7

N 2 

### O Emissions from Field Burning of Agricultural Residues N 2 

### O 0.5

Waste

CH 4 

### Emissions from Landfills CH 4 

### 132.9 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

### A-9 IPCC Source Categories Gas 2007

Emissions

(Tg CO 2 Eq.) Key

Category? ID Criteria Level in which

year(s)?



### CH 4 

### Emissions from Wastewater Treatment CH 4 

### 24.4 • 

### L 2 

### 2007

CH 4 

### Emissions form Composting CH 4 

### 1.7

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 4.9

N 2 

### O Emissions from Composting N 2 

### O 1.8

Land Use, Land Use Change, and Forestry

CO 2 

### from Changes in Forest Carbon Stocks CO 2 

### (910.1) • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Urban Trees CO 2 

### (97.6) • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990, 2007

CO 2 

### Emissions from Land Converted to Grassland CO 2 

### (26.7)

CO 2 

### Emissions from Cropland Remaining Cropland CO 2 

### (11.6) • 

### T 1 

### L 2 

### T 2 

### 1990

CO 2 

### Emissions from Landfilled Yard Trimmings and Food

Scraps CO 2 

### (9.8) • 

### T 1 

### L 2 

### T 2 

### 1990

CO 2 

### Emissions from Grassland Remaining Grassland CO 2 

### (4.7) • 

### L 1 

### T 1 

### L 2 

### T 2 

### 1990

CO 2 

### Emissions from Land Converted to Cropland CO 2 

### 5.9

CO 2 

### Emissions from Wetlands Remaining Wetlands CO 2 

### 1.0

CH 4 

### Emissions from Forest Fires CH 4 

### 29.0 • 

### L 1 

### T 1 

### L 2 

### T 2 

### 2007

N 2 

### O Emissions from Forest Fires N 2 

### O 2.9 • 

### T 2



### N 2 

### O Emissions from Soils N 2 

### O 1.9

N 2 

### O Emissions from Wetlands Remaining Wetlands N 2 

### O +

a Emissions from these source s not included in totals.

+ Does not exceed 0.05 Tg CO 2 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 em issions 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 ke y 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 subc ategories, 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 categor y. 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 em issions 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 so urce categories, including fossil fuel feedstocks, international

bunkers, and emissions from U.S. territories, that are de rived 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 define d 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 as sessments 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 cat egory from the Tier 1 approach is multiplied by its

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

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

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

assessment may differ from those identified by the Tier 1 asse ssment. 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 an d Tier 2 Analysis—Level Assessment, without LULUCF

IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 1,695.9 0.28 0.28 9% 0.026

Mobile Combustion: Road & Other CO 2 

### 1,258.7 0.21 0.49 8% 0.016

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 965.5 0.16 0.64 7% 0.010

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 562.7 0.09 0.74 6% 0.006

Mobile Combustion: Aviation CO 2 

### 179.4 0.03 0.77 8% 0.002

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 158.9 0.03 0.79 54% 0.014

CH 4 

### Emissions from Landfills CH 4 

### 149.2 0.02 0.82 39% 0.010

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 133.2 0.02 0.84 18% 0.004

Fugitive Emissions from Natural Gas Systems CH 4 

### 129.6 0.02 0.86 43% 0.009

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 117.0 0.02 0.88 20% 0.004

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 109.8 0.02 0.90 22% 0.004

Fugitive Emissions from Coal Mining CH 4 

### 84.1 0.01 0.91 24% 0.003

Mobile Combustion: Marine CO 2 

### 46.5 0.01 0.92 8% 0.001

Mobile Combustion: Road & Other N 2 

### O 41.6 0.01 0.93 19% 0.001

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 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 CH 4 

### 33.9 0.01 0.94 144% 0.008

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 33.7 0.01 0.95 43% 0.002

CO 2 

### Emissions from Cement Production CO 2 

### 33.3 0.01 0.95 13% 0.001

CH 4 

### Emissions from Manure Management CH 4 

### 30.4 <0.01 0.96 20% 0.001

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 26.8 <0.01 0.96 22% 0.001

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 23.5 <0.01 0.97 49% 0.002

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 20.0 <0.01 0.97 44% 0.001

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

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 16.8 <0.01 0.98 12% <0.001

N 2 

### O Emissions from Adipic Acid Production N 2 

### O 15.3 <0.01 0.98 20% <0.001

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 12.8 <0.01 0.98 187% 0.004

N 2 

### O Emissions from Manure Management N 2 

### O 12.1 <0.01 0.98 24% <0.001

CO 2 

### Emissions from Lime Production CO 2 

### 11.5 <0.01 0.99 9% <0.001

CO 2 

### Emissions from Waste Incineration CO 2 

### 10.9 <0.01 0.99 27% <0.001

Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 7.4 <0.01 0.99 128% 0.002

### A-11 IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### CH 4 

### Emissions from Rice Cultivation CH 4 

### 7.1 <0.01 0.99 164% 0.002

CO 2 

### Emissions from Aluminum Production CO 2 

### 6.8 <0.01 0.99 4% <0.001

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 6.0 <0.01 0.99 23% <0.001

SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 5.4 <0.01 0.99 13% <0.001

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 5.1 <0.01 0.99 16% <0.001

Mobile Combustion: Road & Other CH 4 

### 4.5 <0.01 0.99 8% <0.001

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 <0.01 1.00 2% <0.001

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 <0.01 1.00 7% <0.001

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 3.7 <0.01 1.00 94% 0.001

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 2.9 <0.01 1.00 9% <0.001

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.2 <0.01 1.00 40% <0.001

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 2.2 <0.01 1.00 12% <0.001

Mobile Combustion: Aviation N 2 

### O 1.7 <0.01 1.00 19% <0.001

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.5 <0.01 1.00 18% <0.001

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.4 <0.01 1.00 22% <0.001

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.2 <0.01 1.00 13% <0.001

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 1.0 <0.01 1.00 8% <0.001

CO 2 

### Emissions from Zinc Production CO 2 

### 0.9 <0.01 1.00 25% <0.001

CH 4 

### Emissions from Petrochemical Production CH 4 

### 0.9 <0.01 1.00 31% <0.001

CH 4 

### Emissions from Field Burning of Agricultural Residues CH 4 

### 0.7 <0.01 1.00 94% <0.001

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.5 <0.01 1.00 191% <0.001

CO 2 

### Emissions from Stationary Combustion - Geothermal

Energy CO 2 

### 0.4 <0.01 1.00 5% <0.001

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.4 <0.01 1.00 144% <0.001

Mobile Combustion: Marine N 2 

### O 0.4 <0.01 1.00 19% <0.001

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.4 <0.01 1.00 10% <0.001

N 2 

### O Emissions from Field Burning of Agricultural Residues N 2 

### O 0.4 <0.01 1.00 85% <0.001

N 2 

### O Emission from Composting N 2 

### O 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

CH 4 

### Emissions from Composting CH 4 

### 0.3 <0.01 1.00 50% <0.001

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 <0.01 1.00 17% <0.001

Mobile Combustion: Aviation CH 4 

### 0.2 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH 4 

### 0.1 <0.01 1.00 8% <0.001

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + <0.01 1.00 10% <0.001

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + <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 a lways positive.

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

+ Does not exceed 0.05 Tg CO 2 Eq.



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

IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 1,695.9 0.24 0.245 9% 0.023

Mobile Combustion: Road & Other CO 2 

### 1,258.7 0.18 0.426 8% 0.014

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 965.5 0.14 0.566 7% 0.009

CO 2 

### from Changes in Forest Carbon Stocks CO 2 

### 661.1 0.10 0.661 19% 0.018

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 562.7 0.08 0.742 6% 0.005

Mobile Combustion: Aviation CO 2 

### 179.4 0.03 0.768 8% 0.002

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 158.9 0.02 0.791 54% 0.012

CH 4 

### Emissions from Landfills CH 4 

### 149.2 0.02 0.813 39% 0.008

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 133.2 0.02 0.832 18% 0.003

Fugitive Emissions from Natural Gas Systems CH 4 

### 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 CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 117.0 0.02 0.867 20% 0.003

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 109.8 0.02 0.883 22% 0.004

Fugitive Emissions from Coal Mining CH 4 

### 84.1 0.01 0.895 24% 0.003

CO 2 

### Emissions from Urban Trees CO 2 

### 60.6 0.01 0.904 21% 0.002

CO 2 

### Emissions from Grassland Remaining Grassland CO 2 

### 46.7 0.01 0.911 54% 0.004

Mobile Combustion: Marine CO 2 

### 46.5 0.01 0.918 8% 0.001

Mobile Combustion: Road & Other N 2 

### O 41.6 0.01 0.924 19% 0.001

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 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 CH 4 

### 33.9 <0.01 0.940 144% 0.007

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 33.7 <0.01 0.945 43% 0.002

CO 2 

### Emissions from Cement Production CO 2 

### 33.3 <0.01 0.949 13% 0.001

CH 4 

### Emissions from Manure Management CH 4 

### 30.4 <0.01 0.954 20% 0.001

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 26.8 <0.01 0.958 22% 0.001

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 23.5 <0.01 0.961 49% 0.002

CO 2 

### Emissions from Landfilled Yard Trimmings and Food

Scraps CO 2 

### 23.5 <0.01 0.964 84% 0.003

CO 2 

### Emissions from Cropland Remaining Cropland CO 2 

### 22.3 <0.01 0.968 56% 0.002

CO 2 

### Emissions from Land Converted to Grassland CO 2 

### 22.3 <0.01 0.971 9% <0.001

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 20.0 <0.01 0.974 44% 0.001

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

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 16.8 <0.01 0.979 12% <0.001

N 2 

### O Emissions from Adipic Acid Production N 2 

### O 15.3 <0.01 0.981 20% <0.001

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 12.8 <0.01 0.983 187% 0.003

N 2 

### O Emissions from Manure Management N 2 

### O 12.1 <0.01 0.985 24% <0.001

CO 2 

### Emissions from Lime Production CO 2 

### 11.5 <0.01 0.986 9% <0.001

CO 2 

### Emissions from Waste Incineration CO 2 

### 10.9 <0.01 0.988 27% <0.001

Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 7.4 <0.01 0.989 128% 0.001

CH 4 

### Emissions from Rice Cultivation CH 4 

### 7.1 <0.01 0.990 164% 0.002

CO 2 

### Emissions from Aluminum Production CO 2 

### 6.8 <0.01 0.991 4% <0.001

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 6.0 <0.01 0.992 23% <0.001

SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 5.4 <0.01 0.993 13% <0.001

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 5.1 <0.01 0.993 16% <0.001

CH 4 

### Emissions from Forest Fires CH 4 

### 4.6 <0.01 0.994 155% 0.001

Mobile Combustion: Road & Other CH 4 

### 4.5 <0.01 0.995 8% <0.001

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 <0.01 0.995 2% <0.001

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 <0.01 0.996 7% <0.001

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 3.7 <0.01 0.996 94% <0.001

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 2.9 <0.01 0.997 9% <0.001

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.2 <0.01 0.997 40% <0.001

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 2.2 <0.01 0.997 12% <0.001

CO 2 

### Emissions from Land Converted to Cropland CO 2 

### 2.2 <0.01 0.998 40% <0.001

Mobile Combustion: Aviation N 2 

### O 1.7 <0.01 0.998 19% <0.001

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.5 <0.01 0.998 18% <0.001

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.4 <0.01 0.998 22% <0.001

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.2 <0.01 0.999 13% <0.001

N 2 

### O Emissions from Soils N 2 

### O 1.0 <0.01 0.999 211% <0.001

CO 2 

### Emissions from Wetlands Remaining Wetlands CO 2 

### 1.0 <0.01 0.999 31% <0.001

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 1.0 <0.01 0.999 8% <0.001

CO 2 

### Emissions from Zinc Production CO 2 

### 0.9 <0.01 0.999 25% <0.001

CH 4 

### Emissions from Petrochemical Production CH 4 

### 0.9 <0.01 0.999 31% <0.001

CH 4 

### Emissions from Field Burning of Agricultural Residues CH 4 

### 0.7 <0.01 0.999 94% <0.001

### A-13 IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### N 2 

### O Emissions from Waste Incineration N 2 

### O 0.5 <0.01 0.999 191% <0.001

N 2 

### O Emissions from Forest Fires N 2 

### O 0.5 <0.01 1.000 152% <0.001

CO 2 

### Emissions from Stationary Combustion - Geothermal

Energy CO 2 

### 0.4 <0.01 1.000 5% <0.001

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.4 <0.01 1.000 144% <0.001

Mobile Combustion: Marine N 2 

### O 0.4 <0.01 1.000 19% <0.001

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.4 <0.01 1.000 10% <0.001

N 2 

### O Emissions from Field Burning of Agricultural Residues N 2 

### O 0.4 <0.01 1.000 85% <0.001

N 2 

### O Emission from Composting N 2 

### O 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

CH 4 

### Emissions from Composting CH 4 

### 0.3 <0.01 1.000 50% <0.001

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 <0.01 1.000 17% <0.001

Mobile Combustion: Aviation CH 4 

### 0.2 <0.01 1.000 8% <0.001

Mobile Combustion: Marine CH 4 

### 0.1 <0.01 1.000 8% <0.001

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + <0.01 1.000 10% <0.001

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + <0.01 1.000 12% <0.001

N 2 

### O Emissions from Wetlands Remaining Wetlands N 2 

### O + <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 a lways positive.

+ Does not exceed 0.05 Tg CO 2 Eq.



### Table A- 6: 2007 Key Source Category Tier 1 an d Tier 2 Analysis—Level As sessment, without LULUCF

IPCC Source Categories Direct

GHG 2007 Estimate

(Tg CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 2,086.5 0.29 0.29 9% 0.028

Mobile Combustion: Road & Other CO 2 

### 1,649.1 0.23 0.53 8% 0.018

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 1,181.1 0.17 0.69 7% 0.011

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 580.4 0.08 0.77 6% 0.005

Mobile Combustion: Aviation CO 2 

### 187.5 0.03 0.80 8% 0.002

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 172.0 0.02 0.82 54% 0.013

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 139.0 0.02 0.84 18% 0.004

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 133.9 0.02 0.86 20% 0.004

CH 4 

### Emissions from Landfills CH 4 

### 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 CH 4 

### 104.7 0.01 0.91 43% 0.006

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 77.4 0.01 0.92 22% 0.002

Fugitive Emissions from Coal Mining CH 4 

### 57.6 0.01 0.93 24% 0.002

Mobile Combustion: Marine CO 2 

### 50.8 0.01 0.94 8% 0.001

CO 2 

### Emissions from Cement Production CO 2 

### 44.5 0.01 0.94 13% 0.001

CH 4 

### Emissions from Manure Management CH 4 

### 44.0 0.01 0.95 20% 0.001

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 35.9 0.01 0.95 136% 0.007

Fugitive Emissions from Petroleum Systems CH 4 

### 28.8 <0.01 0.96 144% 0.006

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 28.7 <0.01 0.96 43% 0.002

Mobile Combustion: Road & Other N 2 

### O 27.9 <0.01 0.97 19% 0.001

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 24.4 <0.01 0.97 49% 0.002

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 21.7 <0.01 0.97 44% 0.001

CO 2 

### Emissions from Waste Incineration CO 2 

### 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

N 2 

### O Emissions from Manure Management N 2 

### O 14.7 <0.01 0.98 24% <0.001

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 14.7 <0.01 0.98 187% 0.004

CO 2 

### Emissions from Lime Production CO 2 

### 14.6 <0.01 0.98 9% <0.001

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 13.8 <0.01 0.99 12% <0.001

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 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 CO 2 Eq.) Tier 1 Level

Assessment Cumulative

Total Uncertainty a Tier 2 Level

Assessment



### Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 6.6 <0.01 0.99 128% 0.001

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 6.2 <0.01 0.99 16% <0.001

CH 4 

### Emissions from Rice Cultivation CH 4 

### 6.2 <0.01 0.99 164% 0.001

N 2 

### O Emissions from Adip ic Acid Production N 2 

### O 5.9 <0.01 0.99 20% <0.001

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 5.7 <0.01 0.99 23% <0.001

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 4.9 <0.01 0.99 94% 0.001

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 4.7 <0.01 0.99 9% <0.001

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 <0.01 0.99 2% <0.001

CO 2 

### Emissions from Aluminum Production CO 2 

### 4.3 <0.01 1.00 4% <0.001

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 <0.01 1.00 7% <0.001

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

SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 3.0 <0.01 1.00 13% <0.001

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.6 <0.01 1.00 40% <0.001

Mobile Combustion: Road & Other CH 4 

### 2.0 <0.01 1.00 8% <0.001

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.9 <0.01 1.00 13% <0.001

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.9 <0.01 1.00 22% <0.001

N 2 

### O Emission from Composting N 2 

### O 1.8 <0.01 1.00 50% <0.001

Mobile Combustion: Aviation N 2 

### O 1.8 <0.01 1.00 19% <0.001

CH 4 

### Emissions from Composting CH 4 

### 1.7 <0.01 1.00 50% <0.001

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 1.6 <0.01 1.00 12% <0.001

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.2 <0.01 1.00 18% <0.001

CH 4 

### Emissions from Petrochemical Production CH 4 

### 1.0 <0.01 1.00 31% <0.001

CH 4 

### Emissions from Field Burning of Agricultural

Residues CH 4 

### 0.9 <0.01 1.00 94% <0.001

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 0.7 <0.01 1.00 8% <0.001

CO 2 

### Emissions from Zinc Production CO 2 

### 0.5 <0.01 1.00 25% <0.001

N 2 

### O Emissions from Field Burning of Agricultural

Residues N 2 

### O 0.5 <0.01 1.00 85% <0.001

Mobile Combustion: Marine N 2 

### O 0.4 <0.01 1.00 19% <0.001

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.4 <0.01 1.00 191% <0.001

CO 2 

### Emissions from Stationary Combustion - Geothermal

Energy CO 2 

### 0.4 <0.01 1.00 5% <0.001

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.3 <0.01 1.00 144% <0.001

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 <0.01 1.00 17% <0.001

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.2 <0.01 1.00 10% <0.001

Mobile Combustion: Aviation CH 4 

### 0.1 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH 4 

### 0.1 <0.01 1.00 8% <0.001

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + <0.01 1.00 12% <0.001

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + <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 a lways positive.

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

+ Does not exceed 0.05 Tg CO 2 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 Uncertainty a Tier 2 Level

Assessment



### CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 2,086.5 0.25 0.25 9% 0.024

Mobile Combustion: Road & Other CO 2 

### 1,649.1 0.20 0.46 8% 0.015

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 1,181.1 0.14 0.60 7% 0.009

CO 2 

### from Changes in Forest Carbon Stocks CO 2 

### 910.1 0.11 0.71 19% 0.021

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 580.4 0.07 0.78 6% 0.004

Mobile Combustion: Aviation CO 2 

### 187.5 0.02 0.80 8% 0.002

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 172.0 0.02 0.82 54% 0.011

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 139.0 0.02 0.84 18% 0.003

### A-15 CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 133.9 0.02 0.86 20% 0.003

CH 4 

### Emissions from Landfills CH 4 

### 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 CH 4 

### 104.7 0.01 0.90 43% 0.006

CO 2 

### Emissions from Urban Trees CO 2 

### 97.6 0.01 0.91 21% 0.002

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 77.4 0.01 0.92 22% 0.002

Fugitive Emissions from Coal Mining CH 4 

### 57.6 0.01 0.93 24% 0.002

Mobile Combustion: Marine CO 2 

### 50.8 0.01 0.93 8% <0.001

CO 2 

### Emissions from Cement Production CO 2 

### 44.5 0.01 0.94 13% 0.001

CH 4 

### Emissions from Manure Management CH 4 

### 44.0 0.01 0.95 20% 0.001

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 35.9 <0.01 0.95 136% 0.006

CH 4 

### Emissions from Forest Fires CH 4 

### 29.0 <0.01 0.95 155% 0.005

Fugitive Emissions from Petroleum Systems CH 4 

### 28.8 <0.01 0.96 144% 0.005

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 28.7 <0.01 0.96 43% 0.002

Mobile Combustion: Road & Other N 2 

### O 27.9 <0.01 0.96 19% 0.001

CO 2 

### Emissions from Land Converted to Grassland CO 2 

### 26.7 <0.01 0.97 9% <0.001

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 24.4 <0.01 0.97 49% 0.001

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 21.7 <0.01 0.97 44% 0.001

CO 2 

### Emissions from Waste Incineration CO 2 

### 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

N 2 

### O Emissions from Manure Management N 2 

### O 14.7 <0.01 0.98 24% <0.001

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 14.7 <0.01 0.98 187% 0.003

CO 2 

### Emissions from Lime Production CO 2 

### 14.6 <0.01 0.98 9% <0.001

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 13.8 <0.01 0.98 12% <0.001

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 12.7 <0.01 0.99 22% <0.001

CO 2 

### Emissions from Cropland Remaining Cropland CO 2 

### 11.6 <0.01 0.99 56% 0.001

CO 2 

### Emissions from Landfilled Yard Trimmings and Food

Scraps CO 2 

### 9.8 <0.01 0.99 84% 0.001

Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 6.6 <0.01 0.99 128% 0.001

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 6.2 <0.01 0.99 16% <0.001

CH 4 

### Emissions from Rice Cultivation CH 4 

### 6.2 <0.01 0.99 164% 0.001

CO 2 

### Emissions from Land Converted to Cropland CO 2 

### 5.9 <0.01 0.99 40% <0.001

N 2 

### O Emissions from Adipic Acid Production N 2 

### O 5.9 <0.01 0.99 20% <0.001

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 5.7 <0.01 0.99 23% <0.001

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 4.9 <0.01 0.99 94% 0.001

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 4.7 <0.01 0.99 9% <0.001

CO 2 

### Emissions from Grassland Remaining Grassland CO 2 

### 4.7 <0.01 0.99 54% <0.001

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 <0.01 0.99 2% <0.001

CO 2 

### Emissions from Aluminum Production CO 2 

### 4.3 <0.01 1.00 4% <0.001

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 <0.01 1.00 7% <0.001

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

SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 3.0 <0.01 1.00 13% <0.001

N 2 

### O Emissions from Forest Fires N 2 

### O 2.9 <0.01 1.00 152% 0.001

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.6 <0.01 1.00 40% <0.001

Mobile Combustion: Road & Other CH 4 

### 2.0 <0.01 1.00 8% <0.001

N 2 

### O Emissions from Soils N 2 

### O 1.9 <0.01 1.00 211% <0.001

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.9 <0.01 1.00 13% <0.001

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.9 <0.01 1.00 22% <0.001

N 2 

### O Emission from Composting N 2 

### O 1.8 <0.01 1.00 50% <0.001

Mobile Combustion: Aviation N 2 

### O 1.8 <0.01 1.00 19% <0.001

CH 4 

### Emissions from Composting CH 4 

### 1.7 <0.01 1.00 50% <0.001

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 1.6 <0.01 1.00 12% <0.001

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.2 <0.01 1.00 18% <0.001

### A-16 DRAFT Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 CH 4 

### Emissions from Petrochemical Production CH 4 

### 1.0 <0.01 1.00 31% <0.001

CO 2 

### Emissions from Wetlands Remaining Wetlands CO 2 

### 1.0 <0.01 1.00 31% <0.001

CH 4 

### Emissions from Field Burning of Agricultural

Residues CH 4 

### 0.9 <0.01 1.00 94% <0.001

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 0.7 <0.01 1.00 8% <0.001

CO 2 

### Emissions from Zinc Production CO 2 

### 0.5 <0.01 1.00 25% <0.001

N 2 

### O Emissions from Field Burning of Agricultural

Residues N 2 

### O 0.5 <0.01 1.00 85% <0.001

Mobile Combustion: Marine N 2 

### O 0.4 <0.01 1.00 19% <0.001

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.4 <0.01 1.00 191% <0.001

CO 2 

### Emissions from Stationary Combustion -

Geothermal Energy CO 2 

### 0.4 <0.01 1.00 5% <0.001

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.3 <0.01 1.00 144% <0.001

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 <0.01 1.00 17% <0.001

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.2 <0.01 1.00 10% <0.001

Mobile Combustion: Aviation CH 4 

### 0.1 <0.01 1.00 8% <0.001

Mobile Combustion: Marine CH 4 

### 0.1 <0.01 1.00 8% <0.001

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + <0.01 1.00 12% <0.001

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + <0.01 1.00 10% <0.001

N 2 

### O Emissions from Wetlands Remaining Wetlands N 2 

### O + <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 a lways positive.

+ Does not exceed 0.05 Tg CO 2 Eq.



### Table A- 8: 1990-2007 Key Source Category Ti er 1 Analysis—Trend Asse ssment, without LULUCF

IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) 2007 Estimate

(Tg CO 2 Eq.) Tier 1 Trend

Assessment Tier 2 Trend

Assessment Percent

Contribution to

Trend (%) Cumulative

Contribution to

Trend (%)



### Mobile Combustion: Road & Other CO 2 

### 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

CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 1695.9 2086.5 0.01 0.001 11.1 41

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 562.7 580.4 0.01 0.001 8.1 49

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 965.5 1181.1 0.01 <0.001 5.6 55

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 109.8 77.4 0.01 0.001 5.3 60

Fugitive Emissions from Natural Gas Systems CH 4 

### 129.6 104.7 0.01 0.002 4.9 65

CH 4 

### Emissions from Landfills CH 4 

### 149.2 132.9 <0.01 0.002 4.4 70

Fugitive Emissions from Coal Mining CH 4 

### 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 CO 2 

### 179.4 187.5 <0.01 <0.001 2.3 79

Mobile Combustion: Road & Other N 2 

### O 41.6 27.9 <0.01 <0.001 2.2 81

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 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

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 133.2 139.0 <0.01 <0.001 1.7 87

Direct N 2 

### O Emissions from Agricultural Soil

Management N 2 

### O 158.9 172.0 <0.01 0.001 1.4 88

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 41.5 35.9 <0.01 0.002 1.3 89

N 2 

### O Emissions from Adip ic Acid Production N 2 

### O 15.3 5.9 <0.01 <0.001 1.3 91

Fugitive Emissions from Petroleum Systems CH 4 

### 33.9 28.8 <0.01 0.002 1.1 92

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 33.7 28.7 <0.01 0.001 1.1 93

CH 4 

### Emissions from Manure Management CH 4 

### 30.4 44.0 <0.01 <0.001 0.9 94

CO 2 

### Emissions from Waste Incineration CO 2 

### 10.9 20.8 <0.01 <0.001 0.8 95

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 16.8 13.8 <0.01 <0.001 0.6 95

CO 2 

### Emissions from Cement Production CO 2 

### 33.3 44.5 <0.01 <0.001 0.6 96

CO 2 

### Emissions from Aluminum Production CO 2 

### 6.8 4.3 <0.01 <0.001 0.4 96

Mobile Combustion: Marine CO 2 

### 46.5 50.8 <0.01 <0.001 0.4 97

### A-17 IPCC Source Categories Direct

GHG 1990 Estimate

(Tg CO 2 Eq.) 2007 Estimate

(Tg CO 2 Eq.) Tier 1 Trend

Assessment Tier 2 Trend

Assessment Percent

Contribution to

Trend (%) Cumulative

Contribution to

Trend (%)



### SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 5.4 3.0 <0.01 <0.001 0.4 97

Mobile Combustion: Road & Other CH 4 

### 4.5 2.0 <0.01 <0.001 0.3 97

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 23.5 24.4 <0.01 <0.001 0.3 98

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 117.0 133.9 <0.01 <0.001 0.3 98

CH 4 

### Emissions from Rice Cultivation CH 4 

### 7.1 6.2 <0.01 <0.001 0.2 98

Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 7.4 6.6 <0.01 <0.001 0.2 98

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 20.0 21.7 <0.01 <0.001 0.2 99

N 2 

### O Emission from Composting N 2 

### O 0.4 1.8 <0.01 <0.001 0.1 99

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 6.0 5.7 <0.01 <0.001 0.1 99

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 2.9 4.7 <0.01 <0.001 0.1 99

CH 4 

### Emissions from Composting CH 4 

### 0.3 1.7 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Lime Production CO 2 

### 11.5 14.6 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 2.2 1.6 <0.01 <0.001 0.1 99

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 4.4 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 4.1 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.5 1.2 <0.01 <0.001 0.1 100

N 2 

### O Emissions from Manure Management N 2 

### O 12.1 14.7 <0.01 <0.001 0.1 100

CO 2 

### Emissions from Zinc Production CO 2 

### 0.9 0.5 <0.01 <0.001 0.1 100

N 2 

### O Emissions from Wastewater Treatment N 2 

### O 3.7 4.9 <0.01 <0.001 0.1 100

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.2 1.9 <0.01 <0.001 0.1 100

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 1.0 0.7 <0.01 <0.001 <0.1 100

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 12.8 14.7 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.4 0.2 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.4 1.9 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation N 2 

### O 1.7 1.8 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 5.1 6.2 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.5 0.4 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.4 0.3 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Field Burning of Agricultural

Residues CH 4 

### 0.7 0.9 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Stationary Combustion -

Geothermal Energy CO 2 

### 0.4 0.4 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 0.3 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Field Burning of Agricultural

Residues N 2 

### O 0.4 0.5 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.2 2.6 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation CH 4 

### 0.2 0.1 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + + <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Petrochemical Production CH 4 

### 0.9 1.0 <0.01 <0.001 <0.1 100

Mobile Combustion: Marine N 2 

### O 0.4 0.4 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + + <0.01 <0.001 <0.1 100

Mobile Combustion: Marine CH 4 

### 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 CO 2 Eq.



### Table A- 9: 1990-2007 Key Source Category Ti er 1 Analysis—Trend A ssessment, with LULUCF

IPCC Source Categories Direct

GHG 1990

Estimate

(Tg CO 2 Eq.) 2007 Estimate

(Tg CO 2 Eq.) Tier 1 Trend

Assessment Tier 2 Trend

Assessment Percent

Contribution to

Trend (%) Cumulative

Contribution to

Trend (%)



### Mobile Combustion: Road & Other CO 2 

### 1258.7 1649.1 0.02 0.001 13.4 13

CO 2 

### from Changes in Forest Carbon Stocks CO 2 

### 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 CO 2 Eq.) 2007 Estimate

(Tg CO 2 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

CO 2 

### Emissions from Stationary Combustion - Oil CO 2 

### 562.7 580.4 0.01 0.001 7.3 40

CO 2 

### Emissions from Stationary Combustion - Coal CO 2 

### 1695.9 2086.5 0.01 0.001 6.6 47

CO 2 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CO 2 

### 109.8 77.4 0.01 0.001 4.4 51

CO 2 

### Emissions from Grassland Remaining Grassland CO 2 

### 46.7 4.7 0.01 0.003 4.3 56

Fugitive Emissions from Natural Gas Systems CH 4 

### 129.6 104.7 0.01 0.002 4.1 60

CH 4 

### Emissions from Landfills CH 4 

### 149.2 132.9 <0.01 0.002 3.7 64

Fugitive Emissions from Coal Mining CH 4 

### 84.1 57.6 <0.01 0.001 3.5 67

CO 2 

### Emissions from Stationary Combustion - Gas CO 2 

### 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

CO 2 

### Emissions from Urban Trees CO 2 

### 60.6 97.6 <0.01 0.001 2.2 75

Mobile Combustion: Aviation CO 2 

### 179.4 187.5 <0.01 <0.001 2.1 77

CH 4 

### Emissions from Forest Fires CH 4 

### 4.6 29.0 <0.01 0.004 2.0 79

Mobile Combustion: Road & Other N 2 

### O 41.6 27.9 <0.01 <0.001 1.8 81

SF 6 

### Emissions from Electrical Transmission and

Distribution SF 6 

### 26.8 12.7 <0.01 <0.001 1.6 82

CH 4 

### Emissions from Enteric Fermentation CH 4 

### 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

CO 2 

### Emissions from Landfilled Yard Trimmings and Food

Scraps CO 2 

### 23.5 9.8 <0.01 0.002 1.5 87

Direct N 2 

### O Emissions from Agricultural Soil Management N 2 

### O 158.9 172.0 <0.01 0.001 1.4 88

CO 2 

### Emissions from Cropland Remaining Cropland CO 2 

### 22.3 11.6 <0.01 0.001 1.2 89

Indirect N 2 

### O Emissions from Applied Nitrogen N 2 

### O 41.5 35.9 <0.01 0.002 1.1 91

N 2 

### O Emissions from Adip ic Acid Production N 2 

### O 15.3 5.9 <0.01 <0.001 1.0 92

Fugitive Emissions from Petroleum Systems CH 4 

### 33.9 28.8 <0.01 0.002 1.0 93

CO 2 

### Emissions from Natural Gas Systems CO 2 

### 33.7 28.7 <0.01 0.001 1.0 93

CH 4 

### Emissions from Manure Management CH 4 

### 30.4 44.0 <0.01 <0.001 0.7 94

CO 2 

### Emissions from Waste Incineration CO 2 

### 10.9 20.8 <0.01 <0.001 0.7 95

CO 2 

### Emissions from Ammonia Production and Urea

Consumption CO 2 

### 16.8 13.8 <0.01 <0.001 0.5 95

CO 2 

### Emissions from Cement Production CO 2 

### 33.3 44.5 <0.01 <0.001 0.4 96

CO 2 

### Emissions from Non-Energy Use of Fuels CO 2 

### 117.0 133.9 <0.01 <0.001 0.4 96

Mobile Combustion: Marine CO 2 

### 46.5 50.8 <0.01 <0.001 0.4 97

CO 2 

### Emissions from Aluminum Production CO 2 

### 6.8 4.3 <0.01 <0.001 0.3 97

CH 4 

### Emissions from Wastewater Treatment CH 4 

### 23.5 24.4 <0.01 <0.001 0.3 97

SF 6 

### Emissions from Magnesium Production and

Processing SF 6 

### 5.4 3.0 <0.01 <0.001 0.3 97

CO 2 

### Emissions from Land Converted to Cropland CO 2 

### 2.2 5.9 <0.01 <0.001 0.3 98

Mobile Combustion: Road & Other CH 4 

### 4.5 2.0 <0.01 <0.001 0.3 98

N 2 

### O Emissions from Forest Fires N 2 

### O 0.5 2.9 <0.01 <0.001 0.2 98

CH 4 

### Emissions from Rice Cultivation CH 4 

### 7.1 6.2 <0.01 <0.001 0.2 98

Non-CO 2 

### Emissions from Stationary Combustion CH 4 

### 7.4 6.6 <0.01 <0.001 0.2 99

N 2 

### O Emissions from Nitric Acid Production N 2 

### O 20.0 21.7 <0.01 <0.001 0.2 99

Fugitive Emissions from Abandoned Underground Coal

Mines CH 4 

### 6.0 5.7 <0.01 <0.001 0.1 99

N 2 

### O Emission from Composting N 2 

### O 0.4 1.8 <0.01 <0.001 0.1 99

CH 4 

### Emissions from Composting CH 4 

### 0.3 1.7 <0.01 <0.001 0.1 99

PFC, HFC, and SF 6 

### Emissions from Semiconductor

Manufacture Several 2.9 4.7 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Ferroalloy Production CO 2 

### 2.2 1.6 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Lime Production CO 2 

### 11.5 14.6 <0.01 <0.001 0.1 99

N 2 

### O Emissions from N 2 

### O Product Uses N 2 

### O 4.4 4.4 <0.01 <0.001 0.1 99

CO 2 

### Emissions from Soda Ash Production and

Consumption CO 2 

### 4.1 4.1 <0.01 <0.001 0.1 99

N 2 

### O Emissions from Soils N 2 

### O 1.0 1.9 <0.01 <0.001 0.1 100

CO 2 

### Emissions from Phosphoric Acid Production CO 2 

### 1.5 1.2 <0.01 <0.001 0.1 100

CO 2 

### Emissions from Zinc Production CO 2 

### 0.9 0.5 <0.01 <0.001 0.1 100

### A-19 IPCC Source Categories Direct

GHG 1990

Estimate

(Tg CO 2 Eq.) 2007 Estimate

(Tg CO 2 Eq.) Tier 1 Trend

Assessment Tier 2 Trend

Assessment Percent

Contribution to

Trend (%) Cumulative

Contribution to

Trend (%)



### N 2 

### O Emissions from Wastewater Treatment N 2 

### O 3.7 4.9 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Titanium Dioxide Production CO 2 

### 1.2 1.9 <0.01 <0.001 <0.1 100

Non-CO 2 

### Emissions from Stationary Combustion N 2 

### O 12.8 14.7 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Iron and Steel Production &

Metallurgical Coke Production CH 4 

### 1.0 0.7 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Manure Management N 2 

### O 12.1 14.7 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Land Converted to Grassland CO 2 

### 22.3 26.7 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Silicon Carbide Production and

Consumption CO 2 

### 0.4 0.2 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation N 2 

### O 1.7 1.8 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Wetlands Remaining Wetlands CO 2 

### 1.0 1.0 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Carbon Dioxide Consumption CO 2 

### 1.4 1.9 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Waste Incineration N 2 

### O 0.5 0.4 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Petroleum Systems CO 2 

### 0.4 0.3 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Limestone and Dolomite Use CO 2 

### 5.1 6.2 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Stationary Combustion -

Geothermal Energy CO 2 

### 0.4 0.4 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Field Burning of Agricultural

Residues CH 4 

### 0.7 0.9 <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Lead Production CO 2 

### 0.3 0.3 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Field Burning of Agricultural

Residues N 2 

### O 0.4 0.5 <0.01 <0.001 <0.1 100

Mobile Combustion: Aviation CH 4 

### 0.2 0.1 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Silicon Carbide Production and

Consumption CH 4 

### + + <0.01 <0.001 <0.1 100

Mobile Combustion: Marine N 2 

### O 0.4 0.4 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Ferroalloy Production CH 4 

### + + <0.01 <0.001 <0.1 100

CO 2 

### Emissions from Petrochemical Production CO 2 

### 2.2 2.6 <0.01 <0.001 <0.1 100

CH 4 

### Emissions from Petrochemical Production CH 4 

### 0.9 1.0 <0.01 <0.001 <0.1 100

Mobile Combustion: Marine CH 4 

### 0.1 0.1 <0.01 <0.001 <0.1 100

N 2 

### O Emissions from Wetlands Remaining Wetlands N 2 

### O + + <0.01 <0.001 <0.1 100

+ Does not exceed 0.05 Tg CO 2 Eq.



## References

Flugsrud, K., W. Irving, and K. Rypdal (1999) Methodologi cal 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 CO 2 

# Emissions from Fossil

Fuel Combustion

2.1. Methodology for Estimating Emissions of CO 2 

# from Fossil Fuel Combustion

Carbon dioxide (CO 2 

### ) 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 CO 2 

### 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 Ta ble 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, a nd 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, indus trial, and other). Because the “other” fuel use includes sale s

to both the commercial and transportation sectors, EIA’s limited transportation el ectricity 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 electr icity 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. territ ories were added to domestic consumption of fossil fuels.

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

reported separately from domestic sectoral consumption, becau se 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 ot her 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 Industria l 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 th erefore 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 fo r 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 CO 2 

### . The synthetic natural gas enters the natural gas distribution system.

Since October 2000, a portion of the CO 2 

### produced by the coal gasification plant has been exported to Canada by pipeline.

The remainder of the CO 2 

### 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 fo r 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 CO 2 

### is not emitted to the

atmosphere in the United States, and therefore the energy used to produce this amount of CO 2 

### 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 CO 2 

### emissions, the amount of ethanol added is subtracted

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

than in EIA sources.

Third, EIA natural gas consumption statistics include “b iomass 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 sli ghtly 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 al so 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 IP CC (IPCC/UNEP/OECD/IEA 1997). There is currently disagreement internationally

as to how these emissions should be allocated, and until th is 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 tran sportation 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 CO 2 

### Emissions

Actual CO 2 

### 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 CO 2 

### Eq.). To convert from

C content to CO 2 

### emissions, the fraction of C that is oxidized wa s 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 electrici ty consumption (see Table A-33). This pro-rated approach

to allocating emissions from electricity generation may overestim ate 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 a nd 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: 1990-2007Table 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 13Total Consumption (TBtu)aAdjustments (TBtu)bUnadjusted NEU ConsumptionFuel TypeRes.Comm.Ind.Trans.Elec.Terr.TotalBunkerFuelInd. Trans. Terr.TotalAdjustedConsumptionTotal Coal 6.071.01,187.8NE20,835.044.522,144.445.322,099.0Residential Coal 6.06.06.0Commercial Coal 71.071.071.0Industrial Other Coal 1,154.91,154.912.41,142.5Transportation CoalNENEElectric Power Coal20,835.020,835.020,835.0U.S. Territory Coal (bit)44.5 44.544.5Natural Gas 4,842.03,080.07,680.9667.07,046.026.723,342.6413.722,929.0Total Petroleum 1,201.1611.29,746.727,505.8659.0710.240,434.01,462.94,923.3152.0100.933,794.9Asphalt & Road Oil 1,197.01,197.01,197.0Aviation Gasoline32.032.032.0Distillate Fuel Oil 698.1 369.11,204.5 6,544.5 92.0 123.7 9,032.0111.311.78,909.0Jet Fuel3,358.0 NA65.1 3,423.1744.12,679.0Kerosene 40.0 9.0 18.05.1 72.172.1LPG 463.0 82.0 2,162.0 26.00.7 2,733.71,540.01,193.6Lubricants 161.0 152.04.9 317.9161.0 152.0 4.9Motor Gasoline 71.8 578.0 16,460.3186.0 17,296.117,296.1Residual Fuel 79.0204.0 933.0 399.0228.9 1,843.9607.51,236.3Other PetroleumAvGas Blend Components 1.81.81.8Crude OilMoGas Blend ComponentsMisc. Products 133.596.0 229.4133.596.0Naphtha (<401 deg. F) 562.5562.5562.5Other Oil (>401 deg. F) 744.1744.1744.1Pentanes Plus 179.4179.4137.142.3Petroleum Coke 0.3 909.0168.01,077.3248.2829.1Still Gas 1,526.91,526.988.41,438.4Special Naphtha 78.078.078.0Unfinished Oils 65.265.265.2Waxes 21.921.921.9Geothermal50.650.650.6TOTAL (All Fuels) 6,049.13,762.218,615.428,172.828,590.6781.485,971.61,462.95,382.3152.0100.978,873.5aExpressed as gross calorific values(i.e., higher heating values).bAdjustments are subtracted from total consumption estimates and include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and international bunkerfuel consumption (see Table A-30).NE (Not Estimated)A-25Table A-11: 2007 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 6.071.01,142.5NE20,835.044.522,099.00.66.8107.4NE1,967.64.12,086.5Residential Coal 6.06.00.60.6Commercial Coal 71.071.06.86.8Industrial Other Coal1,142.51,142.5107.4107.4Transportation CoalNENEElectric Power Coal20,835.020,835.01,967.61,967.6U.S. Territory Coal (bit)44.5 44.54.1 4.1Natural Gas 4,842.03,080.07,267.3667.07,046.026.722,929.0256.9163.4385.635.4373.81.41,216.5Total Petroleum 1,201.1611.24,823.425,890.9659.0609.333,794.983.244.2352.51,852.055.345.32,432.4Asphalt & Road OilAviation Gasoline32.032.02.22.2Distillate Fuel Oil 698.1 369.11,192.8 6,433.2 92.0 123.7 8,909.051.1 27.0 87.3 470.6 6.7 9.0 651.7Jet Fuel2,613.9 NA65.1 2,679.0185.34.6 189.9Kerosene 40.0 9.0 18.05.1 72.12.9 0.7 1.30.4 5.2LPG 463.0 82.0 622.0 26.00.7 1,193.629.2 5.2 39.2 1.60.0 75.3LubricantsMotor Gasoline 71.8 578.0 16,460.3186.0 17,296.15.1 41.0 1,166.713.2 1,225.9Residual Fuel 79.0204.0 325.5 399.0228.9 1,236.36.2 16.1 25.631.4 18.0 97.4Other PetroleumAvGas Blend Components1.81.80.10.1Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus42.342.32.82.8Petroleum Coke 0.3 660.8168.0829.10.0 67.517.284.7Still Gas1,438.41,438.492.492.4Special NaphthaUnfinished Oils65.265.24.94.9WaxesGeothermal50.650.60.40.4TOTAL (All Fuels) 6,049.13,762.213,233.126,557.928,590.6680.578,873.5340.6214.4845.41,887.42,397.250.85,735.8aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-12: 2006 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 5.765.11,213.8NE20,461.443.621,789.50.56.2114.1NE1,932.44.02,057.2Residential Coal 5.75.70.50.5Commercial Coal 65.165.16.26.2Industrial Other Coal1,213.81,213.8114.1114.1Transportation CoalNENEElectric Power Coal20,461.420,461.41,932.41,932.4U.S. Territory Coal (bit)43.6 43.64.0 4.0Natural Gas 4,463.52,899.37,091.0630.76,388.226.121,498.8236.8153.8376.233.5338.91.41,140.7Total Petroleum 1,219.1636.84,841.025,830.7648.1664.933,840.584.546.0354.21,847.455.649.42,437.2Asphalt & Road OilAviation Gasoline33.433.42.32.3Distillate Fuel Oil 694.9 391.61,208.6 6,352.2 73.7 134.7 8,855.750.8 28.6 88.4 464.7 5.4 9.9 647.8Jet Fuel2,609.6 NA69.4 2,679.0185.04.9 189.9Kerosene 66.4 15.2 29.65.5 116.64.8 1.1 2.10.4 8.4LPG 457.8 80.8 619.4 25.80.7 1,184.528.9 5.1 39.1 1.60.0 74.7LubricantsMotor Gasoline 73.7 582.2 16,503.3203.6 17,362.95.2 41.3 1,169.714.4 1,230.6Residual Fuel 75.3195.3 306.3 360.5250.9 1,188.35.9 15.4 24.128.4 19.8 93.6Other PetroleumAvGas Blend Components0.60.60.00.0Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus32.832.82.22.2Petroleum Coke 0.3 672.8213.9886.90.0 68.721.890.6Still Gas1,429.41,429.491.891.8Special NaphthaUnfinished Oils70.370.35.25.2WaxesGeothermal49.749.70.40.4TOTAL (All Fuels) 5,688.23,601.213,145.726,461.427,547.4734.677,178.5321.9206.0844.51,880.92,327.354.85,635.4aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-27Table A-13: 2005 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 8.395.71,236.2NE20,736.940.122,117.20.89.1116.2NE1,958.43.72,088.2Residential Coal 8.38.30.80.8Commercial Coal 95.795.79.19.1Industrial Other Coal1,236.21,236.2116.2116.2Transportation CoalNENEElectric Power Coal20,736.920,736.91,958.41,958.4U.S. Territory Coal (bit)40.1 40.13.7 3.7Natural Gas 4,939.03,073.77,196.3626.36,030.124.321,889.7262.0163.1381.833.2319.91.31,161.4Total Petroleum 1,373.7682.74,500.725,853.21,234.5648.734,293.595.249.6330.01,848.2102.348.22,473.5Asphalt & Road OilAviation Gasoline35.435.42.42.4Distillate Fuel Oil 773.1 405.0 1,129.76,192.2 114.6131.5 8,746.156.6 29.6 82.6 453.0 8.4 9.6 639.8Jet Fuel2,678.7 NA67.7 2,746.4189.94.8 194.7Kerosene 83.8 21.6 39.15.4 149.86.1 1.6 2.80.4 10.8LPG 516.8 91.2 559.2 26.60.7 1,194.432.6 5.7 35.3 1.70.0 75.3LubricantsMotor Gasoline 48.8 388.7 16,663.9198.7 17,300.23.5 27.6 1,181.114.1 1,226.2Residual Fuel 115.8237.4 256.4 876.5244.7 1,730.89.1 18.7 20.269.1 19.3 136.4Other PetroleumAvGas Blend Components8.38.30.60.6Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus45.945.93.13.1Petroleum Coke 0.3 660.1243.5903.90.0 67.424.992.3Still Gas1,429.41,429.491.891.8Special NaphthaUnfinished Oils2.82.80.20.2WaxesGeothermal50.150.10.40.4TOTAL (All Fuels) 6,321.03,852.112,933.126,479.528,051.6713.178,350.5358.0221.8828.01,881.52,381.053.25,723.5aExpressed as gross calorific values (i.e., higher heating values).Adjustments include biofuels,conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-14: 2004 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 11.4102.91,261.8NE20,304.939.121,720.11.19.8118.6NE1,917.63.62,050.7Residential Coal 11.411.41.11.1Commercial Coal 102.9102.99.89.8Industrial Other Coal1,261.81,261.8118.6118.6Transportation CoalNENEElectric Power Coal20,304.920,304.91,917.61,917.6U.S. Territory Coal (bit)39.1 39.13.6 3.6Natural Gas 4,949.23,181.07,729.2603.15,602.424.722,089.6262.6168.8410.132.0297.21.31,172.0Total Petroleum 1,476.4698.24,308.425,586.81,212.4653.633,935.8102.650.8315.91,828.4100.148.62,446.3Asphalt & Road OilAviation Gasoline31.231.22.22.2Distillate Fuel Oil 859.6 437.61,115.2 5,970.0 111.3 134.4 8,628.162.9 32.0 81.6 436.7 8.1 9.8 631.1Jet Fuel2,525.0 NA68.8 2,593.7179.04.9 183.8Kerosene 84.8 20.5 28.26.0 139.56.1 1.5 2.00.4 10.1LPG 532.1 93.9 603.3 18.00.8 1,248.233.5 5.9 38.0 1.10.1 78.7LubricantsMotor Gasoline 23.4 199.5 16,856.2198.6 17,277.71.7 14.1 1,194.714.1 1,224.6Residual Fuel 122.5204.7 186.4 879.0245.0 1,637.69.7 16.1 14.769.3 19.3 129.0Other PetroleumAvGas Blend Components10.610.60.70.7Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus52.152.13.53.5Petroleum Coke 0.3 687.7222.1910.00.0 70.222.792.9Still Gas1,482.61,482.695.295.2Special NaphthaUnfinished Oils(75.6)(75.6)(5.6)(5.6)WaxesGeothermal50.550.50.40.4TOTAL (All Fuels) 6,437.13,982.113,299.426,189.927,170.2717.477,796.0366.2229.3844.61,860.42,315.353.55,669.3aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-29Table A-15: 2003 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 12.482.91,248.9NE20,184.541.121,569.81.27.9117.4NE1,906.23.82,036.5Residential Coal 12.412.41.21.2Commercial Coal 82.982.97.97.9Industrial Other Coal1,248.91,248.9117.4117.4Transportation CoalNENEElectric Power Coal20,184.520,184.51,906.21,906.2U.S. Territory Coal (bit)41.1 41.13.8 3.8Natural Gas 5,206.83,276.98,064.6632.65,256.226.922,464.0276.3173.9427.933.6278.91.41,191.9Total Petroleum 1,503.2714.34,073.724,884.81,205.0621.833,002.8104.251.8299.01,777.298.145.82,376.2Asphalt & Road OilAviation Gasoline30.230.22.12.1Distillate Fuel Oil 868.9 462.41,077.6 5,661.8 161.0 120.5 8,352.363.6 33.8 78.8 414.2 11.8 8.8 611.0Jet Fuel2,480.8 NA76.1 2,556.9175.85.4 181.2Kerosene 70.3 18.6 24.110.7 123.75.1 1.3 1.70.8 8.9LPG 564.0 99.5 523.1 15.610.5 1,212.835.6 6.3 33.0 1.00.7 76.5LubricantsMotor Gasoline 22.3 122.7 16,597.1210.1 16,952.21.6 8.7 1,176.414.9 1,201.5Residual Fuel 111.1176.4 99.1 869.4193.9 1,450.08.8 13.9 7.868.5 15.3 114.3Other PetroleumAvGas Blend Components7.57.50.50.5Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus51.751.73.53.5Petroleum Coke 0.3 663.7174.7838.70.0 67.817.885.6Still Gas1,477.31,477.394.894.8Special NaphthaUnfinished Oils(50.4)(50.4)(3.7)(3.7)WaxesGeothermal49.249.20.40.4TOTAL (All Fuels) 6,722.34,074.113,387.225,517.426,694.9689.877,085.8381.7233.6844.31,810.82,283.651.05,604.9aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-16: 2002 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 12.087.81,243.5NE19,782.917.921,144.21.18.4116.9NE1,868.31.71,996.3Residential Coal 12.012.01.11.1Commercial Coal 87.887.88.48.4Industrial Other Coal1,243.51,243.5116.9116.9Transportation CoalNENEElectric Power Coal19,782.919,782.91,868.31,868.3U.S. Territory Coal (bit)17.9 17.91.7 1.7Natural Gas 4,971.03,208.18,318.2711.15,785.022.823,016.2263.7170.2441.337.7306.91.21,221.2Total Petroleum 1,365.5601.43,873.824,838.1961.3556.832,196.994.443.4283.81,776.079.141.12,317.9Asphalt & Road OilAviation Gasoline33.733.72.32.3Distillate Fuel Oil 762.8 394.01,049.9 5,592.7 127.4 92.8 8,019.755.8 28.8 76.8 409.1 9.3 6.8 586.6Jet Fuel2,448.5 NA61.8 2,510.2173.54.4 177.9Kerosene 59.9 15.9 13.88.2 97.84.3 1.2 1.00.6 7.1LPG 542.8 95.8 579.5 13.511.2 1,242.734.2 6.0 36.6 0.80.7 78.4LubricantsMotor Gasoline 15.6 106.9 16,521.9189.4 16,833.71.1 7.6 1,172.213.4 1,194.4Residual Fuel 79.8146.1 227.9 658.7193.6 1,306.16.3 11.5 18.051.9 15.3 102.9Other PetroleumAvGas Blend Components7.57.50.50.5Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus52.452.43.53.5Petroleum Coke 0.2 650.0175.2825.40.0 66.417.984.3Still Gas1,403.31,403.390.190.1Special NaphthaUnfinished Oils(135.7)(135.7)(10.1)(10.1)WaxesGeothermal49.449.40.40.4TOTAL (All Fuels) 6,348.53,897.313,435.525,549.226,578.7597.676,406.8359.3222.0842.11,813.82,254.744.05,535.8aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-31Table A-17: 2001 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 10.988.31,358.3NE19,613.610.821,081.91.08.4127.7NE1,852.31.01,990.4Residential Coal 10.910.91.01.0Commercial Coal 88.388.38.48.4Industrial Other Coal1,358.31,358.3127.7127.7Transportation CoalNENEElectric Power Coal19,613.619,613.61,852.31,852.3U.S. Territory Coal (bit)10.8 10.81.0 1.0Natural Gas 4,871.93,089.37,882.3655.55,488.322.922,010.1258.5163.9418.234.8291.21.21,167.8Total Petroleum 1,472.4675.83,993.224,379.71,276.6632.232,429.9102.248.8293.21,742.0102.046.82,335.2Asphalt & Road OilAviation Gasoline34.934.92.42.4Distillate Fuel Oil 842.1 471.31,181.6 5,417.4 170.5 109.4 8,192.361.6 34.5 86.4 396.3 12.5 8.0 599.3Jet Fuel2,605.2 NA98.9 2,704.0184.77.0 191.7Kerosene 95.1 31.4 23.20.9 150.66.9 2.3 1.70.1 10.9LPG 535.2 94.5 500.6 12.97.0 1,150.233.8 6.0 31.6 0.80.4 72.6LubricantsMotor Gasoline 8.6 68.1 16,149.8187.6 16,414.20.6 4.8 1,145.213.3 1,164.0Residual Fuel 69.9 146.7 159.5 1,002.8228.4 1,607.25.5 11.6 12.679.0 18.0 126.6Other PetroleumAvGas Blend Components6.16.10.40.4Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus61.661.64.14.1Petroleum Coke 0.2 650.0103.2753.40.0 66.410.576.9Still Gas1,430.71,430.791.991.9Special NaphthaUnfinished Oils(75.4)(75.4)(5.6)(5.6)WaxesGeothermal46.946.90.40.4TOTAL (All Fuels) 6,355.33,853.413,233.825,035.226,425.3665.975,568.8361.8221.2839.11,776.82,245.949.05,493.7aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-18: 2000 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 10.685.81,348.7NE20,219.810.321,675.11.08.2126.8NE1,909.50.92,046.4Residential Coal 10.610.61.01.0Commercial Coal 85.885.88.28.2Industrial Other Coal1,348.71,348.7126.8126.8Transportation CoalNENEElectric Power Coal20,219.820,219.81,909.51,909.5U.S. Territory Coal (bit)10.3 10.30.9 0.9Natural Gas 5,067.23,233.78,524.2671.75,311.012.722,820.4268.8171.6452.335.6281.80.71,210.8Total Petroleum 1,452.6651.93,630.524,668.81,144.3471.732,019.7100.547.2265.51,764.791.534.62,303.9Asphalt & Road OilAviation Gasoline36.336.32.52.5Distillate Fuel Oil 794.3 431.01,025.3 5,395.7 174.8 71.3 7,892.558.1 31.5 75.0 394.7 12.8 5.2 577.3Jet Fuel2,766.4 NA74.1 2,840.5196.15.2 201.3Kerosene 94.6 29.7 15.62.4 142.26.8 2.1 1.10.2 10.3LPG 563.7 99.5 605.3 11.28.0 1,287.735.6 6.3 38.2 0.70.5 81.2LubricantsMotor Gasoline16,015.7185.1 16,200.81,135.713.1 1,148.9Residual Fuel 91.6184.1 443.5 870.8130.9 1,720.87.2 14.5 34.968.6 10.3 135.6Other PetroleumAvGas Blend Components3.83.80.30.3Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus106.5106.57.17.1Petroleum Coke 0.2 655.498.6754.20.0 66.910.177.0Still Gas1,435.61,435.692.292.2Special NaphthaUnfinished Oils(401.2)(401.2)(29.8)(29.8)WaxesGeothermal48.148.10.40.4TOTAL (All Fuels) 6,530.33,971.413,503.325,340.426,723.2494.676,563.3370.4226.9844.61,800.32,283.236.25,561.5aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-33Table A-19: 1999 Energy Consumption Data and CO2 Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 13.9101.61,317.3NE19,279.110.220,722.01.39.7123.8NE1,820.70.91,956.5Residential Coal 13.913.91.31.3Commercial Coal 101.6101.69.79.7Industrial Other Coal1,317.31,317.3123.8123.8Transportation CoalNENEElectric Power Coal19,279.119,279.11,820.71,820.7U.S. Territory Coal (bit)10.2 10.20.9 0.9Natural Gas 4,822.03,110.68,407.9675.04,916.5-21,932.0255.8165.0446.135.8260.91,163.6Total Petroleum 1,377.4582.53,516.623,990.01,211.4461.031,138.995.442.1259.01,713.397.334.02,241.0Asphalt & Road OilAviation Gasoline39.239.22.72.7Distillate Fuel Oil 732.5 388.01,022.8 5,169.8 140.1 79.4 7,532.653.6 28.4 74.8 378.2 10.3 5.8 551.0Jet Fuel2,677.9 NA59.5 2,737.4189.84.2 194.0Kerosene 111.2 26.9 12.83.7 154.78.0 1.9 0.90.3 11.2LPG 533.7 94.2 435.0 13.58.3 1,084.733.8 6.0 27.5 0.90.5 68.6LubricantsMotor Gasoline15,913.9164.0 16,078.01,127.911.6 1,139.6Residual Fuel 73.3150.9 175.7 958.7146.0 1,504.65.8 11.9 13.875.5 11.5 118.6Other PetroleumAvGas Blend Components6.46.40.40.4Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus103.5103.56.96.9Petroleum Coke 0.1 652.0112.5764.60.0 66.611.578.1Still Gas1,421.11,421.191.291.2Special NaphthaUnfinished Oils(287.9)(287.9)(21.3)(21.3)WaxesGeothermal50.650.60.40.4TOTAL (All Fuels) 6,213.33,794.713,241.824,665.025,457.5471.273,843.5352.5216.8828.91,749.12,179.234.95,361.5aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-20: 1998 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 12.4100.61,356.3NE19,215.810.520,695.61.29.6127.5NE1,814.71.01,954.0Residential Coal 12.412.41.21.2Commercial Coal 100.6100.69.69.6Industrial Other Coal1,356.31,356.3127.5127.5Transportation CoalNENEElectric Power Coal19,215.819,215.81,814.71,814.7U.S. Territory Coal (bit)10.5 10.51.0 1.0Natural Gas 4,637.33,076.08,888.2666.64,689.7-21,957.8246.0163.2471.635.4248.81,165.0Total Petroleum 1,242.9585.43,444.623,055.71,306.2445.430,080.386.542.5254.81,645.6105.032.82,167.3Asphalt & Road OilAviation Gasoline35.535.52.52.5Distillate Fuel Oil 701.0 389.51,068.5 4,874.3 135.7 71.9 7,240.851.3 28.5 78.2 356.6 9.9 5.3 529.7Jet Fuel2,484.2 NA59.9 2,544.1176.14.2 180.3Kerosene 108.3 31.2 22.16.3 167.87.8 2.3 1.60.5 12.1LPG 433.6 76.5 303.9 16.65.9 836.627.4 4.8 19.2 1.10.4 52.8LubricantsMotor Gasoline 3.0 15.1 15,566.1160.3 15,744.60.2 1.1 1,103.311.4 1,115.9Residual Fuel 85.2 173.3 78.9 1,047.0141.1 1,525.56.7 13.7 6.282.5 11.1 120.2Other PetroleumAvGas Blend Components4.04.00.30.3Crude OilMoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus89.789.76.06.0Petroleum Coke 0.1 644.8123.6768.40.0 65.812.678.5Still Gas1,437.31,437.392.392.3Special NaphthaUnfinished Oils(313.9)(313.9)(23.3)(23.3)WaxesGeothermal50.450.40.40.4TOTAL (All Fuels) 5,892.63,762.113,689.123,722.225,262.1456.072,784.1333.7215.3853.81,681.02,169.033.85,286.6aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-35Table A-21: 1997 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 15.9128.71,435.6NE18,902.910.420,493.61.512.3134.9NE1,785.21.01,934.9Residential Coal 15.915.91.51.5Commercial Coal 128.7128.712.312.3Industrial Other Coal1,435.61,435.6134.9134.9Transportation CoalNENEElectric Power Coal18,902.918,902.91,785.21,785.2U.S. Territory Coal (bit)10.4 10.41.0 1.0Natural Gas 5,088.03,282.99,174.3789.94,140.3-22,475.4270.0174.2486.841.9219.71,192.5Total Petroleum 1,343.4623.93,801.322,604.2926.8445.329,745.093.545.4279.01,615.074.832.82,140.5Asphalt & Road OilAviation Gasoline39.739.72.72.7Distillate Fuel Oil 789.8 400.91,059.1 4,790.8 110.6 81.6 7,232.757.8 29.3 77.5 350.4 8.1 6.0 529.1Jet Fuel2,509.8 NA62.1 2,571.9177.94.4 182.3Kerosene 92.9 24.6 18.84.0 140.36.7 1.8 1.40.3 10.1LPG 460.7 81.3 463.7 13.46.5 1,025.729.0 5.1 29.2 0.80.4 64.6LubricantsMotor Gasoline 5.8 28.5 15,113.9160.0 15,308.20.4 2.0 1,072.311.4 1,086.1Residual Fuel 111.2235.6 136.5 714.6131.1 1,329.08.8 18.6 10.856.3 10.3 104.7Other PetroleumAvGas Blend Components9.19.10.60.6Crude Oil4.64.60.30.3MoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus30.030.02.02.0Petroleum Coke 0.1 609.7101.6711.40.0 62.310.472.6Still Gas1,445.11,445.192.892.8Special NaphthaUnfinished Oils(102.9)(102.9)(7.6)(7.6)WaxesGeothermal50.250.20.40.4TOTAL (All Fuels) 6,447.34,035.614,411.323,394.124,020.2455.772,764.2365.0231.9900.71,656.92,080.033.75,268.2aExpressed as gross calorific values (i.e., higher heating values).Adjustments include biofuels,conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-22: 1996 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 16.4120.11,409.0NE18,427.610.319,983.41.611.4132.3NE1,739.51.01,885.7Residential Coal 16.416.41.61.6Commercial Coal 120.1120.111.411.4Industrial Other Coal1,409.01,409.0132.3132.3Transportation CoalNENEElectric Power Coal18,427.618,427.61,739.51,739.5U.S. Territory Coal (bit)10.3 10.31.0 1.0Natural Gas 5,350.23,224.89,090.7740.53,875.7-22,281.8283.9171.1482.339.3205.61,182.2Total Petroleum1,436.3699.33,832.022,294.9817.4434.629,514.4100.251.1281.81,594.465.631.92,125.0Asphalt & Road OilAviation Gasoline37.437.42.62.6Distillate Fuel Oil 874.2 455.91,091.8 4,500.4 109.4 76.5 7,108.163.9 33.4 79.9 329.2 8.0 5.6 520.0Jet Fuel2,460.3 NA78.5 2,538.8174.45.6 180.0Kerosene 88.8 21.0 18.33.0 131.16.4 1.5 1.30.2 9.5LPG 473.2 83.5 436.9 14.77.3 1,015.729.8 5.3 27.5 0.90.5 64.1LubricantsMotor Gasoline 1.5 11.2 14,967.3151.4 15,131.40.1 0.8 1,062.510.7 1,074.1Residual Fuel 137.2281.7 314.9 628.4118.0 1,480.110.8 22.2 24.849.5 9.3 116.6Other PetroleumAvGas Blend Components7.07.00.50.5Crude Oil13.713.71.01.0MoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus38.538.52.62.6Petroleum Coke 0.1 608.779.6688.40.0 62.28.170.3Still Gas1,437.11,437.192.392.3Special NaphthaUnfinished Oils(112.8)(112.8)(8.4)(8.4)WaxesGeothermal48.948.90.40.4TOTAL (All Fuels) 6,802.94,044.114,331.723,035.423,169.5445.071,828.4385.6233.6896.51,633.72,011.132.85,193.3aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-37Table A-23: 1995 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 17.4116.31,485.6NE17,465.110.219,094.61.711.1139.6NE1,648.60.91,801.9Residential Coal 17.417.41.71.7Commercial Coal 116.3116.311.111.1Industrial Other Coal1,485.61,485.6139.6139.6Transportation CoalNENEElectric Power Coal17,465.117,465.11,648.61,648.6U.S. Territory Coal (bit)10.2 10.20.9 0.9Natural Gas 4,943.93,090.68,764.3727.84,319.7-21,846.2262.3164.0465.038.6229.21,159.1Total Petroleum1,293.5673.73,508.921,793.2754.6461.828,485.790.549.3257.91,560.160.734.02,052.6Asphalt & Road OilAviation Gasoline39.639.62.72.7Distillate Fuel Oil 814.9 431.3 995.7 4,318.9 108.1 89.5 6,758.459.6 31.6 72.8 315.9 7.9 6.5 494.4Jet Fuel2,410.1 NA75.7 2,485.9170.95.4 176.3Kerosene 74.3 22.1 15.43.6 115.45.4 1.6 1.10.3 8.3LPG 404.2 71.3 432.5 16.75.6 930.525.5 4.5 27.3 1.10.4 58.7LubricantsMotor Gasoline 7.3 81.2 14,620.5146.7 14,855.70.5 5.8 1,038.910.4 1,055.6Residual Fuel 141.5284.7 387.3 566.0140.7 1,520.111.1 22.4 30.544.6 11.1 119.8Other PetroleumAvGas Blend Components5.35.30.40.4Crude Oil14.514.51.11.1MoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus34.534.52.32.3Petroleum Coke 0.1 588.780.6669.40.0 60.18.268.4Still Gas1,377.31,377.388.488.4Special NaphthaUnfinished Oils(320.9)(320.9)(23.8)(23.8)WaxesGeothermal45.645.60.30.3TOTAL (All Fuels) 6,254.73,880.613,758.822,521.022,585.0472.069,472.1354.4224.4862.61,598.71,938.935.05,013.9aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-24: 1994 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 20.7117.11,550.9NE17,259.110.018,957.82.011.1145.8NE1,627.60.91,787.4Residential Coal 20.720.72.02.0Commercial Coal 117.1117.111.111.1Industrial Other Coal1,550.91,550.9145.8145.8Transportation CoalNENEElectric Power Coal17,259.117,259.11,627.61,627.6U.S. Territory Coal (bit)10.0 10.00.9 0.9Natural Gas 4,941.22,952.18,300.7709.73,992.8-20,896.6262.2156.6440.437.7211.81,108.7Total Petroleum1,325.5712.93,652.221,355.91,058.8506.328,611.592.952.4268.21,532.184.437.52,067.5Asphalt & Road OilAviation Gasoline38.138.12.62.6Distillate Fuel Oil 865.1 451.5 985.5 4,164.4 120.1 118.8 6,705.563.3 33.0 72.1 304.6 8.8 8.7 490.5Jet Fuel2,360.9 NA65.8 2,426.7167.54.7 172.2Kerosene 64.9 19.5 16.93.0 104.34.7 1.4 1.20.2 7.5LPG 395.4 69.8 450.8 32.27.3 955.525.0 4.4 28.5 2.00.5 60.3LubricantsMotor Gasoline14,402.0147.4 14,549.41,027.110.5 1,037.6Residual Fuel 171.9368.4 358.1 869.0164.1 1,931.513.5 29.0 28.268.5 12.9 152.2Other PetroleumAvGas Blend Components6.16.10.40.4Crude Oil18.718.71.41.4MoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus80.880.85.45.4Petroleum Coke 0.1 586.869.7656.60.0 59.97.167.1Still Gas1,417.51,417.591.091.0Special NaphthaUnfinished Oils(279.2)(279.2)(20.7)(20.7)WaxesGeothermal53.053.00.40.4TOTAL (All Fuels) 6,287.43,782.113,503.822,065.622,363.7516.368,518.8357.1220.2854.41,569.81,924.238.44,964.0aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-39Table A-25: 1993 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 25.6116.51,543.1NE17,194.49.618,889.22.411.1144.9NE1,620.70.91,780.0Residential Coal 25.625.62.42.4Commercial Coal 116.5116.511.111.1Industrial Other Coal1,543.11,543.1144.9144.9Transportation CoalNENEElectric Power Coal17,194.417,194.41,620.71,620.7U.S. Territory Coal (bit)9.6 9.60.9 0.9Natural Gas 5,054.22,916.48,305.3646.03,552.0-20,473.9268.2154.7440.734.3188.51,086.3Total Petroleum1,358.4704.93,524.220,856.71,123.8459.928,027.995.351.8259.71,495.489.934.12,026.2Asphalt & Road OilAviation Gasoline38.438.42.72.7Distillate Fuel Oil 884.2 447.7 991.1 3,886.9 86.5 104.9 6,401.364.7 32.7 72.5 284.3 6.3 7.7 468.3Jet Fuel2,307.2 NA62.1 2,369.3163.94.4 168.3Kerosene 75.6 14.0 13.13.8 106.55.5 1.0 0.90.3 7.7LPG 398.6 70.3 443.3 19.04.9 936.225.2 4.4 28.0 1.20.3 59.1LubricantsMotor Gasoline14,237.7128.3 14,365.91,014.39.1 1,023.5Residual Fuel 172.7391.5 367.5 958.6155.9 2,046.313.6 30.8 29.075.5 12.3 161.2Other PetroleumAvGas Blend Components0.10.10.00.0Crude Oil21.221.21.61.6MoGas Blend ComponentsMisc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus56.456.43.83.8Petroleum Coke 0.2 601.878.6680.60.0 61.58.069.5Still Gas1,401.81,401.890.090.0Special NaphthaUnfinished Oils(396.0)(396.0)(29.4)(29.4)WaxesGeothermal57.357.30.40.4TOTAL (All Fuels) 6,438.13,737.813,372.721,502.721,927.5469.567,448.3365.9217.7845.31,529.71,899.535.04,893.0aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-26: 1992 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal 25.6116.81,530.8NE16,463.68.818,145.72.511.2143.8NE1,551.10.81,709.3Residential Coal 25.625.62.52.5Commercial Coal 116.8116.811.211.2Industrial Other Coal1,530.81,530.8143.8143.8Transportation CoalNENEElectric Power Coal16,463.616,463.61,551.11,551.1U.S. Territory Coal (bit)8.8 8.80.8 0.8Natural Gas 4,777.72,855.38,203.1610.63,528.1-19,974.9253.5151.5435.232.4187.21,059.8Total Petroleum1,381.9751.03,850.920,274.8990.7444.927,694.397.255.3280.31,453.478.732.91,997.8Asphalt & Road OilAviation Gasoline41.141.12.82.8Distillate Fuel Oil 934.4 483.31,032.0 3,657.7 73.5 91.8 6,272.768.4 35.4 75.5 267.6 5.4 6.7 458.8Jet Fuel2,268.0 NA61.3 2,329.3161.24.4 165.6Kerosene 65.0 11.1 9.83.3 89.24.7 0.8 0.70.2 6.4LPG 382.5 67.5 469.1 18.311.9 949.324.1 4.3 29.6 1.20.7 59.9LubricantsMotor Gasoline13,889.5122.1 14,011.7989.08.7 997.7Residual Fuel 189.1328.4 400.1 872.2154.6 1,944.314.9 25.9 31.568.7 12.2 153.2Other PetroleumAvGas Blend Components0.20.20.00.0Crude Oil27.427.42.02.0MoGas Blend Components75.775.75.45.4Misc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus261.0261.017.517.5Petroleum Coke 0.1 566.645.0611.70.0 57.94.662.5Still Gas1,435.71,435.792.292.2Special NaphthaUnfinished Oils(354.8)(354.8)(26.3)(26.3)WaxesGeothermal55.155.10.40.4TOTAL (All Fuels) 6,185.33,723.213,584.920,885.421,037.5453.765,869.9353.1218.0859.31,485.71,817.433.74,767.4aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-41Table A-27: 1991 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal25.3115.41,580.9NE16,247.97.717,977.42.411.0148.4NE1,530.80.71,693.3Residential Coal 25.325.32.42.4Commercial Coal 115.4115.411.011.0Industrial Other Coal1,580.91,580.9148.4148.4Transportation CoalNENEElectric Power Coal16,247.916,247.91,530.81,530.8U.S. Territory Coal (bit)7.7 7.70.7 0.7Natural Gas 4,642.82,779.77,833.6621.83,391.1-19,269.1246.3147.5415.633.0179.91,022.4Total Petroleum1,386.6837.83,523.419,689.01,198.3425.427,060.597.561.7256.61,409.594.631.31,951.3Asphalt & Road OilAviation Gasoline41.741.72.92.9Distillate Fuel Oil 924.8514.3 1,043.6 3,466.5 83.6 71.46,104.167.6 37.6 76.3 253.6 6.1 5.2 446.5Jet Fuel2,329.3 NA78.2 2,407.5165.75.6 171.3Kerosene 72.3 12.1 11.42.8 98.65.2 0.9 0.80.2 7.1LPG 389.5 68.7 371.4 19.913.8 863.324.6 4.3 23.4 1.30.9 54.5LubricantsMotor Gasoline 30.8 70.0 13,607.2124.7 13,832.62.2 5.0 968.48.9 984.5Residual Fuel 211.9 270.9 224.4 1,085.3134.6 1,927.116.7 21.3 17.785.5 10.6 151.9Other PetroleumAvGas Blend Components(0.1)(0.1)(0.0)(0.0)Crude Oil38.938.92.92.9MoGas Blend Components(25.9)(25.9)(1.8)(1.8)Misc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus249.2249.216.716.7Petroleum Coke539.629.3569.055.13.058.1Still Gas1,404.51,404.590.290.2Special NaphthaUnfinished Oils(450.2)(450.2)(33.3)(33.3)WaxesGeothermal54.554.50.40.4TOTAL (All Fuels) 6,054.73,733.012,938.020,310.920,891.7433.264,361.4346.2220.2820.61,442.51,805.732.04,667.3aExpressed as gross calorific values (i.e., higher heating values).Adjustments include biofuels,conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect 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: 1990-2007Table A-28: 1990 Energy Consumption Data and CO2Emissions from Fossil Fuel Combustion by Fuel Type1 2 3 4 5 6 7 8 9 10 11 12 13 14 15Adjusted Consumption (TBtu)aEmissionsb(Tg CO2Eq.) from Energy UseFuel Type Res.Comm.Ind.Trans.Elec.Terr.TotalRes.Comm.Ind.Trans.Elec.Terr.TotalTotal Coal31.0123.81,593.5NE16,259.07.018,014.32.911.8149.5NE1,531.10.61,695.9Residential Coal 31.031.02.92.9Commercial Coal 123.8123.811.811.8Industrial Other Coal1,593.51,593.5149.5149.5Transportation CoalNENEElectric Power Coal16,259.016,259.01,531.11,531.1U.S. Territory Coal (bit)7.0 7.00.6 0.6Natural Gas 4,473.92,667.77,729.7682.73,325.8-18,879.9237.4141.5410.136.2176.51,001.7Total Petroleum1,382.3828.43,766.320,222.71,289.4374.827,864.097.461.2274.61,448.3101.827.62,010.9Asphalt & Road OilAviation Gasoline45.045.03.13.1Distillate Fuel Oil 953.4 522.31,091.7 3,570.5 96.5 74.0 6,308.469.7 38.2 79.9 261.2 7.1 5.4 461.5Jet Fuel2,477.5 NA61.0 2,538.5176.24.3 180.6Kerosene 63.9 11.8 12.32.6 90.64.6 0.9 0.90.2 6.6LPG 365.0 64.4 406.4 21.614.4 871.923.0 4.1 25.7 1.40.9 55.0LubricantsMotor Gasoline13,807.8101.0 13,908.8982.77.2 989.9Residual Fuel 229.8 364.1 300.3 1,162.6121.8 2,178.718.1 28.7 23.791.6 9.6 171.7Other PetroleumAvGas Blend Components0.20.20.00.0Crude Oil50.950.93.83.8MoGas Blend Components53.753.73.83.8Misc. ProductsNaphtha (<401 deg. F)Other Oil (>401 deg. F)Pentanes Plus167.8167.811.211.2Petroleum Coke536.230.4566.654.83.157.9Still Gas1,451.91,451.993.293.2Special NaphthaUnfinished Oils(369.0)(369.0)(27.3)(27.3)WaxesGeothermal52.752.70.40.4TOTAL (All Fuels) 5,887.23,619.913,089.520,905.520,926.9381.964,810.8337.7214.5834.21,484.51,809.728.34,708.9aExpressed as gross calorific values (i.e., higher heating values). Adjustments include biofuels, conversion of fossil fuels, non-energy use (see Table A-29), and internationalbunker fuel consumption (see Table A-30).bConsumption and/or emissions ofselect fuels are shown as negative due to differences in EIA energy balancing accounting. These are designated with parentheses.NE (Not Estimated)A-43Table A-29: Unadjusted Non-Energy Fuel Consumption (TBtu)Sector/Fuel Type 199019911992199319941995199619971998199920002001200220032004200520062007Industry 4,534.94,571.54,718.84,877.65,223.65,253.05,295.35,571.85,820.26,039.95,652.35,297.15,416.65,332.65,843.65,513.55,588.05,382.3Industrial Coking Coal 0.0 11.942.533.371.775.028.057.184.4 72.582.236.876.463.5173.253.374.733.0Industrial Other Coal 8.2 8.59.510.010.911.311.411.210.4 11.112.411.312.011.911.911.912.412.4Natural Gas to Chemical Plants, Other Uses 299.9 301.1259.4292.4354.0357.2360.3386.8426.6 437.5439.8412.3379.5383.0401.5404.9411.0413.7Asphalt & Road Oil 1,170.2 1,076.51,102.21,149.01,172.91,178.21,175.91,223.61,262.6 1,324.41,275.71,256.91,239.91,219.51,303.81,323.21,261.21,197.0LPG 1,201.4 1,377.91,390.71,351.01,545.71,586.91,652.01,670.41,744.4 1,820.71,665.41,553.41,620.31,545.11,576.41,488.11,516.81,540.0Lubricants 186.3 166.7170.0173.1180.9177.8172.5182.3190.8 192.8189.9174.0171.9159.0161.0160.2156.1161.0Pentanes Plus 82.6 44.861.5275.9257.9303.4316.5298.9204.3 261.4236.7201.6171.4169.1170.4150.3107.3137.1Naphtha (<401 deg. F) 347.8 298.9377.1350.6398.3373.0479.3536.4584.0 502.1613.5493.7582.6613.0749.4698.7628.9562.5Other Oil (>401 deg. F) 753.9 827.3814.5844.1838.6801.0729.6861.3818.7 811.1722.2662.4632.1699.4779.5708.0790.6744.1Still Gas 21.3 22.011.328.421.940.10.02.10.0 16.112.635.857.859.063.567.7123.988.4Petroleum Coke 178.0 153.0231.1123.5136.4132.6148.2117.7213.7 283.8140.7207.6192.1161.0245.9229.0261.3248.2Special Naphtha 107.1 88.0104.6104.681.170.874.572.3107.3 145.497.478.5102.480.551.062.570.178.0Other (Wax/Misc.)Distillate Fuel Oil 7.0 7.17.06.96.88.09.210.411.7 11.711.711.711.711.711.711.711.711.7Waxes 33.3 35.137.340.040.640.648.743.742.4 37.433.136.332.231.030.831.426.121.9Miscellaneous Products 137.8 152.6100.194.7105.997.189.097.8119.0 111.9119.2124.9134.2126.0113.4112.8136.0133.5Transportation 176.0157.5160.5163.5170.8167.9163.0172.1180.2182.1179.4164.3162.4150.1152.1151.3147.4152.0Lubricants 176.0 157.5160.5163.5170.8167.9163.0172.1180.2 182.1179.4164.3162.4150.1152.1151.3147.4152.0U.S. Territories 86.7114.462.674.254.890.8121.0131.6135.0139.3165.580.3138.6127.9110.8107.7110.3100.9Lubricants 0.7 0.61.53.31.92.00.82.51.3 1.416.40.01.59.35.15.25.44.9Other Petroleum (Misc. Prod.) 86.0 113.861.271.053.088.8120.2129.1133.8 138.0149.180.3137.2118.6105.7102.4105.096.0Total 4,797.64,843.34,941.95,115.35,449.35,511.85,579.35,875.56,135.46,361.35,997.15,541.75,717.65,610.76,106.55,772.45,845.75,635.2Note: These values are unadjusted non-energy fuel use provided byEIA. They have not yet been adjusted to remove petroleum feedstock exports and processes accounted for in the Industrial Processes Chapter.Table A-30: International Bunker Fuel Consumption (TBtu)Fuel Type 199019911992199319941995199619971998199920002001200220032004200520062007Marine Residual Fuel Oil 715.7 801.5 669.8 533.9 524.5 523.2 536.4 575.2 594.8 489.7 444.1 426.0 448.9 471.8 553.1 581.0 599.4607.5Marine Distillate Fuel Oil & Other158.0 149.3 145.9 146.6 121.2 125.7 114.1 125.5 158.8 113.6 85.972.4 82.6 103.9 143.6 126.9119.3 111.3Aviation Jet Fuel 652.0 695.7 733.3720.8 793.6 722.1 813.9 798.3872.6 783.9 814.0 820.8 891.9784.6 857.6 796.0 769.8 744.1Total 1,525.71,646.51,549.11,401.41,439.21,371.01,464.51,499.11,626.21,387.21,343.91,319.31,423.31,360.31,554.21,503.91,488.51,462.9

### A-44 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 Table A-31: Key Assumptions for Estimating CO 2 

### 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 fluc tuations in fuel quality (see Table A-32).A-45Table A-32: Annually Variable C Content Coefficients by Year (Tg C/QBtu)Fuel Type 199019911992199319941995199619971998199920002001200220032004200520062007Residential Coal 25.92 26.00 26.1325.97 25.95 26.00 25.92 26.0026.00 26.00 26.00 26.00 26.0026.00 26.00 26.00 26.00 26.00Commercial 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 26.00 26.00Industrial 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 25.63 25.63Electric Power Coal 25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.7625.76 25.76 25.76 25.76 25.7625.76 25.76 25.76 25.76 25.76LPG (energy use) 16.99 16.98 16.99 16.9717.01 17.00 16.99 16.99 16.9916.99 16.98 16.99 16.99 16.99 16.99 16.99 16.99 16.99LPG (non-energy use) 17.21 17.21 17.2117.22 17.22 17.20 17.20 17.1817.23 17.25 17.20 17.21 17.2017.21 17.20 17.19 17.19 17.18Motor Gasoline 16.83 16.84 16.84 16.8016.88 16.87 16.86 16.88 16.8816.84 16.81 16.83 16.82 16.84 16.81 16.81 16.78 16.76Jet Fuel 19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.3319.33 19.34 19.34 19.35 19.33 19.33 19.33 19.33 19.33MoGas 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 19.33 19.33Misc. 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 19.33 19.33Unfinished 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 20.33 20.33Crude Oil 20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.2420.19 20.23 20.29 20.30 20.28 20.33 20.33 20.33 20.33Source: EIA (2008b)Table A-33: Electricity Consumption byEnd-Use Sector (Billion Kilowatt-Hours)End-Use Sector 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007Residential 924 955 936 9951,0081,0431,0831,0761,1301,1451,192 1,202 1,2651,2761,2921,3591,3521,392Commercial 838 855 850 8859139539801,0271,0781,1041,159 1,191 1,2051,1991,2301,2751,3001,343Industrial 1,070 1,071 1,107 1,1161,1541,1631,1861,1941,2121,2301,235 1,159 1,1561,1811,1861,1691,1581,149Transportation 5 5 5 55555555 6 677878Total 2,837 2,886 2,897 3,001 3,081 3,164 3,254 3,3023,425 3,484 3,592 3,5573,632 3,662 3,716 3,8113,817 3,892Note: 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 defi ned 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 meth ods 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.A-47Table A- 34: Carbon Content CoefficientsUsed in this Report (Tg Carbon/QBtu)Fuel Type 1990 1991 1992 19931994199519961997199819992000 2001200220032004200520062007CoalResidential Coala25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.0026.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00 26.00Commercial Coala25.92 26.00 26.13 25.97 25.95 26.00 25.92 26.00 26.00 26.0026.00 26.00 26.00 26.0026.00 26.00 26.00 26.00Industrial Coking Coala25.51 25.51 25.51 25.51 25.52 25.53 25.55 25.56 25.5625.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56 25.56Industrial Other Coala25.58 25.60 25.62 25.61 25.63 25.63 25.61 25.63 25.6325.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63 25.63Utility Coala,b25.68 25.69 25.69 25.71 25.72 25.74 25.74 25.76 25.7625.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76 25.76Natural Gas 14.47 14.47 14.47 14.4714.47 14.47 14.47 14.47 14.4714.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47 14.47PetroleumAsphalt and Road Oil 20.62 20.62 20.6220.62 20.62 20.62 20.62 20.6220.62 20.62 20.62 20.62 20.6220.62 20.62 20.62 20.62 20.62Aviation Gasoline 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.87Distillate Fuel Oil 19.95 19.95 19.9519.95 19.95 19.95 19.95 19.9519.95 19.95 19.95 19.95 19.9519.95 19.95 19.95 19.95 19.95Jet Fuela19.40 19.40 19.39 19.37 19.35 19.34 19.33 19.33 19.3319.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33 19.33Kerosene 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.7219.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72 19.72LPG (energy use)a17.21 17.21 17.21 17.22 17.22 17.20 17.20 17.18 17.2317.25 17.20 17.21 17.20 17.21 17.20 17.19 17.19 17.18LPG (non-energy use)a16.83 16.84 16.84 16.80 16.88 16.87 16.86 16.88 16.8816.84 16.81 16.83 16.82 16.84 16.81 16.81 16.78 16.76Lubricants 20.24 20.24 20.24 20.2420.24 20.24 20.24 20.24 20.2420.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24 20.24Motor Gasolinea19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.3319.33 19.34 19.34 19.35 19.33 19.33 19.33 19.33 19.33Residual 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 21.49 21.49 21.49Other PetroleumAv Gas Blend Comp. 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.8718.87 18.87 18.87 18.87 18.87Mo Gas Blend Compa19.41 19.41 19.42 19.43 19.45 19.38 19.36 19.35 19.3319.33 19.34 19.34 19.35 19.33 19.33 19.33 19.33 19.33Crude Oila20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.2420.19 20.23 20.29 20.30 20.28 20.33 20.33 20.33 20.33Misc. Productsa20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.2420.19 20.23 20.29 20.30 20.28 20.33 20.33 20.33 20.33Misc. Products (Terr.) 20.00 20.0020.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.00 20.0020.00 20.00 20.00 20.00 20.00Naphtha (<401 deg. F) 18.14 18.1418.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.14 18.1418.14 18.14 18.14 18.14 18.14Other oil (>401 deg. F) 19.95 19.9519.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.95 19.9519.95 19.95 19.95 19.95 19.95Pentanes 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 18.24 18.24 18.24Petrochemical Feed. 19.37 19.37 19.3719.37 19.37 19.37 19.37 19.3719.37 19.37 19.37 19.37 19.3719.37 19.37 19.37 19.37 19.37Petroleum 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 27.85 27.85 27.85Still Gas 17.51 17.51 17.51 17.51 17.5117.51 17.51 17.51 17.51 17.51 17.51 17.51 17.51 17.5117.51 17.51 17.51 17.51Special 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 19.86 19.86 19.86Unfinished Oilsa20.16 20.18 20.22 20.22 20.21 20.23 20.25 20.24 20.2420.19 20.23 20.29 20.30 20.28 20.33 20.33 20.33 20.33Waxes 19.81 19.81 19.81 19.81 19.8119.81 19.81 19.81 19.81 19.81 19.81 19.81 19.81 19.8119.81 19.81 19.81 19.81Other Wax and Misc. 19.81 19.81 19.8119.81 19.81 19.81 19.81 19.8119.81 19.81 19.81 19.81 19.8119.81 19.81 19.81 19.81 19.81Geothermal 2.05 2.05 2.05 2.052.05 2.05 2.05 2.05 2.052.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05 2.05aC contents vary annually based on changes in fuel composition.bC content for utility coal used in theelectric power calculations. All coefficients based on higher heating value.Higher heating value (gross heating value) is the total amount ofheat released when a fuel is burned. Coal, crude oil, andnatural gas all include chemical compoundsof carbon and hydrogen. When those fuelsare burned, the carbon and hydrogen combinewith oxygen in the air to produce CO2and water. Some of the energyreleased in burning goes intotransforming the water into steamand is usually lost. The amountof heat spent in transformingthe water into steam is countedas part of gross heat content. Lower heating value (net heating value), in contrast, does not include the heatspent 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 valuefor coal and petroleum products by multiplying by0.95 and for natural gas by multiplying by 0.90. Carbon content coefficients are presented inhigher 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. CO 2 

### 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 co al 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 CO 2 

### ) 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 resi dential and commercial end-use sectors. Because U.S. energy

statistics do not provide the distribution of coal rank consum ed by each consuming sector, it is assumed that each sector

consumes a representative mixture of coal ranks from a partic ular 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.

C

sector 

### = S rank1 

### ×C rank1 

### + S rank2 

### ×C rank2 

### +.... + S rank50 

### ×C rank50



### Where,

C

sector 

### = The C content by consuming sector;

S rank 

### = The portion of consuming sector coal consumption attributed to a given rank in each state;

C rank 

### = 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.A-49Table A-35: Carbon Content Coefficients for Coal byConsuming Sector and CoalRank (Tg C/QBtu) (1990-2007)Consuming Sector 1990 1991 1992199319941995199619971998 1999 20002001200220032004200520062007Electric Power 25.6825.6925.6925.7125.7225.7425.7425.7625.7625.7625.7625.7625.7625.7625.7625.7625.7625.76Industrial Coking 25.5125.5125.5125.5125.5225.5325.5525.5625.5625.5625.5625.5625.5625.5625.5625.5625.5625.56Other Industrial 25.5825.6025.6225.6125.6325.6325.6125.6325.6325.6325.6325.6325.6325.6325.6325.6325.6325.63Residential/ Commercial 25.9226.0026.1325.9725.9526.0025.9226.0026.0026.0026.0026.0026.0026.0026.0026.0026.0026.00Coal RankAnthracite 28.26 28.26 28.2628.2628.2628.2628.2628.2628.26 28.26 28.2628.2628.2628.2628.2628.2628.2628.26pBituminous 25.43 25.45 25.4425.4525.4625.4725.4725.4825.47 25.48 25.4925.4925.4925.4925.4925.4925.4925.49pSub-bituminous 26.50 26.49 26.4926.4826.4926.4926.4926.4926.49 26.49 26.4826.4826.4826.4826.4826.4826.4826.48pLignite 26.19 26.21 26.2226.2126.5226.5126.4726.5026.53 26.55 26.5726.5726.5726.5726.5726.5726.5726.57ppPreliminarySources: C content coefficientsby consuming sector from EIA (2008a). C content coefficients bycoal rank from USGS (1998) andSAIC (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.

N rank 

### = P rank1 

### × C rank1 

### + P rank2 

### × C rank2 

### +...+ P rankn 

### × C rankn



### Where,

N rank 

### = The national C content by rank;

P rank 

### = The portion of U.S. coal production attributed to a given rank in each state; and

C rank 

### = 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 CO 2 

### per MMBtu in Kansas to a high of 105.23 kg CO 2 

### 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 st ates 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 CO 2 

### per MMBtu in Utah to

98.66 kg CO 2 

### 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 Ca rbon Content Coefficients by Rank Across States (Kilograms CO

2 

### 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 (C 2 

### H 6 

### ), propane (C 3 

### H 8 

### ), butane (C 4 

### H 10 

### ), and, to a lesser extent, pentane (C

5 

### H 12 

### )

and hexane (C 6 

### H 14 

### ). Because the NGLs have more C atoms than meth ane (which has only one), their presence increases

the overall C content of natural gas. NGLs have a commerci al value greater than that of methane, and therefore are

usually separated from raw natural gas at gas processing plants a nd sold as separate products. Ethane is typically used as a

petrochemical feedstock, propane and butane have diverse uses, and natural gasoline 1 

### 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 CO 2 

### , nitrogen, helium and

other noble gases, and hydrogen sulfide. The share of non-hydr ocarbon gases is usually less than 5 percent of the total,

but there are individual natural gas reservoirs where the sh are 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 CO 2 

### , 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 CO 2



### .

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 CO 2 

### 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 CO 2 

### ). All natural gas flared for these reason s 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 natu ral gas because of "process upsets," because the gas is

"off spec," or possibly as part of an emissions control syst em. 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 ener gy 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 th ese 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-Qualit y 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 conten ts 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 CO 2 

### (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 com bustion efficiency of the flare. Because EIA estimates the

heat content of flare gas at 1,130 Btu per cubic foot, the av erage 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 abou t

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 Samp les of Pipeline-Quality Natu ral 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 anom alies. 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 butan e) tend to have energy contents greater than 1,000 Btu

per cubic foot, along with higher emissions coefficients. Sa mples 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.

C fuel 

### = (D fuel 

### × S fuel 

### ) / E fuel



### Where,

C fuel 

### = The C content coefficient of the fuel;

D fuel 

### = The density of the fuel;

S fuel 

### = The share of the fuel that is C; and

E fuel 

### = 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 pur e 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 C oefficients 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 natu ral 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 m easuring 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 calcu lated 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 am ong 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 th e 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 percen t non-hydrocarbons by mass. Thus, the C content of heavy

fuel oils can often be several percent lower than that of li ghter 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. Th e 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 C

n 

### H 2n+2 

### . Paraffins include ethane (C 2 

### H 6 

### ), propane

(C 3 

### H 8 

### ), butane (C 4 

### H 10 

### ), and octane (C 8 

### H 18 

### ). 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 C

n 

### H 2n 

### 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 pe rcent olefins. They also have the general formula C

n 

### H 2n 

### , and hence

are also always 85.7 percent C by weight. Propylene (C 3 

### H 6 

### ), 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 (C 6 

### H 6 

### ), toluene (C

7 

### H 8 

### ), and xylene (C 8 

### H 10 

### ). The general formula for aromatics is C

n 

### H 2n-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 (C 10 

### H 2 

### and 94.4 percent C by mass) and anthracene (C 14 

### H 4 

### 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 Hydr ocarbons 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 de nser 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 para meters 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 no rmal butane, each with their own heat co ntent, density and C content, see Table A42.

b Parameters presented are for naphthas with a boiling temperature less than 400 degrees Fahrenheit. Petrochemical feedstocks w ith 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. CO 2 

### 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 Fahrenh eit at the

90-percent recovery point.

### A-57 API gravity, or 111.52 to 112.65 kilograms per barrel, which impl ies 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 Densit y, 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 (MTB E) and tertiary amyl methyl ether (TAME) are often added to standard gasoline

to boost its oxygen content. The increased oxygen redu ces 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 Ma jor Reformulated Fuel Additives

Additive Density (Degrees API) Carbon Sh are (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 derive d 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 as sumed 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 b een 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 esti mated hydrogen content of 14.1 percent (Martel and Angello

1977), and an estimated content of sulfur a nd 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. commercia l 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 ta ken 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 de fault 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 th ose 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 i f

it is consumed in large portions for mobile sources, the C conten t 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 co mpared 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 grav ity 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 genera tion 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 mari ne 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 densit y 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 hy drogen (1). Thus, for example, the C share of propane, C 3 

### H 8 

### , 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 Characterist ics 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 C 2 H 6 16.88 80.0 2.916 16.25

Propane C 3 H 8 12.44 81.8 3.824 17.20

Isobutane C 4 H 10 11.20 82.8 4.162 17.75

n-butane C 4 H 10 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, et hane 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.A-61Table A-43: Consumption and Carbon Content Coefficients of Liquefied Petroleum Gases, 1990-20071990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007Energy Consumption (QBtu)Fuel Use 0.86 0.86 0.94 0.94 0.960.93 1.02 1.03 0.84 1.09 1.29 1.15 1.24 1.211.25 1.20 1.17 1.19Ethane 0.03 0.02 0.030.02 0.02 0.03 0.03 0.050.00 0.00 0.08 0.05 0.050.05 0.05 0.04 0.05 0.05Propane 0.75 0.79 0.84 0.86 0.86 0.86 0.95 0.92 0.800.97 1.08 1.01 1.10 1.07 1.13 1.08 1.06 1.09Butane 0.08 0.05 0.07 0.06 0.07 0.05 0.04 0.05 0.040.11 0.14 0.10 0.09 0.09 0.07 0.07 0.06 0.05Non-Fuel Use1.20 1.38 1.39 1.35 1.55 1.59 1.65 1.67 1.741.82 1.67 1.55 1.62 1.55 1.58 1.49 1.52 1.54Ethane 0.55 0.62 0.620.65 0.65 0.68 0.74 0.710.73 0.82 0.80 0.73 0.770.70 0.74 0.70 0.75 0.78Propane 0.53 0.59 0.61 0.55 0.65 0.67 0.65 0.71 0.770.77 0.66 0.59 0.65 0.63 0.66 0.64 0.62 0.64Butane 0.13 0.17 0.16 0.15 0.25 0.24 0.26 0.25 0.240.22 0.21 0.23 0.21 0.22 0.17 0.15 0.15 0.12Carbon Content (Tg C/QBtu)Fuel Use 17.21 17.21 17.21 17.2217.2217.2017.2017.1817.2317.25 17.2017.2117.2017.2117.2017.2017.20 17.20Non-Fuel Use 16.83 16.84 16.84 16.8016.8816.8716.8616.8816.8816.84 16.8116.8316.8216.8416.8116.8116. 8116.81Sources: 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 engi nes. It is a complex mixture of relatively volatile

hydrocarbons with or without small quantities of additives, blende d 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 emi ssions 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 approxima tely 69 degrees API gravity or 5.884 pounds per gallon. To

estimate the share of C in the fuel, it was assumed that aviatio n 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 hydrocar bon 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. Ta ble A-44 below shows the composition of those samples.

Table A-44: Composition, Energy Content, and Carb on 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 lik ely 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 assume d. 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 betw een 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 includ e 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 coe fficients 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 MM Btu 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 sa me 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 coefficien ts 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 shar e 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 kerose ne-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 extens ive 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 sh owed 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 es timated 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 a nd 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 de rived 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 estima ted 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 petr oleum 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 Gr avity) + (-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 acc ounted 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 de fine, the uncertainty of applying a crude oil C content is

likely to be bounded by the range of petrol eum products described in this chapter at  10 percent.



## Chronology and Explanation of Ch anges in Individual Carbon Conten t 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 sa mples 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 incl uded 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. Howeve r, 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 comp ounds 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 highe r proportions of propane than LPG for non-fuel use, the C

content coefficient for fuel use is about 2 percen t 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.A-69Table A-46: Carbon Content Coefficients for Coal byConsuming Sector and Coal Rank, 1990-2007 [Tg C/QBtu)1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 20022003 200420052006 2007Consuming SectorElectric Power 25.68 25.69 25.6925.7125.7225.7425.7425.7625.76 25.76 25.7625.7625.7625.7625.7625.7625.7625.76Industrial Coking 25.51 25.51 25.5125.5125.5225.5325.5525.5625.56 25.56 25.5625.5625.5625.5625.5625.5625.5625.56Other Industrial 25.58 25.60 25.6225.6125.6325.6325.6125.6325.63 25.63 25.6325.6325.6325.6325.6325.6325.6325.63Residential/Commercial 25.92 26.00 26.1325.9725.9526.0025.9226.0026.00 26.00 26.0026.0026.0026.0026.0026.0026.0026.00Coal RankAnthracite 28.26 28.2628.26 28.26 28.26 28.26 28.2628.26 28.26 28.26 28.26 28.2628.2628.2628.2628.2628.2628.26pBituminous 25.43 25.45 25.44 25.45 25.46 25.47 25.47 25.48 25.47 25.48 25.49 25.4925.4925.4925.4925.4925.4925.49pSub-bituminous 26.50 26.4926.49 26.48 26.49 26.4926.49 26.49 26.49 26.4926.48 26.48 26.48 26.4826.4826.4826.4826.48pLignite 26.19 26.21 26.22 26.21 26.2426.22 26.17 26.20 26.23 26.26 26.30 26.3026.3026.3026.3026.3026.3026.57pp(Preliminary)Sources: C content coefficientsby consuming sector from EIA (2008a). C content coefficients bycoal rank from USGS (1998) andSAIC (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 Ad ministration, Emissions of Gre enhouse Gases in the United States 1987-1992, U.S. Depart ment of

Energy, Washington, DC, November, 1994, DOE/EIA 0573, Appendix A.

### Figure A -2: Estimated and Actual Relationshi ps Between Petroleum Carbon Content Coefficients and Hydrocarbon

Density

Source: Carbon content factors for paraff ins 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 emiss ion factors based on the relationship betwee n density and energy content in U.S. D epartment 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 co mposition in S. Ringen, J. Lanum, and F.P. Miknis, “Calcula ting Heating

Values from the Elemental Com position 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

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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 Th eir 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 Determin ing 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. Chica go, 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 <h ttp://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. Depa rtment 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) “T he 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) “N atural 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 Be tween 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) “Calcula ting 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 Nume rical Data, Physics, Chemistry, and

Technology, New York, NY, McGraw-Hill.

Unzelman, G.H. (1992) “A Sticky Point for Re finers: 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 Emitte d 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 factors 11 

### 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 Coal a 

### 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 Coke c 

### 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 coki ng coal consumption and emission factor dat a are not available. Consumption of coki ng 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 fo r pigments. Consumption of petroleum coke for production of primary aluminum anodes, e lectric arc furnace anodes, titanium

dioxide, ammonia, urea, and ferroalloys is cove red 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 ty pes tracked by EIA are used in organic chemical synthesis and

in other manufacturing processes, and are referred to collectiv ely as “petrochemical feedstocks.” Because the methods and

data used to analyze them overlap, they are handled as a gro up and are discussed first. Discussions of the storage factors

for asphalt and road oil, lubricants, wa xes, 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, natu ral 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 fibe rs, solvents, paints, fertilizers, pharmaceuticals, and food additives are just a fe w of the derivatives of these fuel types. Chemically speaking, these fuels are diverse, ranging from simple natural gas (i.e.,

predominantly CH 4 

### ) 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 CO 2 

### Eq., or 58% percent, of the 361.1 Tg CO 2 

### Eq. of non-energy fuel consumption in 2007. For

2007 the storage factor for the eight fuel categories was 61 per cent. In other words, of the net consumption, 61 percent

was destined for long-term storage in products—including pr oducts subsequently combusted for waste disposal—while

the remaining 39 percent was emitted to the atmosphere directly as CO 2 

### (e.g., through combustion of industrial

byproducts) or indirectly as CO 2 

### precursors (e.g., through evaporative product use). The indirect emissions include a

variety of organic gases such as volatile organic compoun ds (VOCs) and carbon monoxide (CO), which eventually oxidize

into CO 2 

### 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 industr ial 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, bu tylenes, 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 statis tics. 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 publis hed 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 me mbership 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 feed stocks. 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 greate r 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 im ports 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 (thr ough the use of consumption data) or not, and to adjust the

import/export balance accordingly.

The BoC trade statistics are publicly available 13 

### 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 impor ts 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 a ll 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 Petrochem ical Feedstocks, 1990 – 2007 (Tg CO

2 

### 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 ad justed for the quantity of C that is used in the

Industrial Processes sector of the Invent ory. Fossil fuels used for non-energy purposes in industrial processes—and for

which C emissions and storage have been characterized thro ugh 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), ferroal loys (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 re leases 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-fe rtilizer uses, LPG, pentanes plus, naphthas, other oils, sti ll

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 re leases, energy recovery processes, and products were

characterized. The C fate categories are plastics, energy reco very, 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. Toxi c 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 chap ter, but because they are not n ecessarily associated with wast e

management, they were included here. Toxic releases are not a “p roduct” 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 fact or 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 CO 2 

### from waste combustion are accounted for separately in the

Inventory and are discussed in the Waste Co mbustion 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 CO 2 

### Eq. / (148.5 + 93.5) Tg CO 2 

### Eq. = 61%

Table A-49: C Stored and Emitted by Prod ucts from Feedstocks in 2007 (Tg CO

2 

### Eq.)

Product/Waste Type C Stored

(Tg CO 2 Eq.) C Emitted

(Tg CO 2 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 i ndustrial 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 rele ased 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 wa s made that the “missing” carbon was lost through fates leadi ng 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 furthe r

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 CO 2 

### (i.e., a storage factor of zero was applied to these

categories.) The release allocation is not reported for off-si te 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 CO

2 

### Eq.)

Disposal Location Carbon Stored

(Gg CO 2 Eq.) Carbon Emitted

(Gg CO 2 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. NMVO C 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 cl eaning, 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 separate d 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 attr ibuted 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 CO 2 

### Eq., and industrial NMVOC emissions in 2007

were 2.1 Tg CO 2 

### 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 NMVOCs a

NMVOCs (‘000 Short Tons) 1,120 1,181 744 779 759 739

Carbon Content (%) 85% 85% 85% 85% 85% 85%

Carbon Emitted (Tg CO 2 Eq.) 3.2 3.3 2.1 2.2 2.1 2.1

Solvent Evaporation b

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 CO 2 Eq.) 10.8 11.6 9.0 8.0 8.0 8.0

a Includes emissions from chemical and allie d products, petroleum and related industries, and other industrial processes categori es.

b Includes solvent usage and solvent evaporati on emissions from degreasing, graphic arts, dry cleaning, surface coating, other in dustrial 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 in clude total C emissions from the primary aluminum, titanium

dioxide, iron and steel, and ferroalloys production processes. The total C (CO and CO 2 

### ) emissions from oil and gas

production, petroleum refining, and asphalt manufacturing are also accounted for elsewh ere 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 petroc hemical 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 Emissions a

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 industri es, 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 reagen ts, 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 burne d 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 syst em 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 hazard ous 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 or ganics; this formed the basis for assuming the fraction of C

oxidized. Emissions from hazardous waste incineration in 2005 were 1.5 Tg CO 2 

### Eq. Table A-54 lists the CO 2 

### emissions

from hazardous waste incineration.

Table A-53: Assumed Composition of Combus ted 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: CO 2 

### Emitted from Hazardous Waste Incineration (Tg CO

2 

### Eq.)

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

CO 2 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 pe rcent 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, no nmetallic minerals, and other manufacturing sectors. These

“other” fuels include refinery still gas; waste gas; waste oi ls, 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 Stee l 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 ar e addressed elsewhere in the Inventory and not considered as

part of the petrochemical feedstocks energy recovery analys is. The remaining categories of fuels, including waste gas;

waste oils, tars, and related materials; and other uncharacteriz ed 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 CO 2 

### . Petrochemical feedstocks

### A-79 combusted for energy recovery corresponded to 42.7 Tg CO 2 

### Eq. in 1991, 35.8 Tg CO 2 

### Eq. in 1994, 58.7 Tg CO 2 

### Eq. in

1998, and 71.9 Tg CO 2 

### 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 Fuel s Used in Manufacturing/Energy Recovery (Trillion Btu)

Subsector and Industry NAICS CODE Waste Gas a Waste Oils/Tars b Refinery Still

Gas c Net Steam d Other Fuels e

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 g as 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 a ssumed to be same as asphalt/road oil

c Refinery "still gas" fuel consumption is re ported elsewhere in the Inventory and is excluded from the total C content estimate

d Net steam fuel consumption is reported elsewhere in the In ventory 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 Fuel s Burned for Energy Recovery (Tg CO

2 

### 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 pa rt 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 Producti on (Tg dry weight) and C Stored (Tg CO

2 

### Eq.)

Resin Type 2007 Production a

(Tg dry weight) Carbon Stored

(Tg CO 2 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 fo r 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 Polyethyl ene (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% Po lyethylene 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 releas e “Synthetic Rubber Use Growth to Continue Through 2004,

Says IISRP and RMA” (IISRP 2000). The 2001 and 2002 consumpt ion 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 diffe rence 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 produc tion 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 CO

2 

### Eq.)

Fiber Type Production

(Tg) C Content C Stored

(Tg CO 2 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 consum ption 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 Pestic ides (Million lbs.) and C Emitted and Stored (Tg CO

2 

### Eq.) in 2001

Pesticide Use* Active Ingredient

(Million lbs.) C Emitted

(Tg CO 2 Eq.) C Stored

(Tg CO 2 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 CO 2 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-deriv ed, and, as cleansers eventually biodegrade, all of the C was

assumed to be emitted. The first step in estimating C flows wa s 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 impor t 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 speci fic 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.—d ry” 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 tota l value) to estimate the total quantity of dry and liquid 18 

### 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 CO 2 

### Eq. in cleansers for 1997. For 1992 and 2002 the estimates

are 3.6 Tg CO 2 

### Eq. and 5.1 Tg CO 2 

### 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 CO

2 

### 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 co mmercial 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 CO 2 

### or to otherwise biodegrade to CO 2 

### . 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 CO 2 

### . Emissions of CO 2 

### from utilization of antifreeze and deicers are summarized in Table A-64.

Table A-64: C Emitted from Utilizat ion of Antifreeze and Deicers (Tg CO

2 

### 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 gly cerin, 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 wa stewater treatment processes to CO 2 

### or to otherwise biodegrade to CO 2 

### . Certain food

additives, e.g., glycerin, are manufactured both from petr ochemical 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 co mpounds 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

CO 2 

### . Emissions of CO 2 

### from utilization of synthetic food additives are summarized in Table A-65.

Table A-65: C Emitted from Utiliz ation of Food Additives (Tg CO

2 

### 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 CO 2 

### in the

manufacturing process. It is also assumed that the C contai ned in the silicone products is stored, and not emitted as CO 2



### .

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 pr oduction 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 CO 2 

### . 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 CO

2 

### 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 categor y 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 CO 2 

### Eq., standard deviation of 8.0 Tg CO 2 

### Eq., and 95 percent

confidence limits of 64.4 and 95.9 Tg CO 2 

### Eq. This compares with a calculated estimate of 79.9 Tg CO 2 

### 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 disc ussed 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 assume s that all C that is not stored is emitted. As the production

statistics that drive the storage factors are relatively well-cha racterized, this approach yields a result that is probably bia sed

toward understating uncertainty.

As far as specific sources of uncertainty, there are seve ral 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 st orage and emissions—are simila r. 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 CO 2 

### may actually take place on a time-scale of seve ral 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 fact ors are used, this “oxidation factor” may be inherent in the

storage factor applied when calculating emissions from non-ener gy 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) th at 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 CO 2 

### emissions from solvent evaporation and industrial

NMVOC emissions are in the estimates of (a) total emissions a nd (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 th at 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 li kely 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 in tended 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 id entified 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 th ere is uncertainty concerning the selection of a specific

conversion factor for each broad “other” fuel category to conve rt 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 Mexi can 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 avai lable. 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 fo r the C stored in scrap tire synthetic rubber. The

value does not take into account the rubber stored in othe r durable goods, clothing, footwear, and other non-durable goods,

or containers and packaging. This adds uncertainty to the tota l mass balance of C stored. There are also uncertainties as to

the assignment of C content values; however, they are much sm aller than in the case of plastics. There are probably fewer

variations in rubber formulations than in plastics, and the ra nge 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 gene ralization 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 uncerta inty 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 under ground 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 comi ng 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 petrol eum 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 CO 2 

### 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 fact or of almost 100 percent, as discussed below.

Paving is the primary application of asphalt cement, co mprising 86 percent of production. The three types of

asphalt paving produced in the United States are hot mix asphal t (HMA), cut-backs, and emulsified asphalt. HMA, which

makes up 90 percent of total asphalt paving (EPA 2000c), cont ains 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 emission s. This calculation was based on the C content of CO and

phenol, and an assumption of 85 percent C content for PAHs a nd HAPs. The C content of asphalt paving is a function of

(1) the proportion of asphalt cement in asphalt paving, assu med to be 5 percent asphalt cement content based on personal

20 The emission factors are expressed as a function of asphalt pavi ng 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 percen t 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 st orage 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 pr obability 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 comp ares 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 CO 2 

### 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 hi gh VOC emission levels and a related increase in emulsified

asphalt use as a substitute. However, because the C storage f actor 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 appli cations. They can be subdivided into oils and greases,

which differ in terms of physical characteristics (e.g., visc osity), 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 CO 2 

### 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 overa ll 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 CO 2 

### (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 CO 2 

### 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 petroleu m-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 CO 2 

### , only about 3 percent of the C in oil lubricants goes into longterm storage.

Table A-67: Commercial and Environmen tal 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 “R CRA (Resource Conservation and Recovery Act) On-line” web site

(<http://www.epa.gov/rcraonline/>) ha s 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 thes e 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 accor ding 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 averag e 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 ca lculation 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 va riance, 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 CO 2 

### , standard deviation of 1.8 and 95 percent confidence limits of 17.7 and 24.9 Tg

CO 2 

### . This compares with a calculated estimate of 21.4 Tg CO 2



### .

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 su rrounding 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 te mperature, but whose viscosity decreases as temperature

increases. Most commercial waxes are produced from petr oleum 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, a bout 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, pl astics, 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 cont ents 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 Perc ent 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 bas ed on professional judgment, ICF Consulting.

Note: Totals may not sum due to independent rounding.



### Emissive wax end uses include candles, firelogs (synthe tic 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 CO 2 

### . 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 CO 2 

### as heat is applied to these products during use. It is estimated that

50 percent of the C contained in hot melts is stored. Toge ther, candles, firelogs, and hotmelts constitute approximately 30

percent of annual wax production (NPRA 2002).

All of the wax utilized in the production of packaging, co smetics, 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 CO 2 

### from wax during the production of

the product). Wax is used in many different packaging ma terials 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 CO 2 

### ; 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 f actor 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 a ddressed 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 pe rcent 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 CO 2 

### , standard deviation of 0.2 Tg CO 2 

### , and 95 percent confidence

limits of 0.5 and 1.1 Tg CO 2 

### . This compares with a calculated estimate of 0.7 Tg CO 2 

### . 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 re fining byproducts (e.g., aromatic extracts and tars); absorption

oils; ram-jet fuel; petroleum rocket fuel; synthe tic 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 ga s 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 o ils are liquids and are precluded from disposal in landfills.

Because no sequestering end uses of any of the miscellaneou s products subcategories have been identified, a zero percent

storage factor is assigned to miscellaneous products. Acco rding 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 CO 2



### .

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 therefor e 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 bu siness 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 st orage 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 ca lculation 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 as sumptions 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 we re 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 va rious fuels) storage factor of 17 percent. The analysis

produced an emission distribution approximating a normal curve with a mean of 23.5 Tg CO 2 

### and a standard deviation of

5.0 Tg CO 2 

### , and 95 percent confidence limits of13.7 Tg CO 2 

### and 33.0 Tg CO 2 

### . This compares with the Inventory estimate

of 31.9 Tg CO 2 

### , which falls closer to the upper boundary of the confidence limit. The uncertainty analysis results are

driven primarily by the very broad uncer tainty 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 CH 4 

# , N 2 

# O, and Indirect Greenhouse

Gases from Stationary Combustion

Estimates of CH 4 

## and N 2 

## O Emissions

Methane (CH 4 

### ) and nitrous oxide (N 2 

### O) 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 activitie s was grouped by sector: industrial, commercial,

residential, electric power, and U.S. territories. For CH 4 

### and N 2 

### O, 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 cons umption 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 CH 4 

### and N 2 

### O 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, indus trial, and electric power sectors were taken from the 2006 IPCC

Guidelines for National Greenhouse Gas Inventories (IPCC 2006). These N 2 

### O emission factors by fuel type (consistent

across sectors) were also assumed for U.S. territories. The CH 4 

### emission factors by fuel ty pe for U.S. territories were

estimated based on the emission factor for the primary sect or in which each fuel was combusted. Table A-72 provides

emission factors used for each sector and fuel type.



## Estimates of NO

x 

## , CO, and NMVOC Emissions

Emissions estimates for NO

x 

### , CO, and NMVOCs were obtained from preliminary data (EPA 2008) and

disaggregated based on EPA (2003), which, in its final iter ation, 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

NO

x 

### , 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 ac tivities because data to allocate territories’ energy use were

unavailable. For this reason, CH 4 and N 2 O emissions from combustion by U.S. Territori es 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 pr edominantly 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 cate gories presented in EPA (2003) and EPA (2008) is

represented by the following equation:

E

p,s 

### = A

s 

### × EF

p,s 

### × (1 - C

p,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 NO

x 

### , CO, and NMVOCs from stationary

combustion as described above is similar to the methodology recommended by the IPCC (IPCC/UNEP/OECD/IEA 1997).A-99Table A-71: Fuel Consumption by Stationary Combustion for Calculating CH4and N2O Emissions (TBtu)Fuel/End-Use Sector 199019911992199319941995199619971998199920002001200220032004200520062007Coal 18,01417,97718,14618,88918,95819,09519,98320,49420,69620,72221,67521,08221,14421,57021,72022,11721,78922,099Residential 31 25 26 26 21 17 16 16 12 14 11 11 12 12 11 8 6 6Commercial 124 115 117 117 117 116 120 129 101 102 86 88 88 83 103 96 65 71Industrial 1,593 1,581 1,531 1,543 1,551 1,486 1,409 1,436 1,3561,317 1,349 1,358 1,244 1,249 1,262 1,236 1,214 1,143Electric Power 16,259 16,248 16,464 17,194 17,259 17,465 18,428 18,90319,216 19,279 20,220 19,61419,783 20,185 20,305 20,73720,461 20,835U.S. Territories 7 8 9 10 10 10 10 10 11 10 10 11 18 41 39 40 44 45Petroleum 6,6496,3556,3696,0876,1275,5296,0255,9055,7745,8996,0746,6845,9546,6756,8406,9086,4296,327Residential 1,382 1,387 1,382 1,3581,325 1,293 1,436 1,343 1,2431,377 1,453 1,472 1,366 1,503 1,476 1,374 1,219 1,201Commercial 828 838 751 705 713 674 699 624 585 583 652 676 601 714 698 683 637 611Industrial 2,774 2,507 2,800 2,440 2,524 2,346 2,638 2,565 2,1942,267 2,353 2,627 2,469 2,631 2,799 2,969 3,260 3,246Electric Power 1,289 1,198 991 1,124 1,059 755 817927 1,306 1,211 1,144 1,277961 1,205 1,212 1,235 648 659U.S. Territories 375 425 445 460 506 462 435 445 445 461 472 632 557 622 654 649 665 609Natural Gas 18,19718,64719,36419,82820,18721,11821,54121,68521,29121,25722,14921,35522,30521,83121,48621,26320,86822,262Residential 4,474 4,643 4,778 5,0544,941 4,944 5,350 5,088 4,6374,822 5,067 4,872 4,971 5,207 4,949 4,939 4,464 4,842Commercial 2,668 2,780 2,855 2,916 2,952 3,091 3,225 3,283 3,0763,111 3,234 3,089 3,208 3,277 3,181 3,074 2,899 3,080Industrial 7,730 7,834 8,203 8,305 8,301 8,764 9,091 9,174 8,8888,408 8,524 7,882 8,318 8,065 7,729 7,196 7,091 7,267Electric Power 3,3263,391 3,528 3,552 3,993 4,3203,876 4,140 4,690 4,9175,311 5,488 5,785 5,2565,602 6,030 6,388 7,046U.S. Territories 0 0 0 0 0 0 0 0 0 0 13 23 23 27 25 24 26 27Wood 2,2162,2142,3132,2602,3242,3702,4372,3712,1842,2142,2622,0061,9952,0022,1212,1562,1722,165Residential 580 610 640 550 520 520 540 430 380 390 420 370 380 400 410 450 410 460Commercial 66 68 72 76 72 72 76 73 64 67 71 67 69 71 70 70 65 65Industrial 1,442 1,410 1,461 1,484 1,580 1,652 1,683 1,731 1,6031,620 1,636 1,443 1,396 1,363 1,476 1,452 1,515 1,457Electric Power 129 126 140 150 152 125 138 137 137 138 134 126 150 167 165 185 182 184U.S. Territories NE NE NENENENENENENENE NENENENENENENENENE (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: CH 4 

### and N 2 

### O Emission Factors by Fuel Type and Sector (g/GJ) 24

Fuel/End-Use Sector CH 4 N 2 O

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) = 10 9 joules. One joule = 9.486×10 -4 BtuA-101Table A-73: NOx Emissions fromStationary Combustion (Gg)Sector/Fuel Type 1990 1991 1992 1993 1994 19951996 1997 19981999 20002001 2002 20032004 2005 20062007Electric Power 6,0455,9145,9006,0345,9565,7925,581 5,6835,6375,183 4,8294,4534,265 4,0073,7503,4923,1263,017Coal 5,1195,0435,0615,2115,1135,0615,079 5,1184,9324,437 4,1303,8023,634 3,4143,1952,9752,6642,570Fuel Oil 20019215416314887107 131202179 147149142 133125116104100Natural gas 513526526500536510248 277329393 376325310 292273254227219Wood NA NA NA NA NA NA5 62433 363736 3431292625Other FuelsaNA NA NA NA NA NANANA NANANANA NANANA NA NANAInternal Combustion 213152159160159134142 150149141 140140143 134126117105101Industrial 2,5592,4982,5752,6452,6462,6502,666 2,6142,5702,283 2,2782,2961,699 1,6991,7001,7001,6561,616Coal 530517521534546541490 487475475 484518384 384384384374365Fuel Oil 240215222222219224203 196190190 166153114 114114114111108Natural gas 8779299699931,000999900 880869706 710711526 526526526513500Wood NA NA NA NA NA NANANA NANANANA NANANA NA NANAOther Fuelsa119117115113113111109 103104100 10911686 8686868381Internal Combustion 792720749783767774965 948932813 809798591 591591591576562Commercial 336333348360365365360 369347255 257261247 246246245244244Coal 36333537363530 323423 212119 1919191919Fuel Oil 88808484869486 887354 525250 4949494949Natural gas 181191204211215210224 229220156 161165157 156156156155155Wood NA NA NA NA NA NANANA NANANANA NANANA NANAOther Fuelsa31292528282720 212122 222221 2121212121Residential 749829879827817813726 699651441 439446423 421421420418417CoalbNA NA NA NA NA NANANA NANANANA NANANA NA NANAFuel OilbNA NA NA NA NA NANANA NANANANA NANANA NA NANANatural GasbNA NA NA NA NA NANANA NANANANA NANANA NA NANAWood 42454840404427 272725 212221 2121212020Other Fuelsa707784831786778769699 671624416 417424402 401400399397396Total 9,6899,5749,7039,8659,7839,6199,333 9,3649,2068,162 7,8027,4566,634 6,3756,1175,8575,4455,293NA (Not Applicable)a“Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008).bResidential 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: 1990-2007Table 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 2004 2005 2006 2007Electric Power 329317318329336 337369385 410450 439439595 594594593 608624Coal 213212214224 224 227228233 220187 221220298 298298297 305313Fuel Oil 1817141513 91113 1736 272838 373737 3839Natural gas 4646474548 497276 88150 9692125 125125124 128131Wood NA NA NA NA NANA NA NANA NANA NA NANA NA NANA NAOther FuelsaNA NA NA NA NANA 78 3024 313244 444443 4546Internal Combustion 5241434650 525254 5552 636791 919191 9395Industrial 797835866946944 9581,0781,054 1,0441,100 1,1061,1371,149 1,1491,1491,150 1,1421,137Coal 9592929291 8810099 96114 118125127 127127127 126125Fuel Oil 6754586060 644947 4654 484546 464646 4545Natural gas 205257272292306 313307307 305350 355366370 370370370 368366Wood NA NA NA NA NANA NA NANA NANA NA NANA NA NANA NAOther Fuelsa253242239259260 270316302 303286 300321325 325325325 323321Internal Combustion 177189205244228 222305299 294296 285279282 282282282 280279Commercial 205196204207212 211122126 122151 151154177 173170166 167167Coal 1313131413 14 1313 1416 141315 151515 1515Fuel Oil 1616161616 171718 1517 171720 191919 1919Natural gas 4040464849 495859 5781 838497 959391 9292Wood NA NA NA NA NANA NA NANA NANA NA NANA NA NANAOther Fuelsa136128128129134 1323436 3636 363844 434242 4242Residential 3,6683,9654,1953,5863,515 3,8772,3642,361 2,3523,323 2,6442,6483,044 2,9862,9272,868 2,8752,883CoalbNA NA NA NA NANA NA NANA NANA NA NANA NA NANA NAFuel OilbNA NA NA NA NANA NA NANA NANA NA NANA NA NANA NANatural GasbNA NA NA NA NANA NA NANA NANA NA NANA NA NANA NAWood 3,4303,7103,9303,3373,272 3,6292,1332,133 2,1333,094 2,4162,4242,787 2,7342,6802,626 2,6322,639Other Fuelsa238255265249243 248231229 220229 228224257 252247242 243243Total 5,0005,3135,5835,0695,007 5,3833,9343,926 3,9285,024 4,3404,3774,965 4,9024,8414,778 4,7924,811NA (Not Applicable)a“Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008).bResidential 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-103Table A-75: NMVOC Emissions from Stationary Combustion (Gg)Sector/Fuel Type 19901991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007Electric Power 43404041 414044475149 565544444444 4444Coal 24242526 262625262625 272621212121 2121Fuel Oil 5544 423454 444443 44Natural gas 2222 227799 121210101010 1010Wood NANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NAOther FuelsaNANA NA NANA NA ++12 221111 11Internal Combustion 11999 999101010 11118888 88Industrial 165178170169 178187163160159156 157160138138138138 132128Coal 7575 756669 9109999 98Fuel Oil 11101111 111187710 997777 77Natural gas 52544846 576654545452 535447474747 4543Wood NANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NAOther Fuelsa46474546 454533313126 272925252525 2424Internal Combustion 49616060 586063626160 585749494949 4746Commercial 18182022 212122222125 282961605957 5655Coal 1111 111111 111111 11Fuel Oil 3233 333333 446655 55Natural gas 78910 101013131211 141423222221 2122Wood NANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NAOther Fuelsa8778 8855510 91031303030 2826Residential 686739782670 657725788788786815 8378371,3411,2981,2541,211 1,2371,264CoalbNANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NAFuel OilbNANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NANatural GasbNANA NA NANA NA NA NA NA NANA NA NA NA NA NANA NAWood 651704746633 621688756757756794 8098091,2981,2561,2131,171 1,1971,223Other Fuelsa35353636 363733323021 272743424139 4041Total 9129751,012901 8979731,0181,0171,0161,045 1,0771,0811,5851,5401,4951,450 1,4701,492NA (Not Applicable)a“Other Fuels” include LPG, waste oil, coke oven gas, coke, and non-residential wood (EPA 2003, 2008).bResidential 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 CH 4 

# , N 2 

# O, and Indirect Greenhouse

Gases from Mobile Combustion a nd Methodology for and Supplemental

Information on Transportati on-Related GHG Emissions

Estimating CO 2 

### Emissions by Transportation Mode

Transportation-related CO 2 

### 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, CO 2 

### 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), CO 2 

### 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). Th e 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 Plai ns 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 co nsumption 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 bo th of these tables correspond to the figures used to calculate

CO 2 

### 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 CO 2 

### emissions.

### A-105 Table A- 76. Fuel Consumption by Fuel and Vehicl e Type (million gallons unless otherwise specified)

Fuel/Vehicle Type 1990 1995 2000 2005 2006 2007

Motor Gasoline a 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 71,58 0.2 70,502.6

Light-Duty Trucks 34,698.1 44,185.8 50,695.6 54,053.2 55,40 8.5 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 Boats b 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 45,80 1.6 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 36,01 3.2 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 Fuel c 18,381.1 17,922.9 18,866.7 17,983.8 17,48 3.8 17,122.0

Commercial Air craft 14,128.0 14,856.5 15,976.6 14,984.1 14,54 6.7 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 Gasoline b 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 Oil c, 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

LPG c 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

Electricity c 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 t otal gasoline estimates.

c Estimated based on EIA transportation sector energy estimates by fuel type, with bottom-up ac tivity data used for apportionmen t to modes.

d Fluctuations in reported fuel consumpt ion 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 Gasoline a 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 Boats b 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 Fuel c 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 Gasoline c 45 40 36 35 33 32

General Aviation Aircraft 45 40 36 35 33 32

Residual Fuel Oil c, d 300 387 443 256 306 325

Ships and Other Boats 300 387 443 256 306 325

Natural Gas c 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

LPG c 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

Electricity c 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 t otal gasoline estimates.

c Estimated based on EIA transportation sector energy esti mates, with bottom-up data used for apportionment to modes

d Fluctuations in reported fuel consumpt ion may reflect data collection problems.

+ Less than 0.5 million Tbtu

- Unreported or zero



## Estimates of CH 4 

## and N 2 

## O Emissions

Mobile source emissions of greenhouse gases other than CO 2 

### are reported by transport mode (e.g., road, rail,

aviation, and waterborne), vehicle type, and fuel type. Emissions estimates of CH 4 

### and N 2 

### O 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 consumpt ion and vehicle miles traveled (VMT). These estimates were

then multiplied by emission factors, expressed as gram s 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, li ght-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 estimate s 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 s hown for 2007 in Table A- 82. Distributions for 1990-2007 are

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

vehicle fleet according to vehicle registrations by the aver age 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. em ission 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 equippe d 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 N 2 

### O 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 techn ology”

category. However, vehicles are now only separated into gasolin e, diesel, or alternative fuel categories, and gas-electric hybr ids 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 ve hicle age span beginning in year 1999.

28 For further description, see “Definitions of Emission Cont rol Technologies and Standards” section of this annex.

### A-108 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 Step 3: Determine CH 4 

### and N 2 

### O 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 cont rol 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 emissi ons. 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 appr oximate average driving characteristics.

Step 4: Determine the Amount of CH 4 

### and N 2 

### O Emitted by Vehicle, Fuel, and Control Technology Type

Emissions of CH 4 

### and N 2 

### O 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 th at 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 Brow ning, 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 CH 4 

### and N 2 

### O Emission Factors by Vehicle and Alternative Fuel Type

CH 4 

### and N 2 

### O 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,

N 2 

### O and CH 4 

### emissions for AFVs were expressed as a multiplier corresponding to conventional vehicle counterpart

emissions. Emission estimates in these studies represen t 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. Fo r light-duty vehicles, gasoline is generally blended with ethano l or

methanol; some vehicles are also designed to run on gasoline or an alternative fuel – either natural gas or LPG – but not at th e same time,

while other vehicles are designed to run on E85 (85% ethanol) or gasoline, or any mixt ure of the two. Heavy-duty vehicles are m ore

likely to run on a combination of diesel fuel an d either natural gas, LPG, ethanol, or methanol.

### A-109 Step 3: Determine the Amount of CH 4 

### and N 2 

### O Emitted by Vehicle and Fuel Type

Emissions of CH 4 

### and N 2 

### O 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

CH 4 

### and N 2 

### O 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 ship s and other boats (i.e., vessel bunkering) were obtained from

DHS (2008) and EIA (1991 through 2008) for distillate fu el, 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 CO 2 

### 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: pi pelines are a transportation source but are stationary,

not mobile, sources). Data on fuel consumption by all non-transportation mobile sources 31 

### 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 CH 4 

### and N 2 

### O 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 NO

x 

## , CO, and NMVOC Emissions

The emission estimates of NO

x 

### , 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 provid es 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 (10 9 

### 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 defi ned as any vehicle or equipment not used on the traditional road system, but exclu ding

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 (10 9 

### 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 fo r Alternative Fuel On-Road Vehicles (10 9 

### 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 (10 6 

### 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 tru cks are grouped with heavy-duty trucks; they are reported separatel y here because these two categories may

run on a slightly different range of fuel types.



### Table A- 82: Age Distribution by V ehicle/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 (lig ht-duty gasoline vehicles), LD GT (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 Vehicl e Mileage Accumulation per Vehicle a 

### (miles)

Vehicle Age LDGV LDGT HDGV LDDV LDDT HDDV MC b

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 (lig ht-duty gasoline vehicles), LD GT (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 ar e considered to have the same emissions and travel characteristi cs, 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 (lig ht-duty gasoline vehicles), LD GT (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-Ro ad Sources by Fuel Type (million gallons)

Vehicle Type/Year 1990 1995 2000 2005 2006 2007

Aircraft a 18,726.3 18,182.1 20,793.7 20,136.6 19,60 8.6 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 19,33 0.3 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

Equipment b 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 Equipment c 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

Other d 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 49,44 3.9 49,323.7

Sources: AAR (2008), APTA (2007 through 2008 and 2006), BEA (1991 through 2005), Benson (2002 through 2004), DHS (2008), DOC (1 991

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

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 we ll 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 agricultur e.

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 p urposes.



### Table A- 86: Control Technolo gy Assignments for Gasoline Passe nger 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 fo r 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 emissi ons of LEVs, a carmaker can reduce the to tal 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 fo r 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 emissi ons of LEVs, a manufacturer can reduce t he total number of LEVs they need to bui ld to meet a specified emission

average for all of their vehicl es in a given model year.

- Not applicable.



### Table A- 89: Control Technolo gy Assignments for Diesel On-R oad 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 CH 4 

### and N 2 

### O for On-Road Vehicles

N 2 O CH 4 Vehicle Type/Control Technology

(g/mi) (g/mi)

Gasoline Passenger Cars

EPA Tier 2 0.0036 0.0173

Low Emission Vehicles 0.0150 0.0105

EPA Tier 1 a 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 1 a 0.0871 0.0452

EPA Tier 0 a 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 1 a 0.1750 0.0655

EPA Tier 0 a 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 emissi ons control technologies are pr ovided at the end of this annex.



### Table A- 91: Emission Factors for CH 4 

### and N 2 

### O for Alternative Fuel Vehicles

N 2 O (g/mi) CH 4 (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 CH 4 

### and N 2 

### O Emissions from Non-Road Mobile Combustion (g gas/kg fuel)

Vehicle Type/Fuel Type N 2 O CH 4 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 Equipment a

Gasoline 0.08 0.45

Diesel 0.08 0.45

Construction/Mining

Equipment c

Gasoline 0.08 0.18

Diesel 0.08 0.18

Other Non-Road

All “Other” Categories c 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 comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

b Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 equipmen t, airport equipment, commercial

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

### A-117 Table A- 93: NO

x 

### 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-Road a 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

Aircraft b 25 31 40 42 41 40

Agricultural Equipment c 437 478 484 509 494 481

Construction/Mining

Equipment d 641 697 697 731 709 691

Other e 376 442 518 562 546 532

Total 10,920 10,622 10,310 8,757 8,271 7,831

a NO

x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cycl es, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

d Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 equipmen t, airport equipment, commercial

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

Note: Totals may not sum due to independent rounding.

IE = Included Elsewhere



### Table A- 94: CO Emissions fr om 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-Road a 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

Aircraft b 217 224 245 197 186 178

Agricultural Equipment c 581 628 626 524 495 472

Construction/Mining

Equipment d 1,090 1,132 1,047 878 830 792

Other e 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 NO

x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cycl es, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

d Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 equipmen t, airport equipment, commercial

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

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-Road a 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

Aircraft b 28 28 24 21 20 19

Agricultural Equipment c 85 86 76 79 76 73

Construction/Mining

Equipment d 149 152 130 136 131 126

Other e 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 NO

x emissions from alternative fuel on-road vehicl es are included under gasoline and diesel on-road.

b Aircraft estimates include only emissions related to LTO cycl es, and therefore do not include cruise altitude emissions.

c Includes equipment, such as tractors and comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

d Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 equipmen t, airport equipment, commercial

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

Note: Totals may not sum due to independent rounding.

IE = Included Elsewhere



## Definitions of Emission Control Technologies and Standards

The N 2 

### O and CH 4 

### 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 NO

x 

### 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 pa ssenger 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 CO 2 

### and H 2 

### O, reducing NO

x 

### 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" (als o 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 NO

x 

### 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 NO

x 

### 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

NO

x 

### , 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 NO

x 

### 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 cont rolled 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 tran sitional 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 CH 4 

### or N 2 

### O 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 catalys ts and NOx absorbers or selective catalytic reduction

(SCR) systems are designated as aftertreatment control. Th ese 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 info rmation on the contribution of transportation and other

mobile sources to U.S. greenhouse gas em issions. In the main body of the I nventory report, emission estimates are

generally presented by greenhouse gas, with separate discussions of the methodologies used to estimate CO 2 

### , N 2 

### O, CH 4 

### , 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- CO 2 

### 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 CO 2 

### 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

CO 2 

### 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 CO 2 

### from Non-Transpor tation Mobile Sources

The estimates of N 2 

### O and CH 4 

### from fuel combustion presented in the Energy chapter of the inventory include

both transportation sources and other mobile sources. Othe r 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

CO 2 

### 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 CO 2 

### from these other mobile sources, developed from EPA’s NONROAD model and

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

utilized in developing the N 2 

### O and CH 4 

### estimates.

Table A- 96: CO 2 

### Emissions from Non-Transportation Mobile Sources (Tg CO

2 

### Eq.)

Fuel Type/Vehicle Type 1990 1995 2000 2005 2006 2007

Agricultural Equipment a 30.8 36.3 38.4 46.4 48.6 47.9

Construction/Mining

Equipment b 41.6 48.5 54.7 65.2 66.6 67.0

Other Sources c 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 comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

b Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 equipmen t, airport equipment, commercial

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



### Estimation of HFC Emissions from Transpor tation Sources



### In addition to CO 2 

### , N 2 

### O and CH 4 

### 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 CO 2 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 emissi ons from all transportation and other mobile sources in

CO 2 

### equivalents. In total, transportation and mobile sources emitted 2,192.6 Tg CO 2 

### Eq. in 2007, an increase of 31

percent from 1990. These estimates were generated using the estimates of CO 2 

### emissions from transportation sources

reported in the Carbon Dioxide Emissions from Fossil Fuel Combustion section, and CH 4 

### emissions and N 2 

### O 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 CO 2 

### 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 N 2 

### O and CH 4 

### , 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 CO 2 

### emissions. N 2 

### O

and CH 4 

### 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, N 2 

### O and CH 4 

### 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 vehi cle type (from DOE 1993 through 2008). Otherwise, the table and

figure are drawn directly from emission estimates presente d elsewhere in the inventory, and are dependent on the

methodologies presented in Annex 2.1 (for CO 2 

### ), Chapter 4, and Annex 3.8 (for HFCs), and earlier in this Annex (for CH 4



### and N 2 

### O).

Transportation sources include on-road vehicles, aircraft, boa ts 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 CO 2 

### from

lubricants (such as motor oil) used in transportation. T ogether, transportation sources were responsible for 2,000.1 Tg

CO 2 

### 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 ve hicle greenhouse gas emissions, light-duty and medium- and

heavy-duty trucks have been the primary sources of grow th in on-road vehicle emissions. Between 1990 and 2007,

greenhouse gas emissions from passenger cars increased 1 per cent, while emissions from light-duty trucks increased 67

percent, largely due to the increased use of sport-utility ve hicles 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 desp ite 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 construc tion/mining equipment, agricultural equipment, and

industrial/commercial equipment, emitted approximately 192.5 Tg CO 2 

### 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. CH 4 

### and N 2 

### O emissions from these sources

are included in the “Mobile Combustion” section and CO 2 

### 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 emissi ons from transportation and other mobile sources broken

down by greenhouse gas. As this table shows, CO 2 

### accounts for the vast majority of transportation greenhouse gas

emissions (approximately 95 percent in 2007). Emissions of CO 2 

### from transportation and mobile sources increased by

467.1 Tg CO 2 

### Eq. between 1990 and 2007. In contrast, the combined emissions of CH 4 

### and N 2 

### O decreased by 16.1 Tg

CO 2 

### 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 CO 2 

### Eq. in 2007 as these chemicals were phased in as substitutes for ozone depleting

substances. It should be noted, however, that the ozone de pleting 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 li ght-duty vehicles, buses, passenger rail, aircraft (general

aviation and commercial aircraft), recreational boats, and mobi le 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 ha ve 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 CO 2 

### Eq., while freight transportation modes emitted

558.7 Tg CO 2 

### Eq. Since 1990, the rate of growth of greenhouse ga s 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. Greenhou se Gas Emissions from Transport ation and Mobile Sources (Tg CO

2 

### Eq.)

Mode / Vehicle Type /

Fuel Type 1990 1995 2000 2005 2006 2007 Percent

Change

1990-2007

Transportation Total a 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 Boats b 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%

Pipelines c 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 Equipment d 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

Equipment e 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 Equipment f 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 CO 2 from natural gas used to power pipelines; does not include emissions from electricity use or non-CO 2 gases.

d Includes equipment, such as tractors and comb ines, as well as fuel consumption from trucks that are used off-road in agricultu re.

e Includes equipment, such as cranes, dumpers, and excavators, as we ll 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 p urposes.

+ Less than 0.05 Tg CO 2 Eq.

NA = Not Applicable, as there were no HF C 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 CO

2 

### Eq.)

Gas 1990 1995 2000 2005 2006 2007 Percent

Change

1990-2007

CO 2 1,626.1 1,757.5 1,971.6 2,083.9 2,087.4 2,093.1 29%

N 2 O 43.7 53.7 52.8 36.7 33.5 30.1 -31%

CH 4 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 HF C 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 CO

2 

### Eq.)

Table A- 100: Greenhouse Gas Emission s from Passenger Transportation (Tg CO

2 

### 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 fr om Domestic Freight Transportation (Tg CO

2 

### 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 CH 4 

# Emissions from Coal Mining

The methodology for estimating CH 4 

### 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 CH 4 

### 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 CH 4 

### Liberated and CH 4 

### Emitted from Underground Mines

Underground mines generate CH 4 

### from ventilation systems and from degasification systems. Some mines

recover and use CH 4 

### generated from degasification systems, thereby reducing emissions to the atmosphere. Total CH 4 

### emitted from underground mines equals the CH 4 

### liberated from ventilation systems, plus the CH 4 

### liberated from

degasification systems, minus CH 4 

### recovered and used.

### A-125 Step 1.1: Estimate CH 4 

### Liberated from Ventilation Systems

All coal mines with detectable CH 4 

### emissions 33 

### use ventilation systems to ensure that CH 4 

### levels remain within

safe concentrations. Many coal mines do not have detectable levels of CH 4 

### , while others emit several million cubic feet

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

(MSHA) measures CH 4 

### emissions levels at underground mines. MSHA maintains a database of measurement data from

all underground mines with detectable levels of CH 4 

### in their ventilation air. Based on the four quarterly measurements,

MSHA estimates average daily CH 4 

### 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 sh own 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 CH 4 

### emissions were obtained. These mines were assumed to account for

100 percent of CH 4 

### 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 CH 4 

### 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 CH 4 

### 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 CH 4 

### 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 Us ed 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 concentrati ons 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 CH 4 

### Liberated from Degasification Systems

Coal mines use several different types of degasification systems to remove CH 4 

### , including vertical wells and

horizontal boreholes to recover CH 4 

### prior to mining of the coal seam. Gob wells and cross-measure boreholes recover

CH 4 

### 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 CH 4 

### 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 CH 4 

### liberated from their degasification systems. For these mines, this

reported information was used as the estimate. In other cases in which mines sell CH 4 

### recovered from degasification

systems to a pipeline, gas sales were used to estimate CH 4 

### liberated from degasification systems (see Step 1.3). Finally,

for those mines that do not sell CH 4 

### to a pipeline and have not provided information to EPA, CH 4 

### 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 CH 4



### liberated from the mine.

Step 1.3: Estimate CH 4 

### Recovered from Degasification Systems and Utilized (Emissions Avoided)

In 2007, fifteen active coal mines had CH 4 

### recovery and use projects. Thirteen mines sold the recovered CH 4 

### to

a pipeline, one used the CH 4 

### on site to heat mine ventilation air, and one of the coal mines used CH

B 4

B 

### to generate electricity.

One of the mines that sold gas to a pipeline also used CH

B 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 CH 4 

### 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 indi vidual 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 CH 4 

### Emitted from Surface Mines and Post-Mining Activities

Mine-specific data were not available for estimating CH 4 

### 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 CH 4 

### emissions.

Step 2.1: Define the Geographic Resolution of the Analysis and Collect Coal Production Data

The first step in estimating CH 4 

### 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 CH 4 

### content of the surface coal in each

basin. Based on an analysis presented in EPA (1993), surf ace mining emission factors were estimated to be from 1 to 3

times the average in situ CH 4 

### content in the basin. For this analysis, the surface mining emission factor was determined to

be twice the in situ CH 4 

### content in the basin. Furthermore, the post-mining emission factors used were estimated to be 25

to 40 percent of the average in situ CH 4 

### 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 CH 4 

### 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 CH 4 

### Emitted

The total amount of CH 4 

### emitted was calculated by multiplying the coal production in each basin by the

appropriate emission factors.

Total annual CH 4 

### emissions are equal to the sum of underground mine emissions plus surface mine emissions

plus post-mining emissions. Table A- 1 06 and Table A- 107 present estimates of CH 4 

### 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 Ba sin 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 Mont ana, North Dakota, Wyoming

West Interior Basin Arkansas, Iowa, Kans as, 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 Centra l 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 BasinA-128 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A- 104: Annual Coal Production (Thousand Short Tons)Basin 1990199119921993 199419951996199719981999 20002001200220032004200520062007Underground CoalProduction423,556406,344406,335351,056 399,102396,249409,850420,657417,729391,791 372,766380,627357,384352,785367,531368,611359,020351,791N. Appalachia 103,865103,450 105,220 77,032 100,12298,103106,729112,135116,718107,575 105,374107,02598,64398,369106,915111,151107,827106,024Cent. Appalachia 198,412 181,873 177,777 164,845 170,893166,495171,845177,720171,279157,058 150,584152,457137,224130,724128,560123,083117,738110,103Warrior 17,531 17,062 15,944 15,557 14,47117,60518,21718,50517,31614,799 15,89515,17214,91615,37516,11413,29510,73711,462Illinois 69,167 69,947 73,154 55,967 69,05069,00967,04664,72864,46363,529 53,72054,36454,01651,78056,31959,18061,72661,924S. West/Rockies 32,754 31,568 31,670 35,409 41,68142,99443,08844,50345,98346,957 45,74251,19352,12156,11159,01260,86559,67058,815N. Great Plains 1,722 2,418 2,511 2,146 2,7382,0182,7882,8541,7231,673 1,21000322015728402,869West Interior 105 26 59 100 14725137212247200 241416464394410465482594Northwest 0 0 0 0 000000 00000000Surface CoalProduction602,753 587,143 588,944 594,372 634,401 636,726 654,007 669,271 699,608 708,639 700,608 745,306 735,912 717,689 743,553 762,191802,975 793,689N. Appalachia 60,76151,124 50,512 48,641 44,96039,37239,78840,17941,04333,928 34,90835,33430,00827,37028,17428,87328,37626,121Cent. Appalachia 94,343 91,785 95,163 94,433 106,129106,250108,869113,275108,345107,507 110,479116,983111,34099,419103,968112,222118,388116,226Warrior 11,413 10,104 9,775 9,211 8,7957,0366,4205,9635,6974,723 4,2524,7966,3208,4379,74211,59911,88911,410Illinois 72,000 63,483 58,814 50,535 51,86840,37644,75446,86247,71540,474 33,63140,89439,38036,67534,01633,70233,36233,736S. West/Rockies 43,863 42,985 46,052 48,765 49,11946,64343,81448,37449,63550,349 49,58752,18050,00641,23742,55842,75636,79834,310N. Great Plains 249,356 259,194 258,281 275,873 308,279331,367343,404349,612385,438407,683 407,670438,367441,346444,007466,224474,056518,136523,695West Interior 64,310 61,889 63,562 60,574 58,79159,11660,91259,06157,95158,309 54,17050,61350,45953,41151,70652,26352,02146,867Northwest 6,707 6,579 6,785 6,340 6,4606,5666,0465,9455,9825,666 5,9116,1386,9737,3137,1656,7204,0051,324Total Coal Production 1,026,309993,487 995,279 945,428 1,033,5031,032,9751,063,8571,089,9281,118,1321,093,975 1,073,3741,127,6891,093,2961,070,6541,111,1111,130,8021,161,9951,145,478N. Appalachia 164,626154,574 155,732 125,673 145,082137,475146,517152,314157,761141,145 140,282142,360128,731125,739135,089140,024136,203132,143Cent. Appalachia 292,755 273,658 272,940 259,278 277,022272,745280,714290,995279,624262,660 261,063269,440248,564230,143232,528235,305236,126226,328Warrior 28,944 27,166 25,719 24,768 23,26624,64124,63724,46823,01319,499 20,14719,96721,23623,81225,85624,89422,62622,872Illinois 141,167 133,430 131,968 106,502 120,918109,385111,800111,590110,176103,966 87,35195,25893,39688,45590,33592,88295,08895,660S. West/Rockies 76,617 74,553 77,722 84,174 90,80089,63786,90292,87795,61896,207 95,239103,373102,12797,348101,570103,62196,46893,125N. Great Plains 251,078 261,612 260,792 278,019 311,017333,385346,192352,466387,161406,324 408,880438,367441,346444,039466,425474,628518,976526,564West Interior 64,415 61,915 63,621 60,674 58,93859,14161,04959,27358,19858,509 54,41151,02850,92353,80552,11652,72852,50347,462Northwest 6,707 6,579 6,785 6,340 6,4606,5666,0465,9455,9825,665 5,9116,1386,9737,3137,1656,7204,0051,324Source 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.A-129Table A- 105: Coal Surface and Post-Mining CH4Emission Factors (ft3per Short Ton)BasinSurface Averagein situContentUnderground AverageIn situContentSurface MineFactorsPost-MiningSurface FactorsPost MiningUndergroundNorthern Appalachia 59.5138.4119.019.345.0Central Appalachia (WV) 24.9136.849.88.144.5Central Appalachia (VA) 24.9399.149.88.1129.7Central Appalachia (E KY) 24.961.449.88.120.0Warrior 30.7266.761.410.086.7Illinois 34.364.368.611.120.9Rockies (Piceance Basin) 33.1196.466.210.863.8Rockies (Uinta Basin) 16.099.432.05.232.3Rockies (San Juan Basin) 7.3104.814.62.434.1Rockies (Green River Basin) 33.1247.266.210.880.3Rockies (Raton Basin) 33.1127.966.210.841.6N. Great Plains (WY, MT) 20.015.840.06.55.1N. Great Plains (ND) 5.615.811.21.85.1West Interior (Forest City, Cherokee Basins) 34.364.368.611.120.9West Interior (Arkoma Basin) 74.5331.2149.024.2107.6West Interior (Gulf Coast Basin) 11.0127.922.03.641.6Northwest (AK) 16.0160.032.01.852.0Northwest (WA) 16.047.332.05.215.4Sources: 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 Coalbeds: A Potential Energy Source, 1986-88 Gas Research Institute Topical Reports, A Geologic Assessment of Natural Gas from Coal Seams;Surface Mines Emissions Assessment, U.S. EPA Draft Report, November 2005.Table A- 106: Underground Coal Mining CH4Emissions (Billion Cubic Feet)Activity 1990 1991199219931994199519961997 1998 19992000200120022003200420052006 2007Ventilation Output 112 NANA95961009096 94 9287847976837579 81Adjustment Factor for Mine Data*97.8% NANA97.8%97.8%91.4%91.4%100% 97.8% 97.8%97.8%97.8%97.8%97.8%97.8%97.8%97.8% 100%Adjusted Ventilation Output 114 NANA97981099996 96 9489868077847781 81Degasification System Liberated 54 NANA4546474942 49 4045495050504854 37Total Underground Liberated 168 164162142144153148139 146 134134135131127132129135 117Recovered & Used (14) (14)(16)(23)(27)(31)(35)(28) (35) (31)(37)(41)(43)(38)(40)(37)(46) (32)Total 154 150145120117116113115 111 10397948889948989 85* Refer to Table A- 102.Note: Totals may not sum due to independent rounding.Table A- 107: Total Coal Mining CH4Emissions (Billion Cubic Feet)Activity 1990 19911992199319941995199619971998 199920002001200220032004200520062007Underground Mining 154 150145120117116113115111 1039794888994878985Surface Mining 30 2829282928293130 313033323132333534Post-Mining (Underground) 19 1818161717181818 171717161616161515Post-Mining (Surface) 5 55455555 555555566Total 208 200196167168166155169166 156149149141141148141145140Note: Totals may not sum due to independent rounding.A-130 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A- 108: Total Coal Mining CH4Emissions by State (Million Cubic Feet)State 199019941995199619971998199920002001200220032004200520062007Alabama 32,27229,618 33,73529,55630,942 27,11125,70223,342 21,89618,68619,28822,33016,39114,17916,580Alaska 6358 635554 505861 56434056545349Arizona 192222 203177199 192200223 228217205216205139135Arkansas 78 543 442 221114144California 1- - --000 0000000Colorado 10,3259,192 7,5825,9729,189 9,1819,39010,808 11,11712,08213,21612,58213,60813,10211,671Illinois 10,50210,585 11,56310,8768,534 7,8477,8108,542 7,2705,9724,7445,7986,5866,9544,493Indiana 2,7952,495 2,0252,1922,742 2,8782,6502,231 3,3733,4963,8213,5313,7024,0294,347Iowa 304 - --000 0000000Kansas 5723 231929 273316 1416126143433Kentucky 10,95611,259 10,2698,98710,451 10,0059,5619,105 9,3638,4648,0287,9267,4949,1359,278Louisiana 8189 958291 827694 95971039710610580Maryland 519237 237259267 251225331 340401391411421435261Mississippi -- - --0157 43165264256254271253Missouri 21167 445732 303135 29204346483119Montana 1,7491,936 1,8341,7561,906 1,9921,9111,783 1,8201,7381,7191,8531,8701,9312,016New Mexico 451679 586408459 489497464 6301,2801,8642,0523,0012,9702,660North Dakota 380420 392389385 389405407 397401401390390396385Ohio 5,0654,583 4,1894,0684,349 4,3503,9143,519 3,6192,8312,6493,1833,3853,4132,672Oklahoma 285359 323286385 395469454 620660620849877658774Pennsylvania 22,73524,024 25,61126,44030,026 29,49124,86724,830 22,25219,66820,28120,02018,92119,53219,472Tennessee 296101 112143148 11611999 142142124136140117120Texas 1,4261,339 1,3471,4111,364 1,3451,3571,240 1,1521,1571,2151,1731,1751,1651,073Utah 3,5872,626 2,5702,8103,566 3,8593,6332,816 2,0802,7093,4085,2534,7875,4453,678Virginia 46,13726,742 20,05919,77116,851 13,97813,32112,065 11,50611,22711,90611,3898,79010,44210,118Washington 186182 181170167 173153159 172217232210196960West Virginia 49,03930,664 30,55236,38433,554 36,96233,66030,827 33,83331,48228,59929,55030,61229,23728,797Wyoming 8,49610,912 12,18512,83812,994 14,54915,60715,725 17,14717,35217,49718,43518,78420,75220,974Total207,844168,422165,785165,109168,687165,747155,654149,235149,196140,526140,669147,749141,809144,627140,084+ Does not exceed 0.5 Million Cubic FeetNote: The emission estimates provided above are inclusive ofemissions from underground mines, surface mines and post-mining activities. The following states have neither underground nor surface mining and thus report no emissionsas a result of coal mining: Connecticut, Delaware, Florida, Georgia, Hawaii,Idaho, Maine, Massachusetts, Michigan, Minnesota,Nebraska, Nevada, New Hampshire, New Jersey, New York, North Carolina, Oregon, Rhode Island, SouthCarolina, South Dakota, Vermont, and Wisconsin.

### A-131 

# 3.4. Methodology for Estimating CH 4 

# and CO 2 

# Emissions from Natural Gas Systems

The following steps were used to estimate CH 4 

### and non-energy CO 2 

### 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 CH 4 

### and non-energy related (i.e., fugitive, vented and flared) CO 2 

### 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 emissi on inventory for the year 1992. These stages include: field

production, processing, transmissi on 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 activit y 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 CH 4 

### 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 CH 4 

### and non-energy CO 2 

### emissions

from all stages. For most sources, the CH 4 

### emission factors were adjusted for CO 2 

### content when estimating fugitive and

vented non-energy CO 2 

### emissions. In the case of non-energy CO 2 

### 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 dr ivers, including: number of producing wells (EIA 2008a-b, New

Mexico 2008a-b, Texas 2008a-b), number of gas plants (A GA 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 CH 4 

### 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 CH 4 

### reductions reported to the

Natural Gas STAR Program and CH 4 

### reductions resulting from National Emission Standards for Hazardous Air Pollutants

(NESHAP) regulations.

Industry partners report CH 4 

### emission reductions by project to the Natural Gas STAR Program. The reductions

are estimates using actual measurement data or equipment-spec ific emission factors. Before incorporating the reductions

into the Inventory, quality assurance and quality control check s 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 re duction occurred between 1990 and 1992, then the reduction is

added back into the estimate for the appropriate year(s). Th e 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. CH 4 

### 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 a nd natural gas industry, the NESHAP regulation addresses HAPs

from the oil and natural gas production sectors and the natura l 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 pro cessing plants exceeding the threshold natural gas throughput

limit are required to routinely implement Leak Detectio n and Survey (LDAR) programs. The emissions reductions

achieved as a result of NESHAP regulations were estimate d 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 re gulation. The reductions are representative of the control

measures in both the oil and natural gas industry. CH 4 

### emission reductions from the NESHAP regulation are summarized

in 

### Table A- 115

.

Step 4: Estimate CO 2 

### Emissions for Each Year and Stage

The same procedure for estimating CH 4 

### emissions holds true for estimating non-energy related CO 2 

### 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 CH 4 

### , but as shown in 

### Table A- 120 , the natural gas contains, in

some cases, as much as 8 percent CO 2 

### . The same vented and fugitive emissions of natural gas that led to CH 4 

### emissions

also contain a certain volume of CO 2 

### . Accordingly, the CO 2 

### emissions for each sector can be estimated using the same

methane activity and emissions factors for these vented and f ugitive sources. The primary difference is that EPA/GRI

emission factors are adjusted for CO 2 

### content in each sector.

Using the default CH 4 

### content of produced natural gas from EPA/GRI (1996) of 78.8 percent, the corresponding

amount of CO 2 

### emissions from the production sector can be estimated. Each sector of the natural gas system has varying

CO 2 

### contents, similar to the way the CH 4 

### content varies among the sectors. 

### Table A- 120 shows the CO 2 

### content for the

different well types in the production sector of the natural gas system. In the estimation of CO 2 

### emissions from the

production sector, the production sector CH 4 

### emission factors were used as a basis to estimate CO 2 

### emissions; however,

they were converted to CO 2 

### emission factors by multiplication by a conversion factor. This conversion factor is the ratio

of CO 2 

### content in the gas stream (in this case the production concentrations provided in 

### Table A- 120 ) to the

corresponding CH 4 

### content of the same gas stream. The three exceptions to this methodology are CO 2 

### emissions from

flares, acid gas removal units, and condensate tanks. In the case of flare emissions, a direct CO 2 

### 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 CO 2 

### emissions from an acid gas unit in a processing plant is

equal to the difference in CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### emission factor for condensate passing through fixed r oof tanks. The detailed source emission estimates

for CH 4 

### and CO 2 

### from the production sector are presented in 

### Table A- 116 and 

### Table A- 121 , respectively.

In the processing sector, the CO 2 

### content of the natural gas remains the same as the CO 2 

### 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

CO 2 

### 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 CH 4 

### content of natural gas in the processing sector to be 87 percent

CH 4 

### . Consequently, the processing sector CH 4 

### emission factors were proportioned to reflect the CO 2 

### content of either

produced natural gas or pipeline quality gas using the same methodology as the production sector. The detailed source

emission estimates for CH 4 

### and CO 2 

### from the processing sector are presented in 

### Table A- 117 and 

### Table A- 122

, respectively.

For the transmission sector, CO 2 

### 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 CO 2 

### 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 CO 2



### emissions for the transmission sector the CH 4 

### emission factors were proportioned from the 93.4 percent CH 4 

### reported in

EPA/GRI (1996) to reflect the 1 percent CO 2 

### content found in transmission quality natural gas. The detailed source

emissions estimates for CH 4 

### and CO 2 

### 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 CH 4 

### content (93.4 percent) and CO 2 

### content (1 percent) are identical due to the absence of any

further treatment between sector boundaries. Thus, the CH 4 

### emissions factors were converted to CO 2 

### emission factors

using the same methodology as discussed for the transmission sector. The detailed source emission estimates for CH 4 

### and

CO 2 

### for the distribution sector are presented in 

### Table A- 119 and 

### Table A- 124 , respectively.

Because Partners report only CH 4 

### emission reductions to the Natural Gas STAR Program, there was no need to

adjust for the Natural Gas STAR program in the CO 2 

### emissions estimates for any of the sectors in the natural gas system.

The impact of NESHAP regulations on CO 2 

### emission reductions are not currently addressed in the CO 2 

### emission

estimates.A-134 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A- 109: 1992 Data and CH4Emissions (Mg) for the Natural Gas Production StageGRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsNorth EastGas WellsNE - Associated Gas Wells267,836wells NA367,489wells NA-NE - Non-associated Gas Wells (lessUnconventional) 129,407wells 7.5scfd/well 9,752129,157wells 7.54scfd/well6,842NE - Unconventional Gas Wells NANA0wellsscfd/well -Field Separation EquipmentHeaters 260heaters 15.1scfd/heater 39258heaters 15.07scfd/heater27Separators 91,670separators 1.0scfd/sep 87391,701separators 0.95scfd/sep614Dehydrators 1,047dehydrators 23.1scfd/dehy 241686dehydrators 23.05scfd/dehy111Meters/Piping 76,262meters 9.6scfd/meter 7,2847,866meters 9.55scfd/meter528Gathering CompressorsSmall Reciprocating Comp. 129compressors 283.8scfd/comp 366129compressors 283.81scfd/comp258Large Reciprocating Comp. 0compressors/station 16,116.9scfd/comp 016compressors 16,116.93scfd/comp1,813Large Reciprocating Stations NA8,741.2scfd/station 2stations 8,741.17scfd/station123Pipeline Leaks 159,406miles (gathering) 56.3scfd/mile 89,80966,029miles 56.34scfd/mile26,151Drilling and Well CompletionCompletion Flaring 396Completions 777.0scf/comp 3,074187completions/ year 776.96scf/comp3Well Drilling NANA5,227wells 2,695.52scf/well271Normal OperationsPneumatic Device Vents 66,903device 365.7scfd/device 244,66062,770controllers 365.69scfd/device161,368Chemical Injection Pumps 4,659active pumps 262.9scfd/pump 12,246646active pumps 262.87scfd/pump1,193Kimray Pumps 5,176,010MMscf/yr 1,051.5scf/MMscf 54,425,537200,673MMscf/yr 1,051.50scf/MMscf4,064Dehydrator Vents 5,813,634MMscf/yr 292.1scf/MMscf 16,983,331225,222MMscf/yr 292.13scf/MMscf1,267Condensate Tank VentsCondensate Tanks without ControlDevicesNANAscf/bbl 1MMbbl/yr 21.87scf/bbl421Condensate Tanks with Control Devices NANAscf/bbl 1MMbbl/yr 4.37scf/bbl84Compressor Exhaust VentedGas Engines 12,874MMHPhr 0.3scf/HPhr 330.00MMHPhr 0.25scf/HPhr -Well WorkoversGas Wells 4,374workovers/yr 2,601.2scfy/w.o. 2195,618workovers/yr 26,01.18scfy/w.o. 281Well Clean Ups (LP Gas Wells) 53,513LP Gas Wells 52,543.0scfy/LP well 54,15453,471LP Gas Wells 52,543.00scfy/LP well54,111BlowdownsVessel BD 120,022vessels 82.7scfy/vessel 19192,645vessels 82.68scfy/vessel 148Pipeline BD 159,406miles (gathering) 327.5scfy/mile 1,00666,029miles (gathering) 327.53scfy/mile417Compressor BD 8,023compressors 4,000.3scfy/comp 618129compressors 4,000.35scfy/comp10Compressor Starts 8,023compressors 8,949.4scfy/comp 1,383129compressors 8,949.38scfy/comp22UpsetsPressure Relief Valves 248,223PRV 36.0PRV 172247,749PRV 36.04PRV172Mishaps 159,406miles (gathering) 709.1scf/mile 2,17716,507miles 709.12scf/mile225Mid-CentralGas WellsA-135GRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsMC - Associated Gas Wells271,002wells NA70,640wells NA-MC - Non-associated Gas Wells 59,143wells 7.14scfd/well 4,22159,029wells 7.14scfd/well2,962MC - Unconventional Gas Wells 329wells 7.14scfd/well 23329wells 7.14scfd/well17Field Separation EquipmentHeaters 30,124heaters 14.27scfd/heater 4,29824,099heaters 14.27scfd/heater2,417Separators 20,469separators 0.90scfd/sep 18525,821separators 0.90scfd/sep164Dehydrators 14,836dehydrators 91.48scfd/dehy 13,5729,987dehydrators 91.48scfd/dehy6,422Meters/Piping 121,500meters 9.05scfd/meter 10,99082,475meters 9.05scfd/meter5,244Gathering CompressorsSmall Reciprocating Comp. 6,824compressors 268.77scfd/comp 18,3406,707compressors 268.77scfd/comp12,673Large Reciprocating Comp. 39compressors 15,263.01scfd/comp 5,91116compressors 15,263.01scfd/comp1,717Large Reciprocating Stations 5stations 8,278.04scfd/station 4012stations 8,278.04scfd/station116Pipeline Leaks 72,854miles (gathering) 53.35scfd/mile 38,87151,169miles 53.35scfd/mile19,192Drilling and Well CompletionCompletion Flaring 181Completions 735.80scf/comp 1,33186completions/ year 735.80scf/comp1Well Drilling NANA2,402wells 2,552.70scf/well118Normal OperationsPneumatic Device Vents 67,285device 346.32scfd/device 233,01992,064controllers 346.32scfd/device224,137Chemical Injection Pumps 2,129active pumps 248.95scfd/pump 5,3008,429active pumps 248.95scfd/pump14,751Kimray Pumps 2,365,609MMscf/yr 995.78scf/MMscf 23,556,3662,923,053MMscf/yr 995.78scf/MMscf56,061Dehydrator Vents 2,657,024MMscf/yr 276.65scf/MMscf 7,350,6963,280,643MMscf/yr 276.65scf/MMscf17,480Condensate Tank VentsCondensate Tanks without ControlDevicesNANA5MMbbl/yr 21.87scf/bbl1,895Condensate Tanks with Control Devices NANA5MMbbl/yr 4.37scf/bbl379Compressor Exhaust VentedGas Engines 5,884MMHPhr 0.24scf/HPhr 1411,099MMHPhr 0.24scf/HPhr51,502Well WorkoversGas Wells 1,999workovers/yr 2,463.36scfy/w.o. 952,582workovers/yr 2,463.36scfy/w.o.123Well Clean Ups (LP Gas Wells) 24,457LP Gas Wells 49,759.11scfy/LP well 23,43924,574LP Gas Wells 49,759.11scfy/LP well23,551BlowdownsVessel BD 54,854vessels 78.30scfy/vessel 8359,907vessels 78.30scfy/vessel90Pipeline BD 72,854miles (gathering) 310.18scfy/mile 43551,169miles (gathering) 310.18scfy/mile306Compressor BD 3,667compressors 3,788.40scfy/comp 2686,707compressors 3,788.40scfy/comp489Compressor Starts 3,667compressors 8,475.21scfy/comp 5996,707compressors 8,475.21scfy/comp1,095UpsetsPressure Relief Valves 113,446PRV 34.13PRV 75113,860PRV 34.13PRV75Mishaps 72,854miles (gathering) 671.55scf/mile 94212,792miles 671.55scf/mile165Rocky MountainGas WellsRM - Associated Gas Wells214,215wells NA14,142wells NA-RM - Non-associated Gas Wells 26,274wells 32.95scfd/well 8,65826,224wells 32.952scfd/well6,075RM - Unconventional Gas Wells 100wells 6.43scfd/well 6100wells 6.428scfd/well5Field Separation EquipmentHeaters 13,383heaters 52.18scfd/heater 6,98412,004heaters 52.185scfd/heater4,404A-136 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007GRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsSeparators 9,093separators 110.32scfd/sep 10,03213,135separators 110.324scfd/sep10,187Dehydrators 6,591dehydrators 82.40scfd/dehy 5,4314,429dehydrators 82.397scfd/dehy2,565Meters/Piping 53,976meters 47.83scfd/meter 25,81930,941meters 47.835scfd/meter10,405Gathering CompressorsSmall Reciprocating Comp. 3,031compressors 242.09scfd/comp 7,3392,922compressors 242.094scfd/comp4,973Large Reciprocating Comp. 17compressors 13,748.02scfd/comp 2,36516compressors 13,748.015scfd/comp1,546Large Reciprocating Stations 2stations 7,456.37scfd/station 1602stations 7,456.366scfd/station105Pipeline Leaks 32,365miles (gathering) 48.06scfd/mile 15,55443,849miles 48.059scfd/mile14,814Drilling and Well CompletionCompletion Flaring180Completions 662.76scf/comp 53238completions/ year 662.762scf/comp0Well Drilling NANA1,065wells 2,299.323scf/well47Normal OperationsPneumatic Device Vents 20,933device 311.94scfd/device 65,29838,538controllers 311.941scfd/device84,510Chemical Injection Pumps 946active pumps 224.24scfd/pump 2,1214,686active pumps 224.236scfd/pump 7,386Kimray Pumps 1,050,921MMscf/yr 896.94scf/MMscf 9,426,1751,296,285MMscf/yr 896.944scf/MMscf22,393Dehydrator Vents 1,180,383MMscf/yr 249.19scf/MMscf 2,941,4111,454,865MMscf/yr 249.191scf/MMscf6,983Condensate Tank VentsCondensate Tanks without ControlDevicesNANA10MMbbl/yr 21.870scf/bbl4,002Condensate Tanks with Control Devices NANA10MMbbl/yr 4.374scf/bbl800Compressor Exhaust VentedGas Engines 2,614MMHPhr 0.22scf/HPhr 64,922MMHPhr 0.22scf/HPhr20,572Well WorkoversGas Wells 888workovers/yr 2,218.85scfy/w.o. 381,145workovers/yr 2,218.85scfy/w.o.49Well Clean Ups (LP Gas Wells) 10,865LP Gas Wells 44,820.07scfy/LP well 9,37910,898LP Gas Wells 44,820.07scfy/LP well9,408BlowdownsVessel BD 24,369vessels 70.53scfy/vessel 3329,568vessels 70.53scfy/vessel40Pipeline BD 32,365miles (gathering) 279.39scfy/mile 17443,849miles (gathering) 279.39scfy/mile236Compressor BD 1,629compressors 3,412.36scfy/comp 1072,922compressors 3,412.36scfy/comp192Compressor Starts 1,629compressors 7,633.97scfy/comp 2402,922compressors 7,633.97scfy/comp430UpsetsPressure Relief Valves 50,399PRV 30.74PRV 3050,494PRV 30.74PRV30Mishaps 32,365miles (gathering) 604.89scf/mile 37710,962miles 604.89scf/mile128Coal Bed MethanePowder River NANA254,507,484gal produced water0.00Gg/gallon waterdrainage472South WestGas WellsSW - Associated Gas Wells268,479wells NA68,130wells NA-SW - Non-associated Gas Wells 22,554wells 34.40scfd/well 7,75822,510wells 34.40scfd/well5,443SW - Unconventional Gas Wells 99wells NA99wells 0scfd/well -Field Separation EquipmentHeaters 11,488heaters 54.47scfd/heater 6,2586,127heaters 54.47scfd/heater2,346Separators 7,806separators 115.17scfd/sep 8,99012,706separators 115.17scfd/sep10,287Dehydrators 5,658dehydrators 86.01scfd/dehy 4,8663,804dehydrators 86.01scfd/dehy2,300A-137GRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsMeters/Piping 46,333meters 49.93scfd/meter 23,13629,576meters 49.93scfd/meter10,382Gathering CompressorsSmall Reciprocating Comp. 2,602compressors 252.72scfd/comp 6,5763,075compressors 252.72scfd/comp 5,463Large Reciprocating Comp. 15compressors 14,351.40scfd/comp 2,1198compressors 14,351.40scfd/comp807Large Reciprocating Stations 2stations 7,783.62scfd/station 1441stations 7,783.62scfd/station55Pipeline Leaks 27,782miles (gathering) 50.17scfd/mile 13,93839,689miles 50.17scfd/mile13,997Drilling and Well CompletionCompletion Flaring169Completions 691.85scf/comp 47733completions/ year 691.85scf/comp0Well Drilling NANA915wells 2,400.24scf/well42Normal OperationsPneumatic Device Vents 47,064device 325.63scfd/device 153,25630,025controllers 325.63scfd/device68,732Chemical Injection Pumps 812active pumps 234.08scfd/pump 1,9011,379active pumps 234.08scfd/pump2,269Kimray Pumps 902,104MMscf/yr 936.31scf/MMscf 8,446,4891,113,378MMscf/yr 936.31scf/MMscf20,078Dehydrator Vents 1,013,233MMscf/yr 260.13scf/MMscf 2,635,7021,249,582MMscf/yr 260.13scf/MMscf6,260Condensate Tank VentsCondensate Tanks without ControlDevicesNANA3MMbbl/yr 21.87scf/bbl1,053Condensate Tanks with Control Devices NANA3MMbbl/yr 4.37scf/bbl211Compressor Exhaust Vented-Gas Engines 2,244MMHPhr 0.23scf/HPhr 54,228MMHPhr 0.23scf/HPhr18,445Well WorkoversGas Wells 762workovers/yr 2,316.23scfy/w.o. 34984workovers/yr 2,316.23scfy/w.o.44Well Clean Ups (LP Gas Wells) 9,327LP Gas Wells 46,787.16scfy/LP well 8,4049,360LP Gas Wells 46,787.16scfy/LP well8,434BlowdownsVessel BD 20,918vessels 73.62scfy/vessel 3022,637vessels 73.62scfy/vessel32Pipeline BD 27,782miles (gathering) 291.65scfy/mile 15639,689miles (gathering) 291.65scfy/mile223Compressor BD 1,398compressors 3,562.13scfy/comp 963,075compressors 3,562.13scfy/comp211Compressor Starts 1,398compressors 7,969.01scfy/comp 2153,075compressors 7,969.01scfy/comp472UpsetsPressure Relief Valves 43,262PRV 32.09PRV 2743,369PRV 32.09PRV27Mishaps 27,782miles (gathering) 631.44scf/mile 3389,922miles 631.44scf/mile121West CoastGas WellsWC - Associated Gas Wells219,921wells NA19,819wells NA-WC - Non-associated Gas Wells 1,256wells 37.38scfd/well 4701,254wells 37.38scfd/well330WC - Unconventional Gas Wells 0wells 0scfd/well 00wells 0scfd/well -Field Separation EquipmentHeaters 640heaters 59.20scfd/heater 3791,254heaters 59.20scfd/heater522Separators 435separators 125.15scfd/sep 544915separators 125.15scfd/sep805Dehydrators 315dehydrators 93.47scfd/dehy 295211dehydrators 93.47scfd/dehy139Meters/Piping 2,581meters 54.26scfd/meter 1,4012,500meters 54.26scfd/meter954Gathering CompressorsSmall Reciprocating Comp. 145compressors 274.63scfd/comp 3981,456compressors 274.63scfd/comp2,811Large Reciprocating Comp. 1compressors 15,595.89scfd/comp 1288compressors 15,595.89scfd/comp877Large Reciprocating Stations 0stations 8,458.58scfd/station 1stations 8,458.58scfd/station59A-138 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007GRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsPipeline Leaks 1,548miles (gathering) 54.52scfd/mile 84415,771miles 54.52scfd/mile6,044Drilling and Well Completion0Completion Flaring14Completions 751.84scf/comp 292completions/ year 751.84scf/comp0Well Drilling NANA51wells 2,608.38scf/well3Normal OperationsPneumatic Device Vents 10,949device 353.87scfd/device 38,7441,257controllers 353.87scfd/device3,126Chemical Injection Pumps 45active pumps 254.38scfd/pump 115851active pumps 254.38scfd/pump1,523Kimray Pumps 50,254MMscf/yr 1,017.50scf/MMscf 511,34161,753MMscf/yr 1,017.50scf/MMscf1,210Dehydrator Vents 56,445MMscf/yr 282.69scf/MMscf 159,56269,307MMscf/yr 282.69scf/MMscf377Condensate Tank VentsCondensate Tanks without ControlDevicesNANAscf/bbl 0MMbbl/yr 21.87scf/bbl -Condensate Tanks with Control Devices NANAscf/bbl 0MMbbl/yr 4.374scf/bbl -Compressor Exhaust VentedGas Engines 125MMHPhr 0.25scf/HPhr 0234MMHPhr 0.25scf/HPhr1,112Well WorkoversGas Wells 42workovers/yr 2,517.09scfy/w.o. 255workovers/yr 2,517.09scfy/w.o.3Well Clean Ups (LP Gas Wells) 520LP Gas Wells 50,844.36scfy/LP well 509519LP Gas Wells 50,844.36scfy/LP well508BlowdownsVessel BD 1,165vessels 80.01scfy/vessel 22,380vessels 80.01scfy/vessel4Pipeline BD 1,548miles (gathering) 316.94scfy/mile 915,771miles (gathering) 316.94scfy/mile96Compressor BD 78compressors 3,871.02scfy/comp 61,456compressors 3,871.02scfy/comp109Compressor Starts 78compressors 8,660.05scfy/comp 131,456compressors 8,660.05scfy/comp243UpsetsPressure Relief Valves 2,410PRV 34.87PRV 22,405PRV 34.87PRV2Mishaps 1,548miles (gathering) 686.20scf/mile 203,943miles 686.20scf/mile52Gulf CoastGas Wells-GC - Associated Gas Wells235,558wells NA35,376wells NA-GC - Non-associated Gas Wells 37,379wells 38.25scfd/well 14,29837,307wells 38.25scfd/well10,032GC - Unconventional Gas Wells NAwells NA0wells 0scfd/well -Field Separation EquipmentHeaters 19,039heaters 60.58scfd/heater 11,5348,357heaters 60.58scfd/heater3,559Separators 12,937separators 128.07scfd/sep 16,56824,548separators 128.07scfd/sep22,101Dehydrators 9,377dehydrators 95.65scfd/dehy 8,9696,277dehydrators 95.65scfd/dehy4,221Meters/Piping 76,790meters 55.53scfd/meter 42,64144,674meters 55.53scfd/meter17,439Gathering CompressorsSmall Reciprocating Comp. 4,313compressors 281.04scfd/comp 12,1203,022compressors 281.04scfd/comp5,970Large Reciprocating Comp. 24compressors 15,959.44scfd/comp 3,90616compressors 15,959.44scfd/comp1,795Large Reciprocating Stations 3stations 8,655.76scfd/station 2652stations 8,655.76scfd/station122Pipeline Leaks 46,045miles (gathering) 55.79scfd/mile 25,68856,151miles 55.79scfd/mile22,022Drilling and Well CompletionCompletion Flaring 114Completions 769.37scf/comp 87954completions/ year 769.37scf/comp1Well Drilling NANA1,510wells 2,669.18scf/well78Normal OperationsA-139GRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor EmissionsActivity Data Emission Factor EmissionsPneumatic Device Vents 37,709device 362.12scfd/device 136,55025,929controllers 362.12scfd/device66,005Chemical Injection Pumps 1,346active pumps 260.31scfd/pump 3,5031,231active pumps 260.31scfd/pump2,253Kimray Pumps 1,495,101MMscf/yr 1,041.22scf/MMscf 15,567,3101,837,175MMscf/yr 1,041.22scf/MMscf36,843Dehydrator Vents 1,679,280MMscf/yr 289.27scf/MMscf 4,857,7342,061,925MMscf/yr 289.27scf/MMscf11,488Condensate Tank VentsCondensate Tanks without ControlDevices NANA34MMbbl/yr 21.87scf/bbl14,321Condensate Tanks with Control Devices NANA34MMbbl/yr 4.37scf/bbl2,864Compressor Exhaust VentedGas Engines 3,719MMHPhr 0.25scf/HPhr 96,976MMHPhr 0.25scf/HPhr33,846Well WorkoversGas Wells 1,263workovers/yr 2,575.76scfy/w.o. 631,623workovers/yr 2,575.76scfy/w.o.81Well Clean Ups (LP Gas Wells) 14,704LP Gas Wells 52,029.57scfy/LP well 14,73515,445LP Gas Wells 52,029.57scfy/LP well15,477BlowdownsVessel BD 34,668vessels 81.87scfy/vessel 5539,182vessels 81.87scfy/vessel62Pipeline BD 46,045miles (gathering) 324.33scfy/mile 28856,151miles (gathering) 324.33scfy/mile351Compressor BD 2,317compressors 3,961.26scfy/comp 1773,022compressors 3,961.26scfy/comp231Compressor Starts 2,317compressors 8,861.93scfy/comp 3963,022compressors 8,861.93scfy/comp516UpsetsPressure Relief Valves 71,700PRV 35.69PRV 4971,563PRV 35.69PRV49Mishaps 46,045miles (gathering) 702.19scf/mile 62314,038miles 702.19scf/mile190Coal Bed MethaneBlack Warrior NANA2,230wells 0.00Gg/well4,844Offshore PlatformsShallow water Gas Platforms (GoM andPacific)NANA2,210Shallow water gasplatforms19,178.08scfd/platform297,920Deepwater Gas Platforms (GoM andPacific)NANA11Deepwater gasplatforms79,452.05scfd/platform5,9431Emissions are not actually0, but too small to show atthis level of precision.2Emissions from oil wells that produce associated gas are estimated in the PetroleumSystems model. Here the oil wells countis used as a driver only.3NA = not available; i.e. this data isnot available from the specified source.Note: The mapping of regions differs between GRI and NEMS. The GRImaps the states to the following regions: Gulf Coast Onshore, Gulf Coast Offshore, Central Plains (onshore), Atlantic and Great Lakes (onshore), Pacific andMountain (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 toprovide a one-to-one comparison ofactivity factors between the two modeling systems. Hence, the GRI national AF estimateswere allocated to the NEMS regions using the NEMS regional gas well counts to national well count ratios.Table A- 110: 1992 Data and CH4Emissions (Mg) for the Natural Gas Processing StageGRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor Emissions Activity Data Emission Factor EmissionsPlants 726 plants 7,906 scfd/plant 40,349 732 plants 7,906 scfd/plant 40,683Recip. Compressors 4,092 compressors 11,196 scfd/comp 322,068 4,126 compressors11,196 scfd/comp 324,744Centrifugal Compressors 726 compressors 21,230 scfd/comp 108,351 665 compressors21,230 scfd/comp 99,248Compressor ExhaustGas Engines 27,760 MMHPhr 0.24 scf/HPhr128,317 29,642 MMHPhr 0.24 scf/HPhr 137,019Gas Turbines 32,910 MMHPhr 0.01 scf/HPhr 3,612 35,147 MMHPhr 0.01 scf/HPhr 3,859AGR Vents 371 AGR units 6,083 scfd/AGR 15,865 371 AGR units 6,083 scfd/AGR 15,865A-140 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Kimray Pumps 957,900 MMscf/yr 178 scf/MMscf3,279 1,073,845 MMscf/yr 178 scf/MMscf 3,676Dehydrator Vents 8,630,000 MMscf/yr 122 scf/MMscf 20,203 9,674,280 MMscf/yr 122 scf/MMscf 22,648Pneumatic Devices 726 gas plants 164,721 scfy/plant 2,303 732 gas plants164,721 scfy/plant 2,322Blowdowns/Venting 726 gas plants 4,060 Mscfy/plant56,770 732 gas plants4,060 Mscfy/plant 57,239Table A- 111: 1992 Data and CH4Emissions (Mg) for the Natural Gas Transmission StageGRI/EPA Values EPA Adjusted ValuesActivity Activity Data Emission Factor Emissions Activity Data Emission Factor EmissionsPipeline Leaks 284,500 miles 1.55 scfd/ mile3,107 291,468 miles 1.55 scfd/ mile 3,184Compressor Stations(Transmission)Station 1,700 Stations8,778 scfd/station 104,931 1,730 Stations 8,778 scfd/station 106,767Recip Compressor 6,799 Compressors 15,205 scfd/ comp726,742 6,956 Compressors15,205 scfd/ comp 743,554Centrifugal Compressor 681 Compressors 30,305 scfd/ comp 145,081 698 Compressors30,305 scfd/ comp 148,634Compressor Stations (Storage)Station 386 Stations 21,507 scfd/station 58,360 386 Stations21,507 scfd/ comp 58,360Recip Compressor 1,135 Compressors 21,116 scfd/comp168,483 1,135 Compressors 21,116 scfd/ comp 168,483Centrifugal Compressor 111 Compressors 30,573 scfd/comp 23,856 111 Compressors30,573 scfd/ comp 23,857Wells (Storage) 17,999 Stations 114 scfd/well 14,487 17,999 Stations 115 scfd/ comp 14,488M&R (Trans. Co. Interconnect) 2,532Stations 3,984 scfd/station 70,915 2,595 Stations3,984 scfd/ comp 72,667M& R (Farm Taps + Direct Sales) 72,630 Stations 31.20 scfd/station 15,930 76,932 Stations 31.20scfd/station 16,873Dehydrator vents (Transmission) 1,083,926 MMscf/Year 93.72 scf/MMscf 1,956 1,102,878 MMscf/Year 94 Scfd/ comp 1,991Dehydrator vents (Storage) 2,000,001MMscf/Year 117.18 scf/MMscf 4,513 2,000,001 MMscf/Year 117 scf/MMscf 4,514Compressor ExhaustEngines (Transmission) 40,380 MMHPhr 0.24 scf/HPhr 186,652 40,380 MMHPhr 0.24 scf/HPhr 186,653Turbines (Transmission) 9,635 MMHPhr 0.01 scf/HPhr 1,057 9,635 MMHPhr 0.01 scf/HPhr 1,058Engines (Storage) 4,922 MMHPhr 0.24 scf/HPhr 22,751 4,922 MMHPhr 0.24 scf/HPhr 22,751Turbines (Storage) 1,729 MMHPhr 0.01 scf/HPhr 189 1,729 MMHPhr 0.01 scf/HPhr 190Generators (Engines) 1,976 MMHPhr 0.24 scf/HPhr 9,133 1,976 MMHPhr 0.24 scf/HPhr 9,134Generators (Turbines) 23 MMHPhr 0.01 scf/HPhr 3 23 MMHPhr 0.01 scf/HPhr 3Pneumatic Devices Trans + StorPneumatic Devices Trans 68,103 Devices 162,197 scfy/device 212,747 68,103 Devices 162,197 scfy/device 212,748Pneumatic Devices Storage 15,460 Devices 162,197 scfy/device 48,295 15,194 Devices 162,197 scfy/device 47,465Routine Maintenance/UpsetsPipeline venting 284,500 Miles 32 Mscfy/mile173,425 291,468 Miles 32 Mscfy/mile 177,673Station Venting Trans + StorageStation Venting Transmission 1,700CompressorStations 4,359 Mscfy/station 148,206 1,730CompressorStations 4,359 Mscfy/station 145,256Station Venting Storage 386CompressorStations4,359 Mscfy/station 33,651 386CompressorStations4,359 Mscfy/station 32,406LNG StorageLNG Stations NA NA 63 Stations 21,507 scfd/station 9,453LNG Reciprocating Compressors NA NA238 Compressors 21,116 scfd/comp 35,367LNG Centrifugal Compressors NA NA56 Compressors 30,573 scfd/comp 12,090LNG Compressor ExhaustLNG Engines NA NA699 MMHPhr 0.24 scf/HPhr 3,231A-141LNG Turbines NA NA110 MMHPhr 0.01 scf/HPhr 12LNG Station Venting NA NA63 Stations 4,359Mscfy/station 5,249LNG Import TerminalsLNG Stations NA NA1 Stations 21,507 scfd/station 212LNG Reciprocating Compressors NA NA7 Compressors 21,116 scfd/comp 1,009LNG Centrifugal Compressors NA NA1 Compressors 30,573 scfd/comp 258LNG Compressor ExhaustLNG Engines NA NA208 MMHPhr 0.24 scf/HPhr 961LNG Turbines NA NA44 MMHPhr 0.01 scf/HPhr 5LNG Station Venting NA NA1 Stations 4,359 Mscfy/station 118Table A- 112: 1992 Data and CH4Emissions (Mg) for the Natural Gas Distribution StageGRI/EPA ValuesEPA Adjusted ValuesActivityActivity DataEmission FactorEmissionsActivity DataEmission FactorEmissionsPipeline LeaksMains—Cast Iron 55,288 miles 238.7 Mscf/mile-yr 254,178.95 52,917miles 238.70 Mscf/mile-yr 243,279Mains—Unprotected steel 174,657 equiv leaks 51.8 Mscf/leak-yr 174,249.70 99,619 miles110.19 Mscf/mile-yr 211,417Mains—Protected steel 68,308 equiv leaks 20.3 Mscf/leak-yr 26,706.93 469,106 miles 3.07 Mscf/mile-yr 27,709Mains—Plastic 49,226 equiv leaks 99.8 Mscf/leak-yr 94,619.66 267,283 miles9.91 Mscf/mile-yr 51,015Services—Unprotected steel 458,476 equivleaks 20.2 Mscf/leak-yr 178,371.00 7,138,563 services 1.70Mscf/service 233,836Services Protected steel 390,628 equivleaks 9.2 Mscf/leak-yr 69,216.16 19,742,086 services 0.18Mscf/service 67,109Services—Plastic 68,903 equiv leaks2.39 Mscf/leak-yr 3,171.7020,692,674 services0.01 Mscf/service 3,706Services—Copper 7,720 equiv leaks 7.68 Mscf/leak-yr 1,141.92 1,568,685 services0.25 Mscf/service 7,684Meter/Regulator (City Gates)M&R >300 3,460 stations 179.8 scfh/station 104,960.57 3,887 stations 179.80scfh/station 117,926M&R 100-300 13,335 stations 95.6scfh/station 215,085.58 14,187 stations 95.60 scfh/station 228,821M&R <100 7,127 stations 4.31 scfh/station 5,182.567,583 stations 4.31scfh/station 5,514Reg >300 3,995 stations 161.9 scfh/station 109,124.94 4,250 stations 161.90scfh/station 116,094R-Vault >300 2,346 stations1.3 scfh/station 514.55 2,496 stations 1.30 scfh/station 547Reg 100-300 12,273 stations 40.5scfh/station 83,862.1812,857 stations 40.50scfh/station 87,856R-Vault 100-300 5,514 stations0.18 scfh/station 167.46 5,777 stations 0.18 scfh/station 175Reg 40-100 36,328 stations 1.04scfh/station 6,374.34 38,583 stations 1.04 scfh/station 6,770R-Vault 40-100 32,215 stations0.09 scfh/station 470.1534,216 stations0.09 scfh/station 499Reg <40 15,377 stations 0.13scfh/station 345.05 16,360 stations 0.13 scfh/station 367Customer MetersResidential 40,049,306outdoormeters138.5 scfy/meter 106,831.9240,049,306 outdr meters143.27 scfy/meter 110,512Commercial/Industry 4,608,000 meters 47.9 scfy/meter 4,251.13 4,607,983 meters 47.90 scfy/meter 4,251Routine MaintenancePressure Relief Valve Releases 836,760 mile main0.05 Mscf/mile 805.8 888,925 mile main 0.05 Mscf/mile 856Pipeline Blowdown 1,297,569 miles 0.1 Mscfy/mile2,549.10 1,297,569 miles 0.10 Mscfy/mile 2,549UpsetsMishaps (Dig-ins) 1,297,569 miles 1.59 mscfy/mile39,735.97 1,297,569 miles1.59 mscfy/mile 39,736A-142 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A- 113: Key Activity Data DriversVariableUnits199019952001200220032004200520062007Transmission Pipelines Lengthmiles291,990296,947290,456303,541301,816303,084300,686300,350299,920WellsNE—Associated Gas Wells*# wells68,26166,10254,72752,92847,80347,41246,47147,03446,646NE—Non-associated Gas Wells*# wells124,241129,789149,436154,590156,320155,257158,238164,322172,493MC—Associated Gas Wells*# wells64,37972,48367,27865,78665,86465,90265,65265,90369,234MC—Non-associated Gas Wells*# wells53,94065,58563,59567,86170,37772,80971,37973,91480,650RM—Associated Gas Wells*# wells13,74913,74512,14812,44612,49512,72313,25913,43712,013RM—Non-associated Gas Wells*# wells24,33932,66870,45072,43871,23970,77072,57975,17070,249SW—Associated Gas Wells*# wells69,33959,95457,18860,62360,31559,38154,78154,55055,143SW—Non-associated Gas Wells*# wells24,21727,39233,93635,02536,64837,21934,24735,41737,949WC—Associated Gas Wells*# wells20,67219,10920,80822,50322,26322,58421,56222,18922,110WC—Non-associated Gas Wells*# wells1,2921,1141,4341,4151,4591,3881,4241,5031,506GC—Associated Gas Wells*# wells36,27934,79232,54929,88028,07827,67528,09027,31926,234GC—Non-associated Gas Wells*# wells41,75341,97851,18253,19854,24554,54457,60060,71568,188PlatformsGulf of Mexico and Pacific OCS Off-shorePlatforms# platforms3,9423,9814,0744,0584,0103,9383,9153,9223,849GoM and Pacific OCS Deep Water Platforms# platforms172340444650596763Gas Plants# gas plants761675570590574572566571574Distribution Services# of services47,883,08354,644,03357,461,79558,876,41658,537,39561,089,88958,556,33562,255,43563,524,388Steel—Unprotected# of services7,633,5266,151,6535,449,6535,186,1344,840,3474,791,6525,308,3755,642,4705,448,804Steel—Protected# of services19,781,58121,002,45517,911,40217,778,46317,258,71018,147,58715,883,42315,732,03715,756,048Plastic# of services18,879,86526,044,54532,706,75334,547,27435,071,96136,811,10736,152,27739,632,31341,092,515Copper# of services1,588,1111,445,3801,393,9871,364,5451,366,3771,339,5431,212,2601,248,6151,277,021Distribution Mainsmiles944,1571,001,7061,099,1371,133,6251,104,6831,151,9951,093,9091,209,4191,198,585Cast Ironmiles58,29250,62544,28342,02541,09140,60037,37136,97737,669Steel—Unprotectedmiles108,94194,05881,29178,11974,04275,85969,29171,73869,525Steel—Protectedmiles465,538503,288475,329480,982483,782495,861461,459481,811489,815Plasticmiles311,386353,735498,234532,499505,768539,675525,788618,893601,575* NEMS (National Energy Modeling System)projects the production, imports, conversion, consumption,and prices of energy, subject to assumptions on macroeconomic andfinancial factors, world energy markets, resource availability andcosts, behavioral and technological choicecriteria, cost and performance characteristics of energy technologies, and demographics

### A-143 Table A- 114: CH 4 

### 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 prog ram reductions. These numbers are adjusted for reductions prior to the 1992 base year, and do not

include a sunsetting period.



### Table A- 115: CH 4 

### 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 : CH 4 

### Emission Estimates from the Na tural 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 : CH 4 

### Emission Estimates from the Na tural 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 : CH 4 

### Emission Estimates from the Natura l 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 Turbines 1 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 : CH 4 

### Emission Estimates from the Nat ural 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 CO 2 

### 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 : CO 2 

### Emission Estimates from the Na tural 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 Wells 2 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 Flaring 2 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 River 1

- - - - - - - - - -

Black Warrior 1

- - - - - - - - - - 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 Engines 1

- - - - - - - - - -

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 CO 2 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 : CO 2 

### Emission Estimates from the Na tural Gas Processing Plants (Gg)

Activity 1990 1992 1995 2001 2002 2003 2004 2005 2006 2007

Normal Fugitives

Plants – Before CO 2 removal 2.56 2.46 2.27 1.92 1.98 1.93 1.92 1.90 1.92 1.93

Plants – After CO 2 removal 0.57 0.55 0.50 0.43 0.44 0.43 0. 43 0.42 0.43 0.43

Reciprocating Compressors –

Before CO 2 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 CO 2 removal 4.36 4.36 4.54 4.79 4.62 4.66 4.54 4.40 4.52 4.72

Centrifugal Compressors –

Before CO 2 removal 6.01 6.01 6.25 6.60 6.36 6.41 6.25 6.05 6.21 6.49

Centrifugal Compressors –

After CO 2 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 Engines 1

- - - - - - - - - - Gas Turbines 1

- - - - - - - - - - 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 CO 2 emissions not estimated to avoid double counting.



### Table A- 123 : CO 2 

### Emission Estimates from the Natura l 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 Engines 1

- - - - - - - - - -

LNG Turbines 1

- - - - - - - - - - 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 Engines 1

- - - - - - - - - -

LNG Turbines 1

- - - - - - - - - - LNG Station Venting 2 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01 0.01

1 Energy use CO 2 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 : CO 2 

### Emission Estimates from the Natu ral 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 CH 4 

# and CO 2 

# Emissions from Petroleum Systems

The methodology for estimating CH 4 

### and non-combustion CO 2 

### 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 CH 4 

### and twenty-nine

activities that emit non-combustion CO 2 

### 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 CH 4 

### emissions. Crude transportation and refining accounted for the remaining CH 4 

### emissions of less than one half and about

two percent, respectively. Non-combustion CO 2 

### 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 CH 4 

### and

CO 2 

### emissions from petroleum systems.

Step 1: Determine Emission Factors for all Activities

The CH 4 

### emission factors for the majority of the activitie s for 1995 are taken from the 1999 EPA draft report,

which contained the most recent and comprehensive determination of CH 4 

### emission factors for the 64 CH 4 

### -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 so urce type over the period 1990 through 2007. Therefore, the

same emission factors are used for each year throughout this period.

The CO 2 

### emission factors were derived from the corresponding source CH 4 

### emission factors. The amount of CO 2 

### 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 CO 2 

### 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 CO 2 

### emission factors

was to use the existing CH 4 

### emissions factors and multiply them by a conversion factor, which is the ratio of CO 2 

### 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 th e 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 rema ining 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 Administrati on (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 tr ansportation 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 CO 2 

### emission sources, the activity

factors remain the same as for CH 4 

### 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 CH 4 

### emissions from each of the 64 petroleum system activities and CO 2 

### 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 CH 4 

### and CO 2 

### 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 CH 4 

### Emissions from Petroleum Prod uction 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 CH 4 /bbl crude 1,326 MMbbl/yr (non stripper

wells) 7.001 134.6

Pneumatic Devices, High Bleed 330 scfd CH 4 /device 132,803 No. of high-bleed devices 16.015 308.0

Pneumatic Devices, Low Bleed 52 scfd CH 4 /device 246,633 No. of low-bleed devices 4.681 90.02

Chemical Injection Pumps 248 scfd CH 4 /pump 26,774 No. of pumps 2.424 46.62

Vessel Blowdowns 78 scfy CH 4 /vessel 173,315 No. of vessels 0.014 0.260

Compressor Blowdowns 3,775 scf/yr of CH 4 /compressor 2,348 No. of compressors 0.009 0.170

Compressor Starts 8,443 scf/yr. of CH 4 /compressor 2,348 No. of compressors 0.020 0.381

Stripper wells 2,345 scf/yr of CH 4 /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 CH 4 /workover 37,500 Oil well workovers 0.004 0.069

Pipeline Pigging 2.40 scfd of CH 4 /pig station NE No. of crude pig stations NE NE

Offshore Platforms, Shallow water Oil,

fugitive, vented and combusted 54,795 scfd CH 4 /platform 1,514 No. of shallow water oil

platforms 30.288 582.5

Offshore Platforms, Deepwater oil,

fugitive, vented and combusted 260,274 scfd CH 4 /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 CH 4 /separator 10,203 No. of hvy. crude seps. 0.001 0.011

Separators (light crude) 14 scfd CH 4 /separator 92,858 No. of lt. crude seps. 0.470 9.029

Heater/Treaters (light crude) 19 scfd CH 4 /heater 70,254 No. of heater treaters 0.492 9.465

Headers (heavy crude) 1 0.08 scfd CH 4 /header 13,043 No. of hvy. crude hdrs. 0.000 0.007

Headers (light crude) 11 scfd CH 4 /header 40,433 No. of lt. crude hdrs. 0.160 3.081

Floating Roof Tanks 338,306 scf CH 4 /floating roof

tank/yr. 24 No. of floating roof tanks 0.008 0.159

Compressors 100 scfd CH 4 /compressor 2,348 No. of compressors 0.086 1.648

Large Compressors 16,360 scfd CH 4 /compressor 0 No. of large comprs. 0.000 0.000

Sales Areas 41 scf CH 4 /loading 1,540,054 Loadings/year 0.062 1.201

Pipelines NE scfd of CH 4 /mile of

pipeline 14,911 Miles of gathering line NE NE

Well Drilling NE scfd of CH 4 /oil well drilled 16,362 No. of oil wells drilled NE NE

Battery Pumps 0.24 scfd of CH 4 /pump 150,000 No. of battery pumps 0.013 0.253

Combustion Emissions 3.595 69.13

Gas Engines 0.24 scf CH 4 /HP-hr 14,795 MMHP-hr 3.551 68.29

Heaters 0.52 scf CH 4 /bbl 1,848 MMbbl/yr 0.001 0.019

Well Drilling 2,453 scf CH 4 /well drilled 16,362 Oil wells drilled 0.040 0.772

Flares 20 scf CH 4 /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 sma ll to show at this level of precision.

NE: Not estimated for lack of activity factor data



### Table A- 126: 2007 CH 4 

### 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 CH 4 /yr/bbl of crude delivered to refineries 5,532 MMbbl crude feed/yr 0.114 2.192

Truck Loading 0.520 scf CH 4 /yr/bbl of crude transported by truck 65.8 MMbbl trans. by truck 0.034 0.658

Marine Loading 2.544 scf CH 4 /1000 gal. crude marine loadings 20,070,765 1,000 gal./yr loaded 0.051 0.982

Rail Loading 0.520 scf CH 4 /yr/bbl of crude transported by rail 4.0 MMbbl. Crude by rail/yr 0.002 0.040

Pump Station Maintenance 1 36.80 scf CH 4 /station/yr 467 No. of pump stations 0.000 0.000

Pipeline Pigging 39 scfd of CH 4 /pig station 933 No. of pig stations 0.013 0.255

Fugitive Emissions 0.050 0.957

Pump Stations 25 scf CH 4 /mile/yr. 46,658 No. of miles of crude p/l 0.001 0.022

Pipelines NE scf CH 4 /bbl crude transported by pipeline 7,038 MMbbl crude piped NE NE

Floating Roof Tanks 58,965 scf CH 4 /floating roof tank/yr. 824 No. of floating roof tanks 0.049 0.934

Combustion Emissions NE NE

Pump Engine Drivers 0.24 scf CH 4 /hp-hr NE No. of hp-hrs NE NE

Heaters 0.521 scf CH 4 /bbl.burned NE No. of bbl. Burned NE NE

Total 0.264 5.084

1 Emissions are not actually 0, but too sma ll to show at this level of precision.

NE: Not estimated for lack of data



### Table A- 127: 2007 CH 4 

### 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 CH 4 /Mbbl 1,945 Mbbl/cd heavy crude feed 0.015 0.281

System Blowdowns 137 scf CH 4 /Mbbl 15,156 Mbbl/cd refinery feed 0.757 14.55

Asphalt Blowing 2,555 scf CH 4 /Mbbl 456 Mbbl/cd production 0.425 8.169

Fugitive Emissions 0.089 1.718

Fuel Gas System 439 Mscf CH 4 /refinery/yr 149 Refineries 0.065 1.258

Floating Roof Tanks 1 587 scf CH 4 /floating roof tank/yr. 767 No. of floating roof tanks 0.000 0.009

Wastewater Treating 1.88 scf CH 4 /Mbbl 15,156 Mbbl/cd refinery feed 0.010 0.200

Cooling Towers 2.36 scf CH 4 /Mbbl 15,156 Mbbl/cd refinery feed 0.013 0.251

Combustion Emissions 0.095 1.819

Atmospheric Distillation 3.61 scf CH 4 /Mbbl 15,450 Mbbl/cd refinery feed 0.020 0.391

Vacuum Distillation 3.61 scf CH 4 /Mbbl 6,933 Mbbl/cd feed 0.009 0.176

Thermal Operations 6.01 scf CH 4 /Mbbl 2,179 Mbbl/cd feed 0.005 0.092

Catalytic Cracking 5.17 scf CH 4 /Mbbl 5,067 Mbbl/cd feed 0.010 0.184

Catalytic Reforming 7.22 scf CH 4 /Mbbl 3,183 Mbbl/cd feed 0.008 0.161

Catalytic Hydrocracking 7.22 scf CH 4 /Mbbl 1,431 Mbbl/cd feed 0.004 0.073

Hydrorefining 2.17 scf CH 4 /Mbbl 2,172 Mbbl/cd feed 0.002 0.033

Hydrotreating 6.50 scf CH 4 /Mbbl 9,960 Mbbl/cd feed 0.024 0.454

Alkylation/Polymerization 12.6 scf CH 4 /Mbbl 1,082 Mbbl/cd feed 0.005 0.096

Aromatics/Isomeration 1.80 scf CH 4 /Mbbl 992 Mbbl/cd feed 0.001 0.013

Lube Oil Processing 0.00 scf CH 4 /Mbbl 176 Mbbl/cd feed 0.000 0.000

Engines 0.006 scf CH 4 /hp-hr 1,158 MMhp-hr/yr 0.007 0.127

Flares 0.189 scf CH 4 /Mbbl 15,156 Mbbl/cd refinery feed 0.001 0.020

Total 1.380 26.54

1 Emissions are not actually 0, but too sma ll to show at this level of precision.



### Table A- 128: Summary of CH 4 

### 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 CO 2 

### Emissions from Petroleum Pr oduction 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 CO 2 /bbl crude 1,326 MMbbl/yr (non stripper

wells) 4.678 89.96

Pneumatic Devices, High Bleed 6.704 scfd CO 2 /device 132,803 No. of high-bleed devices 0.325 6.249

Pneumatic Devices, Low Bleed 1.055 scfd CO 2 /device 246,633 No. of low-bleed devices 0.095 1.827

Chemical Injection Pumps 5.033 scfd CO 2 /pump 26,774 No. of pumps 0.049 0.946

Vessel Blowdowns 1 1.583 scfy CO 2 /vessel 173,315 No. of vessels 0.000 0.005

Compressor Blowdowns 1 77 scf/yr of CO 2 /compressor 2,348 No. of compressors 0.000 0.003

Compressor Starts 1 171 scf/yr. of CO 2 /compressor 2,348 No. of compressors 0.000 0.008

Stripper wells 48 scf/yr of CO 2 /stripper well 337,804 No. of stripper wells vented 0.016 0.309

Well Completion Venting 1 14.87 scf/completion 14,477 Oil well completions 0.000 0.004

Well Workovers 1 1.95 scf CO 2 /workover 37,500 Oil well workovers 0.000 0.001

Pipeline Pigging NE scfd of CO 2 /pig station NE No. of crude pig stations NE NE

Offshore Platforms, Shallow water Oil,

fugitive, vented and combusted 358 scfd CO 2 /platform 1,514 No. of shallow water oil

platforms 0.198 3.807

Offshore Platforms, Deepwater oil,

fugitive, vented and combusted 1,701 scfd CO 2 /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 CO 2 /separator 10,203 No. of hvy. crude seps. 0.000 0.000

Separators (light crude) 0.281 scfd CO 2 /separator 92,858 No. of lt. crude seps. 0.010 0.183

Heater/Treaters (light crude) 0.319 scfd CO 2 /heater 70,254 No. of heater treaters 0.008 0.157

Headers (heavy crude) 1 0.002 scfd CO 2 /header 13,043 No. of hvy. crude hdrs. 0.000 0.000

Headers (light crude) 0.220 scfd CO 2 /header 40,433 No. of lt. crude hdrs. 0.003 0.063

Floating Roof Tanks 1 17,490 scf CO 2 /floating roof

tank/yr. 24 No. of floating roof tanks 0.000 0.008

Compressors 2.029 scfd CO 2 /compressor 2,348 No. of compressors 0.002 0.033

Large Compressors 332 scfd CO 2 /compressor 0 No. of large comprs. 0.000 0.000

Sales Areas 2.096 scf CO 2 /loading 1,540,054 Loadings/year 0.003 0.062

Pipelines NE scfd of CO 2 /mile of

pipeline 14,911 Miles of gathering line NE NE

Well Drilling NE scfd of CO 2 /oil well drilled 16,362 No. of oil wells drilled NE NE

Battery Pumps 0.012 scfd of CO 2 /pump 150,000 No. of battery pumps 0.001 0.013

Process Upset Emissions 0.003 0.059

Pressure Relief Valves 1 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 sma ll to show at this level of precision.

NE: Not estimated for lack of data

Energy use CO 2 emissions not estimated to avoid doubl e counting with fossil fuel combustion



### Table A- 130: Summary of CO 2 

### 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 CO 2 

# and N 2 

# O Emissions from the Incineration of

Waste

Emissions of CO 2 

### from the incineration of waste include CO 2 

### 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 N 2 

### O. The methodology for calculating emissions from each of these waste incineration sources is

described in this Annex.



## CO 2 

## 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, th e 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 Mu nicipal 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. Abbreviati ons: PET (polyethylene terephthalate), HDPE (high density poly ethylene), PVC (polyvinyl chloride),

LDPE/LLDPE (linear low density polyethyle ne), PP (polypropylene), PS (polystyrene).



### Fossil fuel-based CO 2 

### 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 ty pes of plastics is listed, with the value for “other plastics”

assumed equal to the weighted average of the six categorie s. The fraction oxidized was assumed to be 98 percent.

Table A- 132: 2007 Plastics Incinerated (Gg), Carbon Cont ent (%), 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 CO 2 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.



## CO 2 

## 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 CO 2 

### 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 e qual 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 r ubber products in municipal solid waste.

- Not applicable

Note: Totals may not sum due to independent rounding.



### Table A- 134: Scrap Tire Constituents and CO 2 

### Emissions from Scrap Tire Incineration in 2007

Material Weight of Material

(Tg) Fraction Oxidized

Carbon Content Emissions

(Tg CO 2 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 

## CO 2 

## from Incineration of Synthetic Rubber in Municipal Solid Waste

Similar to the methodology for scrap tires, CO 2 

### 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 an d 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 produc t types (based on the weighted average C content of rubber

consumed for non-tire uses), and a 98 pe rcent 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 CO 2 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 CO 2 Eq.

- Not applicable.



## CO 2 

## from Incineration of Synthetic Fibers

CO 2 

### 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 pr oportions 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 CO 2 

### emissions to the amount of textiles combusted is

shown below.

CO 2 

### 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 CO 2 

### /12 g C)

35 As a sustainably harvested biogenic material, the in cineration of leather is assumed to have no net CO 2 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%



## N 2 

## O from Incineration of Waste

Estimates of N 2 

### O 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 N 2 

### O

from waste incineration are the product of the mass of waste incinerated, an emission factor of N 2 

### O emitted per unit mass

of waste incinerated, and an N 2 

### O 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 N 2 

### O/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 incinerate d 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 25,951,892 a

2002 30,255,120 25,802,917

2003 30,527,280 25,932,940 b

2004 28,585,872 26,037,823

2005 28,685,664 25,974,142 c

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 DoD 36 

### and NATO 37 

### 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 av iation 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 ga s 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 STANA G 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 an d

end in a foreign country), operations conducted from naval vessel s at sea, and operations conducted from U.S. installations

principally over international water in direct support of militar y 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 fu el 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. Th e 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 un derway, 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. CO 2 

### 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. CO 2 

### 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.A-161Table A-139: Transportation Fuels from Domestic Fuel Deliveriesa(Million Gallons)Vehicle Type/Fuel 199019911992199319941995199619971998199920002001200220032004200520062007Aviation 4,598.44,562.83,734.53,610.83,246.23,099.92,941.92,685.62,741.42,635.22,664.42,900.62,609.82,615.02,703.12,338.12,092.02,081.0Total Jet Fuels 4,598.44,562.8 3,734.53,610.83,246.23,099.92,941.92,685.62,741.4 2,635.2 2,664.42,900.62,609.62,614.92,703.12,338.0 2,091.92,080.9JP8285.7283.5 234.5989.41,598.12,182.82,253.12,072.02,122.5 2,066.5 2,122.72,326.22,091.42,094.32,126.21,838.8 1,709.31,618.5JP51,025.41,017.4 832.7805.1723.8691.2615.8552.8515.6 505.5 472.1503.2442.2409.1433.7421.6 325.5376.1Other Jet Fuels3,287.33,261.9 2,667.31,816.3924.3225.972.960.9103.3 63.3 69.671.276.1111.4143.277.6 57.086.3Aviation Gasoline ++ +++++++ + ++0.10.1+0.1 0.10.2Marine 686.8632.6646.2589.4478.6438.9493.3639.8674.2598.9454.4418.4455.8609.1704.5604.9531.6572.8Middle Distillate (MGO) 0.00.0 0.00.00.00.038.547.551.1 49.2 48.333.041.288.171.254.0 45.845.7Naval Distillate (F76) 686.8632.6 646.2589.4478.6438.9449.0583.4608.4 542.9 398.0369.1395.1460.9583.5525.9 453.6516.0Intermediate Fuel Oil (IFO)b0.00.0 0.00.00.00.05.99.014.7 6.7 8.116.319.560.249.925.0 32.211.1Otherc717.1590.4491.7415.1356.1310.9276.9263.3256.8256.0248.2109.8211.1221.2170.9205.6107.3169.0Diesel 93.097.9 103.0108.3113.9119.9126.1132.6139.5 146.8 126.626.657.760.846.456.8 30.647.3Gasoline 624.1492.5 388.7306.8242.1191.1150.8119.093.9 74.1 74.824.727.526.519.424.3 11.719.2Jet Fueld0.00.0 0.00.00.00.00.011.723.4 35.0 46.758.4125.9133.9105.1124.4 65.0102.6Total (Including Bunkers) 6,002.45,785.94,872.34,615.34,080.93,849.83,712.13,588.83,672.43,490.13,367.03,428.83,276.73,445.33,578.53,148.62,730.92,822.8Note: Totals may not sum due to independent rounding.aIncludes fuel consumption in theUnited States and U.S. Territories.bIntermediate fuel oil (IFO 180 and IFO 380) is a blend of distillate and residual fuels. IFO isused by the Military SealiftCommand.cPrior to 2001, gasoline and diesel fueltotals were estimated using data providedby the military Services for 1990 and 1996.The 1991 through 1995 data points were interpolated fromthe Service inventory data. The 1997 through 1999gasoline and diesel fuel data were initiallyextrapolated from the 1996 inventory data.Growth factors used for other diesel and gasoline were 5.2 and -21.1 percent, respectively. However, prior diesel fuel estimates from 1997 through2000 were reduced according to the estimated consumption of jet fuelthat is assumed to have replaced the diesel fuel consumption in land-based vehicles. Data sets for other dieseland gasoline consumed by the military in 2000 wereestimated based on ground fuels consumption trends. This method produced a result that was more consistent with expected consumption for 2000. In 2001, other gasoline anddiesel fuel totals were generated by DESC.dThe 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: 1990-2007Table A-140: Total U.S. MilitaryAviation Bunker Fuel (Million Gallons)Fuel Type/Service 199019911992199319941995199619971998199920002001200220032004200520062007JP8 56.756.346.4145.3224.0300.4308.8292.0306.4301.4307.6341.2309.5305.1309.8285.6262.5249.1Navy 56.7 56.3 46.1 44.640.1 38.3 39.8 46.9 53.855.5 53.4 73.8 86.6 76.3 79.2 70.9 64.7 62.7Air Force + + 0.3 100.8183.9 262.2 269.0 245.1 252.6245.9 254.2 267.4 222.9 228.7 230.6 214.7 197.8 186.5JP5 370.5367.7300.9291.0261.6249.8219.4194.2184.4175.4160.3169.7158.3146.1157.9160.6125.0144.5Navy 365.3 362.5 296.7 286.8257.9 246.3 216.1 191.2 181.4170.6 155.6 163.7 153.0 141.3 153.8 156.9 122.8 141.8Air Force 5.3 5.2 4.34.1 3.7 3.5 3.3 3.03.0 4.8 4.7 6.1 5.34.9 4.1 3.7 2.3 2.7JP4 420.8417.5341.4229.6113.121.51.10.1++++++++++Navy + + + + + + 0.00.00.00.0 0.00.00.00.00.0+ 0.0 0.0Air Force 420.8 417.5 341.4 229.6 113.1 21.5 1.1 0.1 + + + + + + + + + +JAA 13.713.611.110.89.79.210.39.410.810.812.512.613.721.730.015.511.715.6Navy 8.5 8.4 6.9 6.6 6.0 5.7 6.6 5.9 6.6 6.3 7.9 8.0 9.815.5 21.5 11.6 9.1 11.7Air Force 5.3 5.2 4.34.1 3.7 3.5 3.7 3.54.2 4.5 4.5 4.6 3.86.2 8.6 3.9 2.6 3.9JA1 +++++++++++0.10.60.20.50.50.41.1Navy + + + + + + + + + 0.0 + + + + + + + 0.1Air Force + + + + + + + + +0.0 + 0.1 0.6 0.20.5 0.5 0.4 1.0JAB 0.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.00.0Navy 0.0 0.0 0.0 0.0 0.0 0.00.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.00.00.0 0.0Air 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 0.00.0 0.0 0.0Navy Subtotal 430.5427.2349.6338.1303.9290.2262.5244.0241.8232.4216.9245.5249.4233.1254.4239.4196.6216.3Air Force Subtotal 431.3427.9350.2338.6304.4290.7277.0251.7259.9255.2263.5278.1232.7239.9243.7222.9203.1194.0Total 861.8855.1699.9676.7608.4580.9539.5495.6501.7487.5480.4523.6482.1473.0498.1462.3399.7410.3+ 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 199019911992199319941995199619971998199920002001200220032004200520062007Navy—MGO 0.0 0.0 0.00.00.00.030.335.631.939.7 23.822.527.163.756.238.033.031.6Navy—F76 522.4 481.2 491.5448.3364.0333.8331.9441.7474.2466.0 298.6282.6305.6347.8434.4413.1355.9404.1Navy—IFO 0.0 0.0 0.00.00.00.04.67.111.65.3 6.412.915.447.539.419.725.48.8Total 522.4481.2491.5448.3364.0333.8366.8484.3517.7511.0328.8318.0348.2459.0530.0470.7414.3444.4+ 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 Cont ents (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 Conten t 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 CO 2 

### Emissions from Bunker Fuels (Tg CO

2 

### 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 Em issions from Substitution of Ozone

Depleting Substances

Emissions of HFCs and PFCs from the substitution of oz one 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 Amendm ents of 1990, the domestic U.S. production of ODS—

chlorofluorocarbons (CFCs), halons, carbon tetrachloride, meth yl 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 hy drofluorocarbons (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 char acteristics 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 amo unt 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 indu stry. 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 Applian ce Manufacturers, the American Automobile Manufacturers

Association, and many of their member co mpanies, 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 jeopardi ze 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 we ll 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, in stallation, use, or servicing of products. The emissions are

estimated from the emission profile for each vintage of equi pment 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 calcu late 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 eq uipment) 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:

Es j 

### = (l

a 

### + l

s 

### ) × 

# Σ Qc j-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.

l

a 

### = Annual Leak Rate. Average annual leak rate during normal equipment operation (expressed as a

percentage of total chemical charge).

l

s 

### = Service Leak Rate. Average leakage during equipm ent 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 emissi ons are thus a function of the quantity of chemical contained

in the retiring equipment fleet and the proportion of chemical released at disposal:

Ed j 

### = Qc j-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 summ ed to provide an estimate of total emissions.

E j 

### = Es j 

### + Ed j



### 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.A-167Table A- 145: Refrigeration and Air-Conditioning Market Transition AssumptionsPrimary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateCentrifugal ChillersCFC-11 HCFC-123 1993 1993 45% Unknown 0.5%HCFC-22 1991 1993 16% HCFC-22 1996 1996 100% HFC-134a 2000 2010 100%HFC-134a 1992 1993 39% NoneCFC-12 HFC-134a 1992 1994 53% None 0.5%HCFC-22 1991 1994 16% HCFC-22 1996 1996 100% HFC-134a 2000 2010 100%HCFC-123 1993 1994 31% UnknownR-500 HFC-134a 1992 1994 53% None 0.5%HCFC-22 1991 1994 16% HCFC-22 1996 1996 100% HFC-134a 2000 2010 100%HCFC-123 1993 1994 31% UnknownCFC-114 HFC-236fa 1993 1994 100% HFC-236fa1996 1996 100% HFC-134a 1998 2009 100% 0.2%Cold StorageCFC-12 HCFC-22 1990 1993 65% R-404A 1996 2010 75% 2.5%R-507 1996 2010 25%R-404A 1994 1996 26% NoneR-507 1994 1996 9% NoneHCFC-22 HCFC-22 1992 1993 100% R-404A 1996 2009 8% 2.5%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% 2.5%R-507 1996 2010 12%Non-ODP/GWP 1996 2010 50%R-404A 1993 1996 45% NoneR-507 1994 1996 15% NoneCommercial Unitary Air Conditioners (Large)HCFC-22 HCFC-22 1992 1993 100% R-410A 2001 2005 5% None 2.5%R-407C 2006 2009 1% NoneR-410A 2006 2009 9% NoneR-407C 2009 2010 5% NoneR-410A 2009 2010 81% NoneCommercial Unitary Air Conditioners (Small)HCFC-22 HCFC-22 1992 1993 100% R-410A 1996 2000 3% None 2.5%R-410A 2001 2005 18% NoneR-410A 2006 2009 8% NoneA-168 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Primary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateR-410A 2009 2010 71% NoneDehumidifiersHCFC-22 HFC-134a 1997 1997 89% None 0.2%R-410A 2007 2010 11% NoneIce MakersCFC-12 HFC-134a 1993 1995 100% None 2.5%Industrial Process RefrigerationCFC-11 HCFC-123 1992 1994 70% Unknown 2.5%HFC-134a 1992 1994 15% NoneHCFC-22 1991 1994 15% HFC-134a 1995 2010 100%CFC-12 HCFC-22 1991 1994 10% HFC-134a 1995 2010 15% 2.5%R-404A 1995 2010 50%R-410A 1999 2010 20%R-507 1995 2010 15%HCFC-123 1992 1994 35% UnknownHFC-134a 1992 1994 50% NoneR-401A 1995 1996 5% HFC-134a 1997 2000 100% NoneHCFC-22 HCFC-22 1992 1993 100% HFC-134a 1995 2009 2% 2.5%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% 2.8%Mobile Air Conditioners (Light Duty Trucks)CFC-12 HFC-134a 1993 1994 100% 2.8%Mobile Air Conditioners (School and Tour Buses)CFC-12 HCFC-22HFC-134a19941994199519970.5%99.5%HFC-134aNone20062007100%None2.6%Mobile Air Conditioners (Transit Buses and Trains)HCFC-22 HFC-134a 1995 2009 100% None 2.6%HCFC-22HFC-134aR-407C200220022009200950%50%NoneNone2.6%Positive Displacement ChillersA-169Primary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateHCFC-22 HCFC-22 1996 1996 100% HFC-134a 2000 2009 9% R-407C 2010 2020 60% 0.5%R-410A 2010 2020 40%R-407C 2000 2009 1% NoneHFC-134a 2009 2010 81% R-407C 2010 2020 60%R-410A 2010 2020 40%R-407C 2009 2010 9% NoneCFC-12 HCFC-22 1993 1993 100% HFC-134a 2000 2009 9% R-407C 2010 2020 60% 0.2%R-410A 2010 2020 40%R-407C 2000 2009 1% NoneHFC-134a 2009 2010 81% R-407C 2010 2020 60%R-410A 2010 2020 40%R-407C 2009 2010 9% NoneRefrigerated AppliancesCFC-12 HFC-134a 1994 1995 100% HFC-134a 2005 2005 100% None 0.5%Residential Unitary Air ConditionersHCFC-22* HCFC-22 1992 1993 100% HCFC-22 2006 2006 70% R-410A 2007 2010 29% 1.9%R-407C 2010 2010 14%R-410A 2010 2010 57%R-410A 2000 2005 5% R-410A 2006 2006 100%R-410A 2000 2006 5% NoneR-410A 2006 2006 20% NoneRetail Food (Large)CFC-12 R-502 1988 1990 100% HCFC-22 1991 1993 100% R-404A 1995 2000 17.5% 1.7%R-507 1995 2000 7.5%R-404A 2000 2005 31.5%R-507 2000 2005 13.5%R-404A 2005 2010 18%R-507 2005 2010 12%R-502 HCFC-22 1990 1993 100% R-404A 1995 2000 17.5% None 1.7%R-507 1995 2000 7.5% NoneR-404A 2000 2005 31.5% NoneR-507 2000 2005 13.5% NoneR-404A 2005 2010 18% NoneR-507 2005 2010 12% NoneRetail Food (Medium)HCFC-22 R-404A 1995 2000 17.5% None 1.7%R-507 1995 2000 7.5% NoneR-404A 2000 2005 31.5% NoneA-170 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Primary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateR-507 2000 2005 13.5% NoneR-404A 2005 2010 18% NoneR-507 2005 2010 12% NoneRetail Food (Small)CFC-12 HCFC-22 1990 1993 90% HFC-134a 1993 1995 90% CO22010 2010 5% 1.7%R-404A 2000 2009 7.5% NoneR-507 2000 2009 2.5% NoneR-404A 1993 1996 7.5% NoneR-507 1993 1996 2.5% NoneTransport RefrigerationCFC-12 HFC-134a 1993 1995 98% None 2.5%HCFC-22 1993 1995 2% HFC-134a 1995 1999 100%R-502 HFC-134a 1993 1995 55% None 2.5%R-404A 1993 1995 45% NoneWater-Source and Ground-Source Heat PumpsHCFC-22 HCFC-22 1992 1993 100% R-407C 2000 2006 5% None 2.5%R-410A 2000 2006 5% NoneHFC-134a 2000 2009 2% NoneR-407C 2006 2009 2.5% NoneR-410A 2006 2009 4.5% NoneHFC-134a 2009 2010 18% NoneR-407C 2009 2010 22.5% NoneR-410A 2009 2010 40.5% NonePackaged Terminal Air Conditioners and Heat PumpsHCFC-22 HCFC-22 1992 1993 100% R-410A 2006 2009 10% None 2.5%R-410A 2009 2010 90% NoneWindow UnitsHCFC-22 R-407C 2003 2009 3% None 5.0%R-407C 2009 2010 35% NoneR-410A 2003 2009 7% NoneR-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 percentof 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 propella nt 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.

E j 

### = Qc j



### 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 pr oducts, are presented in Table A- 147.

Table A- 147. Aerosol Product Transition Assumptions

Primary Substitute Secondary Substitute Initial

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 Primary Substitute Secondary Substitute Initial

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 Mo del, 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 solven ts to clean items. For example, electronics may need

to be cleaned after production to remove any manufacturing pro cess 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 consid ered “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.

E j 

### = l × Qc j



### 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, coat ings and inks, are presented in Table A- 148.

Table A- 148. Solvent Ma rket Transition Assumptions

Primary Substitute Secondary Substitute Initial

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 Primary Substitute Secondary Substitute Initial

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 hy drocarbons and ammonia, as well as not-in-kind alternatives su ch 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 simi lar 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 be low. 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.

E j 

### = r × 

# Σ Qc j-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 chem ical 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 Extinguish ing 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

C 4 F 10 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 chem ical 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 co ntent upon manufacture, a portion at a constant rate over the

lifetime of the foam, a portion at disposal, and a por tion 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.

Em j 

### = lm × Qc j



### Where:

Em j 

### = Emissions from manufacturing. Total em issions 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.

Eu j 

### = lu × 

# Σ Qc j-i+1 

### for i=1 → k

Where:

Eu

j 

### = 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.

Ed j 

### = ld × Qc j-k



### Where:

Ed

j 

### = 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.

Ep j 

### = lp × 

# Σ Qc j-m 

### for m=k → k + 32

Where:

Ep

j 

### = Emissions from post disposal. Total post-disposa l 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.

E j 

### = Em j 

### + Eu j 

### + Ed j 

### + Ep j



### Where:

E

j 

### = 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 Eu

j 

### = Emissions from Lifetime Losses. Total emissions of a specific chemical in year j due to lifetime losses

during use, by weight.

Ed

j 

### = Emissions from disposal. Total emissions of a specific chemical in year j at disposal, by weight.

Ep

j 

### = 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.A-177Table A- 150. Foam BlowingMarket Transition AssumptionsPrimary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateCommercial Refrigeration FoamCFC-11 HCFC-141b 1989 1996 40% HFC-245fa 2002 2003 80% None 6.0%Non-ODP/GWP 2002 2003 20% NoneHCFC-142b 1989 1996 8% Non-ODP/GWP 2009 2010 80% NoneHFC-245fa 2009 2010 20% NoneHCFC-22 1989 1996 52% Non-ODP/GWP 2009 2010 80% NoneHFC-245fa 2009 2010 20% NoneFlexible PU Foam:Integral Skin FoamCFC-11 HCFC-141b 1989 1990 100% HFC-134a 1993 1996 25% None 2.0%HFC-134a 1994 1996 25% NoneCO21993 1996 25% NoneCO21994 1996 25% NoneFlexible PU Foam: Slabstock Foam, Moulded FoamCFC-11 Non-ODP/GWP 1992 1992 100% None 2.0%Phenolic FoamCFC-11 HCFC-141b 1989 1990 100% Non-ODP/GWP 1992 1992 100% None 2.0%Polyolefin FoamCFC-114 HFC-152a 1989 1993 10% Non-ODP/GWP 2005 2010 100% None 2.0%HCFC-142b 1989 1993 90% Non-ODP/GWP 1994 1996 100% NonePU and PIR Rigid: BoardstockCFC-11 HCFC-141b 1993 1996 100% Non-ODP/GWP 2000 2003 95% None 6.0%HC/HFC-245faBlend2000 2003 5% NonePU Rigid: Domestic Refrigerator and Freezer InsulationCFC-11 HCFC-141b 1993 1996 89% HFC-134a 1996 2003 10% None 3.0%HFC-245fa 2002 2003 85% NoneNon-ODP/GWP 2002 2003 5% NoneHCFC-142b/22Blend1993 1996 1% HFC-245fa 2009 2010 50% NoneHFC-134a 2009 2010 50% NoneHCFC-22 1993 1996 10% HFC-134a 2009 2010 100% NonePU Rigid: One Component FoamCFC-12HCFC-142b/22Blend1989 1996 70% Non-ODP/GWP 2009 2010 80% None 4.0%HFC-134a 2009 2010 10% NoneHFC-152a 2009 2010 10% NoneHCFC-22 1989 1996 30% Non-ODP/GWP 2009 2010 80% NoneHFC-134a 2009 2010 10% NoneHFC-152a 2009 2010 10% NonePU Rigid: Other: Slabstock FoamA-178 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Primary Substitute Secondary Substitute Tertiary SubstituteInitial MarketSegmentName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationName ofSubstituteStartDateDate of FullPenetrationin NewEquipmentMaximumMarketPenetrationGrowthRateCFC-11 HCFC-141b 1989 1996 100% CO21999 2003 45% None 2.0%Non-ODP/GWP 2001 2003 45% NoneHCFC-22 2003 2003 10% Non-ODS/GWP 2009 2010 100%PU Rigid: Sandwich Panels: Continuous and DiscontinuousCFC-11 HCFC-141b 1989 1996 82%HCFC-22/WaterBlend 2001 2003 20% HFC Blend 2009 2010 50% 6.0%Non-ODP/GWP 2009 2010 50%HFC-245fa/CO2Blend 2002 2004 20%Non-ODP/GWP 2001 2004 40% NoneHFC-134a 2002 2004 20% NoneHCFC-22 1989 1996 18%HFC-245fa/CO2Blend2009 2010 40% NoneNon-ODP/GWP 2009 2010 20% NoneCO22009 2010 20% NoneHFC-134a 2009 2010 20% NonePU Rigid: Spray FoamCFC-11 HCFC-141b 1989 1996 100% HFC-245fa 2002 2003 30% None 6.0%HFC-245fa/CO2Blend 2002 2003 60% NoneNon-ODP/GWP 2001 2003 10% NoneXPS: Boardstock FoamCFC-12HCFC-142b/22Blend1989 1994 10% HFC-134a 2009 2010 70% None 2.5%HFC-152a 2009 2010 10% NoneCO22009 2010 10% NoneNon-ODP/GWP 2009 2010 10% NoneHCFC-142b 1989 1994 90% HFC-134a 2009 2010 70% NoneHFC-152a 2009 2010 10% NoneCO22009 2010 10% NoneNon-ODP/GWP 2009 2010 10% NoneXPS: Sheet FoamCFC-12 CO21989 1994 1% None None 2.0%Non-ODP/GWP 1989 1994 99% CO21995 1999 9% NoneHFC-152a 1995 1999 10% None

### A-179 Table A- 151. Emission prof ile 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 as sumption. In the XPS

Sheet/Insulation Board end-use, t he source of emission rates and lifetimes did not yield 100 percent emi ssion; it is unclear at this time whether that was

intentional. In the Rigid PU Appliance Foam end-use, the sourc e of emission rates and lifetimes did not yield 100 percent emis sion; 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 pa thogens during the growing, collecting, storing and

distribution of flowers as well as various foods including gr ains, vegetables and fruits. Currently, the Vintaging Model

assumes that the sterilization s ector 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. Fo r 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.

E j 

### = Qc j



### 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 Vintag ing 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. Valu es for use and emissions are calculated both in metric tons

and in teragrams of CO 2 

### equivalents (Tg CO 2 

### Eq.). The conversion of metric tons of chemical to Tg CO 2 

### 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 th e 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 Bc j 

### = Bc j-1 

### -Qd j 

### +Qp j 

### +E

e 

### -Q

r



### Where:

Bc j 

### = Bank of Chemical. Total bank of a specific chemical in year j , by weight.

Qd j 

### = Quantity of Chemical in Equipment Disposed. Total quantity of a specific chemical in equipment

disposed of in year j , by weight.

Qp j 

### = Quantity of Chemical Penetrating the Market. Total quantity of a specific chemical that is entering the

market in year j , by weight.

E

e 

### = 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.

Q

r



### = 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.A-181Table A- 152. Banks of ODs andODS Substitutes, 1990-2007 (MT)1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007CFC 651,935 713,209 751,697 784,086 795,636 793,517 773,996 750,718 728,247 698,645665,619 634,720 605,686 577,491 550,727 528,331 510,426 495,709HCFC 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 1,156,144 1,182,917 1,209,527 1,231,755HFC 997 4,058 13,466 30,335 49,855 75,182 100,630 126,231 152,926 180,028 206,862 240,352 284,089 328,371 370,874 414,353 460,547 508,693ReferencesIPCC (2006) 2006 IPCC Guidelines for National Greenhouse Gas Inventories. The National Greenhouse Gas Inventories Programme, The Intergovernmental Panel onClimate Change, H.S. Eggleston, L. Buendia, K. Miwa, TNgara, 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 CH 4 

# 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 me thane 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 inform ation 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 averag e of the January and July

populations downloaded from USDA (2008).



### The population transition matrix in the CEFM simulate s the U.S. cattle population over time and provides an

estimate of the population age and weight structure by cattle ty pe 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 Ti er 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 cal ves, 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 st atistics 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 CH 4 

### emissions associated with each life stag e. 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 CH 4 

### emissions (e.g., beef versus dairy). Consequently, ther e is an independent transition matrix for each of three

separate lifecycle phases, 1) calves, 2) replacements and stoc kers, 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 Ca tegories Used for Estimating CH 4 

### 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 th roughout 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 whethe r 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 ‘sto ckers,’ 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 th e transition matrix for stockers is shown in Table A-157.

Table A-157: Example of Monthly Average Po pulations 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 13 00 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), Skogerbo e 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, anim als spend 25 days on a step-up diet to become acclimated to

the new feed type, during this time weight gain is estima ted 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 cate gory (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 CH 4 

### 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 esti mates 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 usi ng the methodology described in Step 1.



### Step 2: Characterize U.S. Cattle Population Diets

To support development of digestible energy (DE, the per cent of gross energy intake digested by the animal) and

CH 4 

### conversion rate (Y

m 

### , the fraction of gross energy converted to CH 4 

### ) 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 Y

m 

### 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 Y

m 

### vary by diet and animal type. The IPCC recommends Y

m 

### 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 Y

m 

### values unique to the United States 40 

### were

developed. Digestible energy and Y

m 

### 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 Y

m 

### 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 performan ce (e.g., initial and final weight, age at start of period), an d

feed characteristics (e.g., chemical composition, habitat, grai n or forage (USDA 1996)). Data used to simulate ruminant

digestion is provided for a particular animal that is then us ed 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 Y

m 

### 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 matt er (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 t he

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 he ifer 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). Y

m 

### values for all grazing beef cattle were set at 6.5 percent based on Johnson (2002).

For feedlot animals, DE and Y

m 

### 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 Y

m 

### are calculated as the average of all state forage and feedlot diet DE and Y

m 

### values. In

response to peer reviewer comments (Johnson 2000), valu es for dairy replacement heifers are based on EPA (1993).

Table A-163 shows the regional DE and Y

m 

### for U.S. cattle in each region for 2007.

40 In some cases, the Y

m 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 a nd assess the

Y

m values associated with different animal performan ce and feed characteristics in the United States.

41 For example, in California the forage DE of 64.7 was used fo r 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 Representati ve Regional Diets (Percent of Diet for each Region) for the Supplemental Diet of

Grazing Beef Cattle

Source of representative regi onal diets: Donovan (1999).

a Note that emissions are currently calculat ed on a state-by-state basis, but diets ar e 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 South central 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 Resu lting DE Values for Each Region

Region a 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 suppl emental diet, Donovan (1999) ; Forage DE, NRC (2000).

a Note that emissions are currently calculat ed on a state-by-state basis, but diets ar e 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 South central 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 Dige stible Energy (DE) and CH

4 

### Conversion Rates (Y

m 

### ) for Cattle in 2007

Animal Type Data California c West Northern

Great Plains Southcentral Northeast Midwest Southeast

Beef Repl. Heif. DE a 65 59 66 64 65 65 64

Y

m b 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

Y

m 5.9% 5.9% 5.6% 6. 4% 6.3% 5.6% 6.9%

Steer Stockers DE 65 59 66 64 65 65 64

Y

m 6.5% 6.5% 6.5% 6. 5% 6.5% 6.5% 6.5%

Heifer Stockers DE 65 59 66 64 65 65 64

Y

m 6.5% 6.5% 6.5% 6. 5% 6.5% 6.5% 6.5%

Steer Feedlot DE 85 85 85 85 85 85 85

Y

m 3.0% 3.0% 3.0% 3. 0% 3.0% 3.0% 3.0%

Heifer Feedlot DE 85 85 85 85 85 85 85

Y

m 3.0% 3.0% 3.0% 3. 0% 3.0% 3.0% 3.0%

Beef Cows DE 63 57 64 62 63 63 62

Y

m 6.5% 6.5% 6.5% 6. 5% 6.5% 6.5% 6.5%

Dairy Cows DE 69 66 69 68 69 69 68

Y

m 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

Y

m 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

Y

m 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 calculat ed on a state-by-state basis, but diets ar e 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 South central 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 CH 4 

### Emissions from Cattle

Emissions by state were estimated in three steps: a) de termine 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 (C

a 

### , 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)



###  Y

m 

### (the fraction of gross energy converted to CH 4 

### )



###  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 cate gory are used (e.g., estimates for feedlot animals do not require

the NE l 

### factor). All net energy equations are provided in IPCC (2006).

42 Zero for feedlot conditions, 0.17 for high quality confined pa sture conditions, and 0.36 for extensive open range or hilly

terrain grazing conditions. C

a 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 bei ng estimated, used in the model

to account for breed potential.

### A-191 

##     

 

    

 

 

 

 

 

  

 

 

     



100 % DE REG NE

REM NE NE NE NE NE

GE g p work l a m



### Where,

GE = Gross energy (MJ/day)

NE

m 

### = Net energy required by the animal for maintenance (MJ/day)

NE

a 

### = Net energy for animal activity (MJ/day)

NE l 

### = Net energy for lactation (MJ/day)

NE work 

### = Net energy for work (MJ/day)

NE

p 

### = Net energy required for pregnancy (MJ/day)

REM = Ratio of net energy available in a diet for maintenance to digestible energy consumed

NE

g 

### = 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

(Y

m 

### ) for each category. This relationship is shown in the following equation:



## 65

. 55 Y ×

m 

## GE DayEmit



Where,

DayEmit = Emission factor (kg CH 4 

### /head/day)

GE = Gross energy intake (MJ/head/day)

Y

m 

### = CH 4 

### conversion rate, which is the fraction of gross energy in feed converted to CH 4 

### (%)

55.65 = A factor for the energy content of methane (MJ/kg CH 4 

### )

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 Nati onal Emission Factors for Cattle by Animal Type (kg CH

4 

### /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 fact or, the yearly emission factor can be divi ded by 365.25 (the average number of days i n a year).



### Step 3c: Estimate Total Emission s

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 Emissions

state 

### = DayEmit

state 

### × Days/Month × SubPop

state



### Where,

DayEmit State 

### = The emission factor for the subcategory and state (kg CH 4 

### /head/day)

Days/Month = The number of days in the month

SubPop State 

### = 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 list ed 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: CH 4 

### 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 consum e mainly milk the IPCC recom mends the use of methane

conversion factor of zero, resulting in no met hane emissions from this subcategory of cattle.



## Emission Estimates from Other Livestock

All livestock population data, except for horses, were ta ken 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 pub lishes 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 CH

4 

### /head/year)

Livestock Type Emission Factor

Sheep 8

Goats 5

Horses 18

Swine 1.5

Source: IPCC (2006)



### Table A-167: CH 4 

### Emissions from Enteric Fermentation (Tg CO

2 

### 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: CH 4 

### 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 CH 4 

# and N 2 

# O Emissions from Manure Management

The following steps were used to estimate methane (CH 4 

### ) and nitrous oxide (N 2 

### O) 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 fatt ened 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 deve loped 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.

Cattle US Cattle US Cattle US 

### January

2 July January

Factor Scaling Cattle   

 

 

  



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 (USD A 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 distribu ting 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 CH 4 

### and N 2 

### O emissions is presented in Table A- 169

.



### Step 2: Waste Characteristics Data

Methane and N 2 

### O emissions calculations are based on the following animal characteristics for each relevant

livestock population:



###  Volatile solids (VS) excretion rate;



###  Maximum methane producing capacity (B

o 

### ) 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, da ta 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 ar e 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 ur inary excretions constitutes manure. The enteric fermentation

model requires the estimation of gross energy intake and its fractional digestibility to estimate enteric CH 4 

### 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 dige stible 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 organi c matter of 20.1 MJ/kg (Garrett and Johnson 1983). The equation

used for calculating VS is as follows:



#  

MJ/kg 20.1 GE 0.02 DE GE (kg) production VS  





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 turk ey 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. Defini tions 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 conduc ted 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 ex clusively 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 a ddition 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 di stribution 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 O ffice 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 (i ncludes 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 mana ged 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 exam ple, if USDA reported that 65 percent of dairies use deep

pits to manage manure and 55 percent of dairies use anaerobi c lagoons to manage manure, it was assumed that 54 percent

(i.e., 65 percent divided by 120 percent) of the manure is ma naged 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 (i ncludes above- and below-ground slurry), anaerobic lagoon,

and solid storage (includes solids separated from liquids).

Some swine operations reported using more than one mana gement 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 li near 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), Pullet s (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 a ugmented 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 distribut ed 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 re mainder of waste is assumed to be deposited in operations

with bedding management.

Step 4: Emission Factor Calculations

Methane conversion factors (MCFs) and N 2 

### O 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 inventor y was assigned a climate classifi cation 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 th e recently revised guidelines from IPCC (IPCC 2006). The van’t

Hoff-Arrhenius equation, with a base temp erature 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,

T 1 

### = 303.15K

T 2 

### = 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 CH 4 

### 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 monthl y 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 CH 4 

### 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 CH 4 

### 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 CH 4 

### generated during the month is equal to the monthly VS consumed multiplied by

the maximum CH 4 

### potential of the waste (B

o 

### ).

The annual MCF is then calculated as:

0 annual annual 4

annual 

### B produced VS generated

CH MCF

 

Where,

MCF annual 

### = Methane conversion factor

VS produced annual 

### = Volatile solids excreted annually

B

o 

### = Maximum CH 4 

### producing potential of the waste      





2 1 1 2 ) (

exp T RT T T E 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 acco unts 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

CH 4 

### . 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 N 2 

### O emission factors for manure management systems (kg N 2 

### O-N/kg excreted N) were set equal to the

most recent default IPCC factors (IPCC 2006), presented in Table A- 176.

Indirect N 2 

### O emission factors account for two fractions of nitrogen losses: volatilization of ammonia (NH 3 

### ) and

NO X 

### (Frac

gas 

### ) and runoff/leaching (Frac runoff/leach 

### ). IPCC default indirect N 2 

### O emission factors were used to estimate

indirect N 2 

### O emissions. These factors are 0.010 kg N 2 

### O-N/kg N for volatilization and 0.0075 kg N 2 

### O/kg N for

runoff/leaching.

EPA developed country-specific estimates of nitrogen losses for Frac

gas 

### and Frac runoff/leach 

### for the U.S. The vast

majority of volatilization losses are NH 3 

### . Although there are also some small losses of NO

X 

### , no quantified estimates were

available for use and those losses are believed to be small (about 1 percent) in comparison to the NH 3 

### losses. Therefore,

Frac

gas 

### 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 Frac runoff/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, Frac runoff/leach 

### was set equal to the runoff loss factor. Nitrogen losses fro m volatilization and runoff/leaching are presented in Table A177.

Step 5: CH 4 

### and N 2 

### O Emission Calculations

To calculate methane emissions, EPA first estimated the amount of volatile solids excreted in manure that is

managed in each WMS:



### 365.25 WMS VS

1000 TAM Population excreted VS Animal State, WMS Animal, State, 

###     

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: 

#       

WMS Animal, State, 0 WMS Animal, State, 4 

### 0.662 MCF B excreted VS CH

Where,

CH 4 

### = CH 4 

### emissions (kg CH 4 

### /yr)

VS excreted WMS, State 

### = The amount of VS excreted in manure managed in each WMS (kg/yr)

B

o 

### = Maximum CH 4 

### producing capacity (m 3 

### CH 4 

### /kg VS)

MCF animal, state, WMS 

### = MCF for the animal group, state and WMS (percent)

0.662 = Density of methane at 25 o 

### C (kg CH 4 

### /m 3 

### CH 4 

### )

### A-200 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 A calculation was developed to estimate the amount of CH 4 

### emitted from anaerobic digestion (AD) systems.

First, AD systems were assumed to produce 90 percent of the maximum CH 4 

### producing capacity. This value is applied for

all climate regions and AD system types. However, the actual amount of CH 4 

### 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 CH 4 

### production from some systems and underestimating CH 4 

### production in other systems. The CH 4 

### production of AD systems is calculated using the equation below:



### 0.90 365.25 0.662 B VS

1000 TAM AD Population AD Production CH 0 System AD System AD 4 

###       

Where,

CH 4 

### Production AD AD system 

### = CH 4 

### 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)

B

o 

### = Maximum CH 4 

### producing capacity (CH4 m 3 

### /kg VS)

0.662 = Density of methane at 25 o 

### C (kg CH 4 

### /m 3 

### CH 4 

### )

365.25 = Days/year

0.90 = CH 4 

### 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 CH 4 

### DE from flaring or burning in an engine was assumed to be 98 percent;

therefore, the amount of CH 4 

### that would not be flared or combusted was assumed to be 2 percent (EPA 2008). The

amount of CH 4 

### produced by systems with anaerobic digestion was calculated with the following equation:



#   

#  

  

#     

 

 

 



   



Systems AD Animal, State, system AD system AD 4 system AD system AD 4

4 

### CE

- 1 AD Production CH DE 1 CE AD Production CH

AD Emissions CH

Where:

CH 4 

### Emissions AD = CH 4 

### emissions from AD systems, (kg/yr)

CH 4 

### Production AD AD system 

### = CH 4 

### production from a particular AD system, (kg/yr)

CE AD 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 CH 4 

### emissions, total N 2 

### O emissions were also estimated from manure management systems. Total

N 2 

### O emissions were calculated by summing direct and indirect N 2 

### O emissions. The first step in estimating direct and

indirect N 2 

### O emissions was calculating the amount of nitrogen excreted in manure and managed in each WMS using the

following equation:



### 365.25 Nex

1000 TAM WMS Population excreted N Animal State, WMS Animal, State, 

###     

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 N 2 

### O emissions were calculated as follows:



###       

  

WMS Animal, State, WMS WMS Animal, State, 2 

### 28 44 EF excreted N O N Direct

Where,

Direct N 2 

### O = Direct N 2 

### O emissions (kg N 2 

### O/yr)

N excreted State, Animal, WMS 

### = The amount of N excreted in manure managed in each WMS for each

animal type (kg/yr)

EF WMS 

### = Direct N 2 

### O emission factor from IPCC guidelines (kg N 2 

### O-N /kg N)

44/28 = Conversion factor of N 2 

### O-N to N 2 

### O

Indirect N 2 

### O emissions were calculated with the following equation:



### 

     

 

     

 

     

       

   

  



WMS Animal, State,

ach runnoff/le WMS ch, runoff/lea

WMS Animal, State, tion volatiliza WMS gas,

WMS Animal, State,

2



### 28 44 EF

100 Frac

excreted N 28 44 EF

100 Frac

excreted N

O N Indirect

Where,

Indirect N 2 

### O = Indirect N 2 

### O emissions (kg N 2 

### O/yr)

N excreted State, Animal, WMS 

### = The amount of N excreted in manure managed in each WMS for each

animal type (kg/yr)

Frac gas,WMS 

### = Nitrogen lost through volatilization in each WMS

Frac runoff/leach,WMS 

### = Nitrogen lost through runoff and leaching in each WMS; data were not

available for leaching so the value reflects only runoff

EF volatilization 

### = Emission factor for volatilization (0.010 kg N 2 

### O-N/kg N)

EF runoff/leach 

### = Emission factor for runoff/leaching (0.0075 kg N 2 

### O-N/kg N)

44/28 = Conversion factor of N 2 

### O-N to N 2 

### O

Emission estimates of CH 4 

### and N 2 

### O 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 presente d by animal type and state in Table A- 180 and Table A181, respectively.A-202 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A- 169: Livestock Population (1,000 Head)Animal Type 1990 1991 1992 1993 199419951996199719981999 20002001200220032004200520062007Dairy Cattle14,14313,98013,83013,76713,56613,50213,30513,13812,99213,02313,06612,96413,00512,97812,80512,93713,14413,246Dairy Cows10,007 9,883 9,714 9,679 9,5049,4919,4109,3099,2009,139 9,2169,1369,1289,1218,9959,0279,1069,141Dairy Heifer4,135 4,097 4,116 4,088 4,0624,0113,8953,8293,7933,884 3,8503,8283,8773,8573,8103,9094,0384,105Swine53,94156,47858,53258,01659,95158,89956,22058,72861,98960,23858,86458,91360,02859,81760,71761,05561,84565,022Market <60 lb.18,359 19,212 19,851 19,434 20,15719,65618,85119,88620,69219,928 19,57419,65919,86319,92920,21620,23220,50521,593Market 60-119 lb.11,734 12,374 12,839 12,656 13,01712,83612,15712,75413,55113,255 12,92612,90013,28413,13813,40013,51313,71714,448Market 120-179 lb.9,440 9,840 10,253 10,334 10,67110,54510,11010,48011,23411,041 10,74810,70811,01311,04611,22811,33811,44012,178Market >180 lb.7,510 7,822 8,333 8,435 8,8248,9378,4638,7689,6719,641 9,3859,4659,7389,7019,9219,99510,11410,672Breeding6,899 7,231 7,255 7,157 7,2826,9266,6396,8406,8416,373 6,2316,1816,1296,0045,9525,9786,0696,131Beef Cattle86,08787,26788,54890,32192,57194,39194,26992,29090,73090,03489,22088,62187,92787,04086,34986,88287,80787,655Feedlot Steers7,338 7,920 7,581 7,984 7,7977,7637,3807,6447,8457,805 8,3388,6228,4237,9448,1748,2988,6098,583Feedlot Heifers3,621 4,035 3,626 3,971 3,9654,0473,9994,3964,4594,587 4,8995,0664,8524,5714,6334,5744,7064,701NOF Bulls2,180 2,198 2,220 2,239 2,3062,3922,3922,3252,2352,241 2,1972,1872,1722,1742,1282,1602,1812,158NOF Calves23,909 23,853 24,118 24,209 24,58625,17025,04224,36324,00123,895 23,50822,95822,57722,27322,00522,07522,18322,078NOF Heifers8,872 8,938 9,520 9,850 10,46910,68010,86910,4819,9989,716 9,3269,1949,2129,3369,2059,4039,5489,537NOF Steers7,490 7,364 8,031 7,935 8,3468,6939,0778,4528,0507,840 7,1906,9467,2497,4517,0757,1907,3577,478NOF Cows32,677 32,960 33,453 34,132 35,10135,64535,50934,62934,14333,950 33,76333,64933,44233,29233,13133,18333,22233,121Sheep11,35811,17410,79710,2019,8368,9898,4658,0247,8257,2477,0366,9086,6236,3216,1056,1356,2306,165Sheep On Feed1,180 1,115 1,187 1,195 1,5371,7712,0422,2942,5362,689 2,9633,2563,1433,0492,9432,9823,0433,029Sheep NOF10,178 10,058 9,610 9,006 8,2987,2186,4235,7305,2894,558 4,0733,6523,4803,2723,1623,1533,1873,136Goats2,5162,5162,5162,4632,4102,3572,3042,2522,3072,3632,4192,4752,5302,5302,5302,5302,5302,530Poultry1,537,0741,594,9441,649,9981,707,4221,769,1351,826,9771,882,0781,926,7901,965,3122,008,6322,033,1232,060,3982,097,6912,085,2682,130,8772,150,4102,154,2362,163,320Hens >1 yr.273,467 280,121 284,500 290,626 298,527299,071303,922312,137321,828330,180 333,593340,317340,209340,979343,922348,203349,888344,492Pullets73,167 76,616 79,870 81,774 79,85381,36981,57290,34495,84597,562 95,15995,65695,289100,346101,42996,80996,596102,301Chickens6,545 6,857 7,113 7,240 7,3697,6377,2437,5497,6829,661 8,0888,1268,3538,4398,2488,2897,9388,109Broilers1,066,209 1,115,845 1,164,089 1,217,147 1,275,9161,331,9401,381,2291,411,6731,442,5931,481,165 1,506,1271,525,4131,562,0151,544,1551,589,2091,613,0911,612,3271,617,855Turkeys117,685 115,504 114,426 110,635 107,469106,960108,112105,08897,36590,064 90,15590,88791,82691,34988,06984,01887,48790,563Horses5,0695,1005,1215,1305,1105,1305,1505,1705,2375,1705,2405,5006,0007,0008,0009,2009,2009,200Note: 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 FeedA-203Table A-170: Waste Characteristics DataAnimal GroupAverageTAM (kg)SourceTotal KjeldahlNitrogen, Nex(kg/day per1,000 kgmass)SourceMaximum MethaneGenerationPotential, Bo(m3CH4/kg VS added)SourceVolatile Solids,VS (kg/day per1,000 kg mass)SourceDairy Cows 604 Safley 20000.44USDA 1996a0.24Morris 1976Table A- 171Moffroid and Pape, 2008Dairy Heifers 476 Safley 20000.31USDA 1996a0.17Bryant et. al. 1976Table A- 171Moffroid and Pape, 2008Feedlot Steers 420 USDA 1996a0.30USDA 1996a0.33Hashimoto 1981Table A- 171Moffroid and Pape, 2008Feedlot Heifers 420 USDA 1996a0.30USDA 1996a0.33Hashimoto 1981Table A- 171Moffroid and Pape, 2008NOF Bulls 750 Shuyler 20000.31USDA 1996a0.17Hashimoto 19816.04USDA 1996aNOF Calves 118 USDA 1996a0.30USDA 1996a0.17Hashimoto 19816.41USDA 1996aNOF Heifers 420 USDA 1996a0.31USDA 1996a0.17Hashimoto 1981Table A- 171Moffroid and Pape, 2008NOF Steers 318 Safley 20000.31USDA 1996a0.17Hashimoto 1981Table A- 171Moffroid and Pape, 2008NOF Cows 533 Safley 20000.33USDA 1996a0.17Hashimoto 1981Table A- 171Moffroid and Pape, 2008Market Swine <60 lbs. 16 Safley 20000.60USDA 1996a0.48Hashimoto 19848.80USDA 1996aMarket Swine 60-119 lbs. 41 Safley 20000.42USDA 1996a0.48Hashimoto 19845.40USDA 1996aMarket Swine 120-179 lbs. 68 Safley 20000.42USDA 1996a0.48Hashimoto 19845.40USDA 1996aMarket Swine >180 lbs. 91 Safley 20000.42USDA 1996a0.48Hashimoto 19845.40USDA 1996aBreeding Swine 198 Safley 20000.24USDA 1996a0.48Hashimoto 19842.60USDA 1996aFeedlot Sheep 25 EPA 19920.42ASAE 19990.36EPA 19929.20ASAE 1999NOF Sheep 80 EPA 19920.42ASAE 19990.19EPA 19929.20ASAE 1999Goats 64 ASAE 19990.45ASAE 19990.17EPA 19929.50ASAE 1999Horses 450 ASAE 19990.30ASAE 19990.33EPA 199210.0ASAE 1999Hens >/= 1 yr 1.8 ASAE 19990.83USDA 1996a0.39Hill 198210.8USDA 1996aPullets 1.8 ASAE 19990.62USDA 1996a0.39Hill 19829.7USDA 1996aOther Chickens 1.8 ASAE 19990.83USDA 1996a0.39Hill 198210.8USDA 1996aBroilers 0.9 ASAE 19991.10USDA 1996a0.36Hill 198415.0USDA 1996aTurkeys 6.8 ASAE 19990.74USDA 1996a0.36Hill 19849.7USDA 1996a

### 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.A-205Table A- 172: 2007 Manure Distribution Among Waste Management Systems by Operation (Percent)Beef Feedlots Dairies1Dairy HeiferFacilitiesSwine Operations1Layer OperationsBroiler and TurkeyOperationsState Dry Lot2Liquid/Slurry2PastureDailySpreadSolidStorageLiquid/SlurryAnaerobicLagoonDeep PitDry Lot2Liquid/Slurry2PastureSolidStorageLiquid/SlurryAnaerobicLagoonDeep PitAnaerobicLagoonPoultrywithoutLitterPasturePoultrywithLitterAlabama 100 1.3 45 17 11 10 17 0 38 0.4 5 4 8 51 32 42 58 1 99Alaska 100 1.3 4 6 28 24 30 8 90 0.9 53 2 13 10 22 25 75 1 99Arizona 100 0.4 0 10 9 20 61 0 90 0.3 14 3 5 50 27 60 40 1 99Arkansas 100 1.3 57 15 11 7 10 1 28 0.3 4 4 12 46 35 0 100 1 99California 100 1.3 1 11 9 21 58 0 88 0.9 13 3 8 47 29 12 88 1 99Colorado 100 0.4 1 1 12 24 62 1 98 0.3 2 5 26 17 50 60 40 1 99Connecticut 100 1.0 6 43 17 20 12 2 51 0.4 57 2 12 9 20 5 95 1 99Delaware 100 1.0 6 44 19 19 10 2 50 0.4 12 4 24 18 42 5 95 1 99Florida 100 1.3 17 22 8 15 39 0 61 0.6 71 1 8 6 14 42 58 1 99Georgia 100 1.3 40 18 10 11 21 0 42 0.4 8 4 10 47 32 42 58 1 99Hawaii 100 1.3 1 0 11 21 67 0 99 1.0 23 3 18 21 34 25 75 1 99Idaho 100 0.4 0 1 12 23 63 1 99 0.3 46 3 15 10 26 60 40 1 99Illinois 100 0.6 5 8 43 26 13 5 87 0.4 2 5 28 15 50 2 98 1 99Indiana 100 0.6 8 13 35 24 16 3 79 0.4 3 5 28 15 50 0 100 1 99Iowa 100 0.6 6 10 41 25 14 4 83 0.4 1 4 12 48 35 0 100 1 99Kansas 100 0.6 3 5 28 33 29 3 92 0.5 2 5 28 13 51 2 98 1 99Kentucky 100 1.0 61 14 14 6 2 2 24 0.2 5 4 12 45 34 5 95 1 99Louisiana 100 1.3 60 14 10 6 9 1 26 0.3 54 2 13 10 22 60 40 1 99Maine 100 1.0 7 45 20 17 9 2 48 0.4 73 1 7 6 13 5 95 1 99Maryland 100 1.0 7 44 23 15 8 3 49 0.4 21 4 21 16 38 5 95 1 99Massachusetts 100 1.0 7 45 24 15 6 3 47 0.4 31 3 19 14 32 5 95 1 99Michigan 100 0.6 3 6 32 33 22 4 91 0.5 5 5 25 17 48 2 98 1 99Minnesota 100 0.6 6 10 44 24 12 5 84 0.4 2 5 26 18 49 0 100 1 99Mississippi 100 1.3 57 15 10 7 10 0 28 0.3 2 4 6 57 31 60 40 1 99Missouri 100 0.6 8 14 48 18 6 5 77 0.4 3 5 28 14 51 0 100 1 99Montana 100 0.4 3 4 25 26 36 6 93 0.3 6 5 25 17 47 60 40 1 99Nebraska 100 0.6 4 6 35 30 21 4 90 0.4 3 5 28 15 50 2 98 1 99Nevada 100 0.4 0 0 11 24 64 0 99 0.3 35 2 4 39 20 0 100 1 99New Hampshire 100 1.0 7 44 21 16 9 3 49 0.4 57 2 12 9 20 5 95 1 99New Jersey 100 1.0 8 45 24 14 6 3 47 0.4 31 3 19 14 33 5 95 1 99New Mexico 100 0.4 0 10 9 19 61 0 90 0.3 92 0 2 2 4 60 40 1 99New York 100 1.0 7 45 20 16 10 2 48 0.4 20 4 21 15 40 5 95 1 99North Carolina 100 1.0 54 15 12 11 6 1 31 0.2 0 4 6 58 31 42 58 1 99North Dakota 100 0.6 6 11 45 22 12 4 83 0.4 9 5 24 17 45 2 98 1 99Ohio 100 0.6 8 14 41 23 11 4 78 0.4 7 4 27 15 47 0 100 1 99Oklahoma 100 0.4 0 6 25 23 40 6 94 0.3 1 4 6 58 31 60 40 1 99Oregon 100 1.3 20 0 13 21 44 2 80 0.8 58 2 12 9 20 25 75 1 99Pennsylvania 100 1.0 9 47 25 12 5 2 44 0.4 5 5 25 18 47 0 100 1 99Rhode Island 100 1.0 9 47 26 12 4 3 44 0.4 56 2 12 9 21 5 95 1 99South Carolina 100 1.3 54 15 8 9 13 0 31 0.3 6 4 9 50 32 60 40 1 99South Dakota 100 0.6 5 8 38 28 18 4 87 0.4 3 5 26 18 48 2 98 1 99A-206 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Beef Feedlots Dairies1Dairy HeiferFacilitiesSwine Operations1Layer OperationsBroiler and TurkeyOperationsState Dry Lot2Liquid/Slurry2PastureDailySpreadSolidStorageLiquid/SlurryAnaerobicLagoonDeep PitDry Lot2Liquid/Slurry2PastureSolidStorageLiquid/SlurryAnaerobicLagoonDeep PitAnaerobicLagoonPoultrywithoutLitterPasturePoultrywithLitterTennessee 100 1.0 59 15 12 9 4 2 26 0.2 10 4 12 41 33 5 95 1 99Texas 100 0.4 0 8 13 24 53 2 92 0.3 7 3 6 54 29 12 88 1 99Utah 100 0.4 1 1 17 26 51 3 98 0.3 1 6 26 17 51 60 40 1 99Vermont 100 1.0 7 44 19 17 10 2 49 0.4 86 1 4 3 7 5 95 1 99Virginia 100 1.0 57 15 12 9 4 2 28 0.2 3 4 7 55 31 5 95 1 99Washington 100 1.3 17 0 11 22 49 1 83 0.8 37 3 17 12 31 12 88 1 99West Virginia 100 1.0 7 45 23 16 7 3 48 0.4 58 2 11 8 21 5 95 1 99Wisconsin 100 0.6 7 12 42 24 12 4 82 0.4 13 4 24 17 42 2 98 1 99Wyoming 100 0.4 7 12 22 23 30 6 81 0.2 3 5 26 17 49 60 40 1 991In the methane inventory for manure management, the percent ofdairy cows and swine with anaerobic digestion systems is estimated using data from EPA’s AgSTAR Program.2Because 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), thepercent of manure that generates emissions is greater than 100 percent.

### A-207 Table A- 173: Manure Management System Descriptions

Manure Management System Description 1

Pasture The manure from pasture and range grazing animals is allowed to lie as is, and is not managed. N 2 O emissions

from deposited manure are covered under the N 2 O from Agricultural Soils category.

Daily Spread Manure is routinely removed from a confinement facility a nd is applied to cropland or pasture within 24 hours of

excretion. N 2 O emissions during storage and treatment are assumed to be zero. N 2 O 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 typi cally found in dry climates but also are used in

humid climates.

Liquid/ Slurry Manure is stored as excreted or with some minimal additi on of water to facilitate handling and is stored in either

tanks or earthen ponds, usually f or 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 th e 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 ot her operational factors. Anaerobic lagoons accumulate

sludge over time, diminishing treatment capacity. Lago ons must be cleaned out once every 5 to 15 years, and

the sludge is typically applied to agricultural lands. T he 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 he lp control the buildup of nondegradabl e 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. T he bedding absorbs moisture and dilutes the

manure produced by the birds. Litter is typically cl eaned out completely once a year. These manure systems

are typically used for all poultry breeder flocks and for th e production of meat type chi ckens (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 syst em 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 Fi nal Revisions to the National

Pollutant Discharge Elimination System Re gulation 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)

Dairy Swine Beef Poultry State

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 Oxid e Emission Factors for 2007 (kg N

2 

### O-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 1 Animal Type Waste Management

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: 1990-2007Table A- 178: Methane Emissions from Livestock Manure Management (Gg)aAnimal Type 1990 1991 19921993199419951996199719981999 20002001200220032004200520062007Dairy Cattle 538 548 536530571597572598637663 701742775798779820833863Dairy Cows 530 540 529522563590565591629655 694734768791772813825855Dairy Heifer 8 8 88887777 77777788Swine 624 676 639680741764730783891847 832851875840835887870940Market Swine 484 524 500534585608582626720692 680696720693692736720785Market <60 lbs. 102 110 104109119121116125141133 131134137133132139137149Market 60-119 lbs. 101 111 105110120124117127144138 136138144138138146144156Market 120-179 lbs. 136 147 140151164170163175201193 189192199193192205200220Market >180 lbs. 145 156 152165182194185198235229 225232240230230245240261Breeding Swine 140 152 139146156155148157170155 152154154147143151149155Beef Cattle 124 124 121122125125124121124123 118121117115114114119116Feedlot Steers 17 16 1415131211111211 1213121111121212Feedlot Heifers 9 9 78776777 88877777NOF Bulls 5 5 55555555 55555555NOF Calves 9 9 99999999 99888898NOF Heifers 14 14 1515161617161616 1515151515151615NOF Steers 9 9 9910101010109 88898899NOF Cows 62 63 6262656665636565 6263616060606260Sheep 7 7 76655554 44444444Goats 1 1 11111111 11111111Poultry 131 132 127131131128126127130125 126130128128127127128130Hens >1 yr. 73 72 7073726968677066 6670676865656666Total Pullets 25 26 2323232221232321 2222222223222325Chickens 4 4 44443343 33443333Broilers 19 20 2021222324252526 2727282728282828Turkeys 10 10 1010999988 88888788Horses 22 22 2121212121212222 2223252934393939aMethane emissions estimates presented in thistable account for anaerobic digestion reductions.A-211Table A- 179: Total (Direct and Indirect) Nitrous Oxide Emissions from Livestock Manure Management (Gg)Animal Type 1990 1991 19921993199419951996199719981999 20002001200220032004200520062007Dairy Cattle 11.2 11.10 11.0611.1611.211.311.211.211.311.5 11.711.711.911.911.812.012.312.5Dairy Cows 6.4 6.36 6.256.316.36.36.46.46.46.4 6.56.56.66.66.56.66.66.7Dairy Heifer 4.8 4.74 4.804.854.95.04.94.84.95.1 5.15.25.45.35.35.55.75.8Swine 4.0 4.18 4.334.374.64.54.44.64.94.8 4.74.74.84.74.84.84.95.1Market Swine 3.0 3.11 3.263.293.53.53.33.53.83.7 3.73.73.83.83.93.93.94.2Market <60 lbs. 0.6 0.59 0.610.600.60.60.60.60.70.6 0.60.60.70.70.70.70.70.7Market 60-119 lbs. 0.6 0.67 0.700.700.70.70.70.70.80.8 0.80.80.80.80.80.80.80.8Market 120-179 lbs. 0.9 0.90 0.940.951.01.01.01.01.11.1 1.01.01.11.11.11.11.11.2Market >180 lbs. 0.9 0.95 1.021.041.11.11.11.11.31.3 1.21.21.31.31.31.31.31.4Breeding Swine 1.0 1.07 1.081.071.11.11.01.11.11.0 1.01.01.01.01.01.01.01.0Beef Cattle 17.8 19.43 18.2219.4319.119.218.519.620.020.1 21.522.221.620.320.820.921.621.6Feedlot Steers 11.9 12.87 12.3212.9812.712.612.012.412.712.7 13.614.013.712.913.313.514.014.0Feedlot Heifers 5.9 6.56 5.896.456.46.66.57.17.27.5 8.08.27.97.47.57.47.67.6Sheep 0.4 0.41 0.440.440.60.70.80.90.91.0 1.11.21.21.11.11.11.11.1Goats 0.1 0.07 0.070.070.10.10.10.10.10.1 0.10.10.10.10.10.10.10.1Poultry 4.7 4.79 4.884.965.15.15.35.35.35.4 5.45.55.65.55.65.65.75.7Hens >1 yr. 1.0 1.02 1.021.031.01.01.01.01.11.1 1.11.11.11.11.11.11.11.1Total Pullets 0.3 0.29 0.290.290.30.30.30.30.30.3 0.30.30.30.30.30.30.30.3Chickens0.00.030.030.030.00.00.00.00.00.00.00.00.00.00.00.00.00.0Broilers2.22.262.362.462.62.72.82.92.93.03.13.13.23.13.23.33.33.3Turkeys1.21.191.181.141.11.11.11.11.00.90.90.90.90.90.90.90.90.9Horses 0.7 0.70 0.710.710.70.70.70.70.70.7 0.70.80.81.01.11.31.31.3

### 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 1.356 1 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 Em issions 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 0.024 9 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 N 2 

# O Emissions from Agricu ltural Soil Management

Nitrous oxide (N 2 

### O) 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 N 2 

### O.

A combination of Tier 1 and Tier 3 approaches was used to estimate direct and indirect N 2 

### O emissions from

agricultural soils. The process-based biogeochemical model DAYCENT (a Tier 3 approach) was used to estimate N 2 

### O

emissions resulting from mineral soil croplands that were us ed to produce major crops, while the IPCC (2006) Tier 1

methodology was applied to estimate N 2 

### O emissions for non-major crop types on mineral soils. The Tier 1 method was

also used to estimate direct N 2 

### O emissions due to drainage and cultivation of organic cropland soils. Direct N 2 

### O

emissions from grasslands were estimated by using a comb ination 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 (NO 3 - 

### ) 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 N 2 

### O emissions resulting from off-site transport of N were then computed using the IPCC

(2006) Tier 1 default emission factor. Overall, the Tier 3 appr oach is used to estimate approximately 85 percent of direct

soil emissions and 70 percent of total soil N 2 

### O 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 N 2 

### O 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 N 2 

### O. 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 N 2 

### O 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 N 2 

### O emissions from soils. Key processes simulated within sub-models of DAYCENT include plant production, organic

matter formation and decomposition, soil water and soil temper ature regimes by layer, and nitrification and denitrification

processes (Figure A- 7). Comparison of model results and pl ot level data show that DAYCENT reliably simulates crop

yields, soil organic matter levels, and trace gas fluxes for a nu mber 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). Th e linear regression of simulated vs. measured emissions for

DAYCENT had higher r 2 

### and a fitted line closer to a perfect 1:1 relationship between measured and modeled N 2 

### O

emissions (Del Grosso et al. 2005, 2008). This is not surpri sing, since DAYCENT includes site-specific factors (climate,

soil properties, and previous management) that influence N 2 

### O emissions. Furthermore, DAYCENT also simulated NO 3 -



### 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 appr oach 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 proces ses, nitrification and denitrification, that result

in N 2 

### O 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 so il profile. The equations and key parameters controlling N 2 

### O

emissions from nitrification and denitrification are described below.

Nitrification is controlled by soil ammonium (NH 4 + 

### ) concentration, water filled pore space (WFPS), temperature

(t), and pH according to the following equation:

Nit = NH 4 

### × K

max 

### × F(t) × F(WFPS) × F(pH)

where,

Nit = the soil nitrification rate (g N/m 2 

### /day)

### A-215 NH 4 

### = the model-derived soil ammonium concentration (g N/m 2 

### )

K

max 

### = the maximum fraction of NH 4 + 

### nitrified (K

max 

### = 0.10/day)

F(t) = the effect of soil temperature on nitrification ( Figure A- 5 

### a)

F(WFPS) = the effect of soil water content and soil texture on nitrification ( Figure A- 5 

### b)

F(pH) = the effect of soil pH on nitrification ( Figure A- 5 

### c)

The current parameterization used in the model assumes that 1.2 percent of nitrified N is converted to N 2 

### O.

N 2 

### O emissions from denitrification are a function of soil NO 3 - 

### concentration, WFPS, heterotrophic (i.e.,

microbial) respiration, and texture. Denitrification is calculated for each soil layer in the profile, and N 2 

### O emissions from

each layer are summed to obtain total soil emissions. The model assumes that denitrification rates are controlled by the

availability of soil NO 3 - 

### (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 functio n

of soil physical properties that influence gas diffusivity, so il WFPS, and oxygen demand. The model selects the minimum

of the NO 3 - 

### and CO 2 

### 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(CO 2 

### ), F(NO 3 

### )] × F(WFPS)

where,

Den = the soil denitrification rate (  g N/g soil/day)

F(CO 2 

### ) = a function relating N gas flux to soil respiration ( Figure A- 6 

### a)

F(NO 3 

### ) = a function relating N gas flux to nitrate levels ( Figure A- 5 

### b)

F(WFPS) = a dimensionless multiplier ( Figure A- 6 

### c).

The x inflection point of F(WFPS) is a function of respiration and soil gas diffusivity at field capacity (D FC 

### ):

x inflection = 0.90 - M(CO 2 

### )

where,

M = a multiplier that is a function of D FC



### .

Respiration has a much stronger effect on the water curve in clay soils with low D FC 

### than in loam or sandy soils

with high D FC 

### (Figure A- 6c). The model assumes th at 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 N 2 

### /N 2 

### O is estimated so that total N gas emissions can be

partitioned between N 2 

### O and N 2 

### :

R N2/N2O 

### = F

r 

### (NO 3 

### /CO 2 

### ) × F

r 

### (WFPS).

where,

R N2/N2O 

### = the ratio of N 2 

### /N 2 

### O

F

r 

### (NO 3 

### /CO 2 

### ) = a function estimating the impact of the ava ilability of electron donor relative to substrate

F

r 

### (WFPS) = a multiplier to account for the effect of soil water on N 2 

### :N 2 

### O.

For F

r 

### (NO 3 

### /CO 2 

### ), as the ratio of electron donor to substrate increases, a higher portion of N gas is assumed to be

in the form of N 2 

### O. For F

r 

### (WFPS), as WFPS increases, a higher portion of N gas is assumed to be in the form of N 2



### .

[End Box]

Figure A- 5: Effect of Soil Temperature, Water-F illed Pore Space, and pH on Nitrification Rates

Figure A- 6: Effect of Soil Nitrite Concentration, Hetero trophic 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 N 2 

### O emissions from cropland and grassland soils, and indirect N 2 

### O

emissions from volatilization, leaching, and runoff from all mana ged 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 th e second, third, and fourth st eps, direct and indirect N 2 

### O

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 N 2 

### O 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 N 2 

### O 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 fo r 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 N 2 

### O 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 ra tes 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 fe rtilizer 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 f or

each region. Mean fertilizer rates and standard deviations fo r 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 Un ited 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 use d

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 fertilizat ion to corn fields in this state).

Managed Livestock Manure 44 

### 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 perman ent 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 gr oundwater 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 we re 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 di vided 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 depe ndence between manure amendments and mineral fertilizer

application.

Manure N that was not applied to major crops and grassla nd 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 DAYCE NT 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 sc hedules 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, Spillm an 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 addi tions, data were not complete, so regional averages were

used to fill spatial gaps in the data sets and inter polation/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 precipi tation, temperature, and other meteorological data at 1

km 2 

### 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 us e 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 km 2 

### resolution for 1980 through 2003. For each county,

DAYMET weather data were selected from the 1 km 2 

### 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. Se parate 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 orga nic 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 subtract ed 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 ma tter 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 assumi ng a ±50 percent uncertainty in the yield estimates (NASS

does not provide uncertainty), along with uncertainties provi ded 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 consiste ncy across the Inventory as these inputs are calculate d 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 region 47 

### 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

N 2 

### O 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 (P RP) soils, and synthetic fertilizer applications. N 2 

### O 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, sh eep, goats, poultry, and horses were derived from multiple

sources as described in the Manure Management section (S ection 6.2) and annex (Annex 3.10). The amount of PRP

manure N deposited on non-federal grasslands (non-federal gra sslands 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 N 2 

### O 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 temp erature, and other factors. Total annual amounts of PRP

manure N, mineral N fertilizer application, manure N amendmen ts, forage legume N and “other N inputs” can be found in

Table A- 190.

Tier 1 Method: Additional Direct Soil N 2 

### O 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 Al aska’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) coldtemperate, moist, (3) sub-tropical, dry, (4) sub-tropical, mois t,

(5) warm temperate, dry, and (6) warm temperate, moist.

### A-221 (Vogelman et al. 2001), respectively. Soil N 2 

### O 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 amende d 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 2 004 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 N 2 

### O 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 N 2 

### O Emissions from Managed Soils of all Land-Use Types and Managed Manure

Systems

Volatilization of N that was applied or deposited as synt hetic fertilizer, livestock manure, sewage sludge, and

other organic amendments leads to emissions of NH 3 

### and NO

x 

### to the atmosphere. In turn, this N is returned to soils

through atmospheric deposition, thereby increasing mineral N availability and enhancing N 2 

### O 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 N 2 

### O. However, N

leaching was assumed to be an insignificant source of indirect N 2 

### O 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 N 2 

### O 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/su rface run-off of N from soils was computed internally

for major crop types and non-federal grasslands. DAYCENT simula tes the processes leading to these losses of N based on

environmental conditions (i.e., weather patterns and soil char acteristics), management impacts (e.g., plowing, irrigation,

harvest), and soil N availability. Note that the DAYCEN T 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. Esti mated leaching losses of N from DAYCENT were not used

in the indirect N 2 

### O calculation if the amount of precipitation plus irrigation did not exceed the potential

evapotranspiration, as recommended by IPCC (2006). Volatili zed losses of N were summed for each day in the annual

cycle to provide an estimate of the amount of N subject to indirect N 2 

### O 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 sl udge 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 NH 3 

### -N+NOx-N/kg N for synthetic fertilizer and 0.05-0.5 kg NH 3 

### -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 NO 3 

### -N/kg

N (IPCC 2006). However, N leaching was assumed to be an insignificant source of indirect N 2 

### O 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 N 2 

### O emissions, discussed in Steps 1a and 1c.

Volatilized N was summed for losses from major crop type s, minor crop types, gra sslands, settlements, and

forest lands. Similarly, the annual amounts of N lost fro m soil profiles through leaching and surface runoff were summed

to obtain the total losses for this pathway.

Step 2: Estimate Direct N 2 

### O Emissions from Cropland Soils

In this step, N 2 

### O 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 N 2 

### O Emissions from Cropland Mineral Soils

Two methods were used to estimate direct N 2 

### O 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 vege tation 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 N 2 

### O 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. Depa rtment 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), di fferent 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 durin g 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, N 2 

### O 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 impr ovement 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 steadil y 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 N 2 

### O 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 weat her 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 1 00 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 N 2 

### O emissions across regions from the past year’s Inventory, using a Neyman allocation (Cochran 1977). A Neyman

allocation is based on the variance in N 2 

### O 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 wh ich 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 pr actices. 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 N 2 

### O 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 N 2 

### O 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 N 2 

### O emissions. This means, for example, that N 2 

### O

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 associat ed 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 N 2 

### O 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 N 2 

### O emissions were assigned to this practice. The

remainder of soil N 2 

### O emissions is reported under “other N inputs,” which includes mineralization due to decomposition

of soil organic matter and litter, as well as asymbiotic fixa tion 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, ma nure 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 N 2 

### O 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 N 2 

### O 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 N 2 O emissions, as it assumes that all N inputs have an

identical chance of being converted to N 2 O. This is unlikely to be the case, but DAYCENT does not track N 2 O emissions by source of

mineral N so this approximation is the only approach that can be used for approximating the portion of N 2 O 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 f or 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 N 2 

### O emissions for the dominant condition, expressed

as a 95 percent confidence interval.

Non-Major Crops: Tier 1 Method

To estimate direct N 2 

### O 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

N 2 

### O-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 N 2 

### O-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 N 2 

### O Emissions Due to Drainage and Cultivation of Organic Cropland Soils

To estimate annual N 2 

### O 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 N 2 

### O-N/ha cultivated) of IPCC (2006)

default emission factors for temperate (8 kg N 2 

### O-N/ha cultivated) and tropical (16 kg N 2 

### O-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 N 2 

### O-N/ha (IPCC 2006).

Step 2c: Estimate Total Direct N 2 

### O Emissions from Cropland Soils

In this step, total direct N 2 

### O 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 N 2 

### O Emissions from Grasslands

DAYCENT was used to estimate direct N 2 

### O emissions from soils in non-federal grasslands (pastures and

rangeland), and the Tier 1 method was used for federal grassl ands. Managed pastures were simulated with DAYCENT by

assuming that the vegetation mix includes forage legumes and gr asses, 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 N 2 

### O-N/kg N from

sludge and 0.02 kg N 2 

### O-N/kg N from manure) to estimate N 2 

### O emissions. This emission estimate was summed with the

DAYCENT simulated emissions to provide the national total for direct N 2 

### O 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 N 2 

### O-N/kg N (IPCC 2006).

Step 4: Estimate Indirect N 2 

### O Emissions for All Land-Use Types

In this step, N 2 

### O emissions were calculated for the two indirect emission pathways (N 2 

### O emissions due to

volatilization, and N 2 

### O emissions due to leaching and runoff of N), which were then summed to yield total indirect N 2 

### O

emissions from croplands, grasslands, forest lands, and settlements.

49 Note that due to lack of data, uncertainties in manage d manure N production, PRP manure N production, other commercial

organic fertilizer amendments, indirect losses of N in the DAYCE NT simulations, and sewage sludge amendments to soils are curre ntly

treated as certain; these sources of uncertain ty 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 N 2 

### O following atmospheric deposition. See Step 1d for additional information about the methods

used to compute N losses due to volatilization. The estima ted N volatilized for all land-use and livestock activities was

multiplied by the IPCC default emission factor of 0.01 kg N 2 

### O-N/kg N (IPCC 2006) to compute total N 2 

### O 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 N 2 

### O-N/kg N (IPCC 2006).

Step 4b: Indirect Emissions Due to Leaching and Runoff

The amount of mineral N (i.e., synthetic fertilizers, comm ercial 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 N 2 

### O-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

N 2 

### O-N/kg N (IPCC 2006).

Step 5: Estimate Total N 2 

### O 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 N 2 

### O vary regionally in both croplands and grasslands as a function of N

inputs, weather, and soil type. About half of the total N 2 

### O 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 N 2 

### O is high in Nevada, Utah, West Virginia and some New

England states (Figure A- 9). Only a small portion of th e 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 conditi ons near the surface that facilitate denitrification and N 2 

### O 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 typi cally lower than those from croplands (Table A- 196,

Figure A- 11, and Figure A- 12) because N inputs tend to be lowe r, 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 intensiv ely managed (legume seeding, fertilization) while western

rangelands receive few, if any, N inputs. Also, rainfall is li mited 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 Mode l and IPCC Tier 1 Method wi th Measurements of Soil N 2 

### O Emissions

### A-226 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 Figure A- 9: Major Crops, Average Annual Direct N 2 

### O Emissions, Estimated Using the DAYCENT Model, 1990-2007 (Metric

Tons CO 2 

### Eq./ha/year)

Figure A- 10: Major Crops, Average Annual N Losses Leading to Indirect N 2 

### O Emissions, Estimated Us ing the DAYCENT Model,

1990-2007 (kg N/ha/year)

Figure A- 11: Grasslands, Average Annual Direct N 2 

### O Emissions, Estimated Using the DAYCENT Model, 1990-2007 (Metric

Tons CO 2 

### Eq./ha/year)

Figure A- 12: Grasslands, Average Annu al N Losses Leading to Indirect N 2 

### O Emissions, Estimated Us ing the DAYCENT Model,

1990-2007 (kg N/ha/year)A-227Table 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 2005 2006 2007Fertilizer 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 7,358 7,000 6,857 7,587Table A- 183: Fate of Livestock Manure Nitrogen (Gg N)Activity 199019911992199319941995199619971998199920002001200220032004200520062007Managed Manure N Applied to MajorCrops and Grasslandsa,b972 1,0411,0531,0561,0179381,0001,0311,084 1,070 1,0771,0891,1471,0941,0901,0428451,206Managed Manure N Applied to NonMajor Cropsb790 797773794849915852892856 883 9069389479209289781,164956Managed Manure N Applied toGrasslands 448 437431417413408401389417 408 408409409414411419422423Pasture, Range, & Paddock Manure N 3,856 3,8753,9593,9924,0684,0944,0723,9293,859 3,823 3,7603,7453,7623,8023,8033,8793,9053,893Total 6,0666,1506,2166,2606,3486,3546,3256,2416,2166,1856,1516,1816,2666,2306,2326,3196,3356,478aAccounts for N volatilized and leached/runoff during treatment, storage and transport before soil application.bIncludes managed manure and daily spread manure amendmentscTotals may not sum exactly due to rounding.Table A- 184: Crop Residue N and Other N Inputsto Major Crops as Simulated by DAYCENT (Gg N)Activity 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007Residue Na1,3581,3131,373 1,3241,3431,4761,4471,5511,4131,6491,5241,4561,4621,5741,4531,3791,4591,443Mineralization &Asymbiotic Fixation 12,36212,33712,120 12,36112,51311,97212,53912,76014,11612,86613,49713,49913,18713,19413,32712,72112,74912,956aResidue N inputs include unharvested fixed N from legumes as well as crop residue N.Table A- 185: Synthetic FertilizerN Added to Non-Major Crops (Gg N)199019911992199319941995199619971998199920002001200220032004200520062007Fertilizer N 2,4462,6592,3973,0572,8653,4232,8243,1333,1753,379 2,9952,9992,9333,3403,4143,7093,6063,098Table A- 186: Other Organic Commercial Fertilizer Consumption on Agricultural Lands (Gg N)199019911992199319941995199619971998199920002001200220032004200520062007Other Commercial Organic Fertilizer Na4865810131412 1197889101215aIncludes dried blood, tankage,compost, other. Excludes dried manure and sewagesludge used as commercial fertilizer to avoid double counting.Table A- 187: Key Assumptions for Production ofNon-Major Crops and Retention of Crop ResiduesAbove-ground Residue Residue N FractionCropDry MatterFraction ofHarvestedProductSlope InterceptRatio ofBelow-groundResidue to Aboveground BiomassAbove-ground Below-groundPeanuts for Nuts 0.94 1.07 1.54 0.20 0.016 0.014Dry Edible Beans 0.90 0.36 0.68 0.19 0.010 0.010Dry Edible Peas 0.91 1.13 0.85 0.19 0.008 0.008Austrian Winter Peas 0.91 1.13 0.85 0.19 0.008 0.008A-228 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Lentils 0.91 1.13 0.85 0.19 0.008 0.008Wrinkled Seed Peas0.91 1.13 0.85 0.19 0.008 0.008Barley 0.89 0.98 0.59 0.22 0.007 0.014Oats 0.89 0.91 0.89 0.25 0.007 0.008Rye 0.88 1.09 0.88 0.22 0.005 0.011Millet 0.90 1.43 0.14 0.22 0.007 0.009Rice 0.89 0.95 2.46 0.16 0.007 0.009Table A- 188: Nitrogen in Crop Residues Retained on Soils Producing Non-Major Crops (Gg N)Crop 1990 1991 19921993199419951996199719981999 2000200120022003200420052006 2007Peanuts for Nuts 64 77 7162706365646866 61716169717763 66Dry Edible Beans 16 17 1414151615161616 15131614131514 14Dry Edible Peas 9 10 9109119111110 10101011141515 16Austrian Winter Peas8 8 88888888 8888888 8Lentils 9 9 99999999 101099101110 10Wrinkled Seed Peas9 9 89898888 8888988 8Barley 112 123 12010610096105969474 86686376765951 59Oats 55 40 4735382928303027 28242327232320 20Rye 9 9 99999899 9889888 8Millet 7 7 77777777 3725654 6Rice 80 82 8980968786919199 941031019710910695 96Total 378 389 392348371343349348352336 331330311333347336297 313Table A- 189: Drained and CultivatedOrganic Soil Area (Thousand Hectares)Year Temperate Area Sub-Tropical Area1990 444 1941991 444 1941992 444 1941993 450 1961994 450 1961995 450 1961996 450 1961997 450 1961998 450 1961999 450 1962000 450 1962001 450 1962002 450 1962003 450 1962004 450 1962005 450 1962006 450 1962007 450 196A-229Table A- 190: Synthetic Fertilizer N, PRP Manure N, Organic Manure N Amendment, Forage Legume N, and Other N Inputs Simulated with the DAYCENT Model (Gg N)Fertilizer Type199019911992199319941995199619971998199920002001200220032004200520062007Fertilizer N 7,966 7,924 8,214 7,8778,1557,8228,4158,2128,1337,908 8,0417,9258,0007,8197,7887,4287,2838,013PRP Manure N 3,479 3,494 3,568 3,6013,6643,6913,6663,5283,4603,431 3,3793,3713,3913,4343,4303,4993,5253,513Managed Manure 1,420 1,478 1,484 1,4741,4311,3461,4011,4201,5011,479 1,4851,4981,5571,5081,5021,4621,2671,630Residue Na6,681 6,592 6,820 6,7906,4096,9356,5636,8066,4587,127 6,4236,4416,1716,5846,4346,4266,5106,512Mineralization &AsymbioticFixation 24,832 24,901 24,684 24,58624,47924,23624,61925,11426,59424,519 25,15925,31924,73624,89225,11224,60024,55324,731aResidue 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 199019911992199319941995199619971998199920002001200220032004200520062007Applied to Agricultural Soils 52 56 59 63 66 69 72 75 78 818486 89 91 94 98101105Other Land Application 25 26 27 27 28 28 29 29 29 30 30 30 30 30 30 313232Surface Disposal 20 20 19 18 17 16 15 14 13 12 10 9 8 6 5 431Total 98101105109111113116118121122124125127128130133136138Note: Totals may not sum due to independent rounding.Table A- 192: Direct N2O Emissions from Cropland Soils (Tg CO2Eq.)Activity 199019911992199319941995199619971998199920002001200220032004200520062007Mineral Soils 103.5113.0104.4112.8115.4111.3119.4117.1120.6115.3116.5124.1116.2114.1120.5119.3117.0119.0Major Crops 85.894.387.092.395.588.499.795.799.292.895.8103.295.791.797.694.892.397.7Synthetic Fertilizer 29.1 31.9 30.4 30.9 32.5 29.9 34.532.1 31.4 30.0 30.8 32.6 31.1 29.2 30.9 30.2 28.9 32.2Managed Manure 3.8 4.4 4.2 4.54.4 3.8 4.4 4.3 4.44.3 4.3 4.8 4.6 4.3 4.5 4.4 3.6 5.0Residue Na5.2 5.5 5.3 5.4 5.7 6.06.3 6.4 5.7 6.7 6.1 6.36.0 6.2 6.1 5.9 6.1 6.1Mineralization and AsymbioticFixation47.8 52.4 47.1 51.5 52.9 48.7 54.4 52.9 57.751.8 54.6 59.5 54.0 52.0 56.1 54.3 53.7 54.4Non-Major Crops 17.618.817.420.519.922.819.721.421.422.520.720.820.522.422.924.524.721.3Synthetic Fertilizer 11.913.0 11.7 14.9 14.0 16.7 13.8 15.3 15.5 16.514.6 14.6 14.3 16.316.6 18.1 17.6 15.1Managed Manure and OtherOrganic Commercial Fertilizer3.9 3.9 3.8 3.9 4.2 4.54.2 4.4 4.2 4.4 4.5 4.64.6 4.5 4.6 4.8 5.7 4.7Residue N 1.8 1.9 1.9 1.7 1.8 1.7 1.7 1.7 1.71.6 1.6 1.6 1.5 1.6 1.7 1.6 1.4 1.5Organic Soils 2.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.9Total* 106.3115.9107.2115.7118.3114.2122.3120.0123.5118.2119.4127.0119.1117.0123.4122.2119.9121.9+ Less than 0.05 Tg CO2Eq.aResidue N inputs include unharvested fixed N from legumes as well as crop residue N.Table A- 193: Direct N2O Emissions from Grasslands (Tg CO2Eq.)199019911992199319941995199619971998199920002001200220032004200520062007A-230 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007DAYCENT48.650.946.048.949.447.452.548.149.944.945.749.650.045.146.747.946.645.9Synthetic Fertilizer 1.0 1.0 1.0 1.0 1.1 1.0 1.11.0 1.0 0.9 0.9 1.1 1.1 0.9 1.0 0.9 0.9 0.9PRP Manure 6.7 7.0 6.5 7.0 7.3 7.0 7.7 6.8 6.96.3 6.5 6.9 7.2 6.5 6.7 7.0 6.8 6.7Managed Manure 1.0 1.0 0.9 1.01.0 0.9 1.0 0.9 0.90.9 0.9 1.0 1.0 0.9 1.0 1.0 1.0 1.0Residue Na12.0 12.4 11.5 12.4 12.0 11.9 12.8 11.9 11.811.9 11.1 12.2 12.0 11.1 11.4 11.8 11.5 11.3Mineralization andAsymbiotic Fixation 27.9 29.4 26.1 27.5 28.0 26.6 30.0 27.529.2 24.8 26.3 28.5 28.725.7 26.7 27.3 26.4 26.0Tier 1 3.94.04.14.14.34.34.34.34.34.24.14.14.04.04.14.24.24.2PRP Manure 3.7 3.7 3.8 3.8 3.9 3.9 4.0 3.9 3.93.8 3.7 3.6 3.6 3.6 3.6 3.7 3.7 3.7Sewage Sludge 0.3 0.3 0.3 0.30.3 0.3 0.4 0.4 0.40.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5Total 52.554.850.153.053.651.656.852.454.249.149.953.654.149.150.852.150.850.1+ Less than 0.05 Tg CO2Eq.aResidue N inputs include unharvested fixed N from legumes as well as crop residue N.Table A- 194: Indirect N2O Emissions (Tg CO2Eq.)Activity 199019911992199319941995199619971998199920002001200220032004200520062007Volatilization and Atm. Deposition 13.513.713.513.614.014.714.014.614.514.514.214.414.314.514.514.715.514.4Croplands 7.8 7.9 7.7 8.0 8.5 8.9 8.4 8.8 8.88.9 9.0 8.9 9.0 9.0 9.1 9.2 10.1 8.9Settlements 0.1 0.1 0.1 0.2 0.2 0.2 0.2 0.2 0.20.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2 0.2Forest Land + + + + + + + + + + + + + + + + + +Grasslands 5.6 5.6 5.6 5.55.4 5.6 5.4 5.6 5.55.4 5.0 5.2 5.1 5.2 5.2 5.2 5.2 5.3Surface Leaching & Run-off 28.021.024.529.017.321.926.419.326.314.221.125.420.122.122.521.622.221.5Croplands 21.3 15.1 17.7 22.1 11.7 15.9 20.4 13.8 20.110.5 16.6 19.1 14.2 16.6 17.3 15.8 16.6 16.0Settlements 0.2 0.2 0.3 0.3 0.3 0.3 0.3 0.3 0.30.3 0.3 0.4 0.4 0.4 0.4 0.4 0.4 0.4Forest Land + + + + + + + 0.1 0.1 0.1 0.1 0.1 0.1 0.10.1 0.1 0.1 0.1Grasslands 6.4 5.7 6.5 6.65.2 5.6 5.7 5.2 5.93.3 4.0 5.8 5.4 5.0 4.7 5.3 5.1 5.0Total 41.534.738.042.631.336.540.433.940.828.635.339.834.536.637.036.337.735.9+ Less than 0.05 Tg CO2Eq.Table A- 195: Total N2O Emissions from Agricultural Soil Management (Tg CO2Eq.)Activity199019911992199319941995199619971998199920002001200220032004200520062007Total Direct 158.9170.7157.4168.7171.9165.8179.1172.4177.7167.2169.2180.6173.1166.2174.2174.4170.7172.0Direct Emissions from MineralCropland Soils 103.5113.0104.4112.8115.4111.3119.4117.1120.6115.3116.5124.1116.2114.1120.5119.3117.0119.0Synthetic Fertilizer 41.0 44.8 42.1 45.8 46.4 46.6 48.347.4 46.9 46.4 45.4 47.2 45.4 45.5 47.5 48.3 46.5 47.3Organic Amendmenta7.6 8.3 8.0 8.4 8.6 8.38.6 8.7 8.6 8.7 8.8 9.49.3 8.8 9.1 9.2 9.3 9.8Residue Nb7.0 7.4 7.2 7.1 7.5 7.78.0 8.1 7.4 8.3 7.7 7.97.5 7.8 7.8 7.5 7.6 7.6Mineralization and Asymbiotic Fixation47.8 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 56.1 54.3 53.7 54.4Direct Emissions from DrainedOrganic Cropland Soils2.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.92.9Direct Emissions from Grasslands* 52.554.850.153.053.651.656.852.454.249.149.953.654.149.150.852.150.850.1Synthetic Mineral Fertilizer 1.0 1.0 1.0 1.0 1.1 1.0 1.11.0 1.0 0.9 0.9 1.1 1.1 0.9 1.0 0.9 0.9 0.9PRP Manure* 10.3 10.7 10.3 10.811.2 10.9 11.7 10.7 10.810.1 10.2 10.6 10.8 10.1 10.3 10.7 10.5 10.4Managed Manure 1.0 1.0 0.9 1.0 1.0 0.9 1.0 0.90.9 0.9 0.9 1.0 1.0 0.9 1.0 1.0 1.01.0Sewage Sludge 0.3 0.3 0.3 0.30.3 0.3 0.4 0.4 0.40.4 0.4 0.4 0.4 0.4 0.5 0.5 0.5 0.5Residueb12.0 12.4 11.5 12.4 12.0 11.9 12.8 11.9 11.811.9 11.1 12.2 12.0 11.1 11.4 11.8 11.5 11.3A-231Mineralization and Asymbiotic Fixation27.9 29.4 26.1 27.5 28.0 26.6 30.0 27.5 29.224.8 26.3 28.5 28.7 25.7 26.7 27.3 26.4 26.0Total Indirect 41.534.738.042.631.336.540.433.940.828.635.339.834.536.637.036.337.735.9Volatilization13.5 13.7 13.5 13.6 14.0 14.7 14.0 14.6 14.514.5 14.2 14.4 14.3 14.5 14.5 14.7 15.5 14.4Leaching/Runoff28.0 21.0 24.5 29.0 17.3 21.9 26.4 19.3 26.314.2 21.1 25.4 20.1 22.1 22.5 21.6 22.2 21.5Total Emissions 200.3205.5195.4211.4203.2202.3219.5206.3218.5195.9204.5220.4207.6202.8211.2210.6208.4207.9+ Less than 0.05 Tg CO2Eq.aOrganic amendment inputs include managed manure amendments, daily spread manure and other commercial organic fertilizer (i.e.,dried blood, tankage, compost, and other).bResidue 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 N 2 

### O Emissions (Direct and Indirect) from Ag ricultural Soil Management by State (Tg CO

2 

### Eq.)

State Croplands 1 Grasslands 2 Settlements 3 Forest Lands 4 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

HI 5 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 cr ops 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 we re not estimated (n.e.) at the state level.

4 Forestland emissions were not esti mated (n.e.) at the state level.

5 N 2 O emissions are not reported for Hawa ii 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 CO 2 

### flux for forests are provided below; full details of selected topics ma y 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 lo cation) 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 Arizon a. 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 sel ected 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 designate d as reserved, or areas where no harv esting 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 th e 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 de liberately combined with crops and/or

livestock.”(Gold et al. 2000). In the Un ited States, there are six categories of ag roforestry practices: riparian forest

buffers, windbreaks, alley cropping, silvopasture , forest farming and special applications. 50 

### These practices are used

to address many issues facing agricultura l 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 agroforest ry practices can be found onlin e at <http://www.unl.edu/nac>.

### A-234 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

### atmospheric CO 2 

### . This occurs directly through CO 2 

### sequestration into woody biomass, and indirectly through

enhancement of agricultural production, trapping wind-b lown and surface runoff sedime nts, and/or reducing CO 2 

### 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 with in 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 ap proximately 90.3 Mt C/year by 2025 (Nair and Nair 2003).

Summing state-level C stocks to calculate Unit ed 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 ba sed on data from two or more forest surveys conducted

several years apart for each state or sub-state. There ar e generally 2 or more survey s available for download for

each state. C stocks are calculated separately for each st ate 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 (t he base year) to present can be adequa tely 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 st ate at a frequency of every 5 to 14 year s. 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 surv eyed 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 Esti mate Statewide Carbon Stocks

State/Substate a 

### Source of Inventory Data, Report/Inventory

Year b 

### Average Year Assigned

to Inventory c



### 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 le ast 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 industri al

wood. Productivity is at a minimum rate of 20 cubic feet of industrial wood per acr e per year. The remaining portion of forest

land is classified as either reserved forest land, which is fo rest 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 y ear.

Forest land includes woodlands, which descri bes forest types consisting primarily of sp ecies that have thei r diameter measured at

root collar, and for which there is no site index equations , nor stocking guides (Woudenberg 2006). There are about 203 millio n

hectares of timberland in the conterminous United States, which represents 81 pe rcent 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), woo dlands (forest land dominated

by woodland species, such as pinyon and juniper, where stocking c annot be determined [USDA Forest Service 2008c]), non-woodland s (used for clarity to

emphasize that woodlands are cla ssified separately), reserv ed (forest land withdraw n from timber utilizat ion through statute, a dministrative regulation, or

designation, Smith et al. (2008)), and non-rese rved (forest land that is not reserved, used for clarity). Some National Forest s are listed individually by name, e.g.,

Payette NF. Oregon and Washington were divided into eastern and west ern forests (east or west of the crest of the Cascade Moun tains). In Oklahoma, east

refers to the eastern 18 counties. Texas and Alaska represen t a portion of each state: eastern Texas and southcentral and sout heast 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 surve ys that were not yet available in the 3.0 format; in these c ases data from the previous

inventory (EPA 2008) were repr ocessed 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 fr om older surveys compiled for the Southern region (CD of South ern data from Linda Heatherly,

FIA Southern Region, July 7, 2004). RPA dat a are periodic national summari es. The year is the nomin al, or reporti ng, year ass ociated with each dataset.

c Average year is based on average measurement year of forest land survey plot s 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 go al 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 th an 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 por tion 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 ar e estimated by linear inte rpolation between survey

years. Similarly, fluxes, or net stock changes, are estim ated 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 le ss than the number of available inventories. Stocks and

fluxes since the most recent survey are based on extrapolation. Extr apolation beyond the most recent survey is

based on an average flux obtained from the last three whole-year interpolated va lues (Smith et al. In preparation). C

stock and flux estimates for each pool are summed over all fore st land in all states as identified in Table A-197 to

form estimates for the United States. Summed net annual ch ange 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.A-241Table A- 198.: Net Annual Changes in Carbon Stocks (Tg C yr-1) in Forest and Harvested Wood Pools, 1990-2007Carbon Pool 199019911992199319941995199619971998199920002001200220032004200520062007Forest(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, 1990-20081990199119921993199419951996199719981999200020012002200320042005200620072008Forest Area (1000 ha)245,697 246,440 247,194 247,922248,594249,240249,798250,312250,858251,345251,732 252,096252,520253,183254,194255,358256,227257,001257,787Carbon PoolForest40,011 40,155 40,311 40,46540,60840,76240,91741,06541,21941,35641,475 41,58441,71741,92342,20342,48842,72642,94243,163Live, aboveground 14,378 14,465 14,560 14,65614,74914,84514,95115,05415,16415,26715,365 15,46215,56415,68415,82915,97416,10216,22216,346Live, belowground 2,860 2,876 2,895 2,9132,9312,9502,9722,9923,0143,0353,055 3,0753,0963,1203,1493,1773,2033,2273,252Dead Wood 2,541 2,545 2,550 2,5542,5592,5672,5752,5822,5892,5932,597 2,6022,6082,6162,6282,6402,6512,6602,670Litter 4,558 4,577 4,596 4,6154,6334,6514,6614,6704,6784,6844,690 4,6944,7034,7224,7474,7724,7944,8134,832Soil Organic Carbon 15,675 15,692 15,710 15,72615,73615,74815,75815,76615,77415,77715,767 15,75215,74615,78115,85115,92515,97716,01916,063Harvested Wood1,783 1,821 1,859 1,8951,9291,9631,9962,0292,0612,0922,124 2,1552,1882,2182,2442,2712,2962,3252,353Products in Use 1,193 1,212 1,231 1,2491,2641,2801,2951,3111,3261,3401,355 1,3681,3821,3951,4041,4131,4231,4361,446SWDS 590 609 628 646665683701718735752769 787805823840857873890907Total Carbon Stock41,794 41,976 42,170 42,36042,53742,72442,91243,09443,28143,44843,599 43,73943,90544,14144,44744,75945,02345,26745,515

### A-242 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 

### Table A- 200 shows average C density values for fore st 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 inventor y 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 are a values in this table are estimated

using the most recent dataset per state (see Table A-1), with an average y ear of 2004. The time series of forest area values r eported in Table A- 199, in

contrast, is constructed following the CCT methods used to constr uct the carbon stock series. T he forest area values reported in Table A- 199 and Table A- 200

would only be identical if all states were measured si multaneously 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 FIAD B 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 es timates, 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 da ta were not collected on in dividual 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 take n 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 st ate, 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. 2 004a, Smith et al. 2007). The conversion factors and

model coefficients are usually categorized by region, and fore st type. Classifications for both region and forest type

are subject to change depending on the pa rticular coefficient set. Thus, region and type are specifically defined for

each set of estimates. Factors are app lied to the survey data at the scale of FIA inventory plots. The results are

estimates of C density (Mg per hectare) for the various fo rest pools. C density for live trees, standing dead trees,

understory vegetation, down dead wood, litter, and soil or ganic 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 func tions 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 hei ght (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 tr ee 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), m odified to reduce error and correspond to common forest

types defined by inventories. Separate estimates are ma de 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 sta ndards. Only trees 5.0 inches d.b.h. and larger are included in growing

stock volume (Smith et al. 2008). The full sets of coeffici ents 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 m 3 

### /ha.

Table A- 201: Coefficients fo r estimating carbon density of live trees (a bove- and below-ground, MgC/ha) by region

and type for plot-level data such as RPA data a



### Region b 

### Forest type group c 

### 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 Co ast (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 t he FISDB Users Guide (USDA Forest Service, 2008d).



### Table A- 202: Coefficients for estima ting carbon density of live trees (abo veground only, MgC/ha) by region and

type for plot-level data such as RPA data a



### Region b 

### Forest type group c 

### 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

Aspen/Birch 3.798 7.914 0.139 0.793 Rocky

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 growi ng-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 Co ast (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 t he 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. Regi ons and forest types are the same cla ssifications described in Smith et al.

(2003). As an example, the basic calculation for unde rstory 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 understo ry vegetation (above- and

belowground, MgC/ha) a 

### by region and forest type. The ratio is mult iplied by tree carbon density on each plot to

produce understory vegetation

Region b 

### Forest Type b 

### A B Maximum

ratio c



### 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

a Prediction of ratio of understory C to liv e tree C is based on the equation: Ratio=ex p(A-B*ln(tree_carbon_tph )), where “ratio” is the ratio of understory C density

to live tree (above-and below- ground) C density, and “tree_carbon_density” is live tree (above-and below- ground) C density in Mg C/ha.

b Regions and types as defined in Smith et al. (2003)

c Maximum ratio: any estimate predicted from the equation that is greater than the maximum ratio is set equal to the maximum rati o.



### 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 un resolved 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 coeffi cients 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 gr eater than 7.5 cm diameter, at transect intersection,

that are not attached to live or standi ng 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 esti mating standing dead tree carbon (MgC/h a) by region and forest type group a



### Region b 

### Forest type group c 

### 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

a Standing dead tree C is based on the equation: mass (Mg/ha) = A* (liv e-tree growing stock volume)^ B. Note that nonstocked stand s are assigned a constant C

density (the value listed under coefficient A). Note that the st anding dead tree equations are for bi omass. To convert to C mas s, multiply by 0.5.

b Regions are PC (CA,OR-West,WA-West), MTN (OR-East,WA-East,ID,MT), MTS (AZ,CO,NM,NV,UT,WY), West (regions PC, MTN, and MTS), NC (IA, IL, IN,

KS, MI, MN, MO, ND, NE, SD, WI), NE (CT, DE, MA, MD, ME, NH, NJ, NY, OH, PA, RI, VT, WV), SC (AL, AR, KY, LA, MS, OK, TN, TX), SE (FL, GA, NC, SC,

VA), and South (regions SC and SE).

c Forest types are described in appendix D of t he FISDB users guide (USDA Forest Service, 2008d). Minor forest types within a re gion that are not explicitly

defined/listed in the table of coefficients are assi gned 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)

Region a 

### Forest type a 

### 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 plot s. 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 desc ribe processes for decay or lo ss of forest floor following

harvest and the net accumulation of new fore st floor material following stand grow th. 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. Li nks 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 de nsities 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 repo rt HWP Contribution usi ng 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 oxidatio n 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 prod uction 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 impor ts and exports are provided in Tabl e 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.A-253Table A-206: Harvested wood products from wood harvested in United States – Annual additions of carbon to stocks and total stocks1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008Net 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)(28.7) (28.3) (29.6) (27.4) (35.9)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) (17.7)Solid wood products (14.4) (11.9)(12.6) (12.2) (12.1) (11.2) (11.5) (11.8) (11.4) (12.1) (11.9) (10.1) (10.7) (10.1) (11.6) (11.9) (10.6) (8.4) (14.4)Paper products (3.3) (3.1) (3.7) (2.8) (3.8) (3.8) (2.6) (3.0) (2.0) (2.0) (1.0) 1.41.20.4(0.8) (0.1) (1.7) (1.7) (3.3)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) (16.3) (16.3) (17.3) (17.3) (18.3)Solid wood products (9.9) (11.1) (9.5) (9.7) (9.8) (10.7) (10.6) (10.3) (10.2)(10.6) (10.7) (10.7)(11.1) (11.1) (11.3)(11.5) (11.6) (11.7) (9.9)Paper products (8.3) (7.7) (7.9) (8.3) (7.7) (6.5) (6.0) (6.9) (7.5) (7.8) (7.3) (6.0) (6.1) (5.1) (5.0) (4.8) (5.6) (5.7) (8.3)Total 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 2,244 2,271 2,296 2,325 2,353Products in use 1,193 1,212 1,231 1,249 1,264 1,280 1,2951,311 1,326 1,340 1,355 1,368 1,382 1,395 1,404 1,413 1,423 1,4361,446Products in SWDS 590 609 628 646 665 683 701 718 735 752 769 787 805 823 840 857 873 890 907Note: 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 WoodProducts Carbon Stocks Using Alternative Accounting ApproachesHWP Contribution to LULUCF Emissions/ removals (Tg CO2Eq.)InventoryYearStock ChangeApproachAtmospheric FlowApproachProductionApproach1990 (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: 1990-2007Table A-208: Harvested Wood ProductsSectoral Background Data for LULUCF- United States (production approach)1A 1B2A2B34 5678InventoryyearAnnual Changein stock of HWPin use fromconsumptionAnnual Changein stock of HWPin SWDS fromconsumptionAnnualChange instock ofHWP in useproducedfromdomesticharvestAnnualChange instock of HWPin SWDSproduced fromdomesticharvestAnnualImports ofwood, andpaperproducts +wood fuel,pulp,recoveredpaper,roundwood/chipsAnnualExports ofwood, andpaperproducts +wood fuel,pulp,recoveredpaper,roundwood/chipsAnnualDomesticHarvestAnnual releaseof carbon tothe atmospherefrom HWPconsumption(from fuelwood& products inuse andproducts inSWDS)Annualrelease ofcarbon to theatmospherefrom HWP(includingfirewood)where woodcame fromdomesticharvest (fromproducts inuse andproducts inSWDS )HWPContribution toAFOLU CO2emissions/removals∆CHWP IU DC∆CHWP SWDS DC∆CHWP IU DH∆CHWP SWDS DHPIMPEXH↑CHWP DC↑CHWP DHGg C /yrGg CO2/yr1990 17,000 18,30017,70018,30012,70015,100 142,300104,500106,400(131,800)1991 13,100 18,60014,90018,80011,60015,700 144,400108,600110,700(123,800)1992 15,700 17,00016,30017,40012,90016,000 139,400103,500105,600(123,800)1993 17,000 17,60015,00018,00014,50014,800 134,60099,700101,600(120,700)1994 18,200 17,20015,90017,50015,70015,700 134,80099,300101,300(122,500)1995 17,300 17,10015,10017,20016,70017,300 137,000102,100104,700(118,400)1996 17,000 16,40014,10016,50016,70016,700 134,500101,100103,900(112,200)1997 18,800 17,10014,70017,30018,00016,900 135,400100,700103,400(117,400)1998 20,300 17,80013,40017,70019,70015,100 135,000101,600103,900(114,100)1999 22,000 18,70014,10018,40021,30015,200 134,900100,200102,500(119,100)2000 20,500 18,60012,80018,00022,40016,200 134,500101,600103,700(112,900)2001 17,300 17,7008,70016,80023,00015,300 128,600101,300103,100(93,500)2002 18,600 18,4009,50017,20024,60015,700 127,60099,400100,800(98,000)2003 19,200 17,5009,70016,20026,00016,300 124,90097,90099,100(94,800)2004 26,400 18,10012,40016,30031,70016,900 130,500100,700101,700(105,400)2005 25,800 18,30012,00016,30031,70017,400 131,700102,000103,400(103,900)2006 19,200 18,80012,30017,30025,70018,800 127,40096,30097,800(108,600)2007 13,000 18,50010,10017,30022,00019,900 123,10093,70095,700(100,400)Note:↑CHWP DC= H + PIM– PEX-∆CHWP IU DC-∆CHWP SWDS DCAND↑CHWP DH= H -∆CHWP IU DH-∆CHWP SWDS DH. Parentheses indicate net C sequestration (i.e., a net removal of C from the atmosphere).

### 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 SW DS 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, dete rmined 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 seco nd criteria is that the WOODCARB II mode l estimate of wood and paper being

discarded to SWDS needed to match EPA estimates of di scards over the period 1990 to 2000. This calibration

strongly influences the estimate of variab le 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 variab les is that products exported from th e 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 ne t fluxes and stocks for harv ested 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 fore st 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 fro m 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. Greenhou se Gas Emissions and Sinks: 1990-2007 distribution functions (PDFs), so that a series of sample values can be randomly sel ected 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 Je nkins et al. (2003) and the species and number of trees

measured on each FIA plot. Plot-lev el 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. De finitions 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 per cent 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 e xpected value for each plot and the mi nimum and mode at 10 percent of the

expected value. The use of these PDFs skewed to the ri ght reflects the assumption that a small proportion of plots

will have relatively high C densities. Joint sampling of PD Fs 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 re flects 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 su rvey are the cumulative sum of random samples from the plot-level PDFs,

which are then appropriately expanded to population estimates. These expected values fo r each carbon pool include

uncertainty associated with sampling, which is also incorp orated in the Monte Carlo simulation. Sampling errors are

determined according to methods descri bed for the FIADB (USDA Forest Servi ce 2008d), are normally distributed,

and are assigned a slight positive correlation between succe ssive 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 inve ntory data are not available, and these surveys are assigned

values consistent with thos e 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 PD F 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 (U SDA Forest Service 2008d) and replaces th e 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

CO 2



### As stated in other sections, the forest inventor y approach implicitly accoun ts 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 CO 2 

### emissions from a disturbance such as fire and

adding those emissions to the net CO 2 

### change in forests would result in double-counting the loss from fire because

the inventory data already reflect the loss. Th ere is interest, however , in the size of the CO 2 

### emissions from

disturbances such as fire. The IPCC (2003) methodology and IPCC (2006) default com bustion factor for wildfire

were employed to estimate emi ssions 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 le vel 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 y ears except 2002, 2004 data were assumed for years 1999 to

2004. For states where data were available for all year s except 2004, 2006 data were assumed for 2003 through

2007. For years where data were ava ilable for all years except 2006, 2004 data were assumed for years 2003 to

2007. Since no 1998 value was availa ble for Alaska, the 1990 value was assu med 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 Illinoi s, 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 lowe r 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 fore stland 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 pr otection 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, ther efore, there is little call for

prescribed burns (Smith 2008a). It was, thus, assumed th at 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/h a was used as average C density for prescribed fires based

on data from the U.S. Forest Service (Smith 2008a). Th us, prescribed fires are estim ated 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 fo r the proportion of carbon

### A-258 Inventory of U.S. Greenhou se Gas Emissions and Sinks: 1990-2007 emitted as CO 2 

### and by 3.67 to yield CO 2 

### units. Total CO 2 

### 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 statis tics and corresponding estimates of carbon and CO 2 

### (Tg/yr)

emissions for wildfires and prescribed fires in the lower 48 states and wildfires in Alaska 1

Lower 48 States Alaska

Wildfires Prescribed Fires Wildfires

Year Reported

area

burned 2

(ha) Forest

area

burned 3

(ha) Carbon

emitted

(Tg/yr) CO 2

emitted

(Tg/yr) Reported

area burned 2

(ha) Forest

area

burned 3

(ha) Carbon

emitted

(Tg/yr) CO 2

emitted

(Tg/yr) Forest

area

burned 4

(acres) Forest

area

burned

(ha) Carbon

emitted

(Tg/yr) CO 2

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 accou nts for the amount

sequestered minus any emissions, includi ng 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 w ildland area under protection esti mates from USDA Forest

Service (1992) and the National Associ ation of State Foresters (2007).

4 Alaska Department of Natural Resources (2008).



### Non-CO 2



### Emissions of non-CO 2 

### gases from forest fires were estimated using the default IPCC (2003) methodology

and default IPCC (2006) combustion factor for wildfires. Emissions estimates for CH 4 

### are calculated by multiplying

the total estimated C emitted from forest burned by ga s-specific emissions ratios an d conversion factors. N 2 

### O

emissions are calculated in the same manner, but are al so multiplied by an N-C ratio of 0.01 as recommended by

IPCC (2003). The equations used are:

CH 4 

### Emissions = (C released) × (emission ratio) × 16/12

N 2 

### O 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-CO 2 

### emissions (Tg/yr) for U.S. forests 1



### Year C emitted (Tg/yr) CH 4 

### emitted

(Tg/yr) N 2 

### O

(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. Greenhou se Gas Emissions and Sinks: 1990-2007 

# 3.13. Methodology for Estimating Net Change s in Carbon Stocks in Mineral and

Organic Soils on C ropland 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, rath er 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 fact or-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, grassl and, forest, and savanna ecosystems, and land-use

transitions between these different la nd 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-grassla nd) 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 la nd 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 simulate d 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 (re presenting 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 m ean 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 at tack, leading to slower decomposition and greater SOM

stabilization in finely textured soils. Soil temperature an d 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 th rough photosynthesis, N uptake, dry matter production,

partitioning of C within the crop or forage, senescence, and mortality. The primary f unction 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 ha rvested 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. Maxi mum 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 precipitati on, 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, or ganic matter addition (e.g., manure amendments), harvest (with

variable residue removal), drainage, irrigation, burning, and grazi ng 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 algorit hms. 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 arch ived 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 fro m 47 experimental sites (Figure A-14). Some biases and

imprecision were found in predictions of soil organic C, whic h 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 So il 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 CO 2 

### 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 ag ricultural 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. Greenhou se 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 factor s 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 ap proach uses an emission factor to estimate annual

losses of CO 2 

### 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-agricultur al 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 sour ces 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 co unty 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 randomi zation 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. Thes e 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 increm ents 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 grassland 53 

### 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 a nd 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 cla ssified 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 (10 6 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 Uses a 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 Uses a 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 (10 6 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 Uses a 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 Uses a 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 percen t

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 eith er grown continuously or in rotation with other crops.

Century has not been fully tested for non-major crops, horticultur al or perennial crops, rice and agricultural use of organic

soils. In addition, Century has not been adequately tested fo r 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 fo r purposes of these calculations, although the specific use is not

identified in the NRI survey (USDA-NRCS 2000).

### A-264 Inventory of U.S. Greenhou se Gas Emissions and Sinks: 1990-2007 Table A-214: Total Land Area Estimated with Tier 2 a 

### and 3 Inventory Approaches (Million Hectares)

Land Areas (10 6 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 pr oduct for the Tier 2 method. Consequently, area data est imates 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 fo r 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 irrigati on 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 mana gement 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 Managem ent System for the Tier 2 Approach (Million Hectares)

Land Areas (10 6 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 Hort icultural 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 syst ems based on drainage (IPCC 2006). Undrained soils are

treated as having no loss of organic C. Drained soils are su bdivided into those used for cultivated cropland, which are

assumed to have high drainage and greater losses of C, an d 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 (10 6 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 Drainag e) 0.33 0.34 0.0971 0.0974 0.19 0.20

Other Land Uses a 0.02 0.01 0.0002 0.0017 0.00 0.02

Total 0.84 0.84 0. 11 0.11 0.39 0.39

a Urban, water, and miscellaneous non-cropland, which are part of the agr icultural land base because these areas were converted f rom 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). CT IC compiles data on cropland area under five tillage

classes by major crop species and year for each county. Beca use 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 tillag e and “reduced” tillage systems as defined by CTIC (1998).

No-till was defined as not disturbing the soil except through the us e of fertilizer and seed drills and where no-till is applie d

to all crops in the rotation. Reduced tillage made up the re mainder 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 proba bility 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 c onstituted < 1 percent of total cropland prior to 1990, there

were no transitions for NT → FT or NT → NT. Uniform probability distributions we re 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. Greenhou se 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 dr aw 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 fe rtilizer 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 f or

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 o n

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 us e 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 availa ble (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 aver aged 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 reduc tion factors were derived from mineral fe rtilization 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 diffe rentiate 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 por tion 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 estima tes, 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 agreemen ts (Euliss and Gleason 2002). While the contracts provide

reasonable estimates of the amount of land restored in th e 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 conver ted to units of N by applying an average N content of 3.9

### A-268 Inventory of U.S. Greenhou se Gas Emissions and Sinks: 1990-2007 percent (McFarland 2001), and disaggregated into use and disp osal 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 dur ing 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. Thes e 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 ch aracteristics needed in the simulation, such as field capacity

and wilting-point water contents, were estimated from soil text ure 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 μ m), silt (2-50 μ m), and clay (< 2 μ m) 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 o xygen) 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 cont erminous 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 A nnual Average

Temperature (  ̊ C)Average Annual Precipitation (mm) Length of Dry Season

(months)

Cold Temperate, Dry < 10 < Po tential 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 t he table for these zones are those that co rrespond to the tropical dry and tropical moi st 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-drain age) is simulated as a management variable.

57 Hydric soils are primarily subject to anaerobic conditions outsi de 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 classifi ed 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 Grou pings 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,

A ridisols, 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 Soils a 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 Su rvey 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 wa s 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 ra te (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 measuremen ts 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. Greenhou se Gas Emissions and Sinks: 1990-2007 pools (Parton et al. 1987). By simulating the historical land us e prior to the inventory period, initial pool distributions ar e

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 th e 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-se ttlement 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 mana gement, particularly the conversion of native vegetation to

agricultural uses. This encompasses a varying time peri od 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 farmi ng 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 operation s 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: Uncertai nty 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 M onte-Carlo Analysis was used with 100 iterations for each NRI

cluster-point in which random draws were made from PDFs fo r 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. Th e 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, tilla ge, 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 matt er amendments, fertilizer rates, inclusion of hay/pasture

in cropping rotations, use of no-till, and inclusion of bare fallo w in the rotation, which were significant at an alpha level o f

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 a ppropriate 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 histor y 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 CO 2 

### Eq. to gains of 64.0 Tg CO 2 

### 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 (9 5% Confidence Interval) for the Land Base Simulated with

the Tier 3 Century Model-Based Approach (Tg CO

2 

### 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 t he annual application of sewage sludge, or the additional Conserva tion Reserve Program enrollment.



### Step 3: Estimate C Stock Changes in Agricultural Lands on Mineral Soils Approximated with the Tier 2 Approach, in

Addition to CO 2 

### 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. Greenhou se 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 in tensification, and land convers ions between cultivated and

native conditions (including set-asides in the Conservation Re serve 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 include d in the analysis. The data were analyzed using linear

mixed-effect modeling, accounting for both fixed and random effect s. Fixed effects included de pth, 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 norma l 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

Cultivated a 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-Aside a (n=142) 1.25 1.31±0.06 1. 26±0.04 1.14±0.06 1.10±0.05

Improved Grassland Factors c

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 Factor c 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 amendment c 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 ra ther than a native condition.

b Default factor was higher for aquic soils at 1.7. The U.S. analysis showed no signifi cant differences between aquic and non-aqu ic 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 st udies addressing the impact of legume

mixtures, irrigation, or manure applications for crop and grassland in the United States, or the impact of wetland rice product ion 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 reclassi fied 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/cm 3 

### ) (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-y r

Note: Mass accumulation rate represents additional gains in C for mineral soils after the first 10 years (Euliss and Gleason 20 02).



### 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 Org anic 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 Temper ate, Moist 1 11.2±2.5 0.25 2.8±0.5 a

Warm Temperate, Dry & Warm Temp erate, Moist 10 14.0± 2.5 2.5 3.5±0.8 a

Sub-Tropical, Dry & Sub-Tropical, Moist 20 14.0±3.3 5 3.5±0.8 a

a There were not enough data available to estimate a U.S. value fo r C losses from grassland. Consequently, estimates are 25 perc ent of the values for cropland, which was

an assumption used for the IPCC defaul t organic soil C losses on grassland.



### Step 3b: Estimate Annual Changes in Mineral Soil Organic C Stocks and CO 2 

### 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### Eq. annually. Minerals soils in Grassland Remaining Grassland had small gains of about 0.2 to 0.3 Tg

CO 2 

### Eq. annually and sequestered from 4.5 to 5.0 Tg CO 2 

### Eq. annually in Land Converted to Grassland . Organic soils lost

about 27.4 to 27.7 Tg CO 2 

### Eq. annually in Cropland Remaining Cropland and 2.4 to 2.6 Tg CO 2 

### Eq. annually in Land

Converted to Cropland , as well as an additional 3.7 to 3.9 Tg CO 2 

### Eq. annually in Grassland Remaining Grassland (Table

A-223) and 0.5 to 0.9 Tg CO 2 

### 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, Re ference Carbon Stocks, and Carbon Loss Rates (Tg CO

2 

### 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. Greenhou se 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. agricu ltural 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 grassla nd (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 CO 2 

### 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 CO 2 

### 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 CO 2 

### Emissions and Removals from Agricultural Lands

The sum of total CO 2 

### emissions and removals from the Tier 3 Century Model Approach (Step 2), Tier 2 IPCC

Methods (Step 3) and additional land-use and management consid erations (Step 4) are presented in Table A-225. Overall,

there was a net accumulation of 138.1 Tg CO 2 

### Eq. in 1990 for agricultural soils, and this rate had decreased by the end of

the reporting period in 2007 to 60.2 Tg CO 2 

### 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 Fi gure 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, M ontana, Oklahoma, North Dakota, and South Dakota (Table

A-226). For organic soils, emission rates were highest in the re gions 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, 2007A-276 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A-224: Assumptions and Calculations toEstimate the Contribution to Soil OrganicCarbon Stocks from Application of Sewage Sludge to Mineral Soils199019911992199319941995199619971998199920002001200220032004200520062007Sewage Sludge NApplied toAgricultural Land(Mg N)a52,198 55,658 59,250 62,97765,96669,00172,08175,19578,35380,932 83,52386,12488,73691,35893,99198,367101,499104,658AssimilativeCapacity(Mg N/ha)b0.120 0.120 0.120 0.1220.1220.1220.1220.1220.1220.122 0.1220.1220.1220.1220.1220.1220.1220.122Area covered byAvailable SewageSludge N (ha)c434,985 463,816 493,746 516,202540,707565,583590,828616,357642,240663,381 684,612705,932727,341748,836770,418806,288831,955857,850Average Annual Rateof C storage (MgC/ha-yr)d0.38 0.38 0.38 0.380.380.380.380.380.380.38 0.380.380.380.380.380.380.380.38Contribution to SoilC (TgCO2/yr)e,f(0.61)(0.65)(0.69)(0.72)(0.75)(0.79)(0.82)(0.86)(0.89)(0.92)(0.95)(0.98)(1.01)(1.04)(1.07)(1.12)(1.16)(1.20)Values in parentheses indicate net C storage.aN applied to soils described in Step 1d.bAssimilative 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).cArea covered by sewage sludge N available for application to soilsis the available N applied atthe assimilative capacity rate. The 1992 assimilative capacity rate was applied to 1990 – 1992 and the 1997 rate was applied to 1993-2007.dAnnual rate of C storage based on national average increase in Cstorage for grazing lands that is attributed to organic matter amendments (0.38 Mg/ha-yr)eContribution to Soil C is estimated as the product of thearea covered by the available sewage sludge N and the average annualC storage attributed to an organic matter amendment.fSome small, undetermined fraction of this applied N is probably not applied to agricultural soils, but instead is applied to forests, home gardens, and other lands.A-277Table A-225: Annual Soil C Stock Change in Cropland Remaining Cropland(CRC), Land Converted to Cropland(LCC), Grassland Remaining Grassland(GRG), and Land

Converted to Grassland(LCG), in U.S. Agricultural Soils (Tg CO2Eq.)199019911992199319941995199619971998199920002001200220032004200520062007Net emissions based on Tier 3 Century-based analysis (Step 2)CRC * (55.2) (57.5) (68.0) (64.2) (62.4)(47.6)(55.0)(54.1)(44.3)(29.7)(54.8)(37.6) (36.1)(42.3)(42.3)(42.3)(42.3)(42.3)LCC (4.4) (4.3) (4.8) (8.7) (12.6)(3.8)(4.5)(4.2)(10.8)(3.3)(4.4)(2.5) (2.0)(0.8)(0.8)(0.8)(0.8)(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)(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)(22.6)(22.6)Net emissions based on the IPCC Tier 2 analysis (Step 3)Mineral SoilsCRC (1.6) (1.6) (1.6) (3.0) (3.0)(3.0)(3.0)(3.0)(3.0)(3.0)(3.0)(3.0) (3.0)(3.0)(3.0)(3.0)(3.0)(3.0)LCC 4.2 4.2 4.2 4.1 4.1 4.1 4.1 4.1 4.1 4.14.1 4.1 4.1 4.14.1 4.1 4.1 4.1GRG (0.3) (0.3)(0.3) (0.2) (0.2)(0.2)(0.2)(0.2)(0.2)(0.2)(0.2)(0.2) (0.2)(0.2)(0.2)(0.2)(0.2)(0.2)LCG (4.5) (4.5) (4.5) (5.0) (5.0)(5.0)(5.0)(5.0)(5.0)(5.0)(5.0)(5.0) (5.0)(5.0)(5.0)(5.0)(5.0)(5.0)Organic SoilsCRC 27.4 27.4 27.4 27.7 27.7 27.7 27.7 27.7 27.727.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7 27.7LCC 2.4 2.4 2.4 2.6 2.6 2.6 2.6 2.6 2.6 2.62.6 2.6 2.6 2.62.6 2.6 2.6 2.6GRG 3.9 3.9 3.9 3.7 3.7 3.7 3.7 3.7 3.7 3.73.7 3.7 3.7 3.73.7 3.7 3.7 3.7LCG 0.5 0.5 0.5 0.9 0.9 0.9 0.9 0.9 0.9 0.90.9 0.9 0.9 0.90.9 0.9 0.9 0.9Additional changes in net emissions from mineral soils basedon application of sewage sludge to agricultural land (Step 4)GRG (0.6) (0.6)(0.7) (0.7) (0.8)(0.8)(0.8)(0.9)(0.9)(0.9)(1.0)(1.0) (1.0)(1.0)(1.1)(1.1)(1.2)(1.2)Additional changes in net emissions from mineral soils based on additional enrollment of CRP land (Step 4)CRC - - - - -------- --(0.4)(0.6)(1.4)(2.0)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)(19.1)(19.7)LCC 2.2 2.3 1.8 (1.9) (5.8)2.9 2.2 2.6 (4.0)3.5 2.4 4.2 4.8 5.9 5.9 5.9 5.9 5.9GRG (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)(4.6)(4.7)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)(26.7)(26.7)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)(44.5)(45.1)

### A-278 DRAFT Inventory of U.S. Greenhous e Gas Emissions an d Sinks: 1990-2007 Table A-226: Soil C Stock Change for Mineral and Organic Soils during 2007 within individual states (Tg CO

2 

### 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 aft er 2003 or sewage sludge application to

soils, which were only estimated at the national scale.

### A-279 

# 3.14. Methodology for Estimating CH 4 

# 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 CH 4 

### and half CO 2



### . 61 

### The amount and rate of CH 4 

### generation depends

upon the quantity and composition of the landfilled material, as well as the surrounding landfill environment.

Not all CH 4 

### generated within a landfill is emitted to the atmosphere. The CH 4 

### can be extracted and either flared

or utilized for energy, thus oxidizing to CO 2 

### during combustion. Of the remaining CH 4 

### , a portion oxidizes to CO 2 

### as it

travels through the top layer of the landfill cover. In general, landfill-related CO 2 

### emissions are of biogenic origin and

primarily result from the decomposition, either aerobic or an aerobic, of organic matter such as food or yard wastes. 62



### To estimate the amount of CH 4 

### 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 charac teristics (e.g., size, aridity, waste density). However, this

information is not available for all landfills in the United States. Consequently, to estimate CH 4 

### 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 CH 4 

### generation rates for U.S. landfills with gas recovery

systems.

From various studies and surveys of the generation and disp osal of solid waste, estimates of the amount of waste

placed in MSW and industrial landfills were developed. A database of measured CH 4 

### 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 CH 4 

### generation potential for use in the first order decay model. In addition, the analysis and other studies

provided estimates of the CH 4 

### 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 CH 4 

### 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, CH 4 

### emissions from industrial landfills were also estimated using the

first order decay model. Total CH 4 

### emissions were estimated by adding the CH 4 

### 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

CH 4 

### emissions from U.S. landfills for the years 1990 th rough 2006 are discussed in greater detail below.

Figure A-19 presents the CH 4 

### 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 CH 4 

### emissions. The table shows BioCycle

estimates of total waste landfilled each year from 1990 thro ugh 2000, 2002, 2004, and 2006 adjusted for U.S. territories.

A linear interpolation was used for 2001, 2003, 2005, and 2007 becau se there were no BioCycle surveys for those years.

Figure A-19: Methane Emissions Resulting fr om Landfilling Municipal and Industrial Waste

Table A-227: Solid Waste in MSW Landfills Contributing to CH 4 

### 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

Generated a 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 nitr ogen, 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 CO 2 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 (2 008).

### A-280 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 Percent of Wastes

Landfilled a 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 Emissions c 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) populati on data and per capita generation rate fr om BioCycle . The data,

originally reported in short tons, are c onverted to metric tons. Estimates show n for 2001 and 2003 are based on an interpolati on 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 CH 4 generation.

c This estimate represents the cumulative amount of waste that has been placed in land fills 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 CH 4 

### Generation at Municipal Solid Waste Landfills

The CH 4 

### generation was estimated from the integrated form of the first order decay (FOD) model using

the procedures and spreadsheets from IPCC (2006) for estimating CH 4 

### 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 CH 4 

### 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 carbo n (DOC), and the decay rate constant (k). The DOC is

determined from the CH 4 

### generation potential (L 0 

### in m 3 

### CH 4 

### /Mg waste), which is discussed in more detail in subsequent

paragraphs, and the following equation:

DOC = [L 0 

### × 6.74 × 10 -4 

### ] ÷ [F × 16/12 × DOC f 

### × MCF]

Where,

DOC = degradable organic carbon (fraction, Gg C/Gg waste),

L 0 

### = CH 4 

### generation potential (m 3 

### CH 4 

### /Mg waste),

6.74 × 10 -4 

### = CH 4 

### density (m 3 

### /Mg),

F = fraction of CH 4 

### by volume in generated landfill gas (equal to 0.5)

16/12 = molecular weight ratio CH 4 

### /C,

DOC f 

### = 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 CH 4 

### generation estimates from MSW landfills is 0.203 based on the CH 4 

### generation

potential of 100 m 3 

### CH 4 

### /Mg waste as described below.

Values for the CH 4 

### generation potential (L

o 

### ) were evaluated from landfill gas recovery data for 52 landfills,

which resulted in a best fit value for L

o 

### of 99 m 3 

### /Mg of waste (RTI 2004) and from other studies. This value compares

favorably with a range of 50 to 162 (midrange of 106) m 3 

### /Mg presented by Peer, Thorneloe, and Epperson (1993); a range

of 87 to 91 m 3 

### /Mg from a detailed analysis of 18 landfills sponsored by the Solid Waste Association of North America

(SWANA 1998); and a value of 100 m 3 

### /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 m 3 

### /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 L 0 

### = 100 m 3 

### /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 re ported a range in values of k from 0.03 to 0.06 yr -1



### based on CH 4 

### 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. populati on 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. Populati on within Precipitation Ranges (%)

Precipitation Range (inches/y ear) 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 precipitat ion 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 us ed based on the recommended IPCC default value for

uncharacterized land disposal (IPCC 2006); this MCF is equiva lent 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 CH 4 

### Generation at Industrial Landfills

Industrial landfills receive waste from factories, proces sing plants, and other manufacturing activities. In

national inventories prior to the 1990 through 2005 inventory, CH 4 

### generation at industrial landfills was estimated as seven

percent of the total CH 4 

### 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 CH 4 

### 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 (2 003) were the primary sources for years 1965 through 2007.

An extrapolation based on U.S. real gross domestic pr oduct was used for years 1940 through 1964. For the food

processing industry, production levels were obtained or develo ped 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 CH 4 

### generation from industrial landfills: 1) quantity of waste that is disposed

in industrial landfills (as a function of production), 2) CH 4 

### generation potential (L 0 

### ) 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 esti mates of annual production to estimate annual waste disposal

in landfills. Estimates for DOC were derived from available da ta (Kraft and Orender, 1993; NCASI 2008; Flores et al.

1999). The DOC value for industrial pulp and paper waste is estimated as 0.20 (L

o 

### of 99 m 3 

### /Mg); the DOC value for

industrial food waste is estimated as 0.26 (L

o 

### of 128 m 3 

### /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 prac tices 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 an d for wastes disposed of in managed landfills, an MCF of 1

was used, based on the recommended IP CC default values (IPCC 2006).

The parameters discussed above were used in the integrated form of the FOD model to estimate CH 4 

### generation

from industrial landfills.

Step 4: Estimate CH 4 

### Emissions Avoided

The estimate of CH 4 

### emissions avoided (e.g., combusted) was base d on landfill-specific data on landfill gas-toenergy (LFGTE) projects and flares. A destruction efficiency of 99 percent was applied to CH 4 

### recovered to estimate CH 4 

### 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 Po llutant 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 CH 4 

### Emissions Avoided Through Landfill Gas-to-Energy (LFGTE) Projects

The quantity of CH 4 

### 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 CH 4 

### reductions, descriptions of the projects, and information on the

methodology used to determine the CH 4 

### reductions. Generally the CH 4 

### reductions for each reporting year were based on

the measured amount of landfill gas collected and the percent CH 4 

### 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 CH 4 

### 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 th e EIA database were dropped to avoid double counting.

Step 4b: Estimate CH 4 

### Emissions Avoided Through Flaring

The quantity of CH 4 

### flared was based on data from the EIA database and on information provided by flaring

equipment vendors. To avoid double-counting, flares associ ated 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 CH 4 

### 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 CH 4 

### 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 capac ity of the flare. An analysis of flare capacity versus

measured CH 4 

### 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 CH 4 

### avoided through

flaring from the flare vendor database was estimated by summing the estimates of CH 4 

### recovered by each flare for each

year.

Step 4c: Reduce CH 4 

### Emissions Avoided Through Flaring

As mentioned in Step 4b, flares in the flare vendor da tabase 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 tw o 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, CH 4 

### avoided would be overestimated, as both the CH 4 

### avoided from flaring and the

LFGTE project would be counted. To avoid overestimating emissions avoided from flaring, the CH 4 

### 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 CH 4 

### avoided due to flaring. This

approach was applied to be conservative in the estimates of CH 4 

### emissions avoided.

Step 5: Estimate CH 4 

### Oxidation

A portion of the CH 4 

### escaping from a landfill oxidizes to CO 2 

### in the top layer of the soil. The amount of

oxidation depends upon the characteristics of the soil and the e nvironment. For purposes of this analysis, it was assumed

that of the CH 4 

### 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 CH 4 

### generation minus recovery for both MSW and industrial landfills.

Step 6: Estimate Total CH 4 

### Emissions

Total CH 4 

### emissions were calculated by adding emissions from MSW and industrial landfills, and subtracting

CH 4 

### recovered and oxidized, as shown in Table A-230.A-284 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A-230: CH4Emissions from Landfills (Gg)Activity 1990 19911992 1993 1994 19951996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007MSW Generation 8,219 8,4438,624 8,788 8,966 9,1329,263 9,381 9,507 9,683 9,854 10,068 10,367 10,754 11,127 11,486 11,813 12,107Industrial Generation 554 563574 585 598 615630 645 660 673 687 701 707 715 720 728 730 735Potential Emissions 8,772 9,0069,197 9,373 9,564 9,7479,893 10,027 10,167 10,356 10,541 10,769 11,074 11,469 11,847 12,214 12,543 12,842Emissions 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) (4,681)(5,170) (5,450) (5,641) (5,812)Landfill Gas-to-Energy (635) (659)(725) (805) (850) (1,064)(1,270) (1,604) (1,921) (2,182)(2,348) (2,550) (2,602) (2,561)(2,679) (2,707) (2,819) (3,062)Flare (242) (376)(441) (545) (746) (1,048)(1,167) (1,341) (1,517) (1,526)(1,722) (1,898) (2,025) (2,119)(2,490) (2,743) (2,822) (2,750)Oxidation at MSW Landfills(734) (741)(746) (744) (737) (702)(683) (644) (607) (598) (578) (562) (574) (607) (596) (604) (617) (629)Oxidation at Industrial Landfills(55) (56)(57) (59) (60) (61)(63) (65) (66) (67) (69) (70) (71) (72) (72) (73) (73) (74)Net Emissions 7,105 7,1737,228 7,221 7,172 6,8716,711 6,374 6,057 5,9845,825 5,689 5,803 6,1106,009 6,088 6,211 6,327Note: 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 othertables throughout the text, MSW generationestimates account for oxidation.Note: Parentheses denote negative values.Figure A- 4: Domestic Greenhouse Gas Emissions by Mode and Vehicle Type, 1990 to 2007 (Tg CO 2 Eq.)

0 500 1,000 1,500 2,000 2,500199019911992199319941995199619971998199920002001200220032004200520062007Tg CO2Eq.

Passenger Cars/Mototcycles Light-Duty Trucks Medium- and Heav y -Dut y Truck s Aircraft Boats/Shi p s, Rail, and Pi p eline s

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 60F(temp)

Soil Temperature

0 0.2 0.4 0.6 0.8

1 1.2F(WFPS)

0 0.2 0.4 0.6 0.8 1

WFPS

0 0.2 0.4 0.6 0.8

1 1.2F(pH)

3 5 4 6 7 9 8 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 400N2+N2O μg N/g soil/day

NO

3 μg N/g soil

0 5 10 15 20 25 30 35 40N2+N2O μg N/g soil/day

0 20 40 60 80 100

CO

2 μg C/g soil/day

0 0.2 0.4 0.6 0.8

1

0 20 40 60 80 100Relative 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 

## NO

3 - 

## NH

4 +



### 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 

## H

2 

## O

soil



## T

soil



### 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 CO

2 

## S,R

h



## PPT,V,L

C:N V

S

C:N

CO

2 

## CO

2 

### CH

4 

### Uptake



## Decomp

R

h 

## S R

h



## S

S NPP 

# DAYCENT MODEL

S=soil type

V=veg type

L=land usey = 1.0765x - 0.6936

R

2 = 0.9587

y = 0.1688x + 3.6132

R

2 = 0.3931

0 5 10 15 20 25 30 35 40

0 1 0 2 0 3 0 4 0

N

2 O gN ha -1 day -1N2O gN ha-1day-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 N

2 

### O Emissions, Estimated Using the DAYCENT Model,

1990–2007 (Metric Tons CO

2 

### Eq./ha/year) Figure A-9

< 0.5 Metric Tons CO

2 Eq./

ha/year

0.5 – 1

1 – 1.5

1.5 – 2

2 – 3

3 – 5

> 5

### Average Annual N Losses Leading to Indirect N

2 

### 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 N

2 

### O Emissions, Estimated Using the DAYCENT Model,

1990–2007 (Metric Tons CO

2 

### Eq./ha/year) Figure A-11

< 0.1 Metric Tons CO

2 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 N

2 

### 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 (gCm-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*QPJOUTJNVMBUF E.PEFMVODFS UBJOUZJTFTUJNBUFEUISPVHIBOFNQJSJDBMCBTFEBQQSPBDI6ODFSUBJOUZJOUIF

MBOESFQSFTFOUBUJPOPG/3*JTFTUJNBUFEGSPNUIFTUBUJTUJDTDPN QJMFEGSPNUIF/3*TVS WFZTUPEFUFSNJOFUIFMBOEBSFBFYQBOTJPOGBDUPST
XIJDIBSFVTFE

UPVQTDBMFEBUBUPUIFOBUJPOBMMFWFM

## Mg C O 2 

## E q./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 : Va l ues great er t han zero r ep re se nt e miss i ons,

and val ues l es s th an zero r epresent s eques tr ati on.

M ap accounts fo r fl uxes a ss ocia t ed wi th t he Ti er 2

and 3 in ve nt ory co mputations. See me thodo logy

fo r additional d et ai ls

. 

### Figure A-17



# Net C Stock Change, per Hectare, for Mineral Soils Under

Agricultural Management, 2007

## Mg C O 2 

## E q./ha/year

> 1

0. 5 to 1

0. 25 to 0.5

0. 1 to 0.2 5

0 to 0.1

No organic so il s Note : Va l ues great er t han zero r ep re se nt e miss i ons

. 

### Figure A-18



# Net C Stock Change, per Hectare, for Organic Soils

Under Agricultural Management, 2007Figure A-19: Methane Emissions Resulting fromLandfilling Municipal and Industrial WasteaBioCycle 2006 for MSW and activity factors for industrial waste.b1960 through 1988 based on EPA 1988 and EPA 1993;1989 through 2006 based on BioCycle 2006.c2006 IPCC Guidelines – First Order Decay Model.dEIA 2007 and flare vendor database.eEIA 2007 and EPA (LMOP) 2007.f2006 IPCC Guidelines; Mancinelli and McKay 1985; Czepiel et al 1996MSW andIndustrialWasteGeneratedaTotalMethaneGeneratedatLandfillscMSW andIndustrialWasteLandfilledbTotalMethaneEmittedNonrecoveredMethaneGeneratedat LandfillsCompostedRecycledIncineratedFlareddOxidizedRecoveredforEnergye10%f

### A-285 

# ANNEX 4 IPCC Reference Approach for

Estimating CO 2 

# Emissions from Fossil

Fuel Combustion

It is possible to estimate carbon dioxide (CO 2 

### ) emissions from fossil fuel co nsumption 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 CO 2 

### 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 CO 2 

### from fuel combustion you are still

asked to complete...the Reference Approach...for verificati on purposes” (IPCC/UNEP/OECD/IEA 1997). This reference

method estimates fossil fuel consumption by adjusting nati onal aggregate fuel production data for imports, exports, and

stock changes rather than relying on end-user consumption su rveys. 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 CO 2 

### 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 conventi on. 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 statis tics. Ethanol is a biofuel, and it is assumed that no net CO 2 

### 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 esti mated separately from U.S. totals. The fifth modification

consists of the addition of U.S. territories data that are ty pically 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, a nd stock change statistics, are presented in Table A-231.

The C content of fuel varies with the fuel's heat c ontent. Therefore, for an accurate estimation of CO 2 

### emissions,

fuel statistics were provided on an energy content basis (e.g., Btu or joules). Because detailed fuel production statistics ar e

typically provided in physical units (as in Table A-231 for 2007), they were converted to units of energy before CO 2 

### 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 estima te "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 + Im ports - 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 suppl y 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, internationa l bunker fuels, and U.S. territory fuel consumption. Bunker

fuels and feedstocks accounted for in the Industrial Processe s 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 chan ges. 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 CO 2 

### 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 CO 2 

### 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 CO 2 

### Emissions

Because the IPCC reporting guidelines recommend that countries report greenhouse gas emissions on a full

molecular weight basis, the final step in estimating CO 2 

### emissions from fossil fuel consumption was converting from units

of C to units of CO 2 

### . Actual C emissions were multiplied by the molecular-to-atomic weight ratio of CO 2 

### to C (44/12) to

obtain total CO 2 

### 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 CO 2 

### from Fossil Fuel Combustion and Carbon Emitted

from Non-Energy Uses of Fossil Fuels Annexes. These two sect ions 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 surv eyed 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 specifi c

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 s ector-specific coal statistics in the Sectoral Approach

(i.e., residential, commercial, industrial coking, i ndustrial 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 CO 2 

### Emissions

Given these differences in energy consumption data, the next step for each methodology involved estimating

emissions of CO 2 

### . 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 accur ate. 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 CO 2 

### 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 Stat es 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., Ng ara 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.A-289Table A-231: 2007 U.S. EnergyStatistics (Physical Units)Fuel Category (Units)Fuel TypeProductionImportsExportsStockChangeAdjustmentBunkersU.S.TerritoriesSolid Fuels (Thousand Short Tons)Anthracite Coal1,557 a aaBituminous Coal534,867 a aaSub-bituminous Coal530,622 a aa440Lignite78,520 a aa3,784Coke2,460 1,444 (52)Unspecified Coal36,347 59,163 2,497 22,7801,996Gas Fuels (Million Cubic Feet)Natural Gas 19,085,2664,601,511 808,883 (176,977)310,41026,000Liquid Fuels (Thousand Barrels)Crude Oil1,848,450 3,661,404 10,006 (17,835)Nat Gas Liquids and LRGs650,794 100,863 25,584 (19,239)174Other Liquids0 546,948 32,049 9,611Motor Gasoline229,736 150,617 46,369 (4,671)110,53235,631Aviation Gasoline117 0 (167)Kerosene1,276 3,148 (569)891Jet Fuel79,034 15,010 329124,249 11,475Distillate Fuel110,997 97,726 (9,707)1218,396 21,235Residual Fuel135,676 120,395 (3,059)7,000 96,634 36,403Naphtha for petrochemical feedstocks34,346 0 205Petroleum Coke11,744 133,582 (128)13,743Other Oil for petrochemical feedstocks54,817 0 (295)35,930Special Naphthas6,083 6,487 (38)Lubricants6,195 21,570 (1,809)807Waxes1,242 1,829 (36)Asphalt/Road Oil14,479 6,757 (6,394)Still Gas0 0 0Misc. Products119 2,365 (359)16,547[a] Included in Unspecified CoalData Sources: Solid and Gas Fuels: EIA (2008a); Liquid Fuels: EIA (1995-2008).A-290 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A-232: Conversion Factors toEnergy Units (Heat Equivalents)Fuel Category (Units)Fuel TypeProductionImportsExportsStockChangeAdjustmentBunkersU.S.TerritoriesSolid Fuels (Million Btu/Short Ton)Anthracite Coal22.57Bituminous Coal23.89Sub-bituminous Coal17.1428.16Lignite12.8712.87Coke25.00 25.4725.00Unspecified25.00 25.9720.8625.5825.14Natural Gas (BTU/Cubic Foot)1,0281,025 1,0091,0281,0261,028Liquid Fuels (Million Btu/Barrel)Crude Oil5.805.98 5.805.805.805.80Nat Gas Liquids and LRGs3.703.70 3.703.703.703.70Other Liquids5.835.83 5.835.835.835.83Motor Gasoline5.225.22 5.225.225.225.225.22Aviation Gasoline5.05 5.055.055.055.05Kerosene5.67 5.675.675.675.67Jet Fuel5.67 5.675.675.675.67Distillate Fuel5.83 5.835.835.835.835.83Residual Oil6.29 6.296.296.296.296.29Naphtha for petrochemical feedstocks5.25 5.255.255.255.25Petroleum Coke6.02 6.026.026.026.026.02Other Oil for petrochemical feedstocks5.83 5.835.835.835.835.83Special Naphthas5.25 5.255.255.255.25Lubricants6.07 6.076.076.076.07Waxes5.54 5.545.545.545.54Asphalt/Road Oil6.64 6.646.646.646.64Still Gas6.00 6.006.006.006.00Misc. Products5.80 5.805.805.805.80Data Sources: Coal and lignite production: EIA (2008b); Unspecified Solid Fuels: EIA (2008a); Coke, Natural Gas and Petroleum Products: EIA (2008a).A-291Table A-233: 2007 Apparent Consumption of Fossil Fuels (TBtu)Fuel CategoryFuel TypeProductionImportsExportsStock ChangeAdjustmentBunkersU.S.TerritoriesApparentConsumptionSolid FuelsAnthracite Coal 35.20.035.2Bituminous Coal 12,778.00.012,778.0Sub-bituminous Coal 9,094.912.40.09,082.5Lignite 1,010.248.70.0961.6Coke 0.0 61.5 36.8 (1.3)0.026.0Unspecified 0.0 908.7 1,536.6 52.1 582.750.2(1,212.5)Gas FuelsNatural Gas 19,619.7 4,716.5 816.2 (181.9)228.726.723,500.0Liquid FuelsCrude Oil 10,721.021,898.9 58.0 (103.4)0.0 0.032,665.3Nat Gas Liquids and LRGs 2,408.6 373.3 94.7 (71.2)0.0 0.62,759.0Other Liquids 0.03,186.0 186.7 56.00.0 0.02,943.3Motor Gasoline 1,199.0 786.1 242.0 (24.4)576.9 0.0 186.01,376.5Aviation Gasoline 0.0 0.6 0.0 (0.8)0.0 0.01.4Kerosene 0.0 7.2 17.8 (3.2)0.0 5.1(2.3)Jet Fuel 0.0 448.1 85.1 1.9704.5 65.1(278.3)Distillate Fuel 0.0 646.6 569.3 (56.5)107.2 123.7150.4Residual Oil 0.0 853.0 756.9 (19.2)44.0 607.5 228.9(307.4)Naphtha for petrochemicalfeedstocks 0.0 180.2 0.0 1.10.0 0.0179.2Petroleum Coke 0.0 70.7 804.7 (0.8)82.8 0.0 0.0(816.0)Other Oil for petrochemicalfeedstocks 0.0 319.3 0.0 (1.7)209.3 0.0 0.0111.7Special Naphthas 0.0 31.9 34.0 (0.2)0.0 0.0(1.9)Lubricants 0.0 37.6 130.8 (11.0)0.0 4.9(77.4)Waxes 0.0 6.9 10.1 (0.2)0.0 0.0(3.1)Asphalt/Road Oil 0.0 96.1 44.8 (42.4)0.0 0.093.7Still Gas 0.0 0.0 0.0 0.00.0 0.00.0Misc. Products 0.0 0.7 13.7 (2.1)0.0 95.985.0Total56,866.5 34,629.9 5,438.3 (409.4)1,785.4 1,419.2 787.084,049.9Note: Totals may not sum due to independent rounding.A-292 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007Table A-234: 2007 Potential CO2EmissionsFuel CategoryFuel TypeApparent Consumption (QBtu) Carbon Coefficients(Tg Carbon/QBtu)Potential Emissions(Tg CO2Eq.)Solid FuelsAnthracite Coal 0.04 28.263.6Bituminous Coal 12.78 25.491,194.3Sub-bituminous Coal 9.08 26.48881.8Lignite 0.96 26.3092.7Coke 0.03 31.003.0Unspecified (1.21)25.34(112.6)Gas FuelsNatural Gas 23.50 14.471,246.8Liquid FuelsCrude Oil 32.67 20.332,434.8Nat Gas Liquids and LRGs 2.76 16.99171.8Other Liquids 2.94 20.33219.4Motor Gasoline 1.38 19.3397.6Aviation Gasoline 0.00 18.870.1Kerosene (0.00)19.72(0.2)Jet Fuel (0.28)19.33(19.7)Distillate Fuel 0.15 19.9511.0Residual Oil (0.31)21.49(24.2)Naphtha for petrochemical feedstocks 0.18 18.1411.9Petroleum Coke (0.82)27.85(83.3)Other Oil for petrochemical feedstocks 0.11 19.958.2Special Naphthas (0.00)19.86(0.1)Lubricants (0.08)20.24(5.7)Waxes (0.00)19.81(0.2)Asphalt/Road Oil 0.09 20.627.1Still Gas 0.00 17.510.0Misc. Products 0.08 20.336.3Total6,144.2Data 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 CO 2 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, Petrochemic al Feedstocks and Waxes/Misc. are not shown because these categori es are aggregates of numerous smaller

components.

Note: Totals may not sum due to independent rounding.



### Table A-236: 2007 Reference Approach CO 2 

### Emissions from Fossil Fuel Consumption (Tg CO

2 

### 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: 1990-2007Table A-237: Fuel Consumption in the United States by Estimating Approach (TBtu)*Approach 199019911992199319941995199619971998199920002001200220032004200520062007Sectoral69,555.869,150.270,756.872,506.373,915.174,938.377,358.878,589.478,869.280,154.282,512.481,063.682,075.082,647.383,851.984,072.882,974.584,458.1Coal 18,022.5 17,997.8 18,197.7 18,932.519,040.419,180.920,022.820,561.820,790.420,805.6 21,769.721,130.021,232.721,645.221,905.222,182.321,876.522,144.4Natural Gas 19,179.8 19,570.1 20,234.3 20,766.321,250.622,203.422,642.122,862.122,384.422,369.5 23,260.222,422.423,395.822,847.022,491.122,294.621,909.723,342.6Petroleum 32,353.5 31,582.3 32,324.8 32,807.533,624.233,553.934,694.035,165.535,694.336,979.1 37,482.537,511.337,446.638,155.139,455.639,595.939,188.238,971.1Reference (Apparent)68,953.168,123.869,651.271,461.673,046.374,070.976,435.277,952.977,970.779,263.581,707.280,709.781,414.281,901.083,600.883,625.782,226.684,049.9Coal 17,602.2 17,400.1 17,724.7 18,259.718,723.718,609.419,517.320,159.620,031.620,080.2 21,038.620,780.820,880.721,179.521,871.322,084.521,626.221,670.7Natural Gas 19,746.7 19,765.4 20,425.2 20,980.821,459.922,411.922,816.222,951.122,531.122,635.1 23,598.722,621.823,360.922,758.622,782.522,448.322,068.123,500.0Petroleum 31,604.1 30,958.3 31,501.3 32,221.032,862.733,049.634,101.734,842.235,408.036,548.2 37,069.937,307.137,172.537,962.838,947.039,092.938,532.338,879.2Difference -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%-0.3%-0.5%-0.9%-0.5%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%-0.2%-0.4%-1.1%-2.1%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%1.3%0.7%0.7%0.7%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%-1.3%-1.3%-1.7%-0.2%* Includes U.S. territories. Does not include international bunker fuels.+ Does not exceed 0.05%.Note: Totals may not sum due to independent rounding.Table A-238: CO2Emissions from Fossil Fuel Combustion by Estimating Approach (Tg CO2Eq.)Approach 199019911992199319941995199619971998199920002001200220032004200520062007Sectoral4,825.54,790.84,890.65,016.25,101.65,151.05,326.85,414.75,450.15,529.15,705.65,629.05,676.55,737.05,818.35,861.25,780.25,869.3Coal 1,696.2 1,694.8 1,714.1 1,783.81,795.21,810.01,889.01,941.21,963.01,964.3 2,055.31,994.62,004.62,043.42,068.82,094.12,065.32,090.3Natural Gas 1,007.71,028.6 1,065.1 1,092.41,116.41,166.71,189.71,200.81,174.61,173.2 1,219.31,176.41,228.71,199.21,179.31,169.61,149.11,224.7Petroleum 2,121.6 2,067.4 2,111.5 2,140.02,190.02,174.32,248.22,272.72,312.52,391.7 2,431.02,458.02,443.22,494.42,570.12,597.52,565.82,554.4Reference (Apparent)4,802.14,743.24,848.24,980.15,083.15,136.45,313.55,429.55,436.15,511.25,696.85,660.65,700.35,758.15,890.05,905.05,801.85,917.1Coal 1,658.1 1,641.2 1,671.3 1,724.91,769.51,760.51,846.01,907.11,898.81,907.7 1,996.81,974.11,983.52,011.12,077.82,097.62,058.02,062.4Natural Gas 1,039.11,040.4 1,076.3 1,105.11,128.81,179.11,200.11,206.91,183.31,188.2 1,238.31,187.31,227.71,196.21,197.01,178.61,157.91,233.9Petroleum 2,105.0 2,061.7 2,100.6 2,150.12,184.82,196.72,267.42,315.42,354.02,415.3 2,461.72,499.22,489.12,550.82,615.22,628.82,585.92,620.7Difference -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%1.2%0.7%0.4%0.8%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%-1.6%0.4%0.2%-0.4%-1.3%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%-0.3%1.5%0.8%0.8%0.8%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%2.3%1.8%1.2%0.8%2.6%* 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 anthropogenic 66 

### 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 (CO 2 

### ) exchange (i.e., uptake or release) by oceans



###  Natural forest fires 67



###  Methane (CH 4 

### ) emissions from wetlands not affected by human induced land-use changes

Some processes or activities may be anthropogenic in or igin but do not result in net emissions of greenhouse

gases, such as the respiration of CO 2 

### 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., CO 2 

### 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.

CO 2 

### 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 CO 2 

### 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 inten tionally or unintentionally are vi ewed as mimicking natural burning

processes that have been suppressed by other human forest manage ment activities. The United States does not consider forest fi res

within its national boundaries to be a net source of greenhouse emissions.

68 Respiration of CO 2 by biological organisms is simply part of the broader global carbon cycle that also includes uptake of

CO 2 by photosynthetic organisms.

### A-296 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 CO 2 

### 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 CO 2 

### . 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 CO 2 

### 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 CO 2 

### is emitted. The assumption that,

in the absence of site specific data, all CO 2 

### 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 CO 2 

### 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 implem ent this guidance and therefore estimates are not yet included

in national totals. Preliminary estimates indicate that the amount of CO 2 

### emitted from EOR operations and CO 2 

### transport

pipelines in 2005 was 35.16 Tg CO 2 

### (35,156 Gg CO 2 

### ). Site-specific monitoring and reporting data for CO 2 

### injection sites

(i.e., EOR operations) were not readily available. Th erefore, these estimates a ssume that all of the CO 2 

### used in EOR

operations is emitted.

CO 2 

### from Natural Gas Processing

CO 2 

### 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 CO 2 

### that must be removed from the natural gas in order for it to

meet pipeline specifications for CO 2 

### content. A fraction of the CO 2 

### 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 CO 2 

### is separated from natural

gas at natural gas processing plants. CO 2 

### removed at gas processing plants is generally vented to the atmosphere, but

several gas processing plants in Wyoming and Texas a nd one gas processing plant in Michigan compress the CO 2 

### separated from natural gas and transport this CO 2 

### by pipeline for use in enhanced oil recovery. CO 2 

### 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 CO 2 

### (5,992 Gg CO 2 

### )

produced from natural gas processing plants (acid gas removal plants) was captured and used in enhanced oil recovery

operations. As discussed under CO 2 

### from Enhanced Oil Recovery (above) all of this CO 2 

### used in EOR operations is

assumed to be emitted.

CO 2 

### 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 una ccounted for natural gas was assumed to be a source of CO 2 

### 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 CO 2 

### emissions.

CO 2 

### 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 petrol eum 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 duri ng 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 se dimentary rocks, usually shales, which can be converted to

petroleum products by distillation.

### A-297 CH 4 

### from the Production of Carbides other than Silicon Carbide

Methane (CH 4 

### ) may be emitted from the production of carbides because the petroleum coke used in the process

contains volatile organic compounds, which form CH 4 

### 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 CH 4 

### 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)

CO 2 

### from Calcium Carbide and Silicon Carbide Production

CO 2 

### is formed by the oxidation of petroleum coke in the production of calcium carbide. These CO 2 

### 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 cons umption to estimate emissions from this source. (See Revised

1996 IPCC Guidelines for National Greenhouse Gas Inventories: Reference Manual, pp. 2.20 – 2.21)

CO 2 

### from Graphite Consumption in Ferroalloy and Steel Production

Emissions from "graphite," "wood" or "biomass" in calculating CO 2 

### emissions from ferroalloy production, iron

and steel production or other "Industrial Processes" included in Ch apter 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 CO 2 

### emission calculation that no wood

or other biogenic C is used in any of these industrial proc esses. 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 am ount 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.

N 2 

### O from Caprolactam Production

Caprolactam is a widely used chemical intermediate, pr imarily to produce nylon-6. All processes for producing

caprolactam involve the catalytic oxidation of ammonia, with N 2 

### O being produced as a by-product. Caprolactam

production could be a significant source of N 2 

### O

 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)

N 2 

### O 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, N 2 

### O emissions may result from this crack ing 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 CH 4 

### from Petroleum Coke Production

Coke production may result in CH 4 

### emissions. Detailed coke production statistics were not available for the

purposes of estimating CH 4 

### 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)

CO 2 

### 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. CO 2 

### 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 CO 2 

### 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)

N 2 

### O from Acrylonitrile Production

Nitrous oxide may be emitted during acrylonitrile production. No methodology was available for estimating

these emissions, and therefore further research is n eeded if these emissions are to be included. (See Revised 1996 IPCC

Guidelines for National Greenhouse Gas Inventories: Reference Manual , p. 2.22)

SF 6 

### from Aluminum Fluxing and Degassing

Occasionally, sulfur hexafluoride (SF 6 

### ) 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 SF 6 

### in the mixture is small and a portion of the SF 6 

### is decomposed in the process (Waite and Bernard

1990, Corns 1990). It has been estimated that 230 Mg of SF 6 

### were used by the aluminum industry in the United States and

Canada (Maiss and Brenninkmeijer 1998); howe ver, this estimate is highly uncertain.

SF 6 

### from Production/Leakage/Breakage of Soundproofed Double-glazed Windows

Sulfur hexafluoride (SF 6 

### ) may be emitted from the production, breakag e, or leakage of soundproof double-glazed

windows. No methodology was available for estimating these emi ssions, and therefore further research is needed if these

emissions are to be included.

SF 6 

### from Production/Leakage/Dismantling of Radar, Tracer and Night Vision Equipment

Sulfur hexafluoride (SF 6 

### ) 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.

SF 6 

### from Applications in Sports Shoes, Tires, and Tennis Balls

Sulfur hexafluoride (SF 6 

### ) 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.

SF 6 

### from Applications to Trace Leakage of Pressure Vessels and Used as a Tracer Gas in Open Air

Sulfur hexafluoride (SF 6 

### ) may be emitted from application involving tracer gasses to detect leakage from

pressure vessels and as a tracer gas in the open air. Al though emissions from this source are believed to be minor,

emissions estimation data and methodologies were not available.

Miscellaneous SF 6 

### 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 ta ndem accelerators, in loudspeaker s, in shock absorbers, and

for certain biomedical applications. Data need to be gath ered and methodologies developed if these emissions are to be

### A-299 estimated. A preliminary global assessment of aggregate em issions from these applications can be found in Maiss, M.

Brenninkmeijer, and C.A.M. Brenninkmeijer (1998).

N 2 

### O 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 (N 2 

### O) 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 CO 2 

### Eq. of N 2 

### O emissions from soils. To estimate the amount of nitrogen deposited by domestic

house animals, only those excretions that remained on land surf aces—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 type 71



### was multiplied by the average amount of nitrogen pe r 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.

CO 2 

### from Non-Hazardous Industrial Waste Combustion

Waste combustion is incorporated in two sections of th e energy chapter of the inventory: in the section on CO 2 

### 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 en ergy recovery. There is one potentially significant category

of waste combustion that is not included in our calculus: i ndustrial non-hazardous waste, burned for disposal (rather than

energy recovery). Data are not readily available for this sour ce; further research is needed to estimate the magnitude of

CO 2 

### emissions.

CH 4 

### from Land-Use Changes Including Wetlands Creation or Destruction

Wetlands are a known source of methane (CH 4 

### ) emissions. When wetlands are destroyed, CH 4 

### emissions may be

reduced. Conversely, when wetlands are created (e.g., during the construction of hydroelectric plants), CH 4 

### emissions

may increase. Grasslands and forestlands may also be weak sinks for CH 4 

### due to the presence of methanotrophic bacteria

that use CH 4 

### as an energy source (i.e., they oxidize CH 4 

### to CO 2 

### ). Currently, an adequate scientific basis for estimating

these emissions and sinks does not exist, and therefore fu rther 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 Al caraz 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 N 2 

### O from Wastewater Treatment and Biological Processes

As a result of nitrification and denitr ification processes, nitrous oxide (N 2 

### O) 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. Nitr ogen-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 N 2 

### O that originates from these

sources is currently estimated under the Wastewater Treat ment source category—based upon average dietary protein

intake and assumptions on co-disposal of N that was not consumed. The portion of emitted N 2 

### O 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.

CH 4 

### from Large and Small Scale Composting

Methane (CH 4 

### ) 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.

CH 4 

### 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 (CH 4 

### ) may be released through the treatment of dredging sludge, remediation of groundwater,

intermediate storage of slaughter waste, production of pr ocess 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. Envi ronmental 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, R ecord 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 (CO 2 

### ) was chosen as this reference gas. Direct effects occur when the gas itself is a

greenhouse gas. Indirect radiative forcing occurs when ch emical 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 CO 2 

### Eq. can be expressed as follows:



#     

 

 

 

   Gg 1,000 Tg GWP gas of Gg Eq CO Tg

2



### Where,

Tg CO 2 

### 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., CO 2 

### , CH 4 

### , N 2 

### O, HFCs, PFCs, and SF 6 

### ) 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, tr opospheric ozone, other indirect greenhouse gases (e.g., NO

x 

### , and NMVOCs), and tropospheric aerosols (e.g., SO 2 

### products and black carbon), however, vary spatially, and

consequently it is difficult to quantify their global radiative fo rcing 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 Atmosp heric Lifetimes (Years) of Gases Used in this Report

Gas Atmospheric Lifetime 100-year GWP a 20-year GWP 500-year GWP

Carbon dioxide (CO 2 ) 50-200 1 1 1

Methane (CH 4 ) b 12  3 21 56 6.5

Nitrous oxide (N 2 O) 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/Ad d.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; Co mmunications from Parties included in Annex I to the Convention: guidelines, schedul e and

process for consideration; Annex: Revised Guidelines for the Preparation of National Communica tions by Parties Included in Ann ex 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

CF 4 50,000 6,500 4,400 10,000

C 2 F 6 10,000 9,200 6,200 14,000

C 4 F 10 2,600 7,000 4,800 10,100

C 6 F 14 3,200 7,400 5,000 10,700

SF 6 3,200 23,900 16,300 34,900

Source: IPCC (1996)

a GWPs used in this report are calc ulated over 100 year time horizon.

b The methane GWP includes the direct effe cts and those indirect effects due to th e production of tropospheric ozone and stratos pheric water vapor. The indirect effect due

to the production of CO 2 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; th erefore, a range of net GWPs is provided for ozone depleting

substances.

Table A-240: Net 100-year Global Warming Pote ntials for Select Ozone Depleting Substances*

Gas Direct Net min Net

max 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

CHCl 3 140 (560) 0

CCl 4 1,800 (3,900) 660

CH 3 Br 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 sub stances (ODSs). However, they are also

potent greenhouse gases. Recognizing the harmful effects of thes e compounds on the ozone layer, in 1987 many governments signe d the Montreal Protocol on Substances that

Deplete the Ozone Layer to limit the production and importation of a number of CFCs and other halogenated co mpounds. 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 in structions for estimati ng 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 th is 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 CO 2 

### as a reference gas; a change in the radiative efficiency of CO 2 

### thus impacts the GWP of all other

greenhouse gases. Since the SAR and TAR, the IPCC has applied an improved calculation of CO 2 

### radiative forcing and an

improved CO 2 

### 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 CO 2 

### is about 8 percent lower than that in the

TAR, the GWPs of the other gases relative to CO 2 

### 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 (CO 2 ) 50-200 5-200 a 5-200 a 1 1 1 NC NC NC NC

Methane (CH 4 ) b 12  3 8.4/12 c 8.7/12 c 21 23 25 2 10% 4 19%

Nitrous oxide (N 2 O) 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

SF 6 3,200 3,200 3200 23,900 22,200 22,800 (1,900) (7%) (1,100) (5%)

CF 4 50,000 50,000 50,000 6,500 5,700 7,390 (800) (12%) 890 14%

C 2 F 6 10,000 10,000 10,000 9,200 11,900 12,200 2,700 29% 3,000 33%

C 3 F 8 2,600 2,600 2,600 7,000 8,600 8,830 1,600 23% 1,830 26%

C 4 F 10 2,600 2,600 2,600 7,000 8,600 8,860 1,600 23% 1,860 27%

c-C 4 F 8 3,200 3,200 3,200 8,700 10,000 10,300 1,300 15% 1,600 18%

C 5 F 12 4,100 4,100 4,100 7,500 8,900 9,160 1,400 19% 1,660 22%

C 6 F 14 3,200 3,200 3,200 7,400 9,000 9,300 1,600 22% 1,900 26%

Others d

NF 3 NA 740 740 NA 10,800 17,200 NA NA NA NA

a No single lifetime can be determined for CO 2 . (See IPCC 2001)

b The methane GWP includes the direct effe cts and those indirect effects due to th e production of tropospheric ozone and stratos pheric water vapor. The indirect effect due

to the production of CO 2 is not included.

c Methane and nitrous oxide have chemical feedback systems that can al ter 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 mean s 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 CO

2 

### Eq.)

Gas Trend from 1990 to 2007 Revisions to Annual Estimates (relative to SAR)

TAR AR4 TAR AR4

SAR TAR AR4 1990 2007

CO 2 1,026.7 1,026.7 1,026.7 NC NC NC NC

CH 4 (31.2) (34.2) (37.2) 58.7 117.4 55.7 111.5

N 2 O (3.1) (3.0) (3.0) (14.2) (12.2) (14.1) (12.1)

HFCs, PFCs, and SF 6 * 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 NF 3 Note: Totals may not sum due to independent rounding. Excl udes 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 CO 2 

### Eq., as compared to 7,150.1 Tg CO 2 

### Eq. when the GWPs from the SAR are used (a 1.5

percent difference). Table A-243 provides a detailed summ ary 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 CO 2 

### 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 CO

2 

### Eq.)

Gas/Source 1990 1995 2000 2005 2006 2007

CO 2 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

CH 4 734.0 733.1 703.7 668.7 692.8 696.8

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 N 2 O 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 SF 6 102.3 116.0 142.3 145.3 147.1 155.4

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

SF 6 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 CO 2 Eq.

a Sinks are only included in net emissions total, and are based par tially on projected activity data. Parentheses indicate nega tive 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 Em issions and Sinks Using TAR vs. AR4 GWPs (Tg CO

2 

### Eq.)

Gas 1990 1995 2000 2005 2006 2007

CO 2 NC NC NC NC NC NC

CH 4 117.4 117.3 112.6 107.0 110.9 111.5

N 2 O (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

SF 6 (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 NF 3 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

CO 2 NC NC NC NC NC NC

CH 4 19.0% 19.0% 19.0% 19.0% 19.0% 19.0%

N 2 O (3.9%) (3.9%) (3.9%) (3.9%) (3.9%) (3.9%)

SF 6 (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 De pleting Substances 0.0% 0.0% 0.0% 0.0% 0.0% 0.0%

HCFC-22 Production b 26.5% 26.5% 26.5% 26.5% 26.5% 26.5%

Semiconductor Manufacture c 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 Manufacture c 28.4% 27.8% 29.1% 39.5% 45.1% 40.0%

Aluminum Production a 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 CF 4 and C 2 F 6

b HFC-23 emitted

c Emissions from HFC-23, CF 4 , C 2 F 6 , C 3 F 8, SF 6 , and the addition of NF 3

Note: Excludes Sinks. Parenthes es 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 comparis on 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 CH 4 

### emissions in this sector. Emissions

from all other sectors were comprised of mainly CO 2 

### or a mix of gases, which moderated the effect of the changes.

Table A-246: Comparison of Emissions by Se ctor using IPCC SAR and AR4 GWP Values (Tg CO

2 

### 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, meth yl 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 comm only 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, al beit in ever decreasing amounts, for many more years. Class

II designated substances, all of which are hydrochlorofluoroc arbons (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 inthe United States is anticipated to increase over the next

several years as equipment that use Class I substances ar e 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 deple tion 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 re quire 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 troposphe re up to about 50 kilometers. Approximately 90 percent of

atmospheric ozone is within the stratosphere. The greatest con centration 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 ki lometers 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 e quipment, 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 Oz one 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 chemic al 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 annua l 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 Substan ces 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 (SO 2 

### ), emitted into the atmosphere through natura l 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 SO 2 

### -derived aerosols on radiative forcing is believed to be negative (IPCC 1996). However, because SO 2 

### is

short-lived and unevenly distributed through the atmosphere, it s 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 SO 2 

### emissions in the United States is the burni ng of sulfur containing fuels, mainly coal.

Metal smelting and other industrial processes also release significant quantities of SO 2 

### . The largest contributor to U.S.

emissions of SO 2 

### is electricity generation, accounting for 71 percent of total SO 2 

### 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 SO 2 

### emissions. Overall, SO 2 

### 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 SO 2 

### can cause significant

increases in acute and chronic respiratory diseases. In addition, once SO 2 

### 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 caus e the acidification of lakes and streams and damage trees.

As a result of these harmful effects, the United States has regulated the emissions of SO 2 

### under the Clean Air Act. The

EPA has also developed a strategy to control these emission s 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: SO 2 

### 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: SO 2 

### 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.



# 6.4. Complete List of Source Categories 1

Chapter/Source Gas(es)

Energy

Fossil Fuel Combustion CO 2 Non-Energy Use of Fossil Fuels CO 2 Stationary Combustion (excluding CO 2 ) CH 4 , N 2 O, CO, NO

x , NMVOC

Mobile Combustion (excluding CO 2 ) CH 4 , N 2 O, CO, NO

x , NMVOC

Coal Mining CH 4 Abandoned Underground Coal Mines CH 4 Natural Gas Systems CH 4 Petroleum Systems CH 4

Waste Incineration CO 2, N 2 O

Industrial Processes

Titanium Dioxide Production CO 2

Aluminum Production CO 2 , CF 4 , C 2 F 6

Iron and Steel Production CO 2 , CH 4 Ferroalloy Production CO 2 , CH 4 Ammonia Production and Urea Consumption CO 2

Cement Production CO 2

Lime Production CO 2

Limestone and Dolomite Use CO 2 Soda Ash Production and Consumption CO 2 Carbon Dioxide Consumption CO 2 Phosphoric Acid Production CO 2

Petrochemical Production CH 4 , CO 2 Silicon Carbide Production and Consumption CH 4 , CO 2

Lead Production CO 2

Zinc Production CO 2 Adipic Acid Production N 2 O

Nitric Acid Production N 2 O

Substitution of Ozone Depl eting Substances HFCs, PFCs a

HCFC-22 Production HFC-23

Semiconductor Manufacture HFCs, PFCs, SF 6 b

Electrical Transmission and Distributing SF 6 Magnesium Production and Processing SF 6

Solvent and Other Product Use CO, NO

x , NMVOC

N 2 O Product Usage N 2 O

Agriculture

Enteric Fermentation CH 4

### A-311 Manure Management CH 4 , N 2 O

Rice Cultivation CH 4 Field Burning of Agricultural Residues CH 4 , N 2 O

Agricultural Soil Management N 2 O, CO, NO

x Land Use, Land-Use Change, and Forestry

CO 2 Flux CO 2 (sink)

Cropland Remaining Cropland CO 2 Settlements Remaining Settlements N 2 O

Forestland Remaining Forestland CH 4 , N 2 O

Wetlands Remaining Wetlands CO 2 , N 2 O

Waste

Landfills CH 4

Wastewater Treatment CH 4, N 2 O

Composting CH 4, N 2 O

a Includes HFC-23, HFC-32, HFC-125, HFC-134a, HFC-143a, HFC-236fa, CF 4 , HFC-152a, HFC-227ea, HFC-245fa, HFC-4310mee, and PFC/PFPEs. 

### 1

b Includes such gases as HFC-23, CF 4 , C 2 F 6 , SF 6

. 

### 2



# 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) 10 2

kilo (k) 10 3

mega (M) 10 6

giga (G) 10 9

tera (T) 10 12

peta (P) 10 15

exa (E) 10 18



## 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 Conversions 83

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 (10 12 

### )

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  10 11 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 Inform ation 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 En vironmental 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 Cent er-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 Administrat ion, 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 (10 12 ) 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 CO 2 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 Perfluoro cyclobutane

C 4 F 10 Perfluorobutane

C 5 F 12 Perfluoropentane

C 6 F 14 Perfluorohexane

CF 3 I Trifluoroiodomethane

CFCl 3 Trichlorofluoromethane (CFC-11)

CF 2 Cl 2 Dichlorodifluoromethane (CFC-12)

CF 3 Cl Chlorotrifluoromethane (CFC-13)

C 2 F 3 Cl 3 Trichlorotrifluoroethane (CFC-113)*

CCl 3 CF 3 CFC-113a*

C 2 F 4 Cl 2 Dichlorotetrafluoroethane (CFC-114)

C 2 F 5 Cl Chloropentafluoroethane (CFC-115)

CHCl 2 F HCFC-21

CHF 2 Cl Chlorodifluoromethane (HCFC-22)

C 2 F 3 HCl 2 HCFC-123

C 2 F 4 HCl HCFC-124

C 2 FH 3 Cl 2 HCFC-141b

C 2 H 3 F 2 Cl HCFC-142b

CF 3 CF 2 CHCl 2 HCFC-225ca

CClF 2 CF 2 CHClF HCFC-225cb

CCl 4 Carbon tetrachloride

CHClCCl 2 Trichloroethylene

CCl 2 CCl 2 Perchloroethylene, tetrachloroethene

CH 3 Cl Methylchloride

CH 3 CCl 3 Methylchloroform

CH 2 Cl 2 Methylenechloride

CHCl 3 Chloroform, trichloromethane

CHF 3 HFC-23

CH 2 F 2 HFC-32

CH 3 F HFC-41

C 2 HF 5 HFC-125

C 2 H 2 F 4 HFC-134

CH 2 FCF 3 HFC-134a

C 2 H 3 F 3 HFC-143*

C 2 H 3 F 3 HFC-143a*

CH 2 FCH 2 F HFC-152*

C 2 H 4 F 2 HFC-152a*

CH 3 CH 2 F HFC-161

C 3 HF 7 HFC-227ea

CF 3 CF 2 CH 2 F HFC-236cb

CF 3 CHFCHF 2 HFC-236ea

C 3 H 2 F 6 HFC-236fa

C 3 H 3 F 5 HFC-245ca

CHF 2 CH 2 CF 3 HFC-245fa

CF 3 CH 2 CF 2 CH 3 HFC-365mfc

C 5 H 2 F 10 HFC-43-10mee

CF 3 OCHF 2 HFE-125

CF 2 HOCF 2 H HFE-134

CH 3 OCF 3 HFE-143a

CF 3 CHFOCF 3 HFE-227ea

### A-318 Inventory of U.S. Greenhouse Gas Emissions and Sinks: 1990-2007 CF 3 CHClOCHF 2 HCFE-235da2

CF 3 CHFOCHF 2 HFE-236ea2

CF 3 CH 2 OCF 3 HFE-236fa

CF 3 CF 2 OCH 3 HFE-245cb2

CHF 2 CH 2 OCF 3 HFE-245fa1

CF 3 CH 2 OCHF 2 HFE-245fa2

CHF 2 CF 2 OCH 3 HFE-254cb2

CF 3 CH 2 OCH 3 HFE-263fb2

CF 3 CF 2 OCF 2 CHF 2 HFE-329mcc2

CF 3 CF 2 OCH 2 CF 3 HFE-338mcf2

CF 3 CF 2 CF 2 OCH 3 HFE-347mcc3

CF 3 CF 2 OCH 2 CHF 2 HFE-347mcf2

CF 3 CHFCF 2 OCH 3 HFE-356mec3

CHF 2 CF 2 CF 2 OCH 3 HFE-356pcc3

CHF 2 CF 2 OCH 2 CHF 2 HFE-356pcf2

CHF 2 CF 2 CH 2 OCHF 2 HFE-356pcf3

CF 3 CF 2 CH 2 OCH 3 HFE-365mcf3

CHF 2 CF 2 OCH 2 CH 3 HFE-374pcf2

C 4 F 9 OCH 3 HFE-7100

C 4 F 9 OC 2 H 5 HFE-7200

CHF 2 OCF 2 OC 2 F 4 OCHF 2 H-Galden 1040x

CHF 2 OCF 2 OCHF 2 HG-10

CHF 2 OCF 2 CF 2 OCHF 2 HG-01

CH 3 OCH 3 Dimethyl ether

CH 2 Br 2 Dibromomethane

CH 2 BrCl Dibromochloromethane

CHBr 3 Tribromomethane

CHBrF 2 Bromodifluoromethane

CH 3 Br Methylbromide

CF 2 BrCl Bromodichloromethane (Halon 1211)

CF 3 Br(CBrF 3 ) Bromotrifluoromethane (Halon 1301)

CF 3 I FIC-13I1

CO Carbon monoxide

CO 2 Carbon dioxide

CaCO 3 Calcium carbonate, Limestone

CaMg(CO 3 ) 2 Dolomite

CaO Calcium oxide, Lime

Cl atomic Chlorine

F Fluorine

Fe Iron

Fe 2 O 3 Ferric oxide

FeSi Ferrosilicon

H, H 2 atomic Hydrogen, molecular Hydrogen

H 2 O Water

H 2 O 2 Hydrogen peroxide

OH Hydroxyl

N, N 2 atomic Nitrogen, molecular Nitrogen

NH 3 Ammonia

NH 4 + Ammonium ion

HNO 3 Nitric acid

NF 3 Nitrogen trifluoride

N 2 O Nitrous oxide

NO Nitric oxide

NO 2 Nitrogen dioxide

NO 3 Nitrate radical

Na Sodium

Na 2 CO 3 Sodium carbonate, soda ash

Na 3 AlF 6 Synthetic cryolite

O, O 2 atomic Oxygen, molecular Oxygen

O 3 Ozone

S atomic Sulfur

H 2 SO 4 Sulfuric acid

SF 6 Sulfur hexafluoride

SF 5 CF 3 Trifluoromethylsulphur pentafluoride

SO 2 Sulfur dioxide

Si Silicon

### A-319 SiC Silicon carbide

SiO 2 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 UN FCCC 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 unc ertainty 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 so me source categories, such as for CO 2 

### Emissions from Fossil Fuel

Combustion, have relatively low level of uncertainty a ssociated with them. However, for some other source

categories, the inventory emission estimates are consider ed less certain. The two major types of uncertainty

associated with these inventory estima tes are (1) model uncertainty, which aris es when the emission and/or removal

estimation models used in developing the inventory esti mates 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 (p rimarily 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 disc ussed 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 quan titative 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 uncertain ty 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 a ssociated source category inventory esti mate 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 pr ocess. For each source category, the analysis highlights

opportunities for changes to data measur ement, data collection, and calculati on 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 uncerta inty 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 es timate of individual source categories and the overall

inventory. Of these, the Tier 2 approach is both more flex ible 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 adop ted. Each of the new Tier 2 analyses reflect additional

detail and characterization of input parameters using statis tical 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 emi ssions 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 un certainty is based on the error pr opagation 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 approximatel y normally distributed, (ii) the coeffici ent 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 vari ables are not normal, (iii) if the estimates of uncertainty

associated with the input vari ables 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 unce rtainty analysis for all so urce categories where sufficient and reliable data are

available to characterize the uncer tainty 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 speci fied) 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 uncer tainty 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 categ ory is expressed as the percentage

deviation above and below the total 2007 emissions estimat ed for that source category. Source category trend

uncertainty is described subsequently in this Appendix.

### A-323 

### Table A-253: Summary Results of So urce Category Uncertainty Analyses

Source Category Base Year

Emissions* 2007

Emissions 2007 Uncertainty

Tg CO 2 

### Eq. Tg CO 2 

### Eq. Low High

CO 2 

### 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 Production d 

### 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 Fuels b 

### 114.3 108.8 NE NE

Wood Biomass and Ethanol Consumption b 

### 219.3 247.8 NE NE

CH 4 

### 616.6 585.3 -10% 18%

Stationary Combustion 7.4 6.6 -34% 128%

Mobile Combustion c 

### 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 Production d 

### 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

N 2 

### O 315.0 311.9 -11% 41%

Stationary Combustion 12.8 14.7 -24% 187%

Mobile Combustion c 

### 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 SF 6 

### 90.5 149.5 -5% 7%

Substitution of Ozone Depleting Substances e 

### 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 CO 2 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 present ed 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 emi ssion estimate presented in the Industr ial Processes chapter of this

Inventory.

e Uncertainty analysis for Substitution of Ozone Depleting Subst ances was conducted for aerosols, foams, solvents, fire extingu ishing agents, and refrigerants,

but not for other remaining categories. Therefore, the emission esti mate presented here does not include “other” ODS substitue s and does not match the

emission estimate presented in the Industria l 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 uncert ainty 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 out put 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 esti mation. However, for all other emission sources (excluding

international bunker fuels, CO 2 

### 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 CO 2 

### 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 appr oximately 7,150 Tg CO 2 

### Eq. The uncertainty interval associated with total CO 2 

### emissions,

which constitute about 85 percent of the total U.S. gree nhouse gas emissions in 2007, ranges from -2 percent to 5

percent of total CO 2 

### emissions estimated. The results indicate that the uncertainty associat ed with the inventory

estimate of the total CH 4 

### emissions ranges from -10 percent to 18 pe rcent, uncertainty associated with the total

inventory N 2 

### O emission estimate ranges from -11 percent to 41 per cent, and uncertainty asso ciated with high GWP

gas emissions ranges from -5 percent to 7 percent.

A summary of the overall quantitative un certainty estimates are shown below.

### A-325 

### Table A-254. Quantitative Uncertainty Assessment of Overall National Inventory Emissions (Tg CO 2 

### Eq. and Percent)

2007 Emission

Estimate Uncertainty Range Relative to Emission

Estimate a 

### Mean b 

### Standard

Deviation

Gas (Tg CO 2 

### Eq.) (Tg CO 2 

### Eq.) (%) (Tg CO 2 

### Eq.)

Lower

Bound c 

### Upper

Bound c 

### Lower

Bound Upper

Bound c



### CO 2 

### 6,103.4 5974.9 6390.0 -2% 5% 6181.5 106.8

CH 4 

### 585.3 527.0 689.0 -10% 18% 599.3 41.3

N 2 

### O 311.9 278.7 440.6 -11% 41% 352.4 42.8

PFC, HFC & SF 6 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 aver age of the simulated emission estimates;

Standard deviation indicates the extent of deviati on of the simulated values from the mean.

c The low and high estimates for total emissions were separatel y calculated through simulations and, hence, the low and high emi ssion 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 CH 4 , N 2 O and high GWP gases used in the inventory emission

calculations for 2007.



### Trend Uncertainty

In addition to estimates of uncertainty associated w ith 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 tre nd uncertainty for a source category is estimated using the sensitivity of

the calculated difference between base year and 2007 emissions to an incrementa l (i.e., 1 percent) increase in one or

both of these values for that source category. The two se nsitivities 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 anal yze 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 assume d 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 tr end uncertainty estimate wa s 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 CO 2 

### Eq. and Percent)

Gas/Source Base

Year* 2007 Emissions

Trend Trend Range a



### (Tg CO 2 

### Eq.) (%) (%)

Lower

Bound Upper

Bound

CO 2 

### 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

Production d 

### 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 Fuels b 

### 114.3 108.8 -5%

Wood Biomass and Ethanol Consumption b 

### 219.3 247.8 13%

CH 4 

### 616.6 585.3 -5% -21% 15%

Stationary Combustion 7.4 6.6 -10% -66% 135%

Mobile Combustion c 

### 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

Production d 

### 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%

N 2 

### O 315.0 311.9 -1% -16% 24%

Stationary Combustion 12.8 14.7 15% -59% 227%

Mobile Combustion c 

### 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 SF 6 * 

### 90.5 149.5 65% 14% 36%

Substitution of Ozone Depleting Substances e 

### 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 SF 6 is reported relative to the base year total of 118 Tg CO 2 Eq., and not the 1990 total of 90.5 Tg CO 2 Eq. For this

reason, the trend uncertainty falls outside of the range of 14% and 36%.

+ Does not exceed 0.05 Tg CO 2 Eq.

a Trend Range represents the 95% confidence interval fo r 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 present ed 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 emi ssion estimate presented in the Industr ial Processes chapter of this

Inventory.

f Uncertainty analysis for Substitution of Ozone Depleting Subst ances was conducted for aerosols, foams, solvents, fire extingu ishing agents, and refrigerants,

but not for other remaining categories. Therefore, the emission esti mate presented here does not include “other” ODS substitue s and does not match the

emission estimate presented in the Industria l Processes chapter of this Inventory.



# 7.3. Planned Improvements

Identifying the sources of uncertainties in the emissi on and sink estimates of the Inventory and quantifying

the magnitude of the associated uncertainty is the crucia l first step towards improving those estimates. Quantitative

assessment of the parameter uncertainty may also prov ide information about the relative importance of input

parameters (such as activity data and emission factors), ba sed on their relative contribution to the uncertainty within

the source category estimates. Such information can be used to prio ritize 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 emissi ons from a variety of sources. For example, the

accuracy of current emissi on factors applied to CH 4 

### and N 2 

### O 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 SF 6 

### from electrical transmission and distribution

is limited due to a lack of activity data regarding national SF 6 

### 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 foll owing 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 PD Fs 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 uncerta inty 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 as sociated 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 th e analysis of trend uncertainty. However, not all of

the simplifying assumptions described in the “Trend Un certainty” section above ma y be eliminated through

this process due to a lack of availability of more appropriate data.



# 7.4. Additional Information on Uncer tainty Analyses by Source

The quantitative uncertainty estimates associated with each emission and sink source category are reported

in each chapter of this Inve ntory following the discussion s 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 orga nized below in the same order as the sources in each chapter of the main

section of this Inventory. To avoid repetition, the followi ng 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 CO 2 

### )

Mobile combustion emissions of CH 4 

### and N 2 

### O 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 an alyzed each year by gove rnment agencies. To

determine the uncertainty associated with the activity data used in the calculations of CH 4 

### and N 2 

### O emissions, the

agencies and the experts that supply th e 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 vehi cles 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 contro l technology types, because the testi ng did not yield sta tistically 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 possibl e sources of error, such as unregi stered 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 uncertain ty within individual source categories was considered to

be higher due to the uncertainty associat ed 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 unc ertainty 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 diff er 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 dist ributions 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 bi odiesel vehicles, because no test data were available,

consistent with the assumptions underlying the ANL GREET model, their N 2 

### O and CH 4 

### 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 CH 4 

### emissions from mobile sources and 14

percent of N 2 

### O 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 examinin g the underlying uncertainty of emission factors and fuel

consumption data, and in the future, EPA will consider co nducting 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 belie ved 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 CH 4 

### emissions from aircraft, an average emi ssion factor is applied to total jet fuel

consumption. This average emission fact or takes into account the fact that CH 4 

### emissions occur only during the

landing and take-off (LTO) cycles, with no CH 4 

### 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 CO 2 

### emissions estimate for Incineration of Waste are 20

percent and -27 percent respectively, and in the N 2 

### O emission estimates are 191 percent and -71 percent

respectively, relative to the respective 2007-Inventory estimat es, at the 95% confidence in terval. The uncertainties in

the waste combustion emission estimates arise from both the as sumptions 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 N 2 

### O emissions. For more information on emission estimates from MSW combustion, please refer to the

Incineration of Waste section of the Ener gy 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 variable s 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 CO 2 

### and N 2 

### O emissions is the

estimate of the MSW combustion rate. The EPA (2000a, 2003, 2005a, 2006 through 2008; Schneider

2007) estimates of materials generated, discarded, a nd combusted carry considerab le uncertainty associated

with the material flows methodology used to generate them. Similarly, the BioCycle (Glenn 1999,

Goldstein and Matdes 2000, Goldst ein 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 N 2 

### O 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 compar able to the earlier results

(Kaufman et al. 2004a, 2004b), introducing further uncertainty.

 Fraction Oxidized. Another source of uncertainty for the CO 2 

### emissions estimate is fraction oxidized.

Municipal waste combustors vary considerably in thei r 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 recen t years for which estimates are available.

 Average Carbon Contents. Average carbon contents were app lied 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 unsp ecified 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 ra nge 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 N 2 

### O Emissions. Because insufficient data exist to provide detailed

estimates of N 2 

### O emissions for individual combustion facilities, the estimates presented exhibit high

uncertainty. The emission factor for N 2 

### O from municipal solid waste combustion facilities used in the

analysis is an average of defa ult values used to estimate N 2 

### O 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

N 2 

### O emissions from MSW combustion facilities in the United States, the estimate of zero percent for N 2 

### O

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 Associatio n (EFMA) reported an emission factor range of 1.15 to 1.30 ton CO 2 

### /ton NH 3 

### , with 1.2 ton CO 2 

### /ton NH 3 

### 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 CO 2 

### /ton NH 3 

### 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 CO 2 

### 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 CO 2



### emissions from fuel use and non-ener gy 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 CO 2 

### /ton NH 3 

### , 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 CO 2 

### 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 CO 2 

### /ton NH 3 

### , corresponding to CO 2 

### emissions estimates that are approximately 1.5 Tg CO 2 

### Eq. below the estimates calculated

using the EFMA emission factor of 1.2 ton CO 2 

### /ton NH 3 

### . 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 CO 2 

### emissions from ammonia

production, rather than EIA data.

Research indicates that there is only one U.S. plan t that manufactures ammoni a from petroleum coke. CO 2 

### 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 CO 2 

### emissions from

ammonia plants are calculated using the emission factor for natural gas feedstock. However, actual emissions may

differ because processes other than catal ytic 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 CO 2 

### for use in EOR. Such CO 2 

### is currently assumed to remain

sequestered (see the section of this chapter on CO 2 

### Consumption); however, time series data for the amount of CO 2 

### recovered from this plant are not av ailable and therefore all of the CO 2 

### produced by this plant is assumed to be

emitted to the atmosphere and allocated to Ammonia Manufacture.

84 

### It appears that the IPCC emission fact or for ammonia production of 1.5 ton CO 2 

### per ton ammonia may include both

CO 2 

### emissions from the natural gas f eedstock to the process and some CO 2 

### 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 Greenhous e 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 inclu de

natural gas used as fuel but that it is unclear whether the fact or for Canada includes natural gas used as fuel. However, the factors

for Norway and Canada are nearly identical (1.5 and 1.6 tons CO 2 

### per ton ammonia, respectively) and it is likely that if one value

does not include fuel use, the other value also does not. Fo r the conventional steam reformi ng process, however, the EFMA

reports an emission factor range for feedstock CO 2 

### 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 CO 2 

### of 0.5 tons per ton. This corresponds to a total CO 2 

### emission factor for the ammonia production

process, including both feedstock CO 2 

### and process heat CO 2 

### , 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 CO 2 

### emission factor of 1.2 ton CO 2 

### 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 emissi on 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 percen t confidence interval. Factors such as the composition of

phosphate rock affect CO 2 

### emissions from phosphoric acid production. For more information on how emissions

estimates were calculated, please refer to the Phosphoric Ac id Production section of the In dustrial Processes chapter.

Only one set of data from the Florida Institute of Phos phate Research (FIPR) was av ailable 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 CO 2 

### ) of phosphate rock could vary ±1 percent, resulting

in a variation in CO 2 

### emissions of ±20 percent.

Organic C is not included in the calculation of CO 2 

### emissions from phosphoric acid production. However,

if, for example, 50 percent of the organic carbon cont ent of the phosphate rock were to be emitted as CO 2 

### in the

phosphoric acid production process, the CO 2 

### 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 CO 2 

### in the calcination process, the CO 2 

### 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, CO 2 

### emissions would

fall 10 percent.

Iron and Steel Production

The uncertainty upper and lower bounds of the CO 2 

### 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 CO 2 

### 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 conf idence interval. The uncertai nties associated with three

variables were estimated for each smelter: (1) the quantity of aluminum produc ed, (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 fracti on). 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 di stribution. 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 CF 4 

### and C 2 

### F 6 

### 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 guid ance for technology-specific (Tie r 2) slope coefficients.

Consequently, the uncertainties values assigned to the technology-specific slope coefficients for CF 4 

### for CWPB and

SWPB were 0.036 and 0.038, respectively, and for C 2 

### F 6 

### for CWPB and SWPB were 0.060 and 0.056, respectively.

(The uncertainty in CF 4 

### emissions for CWPB is reported as 6 percent in IPCC (2001), but was increased to 50

percent in this analysis to better account for measuremen t 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 informa tion 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 Proc esses chapter. Please refer to that s ection 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 percen t confidence interval. An uncertainty of 5 percent was

assigned to the data reported by each participant in the Partne rship. If Partners did not report emissions data during

the current reporting year, SF 6 

### 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 uncertai nty associated with the SF 6 

### 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 la st reported was assumed and a 30% uncer tainty for each subsequent year was

assumed. For those industry processes that are not represented in Partnership, such as permanent mold and wrought

casting, SF 6 

### emissions were estimated using production and consumption statistics reported by USGS and estimated

process-specific emission fact ors (see Table 4-63). The uncertainties a ssociated with the emission factors and

USGS-reported statistics were assumed to be 75 percent an d 25 percent, respectively. Emissions associated with

sand casting activities not entirely captured by the Partnershi p utilized a Partner-reported emission factor with an

uncertainty of 75 percent. In general, where precise quant itative 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 SF 6 

### from electric transmission and distri bution stem from the following three

quantities: (1) emissions from partners, (2) emissions from non-partners, and (3) emissi ons 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 SF 6 

### data was assumed to have an

uncertainty of 10 percent. Based on a Monte Carlo analys is, the cumulative uncertainty of the total partner reported

data was estimated to be 3.6 percent. For partner estim ated 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 unc ertainty, 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 re gression standard error estimate) is estimated to be ±21 percent for small

utilities and ±64 percent for large utilities, while the uncerta inty in the transmission miles is assumed to be 10

percent. For equipment manufacturers, the quantity of SF 6 

### 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 SF 6 

### charged into equipment in 2007 is estimated to have an uncertainty of 70.2 percent,

and is derived from the uncertainty in partner reported ne w 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 para meter (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), rand om 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 pe rtains to the emission estimates pr esented in the Agriculture Manure

Management section of the Agriculture chapter. Please re fer 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

CH 4 

### 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 factor s that contribute to the uncertainty in emission estimates are a lack of

information on the usage of various manure manageme nt systems in each regional location and the exact CH 4 

### 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 CH 4 

### 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 system s (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 CH 4 

### at the same rate as a properly designed lagoon.

In addition, these systems may not be well operated, contri buting 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 methodo logy utilizing the van’t Hoff-Arrheni us equation was developed to more

closely match observed system performan ce and account for the affect of temp erature 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 clim ates, 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 CH 4 

### producing potential of VS excreted by different animal

groups (i.e., B

o 

### ). The B

o 

### values used in the CH 4 

### calculations are published values fo r U.S. animal waste. However,

there are several studies that provide a range of B

o 

### values for certain animals, in cluding dairy and swine. The B

o 

### values chosen for dairy assign separate values for dair y 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 manu re will not produce as much CH 4 

### as manure from a milking cow. However, the data

available for B

o 

### values are sparse, and do not necessarily reflect th e 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 confiden ce 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 wa s 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 uncer tainty. 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 th e rice acreage. Fields are flooded for a variety of reasons: to provide habitat

for waterfowl, to provide ponds for crawfish production, an d to aid in rice straw decomposition. To date, however,

### A-335 

### CH 4 

### 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 ch apter. 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 characteris tics, and N inputs to simulate direct N 2 

### O emissions for each crop- or

grassland type in a county. A joint PDF was used to address the structural uncertainty for direct N 2 

### O 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 volatilizati on, runoff, and leaching of N th at 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-le vel, 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 re ceiving manure amendments; s ee Edmonds et al. 2003).

Together with the tota l area for each crop within a co unty, the result yielded a probab ility that a given crop in a

specific county would either receive ma nure or not in the Monte Carlo analysis. A ratio of manure N available for

application in each year of the invent ory relative to 1997 was used to adju st 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 applie d to the N fertilization rate

accounting for the interaction betwee n fertilization and manure N amendmen ts (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 applicati on 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 N 2 

### O emission estimates from eight agricultural

experiments with 50 treatments. Although the dataset wa s 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 experi mental site, which were needed to estimate appropriate

standard deviations for parameter coefficients. The structural uncertainty esti mator 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 develo pment will be needed to address this uncertainty in model

estimates for grasslands, which is a planned improvement as more soil N 2 

### O measurement data become available for

grassland sites. In general, DAYCENT tended to und erestimate emissions if the rates were above 6 g N 2 

### O/ha/day

(Del Grosso et al., in prep). Model structural uncertain ty was not assessed for N volatilization and leaching/runoff,

because sufficient data from fiel d experiments were not available.

A simple error propagation method (IPCC 2006) was used to estimate uncertainties for direct emissions

estimated with Tier 1 methods, includ ing management of non-major crops (m ineral fertilization, crop residues,

organic fertilizers) and N inputs that were not addressed in the DAYCENT simu lations (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 methodol ogy using the simple erro r propagation method (IPCC

2006). PDFs for the proportion of N subject to volatilization, leaching and runoff, as well as indirect N 2 

### O emission

factors were based on IPCC (2006), and PDFs for the ac tivity 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 N 2 

### O emission factors (IPCC 2006).

Field Burning of Agricultural Residues

The uncertainty upper and lower bounds of the CH 4 

### emissions estimate for Field Burning of Agricultural

Residues were 94 percent and -73 percent, respectively, and of the N 2 

### O 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. Th e 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 spec ific 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 N 2 

### O 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 CH 4 

### 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 US DA 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 regres sion 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 tr ee 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 availa ble. Such extrapolation in troduces 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 relations hips 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 change s in soil C sum to large differences wh en integrated over large areas. The

85 

### With the exception of organic fertilizers and crop yiel ds, 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 discarde d, and rates and limits for d ecay of wood and paper in

SWDS.



## Direct N 2 

## O fluxes from Forest Soils

The uncertainty upper and lower bounds of the emissions estimate for Direct N 2 

### O Fluxes from Forest Soils

were 211 percent and -59 percent, respectively, at the 95 pe rcent confidence interval. Va riables such as the emission

factor for synthetic fertilizer applied to soil, and the area of forest land receiving fertilizer affect direct N 2 

### O fluxes

from Forest Soils. For more information, please refer to that section of the LULUCF ch apter. The uncertainty range

of the IPCC default emission factor for synthetic fertilizer applied to soil, acco rding to IPCC (2006), ranges from 0.3

to 3 percent. Because IPCC does not provide further info rmation 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 uncertain ty 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 pe rtains to the emission estimates presented in the Cropland Remaining

Cropland section of the LULUCF chapte r. 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 confid ence interval. Probability Distribution Functions (PDFs) for

fertilizer were based on survey data for major U.S. crop s, 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, appli cation rates and associated land areas receiving manure amendments; see

Edmonds et al. 2003). Together with the total area for each crop within a co unty, this yielded a probability that a

given crop at a specific NRI point would either receive ma nure or not. A ratio of managed manure N production in

each year of the inventory re lative to 1997 was used to adjust the pr obability of an area r eceiving 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 pro duce the PDF for the Monte Carlo Analys is (i.e., 1980-1 989, 1990-2000). If

soils were amended with manure, a re duction 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 se lected 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 pa rameterization. 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, cro pping, 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-asid e 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 estimat es 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 appropria te standard deviations for parameter coefficients. For

each C stock estimate from the Monte Carlo analysis, the stru ctural uncertainty estimator was applied to adjust the

value accounting for bias and prediction error in the mode led 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 fro m 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 bivari ate normal distribution with a log-ra tio scale, accounting for the negative

dependence among the proportions of land under conventi onal and conservation tillage practices. PDFs for the

agricultural areas receiving manure we re derived assuming a normal distribu tion 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 refe r 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, re presenting 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 rota tions, 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 signi ficant biases in modeled estimates from the Century model. Uncertainty in

land-use and management statistics fro m 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 Stoc k 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 a ssociated 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 St ock Changes section of the LULUCF chapter for more

information on C emissions from organic soils.



## Uncertainties in CO 2 

## 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 re fer 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 CO 2 

### 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 followi ng application to soils, including: di ssolution of liming co nstituents; leaching

of bicarbonates into the soil and transport to the ocean; and emissions to the atmosphe re (West and McBride 2005).

The C accounting behind these emission f actors entails assumptions about severa l uncertain factors. First, it is

uncertain what fraction of agricultural lime is dissolved by nitric acid (HNO 3 

### )—a process that releases CO 2 

### —and

what portion reacts with carbonic acid (H 2 

### CO 3 

### ), resulting in the uptake of CO 2 

### . The fractions can vary depending

on soil pH and N fertilizer use. The second major sour ce of uncertainty is the fraction of bicarbonate (HCO 3 - 

### ) that

leaches through the soil pr ofile and is transported into groundwater, which can eventually be transferred into rivers

and into the ocean. This fraction can vary de pending on the soil pH and whether calcium (Ca 2+ 

### ) and magnesium

(Mg 2+ 

### ) liming constituents that might otherwise accompany HCO 3 - 

### , 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 limest one 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 report ing could lead to an overestimation of limestone and an

underestimation of dolomite. In addition, the total quantity of crus hed 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 perc ent confidence interval. Th e 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 so me 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 aq uaculture (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 curre nt 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, resp ectively, 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 CO 2 

### 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 uncertai nty. Minimal data exist on where and how much sewage

sludge has been applied to U.S. agricultural land and th e 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 agricultura l land, but there is not sufficient information to further

subdivide application among the agricultural land use/land-u se 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 percen t 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 a nd 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 so me 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 inclusi ons 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



## N 2 

## O Fluxes from Settlement Soil

### A-341 

### The uncertainty information below pertains to the emission estimates presented in the N 2 

### O Fluxes from

Settlement Soils section of the LULUCF chapter. Please refer to that section for information about synthetic

fertilizer N, the amounts of sewage slu dge applied to non-agricultural lands, and other variables that affect this

source. The uncertainty upper and lower bounds of the emissions estimate for N 2 

### O 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 emi ssion 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 wa s 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 prac tice 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 su rface disposal used in the Inventory calculations (Biocycle 2000, NRC 2002).

Other

The uncertainty analysis descriptions in this secti on 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 F ood Scrap Carbon Stocks in Landfills

The uncertainty ranges were assigned based on expert ju dgment 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 range s 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 fr om 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 Nati ons 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 Nati ons Environment Programme, Organization for Economic

Co-Operation and Development, International Energy Agency

.